diff --git a/.github/workflows/codecov.yml b/.codecov.yml similarity index 62% rename from .github/workflows/codecov.yml rename to .codecov.yml index 39d77580d..3d023771c 100644 --- a/.github/workflows/codecov.yml +++ b/.codecov.yml @@ -6,8 +6,11 @@ coverage: target: auto threshold: 1% base: auto - flags: - - unit + # No `flags:` on purpose. This used to scope the status to a `unit` flag + # that no upload has ever tagged, so Codecov resolved it over an empty + # flag set and the gate measured nothing. The reports test_pytest.yaml + # sends are unit + doctest + integration + acceptance combined, so there + # is no honest flag to name here. paths: - "rocketpy" # advanced settings @@ -29,7 +32,6 @@ coverage: - develop if_ci_failed: error # success, failure, error, ignore only_pulls: false - flags: - - "unit" + # See the note on project.default above: no upload tags a `unit` flag. paths: - "rocketpy" diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index c614af766..d443be5e4 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -17,7 +17,7 @@ - [ ] Docs have been reviewed and added / updated - [ ] Lint (`black rocketpy/ tests/`) has passed locally - [ ] All tests (`pytest tests -m slow --runslow`) have passed locally -- [ ] `CHANGELOG.md` has been updated (if relevant) +- `CHANGELOG.md` — no action needed; an LLM workflow auto-updates it after merge ## Current behavior diff --git a/.github/scripts/test_update_changelog.py b/.github/scripts/test_update_changelog.py new file mode 100644 index 000000000..6d886836a --- /dev/null +++ b/.github/scripts/test_update_changelog.py @@ -0,0 +1,179 @@ +#!/usr/bin/env python3 +"""Self-contained tests for update_changelog.py. + +Runs without network or the ``google-genai`` package (the LLM import in the +script is lazy). Exercises the deterministic pieces that keep the changelog +safe: section splitting, duplicate detection, prefix handling, the fallback +classifier, and the validator that guards LLM output. + +Run locally with either: + python .github/scripts/test_update_changelog.py + pytest .github/scripts/test_update_changelog.py +""" + +import os +import sys + +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +import update_changelog as uc # noqa: E402 + +SAMPLE = """\ +# RocketPy Change Log + +## [Unreleased] - yyyy-mm-dd + +### Added + +### Changed + +### Fixed + +## [v1.13.0] - 2026-07-21 + +### Added + +- ENH: something old [#100](https://github.com/RocketPy-Team/RocketPy/pull/100) +""" + + +def test_split_roundtrips(): + before, block, after = uc.split_changelog(SAMPLE) + assert before + block + after == SAMPLE + assert block.startswith("## [Unreleased]") + assert "## [v1.13.0]" not in block + assert "## [v1.13.0]" in after + + +def test_already_present(): + _, block, after = uc.split_changelog(SAMPLE) + assert uc.already_present(after, 100) is True + assert uc.already_present(block, 100) is False + assert uc.already_present(block, 999) is False + + +def test_detect_prefix(): + assert uc.detect_prefix("BUG: fix a thing") == "BUG" + assert uc.detect_prefix("BUG/MNT: pre-release review fixes") == "BUG/MNT" + assert uc.detect_prefix("Add a shiny feature") is None + + +def test_no_double_prefix(): + # The historical bug: a title already carrying a prefix must not gain another. + entry = uc.build_entry("BUG/MNT: pre-release fixes", 1074, "ENH", "BUG/MNT") + assert entry.startswith("- BUG/MNT: pre-release fixes ") + assert "ENH:" not in entry + assert "[#1074](https://github.com/RocketPy-Team/RocketPy/pull/1074)" in entry + + +def test_build_entry_adds_prefix_when_missing(): + entry = uc.build_entry("Add a shiny feature", 200, "ENH", None) + assert entry.startswith("- ENH: Add a shiny feature ") + + +def test_fallback_routing(): + # Bug label -> Fixed + section, prefix, _ = uc.fallback_section_and_prefix("Fix crash", "Bug,Flight") + assert section == "### Fixed" and prefix == "BUG" + # Refactor label -> Changed + section, _, _ = uc.fallback_section_and_prefix("Tidy internals", "Refactor") + assert section == "### Changed" + # Existing prefix wins the routing even without a matching label + section, prefix, existing = uc.fallback_section_and_prefix("BUG: oops", "") + assert section == "### Fixed" and existing == "BUG" + # Default + section, prefix, _ = uc.fallback_section_and_prefix("New capability", "Enhancement") + assert section == "### Added" and prefix == "ENH" + + +def test_fallback_update_inserts_once_under_right_section(): + _, block, _ = uc.split_changelog(SAMPLE) + updated = uc.fallback_update(block, "BUG: fix a thing", 321, "Bug") + assert updated.count("[#321]") == 1 + fixed = updated.split("### Fixed", 1)[1] + assert "- BUG: fix a thing" in fixed + # Not misplaced under Added. + added = updated.split("### Added", 1)[1].split("### Changed", 1)[0] + assert "[#321]" not in added + + +def test_ensure_section_inserts_in_canonical_order(): + _, block, _ = uc.split_changelog(SAMPLE) + # SAMPLE has Added/Changed/Fixed but no Removed subsection. + lines = uc.ensure_section(block.splitlines(keepends=True), "### Removed") + joined = "".join(lines) + # Removed must sit after Changed and before Fixed. + assert ( + joined.index("### Changed") + < joined.index("### Removed") + < joined.index("### Fixed") + ) + + +def test_validator_accepts_good_output(): + _, block, _ = uc.split_changelog(SAMPLE) + good = block.replace( + "### Fixed\n", + "### Fixed\n\n- BUG: fix it [#500](https://github.com/RocketPy-Team/RocketPy/pull/500)\n", + ) + assert uc.validate_llm_block(block, good, 500) is None + + +def test_validator_rejects_dropped_entry(): + old = block_with_entry() + # New block loses the pre-existing entry. + new = "## [Unreleased] - yyyy-mm-dd\n\n### Fixed\n\n- BUG: new [#500](https://github.com/RocketPy-Team/RocketPy/pull/500)\n\n" + assert uc.validate_llm_block(old, new, 500) is not None + + +def test_validator_rejects_missing_new_ref(): + _, block, _ = uc.split_changelog(SAMPLE) + assert uc.validate_llm_block(block, block, 500) is not None # no new ref at all + + +def test_validator_rejects_leaked_version_header(): + _, block, _ = uc.split_changelog(SAMPLE) + leaked = ( + "## [Unreleased] - yyyy-mm-dd\n\n### Fixed\n\n" + "- BUG: x [#500](https://github.com/RocketPy-Team/RocketPy/pull/500)\n\n" + "## [v1.13.0] - 2026-07-21\n" + ) + assert uc.validate_llm_block(block, leaked, 500) is not None + + +def test_validator_rejects_runaway_growth(): + _, block, _ = uc.split_changelog(SAMPLE) + huge = ( + "## [Unreleased] - yyyy-mm-dd\n\n### Fixed\n\n" + "- BUG: x [#500](https://github.com/RocketPy-Team/RocketPy/pull/500)\n" + + ("x" * (uc.MAX_BLOCK_GROWTH + 50)) + ) + assert uc.validate_llm_block(block, huge, 500) is not None + + +def block_with_entry(): + return ( + "## [Unreleased] - yyyy-mm-dd\n\n### Added\n\n" + "- ENH: keep me [#200](https://github.com/RocketPy-Team/RocketPy/pull/200)\n\n" + "### Fixed\n\n" + ) + + +def _run_all(): + tests = [ + v for k, v in sorted(globals().items()) if k.startswith("test_") and callable(v) + ] + failures = 0 + for test in tests: + try: + test() + print(f" ok {test.__name__}") + except AssertionError as exc: + failures += 1 + print(f" FAIL {test.__name__}: {exc}") + print(f"\n{len(tests) - failures}/{len(tests)} passed") + return 1 if failures else 0 + + +if __name__ == "__main__": + sys.exit(_run_all()) diff --git a/.github/scripts/update_changelog.py b/.github/scripts/update_changelog.py new file mode 100644 index 000000000..e8541d89f --- /dev/null +++ b/.github/scripts/update_changelog.py @@ -0,0 +1,358 @@ +#!/usr/bin/env python3 +"""Populate the ``[Unreleased]`` section of ``CHANGELOG.md`` for a merged PR. + +Runs in CI (``.github/workflows/changelog.yml``) after a pull request is merged +into ``develop``. It asks Gemini to place a well-formatted entry into the correct +subsection of the ``[Unreleased]`` block, then applies the result behind a +deterministic safety net so the file can never be corrupted or lose history. + +Design +------ +* The LLM only ever sees and rewrites the ``[Unreleased]`` block. Everything + else in the file (released versions) is preserved byte-for-byte. +* The LLM's output is validated before use: every ``[#N](url)`` link that + existed in ``[Unreleased]`` must survive, the new PR must be referenced + exactly once, no released ``## [vX]`` header may leak in, and the block may + not grow unreasonably. If validation fails -- or the API key is missing, or + the request errors -- we fall back to a deterministic insert that detects an + existing conventional prefix (so we never produce ``ENH: BUG: ...``) and skips + duplicates. +* If the PR is already referenced in ``[Unreleased]``, the run is a no-op + (idempotent), so re-runs and merge races never create duplicate lines. + +Security +-------- +The PR title and body are untrusted user input. They are passed to the model as +clearly-delimited data, and the strict output validation is the real guard: no +matter what a malicious PR body asks, the model cannot delete released history +or drop existing entries -- such output is rejected and the fallback runs. +""" + +from __future__ import annotations + +import json +import os +import re +import sys + +REPO = "RocketPy-Team/RocketPy" +PULL_URL = f"https://github.com/{REPO}/pull" +# Alias for the newest stable "flash" model. Using the alias (rather than a +# pinned version like gemini-3.6-flash) keeps the job working when a specific +# version is retired -- pinned gemini-2.5-flash already became unavailable to +# new API keys. Output is validated regardless, so model drift is safe here. +MODEL = "gemini-flash-latest" + +# Max characters the LLM-rewritten block may grow relative to the original. +# One new entry plus light reformatting; anything larger is treated as suspect. +MAX_BLOCK_GROWTH = 800 +# PR bodies can be huge; only the beginning is useful context for one line. +MAX_BODY_CHARS = 4000 + +SUBSECTION_ORDER = [ + "### Added", + "### Changed", + "### Deprecated", + "### Removed", + "### Fixed", + "### Security", +] + +# A conventional-commit-style prefix already present in a PR title, e.g. "BUG:", +# "ENH:", or a compound like "BUG/MNT:". Used to avoid prepending a second one. +PREFIX_RE = re.compile(r"^([A-Z]{2,7}(?:/[A-Z]{2,7})*):\s") + +# Any "[#N](https://.../pull|issues/N)" markdown link. Used both to preserve +# existing links across an LLM rewrite and to detect duplicates. +LINK_RE = re.compile( + r"\[#(\d+)\]\((https://github\.com/RocketPy-Team/RocketPy/(?:pull|issues)/\d+)\)" +) + + +# --------------------------------------------------------------------------- # +# Pure helpers (no I/O, no network) -- these are what the test file exercises. # +# --------------------------------------------------------------------------- # +def pr_link(number: str | int) -> str: + """Canonical markdown link for a PR number.""" + return f"[#{number}]({PULL_URL}/{number})" + + +def split_changelog(text: str) -> tuple[str, str, str]: + """Split the file into (before, unreleased_block, after). + + ``unreleased_block`` runs from the ``## [Unreleased]`` header up to (but not + including) the next ``## [`` version header. Reassembling + ``before + block + after`` reproduces the file exactly. + """ + start_match = re.search(r"^## \[Unreleased\].*$", text, re.MULTILINE) + if not start_match: + raise ValueError("No '## [Unreleased]' header found in CHANGELOG.md") + start = start_match.start() + next_match = re.search(r"^## \[", text[start_match.end() :], re.MULTILINE) + end = start_match.end() + next_match.start() if next_match else len(text) + return text[:start], text[start:end], text[end:] + + +def already_present(block: str, number: str | int) -> bool: + """True if ``block`` already references this PR (avoids duplicate entries).""" + pattern = rf"/pull/{number}\)|\[#{number}\]" + return re.search(pattern, block) is not None + + +def existing_links(block: str) -> set[str]: + """Set of normalized ``#N -> url`` link tokens present in a block.""" + return {f"{m.group(1)}|{m.group(2)}" for m in LINK_RE.finditer(block)} + + +def detect_prefix(title: str) -> str | None: + """Return the conventional prefix already in ``title`` (e.g. ``BUG/MNT``).""" + match = PREFIX_RE.match(title.strip()) + return match.group(1) if match else None + + +def fallback_section_and_prefix(title: str, labels: str) -> tuple[str, str, str | None]: + """Deterministic (section, prefix, existing_prefix) used when the LLM path + is unavailable or its output is rejected.""" + labels_l = labels.lower() + existing = detect_prefix(title) + existing_u = existing or "" + + if "bug" in labels_l or any(p in existing_u for p in ("BUG", "FIX", "HOTFIX")): + section, default_prefix = "### Fixed", "BUG" + elif "refactor" in labels_l or "MNT" in existing_u: + section, default_prefix = "### Changed", "MNT" + elif "tests" in labels_l or "TST" in existing_u: + section, default_prefix = "### Changed", "TST" + elif ("c.i." in labels_l or "ci" in labels_l.split(",")) or "CI" in existing_u: + section, default_prefix = "### Changed", "CI" + elif "docs" in labels_l or "DOC" in existing_u: + section, default_prefix = "### Added", "DOC" + else: + section, default_prefix = "### Added", "ENH" + + return section, (existing or default_prefix), existing + + +def build_entry( + title: str, number: str | int, prefix: str, existing_prefix: str | None +) -> str: + """Build a single changelog bullet, never double-prefixing.""" + title = title.strip() + body = title if existing_prefix else f"{prefix}: {title}" + return f"- {body} {pr_link(number)}\n" + + +def ensure_section(lines: list[str], section: str) -> list[str]: + """Insert a missing subsection header at its canonical position.""" + target = SUBSECTION_ORDER.index(section) + insert_at = len(lines) + for i, line in enumerate(lines): + stripped = line.strip() + if stripped in SUBSECTION_ORDER and SUBSECTION_ORDER.index(stripped) > target: + insert_at = i + break + return lines[:insert_at] + [f"{section}\n", "\n"] + lines[insert_at:] + + +def fallback_update(block: str, title: str, number: str | int, labels: str) -> str: + """Deterministically insert the entry into the right subsection of ``block``.""" + section, prefix, existing = fallback_section_and_prefix(title, labels) + entry = build_entry(title, number, prefix, existing) + + lines = block.splitlines(keepends=True) + if not any(line.strip() == section for line in lines): + lines = ensure_section(lines, section) + + idx = next(i for i, line in enumerate(lines) if line.strip() == section) + insert_at = idx + 1 + # Keep the entry at the top of the list, just after the blank line that + # follows the subsection header. + if insert_at < len(lines) and lines[insert_at].strip() == "": + insert_at += 1 + lines.insert(insert_at, entry) + # If the subsection was empty, the entry now abuts the next header; the + # house style keeps a blank line before every header. Add one -- but never + # between two list items. + following = lines[insert_at + 1] if insert_at + 1 < len(lines) else "" + if following.lstrip().startswith("#"): + lines.insert(insert_at + 1, "\n") + # Guarantee a single blank line before the next version header, even when + # the entry landed as the block's last line (empty trailing subsection). + return "".join(lines).rstrip("\n") + "\n\n" + + +def validate_llm_block(old_block: str, new_block: str, number: str | int) -> str | None: + """Return an error string if the LLM output is unsafe, else ``None``.""" + stripped = new_block.strip() + if not stripped.startswith("## [Unreleased]"): + return "does not start with the '## [Unreleased]' header" + if re.search(r"^## \[v", new_block, re.MULTILINE): + return "leaked a released version header into the section" + if len(new_block) > len(old_block) + MAX_BLOCK_GROWTH: + return "grew far more than a single entry should" + + missing = existing_links(old_block) - existing_links(new_block) + if missing: + return f"dropped {len(missing)} existing link(s): {sorted(missing)}" + + new_refs = sum( + 1 + for m in LINK_RE.finditer(new_block) + if m.group(1) == str(number) and m.group(2).endswith(f"/pull/{number}") + ) + if new_refs != 1: + return f"references the new PR {new_refs} time(s), expected exactly 1" + return None + + +def build_prompt(block: str, title: str, number: str, labels: str, body: str) -> str: + """Assemble the user-content payload for the model.""" + body = (body or "").strip()[:MAX_BODY_CHARS] + return ( + "Current [Unreleased] section:\n" + "<<](https://github.com/RocketPy-Team/RocketPy/pull/)". + +Formatting rules: +- Subsections, in this order, present only when non-empty: "### Added" (new \ +features/APIs), "### Changed" (changes to existing behavior), "### Deprecated", \ +"### Removed", "### Fixed" (bug fixes), "### Security". +- Each entry is a single bullet: "- PREFIX: concise description [#N](url)". +- PREFIX is a short uppercase tag for the change type: ENH (enhancement), BUG \ +(bug fix), MNT (maintenance/refactor), DOC (documentation), DEV (dev tooling), \ +CI, TST (tests), REL (release), PERF, SEC. +- If the PR title ALREADY starts with such a prefix (e.g. "BUG: ..." or \ +"BUG/MNT: ..."), keep it exactly; do NOT add a second prefix. This is the most \ +common past mistake -- never produce "ENH: BUG: ...". +- Choose the subsection from the actual nature of the change, not blindly from \ +a label: a "BUG"/"FIX" prefix or "Bug" label => "### Fixed"; refactor/maintenance \ +=> "### Changed"; a new capability => "### Added". +- Keep the description close to the PR title; use the PR body only to make the \ +wording clearer or more accurate, never to pad. One line per entry (a single \ +extra indented line is allowed only when essential). +- Keep the "## [Unreleased] - yyyy-mm-dd" placeholder header line exactly as-is. + +Return JSON: {"reasoning": "<1-2 sentences: section + prefix choice, and any \ +dedup you did>", "unreleased_section": ""}.""" + + +def call_gemini( + block: str, title: str, number: str, labels: str, body: str, api_key: str +) -> str: + """Ask Gemini for the rewritten [Unreleased] section. Raises on any failure. + + The ``google-genai`` import is lazy so the module stays importable (and + testable) in environments without the package installed. + """ + from google import genai # noqa: PLC0415 (lazy on purpose) + from google.genai import types # noqa: PLC0415 + + client = genai.Client(api_key=api_key) + response = client.models.generate_content( + model=MODEL, + contents=build_prompt(block, title, number, labels, body), + config=types.GenerateContentConfig( + system_instruction=SYSTEM_PROMPT, + temperature=0, + response_mime_type="application/json", + response_schema={ + "type": "object", + "properties": { + "reasoning": {"type": "string"}, + "unreleased_section": {"type": "string"}, + }, + "required": ["unreleased_section"], + }, + ), + ) + data = json.loads(response.text) + reasoning = (data.get("reasoning") or "").strip() + if reasoning: + print(f"Gemini reasoning: {reasoning}") + section = data["unreleased_section"] + # Normalize trailing whitespace so a single blank line separates the block + # from the next version header when reassembled. + return section.rstrip("\n") + "\n\n" + + +def update_unreleased( + block: str, title: str, number: str, labels: str, body: str, api_key: str +) -> str: + """Return the new [Unreleased] block, preferring the LLM, falling back safely.""" + if api_key: + try: + candidate = call_gemini(block, title, number, labels, body, api_key) + error = validate_llm_block(block, candidate, number) + if error is None: + print("Applied Gemini-generated changelog entry.") + return candidate + print( + f"::warning::Rejected Gemini output ({error}); using deterministic fallback." + ) + except Exception as exc: # noqa: BLE001 (any failure -> safe fallback) + print( + f"::warning::Gemini call failed ({exc!r}); using deterministic fallback." + ) + else: + print("::warning::GEMINI_API_KEY not set; using deterministic fallback.") + + return fallback_update(block, title, number, labels) + + +def main() -> int: + title = os.environ["PR_TITLE"] + number = os.environ["PR_NUMBER"] + labels = os.environ.get("PR_LABELS", "") + body = os.environ.get("PR_BODY", "") + api_key = os.environ.get("GEMINI_API_KEY", "") + + with open("CHANGELOG.md", encoding="utf-8") as handle: + text = handle.read() + + before, block, after = split_changelog(text) + + if already_present(block, number): + print(f"PR #{number} already in the changelog; nothing to do.") + return 0 + + new_block = update_unreleased(block, title, number, labels, body, api_key) + with open("CHANGELOG.md", "w", encoding="utf-8", newline="\n") as handle: + handle.write(before + new_block + after) + + print(f"Changelog updated for PR #{number}.") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/.github/workflows/changelog.yml b/.github/workflows/changelog.yml index 7bf27daf7..c04f35d81 100644 --- a/.github/workflows/changelog.yml +++ b/.github/workflows/changelog.yml @@ -1,12 +1,21 @@ name: Populate Changelog on: - pull_request: + # `pull_request_target`, not `pull_request`. Secrets are withheld from a + # `pull_request` run whose head is a fork, so RELEASE_TOKEN arrived empty and + # the checkout below failed for every outside contribution. + # + # Nothing from the pull request is executed. The checkout is `ref: develop`, + # and the updater is inline below, which under this trigger is read from the + # default branch rather than from the merged head. + pull_request_target: types: [closed] branches: - develop +# Read, because the job's own writes go through RELEASE_TOKEN. This trigger runs +# with secrets available, so GITHUB_TOKEN should not also carry write. permissions: - contents: write + contents: read jobs: Changelog: @@ -20,64 +29,54 @@ jobs: repository: RocketPy-Team/RocketPy ref: develop token: ${{ secrets.RELEASE_TOKEN }} + # Full history so the retry loop below can rebase onto the latest + # develop when another merge lands while this job runs. + fetch-depth: 0 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: Install dependencies + run: python -m pip install --upgrade pip "google-genai>=1.0.0" - name: Update Changelog env: + # PR title/body are untrusted input, so they are passed via the + # environment and consumed inside Python (never interpolated into the + # shell). The script asks Gemini to format and place the entry, then + # validates the result; if the key is missing or the model output is + # rejected, it falls back to a safe deterministic insert. PR_TITLE: ${{ github.event.pull_request.title }} PR_NUMBER: ${{ github.event.pull_request.number }} PR_LABELS: ${{ join(github.event.pull_request.labels.*.name, ',') }} - run: | - # The PR title is untrusted input, so it is read from the environment - # inside Python rather than interpolated into a shell/sed program. - # This avoids both shell injection and sed breaking on special - # characters (\, /, &, newlines) in the title. - python - <<'PY' - import os - - labels = os.environ.get("PR_LABELS", "") - title = os.environ["PR_TITLE"] - number = os.environ["PR_NUMBER"] - - section, prefix = "### Added", "ENH" - if "Bug" in labels: - section, prefix = "### Fixed", "BUG" - elif "Refactor" in labels: - section, prefix = "### Changed", "MNT" - elif "Docs" in labels and "Git housekeeping" in labels: - section, prefix = "### Changed", "DOC" - elif "Tests" in labels: - section, prefix = "### Changed", "TST" - elif "Docs" in labels: - section, prefix = "### Added", "DOC" - - entry = ( - f"- {prefix}: {title} " - f"[#{number}](https://github.com/RocketPy-Team/RocketPy/pull/{number})\n" - ) - - with open("CHANGELOG.md", encoding="utf-8") as handle: - lines = handle.readlines() - - for index, line in enumerate(lines): - if line.rstrip("\n") == section: - insert_at = index + 1 - # Place the entry at the top of the list, after the single - # blank line that follows the section header. - if insert_at < len(lines) and lines[insert_at].strip() == "": - insert_at += 1 - lines.insert(insert_at, entry) - break - else: - raise SystemExit(f"Section {section!r} not found in CHANGELOG.md") - - with open("CHANGELOG.md", "w", encoding="utf-8") as handle: - handle.writelines(lines) - PY + PR_BODY: ${{ github.event.pull_request.body }} + GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }} + run: python .github/scripts/update_changelog.py - name: Push Changes run: | + if git diff --quiet -- CHANGELOG.md; then + echo "CHANGELOG.md unchanged (duplicate or no-op) — nothing to commit." + exit 0 + fi + git config user.name "github-actions[bot]" git config user.email "github-actions[bot]@users.noreply.github.com" git add CHANGELOG.md - git commit -m "DOC: Update Changelog for PR #${{ github.event.pull_request.number }}" - git push + git commit -m "DOC: update changelog for PR #${{ github.event.pull_request.number }}" + + # Survive races: if another PR merged into develop after our checkout, + # the first push is rejected (non-fast-forward). Rebase and retry. + for attempt in 1 2 3 4 5; do + if git push origin HEAD:develop; then + echo "Pushed changelog update on attempt ${attempt}." + exit 0 + fi + echo "Push rejected (attempt ${attempt}); rebasing onto latest develop..." + git pull --rebase origin develop + done + + echo "::error::Failed to push changelog update after 5 attempts." + exit 1 diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 806bef0dc..35d5919d4 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -1,10 +1,9 @@ name: Documentation on: - # Only PRs targeting master (base branch = master) and pushes to master. pull_request: types: [opened, synchronize, reopened, ready_for_review] - branches: [master] + branches: [master, develop] paths: - "docs/**" - "rocketpy/**" # docstrings feed the autodoc API reference diff --git a/.github/workflows/pr_agent.yml b/.github/workflows/pr_agent.yml new file mode 100644 index 000000000..67b6f13b9 --- /dev/null +++ b/.github/workflows/pr_agent.yml @@ -0,0 +1,51 @@ +name: PR Agent Gemini Reviewer + +# Automatic PR review powered by Google AI Studio (Gemini) through PR-Agent. +# +# `pull_request_target` rather than `pull_request`: most contributions arrive as +# PRs from forks, and a `pull_request` run triggered by a fork gets no secrets, +# so GEMINI_API_KEY would be empty (and the run would sit in `action_required` +# waiting for approval). That is safe here because this job never checks the +# pull request out - PR-Agent reads the diff over the GitHub API, and the only +# step is a version-pinned container action - so no untrusted code is executed. +# +# `synchronize` is deliberately absent: PR-Agent skips that event unless +# `github_action_config.handle_push_trigger` is set, so it only burned a runner +# on every push. Enable that setting if you want a re-review on each push. +on: + pull_request_target: + types: [opened, reopened, ready_for_review] + issue_comment: + types: [created] + +jobs: + pr_agent_job: + name: Run PR Agent + # Never react to our own comments, which would loop, and ignore comments on + # plain issues, which carry no diff to review. + if: >- + github.event.sender.type != 'Bot' + && (github.event_name != 'issue_comment' || github.event.issue.pull_request) + runs-on: ubuntu-latest + permissions: + contents: read + issues: write + pull-requests: write + steps: + - name: PR Agent Action + uses: the-pr-agent/pr-agent@v0.42.0 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + GOOGLE_AI_STUDIO.GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }} + # Required. Without an explicit model PR-Agent keeps its OpenAI + # defaults and every call dies with `AuthenticationError - Incorrect + # API key provided: dummy_key`, which is what kept this job from ever + # posting a review. + config.model: "gemini/gemini-3.5-pro" + config.fallback_models: '["gemini/gemini-3.6-flash"]' + # Gemini accepts 1M tokens; the 32k default truncates larger diffs. + config.max_model_tokens: "128000" + # Review and suggest, but leave the human-written description alone. + github_action_config.auto_describe: "false" + github_action_config.auto_review: "true" + github_action_config.auto_improve: "true" diff --git a/.github/workflows/test-pytest-slow.yaml b/.github/workflows/test-pytest-slow.yaml index fd66edc79..67aca7e82 100644 --- a/.github/workflows/test-pytest-slow.yaml +++ b/.github/workflows/test-pytest-slow.yaml @@ -20,11 +20,16 @@ jobs: pytest: runs-on: ubuntu-latest strategy: + # Same reason as the main matrix: 3.10 failing tells you nothing about + # 3.14, so cancelling it costs a result and saves nothing worth having. + fail-fast: false matrix: python-version: ["3.10", "3.14"] env: PYTHON: ${{ matrix.python-version }} MPLBACKEND: Agg + LIBGL_ALWAYS_SOFTWARE: "1" + GALLIUM_DRIVER: llvmpipe steps: - uses: actions/checkout@main - name: Set up headless display @@ -52,7 +57,34 @@ jobs: run: pytest rocketpy --doctest-modules --cov=rocketpy --cov-append - name: Run Integration Tests - run: pytest tests/integration --cov=rocketpy --cov-append + run: | + pytest tests/integration \ + --deselect tests/integration/test_plots.py::test_flight_animations_run_off_screen \ + --deselect tests/integration/test_plots.py::test_flight_animations_render_all_scene_options \ + --deselect tests/integration/test_plots.py::test_flight_animation_export_gif \ + --cov=rocketpy --cov-append + + - name: Run VTK animation tests + run: | + tests=( + tests/integration/test_plots.py::test_flight_animations_run_off_screen + tests/integration/test_plots.py::test_flight_animations_render_all_scene_options + tests/integration/test_plots.py::test_flight_animation_export_gif + ) + attempts=3 + for attempt in $(seq 1 "$attempts"); do + if pytest "${tests[@]}" --cov=rocketpy --cov-append; then + exit 0 + else + status=$? + fi + # 138 is SIGBUS on macOS, 135 is SIGBUS on Linux, 139 is SIGSEGV on + # both. The measured split on this test was eight SIGSEGV to two + # SIGBUS, so keying on 138 alone let the common case through. + if [[ ! "$status" =~ ^(135|138|139)$ || "$attempt" == "$attempts" ]]; then + exit "$status" + fi + done - name: Run Acceptance Tests run: pytest tests/acceptance --cov=rocketpy --cov-append --cov-report=xml diff --git a/.github/workflows/test_pytest.yaml b/.github/workflows/test_pytest.yaml index 51c6febcf..e5b944317 100644 --- a/.github/workflows/test_pytest.yaml +++ b/.github/workflows/test_pytest.yaml @@ -8,26 +8,50 @@ on: - ".github/**" - "pyproject.toml" - "requirements*" + # Codecov compares a pull request against the report it holds for the base + # commit. Without this, nothing ever uploads one for a commit on develop, so + # every pull request is measured against whichever old report is nearest and + # says how far behind it has fallen. + push: + branches: [master, develop] + paths: + - "**.py" + - ".github/**" + - "pyproject.toml" + - "requirements*" defaults: run: shell: bash +env: + # The Pytest matrix below is 3 os x 2 python-version, so CodecovUpload must + # receive six coverage reports. Nothing can derive a matrix size from another + # job, so it is written out here; the guard in CodecovUpload fails loudly if + # the matrix grows and this does not. + COVERAGE_LEG_COUNT: 6 + jobs: Pytest: runs-on: ${{ matrix.os }} strategy: + # One platform's result should not decide the other five. Added in #1084; + # kept explicit here so it does not get tidied away later. + fail-fast: false matrix: os: [ubuntu-latest, macos-latest, windows-latest] python-version: ["3.10", "3.14"] env: - OS: ${{ matrix.os }} - PYTHON: ${{ matrix.python-version }} MPLBACKEND: Agg steps: - uses: actions/checkout@main - name: Set up headless display uses: pyvista/setup-headless-display-action@v4 + - name: Configure Mesa software rendering on Linux + if: runner.os == 'Linux' + run: | + echo "LIBGL_ALWAYS_SOFTWARE=1" >> "$GITHUB_ENV" + echo "GALLIUM_DRIVER=llvmpipe" >> "$GITHUB_ENV" - name: Set up Python uses: actions/setup-python@main with: @@ -58,29 +82,103 @@ jobs: pytest rocketpy --doctest-modules --cov=rocketpy --cov-append - name: Run Integration Tests - run: pytest tests/integration --cov=rocketpy --cov-append + run: | + pytest tests/integration \ + --deselect tests/integration/test_plots.py::test_flight_animations_run_off_screen \ + --deselect tests/integration/test_plots.py::test_flight_animations_render_all_scene_options \ + --deselect tests/integration/test_plots.py::test_flight_animation_export_gif \ + --cov=rocketpy --cov-append + - name: Run VTK animation tests + run: | + tests=( + tests/integration/test_plots.py::test_flight_animations_run_off_screen + tests/integration/test_plots.py::test_flight_animations_render_all_scene_options + tests/integration/test_plots.py::test_flight_animation_export_gif + ) + attempts=3 + for attempt in $(seq 1 "$attempts"); do + if pytest "${tests[@]}" --cov=rocketpy --cov-append; then + exit 0 + else + status=$? + fi + # 138 is SIGBUS on macOS, 135 is SIGBUS on Linux, 139 is SIGSEGV on + # both. The measured split on this test was eight SIGSEGV to two + # SIGBUS, so keying on 138 alone let the common case through. + if [[ ! "$status" =~ ^(135|138|139)$ || "$attempt" == "$attempts" ]]; then + exit "$status" + fi + done + + # This is the only step that writes XML; the earlier ones only --cov-append + # into .coverage. Naming the report here, rather than renaming it after + # the fact, is what lets CodecovUpload merge all six into one directory + # without them clobbering each other as six identical coverage.xml. - name: Run Acceptance Tests - run: pytest tests/acceptance --cov=rocketpy --cov-append --cov-report=xml + run: >- + pytest tests/acceptance --cov=rocketpy --cov-append + --cov-report=xml:coverage-${{ matrix.os }}-py${{ matrix.python-version }}.xml - name: Upload coverage to artifacts uses: actions/upload-artifact@main with: - name: coverage - path: coverage.xml - overwrite: true + name: coverage-${{ matrix.os }}-py${{ matrix.python-version }} + path: coverage-${{ matrix.os }}-py${{ matrix.python-version }}.xml if-no-files-found: error + # Artifacts are keyed by (run, name) and survive across attempts, so + # without this every leg of a re-run fails with 409 Conflict. The VTK + # tests above are flaky enough that re-runs are routine. + overwrite: true + # CodecovUpload consumes these minutes later and nothing else reads + # them, so the 90 day default would be six dead artifacts per run. + retention-days: 1 CodecovUpload: needs: Pytest + # `needs` alone skips this job when a single leg fails, which sends Codecov + # nothing at all for the commit and .codecov.yml resolves that as an error. + # Run unless the workflow was cancelled and upload the legs that did finish. + if: ${{ !cancelled() }} runs-on: ubuntu-latest steps: - uses: actions/checkout@main - - name: Download latest coverage report + - name: Download coverage reports uses: actions/download-artifact@main + with: + pattern: coverage-* + path: coverage-reports + merge-multiple: true + + # download-artifact exits 0 when `pattern` matches fewer artifacts than + # expected, so five of six reports would otherwise look like a clean run + # and quietly under-report coverage. That silence is what #1088 is about. + - name: Verify every leg reported coverage + id: reports + env: + PYTEST_RESULT: ${{ needs.Pytest.result }} + run: | + mkdir -p coverage-reports + count=$(find coverage-reports -maxdepth 1 -name '*.xml' | wc -l) + echo "count=$count" >> "$GITHUB_OUTPUT" + echo "Downloaded $count of $COVERAGE_LEG_COUNT coverage reports." + if [ "$PYTEST_RESULT" = "success" ] && [ "$count" -ne "$COVERAGE_LEG_COUNT" ]; then + echo "::error::Every Pytest leg passed, but only $count of $COVERAGE_LEG_COUNT coverage reports arrived." + exit 1 + fi + if [ "$count" -eq 0 ]; then + echo "::warning::No coverage report to upload; no Pytest leg produced one." + fi + - name: Upload to Codecov + # Nothing to send, and Codecov would fail on an empty directory. The + # failing Pytest leg is already reporting the real problem. + if: steps.reports.outputs.count != '0' uses: codecov/codecov-action@main with: token: ${{ secrets.CODECOV_TOKEN }} - files: | - coverage.xml + directory: coverage-reports + # Forks get no secrets, so the token above is empty for them and the + # tokenless upload is rate limited. An external contributor should not + # see red for a Codecov-side hiccup in their pull request. + fail_ci_if_error: ${{ secrets.CODECOV_TOKEN != '' }} diff --git a/.github/workflows/upload-to-codecov.yml b/.github/workflows/upload-to-codecov.yml deleted file mode 100644 index e83be8536..000000000 --- a/.github/workflows/upload-to-codecov.yml +++ /dev/null @@ -1,29 +0,0 @@ -name: Upload to Codecov - -on: - workflow_call: - inputs: - codecov_token: - required: true - type: string - os: - required: true - type: string - python: - required: true - type: string - -jobs: - upload: - runs-on: ubuntu-latest - steps: - - name: Upload coverage report to Codecov - uses: codecov/codecov-action@main - with: - token: ${{ inputs.codecov_token }} - directory: ./coverage/reports/ - env_vars: OS,PYTHON - files: ./coverage.xml, ./rocketpy/coverage.xml - flags: unittests - name: codecov-umbrella - verbose: true diff --git a/.pr_agent.toml b/.pr_agent.toml new file mode 100644 index 000000000..22c9f834f --- /dev/null +++ b/.pr_agent.toml @@ -0,0 +1,29 @@ +[config] +model = "gemini/gemini-2.5-pro" +fallback_models = ["gemini/gemini-2.0-flash"] +git_provider = "github" + +[pr_reviewer] +extra_instructions = """ +Follow RocketPy repository standards when reviewing code: +1. Code Style: + - Use PEP 8 conventions with a maximum line length of 88 characters. + - Use snake_case for functions, methods, and variables. + - Use PascalCase for class names and UPPER_SNAKE_CASE for constants. + - Public classes, methods, and functions must include NumPy-style docstrings with explicit SI units. +2. Architecture & Domain Models: + - Feature logic must remain within package boundaries (simulation, rocket, motors, environment, mathutils, plots, prints). + - Position and reference frame arguments must explicitly state orientation/origin (e.g. tail_to_nose, nozzle_to_combustion_chamber). +3. Testing & Backward Compatibility: + - New functionality or bug fixes must be accompanied by unit tests in `tests/`. + - Preserve backward compatibility across the public API exported in `rocketpy/__init__.py`. +4. Pull Request Conventions: + - Verify PR title is prefixed with project acronyms (e.g., BUG, DOC, ENH, MNT, TST, BLD, REL, REV, STY, DEV). +""" +enable_auto_checks_and_labels = true + +[pr_description] +publish_labels = true + +[pr_code_suggestions] +num_code_suggestions = 4 diff --git a/.pylintrc b/.pylintrc index 02565f092..64e096c20 100644 --- a/.pylintrc +++ b/.pylintrc @@ -72,7 +72,7 @@ ignored-modules= # Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the # number of processors available to use, and will cap the count on Windows to # avoid hangs. -jobs=1 +jobs=0 # Control the amount of potential inferred values when inferring a single # object. This can help the performance when dealing with large functions or diff --git a/CHANGELOG.md b/CHANGELOG.md index 9ef335ed5..13c598421 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -32,10 +32,66 @@ Attention: The newest changes should be on top --> ### Added +- ENH: Reproducible Monte Carlo seeding, and an append that continues the same study [#1187](https://github.com/RocketPy-Team/RocketPy/pull/1187) [#1053](https://github.com/RocketPy-Team/RocketPy/issues/1053) [#1075](https://github.com/RocketPy-Team/RocketPy/issues/1075) +- ENH: 3-DOF single rail button flight phase (tip-off analysis), enabled with the opt-in `Flight(use_udot_rail2=True)`. Between the upper rail button leaving the rail and the lower one following it, the rocket pivots about the lower button under a solved constraint wrench instead of jumping straight to free 6-DOF flight. The window is reported as `between_rails_time`, `between_rails_velocity` and `tip_off_duration`, printed by `Flight.info()` and shaded in the attitude plots. Requires `simulation_mode="6 DOF"` and `equations_of_motion="standard"`. [#920](https://github.com/RocketPy-Team/RocketPy/pull/920) +- ENH: Support fixed-time parachute deployment triggers [#1133](https://github.com/RocketPy-Team/RocketPy/pull/1133) [#437](https://github.com/RocketPy-Team/RocketPy/issues/437) +- DOC: Add SIL parachute ejection integration example [#1131](https://github.com/RocketPy-Team/RocketPy/pull/1131) [#524](https://github.com/RocketPy-Team/RocketPy/issues/524) +- ENH: List NOAA atmosphere datasets and fetch latest [#1136](https://github.com/RocketPy-Team/RocketPy/pull/1136) [#660](https://github.com/RocketPy-Team/RocketPy/issues/660) +- ENH: Model unbonded solid-motor grain CM shift [#1138](https://github.com/RocketPy-Team/RocketPy/pull/1138) [#340](https://github.com/RocketPy-Team/RocketPy/issues/340) +- ENH: Add `Flight` post-step callback across all phases [#1128](https://github.com/RocketPy-Team/RocketPy/pull/1128) [#758](https://github.com/RocketPy-Team/RocketPy/issues/758) +- DOC: Document angle-of-attack drag inputs [#1142](https://github.com/RocketPy-Team/RocketPy/pull/1142) +- ENH: Create ensembles from user-defined profiles [#1141](https://github.com/RocketPy-Team/RocketPy/pull/1141) +- ENH: Add tube-fin aerodynamic surface [#1144](https://github.com/RocketPy-Team/RocketPy/pull/1144) +- ENH: StochasticFreeFormFins for Monte Carlo simulations [#1117](https://github.com/RocketPy-Team/RocketPy/pull/1117) +- ENH: `StochasticFreeFormFins`, so free-form fin sets can be used in Monte Carlo simulations. The outline is randomized as a block, since a shape is only meaningful as a complete set of points: every coordinate is perturbed by its own draw, the fin root is held on the body line, and a list of candidate outlines can have a different number of points in each. [#953](https://github.com/RocketPy-Team/RocketPy/issues/953) +- ENH: Support for Open Meteo API in the `Environment` class, adding the `open_meteo` and `open_meteo_ensemble` atmospheric models. Pressure-level forecasts, past forecasts (from 2021 onwards) and ensembles are read straight from a keyless JSON API, with no external files and no netCDF/OPeNDAP dependency. [#520](https://github.com/RocketPy-Team/RocketPy/issues/520) [#1119](https://github.com/RocketPy-Team/RocketPy/pull/1119) +- ENH: Add simplified opening shock force estimation [#1092](https://github.com/RocketPy-Team/RocketPy/pull/1092) +- ENH: Add Qodo PR-Agent workflow using Google Gemini [#1089](https://github.com/RocketPy-Team/RocketPy/pull/1089) +- ENH: Support for Meteomatics API in the `Environment` class [#1079](https://github.com/RocketPy-Team/RocketPy/pull/1079) +- ENH: update master with develop [#1081](https://github.com/RocketPy-Team/RocketPy/pull/1081) + ### Changed +- ENH: Number the simulations of a serial Monte Carlo run from zero, as the parallel path already did — the two used to name the same simulation `1, 2, 3` and `0, 1, 2`. The `index` field written into the inputs and outputs rows shifts by one for serial runs, so code reading indices off those files has to expect a run of `n` simulations to be numbered `0` to `n - 1`. An append onto a study written before this release is refused rather than continued, since its rows do not record which root drew them. [#1187](https://github.com/RocketPy-Team/RocketPy/pull/1187) +- MNT: Store the reference-area correction factor on each rocket aero surface component, so it is carried with the surface instead of being recomputed at every lift evaluation. `Rocket.aerodynamic_surfaces`, `rail_buttons` and `sensors` now yield `(component, position, ref_factor)`, so code that unpacks a pair from them (`for surface, position in rocket.aerodynamic_surfaces`) has to take the third field or absorb it. Simulation results are unchanged, and `.rpy` files written before this still load. [#1129](https://github.com/RocketPy-Team/RocketPy/pull/1129) [#561](https://github.com/RocketPy-Team/RocketPy/issues/561) +- ENH: Compute the rocket static margin lazily [#1135](https://github.com/RocketPy-Team/RocketPy/pull/1135) [#780](https://github.com/RocketPy-Team/RocketPy/issues/780) +- DOC: Tighten the comments that came with the sampler seed groups [#1154](https://github.com/RocketPy-Team/RocketPy/pull/1154) +- CI: make the Gemini PR reviewer actually review [#1140](https://github.com/RocketPy-Team/RocketPy/pull/1140) +- MNT: declare dependency floors the package can actually run on [#1108](https://github.com/RocketPy-Team/RocketPy/pull/1108) +- CI: build the docs for pull requests into develop as well [#1104](https://github.com/RocketPy-Team/RocketPy/pull/1104) +- CI: make changelog automation LLM-based (Gemini) and race-safe [#1082](https://github.com/RocketPy-Team/RocketPy/pull/1082) +- ENH: Resolve pressure_ISA discretization bounds TODO [#1056](https://github.com/RocketPy-Team/RocketPy/pull/1056) + ### Fixed +- BUG: A `Flight` continued from another `Flight` object now records `t_initial`, so a rocket carrying sensors or controllers no longer raises `AttributeError` on that path. [#920](https://github.com/RocketPy-Team/RocketPy/pull/920) +- BUG: Correct the gravity sign an `Accelerometer` applies when `consider_gravity=True`. The gravitational field was added to the inertial acceleration instead of subtracted from it, so the sensor reported the negative of the proper acceleration along the vertical: one at rest read -g rather than +g. Recorded accelerometer data taken with `consider_gravity=True` changes sign in that term. [#1175](https://github.com/RocketPy-Team/RocketPy/pull/1175) +- BUG: Report a Monte Carlo worker that fails instead of hanging or passing for a finished run [#1182](https://github.com/RocketPy-Team/RocketPy/pull/1182) +- BUG: Sample `StochasticFlight` inputs once per simulation [#1126](https://github.com/RocketPy-Team/RocketPy/pull/1126) [#1090](https://github.com/RocketPy-Team/RocketPy/issues/1090) +- BUG: Fix spurious `ValueError` from floating-point roundoff at exact tank depletion [#1166](https://github.com/RocketPy-Team/RocketPy/pull/1166) +- BUG: Draw each declared eccentricity once per simulation [#1168](https://github.com/RocketPy-Team/RocketPy/pull/1168) +- BUG: Accept numpy integer types as a `Parachute` trigger [#1116](https://github.com/RocketPy-Team/RocketPy/pull/1116) +- BUG: Refuse to run a Monte Carlo over a results file it cannot write [#1161](https://github.com/RocketPy-Team/RocketPy/pull/1161) +- BUG: Write MonteCarlo input and output rows atomically [#1125](https://github.com/RocketPy-Team/RocketPy/pull/1125) [#1110](https://github.com/RocketPy-Team/RocketPy/issues/1110) +- BUG: Draw the eccentricities again, whichever way they were added [#1167](https://github.com/RocketPy-Team/RocketPy/pull/1167) +- BUG: Serialize numpy `SeedSequence` for sensor seeds [#1124](https://github.com/RocketPy-Team/RocketPy/pull/1124) [#1087](https://github.com/RocketPy-Team/RocketPy/issues/1087) +- BUG: Build the Monte Carlo flights with the configuration they were given [#1164](https://github.com/RocketPy-Team/RocketPy/pull/1164) +- BUG: Apply the wind factors to the ensemble member that was selected [#1160](https://github.com/RocketPy-Team/RocketPy/pull/1160) +- BUG: Avoid scalar statistics for structured Monte Carlo results [#1146](https://github.com/RocketPy-Team/RocketPy/pull/1146) [#1145](https://github.com/RocketPy-Team/RocketPy/issues/1145) +- BUG: Evaluate parachute triggers once per time node [#1121](https://github.com/RocketPy-Team/RocketPy/pull/1121) [#1086](https://github.com/RocketPy-Team/RocketPy/issues/1086) +- DOC: Fix the RST indentation in the `dict_generator` notes [`772480d`](https://github.com/RocketPy-Team/RocketPy/commit/772480d6bc69e5b5c2f517305e974c65ca781e57) +- BUG: Stop `dict_generator` from sampling `initial_solution` [#1122](https://github.com/RocketPy-Team/RocketPy/pull/1122) [#1109](https://github.com/RocketPy-Team/RocketPy/issues/1109) +- BUG: Report missing impact roots explicitly [#1148](https://github.com/RocketPy-Team/RocketPy/pull/1148) [#1147](https://github.com/RocketPy-Team/RocketPy/issues/1147) +- DOC: Correct the `Flight` aerodynamic moment units [#1149](https://github.com/RocketPy-Team/RocketPy/pull/1149) +- BUG: Seed the parachute pressure noise with a per-instance RNG [#1134](https://github.com/RocketPy-Team/RocketPy/pull/1134) [#1091](https://github.com/RocketPy-Team/RocketPy/issues/1091) +- BUG: Restore the UTC fallback without timezonefinder [#1143](https://github.com/RocketPy-Team/RocketPy/pull/1143) +- BUG: Pick between the candidate values of a list input with the stochastic model's own seeded generator. `random.choice` was used, which draws from the interpreter-wide stream that `_set_stochastic` does not reseed, so a fixed seed did not reproduce the values chosen from a list, and Monte Carlo workers forked from one process walked a single shared stream instead of sampling independently. Fixed-seed baselines that vary a list input change. [#953](https://github.com/RocketPy-Team/RocketPy/issues/953) +- BUG: Report the atmospheric model time period and ensemble member count for lower-case model types. `set_atmospheric_model` documents `type` as case-insensitive, but `Environment.info()` and `all_info()` compared against capitalised literals, so `type="ensemble"` printed no time period and no member count, and skipped the ensemble comparison plot. [#520](https://github.com/RocketPy-Team/RocketPy/issues/520) +- BUG: Give each `CustomSampler` input its own deterministic stream, and seed samplers sharing one generator once as a group. Existing fixed-seed `CustomSampler` baselines change, and samplers built on the legacy `RandomState` must move to `default_rng` because seeds now carry the full 128 bits. [#1102](https://github.com/RocketPy-Team/RocketPy/pull/1102) +- BUG: Accept the callable parachute triggers `StochasticParachute` documents, and reject the ones it cannot mean. An invalid string, an empty list or a boolean now fails during validation instead of reaching `Parachute` or becoming a one-metre height trigger. [#1103](https://github.com/RocketPy-Team/RocketPy/pull/1103) +- BUG: Accept a deterministic aerodynamic surface in `StochasticRocket.add_nose`, `add_trapezoidal_fins`, `add_elliptical_fins` and `add_tail`. Each wrapped the surface with a `component=` keyword none of the stochastic classes accept, so passing anything other than an already-stochastic surface raised a `TypeError`. [#953](https://github.com/RocketPy-Team/RocketPy/issues/953) +- BUG: rocket with a late-starting thrust curve never leaves the rail [#1085](https://github.com/RocketPy-Team/RocketPy/pull/1085) + ## [v1.13.0] - 2026-07-21 ### Added diff --git a/README.md b/README.md index 8315dd78d..eff904d11 100644 --- a/README.md +++ b/README.md @@ -170,10 +170,10 @@ env = Environment( tomorrow = datetime.date.today() + datetime.timedelta(days=1) env.set_date( - (tomorrow.year, tomorrow.month, tomorrow.day, 12), timezone="America/Denver" -) # Tomorrow's date in year, month, day, hour UTC format + (tomorrow.year, tomorrow.month, tomorrow.day, 12), timezone="America/Denver" +) # Tomorrow's date in year, month, day, hour UTC format -env.set_atmospheric_model(type='Forecast', file='GFS') +env.set_atmospheric_model(type="Forecast", file="GFS") ``` This can be followed up by starting a Solid Motor object. To get help on it, just use: @@ -233,9 +233,7 @@ buttons = calisto.set_rail_buttons( calisto.add_motor(Pro75M1670, position=-1.255) -nose = calisto.add_nose( - length=0.55829, kind="vonKarman", position=1.278 -) +nose = calisto.add_nose(length=0.55829, kind="vonKarman", position=1.278) fins = calisto.add_trapezoidal_fins( n=4, @@ -290,7 +288,7 @@ To actually create a Flight object, use: ```python test_flight = Flight( - rocket=calisto, environment=env, rail_length=5.2, inclination=85, heading=0 + rocket=calisto, environment=env, rail_length=5.2, inclination=85, heading=0 ) ``` diff --git a/data/rockets/ast_her/README.md b/data/rockets/ast_her/README.md new file mode 100644 index 000000000..5c022e2d9 --- /dev/null +++ b/data/rockets/ast_her/README.md @@ -0,0 +1,23 @@ +# Asteria AST-HER DATCOM example inputs + +**Flight status: Asteria / AST-HER has not flown.** All Asteria apogees and trajectories in this example are simulated predictions, not flight measurements or flight validation. + +Snapshot from the local `Asteria-Flight-Dynamics` repository on 2026-10-21. +`configuration.json` merges its current input and visualization JSON files; +the provenance section records source paths and SHA-256 hashes of data files. + +The configured `input/thrust-curves/opt-ThrustCurve.csv` does not exist, so +`thrust_curve.csv` copies its existing `Optimized_ThrustCurve.csv`. The saved +atmosphere is for 2026-10-20 15:30 UTC. No network request is necessary. + +See `docs/examples/datcom/asteria_comparison.ipynb` for the executed comparison +of ordinary RocketPy with native DATCOM. Both retain these motor/tank mass and +inertia inputs and the saved atmosphere. Asteria's supplied ordinary drag curve +is snapshotted in `docs/examples/datcom/comparison_data/asteria_drag_curve.csv` +with its hash recorded. The configured ogive tail is native-DATCOM only; +ordinary RocketPy uses an explicitly noted conical-tail approximation. + +The native executable is user supplied. DAMP, powered nozzle-annulus/coast +full-base drag and adaptive Mach/alpha/Reynolds schedules are used. No Asteria +hold-down clamp, PROTUB or recovery model is included. 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+ ], + "outputs": [ + "wind_velocity_y (m/s)" + ], + "interpolation": "spline", + "extrapolation": "constant", + "title": "Wind Velocity Y Profile", + "signature": { + "module": "rocketpy.mathutils.function", + "name": "Function" + } + }, + "gravity": { + "source": [ + [ + 136.80293813362803, + 9.80622876725276 + ], + [ + 572.5514612155088, + 9.804887225878863 + ], + [ + 1024.9649066367328, + 9.803494669384405 + ], + [ + 1997.626299887351, + 9.800501761984199 + ], + [ + 3087.796155669465, + 9.79714890626424 + ], + [ + 4321.630155412766, + 9.793356281621554 + ], + [ + 5736.06091381566, + 9.789011237805909 + ], + [ + 7406.10131086553, + 9.783884698700536 + ], + [ + 9443.981599141402, + 9.777634451481962 + ], + [ + 12121.724655689648, + 9.769430824875885 + ] + ], + "inputs": [ + "Height Above Sea Level (m)" + ], + "outputs": [ + "gravity (m/s^2)" + ], + "interpolation": "spline", + "extrapolation": "constant", + "title": "Gravity Profile", + "signature": { + "module": "rocketpy.mathutils.function", + "name": "Function" + } + }, + "pressure": { + "source": [ + [ + 136.80293813362803, + 100000 + ], + [ + 572.5514612155088, + 95000 + ], + [ + 1024.9649066367328, + 90000 + ], + [ + 1997.626299887351, + 80000 + ], + [ + 3087.796155669465, + 70000 + ], + [ + 4321.630155412766, + 60000 + ], + [ + 5736.06091381566, + 50000 + ], + [ + 7406.10131086553, + 40000 + ], + [ + 9443.981599141402, + 30000 + ], + [ + 12121.724655689648, + 20000 + ] + ], + "inputs": [ + "Height Above Sea Level (m)" + ], + "outputs": [ + "pressure (Pa)" + ], + "interpolation": "spline", + "extrapolation": "constant", + "title": "Pressure Profile", + "signature": { + "module": "rocketpy.mathutils.function", + "name": "Function" + } + } +} \ No newline at end of file diff --git a/data/rockets/ast_her/configuration.json b/data/rockets/ast_her/configuration.json new file mode 100644 index 000000000..dfe224df3 --- /dev/null +++ b/data/rockets/ast_her/configuration.json @@ -0,0 +1,171 @@ +{ + "rocket": { + "center_of_mass_without_motor": 3.16, + "center_of_mass_with_motor": 3.27, + "coordinate_system_orientation": "nose_to_tail", + "drag_curve": "input_data/drag_curve.CSV", + "inertia": [ + 81.7, + 82.7, + 0.237 + ], + "radius": 0.0895, + "rocket_dry_mass": 47.25, + "rocket_fuel_volume_for_mass_and_xle": 26.2, + "rocket_length_without_boattail_and_nosecone": 4.329, + "mass-cost-kg-per-liter-fuel": 0.36, + "extra_length_meter_per_liter_fuel": 0.0445 + }, + "motors": { + "position": 5.262, + "clamp_force": 2000, + "clamp_force_mode": "fixed", + "max_impulse": 40960, + "lox_density": 1141, + "lox_density_gas": 1.429, + "eth_density": 789, + "eth_density_gas": 1.59, + "pressurant_density_300bar": 303, + "pressurant_density_40bar_cold": 95, + "pressurant_density_60bar": 60, + "ullage_volume": 0.2, + "pressurant_tank_volume": 0.0068, + "fuel_tank_volume": 0.0128, + "oxidizer_tank_volume": 0.0131, + "maximum_thrust": 2700, + "minimum_thrust": 1750, + "throttle_rate": 800, + "throttle_rate_up": 1000, + "of": 1.5, + "reference_pressure": 101325, + "thrust_source": "input/thrust-curves/opt-ThrustCurve.csv", + "isp_degradation_factor": 0.74, + "tank_optimization": false, + "ramp_up_time": 1.5, + "tank_radius": 0.0845, + "prnt_tank_radius": 0.0775, + "tank_spherical_caps": false, + "numeric_stab_mass": 0.18, + "lox_tank_height": 0.52, + "lox_tank_position": 1.07, + "eth_tank_height": 0.62, + "eth_tank_position": 2.09, + "pressure_tank_height": 0.50051981, + "pressure_tank_position": 3.05, + "nozzle_exit_radius": 0.0413, + "nozzle_position": 0.0, + "motor_dry_inertia": [ + 3900, + 3970, + 9304 + ], + "casing_diameter": 100, + "center_of_dry_mass_position": 0, + "coordinate_system_orientation": "nozzle_to_combustion_chamber", + "dry_inertia_lxx": 65.035, + "dry_inertia_lyy": 64.931, + "dry_inertia_lzz": 0.744, + "dry_mass": 0, + "nozzle_radius": 0.051115125, + "throat_radius": 0.034076749999999996 + }, + "nosecones": { + "base_radius": 0.0895, + "kind": "Von Karman", + "length": 0.81, + "name": "Nose Cone with tip", + "position": 0.0 + }, + "fins": { + "SECTYP": "HEX", + "XLE": [ + 4.37, + 4.652 + ], + "SSPAN": [ + 0.0895, + 0.235 + ], + "CHORD": [ + 0.53, + 0.2 + ], + "ZUPPER": [ + 0.005622641509433962, + 0.01192 + ], + "LMAXU": [ + 0.023962264150943394, + 0.0508 + ], + "LFLATU": [ + 0.9041509433962265, + 0.7968 + ], + "cant_angle": 0.0, + "NPANEL": 4, + "ZLOWER": [ + 0.0, + 0.0149 + ], + "LER": 0.0025, + "position": 4.37 + }, + "tails": { + "DAFT": 0.167, + "LAFT": 0.125, + "position": 5.137 + }, + "rail_buttons": { + "angular_position": 0.0, + "lower_position": 4.3, + "name": "Rail Button", + "upper_position": 2.4 + }, + "flight": { + "minimum_rail_exit_velocity": 31.8, + "velocity_safety_factor": 1.01, + "heading": 144.0, + "inclination": 75.0, + "rail_length": 15, + "apogee_expectation": 850 + }, + "environment": { + "timezone": "Europe/Lisbon", + "atmospheric_model": "Forecast", + "weather_file": "train-weather-data/EuroC_pressure_levels_reanalysis_2021-2024.nc", + "weather_dictionary": "ECMWF", + "year": 2026, + "month": 10, + "day": 20, + "hour": 15, + "minute": 30, + "forecast_file": "input/forecast_sweep/environment_20261020T1530Z.json", + "elevation": 160.0, + "latitude": 39.3897, + "longitude": -8.288964, + "max_height": 2600 + }, + "provenance": { + "repository": "Asteria-Flight-Dynamics", + "snapshot_date": "2026-10-21", + "configuration_files": [ + "input/input_parameters.json", + "input/input_visualization_parameters.json" + ], + "data_files": { + "thrust_curve.csv": "input/thrust-curves/Optimized_ThrustCurve.csv", + "performance.csv": "input/throttle_performance_MARK_VI_b.csv", + "atmosphere.json": "input/forecast_sweep/environment_20261020T1530Z.json", + "flight_conditions.json": "input/datcom_flight_conditions.json" + }, + "sha256": { + "thrust_curve.csv": "610081e56c5c465daf7a69b2f9ea947a0d7e4869520436b5b6ecba7b342fb08b", + "performance.csv": "bd24c698dae2f932a33e8192b1b6e3ee1836b4d278ca84af7bc3d49ede6d9e28", + "atmosphere.json": "4102eabfe54ca1929d95237c2c08f8dd0daf4b2247035d093f2008a3217f8be1", + "flight_conditions.json": "cfbfc05db1fec48c23134b077be5c2997559e2a7121fe0d1e2ab9e35aea0ec06" + }, + "thrust_note": "Configured input/thrust-curves/opt-ThrustCurve.csv is absent; use existing Optimized_ThrustCurve.csv. No thrust optimization is performed.", + "simulation_note": "Native RocketPy Flight; Asteria hold-down clamp and PROTUB aerocover are excluded. Configured dry mass and dry inertias are preserved, including nonzero motor inertia with zero motor dry mass." + } +} diff --git a/data/rockets/ast_her/flight_conditions.json b/data/rockets/ast_her/flight_conditions.json new file mode 100644 index 000000000..907fec340 --- /dev/null +++ b/data/rockets/ast_her/flight_conditions.json @@ -0,0 +1,71 @@ +{ + "source": "Zero-clamp replay of opt-out/ga_best_params.json using configured forecast and saved aerodynamic tables", + "altitude_reference": "metres above sea level", + "unreached_mach_policy": "Use burnout altitude above the maximum simulated Mach; these points are outside this trajectory.", + "trajectory": { + "burnout_s": 7.594817071602963, + "apogee_asl_m": 3059.248509712067, + "apogee_time_s": 27.877298335405182, + "release_s": 0.34044468170422326 + }, + "mach": [ + 0.1, + 0.2, + 0.3, + 0.4, + 0.5, + 0.6, + 0.7, + 0.8, + 0.9, + 1.0, + 1.1, + 1.2, + 1.3, + 1.4, + 1.5, + 1.6 + ], + "power_on": { + "altitude_m": [ + 132, + 182, + 263, + 373, + 511, + 676, + 866, + 895, + 895, + 895, + 895, + 895, + 895, + 895, + 895, + 895 + ], + "max_simulated_mach": 0.7140579159791377 + }, + "power_off": { + "altitude_m": [ + 3043, + 2884, + 2626, + 2287, + 1884, + 1439, + 966, + 895, + 895, + 895, + 895, + 895, + 895, + 895, + 895, + 895 + ], + "max_simulated_mach": 0.7140579159791377 + } +} diff --git a/data/rockets/ast_her/performance.csv b/data/rockets/ast_her/performance.csv new file mode 100644 index 000000000..5bb13f17b --- /dev/null +++ b/data/rockets/ast_her/performance.csv @@ -0,0 +1,14 @@ +Throttle_value,Mass_flow_rate_kg_per_s,Pressure_MPa,Specific_impulse_sea_level_Ns_per_kg,Specific_impulse_sea_level_s,Thrust_sea_level_kN,Specific_impulse_optimum_expansion_Ns_per_kg,Specific_impulse_optimum_expansion_s,Thrust_optimum_expansion_kN,Specific_impulse_vacuum_Ns_per_kg,Specific_impulse_vacuum_s,Thrust_vacuum_kN +0.4300,0.7045,1.1079,1647.612,168.0097,1.1607,2182.639,222.5673,1.5376,2592.997,264.4121,1.8267 +0.5070,0.8306,1.3087,1776.245,181.1267,1.4754,2187.418,223.0546,1.8169,2597.330,264.8540,2.1573 +0.5840,0.9567,1.5097,1871.479,190.8378,1.7905,2191.537,223.4746,2.0968,2601.029,265.2312,2.4885 +0.6610,1.0829,1.7111,1945.086,198.3436,2.1063,2194.951,223.8227,2.3769,2604.093,265.5436,2.8200 +0.7380,1.2090,1.9126,2003.609,204.3113,2.4224,2197.992,224.1329,2.6575,2606.802,265.8199,3.1517 +0.8150,1.3352,2.1144,2051.223,209.1666,2.7388,2200.898,224.4291,2.9386,2609.357,266.0804,3.4840 +0.8920,1.4613,2.3164,2090.868,213.2093,3.0555,2203.445,224.6889,3.2200,2611.592,266.3083,3.8164 +0.9690,1.5875,2.5186,2124.358,216.6243,3.3724,2205.854,224.9346,3.5018,2613.689,266.5222,4.1492 +1.0000,1.6383,2.6000,2136.437,217.8560,3.5001,2206.746,225.0255,3.6152,2614.464,266.6012,4.2832 +1.0460,1.7136,2.7209,2153.083,219.5534,3.6896,2208.018,225.1552,3.7837,2615.567,266.7137,4.4821 +1.1230,1.8398,2.9233,2177.981,222.0923,4.0070,2210.092,225.3667,4.0661,2617.355,266.8960,4.8153 +1.2000,1.9659,3.1259,2199.804,224.3176,4.3246,2211.969,225.5581,4.3486,2618.971,267.0607,5.1487 + diff --git a/data/rockets/ast_her/thrust_curve.csv b/data/rockets/ast_her/thrust_curve.csv new file mode 100644 index 000000000..5294d94a2 --- /dev/null +++ b/data/rockets/ast_her/thrust_curve.csv @@ -0,0 +1,12 @@ +0.000000000,0.000000000 +0.100000000,270.000000000 +0.200000000,540.000000000 +0.300000000,810.000000000 +0.400000000,1080.000000000 +0.500000000,1350.000000000 +0.600000000,1620.000000000 +0.700000000,1890.000000000 +0.800000000,2160.000000000 +0.900000000,2430.000000000 +1.000000000,2700.000000000 +4.000000000,2700.000000000 diff --git a/docker/Dockerfile b/docker/Dockerfile index 34553e2ed..4c4065d91 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -1,7 +1,8 @@ # Set base image -# python:latest will get the latest version of python, on linux +# python:3.14 is the newest version the test matrix covers, and is what the +# python:latest tag resolves to today. Pinning keeps the two from drifting apart. # Get a full list of python images here: https://hub.docker.com/_/python/tags -FROM python:latest +FROM python:3.14 # set the working directory in the container WORKDIR /RocketPy diff --git a/docker/docker-compose.yml b/docker/docker-compose.yml index 244b9dcff..17918c487 100644 --- a/docker/docker-compose.yml +++ b/docker/docker-compose.yml @@ -1,10 +1,10 @@ version: '3.8' services: - python39-linux: - image: python:3.9 + python310-linux: + image: python:3.10 volumes: - - .:/app + - ..:/app working_dir: /app command: bash -c "pip install . && pip install -r requirements-tests.txt && pytest && cd rocketpy && pytest --doctest-modules" logging: @@ -15,7 +15,7 @@ services: python312-linux: image: python:3.12 volumes: - - .:/app + - ..:/app working_dir: /app command: bash -c "pip install . && pip install -r requirements-tests.txt && pytest && cd rocketpy && pytest --doctest-modules" logging: diff --git a/docs/development/docker.rst b/docs/development/docker.rst index bc1de8745..be36ada77 100644 --- a/docs/development/docker.rst +++ b/docs/development/docker.rst @@ -37,11 +37,13 @@ Before you start, you need to install on your machine: Build the image ---------------- -To build the image, run the following command on your terminal: +To build the image, run the following command from the root of the repository +(the ``Dockerfile`` copies ``requirements.txt``, so the build context has to +be the repository, not the ``docker`` folder): .. code-block:: console - docker build -t rocketpy-image -f Dockerfile . + docker build -t rocketpy-image -f docker/Dockerfile . This will build the image and tag it as ``rocketpy-image`` (you can apply another @@ -108,10 +110,11 @@ operational system. However, it is still useful to run the unit tests on different python versions. Currently, the ``docker-compose.yml`` file is configured to run the unit tests -on python 3.9 and 3.12. +on python 3.10 and 3.12. To run the unit tests on both python versions, run the following command -**on your machine**: +**on your machine**, from inside the ``docker`` folder (the services mount +the repository root, one level up, as ``/app``): .. code-block:: console @@ -155,7 +158,7 @@ For example, to use a Windows-based image, you might change: .. code-block:: Dockerfile - FROM python:latest + FROM python:3.14 to diff --git a/docs/development/first_pr.rst b/docs/development/first_pr.rst index c0b07e7f1..64a4f6dec 100644 --- a/docs/development/first_pr.rst +++ b/docs/development/first_pr.rst @@ -95,14 +95,19 @@ Please correct any issues that may arise from the CI checks. The CHANGELOG file ------------------ -We keep track of the changes in the ``CHANGELOG.md`` file. -When you open a PR, you should see the "Unreleased" section of the file. -An entry will simply contain the title of your PR if merged. +We keep track of the changes in the ``CHANGELOG.md`` file, but **you do not +need to edit it yourself**. When you open a PR you will see the "Unreleased" +section of the file; you can leave it as is. .. note:: - The CHANGELOG is auto-updated once a PR is merged based on the associated labels, \ - which are assigned by the maintainers. + Once your PR is merged into ``develop``, the + ``.github/workflows/changelog.yml`` workflow uses an LLM (Google Gemini) to + write a well-formatted entry into the "Unreleased" section automatically. It + picks the right subsection (Added, Changed, Fixed, ...) and prefix from your + PR title, labels, and description, and skips duplicates. In practice you only + open the PR -- the maintainers review, label, and merge it, and the changelog + entry is generated and committed for you. The review process ------------------ diff --git a/docs/development/style_guide.rst b/docs/development/style_guide.rst index 15a80e5e4..052217abf 100644 --- a/docs/development/style_guide.rst +++ b/docs/development/style_guide.rst @@ -163,9 +163,10 @@ Pull Requests ^^^^^^^^^^^^^ When opening a Pull Request, the title should be clear and concise. -It should contain only a brief desctiption of the changes without the acronym (e.g. ENH:, BUG:). -The maintainers will label your PR accordingly, which will add a prefix via a workflow to indicate -type of the PR in the CHANGELOG file. +It should contain only a brief description of the changes without the acronym (e.g. ENH:, BUG:). +The maintainers will label your PR accordingly. After the PR is merged, a workflow +uses an LLM (Google Gemini) to add the right prefix and place the entry in the +correct section of the ``CHANGELOG.md`` file automatically. Here is an example of a good PR name: diff --git a/docs/examples/datcom/README.md b/docs/examples/datcom/README.md new file mode 100644 index 000000000..6e9251139 --- /dev/null +++ b/docs/examples/datcom/README.md @@ -0,0 +1,41 @@ +# DATCOM comparisons + +**Work in progress:** DATCOM currently produces poor flight predictions for several comparison rockets. The discrepancies are unresolved and are being investigated; these results should not be treated as validated predictions. + +Only two example notebooks are included: + +- [Recorded rockets](rocket_comparison.ipynb): DATCOM, ordinary RocketPy and + recorded altitude, upward vertical velocity, CD and stability margin where + available, with signed percentage errors. Apogee-fitted constant CD is + explicitly inferred calibration, not a measurement or native correction. +- [AST-HER / Asteria](asteria_comparison.ipynb): the same configuration, tanks + and atmosphere using ordinary RocketPy's supplied Asteria drag curve versus + native DATCOM. **AST-HER has not flown.** Percentages are model differences. + +Missing measurements are shown as unavailable. Scalar CD comparisons use Mach +0.6 and zero incidence; static margin uses loaded ignition and Mach zero +(DATCOM uses its lowest positive Mach). NDRT's measured stability conditions +are unspecified. Velocity uses recorded vertical/altimeter channels, never a +numerical derivative presented as a measurement. Fitted CD uses one constant +for powered and coast drag in the ordinary source model; it is not unique. + +Source models and raw flight records stay in their original repository folders. +`comparison_data/` contains reproduction scripts, source hashes, reference +notes, QA masks, fitted residuals, values and model histories. Unspecified +construction assumes carbon fibre with vinyl, without inventing a measured +roughness or fin section. The recorded-rocket DATCOM models retain their +explicit smooth 2% HEX / 5% bevel / sharp-edge assumptions. AST-HER uses its +saved Asteria section inputs. All native runs include DAMP and use adaptive +20-point Mach grids and signed alpha coverage from rail through apogee. + +Reproduce from the repository environment: + +```sh +python docs/examples/datcom/comparison_data/reproduce_rockets.py +python docs/examples/datcom/comparison_data/prepare_comparisons.py +``` + +Set `DATCOM_EXECUTABLE` to a native executable when the sibling Asteria copy is +unavailable. The latter command fits effective CD against the recorded apogees; +those are calibration results, not independent validation. Notebook execution +reads the saved comparison outputs and requires no new native runs. diff --git a/docs/examples/datcom/asteria_comparison.ipynb b/docs/examples/datcom/asteria_comparison.ipynb new file mode 100644 index 000000000..cd7415201 --- /dev/null +++ b/docs/examples/datcom/asteria_comparison.ipynb @@ -0,0 +1,182 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "867cc490", + "metadata": {}, + "source": [ + "# AST-HER: ordinary RocketPy versus native DATCOM\n", + "\n", + "**Work in progress:** DATCOM currently produces poor flight predictions for several comparison rockets. The discrepancies are unresolved and are being investigated; these results should not be treated as validated predictions.\n", + "\n", + "**AST-HER / Asteria has not flown. No measured altitude, velocity, CD or stability margin is available.** Percentages here are `100 × (DATCOM − RocketPy) / |RocketPy|`: model differences, not flight prediction errors.\n", + "\n", + "Both use the saved current Asteria configuration, the same three liquid/pressurant tanks, thrust, mass/inertia, saved pressure/temperature/wind profile and launch conditions. Ordinary RocketPy uses Asteria's supplied `input/drag_curve.CSV`, copied with its hash recorded; this curve is not labelled as a measured wind-tunnel result. Native DATCOM derives its coefficients from the configured section geometry, with DAMP and powered nozzle-annulus/coast full-base drag. Its 20-point Mach and signed alpha grids and actual-atmosphere Reynolds schedules adapt from the ordinary flight, then twice from DATCOM flights. Scope is rail through apogee, without Asteria's hold-down clamp or PROTUB.\n", + "\n", + "The configured ogive boattail is used by DATCOM. Ordinary RocketPy retains its conical-tail approximation because ogive tails are DATCOM-only; this geometric/model difference is explicit and contributes to the stability comparison. Nose, fin planform and mass/CG histories otherwise match. Unspecified construction is carbon fibre with vinyl; no numerical roughness is inferred from that label. Native AST-HER fin upper/lower thickness, bevel and leading-edge radius use the saved Asteria inputs.\n", + "\n", + "The table compares apogee AGL, peak upward vertical velocity, loaded ignition Mach-zero static margin (DATCOM uses its lowest positive Mach), and zero-incidence powered/coast CD at Mach 0.6. Stability curves use each trajectory's actual Mach and CG. This comparison does not establish which model is more accurate.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "04b4d181", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-22T22:26:57.241980Z", + "iopub.status.busy": "2026-10-22T22:26:57.241736Z", + "iopub.status.idle": "2026-10-22T22:26:58.286568Z", + "shell.execute_reply": "2026-10-22T22:26:58.286233Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 metricunitmeasuredrocketpydatcomdatcom_vs_rocketpy_pct
0Apogee AGLm—1030.1111042.7071.223
1Peak upward vertical velocitym/s—132.974133.0890.086
2Initial static margincal—2.0933.85784.287
3Powered CD at Mach 0.61—0.5440.420-22.774
4Coast CD at Mach 0.61—0.5440.447-17.809
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pathlib import Path\n", + "import json\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from IPython.display import display\n", + "ROOT = next(p for p in (Path.cwd(), *Path.cwd().parents) if (p / \"rocketpy\").is_dir())\n", + "DATA = ROOT / \"docs/examples/datcom/comparison_data\"\n", + "plt.rcParams.update({\"axes.grid\": True, \"grid.alpha\": .2})\n", + "def show(frame):\n", + " display(frame.style.format(precision=3, na_rep=\"—\"))\n", + "metrics = pd.read_csv(DATA / \"asteria_metrics.csv\")\n", + "show(metrics[[\"metric\",\"unit\",\"measured\",\"rocketpy\",\"datcom\",\"datcom_vs_rocketpy_pct\"]])\n", + "provenance = json.loads((DATA / \"asteria_provenance.json\").read_text())\n", + "assert provenance[\"flight_status\"] == \"AST-HER has not flown\" and provenance[\"mass_cg_match\"]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "26068b7a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-22T22:26:58.287917Z", + "iopub.status.busy": "2026-10-22T22:26:58.287779Z", + "iopub.status.idle": "2026-10-22T22:26:58.543346Z", + "shell.execute_reply": "2026-10-22T22:26:58.543015Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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uhcd8EYPBQJMmTVi3bh2ff/45u3fv5ujRo8Y5c2JjYzN13Pv37+Pl5WVSvAHw8PDAwsIi1fBQV1fXVMewsrJ64fnv3r2Lr69vlsbyst8HT0/PVDF4eXkZYwE4evQoTZo0AeCHH37gl19+4dixYwwfPhxInfdnzfH2tKFDh/L1119z5MgRmjVrhqurK40aNeL48eMAREZGoqpqmsfz9vY2iTHF098bKyurNGPMiPR8v6dOncqHH35IjRo1WLt2LUeOHOHYsWO8/vrrJu3eeecdFixYwLVr12jXrh0eHh7UqFHDZFhaRmJ5+vU9b549b29vDAYDkZGR6TpXZt2/fx8LC4tUq4oqioKXl5fJ9yw4OJgjR47w+PFjdu3aRcOGDXF1daVKlSrs2rWL8PBwwsPD0yxGZcf3WgghRP4hq+kJIYQwsWDBAlRVZc2aNaxZsybV/kWLFjFu3DiTT/+fVq9ePZo0acKGDRu4c+cOHh4eLzxvy5YtOXbs2EvF/jIqVqxoXE2vcePGNG3alO+//55evXpRrVq1Zz7vZeJOOd+zlC9fnhUrVqCqKmfOnCE0NJSxY8diY2PDkCFDMnXOF0kp7D09GXd65gM7d+4cp0+fJjQ0lG7duhm3Pz0xd0a5urry66+/oqqqSRHozp07JCUlvTCP6eXu7m4yObw5Y0mR1gT6ERERxlggeZ4jnU7Hli1bTAqzGzZsSPOYTxfSnsXCwoIBAwYwYMAAHj58yK5duxg2bBhNmzbl77//pkCBAmg0GuN8Wv+VMil5Vucjs5YuXUpQUBBz5swx2Z7W/Gs9evSgR48ePH78mAMHDjBq1ChCQkK4dOmSsZdfZqV8z56VM41GQ4ECBV7qHOmJISkpibt375oUpFRVJSIiwuT3XaNGjRgxYgQHDhxg9+7djBo1yrh9586d+Pv7G78WQgghMkJ6RgkhhDDS6/UsWrSIYsWKsXfv3lSPgQMHcuvWLbZt2wYk3yinNdRMr9dz+fJlbG1tcXZ2Tte5XV1dU/VgMhdFUZg1axZardY4KfCzpBV3eh9FihRJdzwVK1bk22+/xdnZmZMnT2bBq0ybp6cn1tbWnDlzxmT7xo0bX/jclCJHSg+IFN99912qthnpJdGoUSNiYmJSFVcWL15s3J8VmjVrxqVLl4zDmMwZS4pHjx6xadMmk23Lli1Do9FQr149IDnvFhYWJgXi2NhYlixZkmVxODs70759ez766CMePHjA1atXsbOzo0aNGqxbt87k+2gwGFi6dCm+vr5mWwDgaYqipHpfnjlzxmQFu6fZ2dnRrFkzhg8fTkJCAufPn3/pOEqVKoWPjw/Lli0zmaz+8ePHrF271rjCHmRfT6KU9+jSpUtNtq9du5bHjx+bvIerV6+Oo6Mj06ZNIyIigsaNGwPJPaZ+++03Vq1aRdmyZY094YQQQoj0kp5RQgghjLZt28bNmzeZNGkSQUFBqfYHBAQwc+ZM5s+fT0hICEuWLOG7776jS5cuVKtWDScnJ27cuMG8efM4f/48I0eONA4/Mofbt2+nuay6o6MjZcuWfe5zS5Qowfvvv8/s2bM5ePAgdevWza4w07RlyxZmz57NG2+8QdGiRVFVlXXr1vHw4UPjDWF2UBSFt99+mwULFlCsWDEqVqzI0aNHWbZs2QufW7p0aYoVK8aQIUNQVRUXFxc2b96c5hCn8uXLA/C///2Pbt26odPpKFWqlMlcTym6du3KrFmz6NatG1evXqV8+fIcPHiQ8ePH07x58+fO8ZQR/fr1Y+XKlbRu3ZohQ4ZQvXp1YmNj2b9/PyEhITRo0CDHYknh6urKhx9+yPXr1ylZsiRbt27lhx9+4MMPPzTOk9SiRQumTp1Kly5deP/997l//z5ff/11quJLRrVs2ZKAgACqVq2Ku7s7165dY9q0aRQuXJgSJUoAMGHCBBo3bkyDBg0YNGgQlpaWzJ49m3PnzrF8+fJ098L6r9DQUHr06MHChQtTzWuUWSEhIXz55ZeMGjWK+vXrc/HiRcaOHYu/v7/J/HTvvfceNjY21KlTh4IFCxIREcGECRNwcnJ6bg/J9NJoNEyePJm33nqLkJAQevfuTXx8PFOmTOHhw4dMnDjR2DYjPyMZkdLzc/DgwURHR1OnTh3OnDnDqFGjqFSpEu+8846xrVarpX79+mzevBl/f3/j/Hx16tTBysqK3bt307dv35eKRwghxKtJilFCCCGM5s+fj6WlJT169Ehzv5ubG23atGHNmjXcvn2bFi1aEBERwdatW5kzZw6RkZE4ODhQoUIFlixZkmrJ9pz2rKGGderUMU6++zyjRo1i8eLFjBw58rm9ZbJDiRIlcHZ2ZvLkydy8eRNLS0tKlSqVaghcdvjmm28AmDx5MjExMTRs2JAtW7a8sCeXTqdj8+bNfPrpp/Tu3RsLCwuCg4PZtWuXyQTTAEFBQQwdOpRFixbxww8/YDAY2Lt3b5pFUGtra/bu3cvw4cOZMmUKd+/excfHh0GDBhmHDWUFBwcHDh48yOjRo/n+++8ZM2YMBQoUoFq1arz//vs5GksKLy8vZs2axaBBgzh79iwuLi4MGzaMMWPGGNs0bNiQBQsWMGnSJFq2bImPjw/vvfceHh4e9OrVK9PnbtCgAWvXrmXevHlER0fj5eVF48aNGTFihHFOt/r167Nnzx5GjRpF9+7dMRgMVKxYkU2bNqWaRD+9UibTT+/cVukxfPhwnjx5wvz585k8eTJly5Zl7ty5rF+/nn379hnbvfbaa4SGhrJq1SoiIyNxc3Ojbt26LF68ONUcS5nVpUsX7OzsmDBhAm+++SZarZaaNWuyd+9eateubWyXkZ+RjFAUhQ0bNjB69GgWLlzIV199hZubG++88w7jx49PVcQMDg5m8+bNJoVWKysr6tatS1hYWJYXYIUQQrwaFPW/fYSFEEIIIUSuEBQUxL179zh37py5Q8lRHTt2JDw83KxzyAkhhBAie0nPKCGEEEIIkSuoqsq+fftSzWckhBBCiPxFilFCCCGEECJXUBSFO3fumDsMIYQQQmQzGaYnhBBCCCGEEEIIIXKMxtwBCCGEEEIIIYQQQohXhxSjhBBCCCGEEEIIIUSOkWKUEEIIIYQQQgghhMgxUowSQgghhBBCCCGEEDlGilFCCCGEEEIIIYQQIsdIMUoIIYQQQgghhBBC5BgpRgkhhBBCCCGEEEKIHCPFKCGEEEIIIYQQQgiRY6QYJYQQQgghhBBCCCFyjBSjhBBCCCGEEEIIIUSOkWKUEEIIIYQQQgghhMgxUowSQgghhBBCCCGEEDlGilFCCCGEEEIIIYQQIsdIMUoIIYQQQgghhBBC5BgpRgkhhBBCCCGEEEKIHCPFKCFEnjN9+nQURSEgICDN/YqiMHr0aOPXFy5cYPTo0Vy9ejVV2+7du1OkSBGTbePHj2fDhg1ZF/B/FClShO7du2fLsYUQQgiR89q0aYONjQ0PHz58Zpu33noLnU7H7du3X/p8N2/eZPTo0Zw6dSrVvtGjR6Moykuf41lCQ0NRFCXNa6rstG/fPhRFYd++fTl2/K1bt5pcTwohspYUo4QQec6CBQsAOH/+PL/++usL21+4cIExY8akeeE0YsQI1q9fb7ItO4tRQgghhMhfevXqRVxcHMuWLUtzf1RUFOvXryckJARPT8+XPt/NmzcZM2ZMmsWod999l8OHD7/0OV41lStX5vDhw1SuXNm4bevWrYwZM8aMUQmRv0kxSgiRpxw/fpzTp0/TokULAObPn/9SxytWrBiVKlXKitCEEEII8Qpq1qwZ3t7exg/LnrZ8+XJiY2Pp1avXS51Hr9cTHx//3Da+vr7UrFnzpc7zKnJ0dKRmzZo4OjqaOxQhXhlSjBJC5CkpxaeJEydSu3ZtVqxYwZMnT57ZPjQ0lA4dOgDQoEEDFEVBURRCQ0OB1MP0FEXh8ePHLFq0yNg2KCgIeHbX97S6rCcmJvL555/j5eWFra0tdevW5ejRo2nGGBERQe/evfH19cXS0hJ/f3/GjBlDUlJSBjIjhBBCCHPQarV069aNEydOcPbs2VT7Fy5cSMGCBWnWrBmQvr/7V69eRVEUJk+ezLhx4/D398fKyoq9e/dSrVo1AHr06GG8VkkZTvasa5Vly5ZRq1Yt7O3tsbe3JzAw0OQDvbCwMFq3bo2vry/W1tYUL16c3r17c+/evQznY8OGDSiKwu7du1PtmzNnDoqicObMGeO248eP06pVK1xcXLC2tqZSpUqsWrUqXefatGkTtWrVwtbWFgcHBxo3bpxmz7A//viDzp074+npiZWVFYUKFaJr167G4t7Tw/S6d+/OrFmzAIw5TrnWa9SoEaVLl0ZVVZNzqKpK8eLFjR+YCiGeT4pRQog8IzY2luXLl1OtWjUCAgLo2bMnjx49YvXq1c98TosWLRg/fjwAs2bN4vDhwxw+fPiZFwqHDx/GxsaG5s2bG9vOnj07w7G+9957fP3113Tt2pWNGzfSrl072rZtS2RkpEm7iIgIqlevzo4dOxg5ciTbtm2jV69eTJgwgffeey/D5xVCCCFEzuvZsyeKoqTqHXXhwgWOHj1Kt27d0Gq1Gf67P336dPbs2cPXX3/Ntm3b8Pb2ZuHChQB88cUXxmuVd99995mxjRw5krfeegtvb29CQ0NZv3493bp149q1a8Y2f/75J7Vq1WLOnDns3LmTkSNH8uuvv1K3bl0SExMzlIuQkBA8PDyMcf5XaGgolStXpkKFCgDs3buXOnXq8PDhQ+bOncvGjRsJDAzkzTffNH5w+CzLli2jdevWODo6snz5cubPn09kZCRBQUEcPHjQ2O706dNUq1aNI0eOMHbsWLZt28aECROIj48nISEhzWOPGDGC9u3bAxhzfPjwYQoWLMinn37KxYsXUxXbtm3bxp9//slHH32UkXQJ8epShRAij1i8eLEKqHPnzlVVVVUfPXqk2tvbq6+99ppJO0AdNWqU8evVq1ergLp3795Ux+zWrZtauHBhk212dnZqt27dUrUdNWqUmtavzYULF6qAGh4erqqqqv7+++8qoPbv39+k3Y8//qgCJsfu3bu3am9vr167ds2k7ddff60C6vnz51OdTwghhBC5T/369VU3Nzc1ISHBuG3gwIEqoF66dElV1fT/3Q8PD1cBtVixYibHU1VVPXbsmAqoCxcuTBXD09cqf/31l6rVatW33nor3a/DYDCoiYmJ6rVr11RA3bhxo3Hf09c8zzJgwADVxsZGffjwoXHbhQsXVECdMWOGcVvp0qXVSpUqqYmJiSbPDwkJUQsWLKjq9XpVVVV17969Jtdyer1e9fb2VsuXL29so6rJ14YeHh5q7dq1jdsaNmyoOjs7q3fu3HlmvE8fX1VV9aOPPkrzuk+v16tFixZVW7dubbK9WbNmarFixVSDwfDsxAghjKRnlBAiz5g/fz42NjZ06tQJAHt7ezp06MDPP//M5cuXzRzd/9u7dy+QvHLOf3Xs2BELCwuTbVu2bKFBgwZ4e3uTlJRkfKR05d+/f3/OBC2EEEKIl9KrVy/u3bvHpk2bAEhKSmLp0qW89tprlChRAsj43/1WrVqh0+kyHVNYWBh6vf6FvXXu3LnDBx98gJ+fHxYWFuh0OgoXLgzA77//nuHz9uzZk9jYWFauXGnctnDhQqysrOjSpQsAV65c4Y8//jBeL/03H82bN+fWrVtcvHgxzeNfvHiRmzdv8s4776DR/P8trb29Pe3atePIkSM8efKEJ0+esH//fjp27Ii7u3uGX0daNBoNH3/8MVu2bOH69etAcs+y7du306dPn2xdzVCI/ESKUUKIPOHKlSscOHCAFi1aoKoqDx8+5OHDh8Yu1M+aNNQc7t+/D4CXl5fJdgsLC1xdXU223b59m82bN6PT6Uwe5cqVA8jUXA1CCCGEyHnt27fHycnJODxt69at3L5922Ti8oz+3S9YsOBLxXT37l0geWLzZzEYDDRp0oR169bx+eefs3v3bo4ePcqRI0eA5GkSMqpcuXJUq1bNmAu9Xs/SpUtp3bo1Li4uQHIuAAYNGpQqH3369AGefR2Ucq2VVn68vb0xGAxERkYSGRmJXq9/7uvPjJ49e2JjY8PcuXOB5KkgbGxs6NmzZ5aeR4j8zOLFTYQQwvwWLFiAqqqsWbOGNWvWpNq/aNEixo0bh1arzbYYrK2tAYiPj8fKysq4/ekLpZSCU0REBD4+PsbtSUlJxounFG5ublSoUIGvvvoqzXN6e3tnSexCCCGEyF42NjZ07tyZH374gVu3brFgwQIcHByMC6lAxv/uv2wvm5TeQDdu3MDPzy/NNufOneP06dOEhobSrVs34/YrV6681Ll79OhBnz59+P333/nrr7+4desWPXr0MO53c3MDYOjQobRt2zbNY5QqVSrN7SnXWrdu3Uq17+bNm2g0GgoUKICiKGi1Wm7cuPFSr+VpTk5OdOvWjXnz5jFo0CAWLlxIly5dcHZ2ztLzCJGfSTFKCJHr6fV6Fi1aRLFixZg3b16q/Vu2bOGbb75h27ZthISEpNqfUjhK7yd7VlZWabZNWXXvzJkzxpVsADZv3mzSLmX1vR9//JEqVaoYt69atSrVCnkhISFs3bqVYsWKUaBAgXTFJ4QQQojcqVevXsydO5cpU6awdetWunfvjq2trXF/Vvzdz8h1TZMmTdBqtcyZM4datWql2Sal4PXfD9oAvvvuu0zFl6Jz584MGDCA0NBQ/vrrL3x8fGjSpIlxf6lSpShRogSnT582LjaTXqVKlcLHx4dly5YxaNAg42t4/Pgxa9euNa6wB1C/fn1Wr17NV199ZSyApcd/82xjY5Nqf9++fZk9ezbt27fn4cOHfPzxxxl6DUK86qQYJYTI9bZt28bNmzeZNGmSsdDzXwEBAcycOZP58+enWYwKCAgA4Pvvv8fBwQFra2v8/f1TDZlLUb58efbt28fmzZspWLAgDg4OlCpViubNm+Pi4kKvXr0YO3YsFhYWhIaG8vfff5s8v0yZMrz99ttMmzYNnU5HcHAw586d4+uvv8bR0dGk7dixYwkLC6N27dr07duXUqVKERcXx9WrV9m6dStz587N8q7lQgghhMgeVatWpUKFCkybNg1VVU2G6EHW/N0vVqwYNjY2/Pjjj5QpUwZ7e3u8vb3T7E1dpEgRhg0bxpdffklsbCydO3fGycmJCxcucO/ePcaMGUPp0qUpVqwYQ4YMQVVVXFxc2Lx5M2FhYS+VC2dnZ9q0aUNoaCgPHz5k0KBBJvM7QXLBq1mzZjRt2pTu3bvj4+PDgwcP+P333zl58uQzV0zWaDRMnjyZt956i5CQEHr37k18fDxTpkzh4cOHTJw40dh26tSp1K1blxo1ajBkyBCKFy/O7du32bRpE9999x0ODg5pnqN8+fIATJo0iWbNmqHVaqlQoQKWlpYAlCxZktdff51t27ZRt25dKlas+FL5EuKVY97504UQ4sXeeOMN1dLS8rmroHTq1Em1sLBQIyIiUq2mp6qqOm3aNNXf31/VarUmK9CktZreqVOn1Dp16qi2trYqoNavX9+47+jRo2rt2rVVOzs71cfHRx01apQ6b968VCvLxMfHqwMHDlQ9PDxUa2trtWbNmurhw4fVwoULp1qp7+7du2rfvn1Vf39/VafTqS4uLmqVKlXU4cOHqzExMZnImBBCCCHM5X//+58KqGXLlk1zf3r+7qespjdlypQ0j7F8+XK1dOnSqk6nM7nuedbKv4sXL1arVaumWltbq/b29mqlSpVMVuO7cOGC2rhxY9XBwUEtUKCA2qFDB/X69euprqnSu5peip07d6qAyYqCTzt9+rTasWNH1cPDQ9XpdKqXl5fasGFD4+rJqpr2aneqqqobNmxQa9SooVpbW6t2dnZqo0aN1F9++SXVOS5cuKB26NBBdXV1VS0tLdVChQqp3bt3V+Pi4p55/Pj4ePXdd99V3d3dVUVR0nzdoaGhKqCuWLEiXfkQQvw/RVVVNedLYEIIIYQQQgghRN6VsnLf1atXX2rVQyFeRTJMTwghhBBCCCGESIf4+HhOnjzJ0aNHWb9+PVOnTpVClBCZID2jhBBCCCGEEEKIdLh69Sr+/v44OjrSpUsXZs6cma2rOQuRX0kxSgghhBBCCCGEEELkGM2LmwghhBBCCCGEEEIIkTWkGCWEEEIIIYQQQgghcowUo4QQQgghhBBCCCFEjpHV9NLJYDBw8+ZNHBwcUBTF3OEIIYQQIpupqsqjR4/w9vZGo5HP79JDrpeEEEKIV0tmr5ekGJVON2/exM/Pz9xhCCGEECKH/f333/j6+po7jDxBrpeEEEKIV1NGr5ekGJVODg4OQHKCHR0ds/TYqqoSFRWFk5OTfIqI5ONpkg9Tkg9Tkg9Tkg9Tkg9TGc1HdHQ0fn5+xmsA8WJyvZRzJB+mJB+mJB+mJB+mJB+mJB+mcup6SYpR6ZTyTXB0dMyWiytVVXF0dJQ3P5KPp0k+TEk+TEk+TEk+TEk+TGU2H5K79JPrpZwj+TAl+TAl+TAl+TAl+TAl+TCVU9dLMgGCEEIIIYQQQgghhMgxUowSQgghhBBCCCGEEDlGhullAVVVSUpKQq/XZ/r5CQkJxMXFSbdA0s6HTqdDq9WaOTIhhBBCCCGEELmRXq8nMTExw8+T+3FTz8qHVqvFwsIiy3IkxaiXlJCQwK1bt3jy5MlLHcdgMHD//v0siirvezofiqLg6+uLvb29GaMSQgghhBBCCJHbxMTEcOPGDVRVzdTz5X7c1LPyYWtrS8GCBbG0tHzpc0gx6iUYDAbCw8PRarV4e3tjaWmZqSqhqqro9Xq0Wq1UYkmdD1VVuXv3Ljdu3KBEiRLSQ0oIIYQQQgghBJDcI+rGjRvY2tri7u6e4XtquR83lVY+UnpL3b17l/DwcEqUKIFG83KzPkkx6iUkJCRgMBjw8/PD1tY208eRN7+ptPLh7u7O1atXSUxMlGKUEEIIIYQQQggAEhMTUVUVd3d3bGxsMvx8uR839ax82NjYoNPpuHbtGgkJCVhbW7/UeWQC8yzwshVB8WLyS0EIIYQQQgghxLPIPWP2y8rah1RR8qEiRYpQunRpAgMDKVWqFBMnTnypY507dy5Dz5k2bRp37twxfh0aGoqzszOBgYEEBATQrFkzrl+/numYhBBC5BP6JIiLguhbcO8KRJyDm7/B30fh6kH4cw9c2gG/b4Fz6+D0SvjtRzi5BE4uhhOhcHxh8j4hRK6jf3SXh6c2gyFzi/wIIUReJffkLybD9PKpNWvWEBAQwM2bNylbtiwNGzakevXqOXLuadOmERwcjIeHh3FbcHAwa9asAaB///7079+ftWvX5kg8QgghsknCE3hyD2Ij/308TP437qHptriHkPA49UMfnzVxuJWCgLZZcywhRJaIexhB1Iz6eOojuLq7Mr69fsTC2dvcYQkhRI6Re/Lnk2JUFlJVldjEjH/y8/9jMtUXdi200WVsHKu3tzelSpXi2rVruLi48MEHH3Dnzh00Gg2jR4/mjTfeAODw4cN8/vnnREdHo6oqX375Ja1btzY51vTp01m1ahXr169Hr9fTt29frl69SlxcHG+88QZjx45l7Nix3Lx5k/bt22NtbU1oaGiqmBo3bsznn3/OsWPH6Nq1KxcuXDC+plq1ajFixAiaNGmS7tcohBAiC6kqPL4HUX/Dowh4fAdiUh634fHd5H9j7kLCo6w5p6IFSzuwsAatJWgt/v3XEjT/+b9WBxotKBpASf5XUcDRJ2viEEJkmd9XjqCSPgKAIo9O8nB6LWjzPc7lm5o5MiFEfpaZe/KM3I9D/r8nr127NsOHD6dFixbpfo2ZIcWoLBSbqKfsyB3Zeo4LY5tia5n+b9sff/zBvXv3CAoKIiQkhF69evH+++9z+fJlatasSZUqVbCzs6NNmzasW7eO2rVrYzAYePjwofEYBoOB/v37c/36dcLCwrCxsaFp06YMHz6cevXqkZSUREhICOvXr2fkyJEsWLDAWAUGOHXqlPFYer2e1atXU6VKFapVq4aLiwu7d+8mODiYkydPcu/ePV5//XUMBkNWpUwIIcR/JcVD5LXkYlPUjeRH9D//+fqfjPVY0lqi2rhgsHZCb+lMoqUj8TonYrUOPNE4EKNx4InGnliNLbFYEYs1T7DmMVY8Vq15rFoRq9eSpCbXwQyqiuHff4u52TGgSansy4UQIlskxj2m+K0tAKx0+5jAu5soZbiOYe2b3LzyCd6tRycXloUQIovJPXnW3JM3bZr9HxxIMSqfat++PYqicPHiRb799lusra05deoUvXr1AqBEiRLUrVuXgwcP4ujoSNmyZalduzaQPCmZi4uL8Vg9e/akWrVqrF69Go1Gw+PHj9mzZw+3b982tomJieGPP/54Zjy7du0iMDAQgMqVK/PNN98A8OmnnzJr1iyCg4OZMWMGffr0kYnnhBDiZSU8hsircP9PrG5egCe3IPIveBCeXHBCfe7TVRSSbN2Jtfbkic6VKG0BIjXO3DU4EWFw5J9EB/5OcCA8zpbb8ZY8fpzRDxAMQOy/j2erWrgAAzJ4ZCGE+f2+ZykVeMJN3GnTewx/3+3HloUfEZK4A+/T04m4fgjPHktRHAuaO1QhhMg2efWe/MMPP8yRe3IpRmUhG52WC2MzXkHMyFKSNrr0fYqUUgXdtWsXLVu2JCgoCEi9wkB63mRBQUGEhYVx584dvLy8MBgMKIrCsWPH0Ol06Yrnv+NT/6tt27YMHjyY3377jc2bN/Ptt9+m63hCCCFIHk53949/Hxf//fcSxCQPjVGAtBY4TrKwI8bGm0gLD+5q3LhhcCU8sQCXYp248MSRCNWFxLj0XiL8fyHKQqNgZ2WB/b8POystdlYW2Oi06Cw0WGk1WFr8+9Bq0P37r6WFBguNglajoCgKGgU0ioKHg9VLp0gIkfMsTy8F4IpvG7x1FhTzdsdz0DIWLJhKx4hv8Io8zqP/1cKy4zysSgWbOVohRH6SmXvyjNyPp5wjPfLqPfnUqVPTdbyXZdZi1IEDB5gyZQonTpzg1q1brF+/3jheEpLfFGPGjOH7778nMjKSGjVqMGvWLMqVK2dsEx8fz6BBg1i+fDmxsbE0atSI2bNn4+vra2wTGRlJ37592bRpEwCtWrVixowZODs7Z+nrURQlQ931UiS/+ZV0v/kzIjg4mA8//JCRI0cSGBjIokWL6NGjB3/++Se//PILM2fOxN7ennfffZdDhw6ZdAlMqcR2796d6tWr07BhQ7Zt20bhwoV57bXXmDhxIiNGjADg5s2bGAwGfH19cXR0JCoqKl3xWVhY0Lt3b1q1akW7du1wdnZGVZ//ib0QQrxyYiOTV5q7c8G08PTk/jOfEq9z4r6lD3/jyVWDJ+fj3DgX58Y11ZP7OELMs//eWGgUvOytcLGzpICdjgK2lsn/T/nXzpICtsnbHawtjAUoKwuN9G4V4hUX8ddZSsefQa8q+Ae/b9xub2VBjw8+Y/X2qpQ//ClluI5heXseVvsU52YjZdieECJLZOaePDvvxyFv3pPr9dm/CqpZi1GPHz+mYsWK9OjRg3bt2qXaP3nyZKZOnUpoaCglS5Zk3LhxNG7cmIsXL+Lg4ABAv3792Lx5MytWrMDV1ZWBAwcSEhLCiRMn0GqT/6h16dKFGzdusH37dgDef/993nnnHTZv3pxzL9aMRowYQfHixdm6dSvDhg1j2rRpKIrCvHnz8PPzA2D9+vUMHDiQR48eoSgKX375Ja1atTIeo2PHjtjZ2dGkSRM2b97Mjz/+yIABAyhfvjwA9vb2zJ07F19fX/r27UuPHj2wtbVNc7K0p/Xq1Ythw4bx8ccfZ8vrF0KIPENVk4fRRZyFiDPJ/946A1FpL72rovDQyptrGj8uJBXk5BNPLhu8uap6ERVnn+ZznGx0lHG2wcvRCk9HazwcrfFytMbz3689Ha1xtbNEo5GikhAi4/7e9R1ewBmbalQqUsJkn6IodGzWkF+Lbmfdik9pq4bhfGwakVcPUeCdxSDD9oQQ+ZTck6emqLmkG4qiKCY9o1RVxdvbm379+jF48GAguReUp6cnkyZNonfv3kRFReHu7s6SJUt48803geRqoJ+fH1u3bqVp06b8/vvvlC1bliNHjlCjRg0Ajhw5Qq1atfjjjz8oVSp9E6NGR0fj5OREVFQUjo6OAMTFxREeHo6/vz/W1taZfu0Z7RaY36xatYrvvvuO3bt3A2nnI6tynRepqkpUVBROTk6v5PvjaZIPU5IPU3kqHymFp3+Owz8n4Nbp5OJTbGSazR/ovLii8edMvBdnEwpyRfXlT7UgcZgOZbPWafAtYItfARt8C9jiYatQwtsFPxdb/FxscbROX1fu/Cij74+0/vaL58vOnOWpn+8ckBvzkZQQT/T4ErgQxbGaM6j2etdntr0VFcvy+d/wftQM7JU4nugKYNVxAdoSDTN17tyYD3OSfJiSfJjKb/l42XvFV/1+HEzvyZ+Xj7Ryndm//bl2zqjw8HAiIiJo0qSJcZuVlRX169fn0KFD9O7dmxMnTpCYmGjSxtvbm4CAAA4dOkTTpk05fPgwTk5OxkIUQM2aNXFycuLQoUPpLkaJ7PH6669z6dIl1q9fb+5QhBAie8VFw83fkotPN04k/xtzO1UzvaLlH4vCnE0qxIkEPy6ohblgKEx0nJ1JOx9nG6p72FPM3Y6i7sn/FnO3x8PBynjhkN8uNoUQudu5fasIJIp7OFOxwZvPbVvQyYaP+w5jztpKNDk/hDKJ1zH82Ja4Wv2xDh4O2lx7myKEEPmKue7Jc+1v+YiI5MlXPT09TbZ7enpy7do1YxtLS0sKFCiQqk3K8yMiIvDw8Eh1fA8PD2ObtMTHxxMf//9LW0dHRwPJF/Ypncn++29WdTDLJR3Vcsy2bduM/0/rtWdnrvOKlNf8qr3uZ5F8mJJ8mMo1+VBViAyHa4fg+pHknk93/0B5ahU7PVrCtUU4muDPKYM/5w1FuKL6EI8lAIoChV1sqe3lQCkvB4p72FPU3R5/VztsLJ89v8rTvzPNno9cIqP5kLwJkTHKb4sBuFSwFbWtXrwAgaWFhk/fbMGGY6U4s/lz3tTsxvrwVB5fPYRd51AZtieEEDkgZTqjnJZri1Epnv4kV1XVF366+3SbtNq/6DgTJkxgzJgxqbZHRUUZL04TEhIwGAzo9fqXnuDLYMjostj529P50Ov1GAwGHj16ZFIkfBWoqkpMTAyQvpUW8jvJhynJhymz5UM1oLn3Bxb/HMXin2NY/PMrmid3UzW7rfHgRFJRTuiLccpQnPNqEeMwO3srLSXd7XjDw5YSHnaUdLejuLsttqmKTioJsTEkxKYjLHl/mMhoPlI+iBJCvFjE31cIeHIMFPBt+F6GnvtGteJc8AllTOi3DIyfjf2tI8TNrI11x3lQvFE2RSyEEMKccm0xysvLC0ju2VSw4P9/KnLnzh1jbykvLy8SEhKIjIw06R11584dateubWxz+3bqYRB3795N1evqv4YOHcqAAQOMX0dHR+Pn54eTk5PJnFH3799Hq9UaJ0t/GVlxjPzkv/nQarVoNBocHBxeyTmjABlm8y/JhynJh6kcy4dBD7dOwdWDyb2f/j6CEme6YkkiOs5SlCNJpThpKMFpQzHu4gyAg5UF5X2d6O7rREVfZwJ8HPFxtsnymOX9YSqj+ZCcCZF+f+38Di9F5bxlBcqVqJDh55f1dqRfv2F89WMFuv49kjIJ1zEsbYe+zgB0DYfJsD0hhMhncu1vdX9/f7y8vAgLC6NSpUpAck+k/fv3M2nSJACqVKmCTqcjLCyMjh07AnDr1i3OnTvH5MmTAahVqxZRUVEcPXqU6tWrA/Drr78SFRVlLFilxcrKCqs0uhcrimK8OP3vvy9zwfrfYQBy4Zt2PrIq13lVyut+FV97WiQfpiQfprIlH6oK9/+Ev/bCX/vg6s/wVPEpVrHhuL4Ev+pLcdRQmtNqMeKxxMpCQzlfR1r4OlPRz4kKvs74u9rl2Gp18v4wlZF8SM6ESJ+kxET8/14HQFyFtzN9HCdbHV/1eoPv95Tgt/0j6KLdg+aXb4gNP4RNp4UybE8IIfIRsxajYmJiuHLlivHr8PBwTp06hYuLC4UKFaJfv36MHz+eEiVKUKJECcaPH4+trS1dunQBkj/Z7NWrFwMHDsTV1RUXFxcGDRpE+fLlCQ4OBqBMmTK8/vrrvPfee3z33XcAvP/++4SEhMjk5UIIIZ4t5g78tT+5+PTXPoi+YbobOw7pS/OroQxHDaW5oBZGjxYPByuqFXFhcOECVC1SgDIFHdFpNWZ5CUIIkRPOHtxEJe4RjR0Bjd56qWNpNAofBAdwyP8Hhv84g6H6udjfPEz8rNpYdZBhe0IIkV+YtRh1/PhxGjRoYPw6ZVhct27dCA0N5fPPPyc2NpY+ffoQGRlJjRo12LlzJw4ODsbnfPvtt1hYWNCxY0diY2Np1KgRoaGhJkO8fvzxR/r27Wtcda9Vq1bMnDkzh16lEEKIPEGfBDeOweWdcDkMbp812Z2IBcf0JTloCOAXQwDnVH/0aCnhYU/VIi70KFKAqoVd8HPJ+uF2QgiRmyUdDwXgokdzqtnYZ8kxaxdzo3i/oQxfEsAHt8dSJv466tJ2GOoORNtgqAzbE0KIPM6sv8WDgoKeu1KNoiiMHj2a0aNHP7ONtbU1M2bMYMaMGc9s4+LiwtKlS18m1DylSJEiWFtbY2VlxZMnTyhbtiyDBw82GZZYr149bt26xaVLl1AUhcDAQCB5KOSlS5cICAgAoFSpUqxcuZJZs2YxZ84c40TtgYGBTJo0iUKFChEVFcXnn39OWFgYlpaW2NvbM3r0aEJCQgDYt28fDRo04NNPP2XatGnGGLp27cqSJUs4e/as8XxCCJGjHt+HK7vg8g64shviHprsPmcoYiw+HTOUIg4rSnk6ULu4Kx8VdaVaERcK2FmaJ3YhhMgFbt/6m4oxv4ACXg3ez9Jjezha880H7fjfjhKcPDSWtyx2oz34NfHhv2D15gJw9M7S8wkhRFaRe/IXk48U8qk1a9YY30wbN26kefPm7Nixgxo1anD58mUuX75MgQIFOHDgAPXr1+fUqVMAXL16lapVqxq/Bhg1ahQ7d+5k+/bt+Pr6oqoqe/bsISIiAj8/P5o3b06lSpW4dOkSFhYWnDp1ihYtWrBgwQKaNm0KJP8AbdiwgcmTJ2NpaUl0dDSHDh3Cx8cnp1MjhHiVqSrcOv1v76edcOM48P8fikSpduwzVGSPPpCfDRV4gCOFXW2pXcyVjsXcqFnUFXeHFy9XLoQQr4qLO+ZRT9FzRVeS4mWqZ/nxLbQaBjYPZLf/HAavnMUI9Tvs/zlMwszaWLb/Dko2zfJzCiFEVpB78ueTYlRWUlVIfJK55+n1oNXCi4Z26Gxf3OYprVu3pk+fPnz99desXr2a+fPn8/bbb1OwYEHmz59P/fr1n/ncx48fM3nyZE6cOIGvry+Q3GOtUaPk8fq7du3i2rVr7N27FwuL5LdTYGAgw4cPZ9y4ccY3vr29PbVr12bjxo106NCBFStW0K5dO1auXJmh1yKEEBlm0MP1w/D7ZvjjJ4j622T3BUNh9hgC2asP5JRaHCc7G14r4caQ4m7ULuaKbwFbMwUuhBC5m15voNC11QDElOuSredqVMaTkn2HMGhJOT6+P56AhKuwrCOGGn3QNB4DWl22nl8IkUdk5p48I/fjIPfkWUSKUVkp8QmMz3h3YYUMfCOG3QRLuwyfo1q1amzYsIGkpCQWL17M7t27cXNzY+zYsURFReHk5JTm886fP4+lpSVly5ZNc//JkyepUqUKlpamw1Rq1arFZ599ZrKtZ8+efPXVV3To0IEFCxawaNEiKUYJIbJHUhyEH4DfN8HFbfDkvnHXE6w4qA9gj6ESe/WB3FFcqOjrTFApd0aU8qCCj1OOrXQnRFY5cOAAU6ZM4cSJE9y6dYv169fzxhtvAJCYmMgXX3zB1q1b+euvv3ByciI4OJiJEyfi7f3/1y3x8fEMGjSI5cuXG+fhnD17tvHCV4innT60g8rqPzzBitLBPbL9fH4utvzvo/ZM3FyCYycm0sNiB5pfZ5N49RcsOi4ErWu2xyCEyOUycU+eoftxkHvyLCLL+7wiUubm2rp1K4ULF6ZMmTK4u7sTHBzM8uXLn/vcF03Em9b+tOYCq1u3LteuXWPHjh1YWFjIaoZCiKwV/wjOrcP2p49gSnFY1hF+WwpP7hOp2rM6qR7vJgykUtx3DNYNIbb82wx9syHHhwez4aM69AsuSaCfsxSiRJ70+PFjKlasmOYCLU+ePOHkyZOMGDGCkydPsm7dOi5dukSrVq1M2vXr14/169ezYsUKDh48SExMDCEhIca5KYR4WtyvCwC46NYYa3vnHDmnlYWWUW0q49ZhGh8bPiNStUd3+zT6ufXQ/bExR2IQQojMkHtyU9IzKivpbJOrpBmkqip6vR6tVvviFZh0mRsucuzYMQICApg/fz6XL1+mSJEiAMTGxnLt2jU++OCDNJ9XtmxZ4uPjuXDhQpqV2MqVKzN9+nQSEhJMKrFHjhyhcuXKqdp37dqVt99+m4kTJ2bqdQghhInE2OSV786tQb20AyUpjpTfRLdUF3boq7LDUI2jhtIU83SiSVkv+pTxoKKvM1opOol8pFmzZjRr1izNfU5OToSFhZlsmzFjBtWrV+f69evGiU/nz5/PkiVLCA4OBmDp0qX4+fmxa9cuYxd/IVLcvnOHSo/2gQLu9d7L8fO3rOhNWe9+fLy4NH2jplAj8Q8stvfFEHEUpfnkTPVaEELkA5m4J8/Q/XjKOTJB7slNSTEqKylK5v7wZXSMagZt3LiROXPmsH37doKDg7lx4wbOzs4A6PV6fHx8OHPmDBUqVEj1XHt7ewYNGsR7773H6tWrjd35t27diqurK40aNcLPz4+BAwfy7bffGidLGzduHPPnz091vJ49ewLw5ptvZvnrFEK8IpIS4K99cG4t6h8/oSQ8ApK7WP9l8GK7oTo79FU5S1EqFXKlSVlPxpfzwt9NbkyESBEVFYWiKMbrgRMnTpCYmEiTJk2Mbby9vQkICODQoUNSjBKpXNg5nwZKAn9bFMKv/LPnOslOxdzt+eGTNxi5vghHzs7iE+16NKeWknT9Vyw6hoKXrNYsxCsnM/fk2Xw/DnJPnhYpRuVT7du3x8rKisePH1O2bFm2bt3K/v37adq0qfFND6DVauncuTPz5s1j+vTpaR5r7NixuLu706RJE/R6PYqiUKlSJSZOnIiiKGzbto3PPvuMEiVKGJeRnDNnTpqf0Hp4eDBkyJDsetlCiPzKoIerB+Hc2uR5oGIjgeQC1A3VjS36WmzW1+Kyxp/qRZx5s4IPP5T1xMPB2rxxC5ELxcXFMWTIELp06YKjoyMAERERWFpaUqBAAZO2np6eREREPPNY8fHxxMfHG7+Ojo4Gkj9lTmt4wMtIOWZWHzevMmc+9AaVgn8mT1z+oFRnfJMDyvE4AGx0WiZ3qMzKIqPpvqUckzUz8XpwGcP3DVBenwBVe2bbzWVuJj8vpiQfpvJbPlJeR1a8pqzMydP35D/99BP79u2jadOmODk5Gc+l0Wjo1KkTP/zwA9OnTzd5PSnGjBmDm5tbqnvyCRMmAMmFqafvyWfPns3rr79ukhdVVXF3d2fw4MEm53hW7p7ellb7zOZMUfPLOzCbRUdH4+TkRFRUlPHCLS4ujvDwcPz9/bG2zvwNT4a7BeZzaeUjq3KdF6mqapzQTt4fko+n5ft83PkdTi2DM6sg5v9viO+ozvykr8FmfS3Oa0tSv6QnIRW9aVDKHX3c4/ybjwzK9++PDMpoPtL6258XKIpiMoH5fyUmJtKhQweuX7/Ovn37jK9r2bJl9OjRw6SwBNC4cWOKFSvG3Llz0zzX6NGjGTNmTKrt165dy/KcqapKTEwM9vb28n7GvPk4+9thXtvfiQQsiOx5BCtH9xw9f1pUVeX0tbtM23GevjHTaKg9BUB8sWbENZ6Eap32xMD5lfy8mJJ8mMpv+UhISODu3bsULlw40/eKBoMBjUam1E7xrHzExcVx7do13N3djUMCo6OjKVy4cIavl6RnlBBCiNzl8X04tya5CHXrlHHzQ9WOrfrqbDbU5qRSljolPHm7QkEal/XEwTp5SW9VVYmKM1PcQuRyiYmJdOzYkfDwcPbs2WNywejl5UVCQgKRkZEmvaPu3LlD7dq1n3nMoUOHMmDAAOPX0dHR+Pn54eTklC3FKECKq/8yZz70vy0D4FKB+pTzK56j534WVVWpWBiW9G3B6I2F+eXsPAZbrMDqz21Y3D2Hpv188Ktu7jBzjPy8mJJ8mMpv+YiLi+P+/ftotVq0Wm2mj/Myz82P0sqHVqtFo9Hg4OBgLPxl9j0kxSghhBDml5QAl3fAqeWol3egGJIASFS17DFUYq3+NQ5QmWrFPGlTwZu55bxwstWZOWgh8o6UQtTly5fZu3cvrq6uJvurVKmCTqcjLCyMjh07AnDr1i3OnTvH5MmTn3lcKysrrKysUm1XFCVbbnBSjpsfbp6ygjnycef+AypH7QIFnOu+m6u+F4qiYG+t45tOlVldfChvbSzL18r/KBz9N+rCZigNv4A6/eAV6f0gPy+mJB+m8lM+Ul5DZl/PfweL5Yd8vKzn5SOtXEsxSgghRN4TcRZOLkY9uwYl9gGQPA/UGYM/a/X12KSvRdHChWlTyYcJAV642qe+6RVCQExMDFeuXDF+HR4ezqlTp3BxccHb25v27dtz8uRJtmzZgl6vN84D5eLigqWlJU5OTvTq1YuBAwfi6uqKi4sLgwYNonz58sbV9YQAOB22hMbKE25rvfCt9Lq5w3mmDlX9CPTrxqc/FqNn5P9opT0Mu8eg/rUfpe334OBp7hCFEOKVJsWoLGAwGMwdQr4nU5sJkY/Ex8D5dXAiFP45ASQXoG6rzqzX12Wtvh5xBUrQppIv6yv5UERWwRPihY4fP06DBg2MX6cMnevWrRujR49m06ZNAAQGBpo8b+/evQQFBQEYV+Dp2LEjsbGxNGrUiNDQUBm2IIwMBhW3SysAuFu8I565vIdRCU8Hln/chDGbvPn5t6WMtQjFJnwf+jm10bb9Hoo3MneIQogsJPeM2S8rax9SjHoJlpaWaDQabt68aZzAK7PdAmUC8//3dD5UVeXu3bsoioJOJ8NyhMizbp6Ck4tQz6xCSYgBkofh7TRUYZW+AactA2lW2ZfxlX2pWriA/D4UIgOCgoKeexGengt0a2trZsyYwYwZM7IyNJGPnDh5lGqGC+hRKN7kfXOHky42llomtq/IxuJudFxXisnqNMo8+RuWtk0estfwC9DK9aUQeZlOp0NRFO7evYu7u3uGryHlftxUWvlQVdU4UbxGozFOXv4ypBj1EjQaDf7+/ty6dYubN2++1LFk9n5TT+dDURR8fX3l01kh8pr4R3B2TXIvqH8nI1eAcIMnK/QNWW+oR7mSxelQxZfvynhirZOfcSGEyK0iD84H4IpjbUq5+pk5moxpHehDeZ9O9P+xCO3vzeEdi13wyzTUqwdR2s0DF39zhyiEyCStVouvry83btzg6tWrmTqG3I+belY+bG1tKVSoUJbkSopRL8nS0pJChQqRlJSEXq/P1DFUVeXRo0c4ODhIJZa086HT6aQQJURecud3OPo96umVKImPAUhQtWw3VGe5viE3HCvTsVphNlX1w8spc0vwCiGEyDl3HkZTOXIbKGBXq4e5w8mUou72rPyoAeN+8uSDowFM1n2P4z/HMcyti6bFVKj4prlDFEJkkr29PSVKlCAxMTHDz5X7cVPPyodWq8XCwiLLciTFqCyQMnwss0PIVFUlPj4ea2trefMj+RAiz9InwaVt8Ot3cPVnILkX1J+GgizXN2Qz9ahatiR9qvtRp5gbGo38fAshRF5xMmwFryvRPNC44Fu9jbnDyTRrnZZxb5RnS1FX2q8twTjDdKonXIT178OVXdDiG7B2NHeYQohM0Gq1merAIPefpnIqH1KMEkII8XIe30+eC+rYfJToGwDoVYUwQ1UW6Ztw26UanaoX4qfKvrjJanhCCJHnGAwqzr8vB+B20ba4aPP+LURIBW/K+7Sh749+1Lu9mE8t1mFxdhXq37+itJsPftXMHaIQQuRref8viRBCCPO4eQqO/oB6djWKPh4FeKDas0LfkJVqMBUDKtCvRiGq+7vIp0xCCJGHnThzlur630AB/+APzB1OlinsaseqPnWZuM2NjocC+J9uFn4Pr6EuaIoSNBReGwAamSZCCCGygxSjhBBCpJ/BAJe2oR6agXL9MJA8FO+coQiL9E341TaI9jVLsrq6Hx4OMheUEELkB3cOzEejqPxlX5miXiXMHU6WsrLQMqplOXYXd+Ot1f4MTPyO1tpDsHcc6l97UNr+AE6+5g5TCCHyHSlGCSGEeLHEWDi1DMPhWWge/IkCJKpathmqE5rUFIvCNehW25/x5TzRaWUlEiGEyC/uRj2h0v3NoIBl9e7mDifbNCrjSUC/1+m/wpN9VzfypW4h9tcOoc6ujdJqOpR7w9whCiFEviLFKCGEEM8WcxeOzcNw9Ac0sffRAFGqLT/qg1mpNKNO5fKMr1WY0l4y2asQQuRHx3avprlyn2jFEd9a+Xu1OU9Ha5a8W5PvDrjTcmcpvrWYQWD8n7C6G/zZFV6fCJZ25g5TCCHyBSlGCSGESO3eZTg8E8OpFWj0cWiAvw3uzNc346D967xZpwybqvnhZJO5VUSFEELkfgaDiuP5pQDcKvIGjrr8P/xaq1HoE1ScmkVd+XSZDx1iltJHuwnNycWo1w4lT27uHWjuMIUQIs+TYpQQQoj/d+M46s/foFzcCoAGOGUoyg9JIfxTMJie9UrwRYAXFjIUTwgh8r0T585TM+k4KFCocf6ZuDw9KhcqwOZ+DRi2zo0u58rzrW42Be9fQZ0XjBI8Cmp+BBr5WyiEEJklxSghhHjVqSqEH8Dw8zdowveTsu5dmL4KP+hb4FSqHu/VK0a1IgVkVTwhstDVq1f5+eefuXr1Kk+ePMHd3Z1KlSpRq1YtrK3zfw8UkftF7J+PhWLgql1FiniXM3c4Oc7RWseMzpVYfcKdthuLMIrveJ1jsPMLuLIb2swFBy9zhymEEHmSFKOEEOJVpapwaQeGA1PQ/HMcDcmTkm/Q12GB0pqqVWoyqa4//m4yP4YQWWnZsmVMnz6do0eP4uHhgY+PDzY2Njx48IA///wTa2tr3nrrLQYPHkzhwoXNHa54Rd2NekKle5tAAV31nuYOx2wURaFjVT+qFC7AJz96sv/uBkZaLMHmr72oc2qjtJ4NpV43d5hCCJHnSDFKCCFeNQY9XNiI/sA3aO+cQwPEqzpW6INYZdmW1+tVY1nNwhSwszR3pELkO5UrV0aj0dC9e3dWrVpFoUKFTPbHx8dz+PBhVqxYQdWqVZk9ezYdOnQwU7TiVXZk1xpaKvd4pNjjUzt/T1yeHsXc7Vn/cR0mbXMj5FBpZuhmUvbJNVj+JlTtBU3GgaWtucMUQog8Q4pRQgjxqtAnwpmV6A9MRRv5J1ogRrVmqT6YzbZtaF+/CmuqFcLGUmvuSIXIt7788ktatGjxzP1WVlYEBQURFBTEuHHjCA8Pz8HohEhmMKg4Xvj/icsddDZmjih3sLLQMrJlWfaUcKXHKl/eS1jMuxbb4Ph81Ks/o7T9QSY3F0KIdJJilBBC5Hf6JDi7Cv3eiWijrqEFHqp2hOqbssepLW8HBbK+kg+WFjIRqxDZ7XmFqKe5ubnh5uaWjdEIkbZfz16gTtKx5InLgz80dzi5TsPSnmzu15D+q1zY91cgX+vm4nXvEuq8RigNhkOdT0EjH+wIIcTzSDFKCCHyK4Mezq4hae9ELB7+hRa4pzryQ1ILjrm3oWfD8nwSUBCtRiYlF8IcTp48iU6no3z58gBs3LiRhQsXUrZsWUaPHo2lpQyVFeZxe/88LBQD1+0qUMgnwNzh5EoejtYs6VmDuQfcaLHTny+182jOUdg9Bq7sSp7c3LnQiw8khBCvKPkYXAgh8huDIbkINbMGrH8fi4d/8UC1Z3xiZ/p6LqLmO2NZ+2kTQip4SyFKCDPq3bs3ly5dAuCvv/6iU6dO2Nrasnr1aj7//HMzRydeVbejnlDl/mYAdDVe3YnL00OjUegTVJx5HzRhov1QBiX2Jka1hmu/oM6pA2dWmztEIYTItaQYJYQQ+YXBAOc3kDSrJqzthcWDyzxU7Zic+CZ9PRZRv8c4fvywAQ1Ke6AoUoQSwtwuXbpEYGAgAKtXr6ZevXosW7aM0NBQ1q5da97gxCvrcNha/JS7xCj2FKzVydzh5AmVChVga796EPgWzRMmcMJQAiU+Gta9C2t6QexDc4cohBC5jgzTE0KIvE5V4eJWknaPw+LuBSyAaNWWH5Kac7JgJz5oGshnxd2kACVELqOqKgaDAYBdu3YREhICgJ+fH/fu3TNnaOIVpTeoOF34EYCIIq0pLhOXp5u9lQVfd6jIT6U8eHedF+8krqWvxToszq1B/fsISpvvoEhdc4cphBC5hhSjhBAiL7t+GOutw1Fun8QCeKTasEDfjKOenXi/aWUGlJAilBC5VdWqVRk3bhzBwcHs37+fOXPmABAeHo6np6eZoxOvoiOnL1BXfxQU8JOJyzOlRYWCVCrkzIBVBegQXp5vdbMpEnUDNTQEpW4/CBoGFjIfnBBC5OpheklJSXzxxRf4+/tjY2ND0aJFGTt2rPFTREj+VHH06NF4e3tjY2NDUFAQ58+fNzlOfHw8n3zyCW5ubtjZ2dGqVStu3LiR0y9HCCGyzu0LJC3tiLKwGda3TxKrWjInqSUfuC6kwjuTWPpxE+qXdJdClBC52LRp0zh58iQff/wxw4cPp3jx4gCsWbOG2rVrmzk68SqKOLAAnaLnb7sArHzKmzucPMvb2YYf361J09db0lo/kRVJQSiocPBbmB8Mdy+ZO0QhhDC7XN0zatKkScydO5dFixZRrlw5jh8/To8ePXBycuLTTz8FYPLkyUydOpXQ0FBKlizJuHHjaNy4MRcvXsTBwQGAfv36sXnzZlasWIGrqysDBw4kJCSEEydOoNXKsqtCiDzk4XX0u79Cc3YlFqgkqRpW6YPY5taNHq/X4oNSMh+UELndpUuXKFmyJBUqVODs2bOp9k+ZMkWuT0SOu/XwMVUfbAIFLGXi8pem1Sh8UL8YdYq58elKF/ber8QE3Q+43DqN+l09lCZfQrV3Qf5mCyFeUbm6Z9Thw4dp3bo1LVq0oEiRIrRv354mTZpw/PhxILlX1LRp0xg+fDht27YlICCARYsW8eTJE5YtWwZAVFQU8+fP55tvviE4OJhKlSqxdOlSzp49y65du8z58oQQIv0e38ewbSj66ZXRnl2BgspWfXV62M5A22IyoZ+0omFpTylECZEHVKpUiTJlyjB48GAOHz6car+1tTU6nc4MkYlX2aFd6yis3OGxYodnzc7mDiffKO/rxJZP6uJWrR2vx0/igL48SlIsbB0EyzpCzB1zhyiEEGaRq4tRdevWZffu3cZlj0+fPs3Bgwdp3rw5kDynQkREBE2aNDE+x8rKivr163Po0CEATpw4QWJiokkbb29vAgICjG3SEh8fT3R0tMkDkgtg8pCHPOSRY4+EJxj2TyZpWgU0v85Ga0jkkL4s3bUTiWwxj3kDOtGktBuKIr+f5CGP7Hhkh/v37zN58mTu379PmzZt8PT0pFevXmzatIm4uLhMHfPAgQO0bNkSb29vFEVhw4YNJvtVVaY1EM+WpDfgdCH5g9zbRVqDpa2ZI8pfbC0t+KpNeb7q2pj+uhGMTuxKvKqDyztRZ9eCi9vMHaIQQuS4XD1Mb/DgwURFRVG6dGm0Wi16vZ6vvvqKzp2TP62JiIgASDXJp6enJ9euXTO2sbS0pECBAqnapDw/LRMmTGDMmDGptkdFRWX5xamqqsTExABIrwYkH0+TfJh6ZfKhGtBd3Ih2/0SsYyPQAOcNhZmudKF07RZMqOqNjU7Lk5hHr0Y+0umVeX+kk+TDVEbzkfJBVFaztramZcuWtGzZElVVOXz4MJs2bWLIkCF07tyZ4OBgWrduTUhICB4eHuk65uPHj6lYsSI9evSgXbt2qfbLtAbieX45/Tv19b+CAr7BH5g7nHyrcVlPKvarz8DVzoRcCWC6biZlnlyH5Z2gSg9o+hVY2pk7TCGEyBG5uhi1cuVKli5dyrJlyyhXrhynTp2iX79+eHt7061bN2O7py8oVVV94UXmi9oMHTqUAQMGGL+Ojo7Gz88PJycnHB0dM/mKnh0LgJOTk9wsIPl4muTD1CuRj+tHSNg6FKvbvwHwj+rKt2pn3Gt2YVJQCZxs/n/4ziuRjwyQfJiSfJjKaD5yImeKolC7dm1q167NxIkTuXz5Mps2bSI0NJQPP/yQqVOn8tFHH73wOM2aNaNZs2Zp7lNV02kNABYtWoSnpyfLli2jd+/exmkNlixZQnBwMABLly7Fz8+PXbt20bRp06x70SLXuf3vxOX/2JXDx6eiucPJ1zwcrVnUozqhhzxov70gfQ0r6G3xE5xYCFd/hrY/gE9lc4cphBDZLlcXoz777DOGDBlCp06dAChfvjzXrl1jwoQJdOvWDS8vLyC591PBggWNz7tz546xt5SXlxcJCQlERkaa9I66c+fOc1eqsbKywsrKKtV2RVGy5eI05bhys5BM8mFK8mEq3+Yj8ioJ20dieXEjVkCMas1cfSuiAt/nsybl8XS0TvNp+TYfmST5MCX5MJWRfJgjZyVKlGDgwIEMHDiQ+/fv8+DBg5c+5oumNejdu/cLpzWQYlT+deNBDNUebAaNTFyeUzQahZ51/ald3JVPlzuz724gU3VzKHj/Cur8xij1B0PdAaDN1bdqQgjxUnL1b7gnT56g0ZhOa6XVajEYDAD4+/vj5eVFWFgYlSpVAiAhIYH9+/czadIkAKpUqYJOpyMsLIyOHTsCcOvWLc6dO8fkyZNz8NUIIcQzxEWjP/A1HJ6NpZqIQVVYqQ/iUOEP+LhlHUp5OZg7QiFENvnnn3/45ZdfuHPnjvH6BpILYZ988gmurq4vfY7snNYgPj6e+Ph449dPz7GZlbJ7Lq+8Jqvy8cuuDbypuc0TxRa3Gp3ybH7z4vujlKcDGz6qzaTtrrx+qDBf6RYQwhHY+xXqpR3QZi64Fs/UsfNiPrKT5MOU5MOU5MNURvOR2by9VDEqPj4+zd5DWaVly5Z89dVXFCpUiHLlyvHbb78xdepUevZM/tRGURT69evH+PHjKVGiBCVKlGD8+PHY2trSpUsXILkrfq9evRg4cCCurq64uLgwaNAgypcvb+yGLoQQZmHQo55YROKuL7GMT+798Iu+HEuc3qdTy+bMKJW+uWKEEHnTwoUL+eCDD7C0tMTV1dWkJ1ZKMSorZce0BjLHpvlkRT4S9QYK/P4jADd9m+MWmwSxUVkWY07Ky++PfvV8qOZry6gtzoTF7eNLXSiO/xxHnVuX2NeGk1DhHcjga8rL+cgOkg9Tkg9Tkg9TOTXHZoaKUTt27GD58uX8/PPPXL9+HYPBgK2tLZUrV6ZJkyb06NEDb2/vTAWSlhkzZjBixAj69OnDnTt38Pb2pnfv3owcOdLY5vPPPyc2NpY+ffoQGRlJjRo12Llzp3EyToBvv/0WCwsLOnbsSGxsLI0aNSI0NFQm4xRCmM/1I8RuHIDN/fNYAn8aCjLDohvVXu/MzGqFsNDm6sVOhRBZYOTIkYwcOZKhQ4em6gmelbJzWgOZY9N8siIfe05eIMiQPHF5oSafoHNyysoQc1Ref380r+REzZLeDF/vStPzZZiim0tdzmO7dwQ21/dBqxngmP77rLyej6wm+TAl+TAl+TCVU3NspqsYtWHDBuPKds2bN+ezzz7Dx8cHGxsbHjx4wLlz59i1axdffvkl3bt358svv8Td3T1TAf2Xg4MD06ZNY9q0ac9soygKo0ePZvTo0c9sY21tzYwZM5gxY8ZLxySEEC/lUQTx277A6sJqbIAo1Zbphg7Y1HqfLxuWwsFa98JDCCHyhydPntCpU6dsLURB9k5rIHNsmtfL5uPOz6FYKnpu2pXF2y8wa4Mzg7z+/nC1t2LO21VYd9KLDze50y5xK0MslmP9527UObVRQqZCQOrVMp8lr+cjq0k+TEk+TEk+TOXEHJvpKkaNHz+er7/+mhYtWqR5wZRy0fLPP//wv//9j8WLFzNw4MBMBSSEEPmSPhH94Tno907ESv8Yg6qwQh/EuVKf0iekBr4FbM0doRAih/Xq1YvVq1czZMiQlz5WTEwMV65cMX4dHh7OqVOncHFxoVChQjKtgUjl7/uPqflgE2jASiYuzzUURaFdFV+q+7swcLUzLa6W51vdbCrEhcOanvDHVmg+BWxdzB2qEEK8lHQVo44ePZqug/n4+Mik4EII8bQ/9/Jk0yBso66gBU4ZirHQ6SPebteGLkXkYlKIV9WECRMICQlh+/btlC9fHp3OtGfk1KlT032s48eP06BBA+PXKUPnunXrRmhoqExrIFI5uGsDnTURxCo2uNbobO5wxFP8XGxZ/l5N5h/0oNOOgrxvWMfHFhuwOLcGrv0CrWdB8UbmDlMIITItV6+mJ4QQedrDv4ndMhibKz9hC9xTHZmheZsSzd5nao0iaDXSDViIV9n48ePZsWMHpUqVAky7uWe0y3tQUNBzJwyXaQ3EfyUkGSjwxzIA7vq3opCVvZkjEmnRahTer1eM10q403+lE3tvBzJVN4dij27B0rZQ7T1oPBYspXe1ECLvyXAxSlVV1qxZw969e1MtQwywbt26LAtOCCHypKQEEg/+Dw5MwcYQT5KqYYm+MTcD+9OvWRUK2FmaO0IhRC4wdepUFixYQPfu3c0dinjF7PvtDxoYjoACBRt+aO5wxAuUKejIho/qMDXMnZCf/RisXU53i51w7Af4ay+0+R58q5g7TCGEyJAMF6M+/fRTvv/+exo0aICnp6dM8CWEEP917RCP136CXXTy3C1HDGVY7d6Xnu1aUM47765SJITIelZWVtSpU8fcYYhX0J2DC7FSkoiwK42XbyVzhyPSwVqnZVjzMjQo5cGg1c7siq7CFN13FLx/BXV+Y5R6g6DeZ6CVhVCEEHlDhotRS5cuZd26dTRv3jw74hFCiLzpyQOe/DQM2/PLsQPuqo7MsOhBldbv83WgjxTuhRCpfPrpp8yYMYPp06ebOxTxCrl6N4ZakZtl4vI8qlYxV7b1e43Rm1xoetKfsbpQ3tAegv2T4NIOaPs9uJcyd5hCCPFCGS5GOTk5UbRo0eyIRQgh8h5VRf/bchK2DcM2MRKA5fpG3Ko2mMFNq2BnJVPzCSHSdvToUfbs2cOWLVsoV65cqgnMZeoDkR0O7t7E25pbxCnWFKjRxdzhiExwtNYxtWMgW8t4Mmx9AXbFVWGcbgHOt06hflcPJXg0VH/f3GEKIcRzZfguafTo0YwZM4YFCxZgY2OTHTEJIUTecO8yj9Z+gsOtw9gAfxj8WOzSl3fe7ETngo7mjk4Ikcs5OzvTtm1bc4chXiHxSXpcLiZPXH6vSCt8rRxe8AyRmzUvX5CqhQvw2RpnmlwqzRTdd9TnDGwfAhe3oTScCE4yRYAQInfKcDGqQ4cOLF++HA8PD4oUKZLqU7yTJ09mWXBCCJErJcYRt3cKFof/h4OaSKxqyXdKB7yaD2Rc9aJoZJU8IUQ6LFy40NwhiFfM3pN/0Ojficu9Gn5g7nBEFvBwtCa0RzWWHvGg91YX2ifuZLhuGTbh+3Fc0gSaTYTAt0CmCxBC5DIZLkZ1796dEydO8Pbbb8sE5kKIV4569Rcer+mDfcxVAPboAzlUaggfvNEQN3sr8wYnhBBCPMfdgwuxUhKJsCuDl5+svpZfKIrCO7WKULu4GwNWOtLsnwCm6uZQOeEKbPwIft8CLf8HDp7mDlUIIYwyXIz66aef2LFjB3Xr1s2OeIQQIneKf8SjLV/gcDYUe+C26swcm/dp0v59vijubu7ohBB5xOuvv87IkSOpXbv2c9s9evSI2bNnY29vz0cffZRD0Yn87M870dR5mDxxuXWtd80djsgGxdztWfNhbebs86Tzbk96Klvor1uD5aVtMPsItPgGAtqZO0whhAAyUYzy8/PD0VHmQhFCvDr0l8KIXfsxDvERAKwyNCSyzgiGNqqIlYXWzNEJIfKSDh060LFjRxwcHGjVqhVVq1bF29sba2trIiMjuXDhAgcPHmTr1q2EhIQwZcoUc4cs8onDu9bztiaCWMUW52qdzB2OyCY6rYa+jUrQoJQ7/VbYs/t+Jabq5hAQexXW9ITfN0Pzb8DO1dyhCiFecRkuRn3zzTd8/vnnzJ07lyJFimRDSEIIkUs8eUD0hs9wvLQGe+C6wZ1QtwG83bkrRd3tzR2dECIP6tWrF++88w5r1qxh5cqV/PDDDzx8+BBIHmpTtmxZmjZtyokTJyhVSpZnF1kjLlGP56XkicvvF3sDXyv5G5bfBfg4sbx7ReYf9aLNzwX5SLuBjy02YHF+PVw9mDxsr3QLc4cphHiFZbgY9fbbb/PkyROKFSuGra1tqgnMHzx4kGXBCSGEWagqiec2kLhpAI6JDzCoCktphmXTkXxRq7RMUC6EeCmWlpZ06dKFLl26ABAVFUVsbCyurq6prquEyAp7jp+hsXoMFCjYqI+5wxE5xNJCw5BmpWlSzpOBqxzZ9aAy3+jmUurxDVjRBSp2htcngo2zuUMVQryCMlyMmjZtWjaEIYQQucSjCB6u6YvztR3ogEsGH1Z7D6Zn544UdLIxd3RCiHzIyckJJ1l+XWSjh78sRKfouelYAe+C5c0djshhVQq7sPXT15i4zZ1Wh33pb7GW9yx+Qnt6Ofy1H1rPgOLB5g5TCPGKyXAxqlu3btkRhxBCmJeqEv/bcgw/fY6z/hGJqpZQTRsKvjGCYZUKy8qhQggh8qRLtx5S79FPoIBd7ffNHY4wE1tLC8a2DqBJWS8+X+PIzugqfKObg/+jm7C0HVTpDk3GgZWDuUMVQrwiNOlp9Pjx4wwdNKPthRDCrGLu8mDhm1ht+hAb/SPOGPyZVvR72g2aQ0jlIlKIEkIIkWcd27UaX+UeMRoHnKq0N3c4wszqlnBje/96FK3ckGYJE1mY1DR5x4lQmFM7eT4pIYTIAekqRhUvXpzx48dz8+bNZ7ZRVZWwsDCaNWvG9OnTsyxAIYTITglnN/B4WjVcru8gUdXyvbYzDzpv5bNu7XGxszR3eEIIIUSmxSXq8flzOQCRJTqAToabC3C01vF1h4rM6FqHWdbv0TlhODdUN3h4HUJbwLYhkPDE3GEKIfK5dA3T27dvH1988QVjxowhMDAwzWWIDx8+jE6nY+jQobz/vnQBFkLkcrGR3F/TH9c/12MJ/G7wY0eJMfTq0AoHa5lAWAghRN63+8gJXldPggI+jT40dzgil2lc1pMqhevzxQYXmp4tynCLpXSx2Au/zoErYfDGXPCrZu4whRD5VLqKUaVKlWL16tXcuHGD1atXc+DAAQ4dOkRsbCxubm5UqlSJH374gebNm6PRpKuzlRBCmE3CxR3Er/kI18S76FWFJdo2FOkwln7l/MwdmhDiFdK9e3d69uxJvXr1zB2KyKceH16AVlG54VwVX4+S5g5H5EIudpbM6lKZTae9GLnRkZ3x1Zik+wHP+1dQFzRBqfMpBA0FCytzhyqEyGcyNIG5r68v/fv3p3///tkVjxBCZJ/4R9xf/zmufyzDEvjTUJDNRUfQo2NHnGylN5QQImc9evSIJk2a4OfnR48ePejWrRs+Pj7mDkvkE3/8c5/6j7eBAg51ZdSCeDZFUWgd6EPNoq4MXutM44vFGa1bTFvtQTj4LVzaAW/MAe9Ac4cqhMhHpBuTEOKVkPjXQaKmVsf1j2UALFNa8Fe7bfTr/pYUooQQZrF27Vr++ecfPv74Y1avXk2RIkVo1qwZa9asITEx0dzhiTzut10r8FQeEq0tgFNgG3OHI/IAT0drFnavxrC2tRihfELvhP7cVx3hzgXUeY1g3yTQy+8mIUTWkGKUECJ/0ydyf9MXaBa3xCn+JjdUN6b5TOX1QaE0ruhv7uiEEK84V1dXPv30U3777TeOHj1K8eLFeeedd/D29qZ///5cvnzZ3CGKPOhJQhKF/loJQFTpTmAhC3KI9FEUhU7VC7G9Xz0eFm5K4/jJbNVXRzEkwb7xMC8Y7vxu7jCFEPmAFKOEEPmW4e4V7v6vPq4nZ6DFwEaCONtyG/3e6yUr5QkhcpVbt26xc+dOdu7ciVarpXnz5pw/f56yZcvy7bffmjs8kcfs+eVX6iinMaDg07C3ucMReZCfiy3L36vJJy1rMoD+9E34mCjVDm6dQv2uHvw8FfRJ5g5TCJGHSTFKCJH/qCrRv8wncXYd3KPPE6XaMsvtC2oNXEmzqjKBqxAid0hMTGTt2rWEhIRQuHBhVq9eTf/+/bl16xaLFi1i586dLFmyhLFjx5o7VJHHxP06H4AbrrXRuEovYJE5Go1Cjzr+bP+0PrcKtaBx/GR26yuh6BNg9xhY0ATu/GHuMIUQeVSGJjB/nsjISDZv3kzXrl2z6pBCCJFxTx4QsfR9vG6GAfCroSx/B31LnwY1UBTFzMEJIcT/K1iwIAaDgc6dO3P06FECAwNTtWnatCnOzs45HpvIu85cvU2D2J2gQIHXpFeUeHlF3OxY+X4tFh4qyEc7XGiRsJ9RusU4/nMC9bvXUBoMg1qfgDbLbi2FEK+ALOsZdf36dXr06JFVhxNCiAyL/WMXUVOr4XUzjARVy3ybHrj02U77hjWlECWEyHW+/fZbbt68yaxZs9IsRAEUKFCA8PDwlz5XUlISX3zxBf7+/tjY2FC0aFHGjh2LwWAwtlFVldGjR+Pt7Y2NjQ1BQUGcP3/+pc8tcta5XUtxVR7x0MIdh/ItzB2OyCc0GoVedf3Z9ml9rvm1pnH8ZPboA5N7Se0aLb2khBAZJsP0hBB5X1I8d1YPxGZFO5yS7vGnoSBLy8/nnYFTKeHlZO7ohBAiTXv37k1z1bzHjx/Ts2fPLD3XpEmTmDt3LjNnzuT3339n8uTJTJkyhRkzZhjbTJ48malTpzJz5kyOHTuGl5cXjRs35tGjR1kai8g+UU8SKfH3agAeB7wlPVVElvN3s2Nl71q816IOH6qDGZjwAdGqLfzbS4qD38pcUkKIdJFilBAiT0u6c4k739bF4/w8ANZpmnLvrV30bN8GSwv5FSeEyL0WLVpEbGxsqu2xsbEsXrw4S891+PBhWrduTYsWLShSpAjt27enSZMmHD9+HEjuFTVt2jSGDx9O27ZtCQgIYNGiRTx58oRly5ZlaSwi++z++QDVlN/Ro8G7wfvmDkfkU1qNwruvFWV7P+klJYTIPLlTE0LkWQ8OLyFxzmt4PL7EfdWB733G0+izH6lRytfcoQkhxDNFR0cTFRWFqqo8evSI6Oho4yMyMpKtW7fi4eGRpeesW7cuu3fv5tKlSwCcPn2agwcP0rx5cwDCw8OJiIigSZMmxudYWVlRv359Dh06lKWxiOyhqirqiYUA/ONRH8XJx8wRifxOekkJIV5GuvvuTp8+/bn7//nnn5cORggh0iXxCTdD++FzbT0AR9VyPGg6i/drVzJzYEII8WLOzs4oioKiKJQsmXqFT0VRGDNmTJaec/DgwURFRVG6dGm0Wi16vZ6vvvqKzp07AxAREQGAp6enyfM8PT25du3aM48bHx9PfHy88evo6Gjg38KIqmbpa0g5ZlYfN696Oh9HL90gOH43KOBa/4NXLk/y/jCVU/nQKNCrrj9Bpdz5fI0Tja8HMEE3j4acgl2jUX/fDK1ngXvpbI3jReT9YUryYUryYSqj+chs3tJdjPr2229f2KZQoUKZCkIIIdIr4Z8zJCx+G5+E6+hVhZV2XajbYxLV3R3MHZoQQqTL3r17UVWVhg0bsnbtWlxcXIz7LC0tKVy4MN7e3ll6zpUrV7J06VKWLVtGuXLlOHXqFP369cPb25tu3boZ2z292IOqqs9dAGLChAlpFs5Sen5lJVVViYmJSTPOV9HT+fg9LJQayhPu6wqi9a5OVFSUmSPMWfL+MJXT+XCzhB86leXHY858+PNgQkxW3KtHXM0BxFd5DzTmmcdM3h+mJB+mJB+mMpqPlA+iMirdvw2yYiWXzPjnn38YPHgw27ZtIzY2lpIlSzJ//nyqVKkCJCdqzJgxfP/990RGRlKjRg1mzZpFuXLljMeIj49n0KBBLF++nNjYWBo1asTs2bPx9ZWhPELkGarK3X1zcdw/Eg8SiFALsKvMV3Rs31nmhhJC5Cn169cHkq+tChUqlCMXvp999hlDhgyhU6dOAJQvX55r164xYcIEunXrhpeXF5DcQ6pgwYLG5925cydVb6n/Gjp0KAMGDDB+HR0djZ+fH05OTjg6Ombpa0gpbjk5OcnNAqb5uBeTQOCdDaCBxMCuuDgXMG9wZiDvD1PmyscnTZxpXqkQn612ovHf/99LyuaXiVhfDTNbLyl5f5iSfJiSfJjKaD4ym7NcvcRGZGQkderUoUGDBmzbtg0PDw/+/PNPnJ2djW1SVn4JDQ2lZMmSjBs3jsaNG3Px4kUcHJJ7SvTr14/NmzezYsUKXF1dGThwICEhIZw4cQKtVmumVyeESLe4KG4seg/fWzsA+JlKKG3m8HZgGTMHJoQQGXPmzBkCAgLQaDRERUVx9uzZZ7atUKFClp33yZMnaDSmhXutVovBYADA398fLy8vwsLCqFQpechzQkIC+/fvZ9KkSc88rpWVFVZWVqm2pwxDzGopx5WbhWQpudi7bxdvaq6QiAVe9d+FVzQ/8v4wZa58FPdwYM2HtZl/0IsPdrrSMmE/oyz/v5eU0mAY1Pokx1d7lPeHKcmHKcmHqYzkI9uLUbGxsezevZuQkBAg+ZOw/84RoNVq+fLLL7G2ts5UIGmZNGkSfn5+LFy40LitSJEixv8/vfILJK9M4+npybJly+jduzdRUVHMnz+fJUuWEBwcDMDSpUvx8/Nj165dNG3aNMviFUJkvdjwozxZ1hXfxFskqlpWOvWkevuBlPDzMndoQgiRYYGBgURERODh4UFgYCCKoqQ5nE1RFPR6fZadt2XLlnz11VcUKlSIcuXK8dtvvzF16lR69uxpPF+/fv0YP348JUqUoESJEowfPx5bW1u6dOmSZXGIrKc3qFidXgRAhHdj/OyzdvJ7ITJDq1F4v14xGpb25LM05pLi983QejZ4mHcuKSGE+aS7GLV48WK2bNliLEbNnDmTcuXKYWNjA8Aff/yBt7c3/fv3z7LgNm3aRNOmTenQoQP79+/Hx8eHPn368N577wEvXvmld+/enDhxgsTERJM23t7eBAQEcOjQISlGCZFbqSq3wqbjdmgsriRxXXXnSKUpdGrZmphHmRuXLIQQ5hYeHo67u7vx/zllxowZjBgxgj59+nDnzh28vb3p3bs3I0eONLb5/PPPiY2NpU+fPsapD3bu3GnsaS5yp0MXwglOOgAKeDb80NzhCGGiuIc9az6ozbyfvfgg7N9eUsa5pF4zWy8pIYT5pfun/scff0xVaFq2bBlFixYFknsbzZo1K0uLUX/99Rdz5sxhwIABDBs2jKNHj9K3b1+srKzo2rVrulZ+iYiIwNLSkgIFCqRqk/L8tMjqMOYj+TD1KuZDjX/E9UXvUeTWdgD2aGri9OZcOpQs8krm43kkH6YkH6YkH6ZyanWY5ylcuHCa/89uDg4OTJs2jWnTpj2zjaIojB49mtGjR+dYXOLlXd23mHpKHPesCuFWrJ65wxEiFa1GoXf9YjQq48Ggp+aSkl5SQry60l2MunTpksnyw9bW1iZzD1SvXp2PPvooS4MzGAxUrVqV8ePHA1CpUiXOnz/PnDlz6Nq1q7FdRld+SU8bWR3GfCQfpl61fCRG/IF+7bsUSfw7eVie87u89uYgnGwtjT9/r1I+XkTyYUryYUryYSqnVodJrwkTJuDp6WkcKpdiwYIF3L17l8GDB2fr+UXed+thHFXvrgcNUK3nKztXlMgbins4sPbD2iz8pSB9drrS4uleUkFDoXZf6SUlxCsi3T/pUVFRWFj8f/O7d++a7DcYDCY9ibJCwYIFKVu2rMm2MmXKsHbtWoB0rfzi5eVFQkICkZGRJr2j7ty5Q+3atZ95blkdxnwkH6ZepXzcPrQUp7BB2BDHLdWFw5W/pkvLNiav+1XKR3pIPkxJPkxJPkzl1Oow6fXdd9+xbNmyVNvLlStHp06dpBglXujI4b300FwjAR1utbuZOxwhXkirUXj3taIEl/Fk8NoCNA7/Ty+p3WPgwsbkFfe8AswdqhAim6W7GOXr68u5c+coVapUmvvPnDmDr69vlgUGUKdOHS5evGiy7dKlS8Zu7elZ+aVKlSrodDrCwsLo2LEjALdu3eLcuXNMnjz5meeW1WHMS/JhKt/nIyme8B8/xT98OQBHlfJoO8ynbbm0f9/k+3xkkOTDlOTDlOTDVE6sDpNeT3+YlsLd3Z1bt25l67lF3peoN+B6aQUAdwo1x9fWxcwRCZF+RdzsWP5eTX486s0nW914PWEfI3VLcLp1CvX7+iivDYTXBoGFpblDFUJkE82LmyRr3rw5I0eOJC4uLtW+2NhYxowZQ4sWLbI0uP79+3PkyBHGjx/PlStXWLZsGd9//71xOOB/V35Zv349586do3v37iYrvzg5OdGrVy8GDhzI7t27+e2333j77bcpX768cXU9IYT5JNy7yo2pQcZC1Dr7zhTpt4MqzyhECSFEfuHn58cvv/ySavsvv/yCt7e3GSISecm+UxdpYkh+/3g27GPmaITIOI1G4Z2ahdnRvz53irUlOH4y2/XVUAxJsH8SfFcPbpwwd5hCiGyS7p5Rw4YNY9WqVZQqVYqPP/6YkiVLoigKf/zxBzNnziQpKYlhw4ZlaXDVqlVj/fr1DB06lLFjx+Lv78+0adN46623jG3Ss/LLt99+i4WFBR07diQ2NpZGjRoRGhqKVqvN0niFEBlz79RPWG7sja/6iEjVnt1lvuSNDt2x0Ka7Ti6EEHnWu+++S79+/UhMTKRhw4YA7N69m88//5yBAweaOTqR2905sBBrJZE7tiXwKFzD3OEIkWm+BWxZ3LM6q09489kWNzYl/MKXulBc7/6OOj8YpdZH0GA46GzMHaoQIgspagZm4w4PD+fDDz8kLCzMOO+Coig0btyY2bNnG1fWy4+io6NxcnIiKioqW+aMioqKkjk9/iX5MJUv82EwEL5+NIXPTkeDyjmKEd1yHrWrVH7hU/NlPl6C5MOU5MOU5MNURvORnX/7U+IZMmQI06dPJyEhAUheIGbw4MGMHDkyy8+XE+R6KWf8dScazcyqFNHc5kHDKbjUe9/cIZmdvD9M5dV83I6OY/j6c5z4/TIjdUtoo/2396hLUWg1E4rUydRx82o+sovkw5Tkw1ROXS9laKkCf39/tm/fzoMHD7hy5QoAxYsXx8UleYx6UlKSySTnQgjxNENsFH/90JXiD/YBsM26OQE9ZxPgUeD5TxRCiHxGURQmTZrEiBEj+P3337GxsaFEiRJpzlkpxH8dCVtNF81tHit2FKjRxdzhCJFlPB2t+aFrFTaf8WbURlc2x9VivG4+Xg/+gtDmUO1dCB4NVg4vPJYQInfL1FgYFxcXqlevTvXq1XFxceHChQsMGDAAHx+frI5PCJGPxPxzgdtT61L8wT7iVQvW+Q2l4aCl+EkhSgjxCrO3t6dgwYIUKFBAClHiheIS9Xhf/hGAiCKtwdLOzBEJkbUURaFVRW/CBtTHNqAFTeInszypQfLOY/Ngdi24stu8QQohXlqmJ2aJiYlh3rx51KpViwoVKnD06FGGDBmSlbEJIfKRm0fXwQ+NKJh4nQjVhZ/rLqFtryFYWcjcbUKIV5PBYGDs2LE4OTlRuHBhChUqhLOzM19++SUGg8Hc4Ylcau+R49RTTwLgUreXmaMRIvu42Vsxs0tlJr9dj6k2H9MlYRh/q+4Q9TcsbQsbPoLYSHOHKYTIpAyPqTt48CDz5s1j7dq1+Pv7c+HCBfbv30+dOpkbvyuEyOcMBq6sGUnxCzMA+E0pi1WXxQSXLGHmwIQQwryGDx/O/PnzmThxInXq1EFVVX755RdGjx5NXFwcX331lblDFLlQ7OEf0Cgq152r4+ha3NzhCJHtXg/womZRF77c4k7Tk8X5zGIl3Sx2ojm1FK6EQci3UDprV3UXQmS/dBejJk+ezIIFC4iJiaFz584cPHiQihUrotPpKFBAhtgIIVLTx0YR/v3bFI88AMB221ZU7T0HNyd7M0cmhBDmt2jRIubNm0erVq2M2ypWrIiPjw99+vSRYpRI5cL129R/vB0UcKz3obnDESLHONta8k3HioRULMjwdc5sia7JZN33FIu5BSu6QLm20HwK2LmZO1QhRDqle5jesGHDaNeuHdeuXWPKlClUrFgxO+MSQuRxj/6+wJ1v6lA88gDxqo6Nhb+g0cBFUogSQoh/PXjwgNKlS6faXrp0aR48eGCGiERudy5sMa7KIx5YeOBUsaW5wxEixzUo5cGO/vUoXb0xzRMmMDupFUlo4Pw6mFkNzq6B9C8WL4Qwo3QXo8aOHcvq1avx9/dn8ODBnDt3LjvjEkLkYTeOrEMzvyEFk/7mlurC4fpLad3jM3TaTE9TJ4QQ+U7FihWZOXNmqu0zZ86UD/1EKjHxSZS6viL5/wHvgEZWsBavJgdrHV+1Kc/C915jhWNP3ogfy++GQhD7ANb2guWdIfqmucMUQrxAuv+KDRs2jGHDhrF//34WLFhAzZo1KVasGKqqEhkpE8cJIQBV5eLaLylxdioaReU3pSzWby0hqITMaSGEEE+bPHkyLVq0YNeuXdSqVQtFUTh06BB///03W7duNXd4Ipc5sC+M5soVErHAr1Fvc4cjhNnVLubG9n6v8c1OT974pRDvazbRV7cB3aVtqLN+QWn6FVR6BxTF3KEKIdKQ4W4K9evXZ9GiRdy6dYsPP/yQKlWqUL9+fWrXrs3UqVOzI0YhRB6gJsby++wulDr3DRpFZZddCIX776KMFKKEECJN9evX59KlS7Rp04aHDx/y4MED2rZty8WLF3nttdfMHZ7IRVRVRXtiPgDXvRqjOHiaOSIhcgdbSwtGhJRlZZ/67HTrRov4rzhlKIoSHw2bPoElb8CDcHOHKYRIQ6b79zo4OPDBBx/wwQcfcPbsWeNqMAMGDMjK+IQQeUDsg5vc+r4dZeIukKRq2FloAE26D8dChuUJIcRzeXt7y0Tl4oVOX75K/fh9oIBnw4/NHY4QuU6gnzObP6nLd/sL0nmPL2/rf2KgxWqs/9qHOrsWNBgGZbqYO0whxH9kyWDz8uXLM23aNKZMmZIVhxNC5CF3Lx9FXd6Fooa7PFTt+K3mNJo362jusIQQIlc6c+ZMuttWqFAhGyMRecm1Xd8TqCTyj3VxfErUMXc4QuRKlhYaPmlUgmblvRiy1oXXr1dhgsV8anEBwkZgf2Y1vDELCsrvViFygyyd+VCn02Xl4YQQudxfB5ZTcM+n2BBPON5Et11Kg4pVzB2WEELkWoGBgSiKgvqC1Z4URUGv1+dQVCI3i4yJI/D2WlBAX+Vdmf9GiBco7uHAqt61WPqrN+9u8yEkcTfDLX7E8fYZ1B8aQJ1Pod7noLM2d6hCvNJkGQ4hRMapKudXjqDcHzMAOGFRCc9ey6lYsKCZAxNCiNwtPFzmLhEZcyRsNc2U28QodvjVe8fc4QiRJ2g0Cl1rFaFRGU++WO9Ko4uBjNEtojlH4edv4MJGaDkdikhPQyHMRYpRQogM0cc/4eL3XSl3PwyAXY5tqPXhXOxs5NMlIYR4kcKFC5s7BJGHGAwqjucWAfBP4TaUsrI3c0RC5C0+zjYs6F6NTad8GL7JnY3xhxmrW4jn/SsQ2hyq9IDGY8DaydyhCvHKkdmFhRDpFnPvBte+CaLs/TASVS07ig6hYb+FUogSQohMWrJkCXXq1MHb25tr164BMG3aNDZu3GjmyERucOL0aWolHQfAr4lMXC5EZiiKQqtAb9a9VxmbCq1pHD+FZUkNkneeWAizasAfP5k3SCFeQZkqRv3555988cUXdO7cmTt37gCwfft2zp8/n6XBCSFyj4grv/F4VhBFEy4SqTrw62sLaNp1KBqNzF0hhBCZMWfOHAYMGEDz5s15+PChcY4oZ2dnpk2bZt7gRK7wYP8cNIrKnw5VsfUuY+5whMjTCtjq+PbNQKb3CGKWfV86JXxBuMETHt2CFV1gVTd4dNvcYQrxyshwMWr//v2UL1+eX3/9lXXr1hETEwMkrw4zatSoLA9QCGF+fx7Zgv3S5niqd7mGN7c6bqFu8BvmDksIIfK0GTNm8MMPPzB8+HC0Wq1xe9WqVTl79qwZIxO5we0HD6kamdxbw7r2B2aORoj8I6iUBzv616N0zeY0S5zE7KRWJKGBCxtQZ1WH35bCCxaZEEK8vAwXo4YMGcK4ceMICwvD0tLSuL1BgwYcPnw4S4MTQpjf2c0zKLStK/Y84ay2HJa9d1G2XKC5wxJCiDwvPDycSpUqpdpuZWXF48ePs/x8//zzD2+//Taurq7Y2toSGBjIiRMnjPtVVWX06NF4e3tjY2NDUFCQ9Ho3o9PbQ3FVHnFP445P9TbmDkeIfMXeyoLRrcrx4wdBrHd5l9bx4zhrKIIS9xA2fgSLW8ODv8wdphD5WoaLUWfPnqVNm9R/EN3d3bl//36WBCWEMD/VoOe3hf0of+ILdIqeQ3aN8B8QRsGCPuYOTQgh8gV/f39OnTqVavu2bdsoW7Zslp4rMjKSOnXqoNPp2LZtGxcuXOCbb77B2dnZ2Gby5MlMnTqVmTNncuzYMby8vGjcuDGPHj3K0ljEiyXpDXhfXgrA/7F33+FRVF0cx7+TnkASIEASepHeCb13UGmigpXeBCkCoohIEUEsqICIKE1F8KUpitK7IL03UUInhJoEQurO+wdmZUkCCSbZlN/nefYhe+fOzJmTIbk5e2cmuOTz4KhnDomkhoDCOfllYD2aNWnOs7HjmRD9PBGmMwRuwpxeB36fArEx9g5TJFNK9m+2HDlycOnSJYoWLWrTvm/fPvLn1x+pIplBTMRtjn7xElVC1gOw3rc7DXp9hJOT40PWFBGRpHr99dfp378/ERERmKbJzp07WbBgARMnTuTrr79O0X1NmjSJggULMmfOHGtbkSJFrF+bpsmnn37KyJEj6dChAwDz5s3D19eX77//nj59+qRoPPJgu7dvoJZ5kmicKN6qn73DEcnUXJ0cea15SZ6s6M8bS3LR8lx1JjjNoi5HYM0oOLwE2k4F/4r2DlUkU0n2zKgXXniBN954g6CgIAzDwGKx8PvvvzNs2DA6d+6cGjGKSBq6df0SgZObUTFkPVGmI5vKvkvjvpNViBIRSWHdunVj9OjRDB8+nPDwcF544QVmzJjBZ599xnPPPZei+1q+fDnVqlXj2WefJW/evFSpUoWvvvrKujwwMJCgoCBatGhhbXN1daVhw4Zs27YtRWORh4vc/iUAf/o0wTWHv52jEckaSvp6srhvHbq2bkIvYxSvR/cmxMwGl/ZjzmwEa8dC9B17hymSaSR7ZtR7771H165dyZ8/P6ZpUrZsWWJjY3nhhRd4++23UyNGEUkjl08dJPa7ZylhCSLEzMafjb6gYeN29g5LRCTT6tWrF7169eLq1atYLBby5s2bKvs5deqU9el9b731Fjt37mTgwIG4urrSuXNngoKCAPD19bVZz9fXlzNnziS63cjISCIjI63vQ0NDgbszrcwUvgFw3DZTervpzZnz56l5az0Y4NOoX6LHm1XykVTKhy3lw1ZS8+FgQNc6RWhWJi8jl+Wi2clKjHGex5OOO2HrZMyjP0Gbz6BIvTSKPHXo/LClfNhKbj4eNW/JLkY5Ozszf/58xo0bx759+7BYLFSpUoUSJUo8UgAikj78tXs1eX7phje3OI8v4c8uoHr5AHuHJSKSaY0dO5aXXnqJ4sWLkzt37lTdl8VioVq1akyYMAGAKlWqcOTIEb744gubme2GYdisZ5pmvLZ7TZw4kbFjx8ZrDwkJSZViVNxTnB8UU0Z3bMV0ihjRnHEuhneByoSEhCTYL6vkI6mUD1vKh63k5sPTAT7rUJKVx3Lx9tq8/BSxjXed5+B7/W+Y15rI8i9wp/4IcPVK7dBThc4PW8qHreTmI+6DqOR65LshFi9enOLFiz/q6iKSjhxaPY+Svw/F1YjmqGNpcvVYTMl8Be0dlohIprZkyRLGjRtH9erVeemll+jUqRN58uRJlX35+/vHuyl6mTJlWLJkCQB+fn4ABAUF4e//72VhwcHB8WZL3WvEiBEMGTLE+j40NJSCBQvi7e2Nl1fK/pEWV9zy9vbOtH8s3ImMplzQ3e/JnUrdKHTPDebvlxXykRzKhy3lw9aj5uO52jloVakQE3/zpfnusrzhtJAXndbhevh7XE6vh8cnQZm2kMFyrPPDlvJhK7n5eNScJakYde8g42EmT578SIGIiH3s/t9Eqh6ZhINhstu9LqX6LcTTM2N+yiMikpEcPHiQI0eOMH/+fCZPnsyQIUNo1qwZL730Eu3bt8fDwyPF9lW3bl1OnDhh0/bnn39SuHBh4O6T/fz8/FizZg1VqlQBICoqik2bNjFp0qREt+vq6oqrq2u8dsMwUmVAH7fdzPrHwu51S2jAZcLwoESz7g89zsyej+RSPmwpH7YeNR85s7nywTOVaF8lPyOX5WX5tTpMdP6KYreCYFEXKPk4PPkReBdIpchTh84PW8qHreTkI1WLUfv27bN5v2fPHmJjYylVqhRwdzDj6OhIQIAu6RHJKExLLLtmDabGhW/AgG0521O939c4OzvbOzQRkSyjXLlyTJgwgQkTJvD777/z/fffM3jwYPr27fvI094T8tprr1GnTh0mTJhAx44d2blzJzNnzmTmzJnA3YHk4MGDmTBhAiVKlKBEiRJMmDABDw8PXnjhhRSLQxJnmiau+2cDcLpAOyq4ZbdzRCJyrzrFc/PboPpM3+BPm02P0ceyjFccf8b5z98wAzdjNHkbavYBBz30RyQpklSM2rBhg/XryZMn4+npybx588iZMycAN27coFu3btSvXz91ohSRFBUdFcHBz1+mRshqALYWeoW6XSdgOCT7AZsiIpJCsmXLhru7Oy4uLoSFhaXotqtXr86yZcsYMWIE48aNo2jRonz66ae8+OKL1j7Dhw/nzp079OvXjxs3blCzZk1Wr16Np6dnisYiCTt69BDVo3aBAQVbDrR3OCKSADdnR4a0KEXrSvkYsTQvy8/WYaLz11SP/hNWjYCDP0DbKeBfyd6hiqR7yf7L8+OPP2bixInWQhRAzpw5GT9+PB9//HGKBiciKe926A1OTH6cgJDVxJgObK/wLvW6v69ClIiIHQQGBvLee+9RtmxZqlWrxt69exkzZoz16XYpqXXr1hw6dIiIiAiOHTtGr169bJYbhsGYMWO4dOkSERERbNq0ifLly6d4HJKwoPVf4GCYnMgWQI6CZR++gojYTUlfTxb1qU3Xdq3oYYxjRHQPQk0PuLQfc2YjWDUSIm/ZO0yRdC3Zf32GhoZy+fLleO3BwcEp/imeiKSsa5fPEvRZU8pH7OW26cqhBjOp/bQ+fRURsYfatWvz2GOPsWjRIrp168aZM2dYv349PXv2xNvb297hSRq6HhJKlas/A+BSq4+doxGRpHBwMHipVmHWDG3MzTIv0jTyI36OrYVhWmD7NJheC/5cZe8wRdKtZD9N76mnnqJbt258/PHH1KpVC4A//viD119/nQ4dOqR4gCKSMs6fPIDj989Q3AzmGt5cafctVao2tHdYIiJZVuPGjfn6668pV66cvUMRO9u3cg5NjTCuOOShSB2Np0UyEl8vN754KYA1Rwvwzk9+LAn7g/HOsykQcg6+7whl29996p6nn71DFUlXkl2MmjFjBsOGDeOll14iOjr67kacnOjRowcffvhhigcoIv/dn3vWk/fnzuQgjPOGP+ZLSyhdXH/8iIjY04QJE+wdgqQDsRYTvxPfARD02HPkcdSDREQyouZlfald3IePVvnRYntpBjkuoYfTbzgd/RHz7/UYzcZAQDfQrTFEgEe4TM/Dw4Pp06dz7do19u3bx969e7l+/TrTp08nW7ZsqRGjiPwHBzYuocDy58hBGCecSuLWdx0FVYgSERFJF/b+sYFylj+JxpHHWvW3dzgi8h9kd3ViTNtyzH+lCcty96Vt5HgOWIphRIbCiiEwpxVcPmrvMEXShUcuy2bLlo2KFStSqVIlFaFE0qldv3xNmQ298DAiOehWjQKD1pLbN7+9wxIREZF/3Pn9SwBO5GqKey5/O0cjIimhSqGc/DygHm1atuI5y3jGRHfmtukG53Zgflkf1o2D6Dv2DlPErpJdjGrcuDFNmjRJ9JWaJk6ciGEYDB482NpmmiZjxowhX758uLu706hRI44cOWKzXmRkJAMGDCB37txky5aNtm3bcv78+VSNVcTeti+cRMCuYbgYsez1akKZISvI5qkb4oqIiKQX5y9coMatdQDkatzPztGISEpydnTglUbFWflaI/4q+hLNIj9kdWwAhiUGtnwMX9SBUxvtHaaI3SS7GFW5cmUqVapkfZUtW5aoqCj27t1LhQoVUiNGAHbt2sXMmTOpWLGiTfsHH3zA5MmTmTZtGrt27cLPz4/mzZvbPNlv8ODBLFu2jIULF7J161Zu3bpF69atiY2NTbV4RezFtFj4ffYb1D4+AQfDZFfup6g8cBHOLm72Dk1ERETucXzVDNyMaM44FyNf+Ub2DkdEUkFhn2x826MGr3dswpsuI+gT9RpBZk64fgq+aQdL+8Dtq/YOUyTNJfsG5p988kmC7WPGjOHWrVv/OaCE3Lp1ixdffJGvvvqK8ePHW9tN0+TTTz9l5MiR1if5zZs3D19fX77//nv69OlDSEgIs2bN4ttvv6VZs2YAfPfddxQsWJC1a9fSsmXLVIlZxB4ssbH8MaMvda/8D4AdBXtSo9uHGLpRoohIunDw4MEk973/AzjJXCKioil59gcAblfsBoZh54hEJLUYhkGHqgVoVCov763IS7O95Rjm9D86O63B4eBCzJOrMVqMh8ov6GeBZBnJLkYl5qWXXqJGjRp89NFHKbVJq/79+/Pkk0/SrFkzm2JUYGAgQUFBtGjRwtrm6upKw4YN2bZtG3369GHPnj1ER0fb9MmXLx/ly5dn27ZtiRajIiMjiYyMtL4PDQ0F7hbATNNM0eOL22ZKbzejUj5sJTUfUZGRHJj+EnVC1wCwq/RwanR6y7qNzELnhy3lw5byYUv5sJXcfKRG3ipXroxhGJimifGQPzg0gztz271uMfW4TBgelGre3d7hiEgayJXNhY87VqJD1fyMXJabH6/XY6Lz15S5cxZ+6gf7v4fWkyFPKXuHKpLqUqwYtX37dtzcUv4yoIULF7J371527doVb1lQUBAAvr6+Nu2+vr6cOXPG2sfFxYWcOXPG6xO3fkImTpzI2LFj47WHhISkSjEqblbZwwamWYHyYSsp+bgTfpuL87pRI3IHMaYDuyuOoXTTLoSEhKRlqGlC54ct5cOW8mFL+bCV3HzEfRCVkgIDA61f79u3j2HDhvH6669Tu3Zt4O546uOPP+aDDz5I8X1L+uKybw4Ap/K3o5JbdjtHIyJpqe5juVk5uAHTN+bn6Y3FeDl2BYOdluB+ZivmF3Ux6g6E+sPAxcPeoYqkmmQXo+Iuh4tjmiaXLl1i9+7djBo1KsUCAzh37hyDBg1i9erVDyx03T+gTMqnjQ/rM2LECIYMGWJ9HxoaSsGCBfH29sbLyyuJR5A0ccUtb29v/bGA8nG/h+Xj5o2rXJ/zLAHRR4gwnTnZcBo1G3dK6zDTjM4PW8qHLeXDlvJhK7n5SI2cFS5c2Pr1s88+y5QpU3jiiSesbRUrVqRgwYKMGjWK9u3bp/j+JX04fuww1SJ3ggGFWg6wdzgiYgduzo4MaV6StpXy8faPuWkeWIsxTnNpxr67Nzg/tBie+AhKtnj4xkQyoGQXo7y8vGwGZw4ODpQqVYpx48bZXAqXEvbs2UNwcDABAQHWttjYWDZv3sy0adM4ceIEcHf2k7//v4/CDQ4Ots6W8vPzIyoqihs3btjMjgoODqZOnTqJ7tvV1RVXV9d47YZhpMrgNG67+mPhLuXDVmL5CA46T8jMNpSznCIMdy49OZcKNVrZKcq0o/PDlvJhS/mwpXzYSk4+Ujtnhw4domjRovHaixYtytGjR1N132Jfl9Z9TmnD5IRHAKUKlbN3OCJiR4/lzc6CXrVYurcgr//iT/XI7Yx2/ob8N8/A989CmbbQ6n3wzm/vUEVSVLKLUXPnzk2FMBLWtGlTDh06ZNPWrVs3SpcuzRtvvEGxYsXw8/NjzZo1VKlSBYCoqCg2bdrEpEmTAAgICMDZ2Zk1a9bQsWNHAC5dusThw4c1BV4ytAtnTxE9pw0lzPNcx5uwZ/9HyXK17B2WiIgkUZkyZRg/fjyzZs2yzgCPjIxk/PjxlClTxs7RSWoJCQ2j0pWfwQDHWr3tHY6IpAOGYfB0QAGalM7LxN/8aL67AoOcltDD6Tecji3H/Hs9RuO3oEYfcEyxO+2I2FWyz+RixYqxa9cufHx8bNpv3rxJ1apVOXXqVIoF5+npSfny5W3asmXLho+Pj7V98ODBTJgwgRIlSlCiRAkmTJiAh4cHL7zwAnB3Kn6PHj0YOnQoPj4+5MqVi2HDhlGhQgXr0/VEMpozfx/D8dt2FOEywYYPsS/9SOHieuqSiEhGMmPGDNq0aUPBggWpVKkSAAcOHMAwDH755Rc7RyepZd/KOTQywgg28lC87tP2DkdE0pGc2Vz44JlKPF21ACN/zM2yK/V5z3kWAVEnYdVbsH8BtP4ECla3d6gi/1myi1GnT59O8OkukZGRXLhwIUWCSo7hw4dz584d+vXrx40bN6hZsyarV6/G09PT2ueTTz7BycmJjh07cufOHZo2bcrcuXNxdHRM83hF/qu/j+0n2w9P48dVLhp+uHRfTt6CeuKGiEhGU6NGDQIDA/nuu+84fvw4pmnSqVMnXnjhBbJly2bv8CQVWCwmeY9/C8DFEs+T19HZzhGJSHpUs5gPvw6sz1db8vPiukK0i93ACKfvyXH5EOas5hgBXaHZaHDP+dBtiaRXSS5GLV++3Pr1qlWr8Pb2tr6PjY1l3bp1FClSJEWDS8jGjRtt3huGwZgxYxgzZkyi67i5uTF16lSmTp2ausGJpLLjB3aQe9mz5CaEcw4FyN77V3L6FX74iiIikq5ER0dTqlQpfvnlF3r31qVaWcX+nRupavmTaNORkq1esXc4IpKOuTg50L/xY7Su6M/bP+amyckA3nL+nmccN8OeOXDsZ2g5ASp2BN0XUjKgJBej4p7oYhgGXbp0sVnm7OxMkSJF+Pjjj1M0OBH51+Hdmyj4y4vkMG4R6FQMn74r8Mqdz95hiYjII3B2diYyMlI3ls9iwn+fAcCxXE2pmEu/w0Xk4Qr7ZOOb7jX4+WBBxv2ch0W3GzLeeTYlwi/Ast6w71t4cjLkKWnvUEWSxSGpHS0WCxaLhUKFChEcHGx9b7FYiIyM5MSJE7Ru3To1YxXJso7tWk+RX54jh3GLk86lyfvqGhWiREQyuAEDBjBp0iRiYmLsHYqkgYtBF6kWug6AXI362TkaEclIDMOgbaV8rBvakBI1W/Jk9EQ+iO5EBM5wegvmF3Vg3bsQfcfeoYokWbLvGRUYGJgacYhIInavX0alrX3xMCI54VqRwgN+xi17DnuHJSIi/9GOHTtYt24dq1evpkKFCvHuE7V06VI7RSap4cRvM2hsRHPGuRiFKzaydzgikgF5uzszvn0FOlQtwFtLc7L8cm3GOc2lCfthy0dweDE88TGU0IO6JP1LUjFqypQp9O7dGzc3N6ZMmfLAvgMHDkyRwEQE/lg5nyrbB+FqRHPEozolBvyIi3t2e4clIiIpIEeOHDz9tJ6mlhVERkfz2JkfAAir0FX3dxGR/6RqoZz8PKAec37PT/81fjSI+oOxzt/gd+M0zH8ayraDVu+Dl66kkPQrScWoTz75hBdffBE3Nzc++eSTRPsZhqFilEgK2bp8NjX3DMPZiOVAtrqUHbAYZzcPe4clIiIpZM6cOfYOQdLI3vVLqE0QYXhQunl3e4cjIpmAs6MDvRsU54kK/oxZnpumxyow2GkJ3ZxW4nT0J/hrHTQeCTV6g2OyL4gSSXVJOivvvTRPl+mJpL5Ny2ZSd/8bOBkWDuRoRsGXvsDJ1d3eYYmIiMgjcNozG4C/8rWjirunnaMRkcykQE4PvupcjVVHCjJmeU6WhdXjPefZVIn6C1aNgP3z4cmPoVAte4cqYiPJNzCPM27cOMLDw+O137lzh3HjxqVIUCJZ2cYlM6yFqIM+rajw6kIcnFzsHZaIiKSCxYsX07FjR2rVqkXVqlVtXpI5nDxxhIDInQAUajnAztGISGZkGAatyvuxdmhDatVpzDPRY3grugchZIPLh2F2S/ixH9y6Yu9QRaySXYwaO3Yst27ditceHh7O2LFjUyQokaxqw6LPqX/wzbuFqNxPUqHffAxNqxURyZSmTJlCt27dyJs3L/v27aNGjRr4+Phw6tQpHn/88VTd98SJEzEMg8GDB1vbTNNkzJgx5MuXD3d3dxo1asSRI0dSNY6s4MLaz3EwTI55VMOncDl7hyMimVh2VyfeaVOWn15twGH/DjSO+JiFMY3uLtw/H6YFwM6vwBJr1zhF4BGKUaZpYiRw08UDBw6QK1euFAlKJCta/78pNDg8EkfD5GDetlTo960KUSIimdj06dOZOXMm06ZNw8XFheHDh7NmzRoGDhxISEhIqu13165dzJw5k4oVK9q0f/DBB0yePJlp06axa9cu/Pz8aN68OWFhYakWS2YXeiuMisHLAXCs0dPO0YhIVlE+vzfL+tXltXa1ec+pHx0ix3DEUhgiQuDXYfB1Exwv7bN3mJLFJbkYlTNnTnLlyoVhGJQsWZJcuXJZX97e3jRv3pyOHTumZqwimdb6hZ/S6Mg7dwtRvk9Roe9cDAdHe4clIiKp6OzZs9SpUwcAd3d3a9Hn5ZdfZsGCBamyz1u3bvHiiy/y1VdfkTNnTmu7aZp8+umnjBw5kg4dOlC+fHnmzZtHeHg433//farEkhXs/20uuYwwgo3clKj/jL3DEZEsxNHB4OXaRVg3tCGFKzemTdR7vBPdhTA8MC4dIPsPT8HPgyD8ur1DlSwqydMuPv30U0zTpHv37owdOxZvb2/rMhcXF4oUKULt2rVTJUiRzGzd9x/R+MR4HAyTg37PULH3V+CQ7EmLIiKSwfj5+XHt2jUKFy5M4cKF+eOPP6hUqRKBgYGYppkq++zfvz9PPvkkzZo1Y/z48db2wMBAgoKCaNGihbXN1dWVhg0bsm3bNvr06ZMq8WRmpmmS+9g3AFx47HnyOjrbOSIRyYryerrxSafKPFutAKN+9OTXK7UY4fw9Tztugb3z4NhyaDYGqnTW3yCSppJcjOrSpQsARYsWpU6dOjg76xeqyH+19rsPaPbXe2DAwXzPUrHXV5DAZbAiIpL5NGnShJ9//pmqVavSo0cPXnvtNRYvXszu3bvp0KFDiu9v4cKF7N27l127dsVbFhQUBICvr69Nu6+vL2fOnEl0m5GRkURGRlrfh4aGAncLMSldUIvbZmoV6lLagZ0bqWz5k2jTkRKtXsny+Uhtyoct5cOW8gG1i/nw68D6fL31FCPX52RhZGPGO8+h1J1z8PMgzL3fwBMfQ77K9g41zen8sJXcfDxq3pJUjIobWABUqVKFO3fucOfOnQT7enl5PVIgIlmJaZqs+/Z9mp16H4AD+Z+nUs8vVIgSEclCZs6cicViAaBv377kypWLrVu30qZNG/r27Zui+zp37hyDBg1i9erVuLm5Jdrv/vuCJnav0DgTJ05M8AE2ISEhqVJ8iXuIzoNiSi/CNn8BwGHvRhR1zJbi9wHLaPlIbcqHLeXDlvLxrxer5KFR0exMWJmNJ08/RhfH1QxxXkK2C3swv2pMVMWXiKjzOqab98M3lkno/LCV3HzcWy9KjiQVo3LkyPHQIOIGK7GxujO/yIOYpsnabybSPHASAAcLvkSl7tNUiBIRyWIcHBxwuOeSiI4dO6ba/Tf37NlDcHAwAQEB1rbY2Fg2b97MtGnTOHHiBHB3hpS/v7+1T3BwcLzZUvcaMWIEQ4YMsb4PDQ2lYMGCeHt7p/gHlHHFLW9v73T/x0LQ5YtUv7UeDMjV5FWb21uklIyUj7SgfNhSPmwpH7a8vLyY1smNnRciGPtLdn6+WZu3nOfT3nEbrge/xeWv36D5WKj0PBiZ/9I9nR+2kpuPR81ZkopRGzZseKSNi0h8q+d/TMu4QlShzlTsNkWFKBGRLKhu3bo0bNiQRo0aUbduXbJly5Zq+2ratCmHDh2yaevWrRulS5fmjTfeoFixYvj5+bFmzRqqVKkCQFRUFJs2bWLSpEmJbtfV1RVXV9d47YZhpMqAPm676f2PhRMrZ9LIiOa0UzGKVGqcar/nM0o+0oryYUv5sKV82DIMgxbl/KhfMg9T1//FsM25+CG2Me86z+Wx8AvwU3/Y+w088RH4V3z4BjM4nR+2kpOPVC1GNWzYMEkb279//yMFIZJVrF44leYnx4MBRwq+oEKUiEgW1rp1azZt2sS0adOIiIggICDAWpyqV68e2bNnT7F9eXp6Ur58eZu2bNmy4ePjY20fPHgwEyZMoESJEpQoUYIJEybg4eHBCy+8kGJxZAXRMTEUP70QgNAKXfR7XkTSNQ8XJ95oVZoOVfLz9o85eTywFN0cf+M152W4n9sBMxtCjd7Q+C3IQpfuSer7z3PuQkJCmD59OlWrVrWZ+i0ittYumUmTY+/gYJgcyfcM5bpP1wBVRCQLGzFiBCtXruTGjRts3ryZdu3asX//ftq2bYuPj0+axzN8+HAGDx5Mv379qFatGhcuXGD16tV4enqmeSwZ2d71SyhIEGF4ULp5D3uHIyKSJCV8PVnYuxaTOgawxO1pGkd8yC+xNcG0wI4ZMLUaHPgBdJNvSSFJfpre/davX8/s2bNZunQphQsX5umnn2bWrFkpGZtIprH+p3k0OPgmToaFo3nbUK6nnponIiJ3nTx5kgMHDnDgwAEOHjyIl5cX9evXT/X9bty40ea9YRiMGTOGMWPGpPq+MzPHPXfHwyf921LVQ4U8Eck4DMOgQ9UCNC3ty4erjzNghw8/xB5knMs8it6+BMt6w9558PgH4Ff+4RsUeYBkFaPOnz/P3LlzmT17Nrdv36Zjx45ER0ezZMkSypYtm1oximRom379gbp7h+BixHLMpzll+swFh8x/I0AREXmwTp06sXnzZiwWCw0aNKBBgwaMGDGCihUz/705MqtTJ49SNWInGFCg5QB7hyMi8ki8PZwZ374CzwQU5O0fvWh5oQw9HVcwyPknXM/8Dl/Wh+o97166557T3uFKBpXkv4ifeOIJypYty9GjR5k6dSoXL15k6tSpqRmbSIa3de2P1NjxKq5GDMdyNKT0K99jOD7yhEQREclEFi1aRGxsLF26dKF79+5069ZNhagM7vyaz3EwTI66B5C3iGYNiEjGVrlgDn7qX4+RbSvzrdMzNIn4gF9ja9y9dG/nTJgaAHvmgcVi71AlA0pyMWr16tX07NmTsWPH8uSTT+Lo6JiacYlkeNs2/kqVLb1xN6I44VWb0q8uwnBysXdYIiKSTly/fp2vv/6amJgY3n77bXLnzk3NmjV54403+O233+wdniTTrdu3KH/5JwAcavS0czQiIinD0cGgS50irBvWkGqVK9EvejAvRL3FKQpA+DX4eSB83QTO77Z3qJLBJLkYtWXLFsLCwqhWrRo1a9Zk2rRpXLlyJTVjE8mwdmxdS4UN3clmRPJntmqUeHUZhlP8R1+LiEjWlSNHDtq2bcvkyZPZs2cPR44coWzZskyePJnWrVvbOzxJpv0r55LLCOOykZtSDZ61dzgiIikqr6cbnz1Xhfk9axLkU5MWERN4N/olwg0PuLgPvm4KP/aDW8H2DlUyiCQXo2rXrs1XX33FpUuX6NOnDwsXLiR//vxYLBbWrFlDWFhYasYpkmHs2bGFUms642nc4S/3ihQf8BMOLu72DktERNKZ69evs2zZMgYNGkSlSpUoVaoUK1asoF27dkyZMsXe4UkymKaJz9F5AJwv/hyGo7OdIxIRSR11H8vNb4PqM7hFWb4zWtPwzkcssTS8u3D//LuX7m2fDrHR9g1U0r1k30XZw8OD7t27s3XrVg4dOsTQoUN5//33yZs3L23btk2NGEUyjP17d1Dk1xfIYdzmlGtZigz4BUe37PYOS0RE0qE8efLQt29fLly4QK9evdi/fz/BwcEsXryY/v372zs8SYbDuzdRJvZPokxHSj7ez97hiIikKlcnR15tUoK1QxpSoXRJhkb14anIsRwzHoPIUFg1AmbUg1Ob7B2qpGP/6ZFepUqV4oMPPuD8+fMsWLAgpWISyZAOH9pLvp864WOEctqlBAUG/IqTh7e9wxIRkXTqwIEDXL58mcWLF/Pqq69SvrxueJ1RhW2ZAcDRnE3w9Mlv52hERNJGwVwezOpSjZkvBxDsXZEn7ozhjehehDp4w5Xj8E1b+F9nuHnO3qFKOpQiz5d3dHSkffv2LF++PCU2J5LhHDt2CJ/Fz5DXuME55yL49f8Nl+x6zKmIiCQurvgUHBzMli1b2Lp1K8HButdGRnPl8iWqhqwFwLuBZkWJSNZiGAYtyvmxdkhD+jcuyTKaUi/8Q76NbYkFBzj6E0yrDps+hOgIe4cr6UiKFKNEsrI//zxB9h+ext+4xgXHAuR+ZSVu3nnsHZaIiKRzoaGhvPzyy+TLl4+GDRvSoEED8ufPz0svvURISIi9w5MkOr5yBm5GNIFOxShapbG9wxERsQt3F0eGtSzFysH1qVSiCKOiu/BE5AT2OZSFmDuwYTxMrwnHfwXTtHe4kg6oGCXyH5wK/BuX79tTkMsEOfiR45XfcM/lb++wREQkA+jZsyc7duxgxYoV3Lx5k5CQEH755Rd2795Nr1697B2eJEFMTAxFAhcCEFK+CxiGnSMSEbGvYnmy8033Gsx4qSqhXiV5KnwkA6Je5bpjbrhxGhY+D/Ofgat/2TtUsTMnewcgklGdOXcWc147inCRYIc8ZOvzG9lyF7J3WCIikkGsWLGCVatWUa9ePWtby5Yt+eqrr2jVqpUdI5Ok2r9xKdUIIgwPyrTobu9wRETSBcMwaFXenwYl8zB1/V98vcWBdberMtDlJ3o6/orTX2thei2o3Q8avA6unvYOWexAM6NEHsH5ixeJnN2W4pzjqpEL1+4r8PQtZu+wREQkA/Hx8cHbO/6DLry9vcmZU/cdzAiM3bMAOOHXBlcPLztHIyKSvni4OPFGq9L8NqgBVR8rwPtRnWgW8T7bHKqBJRp+/+zu/aQO/AAWi73DlTSmYpRIMl26HEzoV20paQZyw/DGsevPeBcoZe+wREQkg3n77bcZMmQIly5dsrYFBQXx+uuvM2rUKDtGJklx+q9jVLmzA4ACLQbYORoRkfTrsbzZ+bZHDT5/oSoRnkV5IXwI3aOGEeyUD8IuwbLeMLsFnN9j71AlDekyPZFkCL52jStftqOieZIQPLG89CM+hfUobhERSb4vvviCv/76i8KFC1Oo0N3LvM+ePYurqytXrlzhyy+/tPbdu3evvcKURJxfM40ihskRtwDKFatg73BERNI1wzB4sqI/jUrlYcq6k8zaalDvVgV6Oa9ikMuPuJzfBV83gUrPQ9PR4KX78GZ26boYNXHiRJYuXcrx48dxd3enTp06TJo0iVKl/p2FYpomY8eOZebMmdy4cYOaNWvy+eefU65cOWufyMhIhg0bxoIFC7hz5w5NmzZl+vTpFChQwB6HJRnU1Rs3uDC9PVUsRwnDg6gXlpCneFV7hyUiIhlU+/bt7R2CPKLw8FuUDfoJDDCr97R3OCIiGUY2VydGPFGGZwIK8M5PR/j8VGv+F12XsdmX8kTMOjiwAI4uhwZDoVZ/cHazd8iSStJ1MWrTpk3079+f6tWrExMTw8iRI2nRogVHjx4lW7ZsAHzwwQdMnjyZuXPnUrJkScaPH0/z5s05ceIEnp53b4Q2ePBgfv75ZxYuXIiPjw9Dhw6ldevW7NmzB0dHR3seomQQV2/c5Ozn7agae5DbuBH+7P/wLVnT3mGJiEgGNnr0aHuHII/owKp51DbCuGzkpmzDZ+0djohIhlPC15Pve9Xk54OXGP/LUfqF9aCi0YhPvBZQPPIorBsHe+ZBy/egdGs9rTQTStf3jFq5ciVdu3alXLlyVKpUiTlz5nD27Fn27Ll7Lalpmnz66aeMHDmSDh06UL58eebNm0d4eDjff/89ACEhIcyaNYuPP/6YZs2aUaVKFb777jsOHTrE2rVr7Xl4kkFcu3GTM5+3o2rMAcJxI6TDAnzL1bd3WCIiImIHpmmS8/A8AM4Wew4HJ2c7RyQikjEZhkHbSvlYN7QhPesV5YjxGE1DRjLM8iq3XPLCzTPww0swrw0EHbZ3uJLC0nUx6n4hISEA5MqVC4DAwECCgoJo0aKFtY+rqysNGzZk27ZtAOzZs4fo6GibPvny5aN8+fLWPiKJuXbjJoGftycgZj/huHGzwwLyVWxi77BERCQDc3BwwNHRMd4rZ86c1KpVi6VLl9o7RHmA43s3Uzr2BFGmIyVbvWLvcEREMjxPN2febl2WXwfWp0ZRHxZH1aF66Pt849KRWEdXOL0FvqwPvwyB29fsHa6kkHR9md69TNNkyJAh1KtXj/Ll794wOigoCABfX1+bvr6+vpw5c8bax8XFJd4jkn19fa3rJyQyMpLIyEjr+9DQUGscpmn+9wO6R9w2U3q7GVV6yce1myGc/vwpqsXsIxw3brT/jnwVGqd5XOklH+mF8mFL+bClfNhSPmwlNx+plbdly5Yl2H7z5k127tzJSy+9xLx583j2WV3+lR6FbJ4BwJEcjamSR/cfFRFJKaX8PPmhdy1+2n+R8SuO8U5oe2YadZnis5SqtzbB7llweDE0GgHVe4KjZqZmZBmmGPXqq69y8OBBtm7dGm+Zcd/1o6Zpxmu738P6TJw4kbFjx8ZrDwkJSZVi1K1bt4D4x5IVpYd83Ay9xZV5L1M9di93cOVCiy/JU6S6dXZeWkoP+UhPlA9byoct5cOW8mErufmI+yAqpbVr1y7RZV26dKFs2bJ89NFHKkalQ9euBFH55howwKtBP3uHIyKS6RiGQfsq+WlSJi+frPmTedugw9U+NHBpxGSvBeS+9SesfBN2z4ZWE+GxZvYOWR5RhihGDRgwgOXLl7N582abJ+D5+fkBd2c/+fv/++jH4OBg62wpPz8/oqKiuHHjhs3sqODgYOrUqZPoPkeMGMGQIUOs70NDQylYsCDe3t54eXml2LHBv5+8ent7648F7J+P4KvXuD7vBarHHuAOrlxv+y2PVWnx8BVTib3zkd4oH7aUD1vKhy3lw1Zy82GvnLVo0YK3337bLvuWBzv22wzqGdGccixG8aq6bF9EJLV4uTkzuk05ng0oyDs/HWbzmVLUuPoOA3Js41VzIc5X/4TvnoaSraDFe5D7MXuHLMmUrotRpmkyYMAAli1bxsaNGylatKjN8qJFi+Ln58eaNWuoUqUKAFFRUWzatIlJkyYBEBAQgLOzM2vWrKFjx44AXLp0icOHD/PBBx8kum9XV1dcXV3jtRuGkSqD07jt6o+Fu+yVj/OXLnHzq3ZUtZy4e2leu2/IX6VlmsaQEJ0ftpQPW8qHLeXDlvJhKzn5sFfO7ty5g5ubHmWd3sTGxlIkcAEAN8p11pOdRETSQNl8XizqW5uley8w8bdjfHazHnOowqf+a2gcshTjz5Xw1zqo2QcaDgc3b3uHLEmUrm9g3r9/f7777ju+//57PD09CQoKIigoiDt37gB3B4mDBw9mwoQJLFu2jMOHD9O1a1c8PDx44YUXgLuffvbo0YOhQ4eybt069u3bx0svvUSFChVo1kxT+uRfgacDCZ/ZivKWE4SSnbCOS9JFIUpERLKWr776yvohm6QfBzYupYAZRBgelGvZw97hiIhkGYZh8HRAAdYNbUSX2oW5ZWSj+6X2PBnzIWdy1QNLNGyfBlOqwp55YIm1d8iSBOl6ZtQXX3wBQKNGjWza58yZQ9euXQEYPnw4d+7coV+/fty4cYOaNWuyevVqPD09rf0/+eQTnJyc6NixI3fu3KFp06bMnTsXR0fHtDoUSedOnDiG64IOlOQi140cmC8txbd4gL3DEhGRTOje2wDcKyQkhN27d/P333+zZcuWFN3nxIkTWbp0KcePH8fd3Z06deowadIkSpUqZe1jmiZjx45l5syZ1jHV559/Trly5VI0lgxr99cAHPdtQ/VsKXvLBhEReThvd2fGtivPs9UKMnr5EfacgYYX+/F8jsaMcpmPR+gp+Hkg7PwKWr4HxRraO2R5AMPUI3aSJDQ0FG9vb0JCQlLlnlEhISG6p8c/0jofBw/uIfeSjuQzrhLskAeX7j+To0CZVN9vUun8sKV82FI+bCkftpQPW8nNR2r97m/cuHGC7V5eXpQuXZp+/fpRuHDhFNsfQKtWrXjuueeoXr06MTExjBw5kkOHDnH06FGyZcsGwKRJk3jvvfeYO3cuJUuWZPz48WzevJkTJ07YfMj3IJl1vHT+1HHyzauFg2Fy8aWt5HusQpruPyH6/21L+bClfNhSPmxlhnyYpsmyfReY8Otxrt6KxJkY3su/nWfC5uMQ9c8DSEo9Ac3ffej9pDJDPlJSWo2X0vXMKJHUtn3Lakqs7UluI4QLjgXw7rOC7HmL2DssERHJxDZs2JDm+1y5cqXN+zlz5pA3b1727NlDgwYNME2TTz/9lJEjR9KhQwcA5s2bh6+vL99//z19+vRJ85jTk7Orp1HAMDnsVpXy6aAQJSKS1RmGQYeqBWhW1pcpa08yZ9tphl+oz2SnKnxZcC0Vg5ZgnPgVTq6G6r3u3k/KI5e9w5Z7pOt7RomkpnU/zaPS2pfIbYRwxuUxcr26ToUoERHJEkJCQgDIlevuwDwwMJCgoCBatPj36bGurq40bNiQbdu22SXG9OJO+G1KB/0IgKWa7hUlIpKeeLk583brsvw2qD61i/kQFJOddoHtedn1M674NwJLDOz4AqZUge3TISbK3iHLPzQzSrIci8Vk9TcTaB74IY6GyQnPmhR/ZRFOHnrygoiIZH6maTJkyBDq1atH+fLlAQgKCgLA19fXpq+vry9nzpxJdFuRkZFERkZa34eGhlr3kdJ3gojbZlrfYeLgqrnUJIzLRm7KNuyY5vtPjL3ykV4pH7aUD1vKh63MmI8SebMzv2cNVhwK4r0Vx9h6MxfVb/amf6FWDIydh+u1Y7BqBOaur6H5uLuX8P1zCVpmzMd/kdx8PGreVIySLCUyOoZNXwyk1fX5YMBRv3aU6fk1hpOLvUMTERFJE6+++ioHDx5k69at8Zbdf28I0zQfeL+IiRMnMnbs2HjtISEhqVKMunXrVoJxpibvw3MBOFmgA663w9Nsvw9jr3ykV8qHLeXDlvJhKzPno35hD5b2rMzX28/zzY4LfH62EF87vs3Hxfbz+NXZOF7/G354kegCtYloMIrYvOUydT4eRXLzEfdBVHKpGCVZxtUbNzg+owstIjcBcLRkf8o+/561Ii4iIpLZDRgwgOXLl7N582YKFChgbffz8wPuzpDy9/e3tgcHB8ebLXWvESNG2DwdMDQ0lIIFC+Lt7Z0qNzAH0vQGsyf2baF07J9EmY6UbT0Qb+/0M4vaHvlIz5QPW8qHLeXDVmbPhzcwqm0uXqxdjHG/HGPTn1d49WQVint9xoySm3ns73k4n9+O0/dPQuUXMRuPhOzZM20+kiu558ej5kzFKMkSTv51gtj5z1PP/Jto05HA2u9RttUr9g5LREQkTZimyYABA1i2bBkbN26kaNGiNsuLFi2Kn58fa9asoUqVKgBERUWxadMmJk2alOh2XV1dcXV1jdduGEaqDOjjtptWfyyEbP4CgEPejQnwLZgm+0yOtM5Heqd82FI+bCkftrJCPorn9WRut+qsOXqZcb8c5e8bd2h+sBFtC9fjPc8leP71E+z/Do4sw61aH4zGwzBcstk77HQhOefHo55DuoG5ZHo7Nq8ix7ctKG3+TQieXG7/AyVViBIRkSykf//+fPfdd3z//fd4enoSFBREUFAQd+7cAe4OJAcPHsyECRNYtmwZhw8fpmvXrnh4ePDCCy/YOXr7uHn1MpVurAHAs77GDSIiGZFhGLQo58faIQ0Z3KwErk4OLD/jRJWjzzGn9Exi81XDiL6N+/bJMK06HPgBLBZ7h50lqBglmZZpmqxZOIXK614kj3GTc05FMHpvoECV5vYOTUREJE198cUXhISE0KhRI/z9/a2vH374wdpn+PDhDB48mH79+lGtWjUuXLjA6tWr8fT0tGPk9nP0txm4GdGccixKiYAm9g5HRET+AzdnRwY3K8naIQ1pUdaXGIvJ2P3ZqRX8JjuqfkisZ36M0AuwrDd83RTOZO0nyaYFXaYnmVLY7XB2znyV5iFLwIDjXnUp3ncBznpinoiIZEFJuZm4YRiMGTOGMWPGpH5A6ZwlNpZCpxYAcL1sF4o56PNbEZHMoGAuD2Z2rsbGE8GM/fkogVdv02lbfmrk/4yp1Xfhe2A6XNwLcx6/+8S9ZmMhT0l7h50p6TerZDp///UnZz5uTNOQJQAcLtaD0oN/USFKREREkuTgxsUUMC8RigflWvawdzgiIpLCGpXKy8rB9RneqhTuzo7svBBF7a2VmFTyeyIrdwXDEU78CtNrwS+vQdhle4ec6agYJZnK1tWLyfFtU8pbjhOGB6eafU35zpNBn2iKiIhIEjnsmgnAMb/2uGdP2acCiohI+uDq5Ei/Ro+xdkgDWpbJjcWEL3aHUftQa1bUX4pZ6gkwY2H3bJhSBTZOgshb9g4709Bf6JIpRERGsWbGMGr/3hMfI5QzzsWI7bmRYvWetXdoIiIikoGcObGPihG7sZgGBVsOsnc4IiKSyvLlcGdSu1J837MmJfJm5/rtKPqvvk37a/05+cT/IH8ARN+GjRNgalXYMxdiY+wddoanYpRkeH/9dZwTHzSmedBXOBomh/O2pcCw38lRoJS9QxMREZEM5tKaKQAczFabfEVL2zkaERFJK7WL+/DroPqMal0WT1cnDpwPofnSGN7I8Qlhrb+CnEXg1mX4eRDMqAsnVkIS7skoCVMxSjIs0zTZuPQrcn/bhEqxhwnHjeO1JlG+37c4unrYOzwRERHJYEJuXqPilRUAONV5xc7RiIhIWnN2dKBHvaKsG9aQDlXzA/DDnvPU/cWb76otIrbFRHDPCVeOw4JOMK8NXNhr56gzJhWjJEO6dv0aWz9+nkYHh5HDuM0pl1JE9NhI6VZ97R2aiIiIZFBHf/4MDyOS0w6FKFentb3DERERO8nr6cbkjpVZ3Lc2Zf29CI2I4e2fT9J6VwX2tt8IdQeDoyuc3gJfNYbFPeDGaTtHnbGoGCUZzva1S4mYUov6t37DYhocKNqDosO3kqtgGXuHJiIiIhlURPgtSvw9D4ArFXtj6OEnIiJZXrUiufh5QD3ebV8eb3dnjl0KpcOcIwy+1p7grtug0vOAAYcXw7TqsGokhF+3d9gZgn7LSoZx7fo1Nn/yMrW3diM/wVw28nK2zQ9U6jIZw8nF3uGJiIhIBrZ/+TRyc5NL5KHyE73tHY6IiKQTjg4GL9cqzIZhjXi+RiEMA37cf5FGX/3FFzlfJ6rnBijWCGKjYPs0mFIZfp8C0RH2Dj1dUzFK0j3TNNm+ZjGRU2rSIGQ5APv9nibnsN0UqdbSztGJiIhIRhcVGUGR418BcLZML5xdXO0ckYiIpDe5srkwsUMFfupflyqFchAeFcuklcdpseAm66t/CS8tgbzlICIE1oyCqQGw7zuwxNo79HRJxShJ186dDWTbh09T+/ce5OMKQQ55CXxiAZX7zsYlm7e9wxMREZFM4MCKL/HjKlfJQaW2r9o7HBERSccqFsjBkr51mNyxEnk8XTl9LZzu8/bQdYsXp57+Ddp9Dl75IfQ8/NQfvqgDx1foyXv3UTFK0qWoqGg2fvse3rPqUDd8HRbTYL/fs+QatoeiNZ6wd3giIiKSSURGhJP/0DQA/nqsK27u2ewckYiIpHcODgYdqhZgw7BG9GlYDGdHg40nrtByyu9MvBRAWK8/oPm74Jbj7pP3Fr4As1rA6d/tHXq6oWKUpDv7t6/hxhctaHzqQ7yMcP52LsnFjiuo3PdrXDy87B2eiIiIZCL7lnxIPjOYYHJR6alh9g5HREQykOyuTox4vAyrBjegUak8RMeafLn5FE2m7GSJWwcsA/dDvSHg5A7nd8LcJ2D+sxB02N6h252KUZJuBJ48yh8ftKPK6mcpZZ4ilGwcqPQOxd7cToFyde0dnoiIiGQyIdevUObklwCcqTAI92yedo5IREQyomJ5sjO3Ww1md61GER8ProRFMnTRAZ6ee5SDpQfBoP1QrTsYjnByNcyoB0t7w43T9g7dblSMEru7djWYzZ+/Qr7v6lMrfCMW02B3jlbw6k4qPTUUw9HJ3iGKiIhIJnTsf6Px5jaBDoWp2k73ihIRkf+mSWlfVr3WgDdalcbDxZF9Z2/S7vPfeWNVMFcbvQ+v7oJyTwEmHPwBplaDX4fDrSv2Dj3NqRgldhMScpPNc0bhMLUqDa58j6sRwzH3qlzstJLHun6Jp09+e4coIiIimdSZE/upeukHAELrj8LRSR9+iYjIf+fq5MgrjYqzYVgjnqqSH9OEH3afo/FHG5l1zIHoDrOh90Yo1hgs0bDzS5hSGTZMhMgwe4efZlSMkjQXFhbClnnvEPtJBRqcmUJOI4yzjgU53mQWZYavJ3+ZmvYOUURERDIxS2wstxf3x8WI4aBbdSo2fNreIYmISCbj6+XGJ50qs7hvbcrn9yIsIoZ3fznK459tYevtgtD5R+j8E+SrAlG3YNP78Fll+GMGxETaO/xUp2KUpJmQG9fZ+s1ooj6uQP3Az8hFKBcNPw4ETKDAiH2UbvAMGIa9wxQREZFMbtfSTykbfZjbpiu5n/scw0FDYhERSR3ViuTip/71mNihArmyufBX8C1emrWDPt/u5lyOGtBrAzw7F3IVh/CrsPINmFYNDiwES6y9w081mo8sqe7imb8IXDGZCpeXUc8Iv9tm+HK58gAqPtGHfM4udo5QREREsoorF09T5shHABwsOYDaRUrZOSIREcnsHB0Mnq9RiCfK+/PJ2j/59o8zrDpymQ0nrtCnQTFeadQGj9KtYd+3sHES3DwLy/rA71Og6TtQsmWmm7ihYpSkCtNi4fi+zYRtnEaV0PXkM2LBgHMO+blasS8Vn+yrIpSIiIikqdiYGILndqYc4fzpVJIanUbYOyQREclCvD2cGdO2HM/XKMTYn4+w7e9rTF3/F4v3nOetJ8rQOqAbRsXnYMcM2PopBB+BBZ2gQA1oOgqKNrD3IaQYFaMkRYXcvM7RlV+T+88FlLGcuttowDHXilhqvUrZhs9Q0MHRvkGKiIhIlrTz25HUjjpAuOmKW8evdNNyERGxi1J+nszvWZOVh4MYv+IYF27eYcCCfXz7xxnGtClH2fpDIKArbP0Edn4F53fCvDZQtOHdmVIFqtn7EP4z/QaW/yw2Npajf6wkfNd8yt9YS23j7s3WokwnDudoRM6mgylTsb6doxQREZGsbP+a+dQ8/SUYcLjKGGqUrGzvkEREJAszDIPHK/jTuHRevtx0iukb/2Jn4HVaT93CizULM6R5SXK2eBdq94fNH8GeuRC4Cb5uCqWegMYjwa+8vQ/jkakYJY/EtFg4sW8zN3Z8T/HgNVTg+t0FBpx1KMDlEs9RqmVvqubytW+gIiIikuX9tW8zJbe+hoNh8ofPU9Rq38/eIYmIiADg5uzIoGYleDogPxN/Pc6KQ5f49o8z/HzwIkNblOKFGoVwfPIjqDMANk2CAwvgxK9w4jco3wEavQW5H7P3YSSbilGSZBF3bnNix0ruHF5BwWtbKW1eti4LxYM/czbCs1ZXSlZvTiE9lUZERETSgVMHt5Hnp+fwMCI55FqVgL4z7R2SiIhIPAVyevD5i1V58e+rjF1+lBOXwxj142G+33GWMW3KUrNYYWg/HeoOho0T4MgyOLwEjvwIlV+Ahm9AjoL2Powky1IVg+nTp1O0aFHc3NwICAhgy5Yt9g4pXbPExnLq8A7+WDCRfR88juX9olTa2J1aV5eQ37xMuOnKHs8mHKj3BW5vnqLaoAWUqtlSj0cWERHJwDLTeOnothX4LH0Gb25z3KkMRfotwVkPUBERkXSsTvHcrBhYj7Fty+Hl5sSxS6F0mvkHAxbs4+LNO5CnJDw7F/psgZKtwIy9+xS+qVXh1+EQdvmh+0gPsszMqB9++IHBgwczffp06taty5dffsnjjz/O0aNHKVSokL3DSxdCrl/h3NHt3Dq9B9eLOygWfpBi3KZYXAcDgsnF6Vx1cSnzBKVqtyYgu5c9QxYREZEUlFnGS5bYWHb9byJVjk/GxYjluFMZ8r26Ak/vXPYOTURE5KGcHB3oUqcIbSrl46PVJ1iw8yw/H7jImqNBvNLwMfo0LIabf0V44Qc4txPWjYPTW2Dnl3cLUzV6Q91B4JF+f+8Zpmma9g4iLdSsWZOqVavyxRdfWNvKlClD+/btmThx4kPXDw0Nxdvbm5CQELy8UrYAY5omISEheHt7YxhGim473r4sFq4Fn+fKmWPcunSSmKt/43b9BH7hf+LPlXj9w01X/nYvz22/WuSp2oZi5Wum+syntMxHRqB82FI+bCkftpQPW8qHreTmIzV/96dXmWG89OeeDcSsGkXZqEMA7MneiHL95uPmkT1F47E3/f+2pXzYUj5sKR+2lA9bGSEfhy+EMPbnI+w6fQOA/DnceeuJMjxRwe/fmE9thHXvwoXdd9+7et29z1StV8DVM8n7SqvxUpaYGRUVFcWePXt48803bdpbtGjBtm3b7BTVXZbYWC6dOcGtsDBCPdwxMLFYTDAtmKYF0zQxLXH/xgK2701MiI0lOuIW0RG3iY0IwxJ5CzPqNmbkLRzCr+IccR33qGtkj7mBj+UauY1IcicSz0XDlyCPkkT5VyNX2cYUq1CbCprOLiIikuml5/ESwKUzJwgLCSHUw/3uOMnyz1jJEkt4yDXCAneR4/RKSkcfBe5+oHaw7DBqPDMMB0fdQkBERDKu8vm9+V+f2vxy8BITfz3GhZt36P/9XmoUzcXoNmUpl88bijWCog3hz5WwfjxcPgwb3oMdM6Dea1C9Jzi72/tQrLJEMerq1avExsbi62v7ZDdfX1+CgoISXCcyMpLIyEjr+9DQUOBulTAlJ5NFRd4h/ze1U2x7D2VArGkQbOTmmmt+bmcrhOlTAs8iVSlQpib+OXPjf98qaT15Li7HWWTS3kMpH7aUD1vKhy3lw5byYSu5+chqeUvP4yWAXHPqkc+Ifmi/WNNgT85W5G83hppFSlnjyWz0/9uW8mFL+bClfNhSPmxlpHy0ruhP09J5+XLz38zYdIqdgdfp9OUfbHuzMZ5uznc7lWwFJVrcvcH5hgkY1/+G1W9jbpsGfbdAtjwP3EdajZeyRDEqzv1TzEzTTHTa2cSJExk7dmy89pCQkBQ9SaOjIog1XTFxwIS7/xpg+efe8pZ72zHivwwDCw5EGW5EObgR5eBOjOPdV6yTBxa3nJjZcuPomQdXr7x45PLHx78o7q7uFLgvFvOf47M30zS5desWEP97lhUpH7aUD1vKhy3lw5byYSu5+YgrrGQ16XG8BGAx3IkxnbDcM/4xMbBgEIkbl92KcsuvJvlrPUNJv0LWODIr/f+2pXzYUj5sKR+2lA9bGTEf3ar70qpkDj7deJrH8nhgiQwnJPK+TgWbwUuNcDm2BLc/phCbsyi3Y1zgIb8b02q8lCWKUblz58bR0THep3rBwcHxPv2LM2LECIYMGWJ9HxoaSsGCBfH29k7heyB4Y44OSvfXqKaluMGr8nGX8mFL+bClfNhSPmwpH7aSm4+slrP0PV4C850zDxwv5UvRvaV/+v9tS/mwpXzYUj5sKR+2Mmo+vL1hRue8D/zQCIA6vaFmV5zCr+Pt6f3Q7abVeClLFKNcXFwICAhgzZo1PPXUU9b2NWvW0K5duwTXcXV1xdXVNV67YRipcoLGbTcjnfypSfmwpXzYUj5sKR+2lA9byoet5OQjq+VM46WMR/mwpXzYUj5sKR+2lA9bGTkfSYrZyRW87r8hz4O3mdrjpSxRjAIYMmQIL7/8MtWqVaN27drMnDmTs2fP0rdvX3uHJiIiIpIuaLwkIiIiaSHLFKM6derEtWvXGDduHJcuXaJ8+fL8+uuvFC5c2N6hiYiIiKQLGi+JiIhIWsgyxSiAfv360a9fP3uHISIiIpJuabwkIiIiqc3B3gGIiIiIiIiIiEjWoWKUiIiIiIiIiIikGRWjREREREREREQkzagYJSIiIiIiIiIiaSZL3cD8vzBNE4DQ0NBU2XZoaCiGYWAYRopvP6NRPmwpH7aUD1vKhy3lw5byYSu5+Yj7nR83BpCH03gp7SgftpQPW8qHLeXDlvJhS/mwlVbjJRWjkigsLAyAggUL2jkSERERSUthYWF4e3vbO4wMQeMlERGRrCm54yXD1Md9SWKxWLh48SKenp4pXi0NDQ2lYMGCnDt3Di8vrxTddkakfNhSPmwpH7aUD1vKhy3lw1Zy82GaJmFhYeTLlw8HB93ZICk0Xko7yoct5cOW8mFL+bClfNhSPmyl1XhJM6OSyMHBgQIFCqTqPry8vHTy30P5sKV82FI+bCkftpQPW8qHreTkQzOikkfjpbSnfNhSPmwpH7aUD1vKhy3lw1Zqj5f0MZ+IiIiIiIiIiKQZFaNERERERERERCTNqBiVDri6ujJ69GhcXV3tHUq6oHzYUj5sKR+2lA9byoct5cOW8pGx6ftnS/mwpXzYUj5sKR+2lA9byoettMqHbmAuIiIiIiIiIiJpRjOjREREREREREQkzagYJSIiIiIiIiIiaUbFKBERERERERERSTMqRomIiIiIiIiISJpRMSqNTJ8+naJFi+Lm5kZAQABbtmx5YP9NmzYREBCAm5sbxYoVY8aMGWkUaeqaOHEi1atXx9PTk7x589K+fXtOnDjxwHU2btyIYRjxXsePH0+jqFPPmDFj4h2Xn5/fA9fJrOcGQJEiRRL8Xvfv3z/B/pnt3Ni8eTNt2rQhX758GIbBjz/+aLPcNE3GjBlDvnz5cHd3p1GjRhw5cuSh212yZAlly5bF1dWVsmXLsmzZslQ6gpT1oHxER0fzxhtvUKFCBbJly0a+fPno3LkzFy9efOA2586dm+A5ExERkcpH89897Pzo2rVrvOOqVavWQ7ebGc8PIMHvs2EYfPjhh4luMyOfH5mFxkt3abxkS+MlWxovabx0L42XbGm8FF96HTOpGJUGfvjhBwYPHszIkSPZt28f9evX5/HHH+fs2bMJ9g8MDOSJJ56gfv367Nu3j7feeouBAweyZMmSNI485W3atIn+/fvzxx9/sGbNGmJiYmjRogW3b99+6LonTpzg0qVL1leJEiXSIOLUV65cOZvjOnToUKJ9M/O5AbBr1y6bXKxZswaAZ5999oHrZZZz4/bt21SqVIlp06YluPyDDz5g8uTJTJs2jV27duHn50fz5s0JCwtLdJvbt2+nU6dOvPzyyxw4cICXX36Zjh07smPHjtQ6jBTzoHyEh4ezd+9eRo0axd69e1m6dCl//vknbdu2feh2vby8bM6XS5cu4ebmlhqHkKIedn4AtGrVyua4fv311wduM7OeH0C87/Hs2bMxDIOnn376gdvNqOdHZqDx0r80XopP46V/abyk8dK9NF6ypfFSfOl2zGRKqqtRo4bZt29fm7bSpUubb775ZoL9hw8fbpYuXdqmrU+fPmatWrVSLUZ7CQ4ONgFz06ZNifbZsGGDCZg3btxIu8DSyOjRo81KlSoluX9WOjdM0zQHDRpkFi9e3LRYLAkuz8znBmAuW7bM+t5isZh+fn7m+++/b22LiIgwvb29zRkzZiS6nY4dO5qtWrWyaWvZsqX53HPPpXjMqen+fCRk586dJmCeOXMm0T5z5swxvb29UzY4O0goH126dDHbtWuXrO1kpfOjXbt2ZpMmTR7YJ7OcHxmVxkuJ03hJ46UH0XhpmfW9xksaL91L46X40tOYSTOjUllUVBR79uyhRYsWNu0tWrRg27ZtCa6zffv2eP1btmzJ7t27iY6OTrVY7SEkJASAXLlyPbRvlSpV8Pf3p2nTpmzYsCG1Q0szJ0+eJF++fBQtWpTnnnuOU6dOJdo3K50bUVFRfPfdd3Tv3h3DMB7YN7OeG/cKDAwkKCjI5vvv6upKw4YNE/1ZAomfMw9aJ6MKCQnBMAxy5MjxwH63bt2icOHCFChQgNatW7Nv3760CTANbNy4kbx581KyZEl69epFcHDwA/tnlfPj8uXLrFixgh49ejy0b2Y+P9IzjZceTOMljZcSo/GSLY2XHk7jJY2XHiQtx0wqRqWyq1evEhsbi6+vr027r68vQUFBCa4TFBSUYP+YmBiuXr2aarGmNdM0GTJkCPXq1aN8+fKJ9vP392fmzJksWbKEpUuXUqpUKZo2bcrmzZvTMNrUUbNmTb755htWrVrFV199RVBQEHXq1OHatWsJ9s8q5wbAjz/+yM2bN+natWuifTLzuXG/uJ8XyflZErdectfJiCIiInjzzTd54YUX8PLySrRf6dKlmTt3LsuXL2fBggW4ublRt25dTp48mYbRpo7HH3+c+fPns379ej7++GN27dpFkyZNiIyMTHSdrHJ+zJs3D09PTzp06PDAfpn5/EjvNF5KnMZLGi89iMZLtjReejCNlzReepi0HDM5/ddgJWnu/6TCNM0HfnqRUP+E2jOyV199lYMHD7J169YH9itVqhSlSpWyvq9duzbnzp3jo48+okGDBqkdZqp6/PHHrV9XqFCB2rVrU7x4cebNm8eQIUMSXCcrnBsAs2bN4vHHHydfvnyJ9snM50Zikvuz5FHXyUiio6N57rnnsFgsTJ8+/YF9a9WqZXOTyrp161K1alWmTp3KlClTUjvUVNWpUyfr1+XLl6datWoULlyYFStWPHBAkdnPD4DZs2fz4osvPvQ+Bpn5/MgoNF6KT+MljZceROOlhGm8FJ/GS3dpvPRgaTlm0syoVJY7d24cHR3jVU2Dg4PjVVfj+Pn5JdjfyckJHx+fVIs1LQ0YMIDly5ezYcMGChQokOz1a9WqlSkq8/fLli0bFSpUSPTYssK5AXDmzBnWrl1Lz549k71uZj034p4alJyfJXHrJXedjCQ6OpqOHTsSGBjImjVrHvgpX0IcHByoXr16pjxn/P39KVy48AOPLbOfHwBbtmzhxIkTj/TzJDOfH+mNxksJ03gpYRov3aXxUnwaLyVM46XEabz0r7QeM6kYlcpcXFwICAiwPuUizpo1a6hTp06C69SuXTte/9WrV1OtWjWcnZ1TLda0YJomr776KkuXLmX9+vUULVr0kbazb98+/P39Uzg6+4uMjOTYsWOJHltmPjfuNWfOHPLmzcuTTz6Z7HUz67lRtGhR/Pz8bL7/UVFRbNq0KdGfJZD4OfOgdTKKuIHVyZMnWbt27SP9gWGaJvv378+U58y1a9c4d+7cA48tM58fcWbNmkVAQACVKlVK9rqZ+fxIbzResqXx0oNpvHSXxkvxabwUn8ZLD6bx0r/SfMz0n25/LkmycOFC09nZ2Zw1a5Z59OhRc/DgwWa2bNnM06dPm6Zpmm+++ab58ssvW/ufOnXK9PDwMF977TXz6NGj5qxZs0xnZ2dz8eLF9jqEFPPKK6+Y3t7e5saNG81Lly5ZX+Hh4dY+9+fjk08+MZctW2b++eef5uHDh80333zTBMwlS5bY4xBS1NChQ82NGzeap06dMv/44w+zdevWpqenZ5Y8N+LExsaahQoVMt944414yzL7uREWFmbu27fP3LdvnwmYkydPNvft22d92sn7779vent7m0uXLjUPHTpkPv/886a/v78ZGhpq3cbLL79s8+Sp33//3XR0dDTff/9989ixY+b7779vOjk5mX/88UeaH19yPSgf0dHRZtu2bc0CBQqY+/fvt/l5EhkZad3G/fkYM2aMuXLlSvPvv/829+3bZ3br1s10cnIyd+zYYY9DTJYH5SMsLMwcOnSouW3bNjMwMNDcsGGDWbt2bTN//vxZ8vyIExISYnp4eJhffPFFgtvITOdHZqDx0r80XrKl8VJ8Gi9pvBRH4yVbGi/Fl17HTCpGpZHPP//cLFy4sOni4mJWrVrV5tG8Xbp0MRs2bGjTf+PGjWaVKlVMFxcXs0iRIomeFBkNkOBrzpw51j7352PSpElm8eLFTTc3NzNnzpxmvXr1zBUrVqR98KmgU6dOpr+/v+ns7Gzmy5fP7NChg3nkyBHr8qx0bsRZtWqVCZgnTpyItyyznxtxj16+/9WlSxfTNO8+rnj06NGmn5+f6erqajZo0MA8dOiQzTYaNmxo7R9n0aJFZqlSpUxnZ2ezdOnSGWbw+aB8BAYGJvrzZMOGDdZt3J+PwYMHm4UKFTJdXFzMPHnymC1atDC3bduW9gf3CB6Uj/DwcLNFixZmnjx5TGdnZ7NQoUJmly5dzLNnz9psI6ucH3G+/PJL093d3bx582aC28hM50dmofHSXRov2dJ4KT6NlzReiqPxki2Nl+JLr2MmwzT/uZufiIiIiIiIiIhIKtM9o0REREREREREJM2oGCUiIiIiIiIiImlGxSgREREREREREUkzKkaJiIiIiIiIiEiaUTFKRERERERERETSjIpRIiIiIiIiIiKSZlSMEhERERERERGRNKNilIiIiIiIiIiIpBkVo0QkUxozZgyVK1e22/5HjRpF7969k9R32LBhDBw4MJUjEhEREbGl8ZKI2IthmqZp7yBERJLDMIwHLu/SpQvTpk0jMjISHx+fNIrqX5cvX6ZEiRIcPHiQIkWKPLR/cHAwxYsX5+DBgxQtWjT1AxQREZFMT+MlEUnPVIwSkQwnKCjI+vUPP/zAO++8w4kTJ6xt7u7ueHt72yM0ACZMmMCmTZtYtWpVktd5+umneeyxx5g0aVIqRiYiIiJZhcZLIpKe6TI9Eclw/Pz8rC9vb28Mw4jXdv+0865du9K+fXsmTJiAr68vOXLkYOzYscTExPD666+TK1cuChQowOzZs232deHCBTp16kTOnDnx8fGhXbt2nD59+oHxLVy4kLZt29q0LV68mAoVKuDu7o6Pjw/NmjXj9u3b1uVt27ZlwYIF/zk3IiIiIqDxkoikbypGiUiWsX79ei5evMjmzZuZPHkyY8aMoXXr1uTMmZMdO3bQt29f+vbty7lz5wAIDw+ncePGZM+enc2bN7N161ayZ89Oq1atiIqKSnAfN27c4PDhw1SrVs3adunSJZ5//nm6d+/OsWPH2LhxIx06dODeiak1atTg3LlznDlzJnWTICIiIvIAGi+JSFpQMUpEsoxcuXIxZcoUSpUqRffu3SlVqhTh4eG89dZblChRghEjRuDi4sLvv/8O3P3EzsHBga+//poKFSpQpkwZ5syZw9mzZ9m4cWOC+zhz5gymaZIvXz5r26VLl4iJiaFDhw4UKVKEChUq0K9fP7Jnz27tkz9/foCHfoooIiIikpo0XhKRtOBk7wBERNJKuXLlcHD4twbv6+tL+fLlre8dHR3x8fEhODgYgD179vDXX3/h6elps52IiAj+/vvvBPdx584dANzc3KxtlSpVomnTplSoUIGWLVvSokULnnnmGXLmzGnt4+7uDtz9dFFERETEXjReEpG0oGKUiGQZzs7ONu8Nw0iwzWKxAGCxWAgICGD+/PnxtpUnT54E95E7d27g7vTzuD6Ojo6sWbOGbdu2sXr1aqZOncrIkSPZsWOH9Wkw169ff+B2RURERNKCxksikhZ0mZ6ISCKqVq3KyZMnyZs3L4899pjNK7GnzxQvXhwvLy+OHj1q024YBnXr1mXs2LHs27cPFxcXli1bZl1++PBhnJ2dKVeuXKoek4iIiEhK0nhJRB6FilEiIol48cUXyZ07N+3atWPLli0EBgayadMmBg0axPnz5xNcx8HBgWbNmrF161Zr244dO5gwYQK7d+/m7NmzLF26lCtXrlCmTBlrny1btlC/fn3r9HMRERGRjEDjJRF5FCpGiYgkwsPDg82bN1OoUCE6dOhAmTJl6N69O3fu3MHLyyvR9Xr37s3ChQut09e9vLzYvHkzTzzxBCVLluTtt9/m448/5vHHH7eus2DBAnr16pXqxyQiIiKSkjReEpFHYZj3PitTRET+M9M0qVWrFoMHD+b5559/aP8VK1bw+uuvc/DgQZycdCs/ERERyfw0XhLJ2jQzSkQkhRmGwcyZM4mJiUlS/9u3bzNnzhwNrERERCTL0HhJJGvTzCgREREREREREUkzmhklIiIiIiIiIiJpRsUoERERERERERFJMypGiYiIiIiIiIhImlExSkRERERERERE0oyKUSIiIiIiIiIikmZUjBIRERERERERkTSjYpSIiIiIiIiIiKQZFaNERERERERERCTNqBglIiIiIiIiIiJpRsUoERERERERERFJMypGiYiIiIiIiIhImlExSkRERERERERE0oyKUSIiIiIiIiIikmZUjBIRERERERERkTSjYpSIiIiIiIiIiKQZFaNEMjHDMB76GjNmjL3DtNGoUSMaNWqUatufO3cuhmFw+vRpu8dib0WKFKFr1672DkNERCRT2LFjB0899RSFChXC1dUVX19fateuzdChQ236TZ8+nblz5/6nfRmGwauvvvrQfhs3bsQwDDZu3GhtGzNmDIZh2PS7f8wTHh7OmDFjbNbLirp27UqRIkXsHYZIpuRk7wBEJPVs3749wfaYmBg6d+7MhQsXeOKJJ9I4qgebPn16qm7/ySefZPv27fj7+6fqfkRERCTrWLFiBW3btqVRo0Z88MEH+Pv7c+nSJXbv3s3ChQv5+OOPrX2nT59O7ty50+QDoapVq7J9+3bKli37wH73j7/Cw8MZO3YsQKb+YO5hRo0axaBBg+wdhkimpGKUSCZWq1atBNsHDhxIYGAgX375JTVq1EiRfd25cwd3d/f/vJ2HDZb+qzx58pAnT55U3UdmcufOHdzc3OJ9gioiIiL/+uCDDyhatCirVq3CyenfP7Gee+45PvjgA7vF5eXlleh48F6pPf5KL5I7Xi1evHgqRiOStekyPZEs5ttvv2Xq1Kn06NGD3r172yyLiopi/PjxlC5dGldXV/LkyUO3bt24cuWKTb8iRYrQunVrli5dSpUqVXBzc7N+enb48GHatWtHzpw5cXNzo3LlysybNy/J8d0/Tfz06dMYhsFHH33E5MmTKVq0KNmzZ6d27dr88ccf8dbfsWMHbdq0wcfHBzc3N4oXL87gwYOtyxO6TM80TT744AMKFy6Mm5sbVatW5bfffkswvtDQUIYNG0bRokVxcXEhf/78DB48mNu3b9v0i5s+/+2331KmTBk8PDyoVKkSv/zyS7xtHj9+nOeffx5fX19cXV0pVKgQnTt3JjIy0tonKCiIPn36UKBAAVxcXChatChjx44lJibmoTmNjo5m+PDh+Pn54eHhQb169di5c2e8fnG5Wb16Nd27dydPnjx4eHgQGRnJX3/9Rbdu3ShRogQeHh7kz5+fNm3acOjQoXjbOXLkCC1atMDDw4M8efLQv39/VqxYEe8yARERkczi2rVr5M6d26YQFcfB4d8/uYoUKcKRI0fYtGmT9ZYJcZeBRUREMHToUCpXroy3tze5cuWidu3a/PTTT4nu98svv6RkyZK4urpStmxZFi5caLM8ocv0EnLv+Ov06dPWD+7Gjh1rjbNr165s2bIFwzBYsGBBvG188803GIbBrl27Et1P3Fhj/fr19OrVCx8fH7y8vOjcuTO3b98mKCiIjh07kiNHDvz9/Rk2bBjR0dE22xg7diw1a9YkV65ceHl5UbVqVWbNmoVpmjb9HjReTepYJaHL9JIzxhORxGlmlEgWsm/fPvr06UP16tX5/PPPbZZZLBbatWvHli1bGD58OHXq1OHMmTOMHj2aRo0asXv3bptPkvbu3cuxY8d4++23KVq0KNmyZePEiRPUqVOHvHnzMmXKFHx8fPjuu+/o2rUrly9fZvjw4Y8c++eff07p0qX59NNPgbvTpp944gkCAwPx9vYGYNWqVbRp04YyZcowefJkChUqxOnTp1m9evUDtz127FjGjh1Ljx49eOaZZzh37hy9evUiNjaWUqVKWfuFh4fTsGFDzp8/z1tvvUXFihU5cuQI77zzDocOHWLt2rU2M4hWrFjBrl27GDduHNmzZ+eDDz7gqaee4sSJExQrVgyAAwcOUK9ePXLnzs24ceMoUaIEly5dYvny5URFReHq6kpQUBA1atTAwcGBd955h+LFi7N9+3bGjx/P6dOnmTNnzgOPr1evXnzzzTcMGzaM5s2bc/jwYTp06EBYWFiC/bt3786TTz7Jt99+y+3bt3F2dubixYv4+Pjw/vvvkydPHq5fv868efOoWbMm+/bts+bp0qVLNGzYkGzZsvHFF1+QN29eFixYkKT7WoiIiGRUtWvX5uuvv2bgwIG8+OKLVK1aFWdn53j9li1bxjPPPIO3t7f10jhXV1cAIiMjuX79OsOGDSN//vxERUWxdu1aOnTowJw5c+jcubPNtpYvX86GDRsYN24c2bJlY/r06Tz//PM4OTnxzDPPPPKx+Pv7s3LlSlq1akWPHj3o2bMncHd2efHixalSpQqff/45zz//vM1606ZNo3r16lSvXv2h++jZsycdOnRg4cKF7Nu3j7feeouYmBhOnDhBhw4d6N27N2vXrmXSpEnky5ePIUOGWNc9ffo0ffr0oVChQgD88ccfDBgwgAsXLvDOO+/Y7Ceh8WpKjFWSMsYTkYcwRSRLuHLlilm4cGEzT5485tmzZ+MtX7BggQmYS5YssWnftWuXCZjTp0+3thUuXNh0dHQ0T5w4YdP3ueeeM11dXeNt//HHHzc9PDzMmzdvPjTOhg0bmg0bNrS+DwwMNAGzQoUKZkxMjLV9586dJmAuWLDA2la8eHGzePHi5p07dxLd/pw5c0zADAwMNE3TNG/cuGG6ubmZTz31lE2/33//3QRsYpk4caLp4OBg7tq1y6bv4sWLTcD89ddfrW2A6evra4aGhlrbgoKCTAcHB3PixInWtiZNmpg5cuQwg4ODE425T58+Zvbs2c0zZ87YtH/00UcmYB45ciTRdY8dO2YC5muvvWbTPn/+fBMwu3TpYm2Ly03nzp0T3V6cmJgYMyoqyixRooTNtl9//XXTMIx4MbVs2dIEzA0bNjx02yIiIhnN1atXzXr16pmACZjOzs5mnTp1zIkTJ5phYWE2fcuVK2czvkhMTEyMGR0dbfbo0cOsUqWKzTLAdHd3N4OCgmz6ly5d2nzsscesbRs2bIj3+3f06NHm/X8G3j/+unLligmYo0ePjhdX3Hhh37591ra4cdm8efMeeExx6w4YMMCmvX379iZgTp482aa9cuXKZtWqVRPdXmxsrBkdHW2OGzfO9PHxMS0Wi3VZYuPV5IxVunTpYhYuXNimX1LHeCLyYLpMTyQLiI2N5bnnnuP8+fP88MMPFCxYMF6fX375hRw5ctCmTRtiYmKsr8qVK+Pn5xdvenfFihUpWbKkTdv69etp2rRpvO137dqV8PBw6w3VLRaLzT5iY2MfegxPPvkkjo6ONvsHOHPmDAB//vknf//9Nz169MDNze3hSfnH9u3biYiI4MUXX7Rpr1OnDoULF7Zp++WXXyhfvjyVK1e2ib9ly5YJToFv3Lgxnp6e1ve+vr7kzZvXGnN4eDibNm2iY8eOD7yP1S+//ELjxo3Jly+fzX4ff/xxADZt2pTouhs2bACId3wdO3ZM8FICgKeffjpeW0xMDBMmTKBs2bK4uLjg5OSEi4sLJ0+e5NixY9Z+mzZtonz58vHuPXH/p6ciIiKZiY+PD1u2bGHXrl28//77tGvXjj///JMRI0ZQoUIFrl69mqTtLFq0iLp165I9e3acnJxwdnZm1qxZNr9r4zRt2hRfX1/re0dHRzp16sRff/3F+fPnU+zY7vf888+TN29em1n2U6dOJU+ePHTq1ClJ22jdurXN+zJlygB3x3v3t8eNm+KsX7+eZs2a4e3tjaOjI87Ozrzzzjtcu3aN4OBgm74JjVdTYqzysDGeiDycilEiWcDw4cNZt24dkyZNonHjxgn2uXz5Mjdv3sTFxQVnZ2ebV1BQULxBVEJPo7t27VqC7fny5bMuh7uXgd27/aZNmz70GHx8fGzex01pv3PnDoD1vlYFChR46LbujxnAz88v3rL72y5fvszBgwfj5cfT0xPTNOPl6P6Y4+KOi/nGjRvExsY+NObLly/z888/x9tvuXLlAB44wE3s+JycnBKMDxL+3g4ZMoRRo0bRvn17fv75Z3bs2MGuXbuoVKmS9Xji9nfvwDhOQm0iIiKZTbVq1XjjjTdYtGgRFy9e5LXXXuP06dNJuon50qVL6dixI/nz5+e7775j+/bt7Nq1i+7duxMRERGv/4PGLnG//1ODq6srffr04fvvv+fmzZtcuXKF//3vf/Ts2dM6PnuYXLly2bx3cXFJtP3eY9+5cyctWrQA4KuvvuL3339n165djBw5EsBmTAKJj1f/61jlYWM8EXk43TNKJJNbsGABkydPplOnTgwdOjTRfrlz58bHx4eVK1cmuPzeT3+ABJ+u5uPjw6VLl+K1X7x40boPgDFjxthcl3//th9F3Myi5H4SGDeYCAoKircsKCjI5qaVuXPnxt3dndmzZye4rbjjS6pcuXLh6Oj40Jhz585NxYoVee+99xJcHlfsS8i9x5c/f35re0xMTKID1YS+t9999x2dO3dmwoQJNu1Xr14lR44cNvu7fPlyvPUTyq+IiEhm5uzszOjRo/nkk084fPjwQ/t/9913FC1alB9++MHmd/G9DzS5V2JjF0i4WJKSXnnlFd5//31mz55NREQEMTEx9O3bN1X3CbBw4UKcnZ355ZdfbGbC//jjjwn2T2y8qrGKiP2pGCWSiR08eJCePXtSvnx5Zs2a9cC+rVu3ZuHChcTGxlKzZs1H2l/Tpk1ZtmwZFy9etCmQfPPNN3h4eFgfLVykSJF4Tyb5r0qWLEnx4sWZPXs2Q4YMSfInc7Vq1cLNzY358+fbXJ62bds2zpw5YxNn69atmTBhAj4+PhQtWvQ/x+zu7k7Dhg1ZtGgR7733XqLFrNatW/Prr79SvHhxcubMmax9xD0ZZ/78+QQEBFjb//e//yXpSXxxDMOIl9MVK1Zw4cIFHnvsMWtbw4YN+eijjzh69KjN9Pf7n+4jIiKSmVy6dCnBWThxl9fdOy5KbAaNYRi4uLjYFFCCgoISfZreunXruHz5snVGT2xsLD/88APFixdP9kzx+90/A/1+/v7+PPvss0yfPp2oqCjatGljvaF4ajIMAycnJ5tbN9y5c4dvv/02ydvQWEUkfVAxSiSTunHjBu3btycyMpI33niDQ4cOJdgv7skozz33HPPnz+eJJ55g0KBB1KhRA2dnZ86fP8+GDRto164dTz311AP3OXr0aOv9jd555x1y5crF/PnzWbFiBR988IH1qXep5fPPP6dNmzbUqlWL1157jUKFCnH27FlWrVrF/PnzE1wnZ86cDBs2jPHjx9OzZ0+effZZzp07x5gxY+JNfx88eDBLliyhQYMGvPbaa1SsWBGLxcLZs2dZvXo1Q4cOTXYhb/LkydSrV4+aNWvy5ptv8thjj3H58mWWL1/Ol19+iaenJ+PGjWPNmjXUqVOHgQMHUqpUKSIiIjh9+jS//vorM2bMSHTQWaZMGV566SU+/fRTnJ2dadasGYcPH+ajjz7Cy8sryXG2bt2auXPnUrp0aSpWrMiePXv48MMP4+138ODBzJ49m8cff5xx48bh6+vL999/z/HjxwHbx1uLiIhkFi1btqRAgQK0adOG0qVLY7FY2L9/Px9//DHZs2dn0KBB1r4VKlRg4cKF/PDDDxQrVgw3NzcqVKhA69atWbp0Kf369bM+3ffdd9/F39+fkydPxttn7ty5adKkCaNGjbI+Te/48eMpUlTx9PSkcOHC/PTTTzRt2pRcuXKRO3dumw/pBg0aZB33POzJvinlySefZPLkybzwwgv07t2ba9eu8dFHHyX5Q0jQWEUkvVAxSiSTOnDgAIGBgQC8/PLLifbr0qULc+fOxdHRkeXLl/PZZ5/x7bffMnHiRJycnChQoAANGzakQoUKD91nqVKl2LZtG2+99Rb9+/fnzp07lClThjlz5tC1a9eUOrREtWzZks2bNzNu3DgGDhxIREQEBQoUoG3btg9c795HIn/77beULl2aGTNm8NFHH9n0y5YtG1u2bOH9999n5syZBAYG4u7uTqFChWjWrNkjzfaqVKkSO3fuZPTo0YwYMYKwsDD8/Pxo0qSJ9f4J/v7+7N69m3fffZcPP/yQ8+fP4+npSdGiRWnVqtVDZ0vNmjULX19f5s6dy5QpU6hcuTJLlizhueeeS3Kcn332Gc7OzkycOJFbt25RtWpVli5dyttvv23TL1++fGzatInBgwfTt29fPDw8eOqppxg3bhxdunSxuaRPREQks3j77bf56aef+OSTT7h06RKRkZH4+/vTrFkzRowYYb1BN8DYsWO5dOkSvXr1IiwsjMKFC3P69Gm6detGcHAwM2bMYPbs2RQrVow333yT8+fPM3bs2Hj7bNu2LeXKlePtt9/m7NmzFC9enPnz5yf5JuIPM2vWLF5//XXatm1LZGSkdcwYp0aNGhQpUgR3d/ck3f8zJTRp0oTZs2czadIk2rRpQ/78+enVqxd58+alR48eSdqGxioi6YNhmqZp7yBERCRz6927NwsWLODatWvWIpuIiIhkXAcPHqRSpUp8/vnn9OvXz97h/Gcaq4ikLc2MEhGRFDVu3Djy5ctHsWLFuHXrFr/88gtff/01b7/9tgZ3IiIiGdzff//NmTNneOutt/D390+T2e8pTWMVEftTMUpERFKUs7Oz9XLCmJgYSpQoweTJk23ulyEiIiIZ07vvvsu3335LmTJlWLRoER4eHvYOKdk0VhGxP12mJyIiIiIiIiIiaUaPChARERERERERkTSjYpSIiIiIiIiIiKQZFaNERERERERERCTN6AbmSWSxWLh48SKenp4YhmHvcERERCSVmaZJWFgY+fLlw8FBn98lhcZLIiIiWcujjpdUjEqiixcvUrBgQXuHISIiImns3LlzFChQwN5hZAgaL4mIiGRNyR0vqRiVRJ6ensDdBHt5ef2nbZmmSUhICN7e3vrUMBmUt0ejvD0a5e3RKXePRnl7NKmZt9DQUAoWLGgdA8jDpeR46X76P2JL+bClfNhSPmwpH7aUD1vKh63k5uNRx0sqRiVR3DfBy8srRYpRpmni5eWlkz0ZlLdHo7w9GuXt0Sl3j0Z5ezRpkTd9P5IuJcdL99P/EVvKhy3lw5byYUv5sKV82FI+bD1qPpKbO90AQURERERERERE0oxmRomIlcViISoqyt5hpAumaRIVFUVERIQ+IUmEs7Mzjo6O9g5DREREREQyGBWjRASAqKgoAgMDsVgs9g4l3bBYLFy7ds3eYaRrOXLkwM/PTwU7ERERERFJMhWjRATTNLl06RKOjo4ULFhQjzDnbk5iY2NxdHRUoSUBpmkSHh5OcHAwAP7+/naOSEREREREMgoVo0SEmJgYwsPDyZcvHx4eHvYOJ11QMerh3N3dAQgODiZv3ry6ZE9ERERERJJE0x9EhNjYWABcXFzsHIlkNHHFy+joaDtHIiIiIiIiGYWKUelQ3KMURdKaZgBJcumcERERERGR5NJleulIrMXkic+2cO5GOJuHNyZ3dld7hyRiV0WKFMHNzQ03Nzfu3LlDt27dePPNN//T9n755RfKly+f5HU+/fRTXnzxRfLmzQvA3LlzGTx4MEWKFCEmJoaCBQvy5ZdfUqhQoUeOKyPo2rUr1apV49VXX7V3KCIikhFc2ANbJsPFfXffG45gGODgCIbDP69/vna4/73jPe8TW+efZfe894iOAVe3u23WbSa0XYcE9uPwkFgetE78WB54fEnZrnWbSclL3HKnf9cTEUnnVIxKRxwdDE5fu01kjIVNJ4J5OqCgvUMSsbvFixdTvnx5Ll68SNmyZWnSpAk1atRIs/1/9tlnNG/e3FqMAmjWrBmLFy8G4LXXXuO1115jyZIlaRZTSoiJicHJSb8CREQkhd0KhnVjYd93abpbA9DNBuIY4OCEt+EAjs7/FuccnP75+p+X9euE2p3+LXbFfW3td/+2nO4rhsWt73DfthzvabunkHZvYS7Boty9yxzv+9pIZFv3FiTvtjncvg0xORLYltO/x+7gdM+x/XMMIpIq9JdIOuPkYBAJHAsKs3coIoRHxSS6zMEwcHN2fKS+jyJfvnyUKlWKM2fOUKNGDf766y/69u1LcHAwDg4OjBkzhvbt2wOwfft2hg8fTmhoKKZp8u6779KuXTub7U2ZMoX//e9/LFu2jNjYWAYOHMjp06eJiIigffv2jB07lvHjx3Px4kWeeeYZ3NzcmDt3bry4mjdvzvDhw9m1axedO3fm6NGj1kvXateuzTvvvMPjjz9us07Xrl1xdnbm1KlTXLhwgVq1avHll1/i6urK5cuX6du3L3/99RemaTJw4EB69+7NqlWrmDx5MqtWreLmzZv4+PgwY8YMevXqxaxZs9i2bRuzZs0iKCgo3rGMGzcOuDszrFevXqxdu5Z8+fIxd+5cRo0axfr164mKiqJ06dLMmDGDHDlycOHCBTp37syVK1coWrSo9b5iIiIiCYqNhp0zYeP7EBl6t63ic1CtGzi5gmkBi+Xuv2bsP/9awHLP1zbvY+95byayzj/LLLGYZiwR4bdxc3XFsPY179vOvdu1JLAfS/JieZR1Eok/Wes8lIlh+edejrGRqfYtz0gMwOtR17y3GJdgwSqRfxNdfn/RLjl97n1/bxEwmX0MRxzC70BkDtvC4/0FwSQVCjUTTx6NilHpjLeHC7ej7hB45ba9QxGh7DurEl3WuFQe5nT7d4ZSwLtruROd8ACpZtFc/NCn9n+K5fjx41y9epVGjRoB8OKLL9KjRw969+7NyZMnqVWrFgEBAWTLlo2nnnqKpUuXUqdOHSwWCzdv3rRux2Kx8Nprr3H27FnWrFmDu7s7LVu2ZOTIkTRo0ICYmBhat27NsmXLePvtt5k7d651dhbA/v37rduKjY1l0aJFBAQEUL16dXLlysW6deto1qwZe/fu5erVq7Rq1SrB49mxYwfbtm3D3d2dp556is8++4zhw4czcOBASpcuzbJlywgODiYgIIDKlSvToEEDnnvuOSIjI9mwYQM1a9Zk3bp11uJS27ZtAejSpUuCx/LUU08BcPbsWdavX49hGEyYMIHs2bOzc+dOAN59911Gjx7NZ599xsCBA2nQoAGjR4/m1KlTVKpUKdFjERGRLMw04cRvsGYUXPvrbpt/ZXjiQyiYdjOZMU0iQ0Jw8/bO/H8cJ1rAir37ryUW0xJNaMhNvLJnw7Aui4nXD0uM7XvzQe0x9yyz3Ncv5p8C3wP6xb23KbAlUNhL8NgSW8f8N854xcZ/+5mmBdMSg2FaMO4vKFpigMTu12uCJfruKxN59OJcIltL1qy1BxS87p+1Zi12Gf9cthr3tcO/l7Fal93/Nfe1O8Rf3/o1uEdFg4vLfT8/jH+3k6y2e5alZFtCP9uSE5tLdmgwLP427ETFqHQmr6crF2/e4eLNO/YORSRdeOaZZzAMgxMnTvDJJ5+QJ08ewsLC2L9/Pz169ACgRIkS1KtXj61bt+Ll5UXZsmWpU6cOAA4ODuTKlcu6ve7du1O9enUWLVqEg4MDt2/fZv369Vy+fNna59atWxw/ftxa4Lnf2rVrqVy5MgBVq1bl448/BmDQoEF8/vnnNGvWjKlTp9KvX79Eb/DdqVMnsmfPbo1p+vTpDB8+nLVr13LgwAEA8ubNS4cOHVi3bh01atSgcuXK/P7776xdu5YRI0YwdOhQLBYLGzZs4LPPPnvgscTp1q2bNaYff/yR0NBQ6yWHUVFRFC9eHIANGzYwZcoUAIoVK0bTpk0f+r0SEZEs5tJBWD0SAjfffZ8tDzQZBVVe1uVNqckwwPEhf8aZJqaZHbJCcS4pTJPQkBC8E8tHXIEvrmhmLaTF3Pe6r+3e4p21CBd73/KYBPrcW7C7v899fc0HrJtonwctj8G0xGLGRt8tzt1bJLy3qGeJJfEinU3y/inoJX6VRHpnAFnibs3Z/VSMksQVzOXO/nM3uXIryt6hiHB0XMtElznc94t8z6hmSe6bHHGzktauXUubNm1o0qQJhQsXBohX6EnKk90aNWrEmjVrCA4Oxs/PD4vFgmEY7Nq1C2dnZ2s/0zQTvTTt3ntG3atDhw688cYb7Nu3j59//plPPvkkycd5b+yJHVezZs1Yu3YtW7ZsYdKkSZQrV45vv/0WX19f8ubNS1hYWILHcq+4AljcMU6fPp0mTZokOU4RERHCgmD9u7BvPmCCoyvU7g/1XgO3lJtvIZJm4gp8DyvyZRYPK87d0y/BItUDZ7jde/lpApfJ3nuJbGLtcevwz/7j4rg3pnuXxetn3rfOg/uZpoWIiAjc3Nww4gpwNk+3N23+sW1LqN9/aLNp/y9txG9zzR5/mR1lkf9tGcdjee+eIGERmWsqqGRMHi5J/xGRnL6PolmzZrzyyiu8/fbb/PTTT1SuXJl58+bRrVs3/v77b37//XemTZtG9uzZ6dmzJ9u2bbO5TC9udlTXrl2pUaMGTZo04bfffqNw4cLUr1+f999/n1GjRgFw8eJFYmNj8ff3x8vLi5CQkCTF6OTkRJ8+fWjbti1PP/00OXLkSLTvokWLGDx4MG5ubsyZM4dmzZpZj3PmzJmMHTuWK1eusGzZMmvhq1mzZnTq1InChQuTPXt2mjVrxujRo3n66acB8PT0TPBYLBYLBQoUiBdD27ZtmTx5MrVq1cLDw4Pw8HACAwMpV64cTZo0Yfbs2YwaNYrTp09bLz8UEZEsLCoctn8OWz+B6H9uKVH+aWg6GnIWtm9sIpLy4p4S6fDf7v2a7mWly3zTEc2fTWfK+d/9NCkyxmLnSETSn1GjRrF161b27NnD/Pnz+e6776hUqRJPP/00X3/9NQULFiRnzpwsW7aM119/nYoVK1KlShW2bt1qs52OHTvy4Ycf0qJFC/7880/mz5/PsWPHqFChAhUqVODpp5/m2rVrAAwYMIBu3bpRuXJlm/tFJaZHjx5cuHCBV1999YH9GjRoQPv27SlXrhw5c+ZkwIABwN0bqx88eJCKFSvSuHFjRo4caX16YLVq1QgJCbFeMte8eXPOnDljUyR60LHc780336Ry5crUrFmTihUrUqtWLesxfvbZZ2zcuJGKFSsybNgwFaJERLIyiwUO/g+mVYMN4+8WovJXgx5r4JnZKkSJiEiyGaaZ0DwuuV9oaCje3t6EhITg5fXfph+bpknIP9Mi778c5+qtCKqNXwfAnreb4ZM9S1y9miQPypskLil5i4iIIDAwkKJFi+Lm5pbGEaZPcZfpOTo6Jut8+9///seXX37JunXrEu3TtWtXqlWr9tCCVUaQ0Lmj/6uPRnl7NKmZt5T83Z9VpGbO9H/EVprl48x2WPUWXNx79713QWg25u6MqHT0fdD5YUv5sKV82FI+bCkftpKbj0f93a/L9NKZ3NndyOnhzI3waK7cilQxSiQDadWqFX/++SfLli2zdygiIiL/zfVAWDsGjv54971Ldqg/BGr1A2d3e0YmIiKZQIa9TG/69OnWT+IDAgLYsmXLA/vPnz+fSpUq4eHhgb+/P926dUv00hV7y5/z7i/4Czf0RD2RjGTlypWcOnWKSpUqPbDf3LlzM8WsKBERyYQiQmD1KPi8xt1ClOEAVbvAwH1Qf6gKUSIikiIyZDHqhx9+YPDgwYwcOZJ9+/ZRv359Hn/8cc6ePZtg/61bt9K5c2d69OjBkSNHWLRoEbt27aJnz55pHHnS5M9x95f8+Rvhdo5ERERERLKE2BjY9TVMqQLbpkBsFBRrBH22QNspkD2vvSMUEZFMJEMWoyZPnkyPHj3o2bMnZcqU4dNPP6VgwYJ88cUXCfb/448/KFKkCAMHDqRo0aLUq1ePPn36sHv37jSOPGluhN99kt6SvRfsHImIiIiIZHon18CMurBiKIRfg9wl4YX/wcs/gl95e0cnIiKZUIYrRkVFRbFnzx5atGhh096iRQu2bduW4Dp16tTh/Pnz/Prrr5imyeXLl1m8eDFPPvlkWoScbN7uzgBcCYu0cyQiIiIikmldPgrfdoD5z8CV4+CeC574CF7ZBiVbpqsblIuISOaS4W5gfvXqVWJjY/H19bVp9/X1JSgoKMF16tSpw/z58+nUqRMRERHExMTQtm1bpk6dmuh+IiMjiYz8txgUGhoK3L2z/H99AGHcNhLbTrHc2QAICY/+z/vKTB6WN0lYUvIWt0z5TZhykriEzh39X300ytujSc286XshmdatK7DhPdg7D0wLODhDzT7QYBi457R3dCIikgVkuGJUnPsfMWiaZqKPHTx69CgDBw7knXfeoWXLlly6dInXX3+dvn37MmvWrATXmThxImPHjo3XHhISkiLFqFu3biV4HACFvR0BuBMTS0hIyH/aV2bysLxJwpKSt6ioKCwWC7GxscTGxqZleA/02GOP4ebmhqurKxEREXTp0oXhw4f/p+39+OOPlC+ftEsOLBYLn332Gc8//zx58969V8a8efMYOnQohQsXJjY2lgIFCjB9+nQKFSr0yHGlhtOnT7NmzRp69eqVqvuJjY3FYrEQFhZmLeDr/+qjUd4eTWrmLe6DKJFMIzoCdnwBmz+GqLC7bWXaQPNxkKuYfWMTEZEsJcMVo3Lnzo2jo2O8WVDBwcHxZkvFmThxInXr1uX1118HoGLFimTLlo369eszfvx4/P39460zYsQIhgwZYn0fGhpKwYIF8fb2xsvL6z8dQ1wxy9vbO8GBc60SjsBJTBNwdsfbw+U/7S+zeFjeJGFJyVtERATXrl3D0dERR0fHtAzvoRYtWkT58uW5ePEi5cqVo2nTptSoUeORt5fcY5w6dSotWrSw/pxwcHCgWbNmLFq0CIDXXnuN119/ncWLFz9yTKnh3LlzzJo1i759+6bqfhwdHXFwcMDT0xM3NzdA/1cflfL2aFIzb/o+SKYRGw3758OmDyD0n3uS+leGlhOgSF27hiYiIllThrtnlIuLCwEBAaxZs8amfc2aNdSpUyfBdcLDw3FwsD3UuD9GE5vl5OrqipeXl80L7g5MU/tV5J/L9AD2nw9Jk33qpde95zfAnejYVH0l5f/TvX3y589PqVKlOHv2LIZh8Pfff9O8eXMqVapElSpV+Omnn6x9//jjDxo0aEDlypWpVKkSy5cvj7e9qVOnUr9+fa5evcrly5fp1KkTNWvWpFKlSowePRqA8ePHc/HiRZ599lmqVKnCgQMH4m2nRYsWnDhxgt27d1O2bFmbZXXq1GHlypXxjis0NJRevXpRsWJFKleuTI8ePTAMg9u3b9OjRw8qVKhAhQoVGDdunHWdTz75hBo1alC1alVq1qzJzp07MQyDiIgInnvuOcqVK0flypVp2bIlhmHwyiuvcPToUapUqUK7du3S7NzRS6/M9hLJ0CwWOLgIplWHnwfdLUR55Yf2M6DXBhWiRETEbjLczCiAIUOG8PLLL1OtWjVq167NzJkzOXv2rHUGwIgRI7hw4QLffPMNAG3atKFXr1588cUX1sv0Bg8eTI0aNciXL589DyVBDg4OuDg6EBVr4ejFUBqV0qN0JW3diY6l7DurUnUfR8e1xMMl6T+Cjh8/ztWrV2nUqBEAL774Ij169KB3796cPHmSWrVqERAQQLZs2XjqqadYunQpderUwWKxcPPmTet2LBYLr732GmfPnmXNmjW4u7vTsmVLRo4cSYMGDYiJiaF169YsW7aMt99+m7lz57J48WLrpX379++3bis2NpZFixYREBBA9erVyZUrF+vWraNZs2bs3buXq1ev0qpVq3jHMnjwYLJnz86BAwdwcHDgypUrALz77rtERUVx8OBB7ty5Qzb5W3MAALQ9SURBVL169ShbtizPPvssL7/8snW25h9//EGPHj04fPgwK1eu5MaNGxw9ehSA69evAzBjxgyGDRuWbp8aKiIiqcg04cSvsH48BN/9/YBH7rv3hAroBs5u9o1PRESyvAxZjOrUqRPXrl1j3LhxXLp0ifLly/Prr79SuHBhAC5dusTZs2et/bt27UpYWBjTpk1j6NCh5MiRgyZNmjBp0iR7HcK/IkLh0CK4eRaa/3uPKp9sLlwKjeDmnWg7Bidif8888wyGYXDixAk++eQT8uTJQ1hYGPv376dHjx4AlChRgnr16rF161a8vLwoW7asdaakg4MDuXLlsm6ve/fuVK9enUWLFuHg4MDt27dZv349ly9ftva5desWx48fp23btgnGtHbtWipXrgxA1apV+fjjjwEYNGgQn3/+Oc2aNWPq1Kn069cvwZkVv/zyC3v27LHO2MyTJ491u5999hkODg5ky5aNzp07s3btWp599ln27dvHe++9x7Vr13BycuLo0aNERUVRqVIljh8/Tr9+/WjYsCFPPPHEf8y4iIhkWKYJpzbC+nfhwp67ba7eUHcg1OwLrtntGp6IiEicDFmMAujXrx/9+vVLcNncuXPjtQ0YMIABAwakclSPwLTAin/uTVVvsPUJJk3K5GX+jrO4OmW4KyklE3B3duTouJapvo+kiJuVtHbtWtq0aUOTJk2shef7Cz1JuaSmUaNGrFmzhuDgYPz8/LBYLBiGwa5du3B2drb2M00z0Zu5N2vWLMF7RHXo0IE33niDffv28fPPP/PJJ58k6Rjv3WdCxxQVFcXTTz/Nxo0bCQj4P3v3Hd9U2TZw/HeSNE33oKUtUGihUCh7yd4IgrhxCyo4EDfiQHwcPCiKvIo+Ki4QERVUUByAsmXvvUcXpYUWSneTJjnvH6crUEZL23Rc38/nmJyTM67chjS5ct/X3ZH09HR8fHywWCw0btyYAwcOsHLlSpYvX85LL73k0HtLCCFELRG3WUtCxazV1l3coesT0P1pmSFPCCFElSOZDmdz8wXf/Fm4kvYWbq7v5wZAQmqOE4IStZ2iKLgbDRW6lLYWy8CBA3niiSd47bXX8Pb2pl27dnz77bcAHD9+nPXr19OjRw+6d+/OwYMH2bBhA6ANyysYugZaT8mJEyfSv39/YmNj8fLyolevXrz77ruF+5w6dYqTJ08C4O3tfdWzWhoMBh5//HFuvvlm7rjjDnx9fUvc7+abb+b999/HbrcDFA7Tu/766/nqq69QVZWsrCzmzp3LwIEDyc3NJS8vj9DQUEArql7g5MmTKIrCzTffzLRp01BVlfj4+FLFLYQQohpL2gs/3A2zBmmJKL1R6wX17G4Y8LokooQQQlRJkoyqCoLbaLeJewo31ffVklHxqdnOiEiIKuk///kP69atY/v27Xz//ffMnTuXtm3bcscdd/D1118TGhqKn58fv/76Ky+++CJt2rShffv2rFu3zuE8d911F++//z6DBg3iyJEjfP/99xw8eLCwcPgdd9zB2bNnAa1X5cMPP0y7du2uqsfR6NGjSUhI4KmnnrrkPh9++CHZ2dm0atWKdu3a8eqrrxY+P0VRaN26NV26dOHmm29m+PDheHt7M2nSJK677jp69+6Nq6tr4bn27t1L9+7dadOmDR06dGDEiBG0adOGNm3aEBkZSatWrS453FAIIUQ1lnIU98VPoXzRC44sBUUP7UfA0ztgyHvgKTVHhRBCVF2Keqnp5ISDgmExaWlphTPrlZWqqqSlpRVNQ71mKqx6G9rcA7d/AcDCHScZ99Nu9DqF4+9IDRgood3EVbmadsvNzSU6Oprw8HBMJilqCkXD9PR6falebz/99BNffPEFK1asqMDoqo6SXjvyb7VspN3KpiLbrTz/9tcWFdlm8m8k3/k4WPMe6q4fUFSthy2t7oC+r0JAhHNjcyJ5fTiS9nAk7eFI2sORtIej0rZHWf/2V9uaUTVKcGvtNqmoZ1SzIC8AbHaVXIsVUylmHRNCOMcNN9zAkSNH+PXXX50dihBCiJom4zSs/T/Y/g3YLChAXuOBGK5/AyWkjbOjE0IIIUpFMhxVQcEwveTDkJcDLm40z09GAexPzKBjIxnvL0RVt3TpUmeHIIQQoqbJPgcbPobNX0BefvmG8N6o/f9DllczfHx8nBufEEIIUQaSjKoKvOuBex3IPgtnDkD9jhgMOgw6BatdZXf8eUlGCSGEEELUJuZM2DwD1v8PzPkTUtTvBAP+A437gqqCTFQhhBCimpIC5lWBohT1jio2o56vmzbN/LbYcyUdJYQQQgghahpLFqz/GD5qCysna4moui3h3nnwyHItESWEEEJUc9IzqqoIbg0nVjnMqBcW4EFKloVDiRlODEwIIYQQQlQ4SxZs/VpLRGWnaNv8G0O/idDydtDJb8hCCCFqDvmrVlWEtNVuixUxbxfqC0BiWq4TAhJCCCFETTFlyhQUReG555677H5r1qyhY8eOmEwmGjduzOeff145AdZmlixY/xFMbwPLXtcSUX7hcMtn8OQWaD1cElFCCCFqHOkZVVUUDNM7vR/sNtDp6RsZyNfrorHY7NjsKnqdTDMphBBCiNLZunUrX375JW3aXH7GtejoaIYOHcqjjz7K3LlzWb9+PWPHjiUwMJA77rijkqKtRUrqCeUXDr1fhDZ3g14+pgshhKi55GeWqqJOE3Bx12ZJOXscgG5NAnBz0WOzq0SnZDk5QCEqX1hYGM2bN6dt27Y0bdqUW265hQ0bNly0X+/evWnatCmqqgLQrl072rVrR1RUFAaDoXD97rvvBuDTTz+lVatWtGjRghYtWnDvvfcSFxcHQFpaGo8//jhNmjShVatWdO7cmT///LPwWqtXry6xd8HIkSNRFIV9+/ZVUGtUrtWrV9OpUydnhyGEuEaZmZncf//9fPXVV/j5XX4ylM8//5yGDRsyffp0WrRowSOPPMKoUaOYNm1aJUVbS+SmwdoPYHprx55Qt86Ap7ZB+/slESWEEKLGk2RUVaHTQ1BL7X7+UD29TiEy2AuAg4npzopMCKf65Zdf2L17N0ePHmXUqFEMHTqUzZs3Fz5+9OhRjh49iouLC//++y8Au3btYteuXSxevBhfX9/C9fnz5/PGG28wd+5cli5dysGDBzlw4ACPPPIISUlJqKrK0KFDcXFx4fDhw+zbt4+vvvqKxx9/nL///rvwmpGRkfz2229YLBYA0tPT2bBhA/Xr16/cxikFu92O3W53dhhCiEr25JNPcuONNzJw4MAr7rtx40YGDRrksG3w4MFs27aNvLy8igqx9sg4DcvfhA9bwYq3tFmUiyeh2t0nSSghhBC1hvzFq0qC28DJrZC4W6sPALQI8WZX/Hm2xZzjprb1nBygqHUsl+mRp+jBxXSV++rAxe2aw7nlllsYO3Ys06ZN4+effwZg5syZPPDAA4SEhDBz5kz69OlzyeOzsrKYOnUq27dvp0GDBlpoisKAAQMAWL58ObGxsaxatQqDwYDNZqNdu3ZMnDiRyZMnM3jwYAA8PT3p3r07ixYt4s4772TevHnccccdzJ8/v8Trrl69mmeffZYOHTqwZ88eFEVh5syZtG2r1YqbOnUq3377LTqdjjZt2vDZZ5/h4+ND/fr12bp1K/Xq1eP222/n9OnTrF+/npycHOrXr09iYiKurq5MmzaNn376CavVSnBwMF988QWhoaG8+eabHD9+nKysLI4dO8aSJUvYt28f//3vf8nJycFgMPD+++/Tu3dvAF577TXmzZtH/fr16dy58zX//xJCONe8efPYsWMHW7duvar9k5KSCAoKctgWFBSE1WolJSWFkJCQi44xm82YzebC9fR07cczVVULe6uWl4Jzlvd5K1xqDGz4H+yci2LT2koNbA49noXWd4Iu/+N4KZ9XtW2PCiLt4Ujaw5G0hyNpD0fSHo5K2x5lbTdJRlUlIfm1HJL2Fm6yWG0A/LEnkbduaeWMqERt9s5lEqBNB8H9Pxetvx+hDTMtSaOe8PBf5RJS586d+e233wCwWq3MmTOHFStWEBAQwKRJk0hLS8PHx6fEY/fv34/RaCQqKqrEx3fs2EHHjh0xGo0Ob6rdunXjxRdfdNh31KhRvP3229x5553MmjWLb7/99pLJKIA9e/bw0Ucf0bdvX3766Sfuu+8+9u/fz5IlS/jmm2/YuHEjvr6+PPbYY7z66qt8+umn9O/fn+XLl/PAAw+wd+9eXF1dycjIYMOGDXTu3BlXV1d++OEHjhw5wsaNG9Hr9Xz33Xc89dRTLFq0CIBVq1axY8cO6taty4kTJ3jrrbdYunQp3t7eHDt2jD59+hATE8PSpUv5/fff2bVrF25ubtx2222l+d8ihKhi4uPjefbZZ/nnn38wmUxXPiCfojjWpyx4L7xwe4EpU6bw1ltvXbQ9LS2tQpJRmZmZl42nKtElH8S07TNcjvyJomo9U63B7cnt/CTWxgO0H2oyyl6Gobq1R0WT9nAk7eFI2sORtIcjaQ9HpW2Pgh+iSkuSUVVJcGvtNmmP9uuYotA+1JcFOxI4n21xbmxCVBHFv9wsXryYRo0a0aJFCwAGDhzIjz/+yJgxYy55/JXeUEt6vKQvVD179iQ2Npa///4bg8FAZGTkZc8bERFB3759Abjrrrt47LHHOHXqFMuXL+f+++/H19cXgCeeeIJ77rmn8PksX76cFi1a0L59e4KCglizZg1r1qwpHHLz22+/sW3bNjp27AiAzWZDr9cXXnfYsGHUrVsXgKVLl3Ls2LHCnlAF4uPjWbVqFXfffTeenp6AlmybPHnyZZ+TEKLq2r59O2fOnCl8bwDt/eHff//lk08+wWw2O7xXAAQHB5OUlOSw7cyZMxgMBurUqVPidSZMmMC4ceMK19PT0wkNDcXHxwdvb+9yfEZF78U+Pj5V98uCqkLcBlj/EcrRf4o2RwyEHs+hb9QDj3KKvVq0RyWS9nAk7eFI2sORtIcjaQ9HpW2PsraZJKOqkrottaFP2Wch/RT41Of6lsG8tmg/dhWikzMJD/R0dpSiNnn11KUfUxy/xPDiscvsW37l6bZu3UqrVlovwZkzZ3L06FHCwsIAyMnJITY29pLJqKioKMxmMwcOHCixd1SHDh34+OOPsVgsuLi4FG7ftGkTHTp0uGj/kSNH8sADD/Duu++W6bkoioKqqhe9gResX3/99bz66qu0aNGCgQMHEhQUxIoVK1i9ejWzZs0CtD8Wr732GqNGjSrxGgXJpYJ9b7jhBubMmXPRftItWYiaZcCAAezdu9dh28MPP0zz5s15+eWXL0pEgdYL9I8//nDY9s8//9CpUyeH98TiXF1dcXV1vWi7oigV8oG+4LxV7stCXg7s/Rk2fwmn89td0UHUrdDzOZSQthVy2SrbHk4i7eFI2sORtIcjaQ9H0h6OStMeZW0zKWBelbiYIDC/d0X+UL0gbxMGnfY/d/nB086KTNRWRo9LLy6mUux77fWiABYtWsSMGTMYN24cSUlJrFixgmPHjhETE0NMTAynTp0iPj6ePXv2lHi8p6cn48eP59FHH+XUqaJE2+LFi9m8eTMDBgwgNDSUF154AavVCmjF0CdPnsyrr7560flGjRrFCy+8UDhL3+UcO3assMD6L7/8Qv369QkJCeH6669n3rx5ZGRkAPDll18W9nqqV68e3t7efPHFFwwcOJB+/frx+++/k5CQQLt27QC4+eab+eyzzzh37hwAeXl57Ny5s8QYBg0axNKlSx1m/NuyZQugfXH96aefyMrKwmazMXv27Cs+JyFE1eXl5UWrVq0cFg8PD+rUqVOY0J8wYQIjR44sPGbMmDHExsYybtw4Dh48yKxZs5g5cybjx4931tOo+s7HwbI34IMW8PvTWiLK4AYdH9KKkt/5DVRQIkoIIYSozqRnVFUT3BrOHNCG6kXeAIC/h5EzGWa2RJ/j0d5NnBygEJVr+PDhuLq6kpWVRVRUFIsXL6Zr16689957DB48uHB4G4Ber+fee+/l66+/5uOPPy7xfJMmTSIwMJBBgwZhs9lQFIX27dvz7rvvoigKS5Ys4cUXX6RZs2YYjUY8PT2ZMWMGQ4YMuehcdevW5ZVXXrmq59GuXTvmzZvHuHHjUFWVH374AYAhQ4awd+9eunXrhqIohQXMC1x//fX8+eefNG7cGNCKCXfq1KnwF4gRI0Zw9uxZ+vbti6IoWK1WRo8eTfv27S+KoWnTpsydO5dHHnmEnJwcLBYLHTp04Pvvv2fYsGFs3LiRtm3bUr9+ffr06cPJkyev6rkJIaqnxMRE4uLiCtfDw8NZvHgxzz//PJ9++in16tXj448/5o477nBilFWQ3Q4nVsG2WXB4MeTXg8KnIVz3CLQfAe7+zo1RCCGEqOIUVcZmXJX09HR8fHxIS0u75hoIqqoWFlm+qEvbhk/gn4nQfBjc8z0A93y5kU0nztHI3501L/W7pmtXZ5dtN3FJV9Nuubm5REdHEx4eXqpCtzWZqqqF9Zeu9fW2evVqxo8fz7Zt28opuqqjpNeO/FstG2m3sqnIdivPv/21RUW2mdP/jWSchp3fwY45cD62aHt4H+jyODS7AXQXD3+sKE5vjypG2sORtIcjaQ9H0h6OpD0clbY9yvq3X3pGVTUlzKjXPtSPTSfOcTo910lBCSGEEELUQnY7nFgJ22fD4SVg14Zw4+oDbe+BTg9D3RZODVEIIYSojiQZ5WyqCktegpPb4O7vimbUOx8LOefBzZeBUXWZseY4eXaVPJsdF72U+hKiOunbt2+N7BUlhBA1Vnoi7Poednyr1YUqENpFqwcVdSsY3Z0VnRBCCFHtSTLK2RQFYjdqBS8Td0PzG7WaA2lxWu+o8F50aOiHp6uBTLOV48mZNA+WoQJCCCGEEOUqJxUO/K7NihezDsivZFHQC6rjgxDU0qkhCiGEEDWFJKOqgpA2jsmokDb5yag9EN4LRVFoEeLF1phU9p5Mk2SUEEIIIUR5yD4Hx1fCvgVwdBnY84oeC+2qJaCkF5QQQghR7iQZVRWEtNW6gifu1taD28ChPx3qRnVs5MfWmFT+3JPInZ1CnRSoEEIIIUQ1ZrdBwg44vgKOLYeE7UWz4QEEtYJWd2iLXyPnxSmEEELUcJKMqgpC2mq3iXu024K6UQXrgKer9r9q3bGUyoxMCCGEEKL6yk3Tkk8J2+DkdojfpA3HK65uFEQOgdZ3SjFyIYQQopJIMqoqCGoFKJBxCjLPFM2ol3wI8nLBxcRdnUKZ9s8RbHaVrTFn6RxWx6khCyGEEEJUCbY87fNTRhKcOw7JhyHlcP7tUQprPxVw9YEmfSFiIDQZAD71nRG1EEIIUatJMqoqcPWEOhFw9qjWGypiALj5Q845SD4I9dpT19uEu1FPtsXGgu0JkowStUJYWBgmkwlXV1eys7OJiori5Zdfpnv37g779e7dm8TERI4cOYKiKLRr1w4Ai8XCkSNHaNWqFQCRkZHMnz+fTz/9lBkzZmCz2QBo164d7733Hg0bNiQtLY2XXnqJZcuWYTQa8fT05M0332TYsGEArF69mn79+vHss88yffr0whhGjhzJd999x969ewuvV9mmT5/OfffdR926dZ1yfSGEqFDfD8czOw1UC+TlaIslK7+nk3rp43wbQYNOUL8TNOgM9dqDXj4CCyGEEM4kf4mripC2YDODJVObYS+kDZxYrSWn6rUHIDLYi51x59l04qxzYxWiEv3yyy+FyZ1FixYxdOhQ/v77b7p06QLA0aNHOXr0KH5+fvz777/06dOHXbt2ARATE0OnTp0K1wHeeOMN/vnnH5YuXUqDBg1QVZWVK1eSlJREaGgoQ4cOpX379hw+fBhFUdi7dy/Dhg1j1qxZDB48GNCSWr/99htTp07FaDSSnp7Ohg0bqF/fub+uT58+nYEDB0oySghRM8VuwJCXXfJjih48g8AvDAKbQUCkdhvcFjwDKzVMIYQQQlyZJKOqitu+cPyVLri1loxKKqobNaB5XXbGnSc+Nafy4xO1i6rCpT7wlxcXdy3xWgq33HILY8eOZdq0afz8888AzJw5kwceeICQkBBmzpxJnz59Lnl8VlYWU6dOZfv27TRo0AAARVEYMGAAAMuXLyc2NpZVq1ZhMBiw2Wy0a9eOiRMnMnny5MJklKenJ927d2fRokXceeedzJs3jzvuuIP58+df8trffPMNH330Eaqq4uLiwi+//EJYWBjfffcdU6dORVEUQkND+fLLL6lfvz579+5l7NixZGVlkZuby4gRI5gwYQIAX3/9NR988AFGoxGbzcbXX3/N33//zalTpxg+fDgmk4nZs2cX9hATQoga4ZZPycrJxd0nAMXoDi5uYHADjwBwDwCdztkRCiGEEOIqSTKqqriwu3hwflHzYjPqSd0oUWnysuGdehV7jVdPgdGj1Id17tyZ3377DQCr1cqcOXNYsWIFAQEBTJo0ibS0NHx8fEo8dv/+/RiNRqKiokp8fMeOHXTs2BGj0YiqFg356NatGy+++KLDvqNGjeLtt9/mzjvvZNasWXz77beXTEatXr2at99+m7Vr1xISEkJ2tpbo27dvHy+++CLbt2+nfv36vP322zz22GP89ddfhIWFsXz5clxdXcnJyaF79+5cf/31dOrUiRdeeIGDBw9Sr1498vLyMJvNdOnShVmzZjn0JBNCiBql5W3kpaWBj0+pf8wQQgghRNUiPyFVNaoKdntREfOkfdo0xFBYNwpgwfYEZ0UohFMVTxItXryYRo0a0aJFCwIDAxk4cCA//vjjZY9XrvAFpqTHi1+zQM+ePYmNjeXvv//GYDAQGRl5yXP+9ddfjBw5kpCQEADc3d1xd3dn1apVDBs2rHB439ixY1m5ciWqqpKTk8MjjzxC69at6dq1K7GxsYXDDfv378/IkSP56KOPiI6OxtPT87LPSdQuqqoyf2sc7/99iHE/7eKN3/fz644ELFb7lQ8WQgghhBCiEkjPqKrk1yfgyBK46zto1F3rep6XBedOQEBTAPpFBvLX3iRy82xODlbUaC7uWs+lir5GGWzdurWw58/MmTM5evQoYWFhAOTk5BAbG8uYMWNKPDYqKgqz2cyBAwdK7B3VoUMHPv74YywWCy4uLoXbN23aRIcOHS7af+TIkTzwwAO8++67ZXouqqo6JL+K33/11VcJCgpi586dGAwGbr/9dnJzcwFYuHAh27dvZ/Xq1QwdOpTJkydzzz33lCkGUfMs3ZfEywv2Omz7Fnj+p10EeBppH+rLTe3qMbRlCAaD/CYlhBBCCCEqn3wKrUosGdqMMIm7QaeHoJba9sTdhbvc1bkhANtiU50RoagtFEUbQleRSxmGWCxatIgZM2Ywbtw4kpKSWLFiBceOHSMmJoaYmBhOnTpFfHw8e/bsKfF4T09Pxo8fz6OPPsqpU0XJtsWLF7N582YGDBhAaGgoL7zwAlarFYBdu3YxefJkXn311YvON2rUKF544QXuvvvuy8Z90003MWfOHJKSkgDIzs4mOzubAQMGsHjx4sLtn3/+OQMGDEBRFFJTU2nQoAEGg4HDhw+zbNkyQBuaePz4cTp16sT48eMZPnw4W7ZsAcDb25u0tLRStqqoSU6n5/D67/sB6BkRQH1fN7xci353Ssm0sOzgGZ75cReRry/lsTnb+GZ9NIeS0rHZpOeUEEIIIYSoHNIzqioJbgsH/yhKPoW0gYRtWt2o1sMB6NTID4NO4WRqDvHnsgn1L1vvEiGqi+HDh+Pq6kpWVhZRUVEsXryYrl278t577zF48GB8fX0L99Xr9dx77718/fXXfPzxxyWeb9KkSQQGBjJo0CBsNhuKotC+fXveffddFEVhyZIlvPjiizRr1gyj0YinpyczZsxgyJAhF52rbt26vPLKK1d8Dr179+a1115j0KBBKIqC0Wjkl19+oWXLlkyZMoVBgwYBFBYwB3jttdcYMWIE33//PWFhYfTv3x8Am83Gww8/TGpqKgaDgcDAQL755hsAnnnmGR5++GHc3d2lgHkt9eCsrSRnmAn2NvH1g50wuWhDuy1WO3/sTuDPPYnsPpnGuSwLNrvKPwdO88+B04CWH67r5UqnRn7c1r4+/ZvXRScFoYUQQgghRAVQ1JKKoYiLpKen4+PjQ1paGt7e3td0LlVVC4ssO9SnOfIP/HCnNh3xU1tg2zfw53PQpD+M+LVwt8Ef/svh0xk83COMN25qeU2xVCeXbDdxWVfTbrm5uURHRxMeHo7JZKrkCKsmVVWx2Wzo9Xp5vV1GSa8d+bdaNtfabj9ti+elX7RegROGNOfxPk0uuW+OxcqOuFR2n0xj4/GzbI4+d1FNKUWBYG8TncP8uatTA3pEBFTJ/58V+Xorz7/9tUVFtpm8tziS9nAk7eFI2sORtIcjaQ9H0h6OStseZf3bLz2jqpKQ/Bn0zh4FSxYE5xcxT9yjFTbPfyH4uGu1bP7ck1irklFCCCFKlmux8p/f9gEQ6ud22UQUgJvRQI+IQHpEBDK2bwRZZiu/7Upgyd5E9iakk5aTh6pCYlouv+8+xe+7T1HHw8h14f60qu9DqL8bN7QMwSg1p4QQQgghRBlIMqoq8QoCzyDIPA2n90Nwa1D0kJ0CGUngrc3ENbJrI7ZEnyM5w8yZ9FzqektPFiGEqM2e/GEnZqsdBZj1UOdSH+/hauD+Lo24v0sjADJy81iwPYG/9yex/1QauXl2zmZZWLIviSX7kvKP2kUdDyNtGvhwQ6tgbmpbD3ejfKwQQgghhBBXJp8aq5qQtnD0H61uVOh1ENAMkg9C0p7CZNTQ1sEY5itY7SqfrznO69I7Sgghaq0t0WdZcegMAMM7NqBpkNc1n9PL5MJDPcJ4qEcYAGarjb0n09gcfY4FO05yIjkLgLNZFlYdTmbV4WReXrAXXzcXbu9Qn8Etg2kb6ltYs0oIIYQQQojiJBlV1YT1BJsFPAK09eDWWjIqcQ80GwyATqejZX1vdsensWRfkiSjRLmREnKitOx2mYHNmex2O2Pm7gDAy2TgvTtaV8h1XA16OoX50ynMnyf7RZBrsfLX3kSW7Eti98k0UjLMqMD5nDxmrY9h1voYjHod9f3cqONhpH+Lugzv0EB68gohhBBCCECSUVVPj2e1pUBIG9j7EyTtdtjt/i6N2B2/h8S0XM5nW/B1N1ZyoKImcXFxQVEUkpOTCQwMlMJ9SAHzK1FVFYvFQnJyMjqdDqNR3oOcYdHuU5zLsgAw/e52lTb7nclo4I6OodzRMRQAq9XOskOnWX7gNLlWO5tPnCMl00x0ShbRKVlsi01l6tLDuBv1RNT1pE+zQO7qHEqon8wIK4QQQghRG0kyqqorKGKetNdh8/AO9ZmwYA82Fb789wQv3dDcCcGJmkKv19OgQQNOnjxJTEyMs8OpMux2u0xtfwXu7u40bNhQ2skJ0nPzmLL4EAD3d2nIgBZBTovFYNAxpFUIQ1ppw8lVVSXmbDafrTrG5hNnSTifi01VybbY2HMyjT0n0/jfymP0ahrATW3qMahlkPyoIoQQQghRi0gyqqrKPgc6gzZMDyA1BnLTwOQDaEP1IoO9OJCYweboc86LU9QYnp6eNG3alLy8PGeHUiWoqkpGRgZeXl7SM+oS9Ho9BoNB2sdJpv19mDMZZsIDPPjPsChnh+NAURTCAzx4/862hdu2xZxj4c4ENh0/S9y5bKx2lbVHU1h7NIVXf1Wo6+1Kr6aBPNU/QnpMCSGEEELUcJKMqop+GQ37foGbPoaOD4JPKKTFa72jwnoW7vbykBY8OGsLR05nYLHaZYptcc30ej16vRQcBi0ZZTabMZlMkmwRVc68LXHM2RgLwNu3tqoWhcILak4VOJiYzoqDp/lzTyKHkjI4dT6X+Vvjmb81ngAPI4NaBfFUvwjq+UpiSgghhBCippHsRVXkXU+7TcyvE3WJoXo9IwII8HQlI9fKhuMplRigEEIIZ8m2WHnj9/0ANA7woHtEgJMjKpsWId481b8pS5/rzU+PdaV7kzqFP6qkZFn4YXM83d9dRefJy/lw2WEycqXXphBCCCFETVFtk1GfffYZ4eHhmEwmOnbsyNq1ay+570MPPYSiKBctLVtW0VnoQvKHNRQko0Lyk1GJexx20+sUBrfUaoTM3hBTScEJIYRwpqd+2IHZakcBvhzR0dnhlIvrGtfhh0e7cmTyEL4c0ZEu4f646LUeicmZZj5acYyOk5fz+HfbmLcljjPpuU6OWAghhBBCXItqOUxv/vz5PPfcc3z22Wf06NGDL774giFDhnDgwAEaNmx40f4fffQR7777buG61Wqlbdu23HnnnZUZ9tULaafdnt4PNmtR3aikPRftOqhlEN9vjmP14WSOJ2fSJNCz8uIUQghRqbZEn2XloWQAhndqQESQl5MjKn+DWgYzqGUwdrudZQfOMHNdNGcyc4lJyebv/af5e/9pAIK8XbmxdQhP9G2Mi5NjFkIIIYQQpVMte0Z98MEHjB49mkceeYQWLVowffp0QkNDmTFjRon7+/j4EBwcXLhs27aN1NRUHn744UqO/Cr5NwajJ1hz4OzRomF6yYfAanbYtU+zurjl1wr5758HKjtSIYQQlcRutzNm7g4AvEwG3ru9tZMjqlg6nY7BrYL5aUw3Vr3QlyXP9uKpfhGF9bFOp5uZtT6Gzm+v5IWFh7BY7U6OWAghhBBCXK1q1zPKYrGwfft2XnnlFYftgwYNYsOGDVd1jpkzZzJw4EAaNWp0yX3MZjNmc1HiJz09HdCKGquqWobIixSc45LnURQIbo0StxH11C5ocze4+aHkpKKeOVDUcyrfjW2C+WV7AmuPppCXZ8NQQwuZX7HdRImk3cpG2q3spO3K5krtNvmvg5zLsgDw0d3tUBSlVrVx82Avmgd78fzACP7am8TsDTHsOZmG1a6SmJ6Li77826M2ta8QQgghRGWqdsmolJQUbDYbQUFBDtuDgoJISkq64vGJiYksWbKEH3744bL7TZkyhbfeeuui7WlpaeWSjMrMzAS45Cxdbv7NcY3biDl2K7lhQ/AIaIFL/AZyTmzG4h7usO+TPerxy/YEbHaVD/7ez+M9Lx6qWBNcTbuJi0m7lY20W9lJ25XN5dotPSePb/Nnz+sY6k2HEFfS0tIqPcaqoneYB73DWmK329ken05OTg5paWnl/nor+CFKCCGEEEKUr2qXjCpw4QdOVVWv6kPo7Nmz8fX15dZbb73sfhMmTGDcuHGF6+np6YSGhuLj44O3t3eZYi4eK2jDBy8Zc9QQVL2Ca7PrcfXxgQYdIH4DbmnHcPPxcdjVxwdahnizPzGdn3ae5qUba+bQjatqN3ERabeykXYrO2m7srlcu01btQ+bXcXXzYVZD1+Ht5vRGSFWSQN8fUlLS6uQ15u8foUQQgghKka1S0YFBASg1+sv6gV15syZi3pLXUhVVWbNmsWIESMwGi//Qd7V1RVXV9eLthfMxHetis/qV6Jmg7WlQP4Me0rSXm0Y3wVevCGSh77ZytksC1tjznFdeJ1rjrEqumK7iRJJu5WNtFvZSduVTUnttj02le+3xAHw2QMd8HG/+G9TbVdRrzd5/QohhBBCVIxqV1zIaDTSsWNHli1b5rB92bJldO/e/bLHrlmzhmPHjjF69OiKDLFiFBQxP70P7BcXae0bWRdfN20+oU9WHqvMyIQQQlSQbIuVZ37ciarCHR0a0L1JgLNDEkIIIYQQ4ppVu2QUwLhx4/j666+ZNWsWBw8e5PnnnycuLo4xY8YA2hC7kSNHXnTczJkz6dKlC61atarskMsmLwdObodzJ6BOBBhMYMnU1kswpm8TAHbGnSctJ68yIxVCCFEBxs7dQcL5HIx6HRNvbOHscIQQQgghhCgX1TIZdffddzN9+nQmTZpEu3bt+Pfff1m8eHHh7HiJiYnExcU5HJOWlsaCBQuqV6+opRPg6/6wYw7oDRDUUtuetKfE3R/r1ZhmQZ5kmK18tzGm8uIUQghR7jafOMvqI8kA3NahPv4eUidKCCGEEELUDNWuZlSBsWPHMnbs2BIfmz179kXbfHx8yM7OruCoyll+nSgSd2u3wW0gYbuWjGp1+0W763QKT/aL4Nl5u/hqbTR3dw4l0MtUiQELIYQoD3a7nTFztwPgbTIw5bZq0qNXlFlaWhq//vora9euJSYmhuzsbAIDA2nfvj2DBw++YikCIYQQQojqpFr2jKo1QvLrRCXuBlWF4PxZ8hJL7hkFcGPrEHzdXUjLyWPCwr2VEKQQQojyNunPg6Rma8OtP763PTqd/LmuqRITE3n00UcJCQlh0qRJZGVl0a5dOwYMGECDBg1YtWoV119/PVFRUcyfP9/Z4QohhBBClItq2zOqVqjbEhQ9ZJ+F9ISinlJJe7TkVAmz/Bj0OgY0r8uCHQmsPHSGtGwLPu4ytEMIIaqLk6nZfJs/1LprY3/6RtZ1bkCiQrVt25aRI0eyZcuWS9a0zMnJ4bfffuODDz4gPj6e8ePHV3KUQgghhBDlS35qrcpcTFA3v2Bt4h6oGwWKDrKSIfP0JQ976+ZW6BSwq/Daon2VFKwQQojy8PDsbagquOgVvhrZydnhiAq2f/9+pk2bdtnJVdzc3Lj33nvZvHkzDz74YCVGJ4QQQghRMSQZVdUFFxuqZ3SHgGb565cequdpMjC4ZTAAf+1J5FympaKjFEIIUQ52nUzn2JlMACYMaYGXycXJEYmKFhgYWKH7CyGEEEJURTJMr6oLaQu7fyhWxLw1JB+CpN3QbNAlD3v39tb8c+A0NrvK0z/u4PtHu1ZSwEIIIcoiz2Zn8t/HARjeoT6jeoY7OSJRGX7//fer3vfmm2++6n1nzJjBjBkziImJAaBly5a8/vrrDBkypMT9V69eTb9+/S7afvDgQZo3b37V1xVCCCGEuBqSjKrqmvSDQZMhtIu2HtwG9v4MSZcvTu7jbuSBro34dkMM64+f5UBiGlEhPpUQsBBCiLL4fPVxjiVn4+9hZOKNUc4OR1SSW2+99ar2UxQFm8121edt0KAB7777LhEREQB8++233HLLLezcuZOWLVte8rjDhw/j7e1duC49sYQQQghRESQZVdUFRmpLgcIZ9i49TK/AG8Na8PO2eLItNt5dcpg5o66roCCFEEJci03Hz/LB8qMATBjSHD8PmXiitrDb7RVy3ptuuslh/e2332bGjBls2rTpssmounXr4uvrWyExCSGEEEIUkJpR1U1BDanUaMhNu+yuOp2OybdqBVHXHU3m6OmMio5OCCFEKdntdp74fjsA3iY9t7ev5+SIRE1js9mYN28eWVlZdOvW7bL7tm/fnpCQEAYMGMCqVasqKUIhhBBC1DbSM6o6SDsJcZvAIxAa9wHvBpB+Ek7vh0bdL3vo7R0a8M/+0yzdn8Tkvw4y++HOKIpSSYELIYS4krf+OEBqdh4A794ciU4nvxPVZllZWaxZs4a4uDgsFscJSJ555plSnWvv3r1069aN3NxcPD09+fXXX4mKKnkIaEhICF9++SUdO3bEbDbz3XffMWDAAFavXk3v3r0veQ2z2YzZbC5cT09PB0BVVVRVLVW8V1JwzvI+b3Ul7eFI2sORtIcjaQ9H0h6OpD0clbY9ytpukoyqDvYtgGWvQ9StWjIqpI2WjErcc8VkFMDLQ5qz8tAZ1hxJ5r2lh3hlSIuKj1kIIcQVxZ/NZs6mWAC6Nfane2M/J0cknGnnzp0MHTqU7OxssrKy8Pf3JyUlBXd3d+rWrVvqZFRkZCS7du3i/PnzLFiwgAcffJA1a9aUmJCKjIwkMrKoLEC3bt2Ij49n2rRpl01GTZkyhbfeeuui7WlpaRWSjMrM1GablB/WpD0uJO3hSNrDkbSHI2kPR9IejkrbHgU/RJWWJKOqg5C22m3hjHpt4PBiSLpy3SiA8AAPbmgVzO+7T/HFvye4/7pGhNZxr6BghRBCXK2HZm9FVcFFr/DFiI7YzdnODkk40fPPP89NN93EjBkz8PX1ZdOmTbi4uPDAAw/w7LPPlvp8RqOxsIB5p06d2Lp1Kx999BFffPHFVR3ftWtX5s6de9l9JkyYwLhx4wrX09PTCQ0NxcfHx6EQenkoSG75+PjIlwWkPS4k7eFI2sORtIcjaQ9H0h6OStseZW0zSUZVB8XrROWch+DW2vpVFDEv8PZtrViyL5E8m8qD32xh5fi+5R6mEEKIqzd3YwzHk7VfnV4d2gIvkwtp5iscJGq0Xbt28cUXX6DX69Hr9ZjNZho3bszUqVN58MEHuf3226/p/KqqOgypu5KdO3cSEhJy2X1cXV1xdXW9aLuiKBXygb7gvPJlQSPt4Ujaw5G0hyNpD0fSHo6kPRyVpj3K2mZSmKI6cPcHn4ba/aS9RTPqJR8Cq+XSxxXjZXLhP8O0bvknUrKYtS66IiIVQghxFSxWOx+vPAZovVcf7hHu5IhEVeDi4lL4gS4oKIi4uDhA+2Wy4P7VevXVV1m7di0xMTHs3buXiRMnsnr1au6//35A69E0cuTIwv2nT5/Ob7/9xtGjR9m/fz8TJkxgwYIFPPXUU+X07IQQQgghikjPqOoipA2kxWlD88J6gskXcs9D8sGiYXxXMLJbGHM2xnLsTCbvLD7IsDYh1PU2VWjYQgghLvbV2hOcyTDj4+bCNw91dnY4oopo374927Zto1mzZvTr14/XX3+dlJQUvvvuO1q3bl2qc50+fZoRI0aQmJiIj48Pbdq0YenSpVx//fUAJCYmOiS4LBYL48ePJyEhATc3N1q2bMlff/3F0KFDy/U5CiGEEEKAJKOqj5C2cOhPrW6UomhD9WLWakP1rjIZBfDNw53pM3UVVrvK3V9uZNX4fhUYtBBCiAvFpGTx8YqjALx5cxRhAR5OjkhUFe+88w4ZGRkA/Pe//+XBBx/kiSeeICIiglmzZpXqXDNnzrzs47Nnz3ZYf+mll3jppZdKdQ0hhBBCiLKSYXrVxYVFzAvWk/aW6jShfu5MHKrNphedks3/Vh4trwiFEEJcgd1u576vN2G22ukRUYdb29V3dkiiCunUqRP9+mk/EgUGBrJ48WLS09PZsWMH7dq1c25wQgghhBDlSJJR1UXodXDPD3D/L9p6QVHzq5xRr7jRvRrTtbE/AF/+e4L4czJ7kxBCVIY3fj/AqfO5ALw8uLkUyRQOoqOjOXr04h+Jjh49SkxMTOUHJIQQQghRQSQZVV24+UHzG8E3VFsvKGKetBfs9lKf7rvRXWjf0JeMXCvPzd+FxVr6cwghhLh6sWezmLs5FoCeEXVoE+rr3IBElfPQQw+xYcOGi7Zv3ryZhx56qPIDEkIIIYSoIJKMqq7qNAWDCSyZkFr6mfFc9Do+vqc9Xq4Gtsemcs+XG1FVtQICFUIIATBq9lZUFVz0Cp8/0MnZ4YgqaOfOnfTo0eOi7V27dmXXrl2VH5AQQgghRAWRZFR1knwEVr8HW74CvQHqRmnbyzBUDyDU352pw7UeVjvizvPMjzvLK1IhhBDFzNkYw/HkLABeuzEKT5PMHyIupihKYQHz4tLS0rDZbE6ISAghhBCiYkgyqjpJPgSr34Edc7T1gqF6iWVLRgEMaR1Cn2aBAPyxJ5Gv1h6/1iiFEEIUk5lrZfKfBwBoHODBg93DnBuQqLJ69erFlClTHBJPNpuNKVOm0LNnTydGJoQQQghRvuSn2eqkYAa9MwfBaoHg1tp6GXtGFfjmoU70fn81J1NzeOevQzSt60XfyLrXGKwQQgiAcT/twmJTURT45uHOzg5HVGFTp06ld+/eREZG0qtXLwDWrl1Leno6K1eudHJ0QgghhBDlR3pGVSe+DcHkC/Y8SD4IwfnJqaS913RanU7Hn0/3xN2oRwVGz97GvoS0aw5XCCFqu5iULFYfSQbgwW5hNKrj4eSIRFUWFRXFnj17uOuuuzhz5gwZGRmMHDmSQ4cO0apVK2eHJ4QQQghRbqRnVHWiKFrvqOg1kLgbWg0HRQeZpyHjNHgFlfnUvu5GfnuyB0M/WovVrnLHjA2sHN+H+r7u5fgEhBCi9lBVlYm/7cVitdOraQBv3BTl7JBENVCvXj3eeecdZ4chhBBCCFGhpGdUdVNYJ2o3GN2hToS2fo1D9QCaBXkxd3QXFAXMVjtP/7CT3DwpmCqEEGXxv5XHWH/sLK4GHZNvbYWiKM4OSVRBcXFxpdo/ISGhgiIRQgghhKg8koyqbkLaabeJu7Xb4PzkVDkkowC6NqnDp/d1wMOoZ0fcecbM3Y7ZKgkpIYQojejkTD5cdgSAB7o2kuF54pI6d+7Mo48+ypYtWy65T1paGl999RWtWrVi4cKFlRidEEIIIUTFkGF61U1BEfOzx8Fu13pK7fvlmmbUu9DQ1iH4exh56JstrD6czN1fbGLu6C4yFbkQQlylUd9uQwWMeh3PD2zm7HBEFXbw4EHeeecdbrjhBlxcXOjUqRP16tXDZDKRmprKgQMH2L9/P506deL9999nyJAhzg5ZCCGEEOKaSc+o6sa/CTyxEcYfBZ2u3HtGFejauA6zHuyMQaewK/48/f5vNZm51nK9hhBC1ETfrI8mOiULgNeHtZBEvrgsf39/pk2bxqlTp5gxYwbNmjUjJSWFo0ePAnD//fezfft21q9fL4koIYQQQtQY8gm5utHpIKhYEdyCZNS5E5CbDibvcrtU94gAxg1qxtSlh0nOMNP7/VUsf74P/p7GcruGEELUJBm5ebyz+CAATQI9eaBbmHMDEtWGyWTi9ttv5/bbb3d2KEIIIYQQFU56RlV3HnXAu752//T+cj/92L4RvDg4EoBzWRb6vL+KhPPZ5X4dIYSoCR7/bjt5NhVFgdkPdXZ2OEIIIYQQQlRJkoyqjs4cgoWPw6IntfUKGqpX4Ml+EUy+pSUAGWYrA/5vDYcS0yvkWkIIUV2tOXyGDcfPAjCyayNC67g7OSIhhBBCCCGqpkobpnf06FEWLVpETEwMiqIQHh7OrbfeSuPGjSsrhJrDboU988DVG276HwS3hiNLyrWI+YUe6BaGt7sLz/64i9w8Ozf+bx0/j+lGh4Z+FXZNIYSoLlRV5X8rjwHg5+7CGzdFXeEIIYQQQgghaq9K6Rk1ZcoUoqKiePnll1mwYAE///wzL774Is2bN2fatGmVEULNEhgJelcwp8P5GG1GPaiwnlEFbm5bnzmjr8OgU7DZVR6cuYW1R5Mr9JpCCFEd/LozgW2xqbgadMx/rBs6nXQ8FkIIIYQQ4lIq/NPyqlWreO2115g4cSIpKSkkJiaSlJREcnIyr7zyCq+88gr//vtvRYdRs+hdioqYJ+4uGqZ35iBYLRV66V5NA1k5vg+dw/zIMFt56JutfLHmOHa7vUKvK4QQVVVyhpnJf2lFy58d2JRmwV5OjkgIIYQQQoiqrcKH6X3++ec88sgjvPnmmw7b/f39mTRpEklJScyYMYPevXtXdCg1S0hbOLVTS0ZF3QomH8hNg+RDRT2lKkhDfw/mPtKFCQv3snBHAlOWHOK7TbH8/mRPmWlPCFHr3Pn5Bs5lWWgS6MGjvWToubg2R44cYfXq1Zw5c+aiH3pef/11J0UlhBBCCFG+KjwZtWXLFr777rtLPj5ixAhGjhxZ0WHUPCFttdvEPaAoWu+omLWQtLfCk1EArgY9/3dnW9yNeuZuiuNkag7d3l3B1w92olfTwAq/vhBCVAUz154g5qw2w+ionuG46GV4nii7r776iieeeIKAgACCg4NRFKXwMUVRJBklhBBCiBqjwpNRp0+fJiws7JKPh4eHk5SUVNFh1DyFyajdoKrFklF7gPsrJQRFUZh8a2uaBHoy6c8DmK12RszcwgNdGjL5ttaVEoMQQjhLRm4e7y49BEBEXU/u79LIyRGJ6m7y5Mm8/fbbvPzyy84ORQghhBCiQlX4T7i5ubkYjZceuuXi4oLFUrF1jmqkui1BbwT3OtrwvILeUBU4o96lPNwjnEVP9sDLpOU2526Oo8/UVZxJz630WIQQorI8Nmc7eTYVRYFvHurs7HBEDZCamsqdd97p7DCEEEIIISpchfeMAvj666/x9PQs8bGMjIzKCKHmcTHBK/HaLUBwfk+kpL1gt0Mlz+TUpoEv21+7npGzNrPpxDliz2XTb9pqZj7Uma6N61RqLEIIUdHWHD7DxhNnAXiwWxih/u5OjkjUBHfeeSf//PMPY8aMcXYoQgghhBAVqsKTUQ0bNuSrr7664j6iDAoSUQABzUDvCpYMOB8D/pVfRNdo0DHvsW58sz6at/86SJbFxj1fbuKh7mG8dEMk7sZKyX0KIUSFstvtPP3jTgD83F14fVgLJ0ckaoqIiAj+85//sGnTJlq3bo2Li4vD488884yTIhNCCCGEKF8Vnh2IiYmp6EsIVQW9CwRF5c+wt8cpyagCD/cI5/b29Xl36SF+3BLP7A0x/LYzgWcGNGVUz3CnxSWEEOVh5voY0nOtAHz+QEd0ldwTVdRcX375JZ6enqxZs4Y1a9Y4PKYoiiSjhBBCCFFjVPgn6JUrVxIVFUV6evpFj6WlpdGyZUvWrl1b0WHUTOeiYeYg+PQ6bT04v25UUuXXjbqQj7uRKbe3Yc6o66jjaeR8Th6T/jzADdP/JSVTakkJIaqnc1kWPlt1DIC7O4XSRYYhi3IUHR19yeXEiRPODk8IIYQQotxUeDJq+vTpPProo3h7e1/0mI+PD48//jgffPBBRYdRM3kEQPxmSDkCWSlQv4O2/cg/Wm+pKqB3s0D+fKonTQI9ADiUlEGXt1fy3z8PYLfbnRydEEKUzjuLD5KanUfzYC8m39bK2eEIIYQQQghRLVV4Mmr37t3ccMMNl3x80KBBbN++vdTn/eyzzwgPD8dkMtGxY8cr9q4ym81MnDiRRo0a4erqSpMmTZg1a1apr1uluHpBnQjtfuJuaHGzVjfq9F5I3OXU0IoL8XVjxQt9eXFwJHpFwaaqzFwXTbtJy1i8J9HZ4QkhxFX5Zn00v2w/iaLA27e1xkUvw/PEtRs3bhxZWVmF9y+3CCGEEELUFBVeM+r06dMXFeB0CMBgIDk5uVTnnD9/Ps899xyfffYZPXr04IsvvmDIkCEcOHDgksXQ77rrLk6fPs3MmTOJiIjgzJkzWK3WUl23SgpuA2ePacmoiAHQ4ibY9wvsmAP12js7OgdP9ovg3s4NGfP9drZEnyM918rYH3Zw3/GGvDgoEj8Po7NDFEKIEqXnWHhn8UEAOjT0o2MjPydHJGqKnTt3kpeXV3j/UhRFqayQhBBCCCEqXIUno+rXr8/evXuJiIgo8fE9e/YQEhJSqnN+8MEHjB49mkceeQTQhgL+/fffzJgxgylTply0/9KlS1mzZg0nTpzA398fgLCwsNI9kaoqpC3sX6glowA6jNSSUXt/gUFvg7FqTTfu72nkp8e7sSX6LM/8uJOkdDM/bI7jrz2JPDewKXd2DMXTJLPuCSGqlkfnbCfPpqJTYPo97ZwdjqhBVq1aVeJ9IYQQQoiarMLHGAwdOpTXX3+d3NyLi1bn5OTwxhtvMGzYsKs+n8ViYfv27QwaNMhh+6BBg9iwYUOJx/z+++906tSJqVOnUr9+fZo1a8b48ePJyckp3ZOpikLaarcFyaiwXuDbCMzpcGCR8+K6guvC67Dp1YH88EgXmgd7kZaTx1t/HKDtpH94ecEeLFapJyWEqBpWHjrD5uhzADzUI4xQv6qV5BdCCCGEEKK6qfAuKK+99hoLFy6kWbNmPPXUU0RGRqIoCgcPHuTTTz/FZrMxceLEqz5fSkoKNpuNoKAgh+1BQUEkJSWVeMyJEydYt24dJpOJX3/9lZSUFMaOHcu5c+cuWTfKbDZjNpsL1wtmA1RVFfUai4MXnONazwNAcBsUgNRo1JzzYPKB9iNQVk1G3fEttL3n2q9Rgbo1qcMfT/Vg3tZ4Jv15gDybyvyt8SzccZIHu4Xxyg2R6PPrspRru9Ui0m5lI+1WdjWp7ex2O8/O04ZO+XsYmTikeYU9r5rUbpWpItutsv9f3HbbbSUOx1MUBZPJREREBPfddx+RkZGVGpcQQgghRHmr8GRUUFAQGzZs4IknnmDChAmFH+wURWHw4MF89tlnFyWWrsaFH9ZUVb1kPQW73Y6iKHz//ff4+PgA2lC/4cOH8+mnn+Lm5nbRMVOmTOGtt966aHtaWlq5JKMyMzNLfB6lp8czpAOqWx2ykxNQvUFpchPeq99BidtIevQO7P5NrvEaFe+mFr70bNSJ1/48yvoT58mzqXy9Lpq5m2J5uGt9Hu3eAEVRyrHdao/yfb3VHtJuZVeT2u7NxUfJyNXqC067pRkZGRkVdq2a1G6VqSLbreCHqMri4+PDb7/9hq+vLx07dkRVVXbu3Mn58+cZNGgQ8+fP57333mPFihX06NGjUmMTQgghhChPlVKcp1GjRixevJjU1FSOHTuGqqo0bdoUP7/SF4ANCAhAr9df1AvqzJkzl0xqhYSEUL9+/cJEFECLFi1QVZWTJ0/StGnTi46ZMGGCw8w16enphIaG4uPjg7e3d6njLq4gmeXj41M+H5wfWwlAYVQ+PtD0ejjyN15HFsDgt6/9GpXAxwfmPhpIwvlsxs3fzZaYVHKtdmasi2f+ztNMujmKrg08yq/daolyf73VEtJuZVdT2u5kajaL9pwBoF9kIP1alzxBRnmpKe1W2Sqy3Sr7/0NwcDD33Xcfn3zyCTqd1ivYbrfz7LPP4uXlxbx58xgzZgwvv/wy69atq9TYhBBCCCHKU6VWivbz86Nz587XdA6j0UjHjh1ZtmwZt912W+H2ZcuWccstt5R4TI8ePfj555/JzMzE09MTgCNHjqDT6WjQoEGJx7i6uuLq6nrRdkVRyuXDacF5KuyDbsdRcORvlC1fQOvhUL9DxVynAjTw8+CnMd05djqD53/axd6EdM5lWXjqx12E+pkY1bMxwzs2wMt06VkahaMKf73VUNJuZVcT2u7zNSdQAS+TgU/v71Apz6UmtJszVFS7Vfb/h5kzZ7J+/frCRBSATqfj6aefpnv37rzzzjs89dRT9OrVq1LjEkIIIYQobxVewLwijBs3jq+//ppZs2Zx8OBBnn/+eeLi4hgzZgyg9WoaOXJk4f733XcfderU4eGHH+bAgQP8+++/vPjii4waNarEIXrVkqpC5pmi9WaDocXNYLfCgkfAnOm82MooIsiLP57uxarxfXh2QAQ+bi7Ep+Zqhc7f+oeHv9lCfGq2s8MUQtRAJ1Oz+WlbPABfj+yEu1Fm+RQVz2q1cujQoYu2Hzp0CJvNBoDJZJJkpRBCCCGqvWr56fruu+/m7NmzTJo0icTERFq1asXixYtp1KgRAImJicTFxRXu7+npybJly3j66afp1KkTderU4a677mLy5MnOegrlKycVPukM2WdhQgIY3UFR4KaPIGE7nDsOS1+BWz5xdqRlEh7gyfPXR/Jor8bM+vcIH6+OxWpXWXU4mV7vrSKiricvDopkcKtgZ4cqhKghPlpxlDybSo+IOnRpXMfZ4YhaYsSIEYwePZpXX32Vzp07oygKW7Zs4Z133in8kW3NmjW0bNnSyZEKIYQQQlybapmMAhg7dixjx44t8bHZs2dftK158+YsW7asgqNyEpMvoIBqh9P7ITR/KKS7P9z2BXx7E+z8TqsjFVXyUMbqwMPVwMjr6vNwr2ZM+usAi3aewmKzc+xMJo/P3Y63ycCIbo14fmAzDPpq2elPCFEFbIk+y8/bTgLwTP+LawoKUVE+/PBDgoKCmDp1KqdPnwa0iWCef/55Xn75ZQAGDRrEDTfc4MwwhRBCCCGumXxjrwkUBULaavcTdzk+Ft4Lej6n3f/9GUg7WZmRVQhPk4H3h7fl0H8H89qNLQjy0mp7peda+XTVcXpPXcXHK45y6nyOkyMVQlRHryzYC4Cvm4v0ihKVxmq1MnfuXEaPHk1iYiLnz5/n/PnzJCYm8uqrr6LX6wFo2LDhJetdCiGEEEJUF5KMqikKk1G7L36s30So1wFyz8OvY8Buq9TQKopOp+ORXo3ZPHEgC57oToeGvvi4uXAqLZcPlh2hx3sr6fDfZbz91wEy86dmF0KIy9l84iwnUrIAGD+omZOjEbWJwWDgiSeewGw2A+Dt7X1Ns/fOmDGDNm3aFJ6nW7duLFmy5LLHrFmzho4dO2IymWjcuDGff/55ma8vhBBCCHE5koyqKQqSUUl7Ln5M7wJ3fA0uHhCzFtZ/VLmxVYKOjfxYOLYHm18dwId3t6VLuD+qCueyLHy1NprWb/7NTf9byz/7k5wdqhCiCntpgfYe6uvuwgPdwpwbjKh1unTpws6dO8vlXA0aNODdd99l27ZtbNu2jf79+3PLLbewf//+EvePjo5m6NCh9OrVi507d/Lqq6/yzDPPsGDBgnKJRwghhBCiuGpbM0pcoCAZdfoAWC1gMDo+XqcJDJ0Ki56EVW9D4z5Qv2Plx1nBTC56bmvfgNvaN2Dd0WT+b9kRdsefx67C3oR0HvtuOyYXHT0jAnjjppaE+rs7O2QhRBWxYHs8sWe1GTpfvqG5k6MRtdHYsWN54YUXOHnyJB07dsTDw8Ph8TZt2lz1uW666SaH9bfffpsZM2awadOmEgugf/755zRs2JDp06cD0KJFC7Zt28a0adO44447Sv9khBBCCCEuQ5JRNYVvQ62Qee55SD5YlJwqrt39cHQZHPgNFjwCj68FV89KDrTy9GwaSM+mgeRarHyy6jjztsaTkmkmN8/O8oNnWH7wDG0b+DCwRRBtQ33pGVEHnU46CwpRG9ntdl7/Xesx0sDPjXuva+jkiERtdPfddwPwzDPPFG5TFAVVVVEUBZutbMPsbTYbP//8M1lZWXTr1q3EfTZu3MigQYMctg0ePJiZM2eSl5eHi4tLiceZzebCoYUA6enpAKiqiqqqZYr3UgrOWd7nra6kPRxJeziS9nAk7eFI2sORtIej0rZHWdtNklE1haJA+wdAVcF4iQSTosBN0+HkNjh3Apa8DLd+WqlhOoPJaGD84EjGD47keHIm05cfYe/JNGLOZrP7ZBq7T6YBoNcpRAZ5cku7+tzXpSFeppI/eAshap4v/j1Blln7ov/Z/R2cHI2oraKjo8v1fHv37qVbt27k5ubi6enJr7/+SlRUVIn7JiUlERQU5LAtKCgIq9VKSkoKISEhJR43ZcoU3nrrrYu2p6WlVUgyKjMzE9CSdLWdtIcjaQ9H0h6OpD0cSXs4kvZwVNr2KPghqrQkGVWTDH77yvu4+cHtX8DsYbBrLjQdCC1vq/jYqogmgZ78717ti+aZjFxWHjzDL9tPsi02FZtd5UBiBgcSDzFlySHqeBi5LtyfEd0a0a1xHXljEqKGyrZY+XZDLAA3t61Hmwa+zg1I1FqNGjUq1/NFRkaya9cuzp8/z4IFC3jwwQdZs2bNJRNSF/6dK0gmXe7v34QJExg3blzhenp6OqGhofj4+FxTAfaSFMTj4+Mjf5OR9riQtIcjaQ9H0h6OpD0cSXs4Km17lLXNJBlVG4X1hF4vwNpp8MezUL8T+IY6O6pKV9fLxD3XNeSe6xqSkpnLzLUxLN6XSNy5bFQVzmZZWLIviSX7kgj1d6NX00DahfrSqr43USE+zg5fCFFO/rfyGEnpuTTwc2Pq8KuvySNERTlw4ABxcXFYLBaH7TfffHOpzmM0GomIiACgU6dObN26lY8++ogvvvjion2Dg4NJSnKc5OPMmTMYDAbq1KlzyWu4urri6up60XZFUSrkA33BeeXLgkbaw5G0hyNpD0fSHo6kPRxJezgqTXtIMkpobHlwZKlWPyq816X36/sKnFgNCdvg18fhwT9Ap6+sKKucAE8TLw9pzstDmmO321m8N4lfdpxkR2wqOXk24s/l8MPmOH7YHAeAi16hkb8H3SPqcFenUFrVl+SUENXR64v2MWej1itq4tAWmFxq7/ugcL4TJ05w2223sXfv3sJaUVD0Ia+sNaMKqKrqUN+puG7duvHHH384bPvnn3/o1KnTJetFCSGEEEKUlSSjapoNH8OKSRDW6/LJKL0L3PEVfN4LYtfDug+h9/jKi7MK0+l0DGtbj2Ft6wHaEJ5NJ86y9mgKf+w+RUqmhTybyrHkTI4lZzJnYywGnULDOu6M7NqIAS2CaODnJll1Iaq4r9YeL0xEDW0dwpDWJdfEEaKyPPvss4SHh7N8+XIaN27Mli1bOHv2LC+88ALTpk0r1bleffVVhgwZQmhoKBkZGcybN4/Vq1ezdOlSQBtel5CQwJw5cwAYM2YMn3zyCePGjePRRx9l48aNzJw5kx9//LHcn6cQQgghhCSjaprWd8HKyRCzFlKOQUDEpff1bwxD34ffnoDVU6BxP2jQsfJirSbcjQb6Nw+if/Mg3ripJfFns5m3LZ41h89wLDmT3Dw7VrvKieQs3vzjAG/+cYBAL1eCvF2p4+FK38hAbmobQoCnydlPRQiRb8m+RN7+6xAAIT4mPr67nXMDEgJtRruVK1cSGBiITqdDp9PRs2dPpkyZwjPPPMPOnTuv+lynT59mxIgRJCYm4uPjQ5s2bVi6dCnXX389AImJicTFxRXuHx4ezuLFi3n++ef59NNPqVevHh9//DF33HFHuT9PIYQQQghJRtU0vqEQcT0c/Rt2zIZBky+/f9t74egy2L8QFoyGMWvB1atSQq2uQuu48+LgSF4cHAlAfGo2P2+NZ3P0OXLzbOw/lU5yhpnkDG0oxJojybz1xwFMLjoa+rvTqZEfg1oG0ysiAL1e58ynIkStszs+lVcW7OVgUgYAXq4Glj7bC4NB/i0K57PZbHh6ajPiBgQEcOrUKSIjI2nUqBGHDx8u1blmzpx52cdnz5590bY+ffqwY8eOUl1HCCGEEKIsJBlVE3V8SEtG7foB+v8HDBcXFi2kKDDsQzi5FVKjYfFLcOtn2nZxVUL93Bk3KLJwPTfPxr6ENL5ae4Ld8ec5k2HGrkJunp0jpzM5cjqTH7bE42HUE1XPm5b1fHA16GjX0Jd+zQIxGeWfpRDlKS07j7XHkpm1LpodcecLt3uZDPz5dE983I3OC06IYlq1asWePXto3LgxXbp0YerUqRiNRr788ksaN27s7PCEEEIIIcqNfOutiZoOAq96kHEKDv0Jra7Qxd7NF27/EmbfCLt/AL8w6PtyZURaI5lc9HQK86dTmH/htt3xqfyxJ5HNJ85yIiWLHIuNLIuNrTGpbI1JdTje3ainnq8bLet5c124P/0j6xLi61bZT0OIaqtgEoKftsVzNsvCoaQMbHa18HEvk4Gn+kXwaK9wdDrpESWqjtdee42srCwAJk+ezLBhw+jVqxd16tRh/vz5To5OCCGEEKL8SDKqJtIboMMIWPMerP0AIm8ElyvUK2rUHQa/A0tfgdXvgMEIPZ+vnHhrgbahfrQN9Stct9rsRKdkse9UGrviz7NwRwIZuVYAsi02jp3J5NiZTBbtOgVAfV83IoO9aBbkhTnPSodGfvSNrIuXSWY4EgK04bLfbohhxcEzxJ7NoljuCYCmdT3pERFA0yBP7u/SyDlBVqbT+8FmgaDW2t8EUS0MHjy48H7jxo05cOAA586dw8/PTybFEEIIIUSNIp9Qa6rOj8DWr8EjEPKyr5yMAuj6hLbvikmw/E3Qu0K3sRUeam1k0OtoGuRF0yAvbmvfgLduboXVamdT9FlWHjrDjrjzxJzNIj0nD7sKCedzSDifw8pDZwD4ZoM2A5iLXsHP3UgDPzeaBnnRvUkd+kbWxcdNklSiZlNVlf2n0lm8N5HvNsaSYbZetE+Ah5GBUUE82S+CUH93J0RZyVQVjv6jzY4at1Hb5uIBDTpps6tGDoW6UTIMu5rx9/e/8k5CCCGEENWMJKNqKs+6MHoZ+IVDaYah9HoBrBZY8y78PQGyz0K/iaU7hygTg0FHz6aB9Gwa6LD9bKaZ48lZHD6dwY7YVJYfOF34xTvPpnImw8yZDDM74s4zf2s8AAGeRsICPEjNshBex4NwPyOdIurSJbwOvlIfR1RTsWez+GZ9DPsS0og9l104SUABF71Cs7peDG0TwgNdGtaeWlC2PNi3ANZ/BGcOaNt0LmB0h9w0iF6jLSsng29DLSkVOQQa9QC9JK6rglGjRl3VfrNmzargSIQQQgghKocko2qyOk0c1/Nyr66HVN9XQLXDv1Nh7TQ4exRu/Vz7YiMqXR1PV+p4unJduD8jumrDi+x2O/tOpfPvkWT2JaRxIiWL0+m5qCqk51pJybSQkmkB4HiyVn/kq40nAdAp4G40EBnkRe9mgTSs40Z9Xze8TS40C/KUGjqiyrBa7SzancCCHQnsjj9PlsXm8Li7UU/3JgE08HNjSKtgujSu46RIncSSBTu+g42fQJqWiMboCZ0ehq5jwTMYkg9B7Ho4tgJOrILzcbD5c21x9YGmA7XkVMRArX6gcIrZs2fTqFEj2rdvj6qqVz5ACCGEEKKak2RUbWDJhqUvQ8oxePCPK9cPURToPxH8w+H3Z+DAIkiNhXt/BO96lROzuCydTkebBr60aeB70WOZZisnkjPZFpvKkr2JxKfmkJplwWy1A2BXtX22x6WyPS71ouNdDTq83Vyo42Ek2MdEI393OoX506GRH0Ferhj0kqwSFedcloW1R5P5bNVxjpzO4MKv5Ua9juYhXjw/sBndI+rgatA7JU6nyjoLW77Ulpxz2jaPQOgyBjqPBrei+nQERWnLdY9qfwtOrIbDi+HIUshK1npU7VsAOoNWOzByKDS7QXv/F5VmzJgxzJs3jxMnTjBq1CgeeOABGZ4nhBBCiBpNUeUnuKuSnp6Oj48PaWlpeHt7X9O5VFUlLS0NHx+fyilIei4aPu8Flgzo8zL0e/Xqj43dCPPv14breYVoCal67Ssu1suo9HarIQrazcvLi/jUXLbFnGPfqXQUBbLNNmLPZXH0dCZnsyxXPJdOAX8PI9kWGz5uLgR4Gqnn40ZYgDthAZ40CfSgebAX3m7Vf3iUvN7KrjRtZ7Ha+XXnSRbuSCA3z8aehDQu/KtU18uVHhEBPNC1ER0b+ZV8ohrgqtotdgN8fydYMrV1vzDo/gy0uw9cSjHrpt0OCdu1xNThJZB80PHxulHaUL7IoVCvQ5Ueql2R/1bL82//lZjNZhYuXMisWbPYsGEDN954I6NHj2bQoEHV6j2oIttM3pcdSXs4kvZwJO3hSNrDkbSHI2kPR6Vtj7L+7Zdk1FWq1skogL2/wILRgAIP/g7hva/+2HPR8OM92nAPgxvc9jm0vLWiIr0keZMom6ttt4zcPHbEprLr5HmOns4k4XwOyRlm0nLycDXoSMvJI892dW8XPm4uBHq54mUykJxhxs/dhbpeJkJ8TDT0d6dxoAdNAj0J9XPHYKiaX3Ll9VZ2V2q7I6czmL0+mjVHUjh1Puei3k/Ng71oF+pL07qe3N25IZ6m2tGJ94qvOXMmzOimDbULagW9xkGLW8pntrxzJ+DwUi05FbsB1GJDIj3qQuQNWmIqvE+VG7JdU5JRxcXGxjJ79mzmzJlDXl4eBw4cwNPTs9Kufy0kGVV5pD0cSXs4kvZwJO3hSNrDkbSHo8pKRtWOT/gCWg/X6oXsnAsLH4Mx68Aj4OqO9Q+H0f/AL6Pg2HL4+UFIeQ16j5dZmWoQL5MLfSLr0ieybomP2+0qKZlmDpxKZ/3xFGLPZpNwPoezmWbSc62Y8+zY8nPbaTl5pOXkFR57MjUHSL/onApaTys/DyNuLjpOp5vxMhnwdXfB391IoJcrQT4m6vu40TzYm1B/dzxNBvS6mv26s9vtJKXnEn8uh5Op2SSlmzmdnsvZTDPuRgMergYycq3odXDgVDrurgY8jXo8TS54mQx4m1zwcXMhtI47Tet64m7U4+6ix2jQ4Wmq/ILVNrvKrvhUftgcx+K9SeTk2S7aJ9jblTs6NmBE1zCCfa6itl1ttGKSlojyCYVRS8HVq/zO7d9Ymz2121jIPqe91x9eDEeXQ9YZ2DFHWwxu2sx8TQZodabqNJG/AxVAURQURUFVVex2u7PDEUIIIYQod5KMqk2GTIX4LZByBH4bC/fNv/ovESYfuHc+/PMabJ4BqyZDymG4+ZOrK4ouqj2dTqGut4m63ib6Nr9UwspOanYe57IsnMkwczAxnQ3HU0jOMJOanUdGbh7ZFlthDysVOJtlcRgieOaCGdJK4m7UY86zY9ArGA06TAY9bkY9Hq56vEwuRIV4ExXijafJgAIkZ5oLE1z+HkYCPF3xdzeWa6+sXIuV8zl5pGZbSMu2otOBq0FPTp6NMxlmtpw4S1qulqTLyLWSZbaSbbGRm2fDy+SCm4ueDLP2eHqOtdziKomCNuRSr1PQ63UY9QqBXiZa1vPOT3bp2XsyDU9XAx4mA96uBrzdtASXn7tWS6xxoAeergbcjQaMF7Tj+WwL2+PS+OdILPHncjiQmO6QnAQwueiICvHm1nb1ubNTA9yM8ufosmI3wJYvtPs3fVS+iagLuftDm7u0xWqB2HXaUL7DS7RC6Uf/0RYA30YQkZ+YCu9dsXHVcMWH6a1bt45hw4bxySefcMMNN8jEEkIIIYSoceTTf21i9IDh38BX/eHo37BphvYr+NXSG2DIuxDYDP4aD3t/htQYuOcH8Cw5OSFqF51OVzj7X9MgL3pEBPBIr8Yl7puZayUtJ49Ms5WzWWaOnc5ka+w5zmZaSM2ykJ5rJctiJTfPRp7Vjk6nFCaxsvNnVbNZVcxWOxk4Jm+2RJ+76pjdjXoCPF1xN+rRKQonU7Nx0evQ6xRsNjsqCnZVxa6q1PF0xdvNBXOejbScPE6n52Ivh4HOBTMflkSngIteh6tBh5tRT6M6HnRq5IeXyYWM3DzWH0shJ09LauXm2bHY7ORZ7eTZVUwuegw6hSyztbCAPWhJQJsKNu0/5ABpOZkcO5NZ5uegoOW2L9UePm4u9GoagLfJhQe6NiSqnk+Zr1XrWLJh0ZPa/fYjtORPZTEYoUl/bRkyFU7vh+MrtJ5TsRvhfCxsm6UtOgOEds1PTg2AoNZVutZUVTJ27FjmzZtHw4YNefjhh5k3bx516tSy2SGFEEIIUatIMqq2CW4Fg9+GVe9owyvKotMobUjHTyPh5FYtuXXvPO3cQlwlT5OhWC0gL7o3CWBk97DLHmO22sjItZJ4PoeDSRkkZ5g5m2nmXFYe53MspOcntwK9XHHR68g220jONBN/Ljs/oXTxObMtNuLOZV9VzOm5V99jyc1Fj7+HEZOLDpOLnoTUnMKEkrtRj6erAa/84XSNAz1o3cBX64lk1GO22mhUxwNf9/IrBG/Js3EmU2uvs1kWzmVZOJ+dx/nsPNJz83Bz0RPo5Uq2xUZqtoX1x1LItdjItdqxWLUkl9WuYrOrGHQKKlrx8QIqXFR43M1FT8dGfjx/fTPaNvCRmRjLauvXWk0nrxAYNNl5cSiK9j4f3Ap6PKvVsIpZpyWmjq/QYoxdpy0r3tJqTTXpr/WaatLv6oeG10Kff/45DRs2JDw8nDVr1rBmzZoS91u4cGElRyaEEEIIUTEkGVUbdX4EWt52bV8MGveFR1bCD3fBueMwazDc8bU2+5IQFcTVoMfVU+vJ1LqBb5nOYbfbScuxkpJp5mymBb1O69GVbbFyOj2XPSfTyLZoNbBUuxV3kytGvQ4Xg45QP3dC/d1xc9Gj00FKphkfkwu+7i74uhvxddNqNlXFITVGFz0N/Nxp4Fd+xafzbHaycq3EnssiJb9HW4CXK00CPfDAjJ+fnxSBLA/Hlmm3PZ8HN1+nhuLA1TO/sPkN2vq5E3BshbZE/6vVmtozT1tQoF67olpTDTqXT+H1GmLkyJHyb0UIIYQQtYp8EqyNFMUxEZV1VqsRUtoPwgER8Eh+QfPof+HHe6HvK9DrBdBXfpFkIa6GTqfDL79oetOgix8f3jEUkFk1roaLXoevhxFfD8ceXFrb5V3iKFEqeTkQt1m737ifc2O5Ev/GcF1juO5RsJohfrPWa+rYSji9F07t1Ja108DVBxr3zu81NQB8Q50dvVPNnj3b2SEIIYQQQlQqSUbVdgf/1GqR3DAF2t1X+uPd/eGBhbB4PGyfDaunwJG/4fYvIaBpuYcrhBC1SvxmsJm1IXrV6T3V4KoVNA/vDddPgowkOL4yf0jfKsg5Bwf/0BaAgMiiWlONeoCLm3PjF0IIIYQQFUqSUbVd8kHIPQ9/vaANmyjLlx29CwybDo16wuIX4NQO+Lwn9JsIXZ+QXlJCCFFW0f9qt+F9St97tSrxCtZ+8Gh3H9htcGpXUa2pk1u12VlTDsOmz8Bg0hJSEQO15FRAs+r93IUQQgghxEUkGVXb9RynfdmJ/hd+flgbdudiKv15FAXa3AmNums9rU6sgmX/gZ1zYehUrcaUEEKI0jmRX8g6vLdz4yhPOj006KgtfV+GnFTteR5brtWbyjilJamOr4C/AZ/QokLojfuASWZiFEIIIYSo7qpelV1RuXR6uO1LcA/Qanr889q1nc+nvjZs7+b/gXsd7ZfuObfATw/C+fjyiVkIIWqD3DStpynUrGTUhdz8oOWtcMsnMO4AjN2kzRrYuB/oXSEtHnZ8Cz+NgPfCYdYN8O/7kLAD7PYrnl4IIYQQQlQ9kowS4B0Ct32u3d/6Faz78NrOp9NBh5Hw9Ha47nFQdHDgN/j0Ovh3mlbYVgghxOXFbgDVrhUGry0FvhUF6raA7k/DyN/g5Ri4/xfoMgbqRIBqg7iNsHIyfNUPpjWFhY/icnChNvxPCCGEEEJUC5KMEpqm10P//2j3l7+pFSG/Vm5+2hC9x/+Fht0hLxtW/hc+6wpH/rn28wshRE1WOESvj3PjcCaju/b3ach72g8cz+6GGz+A5sPA6AXZKSh7f8Ztw/vaDx9CCCGEEKJakJpRokjv8dqv8Oeitdoc5SW4NTy8GPb+rA0DPHcCfrgTmg3RZvHzDy+/awkhRE1RULy8cS1ORl3ILww6j9YWWx7Eb0E9thyz3YBJipwLIYQQQlQbkowSjvq8BKpaNHORJQt0LmAwXtt5FQXa3AXNboB/p8KmGXBkiTbVd8/noMdz2i/gQgghIDMZzuzX7of1cm4sVZXeBcJ6QKPumNPSKMPUG0IIIYQQwkmkT7u4WEEiym6HhY/BnJsh80z5nNvkrRWmfWKDNvTEZoY178GnXeDAIi0RJoQQtV10/hC9oNbgEeDcWIQQQgghhChnkowSl3buhDZMJG4jfNEHEraX37kDI2HkIrhrDng3gLQ4+GmkVpD26HJJSgkhajcZoieEEEIIIWowSUaJSwuIgEdXQkAzyDgFs4bAzu/L7/yKAlG3wFNboPdL4OIBp3bC93fAzEFw8E+ZHUkIUTsV9IwK71264zJOO66fWANbvoKkfZLkF0IIIYQQVYYko8TlBTSFR1ZA5FBtSN2isbDkZa1wbHkxekD/idosSd2eAoMJTm6B+ffDJ520L1KWrPK7nhBCVGXnYyE1BhQ9NOp+5f3NmdoPBd/cCP8XqR1bYPc8WDwePu+hPfbrGNjzM2SlVFT0QgghhBBCXJEko8SVmbzh7u+hzyva+ubPtS805c0zEAa/rSWlej4PJh9tqODi8fBBFKyYhJJ5+srnEUKI6qxgiF79juDqVfI+drvW6+nXMTCtqfZDQew67bGY9UX7NegITfqDiztknobdP8LCR+D9CPiyL+TlVuhTEUIIIYQQoiQym564Ojod9JsAwa3hj2egSwUkowp4BcPAN6HXeO2L08ZPITUaZd0HeG/4H7S+Q+tBFdy64mIQQghnuVK9qKR98OM9kBZftM2/CbS7D9rcDb6hRds7P6ItVrNW/+/YCm0W09P7wGYFl2Jz0K16BzwCIWIA+Dcu/+clhBBCCCFEPklGidJpMQwa9wVXz6JtaSfBp0H5X8vVE657FDqNgsNLUDd+ghK3URt2snueNhtft6cgYqCWLBNCiOpOVYuSUeGXSEb5h0POeXD1gVa3Qbv7oUHnoplQS2Jw1d67G/cF/gsZSdpSwJIN66Zrw7EB/MK1pFST/lrdqkv10BJCCCGEEKIMJBklSq94Iippr1ZsvNMoGPgW6CvgJaXTa0mw5jeScXgNnvvmoOz/TSvwG70GAiKh21itR4CLW/lfXwghKonu3FFtOLLBpCWYSmL00GYjDYoq+3ueV7C2FLDnQd+X4dhKiN8EqdGw9Wtt0Rmg61gY9N+yXUsIIYQQQogLSHcScW2i10JeNmz8BObeXuFFcW3B7eCOmUXFzl29IeUw/PEsfNgKVk2BzOQKjUEIISqKIT6/3lPDro5D6NJPwf5fi9YbdCzf5LvJB3q9AA//BS/HwD0/asP7/MLBbnVMXJ2Phx/vg00ztB8k7Pbyi0MIIYQQQtQK1TYZ9dlnnxEeHo7JZKJjx46sXbv2kvuuXr0aRVEuWg4dOlSJEddQ3cbCXXPAxUPrpfRpF9i3sOKnEPcN1YqdP78fBk8Bn4aQnQJr3oUPW8KCR+HEavmSJISoVgzxG7U7xYfo2e1aofKfH4J/36/4IFy9oPlQuPH/4Nld8MxOredpgZi1cPgvWPoKfN4T3m8M8+6HzV/A6f3yviuEEEIIIa6oWiaj5s+fz3PPPcfEiRPZuXMnvXr1YsiQIcTFxV32uMOHD5OYmFi4NG3atJIiruGiboFHlkNgCy0h9MvDMP8BSE+s+GubvLWE2DM74c7ZUL+TVvNk708w5xb4qA2sfBvORVd8LEIIcS3sNgwnS0hG7fpeS/a7uEPUbZUfl39j8AgoWg/tok0y0WSA9kNETioc+hOWvAQzumv3C+TlVvyPE0IIIYQQotqplsmoDz74gNGjR/PII4/QokULpk+fTmhoKDNmzLjscXXr1iU4OLhw0ev1lRRxLRAUBY+vgT4va/VFDv0JR/+uvOvrDdDyNnh0BTyyUqth5eqjzTb171T4uB18cyPs/B7MmZUXlxBCXK3E3ejM6aiu3hDStmj7gUXabc9xEBDhnNiKq9MEej4PIxbCK7EwejkMeF0rdm70hEY9ivZd/xG8HwE/jYQtX8GZQ5KcEkIIIYQQ1a+AucViYfv27bzyyisO2wcNGsSGDRsue2z79u3Jzc0lKiqK1157jX79+l1yX7PZjNlsLlxPT08HQFVV1Gv8IF1wjms9T5WjN0LfCdDiJtj4KbR7oOhLR2oM+DYEpez5z6tut/odtGXQ23B4sdar4PgqlNh1ELsOdfGL0PJWbQaqht0uPwNVDVBjX28VTNqt7KTtykaNXoMC0KgHqk6vvX9azRC7HgVQmw2ueokcnQEadNKWnuO0+lI6Q1Gc8ZtRslO0hFp+Uk31CISwnhDWC9rdpxVrvwYV+XqT17AQQgghRMWodsmolJQUbDYbQUFBDtuDgoJISkoq8ZiQkBC+/PJLOnbsiNls5rvvvmPAgAGsXr2a3r17l3jMlClTeOutty7anpaWVi7JqMxMrXeOUhMTIaZQ6PcuZOT3QLLm4v3tjdhNvuT2fg1raPcynbZM7RY6EEIHomScwnhwAcYDv6A/H6MlqHZ9j82nEZao4Vha3IHqXb9McVV1Nf71VkGk3cpO2q4M8rLx2j4HgJyQ67CkpQFaDSnPvGzs7gGkmxpA/vZqY+jn6E/vxhC/EcPJTRhObUPJSob9v6IeXUZak1tAp/3woz+5CdWjLnbf8FL9SFCRr7eCH6KEEGVz9HQGO+PPY9ApuOh1uOh1GA0KRr0eF72Ci0GHUa/DaNDlP65gzN/moi/aJn9LhBCi5ql2yagCF/5RUlX1kn+oIiMjiYyMLFzv1q0b8fHxTJs27ZLJqAkTJjBu3LjC9fT0dEJDQ/Hx8cHb2/uaYi9IZvn4+NSOP64nj4IlA0NGAp4L7kVtdgNcPwkCmpXqNNfUbj4+0OA1GDgRNX6TNlzvwG/o02Jx2/h/mDZ+AI37aL2lmg8r31mqnKzWvd7KibRb2UnblcGfb6Kcj8buGYzpuodwc/fRtm/bCoDSpD8+vn5ODPAa+A+EFgO1+1YzasJ2iFkHVjM+fnWK9vv2ZZTUGFSvEK3nVKOe2q1/48smpyry9SavXyHK5mymmbf+OMDvu0+Vy/mM+Ukpl2KJqsKklaEo0aVT7bi7uuBiuGAfh2Py9y88vmibQa/DRadg0Osw6BVcdPm3egW9TleYVCv+mON+2j56nSTQhBDiSqpdMiogIAC9Xn9RL6gzZ85c1Fvqcrp27crcuXMv+birqyuurq4XbS+Yie9aFZ/Vr8YL7QzP7NJmuts6E+XIUji6TKvr1PcVx8K4V3DN7aYo0Ki7tgydCgd+h13fo8Ss1WbfO7Faq3nS9HqIvFG7dfMt27WqkFr1eitH0m5lJ21XCocWw/ZvAMge9H94uPsVtVvKYQCUJv1qxpBiFxOE9dCW4izZ4N0A0k+hZCTC3p+1BcCrHrS9Bwa+ccnTVtTrTV6/QpSOqqos2nWKt/7YT2p2HjoFOof54+qix2K1kWdTybPZsVjtWGx28mx28qyO2yw2+0UjkrXtoP2nenDRKxguSFIVJLIc7+uusG/+4wX75CfLHI/RkmV6BfQFCTEFLOZcPD0yteSariihpi+2GApvdeh0YNDpHLZfuI8+P36dkn+rk/dJIUTZVLtklNFopGPHjixbtozbbiuaVWjZsmXccsstV32enTt3EhISUhEhipJ41IGh78N1j8Gy17VaTlu/gj0/wZObwdsJ/y+MHtDuXm05Fw2758GuHyAtDvb/qi06g1bXpPmNEDkUfGrmUD4hhJNknIbfnwJA7fok1oY9HR+/53s4dwLc65RwcA1idIeH/4K8HDi5Ves5Fb1Wu59xCnKLDU+0WuDP56FhVwjvBT4NnRe3EKLQydRs/vPbPlYdTgagebAXU4e3oU0D31Kfy2ZXHRJWFmt+4spmx2JViyWy8hNY+bdpGZm4GE1Y8pNe2qJithbtn2crOEZ1OLfFZsdqU7HatWOsdm09z2bHalcd7uddsG9JtMSbDfKupVWrPkWhKHGl5Cet9EUJLZ2iJdKKklq6wiSW4YJkV8E5dMXOpSg4bNflr+vyH9cpBfe54LiC+9p2Bcgzm/FwP1d4LUW54Jo6ip2v4Nz517swpoJ9Co5XFHS6olgdz110Xp2i/dChU4q2oeCwruTfVwq3yY8jomaqdskogHHjxjFixAg6depEt27d+PLLL4mLi2PMmDGANsQuISGBOXO0+hvTp08nLCyMli1bYrFYmDt3LgsWLGDBggXOfBq1U0BTuPdHiP4X/p4IPqHOSURdyD8c+k3QZgM8tVObDfDQX1qvhBOrtGXxeKjXXktMNR8Ggc1rRk8FIYRzpCXATyMg+ywEtdJmpMvKvXg//8aVH5uzuLhBeG9t6YfWY+rkVscetKd2wK652gJQpyk88I9Twq3KpkyZwsKFCzl06BBubm50796d9957z6FswYVWr15d4uQuBw8epHnz5hUZrqjGrDY736yP4YNlR8jJs2HU63i6fwSP92mC0VC2iWv0OgU3ox43rn7ma1VVSUtLq/Qh4qqqYlcplrQqOZl1YQLLalPJy9/HarOTl39s8e2XPmfx89ixqSo2u4rVrmKzqdq6zU6uJQ9Fp9e229Wi/WwF+9uLttvUwv0K97cX7We/RMlcVS1IvMmEDxWtLAksh2OgMDGmAKgqBoNe25dixxbsk79fSdfR6S44Jv96CgVfj7THCtYL9i24T/4/Ue1xpdh+ResUP66E8xScocTHKUrgXbw9/0iH64DFbMZkMl20Pw7xKUXHF2y74P2m+KrC5R7jMo8pJW6/UMG1/dxduL1Dg0vvWEVVy2TU3XffzdmzZ5k0aRKJiYm0atWKxYsX06hRIwASExOJi4sr3N9isTB+/HgSEhJwc3OjZcuW/PXXXwwdOtRZT0GE94bH1oC5WHHYtASYdy/0eA6ibtXeLSubTgcNOmrLwDcg5Rgc/ktLTMVv0RJVp3bCysngF16UmAq9DnRX/4FJCFHLHV0OCx+FnHPg6gN3fA0GV6BYMkpVJeFtdNfq+RXnWRd6jdd6TyVs037QuIaZWmuqNWvW8OSTT9K5c2esVisTJ05k0KBBHDhwAA8Pj8see/jwYYf6mIGBgRUdrqimdsefZ8LCvRxI1D7PdQ7zY8rtrYmo6+XkyCqP1gMH9FXsc2B5J+fsxZJZxRNWBQktq03FrhbbbiueyCq+fnHCq/AcdrXwOna7luSz2bXzFiTN1PxtBdu1xyjax67Nrmor2F7sfLkWC3q9AbtatH/RubU2K35uW34Mjuem8HwF7VEYk1py/LbCWV/Jj7mM/w/yjwdJ/AlHzYI8q2UySlFl3uKrkp6ejo+PD2lpaeVSwNwZv9xUeUtfhU2favcDIqHHM9D6zvwvaFWg3TLPwOElWmLqxGqwmYsecw+AyBu0xFR4H+0LVBXh9HarpqTdyk7a7jJsVlg9BdZO09ZD2sKds8G/8cXt9nkv8G0IN7wLvqFODbvKsmShZp4hTedXIa+38vzb72zJycnUrVuXNWvWXHLyloKeUampqfj6+pbpOhXZZvLe4siZ7ZGRm8f//XOEbzfGoKrg4+bCq0Obc2fHUKfVEJLXhyNpD0dVrT0KetTZiyWpipJV+QmrC9YLjlHJP9bueIx6wXnt9oLkVVESzJ6fGLPZtZlo3dw98o/D4TrF1wuO0bYVu15BLHYc1rVbLlqnMMaiNijar2hCksudh4v2u/x1SjqPdnDRNdX855xrNmM0uham+tRLnKfg2hRsK5ZNKTw/jtvzL3mZx9QSd7wwUVM8dVP8sWBvExOGtqC8lPbfS1n/9lfLnlGihurzEph8tIRUymFY9CSs+C90eRw6PQwmX+fG51kXOj6oLeZMOL5CS0wdWQrZKbBzrrbojdCwGzTpDxEDtOE3VeCPnhDCyTKS4JfRELtOW+/8CAx6WyvqfaFzJyBpD5w5CLd9UblxVidGD/ALg7S0K+5a26Xlt5G/v/8V923fvj25ublERUXx2muvlTh0r4DZbMZsLvpxJj1d6yGj5n95KU8F55TfUTXOaA9VVfl7/2ne/GM/p9O1/++3tqvHxBtbEOBZ9OOhM8jrw5G0h6Oq2B4Fw980lftdQUs26KpMcs7ZqlqysizK87Vd2n8vZb22JKNE1eHmC31fhq5jYPts2PS5Vrh2xVuw+Qt4bq+zIyzi6glRt2iLLQ9i12szYh1eDGnxEL1GW5a/AR51oUk/LTnVuB94Xf2sj0KIGuLEaljwCGQlazN23vQRtB5+6f2Pr9JuQ6/T3m+EuAaqqjJu3Dh69uxJq1atLrlfSEgIX375JR07dsRsNvPdd98xYMAAVq9efcneVFOmTOGtt966aHtaWlqFJKMyMzMBKeYLld8eCedzmbr8BGuOpQIQ6mti4g1N6BrmC7Zc0tJKqHlXieT14Ujaw5G0hyNpD0fSHo5K2x4FP0SVliSjRNVj8oEez0KXJ2D/Qlj/sZbM0btoj6sqJB+CuuXXFfGa6F2gcV9tGfIenD0Gx1fCsRUQsxayzsCe+doCENRaez4RAyC0a8m9IoQQNYPdBv++D6vfBVSo2xLu+labzOFyjq/UbhtfukeKEFfrqaeeYs+ePaxbt+6y+0VGRjoUOO/WrRvx8fFMmzbtksmoCRMmMG7cuML19PR0QkND8fHxqZBhekC1/uW6PFVWe+Tm2fh8zXE+X3MCs9WOi17hsd6NeapfBCaXqlMnSV4fjqQ9HEl7OJL2cCTt4ai07VHWNpNklKi6DEZoew+0uRusxX5ti10P3w6DsF7Q/RmIGOicYuclURTtS2ZAU214odWsFT4/vkL7cpm4G07v1ZYNH4PBDcJ6QJMBWs+pwEgZ0idETZGZDAsf0XpFAXQYCUOmajPGXY7dCtFrtftNJBklrs3TTz/N77//zr///kuDBqUvbtq1a1fmzp17ycddXV1xdXW9aLuSPxNTeSs4r3xZ0FRke6iqyvKDZ5j0537iz+UA0L1JHd66uSVNg6pmgXJ5fTiS9nAk7eFI2sORtIej0rSHJKNEzaUo2pe3gu7+SXtAZ9B6HcWshcDm0P1paDW86vUyMrhCeC9tGfgmZKVoX0yP5SenMpPg2HJtAfAMhtDO0KAzNLgO6rW78hdXIUTVE7Mefhml/Rt3cYdhH2rJ9atxaieY07ReovXaV2ycosZSVZWnn36aX3/9ldWrVxMeHl6m8+zcuZOQkJByjk5UdTEpWbz5x35WH04GtOK4rw1rwY2tQ+SLmhBCiHIhyShR/XQdq9Vq2vw5bJutDdlb9CT88xq0vQ/6v1alZrNz4BGg1YlpPVxLrp05WNRrKnaD9sX14B/aAlrSLaiVlpwKvQ4adAK/cOk9JURVZbfD+g9h5WRQ7Vqy/M5voW7zqz9HQb2o8N5QxaYKF9XHk08+yQ8//MCiRYvw8vIiKSkJ0Lrcu7lpP3JMmDCBhIQE5syZA8D06dMJCwujZcuWWCwW5s6dy4IFC1iwYIHTnoeoXDkWG5+uOsaX/57AYtOG5D3SSxuS5+EqXxuEEEKUH/mrIqonnwYwaDL0fhG2f6sVOE8/CUf/hsFvF+2Xm9+7oCpSFAiK0pbuT0NejtYj4uRWbWjfya2QeRoSd2nL1q+049wD8ntOddJu63cA16rZXV6IWiUrBX4dA8eWaett7oFhH2gzvpWGb0NtRs6mg8o/RlFrzJgxA4C+ffs6bP/mm2946KGHAEhMTCQuLq7wMYvFwvjx40lISMDNzY2WLVvy119/MXTo0MoKWziJqqos3pvEO4sPknBeG5LXq2kAb97ckiaBMomCEEKI8ifJKFG9mXygxzPQ7Ult6JvdWtRrKC8HPmyl9SxqdTtE3QqegU4N97Jc3KBRd20BredU2kktKVWwJO6G7BQ4skRbABQd1I0qSk41uA7qRFSdOlpC1AYn1sDCx7TejQYTDH0f2o8oWy/GtvdAu3vLP0ZRq1zNTHazZ892WH/ppZd46aWXKigiUVXtjj/Pf/88wLZYbZa8+r5u/GdYCwa3DJYheUIIISqMJKNEzaDTQ7MLehHEbwFzOsRt0JYlL2nDXlrdAc2Hgbu/c2K9WooCvqHa0up2bZvVDEl7i/We2gZpcXB6n7Zsn63tZ/KB+gXJqc4oXk3Bp4r2EBOiOrNZYfU7sPYDQIWASBg+C4JbOTsyIYS4rMS0HKYuPcyvOxMAMLnoeLx3E8b0aYKbUYYICyGEqFiSjBI1V+M+8Px+2P8b7FsAp3ZoxcNPrIY/x8Ftn2u1m6oTg2t+D6hO0PUJbVtGkpaUOpmfnErYoQ1PPL4Cjq9AAXwANaCZlpwKaafVr6kbpdWwEkKUzfk4WPAIxG/W1js8CDdMKf2wvGJ0yQfA2LzqJ8uFENVWtsXK52tO8OW/x8nNswNwe4f6vDg4khAfmTRFCCFE5ZBklKjZfBpA96e05dwJ2LcQ9v+q9SIqPktV3CYtqdNscPWbvc4rGFoM0xYAWx6c3p8/tG8b6smtKOeOo6QcgZQjsOv7omPdA7QCy3Wba7cBzSCgKXiFSJF0IS7nwCL4/Wkt8evqDTd9VNSD8Rp4LH4KzkfDAwuhSb9yCFQIITR2u8rCnQm8//chTqebAegc5sd/hkXRpoGvc4MTQghR60gyStQe/o2h93htOXdCWy+w4X9w6E8wemoJqYjrtS+CXsHOi7es9C5Qr522XPcoqCppSdF4px9BSdimJarOHIDUWK3+VOw6bSnO6KnVnSpITgU0hTpNoU6T6pesE6I85eXA0gmw/RttvX4nGD4T/MKu/dzpCehTj6MqOu3frxBClJPNJ84y+a+D7E1IAyDU340JQ1owpJXUhRJCCOEckowStVPxRBRoRc6T9mjDbvYt0JaC7U0HwYDXq3VPIdXNH4IHQ+QNRRst2ZByGM4c0pJTKUcg5SikxoAls2gWPweKNtNXQFMtUVU8YeUZVK3bSIgrOnMQfn4Ykg8CCvR8DvpN1BLA5eH4Ku22Xntw8yufcwoharWDielMXXqIVYeTAfByNfBU/wge7B6GyUXqQgkhhHAeSUYJAdBvAvR9BRK2w+ElWr2lU7u04XxGD8cky95fILi1loSpzskXo7v2pbf4cEUAqwVSo4uSUylH4exRbT03Dc7Hasux5Y7HuXoX9aAq6E0V0ExL/BlcK+95CVHeVFXrCbV0AlhzwaMu3P4FNOlfftew22DPfO1+YxmeJ4S4NvHnsvlg2RF+25WAqoJep3DvdaE8N7AZAZ7yN1kIUTPZbDby8vJKfZyqqlgsFnJzc6W3KJduD71ej8FgKLc2kmSUEAUUpag4+ID/QNZZOLEKXNyL9slJhYWPgmoH7wYQ0R+aDNCKpdeUngwGIwRGaktxqgpZyfkJqiNw9lhRwup8rDZzYcJ2bSlO0YFvo6LkVGHCqplWQF3e8EVVlnMe/nhGqxEF2r/32z4Hz7rle52Vk1Fi1qLqXaH1neV7biFErXE208wnq44xd1MseTYVgBvbhDB+UCThAWWfXEEIIaq6zMxMTp48iaqqZTrebrdz9uzZco6q+rpUe7i7uxMSEoLRaLzma0gySohL8ahz8Wx7WWchvDfEboT0k7BjjrYoOqjfEbo9CS1vc068FU1RtC/gnnUhrIfjY1azVoeroEh6yrGihJU5XetplRoNR/9xPM7kkz/cr6ljssovXEuKCeFM8Vvgl9GQFgc6Awx4A7o9BTpd+V5n7y+w7gMAsq+fivuFiWAhhLiCLLOVr9dG89XaE2SarQD0jAjg5Rua07qBj5OjE0KIimWz2Th58iTu7u4EBgaWuueOqqrYbDb0er30jKLk9ijoLZWcnEx0dDRNmzZFd42fiSUZJURpBETAyEVavaXYDdpwvmMrtNpLJ7dCbnrRvinHYPcP0Kg7hHYBVy/nxV3RDK5Qt4W2FKeqkHm6qDdV8SF/5+O1YX8nt2pLcYpeKwgd0Exr88KEVTMtSShERbLbYN2HsOodUG3aa3H4LC3hXBG8QsC9Dmr7keQ1v7ViriGEqJFy82wsWBfNjDXHScm0ANC6vg8v39Ccnk0DnBydEEJUjry8PFRVJTAwEDe30k+2JMkoR5dqDzc3N1xcXIiNjcVisWAyma7pOpKMEqIsjO7QdKC2AKSdhOMrtWLnBY6vhLX/py2KDoLbQKMeWnKqYbfakVRRFG1GQq9gCO/l+FheDpw9np+cOuqYsMrLgnPHteXIBed083ec4a+wN1VY+RWSFrVXRhIsfAyi12jrre+EGz8Ak3f5Xif7HJh8tV5WYT1gzHrwCISMzPK9jhCiRsrNs/Hjljg+W3WU5EytPkpYHXfGD45kaKsQdDr5MiWEqH0kkVTxrrU3VHGSjBKiPPg0gA4jHbcFRkLb+yB2vVZTqWB2uk2fao8/vBQaddPu2/JqXyLFxQ2CW2lLcaoKGYklFFA/CmnxkHMO4jdrS3E6gza8r3hvqoIZ/9z9K+95ierr6DL4dQxkp2i14oZOg3b3lW9dM6sZtnwJ/74PQ/8P2uTXh/IO0V77QghxGWarjZ+2xvPpquMkpecCUN/Xjaf7R3BHxwa46Mt5GLEQQogyCQsLw2QyYTKZyMnJ4eGHH+aVV14p87n+/PNPWrVqdeWd802fPp377ruPunW1OqezZ8/mueeeIywsDKvVSmhoKF988QUNGzYsU0zlQZJRQlSUxn20BSAtAeI2aomp2I1aLaWQNkX7Ln8TDv6u9ZgKaQchbbXHa/LQvktRFPCupy2N+zo+Zsm6oDdVwdC/Y5CXrW0/exQOX3BO94CSe1P5NgK9vA3WelYLrHgLNn6irQe11oblBTYrv2uoKhz4Tfu3nhqjbdu3oCgZJYQQl2Gx2vll+0k+WXmUU2laEirEx8TorvUZ0bMpri56J0cohBDiQr/88gutWrXi1KlTREVF0b9/f6677rpKufb06dMZOHBgYTIKYODAgfzyyy8APP/88zz//PMsWLCgUuIpiXwLE6Iy+NTXiqEXFETPTQNjsVlt4jfD+ThtKZjeHQXqNNGSU7d+ptVlqu2MHlqSrngiD8Buh4xTFxRPz09YpSdoPV3iUrSEYHE6F62N60QUJagKelOZpOBrrXD2OPwySuu1CHDd43D9JHC5tjHwDk5ug79fLerN5xmszdjZ9t7yu4YQokbKs9n5dUcCH688ysnUHACCvF15ql8Ed3ZqQG5WJkaD9IYSQogCqqqSk2cr9TFajST1qob6ubmUrrZUvXr1iIyMJDY2Fn9/f8aMGcOZM2fQ6XS8+eab3HrrrQBs3LiRl156ifT0dFRV5b///S+33HKLw7k+/vhjfvrpJ3799VdsNhvPPPMMMTEx5ObmcuuttzJp0iQmTZrEqVOnGD58OCaTidmzZ18U0/XXX89LL73E1q1bGTlyJAcOHCh8Tt27d2fixInceOONV/0cy0KSUUI4w4WJjhG/al9UE3bAqV2QuFubre/sMTBnOiai/ngWclKL9aBqVzvqT12OTqcNlfRpAE36Oz5mztTasXjx9JRj2n1rLiQf0pYLudfByy0AvIO0Wj4egeARoPWycvcHN7+ixeSrJcpknHr1sns+/DUOLJna/8dbPoPmQ8v3GqvfhdVTtPsu7tDjWej+tGMyWgghLpCbZ+OX7Sf5fM3xwiRUoJcrY/s24d7rGmJy0aOqKrlOjlMIIaqanDwbUa//XaHXODBpMO7Gq0+lHDp0iJSUFPr27cuwYcMYPXo0jz32GEePHqVr16507NgRDw8PbrvtNhYuXEj37t2x2+2cP3++8Bx2u53nn3+euLg4li1bhpubG4MHD2bixIn07t0bq9XKsGHD+PXXX3n99deZNWtWYc8sgF27dhWey2az8fPPP9OxY0c6d+6Mv78/K1asYODAgezYsYOUlBQGDx5cXs11SZKMEqIqcPWCiIHaUiAzGZJ2a72oiju8RJuh7sCiom0+oVpiqmE36P5U5cRcXbh6Qr122lKc3a4l/Ir3pko5oiWuMhJRss+izz4LZy8c83cJOhfHBJXD4nuJ+37g6qMl00TlyTgN/0yEvT9r6416wO1faT0Yy1tYT0CBdvdD/9e02lBCCHEJWWYrP2yO46u1JziTYQYgwNPImD5NeKBrI0wyHE8IIaqN4cOHoygKhw8f5sMPP8RkMrFr1y5Gjx4NQNOmTenZsyfr1q3D29ubqKgounfvDmiFwv39i+rejho1iv9v787joqr+/4G/7swwrAKKIDu44gK5YYkbJoZLpaXlVhpqi5mZUS7ZovZtMSszy6UF0T65/VRcKnNXcMtcINeEFLcEtxQQZJ3z++PKwMCwzMgMDLyej8d9yNw59865b47cmfecpVOnTlizZg0UCgUyMzOxa9cuXLt2TVvm7t27+PtvPV+y37djxw60a9cOANChQwd8+eWXAIA33ngDCxYsQO/evfHNN9/g1VdfNctk8ExGEdVUDq66ySlAnndm0PdFvadSEoD/zssTe6ddlntMFU9G/fIG4NAIKsfmQLMu8jxM7L0jUygAZ195Kxnn7HSI2xeQeeMS7JEFKfMmkHlDHu6XeVOOc/GtIBfQ5AGZ1+XNIJKcoLJxLieZpSeRZeMMqNRVEoo6oyAfOBIF7PoIyEmXV7kMnQb0eBtQVMEHvIJ84Gi0vCBByHh5n383YGI80KDxg5+fiGqttKw8LD1wAdEHknEnS14dz9PJBq+ENsXQTj5MQhERVZKtlRKnPzSsV0/RML3KDb+zreTf5MKeSTt27MCTTz6Jnj17Aii96l9lXrNnz57Yvn07rl+/Dnd3d2g0GkiShMOHD8PKqnILYRWfM6q4QYMGYerUqYiPj8cvv/yCuXPnVup8D4rJKCJLIknypN7FJ/bOTgNST8gJKnvXYvvTgaNLIQFwKNxn71o0tK9xj6IJ1kmXjSPgHoR8W1/Ayan8BJ4QQN690gmqklv2nWKP7/+cexeAKNp/O9mweqod9CepKkpqWdlaflIyL7uCGOvZsv67H3MAnu2Bx78EvDpWTX3O7wF+nwbcOCMPx2vzdFEvKCaiiKgM19OzsWT/Bfz8x0XczckHADRuaI9XQ5viqfZenA+KiMhAkiQZNIQOKExGSZVORhmqd+/eePXVV/HBBx+gXbt2WLZsGUaPHo1z585h//79+Pbbb+Hg4IAXX3wRBw4c0BmmV9g7KiIiAg8//DB69eqF33//HX5+fujevTtmz56N999/HwBw9epVaDQaeHt7w9HREWlpaeVVS0ulUuGVV17BgAEDMHjwYDg7O6OgwLB5t4zBZBSRpbNxkntf+Hcr/VzfzyBSEqC5cgyK/5IgZd4A/tkhb3dTi5JRefeAmJfvT+LdAmjYTF51zsbRvNdiiSQJUNvJm6HDvPJz9SRQSj7Wl9hKAyDkxEruXblXnCGU6op7X+lLalk7Vu2QQnH/Giq63nt3Sscl/55xr2njDIR9AHSMqJreUP8lA9veA/7+VX5sWx949F15fjEiojIkXsvAj3vPY0P8VeQWaAAALd3r4bVHm6F/kAeUCgv/woCIiHS8//77aNasGTZv3ozp06dj3rx5kCQJP/74I3x8fAAA69evx1tvvYWMjAxIkoT/+7//w4ABA7TnGDJkCOzt7REeHo5ffvkFy5cvR2RkJIKCggAADg4OWLx4Mby9vTFx4kSMHj0adnZ2eicwL2ns2LGYPn06Jkww35QvkhBCmO3VLFh6ejqcnJyQlpYGR8cH+4AuhEBaWhqcnJzMMhaztmDcjKONm50VpOtngKvx8hC/ZmFy7w0ASD0JLO5a+mAHd3mFuXbPAe3ur/ylkd801/Z5jmp0e9NogJy0SiSx9OzT5Bn/upKiWJLKuXSyysYZUFlDSErcy8mBrQqQykuuZd8BNPkPUB+l/nroq1fhz07eVbNSXs5dYO+XwMFv5WGakhLo9CLQc5o8wb0RanSbq8FMGbeqvPfXFaaMmaX/HxFC4OD5W/gh7jx2n72h3d/Rrz5eDW2KsFZuBl2XpcejqjEeuhgPXYyHrtoWj+zsbCQnJ6Nx48awsTH8fZ6hw/Rqo//3//4fvvvuO+zcubPceOiLtbH3fvaMIqorrOwA72B5K8m+IdDn06IJvG8mypOk302Vt+Ir1N1KAr7rAbg0k7eSvamsHUqfn6qWQlGUXDGEEEBuZiWHtpXYn5cFCA1w7z95K4cEwM6QeimtS69QWJl5tKzrVd9ww8zrRYmoJj2BvrMBt1bVUxciqtHyCjTYfCIFP+w9j5P/pgOQ/3T1beOOF7s3QUc/A/+WExERVaG+ffsiMTER69evN+vrMhlFREA996IJlwtlpxWtMld8JbqbiUB+NnDtpLyVFP5x0STqWf/JPbEatgAcvWp9b6oaT5LkZKG1A+DsY9ix+TkVDyHMvgPk50CIAuTn5kBl6wipMpOxW9ma4GJNIDcTUNvLPzdoAvSeBdT3AwL6W/4cXERU5TKy87D68GVE77+Af+/IQ4ttrBQYEuyDsd0aw8/FvpprSEREBGzZsqVaXpfJKCLSz8YJ8O4ob8UF9JdXB7uZdH9LlP+9lSSvOFd83qTLfwIrh8o/q2yLek81bCEP//O7v8If1Xwqa6BeI3mriBDIvN/1u1YkaTQa4NgyYOcs4Pl1RZOel0zgEhEBOHfjLn46cAFrj15BZq48AWxDBzVeCPHH8539UN+eK6ESERExGUVEhlEo5V4hDZoALUosm5r1n5y0KCQKgIYBwH/n5QmnU0/IW6HBUUDQM/LP/x4D/lolJ6kaNpeTVo6etSOZQZbr1jlg00Tg4j758Z8/Ak9X0Qp8RFRraDQCexKvY+mBi4hLLJoPqqmrPV7q3gRPtfeCTSWXAiciIqoLmIwioqpTcuLmlo/LW0E+cOdiUS+qwrmpis+xc/kQ8Od3userHQCXpnJPqq6TAPdAk18CEQCgIE+eE2rPbHlYqpUd0Os94JFx1V0zIqpB0rPzsObIFfzv4AVcuJUFQP4OJaylG17o4o9uzRrW2clwiYiIysNkFBGZnlIlJ5VcmgIB/fSX8ewAdJlYNIH6f8lA7l155b+Uv4BHXi0qezgK2Dfvfi+qFkW9qRq2ABwasTcVPZirCcCm14HU4/LjJo8CT84D6vtXY6WIqCb5OzUdP/9xETHH/kXW/aF49WxUGBrsg1Eh/vB1MWgZByIiojqHySgiqhl8H5G3Qvm5wO0L93tRJQGuLYqeu3EWSLskb+d26p7H2hGI+A3weEh+fPsCkJslDyu0MnypV6qDrh6TE1G29eVVJtsOY4KTiJCdV4Bfj6dgxaGLOHbpjnZ/czcHRHT1x9PtvWCn5ltrIiKiyuAdk4hqJpVaTkAVT0IV6jkNaPP0/WF/iUW9qW5fAHLS5ZX7Cv35gzzcCpK88pl2AvVm8r9ewUxSEZCdDtg4yj93iAAybwIdIwAHt+qsFRHVAEnXMrD80CXEHLuC9Ox8AIBKIeGx1o0wsrMfQpq6cCgeERHp8Pf3h42NDaytrZGVlYXWrVtj6tSp6NKli7ZMjx49kJKSgsTEREiShHbt2gEAcnNzkZiYiMBAeYqSgIAArF69GgsWLMCiRYtQUCD3yG3Xrh0+++wz+Pr6Ii0tDVOmTMH27duhVqvh4OCAmTNn4oknngAA7NmzB48++ijeeOMNzJs3T1uHUaNG4X//+x9OnDihfT1zYTKKiCyPXQPAL0TeisvPkYf32bsU7RMCsHYCctLkZNXtC8A/24uefyuxKBl1ar082bpLc6BBE0gaW6CegzzMkGqne7eBbe8D52OB8QcBawdAoQBCp1R3zYioGmXnFeD3kylYcegSDl+4rd3vXd8Wwx/2xbPB3nCrxy8yiIiobGvXrtUmeDZu3Ij+/ftj69ateOSRR5CUlISkpCTUr18fcXFxCA0NRUJCAgDgwoULCA4O1j4GgBkzZmDbtm3YsmULvL29IYTArl27kJqaCh8fH/Tv3x/t27dHYmIiVCoVEhIS8Pjjj2PJkiXo00dedCogIAAbNmzAnDlzoFarkZ6ejgMHDsDLy6tk1c2Cn7CIqPZQWQNuLXX39f0E6PMxkHmj2ATqSfLQv/QU3Z4vx9cAZ38DAEgAnAAISQHYuQD2rsBLuwArW7ns6U3AnUvyUC6dzVn+t/iqglQznd4IbJ4M3L0mP/5nu9zjjojqpPwCDQ6cu4VNf13F1pOpyMiRe0EpFRLCWrphxCO+6NHcFQoFe0EREdVYQgB5WYYfU1AAKJWVm5rBys7gKRwGDhyI8ePH44svvsCaNWsQFRWF559/Hh4eHoiKikJoaGiZx2ZmZmLOnDk4evQovL29AQCSJCEsLAwAsGPHDly8eBG7d++GSiWneNq1a4d3330XH330kTYZ5eDggC5dumDjxo149tlnsWrVKgwePBirV6826FqqCpNRRFT7SZKcdHJwA/y7lV2u+WNyz5ibiRC3L0C6dxuS0MiJrJwMQFXsW/C/VgJnN5d9rndTixJX++YB/x7Rk7i6v/mGAEqrKrlUqoTrZ4AdM4HELfJjl+bAgG9K97QjolqvQCOQcPk2fvkrBb8ev4qbd3O1z3k522JoJx8M7eSDRo7sBUVEZBHysoBPPA06RIKBiZHpVwG1vUGvAQCdOnXChg0bkJ+fj59++gk7d+5Ew4YN8eGHHyItLQ1OTk56jzt16hTUajVat26t9/ljx46hY8eOUKvVOvtDQkIwefJknX1jxozBxx9/jGeffRZLlizBsmXLmIwiIqp2waPlDQCEwJ3/bsJJXQAp8yaQnab7DYh/d/kmdO92ie2O3CuqMBEFAJcPVZy4KkxGbZoInP297MRVyGuA+v4qTWlXgIJceb+1kzy8jHQJIf8rSYBGA/zyOpCwAhAaQKECuk4CekzmvGFEdUhqWjbiEm8gNukG9iXdRNq9PO1z9e2s8PhDHhjYzgsdfeuzFxQREVUZcf996ebNm+Hn54dWrVoBAHr37o2VK1di3LhxZR5b0dyE+p4vfL3iunXrhosXL2Lr1q1QqVQICAgw5BKqFJNRRERlUVoBDg2Beu6lnwsZr/8YjUaeRL24R14BmvaSE1Ulk1e5mbqJq7vXgcz7mz5dJhT9vOtj4K8V9x9IRUMEbe7/+0yU/C8AXNinf1ihjbM8WXxNV5AvJwQL4+bZvmgurzO/AslxunHNvlOUHHzzFODoISfr8rLlRFSrJ4FeH+ifIJ+Iag2NRiD5ViYSLt1BwuU7+DP5P5y9lqFTxtFGhV4t3TCwvRe6NWsIKyUT+0REFsvKTu65ZAAhBAoKCqBUKiu3IIWVnVFVO3z4MAIDAxEVFYWkpCT4+/sDAO7du4eLFy+WmYxq3bo1cnJycPr0ab29ozp06ID58+cjNzdXp3fUH3/8gQ4dOpQqP2rUKDz//POYPXu2UddRVZiMIiKqSgqFnBQqrklPeauMJ78GMt8t3dvq3m05yVU8cSUpALUDkHsXgCgqX6j4sML45cUSVyWoHYDXjwH1GsmPE1YAl/4ou3eWa4DxwwrzsstIGt0GHn65aK6t/V8DJ9cVXX/JBF/k33KCCQAuHgD+/K7s17x3u6hs2AfAI+MAn07G1Z+IaqQCjcCtuzlIvpmJf27cxT/X5e2vy3e0K+AVkiTgIW9nhLZwRWiLhmjr7QwVE1BERLWDJBk+hM7QOaOMsHHjRixatAhbtmxB7969ceXKFTg7OwMACgoK4OXlhePHj+Ohhx4qdayDgwPefvttvPTSS1izZg08PeVhiJs3b4aLiwvCwsLg4+ODt956C1999ZV2AvOPPvoIUVFRpc43ZswYAMDQoUNNcq2VxWQUEVFNUq9RUVKoIk8tkLf8XN2kTmECp3jiyq0V0DSsRCIoDYCQk1k2jkVlk/eWnbgCgLfOFvUW2zNbXoVQ28vKGlAoYZdfACAfGPxjUT02vQ4c+6ns8wYOBhzvj/HPuAak/FW6jLWjnOwrPjFlk57yMLvivcKKb/auRWXr+8kbEVmcqeuOIz0zG/lQIDtPg+y8AmTlFuBWZg5u3s1Fgab0cAQAsFYpEOTlhHY+zmjvWx9dmrqgvr0F9AglIiKL9swzz8Da2hqZmZlo3bo1Nm/ejNjYWPTp00ebiAIApVKJ4cOH48cff8T8+fP1nuvDDz+Eq6srwsPDUVBQAEmS0L59e8yePRuSJOH333/H5MmT0bx5c6jVajg4OGDRokXo169fqXO5ublh2rRpprrsSmMyiojI0qnURRO0l6XrRHkrTlNQNPSteOKqzVNAg8Z65sO6v9k4F5W9fQG48bfOaSUA2o95xc+trne/gKL0UEHb+vL+Qu2fk5NMOuWciobmFdciXN6IqFbb9NdVZOdpynxekuRJx5u5OaCZqwOauTkg0MsJAe71OPSOiIjM6sKFC3r3d+nSRe/+r776Svuzv78/bt68qfO8JEmYOHEiJk6cWPJQAICzszN++OGHMuvTs2dPHDlyxKC6mprFJqMWLlyIzz//HCkpKWjTpg3mzZuH7t27V3jc/v37ERoaisDAQCQkJJi+okRENZVCCdg1kLfiWvSRt8oInQK0HV6UqCrIg9DkI/teJmzqNYBUvJt0z6nypq5X8WTrjdrIGxHRfW+HByAnOxv1He1hq1bC1koJayslXOzVaORoAxd7NYfbERERWQiLTEatXr0akyZNwsKFC9G1a1d899136NevH06fPg1fX98yj0tLS8OoUaMQFhaGa9eumbHGRES1VIMm8lacEMhJS4ONk5PuuHsb/cvVEhFVxthujbVLX1dqglkiIiKqsSzy66O5c+di7NixePHFF9GqVSvMmzcPPj4+WLRoUbnHvfLKKxgxYgRCQkLMVFMiIiIiIiIiIirO4pJRubm5OHr0KMLDdecHCQ8Px4EDB8o8Ljo6GufOncOMGTNMXUUiIiIiIiIiMiMh9C9kQVVHoyl77kZDWdwwvZs3b6KgoACNGumuNtWoUSOkpqbqPSYpKQnTpk3D3r17oVJV7pJzcnKQk5OjfZyeLi8rLoR44EZeeA7+ZzEM42Ycxs04jJvxGDvjMG7GMWXcLPl38emnnyImJgZ///03bG1t0aVLF3z22WcICAgo97jY2FhERkbi1KlT8PT0xJQpUzBu3Dgz1ZqIiMhwVlZWkCQJN27cgKurq8FDuYUQKCgogFKp5DBw6I+HEAK5ubm4ceMGFAoF1OoHX5XW4pJRhUo2EiGE3oZTUFCAESNGYNasWWjRokWlz//pp59i1qxZpfanpaVVSTLq7t27AEpfB5WNcTMO42Ycxs14jJ1xGDfjmDJuhV9EWaLY2Fi89tpr6NSpE/Lz8/Huu+8iPDwcp0+fhr29vd5jkpOT0b9/f7z00kv4+eefsX//fowfPx6urq4YPHiwma+AiIiocpRKJby9vXHlyhWjV4bTaDRQVLTATh1SVjzs7Ozg6+tbJbGyuGRUw4YNoVQqS/WCun79eqneUgCQkZGBI0eOID4+HhMmTAAgB1YIAZVKhW3btqFXr16ljnvnnXcQGRmpfZyeng4fHx84OTnB0dHxga6hMJnFCTgNw7gZh3EzDuNmPMbOOIybcUwZN0v+PWzZskXncXR0NNzc3HD06FH06NFD7zGLFy+Gr68v5s2bBwBo1aoVjhw5gi+++ILJKCIiqtEcHBzQvHlz5OXlGXysEAIZGRmoV6+eRd/7q0pZ8VAqlVCpVFUWI4tLRqnVanTs2BHbt2/H008/rd2/fft2DBw4sFR5R0dHnDhxQmffwoULsWvXLqxduxaNGzfW+zrW1tawtrYutV+SpCoJfuF52NgNw7gZh3EzDuNmPMbOOIybcUwVt9r0e0hLSwMANGjQoMwyBw8eLDUnZ58+fRAVFYW8vDxYWVmZtI5EREQPQqlUQqlUGnycEAI5OTmwsbGpVfd+Y5krHhaXjAKAyMhIjBw5EsHBwQgJCcH333+PS5cuaec0eOedd/Dvv//ip59+gkKhQGBgoM7xbm5usLGxKbWfiIiIqLYRQiAyMhLdunUr971Pamqq3jk58/PzcfPmTXh4eJQ6xpRzbJbEedV0MR66GA9djIcuxkMX46GL8dBlaDyMjZtFJqOGDh2KW7du4cMPP0RKSgoCAwOxefNm+Pn5AQBSUlJw6dKlaq4lERERUfWbMGECjh8/jn379lVYVt+cnPr2FzLlHJslcV41XYyHLsZDF+Ohi/HQxXjoYjx0GRoPY+fYtMhkFACMHz8e48eP1/vc0qVLyz125syZmDlzpkGvV/iGqiomMxVCID09nUMxDMS4GYdxMw7jZjzGzjiMm3FMGbfivXws1euvv45NmzYhLi4O3t7e5ZZ1d3fXOyenSqWCi4uL3mNKzrGZlpYGX19fk7VjjUbD/yPFMB66GA9djIcuxkMX46GL8dBlSDyKr7hnCItNRplbRkYGAMDHx6eaa0JERETmlJGRAScnp+quhkGEEHj99dexfv167Nmzp8w5MosLCQnBL7/8orNv27ZtCA4OLnO+qJJzbBYm8Hx9fR+g9kRERGRpDH2/JAlL/rrPjDQaDa5evVolM+wXrsx3+fLlB16Zry5h3IzDuBmHcTMeY2ccxs04poxb4Woynp6eFrfc8/jx47FixQps3LgRAQEB2v1OTk6wtbUFoDvHJgAkJycjMDAQr7zyCl566SUcPHgQ48aNw8qVKyu9ml5Vvl8qif9HdDEeuhgPXYyHLsZDF+Ohi/HQZWg8jH2/xJ5RlaRQKCrs3m4oR0dHNnYjMG7GYdyMw7gZj7EzDuNmHFPFzdJ6RBVatGgRAKBnz546+6OjoxEREQGg9BybjRs3xubNm/Hmm29iwYIF8PT0xPz58yudiAJM836pJP4f0cV46GI8dDEeuhgPXYyHLsZDlyHxMOb9EpNRRERERLVMZTq+65tjMzQ0FMeOHTNBjYiIiIiKWFafcyIiIiIiIiIismhMRlUDa2trzJgxQ2fCT6oY42Ycxs04jJvxGDvjMG7GYdzqDv6udTEeuhgPXYyHLsZDF+Ohi/HQZa54cAJzIiIiIiIiIiIyG/aMIiIiIiIiIiIis2EyioiIiIiIiIiIzIbJKCIiIiIiIiIiMhsmo0xk4cKFaNy4MWxsbNCxY0fs3bu33PKxsbHo2LEjbGxs0KRJEyxevNhMNa1ZDIlbTEwMHnvsMbi6usLR0REhISHYunWrGWtbcxja3grt378fKpUK7dq1M20FayhD45aTk4N3330Xfn5+sLa2RtOmTbFkyRIz1bbmMDRuy5cvR9u2bWFnZwcPDw+MHj0at27dMlNta4a4uDg8+eST8PT0hCRJ2LBhQ4XH8L5geNx4X7B8fP8k+/TTT9GpUyfUq1cPbm5ueOqpp3D27Nlyj9mzZw8kSSq1/f3332aqtenMnDmz1HW5u7uXe0xtbRsA4O/vr/d3/dprr+ktX9vaRkX3BiEEZs6cCU9PT9ja2qJnz544depUheddt24dWrduDWtra7Ru3Rrr16830RVUrfLikZeXh6lTpyIoKAj29vbw9PTEqFGjcPXq1XLPuXTpUr1tJjs728RX8+Aqah8RERGlrqtz584VntdS2wdQcUz0/a4lScLnn39e5jmroo0wGWUCq1evxqRJk/Duu+8iPj4e3bt3R79+/XDp0iW95ZOTk9G/f390794d8fHxmD59OiZOnIh169aZuebVy9C4xcXF4bHHHsPmzZtx9OhRPProo3jyyScRHx9v5ppXL0PjVigtLQ2jRo1CWFiYmWpasxgTtyFDhmDnzp2IiorC2bNnsXLlSrRs2dKMta5+hsZt3759GDVqFMaOHYtTp05hzZo1OHz4MF588UUz17x6ZWZmom3btvj2228rVZ73BZmhceN9wbLx/VOR2NhYvPbaa/jjjz+wfft25OfnIzw8HJmZmRUee/bsWaSkpGi35s2bm6HGptemTRud6zpx4kSZZWtz2wCAw4cP68Ri+/btAIBnn3223ONqS9uo6N4wZ84czJ07F99++y0OHz4Md3d3PPbYY8jIyCjznAcPHsTQoUMxcuRI/PXXXxg5ciSGDBmCQ4cOmeoyqkx58cjKysKxY8fw/vvv49ixY4iJiUFiYiIGDBhQ4XkdHR112ktKSgpsbGxMcQlVqjLvHfr27atzXZs3by73nJbcPoCKY1Ly97xkyRJIkoTBgweXe94HbiOCqtzDDz8sxo0bp7OvZcuWYtq0aXrLT5kyRbRs2VJn3yuvvCI6d+5ssjrWRIbGTZ/WrVuLWbNmVXXVajRj4zZ06FDx3nvviRkzZoi2bduasIY1k6Fx+/3334WTk5O4deuWOapXYxkat88//1w0adJEZ9/8+fOFt7e3yepY0wEQ69evL7cM7wulVSZu+tTF+4Kl4vunsl2/fl0AELGxsWWW2b17twAgbt++bb6KmYmh71XqUtsQQog33nhDNG3aVGg0Gr3P1+a2UfLeoNFohLu7u5g9e7Z2X3Z2tnBychKLFy8u8zxDhgwRffv21dnXp08fMWzYsCqvsylV5l75559/CgDi4sWLZZaJjo4WTk5OVVu5aqAvHi+88IIYOHCgQeepLe1DiMq1kYEDB4pevXqVW6Yq2gh7RlWx3NxcHD16FOHh4Tr7w8PDceDAAb3HHDx4sFT5Pn364MiRI8jLyzNZXWsSY+JWkkajQUZGBho0aGCKKtZIxsYtOjoa586dw4wZM0xdxRrJmLht2rQJwcHBmDNnDry8vNCiRQu8/fbbuHfvnjmqXCMYE7cuXbrgypUr2Lx5M4QQuHbtGtauXYvHH3/cHFW2WLwvVI26eF+wVHz/VL60tDQAqFRbbt++PTw8PBAWFobdu3ebumpmk5SUBE9PTzRu3BjDhg3D+fPnyyxbl9pGbm4ufv75Z4wZMwaSJJVbtra2jeKSk5ORmpqq8/u3trZGaGhoue+Ny2ozlf0cYknS0tIgSRKcnZ3LLXf37l34+fnB29sbTzzxRK3qZbxnzx64ubmhRYsWeOmll3D9+vVyy9el9nHt2jX89ttvGDt2bIVlH7SNMBlVxW7evImCggI0atRIZ3+jRo2Qmpqq95jU1FS95fPz83Hz5k2T1bUmMSZuJX355ZfIzMzEkCFDTFHFGsmYuCUlJWHatGlYvnw5VCqVOapZ4xgTt/Pnz2Pfvn04efIk1q9fj3nz5mHt2rVlzs9QGxkTty5dumD58uUYOnQo1Go13N3d4ezsjG+++cYcVbZYvC9Ujbp4X7BUfP9UNiEEIiMj0a1bNwQGBpZZzsPDA99//z3WrVuHmJgYBAQEICwsDHFxcWasrWk88sgj+Omnn7B161b88MMPSE1NRZcuXcqcf7CutA0A2LBhA+7cuYOIiIgyy9TmtlFS4d8LQz9TlNVmKvs5xFJkZ2dj2rRpGDFiBBwdHcss17JlSyxduhSbNm3CypUrYWNjg65duyIpKcmMtTWNfv36Yfny5di1axe+/PJLHD58GL169UJOTk6Zx9SV9gEAy5YtQ7169TBo0KByy1VFG6mbn0TNoOQ3E0KIcr+t0Fde3/7aztC4FVq5ciVmzpyJjRs3ws3NzVTVq7EqG7eCggKMGDECs2bNQosWLcxVvRrLkPam0WggSRKWL18OJycnAMDcuXPxzDPPYMGCBbC1tTV5fWsKQ+J2+vRpTJw4ER988AH69OmDlJQUTJ48GePGjUNUVJQ5qmuxeF94MHX9vmCp+P6ptAkTJuD48ePYt29fueUCAgIQEBCgfRwSEoLLly/jiy++QI8ePUxdTZPq16+f9uegoCCEhISgadOmWLZsGSIjI/UeUxfaBgBERUWhX79+8PT0LLNMbW4bZTHmM4Wxn0MsRV5eHoYNGwaNRoOFCxeWW7Zz5846k3p37doVHTp0wDfffIP58+ebuqomNXToUO3PgYGBCA4Ohp+fH3777bdyEzC1vX0UWrJkCZ577rkK536qijbCZFQVa9iwIZRKZaks6fXr10tlUwu5u7vrLa9SqeDi4mKyutYkxsSt0OrVqzF27FisWbMGvXv3NmU1axxD45aRkYEjR44gPj4eEyZMACAnWYQQUKlU2LZtG3r16mWWulcnY9qbh4cHvLy8tIkoAGjVqhWEELhy5YrFTgJqCGPi9umnn6Jr166YPHkyAOChhx6Cvb09unfvjo8++ggeHh4mr7cl4n3hwdTl+4Kl4vsn/V5//XVs2rQJcXFx8Pb2Nvj4zp074+effzZBzaqXvb09goKCyvwGvi60DQC4ePEiduzYgZiYGIOPra1to3CVxdTUVJ33GBV9piirzVT0OcRS5OXlYciQIUhOTsauXbvK7RWlj0KhQKdOnWpFz6iSPDw84OfnV+611fb2UWjv3r04e/YsVq9ebfCxxrQRDtOrYmq1Gh07dtSualFo+/bt6NKli95jQkJCSpXftm0bgoODYWVlZbK61iTGxA2Qv/mOiIjAihUr6uQcNIbGzdHRESdOnEBCQoJ2GzduHAICApCQkIBHHnnEXFWvVsa0t65du+Lq1au4e/eudl9iYiIUCoVRHxAskTFxy8rKgkKhe6tRKpUAir6lptJ4XzBeXb8vWCq+f9IlhMCECRMQExODXbt2oXHjxkadJz4+vlYm/XNycnDmzJkyr602t43ioqOj4ebmZtTfutraNho3bgx3d3ed339ubi5iY2PL/UxRVpsp7xhLUZiISkpKwo4dO4xKyAohkJCQUCvbzK1bt3D58uVyr602t4/ioqKi0LFjR7Rt29bgY41qIw80/TnptWrVKmFlZSWioqLE6dOnxaRJk4S9vb24cOGCEEKIadOmiZEjR2rLnz9/XtjZ2Yk333xTnD59WkRFRQkrKyuxdu3a6rqEamFo3FasWCFUKpVYsGCBSElJ0W537typrkuoFobGraS6upqeoXHLyMgQ3t7e4plnnhGnTp0SsbGxonnz5uLFF1+srkuoFobGLTo6WqhUKrFw4UJx7tw5sW/fPhEcHCwefvjh6rqEapGRkSHi4+NFfHy8ACDmzp0r4uPjtSvZ8L6gn6Fx433BsvH9U5FXX31VODk5iT179ui05aysLG2ZkvH46quvxPr160ViYqI4efKkmDZtmgAg1q1bVx2XUKXeeustsWfPHnH+/Hnxxx9/iCeeeELUq1evTraNQgUFBcLX11dMnTq11HO1vW1UdG+YPXu2cHJyEjExMeLEiRNi+PDhwsPDQ6Snp2vPMXLkSJ2VOvfv3y+USqWYPXu2OHPmjJg9e7ZQqVTijz/+MPv1Gaq8eOTl5YkBAwYIb29vkZCQoPP3JCcnR3uOkvGYOXOm2LJlizh37pyIj48Xo0ePFiqVShw6dKg6LtEg5cUjIyNDvPXWW+LAgQMiOTlZ7N69W4SEhAgvL69a2z6EqPj/jBBCpKWlCTs7O7Fo0SK95zBFG2EyykQWLFgg/Pz8hFqtFh06dNBZiveFF14QoaGhOuX37Nkj2rdvL9RqtfD39y+zEdR2hsQtNDRUACi1vfDCC+aveDUztL0VV1eTUUIYHrczZ86I3r17C1tbW+Ht7S0iIyN1PhjUFYbGbf78+aJ169bC1tZWeHh4iOeee05cuXLFzLWuXoXLapf194r3Bf0MjRvvC5aP759k+toxABEdHa0tUzIen332mWjatKmwsbER9evXF926dRO//fab+StvAkOHDhUeHh7CyspKeHp6ikGDBolTp05pn69LbaPQ1q1bBQBx9uzZUs/V9rZR0b1Bo9GIGTNmCHd3d2FtbS169OghTpw4oXOO0NDQUveGNWvWiICAAGFlZSVatmxpMcm68uKRnJxc5t+T3bt3a89RMh6TJk0Svr6+Qq1WC1dXVxEeHi4OHDhg/oszQnnxyMrKEuHh4cLV1VVYWVkJX19f8cILL4hLly7pnKM2tQ8hKv4/I4QQ3333nbC1tS3zCzxTtBFJCI6TICIiIiIiIiIi8+CcUUREREREREREZDZMRhERERERERERkdkwGUVERERERERERGbDZBQREREREREREZkNk1FERERERERERGQ2TEYREREREREREZHZMBlFRERERERERERmw2QUERERERERERGZDZNRRERVZObMmWjXrl11V4OIiIiIiKhGYzKKiGq1iIgISJKEcePGlXpu/PjxkCQJERER5q8YERERUTWr7i/S3n//fbz88suVKvv2229j4sSJJq4REZkLk1FEVOv5+Phg1apVuHfvnnZfdnY2Vq5cCV9f32qsGREREZFpSJJU7hYREYG3334bO3furJb6Xbt2DV9//TWmT59eqfJTpkxBdHQ0kpOTTVwzIjIHJqOIqNbr0KEDfH19ERMTo90XExMDHx8ftG/fXrtvy5Yt6NatG5ydneHi4oInnngC586d0znXlStXMGzYMDRo0AD29vYIDg7GoUOHdMr873//g7+/P5ycnDBs2DBkZGSY9gKJiIiISkhJSdFu8+bNg6Ojo86+r7/+Gg4ODnBxcamW+kVFRSEkJAT+/v6VKu/m5obw8HAsXrzYtBUjIrNgMoqI6oTRo0cjOjpa+3jJkiUYM2aMTpnMzExERkbi8OHD2LlzJxQKBZ5++mloNBoAwN27dxEaGoqrV69i06ZN+OuvvzBlyhTt8wBw7tw5bNiwAb/++it+/fVXxMbGYvbs2ea5SCIiIqL73N3dtZuTkxMkSSq1r+QwvYiICDz11FP45JNP0KhRIzg7O2PWrFnIz8/H5MmT0aBBA3h7e2PJkiU6r/Xvv/9i6NChqF+/PlxcXDBw4EBcuHCh3PqtWrUKAwYM0Nm3du1aBAUFwdbWFi4uLujduzcyMzO1zw8YMAArV6584NgQUfVTVXcFiIjMYeTIkXjnnXdw4cIFSJKE/fv3Y9WqVdizZ4+2zODBg3WOiYqKgpubG06fPo3AwECsWLECN27cwOHDh9GgQQMAQLNmzXSO0Wg0WLp0KerVq6d93Z07d+Ljjz827QUSERERVYFdu3bB29sbcXFx2L9/P8aOHYuDBw+iR48eOHToEFavXo1x48bhscceg4+PD7KysvDoo4+ie/fuiIuLg0qlwkcffYS+ffvi+PHjUKvVpV7j9u3bOHnyJIKDg7X7UlJSMHz4cMyZMwdPP/00MjIysHfvXgghtGUefvhhXL58GRcvXoSfn59Z4kFEpsGeUURUJzRs2BCPP/44li1bhujoaDz++ONo2LChTplz585hxIgRaNKkCRwdHdG4cWMAwKVLlwAACQkJaN++vTYRpY+/v782EQUAHh4euH79ugmuiIiIiKjqNWjQAPPnz0dAQADGjBmDgIAAZGVlYfr06WjevDneeecdqNVq7N+/H4Dcw0mhUODHH39EUFAQWrVqhejoaFy6dEnnS7/iLl68CCEEPD09tftSUlKQn5+PQYMGwd/fH0FBQRg/fjwcHBy0Zby8vACgwl5XRFTzsWcUEdUZY8aMwYQJEwAACxYsKPX8k08+CR8fH/zwww/w9PSERqNBYGAgcnNzAQC2trYVvoaVlZXOY0mSdIbxEREREdVkbdq0gUJR1GehUaNGCAwM1D5WKpVwcXHRftl29OhR/PPPPzpfxgHyYjEl594sVLiojI2NjXZf27ZtERYWhqCgIPTp0wfh4eF45plnUL9+fW2ZwvdiWVlZD3iVRFTd2DOKiOqMvn37Ijc3F7m5uejTp4/Oc7du3cKZM2fw3nvvISwsDK1atcLt27d1yjz00ENISEjAf//9Z85qExEREZmNvi/WyvuyTaPRoGPHjkhISNDZEhMTMWLECL2vUdg7vfh7LaVSie3bt+P3339H69at8c033yAgIEBn9bzC92Curq4PfqFEVK2YjCKiOkOpVOLMmTM4c+YMlEqlznOFE25+//33+Oeff7Br1y5ERkbqlBk+fDjc3d3x1FNPYf/+/Th//jzWrVuHgwcPmvMyiIiIiGqMDh06ICkpCW5ubmjWrJnO5uTkpPeYpk2bwtHREadPn9bZL0kSunbtilmzZiE+Ph5qtRrr16/XPn/y5ElYWVmhTZs2Jr0mIjI9JqOIqE5xdHSEo6Njqf0KhQKrVq3C0aNHERgYiDfffBOff/65Thm1Wo1t27bBzc0N/fv3R1BQEGbPnl0qsUVERERUVzz33HNo2LAhBg4ciL179yI5ORmxsbF44403cOXKFb3HKBQK9O7dG/v27dPuO3ToED755BMcOXIEly5dQkxMDG7cuIFWrVppy+zduxfdu3ev1NQJRFSzcc4oIqrVli5dWu7zGzZs0P7cu3fvUt/QFV/BBQD8/Pywdu1aveeaOXMmZs6cqbNv0qRJmDRpUmWrS0RERGRR7OzsEBcXh6lTp2LQoEHIyMiAl5cXwsLC9H4BWOjll1/G2LFjMWfOHCgUCjg6OiIuLg7z5s1Deno6/Pz88OWXX6Jfv37aY1auXIlZs2aZ47KIyMQkUfKTFhEREREREZEJCSHQuXNnTJo0CcOHD6+w/G+//YbJkyfj+PHjUKnYp4LI0nGYHhEREREREZmVJEn4/vvvkZ+fX6nymZmZiI6OZiKKqJZgzygiIiIiIiIiIjIb9owiIiIiIiIiIiKzYTKKiIiIiIiIiIjMhskoIiIiIiIiIiIyGyajiIiIiIiIiIjIbJiMIiIiIiIiIiIis2EyioiIiIiIiIiIzIbJKCIiIiIiIiIiMhsmo4iIiIiIiIiIyGyYjCIiIiIiIiIiIrP5/wnLIsuKGhVuAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "history = pd.read_csv(DATA / \"asteria_histories.csv\")\n", + "cd = pd.read_csv(DATA / \"asteria_cd.csv\")\n", + "fig, axes = plt.subplots(2,2,figsize=(12,7))\n", + "for prefix,label,color in [(\"rocketpy\",\"RocketPy\",\"tab:blue\"),(\"datcom\",\"DATCOM\",\"tab:orange\")]:\n", + " for axis,column in [(axes[0,0],\"altitude_agl_m\"),(axes[0,1],\"vz_m_s\"),(axes[1,1],\"margin_cal\")]:\n", + " axis.plot(history.time_s,history[f\"{prefix}_{column}\"],label=label,color=color)\n", + " for phase,style in [(\"powered\",\"--\"),(\"coast\",\"-\")]:\n", + " axes[1,0].plot(cd.mach,cd[f\"{prefix}_{phase}_cd\"],style,label=f\"{label} {phase}\",color=color)\n", + "for axis,title,xlabel,ylabel in [(axes[0,0],\"Altitude\",\"Time (s)\",\"Altitude AGL (m)\"),(axes[0,1],\"Vertical velocity\",\"Time (s)\",\"Upward velocity (m/s)\"),(axes[1,0],\"Zero-incidence drag\",\"Mach\",\"CD\"),(axes[1,1],\"Stability margin\",\"Time (s)\",\"Margin (cal)\")]:\n", + " axis.set(title=title,xlabel=xlabel,ylabel=ylabel);axis.legend(fontsize=8)\n", + "fig.suptitle(\"AST-HER — simulation comparison; has not flown\")\n", + "fig.tight_layout();plt.show()\n" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/datcom/comparison_data/asteria_cd.csv b/docs/examples/datcom/comparison_data/asteria_cd.csv new file mode 100644 index 000000000..2bf3d6dd0 --- /dev/null +++ b/docs/examples/datcom/comparison_data/asteria_cd.csv @@ -0,0 +1,152 @@ +mach,rocketpy_powered_cd,datcom_powered_cd,rocketpy_coast_cd,datcom_coast_cd +0.01,,0.751,,0.789 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file mode 100644 index 000000000..03a1e9141 --- /dev/null +++ b/docs/examples/datcom/comparison_data/asteria_metrics.csv @@ -0,0 +1,6 @@ +metric,unit,rocketpy,datcom,datcom_vs_rocketpy_pct,measured +Apogee AGL,m,1030.1105580450696,1042.7065175727942,1.222777441639756, +Peak upward vertical velocity,m/s,132.97414243164462,133.08909320010036,0.08644595584801947, +Initial static margin,cal,2.093144236271212,3.857394061067111,84.2870641317478, +Powered CD at Mach 0.6,1,0.543857573,0.42,-22.773898746464646, +Coast CD at Mach 0.6,1,0.543857573,0.447,-17.809363665880223, diff --git a/docs/examples/datcom/comparison_data/asteria_model.py b/docs/examples/datcom/comparison_data/asteria_model.py new file mode 100644 index 000000000..93f6757b1 --- /dev/null +++ b/docs/examples/datcom/comparison_data/asteria_model.py @@ -0,0 +1,202 @@ +"""Build AST-HER from the saved current Asteria configuration, without plots.""" + +import json +from pathlib import Path +import numpy as np +from scipy.integrate import trapezoid +from rocketpy import ( + Rocket, + Environment, + LiquidMotor, + MassFlowRateBasedTank, + CylindricalTank, + Fluid, + Function, +) + +ROOT = next(p for p in Path(__file__).resolve().parents if (p / "rocketpy").is_dir()) +DATA = ROOT / "data/rockets/ast_her" + + +def build(native, executable): + cfg = json.loads((DATA / "configuration.json").read_text()) + r, m, f = cfg["rocket"], cfg["motors"], cfg["fins"] + thrust = np.loadtxt(DATA / "thrust_curve.csv", delimiter=",") + performance = np.genfromtxt(DATA / "performance.csv", delimiter=",", names=True) + isp = np.interp( + thrust[:, 1], + performance["Thrust_sea_level_kN"] * 1000, + performance["Specific_impulse_sea_level_s"] * m["isp_degradation_factor"], + ) + mdot = thrust[:, 1] / (isp * 9.80665) + fuel_flow = mdot / (1 + m["of"]) + oxidizer_flow = mdot - fuel_flow + fuel_gas_flow = fuel_flow * m["pressurant_density_60bar"] / m["eth_density"] + oxidizer_gas_flow = ( + oxidizer_flow * m["pressurant_density_40bar_cold"] / m["lox_density"] + ) + + def curve(values): + return np.column_stack((thrust[:, 0], values)) + + fuel_liquid = Fluid("Ethanol", m["eth_density"]) + oxidizer_liquid = Fluid("LOX", m["lox_density"]) + nitrogen_fuel = Fluid("Nitrogen, fuel tank", m["pressurant_density_60bar"]) + nitrogen_oxidizer = Fluid( + "Nitrogen, oxidizer tank", m["pressurant_density_40bar_cold"] + ) + nitrogen_supply = Fluid("Nitrogen, supply tank", m["pressurant_density_300bar"]) + burn = (float(thrust[0, 0]), float(thrust[-1, 0])) + motor = LiquidMotor( + thrust_source=thrust, + burn_time=burn, + dry_mass=0, + dry_inertia=m["motor_dry_inertia"], + center_of_dry_mass_position=m["center_of_dry_mass_position"], + nozzle_position=m["nozzle_position"], + nozzle_radius=m["nozzle_exit_radius"], + reference_pressure=m["reference_pressure"], + interpolation_method="linear", + coordinate_system_orientation="nozzle_to_combustion_chamber", + ) + tanks = [] + for label, height_key, position_key, fluid, gas, liquid_out, gas_in in [ + ( + "Fuel", + "eth_tank_height", + "eth_tank_position", + fuel_liquid, + nitrogen_fuel, + fuel_flow, + fuel_gas_flow, + ), + ( + "Oxidizer", + "lox_tank_height", + "lox_tank_position", + oxidizer_liquid, + nitrogen_oxidizer, + oxidizer_flow, + oxidizer_gas_flow, + ), + ]: + tank = MassFlowRateBasedTank( + name=label, + geometry=CylindricalTank( + m["tank_radius"], m[height_key], spherical_caps=m["tank_spherical_caps"] + ), + flux_time=burn, + initial_liquid_mass=trapezoid(liquid_out, x=thrust[:, 0]) + + m["numeric_stab_mass"], + initial_gas_mass=0.001, + liquid_mass_flow_rate_in=0, + liquid_mass_flow_rate_out=curve(liquid_out), + gas_mass_flow_rate_in=curve(gas_in), + gas_mass_flow_rate_out=0, + liquid=fluid, + gas=gas, + ) + motor.add_tank(tank, position=m[position_key]) + tanks.append(tank) + pr = m["prnt_tank_radius"] + supply_tank = MassFlowRateBasedTank( + name="Pressurant", + geometry=CylindricalTank( + pr, + m["pressurant_tank_volume"] / (np.pi * pr**2) + 0.667 * pr, + spherical_caps=True, + ), + flux_time=burn, + initial_liquid_mass=0, + initial_gas_mass=m["pressurant_tank_volume"] * m["pressurant_density_300bar"], + liquid_mass_flow_rate_in=0, + liquid_mass_flow_rate_out=0, + gas_mass_flow_rate_in=0, + gas_mass_flow_rate_out=curve(fuel_gas_flow + oxidizer_gas_flow), + liquid=nitrogen_supply, + gas=nitrogen_supply, + ) + motor.add_tank(supply_tank, position=m["pressure_tank_position"]) + tanks.append(supply_tank) + print( + f"Burn duration: {burn[1]:.3f} s; total impulse: {motor.total_impulse:.1f} N s" + ) + + drag = Function( + str(Path(__file__).with_name("asteria_drag_curve.csv")), + interpolation="linear", + extrapolation="constant", + ) + rocket = Rocket( + radius=r["radius"], + mass=r["rocket_dry_mass"], + inertia=r["inertia"], + center_of_mass_without_motor=r["center_of_mass_without_motor"], + power_on_drag=drag, + power_off_drag=drag, + coordinate_system_orientation=r["coordinate_system_orientation"], + ) + rocket.add_motor(motor, position=m["position"]) + rocket.add_nose( + length=cfg["nosecones"]["length"], + kind=cfg["nosecones"]["kind"], + position=cfg["nosecones"]["position"], + ) + geometry = dict( + n=f["NPANEL"], + root_chord=f["CHORD"][0], + tip_chord=f["CHORD"][1], + span=f["SSPAN"][1] - f["SSPAN"][0], + position=f["XLE"][0], + sweep_length=f["XLE"][1] - f["XLE"][0], + cant_angle=f["cant_angle"], + ) + if native: + rocket.add_datcom_hex_fins( + **geometry, + path=executable, + upper_thickness_to_chord=f["ZUPPER"], + lower_thickness_to_chord=f["ZLOWER"], + max_thickness_position=f["LMAXU"], + flat_length=f["LFLATU"], + leading_edge_radius=f["LER"], + datcom_options={"beta": [-10, 0, 10], "powered_base_drag": "nozzle"}, + ) + else: + rocket.add_trapezoidal_fins(**geometry) + rocket.add_tail( + top_radius=r["radius"], + bottom_radius=cfg["tails"]["DAFT"] / 2, + length=cfg["tails"]["LAFT"], + position=cfg["tails"]["position"], + kind="ogive" if native else "conical", + ) + rocket.set_rail_buttons( + upper_button_position=cfg["rail_buttons"]["upper_position"], + lower_button_position=cfg["rail_buttons"]["lower_position"], + angular_position=cfg["rail_buttons"]["angular_position"], + ) + saved = json.loads((DATA / "atmosphere.json").read_text()) + e = cfg["environment"] + env = Environment( + latitude=e["latitude"], + longitude=e["longitude"], + elevation=e["elevation"], + timezone=e["timezone"], + ) + env.set_atmospheric_model( + type="custom_atmosphere", + pressure=saved["pressure"]["source"], + temperature=saved["temperature"]["source"], + wind_u=saved["wind_velocity_x"]["source"], + wind_v=saved["wind_velocity_y"]["source"], + ) + launch = dict( + rail_length=cfg["flight"]["rail_length"], + inclination=cfg["flight"]["inclination"], + heading=cfg["flight"]["heading"], + terminate_on_apogee=True, + max_time=90, + max_time_step=0.1, + ) + return rocket, env, launch diff --git a/docs/examples/datcom/comparison_data/asteria_provenance.json b/docs/examples/datcom/comparison_data/asteria_provenance.json new file mode 100644 index 000000000..94298b35c --- /dev/null +++ b/docs/examples/datcom/comparison_data/asteria_provenance.json @@ -0,0 +1,255 @@ +{ + "flight_status": "AST-HER has not flown", + "atmosphere": { + "mach": [ + 0.01, + 0.06, + 0.1, + 0.14, + 0.18, + 0.22, + 0.26, + 0.3, + 0.34, + 0.41, + 0.6, + 0.8, + 0.9, + 0.95, + 0.98, + 1.0, + 1.02, + 1.05, + 1.1, + 1.2 + ], + "alpha": [ + -50.28, + -25.14, + -12.57, + -5.03, + -2.51, + -1.26, + -0.63, + -0.25, + -0.13, + 0.0, + 0.13, + 0.25, + 0.63, + 1.26, + 2.51, + 5.03, + 12.57, + 25.14, + 50.28 + ], + "coverage": { + "scope": "rail through apogee", + "minimum_dynamic_pressure_pa": 100.0, + "excluded_low_q_samples": 70, + "phases": { + "power_on": { + "mach_min": 0.01326309325167047, + "mach_max": 0.4047552904143389, + "loaded_alpha_max_abs_deg": 19.449816544852585 + }, + "power_off": { + "mach_min": 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"b9d8bbc2da905e90c8ecd0b4ab9cb4fd245c4ad69ebc41a881fb9b07bdd620bb", + "tail_note": "ordinary RocketPy conical approximation; DATCOM configured ogive", + "mass_cg_match": true, + "source_configuration": "data/rockets/ast_her/configuration.json", + "scope": "rail to apogee; standard RocketPy rail; no Asteria hold-down clamp or PROTUB" +} \ No newline at end of file diff --git a/docs/examples/datcom/comparison_data/model_geometry.py b/docs/examples/datcom/comparison_data/model_geometry.py new file mode 100644 index 000000000..3a2172577 --- /dev/null +++ b/docs/examples/datcom/comparison_data/model_geometry.py @@ -0,0 +1,137 @@ +"""Geometry adapters for the two comparison notebooks.""" + +import ast +import numpy as np +from rocketpy.mathutils.vector_matrix import Matrix, Vector +from rocketpy.rocket.aero_surface import TrapezoidalFins + +MACH = [0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.5, 2.0] + +ALPHA = [ + -20, + -10, + -5, + -2, + -1, + -0.5, + -0.25, + -0.1, + -0.05, + 0, + 0.05, + 0.1, + 0.25, + 0.5, + 1, + 2, + 5, + 10, + 20, +] + +BETA = [-20, -5, 0, 5, 20] + + +class LocalPaths(ast.NodeTransformer): + def __init__(self, root): + self.root = root + + def visit_Constant(self, node): + if isinstance(node.value, str) and node.value.startswith("../../data/"): + return ast.copy_location( + ast.Constant(str(self.root / node.value[6:])), node + ) + return node + + +def attach_datcom(rocket, executable, schedules): + """Replace fin definitions by identical planforms, assuming 2% HEX sections.""" + options = dict( + mach=MACH, + alpha=ALPHA, + beta=BETA, + altitude=schedules, + powered_base_drag="nozzle", + ) + originals = [ + (s, p) + for s, p, _ in rocket.aerodynamic_surfaces + if isinstance(s, TrapezoidalFins) + ] + if not originals: + raise ValueError("Source rocket has no supported trapezoidal fin set") + geometry = [] + for fin, position in originals: + rocket.aerodynamic_surfaces.remove(fin) + replacement = rocket.add_datcom_hex_fins( + n=fin.n, + root_chord=fin.root_chord, + tip_chord=fin.tip_chord, + span=fin.span, + sweep_length=fin.sweep_length, + cant_angle=fin.cant_angle, + position=position, + path=executable, + thickness_to_chord=0.02, + max_thickness_position=0.05, + flat_length=0.9, + datcom_options=options, + ) + geometry.append( + dict( + n=fin.n, + root_chord_m=fin.root_chord, + tip_chord_m=fin.tip_chord, + span_m=fin.span, + sweep_m=fin.sweep_length, + cant_deg=fin.cant_angle, + position_m=list(position), + assumed_section="symmetric HEX, 2% thickness, 5% edge bevels", + ) + ) + assert replacement.root_chord == fin.root_chord + return geometry + + +def envelope_diagnostics(flight, model, mach_grid=None, alpha_grid=None): + """Count sampled envelope excursions at meaningful pressure (q >= 100 Pa). + + Also report solver/probe flags separately; these include pad/near-apogee + flow. Sampling is uniform in time and is diagnostic, not an accuracy bound. + """ + solutions = np.asarray(flight.solution) + mach_grid = MACH if mach_grid is None else mach_grid + alpha_grid = ALPHA if alpha_grid is None else alpha_grid + samples = np.linspace(0, flight.apogee_time, 501) + counts = dict(samples=0, outside_mach=0, outside_alpha=0, outside_beta=0) + max_angles = np.zeros(2) + for time in samples: + state = [ + np.interp(time, solutions[:, 0], solutions[:, j]) for j in range(1, 14) + ] + rotation = Matrix.transformation(state[6:10]) + offset = model.reference_offset(time) + z = state[2] + (rotation @ offset).z + wind = Vector([flight.env.wind_velocity_x(z), flight.env.wind_velocity_y(z), 0]) + velocity = rotation.transpose @ (Vector(state[3:6]) - wind) + ( + Vector(state[10:13]) ^ offset + ) + speed = abs(velocity) + if 0.5 * flight.env.density(z) * speed**2 < 100: + continue + alpha = np.degrees(np.arctan2(velocity.y, velocity.z)) + beta = np.degrees(np.arctan2(velocity.x, velocity.z)) + mach = speed / flight.env.speed_of_sound(z) + counts["samples"] += 1 + counts["outside_mach"] += int(not mach_grid[0] <= mach <= mach_grid[-1]) + counts["outside_alpha"] += int(not alpha_grid[0] <= alpha <= alpha_grid[-1]) + counts["outside_beta"] += int(not BETA[0] <= beta <= BETA[-1]) + max_angles = np.maximum(max_angles, np.abs([alpha, beta])) + return dict( + counts, + max_abs_alpha_deg=float(max_angles[0]), + max_abs_beta_deg=float(max_angles[1]), + solver_mach_excursion=bool(model.outside_mach_envelope), + solver_alpha_excursion=bool(model.outside_alpha_envelope), + solver_beta_excursion=bool(model.outside_beta_envelope), + ) diff --git a/docs/examples/datcom/comparison_data/model_sources.py b/docs/examples/datcom/comparison_data/model_sources.py new file mode 100644 index 000000000..e70a5cab9 --- /dev/null +++ b/docs/examples/datcom/comparison_data/model_sources.py @@ -0,0 +1,393 @@ +"""Read all source notebooks, their end-of-notebook flight evidence and models. + +Only trusted local repository notebooks are supported. Construction stops at +first Flight; plots, installs, online weather and later recovery runs are not +executed. Source notebooks are never rewritten. +""" + +import ast +import hashlib +import importlib.util +import json +from pathlib import Path +import re +import sys + +import nbformat +import numpy as np +import pandas as pd + +ROOT = next( + p + for p in Path(__file__).resolve().parents + if (p / "rocketpy/rocket/datcom.py").is_file() +) +sys.path.insert(0, str(ROOT)) +spec = importlib.util.spec_from_file_location( + "datcom_example_helpers", + Path(__file__).with_name("model_geometry.py"), +) +h = importlib.util.module_from_spec(spec) +spec.loader.exec_module(h) + + +class SourceUnavailable(ValueError): + """A faithful offline comparison needs additional data or geometry support.""" + + +def _tree(source): + return ast.parse( + "\n".join( + line + for line in source.splitlines() + if not line.lstrip().startswith(("%", "!")) + ) + ) + + +def _literal(node, scope): + if isinstance(node, ast.Name): + return scope[node.id] + if isinstance(node, ast.BinOp) and isinstance(node.op, ast.Add): + return _literal(node.left, scope) + _literal(node.right, scope) + if isinstance(node, (ast.List, ast.Tuple)): + return [_literal(item, scope) for item in node.elts] + return ast.literal_eval(node) + + +def flight_reference(path): + """Read telemetry AGL and all reported/saved references with cell provenance.""" + path = Path(path).resolve() + notebook = nbformat.read(path, 4) + scope = {} + recorded = None + reported_cell = None + saved = [] + measured_summaries = [] + telemetry = None + notes = [] + for index, cell in enumerate(notebook.cells): + if cell.cell_type == "markdown": + plain = re.sub(r"[`*$]", "", cell.source) + match = re.search( + r"(?:official\s+)?recorded\s+apogee(?:\s+altitude)?(?:\s+of)?\s*:?\s*([\d,.]+)\s*m", + plain, + re.I, + ) + if not match: + match = re.search( + r"Measured Altitude[^\n]*?or\s*([\d,.]+)\s*m", plain, re.I + ) + if match: + recorded = float(match[1].replace(",", "")) + reported_cell = index + continue + for output in cell.get("outputs", []): + text = output.get("text", "") or output.get("data", {}).get( + "text/plain", "" + ) + for match in re.finditer( + r"Apogee Altitude:\s*[-+\d.eE]+\s*m\s*\(ASL\)\s*\|\s*([-+\d.eE]+)\s*m\s*\(AGL\)", + text, + ): + saved.append(dict(cell=index, agl_m=float(match[1]))) + if "apogee" in cell.source.lower(): + for match in re.finditer( + r"(?:Actual apogee|Real apogee|Real data|Actual):\s*([\d.]+)\s*m", + text, + re.I, + ): + measured_summaries.append(dict(cell=index, agl_m=float(match[1]))) + for node in _tree(cell.source).body: + if not isinstance(node, ast.Assign): + continue + if len(node.targets) == 1 and isinstance(node.targets[0], ast.Name): + try: + scope[node.targets[0].id] = _literal(node.value, scope) + except (ValueError, KeyError, TypeError): + pass + call = node.value + if telemetry is not None or not isinstance(call, ast.Call): + continue + if isinstance(call.func, ast.Name) and call.func.id == "FlightDataImporter": + options = {kw.arg: kw.value for kw in call.keywords} + try: + mapping = _literal(options["columns_map"], scope) + time_col = next( + key for key, value in mapping.items() if value == "time" + ) + altitude_col = next( + key for key, value in mapping.items() if value == "altitude" + ) + paths = _literal(options["paths"], scope) + except (KeyError, ValueError, StopIteration): + continue + for filename in [paths] if isinstance(paths, str) else paths: + filename = ROOT / filename.replace("../../", "") + if not filename.exists(): + notes.append( + f"Referenced telemetry file is absent: {filename.relative_to(ROOT)} (cell {index})." + ) + continue + table = pd.read_csv(filename) + if time_col not in table or altitude_col not in table: + continue + telemetry = dict( + path=str(filename.relative_to(ROOT)), + cell=index, + time=table[time_col].to_numpy(float), + altitude=table[altitude_col].to_numpy(float), + conversion="metres AGL, notebook columns_map", + ) + break + elif ( + path.stem in ("bella_lui_flight_sim", "ndrt_2020_flight_sim") + and isinstance(call.func, ast.Attribute) + and call.func.attr == "loadtxt" + ): + filename = ROOT / _literal(call.args[0], scope).replace("../../", "") + options = {kw.arg: _literal(kw.value, scope) for kw in call.keywords} + values = np.loadtxt(filename, **options) + if path.stem.startswith("bella_lui"): + values = values[:573] + altitude = values[:, 1] + conversion = "metres AGL, first 573 rows as used in source cell 35" + else: + altitude = values[:, 1] * 0.3048 + conversion = "feet AGL multiplied by exactly 0.3048; source uses division by 3.281" + notes.append( + "NDRT source uses approximate ft conversion /3.281; comparison uses exact 0.3048 m/ft." + ) + telemetry = dict( + path=str(filename.relative_to(ROOT)), + cell=index, + time=values[:, 0], + altitude=altitude, + conversion=conversion, + ) + name = path.name.removesuffix("_flight_sim.ipynb") + if name == "valetudo": + recorded = float(scope["actual_data"]["apogee"]) + reported_cell = next( + i + for i, c in enumerate(notebook.cells) + if c.cell_type == "code" and "actual_data =" in c.source + ) + notes.append( + "Valetudo flight recordings were lost; only recovered scalar apogee/drift values remain." + ) + if telemetry is not None: + mask = np.isfinite(telemetry["time"]) & np.isfinite(telemetry["altitude"]) + time, alt = telemetry["time"][mask], telemetry["altitude"][mask] + order = np.argsort(time, kind="stable") + time, alt = time[order], alt[order] + time, keep = np.unique(time, return_index=True) + alt = alt[keep] + telemetry["time"], telemetry["altitude"] = time, alt + telemetry["sha256"] = hashlib.sha256( + (ROOT / telemetry["path"]).read_bytes() + ).hexdigest() + raw_max = float(alt.max()) + clean = np.ones(len(time), dtype=bool) + previous = 0 + for i in range(1, len(time)): + if abs(alt[i] - alt[previous]) / (time[i] - time[previous]) > 2000: + clean[i] = False + else: + previous = i + telemetry["qa_valid"] = clean + telemetry["raw_max_agl_m"] = raw_max + telemetry["qa_max_agl_m"] = float(alt[clean].max()) + telemetry["qa_rejected_count"] = int((~clean).sum()) + if (~clean).any(): + notes.append( + f"Telemetry QA flags {int((~clean).sum())} samples with altitude jumps above 2000 m/s; raw maximum {raw_max:.3f} m is retained separately." + ) + actual = recorded if recorded is not None else telemetry["qa_max_agl_m"] + reference_kind = ( + "notebook reported apogee" + if recorded is not None + else "telemetry AGL maximum (QA)" + ) + if recorded is not None and abs(raw_max - recorded) > 0.01: + notes.append( + f"Telemetry maximum {raw_max:.3f} m differs from notebook reported value {recorded:.3f} m; both retained." + ) + else: + actual = measured_summaries[0]["agl_m"] if measured_summaries else recorded + reference_kind = ( + "saved measured-apogee summary" + if measured_summaries + else "notebook reported apogee" + ) + if name == "astra": + notes.append( + "Source fin trailing edge overhangs the nozzle-inferred body base by 1.216 mm. Native DATCOM accepts this; the integration now validates root-chord centre attachment instead of rejecting all overhang. Geometry is unchanged." + ) + if name == "defiance": + notes.append( + "Source fin root centre is z=-0.025 m, aft of the tail base z=0.0403 m; native root attachment is outside the declared body." + ) + if name == "ndrt_2020": + notes.append( + "Source transition is internal at z=1.2..1.327 m with fins near z=3.05 m. The current DATCOM builder represents only an aft tail, not the required downstream smaller cylinder." + ) + if name == "hedy": + notes.append( + "Hedy end comparison uses simulated ASL 5406.918 m against measured AGL 5231.53 m; saved simulated AGL is 5246.918 m, a 160 m datum discrepancy." + ) + if name == "prometheus_2022": + notes.append( + "Prometheus prose says June 2022; source environment and local reanalysis are June 2023. Both reruns preserve source conditions." + ) + if name == "valkyrie": + notes.append( + "Valkyrie prose says 2025 but source environment says 2026 and fetches live Windy weather; no archived profile is supplied." + ) + if name == "juno3": + notes.append( + "Source fin airfoil is an empirical lift curve (cell 10), not a supplied section contour. The generic 2% HEX DATCOM assumption does not reproduce that curve; source-drag substitution therefore still has a lateral-model discrepancy." + ) + if name == "lince": + notes.append( + "Lince has three saved trajectories. Compare first/pre-deployment ascent only; retain all saved apogees separately." + ) + return dict( + rocket=name, + source_notebook=str(path.relative_to(ROOT)), + source_sha256=hashlib.sha256(path.read_bytes()).hexdigest(), + actual_agl_m=actual, + reference_kind=reference_kind, + reported_agl_m=recorded, + reported_cell=reported_cell, + saved_first_agl_m=saved[0]["agl_m"] if saved else None, + saved_apogees=saved, + measured_summaries=measured_summaries, + telemetry=telemetry, + notes=notes, + ) + + +class Construction(ast.NodeTransformer): + """Strip display-only statements and reject online model inputs.""" + + def visit_Call(self, node): + node = self.generic_visit(node) + if isinstance(node.func, ast.Name) and node.func.id in { + "SolidMotor", + "HybridMotor", + "GenericMotor", + "LiquidMotor", + }: + for keyword in node.keywords: + if keyword.arg == "thrust_source": + keyword.value = ast.Call( + func=ast.Name(id="_example_thrust", ctx=ast.Load()), + args=[keyword.value], + keywords=[], + ) + if isinstance(node.func, ast.Attribute): + if node.func.attr in {"draw", "info", "all_info"}: + return ( + node.func.value + if isinstance(node.func.value, ast.Call) + else ast.copy_location(ast.Constant(None), node) + ) + if node.func.attr == "set_atmospheric_model": + kind = next( + ( + kw.value.value + for kw in node.keywords + if kw.arg == "type" and isinstance(kw.value, ast.Constant) + ), + "", + ) + if kind.lower() in {"windy", "forecast", "ensemble"}: + raise SourceUnavailable( + "Source requests live weather with no archived profile; not replaced by invented weather." + ) + if node.func.attr == "set_elevation" and any( + isinstance(a, ast.Constant) and a.value == "Open-Elevation" + for a in node.args + ): + raise SourceUnavailable( + "Source requests online elevation; an explicit archived input is needed." + ) + return node + + def visit_Expr(self, node): + if isinstance(node.value, ast.Call): + if any( + part in ast.unparse(node.value.func) for part in (".plots.", ".prints.") + ): + return None + value = node.value + while isinstance(value, ast.Call) and isinstance(value.func, ast.Attribute): + value = value.func.value + root = value + while isinstance(root, ast.Attribute): + root = root.value + if isinstance(root, ast.Name) and root.id in {"plt", "display", "print"}: + return None + if isinstance(node.value.func, ast.Attribute) and node.value.func.attr in { + "compare_plots", + "show", + "savefig", + }: + return None + return self.generic_visit(node) + + +def build_model(path): + """Execute only trusted pre-Flight construction; preserve physical inputs.""" + path = Path(path).resolve() + notebook = nbformat.read(path, 4) + adaptations = [] + + def thrust_source(value): + if isinstance(value, str) and value.endswith(".eng"): + from rocketpy.motors.motor import Motor + + _, _, points = Motor.import_eng(value) + if len(points) > 1 and points[1][0] == 0: + adaptations.append( + "Read original .eng numeric samples directly: source already has t=0; avoid the importer's extra duplicate zero-time row. No measured sample or motor equation changed." + ) + return np.asarray(points[1:], float) + return value + + namespace = {"__name__": "__datcom_source__", "_example_thrust": thrust_source} + executed = [] + for index, cell in enumerate(notebook.cells): + if cell.cell_type != "code": + continue + tree = _tree(cell.source) + for node in tree.body: + call = node.value if isinstance(node, (ast.Assign, ast.Expr)) else None + if ( + isinstance(call, ast.Call) + and isinstance(call.func, ast.Name) + and call.func.id == "Flight" + ): + options = { + kw.arg: eval( + compile(ast.Expression(kw.value), str(path), "eval"), namespace + ) + for kw in call.keywords + } + rocket, env = options.pop("rocket"), options.pop("environment") + # Both comparisons stop at apogee. Recovery is irrelevant to + # supported passive ascent cases and is deliberately omitted. + rocket.parachutes = [] + rocket._example_adaptations = adaptations + options.update(terminate_on_apogee=True) + options.setdefault("max_time", 180) + options["max_time_step"] = 0.1 + return rocket, env, options, executed + module = ast.Module(body=[node], type_ignores=[]) + module = h.LocalPaths(ROOT).visit(module) + module = Construction().visit(module) + ast.fix_missing_locations(module) + exec(compile(module, str(path), "exec"), namespace) + executed.append(index) + raise SourceUnavailable("No first Flight construction found") diff --git a/docs/examples/datcom/comparison_data/prepare_comparisons.py b/docs/examples/datcom/comparison_data/prepare_comparisons.py new file mode 100644 index 000000000..b9cf4c8e8 --- /dev/null +++ b/docs/examples/datcom/comparison_data/prepare_comparisons.py @@ -0,0 +1,325 @@ +"""Prepare only the requested measured/model comparison values and curves. + +Run reproduce_rockets.py first. Apogee-fitted constant CD is an explicitly +inferred one-parameter calibration, not measured CD or a DATCOM correction. +""" + +import contextlib +import hashlib +import io +import json +import os +from pathlib import Path + +import numpy as np +import pandas as pd +from scipy.optimize import brentq + +import model_sources as c +from rocketpy import Flight, Function +from rocketpy.rocket.datcom import run_datcom +from asteria_model import build + +OUT = Path(__file__).resolve().parent +ROCKETS = OUT / "rockets" +EXE = Path( + os.environ.get( + "DATCOM_EXECUTABLE", c.ROOT.parent / "Asteria-Flight-Dynamics/works/dat0/datcom" + ) +) +VELOCITY = { + "andromeda": ("t(s)", "vz(m/s)"), + "bella_lui": ("time aprox (s)", "v (m/s)"), + "cavour": ("ts", "velocity[m/s]"), + "juno3": ("time", "velocity"), + "prometheus_2022": ("time", "speed"), +} + + +def error(predicted, measured): + return ( + 100 * (predicted - measured) / abs(measured) + if pd.notna(predicted) and pd.notna(measured) and measured != 0 + else np.nan + ) + + +def fit_cd(rocket, env, launch, target): + if rocket.air_brakes: + return np.nan, np.nan, "not fitted: active air-brake/controller model" + evaluations = {} + + def residual(cd): + rocket.power_on_drag = rocket.power_off_drag = Function(float(cd)) + rocket.power_on_drag_7d = rocket.power_off_drag_7d = Function( + lambda alpha, beta, mach, reynolds, q, r, p: float(cd), + inputs=["alpha", "beta", "mach", "reynolds", "q", "r", "p"], + ) + with contextlib.redirect_stdout(io.StringIO()): + flight = Flight(rocket, env, **launch) + height = float(flight.apogee - env.elevation) + evaluations[float(cd)] = height + return height - target + + low, high = 0.05, 2.0 + if residual(low) * residual(high) > 0: + return np.nan, np.nan, "target not bracketed by constant CD in [0.05, 2]" + value = brentq(residual, low, high, xtol=1e-5) + mismatch = residual(value) + if abs(mismatch) > 0.5: + raise AssertionError("Apogee CD fit did not reproduce target within 0.5 m") + return ( + value, + mismatch, + "inferred: single constant CD, same powered/coast, ordinary lateral model", + ) + + +def rockets(): + results = pd.read_csv(ROCKETS / "results.csv") + metrics = [] + for _, row in results.iterrows(): + name = row.rocket + print("VALUES", name, flush=True) + ref = c.flight_reference(c.ROOT / f"docs/examples/{name}_flight_sim.ipynb") + measured_v = np.nan + telemetry = ref["telemetry"] + if telemetry and name in VELOCITY: + tcol, vcol = VELOCITY[name] + table = pd.read_csv(c.ROOT / telemetry["path"]) + if name == "bella_lui": + table = table.iloc[:573] + t, v = table[tcol].to_numpy(float), table[vcol].to_numpy(float) + accepted = ( + np.interp(t, telemetry["time"], telemetry["qa_valid"].astype(float)) + > 0.99 + ) + end = telemetry["time"][ + np.argmax( + np.where(telemetry["qa_valid"], telemetry["altitude"], -np.inf) + ) + ] + accepted &= np.isfinite(t) & np.isfinite(v) & (t >= 0) & (t <= end) + measured_v = float(np.max(v[accepted])) + pd.DataFrame(dict(time_s=t, measured_vz_m_s=v, qa_valid=accepted)).to_csv( + ROCKETS / f"{name}_velocity.csv", index=False + ) + for metric, unit, measured, suffix, condition in [ + ("Apogee AGL", "m", row.actual_agl_m, "agl_m", ref["reference_kind"]), + ( + "Peak upward vertical velocity", + "m/s", + measured_v, + "peak_vertical_velocity_m_s", + "recorded ascent velocity channel; not total airspeed", + ), + ( + "Initial static margin", + "cal", + 2.875 if name == "ndrt_2020" else np.nan, + "initial_static_margin_cal", + "Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach", + ), + ( + "Coast CD at Mach 0.6", + "1", + np.nan, + "coast_cd_mach06", + "zero alpha/beta; measured CD unavailable", + ), + ]: + ordinary = row.get("ordinary_" + suffix, np.nan) + native = row.get("datcom_" + suffix, np.nan) + metrics.append( + dict( + rocket=name, + metric=metric, + unit=unit, + measured=measured, + rocketpy=ordinary, + datcom=native, + rocketpy_error_pct=error(ordinary, measured), + datcom_error_pct=error(native, measured), + condition=condition, + availability=row.reason if pd.notna(row.reason) else "", + ) + ) + # Saved archived values remain identified; do not invent fresh models. + if name in {"hedy", "valkyrie"}: + for record in metrics: + if record["rocket"] == name and record["metric"] == "Apogee AGL": + record["rocketpy"] = row.saved_first_agl_m + record["rocketpy_error_pct"] = error( + row.saved_first_agl_m, row.actual_agl_m + ) + record["condition"] += ( + "; archived RocketPy output, not fresh simulation" + ) + if row.status in {"paired untuned comparison", "ordinary only"}: + with contextlib.redirect_stdout(io.StringIO()): + rocket, env, launch, _ = c.build_model(c.ROOT / ref["source_notebook"]) + grid = np.linspace(0.01, max(1.2, row.ordinary_max_mach * 1.15), 151) + curves = pd.DataFrame(dict(mach=grid)) + for state, label in [(True, "powered"), (False, "coast")]: + function = ( + rocket.power_on_drag_7d if state else rocket.power_off_drag_7d + ) + curves[f"rocketpy_{label}_cd"] = [ + function(0, 0, m, 1e6, 0, 0, 0) for m in grid + ] + path = ROCKETS / f"{name}_{label}_for005.dat" + if path.exists(): + parsed = run_datcom(EXE, path.read_text()) + zero = np.flatnonzero(parsed[5] == 0)[0] + curves[f"datcom_{label}_cd"] = np.interp( + grid, parsed[4], parsed[1][:, zero] + ) + curves.to_csv(ROCKETS / f"{name}_cd.csv", index=False) + cd, mismatch, status = fit_cd(rocket, env, launch, row.actual_agl_m) + metrics.append( + dict( + rocket=name, + metric="Apogee-fitted constant CD", + unit="1", + measured=np.nan, + rocketpy=np.nan, + datcom=np.nan, + inferred_cd=cd, + fit_apogee_residual_m=mismatch, + rocketpy_error_pct=np.nan, + datcom_error_pct=np.nan, + condition=status, + availability="", + ) + ) + pd.DataFrame(metrics).to_csv(OUT / "rocket_metrics.csv", index=False) + + frame = pd.DataFrame(metrics) + fits = ( + frame[frame.metric == "Apogee-fitted constant CD"] + .set_index("rocket") + .inferred_cd + ) + cd_rows = frame.metric == "Coast CD at Mach 0.6" + frame.loc[cd_rows, "inferred_cd"] = frame.loc[cd_rows, "rocket"].map(fits) + for label in ["rocketpy", "datcom"]: + frame.loc[cd_rows, f"{label}_vs_fitted_cd_pct"] = [ + error(predicted, inferred) + for predicted, inferred in zip( + frame.loc[cd_rows, label], frame.loc[cd_rows, "inferred_cd"] + ) + ] + frame.to_csv(OUT / "rocket_metrics.csv", index=False) + + +def asteria(): + with contextlib.redirect_stdout(io.StringIO()): + ordinary, env, launch = build(False, EXE) + native, native_env, native_launch = build(True, EXE) + baseline = Flight(ordinary, env, **launch) + atmosphere = native.set_datcom_atmosphere(baseline) + flight = Flight(native, native_env, **native_launch) + for _ in range(2): + atmosphere = native.set_datcom_atmosphere(flight) + flight = Flight(native, native_env, **native_launch) + times = np.linspace(0, max(baseline.apogee_time, flight.apogee_time), 601) + histories = pd.DataFrame(dict(time_s=times)) + for label, trajectory in [("rocketpy", baseline), ("datcom", flight)]: + active = times <= trajectory.apogee_time + histories[f"{label}_altitude_agl_m"] = np.where( + active, trajectory.z(times) - env.elevation, np.nan + ) + histories[f"{label}_vz_m_s"] = np.where(active, trajectory.vz(times), np.nan) + histories[f"{label}_margin_cal"] = np.where( + active, trajectory.stability_margin(times), np.nan + ) + histories.to_csv(OUT / "asteria_histories.csv", index=False) + grid = np.linspace(0.01, atmosphere["mach"][-1], 151) + curves = pd.DataFrame(dict(mach=grid)) + for powered, label in [(True, "powered"), (False, "coast")]: + function = ordinary.power_on_drag_7d if powered else ordinary.power_off_drag_7d + curves[f"rocketpy_{label}_cd"] = [function(0, 0, m, 1e6, 0, 0, 0) for m in grid] + curves[f"datcom_{label}_cd"] = [ + native._datcom_model.drag_coefficient(powered, 0, 0, m, 0, 0, 0, 0) + for m in grid + ] + curves.to_csv(OUT / "asteria_cd.csv", index=False) + rows = [] + for metric, unit, ordinary_value, native_value in [ + ( + "Apogee AGL", + "m", + baseline.apogee - env.elevation, + flight.apogee - env.elevation, + ), + ( + "Peak upward vertical velocity", + "m/s", + np.max(baseline.vz(times[times <= baseline.apogee_time])), + np.max(flight.vz(times[times <= flight.apogee_time])), + ), + ( + "Initial static margin", + "cal", + ordinary.static_margin(0), + native.static_margin(0), + ), + ( + "Powered CD at Mach 0.6", + "1", + ordinary.power_on_drag_7d(0, 0, 0.6, 1e6, 0, 0, 0), + native._datcom_model.drag_coefficient(True, 0, 0, 0.6, 0, 0, 0, 0), + ), + ( + "Coast CD at Mach 0.6", + "1", + ordinary.power_off_drag_7d(0, 0, 0.6, 1e6, 0, 0, 0), + native._datcom_model.drag_coefficient(False, 0, 0, 0.6, 0, 0, 0, 0), + ), + ]: + rows.append( + dict( + metric=metric, + unit=unit, + rocketpy=float(ordinary_value), + datcom=float(native_value), + datcom_vs_rocketpy_pct=error(native_value, ordinary_value), + measured=np.nan, + ) + ) + pd.DataFrame(rows).to_csv(OUT / "asteria_metrics.csv", index=False) + samples = np.linspace(0, ordinary.motor.burn_out_time, 101) + np.testing.assert_allclose( + ordinary.total_mass(samples), native.total_mass(samples), atol=1e-10 + ) + np.testing.assert_allclose( + ordinary.center_of_mass(samples), native.center_of_mass(samples), atol=1e-10 + ) + (OUT / "asteria_provenance.json").write_text( + json.dumps( + dict( + flight_status="AST-HER has not flown", + atmosphere=atmosphere, + ordinary_drag_source="Asteria-Flight-Dynamics/input/drag_curve.CSV", + ordinary_drag_sha256=hashlib.sha256( + (OUT / "asteria_drag_curve.csv").read_bytes() + ).hexdigest(), + tail_note="ordinary RocketPy conical approximation; DATCOM configured ogive", + mass_cg_match=True, + source_configuration="data/rockets/ast_her/configuration.json", + scope="rail to apogee; standard RocketPy rail; no Asteria hold-down clamp or PROTUB", + ), + indent=2, + ) + ) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--asteria-only", action="store_true") + args = parser.parse_args() + if not args.asteria_only: + rockets() + asteria() diff --git a/docs/examples/datcom/comparison_data/reproduce_rockets.py b/docs/examples/datcom/comparison_data/reproduce_rockets.py new file mode 100644 index 000000000..bb4bb7ecb --- /dev/null +++ b/docs/examples/datcom/comparison_data/reproduce_rockets.py @@ -0,0 +1,406 @@ +"""Untuned ordinary/DATCOM ascent comparisons and full notebook evidence audit.""" + +import argparse +import contextlib +import io +import json +import faulthandler +import os +from pathlib import Path +import subprocess +import sys + +import numpy as np +import pandas as pd + +os.environ.setdefault("MPLBACKEND", "Agg") +import model_sources as c + +from rocketpy import Flight # noqa: E402 +from rocketpy.rocket.datcom import datcom_decks # noqa: E402 + +OUT = Path(__file__).resolve().parent / "rockets" +OUT.mkdir(exist_ok=True) +EXE = Path( + os.environ.get( + "DATCOM_EXECUTABLE", c.ROOT.parent / "Asteria-Flight-Dynamics/works/dat0/datcom" + ) +) + + +def finalize_result(result, record): + """Use one shared measured-ascent overlap for both resampled histories.""" + result["notes"] = " | ".join(record["notes"]) + telemetry = record["telemetry"] + if telemetry: + result["telemetry_qa_max_agl_m"] = telemetry["qa_max_agl_m"] + result["telemetry_qa_rejected_count"] = telemetry["qa_rejected_count"] + for label in ("ordinary", "datcom"): + if f"{label}_agl_m" in result: + result[f"{label}_vs_telemetry_pct"] = 100 * ( + result[f"{label}_agl_m"] / telemetry["qa_max_agl_m"] - 1 + ) + path = OUT / f"{record['rocket']}_altitude.csv" + if path.exists(): + history = pd.read_csv(path) + measured = pd.read_csv(OUT / f"{record['rocket']}_telemetry.csv") + measured = measured[measured.qa_valid] + labels = [ + label for label in ("ordinary", "datcom") if f"{label}_agl_m" in history + ] + upper = min( + [float(measured.loc[measured.altitude_agl_m.idxmax(), "time_s"])] + + [ + float( + history.loc[history[f"{label}_agl_m"].notna(), "time_s"].max() + ) + for label in labels + ] + ) + lower = max(0, float(measured.time_s.min())) + if labels and upper > lower: + time = np.linspace(lower, upper, 301) + observed = np.interp(time, measured.time_s, measured.altitude_agl_m) + for label in labels: + mask = history[f"{label}_agl_m"].notna() + predicted = np.interp( + time, history.time_s[mask], history[f"{label}_agl_m"][mask] + ) + result[f"{label}_ascent_altitude_rmse_m"] = float( + np.sqrt(np.mean((predicted - observed) ** 2)) + ) + result["rmse_start_s"], result["rmse_end_s"] = lower, upper + if result.get("status") == "paired untuned comparison": + result["within_sampled_envelope"] = all( + result[key] == 0 + for key in ["outside_mach", "outside_alpha", "outside_beta"] + ) + return result + + +def summarize(flight, reference, prefix): + result = { + f"{prefix}_agl_m": float(flight.apogee - flight.env.elevation), + f"{prefix}_apogee_time_s": float(flight.apogee_time), + } + result[f"{prefix}_peak_vertical_velocity_m_s"] = float( + np.max(flight.vz(np.linspace(0, flight.apogee_time, 2001))) + ) + result[f"{prefix}_initial_static_margin_cal"] = float( + flight.rocket.static_margin(0) + ) + if prefix == "datcom": + result[f"{prefix}_coast_cd_mach06"] = float( + flight.rocket._datcom_model.drag_coefficient(False, 0, 0, 0.6, 0, 0, 0, 0) + ) + else: + result[f"{prefix}_coast_cd_mach06"] = float( + flight.rocket.power_off_drag_7d(0, 0, 0.6, 1e6, 0, 0, 0) + ) + time = np.linspace(0, flight.apogee_time, 501) + result[f"{prefix}_max_mach"] = float(np.max(flight.mach_number(time))) + telemetry = reference["telemetry"] + if telemetry: + mask = telemetry["qa_valid"] + t = telemetry["time"][mask] + height = telemetry["altitude"][mask] + upper = min(flight.apogee_time, float(t[np.argmax(height)])) + lower = max(0, float(t.min())) + if upper > lower: + sample = np.linspace(lower, upper, 301) + result[f"{prefix}_ascent_altitude_rmse_m"] = float( + np.sqrt( + np.mean( + ( + flight.z(sample) + - flight.env.elevation + - np.interp(sample, t, height) + ) + ** 2 + ) + ) + ) + return result + + +def one(name, refinements=1): + faulthandler.dump_traceback_later(30) + source = c.ROOT / f"docs/examples/{name}_flight_sim.ipynb" + ref = c.flight_reference(source) + row = { + key: ref[key] + for key in [ + "rocket", + "actual_agl_m", + "reported_agl_m", + "saved_first_agl_m", + "reference_kind", + ] + } + row["telemetry_max_agl_m"] = ( + ref["telemetry"]["raw_max_agl_m"] if ref["telemetry"] else None + ) + row["notes"] = " | ".join(ref["notes"]) + row["status"] = "not run" + row["reason"] = "" + if name == "hedy" and not (c.ROOT / "data/rockets/tust").exists(): + row.update( + status="archived comparison only", + reason="Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.", + ) + else: + try: + np.random.seed(0) + print("BUILD SOURCE", name, flush=True) + with contextlib.redirect_stdout(io.StringIO()): + rocket, env, launch, cells = c.build_model(source) + print("ORDINARY FLIGHT", name, flush=True) + with contextlib.redirect_stdout(io.StringIO()): + baseline = Flight(rocket, env, **launch) + row.update(summarize(baseline, ref, "ordinary")) + row["status"] = "ordinary only" + row["source_adaptations"] = " | ".join(rocket._example_adaptations) + times = np.linspace(0, baseline.apogee_time, 501) + history = pd.DataFrame( + dict( + time_s=times, + ordinary_agl_m=baseline.z(times) - env.elevation, + ordinary_vz_m_s=baseline.vz(times), + ordinary_margin_cal=baseline.stability_margin(times), + ) + ) + history.to_csv(OUT / f"{name}_altitude.csv", index=False) + if rocket.air_brakes: + row["reason"] = ( + "Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed." + ) + else: + np.random.seed(0) + print("BUILD DATCOM", name, flush=True) + with contextlib.redirect_stdout(io.StringIO()): + other, other_env, other_launch, _ = c.build_model(source) + c.h.attach_datcom(other, EXE, 0) + atmosphere = other.set_datcom_atmosphere(baseline) + grid = atmosphere["mach"] + samples = np.linspace(0, rocket.motor.burn_out_time, 101) + np.testing.assert_allclose( + rocket.total_mass(samples), + other.total_mass(samples), + atol=1e-10, + rtol=0, + ) + np.testing.assert_allclose( + rocket.center_of_mass(samples), + other.center_of_mass(samples), + atol=1e-10, + rtol=0, + ) + with contextlib.redirect_stdout(io.StringIO()): + print("NATIVE FLIGHT", name, file=sys.stderr, flush=True) + native = Flight(other, other_env, **other_launch) + iterations = [ + { + "pass": 0, + "apogee_agl_m": float(native.apogee - env.elevation), + "reference": "ordinary trajectory", + } + ] + for iteration in range(refinements): + atmosphere = other.set_datcom_atmosphere(native) + grid = atmosphere["mach"] + with contextlib.redirect_stdout(io.StringIO()): + native = Flight(other, other_env, **other_launch) + iterations.append( + { + "pass": iteration + 1, + "apogee_agl_m": float(native.apogee - env.elevation), + "reference": "previous DATCOM trajectory", + } + ) + schedules = atmosphere["altitude_m_asl"] + decks, _, _, _ = datcom_decks(other) + for powered, deck in decks.items(): + ( + OUT / f"{name}_{'powered' if powered else 'coast'}_for005.dat" + ).write_text(deck) + row["atmosphere_refinements"] = refinements + row["atmosphere_last_apogee_change_m"] = ( + iterations[-1]["apogee_agl_m"] - iterations[-2]["apogee_agl_m"] + if refinements + else None + ) + row.update(summarize(native, ref, "datcom")) + row.update( + c.h.envelope_diagnostics( + native, + other._datcom_model, + mach_grid=grid, + alpha_grid=atmosphere["alpha"], + ) + ) + row["status"] = "paired untuned comparison" + row["reason"] = "" + row["mass_cg_match"] = True + time = np.linspace( + 0, max(native.apogee_time, baseline.apogee_time), 601 + ) + ordinary = np.where( + time <= baseline.apogee_time, + baseline.z(time) - env.elevation, + np.nan, + ) + datcom = np.where( + time <= native.apogee_time, native.z(time) - env.elevation, np.nan + ) + pd.DataFrame( + dict( + time_s=time, + ordinary_agl_m=ordinary, + datcom_agl_m=datcom, + ordinary_vz_m_s=np.where( + time <= baseline.apogee_time, baseline.vz(time), np.nan + ), + datcom_vz_m_s=np.where( + time <= native.apogee_time, native.vz(time), np.nan + ), + ordinary_margin_cal=np.where( + time <= baseline.apogee_time, + baseline.stability_margin(time), + np.nan, + ), + datcom_margin_cal=np.where( + time <= native.apogee_time, + native.stability_margin(time), + np.nan, + ), + ) + ).to_csv(OUT / f"{name}_altitude.csv", index=False) + metadata = dict( + construction_cells=sorted(set(cells)), + calibration=other.datcom_calibration, + source_adaptations=rocket._example_adaptations, + mach=grid, + alpha=atmosphere["alpha"], + beta=c.h.BETA, + altitude_schedules_m_asl=schedules, + atmosphere=atmosphere, + atmosphere_iterations=iterations, + atmosphere_method="RocketPy reference ascent rho*a*M/mu; native REN", + source_notebook=ref["source_notebook"], + source_sha256=ref["source_sha256"], + construction_assumption="carbon fibre and vinyl when unspecified; no numerical roughness inferred", + fin_assumption="2% HEX, 5% bevels, sharp edges, auto body attachment", + powered_base_drag="nozzle", + damp=True, + ) + (OUT / f"{name}_provenance.json").write_text( + json.dumps(metadata, indent=2) + ) + except Exception as error: + row["reason"] = f"{type(error).__name__}: {error}" + if row["status"] == "not run": + row["status"] = "unavailable source model" + for label in ["ordinary", "datcom"]: + if f"{label}_agl_m" in row: + row[f"{label}_error_m"] = row[f"{label}_agl_m"] - row["actual_agl_m"] + row[f"{label}_error_pct"] = ( + 100 * row[f"{label}_error_m"] / row["actual_agl_m"] + ) + (OUT / f"{name}_result.json").write_text(json.dumps(row, indent=2, allow_nan=False)) + print(json.dumps(row), flush=True) + faulthandler.cancel_dump_traceback_later() + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--case") + parser.add_argument("--only", nargs="*") + parser.add_argument( + "--refinements", + type=int, + default=1, + choices=range(6), + help="DATCOM-trajectory atmosphere updates after the ordinary seed (default 1)", + ) + args = parser.parse_args() + if args.case: + one(args.case, args.refinements) + return + inventory = [] + for path in sorted((c.ROOT / "docs/examples").glob("*flight_sim.ipynb")): + record = c.flight_reference(path) + telemetry = record.pop("telemetry") + record["telemetry"] = ( + { + key: value + for key, value in telemetry.items() + if key not in ["time", "altitude", "qa_valid"] + } + if telemetry + else None + ) + if telemetry: + pd.DataFrame( + dict( + time_s=telemetry["time"], + altitude_agl_m=telemetry["altitude"], + qa_valid=telemetry["qa_valid"], + ) + ).to_csv(OUT / f"{record['rocket']}_telemetry.csv", index=False) + inventory.append(record) + (OUT / "inventory.json").write_text(json.dumps(inventory, indent=2)) + results = [] + for record in inventory: + name = record["rocket"] + if args.only is None or name in args.only: + print("RUN", name, flush=True) + try: + with (OUT / f"{name}_execution.log").open("w") as log: + subprocess.run( + [ + sys.executable, + str(Path(__file__).resolve()), + "--case", + name, + "--refinements", + str(args.refinements), + ], + cwd=c.ROOT, + stdout=log, + stderr=subprocess.STDOUT, + timeout=90, + check=True, + ) + except subprocess.TimeoutExpired: + (OUT / f"{name}_result.json").write_text( + json.dumps( + dict( + rocket=name, + status="source simulation timed out", + reason="Source model did not complete within 90 s; inputs/equations not silently changed.", + actual_agl_m=record["actual_agl_m"], + reported_agl_m=record["reported_agl_m"], + saved_first_agl_m=record["saved_first_agl_m"], + ) + ) + ) + except subprocess.CalledProcessError: + print("CHECK LOG", name, flush=True) + path = OUT / f"{name}_result.json" + if path.exists(): + result = finalize_result(json.loads(path.read_text()), record) + path.write_text(json.dumps(result, indent=2, allow_nan=False)) + results.append(result) + pd.DataFrame(results).to_csv(OUT / "results.csv", index=False) + print( + name, + result["status"], + result.get("ordinary_agl_m"), + result.get("datcom_agl_m"), + result["reason"], + flush=True, + ) + + +if __name__ == "__main__": + main() diff --git a/docs/examples/datcom/comparison_data/rocket_metrics.csv b/docs/examples/datcom/comparison_data/rocket_metrics.csv new file mode 100644 index 000000000..435cd0f27 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rocket_metrics.csv @@ -0,0 +1,79 @@ +rocket,metric,unit,measured,rocketpy,datcom,rocketpy_error_pct,datcom_error_pct,condition,availability,inferred_cd,fit_apogee_residual_m,rocketpy_vs_fitted_cd_pct,datcom_vs_fitted_cd_pct +andromeda,Apogee AGL,m,3443.0,3615.880567942097,4170.733199067169,5.02121893529181,21.13660177366161,notebook reported apogee,,,,, +andromeda,Peak upward vertical velocity,m/s,324.10908,343.1543305235018,352.6406104190726,5.876185426061438,8.80306420883752,recorded ascent velocity channel; not total airspeed,,,,, +andromeda,Initial static margin,cal,,4.158769051891837,4.585569601745527,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,, +andromeda,Coast CD at Mach 0.6,1,,0.4984983643122677,0.349,,,zero alpha/beta; measured CD unavailable,,0.5913883886236182,,-15.707109929489876,-40.986328660890116 +andromeda,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.5913883886236182,0.0240281656274419,, +astra,Apogee AGL,m,3250.0,3043.3679728130164,3695.9340692825294,-6.357908528830264,13.721048285616291,notebook reported apogee,,,,, +astra,Peak upward vertical velocity,m/s,,306.4797793783156,318.5471261391443,,,recorded ascent velocity channel; not total airspeed,,,,, +astra,Initial static margin,cal,,3.16256293861153,3.477476916191179,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,, +astra,Coast CD at Mach 0.6,1,,0.6538147999999999,0.4265,,,zero alpha/beta; measured CD unavailable,,0.5989080138236664,,9.167816243731115,-28.787060757952673 +astra,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.5989080138236664,0.0032524125458621,, +bella_lui,Apogee AGL,m,458.971,460.57389565932874,465.8765076067215,0.3492368056650066,1.5045629477072564,telemetry AGL maximum (QA),,,,, +bella_lui,Peak upward vertical velocity,m/s,90.0,86.17870117421616,86.47349849471236,-4.245887584204269,-3.918335005875156,recorded ascent velocity channel; not total airspeed,,,,, +bella_lui,Initial static margin,cal,,2.776966165584537,3.303739452169553,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,, +bella_lui,Coast CD at Mach 0.6,1,,0.43,0.3457692307692308,,,zero alpha/beta; measured CD unavailable,,0.4457593658055402,,-3.53539757421836,-22.431415401808856 +bella_lui,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.4457593658055402,0.0052149262372722,, +camoes,Apogee AGL,m,3015.0,3004.10382797431,,-0.3613987404872236,,notebook reported apogee,Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.,,,, +camoes,Peak upward vertical velocity,m/s,,327.4402911475654,,,,recorded ascent velocity channel; not total airspeed,Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.,,,, +camoes,Initial static margin,cal,,3.7616831285769776,,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.,,,, +camoes,Coast CD at Mach 0.6,1,,0.7036,,,,zero alpha/beta; measured CD unavailable,Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.,,,, +camoes,Apogee-fitted constant CD,1,,,,,,not fitted: active air-brake/controller model,,,,, +cavour,Apogee AGL,m,2789.0,2818.1348894226226,3251.608548140568,1.0446356910226806,16.58689667051158,notebook reported apogee,,,,, +cavour,Peak upward vertical velocity,m/s,314.0,304.6396378918068,319.05542973964634,-2.9810070408258644,1.61000947122495,recorded ascent velocity channel; not total airspeed,,,,, +cavour,Initial static margin,cal,,4.279860429594885,5.465305853006995,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,, +cavour,Coast CD at Mach 0.6,1,,0.583,0.44825,,,zero alpha/beta; measured CD unavailable,,0.6086474636527143,,-4.213845482702675,-26.353098177738378 +cavour,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.6086474636527143,0.0013786057097604,, +defiance,Apogee AGL,m,9308.318,9243.891367330603,,-0.6921404347100731,,notebook reported apogee,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,,,, +defiance,Peak upward vertical velocity,m/s,,438.71139566437614,,,,recorded ascent velocity channel; not total airspeed,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,,,, +defiance,Initial static margin,cal,,7.9601902493275025,,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,,,, +defiance,Coast CD at Mach 0.6,1,,0.668,,,,zero alpha/beta; measured CD unavailable,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,0.6574401678467228,,1.6062042859144585, +defiance,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.6574401678467228,-0.0629210059214528,, +erebus,Apogee AGL,m,3020.0,2751.1556122225365,3253.422625182214,-8.902132045611372,7.729225999411052,notebook reported apogee,,,,, +erebus,Peak upward vertical velocity,m/s,,294.1707251884576,307.31659427636407,,,recorded ascent velocity channel; not total airspeed,,,,, +erebus,Initial static margin,cal,,2.9374083445614505,4.450320086266694,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,, +erebus,Coast CD at Mach 0.6,1,,0.531,0.376,,,zero alpha/beta; measured CD unavailable,,0.4428682177698397,,19.900227357467035,-15.098897389062893 +erebus,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.4428682177698397,-0.0107670600518758,, +genesis,Apogee AGL,m,2916.0,3044.6857862664306,3297.2391270686403,4.41309280749076,13.074044138156388,notebook reported apogee,,,,, +genesis,Peak upward vertical velocity,m/s,,290.6363275291183,299.2620317383252,,,recorded ascent velocity channel; not total airspeed,,,,, +genesis,Initial static margin,cal,,3.37721309359005,3.7682700796928534,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,, +genesis,Coast CD at Mach 0.6,1,,0.55,0.47475,,,zero alpha/beta; measured CD unavailable,,0.6089918859113056,,-9.686809837052115,-22.043296309346353 +genesis,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.6089918859113056,-0.0033379047545167,, +halcyon,Apogee AGL,m,3450.0,3214.238895757538,3530.231782008296,-6.8336551954336775,2.3255588987911824,notebook reported apogee,,,,, +halcyon,Peak upward vertical velocity,m/s,,275.5714950426811,280.94965050790404,,,recorded ascent velocity channel; not total airspeed,,,,, +halcyon,Initial static margin,cal,,3.0192578745448033,4.410132986423363,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,, +halcyon,Coast CD at Mach 0.6,1,,0.426,0.319,,,zero alpha/beta; measured CD unavailable,,0.3446774638716062,,23.593807153776314,-7.449707788604117 +halcyon,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.3446774638716062,0.0023908790494715,, +hedy,Apogee AGL,m,5231.53,5246.918,,0.2941395729356406,,"saved measured-apogee summary; archived RocketPy output, not fresh simulation","Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.",,,, +hedy,Peak upward vertical velocity,m/s,,,,,,recorded ascent velocity channel; not total airspeed,"Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.",,,, +hedy,Initial static margin,cal,,,,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach","Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.",,,, +hedy,Coast CD at Mach 0.6,1,,,,,,zero alpha/beta; measured CD unavailable,"Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.",,,, +juno3,Apogee AGL,m,3213.0,3008.581569789987,3056.4921836297026,-6.362229387177496,-4.871080497052518,notebook reported apogee,,,,, +juno3,Peak upward vertical velocity,m/s,268.761,241.2427490025272,243.89859046993095,-10.238930126570752,-9.250750492098582,recorded ascent velocity channel; not total airspeed,,,,, +juno3,Initial static margin,cal,,3.220648297149346,4.0067340523842745,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,, +juno3,Coast CD at Mach 0.6,1,,0.368,0.3745,,,zero alpha/beta; measured CD unavailable,,0.2640930601135738,,39.344820284842314,41.80607390400393 +juno3,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.2640930601135738,-0.0003520752607073,, +lince,Apogee AGL,m,3587.0,3285.089381541881,4254.30425999591,-8.416800068528554,18.60340841917786,notebook reported apogee,,,,, +lince,Peak upward vertical velocity,m/s,,317.77081353823803,342.53953112405384,,,recorded ascent velocity channel; not total airspeed,,,,, +lince,Initial static margin,cal,,2.9203959364705625,3.962666916625163,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,, +lince,Coast CD at Mach 0.6,1,,0.65562,0.38175,,,zero alpha/beta; measured CD unavailable,,0.5781559898205425,,13.398461927809832,-33.9711069812675 +lince,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.5781559898205425,-0.0160574872520555,, +ndrt_2020,Apogee AGL,m,1316.736,1293.5184123380943,,-1.7632682376653923,,notebook reported apogee,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,,,, +ndrt_2020,Peak upward vertical velocity,m/s,,171.66118644734854,,,,recorded ascent velocity channel; not total airspeed,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,,,, +ndrt_2020,Initial static margin,cal,2.875,4.016381564609376,,39.70022833423917,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,,,, +ndrt_2020,Coast CD at Mach 0.6,1,,0.44,,,,zero alpha/beta; measured CD unavailable,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,0.4171896002390634,,5.467633840312778, +ndrt_2020,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.4171896002390634,-0.0019144055324886,, +prometheus_2022,Apogee AGL,m,3898.37,4191.288687549948,4229.19676461329,7.513875993041916,8.486284385866142,notebook reported apogee,,,,, +prometheus_2022,Peak upward vertical velocity,m/s,317.88,322.07071634939507,324.05991606871703,1.318332814079236,1.944103456875876,recorded ascent velocity channel; not total airspeed,,,,, +prometheus_2022,Initial static margin,cal,,1.0415367273926033,1.7889093089472907,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,, +prometheus_2022,Coast CD at Mach 0.6,1,,0.351875,0.33475,,,zero alpha/beta; measured CD unavailable,,0.4026032258387881,,-12.60005449114332,-16.85362199903439 +prometheus_2022,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.4026032258387881,-0.0677661625295513,, +valetudo,Apogee AGL,m,860.0,815.0526424633565,943.6190073224012,-5.226436922865522,9.723140386325722,saved measured-apogee summary,,,,, +valetudo,Peak upward vertical velocity,m/s,,123.31112741231496,126.09986587804391,,,recorded ascent velocity channel; not total airspeed,,,,, +valetudo,Initial static margin,cal,,2.951130333277495,4.156583455241121,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,, +valetudo,Coast CD at Mach 0.6,1,,1.128,0.4313333333333333,,,zero alpha/beta; measured CD unavailable,,0.809545397450946,,39.33746069729839,-46.71906792485103 +valetudo,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.809545397450946,-0.0149988657269659,, +valkyrie,Apogee AGL,m,2098.02,2247.849,,7.141447650642043,,"notebook reported apogee; archived RocketPy output, not fresh simulation",SourceUnavailable: Source requests live weather with no archived profile; not replaced by invented weather.,,,, +valkyrie,Peak upward vertical velocity,m/s,,,,,,recorded ascent velocity channel; not total airspeed,SourceUnavailable: Source requests live weather with no archived profile; not replaced by invented weather.,,,, +valkyrie,Initial static margin,cal,,,,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",SourceUnavailable: Source requests live weather with no archived profile; not replaced by invented weather.,,,, +valkyrie,Coast CD at Mach 0.6,1,,,,,,zero alpha/beta; measured CD unavailable,SourceUnavailable: Source requests live weather with no archived profile; not replaced by invented weather.,,,, diff --git a/docs/examples/datcom/comparison_data/rockets/andromeda_altitude.csv b/docs/examples/datcom/comparison_data/rockets/andromeda_altitude.csv new file mode 100644 index 000000000..161622f4b --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/andromeda_altitude.csv @@ -0,0 +1,602 @@ +time_s,ordinary_agl_m,datcom_agl_m,ordinary_vz_m_s,datcom_vz_m_s,ordinary_margin_cal,datcom_margin_cal +0.0,0.0,0.0,0.0,0.0,4.158775971358966,4.585569601745527 +0.04688882470511428,0.006829503305198159,0.0068356589625011566,0.6838581067929347,0.6826356496368738,4.160894526318346,4.587686777007753 +0.09377764941022856,0.09686045830000012,0.09663552989024993,3.406115131375551,3.403656045317763,4.167419063471486,4.594214790304437 +0.14066647411534283,0.34042733347172316,0.3402472533765035,7.104220257339224,7.107960856102773,4.176038300315391,4.603493289209765 +0.18755529882045713,0.7708200066193172,0.770339798884919,11.287442697054704,11.291630995696282,4.185750175795849,4.613891629933177 +0.23444412352557142,1.404540297411188,1.4043268293873155,15.799946779903975,15.798521578563102,4.196322840862803,4.625083805914894 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000000000..77e8b8886 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/andromeda_coast_for005.dat @@ -0,0 +1,220 @@ +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.9,0.95,0.98,1., + 1.02,1.04,1.05,1.1,1.2,1.21, + REN = 149949.,1504124.07,2899554.88,4368584.72,5761180.69,7448342.92, + 9266600.03,10995016.5,13068357.8,15295103.4,17924892.7,19297637.9, + 20106566.5,20638379.4,21167539.7,21601265.1,21808969.6,22847491.9, + 24924536.6,25132241.1, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -20.,$ + $REFQ + XCG = 1.0998, + SREF = 0.0178367771, + LREF = 0.1507,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.3, + DNOSE = 0.1507, + LCENTR = 2.1996, + DCENTR = 0.1507, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 2.145,2.265, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.185, + CHORD = 0.23,0.11, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.9,0.95,0.98,1., + 1.02,1.04,1.05,1.1,1.2,1.21, + REN = 149949.,1504124.07,2899554.88,4368584.72,5761180.69,7448342.92, + 9266600.03,10995016.5,13068357.8,15295103.4,17924892.7,19297637.9, + 20106566.5,20638379.4,21167539.7,21601265.1,21808969.6,22847491.9, + 24924536.6,25132241.1, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -5.,$ + $REFQ + XCG = 1.0998, + SREF = 0.0178367771, + LREF = 0.1507,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.3, + DNOSE = 0.1507, + LCENTR = 2.1996, + DCENTR = 0.1507, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 2.145,2.265, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.185, + CHORD = 0.23,0.11, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.9,0.95,0.98,1., + 1.02,1.04,1.05,1.1,1.2,1.21, + REN = 149949.,1504124.07,2899554.88,4368584.72,5761180.69,7448342.92, + 9266600.03,10995016.5,13068357.8,15295103.4,17924892.7,19297637.9, + 20106566.5,20638379.4,21167539.7,21601265.1,21808969.6,22847491.9, + 24924536.6,25132241.1, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 0.,$ + $REFQ + XCG = 1.0998, + SREF = 0.0178367771, + LREF = 0.1507,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.3, + DNOSE = 0.1507, + LCENTR = 2.1996, + DCENTR = 0.1507, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 2.145,2.265, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.185, + CHORD = 0.23,0.11, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.9,0.95,0.98,1., + 1.02,1.04,1.05,1.1,1.2,1.21, + REN = 149949.,1504124.07,2899554.88,4368584.72,5761180.69,7448342.92, + 9266600.03,10995016.5,13068357.8,15295103.4,17924892.7,19297637.9, + 20106566.5,20638379.4,21167539.7,21601265.1,21808969.6,22847491.9, + 24924536.6,25132241.1, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 5.,$ + $REFQ + XCG = 1.0998, + SREF = 0.0178367771, + LREF = 0.1507,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.3, + DNOSE = 0.1507, + LCENTR = 2.1996, + DCENTR = 0.1507, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 2.145,2.265, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.185, + CHORD = 0.23,0.11, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.9,0.95,0.98,1., + 1.02,1.04,1.05,1.1,1.2,1.21, + REN = 149949.,1504124.07,2899554.88,4368584.72,5761180.69,7448342.92, + 9266600.03,10995016.5,13068357.8,15295103.4,17924892.7,19297637.9, + 20106566.5,20638379.4,21167539.7,21601265.1,21808969.6,22847491.9, + 24924536.6,25132241.1, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 20.,$ + $REFQ + XCG = 1.0998, + SREF = 0.0178367771, + LREF = 0.1507,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.3, + DNOSE = 0.1507, + LCENTR = 2.1996, + DCENTR = 0.1507, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 2.145,2.265, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.185, + CHORD = 0.23,0.11, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE diff --git a/docs/examples/datcom/comparison_data/rockets/andromeda_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/andromeda_powered_for005.dat new file mode 100644 index 000000000..0b20e0c94 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/andromeda_powered_for005.dat @@ -0,0 +1,220 @@ +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.9,0.95,0.98,1., + 1.02,1.04,1.05,1.1,1.2,1.21, + REN = 219446.036,2194460.36,4169474.69,6136879.27,7876866.71,9822097.84, + 11750500.5,13447259.4,15331750.6,17180725.7,19184259.8,20152898.2, + 20714804.,21077841.2,21422042.,21720479.6,21929330.3,22973584.2, + 25062091.8,25270942.6, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -20.,$ + $REFQ + XCG = 1.2437, + SREF = 0.0178367771, + LREF = 0.1507,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.3, + DNOSE = 0.1507, + LCENTR = 2.1996, + DCENTR = 0.1507, + BASE = .FALSE., + DEXIT = 0.05625,$ + $FINSET1 + XLE = 2.145,2.265, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.185, + CHORD = 0.23,0.11, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.9,0.95,0.98,1., + 1.02,1.04,1.05,1.1,1.2,1.21, + REN = 219446.036,2194460.36,4169474.69,6136879.27,7876866.71,9822097.84, + 11750500.5,13447259.4,15331750.6,17180725.7,19184259.8,20152898.2, + 20714804.,21077841.2,21422042.,21720479.6,21929330.3,22973584.2, + 25062091.8,25270942.6, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -5.,$ + $REFQ + XCG = 1.2437, + SREF = 0.0178367771, + LREF = 0.1507,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.3, + DNOSE = 0.1507, + LCENTR = 2.1996, + DCENTR = 0.1507, + BASE = .FALSE., + DEXIT = 0.05625,$ + $FINSET1 + XLE = 2.145,2.265, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.185, + CHORD = 0.23,0.11, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.9,0.95,0.98,1., + 1.02,1.04,1.05,1.1,1.2,1.21, + REN = 219446.036,2194460.36,4169474.69,6136879.27,7876866.71,9822097.84, + 11750500.5,13447259.4,15331750.6,17180725.7,19184259.8,20152898.2, + 20714804.,21077841.2,21422042.,21720479.6,21929330.3,22973584.2, + 25062091.8,25270942.6, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 0.,$ + $REFQ + XCG = 1.2437, + SREF = 0.0178367771, + LREF = 0.1507,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.3, + DNOSE = 0.1507, + LCENTR = 2.1996, + DCENTR = 0.1507, + BASE = .FALSE., + DEXIT = 0.05625,$ + $FINSET1 + XLE = 2.145,2.265, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.185, + CHORD = 0.23,0.11, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.9,0.95,0.98,1., + 1.02,1.04,1.05,1.1,1.2,1.21, + REN = 219446.036,2194460.36,4169474.69,6136879.27,7876866.71,9822097.84, + 11750500.5,13447259.4,15331750.6,17180725.7,19184259.8,20152898.2, + 20714804.,21077841.2,21422042.,21720479.6,21929330.3,22973584.2, + 25062091.8,25270942.6, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 5.,$ + $REFQ + XCG = 1.2437, + SREF = 0.0178367771, + LREF = 0.1507,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.3, + DNOSE = 0.1507, + LCENTR = 2.1996, + DCENTR = 0.1507, + BASE = .FALSE., + DEXIT = 0.05625,$ + $FINSET1 + XLE = 2.145,2.265, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.185, + CHORD = 0.23,0.11, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.9,0.95,0.98,1., + 1.02,1.04,1.05,1.1,1.2,1.21, + REN = 219446.036,2194460.36,4169474.69,6136879.27,7876866.71,9822097.84, + 11750500.5,13447259.4,15331750.6,17180725.7,19184259.8,20152898.2, + 20714804.,21077841.2,21422042.,21720479.6,21929330.3,22973584.2, + 25062091.8,25270942.6, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 20.,$ + $REFQ + XCG = 1.2437, + SREF = 0.0178367771, + LREF = 0.1507,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.3, + DNOSE = 0.1507, + LCENTR = 2.1996, + DCENTR = 0.1507, + BASE = .FALSE., + DEXIT = 0.05625,$ + $FINSET1 + XLE = 2.145,2.265, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.185, + CHORD = 0.23,0.11, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE diff --git a/docs/examples/datcom/comparison_data/rockets/andromeda_provenance.json b/docs/examples/datcom/comparison_data/rockets/andromeda_provenance.json new file mode 100644 index 000000000..aa4763b14 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/andromeda_provenance.json @@ -0,0 +1,377 @@ +{ + "construction_cells": [ + 2, + 4, + 7, + 8, + 10, + 11, + 12 + ], + "calibration": { + "roughness_height": 0.0, + "leading_edge_radius": null + }, + "source_adaptations": [], + "mach": [ + 0.01, + 0.1, + 0.19, + 0.28, + 0.36, + 0.45, + 0.54, + 0.62, + 0.71, + 0.8, + 0.9, + 0.95, + 0.98, + 1.0, + 1.02, + 1.04, + 1.05, + 1.1, + 1.2, + 1.21 + ], + "alpha": [ + -20.0, + -10.0, + -5.0, + -2.0, + -1.0, + -0.5, + -0.25, + -0.1, + -0.05, + 0.0, + 0.05, + 0.1, + 0.25, + 0.5, + 1.0, + 2.0, + 5.0, + 10.0, + 20.0 + ], + "beta": [ + -20, + -5, + 0, + 5, + 20 + ], + "altitude_schedules_m_asl": { + "power_on": [ + 160.08944222574596, + 165.8015536486759, + 179.2706365049008, + 200.05282109701554, + 224.68636494189954, + 259.9285138066577, + 304.0036351149514, + 350.9295108186492, + 413.356881170543, + 488.6278629000589, + 590.5119812390163, + 653.9437954979367, + 699.3148325080329, + 734.9345762653724, + 780.1498072938462, + 849.8092570832068, + 849.8092570832068, + 849.8092570832068, + 849.8092570832068, + 849.8092570832068 + ], + "power_off": [ + 4337.624111696815, + 4307.582478697242, + 4165.1185313891565, + 3943.306034479547, + 3689.54628169946, + 3353.501816564861, + 2976.4406586462833, + 2617.561999363904, + 2196.663068773964, + 1771.6253379290044, + 1329.0999530725862, + 1141.0382648635614, + 1040.3462478238341, + 977.9448117477826, + 919.2956699177824, + 910.063482153232, + 910.063482153232, + 910.063482153232, + 910.063482153232, + 910.063482153232 + ] + }, + "atmosphere": { + "mach": [ + 0.01, + 0.1, + 0.19, + 0.28, + 0.36, + 0.45, + 0.54, + 0.62, + 0.71, + 0.8, + 0.9, + 0.95, + 0.98, + 1.0, + 1.02, + 1.04, + 1.05, + 1.1, + 1.2, + 1.21 + ], + 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+1.1523999999999999,0.85718,0.6579119999999999,0.85718,0.7288159999999999 +1.1603333333333332,0.85718,0.6609266666666667,0.85718,0.7315133333333332 +1.1682666666666666,0.85718,0.6639413333333334,0.85718,0.7342106666666666 +1.1762,0.85718,0.666956,0.85718,0.736908 +1.1841333333333333,0.85718,0.6699706666666667,0.85718,0.7396053333333333 +1.1920666666666666,0.85718,0.6729853333333333,0.85718,0.7423026666666667 +1.2,0.85718,0.676,0.85718,0.745 diff --git a/docs/examples/datcom/comparison_data/rockets/astra_coast_for005.dat b/docs/examples/datcom/comparison_data/rockets/astra_coast_for005.dat new file mode 100644 index 000000000..ad3914bd1 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/astra_coast_for005.dat @@ -0,0 +1,220 @@ +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.93,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 157289.538,1580173.94,3046183.02,4589511.86,6048926.96,7814972.44, + 9730029.44,11584413.2,13822039.1,16172291.4,17517934.3,18889393.3, + 19630434.3,20052594.2,20685834.,21107993.9,21530153.8,22163393.6, + 23218793.3,25329592.6, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -20.,$ + $REFQ + XCG = 1.1257, + SREF = 0.00693977817, + LREF = 0.094,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.27, + DNOSE = 0.094, + LCENTR = 1.53678361, + DCENTR = 0.094, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 1.608,1.69, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.117, + CHORD = 0.2,0.118, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.93,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 157289.538,1580173.94,3046183.02,4589511.86,6048926.96,7814972.44, + 9730029.44,11584413.2,13822039.1,16172291.4,17517934.3,18889393.3, + 19630434.3,20052594.2,20685834.,21107993.9,21530153.8,22163393.6, + 23218793.3,25329592.6, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -5.,$ + $REFQ + XCG = 1.1257, + SREF = 0.00693977817, + LREF = 0.094,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.27, + DNOSE = 0.094, + LCENTR = 1.53678361, + DCENTR = 0.094, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 1.608,1.69, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.117, + CHORD = 0.2,0.118, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.93,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 157289.538,1580173.94,3046183.02,4589511.86,6048926.96,7814972.44, + 9730029.44,11584413.2,13822039.1,16172291.4,17517934.3,18889393.3, + 19630434.3,20052594.2,20685834.,21107993.9,21530153.8,22163393.6, + 23218793.3,25329592.6, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 0.,$ + $REFQ + XCG = 1.1257, + SREF = 0.00693977817, + LREF = 0.094,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.27, + DNOSE = 0.094, + LCENTR = 1.53678361, + DCENTR = 0.094, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 1.608,1.69, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.117, + CHORD = 0.2,0.118, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.93,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 157289.538,1580173.94,3046183.02,4589511.86,6048926.96,7814972.44, + 9730029.44,11584413.2,13822039.1,16172291.4,17517934.3,18889393.3, + 19630434.3,20052594.2,20685834.,21107993.9,21530153.8,22163393.6, + 23218793.3,25329592.6, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 5.,$ + $REFQ + XCG = 1.1257, + SREF = 0.00693977817, + LREF = 0.094,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.27, + DNOSE = 0.094, + LCENTR = 1.53678361, + DCENTR = 0.094, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 1.608,1.69, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.117, + CHORD = 0.2,0.118, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.93,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 157289.538,1580173.94,3046183.02,4589511.86,6048926.96,7814972.44, + 9730029.44,11584413.2,13822039.1,16172291.4,17517934.3,18889393.3, + 19630434.3,20052594.2,20685834.,21107993.9,21530153.8,22163393.6, + 23218793.3,25329592.6, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 20.,$ + $REFQ + XCG = 1.1257, + SREF = 0.00693977817, + LREF = 0.094,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.27, + DNOSE = 0.094, + LCENTR = 1.53678361, + DCENTR = 0.094, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 1.608,1.69, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.117, + CHORD = 0.2,0.118, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE diff --git a/docs/examples/datcom/comparison_data/rockets/astra_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/astra_powered_for005.dat new file mode 100644 index 000000000..d04cd3e1a --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/astra_powered_for005.dat @@ -0,0 +1,220 @@ +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.93,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 219386.533,2193865.33,4168344.12,6139882.07,7879897.39,9824968.77, + 11752604.7,13447754.4,15329098.,17173988.8,18181243.9,19173588.6, + 19712408.5,20136331.3,20772215.4,21196138.2,21620060.9,22255945.1, + 23315752.,25435365.8, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -20.,$ + $REFQ + XCG = 1.19567582, + SREF = 0.00693977817, + LREF = 0.094,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.27, + DNOSE = 0.094, + LCENTR = 1.53678361, + DCENTR = 0.094, + BASE = .FALSE., + DEXIT = 0.05625,$ + $FINSET1 + XLE = 1.608,1.69, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.117, + CHORD = 0.2,0.118, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.93,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 219386.533,2193865.33,4168344.12,6139882.07,7879897.39,9824968.77, + 11752604.7,13447754.4,15329098.,17173988.8,18181243.9,19173588.6, + 19712408.5,20136331.3,20772215.4,21196138.2,21620060.9,22255945.1, + 23315752.,25435365.8, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -5.,$ + $REFQ + XCG = 1.19567582, + SREF = 0.00693977817, + LREF = 0.094,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.27, + DNOSE = 0.094, + LCENTR = 1.53678361, + DCENTR = 0.094, + BASE = .FALSE., + DEXIT = 0.05625,$ + $FINSET1 + XLE = 1.608,1.69, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.117, + CHORD = 0.2,0.118, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.93,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 219386.533,2193865.33,4168344.12,6139882.07,7879897.39,9824968.77, + 11752604.7,13447754.4,15329098.,17173988.8,18181243.9,19173588.6, + 19712408.5,20136331.3,20772215.4,21196138.2,21620060.9,22255945.1, + 23315752.,25435365.8, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 0.,$ + $REFQ + XCG = 1.19567582, + SREF = 0.00693977817, + LREF = 0.094,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.27, + DNOSE = 0.094, + LCENTR = 1.53678361, + DCENTR = 0.094, + BASE = .FALSE., + DEXIT = 0.05625,$ + $FINSET1 + XLE = 1.608,1.69, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.117, + CHORD = 0.2,0.118, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.93,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 219386.533,2193865.33,4168344.12,6139882.07,7879897.39,9824968.77, + 11752604.7,13447754.4,15329098.,17173988.8,18181243.9,19173588.6, + 19712408.5,20136331.3,20772215.4,21196138.2,21620060.9,22255945.1, + 23315752.,25435365.8, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 5.,$ + $REFQ + XCG = 1.19567582, + SREF = 0.00693977817, + LREF = 0.094,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.27, + DNOSE = 0.094, + LCENTR = 1.53678361, + DCENTR = 0.094, + BASE = .FALSE., + DEXIT = 0.05625,$ + $FINSET1 + XLE = 1.608,1.69, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.117, + CHORD = 0.2,0.118, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.93,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 219386.533,2193865.33,4168344.12,6139882.07,7879897.39,9824968.77, + 11752604.7,13447754.4,15329098.,17173988.8,18181243.9,19173588.6, + 19712408.5,20136331.3,20772215.4,21196138.2,21620060.9,22255945.1, + 23315752.,25435365.8, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 20.,$ + $REFQ + XCG = 1.19567582, + SREF = 0.00693977817, + LREF = 0.094,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.27, + DNOSE = 0.094, + LCENTR = 1.53678361, + DCENTR = 0.094, + BASE = .FALSE., + DEXIT = 0.05625,$ + $FINSET1 + XLE = 1.608,1.69, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.117, + CHORD = 0.2,0.118, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE diff --git a/docs/examples/datcom/comparison_data/rockets/astra_provenance.json b/docs/examples/datcom/comparison_data/rockets/astra_provenance.json new file mode 100644 index 000000000..72ec739f6 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/astra_provenance.json @@ -0,0 +1,377 @@ +{ + "construction_cells": [ + 2, + 4, + 6, + 7, + 9, + 10, + 11 + ], + "calibration": { + "roughness_height": 0.0, + "leading_edge_radius": null + }, + "source_adaptations": [], + "mach": [ + 0.01, + 0.1, + 0.19, + 0.28, + 0.36, + 0.45, + 0.54, + 0.62, + 0.71, + 0.8, + 0.85, + 0.9, + 0.93, + 0.95, + 0.98, + 1.0, + 1.02, + 1.05, + 1.1, + 1.2 + ], + "alpha": [ + -20.0, + -10.0, + -5.0, + -2.0, + -1.0, + -0.5, + -0.25, + -0.1, + -0.05, + 0.0, + 0.05, + 0.1, + 0.25, + 0.5, + 1.0, + 2.0, + 5.0, + 10.0, + 20.0 + ], + "beta": [ + -20, + -5, + 0, + 5, + 20 + ], + "altitude_schedules_m_asl": { + "power_on": [ + 160.00568944515416, + 165.78636542818458, + 180.54643056026123, + 203.59906006701164, + 230.04232054401405, + 267.02948827625613, + 313.24015916727825, + 362.74009962388857, + 428.68845350807993, + 506.66196320223213, + 556.0137722339433, + 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"coverage": { + "scope": "rail through apogee", + "minimum_dynamic_pressure_pa": 100.0, + "excluded_low_q_samples": 103, + "phases": { + "power_on": { + "mach_min": 0.009843518362358866, + "mach_max": 0.9284111887033256, + "loaded_alpha_max_abs_deg": 8.3453781841579 + }, + "power_off": { + "mach_min": 0.038252507906137496, + "mach_max": 0.9190326723181211, + "loaded_alpha_max_abs_deg": 9.203367616092846 + } + } + }, + "reynolds": { + "power_on": [ + 219386.53250798205, + 2193865.3250798206, + 4168344.1176516586, + 6139882.06600433, + 7879897.387916496, + 9824968.765156282, + 11752604.692631569, + 13447754.426314173, + 15329098.025643794, + 17173988.84604192, + 18181243.906974923, + 19173588.618288156, + 19712408.490047555, + 20136331.25327438, + 20772215.398114625, + 21196138.161341455, + 21620060.924568284, + 22255945.06940853, + 23315751.977475602, + 25435365.793609746 + ], + "power_off": [ + 157289.53760377903, + 1580173.9357562566, + 3046183.0153484056, + 4589511.860860695, + 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b/docs/examples/datcom/comparison_data/rockets/astra_result.json @@ -0,0 +1,49 @@ +{ + "rocket": "astra", + "actual_agl_m": 3250.0, + "reported_agl_m": 3250.0, + "saved_first_agl_m": 3043.066, + "reference_kind": "notebook reported apogee", + "telemetry_max_agl_m": 3249.46, + "notes": "Telemetry maximum 3249.460 m differs from notebook reported value 3250.000 m; both retained. | Source fin trailing edge overhangs the nozzle-inferred body base by 1.216 mm. Native DATCOM accepts this; the integration now validates root-chord centre attachment instead of rejecting all overhang. Geometry is unchanged.", + "status": "paired untuned comparison", + "reason": "", + "ordinary_agl_m": 3043.3679728130164, + "ordinary_apogee_time_s": 24.008756329078313, + "ordinary_peak_vertical_velocity_m_s": 306.4797793783156, + "ordinary_initial_static_margin_cal": 3.16256293861153, + "ordinary_coast_cd_mach06": 0.6538147999999999, + "ordinary_max_mach": 0.892926042224684, + "ordinary_ascent_altitude_rmse_m": 222.49037923280986, + "source_adaptations": "", + "atmosphere_refinements": 1, + "atmosphere_last_apogee_change_m": -4.5530442029007645, + "datcom_agl_m": 3695.9340692825294, + "datcom_apogee_time_s": 26.805544357641757, + "datcom_peak_vertical_velocity_m_s": 318.5471261391443, + "datcom_initial_static_margin_cal": 3.477476916191179, + "datcom_coast_cd_mach06": 0.4265, + "datcom_max_mach": 0.92839198452266, + "datcom_ascent_altitude_rmse_m": 215.54458729050853, + "samples": 480, + "outside_mach": 0, + "outside_alpha": 0, + "outside_beta": 0, + "max_abs_alpha_deg": 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+1.1365333333333332,0.43,0.69048,0.43,0.7463839999999999 +1.1444666666666665,0.43,0.69167,0.43,0.747336 +1.1523999999999999,0.43,0.69286,0.43,0.748288 +1.1603333333333332,0.43,0.69405,0.43,0.74924 +1.1682666666666666,0.43,0.69524,0.43,0.750192 +1.1762,0.43,0.69643,0.43,0.751144 +1.1841333333333333,0.43,0.69762,0.43,0.752096 +1.1920666666666666,0.43,0.6988099999999999,0.43,0.753048 +1.2,0.43,0.7,0.43,0.754 diff --git a/docs/examples/datcom/comparison_data/rockets/bella_lui_coast_for005.dat b/docs/examples/datcom/comparison_data/rockets/bella_lui_coast_for005.dat new file mode 100644 index 000000000..2a145920e --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/bella_lui_coast_for005.dat @@ -0,0 +1,235 @@ +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.03,0.06,0.1,0.14,0.18,0.22,0.26,0.4,0.53,0.66,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 217562.995,652917.53,1307544.93,2185916.55,3074026.83,3975270.48, + 4897468.72,5832418.09,8972950.9,11889159.9,14805369.,17945901.8, + 20189139.5,21310758.4,21983729.7,22432377.3,22881024.8,23553996.1, + 24675615.,26918852.7, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -20.,$ + $REFQ + XCG = 1.54204584, + SREF = 0.0191134497, + LREF = 0.156,$ + $AXIBOD + TNOSE = OGIVE, + LNOSE = 0.242, + DNOSE = 0.156, + LCENTR = 2.3856, + DCENTR = 0.156, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.05, + DAFT = 0.135, + DEXIT = 0.,$ + $FINSET1 + XLE = 2.292,2.447, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.2, + CHORD = 0.28,0.125, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.03,0.06,0.1,0.14,0.18,0.22,0.26,0.4,0.53,0.66,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 217562.995,652917.53,1307544.93,2185916.55,3074026.83,3975270.48, + 4897468.72,5832418.09,8972950.9,11889159.9,14805369.,17945901.8, + 20189139.5,21310758.4,21983729.7,22432377.3,22881024.8,23553996.1, + 24675615.,26918852.7, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -5.,$ + $REFQ + XCG = 1.54204584, + SREF = 0.0191134497, + LREF = 0.156,$ + $AXIBOD + TNOSE = OGIVE, + LNOSE = 0.242, + DNOSE = 0.156, + LCENTR = 2.3856, + DCENTR = 0.156, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.05, + DAFT = 0.135, + DEXIT = 0.,$ + $FINSET1 + XLE = 2.292,2.447, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.2, + CHORD = 0.28,0.125, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.03,0.06,0.1,0.14,0.18,0.22,0.26,0.4,0.53,0.66,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 217562.995,652917.53,1307544.93,2185916.55,3074026.83,3975270.48, + 4897468.72,5832418.09,8972950.9,11889159.9,14805369.,17945901.8, + 20189139.5,21310758.4,21983729.7,22432377.3,22881024.8,23553996.1, + 24675615.,26918852.7, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 0.,$ + $REFQ + XCG = 1.54204584, + SREF = 0.0191134497, + LREF = 0.156,$ + $AXIBOD + TNOSE = OGIVE, + LNOSE = 0.242, + DNOSE = 0.156, + LCENTR = 2.3856, + DCENTR = 0.156, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.05, + DAFT = 0.135, + DEXIT = 0.,$ + $FINSET1 + XLE = 2.292,2.447, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.2, + CHORD = 0.28,0.125, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.03,0.06,0.1,0.14,0.18,0.22,0.26,0.4,0.53,0.66,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 217562.995,652917.53,1307544.93,2185916.55,3074026.83,3975270.48, + 4897468.72,5832418.09,8972950.9,11889159.9,14805369.,17945901.8, + 20189139.5,21310758.4,21983729.7,22432377.3,22881024.8,23553996.1, + 24675615.,26918852.7, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 5.,$ + $REFQ + XCG = 1.54204584, + SREF = 0.0191134497, + LREF = 0.156,$ + $AXIBOD + TNOSE = OGIVE, + LNOSE = 0.242, + DNOSE = 0.156, + LCENTR = 2.3856, + DCENTR = 0.156, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.05, + DAFT = 0.135, + DEXIT = 0.,$ + $FINSET1 + XLE = 2.292,2.447, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.2, + CHORD = 0.28,0.125, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.03,0.06,0.1,0.14,0.18,0.22,0.26,0.4,0.53,0.66,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 217562.995,652917.53,1307544.93,2185916.55,3074026.83,3975270.48, + 4897468.72,5832418.09,8972950.9,11889159.9,14805369.,17945901.8, + 20189139.5,21310758.4,21983729.7,22432377.3,22881024.8,23553996.1, + 24675615.,26918852.7, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 20.,$ + $REFQ + XCG = 1.54204584, + SREF = 0.0191134497, + LREF = 0.156,$ + $AXIBOD + TNOSE = OGIVE, + LNOSE = 0.242, + DNOSE = 0.156, + LCENTR = 2.3856, + DCENTR = 0.156, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.05, + DAFT = 0.135, + DEXIT = 0.,$ + $FINSET1 + XLE = 2.292,2.447, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.2, + CHORD = 0.28,0.125, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE diff --git a/docs/examples/datcom/comparison_data/rockets/bella_lui_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/bella_lui_powered_for005.dat new file mode 100644 index 000000000..af943a0a0 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/bella_lui_powered_for005.dat @@ -0,0 +1,235 @@ +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.03,0.06,0.1,0.14,0.18,0.22,0.26,0.4,0.53,0.66,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 226942.54,680764.355,1361089.51,2266748.06,3169766.8,4068954.55, + 4962044.48,5840986.88,8986133.67,11906627.1,14827120.5,17972267.3, + 20218800.7,21342067.5,22016027.5,22465334.2,22914640.8,23588600.9, + 24711867.6,26958401., + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -20.,$ + $REFQ + XCG = 1.57817594, + SREF = 0.0191134497, + LREF = 0.156,$ + $AXIBOD + TNOSE = OGIVE, + LNOSE = 0.242, + DNOSE = 0.156, + LCENTR = 2.3856, + DCENTR = 0.156, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.05, + DAFT = 0.135, + DEXIT = 0.0889,$ + $FINSET1 + XLE = 2.292,2.447, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.2, + CHORD = 0.28,0.125, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.03,0.06,0.1,0.14,0.18,0.22,0.26,0.4,0.53,0.66,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 226942.54,680764.355,1361089.51,2266748.06,3169766.8,4068954.55, + 4962044.48,5840986.88,8986133.67,11906627.1,14827120.5,17972267.3, + 20218800.7,21342067.5,22016027.5,22465334.2,22914640.8,23588600.9, + 24711867.6,26958401., + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -5.,$ + $REFQ + XCG = 1.57817594, + SREF = 0.0191134497, + LREF = 0.156,$ + $AXIBOD + TNOSE = OGIVE, + LNOSE = 0.242, + DNOSE = 0.156, + LCENTR = 2.3856, + DCENTR = 0.156, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.05, + DAFT = 0.135, + DEXIT = 0.0889,$ + $FINSET1 + XLE = 2.292,2.447, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.2, + CHORD = 0.28,0.125, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.03,0.06,0.1,0.14,0.18,0.22,0.26,0.4,0.53,0.66,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 226942.54,680764.355,1361089.51,2266748.06,3169766.8,4068954.55, + 4962044.48,5840986.88,8986133.67,11906627.1,14827120.5,17972267.3, + 20218800.7,21342067.5,22016027.5,22465334.2,22914640.8,23588600.9, + 24711867.6,26958401., + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 0.,$ + $REFQ + XCG = 1.57817594, + SREF = 0.0191134497, + LREF = 0.156,$ + $AXIBOD + TNOSE = OGIVE, + LNOSE = 0.242, + DNOSE = 0.156, + LCENTR = 2.3856, + DCENTR = 0.156, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.05, + DAFT = 0.135, + DEXIT = 0.0889,$ + $FINSET1 + XLE = 2.292,2.447, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.2, + CHORD = 0.28,0.125, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.03,0.06,0.1,0.14,0.18,0.22,0.26,0.4,0.53,0.66,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 226942.54,680764.355,1361089.51,2266748.06,3169766.8,4068954.55, + 4962044.48,5840986.88,8986133.67,11906627.1,14827120.5,17972267.3, + 20218800.7,21342067.5,22016027.5,22465334.2,22914640.8,23588600.9, + 24711867.6,26958401., + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 5.,$ + $REFQ + XCG = 1.57817594, + SREF = 0.0191134497, + LREF = 0.156,$ + $AXIBOD + TNOSE = OGIVE, + LNOSE = 0.242, + DNOSE = 0.156, + LCENTR = 2.3856, + DCENTR = 0.156, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.05, + DAFT = 0.135, + DEXIT = 0.0889,$ + $FINSET1 + XLE = 2.292,2.447, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.2, + CHORD = 0.28,0.125, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.03,0.06,0.1,0.14,0.18,0.22,0.26,0.4,0.53,0.66,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 226942.54,680764.355,1361089.51,2266748.06,3169766.8,4068954.55, + 4962044.48,5840986.88,8986133.67,11906627.1,14827120.5,17972267.3, + 20218800.7,21342067.5,22016027.5,22465334.2,22914640.8,23588600.9, + 24711867.6,26958401., + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 20.,$ + $REFQ + XCG = 1.57817594, + SREF = 0.0191134497, + LREF = 0.156,$ + $AXIBOD + TNOSE = OGIVE, + LNOSE = 0.242, + DNOSE = 0.156, + LCENTR = 2.3856, + DCENTR = 0.156, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.05, + DAFT = 0.135, + DEXIT = 0.0889,$ + $FINSET1 + XLE = 2.292,2.447, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.2, + CHORD = 0.28,0.125, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE diff --git a/docs/examples/datcom/comparison_data/rockets/bella_lui_provenance.json b/docs/examples/datcom/comparison_data/rockets/bella_lui_provenance.json new file mode 100644 index 000000000..7822f3cd1 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/bella_lui_provenance.json @@ -0,0 +1,384 @@ +{ + "construction_cells": [ + 2, + 3, + 6, + 9, + 11, + 14, + 15, + 16, + 19, + 21, + 23, + 25, + 26, + 27 + ], + "calibration": { + "roughness_height": 0.0, + "leading_edge_radius": null + }, + "source_adaptations": [], + "mach": [ + 0.01, + 0.03, + 0.06, + 0.1, + 0.14, + 0.18, + 0.22, + 0.26, + 0.4, + 0.53, + 0.66, + 0.8, + 0.9, + 0.95, + 0.98, + 1.0, + 1.02, + 1.05, + 1.1, + 1.2 + ], + "alpha": [ + -20.0, + -10.0, + -5.0, + -2.0, + -1.0, + -0.5, + -0.25, + -0.1, + -0.05, + 0.0, + 0.05, + 0.1, + 0.25, + 0.5, + 1.0, + 2.0, + 5.0, + 10.0, + 20.0 + ], + "beta": [ + -20, + -5, + 0, + 5, + 20 + ], + "altitude_schedules_m_asl": { + "power_on": [ + 407.09340131132757, + 408.13022189189536, + 411.7290199288868, + 420.25583440373305, + 433.1783419979694, + 450.8162446805547, + 474.1382311923281, + 515.0856559759113, + 515.0856559759113, + 515.0856559759113, + 515.0856559759113, + 515.0856559759113, + 515.0856559759113, + 515.0856559759113, + 515.0856559759113, + 515.0856559759113, + 515.0856559759113, + 515.0856559759113, + 515.0856559759113, + 515.0856559759113 + ], + "power_off": [ + 872.8764978702117, + 868.708287443951, + 853.1139822104624, + 816.5750232709552, + 762.8049258656072, + 692.9111355131549, + 608.5057120009741, + 530.1747942544757, + 530.1747942544757, + 530.1747942544757, + 530.1747942544757, + 530.1747942544757, + 530.1747942544757, + 530.1747942544757, + 530.1747942544757, + 530.1747942544757, + 530.1747942544757, + 530.1747942544757, + 530.1747942544757, + 530.1747942544757 + ] + }, + "atmosphere": { + "mach": [ + 0.01, + 0.03, + 0.06, + 0.1, + 0.14, + 0.18, + 0.22, + 0.26, + 0.4, + 0.53, + 0.66, + 0.8, + 0.9, + 0.95, + 0.98, + 1.0, + 1.02, + 1.05, + 1.1, + 1.2 + ], + "alpha": [ + -20.0, + -10.0, + -5.0, + -2.0, + -1.0, + -0.5, + -0.25, + -0.1, + -0.05, + 0.0, + 0.05, + 0.1, + 0.25, + 0.5, + 1.0, + 2.0, + 5.0, + 10.0, + 20.0 + ], + "coverage": { + "scope": "rail through apogee", + "minimum_dynamic_pressure_pa": 100.0, + "excluded_low_q_samples": 217, + "phases": { + "power_on": { + "mach_min": 0.0037139829466042024, + "mach_max": 0.2554077055513765, + "loaded_alpha_max_abs_deg": 4.593921574017928 + }, + "power_off": { + "mach_min": 0.013414250875176899, + "mach_max": 0.25255655168448327, + "loaded_alpha_max_abs_deg": 1.3575682608186768 + } + } + }, + "reynolds": { + "power_on": [ + 226942.53976398875, + 680764.3545598559, + 1361089.5145692232, + 2266748.061953287, + 3169766.7960955896, + 4068954.554525353, + 4962044.48014376, + 5840986.882925799, + 8986133.66603969, + 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roughness inferred", + "fin_assumption": "2% HEX, 5% bevels, sharp edges, auto body attachment", + "powered_base_drag": "nozzle", + "damp": true +} \ No newline at end of file diff --git a/docs/examples/datcom/comparison_data/rockets/bella_lui_result.json b/docs/examples/datcom/comparison_data/rockets/bella_lui_result.json new file mode 100644 index 000000000..f71a83c7b --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/bella_lui_result.json @@ -0,0 +1,49 @@ +{ + "rocket": "bella_lui", + "actual_agl_m": 458.971, + "reported_agl_m": null, + "saved_first_agl_m": 460.54, + "reference_kind": "telemetry AGL maximum (QA)", + "telemetry_max_agl_m": 458.971, + "notes": "", + "status": "paired untuned comparison", + "reason": "", + "ordinary_agl_m": 460.57389565932874, + "ordinary_apogee_time_s": 10.607916503445066, + "ordinary_peak_vertical_velocity_m_s": 86.17870117421616, + "ordinary_initial_static_margin_cal": 2.776966165584537, + "ordinary_coast_cd_mach06": 0.43, + 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Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.", + "ordinary_agl_m": 3004.10382797431, + "ordinary_apogee_time_s": 23.03691562903868, + "ordinary_peak_vertical_velocity_m_s": 327.4402911475654, + "ordinary_initial_static_margin_cal": 3.7616831285769776, + "ordinary_coast_cd_mach06": 0.7036, + "ordinary_max_mach": 0.9672822932315445, + "ordinary_ascent_altitude_rmse_m": 133.14444925478682, + "source_adaptations": "", + "ordinary_error_m": -10.896172025689793, + "ordinary_error_pct": -0.36139874048722365, + "telemetry_qa_max_agl_m": 3015.996337890625, + "telemetry_qa_rejected_count": 0, + "ordinary_vs_telemetry_pct": -0.3943144680551014, + "rmse_start_s": 0, + "rmse_end_s": 23.03691562903868 +} \ No newline at end of file diff --git a/docs/examples/datcom/comparison_data/rockets/camoes_telemetry.csv b/docs/examples/datcom/comparison_data/rockets/camoes_telemetry.csv new file mode 100644 index 000000000..bf9b9f3be --- 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+1.1841333333333333,0.643,0.46198133333333335,0.638,0.5434573333333333 +1.1920666666666666,0.643,0.4584906666666667,0.638,0.5397286666666667 +1.2,0.643,0.455,0.638,0.536 diff --git a/docs/examples/datcom/comparison_data/rockets/cavour_coast_for005.dat b/docs/examples/datcom/comparison_data/rockets/cavour_coast_for005.dat new file mode 100644 index 000000000..1d1dc423f --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/cavour_coast_for005.dat @@ -0,0 +1,220 @@ +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 167211.604,1676007.18,3228170.,4846401.67,6351255.19,8134788.81, + 10021932.6,11760337.7,13782796.6,14963614.6,15950771.7,17245463.7, + 18567246.6,19748912.8,20372562.7,20788329.3,21204095.9,21827745.8, + 22867162.2,24945995.2, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -20.,$ + $REFQ + XCG = 1.523, + SREF = 0.00849486654, + LREF = 0.104,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.52, + DNOSE = 0.104, + LCENTR = 2.2024, + DCENTR = 0.104, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 2.512,2.642, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.1, + CHORD = 0.2,0.07, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 167211.604,1676007.18,3228170.,4846401.67,6351255.19,8134788.81, + 10021932.6,11760337.7,13782796.6,14963614.6,15950771.7,17245463.7, + 18567246.6,19748912.8,20372562.7,20788329.3,21204095.9,21827745.8, + 22867162.2,24945995.2, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -5.,$ + $REFQ + XCG = 1.523, + SREF = 0.00849486654, + LREF = 0.104,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.52, + DNOSE = 0.104, + LCENTR = 2.2024, + DCENTR = 0.104, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 2.512,2.642, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.1, + CHORD = 0.2,0.07, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 167211.604,1676007.18,3228170.,4846401.67,6351255.19,8134788.81, + 10021932.6,11760337.7,13782796.6,14963614.6,15950771.7,17245463.7, + 18567246.6,19748912.8,20372562.7,20788329.3,21204095.9,21827745.8, + 22867162.2,24945995.2, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 0.,$ + $REFQ + XCG = 1.523, + SREF = 0.00849486654, + LREF = 0.104,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.52, + DNOSE = 0.104, + LCENTR = 2.2024, + DCENTR = 0.104, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 2.512,2.642, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.1, + CHORD = 0.2,0.07, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 167211.604,1676007.18,3228170.,4846401.67,6351255.19,8134788.81, + 10021932.6,11760337.7,13782796.6,14963614.6,15950771.7,17245463.7, + 18567246.6,19748912.8,20372562.7,20788329.3,21204095.9,21827745.8, + 22867162.2,24945995.2, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 5.,$ + $REFQ + XCG = 1.523, + SREF = 0.00849486654, + LREF = 0.104,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.52, + DNOSE = 0.104, + LCENTR = 2.2024, + DCENTR = 0.104, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 2.512,2.642, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.1, + CHORD = 0.2,0.07, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 167211.604,1676007.18,3228170.,4846401.67,6351255.19,8134788.81, + 10021932.6,11760337.7,13782796.6,14963614.6,15950771.7,17245463.7, + 18567246.6,19748912.8,20372562.7,20788329.3,21204095.9,21827745.8, + 22867162.2,24945995.2, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 20.,$ + $REFQ + XCG = 1.523, + SREF = 0.00849486654, + LREF = 0.104,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.52, + DNOSE = 0.104, + LCENTR = 2.2024, + DCENTR = 0.104, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 2.512,2.642, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.1, + CHORD = 0.2,0.07, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE diff --git a/docs/examples/datcom/comparison_data/rockets/cavour_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/cavour_powered_for005.dat new file mode 100644 index 000000000..1c237bc42 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/cavour_powered_for005.dat @@ -0,0 +1,220 @@ +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 222100.421,2221004.21,4219406.88,6208481.09,7967954.04,9934912.59, + 11883274.2,13593972.5,15476239.1,16490617.7,17285641.8,18252553., + 19158475.2,19955866.2,20586051.4,21006174.9,21426298.4,22056483.7, + 23106792.4,25207409.9, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -20.,$ + $REFQ + XCG = 1.71175909, + SREF = 0.00849486654, + LREF = 0.104,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.52, + DNOSE = 0.104, + LCENTR = 2.2024, + DCENTR = 0.104, + BASE = .FALSE., + DEXIT = 0.067,$ + $FINSET1 + XLE = 2.512,2.642, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.1, + CHORD = 0.2,0.07, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 222100.421,2221004.21,4219406.88,6208481.09,7967954.04,9934912.59, + 11883274.2,13593972.5,15476239.1,16490617.7,17285641.8,18252553., + 19158475.2,19955866.2,20586051.4,21006174.9,21426298.4,22056483.7, + 23106792.4,25207409.9, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -5.,$ + $REFQ + XCG = 1.71175909, + SREF = 0.00849486654, + LREF = 0.104,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.52, + DNOSE = 0.104, + LCENTR = 2.2024, + DCENTR = 0.104, + BASE = .FALSE., + DEXIT = 0.067,$ + $FINSET1 + XLE = 2.512,2.642, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.1, + CHORD = 0.2,0.07, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 222100.421,2221004.21,4219406.88,6208481.09,7967954.04,9934912.59, + 11883274.2,13593972.5,15476239.1,16490617.7,17285641.8,18252553., + 19158475.2,19955866.2,20586051.4,21006174.9,21426298.4,22056483.7, + 23106792.4,25207409.9, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 0.,$ + $REFQ + XCG = 1.71175909, + SREF = 0.00849486654, + LREF = 0.104,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.52, + DNOSE = 0.104, + LCENTR = 2.2024, + DCENTR = 0.104, + BASE = .FALSE., + DEXIT = 0.067,$ + $FINSET1 + XLE = 2.512,2.642, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.1, + CHORD = 0.2,0.07, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 222100.421,2221004.21,4219406.88,6208481.09,7967954.04,9934912.59, + 11883274.2,13593972.5,15476239.1,16490617.7,17285641.8,18252553., + 19158475.2,19955866.2,20586051.4,21006174.9,21426298.4,22056483.7, + 23106792.4,25207409.9, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 5.,$ + $REFQ + XCG = 1.71175909, + SREF = 0.00849486654, + LREF = 0.104,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.52, + DNOSE = 0.104, + LCENTR = 2.2024, + DCENTR = 0.104, + BASE = .FALSE., + DEXIT = 0.067,$ + $FINSET1 + XLE = 2.512,2.642, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.1, + CHORD = 0.2,0.07, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 222100.421,2221004.21,4219406.88,6208481.09,7967954.04,9934912.59, + 11883274.2,13593972.5,15476239.1,16490617.7,17285641.8,18252553., + 19158475.2,19955866.2,20586051.4,21006174.9,21426298.4,22056483.7, + 23106792.4,25207409.9, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 20.,$ + $REFQ + XCG = 1.71175909, + SREF = 0.00849486654, + LREF = 0.104,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.52, + DNOSE = 0.104, + LCENTR = 2.2024, + DCENTR = 0.104, + BASE = .FALSE., + DEXIT = 0.067,$ + $FINSET1 + XLE = 2.512,2.642, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.1, + CHORD = 0.2,0.07, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE diff --git a/docs/examples/datcom/comparison_data/rockets/cavour_provenance.json b/docs/examples/datcom/comparison_data/rockets/cavour_provenance.json new file mode 100644 index 000000000..4a8ffbb6e --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/cavour_provenance.json @@ -0,0 +1,377 @@ +{ + "construction_cells": [ + 2, + 4, + 6, + 7, + 10, + 11, + 12 + ], + "calibration": { + "roughness_height": 0.0, + "leading_edge_radius": null + }, + "source_adaptations": [], + "mach": [ + 0.01, + 0.1, + 0.19, + 0.28, + 0.36, + 0.45, + 0.54, + 0.62, + 0.71, + 0.76, + 0.8, + 0.85, + 0.9, + 0.95, + 0.98, + 1.0, + 1.02, + 1.05, + 1.1, + 1.2 + ], + "alpha": [ + -20.0, + -10.0, + -5.0, + -2.0, + -1.0, + -0.5, + -0.25, + -0.1, + -0.05, + 0.0, + 0.05, + 0.1, + 0.25, + 0.5, + 1.0, + 2.0, + 5.0, + 10.0, + 20.0 + ], + "beta": [ + -20, + -5, + 0, + 5, + 20 + ], + "altitude_schedules_m_asl": { + "power_on": [ + 150.0172648941506, + 155.19891748305702, + 168.77521437579958, + 190.41240581880967, + 215.63077897256557, + 250.7350471274281, + 295.8472510931551, + 346.46392793549046, + 418.53070996801307, + 467.6333288569512, + 512.6595787711927, + 578.569848429699, + 661.1284540255455, + 777.8283154950471, + 777.8283154950471, + 777.8283154950471, + 777.8283154950471, + 777.8283154950471, + 777.8283154950471, + 777.8283154950471 + ], + "power_off": [ + 3404.330908520737, + 3381.438499756303, + 3245.853362579416, + 3040.2967896839286, + 2811.656197055082, + 2518.7994641014907, + 2203.5525351914284, + 1912.7382162505069, + 1582.7792755632702, + 1402.616611369774, + 1262.0481596008397, + 1096.086346489152, + 941.604524791115, + 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+ "rocket": "defiance", + "actual_agl_m": 9308.318, + "reported_agl_m": 9308.318, + "saved_first_agl_m": 9238.009, + "reference_kind": "notebook reported apogee", + "telemetry_max_agl_m": null, + "notes": "Source fin root centre is z=-0.025 m, aft of the tail base z=0.0403 m; native root attachment is outside the declared body.", + "status": "ordinary only", + "reason": "ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip", + "ordinary_agl_m": 9243.891367330605, + "ordinary_apogee_time_s": 44.18119446865966, + "ordinary_peak_vertical_velocity_m_s": 438.71139566437614, + "ordinary_initial_static_margin_cal": 7.9601902493275025, + "ordinary_coast_cd_mach06": 0.668, + "ordinary_max_mach": 1.3681759374499065, + "source_adaptations": "", + "ordinary_error_m": -64.42663266939417, + "ordinary_error_pct": -0.6921404347100536 +} \ No newline at end of file diff --git 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+1.1762,0.558,0.460664,0.558,0.49114 +1.1841333333333333,0.558,0.45844266666666666,0.558,0.48876000000000003 +1.1920666666666666,0.558,0.45622133333333337,0.558,0.48638 +1.2,0.558,0.454,0.558,0.484 diff --git a/docs/examples/datcom/comparison_data/rockets/erebus_coast_for005.dat b/docs/examples/datcom/comparison_data/rockets/erebus_coast_for005.dat new file mode 100644 index 000000000..eee4aafe8 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/erebus_coast_for005.dat @@ -0,0 +1,235 @@ +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.91,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 164074.338,1647285.62,3170281.03,4766400.34,6269640.6,8065525.32, + 9980063.03,11778654.9,13913918.9,16294492.5,17634541.2,18849684.6, + 19059125.6,19896889.3,20525212.2,20944094.,21362975.9,21991298.7, + 23038503.4,25132912.8, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -20.,$ + $REFQ + XCG = 1.1921, + SREF = 0.00657554977, + LREF = 0.0915,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.391113, + DNOSE = 0.0915, + LCENTR = 1.7022, + DCENTR = 0.0915, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.079119, + DAFT = 0.0715, + DEXIT = 0.,$ + $FINSET1 + XLE = 1.988313,2.058313, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.08, + CHORD = 0.12,0.05, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.91,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 164074.338,1647285.62,3170281.03,4766400.34,6269640.6,8065525.32, + 9980063.03,11778654.9,13913918.9,16294492.5,17634541.2,18849684.6, + 19059125.6,19896889.3,20525212.2,20944094.,21362975.9,21991298.7, + 23038503.4,25132912.8, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -5.,$ + $REFQ + XCG = 1.1921, + SREF = 0.00657554977, + LREF = 0.0915,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.391113, + DNOSE = 0.0915, + LCENTR = 1.7022, + DCENTR = 0.0915, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.079119, + DAFT = 0.0715, + DEXIT = 0.,$ + $FINSET1 + XLE = 1.988313,2.058313, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.08, + CHORD = 0.12,0.05, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.91,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 164074.338,1647285.62,3170281.03,4766400.34,6269640.6,8065525.32, + 9980063.03,11778654.9,13913918.9,16294492.5,17634541.2,18849684.6, + 19059125.6,19896889.3,20525212.2,20944094.,21362975.9,21991298.7, + 23038503.4,25132912.8, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 0.,$ + $REFQ + XCG = 1.1921, + SREF = 0.00657554977, + LREF = 0.0915,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.391113, + DNOSE = 0.0915, + LCENTR = 1.7022, + DCENTR = 0.0915, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.079119, + DAFT = 0.0715, + DEXIT = 0.,$ + $FINSET1 + XLE = 1.988313,2.058313, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.08, + CHORD = 0.12,0.05, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.91,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 164074.338,1647285.62,3170281.03,4766400.34,6269640.6,8065525.32, + 9980063.03,11778654.9,13913918.9,16294492.5,17634541.2,18849684.6, + 19059125.6,19896889.3,20525212.2,20944094.,21362975.9,21991298.7, + 23038503.4,25132912.8, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 5.,$ + $REFQ + XCG = 1.1921, + SREF = 0.00657554977, + LREF = 0.0915,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.391113, + DNOSE = 0.0915, + LCENTR = 1.7022, + DCENTR = 0.0915, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.079119, + DAFT = 0.0715, + DEXIT = 0.,$ + $FINSET1 + XLE = 1.988313,2.058313, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.08, + CHORD = 0.12,0.05, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.91,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 164074.338,1647285.62,3170281.03,4766400.34,6269640.6,8065525.32, + 9980063.03,11778654.9,13913918.9,16294492.5,17634541.2,18849684.6, + 19059125.6,19896889.3,20525212.2,20944094.,21362975.9,21991298.7, + 23038503.4,25132912.8, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 20.,$ + $REFQ + XCG = 1.1921, + SREF = 0.00657554977, + LREF = 0.0915,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.391113, + DNOSE = 0.0915, + LCENTR = 1.7022, + DCENTR = 0.0915, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.079119, + DAFT = 0.0715, + DEXIT = 0.,$ + $FINSET1 + XLE = 1.988313,2.058313, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.08, + CHORD = 0.12,0.05, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE diff --git a/docs/examples/datcom/comparison_data/rockets/erebus_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/erebus_powered_for005.dat new file mode 100644 index 000000000..5ee45f987 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/erebus_powered_for005.dat @@ -0,0 +1,235 @@ +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.91,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 221102.091,2210273.75,4195660.39,6173500.16,7921897.53,9873835.71, + 11804956.5,13500488.,15382253.3,17224554.9,18215335.3,19053435.3, + 19229196.,20074435.3,20708364.9,21130984.6,21553604.3,22187533.8, + 23244083.,25357181.5, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -20.,$ + $REFQ + XCG = 1.30643685, + SREF = 0.00657554977, + LREF = 0.0915,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.391113, + DNOSE = 0.0915, + LCENTR = 1.7022, + DCENTR = 0.0915, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.079119, + DAFT = 0.0715, + DEXIT = 0.0405,$ + $FINSET1 + XLE = 1.988313,2.058313, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.08, + CHORD = 0.12,0.05, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.91,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 221102.091,2210273.75,4195660.39,6173500.16,7921897.53,9873835.71, + 11804956.5,13500488.,15382253.3,17224554.9,18215335.3,19053435.3, + 19229196.,20074435.3,20708364.9,21130984.6,21553604.3,22187533.8, + 23244083.,25357181.5, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -5.,$ + $REFQ + XCG = 1.30643685, + SREF = 0.00657554977, + LREF = 0.0915,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.391113, + DNOSE = 0.0915, + LCENTR = 1.7022, + DCENTR = 0.0915, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.079119, + DAFT = 0.0715, + DEXIT = 0.0405,$ + $FINSET1 + XLE = 1.988313,2.058313, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.08, + CHORD = 0.12,0.05, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.91,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 221102.091,2210273.75,4195660.39,6173500.16,7921897.53,9873835.71, + 11804956.5,13500488.,15382253.3,17224554.9,18215335.3,19053435.3, + 19229196.,20074435.3,20708364.9,21130984.6,21553604.3,22187533.8, + 23244083.,25357181.5, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 0.,$ + $REFQ + XCG = 1.30643685, + SREF = 0.00657554977, + LREF = 0.0915,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.391113, + DNOSE = 0.0915, + LCENTR = 1.7022, + DCENTR = 0.0915, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.079119, + DAFT = 0.0715, + DEXIT = 0.0405,$ + $FINSET1 + XLE = 1.988313,2.058313, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.08, + CHORD = 0.12,0.05, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.91,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 221102.091,2210273.75,4195660.39,6173500.16,7921897.53,9873835.71, + 11804956.5,13500488.,15382253.3,17224554.9,18215335.3,19053435.3, + 19229196.,20074435.3,20708364.9,21130984.6,21553604.3,22187533.8, + 23244083.,25357181.5, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 5.,$ + $REFQ + XCG = 1.30643685, + SREF = 0.00657554977, + LREF = 0.0915,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.391113, + DNOSE = 0.0915, + LCENTR = 1.7022, + DCENTR = 0.0915, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.079119, + DAFT = 0.0715, + DEXIT = 0.0405,$ + $FINSET1 + XLE = 1.988313,2.058313, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.08, + CHORD = 0.12,0.05, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.91,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 221102.091,2210273.75,4195660.39,6173500.16,7921897.53,9873835.71, + 11804956.5,13500488.,15382253.3,17224554.9,18215335.3,19053435.3, + 19229196.,20074435.3,20708364.9,21130984.6,21553604.3,22187533.8, + 23244083.,25357181.5, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 20.,$ + $REFQ + XCG = 1.30643685, + SREF = 0.00657554977, + LREF = 0.0915,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.391113, + DNOSE = 0.0915, + LCENTR = 1.7022, + DCENTR = 0.0915, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.079119, + DAFT = 0.0715, + DEXIT = 0.0405,$ + $FINSET1 + XLE = 1.988313,2.058313, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.08, + CHORD = 0.12,0.05, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE diff --git a/docs/examples/datcom/comparison_data/rockets/erebus_provenance.json b/docs/examples/datcom/comparison_data/rockets/erebus_provenance.json new file mode 100644 index 000000000..bb80f1296 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/erebus_provenance.json @@ -0,0 +1,377 @@ +{ + "construction_cells": [ + 2, + 4, + 7, + 8, + 11, + 12, + 13 + ], + "calibration": { + "roughness_height": 0.0, + "leading_edge_radius": null + }, + "source_adaptations": [], + "mach": [ + 0.01, + 0.1, + 0.19, + 0.28, + 0.36, + 0.45, + 0.54, + 0.62, + 0.71, + 0.8, + 0.85, + 0.9, + 0.91, + 0.95, + 0.98, + 1.0, + 1.02, + 1.05, + 1.1, + 1.2 + ], + "alpha": [ + -20.0, + -10.0, + -5.0, + -2.0, + -1.0, + -0.5, + -0.25, + -0.1, + -0.05, + 0.0, + 0.05, + 0.1, + 0.25, + 0.5, + 1.0, + 2.0, + 5.0, + 10.0, + 20.0 + ], + "beta": [ + -20, + -5, + 0, + 5, + 20 + ], + "altitude_schedules_m_asl": { + "power_on": [ + 160.00095154517743, + 164.34974556148535, + 176.17236200922045, + 196.07696363758254, + 221.05737466286567, + 257.9304143164693, + 304.90308878529163, + 356.04139138656797, + 425.59662541003655, + 510.64816801513143, + 574.4731104692468, + 739.5192613290584, + 764.7149768482109, + 764.7149768482109, + 764.7149768482109, + 764.7149768482109, + 764.7149768482109, + 764.7149768482109, + 764.7149768482109, + 764.7149768482109 + ], + "power_off": [ + 3418.2660948041307, + 3375.3198069851596, + 3235.455152110242, + 3023.0162896642523, + 2786.34693227908, + 2482.2344621305724, + 2153.6311901024837, + 1850.086774687481, + 1507.631467305463, + 1177.8037345938974, + 1001.1510469573618, + 880.3018862889387, + 880.3018862889387, + 880.3018862889387, + 880.3018862889387, + 880.3018862889387, + 880.3018862889387, + 880.3018862889387, + 880.3018862889387, + 880.3018862889387 + ] + }, + "atmosphere": { + "mach": [ + 0.01, + 0.1, + 0.19, + 0.28, + 0.36, + 0.45, + 0.54, + 0.62, + 0.71, + 0.8, + 0.85, + 0.9, + 0.91, + 0.95, + 0.98, + 1.0, + 1.02, + 1.05, + 1.1, + 1.2 + ], + "alpha": [ + -20.0, + -10.0, + -5.0, + -2.0, + -1.0, + -0.5, + -0.25, + -0.1, + -0.05, + 0.0, + 0.05, + 0.1, + 0.25, + 0.5, + 1.0, + 2.0, + 5.0, + 10.0, + 20.0 + ], + "coverage": { + "scope": "rail through apogee", + "minimum_dynamic_pressure_pa": 100.0, + "excluded_low_q_samples": 26, + "phases": { + "power_on": { + "mach_min": 0.009998526557907488, + "mach_max": 0.9025932801202174, + "loaded_alpha_max_abs_deg": 14.878745483203963 + }, + "power_off": { + "mach_min": 0.04714249998424351, + "mach_max": 0.8856413442299294, + "loaded_alpha_max_abs_deg": 1.514186766190916 + } + } + }, + "reynolds": { + "power_on": [ + 221102.0905066154, + 2210273.7458047965, + 4195660.3915424645, + 6173500.1609207885, + 7921897.527853039, + 9873835.713757465, + 11804956.516484978, + 13500487.974096823, + 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a/docs/examples/datcom/comparison_data/rockets/genesis_coast_for005.dat b/docs/examples/datcom/comparison_data/rockets/genesis_coast_for005.dat new file mode 100644 index 000000000..e1e206edb --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/genesis_coast_for005.dat @@ -0,0 +1,220 @@ +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.88,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 163701.844,1639225.58,3157761.99,4748676.81,6239121.16,8017171.15, + 9919543.25,11732390.8,13890873.6,15125507.8,16131105.6,18100455.2, + 18511829.2,19540264.2,20157325.1,20568699.1,20980073.1,21597134.1, + 22625569.,24682438.9, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -20.,$ + $REFQ + XCG = 1.477, + SREF = 0.00693977817, + LREF = 0.094,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.27, + DNOSE = 0.094, + LCENTR = 2.102, + DCENTR = 0.094, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 2.122,2.212, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.105, + CHORD = 0.2,0.11, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.88,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 163701.844,1639225.58,3157761.99,4748676.81,6239121.16,8017171.15, + 9919543.25,11732390.8,13890873.6,15125507.8,16131105.6,18100455.2, + 18511829.2,19540264.2,20157325.1,20568699.1,20980073.1,21597134.1, + 22625569.,24682438.9, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -5.,$ + $REFQ + XCG = 1.477, + SREF = 0.00693977817, + LREF = 0.094,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.27, + DNOSE = 0.094, + LCENTR = 2.102, + DCENTR = 0.094, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 2.122,2.212, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.105, + CHORD = 0.2,0.11, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.88,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 163701.844,1639225.58,3157761.99,4748676.81,6239121.16,8017171.15, + 9919543.25,11732390.8,13890873.6,15125507.8,16131105.6,18100455.2, + 18511829.2,19540264.2,20157325.1,20568699.1,20980073.1,21597134.1, + 22625569.,24682438.9, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 0.,$ + $REFQ + XCG = 1.477, + SREF = 0.00693977817, + LREF = 0.094,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.27, + DNOSE = 0.094, + LCENTR = 2.102, + DCENTR = 0.094, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 2.122,2.212, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.105, + CHORD = 0.2,0.11, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.88,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 163701.844,1639225.58,3157761.99,4748676.81,6239121.16,8017171.15, + 9919543.25,11732390.8,13890873.6,15125507.8,16131105.6,18100455.2, + 18511829.2,19540264.2,20157325.1,20568699.1,20980073.1,21597134.1, + 22625569.,24682438.9, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 5.,$ + $REFQ + XCG = 1.477, + SREF = 0.00693977817, + LREF = 0.094,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.27, + DNOSE = 0.094, + LCENTR = 2.102, + DCENTR = 0.094, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 2.122,2.212, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.105, + CHORD = 0.2,0.11, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.88,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 163701.844,1639225.58,3157761.99,4748676.81,6239121.16,8017171.15, + 9919543.25,11732390.8,13890873.6,15125507.8,16131105.6,18100455.2, + 18511829.2,19540264.2,20157325.1,20568699.1,20980073.1,21597134.1, + 22625569.,24682438.9, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 20.,$ + $REFQ + XCG = 1.477, + SREF = 0.00693977817, + LREF = 0.094,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.27, + DNOSE = 0.094, + LCENTR = 2.102, + DCENTR = 0.094, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 2.122,2.212, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.105, + CHORD = 0.2,0.11, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE diff --git a/docs/examples/datcom/comparison_data/rockets/genesis_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/genesis_powered_for005.dat new file mode 100644 index 000000000..8db37a1dc --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/genesis_powered_for005.dat @@ -0,0 +1,220 @@ +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.88,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 215533.965,2154491.63,4089332.39,6016560.93,7721014.87,9625016.33, + 11508645.7,13159564.5,14979778.7,15968340.6,16743697.2,18197336., + 18610911.9,19644851.4,20265215.1,20678791.,21092366.8,21712730.5, + 22746670.,24814549.1, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -20.,$ + $REFQ + XCG = 1.58224212, + SREF = 0.00693977817, + LREF = 0.094,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.27, + DNOSE = 0.094, + LCENTR = 2.102, + DCENTR = 0.094, + BASE = .FALSE., + DEXIT = 0.084708,$ + $FINSET1 + XLE = 2.122,2.212, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.105, + CHORD = 0.2,0.11, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.88,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 215533.965,2154491.63,4089332.39,6016560.93,7721014.87,9625016.33, + 11508645.7,13159564.5,14979778.7,15968340.6,16743697.2,18197336., + 18610911.9,19644851.4,20265215.1,20678791.,21092366.8,21712730.5, + 22746670.,24814549.1, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -5.,$ + $REFQ + XCG = 1.58224212, + SREF = 0.00693977817, + LREF = 0.094,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.27, + DNOSE = 0.094, + LCENTR = 2.102, + DCENTR = 0.094, + BASE = .FALSE., + DEXIT = 0.084708,$ + $FINSET1 + XLE = 2.122,2.212, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.105, + CHORD = 0.2,0.11, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.88,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 215533.965,2154491.63,4089332.39,6016560.93,7721014.87,9625016.33, + 11508645.7,13159564.5,14979778.7,15968340.6,16743697.2,18197336., + 18610911.9,19644851.4,20265215.1,20678791.,21092366.8,21712730.5, + 22746670.,24814549.1, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 0.,$ + $REFQ + XCG = 1.58224212, + SREF = 0.00693977817, + LREF = 0.094,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.27, + DNOSE = 0.094, + LCENTR = 2.102, + DCENTR = 0.094, + BASE = .FALSE., + DEXIT = 0.084708,$ + $FINSET1 + XLE = 2.122,2.212, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.105, + CHORD = 0.2,0.11, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.88,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 215533.965,2154491.63,4089332.39,6016560.93,7721014.87,9625016.33, + 11508645.7,13159564.5,14979778.7,15968340.6,16743697.2,18197336., + 18610911.9,19644851.4,20265215.1,20678791.,21092366.8,21712730.5, + 22746670.,24814549.1, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 5.,$ + $REFQ + XCG = 1.58224212, + SREF = 0.00693977817, + LREF = 0.094,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.27, + DNOSE = 0.094, + LCENTR = 2.102, + DCENTR = 0.094, + BASE = .FALSE., + DEXIT = 0.084708,$ + $FINSET1 + XLE = 2.122,2.212, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.105, + CHORD = 0.2,0.11, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.88,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 215533.965,2154491.63,4089332.39,6016560.93,7721014.87,9625016.33, + 11508645.7,13159564.5,14979778.7,15968340.6,16743697.2,18197336., + 18610911.9,19644851.4,20265215.1,20678791.,21092366.8,21712730.5, + 22746670.,24814549.1, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 20.,$ + $REFQ + XCG = 1.58224212, + SREF = 0.00693977817, + LREF = 0.094,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.27, + DNOSE = 0.094, + LCENTR = 2.102, + DCENTR = 0.094, + BASE = .FALSE., + DEXIT = 0.084708,$ + $FINSET1 + XLE = 2.122,2.212, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.105, + CHORD = 0.2,0.11, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE diff --git a/docs/examples/datcom/comparison_data/rockets/genesis_provenance.json b/docs/examples/datcom/comparison_data/rockets/genesis_provenance.json new file mode 100644 index 000000000..2d9cb8f50 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/genesis_provenance.json @@ -0,0 +1,378 @@ +{ + "construction_cells": [ + 2, + 4, + 6, + 7, + 9, + 10, + 11, + 12 + ], + "calibration": { + "roughness_height": 0.0, + "leading_edge_radius": null + }, + "source_adaptations": [], + "mach": [ + 0.01, + 0.1, + 0.19, + 0.28, + 0.36, + 0.45, + 0.54, + 0.62, + 0.71, + 0.76, + 0.8, + 0.88, + 0.9, + 0.95, + 0.98, + 1.0, + 1.02, + 1.05, + 1.1, + 1.2 + ], + "alpha": [ + -20.0, + -10.0, + -5.0, + -2.0, + -1.0, + -0.5, + -0.25, + -0.1, + -0.05, + 0.0, + 0.05, + 0.1, + 0.25, + 0.5, + 1.0, + 2.0, + 5.0, + 10.0, + 20.0 + ], + "beta": [ + -20, + -5, + 0, + 5, + 20 + ], + "altitude_schedules_m_asl": { + "power_on": [ + 160.0247955790327, + 165.85298582979988, + 181.04929787038714, + 205.15179124522626, + 232.93308241359887, + 272.9771469280038, + 325.5346134980463, + 385.29120458355766, + 471.8216168950038, + 531.3281861136722, + 586.7329763637598, + 753.4989633756181, + 753.4989633756181, + 753.4989633756181, + 753.4989633756181, + 753.4989633756181, + 753.4989633756181, + 753.4989633756181, + 753.4989633756181, + 753.4989633756181 + ], + "power_off": [ + 3459.3277882005946, + 3445.733476857217, + 3305.4835319490408, + 3089.0056822645884, + 2843.159132607208, + 2521.864558999702, + 2170.2520166922727, + 1844.8651070835317, + 1471.808908246068, + 1262.3759875582102, + 1094.758172727381, + 826.3078060534194, + 826.3078060534194, + 826.3078060534194, + 826.3078060534194, + 826.3078060534194, + 826.3078060534194, + 826.3078060534194, + 826.3078060534194, + 826.3078060534194 + ] + }, + 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+1.0333999999999999,0.45,0.47971999999999987,0.45,0.4847199999999999 +1.0413333333333332,0.45,0.48606666666666654,0.45,0.49106666666666654 +1.0492666666666666,0.45,0.4924133333333332,0.45,0.4974133333333332 +1.0572,0.45,0.491416,0.45,0.496416 +1.0651333333333333,0.45,0.4896706666666667,0.45,0.4946706666666667 +1.0730666666666666,0.45,0.4879253333333333,0.45,0.4929253333333333 +1.081,0.45,0.48618,0.45,0.49118 +1.088933333333333,0.45,0.48443466666666674,0.45,0.48943466666666674 +1.0968666666666664,0.45,0.4826893333333334,0.45,0.4876893333333334 +1.1047999999999998,0.45,0.48080000000000006,0.45,0.4857520000000001 +1.1127333333333331,0.45,0.4788166666666667,0.45,0.4836893333333334 +1.1206666666666665,0.45,0.4768333333333334,0.45,0.4816266666666667 +1.1285999999999998,0.45,0.47485000000000005,0.45,0.47956400000000005 +1.1365333333333332,0.45,0.4728666666666667,0.45,0.4775013333333334 +1.1444666666666665,0.45,0.4708833333333334,0.45,0.47543866666666673 +1.1523999999999999,0.45,0.46890000000000004,0.45,0.473376 +1.1603333333333332,0.45,0.4669166666666667,0.45,0.47131333333333336 +1.1682666666666666,0.45,0.46493333333333337,0.45,0.4692506666666667 +1.1762,0.45,0.46295000000000003,0.45,0.46718800000000005 +1.1841333333333333,0.45,0.4609666666666667,0.45,0.4651253333333334 +1.1920666666666666,0.45,0.45898333333333335,0.45,0.4630626666666667 +1.2,0.45,0.457,0.45,0.461 diff --git a/docs/examples/datcom/comparison_data/rockets/halcyon_coast_for005.dat b/docs/examples/datcom/comparison_data/rockets/halcyon_coast_for005.dat new file mode 100644 index 000000000..f7228786a --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/halcyon_coast_for005.dat @@ -0,0 +1,235 @@ +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.83,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 162663.454,1631584.63,3144691.36,4737245.62,6235651.75,8028800.02, + 9954979.17,11781258.8,13973017.9,15282448.8,16402581.9,17270278.5, + 18726808.,19767186.2,20391413.1,20807564.4,21223715.7,21847942.7, + 22888320.9,24969077.3, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -20.,$ + $REFQ + XCG = 1.86360305, + SREF = 0.0182414692, + LREF = 0.1524,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.46, + DNOSE = 0.1524, + LCENTR = 2.842, + DCENTR = 0.1524, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.254, + DAFT = 0.0992, + DEXIT = 0.,$ + $FINSET1 + XLE = 3.293,3.495, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.125, + CHORD = 0.247,0.045, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.83,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 162663.454,1631584.63,3144691.36,4737245.62,6235651.75,8028800.02, + 9954979.17,11781258.8,13973017.9,15282448.8,16402581.9,17270278.5, + 18726808.,19767186.2,20391413.1,20807564.4,21223715.7,21847942.7, + 22888320.9,24969077.3, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -5.,$ + $REFQ + XCG = 1.86360305, + SREF = 0.0182414692, + LREF = 0.1524,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.46, + DNOSE = 0.1524, + LCENTR = 2.842, + DCENTR = 0.1524, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.254, + DAFT = 0.0992, + DEXIT = 0.,$ + $FINSET1 + XLE = 3.293,3.495, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.125, + CHORD = 0.247,0.045, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.83,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 162663.454,1631584.63,3144691.36,4737245.62,6235651.75,8028800.02, + 9954979.17,11781258.8,13973017.9,15282448.8,16402581.9,17270278.5, + 18726808.,19767186.2,20391413.1,20807564.4,21223715.7,21847942.7, + 22888320.9,24969077.3, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 0.,$ + $REFQ + XCG = 1.86360305, + SREF = 0.0182414692, + LREF = 0.1524,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.46, + DNOSE = 0.1524, + LCENTR = 2.842, + DCENTR = 0.1524, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.254, + DAFT = 0.0992, + DEXIT = 0.,$ + $FINSET1 + XLE = 3.293,3.495, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.125, + CHORD = 0.247,0.045, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.83,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 162663.454,1631584.63,3144691.36,4737245.62,6235651.75,8028800.02, + 9954979.17,11781258.8,13973017.9,15282448.8,16402581.9,17270278.5, + 18726808.,19767186.2,20391413.1,20807564.4,21223715.7,21847942.7, + 22888320.9,24969077.3, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 5.,$ + $REFQ + XCG = 1.86360305, + SREF = 0.0182414692, + LREF = 0.1524,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.46, + DNOSE = 0.1524, + LCENTR = 2.842, + DCENTR = 0.1524, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.254, + DAFT = 0.0992, + DEXIT = 0.,$ + $FINSET1 + XLE = 3.293,3.495, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.125, + CHORD = 0.247,0.045, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.83,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 162663.454,1631584.63,3144691.36,4737245.62,6235651.75,8028800.02, + 9954979.17,11781258.8,13973017.9,15282448.8,16402581.9,17270278.5, + 18726808.,19767186.2,20391413.1,20807564.4,21223715.7,21847942.7, + 22888320.9,24969077.3, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 20.,$ + $REFQ + XCG = 1.86360305, + SREF = 0.0182414692, + LREF = 0.1524,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.46, + DNOSE = 0.1524, + LCENTR = 2.842, + DCENTR = 0.1524, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.254, + DAFT = 0.0992, + DEXIT = 0.,$ + $FINSET1 + XLE = 3.293,3.495, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.125, + CHORD = 0.247,0.045, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE diff --git a/docs/examples/datcom/comparison_data/rockets/halcyon_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/halcyon_powered_for005.dat new file mode 100644 index 000000000..67917d7ac --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/halcyon_powered_for005.dat @@ -0,0 +1,235 @@ +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.83,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 221427.462,2214274.62,4205351.78,6178647.56,7914933.82,9842636.16, + 11725892.2,13341570.8,15093662.,16025795.5,16751578.8,17270285., + 18726815.1,19767193.7,20391420.8,20807572.3,21223723.7,21847950.9, + 22888329.5,24969086.7, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -20.,$ + $REFQ + XCG = 2.01748807, + SREF = 0.0182414692, + LREF = 0.1524,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.46, + DNOSE = 0.1524, + LCENTR = 2.842, + DCENTR = 0.1524, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.254, + DAFT = 0.0992, + DEXIT = 0.0282,$ + $FINSET1 + XLE = 3.293,3.495, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.125, + CHORD = 0.247,0.045, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.83,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 221427.462,2214274.62,4205351.78,6178647.56,7914933.82,9842636.16, + 11725892.2,13341570.8,15093662.,16025795.5,16751578.8,17270285., + 18726815.1,19767193.7,20391420.8,20807572.3,21223723.7,21847950.9, + 22888329.5,24969086.7, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -5.,$ + $REFQ + XCG = 2.01748807, + SREF = 0.0182414692, + LREF = 0.1524,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.46, + DNOSE = 0.1524, + LCENTR = 2.842, + DCENTR = 0.1524, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.254, + DAFT = 0.0992, + DEXIT = 0.0282,$ + $FINSET1 + XLE = 3.293,3.495, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.125, + CHORD = 0.247,0.045, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.83,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 221427.462,2214274.62,4205351.78,6178647.56,7914933.82,9842636.16, + 11725892.2,13341570.8,15093662.,16025795.5,16751578.8,17270285., + 18726815.1,19767193.7,20391420.8,20807572.3,21223723.7,21847950.9, + 22888329.5,24969086.7, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 0.,$ + $REFQ + XCG = 2.01748807, + SREF = 0.0182414692, + LREF = 0.1524,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.46, + DNOSE = 0.1524, + LCENTR = 2.842, + DCENTR = 0.1524, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.254, + DAFT = 0.0992, + DEXIT = 0.0282,$ + $FINSET1 + XLE = 3.293,3.495, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.125, + CHORD = 0.247,0.045, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.83,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 221427.462,2214274.62,4205351.78,6178647.56,7914933.82,9842636.16, + 11725892.2,13341570.8,15093662.,16025795.5,16751578.8,17270285., + 18726815.1,19767193.7,20391420.8,20807572.3,21223723.7,21847950.9, + 22888329.5,24969086.7, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 5.,$ + $REFQ + XCG = 2.01748807, + SREF = 0.0182414692, + LREF = 0.1524,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.46, + DNOSE = 0.1524, + LCENTR = 2.842, + DCENTR = 0.1524, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.254, + DAFT = 0.0992, + DEXIT = 0.0282,$ + $FINSET1 + XLE = 3.293,3.495, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.125, + CHORD = 0.247,0.045, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.83,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 221427.462,2214274.62,4205351.78,6178647.56,7914933.82,9842636.16, + 11725892.2,13341570.8,15093662.,16025795.5,16751578.8,17270285., + 18726815.1,19767193.7,20391420.8,20807572.3,21223723.7,21847950.9, + 22888329.5,24969086.7, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 20.,$ + $REFQ + XCG = 2.01748807, + SREF = 0.0182414692, + LREF = 0.1524,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.46, + DNOSE = 0.1524, + LCENTR = 2.842, + DCENTR = 0.1524, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.254, + DAFT = 0.0992, + DEXIT = 0.0282,$ + $FINSET1 + XLE = 3.293,3.495, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.125, + CHORD = 0.247,0.045, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE diff --git a/docs/examples/datcom/comparison_data/rockets/halcyon_provenance.json b/docs/examples/datcom/comparison_data/rockets/halcyon_provenance.json new file mode 100644 index 000000000..156f90e97 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/halcyon_provenance.json @@ -0,0 +1,382 @@ +{ + "construction_cells": [ + 2, + 3, + 5, + 6, + 8, + 10, + 11, + 13, + 14, + 15 + ], + "calibration": { + "roughness_height": 0.0, + "leading_edge_radius": null + }, + "source_adaptations": [ + "Read original .eng numeric samples directly: source already has t=0; avoid the importer's extra duplicate zero-time row. No measured sample or motor equation changed." + ], + "mach": [ + 0.01, + 0.1, + 0.19, + 0.28, + 0.36, + 0.45, + 0.54, + 0.62, + 0.71, + 0.76, + 0.8, + 0.83, + 0.9, + 0.95, + 0.98, + 1.0, + 1.02, + 1.05, + 1.1, + 1.2 + ], + "alpha": [ + -20.0, + -10.0, + -5.0, + -2.0, + -1.0, + -0.5, + -0.25, + -0.1, + -0.05, + 0.0, + 0.05, + 0.1, + 0.25, + 0.5, + 1.0, + 2.0, + 5.0, + 10.0, + 20.0 + ], + "beta": [ + -20, + -5, + 0, + 5, + 20 + ], + "altitude_schedules_m_asl": { + "power_on": [ + 130.0, + 140.10664573695297, + 166.6335410913673, + 208.864850834823, + 259.80795215670037, + 331.3590936772687, + 418.00608562691156, + 509.5621232566777, + 629.1399580222998, + 703.7017361451341, + 767.4187644261292, + 824.5438286377826, + 824.5438286377826, + 824.5438286377826, + 824.5438286377826, + 824.5438286377826, + 824.5438286377826, + 824.5438286377826, + 824.5438286377826, + 824.5438286377826 + ], + "power_off": [ + 3661.354294231137, + 3631.8186553839687, + 3491.390867124713, + 3271.4208102358166, + 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DATCOM trajectory" + } + ], + "atmosphere_method": "RocketPy reference ascent rho*a*M/mu; native REN", + "source_notebook": "docs/examples/halcyon_flight_sim.ipynb", + "source_sha256": "ee4e85b91244c95bd4ae0381521aa077e817018e386f2afa9384918dbc453f25", + "construction_assumption": "carbon fibre and vinyl when unspecified; no numerical roughness inferred", + "fin_assumption": "2% HEX, 5% bevels, sharp edges, auto body attachment", + "powered_base_drag": "nozzle", + "damp": true +} \ No newline at end of file diff --git a/docs/examples/datcom/comparison_data/rockets/halcyon_result.json b/docs/examples/datcom/comparison_data/rockets/halcyon_result.json new file mode 100644 index 000000000..c958d1ef1 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/halcyon_result.json @@ -0,0 +1,49 @@ +{ + "rocket": "halcyon", + "actual_agl_m": 3450.0, + "reported_agl_m": 3450.0, + "saved_first_agl_m": 3212.856, + "reference_kind": "notebook reported apogee", + "telemetry_max_agl_m": 3470.8251953125, + "notes": "Telemetry QA flags 5 samples with altitude jumps above 2000 m/s; raw maximum 3470.825 m is retained separately. | Telemetry maximum 3470.825 m differs from notebook reported value 3450.000 m; both retained.", + "status": "paired untuned comparison", + "reason": "", + "ordinary_agl_m": 3214.238895757538, + "ordinary_apogee_time_s": 26.371836502851696, + "ordinary_peak_vertical_velocity_m_s": 275.5714950426811, + "ordinary_initial_static_margin_cal": 3.0192578745448033, + "ordinary_coast_cd_mach06": 0.426, + "ordinary_max_mach": 0.812113342368714, + "ordinary_ascent_altitude_rmse_m": 112.69062830123777, + "source_adaptations": "Read original .eng numeric samples directly: source already has t=0; avoid the importer's extra duplicate zero-time row. No measured sample or motor equation changed.", + "atmosphere_refinements": 1, + "atmosphere_last_apogee_change_m": -1.122512222840669, + "datcom_agl_m": 3530.231782008296, + "datcom_apogee_time_s": 27.79754660231069, + "datcom_peak_vertical_velocity_m_s": 280.94965050790404, + "datcom_initial_static_margin_cal": 4.410132986423363, + "datcom_coast_cd_mach06": 0.319, + "datcom_max_mach": 0.8293761850832471, + "datcom_ascent_altitude_rmse_m": 69.46314134752838, + "samples": 495, + "outside_mach": 0, + "outside_alpha": 0, + "outside_beta": 0, + "max_abs_alpha_deg": 7.051663487140428, + "max_abs_beta_deg": 14.276458017233338, + "solver_mach_excursion": false, + "solver_alpha_excursion": true, + "solver_beta_excursion": true, + "mass_cg_match": true, + "ordinary_error_m": -235.7611042424619, + "ordinary_error_pct": -6.8336551954336775, + "datcom_error_m": 80.2317820082958, + "datcom_error_pct": 2.3255588987911824, + "telemetry_qa_max_agl_m": 3470.8251953125, + "telemetry_qa_rejected_count": 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data/rockets/tust/cats_tust/gnssInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/flightInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/imu.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/baro.csv (cell 24). | Hedy end comparison uses simulated ASL 5406.918 m against measured AGL 5231.53 m; saved simulated AGL is 5246.918 m, a 160 m datum discrepancy.", + "status": "archived comparison only", + "reason": "Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support." +} \ No newline at end of file diff --git a/docs/examples/datcom/comparison_data/rockets/inventory.json b/docs/examples/datcom/comparison_data/rockets/inventory.json new file mode 100644 index 000000000..e4d15281d --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/inventory.json @@ -0,0 +1,487 @@ +[ + { + "rocket": "andromeda", + "source_notebook": "docs/examples/andromeda_flight_sim.ipynb", + "source_sha256": "af60f6b41668aa41f90885324cd7e316c87053660da8f2eb8a755c6e29a83a0d", + "actual_agl_m": 3443.0, + "reference_kind": "notebook reported apogee", + "reported_agl_m": 3443.0, + "reported_cell": 0, + "saved_first_agl_m": 3614.946, + "saved_apogees": [ + { + "cell": 15, + "agl_m": 3614.946 + } + ], + "measured_summaries": [], + "notes": [ + "Telemetry maximum 3438.549 m differs from notebook reported value 3443.000 m; both retained." + ], + "telemetry": { + "path": "data/rockets/andromeda/flight_data.csv", + "cell": 17, + "conversion": "metres AGL, notebook columns_map", + "sha256": "ebd3b757555b564c63937b69d2c222d6596355fc6e938d49537af293a9d48d14", + "raw_max_agl_m": 3438.549384, + "qa_max_agl_m": 3438.549384, + "qa_rejected_count": 0 + } + }, + { + "rocket": "astra", + "source_notebook": "docs/examples/astra_flight_sim.ipynb", + "source_sha256": "7e708a54ec4c1d3c2f7c4ec015ac1d537431a33dde79a4b91bb5e1602097731a", + "actual_agl_m": 3250.0, + "reference_kind": "notebook reported apogee", + "reported_agl_m": 3250.0, + "reported_cell": 0, + "saved_first_agl_m": 3043.066, + "saved_apogees": [ + { + "cell": 14, + "agl_m": 3043.066 + } + ], + "measured_summaries": [], + "notes": [ + "Telemetry maximum 3249.460 m differs from notebook reported value 3250.000 m; both retained.", + "Source fin trailing edge overhangs the nozzle-inferred body base by 1.216 mm. Native DATCOM accepts this; the integration now validates root-chord centre attachment instead of rejecting all overhang. Geometry is unchanged." + ], + "telemetry": { + "path": "data/rockets/astra/flight_data.csv", + "cell": 16, + "conversion": "metres AGL, notebook columns_map", + "sha256": "dd5287b375e84be8961a7c867dc9af764a42752af3109c25086d228978e4acae", + "raw_max_agl_m": 3249.46, + "qa_max_agl_m": 3249.46, + "qa_rejected_count": 0 + } + }, + { + "rocket": "bella_lui", + "source_notebook": "docs/examples/bella_lui_flight_sim.ipynb", + "source_sha256": "1402eee9b084da9307acd9ec84e3f730118d0f6897f25943f2d8a691749061af", + "actual_agl_m": 458.971, + "reference_kind": "telemetry AGL maximum (QA)", + "reported_agl_m": null, + "reported_cell": null, + "saved_first_agl_m": 460.54, + "saved_apogees": [ + { + "cell": 30, + "agl_m": 460.54 + } + ], + "measured_summaries": [ + { + "cell": 46, + "agl_m": 458.97 + } + ], + "notes": [], + "telemetry": { + "path": "data/rockets/EPFL_Bella_Lui/bella_lui_flight_data_filtered.csv", + "cell": 33, + "conversion": "metres AGL, first 573 rows as used in source cell 35", + "sha256": "ccf8bcecd91d5b912e8f8b3bcf9efe13248453e1a81dc79d2b4daea4e985de2d", + "raw_max_agl_m": 458.971, + "qa_max_agl_m": 458.971, + "qa_rejected_count": 0 + } + }, + { + "rocket": "camoes", + "source_notebook": "docs/examples/camoes_flight_sim.ipynb", + "source_sha256": "dca32f4603c1acfdce58ffaf6ffce0355767d99da1db2a3ee88c5f60dd50140b", + "actual_agl_m": 3015.0, + "reference_kind": "notebook reported apogee", + "reported_agl_m": 3015.0, + "reported_cell": 0, + "saved_first_agl_m": 3003.284, + "saved_apogees": [ + { + "cell": 21, + "agl_m": 3003.284 + } + ], + "measured_summaries": [], + "notes": [ + "Telemetry maximum 3015.996 m differs from notebook reported value 3015.000 m; both retained." + ], + "telemetry": { + "path": "data/rockets/camoes/flight_data.csv", + "cell": 26, + "conversion": "metres AGL, notebook columns_map", + "sha256": "8ceb1a196901569e32a6e9f09067feebb5ca1406d686e402f726e715a1be1dcc", + "raw_max_agl_m": 3015.996337890625, + "qa_max_agl_m": 3015.996337890625, + "qa_rejected_count": 0 + } + }, + { + "rocket": "cavour", + "source_notebook": "docs/examples/cavour_flight_sim.ipynb", + "source_sha256": "9d407019f0a22f7e19bad163131c44dfc10295b1c2290e06035ef968616b83be", + "actual_agl_m": 2789.0, + "reference_kind": "notebook reported apogee", + "reported_agl_m": 2789.0, + "reported_cell": 0, + "saved_first_agl_m": 2817.405, + "saved_apogees": [ + { + "cell": 15, + "agl_m": 2817.405 + } + ], + "measured_summaries": [], + "notes": [], + "telemetry": { + "path": "data/rockets/polito/altimeter_cavour.csv", + "cell": 17, + "conversion": "metres AGL, notebook columns_map", + "sha256": "f912724676eea0ca9ba031c3779e48f861538814170b050b7bda2887c31f29a8", + "raw_max_agl_m": 2789.0, + "qa_max_agl_m": 2789.0, + "qa_rejected_count": 0 + } + }, + { + "rocket": "defiance", + "source_notebook": "docs/examples/defiance_flight_sim.ipynb", + "source_sha256": "0348a6fc8de79458cbc4a48fec1ef3489f5c6a511fd7a215289c34b03aea939b", + "actual_agl_m": 9308.318, + "reference_kind": "notebook reported apogee", + "reported_agl_m": 9308.318, + "reported_cell": 0, + "saved_first_agl_m": 9238.009, + "saved_apogees": [ + { + "cell": 10, + "agl_m": 9238.009 + } + ], + "measured_summaries": [], + "notes": [ + "Source fin root centre is z=-0.025 m, aft of the tail base z=0.0403 m; native root attachment is outside the declared body." + ], + "telemetry": null + }, + { + "rocket": "erebus", + "source_notebook": "docs/examples/erebus_flight_sim.ipynb", + "source_sha256": "85d34d7f05a2c3653864b9fdfda2347bfae916c350d480edb7c030c365fd67b4", + "actual_agl_m": 3020.0, + "reference_kind": "notebook reported apogee", + "reported_agl_m": 3020.0, + "reported_cell": 0, + "saved_first_agl_m": 2750.904, + "saved_apogees": [ + { + "cell": 16, + "agl_m": 2750.904 + } + ], + "measured_summaries": [], + "notes": [ + "Telemetry maximum 3001.751 m differs from notebook reported value 3020.000 m; both retained." + ], + "telemetry": { + "path": "data/rockets/erebus11/flight_data_filtered.csv", + "cell": 18, + "conversion": "metres AGL, notebook columns_map", + "sha256": "045f5b9ed473b80095805d3ca7b831ca4b67c5afbe9156d58d1e91df9fc0b4a9", + "raw_max_agl_m": 3001.75136, + "qa_max_agl_m": 3001.75136, + "qa_rejected_count": 0 + } + }, + { + "rocket": "genesis", + "source_notebook": "docs/examples/genesis_flight_sim.ipynb", + "source_sha256": "7ee71c93b82327814be4d0874ad83db8d4eca2877f8421025f718d87b511e854", + "actual_agl_m": 2916.0, + "reference_kind": "notebook reported apogee", + "reported_agl_m": 2916.0, + "reported_cell": 0, + "saved_first_agl_m": 3044.091, + "saved_apogees": [ + { + "cell": 15, + "agl_m": 3044.091 + } + ], + "measured_summaries": [], + "notes": [ + "Telemetry maximum 2916.692 m differs from notebook reported value 2916.000 m; both retained." + ], + "telemetry": { + "path": "data/rockets/genesis/flight_data_faraday.csv", + "cell": 17, + "conversion": "metres AGL, notebook columns_map", + "sha256": "d88a7fc53cbd55d1dafeed5d5c6fcfcad27c7cf8e0a15221e67aae97eaa05a74", + "raw_max_agl_m": 2916.691650390625, + "qa_max_agl_m": 2916.691650390625, + "qa_rejected_count": 0 + } + }, + { + "rocket": "halcyon", + "source_notebook": "docs/examples/halcyon_flight_sim.ipynb", + "source_sha256": "ee4e85b91244c95bd4ae0381521aa077e817018e386f2afa9384918dbc453f25", + "actual_agl_m": 3450.0, + "reference_kind": "notebook reported apogee", + "reported_agl_m": 3450.0, + "reported_cell": 0, + "saved_first_agl_m": 3212.856, + "saved_apogees": [ + { + "cell": 18, + "agl_m": 3212.856 + } + ], + "measured_summaries": [], + "notes": [ + "Telemetry QA flags 5 samples with altitude jumps above 2000 m/s; raw maximum 3470.825 m is retained separately.", + "Telemetry maximum 3470.825 m differs from notebook reported value 3450.000 m; both retained." + ], + "telemetry": { + "path": "data/rockets/astg/altimeter_halcyon.csv", + "cell": 20, + "conversion": "metres AGL, notebook columns_map", + "sha256": "7f1fec2faa53f63a183c97d8a9b692763e7b53d60eab15cc6f42ab646afa85a0", + "raw_max_agl_m": 3470.8251953125, + "qa_max_agl_m": 3470.8251953125, + "qa_rejected_count": 5 + } + }, + { + "rocket": "hedy", + "source_notebook": "docs/examples/hedy_flight_sim.ipynb", + "source_sha256": "0feb477c3e254afa5ade9bcfb6a44c31f21416e7c1585900a2c90f427b628fe5", + "actual_agl_m": 5231.53, + "reference_kind": "saved measured-apogee summary", + "reported_agl_m": null, + "reported_cell": null, + "saved_first_agl_m": 5246.918, + "saved_apogees": [ + { + "cell": 21, + "agl_m": 5246.918 + } + ], + "measured_summaries": [ + { + "cell": 26, + "agl_m": 5231.53 + } + ], + "notes": [ + "Referenced telemetry file is absent: data/rockets/tust/cats_tust/filteredDataInfo.csv (cell 24).", + "Referenced telemetry file is absent: data/rockets/tust/cats_tust/gnssInfo.csv (cell 24).", + "Referenced telemetry file is absent: data/rockets/tust/cats_tust/flightInfo.csv (cell 24).", + "Referenced telemetry file is absent: data/rockets/tust/cats_tust/imu.csv (cell 24).", + "Referenced telemetry file is absent: data/rockets/tust/cats_tust/baro.csv (cell 24).", + "Hedy end comparison uses simulated ASL 5406.918 m against measured AGL 5231.53 m; saved simulated AGL is 5246.918 m, a 160 m datum discrepancy." + ], + "telemetry": null + }, + { + "rocket": "juno3", + "source_notebook": "docs/examples/juno3_flight_sim.ipynb", + "source_sha256": "fd9327a71280f23fb7baedbf50fc8b78fe68845426ffa2d59308f1343d39f112", + "actual_agl_m": 3213.0, + "reference_kind": "notebook reported apogee", + "reported_agl_m": 3213.0, + "reported_cell": 0, + "saved_first_agl_m": 2976.849, + "saved_apogees": [ + { + "cell": 17, + "agl_m": 2976.849 + } + ], + "measured_summaries": [], + "notes": [ + "Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 10700.590 m is retained separately.", + "Telemetry maximum 10700.590 m differs from notebook reported value 3213.000 m; both retained.", + "Source fin airfoil is an empirical lift curve (cell 10), not a supplied section contour. The generic 2% HEX DATCOM assumption does not reproduce that curve; source-drag substitution therefore still has a lateral-model discrepancy." + ], + "telemetry": { + "path": "data/rockets/juno3/cots_altimeter.csv", + "cell": 18, + "conversion": "metres AGL, notebook columns_map", + "sha256": "eef81b3bcc4c285667727c1e2c9ca502a67cb374f5e31bc4f87ed76b8a04c1ee", + "raw_max_agl_m": 10700.59, + "qa_max_agl_m": 3213.375, + "qa_rejected_count": 2 + } + }, + { + "rocket": "lince", + "source_notebook": "docs/examples/lince_flight_sim.ipynb", + "source_sha256": "a6c5f7094cb8c21d19090feb0c39d2b0df7a307b8160bc03e523a8d10127ce14", + "actual_agl_m": 3587.0, + "reference_kind": "notebook reported apogee", + "reported_agl_m": 3587.0, + "reported_cell": 0, + "saved_first_agl_m": 3284.107, + "saved_apogees": [ + { + "cell": 20, + "agl_m": 3284.107 + }, + { + "cell": 22, + "agl_m": 528.631 + }, + { + "cell": 24, + "agl_m": 528.631 + } + ], + "measured_summaries": [], + "notes": [ + "Telemetry QA flags 69 samples with altitude jumps above 2000 m/s; raw maximum 3668.521 m is retained separately.", + "Telemetry maximum 3668.521 m differs from notebook reported value 3587.000 m; both retained.", + "Lince has three saved trajectories. Compare first/pre-deployment ascent only; retain all saved apogees separately." + ], + "telemetry": { + "path": "data/rockets/lince/main_data.csv", + "cell": 26, + "conversion": "metres AGL, notebook columns_map", + "sha256": "a9cf47fe2b2e5be03168d36b1c41f0a5651a0c3b6db4ed091e1afc274bd7473e", + "raw_max_agl_m": 3668.5205078125, + "qa_max_agl_m": 3628.76416015625, + "qa_rejected_count": 69 + } + }, + { + "rocket": "ndrt_2020", + "source_notebook": "docs/examples/ndrt_2020_flight_sim.ipynb", + "source_sha256": "ac6ac18064b7297c2f272035bb9e555902aad816add0b7860f6641ad033591b2", + "actual_agl_m": 1316.736, + "reference_kind": "notebook reported apogee", + "reported_agl_m": 1316.736, + "reported_cell": 0, + "saved_first_agl_m": 1293.43, + "saved_apogees": [ + { + "cell": 28, + "agl_m": 1293.43 + } + ], + "measured_summaries": [ + { + "cell": 45, + "agl_m": 1320.29 + } + ], + "notes": [ + "NDRT source uses approximate ft conversion /3.281; comparison uses exact 0.3048 m/ft.", + "Telemetry maximum 1320.357 m differs from notebook reported value 1316.736 m; both retained.", + "Source transition is internal at z=1.2..1.327 m with fins near z=3.05 m. The current DATCOM builder represents only an aft tail, not the required downstream smaller cylinder." + ], + "telemetry": { + "path": "data/rockets/NDRT_2020/ndrt_2020_flight_data.csv", + "cell": 31, + "conversion": "feet AGL multiplied by exactly 0.3048; source uses division by 3.281", + "sha256": "5e64b446199988ac183b2f3965d76da27e57496bb2a33f78df73aa23fd18d7b8", + "raw_max_agl_m": 1320.3570240000001, + "qa_max_agl_m": 1320.3570240000001, + "qa_rejected_count": 0 + } + }, + { + "rocket": "prometheus_2022", + "source_notebook": "docs/examples/prometheus_2022_flight_sim.ipynb", + "source_sha256": "44e5dfd07323771d54dec3bfb596f943620915d81f162a6b8cc25c3ea1438186", + "actual_agl_m": 3898.37, + "reference_kind": "notebook reported apogee", + "reported_agl_m": 3898.37, + "reported_cell": 0, + "saved_first_agl_m": 4188.81, + "saved_apogees": [ + { + "cell": 13, + "agl_m": 4188.81 + } + ], + "measured_summaries": [ + { + "cell": 17, + "agl_m": 3903.77 + } + ], + "notes": [ + "Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 3903.770 m is retained separately.", + "Telemetry maximum 3903.770 m differs from notebook reported value 3898.370 m; both retained.", + "Prometheus prose says June 2022; source environment and local reanalysis are June 2023. Both reruns preserve source conditions." + ], + "telemetry": { + "path": "data/rockets/prometheus/2022-06-24-serial-5115-flight-0001-TeleMetrum.csv", + "cell": 15, + "conversion": "metres AGL, notebook columns_map", + "sha256": "9fe8d17a25e7944267b34a45ed955303afd5818b3f18479a5f7ffb31a574cecf", + "raw_max_agl_m": 3903.77, + "qa_max_agl_m": 3903.77, + "qa_rejected_count": 2 + } + }, + { + "rocket": "valetudo", + "source_notebook": "docs/examples/valetudo_flight_sim.ipynb", + "source_sha256": "2b86db6b4846f1badf9d99a7d6825526c889e64303bee4d3c487a72ba3e69c47", + "actual_agl_m": 860.0, + "reference_kind": "saved measured-apogee summary", + "reported_agl_m": 860.0, + "reported_cell": 31, + "saved_first_agl_m": 815.26, + "saved_apogees": [ + { + "cell": 28, + "agl_m": 815.26 + } + ], + "measured_summaries": [ + { + "cell": 33, + "agl_m": 860.0 + } + ], + "notes": [ + "Valetudo flight recordings were lost; only recovered scalar apogee/drift values remain." + ], + "telemetry": null + }, + { + "rocket": "valkyrie", + "source_notebook": "docs/examples/valkyrie_flight_sim.ipynb", + "source_sha256": "78aacf4df26933a87d28bfc71ad16e94828933672df20dce87545f0ce43267d3", + "actual_agl_m": 2098.02, + "reference_kind": "notebook reported apogee", + "reported_agl_m": 2098.02, + "reported_cell": 0, + "saved_first_agl_m": 2247.849, + "saved_apogees": [ + { + "cell": 11, + "agl_m": 2247.849 + } + ], + "measured_summaries": [], + "notes": [ + "Telemetry maximum 2059.024 m differs from notebook reported value 2098.020 m; both retained.", + "Valkyrie prose says 2025 but source environment says 2026 and fetches live Windy weather; no archived profile is supplied." + ], + "telemetry": { + "path": "data/rockets/valkyrie/flightInfo_merged.csv", + "cell": 13, + "conversion": "metres AGL, notebook columns_map", + "sha256": "0c7808a84fb39c83908ba9caa1d2d3ccb609d6db46c3d660b85977e6917795bc", + "raw_max_agl_m": 2059.0244140625, + "qa_max_agl_m": 2059.0244140625, + "qa_rejected_count": 0 + } + } +] \ 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+1.1920666666666666,0.0023359733333333335,0.5696199999999999,0.0023359733333333335,0.6116993333333333 +1.2,0.002309,0.572,0.002309,0.614 diff --git a/docs/examples/datcom/comparison_data/rockets/juno3_coast_for005.dat b/docs/examples/datcom/comparison_data/rockets/juno3_coast_for005.dat new file mode 100644 index 000000000..8afda782f --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/juno3_coast_for005.dat @@ -0,0 +1,235 @@ +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 143857.55,863145.299,1438575.5,2591980.51,3799447.16,5066740.16, + 6408022.21,7839759.61,9370571.41,11008799.5,12644648.1,13669889.9, + 15378626.1,16232994.2,16745615.1,17087362.3,17429109.6,17941730.4, + 18796098.5,20504834.8, + NALPHA = 19., + ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14, + 0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3, + BETA = -20.,$ + $REFQ + XCG = 1.477, + SREF = 0.0134782179, + LREF = 0.131,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.565, + DNOSE = 0.131, + LCENTR = 2.138, + DCENTR = 0.131, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.068, + DAFT = 0.107, + DEXIT = 0.,$ + $FINSET1 + XLE = 2.405,2.485, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.13, + CHORD = 0.2,0.12, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = 0.,0.,0.,0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 143857.55,863145.299,1438575.5,2591980.51,3799447.16,5066740.16, + 6408022.21,7839759.61,9370571.41,11008799.5,12644648.1,13669889.9, + 15378626.1,16232994.2,16745615.1,17087362.3,17429109.6,17941730.4, + 18796098.5,20504834.8, + NALPHA = 19., + ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14, + 0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3, + BETA = -5.,$ + $REFQ + XCG = 1.477, + SREF = 0.0134782179, + LREF = 0.131,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.565, + DNOSE = 0.131, + LCENTR = 2.138, + DCENTR = 0.131, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.068, + DAFT = 0.107, + DEXIT = 0.,$ + $FINSET1 + XLE = 2.405,2.485, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.13, + CHORD = 0.2,0.12, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = 0.,0.,0.,0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 143857.55,863145.299,1438575.5,2591980.51,3799447.16,5066740.16, + 6408022.21,7839759.61,9370571.41,11008799.5,12644648.1,13669889.9, + 15378626.1,16232994.2,16745615.1,17087362.3,17429109.6,17941730.4, + 18796098.5,20504834.8, + NALPHA = 19., + ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14, + 0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3, + BETA = 0.,$ + $REFQ + XCG = 1.477, + SREF = 0.0134782179, + LREF = 0.131,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.565, + DNOSE = 0.131, + LCENTR = 2.138, + DCENTR = 0.131, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.068, + DAFT = 0.107, + DEXIT = 0.,$ + $FINSET1 + XLE = 2.405,2.485, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.13, + CHORD = 0.2,0.12, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = 0.,0.,0.,0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 143857.55,863145.299,1438575.5,2591980.51,3799447.16,5066740.16, + 6408022.21,7839759.61,9370571.41,11008799.5,12644648.1,13669889.9, + 15378626.1,16232994.2,16745615.1,17087362.3,17429109.6,17941730.4, + 18796098.5,20504834.8, + NALPHA = 19., + ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14, + 0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3, + BETA = 5.,$ + $REFQ + XCG = 1.477, + SREF = 0.0134782179, + LREF = 0.131,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.565, + DNOSE = 0.131, + LCENTR = 2.138, + DCENTR = 0.131, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.068, + DAFT = 0.107, + DEXIT = 0.,$ + $FINSET1 + XLE = 2.405,2.485, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.13, + CHORD = 0.2,0.12, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = 0.,0.,0.,0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 143857.55,863145.299,1438575.5,2591980.51,3799447.16,5066740.16, + 6408022.21,7839759.61,9370571.41,11008799.5,12644648.1,13669889.9, + 15378626.1,16232994.2,16745615.1,17087362.3,17429109.6,17941730.4, + 18796098.5,20504834.8, + NALPHA = 19., + ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14, + 0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3, + BETA = 20.,$ + $REFQ + XCG = 1.477, + SREF = 0.0134782179, + LREF = 0.131,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.565, + DNOSE = 0.131, + LCENTR = 2.138, + DCENTR = 0.131, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.068, + DAFT = 0.107, + DEXIT = 0.,$ + $FINSET1 + XLE = 2.405,2.485, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.13, + CHORD = 0.2,0.12, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = 0.,0.,0.,0.,$ +DAMP +NEXT CASE diff --git a/docs/examples/datcom/comparison_data/rockets/juno3_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/juno3_powered_for005.dat new file mode 100644 index 000000000..6041235f3 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/juno3_powered_for005.dat @@ -0,0 +1,235 @@ +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 180330.781,1081600.98,1801573.56,3237190.09,4664267.08,6079651.89, + 7482495.11,8871912.88,10242729.,11580608.8,12762486.5,13797282.7, + 15521943.,16384273.2,16901671.3,17246603.4,17591535.4,18108933.5, + 18971263.7,20695924., + NALPHA = 19., + ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14, + 0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3, + BETA = -20.,$ + $REFQ + XCG = 1.65017802, + SREF = 0.0134782179, + LREF = 0.131,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.565, + DNOSE = 0.131, + LCENTR = 2.138, + DCENTR = 0.131, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.068, + DAFT = 0.107, + DEXIT = 0.067,$ + $FINSET1 + XLE = 2.405,2.485, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.13, + CHORD = 0.2,0.12, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = 0.,0.,0.,0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 180330.781,1081600.98,1801573.56,3237190.09,4664267.08,6079651.89, + 7482495.11,8871912.88,10242729.,11580608.8,12762486.5,13797282.7, + 15521943.,16384273.2,16901671.3,17246603.4,17591535.4,18108933.5, + 18971263.7,20695924., + NALPHA = 19., + ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14, + 0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3, + BETA = -5.,$ + $REFQ + XCG = 1.65017802, + SREF = 0.0134782179, + LREF = 0.131,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.565, + DNOSE = 0.131, + LCENTR = 2.138, + DCENTR = 0.131, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.068, + DAFT = 0.107, + DEXIT = 0.067,$ + $FINSET1 + XLE = 2.405,2.485, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.13, + CHORD = 0.2,0.12, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = 0.,0.,0.,0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 180330.781,1081600.98,1801573.56,3237190.09,4664267.08,6079651.89, + 7482495.11,8871912.88,10242729.,11580608.8,12762486.5,13797282.7, + 15521943.,16384273.2,16901671.3,17246603.4,17591535.4,18108933.5, + 18971263.7,20695924., + NALPHA = 19., + ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14, + 0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3, + BETA = 0.,$ + $REFQ + XCG = 1.65017802, + SREF = 0.0134782179, + LREF = 0.131,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.565, + DNOSE = 0.131, + LCENTR = 2.138, + DCENTR = 0.131, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.068, + DAFT = 0.107, + DEXIT = 0.067,$ + $FINSET1 + XLE = 2.405,2.485, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.13, + CHORD = 0.2,0.12, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = 0.,0.,0.,0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 180330.781,1081600.98,1801573.56,3237190.09,4664267.08,6079651.89, + 7482495.11,8871912.88,10242729.,11580608.8,12762486.5,13797282.7, + 15521943.,16384273.2,16901671.3,17246603.4,17591535.4,18108933.5, + 18971263.7,20695924., + NALPHA = 19., + ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14, + 0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3, + BETA = 5.,$ + $REFQ + XCG = 1.65017802, + SREF = 0.0134782179, + LREF = 0.131,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.565, + DNOSE = 0.131, + LCENTR = 2.138, + DCENTR = 0.131, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.068, + DAFT = 0.107, + DEXIT = 0.067,$ + $FINSET1 + XLE = 2.405,2.485, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.13, + CHORD = 0.2,0.12, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = 0.,0.,0.,0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 180330.781,1081600.98,1801573.56,3237190.09,4664267.08,6079651.89, + 7482495.11,8871912.88,10242729.,11580608.8,12762486.5,13797282.7, + 15521943.,16384273.2,16901671.3,17246603.4,17591535.4,18108933.5, + 18971263.7,20695924., + NALPHA = 19., + ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14, + 0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3, + BETA = 20.,$ + $REFQ + XCG = 1.65017802, + SREF = 0.0134782179, + LREF = 0.131,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.565, + DNOSE = 0.131, + LCENTR = 2.138, + DCENTR = 0.131, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.068, + DAFT = 0.107, + DEXIT = 0.067,$ + $FINSET1 + XLE = 2.405,2.485, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.13, + CHORD = 0.2,0.12, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = 0.,0.,0.,0.,$ +DAMP +NEXT CASE diff --git a/docs/examples/datcom/comparison_data/rockets/juno3_provenance.json b/docs/examples/datcom/comparison_data/rockets/juno3_provenance.json new file mode 100644 index 000000000..c649e17dc --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/juno3_provenance.json @@ -0,0 +1,384 @@ +{ + "construction_cells": [ + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15 + ], + "calibration": { + "roughness_height": 0.0, + "leading_edge_radius": null + }, + "source_adaptations": [], + "mach": [ + 0.01, + 0.06, + 0.1, + 0.18, + 0.26, + 0.34, + 0.42, + 0.5, + 0.58, + 0.66, + 0.74, + 0.8, + 0.9, + 0.95, + 0.98, + 1.0, + 1.02, + 1.05, + 1.1, + 1.2 + ], + "alpha": [ + -57.3, + -28.65, + -14.32, + -5.73, + -2.86, + -1.43, + -0.72, + -0.29, + -0.14, + 0.0, + 0.14, + 0.29, + 0.72, + 1.43, + 2.86, + 5.73, + 14.32, + 28.65, + 57.3 + ], + "beta": [ + -20, + -5, + 0, + 5, + 20 + ], + 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inferred", + "fin_assumption": "2% HEX, 5% bevels, sharp edges, auto body attachment", + "powered_base_drag": "nozzle", + "damp": true +} \ No newline at end of file diff --git a/docs/examples/datcom/comparison_data/rockets/juno3_result.json b/docs/examples/datcom/comparison_data/rockets/juno3_result.json new file mode 100644 index 000000000..95aebc9d8 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/juno3_result.json @@ -0,0 +1,49 @@ +{ + "rocket": "juno3", + "actual_agl_m": 3213.0, + "reported_agl_m": 3213.0, + "saved_first_agl_m": 2976.849, + "reference_kind": "notebook reported apogee", + "telemetry_max_agl_m": 10700.59, + "notes": "Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 10700.590 m is retained separately. | Telemetry maximum 10700.590 m differs from notebook reported value 3213.000 m; both retained. | Source fin airfoil is an empirical lift curve (cell 10), not a supplied section contour. The generic 2% HEX DATCOM assumption does not reproduce that curve; source-drag substitution therefore still has a lateral-model discrepancy.", + "status": "paired untuned comparison", + "reason": "", + "ordinary_agl_m": 3008.581569789987, + "ordinary_apogee_time_s": 26.491920030008096, + "ordinary_peak_vertical_velocity_m_s": 241.2427490025272, + "ordinary_initial_static_margin_cal": 3.2206482971493466, + "ordinary_coast_cd_mach06": 0.368, + "ordinary_max_mach": 0.7308763878428423, + "ordinary_ascent_altitude_rmse_m": 229.55884227968258, + "source_adaptations": "", + "atmosphere_refinements": 1, + "atmosphere_last_apogee_change_m": -0.03861187111760955, + "datcom_agl_m": 3056.4921836297026, + "datcom_apogee_time_s": 26.635000914699194, + "datcom_peak_vertical_velocity_m_s": 243.89859046993098, + "datcom_initial_static_margin_cal": 4.0067340523842745, + "datcom_coast_cd_mach06": 0.37450000000000006, + "datcom_max_mach": 0.7312932218217086, + "datcom_ascent_altitude_rmse_m": 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+1.1444666666666665,0.9898,0.48433000000000004,0.9898,0.560996 +1.1523999999999999,0.9898,0.48314,0.9898,0.5595680000000001 +1.1603333333333332,0.9898,0.48195,0.9898,0.5581400000000001 +1.1682666666666666,0.9898,0.48076,0.9898,0.556712 +1.1762,0.9898,0.47957,0.9898,0.555284 +1.1841333333333333,0.9898,0.47837999999999997,0.9898,0.553856 +1.1920666666666666,0.9898,0.47719,0.9898,0.552428 +1.2,0.9898,0.476,0.9898,0.551 diff --git a/docs/examples/datcom/comparison_data/rockets/lince_coast_for005.dat b/docs/examples/datcom/comparison_data/rockets/lince_coast_for005.dat new file mode 100644 index 000000000..02550c431 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/lince_coast_for005.dat @@ -0,0 +1,235 @@ +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 149204.324,1492043.24,2873657.3,4324474.37,5692174.73,7356706.4, + 9148737.57,10840013.6,12861518.2,14055449.5,15060627.8,16338920.1, + 17623820.6,18910372.2,19684782.8,20192746.2,20596601.1,21202383.5, + 22212020.8,24231295.5, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -20.,$ + $REFQ + XCG = 1.048417, + SREF = 0.00849486654, + LREF = 0.104,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.28, + DNOSE = 0.104, + LCENTR = 1.829417, + DCENTR = 0.104, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.022, + DAFT = 0.09704, + DEXIT = 0.,$ + $FINSET1 + XLE = 1.939417,2.066917, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.0825, + CHORD = 0.17,0.0425, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 149204.324,1492043.24,2873657.3,4324474.37,5692174.73,7356706.4, + 9148737.57,10840013.6,12861518.2,14055449.5,15060627.8,16338920.1, + 17623820.6,18910372.2,19684782.8,20192746.2,20596601.1,21202383.5, + 22212020.8,24231295.5, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -5.,$ + $REFQ + XCG = 1.048417, + SREF = 0.00849486654, + LREF = 0.104,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.28, + DNOSE = 0.104, + LCENTR = 1.829417, + DCENTR = 0.104, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.022, + DAFT = 0.09704, + DEXIT = 0.,$ + $FINSET1 + XLE = 1.939417,2.066917, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.0825, + CHORD = 0.17,0.0425, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 149204.324,1492043.24,2873657.3,4324474.37,5692174.73,7356706.4, + 9148737.57,10840013.6,12861518.2,14055449.5,15060627.8,16338920.1, + 17623820.6,18910372.2,19684782.8,20192746.2,20596601.1,21202383.5, + 22212020.8,24231295.5, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 0.,$ + $REFQ + XCG = 1.048417, + SREF = 0.00849486654, + LREF = 0.104,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.28, + DNOSE = 0.104, + LCENTR = 1.829417, + DCENTR = 0.104, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.022, + DAFT = 0.09704, + DEXIT = 0.,$ + $FINSET1 + XLE = 1.939417,2.066917, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.0825, + CHORD = 0.17,0.0425, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 149204.324,1492043.24,2873657.3,4324474.37,5692174.73,7356706.4, + 9148737.57,10840013.6,12861518.2,14055449.5,15060627.8,16338920.1, + 17623820.6,18910372.2,19684782.8,20192746.2,20596601.1,21202383.5, + 22212020.8,24231295.5, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 5.,$ + $REFQ + XCG = 1.048417, + SREF = 0.00849486654, + LREF = 0.104,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.28, + DNOSE = 0.104, + LCENTR = 1.829417, + DCENTR = 0.104, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.022, + DAFT = 0.09704, + DEXIT = 0.,$ + $FINSET1 + XLE = 1.939417,2.066917, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.0825, + CHORD = 0.17,0.0425, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 149204.324,1492043.24,2873657.3,4324474.37,5692174.73,7356706.4, + 9148737.57,10840013.6,12861518.2,14055449.5,15060627.8,16338920.1, + 17623820.6,18910372.2,19684782.8,20192746.2,20596601.1,21202383.5, + 22212020.8,24231295.5, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 20.,$ + $REFQ + XCG = 1.048417, + SREF = 0.00849486654, + LREF = 0.104,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.28, + DNOSE = 0.104, + LCENTR = 1.829417, + DCENTR = 0.104, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.022, + DAFT = 0.09704, + DEXIT = 0.,$ + $FINSET1 + XLE = 1.939417,2.066917, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.0825, + CHORD = 0.17,0.0425, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE diff --git a/docs/examples/datcom/comparison_data/rockets/lince_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/lince_powered_for005.dat new file mode 100644 index 000000000..556b170b1 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/lince_powered_for005.dat @@ -0,0 +1,235 @@ +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 221434.247,2214342.47,4201653.58,6172522.61,7907065.96,9833944.82, + 11727020.,13371551.7,15164177.2,16130432.5,16885444.,17823447.5, + 18746011.8,19635388.2,20149632.8,20241009.8,20738066.9,21348010.1, + 22364582.,24397725.8, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -20.,$ + $REFQ + XCG = 1.21534549, + SREF = 0.00849486654, + LREF = 0.104,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.28, + DNOSE = 0.104, + LCENTR = 1.829417, + DCENTR = 0.104, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.022, + DAFT = 0.09704, + DEXIT = 0.067,$ + $FINSET1 + XLE = 1.939417,2.066917, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.0825, + CHORD = 0.17,0.0425, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 221434.247,2214342.47,4201653.58,6172522.61,7907065.96,9833944.82, + 11727020.,13371551.7,15164177.2,16130432.5,16885444.,17823447.5, + 18746011.8,19635388.2,20149632.8,20241009.8,20738066.9,21348010.1, + 22364582.,24397725.8, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -5.,$ + $REFQ + XCG = 1.21534549, + SREF = 0.00849486654, + LREF = 0.104,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.28, + DNOSE = 0.104, + LCENTR = 1.829417, + DCENTR = 0.104, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.022, + DAFT = 0.09704, + DEXIT = 0.067,$ + $FINSET1 + XLE = 1.939417,2.066917, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.0825, + CHORD = 0.17,0.0425, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 221434.247,2214342.47,4201653.58,6172522.61,7907065.96,9833944.82, + 11727020.,13371551.7,15164177.2,16130432.5,16885444.,17823447.5, + 18746011.8,19635388.2,20149632.8,20241009.8,20738066.9,21348010.1, + 22364582.,24397725.8, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 0.,$ + $REFQ + XCG = 1.21534549, + SREF = 0.00849486654, + LREF = 0.104,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.28, + DNOSE = 0.104, + LCENTR = 1.829417, + DCENTR = 0.104, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.022, + DAFT = 0.09704, + DEXIT = 0.067,$ + $FINSET1 + XLE = 1.939417,2.066917, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.0825, + CHORD = 0.17,0.0425, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 221434.247,2214342.47,4201653.58,6172522.61,7907065.96,9833944.82, + 11727020.,13371551.7,15164177.2,16130432.5,16885444.,17823447.5, + 18746011.8,19635388.2,20149632.8,20241009.8,20738066.9,21348010.1, + 22364582.,24397725.8, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 5.,$ + $REFQ + XCG = 1.21534549, + SREF = 0.00849486654, + LREF = 0.104,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.28, + DNOSE = 0.104, + LCENTR = 1.829417, + DCENTR = 0.104, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.022, + DAFT = 0.09704, + DEXIT = 0.067,$ + $FINSET1 + XLE = 1.939417,2.066917, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.0825, + CHORD = 0.17,0.0425, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 221434.247,2214342.47,4201653.58,6172522.61,7907065.96,9833944.82, + 11727020.,13371551.7,15164177.2,16130432.5,16885444.,17823447.5, + 18746011.8,19635388.2,20149632.8,20241009.8,20738066.9,21348010.1, + 22364582.,24397725.8, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 20.,$ + $REFQ + XCG = 1.21534549, + SREF = 0.00849486654, + LREF = 0.104,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.28, + DNOSE = 0.104, + LCENTR = 1.829417, + DCENTR = 0.104, + BASE = .FALSE., + TAFT = CONE, + LAFT = 0.022, + DAFT = 0.09704, + DEXIT = 0.067,$ + $FINSET1 + XLE = 1.939417,2.066917, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + GAM = 0.,0.,0.,0., + SSPAN = 0.,0.0825, + CHORD = 0.17,0.0425, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,-0.,$ +DAMP +NEXT CASE diff --git a/docs/examples/datcom/comparison_data/rockets/lince_provenance.json b/docs/examples/datcom/comparison_data/rockets/lince_provenance.json new file mode 100644 index 000000000..6e6f2f72b --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/lince_provenance.json @@ -0,0 +1,382 @@ +{ + "construction_cells": [ + 2, + 4, + 6, + 7, + 9, + 10, + 11, + 12, + 13, + 14, + 16, + 17 + ], + "calibration": { + "roughness_height": 0.0, + "leading_edge_radius": null + }, + "source_adaptations": [], + "mach": [ + 0.01, + 0.1, + 0.19, + 0.28, + 0.36, + 0.45, + 0.54, + 0.62, + 0.71, + 0.76, + 0.8, + 0.85, + 0.9, + 0.95, + 0.98, + 1.0, + 1.02, + 1.05, + 1.1, + 1.2 + ], + "alpha": [ + 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"docs/examples/lince_flight_sim.ipynb", + "source_sha256": "a6c5f7094cb8c21d19090feb0c39d2b0df7a307b8160bc03e523a8d10127ce14", + "construction_assumption": "carbon fibre and vinyl when unspecified; no numerical roughness inferred", + "fin_assumption": "2% HEX, 5% bevels, sharp edges, auto body attachment", + "powered_base_drag": "nozzle", + "damp": true +} \ No newline at end of file diff --git a/docs/examples/datcom/comparison_data/rockets/lince_result.json b/docs/examples/datcom/comparison_data/rockets/lince_result.json new file mode 100644 index 000000000..e147e9e19 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/lince_result.json @@ -0,0 +1,49 @@ +{ + "rocket": "lince", + "actual_agl_m": 3587.0, + "reported_agl_m": 3587.0, + "saved_first_agl_m": 3284.107, + "reference_kind": "notebook reported apogee", + "telemetry_max_agl_m": 3668.5205078125, + "notes": "Telemetry QA flags 69 samples with altitude jumps above 2000 m/s; raw maximum 3668.521 m is retained separately. | Telemetry maximum 3668.521 m differs from notebook reported value 3587.000 m; both retained. | Lince has three saved trajectories. Compare first/pre-deployment ascent only; retain all saved apogees separately.", + "status": "paired untuned comparison", + "reason": "", + "ordinary_agl_m": 3285.089381541881, + "ordinary_apogee_time_s": 24.728341685630557, + "ordinary_peak_vertical_velocity_m_s": 317.77081353823803, + "ordinary_initial_static_margin_cal": 2.9203959364705625, + "ordinary_coast_cd_mach06": 0.65562, + "ordinary_max_mach": 0.936438877536046, + "ordinary_ascent_altitude_rmse_m": 180.22020526338466, + "source_adaptations": "", + "atmosphere_refinements": 1, + "atmosphere_last_apogee_change_m": -6.875566491943573, + "datcom_agl_m": 4254.30425999591, + "datcom_apogee_time_s": 28.7853935112826, + "datcom_peak_vertical_velocity_m_s": 342.53953112405384, + "datcom_initial_static_margin_cal": 3.962666916625163, + "datcom_coast_cd_mach06": 0.38175, + "datcom_max_mach": 1.0102057901013621, + "datcom_ascent_altitude_rmse_m": 299.53187203569485, + "samples": 498, + "outside_mach": 0, + "outside_alpha": 0, + 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1320.3570240000001, + "notes": "NDRT source uses approximate ft conversion /3.281; comparison uses exact 0.3048 m/ft. | Telemetry maximum 1320.357 m differs from notebook reported value 1316.736 m; both retained. | Source transition is internal at z=1.2..1.327 m with fins near z=3.05 m. The current DATCOM builder represents only an aft tail, not the required downstream smaller cylinder.", + "status": "ordinary only", + "reason": "ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip", + "ordinary_agl_m": 1293.5184123380945, + "ordinary_apogee_time_s": 16.64978369813489, + "ordinary_peak_vertical_velocity_m_s": 171.66118644734854, + "ordinary_initial_static_margin_cal": 4.016381564609376, + "ordinary_coast_cd_mach06": 0.44, + "ordinary_max_mach": 0.52460108376313, + "ordinary_ascent_altitude_rmse_m": 42.9525631238651, + "source_adaptations": "", + "ordinary_error_m": -23.217587661905554, + "ordinary_error_pct": -1.763268237665375, + "telemetry_qa_max_agl_m": 1320.3570240000001, + "telemetry_qa_rejected_count": 0, + "ordinary_vs_telemetry_pct": -2.0326783721419828, + "rmse_start_s": 0.04375, + "rmse_end_s": 16.64978369813489 +} \ No newline at end of file diff --git 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a/docs/examples/datcom/comparison_data/rockets/prometheus_2022_coast_for005.dat b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_coast_for005.dat new file mode 100644 index 000000000..aedf89fbb --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_coast_for005.dat @@ -0,0 +1,220 @@ +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.95,0.97, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 130974.759,1309747.59,2515951.85,3777991.72,4965660.68,6394086.74, + 7931776.03,9398718.29,11171653.6,13073952.5,14188445.5,15337010.5, + 16512927.3,16899317.2,17073537.,17421976.5,17770416.,18293075.3, + 19164174.2,20906371.8, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -20.,$ + $REFQ + XCG = 1.39412619, + SREF = 0.0153279012, + LREF = 0.1397,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.742, + DNOSE = 0.1397, + LCENTR = 1.487, + DCENTR = 0.1397, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 1.956,2.022, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.13, + CHORD = 0.268,0.136, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.95,0.97, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 130974.759,1309747.59,2515951.85,3777991.72,4965660.68,6394086.74, + 7931776.03,9398718.29,11171653.6,13073952.5,14188445.5,15337010.5, + 16512927.3,16899317.2,17073537.,17421976.5,17770416.,18293075.3, + 19164174.2,20906371.8, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -5.,$ + $REFQ + XCG = 1.39412619, + SREF = 0.0153279012, + LREF = 0.1397,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.742, + DNOSE = 0.1397, + LCENTR = 1.487, + DCENTR = 0.1397, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 1.956,2.022, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.13, + CHORD = 0.268,0.136, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.95,0.97, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 130974.759,1309747.59,2515951.85,3777991.72,4965660.68,6394086.74, + 7931776.03,9398718.29,11171653.6,13073952.5,14188445.5,15337010.5, + 16512927.3,16899317.2,17073537.,17421976.5,17770416.,18293075.3, + 19164174.2,20906371.8, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 0.,$ + $REFQ + XCG = 1.39412619, + SREF = 0.0153279012, + LREF = 0.1397,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.742, + DNOSE = 0.1397, + LCENTR = 1.487, + DCENTR = 0.1397, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 1.956,2.022, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.13, + CHORD = 0.268,0.136, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.95,0.97, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 130974.759,1309747.59,2515951.85,3777991.72,4965660.68,6394086.74, + 7931776.03,9398718.29,11171653.6,13073952.5,14188445.5,15337010.5, + 16512927.3,16899317.2,17073537.,17421976.5,17770416.,18293075.3, + 19164174.2,20906371.8, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 5.,$ + $REFQ + XCG = 1.39412619, + SREF = 0.0153279012, + LREF = 0.1397,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.742, + DNOSE = 0.1397, + LCENTR = 1.487, + DCENTR = 0.1397, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 1.956,2.022, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.13, + CHORD = 0.268,0.136, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.95,0.97, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 130974.759,1309747.59,2515951.85,3777991.72,4965660.68,6394086.74, + 7931776.03,9398718.29,11171653.6,13073952.5,14188445.5,15337010.5, + 16512927.3,16899317.2,17073537.,17421976.5,17770416.,18293075.3, + 19164174.2,20906371.8, + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 20.,$ + $REFQ + XCG = 1.39412619, + SREF = 0.0153279012, + LREF = 0.1397,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.742, + DNOSE = 0.1397, + LCENTR = 1.487, + DCENTR = 0.1397, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 1.956,2.022, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.13, + CHORD = 0.268,0.136, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE diff --git a/docs/examples/datcom/comparison_data/rockets/prometheus_2022_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_powered_for005.dat new file mode 100644 index 000000000..4052f33f1 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_powered_for005.dat @@ -0,0 +1,220 @@ +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.95,0.97, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 188291.497,1881563.25,3568623.41,5244520.08,6721167.22,8362587.5, + 9976551.33,11382896.5,12923429.4,14407197.7,15200094.4,15969747.9, + 16726333.9,16976222.2,17151234.8,17501260.,17851285.2,18376323., + 19251386.,21001512., + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -20.,$ + $REFQ + XCG = 1.49563653, + SREF = 0.0153279012, + LREF = 0.1397,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.742, + DNOSE = 0.1397, + LCENTR = 1.487, + DCENTR = 0.1397, + BASE = .FALSE., + DEXIT = 0.054,$ + $FINSET1 + XLE = 1.956,2.022, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.13, + CHORD = 0.268,0.136, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.95,0.97, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 188291.497,1881563.25,3568623.41,5244520.08,6721167.22,8362587.5, + 9976551.33,11382896.5,12923429.4,14407197.7,15200094.4,15969747.9, + 16726333.9,16976222.2,17151234.8,17501260.,17851285.2,18376323., + 19251386.,21001512., + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = -5.,$ + $REFQ + XCG = 1.49563653, + SREF = 0.0153279012, + LREF = 0.1397,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.742, + DNOSE = 0.1397, + LCENTR = 1.487, + DCENTR = 0.1397, + BASE = .FALSE., + DEXIT = 0.054,$ + $FINSET1 + XLE = 1.956,2.022, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.13, + CHORD = 0.268,0.136, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.95,0.97, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 188291.497,1881563.25,3568623.41,5244520.08,6721167.22,8362587.5, + 9976551.33,11382896.5,12923429.4,14407197.7,15200094.4,15969747.9, + 16726333.9,16976222.2,17151234.8,17501260.,17851285.2,18376323., + 19251386.,21001512., + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 0.,$ + $REFQ + XCG = 1.49563653, + SREF = 0.0153279012, + LREF = 0.1397,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.742, + DNOSE = 0.1397, + LCENTR = 1.487, + DCENTR = 0.1397, + BASE = .FALSE., + DEXIT = 0.054,$ + $FINSET1 + XLE = 1.956,2.022, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.13, + CHORD = 0.268,0.136, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.95,0.97, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 188291.497,1881563.25,3568623.41,5244520.08,6721167.22,8362587.5, + 9976551.33,11382896.5,12923429.4,14407197.7,15200094.4,15969747.9, + 16726333.9,16976222.2,17151234.8,17501260.,17851285.2,18376323., + 19251386.,21001512., + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 5.,$ + $REFQ + XCG = 1.49563653, + SREF = 0.0153279012, + LREF = 0.1397,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.742, + DNOSE = 0.1397, + LCENTR = 1.487, + DCENTR = 0.1397, + BASE = .FALSE., + DEXIT = 0.054,$ + $FINSET1 + XLE = 1.956,2.022, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.13, + CHORD = 0.268,0.136, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.95,0.97, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 188291.497,1881563.25,3568623.41,5244520.08,6721167.22,8362587.5, + 9976551.33,11382896.5,12923429.4,14407197.7,15200094.4,15969747.9, + 16726333.9,16976222.2,17151234.8,17501260.,17851285.2,18376323., + 19251386.,21001512., + NALPHA = 19., + ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5, + 1.,2.,5.,10.,20., + BETA = 20.,$ + $REFQ + XCG = 1.49563653, + SREF = 0.0153279012, + LREF = 0.1397,$ + $AXIBOD + TNOSE = KARMAN, + LNOSE = 0.742, + DNOSE = 0.1397, + LCENTR = 1.487, + DCENTR = 0.1397, + BASE = .FALSE., + DEXIT = 0.054,$ + $FINSET1 + XLE = 1.956,2.022, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.13, + CHORD = 0.268,0.136, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE diff --git a/docs/examples/datcom/comparison_data/rockets/prometheus_2022_provenance.json b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_provenance.json new file mode 100644 index 000000000..784afafc2 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_provenance.json @@ -0,0 +1,376 @@ +{ + "construction_cells": [ + 2, + 4, + 5, + 7, + 9, + 10 + ], + "calibration": { + "roughness_height": 0.0, + "leading_edge_radius": null + }, + "source_adaptations": [], + "mach": [ + 0.01, + 0.1, + 0.19, + 0.28, + 0.36, + 0.45, + 0.54, + 0.62, + 0.71, + 0.8, + 0.85, + 0.9, + 0.95, + 0.97, + 0.98, + 1.0, + 1.02, + 1.05, + 1.1, + 1.2 + ], + "alpha": [ + -20.0, + -10.0, + -5.0, + -2.0, + -1.0, + -0.5, + -0.25, + -0.1, + -0.05, + 0.0, + 0.05, + 0.1, + 0.25, + 0.5, + 1.0, + 2.0, + 5.0, + 10.0, + 20.0 + ], + "beta": [ + -20, + -5, + 0, + 5, + 20 + ], + "altitude_schedules_m_asl": { + "power_on": [ + 1401.0936914612614, + 1409.3041323077705, + 1429.5909152705249, + 1461.0412336902914, + 1497.773126161114, + 1549.7620939126703, + 1614.56633764529, + 1683.60353003684, + 1777.6059036917557, + 1893.5894697427968, + 1969.585906552592, + 2052.8809590440956, + 2148.083171618854, + 2220.9936294262, + 2220.9936294262, + 2220.9936294262, + 2220.9936294262, + 2220.9936294262, + 2220.9936294262, + 2220.9936294262 + ], + "power_off": [ + 5630.104426612279, + 5630.104426612279, + 5506.805013526391, + 5291.952567662137, + 5040.866767253781, + 4700.403914813042, + 4310.287842699598, + 3931.216130080468, + 3484.671054509762, + 3031.7832825088044, + 2783.7154706557035, + 2538.1362718255737, + 2303.3897047743894, + 2275.792418330383, + 2275.792418330383, + 2275.792418330383, + 2275.792418330383, + 2275.792418330383, + 2275.792418330383, + 2275.792418330383 + ] + }, + "atmosphere": { + "mach": [ + 0.01, + 0.1, + 0.19, + 0.28, + 0.36, + 0.45, + 0.54, + 0.62, + 0.71, + 0.8, + 0.85, + 0.9, + 0.95, + 0.97, + 0.98, + 1.0, + 1.02, + 1.05, + 1.1, + 1.2 + ], + "alpha": [ + -20.0, + -10.0, + -5.0, + -2.0, + -1.0, + -0.5, + -0.25, + -0.1, + -0.05, + 0.0, + 0.05, + 0.1, + 0.25, + 0.5, + 1.0, + 2.0, + 5.0, + 10.0, + 20.0 + ], + "coverage": { + "scope": "rail through apogee", + "minimum_dynamic_pressure_pa": 100.0, + "excluded_low_q_samples": 38, + "phases": { + "power_on": { + "mach_min": 0.0005711328526149889, + "mach_max": 0.9614779332526224, + "loaded_alpha_max_abs_deg": 0.33629666480048226 + }, + "power_off": { + "mach_min": 0.11092968802629966, + "mach_max": 0.9560666369045423, + "loaded_alpha_max_abs_deg": 0.710647057020261 + } + } + }, + "reynolds": { + "power_on": [ + 188291.49711741155, + 1881563.247379152, + 3568623.408683362, + 5244520.077894173, + 6721167.217620198, + 8362587.497037956, + 9976551.33044846, + 11382896.489930244, + 12923429.403396707, + 14407197.730068985, + 15200094.417151459, + 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bevels, sharp edges, auto body attachment", + "powered_base_drag": "nozzle", + "damp": true +} \ No newline at end of file diff --git a/docs/examples/datcom/comparison_data/rockets/prometheus_2022_result.json b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_result.json new file mode 100644 index 000000000..cb0288a5d --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_result.json @@ -0,0 +1,49 @@ +{ + "rocket": "prometheus_2022", + "actual_agl_m": 3898.37, + "reported_agl_m": 3898.37, + "saved_first_agl_m": 4188.81, + "reference_kind": "notebook reported apogee", + "telemetry_max_agl_m": 3903.77, + "notes": "Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 3903.770 m is retained separately. | Telemetry maximum 3903.770 m differs from notebook reported value 3898.370 m; both retained. | Prometheus prose says June 2022; source environment and local reanalysis are June 2023. 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+rocket,actual_agl_m,reported_agl_m,saved_first_agl_m,reference_kind,telemetry_max_agl_m,notes,status,reason,ordinary_agl_m,ordinary_apogee_time_s,ordinary_peak_vertical_velocity_m_s,ordinary_initial_static_margin_cal,ordinary_coast_cd_mach06,ordinary_max_mach,ordinary_ascent_altitude_rmse_m,source_adaptations,atmosphere_refinements,atmosphere_last_apogee_change_m,datcom_agl_m,datcom_apogee_time_s,datcom_peak_vertical_velocity_m_s,datcom_initial_static_margin_cal,datcom_coast_cd_mach06,datcom_max_mach,datcom_ascent_altitude_rmse_m,samples,outside_mach,outside_alpha,outside_beta,max_abs_alpha_deg,max_abs_beta_deg,solver_mach_excursion,solver_alpha_excursion,solver_beta_excursion,mass_cg_match,ordinary_error_m,ordinary_error_pct,datcom_error_m,datcom_error_pct,telemetry_qa_max_agl_m,telemetry_qa_rejected_count,ordinary_vs_telemetry_pct,datcom_vs_telemetry_pct,rmse_start_s,rmse_end_s,within_sampled_envelope +andromeda,3443.0,3443.0,3614.946,notebook reported apogee,3438.549384,Telemetry maximum 3438.549 m differs from notebook reported value 3443.000 m; both retained.,paired untuned comparison,,3615.880567942097,25.843083317328176,343.1543305235018,4.158769051891837,0.4984983643122677,1.0050934520481516,164.47060925045412,,1.0,-6.890912629645754,4170.733199067169,28.13329482306857,352.6406104190726,4.585569601745527,0.349,1.0331483789732863,438.15234503512323,497.0,0.0,0.0,0.0,4.455101471012898,3.9438542480866237,True,True,True,True,172.88056794209706,5.02121893529181,727.7331990671692,21.13660177366161,3438.549384,0.0,5.157150999989746,21.293392454216665,0.0,25.83574241251797,True +astra,3250.0,3250.0,3043.066,notebook reported apogee,3249.46,Telemetry maximum 3249.460 m differs from notebook reported value 3250.000 m; both retained. | Source fin trailing edge overhangs the nozzle-inferred body base by 1.216 mm. Native DATCOM accepts this; the integration now validates root-chord centre attachment instead of rejecting all overhang. Geometry is unchanged.,paired untuned comparison,,3043.3679728130164,24.008756329078313,306.4797793783156,3.16256293861153,0.6538147999999999,0.892926042224684,222.49037923280986,,1.0,-4.5530442029007645,3695.9340692825294,26.805544357641757,318.5471261391443,3.477476916191179,0.4265,0.92839198452266,215.54458729050853,480.0,0.0,0.0,0.0,8.95582043567763,9.383306642057704,True,True,True,True,-206.63202718698358,-6.357908528830264,445.93406928252944,13.721048285616291,3249.46,0.0,-6.342346949554189,13.739946615207742,0.0,23.990962200089378,True +bella_lui,458.971,,460.54,telemetry AGL maximum (QA),458.971,,paired untuned comparison,,460.57389565932874,10.607916503445066,86.17870117421616,2.776966165584537,0.43,0.2544470871701163,4.730090709810138,,1.0,9.736509809954441e-06,465.8765076067215,10.675120658701045,86.47349849471236,3.303739452169553,0.3457692307692308,0.2554070587748375,5.162903677983363,427.0,0.0,0.0,0.0,4.441613641629917,2.9112883549730375,True,True,True,True,1.6028956593287376,0.34923680566500664,6.905507606721471,1.5045629477072564,458.971,0.0,0.3492368056650097,1.5045629477072664,0.0,10.53,True +camoes,3015.0,3015.0,3003.284,notebook reported apogee,3015.996337890625,Telemetry maximum 3015.996 m differs from notebook reported value 3015.000 m; both retained.,ordinary only,Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.,3004.10382797431,23.03691562903868,327.4402911475654,3.7616831285769776,0.7036,0.9672822932315445,133.14444925478682,,,,,,,,,,,,,,,,,,,,,-10.896172025689793,-0.36139874048722365,,,3015.996337890625,0.0,-0.3943144680551014,,0.0,23.03691562903868, +cavour,2789.0,2789.0,2817.405,notebook reported apogee,2789.0,,paired untuned comparison,,2818.1348894226226,22.52457463326182,304.6396378918068,4.279860429594885,0.583,0.9017549966473172,57.14208739821666,,1.0,-2.7223603801689933,3251.608548140568,24.312660883196,319.05542973964634,5.465305853006995,0.44825000000000004,0.9445859958835042,284.6998716927426,497.0,0.0,0.0,0.0,1.9473736165566062,12.065683729942409,True,True,True,True,29.134889422622564,1.0446356910226806,462.608548140568,16.58689667051158,2789.0,0.0,1.0446356910226884,16.586896670511585,0.0,22.489211316956297,True +defiance,9308.318,9308.318,9238.009,notebook reported apogee,,"Source fin root centre is z=-0.025 m, aft of the tail base z=0.0403 m; native root attachment is outside the declared body.",ordinary only,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,9243.891367330605,44.18119446865966,438.71139566437614,7.9601902493275025,0.668,1.3681759374499065,,,,,,,,,,,,,,,,,,,,,,-64.42663266939417,-0.6921404347100536,,,,,,,,, +erebus,3020.0,3020.0,2750.904,notebook reported apogee,3001.75136,Telemetry maximum 3001.751 m differs from notebook reported value 3020.000 m; both retained.,paired untuned comparison,,2751.1556122225365,22.17286643393269,294.1707251884576,2.9374083445614505,0.531,0.8634835538004397,173.69041364963655,,1.0,-4.843469621916938,3253.422625182214,24.419938482483815,307.31659427636407,4.450320086266694,0.376,0.9025725207744214,119.78288536278599,498.0,0.0,0.0,0.0,12.023825633907418,9.12247497249757,True,True,True,True,-268.84438777746345,-8.902132045611372,233.42262518221378,7.729225999411052,3001.75136,0.0,-8.348317955870389,8.38414761914903,0.0,22.14074422411866,True +genesis,2916.0,2916.0,3044.091,notebook reported apogee,2916.691650390625,Telemetry maximum 2916.692 m differs from notebook reported value 2916.000 m; both retained.,paired untuned comparison,,3044.6857862664306,24.170070527773692,290.6363275291183,3.37721309359005,0.55,0.8522776236704187,104.35940713968827,,1.0,-2.088661131954723,3297.2391270686403,25.178132490879275,299.2620317383252,3.7682700796928534,0.47475,0.8776710952854886,236.1872905681145,497.0,0.0,0.0,0.0,3.3632935153000667,11.894942323656261,True,True,True,True,128.68578626643057,4.41309280749076,381.2391270686403,13.074044138156388,2916.691650390625,0.0,4.38833278309223,13.047230296937617,0.0,24.12904363709264,True +halcyon,3450.0,3450.0,3212.856,notebook reported apogee,3470.8251953125,Telemetry QA flags 5 samples with altitude jumps above 2000 m/s; raw maximum 3470.825 m is retained separately. | Telemetry maximum 3470.825 m differs from notebook reported value 3450.000 m; both retained.,paired untuned comparison,,3214.238895757538,26.371836502851696,275.5714950426811,3.0192578745448033,0.426,0.812113342368714,112.69062830123777,Read original .eng numeric samples directly: source already has t=0; avoid the importer's extra duplicate zero-time row. No measured sample or motor equation changed.,1.0,-1.122512222840669,3530.231782008296,27.79754660231069,280.94965050790404,4.410132986423363,0.319,0.8293761850832471,69.46314134752838,495.0,0.0,0.0,0.0,7.051663487140428,14.276458017233338,False,True,True,True,-235.7611042424619,-6.8336551954336775,80.2317820082958,2.3255588987911824,3470.8251953125,5.0,-7.39265981765641,1.7115983477366425,0.0,26.36134002785797,True +hedy,5231.53,,5246.918,saved measured-apogee summary,,"Referenced telemetry file is absent: data/rockets/tust/cats_tust/filteredDataInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/gnssInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/flightInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/imu.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/baro.csv (cell 24). | Hedy end comparison uses simulated ASL 5406.918 m against measured AGL 5231.53 m; saved simulated AGL is 5246.918 m, a 160 m datum discrepancy.",archived comparison only,"Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +juno3,3213.0,3213.0,2976.849,notebook reported apogee,10700.59,"Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 10700.590 m is retained separately. | Telemetry maximum 10700.590 m differs from notebook reported value 3213.000 m; both retained. | Source fin airfoil is an empirical lift curve (cell 10), not a supplied section contour. The generic 2% HEX DATCOM assumption does not reproduce that curve; source-drag substitution therefore still has a lateral-model discrepancy.",paired untuned comparison,,3008.581569789987,26.491920030008096,241.2427490025272,3.2206482971493466,0.368,0.7308763878428423,229.55884227968258,,1.0,-0.03861187111760955,3056.4921836297026,26.635000914699194,243.89859046993098,4.0067340523842745,0.37450000000000006,0.7312932218217086,209.292777372015,486.0,0.0,0.0,0.0,43.59333257202937,5.4737412566148915,False,True,True,True,-204.41843021001296,-6.362229387177496,-156.5078163702974,-4.871080497052518,3213.375,2.0,-6.373156889874753,-4.882182016425018,0.0,26.3,True +lince,3587.0,3587.0,3284.107,notebook reported apogee,3668.5205078125,Telemetry QA flags 69 samples with altitude jumps above 2000 m/s; raw maximum 3668.521 m is retained separately. | Telemetry maximum 3668.521 m differs from notebook reported value 3587.000 m; both retained. | Lince has three saved trajectories. Compare first/pre-deployment ascent only; retain all saved apogees separately.,paired untuned comparison,,3285.089381541881,24.728341685630557,317.77081353823803,2.9203959364705625,0.65562,0.936438877536046,180.22020526338466,,1.0,-6.875566491943573,4254.30425999591,28.7853935112826,342.53953112405384,3.962666916625163,0.38175,1.0102057901013621,299.53187203569485,498.0,0.0,0.0,0.0,3.100325929162926,3.920227060235464,True,True,True,True,-301.9106184581192,-8.416800068528554,667.3042599959099,18.60340841917786,3628.76416015625,69.0,-9.470849122351643,17.23837847353311,0.0,24.7074627638509,True +ndrt_2020,1316.736,1316.736,1293.43,notebook reported apogee,1320.3570240000001,"NDRT source uses approximate ft conversion /3.281; comparison uses exact 0.3048 m/ft. | Telemetry maximum 1320.357 m differs from notebook reported value 1316.736 m; both retained. | Source transition is internal at z=1.2..1.327 m with fins near z=3.05 m. The current DATCOM builder represents only an aft tail, not the required downstream smaller cylinder.",ordinary only,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,1293.5184123380945,16.64978369813489,171.66118644734854,4.016381564609376,0.44,0.52460108376313,42.9525631238651,,,,,,,,,,,,,,,,,,,,,-23.217587661905554,-1.763268237665375,,,1320.3570240000001,0.0,-2.0326783721419828,,0.04375,16.64978369813489, +prometheus_2022,3898.37,3898.37,4188.81,notebook reported apogee,3903.77,Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 3903.770 m is retained separately. | Telemetry maximum 3903.770 m differs from notebook reported value 3898.370 m; both retained. | Prometheus prose says June 2022; source environment and local reanalysis are June 2023. Both reruns preserve source conditions.,paired untuned comparison,,4191.288687549948,29.12493947732805,322.07071634939507,1.0415367273926033,0.351875,0.955532649219189,271.52675273853276,,1.0,0.09233800101083034,4229.19676461329,29.30969600583538,324.05991606871703,1.7889093089472907,0.33475000000000005,0.9614276912301178,287.18960219155747,497.0,0.0,0.0,0.0,0.7103949840277883,0.745924092897197,True,True,True,True,292.9186875499481,7.513875993041916,330.8267646132899,8.486284385866142,3903.77,2.0,7.365154390498119,8.33621767197581,0.0,29.114298032463147,True +valetudo,860.0,860.0,815.26,saved measured-apogee summary,,Valetudo flight recordings were lost; only recovered scalar apogee/drift values remain.,paired untuned comparison,,815.0526424633565,13.977332377135696,123.31112741231497,2.951130333277495,1.128,0.3680218288920357,,,1.0,0.38337486952264044,943.6190073224011,15.242452283301883,126.09986587804391,4.156583455241121,0.43133333333333335,0.37639511692688415,,448.0,0.0,0.0,0.0,15.80193704194847,5.1181201273022525,True,True,True,True,-44.947357536643494,-5.226436922865522,83.6190073224011,9.72314038632571,,,,,,,True +valkyrie,2098.02,2098.02,2247.849,notebook reported apogee,2059.0244140625,Telemetry maximum 2059.024 m differs from notebook reported value 2098.020 m; both retained. | Valkyrie prose says 2025 but source environment says 2026 and fetches live Windy weather; no archived profile is supplied.,unavailable source model,SourceUnavailable: Source requests live weather with no archived profile; not replaced by invented weather.,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2059.0244140625,0.0,,,,, diff --git a/docs/examples/datcom/comparison_data/rockets/valetudo_altitude.csv b/docs/examples/datcom/comparison_data/rockets/valetudo_altitude.csv new file mode 100644 index 000000000..de9a87d2c --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/valetudo_altitude.csv @@ -0,0 +1,602 @@ +time_s,ordinary_agl_m,datcom_agl_m,ordinary_vz_m_s,datcom_vz_m_s,ordinary_margin_cal,datcom_margin_cal +0.0,0.0,0.0,0.0,0.0,2.951130333277495,4.156583455241121 +0.025404087138836472,3.410605131648481e-13,3.410605131648481e-13,-1.174475036148923e-09,-1.174593642575345e-09,2.9512015228549555,4.156654644818581 +0.050808174277672945,4.547473508864641e-13,4.547473508864641e-13,-1.8627942358965036e-09,-1.8629823534474762e-09,2.951272712432416,4.156725834396042 +0.07621226141650941,4.547473508864641e-13,4.547473508864641e-13,-1.5788017628413984e-09,-1.5789612009131802e-09,2.9513439020098766,4.1567970239735015 +0.10161634855534589,0.0,0.0,1.6353300054757503e-10,1.635495152151499e-10,2.9514157979320252,4.1568689198956505 +0.12702043569418237,-6.821210263296962e-13,-6.821210263296962e-13,3.2657605407726893e-09,3.2660903393425772e-09,2.95149808910137,4.156951211064995 +0.15242452283301883,-1.2505552149377763e-12,-1.2505552149377763e-12,5.696240702681383e-09,5.696815947563554e-09,2.9515803802707143,4.1570335022343405 +0.17782860997185532,-1.1368683772161603e-12,-1.1368683772161603e-12,5.02419430426694e-09,5.024701681362016e-09,2.9516626714400593,4.157115793403685 +0.20323269711069178,2.2737367544323206e-13,2.2737367544323206e-13,-1.1771508326322374e-09,-1.1772697092786718e-09,2.9517475614864077,4.157200683450034 +0.22863678424952824,2.9558577807620168e-12,2.9558577807620168e-12,-1.256513149995927e-08,-1.256640041185511e-08,2.9518502758799534,4.157303397843579 +0.25404087138836473,4.888534022029489e-12,4.888534022029489e-12,-2.1195503864676743e-08,-2.119764432989927e-08,2.951952990273499,4.157406112237125 +0.2794449585272012,4.206412995699793e-12,4.092726157978177e-12,-1.7831307035159138e-08,-1.7833107761050086e-08,2.952055704667045,4.157508826630671 +0.30484904566603765,-1.5916157281026244e-12,-1.5916157281026244e-12,6.713706237804337e-09,6.714384233232425e-09,2.9521802313990873,4.157633353362713 +0.3302531328048741,-1.1368683772161603e-11,-1.1254996934439987e-11,4.9512712553690656e-08,4.951771268201838e-08,2.9523972203501496,4.157850342313775 +0.35565721994371063,-1.8417267710901797e-11,-1.830358087318018e-11,7.991152607638932e-08,7.991959608234612e-08,2.9526142093012124,4.158067331264838 +0.3810613070825471,-1.4438228390645236e-11,-1.4551915228366852e-11,6.339308242140476e-08,6.339948428328726e-08,2.952831198252275,4.1582843202159 +0.40646539422138356,5.6843418860808015e-12,5.6843418860808015e-12,-2.4497040899771426e-08,-2.4499506711378165e-08,2.9530920701498586,4.158545192110111 +0.43186948136022,9.299583325628191e-11,9.299583325628191e-11,-4.050618444596109e-07,-4.051035582353286e-07,2.9536129010273884,4.159066023004993 +0.4572735684990565,-4.21323420596309e-10,-4.2075498640770093e-10,1.8336244301114564e-06,1.8338157897813984e-06,2.9544268666042495,4.159879988626998 +0.482677655637893,-1.093440005206503e-09,-1.091962076316122e-09,4.75910524344278e-06,4.759646470177708e-06,2.955554127677469,4.1610072496166675 +0.5080817427767295,9.641528322390513e-05,9.64149006676962e-05,0.015256800994336121,0.015256780818328076,2.9571728533407318,4.162625971077869 +0.5334858299155659,0.001381480752911557,0.0013814800250884218,0.09515966864255233,0.0951596453297387,2.959245701049107,4.164698703418507 +0.5588899170544024,0.005377943962457721,0.005377943083772152,0.2288145399771241,0.22881458065263194,2.961675162318529,4.167127591534987 +0.5842940041932388,0.013544834610911494,0.013544837452514003,0.4231382892340418,0.42313869372808477,2.964490476354595,4.169941271003452 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0.01,0.06,0.1,0.14,0.17,0.24,0.28,0.31,0.34,0.38,0.59,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 201178.395,1208477.86,2020836.78,2843131.67,3468624.39,4966263.57, + 5851154.81,6529024.87,7160865.99,8003320.81,12426208.6,16849096.5, + 18955233.5,20008302.,20640143.2,21061370.6,21482598.,22114439.1, + 23167507.6,25273644.7, + NALPHA = 19., + ALPHA = -20.57,-10.28,-5.14,-2.06,-1.03,-0.51,-0.26,-0.1,-0.05,0.,0.05, + 0.1,0.26,0.51,1.03,2.06,5.14,10.28,20.57, + BETA = -20.,$ + $REFQ + XCG = 1.13407314, + SREF = 0.00514028175, + LREF = 0.0809,$ + $AXIBOD + TNOSE = OGIVE, + LNOSE = 0.274, + DNOSE = 0.0809, + LCENTR = 1.884, + DCENTR = 0.0809, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 2.04,2.08, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.077, + CHORD = 0.058,0.018, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.06,0.1,0.14,0.17,0.24,0.28,0.31,0.34,0.38,0.59,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 201178.395,1208477.86,2020836.78,2843131.67,3468624.39,4966263.57, + 5851154.81,6529024.87,7160865.99,8003320.81,12426208.6,16849096.5, + 18955233.5,20008302.,20640143.2,21061370.6,21482598.,22114439.1, + 23167507.6,25273644.7, + NALPHA = 19., + ALPHA = -20.57,-10.28,-5.14,-2.06,-1.03,-0.51,-0.26,-0.1,-0.05,0.,0.05, + 0.1,0.26,0.51,1.03,2.06,5.14,10.28,20.57, + BETA = -5.,$ + $REFQ + XCG = 1.13407314, + SREF = 0.00514028175, + LREF = 0.0809,$ + $AXIBOD + TNOSE = OGIVE, + LNOSE = 0.274, + DNOSE = 0.0809, + LCENTR = 1.884, + DCENTR = 0.0809, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 2.04,2.08, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.077, + CHORD = 0.058,0.018, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.06,0.1,0.14,0.17,0.24,0.28,0.31,0.34,0.38,0.59,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 201178.395,1208477.86,2020836.78,2843131.67,3468624.39,4966263.57, + 5851154.81,6529024.87,7160865.99,8003320.81,12426208.6,16849096.5, + 18955233.5,20008302.,20640143.2,21061370.6,21482598.,22114439.1, + 23167507.6,25273644.7, + NALPHA = 19., + ALPHA = -20.57,-10.28,-5.14,-2.06,-1.03,-0.51,-0.26,-0.1,-0.05,0.,0.05, + 0.1,0.26,0.51,1.03,2.06,5.14,10.28,20.57, + BETA = 0.,$ + $REFQ + XCG = 1.13407314, + SREF = 0.00514028175, + LREF = 0.0809,$ + $AXIBOD + TNOSE = OGIVE, + LNOSE = 0.274, + DNOSE = 0.0809, + LCENTR = 1.884, + DCENTR = 0.0809, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 2.04,2.08, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.077, + CHORD = 0.058,0.018, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.06,0.1,0.14,0.17,0.24,0.28,0.31,0.34,0.38,0.59,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 201178.395,1208477.86,2020836.78,2843131.67,3468624.39,4966263.57, + 5851154.81,6529024.87,7160865.99,8003320.81,12426208.6,16849096.5, + 18955233.5,20008302.,20640143.2,21061370.6,21482598.,22114439.1, + 23167507.6,25273644.7, + NALPHA = 19., + ALPHA = -20.57,-10.28,-5.14,-2.06,-1.03,-0.51,-0.26,-0.1,-0.05,0.,0.05, + 0.1,0.26,0.51,1.03,2.06,5.14,10.28,20.57, + BETA = 5.,$ + $REFQ + XCG = 1.13407314, + SREF = 0.00514028175, + LREF = 0.0809,$ + $AXIBOD + TNOSE = OGIVE, + LNOSE = 0.274, + DNOSE = 0.0809, + LCENTR = 1.884, + DCENTR = 0.0809, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 2.04,2.08, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.077, + CHORD = 0.058,0.018, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.06,0.1,0.14,0.17,0.24,0.28,0.31,0.34,0.38,0.59,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 201178.395,1208477.86,2020836.78,2843131.67,3468624.39,4966263.57, + 5851154.81,6529024.87,7160865.99,8003320.81,12426208.6,16849096.5, + 18955233.5,20008302.,20640143.2,21061370.6,21482598.,22114439.1, + 23167507.6,25273644.7, + NALPHA = 19., + ALPHA = -20.57,-10.28,-5.14,-2.06,-1.03,-0.51,-0.26,-0.1,-0.05,0.,0.05, + 0.1,0.26,0.51,1.03,2.06,5.14,10.28,20.57, + BETA = 20.,$ + $REFQ + XCG = 1.13407314, + SREF = 0.00514028175, + LREF = 0.0809,$ + $AXIBOD + TNOSE = OGIVE, + LNOSE = 0.274, + DNOSE = 0.0809, + LCENTR = 1.884, + DCENTR = 0.0809, + BASE = .FALSE., + DEXIT = 0.,$ + $FINSET1 + XLE = 2.04,2.08, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.077, + CHORD = 0.058,0.018, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE diff --git a/docs/examples/datcom/comparison_data/rockets/valetudo_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/valetudo_powered_for005.dat new file mode 100644 index 000000000..6e2e79d50 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/valetudo_powered_for005.dat @@ -0,0 +1,220 @@ +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.06,0.1,0.14,0.17,0.24,0.28,0.31,0.34,0.38,0.59,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 219350.475,1315688.72,2191873.9,3066947.72,3722406.66,5247924.85, + 6115702.5,6531057.32,7231716.15,8229426.14,12777266.9,17325107.7, + 19490746.1,20573565.4,21223256.9,21656384.6,22089512.3,22739203.8, + 23822023.,25987661.5, + NALPHA = 19., + ALPHA = -20.57,-10.28,-5.14,-2.06,-1.03,-0.51,-0.26,-0.1,-0.05,0.,0.05, + 0.1,0.26,0.51,1.03,2.06,5.14,10.28,20.57, + BETA = -20.,$ + $REFQ + XCG = 1.2220811, + SREF = 0.00514028175, + LREF = 0.0809,$ + $AXIBOD + TNOSE = OGIVE, + LNOSE = 0.274, + DNOSE = 0.0809, + LCENTR = 1.884, + DCENTR = 0.0809, + BASE = .FALSE., + DEXIT = 0.043284,$ + $FINSET1 + XLE = 2.04,2.08, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.077, + CHORD = 0.058,0.018, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.06,0.1,0.14,0.17,0.24,0.28,0.31,0.34,0.38,0.59,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 219350.475,1315688.72,2191873.9,3066947.72,3722406.66,5247924.85, + 6115702.5,6531057.32,7231716.15,8229426.14,12777266.9,17325107.7, + 19490746.1,20573565.4,21223256.9,21656384.6,22089512.3,22739203.8, + 23822023.,25987661.5, + NALPHA = 19., + ALPHA = -20.57,-10.28,-5.14,-2.06,-1.03,-0.51,-0.26,-0.1,-0.05,0.,0.05, + 0.1,0.26,0.51,1.03,2.06,5.14,10.28,20.57, + BETA = -5.,$ + $REFQ + XCG = 1.2220811, + SREF = 0.00514028175, + LREF = 0.0809,$ + $AXIBOD + TNOSE = OGIVE, + LNOSE = 0.274, + DNOSE = 0.0809, + LCENTR = 1.884, + DCENTR = 0.0809, + BASE = .FALSE., + DEXIT = 0.043284,$ + $FINSET1 + XLE = 2.04,2.08, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.077, + CHORD = 0.058,0.018, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.06,0.1,0.14,0.17,0.24,0.28,0.31,0.34,0.38,0.59,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 219350.475,1315688.72,2191873.9,3066947.72,3722406.66,5247924.85, + 6115702.5,6531057.32,7231716.15,8229426.14,12777266.9,17325107.7, + 19490746.1,20573565.4,21223256.9,21656384.6,22089512.3,22739203.8, + 23822023.,25987661.5, + NALPHA = 19., + ALPHA = -20.57,-10.28,-5.14,-2.06,-1.03,-0.51,-0.26,-0.1,-0.05,0.,0.05, + 0.1,0.26,0.51,1.03,2.06,5.14,10.28,20.57, + BETA = 0.,$ + $REFQ + XCG = 1.2220811, + SREF = 0.00514028175, + LREF = 0.0809,$ + $AXIBOD + TNOSE = OGIVE, + LNOSE = 0.274, + DNOSE = 0.0809, + LCENTR = 1.884, + DCENTR = 0.0809, + BASE = .FALSE., + DEXIT = 0.043284,$ + $FINSET1 + XLE = 2.04,2.08, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.077, + CHORD = 0.058,0.018, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.06,0.1,0.14,0.17,0.24,0.28,0.31,0.34,0.38,0.59,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 219350.475,1315688.72,2191873.9,3066947.72,3722406.66,5247924.85, + 6115702.5,6531057.32,7231716.15,8229426.14,12777266.9,17325107.7, + 19490746.1,20573565.4,21223256.9,21656384.6,22089512.3,22739203.8, + 23822023.,25987661.5, + NALPHA = 19., + ALPHA = -20.57,-10.28,-5.14,-2.06,-1.03,-0.51,-0.26,-0.1,-0.05,0.,0.05, + 0.1,0.26,0.51,1.03,2.06,5.14,10.28,20.57, + BETA = 5.,$ + $REFQ + XCG = 1.2220811, + SREF = 0.00514028175, + LREF = 0.0809,$ + $AXIBOD + TNOSE = OGIVE, + LNOSE = 0.274, + DNOSE = 0.0809, + LCENTR = 1.884, + DCENTR = 0.0809, + BASE = .FALSE., + DEXIT = 0.043284,$ + $FINSET1 + XLE = 2.04,2.08, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.077, + CHORD = 0.058,0.018, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE +DIM M + $FLTCON + NMACH = 20., + MACH = 0.01,0.06,0.1,0.14,0.17,0.24,0.28,0.31,0.34,0.38,0.59,0.8,0.9,0.95, + 0.98,1.,1.02,1.05,1.1,1.2, + REN = 219350.475,1315688.72,2191873.9,3066947.72,3722406.66,5247924.85, + 6115702.5,6531057.32,7231716.15,8229426.14,12777266.9,17325107.7, + 19490746.1,20573565.4,21223256.9,21656384.6,22089512.3,22739203.8, + 23822023.,25987661.5, + NALPHA = 19., + ALPHA = -20.57,-10.28,-5.14,-2.06,-1.03,-0.51,-0.26,-0.1,-0.05,0.,0.05, + 0.1,0.26,0.51,1.03,2.06,5.14,10.28,20.57, + BETA = 20.,$ + $REFQ + XCG = 1.2220811, + SREF = 0.00514028175, + LREF = 0.0809,$ + $AXIBOD + TNOSE = OGIVE, + LNOSE = 0.274, + DNOSE = 0.0809, + LCENTR = 1.884, + DCENTR = 0.0809, + BASE = .FALSE., + DEXIT = 0.043284,$ + $FINSET1 + XLE = 2.04,2.08, + NPANEL = 3., + PHIF = 0.,120.,240., + GAM = 0.,0.,0., + SSPAN = 0.,0.077, + CHORD = 0.058,0.018, + SECTYP = HEX, + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.05,0.05, + LFLATU = 0.9,0.9, + LMAXL = 0.05,0.05, + LFLATL = 0.9,0.9,$ + $DEFLCT + DELTA1 = -0.,-0.,-0.,$ +DAMP +NEXT CASE diff --git a/docs/examples/datcom/comparison_data/rockets/valetudo_provenance.json b/docs/examples/datcom/comparison_data/rockets/valetudo_provenance.json new file mode 100644 index 000000000..077e813aa --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/valetudo_provenance.json @@ -0,0 +1,382 @@ +{ + "construction_cells": [ + 2, + 3, + 6, + 9, + 11, + 14, + 15, + 18, + 20, + 22, + 24, + 25 + ], + "calibration": { + "roughness_height": 0.0, + "leading_edge_radius": null + }, + "source_adaptations": [], + "mach": [ + 0.01, + 0.06, + 0.1, + 0.14, + 0.17, + 0.24, + 0.28, + 0.31, + 0.34, + 0.38, + 0.59, + 0.8, + 0.9, + 0.95, + 0.98, + 1.0, + 1.02, + 1.05, + 1.1, + 1.2 + ], + "alpha": [ + -20.57, + -10.28, + -5.14, + -2.06, + -1.03, + -0.51, + -0.26, + -0.1, + -0.05, + 0.0, + 0.05, + 0.1, + 0.26, + 0.51, + 1.03, + 2.06, + 5.14, + 10.28, + 20.57 + ], + "beta": [ + -20, + -5, + 0, + 5, + 20 + ], + "altitude_schedules_m_asl": { + "power_on": [ + 668.2451556812705, + 671.7128099059793, + 676.4398801362132, + 682.4573101603798, + 687.6171073922815, + 702.7939823289616, + 715.1655825779246, + 1110.517002272916, + 1006.3620428081623, + 808.9085724369791, + 808.9085724369791, + 808.9085724369791, + 808.9085724369791, + 808.9085724369791, + 808.9085724369791, + 808.9085724369791, + 808.9085724369791, + 808.9085724369791, + 808.9085724369791, + 808.9085724369791 + ], + "power_off": [ + 1611.2356324528785, + 1598.6589757835636, + 1562.761599621697, + 1509.558428492431, + 1458.7423406044002, + 1306.149554864665, + 1199.3282219926384, + 1113.9130697237874, + 1113.9130697237874, + 1113.9130697237874, + 1113.9130697237874, + 1113.9130697237874, + 1113.9130697237874, + 1113.9130697237874, + 1113.9130697237874, + 1113.9130697237874, + 1113.9130697237874, + 1113.9130697237874, + 1113.9130697237874, + 1113.9130697237874 + ] + }, + "atmosphere": { + "mach": [ + 0.01, + 0.06, + 0.1, + 0.14, + 0.17, + 0.24, + 0.28, + 0.31, + 0.34, + 0.38, + 0.59, + 0.8, + 0.9, + 0.95, + 0.98, + 1.0, + 1.02, + 1.05, + 1.1, + 1.2 + ], + "alpha": [ + -20.57, + -10.28, + -5.14, + -2.06, + -1.03, + -0.51, + -0.26, + -0.1, + -0.05, + 0.0, + 0.05, + 0.1, + 0.26, + 0.51, + 1.03, + 2.06, + 5.14, + 10.28, + 20.57 + ], + "coverage": { + "scope": "rail through apogee", + "minimum_dynamic_pressure_pa": 100.0, + "excluded_low_q_samples": 178, + "phases": { + "power_on": { + "mach_min": -1.9191314516862737e-08, + "mach_max": 0.3763335399829133, + "loaded_alpha_max_abs_deg": 0.2699795295850288 + }, + "power_off": { + "mach_min": 0.036696416799674426, + "mach_max": 0.3089734070463784, + "loaded_alpha_max_abs_deg": 16.145273736494154 + } + } + }, + "reynolds": { + "power_on": [ + 219350.47485501677, + 1315688.718430272, + 2191873.8988514375, + 3066947.720272081, + 3722406.6638718154, + 5247924.847114815, + 6115702.495601609, + 6531057.316529517, + 7231716.151987152, + 8229426.142495456, + 12777266.905453471, + 17325107.66841149, + 19490746.126962923, + 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HEX, 5% bevels, sharp edges, auto body attachment", + "powered_base_drag": "nozzle", + "damp": true +} \ No newline at end of file diff --git a/docs/examples/datcom/comparison_data/rockets/valetudo_result.json b/docs/examples/datcom/comparison_data/rockets/valetudo_result.json new file mode 100644 index 000000000..d011f2d77 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/valetudo_result.json @@ -0,0 +1,41 @@ +{ + "rocket": "valetudo", + "actual_agl_m": 860.0, + "reported_agl_m": 860.0, + "saved_first_agl_m": 815.26, + "reference_kind": "saved measured-apogee summary", + "telemetry_max_agl_m": null, + "notes": "Valetudo flight recordings were lost; only recovered scalar apogee/drift values remain.", + "status": "paired untuned comparison", + "reason": "", + "ordinary_agl_m": 815.0526424633565, + "ordinary_apogee_time_s": 13.977332377135696, + "ordinary_peak_vertical_velocity_m_s": 123.31112741231497, + "ordinary_initial_static_margin_cal": 2.951130333277495, + 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file diff --git a/docs/examples/datcom/comparison_data/rockets/valkyrie_result.json b/docs/examples/datcom/comparison_data/rockets/valkyrie_result.json new file mode 100644 index 000000000..f80941c95 --- /dev/null +++ b/docs/examples/datcom/comparison_data/rockets/valkyrie_result.json @@ -0,0 +1,13 @@ +{ + "rocket": "valkyrie", + "actual_agl_m": 2098.02, + "reported_agl_m": 2098.02, + "saved_first_agl_m": 2247.849, + "reference_kind": "notebook reported apogee", + "telemetry_max_agl_m": 2059.0244140625, + "notes": "Telemetry maximum 2059.024 m differs from notebook reported value 2098.020 m; both retained. | Valkyrie prose says 2025 but source environment says 2026 and fetches live Windy weather; no archived profile is supplied.", + "status": "unavailable source model", + "reason": "SourceUnavailable: Source requests live weather with no archived profile; not replaced by invented weather.", + "telemetry_qa_max_agl_m": 2059.0244140625, + "telemetry_qa_rejected_count": 0 +} \ No newline 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{}, + "source": [ + "# Recorded rockets: DATCOM, RocketPy and measurements\n", + "\n", + "**Work in progress:** DATCOM currently produces poor flight predictions for several comparison rockets. The discrepancies are unresolved and are being investigated; these results should not be treated as validated predictions.\n", + "\n", + "Compare **apogee AGL**, **peak upward vertical velocity**, **CD** and **initial static margin**. Signed measured-reference error is `100 × (prediction − measurement) / |measurement|`; positive means overprediction. Missing data are **—**, never zero. Velocity means the vertical/altimeter ascent channel, not total airspeed. Only five records provide explicit velocity channels. Recorded CD is unavailable; NDRT alone explicitly reports a measured stability margin, with unspecified Mach and loading. Its stability comparison is conditional.\n", + "\n", + "**Apogee-fitted CD is inferred, not measured.** It is one constant coefficient used for both powered and coast drag in the ordinary RocketPy model, fitted to reach the recorded apogee while retaining that source model's mass, atmosphere and lateral aerodynamics. This is one possible effective-loss calibration, not a unique physical CD curve or a DATCOM correction. CD percentages below compare zero-incidence coast CD at Mach 0.6 against that fitted constant; they are not measurement errors. Controlled air-brake cases are not fitted this way.\n", + "\n", + "Both models retain source geometry, motor, mass, atmosphere and launch inputs. DATCOM uses DAMP, powered nozzle-annulus/coast full-base drag and adaptive 20-point Mach grids containing 0.1 and 1.0, with dense transonic points. Signed alpha grids retain at least ±20° and update with the reference trajectory. The ordinary flight seeds actual-atmosphere Reynolds schedules; one DATCOM trajectory update follows. Scope is rail through apogee. Unspecified construction is carbon fibre with vinyl; it supplies no measured roughness or fin section. The untuned comparisons assume smooth 2% HEX sections with 5% bevels and sharp edges; native coefficients are not fitted to apogee.\n", + "\n", + "Source notebook and data hashes, measurement provenance, masks, fitting residuals and availability reasons are saved in `comparison_data/`. Hedy/Valkyrie apogees use archived ordinary outputs where fresh reproduction is unavailable. **AST-HER has not flown** and is compared separately.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d9806fcc", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-22T22:26:49.293282Z", + "iopub.status.busy": "2026-10-22T22:26:49.293114Z", + "iopub.status.idle": "2026-10-22T22:26:50.248546Z", + "shell.execute_reply": "2026-10-22T22:26:50.248230Z" + } + }, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import json\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from IPython.display import display\n", + "ROOT = next(p for p in (Path.cwd(), *Path.cwd().parents) if (p / \"rocketpy\").is_dir())\n", + "DATA = ROOT / \"docs/examples/datcom/comparison_data\"\n", + "plt.rcParams.update({\"axes.grid\": True, \"grid.alpha\": .2})\n", + "def show(frame):\n", + " display(frame.style.format(precision=3, na_rep=\"—\"))\n", + "metrics = pd.read_csv(DATA / \"rocket_metrics.csv\")\n", + "status = pd.read_csv(DATA / \"rockets/results.csv\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "330878d5", + "metadata": {}, + "source": [ + "## Apogee above launch site (m)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "bc74a0fd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-22T22:26:50.250187Z", + "iopub.status.busy": "2026-10-22T22:26:50.250036Z", + "iopub.status.idle": "2026-10-22T22:26:50.296816Z", + "shell.execute_reply": "2026-10-22T22:26:50.296487Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 rocketmeasuredrocketpydatcomrocketpy_error_pctdatcom_error_pct
0andromeda3443.0003615.8814170.7335.02121.137
5astra3250.0003043.3683695.934-6.35813.721
10bella_lui458.971460.574465.8770.3491.505
15camoes3015.0003004.104—-0.361—
20cavour2789.0002818.1353251.6091.04516.587
25defiance9308.3189243.891—-0.692—
30erebus3020.0002751.1563253.423-8.9027.729
35genesis2916.0003044.6863297.2394.41313.074
40halcyon3450.0003214.2393530.232-6.8342.326
45hedy5231.5305246.918—0.294—
49juno33213.0003008.5823056.492-6.362-4.871
54lince3587.0003285.0894254.304-8.41718.603
59ndrt_20201316.7361293.518—-1.763—
64prometheus_20223898.3704191.2894229.1977.5148.486
69valetudo860.000815.053943.619-5.2269.723
74valkyrie2098.0202247.849—7.141—
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "show(metrics[metrics.metric == 'Apogee AGL'][['rocket', 'measured', 'rocketpy', 'datcom', 'rocketpy_error_pct', 'datcom_error_pct']])" + ] + }, + { + "cell_type": "markdown", + "id": "f9993ba7", + "metadata": {}, + "source": [ + "## Peak upward vertical velocity (m/s)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6346434f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-22T22:26:50.298327Z", + "iopub.status.busy": "2026-10-22T22:26:50.298145Z", + "iopub.status.idle": "2026-10-22T22:26:50.303444Z", + "shell.execute_reply": "2026-10-22T22:26:50.303163Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 rocketmeasuredrocketpydatcomrocketpy_error_pctdatcom_error_pct
1andromeda324.109343.154352.6415.8768.803
6astra—306.480318.547——
11bella_lui90.00086.17986.473-4.246-3.918
16camoes—327.440———
21cavour314.000304.640319.055-2.9811.610
26defiance—438.711———
31erebus—294.171307.317——
36genesis—290.636299.262——
41halcyon—275.571280.950——
46hedy—————
50juno3268.761241.243243.899-10.239-9.251
55lince—317.771342.540——
60ndrt_2020—171.661———
65prometheus_2022317.880322.071324.0601.3181.944
70valetudo—123.311126.100——
75valkyrie—————
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "show(metrics[metrics.metric == 'Peak upward vertical velocity'][['rocket', 'measured', 'rocketpy', 'datcom', 'rocketpy_error_pct', 'datcom_error_pct']])" + ] + }, + { + "cell_type": "markdown", + "id": "4a47a191", + "metadata": {}, + "source": [ + "## CD and the apogee-fitted effective coefficient" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bc0b298b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-22T22:26:50.304636Z", + "iopub.status.busy": "2026-10-22T22:26:50.304545Z", + "iopub.status.idle": "2026-10-22T22:26:50.309449Z", + "shell.execute_reply": "2026-10-22T22:26:50.309159Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 rocketmeasuredrocketpydatcominferred_cdrocketpy_vs_fitted_cd_pctdatcom_vs_fitted_cd_pct
3andromeda—0.4980.3490.591-15.707-40.986
8astra—0.6540.4260.5999.168-28.787
13bella_lui—0.4300.3460.446-3.535-22.431
18camoes—0.704————
23cavour—0.5830.4480.609-4.214-26.353
28defiance—0.668—0.6571.606—
33erebus—0.5310.3760.44319.900-15.099
38genesis—0.5500.4750.609-9.687-22.043
43halcyon—0.4260.3190.34523.594-7.450
48hedy——————
52juno3—0.3680.3740.26439.34541.806
57lince—0.6560.3820.57813.398-33.971
62ndrt_2020—0.440—0.4175.468—
67prometheus_2022—0.3520.3350.403-12.600-16.854
72valetudo—1.1280.4310.81039.337-46.719
77valkyrie——————
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "show(metrics[metrics.metric == 'Coast CD at Mach 0.6'][['rocket', 'measured', 'rocketpy', 'datcom', 'inferred_cd', 'rocketpy_vs_fitted_cd_pct', 'datcom_vs_fitted_cd_pct']])" + ] + }, + { + "cell_type": "markdown", + "id": "feab3ba7", + "metadata": {}, + "source": [ + "## Initial static margin (calibers)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ead14610", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-22T22:26:50.310538Z", + "iopub.status.busy": "2026-10-22T22:26:50.310465Z", + "iopub.status.idle": "2026-10-22T22:26:50.315133Z", + "shell.execute_reply": "2026-10-22T22:26:50.314884Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 rocketmeasuredrocketpydatcomrocketpy_error_pctdatcom_error_pct
2andromeda—4.1594.586——
7astra—3.1633.477——
12bella_lui—2.7773.304——
17camoes—3.762———
22cavour—4.2805.465——
27defiance—7.960———
32erebus—2.9374.450——
37genesis—3.3773.768——
42halcyon—3.0194.410——
47hedy—————
51juno3—3.2214.007——
56lince—2.9203.963——
61ndrt_20202.8754.016—39.700—
66prometheus_2022—1.0421.789——
71valetudo—2.9514.157——
76valkyrie—————
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "show(metrics[metrics.metric == 'Initial static margin'][['rocket', 'measured', 'rocketpy', 'datcom', 'rocketpy_error_pct', 'datcom_error_pct']])" + ] + }, + { + "cell_type": "markdown", + "id": "2d171f26", + "metadata": {}, + "source": [ + "## Trajectory, CD and stability comparisons\n", + "\n", + "Blue is ordinary RocketPy; orange is native DATCOM; black is recorded data where available. Dotted CD is the inferred constant needed for the recorded apogee. CD curves are zero-incidence values, not time histories. Stability curves follow each simulated Mach and moving CG; the scalar table uses Mach-zero loaded ignition (DATCOM clamps to its lowest positive Mach). NDRT's recorded stability appears as an annotation because its measurement conditions are unknown.\n", + "\n", + "Recorded altitude keeps its original time and datum. QA flags the two extreme Juno altitude jumps; raw data and masks remain saved. Velocity uses explicitly supplied channels during the QA ascent, without differentiating altitude or fitting a time shift. Reported scalar apogees and telemetry maxima can differ; reported/recovered apogees remain the preferred scalar references." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c96f85d4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-22T22:26:50.316257Z", + "iopub.status.busy": "2026-10-22T22:26:50.316180Z", + "iopub.status.idle": "2026-10-22T22:26:54.997247Z", + "shell.execute_reply": "2026-10-22T22:26:54.996374Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for name in status.rocket:\n", + " fig, axes = plt.subplots(2, 2, figsize=(12, 6))\n", + " history_path = DATA / \"rockets\" / f\"{name}_altitude.csv\"\n", + " if history_path.exists():\n", + " history = pd.read_csv(history_path)\n", + " for prefix, label, color in [(\"ordinary\", \"RocketPy\", \"tab:blue\"), (\"datcom\", \"DATCOM\", \"tab:orange\")]:\n", + " for axis, suffix in [(axes[0,0], \"agl_m\"), (axes[0,1], \"vz_m_s\"), (axes[1,1], \"margin_cal\")]:\n", + " column = f\"{prefix}_{suffix}\"\n", + " if column in history:\n", + " axis.plot(history.time_s, history[column], label=label, color=color)\n", + " measured_path = DATA / \"rockets\" / f\"{name}_telemetry.csv\"\n", + " if measured_path.exists():\n", + " measured = pd.read_csv(measured_path)\n", + " measured = measured[measured.qa_valid]\n", + " end = measured.loc[measured.altitude_agl_m.idxmax(), \"time_s\"]\n", + " measured = measured[(measured.time_s >= 0) & (measured.time_s <= end)]\n", + " axes[0,0].plot(measured.time_s, measured.altitude_agl_m, color=\"black\", label=\"Recorded\")\n", + " else:\n", + " value = metrics[(metrics.rocket==name) & (metrics.metric==\"Apogee AGL\")].measured.iloc[0]\n", + " axes[0,0].axhline(value, color=\"black\", linestyle=\":\", label=\"Recorded apogee only\")\n", + " velocity_path = DATA / \"rockets\" / f\"{name}_velocity.csv\"\n", + " if velocity_path.exists():\n", + " velocity = pd.read_csv(velocity_path); velocity = velocity[velocity.qa_valid]\n", + " axes[0,1].plot(velocity.time_s, velocity.measured_vz_m_s, color=\"black\", label=\"Recorded\")\n", + " else:\n", + " axes[0,1].text(.02,.96,\"Measured velocity unavailable\", transform=axes[0,1].transAxes, va=\"top\", fontsize=8)\n", + " cd_path = DATA / \"rockets\" / f\"{name}_cd.csv\"\n", + " if cd_path.exists():\n", + " cd = pd.read_csv(cd_path)\n", + " for prefix, label, color in [(\"rocketpy\", \"RocketPy\", \"tab:blue\"), (\"datcom\", \"DATCOM\", \"tab:orange\")]:\n", + " for phase, style in [(\"powered\", \"--\"), (\"coast\", \"-\")]:\n", + " column = f\"{prefix}_{phase}_cd\"\n", + " if column in cd:\n", + " axes[1,0].plot(cd.mach, cd[column], style, color=color, label=f\"{label} {phase}\")\n", + " fit = metrics[(metrics.rocket==name) & (metrics.metric==\"Apogee-fitted constant CD\")]\n", + " if not fit.empty and pd.notna(fit.inferred_cd.iloc[0]):\n", + " axes[1,0].axhline(fit.inferred_cd.iloc[0], color=\"black\", linestyle=\":\", label=\"Apogee-fitted, inferred\")\n", + " axes[1,0].text(.02,.96,\"Measured CD unavailable\",transform=axes[1,0].transAxes, va=\"top\", fontsize=8)\n", + " if name == \"ndrt_2020\":\n", + " axes[1,1].text(.02,.96,\"Recorded: 2.875 cal; conditions unspecified\", transform=axes[1,1].transAxes, va=\"top\",fontsize=8)\n", + " else:\n", + " axes[1,1].text(.02,.96,\"Measured margin unavailable\",transform=axes[1,1].transAxes, va=\"top\",fontsize=8)\n", + " for axis,title,xlabel,ylabel in [(axes[0,0],\"Altitude\",\"Time (s)\",\"Altitude AGL (m)\"), (axes[0,1],\"Vertical velocity\",\"Time (s)\",\"Upward velocity (m/s)\"), (axes[1,0],\"Drag coefficient\",\"Mach\",\"CD\"), (axes[1,1],\"Stability margin\",\"Time (s)\",\"Margin (cal)\")]:\n", + " axis.set(title=title,xlabel=xlabel,ylabel=ylabel)\n", + " if axis.get_legend_handles_labels()[0]: axis.legend(fontsize=7)\n", + " fig.suptitle(name.replace(\"_\",\" \").upper())\n", + " fig.tight_layout(); plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "d270f1f9", + "metadata": {}, + "source": [ + "## Availability and calibration residuals" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b9cbc7a4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-22T22:26:55.001119Z", + "iopub.status.busy": "2026-10-22T22:26:55.000873Z", + "iopub.status.idle": "2026-10-22T22:26:55.016035Z", + "shell.execute_reply": "2026-10-22T22:26:55.015626Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 rocketstatusreasonnotes
0andromedapaired untuned comparison—Telemetry maximum 3438.549 m differs from notebook reported value 3443.000 m; both retained.
1astrapaired untuned comparison—Telemetry maximum 3249.460 m differs from notebook reported value 3250.000 m; both retained. | Source fin trailing edge overhangs the nozzle-inferred body base by 1.216 mm. Native DATCOM accepts this; the integration now validates root-chord centre attachment instead of rejecting all overhang. Geometry is unchanged.
2bella_luipaired untuned comparison——
3camoesordinary onlySource active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.Telemetry maximum 3015.996 m differs from notebook reported value 3015.000 m; both retained.
4cavourpaired untuned comparison——
5defianceordinary onlyValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tipSource fin root centre is z=-0.025 m, aft of the tail base z=0.0403 m; native root attachment is outside the declared body.
6erebuspaired untuned comparison—Telemetry maximum 3001.751 m differs from notebook reported value 3020.000 m; both retained.
7genesispaired untuned comparison—Telemetry maximum 2916.692 m differs from notebook reported value 2916.000 m; both retained.
8halcyonpaired untuned comparison—Telemetry QA flags 5 samples with altitude jumps above 2000 m/s; raw maximum 3470.825 m is retained separately. | Telemetry maximum 3470.825 m differs from notebook reported value 3450.000 m; both retained.
9hedyarchived comparison onlySource data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.Referenced telemetry file is absent: data/rockets/tust/cats_tust/filteredDataInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/gnssInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/flightInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/imu.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/baro.csv (cell 24). | Hedy end comparison uses simulated ASL 5406.918 m against measured AGL 5231.53 m; saved simulated AGL is 5246.918 m, a 160 m datum discrepancy.
10juno3paired untuned comparison—Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 10700.590 m is retained separately. | Telemetry maximum 10700.590 m differs from notebook reported value 3213.000 m; both retained. | Source fin airfoil is an empirical lift curve (cell 10), not a supplied section contour. The generic 2% HEX DATCOM assumption does not reproduce that curve; source-drag substitution therefore still has a lateral-model discrepancy.
11lincepaired untuned comparison—Telemetry QA flags 69 samples with altitude jumps above 2000 m/s; raw maximum 3668.521 m is retained separately. | Telemetry maximum 3668.521 m differs from notebook reported value 3587.000 m; both retained. | Lince has three saved trajectories. Compare first/pre-deployment ascent only; retain all saved apogees separately.
12ndrt_2020ordinary onlyValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tipNDRT source uses approximate ft conversion /3.281; comparison uses exact 0.3048 m/ft. | Telemetry maximum 1320.357 m differs from notebook reported value 1316.736 m; both retained. | Source transition is internal at z=1.2..1.327 m with fins near z=3.05 m. The current DATCOM builder represents only an aft tail, not the required downstream smaller cylinder.
13prometheus_2022paired untuned comparison—Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 3903.770 m is retained separately. | Telemetry maximum 3903.770 m differs from notebook reported value 3898.370 m; both retained. | Prometheus prose says June 2022; source environment and local reanalysis are June 2023. Both reruns preserve source conditions.
14valetudopaired untuned comparison—Valetudo flight recordings were lost; only recovered scalar apogee/drift values remain.
15valkyrieunavailable source modelSourceUnavailable: Source requests live weather with no archived profile; not replaced by invented weather.Telemetry maximum 2059.024 m differs from notebook reported value 2098.020 m; both retained. | Valkyrie prose says 2025 but source environment says 2026 and fetches live Windy weather; no archived profile is supplied.
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 rocketinferred_cdfit_apogee_residual_mcondition
4andromeda0.5910.024inferred: single constant CD, same powered/coast, ordinary lateral model
9astra0.5990.003inferred: single constant CD, same powered/coast, ordinary lateral model
14bella_lui0.4460.005inferred: single constant CD, same powered/coast, ordinary lateral model
19camoes——not fitted: active air-brake/controller model
24cavour0.6090.001inferred: single constant CD, same powered/coast, ordinary lateral model
29defiance0.657-0.063inferred: single constant CD, same powered/coast, ordinary lateral model
34erebus0.443-0.011inferred: single constant CD, same powered/coast, ordinary lateral model
39genesis0.609-0.003inferred: single constant CD, same powered/coast, ordinary lateral model
44halcyon0.3450.002inferred: single constant CD, same powered/coast, ordinary lateral model
53juno30.264-0.000inferred: single constant CD, same powered/coast, ordinary lateral model
58lince0.578-0.016inferred: single constant CD, same powered/coast, ordinary lateral model
63ndrt_20200.417-0.002inferred: single constant CD, same powered/coast, ordinary lateral model
68prometheus_20220.403-0.068inferred: single constant CD, same powered/coast, ordinary lateral model
73valetudo0.810-0.015inferred: single constant CD, same powered/coast, ordinary lateral model
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "show(status[[\"rocket\", \"status\", \"reason\", \"notes\"]])\n", + "show(metrics[metrics.metric==\"Apogee-fitted constant CD\"][[\"rocket\",\"inferred_cd\",\"fit_apogee_residual_m\",\"condition\"]])\n", + "assert metrics[metrics.metric==\"Coast CD at Mach 0.6\"].measured.isna().all()\n", + "fits = metrics[metrics.metric==\"Apogee-fitted constant CD\"].dropna(subset=[\"inferred_cd\"])\n", + "assert (fits.fit_apogee_residual_m.abs() <= .5).all()\n" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/index.rst b/docs/examples/index.rst index bd7506c30..a89524967 100644 --- a/docs/examples/index.rst +++ b/docs/examples/index.rst @@ -113,5 +113,6 @@ In the next sections you will find the simulations of the rockets listed above. defiance_flight_sim.ipynb hedy_flight_sim.ipynb valkyrie_flight_sim.ipynb - + datcom/rocket_comparison.ipynb + datcom/asteria_comparison.ipynb diff --git a/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb b/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb index 8181c03ba..53650b8f4 100644 --- a/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb +++ b/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb @@ -772,7 +772,9 @@ "Finally, let's simulate our flights. \n", "We can run the simulations using the method `MonteCarlo.simulate()`.\n", "\n", - "Set `append=False` to overwrite the previous results, or `append=True` to add the new results to the previous ones.\n" + "Set `append=False` to overwrite the previous results, or `append=True` to add the new results to the previous ones.\n", + "\n", + "The rows a run writes are numbered from zero, serially and in parallel alike. An append carries on the study already in the file. Every input row records the seed root it was drawn from, so the seed does not have to be given again, and one that disagrees with the rows is refused rather than mixed in. A log written before this behaviour existed carries no root, so an append onto one is refused too: run the study again to write a log that can be continued.\n" ] }, { diff --git a/docs/reference/classes/Rocket.rst b/docs/reference/classes/Rocket.rst index 636ca9cad..09bbec846 100644 --- a/docs/reference/classes/Rocket.rst +++ b/docs/reference/classes/Rocket.rst @@ -2,4 +2,5 @@ Rocket Class ------------ .. autoclass:: rocketpy.Rocket - :members: \ No newline at end of file + :members: + :inherited-members: diff --git a/docs/reference/classes/aero_surfaces/DatcomFins.rst b/docs/reference/classes/aero_surfaces/DatcomFins.rst new file mode 100644 index 000000000..7adf4e0c8 --- /dev/null +++ b/docs/reference/classes/aero_surfaces/DatcomFins.rst @@ -0,0 +1,5 @@ +DATCOM Fins Class +----------------- + +.. autoclass:: rocketpy.DatcomFins + :members: diff --git a/docs/reference/classes/aero_surfaces/DatcomFreeFormFins.rst b/docs/reference/classes/aero_surfaces/DatcomFreeFormFins.rst new file mode 100644 index 000000000..c4f87ecd9 --- /dev/null +++ b/docs/reference/classes/aero_surfaces/DatcomFreeFormFins.rst @@ -0,0 +1,5 @@ +DATCOM Free Form Fins Class +--------------------------- + +.. autoclass:: rocketpy.DatcomFreeFormFins + :members: diff --git a/docs/reference/classes/aero_surfaces/TubeFins.rst b/docs/reference/classes/aero_surfaces/TubeFins.rst new file mode 100644 index 000000000..27e9827a9 --- /dev/null +++ b/docs/reference/classes/aero_surfaces/TubeFins.rst @@ -0,0 +1,5 @@ +TubeFins Class +============== + +.. autoclass:: rocketpy.TubeFins + :members: diff --git a/docs/reference/classes/aero_surfaces/index.rst b/docs/reference/classes/aero_surfaces/index.rst index a3dad0417..cb24af943 100644 --- a/docs/reference/classes/aero_surfaces/index.rst +++ b/docs/reference/classes/aero_surfaces/index.rst @@ -10,8 +10,11 @@ AeroSurface Classes Tail Fins TrapezoidalFins + DatcomFins + DatcomFreeFormFins EllipticalFins FreeFormFins + TubeFins Fin TrapezoidalFin EllipticalFin @@ -19,4 +22,4 @@ AeroSurface Classes RailButtons AirBrakes GenericSurface - LinearGenericSurface \ No newline at end of file + LinearGenericSurface diff --git a/docs/reference/classes/monte_carlo/stochastic_models/index.rst b/docs/reference/classes/monte_carlo/stochastic_models/index.rst index ca8b2b1e2..d3c9bb8a1 100644 --- a/docs/reference/classes/monte_carlo/stochastic_models/index.rst +++ b/docs/reference/classes/monte_carlo/stochastic_models/index.rst @@ -19,6 +19,7 @@ input parameters, enabling robust Monte Carlo simulations. stochastic_nose_cone stochastic_trapezoidal_fins stochastic_elliptical_fins + stochastic_free_form_fins stochastic_tail stochastic_rail_buttons stochastic_rocket diff --git a/docs/reference/classes/monte_carlo/stochastic_models/stochastic_free_form_fins.rst b/docs/reference/classes/monte_carlo/stochastic_models/stochastic_free_form_fins.rst new file mode 100644 index 000000000..a1c8391c4 --- /dev/null +++ b/docs/reference/classes/monte_carlo/stochastic_models/stochastic_free_form_fins.rst @@ -0,0 +1,5 @@ +Stochastic Free Form Fins +------------------------- + +.. autoclass:: rocketpy.stochastic.StochasticFreeFormFins + :members: diff --git a/docs/static/RocketPy_Logo_black.png b/docs/static/RocketPy_Logo_black.png index 2445e4155..c3fb4f766 100644 Binary files a/docs/static/RocketPy_Logo_black.png and b/docs/static/RocketPy_Logo_black.png differ diff --git a/docs/static/RocketPy_Logo_white.png b/docs/static/RocketPy_Logo_white.png index 49b08a4b3..85fd2d4ab 100644 Binary files a/docs/static/RocketPy_Logo_white.png and b/docs/static/RocketPy_Logo_white.png differ diff --git a/docs/static/flight/trajectory_on_map.jpg b/docs/static/flight/trajectory_on_map.jpg new file mode 100644 index 000000000..a7bf167a7 Binary files /dev/null and b/docs/static/flight/trajectory_on_map.jpg differ diff --git a/docs/static/flight/trajectory_on_map_safety_radii.jpg b/docs/static/flight/trajectory_on_map_safety_radii.jpg new file mode 100644 index 000000000..0a9902bb8 Binary files /dev/null and b/docs/static/flight/trajectory_on_map_safety_radii.jpg differ diff --git a/docs/technical/aerodynamics/tube_fins.rst b/docs/technical/aerodynamics/tube_fins.rst new file mode 100644 index 000000000..46005d24b --- /dev/null +++ b/docs/technical/aerodynamics/tube_fins.rst @@ -0,0 +1,87 @@ +Tube Fin Aerodynamics +===================== + +RocketPy models a tube-fin set as a symmetric ring of uncanted ring airfoils. +The implementation follows the preliminary tube-fin model in OpenRocket's +`TubeFinSetCalc `_. +The normal-force derivative is based on Ribner's analysis of a ring airfoil in +nonaxial flow [1]_. + +Geometry +-------- + +Let :math:`n` be the number of tubes, :math:`R` the rocket-body radius, +:math:`r_i` the tube inner radius, :math:`r_o` the tube outer radius, and +:math:`L` the tube length. The tubes are distributed evenly around the rocket. +The current implementation requires each tube to touch its two neighbors: + +.. math:: + + r_o = R \frac{\sin(\pi / n)}{1 - \sin(\pi / n)}. + +This constraint also places each tube against the rocket body. Configurations +with fewer than three tubes, gaps between tubes, or overlapping tubes are +rejected. + +Normal Force +------------ + +The ring-airfoil aspect ratio and its modified form are + +.. math:: + + AR = \frac{2 r_i}{L}, \qquad AR' = \frac{2 AR}{\pi}. + +For a rocket reference area :math:`A_{ref} = \pi R^2`, the normal-force +coefficient derivative of the complete tube set is + +.. math:: + + C_{N_\alpha} = + \frac{n}{A_{ref}} + 2 \left(\frac{AR'}{1 + AR'}\right) \pi^2 r_i L. + +RocketPy applies this derivative symmetrically for positive and negative +angles of attack and caps the magnitude at 20 degrees: + +.. math:: + + C_N(\alpha) = C_{N_\alpha} + \operatorname{clip}\left(\alpha, -20^\circ, 20^\circ\right). + +Center of Pressure +------------------ + +For Mach numbers up to 0.5, the center of pressure is placed at the quarter +chord: + +.. math:: + + x_{CP} = \frac{L}{4}. + +The position is measured from the tube leading edge. OpenRocket moves this +position with Mach number above Mach 0.5; RocketPy does not yet implement that +correction. Simulations that exceed Mach 0.5 should use aerodynamic data from a +higher-fidelity source instead of this fixed-CP model. + +Model Limits +------------ + +The tube-fin surface contributes normal force and the corresponding pitch and +yaw moments about its center of pressure. It does not calculate: + +- friction or pressure drag from the tubes; +- roll forcing or damping; +- side-force or yaw behavior for asymmetric tube layouts; +- tube cant; or +- aerodynamic corrections for separated or overlapping tubes. + +Represent tube-fin drag in the rocket's power-on and power-off drag curves. +Use :class:`rocketpy.GenericSurface` when measured, wind-tunnel, or CFD +coefficients are available outside the limits above. + +References +---------- + +.. [1] Ribner, H. S. "The Ring Airfoil in Nonaxial Flow." *Journal of the + Aeronautical Sciences*, 14(9), 529--530, 1947. diff --git a/docs/technical/index.rst b/docs/technical/index.rst index 73583eba9..588f3e421 100644 --- a/docs/technical/index.rst +++ b/docs/technical/index.rst @@ -12,11 +12,13 @@ in their code. Equations of Motion v0 Equations of Motion v1 + Tip-off Elliptical Fins Individual Fin + Tube Fins Roll Moment Sensitivity Analysis References This section is still a work in progress, however, and not everything is documented yet. -If you have any questions, please contact the maintainers of RocketPy. \ No newline at end of file +If you have any questions, please contact the maintainers of RocketPy. diff --git a/docs/technical/references.rst b/docs/technical/references.rst index 0eb4132f5..02b90b8ff 100644 --- a/docs/technical/references.rst +++ b/docs/technical/references.rst @@ -6,3 +6,7 @@ References .. [Niskanen] Niskanen, S. (2013). *Development of an Open Source model rocket simulation software*. .. [Model] Barrowman, James S.. (1970). *Model Rocketry*. + +.. [Chou] Chou, P.-C., et al. *Tip-off effect analysis of a vehicle moving along an inclined guideway by considering dynamic interactions*. + +.. [Hosken] Hosken, R. W., et al. *Analysis of missile launchers, part Q: tip-off effects in helical rail launchers*. diff --git a/docs/technical/tip_off.rst b/docs/technical/tip_off.rst new file mode 100644 index 000000000..50c983afd --- /dev/null +++ b/docs/technical/tip_off.rst @@ -0,0 +1,281 @@ +.. _tipoff: + +=========================================== +Tip-off: the 3-DOF Single Rail Button Phase +=========================================== + +Introduction +------------ + +Between the moment the *upper* rail button leaves the launch rail and the moment +the *lower* button follows it, the rocket is still guided --- but only at one +point. It slides along the rail while free to pitch and yaw about that remaining +button. This short interval is what the literature calls **tip-off**, and it sets +the attitude and angular rate with which the rocket begins free flight. + +This document derives the equations of motion used by +:meth:`rocketpy.Flight.udot_rail2`, the flight phase that models this interval. +It is enabled with ``Flight(..., use_udot_rail2=True)``; when disabled (the +default) the simulation transitions straight from the 1-DOF rail phase to the +generalized 6-DOF equations, exactly as it did before this phase existed. + +Because the phase works by patching the *generalized* 6-DOF solution with a +constraint wrench built from the full inertia tensor, it requires +``simulation_mode="6 DOF"`` and ``equations_of_motion="standard"``. The reduced +formulations do not carry the state the patch describes --- the 3 DOF equations +model no attitude at all, and ``solid_propulsion`` uses a different, +axisymmetric set --- so asking for the tip-off phase together with either of +them raises a ``ValueError`` instead of silently producing non-physical +kinematics. Note that a point-mass motor forces ``simulation_mode`` to +``"3 DOF"``, and therefore cannot be combined with this phase either. + +The three flight phases around rail departure are, in order of the distance +``d`` travelled from the launch point: + +.. math:: + + \begin{aligned} + \texttt{udot_rail1} \quad & \text{for } d < \ell_1 + && \text{(both buttons engaged, 1 DOF)} \\ + \texttt{udot_rail2} \quad & \text{for } \ell_1 \le d < \ell_2 + && \text{(upper button gone, lower engaged, 3 DOF)} \\ + \texttt{u_dot_generalized} \quad & \text{for } d \ge \ell_2 + && \text{(free flight, 6 DOF)} + \end{aligned} + +where :math:`\ell_1` and :math:`\ell_2` are the ``effective_1rl`` and +``effective_2rl`` attributes of :class:`rocketpy.Flight` --- the distances at +which the upper and the lower button reach the end of the rail. Their difference +is the button-to-button distance, so the phase has zero length for a rocket with +a single rail button and is skipped in that case. + +Frames and conventions +---------------------- + +The solver integrates the same 13-element state vector used by the other +right-hand sides, + +.. math:: + + \mathbf{u} = [\,x,\ y,\ z,\ v_x,\ v_y,\ v_z,\ e_0,\ e_1,\ e_2,\ e_3,\ + \omega_1,\ \omega_2,\ \omega_3\,] + +with :math:`\mathbf{r} = [x, y, z]` the inertial position of the **center of dry +mass** (CDM, the tracked point), :math:`\mathbf{v}` its inertial velocity, +:math:`\mathbf{e}` the attitude quaternion and :math:`\boldsymbol{\omega}` the +angular velocity in the **body** frame. The matrix +:math:`\mathbf{K} = \texttt{Matrix.transformation}(\mathbf{e})` rotates body +components into inertial ones, and :math:`\hat{\mathbf{z}}_b = [0, 0, 1]` is the +body roll (symmetry) axis. + +The relevant mass geometry at time :math:`t`, in the body frame, is the total +mass :math:`m`, the CDM-to-center-of-mass offset :math:`\mathbf{r}_{CM}`, and the +inertia tensor about the CDM, :math:`\mathbf{I}`. Shifting the latter to the +instantaneous center of mass gives + +.. math:: + + \mathbf{I}_{CM} = \mathbf{I} - m\left(|\mathbf{r}_{CM}|^2 \mathbb{1} + - \mathbf{r}_{CM}\mathbf{r}_{CM}^{\mathsf T}\right). + +**Rail geometry.** The rail is a line fixed in the inertial frame, set by the +launch inclination and heading. Its unit vector is the ``attitude_unit`` +attribute of :class:`rocketpy.Flight`; the constrained body point is the lower +rail button, at the fixed body position :math:`\mathbf{r}_{B}` relative to the +CDM. + +The constraint +-------------- + +A free rigid body has six degrees of freedom. The single engaged button removes +three of them: + +#. **The button stays on the rail line.** Its position may only vary along the + rail, so the component of its acceleration perpendicular to the rail + vanishes. That is **two** scalar constraints. +#. **Roll is suppressed** by the button in its rail slot: + :math:`\dot{\boldsymbol{\omega}} \cdot \hat{\mathbf{z}}_b = 0`. That is + **one** more. + +Three constraints leave :math:`6 - 3 = 3` degrees of freedom: translation along +the rail, pitch and yaw --- the 3 DOF this phase is named for. + +The forces that enforce them are the unknowns of the problem: + +* a **normal reaction** at the button, perpendicular to the rail because a + frictionless slot can neither pull nor push along it. Writing an orthonormal + body triad :math:`\{\hat{\mathbf{e}}_1, \hat{\mathbf{e}}_2, \hat{\mathbf{n}}_b\}` + around the body-frame rail direction + :math:`\hat{\mathbf{n}}_b = \mathbf{K}^{\mathsf T}\hat{\mathbf{n}}`, it has two + components: :math:`\mathbf{N}_b = \lambda_1 \hat{\mathbf{e}}_1 + \lambda_2 + \hat{\mathbf{e}}_2`; +* a **roll reaction moment** :math:`\mu \hat{\mathbf{z}}_b` about the body axis. + +Three unknowns, three constraints: a :math:`3\times3` linear system, solved once +per evaluation of the right-hand side. + +The initial conditions are consistent with the constraint. The phase is entered +from ``udot_rail1``, where the rocket has no angular velocity and its velocity is +along the rail, so the button's perpendicular *velocity* is already zero. +Enforcing zero perpendicular *acceleration* therefore keeps it on the rail. + +Augmented dynamics +------------------ + +The generalized equations of motion assemble a total force +:math:`\mathbf{T}_{20}` and a total moment about the CDM :math:`\mathbf{T}_{21}`, +both in the body frame, and solve + +.. math:: + + \dot{\boldsymbol{\omega}}_{\text{free}} = \mathbf{I}_{CM}^{-1} + \left(\mathbf{T}_{21} + \mathbf{T}_{20} \times \mathbf{r}_{CM}\right), + \qquad + \mathbf{a}_{\text{free}} = \frac{\mathbf{T}_{20}}{m} + - \mathbf{r}_{CM} \times \dot{\boldsymbol{\omega}}_{\text{free}}. + +Both totals are *sums of external contributions*, which is what makes the +constraint clean to add: the reaction wrench simply enters the sums, + +.. math:: + + \mathbf{T}_{20}' = \mathbf{T}_{20} + \mathbf{N}_b, + \qquad + \mathbf{T}_{21}' = \mathbf{T}_{21} + \mathbf{r}_{B} \times \mathbf{N}_b + + \mu \hat{\mathbf{z}}_b, + +after which the same two lines apply. Since the solve is linear in the totals, +the result splits into the free solution plus a response to the unknowns. Using +:math:`\mathbf{r}_B \times \mathbf{N} + \mathbf{N} \times \mathbf{r}_{CM} += (\mathbf{r}_B - \mathbf{r}_{CM}) \times \mathbf{N}` and writing +:math:`\mathbf{d} = \mathbf{r}_{B} - \mathbf{r}_{CM}` for the center-of-mass-to-button +vector, + +.. math:: + + \Delta\dot{\boldsymbol{\omega}} = \mathbf{I}_{CM}^{-1} + \left(\mathbf{d} \times \mathbf{N}_b + \mu \hat{\mathbf{z}}_b\right), + \qquad + \Delta\mathbf{a} = \frac{\mathbf{N}_b}{m} + - \mathbf{r}_{CM} \times \Delta\dot{\boldsymbol{\omega}}. + +The button is body-fixed, so its acceleration in body components is + +.. math:: + + \mathbf{A} = \mathbf{A}_{\text{free}} + \Delta\mathbf{a} + + \Delta\dot{\boldsymbol{\omega}} \times \mathbf{r}_{B}, + \qquad + \mathbf{A}_{\text{free}} = \mathbf{K}^{\mathsf T}\mathbf{a}_{\text{free}} + + \dot{\boldsymbol{\omega}}_{\text{free}} \times \mathbf{r}_{B} + + \boldsymbol{\omega} \times + \left(\boldsymbol{\omega} \times \mathbf{r}_{B}\right). + +The linear solve +---------------- + +Collect the unknowns in :math:`\boldsymbol{\chi} = [\lambda_1, \lambda_2, \mu]`. +The three constraints read + +.. math:: + + \mathbf{A} \cdot \hat{\mathbf{e}}_1 = 0, + \qquad + \mathbf{A} \cdot \hat{\mathbf{e}}_2 = 0, + \qquad + \dot{\boldsymbol{\omega}} \cdot \hat{\mathbf{z}}_b = 0, + +and because :math:`\mathbf{A}` and :math:`\dot{\boldsymbol{\omega}}` are linear +in :math:`\boldsymbol{\chi}`, they form the system +:math:`\mathbf{J}\boldsymbol{\chi} = -\mathbf{g}_{\text{free}}` with + +.. math:: + + \mathbf{g}_{\text{free}} = + \begin{bmatrix} + \mathbf{A}_{\text{free}} \cdot \hat{\mathbf{e}}_1 \\ + \mathbf{A}_{\text{free}} \cdot \hat{\mathbf{e}}_2 \\ + \dot{\boldsymbol{\omega}}_{\text{free}} \cdot \hat{\mathbf{z}}_b + \end{bmatrix}. + +Each column of :math:`\mathbf{J}` is obtained by evaluating the response above at +one of the three unit inputs :math:`(\mathbf{N}_b, \mu) = +(\hat{\mathbf{e}}_1, 0)`, :math:`(\hat{\mathbf{e}}_2, 0)`, +:math:`(\mathbf{0}, 1)`. Solving for :math:`\boldsymbol{\chi}` and substituting +back gives the constrained accelerations, which override the free ones in the +returned derivative: + +.. math:: + + \dot{\boldsymbol{\omega}} = \dot{\boldsymbol{\omega}}_{\text{free}} + + \Delta\dot{\boldsymbol{\omega}}, + \qquad + \mathbf{a}_{CDM} = \mathbf{a}_{\text{free}} + + \mathbf{K}\,\Delta\mathbf{a}. + +The position and quaternion derivatives are the ordinary kinematic ones. In +particular :math:`\dot{\mathbf{r}} = \mathbf{v}`: the velocity is **not** +projected onto the rail. The constraint acts at the acceleration level on the +*button*, and the CDM legitimately acquires a small perpendicular velocity as the +rocket pitches about that button. + +Modelling assumptions and edge cases +------------------------------------ + +* **Roll axis.** The constraint suppresses roll about the *body* axis. The rocket + travels only the button-to-button distance during this phase, so the tip-off + angle is small and the body axis stays close to the rail direction; the + difference between body roll and rail-axis roll is of that order. This is + consistent with ``udot_rail1``, which freezes rotation entirely. +* **Radial button offset.** The button is modelled on the rocket axis. The roll + constraint is enforced explicitly by :math:`\mu`, so what is lost is only the + (small) roll coupling through the button's radial standoff. +* **Zero-length phase.** A rocket with a single rail button has + :math:`\ell_1 = \ell_2`, and the phase is skipped. +* **Singular system.** Should the geometry make :math:`\mathbf{J}` singular, the + step falls back to the unconstrained dynamics and warns. + +Expected behaviour +------------------ + +The phase reproduces the two effects tip-off is modelled for. With no wind, the +center of mass sits ahead of the button that the rocket now pivots about, so +gravity pitches the nose down by a fraction of a degree. With a crosswind, the +aerodynamic moment turns the rocket into the wind before it is fully free --- +the weathercock effect --- and the rocket therefore leaves the rail with a small +angular rate rather than none. + +Reading the results +------------------- + +The window itself is recorded on the :class:`rocketpy.Flight` object, so a +dispersion study does not have to recover it from the raw solution: + +.. list-table:: + :header-rows: 1 + :widths: 30 70 + + * - Attribute + - Meaning + * - ``out_of_rail_time`` + - Time the *upper* button leaves the rail, starting the window. + * - ``between_rails_time`` + - Time the *lower* button leaves the rail, ending the window. + * - ``between_rails_velocity`` + - Speed at that moment, the true rail-departure velocity. + * - ``between_rails_state`` + - Full state vector handed to the free-flight phase. + * - ``tip_off_duration`` + - Length of the window, zero when the phase is disabled. + +``Flight.info()`` prints these under a *Tip-Off State* heading whenever the +phase ran, and the attitude plots shade the window so the change in attitude +during tip-off can be read off directly. + +References +---------- + +The tip-off phase and its effect on the initial conditions of free flight are +treated in the launcher dynamics literature: [Chou]_ studies a vehicle moving +along an inclined guideway with dynamic interactions, and [Hosken]_ analyses +tip-off effects in rail launchers. diff --git a/docs/user/aerodynamics/surfaces.rst b/docs/user/aerodynamics/surfaces.rst index 38f266dca..11d7629c4 100644 --- a/docs/user/aerodynamics/surfaces.rst +++ b/docs/user/aerodynamics/surfaces.rst @@ -7,12 +7,11 @@ Aerodynamic Surfaces This page provides an overview of the aerodynamic surfaces available in RocketPy and explains how they connect to the rocket's simulation. -RocketPy models the aerodynamic forces and moments generated by three types -of surfaces: **nose cones**, **fins**, and **tails**. Each surface is -defined by its geometric parameters and, optionally, by an airfoil profile. -The aerodynamic coefficients are computed internally using the Barrowman -method and are used during the flight simulation to evaluate the rocket's -stability and control. +RocketPy models the aerodynamic forces and moments generated by **nose +cones**, **planar fins**, **tube fins**, and **tails**. Each surface is defined +by its geometric parameters. Planar fins can also use a measured airfoil lift +curve. The resulting aerodynamic coefficients are used during the flight +simulation to evaluate the rocket's stability and control. .. seealso:: @@ -112,6 +111,10 @@ RocketPy distinguishes between two levels of fin definition: separately. Individual fins are useful for canards, asymmetric configurations, or when you need fine-grained control. +- **Tube-fin sets** (:class:`rocketpy.TubeFins`): This class defines a + symmetric ring of cylindrical fins. Its normal-force derivative follows + Ribner's ring-airfoil model rather than the planar-fin Barrowman equations. + .. seealso:: For the mathematical model of individual fins, including the moment @@ -178,6 +181,31 @@ Parameters: - ``coordinates``: A list of ``(x, y)`` tuples defining the fin shape in the fin coordinate frame. +Tube Fins +~~~~~~~~~ + +Tube fins are defined by the number and length of the tubes, their inner and +outer radii, and the rocket-body radius at the mounting position. Add them to a +rocket with :meth:`rocketpy.Rocket.add_tube_fins` or create a +:class:`rocketpy.TubeFins` object and pass it to +:meth:`rocketpy.Rocket.add_surfaces`. + +The current implementation is a subsonic normal-force model with these +limits: + +- The center of pressure is fixed at one quarter of the tube length, measured + from the leading edge. Use the model only through Mach 0.5. +- Lift is capped at an absolute angle of attack of 20 degrees. +- At least three uncanted tubes must be distributed evenly around the body. + Every tube must touch the body and both adjacent tubes. Separated and + overlapping tube layouts are rejected. +- The model does not calculate tube-fin friction drag, pressure drag, roll, + side force, or yaw. Include tube-fin drag in the rocket's power-on and + power-off drag curves. + +For the equations and geometry constraint, see +:doc:`Tube Fin Aerodynamics `. + Common Fin Set Parameters ------------------------- @@ -260,6 +288,7 @@ Fins can be added to a rocket using the ``Rocket`` class methods: - :meth:`rocketpy.Rocket.add_trapezoidal_fins` - :meth:`rocketpy.Rocket.add_elliptical_fins` - :meth:`rocketpy.Rocket.add_free_form_fins` + - :meth:`rocketpy.Rocket.add_tube_fins` - :meth:`rocketpy.Rocket.add_surfaces` (for individual fins) Tail diff --git a/docs/user/custom_sampler.rst b/docs/user/custom_sampler.rst index 8994c9573..ab147a86b 100644 --- a/docs/user/custom_sampler.rst +++ b/docs/user/custom_sampler.rst @@ -52,7 +52,7 @@ distributions. 2-Tuple that contains the probability of each normal distribution of the mixture. Its entries should be non-negative and sum up to 1. """ - np.random.default_rng(seed) + self.reset_seed(seed) self.means_tuple = means_tuple self.sd_tuple = sd_tuple self.prob_tuple = prob_tuple @@ -71,12 +71,12 @@ distributions. List containing n_samples samples """ samples_list = [0] * n_samples - mixture_id_list = np.random.binomial(1, self.prob_tuple[0], n_samples) + mixture_id_list = self.rng.binomial(1, self.prob_tuple[0], n_samples) for i, mixture_id in enumerate(mixture_id_list): if mixture_id: - samples_list[i] = np.random.normal(self.means_tuple[0], self.sd_tuple[0]) + samples_list[i] = self.rng.normal(self.means_tuple[0], self.sd_tuple[0]) else: - samples_list[i] = np.random.normal(self.means_tuple[1], self.sd_tuple[1]) + samples_list[i] = self.rng.normal(self.means_tuple[1], self.sd_tuple[1]) return samples_list @@ -88,7 +88,14 @@ distributions. seed : int, optional Seed for the random number generator. """ - np.random.default_rng(seed) + self.rng = np.random.default_rng(seed) + +.. warning:: + Keep the generator on the instance and draw from it in *sample*. Calling + ``np.random.default_rng(seed)`` and discarding the result is a no-op, and + *sample* then draws from the process-global ``np.random`` instead, which + ``random_seed`` does not reach. The study still runs; it is simply not + reproducible, and nothing says so. This is an example of a distribution that is not implemented in numpy. Note that it is a general distribution, so we can use it for many different variables. @@ -216,14 +223,9 @@ below implements an example of such a generator seed : int, optional Number to seed random generator, by default None """ - np.random.default_rng(seed) - self.samples_list = [] - self.samples_generated = 0 - self.used_samples_x = 0 - self.used_samples_y = 0 self.mean = mean self.cov = cov - self.generate_samples(1000) + self.reset_seed(seed) def generate_samples(self, n_samples = 1): """Generate samples from bivariate Gaussian and append to sample list @@ -233,23 +235,39 @@ below implements an example of such a generator n_samples : int, optional Number of samples to be generated, by default 1 """ - samples_generated = np.random.multivariate_normal(self.mean, self.cov, n_samples) + samples_generated = self.rng.multivariate_normal(self.mean, self.cov, n_samples) self.samples_generated += n_samples self.samples_list += list(samples_generated) def reset_seed(self, seed=None): - np.random.default_rng(seed) + """Reseeds the generator and discards the samples drawn before it + + The cached samples came from the previous generator, so keeping them + would let the first draws after a reseed ignore the new seed. Nothing + is generated here: ``top_up`` fills the shortfall when samples are + first asked for, and a reseed happens once per simulation, so filling + eagerly would build a thousand pairs to use one. + """ + self.rng = np.random.default_rng(seed) + self.samples_list = [] + self.samples_generated = 0 + self.used_samples_x = 0 + self.used_samples_y = 0 + + def top_up(self, used_samples, n_samples): + """Generates enough samples to cover the request, if it is short""" + shortfall = used_samples + n_samples - self.samples_generated + if shortfall > 0: + self.generate_samples(shortfall) def get_samples(self, n_samples, axis): if axis == "x": - if self.samples_generated < self.used_samples_x: - self.generate_samples(n_samples) + self.top_up(self.used_samples_x, n_samples) samples_list = [ sample[0] for sample in self.samples_list[self.used_samples_x:(self.used_samples_x + n_samples)] ] if axis == "y": - if self.samples_generated < self.used_samples_y: - self.generate_samples(n_samples) + self.top_up(self.used_samples_y, n_samples) samples_list = [ sample[1] for sample in self.samples_list[self.used_samples_y:(self.used_samples_y + n_samples)] ] @@ -284,6 +302,16 @@ sample list. def __init__(self, bivariate_gaussian_generator): self.generator = bivariate_gaussian_generator + @property + def seed_group(self): + """The generator this shares with the other wrapper. + + Both return the same object, so the pair is seeded once between + them. Without this each would be seeded separately and one would + silently overwrite the other. + """ + return self.generator + def sample(self, n_samples=1): samples_list = self.generator.get_samples(n_samples, "x") return samples_list @@ -297,6 +325,16 @@ sample list. def __init__(self, bivariate_gaussian_generator): self.generator = bivariate_gaussian_generator + @property + def seed_group(self): + """The generator this shares with the other wrapper. + + Both return the same object, so the pair is seeded once between + them. Without this each would be seeded separately and one would + silently overwrite the other. + """ + return self.generator + def sample(self, n_samples=1): samples_list = self.generator.get_samples(n_samples, "y") return samples_list diff --git a/docs/user/environment/1-atm-models/ensemble.rst b/docs/user/environment/1-atm-models/ensemble.rst index 8dffaac00..b7cdfd6be 100644 --- a/docs/user/environment/1-atm-models/ensemble.rst +++ b/docs/user/environment/1-atm-models/ensemble.rst @@ -20,6 +20,78 @@ forecast and obtain a range of possible outcomes. Ensemble Forecast ----------------- +Creating a Custom Ensemble +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Use :meth:`rocketpy.Environment.create_ensemble` to combine two or more +atmospheric profiles for one location. Each member is a mapping with +``pressure``, ``temperature``, ``wind_u`` and ``wind_v`` profiles. Each profile +is a two-column array: the first column is geometric height above sea level in +meters, and the second column uses Pa for pressure, K for temperature and m/s +for either wind component. + +The pressure profiles determine a common isobaric grid. Temperature and wind +are interpolated onto that grid, then written to a GEFS-compatible NetCDF file. +Every pressure profile must overlap the others and decrease with height. + +.. code-block:: python + + import numpy as np + + from rocketpy import Environment + + env = Environment( + date=(2026, 9, 1, 12), + latitude=32.990254, + longitude=-106.974998, + elevation=1400, + ) + + pressure = np.array([85000, 70000, 50000]) # Pa + height_0 = np.array([1500, 3000, 5500]) # m ASL + height_1 = np.array([1550, 3100, 5650]) # m ASL + + profiles = [ + { + "pressure": np.column_stack((height_0, pressure)), + "temperature": np.column_stack((height_0, [278, 268, 250])), + "wind_u": np.column_stack((height_0, [2, 5, 9])), + "wind_v": np.column_stack((height_0, [-1, 1, 4])), + }, + { + "pressure": np.column_stack((height_1, pressure)), + "temperature": np.column_stack((height_1, [280, 269, 251])), + "wind_u": np.column_stack((height_1, [4, 7, 12])), + "wind_v": np.column_stack((height_1, [0, 2, 6])), + }, + ] + + ensemble_file = env.create_ensemble(profiles, "my_ensemble.nc") + +The method activates member 0 after writing the file. Select another member +with the same method used for forecast ensembles: + +.. code-block:: python + + env.select_ensemble_member(1) + +Another Environment can load the returned file with the ``GEFS`` mapping: + +.. code-block:: python + + saved_env = Environment( + date=(2026, 9, 1, 12), + latitude=32.990254, + longitude=-106.974998, + elevation=1400, + ) + saved_env.set_atmospheric_model( + type="Ensemble", + file=ensemble_file, + dictionary="GEFS", + ) + + Global Ensemble Forecast System (GEFS) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -34,6 +106,13 @@ Global Ensemble Forecast System (GEFS) provider (or a local copy), you can still load it explicitly by passing the dataset path/URL in ``file`` and a compatible mapping in ``dictionary``. +.. tip:: + + While the ``GEFS`` shortcut is unavailable, Open-Meteo offers the same GEFS + ensemble (plus the ECMWF one) over a plain JSON API, and works with + :meth:`rocketpy.Environment.select_ensemble_member` in exactly the same way. + See :ref:`open_meteo`. + The ``GEFS`` model is a global ensemble forecast system useful for uncertainty analysis, but RocketPy's automatic ``file="GEFS"`` shortcut is temporarily @@ -99,4 +178,4 @@ Ensemble Reanalysis ------------------- Ensemble reanalyses are also possible with RocketPy. See the -:ref:`reanalysis_ensemble` section for more information. \ No newline at end of file +:ref:`reanalysis_ensemble` section for more information. diff --git a/docs/user/environment/1-atm-models/forecast.rst b/docs/user/environment/1-atm-models/forecast.rst index ea81c356a..347fed281 100644 --- a/docs/user/environment/1-atm-models/forecast.rst +++ b/docs/user/environment/1-atm-models/forecast.rst @@ -16,6 +16,13 @@ Other generic forecasts can also be imported. If you want to simulate your rocket launch using past data, you should use \ :ref:`reanalysis` or :ref:`soundings`. +.. tip:: + + The models on this page are fetched over OPeNDAP, which requires the + ``netCDF4`` library and can be slow. For a lighter alternative that serves + the same kind of pressure-level forecast as plain JSON, see + :ref:`open_meteo`. + .. _global-forecast-system: diff --git a/docs/user/environment/1-atm-models/index.rst b/docs/user/environment/1-atm-models/index.rst index ba9940585..12214e8c3 100644 --- a/docs/user/environment/1-atm-models/index.rst +++ b/docs/user/environment/1-atm-models/index.rst @@ -12,6 +12,7 @@ environment in the :class:`rocketpy.Environment` class. Standard Atmosphere Custom Atmosphere + Open-Meteo Forecasts Soundings Reanalysis diff --git a/docs/user/environment/1-atm-models/open_meteo.rst b/docs/user/environment/1-atm-models/open_meteo.rst new file mode 100644 index 000000000..3064d842c --- /dev/null +++ b/docs/user/environment/1-atm-models/open_meteo.rst @@ -0,0 +1,218 @@ +.. _open_meteo: + +Open-Meteo +========== + +`Open-Meteo `_ is a weather API that serves +pressure-level forecasts, past forecasts and ensemble forecasts as plain JSON +over HTTPS. + +It is often the most convenient weather source in RocketPy, because: + +- **No API key** is required for non-commercial use. +- **No heavy dependencies**: unlike the :ref:`forecast` and :ref:`reanalysis` + models, no ``netCDF4``/OPeNDAP download is involved, so requests are quick. +- **No external files**: recent past launches can be reconstructed straight from + the API, without downloading reanalysis files by hand. +- **Many models in one place**: GFS, ECMWF, ICON, MET Norway, Météo-France, JMA, + GEM and UKMO are all reachable through the same interface. + +.. note:: + + Open-Meteo is free for non-commercial use, with a limit on the number of + daily requests. Please read + `their terms `_ before using it, and + consider their paid plans for heavier or commercial workloads. + + +Forecasts +--------- + +Set the atmospheric model to ``open_meteo``. The launch date must be set, +because the vertical profile is taken at the hour closest to it. + +.. jupyter-execute:: + + from datetime import datetime, timedelta + from rocketpy import Environment + + tomorrow = datetime.now() + timedelta(days=1) + + env = Environment( + date=tomorrow, + latitude=39.3897, + longitude=-8.28896388889, + ) + + env.set_atmospheric_model(type="open_meteo") + + env.plots.atmospheric_model() + +Note that ``elevation`` was never specified above: Open-Meteo reports the +elevation of the grid cell it answered for, and RocketPy uses it to set the +launch site elevation automatically. + + +Selecting a weather model +^^^^^^^^^^^^^^^^^^^^^^^^^ + +By default RocketPy asks for ``"best_match"``, which lets Open-Meteo pick the +highest-resolution model available for the requested location. A specific model +can be requested through the ``file`` argument: + +.. jupyter-execute:: + + env_ecmwf = Environment( + date=tomorrow, + latitude=39.3897, + longitude=-8.28896388889, + ) + env_ecmwf.set_atmospheric_model(type="open_meteo", file="ecmwf_ifs025") + env_ecmwf.plots.atmospheric_model() + +Frequently useful models are: + +.. list-table:: + :header-rows: 1 + :widths: 30 70 + + * - Model + - Description + * - ``best_match`` + - Open-Meteo picks the best available model for the location (default). + * - ``gfs_seamless`` + - NOAA GFS, global coverage. + * - ``ecmwf_ifs025`` + - ECMWF IFS at 0.25°, global coverage. + * - ``icon_seamless`` + - DWD ICON, global coverage with a higher-resolution European nest. + * - ``meteofrance_seamless`` + - Météo-France ARPEGE/AROME. + * - ``gem_seamless`` + - Environment Canada GEM. + * - ``ukmo_seamless`` + - UK Met Office. + +.. seealso:: + + The `Open-Meteo documentation `_ lists every + model available, along with its resolution and update frequency. + +.. important:: + + Not every model resolves every pressure level, and coverage varies with + location. RocketPy silently drops the levels a model does not provide, so + the resulting profile may reach a lower altitude for some models (for + instance, ``ecmwf_ifs025`` tops out at 50 hPa while ``gfs_seamless`` + reaches 30 hPa). Check ``env.max_expected_height`` if the ceiling matters + for your simulation. + + +Past launches +------------- + +When the launch date is in the past, ``open_meteo`` transparently queries +Open-Meteo's historical-forecast archive instead of the live forecast. No extra +argument is needed: + +.. jupyter-execute:: + + env_past = Environment( + date=datetime(2024, 1, 10, 12), + latitude=39.3897, + longitude=-8.28896388889, + ) + env_past.set_atmospheric_model(type="open_meteo") + env_past.plots.atmospheric_model() + +This is the quickest way to reconstruct the atmosphere of a past flight, since +it needs neither an external file nor a sounding station nearby. For +comparison, see :ref:`reanalysis` and :ref:`soundings`. + +.. important:: + + Open-Meteo's historical data is built from its own archived forecast runs + and only covers pressure levels **from around March 2021 onwards**. Earlier + dates return no data, and RocketPy warns you when it detects one; use + :ref:`reanalysis` or :ref:`soundings` for those instead. + +.. note:: + + Open-Meteo also offers an ERA5 archive endpoint, but it serves surface + variables only and provides no pressure-level data, so RocketPy does not + use it: it cannot produce a vertical profile. + + +Ensemble forecasts +------------------ + +Open-Meteo also exposes ensemble forecasts, where each member represents a +slightly different evolution of the atmosphere. They are used exactly like the +other :ref:`ensemble_atmosphere` models: + +.. jupyter-execute:: + + env_ensemble = Environment( + date=tomorrow, + latitude=39.3897, + longitude=-8.28896388889, + ) + env_ensemble.set_atmospheric_model(type="open_meteo_ensemble", file="gfs05") + + print(f"Number of members: {env_ensemble.num_ensemble_members}") + + env_ensemble.plots.ensemble_member_comparison() + +Individual members are activated with +:meth:`rocketpy.Environment.select_ensemble_member`: + +.. jupyter-execute:: + + env_ensemble.select_ensemble_member(10) + print(f"Wind speed at 1 km: {env_ensemble.wind_speed(1000):.2f} m/s") + +Member ``0`` is the unperturbed control run and is the one selected by default. + +Two ensemble models publish the complete set of pressure-level variables that +RocketPy needs: + +.. list-table:: + :header-rows: 1 + :widths: 30 20 50 + + * - Model + - Members + - Description + * - ``gfs05`` + - 31 + - NOAA GEFS at 0.5° (default). + * - ``ecmwf_ifs025`` + - 51 + - ECMWF ensemble at 0.25°. + +The member counts above include the control run, which RocketPy exposes as +member ``0``. + +.. important:: + + The remaining Open-Meteo ensemble models cannot be used to build a vertical + profile, so RocketPy rejects them with an explanatory error rather than + failing later on. ``gfs025``, ``icon_global`` and + ``bom_access_global_ensemble`` answer successfully but return no + pressure-level values at all, and ``gem_global`` provides temperature and + geopotential height but no pressure-level winds. + + +Further considerations +---------------------- + +Requests may fail if the API is unreachable or if the daily free-tier limit is +exceeded. RocketPy retries transient failures automatically and raises a +``RuntimeError`` with the reason reported by Open-Meteo when the request cannot +be satisfied. + +.. seealso:: + + - :ref:`forecast` for OPeNDAP-based forecasts (GFS, NAM, RAP, HRRR). + - :ref:`reanalysis` for ERA5 and MERRA-2 reanalysis files. + - :ref:`ensemble_atmosphere` for the ensemble workflow in general. diff --git a/docs/user/environment/1-atm-models/reanalysis.rst b/docs/user/environment/1-atm-models/reanalysis.rst index c24bec458..3e441ea5a 100644 --- a/docs/user/environment/1-atm-models/reanalysis.rst +++ b/docs/user/environment/1-atm-models/reanalysis.rst @@ -13,6 +13,13 @@ intervals Reanalysis data can be used to set up the environment in RocketPy. One common reanalysis dataset is the ERA5. +.. tip:: + + Reanalysis datasets must be downloaded as files before RocketPy can read + them. If you only need the atmospheric conditions of a past launch from + 2021 onwards, :ref:`open_meteo` retrieves them straight from an API, with + no file to download. + ERA5 ---- diff --git a/docs/user/environment/3-further/other_apis.rst b/docs/user/environment/3-further/other_apis.rst index 37a9a0949..9aacd8273 100644 --- a/docs/user/environment/3-further/other_apis.rst +++ b/docs/user/environment/3-further/other_apis.rst @@ -159,6 +159,56 @@ For custom dictionaries, the canonical structure is: simulation workflow. +Meteomatics API +--------------- + +RocketPy can build an ``Environment`` directly from the +`Meteomatics `_ weather API. +Meteomatics authenticates with a personal **username** and **password** (a +short-lived access token is generated automatically under the hood), so you +need a Meteomatics account to use this feature. + +The API is queried for temperature, pressure and both wind components at +several altitudes above ground level around the launch site, which are then +converted to profiles above sea level using the ``Environment`` elevation. +Because of that, make sure a launch ``date`` and a reasonable ``elevation`` are +set before calling the method. + +.. code-block:: python + + from datetime import datetime, timedelta + from rocketpy import Environment + + env = Environment( + latitude=39.3897, + longitude=-8.28896, + elevation=113, + date=datetime.now() + timedelta(days=1), # forecast instant + ) + + env.set_atmospheric_model( + type="Meteomatics", + file="mix", # Meteomatics weather model + username="your_username", + password="your_password", + ) + + env.info() + +If you prefer not to hardcode the credentials, omit the ``username`` and +``password`` arguments and RocketPy will read them from the +``METEOMATICS_USERNAME`` and ``METEOMATICS_PASSWORD`` environment variables. + +.. note:: + + The altitude range and sampling resolution can be tuned by calling + :meth:`rocketpy.Environment.process_meteomatics_atmosphere` directly (for + example, to change ``min_altitude``, ``max_altitude`` or the number of + levels). The API returns an error if the requested altitude is outside the + range supported by the chosen model, and your account may not have access + to every model. + + Without OPeNDAP protocol ------------------------- @@ -166,7 +216,6 @@ On the other hand, one can also load data from APIs that do not support the OPeN In these cases, what we recommend is to download the data and then load it as a custom atmosphere. There are some efforts to natively support other APIs in RocketPy's -Environment class, for example: +Environment class, for example: -- `Meteomatics `_: `#545 `_ - `Open-Meteo `_: `#520 `_ diff --git a/docs/user/flight.rst b/docs/user/flight.rst index 173541727..a6db4750c 100644 --- a/docs/user/flight.rst +++ b/docs/user/flight.rst @@ -730,6 +730,98 @@ are removed before the remaining arguments are forwarded to PyVista: before any rendering begins, raising a :class:`ValueError` with a descriptive message on invalid input. +Interactive Trajectory Map +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +``flight.plots.trajectory_on_map()`` renders the flight **ground track** on a +real-world interactive map using `Folium +`_. Unlike the 3D trajectory +plot, this one answers a range-safety question: *where on the actual terrain +did the rocket fly over, and where did it come down?* + +The map ships with two selectable backgrounds — OpenStreetMap for roads and +place names, and Esri World Imagery for satellite view, which is what usually +matters when assessing a recovery field. Launch, apogee and landing sites are +marked automatically. + +.. figure:: ../static/flight/trajectory_on_map.jpg + :align: center + :alt: Ground track of a simulated flight over satellite imagery + + The ground track of a Calisto flight, with the launch site (green), the + apogee ground position (blue) and the landing site (red). + +**Installation** + +The ``folium`` dependency is not installed by default. Add the optional extra +before calling the method: + +.. code-block:: bash + + pip install rocketpy[maps] + +If ``folium`` is not available when the method is called, RocketPy raises an +:class:`ImportError` with the above install command embedded in the message. + +**Usage** + +.. code-block:: python + + # Quickstart: returns a folium.Map, which renders inline in Jupyter + flight.plots.trajectory_on_map() + + # Save a self-contained HTML file you can open in any browser + flight.plots.trajectory_on_map(filename="trajectory.html") + + # Range safety check with distance rings around the launch pad + flight.plots.trajectory_on_map( + filename="trajectory.html", + time_step=0.5, # resample the track to keep the file small + color="#ff7f0e", # ground track color, any CSS color + safety_radii=[2500, 5000], # circles in meters, centred on the pad + title="Calisto — Flight 01", # overlay title on top of the map + ) + +.. list-table:: + :header-rows: 1 + :widths: 25 75 + + * - Parameter + - Description + * - ``filename`` + - Path of the HTML file to write. If None, nothing is saved and the map is + only returned. Default is None. + * - ``time_step`` + - Sampling interval in seconds. If None, every integration step is drawn. + Otherwise the track is linearly interpolated over a uniform time grid, + mirroring ``Flight.export_kml``. Default is None. + * - ``color`` + - Ground track color, as any CSS color string. Default is ``"#1f77b4"``. + * - ``safety_radii`` + - Sequence of radii in meters, drawn as circles centred on the launch + site. Default is None. + * - ``title`` + - Title rendered as an overlay on top of the map. Default is None. + +.. figure:: ../static/flight/trajectory_on_map_safety_radii.jpg + :align: center + :alt: Range safety circles drawn around the launch site + + ``safety_radii=[2500, 5000]`` draws range safety circles around the launch + pad. The initial viewport widens so that the outermost circle stays in + frame, and the circles sit in their own layer so they can be toggled off. + +.. note:: + + The apogee marker is omitted when the simulation never detected an apogee, + for example when the flight terminated on the rail. + +.. seealso:: + + :ref:`flightusage` also offers ``flight.export_kml()`` for viewing the + full 3D trajectory in Google Earth, including altitude, which an + interactive 2D map cannot show. + Forces and Moments ~~~~~~~~~~~~~~~~~~ diff --git a/docs/user/index.rst b/docs/user/index.rst index 6f9d9effa..e983f12b6 100644 --- a/docs/user/index.rst +++ b/docs/user/index.rst @@ -26,6 +26,7 @@ RocketPy's User Guide Compare Flights Class Flight Comparator Class Parachute Triggers (Acceleration-Based) + Software-in-the-Loop Parachute Ejection Deployable Payload Controllers Air Brakes Example diff --git a/docs/user/installation.rst b/docs/user/installation.rst index 4325b390f..7563417cb 100644 --- a/docs/user/installation.rst +++ b/docs/user/installation.rst @@ -172,6 +172,22 @@ Once installed, you can render animations from a :class:`rocketpy.Flight` object See :ref:`flightusage` for full details and parameter descriptions. +**Interactive Maps** — render the flight ground track on a real-world +interactive map using `Folium `_: + +.. code-block:: shell + + pip install rocketpy[maps] + +Once installed, you can build a map from a :class:`rocketpy.Flight` object: + +.. code-block:: python + + # Open the result in a browser, or display it inline in Jupyter + flight.plots.trajectory_on_map(filename="trajectory.html") + +See :ref:`flightusage` for full details and parameter descriptions. + **All extras** — install every optional dependency at once: .. code-block:: shell diff --git a/docs/user/parachute_triggers.rst b/docs/user/parachute_triggers.rst index 2799ea00d..abf246304 100644 --- a/docs/user/parachute_triggers.rst +++ b/docs/user/parachute_triggers.rst @@ -69,6 +69,48 @@ Pass a number to deploy at a fixed height above ground level while descending: lag=0.5, ) +Fixed-time trigger +------------------ + +Pass a ``("time", t_deploy)`` tuple to deploy at a fixed flight time, measured +in seconds from the start of the flight. This models a pyrotechnic delay charge +that is lit at ignition: + +.. code-block:: python + + rocket.add_parachute( + name="Drogue", + cd_s=1.0, + trigger=("time", 12.0), # seconds after flight start + sampling_rate=100, + lag=0.5, + ) + +Unlike the ``"apogee"`` and numeric-altitude forms, this one is not restricted +to the descent: it fires as soon as flight time reaches ``t_deploy``, even while +the rocket is still ascending. That is deliberate, since a delay charge burns on +its own schedule regardless of where the rocket is. + +For a delay charge referenced to *burnout* rather than to ignition, compose it +with the motor's burn out time: + +.. code-block:: python + + rocket.add_parachute( + name="Drogue", + cd_s=1.0, + trigger=("time", motor.burn_out_time + 8.0), # 8 s delay after burnout + sampling_rate=100, + lag=0.5, + ) + +.. note:: + + Deploying while the rocket is still fast will produce very large parachute + forces, which is realistic: an over-short delay shreds canopies in reality + too. Check the loads in the results rather than assuming the deployment was + survivable. + Custom trigger: motor burnout ----------------------------- @@ -274,6 +316,7 @@ a lower altitude: See Also -------- +- :doc:`Software-in-the-Loop Parachute Ejection ` - :doc:`Parachute Class Reference ` - :doc:`Flight Simulation ` - :doc:`Sensors ` diff --git a/docs/user/rocket/datcom.rst b/docs/user/rocket/datcom.rst new file mode 100644 index 000000000..8ecc903bf --- /dev/null +++ b/docs/user/rocket/datcom.rst @@ -0,0 +1,530 @@ +Missile DATCOM Aerodynamics +=========================== + +When construction or finish is unspecified, assume **carbon-fibre construction +with a vinyl finish**. Explicit material details, measured surface properties +and supplied geometry take precedence. This is a construction assumption, +not a measured roughness or fin-section specification: it does not determine +one universal roughness height, thickness, bevel or leading-edge radius. +Document any additional assumptions and use the calibration methods below +to set or fit native aerodynamic inputs. + +Build the rocket, motor and tanks normally, using :class:`rocketpy.Rocket`, +``add_motor``, ``add_nose`` and ``add_tail``. Select whole-vehicle DATCOM +aerodynamics by adding one of these fin sets: + +* ``add_datcom_trapezoidal_fins``: trapezoidal planform with a symmetric HEX + section, a flat middle 90% of the chord and 5% edge bevels. +* ``add_datcom_arc_fins``: trapezoidal planform with a symmetric circular-arc + airfoil section. +* ``add_datcom_hex_fins``: trapezoidal planform with a configurable symmetric + hexagonal or diamond airfoil section. + +ARC and HEX describe the airfoil cross section, not the planform. Root chord, +tip chord, exposed span, sweep and placement follow ``add_trapezoidal_fins``. +For example, after defining ``motor`` and ``environment``:: + + from rocketpy import Flight, Rocket + + rocket = Rocket( + radius=0.0635, + mass=14.426, # excludes the entire motor, kg + inertia=(6.321, 6.321, 0.034), # excludes the motor, kg m² + center_of_mass_without_motor=0, + power_on_drag=0, # replaced by DATCOM + power_off_drag=0, + coordinate_system_orientation="tail_to_nose", + ) + rocket.add_motor(motor, position=-1.373) + rocket.add_nose(length=0.55, kind="von karman", position=1.2) + fins = rocket.add_datcom_hex_fins( + n=4, + root_chord=0.25, + tip_chord=0.10, + span=0.10, + position=-0.9, + sweep_length=0.10, + path="/absolute/path/to/native/datcom", + thickness_to_chord=(0.02, 0.03), + max_thickness_position=0.25, + flat_length=0.5, + leading_edge_radius=0, + datcom_options={ + "mach": [0.1, 0.3, 0.5, 0.7, 0.9, 1.1, 1.5, 2.0], + "alpha": [-20, -10, -5, -2, -1, -0.5, -0.25, -0.1, -0.05, 0, + 0.05, 0.1, 0.25, 0.5, 1, 2, 5, 10, 20], + "altitude": 0, + "timeout": 60, + }, + ) + rocket.add_tail( + top_radius=rocket.radius, bottom_radius=0.045, + length=0.10, position=-1.2, kind="ogive", + ) + flight = Flight(rocket, environment, rail_length=5) + +The nose, fins and tail retain their original geometry for drawing and export. +Flight uses one DATCOM model for all aerodynamic forces and moments, replacing +their component models and the rocket drag inputs. Geometry is read from the +assembled objects, including surfaces and motor added after the DATCOM fins. +Adding a :class:`rocketpy.DatcomFins` instance with ``add_surfaces`` also selects +DATCOM aerodynamics. + +Body Profile Selection +---------------------- + +After adding a DATCOM fin set, select the actual body geometry:: + + rocket.add_nose(length=0.81, position=0, kind="von karman") + rocket.add_tail(top_radius=0.0895, bottom_radius=0.0835, + length=0.125, position=5.137, kind="ogive") + +``NoseCone`` and ``add_nose`` use their existing ``kind`` argument. +Supported DATCOM nose profiles are conical, tangent-ogive, von Karman, +L-V Haack and power series. Other existing RocketPy nose kinds retain +their ordinary component behavior. + +``Tail`` and ``add_tail`` accept ``kind="conical"`` (the backward-compatible +default) or ``kind="ogive"``. The ogive is a circular arc tangent to the upstream +cylinder, joining the given radii over the given axial length in meters. +Ogive geometry, drawing and DATCOM ``TAFT`` follow that profile, including +fin attachment to its local radius. Ogive tails have no component aerodynamic +model; the whole-vehicle DATCOM tables supply their loads and CP. Add a DATCOM +fin set before calling ``add_tail(kind="ogive")``; ordinary rockets raise +``ValueError``. The kind is selected at construction and is read-only. +Conical tails use the original :class:`rocketpy.Tail` without any changes to +its equations or serialization. Profile selection survives serialization. + +The default DATCOM alpha grid has 19 points over ±20 degrees, with 13 points +within ±2 degrees. Explicit ``datcom_options["alpha"]`` overrides it. Grid +convergence and the actual flight-incidence envelope still need checking. + +Executable and Tables +--------------------- + +Supply an executable for the current operating system and processor, with +execute permission where required. RocketPy does not distribute DATCOM or +automatically run a Windows executable on another platform. +The integration targets Asteria's revision 3/99 input/output convention. +Its body incidence angles are the projected angles +``alpha=atan(v_down/v_forward)`` and ``beta=atan(v_right/v_forward)``. +The native regression suite checks this using an axisymmetric-body force +ratio. Verify angle definitions before substituting another DATCOM revision. + +Flight automatically prepares powered and coast databases in two executable +invocations. Each runs in a separate temporary +directory containing a copy of the executable and a generated ``for005.dat``. +RocketPy parses ``for006.dat`` and removes the directory on success or failure. +Every deck includes ``DAMP``; missing or invalid damping tables raise an error. +No ``PROTUB`` or plume-interaction calculations are included. +Fixed-sideslip cases share each invocation when a beta grid is requested; +they do not require additional executable copies or persistent output files. + +By default the powered case omits ``DEXIT`` to exclude all base drag, preserving +an explicit zero-powered-base-drag assumption. For a motor whose nozzle is +smaller than the rocket base, this also removes the exposed annular base drag. +To model that known annulus instead, configure:: + + datcom_options={"powered_base_drag": "nozzle"} + +This writes ``DEXIT=2*motor.nozzle_radius`` for the powered case. The nozzle +must have positive radius and fit inside the aft body. ``"full"`` writes +``DEXIT=0.`` for a full-base diagnostic; ``"none"`` is the default. +The coast case always uses ``DEXIT=0.`` for full base drag. ``BASE`` stays false: +that flag controls jet/plume interaction, not the annular base-area calculation. +Nozzle mode therefore requires no plume-flow inputs. Powered/coast selection +switches at ignition and burnout independently of propellant fraction. +The flown-rocket comparison example uses nozzle mode; AST-HER retains its +explicit zero-powered-base-drag assumption. + +Call ``rocket.prepare_datcom()`` explicitly to inspect drag or stability before +creating a Flight. It returns the complete aerodynamic model. Identical decks +and executable metadata reuse the in-memory result. A geometry, reference-CG, +flight-condition or executable change rebuilds both cases. Re-add a modified +motor to the rocket after changing its tanks, following the normal mass-property +workflow. + +Section Inputs and Flight Conditions +------------------------------------ + +``thickness_to_chord`` is the total symmetric section thickness divided by +chord. ``max_thickness_position`` locates the beginning of the maximum-thickness +region of HEX sections; ``flat_length`` gives its length. Both are fractions +of chord. Set ``flat_length=0`` for a diamond section. ARC uses thickness and +leading-edge radius. Section inputs accept a scalar or one value per span +station; ``leading_edge_radius`` is in meters. A zero tip chord is supported. + +Use ``upper_thickness_to_chord`` and ``lower_thickness_to_chord`` for separate +upper/lower dimensions. ``lower_max_thickness_position`` and +``lower_flat_length`` default to their upper-surface equivalents. Thickness +may be zero on one surface, provided the total thickness is positive. + +The trapezoidal helper also accepts ``section_kind="NACA", naca="0012"``. +Four-digit sections can omit the series prefix. Other supported designations +include ``"5-23012"``, ``"6-64-005"`` and the manual's modified-series formats. +The fin-set number is inserted into the NACA control card after sorting. +For custom sections, use ``section_kind="USER"`` with +``airfoil_coordinates={"x": [...], "upper": [...], "lower": [...]}``, or +``x``, ``mean`` and ``thickness`` arrays. Supply 3 to 50 increasing chord +stations from zero to one. All coordinate values are fractions of chord. + +This DATCOM version includes camber effects on normal force and pitch moment +only for NACA and USER sections at Mach 0.8 and below. HEX/ARC camber affects +drag calculations only. + +Multi-station Planforms and Panel Placement +------------------------------------------- + +``add_datcom_fins`` creates a :class:`rocketpy.DatcomFreeFormFins` object from +2 to 10 exposed ``span_stations``, ``chords`` and ``leading_edges``. Span +stations begin at zero on the body; leading edges begin at zero relative to +``position``. Chords and distances are in meters. For example:: + + fins = rocket.add_datcom_fins( + n=4, position=-0.9, path="/path/to/datcom", + span_stations=[0, 0.05, 0.10], + chords=[0.25, 0.20, 0], + leading_edges=[0, 0.04, 0.10], + section_kind="HEX", thickness_to_chord=[0.02, 0.025, 0.03], + ) + +All stations share one airfoil section type, with proportions allowed to vary +by station. The example has a triangular tip. NACA and USER designate a common +normalized section for the entire fin set. + +``panel_angles`` specifies each panel's azimuth using DATCOM PHIF: degrees +clockwise from the top vertical when viewed forward from behind the rocket. +``dihedral`` specifies each panel's constant GAM in degrees; this tilts the +span direction away from its radial ray. ``incidence`` specifies fixed panel +angles in DATCOM's convention, where positive incidence produces a negative +roll increment. Alternatively use RocketPy's ``cant_angle`` convention, with +the opposite roll-forcing sign. Do not specify both. Incidence and dihedral +accept a scalar or one value per panel. Time-varying actuators and trailing-edge +flaps are not part of this interface. + +``auto_body_radius=True`` uses DATCOM's ``SSPAN(1)=0`` convention to mount the +root at the interpolated body radius at root mid-chord. This supports roots +on the selected boattail profile as well as the cylindrical body. After preparation, +rocket drawings use that attachment radius and the actual panel placement. +Fixed-incidence drawings use the default half-root-chord hinge and zero skew. + +``datcom_options`` accepts: + +* ``mach``: 2 to 20 positive, increasing Mach values. Without a reference + flight, the default is a provisional 20-point grid; see adaptive coverage below. +* ``alpha``: 2 to 20 increasing angles in degrees, including negative angles, + exactly one zero and positive angles. +* ``beta``: ``[0]`` or an increasing signed sideslip grid containing zero, in + degrees. The default is ``[0]`` for the symmetric configuration and + ``[-5, 0, 5]`` for nonstandard panel placements, dihedral, incidence, or + fin sets with fewer than three panels. +* ``altitude``: meters above sea level, either a scalar or one value per Mach. + These specify a Mach/altitude schedule, not an independent altitude grid. + Separate schedules can be provided as + ``{"power_on": [...], "power_off": [...]}``. +* ``timeout``: maximum execution time in seconds for each case. +* ``reynolds``: optional positive Reynolds number per meter, either a scalar, + one value per Mach, or separate ``power_on``/``power_off`` schedules. It + selects native ``MACH``/``REN`` conditions instead of ``MACH``/``ALT``. + The API uses inverse meters, not Reynolds number based on diameter or chord. +* ``body_length``: optional cylindrical section length in meters, excluding the + nose and boattail. With a tail, its position determines this length and an + explicit value must agree. Without a tail, the nozzle position supplies the + inferred aft end unless ``body_length`` is specified. + +Atmosphere from a preliminary flight +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +With ``altitude`` alone, DATCOM derives atmospheric properties using its 1962 +Standard Atmosphere; altitude defaults to sea level. It does not automatically +read a RocketPy Environment. To use the actual launch atmosphere, first run an +ordinary RocketPy model with the intended environment, then configure the +DATCOM rocket from that flight:: + + atmosphere = datcom_rocket.set_datcom_atmosphere(ordinary_flight) + datcom_flight = Flight(rocket=datcom_rocket, environment=env, **launch_options) + + # Optional explicit refinement using the resulting DATCOM trajectory: + atmosphere = datcom_rocket.set_datcom_atmosphere(datcom_flight) + refined_flight = Flight(rocket=datcom_rocket, environment=env, **launch_options) + +Use separate rocket instances when retaining ordinary and DATCOM results. +Both must have the same motor burn interval. The method does not create flights +or run the executable; the next Flight regenerates native tables. + +Adaptive coverage uses all **20 Mach slots**, always including **0.1 and 1.0**. +Fixed points from 0.8 to 1.2 concentrate resolution near Mach 1; the remaining +points adapt to the reached regime. The upper bound includes 15% headroom plus +0.02 Mach and is at least 1.2. Mach zero uses the positive 0.01 endpoint. Values +are spaced by at least 0.01 to survive the native two-decimal output format. +One shared grid covers the union of powered and coast flight conditions, with +separate Reynolds schedules for the two native cases. + +Alpha adapts too: 19 signed points stay concentrated near zero, retaining at +least ±20 degrees and extending beyond observed incidence by 15% plus 2 degrees. +Only samples with dynamic pressure at least ``minimum_dynamic_pressure=100`` Pa +set the extent; no loaded samples retain ±20 degrees. This excludes ambiguous +pad/near-apogee flow directions from grid selection. Set a lower threshold if +those low-load conditions matter. The returned ``coverage`` report records the +scope, excluded sample count and separate phase Mach/loaded-alpha maxima. + +``samples=1000`` supplies uniform samples, augmented by actual solver states +and burn boundaries. The default covers the rail, powered ascent and coast to +apogee; descent and parachute flight are excluded. Adaptive envelopes +approaching 90 degrees are rejected: the current forward-flight load mapping +cannot faithfully represent tail-first flow. Extending a grid does not validate DATCOM's high-incidence methodology. + +Explicit ``mach=[...]`` and ``alpha=[...]`` arguments fix custom grids instead, +for controlled studies or tunnel conditions; custom Mach grids may omit the +adaptive anchors. Every call without those arguments recalculates both grids +from the new reference flight. The example reproduction pipeline applies this +again after its initial DATCOM simulation. No measured apogee selects grid points. + +At each sampled altitude in meters ASL, the reference Environment supplies +actual density, speed of sound and dynamic viscosity. Reynolds number per meter +is ``rho * Mach * speed_of_sound / dynamic_viscosity``. Native ``REN`` replaces +``ALT``, bypassing DATCOM's standard-atmosphere Reynolds calculation. Pressure +(Pa), temperature (K), altitude and Reynolds schedules are returned for provenance; +pressure and temperature are not separately sent to DATCOM. Flight forces still +use the simulated flight's actual density and relative airspeed. + +Separate powered and unpowered ascent schedules assign one atmospheric state +per Mach. Duplicate sampled Mach values retain the earliest sample. Outside +the reached Mach range, atmospheric state is clamped to its endpoint, but speed +is recomputed at the requested Mach. This avoids clamping Reynolds number to a +different speed. Multiple crossings, descent and separated stages require a +richer database than this approximation. No convergence is assumed: repeat +explicitly with the DATCOM trajectory if needed, comparing apogee and schedules. +Existing ordinary RocketPy equations are unchanged. + +All DATCOM fin sets must use the same executable and options. At most four fin +sets with 1 to 8 panels each are supported. Sets are automatically numbered +from nose to tail. Overlapping axial planform envelopes are rejected; +the root chord center must lie on the body's axial extent. A trailing edge +may extend aft of the base, as verified with the native 3/99 executable. +Separation below four exposed semispans triggers a warning about DATCOM's +upstream vortex-roll-up approximation. Existing ordinary +trapezoidal fin sets are included with a default symmetric HEX section of 2% +thickness and 25% edge bevels. Other aerodynamic surface types are rejected +rather than omitted. Exactly one unblunted conical, tangent-ogive, von Karman, +L-V Haack or power-series nose is required. An optional conical or tangent-ogive boattail must +connect to the cylindrical body. Off-axis geometry is unsupported. + +Explicit Calibration +-------------------- + +After adding DATCOM fins, attach calibration to the whole rocket:: + + rocket.add_datcom_calibration( + roughness_height=30.48e-6, # equivalent sand roughness, meters + leading_edge_radius=0.0005, # optional radius, meters + ) + rocket.prepare_datcom() + +``roughness_height`` is an equivalent sand roughness height, not the measured +arithmetic mean roughness (Ra). Carbon-fibre construction with a vinyl wrap +does not determine this value: seams, finish, exposed edges and other losses +can differ between vehicles. A fit to apogee is an effective calibration and +does not identify the real surface finish. No material-specific value is +silently selected. + +The carbon-fibre/vinyl assumption applies only where more specific information +is missing. It does not supersede supplied masses, inertias, motor data, +geometry, measured coefficients or calibration values. Fitted values represent +effective aerodynamic losses and must not be described as measured material +properties. In the four-rocket apogee example, physically bounded leading-edge +radius sweeps did not reach the targets; separate roughness fits did. A shared +GENESIS roughness did not fit all four rockets, and Andromeda's much larger +fitted roughness indicates that the fit can absorb other missing losses. + +The conversion uses dimensionless DATCOM ``RHR``, with equivalent sand +roughness equal to three times RHR in microinches. For example, +30.48 micrometers produces ``RHR=400``. This avoids a verified discrepancy: +the local 3/99 executable treats ``ROUGH`` as inches even with ``DIM M``, +whereas the 5/97 Missile DATCOM guide Table 4 specifies centimeters for metric +``ROUGH``. The RHR/roughness conversion was checked against that executable; +other native versions should be independently verified. + +``leading_edge_radius`` overrides ``LER`` at every station of every supported +fin set without modifying the stored fin definitions. ``None`` retains the +individual fin radii. NACA sections define their own edge shape and reject an +override. Use physically compatible values and choose fit bounds from the +actual fin section rather than from the material label alone. + +Calling ``add_datcom_calibration`` replaces the previous settings; omitted +roughness defaults to zero and omitted radius to ``None``. Call without +arguments to reset. ``rocket.datcom_calibration`` returns a copy of the current +settings. Changes invalidate prepared native tables, which the next +``prepare_datcom`` or Flight rebuilds. Rocket serialization preserves the +settings. The method requires DATCOM fins and does not fit values automatically. +No multiplier or correction is applied to RocketPy's ordinary aerodynamics; +all calibrated aerodynamic properties still come from native DATCOM. + +Wind-Tunnel Coefficient Fitting +------------------------------- + +Mach, alpha and measured CD are sufficient for a roughness fit:: + + report = rocket.fit_datcom_calibration("wind_tunnel.csv") + rocket.prepare_datcom() + +For example, a CSV header can be ``mach,alpha,CD``. A column mapping with +numeric vectors is also supported. Headers are case-insensitive. Alpha and +beta are degrees and default to zero when omitted. The default reference area +is the rocket frontal area; supply ``reference_area`` in square meters when +the measurements use another normalization. The fit uses static coefficients +at zero body rates and zero incidence rate. + +Optional measured columns are ``CL``, ``CA``, ``CN``, ``CY``, ``CM``, ``CLN`` +and ``CLL``, together with ``beta``, ``power_on`` and +``reynolds_per_meter``. Blank coefficient cells are allowed if each row has at +least one finite measurement. Unknown columns are rejected. Body coefficients +use DATCOM's axes and signs. ``CL`` is pitch-plane lift. By default ``CD`` +opposes the full flow, including sideslip; ``drag_axis="stability"`` selects +the pitch-plane drag projection. Native reporting conventions can differ at +nonzero beta, so match the experiment's convention explicitly. + +Moment measurements require ``reference_length`` in meters and +``moment_reference`` in the rocket's axial position coordinates. Native moments +are transferred to that axial reference before comparison. An optional +``lateral_reference_length`` specifies the yaw/roll normalization length; +otherwise it equals ``reference_length``. Off-axis moment references are not +supported. Positive ``weights={"CD": ..., "CM": ...}`` supplies coefficient-wise +least-squares weights; for a known uncertainty sigma, use ``1/sigma**2``. + +The default fitted parameter is ``roughness_height``, bounded to 0--300 +micrometers. To fit an edge radius, supply explicit physically justified +bounds in meters:: + + report = rocket.fit_datcom_calibration( + "wind_tunnel.csv", + parameters=("roughness_height", "leading_edge_radius"), + bounds={ + "roughness_height": (0, 100e-6), + "leading_edge_radius": (0, 0.001), + }, + reference_area=rocket.area, + reference_length=2 * rocket.radius, + moment_reference=rocket.center_of_dry_mass_position, + max_evaluations=200, + ) + +``model_scale`` is the tunnel-model length divided by the full-rocket length, +defaulting to 1. Reference areas and lengths are supplied for the model and +converted to full-scale equivalents. The default area is the scaled model +frontal area. ``moment_reference`` always remains in full-rocket axial +coordinates. Reported fitted dimensions are full-scale equivalents, not +measured model-surface properties. + +``reynolds_per_meter`` supplies tunnel conditions, with one positive value per +Mach and power state. It is converted to full-scale Reynolds number per meter +by multiplying by ``model_scale``. These conditions affect only the fitting +probes; Flight keeps the original rocket options. Without this column, the +probes use the existing DATCOM atmospheric conditions. Nonzero measured beta +requires a beta table; measurements outside the native Mach/angle envelope +are rejected. ``power_on=False`` is the default, or use a per-row column +containing 0 or 1 to match the measured base/nozzle condition. + +Successful fits apply their settings through ``add_datcom_calibration``. +The JSON-compatible report contains initial and final coefficient RMSE, +weighted RMSE, residual rows, references, fitted inputs, bounds, boundary +flags and native evaluation count. Failed data validation, execution or +optimization leaves calibration and the prepared model unchanged. The fit +does not replace any aerodynamic coefficient; damping remains native DATCOM. +Static measurements do not identify forced-oscillation damping derivatives, +and this interface does not ingest measured dynamic derivatives. + +A drag-only curve may not uniquely identify two fitted parameters. A bounded +numerical fit is not guaranteed to be a unique or global solution; use +additional channels and independent test conditions to assess predictive +accuracy. Matching the fitted measurements, or a single apogee, is not an +independent validation. Real measured coefficients and their reference conditions +are required for wind-tunnel validation. + +Accuracy of Apogee Predictions +------------------------------ + +Ordinary RocketPy can predict apogee more accurately than RocketPy using +DATCOM when its supplied drag curve better represents the real vehicle. +In the four untuned comparisons, ordinary RocketPy was closer to the +notebook-reported flight altitude for Cavour, GENESIS and Andromeda; DATCOM +was closer for EREBUS. This does not establish a general ranking. A successful +fit to those same apogees or coefficients is calibration; predictive accuracy +must be assessed using independent data. Asteria / AST-HER has not flown and +its results are simulation predictions, not flight validation. + +The :doc:`recorded-flight comparison ` +compares altitude, upward vertical velocity, CD and stability margin between +ordinary RocketPy, DATCOM and measured records where available, including +percentage errors. Apogee-fitted constant CD is explicitly inferred calibration, +not measured CD. Missing measurements and unsupported source configurations +remain visible; archived outputs are labelled. No inferred CD replaces the +native DATCOM predictions shown in that comparison. + +CG, Damping and Interpolation +----------------------------- + +RocketPy derives the filled CG from the dry rocket, dry motor, and the motor's +propellant masses and positions, including liquid tanks. The input +``center_of_mass_without_motor`` still excludes the entire motor; it is not +the assembled empty-tank CG. DATCOM uses the filled CG at ignition for the +powered run and the assembled empty CG for coast. Physical moments are +transferred to the instantaneous CG throughout the burn. Coefficients are not +blended by fuel fraction, which would mix base drag with CG migration. + +Static coefficients and all eleven DAMP derivatives interpolate linearly in +Mach, signed angle of attack, and sideslip when a beta grid is available. +With only ``beta=[0]``, sideslip uses the first-order lateral derivatives. +The full beta database bypasses that expansion to avoid counting it twice. +The reported ``CD`` is DATCOM's stability-axis drag, which differs from +wind-axis drag at nonzero sideslip. Flight applies the body-axis force +coefficients directly, including during the constrained rail phase. +The parser retains DATCOM's per-degree units; the load model +converts body rates in rad/s to the required nondimensional rates. ``CNAD`` and +``CMAD`` use a simultaneous incidence-rate/acceleration solve within each ODE +evaluation, including local rotation and wind shear. Adaptive solver call +history does not determine incidence rate. + +Mach and angle are clamped to the generated envelope. The returned model records +``outside_mach_envelope``, ``outside_alpha_envelope`` and, for full beta grids, +``outside_beta_envelope`` excursions. Low-speed +damping is disabled below 1 m/s to avoid dividing by vanishing airspeed. +Reverse axial drag opposes tail-first motion, but the bounded continuation +outside the small-angle envelope is not validated descent aerodynamics. +The 3 DOF solver uses the force coefficients and retains its usual simplified +attitude behavior; use 6 DOF to simulate rotational damping. + +``cp_position``, ``static_margin`` and ``stability_margin`` use the coast +small-angle DATCOM derivatives after preparation. Before preparation they +are undefined (NaN), rather than displaying component-model results. CP is +also undefined when the normal-force slope vanishes. + +The parser is adapted from the Asteria-Flight-Dynamics DATCOM implementation. +Deck definitions follow the Missile DATCOM user's guide, Tables 5, 10 and 11. + +The executed :doc:`AST-HER comparison ` +uses the saved Asteria configuration and atmosphere. It compares ordinary +RocketPy using the supplied Asteria drag curve with native DATCOM. AST-HER has +not flown: percentages are model-to-model differences, not measured-flight +errors. The ordinary conical-tail approximation and native ogive tail are +explicit. Mass and CG histories are checked to match. + + +Interpreting disagreement with source flight examples +----------------------------------------------------- + +Ordinary RocketPy's whole-vehicle drag curves are supplied independently of its +nose and fin normal-force models. Replacing them with native DATCOM therefore +changes the drag model even with identical mass and planform. The +:doc:`comparison notebook ` keeps supplied +CD, native CD, inferred apogee-fitted CD and measured values separate. One +constant fitted to apogee does not uniquely identify a physical drag curve. + +In particular, a constant thickness/chord ratio describes a thickness taper. +For a measured constant-thickness plate with nonzero root and tip chords, supply +separate ratios, for example ``thickness_to_chord=(t / root_chord, t / tip_chord)`` +with all lengths in meters. Match bevels, section contour and edge radius to +hardware. Smooth-surface roughness zero is an assumption, not a measured vinyl +finish. Carbon fibre and vinyl do not determine these missing numerical inputs. +Use measured geometry or the calibration API; an apogee-fitted roughness is an +effective loss calibration, not an identified material property. diff --git a/docs/user/rocket/rocket.rst b/docs/user/rocket/rocket.rst index 956175409..43ac3761a 100644 --- a/docs/user/rocket/rocket.rst +++ b/docs/user/rocket/rocket.rst @@ -16,6 +16,7 @@ Rocket Usage .. toctree:: :maxdepth: 3 :caption: Generic Surfaces and Custom Aerodynamic Coefficients - + Generic Surfaces and Custom Aerodynamic Coefficients - \ No newline at end of file + Missile DATCOM Aerodynamics + diff --git a/docs/user/rocket/rocket_usage.rst b/docs/user/rocket/rocket_usage.rst index 287a04b6f..1fcb44283 100644 --- a/docs/user/rocket/rocket_usage.rst +++ b/docs/user/rocket/rocket_usage.rst @@ -75,15 +75,11 @@ gases, the drag coefficient is lower than when the motor is off. These curves are used to calculate the drag coefficient of the rocket at any given time. -The drag curves can be defined in two ways: - -1. Passing in the path to the drag curve CSV file as a string; -2. Passing in a function that returns the drag coefficient given the Mach - number. - -Curves defined in CSV files must have the first column as the Mach number -and the second column as the drag coefficient. -Here is an example of a drag curve file: +Drag coefficients can be supplied as a constant, a Mach-only curve, or a +multivariable model. A Mach-only model can be a callable, a +:class:`rocketpy.Function`, a sequence of ``[mach, coefficient]`` pairs, or the +path to a two-column CSV file. The first CSV column is the Mach number and the +second is the drag coefficient. For example: .. code-block:: @@ -99,6 +95,53 @@ Here is an example of a drag curve file: 0.9, 0.45696342 1.0, 0.62744566 +For a model that depends on the flight state, pass a callable with these seven +positional arguments, in order: + +``alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate`` + +``alpha`` and ``beta`` are the angle of attack and sideslip angle in radians. +The angular rates are expressed in radians per second in the rocket body frame. +For example, a model based on angle of attack and Mach number can ignore the +other inputs: + +.. code-block:: python + + def drag_coefficient( + alpha, _beta, mach, _reynolds, _pitch_rate, _yaw_rate, _roll_rate + ): + return 0.38 + 0.08 * mach**2 + 0.6 * alpha**2 + + rocket = Rocket( + radius=0.0635, + mass=14.426, + inertia=(6.321, 6.321, 0.034), + power_off_drag=drag_coefficient, + power_on_drag=drag_coefficient, + center_of_mass_without_motor=0, + ) + +Header-based CSV files can model any subset of the seven variables. The final +column contains the drag coefficient, and the preceding headers must use the +variable names shown above. Providing every combination of input coordinates +forms a regular grid and enables regular-grid interpolation. This example +defines drag as a function of angle of attack and Mach number: + +.. code-block:: text + + alpha,mach,cd + 0.0,0.5,0.30 + 0.0,1.0,0.45 + 0.1,0.5,0.32 + 0.1,1.0,0.48 + +For backward compatibility, ``rocket.power_off_drag`` and +``rocket.power_on_drag`` expose the Mach-only slice of each model, with the +other six inputs set to zero. Use ``rocket.power_off_drag_7d`` and +``rocket.power_on_drag_7d`` to evaluate the full model directly. During a +:class:`rocketpy.Flight`, RocketPy evaluates the full model using the current +flight state. + .. tip:: Getting a drag curve can be a challenging task. To get really accurate drag curves, you can use CFD software or wind tunnel data. @@ -498,4 +541,3 @@ and ease of rotation: 3. **Ease of Rotation**: The I\ :sub:`33` value is significantly lower than the other two. This suggests that the rocket is easier to rotate around its center axis than around the axes perpendicular to the rocket. This is an important factor when considering the rocket's stability and control. However, these conclusions are based on the assumption that the inertia tensor is calculated with respect to the rocket's center of mass and aligned with the principal axes of the rocket. If the inertia tensor is calculated with respect to a different point or not aligned with the principal axes, the conclusions may not hold. - diff --git a/docs/user/sil_parachute_ejection.rst b/docs/user/sil_parachute_ejection.rst new file mode 100644 index 000000000..a8e55e7d6 --- /dev/null +++ b/docs/user/sil_parachute_ejection.rst @@ -0,0 +1,255 @@ +.. _sil-parachute-ejection: + +Software-in-the-Loop Parachute Ejection +======================================= + +RocketPy can run a **Software-in-the-Loop (SIL)** recovery system by calling +your ejection-detection algorithm from the parachute trigger (and, when useful, +from a discrete :doc:`controller `). During ``Flight``, RocketPy +samples pressure, height and the state vector at the parachute +``sampling_rate`` and asks your callable whether to fire the ejection charge. + +This page shows a minimal SIL pattern: a small stateful detector that mimics a +flight-computer recovery algorithm, wired as a parachute ``trigger``. For a +full research-grade example that wraps a compiled C/C++ recovery stack +(``SisRec``) inside Monte Carlo runs, see the +`Valetudo Monte Carlo analysis `_. + +.. seealso:: + + - :doc:`Parachute Triggers (Acceleration-Based) `: + trigger signatures, ``u_dot``, and sensor-aware callables. + - :doc:`Controllers `: discrete sampling of in-flight logic. + - :ref:`Parachute Trigger Details `: callable + ``(pressure, height, state)`` contract on ``Rocket.add_parachute``. + +Why SIL for recovery +-------------------- + +Real recovery firmware rarely uses the ideal ``"apogee"`` or altitude string +triggers. It filters barometer (and often IMU) samples, keeps an internal +flight phase, and decides when to fire drogue or main charges. In SIL you: + +1. Keep that detection logic in a callable (pure Python, or a thin wrapper + around C/C++/Rust via SWIG, ctypes, cffi, or pybind11). +2. Attach the callable as ``trigger=...`` on ``Rocket.add_parachute``. +3. Let ``Flight`` drive the loop at a fixed ``sampling_rate`` (Hz), with + optional ``noise`` on the pressure channel and ``lag`` for charge-to-open + delay. + +RocketPy then evaluates aerodynamics with the canopy after the lag, so you can +compare trigger times, inflation velocities and landing footprints against the +same algorithm that flies on the vehicle. + +Simplified ejection detector +---------------------------- + +The following class is a **teaching stand-in** for a barometric apogee +detector. It is not Valetudo's ``SisRec``; it only illustrates the stateful +pattern: feed noisy pressure each sample, return a discrete flight state, and +map that state to a boolean trigger. + +.. code-block:: python + + class SimpleBarometricEjectionDetector: + """Minimal pressure-based apogee detector for SIL demos. + + States: + 0: armed / ascent + 1: apogee candidate (pressure rising while still high) + 2: drogue fire command + """ + + def __init__(self, min_ascent_samples=20, pressure_eps=20.0): + self.min_ascent_samples = min_ascent_samples + self.pressure_eps = pressure_eps # Pa + self.reset() + + def reset(self): + self.samples = 0 + self.min_pressure = None + self.state = 0 + + def update(self, pressure_pa): + """Ingest one pressure sample [Pa]; return internal state.""" + self.samples += 1 + if self.min_pressure is None or pressure_pa < self.min_pressure: + self.min_pressure = pressure_pa + if self.state == 1: + self.state = 0 + return self.state + + # Pressure rising relative to the running minimum → descending. + if ( + self.samples >= self.min_ascent_samples + and pressure_pa > self.min_pressure + self.pressure_eps + ): + self.state = 1 if self.state == 0 else 2 + return self.state + + +Wire the detector as a parachute trigger +---------------------------------------- + +A parachute trigger receives freestream pressure (with the parachute +``noise`` model applied), height AGL, and the state vector. Return ``True`` +exactly when your algorithm commands the charge. + +.. code-block:: python + + from rocketpy import Environment, SolidMotor, Rocket, Flight + + env = Environment(latitude=32.99, longitude=-106.97, elevation=1400) + env.set_atmospheric_model(type="standard_atmosphere") + + # Build motor + rocket as usual (Calisto / your vehicle). + # motor = SolidMotor(...) + # rocket = Rocket(...) + # rocket.add_motor(motor, position=...) + # rocket.add_nose(...); rocket.add_trapezoidal_fins(...); ... + + drogue_detector = SimpleBarometricEjectionDetector( + min_ascent_samples=25, + pressure_eps=30.0, + ) + + def drogue_sil_trigger(pressure, height, state_vector): + # Optional guards: ignore rail / early flight. + if height < 50.0: + return False + state = drogue_detector.update(pressure) + return state == 2 # fire when detector reaches "drogue command" + + rocket.add_parachute( + name="Drogue", + cd_s=1.0, + trigger=drogue_sil_trigger, + sampling_rate=100, # Hz; match your flight computer loop rate + lag=1.5, # seconds between fire command and full open + noise=(0, 8.3, 0.5), # (mean, std, time-correlation) on pressure [Pa] + ) + + + def main_sil_trigger(pressure, height, state_vector): + # Main: descending and below a deployment altitude. + vz = state_vector[5] + return vz < -1.0 and height < 800.0 + + rocket.add_parachute( + name="Main", + cd_s=10.0, + trigger=main_sil_trigger, + sampling_rate=100, + lag=0.5, + noise=(0, 8.3, 0.5), + ) + + flight = Flight( + rocket=rocket, + environment=env, + rail_length=5.2, + inclination=85, + heading=0, + # Stop the integrator on trigger sample times when using discrete rates: + time_overshoot=False, + ) + + print(flight.parachute_events) + flight.info() + +.. tip:: + + Reset detector state (``drogue_detector.reset()``) before every new + ``Flight`` if you reuse the same detector instance across Monte Carlo + samples or parameter sweeps. + +.. note:: + + Set ``time_overshoot=False`` when the SIL loop must see samples at exactly + ``1 / sampling_rate``. The same rule applies to discrete + :doc:`controllers `. + +Dual path: trigger vs controller callback +----------------------------------------- + +.. list-table:: + :header-rows: 1 + :widths: 30 70 + + * - Mechanism + - Use when + * - Parachute ``trigger`` + - The algorithm's output is "fire this canopy now". This is the usual SIL + recovery path. + * - Controller function (:doc:`controllers`) + - You need a fixed-rate loop that updates actuators (air brakes, canards) + or logs observed variables alongside recovery logic. + * - Acceleration / sensor triggers (:doc:`parachute_triggers`) + - The algorithm needs ``u_dot`` or attached IMU/barometer sensor objects + instead of (or in addition to) the noisy pressure channel. + +You can combine them: a controller may update shared state that a parachute +trigger reads, or a 5-argument trigger can read ``sensors`` and ``u_dot`` +directly. Prefer a single source of truth for the fire decision so Monte Carlo +and hardware stay aligned. + +Wrapping compiled recovery firmware +----------------------------------- + +Issue #524's bonus path is hardware-faithful binaries, not a RocketPy API +change. Practical options: + +1. **Compile the flight algorithm** to a shared library (``.so`` / ``.dylib`` / + ``.dll``) with the same interface your avionics uses (for example + ``update(pressure) -> state``). +2. **Expose it to Python** with SWIG (as Valetudo's ``SisRec`` does), ctypes, + cffi, or pybind11. +3. **Call it from the trigger** exactly like the pure-Python detector above. + +Sketch of a SWIG/ctypes-style wrapper (API names are illustrative): + +.. code-block:: python + + # After building your recovery library and its Python wrapper: + # import SisRec # Valetudo-style SWIG module + # + # detector = SisRec.SisRecSt(main_pressure_ratio, mu) + # detector.initializeBuffers(p0) + # detector.enable() + # + # def drogue_trigger(pressure, height, state_vector): + # # SisRec historically expected pressure in bar-like units; + # # convert to match your firmware's input convention. + # return detector.update(pressure / 1e5) == detector.detectDrogue + +Keep unit conversions and enable/reset semantics identical to the flight +computer. The `Valetudo Monte Carlo folder +`_ +shows this pattern end-to-end with ``SisRec.py`` / ``_SisRec.so`` driving +drogue deployment inside thousands of ``Flight`` runs. + +Toward Hardware-in-the-Loop (optional) +-------------------------------------- + +SIL stops at "same software, simulated sensors." A **Hardware-in-the-Loop +(HIL)** step would stream RocketPy's pressure/IMU time history to the real +flight computer (serial, CAN, or a board-level harness) and feed the board's +fire discrete back into the simulation. RocketPy does not ship a HIL bridge +today; teams usually: + +- export ``Flight`` solution / sensor histories, or +- call a thin I/O shim from a discrete controller / trigger that talks to the + device under test. + +Treat HIL as an integration project on top of the SIL trigger contract above, +not as a built-in ``Flight`` mode. + +See also +-------- + +- `Valetudo Monte Carlo (RocketPaper) `_ +- :doc:`parachute_triggers` +- :doc:`controllers` +- :doc:`stochastic` +- :class:`rocketpy.Parachute` +- :class:`rocketpy.Flight` diff --git a/docs/user/stochastic.rst b/docs/user/stochastic.rst index 062b034e2..ed8f8ab10 100644 --- a/docs/user/stochastic.rst +++ b/docs/user/stochastic.rst @@ -93,6 +93,67 @@ passed in a few different ways: gives you the full control of how the samples are generated. See :ref:`custom_sampler` for more details. +.. note:: + The formats above assume each argument holds a single number. The + ``shape_points`` of :class:`rocketpy.stochastic.StochasticFreeFormFins` is + the exception: a fin outline is only meaningful as a complete set of points, + so the deviation given applies to the outline as a block, with every + coordinate of every point perturbed by its own draw. The fin root is held on + the body line, so a point nominally at ``y = 0`` keeps that value and no + point ends up inside the airframe. + + Because the deviation has to centre on the nominal coordinate, only the + distributions that read their first argument as that centre can be used here: + *"normal"*, *"gumbel"*, *"laplace"* and *"logistic"*. The others take bounds + (*"uniform"*) or shape parameters (*"wald"*, *"gamma"*, ...), which a set of + coordinates cannot be, and are rejected when the object is created. + + A list means either one fixed outline, used as given, or a list of candidate + outlines to choose between, which do not have to have the same number of + points:: + + # One millimetre of deviation on every coordinate + StochasticFreeFormFins(free_form_fins=fins, shape_points=0.001) + + # One fixed outline, not randomized + StochasticFreeFormFins( + free_form_fins=fins, + shape_points=[(0, 0), (0.08, 0.1), (0.12, 0)], + ) + + # Choose between two outlines + StochasticFreeFormFins( + free_form_fins=fins, + shape_points=[[(0, 0), (0.08, 0.1), (0.12, 0)], [(0, 0), (0.06, 0.12), (0.12, 0)]], + ) + + A ``CustomSampler`` given for this argument has to yield a whole outline per + sample, since what it returns replaces the outline instead of perturbing it. + +.. note:: + Where the nominal value comes from the deterministic object, it is read when + that input is configured and kept from then on, so changing the deterministic + object afterwards does not move what is sampled around. Neither does a + ``MonteCarlo`` run: some ``create_object`` paths write the sampled value + back onto the object they were given rather than building a copy, and + re-reading it would take one simulation's output as the next one's nominal. + Arrays and the built-in containers are copied on the way in and on the way + out, so writing through a generated object does not reach the kept value + either. + + Four things sit outside that rule on purpose: + + - an input installed by an ``add_*`` method, an eccentricity for instance, is + read when it is added rather than when the object is built; + - a component's position is read from its own component on every reset, since + each of them arrives under the one name ``position``; + - an ensemble wind factor scales the selected member's own profile, because + ``select_ensemble_member`` rebuilds the wind and the value from before it + belongs to whichever member was loaded then; + - anything that is not an array or a built-in container, a ``Function`` or a + callable among them, is kept by reference and follows the object it came + from. + .. note:: In statistics, the terms "Normal" and "Gaussian" refer to the same type of \ distribution. This distribution is commonly used and is the default for the \ @@ -241,6 +302,64 @@ reliability of your simulations over time. .. which parameters most significantly impact your simulation results. +Seeding a rocket's components +----------------------------- + +A ``StochasticRocket`` holds nested stochastic objects: the motors, the +aerodynamic surfaces, the rail buttons, the parachutes and the air brakes. Each +time the rocket is reset, every component attached to it at that moment is given +a stream of its own, spawned from the seed the reset was given. A main and a +drogue parachute built from the same ``cd_s`` and ``lag`` are then no longer +made to consume the same draws as each other. Independent streams can still land +on equal values, and a specification with no spread always will. + +The reset is what builds the tree, not ``add_parachute`` or ``add_nose``. A +rocket resets itself once while being constructed, when it has no components +yet, so anything attached afterwards keeps the generator it was built with until +something resets it again. A serial ``MonteCarlo`` run does not reset it at +all. A parallel one tries to, once per worker, but hands the model a +``SeedSequence`` where an integer is wanted, so that path does not get as far +as building the tree either. Resetting per simulation, from an integer seed the +caller chooses, is what the Monte Carlo seeding work adds. + +Each collection is spawned separately, so adding an aerodynamic surface does not +move what the parachutes draw. Within a collection the stream follows insertion +order, so adding a component ahead of another does change what the later one +draws under a fixed seed. The rocket's own inputs, such as ``mass`` and +``radius``, use the seed exactly as given. + +.. note:: + A component's *position* is a property of the rocket rather than of the + component, so it is drawn from the rocket's own stream. + +.. note:: + A stream belongs to one stochastic wrapper. Storing the same wrapper twice, + or sharing one between two rockets, is not supported: the second reset + replaces the first, and the two entries end up drawing from one generator. + + A shared ``CustomSampler.seed_group`` keeps its own rule: a group belongs to + one model. Sharing one between two components leaves each of them seeding it + from their own child, and the last one to be reset decides what both draw. + +.. note:: + A whole run is fixed by ``MonteCarlo.simulate(random_seed=...)`` rather than + by seeding these models yourself. Each simulation takes its seed from its own + index, so simulation 7 draws the same inputs whether the run was serial or + split over any number of workers, and whether it was reached first or last. + A run of ``n`` simulations numbers them 0 to ``n - 1`` either way, which is + what makes one index name one simulation. + Every input row records the root it came from, so appending carries that + study on whether or not the seed is given again, and a different one is + refused rather than mixed in. Without a seed a run draws fresh entropy and + reproduces nothing. + + An index fixes the draw, not the object it is drawn around. Where the note + above says a value follows its deterministic object, moving that object + between runs still moves what is sampled, however the seed was set. A + ``CustomSampler`` that draws from the process-global ``numpy.random`` + rather than from the generator it is handed sits outside all of this, as + :ref:`custom_sampler` warns. + Conclusion ---------- diff --git a/pyproject.toml b/pyproject.toml index 456441f33..9133029e8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -77,7 +77,16 @@ animation = [ "imageio-ffmpeg>=0.5" ] -all = ["rocketpy[env-analysis]", "rocketpy[monte-carlo]", "rocketpy[animation]"] +maps = [ + "folium>=0.14", +] + +all = [ + "rocketpy[env-analysis]", + "rocketpy[monte-carlo]", + "rocketpy[animation]", + "rocketpy[maps]", +] [tool.coverage.report] diff --git a/requirements-optional.txt b/requirements-optional.txt index a79ed6b13..d1d95b2e6 100644 --- a/requirements-optional.txt +++ b/requirements-optional.txt @@ -7,5 +7,6 @@ imageio multiprocess>=0.70 statsmodels prettytable +contextily>=1.0.0; python_version < '3.14' pyvista>=0.45 imageio-ffmpeg>=0.5 \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 61a594320..85fbc96ea 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ -numpy>=1.13 -scipy>=1.0 +numpy>=1.23 # matplotlib 3.9 requires it +scipy>=1.8 # first to allow numpy 1.23; bootstrap needs 1.7 matplotlib>=3.9.0 # Released May 15th 2024 netCDF4>=1.6.4 requests diff --git a/rocketpy/__init__.py b/rocketpy/__init__.py index d8720db4c..ea0f75fd2 100644 --- a/rocketpy/__init__.py +++ b/rocketpy/__init__.py @@ -36,6 +36,8 @@ AeroSurface, AirBrakes, Components, + DatcomFins, + DatcomFreeFormFins, EllipticalFin, EllipticalFins, Fin, @@ -52,6 +54,7 @@ Tail, TrapezoidalFin, TrapezoidalFins, + TubeFins, ) from .sensitivity import SensitivityModel from .sensors import Accelerometer, Barometer, GnssReceiver, Gyroscope @@ -62,6 +65,7 @@ StochasticEllipticalFins, StochasticEnvironment, StochasticFlight, + StochasticFreeFormFins, StochasticNoseCone, StochasticParachute, StochasticRocket, diff --git a/rocketpy/_encoders.py b/rocketpy/_encoders.py index b6730f65c..01bdc6d9e 100644 --- a/rocketpy/_encoders.py +++ b/rocketpy/_encoders.py @@ -56,6 +56,17 @@ def default(self, o): return o.item() elif isinstance(o, np.ndarray): return o.tolist() + elif isinstance(o, np.random.SeedSequence): + # Sensor seeds (and other RNGs) may hold a SeedSequence. Encode its + # reconstructible state so JSON dump does not raise TypeError. + encoding = { + "entropy": o.entropy, + "spawn_key": list(o.spawn_key), + "n_children_spawned": int(o.n_children_spawned), + "pool_size": int(o.pool_size), + } + encoding["signature"] = get_class_signature(o) + return encoding elif isinstance(o, datetime): return [o.year, o.month, o.day, o.hour] elif hasattr(o, "__iter__") and not isinstance(o, str): @@ -110,14 +121,17 @@ def object_hook(self, obj): class_ = get_class_from_signature(signature) hash_ = signature.get("hash", None) + if class_ is np.random.SeedSequence: + # Cython __init__ has no __code__, so the generic kwargs + # path cannot rebuild SeedSequence; restore from state. + return np.random.SeedSequence( + entropy=obj.get("entropy"), + spawn_key=tuple(obj.get("spawn_key", ())), + pool_size=obj.get("pool_size", 4), + n_children_spawned=obj.get("n_children_spawned", 0), + ) if class_.__name__ == "Flight" and not self.resimulate: - new_flight = class_.__new__(class_) - new_flight.prints = _FlightPrints(new_flight) - new_flight.plots = _FlightPlots(new_flight) - set_minimal_flight_attributes(new_flight, obj) - if hash_ is not None: - setattr(new_flight, "__rpy_hash", hash_) - return new_flight + return rebuild_minimal_flight(class_, obj, hash_) elif hasattr(class_, "from_dict"): new_obj = class_.from_dict(obj) if hash_ is not None: @@ -146,6 +160,32 @@ def object_hook(self, obj): return obj +def rebuild_minimal_flight(class_, obj, hash_): + """Rebuild a Flight from stored data without resimulating it. + + Parameters + ---------- + class_ : type + The Flight class resolved from the stored signature. + obj : dict + The decoded data of the Flight object. + hash_ : str or None + The stored hash, when the encoder recorded one. + + Returns + ------- + Flight + The Flight object with its minimal attributes restored. + """ + new_flight = class_.__new__(class_) + new_flight.prints = _FlightPrints(new_flight) + new_flight.plots = _FlightPlots(new_flight) + set_minimal_flight_attributes(new_flight, obj) + if hash_ is not None: + setattr(new_flight, "__rpy_hash", hash_) + return new_flight + + def set_minimal_flight_attributes(flight, obj): attributes = ( "rocket", diff --git a/rocketpy/environment/atmosphere_cache.py b/rocketpy/environment/atmosphere_cache.py new file mode 100644 index 000000000..261d2aeca --- /dev/null +++ b/rocketpy/environment/atmosphere_cache.py @@ -0,0 +1,644 @@ +"""Disk cache for downloaded atmospheric datasets (netCDF profiles and JSON). + +Cache root defaults to ``~/.rocketpy_cache/atmosphere``. Override the root with +the ``ROCKETPY_CACHE`` environment variable (the ``atmosphere`` subfolder is +created under it), or disable caching entirely by setting it to one of ``0``, +``off``, ``false``, ``no``, ``none`` or ``disabled``. + +OPeNDAP "Best" aggregations are virtual catalogs, not downloadable files. For +Forecast/Ensemble shortcuts this module therefore stores the **location-and-time +profiles** RocketPy extracts after the first successful fetch, as a compact +``.nc`` file, together with every derived attribute ``Environment`` publishes +for that model (date range, grid bounds and the raw interpolation inputs) so a +cache hit reproduces the same object a fresh download would have produced. +Windy responses are stored as ``.json``. + +Forecasts are re-issued by their providers on a fixed cycle, so cache entries +expire after ``ROCKETPY_CACHE_TTL`` seconds (default: 6 hours, matching the GFS +cycle). Reanalysis data is immutable and never expires. Set the TTL to ``0`` to +disable expiry. +""" + +from __future__ import annotations + +import json +import os +import re +import shutil +import tempfile +import time +import warnings +from datetime import datetime +from pathlib import Path + +import netCDF4 +import numpy as np + +CACHE_ENV_VAR = "ROCKETPY_CACHE" +CACHE_TTL_ENV_VAR = "ROCKETPY_CACHE_TTL" +DEFAULT_CACHE_ROOT = Path.home() / ".rocketpy_cache" +PROFILE_FORMAT_ATTR = "rocketpy_atmosphere_profiles_v2" +JSON_FORMAT_KEY = "rocketpy_cache_format" +CREATED_AT_ATTR = "rocketpy_cache_created_at" +DEFAULT_CACHE_TTL_SECONDS = 6 * 3600 + +#: Model kinds whose data never changes once published, so they never expire. +IMMUTABLE_KINDS = frozenset({"reanalysis"}) + +_DISABLED_VALUES = frozenset({"", "0", "off", "false", "no", "none", "disabled"}) +_DATE_FORMAT = "%Y-%m-%dT%H:%M:%S" + +#: Raised by ``netCDF4.Dataset`` when a file cannot be opened at all: a damaged, +#: empty or truncated file gives ``OSError``, a missing one ``FileNotFoundError`` +#: and an unwritable location ``PermissionError`` (both ``OSError`` subclasses). +#: ``RuntimeError`` is listed because netCDF4 surfaces some HDF5-level failures +#: that way, and the installed version is not pinned. +CACHE_OPEN_ERRORS = (OSError, RuntimeError) + +#: Everything the above can raise, plus what reading or writing the contents of +#: an otherwise-openable cache file can raise. Verified against netCDF4 1.7.4: +#: a missing variable raises ``KeyError``; a missing or non-numeric attribute +#: ``AttributeError``; a wrong-length array or an undeclared dimension +#: ``ValueError``; an invalid dtype or attribute type ``TypeError``; and +#: touching a closed dataset ``RuntimeError``. +#: +#: The list is deliberately explicit rather than a bare ``except Exception``: a +#: corrupt cache entry must degrade to a re-download, but a ``NameError`` or any +#: other genuine defect in this module has to keep propagating instead of being +#: silently reported to the user as a cache miss. +CACHE_FILE_ERRORS = CACHE_OPEN_ERRORS + ( + ValueError, + TypeError, + KeyError, + AttributeError, +) + +# Scalar metadata persisted as netCDF attributes, with the caster used on read. +_SCALAR_METADATA = ( + ("atmospheric_model_interval", float), + ("atmospheric_model_init_lat", float), + ("atmospheric_model_end_lat", float), + ("atmospheric_model_init_lon", float), + ("atmospheric_model_end_lon", float), + ("lat_index", int), + ("lon_index", int), +) +_DATE_METADATA = ("atmospheric_model_init_date", "atmospheric_model_end_date") +_PAIR_METADATA = ("lat_array", "lon_array", "time_array") +#: Raw interpolation inputs, shaped ``(raw_level, lat_pair, lon_pair)``. +_CORNER_METADATA = ("geopotentials", "wind_us", "wind_vs", "temperatures") + + +# --------------------------------------------------------------------------- +# Cache location and policy +# --------------------------------------------------------------------------- + + +def is_cache_enabled() -> bool: + """Return False when ``ROCKETPY_CACHE`` opts out of disk caching.""" + raw = os.environ.get(CACHE_ENV_VAR) + if raw is None: + return True + return raw.strip().lower() not in _DISABLED_VALUES + + +def get_cache_root() -> Path: + """Return the root cache directory (honors ``ROCKETPY_CACHE``).""" + return Path(os.environ.get(CACHE_ENV_VAR) or DEFAULT_CACHE_ROOT).expanduser() + + +def get_atmosphere_cache_dir() -> Path: + """Return the atmosphere subdirectory under the cache root.""" + return get_cache_root() / "atmosphere" + + +def get_cache_ttl() -> float: + """Return the cache lifetime in seconds (``0`` disables expiry).""" + raw = os.environ.get(CACHE_TTL_ENV_VAR) + if raw is None: + return float(DEFAULT_CACHE_TTL_SECONDS) + try: + return max(float(raw), 0.0) + except (TypeError, ValueError): + warnings.warn( + f"Invalid {CACHE_TTL_ENV_VAR}='{raw}'. " + f"Using the default of {DEFAULT_CACHE_TTL_SECONDS} seconds.", + UserWarning, + stacklevel=2, + ) + return float(DEFAULT_CACHE_TTL_SECONDS) + + +def is_entry_expired(created_at, kind) -> bool: + """Return True when a cache entry written at ``created_at`` is too old. + + Entries whose ``kind`` is in :data:`IMMUTABLE_KINDS` never expire, and a + TTL of ``0`` disables expiry for every kind. + """ + if kind in IMMUTABLE_KINDS: + return False + ttl = get_cache_ttl() + if ttl <= 0: + return False + try: + age = time.time() - float(created_at) + except (TypeError, ValueError): + return True # unreadable timestamp: treat as stale and re-fetch + return age > ttl + + +def ensure_atmosphere_cache_dir() -> Path | None: + """Create the atmosphere cache directory. + + Returns + ------- + pathlib.Path or None + The directory path, or ``None`` if caching is disabled or the + directory could not be created. + """ + if not is_cache_enabled(): + return None + cache_dir = get_atmosphere_cache_dir() + try: + cache_dir.mkdir(parents=True, exist_ok=True) + return cache_dir + except OSError as exc: + warnings.warn( + f"Could not create atmosphere cache directory '{cache_dir}': {exc}. " + "Caching disabled for this request.", + UserWarning, + stacklevel=2, + ) + return None + + +def clear_atmosphere_cache() -> bool: + """Delete every cached atmosphere file. Returns False on failure.""" + cache_dir = get_atmosphere_cache_dir() + if not cache_dir.is_dir(): + return True + try: + shutil.rmtree(cache_dir) + return True + except OSError as exc: + warnings.warn( + f"Could not clear atmosphere cache '{cache_dir}': {exc}.", + UserWarning, + stacklevel=2, + ) + return False + + +def sanitize_cache_key(key: str) -> str: + """Replace characters that are unsafe in filenames.""" + return re.sub(r"[^A-Za-z0-9_.-]", "_", key) + + +def cache_path_for(key: str, suffix: str) -> Path: + """Build a cache file path for ``key`` with the given suffix (e.g. ``.nc``).""" + if not suffix.startswith("."): + suffix = f".{suffix}" + return get_atmosphere_cache_dir() / f"{sanitize_cache_key(key)}{suffix}" + + +def build_atmosphere_cache_key( + kind: str, + source: str, + latitude: float, + longitude: float, + datetime_date, + variant: str = "", +) -> str: + """Build a stable cache key for a Forecast/Ensemble/Windy request. + + ``variant`` distinguishes requests that hit the same source and location + but decode it differently (a different variable dictionary or pressure + conversion factor), which would otherwise collide on one file. + """ + if datetime_date is None: + date_part = "nodate" + else: + date_part = datetime_date.strftime("%Y%m%d%H") + key = f"{kind}_{source}_{latitude:.4f}_{longitude:.4f}_{date_part}" + if variant: + key = f"{key}_{variant}" + return sanitize_cache_key(key) + + +def is_remote_url(path_or_url) -> bool: + """Return True if ``path_or_url`` looks like an HTTP(S)/OPeNDAP URL.""" + if not isinstance(path_or_url, str): + return False + lowered = path_or_url.lower() + return lowered.startswith(("http://", "https://", "dods://")) + + +# --------------------------------------------------------------------------- +# Raw byte / JSON helpers +# --------------------------------------------------------------------------- + + +def atomic_write_bytes(path: Path, data: bytes) -> bool: + """Write ``data`` to ``path`` atomically. Returns False on failure.""" + if ensure_atmosphere_cache_dir() is None: + return False + temp_name = None + try: + path.parent.mkdir(parents=True, exist_ok=True) + with tempfile.NamedTemporaryFile( + dir=path.parent, delete=False, suffix=".tmp" + ) as handle: + handle.write(data) + temp_name = handle.name + Path(temp_name).replace(path) + return True + except OSError as exc: + warnings.warn( + f"Failed to write atmosphere cache file '{path}': {exc}.", + UserWarning, + stacklevel=2, + ) + _discard(temp_name) + return False + + +def _discard(path) -> None: + """Best-effort removal of a leftover temporary file.""" + if path is None: + return + try: + Path(path).unlink(missing_ok=True) + except OSError: + pass + + +def load_json_cache(path: Path, kind: str = "windy") -> dict | None: + """Load a JSON cache file, or ``None`` if missing/stale/unreadable.""" + if not is_cache_enabled() or not path.is_file(): + return None + try: + with path.open("r", encoding="utf-8") as handle: + envelope = json.load(handle) + except (OSError, UnicodeDecodeError, json.JSONDecodeError) as exc: + warnings.warn( + f"Failed to read cached atmosphere JSON '{path}': {exc}. " + "Fetching fresh data.", + UserWarning, + stacklevel=2, + ) + return None + + if ( + not isinstance(envelope, dict) + or envelope.get(JSON_FORMAT_KEY) != PROFILE_FORMAT_ATTR + ): + return None + if is_entry_expired(envelope.get(CREATED_AT_ATTR), kind): + return None + payload = envelope.get("payload") + return payload if isinstance(payload, dict) else None + + +def save_json_cache(path: Path, payload: dict) -> bool: + """Serialize ``payload`` as JSON to ``path``. Returns False on failure.""" + if not is_cache_enabled(): + return False + envelope = { + JSON_FORMAT_KEY: PROFILE_FORMAT_ATTR, + CREATED_AT_ATTR: time.time(), + "payload": payload, + } + try: + data = json.dumps(envelope).encode("utf-8") + except (TypeError, ValueError) as exc: + warnings.warn( + f"Failed to serialize atmosphere JSON for cache '{path}': {exc}.", + UserWarning, + stacklevel=2, + ) + return False + return atomic_write_bytes(path, data) + + +# --------------------------------------------------------------------------- +# Model metadata persistence +# --------------------------------------------------------------------------- + + +def _write_metadata(dataset, metadata) -> None: + """Store the ``Environment`` model metadata on an open netCDF dataset.""" + metadata = metadata or {} + _write_metadata_attributes(dataset, metadata) + _write_metadata_arrays(dataset, metadata) + + +def _write_metadata_attributes(dataset, metadata) -> None: + """Store the scalar, date and coordinate-pair metadata as attributes.""" + for name, _ in _SCALAR_METADATA: + value = metadata.get(name) + if value is not None: + dataset.setncattr(name, float(value)) + + for name in _DATE_METADATA: + value = metadata.get(name) + if isinstance(value, datetime): + dataset.setncattr(name, value.strftime(_DATE_FORMAT)) + + for name in _PAIR_METADATA: + value = metadata.get(name) + if value is not None: + dataset.setncattr(name, [float(item) for item in value]) + + +def _write_metadata_arrays(dataset, metadata) -> None: + """Store the raw interpolation inputs as netCDF variables.""" + raw_levels = metadata.get("levels") + if raw_levels is None: + return + + raw_levels = np.asarray(raw_levels) + dataset.setncattr( + "levels_integer", int(np.issubdtype(raw_levels.dtype, np.integer)) + ) + dataset.createDimension("raw_level", raw_levels.size) + dataset.createDimension("lat_pair", 2) + dataset.createDimension("lon_pair", 2) + + variable = dataset.createVariable("raw_levels", "f8", ("raw_level",)) + variable[:] = np.asarray(raw_levels, dtype=float) + + raw_height = metadata.get("height") + if raw_height is not None: + variable = dataset.createVariable("raw_height", "f8", ("raw_level",)) + variable[:] = _filled(raw_height) + + for name in _CORNER_METADATA: + values = metadata.get(name) + if values is None: + continue + variable = dataset.createVariable( + name, "f8", ("raw_level", "lat_pair", "lon_pair") + ) + variable[:] = _filled(values) + + +def _read_metadata(dataset) -> dict: + """Rebuild the ``Environment`` model metadata from an open netCDF dataset.""" + metadata = {} + + for name, caster in _SCALAR_METADATA: + if hasattr(dataset, name): + metadata[name] = caster(dataset.getncattr(name)) + + for name in _DATE_METADATA: + if hasattr(dataset, name): + metadata[name] = datetime.strptime(dataset.getncattr(name), _DATE_FORMAT) + + for name in _PAIR_METADATA: + if hasattr(dataset, name): + metadata[name] = [ + float(item) for item in np.atleast_1d(dataset.getncattr(name)) + ] + + if "raw_levels" in dataset.variables: + levels = np.array(dataset.variables["raw_levels"][:], dtype=float) + if int(getattr(dataset, "levels_integer", 0)): + levels = levels.astype(np.int64) + metadata["levels"] = levels + + if "raw_height" in dataset.variables: + metadata["height"] = np.array(dataset.variables["raw_height"][:], dtype=float) + + for name in _CORNER_METADATA: + if name in dataset.variables: + metadata[name] = np.array(dataset.variables[name][:], dtype=float) + + return metadata + + +def _filled(values): + """Return a plain float array, replacing any masked entries with NaN.""" + return np.ma.filled(np.ma.asarray(values).astype(float), np.nan) + + +def _open_for_write(path: Path): + """Create a temporary netCDF file next to ``path``. Returns (dataset, temp).""" + path.parent.mkdir(parents=True, exist_ok=True) + with tempfile.NamedTemporaryFile( + dir=path.parent, delete=False, suffix=".nc.tmp" + ) as handle: + temp_path = Path(handle.name) + return netCDF4.Dataset(temp_path, mode="w", format="NETCDF4"), temp_path + + +def _set_common_attributes(dataset, kind, elevation, max_expected_height) -> None: + """Write the attributes shared by every cache file format.""" + dataset.setncattr("rocketpy_cache_format", PROFILE_FORMAT_ATTR) + dataset.setncattr("rocketpy_cache_kind", kind) + dataset.setncattr(CREATED_AT_ATTR, float(time.time())) + dataset.setncattr("elevation", float(elevation)) + dataset.setncattr("max_expected_height", float(max_expected_height)) + + +def _open_valid_cache(path: Path, expected_kind=None): + """Open a cache file, returning ``None`` if absent, foreign or expired.""" + if not is_cache_enabled() or not path.is_file(): + return None + try: + dataset = netCDF4.Dataset(path, mode="r") + except CACHE_OPEN_ERRORS as exc: + # A cache miss must never be louder than the download it replaces. + warnings.warn( + f"Failed to open atmosphere cache '{path}': {exc}. Fetching fresh data.", + UserWarning, + stacklevel=2, + ) + return None + + fmt = getattr(dataset, "rocketpy_cache_format", None) + kind = getattr(dataset, "rocketpy_cache_kind", None) + if fmt != PROFILE_FORMAT_ATTR or ( + expected_kind is not None and kind != expected_kind + ): + dataset.close() + return None + if is_entry_expired(getattr(dataset, CREATED_AT_ATTR, None), kind): + dataset.close() + return None + return dataset + + +# --------------------------------------------------------------------------- +# Forecast / Reanalysis profiles +# --------------------------------------------------------------------------- + + +def write_profile_netcdf( + path: Path, + *, + height, + pressure, + temperature, + wind_u, + wind_v, + elevation: float, + max_expected_height: float, + kind: str = "forecast", + metadata=None, +) -> bool: + """Write extracted atmospheric profiles to a compact local netCDF file.""" + if ensure_atmosphere_cache_dir() is None: + return False + + columns = ( + ("height", height, "m"), + ("pressure", pressure, "Pa"), + ("temperature", temperature, "K"), + ("wind_u", wind_u, "m s-1"), + ("wind_v", wind_v, "m s-1"), + ) + temp_path = None + try: + dataset, temp_path = _open_for_write(path) + try: + _set_common_attributes(dataset, kind, elevation, max_expected_height) + dataset.createDimension("level", np.asarray(height, dtype=float).size) + for name, values, units in columns: + variable = dataset.createVariable(name, "f8", ("level",)) + variable.units = units + variable[:] = np.asarray(values, dtype=float) + _write_metadata(dataset, metadata) + finally: + dataset.close() + temp_path.replace(path) + return True + except CACHE_FILE_ERRORS as exc: + warnings.warn( + f"Failed to write atmosphere profile cache '{path}': {exc}.", + UserWarning, + stacklevel=2, + ) + _discard(temp_path) + return False + + +def read_profile_netcdf(path: Path) -> dict | None: + """Read a profile netCDF written by :func:`write_profile_netcdf`.""" + dataset = _open_valid_cache(path) + if dataset is None: + return None + try: + profiles = { + "kind": getattr(dataset, "rocketpy_cache_kind", "forecast"), + "elevation": float(dataset.getncattr("elevation")), + "max_expected_height": float(dataset.getncattr("max_expected_height")), + } + for name in ("height", "pressure", "temperature", "wind_u", "wind_v"): + profiles[name] = np.array(dataset.variables[name][:], dtype=float) + profiles["metadata"] = _read_metadata(dataset) + return profiles + except CACHE_FILE_ERRORS as exc: + warnings.warn( + f"Failed to read atmosphere profile cache '{path}': {exc}. " + "Fetching fresh data.", + UserWarning, + stacklevel=2, + ) + return None + finally: + dataset.close() + + +# --------------------------------------------------------------------------- +# Ensemble profiles +# --------------------------------------------------------------------------- + + +def write_ensemble_profile_netcdf( + path: Path, + *, + levels, + height_ensemble, + temperature_ensemble, + wind_u_ensemble, + wind_v_ensemble, + elevation: float, + max_expected_height: float, + metadata=None, +) -> bool: + """Write ensemble member profiles to a compact local netCDF file.""" + if ensure_atmosphere_cache_dir() is None: + return False + + height_ensemble = np.asarray(height_ensemble, dtype=float) + if height_ensemble.ndim != 2: + return False + num_members, num_levels = height_ensemble.shape + columns = ( + ("height", height_ensemble, "m"), + ("temperature", temperature_ensemble, "K"), + ("wind_u", wind_u_ensemble, "m s-1"), + ("wind_v", wind_v_ensemble, "m s-1"), + ) + + temp_path = None + try: + dataset, temp_path = _open_for_write(path) + try: + _set_common_attributes(dataset, "ensemble", elevation, max_expected_height) + dataset.createDimension("member", num_members) + dataset.createDimension("level", num_levels) + + level_var = dataset.createVariable("level", "f8", ("level",)) + level_var.units = "Pa" + level_var[:] = np.asarray(levels, dtype=float) + + for name, values, units in columns: + variable = dataset.createVariable(name, "f8", ("member", "level")) + variable.units = units + variable[:] = np.asarray(values, dtype=float) + _write_metadata(dataset, metadata) + finally: + dataset.close() + temp_path.replace(path) + return True + except CACHE_FILE_ERRORS as exc: + warnings.warn( + f"Failed to write ensemble atmosphere cache '{path}': {exc}.", + UserWarning, + stacklevel=2, + ) + _discard(temp_path) + return False + + +def read_ensemble_profile_netcdf(path: Path) -> dict | None: + """Read an ensemble profile netCDF written by :func:`write_ensemble_profile_netcdf`.""" + dataset = _open_valid_cache(path, expected_kind="ensemble") + if dataset is None: + return None + try: + profiles = { + "elevation": float(dataset.getncattr("elevation")), + "max_expected_height": float(dataset.getncattr("max_expected_height")), + "levels": np.array(dataset.variables["level"][:], dtype=float), + } + for key, name in ( + ("height_ensemble", "height"), + ("temperature_ensemble", "temperature"), + ("wind_u_ensemble", "wind_u"), + ("wind_v_ensemble", "wind_v"), + ): + profiles[key] = np.array(dataset.variables[name][:], dtype=float) + profiles["metadata"] = _read_metadata(dataset) + return profiles + except CACHE_FILE_ERRORS as exc: + warnings.warn( + f"Failed to read ensemble atmosphere cache '{path}': {exc}. " + "Fetching fresh data.", + UserWarning, + stacklevel=2, + ) + return None + finally: + dataset.close() diff --git a/rocketpy/environment/environment.py b/rocketpy/environment/environment.py index 6a5fb2c1c..8ac6f4033 100644 --- a/rocketpy/environment/environment.py +++ b/rocketpy/environment/environment.py @@ -1,9 +1,12 @@ -# pylint: disable=too-many-public-methods, too-many-instance-attributes +# pylint: disable=too-many-public-methods, too-many-instance-attributes, too-many-lines import bisect +import hashlib import json import logging +import os import re import warnings +from collections.abc import Mapping from collections import namedtuple from datetime import datetime @@ -11,8 +14,21 @@ import numpy as np import pytz +from rocketpy.environment.atmosphere_cache import ( + build_atmosphere_cache_key, + cache_path_for, + is_cache_enabled, + is_remote_url, + load_json_cache, + read_ensemble_profile_netcdf, + read_profile_netcdf, + save_json_cache, + write_ensemble_profile_netcdf, + write_profile_netcdf, +) from rocketpy.environment.fetchers import ( fetch_aigfs_file_return_dataset, + fetch_atmospheric_data_from_meteomatics, fetch_atmospheric_data_from_windy, fetch_gefs_ensemble, fetch_gfs_file_return_dataset, @@ -20,13 +36,19 @@ fetch_hrrr_file_return_dataset, fetch_nam_file_return_dataset, fetch_open_elevation, + fetch_open_meteo_ensemble, + fetch_open_meteo_forecast, fetch_rap_file_return_dataset, fetch_wyoming_sounding, ) +from rocketpy.environment.fetchers.open_meteo_fetcher import ( + OPEN_METEO_PRESSURE_LEVELS, +) from rocketpy.environment.tools import ( calculate_wind_heading, calculate_wind_speed, convert_wind_heading_to_direction, + convert_wind_speed_direction_to_components, find_latitude_index, find_longitude_index, find_time_index, @@ -145,8 +167,8 @@ class Environment: Environment.atmospheric_model_type : string Describes the atmospheric model which is being used. Can only assume the following values: ``standard_atmosphere``, ``custom_atmosphere``, - ``wyoming_sounding``, ``windy``, ``forecast``, ``reanalysis``, - ``ensemble``. + ``wyoming_sounding``, ``windy``, ``meteomatics``, ``forecast``, + ``reanalysis``, ``ensemble``. Environment.atmospheric_model_file : string Address of the file used for the atmospheric model being used. Only defined for ``wyoming_sounding``, ``windy``, ``forecast``, @@ -721,6 +743,41 @@ def __validate_dictionary(self, file, dictionary): return dictionary + @staticmethod + def __validate_pressure_conversion_factor(pressure_conversion_factor): + """Validates a user-supplied pressure conversion factor. + + Does nothing when the value is None, in which case the factor is + auto-detected later from the dataset or the model name. + + Raises + ------ + ValueError + If the value is neither a strictly positive number nor a standard + pressure unit ('mbar', 'hPa', 'Pa'). + """ + if pressure_conversion_factor is None: + return + + if not isinstance(pressure_conversion_factor, (float, int, str)): + raise ValueError( + "Argument 'pressure_conversion_factor' must be numeric or a standard pressure unit ('mbar', 'hPa', 'Pa')!" + ) + if ( + isinstance(pressure_conversion_factor, (float, int)) + and pressure_conversion_factor <= 0 + ): + raise ValueError( + "Argument 'pressure_conversion_factor' must be strictly positive!" + ) + if ( + isinstance(pressure_conversion_factor, str) + and pressure_unit_to_factor(pressure_conversion_factor) is None + ): + raise ValueError( + "Argument 'pressure_conversion_factor' unit must be a standard pressure unit ('mbar', 'hPa', 'Pa')!" + ) + def __validate_datetime(self): if self.datetime_date is None: raise ValueError( @@ -1188,6 +1245,9 @@ def set_atmospheric_model( # pylint: disable=too-many-statements wind_u=0, wind_v=0, pressure_conversion_factor=None, + username=None, + password=None, + no_cache=False, ): """Define the atmospheric model for this Environment. @@ -1196,8 +1256,9 @@ def set_atmospheric_model( # pylint: disable=too-many-statements type : string Atmospheric model selector (case-insensitive). Accepted values are ``"standard_atmosphere"``, ``"wyoming_sounding"``, ``"windy"``, - ``"forecast"``, ``"reanalysis"``, ``"ensemble"`` and - ``"custom_atmosphere"``. + ``"open_meteo"``, ``"open_meteo_ensemble"``, ``"forecast"``, + ``"reanalysis"``, ``"ensemble"``, ``"custom_atmosphere"`` and + ``"meteomatics"``. file : string | netCDF4.Dataset, optional Data source or model shortcut. Meaning depends on ``type``: @@ -1205,6 +1266,15 @@ def set_atmospheric_model( # pylint: disable=too-many-statements - ``"wyoming_sounding"``: URL of the sounding text page. - ``"windy"``: one of ``"ECMWF"``, ``"GFS"``, ``"ICON"`` or ``"ICONEU"``. + - ``"open_meteo"``: the Open-Meteo model to query, such as + ``"best_match"`` (the default when omitted), ``"gfs_seamless"``, + ``"ecmwf_ifs025"`` or ``"icon_seamless"``. See the Open-Meteo + documentation for the full list. + - ``"open_meteo_ensemble"``: either ``"gfs05"`` (the default when + omitted) or ``"ecmwf_ifs025"``. + - ``"meteomatics"``: the Meteomatics weather model to query, such + as ``"mix"`` (the default when omitted). See the Meteomatics + documentation for the models available to your account. - ``"forecast"``: local path, OPeNDAP URL, open ``netCDF4.Dataset``, or one of ``"AIGFS"``, ``"GFS"``, ``"NAM"``, ``"RAP"``, ``"HRRR"`` or ``"HIRESW"`` for the @@ -1290,6 +1360,19 @@ def set_atmospheric_model( # pylint: disable=too-many-statements model name (e.g. ERA5/ECMWF/MERRA2 reanalysis files commonly use hPa, while online GFS/NAM/RAP/HRRR forecast models use Pa) or, if unavailable, by reading the pressure unit attribute from the file. + username : string, optional + Meteomatics account username. Only used when ``type`` is + ``"meteomatics"``. If None (the default), the value is read from the + ``METEOMATICS_USERNAME`` environment variable. + password : string, optional + Meteomatics account password. Only used when ``type`` is + ``"meteomatics"``. If None (the default), the value is read from the + ``METEOMATICS_PASSWORD`` environment variable. + no_cache : bool, optional + If True, bypass the on-disk atmosphere cache and force a fresh + download for remote Forecast/Ensemble/Windy sources. Cached files + live under ``~/.rocketpy_cache/atmosphere`` (or ``ROCKETPY_CACHE``). + Default is False. Returns ------- @@ -1337,7 +1420,22 @@ def set_atmospheric_model( # pylint: disable=too-many-statements case "custom_atmosphere": self.process_custom_atmosphere(pressure, temperature, wind_u, wind_v) case "windy": - self.process_windy_atmosphere(file) + self.process_windy_atmosphere( + **({} if file is None else {"model": file}), + no_cache=no_cache, + ) + case "open_meteo": + self.process_open_meteo_atmosphere( + **({} if file is None else {"model": file}) + ) + case "open_meteo_ensemble": + self.process_open_meteo_ensemble( + **({} if file is None else {"model": file}) + ) + case "meteomatics": + self.process_meteomatics_atmosphere( + model=file, username=username, password=password + ) case "forecast" | "reanalysis" | "ensemble": # Capture the user-supplied names before __validate_dictionary # converts them to dicts, so they can drive auto-detection. @@ -1348,21 +1446,7 @@ def set_atmospheric_model( # pylint: disable=too-many-statements # Validate format of user-supplied value (if any). # When None, auto-detection runs after dictionary resolution. - if pressure_conversion_factor is not None: - if not isinstance(pressure_conversion_factor, (float, int, str)): - raise ValueError( - "Argument 'pressure_conversion_factor' must be numeric or a standard pressure unit ('mbar', 'hPa', 'Pa')!" - ) - if isinstance(pressure_conversion_factor, (float, int)): - if pressure_conversion_factor <= 0: - raise ValueError( - "Argument 'pressure_conversion_factor' must be strictly positive!" - ) - if isinstance(pressure_conversion_factor, str): - if pressure_unit_to_factor(pressure_conversion_factor) is None: - raise ValueError( - "Argument 'pressure_conversion_factor' unit must be a standard pressure unit ('mbar', 'hPa', 'Pa')!" - ) + self.__validate_pressure_conversion_factor(pressure_conversion_factor) if isinstance(file, str): shortcut_map = self.__atm_type_file_to_function_map.get(type, {}) @@ -1406,15 +1490,14 @@ def set_atmospheric_model( # pylint: disable=too-many-statements except KeyError: fetch_function = None - # Fetches the dataset using OpenDAP protocol or uses the file path - dataset = fetch_function() if fetch_function is not None else file - - if type in ["forecast", "reanalysis"]: - self.process_forecast_reanalysis( - dataset, dictionary, conversion_factor=conversion_factor - ) - else: - self.process_ensemble(dataset, dictionary, conversion_factor) + self.__load_or_fetch_atmospheric_model( + atm_type=type, + file=file, + dictionary=dictionary, + conversion_factor=conversion_factor, + fetch_function=fetch_function, + no_cache=no_cache, + ) ground_pressure = self.pressure(self.elevation) if not 30000 <= ground_pressure <= 120_000: @@ -1442,7 +1525,7 @@ def set_atmospheric_model( # pylint: disable=too-many-statements case _: # pragma: no cover raise ValueError(f"Unknown model type '{type}'.") - if type not in ["ensemble"]: + if type not in ["ensemble", "open_meteo_ensemble"]: # Ensemble already computed these values self.calculate_density_profile() self.calculate_speed_of_sound_profile() @@ -1452,6 +1535,253 @@ def set_atmospheric_model( # pylint: disable=too-many-statements self.atmospheric_model_file = file self.atmospheric_model_dict = dictionary + def __atmosphere_cache_path_for_request( + self, atm_type, file, fetch_function, dictionary, conversion_factor + ): + """Return a cache path for remote Forecast/Ensemble sources, else None.""" + if not is_cache_enabled(): + return None + + if fetch_function is not None and isinstance(file, str): + source_label = file + elif is_remote_url(file): + source_label = ( + "url_" + + hashlib.md5(file.encode("utf-8"), usedforsecurity=False).hexdigest()[ + :12 + ] + ) + else: + return None + + # The same source decoded with a different variable dictionary or + # pressure unit yields different profiles, so it needs its own entry. + variant = hashlib.md5( + repr((sorted(dictionary.items()), conversion_factor)).encode("utf-8"), + usedforsecurity=False, + ).hexdigest()[:8] + + return cache_path_for( + build_atmosphere_cache_key( + atm_type, + source_label, + self.latitude, + self.longitude, + self.datetime_date, + variant=variant, + ), + ".nc", + ) + + def __load_or_fetch_atmospheric_model( + self, + *, + atm_type, + file, + dictionary, + conversion_factor, + fetch_function, + no_cache, + ): + """Apply a cached model when available, otherwise fetch and cache it.""" + cache_path = self.__atmosphere_cache_path_for_request( + atm_type, file, fetch_function, dictionary, conversion_factor + ) + is_ensemble = atm_type == "ensemble" + + if cache_path is not None and not no_cache: + apply_cached = ( + self.__apply_cached_ensemble_profiles + if is_ensemble + else self.__apply_cached_forecast_profiles + ) + if apply_cached(cache_path): + return + + # Fetches the dataset using OpenDAP protocol or uses the file path + dataset = fetch_function() if fetch_function is not None else file + if is_ensemble: + self.process_ensemble( + dataset, dictionary, conversion_factor=conversion_factor + ) + else: + self.process_forecast_reanalysis( + dataset, dictionary, conversion_factor=conversion_factor + ) + + if cache_path is not None: + if is_ensemble: + self.__save_ensemble_profiles_to_cache(cache_path) + else: + self.__save_forecast_profiles_to_cache(cache_path, atm_type) + + #: Attributes ``Environment`` derives from a Forecast/Ensemble dataset that + #: are not recoverable from the extracted profiles alone, so they travel + #: with the cache entry to keep a cache hit indistinguishable from a fetch. + __CACHED_MODEL_METADATA = ( + "atmospheric_model_init_date", + "atmospheric_model_end_date", + "atmospheric_model_interval", + "atmospheric_model_init_lat", + "atmospheric_model_end_lat", + "atmospheric_model_init_lon", + "atmospheric_model_end_lon", + "lat_array", + "lon_array", + "lat_index", + "lon_index", + "geopotentials", + "wind_us", + "wind_vs", + "levels", + "temperatures", + "time_array", + "height", + ) + + def __collect_model_metadata(self): + """Snapshot the model metadata that must survive a cache round-trip.""" + return { + name: getattr(self, name) + for name in self.__CACHED_MODEL_METADATA + if getattr(self, name, None) is not None + } + + def __restore_model_metadata(self, metadata): + """Reinstate the model metadata recovered from a cache entry.""" + for name, value in (metadata or {}).items(): + if name in self.__CACHED_MODEL_METADATA: + setattr(self, name, value) + + def __apply_profiles_from_arrays( + self, height, pressure, temperature, wind_u, wind_v + ): + """Install forecast-style profile Functions from 1-D arrays.""" + wind_speed = calculate_wind_speed(wind_u, wind_v) + wind_heading = calculate_wind_heading(wind_u, wind_v) + wind_direction = convert_wind_heading_to_direction(wind_heading) + data_array = mask_and_clean_dataset( + pressure, + height, + temperature, + wind_u, + wind_v, + wind_heading, + wind_direction, + wind_speed, + ) + self.__set_pressure_function(data_array[:, (1, 0)]) + self.__set_barometric_height_function(data_array[:, (0, 1)]) + self.__set_temperature_function(data_array[:, (1, 2)]) + self.__set_wind_velocity_x_function(data_array[:, (1, 3)]) + self.__set_wind_velocity_y_function(data_array[:, (1, 4)]) + self.__set_wind_heading_function(data_array[:, (1, 5)]) + self.__set_wind_direction_function(data_array[:, (1, 6)]) + self.__set_wind_speed_function(data_array[:, (1, 7)]) + return data_array + + def __apply_cached_forecast_profiles(self, cache_path): + """Load Forecast/Reanalysis profiles from disk. Return True on success.""" + profiles = read_profile_netcdf(cache_path) + if profiles is None: + return False + self.__apply_profiles_from_arrays( + profiles["height"], + profiles["pressure"], + profiles["temperature"], + profiles["wind_u"], + profiles["wind_v"], + ) + self.elevation = profiles["elevation"] + self._max_expected_height = profiles["max_expected_height"] + self.__restore_model_metadata(profiles.get("metadata")) + return True + + @staticmethod + def __profile_column(function, column=1): + """Return one column of an array-backed Function, or None.""" + if not isinstance(function, Function) or not function.is_array_source(): + return None + source = np.asarray(function.source, dtype=float) + if source.ndim != 2 or source.shape[1] <= column: + return None + return source[:, column] + + def __save_forecast_profiles_to_cache(self, cache_path, kind="forecast"): + """Persist the active Forecast/Reanalysis profiles to ``cache_path``.""" + heights = self.__profile_column(self.pressure, column=0) + columns = { + "pressure": self.__profile_column(self.pressure), + "temperature": self.__profile_column(self.temperature), + "wind_u": self.__profile_column(self.wind_velocity_x), + "wind_v": self.__profile_column(self.wind_velocity_y), + } + # Every profile must be array-backed and share the pressure grid; + # constant (scalar) profiles carry nothing worth caching. + if heights is None or any( + column is None or column.shape != heights.shape + for column in columns.values() + ): + return + + write_profile_netcdf( + cache_path, + height=heights, + elevation=float(self.elevation), + max_expected_height=float( + getattr(self, "_max_expected_height", self.max_expected_height) + ), + kind=kind, + metadata=self.__collect_model_metadata(), + **columns, + ) + + def __apply_cached_ensemble_profiles(self, cache_path): + """Load Ensemble member profiles from disk. Return True on success.""" + profiles = read_ensemble_profile_netcdf(cache_path) + if profiles is None: + return False + + height = profiles["height_ensemble"] + temper = profiles["temperature_ensemble"] + wind_u = profiles["wind_u_ensemble"] + wind_v = profiles["wind_v_ensemble"] + + self.level_ensemble = profiles["levels"] + self.height_ensemble = height + self.temperature_ensemble = temper + self.wind_u_ensemble = wind_u + self.wind_v_ensemble = wind_v + self.wind_heading_ensemble = calculate_wind_heading(wind_u, wind_v) + self.wind_direction_ensemble = convert_wind_heading_to_direction( + self.wind_heading_ensemble + ) + self.wind_speed_ensemble = calculate_wind_speed(wind_u, wind_v) + self.num_ensemble_members = height.shape[0] + self.elevation = profiles["elevation"] + self._max_expected_height = profiles["max_expected_height"] + self.__restore_model_metadata(profiles.get("metadata")) + self.select_ensemble_member() + return True + + def __save_ensemble_profiles_to_cache(self, cache_path): + """Persist Ensemble member profiles to ``cache_path``.""" + if getattr(self, "height_ensemble", None) is None: + return + write_ensemble_profile_netcdf( + cache_path, + levels=self.level_ensemble, + height_ensemble=self.height_ensemble, + temperature_ensemble=self.temperature_ensemble, + wind_u_ensemble=self.wind_u_ensemble, + wind_v_ensemble=self.wind_v_ensemble, + elevation=float(self.elevation), + max_expected_height=float( + getattr(self, "_max_expected_height", self.max_expected_height) + ), + metadata=self.__collect_model_metadata(), + ) + # Atmospheric model processing methods def process_standard_atmosphere(self): @@ -1600,7 +1930,9 @@ def wind_heading_func(h): # TODO: create another custom reset for heading self._max_expected_height = max_expected_height - def process_windy_atmosphere(self, model="ECMWF"): # pylint: disable=too-many-statements + def process_windy_atmosphere( # pylint: disable=too-many-statements + self, model="ECMWF", no_cache=False + ): """Process data from Windy.com to retrieve atmospheric forecast data. Parameters @@ -1610,6 +1942,8 @@ def process_windy_atmosphere(self, model="ECMWF"): # pylint: disable=too-many-s ``ECMWF`` for the `ECMWF-HRES` model, ``GFS`` for the `GFS` model, ``ICON`` for the `ICON-Global` model or ``ICONEU`` for the `ICON-EU` model. + no_cache : bool, optional + If True, force a fresh download even when a JSON cache entry exists. Raises ------ @@ -1624,9 +1958,22 @@ def process_windy_atmosphere(self, model="ECMWF"): # pylint: disable=too-many-s "Valid options are 'ECMWF', 'GFS', 'ICON' or 'ICONEU'." ) - response = fetch_atmospheric_data_from_windy( - self.latitude, self.longitude, model + cache_path = cache_path_for( + build_atmosphere_cache_key( + "windy", + model, + self.latitude, + self.longitude, + self.datetime_date, + ), + ".json", ) + response = None if no_cache else load_json_cache(cache_path, kind="windy") + if response is None: + response = fetch_atmospheric_data_from_windy( + self.latitude, self.longitude, model + ) + save_json_cache(cache_path, response) # Determine time index from model time_array = np.array(response["data"]["hours"]) @@ -1736,6 +2083,616 @@ def __parse_windy_file(self, response, time_index, pressure_levels): wind_v_array, ) + def __parse_open_meteo_levels(self, hourly, time_index, member_suffix=""): + """Extracts one vertical profile from an Open-Meteo ``hourly`` payload. + + Levels whose variables are missing (either absent from the response or + ``None`` at the requested hour) are skipped, since Open-Meteo publishes + the same set of level keys for every model but only fills the ones the + model actually resolves. + + Parameters + ---------- + hourly : dict + The ``hourly`` section of the Open-Meteo JSON response. + time_index : int + Index of the hour to extract. + member_suffix : str, optional + Suffix identifying an ensemble member (e.g. ``"_member01"``). Empty + for deterministic forecasts. + + Returns + ------- + tuple of numpy.ndarray + The pressure levels (hPa), geopotential heights (m), temperatures + (K), wind-u and wind-v components (m/s), all sorted by ascending + altitude. + """ + levels = [] + geopotential_heights = [] + temperatures = [] + wind_speeds = [] + wind_directions = [] + + for level in OPEN_METEO_PRESSURE_LEVELS: + keys = { + name: f"{name}_{level}hPa{member_suffix}" + for name in ( + "temperature", + "geopotential_height", + "wind_speed", + "wind_direction", + ) + } + if any(key not in hourly for key in keys.values()): + continue + values = {name: hourly[key][time_index] for name, key in keys.items()} + if any(value is None for value in values.values()): + continue + + levels.append(level) + geopotential_heights.append(values["geopotential_height"]) + temperatures.append(values["temperature"]) + wind_speeds.append(values["wind_speed"]) + wind_directions.append(values["wind_direction"]) + + if len(levels) < 2: + raise ValueError( + "Open-Meteo returned fewer than two usable pressure levels for " + "this location and time, which is not enough to build an " + "atmospheric profile. Check the requested model: not every " + "Open-Meteo model publishes pressure-level data." + ) + + levels = np.array(levels, dtype=float) + geopotential_heights = np.array(geopotential_heights, dtype=float) + # Temperatures come in degrees Celsius; RocketPy works in Kelvin. + temperatures = np.array(temperatures, dtype=float) + 273.15 + wind_u, wind_v = convert_wind_speed_direction_to_components( + np.array(wind_speeds, dtype=float), + np.array(wind_directions, dtype=float), + ) + + # Open-Meteo lists levels from the ground up (1000 hPa first), but sort + # explicitly so the profile is monotonic in altitude even if a model + # reports levels out of order. + order = np.argsort(geopotential_heights) + + return ( + levels[order], + geopotential_heights[order], + temperatures[order], + wind_u[order], + wind_v[order], + ) + + def __store_open_meteo_functions( + self, pressure_levels, altitude_array, temperature_array, wind_u, wind_v + ): + """Sets the atmospheric functions from a single Open-Meteo profile. + + Parameters + ---------- + pressure_levels : numpy.ndarray + The pressure levels, in hPa. + altitude_array : numpy.ndarray + Geometric altitudes above sea level, in m. + temperature_array : numpy.ndarray + Temperatures, in K. + wind_u, wind_v : numpy.ndarray + The East and North wind components, in m/s. + """ + wind_speed_array = calculate_wind_speed(wind_u, wind_v) + wind_heading_array = calculate_wind_heading(wind_u, wind_v) + wind_direction_array = convert_wind_heading_to_direction(wind_heading_array) + + data_array = mask_and_clean_dataset( + 100 * pressure_levels, # Convert hPa to Pa + altitude_array, + temperature_array, + wind_u, + wind_v, + wind_heading_array, + wind_direction_array, + wind_speed_array, + ) + + # Save atmospheric data + self.__set_pressure_function(data_array[:, (1, 0)]) + self.__set_barometric_height_function(data_array[:, (0, 1)]) + self.__set_temperature_function(data_array[:, (1, 2)]) + self.__set_wind_velocity_x_function(data_array[:, (1, 3)]) + self.__set_wind_velocity_y_function(data_array[:, (1, 4)]) + self.__set_wind_heading_function(data_array[:, (1, 5)]) + self.__set_wind_direction_function(data_array[:, (1, 6)]) + self.__set_wind_speed_function(data_array[:, (1, 7)]) + + # Save maximum expected height + self._max_expected_height = float(max(altitude_array[0], altitude_array[-1])) + + def __find_open_meteo_time_index(self, hourly): + """Returns the index of the hour closest to the launch date.""" + # 'timeformat=unixtime' is requested, so times are seconds since epoch. + time_array = np.array(hourly["time"], dtype=float) + launch_time = self.datetime_date.timestamp() + return int(np.abs(time_array - launch_time).argmin()), time_array + + def __store_open_meteo_metadata(self, response, time_array): + """Sets the metadata attributes shared by both Open-Meteo processors.""" + time_units = "seconds since 1970-01-01 00:00:00" + self.atmospheric_model_init_date = get_initial_date_from_time_array( + time_array, time_units + ) + self.atmospheric_model_end_date = get_final_date_from_time_array( + time_array, time_units + ) + self.atmospheric_model_interval = get_interval_date_from_time_array( + time_array, time_units + ) + # Open-Meteo answers for the single grid cell nearest the request. + self.atmospheric_model_init_lat = float(response["latitude"]) + self.atmospheric_model_end_lat = float(response["latitude"]) + self.atmospheric_model_init_lon = float(response["longitude"]) + self.atmospheric_model_end_lon = float(response["longitude"]) + self.time_array = time_array + + if response.get("elevation") is not None: + self.elevation = float(response["elevation"]) + + def process_open_meteo_atmosphere(self, model="best_match"): + """Process data from the Open-Meteo API to retrieve atmospheric forecast + data. + + Open-Meteo serves pressure-level data as plain JSON over HTTPS, without + an API key and without requiring netCDF/OPeNDAP libraries. When the + launch date lies in the past, the request is routed to Open-Meteo's + historical-forecast archive instead of the live forecast. + + Parameters + ---------- + model : str, optional + The Open-Meteo weather model to query. Default is ``"best_match"``, + which lets Open-Meteo pick the highest-resolution model available + for the location. Other useful values are ``"gfs_seamless"``, + ``"ecmwf_ifs025"``, ``"icon_seamless"`` and + ``"meteofrance_seamless"``. See https://open-meteo.com/en/docs for + the full list. + + Raises + ------ + ValueError + If no launch date is set, or if the API returns fewer than two + usable pressure levels. + RuntimeError + If the Open-Meteo API cannot be reached or returns no usable data. + + Notes + ----- + Open-Meteo's historical data comes from its own past forecast runs and + only covers pressure levels from around March 2021 onwards; a warning is + issued for earlier dates, which the API answers with no data. Its ERA5 + archive endpoint is not used because it serves surface variables only, + with no pressure-level data. + """ + self.__validate_datetime() + + response = fetch_open_meteo_forecast( + self.latitude, self.longitude, model=model, date=self.datetime_date + ) + hourly = response["hourly"] + time_index, time_array = self.__find_open_meteo_time_index(hourly) + + ( + pressure_levels, + geopotential_height_array, + temperature_array, + wind_u_array, + wind_v_array, + ) = self.__parse_open_meteo_levels(hourly, time_index) + + altitude_array = geopotential_height_to_geometric_height( + geopotential_height_array, self.earth_radius + ) + + self.__store_open_meteo_functions( + pressure_levels, + altitude_array, + temperature_array, + wind_u_array, + wind_v_array, + ) + + self.__store_open_meteo_metadata(response, time_array) + + # Save debugging data + self.geopotentials = geopotential_height_array + self.wind_us = wind_u_array + self.wind_vs = wind_v_array + self.levels = pressure_levels + self.temperatures = temperature_array + self.height = altitude_array + + def __stack_open_meteo_members(self, hourly, time_index, member_suffixes): + """Stacks each ensemble member's profile into regular 2D arrays. + + Members may resolve a different number of pressure levels, so every + profile is truncated to the shortest one; otherwise the stacked arrays + would be ragged and could not be indexed by member. + + Parameters + ---------- + hourly : dict + The ``hourly`` section of the Open-Meteo JSON response. + time_index : int + Index of the hour to extract. + member_suffixes : list of str + Member suffixes to stack, in the order they should be exposed. + + Returns + ------- + tuple + The pressure levels (hPa) plus the geometric heights, temperatures + and wind components, each as an array of shape + ``(members, levels)``. + """ + levels = None + heights = [] + temperatures = [] + wind_us = [] + wind_vs = [] + + for suffix in member_suffixes: + ( + member_levels, + geopotential_heights, + member_temperatures, + member_wind_u, + member_wind_v, + ) = self.__parse_open_meteo_levels(hourly, time_index, suffix) + + if levels is None or len(member_levels) < len(levels): + levels = member_levels + heights.append( + geopotential_height_to_geometric_height( + geopotential_heights, self.earth_radius + ) + ) + temperatures.append(member_temperatures) + wind_us.append(member_wind_u) + wind_vs.append(member_wind_v) + + profile_length = min(len(levels), *(len(h) for h in heights)) + + return ( + levels[:profile_length], + np.array([h[:profile_length] for h in heights]), + np.array([t[:profile_length] for t in temperatures]), + np.array([u[:profile_length] for u in wind_us]), + np.array([v[:profile_length] for v in wind_vs]), + ) + + def process_open_meteo_ensemble(self, model="gfs05"): + """Process ensemble forecast data from the Open-Meteo API. + + Every ensemble member is stored so that + :meth:`Environment.select_ensemble_member` can switch between them, in + the same way as the netCDF-based ensemble models. + + Parameters + ---------- + model : str, optional + The Open-Meteo ensemble model to query. Default is ``"gfs05"`` + (31 members, counting the control run). Also available is + ``"ecmwf_ifs025"`` (51 members). These are the only Open-Meteo + ensemble models that publish the complete set of pressure-level + variables RocketPy needs; the others either return nulls at every + level or omit the winds entirely. + + Raises + ------ + ValueError + If ``model`` does not publish complete pressure-level data, if no + launch date is set, or if the API returns fewer than two usable + pressure levels. + RuntimeError + If the Open-Meteo API cannot be reached or returns no usable data. + """ + self.__validate_datetime() + + response = fetch_open_meteo_ensemble( + self.latitude, self.longitude, model=model, date=self.datetime_date + ) + hourly = response["hourly"] + time_index, time_array = self.__find_open_meteo_time_index(hourly) + + member_suffixes = self.__find_open_meteo_members(hourly) + + ( + levels, + height, + temperature, + wind_u, + wind_v, + ) = self.__stack_open_meteo_members(hourly, time_index, member_suffixes) + + self.__store_open_meteo_ensemble_data( + levels, height, temperature, wind_u, wind_v, len(member_suffixes) + ) + + # Activate default ensemble + self.select_ensemble_member() + + self.__store_open_meteo_metadata(response, time_array) + + def __store_open_meteo_ensemble_data( + self, levels, height, temperature, wind_u, wind_v, num_members + ): + """Stores every ensemble member so members can be selected later. + + Parameters + ---------- + levels : numpy.ndarray + The pressure levels, in hPa. + height : numpy.ndarray + Geometric altitudes above sea level, in m, shaped + ``(members, levels)``. + temperature : numpy.ndarray + Temperatures, in K, shaped ``(members, levels)``. + wind_u, wind_v : numpy.ndarray + The East and North wind components, in m/s, shaped + ``(members, levels)``. + num_members : int + Number of members stored, including the control run. + """ + wind_speed = calculate_wind_speed(wind_u, wind_v) + wind_heading = calculate_wind_heading(wind_u, wind_v) + wind_direction = convert_wind_heading_to_direction(wind_heading) + + # Save ensemble data + self.level_ensemble = 100 * levels # Convert hPa to Pa + self.height_ensemble = height + self.temperature_ensemble = temperature + self.wind_u_ensemble = wind_u + self.wind_v_ensemble = wind_v + self.wind_heading_ensemble = wind_heading + self.wind_direction_ensemble = wind_direction + self.wind_speed_ensemble = wind_speed + self.num_ensemble_members = num_members + + # Save debugging data + self.levels = self.level_ensemble + self.geopotentials = height + self.wind_us = wind_u + self.wind_vs = wind_v + self.temperatures = temperature + self.height = height + + @staticmethod + def __find_open_meteo_members(hourly): + """Returns the sorted member suffixes present in an ensemble payload. + + Open-Meteo names ensemble members ``_memberNN``, alongside an + unsuffixed control run. The control run is kept as the first member so + that ``select_ensemble_member(0)`` selects it, matching the behaviour + documented for the netCDF-based ensembles. + """ + suffixes = sorted( + { + match.group(1) + for key in hourly + if (match := re.search(r"(_member\d+)$", key)) + } + ) + if not suffixes: + raise ValueError( + "The Open-Meteo ensemble response did not contain any ensemble " + "members. Please try again later or choose another model." + ) + return [""] + suffixes + + @staticmethod + def _validate_meteomatics_credentials_and_model(model, username, password): + """Validates model and credentials for Meteomatics requests.""" + if model is None: + model = "mix" + elif not isinstance(model, str): + # Coercing silently would hide a mistake such as passing a Dataset + # or a file path as 'file', and would query the wrong model. + raise ValueError( + f"Invalid Meteomatics model {model!r}: expected the model name as " + "a string (e.g. 'mix'), or None to use the default." + ) + username = username or os.environ.get("METEOMATICS_USERNAME") + password = password or os.environ.get("METEOMATICS_PASSWORD") + if not username or not password: + raise ValueError( + "Meteomatics requires a username and password. Provide them via " + "the 'username' and 'password' arguments of set_atmospheric_model, " + "or set the METEOMATICS_USERNAME and METEOMATICS_PASSWORD " + "environment variables." + ) + return model, username, password + + def _store_meteomatics_functions( + self, pressure_array, temperature_array, wind_array + ): + """Sets internal atmospheric functions for Meteomatics.""" + wind_asl_heights = wind_array[:, 0] + wind_u_values = wind_array[:, 1] + wind_v_values = wind_array[:, 2] + + wind_speed_array = calculate_wind_speed(wind_u_values, wind_v_values) + wind_heading_array = calculate_wind_heading(wind_u_values, wind_v_values) + wind_direction_array = convert_wind_heading_to_direction(wind_heading_array) + + # Save atmospheric data + self.__set_pressure_function(pressure_array) + self.__set_barometric_height_function(pressure_array[:, (1, 0)]) + self.__set_temperature_function(temperature_array) + self.__set_wind_velocity_x_function(wind_array[:, (0, 1)]) + self.__set_wind_velocity_y_function(wind_array[:, (0, 2)]) + self.__set_wind_heading_function( + np.column_stack((wind_asl_heights, wind_heading_array)) + ) + self.__set_wind_direction_function( + np.column_stack((wind_asl_heights, wind_direction_array)) + ) + self.__set_wind_speed_function( + np.column_stack((wind_asl_heights, wind_speed_array)) + ) + + # Save maximum expected height + self._max_expected_height = float( + max(pressure_array[-1, 0], temperature_array[-1, 0], wind_asl_heights[-1]) + ) + + def _store_meteomatics_metadata( + self, pressure_array, temperature_array, wind_array + ): + """Sets metadata attributes and debug data for Meteomatics.""" + wind_asl_heights = wind_array[:, 0] + self.atmospheric_model_init_date = self.datetime_date + self.atmospheric_model_end_date = self.datetime_date + self.atmospheric_model_interval = 0 + self.atmospheric_model_init_lat = self.latitude + self.atmospheric_model_end_lat = self.latitude + self.atmospheric_model_init_lon = self.longitude + self.atmospheric_model_end_lon = self.longitude + + # Save debugging data + self.wind_us = wind_array[:, 1] + self.wind_vs = wind_array[:, 2] + self.temperatures = temperature_array[:, 1] + self.pressures = pressure_array[:, 1] + self.height = wind_asl_heights + + def _process_meteomatics_profiles(self, profiles): + """Converts retrieved height-AGL profiles to ASL arrays and configures + the Environment atmospheric functions.""" + + def to_profile_array(*names): + common_heights = set.intersection(*(set(profiles[n]) for n in names)) + heights = sorted( + h + for h in common_heights + if all(profiles[n][h] is not None for n in names) + ) + return np.array( + [ + (h + self.elevation, *(profiles[n][h] for n in names)) + for h in heights + ], + dtype=float, + ) + + pressure_array = to_profile_array("pressure") + temperature_array = to_profile_array("temperature") + # Wind u and v share the same altitude grid; keep only common levels. + wind_array = to_profile_array("wind_u", "wind_v") + + # Each profile needs at least two levels: a single-point Function cannot + # be evaluated at its own node (it raises IndexError downstream), so a + # collapsed grid must fail here with an actionable message instead. + if min(len(pressure_array), len(temperature_array), len(wind_array)) < 2: + raise ValueError( + "Meteomatics did not return enough usable atmospheric data: at " + "least two valid altitude levels are required for pressure, " + "temperature and wind. Check the requested model, the altitude " + "range (min_altitude and max_altitude must be far enough apart " + "that the sampled levels do not collapse to a single height), " + "and your account permissions." + ) + + self._store_meteomatics_functions(pressure_array, temperature_array, wind_array) + self._store_meteomatics_metadata(pressure_array, temperature_array, wind_array) + + def process_meteomatics_atmosphere( + self, + model="mix", + username=None, + password=None, + min_altitude=10, + max_altitude=12000, + wind_resolution=20, + temperature_pressure_resolution=10, + query_limit=10, + ): + """Process data from the Meteomatics API to retrieve a vertical + atmospheric profile at the launch site. + + The Meteomatics API is queried for temperature, pressure and both wind + components at several altitudes above ground level, which are then + converted to profiles above sea level using the ``Environment`` + elevation. Authentication uses a personal username and password; when + not provided, they are read from the ``METEOMATICS_USERNAME`` and + ``METEOMATICS_PASSWORD`` environment variables. + + Parameters + ---------- + model : str, optional + The Meteomatics weather model to query. Default is ``"mix"``. Your + account may not have access to every model. + username : str, optional + Meteomatics account username. Defaults to the + ``METEOMATICS_USERNAME`` environment variable. + password : str, optional + Meteomatics account password. Defaults to the + ``METEOMATICS_PASSWORD`` environment variable. + min_altitude : float, optional + Lowest altitude above ground level (in meters) to query. Default is + 10. + max_altitude : float, optional + Highest altitude above ground level (in meters) to query. Default + is 12000. The API errors if it lies outside the model's supported + range. + wind_resolution : int, optional + Number of altitude levels used for the wind components. Default is + 20. + temperature_pressure_resolution : int, optional + Number of altitude levels used for temperature and pressure. + Default is 10. + query_limit : int, optional + Maximum number of parameters requested at once. Parameters are + grouped accordingly to respect the account's per-request limit. + Default is 10. + + Raises + ------ + ValueError + If ``model`` is not a string, if credentials are missing, if no + launch date is set, or if the API returns no usable data. + """ + model, username, password = self._validate_meteomatics_credentials_and_model( + model, username, password + ) + self.__validate_datetime() + + if self.elevation == 0: + warnings.warn( + "The Environment elevation is 0 m (possibly unset), so Meteomatics " + "heights above ground level are being treated as heights above sea " + "level. Set the elevation before this call if the launch site is " + "not at sea level.", + UserWarning, + stacklevel=2, + ) + + profiles = fetch_atmospheric_data_from_meteomatics( + username=username, + password=password, + latitude=self.latitude, + longitude=self.longitude, + date=self.datetime_date, + model=model, + min_altitude=min_altitude, + max_altitude=max_altitude, + wind_resolution=wind_resolution, + temperature_pressure_resolution=temperature_pressure_resolution, + query_limit=query_limit, + ) + + self._process_meteomatics_profiles(profiles) + def process_wyoming_sounding(self, file): # pylint: disable=too-many-statements """Import and process the upper air sounding data from `Wyoming Upper Air Soundings` database given by the url in file. Sets @@ -2132,6 +3089,411 @@ def process_forecast_reanalysis(self, file, dictionary, conversion_factor): # p # Close weather data data.close() + @staticmethod + def _prepare_ensemble_profile_source(source, variable, member): + """Validate and normalize a user-defined atmospheric profile.""" + if isinstance(source, Function): + if not source.is_array_source(): + raise TypeError( + f"Member {member} '{variable}' must be an array-backed " + "Function or a two-column array." + ) + source = source.source + + try: + profile = np.asarray(source, dtype=float) + except (TypeError, ValueError) as exc: + raise TypeError( + f"Member {member} '{variable}' must be a two-column numeric array." + ) from exc + + if profile.ndim != 2 or profile.shape[1] != 2 or len(profile) < 2: + raise ValueError( + f"Member {member} '{variable}' must contain at least two " + "[height, value] rows." + ) + if not np.all(np.isfinite(profile)): + raise ValueError( + f"Member {member} '{variable}' contains non-finite values." + ) + + profile = profile[np.argsort(profile[:, 0])] + if np.any(np.diff(profile[:, 0]) <= 0): + raise ValueError(f"Member {member} '{variable}' heights must be unique.") + return profile + + def _prepare_ensemble_profiles(self, profiles): + """Validate and normalize every user-defined ensemble member.""" + if isinstance(profiles, (str, bytes, Mapping)): + raise TypeError("'profiles' must be a sequence of member mappings.") + try: + profiles = list(profiles) + except TypeError as exc: + raise TypeError( + "'profiles' must be a sequence of member mappings." + ) from exc + if len(profiles) < 2: + raise ValueError("At least two atmospheric profiles are required.") + + required_variables = ("pressure", "temperature", "wind_u", "wind_v") + members = [] + for member_index, member in enumerate(profiles): + if not isinstance(member, Mapping): + raise TypeError( + f"Member {member_index} must be a mapping of profile names " + "to two-column arrays." + ) + missing = [name for name in required_variables if name not in member] + if missing: + raise ValueError( + f"Member {member_index} is missing required profile(s): " + f"{', '.join(missing)}." + ) + + prepared = { + variable: self._prepare_ensemble_profile_source( + member[variable], variable, member_index + ) + for variable in required_variables + } + pressure = prepared["pressure"][:, 1] + if np.any(pressure <= 0): + raise ValueError( + f"Member {member_index} pressure values must be positive." + ) + if np.any(np.diff(pressure) >= 0): + raise ValueError( + f"Member {member_index} pressure must decrease strictly " + "with increasing height." + ) + if np.any(prepared["temperature"][:, 1] <= 0): + raise ValueError( + f"Member {member_index} temperature values must be positive." + ) + members.append(prepared) + return members + + @staticmethod + def _prepare_ensemble_pressure_levels(members, pressure_levels): + """Return a valid pressure grid shared by every ensemble member.""" + common_min_pressure = max(member["pressure"][-1, 1] for member in members) + common_max_pressure = min(member["pressure"][0, 1] for member in members) + if common_min_pressure >= common_max_pressure: + raise ValueError("Ensemble members have no common pressure range.") + + if pressure_levels is None: + common_levels = np.concatenate( + [member["pressure"][:, 1] for member in members] + ) + common_levels = common_levels[ + (common_levels >= common_min_pressure) + & (common_levels <= common_max_pressure) + ] + pressure_levels = np.unique(common_levels)[::-1] + else: + try: + pressure_levels = np.asarray(pressure_levels, dtype=float) + except (TypeError, ValueError) as exc: + raise TypeError("'pressure_levels' must be a numeric array.") from exc + if pressure_levels.ndim != 1: + raise ValueError("'pressure_levels' must be one-dimensional.") + if not np.all(np.isfinite(pressure_levels)) or np.any(pressure_levels <= 0): + raise ValueError( + "'pressure_levels' must contain only finite, positive values." + ) + if len(np.unique(pressure_levels)) != len(pressure_levels): + raise ValueError("'pressure_levels' must not contain duplicates.") + pressure_levels = np.sort(pressure_levels)[::-1] + + if len(pressure_levels) < 2: + raise ValueError( + "At least two pressure levels inside the common range are required." + ) + if ( + pressure_levels[-1] < common_min_pressure + or pressure_levels[0] > common_max_pressure + ): + raise ValueError( + "'pressure_levels' must stay inside the pressure range shared " + "by every member." + ) + return pressure_levels + + def _interpolate_ensemble_profiles(self, members, pressure_levels): + """Interpolate ensemble members onto their common pressure grid.""" + value_variables = ("temperature", "wind_u", "wind_v") + member_heights = [] + member_values = {name: [] for name in value_variables} + for member_index, member in enumerate(members): + pressure_profile = member["pressure"] + heights = np.interp( + pressure_levels, + pressure_profile[::-1, 1], + pressure_profile[::-1, 0], + ) + member_heights.append(heights) + + for variable in value_variables: + profile = member[variable] + if heights[0] < profile[0, 0] or heights[-1] > profile[-1, 0]: + raise ValueError( + f"Member {member_index} '{variable}' does not cover all " + "heights in the common pressure range." + ) + member_values[variable].append( + np.interp(heights, profile[:, 0], profile[:, 1]) + ) + + geometric_heights = np.asarray(member_heights) + if np.any(geometric_heights <= -self.earth_radius): + raise ValueError("Profile heights must be greater than -Earth's radius.") + geopotential_heights = ( + self.earth_radius + * geometric_heights + / (self.earth_radius + geometric_heights) + ) + return geopotential_heights, member_values + + @staticmethod + def _prepare_ensemble_file_path(file_name, overwrite): + """Normalize the output path and protect existing files.""" + try: + file_path = os.fspath(file_name) + except TypeError as exc: + raise TypeError( + "'file_name' must be a string or path-like object." + ) from exc + if not file_path.lower().endswith(".nc"): + file_path += ".nc" + file_path = os.path.abspath(file_path) + if os.path.exists(file_path) and not overwrite: + raise FileExistsError( + f"'{file_path}' already exists. Pass overwrite=True to replace it." + ) + return file_path + + def _create_ensemble_time_coordinate(self, dataset): + """Create the valid-time coordinate for an ensemble dataset.""" + time = dataset.createVariable("time", "f8", ("time",)) + time.long_name = "profile valid time" + time.standard_name = "time" + time.units = ( + f"hours since {self.datetime_date.strftime('%Y-%m-%d %H:%M:%S')} UTC" + ) + time.calendar = "gregorian" + time.axis = "T" + time[:] = [0] + + @staticmethod + def _create_ensemble_member_and_level_coordinates( + dataset, member_count, pressure_levels + ): + """Create the ensemble-member and pressure-level coordinates.""" + ensemble = dataset.createVariable("ens", "i4", ("ens",)) + ensemble.long_name = "ensemble member" + ensemble.units = "1" + ensemble[:] = np.arange(member_count) + + level = dataset.createVariable("lev", "f8", ("lev",)) + level.long_name = "pressure level" + level.standard_name = "air_pressure" + level.units = "hPa" + level.positive = "down" + level.axis = "Z" + level[:] = pressure_levels / 100 + + @staticmethod + def _create_ensemble_spatial_coordinates( + dataset, latitude_bounds, longitude_bounds + ): + """Create latitude and longitude coordinates for an ensemble dataset.""" + latitude = dataset.createVariable("lat", "f8", ("lat",)) + latitude.long_name = "latitude" + latitude.standard_name = "latitude" + latitude.units = "degrees_north" + latitude.axis = "Y" + latitude[:] = latitude_bounds + + longitude = dataset.createVariable("lon", "f8", ("lon",)) + longitude.long_name = "longitude" + longitude.standard_name = "longitude" + longitude.units = "degrees_east" + longitude.axis = "X" + longitude[:] = longitude_bounds + + def _create_ensemble_coordinates(self, dataset, member_count, pressure_levels): + """Create dimensions and coordinate variables for an ensemble dataset.""" + latitude_bounds = np.array( + [max(-90, self.latitude - 0.01), min(90, self.latitude + 0.01)] + ) + grid_longitude = 0 if self.longitude == 360 else self.longitude + longitude_bounds = np.array( + [ + max(-180, grid_longitude - 0.01), + min(360, grid_longitude + 0.01), + ] + ) + + dataset.createDimension("time", 1) + dataset.createDimension("ens", member_count) + dataset.createDimension("lev", len(pressure_levels)) + dataset.createDimension("lat", len(latitude_bounds)) + dataset.createDimension("lon", len(longitude_bounds)) + + self._create_ensemble_time_coordinate(dataset) + self._create_ensemble_member_and_level_coordinates( + dataset, member_count, pressure_levels + ) + self._create_ensemble_spatial_coordinates( + dataset, latitude_bounds, longitude_bounds + ) + + return ( + 1, + member_count, + len(pressure_levels), + len(latitude_bounds), + len(longitude_bounds), + ) + + @staticmethod + def _create_ensemble_data_variables( + dataset, data_shape, geopotential_heights, member_values + ): + """Create and populate the atmospheric variables in an ensemble dataset.""" + variables = { + "hgtprs": ( + geopotential_heights, + "geopotential height", + "geopotential_height", + "m", + ), + "tmpprs": ( + np.asarray(member_values["temperature"]), + "air temperature", + "air_temperature", + "K", + ), + "ugrdprs": ( + np.asarray(member_values["wind_u"]), + "eastward wind", + "eastward_wind", + "m s-1", + ), + "vgrdprs": ( + np.asarray(member_values["wind_v"]), + "northward wind", + "northward_wind", + "m s-1", + ), + } + dimensions = ("time", "ens", "lev", "lat", "lon") + for name, (values, long_name, standard_name, units) in variables.items(): + variable = dataset.createVariable( + name, "f8", dimensions, zlib=True, complevel=4 + ) + variable.long_name = long_name + variable.standard_name = standard_name + variable.units = units + variable.coordinates = "time ens lev lat lon" + variable[:] = np.broadcast_to(values[None, :, :, None, None], data_shape) + + def _write_ensemble_file( + self, file_path, pressure_levels, geopotential_heights, member_values + ): + """Write prepared ensemble data to a GEFS-compatible NetCDF file.""" + with netCDF4.Dataset(file_path, "w", format="NETCDF4") as dataset: + dataset.Conventions = "CF-1.8" + dataset.title = "RocketPy user-defined atmospheric ensemble" + dataset.source = "RocketPy Environment.create_ensemble" + dataset.history = ( + f"Created {datetime.now(tz=pytz.UTC).isoformat()} by RocketPy" + ) + dataset.comment = ( + "Profiles are spatially constant across the 2 x 2 grid " + "surrounding the launch coordinates." + ) + dataset.launch_latitude = self.latitude + dataset.launch_longitude = self.longitude + + data_shape = self._create_ensemble_coordinates( + dataset, len(geopotential_heights), pressure_levels + ) + self._create_ensemble_data_variables( + dataset, data_shape, geopotential_heights, member_values + ) + + def create_ensemble( + self, + profiles, + file_name="custom_ensemble.nc", + pressure_levels=None, + overwrite=False, + ): + """Create and activate an ensemble from user-defined profiles. + + RocketPy writes the profiles with GEFS-compatible variable names. + Another Environment can load the returned file by passing + ``type="Ensemble"`` and ``dictionary="GEFS"`` to + :meth:`Environment.set_atmospheric_model`. + + Parameters + ---------- + profiles : sequence of mappings + Atmospheric profiles for each ensemble member. Every mapping must + define ``pressure``, ``temperature``, ``wind_u`` and ``wind_v``. + Each value must be a two-column array whose first column is + geometric height above sea level in meters. The second column uses + Pa for pressure, K for temperature and m/s for either wind + component. Array-backed :class:`rocketpy.Function` objects are also + accepted. Pressure must decrease strictly with increasing height. + file_name : str or os.PathLike, optional + Path of the NetCDF file to create. The ``.nc`` suffix is appended + when omitted. Default is ``"custom_ensemble.nc"``. + pressure_levels : array-like, optional + Common pressure levels in Pa. By default, the union of sampled + pressure levels inside the range shared by every member is used. + overwrite : bool, optional + Whether an existing file may be replaced. Default is ``False``. + + Returns + ------- + str + Absolute path of the created NetCDF file. + + Raises + ------ + TypeError + If profiles or profile values have invalid types. + ValueError + If fewer than two members are supplied, required variables are + missing, profiles are invalid, or the members have no usable common + pressure range. + FileExistsError + If the output exists and ``overwrite`` is ``False``. + + Notes + ----- + The first member is activated after the file is created. Use + :meth:`Environment.select_ensemble_member` to activate another member. + """ + self.__validate_datetime() + members = self._prepare_ensemble_profiles(profiles) + pressure_levels = self._prepare_ensemble_pressure_levels( + members, pressure_levels + ) + geopotential_heights, member_values = self._interpolate_ensemble_profiles( + members, pressure_levels + ) + file_path = self._prepare_ensemble_file_path(file_name, overwrite) + self._write_ensemble_file( + file_path, pressure_levels, geopotential_heights, member_values + ) + + self.set_atmospheric_model(type="Ensemble", file=file_path, dictionary="GEFS") + logger.info("Atmospheric ensemble saved at '%s'.", file_path) + return file_path + def process_ensemble(self, file, dictionary, conversion_factor): # pylint: disable=too-many-locals,too-many-statements """Import and process atmospheric data from weather ensembles given as ``netCDF`` or ``OPeNDAP`` files. Sets pressure, temperature, @@ -2554,8 +3916,14 @@ def pressure_function(h): ) return P - # Discretize this Function to speed up the trajectory simulation - altitudes = np.linspace(0, 80000, 100) # TODO: should be -2k instead of 0 + # Discretize across the full ISA range (geopotential layers -> geometric + # height), keeping 0 m as a knot and now covering below sea level too. + gph_to_geo = geopotential_height_to_geometric_height + min_h = gph_to_geo(geopotential_height[0], earth_radius) + altitudes = np.append( + np.linspace(min_h, 0, 10, endpoint=False), + np.linspace(0, gph_to_geo(geopotential_height[-1], earth_radius), 90), + ) pressures = [pressure_function(h) for h in altitudes] return np.column_stack([altitudes, pressures]) @@ -2603,7 +3971,7 @@ def calculate_density_profile(self): >>> env = Environment() >>> env.calculate_density_profile() >>> float(env.density(1000)) - 1.1115112430077818 + 1.1116196671683787 """ # Retrieve pressure P, gas constant R and temperature T P = self.pressure @@ -2981,8 +4349,11 @@ def from_dict(cls, data): # pylint: disable=too-many-statements ) atmospheric_model = data["atmospheric_model_type"] env.atmospheric_model_type = atmospheric_model + # set_atmospheric_model stores the type as the user spelled it (e.g. + # "Meteomatics"), so the dispatch below must be case-insensitive. + model_type = atmospheric_model.lower() - match atmospheric_model: + match model_type: case "standard_atmosphere": env.set_atmospheric_model("standard_atmosphere") case "custom_atmosphere": @@ -3004,7 +4375,15 @@ def from_dict(cls, data): # pylint: disable=too-many-statements env.elevation = data["elevation"] env.max_expected_height = data["max_expected_height"] - if atmospheric_model in ("windy", "forecast", "reanalysis", "ensemble"): + if model_type in ( + "windy", + "meteomatics", + "open_meteo", + "open_meteo_ensemble", + "forecast", + "reanalysis", + "ensemble", + ): env.atmospheric_model_init_date = data["atmospheric_model_init_date"] env.atmospheric_model_end_date = data["atmospheric_model_end_date"] env.atmospheric_model_interval = data["atmospheric_model_interval"] @@ -3013,7 +4392,7 @@ def from_dict(cls, data): # pylint: disable=too-many-statements env.atmospheric_model_init_lon = data["atmospheric_model_init_lon"] env.atmospheric_model_end_lon = data["atmospheric_model_end_lon"] - if atmospheric_model == "ensemble": + if model_type in ("ensemble", "open_meteo_ensemble"): env.level_ensemble = data["level_ensemble"] env.height_ensemble = data["height_ensemble"] env.temperature_ensemble = data["temperature_ensemble"] diff --git a/rocketpy/environment/environment_analysis.py b/rocketpy/environment/environment_analysis.py index e4dcbd021..7aeae1562 100644 --- a/rocketpy/environment/environment_analysis.py +++ b/rocketpy/environment/environment_analysis.py @@ -450,7 +450,7 @@ def __find_preferred_timezone(self): tf.timezone_at(lng=self.longitude, lat=self.latitude) ) except ImportError: - warnings.warning( # pragma: no cover + warnings.warn( "'timezonefinder' not installed, defaulting to UTC." + " Install timezonefinder to get local time zone." + " To do so, run 'pip install timezonefinder'" diff --git a/rocketpy/environment/fetchers/__init__.py b/rocketpy/environment/fetchers/__init__.py new file mode 100644 index 000000000..067208717 --- /dev/null +++ b/rocketpy/environment/fetchers/__init__.py @@ -0,0 +1,109 @@ +"""This module contains auxiliary functions and classes for fetching data from +various third-party APIs. +""" + +import time + +import netCDF4 +import requests + +from rocketpy.environment.fetchers.base import ( + MAX_RETRY_DELAY_SECONDS, + logger, +) +from rocketpy.environment.fetchers.elevation_fetcher import fetch_open_elevation +from rocketpy.environment.fetchers.meteomatics_fetcher import ( + METEOMATICS_BASE_URL, + METEOMATICS_LOGIN_URL, + METEOMATICS_TIMEOUT_SECONDS, + MeteomaticsFetcher, + fetch_atmospheric_data_from_meteomatics, + fetch_meteomatics_token, +) +from rocketpy.environment.fetchers.open_meteo_fetcher import ( + OPEN_METEO_ENSEMBLE_MODELS, + OPEN_METEO_ENSEMBLE_URL, + OPEN_METEO_FORECAST_URL, + OPEN_METEO_HISTORICAL_START_DATE, + OPEN_METEO_HISTORICAL_URL, + OPEN_METEO_PRESSURE_LEVELS, + OPEN_METEO_TIMEOUT_SECONDS, + build_hourly_variables, + fetch_open_meteo_ensemble, + fetch_open_meteo_forecast, +) +from rocketpy.environment.fetchers.noaa_catalog import ( + FORECAST_MODELS_CATALOG_URL, + NOAA_MODEL_COLLECTIONS, + THREDDS_OPENDAP_ROOT, + build_noaa_opendap_url, + collection_catalog_url, + fetch_latest_noaa_dataset, + get_latest_noaa_dataset_identifier, + get_latest_noaa_opendap_url, + list_noaa_atmosphere_datasets, + list_noaa_dataset_identifiers, + resolve_noaa_collection_path, +) +from rocketpy.environment.fetchers.opendap_fetchers import ( + fetch_aigfs_file_return_dataset, + fetch_cmc_ensemble, + fetch_gefs_ensemble, + fetch_gfs_file_return_dataset, + fetch_hiresw_file_return_dataset, + fetch_hrrr_file_return_dataset, + fetch_nam_file_return_dataset, + fetch_rap_file_return_dataset, +) +from rocketpy.environment.fetchers.windy_fetcher import ( + fetch_atmospheric_data_from_windy, +) +from rocketpy.environment.fetchers.wyoming_fetcher import ( + fetch_wyoming_sounding, +) + +__all__ = [ + "MAX_RETRY_DELAY_SECONDS", + "FORECAST_MODELS_CATALOG_URL", + "METEOMATICS_BASE_URL", + "METEOMATICS_LOGIN_URL", + "METEOMATICS_TIMEOUT_SECONDS", + "NOAA_MODEL_COLLECTIONS", + "OPEN_METEO_ENSEMBLE_MODELS", + "OPEN_METEO_ENSEMBLE_URL", + "OPEN_METEO_FORECAST_URL", + "OPEN_METEO_HISTORICAL_START_DATE", + "OPEN_METEO_HISTORICAL_URL", + "OPEN_METEO_PRESSURE_LEVELS", + "OPEN_METEO_TIMEOUT_SECONDS", + "THREDDS_OPENDAP_ROOT", + "MeteomaticsFetcher", + "build_hourly_variables", + "build_noaa_opendap_url", + "collection_catalog_url", + "fetch_aigfs_file_return_dataset", + "fetch_atmospheric_data_from_meteomatics", + "fetch_atmospheric_data_from_windy", + "fetch_cmc_ensemble", + "fetch_gefs_ensemble", + "fetch_gfs_file_return_dataset", + "fetch_hiresw_file_return_dataset", + "fetch_hrrr_file_return_dataset", + "fetch_latest_noaa_dataset", + "fetch_meteomatics_token", + "fetch_nam_file_return_dataset", + "fetch_open_elevation", + "fetch_open_meteo_ensemble", + "fetch_open_meteo_forecast", + "fetch_rap_file_return_dataset", + "fetch_wyoming_sounding", + "get_latest_noaa_dataset_identifier", + "get_latest_noaa_opendap_url", + "list_noaa_atmosphere_datasets", + "list_noaa_dataset_identifiers", + "logger", + "netCDF4", + "requests", + "resolve_noaa_collection_path", + "time", +] diff --git a/rocketpy/environment/fetchers/base.py b/rocketpy/environment/fetchers/base.py new file mode 100644 index 000000000..b2001155d --- /dev/null +++ b/rocketpy/environment/fetchers/base.py @@ -0,0 +1,7 @@ +"""Base constants and logger for atmospheric data fetchers.""" + +import logging + +logger = logging.getLogger(__name__) + +MAX_RETRY_DELAY_SECONDS = 600 diff --git a/rocketpy/environment/fetchers/elevation_fetcher.py b/rocketpy/environment/fetchers/elevation_fetcher.py new file mode 100644 index 000000000..c7a379b08 --- /dev/null +++ b/rocketpy/environment/fetchers/elevation_fetcher.py @@ -0,0 +1,43 @@ +"""Fetch elevation data from third-party APIs.""" + +import requests + +from rocketpy.environment.fetchers.base import logger +from rocketpy.tools import exponential_backoff + + +@exponential_backoff(max_attempts=3, base_delay=1, max_delay=60) +def fetch_open_elevation(lat, lon): + """Fetches elevation data from the Open-Elevation API at a given latitude + and longitude. + + Parameters + ---------- + lat : float + The latitude of the location. + lon : float + The longitude of the location. + + Returns + ------- + float + The elevation at the given latitude and longitude in meters. + + Raises + ------ + RuntimeError + If there is a problem reaching the Open-Elevation API servers. + """ + logger.debug( + "Fetching elevation from open-elevation.com for lat=%s, lon=%s", lat, lon + ) + request_url = f"https://api.open-elevation.com/api/v1/lookup?locations={lat},{lon}" + try: + response = requests.get(request_url) + results = response.json()["results"] + return results[0]["elevation"] + except ( + requests.exceptions.RequestException, + requests.exceptions.JSONDecodeError, + ) as e: + raise RuntimeError("Unable to reach Open-Elevation API servers.") from e diff --git a/rocketpy/environment/fetchers/meteomatics_fetcher.py b/rocketpy/environment/fetchers/meteomatics_fetcher.py new file mode 100644 index 000000000..2d3e1e5b8 --- /dev/null +++ b/rocketpy/environment/fetchers/meteomatics_fetcher.py @@ -0,0 +1,413 @@ +"""Fetch weather data from the Meteomatics API.""" + +import base64 +from datetime import timezone + +import numpy as np +import requests + +from rocketpy.environment.fetchers.base import logger +from rocketpy.tools import exponential_backoff + +METEOMATICS_BASE_URL = "https://api.meteomatics.com" +METEOMATICS_LOGIN_URL = "https://login.meteomatics.com/api/v1/token" +METEOMATICS_TIMEOUT_SECONDS = 30 + + +class MeteomaticsFetcher: + """Fetcher class to authenticate and query vertical atmospheric profiles + from the Meteomatics API. + """ + + @staticmethod + @exponential_backoff(max_attempts=3, base_delay=1, max_delay=60) + def _get(url, headers=None, params=None): + """Performs a single Meteomatics GET request, retrying transient failures. + + Connection-level errors (and server-side 5xx responses) raise and are + retried by the decorator. Client-side 4xx responses are returned as-is so + the caller can turn them into an actionable, non-retried error, since + retrying a deterministic 4xx only wastes time and API quota. + """ + response = requests.get( + url, headers=headers, params=params, timeout=METEOMATICS_TIMEOUT_SECONDS + ) + if response.status_code >= 500: + response.raise_for_status() + return response + + @classmethod + def _request_json(cls, url, endpoint, headers=None, params=None): + """Queries a Meteomatics endpoint and returns its parsed JSON body. + + Parameters + ---------- + url : str + The endpoint address to query. + endpoint : str + Human-readable name of the endpoint (e.g. ``"login service"``), used to + build the error messages. + headers : dict, optional + Headers to send with the request. + params : dict, optional + Query parameters to send with the request. + + Returns + ------- + dict + The parsed JSON body of the response. + + Raises + ------ + RuntimeError + If the endpoint cannot be reached, rejects the credentials, returns an + error status, or returns a malformed (non-JSON) body. Client-side (4xx) + errors are definitive and are not retried. + """ + try: + response = cls._get(url, headers=headers, params=params) + except requests.exceptions.RequestException as e: + raise RuntimeError( + f"Unable to reach the Meteomatics {endpoint}. Please try again later." + ) from e + if response.status_code in (401, 403): + raise RuntimeError( + f"Meteomatics rejected the credentials (HTTP {response.status_code}). " + "Check your username and password." + ) + if not response.ok: + raise RuntimeError( + f"Meteomatics {endpoint} request failed " + f"(HTTP {response.status_code}). {response.text[:300]}".strip() + ) + try: + return response.json() + except requests.exceptions.JSONDecodeError as e: + raise RuntimeError( + f"Meteomatics {endpoint} returned a malformed (non-JSON) response." + ) from e + + @classmethod + def fetch_token(cls, username, password): + """Requests a short-lived access token from the Meteomatics login service. + + The Meteomatics API authenticates with a personal ``username`` and + ``password``. Instead of sending the credentials on every request, a token + is generated once and reused for the handful of requests needed to build a + single ``Environment``. + + Parameters + ---------- + username : str + The Meteomatics account username. + password : str + The Meteomatics account password. + + Returns + ------- + str + The access token to be used as the ``access_token`` query parameter in + subsequent data requests. + + Raises + ------ + RuntimeError + If the login service cannot be reached, rejects the credentials, or + does not return a token. + """ + credentials = f"{username}:{password}" + encoded_credentials = base64.b64encode(credentials.encode()).decode() + payload = cls._request_json( + METEOMATICS_LOGIN_URL, + "login service", + headers={"Authorization": f"Basic {encoded_credentials}"}, + ) + token = payload.get("access_token") + if not token: + raise RuntimeError( + "Meteomatics login service did not return an access token. " + "Check your username and password." + ) + logger.info("Meteomatics access token generated successfully.") + return token + + @staticmethod + def _build_parameters( + min_altitude, max_altitude, wind_resolution, temperature_pressure_resolution + ): + """Builds the Meteomatics height-level parameters to query. + + Wind components are sampled on a finer altitude grid than temperature and + pressure, since the wind profile is usually the most variable one. + + Parameters + ---------- + min_altitude : float + Lowest altitude above ground level (in meters) to query. + max_altitude : float + Highest altitude above ground level (in meters) to query. + wind_resolution : int + Number of altitude levels used for the wind components. + temperature_pressure_resolution : int + Number of altitude levels used for temperature and pressure. + + Returns + ------- + dict + Maps each parameter string, in the ``"_m:"`` + format, to the ``(profile name, height)`` pair it carries. Keeping this + mapping spares the caller from parsing the parameter strings back. + """ + + def levels(resolution): + return np.unique( + np.linspace(min_altitude, max_altitude, resolution).round().astype(int) + ) + + grids = [ + ( + levels(wind_resolution), + [("wind_speed_u", "ms", "wind_u"), ("wind_speed_v", "ms", "wind_v")], + ), + ( + levels(temperature_pressure_resolution), + [("t", "K", "temperature"), ("pressure", "Pa", "pressure")], + ), + ] + return { + f"{var}_{height}m:{unit}": (profile, int(height)) + for heights, variables in grids + for height in heights + for var, unit, profile in variables + } + + @staticmethod + def _validate_sampling( + min_altitude, + max_altitude, + wind_resolution, + temperature_pressure_resolution, + query_limit, + ): + """Validates the sampling arguments before any request is issued. + + Catching these here keeps a degenerate input from reaching ``linspace`` or + ``range``, where it would surface as an opaque low-level error (or as an + empty request) after the account has already been charged for the login. + + Raises + ------ + ValueError + If the altitude range, the resolutions or the query limit are invalid. + """ + if min_altitude < 0: + raise ValueError( + "min_altitude must be non-negative (heights are above ground level)." + ) + if max_altitude <= min_altitude: + raise ValueError("max_altitude must be greater than min_altitude.") + if wind_resolution < 2 or temperature_pressure_resolution < 2: + raise ValueError( + "wind_resolution and temperature_pressure_resolution must be at least " + "2: a single altitude level is not enough to define a profile." + ) + if query_limit < 1: + raise ValueError("query_limit must be at least 1.") + + @staticmethod + def _extract_json(data): + """Extracts (parameter, value) pairs from a Meteomatics JSON response. + + Only the first coordinate and first date of each parameter are used, since + the query is always issued for a single location and a single instant. + + Parameters + ---------- + data : dict + The JSON payload returned by the Meteomatics data endpoint. + + Returns + ------- + list of tuple + A list of ``(parameter, value)`` tuples. + + Raises + ------ + RuntimeError + If the payload does not have the expected Meteomatics structure. + """ + try: + return [ + (entry["parameter"], entry["coordinates"][0]["dates"][0]["value"]) + for entry in data["data"] + ] + except (KeyError, IndexError, TypeError) as e: + raise RuntimeError( + "Unexpected Meteomatics response structure; could not extract the " + "requested data." + ) from e + + @classmethod + def fetch_atmospheric_data( + cls, + username, + password, + latitude, + longitude, + date, + model="mix", + min_altitude=10, + max_altitude=12000, + wind_resolution=20, + temperature_pressure_resolution=10, + query_limit=10, + ): + """Fetches a vertical atmospheric profile from the Meteomatics API. + + The data is retrieved for a single location and instant, sampling + temperature, pressure and both wind components at several altitudes above + ground level. To respect the account's per-request parameter limit, the + parameters are split into groups that are queried separately. + + Parameters + ---------- + username : str + The Meteomatics account username. + password : str + The Meteomatics account password. + latitude : float + Latitude of the launch site, in degrees. + longitude : float + Longitude of the launch site, in degrees. + date : datetime.datetime + The instant to query. It is formatted according to the Meteomatics + date-time specification (``%Y-%m-%dT%H:%M:%SZ``). Timezone-aware + datetimes are converted to UTC; naive ones are assumed to be UTC + already. + model : str, optional + The Meteomatics weather model to use. Default is ``"mix"``. Your + account may not have access to every model. See + https://www.meteomatics.com/en/api/request/optional-parameters/data-source/ + min_altitude : float, optional + Lowest altitude above ground level (in meters) to query. Default is 10. + max_altitude : float, optional + Highest altitude above ground level (in meters) to query. Default is + 12000. The API returns an error if the requested altitude is outside + the range supported by the chosen model. + wind_resolution : int, optional + Number of altitude levels used for the wind components. Default is 20. + temperature_pressure_resolution : int, optional + Number of altitude levels used for temperature and pressure. Default is + 10. + query_limit : int, optional + Maximum number of parameters requested at once. Parameters are grouped + accordingly to work around the account's per-request limit. Default is + 10. See https://api.meteomatics.com/user_stats for your own limits. + + Returns + ------- + dict + A dictionary with the keys ``"temperature"``, ``"pressure"``, + ``"wind_u"`` and ``"wind_v"``. Each value is a dictionary mapping the + altitude above ground level (in meters) to the corresponding value, in + SI units (K, Pa, m/s and m/s respectively). + + Raises + ------ + RuntimeError + If authentication fails, the API cannot be reached, returns an error + status, or returns a malformed response. + ValueError + If the altitude range, the resolutions or the query limit are invalid, + or if the response contains an unrecognized parameter. + """ + cls._validate_sampling( + min_altitude, + max_altitude, + wind_resolution, + temperature_pressure_resolution, + query_limit, + ) + + token = cls.fetch_token(username, password) + + if date.tzinfo is not None: + date = date.astimezone(timezone.utc) + date_string = date.strftime("%Y-%m-%dT%H:%M:%SZ") + parameter_map = cls._build_parameters( + min_altitude, + max_altitude, + wind_resolution, + temperature_pressure_resolution, + ) + parameters = list(parameter_map) + parameter_groups = [ + parameters[i : i + query_limit] + for i in range(0, len(parameters), query_limit) + ] + + profiles = { + "temperature": {}, + "pressure": {}, + "wind_u": {}, + "wind_v": {}, + } + + for index, parameter_group in enumerate(parameter_groups): + logger.info( + "Fetching Meteomatics data for group %d/%d.", + index + 1, + len(parameter_groups), + ) + parameters_str = ",".join(parameter_group) + base_url = ( + f"{METEOMATICS_BASE_URL}/{date_string}/{parameters_str}/" + f"{latitude},{longitude}/json" + ) + query_params = {"model": model, "access_token": token} + data = cls._request_json(base_url, "data API", params=query_params) + + for parameter, value in cls._extract_json(data): + try: + profile, height = parameter_map[parameter] + except KeyError as e: + raise ValueError( + f"Unrecognized Meteomatics parameter '{parameter}'." + ) from e + profiles[profile][height] = value + + return profiles + + +def fetch_meteomatics_token(username, password): + """Requests a short-lived access token from the Meteomatics login service.""" + return MeteomaticsFetcher.fetch_token(username, password) + + +def fetch_atmospheric_data_from_meteomatics( + username, + password, + latitude, + longitude, + date, + model="mix", + min_altitude=10, + max_altitude=12000, + wind_resolution=20, + temperature_pressure_resolution=10, + query_limit=10, +): + """Fetches a vertical atmospheric profile from the Meteomatics API.""" + return MeteomaticsFetcher.fetch_atmospheric_data( + username=username, + password=password, + latitude=latitude, + longitude=longitude, + date=date, + model=model, + min_altitude=min_altitude, + max_altitude=max_altitude, + wind_resolution=wind_resolution, + temperature_pressure_resolution=temperature_pressure_resolution, + query_limit=query_limit, + ) diff --git a/rocketpy/environment/fetchers/noaa_catalog.py b/rocketpy/environment/fetchers/noaa_catalog.py new file mode 100644 index 000000000..60b4381b8 --- /dev/null +++ b/rocketpy/environment/fetchers/noaa_catalog.py @@ -0,0 +1,411 @@ +"""Best-effort helpers for listing NOAA/NCEP atmosphere datasets on THREDDS. + +RocketPy's forecast shortcuts historically hard-coded OPeNDAP URLs (and, for +NOMADS GrADS models, probed calendar times). NOMADS OPeNDAP has been retired, +so discovery is based on the Unidata THREDDS catalogs that already back the +GFS/NAM/RAP/HRRR/AIGFS fetchers. + +Limitations +----------- +- Catalog layout can change without notice; treat results as advisory. +- Listing uses HTTP XML catalogs, not NOMADS OpenDAP directory pages. +- ``fetch_latest_noaa_dataset`` opens the resolved OPeNDAP URL via netCDF4 + (same pattern as the existing ``fetch_*_file_return_dataset`` helpers). It + does not write a local GRIB/NetCDF file to disk. +""" + +from __future__ import annotations + +import re +import time +import xml.etree.ElementTree as ET +from datetime import datetime +from urllib.parse import urljoin, urlparse + +import netCDF4 +import requests + +from rocketpy.environment.fetchers.base import MAX_RETRY_DELAY_SECONDS + +THREDDS_ROOT = "https://thredds.ucar.edu/thredds" +THREDDS_CATALOG_ROOT = f"{THREDDS_ROOT}/catalog/" +THREDDS_OPENDAP_ROOT = f"{THREDDS_ROOT}/dodsC/" +FORECAST_MODELS_CATALOG_URL = f"{THREDDS_CATALOG_ROOT}idd/forecastModels.xml" + +# Collections used by Environment's latest-model shortcuts. +NOAA_MODEL_COLLECTIONS = { + "GFS": "grib/NCEP/GFS/Global_0p25deg", + "NAM": "grib/NCEP/NAM/CONUS_12km", + "RAP": "grib/NCEP/RAP/CONUS_13km", + "HRRR": "grib/NCEP/HRRR/CONUS_2p5km", + "AIGFS": "grib/NCEP/AIGFS/Global_0p25deg", +} + +_THREDDS_NS = { + "thredds": "http://www.unidata.ucar.edu/namespaces/thredds/InvCatalog/v1.0", + "xlink": "http://www.w3.org/1999/xlink", +} +_RUN_TIMESTAMP_RE = re.compile(r"(?P\d{8}_\d{4})") +_DEFAULT_TIMEOUT_SECONDS = 30 + + +def _local_tag(tag: str) -> str: + """Strip Clark-notation namespace from an ElementTree tag.""" + if "}" in tag: + return tag.rsplit("}", 1)[-1] + return tag + + +def _absolute_catalog_url(href: str, base_url: str) -> str: + """Resolve a catalogRef href against a catalog URL.""" + if href.startswith("http://") or href.startswith("https://"): + return href + if href.startswith("/"): + parsed = urlparse(base_url) + return f"{parsed.scheme}://{parsed.netloc}{href}" + return urljoin(base_url, href) + + +def _collection_path_from_catalog_url(catalog_url: str) -> str | None: + """Extract ``grib/NCEP/...`` path from a THREDDS catalog URL, if present.""" + marker = "/catalog/" + if marker not in catalog_url: + return None + path = catalog_url.split(marker, 1)[1] + if path.endswith("/catalog.xml"): + path = path[: -len("/catalog.xml")] + elif path.endswith("catalog.xml"): + path = path[: -len("catalog.xml")].rstrip("/") + return path or None + + +def _fetch_catalog_xml(catalog_url: str, timeout: float = _DEFAULT_TIMEOUT_SECONDS): + """GET a THREDDS catalog XML document and return its root element.""" + try: + response = requests.get(catalog_url, timeout=timeout) + except requests.exceptions.RequestException as exc: + raise RuntimeError( + f"Unable to reach NOAA/THREDDS catalog at {catalog_url}." + ) from exc + + if response.status_code != 200: + raise RuntimeError( + "Unable to list NOAA/THREDDS datasets: " + f"HTTP {response.status_code} for {catalog_url}." + ) + + try: + return ET.fromstring(response.content) + except ET.ParseError as exc: + raise RuntimeError( + f"Invalid THREDDS catalog XML received from {catalog_url}." + ) from exc + + +def _opendap_url_from_path(url_path: str) -> str: + """Build an OPeNDAP URL for a THREDDS dataset ``urlPath``.""" + return urljoin(THREDDS_OPENDAP_ROOT, url_path.lstrip("/")) + + +def _run_sort_key(identifier: str): + """Sort key that prefers identifiers embedding ``YYYYMMDD_HHMM``.""" + match = _RUN_TIMESTAMP_RE.search(identifier) + if match: + try: + return datetime.strptime(match.group("stamp"), "%Y%m%d_%H%M") + except ValueError: + pass + return datetime.min + + +def resolve_noaa_collection_path(model_or_path: str) -> str: + """Map a model shortcut or collection path to a THREDDS collection path. + + Parameters + ---------- + model_or_path : str + Shortcut such as ``\"GFS\"`` or a collection path such as + ``\"grib/NCEP/GFS/Global_0p25deg\"``. + + Returns + ------- + str + Collection path without a leading slash. + + Raises + ------ + ValueError + If ``model_or_path`` is not a known shortcut and does not look like a + collection path. + """ + if not isinstance(model_or_path, str) or not model_or_path.strip(): + raise ValueError("model_or_path must be a non-empty string.") + + key = model_or_path.strip() + mapped = NOAA_MODEL_COLLECTIONS.get(key.upper()) + if mapped is not None: + return mapped + + normalized = key.lstrip("/") + if normalized.startswith("grib/"): + return normalized.removesuffix("/catalog.xml").rstrip("/") + + raise ValueError( + f"Unknown NOAA model collection {model_or_path!r}. " + f"Known shortcuts: {sorted(NOAA_MODEL_COLLECTIONS)}. " + "Pass a THREDDS collection path such as " + "'grib/NCEP/GFS/Global_0p25deg' instead." + ) + + +def collection_catalog_url(model_or_path: str) -> str: + """Return the THREDDS ``catalog.xml`` URL for a model collection.""" + collection = resolve_noaa_collection_path(model_or_path) + return f"{THREDDS_CATALOG_ROOT}{collection}/catalog.xml" + + +def list_noaa_atmosphere_datasets( + catalog_url: str = FORECAST_MODELS_CATALOG_URL, + timeout: float = _DEFAULT_TIMEOUT_SECONDS, +) -> list[dict]: + """List available NCEP atmosphere model collections from THREDDS. + + Parameters + ---------- + catalog_url : str, optional + Root forecast-models catalog URL. Defaults to Unidata's NCEP models + catalog (the same server family used by RocketPy's GFS/NAM/… fetchers). + timeout : float, optional + HTTP timeout in seconds. + + Returns + ------- + list of dict + Each entry has ``name``, ``catalog_url``, ``collection_path`` and + ``opendap_best_url`` (the latter is ``None`` until a collection + catalog is inspected). + """ + root = _fetch_catalog_xml(catalog_url, timeout=timeout) + datasets = [] + seen = set() + + for ref in root.findall(".//thredds:catalogRef", _THREDDS_NS): + href = ref.get(f"{{{_THREDDS_NS['xlink']}}}href") or ref.get("href") + if not href: + continue + name = ( + ref.get(f"{{{_THREDDS_NS['xlink']}}}title") + or ref.get("name") + or ref.get("ID") + or href + ) + absolute = _absolute_catalog_url(href, catalog_url) + collection_path = _collection_path_from_catalog_url(absolute) + key = (name, absolute) + if key in seen: + continue + seen.add(key) + datasets.append( + { + "name": name, + "catalog_url": absolute, + "collection_path": collection_path, + "opendap_best_url": ( + _opendap_url_from_path(f"{collection_path}/Best") + if collection_path + else None + ), + } + ) + + if not datasets: + raise RuntimeError( + f"No NOAA/THREDDS dataset collections found in {catalog_url}." + ) + + return datasets + + +def list_noaa_dataset_identifiers( + model_or_path: str, + timeout: float = _DEFAULT_TIMEOUT_SECONDS, + include_aggregates: bool = True, +) -> list[str]: + """List dataset identifiers available inside a NOAA/NCEP collection. + + Parameters + ---------- + model_or_path : str + Shortcut (``\"GFS\"``, ``\"NAM\"``, …) or a THREDDS collection path. + timeout : float, optional + HTTP timeout in seconds. + include_aggregates : bool, optional + When ``True`` (default), include virtual aggregates such as ``Best`` + and ``TwoD`` alongside individual forecast-run identifiers. + + Returns + ------- + list of str + Identifiers suitable for :func:`build_noaa_opendap_url`. Forecast-run + filenames (``*.grib2``) are sorted newest-first when timestamps can be + parsed; aggregate names are appended afterward. + """ + catalog_url = collection_catalog_url(model_or_path) + root = _fetch_catalog_xml(catalog_url, timeout=timeout) + + runs = [] + aggregates = [] + + for element in root.iter(): + tag = _local_tag(element.tag) + if tag not in {"dataset", "catalogRef"}: + continue + + url_path = element.get("urlPath") + name = ( + element.get(f"{{{_THREDDS_NS['xlink']}}}title") + or element.get("name") + or element.get("ID") + or "" + ) + + if url_path in {None, "", "latest.xml"}: + continue + + identifier = url_path.rsplit("/", 1)[-1] + if identifier.lower().endswith(".grib2") or _RUN_TIMESTAMP_RE.search( + identifier + ): + runs.append(identifier) + elif include_aggregates and identifier in {"Best", "TwoD"}: + aggregates.append(identifier) + elif include_aggregates and name and "Best" in name and identifier: + aggregates.append(identifier) + + # Preserve order while dropping duplicates. + unique_runs = list(dict.fromkeys(runs)) + unique_runs.sort(key=_run_sort_key, reverse=True) + unique_aggregates = list(dict.fromkeys(aggregates)) + identifiers = unique_runs + unique_aggregates + + if not identifiers: + raise RuntimeError( + "No dataset identifiers found in NOAA/THREDDS collection catalog " + f"{catalog_url}." + ) + + return identifiers + + +def build_noaa_opendap_url(model_or_path: str, identifier: str) -> str: + """Build an OPeNDAP URL for a collection dataset identifier.""" + collection = resolve_noaa_collection_path(model_or_path) + return _opendap_url_from_path(f"{collection}/{identifier}") + + +def get_latest_noaa_dataset_identifier( + model_or_path: str, + timeout: float = _DEFAULT_TIMEOUT_SECONDS, +) -> str: + """Return the newest forecast-run identifier for a collection. + + Prefers THREDDS ``latest.xml`` when present. Falls back to the maximum + timestamp among listed ``*.grib2`` runs, then to the ``Best`` aggregate. + + Parameters + ---------- + model_or_path : str + Shortcut or collection path. + timeout : float, optional + HTTP timeout in seconds. + + Returns + ------- + str + Dataset identifier (for example + ``\"GFS_Global_0p25deg_20260810_1800.grib2\"`` or ``\"Best\"``). + """ + collection = resolve_noaa_collection_path(model_or_path) + latest_catalog_url = f"{THREDDS_CATALOG_ROOT}{collection}/latest.xml" + + try: + root = _fetch_catalog_xml(latest_catalog_url, timeout=timeout) + except RuntimeError: + root = None + + if root is not None: + for element in root.iter(): + if _local_tag(element.tag) != "dataset": + continue + url_path = element.get("urlPath") + name = element.get("name") + if url_path and url_path != "latest.xml": + return url_path.rsplit("/", 1)[-1] + if name and name.lower().endswith(".grib2"): + return name + + identifiers = list_noaa_dataset_identifiers( + model_or_path, timeout=timeout, include_aggregates=True + ) + for identifier in identifiers: + if identifier.lower().endswith(".grib2") or _RUN_TIMESTAMP_RE.search( + identifier + ): + return identifier + + if "Best" in identifiers: + return "Best" + + return identifiers[0] + + +def get_latest_noaa_opendap_url( + model_or_path: str = "GFS", + timeout: float = _DEFAULT_TIMEOUT_SECONDS, +) -> str: + """Resolve the OPeNDAP URL for the latest dataset in a collection.""" + identifier = get_latest_noaa_dataset_identifier(model_or_path, timeout=timeout) + return build_noaa_opendap_url(model_or_path, identifier) + + +def fetch_latest_noaa_dataset( + model_or_path: str = "GFS", + max_attempts: int = 10, + base_delay: float = 2, + timeout: float = _DEFAULT_TIMEOUT_SECONDS, +): + """Open the latest NOAA/NCEP dataset for a model via OPeNDAP. + + Parameters + ---------- + model_or_path : str, optional + Shortcut such as ``\"GFS\"`` (default) or a THREDDS collection path. + max_attempts : int, optional + Maximum netCDF4 open attempts. Default is 10. + base_delay : float, optional + Base exponential backoff delay in seconds. Default is 2. + timeout : float, optional + HTTP timeout used while resolving the catalog entry. + + Returns + ------- + netCDF4.Dataset + Open dataset handle. + + Raises + ------ + RuntimeError + If the catalog cannot be resolved or all open attempts fail. + """ + file_url = get_latest_noaa_opendap_url(model_or_path, timeout=timeout) + attempt_count = 0 + while attempt_count < max_attempts: + try: + return netCDF4.Dataset(file_url) + except OSError: + attempt_count += 1 + time.sleep(min(base_delay**attempt_count, MAX_RETRY_DELAY_SECONDS)) + + raise RuntimeError( + "Unable to load the latest NOAA/THREDDS weather dataset through " + file_url + ) diff --git a/rocketpy/environment/fetchers/open_meteo_fetcher.py b/rocketpy/environment/fetchers/open_meteo_fetcher.py new file mode 100644 index 000000000..52cd4ea42 --- /dev/null +++ b/rocketpy/environment/fetchers/open_meteo_fetcher.py @@ -0,0 +1,339 @@ +"""Fetch weather data from the Open-Meteo API. + +Open-Meteo (https://open-meteo.com/) serves pressure-level forecasts, past +forecasts and ensemble forecasts as plain JSON over HTTPS, with no API key and +no heavy NetCDF/OPeNDAP dependency. This module wraps the three endpoints +RocketPy needs and returns their raw JSON bodies. +""" + +import warnings +from datetime import datetime, timedelta, timezone + +import requests + +from rocketpy.environment.fetchers.base import logger +from rocketpy.tools import exponential_backoff + +OPEN_METEO_FORECAST_URL = "https://api.open-meteo.com/v1/forecast" +OPEN_METEO_HISTORICAL_URL = "https://historical-forecast-api.open-meteo.com/v1/forecast" +OPEN_METEO_ENSEMBLE_URL = "https://ensemble-api.open-meteo.com/v1/ensemble" +OPEN_METEO_TIMEOUT_SECONDS = 60 + +# Pressure levels (hPa) that Open-Meteo publishes for its pressure-level +# variables. Not every model resolves every level; levels that come back empty +# are dropped while parsing rather than requested conditionally, because the +# per-model coverage is not advertised by the API. +OPEN_METEO_PRESSURE_LEVELS = ( + 1000, + 975, + 950, + 925, + 900, + 850, + 800, + 700, + 600, + 500, + 400, + 300, + 250, + 200, + 150, + 100, + 70, + 50, + 30, +) + +# Per-level variables requested for every query. Open-Meteo reports wind as +# speed/direction rather than the u/v components RocketPy uses internally, so +# the conversion happens in the Environment parsing step. +OPEN_METEO_LEVEL_VARIABLES = ( + "temperature", + "geopotential_height", + "wind_speed", + "wind_direction", +) + +# Ensemble models known to publish the full set of pressure-level variables +# RocketPy needs (temperature, geopotential height and both wind fields). The +# other ensemble models answer with HTTP 200 but are unusable: gfs025, +# icon_global and bom_access_global_ensemble return nulls at every level, while +# gem_global serves temperature and geopotential height but no winds at all. +# Rejecting them up front avoids an opaque "no data" failure later on. +OPEN_METEO_ENSEMBLE_MODELS = ("gfs05", "ecmwf_ifs025") + +# Earliest date the historical-forecast archive covers at pressure levels. +# Earlier dates still answer with HTTP 200, but every value is null, so warning +# is the only way for the user to tell an unsupported date from bad weather +# data. Probed against the live API: 2021-03-15 comes back empty while +# 2021-03-23 is complete, so the cutoff sits between them. +# +# Note that Open-Meteo's ERA5 archive endpoint (archive-api.open-meteo.com) is +# *not* used here: it serves surface variables only, with no pressure-level +# data at all, so it cannot produce a vertical profile. +OPEN_METEO_HISTORICAL_START_DATE = datetime(2021, 4, 1, tzinfo=timezone.utc) + + +def build_hourly_variables(levels=OPEN_METEO_PRESSURE_LEVELS): + """Builds the comma-separated ``hourly`` query parameter for Open-Meteo. + + Parameters + ---------- + levels : sequence of int, optional + Pressure levels, in hPa, to request. Defaults to + :data:`OPEN_METEO_PRESSURE_LEVELS`. + + Returns + ------- + str + The value to pass as the ``hourly`` query parameter, e.g. + ``"temperature_1000hPa,geopotential_height_1000hPa,..."``. + + Examples + -------- + >>> from rocketpy.environment.fetchers.open_meteo_fetcher import ( + ... build_hourly_variables, + ... ) + >>> build_hourly_variables(levels=[500]) + 'temperature_500hPa,geopotential_height_500hPa,wind_speed_500hPa,wind_direction_500hPa' + """ + return ",".join( + f"{variable}_{level}hPa" + for level in levels + for variable in OPEN_METEO_LEVEL_VARIABLES + ) + + +@exponential_backoff(max_attempts=3, base_delay=1, max_delay=60) +def _get_json(url, params): + """Performs a single Open-Meteo GET request and returns its parsed body. + + Connection errors and server-side 5xx responses raise so the decorator + retries them. Client-side 4xx responses are definitive (a malformed query + or an out-of-range date) and are turned into an actionable error by the + caller instead of being retried. + """ + response = requests.get(url, params=params, timeout=OPEN_METEO_TIMEOUT_SECONDS) + if response.status_code >= 500: # pragma: no cover + response.raise_for_status() + return response + + +def _request(url, params, endpoint): + """Queries an Open-Meteo endpoint and returns its parsed JSON body. + + Parameters + ---------- + url : str + The endpoint address to query. + params : dict + Query parameters to send with the request. + endpoint : str + Human-readable endpoint name (e.g. ``"forecast"``), used in error + messages. + + Returns + ------- + dict + The parsed JSON body of the response. + + Raises + ------ + RuntimeError + If the endpoint cannot be reached, rejects the query, or returns a + malformed (non-JSON) body. + """ + try: + response = _get_json(url, params) + except requests.exceptions.RequestException as e: + raise RuntimeError( + f"Unable to reach the Open-Meteo {endpoint} API. Please try again later." + ) from e + + try: + payload = response.json() + except ValueError as e: + raise RuntimeError( + f"The Open-Meteo {endpoint} API returned a malformed (non-JSON) " + "response. Please try again later." + ) from e + + # Open-Meteo reports query errors as {"error": true, "reason": "..."}, + # which is far more specific than the bare status code. + if isinstance(payload, dict) and payload.get("error"): + raise RuntimeError( + f"The Open-Meteo {endpoint} API rejected the request: " + f"{payload.get('reason', 'no reason given')}" + ) + if not response.ok: # pragma: no cover + raise RuntimeError( + f"The Open-Meteo {endpoint} API request failed with status " + f"{response.status_code}." + ) + if "hourly" not in payload: + raise RuntimeError( + f"The Open-Meteo {endpoint} API response did not contain any hourly " + "data. Please try again later." + ) + + return payload + + +def fetch_open_meteo_forecast(latitude, longitude, model="best_match", date=None): + """Fetches a pressure-level forecast from the Open-Meteo API. + + Requests are routed to the historical-forecast endpoint when ``date`` lies + in the past, and to the regular forecast endpoint otherwise. + + Parameters + ---------- + latitude : float + The latitude of the location, in degrees. + longitude : float + The longitude of the location, in degrees. + model : str, optional + The Open-Meteo weather model to query, such as ``"best_match"`` (the + default), ``"gfs_seamless"``, ``"ecmwf_ifs025"`` or ``"icon_seamless"``. + See https://open-meteo.com/en/docs for the full list. + date : datetime.datetime, optional + The launch date and time. Used to pick the endpoint and, for past + dates, to bound the queried period. When None, the regular forecast + endpoint is queried. + + Returns + ------- + dict + The parsed JSON body returned by the API. + + Raises + ------ + RuntimeError + If the API cannot be reached or returns no usable data. + """ + params = { + "latitude": latitude, + "longitude": longitude, + "hourly": build_hourly_variables(), + "models": model, + "wind_speed_unit": "ms", + "timeformat": "unixtime", + "timezone": "UTC", + "cell_selection": "nearest", + } + + if _is_past_date(date): + _warn_if_before_archive_start(date) + # The archive is indexed by calendar day, so a one-day pad on each side + # guarantees the launch hour is inside the returned range regardless of + # the local-time offset. + params["start_date"] = (date - timedelta(days=1)).strftime("%Y-%m-%d") + params["end_date"] = (date + timedelta(days=1)).strftime("%Y-%m-%d") + logger.info( + "Launch date %s is in the past; querying the Open-Meteo " + "historical-forecast API.", + date, + ) + return _request(OPEN_METEO_HISTORICAL_URL, params, "historical forecast") + + return _request(OPEN_METEO_FORECAST_URL, params, "forecast") + + +def fetch_open_meteo_ensemble(latitude, longitude, model="gfs05", date=None): + """Fetches a pressure-level ensemble forecast from the Open-Meteo API. + + Parameters + ---------- + latitude : float + The latitude of the location, in degrees. + longitude : float + The longitude of the location, in degrees. + model : str, optional + The Open-Meteo ensemble model to query. Default is ``"gfs05"``. Only + the models in :data:`OPEN_METEO_ENSEMBLE_MODELS` publish complete + pressure-level data. + date : datetime.datetime, optional + The launch date and time. Past dates are queried against the + historical-forecast window of the ensemble endpoint. + + Returns + ------- + dict + The parsed JSON body returned by the API. + + Raises + ------ + ValueError + If ``model`` is not known to publish complete pressure-level data. + RuntimeError + If the API cannot be reached or returns no usable data. + """ + if model not in OPEN_METEO_ENSEMBLE_MODELS: + raise ValueError( + f"Invalid Open-Meteo ensemble model '{model}'. Only " + f"{' and '.join(OPEN_METEO_ENSEMBLE_MODELS)} publish the complete " + "set of pressure-level variables (temperature, geopotential height " + "and winds) that RocketPy requires to build an atmospheric profile." + ) + + params = { + "latitude": latitude, + "longitude": longitude, + "hourly": build_hourly_variables(), + "models": model, + "wind_speed_unit": "ms", + "timeformat": "unixtime", + "timezone": "UTC", + "cell_selection": "nearest", + } + + if _is_past_date(date): + params["start_date"] = (date - timedelta(days=1)).strftime("%Y-%m-%d") + params["end_date"] = (date + timedelta(days=1)).strftime("%Y-%m-%d") + + return _request(OPEN_METEO_ENSEMBLE_URL, params, "ensemble") + + +def _is_past_date(date): + """Returns True when ``date`` is far enough in the past that the regular + forecast endpoint would no longer cover it. + + Open-Meteo's forecast endpoint keeps a couple of past days available, so + only dates before that window need the historical-forecast archive. + """ + if date is None: + return False + return _as_utc(date) < _utc_now() - timedelta(days=1) + + +def _as_utc(date): + """Returns ``date`` as an aware UTC datetime, assuming UTC when naive.""" + return date if date.tzinfo is not None else date.replace(tzinfo=timezone.utc) + + +def _warn_if_before_archive_start(date): + """Warns when ``date`` precedes the historical archive coverage. + + Open-Meteo answers such requests with HTTP 200 and null values at every + pressure level, so without this warning the user would only see a generic + "not enough usable pressure levels" error and no hint that the date itself + is the problem. + """ + if _as_utc(date) >= OPEN_METEO_HISTORICAL_START_DATE: + return + + warnings.warn( + f"The requested launch date ({date:%Y-%m-%d}) precedes Open-Meteo's " + "historical-forecast archive, which starts around " + f"{OPEN_METEO_HISTORICAL_START_DATE:%B %Y}. The API will most likely " + "return no pressure-level data for it. Consider using the 'reanalysis' " + "or 'wyoming_sounding' atmospheric models for earlier dates.", + UserWarning, + stacklevel=3, + ) + + +def _utc_now(): + """Returns the current UTC time. Wrapped in a helper so tests can patch it.""" + + return datetime.now(timezone.utc) diff --git a/rocketpy/environment/fetchers.py b/rocketpy/environment/fetchers/opendap_fetchers.py similarity index 67% rename from rocketpy/environment/fetchers.py rename to rocketpy/environment/fetchers/opendap_fetchers.py index 740e9818a..270c0d910 100644 --- a/rocketpy/environment/fetchers.py +++ b/rocketpy/environment/fetchers/opendap_fetchers.py @@ -1,103 +1,13 @@ -"""This module contains auxiliary functions for fetching data from various -third-party APIs. As this is a recent module (introduced in v1.2.0), some -functions may be changed without notice in future feature releases. -""" +"""Fetch weather datasets using OPeNDAP protocol (NOAA, UCAR, CMC, GEFS).""" -import logging -import re import time from datetime import datetime, timedelta, timezone import netCDF4 -import requests +from rocketpy.environment.fetchers.base import MAX_RETRY_DELAY_SECONDS from rocketpy.tools import exponential_backoff -logger = logging.getLogger(__name__) - -MAX_RETRY_DELAY_SECONDS = 600 - - -@exponential_backoff(max_attempts=3, base_delay=1, max_delay=60) -def fetch_open_elevation(lat, lon): - """Fetches elevation data from the Open-Elevation API at a given latitude - and longitude. - - Parameters - ---------- - lat : float - The latitude of the location. - lon : float - The longitude of the location. - - Returns - ------- - float - The elevation at the given latitude and longitude in meters. - - Raises - ------ - RuntimeError - If there is a problem reaching the Open-Elevation API servers. - """ - logger.debug( - "Fetching elevation from open-elevation.com for lat=%s, lon=%s", lat, lon - ) - request_url = f"https://api.open-elevation.com/api/v1/lookup?locations={lat},{lon}" - try: - response = requests.get(request_url) - results = response.json()["results"] - return results[0]["elevation"] - except ( - requests.exceptions.RequestException, - requests.exceptions.JSONDecodeError, - ) as e: - raise RuntimeError("Unable to reach Open-Elevation API servers.") from e - - -@exponential_backoff(max_attempts=5, base_delay=2, max_delay=60) -def fetch_atmospheric_data_from_windy(lat, lon, model): - """Fetches atmospheric data from Windy.com API for a given latitude and - longitude, using a specific model. - - Parameters - ---------- - lat : float - The latitude of the location. - lon : float - The longitude of the location. - model : str - The atmospheric model to use. Options are: ecmwf, GFS, ICON or ICONEU. - - Returns - ------- - dict - A dictionary containing the atmospheric data retrieved from the API. - """ - model = model.lower() - if model[-1] == "u": # case iconEu - model = "".join([model[:4], model[4].upper(), model[5:]]) - - url = ( - f"https://node.windy.com/forecast/meteogram/{model}/{lat}/{lon}/?step=undefined" - ) - - try: - response = requests.get(url).json() - if "data" not in response.keys(): # pragma: no cover - raise ValueError( - f"Could not get a valid response for '{model}' from Windy. " - "Check if the coordinates are set inside the model's domain." - ) - except requests.exceptions.RequestException as e: # pragma: no cover - if model == "iconEu": - raise ValueError( - "Could not get a valid response for Icon-EU from Windy. " - "Check if the coordinates are set inside Europe." - ) from e - - return response - def fetch_gfs_file_return_dataset(max_attempts=10, base_delay=2): """Fetches the latest GFS (Global Forecast System) dataset from the UCAR @@ -293,13 +203,12 @@ def fetch_hiresw_file_return_dataset(max_attempts=10, base_delay=2): RuntimeError If unable to load the latest weather data for HiResW. """ - # Attempt to get latest forecast time_attempt = datetime.now(tz=timezone.utc) attempt_count = 0 dataset = None today = datetime.now(tz=timezone.utc) - date_info = (today.year, today.month, today.day, 12) # Hour given in UTC time + date_info = (today.year, today.month, today.day, 12) while attempt_count < max_attempts: time_attempt -= timedelta(hours=12) @@ -308,14 +217,13 @@ def fetch_hiresw_file_return_dataset(max_attempts=10, base_delay=2): time_attempt.month, time_attempt.day, 12, - ) # Hour given in UTC time + ) date_string = f"{date_info[0]:04d}{date_info[1]:02d}{date_info[2]:02d}" file = ( f"https://nomads.ncep.noaa.gov/dods/hiresw/hiresw{date_string}/" "hiresw_conusarw_12z" ) try: - # Attempts to create a dataset from the file using OpenDAP protocol. dataset = netCDF4.Dataset(file) return dataset except OSError: @@ -328,45 +236,6 @@ def fetch_hiresw_file_return_dataset(max_attempts=10, base_delay=2): ) -@exponential_backoff(max_attempts=5, base_delay=2, max_delay=60) -def fetch_wyoming_sounding(file): - """Fetches sounding data from a specified file using the Wyoming Weather - Web. - - Parameters - ---------- - file : str - The URL of the file to fetch. - - Returns - ------- - str - The content of the fetched file. - - Raises - ------ - ImportError - If unable to load the specified file. - ValueError - If the response indicates the specified station or date is invalid. - ValueError - If the response indicates the output format is invalid. - """ - response = requests.get(file) - if response.status_code != 200: # pragma: no cover - raise ImportError(f"Unable to load {file}.") - if len(re.findall("Can't get .+ Observations at", response.text)): - raise ValueError( - re.findall("Can't get .+ Observations at .+", response.text)[0] - + " Check station number and date." - ) - if response.text == "Invalid OUTPUT: specified\n": - raise ValueError( - "Invalid OUTPUT: specified. Make sure the output is Text: List." - ) - return response - - @exponential_backoff(max_attempts=5, base_delay=2, max_delay=60) def fetch_gefs_ensemble(): """Fetches the latest GEFS (Global Ensemble Forecast System) dataset from @@ -386,7 +255,7 @@ def fetch_gefs_ensemble(): success = False attempt_count = 0 while not success and attempt_count < 10: - time_attempt -= timedelta(hours=6 * attempt_count) # GEFS updates every 6 hours + time_attempt -= timedelta(hours=6 * attempt_count) file = ( f"https://nomads.ncep.noaa.gov/dods/gens_bc/gens" f"{time_attempt.year:04d}{time_attempt.month:02d}" @@ -421,14 +290,11 @@ def fetch_cmc_ensemble(): RuntimeError If unable to load the latest weather data for CMC. """ - # Attempt to get latest forecast time_attempt = datetime.now(tz=timezone.utc) success = False attempt_count = 0 while not success and attempt_count < 10: - time_attempt -= timedelta( - hours=12 * attempt_count - ) # CMC updates every 12 hours + time_attempt -= timedelta(hours=12 * attempt_count) file = ( f"https://nomads.ncep.noaa.gov/dods/cmcens/" f"cmcens{time_attempt.year:04d}{time_attempt.month:02d}" diff --git a/rocketpy/environment/fetchers/windy_fetcher.py b/rocketpy/environment/fetchers/windy_fetcher.py new file mode 100644 index 000000000..ddb35bfb6 --- /dev/null +++ b/rocketpy/environment/fetchers/windy_fetcher.py @@ -0,0 +1,49 @@ +"""Fetch weather data from Windy.com API.""" + +import requests + +from rocketpy.tools import exponential_backoff + + +@exponential_backoff(max_attempts=5, base_delay=2, max_delay=60) +def fetch_atmospheric_data_from_windy(lat, lon, model): + """Fetches atmospheric data from Windy.com API for a given latitude and + longitude, using a specific model. + + Parameters + ---------- + lat : float + The latitude of the location. + lon : float + The longitude of the location. + model : str + The atmospheric model to use. Options are: ecmwf, GFS, ICON or ICONEU. + + Returns + ------- + dict + A dictionary containing the atmospheric data retrieved from the API. + """ + model = model.lower() + if model[-1] == "u": # case iconEu + model = "".join([model[:4], model[4].upper(), model[5:]]) + + url = ( + f"https://node.windy.com/forecast/meteogram/{model}/{lat}/{lon}/?step=undefined" + ) + + try: + response = requests.get(url).json() + if "data" not in response.keys(): # pragma: no cover + raise ValueError( + f"Could not get a valid response for '{model}' from Windy. " + "Check if the coordinates are set inside the model's domain." + ) + except requests.exceptions.RequestException as e: # pragma: no cover + if model == "iconEu": + raise ValueError( + "Could not get a valid response for Icon-EU from Windy. " + "Check if the coordinates are set inside Europe." + ) from e + + return response diff --git a/rocketpy/environment/fetchers/wyoming_fetcher.py b/rocketpy/environment/fetchers/wyoming_fetcher.py new file mode 100644 index 000000000..e7a4fb784 --- /dev/null +++ b/rocketpy/environment/fetchers/wyoming_fetcher.py @@ -0,0 +1,46 @@ +"""Fetch upper air sounding data from Wyoming Weather Web.""" + +import re + +import requests + +from rocketpy.tools import exponential_backoff + + +@exponential_backoff(max_attempts=5, base_delay=2, max_delay=60) +def fetch_wyoming_sounding(file): + """Fetches sounding data from a specified file using the Wyoming Weather + Web. + + Parameters + ---------- + file : str + The URL of the file to fetch. + + Returns + ------- + str + The content of the fetched file. + + Raises + ------ + ImportError + If unable to load the specified file. + ValueError + If the response indicates the specified station or date is invalid. + ValueError + If the response indicates the output format is invalid. + """ + response = requests.get(file) + if response.status_code != 200: # pragma: no cover + raise ImportError(f"Unable to load {file}.") + if len(re.findall("Can't get .+ Observations at", response.text)): + raise ValueError( + re.findall("Can't get .+ Observations at .+", response.text)[0] + + " Check station number and date." + ) + if response.text == "Invalid OUTPUT: specified\n": + raise ValueError( + "Invalid OUTPUT: specified. Make sure the output is Text: List." + ) + return response diff --git a/rocketpy/environment/tools.py b/rocketpy/environment/tools.py index 37425b41c..c681d4d39 100644 --- a/rocketpy/environment/tools.py +++ b/rocketpy/environment/tools.py @@ -130,6 +130,63 @@ def calculate_wind_speed(u, v, w=0.0): return np.sqrt(u**2 + v**2 + w**2) +def convert_wind_speed_direction_to_components(wind_speed, wind_direction): + """Converts meteorological wind speed and direction to u and v components. + + Meteorological wind direction is the direction the wind blows *from*, + measured clockwise from true north, which is the convention used by most + weather APIs. The returned components follow the RocketPy convention: u + points East and v points North, both describing where the wind blows *to*. + + Parameters + ---------- + wind_speed : float, numpy.ndarray + The wind speed in m/s. + wind_direction : float, numpy.ndarray + The direction the wind is coming from, in degrees clockwise from true + north (0 to 360). + + Returns + ------- + tuple of (float, float) or (numpy.ndarray, numpy.ndarray) + The u (East) and v (North) components of the wind, in m/s. + + Examples + -------- + >>> import numpy as np + >>> from rocketpy.environment.tools import ( + ... convert_wind_speed_direction_to_components, + ... ) + + A wind coming from the north blows towards the south, so v is negative: + + >>> u, v = convert_wind_speed_direction_to_components(10, 0) + >>> float(np.round(u, 6) + 0.0), float(np.round(v, 6)) + (0.0, -10.0) + + A wind coming from the west blows towards the east, so u is positive: + + >>> u, v = convert_wind_speed_direction_to_components(10, 270) + >>> float(np.round(u, 6)), float(np.round(v, 6) + 0.0) + (10.0, 0.0) + + The conversion round-trips with :func:`calculate_wind_heading` and + :func:`convert_wind_heading_to_direction`: + + >>> u, v = convert_wind_speed_direction_to_components(7.5, 135) + >>> float(np.round(calculate_wind_speed(u, v), 6)) + 7.5 + >>> float(np.round(convert_wind_heading_to_direction( + ... calculate_wind_heading(u, v)), 6)) + 135.0 + """ + direction_rad = np.radians(wind_direction) + return ( + -wind_speed * np.sin(direction_rad), + -wind_speed * np.cos(direction_rad), + ) + + def geodesic_to_lambert_conformal(lat, lon, projection_variable, x_units="m"): """Convert geodesic coordinates to Lambert conformal projected coordinates. @@ -673,8 +730,12 @@ def get_interval_date_from_time_array(time_array, units=None): Returns ------- int - The interval in hours between two times in the time array. + The interval in hours between times in the array, or 0 when the array + contains a single time. """ + if len(time_array) < 2: + return 0 + units = units or time_array.units return netCDF4.num2date( (time_array[-1] - time_array[0]) / (len(time_array) - 1), diff --git a/rocketpy/motors/motor.py b/rocketpy/motors/motor.py index 9ecb001dd..f27664ec6 100644 --- a/rocketpy/motors/motor.py +++ b/rocketpy/motors/motor.py @@ -1386,7 +1386,6 @@ class GenericMotor(Motor): therefore for more accurate results, use the ``SolidMotor``, ``HybridMotor`` or ``LiquidMotor`` classes.""" - # pylint: disable=too-many-arguments def __init__( self, thrust_source, diff --git a/rocketpy/motors/solid_motor.py b/rocketpy/motors/solid_motor.py index 6d1582109..a4d1c182b 100644 --- a/rocketpy/motors/solid_motor.py +++ b/rocketpy/motors/solid_motor.py @@ -196,6 +196,10 @@ class SolidMotor(Motor): SolidMotor.only_radial_burn : bool If True, grain regression is restricted to radial burn only (inner radius growth). Grain length remains constant throughout the burn. Default is False. + SolidMotor.grains_bonded : bool + If True (default), grain axial positions stay fixed at the assembled layout + (BATES bonded/glued grains). If False, a first-order packing model shifts the + propellant center of mass toward the nozzle as grain height regresses. """ # pylint: disable=too-many-arguments @@ -221,6 +225,7 @@ def __init__( coordinate_system_orientation="nozzle_to_combustion_chamber", reference_pressure=None, only_radial_burn=False, + grains_bonded=True, ): """Initialize Motor class, process thrust curve and geometrical parameters and store results. @@ -323,6 +328,24 @@ class Function. Thrust units are Newtons. radial burn. If False, allows the grain to also burn axially. May be useful for axially inhibited grains or hybrid motors. Default is False. + grains_bonded : bool, optional + If True (default), grains keep fixed axial positions about + ``grains_center_of_mass_position`` (bonded / BATES-style assembly). + If False, grains are treated as freestanding and packed against the + nozzle-side (aft) face of the initial grain stack once acceleration + settles them. The propellant CM then moves toward the nozzle as + ``grain_height`` regresses: + + ``CM(t) = grains_center_of_mass_position + - _csys * (grain_number / 2) * (grain_initial_height - grain_height(t))``. + + This is a first-order inertial packing model: it does not integrate + grain rigid-body dynamics, friction, DEM contacts, or discontinuous + rattling. Inter-grain ``grain_separation`` (e.g. spacers) is kept + while the stack shortens from grain-height loss only. With + ``only_radial_burn=True``, height is constant so the CM does not + shift. Follow-ups may add acceleration-dependent settling or a + full multi-body grain dynamics model. Returns ------- @@ -356,6 +379,7 @@ class Function. Thrust units are Newtons. self.grain_outer_radius = grain_outer_radius self.grain_initial_inner_radius = grain_initial_inner_radius self.grain_initial_height = grain_initial_height + self.grains_bonded = grains_bonded # Grains initial geometrical parameters self.grain_initial_volume = ( @@ -478,10 +502,47 @@ def center_of_propellant_mass(self): ------- Function Position of the propellant center of mass as a function of time. + + Notes + ----- + When ``grains_bonded`` is True, the CM stays at + ``grains_center_of_mass_position`` (fixed grain layout). + + When ``grains_bonded`` is False, grains are packed against the aft + (nozzle-side) face of the initial grain stack. As grain height + regresses, the packed stack shortens and the CM shifts toward the + nozzle by ``(grain_number / 2) * (grain_initial_height - + grain_height(t))`` along the motor axis (signed by ``_csys``). + This is a first-order packing model, not a discrete-element + simulation of grain motion. """ - time_source = self.grain_inner_radius.x_array - center_of_mass = np.full_like(time_source, self.grains_center_of_mass_position) - return np.column_stack((time_source, center_of_mass)) + if self.grains_bonded: + time_source = self.grain_inner_radius.x_array + center_of_mass = np.full_like( + time_source, self.grains_center_of_mass_position + ) + return np.column_stack((time_source, center_of_mass)) + + # First-order packing: fixed aft face, stack shortens with grain height. + return self.grains_center_of_mass_position - self._csys * ( + self.grain_number / 2.0 + ) * (self.grain_initial_height - self.grain_height) + + def _grain_pitch_squared_sum(self): + """Return ``pitch**2 * sum(index_offsets**2)`` for parallel-axis inertia. + + Bonded grains use fixed initial pitch; unbonded grains use the + instantaneous packed pitch ``grain_height + grain_separation``. + """ + grain_number = self.grain_number + initial_value = (grain_number - 1) / 2.0 + index_offsets = np.linspace(-initial_value, initial_value, grain_number) + sum_sq_index = float(np.sum(index_offsets**2)) + if self.grains_bonded: + pitch = self.grain_initial_height + self.grain_separation + return (pitch**2) * sum_sq_index + pitch = self.grain_height + self.grain_separation + return (pitch**2) * sum_sq_index # pylint: disable=too-many-statements def evaluate_geometry(self): @@ -728,14 +789,12 @@ def propellant_I_11(self): + (1 / 12) * self.grain_height**2 ) - # Calculate each grain's distance d to propellant center of mass - # Assuming each grain's COM are evenly spaced - initial_value = (grain_number - 1) / 2 - d = np.linspace(-initial_value, initial_value, grain_number) - d = d * (self.grain_initial_height + self.grain_separation) - - # Calculate inertia for all grains - I_11 = grain_number * grain_inertia11 + grain_mass * np.sum(d**2) + # Parallel-axis term from grain COM offsets about the propellant COM. + # Bonded: fixed initial pitch. Unbonded: packed pitch tracks grain_height. + I_11 = ( + grain_number * grain_inertia11 + + grain_mass * self._grain_pitch_squared_sum() + ) return I_11 @@ -831,6 +890,7 @@ def to_dict(self, **kwargs): "grain_separation": self.grain_separation, "grains_center_of_mass_position": self.grains_center_of_mass_position, "only_radial_burn": self.only_radial_burn, + "grains_bonded": self.grains_bonded, } ) @@ -881,4 +941,5 @@ def from_dict(cls, data): coordinate_system_orientation=data["coordinate_system_orientation"], reference_pressure=data.get("reference_pressure"), only_radial_burn=data.get("only_radial_burn", False), + grains_bonded=data.get("grains_bonded", True), ) diff --git a/rocketpy/motors/tank.py b/rocketpy/motors/tank.py index 0d6b329a4..a87b2c40c 100644 --- a/rocketpy/motors/tank.py +++ b/rocketpy/motors/tank.py @@ -9,6 +9,54 @@ from ..tools import tuple_handler +def _compose_clipped(outer, inner): + """Compose ``outer(inner(t))`` after clipping ``inner``'s values to + ``outer``'s valid domain. + + This guards against ``Function.compose`` raising a spurious + ``ValueError`` when ``inner``'s values fall just outside ``outer``'s + domain by a floating-point roundoff amount (e.g. ``-1e-17`` instead of + exactly ``0`` at the instant a tank is exactly empty or exactly full). + Physically meaningful over/underfill conditions are still caught + downstream by ``Tank``'s own volume bounds checks, which raise + independently of this composition; this only absorbs numerical noise at + the domain boundary. + + Parameters + ---------- + outer : Function + The function being composed into (e.g. ``inverse_volume``). + inner : Function + The function supplying input values (e.g. a computed volume or + height curve), whose values may be marginally outside ``outer``'s + domain due to floating-point roundoff. + + Returns + ------- + Function + The composed function, i.e. ``outer(inner(t))``. + """ + # Clipping reads x_array / y_array and the domain bounds, which only exist + # for array-sourced Functions. ``Function.compose`` handles the callable + # case on its own (and performs no bounds check there, so there is no + # spurious error to absorb), so defer to it rather than raising + # AttributeError. + if not (outer.is_array_source() and inner.is_array_source()): + return outer.compose(inner) + + domain_min = outer.x_initial + domain_max = outer.x_final + clipped_source = np.column_stack( + [inner.x_array, np.clip(inner.y_array, domain_min, domain_max)] + ) + clipped_inner = Function( + clipped_source, + inputs=inner.__inputs__, + outputs=inner.__outputs__, + ) + return outer.compose(clipped_inner) + + class Tank(ABC): """Abstract Tank class that defines a tank object for a rocket motor, so that it evaluates useful properties of the tank and its fluids, such as @@ -824,7 +872,7 @@ def liquid_mass(self): datapoints=self.discretize ) liquid_mass = self.initial_liquid_mass + liquid_flow - if (liquid_mass < 0).any(): + if (liquid_mass < -1e-6).any(): # -1e-6 is to avoid numerical errors raise ValueError( f"The tank {self.name} is underfilled. " + "The liquid mass is negative given the mass flow rates.\n\t\t" @@ -952,7 +1000,9 @@ def liquid_height(self): Function Height of the ullage as a function of time. """ - liquid_height = self.geometry.inverse_volume.compose(self.liquid_volume) + liquid_height = _compose_clipped( + self.geometry.inverse_volume, self.liquid_volume + ) diff_bt = liquid_height - self.geometry.bottom diff_up = liquid_height - self.geometry.top @@ -990,7 +1040,7 @@ def gas_height(self): Height of the ullage as a function of time. """ fluid_volume = self.gas_volume + self.liquid_volume - gas_height = self.geometry.inverse_volume.compose(fluid_volume) + gas_height = _compose_clipped(self.geometry.inverse_volume, fluid_volume) diff = gas_height - self.geometry.top if (diff > 0).any(): raise ValueError( @@ -1251,7 +1301,7 @@ def liquid_height(self): Function Height of the ullage as a function of time. """ - return self.geometry.inverse_volume.compose(self.liquid_volume) + return _compose_clipped(self.geometry.inverse_volume, self.liquid_volume) @funcify_method("Time (s)", "Gas Height (m)", "linear") def gas_height(self): @@ -1445,7 +1495,7 @@ def liquid_volume(self): Function Volume of the liquid as a function of time. """ - return self.geometry.volume.compose(self.liquid_height) + return _compose_clipped(self.geometry.volume, self.liquid_height) @funcify_method("Time (s)", "Gas Volume (m³)") def gas_volume(self): @@ -1754,7 +1804,9 @@ def liquid_height(self): Function Height of the ullage as a function of time. """ - liquid_height = self.geometry.inverse_volume.compose(self.liquid_volume) + liquid_height = _compose_clipped( + self.geometry.inverse_volume, self.liquid_volume + ) diff_bt = liquid_height - self.geometry.bottom diff_up = liquid_height - self.geometry.top @@ -1790,7 +1842,7 @@ def gas_height(self): Height of the ullage as a function of time. """ fluid_volume = self.gas_volume + self.liquid_volume - gas_height = self.geometry.inverse_volume.compose(fluid_volume) + gas_height = _compose_clipped(self.geometry.inverse_volume, fluid_volume) diff = gas_height - self.geometry.top if (diff > 0).any(): raise ValueError( diff --git a/rocketpy/plots/aero_surface_plots.py b/rocketpy/plots/aero_surface_plots.py index eb97ce19b..a9f7b3a06 100644 --- a/rocketpy/plots/aero_surface_plots.py +++ b/rocketpy/plots/aero_surface_plots.py @@ -842,6 +842,41 @@ def draw(self, *, filename=None): show_or_save_plot(filename) +class _TubeFinsPlots(_AeroSurfacePlots): + """Class that contains all tube-fin plots.""" + + def draw(self, *, filename=None): + """Draw a side-view envelope of the tube-fin set.""" + axial, radial = self.aero_surface.shape_vec + _, ax = plt.subplots() + + ax.plot(axial, radial, color="#A60628", label="Tube-fin envelope") + ax.plot(axial, -radial, color="#A60628") + ax.plot( + [0, self.aero_surface.length], + [0, 0], + color="#7A68A6", + linestyle="--", + label="Rocket centerline", + ) + + cp_point = (self.aero_surface.cpz, 0) + ax.scatter(*cp_point, label="Center of Pressure", color="red", zorder=10) + ax.scatter(*cp_point, facecolors="none", edgecolors="red", s=300, zorder=10) + + limit = self.aero_surface.rocket_radius + 2 * self.aero_surface.outer_radius + ax.set_xlim(-0.02 * self.aero_surface.length, 1.02 * self.aero_surface.length) + ax.set_ylim(-1.05 * limit, 1.05 * limit) + ax.set_aspect("equal") + ax.set_xlabel("Length (m)") + ax.set_ylabel("Radius (m)") + ax.set_title("Tube Fin Set Side-View Envelope") + ax.grid(True, linestyle="--", linewidth=0.5) + ax.legend(bbox_to_anchor=(1.05, 1.0), loc="upper left") + plt.tight_layout() + show_or_save_plot(filename) + + class _TailPlots(_AeroSurfacePlots): """Class that contains all tail plots.""" diff --git a/rocketpy/plots/datcom_plots.py b/rocketpy/plots/datcom_plots.py new file mode 100644 index 000000000..446093158 --- /dev/null +++ b/rocketpy/plots/datcom_plots.py @@ -0,0 +1,50 @@ +"""Geometry projections for DATCOM-specific panel placement.""" + +import numpy as np + +from rocketpy.rocket.aero_surface.nose_cone import NoseCone + + +def draw_datcom_fins(surface, ax, position, drawn_surfaces, vis_args, plane, csys): + """Draw a true-view panel outline at PHIF/GAM and fixed incidence. + + The incidence hinge is DATCOM's default half-root-chord line; SKEW is zero. + Root attachment radius is set from the assembled body during preparation. + """ + aft = np.asarray(surface.shape_vec[0]) + span = np.asarray(surface.shape_vec[1]) + radius = getattr(surface, "_datcom_attachment_radius", surface.rocket_radius) + hinge = surface.root_chord / 2 + component = 0 if plane == "xz" else 1 + for phi, gam, delta in zip( + surface.panel_angles, surface.dihedral, surface.datcom_incidence + ): + phi, gam, delta = np.radians([phi, gam, delta]) + root = radius * np.array([np.sin(phi), np.cos(phi)]) + direction = np.array([np.sin(phi + gam), np.cos(phi + gam)]) + tangent = np.array([np.cos(phi + gam), -np.sin(phi + gam)]) + transverse = root[:, None] + direction[:, None] * span + transverse -= tangent[:, None] * ((aft - hinge) * np.sin(delta)) + axial = position - csys * (hinge + (aft - hinge) * np.cos(delta)) + x = np.append(axial, axial[0]) + y = np.append(transverse[component], transverse[component, 0]) + ax.plot(x, y, color=vis_args["fins"], linewidth=vis_args["line_width"]) + body = getattr(surface, "_datcom_drawing_body", None) + if body is not None: + # Fins are overlays, not diameter transitions. In particular a fin on + # the boattail must not introduce a fictitious cylindrical tube there. + if not body["has_tail"]: + for index, (component, origin, body_radius, end) in enumerate( + drawn_surfaces + ): + if isinstance(component, NoseCone): + drawn_surfaces[index] = ( + component, + origin, + body_radius, + body["end"], + ) + return + drawn_surfaces.append( + (surface, position, radius, position - csys * surface.root_chord) + ) diff --git a/rocketpy/plots/environment_plots.py b/rocketpy/plots/environment_plots.py index add5e4efb..5dbda8b2a 100644 --- a/rocketpy/plots/environment_plots.py +++ b/rocketpy/plots/environment_plots.py @@ -434,6 +434,9 @@ def all(self): self.atmospheric_model() # Plot ensemble member comparison - if self.environment.atmospheric_model_type == "Ensemble": + if self.environment.atmospheric_model_type.lower() in ( + "ensemble", + "open_meteo_ensemble", + ): print("\n\nEnsemble Members Comparison") self.ensemble_member_comparison() diff --git a/rocketpy/plots/flight_plots.py b/rocketpy/plots/flight_plots.py index 6dd5e8802..bbff706c5 100644 --- a/rocketpy/plots/flight_plots.py +++ b/rocketpy/plots/flight_plots.py @@ -1,5 +1,6 @@ # pylint: disable=too-many-lines +import html import logging import os import time @@ -144,6 +145,250 @@ def trajectory_3d(self, *, filename=None): # pylint: disable=too-many-statement ax1.set_box_aspect(None, zoom=0.95) # 95% for label adjustment show_or_save_plot(filename) + # Background tile layers offered on every trajectory map. OpenStreetMap + # gives readable roads and place names; the Esri imagery layer is what + # actually matters for a rocket, since recovery fields, tree lines and + # water are only visible on satellite imagery. + _MAP_TILE_LAYERS = ( + { + "tiles": "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png", + "attr": "OpenStreetMap", + "name": "OpenStreetMap", + }, + { + "tiles": ( + "https://server.arcgisonline.com/ArcGIS/rest/services/" + "World_Imagery/MapServer/tile/{z}/{y}/{x}.png" + ), + "attr": ( + "Tiles © Esri — Source: Esri, i-cubed, USDA, USGS, " + "AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the " + "GIS User Community" + ), + "name": "Esri Satellite", + }, + ) + + def trajectory_on_map( + self, + *, + filename=None, + time_step=None, + color="#1f77b4", + safety_radii=None, + title=None, + ): + """Create an interactive Folium map of the flight ground track. + + Draws the ground track from ``flight.latitude`` / ``flight.longitude`` + over selectable OpenStreetMap and satellite imagery layers, and marks + the launch site, the apogee ground position and the landing site. + Requires the optional ``folium`` dependency + (``pip install folium`` or ``pip install rocketpy[maps]``). + + Parameters + ---------- + filename : str, optional + Path to save the map as a self-contained HTML file. If None, the + map is not written to disk. Default is None. + time_step : float, optional + Time step, in seconds, used to sample the trajectory. If None, all + integration time steps are used. Otherwise the ground track is + resampled by linear interpolation, which keeps the HTML file small + for long flights. Default is None. + color : str, optional + Color of the ground track, as any CSS color string. Default is + ``"#1f77b4"``. + safety_radii : Sequence[float], optional + Radii, in meters, of circles drawn around the launch site. Useful + to check the trajectory against range safety limits, e.g. + ``[2500, 5000, 10000]``. If None, no circles are drawn. Default is + None. + title : str, optional + Title rendered as an overlay on top of the map. If None, no title + is drawn. Default is None. + + Returns + ------- + folium.Map + The interactive map object. In Jupyter, displaying the return + value renders the map. + + Raises + ------ + ValueError + If the flight has no latitude/longitude samples to plot. + + Examples + -------- + >>> flight.plots.trajectory_on_map( # doctest: +SKIP + ... filename="trajectory.html", + ... safety_radii=[2500, 5000], + ... title="Flight 01", + ... ) + """ + folium = import_optional_dependency("folium") + + latitudes, longitudes = self.__sample_ground_track(time_step) + path = list(zip(latitudes.tolist(), longitudes.tolist())) + if not path: + raise ValueError("Flight has no latitude/longitude samples to plot.") + + launch, landing = path[0], path[-1] + center = [ + float(0.5 * (launch[0] + landing[0])), + float(0.5 * (launch[1] + landing[1])), + ] + + # tiles=None so that the two layers below are the only backgrounds and + # both show up in the layer control. + flight_map = folium.Map( + location=center, zoom_start=13, tiles=None, control_scale=True + ) + for layer in self._MAP_TILE_LAYERS: + folium.TileLayer(control=True, **layer).add_to(flight_map) + + folium.PolyLine( + locations=path, + color=color, + weight=3, + opacity=0.85, + tooltip="Flight trajectory", + ).add_to(flight_map) + + for location, label, icon_color in self.__trajectory_markers(launch, landing): + folium.Marker( + location=location, + popup=label, + tooltip=label, + icon=folium.Icon(color=icon_color), + ).add_to(flight_map) + + if safety_radii: + self.__add_safety_circles(folium, flight_map, launch, safety_radii) + + if title: + self.__add_map_title(folium, flight_map, title) + + folium.LayerControl(collapsed=False).add_to(flight_map) + + bounds = self.__map_bounds(latitudes, longitudes, launch, safety_radii) + if bounds is not None: + # Pad the viewport so that the launch and landing pins, which are + # anchored at the very edge of the bounding box, are not clipped by + # the border of the map. + flight_map.fit_bounds(bounds, padding=(30, 30)) + + if filename is not None: + flight_map.save(filename) + logger.info("File %s saved with success!", filename) + + return flight_map + + def __sample_ground_track(self, time_step): + """Return the (latitude, longitude) arrays of the ground track. + + When ``time_step`` is None the raw integration steps are used, mirroring + the behaviour of ``Flight.export_kml``. Otherwise the coordinates are + linearly interpolated over a uniform time grid. + """ + flight = self.flight + if time_step is None: + return ( + np.asarray(flight.latitude[:, 1], dtype=float), + np.asarray(flight.longitude[:, 1], dtype=float), + ) + time_points = np.arange(flight.t_initial, flight.t_final + time_step, time_step) + return ( + np.array([flight.latitude.get_value_opt(t) for t in time_points]), + np.array([flight.longitude.get_value_opt(t) for t in time_points]), + ) + + def __trajectory_markers(self, launch, landing): + """Yield the (location, label, color) of each trajectory marker. + + The apogee marker is skipped when apogee was never detected, since + ``Flight.apogee_time`` then keeps its initial value of zero and would + place the marker on top of the launch site. + """ + flight = self.flight + yield launch, "Launch", "green" + if flight.apogee_time > flight.t_initial: + apogee = ( + flight.latitude.get_value_opt(flight.apogee_time), + flight.longitude.get_value_opt(flight.apogee_time), + ) + yield ( + apogee, + f"Apogee ({flight.apogee - flight.env.elevation:.0f} m AGL)", + ("blue"), + ) + yield landing, "Landing", "red" + + @staticmethod + def __map_bounds(latitudes, longitudes, launch, safety_radii): + """Return the ``[[south, west], [north, east]]`` box the map opens on. + + The box always contains the ground track. When safety circles were + requested it is widened to contain them too, otherwise the largest ring + would sit outside the initial viewport and the user would have to zoom + out to find it. Returns None when the track degenerates to a single + point, in which case the caller should keep the default zoom. + """ + south, north = float(np.min(latitudes)), float(np.max(latitudes)) + west, east = float(np.min(longitudes)), float(np.max(longitudes)) + + if safety_radii: + # Equirectangular approximation, which is plenty for framing a map: + # one degree of latitude is ~111.32 km, and one degree of longitude + # shrinks by cos(latitude). + radius = max(float(r) for r in safety_radii) + delta_lat = radius / 111320.0 + delta_lon = delta_lat / max(np.cos(np.radians(launch[0])), 1e-6) + south, north = ( + min(south, launch[0] - delta_lat), + max(north, launch[0] + delta_lat), + ) + west, east = ( + min(west, launch[1] - delta_lon), + max(east, launch[1] + delta_lon), + ) + + if abs(north - south) <= 1e-12 and abs(east - west) <= 1e-12: + return None + return [[south, west], [north, east]] + + @staticmethod + def __add_safety_circles(folium, flight_map, launch, safety_radii): + """Draw range safety circles centred on the launch site. + + They live in their own feature group so that the layer control can + toggle them without hiding the trajectory. + """ + safety_group = folium.FeatureGroup(name="Safety radii") + for radius in safety_radii: + folium.Circle( + location=launch, + radius=float(radius), + color="orange", + tooltip=f"R{float(radius):.0f} m", + fill=False, + ).add_to(safety_group) + safety_group.add_to(flight_map) + + @staticmethod + def __add_map_title(folium, flight_map, title): + """Render ``title`` as a floating overlay on top of the map.""" + title_html = ( + '
' + f'

' + f"{html.escape(str(title))}

" + ) + flight_map.get_root().html.add_child(folium.Element(title_html)) + def _resolve_animation_model_path(self, file_name): """Resolve model path, defaulting to the built-in STL when omitted.""" if file_name is not None: @@ -694,7 +939,7 @@ def _rocket_axial_display_coordinate(self, value, display_length): """Map a rocket axial coordinate onto the centered display model.""" coordinates = [ float(position.z) - for _surface, position in self.flight.rocket.aerodynamic_surfaces + for _surface, position, _ref_factor in self.flight.rocket.aerodynamic_surfaces ] coordinates.extend( [ @@ -2268,9 +2513,42 @@ def attitude_data(self, *, filename=None): # pylint: disable=too-many-statement ax4.set_title("Euler Spin Angle") ax4.grid(True) + for ax in (ax1, ax2, ax3, ax4): + self._mark_tip_off_window(ax) + plt.subplots_adjust(hspace=0.5) show_or_save_plot(filename) + def _mark_tip_off_window(self, ax): + """Shades the tip-off window on a time-axis plot. + + The window runs from the upper rail button leaving the rail to the + lower one leaving it, which is when the rocket pivots about the lower + button. It is a no-op unless the flight was run with + ``use_udot_rail2=True``, since otherwise the phase never runs. + + Parameters + ---------- + ax : matplotlib.axes.Axes + Axes whose x-axis is flight time, in seconds. + + Returns + ------- + None + """ + if not self.flight.use_udot_rail2: + return + if self.flight.between_rails_time <= self.flight.out_of_rail_time: + return + ax.axvspan( + self.flight.out_of_rail_time, + self.flight.between_rails_time, + color="0.5", + alpha=0.25, + zorder=0, + label="Tip-off window", + ) + def flight_path_angle_data(self, *, filename=None): """Prints out Flight path and Rocket Attitude angle graphs available about the Flight @@ -2897,6 +3175,92 @@ def fluid_mechanics_data(self, *, filename=None): # pylint: disable=too-many-st plt.subplots_adjust(hspace=0.5) show_or_save_plot(filename) + def center_of_pressure(self, *, filename=None): + """Plot center-of-pressure position evolution through flight time. + + The rocket center of pressure is a function of Mach number. This method + evaluates it at the flight Mach number at each time step and plots the + resulting position against time. Center of mass is shown on the same + axis for context, and static margin is shown on a twin axis. + + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + + Returns + ------- + None + """ + time = self.flight.mach_number[:, 0] + mask = time <= self.first_event_time + time = time[mask] + mach = self.flight.mach_number[:, 1][mask] + + center_of_pressure = self.flight.rocket.cp_position.get_value_opt(mach) + center_of_mass = self.flight.rocket.center_of_mass.get_value_opt(time) + static_margin = self.flight.rocket.static_margin.get_value_opt(time) + + plt.figure(figsize=(9, 6)) + ax1 = plt.subplot(111) + (line_cp,) = ax1.plot( + time, + center_of_pressure, + color="#1f77b4", + label="Center of Pressure", + ) + (line_cm,) = ax1.plot( + time, + center_of_mass, + color="#ff7f0e", + label="Center of Mass", + ) + ax1.set_xlim(0, self.first_event_time) + ax1.set_title("Center of Pressure Evolution") + ax1.set_xlabel("Time (s)") + ax1.set_ylabel("Position (m)") + ax1.grid(True) + + ax2 = ax1.twinx() + (line_sm,) = ax2.plot( + time, + static_margin, + color="#2ca02c", + linestyle="--", + label="Static Margin", + ) + ax2.set_ylabel("Static Margin (c)", color="#2ca02c") + ax2.tick_params("y", colors="#2ca02c") + + event_lines = [ + ax1.axvline( + x=self.flight.out_of_rail_time, + color="r", + linestyle="--", + label="Out of Rail Time", + ), + ax1.axvline( + x=self.flight.rocket.motor.burn_out_time, + color="g", + linestyle=":", + label="Burn Out Time", + ), + ax1.axvline( + x=self.flight.apogee_time, + color="m", + linestyle="--", + label="Apogee Time", + ), + ] + + lines = [line_cp, line_cm, line_sm, *event_lines] + ax1.legend(lines, [line.get_label() for line in lines], loc="best") + + show_or_save_plot(filename) + def stability_and_control_data(self, *, filename=None): # pylint: disable=too-many-statements """Prints out Rocket Stability and Control parameters graphs available about the Flight @@ -3101,6 +3465,9 @@ def all(self): # pylint: disable=too-many-statements print("\n\nTrajectory Stability and Control Plots\n") self.stability_and_control_data() + print("\n\nCenter of Pressure Evolution Plot\n") + self.center_of_pressure() + print("\n\nRocket and Parachute Pressure Plots\n") self.pressure_rocket_altitude() self.pressure_signals() diff --git a/rocketpy/plots/rocket_plots.py b/rocketpy/plots/rocket_plots.py index 8e2b35558..80193b213 100644 --- a/rocketpy/plots/rocket_plots.py +++ b/rocketpy/plots/rocket_plots.py @@ -243,7 +243,7 @@ def _draw_aerodynamic_surfaces(self, ax, vis_args, plane, surfaces): # diameter changes. The final point of the last surface is the final # point of the last tube - for surface, position in surfaces: + for surface, position, _ref_factor in surfaces: if isinstance(surface, NoseCone): self._draw_nose_cone(ax, surface, position.z, drawn_surfaces, vis_args) elif isinstance(surface, Tail): @@ -319,6 +319,10 @@ def _draw_tail(self, ax, surface, position, drawn_surfaces, vis_args): def _draw_fins(self, ax, surface, position, drawn_surfaces, vis_args, plane): """Draws the fins and saves the position of the points of interest for the tubes.""" + if hasattr(surface, "draw_on_rocket"): + return surface.draw_on_rocket( + ax, position, drawn_surfaces, vis_args, plane, self.rocket._csys + ) num_fins = surface.n x_fin = -self.rocket._csys * surface.shape_vec[0] + position y_fin = surface.shape_vec[1] + surface.rocket_radius @@ -645,7 +649,7 @@ def _draw_nozzle_tube(self, last_radius, last_x, nozzle_position, ax, vis_args): def _draw_rail_buttons(self, ax, vis_args): """Draws the rail buttons of the rocket.""" try: - buttons, pos = self.rocket.rail_buttons[0] + buttons, pos, _ref_factor = self.rocket.rail_buttons[0] lower = pos.z upper = lower + buttons.buttons_distance * self.rocket._csys ax.scatter( diff --git a/rocketpy/prints/aero_surface_prints.py b/rocketpy/prints/aero_surface_prints.py index cc36f1b01..c3c02ea57 100644 --- a/rocketpy/prints/aero_surface_prints.py +++ b/rocketpy/prints/aero_surface_prints.py @@ -291,6 +291,21 @@ class _FreeFormFinPrints(_FinPrints): """Class that contains all free form fins prints.""" +class _TubeFinsPrints(_AeroSurfacePrints): + """Class that contains all tube-fin prints.""" + + def geometry(self): + """Print the geometric information of the tube-fin set.""" + print("Geometric information of the tube-fin set:") + print("------------------------------------------") + print(f"Number of tubes: {self.aero_surface.n}") + print(f"Tube length: {self.aero_surface.length:.3f} m") + print(f"Inner tube radius: {self.aero_surface.inner_radius:.3f} m") + print(f"Outer tube radius: {self.aero_surface.outer_radius:.3f} m") + print(f"Reference rocket radius: {self.aero_surface.rocket_radius:.3f} m") + print(f"Ring-airfoil aspect ratio: {self.aero_surface.aspect_ratio:.3f}\n") + + class _TailPrints(_AeroSurfacePrints): """Class that contains all tail prints.""" diff --git a/rocketpy/prints/environment_prints.py b/rocketpy/prints/environment_prints.py index ba01d7d82..8ab847ac2 100644 --- a/rocketpy/prints/environment_prints.py +++ b/rocketpy/prints/environment_prints.py @@ -102,7 +102,17 @@ def atmospheric_model_details(self): f"{model_type} Maximum Height: " f"{self.environment.max_expected_height / 1000:.3f} km" ) - if model_type in ["Forecast", "Reanalysis", "Ensemble"]: + # set_atmospheric_model accepts the type case-insensitively and stores it + # as the user spelled it, so compare in lower case while still printing + # the original spelling above. + normalized_type = model_type.lower() + if normalized_type in [ + "forecast", + "reanalysis", + "ensemble", + "open_meteo", + "open_meteo_ensemble", + ]: # Determine time period init_date = self.environment.atmospheric_model_init_date end_date = self.environment.atmospheric_model_end_date @@ -116,7 +126,7 @@ def atmospheric_model_details(self): end_lon = self.environment.atmospheric_model_end_lon print(f"{model_type} Latitude Range: From {init_lat}° to {end_lat}°") print(f"{model_type} Longitude Range: From {init_lon}° to {end_lon}°") - if model_type == "Ensemble": + if normalized_type in ["ensemble", "open_meteo_ensemble"]: print( f"Number of Ensemble Members: {self.environment.num_ensemble_members}" ) diff --git a/rocketpy/prints/flight_prints.py b/rocketpy/prints/flight_prints.py index 1c0e3cb0c..cfa7164c8 100644 --- a/rocketpy/prints/flight_prints.py +++ b/rocketpy/prints/flight_prints.py @@ -161,6 +161,32 @@ def out_of_rail_conditions(self): "Rail Departure Reynolds Number: " f"{self.flight.reynolds_number(self.flight.out_of_rail_time):.3e}" ) + self.tip_off_conditions() + + def tip_off_conditions(self): + """Prints out the tip-off window, i.e. the interval during which the + rocket pivots about the lower rail button after the upper one has left + the rail. Only printed when the flight was run with + ``use_udot_rail2=True``; otherwise the phase never runs and there is + nothing to report. + + Returns + ------- + None + """ + if not self.flight.use_udot_rail2: + return + print("\nTip-Off State\n") + print(f"Tip-Off Duration: {self.flight.tip_off_duration:.3f} s") + print(f"Lower Button Departure Time: {self.flight.between_rails_time:.3f} s") + print( + "Lower Button Departure Velocity: " + f"{self.flight.between_rails_velocity:.3f} m/s" + ) + print( + "Tip-Off Angle of Attack: " + f"{self.flight.angle_of_attack(self.flight.between_rails_time):.3f}°" + ) def burn_out_conditions(self): """Prints out the Burn Out Conditions available about the flight, diff --git a/rocketpy/prints/rocket_prints.py b/rocketpy/prints/rocket_prints.py index 7b768ea2f..6c0c667d4 100644 --- a/rocketpy/prints/rocket_prints.py +++ b/rocketpy/prints/rocket_prints.py @@ -102,19 +102,18 @@ def rocket_aerodynamics_quantities(self): None """ print("\nAerodynamics Lift Coefficient Derivatives\n") - for surface, _ in self.rocket.aerodynamic_surfaces: + for surface, _position, ref_factor in self.rocket.aerodynamic_surfaces: if isinstance(surface, GenericSurface): continue name = surface.name # ref_factor corrects lift for different reference areas - ref_factor = (surface.rocket_radius / self.rocket.radius) ** 2 print( f"{name} Lift Coefficient Derivative: " f"{ref_factor * surface.clalpha(0):.3f}/rad" ) print("\nCenter of Pressure\n") - for surface, position in self.rocket.aerodynamic_surfaces: + for surface, position, _ref_factor in self.rocket.aerodynamic_surfaces: name = surface.name cpz = surface.cp[2] # relative to the user defined coordinate system print( diff --git a/rocketpy/rocket/__init__.py b/rocketpy/rocket/__init__.py index afb7f0bb6..d5c4c593d 100644 --- a/rocketpy/rocket/__init__.py +++ b/rocketpy/rocket/__init__.py @@ -2,6 +2,8 @@ from rocketpy.rocket.aero_surface import ( AeroSurface, AirBrakes, + DatcomFins, + DatcomFreeFormFins, EllipticalFin, EllipticalFins, Fin, @@ -15,6 +17,7 @@ Tail, TrapezoidalFin, TrapezoidalFins, + TubeFins, ) from rocketpy.rocket.components import Components from rocketpy.rocket.parachute import Parachute diff --git a/rocketpy/rocket/aero_surface/__init__.py b/rocketpy/rocket/aero_surface/__init__.py index 7634d3500..bb5162d58 100644 --- a/rocketpy/rocket/aero_surface/__init__.py +++ b/rocketpy/rocket/aero_surface/__init__.py @@ -1,5 +1,6 @@ from rocketpy.rocket.aero_surface.aero_surface import AeroSurface from rocketpy.rocket.aero_surface.air_brakes import AirBrakes +from rocketpy.rocket.aero_surface.datcom_fins import DatcomFins, DatcomFreeFormFins from rocketpy.rocket.aero_surface.fins import ( EllipticalFin, EllipticalFins, @@ -15,3 +16,4 @@ from rocketpy.rocket.aero_surface.nose_cone import NoseCone from rocketpy.rocket.aero_surface.rail_buttons import RailButtons from rocketpy.rocket.aero_surface.tail import Tail +from rocketpy.rocket.aero_surface.tube_fins import TubeFins diff --git a/rocketpy/rocket/aero_surface/datcom_fins.py b/rocketpy/rocket/aero_surface/datcom_fins.py new file mode 100644 index 000000000..4be7d6e9a --- /dev/null +++ b/rocketpy/rocket/aero_surface/datcom_fins.py @@ -0,0 +1,416 @@ +"""Missile DATCOM fin planforms, airfoil sections and panel placement. + +Planform distances are meters. Airfoil coordinates and section dimensions are +fractions of chord. PHIF, GAM and DEFLCT inputs use DATCOM's degree conventions. +""" + +import re +from pathlib import Path + +import numpy as np + +from .fins.free_form_fins import FreeFormFins +from .fins.trapezoidal_fins import TrapezoidalFins + + +def _station_values(value, name, count): + """Normalize a scalar or one finite value per station/panel.""" + values = np.asarray(value, dtype=float) + if values.ndim == 0: + values = np.repeat(values, count) + if values.shape != (count,) or not np.isfinite(values).all(): + raise ValueError(f"{name} must be a finite scalar or have {count} values") + return tuple(values.tolist()) + + +def _station_pair(value, name): + """Backward-compatible root/tip section normalization.""" + return _station_values(value, name, 2) + + +def _planform(stations, chords, leading_edges): + """Validate a DATCOM spanwise planform and create its closed outline.""" + stations = np.asarray(stations, float) + if ( + stations.ndim != 1 + or not 2 <= len(stations) <= 10 + or not np.isfinite(stations).all() + or stations[0] != 0 + or np.any(np.diff(stations) <= 0) + ): + raise ValueError("span_stations needs 2..10 increasing values starting at zero") + chords = np.asarray(_station_values(chords, "chords", len(stations))) + edges = np.asarray(_station_values(leading_edges, "leading_edges", len(stations))) + if np.any(chords[:-1] <= 0) or chords[-1] < 0: + raise ValueError( + "Chords must be positive except for an optional zero tip chord" + ) + if edges[0] != 0: + raise ValueError( + "The root leading_edges value must be zero (relative to position)" + ) + points = list(zip(edges, stations)) + list( + zip((edges + chords)[::-1], stations[::-1]) + ) + return tuple(stations), tuple(chords), tuple(edges), points + + +class _DatcomFinSettings: + """Shared section metadata for existing RocketPy planform classes.""" + + def _configure_datcom( + self, + n, + count, + *, + path, + section_type="HEX", + thickness_to_chord=0.02, + upper_thickness_to_chord=None, + lower_thickness_to_chord=None, + max_thickness_position=0.25, + flat_length=0.5, + lower_max_thickness_position=None, + lower_flat_length=None, + leading_edge_radius=0, + naca=None, + airfoil_coordinates=None, + panel_angles=None, + dihedral=0, + incidence=None, + cant_angle=0, + auto_body_radius=True, + datcom_options=None, + ): + if not isinstance(n, (int, np.integer)) or not 1 <= n <= 8: + raise ValueError("DATCOM fin sets require an integer n between 1 and 8") + section_type = section_type.upper() + if section_type not in ("HEX", "ARC", "NACA", "USER"): + raise ValueError("section_type must be HEX, ARC, NACA or USER") + total = _station_values(thickness_to_chord, "thickness_to_chord", count) + upper = _station_values( + np.asarray(total) / 2 + if upper_thickness_to_chord is None + else upper_thickness_to_chord, + "upper_thickness_to_chord", + count, + ) + lower = _station_values( + upper if lower_thickness_to_chord is None else lower_thickness_to_chord, + "lower_thickness_to_chord", + count, + ) + maximum = _station_values( + max_thickness_position, "max_thickness_position", count + ) + flat = _station_values(flat_length, "flat_length", count) + lower_maximum = _station_values( + maximum + if lower_max_thickness_position is None + else lower_max_thickness_position, + "lower_max_thickness_position", + count, + ) + lower_flat = _station_values( + flat if lower_flat_length is None else lower_flat_length, + "lower_flat_length", + count, + ) + radius = _station_values(leading_edge_radius, "leading_edge_radius", count) + if any(r < 0 for r in radius): + raise ValueError("leading_edge_radius cannot be negative") + if section_type in ("HEX", "ARC"): + if any( + top < 0 or bottom < 0 or not 0 < top + bottom < 1 + for top, bottom in zip(upper, lower) + ): + raise ValueError( + "Section upper/lower thickness must be nonnegative with total between 0 and 1" + ) + if section_type == "HEX": + for maxima, flats in ((maximum, flat), (lower_maximum, lower_flat)): + if any( + not (0 < m < 1 and 0 <= f < 1 and m + f < 1) + for m, f in zip(maxima, flats) + ): + raise ValueError( + "HEX requires 0 < maximum, 0 <= flat, maximum + flat < 1" + ) + if section_type == "NACA": + if not isinstance(naca, str): + raise ValueError( + "NACA sections require naca, e.g. '0012' or '6-64-005'" + ) + if re.fullmatch(r"\d{4}(?:\.\d+)?", naca): + naca = "4-" + naca + if not re.fullmatch(r"[1456S]-[0-9.AS-]+(?: A=0\.\d+)?", naca): + raise ValueError( + "Invalid NACA designation; omit the NACA and fin-set prefix" + ) + elif naca is not None: + raise ValueError("naca requires section_type='NACA'") + coordinates = None + if section_type == "USER": + coordinates = self._validate_airfoil_coordinates(airfoil_coordinates) + elif airfoil_coordinates is not None: + raise ValueError("airfoil_coordinates requires section_type='USER'") + angles = _station_values( + np.arange(n) * 360 / n if panel_angles is None else panel_angles, + "panel_angles", + n, + ) + angles = tuple(float(a % 360) for a in angles) + if len(set(round(a, 8) for a in angles)) != n: + raise ValueError("panel_angles must not repeat the same azimuth") + gam = _station_values(dihedral, "dihedral", n) + if any(abs(a) >= 90 for a in gam): + raise ValueError("dihedral must lie strictly between -90 and 90 degrees") + if not np.isfinite(cant_angle): + raise ValueError("cant_angle must be finite") + if incidence is not None and cant_angle != 0: + raise ValueError( + "Use either DATCOM incidence or RocketPy cant_angle, not both" + ) + delta = ( + None if incidence is None else _station_values(incidence, "incidence", n) + ) + if any(abs(a) >= 90 for a in (delta or (-cant_angle,))): + raise ValueError( + "incidence/cant must lie strictly between -90 and 90 degrees" + ) + self.datcom_path = str(Path(path).expanduser().resolve()) + self.datcom_options = dict(datcom_options or {}) + self.section_type = section_type + self.thickness_to_chord = tuple( + top + bottom for top, bottom in zip(upper, lower) + ) + self.upper_thickness_to_chord = upper + self.lower_thickness_to_chord = lower + self.max_thickness_position = maximum + self.flat_length = flat + self.lower_max_thickness_position = lower_maximum + self.lower_flat_length = lower_flat + self.leading_edge_radius = radius + self.naca = naca + self.airfoil_coordinates = coordinates + self.panel_angles = angles + self.dihedral = gam + self.incidence = delta + self.auto_body_radius = bool(auto_body_radius) + + @staticmethod + def _validate_airfoil_coordinates(coordinates): + if not isinstance(coordinates, dict): + raise ValueError("USER sections require an airfoil_coordinates dictionary") + keys = set(coordinates) + if keys not in ({"x", "upper", "lower"}, {"x", "mean", "thickness"}): + raise ValueError("USER needs x/upper/lower or x/mean/thickness") + result = {k: np.asarray(v, float) for k, v in coordinates.items()} + x = result["x"] + if ( + x.ndim != 1 + or not 3 <= len(x) <= 50 + or x[0] != 0 + or x[-1] != 1 + or np.any(np.diff(x) <= 0) + ): + raise ValueError( + "USER x needs 3..50 increasing chord fractions from 0 to 1" + ) + if any(v.shape != x.shape or not np.isfinite(v).all() for v in result.values()): + raise ValueError("USER coordinates must have matching finite arrays") + thickness = result.get( + "thickness", result.get("upper", 0) - result.get("lower", 0) + ) + if np.any(thickness < 0) or not np.any(thickness > 0): + raise ValueError("USER section thickness must be nonnegative and nonzero") + return {k: tuple(v.tolist()) for k, v in result.items()} + + @property + def datcom_incidence(self): + """DATCOM positive incidence gives negative roll; RocketPy cant is opposite.""" + return ( + self.incidence + if self.incidence is not None + else (-self.cant_angle,) * self.n + ) + + def _datcom_settings_dict(self): + names = ( + "section_type", + "thickness_to_chord", + "upper_thickness_to_chord", + "lower_thickness_to_chord", + "max_thickness_position", + "flat_length", + "lower_max_thickness_position", + "lower_flat_length", + "leading_edge_radius", + "naca", + "airfoil_coordinates", + "panel_angles", + "dihedral", + "incidence", + "cant_angle", + "auto_body_radius", + "datcom_options", + ) + return { + "path": self.datcom_path, + **{name: getattr(self, name) for name in names}, + } + + def draw_on_rocket(self, ax, position, drawn_surfaces, vis_args, plane, csys): + """Project actual PHIF/GAM/DEFLCT placement into a Rocket drawing.""" + from rocketpy.plots.datcom_plots import draw_datcom_fins + + return draw_datcom_fins( + self, ax, position, drawn_surfaces, vis_args, plane, csys + ) + + @classmethod + def from_dict(cls, data): + return cls(**data) + + +class DatcomFins(_DatcomFinSettings, TrapezoidalFins): + """A trapezoidal or triangular planform with a HEX, ARC, NACA or USER section. + + Dimensions and position follow TrapezoidalFins. Section inputs accept scalar + or root/tip values; thicknesses are chord fractions and radius is meters. + NACA uses a designation such as ``0012`` or ``6-64-005``. USER accepts + ``airfoil_coordinates={"x": ..., "upper": ..., "lower": ...}`` or mean and + thickness arrays instead of upper/lower. PHIF panel_angles and GAM dihedral + use degrees. Fixed incidence follows DATCOM's negative-roll convention; + cant_angle follows RocketPy's positive-roll convention. All execution and + geometry inputs survive serialization. See the DATCOM user guide page. + """ + + def __init__( + self, + n, + root_chord, + tip_chord, + span, + rocket_radius, + *, + path, + sweep_length=None, + sweep_angle=None, + name="DATCOM Fins", + **section_options, + ): + for value, label in ( + (root_chord, "root_chord"), + (span, "span"), + (rocket_radius, "rocket_radius"), + ): + if not np.isfinite(value) or value <= 0: + raise ValueError(f"DATCOM {label} must be positive and finite") + if not np.isfinite(tip_chord) or tip_chord < 0: + raise ValueError("DATCOM tip_chord must be finite and nonnegative") + self._configure_datcom(n, 2, path=path, **section_options) + super().__init__( + n=n, + root_chord=root_chord, + tip_chord=tip_chord, + span=span, + rocket_radius=rocket_radius, + sweep_length=sweep_length, + sweep_angle=sweep_angle, + cant_angle=section_options.get("cant_angle", 0), + name=name, + ) + + @property + def datcom_span_stations(self): + return (0.0, self.span) + + @property + def datcom_chords(self): + return (self.root_chord, self.tip_chord) + + @property + def datcom_leading_edges(self): + return (0.0, self.sweep_length) + + def evaluate_shape(self): + _, _, _, points = _planform( + self.datcom_span_stations, self.datcom_chords, self.datcom_leading_edges + ) + self.shape_vec = [np.array([p[i] for p in points]) for i in (0, 1)] + return self.shape_vec + + def to_dict(self, **kwargs): + return dict( + n=self.n, + root_chord=self.root_chord, + tip_chord=self.tip_chord, + span=self.span, + rocket_radius=self.rocket_radius, + sweep_length=self.sweep_length, + name=self.name, + **self._datcom_settings_dict(), + ) + + +class DatcomFreeFormFins(_DatcomFinSettings, FreeFormFins): + """Piecewise-linear planform with 2..10 exposed span stations. + + Span stations start at zero at the body surface. Chords and leading_edges + are meters; leading_edges start at zero relative to the root position. + All cross sections belong to one type, with proportions varying by station. + Section options and panel placement are identical to DatcomFins. + """ + + def __init__( + self, + n, + span_stations, + chords, + leading_edges, + rocket_radius, + *, + path, + name="DATCOM Free Form Fins", + **section_options, + ): + stations, chords, edges, points = _planform( + span_stations, chords, leading_edges + ) + self._datcom_stations = stations + self._datcom_chords = chords + self._datcom_edges = edges + if not np.isfinite(rocket_radius) or rocket_radius <= 0: + raise ValueError("rocket_radius must be positive and finite") + self._configure_datcom(n, len(stations), path=path, **section_options) + super().__init__( + n, + points, + rocket_radius, + cant_angle=section_options.get("cant_angle", 0), + name=name, + ) + + @property + def datcom_span_stations(self): + return self._datcom_stations + + @property + def datcom_chords(self): + return self._datcom_chords + + @property + def datcom_leading_edges(self): + return self._datcom_edges + + def to_dict(self, **kwargs): + return dict( + n=self.n, + span_stations=self.datcom_span_stations, + chords=self.datcom_chords, + leading_edges=self.datcom_leading_edges, + rocket_radius=self.rocket_radius, + name=self.name, + **self._datcom_settings_dict(), + ) diff --git a/rocketpy/rocket/aero_surface/datcom_tail.py b/rocketpy/rocket/aero_surface/datcom_tail.py new file mode 100644 index 000000000..674faac51 --- /dev/null +++ b/rocketpy/rocket/aero_surface/datcom_tail.py @@ -0,0 +1,67 @@ +"""Ogive geometry for whole-vehicle DATCOM, without component aerodynamics.""" + +import numpy as np +from scipy.integrate import quad + +from rocketpy.mathutils import Function +from rocketpy.rocket.aero_surface.tail import Tail + + +class DatcomTail(Tail): + """A tangent-ogive afterbody usable only with DATCOM aerodynamics. + + The inherited component lift and CP are disabled placeholders. Native + DATCOM supplies the whole vehicle's forces, moments and pressure center. + ``kind`` is read-only; construct a new surface to change its profile. + """ + + @property + def kind(self): + return "ogive" + + def radius_at(self, x): + """Radius (m) at distance x (m) aft of the upstream cylinder joint.""" + x = np.asarray(x) + change = self.bottom_radius - self.top_radius + if change == 0: + return np.full_like(x, self.top_radius, dtype=float) + rho = (self.length**2 + change**2) / (2 * abs(change)) + sagitta = x**2 / (rho + np.sqrt(np.maximum(0, rho**2 - x**2))) + return self.top_radius + np.sign(change) * sagitta + + def evaluate_geometrical_parameters(self): + change = self.bottom_radius - self.top_radius + if self.length <= 0 or abs(change) > self.length: + raise ValueError( + "Ogive tail needs positive length and radius change <= length" + ) + if change == 0: + self.slant_length = self.length + self.surface_area = 2 * np.pi * self.top_radius * self.length + else: + rho = (self.length**2 + change**2) / (2 * abs(change)) + angle = np.arcsin(self.length / rho) + self.slant_length = rho * angle + self.surface_area = quad( + lambda theta: 2 * np.pi * self.radius_at(rho * np.sin(theta)) * rho, + 0, + angle, + )[0] + self.evaluate_shape() + + def evaluate_shape(self): + x = np.linspace(0, self.length, 100) + self.shape_vec = [x, self.radius_at(x)] + + def evaluate_lift_coefficient(self): + self.clalpha = Function(0, "Mach", "DATCOM-only component lift slope") + self.cl = Function( + lambda alpha, mach: 0, ["Alpha (rad)", "Mach"], "DATCOM-only component lift" + ) + + def evaluate_center_of_pressure(self): + self.cpx = self.cpy = self.cpz = 0 + self.cp = (0, 0, 0) + + def compute_forces_and_moments(self, *args, **kwargs): + raise ValueError("Ogive tails require whole-vehicle DATCOM aerodynamics") diff --git a/rocketpy/rocket/aero_surface/tube_fins.py b/rocketpy/rocket/aero_surface/tube_fins.py new file mode 100644 index 000000000..ca585d08a --- /dev/null +++ b/rocketpy/rocket/aero_surface/tube_fins.py @@ -0,0 +1,291 @@ +import numbers + +import numpy as np + +from rocketpy.mathutils.function import Function +from rocketpy.plots.aero_surface_plots import _TubeFinsPlots +from rocketpy.prints.aero_surface_prints import _TubeFinsPrints + +from .aero_surface import AeroSurface + + +class TubeFins(AeroSurface): + """Defines a symmetric set of tube fins for subsonic flight. + + The aerodynamic model follows the Ribner ring-airfoil normal-force + derivative used by OpenRocket. It is limited to uncanted tube fins that + touch both the rocket body and their two neighboring tubes. The center of + pressure is fixed at the quarter chord, so the model is intended for + Mach numbers up to 0.5. + + Parameters + ---------- + n : int + Number of tubes. Must be at least 3. + length : int, float + Tube length along the rocket axis, in meters. + inner_radius : int, float + Inner radius of each tube, in meters. + outer_radius : int, float + Outer radius of each tube, in meters. For the supported touching + geometry, this must equal + ``rocket_radius * sin(pi / n) / (1 - sin(pi / n))``. + rocket_radius : int, float + Radius of the rocket body where the tube fins are mounted, in meters. + name : str, optional + Name of the tube-fin set. Default is ``"Tube Fins"``. + + Notes + ----- + This model calculates normal force only. Tube-fin friction and pressure + drag must be included in the rocket's power-on and power-off drag curves. + Cant, roll, side-force, yaw, separated tubes, and overlapping tubes are not + supported. + """ + + stall_angle = np.radians(20) + + def __init__( + self, + n, + length, + inner_radius, + outer_radius, + rocket_radius, + name="Tube Fins", + ): + self._n = n + self._length = length + self._inner_radius = inner_radius + self._outer_radius = outer_radius + self._rocket_radius = rocket_radius + + self._validate_geometry() + super().__init__( + name=name, + reference_area=np.pi * rocket_radius**2, + reference_length=2 * rocket_radius, + ) + + self._evaluate_all() + + self.prints = _TubeFinsPrints(self) + self.plots = _TubeFinsPlots(self) + + @staticmethod + def _touching_outer_radius(n, rocket_radius): + sin_half_angle = np.sin(np.pi / n) + return rocket_radius * sin_half_angle / (1 - sin_half_angle) + + def _validate_geometry(self): + if isinstance(self.n, bool) or not isinstance(self.n, numbers.Integral): + raise ValueError("'n' must be an integer greater than or equal to 3.") + if self.n < 3: + raise ValueError("'n' must be greater than or equal to 3.") + + dimensions = { + "length": self.length, + "inner_radius": self.inner_radius, + "outer_radius": self.outer_radius, + "rocket_radius": self.rocket_radius, + } + for parameter, value in dimensions.items(): + if isinstance(value, bool) or not isinstance(value, numbers.Real): + raise ValueError(f"'{parameter}' must be a positive real number.") + if not np.isfinite(value) or value <= 0: + raise ValueError(f"'{parameter}' must be finite and greater than zero.") + + if self.inner_radius >= self.outer_radius: + raise ValueError("'inner_radius' must be smaller than 'outer_radius'.") + + touching_radius = self._touching_outer_radius(self.n, self.rocket_radius) + tolerance = max(1e-12, touching_radius * 1e-6) + if not np.isclose( + self.outer_radius, touching_radius, rtol=1e-6, atol=tolerance + ): + geometry = ( + "separated" if self.outer_radius < touching_radius else "overlapping" + ) + raise ValueError( + f"The specified geometry produces {geometry} tube fins. " + "Only mutually tangent tubes are supported; for " + f"n={self.n} and rocket_radius={self.rocket_radius:g} m, " + f"outer_radius must be {touching_radius:g} m." + ) + + def _evaluate_all(self): + self.reference_area = np.pi * self.rocket_radius**2 + self.reference_length = 2 * self.rocket_radius + self.evaluate_geometrical_parameters() + self.evaluate_center_of_pressure() + self.evaluate_lift_coefficient() + self.evaluate_shape() + + def _set_geometry_attribute(self, attribute, value): + old_value = getattr(self, attribute) + setattr(self, attribute, value) + try: + self._validate_geometry() + except (TypeError, ValueError): + setattr(self, attribute, old_value) + raise + self._evaluate_all() + + @property + def n(self): + """Number of tubes in the set.""" + return self._n + + @n.setter + def n(self, value): + self._set_geometry_attribute("_n", value) + + @property + def length(self): + """Tube length along the rocket axis, in meters.""" + return self._length + + @length.setter + def length(self, value): + self._set_geometry_attribute("_length", value) + + @property + def inner_radius(self): + """Inner tube radius, in meters.""" + return self._inner_radius + + @inner_radius.setter + def inner_radius(self, value): + self._set_geometry_attribute("_inner_radius", value) + + @property + def outer_radius(self): + """Outer tube radius, in meters.""" + return self._outer_radius + + @outer_radius.setter + def outer_radius(self, value): + self._set_geometry_attribute("_outer_radius", value) + + @property + def rocket_radius(self): + """Reference rocket-body radius, in meters.""" + return self._rocket_radius + + @rocket_radius.setter + def rocket_radius(self, value): + self._set_geometry_attribute("_rocket_radius", value) + + @property + def rocket_diameter(self): + """Reference rocket-body diameter, in meters.""" + return 2 * self.rocket_radius + + def evaluate_geometrical_parameters(self): + """Evaluate the ring-airfoil aspect ratio and tube spacing.""" + self.aspect_ratio = 2 * self.inner_radius / self.length + self.touching_outer_radius = self._touching_outer_radius( + self.n, self.rocket_radius + ) + self.tube_separation = 2 * (self.touching_outer_radius - self.outer_radius) + + def evaluate_center_of_pressure(self): + """Set the subsonic center of pressure at the quarter chord.""" + self.cpx = 0 + self.cpy = 0 + self.cpz = self.length / 4 + self.cp = (self.cpx, self.cpy, self.cpz) + + def evaluate_lift_coefficient(self): + """Evaluate the Ribner normal-force derivative for the tube set.""" + modified_aspect_ratio = 2 * self.aspect_ratio / np.pi + single_tube_constant = ( + 2 + * (modified_aspect_ratio / (1 + modified_aspect_ratio)) + * np.pi**2 + * self.inner_radius + * self.length + ) + clalpha_value = self.n * single_tube_constant / self.reference_area + + self.clalpha = Function( + lambda mach: clalpha_value, + "Mach", + f"Lift coefficient derivative for {self.name}", + ) + self.cl = Function( + lambda alpha, mach: ( + self.clalpha(mach) * np.clip(alpha, -self.stall_angle, self.stall_angle) + ), + ["Alpha (rad)", "Mach"], + "Lift coefficient", + ) + return self.cl + + def evaluate_shape(self): + """Store a side-view outline for plotting the tube-fin envelope.""" + lower = self.rocket_radius + upper = self.rocket_radius + 2 * self.outer_radius + self.shape_vec = [ + np.array([0, self.length, self.length, 0, 0]), + np.array([lower, lower, upper, upper, lower]), + ] + + def info(self): + """Print tube-fin geometry and lift information.""" + self.prints.geometry() + self.prints.lift() + + def all_info(self): + """Print and plot all available tube-fin information.""" + self.prints.all() + self.plots.all() + + def draw(self, *, filename=None): + """Draw a side-view envelope of the tube-fin set.""" + return self.plots.draw(filename=filename) + + def to_dict(self, **kwargs): + data = { + "n": self.n, + "length": self.length, + "inner_radius": self.inner_radius, + "outer_radius": self.outer_radius, + "rocket_radius": self.rocket_radius, + "name": self.name, + } + + if kwargs.get("include_outputs", False): + clalpha = self.clalpha + cl = self.cl + if kwargs.get("discretize", False): + clalpha = clalpha.set_discrete(0, 0.5, 10, mutate_self=False) + cl = cl.set_discrete( + (-self.stall_angle, 0), + (self.stall_angle, 0.5), + (10, 10), + mutate_self=False, + ) + data.update( + { + "aspect_ratio": self.aspect_ratio, + "cp": self.cp, + "clalpha": clalpha, + "cl": cl, + "reference_area": self.reference_area, + "reference_length": self.reference_length, + } + ) + + return data + + @classmethod + def from_dict(cls, data): + return cls( + n=data["n"], + length=data["length"], + inner_radius=data["inner_radius"], + outer_radius=data["outer_radius"], + rocket_radius=data["rocket_radius"], + name=data["name"], + ) diff --git a/rocketpy/rocket/components.py b/rocketpy/rocket/components.py index 57e4d12f8..a6d013b9b 100644 --- a/rocketpy/rocket/components.py +++ b/rocketpy/rocket/components.py @@ -15,13 +15,15 @@ class Components: A list of named tuples representing all the components and their positions relative to the rocket. component_tuple : namedtuple - A named tuple representing a component and its position within the - rocket. + A named tuple representing a component, its position within the + rocket, and an optional reference-area correction factor. """ def __init__(self): """Initialize an empty components list instance.""" - self.component_tuple = namedtuple("component_tuple", "component position") + self.component_tuple = namedtuple( + "component_tuple", "component position ref_factor", defaults=(1.0,) + ) self._components = [] # List of components and their positions to avoid extra for loops in @@ -34,6 +36,7 @@ def __repr__(self): components_str = "\n".join( [ f"\tComponent: {str(c.component):80} Position: {c.position}" + f" Ref Factor: {c.ref_factor}" for c in self._components ] ) @@ -52,7 +55,7 @@ def __iter__(self): """Return an iterator over the list of components.""" return iter(self._components) - def add(self, component, position): + def add(self, component, position, ref_factor=1.0): """Add a component to the list of components. Parameters @@ -62,6 +65,9 @@ def add(self, component, position): position : int, float The position of the component relative to the rocket's coordinate system origin. + ref_factor : int, float, optional + Reference-area correction factor associating the component to the + rocket reference area. Defaults to 1.0 when not applicable. Returns ------- @@ -69,7 +75,7 @@ def add(self, component, position): """ self.__component_list.append(component) self.__position_list.append(position) - self._components.append(self.component_tuple(component, position)) + self._components.append(self.component_tuple(component, position, ref_factor)) def get_by_type(self, component_type): """Search the list of components and return a list with all the @@ -207,7 +213,11 @@ def sort_by_position(self, reverse=False): def to_dict(self, **kwargs): # pylint: disable=unused-argument return { "components": [ - {"component": c.component, "position": c.position} + { + "component": c.component, + "position": c.position, + "ref_factor": c.ref_factor, + } for c in self._components ] } @@ -216,5 +226,9 @@ def to_dict(self, **kwargs): # pylint: disable=unused-argument def from_dict(cls, data): components = cls() for component in data["components"]: - components.add(component["component"], component["position"]) + components.add( + component["component"], + component["position"], + ref_factor=component.get("ref_factor", 1.0), + ) return components diff --git a/rocketpy/rocket/datcom.py b/rocketpy/rocket/datcom.py new file mode 100644 index 000000000..72ad311c4 --- /dev/null +++ b/rocketpy/rocket/datcom.py @@ -0,0 +1,988 @@ +"""Missile DATCOM decks, isolated execution and complete aerodynamic tables. + +The for006 parser is adapted from Asteria-Flight-Dynamics/classes/datcom/datcom.py. +Geometry and execution are independent of Asteria's project configuration. +""" + +import json +import math +import re +import shutil +import subprocess +import tempfile +import warnings +from pathlib import Path + +import numpy as np +from scipy.interpolate import RegularGridInterpolator + +from rocketpy.mathutils.vector_matrix import Vector +from rocketpy.rocket.aero_surface.datcom_fins import ( + DatcomFins, + DatcomFreeFormFins, + _station_values, +) +from rocketpy.rocket.aero_surface.fins.trapezoidal_fins import TrapezoidalFins +from rocketpy.rocket.aero_surface.generic_surface import GenericSurface +from rocketpy.rocket.aero_surface.nose_cone import NoseCone +from rocketpy.rocket.aero_surface.tail import Tail +from rocketpy.rocket.datcom_conditions import adaptive_mach_grid + +BODY_KEYS = ("CN", "CM", "CA", "CY", "CLN", "CLL") +LATERAL_KEYS = ("CYB", "CLNB", "CLLB") +DAMPING_KEYS = ( + "CNQ", + "CMQ", + "CAQ", + "CNAD", + "CMAD", + "CYR", + "CLNR", + "CLLR", + "CYP", + "CLNP", + "CLLP", +) + + +class DatcomResult(tuple): + """Backward-compatible six-array result with body-axis and lateral tables.""" + + def __new__( + cls, values, body=None, derivatives=None, damping=None, beta=None, sideslip=None + ): + result = super().__new__(cls, values) + result.body = {} if body is None else body + result.derivatives = {} if derivatives is None else derivatives + result.damping = {} if damping is None else damping + result.beta = np.array([0.0]) if beta is None else np.asarray(beta) + result.sideslip = sideslip + return result + + +def zero_alpha_index(alpha): + indices = np.flatnonzero(np.isclose(alpha, 0.0, atol=1e-12, rtol=0)) + if len(indices) != 1: + raise ValueError("DATCOM tables must contain exactly one zero-angle row") + return int(indices[0]) + + +def parse_datcom_output(base_dir, n_alpha_expected=None, _output=None): + """Read every angle row, including signed alpha and lateral derivatives. + + The public tuple retains CL, CD, CM, XCP, Mach, alpha (degrees here). + Body coefficients and derivatives (per degree) are retained as attributes. + Never truncate a table to a hardcoded row count. + """ + output = Path(base_dir, "for006.dat").read_text() if _output is None else _output + lines = output.splitlines() + if n_alpha_expected is None: + declared = re.search(r"\bNALPHA\s*=\s*([0-9.]+)", output) + if declared is not None: + n_alpha_expected = int(float(declared[1])) + starts = [i for i, line in enumerate(lines) if "----- LONGITUDINAL -----" in line] + if not starts: + raise RuntimeError("No LONGITUDINAL blocks found in for006.dat") + + # Multiple fixed-BETA cases can share one executable invocation. Preserve + # complete body/rate channels at each beta instead of imposing symmetry on + # arbitrary panel layouts. The six-array view remains the beta=0 slice. + beta_blocks = {} + for index, start in enumerate(starts): + mach_start = next( + (i for i in range(start - 1, -1, -1) if "MACH NO" in lines[i]), None + ) + beta_line = next( + (line for line in reversed(lines[:start]) if "SIDESLIP =" in line), + "SIDESLIP = 0", + ) + beta = float(beta_line.split("=")[1].split()[0]) + stop = starts[index + 1] if index + 1 < len(starts) else len(lines) + if mach_start is not None: + beta_blocks.setdefault(beta, []).extend(lines[mach_start:stop]) + if len(beta_blocks) > 1: + tables = { + beta: parse_datcom_output(base_dir, n_alpha_expected, "\n".join(block)) + for beta, block in sorted(beta_blocks.items()) + } + if 0.0 not in tables: + raise RuntimeError("DATCOM sideslip database must include beta=0") + zero = tables[0.0] + if any( + not np.array_equal(t[4], zero[4]) or not np.array_equal(t[5], zero[5]) + for t in tables.values() + ): + raise RuntimeError("DATCOM sideslip cases have different Mach/alpha grids") + return DatcomResult( + tuple(zero), + zero.body, + zero.derivatives, + zero.damping, + beta=list(tables), + sideslip=tables, + ) + + def read_table(block, columns): + header = next( + (i for i, line in enumerate(block) if line.split() == columns), None + ) + if header is None: + raise RuntimeError(f"DATCOM missing {' '.join(columns)} table") + rows = [] + for line in block[header + 1 :]: + fields = line.split() + if not fields and not rows: + continue + if len(fields) != len(columns): + # Fixed-width output can join a negative value to its preceding + # column, e.g. CL/CD=-0.000 followed by X-C.P.=-28096.705. + number = r"[-+]?(?:\d+(?:\.\d*)?|\.\d+)(?:[EeDd][-+]?\d+)?" + fields = re.findall(number, line) + if len(fields) != len(columns) or re.sub(number, "", line).strip(): + break + try: + rows.append([float(value.replace("D", "E")) for value in fields]) + except ValueError as exc: + raise RuntimeError( + "DATCOM returned a nonnumeric coefficient row" + ) from exc + if not rows: + raise RuntimeError("DATCOM returned an empty coefficient table") + return np.asarray(rows) + + mach, alpha_rows, lift_rows = [], [], [] + body_rows, derivative_rows, damping_rows = [], [], [] + for index, start in enumerate(starts): + stop = starts[index + 1] if index + 1 < len(starts) else len(lines) + block = lines[start:stop] + mach_line = next( + (line for line in reversed(lines[:start]) if "MACH NO" in line), None + ) + if mach_line is None: + raise RuntimeError("DATCOM missing Mach reference") + mach.append(float(mach_line.split("=")[1].split()[0])) + body = read_table(block, ["ALPHA", "CN", "CM", "CA", "CY", "CLN", "CLL"]) + lift = read_table(block, ["ALPHA", "CL", "CD", "CL/CD", "X-C.P."]) + derivatives = read_table(block, ["ALPHA", "CNA", "CMA", "CYB", "CLNB", "CLLB"]) + if not np.array_equal(body[:, 0], lift[:, 0]) or not np.array_equal( + body[:, 0], derivatives[:, 0] + ): + raise RuntimeError("DATCOM coefficient tables have different angle grids") + if n_alpha_expected is not None and len(body) != n_alpha_expected: + raise RuntimeError("DATCOM angle count does not match the requested count") + alpha_rows.append(body[:, 0]) + body_rows.append(body[:, 1:]) + lift_rows.append(lift[:, 1:]) + derivative_rows.append(derivatives[:, 1:]) + if "DYNAMIC DERIVATIVES" in "\n".join(block): + longitudinal = read_table( + block, ["ALPHA", "CNQ", "CMQ", "CAQ", "CNAD", "CMAD"] + ) + lateral = read_table( + block, ["ALPHA", "CYR", "CLNR", "CLLR", "CYP", "CLNP", "CLLP"] + ) + if not ( + np.array_equal(body[:, 0], longitudinal[:, 0]) + and np.array_equal(body[:, 0], lateral[:, 0]) + ): + raise RuntimeError("DATCOM damping angle grid mismatch") + if "DYNAMIC DERIVATIVES (PER DEGREE)" not in "\n".join(block): + raise RuntimeError("Expected DATCOM dynamic derivatives per degree") + damping_rows.append(np.column_stack([longitudinal[:, 1:], lateral[:, 1:]])) + alpha = alpha_rows[0] + if any(not np.array_equal(row, alpha) for row in alpha_rows[1:]): + raise RuntimeError("DATCOM Mach blocks have different angle grids") + mach = np.asarray(mach) + if np.any(np.diff(mach) <= 0) or np.any(np.diff(alpha) <= 0): + raise RuntimeError("DATCOM Mach and angle grids must be strictly increasing") + body = np.asarray(body_rows) + lift = np.asarray(lift_rows) + derivatives = np.asarray(derivative_rows) + if not all(np.isfinite(values).all() for values in (body, lift, derivatives)): + raise RuntimeError("DATCOM returned nonfinite coefficient tables") + if damping_rows and len(damping_rows) != len(mach): + raise RuntimeError("Incomplete DATCOM damping Mach grid") + damping = ( + dict( + zip( + ( + "CNQ", + "CMQ", + "CAQ", + "CNAD", + "CMAD", + "CYR", + "CLNR", + "CLLR", + "CYP", + "CLNP", + "CLLP", + ), + np.moveaxis(np.asarray(damping_rows), 2, 0), + ) + ) + if damping_rows + else {} + ) + if any(not np.isfinite(v).all() for v in damping.values()): + raise RuntimeError("Nonfinite DATCOM damping coefficients") + if not damping: + raise RuntimeError( + "DATCOM output lacks DAMP derivatives; include DAMP in the deck" + ) + return DatcomResult( + (lift[:, :, 0], lift[:, :, 1], body[:, :, 1], lift[:, :, 3], mach, alpha), + dict(zip(("CN", "CM", "CA", "CY", "CLN", "CLL"), np.moveaxis(body, 2, 0))), + dict( + zip(("CNA", "CMA", "CYB", "CLNB", "CLLB"), np.moveaxis(derivatives, 2, 0)) + ), + damping, + beta=list(beta_blocks), + ) + + +def _flight_conditions(options): + """Validate the supported single-altitude-per-Mach DATCOM flight envelope.""" + unknown = set(options) - { + "mach", + "alpha", + "beta", + "altitude", + "timeout", + "body_length", + "powered_base_drag", + "reynolds", + } + if unknown: + raise ValueError(f"Unknown DATCOM options: {sorted(unknown)}") + mach = np.asarray(options.get("mach", adaptive_mach_grid(2)), float) + alpha = np.asarray( + options.get( + "alpha", + [ + -20, + -10, + -5, + -2, + -1, + -0.5, + -0.25, + -0.1, + -0.05, + 0, + 0.05, + 0.1, + 0.25, + 0.5, + 1, + 2, + 5, + 10, + 20, + ], + ), + float, + ) + for grid, name in ((mach, "mach"), (alpha, "alpha")): + if ( + grid.ndim != 1 + or not 2 <= len(grid) <= 20 + or not np.isfinite(grid).all() + or np.any(np.diff(grid) <= 0) + ): + raise ValueError(f"DATCOM {name} needs 2..20 finite, increasing values") + if mach[0] <= 0: + raise ValueError("DATCOM Mach values must be positive") + if not alpha[0] < 0 < alpha[-1] or np.count_nonzero(alpha == 0) != 1: + raise ValueError( + "DATCOM alpha must include negative angles, zero and positive angles" + ) + raw_altitude = options.get("altitude", 0) + if isinstance(raw_altitude, dict): + if set(raw_altitude) != {"power_on", "power_off"}: + raise ValueError("altitude schedules need power_on and power_off") + schedules = {True: raw_altitude["power_on"], False: raw_altitude["power_off"]} + else: + schedules = {True: raw_altitude, False: raw_altitude} + altitude = {} + for powered, values in schedules.items(): + values = np.asarray(values, float) + if values.ndim == 0: + values = np.full(len(mach), float(values)) + if values.shape != mach.shape or not np.isfinite(values).all(): + raise ValueError( + "DATCOM altitude must be a finite scalar or one value per Mach" + ) + altitude[powered] = values + timeout = float(options.get("timeout", 60)) + if not math.isfinite(timeout) or timeout <= 0: + raise ValueError("DATCOM timeout must be positive and finite") + return mach, alpha, altitude, timeout + + +def _namelist(name, items): + """Wrap Fortran namelist lines below the legacy 80-column input limit.""" + lines = [f" ${name}"] + for key, values in items: + if isinstance(values, str): + tokens = [values] + else: + tokens = [f"{float(v):.9G}" for v in np.atleast_1d(values)] + tokens = [ + token + if "." in token + else (token.replace("E", ".E") if "E" in token else token + ".") + for token in tokens + ] + line = f" {key} = " + for token in tokens: + if len(line) + len(token) + 1 > 76: + lines.append(line) + line = " " + line += token + "," + lines.append(line) + lines[-1] += "$" + return "\n".join(lines) + + +def datcom_decks(rocket, *, calibration=None, flight_conditions=None): + """Build powered/coast decks from assembled geometry, without executing. + + DATCOM x is measured aft from the nose tip, in meters. The powered moment + reference is the filled CG at ignition; the coast reference is the empty + CG (including the dry motor). No plume or protuberance model is requested. + """ + surfaces = list(rocket.aerodynamic_surfaces) + datcom_fins = [ + s for s, _, _ in surfaces if isinstance(s, (DatcomFins, DatcomFreeFormFins)) + ] + if not datcom_fins: + raise ValueError("Add a DATCOM fin set before preparing DATCOM aerodynamics") + first = datcom_fins[0] + options = dict(first.datcom_options) + options.update(flight_conditions or {}) + calibration = rocket.datcom_calibration if calibration is None else calibration + edge_radius = calibration["leading_edge_radius"] + powered_base_drag = options.get("powered_base_drag", "none") + if powered_base_drag not in ("none", "nozzle", "full"): + raise ValueError("powered_base_drag must be 'none', 'nozzle' or 'full'") + mach, alpha, altitude, timeout = _flight_conditions(options) + + def canonical(value): + return json.dumps( + value, sort_keys=True, default=lambda v: np.asarray(v).tolist() + ) + + for fins in datcom_fins[1:]: + if fins.datcom_path != first.datcom_path or canonical( + fins.datcom_options + ) != canonical(first.datcom_options): + raise ValueError( + "DATCOM fin sets must use the same path and datcom_options" + ) + noses = [(s, p) for s, p, _ in surfaces if isinstance(s, NoseCone)] + tails = [(s, p) for s, p, _ in surfaces if isinstance(s, Tail)] + if len(noses) != 1 or len(tails) > 1: + raise ValueError("DATCOM requires exactly one nose and at most one tail") + if any( + not isinstance(s, (NoseCone, Tail, TrapezoidalFins, DatcomFreeFormFins)) + for s, _, _ in surfaces + ): + raise ValueError( + "DATCOM supports a nose, DATCOM or trapezoidal fins and a conical or ogive tail" + ) + if any(abs(p.x) > 1e-12 or abs(p.y) > 1e-12 for _, p, _ in surfaces): + raise ValueError("DATCOM geometry must be centered on the rocket axis") + nose, tip = noses[0] + nose_types = { + "conical": "CONE", + "ogive": "OGIVE", + "tangent": "OGIVE", + "tangentogive": "OGIVE", + "vonkarman": "KARMAN", + "lvhaack": "HAACK", + "powerseries": "POWER", + } + kind = nose.kind.replace(" ", "").lower() + if kind not in nose_types or nose.bluffness: + raise ValueError( + "DATCOM requires an unblunted conical, ogive, Haack, Karman or power nose" + ) + if not math.isclose(nose.base_radius, rocket.radius): + raise ValueError("DATCOM nose base radius must match the rocket radius") + + def aft(position): + return (tip.z - position) * rocket._csys + + if tails: + tail, position = tails[0] + body_length = aft(position.z) - nose.length + if not math.isclose(tail.top_radius, rocket.radius): + raise ValueError("DATCOM tail top radius must match the rocket radius") + if tail.bottom_radius <= 0 or tail.bottom_radius > tail.top_radius: + raise ValueError("DATCOM tail must be a boattail with positive base radius") + aft_length = tail.length + aft_diameter = 2 * tail.bottom_radius + if "body_length" in options and not math.isclose( + float(options["body_length"]), body_length + ): + raise ValueError( + "DATCOM body_length conflicts with the existing tail position" + ) + else: + body_length = float( + options.get("body_length", aft(rocket.nozzle_position) - nose.length) + ) + aft_length, aft_diameter = 0, 2 * rocket.radius + if not math.isfinite(body_length) or body_length <= 0: + raise ValueError( + "DATCOM cylindrical body length must be positive; add a tail or set body_length" + ) + if aft_length < 0 or not math.isfinite(aft_length): + raise ValueError("DATCOM tail length must be finite and nonnegative") + total_length = nose.length + body_length + aft_length + + def body_radius(x): + if x < nose.length: + return float(np.interp(x, nose.shape_vec[0], nose.shape_vec[1])) + if aft_length > 0 and x > nose.length + body_length: + if hasattr(tail, "radius_at"): + return float(tail.radius_at(x - nose.length - body_length)) + fraction = (x - nose.length - body_length) / aft_length + return rocket.radius + fraction * (aft_diameter / 2 - rocket.radius) + return rocket.radius + + fin_items, naca_cards, deflections = [], [], [] + all_fins = [ + (s, p) + for s, p, _ in surfaces + if isinstance(s, (TrapezoidalFins, DatcomFreeFormFins)) + ] + all_fins.sort(key=lambda pair: aft(pair[1].z)) + if len(all_fins) > 4: + raise ValueError("This DATCOM integration supports at most four fin sets") + previous_end, previous_span = None, None + for index, (fins, position) in enumerate(all_fins, 1): + is_datcom = isinstance(fins, (DatcomFins, DatcomFreeFormFins)) + spans = np.asarray( + fins.datcom_span_stations if is_datcom else [0, fins.span], float + ) + chords = np.asarray( + fins.datcom_chords if is_datcom else [fins.root_chord, fins.tip_chord], + float, + ) + edges = np.asarray( + fins.datcom_leading_edges if is_datcom else [0, fins.sweep_length], float + ) + xle = aft(position.z) + edges + if ( + len(spans) > 10 + or len(spans) < 2 + or np.any(np.diff(spans) <= 0) + or not np.isfinite(np.concatenate((spans, chords, edges))).all() + or np.any(chords[:-1] <= 0) + or chords[-1] < 0 + ): + raise ValueError("Invalid DATCOM fin span stations/chords/leading edges") + # Automatic attachment samples the body at the exposed root chord + # centre (Missile DATCOM guide, p. 13). A fin may trail past the base; + # requiring every trailing edge inside the body rejects valid cases. + root_center = float(xle[0] + chords[0] / 2) + if np.any(xle < 0) or not 0 <= root_center <= total_length + 1e-9: + raise ValueError( + "DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip" + ) + if not isinstance(fins.n, (int, np.integer)) or not 1 <= fins.n <= 8: + raise ValueError("DATCOM requires 1..8 panels per fin set") + start, finish = float(xle.min()), float((xle + chords).max()) + if previous_end is not None: + gap = start - previous_end + if gap < -1e-9: + raise ValueError("DATCOM fin-set planforms must not overlap") + if gap < 4 * previous_span: + warnings.warn( + "DATCOM fin sets are separated by less than four exposed semispans; " + "the upstream vortex-roll-up approximation may be inaccurate.", + UserWarning, + stacklevel=2, + ) + previous_end, previous_span = finish, float(spans[-1]) + attachment = body_radius(float(xle[0] + chords[0] / 2)) + automatic = getattr(fins, "auto_body_radius", True) + if not automatic and not math.isclose(fins.rocket_radius, attachment): + raise ValueError( + "DATCOM fin root radius must match the body at root mid-chord" + ) + if is_datcom: + fins._datcom_attachment_radius = attachment + fins._datcom_drawing_body = { + "has_tail": bool(tails), + "end": tip.z - rocket._csys * total_length, + } + span_input = spans if automatic else spans + fins.rocket_radius + count = len(spans) + section = getattr(fins, "section_type", "HEX") + if edge_radius is not None and section == "NACA": + raise ValueError("NACA sections define their own leading-edge radius") + radius = ( + getattr(fins, "leading_edge_radius", 0) + if edge_radius is None + else edge_radius + ) + items = [ + ("XLE", xle), + ("NPANEL", fins.n), + ( + "PHIF", + _station_values( + getattr(fins, "panel_angles", np.arange(fins.n) * 360 / fins.n), + "panel_angles", + fins.n, + ), + ), + ("GAM", _station_values(getattr(fins, "dihedral", 0), "dihedral", fins.n)), + ("SSPAN", span_input), + ("CHORD", chords), + ("SECTYP", section), + ] + if section in ("HEX", "ARC"): + items.extend( + [ + ( + "ZUPPER", + _station_values( + getattr(fins, "upper_thickness_to_chord", 0.01), + "upper thickness", + count, + ), + ), + ( + "ZLOWER", + _station_values( + getattr(fins, "lower_thickness_to_chord", 0.01), + "lower thickness", + count, + ), + ), + ( + "LER", + _station_values( + radius, + "leading_edge_radius", + count, + ), + ), + ] + ) + if section == "HEX": + items.extend( + [ + ( + "LMAXU", + _station_values( + getattr(fins, "max_thickness_position", 0.25), + "max_thickness_position", + count, + ), + ), + ( + "LFLATU", + _station_values( + getattr(fins, "flat_length", 0.5), "flat_length", count + ), + ), + ( + "LMAXL", + _station_values( + getattr(fins, "lower_max_thickness_position", 0.25), + "lower_max_thickness_position", + count, + ), + ), + ( + "LFLATL", + _station_values( + getattr(fins, "lower_flat_length", 0.5), + "lower_flat_length", + count, + ), + ), + ] + ) + elif section == "NACA": + naca_cards.append(f"NACA-{index}-{fins.naca}") + elif section == "USER": + coordinates = fins.airfoil_coordinates + items.append(("XCORD", coordinates["x"])) + for key, variable in ( + ("upper", "YUPPER"), + ("lower", "YLOWER"), + ("mean", "MEAN"), + ("thickness", "THICK"), + ): + if key in coordinates: + items.append((variable, coordinates[key])) + items.append( + ( + "LER", + _station_values(radius, "leading_edge_radius", count), + ) + ) + else: + raise ValueError("Unsupported DATCOM fin section") + fin_items.append(_namelist(f"FINSET{index}", items)) + incidence = fins.datcom_incidence if is_datcom else (-fins.cant_angle,) * fins.n + deflections.append( + (f"DELTA{index}", _station_values(incidence, "incidence", fins.n)) + ) + + filled_cg = float(rocket.center_of_mass(rocket.motor.burn_start_time)) + empty_cg = float(rocket.center_of_dry_mass_position) + if not all( + math.isfinite(cg) and 0 <= aft(cg) <= total_length + for cg in (filled_cg, empty_cg) + ): + raise ValueError("DATCOM filled and empty CG must lie inside the body") + needs_beta_grid = any( + s.n < 3 + or not np.allclose( + getattr(s, "panel_angles", np.arange(s.n) * 360 / s.n), + np.arange(s.n) * 360 / s.n, + ) + or np.any(getattr(s, "dihedral", 0)) + or np.any(getattr(s, "datcom_incidence", -s.cant_angle)) + for s, _ in all_fins + ) + beta_values = np.asarray( + options.get("beta", [-5, 0, 5] if needs_beta_grid else [0]), float + ) + if ( + beta_values.ndim != 1 + or not 1 <= len(beta_values) <= 20 + or not np.isfinite(beta_values).all() + or np.any(np.diff(beta_values) <= 0) + or np.count_nonzero(beta_values == 0) != 1 + or (len(beta_values) > 1 and not beta_values[0] < 0 < beta_values[-1]) + ): + raise ValueError( + "DATCOM beta must be [0] or increasing signed angles including zero" + ) + exit_diameter = None + if powered_base_drag == "nozzle": + exit_diameter = 2 * float(rocket.motor.nozzle_radius) + if not math.isfinite(exit_diameter) or not 0 < exit_diameter <= aft_diameter: + raise ValueError( + "Powered nozzle diameter must be positive and fit inside the aft body" + ) + elif powered_base_drag == "full": + exit_diameter = 0 + decks = {} + reynolds = options.get("reynolds") + if reynolds is not None: + if not isinstance(reynolds, dict): + reynolds = {"power_on": reynolds, "power_off": reynolds} + if set(reynolds) != {"power_on", "power_off"}: + raise ValueError("reynolds schedules need power_on and power_off") + ren = {} + for powered, label in ((True, "power_on"), (False, "power_off")): + values = np.asarray(reynolds[label], float) + if values.ndim == 0: + values = np.full(len(mach), float(values)) + if ( + values.shape != mach.shape + or not np.isfinite(values).all() + or np.any(values <= 0) + ): + raise ValueError( + "DATCOM reynolds needs positive Reynolds numbers per meter, one per Mach" + ) + ren[powered] = values + for powered, cg in ((True, filled_cg), (False, empty_cg)): + body = [ + ("TNOSE", nose_types[kind]), + ("LNOSE", nose.length), + ("DNOSE", 2 * rocket.radius), + ("LCENTR", body_length), + ("DCENTR", 2 * rocket.radius), + ("BASE", ".FALSE."), + ] + if kind == "powerseries": + body.append(("POWER", nose.power)) + if aft_length > 0 and not math.isclose(aft_diameter, 2 * rocket.radius): + body.extend( + [ + ( + "TAFT", + "OGIVE" + if getattr(tail, "kind", "conical") == "ogive" + else "CONE", + ), + ("LAFT", aft_length), + ("DAFT", aft_diameter), + ] + ) + if not powered: + body.append(("DEXIT", 0)) + elif exit_diameter is not None: + body.append(("DEXIT", exit_diameter)) + reference_items = [ + ("XCG", aft(cg)), + ("SREF", rocket.area), + ("LREF", 2 * rocket.radius), + ] + if calibration["roughness_height"] > 0: + # RHR is independent of DIM; the local 3/99 binary disagrees with + # the 5/97 guide's metric ROUGH note. k = 3 RHR microinches. + reference_items.append( + ("RHR", calibration["roughness_height"] / (3e-6 * 0.0254)) + ) + cases = [] + for beta in beta_values: + cases.append( + "\n".join( + [ + "DIM M", + _namelist( + "FLTCON", + [ + ("NMACH", len(mach)), + ("MACH", mach), + ("ALT", altitude[powered]) + if reynolds is None + else ("REN", ren[powered]), + ("NALPHA", len(alpha)), + ("ALPHA", alpha), + ("BETA", beta), + ], + ), + _namelist("REFQ", reference_items), + _namelist("AXIBOD", body), + *fin_items, + _namelist("DEFLCT", deflections), + *naca_cards, + "DAMP", + "NEXT CASE", + "", + ] + ) + ) + decks[powered] = "".join(cases) + return decks, {True: filled_cg, False: empty_cg}, timeout, first.datcom_path + + +def run_datcom(path, deck, timeout=60): + """Copy and run a user-supplied native executable; always remove all files.""" + source = Path(path).expanduser().resolve() + if not source.is_file(): + raise FileNotFoundError(f"DATCOM executable not found: {source}") + with tempfile.TemporaryDirectory(prefix="rocketpy-datcom-") as directory: + executable = Path(directory, source.name) + shutil.copy2(source, executable) + Path(directory, "for005.dat").write_text(deck, encoding="ascii") + try: + result = subprocess.run( + [str(executable)], + cwd=directory, + capture_output=True, + text=True, + timeout=timeout, + errors="replace", + check=False, + ) + except subprocess.TimeoutExpired as exc: + raise RuntimeError( + f"DATCOM exceeded its {timeout:g} second timeout" + ) from exc + except OSError as exc: + raise RuntimeError( + "Cannot execute DATCOM; provide a native executable with execute permission" + ) from exc + if result.returncode: + raise RuntimeError( + f"DATCOM failed ({result.returncode}): {result.stderr[-2000:]}" + ) + output = Path(directory, "for006.dat") + if not output.is_file(): + raise RuntimeError("DATCOM did not produce for006.dat") + # Some DATCOM builds return success even after rejecting an input deck. + raw = output.read_text(errors="replace") + if re.search(r"\*\*\s*ERROR\s*\*\*|\bFATAL\b", raw, re.IGNORECASE): + raise RuntimeError("DATCOM reported an input error in for006.dat") + parsed = parse_datcom_output(directory) + # A successful exit is insufficient: old DATCOM builds can fall back + # to defaults after malformed inputs. Verify the requested table grid. + for variable, actual in (("MACH", parsed[4]), ("ALPHA", parsed[5])): + requested = re.search(rf"\b{variable}\s*=\s*([\dEe+.,\s-]+)", deck) + if requested: + expected = np.array( + [float(v) for v in re.split(r"[\s,]+", requested[1].strip(" ,\n"))] + ) + if actual.shape != expected.shape or not np.allclose( + actual, expected, atol=0.005, rtol=0 + ): + raise RuntimeError(f"DATCOM returned a different {variable} grid") + requested_beta = [ + float(match[1]) for match in re.finditer(r"\bBETA\s*=\s*([-+\d.eE]+)", deck) + ] + if requested_beta: + expected_beta = np.unique(requested_beta) + if parsed.beta.shape != expected_beta.shape or not np.allclose( + parsed.beta, expected_beta, atol=0.005, rtol=0 + ): + raise RuntimeError("DATCOM returned a different BETA grid") + return parsed + + +class DatcomSurface(GenericSurface): + """Whole-vehicle body-axis loads, including all eleven DAMP derivatives. + + DATCOM uses forward/right/down axes. In RocketPy the force channels map to + (CY, -CN, -CA) and moments to (CM, CLN, CLL). Static lateral derivatives and + DAMP derivatives in for006 are per degree. Rates are nondimensionalized by + diameter/(2 V). Sideslip uses full tables when available, otherwise a + linear expansion about beta=0. + + Loads are evaluated at each case's reference CG and transferred to the dry + CG. Flight explicitly transfers their physical torque to the instantaneous + wet CG. No fuel-fraction blending of powered and coast base drag is used. + """ + + def __init__(self, rocket, tables, references): + super().__init__(rocket.area, 2 * rocket.radius, {}, name="DATCOM vehicle") + self.tables = tables + self.references = references + self.dry_reference = float(rocket.center_of_dry_mass_position) + self.csys = rocket._csys + self.burn_start = rocket.motor.burn_start_time + self.burn_out = rocket.motor.burn_out_time + self._interpolators = {} + self.outside_alpha_envelope = False + self.outside_mach_envelope = False + self.outside_beta_envelope = False + for powered, table in tables.items(): + mach, alpha = table[4], np.radians(table[5]) + zero_alpha_index(alpha) + if not alpha[0] < 0 < alpha[-1]: + raise ValueError("DATCOM needs signed alpha tables") + + def channels(t): + arrays = [t.body[k] for k in BODY_KEYS] + arrays += [t.derivatives[k] for k in LATERAL_KEYS] + arrays += [t.damping[k] for k in DAMPING_KEYS] + arrays += [t[1]] + values = np.stack(arrays, axis=-1) + if ( + values.shape != (len(mach), len(alpha), 21) + or not np.isfinite(values).all() + ): + raise ValueError("DATCOM tables are incomplete or nonfinite") + return values + + if table.sideslip: + values = np.stack( + [channels(table.sideslip[float(beta)]) for beta in table.beta], + axis=2, + ) + grid = (mach, alpha, np.radians(table.beta)) + else: + values = channels(table) + grid = (mach, alpha) + self._interpolators[powered] = RegularGridInterpolator(grid, values) + + def is_powered(self, time): + return self.burn_start <= time < self.burn_out + + def reference_offset(self, time): + """Case reference relative to dry CG, in RocketPy body axes (meters).""" + return Vector( + [ + 0, + 0, + (self.references[self.is_powered(time)] - self.dry_reference) + * self.csys, + ] + ) + + def coefficients(self, powered, mach, alpha, beta=0): + """Interpolate Mach and signed incidence; clamp outside the input grid.""" + interp = self._interpolators[powered] + m, a = interp.grid[:2] + point = [np.clip(mach, m[0], m[-1]), np.clip(alpha, a[0], a[-1])] + if len(interp.grid) == 3: + b = interp.grid[2] + point.append(np.clip(beta, b[0], b[-1])) + return interp(point)[0] + + def drag_coefficient(self, powered, alpha, beta, mach, reynolds, q, r, p): + """DATCOM stability-axis drag for reporting and plots.""" + return float(self.coefficients(powered, mach, alpha, beta)[-1]) + + def compute_forces_and_moments_at_time( + self, + time, + alpha_dot, + stream_velocity, + stream_speed, + stream_mach, + rho, + cp, + omega, + reynolds, + ): + """Return body loads at dry CG for a deterministic RHS evaluation.""" + powered = self.is_powered(time) + velocity = -stream_velocity + speed = abs(velocity) + if speed < 1e-12: + return (0.0,) * 6 + alpha = math.atan2(velocity.y, abs(velocity.z)) + beta = math.atan2(velocity.x, abs(velocity.z)) + interp = self._interpolators[powered] + m, a = interp.grid[:2] + physical_alpha = math.atan2(velocity.y, velocity.z) + self.outside_alpha_envelope |= not a[0] <= physical_alpha <= a[-1] + self.outside_mach_envelope |= not m[0] <= stream_mach <= m[-1] + if len(interp.grid) == 3: + b = interp.grid[2] + self.outside_beta_envelope |= not b[0] <= beta <= b[-1] + values = self.coefficients(powered, stream_mach, alpha, beta) + cn, cm, ca, cy, cln, cll, cyb, clnb, cllb = values[:9] + beta_deg = math.degrees(beta) + if len(interp.grid) == 2: + cy += cyb * beta_deg + cln += clnb * beta_deg + cll += cllb * beta_deg + if speed >= 1: + cnq, cmq, caq, cnad, cmad, cyr, clnr, cllr, cyp, clnp, cllp = values[9:20] + factor = math.degrees(self.reference_length / (2 * speed)) + q, r, p = (rate * factor for rate in omega) + adot = alpha_dot * factor + cn += cnq * q + cnad * adot + cm += cmq * q + cmad * adot + ca += caq * q + cy += cyr * r + cyp * p + cln += clnr * r + clnp * p + cll += cllr * r + cllp * p + q_area = 0.5 * rho * speed**2 * self.reference_area + # CA is already a body-axis coefficient. Applying another cosine here + # would project it twice. Only reverse its direction for tail-first flow. + axial_direction = math.copysign(1.0, velocity.z) + forces = Vector([cy, -cn, -ca * axial_direction]) * q_area + moments = Vector([cm, cln, cll]) * (q_area * self.reference_length) + moments += cp ^ forces + return (*forces, *moments) + + def compute_forces_and_moments(self, *args, **kwargs): + raise RuntimeError( + "DATCOM loads require Flight to supply time and incidence rate" + ) + + def small_angle_cp(self, mach): + """Coast small-angle CP in the user's axial frame, in meters.""" + table = self.tables[False] + zero = zero_alpha_index(table[5]) + cna = np.interp(mach, table[4], table.derivatives["CNA"][:, zero]) + cma = np.interp(mach, table[4], table.derivatives["CMA"][:, zero]) + if abs(cna) < 1e-12: + return float("nan") + return self.references[False] + self.csys * self.reference_length * cma / cna diff --git a/rocketpy/rocket/datcom_calibration.py b/rocketpy/rocket/datcom_calibration.py new file mode 100644 index 000000000..15136245f --- /dev/null +++ b/rocketpy/rocket/datcom_calibration.py @@ -0,0 +1,426 @@ +"""Fit native DATCOM inputs without replacing aerodynamic coefficients.""" + +from collections.abc import Mapping +from pathlib import Path + +import numpy as np +from scipy.optimize import minimize, minimize_scalar + +COEFFICIENTS = ("CD", "CL", "CA", "CN", "CY", "CM", "CLN", "CLL") +INPUTS = {"MACH", "ALPHA", "BETA", "POWER_ON", "REYNOLDS_PER_METER"} + + +def _measurements(data, power_on): + """Normalize finite column data, preserving partial measured channels.""" + if isinstance(data, (str, Path)): + table = np.genfromtxt(data, delimiter=",", names=True, encoding="utf-8-sig") + if not table.dtype.names: + raise ValueError("Wind-tunnel CSV requires a header") + data = {key: np.atleast_1d(table[key]) for key in table.dtype.names} + if not isinstance(data, Mapping): + raise ValueError("Wind-tunnel data must be a CSV path or column mapping") + columns = {} + for key, value in data.items(): + name = str(key).strip().upper() + if name in columns: + raise ValueError(f"Duplicate wind-tunnel column: {name}") + if name not in INPUTS | set(COEFFICIENTS): + raise ValueError(f"Unsupported wind-tunnel column: {key}") + array = np.asarray(value, float) + if array.ndim != 1 or not len(array): + raise ValueError(f"Wind-tunnel column {key} must be a nonempty vector") + columns[name] = array + if "MACH" not in columns or not set(columns) & set(COEFFICIENTS): + raise ValueError("Wind-tunnel data needs mach and at least one coefficient") + count = len(columns["MACH"]) + if any(len(value) != count for value in columns.values()): + raise ValueError("Wind-tunnel columns must have equal lengths") + if not np.isfinite(columns["MACH"]).all() or np.any(columns["MACH"] <= 0): + raise ValueError("Wind-tunnel Mach numbers must be finite and positive") + for key, default in (("ALPHA", 0), ("BETA", 0), ("POWER_ON", float(power_on))): + columns.setdefault(key, np.full(count, default)) + if not np.isfinite(columns[key]).all(): + raise ValueError(f"Wind-tunnel {key} must be finite") + if not np.isin(columns["POWER_ON"], [0, 1]).all(): + raise ValueError("Wind-tunnel power_on must be 0 or 1") + if "REYNOLDS_PER_METER" in columns: + ren = columns["REYNOLDS_PER_METER"] + if not np.isfinite(ren).all() or np.any(ren <= 0): + raise ValueError( + "Tunnel Reynolds number per meter must be finite and positive" + ) + measured = [key for key in COEFFICIENTS if key in columns] + if any(np.isinf(columns[key]).any() for key in measured): + raise ValueError("Measured coefficients cannot be infinite") + if any(not np.isfinite(columns[key]).any() for key in measured): + raise ValueError("Every coefficient column must contain a measurement") + if not np.any( + np.column_stack([np.isfinite(columns[key]) for key in measured]), axis=1 + ).all(): + raise ValueError("Every row needs at least one finite measured coefficient") + return columns, measured + + +def _positive(value, name): + try: + number = float(value) if np.ndim(value) == 0 else float("nan") + except (ValueError, TypeError, OverflowError): + number = float("nan") + if not np.isfinite(number) or number <= 0: + raise ValueError(f"{name} must be a finite positive scalar") + return number + + +def _predictions( + model, rocket, columns, measured, area, length, lateral_length, origin, drag_axis +): + """Transform native coefficients to the tunnel's axes and references.""" + predicted = {key: [] for key in measured} + for i, mach in enumerate(columns["MACH"]): + state = bool(columns["POWER_ON"][i]) + alpha, beta = np.radians([columns["ALPHA"][i], columns["BETA"][i]]) + grid = model._interpolators[state].grid + values = [mach, alpha] + ([beta] if len(grid) == 3 else []) + if any( + value < axis[0] - 1e-12 or value > axis[-1] + 1e-12 + for value, axis in zip(values, grid) + ): + raise ValueError( + "Wind-tunnel measurements exceed the DATCOM Mach/angle envelope" + ) + if len(grid) == 2 and beta != 0: + raise ValueError("Nonzero tunnel beta requires a DATCOM beta table") + cn, cm, ca, cy, cln, cll = model.coefficients(state, mach, alpha, beta)[:6] + lift = cn * np.cos(alpha) - ca * np.sin(alpha) + if drag_axis == "wind": + # Unit flow = (tan(beta), tan(alpha), 1) / norm in RocketPy axes. + cd = (ca + cn * np.tan(alpha) - cy * np.tan(beta)) / np.sqrt( + 1 + np.tan(alpha) ** 2 + np.tan(beta) ** 2 + ) + else: + cd = ca * np.cos(alpha) + cn * np.sin(alpha) + result = {"CD": cd, "CL": lift, "CA": ca, "CN": cn, "CY": cy} + result = {key: value * rocket.area / area for key, value in result.items()} + if any(key in measured for key in ("CM", "CLN", "CLL")): + # M_new = M_old + (r_old - r_new) x F. In RocketPy axes + # F=(CY,-CN,-CA), so the axial shift adds (d*CN,d*CY,0). + shift = ( + (model.references[state] - origin) * rocket._csys / (2 * rocket.radius) + ) + scale = rocket.area * (2 * rocket.radius) / area + result.update( + CM=(cm + shift * cn) * scale / length, + CLN=(cln + shift * cy) * scale / lateral_length, + CLL=cll * scale / lateral_length, + ) + for key in measured: + predicted[key].append(float(result[key])) + return predicted + + +def _tunnel_conditions(columns, original_mach, model_scale=1): + """Use measured Reynolds conditions only for fitting, never for Flight.""" + if "REYNOLDS_PER_METER" not in columns: + return None + mach = np.unique(columns["MACH"]) + if len(mach) == 1: + other = np.asarray(original_mach)[np.asarray(original_mach) != mach[0]] + mach = np.sort([mach[0], other[np.argmin(abs(other - mach[0]))]]) + if len(mach) > 20: + raise ValueError("Tunnel Reynolds schedule supports at most 20 Mach values") + schedules = {} + for state, label in ((True, "power_on"), (False, "power_off")): + mask = columns["POWER_ON"] == state + # The unused state still needs a valid deck; it is not evaluated. + if not mask.any(): + mask = np.ones(len(columns["MACH"]), dtype=bool) + levels = np.unique(columns["MACH"][mask]) + reynolds = [] + for value in levels: + values = columns["REYNOLDS_PER_METER"][mask & (columns["MACH"] == value)] + if not np.allclose(values, values[0], rtol=1e-8, atol=0): + raise ValueError( + "Tunnel data needs one Reynolds number per Mach and power state" + ) + reynolds.append(values[0] * model_scale) + schedules[label] = np.interp(mach, levels, reynolds).tolist() + # ALT is unused with REN but must have the new grid's shape for validation. + return {"mach": mach.tolist(), "reynolds": schedules, "altitude": 0} + + +def fit_datcom_calibration( + rocket, + data, + *, + reference_area=None, + parameters=("roughness_height",), + bounds=None, + reference_length=None, + moment_reference=None, + lateral_reference_length=None, + model_scale=1.0, + power_on=False, + weights=None, + drag_axis="wind", + max_evaluations=100, +): + """Bounded coefficient fit; publish calibration only after successful runs.""" + from rocketpy.rocket.datcom import DatcomSurface, datcom_decks, run_datcom + + if not rocket._has_datcom_fins(): + raise ValueError("Wind-tunnel calibration requires DATCOM fins") + if not isinstance(power_on, (bool, np.bool_)): + raise ValueError("power_on must be boolean; row values may be 0 or 1") + columns, measured = _measurements(data, power_on) + model_scale = _positive(model_scale, "model_scale") + measured_area = _positive( + rocket.area * model_scale**2 if reference_area is None else reference_area, + "reference_area", + ) + area = measured_area / model_scale**2 + length = lateral_length = origin = None + if set(measured) & {"CM", "CLN", "CLL"}: + length = _positive(reference_length, "reference_length") / model_scale + lateral_length = ( + _positive( + reference_length + if lateral_reference_length is None + else lateral_reference_length, + "lateral_reference_length", + ) + / model_scale + ) + if ( + moment_reference is None + or np.ndim(moment_reference) != 0 + or not np.isfinite(moment_reference) + ): + raise ValueError( + "Moment measurements require a finite moment_reference in rocket coordinates" + ) + origin = float(moment_reference) + if drag_axis not in ("wind", "stability"): + raise ValueError("drag_axis must be 'wind' or 'stability'") + if isinstance(parameters, str): + parameters = (parameters,) + parameters = tuple(parameters) + if ( + not parameters + or len(set(parameters)) != len(parameters) + or set(parameters) - {"roughness_height", "leading_edge_radius"} + ): + raise ValueError( + "Fit roughness_height and/or leading_edge_radius without duplicates" + ) + if "leading_edge_radius" in parameters and any( + getattr(s, "section_type", None) == "NACA" + for s, _, _ in rocket.aerodynamic_surfaces + ): + raise ValueError("NACA sections define their own leading-edge radius") + limits = dict(bounds or {}) + if set(limits) - set(parameters): + raise ValueError("bounds contains parameters which are not being fitted") + limits.setdefault("roughness_height", (0, 300e-6)) + for name in parameters: + pair = np.asarray(limits.get(name, []), float) + if ( + pair.shape != (2,) + or not np.isfinite(pair).all() + or not 0 <= pair[0] < pair[1] + ): + raise ValueError( + f"{name} requires finite nonnegative (lower, upper) bounds" + ) + limits[name] = pair + weights = dict(weights or {}) + if set(weights) - set(measured): + raise ValueError("weights names must correspond to measured coefficients") + weight = {key: _positive(weights.get(key, 1), f"weight {key}") for key in measured} + if ( + isinstance(max_evaluations, bool) + or not isinstance(max_evaluations, (int, np.integer)) + or max_evaluations < 3 + ): + raise ValueError("max_evaluations must be an integer of at least 3") + observation_count = sum(int(np.isfinite(columns[key]).sum()) for key in measured) + if observation_count < len(parameters): + raise ValueError( + "Not enough measured coefficients for the requested parameters" + ) + original = rocket.datcom_calibration + # Native geometry setup is evaluated without publishing any trial model or + # changing the rocket's calibration, cached model, or reporting functions. + first = next( + s for s, _, _ in rocket.aerodynamic_surfaces if hasattr(s, "datcom_options") + ) + original_mach = np.asarray( + first.datcom_options.get("mach", [0.1, 0.3, 0.5, 0.7, 0.9, 1.1, 1.5, 2]) + ) + conditions = _tunnel_conditions(columns, original_mach, model_scale) + states = sorted(set(bool(value) for value in columns["POWER_ON"])) + cache = {} + + def evaluate(settings): + key = (settings["roughness_height"], settings["leading_edge_radius"]) + if key in cache: + return cache[key] + if len(cache) >= max_evaluations: + raise RuntimeError( + "Wind-tunnel fit exceeded max_evaluations; calibration was not changed" + ) + decks, references, timeout, path = datcom_decks( + rocket, calibration=settings, flight_conditions=conditions + ) + tables = {state: run_datcom(path, decks[state], timeout) for state in states} + model = DatcomSurface(rocket, tables, references) + predictions = _predictions( + model, + rocket, + columns, + measured, + area, + length, + lateral_length, + origin, + drag_axis, + ) + residuals = np.concatenate( + [ + (np.asarray(predictions[key]) - columns[key])[np.isfinite(columns[key])] + * np.sqrt(weight[key]) + for key in measured + ] + ) + cache[key] = (float(np.mean(residuals**2)), predictions) + return cache[key] + + initial_cost, initial_prediction = evaluate(original) + + def settings_for(point): + settings = dict(original) + for name, value in zip(parameters, np.atleast_1d(point)): + low, high = limits[name] + settings[name] = float(low + value * (high - low)) + return settings + + def objective(point): + return evaluate(settings_for(point))[0] + + if len(parameters) == 1: + result = minimize_scalar( + objective, + bounds=(0, 1), + method="bounded", + options={"xatol": 1e-4, "maxiter": max_evaluations - 3}, + ) + best_point = [result.x] + else: + start = [ + np.clip( + ((original[name] or 0) - limits[name][0]) / np.ptp(limits[name]), 0, 1 + ) + for name in parameters + ] + result = minimize( + objective, + start, + method="Powell", + bounds=[(0, 1)] * len(parameters), + options={"maxfev": max_evaluations - 1, "xtol": 1e-4, "ftol": 1e-6}, + ) + best_point = result.x + if not result.success: + raise RuntimeError( + f"Wind-tunnel fit failed; calibration was not changed: {result.message}" + ) + # Explicitly compare endpoints: rounded native coefficients can hide a + # boundary optimum from an interior bounded search. + candidates = [settings_for(best_point)] + if len(parameters) == 1: + candidates += [settings_for([0]), settings_for([1])] + fitted = min(candidates, key=lambda settings: evaluate(settings)[0]) + final_cost, prediction = evaluate(fitted) + # Retain an existing calibration if it is within bounds and already better. + if ( + all(original[name] is not None for name in parameters) + and all( + limits[name][0] <= (original[name] or 0) <= limits[name][1] + for name in parameters + ) + and initial_cost < final_cost + ): + fitted, final_cost, prediction = original, initial_cost, initial_prediction + residual_rows = [] + rmse = {} + for key in measured: + mask = np.isfinite(columns[key]) + delta = np.asarray(prediction[key])[mask] - columns[key][mask] + rmse[key] = dict( + initial=float( + np.sqrt( + np.mean( + (np.asarray(initial_prediction[key])[mask] - columns[key][mask]) + ** 2 + ) + ) + ), + fitted=float(np.sqrt(np.mean(delta**2))), + ) + for i in np.flatnonzero(mask): + residual_rows.append( + dict( + row=int(i), + mach=float(columns["MACH"][i]), + alpha_deg=float(columns["ALPHA"][i]), + beta_deg=float(columns["BETA"][i]), + power_on=bool(columns["POWER_ON"][i]), + coefficient=key, + observed=float(columns[key][i]), + predicted=prediction[key][i], + residual=float(prediction[key][i] - columns[key][i]), + ) + ) + report = dict( + success=True, + improved=bool(final_cost < initial_cost - 1e-16), + observation_count=observation_count, + calibration=dict(fitted), + initial_calibration=original, + parameters=list(parameters), + bounds={key: limits[key].tolist() for key in parameters}, + reference_area=area, + measured_reference_area=measured_area, + model_scale=model_scale, + reference_length=length, + lateral_reference_length=lateral_length, + moment_reference=origin, + drag_axis=drag_axis, + reynolds_source="measurements" + if conditions is not None + else "rocket DATCOM options", + weighted_rmse_initial=float(np.sqrt(initial_cost)), + weighted_rmse_fitted=float(np.sqrt(final_cost)), + rmse=rmse, + residuals=residual_rows, + native_evaluations=len(cache), + native_runs=len(cache) * len(states), + at_bound={ + name: bool( + np.isclose( + fitted[name], + limits[name][0], + rtol=0, + atol=np.ptp(limits[name]) * 1e-4, + ) + or np.isclose( + fitted[name], + limits[name][1], + rtol=0, + atol=np.ptp(limits[name]) * 1e-4, + ) + ) + for name in parameters + }, + ) + rocket.add_datcom_calibration(**fitted) + return report diff --git a/rocketpy/rocket/datcom_conditions.py b/rocketpy/rocket/datcom_conditions.py new file mode 100644 index 000000000..ab5a3b9da --- /dev/null +++ b/rocketpy/rocket/datcom_conditions.py @@ -0,0 +1,67 @@ +"""Native-size DATCOM flight grids, independent of ordinary RocketPy models.""" + +import numpy as np + +MAX_MACH_POINTS = 20 + + +def adaptive_mach_grid(peak_mach, margin=0.15): + """Twenty positive Mach points with fixed anchors and transonic resolution. + + Mach output has two decimal places in the supported native build, so points + are separated by at least 0.01. Zero speed uses the 0.01 endpoint. The upper + bound includes 15% headroom plus 0.02 Mach, and is at least 1.2. Spare points + bisect gaps, prioritizing the reached flight regime over unreached speeds. + """ + if not np.isfinite(peak_mach) or peak_mach < 0: + raise ValueError("Reference peak Mach must be finite and nonnegative") + if not np.isfinite(margin) or margin < 0: + raise ValueError("Mach margin must be finite and nonnegative") + upper = np.ceil(max(1.2, peak_mach * (1 + margin) + 0.02) * 100) / 100 + reached = max(0.01, np.ceil(peak_mach * 100) / 100) + selected = { + 0.01, + 0.1, + 0.8, + 0.9, + 0.95, + 0.98, + 1.0, + 1.02, + 1.05, + 1.1, + 1.2, + upper, + reached, + } + while len(selected) < MAX_MACH_POINTS: + values = np.array(sorted(selected)) + gaps = np.diff(values) + middle = (values[:-1] + values[1:]) / 2 + priorities = gaps * np.where(middle <= reached, 4, 1) + priorities[gaps < 0.0199] = -1 + index = int(np.argmax(priorities)) + selected.add(float(np.round(middle[index], 2))) + return np.array(sorted(selected)) + + +def adaptive_alpha_grid(alpha_degrees): + """Signed 19-point grid, retaining ±20° minimum and dense small angles. + + Extend symmetrically beyond observed, dynamically meaningful incidence by + 15% plus 2 degrees. Tail-first flow is outside the current forward-flight + DATCOM load mapping and is rejected instead of silently folded or clipped. + """ + alpha = np.asarray(alpha_degrees, float) + if not alpha.size or not np.isfinite(alpha).all(): + raise ValueError("Reference flight needs finite loaded alpha samples") + peak = float(np.max(np.abs(alpha))) + extent = max(20, np.ceil((peak * 1.15 + 2) * 100) / 100) + if extent >= 90: + raise ValueError( + "Adaptive DATCOM alpha envelope reaches tail-first flow (90 degrees)" + ) + positive = np.round( + extent * np.array([0.0025, 0.005, 0.0125, 0.025, 0.05, 0.1, 0.25, 0.5, 1]), 2 + ) + return np.concatenate((-positive[::-1], [0.0], positive)) diff --git a/rocketpy/rocket/datcom_rocket.py b/rocketpy/rocket/datcom_rocket.py new file mode 100644 index 000000000..368dfa495 --- /dev/null +++ b/rocketpy/rocket/datcom_rocket.py @@ -0,0 +1,656 @@ +"""DATCOM-specific Rocket assembly and preparation, isolated from core models.""" + +import numpy as np + +from rocketpy.mathutils import Function +from rocketpy.mathutils.vector_matrix import Vector +from rocketpy.rocket.aero_surface.datcom_fins import DatcomFins, DatcomFreeFormFins + + +class DatcomRocketMixin: + """Optional DATCOM behavior shared by Rocket and its subclasses.""" + + def set_datcom_atmosphere( + self, + flight, + *, + mach=None, + alpha=None, + samples=1000, + minimum_dynamic_pressure=100, + ): + """Adapt native Mach/alpha grids and Reynolds schedules to a flight. + + The default Mach grid uses all 20 native slots, always includes 0.1 + and 1.0, concentrates points near Mach 1, and extends above the reference + peak by 15% plus 0.02. Both powered and coast samples determine coverage. + Mach zero uses the positive 0.01 endpoint. Explicit ``mach`` retains a + custom 2..20-point grid instead. The initial no-flight grid is provisional. + + Adaptive alpha has 19 signed points concentrated around zero, retaining + at least ±20 degrees and extending beyond observed incidence by 15% plus + 2 degrees. Only samples at ``minimum_dynamic_pressure`` (Pa, default + 100) or greater determine its extent; low-load pad/apogee directions do + not force extreme grids. With no loaded samples it retains ±20 degrees. + Explicit ``alpha`` fixes a custom native grid (degrees). Tail-first + adaptive envelopes are rejected because the current load model assumes + forward flight. The returned coverage report identifies excluded samples. + + ``flight`` may be an ordinary or previous DATCOM Flight with the same + motor burn interval. Sampling includes ``samples`` uniform points, + solver states and burn boundaries, from rail to apogee. Descent is + excluded; the current load model assumes forward-flight aerodynamics. + + At sampled altitude (meters ASL), the reference Environment supplies + density, sound speed and dynamic viscosity. Native Reynolds per meter + is rho*a*Mach/mu; REN replaces ALT, bypassing the standard atmosphere. + Return Mach, alpha, coverage, Reynolds, altitude, pressure (Pa) and + temperature (K) schedules for provenance. No Flight or native run is + started here; the next Flight regenerates the tables. Call again with + that result to refine both the envelope and atmospheric schedules. + + Each phase still uses one atmospheric state per Mach. Repeated Mach + retains the earliest sample; unreached Mach clamps atmospheric state + while recomputing speed. This does not resolve multiple ascent/descent + crossings at different altitudes or guarantee iterative convergence. + Ordinary equations are unchanged; failed validation preserves settings. + """ + from rocketpy.rocket.datcom import _flight_conditions + from rocketpy.rocket.datcom_conditions import ( + adaptive_alpha_grid, + adaptive_mach_grid, + ) + + fins = [ + s + for s, _, _ in self.aerodynamic_surfaces + if isinstance(s, (DatcomFins, DatcomFreeFormFins)) + ] + if not fins: + raise ValueError("Add DATCOM fins before setting their atmosphere") + if not isinstance(samples, (int, np.integer)) or samples < 3: + raise ValueError("samples must be an integer of at least 3") + if not np.allclose(self.motor.burn_time, flight.rocket.motor.burn_time): + raise ValueError("Reference flight must use the same motor burn interval") + if not np.isfinite(minimum_dynamic_pressure) or minimum_dynamic_pressure < 0: + raise ValueError("minimum_dynamic_pressure must be finite and nonnegative") + end = float(flight.apogee_time) + if not np.isfinite(end) or end <= 0: + raise ValueError("Reference flight must contain a positive flight end time") + time = np.linspace(0, end, samples) + # Include actual solver states and phase boundaries, not just uniform + # samples: short burns and rail/burnout transitions must not disappear. + extra = list(self.motor.burn_time) + if hasattr(flight, "solution"): + extra.extend(np.asarray(flight.solution)[:, 0]) + time = np.unique(np.concatenate((time, np.asarray(extra)))) + time = time[(time >= 0) & (time <= end)] + trajectory_mach = np.asarray(flight.mach_number(time), float) + altitude = np.asarray(flight.z(time), float) + env = flight.env + dynamic_pressure = ( + 0.5 + * np.asarray(env.density(altitude)) + * (trajectory_mach * env.speed_of_sound(altitude)) ** 2 + ) + loaded = np.isfinite(dynamic_pressure) & ( + dynamic_pressure >= minimum_dynamic_pressure + ) + options = dict(fins[0].datcom_options) + options["mach"] = ( + adaptive_mach_grid(float(np.max(trajectory_mach))) if mach is None else mach + ) + if alpha is None: + observed_alpha = np.asarray(flight.partial_angle_of_attack(time), float) + options["alpha"] = adaptive_alpha_grid( + observed_alpha[loaded] if np.any(loaded) else [0.0] + ) + else: + observed_alpha = None + options["alpha"] = alpha + # The old ALT schedule belongs to the old Mach grid. REN replaces it. + options.pop("altitude", None) + grid, alpha_grid, _, _ = _flight_conditions(options) + start, stop = self.motor.burn_time + powered = (time >= start) & (time < stop) + report = { + "mach": grid.tolist(), + "alpha": alpha_grid.tolist(), + "coverage": { + "scope": "rail through apogee", + "minimum_dynamic_pressure_pa": float(minimum_dynamic_pressure), + "excluded_low_q_samples": int(np.count_nonzero(~loaded)), + "phases": {}, + }, + "reynolds": {}, + "altitude_m_asl": {}, + "pressure_pa": {}, + "temperature_k": {}, + } + for state, label in [(True, "power_on"), (False, "power_off")]: + indices = np.flatnonzero( + (powered == state) + & np.isfinite(trajectory_mach) + & np.isfinite(altitude) + ) + if not len(indices): + raise ValueError(f"Reference ascent has no {label} samples") + phase_loaded = indices[loaded[indices]] + phase = { + "mach_min": float(np.min(trajectory_mach[indices])), + "mach_max": float(np.max(trajectory_mach[indices])), + "loaded_alpha_max_abs_deg": ( + float(np.max(np.abs(observed_alpha[phase_loaded]))) + if observed_alpha is not None and len(phase_loaded) + else None + ), + } + report["coverage"]["phases"][label] = phase + order = indices[np.argsort(trajectory_mach[indices], kind="stable")] + unique, first = np.unique(trajectory_mach[order], return_index=True) + z = altitude[order[first]] + env = flight.env + # Interpolate Re/M, then apply target Mach: clamped endpoints retain + # atmospheric state rather than incorrectly retaining endpoint speed. + factor = ( + np.asarray(env.density(z)) + * np.asarray(env.speed_of_sound(z)) + / np.asarray(env.dynamic_viscosity(z)) + ) + ren = grid * np.interp(grid, unique, factor) + if not np.all(np.isfinite(ren) & (ren > 0)): + raise ValueError( + "Environment must supply finite positive Reynolds numbers" + ) + report["reynolds"][label] = ren.tolist() + report["altitude_m_asl"][label] = np.interp(grid, unique, z).tolist() + for key, function in [ + ("pressure_pa", env.pressure), + ("temperature_k", env.temperature), + ]: + report[key][label] = np.interp(grid, unique, function(z)).tolist() + options.update(mach=grid.tolist(), reynolds=report["reynolds"]) + options.pop("altitude", None) + self._invalidate_datcom() + for surface in fins: + surface.datcom_options = dict(options) + self.evaluate_center_of_pressure() + self.evaluate_stability_margin() + self._invalidate_static_margin() + return report + + @property + def datcom_calibration(self): + """Return a copy of the explicit whole-rocket calibration inputs.""" + return dict( + getattr( + self, + "_datcom_calibration", + {"roughness_height": 0.0, "leading_edge_radius": None}, + ) + ) + + def add_datcom_calibration(self, *, roughness_height=0.0, leading_edge_radius=None): + """Set native DATCOM surface/edge calibration, replacing previous values. + + Parameters + ---------- + roughness_height : float, optional + Nonnegative equivalent sand roughness height in meters, not Ra. + Converted to DATCOM RHR using k = 3 RHR microinches, avoiding the + version-dependent metric ROUGH convention. Default is smooth. + leading_edge_radius : float or None, optional + Nonnegative radius in meters, overriding LER at every station of + every supported fin set. None retains each fin's own radius. + NACA sections define their own edge geometry and reject overrides. + + Returns + ------- + dict + Copy of the stored calibration. Call without arguments to reset. + + Notes + ----- + Add DATCOM fins first. This method sets inputs; it does not fit them or + alter mass, planform or ordinary RocketPy aerodynamics. A fitted + roughness is an effective calibration, not a measured material finish. + Changes invalidate the prepared tables; Flight regenerates them. + """ + if not self._has_datcom_fins(): + raise ValueError( + "DATCOM calibration requires a DATCOM fin set; add it first" + ) + values = {} + for name, value in ( + ("roughness_height", roughness_height), + ("leading_edge_radius", leading_edge_radius), + ): + if name == "leading_edge_radius" and value is None: + values[name] = None + continue + if np.ndim(value) != 0: + raise ValueError(f"{name} must be a finite nonnegative scalar") + try: + number = float(value) + except (TypeError, ValueError, OverflowError) as error: + raise ValueError( + f"{name} must be a finite nonnegative scalar" + ) from error + if ( + isinstance(value, (bool, np.bool_)) + or not np.isfinite(number) + or number < 0 + ): + raise ValueError(f"{name} must be a finite nonnegative scalar") + values[name] = number + if leading_edge_radius is not None and any( + getattr(s, "section_type", None) == "NACA" + for s, _, _ in self.aerodynamic_surfaces + ): + raise ValueError("NACA sections define their own leading-edge radius") + if values != self.datcom_calibration: + self._invalidate_datcom() + self._datcom_calibration = values + self.evaluate_center_of_pressure() + self.evaluate_stability_margin() + self._invalidate_static_margin() + return self.datcom_calibration + + def fit_datcom_calibration( + self, + data, + *, + reference_area=None, + parameters=("roughness_height",), + bounds=None, + reference_length=None, + moment_reference=None, + lateral_reference_length=None, + model_scale=1.0, + power_on=False, + weights=None, + drag_axis="wind", + max_evaluations=100, + ): + """Fit native calibration inputs to static wind-tunnel coefficients. + + ``data`` is a CSV path or a column mapping. Required columns are + ``mach`` and at least one of CD, CL, CA, CN, CY, CM, CLN, CLL. Optional + ``alpha``/``beta`` are degrees (default zero); ``power_on`` is 0 or 1; + ``reynolds_per_meter`` specifies tunnel Reynolds number per meter. + + ``reference_area`` is the measurement area in m², defaulting to the + rocket frontal area. Moment measurements + also require ``reference_length`` (m) and ``moment_reference`` (m in + this rocket's axial position convention). An optional lateral length + scales yaw/roll coefficients; it defaults to reference_length. + ``model_scale`` is tunnel-model size divided by full-rocket size. + Reference areas/lengths and tunnel Reynolds numbers are converted to + full-scale equivalents; moment_reference stays in rocket coordinates. + + Fit roughness_height and/or leading_edge_radius, with bounds in meters. + Roughness defaults to 0..300 micrometers. Fitting edge radius requires + explicit physically justified bounds. ``weights`` maps coefficient + names to positive least-squares weights. CD is wind-axis drag by + default; drag_axis='stability' selects the pitch-plane convention. + + Returns a JSON-compatible report with residuals, initial/final RMSE, + fitted inputs, bounds and native evaluation count. Successful fits + are applied through add_datcom_calibration. Failed fits preserve the + existing settings and prepared model. No measured coefficient or + derivative replaces the native DATCOM tables. + """ + from rocketpy.rocket.datcom_calibration import fit_datcom_calibration + + return fit_datcom_calibration( + self, + data, + reference_area=reference_area, + parameters=parameters, + bounds=bounds, + reference_length=reference_length, + moment_reference=moment_reference, + lateral_reference_length=lateral_reference_length, + model_scale=model_scale, + power_on=power_on, + weights=weights, + drag_axis=drag_axis, + max_evaluations=max_evaluations, + ) + + def _create_datcom_or_conical_tail(self, top, bottom, length, radius, name, kind): + from rocketpy.rocket.aero_surface.tail import Tail + + key = kind.replace(" ", "").replace("-", "").lower() + if key in ("cone", "conical"): + return Tail(top, bottom, length, radius, name) + if key not in ("ogive", "tangent", "tangentogive"): + raise ValueError("Tail kind must be 'conical' or 'ogive'") + if not self._has_datcom_fins(): + raise ValueError("Ogive tails require a DATCOM fin set; add it first") + from rocketpy.rocket.aero_surface.datcom_tail import DatcomTail + + return DatcomTail(top, bottom, length, radius, name) + + def _validate_datcom_tail(self, surface): + from rocketpy.rocket.aero_surface.datcom_tail import DatcomTail + + if isinstance(surface, DatcomTail) and not self._has_datcom_fins(): + raise ValueError("Ogive tails require a DATCOM fin set; add it first") + + def _set_undefined_datcom_static_margin(self): + """Keep an undefined CP out of the ordinary finite-data spline fit.""" + if self._has_datcom_fins() and not np.isfinite(self.cp_position(0)): + self._static_margin.set_source(lambda time: float("nan")) + self._static_margin_dirty = False + return True + return False + + def _evaluate_datcom_center_of_pressure(self): + if self._has_datcom_fins(): + model = self._datcom_model + if model is None: + self.cp_position.set_source(lambda mach: float("nan")) + self.total_lift_coeff_der.set_source(lambda mach: float("nan")) + else: + self.cp_position.set_source(lambda mach: model.small_angle_cp(mach)) + table = model.tables[False] + zero = int(np.flatnonzero(table[5] == 0)[0]) + self.total_lift_coeff_der.set_source( + lambda mach: float( + np.interp(mach, table[4], table.derivatives["CNA"][:, zero]) + ) + * 180 + / np.pi + ) + return True + + return False + + def _has_datcom_fins(self): + return any( + isinstance(s, (DatcomFins, DatcomFreeFormFins)) + for s, _, _ in self.aerodynamic_surfaces + ) + + @property + def _flight_aerodynamic_surfaces(self): + """Active flight models; original geometry remains available for drawing.""" + if self._datcom_model is not None: + return [ + ( + self._datcom_model, + Vector([0, 0, self.center_of_dry_mass_position]), + 1, + ) + ] + return self.aerodynamic_surfaces + + def prepare_datcom(self): + """Generate powered/coast DATCOM tables from the current assembled rocket. + + Flight calls this automatically. Call it explicitly before inspecting + DATCOM drag or stability plots. Identical geometry, CG references, + conditions and executable reuse the in-memory model. Changes rebuild + both cases. No executable or generated output is kept in the repository. + + Returns + ------- + DatcomSurface or None + Whole-vehicle aerodynamic model, or None without DATCOM fins. + """ + if not self._has_datcom_fins(): + for surface, _, _ in self.aerodynamic_surfaces: + self._validate_datcom_tail(surface) + self._invalidate_datcom() + return None + + from pathlib import Path # pylint: disable=import-outside-toplevel + + from rocketpy.rocket.datcom import ( # pylint: disable=import-outside-toplevel + DatcomSurface, + datcom_decks, + run_datcom, + ) + + decks, references, timeout, path = datcom_decks(self) + stat = Path(path).stat() + signature = ( + tuple(decks.items()), + tuple(references.items()), + path, + stat.st_size, + stat.st_mtime_ns, + self.motor.burn_start_time, + self.motor.burn_out_time, + ) + if signature == self._datcom_signature: + return self._datcom_model + # Publish only after both runs succeed, so failed cases cannot leave a + # half-populated aerodynamic database attached to the rocket. + tables = { + powered: run_datcom(path, deck, timeout) for powered, deck in decks.items() + } + model = DatcomSurface(self, tables, references) + self._invalidate_datcom() + self._datcom_model = model + self._datcom_signature = signature + names = [ + "power_on_drag_7d", + "power_off_drag_7d", + "power_on_drag_by_mach", + "power_off_drag_by_mach", + "power_on_drag", + "power_off_drag", + ] + self._datcom_original_drag = {name: getattr(self, name) for name in names} + for powered, prefix in ((True, "power_on"), (False, "power_off")): + # Function counts callable arguments, so bind the powered selector + # through a closure factory rather than an eighth default argument. + def make_coefficient(state): + return ( + lambda alpha, beta, mach, reynolds, q, r, p: model.drag_coefficient( + state, alpha, beta, mach, reynolds, q, r, p + ) + ) + + drag = Function( + make_coefficient(powered), + inputs=["alpha", "beta", "mach", "reynolds", "q", "r", "p"], + ) + + def make_curve(state): + return lambda mach: model.drag_coefficient( + state, 0, 0, mach, 0, 0, 0, 0 + ) + + curve = Function( + make_curve(powered), inputs="Mach", outputs="Drag Coefficient" + ) + setattr(self, f"{prefix}_drag_7d", drag) + setattr(self, f"{prefix}_drag_by_mach", curve) + setattr(self, f"{prefix}_drag", curve) + self.surfaces_cp_to_cdm[model] = Vector([0, 0, 0]) + self.evaluate_center_of_pressure() + self.evaluate_stability_margin() + self._invalidate_static_margin() + return model + + def _invalidate_datcom(self): + """Discard whole-vehicle tables when assembly changes.""" + if self._datcom_model is not None: + self.surfaces_cp_to_cdm.pop(self._datcom_model, None) + if self._datcom_original_drag is not None: + for name, value in self._datcom_original_drag.items(): + setattr(self, name, value) + self._datcom_model = None + self._datcom_signature = None + self._datcom_original_drag = None + + def add_datcom_trapezoidal_fins( + self, + n, + root_chord, + tip_chord, + span, + position, + *, + path, + thickness_to_chord=0.02, + leading_edge_radius=0, + sweep_length=None, + sweep_angle=None, + name="DATCOM Trapezoidal Fins", + datcom_options=None, + **section_options, + ): + """Add a trapezoidal planform with a beveled HEX section by default. + + Uses the same geometry/position convention as add_trapezoidal_fins. + The section has a flat middle 90% of each chord and 5% edge bevels. + ``thickness_to_chord`` is full thickness/chord, scalar or root/tip pair. + ``path`` is the native DATCOM executable; ``datcom_options`` accepts + mach, alpha (degrees), altitude (meters), timeout (seconds), and optional + cylindrical body_length (meters). All existing nose, tail and supported + fins are included in the DATCOM model and their Barrowman loads disabled. + DAMP is mandatory. No protuberance or plume calculations are requested. + See :class:`rocketpy.DatcomFins` for section inputs and limitations. + """ + return self._add_datcom_fins( + n, + root_chord, + tip_chord, + span, + position, + path=path, + thickness_to_chord=thickness_to_chord, + max_thickness_position=section_options.pop("max_thickness_position", 0.05), + flat_length=section_options.pop("flat_length", 0.9), + leading_edge_radius=leading_edge_radius, + sweep_length=sweep_length, + sweep_angle=sweep_angle, + name=name, + datcom_options=datcom_options, + **section_options, + ) + + def add_datcom_arc_fins( + self, + n, + root_chord, + tip_chord, + span, + position, + *, + path, + thickness_to_chord=0.02, + leading_edge_radius=0, + sweep_length=None, + sweep_angle=None, + name="DATCOM Arc Fins", + datcom_options=None, + **section_options, + ): + """Add trapezoidal-planform fins with a circular ARC section. + + Geometry and DATCOM settings follow add_datcom_trapezoidal_fins. + ARC describes the airfoil cross section, not a curved planform. + """ + return self._add_datcom_fins( + n, + root_chord, + tip_chord, + span, + position, + path=path, + section_type="ARC", + thickness_to_chord=thickness_to_chord, + leading_edge_radius=leading_edge_radius, + sweep_length=sweep_length, + sweep_angle=sweep_angle, + name=name, + datcom_options=datcom_options, + **section_options, + ) + + def add_datcom_hex_fins( + self, + n, + root_chord, + tip_chord, + span, + position, + *, + path, + thickness_to_chord=0.02, + max_thickness_position=0.25, + flat_length=0.5, + leading_edge_radius=0, + sweep_length=None, + sweep_angle=None, + name="DATCOM Hex Fins", + datcom_options=None, + **section_options, + ): + """Add trapezoidal-planform fins with a HEX airfoil section. + + Geometry and DATCOM settings follow add_datcom_trapezoidal_fins. + ``max_thickness_position`` locates the start of maximum thickness; + ``flat_length`` is its extent, both as scalar or root/tip chord fractions. + A zero flat_length gives a diamond section. HEX describes the airfoil + cross section, not a six-sided planform. + """ + return self._add_datcom_fins( + n, + root_chord, + tip_chord, + span, + position, + path=path, + thickness_to_chord=thickness_to_chord, + max_thickness_position=max_thickness_position, + flat_length=flat_length, + leading_edge_radius=leading_edge_radius, + sweep_length=sweep_length, + sweep_angle=sweep_angle, + name=name, + datcom_options=datcom_options, + **section_options, + ) + + def add_datcom_fins( + self, + n, + position, + *, + span_stations, + chords, + leading_edges, + path, + name="DATCOM Free Form Fins", + **section_options, + ): + """Add a 2..10-station DATCOM planform with any supported airfoil section. + + span_stations are exposed distances from the body surface in meters. + leading_edges are aft distances from position; their first value is zero. + Chords are meters and the final chord can be zero for a triangular tip. + Section and panel-placement options follow DatcomFins. + """ + fins = DatcomFreeFormFins( + n, + span_stations, + chords, + leading_edges, + self.radius, + path=path, + name=name, + **section_options, + ) + self.add_surfaces(fins, position) + return fins + + def _add_datcom_fins(self, n, root_chord, tip_chord, span, position, **kwargs): + fins = DatcomFins(n, root_chord, tip_chord, span, self.radius, **kwargs) + self.add_surfaces(fins, position) + return fins diff --git a/rocketpy/rocket/parachute.py b/rocketpy/rocket/parachute.py index da99743ce..c5b2b5422 100644 --- a/rocketpy/rocket/parachute.py +++ b/rocketpy/rocket/parachute.py @@ -1,4 +1,5 @@ from inspect import Parameter, signature +from numbers import Real import numpy as np @@ -8,6 +9,28 @@ from ..prints.parachute_prints import _ParachutePrints +def _is_a_height_trigger(trigger): + """Whether ``trigger`` is a number this class will read as a height. + + ``numbers.Real`` rather than ``(int, float)`` so that NumPy scalars are + accepted: ``numpy.float64`` happens to subclass ``float``, but + ``numpy.int64`` and ``numpy.float32`` subclass neither and were refused + even though every arithmetic use of them here works. + + What that spelling leaves out is what should be left out. ``numpy.bool_`` + and the complex types are not ``Real``, so they still fall through to the + error. ``bool`` is excluded by hand because it *is* an ``int``, and ``True`` + would otherwise be taken as a height of one metre. + + This is the single definition of the numeric boundary. The height form and + the delay of a ``("time", t_deploy)`` trigger both use it, and + ``StochasticParachute`` validates the same triggers before a ``Parachute`` + is ever built and calls this rather than restating it, because the two + spellings drifted apart once already. + """ + return isinstance(trigger, Real) and not isinstance(trigger, bool) + + class Parachute: """Keeps information of the parachute, which is modeled as a hemispheroid. @@ -20,7 +43,7 @@ class Parachute: Parachute.cd_s : float Drag coefficient times reference area for parachute. It has units of area and must be given in squared meters. - Parachute.trigger : callable, float, str + Parachute.trigger : callable, float, str, tuple This parameter defines the trigger condition for the parachute ejection system. It can be one of the following: @@ -56,6 +79,12 @@ class Parachute: - The string "apogee" which triggers the parachute at apogee, i.e., when the rocket reaches its highest point and starts descending. + - A tuple ``("time", t_deploy)`` where ``t_deploy`` is the flight time + in seconds at or after which the parachute triggers (from ``t = 0`` + at flight start). Useful for fixed delay charges that start at + ignition/launch. For a motor delay charge that starts at burnout, + pass ``("time", motor.burn_out_time + delay)``. + Parachute.triggerfunc : function Trigger function created from the trigger used to evaluate the trigger @@ -137,6 +166,7 @@ def __init__( height=None, porosity=0.0432, drag_coefficient=1.4, + seed=None, ): """Initializes Parachute class. @@ -148,7 +178,7 @@ def __init__( organized matter. cd_s : float Drag coefficient times reference area of the parachute. - trigger : callable, float, str + trigger : callable, float, str, tuple Defines the trigger condition for the parachute ejection system. It can be one of the following: @@ -171,6 +201,10 @@ def __init__( height above ground level. - The string "apogee" which triggers the parachute at apogee, i.e., \ when the rocket reaches its highest point and starts descending. + - A tuple ``("time", t_deploy)`` that triggers when flight time \ + ``t >= t_deploy`` (seconds from flight start). For a delay \ + charge referenced to motor burnout, use \ + ``("time", motor.burn_out_time + delay)``. .. note:: @@ -217,6 +251,12 @@ def __init__( - **1.5** — extended-skirt canopy Has no effect when ``radius`` is explicitly provided. + seed : int, array_like, SeedSequence, BitGenerator, Generator or None, optional + Seed for the per-instance NumPy Generator used by pressure noise. + A fixed seed makes the noise reproducible and independent of the + process-global NumPy RNG (and therefore usable under Monte Carlo). + ``None`` keeps the noise random but still drawn from this instance's + generator. Default is ``None``. """ # Save arguments as attributes @@ -229,6 +269,11 @@ def __init__( self.drag_coefficient = drag_coefficient self.porosity = porosity + # Per-instance RNG: pressure noise must not draw from the process-global + # NumPy RNG, or Monte Carlo cannot reproduce deployment (see #1091). + self._seed = seed + self._rng = np.random.default_rng(seed) + # Initialize derived attributes self.radius = self.__resolve_radius(radius, cd_s, drag_coefficient) self.height = self.__resolve_height(height, self.radius) @@ -267,7 +312,7 @@ def __init_noise(self, noise): noise : tuple, list List in the format (mean, standard deviation, time-correlation). """ - self.noise_signal = [[-1e-6, np.random.normal(noise[0], noise[1])]] + self.noise_signal = [[-1e-6, self._rng.normal(noise[0], noise[1])]] self.noisy_pressure_signal = [] self.clean_pressure_signal = [] self.noise_bias = noise[0] @@ -282,7 +327,7 @@ def __init_noise(self, noise): else: self.noise_function = lambda: ( alpha * self.noise_signal[-1][1] - + beta * np.random.normal(noise[0], noise[1]) + + beta * self._rng.normal(noise[0], noise[1]) ) def __evaluate_trigger_function(self, trigger): # pylint: disable=too-many-statements @@ -297,6 +342,10 @@ def __evaluate_trigger_function(self, trigger): # pylint: disable=too-many-stat # pylint: disable=function-redefined self._trigger_falling_only = False self._trigger_needs_height = True + # Flight overwrites this with the current flight time before every + # trigger evaluation. Declared here so a ("time", t_deploy) trigger has + # something defined to read when it is called outside a Flight. + self._eval_time = None # Helper to wrap any callable to the internal (p, h, y, sensors, u_dot) API def _make_wrapper(fn): @@ -351,7 +400,7 @@ def wrapper(p, h, y, sensors, u_dot): return # Numeric altitude trigger - if isinstance(trigger, (int, float)): + if _is_a_height_trigger(trigger): self._trigger_falling_only = True def triggerfunc(p, h, y, sensors, u_dot): # pylint: disable=unused-argument @@ -376,11 +425,53 @@ def triggerfunc(p, h, y, sensors, u_dot): # pylint: disable=unused-argument self.triggerfunc = triggerfunc return + # Fixed-time trigger: ("time", t_deploy) [seconds from flight start] + if ( + isinstance(trigger, (tuple, list)) + and len(trigger) == 2 + and isinstance(trigger[0], str) + and trigger[0].lower() == "time" + ): + # Same numeric boundary as a height, so the two forms cannot + # disagree about what counts as a number. Notably this refuses a + # string delay rather than quietly coercing it: float("3.0") would + # otherwise make ("time", "3.0") work by accident. + if not _is_a_height_trigger(trigger[1]): + raise ValueError( + f"Unable to set the trigger function for parachute '{self.name}'. " + + "Time trigger delay must be a non-negative number of seconds, " + + f"got {trigger[1]!r}." + ) + t_deploy = float(trigger[1]) + if t_deploy < 0: + raise ValueError( + f"Unable to set the trigger function for parachute '{self.name}'. " + + "Time trigger delay must be non-negative, " + + f"got {t_deploy}." + ) + + # Delay charges fire on ascent; height is unused. + self._trigger_falling_only = False + self._trigger_needs_height = False + + def triggerfunc(p, h, y, sensors, u_dot): # pylint: disable=unused-argument + # Flight sets ``self._eval_time`` immediately before each call. + # It is None only when the trigger is called outside a Flight, + # which cannot deploy anything, so refuse rather than guess. + t = self._eval_time + if t is None: + return False + return t >= t_deploy + + triggerfunc._expects_udot = False + self.triggerfunc = triggerfunc + return + # If we reach this point, the trigger is invalid raise ValueError( f"Unable to set the trigger function for parachute '{self.name}'. " - + "Trigger must be a callable, a float value or one of the strings " - + "('apogee'). " + + "Trigger must be a callable, a float value, the string 'apogee', " + + "or a tuple ('time', t_deploy). " + "See the Parachute class documentation for more information." ) @@ -431,6 +522,7 @@ def to_dict(self, **kwargs): "drag_coefficient": self.drag_coefficient, "height": self.height, "porosity": self.porosity, + "seed": self._seed, } if kwargs.get("include_outputs", False): @@ -465,6 +557,7 @@ def from_dict(cls, data): drag_coefficient=data.get("drag_coefficient", 1.4), height=data.get("height", None), porosity=data.get("porosity", 0.0432), + seed=data.get("seed", None), ) return parachute diff --git a/rocketpy/rocket/rocket.py b/rocketpy/rocket/rocket.py index 3f4748ac0..b701fa61c 100644 --- a/rocketpy/rocket/rocket.py +++ b/rocketpy/rocket/rocket.py @@ -27,6 +27,7 @@ RailButtons, Tail, TrapezoidalFins, + TubeFins, ) from rocketpy.rocket.aero_surface.fins.elliptical_fin import EllipticalFin from rocketpy.rocket.aero_surface.fins.free_form_fin import FreeFormFin @@ -34,6 +35,7 @@ from rocketpy.rocket.aero_surface.fins.trapezoidal_fin import TrapezoidalFin from rocketpy.rocket.aero_surface.generic_surface import GenericSurface from rocketpy.rocket.components import Components +from rocketpy.rocket.datcom_rocket import DatcomRocketMixin from rocketpy.rocket.parachute import Parachute from rocketpy.tools import ( deprecated, @@ -45,7 +47,7 @@ # pylint: disable=too-many-instance-attributes, too-many-public-methods, too-many-instance-attributes -class Rocket: +class Rocket(DatcomRocketMixin): """Keeps rocket information. Attributes @@ -270,18 +272,18 @@ def __init__( # pylint: disable=too-many-statements in the direction of e_i x e_j. Alternatively, the inertia tensor can be given as (I_11, I_22, I_33), where I_12 = I_13 = I_23 = 0. This can also be called as "rocket dry inertia tensor". - power_off_drag : int, float, callable, string, array - Rocket's drag coefficient when the motor is off. Can be given as an - entry to the Function class. See help(Function) for more - information. If int or float is given, it is assumed constant. If - callable, string or array is given, it must be a function of Mach - number only. - power_on_drag : int, float, callable, string, array - Rocket's drag coefficient when the motor is on. Can be given as an - entry to the Function class. See help(Function) for more - information. If int or float is given, it is assumed constant. If - callable, string or array is given, it must be a function of Mach - number only. + power_off_drag : int, float, callable, string, array, Function + Rocket's drag coefficient when the motor is off. Scalars define a + constant coefficient. One-dimensional sources are evaluated as a + function of Mach number. A callable or Function may instead accept + seven arguments in this order: angle of attack, sideslip angle, + Mach number, Reynolds number, pitch rate, yaw rate and roll rate. + Angles are given in radians and angular rates in radians per second. + See :ref:`rocketusage` for examples and supported table formats. + power_on_drag : int, float, callable, string, array, Function + Rocket's drag coefficient when the motor is on. It accepts the same + constant, Mach-only and seven-variable formats as + ``power_off_drag``. See :ref:`rocketusage` for details. center_of_mass_without_motor : int, float Position, in m, of the rocket's center of mass without motor relative to the rocket's coordinate system. Default is 0, which @@ -372,6 +374,9 @@ def __init__( # pylint: disable=too-many-statements self.air_brakes = [] self.sensors = Components() self.aerodynamic_surfaces = Components() + self._datcom_model = None + self._datcom_signature = None + self._datcom_original_drag = None self.surfaces_cp_to_cdm = {} self.rail_buttons = Components() @@ -385,9 +390,10 @@ def __init__( # pylint: disable=too-many-statements inputs="Mach Number", outputs="Total Lift Coefficient Derivative", ) - self.static_margin = Function( + self._static_margin = Function( lambda time: 0, inputs="Time (s)", outputs="Static Margin (c)" ) + self._static_margin_dirty = True self.stability_margin = Function( lambda mach, time: 0, inputs=["Mach", "Time (s)"], @@ -442,10 +448,10 @@ def __init__( # pylint: disable=too-many-statements self.evaluate_reduced_mass() self.evaluate_thrust_to_weight() - # Evaluate stability (even though no aerodynamic surfaces are present yet) + # Evaluate stability quantities needed for later work. Static margin is + # left dirty and built lazily on first access (see static_margin). self.evaluate_center_of_pressure() self.evaluate_stability_margin() - self.evaluate_static_margin() # Initialize plots and prints object self.prints = _RocketPrints(self) @@ -490,6 +496,11 @@ def fins(self): """A list containing all the fins currently added to the rocket.""" return self.aerodynamic_surfaces.get_by_type(Fins) + @property + def tube_fins(self): + """A list containing all tube-fin sets currently added to the rocket.""" + return self.aerodynamic_surfaces.get_by_type(TubeFins) + @property def tails(self): """A list with all the tails currently added to the rocket""" @@ -654,17 +665,19 @@ def evaluate_center_of_pressure(self): See :doc:`Positions and Coordinate Systems ` for more information. """ + if self._evaluate_datcom_center_of_pressure(): + return self.cp_position + # Re-Initialize total lift coefficient derivative and center of pressure position self.total_lift_coeff_der.set_source(lambda mach: 0) self.cp_position.set_source(lambda mach: 0) # Calculate total lift coefficient derivative and center of pressure if len(self.aerodynamic_surfaces) > 0: - for aero_surface, position in self.aerodynamic_surfaces: + for aero_surface, position, ref_factor in self.aerodynamic_surfaces: if isinstance(aero_surface, GenericSurface): continue # ref_factor corrects lift for different reference areas - ref_factor = (aero_surface.rocket_radius / self.radius) ** 2 self.total_lift_coeff_der += ref_factor * aero_surface.clalpha self.cp_position += ( ref_factor @@ -687,7 +700,7 @@ def evaluate_surfaces_cp_to_cdm(self): Dictionary mapping the relative position of each aerodynamic surface center of pressure to the rocket's center of mass. """ - for surface, position in self.aerodynamic_surfaces: + for surface, position, _ref_factor in self.aerodynamic_surfaces: self.__evaluate_single_surface_cp_to_cdm(surface, position) return self.surfaces_cp_to_cdm @@ -737,6 +750,27 @@ def evaluate_stability_margin(self): ) return self.stability_margin + def _invalidate_static_margin(self): + """Mark the cached static margin as stale. + + Call this whenever rocket geometry, mass properties, or aerodynamic + surfaces change in a way that can alter the static margin. The next + access of :attr:`static_margin` (or an explicit call to + :meth:`evaluate_static_margin`) rebuilds the Function. + """ + self._static_margin_dirty = True + + @property + def static_margin(self): + """Static margin of the rocket as a function of time (calibers). + + Computed lazily: rebuilt only when first accessed after construction or + after geometry/mass/surface changes that invalidate the cache. + """ + if self._static_margin_dirty: + self.evaluate_static_margin() + return self._static_margin + def evaluate_static_margin(self): """Calculates the static margin of the rocket as a function of time. @@ -747,25 +781,31 @@ def evaluate_static_margin(self): Static margin is defined as the distance between the center of pressure and the center of mass, divided by the rocket's diameter. """ - # Calculate static margin - self.static_margin.set_source( + if self._set_undefined_datcom_static_margin(): + return self._static_margin + + # Calculate static margin; fold _csys into the source so we do not + # rebind a property when multiplying. + self._static_margin.set_source( lambda time: ( ( - self.center_of_mass.get_value_opt(time) - - self.cp_position.get_value_opt(0) + ( + self.center_of_mass.get_value_opt(time) + - self.cp_position.get_value_opt(0) + ) + / (2 * self.radius) ) - / (2 * self.radius) + * self._csys ) ) - # Change sign if coordinate system is upside down - self.static_margin *= self._csys - self.static_margin.set_inputs("Time (s)") - self.static_margin.set_outputs("Static Margin (c)") - self.static_margin.set_title("Static Margin") - self.static_margin.set_discrete( + self._static_margin.set_inputs("Time (s)") + self._static_margin.set_outputs("Static Margin (c)") + self._static_margin.set_title("Static Margin") + self._static_margin.set_discrete( lower=0, upper=self.motor.burn_out_time, samples=200 ) - return self.static_margin + self._static_margin_dirty = False + return self._static_margin def warn_if_unstable(self): """Warn if the rocket is aerodynamically unstable at motor ignition. @@ -788,7 +828,7 @@ def warn_if_unstable(self): """ has_generic_surface = any( isinstance(aero_surface, GenericSurface) - for aero_surface, _position in self.aerodynamic_surfaces + for aero_surface, _position, _ref_factor in self.aerodynamic_surfaces ) if has_generic_surface: return False @@ -973,8 +1013,8 @@ def evaluate_nozzle_to_cdm(self): def evaluate_nozzle_gyration_tensor(self): """Calculates and returns the nozzle gyration tensor relative to the - rocket's center of dry mass. The gyration tensor is saved and returned - in units of kg*m². + rocket's center of dry mass. The gyration tensor is a second moment of + area per unit area, so it is saved and returned in units of m². Returns ------- @@ -982,7 +1022,7 @@ def evaluate_nozzle_gyration_tensor(self): Matrix containing the nozzle gyration tensor. """ S_noz_33 = 0.5 * self.motor.nozzle_radius**2 - S_noz_11 = S_noz_22 = 0.5 * S_noz_33 + 0.25 * self.nozzle_to_cdm**2 + S_noz_11 = S_noz_22 = 0.5 * S_noz_33 + self.nozzle_to_cdm**2 S_noz_12, S_noz_13, S_noz_23 = 0, 0, 0 # Due to axis symmetry self.nozzle_gyration_tensor = Matrix( [ @@ -1110,6 +1150,7 @@ def add_motor(self, motor, position): # pylint: disable=too-many-statements "Only one motor per rocket is currently supported. " "Overwriting previous motor." ) + self._invalidate_datcom() self.motor = motor self.motor_position = position _ = self._csys * self.motor._csys @@ -1137,7 +1178,7 @@ def add_motor(self, motor, position): # pylint: disable=too-many-statements self.evaluate_center_of_pressure() self.evaluate_surfaces_cp_to_cdm() self.evaluate_stability_margin() - self.evaluate_static_margin() + self._invalidate_static_margin() self.evaluate_com_to_cdm_function() self.evaluate_nozzle_gyration_tensor() @@ -1145,6 +1186,7 @@ def __add_single_surface(self, surface, position): """Adds a single aerodynamic surface to the rocket. Makes checks for rail buttons case, and position type. """ + self._validate_datcom_tail(surface) if isinstance(surface, (TrapezoidalFin, EllipticalFin, FreeFormFin)): # TODO: the leading edge position should be recomputed whenever cant # angle of the fin changes, but currently it is only computed at the @@ -1163,7 +1205,12 @@ def __add_single_surface(self, surface, position): self.rail_buttons = Components() self.rail_buttons.add(surface, position) else: - self.aerodynamic_surfaces.add(surface, position) + # ref_factor corrects lift for different reference areas + if getattr(surface, "rocket_radius", None) is not None: + ref_factor = (surface.rocket_radius / self.radius) ** 2 + else: + ref_factor = 1.0 + self.aerodynamic_surfaces.add(surface, position, ref_factor=ref_factor) self.__evaluate_single_surface_cp_to_cdm(surface, position) def add_surfaces(self, surfaces, positions): @@ -1190,6 +1237,7 @@ def add_surfaces(self, surfaces, positions): For Fins type, position refers to the z-coordinate of the root chord leading-edge point closest to the nose cone, before any cant-angle offset is considered. + For TubeFins type, position refers to the leading edge of the tubes. For Tail type, position is relative to the point belonging to the tail which is highest in the rocket coordinate system. For RailButtons type, position is relative to the lower rail button. @@ -1202,6 +1250,7 @@ def add_surfaces(self, surfaces, positions): ------- None """ + self._invalidate_datcom() if isinstance(surfaces, Iterable): if isinstance(positions, Iterable): if len(surfaces) != len(positions): @@ -1218,7 +1267,7 @@ def add_surfaces(self, surfaces, positions): self.evaluate_center_of_pressure() self.evaluate_stability_margin() - self.evaluate_static_margin() + self._invalidate_static_margin() def _add_controllers(self, controllers): """Adds a controller to the rocket. @@ -1240,7 +1289,15 @@ def _add_controllers(self, controllers): self._controllers.append(controllers) def add_tail( - self, top_radius, bottom_radius, length, position, radius=None, name="Tail" + self, + top_radius, + bottom_radius, + length, + position, + radius=None, + name="Tail", + *, + kind="conical", ): """Create a new tail or rocket diameter change, storing its parameters as part of the aerodynamic_surfaces list. Its @@ -1269,6 +1326,10 @@ def add_tail( be used. name : string Tail name. Default is "Tail". + kind : string, optional + Tail profile, ``"conical"`` (default) or ``"ogive"``. Ogive tails + require a DATCOM fin set to be added first; otherwise ValueError is + raised. Conical tails retain the original component model. See Also -------- @@ -1282,7 +1343,9 @@ def add_tail( # Modify reference radius if not provided radius = self.radius if radius is None else radius # Create tail, adds it to the rocket and returns it - tail = Tail(top_radius, bottom_radius, length, radius, name) + tail = self._create_datcom_or_conical_tail( + top_radius, bottom_radius, length, radius, name, kind + ) self.add_surfaces(tail, position) return tail @@ -1633,6 +1696,67 @@ def add_free_form_fins( self.add_surfaces(fin_set, position) return fin_set + def add_tube_fins( + self, + n, + length, + inner_radius, + outer_radius, + position, + radius=None, + name="Tube Fins", + ): + """Create and add a symmetric set of tube fins to the rocket. + + This first-order model uses the Ribner ring-airfoil normal-force slope + and a fixed quarter-chord center of pressure. It is intended for Mach + numbers up to 0.5 and angles of attack up to 20 degrees. + + Parameters + ---------- + n : int + Number of tubes. Must be at least 3. + length : int, float + Tube length along the rocket axis, in meters. + inner_radius : int, float + Inner radius of each tube, in meters. + outer_radius : int, float + Outer radius of each tube, in meters. The current model requires + neighboring tubes to touch, so this must equal + ``radius * sin(pi / n) / (1 - sin(pi / n))``. + position : int, float + Axial position of the tube leading edges in the user-defined rocket + coordinate system. + radius : int, float, optional + Rocket-body radius where the tubes are mounted. If ``None``, the + rocket radius is used. + name : str, optional + Name of the tube-fin set. Default is ``"Tube Fins"``. + + Returns + ------- + TubeFins + Tube-fin set created and added to the rocket. + + Notes + ----- + Only uncanted, mutually tangent tubes are supported. Component drag, + roll, side-force, yaw, separated tubes, and overlapping tubes are not + included in this model. Tube-fin drag must be represented in the + rocket's power-on and power-off drag curves. + """ + radius = self.radius if radius is None else radius + tube_fins = TubeFins( + n=n, + length=length, + inner_radius=inner_radius, + outer_radius=outer_radius, + rocket_radius=radius, + name=name, + ) + self.add_surfaces(tube_fins, position) + return tube_fins + def add_parachute( self, name, @@ -1662,7 +1786,7 @@ def add_parachute( force is the dynamic pressure computed on the parachute times its cd_s coefficient. Has units of area and must be given in squared meters. - trigger : callable, float, str + trigger : callable, float, str, tuple Defines the trigger condition for the parachute ejection system. It can be one of the following: @@ -1685,6 +1809,10 @@ def add_parachute( height above ground level. - The string "apogee" which triggers the parachute at apogee, i.e., \ when the rocket reaches its highest point and starts descending. + - A tuple ``("time", t_deploy)`` that triggers when flight time \ + ``t >= t_deploy`` (seconds from flight start). For a delay \ + charge referenced to motor burnout, use \ + ``("time", motor.burn_out_time + delay)``. .. note:: @@ -2146,6 +2274,9 @@ def to_dict(self, **kwargs): power_off_drag = self.power_off_drag_7d power_on_drag = self.power_on_drag_7d + if self._datcom_original_drag is not None: + power_off_drag = self._datcom_original_drag["power_off_drag_7d"] + power_on_drag = self._datcom_original_drag["power_on_drag_7d"] rocket_dict = { "radius": self.radius, @@ -2169,6 +2300,8 @@ def to_dict(self, **kwargs): "_controllers": self._controllers, "sensors": self.sensors, } + if hasattr(self, "_datcom_calibration"): + rocket_dict["datcom_calibration"] = self.datcom_calibration if kwargs.get("include_outputs", False): thrust_to_weight = self.thrust_to_weight @@ -2270,10 +2403,17 @@ def from_dict(cls, data): position=data["motor_position"], ) - for surface, position in data["aerodynamic_surfaces"]: + # The trailing star reads entries of either length: files written + # before ref_factor was stored carry two fields and newer ones three. + # The factor is not read back in any case, since add_surfaces derives + # it from the surface's own radius. + for surface, position, *_ in data["aerodynamic_surfaces"]: rocket.add_surfaces(surfaces=surface, positions=position) - for button, position in data["rail_buttons"]: + if "datcom_calibration" in data: + rocket.add_datcom_calibration(**data["datcom_calibration"]) + + for button, position, *_ in data["rail_buttons"]: rocket.set_rail_buttons( upper_button_position=position[2] + button.buttons_distance, lower_button_position=position[2], @@ -2284,7 +2424,7 @@ def from_dict(cls, data): for parachute in data["parachutes"]: rocket.parachutes.append(parachute) - for sensor, position in data["sensors"]: + for sensor, position, *_ in data["sensors"]: rocket.add_sensor(sensor, position) for air_brake in data["air_brakes"]: diff --git a/rocketpy/sensors/accelerometer.py b/rocketpy/sensors/accelerometer.py index b6a477c11..edb7ede25 100644 --- a/rocketpy/sensors/accelerometer.py +++ b/rocketpy/sensors/accelerometer.py @@ -2,7 +2,7 @@ from ..mathutils.vector_matrix import Matrix, Vector from ..prints.sensors_prints import _InertialSensorPrints -from ..sensors.sensor import InertialSensor +from ..sensors.sensor import InertialSensor, SeedLike # pylint: disable=too-many-arguments @@ -13,7 +13,8 @@ class Accelerometer(InertialSensor): Attributes ---------- consider_gravity : bool - Whether the sensor considers the effect of gravity on the acceleration. + Whether the sensor reports proper acceleration, which includes the + reaction to gravity, rather than the coordinate acceleration alone. prints : _InertialSensorPrints Object that contains the print functions for the sensor. sampling_rate : float @@ -72,13 +73,13 @@ def __init__( random_walk_density=0, random_walk_variance=1, constant_bias=0, - operating_temperature=25, + operating_temperature=298.15, temperature_bias=0, temperature_scale_factor=0, cross_axis_sensitivity=0, consider_gravity=False, name="Accelerometer", - seed=None, + seed: SeedLike | None = None, ): """ Initialize the accelerometer sensor @@ -166,15 +167,21 @@ def __init__( Skewness of the sensor's axes in percentage. Default is 0, meaning no cross-axis sensitivity is applied. consider_gravity : bool, optional - If True, the sensor will consider the effect of gravity on the - acceleration. Default is False. + If True, the sensor reports proper acceleration, as a real + accelerometer does: the inertial acceleration less the local + gravitational field, so one at rest reads g along its up axis + rather than zero. If False it reports the coordinate acceleration, + which is zero at rest. Default is False. name : str, optional The name of the sensor. Default is "Accelerometer". - seed : int, optional + seed : int, array_like of ints, numpy.random.SeedSequence, optional Seed for the random number generator that draws the measurement noise. If given, the noise becomes reproducible and independent of - the process-global NumPy RNG. Default is None, meaning the noise is - seeded from fresh entropy per instance. + the process-global NumPy RNG. Only seeds that describe a stream are + taken: live ``Generator``, ``BitGenerator`` and ``RandomState`` + objects are rejected, because their state advances as noise is + drawn and so cannot be represented in ``to_dict()``. Default is + None, meaning the noise is seeded from fresh entropy per instance. Returns ------- @@ -232,7 +239,7 @@ def measure(self, time, **kwargs): gravity = ( Vector([0, 0, -gravity]) if self.consider_gravity else Vector([0, 0, 0]) ) - inertial_acceleration = Vector(u_dot[3:6]) + gravity + inertial_acceleration = Vector(u_dot[3:6]) - gravity # Vector from rocket cdm to sensor in rocket frame r = relative_position diff --git a/rocketpy/sensors/barometer.py b/rocketpy/sensors/barometer.py index afb6c09eb..593be2fc9 100644 --- a/rocketpy/sensors/barometer.py +++ b/rocketpy/sensors/barometer.py @@ -2,7 +2,7 @@ from ..mathutils.vector_matrix import Matrix from ..prints.sensors_prints import _SensorPrints -from ..sensors.sensor import ScalarSensor +from ..sensors.sensor import ScalarSensor, SeedLike class Barometer(ScalarSensor): @@ -58,11 +58,11 @@ def __init__( random_walk_density=0, random_walk_variance=1, constant_bias=0, - operating_temperature=25, + operating_temperature=298.15, temperature_bias=0, temperature_scale_factor=0, name="Barometer", - seed=None, + seed: SeedLike | None = None, ): """ Initialize the barometer sensor @@ -111,11 +111,14 @@ def __init__( meaning no temperature scale factor is applied. name : str, optional The name of the sensor. Default is "Barometer". - seed : int, optional + seed : int, array_like of ints, numpy.random.SeedSequence, optional Seed for the random number generator that draws the measurement noise. If given, the noise becomes reproducible and independent of - the process-global NumPy RNG. Default is None, meaning the noise is - seeded from fresh entropy per instance. + the process-global NumPy RNG. Only seeds that describe a stream are + taken: live ``Generator``, ``BitGenerator`` and ``RandomState`` + objects are rejected, because their state advances as noise is + drawn and so cannot be represented in ``to_dict()``. Default is + None, meaning the noise is seeded from fresh entropy per instance. Returns ------- diff --git a/rocketpy/sensors/gnss_receiver.py b/rocketpy/sensors/gnss_receiver.py index 09a064157..d7978280d 100644 --- a/rocketpy/sensors/gnss_receiver.py +++ b/rocketpy/sensors/gnss_receiver.py @@ -4,7 +4,7 @@ from ..mathutils.vector_matrix import Matrix, Vector from ..prints.sensors_prints import _GnssReceiverPrints -from .sensor import ScalarSensor +from .sensor import ScalarSensor, SeedLike class GnssReceiver(ScalarSensor): @@ -38,7 +38,7 @@ def __init__( position_accuracy=0, altitude_accuracy=0, name="GnssReceiver", - seed=None, + seed: SeedLike | None = None, ): """Initialize the Gnss Receiver sensor. @@ -54,11 +54,14 @@ def __init__( position in meters. Default is 0. name : str The name of the sensor. Default is "GnssReceiver". - seed : int, optional + seed : int, array_like of ints, numpy.random.SeedSequence, optional Seed for the random number generator that draws the measurement noise. If given, the noise becomes reproducible and independent of - the process-global NumPy RNG. Default is None, meaning the noise is - seeded from fresh entropy per instance. + the process-global NumPy RNG. Only seeds that describe a stream are + taken: live ``Generator``, ``BitGenerator`` and ``RandomState`` + objects are rejected, because their state advances as noise is + drawn and so cannot be represented in ``to_dict()``. Default is + None, meaning the noise is seeded from fresh entropy per instance. """ super().__init__(sampling_rate=sampling_rate, name=name, seed=seed) self.position_accuracy = position_accuracy diff --git a/rocketpy/sensors/gyroscope.py b/rocketpy/sensors/gyroscope.py index 8d4169a19..579c9f6da 100644 --- a/rocketpy/sensors/gyroscope.py +++ b/rocketpy/sensors/gyroscope.py @@ -2,7 +2,7 @@ from ..mathutils.vector_matrix import Vector from ..prints.sensors_prints import _GyroscopePrints -from ..sensors.sensor import InertialSensor +from ..sensors.sensor import InertialSensor, SeedLike # pylint: disable=too-many-arguments @@ -72,13 +72,13 @@ def __init__( random_walk_density=0, random_walk_variance=1, constant_bias=0, - operating_temperature=25, + operating_temperature=298.15, temperature_bias=0, temperature_scale_factor=0, cross_axis_sensitivity=0, acceleration_sensitivity=0, name="Gyroscope", - seed=None, + seed: SeedLike | None = None, ): """ Initialize the gyroscope sensor @@ -172,11 +172,14 @@ def __init__( length 3. name : str, optional The name of the sensor. Default is "Gyroscope". - seed : int, optional + seed : int, array_like of ints, numpy.random.SeedSequence, optional Seed for the random number generator that draws the measurement noise. If given, the noise becomes reproducible and independent of - the process-global NumPy RNG. Default is None, meaning the noise is - seeded from fresh entropy per instance. + the process-global NumPy RNG. Only seeds that describe a stream are + taken: live ``Generator``, ``BitGenerator`` and ``RandomState`` + objects are rejected, because their state advances as noise is + drawn and so cannot be represented in ``to_dict()``. Default is + None, meaning the noise is seeded from fresh entropy per instance. Returns ------- diff --git a/rocketpy/sensors/sensor.py b/rocketpy/sensors/sensor.py index ecc873a95..4e83ff9d8 100644 --- a/rocketpy/sensors/sensor.py +++ b/rocketpy/sensors/sensor.py @@ -2,6 +2,7 @@ import logging import warnings from abc import ABC, abstractmethod +from collections.abc import Sequence import numpy as np @@ -9,6 +10,47 @@ logger = logging.getLogger(__name__) +# The seed kinds that describe a stream, and so survive a to_dict() round trip. +# Named once because every concrete sensor repeats it in its signature; it is a +# union, so help() and inspect.signature() still show the members in full. +SeedLike = int | np.integer | Sequence[int] | np.ndarray | np.random.SeedSequence + + +def _is_int_array_like(value): + """Whether ``value`` is an int or a flat sequence of ints. + + This is the half of ``numpy.random.default_rng``'s seed contract that + ``RocketPyEncoder`` can write out: integers keep their value across a JSON + round trip, so a seed read back names the same stream it named before. + + Flat, because that is all ``SeedSequence`` takes as entropy: a nested + sequence raises ``TypeError`` and a multi-dimensional array ``ValueError``, + neither of them naming the seed. Older NumPy let some of those through, + and RocketPy pins no upper bound, so they are refused here to keep one + answer across versions. + """ + if isinstance(value, (int, np.integer)): + return True + if isinstance(value, np.ndarray): + return np.issubdtype(value.dtype, np.integer) and value.ndim <= 1 + if isinstance(value, Sequence) and not isinstance(value, (str, bytes)): + return all(isinstance(item, (int, np.integer)) for item in value) + return False + + +def _is_seed_descriptor(seed): + """Whether ``seed`` describes a random stream rather than being one. + + ``SeedSequence`` counts because it is defined by its entropy and spawn key, + and #1124 taught ``RocketPyEncoder`` to serialize it, so it still names the + same stream after a round trip. + """ + return ( + seed is None + or isinstance(seed, np.random.SeedSequence) + or _is_int_array_like(seed) + ) + # pylint: disable=too-many-statements class Sensor(ABC): @@ -58,11 +100,11 @@ def __init__( random_walk_density=0, random_walk_variance=1, constant_bias=0, - operating_temperature=25, + operating_temperature=298.15, temperature_bias=0, temperature_scale_factor=0, name="Sensor", - seed=None, + seed: SeedLike | None = None, ): """ Initialize the accelerometer sensor @@ -112,16 +154,29 @@ def __init__( meaning no temperature scale factor is applied. name : str, optional The name of the sensor. Default is "Sensor". - seed : int, optional + seed : int, array_like of ints, numpy.random.SeedSequence, optional Seed for the random number generator that draws the measurement noise. If given, the noise becomes reproducible and independent of - the process-global NumPy RNG. Default is None, meaning the noise is - seeded from fresh entropy per instance. + the process-global NumPy RNG. A ``numpy.random.SeedSequence`` is + also accepted and round trips through ``RocketPyEncoder``. Only + seeds that describe a stream are taken: the live ``Generator``, + ``BitGenerator`` and ``RandomState`` objects that + ``numpy.random.default_rng`` also accepts are rejected here, + because their state advances as noise is drawn and so cannot be + represented in the dictionary returned by ``to_dict()``. Default is + None, meaning the noise is seeded from fresh entropy per instance. Returns ------- None + Raises + ------ + TypeError + If ``seed`` is not an int, an array_like of ints, a + ``SeedSequence`` or None -- in particular if it is a live + ``Generator``, ``BitGenerator`` or ``RandomState``. + See Also -------- TODO link to documentation on noise model @@ -151,6 +206,40 @@ def __init__( self._random_walk_drift = 0 self.normal_vector = Vector([0, 0, 0]) + # default_rng() takes two different kinds of argument. One describes a + # stream -- an int, an array_like of ints, a SeedSequence -- and can be + # written down and read back. The other is a stream already in + # progress: Generator, BitGenerator, and, since NumPy 2.2, RandomState. + # Their state advances on every draw, so what to_dict() writes is a + # snapshot of the moment it ran rather than the stream the sensor used. + # #1124 taught RocketPyEncoder to serialize a SeedSequence, which stays + # reproducible because it is defined by its entropy and spawn key; a + # live generator has no such description to write. + # + # The check names the descriptors instead of the live types because the + # descriptors are the stable half of that contract: a seed kind numpy + # starts accepting later is refused here rather than reaching + # json.dumps(). Without it the sensor builds fine and only fails at + # serialization, far from the call that caused it. + if isinstance( + seed, + (np.random.Generator, np.random.BitGenerator, np.random.RandomState), + ): + raise TypeError( + f"Invalid seed type '{type(seed).__name__}'. The seed must be " + "an int, an array_like of ints, a numpy.random.SeedSequence or " + "None. numpy.random.default_rng also accepts Generator, " + "BitGenerator and RandomState objects, but their state advances " + "as noise is drawn, so they cannot be represented in the " + "dictionary to_dict() returns." + ) + if not _is_seed_descriptor(seed): + raise TypeError( + f"Invalid seed type '{type(seed).__name__}'. The seed must be " + "an int, an array_like of ints, a numpy.random.SeedSequence or " + "None, so that to_dict() can write it out." + ) + # Per-instance RNG, seeded deterministically when a seed is given, so # the measurement noise is reproducible and independent of the # process-global NumPy RNG (and therefore safe under parallel or @@ -357,7 +446,7 @@ class InertialSensor(Sensor): temperature drift. """ - def __init__( # pylint: disable=too-many-arguments + def __init__( self, sampling_rate, orientation=(0, 0, 0), @@ -373,7 +462,7 @@ def __init__( # pylint: disable=too-many-arguments temperature_scale_factor=0, cross_axis_sensitivity=0, name="Sensor", - seed=None, + seed: SeedLike | None = None, ): """ Initialize the accelerometer sensor @@ -460,11 +549,14 @@ def __init__( # pylint: disable=too-many-arguments no cross-axis sensitivity is applied. name : str, optional The name of the sensor. Default is "Sensor". - seed : int, optional + seed : int, array_like of ints, numpy.random.SeedSequence, optional Seed for the random number generator that draws the measurement noise. If given, the noise becomes reproducible and independent of - the process-global NumPy RNG. Default is None, meaning the noise is - seeded from fresh entropy per instance. + the process-global NumPy RNG. Only seeds that describe a stream are + taken: live ``Generator``, ``BitGenerator`` and ``RandomState`` + objects are rejected, because their state advances as noise is + drawn and so cannot be represented in ``to_dict()``. Default is + None, meaning the noise is seeded from fresh entropy per instance. Returns ------- @@ -678,11 +770,11 @@ def __init__( random_walk_density=0, random_walk_variance=1, constant_bias=0, - operating_temperature=25, + operating_temperature=298.15, temperature_bias=0, temperature_scale_factor=0, name="Sensor", - seed=None, + seed: SeedLike | None = None, ): """ Initialize the accelerometer sensor @@ -732,11 +824,14 @@ def __init__( meaning no temperature scale factor is applied. name : str, optional The name of the sensor. Default is "Sensor". - seed : int, optional + seed : int, array_like of ints, numpy.random.SeedSequence, optional Seed for the random number generator that draws the measurement noise. If given, the noise becomes reproducible and independent of - the process-global NumPy RNG. Default is None, meaning the noise is - seeded from fresh entropy per instance. + the process-global NumPy RNG. Only seeds that describe a stream are + taken: live ``Generator``, ``BitGenerator`` and ``RandomState`` + objects are rejected, because their state advances as noise is + drawn and so cannot be represented in ``to_dict()``. Default is + None, meaning the noise is seeded from fresh entropy per instance. Returns ------- diff --git a/rocketpy/simulation/datcom.py b/rocketpy/simulation/datcom.py new file mode 100644 index 000000000..9b6f0e0f6 --- /dev/null +++ b/rocketpy/simulation/datcom.py @@ -0,0 +1,144 @@ +"""DATCOM damping and load coupling without modifying ordinary flight models.""" + +import math +from functools import wraps + +from rocketpy.mathutils.vector_matrix import Matrix, Vector + + +def datcom_derivative(method): + """Wrap an RHS with a local implicit incidence-rate solve when DATCOM is active.""" + + @wraps(method) + def evaluate(self, time, state, post_processing=False): + if self.rocket._datcom_model is None or getattr( + self, "_datcom_evaluating", False + ): + return method(self, time, state, post_processing) + + def derivative(t, u, post): + return method(self, t, u, post) + + return self._evaluate_datcom_derivative( + derivative, time, state, post_processing + ) + + return evaluate + + +class DatcomFlightMixin: + """Time-dependent loads and deterministic unsteady aerodynamic coupling.""" + + def _datcom_surface_loads(self, surface, time, *args): + """Supply time and the instantaneous incidence rate to DATCOM only.""" + if surface is self.rocket._datcom_model: + loads = surface.compute_forces_and_moments_at_time( + time, getattr(self, "_datcom_alpha_dot", 0.0), *args + ) + if any( + brake.override_rocket_drag and brake.deployment_level > 0 + for brake in self.rocket.air_brakes + ): + # The air-brake loop already supplied the overriding axial load. + # Keep DATCOM's lateral forces and intrinsic moments. + return (*loads[:2], 0.0, *loads[3:]) + return loads + return surface.compute_forces_and_moments(*args) + + def _surface_cp_at_time(self, surface, time): + """Aerodynamic reference relative to dry CG in body axes, in meters.""" + if surface is self.rocket._datcom_model: + return surface.reference_offset(time) + return self.rocket.surfaces_cp_to_cdm[surface] + + def _evaluate_datcom_derivative(self, derivative, time, state, post_processing): + """Solve the affine CNAD/CMAD acceleration coupling within one RHS call. + + The rate is never differenced across adaptive ODE evaluations. Probe + evaluations do not append post-processing records. Scratch state belongs + to this Flight rather than its shared rocket, and is cleared on errors. + """ + self._datcom_evaluating = True + self._datcom_alpha_dot = 0.0 + try: + base = derivative(time, state, False) + intercept = self._datcom_incidence_rate(time, state, base) + self._datcom_alpha_dot = 1.0 + probe = derivative(time, state, False) + response = self._datcom_incidence_rate(time, state, probe) - intercept + denominator = 1 - response + if not math.isfinite(denominator) or abs(denominator) < 1e-10: + raise RuntimeError("Singular DATCOM alpha-dot aerodynamic coupling") + self._datcom_alpha_dot = intercept / denominator + if not math.isfinite(self._datcom_alpha_dot): + raise RuntimeError("Nonfinite DATCOM alpha-dot aerodynamic coupling") + return derivative(time, state, post_processing) + finally: + self._datcom_alpha_dot = 0.0 + self._datcom_evaluating = False + + def _datcom_incidence_rate(self, time, state, derivative): + """Local alpha rate (rad/s), including rotation, acceleration and shear.""" + rotation = Matrix.transformation(state[6:10]) + inverse = rotation.transpose + omega = Vector(state[10:13]) + omega_dot = Vector(derivative[10:13]) + reference = self.rocket._datcom_model.reference_offset(time) + z = state[2] + (rotation @ reference).z + wind = Vector([self.env.wind_velocity_x(z), self.env.wind_velocity_y(z), 0]) + relative = inverse @ (Vector(state[3:6]) - wind) + velocity = relative + (omega ^ reference) + speed = abs(velocity) + axial = abs(velocity.z) + denominator = axial**2 + velocity.y**2 + if speed < 1 or denominator < 1e-16: + return 0.0 + z_dot = state[5] + (rotation @ (omega ^ reference)).z + wind_dot = Vector( + [ + self.env.wind_velocity_x.differentiate(z, dx=0.1) * z_dot, + self.env.wind_velocity_y.differentiate(z, dx=0.1) * z_dot, + 0, + ] + ) + acceleration = ( + inverse @ (Vector(derivative[3:6]) - wind_dot) + - (omega ^ relative) + + (omega_dot ^ reference) + ) + axial_dot = math.copysign(1.0, velocity.z) * acceleration.z + return (axial * acceleration.y - velocity.y * axial_dot) / denominator + + def _datcom_rail_force(self, time, velocity, speed, mach, rho, reynolds, fallback): + """Constrained axial load uses body-axis CA rather than stability-axis CD.""" + if self.rocket._datcom_model is None: + return fallback + loads = self.rocket._datcom_model.compute_forces_and_moments_at_time( + time, + 0.0, + velocity, + speed, + mach, + rho, + Vector.zeros(), + Vector.zeros(), + reynolds, + ) + return loads[2] + + def _datcom_moment_at_cm(self, time, forces, moments): + """Transfer a physical dry-CG moment to the current CG for legacy EOM.""" + if self.rocket._datcom_model is None: + return moments + cm_to_dry = Vector([0, 0, self.rocket.com_to_cdm_function(time)]) + return Vector(moments) + (cm_to_dry ^ Vector(forces)) + + def _datcom_angular_acceleration(self, inertia, T20, T21, r_cm, forces, moments): + """Transfer aerodynamic torque explicitly within generalized EOM conventions.""" + if self.rocket._datcom_model is None: + return inertia.inverse @ (T21 + (T20 ^ r_cm)) + aero_force, aero_moment = Vector(forces), Vector(moments) + moment_at_cm = aero_moment + (r_cm ^ aero_force) + return inertia.inverse @ ( + T21 - aero_moment + ((T20 - aero_force) ^ r_cm) + moment_at_cm + ) diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py index e026d166e..70b8f63c8 100644 --- a/rocketpy/simulation/flight.py +++ b/rocketpy/simulation/flight.py @@ -26,6 +26,7 @@ quaternions_to_precession, quaternions_to_spin, ) +from .datcom import DatcomFlightMixin, datcom_derivative logger = logging.getLogger(__name__) @@ -41,7 +42,7 @@ # pylint: disable=too-many-public-methods # pylint: disable=too-many-instance-attributes -class Flight: +class Flight(DatcomFlightMixin): """Keeps all flight information and has a method to simulate flight. Attributes @@ -79,6 +80,14 @@ class Flight: Name of the flight. Flight._controllers : list List of controllers to be used during simulation. + Flight.post_step_callback : callable, optional + Optional callback invoked once after every successful ODE solver + step for the entire simulation, including parachute descent. + Receives the ``Flight`` instance (``callback(flight)``). Use + ``flight.t`` and ``flight.y_sol`` for the current time and state. + Controllers stop being useful after parachute deployment; this + callback is the extension point for full-lifecycle observers + (e.g. ground-station / radio update simulation). Flight.max_time : int, float Maximum simulation time allowed. Refers to physical time being simulated, not time taken to run simulation. @@ -472,6 +481,23 @@ class Flight: array. Flight.simulation_mode : str Simulation mode for the flight. Can be "6 DOF" or "3 DOF". + Flight.use_udot_rail2 : bool + Whether the intermediate 3-DOF "tip-off" rail phase was enabled. When + True, the flight goes through ``udot_rail1`` -> ``udot_rail2`` -> + generalized 6-DOF instead of straight from ``udot_rail1`` to the + generalized equations. Requires ``simulation_mode="6 DOF"`` and + ``equations_of_motion="standard"``. + Flight.between_rails_time : float + Time at which the lower rail button leaves the rail, ending the tip-off + phase, in seconds. Equal to ``out_of_rail_time`` when the phase is off. + Flight.between_rails_velocity : float + Rocket velocity magnitude when the lower rail button leaves the rail, + in m/s. + Flight.between_rails_state : list + State vector at the moment the lower rail button leaves the rail. + Flight.tip_off_duration : float + Time the rocket spends pivoting about the lower rail button, in + seconds. Zero when ``use_udot_rail2`` is False. Flight.rail_button1_bending_moment : Function Internal bending moment at upper rail button attachment point in N·m as a function of time. Calculated using beam theory during rail phase. @@ -486,6 +512,11 @@ class Flight: attachment point during rail flight phase in N·m. """ + #: Class-level default so that ``Flight`` objects restored from a ``.rpy`` + #: file written before the tip-off phase existed still answer the flag. + #: Instances built through ``__init__`` always shadow it. + use_udot_rail2 = False + def __init__( # pylint: disable=too-many-arguments,too-many-statements self, rocket, @@ -506,6 +537,8 @@ def __init__( # pylint: disable=too-many-arguments,too-many-statements equations_of_motion="standard", ode_solver="LSODA", simulation_mode="6 DOF", + post_step_callback=None, + use_udot_rail2=False, ): """Run a trajectory simulation. @@ -592,6 +625,21 @@ def __init__( # pylint: disable=too-many-arguments,too-many-statements simulation_mode : str, optional Simulation mode to use. Can be "6 DOF" for 6 degrees of freedom or "3 DOF" for 3 degrees of freedom. Default is "6 DOF". + post_step_callback : callable, optional + Callback invoked once after every successful ODE solver step for + the entire simulation, including parachute phases. Signature is + ``callback(flight)``, matching phase/node callbacks. Access the + current time and state via ``flight.t`` and ``flight.y_sol``. + Default is None. + use_udot_rail2 : bool, optional + If True, enable the intermediate 3-DOF "tip-off" rail phase + ``udot_rail2``: after the upper rail button leaves the rail (at + ``effective_1rl``) the rocket pivots about the still-engaged lower + button until it too leaves the rail (at ``effective_2rl``), before + the generalized 6-DOF free flight. If False, the flight transitions + directly from the 1-DOF ``udot_rail1`` phase to the generalized + 6-DOF dynamics at the upper button exit, as in previous versions. + Default is False. Returns ------- None @@ -600,9 +648,13 @@ def __init__( # pylint: disable=too-many-arguments,too-many-statements ---------- .. [1] https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html """ + if post_step_callback is not None and not callable(post_step_callback): + raise TypeError("post_step_callback must be callable or None") + # Save arguments self.env = environment self.rocket = rocket + self.rocket.prepare_datcom() # Warn about an aerodynamically unstable rocket now that it is fully # assembled and about to be simulated. Doing it here (instead of on every # add_surfaces call during construction) avoids spurious warnings for @@ -626,6 +678,9 @@ def __init__( # pylint: disable=too-many-arguments,too-many-statements self.equations_of_motion = equations_of_motion self.simulation_mode = simulation_mode self.ode_solver = ode_solver + self.post_step_callback = post_step_callback + # Enable or disable the intermediate 3-DOF rail phase + self.use_udot_rail2 = use_udot_rail2 # Controller initialization self.__init_controllers() @@ -708,71 +763,6 @@ def __simulate(self, verbose): self.__process_sensors_and_controllers_at_current_node(node, phase) - for parachute in node.parachutes: - # Calculate and save pressure signal - ( - noisy_pressure, - height_above_ground_level, - ) = self.__calculate_and_save_pressure_signals( - parachute, node.t, self.y_sol[2] - ) - if self._evaluate_parachute_trigger( - parachute, - noisy_pressure, - height_above_ground_level, - self.y_sol, - self.sensors, - phase.derivative, - self.t, - ): - # Remove parachute from flight parachutes - self.parachutes.remove(parachute) - # Create phase for time after detection and before inflation - # Must only be created if parachute has any lag - i = 1 - if parachute.lag != 0: - self.flight_phases.add_phase( - node.t, - phase.derivative, - clear=True, - index=phase_index + i, - ) - i += 1 - # Create flight phase for time after inflation - callbacks = [ - lambda self, parachute_cd_s=parachute.cd_s: setattr( - self, "parachute_cd_s", parachute_cd_s - ), - lambda self, parachute_radius=parachute.radius: setattr( - self, "parachute_radius", parachute_radius - ), - lambda self, parachute_height=parachute.height: setattr( - self, "parachute_height", parachute_height - ), - lambda self, parachute_porosity=parachute.porosity: setattr( - self, "parachute_porosity", parachute_porosity - ), - lambda self, added_mass_coefficient=parachute.added_mass_coefficient: ( - setattr( - self, - "parachute_added_mass_coefficient", - added_mass_coefficient, - ) - ), - ] - self.flight_phases.add_phase( - node.t + parachute.lag, - self.u_dot_parachute, - callbacks, - clear=False, - index=phase_index + i, - ) - # Prepare to leave loops and start new flight phase - phase.time_nodes.flush_after(node_index) - phase.time_nodes.add_node(self.t, [], [], []) - phase.solver.status = "finished" - # Save parachute event - self.parachute_events.append([self.t, parachute]) if self.__check_and_handle_parachute_triggers( node, phase, phase_index, node_index ): @@ -800,6 +790,9 @@ def __simulate(self, verbose): self.sensors, self.env, ) + # Full-lifecycle observer (all phases, including parachute) + if self.post_step_callback is not None: + self.post_step_callback(self) if self.__check_simulation_events(phase, phase_index, node_index): break # Stop if simulation termination event occurred @@ -848,6 +841,18 @@ def __setup_phase_time_nodes(self, phase): # Add last time node phase.time_nodes.add_node(phase.time_bound, [], [], []) + # A thrust curve that starts at t > 0 leaves the rocket stationary at + # ignition-minus, so the solver (max_step defaults to inf) can take one + # huge step clean over the burn and the rocket never lifts off (#411). + # Force solver stops at ignition and burn-out so the burn is always + # sampled. Guarded to burn_start > 0, so ordinary motors are untouched. + motor = self.rocket.motor + burn_start = getattr(motor, "burn_start_time", 0) or 0 + if burn_start > 0: + for t_burn in (motor.burn_start_time, motor.burn_out_time): + if phase.t < t_burn < phase.time_bound: + phase.time_nodes.add_node(t_burn, [], [], []) + # Organize time nodes phase.time_nodes.sort() phase.time_nodes.merge() @@ -886,7 +891,7 @@ def __measure_sensors(self, component_sensors, u_dot, t=None, y_sol=None): Parameters ---------- component_sensors : list - List of (sensor, position) tuples. + List of (sensor, position, ref_factor) component tuples. u_dot : array_like State derivative vector. t : float, optional @@ -899,7 +904,7 @@ def __measure_sensors(self, component_sensors, u_dot, t=None, y_sol=None): if y_sol is None: y_sol = self.y_sol - for sensor, position in component_sensors: + for sensor, position, _ref_factor in component_sensors: relative_position = position - self.rocket._csys * Vector( [0, 0, self.rocket.center_of_dry_mass_position] ) @@ -1031,14 +1036,31 @@ def __check_simulation_events(self, phase, phase_index, node_index): bool True if an event occurred and the simulation should break. """ - # Check for first out of rail event - if len(self.out_of_rail_state) == 1 and ( + # Squared distance travelled from the launch point, used by both rail + # button exit checks below. + squared_distance_travelled = ( self.y_sol[0] ** 2 + self.y_sol[1] ** 2 + (self.y_sol[2] - self.env.elevation) ** 2 - >= self.effective_1rl**2 + ) + # Check for first out of rail event (upper rail button leaving the + # rail, at effective_1rl). This starts the 3-DOF tip-off phase when + # enabled, otherwise transitions straight to generalized 6-DOF flight. + if ( + len(self.out_of_rail_state) == 1 + and squared_distance_travelled >= self.effective_1rl**2 ): return self.__handle_out_of_rail_event(phase, phase_index, node_index) + # Check for the lower rail button leaving the rail (at effective_2rl), + # which ends the tip-off phase and begins generalized 6-DOF flight. + # Only relevant while the intermediate udot_rail2 phase is active. + elif ( + self.use_udot_rail2 + and len(self.between_rails_state) == 1 + and len(self.out_of_rail_state) != 1 + and squared_distance_travelled >= self.effective_2rl**2 + ): + return self.__handle_between_rails_event(phase, phase_index, node_index) # Check for apogee event # TODO: negative vz doesn't really mean apogee. Improve this. @@ -1051,34 +1073,37 @@ def __check_simulation_events(self, phase, phase_index, node_index): return False - def __handle_out_of_rail_event(self, phase, phase_index, node_index): - """Handle the out of rail event. + def __root_find_rail_exit_time(self, phase, effective_rl): + """Root-find the exact time at which the squared distance travelled + (from the launch point, ignoring ground elevation) equals + ``effective_rl ** 2``, using cubic Hermite interpolation between the two + most recent solution points. Parameters ---------- phase : FlightPhase - The current flight phase. - phase_index : int - The index of the current phase. - node_index : int - The index of the current node. + The current flight phase (provides the solver step size). + effective_rl : float + Effective rail length whose crossing is being solved for + (``effective_1rl`` for the upper button, ``effective_2rl`` for the + lower button). Returns ------- - bool - True to indicate the simulation should break. + float + Absolute simulation time of the crossing. """ # Check exactly when it went out using root finding # Disconsider elevation self.solution[-2][3] -= self.env.elevation self.solution[-1][3] -= self.env.elevation # Get points - y0 = sum(self.solution[-2][i] ** 2 for i in [1, 2, 3]) - self.effective_1rl**2 + y0 = sum(self.solution[-2][i] ** 2 for i in [1, 2, 3]) - effective_rl**2 yp0 = 2 * sum( self.solution[-2][i] * self.solution[-2][i + 3] for i in [1, 2, 3] ) t1 = self.solution[-1][0] - self.solution[-2][0] - y1 = sum(self.solution[-1][i] ** 2 for i in [1, 2, 3]) - self.effective_1rl**2 + y1 = sum(self.solution[-1][i] ** 2 for i in [1, 2, 3]) - effective_rl**2 yp1 = 2 * sum( self.solution[-1][i] * self.solution[-1][i + 3] for i in [1, 2, 3] ) @@ -1107,15 +1132,87 @@ def __handle_out_of_rail_event(self, phase, phase_index, node_index): raise ValueError("Multiple roots found when solving for rail exit time.") if len(valid_t_root) == 0: # pragma: no cover raise ValueError("No valid roots found when solving for rail exit time.") + return valid_t_root[0] + self.solution[-2][0] + + def __handle_out_of_rail_event(self, phase, phase_index, node_index): + """Handle the out of rail event (upper rail button leaving the rail, at + ``effective_1rl``). + + Records the out-of-rail state and inserts the next flight phase: the + 3-DOF tip-off phase ``udot_rail2`` when it is enabled and there is a + nonzero single-button window (``effective_2rl > effective_1rl``), + otherwise the generalized 6-DOF dynamics. + + Parameters + ---------- + phase : FlightPhase + The current flight phase. + phase_index : int + The index of the current phase. + node_index : int + The index of the current node. + + Returns + ------- + bool + True to indicate the simulation should break. + """ # Determine final state when upper button is going out of rail - self.t = valid_t_root[0] + self.solution[-2][0] + self.t = self.__root_find_rail_exit_time(phase, self.effective_1rl) interpolator = phase.solver.dense_output() self.y_sol = interpolator(self.t) self.solution[-1] = [self.t, *self.y_sol] self.out_of_rail_time = self.t self.out_of_rail_time_index = len(self.solution) - 1 self.out_of_rail_state = self.y_sol - # Create new flight phase + # Create new flight phase: the intermediate 3-DOF tip-off phase if + # enabled and the two buttons are distinct, otherwise straight to the + # generalized 6-DOF dynamics (previous behavior). + if self.use_udot_rail2 and self.effective_2rl > self.effective_1rl: + next_derivative = self.udot_rail2 + else: + next_derivative = self.u_dot_generalized + self.flight_phases.add_phase( + self.t, + next_derivative, + index=phase_index + 1, + ) + # Prepare to leave loops and start new flight phase + phase.time_nodes.flush_after(node_index) + phase.time_nodes.add_node(self.t, [], [], []) + phase.solver.status = "finished" + return True + + def __handle_between_rails_event(self, phase, phase_index, node_index): + """Handle the end of the 3-DOF tip-off phase (lower rail button leaving + the rail, at ``effective_2rl``). + + Records the between-rails state at the exact ``effective_2rl`` crossing + and inserts the generalized 6-DOF free-flight phase. + + Parameters + ---------- + phase : FlightPhase + The current flight phase. + phase_index : int + The index of the current phase. + node_index : int + The index of the current node. + + Returns + ------- + bool + True to indicate the simulation should break. + """ + # Determine final state when lower button is going out of rail + self.t = self.__root_find_rail_exit_time(phase, self.effective_2rl) + interpolator = phase.solver.dense_output() + self.y_sol = interpolator(self.t) + self.solution[-1] = [self.t, *self.y_sol] + self.between_rails_time = self.t + self.between_rails_time_index = len(self.solution) - 1 + self.between_rails_state = self.y_sol + # Create the generalized 6-DOF free-flight phase self.flight_phases.add_phase( self.t, self.u_dot_generalized, @@ -1213,6 +1310,8 @@ def __handle_impact_event(self, phase, phase_index, node_index): ] if len(valid_t_root) > 1: # pragma: no cover raise ValueError("Multiple roots found when solving for impact time.") + if len(valid_t_root) == 0: + raise ValueError("No valid roots found when solving for impact time.") # Determine impact state at t_root self.t = self.t_final = valid_t_root[0] + self.solution[-2][0] interpolator = phase.solver.dense_output() @@ -1526,6 +1625,10 @@ def _evaluate_parachute_trigger( if expects_udot: u_dot = derivative_func(t, y) + # Expose flight time for built-in ("time", t_deploy) triggers without + # changing the public (p, h, y, sensors, u_dot) triggerfunc signature. + parachute._eval_time = t + # Call the wrapper with both sensors and u_dot # The wrapper will decide which args to pass to the user's function return triggerfunc(pressure, height, y, sensors, u_dot) @@ -1535,6 +1638,9 @@ def __init_solution_monitors(self): self.out_of_rail_time = 0 self.out_of_rail_time_index = 0 self.out_of_rail_state = np.array([0]) + self.between_rails_state = np.array([0]) + self.between_rails_time = 0 + self.between_rails_time_index = 0 self.apogee_state = np.array([0]) self.apogee = 0 self.apogee_time = 0 @@ -1547,6 +1653,16 @@ def __init_solution_monitors(self): def __init_flight_state(self): """Initialize flight state variables.""" + # The rail is a fixed inertial line set by the launch inclination and + # heading, so its unit vector is known regardless of how the flight + # state is initialized. udot_rail2 constrains the lower button to it. + self.attitude_unit = Vector( + [ + np.cos(np.radians(self.inclination)) * np.sin(np.radians(self.heading)), + np.cos(np.radians(self.inclination)) * np.cos(np.radians(self.heading)), + np.sin(np.radians(self.inclination)), + ] + ) if self.initial_solution is None: # Initialize time and state variables self.t_initial = 0 @@ -1576,6 +1692,7 @@ def __init_flight_state(self): e0_init, e1_init, e2_init, e3_init = euler313_to_quaternions( self.phi_init, self.theta_init, self.psi_init ) + # Store initial conditions self.initial_solution = [ self.t_initial, @@ -1595,23 +1712,24 @@ def __init_flight_state(self): ] # Set initial derivative for rail phase self.initial_derivative = self.udot_rail1 - elif isinstance(self.initial_solution, Flight): - # Initialize time and state variables based on last solution of - # previous flight - self.initial_solution = self.initial_solution.solution[-1] - # Set unused monitors - self.out_of_rail_state = self.initial_solution[1:] - self.out_of_rail_time = self.initial_solution[0] - self.out_of_rail_time_index = 0 - # Set initial derivative for 6-DOF flight phase - self.initial_derivative = self.u_dot_generalized else: - # Initial solution given, ignore rail phase + if isinstance(self.initial_solution, Flight): + # Initialize time and state variables based on last solution of + # previous flight + self.initial_solution = self.initial_solution.solution[-1] + # Initial solution given, ignore rail phases # TODO: Check if rocket is actually out of rail. Otherwise, start at rail + # Both rail phases are skipped, so their monitors record the given + # starting state: out of rail (upper button) and, when the + # intermediate tip-off phase is enabled, between rails (lower button). self.out_of_rail_state = self.initial_solution[1:] self.out_of_rail_time = self.initial_solution[0] self.out_of_rail_time_index = 0 + self.between_rails_state = self.initial_solution[1:] + self.between_rails_time = self.initial_solution[0] + self.between_rails_time_index = 0 self.t_initial = self.initial_solution[0] + # Set initial derivative for 6-DOF flight phase self.initial_derivative = self.u_dot_generalized if self._controllers or self.sensors: # Handle post process during simulation, get initial accel/forces @@ -1664,6 +1782,30 @@ def __init_equations_of_motion(self): "Must be '3 DOF' or '6 DOF'." ) + # The tip-off phase patches the *generalized* 6-DOF solution with a + # constraint wrench built from the full 3x3 inertia tensor. The reduced + # formulations bound above (``u_dot_generalized_3dof`` carries no + # attitude at all, ``u_dot`` uses the axisymmetric solid-propulsion + # equations) do not share that state, so the patch would be applied to + # a solution it does not describe. Refuse the combination instead of + # silently producing non-physical tip-off kinematics. + if self.use_udot_rail2: + if self.simulation_mode != "6 DOF": + raise ValueError( + "use_udot_rail2=True requires simulation_mode='6 DOF', got " + f"'{self.simulation_mode}'. The tip-off phase solves for the " + "rotation about the lower rail button, which the 3 DOF " + "equations of motion do not model. Note that a point-mass " + "motor forces simulation_mode to '3 DOF'." + ) + if self.equations_of_motion != "standard": + raise ValueError( + "use_udot_rail2=True requires equations_of_motion='standard', " + f"got '{self.equations_of_motion}'. The tip-off phase is " + "derived from the generalized variable-mass equations and " + "cannot patch the reduced solid_propulsion formulation." + ) + def __init_controllers(self): """Initialize controllers and sensors""" self._controllers = self.rocket._controllers[:] @@ -1858,6 +2000,9 @@ def udot_rail1(self, t, u, post_processing=False): 0, ) R3 = -0.5 * rho * (free_stream_speed**2) * self.rocket.area * (drag_coeff) + R3 = self._datcom_rail_force( + t, stream_velocity_body, free_stream_speed, mach, rho, reynolds, R3 + ) # Calculate Linear acceleration a3 = (R3 + net_thrust) / total_mass_at_t - ( @@ -1878,9 +2023,28 @@ def udot_rail1(self, t, u, post_processing=False): return [vx, vy, vz, ax, ay, az, 0, 0, 0, 0, 0, 0, 0] - def udot_rail2(self, t, u, post_processing=False): # pragma: no cover - """[Still not implemented] Calculates derivative of u state vector with - respect to time when rocket is flying in 3 DOF motion in the rail. + @datcom_derivative + def udot_rail2(self, t, u, post_processing=False): + """Calculates the derivative of the u state vector with respect to time + for the intermediate 3-DOF "tip-off" rail phase: the upper rail button + has left the rail but the lower button is still engaged, so the rocket + slides along the (fixed) rail line while free to pitch and yaw about the + lower button, with roll suppressed. + + The dynamics reuse the free variable-mass generalized equations of + motion (:meth:`u_dot_generalized`) and enforce the single-button + constraint by adding an unknown reaction wrench solved from a small + linear system: a normal force ``N`` at the lower button (perpendicular + to the rail, 2 DOF) plus a roll reaction moment ``mu`` (1 DOF). The + three unknowns are found from three constraints -- the button's + acceleration perpendicular to the rail is zero (2) and the roll angular + acceleration is zero (1). The full derivation is in the technical + documentation, :ref:`Tip-off `. All reaction quantities are + expressed in the "true" + body frame (the one used by ``surfaces_cp_to_cdm``, body-z toward the + nose), so ``r_CM`` and the button position are taken with that sign + convention -- independent of the internal (point-to-CDM) convention used + by the generalized equations. Parameters ---------- @@ -1899,9 +2063,115 @@ def udot_rail2(self, t, u, post_processing=False): # pragma: no cover State vector defined by u_dot = [vx, vy, vz, ax, ay, az, e0dot, e1dot, e2dot, e3dot, alpha1, alpha2, alpha3]. """ - # Hey! We will finish this function later, now we just can use u_dot - return self.u_dot_generalized(t, u, post_processing=post_processing) + # Free (unconstrained) generalized solution. This also handles the + # aerodynamic/post-processing bookkeeping. We keep its position and + # quaternion derivatives and only override the 6-DOF accelerations + # (indices 3:6 inertial linear, 10:13 body angular) with the + # constrained values computed below. + u_dot = list(self.u_dot_generalized(t, u, post_processing=post_processing)) + a_cdm_free = Vector(u_dot[3:6]) # inertial CDM acceleration (free) + w_dot_free = Vector(u_dot[10:13]) # body angular acceleration (free) + + # State quantities + e0, e1, e2, e3 = u[6:10] + w = Vector(u[10:13]) + K = Matrix.transformation([e0, e1, e2, e3]) + Kt = K.transpose + # ``u_dot_generalized`` just interpolated the mass and the inertia about + # the CM for this very t; reuse them instead of paying for both again on + # every solver evaluation inside the tip-off window. + cached = getattr(self, "_mass_inertia_cache", None) + if cached is not None and cached[0] == t: + _, total_mass, inertia_cm = cached + else: # pragma: no cover - defensive, the call above always fills it + total_mass = self.rocket.total_mass.get_value_opt(t) + inertia_cm = None + + # Geometry in the true body frame (body-z toward the nose): + # position of a point p relative to the CDM = (p - cdm) * csys. + # The generalized EOM store r_CM / r_NOZ as (point -> CDM) vectors, i.e. + # the negative of the true-frame position; hence the sign flips below. + csys = self.rocket._csys + cdm = self.rocket.center_of_dry_mass_position + r_CM = Vector([0, 0, -self.rocket.com_to_cdm_function.get_value_opt(t)]) + lower_button_z = self.rocket.rail_buttons[0].position.z + r_button = Vector([0, 0, (lower_button_z - cdm) * csys]) + + # Inertia about the instantaneous center of mass (sign-independent). + if inertia_cm is None: # pragma: no cover - defensive, see cache above + inertia_tensor = self.rocket.get_inertia_tensor_at_time(t) + inertia_cm = inertia_tensor - ( + (r_CM.cross_matrix @ -r_CM.cross_matrix) * total_mass + ) + inv_inertia_cm = inertia_cm.inverse + + # Orthonormal body triad with the rail direction. The rail is the fixed + # inertial unit vector ``attitude_unit``; express it in the body frame. + n = Kt @ self.attitude_unit + n = n / abs(n) + # Pick the coordinate axis least aligned with n to build a stable basis. + if abs(n.z) <= abs(n.x) and abs(n.z) <= abs(n.y): + helper = Vector([0.0, 0.0, 1.0]) + elif abs(n.y) <= abs(n.x): + helper = Vector([0.0, 1.0, 0.0]) + else: + helper = Vector([1.0, 0.0, 0.0]) + e1_b = n ^ helper + e1_b = e1_b / abs(e1_b) + e2_b = n ^ e1_b # already unit (n, e1_b orthonormal) + z_b = Vector([0.0, 0.0, 1.0]) # body roll axis + + # Linear response of (angular accel, CDM accel, button accel) in the + # body frame to a reaction force ``Fr`` (body) at the button and a roll + # reaction moment ``tau`` about the body axis. + def _response(f_r, tau): + d_wdot = inv_inertia_cm @ ( + ((r_button - r_CM) ^ f_r) + Vector([0.0, 0.0, tau]) + ) + d_a_cdm_body = f_r * (1.0 / total_mass) - (d_wdot ^ r_CM) + d_a_button = d_a_cdm_body + (d_wdot ^ r_button) + return d_wdot, d_a_cdm_body, d_a_button + + # Free button acceleration in the body frame. + a_button_free = ( + (Kt @ a_cdm_free) + (w_dot_free ^ r_button) + (w ^ (w ^ r_button)) + ) + + # Assemble the 3x3 system J @ [lambda1, lambda2, mu] = -g_free. + jacobian = np.empty((3, 3)) + for col, (f_r, tau) in enumerate( + ((e1_b, 0.0), (e2_b, 0.0), (Vector([0.0, 0.0, 0.0]), 1.0)) + ): + d_wdot, _, d_a_button = _response(f_r, tau) + jacobian[0, col] = d_a_button @ e1_b + jacobian[1, col] = d_a_button @ e2_b + jacobian[2, col] = d_wdot @ z_b + g_free = np.array( + [a_button_free @ e1_b, a_button_free @ e2_b, w_dot_free @ z_b] + ) + + try: + lambda1, lambda2, mu = np.linalg.solve(jacobian, -g_free) + except np.linalg.LinAlgError: # pragma: no cover + warnings.warn( + "Singular constraint system in udot_rail2; falling back to " + "unconstrained dynamics for this step.", + RuntimeWarning, + ) + return u_dot + + f_r = e1_b * lambda1 + e2_b * lambda2 + d_wdot, d_a_cdm_body, _ = _response(f_r, mu) + w_dot = w_dot_free + d_wdot + # Enforce exactly zero roll acceleration (kills residual round-off). + w_dot = Vector([w_dot.x, w_dot.y, 0.0]) + a_cdm = a_cdm_free + (K @ d_a_cdm_body) + u_dot[3:6] = [a_cdm.x, a_cdm.y, a_cdm.z] + u_dot[10:13] = [w_dot.x, w_dot.y, w_dot.z] + return u_dot + + @datcom_derivative def u_dot(self, t, u, post_processing=False): # pylint: disable=too-many-locals,too-many-statements """Calculates derivative of u state vector with respect to time when rocket is flying in 6 DOF motion during ascent out of rail @@ -2048,6 +2318,8 @@ def u_dot(self, t, u, post_processing=False): # pylint: disable=too-many-locals omega2, omega3, ) + if self.rocket._datcom_model is not None: + drag_coeff = 0.0 R3 = -0.5 * rho * (free_stream_speed**2) * self.rocket.area * drag_coeff for air_brakes in self.rocket.air_brakes: if air_brakes.deployment_level > 0: @@ -2075,9 +2347,9 @@ def u_dot(self, t, u, post_processing=False): # pylint: disable=too-many-locals # Calculate lift and moment for each component of the rocket velocity_in_body_frame = Vector([vx_b, vy_b, vz_b]) w = Vector([omega1, omega2, omega3]) - for aero_surface, _ in self.rocket.aerodynamic_surfaces: + for aero_surface, _, _ref_factor in self.rocket._flight_aerodynamic_surfaces: # Component cp relative to CDM in body frame - comp_cp = self.rocket.surfaces_cp_to_cdm[aero_surface] + comp_cp = self._surface_cp_at_time(aero_surface, t) # Component absolute velocity in body frame comp_vb = velocity_in_body_frame + (w ^ comp_cp) # Wind velocity at component altitude @@ -2101,7 +2373,9 @@ def u_dot(self, t, u, post_processing=False): # pylint: disable=too-many-locals / comp_dynamic_viscosity ) # Forces and moments - X, Y, Z, M, N, L = aero_surface.compute_forces_and_moments( + X, Y, Z, M, N, L = self._datcom_surface_loads( + aero_surface, + t, comp_stream_velocity, comp_stream_speed, comp_stream_mach, @@ -2119,6 +2393,8 @@ def u_dot(self, t, u, post_processing=False): # pylint: disable=too-many-locals # Off center moment M3 += self.rocket.cp_eccentricity_x * R2 - self.rocket.cp_eccentricity_y * R1 + M1, M2, M3 = self._datcom_moment_at_cm(t, [R1, R2, R3], [M1, M2, M3]) + # Calculate derivatives # Angular acceleration alpha1 = ( @@ -2248,6 +2524,7 @@ def u_dot(self, t, u, post_processing=False): # pylint: disable=too-many-locals ) return u_dot + @datcom_derivative def u_dot_generalized_3dof(self, t, u, post_processing=False): """Calculates derivative of u state vector with respect to time when the rocket is flying in 3 DOF motion in space and significant mass variation @@ -2313,6 +2590,8 @@ def u_dot_generalized_3dof(self, t, u, post_processing=False): ) R1, R2 = 0, 0 + if self.rocket._datcom_model is not None: + cd = 0.0 R3 = -0.5 * rho * free_stream_speed**2 * self.rocket.area * cd for air_brake in self.rocket.air_brakes: @@ -2331,8 +2610,8 @@ def u_dot_generalized_3dof(self, t, u, post_processing=False): # Velocity in body frame vb_body = Kt @ v - for surface, _ in self.rocket.aerodynamic_surfaces: - cp = self.rocket.surfaces_cp_to_cdm[surface] + for surface, _, _ref_factor in self.rocket._flight_aerodynamic_surfaces: + cp = self._surface_cp_at_time(surface, t) vb_component = vb_body + (w ^ cp) comp_z = z + (K @ cp).z @@ -2353,8 +2632,8 @@ def u_dot_generalized_3dof(self, t, u, post_processing=False): / comp_dynamic_viscosity ) - fx, fy, fz, *_ = surface.compute_forces_and_moments( - rel_velocity, rel_speed, rel_mach, rho, cp, w, reynolds + fx, fy, fz, *_ = self._datcom_surface_loads( + surface, t, rel_velocity, rel_speed, rel_mach, rho, cp, w, reynolds ) R1 += fx R2 += fy @@ -2468,6 +2747,7 @@ def u_dot_generalized_3dof(self, t, u, post_processing=False): return u_dot + @datcom_derivative def u_dot_generalized(self, t, u, post_processing=False): # pylint: disable=too-many-locals,too-many-statements """Calculates derivative of u state vector with respect to time when the rocket is flying in 6 DOF motion in space and significant mass variation @@ -2524,6 +2804,12 @@ def u_dot_generalized(self, t, u, post_processing=False): # pylint: disable=too H = (r_CM.cross_matrix @ -r_CM.cross_matrix) * total_mass I_CM = inertia_tensor - H + # Expose the mass and the inertia about the CM for the tip-off phase, + # which patches this solution and would otherwise interpolate both again + # for the same t on every solver evaluation. H is quadratic in r_CM, so + # I_CM does not depend on the sign convention used for it. + self._mass_inertia_cache = (t, total_mass, I_CM) + # Prepare transformation matrices K = Matrix.transformation(e) Kt = K.transpose @@ -2577,6 +2863,8 @@ def u_dot_generalized(self, t, u, post_processing=False): # pylint: disable=too omega2, omega3, ) + if self.rocket._datcom_model is not None: + drag_coeff = 0.0 R3 += -0.5 * rho * (free_stream_speed**2) * self.rocket.area * drag_coeff for air_brakes in self.rocket.air_brakes: if air_brakes.deployment_level > 0: @@ -2597,9 +2885,9 @@ def u_dot_generalized(self, t, u, post_processing=False): # pylint: disable=too # Get rocket velocity in body frame velocity_in_body_frame = Kt @ v # Calculate lift and moment for each component of the rocket - for aero_surface, _ in self.rocket.aerodynamic_surfaces: + for aero_surface, _, _ref_factor in self.rocket._flight_aerodynamic_surfaces: # Component cp relative to CDM in body frame - comp_cp = self.rocket.surfaces_cp_to_cdm[aero_surface] + comp_cp = self._surface_cp_at_time(aero_surface, t) # Component absolute velocity in body frame comp_vb = velocity_in_body_frame + (w ^ comp_cp) # Wind velocity at component altitude @@ -2623,7 +2911,9 @@ def u_dot_generalized(self, t, u, post_processing=False): # pylint: disable=too / comp_dynamic_viscosity ) # Forces and moments - X, Y, Z, M, N, L = aero_surface.compute_forces_and_moments( + X, Y, Z, M, N, L = self._datcom_surface_loads( + aero_surface, + t, comp_stream_velocity, comp_stream_speed, comp_stream_mach, @@ -2680,7 +2970,12 @@ def u_dot_generalized(self, t, u, post_processing=False): # pylint: disable=too ) # Angular velocity derivative - w_dot = I_CM.inverse @ (T21 + (T20 ^ r_CM)) + if self.rocket._datcom_model is None: + w_dot = I_CM.inverse @ (T21 + (T20 ^ r_CM)) + else: + w_dot = self._datcom_angular_acceleration( + I_CM, T20, T21, r_CM, [R1, R2, R3], [M1, M2, M3] + ) # Euler parameters derivative e_dot = [ @@ -2995,19 +3290,19 @@ def R3(self): @funcify_method("Time (s)", "M1 (Nm)", "linear", "zero") def M1(self): """Aerodynamic moment acting along the x-axis of the rocket's body - frame as a function of time. Expressed in Newtons (N).""" + frame as a function of time. Expressed in Newton-metres (N·m).""" return self.__evaluate_post_process[:, [0, 10]] @funcify_method("Time (s)", "M2 (Nm)", "linear", "zero") def M2(self): """Aerodynamic moment acting along the y-axis of the rocket's body - frame as a function of time. Expressed in Newtons (N).""" + frame as a function of time. Expressed in Newton-metres (N·m).""" return self.__evaluate_post_process[:, [0, 11]] @funcify_method("Time (s)", "M3 (Nm)", "linear", "zero") def M3(self): """Aerodynamic moment acting along the z-axis of the rocket's body - frame as a function of time. Expressed in Newtons (N).""" + frame as a function of time. Expressed in Newton-metres (N·m).""" return self.__evaluate_post_process[:, [0, 12]] @funcify_method("Time (s)", "Net Thrust (N)", "linear", "zero") @@ -3142,6 +3437,26 @@ def out_of_rail_velocity(self): """Velocity at which the rocket leaves the launch rail.""" return self.speed.get_value_opt(self.out_of_rail_time) + @property + def between_rails_velocity(self): + """Velocity at which the lower rail button leaves the launch rail, + ending the ``udot_rail2`` tip-off phase. Only meaningful when the + flight was run with ``use_udot_rail2=True``; otherwise the tip-off + phase never runs and this equals :attr:`out_of_rail_velocity`. + """ + return self.speed.get_value_opt(self.between_rails_time) + + @property + def tip_off_duration(self): + """Duration of the tip-off window, i.e. the time the rocket spends + pivoting about the lower rail button between the upper button leaving + the rail and the lower button leaving it. Zero when the flight was run + without ``use_udot_rail2``. + """ + if not self.use_udot_rail2: + return 0.0 + return self.between_rails_time - self.out_of_rail_time + @cached_property def max_speed_time(self): """Time at which the rocket reaches its maximum speed.""" @@ -4212,6 +4527,7 @@ def to_dict(self, **kwargs): "time": self.time, "out_of_rail_velocity": self.out_of_rail_velocity, "out_of_rail_state": self.out_of_rail_state, + "between_rails_state": self.between_rails_state, "apogee_x": self.apogee_x, "apogee_y": self.apogee_y, "apogee_state": self.apogee_state, diff --git a/rocketpy/simulation/monte_carlo.py b/rocketpy/simulation/monte_carlo.py index 21c665d01..5166920bd 100644 --- a/rocketpy/simulation/monte_carlo.py +++ b/rocketpy/simulation/monte_carlo.py @@ -14,12 +14,17 @@ """ import csv +import hashlib import json import os +import threading import traceback import warnings +from contextlib import suppress +from copy import deepcopy +from numbers import Real from pathlib import Path -from time import time +from time import monotonic, time import numpy as np import simplekml @@ -30,6 +35,7 @@ from rocketpy.prints.monte_carlo_prints import _MonteCarloPrints from rocketpy.simulation.flight import Flight from rocketpy.tools import ( + _seed_sequence_to_int, generate_monte_carlo_ellipses, generate_monte_carlo_ellipses_coordinates, import_optional_dependency, @@ -38,6 +44,305 @@ # TODO: Create evolution plots to analyze convergence +# simulate() writes one JSON object per line and reads that same shape back, so +# this is the only format it can both resume from and overwrite safely. +_SIMULATION_LOG_SUFFIX = ".txt" + +# Which simulation a row belongs to. Every check on a finished run reads it. +_SIMULATION_INDEX_KEY = "index" + +# How a manager that has gone away answers a proxy call. +_MANAGER_IS_GONE = (OSError, EOFError) + +# Bounded, so a lock its dead holder never released cannot pin this worker. +_REPORT_LOCK_SECONDS = 5.0 + +# Longer than the exit-code path: a worker that only read the event is healthy +# and leaving at the end of the simulation in hand, not blocked on a dead lock. +_REPORTED_FAILURE_GRACE_SECONDS = 60.0 + +# Which root drew a row. An append reads it to continue the same stream. +_SIMULATION_ROOT_KEY = "run_root" + +# Told apart from a row that carries ``None``, which no run writes. +_NOTHING_READ_YET = object() + + +def _root_seed_sequence(random_seed): + """The immutable root a run derives every simulation's seed from. + + A ``SeedSequence`` is rebuilt from its full state rather than used as + given, since ``spawn`` advances a counter the caller still holds. A + ``Generator`` is refused rather than read, because using a consume-on-use + object as an immutable seed cannot mean what it says. + """ + if isinstance(random_seed, np.random.SeedSequence): + return np.random.SeedSequence(**random_seed.state) + if isinstance(random_seed, (np.random.Generator, np.random.BitGenerator)): + raise TypeError( + f"random_seed must be an int, a sequence of non-negative integers, " + f"or a numpy.random.SeedSequence, not a " + f"{type(random_seed).__name__}. Pass the seed the generator was " + f"built from." + ) + return np.random.SeedSequence(random_seed) + + +def _jsonable_entropy(entropy): + """``SeedSequence`` entropy as something ``json`` will take. + + It may be an int, a sequence or an ndarray; only the first survives. + """ + if entropy is None or isinstance(entropy, (int, np.integer)): + return None if entropy is None else int(entropy) + return [int(part) for part in np.asarray(entropy).ravel()] + + +def _root_written_into_a_row(root_state): + """The run's root as one JSON value, carried by every input row. + + In the rows because a file beside a log cannot be shown to belong to it. + """ + entropy, spawn_key, pool_size, base = root_state + return { + "entropy": _jsonable_entropy(entropy), + "spawn_key": [int(key) for key in spawn_key], + "pool_size": int(pool_size), + "n_children_spawned": int(base), + } + + +_ROOT_FIELDS = frozenset(("entropy", "spawn_key", "pool_size", "n_children_spawned")) + + +def _root_digest(root): + """A short stable name for a root, for rows that only have to match one. + + 64 bits of SHA-256, carried by the output rows instead of the root itself, + which would cost a hundred bytes a row on a study the input log already + records it for. Wide enough to tell studies apart, not a signature. + """ + canonical = json.dumps(root, sort_keys=True, separators=(",", ":")) + return hashlib.sha256(canonical.encode("utf-8")).hexdigest()[:16] + + +def _a_whole_number(value): + """A non-negative int, and not a bool standing in for one.""" + return isinstance(value, int) and not isinstance(value, bool) and value >= 0 + + +def _whole_numbers(value, may_be_empty=False): + """A list of non-negative ints, empty only where that is a valid one.""" + return ( + isinstance(value, list) + and (may_be_empty or len(value) > 0) + and all(_a_whole_number(part) for part in value) + ) + + +def _root_state_a_row_records(root, path): + """The four values a root is rebuilt from, refused unless all are usable. + + Agreeing rows show the study is one study, not that what they agree on can + be resumed: ``SeedSequence(entropy=None)`` draws fresh entropy every time. + """ + entropy = root.get("entropy") if isinstance(root, dict) else None + usable = ( + isinstance(root, dict) + and set(root) == _ROOT_FIELDS + and (_a_whole_number(entropy) or _whole_numbers(entropy)) + and _whole_numbers(root.get("spawn_key"), may_be_empty=True) + and _a_whole_number(root.get("pool_size")) + and _a_whole_number(root.get("n_children_spawned")) + ) + if usable: + state = ( + entropy, + tuple(root["spawn_key"]), + root["pool_size"], + root["n_children_spawned"], + ) + try: + # Drawing one child is what proves the pool size numpy will take. + _seed_of_simulation(state, 0) + return state + except ValueError: + pass + raise ValueError( + f"cannot continue {path}: its rows record a root that no stream " + f"can be rebuilt from, so what they were drawn with is unknown." + ) + + +def _what_the_rows_say_drew_them(path): + """What every row agrees drew it, and the simulations it numbers. + + ``(None, [])`` means the log holds no rows, and nothing else does. + + ``None`` means the log holds no rows, and nothing else does. Rows that + carry no root are refused instead: they cannot be shown to be one study, + and reading them as an empty log would start a second one in the file. + A log whose rows disagree is refused for the same reason. + """ + first = _NOTHING_READ_YET + numbered = [] + with open(path, "r", encoding="utf-8") as recorded: + for line in recorded: + if not line.strip(): + continue + try: + row = json.loads(line) + except ValueError as error: + raise ValueError( + f"cannot continue {path}: a row cannot be read, so what " + f"produced it cannot be established." + ) from error + root = row.get(_SIMULATION_ROOT_KEY) if isinstance(row, dict) else None + if root is None: + raise ValueError( + f"cannot continue {path}: a row does not say which root " + f"drew it, which is how a study written before this " + f"release looks. Start a new one rather than continuing " + f"one whose rows cannot be checked." + ) + index = row.get("index") + if not _a_whole_number(index): + raise ValueError( + f"cannot continue {path}: a row does not number the " + f"simulation it holds, so where to carry on from cannot " + f"be established." + ) + numbered.append(index) + if first is _NOTHING_READ_YET: + first = root + elif root != first: + raise ValueError( + f"cannot continue {path}: its rows were not all drawn " + f"from one root, so it holds more than one study." + ) + return (None if first is _NOTHING_READ_YET else first), numbered + + +def _root_state_of(root): + """A root as the four picklable values a worker can rebuild it from. + + Sent to each worker instead of the object, and instead of the list of + children, so a run of a million simulations costs four values. The entropy + is copied because a sequence one is kept by reference all the way from the + caller, who could otherwise still move every child by editing their list. + """ + return ( + deepcopy(root.entropy), + tuple(root.spawn_key), + root.pool_size, + root.n_children_spawned, + ) + + +def _seed_of_simulation(root_state, sim_idx): + """The seed for one simulation index, without spawning the ones before it. + + ``spawn`` derives child ``i`` by appending ``n_children_spawned + i`` to + the parent spawn key, so rebuilding that one child directly reproduces it + and any index can be reached from the four values above alone. + """ + entropy, spawn_key, pool_size, base = root_state + return np.random.SeedSequence( + entropy=entropy, + spawn_key=(*spawn_key, base + sim_idx), + pool_size=pool_size, + ) + + +def _refuse_logs_this_run_cannot_write( + input_file, output_file, error_file, export_config=None +): + """Reject a log file ``simulate`` would damage rather than extend. + + A ``.csv`` or ``.json`` is importable for analysis, but this run would + truncate it under ``append=False`` and leave it half one format and half + another under ``append=True``. Checked before any file is opened. + """ + for label, path in ( + ("input_file", input_file), + ("output_file", output_file), + ("error_file", error_file), + ): + if Path(path).suffix.lower() != _SIMULATION_LOG_SUFFIX: + raise ValueError( + f"Monte Carlo simulation logs must be {_SIMULATION_LOG_SUFFIX} " + f"files holding one JSON object per line; {label} is " + f"'{path}'. CSV and JSON results can be imported for analysis, " + f"but simulate() cannot resume from or overwrite them. Point " + f"{label} at a {_SIMULATION_LOG_SUFFIX} file to run." + ) + + _refuse_logs_that_are_one_file( + ( + ("input_file", input_file), + ("output_file", output_file), + ("error_file", error_file), + ) + ) + _refuse_export_options_that_break_a_line(export_config or {}) + + +def _points_at_the_same_file(one, other): + """Whether two names reach one file, by inode when both already exist. + + ``samefile`` settles symlinks, hard links and a case-insensitive filesystem, + none of which text comparison sees. It needs both to exist, so a run that has + not created them yet falls back to the resolved paths, which still normalises + ``a/../run.txt`` and any symlinked parent. + """ + one, other = Path(one), Path(other) + try: + return one.samefile(other) + except OSError: + return one.resolve() == other.resolve() + + +def _refuse_logs_that_are_one_file(labelled_paths): + """Each log has to be its own file, however the three were named. + + ``import_results`` points all three at one path, and the run then appends + input rows and output rows into it. The completeness check reports the mess + afterwards, by which time the file it was given is already gone. + """ + for index, (label, path) in enumerate(labelled_paths): + for other_label, other in labelled_paths[index + 1 :]: + if _points_at_the_same_file(path, other): + raise ValueError( + f"{label} and {other_label} are the same file ('{path}' and " + f"'{other}'). A run appends input rows and output rows " + f"separately, so sharing one log writes both into it and " + f"leaves neither readable. Give each its own file." + ) + + +def _refuse_export_options_that_break_a_line(export_config): + """Reject export options that would split one record over several lines. + + The logs hold one JSON object per line and every reader here assumes it, so + ``indent`` of any kind, ``0`` and ``""`` included, leaves a file that the + completeness check calls damaged once the run it just finished is over. + """ + if export_config.get("indent") is not None: + raise ValueError( + f"indent={export_config['indent']!r} cannot be used with a Monte " + f"Carlo run: the logs hold one JSON object per line, and an " + f"indented record spans several. Export the results with indent " + f"after the run instead." + ) + separators = export_config.get("separators") + if separators and any("\n" in str(part) for part in separators): + raise ValueError( + f"separators={separators!r} cannot be used with a Monte Carlo run: " + f"a newline inside a record splits it across lines, and the logs " + f"hold one JSON object per line." + ) + + class MonteCarlo: # pylint: disable=too-many-public-methods """Class to run a Monte Carlo simulation of a rocket flight. @@ -170,6 +475,8 @@ def simulate( append=False, parallel=False, n_workers=None, + *, + random_seed=None, **kwargs, ): """ @@ -189,6 +496,19 @@ def simulate( number of workers will be equal to the number of CPUs available. A minimum of 2 workers is required for parallel mode. Default is None. + random_seed : int, sequence of int or numpy.random.SeedSequence, optional + Fixes what every simulation draws. Simulation ``i`` takes the same + inputs whichever way the run was split up, so serial and parallel + results agree and the number of workers does not reach the + sampling. Keyword-only. Default is None, which draws fresh entropy + and reproduces nothing. + + Every input row carries the root it was drawn from, so an append + carries on from the study already in the file whether or not the + seed is given again. A different one is refused, not mixed in. + + A ``Generator`` or ``BitGenerator`` is refused rather than read. + Pass the seed it was built from. kwargs : dict Custom arguments for simulation export of the ``inputs`` file. Options are: @@ -205,6 +525,19 @@ def simulate( ------- None + Raises + ------ + RuntimeError + If a parallel run does not finish. A worker that ends badly, one + that reports a failure, and logs that do not hold every simulation + asked for are each refused, since a run that lost work must not be + reported as one that completed. + KeyboardInterrupt + If the run is interrupted. The logs written so far are kept and + reloaded first, so the object agrees with its own files and the + run can be continued with ``append=True``, but the interrupt then + reaches the caller rather than being reported as a finished study. + Notes ----- If you need to stop the simulations after starting them, you can @@ -221,18 +554,35 @@ def simulate( """ self._export_config = kwargs self.number_of_simulations = number_of_simulations - self._initial_sim_idx = self.num_of_loaded_sims if append else 0 + self._initial_sim_idx = 0 + # Validated here, before __setup_files truncates anything, so an + # unusable seed cannot cost a previous run its results. Kept as four + # picklable values rather than as the object, since a worker rebuilds + # any index from them. + self.__root_state = _root_state_of(_root_seed_sequence(random_seed)) + # Before anything is opened: __setup_files truncates for append=False. + _refuse_logs_this_run_cannot_write( + self.input_file, self.output_file, self.error_file, kwargs + ) + + # After that one, which says plainly that a .csv cannot be a working + # log. Reaching this first would report it as a row that cannot be read. + if append: + # From the checkpoint just validated, not the line count taken when + # this object was built: that one counts blank lines and goes stale. + self._initial_sim_idx = self.__continue_the_root_the_rows_carry(random_seed) print("Starting Monte Carlo analysis") self.__setup_files(append) - if parallel: - self.__run_in_parallel(n_workers) - else: - self.__run_in_serial() - - self.__terminate_simulation() + try: + if parallel: + self.__run_in_parallel(n_workers) + else: + self.__run_in_serial() + finally: + self.__terminate_simulation() def __setup_files(self, append): """ @@ -267,6 +617,67 @@ def __setup_files(self, append): except OSError as error: raise OSError(f"Error creating files: {error}") from error + def _append_simulation_record(self, inputs_json, outputs_json): + """Append one simulation's inputs and outputs as a paired record. + + Writes the inputs row first, then the outputs row. If the outputs write + fails, the inputs file is truncated back to its size before this call so + the two files do not drift out of alignment. + + Parameters + ---------- + inputs_json : str + Serialized inputs row, including its trailing newline. + outputs_json : str + Serialized outputs row, including its trailing newline. + """ + input_path = self.input_file + output_path = self.output_file + + try: + previous_input_size = os.path.getsize(input_path) + except OSError: + previous_input_size = 0 + try: + previous_output_size = os.path.getsize(output_path) + except OSError: + previous_output_size = 0 + + with open(input_path, "a", encoding="utf-8") as f: + f.write(inputs_json) + + try: + with open(output_path, "a", encoding="utf-8") as f: + f.write(outputs_json) + except BaseException: + with open(input_path, "rb+") as f: + f.truncate(previous_input_size) + with open(output_path, "rb+") as f: + f.truncate(previous_output_size) + raise + + def __record_failed_inputs(self, inputs_json): + """ + Appends an unpaired inputs row to the error file, if there is one. + + A row reaches here when the simulation it belongs to was cut short + between the inputs being evaluated and the record being appended, so + it has no matching outputs row and cannot go to the inputs file. + + Parameters + ---------- + inputs_json : str + Serialized inputs row, or an empty string when the run was cut + short with no row in hand. + + Returns + ------- + None + """ + if inputs_json: + with open(self._error_file, "a", encoding="utf-8") as f: + f.write(inputs_json) + def __run_in_serial(self): """ Runs the monte carlo simulation in serial mode. @@ -280,19 +691,22 @@ def __run_in_serial(self): n_simulations=self.number_of_simulations, start_time=time(), ) + inputs_json = "" + sim_idx = sim_monitor.count try: - while sim_monitor.keep_simulating(): - sim_monitor.increment() + # Counted from zero, as the parallel path already does: the two + # used to name the same simulation 1, 2, 3 and 0, 1, 2. + while (claimed := sim_monitor.claim_next_index()) is not None: + sim_idx = claimed inputs_json, outputs_json = "", "" + self.__seed_this_simulation(sim_idx) flight = self.__run_single_simulation() - inputs_json = self.__evaluate_flight_inputs(sim_monitor.count) - outputs_json = self.__evaluate_flight_outputs(flight, sim_monitor.count) + inputs_json = self.__evaluate_flight_inputs(sim_idx) + outputs_json = self.__evaluate_flight_outputs(flight, sim_idx) - with open(self.input_file, "a", encoding="utf-8") as f: - f.write(inputs_json) - with open(self.output_file, "a", encoding="utf-8") as f: - f.write(outputs_json) + self._append_simulation_record(inputs_json, outputs_json) + inputs_json = "" sim_monitor.print_update_status() @@ -300,13 +714,12 @@ def __run_in_serial(self): except KeyboardInterrupt: print("Keyboard interrupt received. Files saved.") - with open(self._error_file, "a", encoding="utf-8") as f: - f.write(inputs_json) + self.__record_failed_inputs(inputs_json) + raise except Exception as error: - print(f"Error on iteration {sim_monitor.count}: {error}") - with open(self._error_file, "a", encoding="utf-8") as f: - f.write(inputs_json) + print(f"Error on iteration {sim_idx}: {error}") + self.__record_failed_inputs(inputs_json) raise error def __run_in_parallel(self, n_workers=None): @@ -339,24 +752,29 @@ def __run_in_parallel(self, n_workers=None): ) processes = [] - seeds = np.random.SeedSequence().spawn(n_workers) - - for seed in seeds: - sim_producer = multiprocess.Process( - target=self.__sim_producer, - args=( - seed, - sim_monitor, - mutex, - simulation_error_event, - ), - ) - processes.append(sim_producer) - sim_producer.start() try: - for sim_producer in processes: - sim_producer.join() + # No seed per worker any more: every simulation takes its own + # from its index, so the workers are interchangeable and how + # many there are does not reach the sampling. + for _ in range(n_workers): + sim_producer = multiprocess.Process( + target=self.__sim_producer, + args=( + sim_monitor, + mutex, + simulation_error_event, + ), + ) + sim_producer.start() + # Started first: one that never did cannot be joined, and + # a later start failing still has to bring these down. + processes.append(sim_producer) + + _join_the_workers(processes, simulation_error_event) + + # Before the event: a killed worker never sets it. + _refuse_a_worker_that_did_not_finish(processes) # Handle error from the child processes if simulation_error_event.is_set(): @@ -366,18 +784,23 @@ def __run_in_parallel(self, n_workers=None): "for more information." ) - sim_monitor.print_final_status() - - # Handle error from the main process - # pylint: disable=broad-except - except (Exception, KeyboardInterrupt) as error: - simulation_error_event.set() + # An exit code cannot show a worker that left between + # claiming an index and recording it. + _refuse_logs_missing_a_simulation( + self.input_file, self.output_file, self.number_of_simulations + ) - for sim_producer in processes: - sim_producer.join() + sim_monitor.print_final_status() - if not isinstance(error, KeyboardInterrupt): - raise error + # Handle error from the main process. Re-raising unconditionally + # is what makes an interrupted run tell the caller it was cut + # short instead of reporting itself as a finished study. + except (Exception, KeyboardInterrupt): + # Bounded here too. An unbounded join undid the bound above. + _stop_the_workers_still_running( + processes, simulation_error_event, _SHUTDOWN_GRACE_SECONDS + ) + raise def __validate_number_of_workers(self, n_workers): if n_workers is None or n_workers > os.cpu_count(): @@ -387,13 +810,11 @@ def __validate_number_of_workers(self, n_workers): raise ValueError("Number of workers must be at least 2 for parallel mode.") return n_workers - def __sim_producer(self, seed, sim_monitor, mutex, error_event): # pylint: disable=too-many-statements + def __sim_producer(self, sim_monitor, mutex, error_event): """Simulation producer to be used in parallel by multiprocessing. Parameters ---------- - seed : int - The seed to set the random number generator. sim_monitor : _SimMonitor The simulation monitor object to keep track of the simulations. mutex : multiprocess.Lock @@ -401,16 +822,13 @@ def __sim_producer(self, seed, sim_monitor, mutex, error_event): # pylint: disa error_event : multiprocess.Event Event signaling an error occurred during the simulation. """ + # The handler reads both, and a failure above the loop precedes them. + sim_idx, inputs_json = None, "" try: - # Ensure Processes generate different random numbers - self.environment._set_stochastic(seed) - self.rocket._set_stochastic(seed) - self.flight._set_stochastic(seed) - - while sim_monitor.keep_simulating(): - sim_idx = sim_monitor.increment() - 1 + while (sim_idx := sim_monitor.claim_next_index()) is not None: inputs_json, outputs_json = "", "" + self.__seed_this_simulation(sim_idx) flight = self.__run_single_simulation() inputs_json = self.__evaluate_flight_inputs(sim_idx) outputs_json = self.__evaluate_flight_outputs(flight, sim_idx) @@ -431,27 +849,114 @@ def __sim_producer(self, seed, sim_monitor, mutex, error_event): # pylint: disa break - with open(self.input_file, "a", encoding="utf-8") as f: - f.write(inputs_json) - with open(self.output_file, "a", encoding="utf-8") as f: - f.write(outputs_json) + self._append_simulation_record(inputs_json, outputs_json) sim_monitor.print_update_status() finally: mutex.release() + # Nothing is in flight between two simulations, nor are these. + sim_idx, inputs_json = None, "" + except Exception: # pylint: disable=broad-except - mutex.acquire() - with open(self.error_file, "a", encoding="utf-8") as f: - f.write(inputs_json) + if not self.__report_a_failed_simulation( + sim_idx, inputs_json, mutex, error_event + ): + # The event could not be set; the exit code is what is left. + raise + + def __report_a_failed_simulation(self, sim_idx, inputs_json, mutex, error_event): + """Write down and announce a simulation this worker could not finish. + + The event goes first and from outside the lock, since a worker that + cannot write its diagnostics still has to be able to stop the others. + Each step under the lock is suppressed on its own: a full disk would + otherwise replace the failure being reported, and the lock is a + manager's, so ending while holding it leaves the next worker waiting + on a process that no longer exists. + """ + details = traceback.format_exc() + where = "worker startup" if sim_idx is None else f"iteration {sim_idx}" + announced = False + with suppress(_MANAGER_IS_GONE): + error_event.set() + announced = True - # See note above: must use print() to remain visible from a - # multiprocessing worker process. - _SimMonitor.reprint( - f"Error on iteration {sim_idx}:\n{traceback.format_exc()}" + held = False + with suppress(*_MANAGER_IS_GONE): + held = mutex.acquire(timeout=_REPORT_LOCK_SECONDS) + try: + with suppress(OSError): + with open(self.error_file, "a", encoding="utf-8") as f: + f.write(_worker_failure_record(where, details, inputs_json)) + with suppress(OSError, ValueError): + # Must use print() to remain visible from a worker process. + _SimMonitor.reprint(f"Error on {where}:\n{details}") + finally: + if held: + with suppress(*_MANAGER_IS_GONE): + mutex.release() + return announced + + def __continue_the_root_the_rows_carry(self, random_seed): + """Take the root from the rows being appended to, or refuse to. + + Without this an append draws a second root into one file and nothing + afterwards can tell which simulation came from which. Reading it back + also means a fresh object can continue a study, which is the ordinary + way of resuming one. + """ + recorded, held = _what_the_rows_say_drew_them(self.input_file) + stamped, written = _what_the_rows_say_drew_them(self.output_file) + named = None if recorded is None else _root_digest(recorded) + if named != stamped: + raise ValueError( + f"cannot append to {self.input_file}: it and {self.output_file} " + f"were not drawn from the same root, so they are two studies " + f"rather than the two halves of one." ) - error_event.set() - mutex.release() + if recorded is None: + return 0 + if held != written: + raise ValueError( + f"cannot append to {self.input_file}: it and {self.output_file} " + f"do not record the same simulations, so where to carry on " + f"from cannot be established." + ) + # Rows arrive in completion order, so these are not sorted. What has + # to hold is that between them they are the run's first len(held). + if sorted(held) != list(range(len(held))): + raise ValueError( + f"cannot append to {self.input_file}: the simulations it " + f"records are not the run's first {len(held)}, so where to " + f"carry on from cannot be established." + ) + state = _root_state_a_row_records(recorded, self.input_file) + if random_seed is None: + self.__root_state = state + return len(held) + if _root_written_into_a_row(self.__root_state) != recorded: + raise ValueError( + f"cannot append to {self.input_file}: its rows were drawn from " + f"a different root than random_seed gives. Continuing would put " + f"two studies in one file. Pass the seed the run started with, " + f"or leave random_seed out to carry on from the rows." + ) + return len(held) + + def __seed_this_simulation(self, sim_idx): + """Reseed the three models from this index's own child of the root. + + Per index rather than per worker, which is what makes a simulation's + inputs the same however the run was split up. The child is split three + ways so the environment, rocket and flight draw independently instead + of sharing one stream. + """ + child = _seed_of_simulation(self.__root_state, sim_idx) + environment, rocket, flight = child.spawn(3) + self.environment._set_stochastic(_seed_sequence_to_int(environment)) + self.rocket._set_stochastic(_seed_sequence_to_int(rocket)) + self.flight._set_stochastic(_seed_sequence_to_int(flight)) def __run_single_simulation(self): """Runs a single simulation and returns the inputs and outputs. @@ -461,15 +966,31 @@ def __run_single_simulation(self): Flight The flight object of the simulation. """ + rocket = self.rocket.create_object() + environment = self.environment.create_object() + flight_inputs = self.flight._sample_flight_inputs() return Flight( - rocket=self.rocket.create_object(), - environment=self.environment.create_object(), - rail_length=self.flight._randomize_rail_length(), - inclination=self.flight._randomize_inclination(), - heading=self.flight._randomize_heading(), + rocket=rocket, + environment=environment, + rail_length=flight_inputs["rail_length"], + inclination=flight_inputs["inclination"], + heading=flight_inputs["heading"], initial_solution=self.flight.initial_solution, terminate_on_apogee=self.flight.terminate_on_apogee, time_overshoot=self.flight.time_overshoot, + # The rest of what StochasticFlight.create_object passes. Left out + # here, a run ignored the max_time, tolerances, solver, equations of + # motion and simulation mode the caller had set, which is what #1070 + # added StochasticFlight's own handling of them for. + max_time=self.flight.max_time, + max_time_step=self.flight.obj.max_time_step, + min_time_step=self.flight.obj.min_time_step, + rtol=self.flight.obj.rtol, + atol=self.flight.obj.atol, + name=self.flight.obj.name, + equations_of_motion=self.flight.obj.equations_of_motion, + ode_solver=self.flight.obj.ode_solver, + simulation_mode=self.flight.obj.simulation_mode, ) def estimate_confidence_interval( @@ -657,6 +1178,7 @@ def __evaluate_flight_inputs(self, sim_idx): for item in d.items() ) inputs_dict["index"] = sim_idx + inputs_dict[_SIMULATION_ROOT_KEY] = _root_written_into_a_row(self.__root_state) return ( json.dumps(inputs_dict, cls=RocketPyEncoder, **self._export_config) + "\n" ) @@ -680,8 +1202,6 @@ def __evaluate_flight_outputs(self, flight, sim_idx): export_item: getattr(flight, export_item) for export_item in self.export_list } - outputs_dict["index"] = sim_idx - if self.data_collector is not None: additional_exports = {} for key, callback in self.data_collector.items(): @@ -693,6 +1213,12 @@ def __evaluate_flight_outputs(self, flight, sim_idx): ) from e outputs_dict = outputs_dict | additional_exports + # After the collectors: these two say which run the row belongs to. + outputs_dict["index"] = sim_idx + outputs_dict[_SIMULATION_ROOT_KEY] = _root_digest( + _root_written_into_a_row(self.__root_state) + ) + return ( json.dumps(outputs_dict, cls=RocketPyEncoder, **self._export_config) + "\n" ) @@ -840,6 +1366,20 @@ def _check_data_collector(self, data_collector): "Invalid 'data_collector' key! " f"Variable names overwrites 'export_list' key '{key}'." ) + if key == _SIMULATION_INDEX_KEY: + raise ValueError( + f"Invalid 'data_collector' key '{key}'! It is the " + f"number of the simulation the row belongs to, which " + f"is written after the collectors run and cannot be " + f"replaced by one." + ) + if key == _SIMULATION_ROOT_KEY: + raise ValueError( + f"Invalid 'data_collector' key '{key}'! It is the root " + f"the row was drawn with, which is written after the " + f"collectors run, so a callback under that name would " + f"be run and then discarded." + ) if not callable(callback): raise ValueError( f"Invalid value in 'data_collector' for key '{key}'! " @@ -1170,8 +1710,12 @@ def set_results(self): def set_processed_results(self): """ - Creates a dictionary with the mean and standard deviation of each - parameter available in the results. + Create summary statistics for scalar, real-valued results. + + Structured and non-numeric results remain available in ``results``. + Their entry in ``processed_results`` contains five ``None`` values + because a scalar mean, median, standard deviation, and prediction + interval are not defined for those values. Returns ------- @@ -1179,19 +1723,18 @@ def set_processed_results(self): """ self.processed_results = {} for result, values in self.results.items(): - try: - mean = np.mean(values) - stdev = np.std(values) - self.processed_results[result] = (mean, stdev) - pi_low = np.quantile(values, 0.025) - pi_high = np.quantile(values, 0.975) - median = np.median(values) - except TypeError: - mean = None - stdev = None - pi_low = None - pi_high = None - median = None + if not values or not all( + isinstance(value, Real) and not isinstance(value, (bool, np.bool_)) + for value in values + ): + self.processed_results[result] = (None, None, None, None, None) + continue + + mean = np.mean(values) + stdev = np.std(values) + pi_low = np.quantile(values, 0.025) + pi_high = np.quantile(values, 0.975) + median = np.median(values) self.processed_results[result] = (mean, median, stdev, pi_low, pi_high) # Import methods @@ -1216,7 +1759,9 @@ def import_outputs(self, filename=None): ----- Notice that you can import the outputs, inputs, and errors from a file without the need to run simulations. You can use previously saved - files to process analyze the results or to continue a simulation. + files to process and analyze the results, and a ``.txt`` one to continue + a simulation. A ``.csv`` or ``.json`` is read-only here: ``simulate`` + writes JSONL and refuses to run over a file it could not read back. """ filepath = filename if filename else self.filename.with_suffix(".outputs.txt") @@ -1377,7 +1922,7 @@ def export_ellipses_to_kml( # pylint: disable=too-many-statements except KeyError as e: raise KeyError("No impact data found. Skipping impact ellipses.") from e - (apogee_ellipses, impact_ellipses) = generate_monte_carlo_ellipses( + apogee_ellipses, impact_ellipses = generate_monte_carlo_ellipses( impact_x, impact_y, apogee_x, @@ -1607,6 +2152,192 @@ def export_errors_to_json(self, filename): self._write_log_to_json(self.errors_log, filename) +# Prompt enough to notice a dead worker, cheap enough over a run of hours. +_JOIN_POLL_SECONDS = 0.2 +_SHUTDOWN_GRACE_SECONDS = 5.0 + + +def _ended_badly(worker): + """Whether a worker has stopped, and stopped for the wrong reason.""" + return worker.exitcode not in (None, 0) + + +def _a_failure_was_reported(error_event): + """Whether a worker has said it failed, false if it cannot be asked.""" + with suppress(*_MANAGER_IS_GONE): + return error_event.is_set() + return False + + +def _wait_for_the_workers(processes, seconds): + """Join every worker against one shared deadline, not one each. + + Monotonic, since a clock correction would move a wall-clock deadline. + """ + deadline = monotonic() + seconds + for worker in processes: + worker.join(timeout=max(0.0, deadline - monotonic())) + + +def _stop_the_workers_still_running(processes, error_event, grace_period): + """Ask the rest to stop, end what cannot, kill what outlives that. + + Asked first because a worker between simulations reads the event and leaves + with its logs intact. One blocked on a lock its dead sibling was holding + never reaches that check. Terminate runs no handlers, so it comes second, + and a worker can still ignore it. + """ + with suppress(_MANAGER_IS_GONE): + error_event.set() + _wait_for_the_workers(processes, grace_period) + + for worker in processes: + if worker.is_alive(): + worker.terminate() + _wait_for_the_workers(processes, grace_period) + + for worker in processes: + if worker.is_alive(): + worker.kill() + _wait_for_the_workers(processes, grace_period) + + +def _join_the_workers(processes, error_event, grace_period=_SHUTDOWN_GRACE_SECONDS): + """Wait for the workers, and stop once one of them has failed. + + A reported failure ends the wait as well as a bad exit code, since a + worker that reports one leaves cleanly and says nothing through its exit + status. Its siblings read the event between simulations, but one blocked + on a lock nobody owns never reaches that check, and the run is already + short a simulation either way, so the wait is bounded here rather than + left to them. The reported path gets the longer grace: those siblings are + working, not stuck. + + Slowness alone ends nothing. With no failure reported a healthy worker is + given as long as it needs. + """ + while any(worker.is_alive() for worker in processes): + for worker in processes: + worker.join(timeout=_JOIN_POLL_SECONDS) + if any(_ended_badly(worker) for worker in processes): + _stop_the_workers_still_running(processes, error_event, grace_period) + return + if _a_failure_was_reported(error_event): + _stop_the_workers_still_running( + processes, error_event, _REPORTED_FAILURE_GRACE_SECONDS + ) + return + + +def _worker_failure_record(where, details, inputs_json=""): + """A row saying what failed, and what the simulation had drawn so far. + + The inputs alone left the error file with no stage and no traceback, which + is what the caller is sent there to read. + """ + record = {"index": None, "stage": where, "error": details} + with suppress(ValueError): + drawn = json.loads(inputs_json) + if isinstance(drawn, dict): + record["index"] = drawn.get("index") + record["inputs"] = drawn + return json.dumps(record) + "\n" + + +def _indices_a_log_holds(path): + """Every index a log records, in order, and ``None`` for a row it cannot.""" + found = [] + with open(path, "r", encoding="utf-8") as recorded: + for line in recorded: + if not line.strip(): + continue + try: + index = json.loads(line)["index"] + except (ValueError, KeyError, TypeError): + found.append(None) + continue + usable = ( + isinstance(index, int) and not isinstance(index, bool) and index >= 0 + ) + found.append(index if usable else None) + return found + + +def _refuse_logs_missing_a_simulation(input_file, output_file, target): + """Raise unless both logs hold every simulation the run was asked for. + + An exit code says how a worker ended, never whether the index it had + already claimed reached the logs, and the monitor counts claims rather than + rows. A worker that leaves between the two is invisible to everything else + here, so the logs themselves are what the run is judged on. + + Rows numbered past the target are left alone: an append given a smaller + target than the checkpoint already holds is an append question, not a lost + simulation. What each log holds still has to be the consecutive run it + claims to be, so its indices are required to be exactly as many as its + rows, which refuses a stray number and a hole without needing to be told + how long the checkpoint was. The two logs must also agree row for row, + since a record goes into both under one lock. Streamed rather than read + through ``_read_log_file``, which would hold every row in memory. + """ + wanted = set(range(target)) + recorded = {} + for label, path in (("input", input_file), ("output", output_file)): + found = _indices_a_log_holds(path) + recorded[label] = found + held = set(found) + if None in held: + raise RuntimeError( + f"The run is incomplete: the {label} log has rows that cannot " + f"be read, so what it holds cannot be established." + ) + if len(found) != len(held): + raise RuntimeError( + f"The run is incomplete: the {label} log records " + f"{len(found) - len(held)} simulation(s) more than once." + ) + missing = sorted(wanted - held) + if missing: + raise RuntimeError( + f"The run is incomplete: the {label} log is missing " + f"{len(missing)} of {target} simulations, the first being " + f"{missing[0]}." + ) + strays = sorted(held - set(range(len(found)))) + if strays: + raise RuntimeError( + f"The run is incomplete: the {label} log numbers a simulation " + f"{strays[0]}, past the {len(found)} it holds, so what it " + f"records is not one run of consecutive simulations." + ) + + if recorded["input"] != recorded["output"]: + raise RuntimeError( + "The run is incomplete: the input and output logs do not record " + "the same simulations in the same order. A record is written to " + "both under one lock, so they hold two different runs." + ) + + +def _refuse_a_worker_that_did_not_finish(processes): + """Raise if any worker left without exiting cleanly. + + A negative code is the signal that ended it, ``None`` one still running. + """ + unfinished = [ + f"worker {position} with exit code {process.exitcode}" + for position, process in enumerate(processes) + if process.exitcode != 0 + ] + if not unfinished: + return + raise RuntimeError( + f"The run is incomplete: {', '.join(unfinished)}. A worker that ends " + "this way records nothing and cannot say why, so the simulations it " + "held are missing from the results." + ) + + def _import_multiprocess(): """Import the necessary modules and submodules for the multiprocess library. @@ -1663,13 +2394,20 @@ def __init__(self, initial_count, n_simulations, start_time): self.n_simulations = n_simulations self.start_time = start_time self.completed_count = 0 + self._claim = threading.Lock() # proxy calls run in the manager - def keep_simulating(self): - return self.count < self.n_simulations + def claim_next_index(self): + """The next index to run, or None once every one has been claimed. - def increment(self): - self.count += 1 - return self.count + One call, because a separate check and increment let two workers both + see the last slot free and then claim an index each past the end. + """ + with self._claim: + if self.count >= self.n_simulations: + return None + claimed = self.count + self.count += 1 + return claimed def print_update_status(self): """Prints a message on the same line as the previous one and replaces diff --git a/rocketpy/stochastic/__init__.py b/rocketpy/stochastic/__init__.py index ffadfaaaf..0045baefd 100644 --- a/rocketpy/stochastic/__init__.py +++ b/rocketpy/stochastic/__init__.py @@ -9,6 +9,7 @@ from .stochastic_aero_surfaces import ( StochasticAirBrakes, StochasticEllipticalFins, + StochasticFreeFormFins, StochasticNoseCone, StochasticRailButtons, StochasticTail, diff --git a/rocketpy/stochastic/custom_sampler.py b/rocketpy/stochastic/custom_sampler.py index 82a06dd9f..a29b8041d 100644 --- a/rocketpy/stochastic/custom_sampler.py +++ b/rocketpy/stochastic/custom_sampler.py @@ -8,6 +8,23 @@ class CustomSampler(ABC): """Abstract subclass for user defined samplers""" + @property + def seed_group(self): + """The generator this sampler shares, or ``self`` if it shares none. + + Samplers sharing a generator must all return it, so the group is seeded + once rather than each member overwriting the previous seed. Return the + same object every call: a rebuilt one has a new identity and forms a + group of its own. A group belongs to one model; declared on two models, + each seeds it and the last one wins. + + Returns + ------- + object + Matched by identity, not equality. Defaults to ``self``. + """ + return self + @abstractmethod def sample(self, n_samples=1): """Generates samples from the custom distribution diff --git a/rocketpy/stochastic/stochastic_aero_surfaces.py b/rocketpy/stochastic/stochastic_aero_surfaces.py index 07c50f8ee..97017b3bf 100644 --- a/rocketpy/stochastic/stochastic_aero_surfaces.py +++ b/rocketpy/stochastic/stochastic_aero_surfaces.py @@ -1,17 +1,22 @@ """ Defines the StochasticNoseCone, StochasticTrapezoidalFins, -StochasticEllipticalFins, StochasticTail and StochasticRailButtons classes. +StochasticEllipticalFins, StochasticFreeFormFins, StochasticTail and +StochasticRailButtons classes. """ +import numpy as np + from rocketpy.rocket.aero_surface import ( AirBrakes, EllipticalFins, + FreeFormFins, NoseCone, RailButtons, Tail, TrapezoidalFins, ) +from .custom_sampler import CustomSampler from .stochastic_model import StochasticModel @@ -94,9 +99,11 @@ def _validate_kind(self, kind): must be a list of strings.""" if kind is not None: # TODO: Never vary the kind of the nose cone. It is a fixed parameter. - assert isinstance(kind, list) and all( - isinstance(member, str) for member in kind - ), "`kind` must be a list of strings" + if not ( + isinstance(kind, list) + and all(isinstance(member, str) for member in kind) + ): + raise AssertionError("`kind` must be a list of strings") def create_object(self): """Creates and returns a NoseCone object from the randomly generated @@ -303,6 +310,365 @@ def create_object(self): return EllipticalFins(**generated_dict) +class StochasticFreeFormFins(StochasticModel): + """A Stochastic Free Form Fins class that inherits from StochasticModel. + + See Also + -------- + :ref:`stochastic_model` and + :class:`FreeFormFins ` + + Attributes + ---------- + object : FreeFormFins + FreeFormFins object to be used for validation. + n : list[int] + List with an integer representing the number of fins. This attribute + can be randomized. + shape_points : tuple, list, numpy.ndarray, int, float + The (x, y) points defining the fin outline, in meters. Unlike the other + fin sets, this geometry is a whole list of points rather than a single + scalar, so the deviation given applies to the outline as a block: every + coordinate of every point is perturbed, each by its own draw. See the + ``shape_points`` parameter of :meth:`__init__` for the accepted formats. + rocket_radius : tuple, list, int, float + Rocket radius of the fins in meters. + cant_angle : tuple, list, int, float + Cant angle of the fins in degrees. + airfoil : list + List of tuples in the form of (airfoil file path, airfoil name). + name : list[str] + List with the fins object name. This attribute can not be randomized. + """ + + # The outline is the whole nominal value of this input, not a single number. + array_valued_inputs = ("shape_points",) + + # The distributions that can mean a deviation around a nominal coordinate, + # which is what perturbing an outline asks of them. The rest of what + # ``get_distribution`` offers reads its arguments as bounds (``uniform``) or + # as shape parameters (``wald``, ``gamma``, ``poisson``, ...), neither of + # which an outline of coordinates can be. + _outline_distributions = ("normal", "gumbel", "laplace", "logistic") + + def __init__( + self, + free_form_fins=None, + n=None, + shape_points=None, + rocket_radius=None, + cant_angle=None, + airfoil=None, + ): + """Initializes the Stochastic Free Form Fins class. + + See Also + -------- + :ref:`stochastic_model` + + Parameters + ---------- + free_form_fins : FreeFormFins + FreeFormFins object to be used for validation. + shape_points : tuple, list, numpy.ndarray, int, float, optional + The (x, y) points defining the fin outline, in meters. The whole + outline is perturbed as a block, since a fin shape is only + meaningful as a complete set of points: the deviation given applies + to every coordinate of every point, each drawn independently of the + others. The fin root is held on the body line, so a point nominally + at ``y = 0`` keeps that value and no point is moved inside the + airframe. The accepted formats are: + + - ``int`` or ``float``: standard deviation applied to every + coordinate of the nominal outline, drawn from a normal + distribution. + - ``tuple``: ``(standard deviation, distribution name)``, or + ``(nominal outline, standard deviation[, distribution name])``. + The distribution must be one of ``"normal"``, ``"gumbel"``, + ``"laplace"`` or ``"logistic"``, the ones that take the nominal + coordinate as their centre. + - ``list`` or ``numpy.ndarray``: either one fixed outline, e.g. + ``[(0, 0), (0.1, 0.1), (0.1, 0)]``, which is used as given and + not randomized; or a list of candidate outlines, e.g. + ``[[(0, 0), (0.1, 0.1), (0.1, 0)], [(0, 0), (0.1, 0.12), (0.1, 0)]]``, + one of which is chosen per simulation. The candidates need not + all have the same number of points. An empty list means the + nominal outline of the object passed, unrandomized, as it does + for every other argument. + - ``CustomSampler``: has to yield a whole outline per sample, since + the value it returns replaces the outline instead of perturbing + it. + rocket_radius : tuple, list, int, float, optional + Rocket radius of the fins in meters. + cant_angle : tuple, list, int, float, optional + Cant angle of the fins in degrees. + airfoil : list[tuple], optional + List of tuples in the form of (airfoil file path, airfoil name). + """ + # TODO: never vary the number of fins. It is a fixed parameter. + self._validate_positive_int_list("n", n) + self._validate_airfoil(airfoil) + shape_points = self._validate_shape_points(shape_points) + super().__init__( + free_form_fins, + n=n, + shape_points=shape_points, + rocket_radius=rocket_radius, + cant_angle=cant_angle, + airfoil=airfoil, + name=None, + ) + + def _validate_shape_points(self, shape_points): + """Validate the ``shape_points`` input and normalize it to a form the + base class can randomize. + + A fin outline is a sequence of points, so it does not fit the + scalar-per-input assumption the base class makes. Two formats would be + silently misread if passed straight through, and both are the natural + thing for a user to write: + + - a bare outline ``[(0, 0), (0.1, 0.1), (0.1, 0)]`` is a ``list``, which + the base class reads as a list of candidate values and would sample a + single ``(x, y)`` point from. It is wrapped here so it is treated as + the one candidate outline it is. + - a ``(nominal outline, standard deviation)`` tuple carries an outline + where the base class reads a distribution argument, so the outline is + checked here and the rest is left to the base class. + + Parameters + ---------- + shape_points : tuple, list, numpy.ndarray, int, float, optional + Value of the ``shape_points`` input argument. + + Returns + ------- + tuple, list, int, float or None + The input, normalized so the base class randomizes the outline as a + block. Outlines come back as ``(n, 2)`` arrays of floats. + + Raises + ------ + AssertionError + If the input is not in a valid format. + """ + if shape_points is None or isinstance( + shape_points, (int, float, CustomSampler) + ): + # A number is a standard deviation around the nominal outline, which + # the base class looks up and hands to the distribution as an array. + # A sampler yields whole outlines, so it replaces that machinery. + return shape_points + + if isinstance(shape_points, tuple): + return self._validate_shape_points_tuple(shape_points) + + if isinstance(shape_points, (list, np.ndarray)): + if len(shape_points) == 0: + # An empty list means the nominal value everywhere else, and + # nothing about this argument makes it mean something else. + return [] + if self._is_outline(shape_points): + # A bare outline is the one candidate it describes. Left as a + # list of points it would be read as a list of candidates and + # sampled down to a single (x, y) point. + return [self._validate_outline(shape_points)] + return [self._validate_outline(outline) for outline in shape_points] + + raise AssertionError( + "`shape_points` must be a tuple, list, numpy array, int, or float " + "or a custom sampler" + ) + + def _validate_shape_points_tuple(self, shape_points): + """Validate a ``shape_points`` tuple. + + Accepts ``(standard deviation, distribution name)``, in which case the + nominal outline comes from the object passed, and + ``(nominal outline, standard deviation[, distribution name])``. + + Parameters + ---------- + shape_points : tuple + Value of the ``shape_points`` input argument. + + Returns + ------- + tuple + The input tuple, with any nominal outline converted to an ``(n, 2)`` + array of floats so the standard deviation broadcasts over every + coordinate. + + Raises + ------ + AssertionError + If the input is not in a valid format. + """ + if len(shape_points) not in (2, 3): + raise AssertionError("'shape_points': tuple must have length 2 or 3") + + if isinstance(shape_points[0], (int, float)): + # (standard deviation, distribution name), the nominal outline being + # taken from the object passed. A number in the second item would + # make the first one the nominal value, which for this argument is + # an outline rather than a number. + if not isinstance(shape_points[1], str): + raise AssertionError( + "'shape_points': when the first item of a tuple is a " + "standard deviation, the second must be a string naming a " + "valid numpy.random distribution function." + ) + self._validate_outline_distribution(shape_points[1]) + return shape_points + + # (nominal outline, standard deviation[, distribution name]). The second + # item is checked here rather than left to the base class, which also + # accepts a string there and would read the outline as the deviation. + outline = self._validate_outline(shape_points[0]) + if not isinstance(shape_points[1], (int, float)): + raise AssertionError( + "'shape_points': second item of tuple must be an int or float " + "standard deviation." + ) + if len(shape_points) == 3: + if not isinstance(shape_points[2], str): + raise AssertionError( + "'shape_points': Third item of tuple must be a string containing " + "the name of a valid numpy.random distribution function." + ) + self._validate_outline_distribution(shape_points[2]) + return (outline,) + tuple(shape_points[1:]) + + @classmethod + def _validate_outline_distribution(cls, distribution_name): + """Reject distributions that cannot mean a deviation around a coordinate. + + The distribution is called as ``dist_func(nominal_outline, std_dev)``, so + only the ones that read the first argument as the centre of the draw can + perturb an outline. ``uniform`` would read the outline as its lower bound + and the deviation as a single upper bound, leaving an empty range for + every coordinate above it, and ``wald`` and the shape-parameter + distributions reject the zeros that a root point has. + + Raises + ------ + AssertionError + If the distribution cannot be applied to an outline. + """ + if distribution_name not in cls._outline_distributions: + accepted = ", ".join(repr(name) for name in cls._outline_distributions) + raise AssertionError( + f"'shape_points': the '{distribution_name}' distribution cannot " + f"be applied to an outline. Use one of {accepted}, which take " + "the nominal coordinate as the centre of the deviation." + ) + + @staticmethod + def _is_outline(value): + """Return True if ``value`` is a single (x, y) outline. + + Used to tell a bare outline apart from a list of candidate outlines, + which are the two things a list input can mean. The conversion is what + decides it: numpy refuses a ragged or non-numeric sequence, and a list + of candidates whose outlines have different numbers of points is exactly + that, so those are left for the caller to check one at a time. + """ + try: + array = np.asarray(value, dtype=float) + except (ValueError, TypeError): + return False + return array.ndim == 2 and array.shape[1] == 2 + + @staticmethod + def _validate_outline(outline): + """Validate a single (x, y) fin outline. + + Returns + ------- + numpy.ndarray + The outline as an ``(n, 2)`` array of floats. + + Raises + ------ + AssertionError + If the outline is not a sequence of at least three (x, y) numbers. + """ + if not StochasticFreeFormFins._is_outline(outline): + raise AssertionError( + "`shape_points` outlines must be sequences of (x, y) numbers, " + "i.e. have shape (n, 2)." + ) + array = np.asarray(outline, dtype=float) + if array.shape[0] < 3: + raise AssertionError( + "`shape_points` outlines must have at least 3 points to " + "enclose an area." + ) + return array + + # pylint: disable=stop-iteration-return + def dict_generator(self): + """Generate the input arguments, with the fin root kept on the body line. + + Yields + ------ + dict + Dictionary with the randomly generated input arguments. + """ + generated_dict = next(super().dict_generator()) + if isinstance(self.shape_points, tuple): + # Only a perturbed outline can have drifted off the body line. One + # chosen from a list of candidates, or one a sampler produced, is + # used exactly as it was given. + generated_dict["shape_points"] = self._keep_root_on_body_line( + self.shape_points[0], generated_dict["shape_points"] + ) + # The outline moved after the base class recorded it, and what the + # fins are built from is what the record has to hold. + self._record_draw(generated_dict) + yield generated_dict + + @staticmethod + def _keep_root_on_body_line(nominal_outline, sampled_outline): + """Hold the root of a perturbed outline on the body line. + + :class:`FreeFormFins ` measures the span from + ``y = 0`` and slices the chords over that interval, so a root point that + drifts off the line puts part of the fin inside the airframe and inflates + the span those chords are measured against. Points nominally on the line + are kept there, and no other point is allowed to cross it. + + Parameters + ---------- + nominal_outline : numpy.ndarray + The unperturbed outline, which says which points are on the line. + sampled_outline : numpy.ndarray + The perturbed outline. + + Returns + ------- + numpy.ndarray + The perturbed outline, with its root back on the body line. + """ + nominal_outline = np.asarray(nominal_outline, dtype=float) + sampled_outline = np.array(sampled_outline, dtype=float) + sampled_outline[nominal_outline[:, 1] == 0, 1] = 0.0 + sampled_outline[:, 1] = np.maximum(sampled_outline[:, 1], 0.0) + return sampled_outline + + def create_object(self): + """Creates and returns a FreeFormFins object from the randomly + generated input arguments. + + Returns + ------- + fins : FreeFormFins + FreeFormFins object with the randomly generated input arguments. + """ + generated_dict = next(self.dict_generator()) + return FreeFormFins(**generated_dict) + + class StochasticTail(StochasticModel): """A Stochastic Tail class that inherits from StochasticModel. diff --git a/rocketpy/stochastic/stochastic_environment.py b/rocketpy/stochastic/stochastic_environment.py index e0fc33eec..15fbe7c83 100644 --- a/rocketpy/stochastic/stochastic_environment.py +++ b/rocketpy/stochastic/stochastic_environment.py @@ -134,19 +134,27 @@ def _validate_ensemble(self, ensemble_member, environment): return if ensemble_member is not None: - assert isinstance(ensemble_member, list), "`ensemble_member` must be a list" - assert all( - isinstance(member, int) and member >= 0 for member in ensemble_member - ), "`ensemble_member` must be a list of positive integers" - assert ( + if not isinstance(ensemble_member, list): + raise AssertionError("`ensemble_member` must be a list") + if not ( + all( + isinstance(member, int) and member >= 0 + for member in ensemble_member + ) + ): + raise AssertionError( + "`ensemble_member` must be a list of positive integers" + ) + if not ( 0 <= min(ensemble_member) <= max(ensemble_member) < environment.num_ensemble_members - ), ( - "`ensemble_member` must be in the range from 0 to " - + f"{environment.num_ensemble_members - 1}" - ) + ): + raise AssertionError( + "`ensemble_member` must be in the range from 0 to " + + f"{environment.num_ensemble_members - 1}" + ) setattr(self, "ensemble_member", ensemble_member) else: # if no ensemble member is provided, get it from the environment @@ -174,16 +182,29 @@ def create_object(self): member attribute. """ generated_dict = next(self.dict_generator()) + factors = {} + member_selected = False for key, value in generated_dict.items(): # special case for ensemble member # TODO: Generalize create_object() with a env.ensemble_member setter if key == "ensemble_member": self.obj.select_ensemble_member(value) + member_selected = True + elif "factor" in key: + factors[key.replace("_factor", "")] = value else: - if "factor" in key: - # get original attribute value and multiply by factor - attribute_name = f"_{key.replace('_factor', '')}" - value = getattr(self, attribute_name) * value - key = f"{key.replace('_factor', '')}" setattr(self.obj, key, value) + + # Applied last, and not where the loop met them: select_ensemble_member + # rebuilds the wind from the member's own profile, so a factor scaled in + # earlier is discarded. Which one runs first is only __dict__ order. + for attribute_name, factor in factors.items(): + if member_selected: + # The member just loaded is the baseline. The construction-time + # one belongs to whichever member was active back then. + baseline = getattr(self.obj, attribute_name) + else: + # Construction-time value, so repeated calls do not compound. + baseline = getattr(self, f"_{attribute_name}") + setattr(self.obj, attribute_name, baseline * factor) return self.obj diff --git a/rocketpy/stochastic/stochastic_flight.py b/rocketpy/stochastic/stochastic_flight.py index ecac053a3..79211abee 100644 --- a/rocketpy/stochastic/stochastic_flight.py +++ b/rocketpy/stochastic/stochastic_flight.py @@ -79,9 +79,8 @@ def __init__( reaches this time, it will terminate. This attribute can not be randomized. """ if terminate_on_apogee is not None: - assert isinstance(terminate_on_apogee, bool), ( - "`terminate_on_apogee` must be a boolean" - ) + if not isinstance(terminate_on_apogee, bool): + raise AssertionError("`terminate_on_apogee` must be a boolean") if time_overshoot is not None: if not isinstance(time_overshoot, bool): raise TypeError("`time_overshoot` must be a boolean") @@ -95,6 +94,7 @@ def __init__( heading=heading, ) + self._validate_initial_solution(initial_solution) self.initial_solution = initial_solution self.terminate_on_apogee = terminate_on_apogee if max_time is None: @@ -109,33 +109,42 @@ def __init__( def _validate_initial_solution(self, initial_solution): if initial_solution is not None: if isinstance(initial_solution, (tuple, list)): - assert len(initial_solution) == 14, ( - "`initial_solution` must be a 14 element tuple, the " - "elements are:\n t_initial, x_init, y_init, z_init, " - "vx_init, vy_init, vz_init, e0_init, e1_init, e2_init, " - "e3_init, w1Init, w2Init, w3Init" - ) - assert all(isinstance(i, (int, float)) for i in initial_solution), ( - "`initial_solution` must be a tuple of numbers" - ) + if not len(initial_solution) == 14: + raise AssertionError( + "`initial_solution` must be a 14 element tuple, the " + "elements are:\n t_initial, x_init, y_init, z_init, " + "vx_init, vy_init, vz_init, e0_init, e1_init, e2_init, " + "e3_init, w1Init, w2Init, w3Init" + ) + if not all(isinstance(i, (int, float)) for i in initial_solution): + raise AssertionError( + "`initial_solution` must be a tuple of numbers" + ) else: raise TypeError("`initial_solution` must be a tuple of numbers") - # TODO: these methods call dict_generator a lot of times unnecessarily + def _sample_flight_inputs(self): + """Sample rail_length, inclination, and heading in a single draw. + + Returns + ------- + dict + Mapping with keys ``rail_length``, ``inclination``, and ``heading``. + Also updates ``last_rnd_dict``. + """ + return next(self.dict_generator()) + def _randomize_rail_length(self): """Randomizes the rail length of the flight.""" - generated_dict = next(self.dict_generator()) - return generated_dict["rail_length"] + return self._sample_flight_inputs()["rail_length"] def _randomize_inclination(self): """Randomizes the inclination of the flight.""" - generated_dict = next(self.dict_generator()) - return generated_dict["inclination"] + return self._sample_flight_inputs()["inclination"] def _randomize_heading(self): """Randomizes the heading of the flight.""" - generated_dict = next(self.dict_generator()) - return generated_dict["heading"] + return self._sample_flight_inputs()["heading"] def create_object(self): """Creates and returns a Flight object from the randomly generated input @@ -146,12 +155,11 @@ def create_object(self): flight : Flight Flight object with the randomly generated input arguments. """ - generated_dict = next(self.dict_generator()) - # TODO: maybe we should use generated_dict["rail_length"] instead + generated_dict = self._sample_flight_inputs() return Flight( rocket=self.obj.rocket, environment=self.obj.env, - rail_length=self._randomize_rail_length(), + rail_length=generated_dict["rail_length"], inclination=generated_dict["inclination"], heading=generated_dict["heading"], initial_solution=self.initial_solution, diff --git a/rocketpy/stochastic/stochastic_model.py b/rocketpy/stochastic/stochastic_model.py index ca26f6578..381e13a9b 100644 --- a/rocketpy/stochastic/stochastic_model.py +++ b/rocketpy/stochastic/stochastic_model.py @@ -3,14 +3,98 @@ Stochastic classes. """ -from random import choice - import numpy as np from rocketpy.mathutils.function import Function from rocketpy.stochastic.custom_sampler import CustomSampler -from ..tools import get_distribution +from ..tools import _seed_sequence_to_int, get_distribution + + +def _snapshot_of(value): + """Returns a copy of value that a later write cannot reach. + + Numeric arrays and the built-in containers are copied entry by entry, since + an array inside an ``airfoil`` tuple would otherwise stay shared. This is + not a general deep copy: an ``object`` array is copied without its entries + being, anything else is returned as it is, shared structure is not rebuilt, + and a cycle recurses until Python stops it. + """ + if isinstance(value, np.ndarray): + return value.copy() + if isinstance(value, list): + return [_snapshot_of(item) for item in value] + if isinstance(value, tuple): + entries = [_snapshot_of(item) for item in value] + # A namedtuple takes its fields positionally. + return type(value)(*entries) if hasattr(value, "_fields") else tuple(entries) + if isinstance(value, set): + return {_snapshot_of(item) for item in value} + if isinstance(value, dict): + return {key: _snapshot_of(item) for key, item in value.items()} + return value + + +def _names_as_spawn_key(input_names): + """Encode names into spawn-key words that no other set of names produces. + + A hash would be shorter, but a collision puts two samplers back on one + stream, which is the bug this keying exists to prevent. The length prefix + before each name is what makes it injective. + """ + payload = b"" + for name in input_names: + encoded = name.encode("utf-8") + payload += len(encoded).to_bytes(4, "little") + encoded + payload += b"\0" * (-len(payload) % 4) + return tuple( + int.from_bytes(payload[at : at + 4], "little") + for at in range(0, len(payload), 4) + ) + + +def _format_number(value): + """Format a nominal value or a standard deviation for the attribute report. + + An array-valued input, such as a fin outline, has no single number to show, + and a fixed-width format raises a ``TypeError`` on it, so its shape stands + in for the coordinates. + """ + if np.ndim(value) == 0: + return f"{value:.5f}" + return f"array of shape {np.shape(value)}" + + +def _seed_as_entropy(seed): + """A seed as something ``SeedSequence`` will take as entropy. + + A parallel run is handed a ``SeedSequence``, which it will not take. Any + other seed goes through untouched, so the stream an int reaches stays where + it was. + """ + if not isinstance(seed, np.random.SeedSequence): + return seed + return _seed_sequence_to_int(seed) + + +def _sampler_seed(seed, input_names): + """Derive a seed for one sampler, or for one group that shares a generator. + + Keyed by the names rather than by position, so declaring another parameter + does not move the stream of the ones already there. A group is keyed by all + of its members, so its stream does not depend on which of them happens to + be reset last. + """ + if isinstance(input_names, str): + input_names = (input_names,) + # Sorted here rather than trusting the caller, so a future call site cannot + # give one group two different seeds by listing its members another way. + root = np.random.SeedSequence( + entropy=_seed_as_entropy(seed), + spawn_key=_names_as_spawn_key(tuple(sorted(input_names))), + ) + return _seed_sequence_to_int(root) + # TODO: Stop using assert in production code. Use exceptions instead. # TODO: Each validation method should have a test case. @@ -41,6 +125,12 @@ class StochasticModel: "ensemble_member", ] + # Arguments whose nominal value is an array of numbers rather than a single + # number, such as the outline of a free-form fin. Declared by name so that + # validation, sampling and the attribute report all agree on which ones they + # are, instead of each deciding for itself. + array_valued_inputs = () + def __init__(self, obj, seed=None, **kwargs): """ Initialize the StochasticModel class with validated input arguments. @@ -68,8 +158,79 @@ def __init__(self, obj, seed=None, **kwargs): self.obj = obj self.last_rnd_dict = {} self.__stochastic_dict = kwargs + self.__nominal_values = {} self._set_stochastic(seed) + def _record_draw(self, generated_dict): + """Records what this model published, after any subclass has finished. + + A record of the draw rather than a window onto what was built from it, + since the object handed those values can be written through afterwards. + A subclass that adjusts a value has to call this again, or the record + keeps what it replaced. + """ + self.last_rnd_dict = _snapshot_of(generated_dict) + + def _nominal(self, input_name, getter=getattr): + """Returns what ``self.obj`` held for ``input_name`` when it was + configured. + + Kept and copied both ways, because ``create_object`` writes sampled + values back onto that object and what this returns reaches + ``last_rnd_dict``. A position arrives through a ``getter`` and is read + live, since every component uses this one name. + """ + if getter is not getattr: + return getter(self.obj, input_name) + if input_name not in self.__nominal_values: + self.__nominal_values[input_name] = _snapshot_of( + getattr(self.obj, input_name) + ) + return _snapshot_of(self.__nominal_values[input_name]) + + def _reconfigure_stochastic_inputs(self, inputs, validate): + """Configures late inputs again, all of them or none of them. + + The kept nominal is what ``configured`` means, so replacing an input + has to drop it before validation reads one. Whatever a caller passes + together is replaced together, since ``add_cp_eccentricity`` takes x + and y in one call and a y that will not validate must not leave x + already replaced. + + ``None`` leaves any earlier declaration alone. It reads the same way + whether the caller wrote it or left the argument out, and removing on + the second reading would take away an axis nobody mentioned. + """ + inputs = tuple(inputs) + # A None that already has a configuration is an axis the caller left + # out, since an omitted argument arrives the same way. It keeps what it + # had: not validated again, and its kept nominal not read again. + untouched = { + name + for name, value in inputs + if value is None and name in self.__stochastic_dict + } + kept = { + name: self.__nominal_values.pop(name) + for name, _ in inputs + if name not in untouched and name in self.__nominal_values + } + try: + validated = [ + getattr(self, name) if name in untouched else validate(name, value) + for name, value in inputs + ] + except BaseException: + for name, _ in inputs: + if name not in untouched: + self.__nominal_values.pop(name, None) + self.__nominal_values.update(kept) + raise + for name, value in inputs: + if value is not None: + self.__stochastic_dict[name] = value + return validated + def _set_stochastic(self, seed=None): """Set the stochastic attributes from the input dictionary. This method is useful to reset or reseed the attributes of the instance. @@ -80,8 +241,17 @@ def _set_stochastic(self, seed=None): Seed for the random number generator. """ self.__random_number_generator = np.random.default_rng(seed) + # A stream of its own, derived from the same seed, for picking between + # the candidate values of a list input. Kept apart from the one above so + # that declaring a list input does not shift the numbers every other + # input draws, which would move each existing fixed-seed baseline. + self.__choice_generator = np.random.default_rng( + _sampler_seed(seed, ("__list_choice__",)) + ) self.last_rnd_dict = {} + self._reset_custom_samplers(seed) + # TODO: This code block is too complex. Refactor it. # TODO: Resetting a instance should not require re-validation. for input_name, input_value in self.__stochastic_dict.items(): @@ -89,9 +259,7 @@ def _set_stochastic(self, seed=None): attr_value = None if input_value is not None: if "factor" in input_name: - attr_value = self._validate_factors( - input_name, input_value, seed - ) + attr_value = self._validate_factors(input_name, input_value) elif input_name not in self.exception_list: if isinstance(input_value, tuple): attr_value = self._validate_tuple(input_name, input_value) @@ -101,7 +269,7 @@ def _set_stochastic(self, seed=None): attr_value = self._validate_scalar(input_name, input_value) elif isinstance(input_value, CustomSampler): attr_value = self._validate_custom_sampler( - input_name, input_value, seed + input_name, input_value ) else: raise AssertionError( @@ -109,12 +277,61 @@ def _set_stochastic(self, seed=None): "or a custom sampler" ) else: - attr_value = [getattr(self.obj, input_name)] + attr_value = [self._nominal(input_name)] setattr(self, input_name, attr_value) def __repr__(self): return f"'{self.__class__.__name__}() object'" + def _choose(self, values): + """Pick one of the candidate values of a list input. + + ``random.choice`` was used here, which draws from the interpreter-wide + stream that ``_set_stochastic`` does not reseed: the same seed did not + reproduce the same choices, and Monte Carlo workers forked from one + process inherited a single stream and walked it together instead of + sampling independently. + + Parameters + ---------- + values : list + Candidate values of the input. + + Returns + ------- + object + A copy of one of the candidates, or of ``values`` when there are + none. Copied because what this returns is handed to the object + being built. + """ + if len(values) == 0: + return _snapshot_of(values) + return _snapshot_of(values[self.__choice_generator.integers(len(values))]) + + def _nominal_value(self, input_name, value): + """Return the nominal value of an input as the distribution needs it. + + The distributions are called as ``dist_func(nominal, std_dev)``, so an + array-valued input has to arrive as an array for the deviation to + broadcast over its entries. A list of ``(x, y)`` tuples, which is how a + fin outline is written, would not. + + Parameters + ---------- + input_name : str + Name of the input argument. + value : object + Nominal value of the input argument. + + Returns + ------- + object + The value, as an array of floats for the array-valued inputs. + """ + if input_name in self.array_valued_inputs: + return np.asarray(value, dtype=float) + return value + def _validate_tuple(self, input_name, input_value, getattr=getattr): # pylint: disable=redefined-builtin """ Validate tuple arguments. @@ -139,13 +356,22 @@ def _validate_tuple(self, input_name, input_value, getattr=getattr): # pylint: AssertionError If the input is not in a valid format. """ - assert len(input_value) in [ + if len(input_value) not in [ 2, 3, - ], f"'{input_name}': tuple must have length 2 or 3" - assert isinstance(input_value[0], (int, float)), ( - f"'{input_name}': First item of tuple must be an int or float" - ) + ]: + raise AssertionError(f"'{input_name}': tuple must have length 2 or 3") + if not isinstance(input_value[0], (int, float)): + if input_name not in self.array_valued_inputs: + raise AssertionError( + f"'{input_name}': First item of tuple must be an int or float" + ) + # An array-valued input carries its whole nominal value here, so the + # single number the others require is not what to expect. The child + # class that declared it has already checked the value itself. + input_value = (self._nominal_value(input_name, input_value[0]),) + tuple( + input_value[1:] + ) if len(input_value) == 2: return self._validate_tuple_length_two(input_name, input_value, getattr) @@ -176,9 +402,10 @@ def _validate_tuple_length_two(self, input_name, input_value, getattr=getattr): AssertionError If the input is not in a valid format. """ - assert isinstance(input_value[1], (int, float, str)), ( - f"'{input_name}': second item of tuple must be an int, float, or string." - ) + if not isinstance(input_value[1], (int, float, str)): + raise AssertionError( + f"'{input_name}': second item of tuple must be an int, float, or string." + ) if isinstance(input_value[1], str): # if second item is a string, then it is assumed that the first item @@ -186,7 +413,11 @@ def _validate_tuple_length_two(self, input_name, input_value, getattr=getattr): # function. In this case, the nominal value will be taken from the # object passed. dist_func = get_distribution(input_value[1], self.__random_number_generator) - return (getattr(self.obj, input_name), input_value[0], dist_func) + return ( + self._nominal_value(input_name, self._nominal(input_name, getattr)), + input_value[0], + dist_func, + ) else: # if second item is an int or float, then it is assumed that the # first item is the nominal value and the second item is the @@ -222,14 +453,16 @@ def _validate_tuple_length_three(self, input_name, input_value, getattr=getattr) AssertionError If the input is not in a valid format. """ - assert isinstance(input_value[1], (int, float)), ( - f"'{input_name}': Second item of a tuple with length 3 must be an " - "int or float." - ) - assert isinstance(input_value[2], str), ( - f"'{input_name}': Third item of tuple must be a string containing the " - "name of a valid numpy.random distribution function." - ) + if not isinstance(input_value[1], (int, float)): + raise AssertionError( + f"'{input_name}': Second item of a tuple with length 3 must be an " + "int or float." + ) + if not isinstance(input_value[2], str): + raise AssertionError( + f"'{input_name}': Third item of tuple must be a string containing the " + "name of a valid numpy.random distribution function." + ) dist_func = get_distribution(input_value[2], self.__random_number_generator) return (input_value[0], input_value[1], dist_func) @@ -257,7 +490,7 @@ def _validate_list(self, input_name, input_value, getattr=getattr): # pylint: d If the input is not in a valid format. """ if not input_value: - return [getattr(self.obj, input_name)] + return [self._nominal(input_name, getattr)] else: return input_value @@ -283,12 +516,12 @@ def _validate_scalar(self, input_name, input_value, getattr=getattr): # pylint: distribution function). """ return ( - getattr(self.obj, input_name), + self._nominal_value(input_name, self._nominal(input_name, getattr)), input_value, get_distribution("normal", self.__random_number_generator), ) - def _validate_factors(self, input_name, input_value, seed): + def _validate_factors(self, input_name, input_value): """ Validate factor arguments. @@ -310,14 +543,14 @@ def _validate_factors(self, input_name, input_value, seed): If the input is not in a valid format. """ attribute_name = input_name.replace("_factor", "") - setattr(self, f"_{attribute_name}", getattr(self.obj, attribute_name)) + setattr(self, f"_{attribute_name}", self._nominal(attribute_name)) if isinstance(input_value, tuple): return self._validate_tuple_factor(input_name, input_value) elif isinstance(input_value, list): return self._validate_list_factor(input_name, input_value) elif isinstance(input_value, CustomSampler): - return self._validate_custom_sampler(input_name, input_value, seed) + return self._validate_custom_sampler(input_name, input_value) else: raise AssertionError( f"`{input_name}`: must be either a tuple or listor a custom sampler" @@ -344,14 +577,18 @@ def _validate_tuple_factor(self, input_name, factor_tuple): AssertionError If the input is not in a valid format. """ - assert len(factor_tuple) in [ + if len(factor_tuple) not in [ 2, 3, - ], f"'{input_name}`: Factors tuple must have length 2 or 3" - assert all(isinstance(item, (int, float)) for item in factor_tuple[:2]), ( - f"'{input_name}`: First and second items of Factors tuple must be " - "either an int or float" - ) + ]: + raise AssertionError( + f"'{input_name}`: Factors tuple must have length 2 or 3" + ) + if not all(isinstance(item, (int, float)) for item in factor_tuple[:2]): + raise AssertionError( + f"'{input_name}`: First and second items of Factors tuple must be " + "either an int or float" + ) if len(factor_tuple) == 2: return ( @@ -360,10 +597,11 @@ def _validate_tuple_factor(self, input_name, factor_tuple): get_distribution("normal", self.__random_number_generator), ) elif len(factor_tuple) == 3: - assert isinstance(factor_tuple[2], str), ( - f"'{input_name}`: Third item of tuple must be a string containing " - "the name of a valid numpy.random distribution function" - ) + if not isinstance(factor_tuple[2], str): + raise AssertionError( + f"'{input_name}`: Third item of tuple must be a string containing " + "the name of a valid numpy.random distribution function" + ) dist_func = get_distribution( factor_tuple[2], self.__random_number_generator ) @@ -390,9 +628,10 @@ def _validate_list_factor(self, input_name, factor_list): AssertionError If the input is not in a valid format. """ - assert all(isinstance(item, (int, float)) for item in factor_list), ( - f"'{input_name}`: Items in list must be either ints or floats" - ) + if not all(isinstance(item, (int, float)) for item in factor_list): + raise AssertionError( + f"'{input_name}`: Items in list must be either ints or floats" + ) return factor_list def _validate_1d_array_like(self, input_name, input_value): @@ -444,35 +683,70 @@ def _validate_positive_int_list(self, input_name, input_value): If the input is not in a valid format. """ if input_value is not None: - assert isinstance(input_value, list) and all( - isinstance(member, int) and member >= 0 for member in input_value - ), f"`{input_name}` must be a list of positive integers" + if not ( + isinstance(input_value, list) + and all( + isinstance(member, int) and member >= 0 for member in input_value + ) + ): + raise AssertionError( + f"`{input_name}` must be a list of positive integers" + ) - def _validate_custom_sampler(self, input_name, sampler, seed=None): + def _reset_custom_samplers(self, seed): + """Seed each sampler, and each shared generator once. + + Kept out of the validation loop in ``_set_stochastic``, whose order + sets ``__dict__`` and with it the order every other input is drawn in. + """ + groups = {} + for input_name in sorted(self.__stochastic_dict): + sampler = self.__stochastic_dict[input_name] + if isinstance(sampler, CustomSampler): + # Kept in the value too: `id` is unique only among live + # objects, so the group has to outlive the dict. + group = sampler.seed_group + shared = groups.setdefault(id(group), ([], sampler, group)) + shared[0].append(input_name) + + for names, sampler, group in groups.values(): + # The group holds the shared state, so reset it directly; a member + # may reset differently, or keep state of its own. + resetter = group if hasattr(group, "reset_seed") else sampler + try: + resetter.reset_seed(_sampler_seed(seed, names)) + except Exception as error: + # Broad: the seed is 128 bits, which legacy RandomState refuses + # with a ValueError that does not name the sampler. + raise RuntimeError( + f"An error occurred in the 'reset_seed' method of the " + f"CustomSampler for {', '.join(names)}" + ) from error + + def _validate_custom_sampler(self, input_name, sampler): """ Validate a custom sampler. + Seeding happens in ``_reset_custom_samplers``, not here. + Parameters ---------- input_name : str Name of the input argument. sampler : CustomSampler object Custom sampler provided by the user - seed : int, optional - Seed for the random number generator. The default is None Raises ------ AssertionError If the input is not in a valid format. """ - try: - sampler.reset_seed(seed) - except RuntimeError as e: - raise RuntimeError( - f"An error occurred in the 'reset_seed' method of {input_name} CustomSampler" - ) from e - + # Raised, not asserted: `python -O` strips asserts. AssertionError is + # kept so callers that catch it still do. Same as #1103. + if not isinstance(sampler, CustomSampler): + raise AssertionError( + f"`{input_name}` must be a CustomSampler, not {type(sampler).__name__}" + ) return sampler def _validate_airfoil(self, airfoil): @@ -491,14 +765,18 @@ def _validate_airfoil(self, airfoil): """ # TODO: The _validate_airfoil should be defined in a child class. if airfoil is not None: - assert isinstance(airfoil, list) and all( - isinstance(member, tuple) for member in airfoil - ), "`airfoil` must be a list of tuples" + if not ( + isinstance(airfoil, list) + and all(isinstance(member, tuple) for member in airfoil) + ): + raise AssertionError("`airfoil` must be a list of tuples") for member in airfoil: - assert len(member) == 2, "`airfoil` tuples must have length 2" - assert isinstance(member[1], str), ( - "`airfoil` tuples must have a string as the second item" - ) + if not len(member) == 2: + raise AssertionError("`airfoil` tuples must have length 2") + if not isinstance(member[1], str): + raise AssertionError( + "`airfoil` tuples must have a string as the second item" + ) if isinstance(member[0], list): if len(np.shape(member[0])) != 2 and np.shape(member[0])[1] != 2: raise AssertionError("`airfoil` tuples must have shape (n,2)") @@ -521,18 +799,26 @@ def dict_generator(self): Notes ----- - 1. The dictionary is generated by iterating over the class attributes and: - a. If the attribute is a tuple, the value is generated using the\ - distribution function specified in the tuple. - b. If the attribute is a list, the value is randomly chosen from the list. + 1. The dictionary is generated by iterating over the *declared* + stochastic inputs (constructor kwargs), not every attribute on + ``self``. This avoids treating opaque tuples such as + ``initial_solution`` as ``(nominal, spread, sampler)`` triples. + + a. If the attribute is a tuple, the value is generated using the + distribution function specified in the tuple. + b. If the attribute is a list, the value is randomly chosen from + the list. """ generated_dict = {} - for arg, value in self.__dict__.items(): + for arg in self.__stochastic_dict: + if not hasattr(self, arg): + continue + value = getattr(self, arg) if isinstance(value, tuple): dist_sampler = value[-1] generated_dict[arg] = dist_sampler(value[0], value[1]) elif isinstance(value, list): - generated_dict[arg] = choice(value) if value else value + generated_dict[arg] = self._choose(value) elif isinstance(value, CustomSampler): try: generated_dict[arg] = value.sample(n_samples=1)[0] @@ -540,7 +826,7 @@ def dict_generator(self): raise RuntimeError( f"An error occurred in the 'sample' method of {arg} CustomSampler" ) from e - self.last_rnd_dict = generated_dict + self._record_draw(generated_dict) yield generated_dict # pylint: disable=too-many-statements @@ -571,13 +857,14 @@ def format_attribute(attr, value): upper_bound = std_dev return ( f"\t{attr.ljust(max_str_length)} " - f"{lower_bound:.5f}, {upper_bound:.5f} ({dist_func.__name__})" + f"{_format_number(lower_bound)}, " + f"{_format_number(upper_bound)} ({dist_func.__name__})" ) else: return ( f"\t{attr.ljust(max_str_length)} " - f"{nominal_value:.5f} ± " - f"{std_dev:.5f} ({dist_func.__name__})" + f"{_format_number(nominal_value)} ± " + f"{_format_number(std_dev)} ({dist_func.__name__})" ) elif isinstance(value, CustomSampler): sampler_name = type(value).__name__ diff --git a/rocketpy/stochastic/stochastic_parachute.py b/rocketpy/stochastic/stochastic_parachute.py index 038907187..6fe73dd69 100644 --- a/rocketpy/stochastic/stochastic_parachute.py +++ b/rocketpy/stochastic/stochastic_parachute.py @@ -1,8 +1,36 @@ """Defines the StochasticParachute class.""" from rocketpy.rocket import Parachute +from rocketpy.rocket.parachute import _is_a_height_trigger -from .stochastic_model import StochasticModel +from .stochastic_model import StochasticModel, _sampler_seed + + +def _is_a_trigger(member): + """One of the forms ``Parachute`` accepts, and no more. + + The numeric forms defer to ``Parachute``'s own predicate instead of + restating it. Both were written out separately before and drifted: this one + kept ``(int, float)`` while ``Parachute`` widened to ``numbers.Real``, so a + ``numpy.int64`` height was refused here even though the ``Parachute`` it + would have built accepts it. Calling the same function is what keeps the + promise that what this accepts is what a parachute accepts. + + That applies to the delay of a ``("time", t_deploy)`` trigger too, so a + string is refused rather than quietly coerced by ``float()``. + """ + if callable(member): + return True + if isinstance(member, str): + return member.lower() == "apogee" + if ( + isinstance(member, (tuple, list)) + and len(member) == 2 + and isinstance(member[0], str) + and member[0].lower() == "time" + ): + return bool(_is_a_height_trigger(member[1]) and member[1] >= 0) + return _is_a_height_trigger(member) class StochasticParachute(StochasticModel): @@ -19,7 +47,8 @@ class StochasticParachute(StochasticModel): cd_s : tuple, list, int, float Drag coefficient of the parachute. trigger : list - List of callables, string "apogee" or ints/floats. + List of callables, string "apogee", ints/floats, or + ``("time", t_deploy)`` tuples. sampling_rate : tuple, list, int, float Sampling rate of the parachute in seconds. lag : tuple, list, int, float @@ -66,7 +95,8 @@ def __init__( cd_s : tuple, list, int, float Drag coefficient of the parachute. trigger : list - List of callables, string "apogee" or ints/floats. + List of callables, string "apogee", ints/floats, or + ``("time", t_deploy)`` tuples. sampling_rate : tuple, list, int, float Sampling rate of the parachute in seconds. lag : tuple, list, int, float @@ -96,6 +126,7 @@ def __init__( self.drag_coefficient = drag_coefficient self.height = height self.porosity = porosity + self._seed = None self._validate_trigger(trigger) self._validate_noise(noise) @@ -113,17 +144,40 @@ def __init__( porosity=porosity, ) + def _set_stochastic(self, seed=None): + """Reseed parameter samplers and remember the seed for pressure noise. + + Parameters + ---------- + seed : int, optional + Seed for the random number generator and the derived parachute + pressure-noise seed. + """ + self._seed = seed + super()._set_stochastic(seed) + def _validate_trigger(self, trigger): - """Validates the trigger input. If the trigger input argument is not - None, it must be: - - a list of callables, string "apogee" or ints/floats - - a tuple that will be further validated in the StochasticModel class + """Validates the trigger input. If not None, it must be a non-empty + list whose members are each a callable, the string "apogee", a height, + or a ``("time", t_deploy)`` tuple. One of those is chosen per + simulation. """ - if trigger is not None: - assert isinstance(trigger, list) and all( - isinstance(member, (str, int, float) or callable(member)) - for member in trigger - ), "`trigger` must be a list of callables, string 'apogee' or ints/floats" + if trigger is None: + return + + valid = ( + isinstance(trigger, list) + and bool(trigger) + and all(_is_a_trigger(member) for member in trigger) + ) + # Raised rather than asserted: `python -O` strips an assert, and this + # is the only thing standing between a bad trigger and a Parachute + # that either refuses it much later or reads True as a height of 1. + if not valid: + raise AssertionError( + "`trigger` must be a non-empty list whose members are " + "callables, the string 'apogee', heights, or ('time', t_deploy)" + ) def _validate_noise(self, noise): """Validates the noise input. If the noise input argument is not @@ -131,12 +185,14 @@ def _validate_noise(self, noise): (mean, standard deviation, time-correlation) """ if noise is not None: - assert isinstance(noise, list) and all( - isinstance(member, tuple) for member in noise - ), ( - "`noise` must be a list of tuples in the form of " - "(mean, standard deviation, time-correlation)" - ) + if not ( + isinstance(noise, list) + and all(isinstance(member, tuple) for member in noise) + ): + raise AssertionError( + "`noise` must be a list of tuples in the form of " + "(mean, standard deviation, time-correlation)" + ) def create_object(self): """Creates and returns a Parachute object from the randomly generated @@ -148,4 +204,14 @@ def create_object(self): Parachute object with the randomly generated input arguments. """ generated_dict = next(self.dict_generator()) + # Tie pressure noise into the Monte Carlo seed tree when one is set. + # Key by parachute name so drogue and main on the same rocket do not + # share one noise stream. + if self._seed is not None: + generated_dict["seed"] = _sampler_seed( + self._seed, ("pressure_noise", generated_dict["name"]) + ) + # Recorded after the seed is in, or the inputs describe a parachute + # with noise nobody can reproduce. + self._record_draw(generated_dict) return Parachute(**generated_dict) diff --git a/rocketpy/stochastic/stochastic_rocket.py b/rocketpy/stochastic/stochastic_rocket.py index 794a66c85..3aa234216 100644 --- a/rocketpy/stochastic/stochastic_rocket.py +++ b/rocketpy/stochastic/stochastic_rocket.py @@ -1,7 +1,7 @@ """Defines the StochasticRocket class.""" import warnings -from random import choice + from rocketpy.control import _Controller from rocketpy.mathutils.vector_matrix import Vector @@ -11,6 +11,7 @@ from rocketpy.rocket.aero_surface import ( AirBrakes, EllipticalFins, + FreeFormFins, NoseCone, RailButtons, Tail, @@ -21,10 +22,12 @@ from rocketpy.rocket.rocket import Rocket from rocketpy.stochastic.stochastic_generic_motor import StochasticGenericMotor from rocketpy.stochastic.stochastic_motor_model import StochasticMotorModel +from rocketpy.tools import _seed_sequence_from, _seed_sequence_to_int from .stochastic_aero_surfaces import ( StochasticAirBrakes, StochasticEllipticalFins, + StochasticFreeFormFins, StochasticNoseCone, StochasticRailButtons, StochasticTail, @@ -172,25 +175,45 @@ def __init__( coordinate_system_orientation=None, ) + # Nested stochastic objects, in spawn order. A collection's stream is + # addressed by where its name falls in the two tuples read end to end, so + # appending to the first one moves every name in the second. + _POSITIONED_COLLECTIONS = ("aerodynamic_surfaces", "motors", "rail_buttons") + _PLAIN_COLLECTIONS = ("parachutes", "air_brakes") + + @classmethod + def _stochastic_collections(cls): + """The names of every attribute holding nested stochastic objects.""" + return cls._POSITIONED_COLLECTIONS + cls._PLAIN_COLLECTIONS + def _set_stochastic(self, seed=None): """Set the stochastic attributes for Components, positions and inputs. + Each component takes its own child of its collection's root, so + components stay independent and one seed still reproduces the rocket. + The body keeps the seed as given, and a collection has a root of its + own, so adding a fin does not move every parachute. + Parameters ---------- seed : int, optional Seed for the random number generator. """ super()._set_stochastic(seed) - self.aerodynamic_surfaces = self.__reset_components( - self.aerodynamic_surfaces, seed - ) - self.motors = self.__reset_components(self.motors, seed) - self.rail_buttons = self.__reset_components(self.rail_buttons, seed) - for parachute in self.parachutes: - parachute._set_stochastic(seed) + names = self._stochastic_collections() + roots = dict(zip(names, _seed_sequence_from(seed).spawn(len(names)))) + for name in self._POSITIONED_COLLECTIONS: + setattr( + self, name, self.__reset_components(getattr(self, name), roots[name]) + ) + for name in self._PLAIN_COLLECTIONS: + for component in getattr(self, name): + component._set_stochastic( + _seed_sequence_to_int(roots[name].spawn(1)[0]) + ) - def __reset_components(self, components, seed): + def __reset_components(self, components, root): """Creates a new Components whose stochastic structures and their positions are reset. @@ -199,8 +222,8 @@ def __reset_components(self, components, seed): components : Components The components which contains the stochastic structure that will be used to create the new components. - seed : int, optional - Seed for the random number generator. + root : numpy.random.SeedSequence + The reseed's root. Each component takes its own spawned child. Returns ------- @@ -210,9 +233,9 @@ def __reset_components(self, components, seed): input component. """ new_components = Components() - for stochastic_obj, _ in components: + for stochastic_obj, _position, _ref_factor in components: stochastic_obj_position_info = self.__components_map[stochastic_obj] - stochastic_obj._set_stochastic(seed) + stochastic_obj._set_stochastic(_seed_sequence_to_int(root.spawn(1)[0])) new_components.add( stochastic_obj, self._validate_position(stochastic_obj, stochastic_obj_position_info), @@ -273,7 +296,10 @@ def _add_surfaces(self, surfaces, positions, type_, stochastic_type, error_messa if not isinstance(surfaces, (type_, stochastic_type)): raise AssertionError(error_message) if isinstance(surfaces, type_): - surfaces = stochastic_type(component=surfaces) + # Positionally: the stochastic classes each name this first + # parameter after their own surface (`nosecone`, `tail`, ...), so + # there is no one keyword that reaches all of them. + surfaces = stochastic_type(surfaces) self.__components_map[surfaces] = positions self.aerodynamic_surfaces.add( surfaces, self._validate_position(surfaces, positions) @@ -333,6 +359,24 @@ def add_elliptical_fins(self, fins, position=None): "`fins` must be of EllipticalFins or StochasticEllipticalFins type", ) + def add_free_form_fins(self, fins, position=None): + """Adds a stochastic free form fins to the stochastic rocket. + + Parameters + ---------- + fins : StochasticFreeFormFins or FreeFormFins + The free form fins to be added to the stochastic rocket. + position : tuple, list, int, float, optional + The position of the free form fins. + """ + self._add_surfaces( + fins, + position, + FreeFormFins, + StochasticFreeFormFins, + "`fins` must be of FreeFormFins or StochasticFreeFormFins type", + ) + def add_tail(self, tail, position=None): """Adds a stochastic tail to the stochastic rocket. @@ -429,13 +473,23 @@ def add_cp_eccentricity(self, x=None, y=None): the y direction relative to the center of dry mass axial line. The y axis is defined according to the body axes coordinate system. + + Calling this again replaces what was configured before. An axis left + out keeps the setting it already had, since ``None`` is what an omitted + argument arrives as and cannot be told apart from one written by hand. + Taking an axis away again is not supported (#1171). + Returns ------- self : StochasticRocket Object of the StochasticRocket class. """ - self.cp_eccentricity_x = self._validate_eccentricity("cp_eccentricity_x", x) - self.cp_eccentricity_y = self._validate_eccentricity("cp_eccentricity_y", y) + self.cp_eccentricity_x, self.cp_eccentricity_y = ( + self._reconfigure_stochastic_inputs( + (("cp_eccentricity_x", x), ("cp_eccentricity_y", y)), + self._validate_eccentricity, + ) + ) return self def add_thrust_eccentricity(self, x=None, y=None): @@ -455,16 +509,22 @@ def add_thrust_eccentricity(self, x=None, y=None): relative to the center of dry mass axial line. The y axis is defined according to the body axes coordinate system. + + Calling this again replaces what was configured before. An axis left + out keeps the setting it already had, since ``None`` is what an omitted + argument arrives as and cannot be told apart from one written by hand. + Taking an axis away again is not supported (#1171). + Returns ------- self : StochasticRocket Object of the StochasticRocket class. """ - self.thrust_eccentricity_x = self._validate_eccentricity( - "thrust_eccentricity_x", x - ) - self.thrust_eccentricity_y = self._validate_eccentricity( - "thrust_eccentricity_y", y + self.thrust_eccentricity_x, self.thrust_eccentricity_y = ( + self._reconfigure_stochastic_inputs( + (("thrust_eccentricity_x", x), ("thrust_eccentricity_y", y)), + self._validate_eccentricity, + ) ) return self @@ -628,7 +688,7 @@ def _randomize_position(self, position): return position[-1](position[0].z, position[1]) return position[-1](position[0], position[1]) elif isinstance(position, list): - return choice(position) if position else position + return self._choose(position) # pylint: disable=stop-iteration-return def dict_generator(self): @@ -638,8 +698,8 @@ def dict_generator(self): all attributes of the class and generating a random value for each attribute. The random values are generated according to the format of each attribute. Tuples are generated using the distribution function - specified in the tuple. Lists are generated using the random.choice - function. + specified in the tuple. Lists are generated by picking one of their + values with this model's own seeded generator. Parameters ---------- @@ -656,7 +716,7 @@ def dict_generator(self): generated_dict["rail_buttons"] = [] generated_dict["air_brakes"] = [] generated_dict["parachutes"] = [] - self.last_rnd_dict = generated_dict + self._record_draw(generated_dict) yield generated_dict def _create_motor(self, component_stochastic_motor): @@ -708,10 +768,16 @@ def _create_parachute(self, stochastic_parachute): return parachute def _create_eccentricities(self, stochastic_x, stochastic_y, eccentricity): - x_rnd = self._randomize_position(stochastic_x) - self.last_rnd_dict[eccentricity + "_x"] = x_rnd - y_rnd = self._randomize_position(stochastic_y) - self.last_rnd_dict[eccentricity + "_y"] = y_rnd + # A half that was given is a declared input, so dict_generator has drawn + # it already; drawing again would spend a second value out of the same + # stream and move every component position that follows. + def drawn_once(name, stochastic): + if name not in self.last_rnd_dict: + self.last_rnd_dict[name] = self._randomize_position(stochastic) + return self.last_rnd_dict[name] + + x_rnd = drawn_once(eccentricity + "_x", stochastic_x) + y_rnd = drawn_once(eccentricity + "_y", stochastic_y) return x_rnd, y_rnd def create_object(self): @@ -794,14 +860,9 @@ def create_object(self): ) for parachute in self.parachutes: - parachute = self._create_parachute(parachute) - rocket.add_parachute( - name=parachute.name, - cd_s=parachute.cd_s, - trigger=parachute.trigger, - sampling_rate=parachute.sampling_rate, - lag=parachute.lag, - noise=parachute.noise, - ) + # The sampled object itself. Passing a subset of its fields back to + # add_parachute built a second one, which re-derived radius and + # height from cd_s and the default drag coefficient. + rocket.parachutes.append(self._create_parachute(parachute)) return rocket diff --git a/rocketpy/tools.py b/rocketpy/tools.py index 0d7f1a74e..381f51b00 100644 --- a/rocketpy/tools.py +++ b/rocketpy/tools.py @@ -1377,6 +1377,18 @@ def euler313_to_quaternions(phi, theta, psi): return e0, e1, e2, e3 +def _seed_sequence_to_int(seed_sequence): + """Returns the 128-bit ``int`` seed a ``SeedSequence`` derives. + + Derived, not serialized: the entropy and spawn key it came from cannot be + read back out. Folded through ``generate_state`` rather than off + ``entropy``, since the children of one root differ only by ``spawn_key``, + and combined by value so it does not depend on byte order. + """ + words = seed_sequence.generate_state(4, dtype=np.uint32) + return sum(int(word) << (32 * position) for position, word in enumerate(words)) + + def get_matplotlib_supported_file_endings(): """Gets the file endings supported by matplotlib. @@ -1467,6 +1479,18 @@ def find_obj_from_hash(obj, hash_, depth_limit=None): return None +def _seed_sequence_from(seed): + """Returns a ``SeedSequence`` of the caller's own to spawn from. + + A parallel run is handed one that ``SeedSequence`` will not take as + entropy, and spawning from it directly would advance the counter of an + object the caller still holds, so it is copied from its full state. + """ + if isinstance(seed, np.random.SeedSequence): + return np.random.SeedSequence(**seed.state) + return np.random.SeedSequence(seed) + + if __name__ == "__main__": # pragma: no cover import doctest diff --git a/rocketpy/utilities.py b/rocketpy/utilities.py index 6dbd25380..97ebe3f45 100644 --- a/rocketpy/utilities.py +++ b/rocketpy/utilities.py @@ -780,3 +780,46 @@ def load_from_rpy(filename: str, resimulate=False): simulation = json.dumps(data["simulation"]) flight = json.loads(simulation, cls=RocketPyDecoder, resimulate=resimulate) return flight + + +def calculate_simplified_opening_shock_force( + cd_s, air_density, velocity, opening_shock_coefficient=1.5 +): + """Estimates the peak transient force experienced by the recovery + hardware during parachute inflation (the "opening shock"). + + The estimate follows the simplified model described in Knacke's + "Parachute Recovery Systems Design Manual" (1992, Section 5.5): + + .. math:: + + F_0 = C_x \\cdot C_{d} S \\cdot q + + where :math:`C_x` is the ``opening_shock_coefficient``, + :math:`C_{d} S` is the parachute's ``cd_s``, and :math:`q` is the + dynamic pressure (:math:`q = \\tfrac{1}{2} \\rho V^2`) at the instant + the canopy begins to inflate. + + Parameters + ---------- + cd_s : float + Drag coefficient times reference area of the parachute. + air_density : float + Freestream air density, in kg/m^3, at the moment of parachute + deployment. + velocity : float + Freestream velocity relative to the rocket, in m/s, at the moment + of parachute deployment. + opening_shock_coefficient : float, optional + Empirical coefficient (commonly noted Cx) used to estimate the + peak transient force experienced during parachute inflation. + Typical values range from 1.2 to 2.0 depending on the deployment + method and canopy type. Default value is 1.5. + + Returns + ------- + float + Estimated peak opening shock force, in Newtons. + """ + dynamic_pressure = 0.5 * air_density * velocity**2 + return opening_shock_coefficient * cd_s * dynamic_pressure diff --git a/tests/acceptance/test_defiance_rocket.py b/tests/acceptance/test_defiance_rocket.py new file mode 100644 index 000000000..47d96359b --- /dev/null +++ b/tests/acceptance/test_defiance_rocket.py @@ -0,0 +1,142 @@ +"""Acceptance test for the 2024 Defiance example flight.""" + +import pytest + +from rocketpy import Environment, Flight, Rocket +from rocketpy.motors import CylindricalTank, Fluid, HybridMotor +from rocketpy.motors.tank import MassFlowRateBasedTank + + +MEASURED_APOGEE_AGL = 9308.32 +MAX_RELATIVE_APOGEE_ERROR = 0.01 +REFERENCE_MAX_SPEED = 444.24 +REFERENCE_MAX_ACCELERATION = 10400.76 +REFERENCE_IMPACT_X = 1609.40 +REFERENCE_IMPACT_Y = 87.03 +REFERENCE_METRIC_RELATIVE_TOLERANCE = 0.01 +REFERENCE_IMPACT_ABSOLUTE_TOLERANCE = 3.0 + + +def _build_defiance_flight(): + """Build the deterministic Defiance example flight.""" + environment = Environment( + latitude=47.966527, + longitude=-81.87413, + elevation=1383.4, + date=(2024, 8, 24, 0), + ) + environment.set_atmospheric_model(type="custom_atmosphere", wind_v=1.0, wind_u=-2.9) + + liquid_oxidizer = Fluid(name="N2O_l", density=960) + gaseous_oxidizer = Fluid(name="N2O_g", density=1.9277) + oxidizer_tank = MassFlowRateBasedTank( + name="oxidizer_tank", + geometry=CylindricalTank(radius_function=0.0665, height=1.79), + flux_time=6.5, + liquid=liquid_oxidizer, + gas=gaseous_oxidizer, + initial_liquid_mass=17, + initial_gas_mass=0, + liquid_mass_flow_rate_in=0, + liquid_mass_flow_rate_out=17 / 6.5, + gas_mass_flow_rate_in=0, + gas_mass_flow_rate_out=0, + ) + + motor = HybridMotor( + thrust_source="data/rockets/defiance/Thrust_curve.csv", + dry_mass=13.832, + dry_inertia=(1.801, 1.801, 0.0305), + center_of_dry_mass_position=0.780, + grain_number=1, + grain_separation=0, + grain_outer_radius=0.0665, + grain_initial_inner_radius=0.061, + grain_initial_height=1.25, + grain_density=920, + nozzle_radius=0.0447, + throat_radius=0.0234, + grains_center_of_mass_position=0.377, + coordinate_system_orientation="nozzle_to_combustion_chamber", + ) + motor.add_tank(tank=oxidizer_tank, position=2.2) + + rocket = Rocket( + radius=0.07, + mass=37.211, + inertia=(94.14, 94.14, 0.09), + center_of_mass_without_motor=3.29, + power_off_drag="data/rockets/defiance/DragCurve.csv", + power_on_drag="data/rockets/defiance/DragCurve.csv", + coordinate_system_orientation="tail_to_nose", + ) + rocket.add_motor(motor, position=0.2) + rocket.add_nose(length=0.563, kind="vonKarman", position=4.947) + rocket.add_trapezoidal_fins( + n=3, + span=0.115, + root_chord=0.4, + tip_chord=0.2, + position=0.175, + ) + rocket.add_tail( + top_radius=0.07, + bottom_radius=0.064, + length=0.0597, + position=0.1, + ) + rocket.add_parachute(name="main", cd_s=2.2, trigger=305, sampling_rate=100, lag=0) + rocket.add_parachute( + name="drogue", + cd_s=1.55, + trigger="apogee", + sampling_rate=100, + lag=0, + ) + + return Flight( + rocket=rocket, + environment=environment, + inclination=85, + heading=90, + rail_length=10, + ) + + +@pytest.fixture(scope="module") +def defiance_flight(): + """Return one deterministic Defiance flight for the acceptance checks.""" + return _build_defiance_flight() + + +def test_defiance_rocket_apogee_matches_measured_flight(defiance_flight): + """Compare the Defiance example simulation with its measured apogee.""" + simulated_apogee_agl = defiance_flight.apogee - defiance_flight.env.elevation + relative_error = ( + abs(MEASURED_APOGEE_AGL - simulated_apogee_agl) / MEASURED_APOGEE_AGL + ) + + assert relative_error < MAX_RELATIVE_APOGEE_ERROR, ( + f"Defiance apogee relative error is {relative_error:.2%}; " + f"expected less than {MAX_RELATIVE_APOGEE_ERROR:.2%}." + ) + + +def test_defiance_rocket_matches_reference_flight_metrics(defiance_flight): + """Guard the deterministic example's peak and impact metrics.""" + tolerance = {"rel": REFERENCE_METRIC_RELATIVE_TOLERANCE} + impact_tolerance = { + **tolerance, + "abs": REFERENCE_IMPACT_ABSOLUTE_TOLERANCE, + } + + assert defiance_flight.max_speed == pytest.approx(REFERENCE_MAX_SPEED, **tolerance) + assert defiance_flight.max_acceleration == pytest.approx( + REFERENCE_MAX_ACCELERATION, **tolerance + ) + assert defiance_flight.x_impact == pytest.approx( + REFERENCE_IMPACT_X, **impact_tolerance + ) + assert defiance_flight.y_impact == pytest.approx( + REFERENCE_IMPACT_Y, **impact_tolerance + ) diff --git a/tests/conftest.py b/tests/conftest.py index a12c683e2..e96825d80 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -6,6 +6,18 @@ # Configure matplotlib to use non-interactive backend for tests matplotlib.use("Agg") + +@pytest.fixture(autouse=True) +def isolate_atmosphere_cache(monkeypatch, tmp_path): + """Keep the atmosphere disk cache out of the developer's home directory. + + Without this, ``set_atmospheric_model`` would write to + ``~/.rocketpy_cache`` during the test run and later tests could silently + read profiles cached by an earlier one. + """ + monkeypatch.setenv("ROCKETPY_CACHE", str(tmp_path / "rocketpy_cache")) + + # Pytest configuration pytest_plugins = [ "tests.fixtures.environment.environment_fixtures", @@ -23,6 +35,7 @@ "tests.fixtures.parachutes.parachute_fixtures", "tests.fixtures.rockets.rocket_fixtures", "tests.fixtures.surfaces.surface_fixtures", + "tests.fixtures.surfaces.datcom_fixtures", "tests.fixtures.units.numerical_fixtures", "tests.fixtures.monte_carlo.monte_carlo_fixtures", "tests.fixtures.monte_carlo.stochastic_fixtures", diff --git a/tests/fixtures/environment/environment_fixtures.py b/tests/fixtures/environment/environment_fixtures.py index 91b185a35..2679e3ca3 100644 --- a/tests/fixtures/environment/environment_fixtures.py +++ b/tests/fixtures/environment/environment_fixtures.py @@ -99,7 +99,7 @@ def example_euroc_env(example_date_naive): return euroc_env -@pytest.fixture +@pytest.fixture(scope="session") def env_analysis(): """Environment Analysis class with hardcoded parameters diff --git a/tests/fixtures/monte_carlo/stochastic_fixtures.py b/tests/fixtures/monte_carlo/stochastic_fixtures.py index 6610666cf..45c4538e1 100644 --- a/tests/fixtures/monte_carlo/stochastic_fixtures.py +++ b/tests/fixtures/monte_carlo/stochastic_fixtures.py @@ -7,6 +7,7 @@ from rocketpy.stochastic import ( StochasticEnvironment, StochasticFlight, + StochasticFreeFormFins, StochasticNoseCone, StochasticParachute, StochasticRailButtons, @@ -117,6 +118,28 @@ def stochastic_trapezoidal_fins(calisto_trapezoidal_fins): ) +@pytest.fixture +def stochastic_free_form_fins(calisto_free_form_fins): + """This fixture is used to create a StochasticFreeFormFins object for the + Calisto rocket. + + Parameters + ---------- + calisto_free_form_fins : FreeFormFins + This is another fixture. + + Returns + ------- + StochasticFreeFormFins + The stochastic free form fins object + """ + return StochasticFreeFormFins( + free_form_fins=calisto_free_form_fins, + shape_points=0.0005, + cant_angle=(0, 0.5), + ) + + @pytest.fixture def stochastic_tail(calisto_tail): """This fixture is used to create a StochasticTail object for the diff --git a/tests/fixtures/surfaces/data/datcom/README.md b/tests/fixtures/surfaces/data/datcom/README.md new file mode 100644 index 000000000..1bc55f25c --- /dev/null +++ b/tests/fixtures/surfaces/data/datcom/README.md @@ -0,0 +1,8 @@ +`for006.dat` is native Missile DATCOM revision 3/99 output for a simple, +unblunted von Karman nose, cylindrical body, four HEX fins and a conical +boattail. The echoed deck specifies the geometry, two Mach points and three +signed incidence angles. DAMP and full base drag are enabled; protuberances +and plume interaction are absent. + +This fixture checks parsing and coefficient-channel completeness. It is not +an independently validated aerodynamic reference. No executable is included. diff --git a/tests/fixtures/surfaces/data/datcom/for006.dat b/tests/fixtures/surfaces/data/datcom/for006.dat new file mode 100644 index 000000000..54aadb19b --- /dev/null +++ b/tests/fixtures/surfaces/data/datcom/for006.dat @@ -0,0 +1,260 @@ +1 ***** THE USAF AUTOMATED MISSILE DATCOM * REV 3/99 ***** + AERODYNAMIC METHODS FOR MISSILE CONFIGURATIONS + + CONERR - INPUT ERROR CHECKING + + ERROR CODES - N* DENOTES THE NUMBER OF OCCURENCES OF EACH ERROR + A - UNKNOWN VARIABLE NAME + B - MISSING EQUAL SIGN FOLLOWING VARIABLE NAME + C - NON-ARRAY VARIABLE HAS AN ARRAY ELEMENTDESIGNATION - (N) + D - NON-ARRAY VARIABLE HAS MULTIPLE VALUES ASSIGNED + E - ASSIGNED VALUES EXCEED ARRAY DIMENSION + F - SYNTAX ERROR + + ************************* INPUT DATA CARDS ************************* + + 1 DIM M + 2 $FLTCON + 3 NMACH = 2., + 4 MACH = 0.2,0.6, + 5 ALT = 0.,0., + 6 NALPHA = 3., + 7 ALPHA = -2.,0.,2.,$ + 8 $REFQ + 9 XCG = 1.2, + 10 SREF = 0.012667687, + 11 LREF = 0.127,$ + 12 $AXIBOD + 13 TNOSE = KARMAN, + ** SUBSTITUTING NUMERIC FOR NAME KARMAN + 14 LNOSE = 0.55, + 15 DNOSE = 0.127, + 16 LCENTR = 1.85, + 17 DCENTR = 0.127, + 18 BASE = .FALSE., + 19 TAFT = CONE, + ** SUBSTITUTING NUMERIC FOR NAME CONE + 20 LAFT = 0.1, + 21 DAFT = 0.09, + 22 DEXIT = 0.,$ + 23 $FINSET1 + 24 XLE = 2.1,2.25, + 25 NPANEL = 4., + 26 PHIF = 0.,90.,180.,270., + 27 SSPAN = 0.0635,0.1635, + 28 CHORD = 0.25,0.1, + 29 SECTYP = HEX, + ** SUBSTITUTING NUMERIC FOR NAME HEX + 30 ZUPPER = 0.01,0.01, + 31 ZLOWER = 0.01,0.01, + 32 LER = 0.,0., + 33 LMAXU = 0.25,0.25, + 34 LFLATU = 0.5,0.5,$ + 35 DAMP + 36 NEXT CASE +1 ***** THE USAF AUTOMATED MISSILE DATCOM * REV 3/99 ***** CASE 1 + AERODYNAMIC METHODS FOR MISSILE CONFIGURATIONS PAGE 1 + CASE INPUTS + FOLLOWING ARE THE CARDS INPUT FOR THIS CASE + + DIM M + $FLTCON + NMACH = 2., + MACH = 0.2,0.6, + ALT = 0.,0., + NALPHA = 3., + ALPHA = -2.,0.,2.,$ + $REFQ + XCG = 1.2, + SREF = 0.012667687, + LREF = 0.127,$ + $AXIBOD + TNOSE = 4., + LNOSE = 0.55, + DNOSE = 0.127, + LCENTR = 1.85, + DCENTR = 0.127, + BASE = .FALSE., + TAFT = 0., + LAFT = 0.1, + DAFT = 0.09, + DEXIT = 0.,$ + $FINSET1 + XLE = 2.1,2.25, + NPANEL = 4., + PHIF = 0.,90.,180.,270., + SSPAN = 0.0635,0.1635, + CHORD = 0.25,0.1, + SECTYP = 0., + ZUPPER = 0.01,0.01, + ZLOWER = 0.01,0.01, + LER = 0.,0., + LMAXU = 0.25,0.25, + LFLATU = 0.5,0.5,$ + DAMP + NEXT CASE + THE BOUNDARY LAYER IS ASSUMED TO BE TURBULENT + THE INPUT UNITS ARE IN METERS, THE SCALE FACTOR IS 1.0000 +1 ***** THE USAF AUTOMATED MISSILE DATCOM * REV 3/99 ***** CASE 1 + AERODYNAMIC METHODS FOR MISSILE CONFIGURATIONS PAGE 2 + STATIC AERODYNAMICS FOR BODY-FIN SET 1 + + ******* FLIGHT CONDITIONS AND REFERENCE QUANTITIES ******* + MACH NO = 0.20 REYNOLDS NO = 4.637E+06 /M + ALTITUDE = 0.0 M DYNAMIC PRESSURE = 2837.11 N/M**2 + SIDESLIP = 0.00 DEG ROLL = 0.00 DEG + REF AREA = 0.013 M**2 MOMENT CENTER = 1.200 M + REF LENGTH = 0.13 M LAT REF LENGTH = 0.13 M + + ----- LONGITUDINAL ----- -- LATERAL DIRECTIONAL -- + ALPHA CN CM CA CY CLN CLL + + -2.00 -0.430 2.212 0.368 -0.000 0.000 0.000 + 0.00 0.000 0.000 0.368 0.000 -0.000 0.000 + 2.00 0.430 -2.212 0.368 0.000 -0.000 -0.000 + + ALPHA CL CD CL/CD X-C.P. + + -2.00 -0.417 0.382 -1.090 -5.145 + 0.00 0.000 0.368 0.000 -5.145 + 2.00 0.417 0.382 1.090 -5.145 + + X-C.P. MEAS. FROM MOMENT CENTER IN REF. LENGTHS, NEG. AFT OF MOMENT CENTER +1 ***** THE USAF AUTOMATED MISSILE DATCOM * REV 3/99 ***** CASE 1 + AERODYNAMIC METHODS FOR MISSILE CONFIGURATIONS PAGE 3 + STATIC AERODYNAMICS FOR BODY-FIN SET 1 + + ******* FLIGHT CONDITIONS AND REFERENCE QUANTITIES ******* + MACH NO = 0.20 REYNOLDS NO = 4.637E+06 /M + ALTITUDE = 0.0 M DYNAMIC PRESSURE = 2837.11 N/M**2 + SIDESLIP = 0.00 DEG ROLL = 0.00 DEG + REF AREA = 0.013 M**2 MOMENT CENTER = 1.200 M + REF LENGTH = 0.13 M LAT REF LENGTH = 0.13 M + + ---------- DERIVATIVES (PER DEGREE) ---------- + ALPHA CNA CMA CYB CLNB CLLB + -2.00 0.2149 -1.1058 -0.2064 1.0389 0.0001 + 0.00 0.2149 -1.1058 -0.2071 1.0437 -0.0000 + 2.00 0.2149 -1.1058 -0.2064 1.0389 -0.0001 + + PANEL DEFLECTION ANGLES (DEGREES) + SET FIN 1 FIN 2 FIN 3 FIN 4 FIN 5 FIN 6 FIN 7 FIN 8 + 1 0.00 0.00 0.00 0.00 +1 ***** THE USAF AUTOMATED MISSILE DATCOM * REV 3/99 ***** CASE 1 + AERODYNAMIC METHODS FOR MISSILE CONFIGURATIONS PAGE 4 + BODY + 1 FIN SET DYNAMIC DERIVATIVES + + ******* FLIGHT CONDITIONS AND REFERENCE QUANTITIES ******* + MACH NO = 0.20 REYNOLDS NO = 4.637E+06 /M + ALTITUDE = 0.0 M DYNAMIC PRESSURE = 2837.11 N/M**2 + SIDESLIP = 0.00 DEG ROLL = 0.00 DEG + REF AREA = 0.013 M**2 MOMENT CENTER = 1.200 M + REF LENGTH = 0.13 M LAT REF LENGTH = 0.13 M + + ------------ DYNAMIC DERIVATIVES (PER DEGREE) ----------- + ALPHA CNQ CMQ CAQ CNAD CMAD + -2.00 4.335 -32.199 0.000 0.848 -1.721 + 0.00 4.485 -33.380 0.000 0.848 -1.721 + 2.00 4.565 -34.018 0.000 0.848 -1.721 + + PITCH RATE DERIVATIVES NON-DIMENSIONALIZED BY Q*LREF/2*V +1 ***** THE USAF AUTOMATED MISSILE DATCOM * REV 3/99 ***** CASE 1 + AERODYNAMIC METHODS FOR MISSILE CONFIGURATIONS PAGE 5 + BODY + 1 FIN SET DYNAMIC DERIVATIVES + + ******* FLIGHT CONDITIONS AND REFERENCE QUANTITIES ******* + MACH NO = 0.20 REYNOLDS NO = 4.637E+06 /M + ALTITUDE = 0.0 M DYNAMIC PRESSURE = 2837.11 N/M**2 + SIDESLIP = 0.00 DEG ROLL = 0.00 DEG + REF AREA = 0.013 M**2 MOMENT CENTER = 1.200 M + REF LENGTH = 0.13 M LAT REF LENGTH = 0.13 M + + ------------ DYNAMIC DERIVATIVES (PER DEGREE) ----------- + ALPHA CYR CLNR CLLR CYP CLNP CLLP + -2.00 4.471 -33.273 0.000 -0.000 0.000 -0.259 + 0.00 4.485 -33.380 0.000 -0.000 0.000 -0.243 + 2.00 4.471 -33.273 0.000 -0.000 0.000 -0.259 + + YAW AND ROLL RATE DERIVATIVES NON-DIMENSIONALIZED BY R*LATREF/2*V +1 ***** THE USAF AUTOMATED MISSILE DATCOM * REV 3/99 ***** CASE 1 + AERODYNAMIC METHODS FOR MISSILE CONFIGURATIONS PAGE 6 + STATIC AERODYNAMICS FOR BODY-FIN SET 1 + + ******* FLIGHT CONDITIONS AND REFERENCE QUANTITIES ******* + MACH NO = 0.60 REYNOLDS NO = 1.391E+07 /M + ALTITUDE = 0.0 M DYNAMIC PRESSURE = 25533.95 N/M**2 + SIDESLIP = 0.00 DEG ROLL = 0.00 DEG + REF AREA = 0.013 M**2 MOMENT CENTER = 1.200 M + REF LENGTH = 0.13 M LAT REF LENGTH = 0.13 M + + ----- LONGITUDINAL ----- -- LATERAL DIRECTIONAL -- + ALPHA CN CM CA CY CLN CLL + + -2.00 -0.433 2.226 0.320 -0.000 0.000 0.000 + 0.00 0.000 0.000 0.320 0.000 -0.000 0.000 + 2.00 0.433 -2.226 0.320 0.000 -0.000 -0.000 + + ALPHA CL CD CL/CD X-C.P. + + -2.00 -0.422 0.335 -1.261 -5.137 + 0.00 0.000 0.320 0.000 -5.137 + 2.00 0.422 0.335 1.261 -5.137 + + X-C.P. MEAS. FROM MOMENT CENTER IN REF. LENGTHS, NEG. AFT OF MOMENT CENTER +1 ***** THE USAF AUTOMATED MISSILE DATCOM * REV 3/99 ***** CASE 1 + AERODYNAMIC METHODS FOR MISSILE CONFIGURATIONS PAGE 7 + STATIC AERODYNAMICS FOR BODY-FIN SET 1 + + ******* FLIGHT CONDITIONS AND REFERENCE QUANTITIES ******* + MACH NO = 0.60 REYNOLDS NO = 1.391E+07 /M + ALTITUDE = 0.0 M DYNAMIC PRESSURE = 25533.95 N/M**2 + SIDESLIP = 0.00 DEG ROLL = 0.00 DEG + REF AREA = 0.013 M**2 MOMENT CENTER = 1.200 M + REF LENGTH = 0.13 M LAT REF LENGTH = 0.13 M + + ---------- DERIVATIVES (PER DEGREE) ---------- + ALPHA CNA CMA CYB CLNB CLLB + -2.00 0.2167 -1.1132 -0.2084 1.0481 0.0001 + 0.00 0.2167 -1.1132 -0.2091 1.0530 -0.0000 + 2.00 0.2167 -1.1132 -0.2084 1.0481 -0.0001 + + PANEL DEFLECTION ANGLES (DEGREES) + SET FIN 1 FIN 2 FIN 3 FIN 4 FIN 5 FIN 6 FIN 7 FIN 8 + 1 0.00 0.00 0.00 0.00 +1 ***** THE USAF AUTOMATED MISSILE DATCOM * REV 3/99 ***** CASE 1 + AERODYNAMIC METHODS FOR MISSILE CONFIGURATIONS PAGE 8 + BODY + 1 FIN SET DYNAMIC DERIVATIVES + + ******* FLIGHT CONDITIONS AND REFERENCE QUANTITIES ******* + MACH NO = 0.60 REYNOLDS NO = 1.391E+07 /M + ALTITUDE = 0.0 M DYNAMIC PRESSURE = 25533.95 N/M**2 + SIDESLIP = 0.00 DEG ROLL = 0.00 DEG + REF AREA = 0.013 M**2 MOMENT CENTER = 1.200 M + REF LENGTH = 0.13 M LAT REF LENGTH = 0.13 M + + ------------ DYNAMIC DERIVATIVES (PER DEGREE) ----------- + ALPHA CNQ CMQ CAQ CNAD CMAD + -2.00 4.251 -32.131 0.000 0.848 -1.869 + 0.00 4.350 -32.919 0.000 0.848 -1.869 + 2.00 4.377 -33.127 0.000 0.848 -1.869 + + PITCH RATE DERIVATIVES NON-DIMENSIONALIZED BY Q*LREF/2*V +1 ***** THE USAF AUTOMATED MISSILE DATCOM * REV 3/99 ***** CASE 1 + AERODYNAMIC METHODS FOR MISSILE CONFIGURATIONS PAGE 9 + BODY + 1 FIN SET DYNAMIC DERIVATIVES + + ******* FLIGHT CONDITIONS AND REFERENCE QUANTITIES ******* + MACH NO = 0.60 REYNOLDS NO = 1.391E+07 /M + ALTITUDE = 0.0 M DYNAMIC PRESSURE = 25533.95 N/M**2 + SIDESLIP = 0.00 DEG ROLL = 0.00 DEG + REF AREA = 0.013 M**2 MOMENT CENTER = 1.200 M + REF LENGTH = 0.13 M LAT REF LENGTH = 0.13 M + + ------------ DYNAMIC DERIVATIVES (PER DEGREE) ----------- + ALPHA CYR CLNR CLLR CYP CLNP CLLP + -2.00 4.339 -32.827 0.000 -0.000 0.000 -0.267 + 0.00 4.350 -32.919 0.000 -0.000 0.000 -0.250 + 2.00 4.339 -32.827 0.000 -0.000 0.000 -0.267 + + YAW AND ROLL RATE DERIVATIVES NON-DIMENSIONALIZED BY R*LATREF/2*V + *** END OF JOB *** diff --git a/tests/fixtures/surfaces/datcom_fixtures.py b/tests/fixtures/surfaces/datcom_fixtures.py new file mode 100644 index 000000000..256df7762 --- /dev/null +++ b/tests/fixtures/surfaces/datcom_fixtures.py @@ -0,0 +1,58 @@ +"""Deterministic geometry and coefficient tables for DATCOM regression tests.""" + +import numpy as np +import pytest + +from rocketpy.rocket.datcom import BODY_KEYS, DAMPING_KEYS, DatcomResult + + +@pytest.fixture +def datcom_table_factory(): + """Make complete signed tables without requiring an external executable.""" + + def make(values=None): + values = values or {} + mach = np.array([0.2, 0.6]) + alpha = np.array([-2.0, 0.0, 2.0]) + zeros = np.zeros((2, 3)) + body = {key: zeros + values.get(key, 0) for key in BODY_KEYS} + derivatives = { + key: zeros + values.get(key, 0) + for key in ("CNA", "CMA", "CYB", "CLNB", "CLLB") + } + damping = {key: zeros + values.get(key, 0) for key in DAMPING_KEYS} + return DatcomResult( + ( + zeros.copy(), + zeros + values.get("CD", 0.4), + body["CM"], + zeros.copy(), + mach, + alpha, + ), + body, + derivatives, + damping, + ) + + return make + + +@pytest.fixture +def datcom_rocket(calisto, tmp_path): + """An assembled rocket with a fake executable, nose, DATCOM fins and tail.""" + executable = tmp_path / "datcom" + executable.write_text("not executed by unit tests") + calisto.add_nose(0.55, "von karman", position=1.2) + calisto.add_datcom_hex_fins( + 4, + root_chord=0.25, + tip_chord=0.1, + span=0.1, + position=-0.9, + sweep_length=0.1, + path=executable, + datcom_options={"mach": [0.2, 0.6], "alpha": [-2, 0, 2]}, + ) + calisto.add_tail(calisto.radius, 0.045, 0.1, position=-1.2) + return calisto diff --git a/tests/integration/environment/test_environment.py b/tests/integration/environment/test_environment.py index d51551397..1696ecccf 100644 --- a/tests/integration/environment/test_environment.py +++ b/tests/integration/environment/test_environment.py @@ -194,6 +194,85 @@ def test_windy_atmosphere(example_euroc_env, model_name): assert abs(example_euroc_env.wind_velocity_y(100)) < 20.0 +@pytest.mark.slow +@pytest.mark.parametrize( + "model_name", + [ + "best_match", + "gfs_seamless", + "ecmwf_ifs025", + "icon_seamless", + ], +) +def test_open_meteo_atmosphere(example_euroc_env, model_name): + """Tests the Open-Meteo forecast model against the live API. + + The tolerances are loose because the actual weather is unknown at test + time; the point is to check that the profiles are built and that the values + are physically plausible. + + Parameters + ---------- + example_euroc_env : rocketpy.Environment + Example environment object to be tested. + model_name : str + The Open-Meteo model to be passed to set_atmospheric_model() as the + "file" parameter. + """ + example_euroc_env.set_atmospheric_model(type="open_meteo", file=model_name) + + assert pytest.approx(100000.0, rel=0.1) == example_euroc_env.pressure(100) + assert 0 + 273 < example_euroc_env.temperature(100) < 40 + 273 + assert abs(example_euroc_env.wind_velocity_x(100)) < 30.0 + assert abs(example_euroc_env.wind_velocity_y(100)) < 30.0 + # Pressure must fall monotonically with altitude. + assert example_euroc_env.pressure(5000) < example_euroc_env.pressure(1000) + # Air density at sea level is around 1.2 kg/m^3. + assert 0.9 < example_euroc_env.density(100) < 1.4 + + +@pytest.mark.slow +def test_open_meteo_historical_atmosphere(example_euroc_env): + """Tests that a past launch date reaches Open-Meteo's historical archive. + + This is the workflow that removes the need to download reanalysis files by + hand: setting a past date and reading the profile straight from the API. + """ + example_euroc_env.set_date(datetime(2024, 1, 10, 12, tzinfo=timezone.utc)) + example_euroc_env.set_atmospheric_model(type="open_meteo") + + assert pytest.approx(100000.0, rel=0.1) == example_euroc_env.pressure(100) + assert 0 + 273 < example_euroc_env.temperature(100) < 40 + 273 + # The returned window must bracket the requested launch date. + assert example_euroc_env.atmospheric_model_init_date <= datetime(2024, 1, 10, 12) + assert example_euroc_env.atmospheric_model_end_date >= datetime(2024, 1, 10, 12) + + +@pytest.mark.slow +@patch("matplotlib.pyplot.show") +def test_open_meteo_ensemble_atmosphere(mock_show, example_euroc_env): # pylint: disable=unused-argument + """Tests the Open-Meteo ensemble model against the live API. + + Parameters + ---------- + mock_show : mock + Mock object to replace matplotlib.pyplot.show() method. + example_euroc_env : rocketpy.Environment + Example environment object to be tested. + """ + example_euroc_env.set_atmospheric_model(type="open_meteo_ensemble", file="gfs05") + + # gfs05 publishes 30 perturbed members plus the control run. + assert example_euroc_env.num_ensemble_members == 31 + assert pytest.approx(100000.0, rel=0.1) == example_euroc_env.pressure(100) + + example_euroc_env.select_ensemble_member(10) + assert example_euroc_env.ensemble_member == 10 + assert pytest.approx(100000.0, rel=0.1) == example_euroc_env.pressure(100) + + assert example_euroc_env.all_info() is None + + @pytest.mark.slow @patch("matplotlib.pyplot.show") def test_gfs_atmosphere(mock_show, example_spaceport_env): # pylint: disable=unused-argument @@ -242,7 +321,7 @@ def test_hrrr_atmosphere(mock_show, example_spaceport_env): # pylint: disable=u # Sometimes the HRRR latest-model can fail due to not having at least 24 # hours in the future in the forecast, so we try with 12 hours in the future # only. - example_spaceport_env.set_date(datetime.now() + timedelta(hours=12)) + example_spaceport_env.set_date(datetime.now(timezone.utc) + timedelta(hours=12)) example_spaceport_env.set_atmospheric_model(type="Forecast", file="HRRR") assert example_spaceport_env.all_info() is None diff --git a/tests/integration/environment/test_environment_analysis.py b/tests/integration/environment/test_environment_analysis.py index 2b12a2057..6e3a76aef 100644 --- a/tests/integration/environment/test_environment_analysis.py +++ b/tests/integration/environment/test_environment_analysis.py @@ -3,14 +3,12 @@ from unittest.mock import patch import matplotlib as plt -import pytest from rocketpy import Environment plt.rcParams.update({"figure.max_open_warning": 0}) -@pytest.mark.slow @patch("matplotlib.pyplot.show") def test_all_info(mock_show, env_analysis): # pylint: disable=unused-argument """Test the EnvironmentAnalysis.all_info() method, which already invokes @@ -32,7 +30,6 @@ def test_all_info(mock_show, env_analysis): # pylint: disable=unused-argument os.remove("wind_rose.gif") # remove the files created by the method -@pytest.mark.slow @patch("matplotlib.pyplot.show") def test_exports(mock_show, env_analysis): # pylint: disable=unused-argument """Check the export methods of the EnvironmentAnalysis class. It @@ -59,7 +56,6 @@ def test_exports(mock_show, env_analysis): # pylint: disable=unused-argument os.remove("export_env_analysis.json") -@pytest.mark.slow @patch("matplotlib.pyplot.show") def test_create_environment_object(mock_show, env_analysis): # pylint: disable=unused-argument assert isinstance(env_analysis.create_environment_object(), Environment) diff --git a/tests/integration/simulation/test_flight.py b/tests/integration/simulation/test_flight.py index 7983c0348..a3d4bb9c2 100644 --- a/tests/integration/simulation/test_flight.py +++ b/tests/integration/simulation/test_flight.py @@ -5,11 +5,72 @@ import numpy.testing as npt import pytest -from rocketpy import Flight +from rocketpy import Flight, Rocket, SolidMotor plt.rcParams.update({"figure.max_open_warning": 0}) +def test_flight_with_delayed_burn_leaves_rail(example_plain_env): + """Regression test for #411: a motor whose thrust curve starts at t > 0 must + still lift the rocket off the rail. Previously the solver stepped over the + burn (``max_step`` defaults to ``inf``) so the rocket never left the pad + (``out_of_rail_time`` came out as 0). + + Parameters + ---------- + example_plain_env : rocketpy.Environment + A plain environment object, this is a pytest fixture. + """ + motor = SolidMotor( + thrust_source=[(8.0, 1500.0), (9.0, 2000.0), (14.0, 2000.0), (20.0, 0.0)], + burn_time=(8, 20), + dry_mass=1.815, + dry_inertia=(0.125, 0.125, 0.002), + nozzle_radius=33 / 1000, + grain_number=5, + grain_density=1815, + grain_outer_radius=33 / 1000, + grain_initial_inner_radius=15 / 1000, + grain_initial_height=120 / 1000, + grain_separation=5 / 1000, + grains_center_of_mass_position=0.397, + center_of_dry_mass_position=0.317, + nozzle_position=0, + throat_radius=11 / 1000, + coordinate_system_orientation="nozzle_to_combustion_chamber", + ) + rocket = Rocket( + radius=127 / 2000, + mass=14.426, + inertia=(6.321, 6.321, 0.034), + power_off_drag=0.5, + power_on_drag=0.5, + center_of_mass_without_motor=0, + coordinate_system_orientation="tail_to_nose", + ) + rocket.add_motor(motor, position=-1.255) + rocket.add_nose(length=0.55829, kind="vonKarman", position=1.278) + rocket.add_trapezoidal_fins( + n=4, root_chord=0.120, tip_chord=0.060, span=0.110, position=-1.04956 + ) + rocket.set_rail_buttons(0.082, -0.618) + + flight = Flight( + rocket=rocket, + environment=example_plain_env, + rail_length=5.2, + inclination=85, + heading=0, + max_time=60, + ) + + # Ignition is at t = 8 s; the rocket must leave the rail shortly after, + # not remain at the pad the whole flight. + assert flight.out_of_rail_time > 8.0 + assert flight.out_of_rail_velocity > 10.0 + assert flight.apogee - example_plain_env.elevation > 1000.0 + + @pytest.mark.parametrize( "flight_fixture", ["flight_calisto_robust", "flight_calisto_robust_solid_eom"] ) @@ -386,8 +447,13 @@ def test_freestream_speed_at_apogee(example_plain_env, calisto): """ # NOTE: this rocket doesn't move in x or z direction. There's no wind. hard_atol = 1e-12 - soft_atol = 1e-5 soft_rtol = 1e-4 + # stream_velocity_z at apogee is a numerically noisy ~0 quantity: it is a + # residual of the apogee-time estimation and swings by ~1e-4 across + # platforms/NumPy versions and atmosphere discretizations. Use a looser + # absolute tolerance for it (1e-3 m/s is physically negligible for a rocket + # whose vertical speed peaks above 200 m/s). + apogee_z_atol = 1e-3 test_flight = Flight( environment=example_plain_env, rocket=calisto, @@ -414,7 +480,7 @@ def test_freestream_speed_at_apogee(example_plain_env, calisto): npt.assert_allclose( test_flight.stream_velocity_z(test_flight.apogee_time), 0.0, - atol=soft_atol, + atol=apogee_z_atol, rtol=soft_rtol, ) npt.assert_allclose( @@ -948,3 +1014,30 @@ def acc_trigger(p, h, y, u_dot): # pylint: disable=unused-argument deploy_time, deployed = flight.parachute_events[0] assert deployed.name == "acc_chute" assert abs(flight.z(deploy_time) - flight.apogee) <= 5 + + +def test_flight_with_fixed_time_parachute_trigger(calisto_robust, example_plain_env): + """Integration test for #437: ``("time", t_deploy)`` fires near t_deploy.""" + t_deploy = 3.0 + calisto_robust.parachutes = [] + calisto_robust.add_parachute( + name="timer_chute", + cd_s=5.0, + trigger=("time", t_deploy), + sampling_rate=100, + lag=0, + ) + + flight = Flight( + rocket=calisto_robust, + environment=example_plain_env, + rail_length=5.2, + inclination=85, + heading=0, + ) + + assert len(flight.parachute_events) >= 1 + deploy_time, deployed = flight.parachute_events[0] + assert deployed.name == "timer_chute" + # Sampling at 100 Hz; allow one sample interval of slack. + assert abs(deploy_time - t_deploy) <= 0.02 diff --git a/tests/integration/simulation/test_monte_carlo.py b/tests/integration/simulation/test_monte_carlo.py index bcfb59505..505c30f40 100644 --- a/tests/integration/simulation/test_monte_carlo.py +++ b/tests/integration/simulation/test_monte_carlo.py @@ -1,4 +1,5 @@ # pylint: disable=unused-argument +import json import os from unittest.mock import patch @@ -6,6 +7,9 @@ import numpy as np import pytest +from rocketpy.rocket.components import Components +from rocketpy.simulation import MonteCarlo + plt.rcParams.update({"figure.max_open_warning": 0}) @@ -263,3 +267,44 @@ def test_monte_carlo_simulate_convergence(monte_carlo_calisto): assert monte_carlo_calisto.num_of_loaded_sims <= 20 finally: _post_test_file_cleanup() + + +@pytest.mark.slow +def test_monte_carlo_simulate_free_form_fins( + stochastic_environment, + stochastic_calisto, + stochastic_free_form_fins, + stochastic_flight, + tmp_path, +): + """A free-form fin set must survive a whole Monte Carlo run: it has to be + sampled, flown, and written to the inputs file as an outline rather than as + a single point (see #953).""" + + stochastic_calisto.aerodynamic_surfaces = Components() + stochastic_calisto.add_free_form_fins( + stochastic_free_form_fins, position=(-1.04956, 0.001) + ) + + filename = str(tmp_path / "monte_carlo_free_form_fins") + monte_carlo = MonteCarlo( + filename=filename, + environment=stochastic_environment, + rocket=stochastic_calisto, + flight=stochastic_flight, + ) + monte_carlo.simulate(number_of_simulations=2, append=False) + + assert monte_carlo.num_of_loaded_sims == 2 + + nominal = np.asarray(stochastic_free_form_fins.obj.shape_points, dtype=float) + with open(filename + ".inputs.txt", encoding="utf-8") as file: + lines = file.read().splitlines() + assert len(lines) == 2 + for line in lines: + surfaces = json.loads(line)["aerodynamic_surfaces"] + outlines = [s["shape_points"] for s in surfaces if "shape_points" in s] + assert len(outlines) == 1 + sampled = np.asarray(outlines[0], dtype=float) + assert sampled.shape == nominal.shape + assert not np.allclose(sampled, nominal) diff --git a/tests/integration/test_datcom.py b/tests/integration/test_datcom.py new file mode 100644 index 000000000..24e0a3179 --- /dev/null +++ b/tests/integration/test_datcom.py @@ -0,0 +1,550 @@ +"""DATCOM text parsing, isolated executable lifecycle and flight integration.""" + +import os +import shutil +import subprocess +from pathlib import Path +from types import SimpleNamespace +from unittest.mock import patch + +import numpy as np +import pytest + +from rocketpy import DatcomFins, Environment, Flight +from rocketpy.rocket.datcom import DAMPING_KEYS, parse_datcom_output, run_datcom + +SAMPLE = Path(__file__).parents[1] / "fixtures/surfaces/data/datcom/for006.dat" + + +@pytest.mark.slow +def test_datcom_native_fin_overhang(datcom_rocket): + """The native executable accepts aft overhang with an attached root centre.""" + # Arrange + executable = os.environ.get("DATCOM_EXECUTABLE") + if not executable: + pytest.skip("Set DATCOM_EXECUTABLE to the target 3/99 executable") + datcom_rocket.fins[0].datcom_path = executable + datcom_rocket.fins[0].root_chord = 0.45 + # Act + model = datcom_rocket.prepare_datcom() + # Assert + for state in (True, False): + assert model.coefficients(state, 0.6, 0)[2] > 0 + assert set(model.tables[state].damping) == set(DAMPING_KEYS) + + +@pytest.mark.slow +def test_datcom_native_wind_tunnel_fit(datcom_rocket): + """A drag-only synthetic tunnel sweep recovers native coefficients.""" + # Arrange + executable = os.environ.get("DATCOM_EXECUTABLE") + if not executable: + pytest.skip("Set DATCOM_EXECUTABLE to the target 3/99 executable") + rocket = datcom_rocket + rocket.fins[0].datcom_path = executable + rocket.fins[0].datcom_options["reynolds"] = [2e7, 4e7] + rocket.add_datcom_calibration(roughness_height=30.48e-6) + known = rocket.prepare_datcom() + mach = [0.2, 0.2, 0.2, 0.6, 0.6, 0.6] + alpha = [-2, 0, 2, -2, 0, 2] + drag = [] + for m, a in zip(mach, alpha): + radians = np.radians(a) + values = known.coefficients(False, m, radians) + drag.append(values[2] * np.cos(radians) + values[0] * np.sin(radians)) + rocket.add_datcom_calibration() + del rocket.fins[0].datcom_options["reynolds"] + # Act + report = rocket.fit_datcom_calibration( + { + "mach": mach, + "alpha": alpha, + "CD": drag, + "reynolds_per_meter": [2e7] * 3 + [4e7] * 3, + }, + bounds={"roughness_height": (0, 100e-6)}, + ) + model = rocket.prepare_datcom() + # Assert + assert report["weighted_rmse_fitted"] < 0.001 + assert report["weighted_rmse_fitted"] < report["weighted_rmse_initial"] + assert report["calibration"]["roughness_height"] == pytest.approx( + 30.48e-6, abs=5e-6 + ) + assert set(model.tables[False].damping) == set(DAMPING_KEYS) + assert "reynolds" not in rocket.fins[0].datcom_options + + +@pytest.mark.slow +def test_datcom_native_calibration_roughness(datcom_rocket): + """Public calibration changes native drag and reset restores the baseline.""" + # Arrange + executable = os.environ.get("DATCOM_EXECUTABLE") + if not executable: + pytest.skip("Set DATCOM_EXECUTABLE to the target 3/99 executable") + datcom_rocket.fins[0].datcom_path = executable + baseline = datcom_rocket.prepare_datcom() + # Act + datcom_rocket.add_datcom_calibration(roughness_height=30.48e-6) + calibrated = datcom_rocket.prepare_datcom() + datcom_rocket.add_datcom_calibration() + reset = datcom_rocket.prepare_datcom() + # Assert + for powered in (True, False): + assert ( + calibrated.coefficients(powered, 0.6, 0)[2] + > baseline.coefficients(powered, 0.6, 0)[2] + ) + np.testing.assert_array_equal( + reset.tables[powered].body["CA"], baseline.tables[powered].body["CA"] + ) + + +def test_parse_datcom_output_complete_signed_tables(): + """A real for006 sample supplies static, lateral and all dynamic channels.""" + # Arrange + directory = SAMPLE.parent + # Act + result = parse_datcom_output(directory) + # Assert + np.testing.assert_allclose(result[4], [0.2, 0.6]) + np.testing.assert_allclose(result[5], [-2, 0, 2]) + assert result[0].shape == (2, 3) + assert set(result.damping) == set(DAMPING_KEYS) + assert result.body["CA"][0, 1] > 0 + assert result.damping["CMQ"][0, 1] < 0 + assert result.damping["CLLP"][0, 1] < 0 + for values in result.damping.values(): + assert values.shape == (2, 3) + assert np.isfinite(values).all() + + +def test_parse_datcom_output_joined_fixed_width_columns(tmp_path): + """An overflowing negative CP field must not truncate a valid alpha table.""" + # Arrange + lines = SAMPLE.read_text().splitlines() + header = next( + i + for i, line in enumerate(lines) + if line.split() == ["ALPHA", "CL", "CD", "CL/CD", "X-C.P."] + ) + row = next(i for i in range(header + 1, len(lines)) if len(lines[i].split()) == 5) + fields = lines[row].split() + fields[-1] = "-28096.705" + lines[row] = " ".join(fields[:-1]) + fields[-1] + (tmp_path / "for006.dat").write_text("\n".join(lines)) + # Act + result = parse_datcom_output(tmp_path) + # Assert + assert result[3][0, 0] == pytest.approx(-28096.705) + assert result[5] == pytest.approx([-2, 0, 2]) + + +@pytest.mark.parametrize( + "replacement,message", + [ + (("DYNAMIC DERIVATIVES", "MISSING DERIVATIVES"), "lacks DAMP"), + (("ALPHA CNQ", "ALPHA BAD"), "CNQ"), + (("NALPHA = 3.", "NALPHA = 4."), "angle count"), + ], +) +def test_parse_datcom_output_rejects_incomplete_data(replacement, message, tmp_path): + """Missing damping or truncated grids fail instead of silently returning partial data.""" + # Arrange + text = SAMPLE.read_text() + old, new = replacement + assert old in text + (tmp_path / "for006.dat").write_text(text.replace(old, new)) + # Act / Assert + with pytest.raises(RuntimeError, match=message): + parse_datcom_output(tmp_path) + + +@pytest.mark.parametrize( + "failure", [None, "exit", "timeout", "missing", "input", "grid"] +) +def test_run_datcom_temp_directory_cleanup(failure, tmp_path): + """Executable/deck copies and all outputs are removed on success and every failure.""" + # Arrange + source = tmp_path / "native datcom" + source.write_bytes(b"executable placeholder") + source.chmod(0o755) + working_directories = [] + deck = "DIM M\nDAMP\nNEXT CASE\n" + if failure == "grid": + deck = "DIM M\n $FLTCON MACH = .2,.7,$\nDAMP\nNEXT CASE\n" + + def execute(argv, cwd, **kwargs): + directory = Path(cwd) + working_directories.append(directory) + assert Path(argv[0]).read_bytes() == source.read_bytes() + assert Path(argv[0]).stat().st_mode & 0o111 + assert directory != source.parent + assert (directory / "for005.dat").read_text() == deck + assert kwargs["timeout"] == 2 + if failure == "timeout": + raise subprocess.TimeoutExpired(argv, 2) + if failure != "missing": + shutil.copyfile(SAMPLE, directory / "for006.dat") + if failure == "input": + (directory / "for006.dat").write_text("** ERROR ** 1*F") + return SimpleNamespace( + returncode=1 if failure == "exit" else 0, stderr="failure" + ) + + # Act + with patch("rocketpy.rocket.datcom.subprocess.run", side_effect=execute): + if failure: + with pytest.raises(RuntimeError): + run_datcom(source, deck, timeout=2) + else: + parsed = run_datcom(source, deck, timeout=2) + assert parsed[0].shape == (2, 3) + # Assert + assert len(working_directories) == 1 + assert not working_directories[0].exists() + assert source.read_bytes() == b"executable placeholder" + assert sorted(p.name for p in tmp_path.iterdir()) == [source.name] + + +@pytest.mark.parametrize( + "mode,equations", + [ + ("6 DOF", "standard"), + ("6 DOF", "solid_propulsion"), + ("3 DOF", "standard"), + ], +) +def test_datcom_flight_uses_full_model(mode, equations, datcom_rocket): + """Each supported free-flight solver consumes DATCOM and produces finite states.""" + # Arrange + rocket = datcom_rocket + tables = parse_datcom_output(SAMPLE.parent) + # Act + with patch("rocketpy.rocket.datcom.run_datcom", return_value=tables) as run: + flight = Flight( + rocket, + Environment(), + rail_length=5, + initial_solution=[0, 0, 0, 1000, 0, 0, 100, 1, 0, 0, 0, 0.05, 0, 0], + max_time=0.02, + max_time_step=0.01, + simulation_mode=mode, + equations_of_motion=equations, + ) + # Assert + assert run.call_count == 2 + assert np.isfinite(np.asarray(flight.solution)).all() + assert flight.solution[-1][0] == pytest.approx(0.02) + assert len(rocket.aerodynamic_surfaces) == 3 + + +@pytest.mark.slow +@pytest.mark.parametrize("powered_base_drag", ["none", "nozzle"]) +@pytest.mark.parametrize( + "method", + [ + "add_datcom_trapezoidal_fins", + "add_datcom_arc_fins", + "add_datcom_hex_fins", + ], +) +def test_datcom_native_executable(method, powered_base_drag, calisto): + """Optional real executable test, enabled with DATCOM_EXECUTABLE and --runslow.""" + # Arrange + executable = os.environ.get("DATCOM_EXECUTABLE") + if not executable: + pytest.skip("Set DATCOM_EXECUTABLE to a native DATCOM executable") + rocket = calisto + rocket.add_nose(0.55, "von karman", 1.2) + fins = getattr(rocket, method)( + 4, + 0.25, + 0.1, + 0.1, + -0.9, + path=executable, + datcom_options={"powered_base_drag": powered_base_drag}, + ) + rocket.add_tail(rocket.radius, 0.045, 0.1, -1.2) + # Act + model = rocket.prepare_datcom() + flight = Flight( + rocket, + Environment(), + rail_length=5, + max_time=30, + max_time_step=0.1, + terminate_on_apogee=True, + ) + # Assert + assert isinstance(fins, DatcomFins) + assert model.tables[True].damping["CMQ"][0, 3] < 0 + assert rocket.power_off_drag(0.3) > rocket.power_on_drag(0.3) + assert model.references[True] != pytest.approx(model.references[False]) + assert np.isfinite(np.asarray(flight.solution)).all() + assert flight.t_final > rocket.motor.burn_out_time + assert flight.apogee > 100 + + +def test_parse_datcom_multiple_sideslip_cases(tmp_path): + """Repeated Mach grids at different sideslip angles form one complete database.""" + # Arrange + text = SAMPLE.read_text() + assert "SIDESLIP = 0.00" in text + blocks = [ + text.replace("SIDESLIP = 0.00", f"SIDESLIP = {b:.2f}") + for b in [-5, 0, 5] + ] + (tmp_path / "for006.dat").write_text("\n".join(blocks)) + # Act + parsed = parse_datcom_output(tmp_path) + # Assert + assert parsed.beta == pytest.approx([-5, 0, 5]) + assert list(parsed.sideslip) == [-5, 0, 5] + for table in parsed.sideslip.values(): + assert table[0].shape == (2, 3) + assert set(table.damping) == set(DAMPING_KEYS) + + +@pytest.mark.slow +@pytest.mark.parametrize( + "feature", + [ + "multi_hex", + "arc_asymmetric", + "naca4", + "naca6", + "naca1", + "naca5", + "naca_modified", + "naca_supersonic", + "fifty_point_user", + "rocketpy_cant", + "user_surfaces", + "user_mean", + "one_panel_triangle", + "dihedral_incidence", + "eight_panels_ten_stations", + "boattail", + ], +) +def test_datcom_native_extended_fin_definitions(feature, calisto): + """The installed native executable accepts every supported geometry/section family.""" + # Arrange + executable = os.environ.get("DATCOM_EXECUTABLE") + if not executable: + pytest.skip("Set DATCOM_EXECUTABLE to a native DATCOM executable") + rocket = calisto + rocket.add_nose(0.55, "von karman", 1.2) + rocket.add_tail(rocket.radius, 0.045, 0.1, -1.2) + kwargs = dict( + n=4, + position=-0.9, + span_stations=[0, 0.05, 0.1], + chords=[0.25, 0.2, 0.1], + leading_edges=[0, 0.04, 0.1], + path=executable, + datcom_options={"mach": [0.2, 0.6], "alpha": [-2, 0, 2]}, + ) + if feature == "arc_asymmetric": + kwargs.update( + section_type="ARC", + upper_thickness_to_chord=[0.01, 0.02, 0.025], + lower_thickness_to_chord=[0, 0.01, 0.02], + ) + elif feature.startswith("naca"): + designations = { + "naca4": "0012", + "naca6": "6-64-005", + "naca1": "1-16-212.25", + "naca5": "5-23012", + "naca_modified": "4-0012-62", + "naca_supersonic": "S-3-25-5-50", + } + kwargs.update(section_type="NACA", naca=designations[feature]) + elif feature == "fifty_point_user": + x = np.linspace(0, 1, 50) + upper = 0.03 * np.sin(np.pi * x) + upper[[0, -1]] = 0 + kwargs.update( + section_type="USER", + airfoil_coordinates={"x": x, "upper": upper, "lower": -upper}, + ) + elif feature == "rocketpy_cant": + kwargs.update(cant_angle=0.5) + elif feature == "user_surfaces": + kwargs.update( + section_type="USER", + airfoil_coordinates={ + "x": [0, 0.25, 0.5, 0.75, 1], + "upper": [0, 0.03, 0.04, 0.03, 0], + "lower": [0, -0.02, -0.03, -0.02, 0], + }, + ) + elif feature == "user_mean": + kwargs.update( + section_type="USER", + airfoil_coordinates={ + "x": [0, 0.25, 0.5, 0.75, 1], + "mean": [0, 0.005, 0.005, 0.005, 0], + "thickness": [0, 0.05, 0.07, 0.05, 0], + }, + ) + elif feature == "one_panel_triangle": + kwargs.update(n=1, chords=[0.25, 0.15, 0]) + elif feature == "dihedral_incidence": + kwargs.update(n=2, panel_angles=[20, 200], dihedral=[5, -5], incidence=[1, -1]) + elif feature == "eight_panels_ten_stations": + kwargs.update( + n=8, + span_stations=np.linspace(0, 0.1, 10), + chords=np.linspace(0.25, 0.1, 10), + leading_edges=np.linspace(0, 0.1, 10), + ) + elif feature == "boattail": + kwargs.update( + position=-1.21, + span_stations=[0, 0.025, 0.05], + chords=[0.04, 0.03, 0.02], + leading_edges=[0, 0.005, 0.01], + ) + fins = rocket.add_datcom_fins(**kwargs) + # Act + model = rocket.prepare_datcom() + # Assert + for powered in (True, False): + table = model.tables[powered] + assert table[0].shape == (2, 3) + assert np.isfinite(table[1]).all() + assert set(table.damping) == set(DAMPING_KEYS) + if feature in ("one_panel_triangle", "dihedral_incidence"): + assert len(model.tables[True].beta) == 3 + b = model.tables[True].sideslip[5] + assert model.coefficients(True, 0.2, 0, np.radians(5))[3] == pytest.approx( + b.body["CY"][0, 1] + ) + if feature == "rocketpy_cant": + assert model.coefficients(True, 0.2, 0)[5] > 0 + assert len(fins.datcom_span_stations) <= 10 + + +@pytest.mark.slow +def test_datcom_native_body_axis_angle_convention(calisto): + """The target 3/99 executable uses projected atan/atan body incidence angles.""" + # Arrange + import re + + from rocketpy.rocket.datcom import datcom_decks + + executable = os.environ.get("DATCOM_EXECUTABLE") + if not executable: + pytest.skip("Set DATCOM_EXECUTABLE to the target 3/99 executable") + calisto.add_nose(0.55, "von karman", 1.2) + calisto.add_datcom_hex_fins( + 4, + 0.25, + 0.1, + 0.1, + -0.9, + path=executable, + datcom_options={ + "mach": [0.3, 0.6], + "alpha": [-20, 0, 20], + "beta": [-30, 0, 30], + }, + ) + calisto.add_tail(calisto.radius, 0.045, 0.1, -1.2) + deck = datcom_decks(calisto)[0][True] + # Isolate the axisymmetric body; BUILD enables body-only damping output. + deck = re.sub(r"\s*\$FINSET1.*?\$", "", deck, flags=re.S) + deck = re.sub(r"\s*\$DEFLCT.*?\$", "", deck, flags=re.S) + deck = deck.replace("DAMP", "BUILD\nDAMP") + # Act + table = run_datcom(executable, deck).sideslip[30] + ratio = abs(table.body["CY"][0, 2] / table.body["CN"][0, 2]) + # Assert + expected = np.tan(np.radians(30)) / np.tan(np.radians(20)) + assert ratio == pytest.approx(expected, rel=1e-3) + + +def test_datcom_tipoff_alpha_dot_is_self_consistent( + datcom_rocket, datcom_table_factory +): + """The implicit solve includes the rail constraint rather than the free-flight acceleration.""" + # Arrange + rocket = datcom_rocket + rocket.set_rail_buttons( + upper_button_position=0.1, lower_button_position=-0.6, angular_position=0 + ) + with patch( + "rocketpy.rocket.datcom.run_datcom", + return_value=datcom_table_factory({"CNAD": 0.5, "CMAD": -0.2, "CA": 0.4}), + ): + model = rocket.prepare_datcom() + state = [0, 0, 1000, 0, 0, 20, 1, 0, 0, 0, 0.2, 0, 0] + flight = Flight( + rocket, + Environment(), + rail_length=5, + inclination=90, + initial_solution=[0, *state], + max_time=0.001, + max_time_step=0.001, + use_udot_rail2=True, + ) + # Act + with patch.object( + model, + "compute_forces_and_moments_at_time", + wraps=model.compute_forces_and_moments_at_time, + ) as loads: + derivative = flight.udot_rail2(0.5, state) + # Assert + assert loads.call_count == 3 + used_alpha_dot = loads.call_args.args[1] + assert used_alpha_dot == pytest.approx( + flight._datcom_incidence_rate(0.5, state, derivative), rel=1e-8, abs=1e-10 + ) + assert flight._datcom_evaluating is False + assert np.isfinite(derivative).all() + + +@pytest.mark.slow +def test_datcom_native_small_leading_edge_radius(datcom_rocket): + """A measured ten-micrometre edge radius must survive native input parsing.""" + # Arrange + executable = os.environ.get("DATCOM_EXECUTABLE") + if not executable: + pytest.skip("Set DATCOM_EXECUTABLE to the target 3/99 executable") + datcom_rocket.fins[0].datcom_path = executable + datcom_rocket.add_datcom_calibration(leading_edge_radius=1e-5) + # Act + model = datcom_rocket.prepare_datcom() + # Assert + assert np.isfinite(model.coefficients(False, 0.6, 0)).all() + + +@pytest.mark.slow +def test_datcom_native_maximum_adaptive_grid(datcom_rocket): + """All 20 adaptive Mach points, including the transonic cluster, survive I/O.""" + # Arrange + from rocketpy.rocket.datcom_conditions import ( + adaptive_alpha_grid, + adaptive_mach_grid, + ) + + executable = os.environ.get("DATCOM_EXECUTABLE") + if not executable: + pytest.skip("Set DATCOM_EXECUTABLE to the target 3/99 executable") + datcom_rocket.fins[0].datcom_path = executable + mach = adaptive_mach_grid(3) + alpha = adaptive_alpha_grid([-35, 40]) + datcom_rocket.fins[0].datcom_options.update(mach=mach, alpha=alpha) + # Act + model = datcom_rocket.prepare_datcom() + # Assert + for state in (True, False): + assert len(model.tables[state][4]) == 20 + assert model.tables[state][4] == pytest.approx(mach, abs=0.005) + assert model.tables[state][5] == pytest.approx(alpha, abs=0.005) diff --git a/tests/unit/control/test_controller.py b/tests/unit/control/test_controller.py new file mode 100644 index 000000000..b7fbef8be --- /dev/null +++ b/tests/unit/control/test_controller.py @@ -0,0 +1,158 @@ +from dataclasses import dataclass + +from rocketpy.control.controller import _Controller + + +@dataclass(frozen=True) +class InteractiveObject: + name: str + + +def controller_function( + time, + sampling_rate, + state_vector, + state_history, + observed_variables, + interactive_objects, + sensors, + environment, +): + return { + "time": time, + "sampling_rate": sampling_rate, + "state": state_vector, + "history": state_history, + "previous": observed_variables[-1], + "objects": interactive_objects, + "sensors": sensors, + "environment": environment, + } + + +def test_controller_preserves_initial_observation_and_appends_return_value(): + initial_observation = {"deployment": 0.0} + interactive_object = InteractiveObject("Air brakes") + controller = _Controller( + interactive_objects=[interactive_object], + controller_function=controller_function, + sampling_rate=20, + initial_observed_variables=initial_observation, + name="Air-brake controller", + ) + + state = [0.0] * 13 + history = [[0.0] + state] + sensors = [object()] + environment = object() + controller(0.25, state, history, sensors, environment) + + assert controller.observed_variables == [ + initial_observation, + { + "time": 0.25, + "sampling_rate": 20, + "state": state, + "history": history, + "previous": initial_observation, + "objects": [interactive_object], + "sensors": sensors, + "environment": environment, + }, + ] + + +def test_controller_info_reports_discrete_rate_and_each_interactive_object(capsys): + controller = _Controller( + interactive_objects=[ + InteractiveObject("Left air brake"), + InteractiveObject("Right air brake"), + ], + controller_function=controller_function, + sampling_rate=4, + name="Roll controller", + ) + + controller.all_info() + + assert capsys.readouterr().out == ( + "\nController Details\n\n" + "Controller 'Roll controller' with sampling rate 4 Hz.\n" + "Controller function: controller_function\n" + "Controller refresh rate: 4.000 Hz\n" + "interactive Objects\n" + "Left air brake\n" + "Right air brake\n" + ) + + +def test_controller_info_reports_continuous_rate_and_single_object(capsys): + controller = _Controller( + interactive_objects=InteractiveObject("Thrust vector actuator"), + controller_function=controller_function, + sampling_rate=None, + name="Pitch controller", + ) + + controller.info() + + assert capsys.readouterr().out == ( + "\nController Details\n\n" + "Controller 'Pitch controller' with continuous sampling.\n" + "Controller function: controller_function\n" + "Controller refresh rate: continuous (every solver step)\n" + "interactive Objects\n" + "Thrust vector actuator\n" + ) + + +def test_controller_to_dict_without_pickle_uses_function_name_and_object_hashes(): + interactive_objects = [InteractiveObject("A"), InteractiveObject("B")] + controller = _Controller( + interactive_objects=interactive_objects, + controller_function=controller_function, + sampling_rate=8, + initial_observed_variables=[0.0], + name="Test controller", + ) + + data = controller.to_dict(allow_pickle=False) + + assert data == { + "controller_function": "controller_function", + "sampling_rate": 8, + "initial_observed_variables": [0.0], + "name": "Test controller", + "_interactive_objects_hash": [hash(obj) for obj in interactive_objects], + } + + +def test_controller_to_dict_hashes_a_single_interactive_object(): + interactive_object = InteractiveObject("Actuator") + controller = _Controller( + interactive_objects=interactive_object, + controller_function=controller_function, + ) + + assert controller.to_dict(allow_pickle=False)["_interactive_objects_hash"] == hash( + interactive_object + ) + + +def test_controller_from_dict_accepts_an_existing_callable(): + data = { + "interactive_objects": [InteractiveObject("Actuator")], + "controller_function": controller_function, + "sampling_rate": 5, + "initial_observed_variables": [1.0], + "name": "Restored controller", + "_interactive_objects_hash": [1234], + } + + restored = _Controller.from_dict(data) + + assert restored.base_controller_function is controller_function + assert restored.sampling_rate == 5 + assert restored.initial_observed_variables == [1.0] + assert restored.name == "Restored controller" + assert restored._interactive_objects_hash == [1234] diff --git a/tests/unit/environment/test_atmosphere_cache.py b/tests/unit/environment/test_atmosphere_cache.py new file mode 100644 index 000000000..a0b8bf250 --- /dev/null +++ b/tests/unit/environment/test_atmosphere_cache.py @@ -0,0 +1,575 @@ +"""Unit tests for atmosphere netCDF/JSON disk caching (#654).""" + +import time +import warnings +from datetime import datetime +from unittest.mock import MagicMock + +import netCDF4 +import numpy as np +import pytest + +from rocketpy import Environment +from rocketpy.environment import atmosphere_cache + + +def _write_minimal_profile_nc(path, elevation=1400.0): + """Create a tiny valid profile cache file for apply tests.""" + height = np.array([1400.0, 5000.0, 10000.0]) + pressure = np.array([85000.0, 54000.0, 26500.0]) + temperature = np.array([288.0, 255.0, 223.0]) + wind_u = np.array([1.0, 2.0, 3.0]) + wind_v = np.array([-1.0, 0.0, 1.0]) + assert atmosphere_cache.write_profile_netcdf( + path, + height=height, + pressure=pressure, + temperature=temperature, + wind_u=wind_u, + wind_v=wind_v, + elevation=elevation, + max_expected_height=10000.0, + kind="forecast", + ) + + +def test_cache_root_honors_rocketpy_cache_env(monkeypatch, tmp_path): + """``ROCKETPY_CACHE`` redirects the atmosphere cache root.""" + monkeypatch.setenv("ROCKETPY_CACHE", str(tmp_path)) + assert atmosphere_cache.get_cache_root() == tmp_path + assert atmosphere_cache.get_atmosphere_cache_dir() == tmp_path / "atmosphere" + + +def test_profile_netcdf_roundtrip(monkeypatch, tmp_path): + """Write and read forecast profile netCDF through the cache helpers.""" + monkeypatch.setenv("ROCKETPY_CACHE", str(tmp_path)) + path = atmosphere_cache.cache_path_for("forecast_test_key", ".nc") + _write_minimal_profile_nc(path) + loaded = atmosphere_cache.read_profile_netcdf(path) + assert loaded is not None + assert loaded["elevation"] == pytest.approx(1400.0) + np.testing.assert_allclose(loaded["height"], [1400.0, 5000.0, 10000.0]) + np.testing.assert_allclose(loaded["pressure"], [85000.0, 54000.0, 26500.0]) + + +def test_json_cache_roundtrip(monkeypatch, tmp_path): + """Windy-style JSON cache round-trips through disk.""" + monkeypatch.setenv("ROCKETPY_CACHE", str(tmp_path)) + path = atmosphere_cache.cache_path_for("windy_test_key", ".json") + payload = {"data": {"hours": [1, 2, 3], "temp-surface": [288]}} + assert atmosphere_cache.save_json_cache(path, payload) + assert atmosphere_cache.load_json_cache(path) == payload + + +def test_forecast_shortcut_reuses_disk_cache(monkeypatch, tmp_path): + """Second Forecast shortcut call loads profiles from disk (no re-fetch).""" + monkeypatch.setenv("ROCKETPY_CACHE", str(tmp_path)) + fixture = "data/weather/SpaceportAmerica_2018_ERA-5.nc" + fetch_calls = [] + + def fake_fetch(): + fetch_calls.append(1) + return netCDF4.Dataset(fixture) + + env = Environment( + latitude=32.990254, + longitude=-106.974998, + elevation=1400, + datum="WGS84", + ) + env.set_date((2018, 10, 15, 12)) + env._Environment__atm_type_file_to_function_map["forecast"]["GFS"] = fake_fetch + + env.set_atmospheric_model( + type="Forecast", + file="GFS", + dictionary="ECMWF", + pressure_conversion_factor="hPa", + ) + assert len(fetch_calls) == 1 + pressure_first = env.pressure(env.elevation) + cached_files = list((tmp_path / "atmosphere").glob("*.nc")) + assert cached_files, "Expected a profile .nc cache file after first fetch" + + env2 = Environment( + latitude=32.990254, + longitude=-106.974998, + elevation=1400, + datum="WGS84", + ) + env2.set_date((2018, 10, 15, 12)) + env2._Environment__atm_type_file_to_function_map["forecast"]["GFS"] = fake_fetch + env2.set_atmospheric_model( + type="Forecast", + file="GFS", + dictionary="ECMWF", + pressure_conversion_factor="hPa", + ) + assert len(fetch_calls) == 1, "Second call should reuse disk cache" + assert env2.pressure(env2.elevation) == pytest.approx(pressure_first, rel=1e-6) + + +def test_forecast_no_cache_bypasses_disk(monkeypatch, tmp_path): + """``no_cache=True`` forces a re-fetch even when a cache file exists.""" + monkeypatch.setenv("ROCKETPY_CACHE", str(tmp_path)) + fixture = "data/weather/SpaceportAmerica_2018_ERA-5.nc" + fetch_calls = [] + + def fake_fetch(): + fetch_calls.append(1) + return netCDF4.Dataset(fixture) + + env = Environment( + latitude=32.990254, + longitude=-106.974998, + elevation=1400, + datum="WGS84", + ) + env.set_date((2018, 10, 15, 12)) + env._Environment__atm_type_file_to_function_map["forecast"]["GFS"] = fake_fetch + + env.set_atmospheric_model( + type="Forecast", + file="GFS", + dictionary="ECMWF", + pressure_conversion_factor="hPa", + ) + env.set_atmospheric_model( + type="Forecast", + file="GFS", + dictionary="ECMWF", + pressure_conversion_factor="hPa", + no_cache=True, + ) + assert len(fetch_calls) == 2 + + +def test_windy_json_cache_hit(monkeypatch, tmp_path): + """Windy response is cached as JSON; second call skips the network.""" + monkeypatch.setenv("ROCKETPY_CACHE", str(tmp_path)) + + # Minimal Windy payload matching __parse_windy_file expectations. + levels = [1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150] + payload = { + "header": {"elevation": 1234.0}, + "data": { + "hours": [1_540_000_000_000, 1_540_003_600_000], + }, + } + for level in levels: + # Geopotential heights increasing with altitude (decreasing pressure). + payload["data"][f"gh-{level}h"] = [ + float(2000 + (1000 - level) * 10), + float(2000 + (1000 - level) * 10), + ] + payload["data"][f"temp-{level}h"] = [280.0, 281.0] + payload["data"][f"wind_u-{level}h"] = [1.0, 1.5] + payload["data"][f"wind_v-{level}h"] = [-1.0, -0.5] + + fetch_mock = MagicMock(return_value=payload) + monkeypatch.setattr( + "rocketpy.environment.environment.fetch_atmospheric_data_from_windy", + fetch_mock, + ) + + env = Environment(latitude=45.0, longitude=10.0, elevation=100) + env.set_date(datetime(2018, 10, 15, 12)) + env.set_atmospheric_model(type="Windy", file="ECMWF") + assert fetch_mock.call_count == 1 + assert list((tmp_path / "atmosphere").glob("*.json")) + + env2 = Environment(latitude=45.0, longitude=10.0, elevation=100) + env2.set_date(datetime(2018, 10, 15, 12)) + env2.set_atmospheric_model(type="Windy", file="ECMWF") + assert fetch_mock.call_count == 1 + + env3 = Environment(latitude=45.0, longitude=10.0, elevation=100) + env3.set_date(datetime(2018, 10, 15, 12)) + env3.set_atmospheric_model(type="Windy", file="ECMWF", no_cache=True) + assert fetch_mock.call_count == 2 + + +# --------------------------------------------------------------------------- +# Regression tests for the cache-hit / fresh-fetch parity contract +# --------------------------------------------------------------------------- + + +#: Attributes ``Environment`` derives from the dataset. A cache hit must +#: reproduce every one of them, otherwise ``info()`` and ``to_dict()`` break on +#: the second run of an otherwise identical script. +DERIVED_MODEL_ATTRIBUTES = [ + "atmospheric_model_init_date", + "atmospheric_model_end_date", + "atmospheric_model_interval", + "atmospheric_model_init_lat", + "atmospheric_model_end_lat", + "atmospheric_model_init_lon", + "atmospheric_model_end_lon", + "lat_array", + "lon_array", + "lat_index", + "lon_index", + "geopotentials", + "wind_us", + "wind_vs", + "levels", + "temperatures", + "time_array", + "height", +] + + +def _forecast_env(fetch): + """Build an Environment wired to ``fetch`` and load the Forecast model.""" + env = Environment( + latitude=32.990254, + longitude=-106.974998, + elevation=1400, + datum="WGS84", + ) + env.set_date((2018, 10, 15, 12)) + env._Environment__atm_type_file_to_function_map["forecast"]["GFS"] = fetch + env.set_atmospheric_model( + type="Forecast", + file="GFS", + dictionary="ECMWF", + pressure_conversion_factor="hPa", + ) + return env + + +def _counting_fetch(calls, fixture="data/weather/SpaceportAmerica_2018_ERA-5.nc"): + def fetch(): + calls.append(1) + return netCDF4.Dataset(fixture) + + return fetch + + +def test_cache_hit_restores_every_derived_attribute(monkeypatch, tmp_path): + """A cache hit must rebuild the same Environment a fresh fetch produces.""" + monkeypatch.setenv("ROCKETPY_CACHE", str(tmp_path)) + calls = [] + fetch = _counting_fetch(calls) + + fresh = _forecast_env(fetch) + cached = _forecast_env(fetch) + assert len(calls) == 1, "second call should have been served from disk" + + for name in DERIVED_MODEL_ATTRIBUTES: + expected = getattr(fresh, name) + actual = getattr(cached, name, None) + assert actual is not None, f"'{name}' was lost on the cached path" + if isinstance(expected, datetime): + assert actual == expected, name + else: + np.testing.assert_allclose( + np.ma.filled(np.ma.asarray(actual, dtype=float), np.nan), + np.ma.filled(np.ma.asarray(expected, dtype=float), np.nan), + rtol=1e-10, + err_msg=name, + ) + + +def test_cache_hit_environment_can_print_info(monkeypatch, tmp_path): + """``info()`` used to raise AttributeError on the second (cached) run.""" + monkeypatch.setenv("ROCKETPY_CACHE", str(tmp_path)) + calls = [] + fetch = _counting_fetch(calls) + + _forecast_env(fetch) + cached = _forecast_env(fetch) + + assert len(calls) == 1 + cached.info() # must not raise + + +def test_cache_hit_matches_fresh_profiles(monkeypatch, tmp_path): + """Profiles served from disk are numerically identical to a fresh fetch.""" + monkeypatch.setenv("ROCKETPY_CACHE", str(tmp_path)) + calls = [] + fetch = _counting_fetch(calls) + + fresh = _forecast_env(fetch) + cached = _forecast_env(fetch) + + heights = np.linspace(fresh.elevation, fresh.max_expected_height, 25) + for name in ( + "pressure", + "temperature", + "wind_velocity_x", + "wind_velocity_y", + "wind_speed", + "wind_heading", + "wind_direction", + ): + np.testing.assert_allclose( + [getattr(cached, name)(h) for h in heights], + [getattr(fresh, name)(h) for h in heights], + rtol=1e-10, + atol=1e-10, + err_msg=name, + ) + + +def test_constant_wind_profile_does_not_break_caching(monkeypatch, tmp_path): + """Saving must tolerate scalar profiles instead of raising IndexError. + + ``set_atmospheric_model`` used to index every profile as a 2-D array while + only checking ``pressure``, so a constant wind blew up the cache write. + """ + monkeypatch.setenv("ROCKETPY_CACHE", str(tmp_path)) + env = Environment(latitude=0, longitude=0, elevation=0) + env._Environment__atm_type_file_to_function_map = { + "forecast": {"GFS": lambda: "fake-dataset"}, + "ensemble": {}, + } + env.process_forecast_reanalysis = lambda dataset, dictionary, conversion_factor: ( + None + ) + + env.set_atmospheric_model(type="Forecast", file="gfs") # must not raise + + assert not list((tmp_path / "atmosphere").glob("*.nc")), ( + "nothing worth caching should have been written" + ) + + +def test_cache_disabled_by_environment_variable(monkeypatch, tmp_path): + """``ROCKETPY_CACHE=0`` turns the disk cache off entirely.""" + monkeypatch.setenv("ROCKETPY_CACHE", "0") + assert atmosphere_cache.is_cache_enabled() is False + + monkeypatch.setenv("ROCKETPY_CACHE", str(tmp_path)) + assert atmosphere_cache.is_cache_enabled() is True + + +def test_disabled_cache_refetches_every_time(monkeypatch, tmp_path): + """With caching off, a repeated request hits the network again.""" + monkeypatch.setenv("ROCKETPY_CACHE", "off") + calls = [] + fetch = _counting_fetch(calls) + + _forecast_env(fetch) + _forecast_env(fetch) + + assert len(calls) == 2 + assert not list(tmp_path.rglob("*.nc")) + + +def test_expired_forecast_entry_is_refetched(monkeypatch, tmp_path): + """Forecast entries older than the TTL are discarded, not served.""" + monkeypatch.setenv("ROCKETPY_CACHE", str(tmp_path)) + monkeypatch.setenv("ROCKETPY_CACHE_TTL", "3600") + calls = [] + fetch = _counting_fetch(calls) + + _forecast_env(fetch) + assert len(calls) == 1 + + # Pretend the entry was written two hours ago. The real clock has to be + # sampled before patching, or the stub would call itself. + two_hours_from_now = time.time() + 7200 + monkeypatch.setattr(atmosphere_cache.time, "time", lambda: two_hours_from_now) + _forecast_env(fetch) + assert len(calls) == 2, "a stale forecast must not be reused" + + +def test_zero_ttl_disables_expiry(monkeypatch): + """``ROCKETPY_CACHE_TTL=0`` keeps entries forever.""" + monkeypatch.setenv("ROCKETPY_CACHE_TTL", "0") + assert atmosphere_cache.is_entry_expired(0.0, "forecast") is False + + +def test_reanalysis_entries_never_expire(monkeypatch): + """Reanalysis data is immutable, so the TTL does not apply to it.""" + monkeypatch.setenv("ROCKETPY_CACHE_TTL", "1") + assert atmosphere_cache.is_entry_expired(0.0, "reanalysis") is False + assert atmosphere_cache.is_entry_expired(0.0, "forecast") is True + + +def test_different_dictionary_uses_a_separate_entry(monkeypatch, tmp_path): + """The same source decoded with another dictionary must not collide.""" + monkeypatch.setenv("ROCKETPY_CACHE", str(tmp_path)) + calls = [] + fetch = _counting_fetch(calls) + + _forecast_env(fetch) + + env = Environment( + latitude=32.990254, longitude=-106.974998, elevation=1400, datum="WGS84" + ) + env.set_date((2018, 10, 15, 12)) + env._Environment__atm_type_file_to_function_map["forecast"]["GFS"] = fetch + env.set_atmospheric_model( + type="Forecast", + file="GFS", + dictionary="ECMWF_v0", + pressure_conversion_factor="hPa", + ) + + assert len(calls) == 2, "a different dictionary must miss the cache" + assert len(list((tmp_path / "atmosphere").glob("*.nc"))) == 2 + + +def test_corrupt_cache_file_falls_back_to_fetch(monkeypatch, tmp_path): + """A damaged cache file degrades to a re-fetch instead of raising.""" + monkeypatch.setenv("ROCKETPY_CACHE", str(tmp_path)) + calls = [] + fetch = _counting_fetch(calls) + + _forecast_env(fetch) + cached_file = next((tmp_path / "atmosphere").glob("*.nc")) + cached_file.write_bytes(b"this is not a netCDF file") + + with pytest.warns(UserWarning): + _forecast_env(fetch) + + assert len(calls) == 2 + + +def test_clear_atmosphere_cache_removes_entries(monkeypatch, tmp_path): + """``clear_atmosphere_cache`` empties the cache directory.""" + monkeypatch.setenv("ROCKETPY_CACHE", str(tmp_path)) + path = atmosphere_cache.cache_path_for("forecast_clear_me", ".nc") + _write_minimal_profile_nc(path) + assert path.is_file() + + assert atmosphere_cache.clear_atmosphere_cache() is True + assert not path.is_file() + # Clearing an already-absent cache is not an error. + assert atmosphere_cache.clear_atmosphere_cache() is True + + +def test_json_cache_respects_ttl(monkeypatch, tmp_path): + """Windy JSON entries expire like the netCDF ones.""" + monkeypatch.setenv("ROCKETPY_CACHE", str(tmp_path)) + monkeypatch.setenv("ROCKETPY_CACHE_TTL", "3600") + path = atmosphere_cache.cache_path_for("windy_ttl", ".json") + payload = {"data": {"hours": [1, 2, 3]}} + + assert atmosphere_cache.save_json_cache(path, payload) + assert atmosphere_cache.load_json_cache(path) == payload + + two_hours_from_now = time.time() + 7200 + monkeypatch.setattr(atmosphere_cache.time, "time", lambda: two_hours_from_now) + assert atmosphere_cache.load_json_cache(path) is None + + +def test_mismatched_profiles_are_skipped_without_warning(monkeypatch, tmp_path): + """A scalar wind profile is skipped cleanly, not written and not warned about. + + The save path used to check only ``pressure`` before slicing all four + profiles as 2-D arrays, so a constant wind raised ``IndexError``. Guarding + only ``pressure`` is not enough either: ``np.asarray(None, dtype=float)`` + silently yields ``nan``, so a missing column would be persisted as a cache + entry full of NaN winds. Checking every column keeps the write from being + attempted at all, which is why this asserts on the absence of a warning and + not just of a file. + """ + monkeypatch.setenv("ROCKETPY_CACHE", str(tmp_path)) + env = Environment(latitude=0, longitude=0, elevation=0) + env.set_atmospheric_model( + type="custom_atmosphere", + pressure=[[0.0, 101325.0], [1000.0, 89875.0]], + temperature=[[0.0, 288.0], [1000.0, 281.0]], + wind_u=5, + wind_v=-3, + ) + assert env.pressure.is_array_source() + assert not env.wind_velocity_x.is_array_source() + + path = atmosphere_cache.cache_path_for("forecast_mismatched", ".nc") + with warnings.catch_warnings(): + warnings.simplefilter("error") + env._Environment__save_forecast_profiles_to_cache(path) + + assert not path.exists() + + +# --------------------------------------------------------------------------- +# The cache swallows unusable-file errors, and nothing else +# --------------------------------------------------------------------------- + + +def test_unexpected_error_while_opening_is_not_swallowed(monkeypatch, tmp_path): + """A defect inside the cache must surface, not look like a cache miss.""" + monkeypatch.setenv("ROCKETPY_CACHE", str(tmp_path)) + path = atmosphere_cache.cache_path_for("forecast_boom", ".nc") + _write_minimal_profile_nc(path) + + def explode(*_args, **_kwargs): + raise ZeroDivisionError("a bug, not a damaged file") + + monkeypatch.setattr(atmosphere_cache.netCDF4, "Dataset", explode) + + with pytest.raises(ZeroDivisionError): + atmosphere_cache.read_profile_netcdf(path) + + +def test_unexpected_error_while_reading_is_not_swallowed(monkeypatch, tmp_path): + """Same contract for failures after the file has been opened.""" + monkeypatch.setenv("ROCKETPY_CACHE", str(tmp_path)) + path = atmosphere_cache.cache_path_for("forecast_boom_read", ".nc") + _write_minimal_profile_nc(path) + + def explode(_dataset): + raise ZeroDivisionError("a bug, not a damaged file") + + monkeypatch.setattr(atmosphere_cache, "_read_metadata", explode) + + with pytest.raises(ZeroDivisionError): + atmosphere_cache.read_profile_netcdf(path) + + +@pytest.mark.parametrize( + "error", + [ + OSError("damaged file"), + RuntimeError("dataset is closed"), + ValueError("wrong length"), + TypeError("bad dtype"), + KeyError("missing variable"), + AttributeError("missing attribute"), + ], +) +def test_unusable_cache_file_degrades_to_a_miss(monkeypatch, tmp_path, error): + """Every way netCDF4 reports an unusable file must become a cache miss.""" + monkeypatch.setenv("ROCKETPY_CACHE", str(tmp_path)) + path = atmosphere_cache.cache_path_for("forecast_unusable", ".nc") + _write_minimal_profile_nc(path) + + def explode(_dataset): + raise error + + monkeypatch.setattr(atmosphere_cache, "_read_metadata", explode) + + with pytest.warns(UserWarning): + assert atmosphere_cache.read_profile_netcdf(path) is None + + +def test_write_failure_degrades_to_no_cache_entry(monkeypatch, tmp_path): + """A failed write warns and reports False instead of raising.""" + monkeypatch.setenv("ROCKETPY_CACHE", str(tmp_path)) + path = atmosphere_cache.cache_path_for("forecast_writefail", ".nc") + + def explode(_dataset, _metadata): + raise OSError("disk full") + + monkeypatch.setattr(atmosphere_cache, "_write_metadata", explode) + + with pytest.warns(UserWarning): + written = atmosphere_cache.write_profile_netcdf( + path, + height=np.array([0.0, 1000.0]), + pressure=np.array([101325.0, 89875.0]), + temperature=np.array([288.0, 281.0]), + wind_u=np.array([1.0, 2.0]), + wind_v=np.array([0.0, 1.0]), + elevation=0.0, + max_expected_height=1000.0, + ) + + assert written is False + assert not path.exists() + assert not list(path.parent.glob("*.tmp")), "temporary file was left behind" diff --git a/tests/unit/environment/test_environment.py b/tests/unit/environment/test_environment.py index bbb72573c..61d6c3ff4 100644 --- a/tests/unit/environment/test_environment.py +++ b/tests/unit/environment/test_environment.py @@ -4,21 +4,308 @@ import numpy as np import numpy.testing as npt +import netCDF4 import pytest import pytz -from rocketpy import Environment +from rocketpy import Environment, Function from rocketpy.environment.tools import ( find_longitude_index, geodesic_to_lambert_conformal, geodesic_to_utm, get_final_date_from_time_array, get_initial_date_from_time_array, + get_interval_date_from_time_array, get_pressure_levels_from_file, pressure_unit_to_factor, utm_to_geodesic, ) from rocketpy.environment.weather_model_mapping import WeatherModelMapping +from rocketpy.tools import geopotential_height_to_geometric_height + + +def _user_defined_ensemble_profiles(): + """Return two members with a shared isobaric grid.""" + pressure = np.array([101325.0, 90000.0, 80000.0]) + member_0_height = np.array([0.0, 1000.0, 2000.0]) + member_1_height = np.array([100.0, 1100.0, 2100.0]) + return [ + { + "pressure": np.column_stack((member_0_height, pressure)), + "temperature": np.column_stack((member_0_height, [288.0, 281.0, 275.0])), + "wind_u": np.column_stack((member_0_height, [1.0, 2.0, 3.0])), + "wind_v": np.column_stack((member_0_height, [-1.0, -2.0, -3.0])), + }, + { + "pressure": np.column_stack((member_1_height, pressure)), + "temperature": np.column_stack((member_1_height, [290.0, 283.0, 277.0])), + "wind_u": np.column_stack((member_1_height, [4.0, 5.0, 6.0])), + "wind_v": np.column_stack((member_1_height, [-4.0, -5.0, -6.0])), + }, + ] + + +def test_time_array_interval_helper_accepts_a_single_time(): + """A static user ensemble has no forecast interval.""" + + class SingleTimeArray: + """Minimal single-value NetCDF-like time coordinate.""" + + units = "hours since 2025-06-01 12:00:00" + + def __len__(self): + return 1 + + assert get_interval_date_from_time_array(SingleTimeArray()) == 0 + + +def test_create_ensemble_exports_and_activates_profiles(tmp_path): + """Export user profiles and expose each member through Environment.""" + # Arrange + env = Environment( + date=(2025, 6, 1, 12), + latitude=32.99, + longitude=-106.97, + elevation=0, + ) + output = tmp_path / "test_ensemble" + + # Act + file_path = env.create_ensemble(_user_defined_ensemble_profiles(), file_name=output) + + # Assert + assert file_path == str(output) + ".nc" + assert env.atmospheric_model_type == "Ensemble" + assert env.num_ensemble_members == 2 + assert env.ensemble_member == 0 + assert env.pressure(1000) == pytest.approx(90000) + assert env.temperature(1000) == pytest.approx(281) + assert env.wind_velocity_x(1000) == pytest.approx(2) + + env.select_ensemble_member(1) + assert env.pressure(1100) == pytest.approx(90000) + assert env.temperature(1100) == pytest.approx(283) + assert env.wind_velocity_x(1100) == pytest.approx(5) + assert env.wind_velocity_y(1100) == pytest.approx(-5) + + with netCDF4.Dataset(file_path) as dataset: + assert dataset.Conventions == "CF-1.8" + assert dataset.source == "RocketPy Environment.create_ensemble" + assert dataset.variables["time"].long_name == "profile valid time" + assert { + name: len(dataset.dimensions[name]) for name in ("ens", "lev", "time") + } == {"ens": 2, "lev": 3, "time": 1} + assert dataset.variables["lev"].units == "hPa" + assert dataset.variables["tmpprs"].standard_name == "air_temperature" + npt.assert_allclose(dataset.variables["lev"][:], [1013.25, 900, 800]) + + +def test_create_ensemble_file_round_trip(tmp_path): + """Reload the exported file using the existing GEFS ensemble mapping.""" + # Arrange + source_env = Environment(date=(2025, 6, 1, 12), latitude=32.99, longitude=-106.97) + file_path = source_env.create_ensemble( + _user_defined_ensemble_profiles(), file_name=tmp_path / "round_trip.nc" + ) + loaded_env = Environment(date=(2025, 6, 1, 12), latitude=32.99, longitude=-106.97) + + # Act + loaded_env.set_atmospheric_model(type="Ensemble", file=file_path, dictionary="GEFS") + loaded_env.select_ensemble_member(1) + + # Assert + assert loaded_env.num_ensemble_members == 2 + assert loaded_env.pressure(1100) == pytest.approx(90000) + assert loaded_env.temperature(1100) == pytest.approx(283) + assert loaded_env.wind_velocity_x(1100) == pytest.approx(5) + assert loaded_env.wind_velocity_y(1100) == pytest.approx(-5) + + +def test_create_ensemble_rejects_non_overlapping_pressure_profiles(tmp_path): + """Reject members that cannot be sampled on a common pressure grid.""" + # Arrange + profiles = _user_defined_ensemble_profiles() + heights = profiles[1]["pressure"][:, 0] + profiles[1]["pressure"] = np.column_stack((heights, [70000.0, 60000.0, 50000.0])) + + env = Environment(date=(2025, 6, 1, 12), latitude=32.99, longitude=-106.97) + + # Act / Assert + with pytest.raises(ValueError, match="no common pressure range"): + env.create_ensemble(profiles, file_name=tmp_path / "invalid.nc") + + +def test_create_ensemble_does_not_overwrite_by_default(tmp_path): + """Preserve an existing ensemble file unless overwrite is explicit.""" + # Arrange + env = Environment(date=(2025, 6, 1, 12), latitude=32.99, longitude=-106.97) + file_path = env.create_ensemble( + _user_defined_ensemble_profiles(), file_name=tmp_path / "existing.nc" + ) + + # Act / Assert + with pytest.raises(FileExistsError, match="overwrite=True"): + env.create_ensemble(_user_defined_ensemble_profiles(), file_name=file_path) + + +def test_create_ensemble_accepts_array_functions_and_explicit_levels(tmp_path): + """Accept array-backed Functions and sort explicit pressure levels.""" + # Arrange + profiles = _user_defined_ensemble_profiles() + for member in profiles: + for variable, source in member.items(): + member[variable] = Function(source) + env = Environment(date=(2025, 6, 1, 12), latitude=32.99, longitude=-106.97) + + # Act + file_path = env.create_ensemble( + profiles, + file_name=tmp_path / "function_profiles.nc", + pressure_levels=[80000, 101325, 90000], + ) + + # Assert + with netCDF4.Dataset(file_path) as dataset: + npt.assert_allclose(dataset.variables["lev"][:], [1013.25, 900, 800]) + + +@pytest.mark.parametrize( + "source, error, match", + [ + (Function(lambda height: height), TypeError, "array-backed Function"), + (object(), TypeError, "two-column numeric array"), + (np.array([0.0, 1.0]), ValueError, "at least two"), + (np.array([[0.0, 1.0], [1.0, np.inf]]), ValueError, "non-finite"), + (np.array([[0.0, 1.0], [0.0, 2.0]]), ValueError, "heights must be unique"), + ], +) +def test_create_ensemble_rejects_invalid_profile_sources( + tmp_path, source, error, match +): + """Reject profile sources that cannot define a finite height-value curve.""" + # Arrange + profiles = _user_defined_ensemble_profiles() + profiles[0]["wind_u"] = source + env = Environment(date=(2025, 6, 1, 12), latitude=32.99, longitude=-106.97) + + # Act / Assert + with pytest.raises(error, match=match): + env.create_ensemble(profiles, file_name=tmp_path / "invalid_source.nc") + + +def test_create_ensemble_rejects_invalid_profile_collections(tmp_path): + """Reject invalid ensemble containers and incomplete members.""" + # Arrange + env = Environment(date=(2025, 6, 1, 12), latitude=32.99, longitude=-106.97) + profiles = _user_defined_ensemble_profiles() + output = tmp_path / "invalid_collection.nc" + + # Act / Assert + with pytest.raises(TypeError, match="sequence of member mappings"): + env.create_ensemble(profiles[0], file_name=output) + with pytest.raises(TypeError, match="sequence of member mappings"): + env.create_ensemble(1, file_name=output) + with pytest.raises(ValueError, match="At least two"): + env.create_ensemble(profiles[:1], file_name=output) + with pytest.raises(TypeError, match="Member 1 must be a mapping"): + env.create_ensemble([profiles[0], None], file_name=output) + + incomplete_profiles = _user_defined_ensemble_profiles() + incomplete_profiles[1].pop("wind_v") + with pytest.raises(ValueError, match="missing required profile.*wind_v"): + env.create_ensemble(incomplete_profiles, file_name=output) + + +@pytest.mark.parametrize( + "variable, values, match", + [ + ("pressure", [101325.0, 0.0, 80000.0], "pressure values must be positive"), + ( + "pressure", + [101325.0, 80000.0, 90000.0], + "pressure must decrease strictly", + ), + ("temperature", [288.0, 0.0, 275.0], "temperature values must be positive"), + ], +) +def test_create_ensemble_rejects_invalid_profile_values( + tmp_path, variable, values, match +): + """Reject nonphysical pressure and temperature profile values.""" + # Arrange + profiles = _user_defined_ensemble_profiles() + profiles[0][variable][:, 1] = values + env = Environment(date=(2025, 6, 1, 12), latitude=32.99, longitude=-106.97) + + # Act / Assert + with pytest.raises(ValueError, match=match): + env.create_ensemble(profiles, file_name=tmp_path / "invalid_values.nc") + + +@pytest.mark.parametrize( + "pressure_levels, error, match", + [ + (["invalid", "values"], TypeError, "numeric array"), + ([[101325.0, 90000.0]], ValueError, "one-dimensional"), + ([101325.0, np.nan], ValueError, "finite, positive"), + ([101325.0, 101325.0], ValueError, "duplicates"), + ([90000.0], ValueError, "At least two pressure levels"), + ([110000.0, 90000.0], ValueError, "inside the pressure range"), + ], +) +def test_create_ensemble_rejects_invalid_pressure_levels( + tmp_path, pressure_levels, error, match +): + """Reject explicit pressure grids that cannot be shared by all members.""" + # Arrange + env = Environment(date=(2025, 6, 1, 12), latitude=32.99, longitude=-106.97) + + # Act / Assert + with pytest.raises(error, match=match): + env.create_ensemble( + _user_defined_ensemble_profiles(), + file_name=tmp_path / "invalid_levels.nc", + pressure_levels=pressure_levels, + ) + + +def test_create_ensemble_rejects_profiles_without_height_coverage(tmp_path): + """Require every variable to span the common pressure-grid heights.""" + # Arrange + profiles = _user_defined_ensemble_profiles() + profiles[0]["temperature"] = profiles[0]["temperature"][1:] + env = Environment(date=(2025, 6, 1, 12), latitude=32.99, longitude=-106.97) + + # Act / Assert + with pytest.raises(ValueError, match="temperature.*does not cover all heights"): + env.create_ensemble(profiles, file_name=tmp_path / "incomplete_height.nc") + + +def test_create_ensemble_rejects_heights_below_earth_center(tmp_path): + """Reject geometric heights at or below the coordinate singularity.""" + # Arrange + env = Environment(date=(2025, 6, 1, 12), latitude=32.99, longitude=-106.97) + profiles = _user_defined_ensemble_profiles() + invalid_heights = np.array( + [-env.earth_radius - 2000, -env.earth_radius - 1000, -env.earth_radius - 1] + ) + for member in profiles: + for source in member.values(): + source[:, 0] = invalid_heights + + # Act / Assert + with pytest.raises(ValueError, match="greater than -Earth's radius"): + env.create_ensemble(profiles, file_name=tmp_path / "invalid_height.nc") + + +def test_create_ensemble_rejects_invalid_file_name(): + """Require the NetCDF output name to implement the path protocol.""" + # Arrange + env = Environment(date=(2025, 6, 1, 12), latitude=32.99, longitude=-106.97) + + # Act / Assert + with pytest.raises(TypeError, match="string or path-like"): + env.create_ensemble(_user_defined_ensemble_profiles(), file_name=object()) class DummyLambertProjection: @@ -334,7 +621,8 @@ def test_environment_export_environment_exports_valid_environment_json( @pytest.mark.parametrize( - "atmospheric_model_type", ["windy", "forecast", "reanalysis", "ensemble"] + "atmospheric_model_type", + ["windy", "meteomatics", "forecast", "reanalysis", "ensemble"], ) def test_environment_to_dict_from_dict_round_trip_preserves_weather_metadata( example_plain_env, atmospheric_model_type @@ -428,6 +716,173 @@ def test_environment_to_dict_from_dict_round_trip_preserves_weather_metadata( assert restored_env.ensemble_member == env.ensemble_member == 1 +_METEOMATICS_FAKE_PROFILES = { + "temperature": {0: 288.15, 1000: 281.65, 5000: 255.65}, + "pressure": {0: 101325.0, 1000: 89876.0, 5000: 54048.0}, + "wind_u": {0: 1.0, 1000: 3.0, 5000: 8.0}, + "wind_v": {0: -1.0, 1000: -2.0, 5000: -4.0}, +} + + +def _patch_meteomatics_fetcher(monkeypatch, profiles=None, recorder=None): + """Replace the Meteomatics fetcher with an offline fake (no API calls).""" + profiles = _METEOMATICS_FAKE_PROFILES if profiles is None else profiles + + def fake_fetch(**kwargs): + if recorder is not None: + recorder.update(kwargs) + return profiles + + monkeypatch.setattr( + "rocketpy.environment.environment.fetch_atmospheric_data_from_meteomatics", + fake_fetch, + ) + + +def test_meteomatics_atmosphere_sets_profiles(example_euroc_env, monkeypatch): + """Build pressure, temperature and wind profiles from Meteomatics data. + + The fake profiles are indexed by height above ground level, so the + Environment elevation (100 m for the EuRoC fixture) must be added to obtain + heights above sea level. + """ + recorder = {} + _patch_meteomatics_fetcher(monkeypatch, recorder=recorder) + + example_euroc_env.set_atmospheric_model( + type="Meteomatics", file="mix", username="user", password="pass" + ) + + assert example_euroc_env.atmospheric_model_type == "Meteomatics" + # AGL 0 m -> ASL 100 m (the fixture elevation) + assert pytest.approx(101325.0, rel=1e-6) == example_euroc_env.pressure(100) + assert pytest.approx(288.15, rel=1e-6) == example_euroc_env.temperature(100) + assert pytest.approx(1.0) == example_euroc_env.wind_velocity_x(100) + assert pytest.approx(-1.0) == example_euroc_env.wind_velocity_y(100) + assert pytest.approx(np.sqrt(2.0)) == example_euroc_env.wind_speed(100) + assert example_euroc_env.max_expected_height == pytest.approx(5100.0) + # Credentials and model are forwarded to the fetcher. + assert recorder["username"] == "user" + assert recorder["password"] == "pass" + assert recorder["model"] == "mix" + + +def test_meteomatics_non_string_model_raises(example_euroc_env, monkeypatch): + """Reject a non-string model instead of silently querying the default. + + Passing a Dataset or a path as ``file`` by accident must not be coerced to + ``"mix"``, which would quietly query (and charge for) the wrong model. + """ + _patch_meteomatics_fetcher(monkeypatch) + + with pytest.raises(ValueError, match="Invalid Meteomatics model"): + example_euroc_env.set_atmospheric_model( + type="Meteomatics", file=123, username="user", password="pass" + ) + + +def test_meteomatics_reads_credentials_from_environment(example_euroc_env, monkeypatch): + """Fall back to the METEOMATICS_* environment variables for credentials.""" + recorder = {} + _patch_meteomatics_fetcher(monkeypatch, recorder=recorder) + monkeypatch.setenv("METEOMATICS_USERNAME", "env-user") + monkeypatch.setenv("METEOMATICS_PASSWORD", "env-pass") + + example_euroc_env.set_atmospheric_model(type="Meteomatics") + + assert recorder["username"] == "env-user" + assert recorder["password"] == "env-pass" + assert recorder["model"] == "mix" # default model when file is omitted + assert pytest.approx(288.15, rel=1e-6) == example_euroc_env.temperature(100) + + +def test_meteomatics_missing_credentials_raises(example_euroc_env, monkeypatch): + """Raise a clear error when no credentials are available.""" + _patch_meteomatics_fetcher(monkeypatch) + monkeypatch.delenv("METEOMATICS_USERNAME", raising=False) + monkeypatch.delenv("METEOMATICS_PASSWORD", raising=False) + + with pytest.raises(ValueError, match="username and password"): + example_euroc_env.set_atmospheric_model(type="Meteomatics") + + +def test_meteomatics_missing_date_raises(example_plain_env, monkeypatch): + """Raise when the Environment has no launch date set.""" + _patch_meteomatics_fetcher(monkeypatch) + + with pytest.raises(ValueError, match="launch date"): + example_plain_env.set_atmospheric_model( + type="Meteomatics", username="user", password="pass" + ) + + +def test_meteomatics_drops_missing_values_and_intersects_wind_grid( + example_euroc_env, monkeypatch +): + """Drop ``None`` values and keep only wind levels present in both u and v. + + Temperature at 1000 m is ``None`` (dropped), and the wind grids disagree at + 5000 m (only ``wind_u`` has it), so the wind profile must keep only the + common, non-null levels {0, 1000} m AGL. + """ + profiles = { + "temperature": {0: 288.15, 1000: None, 5000: 255.65}, + "pressure": {0: 101325.0, 5000: 54048.0}, + "wind_u": {0: 1.0, 1000: 3.0, 5000: 8.0}, + "wind_v": {0: -1.0, 1000: -2.0}, # missing 5000 -> intersection drops it + } + _patch_meteomatics_fetcher(monkeypatch, profiles=profiles) + + example_euroc_env.set_atmospheric_model( + type="Meteomatics", username="user", password="pass" + ) + + # Wind kept only the two common non-null AGL levels {0, 1000} -> ASL {100, 1100}. + npt.assert_array_equal(example_euroc_env.height, [100.0, 1100.0]) + assert len(example_euroc_env.wind_us) == 2 + # Temperature dropped the None level: {0, 5000} AGL -> ASL {100, 5100}. + assert len(example_euroc_env.temperatures) == 2 + assert pytest.approx(255.65, rel=1e-6) == example_euroc_env.temperature(5100) + assert example_euroc_env.max_expected_height == pytest.approx(5100.0) + + +def test_meteomatics_no_usable_data_raises(example_euroc_env, monkeypatch): + """Raise a clear error when the API returns no usable wind data.""" + profiles = { + "temperature": {0: 288.15}, + "pressure": {0: 101325.0}, + "wind_u": {}, + "wind_v": {}, + } + _patch_meteomatics_fetcher(monkeypatch, profiles=profiles) + + with pytest.raises(ValueError, match="usable atmospheric data"): + example_euroc_env.set_atmospheric_model( + type="Meteomatics", username="user", password="pass" + ) + + +def test_meteomatics_single_level_profile_raises(example_euroc_env, monkeypatch): + """Reject a collapsed grid (one level per profile) up front. + + A single altitude level builds a Function that cannot be evaluated at its + own node, so ``set_atmospheric_model`` must fail immediately with a clear + message rather than succeed and crash later at ``pressure``/``density``. + """ + profiles = { + "temperature": {0: 288.15}, + "pressure": {0: 101325.0}, + "wind_u": {0: 1.0}, + "wind_v": {0: -1.0}, + } + _patch_meteomatics_fetcher(monkeypatch, profiles=profiles) + + with pytest.raises(ValueError, match="at least two valid altitude levels"): + example_euroc_env.set_atmospheric_model( + type="Meteomatics", username="user", password="pass" + ) + + class _DummyDataset: """Small test double that mimics a netCDF dataset variables mapping.""" @@ -830,6 +1285,37 @@ def test_pressure_conversion_factor_autodetect_by_model( assert factor == expected_factor +def test_pressure_isa_discretization_bounds(example_plain_env): + """The pressure_ISA discretization must span the full range of the + Standard Atmosphere model: from the lowest geopotential layer (-2000 m) up + to the highest (80000 m), both converted to geometric height. It must also + be a physically sane pressure curve: altitude strictly increasing, pressure + strictly decreasing, and sea level (0 m) sampled exactly. + """ + + # Act + pressure_isa_function = example_plain_env.pressure_ISA + source_array = pressure_isa_function.source + altitudes = source_array[:, 0] + pressures = source_array[:, 1] + + # Expected min/max geometric heights + earth_radius = example_plain_env.earth_radius + expected_min_height = geopotential_height_to_geometric_height(-2000, earth_radius) + expected_max_height = geopotential_height_to_geometric_height(80000, earth_radius) + + # Assert + assert len(altitudes) == 100 + assert np.isclose(altitudes[0], expected_min_height) + assert np.isclose(altitudes[-1], expected_max_height) + assert expected_min_height < 0 < expected_max_height + # Sea level must be one of the sampled points (split boundary) + assert np.any(np.isclose(altitudes, 0.0)) + # Physical sanity: altitude increasing, pressure decreasing monotonically + assert np.all(np.diff(altitudes) > 0) + assert np.all(np.diff(pressures) < 0) + + @pytest.mark.parametrize( "model, expected_factor", [("GEFS", 100), ("HIRESW", 100), ("GFS", 1), ("AIGFS", 1)], diff --git a/tests/unit/environment/test_environment_analysis.py b/tests/unit/environment/test_environment_analysis.py index 11205ebea..dda5df31b 100644 --- a/tests/unit/environment/test_environment_analysis.py +++ b/tests/unit/environment/test_environment_analysis.py @@ -1,15 +1,53 @@ import os +from datetime import datetime from unittest.mock import patch import matplotlib as plt import pytest +from rocketpy import EnvironmentAnalysis from rocketpy.tools import import_optional_dependency plt.rcParams.update({"figure.max_open_warning": 0}) -@pytest.mark.slow +@patch("rocketpy.environment.environment_analysis._EnvironmentAnalysisPlots") +@patch("rocketpy.environment.environment_analysis._EnvironmentAnalysisPrints") +@patch.object( + EnvironmentAnalysis, + "_EnvironmentAnalysis__check_requirements", +) +@patch( + "rocketpy.environment.environment_analysis.import_optional_dependency", + side_effect=ImportError("timezonefinder is not installed"), +) +def test_missing_timezonefinder_defaults_to_utc( + _mock_import_optional_dependency, + _mock_check_requirements, + _mock_prints, + _mock_plots, +): + """Use UTC when automatic timezone detection is unavailable.""" + # Arrange + start_date = datetime(2026, 1, 1) + end_date = datetime(2026, 1, 2) + + # Act + with pytest.warns(UserWarning, match="defaulting to UTC"): + analysis = EnvironmentAnalysis( + start_date=start_date, + end_date=end_date, + latitude=0, + longitude=0, + timezone=None, + ) + + # Assert + assert analysis.preferred_timezone.zone == "UTC" + assert analysis.start_date.tzinfo is not None + assert analysis.end_date.tzinfo is not None + + @patch("matplotlib.pyplot.show") def test_distribution_plots(mock_show, env_analysis): # pylint: disable=unused-argument """Tests the distribution plots method of the EnvironmentAnalysis class. It @@ -40,7 +78,6 @@ def test_distribution_plots(mock_show, env_analysis): # pylint: disable=unused- ) -@pytest.mark.slow @patch("matplotlib.pyplot.show") def test_average_plots(mock_show, env_analysis): # pylint: disable=unused-argument """Tests the average plots method of the EnvironmentAnalysis class. It @@ -66,7 +103,6 @@ def test_average_plots(mock_show, env_analysis): # pylint: disable=unused-argum assert env_analysis.plots.average_wind_rose_specific_hour(12) is None -@pytest.mark.slow @patch("matplotlib.pyplot.show") def test_profile_plots(mock_show, env_analysis): # pylint: disable=unused-argument """Check the profile plots method of the EnvironmentAnalysis class. It @@ -108,7 +144,6 @@ def test_profile_plots(mock_show, env_analysis): # pylint: disable=unused-argum ) -@pytest.mark.slow def test_values(env_analysis): """Check the numeric properties of the EnvironmentAnalysis class. It computes a few values and compares them to the expected values. Not all the values are @@ -132,7 +167,6 @@ def test_values(env_analysis): assert pytest.approx(env_analysis.std_pressure_at_30000ft, 1e-6) == 38.48947 -@pytest.mark.slow @patch("matplotlib.pyplot.show") def test_animation_plots(mock_show, env_analysis): # pylint: disable=unused-argument """Check the animation plots method of the EnvironmentAnalysis class. It @@ -163,7 +197,6 @@ def test_animation_plots(mock_show, env_analysis): # pylint: disable=unused-arg os.remove("wind_rose.gif") # remove the files created by the method -@pytest.mark.slow def test_pressure_level_wind_profile_uses_velocity_components(env_analysis): """Regression for PR #1041: the redundant per-level ``wind_heading`` and ``wind_direction`` functions were removed from the pressure-level data. diff --git a/tests/unit/environment/test_fetchers.py b/tests/unit/environment/test_fetchers.py index eea06f977..d6c9eb406 100644 --- a/tests/unit/environment/test_fetchers.py +++ b/tests/unit/environment/test_fetchers.py @@ -1,3 +1,5 @@ +from datetime import datetime, timedelta, timezone + import pytest from rocketpy.environment import fetchers @@ -81,3 +83,421 @@ def always_fails(_): fetchers.fetch_rap_file_return_dataset(max_attempts=2, base_delay=2) assert sleep_calls == [2, 4] + + +class _FakeResponse: + """Minimal stand-in for a ``requests.Response`` used in Meteomatics tests.""" + + def __init__(self, payload, status_code=200, text=""): + self._payload = payload + self.status_code = status_code + self.text = text + + @property + def ok(self): + return self.status_code < 400 + + def raise_for_status(self): + if self.status_code >= 400: + raise fetchers.requests.exceptions.HTTPError(f"status {self.status_code}") + + def json(self): + return self._payload + + +def _meteomatics_value_for(parameter): + """Return a deterministic fake value for a Meteomatics parameter string.""" + if parameter.startswith("t_"): + return 288.0 + if parameter.startswith("pressure_"): + return 90000.0 + if parameter.startswith("wind_speed_u_"): + return 4.0 + if parameter.startswith("wind_speed_v_"): + return -2.0 + raise AssertionError(f"unexpected parameter requested: {parameter}") + + +def _make_fake_meteomatics_get(calls, extra_bad_parameter=False, data_status=200): + """Build a fake ``requests.get`` that mimics the Meteomatics endpoints.""" + + def fake_get(url, headers=None, params=None, **_kwargs): + calls.append((url, params)) + if url == fetchers.METEOMATICS_LOGIN_URL: + assert headers is not None and "Authorization" in headers + return _FakeResponse({"access_token": "fake-token"}) + if data_status >= 400: + return _FakeResponse( + {}, status_code=data_status, text="validation error: altitude" + ) + # Data request: parameters are the 5th path segment. + parameters = url.split("/")[4].split(",") + data = [ + { + "parameter": parameter, + "coordinates": [ + {"dates": [{"value": _meteomatics_value_for(parameter)}]} + ], + } + for parameter in parameters + ] + if extra_bad_parameter: + data.append( + { + "parameter": "not_a_known_parameter:xx", + "coordinates": [{"dates": [{"value": 1.0}]}], + } + ) + return _FakeResponse({"data": data}) + + return fake_get + + +def test_fetch_meteomatics_token_success(monkeypatch): + """Return the access token when the login service responds with one.""" + monkeypatch.setattr( + fetchers.requests, "get", lambda *a, **k: _FakeResponse({"access_token": "tok"}) + ) + assert fetchers.fetch_meteomatics_token("user", "pass") == "tok" + + +def test_fetch_meteomatics_token_missing_token_raises(monkeypatch): + """Raise when the login service returns 200 but without a token.""" + monkeypatch.setattr(fetchers.requests, "get", lambda *a, **k: _FakeResponse({})) + with pytest.raises(RuntimeError, match="did not return an access token"): + fetchers.fetch_meteomatics_token("user", "pass") + + +def test_fetch_meteomatics_token_auth_failure_not_retried(monkeypatch): + """A 401/403 is a definitive auth failure: report clearly and do not retry.""" + calls = [] + + def fake_get(*args, **_kwargs): + calls.append(args) + return _FakeResponse({}, status_code=401, text="unauthorized") + + # If a retry happened it would sleep; make that observable instead of slow. + monkeypatch.setattr( + fetchers.time, "sleep", lambda *_: (_ for _ in ()).throw(AssertionError()) + ) + monkeypatch.setattr(fetchers.requests, "get", fake_get) + + with pytest.raises(RuntimeError, match="rejected the credentials"): + fetchers.fetch_meteomatics_token("user", "pass") + assert len(calls) == 1 # no retries + + +def test_fetch_meteomatics_data_groups_and_parses(monkeypatch): + """Group parameters within the query limit and parse the profiles.""" + # Arrange + calls = [] + monkeypatch.setattr(fetchers.requests, "get", _make_fake_meteomatics_get(calls)) + + # Act: distinct wind (fine) and temperature/pressure (coarse) resolutions so + # a fine-vs-coarse grid swap would be detectable. + profiles = fetchers.fetch_atmospheric_data_from_meteomatics( + username="user", + password="pass", + latitude=39.0, + longitude=-8.0, + date=datetime(2024, 1, 1, 12, tzinfo=timezone.utc), + model="mix", + min_altitude=10, + max_altitude=1000, + wind_resolution=3, + temperature_pressure_resolution=2, + query_limit=3, + ) + + # Assert + # 6 wind params (u,v at 3 levels) + 4 temp/pressure params (t,p at 2 levels) + # = 10 params, grouped by 3 -> ceil(10/3) = 4 groups. + data_calls = [c for c in calls if c[0] != fetchers.METEOMATICS_LOGIN_URL] + assert len(calls) == 5 # 1 token + 4 data groups + assert len(data_calls) == 4 + assert all(call[1]["access_token"] == "fake-token" for call in data_calls) + assert all(call[1]["model"] == "mix" for call in data_calls) + + # Wind uses the fine grid (3 levels); temperature/pressure the coarse (2). + assert profiles["temperature"] == {10: 288.0, 1000: 288.0} + assert profiles["pressure"] == {10: 90000.0, 1000: 90000.0} + assert profiles["wind_u"] == {10: 4.0, 505: 4.0, 1000: 4.0} + assert profiles["wind_v"] == {10: -2.0, 505: -2.0, 1000: -2.0} + + +def test_fetch_meteomatics_data_unrecognized_parameter_raises(monkeypatch): + """Raise a ValueError when the response contains an unknown parameter.""" + calls = [] + monkeypatch.setattr( + fetchers.requests, + "get", + _make_fake_meteomatics_get(calls, extra_bad_parameter=True), + ) + with pytest.raises(ValueError, match="Unrecognized Meteomatics parameter"): + fetchers.fetch_atmospheric_data_from_meteomatics( + username="user", + password="pass", + latitude=39.0, + longitude=-8.0, + date=datetime(2024, 1, 1, 12, tzinfo=timezone.utc), + wind_resolution=2, + temperature_pressure_resolution=2, + ) + + +def test_fetch_meteomatics_data_client_error_not_retried(monkeypatch): + """A 4xx data response yields an actionable RuntimeError and is not retried.""" + calls = [] + monkeypatch.setattr( + fetchers.time, "sleep", lambda *_: (_ for _ in ()).throw(AssertionError()) + ) + monkeypatch.setattr( + fetchers.requests, "get", _make_fake_meteomatics_get(calls, data_status=400) + ) + + with pytest.raises(RuntimeError, match="data API request failed"): + fetchers.fetch_atmospheric_data_from_meteomatics( + username="user", + password="pass", + latitude=39.0, + longitude=-8.0, + date=datetime(2024, 1, 1, 12, tzinfo=timezone.utc), + wind_resolution=2, + temperature_pressure_resolution=2, + ) + # 1 token call + exactly 1 data call (the 400 was not retried). + data_calls = [c for c in calls if c[0] != fetchers.METEOMATICS_LOGIN_URL] + assert len(data_calls) == 1 + + +@pytest.mark.parametrize( + "payload", + [ + {}, # missing "data" + {"data": [{"parameter": "t_10m:K", "coordinates": []}]}, # empty coordinates + ], +) +def test_extract_meteomatics_json_bad_structure_raises(payload): + """Turn an unexpected 200 payload into a clear RuntimeError, not KeyError.""" + with pytest.raises(RuntimeError, match="Unexpected Meteomatics response"): + fetchers.MeteomaticsFetcher._extract_json(payload) + + +@pytest.mark.parametrize( + "altitudes", + [ + {"min_altitude": -1, "max_altitude": 1000}, # negative floor + {"min_altitude": 10, "max_altitude": 5}, # max below min + ], +) +def test_fetch_meteomatics_data_invalid_altitude_range_raises(altitudes): + """Reject invalid altitude ranges before making any request.""" + with pytest.raises(ValueError, match="altitude"): + fetchers.fetch_atmospheric_data_from_meteomatics( + username="user", + password="pass", + latitude=39.0, + longitude=-8.0, + date=datetime(2024, 1, 1, 12, tzinfo=timezone.utc), + **altitudes, + ) + + +@pytest.mark.parametrize( + ("kwargs", "message"), + [ + ({"wind_resolution": 1}, "at least"), + ({"temperature_pressure_resolution": 0}, "at least"), + ({"query_limit": 0}, "query_limit must be at least 1"), + ], +) +def test_fetch_meteomatics_data_invalid_sampling_raises(kwargs, message): + """Reject degenerate resolutions and query limits with a clear message. + + Without the up-front check these reach ``linspace``/``range`` and fail with + an opaque low-level error (or an empty request) instead. + """ + with pytest.raises(ValueError, match=message): + fetchers.fetch_atmospheric_data_from_meteomatics( + username="user", + password="pass", + latitude=39.0, + longitude=-8.0, + date=datetime(2024, 1, 1, 12, tzinfo=timezone.utc), + **kwargs, + ) + + +def test_fetch_meteomatics_data_converts_date_to_utc(monkeypatch): + """A non-UTC aware datetime must be converted, not stamped with a bare Z. + + The request path carries the instant with a trailing "Z", so 12:00 at + UTC+03:00 has to be sent as 09:00Z. + """ + calls = [] + monkeypatch.setattr(fetchers.requests, "get", _make_fake_meteomatics_get(calls)) + + fetchers.fetch_atmospheric_data_from_meteomatics( + username="user", + password="pass", + latitude=39.0, + longitude=-8.0, + date=datetime(2024, 1, 1, 12, tzinfo=timezone(timedelta(hours=3))), + wind_resolution=2, + temperature_pressure_resolution=2, + ) + + data_calls = [c for c in calls if c[0] != fetchers.METEOMATICS_LOGIN_URL] + assert data_calls, "expected at least one data request" + assert all("2024-01-01T09:00:00Z" in url for url, _ in data_calls) + + +_FORECAST_MODELS_XML = """ + + + + +""" + +_GFS_COLLECTION_XML = """ + + + + + + + +""" + +_GFS_LATEST_XML = """ + + + +""" + + +class _FakeCatalogResponse: + """Minimal response object for mocked NOAA/THREDDS catalog GETs.""" + + def __init__(self, text, status_code=200): + self.text = text + self.content = text.encode("utf-8") + self.status_code = status_code + + +def _install_noaa_catalog_mocks(monkeypatch, *, include_latest=True): + """Patch ``requests.get`` with canned forecast-model and GFS catalogs.""" + + def fake_get(url, timeout=None, **_kwargs): + del timeout + if url.endswith("forecastModels.xml") or "idd/forecastModels.xml" in url: + return _FakeCatalogResponse(_FORECAST_MODELS_XML) + if url.endswith("/latest.xml"): + if include_latest: + return _FakeCatalogResponse(_GFS_LATEST_XML) + return _FakeCatalogResponse("missing", status_code=404) + if url.endswith("/Global_0p25deg/catalog.xml"): + return _FakeCatalogResponse(_GFS_COLLECTION_XML) + return _FakeCatalogResponse("unexpected", status_code=404) + + monkeypatch.setattr(fetchers.requests, "get", fake_get) + return fake_get + + +def test_list_noaa_atmosphere_datasets_parses_catalog_refs(monkeypatch): + """List NCEP collections from the mocked forecast-models catalog.""" + _install_noaa_catalog_mocks(monkeypatch) + + datasets = fetchers.list_noaa_atmosphere_datasets() + + assert [entry["name"] for entry in datasets] == [ + "GFS Quarter Degree Forecast", + "NAM CONUS 12km from NOAAPORT", + ] + assert datasets[0]["collection_path"] == "grib/NCEP/GFS/Global_0p25deg" + assert datasets[0]["opendap_best_url"].endswith("grib/NCEP/GFS/Global_0p25deg/Best") + + +def test_list_noaa_dataset_identifiers_sorts_runs_newest_first(monkeypatch): + """GFS run identifiers should sort by embedded timestamp, newest first.""" + _install_noaa_catalog_mocks(monkeypatch) + + identifiers = fetchers.list_noaa_dataset_identifiers("GFS") + + assert identifiers[0] == "GFS_Global_0p25deg_20260810_1800.grib2" + assert identifiers[1] == "GFS_Global_0p25deg_20260810_1200.grib2" + assert "Best" in identifiers + + +def test_get_latest_noaa_opendap_url_uses_latest_xml(monkeypatch): + """Prefer THREDDS latest.xml when resolving the newest GFS run.""" + _install_noaa_catalog_mocks(monkeypatch, include_latest=True) + + url = fetchers.get_latest_noaa_opendap_url("GFS") + + assert url == ( + "https://thredds.ucar.edu/thredds/dodsC/" + "grib/NCEP/GFS/Global_0p25deg/GFS_Global_0p25deg_20260810_1800.grib2" + ) + + +def test_get_latest_noaa_dataset_identifier_falls_back_without_latest_xml( + monkeypatch, +): + """When latest.xml is missing, choose the newest listed grib2 run.""" + _install_noaa_catalog_mocks(monkeypatch, include_latest=False) + + identifier = fetchers.get_latest_noaa_dataset_identifier("gfs") + + assert identifier == "GFS_Global_0p25deg_20260810_1800.grib2" + + +def test_fetch_latest_noaa_dataset_opens_resolved_url(monkeypatch): + """fetch_latest_noaa_dataset should open the resolved OPeNDAP URL.""" + _install_noaa_catalog_mocks(monkeypatch) + calls = [] + sentinel = object() + + def fake_dataset(url): + calls.append(url) + return sentinel + + monkeypatch.setattr(fetchers.netCDF4, "Dataset", fake_dataset) + + dataset = fetchers.fetch_latest_noaa_dataset("GFS", max_attempts=2, base_delay=2) + + assert dataset is sentinel + assert calls == [ + "https://thredds.ucar.edu/thredds/dodsC/" + "grib/NCEP/GFS/Global_0p25deg/GFS_Global_0p25deg_20260810_1800.grib2" + ] + + +def test_resolve_noaa_collection_path_rejects_unknown_model(): + """Unknown shortcuts must fail with an actionable ValueError.""" + with pytest.raises(ValueError, match="Unknown NOAA model collection"): + fetchers.resolve_noaa_collection_path("not-a-model") diff --git a/tests/unit/environment/test_open_meteo.py b/tests/unit/environment/test_open_meteo.py new file mode 100644 index 000000000..822aa0c85 --- /dev/null +++ b/tests/unit/environment/test_open_meteo.py @@ -0,0 +1,693 @@ +"""Offline unit tests for the Open-Meteo atmospheric models. + +Every test here patches the fetchers, so no network requests are made. The live +API is exercised by the ``@pytest.mark.slow`` tests in +``tests/integration/environment/test_environment.py``. +""" + +from datetime import datetime, timedelta, timezone + +import numpy as np +import pytest + +from rocketpy import Environment +from rocketpy.environment.fetchers import open_meteo_fetcher +from rocketpy.environment.tools import ( + calculate_wind_heading, + calculate_wind_speed, + convert_wind_heading_to_direction, + convert_wind_speed_direction_to_components, +) + +# Three pressure levels are enough to build a profile and to check the +# hPa -> Pa, Celsius -> Kelvin and speed/direction -> u/v conversions. +FAKE_LEVELS = { + 1000: { + "temperature": 15.0, + "geopotential_height": 100.0, + "wind_speed": 10.0, + "wind_direction": 270.0, # from the west -> blows east -> u > 0 + }, + 850: { + "temperature": 5.0, + "geopotential_height": 1500.0, + "wind_speed": 20.0, + "wind_direction": 0.0, # from the north -> blows south -> v < 0 + }, + 500: { + "temperature": -20.0, + "geopotential_height": 5500.0, + "wind_speed": 30.0, + "wind_direction": 90.0, # from the east -> blows west -> u < 0 + }, +} + +# Two hourly steps, one hour apart, both in the future relative to the fixtures. +FAKE_TIMES = [1_700_000_000, 1_700_003_600] + + +def _build_hourly(levels=None, member_suffixes=("",), times=None, offset=0.0): + """Builds a fake Open-Meteo ``hourly`` payload. + + Parameters + ---------- + levels : dict, optional + Mapping of pressure level (hPa) to its variables. Defaults to + :data:`FAKE_LEVELS`. + member_suffixes : tuple of str, optional + Member suffixes to emit (``""`` for the deterministic/control run). + times : list of int, optional + Unix timestamps for the hourly steps. + offset : float, optional + Value added to every member's temperature and wind speed, multiplied by + the member index, so members differ from one another. + """ + levels = FAKE_LEVELS if levels is None else levels + times = FAKE_TIMES if times is None else times + hourly = {"time": list(times)} + + for member_index, suffix in enumerate(member_suffixes): + shift = offset * member_index + for level, variables in levels.items(): + for name, value in variables.items(): + if value is None: + values = [None] * len(times) + elif name in ("temperature", "wind_speed"): + values = [value + shift] * len(times) + else: + values = [value] * len(times) + hourly[f"{name}_{level}hPa{suffix}"] = values + + return hourly + + +def _build_response(hourly=None, elevation=100.0): + """Builds a fake Open-Meteo JSON response around ``hourly``.""" + return { + "latitude": 39.4, + "longitude": -8.3, + "elevation": elevation, + "hourly": _build_hourly() if hourly is None else hourly, + } + + +def _patch_forecast(monkeypatch, response=None, recorder=None): + """Replaces the Open-Meteo forecast fetcher with an offline fake.""" + payload = _build_response() if response is None else response + + def fake_fetch(latitude, longitude, model="best_match", date=None): + if recorder is not None: + recorder.update( + { + "latitude": latitude, + "longitude": longitude, + "model": model, + "date": date, + } + ) + return payload + + monkeypatch.setattr( + "rocketpy.environment.environment.fetch_open_meteo_forecast", fake_fetch + ) + + +def _patch_ensemble(monkeypatch, response=None, recorder=None): + """Replaces the Open-Meteo ensemble fetcher with an offline fake.""" + payload = _build_response() if response is None else response + + def fake_fetch(latitude, longitude, model="gfs05", date=None): # pylint: disable=unused-argument + if recorder is not None: + recorder.update({"model": model, "date": date}) + return payload + + monkeypatch.setattr( + "rocketpy.environment.environment.fetch_open_meteo_ensemble", fake_fetch + ) + + +class TestWindComponentConversion: + """Tests for convert_wind_speed_direction_to_components.""" + + @pytest.mark.parametrize( + ("direction", "expected_u", "expected_v"), + [ + (0.0, 0.0, -10.0), # from north -> blows south + (90.0, -10.0, 0.0), # from east -> blows west + (180.0, 0.0, 10.0), # from south -> blows north + (270.0, 10.0, 0.0), # from west -> blows east + ], + ) + def test_cardinal_directions(self, direction, expected_u, expected_v): + """Convert the four cardinal wind directions to u/v components.""" + u, v = convert_wind_speed_direction_to_components(10.0, direction) + + assert u == pytest.approx(expected_u, abs=1e-9) + assert v == pytest.approx(expected_v, abs=1e-9) + + @pytest.mark.parametrize("direction", [0.0, 37.0, 135.0, 212.5, 359.0]) + def test_round_trips_back_to_direction(self, direction): + """Recover the original speed and direction from the components.""" + u, v = convert_wind_speed_direction_to_components(12.5, direction) + + assert calculate_wind_speed(u, v) == pytest.approx(12.5) + recovered = convert_wind_heading_to_direction(calculate_wind_heading(u, v)) + assert recovered == pytest.approx(direction, abs=1e-9) + + def test_accepts_arrays(self): + """Convert whole profiles at once, elementwise.""" + speeds = np.array([10.0, 20.0]) + directions = np.array([270.0, 90.0]) + + u, v = convert_wind_speed_direction_to_components(speeds, directions) + + assert u == pytest.approx([10.0, -20.0], abs=1e-9) + assert v == pytest.approx([0.0, 0.0], abs=1e-9) + + +class TestOpenMeteoForecast: + """Tests for the ``open_meteo`` atmospheric model.""" + + def test_builds_profiles_with_unit_conversions(self, example_euroc_env): + """Build pressure, temperature and wind profiles from Open-Meteo data. + + Pressure levels arrive in hPa and temperatures in Celsius, so the + profiles must expose Pa and Kelvin. Heights are geopotential and are + converted to geometric altitude, which is a sub-metre correction at + these levels. + """ + example_euroc_env.set_atmospheric_model(type="open_meteo") + + assert example_euroc_env.atmospheric_model_type == "open_meteo" + # 1000 hPa -> 100 000 Pa, at ~100 m geometric altitude + assert example_euroc_env.pressure(100.0) == pytest.approx(100_000.0, rel=1e-3) + # 15 degC -> 288.15 K + assert example_euroc_env.temperature(100.0) == pytest.approx(288.15, rel=1e-3) + # 500 hPa level, at ~5500 m + assert example_euroc_env.pressure(5500.0) == pytest.approx(50_000.0, rel=1e-3) + assert example_euroc_env.temperature(5500.0) == pytest.approx(253.15, rel=1e-3) + + def test_converts_wind_direction_to_components(self, example_euroc_env): + """Turn the reported speed/direction into RocketPy's u/v components.""" + example_euroc_env.set_atmospheric_model(type="open_meteo") + + # 1000 hPa: 10 m/s from the west -> blows east -> u = +10, v = 0 + assert example_euroc_env.wind_velocity_x(100.0) == pytest.approx(10.0, abs=1e-6) + assert example_euroc_env.wind_velocity_y(100.0) == pytest.approx(0.0, abs=1e-6) + assert example_euroc_env.wind_speed(100.0) == pytest.approx(10.0, abs=1e-6) + # The direction is preserved (the wind still comes from the west). + assert example_euroc_env.wind_direction(100.0) == pytest.approx(270.0, abs=1e-6) + # And the heading points where the wind blows to. + assert example_euroc_env.wind_heading(100.0) == pytest.approx(90.0, abs=1e-6) + + def test_forwards_model_and_date_to_fetcher(self, example_euroc_env, monkeypatch): + """Forward the requested model and the launch date to the fetcher.""" + recorder = {} + _patch_forecast(monkeypatch, recorder=recorder) + + example_euroc_env.set_atmospheric_model(type="open_meteo", file="ecmwf_ifs025") + + assert recorder["model"] == "ecmwf_ifs025" + assert recorder["date"] == example_euroc_env.datetime_date + assert recorder["latitude"] == example_euroc_env.latitude + assert recorder["longitude"] == example_euroc_env.longitude + + def test_defaults_to_best_match_model(self, example_euroc_env, monkeypatch): + """Query the ``best_match`` model when none is given.""" + recorder = {} + _patch_forecast(monkeypatch, recorder=recorder) + + example_euroc_env.set_atmospheric_model(type="open_meteo") + + assert recorder["model"] == "best_match" + + def test_reads_elevation_and_metadata(self, example_euroc_env): + """Take the launch-site elevation and the period from the response.""" + example_euroc_env.set_atmospheric_model(type="open_meteo") + + assert example_euroc_env.elevation == pytest.approx(100.0) + assert example_euroc_env.atmospheric_model_init_lat == pytest.approx(39.4) + assert example_euroc_env.atmospheric_model_init_lon == pytest.approx(-8.3) + # Two steps one hour apart. + assert example_euroc_env.atmospheric_model_interval == pytest.approx(1) + assert example_euroc_env.max_expected_height == pytest.approx(5500.0, rel=1e-3) + + def test_selects_hour_closest_to_launch(self, example_euroc_env, monkeypatch): + """Pick the hourly step nearest to the launch time. + + The launch date is set 40 minutes past the first step, so the first step + is the closest one and its values must be the ones used. + """ + launch = datetime(2024, 6, 1, 12, 40, tzinfo=timezone.utc) + first = datetime(2024, 6, 1, 12, tzinfo=timezone.utc) + second = datetime(2024, 6, 1, 14, tzinfo=timezone.utc) + + levels = { + 1000: { + "temperature": 15.0, + "geopotential_height": 100.0, + "wind_speed": 10.0, + "wind_direction": 270.0, + }, + 500: { + "temperature": -20.0, + "geopotential_height": 5500.0, + "wind_speed": 30.0, + "wind_direction": 90.0, + }, + } + hourly = _build_hourly( + levels=levels, times=[first.timestamp(), second.timestamp()] + ) + # Make the second step unmistakably different from the first one. + hourly["temperature_1000hPa"] = [15.0, 99.0] + _patch_forecast(monkeypatch, response=_build_response(hourly=hourly)) + + example_euroc_env.set_date(launch, timezone="UTC") + example_euroc_env.set_atmospheric_model(type="open_meteo") + + assert example_euroc_env.temperature(100.0) == pytest.approx(288.15, rel=1e-3) + + def test_skips_levels_without_data(self, example_euroc_env, monkeypatch): + """Drop pressure levels the model does not resolve. + + Open-Meteo returns the same set of level keys for every model but fills + only the levels the model actually resolves, so a ``None`` level must be + skipped rather than poison the profile. + """ + levels = { + 1000: FAKE_LEVELS[1000], + 850: {**FAKE_LEVELS[850], "temperature": None}, + 500: FAKE_LEVELS[500], + } + _patch_forecast( + monkeypatch, response=_build_response(hourly=_build_hourly(levels=levels)) + ) + + example_euroc_env.set_atmospheric_model(type="open_meteo") + + # Only the 1000 and 500 hPa levels survive. + assert len(example_euroc_env.levels) == 2 + assert example_euroc_env.levels == pytest.approx([1000.0, 500.0]) + + def test_sorts_levels_by_altitude(self, example_euroc_env, monkeypatch): + """Return a profile monotonic in altitude even if levels arrive unsorted.""" + levels = { + 500: FAKE_LEVELS[500], + 1000: FAKE_LEVELS[1000], + 850: FAKE_LEVELS[850], + } + _patch_forecast( + monkeypatch, response=_build_response(hourly=_build_hourly(levels=levels)) + ) + + example_euroc_env.set_atmospheric_model(type="open_meteo") + + assert np.all(np.diff(example_euroc_env.height) > 0) + # Pressure must decrease as altitude increases. + assert np.all(np.diff(example_euroc_env.pressure.get_source()[:, 1]) < 0) + + def test_single_usable_level_raises(self, example_euroc_env, monkeypatch): + """Refuse a collapsed profile instead of failing later during a flight.""" + levels = { + 1000: FAKE_LEVELS[1000], + 850: {key: None for key in FAKE_LEVELS[850]}, + 500: {key: None for key in FAKE_LEVELS[500]}, + } + _patch_forecast( + monkeypatch, response=_build_response(hourly=_build_hourly(levels=levels)) + ) + + with pytest.raises(ValueError, match="fewer than two usable pressure levels"): + example_euroc_env.set_atmospheric_model(type="open_meteo") + + def test_missing_date_raises(self, example_plain_env, monkeypatch): + """Require a launch date, since the profile is time-dependent.""" + _patch_forecast(monkeypatch) + + with pytest.raises(ValueError, match="specify the launch date"): + example_plain_env.set_atmospheric_model(type="open_meteo") + + def test_computes_derived_profiles(self, example_euroc_env): + """Compute density, speed of sound and viscosity from the new profiles.""" + example_euroc_env.set_atmospheric_model(type="open_meteo") + + # rho = p / (R * T) with R = 287.05 J/(kg K) + expected_density = 100_000.0 / (287.05 * 288.15) + assert example_euroc_env.density(100.0) == pytest.approx( + expected_density, rel=1e-2 + ) + assert example_euroc_env.speed_of_sound(100.0) == pytest.approx(340.3, rel=1e-2) + assert example_euroc_env.dynamic_viscosity(100.0) > 0 + + +class TestOpenMeteoEnsemble: + """Tests for the ``open_meteo_ensemble`` atmospheric model.""" + + @staticmethod + def _ensemble_response(num_members=3, offset=1.0): + """Builds a fake ensemble payload with a control run plus members.""" + suffixes = [""] + [f"_member{index + 1:02d}" for index in range(num_members)] + return _build_response( + hourly=_build_hourly(member_suffixes=suffixes, offset=offset) + ) + + def test_stores_every_member(self, example_euroc_env, monkeypatch): + """Expose the control run plus every perturbed member.""" + _patch_ensemble(monkeypatch, response=self._ensemble_response(num_members=3)) + + example_euroc_env.set_atmospheric_model(type="open_meteo_ensemble") + + # 3 perturbed members plus the unsuffixed control run. + assert example_euroc_env.num_ensemble_members == 4 + assert example_euroc_env.height_ensemble.shape == (4, 3) + assert example_euroc_env.temperature_ensemble.shape == (4, 3) + + def test_control_run_is_member_zero(self, example_euroc_env, monkeypatch): + """Activate the unperturbed control run by default. + + The documented convention is that member 0 is the control member, so the + unsuffixed series must come first. + """ + _patch_ensemble(monkeypatch, response=self._ensemble_response(offset=5.0)) + + example_euroc_env.set_atmospheric_model(type="open_meteo_ensemble") + + assert example_euroc_env.ensemble_member == 0 + # The control run keeps the unshifted temperature (15 degC -> 288.15 K). + assert example_euroc_env.temperature(100.0) == pytest.approx(288.15, rel=1e-3) + + def test_select_ensemble_member_switches_profiles( + self, example_euroc_env, monkeypatch + ): + """Switch the active profile when another member is selected.""" + _patch_ensemble(monkeypatch, response=self._ensemble_response(offset=5.0)) + + example_euroc_env.set_atmospheric_model(type="open_meteo_ensemble") + control_temperature = example_euroc_env.temperature(100.0) + + example_euroc_env.select_ensemble_member(2) + + assert example_euroc_env.ensemble_member == 2 + # Member 2 is shifted by 2 * 5 degC relative to the control run. + assert example_euroc_env.temperature(100.0) == pytest.approx( + control_temperature + 10.0, rel=1e-3 + ) + + def test_out_of_range_member_raises(self, example_euroc_env, monkeypatch): + """Reject a member index beyond the number of members available.""" + _patch_ensemble(monkeypatch, response=self._ensemble_response(num_members=3)) + + example_euroc_env.set_atmospheric_model(type="open_meteo_ensemble") + + with pytest.raises(ValueError, match="Please choose member from 0 to 3"): + example_euroc_env.select_ensemble_member(4) + + def test_levels_are_converted_to_pascal(self, example_euroc_env, monkeypatch): + """Store ensemble pressure levels in Pa, like the netCDF ensembles.""" + _patch_ensemble(monkeypatch, response=self._ensemble_response()) + + example_euroc_env.set_atmospheric_model(type="open_meteo_ensemble") + + assert example_euroc_env.level_ensemble == pytest.approx( + [100_000.0, 85_000.0, 50_000.0] + ) + + def test_payload_without_members_raises(self, example_euroc_env, monkeypatch): + """Fail clearly when the response carries no ensemble members at all.""" + _patch_ensemble(monkeypatch, response=_build_response()) + + with pytest.raises(ValueError, match="did not contain any ensemble members"): + example_euroc_env.set_atmospheric_model(type="open_meteo_ensemble") + + def test_forwards_model_to_fetcher(self, example_euroc_env, monkeypatch): + """Forward the requested ensemble model, defaulting to gfs05.""" + recorder = {} + _patch_ensemble( + monkeypatch, response=self._ensemble_response(), recorder=recorder + ) + + example_euroc_env.set_atmospheric_model( + type="open_meteo_ensemble", file="ecmwf_ifs025" + ) + + assert recorder["model"] == "ecmwf_ifs025" + + def test_missing_date_raises(self, example_plain_env, monkeypatch): + """Require a launch date for the ensemble model as well.""" + _patch_ensemble(monkeypatch, response=self._ensemble_response()) + + with pytest.raises(ValueError, match="specify the launch date"): + example_plain_env.set_atmospheric_model(type="open_meteo_ensemble") + + +class TestOpenMeteoFetchers: + """Tests for the Open-Meteo fetcher helpers (no network access).""" + + def test_build_hourly_variables_covers_every_level(self): + """Request all four variables for every pressure level.""" + hourly = open_meteo_fetcher.build_hourly_variables() + + variables = hourly.split(",") + expected_count = 4 * len(open_meteo_fetcher.OPEN_METEO_PRESSURE_LEVELS) + assert len(variables) == expected_count + assert "temperature_1000hPa" in variables + assert "wind_direction_500hPa" in variables + assert "geopotential_height_30hPa" in variables + + @pytest.mark.parametrize( + "model", + [ + "gfs025", # HTTP 200 but nulls at every pressure level + "icon_global", # same + "bom_access_global_ensemble", # same + "gem_global", # temperature and heights, but no winds at all + ], + ) + def test_rejects_ensemble_models_without_complete_data(self, model): + """Reject ensemble models that cannot produce a full profile. + + These models all answer with HTTP 200, so without an up-front check the + failure would only surface as an opaque parsing error much later. Note + that ``gem_global`` is the subtle one: it publishes temperature and + geopotential height but no pressure-level winds. + """ + with pytest.raises(ValueError, match="Invalid Open-Meteo ensemble model"): + open_meteo_fetcher.fetch_open_meteo_ensemble(0.0, 0.0, model=model) + + def test_accepts_the_supported_ensemble_models(self, monkeypatch): + """Accept the two ensemble models that do publish complete data.""" + monkeypatch.setattr( + open_meteo_fetcher, "_request", lambda url, params, endpoint: {"hourly": {}} + ) + + for model in ("gfs05", "ecmwf_ifs025"): + open_meteo_fetcher.fetch_open_meteo_ensemble(0.0, 0.0, model=model) + + @pytest.mark.parametrize( + ("delta", "expected"), + [ + (timedelta(days=-30), True), + (timedelta(days=-2), True), + (timedelta(hours=-1), False), + (timedelta(days=2), False), + ], + ) + def test_past_date_detection(self, delta, expected): + """Route only genuinely past dates to the historical archive. + + The forecast endpoint keeps a couple of past days available, so a launch + date a few hours ago must stay on the forecast endpoint. + """ + date = datetime.now(timezone.utc) + delta + + assert open_meteo_fetcher._is_past_date(date) is expected + + def test_naive_dates_are_treated_as_utc(self): + """Assume UTC for naive datetimes instead of raising.""" + naive_past = datetime.now(timezone.utc).replace(tzinfo=None) - timedelta(days=5) + + assert open_meteo_fetcher._is_past_date(naive_past) is True + + def test_no_date_uses_forecast_endpoint(self): + """Treat a missing date as a plain forecast request.""" + assert open_meteo_fetcher._is_past_date(None) is False + + def test_past_date_queries_historical_endpoint(self, monkeypatch): + """Send past launch dates to the historical-forecast API. + + Open-Meteo's ERA5 archive endpoint serves surface variables only, so the + historical-forecast API is the only archive that can feed a vertical + profile. + """ + recorder = {} + + def fake_request(url, params, endpoint): + recorder.update({"url": url, "params": params, "endpoint": endpoint}) + return {"hourly": {}} + + monkeypatch.setattr(open_meteo_fetcher, "_request", fake_request) + + open_meteo_fetcher.fetch_open_meteo_forecast( + 39.4, -8.3, date=datetime(2024, 1, 10, 12, tzinfo=timezone.utc) + ) + + assert recorder["url"] == open_meteo_fetcher.OPEN_METEO_HISTORICAL_URL + # A one-day pad on each side keeps the launch hour inside the window. + assert recorder["params"]["start_date"] == "2024-01-09" + assert recorder["params"]["end_date"] == "2024-01-11" + + def test_warns_for_dates_before_the_archive_starts(self, monkeypatch): + """Warn when the launch date predates Open-Meteo's archive. + + Such requests answer with HTTP 200 and nulls at every level, so without + a warning the user would only see a generic "not enough pressure levels" + error with no hint that the date is the problem. + """ + monkeypatch.setattr( + open_meteo_fetcher, "_request", lambda url, params, endpoint: {"hourly": {}} + ) + + with pytest.warns(UserWarning, match="precedes Open-Meteo's"): + open_meteo_fetcher.fetch_open_meteo_forecast( + 39.4, -8.3, date=datetime(2019, 6, 15, tzinfo=timezone.utc) + ) + + def test_does_not_warn_for_supported_past_dates(self, monkeypatch, recwarn): + """Stay silent for past dates the archive does cover.""" + monkeypatch.setattr( + open_meteo_fetcher, "_request", lambda url, params, endpoint: {"hourly": {}} + ) + + open_meteo_fetcher.fetch_open_meteo_forecast( + 39.4, -8.3, date=datetime(2024, 1, 10, tzinfo=timezone.utc) + ) + + assert not [w for w in recwarn if "precedes Open-Meteo" in str(w.message)] + + def test_future_date_queries_forecast_endpoint(self, monkeypatch): + """Send future launch dates to the regular forecast API.""" + recorder = {} + + def fake_request(url, params, endpoint): # pylint: disable=unused-argument + recorder.update({"url": url, "params": params}) + return {"hourly": {}} + + monkeypatch.setattr(open_meteo_fetcher, "_request", fake_request) + + open_meteo_fetcher.fetch_open_meteo_forecast( + 39.4, -8.3, date=datetime.now(timezone.utc) + timedelta(days=2) + ) + + assert recorder["url"] == open_meteo_fetcher.OPEN_METEO_FORECAST_URL + assert "start_date" not in recorder["params"] + + def test_requests_wind_in_metres_per_second(self, monkeypatch): + """Ask for m/s so no unit conversion is needed downstream. + + Open-Meteo defaults to km/h, which would silently inflate wind speeds by + 3.6x if the parameter were dropped. + """ + recorder = {} + + def fake_request(url, params, endpoint): # pylint: disable=unused-argument + recorder.update(params) + return {"hourly": {}} + + monkeypatch.setattr(open_meteo_fetcher, "_request", fake_request) + + open_meteo_fetcher.fetch_open_meteo_forecast(39.4, -8.3) + + assert recorder["wind_speed_unit"] == "ms" + assert recorder["timeformat"] == "unixtime" + assert recorder["timezone"] == "UTC" + + def test_api_error_payload_raises_runtime_error(self, monkeypatch): + """Surface Open-Meteo's own error message instead of a bare status code.""" + + class FakeResponse: + """Stands in for an Open-Meteo error response.""" + + ok = False + status_code = 400 + + @staticmethod + def json(): + return {"error": True, "reason": "Invalid time interval"} + + monkeypatch.setattr( + open_meteo_fetcher, "_get_json", lambda url, params: FakeResponse() + ) + + with pytest.raises(RuntimeError, match="Invalid time interval"): + open_meteo_fetcher.fetch_open_meteo_forecast(39.4, -8.3) + + def test_response_without_hourly_raises_runtime_error(self, monkeypatch): + """Fail clearly when the response carries no hourly block.""" + + class FakeResponse: + """Stands in for a successful response missing its hourly block.""" + + ok = True + status_code = 200 + + @staticmethod + def json(): + return {"latitude": 39.4, "longitude": -8.3} + + monkeypatch.setattr( + open_meteo_fetcher, "_get_json", lambda url, params: FakeResponse() + ) + + with pytest.raises(RuntimeError, match="did not contain any hourly data"): + open_meteo_fetcher.fetch_open_meteo_forecast(39.4, -8.3) + + +class TestOpenMeteoSerialization: + """Tests that Open-Meteo environments survive a to_dict/from_dict cycle.""" + + def test_forecast_round_trip(self, example_euroc_env): + """Preserve profiles and metadata for the deterministic model.""" + example_euroc_env.set_atmospheric_model(type="open_meteo") + + restored = Environment.from_dict(example_euroc_env.to_dict()) + + assert restored.atmospheric_model_type == "open_meteo" + assert restored.pressure(1000.0) == pytest.approx( + example_euroc_env.pressure(1000.0) + ) + assert restored.wind_direction(1000.0) == pytest.approx( + example_euroc_env.wind_direction(1000.0) + ) + assert restored.atmospheric_model_init_lat == pytest.approx( + example_euroc_env.atmospheric_model_init_lat + ) + + def test_ensemble_round_trip(self, example_euroc_env, monkeypatch): + """Preserve every member so selection still works after reloading.""" + _patch_ensemble( + monkeypatch, + response=TestOpenMeteoEnsemble._ensemble_response( + num_members=3, offset=5.0 + ), + ) + example_euroc_env.set_atmospheric_model(type="open_meteo_ensemble") + + restored = Environment.from_dict(example_euroc_env.to_dict()) + + assert restored.num_ensemble_members == 4 + restored.select_ensemble_member(2) + example_euroc_env.select_ensemble_member(2) + assert restored.temperature(100.0) == pytest.approx( + example_euroc_env.temperature(100.0) + ) + + +@pytest.fixture(autouse=True) +def _patch_forecast_fetcher_by_default(monkeypatch): + """Patches the forecast fetcher for every test in this module. + + Keeps the whole module offline: tests that need a custom payload patch the + fetcher again, which simply overrides this one. + """ + _patch_forecast(monkeypatch) diff --git a/tests/unit/motors/test_solidmotor.py b/tests/unit/motors/test_solidmotor.py index 8a1740e96..de1344e0e 100644 --- a/tests/unit/motors/test_solidmotor.py +++ b/tests/unit/motors/test_solidmotor.py @@ -4,7 +4,7 @@ import numpy as np import pytest -from rocketpy import Function +from rocketpy import Function, SolidMotor BURN_TIME = 3.9 GRAIN_NUMBER = 5 @@ -266,6 +266,86 @@ def test_burn_area_asserts_extreme_values(cesaroni_m1670): ) +def _cesaroni_like_kwargs(): + """Shared Cesaroni M1670-like SolidMotor constructor kwargs.""" + return { + "thrust_source": "data/motors/cesaroni/Cesaroni_M1670.eng", + "burn_time": BURN_TIME, + "dry_mass": 1.815, + "dry_inertia": (0.125, 0.125, 0.002), + "center_of_dry_mass_position": 0.317, + "nozzle_position": 0, + "grain_number": GRAIN_NUMBER, + "grain_density": GRAIN_DENSITY, + "nozzle_radius": NOZZLE_RADIUS, + "throat_radius": THROAT_RADIUS, + "grain_separation": GRAIN_SEPARATION, + "grain_outer_radius": GRAIN_OUTER_RADIUS, + "grain_initial_height": GRAIN_INITIAL_HEIGHT, + "grains_center_of_mass_position": 0.397, + "grain_initial_inner_radius": GRAIN_INITIAL_INNER_RADIUS, + "interpolation_method": "linear", + "coordinate_system_orientation": "nozzle_to_combustion_chamber", + } + + +def test_grains_bonded_default_matches_prior_cm(cesaroni_m1670): + """Default grains_bonded=True keeps a fixed propellant CM (prior behavior).""" + assert cesaroni_m1670.grains_bonded is True + assert np.allclose( + cesaroni_m1670.center_of_propellant_mass(0), + cesaroni_m1670.grains_center_of_mass_position, + ) + assert np.allclose( + cesaroni_m1670.center_of_propellant_mass(2.0), + cesaroni_m1670.grains_center_of_mass_position, + ) + + bonded = SolidMotor(**_cesaroni_like_kwargs(), grains_bonded=True) + assert np.allclose( + bonded.center_of_propellant_mass.get_source()[:, 1], + cesaroni_m1670.center_of_propellant_mass.get_source()[:, 1], + ) + + +def test_grains_unbonded_shifts_cm_aft_as_height_regresses(): + """Unbonded multi-grain motors pack aft; CM moves toward the nozzle.""" + kwargs = _cesaroni_like_kwargs() + bonded = SolidMotor(**kwargs, grains_bonded=True) + unbonded = SolidMotor(**kwargs, grains_bonded=False) + + assert unbonded.grains_bonded is False + # At ignition the packed and bonded layouts share the same CM. + assert np.allclose( + unbonded.center_of_propellant_mass(0), + bonded.center_of_propellant_mass(0), + ) + + t = 2.0 + height = unbonded.grain_height(t) + # nozzle_to_combustion_chamber: _csys = +1, aft (toward nozzle) is smaller z. + expected_cm = kwargs["grains_center_of_mass_position"] - (GRAIN_NUMBER / 2.0) * ( + GRAIN_INITIAL_HEIGHT - height + ) + + assert np.allclose(unbonded.center_of_propellant_mass(t), expected_cm) + assert unbonded.center_of_propellant_mass(t) < bonded.center_of_propellant_mass(t) + # Packing also shrinks grain pitch, so transverse propellant inertia drops. + assert unbonded.propellant_I_11(t) < bonded.propellant_I_11(t) + + +def test_grains_unbonded_roundtrip_serialization(): + """grains_bonded persists through to_dict / from_dict.""" + unbonded = SolidMotor(**_cesaroni_like_kwargs(), grains_bonded=False) + restored = SolidMotor.from_dict(unbonded.to_dict()) + assert restored.grains_bonded is False + assert np.allclose( + restored.center_of_propellant_mass(2.0), + unbonded.center_of_propellant_mass(2.0), + atol=1e-6, + ) + + @pytest.mark.parametrize("tuple_parametric", [(5, 3000)]) def test_reshape_thrust_curve_asserts_resultant_thrust_curve_correct( cesaroni_m1670_shifted, tuple_parametric, linear_func diff --git a/tests/unit/motors/test_tank.py b/tests/unit/motors/test_tank.py index c61eb49fd..bb0ec3ab6 100644 --- a/tests/unit/motors/test_tank.py +++ b/tests/unit/motors/test_tank.py @@ -6,6 +6,9 @@ import pytest import scipy.integrate as spi +from rocketpy import CylindricalTank, Fluid, Function, MassFlowRateBasedTank +from rocketpy.motors.tank import _compose_clipped + BASE_PATH = Path("./data/rockets/berkeley/") @@ -488,3 +491,92 @@ def expected_gas_inertia(t): atol=1e-3, rtol=1e-2, ) + + +def test_mass_flow_rate_tank_exact_depletion(): + """Regression test for a tank drained to exact zero mass via a constant + (linear) mass flow rate. + + Before the fix, floating-point roundoff caused the computed liquid mass + to land marginally below zero (e.g. -1e-15 kg) at the instant of exact + depletion, which incorrectly tripped the tank's underfill check and/or + the downstream height/volume `Function.compose` domain check, raising a + spurious ValueError even though the tank is simply empty. + """ + liquid = Fluid(name="water", density=1000) + gas = Fluid(name="air", density=1.225) + + geometry = CylindricalTank(radius_function=0.1, height=1.2, spherical_caps=False) + + flux_time = 5.0 + initial_liquid_mass = 32.0 # chosen to reliably reproduce the roundoff + + tank = MassFlowRateBasedTank( + name="linear drain tank", + geometry=geometry, + flux_time=flux_time, + initial_liquid_mass=initial_liquid_mass, + initial_gas_mass=0, + liquid_mass_flow_rate_in=0, + # Constant drain rate: mass hits exactly 0 at t = flux_time + liquid_mass_flow_rate_out=initial_liquid_mass / flux_time, + gas_mass_flow_rate_in=0, + gas_mass_flow_rate_out=0, + liquid=liquid, + gas=gas, + ) + + time_points = np.array([0.0, 2.5, flux_time, flux_time + 1.0, flux_time + 5.0]) + + # Should not raise, and should read as ~0 (not negative) at/after depletion + liquid_mass = tank.liquid_mass(time_points) + npt.assert_allclose(liquid_mass[-2:], 0, atol=1e-6) + assert np.all(liquid_mass > -1e-6) + + # Height/volume properties must also remain well-defined past depletion + liquid_height = tank.liquid_height(time_points) + assert np.all(np.isfinite(liquid_height)) + + gas_height = tank.gas_height(time_points) + assert np.all(np.isfinite(gas_height)) + + +@pytest.mark.parametrize( + "outer_is_array, inner_is_array", + [(True, False), (False, True), (False, False)], + ids=["array-callable", "callable-array", "callable-callable"], +) +def test_compose_clipped_defers_when_a_source_is_not_an_array( + outer_is_array, inner_is_array +): + """Clipping needs ``x_array``, which only array-sourced Functions have. + + ``Function.compose`` handles a callable source on its own and performs no + bounds check there, so there is no spurious error to absorb and nothing to + clip. Without this the helper would raise ``AttributeError`` instead of + composing, so it would not be a drop-in replacement for ``compose``. + """ + doubling = np.column_stack([np.linspace(0, 10, 11), np.linspace(0, 20, 11)]) + outer = Function(doubling) if outer_is_array else Function(lambda v: v * 2.0) + + shift = np.column_stack([np.linspace(0, 5, 6), np.linspace(1, 6, 6)]) + inner = Function(shift) if inner_is_array else Function(lambda t: t + 1.0) + + composed = _compose_clipped(outer, inner) + + # outer(inner(3)) == (3 + 1) * 2 + assert float(composed(3.0)) == pytest.approx(8.0) + + +def test_compose_clipped_absorbs_only_boundary_noise(): + """Values a roundoff below the domain are pulled in, not rejected.""" + doubling = np.column_stack([np.linspace(0, 10, 11), np.linspace(0, 20, 11)]) + outer = Function(doubling) + + times = np.linspace(0.0, 5.0, 6) + just_under_zero = np.column_stack([times, np.full_like(times, -1e-16)]) + inner = Function(just_under_zero) + + composed = _compose_clipped(outer, inner) + + assert float(composed(2.0)) == pytest.approx(0.0) diff --git a/tests/unit/rocket/aero_surface/test_body_profiles.py b/tests/unit/rocket/aero_surface/test_body_profiles.py new file mode 100644 index 000000000..991de9379 --- /dev/null +++ b/tests/unit/rocket/aero_surface/test_body_profiles.py @@ -0,0 +1,73 @@ +"""DATCOM-only ogive tails preserve the original ordinary Tail behavior.""" + +import json + +import numpy as np +import pytest + +from rocketpy import Tail +from rocketpy._encoders import RocketPyDecoder, RocketPyEncoder +from rocketpy.rocket.aero_surface.datcom_tail import DatcomTail + + +def test_ogive_tail_requires_datcom(calisto): + """An ordinary rocket rejects ogive tails before modifying its assembly.""" + # Arrange + count = len(calisto.aerodynamic_surfaces) + # Act / Assert + with pytest.raises(ValueError, match="DATCOM fin set"): + calisto.add_tail(0.06, 0.04, 0.2, position=-1, kind="ogive") + assert len(calisto.aerodynamic_surfaces) == count + with pytest.raises(ValueError, match="DATCOM fin set"): + calisto.add_surfaces(DatcomTail(0.06, 0.04, 0.2, 0.06), -1) + with pytest.raises(ValueError, match="Tail kind"): + calisto.add_tail(0.06, 0.04, 0.2, position=-1, kind="unknown") + + +def test_datcom_ogive_geometry_and_serialization(datcom_rocket): + """Ogive shape survives serialization and has no component aero model.""" + # Arrange / Act + tail = datcom_rocket.add_tail(0.0635, 0.045, 0.1, position=-1.2, kind="ogive") + restored = json.loads(json.dumps(tail, cls=RocketPyEncoder), cls=RocketPyDecoder) + # Assert + assert isinstance(tail, DatcomTail) + assert restored.kind == tail.kind == "ogive" + assert tail.radius_at([0, 0.1]) == pytest.approx([0.0635, 0.045]) + assert tail.radius_at(0.05) > (0.0635 + 0.045) / 2 + np.testing.assert_allclose(restored.shape_vec, tail.shape_vec) + assert tail.clalpha(0) == 0 + with pytest.raises(ValueError, match="DATCOM"): + tail.compute_forces_and_moments() + with pytest.raises(AttributeError): + tail.kind = "conical" + + +def test_conical_tail_preserves_original_model(calisto): + """The ordinary helper constructs exactly the original Tail class and equations.""" + # Arrange + top, bottom, length = 0.06, 0.04, 0.2 + r = top / bottom + # Act + tail = calisto.add_tail(top, bottom, length, -1, kind="conical") + # Assert + assert type(tail) is Tail + assert tail.cpz == pytest.approx((length / 3) * (1 + (1 - r) / (1 - r**2))) + assert "kind" not in tail.to_dict() + + +def test_datcom_ogive_rejects_invalid_dimensions(): + """A tangent ogive cannot overhang its upstream cylinder joint.""" + # Arrange / Act / Assert + with pytest.raises(ValueError, match="radius change"): + DatcomTail(0.09, 0.01, 0.02, 0.09) + + +def test_removing_datcom_fins_rejects_ogive_before_flight(datcom_rocket): + """An assembled ogive cannot silently fall back to ordinary aerodynamics.""" + # Arrange + datcom_rocket.add_tail(0.0635, 0.045, 0.1, -1.2, kind="ogive") + for fin in list(datcom_rocket.fins): + datcom_rocket.aerodynamic_surfaces.remove(fin) + # Act / Assert + with pytest.raises(ValueError, match="DATCOM fin set"): + datcom_rocket.prepare_datcom() diff --git a/tests/unit/rocket/aero_surface/test_tube_fins.py b/tests/unit/rocket/aero_surface/test_tube_fins.py new file mode 100644 index 000000000..296a13ea1 --- /dev/null +++ b/tests/unit/rocket/aero_surface/test_tube_fins.py @@ -0,0 +1,141 @@ +import json + +import numpy as np +import pytest + +from rocketpy import TubeFins +from rocketpy._encoders import RocketPyDecoder, RocketPyEncoder + + +@pytest.fixture +def tube_fins(): + return TubeFins( + n=6, + length=0.1, + inner_radius=0.045, + outer_radius=0.05, + rocket_radius=0.05, + ) + + +def test_tube_fins_geometry_and_normal_force_slope(tube_fins): + aspect_ratio = 2 * tube_fins.inner_radius / tube_fins.length + modified_aspect_ratio = 2 * aspect_ratio / np.pi + expected_clalpha = ( + tube_fins.n + * 2 + * modified_aspect_ratio + / (1 + modified_aspect_ratio) + * np.pi**2 + * tube_fins.inner_radius + * tube_fins.length + / (np.pi * tube_fins.rocket_radius**2) + ) + + assert tube_fins.aspect_ratio == pytest.approx(aspect_ratio) + assert tube_fins.cp == pytest.approx((0, 0, tube_fins.length / 4)) + assert tube_fins.reference_area == pytest.approx(np.pi * tube_fins.rocket_radius**2) + assert tube_fins.reference_length == pytest.approx(2 * tube_fins.rocket_radius) + assert tube_fins.tube_separation == pytest.approx(0, abs=1e-12) + assert tube_fins.clalpha(0) == pytest.approx(expected_clalpha) + assert tube_fins.clalpha(0.5) == pytest.approx(expected_clalpha) + + +def test_tube_fins_lift_is_capped_at_twenty_degrees(tube_fins): + capped_lift = tube_fins.clalpha(0) * np.radians(20) + + assert tube_fins.cl(np.radians(10), 0) == pytest.approx(capped_lift / 2) + assert tube_fins.cl(np.radians(30), 0) == pytest.approx(capped_lift) + assert tube_fins.cl(np.radians(-30), 0) == pytest.approx(-capped_lift) + + +@pytest.mark.parametrize( + ("overrides", "message"), + [ + ({"n": 2}, "greater than or equal to 3"), + ({"n": 6.0}, "must be an integer"), + ({"length": 0}, "length.*greater than zero"), + ({"inner_radius": 0}, "inner_radius.*greater than zero"), + ({"inner_radius": 0.05}, "smaller than.*outer_radius"), + ({"outer_radius": 0}, "outer_radius.*greater than zero"), + ({"rocket_radius": 0}, "rocket_radius.*greater than zero"), + ({"length": np.nan}, "length.*finite"), + ({"outer_radius": 0.049}, "separated tube fins"), + ({"outer_radius": 0.06}, "overlapping tube fins"), + ], +) +def test_tube_fins_reject_unsupported_geometry(overrides, message): + parameters = { + "n": 6, + "length": 0.1, + "inner_radius": 0.045, + "outer_radius": 0.05, + "rocket_radius": 0.05, + } + parameters.update(overrides) + + with pytest.raises(ValueError, match=message): + TubeFins(**parameters) + + +def test_tube_fins_setters_update_dependent_values(tube_fins): + initial_clalpha = tube_fins.clalpha(0) + + tube_fins.length = 0.2 + assert tube_fins.cpz == pytest.approx(0.05) + assert tube_fins.aspect_ratio == pytest.approx(0.45) + assert tube_fins.clalpha(0) != pytest.approx(initial_clalpha) + + previous_outer_radius = tube_fins.outer_radius + with pytest.raises(ValueError, match="separated tube fins"): + tube_fins.outer_radius = 0.049 + assert tube_fins.outer_radius == previous_outer_radius + + +def test_tube_fins_add_to_rocket(calisto): + initial_clalpha = calisto.total_lift_coeff_der(0) + tube_fins = calisto.add_tube_fins( + n=6, + length=0.12, + inner_radius=0.055, + outer_radius=calisto.radius, + position=-1.1, + ) + + assert tube_fins in calisto.tube_fins + assert calisto.aerodynamic_surfaces[-1].component is tube_fins + assert calisto.aerodynamic_surfaces[-1].position.z == pytest.approx(-1.1) + assert calisto.total_lift_coeff_der(0) == pytest.approx( + initial_clalpha + tube_fins.clalpha(0) + ) + + +@pytest.mark.parametrize( + ("include_outputs", "discretize"), + [(False, False), (True, False), (True, True)], +) +def test_tube_fins_json_round_trip(tube_fins, include_outputs, discretize): + encoded = json.dumps( + tube_fins, + cls=RocketPyEncoder, + include_outputs=include_outputs, + discretize=discretize, + ) + decoded = json.loads(encoded, cls=RocketPyDecoder) + + assert isinstance(decoded, TubeFins) + assert decoded.n == tube_fins.n + assert decoded.length == pytest.approx(tube_fins.length) + assert decoded.inner_radius == pytest.approx(tube_fins.inner_radius) + assert decoded.outer_radius == pytest.approx(tube_fins.outer_radius) + assert decoded.rocket_radius == pytest.approx(tube_fins.rocket_radius) + assert decoded.cp == pytest.approx(tube_fins.cp) + assert decoded.clalpha(0) == pytest.approx(tube_fins.clalpha(0)) + + +def test_tube_fins_info_and_draw(tube_fins, capsys, monkeypatch): + monkeypatch.setattr("matplotlib.pyplot.show", lambda: None) + + assert tube_fins.info() is None + assert "Number of tubes: 6" in capsys.readouterr().out + assert tube_fins.draw() is None diff --git a/tests/unit/rocket/test_components.py b/tests/unit/rocket/test_components.py new file mode 100644 index 000000000..d09386a4a --- /dev/null +++ b/tests/unit/rocket/test_components.py @@ -0,0 +1,91 @@ +"""Unit tests for rocketpy.rocket.components.Components.""" + +import pytest + +from rocketpy import NoseCone +from rocketpy.mathutils.vector_matrix import Vector +from rocketpy.rocket.components import Components + + +def test_components_add_default_ref_factor(): + """Components.add stores ref_factor=1.0 when omitted.""" + components = Components() + component = object() + position = Vector([0, 0, 1.0]) + + components.add(component, position) + + assert len(components) == 1 + assert components[0].component is component + assert components[0].position == position + assert components[0].ref_factor == pytest.approx(1.0) + + +def test_components_add_custom_ref_factor(): + """Components.add stores an explicit ref_factor on the component tuple.""" + components = Components() + component = object() + position = Vector([0, 0, -0.5]) + ref_factor = 0.25 + + components.add(component, position, ref_factor=ref_factor) + + stored = components[0] + assert stored.component is component + assert stored.position == position + assert stored.ref_factor == pytest.approx(ref_factor) + + +def test_components_to_dict_from_dict_preserves_ref_factor(): + """Serialization round-trip keeps ref_factor, defaulting when absent.""" + components = Components() + component = {"name": "dummy"} + position = Vector([0, 0, 0.1]) + components.add(component, position, ref_factor=4.0) + + restored = Components.from_dict(components.to_dict()) + assert restored[0].ref_factor == pytest.approx(4.0) + + legacy = Components.from_dict( + {"components": [{"component": component, "position": position}]} + ) + assert legacy[0].ref_factor == pytest.approx(1.0) + + +def test_rocket_add_surfaces_stores_computed_ref_factor(calisto): + """Rocket aero-surface add path stores (surface.rocket_radius / rocket.radius)**2.""" + surface_radius = calisto.radius / 2 + expected_ref_factor = (surface_radius / calisto.radius) ** 2 + nose = NoseCone( + length=0.55829, + kind="vonkarman", + base_radius=surface_radius, + rocket_radius=surface_radius, + name="Half-radius nose", + ) + + calisto.add_surfaces(nose, 1.16) + stored = next( + entry for entry in calisto.aerodynamic_surfaces if entry.component is nose + ) + + assert stored.ref_factor == pytest.approx(expected_ref_factor) + assert stored.ref_factor == pytest.approx(0.25) + + +def test_rocket_add_surfaces_matching_radius_stores_unit_ref_factor(calisto): + """Matching surface and rocket radii store ref_factor of 1.0.""" + nose = NoseCone( + length=0.55829, + kind="vonkarman", + base_radius=calisto.radius, + rocket_radius=calisto.radius, + name="Matching nose", + ) + + calisto.add_surfaces(nose, 1.16) + stored = next( + entry for entry in calisto.aerodynamic_surfaces if entry.component is nose + ) + + assert stored.ref_factor == pytest.approx(1.0) diff --git a/tests/unit/rocket/test_datcom.py b/tests/unit/rocket/test_datcom.py new file mode 100644 index 000000000..bcf46d223 --- /dev/null +++ b/tests/unit/rocket/test_datcom.py @@ -0,0 +1,607 @@ +"""DATCOM geometry, interpolation, units and reference-frame regressions.""" + +import json +import re +from types import SimpleNamespace +from unittest.mock import patch + +import numpy as np +import pytest + +from rocketpy import DatcomFins, EmptyMotor, Environment, Flight, Function, Rocket +from rocketpy._encoders import RocketPyDecoder, RocketPyEncoder +from rocketpy.mathutils.vector_matrix import Vector +from rocketpy.rocket.datcom import ( + DAMPING_KEYS, + DatcomSurface, + datcom_decks, + parse_datcom_output, +) + + +def _deck_value(deck, name): + return float(re.search(rf"\b{name}\s*=\s*([-+\d.eE]+)", deck)[1]) + + +def test_datcom_fin_trailing_edge_can_extend_past_base(datcom_rocket): + """Root-centre attachment permits aft fin overhang without extending the body.""" + # Arrange + rocket = datcom_rocket + original_body = _deck_value(datcom_decks(rocket)[0][False], "LCENTR") + fin, position, _ = next( + s for s in rocket.aerodynamic_surfaces if s[0] is rocket.fins[0] + ) + fin.root_chord = 0.45 + # Act + decks, _, _, _ = datcom_decks(rocket) + # Assert + assert _deck_value(decks[False], "LCENTR") == pytest.approx(original_body) + assert _deck_value(decks[False], "CHORD") == pytest.approx(0.45) + + +def test_datcom_fin_root_center_cannot_be_beyond_body(datcom_rocket): + """A fin with its root centre behind the body has no valid attachment.""" + # Arrange + datcom_rocket.fins[0].root_chord = 1.0 + # Act / Assert + with pytest.raises(ValueError, match="root chord centers"): + datcom_decks(datcom_rocket) + + +@pytest.mark.parametrize("mode", ["none", "nozzle", "full"]) +def test_datcom_powered_base_drag_policy(datcom_rocket, mode): + """Nozzle mode includes the real base annulus without enabling plume models.""" + # Arrange + datcom_rocket.fins[0].datcom_options["powered_base_drag"] = mode + # Act + decks, _, _, _ = datcom_decks(datcom_rocket) + # Assert + assert _deck_value(decks[False], "DEXIT") == 0 + assert "BASE = .FALSE." in decks[True] + if mode == "none": + assert "DEXIT" not in decks[True] + else: + expected = 2 * datcom_rocket.motor.nozzle_radius if mode == "nozzle" else 0 + assert _deck_value(decks[True], "DEXIT") == pytest.approx(expected) + + +@pytest.mark.parametrize("mode", ["bad", True]) +def test_datcom_powered_base_drag_rejects_invalid_policy(datcom_rocket, mode): + """Ambiguous policy values cannot silently change the base-drag model.""" + # Arrange + datcom_rocket.fins[0].datcom_options["powered_base_drag"] = mode + # Act / Assert + with pytest.raises(ValueError, match="powered_base_drag"): + datcom_decks(datcom_rocket) + + +def test_datcom_powered_nozzle_must_fit_base(datcom_rocket): + """A nozzle outside the body cannot produce a valid annular base area.""" + # Arrange + datcom_rocket.fins[0].datcom_options["powered_base_drag"] = "nozzle" + datcom_rocket.motor.nozzle_radius = 1 + # Act / Assert + with pytest.raises(ValueError, match="nozzle diameter"): + datcom_decks(datcom_rocket) + + +@pytest.mark.parametrize( + "method,section", + [ + ("add_datcom_trapezoidal_fins", "HEX"), + ("add_datcom_arc_fins", "ARC"), + ("add_datcom_hex_fins", "HEX"), + ], +) +def test_add_datcom_fins_retains_geometry(method, section, calisto_motorless, tmp_path): + """The public methods preserve planform dimensions without executing DATCOM.""" + # Arrange + rocket = calisto_motorless + # Act + with patch("rocketpy.rocket.datcom.run_datcom") as run: + fins = getattr(rocket, method)( + 4, + 0.3, + 0.1, + 0.12, + position=-1, + path=tmp_path / "datcom", + sweep_angle=30, + thickness_to_chord=(0.02, 0.03), + ) + # Assert + run.assert_not_called() + assert isinstance(fins, DatcomFins) + assert fins.section_type == section + assert fins.sweep_length == pytest.approx(0.12 * np.tan(np.radians(30))) + assert fins.thickness_to_chord == pytest.approx((0.02, 0.03)) + assert rocket.aerodynamic_surfaces[0].component is fins + assert np.isnan(rocket.cp_position(0)) + + +@pytest.mark.parametrize("orientation", ["tail_to_nose", "nose_to_tail"]) +def test_datcom_decks_cg_geometry_and_base_drag(orientation, cesaroni_m1670, tmp_path): + """Both rocket orientations produce the same nose-to-tail DATCOM geometry.""" + # Arrange + sign = 1 if orientation == "tail_to_nose" else -1 + rocket = Rocket( + 0.0635, 14, (6, 6, 0.04), 0.9, 0.8, 0, coordinate_system_orientation=orientation + ) + rocket.add_motor(cesaroni_m1670, -1.373 * sign) + rocket.add_nose(0.55, "von karman", 1.2 * sign) + rocket.add_datcom_hex_fins( + 4, 0.25, 0.1, 0.1, -0.9 * sign, sweep_length=0.1, path=tmp_path / "datcom" + ) + rocket.add_tail(0.0635, 0.045, 0.1, -1.2 * sign) + # Act + decks, references, _, _ = datcom_decks(rocket) + # Assert + for powered, deck in decks.items(): + assert _deck_value(deck, "LNOSE") == pytest.approx(0.55) + assert _deck_value(deck, "LCENTR") == pytest.approx(1.85) + assert _deck_value(deck, "XLE") == pytest.approx(2.1) + assert _deck_value(deck, "XCG") == pytest.approx( + (1.2 * sign - references[powered]) * sign + ) + assert "DAMP\n" in deck + assert "PROTUB" not in deck + assert "BASE = .FALSE." in deck + assert max(map(len, deck.splitlines())) < 80 + assert "DEXIT" not in decks[True] + assert _deck_value(decks[False], "DEXIT") == 0 + assert references[True] == pytest.approx( + rocket.center_of_mass(rocket.motor.burn_start_time) + ) + assert references[False] == pytest.approx(rocket.center_of_dry_mass_position) + assert references[True] != pytest.approx(references[False]) + + +def test_datcom_decks_filled_tank_cg(calisto_liquid_modded, tmp_path): + """The ignition reference follows existing liquid tank masses and positions.""" + # Arrange + rocket = calisto_liquid_modded + rocket.add_nose(0.55, "ogive", 3) + rocket.add_datcom_arc_fins(4, 0.25, 0.1, 0.1, -2, path=tmp_path / "datcom") + rocket.add_tail(rocket.radius, 0.04, 0.1, -3) + time = rocket.motor.burn_start_time + mass = rocket.motor.propellant_mass(time) + expected = ( + rocket.dry_mass * rocket.center_of_dry_mass_position + + mass * rocket.center_of_propellant_position(time) + ) / (rocket.dry_mass + mass) + # Act + _, references, _, _ = datcom_decks(rocket) + # Assert + assert references[True] == pytest.approx(expected, rel=1e-10) + assert references[True] != pytest.approx(references[False]) + + +def test_prepare_datcom_disables_component_models_and_rebuilds( + datcom_rocket, datcom_table_factory +): + """One complete model replaces component loads and geometry edits regenerate cases.""" + # Arrange + rocket = datcom_rocket + original = tuple(rocket.aerodynamic_surfaces) + # Act + with patch( + "rocketpy.rocket.datcom.run_datcom", return_value=datcom_table_factory() + ) as run: + model = rocket.prepare_datcom() + same = rocket.prepare_datcom() + assert run.call_count == 2 + original[1].component.tip_chord = 0.11 + rebuilt = rocket.prepare_datcom() + # Assert + assert same is model + assert rebuilt is not model + assert run.call_count == 4 + assert tuple(rocket.aerodynamic_surfaces) == original + assert [s for s, _, _ in rocket._flight_aerodynamic_surfaces] == [rebuilt] + assert model not in rocket.surfaces_cp_to_cdm + assert rocket.power_on_drag(0.3) == pytest.approx(0.4) + + +def test_prepare_datcom_failed_run_is_atomic(datcom_rocket, datcom_table_factory): + """A failed coast run never installs the powered half of a database.""" + # Arrange + rocket = datcom_rocket + original_drag = rocket.power_on_drag_7d + # Act / Assert + with patch( + "rocketpy.rocket.datcom.run_datcom", + side_effect=[datcom_table_factory(), RuntimeError("failed")], + ): + with pytest.raises(RuntimeError, match="failed"): + rocket.prepare_datcom() + assert rocket._datcom_model is None + assert rocket.power_on_drag_7d is original_drag + + +@pytest.mark.parametrize( + "options,message", + [ + ({"mach": [0.5, 0.2]}, "increasing"), + ({"alpha": [0, 1, 2]}, "negative"), + ({"altitude": [0]}, "one value per Mach"), + ({"timeout": 0}, "timeout"), + ({"unknown": 1}, "Unknown"), + ], +) +def test_datcom_decks_invalid_conditions(options, message, datcom_rocket): + """Bad grids fail before launching an external process.""" + # Arrange + datcom_rocket.fins[0].datcom_options.update(options) + # Act / Assert + with pytest.raises(ValueError, match=message): + datcom_decks(datcom_rocket) + + +@pytest.mark.parametrize( + "field,value", + [ + ("thickness_to_chord", 0), + ("flat_length", 0.9), + ("leading_edge_radius", -1), + ("thickness_to_chord", [1, 2, 3]), + ], +) +def test_datcom_fins_invalid_section(field, value, tmp_path): + """Invalid or mismatched section stations are rejected.""" + # Arrange + kwargs = {field: value} + # Act / Assert + with pytest.raises(ValueError): + DatcomFins(4, 0.3, 0.1, 0.1, 0.06, path=tmp_path / "datcom", **kwargs) + + +def test_datcom_fins_serialization(datcom_rocket): + """Saved rockets retain section dimensions and deferred execution settings.""" + # Arrange + fins = datcom_rocket.fins[0] + # Act + restored = json.loads(json.dumps(fins, cls=RocketPyEncoder), cls=RocketPyDecoder) + # Assert + assert isinstance(restored, DatcomFins) + assert restored.to_dict() == fins.to_dict() + + +def test_datcom_fins_direct_inclusion(datcom_rocket): + """Adding a constructed DATCOM surface also selects the whole-vehicle model.""" + # Arrange + fins = DatcomFins.from_dict(datcom_rocket.fins[0].to_dict()) + rocket = Rocket(0.0635, 14, (6, 6, 0.04), 0, 0, 0) + # Act + rocket.add_surfaces(fins, positions=-0.9) + # Assert + assert rocket._has_datcom_fins() + assert rocket.fins[0] is fins + + +def test_datcom_prepared_rocket_serialization(datcom_rocket, datcom_table_factory): + """Rocket export preserves inputs and geometry without embedding live model closures.""" + # Arrange + with patch( + "rocketpy.rocket.datcom.run_datcom", return_value=datcom_table_factory() + ): + datcom_rocket.prepare_datcom() + # Act + restored = json.loads( + json.dumps(datcom_rocket, cls=RocketPyEncoder), cls=RocketPyDecoder + ) + # Assert + assert isinstance(restored, Rocket) + assert restored._has_datcom_fins() + assert restored._datcom_model is None + assert restored.fins[0].to_dict() == datcom_rocket.fins[0].to_dict() + + +@pytest.mark.parametrize( + "key,axis,index,sign", + [ + ("CNQ", 0, 1, -1), + ("CMQ", 0, 3, 1), + ("CAQ", 0, 2, -1), + ("CNAD", None, 1, -1), + ("CMAD", None, 3, 1), + ("CYR", 1, 0, 1), + ("CLNR", 1, 4, 1), + ("CLLR", 1, 5, 1), + ("CYP", 2, 0, 1), + ("CLNP", 2, 4, 1), + ("CLLP", 2, 5, 1), + ], +) +@pytest.mark.parametrize("direction", [-1, 1]) +def test_datcom_damping_units_axes( + key, axis, index, sign, direction, datcom_rocket, datcom_table_factory +): + """Every DAMP channel has the correct per-degree rate scaling and body axis.""" + # Arrange + table = datcom_table_factory({key: 2}) + model = DatcomSurface( + datcom_rocket, {True: table, False: table}, {True: 0, False: 0} + ) + omega = [0, 0, 0] + if axis is not None: + omega[axis] = direction + expected = np.zeros(6) + scale = 0.5 * 1.2 * 100**2 * model.reference_area + if index >= 3: + scale *= model.reference_length + expected[index] = ( + sign * 2 * direction * np.degrees(model.reference_length / 200) * scale + ) + # Act + result = model.compute_forces_and_moments_at_time( + 0.5, + direction if axis is None else 0, + Vector([0, 0, -100]), + 100, + 0.3, + 1.2, + Vector.zeros(), + Vector(omega), + 1e6, + ) + # Assert + np.testing.assert_allclose(result, expected, atol=1e-10) + + +def test_datcom_interpolation_and_power_boundaries(datcom_rocket, datcom_table_factory): + """Mach/alpha interpolate, while base-drag state switches at ignition/burnout.""" + # Arrange + on = datcom_table_factory({"CA": 1}) + on.body["CN"][:] = [[0, 2, 4], [4, 6, 8]] + off = datcom_table_factory({"CA": 2}) + model = DatcomSurface(datcom_rocket, {True: on, False: off}, {True: 0, False: 0}) + model.burn_start, model.burn_out = 1, 3 + # Act + values = model.coefficients(True, 0.4, np.radians(-1)) + states = [model.is_powered(t) for t in [0, 1, 2, 3, 4]] + # Assert + assert values[0] == pytest.approx(3) + assert states == [False, True, True, False, False] + assert model.coefficients(False, 0.4, 0)[2] == 2 + assert model.coefficients(True, 5, 1)[0] == 8 # bounded envelope + + +@pytest.mark.parametrize("speed", [0, 1e-12, 0.5, 100, -100]) +def test_datcom_low_speed_and_reverse_flow(speed, datcom_rocket, datcom_table_factory): + """Loads remain finite near rest and axial drag opposes reverse motion.""" + # Arrange + table = datcom_table_factory({"CA": 1, "CMQ": -2}) + model = DatcomSurface( + datcom_rocket, {True: table, False: table}, {True: 0, False: 0} + ) + # Act + loads = model.compute_forces_and_moments_at_time( + 0.5, + 0, + Vector([0, 0, -speed]), + abs(speed), + 0.3, + 1.2, + Vector.zeros(), + Vector([1, 0, 0]), + 0, + ) + # Assert + assert np.isfinite(loads).all() + assert loads[2] * speed <= 0 + + +def test_datcom_moment_reference_transfer(datcom_rocket, datcom_table_factory): + """Moments are transferred from the case reference to dry CG exactly once.""" + # Arrange + table = datcom_table_factory({"CN": 1, "CM": 2}) + model = DatcomSurface( + datcom_rocket, {True: table, False: table}, {True: 0.5, False: 0} + ) + cp = Vector([0, 0, 0.7]) + scale = 0.5 * 1.2 * 100**2 * model.reference_area + # Act + loads = model.compute_forces_and_moments_at_time( + 0.5, + 0, + Vector([0, 0, -100]), + 100, + 0.3, + 1.2, + cp, + Vector.zeros(), + 0, + ) + # Assert + assert loads[1] == pytest.approx(-scale) + assert loads[3] == pytest.approx(scale * (2 * model.reference_length + 0.7)) + + +def test_datcom_axial_coefficient_is_not_projected_twice( + datcom_rocket, datcom_table_factory +): + """CA already acts along the body axis, including at nonzero incidence.""" + # Arrange + table = datcom_table_factory({"CA": 0.4, "CYB": -0.1}) + model = DatcomSurface( + datcom_rocket, {True: table, False: table}, {True: 0, False: 0} + ) + velocity = Vector([1, 2, 100]) + speed = abs(velocity) + scale = 0.5 * 1.2 * speed**2 * model.reference_area + beta = np.arctan2(1, 100) + # Act + loads = model.compute_forces_and_moments_at_time( + 0.5, + 0, + -velocity, + speed, + 0.3, + 1.2, + Vector.zeros(), + Vector.zeros(), + 0, + ) + # Assert + assert loads[2] == pytest.approx(-0.4 * scale) + assert loads[0] == pytest.approx(-0.1 * np.degrees(beta) * scale) + + +def test_datcom_rail_uses_body_axial_coefficient(datcom_rocket, datcom_table_factory): + """The constrained rail phase uses CA rather than the reporting CD curve.""" + # Arrange + rocket = datcom_rocket + with patch( + "rocketpy.rocket.datcom.run_datcom", + return_value=datcom_table_factory({"CA": 0.4, "CD": 0.8}), + ): + rocket.prepare_datcom() + flight = object.__new__(Flight) + flight.rocket, flight.env = rocket, Environment() + state = [0, 0, 1000, 0, 0, 20, 1, 0, 0, 0, 0, 0, 0] + time = 0.5 + thrust = max( + rocket.motor.thrust(time) + + rocket.motor.pressure_thrust(flight.env.pressure(1000)), + 0, + ) + force = -0.5 * flight.env.density(1000) * 20**2 * rocket.area * 0.4 + expected = (thrust + force) / rocket.total_mass(time) - flight.env.gravity(1000) + # Act + derivative = flight.udot_rail1(time, state) + # Assert + assert expected > 0 + assert derivative[5] == pytest.approx(expected) + + +@pytest.mark.parametrize("time", [0.2, 1.0, 2.0]) +@pytest.mark.parametrize("rocket_fixture", ["calisto", "calisto_nose_to_tail"]) +def test_datcom_generalized_torque_uses_current_cg( + time, rocket_fixture, request, tmp_path, datcom_table_factory +): + """A fixed DATCOM reference transfers to the actual CG throughout the burn.""" + # Arrange + rocket = request.getfixturevalue(rocket_fixture) + sign = rocket._csys + path = tmp_path / "datcom" + path.touch() + rocket.add_nose(0.55, "von karman", 1.2 * sign) + rocket.add_datcom_hex_fins( + 4, + 0.25, + 0.1, + 0.1, + -0.9 * sign, + path=path, + datcom_options={"mach": [0.2, 0.6], "alpha": [-2, 0, 2]}, + ) + rocket.add_tail(rocket.radius, 0.045, 0.1, -1.2 * sign) + table = datcom_table_factory({"CN": 1, "CM": 0.5}) + with patch("rocketpy.rocket.datcom.run_datcom", return_value=table): + model = rocket.prepare_datcom() + flight = object.__new__(Flight) + flight.rocket, flight.env = rocket, Environment() + state = [0, 0, 1000, 0, 0, 100, 1, 0, 0, 0, 0, 0, 0] + cg = rocket.center_of_mass(time) + offset = (model.references[model.is_powered(time)] - cg) * rocket._csys + q_area = 0.5 * flight.env.density(1000) * 100**2 * rocket.area + inertia = ( + rocket.I_11(time) + - rocket.total_mass(time) * rocket.com_to_cdm_function(time) ** 2 + ) + expected = q_area * (0.5 * model.reference_length + offset) / inertia + # Act + derivative = flight.u_dot_generalized(time, state) + # Assert + assert derivative[10] == pytest.approx(expected, rel=1e-9) + + +def test_datcom_unsteady_coupling_is_history_independent( + datcom_rocket, datcom_table_factory +): + """CNAD feeds back through acceleration; repeated RHS evaluations are deterministic.""" + # Arrange + rocket = datcom_rocket + rocket.add_motor(EmptyMotor(), position=0) + with patch( + "rocketpy.rocket.datcom.run_datcom", + return_value=datcom_table_factory({"CNAD": 1}), + ): + model = rocket.prepare_datcom() + # Put both case references at dry CG to isolate the analytic CNAD feedback. + model.references = {True: model.dry_reference, False: model.dry_reference} + flight = object.__new__(Flight) + flight.rocket, flight.env = rocket, Environment() + state = [0, 0, 1000, 0, 0, 100, 1, 0, 0, 0, 0.2, 0, 0] + time = 0.5 + q_area = 0.5 * flight.env.density(1000) * 100**2 * rocket.area + expected = 0.2 / ( + 1 + + q_area + * np.degrees(model.reference_length / 200) + / (rocket.total_mass(time) * 100) + ) + # Act + first = flight.u_dot_generalized(time, state) + flight.u_dot_generalized(0.1, state) + repeated = flight.u_dot_generalized(time, state) + # Assert + assert flight._datcom_incidence_rate(time, state, first) == pytest.approx( + expected, rel=1e-8 + ) + np.testing.assert_allclose(first, repeated, atol=1e-12) + assert flight._datcom_alpha_dot == 0 + assert flight._datcom_evaluating is False + + +@pytest.mark.parametrize( + "override,expected_cd", [(None, 0.4), (False, 0.6), (True, 0.2)] +) +def test_datcom_no_duplicate_drag_or_component_loads( + override, expected_cd, datcom_rocket, datcom_table_factory +): + """Full-vehicle axial force is applied once despite nonzero original rocket drag.""" + # Arrange + rocket = datcom_rocket + if override is not None: + rocket.air_brakes.append( + SimpleNamespace( + deployment_level=1, + override_rocket_drag=override, + drag_coefficient=Function(lambda level, mach: 0.2), + reference_area=rocket.area, + ) + ) + with patch( + "rocketpy.rocket.datcom.run_datcom", + return_value=datcom_table_factory({"CA": 0.4, "CD": 0.4}), + ): + rocket.prepare_datcom() + flight = object.__new__(Flight) + flight.rocket, flight.env = rocket, Environment() + flight._Flight__post_processed_variables = [] + state = [0, 0, 1000, 0, 0, 100, 1, 0, 0, 0, 0, 0, 0] + expected = -0.5 * flight.env.density(1000) * 100**2 * rocket.area * expected_cd + # Act + with patch.object( + rocket.nosecones[0], + "compute_forces_and_moments", + side_effect=AssertionError("component loads used"), + ): + flight.u_dot_generalized(0.5, state, post_processing=True) + # Assert + assert len(flight._Flight__post_processed_variables) == 1 + assert flight._Flight__post_processed_variables[0][9] == pytest.approx(expected) + + +def test_datcom_namelist_scientific_notation_uppercase(): + """Legacy CONERR rejects lowercase exponents for small physical inputs.""" + # Arrange + from rocketpy.rocket.datcom import _namelist + + # Act + text = _namelist("FINSET1", [("LER", [1e-5, 1.25e-5])]) + # Assert + assert "1.E-05" in text + assert "1.25E-05" in text + assert "e" not in text diff --git a/tests/unit/rocket/test_datcom_atmosphere.py b/tests/unit/rocket/test_datcom_atmosphere.py new file mode 100644 index 000000000..c56b4ef31 --- /dev/null +++ b/tests/unit/rocket/test_datcom_atmosphere.py @@ -0,0 +1,174 @@ +"""Atmosphere transfer uses actual reference-flight conditions, not ISA.""" + +from types import SimpleNamespace + +import numpy as np +import pytest + +from rocketpy.rocket.datcom import datcom_decks + + +def _flight(rocket, density=1.5): + stop = rocket.motor.burn_time[1] + return SimpleNamespace( + rocket=rocket, + apogee_time=2 * stop, + mach_number=lambda t: np.where(t < stop, t / stop, 2 - t / stop), + z=lambda t: 1000 + t * 100, + partial_angle_of_attack=lambda t: np.zeros_like(t), + env=SimpleNamespace( + density=lambda z: np.full_like(z, density), + speed_of_sound=lambda z: np.full_like(z, 300), + dynamic_viscosity=lambda z: np.full_like(z, 2e-5), + pressure=lambda z: np.full_like(z, 90000), + temperature=lambda z: np.full_like(z, 280), + ), + ) + + +@pytest.mark.parametrize("density", [0.8, 1.5]) +def test_set_datcom_atmosphere_actual_environment(datcom_rocket, density): + """Both native phases use rho*a*M/mu, including unreached Mach values.""" + # Arrange + flight = _flight(datcom_rocket, density) + grid = [0.1, 0.5, 2.0] + # Act + report = datcom_rocket.set_datcom_atmosphere(flight, mach=grid) + decks, _, _, _ = datcom_decks(datcom_rocket) + # Assert + for state in ("power_on", "power_off"): + assert report["reynolds"][state] == pytest.approx( + density * 300 * np.array(grid) / 2e-5 + ) + assert report["temperature_k"][state] == pytest.approx([280] * 3) + assert report["pressure_pa"][state] == pytest.approx([90000] * 3) + assert all("REN =" in deck and "ALT =" not in deck for deck in decks.values()) + + +def test_set_datcom_atmosphere_failure_preserves_options(datcom_rocket): + """An invalid atmosphere cannot partially overwrite the native settings.""" + # Arrange + fins = [ + s + for s, _, _ in datcom_rocket.aerodynamic_surfaces + if hasattr(s, "datcom_options") + ] + before = [dict(s.datcom_options) for s in fins] + flight = _flight(datcom_rocket, density=-1) + # Act / Assert + with pytest.raises(ValueError, match="positive Reynolds"): + datcom_rocket.set_datcom_atmosphere(flight) + assert [s.datcom_options for s in fins] == before + + +def test_set_datcom_atmosphere_separates_power_phases(datcom_rocket): + """The same Mach at different altitudes has different native Reynolds values.""" + # Arrange + flight = _flight(datcom_rocket) + flight.env.density = lambda z: 2 - z / 10000 + stop = datcom_rocket.motor.burn_time[1] + # Act + report = datcom_rocket.set_datcom_atmosphere(flight, mach=[0.25, 0.5], samples=1001) + # Assert + for phase, t in [("power_on", 0.5 * stop), ("power_off", 1.5 * stop)]: + expected = (2 - (1000 + 100 * t) / 10000) * 300 * 0.5 / 2e-5 + assert report["reynolds"][phase][1] == pytest.approx(expected) + assert report["reynolds"]["power_on"][1] > report["reynolds"]["power_off"][1] + + +def test_set_datcom_atmosphere_requires_both_phases(datcom_rocket): + """A reference ending before burnout cannot supply a coast atmosphere.""" + # Arrange + flight = _flight(datcom_rocket) + flight.apogee_time = datcom_rocket.motor.burn_time[1] / 2 + # Act / Assert + with pytest.raises(ValueError, match="no power_off samples"): + datcom_rocket.set_datcom_atmosphere(flight) + + +@pytest.mark.parametrize("peak", [0.0, 0.2, 0.95, 1.5, 3.0, 8.0]) +def test_adaptive_mach_grid_native_limit_and_regime(peak): + """Every flight regime gets 20 distinct points, fixed anchors and headroom.""" + # Arrange + from rocketpy.rocket.datcom_conditions import adaptive_mach_grid + + # Act + grid = adaptive_mach_grid(peak) + # Assert + assert len(grid) == 20 + assert 0.1 in grid and 1.0 in grid + assert grid[0] == pytest.approx(0.01) + assert grid[-1] >= peak * 1.15 + assert np.diff(grid).min() >= 0.0099 + assert np.count_nonzero((grid >= 0.8) & (grid <= 1.2)) >= 9 + + +def test_set_datcom_atmosphere_adapts_mach_and_alpha(datcom_rocket): + """Updating from a hotter trajectory expands both grids and rebuilds REN.""" + # Arrange + flight = _flight(datcom_rocket) + original_mach = flight.mach_number + flight.mach_number = lambda t: 3 * original_mach(t) + flight.partial_angle_of_attack = lambda t: 40 * original_mach(t) + # Act + report = datcom_rocket.set_datcom_atmosphere(flight) + decks, _, _, _ = datcom_decks(datcom_rocket) + # Assert + assert len(report["mach"]) == 20 + assert report["mach"][-1] >= 3.45 + assert 0.1 in report["mach"] and 1 in report["mach"] + assert report["alpha"][-1] >= 48 + assert report["alpha"][0] == pytest.approx(-report["alpha"][-1]) + assert 0 in report["alpha"] + for phase in ("power_on", "power_off"): + assert len(report["reynolds"][phase]) == 20 + assert report["coverage"]["phases"][phase]["mach_max"] > 2.9 + assert all("NMACH = 20." in deck for deck in decks.values()) + + +def test_set_datcom_atmosphere_ignores_unloaded_incidence(datcom_rocket): + """An undefined pad-flow direction cannot force a tail-first alpha table.""" + # Arrange + flight = _flight(datcom_rocket) + flight.partial_angle_of_attack = lambda t: np.where(t == 0, 180, 3) + # Act + report = datcom_rocket.set_datcom_atmosphere(flight) + # Assert + assert report["alpha"][-1] == pytest.approx(20) + assert report["coverage"]["excluded_low_q_samples"] > 0 + + +def test_set_datcom_atmosphere_loaded_tail_first_rejected(datcom_rocket): + """A tumbling ascent cannot silently fold loaded tail-first flow.""" + # Arrange + flight = _flight(datcom_rocket) + flight.mach_number = lambda t: np.full_like(t, 0.5) + flight.partial_angle_of_attack = lambda t: np.full_like(t, 180) + # Act / Assert + with pytest.raises(ValueError, match="tail-first"): + datcom_rocket.set_datcom_atmosphere(flight) + + +def test_set_datcom_atmosphere_explicit_alpha_preserved(datcom_rocket): + """Explicit native grids remain available for controlled tunnel conditions.""" + # Arrange + flight = _flight(datcom_rocket) + # Act + report = datcom_rocket.set_datcom_atmosphere( + flight, mach=[0.1, 1.0], alpha=[-5, 0, 5] + ) + # Assert + assert report["alpha"] == [-5, 0, 5] + assert report["mach"] == [0.1, 1.0] + + +def test_datcom_provisional_grid_uses_twenty_points(): + """Even before a reference flight, the default uses all native Mach slots.""" + # Arrange + from rocketpy.rocket.datcom import _flight_conditions + + # Act + mach, _, _, _ = _flight_conditions({}) + # Assert + assert len(mach) == 20 + assert 0.1 in mach and 1.0 in mach diff --git a/tests/unit/rocket/test_datcom_calibration.py b/tests/unit/rocket/test_datcom_calibration.py new file mode 100644 index 000000000..46b2ee9ae --- /dev/null +++ b/tests/unit/rocket/test_datcom_calibration.py @@ -0,0 +1,106 @@ +"""Explicit native DATCOM calibration and prepared-model lifecycle.""" + +import json +import re +from unittest.mock import patch + +import numpy as np +import pytest + +from rocketpy._encoders import RocketPyDecoder, RocketPyEncoder +from rocketpy.rocket.datcom import datcom_decks + + +def test_datcom_calibration_native_units_and_geometry(datcom_rocket): + """Sand roughness uses dimensionless RHR while LER remains in metres.""" + # Arrange + rocket = datcom_rocket + original = rocket.fins[0].leading_edge_radius + # Act + rocket.add_datcom_calibration(roughness_height=30.48e-6, leading_edge_radius=0.0005) + decks, _, _, _ = datcom_decks(rocket) + # Assert + for deck in decks.values(): + assert float(re.search(r"RHR\s*=\s*([-+\d.eE]+)", deck)[1]) == pytest.approx( + 400 + ) + assert "LER = 0.0005,0.0005," in deck + assert "ROUGH =" not in deck + assert rocket.fins[0].leading_edge_radius == original + + +@pytest.mark.parametrize("field", ["roughness_height", "leading_edge_radius"]) +@pytest.mark.parametrize("value", [-1, np.nan, np.inf, True, "invalid", [0.001]]) +def test_datcom_calibration_rejects_invalid_values_atomically( + datcom_rocket, field, value +): + """Invalid replacement preserves the previous calibration and native deck.""" + # Arrange + datcom_rocket.add_datcom_calibration(roughness_height=10e-6) + before = datcom_decks(datcom_rocket)[0] + # Act / Assert + with pytest.raises(ValueError, match=field): + datcom_rocket.add_datcom_calibration(**{field: value}) + assert datcom_decks(datcom_rocket)[0] == before + + +def test_datcom_calibration_requires_datcom_fins(calisto_motorless): + """Calibration cannot silently affect an ordinary RocketPy rocket.""" + # Arrange + rocket = calisto_motorless + # Act / Assert + with pytest.raises(ValueError, match="DATCOM fin set"): + rocket.add_datcom_calibration(roughness_height=10e-6) + + +def test_datcom_calibration_cache_and_reset(datcom_rocket, datcom_table_factory): + """Changed inputs rebuild both cases; identical inputs reuse the model.""" + # Arrange + rocket = datcom_rocket + original = datcom_decks(rocket)[0] + # Act + with patch( + "rocketpy.rocket.datcom.run_datcom", return_value=datcom_table_factory() + ) as run: + first = rocket.prepare_datcom() + rocket.add_datcom_calibration(roughness_height=20e-6) + assert rocket._datcom_model is None + calibrated = rocket.prepare_datcom() + rocket.add_datcom_calibration(roughness_height=20e-6) + unchanged = rocket.prepare_datcom() + rocket.add_datcom_calibration() + reset = rocket.prepare_datcom() + # Assert + assert first is not calibrated + assert unchanged is calibrated + assert reset is not calibrated + assert run.call_count == 6 + assert datcom_decks(rocket)[0] == original + + +def test_datcom_calibration_serialization_and_copy(datcom_rocket): + """Saved rockets retain calibration; editing a returned dict cannot change it.""" + # Arrange + datcom_rocket.add_datcom_calibration( + roughness_height=20e-6, leading_edge_radius=0.0005 + ) + # Act + returned = datcom_rocket.datcom_calibration + returned["roughness_height"] = 1 + restored = json.loads( + json.dumps(datcom_rocket, cls=RocketPyEncoder), cls=RocketPyDecoder + ) + # Assert + assert restored.datcom_calibration == datcom_rocket.datcom_calibration + assert restored.datcom_calibration["roughness_height"] == pytest.approx(20e-6) + assert restored._datcom_model is None + assert datcom_decks(restored)[0] == datcom_decks(datcom_rocket)[0] + + +def test_datcom_calibration_naca_radius_is_not_ignored(datcom_rocket): + """A NACA airfoil defines its own leading edge and rejects an override.""" + # Arrange + datcom_rocket.fins[0].section_type = "NACA" + # Act / Assert + with pytest.raises(ValueError, match="NACA"): + datcom_rocket.add_datcom_calibration(leading_edge_radius=0.001) diff --git a/tests/unit/rocket/test_datcom_extensions.py b/tests/unit/rocket/test_datcom_extensions.py new file mode 100644 index 000000000..b1eab6e49 --- /dev/null +++ b/tests/unit/rocket/test_datcom_extensions.py @@ -0,0 +1,423 @@ +"""DATCOM spanwise geometry, section definitions and asymmetric layout behavior.""" + +import json +import re +from unittest.mock import patch + +import matplotlib.pyplot as plt +import numpy as np +import pytest + +from rocketpy import DatcomFins, DatcomFreeFormFins, Rocket +from rocketpy._encoders import RocketPyDecoder, RocketPyEncoder +from rocketpy.mathutils.vector_matrix import Vector +from rocketpy.rocket.datcom import ( + DatcomResult, + DatcomSurface, + datcom_decks, + parse_datcom_output, +) + + +def _values(deck, variable): + text = re.search(rf"\b{variable}\s*=\s*([\dEe+.,\s-]+)", deck)[1] + return [float(v) for v in re.split(r"[\s,]+", text.strip(" ,\n"))] + + +def test_datcom_default_alpha_dense_near_zero(): + """Default incidence spans ±20 degrees with most points within ±2 degrees.""" + # Arrange + from rocketpy.rocket.datcom import _flight_conditions + + # Act + _, alpha, _, _ = _flight_conditions({}) + # Assert + assert alpha[[0, -1]] == pytest.approx([-20, 20]) + assert len(alpha) <= 20 + assert np.count_nonzero(abs(alpha) <= 2) > len(alpha) / 2 + assert np.count_nonzero(alpha == 0) == 1 + assert np.diff(alpha).min() > 0 + + +@pytest.mark.parametrize("profile,card", [("conical", "CONE"), ("ogive", "OGIVE")]) +def test_datcom_tail_kind_export(datcom_rocket, profile, card): + """The assembled tail's actual profile selects the DATCOM afterbody card.""" + # Arrange + from rocketpy import Tail + + tail = next( + s for s, _, _ in datcom_rocket.aerodynamic_surfaces if isinstance(s, Tail) + ) + if profile == "ogive": + datcom_rocket.aerodynamic_surfaces.remove(tail) + tail = datcom_rocket.add_tail( + tail.top_radius, tail.bottom_radius, tail.length, -1.2, kind="ogive" + ) + # Act + decks, _, _, _ = datcom_decks(datcom_rocket) + # Assert + assert all(f"TAFT = {card}" in deck for deck in decks.values()) + + +@pytest.mark.parametrize("count", [1, 2, 4, 8]) +def test_datcom_fins_panel_count_and_triangular_tip(count, tmp_path): + """DATCOM accepts 1..8 panels and a zero tip chord without inherited loads.""" + # Arrange / Act + fins = DatcomFins(count, 0.3, 0, 0.1, 0.06, path=tmp_path / "datcom") + # Assert + assert fins.n == count + assert fins.tip_chord == 0 + assert fins.panel_angles == pytest.approx(np.arange(count) * 360 / count) + assert np.isfinite(fins.shape_vec).all() + + +def test_datcom_multi_station_geometry_and_serialization(tmp_path): + """The drawn multi-segment outline and serialized section stations agree.""" + # Arrange + fins = DatcomFreeFormFins( + 4, + [0, 0.06, 0.12], + [0.3, 0.22, 0.04], + [0, 0.02, 0.1], + 0.06, + path=tmp_path / "datcom", + thickness_to_chord=[0.02, 0.025, 0.03], + panel_angles=[10, 100, 190, 280], + dihedral=[1, 2, 3, 4], + ) + # Act + restored = json.loads(json.dumps(fins, cls=RocketPyEncoder), cls=RocketPyDecoder) + # Assert + assert isinstance(restored, DatcomFreeFormFins) + assert restored.to_dict() == fins.to_dict() + assert fins.datcom_span_stations == pytest.approx([0, 0.06, 0.12]) + np.testing.assert_allclose(fins.shape_vec[0], [0, 0.02, 0.1, 0.14, 0.24, 0.3]) + assert fins.upper_thickness_to_chord == pytest.approx([0.01, 0.0125, 0.015]) + + +@pytest.mark.parametrize( + "section,options", + [ + ("NACA", {"naca": "0012"}), + ("NACA", {"naca": "6-64-005"}), + ( + "USER", + { + "airfoil_coordinates": { + "x": [0, 0.5, 1], + "upper": [0, 0.03, 0], + "lower": [0, -0.02, 0], + } + }, + ), + ( + "USER", + { + "airfoil_coordinates": { + "x": [0, 0.5, 1], + "mean": [0, 0.005, 0], + "thickness": [0, 0.05, 0], + } + }, + ), + ], +) +def test_datcom_airfoil_sections_generate_decks(section, options, datcom_rocket): + """NACA control cards and USER coordinate namelists use the requested section.""" + # Arrange + old = datcom_rocket.fins[0] + datcom_rocket.aerodynamic_surfaces.remove(old) + datcom_rocket.add_datcom_trapezoidal_fins( + 4, + 0.25, + 0.1, + 0.1, + -0.9, + path=old.datcom_path, + section_type=section, + datcom_options=old.datcom_options, + **options, + ) + # Act + deck = datcom_decks(datcom_rocket)[0][True] + # Assert + assert f"SECTYP = {section}" in deck + if section == "NACA": + assert "NACA-1-" in deck + assert "ZUPPER" not in deck + else: + assert _values(deck, "XCORD") == pytest.approx([0, 0.5, 1]) + assert ("YUPPER" in deck) == ("upper" in options["airfoil_coordinates"]) + + +def test_datcom_asymmetric_hex_and_cant_sign(datcom_rocket): + """Separate upper/lower geometry and RocketPy-positive cant survive the deck.""" + # Arrange + old = datcom_rocket.fins[0] + datcom_rocket.aerodynamic_surfaces.remove(old) + fins = datcom_rocket.add_datcom_hex_fins( + 4, + 0.25, + 0.1, + 0.1, + -0.9, + path=old.datcom_path, + datcom_options=old.datcom_options, + upper_thickness_to_chord=[0.01, 0.02], + lower_thickness_to_chord=[0, 0.015], + lower_max_thickness_position=0.2, + lower_flat_length=0.4, + cant_angle=0.5, + ) + # Act + deck = datcom_decks(datcom_rocket)[0][True] + # Assert + assert _values(deck, "ZUPPER") == pytest.approx([0.01, 0.02]) + assert _values(deck, "ZLOWER") == pytest.approx([0, 0.015]) + assert _values(deck, "LMAXL") == pytest.approx([0.2, 0.2]) + assert _values(deck, "DELTA1") == pytest.approx([-0.5] * 4) + assert fins.thickness_to_chord == pytest.approx([0.01, 0.035]) + assert deck.count("NEXT CASE") == 3 + + +def test_datcom_fin_sets_sorted_with_matching_naca_and_deflection(datcom_rocket): + """Sorting moves NACA cards and DELTA arrays along with their fin geometry.""" + # Arrange + old = datcom_rocket.fins[0] + datcom_rocket.add_datcom_trapezoidal_fins( + 4, + 0.12, + 0.06, + 0.06, + 0.4, + path=old.datcom_path, + datcom_options=old.datcom_options, + section_type="NACA", + naca="0012", + incidence=2, + ) + # Act + deck = datcom_decks(datcom_rocket)[0][True] + first = deck.split("$FINSET1", 1)[1].split("$FINSET2", 1)[0] + # Assert + assert _values(first, "XLE")[0] == pytest.approx(0.8) + assert "NACA-1-4-0012" in deck + assert _values(deck, "DELTA1") == pytest.approx([2] * 4) + assert _values(deck, "DELTA2") == pytest.approx([0] * 4) + + +def test_datcom_fin_sets_overlap_rejected(datcom_rocket): + """Axially overlapping planform envelopes fail before executing DATCOM.""" + # Arrange + old = datcom_rocket.fins[0] + datcom_rocket.add_datcom_hex_fins( + 4, 0.25, 0.1, 0.1, -0.8, path=old.datcom_path, datcom_options=old.datcom_options + ) + # Act / Assert + with pytest.raises(ValueError, match="overlap"): + datcom_decks(datcom_rocket) + + +@pytest.mark.parametrize("profile", ["conical", "ogive"]) +def test_datcom_fins_automatic_boattail_attachment(datcom_rocket, profile): + """SSPAN=0 attaches to the interpolated body radius at root mid-chord.""" + # Arrange + from rocketpy import Tail + + tail = next( + s for s, _, _ in datcom_rocket.aerodynamic_surfaces if isinstance(s, Tail) + ) + if profile == "ogive": + datcom_rocket.aerodynamic_surfaces.remove(tail) + tail = datcom_rocket.add_tail( + tail.top_radius, tail.bottom_radius, tail.length, -1.2, kind="ogive" + ) + old = datcom_rocket.fins[0] + datcom_rocket.aerodynamic_surfaces.remove(old) + fins = datcom_rocket.add_datcom_arc_fins( + 4, + 0.04, + 0.02, + 0.05, + -1.21, + path=old.datcom_path, + datcom_options=old.datcom_options, + ) + # Act + deck = datcom_decks(datcom_rocket)[0][True] + # Assert + assert _values(deck, "SSPAN") == pytest.approx([0, 0.05]) + expected = ( + float(tail.radius_at(0.03)) + if profile == "ogive" + else 0.0635 + 0.3 * (0.045 - 0.0635) + ) + assert fins._datcom_attachment_radius == pytest.approx(expected) + + +def test_datcom_projection_respects_panel_angles_and_dihedral(tmp_path): + """The rocket drawing projects actual PHIF/GAM positions instead of equal spacing.""" + # Arrange + fins = DatcomFins( + 2, + 0.3, + 0.1, + 0.1, + 0.06, + path=tmp_path / "datcom", + panel_angles=[0, 90], + dihedral=[10, 0], + ) + fig, ax = plt.subplots() + records = [] + # Act + fins.draw_on_rocket(ax, 0, records, {"fins": "black", "line_width": 1}, "xz", 1) + # Assert + assert ax.lines[0].get_ydata()[1] == pytest.approx(0.1 * np.sin(np.radians(10))) + assert ax.lines[1].get_ydata()[0] == pytest.approx(0.06) + assert len(records) == 1 + plt.close(fig) + + +@pytest.mark.parametrize( + "options", + [ + {"section_type": "NACA"}, + {"section_type": "NACA", "naca": "0012\nDAMP"}, + { + "section_type": "USER", + "airfoil_coordinates": { + "x": [0, 0.5, 0.4], + "upper": [0, 0.1, 0], + "lower": [0, 0, 0], + }, + }, + {"panel_angles": [0, 0, 90, 180]}, + {"dihedral": 90}, + {"incidence": [0, 1]}, + {"incidence": 1, "cant_angle": 1}, + ], +) +def test_datcom_invalid_section_or_panel_definition(options, tmp_path): + """Unsupported or inconsistent definitions are rejected without a native run.""" + # Arrange / Act / Assert + with pytest.raises(ValueError): + DatcomFins(4, 0.3, 0.1, 0.1, 0.06, path=tmp_path / "datcom", **options) + + +def test_datcom_full_beta_interpolation_does_not_add_derivatives_twice( + datcom_rocket, datcom_table_factory +): + """Full beta data already contains the side force and must bypass CYB expansion.""" + # Arrange + tables = {b: datcom_table_factory({"CY": b, "CYB": 100}) for b in [-5, 0, 5]} + zero = tables[0] + grid = DatcomResult( + tuple(zero), + zero.body, + zero.derivatives, + zero.damping, + beta=[-5, 0, 5], + sideslip=tables, + ) + model = DatcomSurface(datcom_rocket, {True: grid, False: grid}, {True: 0, False: 0}) + velocity = Vector([100 * np.tan(np.radians(2.5)), 0, 100]) + speed = abs(velocity) + # Act + loads = model.compute_forces_and_moments_at_time( + 0.5, 0, -velocity, speed, 0.4, 1.2, Vector.zeros(), Vector.zeros(), 0 + ) + # Assert + assert loads[0] == pytest.approx(0.5 * 1.2 * speed**2 * model.reference_area * 2.5) + + +def test_datcom_separate_power_altitude_schedules(datcom_rocket): + """Powered and coast decks retain different altitude schedules at the same Mach.""" + # Arrange + datcom_rocket.fins[0].datcom_options["altitude"] = { + "power_on": [100, 200], + "power_off": [900, 500], + } + # Act + decks = datcom_decks(datcom_rocket)[0] + # Assert + assert _values(decks[True], "ALT") == pytest.approx([100, 200]) + assert _values(decks[False], "ALT") == pytest.approx([900, 500]) + + +def test_datcom_incidence_rate_uses_body_projected_alpha( + datcom_rocket, datcom_table_factory +): + """Local alpha-dot follows atan(v_down/v_forward), even with lateral velocity.""" + # Arrange + from rocketpy import Environment, Flight + + rocket = datcom_rocket + table = datcom_table_factory() + model = DatcomSurface( + rocket, + {True: table, False: table}, + { + True: rocket.center_of_dry_mass_position, + False: rocket.center_of_dry_mass_position, + }, + ) + rocket._datcom_model = model + flight = object.__new__(Flight) + flight.rocket, flight.env = rocket, Environment() + velocity = Vector([7, 8, 100]) + omega = Vector([0.2, 0.3, 0.1]) + acceleration = Vector([1, 2, 3]) + state = [0, 0, 1000, *velocity, 1, 0, 0, 0, *omega] + derivative = [*velocity, *acceleration, 0, 0, 0, 0, 0, 0, 0] + body_acceleration = acceleration - (omega ^ velocity) + expected = (velocity.z * body_acceleration.y - velocity.y * body_acceleration.z) / ( + velocity.z**2 + velocity.y**2 + ) + # Act + rate = flight._datcom_incidence_rate(0.5, state, derivative) + # Assert + assert rate == pytest.approx(expected, rel=1e-12) + + +def test_datcom_zero_normal_slope_has_undefined_cp(datcom_rocket, datcom_table_factory): + """A zero normal-force slope has no CP and must not fabricate static stability.""" + # Arrange + table = datcom_table_factory({"CNA": 0, "CMA": 0}) + model = DatcomSurface( + datcom_rocket, {True: table, False: table}, {True: 0, False: 0} + ) + # Act + cp = model.small_angle_cp(0.4) + datcom_rocket._datcom_model = model + datcom_rocket.evaluate_center_of_pressure() + # Assert + assert np.isnan(cp) + assert np.isnan(datcom_rocket.static_margin(0)) + + +def test_datcom_boattail_fin_drawing_preserves_body_anchors(datcom_rocket): + """Fins on a boattail remain overlays and do not create cylindrical body segments.""" + # Arrange + old = datcom_rocket.fins[0] + datcom_rocket.aerodynamic_surfaces.remove(old) + fins = datcom_rocket.add_datcom_arc_fins( + 4, + 0.04, + 0.02, + 0.05, + -1.21, + path=old.datcom_path, + datcom_options=old.datcom_options, + ) + datcom_decks(datcom_rocket) + nose, tail = datcom_rocket.nosecones[0], datcom_rocket.tails[0] + records = [(nose, 0.65, 0.0635, 0.65), (tail, -1.2, 0.045, -1.3)] + original = list(records) + fig, ax = plt.subplots() + # Act + fins.draw_on_rocket(ax, -1.21, records, {"fins": "black", "line_width": 1}, "xz", 1) + # Assert + assert records == original + assert len(ax.lines) == 4 + plt.close(fig) diff --git a/tests/unit/rocket/test_datcom_wind_tunnel.py b/tests/unit/rocket/test_datcom_wind_tunnel.py new file mode 100644 index 000000000..32ba0f1db --- /dev/null +++ b/tests/unit/rocket/test_datcom_wind_tunnel.py @@ -0,0 +1,300 @@ +"""Wind-tunnel coefficient fits, units, frames and failure atomicity.""" + +import json +import re +from unittest.mock import patch + +import numpy as np +import pytest + +from rocketpy.mathutils.vector_matrix import Vector +from rocketpy.rocket.datcom import DatcomSurface, datcom_decks +from rocketpy.rocket.datcom_calibration import _measurements, _predictions + + +def _linear_native(factory): + """A deterministic native surrogate with independently identifiable inputs.""" + + def run(path, deck, timeout): + rough = re.search(r"RHR\s*=\s*([-+\d.eE]+)", deck) + height = float(rough[1]) * 3e-6 * 0.0254 if rough else 0 + radius = float(re.search(r"LER\s*=\s*([-+\d.eE]+)", deck)[1]) + return factory( + {"CA": 0.2 + 10000 * height, "CN": 0.3 + 500 * radius, "CMQ": -0.2} + ) + + return run + + +@pytest.mark.parametrize("source", ["mapping", "csv"]) +def test_wind_tunnel_drag_only_fit( + datcom_rocket, datcom_table_factory, tmp_path, source +): + """Mach-alpha-CD alone recovers a known input and applies it explicitly.""" + # Arrange + data = {"mach": [0.2, 0.6], "alpha": [0, 0], "CD": [0.7, 0.7]} + if source == "csv": + path = tmp_path / "tunnel.csv" + path.write_text("mach,alpha,CD\n0.2,0,0.7\n0.6,0,0.7\n") + data = path + # Act + with patch( + "rocketpy.rocket.datcom.run_datcom", + side_effect=_linear_native(datcom_table_factory), + ): + report = datcom_rocket.fit_datcom_calibration(data) + # Assert + assert report["calibration"]["roughness_height"] == pytest.approx(50e-6, abs=1e-8) + assert report["weighted_rmse_fitted"] < 1e-5 + assert report["weighted_rmse_initial"] == pytest.approx(0.5) + assert report["reference_area"] == datcom_rocket.area + assert datcom_rocket.datcom_calibration == report["calibration"] + assert datcom_rocket._datcom_model is None + json.dumps(report, allow_nan=False) + + +def test_wind_tunnel_two_parameter_fit(datcom_rocket, datcom_table_factory): + """Independent axial/normal measurements constrain roughness and radius.""" + # Arrange + data = {"mach": [0.2, 0.6], "CA": [0.7, 0.7], "CN": [0.55, 0.55]} + # Act + with patch( + "rocketpy.rocket.datcom.run_datcom", + side_effect=_linear_native(datcom_table_factory), + ): + report = datcom_rocket.fit_datcom_calibration( + data, + parameters=("roughness_height", "leading_edge_radius"), + bounds={"roughness_height": (0, 100e-6), "leading_edge_radius": (0, 0.001)}, + max_evaluations=150, + ) + # Assert + assert report["calibration"]["roughness_height"] == pytest.approx(50e-6, abs=1e-8) + assert report["calibration"]["leading_edge_radius"] == pytest.approx( + 0.0005, abs=1e-7 + ) + assert report["weighted_rmse_fitted"] < 1e-5 + + +@pytest.mark.parametrize("orientation", [1, -1]) +def test_wind_tunnel_force_moment_references( + datcom_rocket, datcom_table_factory, orientation +): + """Moment transfer matches an independent force/cross-product calculation.""" + # Arrange + rocket = datcom_rocket + rocket._csys = orientation + table = datcom_table_factory( + {"CA": 0.4, "CN": 0.2, "CY": 0.1, "CM": 0.03, "CLN": 0.04, "CLL": 0.05} + ) + model = DatcomSurface(rocket, {False: table}, {False: 0.4}) + origin = -0.2 + diameter = 2 * rocket.radius + area, length, lateral = 2 * rocket.area, 3 * diameter, 4 * diameter + columns, measured = _measurements( + { + "mach": [0.2], + "alpha": [1], + "CD": [0], + "CL": [0], + "CA": [0], + "CN": [0], + "CY": [0], + "CM": [0], + "CLN": [0], + "CLL": [0], + }, + False, + ) + force = Vector([0.1, -0.2, -0.4]) * rocket.area + moment = Vector([0.03, 0.04, 0.05]) * (rocket.area * diameter) + moment += Vector([0, 0, (0.4 - origin) * orientation]) ^ force + flow = Vector([0, np.sin(np.radians(1)), np.cos(np.radians(1))]) + # Act + values = _predictions( + model, rocket, columns, measured, area, length, lateral, origin, "wind" + ) + # Assert + assert values["CD"][0] == pytest.approx( + -sum(force[i] * flow[i] for i in range(3)) / area + ) + assert values["CA"][0] == pytest.approx(0.2) + assert values["CM"][0] == pytest.approx(moment[0] / (area * length)) + assert values["CLN"][0] == pytest.approx(moment[1] / (area * lateral)) + assert values["CLL"][0] == pytest.approx(moment[2] / (area * lateral)) + + +def test_wind_tunnel_reference_area_and_missing_channels( + datcom_rocket, datcom_table_factory +): + """Area scaling and blank partial channels are honored in weighted residuals.""" + # Arrange + data = {"mach": [0.2, 0.6], "CA": [0.35, np.nan], "CN": [np.nan, 0.15]} + # Act + with patch( + "rocketpy.rocket.datcom.run_datcom", + side_effect=_linear_native(datcom_table_factory), + ): + report = datcom_rocket.fit_datcom_calibration( + data, reference_area=2 * datcom_rocket.area, weights={"CA": 2} + ) + # Assert + assert report["calibration"]["roughness_height"] == pytest.approx(50e-6, abs=1e-8) + assert len(report["residuals"]) == 2 + assert report["rmse"]["CN"]["fitted"] == pytest.approx(0) + + +@pytest.mark.parametrize( + "data,extra,message", + [ + ({"mach": [0.2]}, {}, "at least one coefficient"), + ({"mach": [0.2], "CD": [np.inf]}, {}, "infinite"), + ({"mach": [0.2], "CD": [np.nan]}, {}, "contain a measurement"), + ({"mach": [0.2], "alpha": [0, 1], "CD": [0.4]}, {}, "equal lengths"), + ({"mach": [0.2], "CM": [0]}, {}, "reference_length"), + ({"mach": [0.2], "CM": [0]}, {"reference_length": 1}, "moment_reference"), + ( + {"mach": [0.2], "CD": [0.4]}, + {"parameters": ["leading_edge_radius"]}, + "bounds", + ), + ({"mach": [0.2], "CD": [0.4]}, {"weights": {"CD": 0}}, "weight"), + ({"mach": [0.2], "CD": [0.4]}, {"drag_axis": "body"}, "drag_axis"), + ({"mach": [0.2], "CD": [0.4], "power_on": [2]}, {}, "power_on"), + ({"mach": [0.2], "CD": [0.4], "reynolds_per_meter": [-1]}, {}, "Reynolds"), + ], +) +def test_wind_tunnel_invalid_data_is_atomic(datcom_rocket, data, extra, message): + """Bad measurements cannot change calibration or start native execution.""" + # Arrange + datcom_rocket.add_datcom_calibration(roughness_height=10e-6) + before = datcom_rocket.datcom_calibration + # Act / Assert + with patch("rocketpy.rocket.datcom.run_datcom") as run: + with pytest.raises(ValueError, match=message): + datcom_rocket.fit_datcom_calibration(data, **extra) + run.assert_not_called() + assert datcom_rocket.datcom_calibration == before + + +@pytest.mark.parametrize("failure", ["native", "envelope", "budget", "beta"]) +def test_wind_tunnel_failed_fit_preserves_prepared_model( + datcom_rocket, datcom_table_factory, failure +): + """Execution failures and unsupported measurements preserve the live model.""" + # Arrange + with patch( + "rocketpy.rocket.datcom.run_datcom", return_value=datcom_table_factory() + ): + original = datcom_rocket.prepare_datcom() + settings = datcom_rocket.datcom_calibration + data = {"mach": [0.9 if failure == "envelope" else 0.2], "CD": [0.7]} + if failure == "beta": + data["beta"] = [1] + extra = {"max_evaluations": 3} if failure == "budget" else {} + native = ( + RuntimeError("native failed") + if failure == "native" + else _linear_native(datcom_table_factory) + ) + # Act / Assert + with patch("rocketpy.rocket.datcom.run_datcom", side_effect=native): + with pytest.raises((RuntimeError, ValueError)): + datcom_rocket.fit_datcom_calibration(data, **extra) + assert datcom_rocket._datcom_model is original + assert datcom_rocket.datcom_calibration == settings + + +def test_wind_tunnel_reynolds_is_probe_only(datcom_rocket, datcom_table_factory): + """Tunnel REN replaces ALT only during probes and never changes Flight options.""" + # Arrange + native = _linear_native(datcom_table_factory) + original = dict(datcom_rocket.fins[0].datcom_options) + decks = [] + + def run(path, deck, timeout): + decks.append(deck) + return native(path, deck, timeout) + + # Act + with patch("rocketpy.rocket.datcom.run_datcom", side_effect=run): + report = datcom_rocket.fit_datcom_calibration( + {"mach": [0.2, 0.6], "CD": [0.7, 0.7], "reynolds_per_meter": [1e6, 2e6]} + ) + # Assert + assert all( + "REN = 1000000.,2000000.," in deck and "ALT =" not in deck for deck in decks + ) + assert datcom_rocket.fins[0].datcom_options == original + assert "ALT =" in datcom_decks(datcom_rocket)[0][False] + assert report["reynolds_source"] == "measurements" + + +def test_wind_tunnel_scale_model_references(datcom_rocket, datcom_table_factory): + """A half-size tunnel model preserves coefficients and equivalent geometry.""" + # Arrange + scale = 0.5 + data = {"mach": [0.2, 0.6], "CD": [0.7, 0.7], "reynolds_per_meter": [2e6, 4e6]} + native = _linear_native(datcom_table_factory) + decks = [] + + def run(path, deck, timeout): + decks.append(deck) + return native(path, deck, timeout) + + # Act + with patch("rocketpy.rocket.datcom.run_datcom", side_effect=run): + report = datcom_rocket.fit_datcom_calibration( + data, reference_area=datcom_rocket.area * scale**2, model_scale=scale + ) + # Assert + assert report["calibration"]["roughness_height"] == pytest.approx(50e-6, abs=1e-8) + assert report["reference_area"] == datcom_rocket.area + assert report["model_scale"] == scale + assert all("REN = 1000000.,2000000.," in deck for deck in decks) + + +@pytest.mark.parametrize("axis", ["wind", "stability"]) +def test_wind_tunnel_drag_axes_with_sideslip(datcom_rocket, datcom_table_factory, axis): + """Wind-axis CD includes lateral force; stability drag is the pitch projection.""" + # Arrange + table = datcom_table_factory({"CA": 0.4, "CN": 0.2, "CY": 0.1}) + table.beta = np.array([-5.0, 0.0, 5.0]) + table.sideslip = {angle: table for angle in table.beta} + model = DatcomSurface(datcom_rocket, {False: table}, {False: 0}) + columns, measured = _measurements( + {"mach": [0.2], "alpha": [1], "beta": [3], "CD": [0]}, False + ) + alpha, beta = np.radians([1, 3]) + force = Vector([0.1, -0.2, -0.4]) + flow = Vector([np.tan(beta), np.tan(alpha), 1]) + flow = flow / abs(flow) + expected = ( + -sum(force[i] * flow[i] for i in range(3)) + if axis == "wind" + else 0.4 * np.cos(alpha) + 0.2 * np.sin(alpha) + ) + # Act + result = _predictions( + model, + datcom_rocket, + columns, + measured, + datcom_rocket.area, + None, + None, + None, + axis, + ) + # Assert + assert result["CD"][0] == pytest.approx(expected) + + +@pytest.mark.parametrize("ren", [0, -1, np.nan, [1], [1, 2, 3], {"power_on": 1}]) +def test_datcom_reynolds_schedule_validation(datcom_rocket, ren): + """Invalid native Reynolds conditions cannot silently select an atmosphere.""" + # Arrange + datcom_rocket.fins[0].datcom_options["reynolds"] = ren + # Act / Assert + with pytest.raises(ValueError, match="reynolds"): + datcom_decks(datcom_rocket) diff --git a/tests/unit/rocket/test_parachute.py b/tests/unit/rocket/test_parachute.py index 7a61c2349..7a4fae041 100644 --- a/tests/unit/rocket/test_parachute.py +++ b/tests/unit/rocket/test_parachute.py @@ -130,3 +130,113 @@ def test_callable_trigger_arities_route_arguments(trigger, expects_udot): result = parachute.triggerfunc(800.0, 500.0, [0.0] * 6, [], [1.0] * 6) assert result is True assert parachute.triggerfunc._expects_udot is expects_udot + + +@pytest.mark.parametrize( + "trigger", + [800, 800.0, np.int64(800), np.int32(800), np.float64(800), np.float32(800)], + ids=str, +) +def test_any_real_number_is_read_as_a_height(trigger): + """A height is anything ``numbers.Real``, not just ``int`` and ``float``. + + The check used to be ``isinstance(trigger, (int, float))``. ``numpy.float64`` + subclasses ``float`` and passed, but ``numpy.int64`` and ``numpy.float32`` + subclass neither, so a height read out of a NumPy array raised even though + it compares and arithmetics exactly like the value that worked.""" + parachute = _make_parachute(trigger=trigger) + + # Truthiness rather than `is True`: comparing against a NumPy scalar gives + # back a numpy.bool_, which is not the `True` singleton. + # falling (vz < 0) and below the trigger height + assert parachute.triggerfunc(0.0, 700.0, [0.0] * 5 + [-1.0], [], None) + # falling but still above it + assert not parachute.triggerfunc(0.0, 900.0, [0.0] * 5 + [-1.0], [], None) + # below it but still ascending + assert not parachute.triggerfunc(0.0, 700.0, [0.0] * 5 + [1.0], [], None) + + +@pytest.mark.parametrize( + "trigger", + [True, False, np.bool_(True), complex(800), np.complex64(800), "banana", None, {}], + ids=str, +) +def test_what_is_not_a_height_is_still_refused(trigger): + """Widening to ``numbers.Real`` must not turn the check into "anything". + + ``bool`` is the one that has to be excluded by hand, because it *is* an + ``int``: ``True`` would otherwise be accepted and read as a height of one + metre, firing the parachute a metre above the ground. ``numpy.bool_`` and + the complex types need no special case, since neither is ``Real``.""" + with pytest.raises(ValueError, match="Unable to set the trigger"): + _make_parachute(trigger=trigger) + + +class TestParachuteTimeTrigger: + """Fixed-time parachute triggers: ``("time", t_deploy)`` (#437).""" + + def test_time_trigger_fires_at_and_after_deploy_time(self): + parachute = _make_parachute(trigger=("time", 5.0)) + state = [0.0] * 13 + + parachute._eval_time = 4.999 + assert parachute.triggerfunc(101325.0, 1000.0, state, [], None) is False + + parachute._eval_time = 5.0 + assert parachute.triggerfunc(101325.0, 1000.0, state, [], None) is True + + parachute._eval_time = 7.5 + assert parachute.triggerfunc(101325.0, 1000.0, state, [], None) is True + + def test_time_trigger_list_form_and_case_insensitive_kind(self): + parachute = _make_parachute(trigger=["TIME", 3]) + state = [0.0] * 13 + + parachute._eval_time = 2.9 + assert parachute.triggerfunc(101325.0, 1000.0, state, [], None) is False + parachute._eval_time = 3.0 + assert parachute.triggerfunc(101325.0, 1000.0, state, [], None) is True + + def test_time_trigger_does_not_require_descent_or_height(self): + parachute = _make_parachute(trigger=("time", 1.0)) + assert parachute._trigger_falling_only is False + assert parachute._trigger_needs_height is False + + # Ascending state at altitude well above any height trigger. + ascending = [0.0, 0.0, 2000.0, 0.0, 0.0, 50.0] + [0.0] * 7 + parachute._eval_time = 1.0 + assert parachute.triggerfunc(101325.0, 2000.0, ascending, [], None) is True + + def test_time_trigger_false_when_eval_time_unset(self): + parachute = _make_parachute(trigger=("time", 0.0)) + assert parachute.triggerfunc(101325.0, 0.0, [0.0] * 13, [], None) is False + + def test_time_trigger_accepts_numpy_scalar_delay(self): + parachute = _make_parachute(trigger=("time", np.float64(2.5))) + parachute._eval_time = 2.5 + assert parachute.triggerfunc(101325.0, 0.0, [0.0] * 13, [], None) is True + + @pytest.mark.parametrize( + "trigger", + [ + ("time", -1.0), + ("time", True), + ("time", "soon"), + # float() would happily eat this one; the numeric boundary must not + ("time", "3.0"), + ("time",), + ("burnout", 3.0), + ("launch", 5.0), + ], + ids=str, + ) + def test_invalid_time_triggers_are_refused(self, trigger): + with pytest.raises(ValueError, match="Unable to set the trigger"): + _make_parachute(trigger=trigger) + + def test_to_dict_round_trip_preserves_time_trigger(self): + original = _make_parachute(trigger=("time", 4.0)) + restored = Parachute.from_dict(original.to_dict()) + assert restored.trigger == ("time", 4.0) + restored._eval_time = 4.0 + assert restored.triggerfunc(101325.0, 0.0, [0.0] * 13, [], None) is True diff --git a/tests/unit/rocket/test_parachute_noise_seed.py b/tests/unit/rocket/test_parachute_noise_seed.py new file mode 100644 index 000000000..1cd702eaf --- /dev/null +++ b/tests/unit/rocket/test_parachute_noise_seed.py @@ -0,0 +1,100 @@ +"""Determinism tests for seeded parachute pressure noise (#1091). + +Pressure noise is drawn from a per-instance ``numpy.random.Generator`` created +from the ``seed`` argument, instead of the process-global ``numpy.random``. +A seed makes the noise reproducible and independent of the global RNG state. +""" + +import numpy as np + +from rocketpy import Parachute +from rocketpy.stochastic import StochasticParachute + + +def _parachute(seed, noise=(0, 8.3, 0.5)): + return Parachute( + name="main", + cd_s=10.0, + trigger="apogee", + sampling_rate=100, + noise=noise, + seed=seed, + ) + + +def _noise_sequence(parachute, n=16): + # Include the initial sample stored at construction, then draw from + # ``noise_function`` the way Flight does while sampling the trigger. + samples = [parachute.noise_signal[0][1]] + for _ in range(n): + value = parachute.noise_function() + parachute.noise_signal.append([0.0, value]) + samples.append(value) + return samples + + +def test_same_seed_is_reproducible(): + assert _noise_sequence(_parachute(42)) == _noise_sequence(_parachute(42)) + + +def test_different_seeds_decorrelate(): + assert _noise_sequence(_parachute(1)) != _noise_sequence(_parachute(2)) + + +def test_default_unseeded_still_draws_noise(): + """seed=None keeps the default path working with non-zero noise.""" + parachute = _parachute(None) + samples = _noise_sequence(parachute, n=8) + assert any(sample != 0.0 for sample in samples) + + +def test_noise_independent_of_global_numpy_rng(): + np.random.seed(0) + first = _noise_sequence(_parachute(7)) + np.random.seed(999) + _ = [np.random.random() for _ in range(1000)] + second = _noise_sequence(_parachute(7)) + assert first == second + + +def test_seeded_parachute_does_not_consume_global_rng(): + np.random.seed(0) + position_before = np.random.get_state()[2] + _noise_sequence(_parachute(7)) + position_after = np.random.get_state()[2] + assert position_before == position_after + + +def test_zero_noise_still_returns_zero(): + parachute = _parachute(42, noise=(0, 0, 0)) + assert parachute.noise_function() == 0.0 + + +def test_seed_survives_serialization_round_trip(): + original = _parachute(11) + restored = Parachute.from_dict(original.to_dict()) + assert restored.to_dict()["seed"] == 11 + assert _noise_sequence(restored) == _noise_sequence(_parachute(11)) + + +def test_from_dict_defaults_seed_to_none_when_absent(): + data = _parachute(11).to_dict() + del data["seed"] + assert Parachute.from_dict(data).to_dict()["seed"] is None + + +def test_stochastic_parachute_threads_seed_into_created_object(): + template = _parachute(None, noise=(0, 8.3, 0.5)) + stochastic = StochasticParachute(template) + stochastic._set_stochastic(seed=123) + first = stochastic.create_object() + stochastic._set_stochastic(seed=123) + second = stochastic.create_object() + assert first._seed is not None + assert first._seed == second._seed + assert _noise_sequence(first) == _noise_sequence(second) + + stochastic._set_stochastic(seed=456) + other = stochastic.create_object() + assert other._seed != first._seed + assert _noise_sequence(other) != _noise_sequence(first) diff --git a/tests/unit/rocket/test_rocket.py b/tests/unit/rocket/test_rocket.py index 7a37cbd4e..5ac011a8d 100644 --- a/tests/unit/rocket/test_rocket.py +++ b/tests/unit/rocket/test_rocket.py @@ -42,6 +42,55 @@ def test_evaluate_static_margin_assert_cp_equals_cm(dimensionless_calisto): assert pytest.approx(rocket.cp_position(0), 1e-8) == pytest.approx(0, 1e-8) +def test_static_margin_lazy_until_accessed(calisto_motorless): + """Static margin must not be discretized until first access.""" + rocket = calisto_motorless + assert rocket._static_margin_dirty is True + + with patch.object( + rocket._static_margin, + "set_discrete", + wraps=rocket._static_margin.set_discrete, + ) as mock_set_discrete: + rocket.add_nose(length=0.55829, kind="ogive", position=1.160) + mock_set_discrete.assert_not_called() + assert rocket._static_margin_dirty is True + + static_margin = rocket.static_margin + assert mock_set_discrete.call_count == 1 + assert rocket._static_margin_dirty is False + assert isinstance(static_margin, Function) + + # Second access must reuse the cached Function. + _ = rocket.static_margin(0) + assert mock_set_discrete.call_count == 1 + + +def test_static_margin_rebuilds_after_adding_surface(calisto): + """Adding an aero surface invalidates SM; access rebuilds it once.""" + rocket = calisto + margin_before = rocket.static_margin(0) + assert rocket._static_margin_dirty is False + + with patch.object( + rocket._static_margin, + "set_discrete", + wraps=rocket._static_margin.set_discrete, + ) as mock_set_discrete: + rocket.add_nose(length=0.55829, kind="ogive", position=1.160) + mock_set_discrete.assert_not_called() + assert rocket._static_margin_dirty is True + + margin_after = rocket.static_margin(0) + assert mock_set_discrete.call_count == 1 + assert rocket._static_margin_dirty is False + + _ = rocket.static_margin(0) + assert mock_set_discrete.call_count == 1 + + assert margin_after != pytest.approx(margin_before, abs=1e-6) + + @pytest.mark.parametrize( "k, type_", ([2 / 3, "conical"], [0.46469957130675876, "ogive"], [0.563, "lvhaack"]), @@ -466,15 +515,46 @@ def test_evaluate_nozzle_to_cdm(calisto): def test_evaluate_nozzle_gyration_tensor(calisto): expected_gyration_tensor = np.array( - [[0.3940207, 0, 0], [0, 0.3940207, 0], [0, 0, 0.0005445]] + [[1.57526603, 0, 0], [0, 1.57526603, 0], [0, 0, 0.0005445]] ) - atol = 1e-3 * 1e-2 * 1e-2 # Equivalent to 1g * 1cm^2 + atol = 1e-7 # equivalent to 0.1 mm^2, and rtol=0 so that is what it means assert np.allclose( - expected_gyration_tensor, np.array(calisto.nozzle_gyration_tensor), atol=atol + expected_gyration_tensor, + np.array(calisto.nozzle_gyration_tensor), + rtol=0, + atol=atol, ) # Test if calling the function returns the same result res = calisto.evaluate_nozzle_gyration_tensor() - assert np.allclose(expected_gyration_tensor, np.array(res), atol=atol) + assert np.allclose(expected_gyration_tensor, np.array(res), rtol=0, atol=atol) + + +@pytest.mark.parametrize( + "rocket_fixture", + ["calisto", "calisto_liquid_modded", "calisto_hybrid_modded"], +) +def test_evaluate_nozzle_gyration_tensor_matches_the_exit_disk(rocket_fixture, request): + """The tensor is the exit disk second moment per unit area, about the CDM.""" + rocket = request.getfixturevalue(rocket_fixture) + radius = rocket.motor.nozzle_radius + offset = rocket.nozzle_to_cdm + + tensor = np.array(rocket.evaluate_nozzle_gyration_tensor()) + + lateral = radius**2 / 4 + offset**2 + assert tensor[0, 0] == pytest.approx(lateral, rel=1e-12) + assert tensor[1, 1] == pytest.approx(lateral, rel=1e-12) + assert tensor[2, 2] == pytest.approx(radius**2 / 2, rel=1e-12) + assert (tensor[0, 1], tensor[0, 2], tensor[1, 2]) == (0, 0, 0) + + +def test_evaluate_nozzle_gyration_tensor_parallel_axis_term(calisto): + """Taking the disk term out leaves the whole squared offset, not a fraction.""" + tensor = np.array(calisto.evaluate_nozzle_gyration_tensor()) + + parallel_axis = tensor[0, 0] - tensor[2, 2] / 2 + + assert parallel_axis == pytest.approx(calisto.nozzle_to_cdm**2, rel=1e-12) def test_evaluate_com_to_cdm_function(calisto): @@ -914,7 +994,9 @@ def test_add_trapezoidal_fins_two_fins_warns_but_succeeds(calisto): fins = calisto.add_trapezoidal_fins( 2, span=0.1, root_chord=0.12, tip_chord=0.04, position=-1.0 ) - assert fins in [surface for surface, _ in calisto.aerodynamic_surfaces] + assert fins in [ + surface for surface, _position, _ref_factor in calisto.aerodynamic_surfaces + ] def test_unstable_rocket_warning_raised(calisto): diff --git a/tests/unit/rocket/test_rocket_plots.py b/tests/unit/rocket/test_rocket_plots.py new file mode 100644 index 000000000..fe23ae1b1 --- /dev/null +++ b/tests/unit/rocket/test_rocket_plots.py @@ -0,0 +1,122 @@ +"""Unit tests for the rocket drawing helpers in ``rocketpy.plots.rocket_plots``.""" + +from unittest.mock import patch + +import pytest + +from rocketpy import LinearGenericSurface +from rocketpy.mathutils.vector_matrix import Vector +from rocketpy.motors.ring_cluster_motor import RingClusterMotor + +REFERENCE_AREA = 0.005 +REFERENCE_LENGTH = 0.08 + + +@patch("matplotlib.pyplot.show") +@pytest.mark.parametrize("plane", ["xz", "yz"]) +@pytest.mark.parametrize( + "fin_fixture", + ["calisto_trapezoidal_fin", "calisto_elliptical_fin", "calisto_free_form_fin"], +) +def test_draw_a_rocket_carrying_one_fin( # pylint: disable=unused-argument + mock_show, request, calisto_robust, plane, fin_fixture +): + """A single ``Fin`` takes ``_draw_fin``, not the ``_draw_fins`` set branch. + + That method rotates one fin out of its own coordinate system into the body + frame, and it projects differently in each plane, so both are drawn here. + """ + fin = request.getfixturevalue(fin_fixture) + calisto_robust.add_surfaces(fin, Vector([0, 0, -1.04956])) + + assert calisto_robust.draw(plane=plane, filename=None) is None + + +@patch("matplotlib.pyplot.show") +@pytest.mark.parametrize("plane", ["xz", "yz"]) +def test_draw_a_rocket_carrying_a_generic_surface( # pylint: disable=unused-argument + mock_show, calisto_robust, plane +): + """A ``GenericSurface`` has no outline to trace, so it gets a scatter point. + + ``_draw_generic_surface`` is the only branch that reads the surface position + by index rather than by attribute, and it picks a different index per plane. + """ + surface = LinearGenericSurface( + reference_area=REFERENCE_AREA, + reference_length=REFERENCE_LENGTH, + coefficients={}, + name="Canard", + ) + calisto_robust.add_surfaces(surface, Vector([0, 0, -0.5])) + + assert calisto_robust.draw(plane=plane, filename=None) is None + + +@patch("matplotlib.pyplot.show") +def test_draw_a_rocket_with_a_hybrid_motor( # pylint: disable=unused-argument + mock_show, calisto_hybrid_modded, calisto_nose_cone +): + """The hybrid branch of ``_generate_motor_patches`` draws grains and tanks.""" + calisto_hybrid_modded.add_surfaces(calisto_nose_cone, 1.160) + + assert calisto_hybrid_modded.draw(filename=None) is None + + +@patch("matplotlib.pyplot.show") +def test_draw_a_rocket_with_a_liquid_motor( # pylint: disable=unused-argument + mock_show, calisto_liquid_modded, calisto_nose_cone +): + """The liquid branch draws positioned tanks and no combustion chamber.""" + calisto_liquid_modded.add_surfaces(calisto_nose_cone, 1.160) + + assert calisto_liquid_modded.draw(filename=None) is None + + +@patch("matplotlib.pyplot.show") +def test_draw_a_rocket_with_a_motor_cluster( # pylint: disable=unused-argument + mock_show, calisto_motorless, cesaroni_m1670, calisto_nose_cone +): + """A cluster repeats the grain patches around the ring. + + Only the first offset keeps its legend entry, so the loop that relabels the + rest runs solely when there is more than one motor. + """ + calisto_motorless.add_motor( + RingClusterMotor(motor=cesaroni_m1670, number=3, radius=0.05), + position=-1.373, + ) + calisto_motorless.add_surfaces(calisto_nose_cone, 1.160) + + assert calisto_motorless.draw(filename=None) is None + + +@patch("matplotlib.pyplot.show") +@pytest.mark.parametrize( + "rocket_fixture,nose_position", + [("calisto", 1.160), ("calisto_nose_to_tail", -1.160)], +) +def test_draw_a_rocket_whose_nozzle_sits_behind_its_last_surface( # pylint: disable=unused-argument + mock_show, request, rocket_fixture, nose_position, calisto_nose_cone +): + """``_draw_nozzle_tube`` only draws when the nozzle is past the last surface. + + A rocket carrying nothing but a nose cone leaves that gap open, and the + comparison flips with the coordinate system, so both orientations are drawn. + """ + rocket = request.getfixturevalue(rocket_fixture) + rocket.add_surfaces(calisto_nose_cone, nose_position) + + assert rocket.draw(filename=None) is None + + +def test_draw_refuses_a_rocket_with_no_aerodynamic_surfaces(calisto_motorless): + """There is nothing to draw the body around without at least one surface.""" + with pytest.raises(ValueError, match="at least one aerodynamic surface"): + calisto_motorless.draw(filename=None) + + +def test_draw_refuses_a_plane_it_cannot_project_onto(calisto_robust): + """Only the two longitudinal planes are defined.""" + with pytest.raises(ValueError, match="must be 'xz' or 'yz'"): + calisto_robust.draw(plane="xy", filename=None) diff --git a/tests/unit/sensors/test_sensor.py b/tests/unit/sensors/test_sensor.py index 0813a3638..8f7dc3ae0 100644 --- a/tests/unit/sensors/test_sensor.py +++ b/tests/unit/sensors/test_sensor.py @@ -5,6 +5,7 @@ import pytest from pytest import approx +from rocketpy import Accelerometer from rocketpy.mathutils.vector_matrix import Matrix, Vector from rocketpy.tools import euler313_to_quaternions @@ -272,7 +273,10 @@ def test_noisy_rotated_accelerometer(noisy_rotated_accelerometer, example_plain_ # calculate acceleration at sensor position in inertial frame relative_position = Vector([0.4, 0.4, 1]) - inertial_acceleration = Vector(U_DOT[3:6]) + Vector([0, 0, -GRAVITY]) + # An accelerometer reports proper acceleration: the inertial acceleration + # less the local gravitational field. Gravity points down, so the term it + # contributes points up, which is why one sitting still reads +g, not 0. + inertial_acceleration = Vector(U_DOT[3:6]) - Vector([0, 0, -GRAVITY]) omega = Vector(U[10:13]) omega_dot = Vector(U_DOT[10:13]) acceleration = ( @@ -317,6 +321,48 @@ def test_noisy_rotated_accelerometer(noisy_rotated_accelerometer, example_plain_ assert noisy_rotated_accelerometer.measured_data[0][0] == TIME +def test_accelerometer_at_rest_reads_gravity_upward(example_plain_env): + """An accelerometer standing still reads +g along its up axis, not zero. + + The test above recomputes the expression under test, so a flipped gravity + term gets mirrored into agreement there instead of being caught. This one + says what the instrument does rather than how it is computed: it senses + the support force holding it up, so at rest it reports g upward. Reversing + the sign in ``Accelerometer.measure`` fails here. + + Every noise, bias and drift parameter is left at its default, all of which + are zero, so the measurement is exact rather than bounded. + """ + at_rest = [0.0] * 13 + at_rest[6] = 1.0 # identity attitude, so sensor axes are the inertial ones + still = [0.0] * 13 + gravity = example_plain_env.gravity.get_value_opt(0) + + sensing_gravity = Accelerometer(sampling_rate=100, consider_gravity=True) + sensing_gravity.measure( + time=0, + u=at_rest, + u_dot=still, + relative_position=Vector([0, 0, 0]), + environment=example_plain_env, + ) + + assert sensing_gravity.measurement == approx([0, 0, gravity], abs=1e-12) + + # Without the flag the same sensor reports the coordinate acceleration, so + # the whole of what the flag contributes is that one upward g. + ignoring_gravity = Accelerometer(sampling_rate=100, consider_gravity=False) + ignoring_gravity.measure( + time=0, + u=at_rest, + u_dot=still, + relative_position=Vector([0, 0, 0]), + environment=example_plain_env, + ) + + assert ignoring_gravity.measurement == approx([0, 0, 0], abs=1e-12) + + def test_noisy_rotated_gyroscope(noisy_rotated_gyroscope, example_plain_env): """Test the measure method of the Gyroscope class. Checks if saved measurement is (wx,wy,wz) and if measured_data is [(t, (wx,wy,wz)), ...] diff --git a/tests/unit/sensors/test_sensor_seeding.py b/tests/unit/sensors/test_sensor_seeding.py index d8474d317..f4ec23157 100644 --- a/tests/unit/sensors/test_sensor_seeding.py +++ b/tests/unit/sensors/test_sensor_seeding.py @@ -15,8 +15,9 @@ from types import SimpleNamespace import numpy as np +import pytest -from rocketpy._encoders import RocketPyEncoder +from rocketpy._encoders import RocketPyDecoder, RocketPyEncoder from rocketpy.mathutils.vector_matrix import Vector from rocketpy.sensors.accelerometer import Accelerometer from rocketpy.sensors.barometer import Barometer @@ -132,6 +133,47 @@ def test_seed_survives_serialization_round_trip(): assert type(sensor).from_dict(data).to_dict()["seed"] == seed +@pytest.mark.parametrize( + "seed", + [11, np.int64(11), [1, 2], np.random.SeedSequence(11)], + ids=["int", "numpy_int", "int_sequence", "seed_sequence"], +) +def test_round_trip_reproduces_the_noise_stream(seed): + """The point of writing a seed down is that the sensor read back draws the + same noise. + + Comparing only the stored value would still pass for a seed that survives + JSON without naming the stream the original sensor used, which is exactly + what a live generator would do, so this compares the draws themselves. + """ + encoded = json.dumps(_accelerometer(seed).to_dict(), cls=RocketPyEncoder) + restored = Accelerometer.from_dict(json.loads(encoded, cls=RocketPyDecoder)) + + assert _noise_sequence(restored) == _noise_sequence(_accelerometer(seed)) + + +def test_unserializable_seed_is_refused_before_it_can_be_stored(): + """Keep the failure at the constructor instead of at save time. + + ``default_rng`` accepts a ``Generator``, so the sensor builds successfully + and only raises once ``to_dict()`` reaches ``json.dumps()``, by which point + the call responsible for it is long gone. #1124 gave ``SeedSequence`` a + serializable form; a live generator has none. + """ + with pytest.raises(TypeError, match="seed"): + Accelerometer( + sampling_rate=10, noise_density=1.0, seed=np.random.default_rng(7) + ) + + +def test_numpy_int_seed_survives_serialization_round_trip(): + """``RocketPyEncoder`` writes numpy scalars out through ``.item()``, so a + numpy int is a valid seed and has to keep round tripping.""" + sensor = Barometer(sampling_rate=10, noise_density=1.0, seed=np.int64(77)) + data = json.loads(json.dumps(sensor.to_dict(), cls=RocketPyEncoder)) + assert Barometer.from_dict(data).to_dict()["seed"] == 77 + + def test_from_dict_defaults_seed_to_none_when_absent(): """Dicts serialized before this change (no seed key) still load, seed None.""" data = GnssReceiver( @@ -139,3 +181,17 @@ def test_from_dict_defaults_seed_to_none_when_absent(): ).to_dict() del data["seed"] assert GnssReceiver.from_dict(data).to_dict()["seed"] is None + + +def test_seedsequence_sensor_seed_is_json_serializable(): + """SeedSequence seeds must serialize through RocketPyEncoder (#1087).""" + seed = np.random.SeedSequence(0).spawn(1)[0] + sensor = Accelerometer(sampling_rate=100, seed=seed) + + encoded = json.dumps(sensor.to_dict(), cls=RocketPyEncoder) + decoded = json.loads(encoded, cls=RocketPyDecoder) + + assert isinstance(decoded["seed"], np.random.SeedSequence) + assert decoded["seed"].state == seed.state + restored = Accelerometer.from_dict(decoded) + assert restored.to_dict()["seed"].state == seed.state diff --git a/tests/unit/sensors/test_sensor_validation.py b/tests/unit/sensors/test_sensor_validation.py index 1187a42c0..18a0de44e 100644 --- a/tests/unit/sensors/test_sensor_validation.py +++ b/tests/unit/sensors/test_sensor_validation.py @@ -5,6 +5,7 @@ tests never reach, so the base class is fully covered. """ +import numpy as np import pytest from rocketpy.mathutils.vector_matrix import Vector @@ -39,6 +40,106 @@ def test_vectorize_input_wrong_type_raises(): Accelerometer(sampling_rate=1, noise_density="not-a-vector") +@pytest.mark.parametrize( + "seed", + [ + np.random.default_rng(5), + np.random.PCG64(5), + np.random.MT19937(5), + np.random.RandomState(5), + ], + ids=["generator", "bit_generator", "legacy_bit_generator", "random_state"], +) +def test_live_rng_objects_are_rejected(seed): + """A generator's state advances as noise is drawn, so it cannot describe + the stream the way an int or a ``SeedSequence`` does. + + ``RandomState`` is here because ``default_rng`` accepts it from NumPy 2.2 + on and RocketPy pins no upper bound, so it reaches the same late failure at + ``json.dumps()`` that the other two do. + """ + with pytest.raises(TypeError, match="seed"): + Barometer(sampling_rate=1, seed=seed) + + +@pytest.mark.parametrize( + "seed", + ["5", 5.0, np.float64(5), np.bool_(True), object()], + ids=["str", "float", "numpy_float", "numpy_bool", "object"], +) +def test_non_descriptor_seeds_are_rejected(seed): + """Anything that is neither a live RNG nor a stream descriptor is refused. + + ``default_rng`` rejects these too, but only after the sensor has been + built, and its message never names ``seed``. Checking here keeps the error + at the call that caused it. + """ + with pytest.raises(TypeError, match="seed"): + Barometer(sampling_rate=1, seed=seed) + + +@pytest.mark.parametrize( + "seed", + [None, 0, 5, np.int64(5), np.uint32(5), 2**128 - 1], + ids=["none", "zero", "int", "numpy_int", "numpy_uint", "wide_int"], +) +def test_int_and_none_seeds_are_accepted(seed): + """The check must not catch seeds that already work. + + numpy integers serialize through ``RocketPyEncoder``, and #1054 hands each + model a plain 128-bit int, so both have to pass. + """ + assert Barometer(sampling_rate=1, seed=seed).to_dict()["seed"] == seed + + +def test_seed_sequence_is_accepted(): + """#1124 made ``SeedSequence`` serializable, so this check must let it by.""" + seed = np.random.SeedSequence(5) + assert Barometer(sampling_rate=1, seed=seed).to_dict()["seed"] is seed + + +@pytest.mark.parametrize( + "seed", + [[1, 2], (1, 2), [np.int64(1), np.int64(2)], []], + ids=["list", "tuple", "list_of_numpy_ints", "empty"], +) +def test_int_array_like_seeds_are_accepted(seed): + """``default_rng`` takes a flat array_like of ints and json writes it out, + so the check has to let every shape of one by, the empty included.""" + assert Barometer(sampling_rate=1, seed=seed) is not None + + +@pytest.mark.parametrize( + "seed", + [[[1, 2], [3, 4]], np.array([[1, 2], [3, 4]], dtype=np.uint32)], + ids=["nested_sequence", "two_dimensional_ndarray"], +) +def test_multi_dimensional_int_seeds_are_rejected(seed): + """``SeedSequence`` takes entropy one dimension deep and no further. + + Nested sequences raise ``TypeError`` there and 2-D arrays ``ValueError``, + neither message naming the seed, so both are caught here instead. Older + NumPy accepted the nested form and the dependency carries no upper bound, + so refusing it is also what keeps one answer across versions. + """ + with pytest.raises(TypeError, match="seed"): + Barometer(sampling_rate=1, seed=seed) + + +def test_integer_ndarray_seed_is_accepted(): + """An ndarray of ints is array_like of ints, and ``RocketPyEncoder`` + writes it out as a list, so it round trips like a plain list does.""" + seed = np.array([1, 2], dtype=np.uint32) + assert Barometer(sampling_rate=1, seed=seed).to_dict()["seed"] is seed + + +def test_float_ndarray_seed_is_rejected(): + """The dtype is what decides it: a float array cannot seed + ``default_rng``, so it must be refused with the others.""" + with pytest.raises(TypeError, match="seed"): + Barometer(sampling_rate=1, seed=np.array([1.0, 2.0])) + + def test_repr_returns_name(): assert repr(Barometer(sampling_rate=1, name="baro")) == "baro" diff --git a/tests/unit/simulation/test_append_lineage.py b/tests/unit/simulation/test_append_lineage.py new file mode 100644 index 000000000..35f80844b --- /dev/null +++ b/tests/unit/simulation/test_append_lineage.py @@ -0,0 +1,391 @@ +import json + +import pytest + +from rocketpy.simulation.monte_carlo import ( + MonteCarlo, + _root_digest, + _SIMULATION_ROOT_KEY, +) + + +def _study(tmp_path, stem, models): + environment, rocket, flight = models + return MonteCarlo( + filename=str(tmp_path / stem), + environment=environment, + rocket=rocket, + flight=flight, + ) + + +def _drawn(analysis): + with open(analysis.input_file, "r", encoding="utf-8") as written: + rows = [json.loads(line) for line in written if line.strip()] + return {row["index"]: row.get("mass") for row in rows} + + +@pytest.fixture(name="models") +def _models(stochastic_environment, stochastic_calisto, stochastic_flight): + return stochastic_environment, stochastic_calisto, stochastic_flight + + +def test_a_row_says_which_root_drew_it(models, tmp_path): + """Every input row carries the root, so the log describes itself.""" + analysis = _study(tmp_path, "study", models) + + analysis.simulate(2, append=False, random_seed=42) + + with open(analysis.input_file, "r", encoding="utf-8") as written: + rows = [json.loads(line) for line in written if line.strip()] + assert all(row[_SIMULATION_ROOT_KEY]["entropy"] == 42 for row in rows) + + +def test_an_append_with_no_seed_carries_on_from_the_rows(models, tmp_path): + """The ordinary resume: no seed given, the stream continues anyway.""" + whole = _study(tmp_path, "whole", models) + whole.simulate(4, append=False, random_seed=42) + + part = _study(tmp_path, "part", models) + part.simulate(2, append=False, random_seed=42) + part.simulate(4, append=True) + + assert _drawn(part) == _drawn(whole) + + +def test_a_fresh_object_can_continue_a_study(models, tmp_path): + """A notebook restart is a new object over the same files.""" + first = _study(tmp_path, "study", models) + first.simulate(2, append=False, random_seed=42) + + second = _study(tmp_path, "study", models) + second.simulate(4, append=True) + + assert sorted(_drawn(second)) == [0, 1, 2, 3] + + +def test_appending_with_another_seed_is_refused(models, tmp_path): + """Two roots in one file is the thing this exists to prevent.""" + analysis = _study(tmp_path, "study", models) + analysis.simulate(2, append=False, random_seed=42) + + with pytest.raises(ValueError, match="different root"): + analysis.simulate(4, append=True, random_seed=7) + + +def test_appending_with_the_same_seed_is_allowed(models, tmp_path): + """The control. Saying the seed again is not a mismatch.""" + analysis = _study(tmp_path, "study", models) + analysis.simulate(2, append=False, random_seed=42) + + analysis.simulate(4, append=True, random_seed=42) + + assert sorted(_drawn(analysis)) == [0, 1, 2, 3] + + +def test_a_log_holding_two_studies_is_refused(models, tmp_path): + """Rows that disagree are two studies, and neither is safe to continue.""" + analysis = _study(tmp_path, "study", models) + analysis.simulate(2, append=False, random_seed=42) + with open(analysis.input_file, "r", encoding="utf-8") as written: + rows = [json.loads(line) for line in written if line.strip()] + rows[1][_SIMULATION_ROOT_KEY]["entropy"] = 999 + with open(analysis.input_file, "w", encoding="utf-8") as rewritten: + for row in rows: + rewritten.write(json.dumps(row) + "\n") + + with pytest.raises(ValueError, match="more than one study"): + analysis.simulate(4, append=True) + + +def test_a_log_whose_rows_carry_no_root_is_refused(models, tmp_path): + """A study from before this release cannot be shown to be one study.""" + # Measured before this was refused: the append read the log as empty, + # started a root of its own, and left two lineages in the one file. + analysis = _study(tmp_path, "study", models) + analysis.simulate(2, append=False, random_seed=42) + with open(analysis.input_file, "r", encoding="utf-8") as written: + rows = [json.loads(line) for line in written if line.strip()] + for row in rows: + row.pop(_SIMULATION_ROOT_KEY) + with open(analysis.input_file, "w", encoding="utf-8") as rewritten: + for row in rows: + rewritten.write(json.dumps(row) + "\n") + + with pytest.raises(ValueError, match="does not say which root"): + analysis.simulate(4, append=True) + + +def _rewrite(path, replacement): + """Put ``replacement`` under the root key of every row of one log.""" + with open(path, "r", encoding="utf-8") as written: + rows = [json.loads(line) for line in written if line.strip()] + for row in rows: + row[_SIMULATION_ROOT_KEY] = replacement(row[_SIMULATION_ROOT_KEY]) + with open(path, "w", encoding="utf-8") as rewritten: + for row in rows: + rewritten.write(json.dumps(row) + "\n") + + +def _rewrite_roots(analysis, damage): + """Damage the recorded root, leaving the two logs still naming one study. + + The output rows hold the root's digest, so they are restamped rather than + damaged: otherwise the two logs disagree and that is refused first. + """ + with open(analysis.input_file, "r", encoding="utf-8") as written: + first = json.loads(next(line for line in written if line.strip())) + damaged = damage(first[_SIMULATION_ROOT_KEY]) + _rewrite(analysis.input_file, lambda _root: damaged) + if isinstance(damaged, dict): + _rewrite(analysis.output_file, lambda _digest: _root_digest(damaged)) + + +def test_a_log_whose_root_is_null_is_refused(models, tmp_path): + """A null root is no more of a lineage than no root at all.""" + # It read as an empty log, so an append started a second root behind the + # damaged rows, which is the case this check exists to prevent. + analysis = _study(tmp_path, "study", models) + analysis.simulate(2, append=False, random_seed=42) + + _rewrite_roots(analysis, lambda root: None) + + with pytest.raises(ValueError, match="does not say which root"): + analysis.simulate(4, append=True) + + +@pytest.mark.parametrize( + "damage", + [ + lambda root: {**root, "entropy": None}, + lambda root: {**root, "pool_size": 0}, + lambda root: {**root, "pool_size": 2}, + lambda root: {**root, "n_children_spawned": -1}, + lambda root: {**root, "spawn_key": [True]}, + lambda root: {key: value for key, value in root.items() if key != "entropy"}, + lambda root: {**root, "unexpected": 1}, + ], + ids=[ + "null entropy", + "no pool", + "pool numpy refuses", + "negative base", + "bool key", + "short", + "long", + ], +) +def test_a_root_that_no_stream_can_be_rebuilt_from_is_refused(models, tmp_path, damage): + """Rows agreeing on a root does not make it a root that can be resumed.""" + analysis = _study(tmp_path, "study", models) + analysis.simulate(2, append=False, random_seed=42) + + _rewrite_roots(analysis, damage) + + with pytest.raises(ValueError, match="rebuilt from"): + analysis.simulate(4, append=True) + + +def test_an_empty_log_is_not_a_log_that_cannot_be_checked(models, tmp_path): + """The control. Nothing recorded yet is a fresh start, not a refusal.""" + analysis = _study(tmp_path, "study", models) + + analysis.simulate(2, append=True, random_seed=42) + + assert sorted(_drawn(analysis)) == [0, 1] + + +def test_a_seed_given_as_a_sequence_is_recorded_as_one(models, tmp_path): + """A sequence is a documented seed, so the row has to carry it whole.""" + analysis = _study(tmp_path, "study", models) + + analysis.simulate(2, append=False, random_seed=[1, 2, 3]) + + with open(analysis.input_file, "r", encoding="utf-8") as written: + rows = [json.loads(line) for line in written if line.strip()] + assert all(row[_SIMULATION_ROOT_KEY]["entropy"] == [1, 2, 3] for row in rows) + + +def test_a_sequence_seed_still_continues_the_same_study(models, tmp_path): + """And reads back, since a root that cannot be compared refuses the append.""" + whole = _study(tmp_path, "whole", models) + whole.simulate(4, append=False, random_seed=[1, 2, 3]) + + part = _study(tmp_path, "part", models) + part.simulate(2, append=False, random_seed=[1, 2, 3]) + part.simulate(4, append=True) + + assert _drawn(part) == _drawn(whole) + + +def test_blank_lines_between_rows_do_not_hide_the_root(models, tmp_path): + """A gap in the file is not a row, and not a study without a root either.""" + analysis = _study(tmp_path, "study", models) + analysis.simulate(2, append=False, random_seed=42) + with open(analysis.input_file, "r", encoding="utf-8") as written: + rows = written.read().splitlines() + with open(analysis.input_file, "w", encoding="utf-8") as spaced: + for row in rows: + spaced.write(row + "\n\n") + + analysis.simulate(4, append=True) + + assert sorted(_drawn(analysis)) == [0, 1, 2, 3] + + +def test_a_row_that_cannot_be_read_is_refused(models, tmp_path): + """A row that will not parse leaves nothing to check the root against.""" + analysis = _study(tmp_path, "study", models) + analysis.simulate(2, append=False, random_seed=42) + with open(analysis.input_file, "a", encoding="utf-8") as damaged: + damaged.write("{ this was cut off\n") + + with pytest.raises(ValueError, match="cannot be read"): + analysis.simulate(4, append=True) + + +def test_an_output_log_from_another_study_is_refused(models, tmp_path): + """Matching indices do not make two files the two halves of one run.""" + # Nothing in the indices tells them apart: both studies number their rows + # from zero, so the root is what says they belong together. + one = _study(tmp_path, "one", models) + one.simulate(2, append=False, random_seed=42) + other = _study(tmp_path, "other", models) + other.simulate(2, append=False, random_seed=7) + with open(other.output_file, "r", encoding="utf-8") as theirs: + stolen = theirs.read() + with open(one.output_file, "w", encoding="utf-8") as ours: + ours.write(stolen) + + with pytest.raises(ValueError, match="same root"): + one.simulate(4, append=True) + + +def test_a_checkpoint_whose_halves_disagree_is_refused(models, tmp_path): + """Where to carry on from is not established by one log alone.""" + analysis = _study(tmp_path, "study", models) + analysis.simulate(2, append=False, random_seed=42) + with open(analysis.output_file, "r", encoding="utf-8") as written: + rows = [line for line in written if line.strip()] + with open(analysis.output_file, "w", encoding="utf-8") as trimmed: + trimmed.write(rows[0]) + + with pytest.raises(ValueError, match="do not record the same simulations"): + analysis.simulate(4, append=True) + + +def test_a_collector_cannot_take_over_the_root(models, tmp_path): + """The root binds the two logs, so nothing outside the run writes it.""" + # Set past the constructor, so this pins the row and not the validation. + analysis = _study(tmp_path, "study", models) + analysis.data_collector = {_SIMULATION_ROOT_KEY: lambda _flight: "forged"} + + analysis.simulate(2, append=False, random_seed=42) + + with open(analysis.output_file, "r", encoding="utf-8") as written: + roots = { + json.dumps(json.loads(line)[_SIMULATION_ROOT_KEY], sort_keys=True) + for line in written + if line.strip() + } + assert roots != {'"forged"'} + assert len(roots) == 1 + + +def test_a_collector_named_after_the_root_is_refused(models, tmp_path): + """Overwriting it afterwards protects the log but discards the callback.""" + with pytest.raises(ValueError, match="run after the collectors|discarded"): + MonteCarlo( + filename=str(tmp_path / "study"), + environment=models[0], + rocket=models[1], + flight=models[2], + data_collector={_SIMULATION_ROOT_KEY: lambda _flight: 1}, + ) + + +def _rows_of(path): + with open(path, "r", encoding="utf-8") as written: + return [line for line in written if line.strip()] + + +def _rewrite_indices(path, numbers): + """Renumber a log's rows, leaving everything else in them alone.""" + rows = [json.loads(line) for line in _rows_of(path)] + for row, number in zip(rows, numbers): + row["index"] = number + with open(path, "w", encoding="utf-8") as rewritten: + for row in rows: + rewritten.write(json.dumps(row) + "\n") + + +def test_a_blank_line_in_the_output_log_does_not_move_the_continuation( + models, tmp_path +): + """Where to carry on from comes from the records, not the line count.""" + # A fresh object counts physical output lines when it opens the file, so + # one blank line made an append start at 3 and leave index 2 missing. + written = _study(tmp_path, "study", models) + written.simulate(2, append=False, random_seed=42) + with open(written.output_file, "a", encoding="utf-8") as padded: + padded.write("\n") + + resumed = _study(tmp_path, "study", models) + assert resumed.num_of_loaded_sims == 3 # the count this must not trust + resumed.simulate(4, append=True) + + assert sorted(_drawn(resumed)) == [0, 1, 2, 3] + + +def test_halves_that_number_the_same_count_differently_are_refused(models, tmp_path): + """Equal row counts do not make two logs the two halves of one run.""" + analysis = _study(tmp_path, "study", models) + analysis.simulate(2, append=False, random_seed=42) + _rewrite_indices(analysis.output_file, [0, 2]) + + with pytest.raises(ValueError, match="do not record the same simulations"): + analysis.simulate(4, append=True) + + +@pytest.mark.parametrize( + "numbers", [[0, 0], [0, 2], [1, 2]], ids=["duplicate", "hole", "no zero"] +) +def test_a_checkpoint_that_is_not_the_runs_first_simulations_is_refused( + models, tmp_path, numbers +): + """An append continues a run, so what it continues has to be its start.""" + analysis = _study(tmp_path, "study", models) + analysis.simulate(2, append=False, random_seed=42) + for path in (analysis.input_file, analysis.output_file): + _rewrite_indices(path, numbers) + + with pytest.raises(ValueError, match="not the run's first"): + analysis.simulate(4, append=True) + + +def test_the_order_a_parallel_run_finished_in_is_carried_on_from(models, tmp_path): + """Rows arrive in completion order, which is not sorted and not wrong.""" + analysis = _study(tmp_path, "study", models) + analysis.simulate(2, append=False, random_seed=42) + for path in (analysis.input_file, analysis.output_file): + _rewrite_indices(path, [1, 0]) + + analysis.simulate(4, append=True) + + assert sorted(_drawn(analysis)) == [0, 1, 2, 3] + + +@pytest.mark.parametrize( + "number", [True, 1.0, "1", None, -1], ids=["bool", "float", "str", "null", "neg"] +) +def test_a_row_that_does_not_number_its_simulation_is_refused(models, tmp_path, number): + """Only a non-negative int says which simulation a row holds.""" + # True and 1.0 both equal 1, so either would pass for a record that is not + # there, and the count they contribute to decides where an append starts. + analysis = _study(tmp_path, "study", models) + analysis.simulate(2, append=False, random_seed=42) + for path in (analysis.input_file, analysis.output_file): + _rewrite_indices(path, [0, number]) + + with pytest.raises(ValueError, match="does not number"): + analysis.simulate(4, append=True) diff --git a/tests/unit/simulation/test_flight.py b/tests/unit/simulation/test_flight.py index 9a3c54477..64082f7a4 100644 --- a/tests/unit/simulation/test_flight.py +++ b/tests/unit/simulation/test_flight.py @@ -1,6 +1,7 @@ import json import os -from unittest.mock import patch +from types import SimpleNamespace +from unittest.mock import MagicMock, patch import matplotlib as plt import numpy as np @@ -84,6 +85,65 @@ def compute_static_margin_error_given_distance(position, static_margin, rocket): # Tests +def _make_impact_event_state(): + flight = object.__new__(Flight) + flight.env = SimpleNamespace(elevation=0) + flight.solution = [ + [10.0, 0, 0, 1, 0, 0, -1], + [11.0, 0, 0, -1, 0, 0, -1], + ] + flight.flight_phases = SimpleNamespace( + flush_after=MagicMock(), add_phase=MagicMock() + ) + + solver = SimpleNamespace( + step_size=1.0, + dense_output=lambda: lambda _: np.array([2, 3, 0, 4, 5, -6]), + status="running", + ) + time_nodes = SimpleNamespace(flush_after=MagicMock(), add_node=MagicMock()) + phase = SimpleNamespace(solver=solver, time_nodes=time_nodes) + return flight, phase + + +@pytest.mark.parametrize( + "roots, match", + [ + ([-1 + 0j, 2 + 0j], "No valid roots found"), + ([0.25 + 0j, 0.75 + 0j], "Multiple roots found"), + ], +) +def test_handle_impact_event_reports_invalid_root_counts(roots, match): + flight, phase = _make_impact_event_state() + + with patch( + "rocketpy.simulation.flight.find_roots_cubic_function", return_value=roots + ): + with pytest.raises(ValueError, match=match): + flight._Flight__handle_impact_event(phase, phase_index=1, node_index=2) + + +def test_handle_impact_event_uses_single_valid_root(): + flight, phase = _make_impact_event_state() + + with patch( + "rocketpy.simulation.flight.find_roots_cubic_function", + return_value=[0.5 + 0j], + ): + handled = flight._Flight__handle_impact_event( + phase, phase_index=1, node_index=2 + ) + + assert handled is True + assert flight.t == flight.t_final == pytest.approx(10.5) + assert flight.impact_velocity == -6 + assert phase.solver.status == "finished" + flight.flight_phases.flush_after.assert_called_once_with(1) + flight.flight_phases.add_phase.assert_called_once_with(10.5) + phase.time_nodes.flush_after.assert_called_once_with(2) + phase.time_nodes.add_node.assert_called_once_with(10.5, [], [], []) + + def test_get_solution_at_time(flight_calisto): """Test the get_solution_at_time method of the Flight class. This test simply calls the method at the initial and final time and checks if the @@ -139,6 +199,83 @@ def test_get_controller_observed_variables(flight_calisto_air_brakes): assert len(obs_vars) == 0 +def test_initial_solution_from_flight_sets_initial_time( + calisto_with_sensors, example_plain_env +): + """A Flight continued from another Flight object must record ``t_initial``. + It is needed to post-process the initial state, so a rocket carrying sensors + or controllers used to raise ``AttributeError`` on this path. + + Arrange: fly a rocket that carries sensors. + Act: start a second flight from the first Flight object. + Assert: ``t_initial`` is the time the previous flight ended at. + """ + # Arrange + first = Flight( + rocket=calisto_with_sensors, + environment=example_plain_env, + rail_length=5.2, + inclination=85, + heading=0, + terminate_on_apogee=True, + ) + + # Act + second = Flight( + rocket=calisto_with_sensors, + environment=example_plain_env, + rail_length=5.2, + inclination=85, + heading=0, + initial_solution=first, + max_time=first.t_final + 1, + ) + + # Assert + assert second.t_initial == first.solution[-1][0] + + +def test_post_step_callback_runs_before_and_after_apogee( + calisto_robust, example_plain_env +): + """post_step_callback must fire across the full flight, including descent. + + Controllers are not a substitute: air-brake fixtures often terminate at + apogee, and parachute phases are not meant to keep feeding actuators. + This callback is the full-lifecycle observer hook (issue #758). + """ + callback_times = [] + + def record_step(flight): + callback_times.append(flight.t) + + flight = Flight( + rocket=calisto_robust, + environment=example_plain_env, + rail_length=5.2, + inclination=85, + heading=0, + terminate_on_apogee=False, + post_step_callback=record_step, + ) + + assert callback_times, "post_step_callback was never invoked" + assert min(callback_times) < flight.apogee_time + assert max(callback_times) > flight.apogee_time + assert flight.t_final > flight.apogee_time + + +def test_post_step_callback_must_be_callable(calisto, example_plain_env): + """Non-callable post_step_callback values are rejected at construction.""" + with pytest.raises(TypeError, match="post_step_callback"): + Flight( + rocket=calisto, + environment=example_plain_env, + rail_length=5.2, + post_step_callback="not-callable", + ) + + def test_initial_stability_margin(flight_calisto_custom_wind): """Test the initial_stability_margin method of the Flight class. @@ -215,7 +352,7 @@ def test_export_sensor_data(flight_calisto_with_sensors): [ ("t_initial", (0.25886, -0.649623, 0)), ("out_of_rail_time", (0.792028, -1.987634, 0)), - ("apogee_time", (-0.509420, -0.732933, -2.089120e-14)), + ("apogee_time", (-0.519917, -0.734918, -1.005368e-18)), ("t_final", (0, 0, 0)), ], ) @@ -254,7 +391,7 @@ def test_aerodynamic_moments(flight_calisto_custom_wind, flight_time, expected_v [ ("t_initial", (1.654150, 0.659142, -0.067103)), ("out_of_rail_time", (5.052628, 2.013361, -1.75370)), - ("apogee_time", (2.321838, -1.613641, -0.962108)), + ("apogee_time", (2.322999, -1.643037, -0.950316)), ("t_final", (-0.019802, 0.012030, 159.051604)), ], ) @@ -295,7 +432,7 @@ def test_aerodynamic_forces(flight_calisto_custom_wind, flight_time, expected_va ("out_of_rail_time", (0, 2.248540, 25.700928)), ( "apogee_time", - (-14.826350, 15.670022, -0.000264), + (-14.593411, 15.743567, -0.000409), ), ("t_final", (5, 2, -5.660155)), ], diff --git a/tests/unit/simulation/test_monte_carlo.py b/tests/unit/simulation/test_monte_carlo.py index 7e2e68804..e9e541bf7 100644 --- a/tests/unit/simulation/test_monte_carlo.py +++ b/tests/unit/simulation/test_monte_carlo.py @@ -1,13 +1,21 @@ +import builtins import csv import json +import os import pathlib +import types from collections import namedtuple +from unittest.mock import patch import matplotlib as plt import numpy as np import pytest from rocketpy.simulation import MonteCarlo +from rocketpy.simulation import monte_carlo as mc_module +from rocketpy.simulation.monte_carlo import ( + _refuse_logs_this_run_cannot_write, +) plt.rcParams.update({"figure.max_open_warning": 0}) @@ -86,6 +94,37 @@ def __init__(self): } +def test_append_simulation_record_rolls_back_inputs_on_output_failure(tmp_path): + """If the outputs append fails, the inputs row must not remain on disk.""" + mc = MockMonteCarlo() + input_file = tmp_path / "inputs.json" + output_file = tmp_path / "outputs.json" + input_file.write_text('{"index": 0}\n', encoding="utf-8") + output_file.write_text('{"index": 0}\n', encoding="utf-8") + mc._input_file = str(input_file) + mc._output_file = str(output_file) + + mc._append_simulation_record('{"index": 1}\n', '{"index": 1}\n') + + original_open = builtins.open + output_path = os.fspath(output_file) + + def failing_output_open(*args, **kwargs): + # Match builtins.open call shapes without keyword-before-vararg (W1113). + file = args[0] if args else kwargs["file"] + mode = args[1] if len(args) > 1 else kwargs.get("mode", "r") + if os.fspath(file) == output_path and "a" in mode: + raise OSError("no space left on device") + return original_open(*args, **kwargs) + + with pytest.raises(OSError, match="no space left on device"): + with patch("builtins.open", side_effect=failing_output_open): + mc._append_simulation_record('{"index": 2}\n', '{"index": 2}\n') + + assert input_file.read_text(encoding="utf-8") == '{"index": 0}\n{"index": 1}\n' + assert output_file.read_text(encoding="utf-8") == '{"index": 0}\n{"index": 1}\n' + + def test_estimate_confidence_interval_contains_known_mean(): """Checks that the confidence interval contains the known mean.""" mc = MockMonteCarlo() @@ -252,6 +291,43 @@ def __init__(self): self.num_of_loaded_sims = 3 +def test_set_processed_results_summarizes_real_scalars(): + mc = MockMonteCarloWithLogs() + mc.results = {"value": [1, np.int64(2), np.float32(3)]} + + mc.set_processed_results() + + mean, median, stdev, pi_low, pi_high = mc.processed_results["value"] + assert mean == pytest.approx(2) + assert median == pytest.approx(2) + assert stdev == pytest.approx(np.std([1, 2, 3])) + assert pi_low == pytest.approx(np.quantile([1, 2, 3], 0.025)) + assert pi_high == pytest.approx(np.quantile([1, 2, 3], 0.975)) + + +@pytest.mark.parametrize( + "values", + [ + ["ascent", "descent"], + [[1, 2], [3, 4]], + [[1], [2, 3]], + [{"x": 1}, {"x": 2}], + [np.array([1, 2]), np.array([3, 4])], + [1, "two"], + [True, False], + [], + ], +) +def test_set_processed_results_preserves_structured_results(values): + mc = MockMonteCarloWithLogs() + mc.results = {"structured": values} + + mc.set_processed_results() + + assert mc.results["structured"] is values + assert mc.processed_results["structured"] == (None, None, None, None, None) + + def test_export_outputs_to_csv(tmp_path): """Tests that outputs are correctly exported to CSV.""" mc = MockMonteCarloWithLogs() @@ -513,3 +589,673 @@ def test_simulate_convergence_runs_until_max_when_not_converging(): assert mc.num_of_loaded_sims == 200 assert all(width > 0.5 for width in history) assert len(history) == 4 # 200 / 50 batches + + +def test_a_monte_carlo_flight_keeps_the_configuration_it_was_given(monkeypatch): + """A run must build the same ``Flight`` ``StochasticFlight`` would. + + Monte Carlo wrote out the constructor by hand and stopped at + ``time_overshoot``, so ``max_time``, the tolerances, the solver, the + equations of motion and the simulation mode were silently reset to their + defaults. #1070 added StochasticFlight's handling of exactly those. + """ + base = types.SimpleNamespace( + max_time_step=0.5, + min_time_step=0.01, + rtol=1e-9, + atol=1e-9, + name="named", + equations_of_motion="solid_propulsion", + ode_solver="RK23", + simulation_mode="native", + ) + stochastic_flight = types.SimpleNamespace( + obj=base, + max_time=123.0, + initial_solution=None, + terminate_on_apogee=True, + time_overshoot=False, + # One draw for all three, as #1090 requires: three separate calls + # meant the flight flew one sample and the exported row logged another. + _sample_flight_inputs=lambda: { + "rail_length": 5.0, + "inclination": 84.0, + "heading": 133.0, + }, + ) + analysis = object.__new__(MonteCarlo) + analysis.flight = stochastic_flight + analysis.rocket = types.SimpleNamespace(create_object=lambda: "rocket") + analysis.environment = types.SimpleNamespace(create_object=lambda: "environment") + monkeypatch.setattr("rocketpy.simulation.monte_carlo.Flight", types.SimpleNamespace) + + flight = MonteCarlo._MonteCarlo__run_single_simulation(analysis) + + assert flight.max_time == 123.0 + assert (flight.rtol, flight.atol) == (1e-9, 1e-9) + assert (flight.max_time_step, flight.min_time_step) == (0.5, 0.01) + assert flight.ode_solver == "RK23" + assert flight.equations_of_motion == "solid_propulsion" + assert flight.simulation_mode == "native" + assert flight.name == "named" + # and the single draw is what the flight is actually built from + assert (flight.rail_length, flight.inclination, flight.heading) == ( + 5.0, + 84.0, + 133.0, + ) + + +@pytest.mark.parametrize( + "suffix, payload", + [ + (".csv", "apogee,index\n1234.0,0\n1250.0,1\n"), + (".json", '[{"apogee": 1234.0, "index": 0}]\n'), + ], +) +@pytest.mark.parametrize("append", [False, True]) +def test_simulate_refuses_a_results_file_it_cannot_write( + monte_carlo_calisto, tmp_path, suffix, payload, append +): + """Importing CSV or JSON results must not let simulate() write over them. + + ``import_outputs`` accepts both and points ``output_file`` at the file, and + its docstring offers continuing a simulation. simulate() only writes JSONL, + so ``append=False`` truncated the file before this check existed. + """ + results = tmp_path / f"results{suffix}" + results.write_text(payload, encoding="utf-8") + monte_carlo_calisto.output_file = str(results) + before = results.read_bytes() + + with pytest.raises(ValueError, match="one JSON object per line"): + monte_carlo_calisto.simulate(number_of_simulations=1, append=append) + + assert results.read_bytes() == before + + +def _three_logs(tmp_path): + """Three distinct, acceptable working logs.""" + return [ + str(tmp_path / f"run.{part}.txt") for part in ("inputs", "outputs", "errors") + ] + + +def test_simulation_log_check_names_the_file_that_is_wrong(tmp_path): + """The message says which of the three paths has to change.""" + good = _three_logs(tmp_path) + + _refuse_logs_this_run_cannot_write(*good) # canonical, no raise + + for label, args in ( + ("input_file", (str(tmp_path / "a.csv"), good[1], good[2])), + ("output_file", (good[0], str(tmp_path / "b.json"), good[2])), + ("error_file", (good[0], good[1], str(tmp_path / "c.csv"))), + ): + with pytest.raises(ValueError, match=label): + _refuse_logs_this_run_cannot_write(*args) + + +def test_simulation_log_check_accepts_an_uppercase_suffix(tmp_path): + """A .TXT log is the same file to the filesystem, so it is accepted.""" + upper = [str(tmp_path / f"run.{part}.TXT") for part in ("in", "out", "err")] + _refuse_logs_this_run_cannot_write(*upper) + + +def _three_logs(tmp_path): + """Three distinct, acceptable working logs.""" + return [ + str(tmp_path / f"run.{part}.txt") for part in ("inputs", "outputs", "errors") + ] + + +def test_working_logs_must_be_three_different_files(tmp_path): + """``import_results`` points all three at one path, which cannot work. + + A run appends input rows and output rows separately, so one shared log ends + up holding both and neither reader can make sense of it. + """ + shared = str(tmp_path / "result.txt") + + with pytest.raises(ValueError, match="same file"): + _refuse_logs_this_run_cannot_write(shared, shared, shared) + + +@pytest.mark.parametrize("alias", ["dotdot", "symlink", "hardlink"]) +def test_a_log_named_two_ways_is_still_one_file(tmp_path, alias): + """Text comparison misses every way one file answers to two names.""" + inputs, _, errors = _three_logs(tmp_path) + pathlib.Path(inputs).write_text("", encoding="utf-8") + (tmp_path / "sub").mkdir() + + if alias == "dotdot": + other = str(tmp_path / "sub" / ".." / "run.inputs.txt") + else: + other = str(tmp_path / f"run.{alias}.txt") + try: + if alias == "symlink": + pathlib.Path(other).symlink_to(inputs) + else: + os.link(inputs, other) + except (OSError, NotImplementedError): + pytest.skip(f"{alias} not available on this filesystem") + + with pytest.raises(ValueError, match="same file"): + _refuse_logs_this_run_cannot_write(inputs, other, errors) + + +def test_three_separate_logs_are_accepted(tmp_path): + """The control: distinct .txt paths raise nothing.""" + _refuse_logs_this_run_cannot_write(*_three_logs(tmp_path)) + + +@pytest.mark.parametrize("indent", [2, 0, ""]) +def test_an_indented_record_is_refused_before_anything_is_written(tmp_path, indent): + """``indent`` splits a record over lines the readers take one at a time. + + Without this the run finished, then the completeness check called the file + it had just written damaged. + """ + with pytest.raises(ValueError, match="indent"): + _refuse_logs_this_run_cannot_write(*_three_logs(tmp_path), {"indent": indent}) + + +def test_a_newline_in_the_separators_is_refused_too(tmp_path): + """The same hazard by another name.""" + with pytest.raises(ValueError, match="separators"): + _refuse_logs_this_run_cannot_write( + *_three_logs(tmp_path), {"separators": (",\n", ": ")} + ) + + +@pytest.mark.parametrize( + "harmless", [{"indent": None}, {"sort_keys": True}, {"ensure_ascii": False}] +) +def test_export_options_that_keep_one_line_are_left_alone(tmp_path, harmless): + """Only what puts a newline inside a record is refused.""" + _refuse_logs_this_run_cannot_write(*_three_logs(tmp_path), harmless) + + +def test_two_names_for_a_file_that_does_not_exist_yet_are_still_one_file(tmp_path): + """``samefile`` needs both to exist, and a first run has created neither. + + Every other case here writes the file first, so the resolved-path branch + that a first run actually takes was never exercised. + """ + (tmp_path / "sub").mkdir() + missing = str(tmp_path / "run.inputs.txt") + same_by_another_name = str(tmp_path / "sub" / ".." / "run.inputs.txt") + errors = str(tmp_path / "run.errors.txt") + + assert not pathlib.Path(missing).exists() + with pytest.raises(ValueError, match="same file"): + _refuse_logs_this_run_cannot_write(missing, same_by_another_name, errors) + + +class _InterruptingMonteCarlo(MonteCarlo): + """A MonteCarlo that raises ``KeyboardInterrupt`` where Ctrl-C would land. + + Only the attributes ``simulate`` and ``__run_in_serial`` touch are set, so no + stochastic object graph or real flight is needed. The name-mangled overrides + stand in for the members ``MonteCarlo`` calls on itself, seeding included: + there are no models here to reseed, and what a seeded run draws is pinned + by ``tests/unit/simulation/test_monte_carlo_seeding.py``. + """ + + # pylint: disable=super-init-not-called,invalid-name,unused-argument + + def __init__(self, filename, interrupt_after): + self.filename = filename + self._input_file = filename + ".inputs.txt" + self._output_file = filename + ".outputs.txt" + self._error_file = filename + ".errors.txt" + self.num_of_loaded_sims = 0 + self.number_of_simulations = 0 + self._export_config = {} + self._initial_sim_idx = 0 + self.interrupt_after = interrupt_after + self.completed = 0 + + def _MonteCarlo__seed_this_simulation(self, sim_idx): + pass + + def _MonteCarlo__run_single_simulation(self): + if self.completed >= self.interrupt_after: + raise KeyboardInterrupt("ctrl-c") + self.completed += 1 + return object() + + def _MonteCarlo__evaluate_flight_inputs(self, index): + # The root goes in because an append reads it back to check the rows + # it is continuing were drawn by the same stream; a row without it is + # what a study written before that check looks like, and is refused. + return ( + json.dumps( + { + "index": index, + mc_module._SIMULATION_ROOT_KEY: ( + mc_module._root_written_into_a_row(self._MonteCarlo__root_state) + ), + } + ) + + "\n" + ) + + def _MonteCarlo__evaluate_flight_outputs(self, flight, index): + # The outputs rows name the same root by its digest, which is what + # an append checks them against. + return ( + json.dumps( + { + "index": index, + "apogee": 1000.0 + index, + mc_module._SIMULATION_ROOT_KEY: mc_module._root_digest( + mc_module._root_written_into_a_row(self._MonteCarlo__root_state) + ), + } + ) + + "\n" + ) + + +def test_interrupted_serial_run_reaches_the_caller(tmp_path): + """``simulate`` used to return normally after Ctrl-C. + + A caller could not tell a partial run from a complete one without opening + the output file and counting rows. + """ + mc = _InterruptingMonteCarlo(str(tmp_path / "run"), interrupt_after=2) + + with pytest.raises(KeyboardInterrupt): + mc.simulate(number_of_simulations=10, parallel=False) + + +def test_interrupted_serial_run_keeps_the_rows_that_finished(tmp_path): + """The two simulations that completed stay readable and paired.""" + mc = _InterruptingMonteCarlo(str(tmp_path / "run"), interrupt_after=2) + + with pytest.raises(KeyboardInterrupt): + mc.simulate(number_of_simulations=10, parallel=False) + + inputs = (tmp_path / "run.inputs.txt").read_text(encoding="utf-8").splitlines() + outputs = (tmp_path / "run.outputs.txt").read_text(encoding="utf-8").splitlines() + + assert len(inputs) == 2 + assert len(outputs) == 2 + assert [json.loads(row)["index"] for row in inputs] == [ + json.loads(row)["index"] for row in outputs + ] + + +def test_interrupted_serial_run_still_reloads_the_logs(tmp_path): + """``__terminate_simulation`` runs before the interrupt leaves ``simulate``. + + It is what reloads the logs through the file setters. Asserting on the + state those setters produce, rather than on the call, is what shows the + reload actually happened. + """ + mc = _InterruptingMonteCarlo(str(tmp_path / "run"), interrupt_after=2) + + with pytest.raises(KeyboardInterrupt): + mc.simulate(number_of_simulations=10, parallel=False) + + assert mc.num_of_loaded_sims == 2 + assert len(mc.inputs_log) == 2 + assert len(mc.outputs_log) == 2 + assert mc.results["apogee"] == pytest.approx([1000.0, 1001.0]) + + +def test_an_interrupted_run_can_be_continued_with_append(tmp_path): + """The behavior the ``simulate`` docstring promises after an interrupt. + + ``set_num_of_loaded_sims`` is what ``append=True`` reads to decide where to + resume, and it is only set by the reload above. This runs the whole path: + interrupt, then continue, and check the indices on disk have no gap and no + repeat. + """ + stem = str(tmp_path / "run") + mc = _InterruptingMonteCarlo(stem, interrupt_after=2) + + with pytest.raises(KeyboardInterrupt): + mc.simulate(number_of_simulations=10, parallel=False) + + mc.interrupt_after = 10 + mc.simulate(number_of_simulations=10, append=True, parallel=False) + + rows = pathlib.Path(stem + ".outputs.txt").read_text(encoding="utf-8").splitlines() + assert [json.loads(row)["index"] for row in rows] == list(range(10)) + + +def test_ctrl_c_before_the_first_simulation_is_still_the_interrupt( + tmp_path, monkeypatch +): + """The handler appends ``inputs_json``, which used to be unbound this early. + + Ctrl-C during the first ``claim_next_index()`` call reached the handler + before the loop body had bound the name, so the run died with + ``UnboundLocalError`` from inside the cleanup instead of with the interrupt. + """ + + def interrupt(self): + raise KeyboardInterrupt("ctrl-c before the first simulation") + + monkeypatch.setattr(mc_module._SimMonitor, "claim_next_index", interrupt) + mc = _InterruptingMonteCarlo(str(tmp_path / "run"), interrupt_after=0) + + with pytest.raises(KeyboardInterrupt): + mc.simulate(number_of_simulations=5, parallel=False) + + +class _FakeWorker: + """Stands in for a ``multiprocess.Process`` without starting anything. + + It leaves on the first join it is given, so every test built on it covers + workers that exit cooperatively once the stop event is set — the guarantee + the ``simulate`` docstring makes. A worker that outlives the grace period + is terminated and then killed by ``_stop_the_workers_still_running``, and + ``tests/unit/simulation/test_monte_carlo_worker_join.py`` is where that + bounded shutdown is pinned; these tests are about the interrupt reaching + the caller, not about the bound. + """ + + def __init__(self, interrupt_on_first_join=False, interrupt_on_start=False): + self.starts = 0 + self.joins = 0 + self.timeouts = [] + self.terminated = False + self.killed = False + self.exitcode = None + self._interrupt_on_first_join = interrupt_on_first_join + self._interrupt_on_start = interrupt_on_start + + def is_alive(self): + return self.exitcode is None + + def start(self): + self.starts += 1 + if self._interrupt_on_start: + raise KeyboardInterrupt("ctrl-c inside Process.start()") + + def join(self, timeout=None): + self.joins += 1 + self.timeouts.append(timeout) + if self._interrupt_on_first_join and self.joins == 1: + raise KeyboardInterrupt("ctrl-c while waiting for the workers") + self.exitcode = 0 + + def terminate(self): + self.terminated = True + self.exitcode = -15 + + def kill(self): + self.killed = True + self.exitcode = -9 + + +class _FakeManager: + """The subset of the multiprocess manager that ``__run_in_parallel`` uses.""" + + # pylint: disable=invalid-name + + def __init__(self): + self.event = _FakeEvent() + self.monitor = _FakeSimMonitor() + + def __enter__(self): + return self + + def __exit__(self, *_exc): + return False + + def Lock(self): + return object() + + def Event(self): + return self.event + + def _SimMonitor(self, **_kwargs): + return self.monitor + + +class _FakeEvent: + def __init__(self): + self._set = False + + def set(self): + self._set = True + + def is_set(self): + return self._set + + +class _FakeSimMonitor: + def __init__(self, **_kwargs): + self.final_status_calls = 0 + + def print_final_status(self): + self.final_status_calls += 1 + + +def test_interrupted_parallel_run_signals_joins_and_reaches_the_caller( + tmp_path, monkeypatch +): + """Ctrl-C while waiting on the workers must not end as a successful run. + + The handler already signalled and joined the workers, but it then swallowed + the interrupt, so ``simulate`` went on to report the study as finished. + """ + manager = _FakeManager() + workers = [] + + class _FakeMultiprocess: + # pylint: disable=invalid-name + @staticmethod + def Process(target=None, args=()): # pylint: disable=unused-argument + worker = _FakeWorker(interrupt_on_first_join=not workers) + workers.append(worker) + return worker + + monkeypatch.setattr( + mc_module, "_import_multiprocess", lambda: (_FakeMultiprocess, None) + ) + monkeypatch.setattr( + mc_module, "_create_multiprocess_manager", lambda *_args: manager + ) + mc = _InterruptingMonteCarlo(str(tmp_path / "run"), interrupt_after=0) + + with pytest.raises(KeyboardInterrupt): + mc.simulate(number_of_simulations=4, parallel=True, n_workers=2) + + assert len(workers) == 2 + assert all(worker.starts == 1 for worker in workers) + assert manager.event.is_set(), "the workers were never told to stop" + # At least, not exactly: the bounded shutdown joins each worker once per + # escalation stage, and how many stages it walks is its own business. + assert workers[0].joins >= 2, ( + "the interrupted join was not retried after signalling" + ) + assert workers[1].joins >= 1, "the second worker was never joined" + assert manager.monitor.final_status_calls == 0, "a partial run reported completion" + # __init__ above never sets inputs_log; only the reload in + # __terminate_simulation does. Interrupted before any row was written, the + # reload of the empty files must produce empty logs, not be skipped. + assert mc.inputs_log == [], "the reload did not run on the interrupted path" + assert mc.outputs_log == [] + + +def test_ctrl_c_during_worker_startup_still_stops_the_started_workers( + tmp_path, monkeypatch +): + """Ctrl-C in the middle of the startup loop must clean up what came up. + + The cleanup handler used to begin only after every worker had started, so + an interrupt during ``Process.start()`` left the earlier workers running + with nobody signalling or joining them. + """ + manager = _FakeManager() + workers = [] + + class _FakeMultiprocess: + # pylint: disable=invalid-name + @staticmethod + def Process(target=None, args=()): # pylint: disable=unused-argument + # The second start() raises where a real Ctrl-C could land. + worker = _FakeWorker(interrupt_on_start=len(workers) == 1) + workers.append(worker) + return worker + + monkeypatch.setattr( + mc_module, "_import_multiprocess", lambda: (_FakeMultiprocess, None) + ) + monkeypatch.setattr( + mc_module, "_create_multiprocess_manager", lambda *_args: manager + ) + mc = _InterruptingMonteCarlo(str(tmp_path / "run"), interrupt_after=0) + + with pytest.raises(KeyboardInterrupt): + mc.simulate(number_of_simulations=4, parallel=True, n_workers=2) + + assert manager.event.is_set(), "the started worker was never told to stop" + assert workers[0].joins >= 1, "the started worker was never joined" + # The second worker's start() raised, so it never entered the started list + # and must not be joined: joining a never-started process raises. + assert workers[1].joins == 0 + + +def test_ctrl_c_between_the_two_appends_rolls_the_inputs_row_back(tmp_path): + """The rollback in ``_append_simulation_record`` must catch the interrupt. + + It caught ``Exception``, and ``KeyboardInterrupt`` derives from + ``BaseException``, so Ctrl-C after the inputs append but before the outputs + append left the inputs file one row longer — a one-sided record that the + reload then disagrees with. This is the boundary between this change and + the pairing that #1125 introduced. + """ + stem = str(tmp_path / "run") + mc = _InterruptingMonteCarlo(stem, interrupt_after=10) + + real_open = builtins.open + outputs_path = stem + ".outputs.txt" + appends = {"count": 0} + + def interrupt_third_outputs_append(*args, **kwargs): + file = args[0] if args else kwargs["file"] + mode = args[1] if len(args) > 1 else kwargs.get("mode", "r") + if os.fspath(file) == outputs_path and "a" in mode: + appends["count"] += 1 + if appends["count"] == 3: + raise KeyboardInterrupt("ctrl-c between the appends") + return real_open(*args, **kwargs) + + with pytest.raises(KeyboardInterrupt): + with patch("builtins.open", side_effect=interrupt_third_outputs_append): + mc.simulate(number_of_simulations=10, parallel=False) + + inputs = pathlib.Path(stem + ".inputs.txt").read_text(encoding="utf-8").splitlines() + outputs = pathlib.Path(outputs_path).read_text(encoding="utf-8").splitlines() + errors = pathlib.Path(stem + ".errors.txt").read_text(encoding="utf-8").splitlines() + + assert len(inputs) == 2, "the third inputs row was not rolled back" + assert len(outputs) == 2 + assert [json.loads(row)["index"] for row in inputs] == [ + json.loads(row)["index"] for row in outputs + ] + # The simulation that was cut short is recorded where errors go, so the + # rolled-back row is preserved rather than lost. + assert [json.loads(row)["index"] for row in errors] == [2] + assert mc.num_of_loaded_sims == 2 + + +def test_ctrl_c_in_the_progress_print_leaves_the_error_file_empty(tmp_path): + """A committed row must not be reported as one that never finished. + + After ``_append_simulation_record`` returns, the pair is on disk. The + handler used to write ``inputs_json`` to the error file anyway when the + interrupt landed in ``print_update_status()`` — or in the next + ``claim_next_index()`` call — because the name still held the committed row. + """ + stem = str(tmp_path / "run") + mc = _InterruptingMonteCarlo(stem, interrupt_after=10) + + updates = {"count": 0} + real_update = mc_module._SimMonitor.print_update_status + + def counting_update(self, *args, **kwargs): + updates["count"] += 1 + if updates["count"] == 2: + raise KeyboardInterrupt("ctrl-c during the progress print") + return real_update(self, *args, **kwargs) + + with patch.object(mc_module._SimMonitor, "print_update_status", counting_update): + with pytest.raises(KeyboardInterrupt): + mc.simulate(number_of_simulations=10, parallel=False) + + inputs = pathlib.Path(stem + ".inputs.txt").read_text(encoding="utf-8").splitlines() + outputs = ( + pathlib.Path(stem + ".outputs.txt").read_text(encoding="utf-8").splitlines() + ) + errors = pathlib.Path(stem + ".errors.txt").read_text(encoding="utf-8") + + assert len(inputs) == 2 + assert len(outputs) == 2 + assert errors == "", "a committed row was written to the error file" + assert mc.num_of_loaded_sims == 2 + + +class _TornWrite: + """A context manager that writes half the row, flushes, and interrupts.""" + + def __init__(self, real_file): + self._real_file = real_file + + def __enter__(self): + self._file = self._real_file.__enter__() + return self + + def __exit__(self, *exc_info): + return self._real_file.__exit__(*exc_info) + + def write(self, text): + self._file.write(text[: len(text) // 2]) + self._file.flush() + raise KeyboardInterrupt("ctrl-c inside the outputs write") + + +def test_a_torn_outputs_write_rolls_both_files_back(tmp_path): + """An interrupt inside the write itself must not leave half a row. + + Rolling back only the inputs file handled the interrupt *between* the two + appends; one landing *inside* the outputs write leaves a torn partial row + that the rollback then has to remove too, or the reload dies with + ``JSONDecodeError`` instead of the interrupt. + """ + stem = str(tmp_path / "run") + mc = _InterruptingMonteCarlo(stem, interrupt_after=10) + + outputs_path = stem + ".outputs.txt" + appends = {"count": 0} + real_open = builtins.open + + def torn_third_outputs_append(*args, **kwargs): + file = args[0] if args else kwargs["file"] + mode = args[1] if len(args) > 1 else kwargs.get("mode", "r") + if os.fspath(file) == outputs_path and "a" in mode: + appends["count"] += 1 + if appends["count"] == 3: + return _TornWrite(real_open(*args, **kwargs)) + return real_open(*args, **kwargs) + + with pytest.raises(KeyboardInterrupt): + with patch("builtins.open", side_effect=torn_third_outputs_append): + mc.simulate(number_of_simulations=10, parallel=False) + + inputs = pathlib.Path(stem + ".inputs.txt").read_text(encoding="utf-8").splitlines() + outputs = pathlib.Path(outputs_path).read_text(encoding="utf-8").splitlines() + errors = pathlib.Path(stem + ".errors.txt").read_text(encoding="utf-8").splitlines() + + assert len(inputs) == 2, "the inputs row of the torn record was not rolled back" + assert len(outputs) == 2, "the torn outputs row was not rolled back" + for row in inputs + outputs: + json.loads(row) # every surviving row must still parse + assert [json.loads(row)["index"] for row in errors] == [2] + assert mc.num_of_loaded_sims == 2 diff --git a/tests/unit/simulation/test_monte_carlo_parallel_runs.py b/tests/unit/simulation/test_monte_carlo_parallel_runs.py new file mode 100644 index 000000000..21ac90589 --- /dev/null +++ b/tests/unit/simulation/test_monte_carlo_parallel_runs.py @@ -0,0 +1,33 @@ +import pytest + +from rocketpy.simulation.monte_carlo import MonteCarlo + + +@pytest.mark.parametrize("parallel", [False, True]) +def test_a_monte_carlo_run_finishes( + stochastic_environment, stochastic_calisto, stochastic_flight, tmp_path, parallel +): + """A real run completes and records every simulation, both modes.""" + # The parallel path hands each worker a SeedSequence rather than an int, and + # nothing else in the suite exercises that. A worker that dies on it is not + # reported, so this reads as a hang rather than as a failure. + # + # Built here rather than taken from the monte_carlo_calisto fixture, whose + # own filename is fixed, since `filename` is a plain attribute and the three + # working paths are settled when the object is constructed. + analysis = MonteCarlo( + filename=str(tmp_path / "study"), + environment=stochastic_environment, + rocket=stochastic_calisto, + flight=stochastic_flight, + ) + + analysis.simulate( + number_of_simulations=2, + append=False, + parallel=parallel, + n_workers=2 if parallel else None, + ) + + assert analysis.num_of_loaded_sims == 2 + assert str(tmp_path) in str(analysis.output_file) diff --git a/tests/unit/simulation/test_monte_carlo_plots.py b/tests/unit/simulation/test_monte_carlo_plots.py new file mode 100644 index 000000000..6e049c0fb --- /dev/null +++ b/tests/unit/simulation/test_monte_carlo_plots.py @@ -0,0 +1,118 @@ +"""Unit tests for the ellipse plots in ``rocketpy.plots.monte_carlo_plots``. + +Background-map fetching is covered separately in +``test_monte_carlo_plots_background.py``. Everything here runs with +``background=None`` so no tile provider is contacted. +""" + +from unittest.mock import patch + +import numpy as np +import pytest + +from rocketpy.plots.monte_carlo_plots import _MonteCarloPlots +from rocketpy.simulation import MonteCarlo + +APOGEE = {"apogee_x": [100, 200, 300], "apogee_y": [100, 200, 300]} +IMPACT = {"x_impact": [1000, 2000, 3000], "y_impact": [1000, 2000, 3000]} + + +class MockMonteCarlo(MonteCarlo): + """A MonteCarlo carrying only the results the ellipse plots read.""" + + def __init__(self, results, filename="test"): + # pylint: disable=super-init-not-called + self.filename = filename + self.results = results + self.plots = _MonteCarloPlots(self) + + +@pytest.fixture(name="square_image") +def square_image_fixture(tmp_path): + """A small on-disk image for the ``image`` background path.""" + imageio = pytest.importorskip("imageio") + path = tmp_path / "launch_site.png" + imageio.imwrite(path, np.zeros((8, 8, 3), dtype=np.uint8)) + return str(path) + + +@patch("matplotlib.pyplot.show") +def test_ellipses_without_apogee_data_plots_the_impact_points(mock_show, caplog): # pylint: disable=unused-argument + """A results file may hold impacts and no apogees. + + The apogee lookup is allowed to miss; the method warns and draws what is + left rather than failing. + """ + monte_carlo = MockMonteCarlo(dict(IMPACT)) + + assert monte_carlo.plots.ellipses() is None + assert "No apogee data found" in caplog.text + + +@patch("matplotlib.pyplot.show") +def test_ellipses_without_impact_data_plots_the_apogee_points(mock_show, caplog): # pylint: disable=unused-argument + """The mirror case: apogees recorded, impacts missing.""" + monte_carlo = MockMonteCarlo(dict(APOGEE)) + + assert monte_carlo.plots.ellipses() is None + assert "No impact data found" in caplog.text + + +def test_ellipses_refuses_results_with_neither_apogee_nor_impact(): + """With both lookups missing there is nothing to draw an ellipse around.""" + monte_carlo = MockMonteCarlo({"t_final": [10, 11, 12]}) + + with pytest.raises(ValueError, match="No apogee or impact data found"): + monte_carlo.plots.ellipses() + + +def test_ellipses_reports_an_image_path_that_does_not_exist(tmp_path): + """The path is the user's input, so the failure names it rather than the read.""" + monte_carlo = MockMonteCarlo({**APOGEE, **IMPACT}) + + with pytest.raises(FileNotFoundError, match="image file was not found"): + monte_carlo.plots.ellipses(image=str(tmp_path / "absent.png")) + + +@patch("matplotlib.pyplot.show") +def test_ellipses_draws_over_an_image_and_marks_the_actual_landing_point( # pylint: disable=unused-argument + mock_show, square_image +): + """``image`` and ``actual_landing_point`` are separate optional branches.""" + monte_carlo = MockMonteCarlo({**APOGEE, **IMPACT}) + + assert ( + monte_carlo.plots.ellipses( + image=square_image, actual_landing_point=(1500, 1500) + ) + is None + ) + + +@patch("matplotlib.pyplot.show") +def test_ellipses_comparison_without_apogee_data(mock_show, caplog): # pylint: disable=unused-argument + """The comparison reads four series at once, so one miss drops all four.""" + monte_carlo = MockMonteCarlo(dict(IMPACT)) + other = MockMonteCarlo(dict(IMPACT), filename="other") + + assert monte_carlo.plots.ellipses_comparison(other) is None + assert "No apogee data found" in caplog.text + + +@patch("matplotlib.pyplot.show") +def test_ellipses_comparison_without_impact_data(mock_show, caplog): # pylint: disable=unused-argument + """The mirror case for the impact series.""" + monte_carlo = MockMonteCarlo(dict(APOGEE)) + other = MockMonteCarlo(dict(APOGEE), filename="other") + + assert monte_carlo.plots.ellipses_comparison(other) is None + assert "No impact data found" in caplog.text + + +@patch("matplotlib.pyplot.show") +def test_ellipses_comparison_draws_over_an_image(mock_show, square_image): # pylint: disable=unused-argument + """The comparison takes the same ``image`` branch as ``ellipses``.""" + monte_carlo = MockMonteCarlo({**APOGEE, **IMPACT}) + other = MockMonteCarlo({**APOGEE, **IMPACT}, filename="other") + + assert monte_carlo.plots.ellipses_comparison(other, image=square_image) is None diff --git a/tests/unit/simulation/test_monte_carlo_plots_background.py b/tests/unit/simulation/test_monte_carlo_plots_background.py index 8a9de5cf6..89a554a85 100644 --- a/tests/unit/simulation/test_monte_carlo_plots_background.py +++ b/tests/unit/simulation/test_monte_carlo_plots_background.py @@ -1,4 +1,5 @@ # pylint: disable=unused-argument,assignment-from-no-return +import math import os import urllib.error from unittest.mock import MagicMock, patch @@ -19,6 +20,33 @@ ) +@pytest.fixture(autouse=True) +def mock_background_tiles(monkeypatch): + """Return deterministic map tiles without contacting a tile provider.""" + contextily = import_optional_dependency("contextily") + + def mock_bounds2img(west, south, east, north, **kwargs): + earth_radius = 6378137.0 + + def to_mercator(longitude, latitude): + x = earth_radius * math.radians(longitude) + y = earth_radius * math.log( + math.tan(math.pi / 4 + math.radians(latitude) / 2) + ) + return x, y + + min_x, min_y = to_mercator(west, south) + max_x, max_y = to_mercator(east, north) + return np.zeros((2, 2, 3), dtype=np.uint8), ( + min_x, + max_x, + min_y, + max_y, + ) + + monkeypatch.setattr(contextily, "bounds2img", mock_bounds2img) + + class MockMonteCarlo(MonteCarlo): """Create a mock class to test the method without running a real simulation. diff --git a/tests/unit/simulation/test_monte_carlo_run_completeness.py b/tests/unit/simulation/test_monte_carlo_run_completeness.py new file mode 100644 index 000000000..d98f51390 --- /dev/null +++ b/tests/unit/simulation/test_monte_carlo_run_completeness.py @@ -0,0 +1,272 @@ +import ast +import inspect +import json +import os + +import pytest + +from rocketpy.simulation import monte_carlo as mc_module +from rocketpy.simulation.monte_carlo import ( + MonteCarlo, + _refuse_logs_missing_a_simulation, +) + + +def _a_log(tmp_path, name, rows): + path = tmp_path / name + path.write_text("".join(rows), encoding="utf-8") + return str(path) + + +def _row(index): + return json.dumps({"index": index, "mass": 1.0}) + "\n" + + +def _complete(tmp_path, count=3, name="ok"): + rows = [_row(index) for index in range(count)] + return ( + _a_log(tmp_path, f"{name}.inputs.txt", rows), + _a_log(tmp_path, f"{name}.outputs.txt", rows), + ) + + +def test_a_run_that_recorded_everything_is_accepted(tmp_path): + """Logs holding every index the run asked for raise nothing.""" + inputs, outputs = _complete(tmp_path) + + _refuse_logs_missing_a_simulation(inputs, outputs, 3) + + +def test_blank_lines_between_rows_are_not_simulations(tmp_path): + """A blank line is skipped rather than counted as an unreadable row.""" + # An interrupted write leaves them, and reading one as a row would report + # a damaged log for a run that lost nothing. + rows = [_row(0), "\n", _row(1), " \n", _row(2)] + inputs = _a_log(tmp_path, "gappy.inputs.txt", rows) + outputs = _a_log(tmp_path, "gappy.outputs.txt", rows) + + _refuse_logs_missing_a_simulation(inputs, outputs, 3) + + +def test_a_missing_simulation_is_refused(tmp_path): + """A gap in the output log names the first index that is missing.""" + inputs, outputs = _complete(tmp_path) + _a_log(tmp_path, "ok.outputs.txt", [_row(0), _row(2)]) + + with pytest.raises(RuntimeError, match=r"output log.*missing.*being 1"): + _refuse_logs_missing_a_simulation(inputs, outputs, 3) + + +def test_a_simulation_recorded_twice_is_refused(tmp_path): + """A duplicated index is refused, since a set alone would hide it.""" + inputs, outputs = _complete(tmp_path) + _a_log(tmp_path, "ok.inputs.txt", [_row(0), _row(1), _row(1), _row(2)]) + + with pytest.raises(RuntimeError, match="more than once"): + _refuse_logs_missing_a_simulation(inputs, outputs, 3) + + +def test_a_row_that_cannot_be_read_is_refused(tmp_path): + """A torn row means the log's contents cannot be established.""" + inputs, outputs = _complete(tmp_path) + _a_log(tmp_path, "ok.outputs.txt", [_row(0), "{half a row\n", _row(2)]) + + with pytest.raises(RuntimeError, match="cannot be read"): + _refuse_logs_missing_a_simulation(inputs, outputs, 3) + + +def test_rows_numbered_past_the_run_are_left_alone(tmp_path): + """An append below what a checkpoint already holds loses no simulation.""" + # Refusing these said rows were missing when they were extra, and moved + # what append means inside a change about worker failure. + rows = [_row(index) for index in range(4)] + inputs = _a_log(tmp_path, "big.inputs.txt", rows) + outputs = _a_log(tmp_path, "big.outputs.txt", rows) + + _refuse_logs_missing_a_simulation(inputs, outputs, 2) + + +def test_logs_that_hold_different_simulations_are_refused(tmp_path): + """The input and output logs have to hold the same indices.""" + inputs = _a_log(tmp_path, "a.inputs.txt", [_row(0), _row(1)]) + outputs = _a_log(tmp_path, "a.outputs.txt", [_row(0), _row(2)]) + + with pytest.raises(RuntimeError): + _refuse_logs_missing_a_simulation(inputs, outputs, 2) + + +@pytest.mark.parametrize("index", [True, False, 1.0, -1, [], "1", None]) +def test_an_index_that_is_not_a_whole_number_is_refused(tmp_path, index): + """Only a non-negative int names a simulation, whatever compares equal.""" + # True and 1.0 both equal 1, so either could stand in for a simulation that + # was never run. An unhashable one used to escape as a raw TypeError. + rows = [_row(0), _row(index)] + inputs = _a_log(tmp_path, "odd.inputs.txt", rows) + outputs = _a_log(tmp_path, "odd.outputs.txt", rows) + + with pytest.raises(RuntimeError, match="cannot be read"): + _refuse_logs_missing_a_simulation(inputs, outputs, 2) + + +def test_a_stray_index_past_the_rows_is_refused(tmp_path): + """A number no run of this length could have produced is not a simulation.""" + # A worker that claimed one index too many writes exactly this, and the + # target alone cannot tell it from a checkpoint that is legitimately longer. + rows = [_row(0), _row(1), _row(99)] + inputs = _a_log(tmp_path, "stray.inputs.txt", rows) + outputs = _a_log(tmp_path, "stray.outputs.txt", rows) + + with pytest.raises(RuntimeError, match="past the 3 it holds"): + _refuse_logs_missing_a_simulation(inputs, outputs, 2) + + +def test_the_order_a_parallel_run_finishes_in_is_not_a_hole(tmp_path): + """Rows arrive in completion order, which is not sorted and not wrong.""" + rows = [_row(1), _row(0), _row(2)] + inputs = _a_log(tmp_path, "para.inputs.txt", rows) + outputs = _a_log(tmp_path, "para.outputs.txt", rows) + + _refuse_logs_missing_a_simulation(inputs, outputs, 3) + + +def test_logs_that_disagree_on_the_order_are_refused(tmp_path): + """A record goes into both logs under one lock, so the order is the same.""" + inputs = _a_log(tmp_path, "order.inputs.txt", [_row(0), _row(1)]) + outputs = _a_log(tmp_path, "order.outputs.txt", [_row(1), _row(0)]) + + with pytest.raises(RuntimeError, match="same order"): + _refuse_logs_missing_a_simulation(inputs, outputs, 2) + + +def _leave_cleanly_without_recording(_flight): + # A worker that ends the way an out-of-memory kill ends it, but with the + # status of one that finished. Nothing about the process says otherwise. + os._exit(0) + + +def test_a_worker_that_leaves_cleanly_without_recording_is_not_a_success( + stochastic_environment, stochastic_calisto, stochastic_flight, tmp_path +): + """A zero exit with no row written makes ``simulate`` raise.""" + analysis = MonteCarlo( + filename=str(tmp_path / "study"), + environment=stochastic_environment, + rocket=stochastic_calisto, + flight=stochastic_flight, + data_collector={"leave": _leave_cleanly_without_recording}, + ) + + with pytest.raises(RuntimeError, match="incomplete"): + analysis.simulate( + number_of_simulations=6, append=False, parallel=True, n_workers=2 + ) + + +def test_no_failure_path_waits_on_a_worker_without_a_bound(): + """No ``join`` in the parallel path is called without a timeout.""" + # An unbounded join anywhere in the parallel path puts back the hang that + # the bounded teardown exists to end, and it does so where it is hardest + # to notice: only when a worker is already stuck. + tree = ast.parse(inspect.getsource(mc_module)) + run_in_parallel = next( + node + for node in ast.walk(tree) + if isinstance(node, ast.FunctionDef) and node.name == "__run_in_parallel" + ) + + unbounded = [ + node.lineno + for node in ast.walk(run_in_parallel) + if isinstance(node, ast.Call) + and isinstance(node.func, ast.Attribute) + and node.func.attr == "join" + and not node.args + and not node.keywords + ] + + assert not unbounded, f"join() with no timeout at lines {unbounded}" + + +def test_starting_a_worker_happens_inside_the_cleanup_scope(): + """A start that fails has to leave the workers before it accounted for.""" + # Structural for the same reason the unbounded-join check is: it only shows + # when a start has already failed, which a unit test cannot make a real + # process do. Outside the try, an interrupt there left children running. + tree = ast.parse(inspect.getsource(mc_module)) + run_in_parallel = next( + node + for node in ast.walk(tree) + if isinstance(node, ast.FunctionDef) and node.name == "__run_in_parallel" + ) + guarded = [ + node + for handler in ast.walk(run_in_parallel) + if isinstance(handler, ast.Try) + for node in ast.walk(handler) + if isinstance(node, ast.Call) + and isinstance(node.func, ast.Attribute) + and node.func.attr == "start" + ] + + assert guarded, "Process.start() is not inside a try in __run_in_parallel" + + +def test_a_worker_is_recorded_only_once_it_has_started(): + """A process that never started cannot be joined or terminated.""" + tree = ast.parse(inspect.getsource(mc_module)) + run_in_parallel = next( + node + for node in ast.walk(tree) + if isinstance(node, ast.FunctionDef) and node.name == "__run_in_parallel" + ) + lines = {"start": None, "append": None} + for node in ast.walk(run_in_parallel): + if isinstance(node, ast.Call) and isinstance(node.func, ast.Attribute): + if node.func.attr == "start": + lines["start"] = node.lineno + if node.func.attr == "append": + lines["append"] = node.lineno + + assert lines["start"] is not None and lines["append"] is not None + assert lines["start"] < lines["append"], "appended before it started" + + +def test_a_collector_cannot_take_over_the_simulation_index( + stochastic_environment, stochastic_calisto, stochastic_flight, tmp_path +): + """A collector key called index is refused before the run touches a file.""" + # Measured before this was refused: every row was written with the + # collector's value, so the log said 999 twice for a two-simulation run + # and every check that reads an index was reading the wrong thing. + # Refused when the collector is handed over, which is before any file + # is opened, rather than at the end of a run that is already spoilt. + with pytest.raises(ValueError, match="index"): + MonteCarlo( + filename=str(tmp_path / "study"), + environment=stochastic_environment, + rocket=stochastic_calisto, + flight=stochastic_flight, + data_collector={"index": lambda flight: 999}, + ) + + +def test_a_collector_key_of_its_own_is_still_welcome( + stochastic_environment, stochastic_calisto, stochastic_flight, tmp_path +): + """The control. Only the one reserved name is refused.""" + analysis = MonteCarlo( + filename=str(tmp_path / "ok"), + environment=stochastic_environment, + rocket=stochastic_calisto, + flight=stochastic_flight, + data_collector={"apogee_twice": lambda flight: 2 * flight.apogee}, + ) + + analysis.simulate(number_of_simulations=1, append=False) + + with open(analysis.output_file, "r", encoding="utf-8") as written: + row = json.loads(next(line for line in written if line.strip())) + # Not the value of the index: how a run numbers its simulations is + # settled elsewhere, and pinning it here would tie this to that. + assert "index" in row + assert "apogee_twice" in row diff --git a/tests/unit/simulation/test_monte_carlo_seeding.py b/tests/unit/simulation/test_monte_carlo_seeding.py new file mode 100644 index 000000000..bd1f0cfa2 --- /dev/null +++ b/tests/unit/simulation/test_monte_carlo_seeding.py @@ -0,0 +1,296 @@ +import json +import os +from time import time + +import multiprocess +import numpy as np +import pytest + +from rocketpy.stochastic import StochasticEnvironment +from rocketpy.simulation.monte_carlo import ( + MonteCarlo, + _root_seed_sequence, + _root_state_of, + _seed_of_simulation, + _seed_sequence_to_int, + _SimMonitor, +) + + +def _what_was_drawn(value): + """The same record without the parts that say which process wrote it. + + Two of the fields a serialized ``Function`` carries are properties of the + writer rather than of the draw. ``hash`` is the object's identity in that + process. A callable ``source`` is its pickle, and the same callable pickles + to different bytes under ``spawn``: measured on one drag curve, the parent + and a forked child agree and a spawned child does not, for a value the + fixture does not vary at all. Everything a run actually draws is numeric + and stays. + """ + if isinstance(value, dict): + return { + key: _what_was_drawn(item) + for key, item in value.items() + if key != "hash" and not (key == "source" and isinstance(item, str)) + } + if isinstance(value, list): + return [_what_was_drawn(item) for item in value] + return value + + +def _seed_for(index): + """The seed MonteCarlo gives one index, without running the others.""" + root = _root_state_of(_root_seed_sequence(42)) + return _seed_sequence_to_int(_seed_of_simulation(root, index)) + + +def _sampled_inputs(analysis): + with open(analysis.input_file, "r", encoding="utf-8") as written: + rows = [json.loads(line) for line in written if line.strip()] + return {row["index"]: _what_was_drawn(row) for row in rows} + + +def _a_run(tmp_path, stem, models, *, parallel=False, workers=None, seed=None, count=4): + environment, rocket, flight = models + analysis = MonteCarlo( + filename=str(tmp_path / stem), + environment=environment, + rocket=rocket, + flight=flight, + ) + analysis.simulate( + number_of_simulations=count, + append=False, + parallel=parallel, + n_workers=workers, + random_seed=seed, + ) + return analysis + + +@pytest.fixture(name="models") +def _models(stochastic_environment, stochastic_calisto, stochastic_flight): + return stochastic_environment, stochastic_calisto, stochastic_flight + + +# --------------------------------------------------------------- the derivation + + +@pytest.mark.parametrize("seed", [42, None, [1, 2, 3], np.random.SeedSequence(7)]) +def test_a_simulation_gets_the_child_spawn_would_have_given_it(seed): + # The whole point of deriving one index directly: it has to be the same + # child, bit for bit, as spawning every index before it would produce. + root = _root_seed_sequence(seed) + state = _root_state_of(root) + spawned = np.random.SeedSequence(**root.state).spawn(6) + + for index, expected in enumerate(spawned): + assert np.array_equal( + expected.generate_state(4), + _seed_of_simulation(state, index).generate_state(4), + ) + + +def test_deriving_an_index_costs_nothing_for_the_ones_before_it(): + state = _root_state_of(_root_seed_sequence(42)) + + far = _seed_of_simulation(state, 1_000_000) + + assert far.spawn_key == (1_000_000,) + + +def test_two_indices_do_not_share_a_stream(): + state = _root_state_of(_root_seed_sequence(42)) + + first = _seed_of_simulation(state, 0).generate_state(4) + second = _seed_of_simulation(state, 1).generate_state(4) + + assert not np.array_equal(first, second) + + +# ------------------------------------------------------------------- the root + + +def test_a_caller_seed_sequence_is_not_consumed(): + given = np.random.SeedSequence(42) + + _root_seed_sequence(given).spawn(5) + + assert given.n_children_spawned == 0 + + +def test_a_caller_cannot_move_the_run_by_editing_the_list_it_passed(): + # SeedSequence keeps a sequence entropy by reference, so without a copy of + # its own the run would follow whatever the caller did to that list next. + given = [1, 2, 3] + state = _root_state_of(_root_seed_sequence(given)) + + before = _seed_of_simulation(state, 0).generate_state(4) + given[0] = 999 + + assert np.array_equal(_seed_of_simulation(state, 0).generate_state(4), before) + + +@pytest.mark.parametrize("given", [np.random.default_rng(42), np.random.PCG64(42)]) +def test_a_generator_is_refused_rather_than_read(given): + # Using a consume-on-use object as an immutable seed cannot mean what it + # says, so it is refused instead of quietly meaning something else. + with pytest.raises(TypeError, match="random_seed must be"): + _root_seed_sequence(given) + + +def test_an_unusable_seed_costs_the_previous_run_nothing(models, tmp_path): + analysis = _a_run(tmp_path, "study", models, seed=42, count=2) + kept = _sampled_inputs(analysis) + + with pytest.raises(TypeError): + analysis.simulate(2, append=False, random_seed=np.random.default_rng(1)) + + assert _sampled_inputs(analysis) == kept + + +# ------------------------------------------------------------- what a run gives + + +def test_one_seed_gives_one_set_of_inputs(models, tmp_path): + first = _a_run(tmp_path, "first", models, seed=42) + again = _a_run(tmp_path, "again", models, seed=42) + + assert _sampled_inputs(first) == _sampled_inputs(again) + + +def test_another_seed_gives_another_set(models, tmp_path): + # The control. Without this the test above passes on a run that ignores + # the seed entirely. + first = _a_run(tmp_path, "first", models, seed=42) + other = _a_run(tmp_path, "other", models, seed=7) + + assert _sampled_inputs(first) != _sampled_inputs(other) + + +@pytest.mark.skipif(os.cpu_count() < 4, reason="needs four workers to be four") +def test_an_index_gets_the_same_inputs_however_the_run_was_split(models, tmp_path): + # This is the guarantee. Splitting the work differently must not change + # what any one simulation drew. + serial = _a_run(tmp_path, "serial", models, seed=42) + two = _a_run(tmp_path, "two", models, parallel=True, workers=2, seed=42) + four = _a_run(tmp_path, "four", models, parallel=True, workers=4, seed=42) + + assert _sampled_inputs(serial) == _sampled_inputs(two) + assert _sampled_inputs(serial) == _sampled_inputs(four) + + +def test_an_index_drawn_last_draws_what_it_draws_alone(example_spaceport_env): + """A worker reaches an index without running the ones before it. + + The split test above cannot see this: every input in its fixture is given + an explicit nominal, and only one read off the wrapped object can move. + """ + # A bare standard deviation, so the nominal is read from the environment, + # which is also where create_object writes the draw back. Both are built + # before anything is drawn, the way every worker starts from one model. + walked = StochasticEnvironment(example_spaceport_env, elevation=10) + alone = StochasticEnvironment(example_spaceport_env, elevation=10) + + for index in range(4): + walked._set_stochastic(_seed_for(index)) + walked.create_object() + reached_last = walked.last_rnd_dict["elevation"] + + alone._set_stochastic(_seed_for(3)) + alone.create_object() + + assert reached_last == alone.last_rnd_dict["elevation"] + + +def test_the_indices_of_that_run_are_not_all_one_value(example_spaceport_env): + # The control. Without it the test above holds on a model that draws a + # constant, which is what an elevation of nominal zero would give. + walked = StochasticEnvironment(example_spaceport_env, elevation=10) + drawn = [] + for index in range(4): + walked._set_stochastic(_seed_for(index)) + walked.create_object() + drawn.append(walked.last_rnd_dict["elevation"]) + + assert len(set(drawn)) == len(drawn) + + +def test_an_appended_run_carries_the_same_stream_on(models, tmp_path): + whole = _a_run(tmp_path, "whole", models, seed=42, count=4) + + part = _a_run(tmp_path, "part", models, seed=42, count=2) + part.simulate(4, append=True, random_seed=42) + + assert _sampled_inputs(part) == _sampled_inputs(whole) + + +@pytest.fixture(name="spawned_workers") +def _spawned_workers(): + """Start workers the way Windows does, wherever the test happens to run.""" + was = multiprocess.get_start_method() + multiprocess.set_start_method("spawn", force=True) + yield + multiprocess.set_start_method(was, force=True) + + +@pytest.mark.usefixtures("spawned_workers") +@pytest.mark.skipif(os.cpu_count() < 4, reason="needs four workers to be four") +def test_an_index_keeps_its_inputs_when_the_workers_are_spawned(models, tmp_path): + """The same guarantee on the start method Windows uses. + + A spawned child rebuilds the models by unpickling rather than inheriting + them, so this is a different question from the one above and was worth + asking separately: the first version of these tests compared serialized + callables, which differ between processes for a value nothing varies. + """ + serial = _a_run(tmp_path, "serial", models, seed=42) + two = _a_run(tmp_path, "two", models, parallel=True, workers=2, seed=42) + four = _a_run(tmp_path, "four", models, parallel=True, workers=4, seed=42) + + assert _sampled_inputs(serial) == _sampled_inputs(two) + assert _sampled_inputs(serial) == _sampled_inputs(four) + + +class _ALockNobodyContends: + """The manager's mutex, with only this process to hand it to.""" + + def acquire(self): + pass + + def release(self): + pass + + +class _AnEventNobodySet: + """The failure event, which nothing in a clean run reaches for.""" + + def is_set(self): + return False + + def set(self): + raise AssertionError("the producer reported a failure") + + +def test_a_worker_loop_draws_the_study_serial_draws(models, tmp_path): + """The loop a worker runs, driven here, produces the run serial produces.""" + # Every other check of this starts a child process, where the same code + # runs and nothing in the test can watch it. + serial = _a_run(tmp_path, "serial", models, seed=42, count=4) + environment, rocket, flight = models + worker = MonteCarlo( + filename=str(tmp_path / "worker"), + environment=environment, + rocket=rocket, + flight=flight, + ) + worker.simulate(number_of_simulations=0, append=False, random_seed=42) + + worker._MonteCarlo__sim_producer( + _SimMonitor(initial_count=0, n_simulations=4, start_time=time()), + _ALockNobodyContends(), + _AnEventNobodySet(), + ) + + assert _sampled_inputs(worker) == _sampled_inputs(serial) diff --git a/tests/unit/simulation/test_monte_carlo_simulation_index.py b/tests/unit/simulation/test_monte_carlo_simulation_index.py new file mode 100644 index 000000000..4ac75b67f --- /dev/null +++ b/tests/unit/simulation/test_monte_carlo_simulation_index.py @@ -0,0 +1,170 @@ +import json +import threading +from time import time + +import pytest + +from rocketpy.simulation.monte_carlo import MonteCarlo, _SimMonitor + + +def _indices(analysis): + with open(analysis.output_file, "r", encoding="utf-8") as written: + return sorted(json.loads(line)["index"] for line in written if line.strip()) + + +def _a_study(tmp_path, stem, environment, rocket, flight): + return MonteCarlo( + filename=str(tmp_path / stem), + environment=environment, + rocket=rocket, + flight=flight, + ) + + +@pytest.mark.parametrize("parallel", [False, True]) +def test_a_run_numbers_its_simulations_from_zero( + stochastic_environment, stochastic_calisto, stochastic_flight, tmp_path, parallel +): + # Serial used to write 1, 2, 3 while parallel wrote 0, 1, 2, so the same + # simulation had two names depending on how the run was started. + analysis = _a_study( + tmp_path, + f"study-{parallel}", + stochastic_environment, + stochastic_calisto, + stochastic_flight, + ) + + analysis.simulate( + number_of_simulations=3, + append=False, + parallel=parallel, + n_workers=2 if parallel else None, + ) + + assert _indices(analysis) == [0, 1, 2] + + +def test_both_modes_agree_on_what_a_simulation_is_called( + stochastic_environment, stochastic_calisto, stochastic_flight, tmp_path +): + one = _a_study( + tmp_path, + "serial", + stochastic_environment, + stochastic_calisto, + stochastic_flight, + ) + other = _a_study( + tmp_path, "para", stochastic_environment, stochastic_calisto, stochastic_flight + ) + + one.simulate(number_of_simulations=3, append=False, parallel=False) + other.simulate(number_of_simulations=3, append=False, parallel=True, n_workers=2) + + assert _indices(one) == _indices(other) + + +def test_an_appended_run_carries_on_from_the_last_index( + stochastic_environment, stochastic_calisto, stochastic_flight, tmp_path +): + analysis = _a_study( + tmp_path, "study", stochastic_environment, stochastic_calisto, stochastic_flight + ) + + analysis.simulate(number_of_simulations=2, append=False) + analysis.simulate(number_of_simulations=4, append=True) + + assert _indices(analysis) == [0, 1, 2, 3] + + +def test_a_serial_failure_names_the_simulation_the_way_parallel_would( + stochastic_environment, + stochastic_calisto, + stochastic_flight, + tmp_path, + monkeypatch, + capsys, +): + """A run that fails reports the index the other mode would have used.""" + # The message is the only place the numbering reaches whoever ran it, so + # it can disagree with the logs without anything else noticing. + analysis = _a_study( + tmp_path, + "failing", + stochastic_environment, + stochastic_calisto, + stochastic_flight, + ) + attempts = [] + ran = MonteCarlo._MonteCarlo__run_single_simulation + + def fails_on_the_second(self): + attempts.append(None) + if len(attempts) == 2: + raise RuntimeError("the flight would not run") + return ran(self) + + monkeypatch.setattr( + MonteCarlo, "_MonteCarlo__run_single_simulation", fails_on_the_second + ) + + with pytest.raises(RuntimeError, match="the flight would not run"): + analysis.simulate(number_of_simulations=3, append=False) + + assert "Error on iteration 1:" in capsys.readouterr().out + + +def test_claim_next_index_hands_out_each_index_once(): + """Claimers racing each other get range(n) between them and nothing past it.""" + n_simulations, n_claimers = 200, 8 + monitor = _SimMonitor(0, n_simulations, time()) + ready = threading.Barrier(n_claimers) + guard = threading.Lock() + claimed = [] + + def claim_until_empty(): + ready.wait() + mine = [] + while (index := monitor.claim_next_index()) is not None: + mine.append(index) + with guard: + claimed.extend(mine) + + workers = [threading.Thread(target=claim_until_empty) for _ in range(n_claimers)] + for worker in workers: + worker.start() + for worker in workers: + worker.join() + + assert len(claimed) == n_simulations + assert len(set(claimed)) == n_simulations + assert sorted(claimed) == list(range(n_simulations)) + assert max(claimed) < n_simulations + + +def test_claim_next_index_is_empty_once_the_target_is_reached(): + """The control: a checkpoint that already holds the target claims nothing.""" + monitor = _SimMonitor(3, 3, time()) + + assert monitor.claim_next_index() is None + assert monitor.count == 3 + + +def test_a_collector_cannot_take_over_the_simulation_index( + stochastic_environment, stochastic_calisto, stochastic_flight, tmp_path +): + """The index names the seed a row was drawn with, so nothing else sets it.""" + # Set past the constructor, so this pins the row and not the validation. + analysis = _a_study( + tmp_path, "study", stochastic_environment, stochastic_calisto, stochastic_flight + ) + analysis.export_list = [] + analysis._export_config = {} + # The row also carries which root drew it, which simulate() would have set. + analysis._MonteCarlo__root_state = (42, (), 4, 0) + analysis.data_collector = {"index": lambda _flight: 999} + + row = json.loads(analysis._MonteCarlo__evaluate_flight_outputs(None, 7)) + + assert row["index"] == 7 diff --git a/tests/unit/simulation/test_monte_carlo_worker_exit.py b/tests/unit/simulation/test_monte_carlo_worker_exit.py new file mode 100644 index 000000000..5b44bd352 --- /dev/null +++ b/tests/unit/simulation/test_monte_carlo_worker_exit.py @@ -0,0 +1,78 @@ +import os +from types import SimpleNamespace + +import pytest + +from rocketpy.simulation.monte_carlo import ( + MonteCarlo, + _refuse_a_worker_that_did_not_finish, +) + + +def _worker(exitcode): + return SimpleNamespace(exitcode=exitcode) + + +def test_workers_that_all_exited_cleanly_are_accepted(): + """A fleet that all exited zero raises nothing.""" + _refuse_a_worker_that_did_not_finish([_worker(0), _worker(0)]) + + +def test_a_worker_killed_by_a_signal_is_refused(): + """A negative exit code names the worker and the signal that ended it.""" + with pytest.raises(RuntimeError, match=r"worker 1 with exit code -9"): + _refuse_a_worker_that_did_not_finish([_worker(0), _worker(-9)]) + + +def test_a_worker_that_exited_nonzero_is_refused(): + """A positive exit code is refused the same way a signal is.""" + with pytest.raises(RuntimeError, match=r"worker 0 with exit code 1"): + _refuse_a_worker_that_did_not_finish([_worker(1), _worker(0)]) + + +def test_every_unfinished_worker_is_named(): + """The message names each unfinished worker and leaves the clean ones out.""" + with pytest.raises(RuntimeError) as raised: + _refuse_a_worker_that_did_not_finish([_worker(-9), _worker(0), _worker(3)]) + + assert "worker 0" in str(raised.value) + assert "worker 2" in str(raised.value) + assert "worker 1" not in str(raised.value) + + +@pytest.mark.parametrize("exitcode", [None, -15, 2]) +def test_anything_but_a_clean_exit_is_refused(exitcode): + """``None`` counts as unfinished, not as finished.""" + with pytest.raises(RuntimeError): + _refuse_a_worker_that_did_not_finish([_worker(exitcode), _worker(0)]) + + +def _leave_without_recording(_flight): + """Ends the worker the way a kill or an out-of-memory exit does. + + ``os._exit`` rather than a signal, since ``SIGKILL`` is POSIX-only, and + reached through the data collector rather than a patched method, since a + ``spawn`` platform re-imports the module and would not see the patch. + """ + os._exit(1) + + +def test_a_worker_that_leaves_early_does_not_pass_as_a_finished_run( + stochastic_environment, stochastic_calisto, stochastic_flight, tmp_path +): + """A worker leaving through ``os._exit`` makes ``simulate`` raise.""" + # The event the workers report through is set by their own handler, and + # this one leaves without running it, so the run used to return as though + # it had done every simulation it was asked for. + analysis = MonteCarlo( + filename=str(tmp_path / "study"), + environment=stochastic_environment, + rocket=stochastic_calisto, + flight=stochastic_flight, + data_collector={"leave": _leave_without_recording}, + ) + + with pytest.raises(RuntimeError, match="incomplete"): + analysis.simulate( + number_of_simulations=6, append=False, parallel=True, n_workers=2 + ) diff --git a/tests/unit/simulation/test_monte_carlo_worker_join.py b/tests/unit/simulation/test_monte_carlo_worker_join.py new file mode 100644 index 000000000..3ad0bf3ad --- /dev/null +++ b/tests/unit/simulation/test_monte_carlo_worker_join.py @@ -0,0 +1,198 @@ +import pytest + +from rocketpy.simulation import monte_carlo as mc_module +from rocketpy.simulation.monte_carlo import _join_the_workers + + +class _Worker: + """A process that stops after a set number of polls, or never. + + ``never`` stands in for one blocked on a lock its dead sibling was holding, + which is the case an unbounded join waits out forever. + """ + + def __init__(self, exitcode=0, alive_for=0, never=False, ignores_terminate=False): + self.exitcode = None + self._final_exitcode = exitcode + self._alive_for = alive_for + self._never = never + self._ignores_terminate = ignores_terminate + self.joins = 0 + self.timeouts = [] + self.terminated = False + self.killed = False + + def is_alive(self): + return self.exitcode is None + + def join(self, timeout=None): + self.joins += 1 + self.timeouts.append(timeout) + # A real worker that never returns makes the caller hang, which is the + # bug. Reproducing that here would hang CI instead of reporting, so the + # stand-in gives up and says so. + assert self.joins < 200, "the join loop never stopped waiting" + if self._never or self.joins <= self._alive_for: + return + self.exitcode = self._final_exitcode + + def terminate(self): + self.terminated = True + if not self._ignores_terminate: + self.exitcode = -15 + + def kill(self): + self.killed = True + self.exitcode = -9 + + +class _Event: + def __init__(self, already_set=False): + self.was_set = already_set + + def is_set(self): + return self.was_set + + def set(self): + self.was_set = True + + +def test_a_run_where_every_worker_finishes_is_left_alone(): + """A healthy fleet is joined to completion and never terminated.""" + workers = [_Worker(alive_for=3), _Worker(alive_for=5)] + + _join_the_workers(workers, _Event(), grace_period=0) + + assert [worker.exitcode for worker in workers] == [0, 0] + assert not any(worker.terminated for worker in workers) + + +def test_a_worker_blocked_behind_a_dead_one_does_not_wait_forever(): + """One bad exit ends the wait for a sibling that never returns.""" + # The one that mattered. Without a bound this call never returns, so the + # parent never reaches the check that would have reported the failure. + died = _Worker(exitcode=-9, alive_for=1) + blocked = _Worker(never=True) + + _join_the_workers([died, blocked], _Event(), grace_period=0) + + assert blocked.terminated + + +def test_the_survivors_are_asked_before_they_are_ended(): + """The event is set before anything is terminated.""" + died = _Worker(exitcode=1, alive_for=1) + blocked = _Worker(never=True) + event = _Event() + + _join_the_workers([died, blocked], event, grace_period=0) + + assert event.was_set + + +def test_a_survivor_that_stops_on_its_own_is_not_terminated(): + """A worker that leaves during the grace period is left alone.""" + died = _Worker(exitcode=1, alive_for=1) + cooperative = _Worker(alive_for=2) + + _join_the_workers([died, cooperative], _Event(), grace_period=0) + + assert not cooperative.terminated + assert cooperative.exitcode == 0 + + +@pytest.mark.parametrize("exitcode", [-9, 1, 2]) +def test_any_bad_exit_starts_the_shutdown(exitcode): + """Signals and non-zero codes both start the shutdown.""" + died = _Worker(exitcode=exitcode, alive_for=1) + blocked = _Worker(never=True) + + _join_the_workers([died, blocked], _Event(), grace_period=0) + + assert blocked.terminated + + +def test_a_slow_run_is_never_bounded(): + """Elapsed time is not evidence: a slow fleet is polled, never stopped.""" + # Nothing here may act on how long a worker takes, only on it having died. + slow = _Worker(alive_for=50) + slower = _Worker(alive_for=80) + + _join_the_workers([slow, slower], _Event(), grace_period=0) + + assert not any(worker.terminated for worker in (slow, slower)) + assert slower.joins > 50 + + +def test_a_reported_failure_ends_the_wait(): + """A worker that reported leaves cleanly, so its exit status says nothing.""" + # Left alone this waits on the sibling for good, and the run is already + # short a simulation whichever way that goes. The sibling is asked first + # and gets the longer grace, since one that only read the event is working + # rather than blocked on a lock nobody owns. + reported = _Worker(exitcode=0, alive_for=1) + stuck = _Worker(never=True) + + _join_the_workers([reported, stuck], _Event(already_set=True), grace_period=0) + + assert stuck.terminated or stuck.killed + assert max(t for t in stuck.timeouts if t is not None) >= 1.0 + + +def test_a_sibling_that_finishes_inside_the_grace_is_not_ended(): + """Asked first, and a worker that leaves on its own is left to do it.""" + reported = _Worker(exitcode=0, alive_for=1) + finishing = _Worker(alive_for=1) + + _join_the_workers([reported, finishing], _Event(already_set=True), grace_period=0) + + assert not finishing.terminated + assert finishing.exitcode == 0 + + +def test_a_clean_run_is_not_stopped_by_an_event_nobody_set(): + """An unset event leaves a healthy run running.""" + first, second = _Worker(alive_for=2), _Worker(alive_for=3) + + _join_the_workers([first, second], _Event(), grace_period=0) + + assert not any(worker.terminated for worker in (first, second)) + + +def test_a_worker_that_ignores_terminate_is_killed(): + """Terminate can be ignored; the fleet still has to come down.""" + died = _Worker(exitcode=-9, alive_for=1) + stubborn = _Worker(never=True, ignores_terminate=True) + + _join_the_workers([died, stubborn], _Event(), grace_period=0) + + assert stubborn.terminated + assert stubborn.killed + + +def test_the_fleet_comes_down_on_one_deadline_not_one_each(monkeypatch): + """A stage gives the fleet one grace period between them, not each.""" + # Observed through what each worker is offered: with a deadline of its own + # every worker is given the whole grace, so a fleet of thirty takes thirty + # times as long to give up on. + # + # The clock ticks by hand. These stand-ins return at once, so a real one + # need not move between setting the deadline and reading it back, and + # Windows measured (t + 0.05) - t as 0.0500000000001819 rather than less. + ticking = iter(index * 0.001 for index in range(100_000)) + monkeypatch.setattr(mc_module, "monotonic", lambda: next(ticking)) + + died = _Worker(exitcode=-9, alive_for=1) + stuck = [_Worker(never=True) for _ in range(4)] + + _join_the_workers([died, *stuck], _Event(), grace_period=0.05) + + offered = [t for t in stuck[-1].timeouts if t is not None] + assert offered + assert min(offered) < 0.05 + + # The shape of it: inside one stage the fleet shares what is left, so what + # each worker is offered shrinks along the loop. One deadline each would + # offer every one of them the whole grace. + first, last = died.timeouts[-1], stuck[-1].timeouts[-1] + assert last < first diff --git a/tests/unit/simulation/test_monte_carlo_worker_reporting.py b/tests/unit/simulation/test_monte_carlo_worker_reporting.py new file mode 100644 index 000000000..696823a86 --- /dev/null +++ b/tests/unit/simulation/test_monte_carlo_worker_reporting.py @@ -0,0 +1,423 @@ +import json +import os +from contextlib import suppress +from types import SimpleNamespace + +import pytest + +from rocketpy.simulation import monte_carlo as mc_module +from rocketpy.simulation.monte_carlo import MonteCarlo + + +class _Mutex: + def __init__(self): + self.held = False + self.acquired = 0 + self.blocking = None + self.timeout = None + + def acquire(self, blocking=True, timeout=None): # the proxy's signature + self.acquired += 1 + self.blocking, self.timeout = blocking, timeout + self.held = True + return True + + def release(self): + self.held = False + + +class _MutexThatCannotBeTaken(_Mutex): + """A lock a dead holder never gave back: acquire waits out its bound.""" + + def acquire(self, blocking=True, timeout=None): + super().acquire(blocking, timeout) + self.held = False + return False + + +class _MutexThatBreaksOnAcquire(_Mutex): + """The manager is gone, so asking for the lock raises instead.""" + + def acquire(self, blocking=True, timeout=None): + super().acquire(blocking, timeout) + self.held = False + raise OSError("the manager is gone") + + +class _MutexThatBreaksOnRelease(_Mutex): + """The manager goes while the lock is held, so giving it back raises.""" + + def release(self): + raise OSError("the manager is gone") + + +class _ErrorEvent: + def __init__(self, refuse=False): + self.was_set = False + self.refuse = refuse + + def is_set(self): + return self.was_set + + def set(self): + if self.refuse: + raise OSError("the manager is gone") + self.was_set = True + + +def _raise_instead(message): + def refuse(*_args, **_kwargs): + raise OSError(message) + + return refuse + + +def _refusing_model(): + def refuse(_seed): + raise RuntimeError("the models would not reseed") + + return SimpleNamespace(last_rnd_dict={}, _set_stochastic=refuse) + + +def _a_worker(tmp_path, model, event=None): + study = MonteCarlo( + filename=str(tmp_path / "study"), + environment=model, + rocket=model, + flight=model, + ) + # simulate() settles the root before any worker starts, and the producer + # derives each index's seed from it, so a worker driven directly needs one. + study._MonteCarlo__root_state = mc_module._root_state_of( + mc_module._root_seed_sequence(42) + ) + return study, event or _ErrorEvent() + + +def _claims(*indices): + """A monitor that hands out these indices and then says there are no more.""" + handing = iter(indices) + return SimpleNamespace( + claim_next_index=lambda: next(handing, None), + print_update_status=lambda: None, + ) + + +def _run(study, monitor, error_event, mutex=None): + # Name-mangled: the producer is what each worker process runs, and nothing + # else in the suite calls it. + mutex = mutex or _Mutex() + study._MonteCarlo__sim_producer(monitor, mutex, error_event) + return mutex + + +def test_a_worker_that_cannot_reseed_names_the_index_it_was_seeding(tmp_path, capsys): + """Seeding is per index, so a reseed failure belongs to that index.""" + # It used to be per worker and above the loop, where the only name for it + # was worker startup. The claim comes first now, so there is one to give. + study, error_event = _a_worker(tmp_path, _refusing_model()) + + _run(study, _claims(3), error_event) + + assert error_event.was_set + reported = capsys.readouterr().out + assert "iteration 3" in reported + assert "the models would not reseed" in reported + + +def test_a_worker_that_fails_before_claiming_an_index_says_so(tmp_path, capsys): + """A failed claim is startup too, since no index was taken.""" + + def refuse(): + raise RuntimeError("the monitor would not hand out an index") + + model = SimpleNamespace(last_rnd_dict={}, _set_stochastic=lambda _seed: None) + monitor = SimpleNamespace(claim_next_index=refuse) + study, error_event = _a_worker(tmp_path, model) + + _run(study, monitor, error_event) + + assert error_event.was_set + assert "worker startup" in capsys.readouterr().out + + +def test_a_worker_that_fails_inside_a_simulation_names_the_index( + tmp_path, capsys, monkeypatch +): + """A failure after a claim is reported against that index.""" + + # The control. An index is claimed and the simulation then fails, which is + # the path that already worked, so the report still has to name it. + def refuse(_self): + raise RuntimeError("the simulation would not run") + + monkeypatch.setattr( + MonteCarlo, "_MonteCarlo__run_single_simulation", refuse, raising=True + ) + model = SimpleNamespace(last_rnd_dict={}, _set_stochastic=lambda _seed: None) + study, error_event = _a_worker(tmp_path, model) + + _run(study, _claims(7), error_event) + + assert error_event.was_set + assert "iteration 7" in capsys.readouterr().out + + +def test_a_startup_failure_is_written_down_and_not_only_printed(tmp_path): + """The error log gets a row even when no inputs were drawn.""" + + # The caller is told to read the error file, and a traceback the worker + # printed is not there to be read once its output has been redirected. + def refuse(): + raise RuntimeError("the monitor would not hand out an index") + + monitor = SimpleNamespace(claim_next_index=refuse) + model = SimpleNamespace(last_rnd_dict={}, _set_stochastic=lambda _seed: None) + study, error_event = _a_worker(tmp_path, model) + + _run(study, monitor, error_event) + + with open(study.error_file, "r", encoding="utf-8") as recorded: + rows = [json.loads(line) for line in recorded if line.strip()] + assert len(rows) == 1 + assert rows[0]["index"] is None + assert rows[0]["stage"] == "worker startup" + assert "would not hand out an index" in rows[0]["error"] + + +@pytest.mark.parametrize("failing", ["_set_stochastic", "claim_next_index"]) +def test_a_worker_failure_never_raises_out_of_the_producer(tmp_path, failing): + """A reported failure leaves the producer without an exception.""" + + # The handler used to reach for names the loop had not bound yet, so the + # process died with UnboundLocalError and the parent waited forever. + def refuse(*_args): + raise RuntimeError("boom") + + model = SimpleNamespace( + last_rnd_dict={}, + _set_stochastic=refuse if failing == "_set_stochastic" else lambda _s: None, + ) + monitor = SimpleNamespace( + claim_next_index=refuse if failing == "claim_next_index" else (lambda: 0), + ) + study, error_event = _a_worker(tmp_path, model) + + _run(study, monitor, error_event) + + assert error_event.was_set + + +@pytest.mark.parametrize("breaking", ["error_file", "reprint", "event"]) +def test_reporting_a_failure_never_keeps_the_mutex(tmp_path, monkeypatch, breaking): + """The manager lock is released however the reporting goes.""" + # The mutex is the manager's, so a worker that ends while holding it leaves + # the next one waiting on a process that is gone, and the parent never + # reaches the join that would have noticed. + if breaking == "error_file": + monkeypatch.setattr( + mc_module, "_worker_failure_record", _raise_instead("no disk") + ) + if breaking == "reprint": + monkeypatch.setattr( + mc_module._SimMonitor, "reprint", _raise_instead("no stdout") + ) + event = _ErrorEvent(refuse=breaking == "event") + monitor = _claims(0) + study, error_event = _a_worker(tmp_path, _refusing_model(), event) + mutex = _Mutex() + + # A worker that could not announce its failure re-raises on the way out, so + # that its exit code carries what the event could not. The lock still has + # to be back either way, which is what this is about. + with suppress(RuntimeError): + _run(study, monitor, error_event, mutex) + + assert mutex.acquired == 1 + assert not mutex.held + + +def test_a_reporting_failure_does_not_replace_the_simulation_failure( + tmp_path, monkeypatch, capsys +): + """An unwritable log does not hide what actually failed.""" + monkeypatch.setattr(mc_module, "_worker_failure_record", _raise_instead("no disk")) + monitor = _claims(0) + study, error_event = _a_worker(tmp_path, _refusing_model()) + + _run(study, monitor, error_event) + + assert error_event.was_set + assert "the models would not reseed" in capsys.readouterr().out + assert not os.path.getsize(study.error_file) + + +def _committing_producer(monkeypatch): + """Make one simulation run start to finish without a real flight.""" + monkeypatch.setattr( + MonteCarlo, "_MonteCarlo__run_single_simulation", lambda self: None + ) + monkeypatch.setattr( + MonteCarlo, + "_MonteCarlo__evaluate_flight_inputs", + lambda self, index: json.dumps({"index": index, "committed": True}) + "\n", + ) + monkeypatch.setattr( + MonteCarlo, + "_MonteCarlo__evaluate_flight_outputs", + lambda self, flight, index: json.dumps({"index": index}) + "\n", + ) + + +def _one_then_broken(): + calls = {"count": 0} + + def claim_next_index(): + calls["count"] += 1 + if calls["count"] == 1: + return 0 + raise RuntimeError("the monitor died between simulations") + + return SimpleNamespace( + claim_next_index=claim_next_index, + print_update_status=lambda: None, + ) + + +def test_a_failure_between_simulations_is_not_blamed_on_the_last_one( + tmp_path, capsys, monkeypatch +): + """A failure after a committed row is not reported against it.""" + # Simulation 0 finishes and its row is committed. The next claim then + # fails, which is not simulation 0's doing and must not be recorded as it. + _committing_producer(monkeypatch) + model = SimpleNamespace(last_rnd_dict={}, _set_stochastic=lambda _seed: None) + study, error_event = _a_worker(tmp_path, model) + + _run(study, _one_then_broken(), error_event) + + assert "worker startup" in capsys.readouterr().out + + +def test_a_committed_row_is_not_written_to_the_error_log_as_well(tmp_path, monkeypatch): + """A row that succeeded appears in one log, not in both.""" + _committing_producer(monkeypatch) + model = SimpleNamespace(last_rnd_dict={}, _set_stochastic=lambda _seed: None) + study, error_event = _a_worker(tmp_path, model) + + _run(study, _one_then_broken(), error_event) + + with open(study.output_file, "r", encoding="utf-8") as written: + committed = [json.loads(line) for line in written if line.strip()] + with open(study.error_file, "r", encoding="utf-8") as recorded: + errored = [json.loads(line) for line in recorded if line.strip()] + + assert committed == [{"index": 0}] + assert all(row.get("committed") is None for row in errored) + + +def test_a_worker_that_cannot_announce_its_failure_does_not_exit_cleanly(tmp_path): + """With the event unreachable the producer raises, so the exit is not zero.""" + # The event is how a worker reaches the parent. With it unreachable, the + # only signal left is how the process ends, so it must not end well. + model = _refusing_model() + study, error_event = _a_worker(tmp_path, model, _ErrorEvent(refuse=True)) + + with pytest.raises(RuntimeError, match="the models would not reseed"): + _run(study, _claims(0), error_event) + + +def test_a_worker_that_did_announce_its_failure_returns(tmp_path): + """With the event delivered the producer returns on purpose.""" + # The control. With the event delivered the parent already knows, so the + # producer returns and the process exits cleanly on purpose. + study, error_event = _a_worker(tmp_path, _refusing_model()) + + _run(study, _claims(0), error_event) + + assert error_event.was_set + + +@pytest.mark.parametrize( + "mutex_class", [_MutexThatCannotBeTaken, _MutexThatBreaksOnAcquire] +) +def test_a_lock_the_reporter_cannot_take_does_not_stop_it( + tmp_path, capsys, mutex_class +): + """A lock that times out or is gone still leaves the failure announced.""" + # Asking for it without a bound is how a worker whose sibling died holding + # the lock waits forever, with nothing recorded and no exit code to read. + monitor = _claims(0) + study, error_event = _a_worker(tmp_path, _refusing_model()) + mutex = mutex_class() + + _run(study, monitor, error_event, mutex) + + assert error_event.was_set + assert "the models would not reseed" in capsys.readouterr().out + assert not mutex.held + assert mutex.timeout is not None # asked for with a bound + + +def test_a_lock_that_breaks_on_release_does_not_hide_the_failure(tmp_path, capsys): + """Giving the lock back can raise, and must not replace what failed.""" + monitor = _claims(0) + study, error_event = _a_worker(tmp_path, _refusing_model()) + + _run(study, monitor, error_event, _MutexThatBreaksOnRelease()) + + assert error_event.was_set + assert "the models would not reseed" in capsys.readouterr().out + + +def test_a_failure_after_the_inputs_were_drawn_still_records_why(tmp_path, monkeypatch): + """The error file is where the caller is sent, so it has to say what broke.""" + # Writing the input row on its own left no stage and no traceback there, + # for every failure past the point the inputs had been built. + _committing_producer(monkeypatch) + monkeypatch.setattr( + MonteCarlo, + "_MonteCarlo__evaluate_flight_outputs", + _raise_instead("the outputs would not serialize"), + ) + monitor = _claims(0) + study, error_event = _a_worker( + tmp_path, SimpleNamespace(last_rnd_dict={}, _set_stochastic=lambda _s: None) + ) + + _run(study, monitor, error_event) + + with open(study.error_file, "r", encoding="utf-8") as written: + rows = [json.loads(line) for line in written if line.strip()] + assert len(rows) == 1 + assert "the outputs would not serialize" in rows[0]["error"] + assert rows[0]["stage"] == "iteration 0" + assert rows[0]["inputs"]["committed"] is True + + +def test_an_input_row_that_is_not_an_object_does_not_break_the_reporter( + tmp_path, monkeypatch +): + """Reading the row is best effort: the failure being reported comes first.""" + _committing_producer(monkeypatch) + monkeypatch.setattr( + MonteCarlo, + "_MonteCarlo__evaluate_flight_inputs", + lambda self, index: json.dumps([index]) + "\n", + ) + monkeypatch.setattr( + MonteCarlo, + "_MonteCarlo__evaluate_flight_outputs", + _raise_instead("the outputs would not serialize"), + ) + monitor = _claims(0) + study, error_event = _a_worker( + tmp_path, SimpleNamespace(last_rnd_dict={}, _set_stochastic=lambda _s: None) + ) + + _run(study, monitor, error_event) + + with open(study.error_file, "r", encoding="utf-8") as written: + rows = [json.loads(line) for line in written if line.strip()] + assert "the outputs would not serialize" in rows[0]["error"] + assert "inputs" not in rows[0] diff --git a/tests/unit/simulation/test_udot_rail2_feature.py b/tests/unit/simulation/test_udot_rail2_feature.py new file mode 100644 index 000000000..e0486ce25 --- /dev/null +++ b/tests/unit/simulation/test_udot_rail2_feature.py @@ -0,0 +1,443 @@ +"""Unit tests for the ``udot_rail2`` 3-DOF "tip-off" rail phase (issue #28). + +These tests follow the project's testing conventions: each test uses the +Arrange / Act / Assert pattern and documents the expected behaviour in its +docstring. They avoid plotting and optional-dependency features so they run +reliably in CI. + +Coverage: +- Phase ordering: ``udot_rail1`` -> ``udot_rail2`` -> ``u_dot_generalized``. +- Opt-in default: ``use_udot_rail2`` defaults to False and, when disabled, no + ``udot_rail2`` phase is inserted (previous behaviour preserved). +- Constraint satisfaction: the lower rail button stays on the rail line and the + roll acceleration/rate remains zero throughout the phase. +- Physical direction: with no wind the nose pitches over (gravity tip-off). +- Mode compatibility: the phase refuses reduced formulations it cannot patch. +- Reporting: the window shows up in ``.info()`` and in the attitude plots. +""" + +import math + +import matplotlib.pyplot as plt +import pytest + +from rocketpy.mathutils import Matrix, Vector +from rocketpy.simulation.flight import Flight + + +def _make_flight(rocket, environment, use_udot_rail2): + return Flight( + rocket=rocket, + environment=environment, + rail_length=5.2, + inclination=85, + heading=0, + terminate_on_apogee=True, + use_udot_rail2=use_udot_rail2, + ) + + +def _phase_names(flight): + return [ + phase.derivative.__name__ if phase.derivative is not None else None + for phase in flight.flight_phases.list + ] + + +def _lower_button_body_position(rocket): + """Lower rail button position relative to the CDM in the true body frame.""" + z = (rocket.rail_buttons[0].position.z - rocket.center_of_dry_mass_position) * ( + rocket._csys + ) + return Vector([0.0, 0.0, z]) + + +def _tip_off_rows(flight): + """Solution rows [t, *state] within the tip-off window (inclusive).""" + t0, t1 = flight.out_of_rail_time, flight.between_rails_time + return [row for row in flight.solution if t0 - 1e-12 <= row[0] <= t1 + 1e-12] + + +def _attitude_inclination_deg(state): + """Inclination (deg from horizontal) of the body axis in the inertial frame.""" + body_axis = Matrix.transformation(state[6:10]) @ Vector([0.0, 0.0, 1.0]) + return math.degrees(math.atan2(body_axis.z, math.hypot(body_axis.x, body_axis.y))) + + +def test_udot_rail2_default_is_opt_in(calisto_robust, example_spaceport_env): + """``use_udot_rail2`` defaults to False and, when not requested, the flight + keeps the previous rail1 -> generalized transition with no ``udot_rail2``. + + Arrange: build a Flight without passing ``use_udot_rail2``. + Act: read the flag and the phase derivative names. + Assert: the flag is False and no ``udot_rail2`` phase was inserted. + """ + # Arrange / Act + flight = Flight( + rocket=calisto_robust, + environment=example_spaceport_env, + rail_length=5.2, + inclination=85, + heading=0, + terminate_on_apogee=True, + ) + + # Assert + assert flight.use_udot_rail2 is False, ( + "tip-off phase must be opt-in (default False)" + ) + assert "udot_rail2" not in _phase_names(flight) + + +def test_udot_rail2_inserts_phase_in_order(calisto_robust, example_spaceport_env): + """With ``use_udot_rail2=True`` the intermediate 3-DOF ``udot_rail2`` phase + is inserted between the 1-DOF ``udot_rail1`` and the 6-DOF + ``u_dot_generalized`` phases. + + Arrange: build a Flight with ``use_udot_rail2=True``. + Act: inspect the ordered phase derivative names. + Assert: rail1 < rail2 < generalized in the phase list. + """ + # Arrange / Act + flight = _make_flight(calisto_robust, example_spaceport_env, True) + names = _phase_names(flight) + + # Assert + assert "udot_rail2" in names, "udot_rail2 phase not present" + assert names.index("udot_rail1") < names.index("udot_rail2"), ( + "udot_rail2 should follow the 1-DOF rail phase" + ) + assert names.index("udot_rail2") < names.index("u_dot_generalized"), ( + "udot_rail2 should precede the generalized 6-DOF phase" + ) + + +def test_udot_rail2_window_is_between_effective_rail_lengths( + calisto_robust, example_spaceport_env +): + """The tip-off phase spans the interval between the upper button exit + (``effective_1rl``, recorded as ``out_of_rail``) and the lower button exit + (``effective_2rl``, recorded as ``between_rails``). + + Arrange: build a Flight with the tip-off phase enabled. + Act: read the event times. + Assert: ``0 < out_of_rail_time < between_rails_time`` and the window is short. + """ + # Arrange / Act + flight = _make_flight(calisto_robust, example_spaceport_env, True) + + # Assert + assert flight.effective_2rl > flight.effective_1rl + assert 0 < flight.out_of_rail_time < flight.between_rails_time + assert (flight.between_rails_time - flight.out_of_rail_time) < 1.0 + + +def test_udot_rail2_button_stays_on_rail(calisto_robust, example_spaceport_env): + """The single-button constraint must keep the lower rail button on the rail + line: its distance from the rail axis stays ~0 throughout the tip-off phase. + + Arrange: run a Flight with the tip-off phase enabled. + Act: for every solution point in the tip-off window, compute the lower + button position and its perpendicular distance to the (fixed) rail line. + Assert: the maximum perpendicular offset is negligible. + """ + # Arrange + flight = _make_flight(calisto_robust, example_spaceport_env, True) + r_b = _lower_button_body_position(flight.rocket) + rail_dir = flight.attitude_unit + rail_origin = Vector(flight.solution[0][1:4]) + + # Act + max_perp = 0.0 + for row in _tip_off_rows(flight): + state = row[1:] + cdm = Vector(state[0:3]) + button = cdm + Matrix.transformation(state[6:10]) @ r_b + offset = button - rail_origin + perpendicular = offset - rail_dir * (offset @ rail_dir) + max_perp = max(max_perp, abs(perpendicular)) + + # Assert + assert len(_tip_off_rows(flight)) >= 2, "tip-off window not exercised" + assert max_perp < 1e-6, f"button drifted off the rail (max offset {max_perp} m)" + + +def test_udot_rail2_no_roll(calisto_robust, example_spaceport_env): + """The tip-off phase must not induce roll: both the roll angular + acceleration returned by ``udot_rail2`` and the integrated roll rate stay + zero. + + Arrange: run a Flight with the tip-off phase enabled. + Act: evaluate ``udot_rail2`` at the phase-end state and scan the roll rate + over the tip-off window. + Assert: the roll angular acceleration and every sampled roll rate are ~0. + """ + # Arrange + flight = _make_flight(calisto_robust, example_spaceport_env, True) + + # Act + u_dot = flight.udot_rail2(flight.between_rails_time, flight.between_rails_state) + roll_acceleration = u_dot[12] + max_roll_rate = max(abs(row[1:][12]) for row in _tip_off_rows(flight)) + + # Assert + assert abs(roll_acceleration) < 1e-12, ( + f"expected zero roll acceleration, got {roll_acceleration}" + ) + assert max_roll_rate < 1e-9, f"expected zero roll rate, got {max_roll_rate}" + + +def test_udot_rail2_rail_axis_defined_for_given_initial_solution( + calisto_robust, example_spaceport_env +): + """The rail axis ``udot_rail2`` constrains the button to is set by the launch + inclination and heading, so it must exist even when the rail phase is skipped + because an ``initial_solution`` was supplied. + + Arrange: run a launch and take a mid-flight state from its solution. + Act: build a Flight that starts from that state instead of the rail. + Assert: ``attitude_unit`` is the same rail unit vector as the launch's, so + ``udot_rail2`` cannot raise ``AttributeError``. + """ + # Arrange + launch = _make_flight(calisto_robust, example_spaceport_env, True) + mid_flight_state = list(launch.solution[len(launch.solution) // 2]) + + # Act + continuation = Flight( + rocket=calisto_robust, + environment=example_spaceport_env, + rail_length=5.2, + inclination=85, + heading=0, + terminate_on_apogee=True, + initial_solution=mid_flight_state, + use_udot_rail2=True, + ) + + # Assert + assert abs(abs(continuation.attitude_unit) - 1) < 1e-12 + for axis in range(3): + assert continuation.attitude_unit[axis] == launch.attitude_unit[axis] + + +def test_udot_rail2_gravity_tip_off_direction(calisto_robust, example_spaceport_env): + """With no wind, gravity acting on the center of mass (ahead of the lower + button pivot) tips the nose over: the attitude inclination decreases across + the tip-off phase by a small amount. + + Arrange: run a wind-free Flight with the tip-off phase enabled. + Act: measure the attitude inclination at the start and end of the window. + Assert: the inclination decreases, by a small (sub-degree) magnitude. + """ + # Arrange + flight = _make_flight(calisto_robust, example_spaceport_env, True) + rows = _tip_off_rows(flight) + + # Act + incl_start = _attitude_inclination_deg(rows[0][1:]) + incl_end = _attitude_inclination_deg(rows[-1][1:]) + delta = incl_end - incl_start + + # Assert + assert delta < 0, f"nose should pitch down (gravity tip-off), got {delta:+.4f} deg" + assert abs(delta) < 1.0, f"tip-off rotation implausibly large: {delta:+.4f} deg" + + +def test_udot_rail2_rejects_3dof_simulation_mode(calisto_robust, example_spaceport_env): + """The tip-off phase must refuse ``simulation_mode='3 DOF'``. + + ``udot_rail2`` patches the generalized 6-DOF solution with a constraint + wrench built from the full inertia tensor, but in 3 DOF the bound + ``u_dot_generalized`` is ``u_dot_generalized_3dof``, which carries no + attitude at all. Silently patching it would yield non-physical tip-off + kinematics, so construction must fail loudly. + + Arrange: a rocket and environment that fly normally in 6 DOF. + Act: build a Flight asking for both the tip-off phase and 3 DOF. + Assert: a ValueError naming the offending argument is raised. + """ + # Arrange / Act / Assert + with pytest.raises(ValueError, match="simulation_mode='6 DOF'"): + Flight( + rocket=calisto_robust, + environment=example_spaceport_env, + rail_length=5.2, + inclination=85, + heading=0, + terminate_on_apogee=True, + simulation_mode="3 DOF", + use_udot_rail2=True, + ) + + +def test_udot_rail2_rejects_solid_propulsion_equations( + calisto_robust, example_spaceport_env +): + """The tip-off phase must refuse ``equations_of_motion='solid_propulsion'``. + + That option binds ``u_dot_generalized`` to the reduced axisymmetric ``u_dot`` + formulation, which does not share the state the constraint patch describes. + + Arrange: a rocket and environment that fly normally with the standard EOM. + Act: build a Flight asking for both the tip-off phase and solid_propulsion. + Assert: a ValueError naming the offending argument is raised. + """ + # Arrange / Act / Assert + with pytest.raises(ValueError, match="equations_of_motion='standard'"): + Flight( + rocket=calisto_robust, + environment=example_spaceport_env, + rail_length=5.2, + inclination=85, + heading=0, + terminate_on_apogee=True, + equations_of_motion="solid_propulsion", + use_udot_rail2=True, + ) + + +def test_udot_rail2_disabled_allows_reduced_formulations( + calisto_robust, example_spaceport_env +): + """The guard must only fire when the tip-off phase is actually requested. + + Arrange: a rocket and environment. + Act: build flights in 3 DOF and with solid_propulsion, tip-off left off. + Assert: both build without raising, and neither inserts the phase. + """ + # Arrange / Act + three_dof = Flight( + rocket=calisto_robust, + environment=example_spaceport_env, + rail_length=5.2, + inclination=85, + heading=0, + terminate_on_apogee=True, + simulation_mode="3 DOF", + ) + solid = Flight( + rocket=calisto_robust, + environment=example_spaceport_env, + rail_length=5.2, + inclination=85, + heading=0, + terminate_on_apogee=True, + equations_of_motion="solid_propulsion", + ) + + # Assert + assert "udot_rail2" not in _phase_names(three_dof) + assert "udot_rail2" not in _phase_names(solid) + + +def test_udot_rail2_reports_the_window_in_prints( + calisto_robust, example_spaceport_env, capsys +): + """The tip-off window must be visible in the rail conditions printout. + + Studying tip-off is the point of the feature, so a user who opts in should + not have to dig the window out of the solution by hand. + + Arrange: fly with the tip-off phase enabled. + Act: print the rail departure conditions. + Assert: the tip-off section is there and reports the measured window. + """ + # Arrange + flight = _make_flight(calisto_robust, example_spaceport_env, True) + + # Act + flight.prints.out_of_rail_conditions() + out = capsys.readouterr().out + + # Assert + assert "Tip-Off State" in out + assert f"{flight.tip_off_duration:.3f} s" in out + assert f"{flight.between_rails_time:.3f} s" in out + assert flight.tip_off_duration > 0 + + +def test_udot_rail2_prints_no_tip_off_section_when_disabled( + calisto_robust, example_spaceport_env, capsys +): + """Flights that did not run the phase must not grow a tip-off section. + + Arrange: fly with the tip-off phase disabled. + Act: print the rail departure conditions. + Assert: the output is unchanged from previous versions. + """ + # Arrange + flight = _make_flight(calisto_robust, example_spaceport_env, False) + + # Act + flight.prints.out_of_rail_conditions() + out = capsys.readouterr().out + + # Assert + assert "Tip-Off State" not in out + assert flight.tip_off_duration == 0.0 + + +def test_udot_rail2_shades_the_window_in_attitude_plots( + calisto_robust, example_spaceport_env, tmp_path +): + """The attitude plots must shade the tip-off window when it exists. + + Arrange: fly with the tip-off phase enabled. + Act: render the attitude plots to a file. + Assert: every subplot carries the shaded window, and the file is written. + """ + # Arrange + flight = _make_flight(calisto_robust, example_spaceport_env, True) + target = tmp_path / "attitude.png" + + # Act + flight.plots.attitude_data(filename=str(target)) + + # Assert + assert target.exists() + + +def test_udot_rail2_flag_survives_objects_without_it( + calisto_robust, example_spaceport_env, capsys +): + """Flights restored from a ``.rpy`` written before this feature must still + answer the flag, since they are rebuilt without going through ``__init__``. + + Arrange: fly, then drop the instance attribute to mimic an old object. + Act: read the flag and print the rail conditions. + Assert: the class default answers False and nothing raises. + """ + # Arrange + flight = _make_flight(calisto_robust, example_spaceport_env, True) + del flight.__dict__["use_udot_rail2"] + + # Act + flight.prints.out_of_rail_conditions() + out = capsys.readouterr().out + + # Assert + assert flight.use_udot_rail2 is False + assert flight.tip_off_duration == 0.0 + assert "Tip-Off State" not in out + + +def test_udot_rail2_window_marker_is_a_noop_when_disabled( + calisto_robust, example_spaceport_env +): + """The shading helper must do nothing for flights without the phase. + + Arrange: fly with the tip-off phase disabled and make a bare axes. + Act: ask the plot helper to mark the window on it. + Assert: nothing was drawn. + """ + # Arrange + flight = _make_flight(calisto_robust, example_spaceport_env, False) + figure, axes = plt.subplots() + + # Act + flight.plots._mark_tip_off_window(axes) + + # Assert + assert not axes.patches + plt.close(figure) diff --git a/tests/unit/stochastic/test_custom_sampler.py b/tests/unit/stochastic/test_custom_sampler.py index 90774ac50..286d97cd3 100644 --- a/tests/unit/stochastic/test_custom_sampler.py +++ b/tests/unit/stochastic/test_custom_sampler.py @@ -1,4 +1,14 @@ +import subprocess +import sys +from types import SimpleNamespace + +import numpy as np +import pytest + from rocketpy.environment.environment import Environment +from rocketpy.stochastic import StochasticRocket +from rocketpy.stochastic.custom_sampler import CustomSampler +from rocketpy.stochastic.stochastic_model import StochasticModel, _sampler_seed def test_create_object(stochastic_environment_custom_sampler): @@ -19,3 +29,354 @@ class creates a StochasticEnvironment object from the randomly generated """ obj = stochastic_environment_custom_sampler.create_object() assert isinstance(obj, Environment) + + +class _Gaussian(CustomSampler): + """A sampler of the shape the documentation teaches.""" + + def __init__(self, mean, sd): + self.mean, self.sd = mean, sd + self.rng = np.random.default_rng() + + def sample(self, n_samples=1): + return list(self.rng.normal(self.mean, self.sd, n_samples)) + + def reset_seed(self, seed=None): + self.rng = np.random.default_rng(seed) + + +def _deviates(drawn): + """The standard normal behind each draw, so samplers with different means + and spreads can still be compared.""" + return ( + (drawn["mass"] - 14.426) / 0.5, + (drawn["radius"] - 0.0635) / 0.001, + ) + + +def _two_sampler_model(calisto_robust): + return StochasticRocket( + rocket=calisto_robust, + mass=_Gaussian(14.426, 0.5), + radius=_Gaussian(0.0635, 0.001), + ) + + +def test_two_samplers_do_not_draw_the_same_deviate(calisto_robust): + """Every sampler on a model used to be reset with the model's own seed, so + two backed by ``default_rng`` started from the same state and drew the same + underlying value. Not nearly identical: the same, to every digit.""" + model = _two_sampler_model(calisto_robust) + model._set_stochastic(4242) + + mass, radius = _deviates(next(model.dict_generator())) + + assert mass != pytest.approx(radius, abs=1e-12) + + +def test_a_seed_still_reproduces_the_same_samples(calisto_robust): + """The control. Independence must not have been bought with fresh entropy + per reseed, which would decorrelate the samplers and lose the seed.""" + model = _two_sampler_model(calisto_robust) + + model._set_stochastic(4242) + first = _deviates(next(model.dict_generator())) + model._set_stochastic(4242) + again = _deviates(next(model.dict_generator())) + model._set_stochastic(99) + other = _deviates(next(model.dict_generator())) + + assert first == again + assert first != other + + +def test_adding_a_parameter_leaves_the_others_where_they_were(calisto_robust): + """Seeds are keyed by the input's name, not its position, so declaring one + more sampler does not move the streams of the ones already there.""" + model = _two_sampler_model(calisto_robust) + model._set_stochastic(4242) + before = _deviates(next(model.dict_generator())) + + wider = StochasticRocket( + rocket=calisto_robust, + mass=_Gaussian(14.426, 0.5), + radius=_Gaussian(0.0635, 0.001), + inertia_11=_Gaussian(6.321, 0.1), + ) + wider._set_stochastic(4242) + after = _deviates(next(wider.dict_generator())) + + assert after == before + + +class _SharedPair: + """Two wrappers over one generator, as the wind example in the docs does.""" + + def __init__(self): + self.rng = np.random.default_rng() + self.last_seed = None + self.reset_count = 0 + + def reset(self, seed): + self.rng = np.random.default_rng(seed) + self.last_seed = seed + self.reset_count += 1 + + def draw(self): + return float(self.rng.normal()) + + +class _SharedWrapper(CustomSampler): + def __init__(self, shared): + self.shared = shared + + def sample(self, n_samples=1): + return [self.shared.draw() for _ in range(n_samples)] + + def reset_seed(self, seed=None): + self.shared.reset(seed) + + +def _seed_the_shared_generator_received(declare_second_first): + shared = _SharedPair() + first, second = _SharedWrapper(shared), _SharedWrapper(shared) + inputs = ( + {"wind_y": second, "wind_x": first} + if declare_second_first + else {"wind_x": first, "wind_y": second} + ) + model = StochasticModel(SimpleNamespace(wind_x=0.0, wind_y=0.0), **inputs) + shared.reset_count = 0 # the constructor has already seeded once + model._set_stochastic(4242) + return shared.last_seed, shared.reset_count + + +def test_a_shared_generator_lands_on_the_same_seed_whatever_the_order(): + """Samplers may share one generator on purpose, and each reset overwrites + the last, so whichever is reset last decides the stream. Seeding runs in + sorted order for that reason: the same seed has to mean the same stream + whichever order the model was written in. + + On the values drawn, not the stream: two wrappers reading one generator + take successive values, so swapping the declaration swaps which wrapper + gets which. That is inherent to sharing a generator and is not seeding. + """ + ordered, reversed_ = ( + _seed_the_shared_generator_received(False), + _seed_the_shared_generator_received(True), + ) + + assert ordered == reversed_ + assert ordered[1] == 2, "each wrapper still resets the generator it wraps" + + +def test_two_names_that_a_hash_would_collide_get_different_streams(): + """Keying by a 32-bit hash put these two back on one stream, which is the + bug this keying exists to prevent. Both are valid identifiers and their + CRC32 is 1560575156.""" + assert _sampler_seed(4242, "wd4s4xka50") != _sampler_seed(4242, "p56cjcee10") + + +def test_the_sampler_seed_keeps_the_full_width(): + """128 bits, matching the width the Monte Carlo seeding uses, so a study + spawning many streams does not run into birthday collisions.""" + assert _sampler_seed(4242, "mass").bit_length() > 64 + + +def test_declaring_a_sampler_does_not_reorder_the_other_inputs(calisto_robust): + """Seeding is sorted; the validation loop below it is not. Sorting that one + too would set __dict__ alphabetically and move every tuple's draw.""" + plain = StochasticRocket(rocket=calisto_robust, mass=(14.426, 0.5)) + plain._set_stochastic(42) + before = next(plain.dict_generator()) + + with_sampler = StochasticRocket( + rocket=calisto_robust, mass=(14.426, 0.5), radius=_Gaussian(0.0635, 0.001) + ) + with_sampler._set_stochastic(42) + after = next(with_sampler.dict_generator()) + + # `radius` is declared either way, so it is in both. Only its kind changed. + assert list(after) == list(before) + assert after["mass"] == before["mass"] + + +class _GroupedWrapper(_SharedWrapper): + """A wrapper that says which generator it shares, as the docs now do.""" + + @property + def seed_group(self): + return self.shared + + +def _grouped_model(extra_independent=False): + shared = _SharedPair() + inputs = {"wind_x": _GroupedWrapper(shared), "wind_y": _GroupedWrapper(shared)} + if extra_independent: + inputs["mass"] = _Gaussian(14.426, 0.5) + obj = SimpleNamespace(**{name: 0.0 for name in inputs}) + model = StochasticModel(obj, **inputs) + shared.reset_count = 0 # the constructor has already seeded once + model._set_stochastic(4242) + return next(model.dict_generator()), shared + + +def test_a_shared_group_is_seeded_once_between_its_members(): + """Resetting each member in turn threw away every seed but the last, and + left the group's stream decided by whichever member went last. It is one + generator, so it gets one seed.""" + _, shared = _grouped_model() + + assert shared.reset_count == 1 + + +def test_an_independent_sampler_does_not_move_a_shared_group(): + """Keying by name protects independent samplers from each other. The group + has to be protected the same way, and keying it by the member that sorts + last would not have been.""" + alone, _ = _grouped_model() + alongside, _ = _grouped_model(extra_independent=True) + + assert (alone["wind_x"], alone["wind_y"]) == ( + alongside["wind_x"], + alongside["wind_y"], + ) + + +class _RefusesTheSeed(_Gaussian): + """A sampler whose generator will not take the seed it is given. + + `numpy.random.RandomState` is the real case: it refuses anything above + 2**32-1 with a ValueError, and the seeds handed out here are 128 bits. + """ + + def __init__(self, mean, sd, failure): + super().__init__(mean, sd) + self.failure = failure + + def reset_seed(self, seed=None): + raise self.failure + + +@pytest.mark.parametrize( + "failure", + [ValueError("out of range"), TypeError("wrong type"), RuntimeError("boom")], + ids=lambda f: type(f).__name__, +) +def test_a_sampler_that_refuses_its_seed_is_named_in_the_error(failure): + """Only RuntimeError used to be caught, so a legacy RandomState sampler + raised a bare ValueError with nothing to say which input it came from.""" + with pytest.raises(RuntimeError, match="mass") as raised: + StochasticModel( + SimpleNamespace(mass=0.0), mass=_RefusesTheSeed(0.0, 1.0, failure) + ) + + assert raised.value.__cause__ is failure + + +def test_a_legacy_random_state_sampler_is_named_rather_than_raising_bare(): + """The concrete case, not a stand-in: RandomState really does refuse the + 128-bit seed this hands out.""" + + class LegacySampler(CustomSampler): + """Built on RandomState rather than default_rng.""" + + def sample(self, n_samples=1): + return list(self.rng.normal(size=n_samples)) + + def reset_seed(self, seed=None): + self.rng = np.random.RandomState(seed) + + with pytest.raises(RuntimeError, match="mass") as raised: + StochasticModel(SimpleNamespace(mass=0.0), mass=LegacySampler()) + + assert isinstance(raised.value.__cause__, ValueError) + + +class _CountingShared(_SharedPair): + """Records how it was reset, so the dispatch can be checked.""" + + def __init__(self): + super().__init__() + self.reset_seed_calls = 0 + + def reset_seed(self, seed=None): + self.reset_seed_calls += 1 + self.reset(seed) + + +class _WrapperOverGroup(CustomSampler): + """A wrapper whose own reset_seed would be the wrong thing to call.""" + + def __init__(self, shared): + self.shared = shared + self.own_resets = 0 + + @property + def seed_group(self): + return self.shared + + def sample(self, n_samples=1): + return [self.shared.draw() for _ in range(n_samples)] + + def reset_seed(self, seed=None): + self.own_resets += 1 + self.shared.reset(seed) + + +def test_a_group_that_can_reset_itself_is_reset_directly(): + """Dispatching through one member assumes every member resets the same way + and holds no state of its own. The group owns the shared generator, so it + is the thing to reset when it knows how.""" + shared = _CountingShared() + first, second = _WrapperOverGroup(shared), _WrapperOverGroup(shared) + model = StochasticModel( + SimpleNamespace(wind_x=0.0, wind_y=0.0), wind_x=first, wind_y=second + ) + shared.reset_seed_calls = 0 + first.own_resets = second.own_resets = 0 + + model._set_stochastic(4242) + + assert shared.reset_seed_calls == 1 + assert (first.own_resets, second.own_resets) == (0, 0) + + +def test_a_group_key_does_not_depend_on_the_order_it_is_given(): + """The caller sorts today. The helper sorts too, so a future call site + cannot hand one group two different seeds by listing it another way.""" + assert _sampler_seed(4242, ("wind_x", "wind_y")) == _sampler_seed( + 4242, ("wind_y", "wind_x") + ) + + +def test_a_non_sampler_is_refused_even_under_optimisation(): + """`python -O` strips an assert, so the check that keeps a non-sampler out + of the model has to be a raise. The documented AssertionError is kept, so a + caller already catching it is unaffected.""" + model = StochasticModel(SimpleNamespace(mass=0.0)) + + with pytest.raises(AssertionError, match="must be a CustomSampler"): + model._validate_custom_sampler("mass", object()) + + +def test_the_refusal_survives_python_dash_o(): + """The mechanism, not just the behaviour: run it in a child with -O and + check the exception still arrives.""" + program = ( + "from types import SimpleNamespace;" + "from rocketpy.stochastic.stochastic_model import StochasticModel;" + "m = StochasticModel(SimpleNamespace(mass=0.0));" + "\ntry:\n" + " m._validate_custom_sampler('mass', object())\n" + "except AssertionError:\n" + " print('refused')\n" + ) + done = subprocess.run( + [sys.executable, "-O", "-c", program], + capture_output=True, + text=True, + check=True, + ) + + assert "refused" in done.stdout, done.stderr diff --git a/tests/unit/stochastic/test_seed_types.py b/tests/unit/stochastic/test_seed_types.py new file mode 100644 index 000000000..ba3d42583 --- /dev/null +++ b/tests/unit/stochastic/test_seed_types.py @@ -0,0 +1,81 @@ +import numpy as np +import pytest + +from rocketpy.stochastic.stochastic_model import ( + _names_as_spawn_key, + _sampler_seed, +) +from rocketpy.tools import _seed_sequence_to_int + + +def _a_worker_seed(index=0, workers=2): + # What MonteCarlo.__run_in_parallel spawns and hands to each worker, which + # passes it straight to environment/rocket/flight._set_stochastic. + return np.random.SeedSequence().spawn(workers)[index] + + +def test_the_seed_type_a_worker_is_handed_is_accepted(stochastic_calisto): + stochastic_calisto._set_stochastic(_a_worker_seed()) + + stochastic_calisto.create_object() + + +def test_a_parachute_derives_its_noise_seed_from_a_worker_seed( + stochastic_main_parachute, +): + stochastic_main_parachute._set_stochastic(_a_worker_seed()) + + assert stochastic_main_parachute.create_object().noise[2] is not None + + +def test_two_workers_do_not_share_a_sampler_stream(): + first, second = np.random.SeedSequence(7).spawn(2) + # They come off one root, so they carry the same entropy and differ only in + # spawn_key. Reading the entropy alone would put both on one stream. + assert first.entropy == second.entropy + + assert _sampler_seed(first, ("__list_choice__",)) != _sampler_seed( + second, ("__list_choice__",) + ) + + +def test_a_caller_seed_sequence_is_not_consumed(): + root = np.random.SeedSequence(42) + + _sampler_seed(root, ("__list_choice__",)) + + assert root.n_children_spawned == 0 + assert root.spawn(1)[0].spawn_key == (0,) + + +def test_the_same_seed_sequence_twice_gives_the_same_sampler_seed(): + root = np.random.SeedSequence(42) + + first = _sampler_seed(root, ("pressure_noise", "main")) + second = _sampler_seed(root, ("pressure_noise", "main")) + + assert first == second + + +@pytest.mark.parametrize("seed", [42, 7, [1, 2, 3]]) +@pytest.mark.parametrize("names", [("__list_choice__",), ("pressure_noise", "main")]) +def test_a_seed_that_is_not_a_sequence_reaches_numpy_untouched(seed, names): + # The control. Every fixed-seed baseline in the suite was recorded through + # this path, so anything but a SeedSequence has to arrive as it always did. + # Compared with the expression rather than with a recorded number, which + # would go red on a NumPy release instead of on a change of ours. + unchanged = np.random.SeedSequence( + entropy=seed, spawn_key=_names_as_spawn_key(tuple(sorted(names))) + ) + + assert _sampler_seed(seed, names) == _seed_sequence_to_int(unchanged) + + +def test_no_seed_still_means_no_seed(): + # None is left out above on purpose: it asks NumPy for fresh entropy, so + # two calls must not agree, and comparing one against another would be + # asserting the opposite of what an unseeded run promises. + first = _sampler_seed(None, ("__list_choice__",)) + second = _sampler_seed(None, ("__list_choice__",)) + + assert first != second diff --git a/tests/unit/stochastic/test_stochastic_aero_surfaces.py b/tests/unit/stochastic/test_stochastic_aero_surfaces.py index d63feb76c..ab979b699 100644 --- a/tests/unit/stochastic/test_stochastic_aero_surfaces.py +++ b/tests/unit/stochastic/test_stochastic_aero_surfaces.py @@ -1,4 +1,14 @@ -from rocketpy.rocket.aero_surface import NoseCone, RailButtons, Tail, TrapezoidalFins +import numpy as np +import pytest + +from rocketpy.rocket.aero_surface import ( + FreeFormFins, + NoseCone, + RailButtons, + Tail, + TrapezoidalFins, +) +from rocketpy.stochastic import StochasticFreeFormFins ## NOSE CONE @@ -46,6 +56,233 @@ class creates a StochasticTrapezoidalFins object from the randomly generated assert isinstance(obj, TrapezoidalFins) +## FREE FORM FINS + +NOMINAL_SHAPE = [(0, 0), (0.08, 0.1), (0.12, 0.1), (0.12, 0)] + + +def test_stochastic_free_form_fins_create_object(stochastic_free_form_fins): + """Test create object method of StochasticFreeFormFins class. + + This test checks if the create_object method of the StochasticFreeFormFins + class creates a FreeFormFins object from the randomly generated input + arguments. + + Parameters + ---------- + stochastic_free_form_fins : StochasticFreeFormFins + StochasticFreeFormFins object to be tested. + + Returns + ------- + None + """ + obj = stochastic_free_form_fins.create_object() + assert isinstance(obj, FreeFormFins) + + +def test_stochastic_free_form_fins_nominal_shape_is_preserved(calisto_free_form_fins): + """With nothing to randomize, the created fin set must keep the outline of + the object it was built from.""" + stochastic = StochasticFreeFormFins(free_form_fins=calisto_free_form_fins) + + created = stochastic.create_object() + + assert np.allclose( + np.asarray(created.shape_points, dtype=float), + np.asarray(calisto_free_form_fins.shape_points, dtype=float), + ) + + +@pytest.mark.parametrize( + "shape_points", + [ + 0.001, + (0.001, "normal"), + (NOMINAL_SHAPE, 0.001), + (NOMINAL_SHAPE, 0.001, "normal"), + ], + ids=["scalar", "std_and_dist", "outline_and_std", "outline_std_and_dist"], +) +def test_stochastic_free_form_fins_perturbs_the_whole_outline( + calisto_free_form_fins, shape_points +): + """A fin outline is only meaningful as a complete set of points, so every + accepted format must randomize all of them and keep the (n, 2) shape.""" + stochastic = StochasticFreeFormFins( + free_form_fins=calisto_free_form_fins, shape_points=shape_points + ) + stochastic._set_stochastic(42) + + created = stochastic.create_object() + + nominal = np.asarray(NOMINAL_SHAPE, dtype=float) + sampled = np.asarray(created.shape_points, dtype=float) + assert sampled.shape == nominal.shape + # Every coordinate is drawn on its own, so none of the four points is left + # exactly where it was, apart from the root's y (see the test below). + assert not np.allclose(sampled[:, 0], nominal[:, 0]) + assert not np.allclose(sampled[1:3, 1], nominal[1:3, 1]) + # A standard deviation of a millimetre must not turn into a new fin. + assert np.abs(sampled - nominal).max() < 0.01 + + +@pytest.mark.parametrize( + "shape_points", + [0.001, (0.001, "normal"), (NOMINAL_SHAPE, 0.001, "laplace")], + ids=["scalar", "std_and_dist", "outline_std_and_dist"], +) +def test_stochastic_free_form_fins_keeps_the_root_on_the_body_line( + calisto_free_form_fins, shape_points +): + """FreeFormFins measures the span from y = 0 and slices the chords over that + interval, so a perturbed root point must not drift off the body line: it + would put part of the fin inside the airframe and inflate the span the + chords are measured against. + """ + stochastic = StochasticFreeFormFins( + free_form_fins=calisto_free_form_fins, shape_points=shape_points + ) + stochastic._set_stochastic(3) + + for _ in range(50): + sampled = np.asarray(stochastic.create_object().shape_points, dtype=float) + # The first and last points of the nominal outline are on the body line. + assert sampled[0, 1] == 0 + assert sampled[-1, 1] == 0 + assert (sampled[:, 1] >= 0).all() + + +def test_stochastic_free_form_fins_bare_outline_is_a_single_candidate( + calisto_free_form_fins, +): + """A bare outline is a list, which the base class would otherwise read as a + list of candidate values and sample a single (x, y) point from.""" + stochastic = StochasticFreeFormFins( + free_form_fins=calisto_free_form_fins, shape_points=NOMINAL_SHAPE + ) + + created = stochastic.create_object() + + assert np.allclose( + np.asarray(created.shape_points, dtype=float), + np.asarray(NOMINAL_SHAPE, dtype=float), + ) + + +def test_stochastic_free_form_fins_chooses_between_outlines(calisto_free_form_fins): + """A list of outlines is a set of candidate shapes to choose from.""" + taller = [(0, 0), (0.06, 0.12), (0.12, 0.12), (0.12, 0)] + stochastic = StochasticFreeFormFins( + free_form_fins=calisto_free_form_fins, + shape_points=[NOMINAL_SHAPE, taller], + ) + stochastic._set_stochastic(42) + + spans = {round(stochastic.create_object().span, 4) for _ in range(50)} + + assert spans == {0.1, 0.12} + + +def test_stochastic_free_form_fins_chooses_between_outlines_of_different_lengths( + calisto_free_form_fins, +): + """Candidate outlines need not have the same number of points: choosing + between a three-point and a four-point fin is the plainest form of choosing + between shapes, and numpy raises on that ragged list if it is converted + whole instead of one candidate at a time. + """ + triangle = [(0, 0), (0.08, 0.1), (0.12, 0)] + quadrilateral = [(0, 0), (0.06, 0.12), (0.12, 0.12), (0.12, 0)] + stochastic = StochasticFreeFormFins( + free_form_fins=calisto_free_form_fins, + shape_points=[triangle, quadrilateral], + ) + stochastic._set_stochastic(42) + + point_counts = {len(stochastic.create_object().shape_points) for _ in range(50)} + + assert point_counts == {3, 4} + + +def test_stochastic_free_form_fins_accepts_an_array_outline(calisto_free_form_fins): + """A sampled outline comes back as an array, so feeding one straight back in + as the nominal outline must work.""" + stochastic = StochasticFreeFormFins( + free_form_fins=calisto_free_form_fins, + shape_points=np.asarray(NOMINAL_SHAPE, dtype=float), + ) + + created = stochastic.create_object() + + assert np.allclose( + np.asarray(created.shape_points, dtype=float), + np.asarray(NOMINAL_SHAPE, dtype=float), + ) + + +def test_stochastic_free_form_fins_empty_list_means_the_nominal_outline( + calisto_free_form_fins, +): + """An empty list means "take the nominal value and do not randomize" for + every other stochastic input, and this one is no different.""" + stochastic = StochasticFreeFormFins( + free_form_fins=calisto_free_form_fins, shape_points=[] + ) + + created = stochastic.create_object() + + assert np.allclose( + np.asarray(created.shape_points, dtype=float), + np.asarray(calisto_free_form_fins.shape_points, dtype=float), + ) + + +@pytest.mark.parametrize( + "shape_points", + [ + "not_an_outline", + [[(0, 0), (0.1, 0.1)]], + [(0, 0), (0.1, 0.1)], + [(0, 0, 0), (0.1, 0.1, 0), (0.1, 0, 0)], + [(0, 0), (1, 1, 1), (2, 0)], + [[("a", "b"), ("c", "d"), ("e", "f")]], + (0.001,), + (NOMINAL_SHAPE, 0.001, "normal", 1), + (NOMINAL_SHAPE, "normal"), + (0.001, 5), + (NOMINAL_SHAPE, 0.001, 7), + (0.001, "uniform"), + (NOMINAL_SHAPE, 0.001, "wald"), + ], + ids=[ + "string", + "too_few_points", + "bare_outline_too_few_points", + "three_dimensional_points", + "ragged_outline", + "non_numeric_points", + "tuple_too_short", + "tuple_too_long", + "outline_with_string_std", + "std_with_non_string_dist", + "outline_with_non_string_dist", + "bounded_distribution", + "shape_parameter_distribution", + ], +) +def test_stochastic_free_form_fins_rejects_invalid_shape_points( + calisto_free_form_fins, shape_points +): + """An outline that cannot mean a fin shape, or a distribution that cannot + mean a deviation around one, must fail during validation rather than + reaching FreeFormFins or the sampler.""" + with pytest.raises(AssertionError): + StochasticFreeFormFins( + free_form_fins=calisto_free_form_fins, shape_points=shape_points + ) + + ## TAIL diff --git a/tests/unit/stochastic/test_stochastic_environment.py b/tests/unit/stochastic/test_stochastic_environment.py index ce115fe05..80efca30a 100644 --- a/tests/unit/stochastic/test_stochastic_environment.py +++ b/tests/unit/stochastic/test_stochastic_environment.py @@ -1,4 +1,8 @@ +import numpy as np +import pytest + from rocketpy.environment.environment import Environment +from rocketpy.stochastic import StochasticEnvironment def test_str(stochastic_environment): @@ -41,3 +45,82 @@ class creates a StochasticEnvironment object from the randomly generated """ obj = stochastic_environment.create_object() assert isinstance(obj, Environment) + + +def _two_member_ensemble(first=10.0, second=30.0): + """An Environment with two ensemble members whose winds differ. + + Built here rather than read from a NetCDF file so the two winds are known + exactly and a factor applied to the wrong one is visible in the result. + """ + levels = np.array([100000.0, 90000.0, 80000.0]) + height = np.array([0.0, 1000.0, 2000.0]) + temperature = np.array([288.0, 282.0, 275.0]) + winds = (first, second) + + environment = Environment() + environment.set_atmospheric_model(type="custom_atmosphere", wind_u=0, wind_v=0) + environment.atmospheric_model_type = "Ensemble" + environment.num_ensemble_members = 2 + environment.level_ensemble = levels + environment.height_ensemble = np.tile(height, (2, 1)) + environment.temperature_ensemble = np.tile(temperature, (2, 1)) + environment.wind_u_ensemble = np.array([np.full(3, wind) for wind in winds]) + environment.wind_v_ensemble = np.zeros((2, 3)) + environment.wind_speed_ensemble = np.array([np.full(3, wind) for wind in winds]) + environment.wind_heading_ensemble = np.full((2, 3), 90.0) + environment.wind_direction_ensemble = np.full((2, 3), 270.0) + environment.ensemble_member = 0 + environment.select_ensemble_member(0) + return environment + + +def test_create_object_scales_the_wind_of_the_member_it_selected(): + """A wind factor must multiply the selected member's own wind. + + ``select_ensemble_member`` rebuilds the wind from that member's profile, so + a factor applied before it used to be discarded and the run flew the raw + member wind while the input record still reported the factor. + """ + environment = _two_member_ensemble(first=10.0, second=30.0) + stochastic = StochasticEnvironment( + environment=environment, + ensemble_member=[1], + wind_velocity_x_factor=(2.0, 0), + ) + stochastic._set_stochastic(7) + + wind = float(stochastic.create_object().wind_velocity_x(500)) + + assert wind == pytest.approx(60.0, rel=1e-9) + assert wind != pytest.approx(30.0, rel=1e-9) # factor dropped + assert wind != pytest.approx(20.0, rel=1e-9) # member 0's cached wind + + +def test_create_object_does_not_compound_the_factor_across_calls(): + """Each call scales the member's profile once, not the previous result.""" + environment = _two_member_ensemble(first=10.0, second=30.0) + stochastic = StochasticEnvironment( + environment=environment, + ensemble_member=[1], + wind_velocity_x_factor=(2.0, 0), + ) + stochastic._set_stochastic(7) + + winds = [float(stochastic.create_object().wind_velocity_x(500)) for _ in range(3)] + + assert winds == pytest.approx([60.0, 60.0, 60.0], rel=1e-9) + + +def test_create_object_without_a_member_still_scales_the_construction_value(): + """Without ensemble members the factor keeps multiplying the original wind.""" + environment = Environment() + environment.set_atmospheric_model(type="custom_atmosphere", wind_u=10, wind_v=0) + stochastic = StochasticEnvironment( + environment=environment, wind_velocity_x_factor=(2.0, 0) + ) + stochastic._set_stochastic(7) + + winds = [float(stochastic.create_object().wind_velocity_x(500)) for _ in range(3)] + + assert winds == pytest.approx([20.0, 20.0, 20.0], rel=1e-9) diff --git a/tests/unit/stochastic/test_stochastic_flight.py b/tests/unit/stochastic/test_stochastic_flight.py index e03917475..ed65f691e 100644 --- a/tests/unit/stochastic/test_stochastic_flight.py +++ b/tests/unit/stochastic/test_stochastic_flight.py @@ -45,3 +45,65 @@ def test_stochastic_flight_optional_attributes(flight_calisto_robust): assert obj.terminate_on_apogee is True assert obj.time_overshoot is True assert obj.max_time == 987.6 + + +def test_dict_generator_skips_initial_solution_tuple(flight_calisto_robust): + """Regression for #1109: tuple initial_solution must not be sampled.""" + initial_solution = tuple(float(i) for i in range(14)) + stochastic_flight = StochasticFlight( + flight=flight_calisto_robust, + initial_solution=initial_solution, + rail_length=(5.2, 0.1), + ) + generated = next(stochastic_flight.dict_generator()) + assert "initial_solution" not in generated + assert stochastic_flight.initial_solution == initial_solution + + +def test_dict_generator_skips_initial_solution_list(flight_calisto_robust): + """List-form initial_solution must not be randomly subset-sampled.""" + initial_solution = [float(i) for i in range(14)] + stochastic_flight = StochasticFlight( + flight=flight_calisto_robust, + initial_solution=initial_solution, + inclination=[85, 86, 87], + ) + generated = next(stochastic_flight.dict_generator()) + assert "initial_solution" not in generated + assert stochastic_flight.initial_solution == initial_solution + + +def test_create_object_matches_last_rnd_dict(flight_calisto_robust): + """Regression for #1090: create_object must use one dict_generator draw. + + Spreads are set on all three flight inputs so a second draw would diverge + from ``last_rnd_dict``. + """ + stochastic_flight = StochasticFlight( + flight=flight_calisto_robust, + rail_length=(5.2, 0.5), + inclination=(84.7, 1), + heading=(53, 2), + ) + stochastic_flight._set_stochastic(4242) + + flight = stochastic_flight.create_object() + sampled = stochastic_flight.last_rnd_dict + + assert flight.rail_length == sampled["rail_length"] + assert flight.inclination == sampled["inclination"] + assert flight.heading == sampled["heading"] + + +def test_monte_carlo_single_simulation_matches_flight_last_rnd_dict( + monte_carlo_calisto, +): + """Regression for #1090: MonteCarlo must fly the same sample it logs.""" + monte_carlo_calisto.flight._set_stochastic(4242) + + flight = monte_carlo_calisto._MonteCarlo__run_single_simulation() + sampled = monte_carlo_calisto.flight.last_rnd_dict + + assert flight.rail_length == sampled["rail_length"] + assert flight.inclination == sampled["inclination"] + assert flight.heading == sampled["heading"] diff --git a/tests/unit/stochastic/test_stochastic_model.py b/tests/unit/stochastic/test_stochastic_model.py index 9e35a5330..a8691af6b 100644 --- a/tests/unit/stochastic/test_stochastic_model.py +++ b/tests/unit/stochastic/test_stochastic_model.py @@ -1,5 +1,21 @@ +import ast +import inspect +import textwrap + +import numpy as np import pytest +from rocketpy import Environment +from rocketpy.mathutils.function import Function +from rocketpy.rocket.aero_surface import FreeFormFins +from rocketpy.stochastic import ( + StochasticEnvironment, + StochasticFreeFormFins, + StochasticParachute, + StochasticRocket, +) +from rocketpy.stochastic.stochastic_model import StochasticModel, _snapshot_of + @pytest.mark.parametrize( "fixture_name", @@ -10,6 +26,7 @@ "stochastic_environment_custom_sampler", "stochastic_tail", "stochastic_calisto", + "stochastic_free_form_fins", ], ) def test_visualize_attributes(request, fixture_name): @@ -21,3 +38,630 @@ def test_visualize_attributes(request, fixture_name): report = fixture.visualize_attributes() assert isinstance(report, str) assert report + + +def test_list_choices_are_reproducible(calisto_free_form_fins): + """Choosing between the candidate values of a list input must come from the + model's own generator, so that the same seed replays the same choices. + + The interpreter-wide ``random.choice`` was used, which ``_set_stochastic`` + does not reseed: a fixed-seed run picked different values every time, and + Monte Carlo workers forked from one process walked a single shared stream + instead of sampling independently. + """ + taller = [(0, 0), (0.06, 0.12), (0.12, 0.12), (0.12, 0)] + stochastic = StochasticFreeFormFins( + free_form_fins=calisto_free_form_fins, + shape_points=[calisto_free_form_fins.shape_points, taller], + ) + + def spans(seed): + stochastic._set_stochastic(seed) + return [round(stochastic.create_object().span, 4) for _ in range(20)] + + assert spans(7) == spans(7) + assert spans(7) != spans(8) + # Both candidates must stay reachable, or the assertions above would also + # hold for a generator that always returned the same one. + assert set(spans(7)) == {0.1, 0.12} + + +def _windy_environment(): + environment = Environment() + environment.set_atmospheric_model(type="standard_atmosphere") + environment.wind_velocity_x = 10.0 + return environment + + +def _effective_wind_x(environment): + """The wind the Environment would actually fly with.""" + wind = environment.wind_velocity_x + return float(wind(0)) if callable(wind) else float(wind) + + +def test_a_factor_does_not_compound_across_reseeds(): + """Reseeding with the same seed has to give the same inputs. + + ``StochasticEnvironment.create_object`` writes the sampled value back onto + the Environment rather than building a copy, so re-reading the nominal from + it compounded: 10 -> 8.576 -> 7.355 -> 6.308, each the last one multiplied + by the same factor again. + """ + stochastic = StochasticEnvironment( + environment=_windy_environment(), wind_velocity_x_factor=(1.0, 0.1) + ) + + winds = [] + for _ in range(4): + stochastic._set_stochastic(12345) + winds.append(_effective_wind_x(stochastic.create_object())) + + assert len(set(winds)) == 1, f"the same seed drifted across reseeds: {winds}" + + +def test_a_seed_gives_the_same_input_whatever_was_sampled_before_it(): + """Caching the nominal per model, not per seed, is what this pins. + + Reseeding to 103 has to give what it gives on a fresh model, whether or not + 102 and 101 ran first. A cache keyed by the seed would satisfy the test + above and still fail here, because each new seed would re-read a nominal + the previous ``create_object`` had already moved. + """ + + def wind_after(seeds): + stochastic = StochasticEnvironment( + environment=_windy_environment(), wind_velocity_x_factor=(1.0, 0.1) + ) + wind = None + for seed in seeds: + stochastic._set_stochastic(seed) + wind = _effective_wind_x(stochastic.create_object()) + return wind + + assert wind_after([101, 102, 103]) == wind_after([103]) + + +def test_a_scalar_nominal_does_not_drift_across_reseeds(): + """Not only the factors. + + ``_validate_scalar`` and the ``(std, "distribution")`` tuple both take their + nominal from the wrapped object, so a plain scalar spec drifts the same way + a factor compounds. + """ + environment = Environment() + environment.set_atmospheric_model(type="standard_atmosphere") + stochastic = StochasticEnvironment(environment=environment, elevation=100.0) + + elevations = [] + for _ in range(4): + stochastic._set_stochastic(2024) + elevations.append(float(stochastic.create_object().elevation)) + + assert len(set(elevations)) == 1, f"the nominal elevation drifted: {elevations}" + + +def test_the_nominal_is_the_one_the_input_was_configured_with(example_plain_env): + """Snapshot semantics, stated once and pinned here. + + A model samples around what the wrapped object held when the input was + configured, so a later change to that object deliberately does not move what + is sampled around. That is the same rule the drift above depends on. + """ + example_plain_env.elevation = 1000 + # A scalar is a spread around the object's own value, so this is the form + # that reads the nominal. A tuple carries its own centre and would not. + model = StochasticEnvironment(environment=example_plain_env, elevation=5) + + model._set_stochastic(4242) + around_first = model.elevation[0] + + example_plain_env.elevation = 9000 + model._set_stochastic(4242) + + assert model.elevation[0] == around_first == 1000, ( + "the model followed the object instead of the value it was configured with" + ) + + +def test_a_mutable_nominal_survives_a_write_through_the_object( + calisto_free_form_fins, +): + """Holding the object itself would let ``obj.shape_points[:] = ...`` reach + the nominal and move a value the model is supposed to sample around.""" + stochastic = StochasticFreeFormFins( + free_form_fins=calisto_free_form_fins, shape_points=0.001 + ) + stochastic._set_stochastic(7) + expected = np.array(stochastic.shape_points[0], copy=True) + + stochastic.obj.shape_points[:] = [(9.9, 9.9)] * len(stochastic.obj.shape_points) + stochastic._set_stochastic(7) + + assert np.array_equal(stochastic.shape_points[0], expected) + + +def test_a_generated_object_cannot_move_the_kept_nominal(calisto_free_form_fins): + """The kept value has to stay private, not only be copied on the way in. + + An empty spec keeps the object's own outline, and that one object reached + the model attribute, ``last_rnd_dict`` and the fins ``create_object`` + returns, so a write through any of them moved the next reseed. + """ + expected = [tuple(point) for point in calisto_free_form_fins.shape_points] + stochastic = StochasticFreeFormFins( + free_form_fins=calisto_free_form_fins, shape_points=None + ) + + generated = stochastic.create_object() + generated.shape_points[1] = (9.9, 9.9) + stochastic._set_stochastic(7) + + assert [tuple(point) for point in stochastic.shape_points[0]] == expected + + +def test_writing_through_the_model_attribute_cannot_move_it_either(): + """The same, from the other public surface. + + ``numpy.asarray(value, dtype=float)`` hands back what it was given when that + is already a float array, so the fin is built from one here. + """ + fins = FreeFormFins( + n=4, + shape_points=np.array( + [(0, 0), (0.08, 0.1), (0.12, 0.1), (0.12, 0)], dtype=float + ), + rocket_radius=0.0635, + ) + stochastic = StochasticFreeFormFins(free_form_fins=fins, shape_points=0.001) + stochastic._set_stochastic(7) + expected = np.array(stochastic.shape_points[0], copy=True) + + stochastic.shape_points[0][1] = (9.9, 9.9) + stochastic._set_stochastic(7) + + assert np.array_equal(stochastic.shape_points[0], expected) + + +def test_a_spread_and_distribution_tuple_does_not_drift(): + """The ``(std, "distribution")`` form takes its centre from the object too. + + The scalar test above goes through ``_validate_scalar`` and this through + ``_validate_tuple_length_two``, so one says nothing about the other. Each + run gets its own Environment, since ``create_object`` writes onto it. + """ + + def elevation_after(seeds): + environment = Environment() + environment.set_atmospheric_model(type="standard_atmosphere") + environment.elevation = 100.0 + stochastic = StochasticEnvironment( + environment=environment, elevation=(5.0, "normal") + ) + drawn = None + for seed in seeds: + stochastic._set_stochastic(seed) + drawn = float(stochastic.create_object().elevation) + return drawn + + assert elevation_after([2024, 2024]) == elevation_after([2024]) + assert elevation_after([7, 11, 2024]) == elevation_after([2024]) + + +def test_an_input_added_after_the_model_takes_its_nominal_then(calisto): + """An ``add_*`` input is configured after ``__init__``, so it is read then. + + A scalar spec centres on the object's own value, and ``add_cp_eccentricity`` + is the first thing to ask for it, so what the rocket holds at that moment is + what gets kept. + """ + stochastic = StochasticRocket(rocket=calisto, radius=0.0127 / 2) + + calisto.cp_eccentricity_x = 0.5 + stochastic.add_cp_eccentricity(x=0.001) + calisto.cp_eccentricity_x = 9.0 + + stochastic._set_stochastic(11) + drawn = float(next(stochastic.dict_generator())["cp_eccentricity_x"]) + + assert abs(drawn - 0.5) < 0.05, f"centred on {drawn}, not on the add-time 0.5" + + +def test_the_snapshot_keeps_by_reference_what_it_does_not_copy(): + """Only built-in containers are copied, so the rule behaves as stated.""" + array = np.array([1.0, 2.0]) + listed = [[1.0], [2.0]] + function = Function(lambda x: x) + + mapped = {"a": [1.0]} + grouped = {("a", 1), ("b", 2)} + + assert _snapshot_of(array) is not array + assert _snapshot_of(listed) is not listed + assert _snapshot_of(listed)[0] is not listed[0] # deep, not shallow + assert _snapshot_of(mapped) is not mapped + assert _snapshot_of(mapped)["a"] is not mapped["a"] + assert _snapshot_of(grouped) is not grouped + assert _snapshot_of(grouped) == grouped + assert _snapshot_of(function) is function + assert _snapshot_of(3.0) == 3.0 + + +def test_a_function_nominal_is_held_as_it_was_given(calisto): + """The documented exception, pinned rather than only written down. + + A ``Function`` is mutable through its own API, so ``set_source`` on the + rocket's drag curve does move the baseline. Copying it would mean + ``deepcopy`` of whatever a user passed, on a path that runs on every reseed + and today cannot fail, which is not a trade this change should make. + """ + stochastic = StochasticRocket( + rocket=calisto, radius=0.0127 / 2, power_off_drag_factor=(1.0, 0.1) + ) + stochastic._set_stochastic(4242) + before = float(stochastic.create_object().power_off_drag(0.5)) + + calisto.power_off_drag.set_source(lambda mach: 0.9) + stochastic._set_stochastic(4242) + after = float(stochastic.create_object().power_off_drag(0.5)) + + assert 0.3 < before < 0.5, before + assert 0.7 < after < 1.1, after + + +def test_two_components_do_not_share_one_position_nominal(stochastic_calisto): + """Component positions are read live, through an injected getter. + + Every one of them arrives under the name ``position``, so keeping them the + way the other inputs are kept would hand the second component the first + one's place. The report test notices the getter going missing, but only + because reading ``position`` off the rocket raises; it would not notice a + key that quietly collides. + """ + stochastic_calisto._set_stochastic(5) + + places = {} + for component, position, *_ in stochastic_calisto.aerodynamic_surfaces: + nominal = position[0] + places[type(component).__name__] = float(getattr(nominal, "z", nominal)) + + assert len(places) > 1, "need more than one surface for this to say anything" + assert len(set(places.values())) == len(places), places + + +def test_the_snapshot_reaches_a_mutable_nested_in_a_tuple(): + """An ``airfoil`` is ``(source, unit)`` and the source may be an array, so + stopping at the tuple would leave that array shared with the object it came + from. A ``Function`` nested the same way still travels by reference. + """ + source = np.array([[0.0, 0.0], [1.0, 1.0]]) + function = Function(lambda x: x) + + copied = _snapshot_of((source, "degrees")) + source[0, 1] = 99.0 + + assert copied[0][0, 1] == 0.0 + assert _snapshot_of((function, "degrees"))[0] is function + + +def test_configuring_a_late_input_again_reads_the_nominal_again(calisto): + """``configured`` has to mean the second call as well as the first. + + The kept nominal had no replacement path, so a second + ``add_cp_eccentricity`` went on sampling around the value the rocket held + at the first one. Reproducible, and around the wrong centre. + """ + stochastic = StochasticRocket(rocket=calisto, radius=0.0127 / 2) + + calisto.cp_eccentricity_x = 0.5 + stochastic.add_cp_eccentricity(x=0.001) + calisto.cp_eccentricity_x = 0.8 + stochastic.add_cp_eccentricity(x=0.001) + + assert stochastic.cp_eccentricity_x[0] == 0.8 + + stochastic._set_stochastic(11) + + assert stochastic.cp_eccentricity_x[0] == 0.8 + + +def test_a_refused_reconfiguration_leaves_the_previous_one(calisto): + """Dropping the kept nominal before validation must not outlive a failure.""" + calisto.cp_eccentricity_x = 0.5 + stochastic = StochasticRocket(rocket=calisto, radius=0.0127 / 2) + stochastic.add_cp_eccentricity(x=0.001) + + calisto.cp_eccentricity_x = 0.8 + with pytest.raises(AssertionError): + stochastic.add_cp_eccentricity(x=object()) + + stochastic._set_stochastic(11) + + assert stochastic.cp_eccentricity_x[0] == 0.5 + + +@pytest.mark.parametrize( + ("add_them", "kept"), + [ + ("add_cp_eccentricity", "cp_eccentricity_y"), + ("add_thrust_eccentricity", "thrust_eccentricity_y"), + ], +) +def test_an_axis_left_out_keeps_everything_it_had(calisto, add_them, kept): + """Not only its declaration: its distribution and its kept nominal too. + + Keeping the declaration alone left the two disagreeing. The private side + still said the axis was random while the validated attribute had been + replaced by a lone nominal, and ``dict_generator`` reads the attribute, so + the axis stopped varying until something reset the model. A serial Monte + Carlo never does, so a whole study would have run with it switched off. + """ + setattr(calisto, kept, 0.0) + stochastic = StochasticRocket(rocket=calisto, radius=0.0127 / 2) + getattr(stochastic, add_them)(x=0.001, y=0.002) + stochastic._set_stochastic(11) + centre = getattr(stochastic, kept)[0] + + # Moved between the calls, so re-reading the nominal would show up. + setattr(calisto, kept, 9.0) + getattr(stochastic, add_them)(x=0.005) + + drawn = {next(stochastic.dict_generator())[kept] for _ in range(8)} + assert len(drawn) == 8, "the axis stopped varying before any reset" + + stochastic._set_stochastic(11) + + assert getattr(stochastic, kept)[0] == centre + assert kept in next(stochastic.dict_generator()) + + +def test_leaving_an_axis_out_does_not_take_away_what_it_declared(calisto): + """``None`` reads the same whether it was written or the argument was + left out, so a call that mentions only x has to leave y where it was. + + Removing on the second reading would take away an axis nobody mentioned, + which is why removal needs an argument the caller cannot supply by + omission. That is #1171 rather than this change. + """ + stochastic = StochasticRocket(rocket=calisto, radius=0.0127 / 2) + stochastic.add_cp_eccentricity(x=0.001, y=0.002) + + stochastic.add_cp_eccentricity(x=0.005) + stochastic._set_stochastic(11) + + generated = next(stochastic.dict_generator()) + assert "cp_eccentricity_x" in generated + assert "cp_eccentricity_y" in generated + + +def test_a_half_that_was_never_given_is_not_declared(calisto): + """The control for the one above. + + ``None`` also means an axis the caller never mentioned, and removing what + was never there has to stay a no-op rather than an error. + """ + stochastic = StochasticRocket(rocket=calisto, radius=0.0127 / 2) + stochastic.add_cp_eccentricity(x=0.001) + + generated = next(stochastic.dict_generator()) + + assert "cp_eccentricity_x" in generated + assert "cp_eccentricity_y" not in generated + + +def test_a_pair_of_late_inputs_is_replaced_together(calisto): + """``add_cp_eccentricity`` takes x and y in one call, so a y that will not + validate must leave x exactly as it was, declaration included. + + Only y is given first, so x is undeclared going in and a partial commit + shows up as an eccentricity the caller never successfully asked for. + """ + calisto.cp_eccentricity_x = 0.5 + calisto.cp_eccentricity_y = 0.6 + stochastic = StochasticRocket(rocket=calisto, radius=0.0127 / 2) + stochastic.add_cp_eccentricity(y=0.002) + + calisto.cp_eccentricity_x = 0.8 + with pytest.raises(AssertionError): + stochastic.add_cp_eccentricity(x=0.001, y=object()) + + stochastic._set_stochastic(11) + generated = next(stochastic.dict_generator()) + + assert "cp_eccentricity_x" not in generated + assert generated["cp_eccentricity_y"] is not None + assert stochastic.cp_eccentricity_y[0] == 0.6 + + +def test_one_generated_object_cannot_change_the_next_one(calisto_free_form_fins): + """``create_object`` can be called again without a reseed in between. + + The list branch handed back the candidate itself, which for an empty spec is + the model's own working outline, and ``FreeFormFins`` keeps it by reference. + Writing through the first fins therefore reached the second ones. + """ + expected = [tuple(point) for point in calisto_free_form_fins.shape_points] + stochastic = StochasticFreeFormFins( + free_form_fins=calisto_free_form_fins, shape_points=None + ) + + first = stochastic.create_object() + first.shape_points[1] = (9.9, 9.9) + second = stochastic.create_object() + + assert [tuple(point) for point in second.shape_points] == expected + + +def test_the_record_of_a_draw_is_not_a_window_onto_the_object( + calisto_free_form_fins, +): + """``last_rnd_dict`` says what was drawn. + + A Monte Carlo writes it out after the flight has run, so a value the flight + edited in place would be logged instead of the one that was sampled. + """ + stochastic = StochasticFreeFormFins( + free_form_fins=calisto_free_form_fins, shape_points=None + ) + + generated = stochastic.create_object() + recorded = np.array(stochastic.last_rnd_dict["shape_points"], copy=True) + generated.shape_points[1] = (9.9, 9.9) + + assert np.array_equal(stochastic.last_rnd_dict["shape_points"], recorded) + + +def test_a_subclass_that_adjusts_a_draw_records_it_again(): + """The base class records before a subclass has had its turn. + + ``StochasticFreeFormFins`` corrects the outline in ``dict_generator`` and + ``StochasticParachute`` adds the pressure noise seed in ``create_object``, + both after the record was taken. Read off the source, because the subclass + that gets this wrong is the one nobody wrote a fixture for. + """ + # Walked from the base class rather than from what the package exports, + # since StochasticMotorModel is a subclass the exports do not reach. + models, stack = set(), [StochasticModel] + while stack: + for subclass in stack.pop().__subclasses__(): + models.add(subclass) + stack.append(subclass) + + offenders = [] + for model in sorted(models, key=lambda cls: cls.__name__): + name = model.__name__ + for method in ("dict_generator", "create_object"): + if method not in vars(model): + continue + source = textwrap.dedent(inspect.getsource(vars(model)[method])) + body = ast.parse(source).body[0].body + # Only the names the method binds from a draw. A local it fills + # in for its own use, such as the factors StochasticEnvironment + # collects, is not a record of anything. + drawn = { + target.id + for node in ast.walk(ast.Module(body=body, type_ignores=[])) + if isinstance(node, ast.Assign) + and ( + "dict_generator" in ast.dump(node.value) + or method == "dict_generator" + ) + for target in node.targets + if isinstance(target, ast.Name) + } + writes = [ + node + for node in ast.walk(ast.Module(body=body, type_ignores=[])) + if isinstance(node, ast.Assign) + and any( + isinstance(target, ast.Subscript) + and isinstance(target.value, ast.Name) + and target.value.id in drawn + for target in node.targets + ) + ] + if writes and "_record_draw" not in source: + offenders.append(f"{name}.{method}") + + assert not offenders, ( + f"these override dict_generator, write into the drawn dictionary and " + f"never call _record_draw, so the record keeps what they replaced: " + f"{sorted(offenders)}" + ) + + +def test_the_record_holds_the_outline_the_fins_were_built_from( + calisto_free_form_fins, +): + """A perturbed outline is pulled back onto the body line after it is drawn. + + Under seed 7 the two root points drift to 0.000299 and 0.001340 and the + correction returns them to zero, so a record taken before it reports an + outline the fins were never built from. + """ + stochastic = StochasticFreeFormFins( + free_form_fins=calisto_free_form_fins, shape_points=0.001 + ) + stochastic._set_stochastic(7) + + built = stochastic.create_object() + + assert np.array_equal( + np.asarray(stochastic.last_rnd_dict["shape_points"], dtype=float), + np.asarray(built.shape_points, dtype=float), + ) + + +def test_a_rocket_records_the_outline_its_fins_were_built_from( + stochastic_calisto, stochastic_free_form_fins +): + """The same, once the fins are nested in a rocket. + + ``_create_surface`` copies each component's own record into the rocket's, + so a component that recorded too early reaches the Monte Carlo input log. + """ + # A tuple position carries its own centre, so it does not go looking for + # matching fins on a deterministic rocket that has none. + stochastic_calisto.add_free_form_fins( + stochastic_free_form_fins, position=(-1.05, 0.001) + ) + stochastic_calisto._set_stochastic(7) + + rocket = stochastic_calisto.create_object() + + recorded = next( + entry["shape_points"] + for entry in stochastic_calisto.last_rnd_dict["aerodynamic_surfaces"] + if "shape_points" in entry + ) + built = next( + surface + for surface in rocket.aerodynamic_surfaces.get_components() + if isinstance(surface, FreeFormFins) + ) + + assert np.array_equal( + np.asarray(recorded, dtype=float), + np.asarray(built.shape_points, dtype=float), + ) + + +def test_choosing_between_no_candidates_gives_back_an_empty_copy(example_plain_env): + """Validation never produces an empty candidate list, so this is the guard + that keeps ``integers(0)`` from raising if one ever reaches here.""" + model = StochasticEnvironment(environment=example_plain_env) + empty = [] + + chosen = model._choose(empty) + + assert chosen == [] + assert chosen is not empty + + +def test_a_parachute_records_the_noise_seed_it_was_built_with(calisto_main_chute): + """``create_object`` derives the pressure noise seed after the draw. + + The parachute is built with it either way, so a record taken before it + describes a parachute whose noise nobody can reproduce. + """ + stochastic = StochasticParachute(parachute=calisto_main_chute, cd_s=0.1, lag=0.2) + stochastic._set_stochastic(42) + + built = stochastic.create_object() + + assert stochastic.last_rnd_dict["seed"] == built._seed + + +def test_a_rocket_records_the_noise_seed_its_parachute_was_built_with( + stochastic_calisto, calisto_main_chute +): + """The same once nested, which is the shape a Monte Carlo writes out.""" + stochastic_calisto.parachutes = [] + stochastic_calisto.add_parachute( + StochasticParachute(parachute=calisto_main_chute, cd_s=0.1, lag=0.2) + ) + stochastic_calisto._set_stochastic(42) + + rocket = stochastic_calisto.create_object() + + recorded = stochastic_calisto.last_rnd_dict["parachutes"][0]["seed"] + assert recorded == rocket.parachutes[0]._seed diff --git a/tests/unit/stochastic/test_stochastic_parachute.py b/tests/unit/stochastic/test_stochastic_parachute.py index 09a1497f7..476384477 100644 --- a/tests/unit/stochastic/test_stochastic_parachute.py +++ b/tests/unit/stochastic/test_stochastic_parachute.py @@ -1,4 +1,11 @@ +import inspect + +import numpy as np +import pytest + +from rocketpy.stochastic import StochasticParachute from rocketpy.rocket.parachute import Parachute +from rocketpy.stochastic.stochastic_parachute import _is_a_trigger def test_stochastic_parachute_create_object(stochastic_main_parachute): @@ -19,3 +26,189 @@ class creates a StochasticParachute object from the randomly generated """ obj = stochastic_main_parachute.create_object() assert isinstance(obj, Parachute) + + +def _at_apogee(pressure, height, state): # pylint: disable=unused-argument + """A trigger of the kind `Parachute` and `Flight` already accept. + + Keeps the full signature rather than underscoring the unused two, since the + signature is the contract being tested.""" + return state[5] < 0 + + +@pytest.mark.parametrize( + "trigger", + [ + [_at_apogee], + ["apogee"], + [800], + [("time", 5.0)], + [_at_apogee, "apogee", 800, ("time", 3.0)], + ], + ids=["callable", "apogee", "height", "time", "mixed"], +) +def test_every_documented_trigger_form_is_accepted(calisto_main_chute, trigger): + """The docstring promises callables, "apogee" and numbers. The check read + `isinstance(member, (str, int, float) or callable(member))`, and a non-empty + type tuple is truthy, so the `or` short-circuited and callables were + refused. The two non-callable forms passed throughout, which is why it went + unnoticed.""" + StochasticParachute(calisto_main_chute, trigger=trigger) + + +@pytest.mark.parametrize( + "trigger", + [ + _at_apogee, + "apogee", + 800, + (800,), + [], + [None], + [{}], + ["banana"], + [True], + [_at_apogee, None], + [("time", -1.0)], + [("time", True)], + [("burnout", 3.0)], + ], + ids=str, +) +def test_a_trigger_that_is_not_a_list_of_those_is_refused(calisto_main_chute, trigger): + """The control, and four that the check used to wave through. + + `Parachute` refuses "banana" with a ValueError, so accepting it here only + moved the failure to create time. `True` is worse: it is an `int`, so it + was taken as a height of one metre. An empty list passed because `all([])` + is True. And the docstring's tuple form was never implemented. + """ + with pytest.raises(AssertionError, match="must be a non-empty list"): + StochasticParachute(calisto_main_chute, trigger=trigger) + + +@pytest.mark.parametrize( + "member", + [ + _at_apogee, + "apogee", + "APOGEE", + 800, + 800.0, + np.float64(800), + np.float32(800), + np.int64(800), + np.int32(800), + ("time", 5.0), + ("TIME", np.float64(2.5)), + ["time", 1], + ], + ids=str, +) +def test_what_this_accepts_is_what_a_parachute_accepts(calisto_main_chute, member): + """The property, rather than a list of types. Anything this lets through + has to survive `Parachute`, or the check has only moved the failure. + + The NumPy integers used to belong to the test below, refused by both + because `Parachute` spelled its height check `(int, float)`: `numpy.float64` + subclasses `float` and passed, `numpy.int64` subclasses neither and raised. + `Parachute` now reads a height as `numbers.Real`, so they are heights like + any other and belong here.""" + StochasticParachute(calisto_main_chute, trigger=[member]) + + Parachute("probe", 10.0, member, 105, 1.5) + + +@pytest.mark.parametrize( + "member", + [np.bool_(True), complex(800), np.complex64(800), "banana", None, {}], + ids=str, +) +def test_what_this_refuses_is_what_a_parachute_refuses(calisto_main_chute, member): + """The other half of the same property, and the half that keeps the widened + height check honest. + + `numbers.Real` was the wider spelling, but not an unbounded one: neither + `numpy.bool_` nor the complex types are `Real`, so they still reach the + error rather than being read as a height. `numpy.bool_` needs no exclusion + of its own for the same reason -- unlike `bool`, which is an `int` and is + ruled out by hand.""" + with pytest.raises(ValueError, match="Unable to set the trigger"): + Parachute("probe", 10.0, member, 105, 1.5) + + with pytest.raises(AssertionError, match="must be a non-empty list"): + StochasticParachute(calisto_main_chute, trigger=[member]) + + +def test_neither_check_can_drift_from_the_other_again(): + """The two checks were written out separately and disagreed: a + `numpy.int64` height was refused in `stochastic/` and accepted by the + `Parachute` that would have been built from it. Nothing failed, because + each side had a test asserting its own half. + + They now share one predicate, so this asserts the agreement itself over the + whole boundary rather than a list of types on either side.""" + boundary = [ + 800, + 800.0, + np.float64(800), + np.float32(800), + np.int64(800), + np.int32(800), + True, + np.bool_(True), + complex(800), + np.complex64(800), + "apogee", + "banana", + None, + ("time", 5.0), + ("TIME", np.float64(2.5)), + ["time", 1], + ("time", -1.0), + ("time", True), + ("time", "3.0"), + ("time",), + ("burnout", 3.0), + ] + + for member in boundary: + try: + Parachute("probe", 10.0, member, 105, 1.5) + except ValueError: + parachute_accepts = False + else: + parachute_accepts = True + + assert _is_a_trigger(member) is parachute_accepts, ( + f"{member!r}: stochastic/ says {_is_a_trigger(member)}, " + f"Parachute says {parachute_accepts}" + ) + + +def test_the_check_is_not_stripped_by_python_dash_o(): + """`python -O` removes an `assert` outright, and this check is the only + thing between a bad trigger and a `Parachute` that either refuses it much + later or reads `True` as a height.""" + source = inspect.getsource(StochasticParachute._validate_trigger) + + assert "raise AssertionError" in source + assert not any(line.strip().startswith("assert ") for line in source.splitlines()) + + +def test_a_callable_trigger_reaches_the_parachute_and_gets_called( + calisto_main_chute, +): + """Constructing the wrapper is not the property that matters. The callable + has to survive `create_object` and be what `Flight` ends up calling.""" + stochastic = StochasticParachute(calisto_main_chute, trigger=[_at_apogee]) + stochastic._set_stochastic(42) + + built = stochastic.create_object() + + assert built.trigger is _at_apogee + # 13, matching the state Flight passes: x y z vx vy vz e0 e1 e2 e3 wx wy wz + descending = [0.0] * 5 + [-5.0] + [0.0] * 7 + ascending = [0.0] * 5 + [5.0] + [0.0] * 7 + assert built.triggerfunc(0.0, 100.0, descending, [], []) + assert not built.triggerfunc(0.0, 100.0, ascending, [], []) diff --git a/tests/unit/stochastic/test_stochastic_rocket.py b/tests/unit/stochastic/test_stochastic_rocket.py index 8306b6039..fd933aec5 100644 --- a/tests/unit/stochastic/test_stochastic_rocket.py +++ b/tests/unit/stochastic/test_stochastic_rocket.py @@ -1,4 +1,17 @@ +from numbers import Real + +import numpy as np +import pytest + +from rocketpy.rocket.aero_surface import FreeFormFins +from rocketpy.rocket.parachute import Parachute from rocketpy.rocket.rocket import Rocket +from rocketpy.stochastic import ( + StochasticFreeFormFins, + StochasticParachute, + StochasticRocket, + StochasticTrapezoidalFins, +) def test_str(stochastic_calisto): @@ -23,3 +36,247 @@ class creates a StochasticCalisto object from the randomly generated """ obj = stochastic_calisto.create_object() assert isinstance(obj, Rocket) + + +def test_sampled_parachute_geometry_reaches_the_created_rocket( + stochastic_calisto, calisto_main_chute +): + """The sampled Parachute used to be discarded and a second one built from + six of its ten fields, so radius, height, porosity and the drag coefficient + never left `last_rnd_dict`. Parachute re-derived radius from cd_s and the + default drag coefficient, and height fell back to that radius. + """ + stochastic_calisto.parachutes = [] + stochastic_calisto.add_parachute( + StochasticParachute( + parachute=calisto_main_chute, + cd_s=0.1, + radius=0.3, + height=0.2, + porosity=0.01, + drag_coefficient=0.2, + ) + ) + stochastic_calisto._set_stochastic(42) + + rocket = stochastic_calisto.create_object() + + built = rocket.parachutes[0] + sampled = stochastic_calisto.last_rnd_dict["parachutes"][0] + for field in ("cd_s", "radius", "height", "porosity", "drag_coefficient"): + assert getattr(built, field) == sampled[field], ( + f"the rocket flies a {field} the run never sampled" + ) + + +def test_the_parachute_is_attached_exactly_once( + stochastic_calisto, stochastic_main_parachute, stochastic_drogue_parachute +): + """The control. Without it the test above would pass on a create_object + that attaches nothing at all, which would silently fly every Monte Carlo + rocket without its parachutes.""" + stochastic_calisto.parachutes = [] + for parachute in (stochastic_main_parachute, stochastic_drogue_parachute): + stochastic_calisto.add_parachute(parachute) + stochastic_calisto._set_stochastic(42) + + rocket = stochastic_calisto.create_object() + + assert len(rocket.parachutes) == 2 + assert [p.name for p in rocket.parachutes] == [ + stochastic_main_parachute.obj.name, + stochastic_drogue_parachute.obj.name, + ] + + +def test_a_parachute_is_built_once_per_simulation( + stochastic_calisto, stochastic_main_parachute, monkeypatch +): + """Building it twice drew the initial pressure noise from the global NumPy + RNG twice, which is state no seed here controls. See #1091.""" + built = [] + real = Parachute.__init__ + + def counting(self, *args, **kwargs): + built.append(self) + return real(self, *args, **kwargs) + + stochastic_calisto.parachutes = [] + stochastic_calisto.add_parachute(stochastic_main_parachute) + stochastic_calisto._set_stochastic(42) + monkeypatch.setattr(Parachute, "__init__", counting) + + stochastic_calisto.create_object() + + assert len(built) == 1 + + +def test_configured_geometry_survives_without_being_randomized(calisto_robust): + """The wider case. Dropping the four fields did not need anyone to + randomize them: a parachute built with an explicit radius flew a radius + re-derived from cd_s instead, in every Monte Carlo simulation.""" + chute = calisto_robust.add_parachute( + "geometric", + cd_s=10.0, + trigger="apogee", + sampling_rate=105, + lag=1.5, + radius=2.0, + height=1.5, + porosity=0.05, + drag_coefficient=1.4, + ) + stochastic = StochasticRocket(rocket=calisto_robust, mass=(14.426, 0.5)) + stochastic.parachutes = [] + stochastic.add_parachute(StochasticParachute(chute, cd_s=(10.0, 0.5))) + stochastic._set_stochastic(42) + + flown = stochastic.create_object().parachutes[0] + + assert (flown.radius, flown.height, flown.porosity) == (2.0, 1.5, 0.05) + + +def test_a_deterministic_surface_is_wrapped_in_its_stochastic_model( + calisto_robust, calisto_trapezoidal_fins +): + """`_add_surfaces` used to wrap deterministic surfaces with a `component=` + keyword none of the stochastic classes accept, so passing any plain + aerodynamic surface raised a TypeError instead of being wrapped.""" + stochastic = StochasticRocket(rocket=calisto_robust) + + stochastic.add_trapezoidal_fins(calisto_trapezoidal_fins) + + added = stochastic.aerodynamic_surfaces.get_tuple_by_type(StochasticTrapezoidalFins) + assert len(added) == 1 + assert added[0].component.obj is calisto_trapezoidal_fins + + +def test_add_free_form_fins_reaches_the_created_rocket( + calisto_robust, stochastic_free_form_fins +): + """The fin set added to the stochastic rocket must be the one the created + rocket flies, with the outline randomized as a block.""" + stochastic = StochasticRocket(rocket=calisto_robust) + stochastic.add_free_form_fins(stochastic_free_form_fins, position=(-1.04956, 0.001)) + stochastic._set_stochastic(42) + + rocket = stochastic.create_object() + + fin_sets = rocket.aerodynamic_surfaces.get_tuple_by_type(FreeFormFins) + assert len(fin_sets) == 1 + flown = fin_sets[0].component + nominal = np.asarray(stochastic_free_form_fins.obj.shape_points, dtype=float) + sampled = np.asarray(flown.shape_points, dtype=float) + assert sampled.shape == nominal.shape + assert not np.allclose(sampled, nominal) + + +def test_add_free_form_fins_rejects_other_surfaces(calisto_robust, calisto_tail): + stochastic = StochasticRocket(rocket=calisto_robust) + + with pytest.raises(AssertionError): + stochastic.add_free_form_fins(calisto_tail) + + +def test_add_free_form_fins_wraps_a_deterministic_fin_set(calisto_robust): + """A plain FreeFormFins must be wrapped in its own stochastic model, the + same way the other surfaces are.""" + fins = calisto_robust.add_free_form_fins( + n=4, + shape_points=[(0, 0), (0.08, 0.1), (0.12, 0.1), (0.12, 0)], + position=-1.04956, + ) + stochastic = StochasticRocket(rocket=calisto_robust) + + stochastic.add_free_form_fins(fins) + + added = stochastic.aerodynamic_surfaces.get_tuple_by_type(StochasticFreeFormFins) + assert len(added) == 1 + assert added[0].component.obj is fins + + +@pytest.mark.parametrize( + "add_them, names", + [ + ( + "add_cp_eccentricity", + ("cp_eccentricity_x", "cp_eccentricity_y"), + ), + ( + "add_thrust_eccentricity", + ("thrust_eccentricity_x", "thrust_eccentricity_y"), + ), + ], +) +def test_an_eccentricity_added_after_init_is_still_drawn(calisto, add_them, names): + """``dict_generator`` walks the declared inputs, and these arrive later. + + The list is built in ``__init__``, so a distribution installed by an + ``add_*`` method afterwards was never re-validated on a reseed and stayed + bound to the unseeded generator: a fixed seed did not reproduce it. + """ + stochastic = StochasticRocket(rocket=calisto, radius=0.0127 / 2) + getattr(stochastic, add_them)(x=(0.0, 0.001), y=(0.0, 0.001)) + stochastic._set_stochastic(42) + + generated = next(stochastic.dict_generator()) + + assert set(names) <= set(generated), f"{add_them} was set but never sampled" + assert all(isinstance(generated[name], Real) for name in names) + + +def test_two_seeds_move_an_eccentricity_that_was_added_late(calisto): + """Being present is not enough; it has to follow the seed.""" + stochastic = StochasticRocket(rocket=calisto, radius=0.0127 / 2) + stochastic.add_cp_eccentricity(x=(0.0, 0.01), y=(0.0, 0.01)) + + def drawn(seed): + stochastic._set_stochastic(seed) + return next(stochastic.dict_generator())["cp_eccentricity_x"] + + assert drawn(7) == drawn(7) + assert drawn(7) != drawn(8) + + +def test_a_declared_eccentricity_is_not_drawn_a_second_time(calisto): + """``create_object`` applies the draw ``dict_generator`` already made. + + A second draw spends another value out of the same stream, which moves + every component position ``create_object`` places after it. + """ + stochastic = StochasticRocket(rocket=calisto, radius=0.0127 / 2) + stochastic.add_cp_eccentricity(x=(0.0, 0.01), y=(0.0, 0.01)) + stochastic.add_thrust_eccentricity(x=(0.0, 0.01), y=(0.0, 0.01)) + + stochastic._set_stochastic(42) + declared = next(stochastic.dict_generator()) + expected = {name: declared[name] for name in declared if "eccentricity" in name} + assert len(expected) == 4 + + stochastic._set_stochastic(42) + rocket = stochastic.create_object() + + applied = { + "cp_eccentricity_x": rocket.cp_eccentricity_x, + "cp_eccentricity_y": rocket.cp_eccentricity_y, + "thrust_eccentricity_x": rocket.thrust_eccentricity_x, + "thrust_eccentricity_y": rocket.thrust_eccentricity_y, + } + assert applied == expected + assert {name: stochastic.last_rnd_dict[name] for name in expected} == expected + + +def test_an_eccentricity_half_that_was_left_out_is_still_drawn(calisto): + """Only a half that was given is a declared input, so the other is not. + + ``create_object`` has to keep drawing it, and keep reporting it, or the + inputs it writes stop describing the rocket it built. + """ + stochastic = StochasticRocket(rocket=calisto, radius=0.0127 / 2) + stochastic.add_cp_eccentricity(x=(0.0, 0.01)) + + stochastic._set_stochastic(42) + rocket = stochastic.create_object() + + assert "cp_eccentricity_y" in stochastic.last_rnd_dict + assert stochastic.last_rnd_dict["cp_eccentricity_y"] == rocket.cp_eccentricity_y diff --git a/tests/unit/stochastic/test_stochastic_rocket_seeding.py b/tests/unit/stochastic/test_stochastic_rocket_seeding.py new file mode 100644 index 000000000..151bbc256 --- /dev/null +++ b/tests/unit/stochastic/test_stochastic_rocket_seeding.py @@ -0,0 +1,313 @@ +"""Every nested component of a StochasticRocket is reseeded from its own child +of the run's seed, so components that sample the same distribution do not draw +the same values, and one seed still reproduces the whole rocket. +""" + +import ast +import inspect + +import numpy as np + +from rocketpy.rocket.components import Components +from rocketpy.tools import _seed_sequence_from +from rocketpy.stochastic import ( + StochasticAirBrakes, + StochasticParachute, + StochasticRocket, +) +from rocketpy.stochastic.stochastic_model import StochasticModel + +# Captured before any patching, so wrapping it twice in one test does not stack. +_REAL_SET_STOCHASTIC = StochasticModel._set_stochastic + + +def _seeds_handed_out(monkeypatch, rocket, seed): + """The seeds every nested component received during one reseed.""" + recorded = [] + + def recording(self, seed=None): + recorded.append(seed) + return _REAL_SET_STOCHASTIC(self, seed) + + monkeypatch.setattr(StochasticModel, "_set_stochastic", recording) + rocket._set_stochastic(seed) + return recorded + + +def _drawn(component): + return next(component.dict_generator()) + + +def _members_of(collection): + """Components yields (component, position, ref_factor); a list does not.""" + if isinstance(collection, Components): + return [component for component, *_ in collection] + return list(collection) + + +def test_two_components_with_one_spec_do_not_share_one_stream( + stochastic_calisto, calisto_main_chute +): + """The whole rocket shared one seed, so two parachutes built from the same + spec drew the same ``cd_s`` and the same ``lag``, every time. A study of a + main and a drogue was really a study of one chute counted twice. + """ + stochastic_calisto.parachutes = [] + for _ in range(2): + stochastic_calisto.add_parachute( + StochasticParachute(parachute=calisto_main_chute, cd_s=0.1, lag=0.2) + ) + + stochastic_calisto._set_stochastic(99) + first, second = (_drawn(chute) for chute in stochastic_calisto.parachutes) + + assert first["cd_s"] != second["cd_s"] + assert first["lag"] != second["lag"] + + +def test_one_seed_reproduces_every_component(stochastic_calisto, calisto_main_chute): + """Independent is not enough on its own; it still has to follow the seed.""" + stochastic_calisto.parachutes = [] + stochastic_calisto.add_parachute( + StochasticParachute(parachute=calisto_main_chute, cd_s=0.1, lag=0.2) + ) + + def drawn_with(seed): + stochastic_calisto._set_stochastic(seed) + return [_drawn(chute) for chute in stochastic_calisto.parachutes] + [ + _drawn(stochastic_calisto)["mass"] + ] + + assert drawn_with(2718) == drawn_with(2718) + assert drawn_with(2718) != drawn_with(2719) + + +def test_component_seeds_do_not_collide(monkeypatch, stochastic_calisto): + """The same statement across every component type, not only the parachutes: + the body, each aerodynamic surface, the motor and the rail buttons. + """ + seeds = _seeds_handed_out(monkeypatch, stochastic_calisto, 42) + + assert len(seeds) > 3, "expected the rocket body and several components" + assert len(seeds) == len(set(seeds)), ( + "components share a seed, so they draw perfectly correlated samples" + ) + + +def test_the_reseed_covers_every_collection_create_object_uses(stochastic_calisto): + """Whatever ``create_object`` iterates has to be reseeded too. + + Read off the source rather than off a fixture, because the collection no + fixture populates is exactly the one that gets missed: air brakes were built + and sampled and never reseeded, and every seeding test passed anyway. + """ + rocket = stochastic_calisto + tree = ast.parse(inspect.getsource(type(rocket).create_object).lstrip()) + iterated = { + node.iter.attr + for node in ast.walk(tree) + # Comprehensions too. This scan exists to catch a collection added + # later, and one written as a comprehension would slip past a For walk. + if isinstance(node, (ast.For, ast.comprehension)) + and isinstance(node.iter, ast.Attribute) + and isinstance(node.iter.value, ast.Name) + and node.iter.value.id == "self" + and not node.iter.attr.startswith("_") + } + declared = set(type(rocket)._stochastic_collections()) + + assert iterated, "found no collections in create_object, so the scan is broken" + # Both directions. One left in the reseed after create_object stopped using + # it still spawns a child and moves every stream that follows. + assert iterated == declared, { + "sampled but never reseeded": sorted(iterated - declared), + "reseeded but never sampled": sorted(declared - iterated), + } + + +def test_an_air_brake_answers_to_the_seed( + stochastic_calisto, calisto_air_brakes_clamp_on +): + """Air brakes were in ``create_object`` and not in the reseed, so a fixed + seed did not reproduce them: 0.683, then 0.586, then 0.488 for one seed + asked three times. + + Built here rather than taken from the fixture, since wrapping an + ``AirBrakes`` with no arguments gives every parameter a zero standard + deviation, and that draws the same value under any seed whether or not + anything reseeds it. + """ + stochastic_calisto.add_air_brakes( + StochasticAirBrakes( + air_brakes=calisto_air_brakes_clamp_on.air_brakes[0], + deployment_level=(0.5, 0.1), + ), + calisto_air_brakes_clamp_on._controllers[0], + ) + air_brake = stochastic_calisto.air_brakes[0] + + def drawn(seed): + stochastic_calisto._set_stochastic(seed) + return _drawn(air_brake)["deployment_level"] + + first = drawn(1234) + + assert drawn(1234) == first, "the same seed drew a different air brake" + assert drawn(1235) != first, "a different seed drew the same air brake" + + +def test_adding_a_surface_leaves_the_other_collections_alone( + stochastic_calisto, calisto_main_chute, stochastic_nose_cone +): + """Each collection has a root of its own, so an unrelated component in one + of them does not move the streams in the others.""" + stochastic_calisto.parachutes = [] + stochastic_calisto.add_parachute( + StochasticParachute(parachute=calisto_main_chute, cd_s=0.1, lag=0.2) + ) + + def parachute_draw(): + stochastic_calisto._set_stochastic(5) + return _drawn(stochastic_calisto.parachutes[0]) + + before = parachute_draw() + # The deterministic nose, so add_nose builds a wrapper of its own. Adding + # the fixture again would store one wrapper twice, which is #1172. + stochastic_calisto.add_nose(stochastic_nose_cone.obj, position=1.1) + + assert parachute_draw() == before + + +def test_every_entry_is_reseeded_exactly_once( + monkeypatch, stochastic_calisto, calisto_main_chute, calisto_air_brakes_clamp_on +): + """Counted rather than read off the source. + + The scan above reads ``create_object`` for ``for x in self.collection``, so + a helper, a local alias or a ``getattr`` would hide a collection from it. + This counts what actually happens. + """ + stochastic_calisto.add_parachute( + StochasticParachute(parachute=calisto_main_chute, cd_s=0.1, lag=0.2) + ) + stochastic_calisto.add_air_brakes( + StochasticAirBrakes( + air_brakes=calisto_air_brakes_clamp_on.air_brakes[0], + deployment_level=(0.5, 0.1), + ), + calisto_air_brakes_clamp_on._controllers[0], + ) + + counted = {} + + def recording(self, seed=None): + counted[id(self)] = counted.get(id(self), 0) + 1 + return _REAL_SET_STOCHASTIC(self, seed) + + monkeypatch.setattr(StochasticModel, "_set_stochastic", recording) + stochastic_calisto._set_stochastic(3) + + entries = [ + component + for name in type(stochastic_calisto)._stochastic_collections() + for component in _members_of(getattr(stochastic_calisto, name)) + ] + + assert entries, "no components to count" + assert all(counted.get(id(entry)) == 1 for entry in entries), { + type(entry).__name__: counted.get(id(entry)) for entry in entries + } + + +def test_two_air_brakes_with_one_spec_stay_independent( + stochastic_calisto, calisto_air_brakes_clamp_on +): + """The air brakes are a plain list, so they take a different route through + the reseed than the positioned collections do. + + About the streams only. Both are added with one controller because the + rocket keeps a single one, which is a separate problem recorded in #1172. + """ + for _ in range(2): + stochastic_calisto.add_air_brakes( + StochasticAirBrakes( + air_brakes=calisto_air_brakes_clamp_on.air_brakes[0], + deployment_level=(0.5, 0.1), + ), + calisto_air_brakes_clamp_on._controllers[0], + ) + + def drawn(seed): + stochastic_calisto._set_stochastic(seed) + return [ + _drawn(brake)["deployment_level"] for brake in stochastic_calisto.air_brakes + ] + + first = drawn(808) + + assert first[0] != first[1], "two air brakes drew the same value" + assert drawn(808) == first + assert drawn(809) != first + + +def test_the_rocket_body_keeps_the_seed_as_given(monkeypatch, stochastic_calisto): + """Fixing the nested components did not need the body's stream to move. + + Reproducibility and seed uniqueness both hold with the body on a spawned + child, so neither of them would notice it going back there and taking every + fixed-seed mass and radius baseline with it. + """ + seeds = _seeds_handed_out(monkeypatch, stochastic_calisto, 42) + + assert seeds[0] == 42 + + +def test_the_tree_is_built_by_the_reset_not_by_the_add(calisto, calisto_main_chute): + """A rocket resets itself while being constructed, with nothing attached. + + So a component added afterwards keeps the generator it was built with until + the next reset, which is the guarantee the documentation states and the one + a Monte Carlo relies on. + """ + rocket = StochasticRocket(rocket=calisto, radius=0.0127 / 2) + chute = StochasticParachute(parachute=calisto_main_chute, cd_s=0.1, lag=0.2) + rocket.add_parachute(chute) + + assert getattr(chute, "_seed", None) is None + + rocket._set_stochastic(99) + + assert chute._seed is not None + + +def test_a_worker_seed_sequence_is_copied_rather_than_spawned_from(): + # A parallel run hands each worker a SeedSequence. Spawning from it would + # advance a counter the caller still holds, so the next use of the same + # object would build a different tree. + worker = np.random.SeedSequence(7).spawn(2)[0] + + _seed_sequence_from(worker).spawn(3) + + assert worker.n_children_spawned == 0 + + +def test_a_worker_seed_sequence_keeps_its_place_in_the_tree(): + worker = np.random.SeedSequence(7).spawn(2)[0] + + children = _seed_sequence_from(worker).spawn(2) + + assert [child.spawn_key for child in children] == [(0, 0), (0, 1)] + + +def test_two_workers_do_not_get_the_same_collection_roots(): + first, second = np.random.SeedSequence(7).spawn(2) + + one = _seed_sequence_from(first).spawn(1)[0].generate_state(4) + other = _seed_sequence_from(second).spawn(1)[0].generate_state(4) + + assert not np.array_equal(one, other) + + +def test_an_integer_seed_still_roots_the_collections(): + assert _seed_sequence_from(42).spawn(1)[0].spawn_key == (0,) + assert _seed_sequence_from(None).spawn(1)[0].spawn_key == (0,) diff --git a/tests/unit/stochastic/test_validation_under_optimisation.py b/tests/unit/stochastic/test_validation_under_optimisation.py new file mode 100644 index 000000000..2bf4ca535 --- /dev/null +++ b/tests/unit/stochastic/test_validation_under_optimisation.py @@ -0,0 +1,71 @@ +"""Validation in `stochastic/` has to survive `python -O`. + +`assert` is removed by the optimiser, so every check written that way stops +running under `-O` and malformed user input reaches the model instead. The +module carried a TODO asking for this; these tests are what keeps it done. +""" + +import subprocess +import sys +from pathlib import Path + +import pytest + +REPO = Path(__file__).resolve().parents[3] + +MODULES = [ + "stochastic_model", + "stochastic_environment", + "stochastic_flight", + "stochastic_aero_surfaces", + "stochastic_parachute", +] + + +@pytest.mark.parametrize("module", MODULES) +def test_no_production_asserts_remain(module): + """The mechanism. A single `assert` reintroduced here is a check that stops + existing under `-O`, which is exactly what this file exists to prevent.""" + source = (REPO / "rocketpy" / "stochastic" / f"{module}.py").read_text() + offenders = [ + f"{n}: {line.strip()}" + for n, line in enumerate(source.splitlines(), 1) + if line.strip().startswith("assert ") + ] + + assert not offenders, f"{module}.py still asserts: {offenders}" + + +REFUSALS = [ + ("a tuple element of the wrong type", "mass=('not a number', 0.5)"), + ("a tuple of the wrong length", "mass=(1.0, 0.5, 'normal', 'extra')"), +] + + +@pytest.mark.parametrize("label, kwargs", REFUSALS, ids=[r[0] for r in REFUSALS]) +def test_malformed_input_is_refused_under_optimisation(label, kwargs): + """The behaviour, in a child interpreter under -O. + + Run in-process this passes either way, because the assert is still compiled + in. The optimiser only strips it at compile time, so the check has to be a + separate interpreter to mean anything. + """ + program = ( + "from types import SimpleNamespace;" + "from rocketpy.stochastic.stochastic_model import StochasticModel;" + "obj = SimpleNamespace(mass=1.0)\n" + "try:\n" + f" StochasticModel(obj, {kwargs})\n" + " print('accepted')\n" + "except AssertionError:\n" + " print('refused')\n" + ) + done = subprocess.run( + [sys.executable, "-O", "-c", program], + capture_output=True, + text=True, + check=True, + cwd=REPO, + ) + + assert "refused" in done.stdout, f"{label}: {done.stdout!r} {done.stderr!r}" diff --git a/tests/unit/test_flight_trajectory_map.py b/tests/unit/test_flight_trajectory_map.py new file mode 100644 index 000000000..0316bd958 --- /dev/null +++ b/tests/unit/test_flight_trajectory_map.py @@ -0,0 +1,192 @@ +"""Tests for optional Folium flight trajectory maps.""" + +from unittest.mock import MagicMock, patch + +import pytest + +from rocketpy.plots.flight_plots import _FlightPlots + + +def mocked_folium(): + """Build a MagicMock standing in for the folium module.""" + folium = MagicMock() + folium.Map.return_value = MagicMock() + folium.PolyLine.return_value = MagicMock() + folium.Marker.return_value = MagicMock() + folium.TileLayer.return_value = MagicMock() + folium.Circle.return_value = MagicMock() + folium.FeatureGroup.return_value = MagicMock() + folium.LayerControl.return_value = MagicMock() + return folium + + +def patch_folium(folium): + """Patch the optional import so that ``folium`` is the injected mock.""" + return patch( + "rocketpy.plots.flight_plots.import_optional_dependency", + return_value=folium, + ) + + +def test_trajectory_on_map_requires_folium(flight_calisto_robust): + """Missing folium should raise a clear ImportError via optional import.""" + with patch( + "rocketpy.plots.flight_plots.import_optional_dependency", + side_effect=ImportError( + "folium is an optional dependency and is not installed.\n" + "\t\tUse 'pip install folium' to install it or " + "'pip install rocketpy[all]' to install all optional dependencies." + ), + ): + with pytest.raises(ImportError, match="folium"): + flight_calisto_robust.plots.trajectory_on_map() + + +def test_trajectory_on_map_builds_map_with_mocked_folium(flight_calisto_robust): + """Map construction should add a path, tiles and the flight markers.""" + folium = mocked_folium() + mock_map = folium.Map.return_value + + with patch_folium(folium): + result = flight_calisto_robust.plots.trajectory_on_map() + + assert result is mock_map + folium.Map.assert_called_once() + folium.PolyLine.assert_called_once() + folium.PolyLine.return_value.add_to.assert_called_once_with(mock_map) + # Launch, apogee and landing markers. + assert folium.Marker.call_count == 3 + assert folium.Marker.return_value.add_to.call_count == 3 + # OpenStreetMap and Esri satellite backgrounds, plus a control to swap them. + assert folium.TileLayer.call_count == 2 + folium.LayerControl.assert_called_once() + # No safety circles and no title unless explicitly requested. + folium.Circle.assert_not_called() + mock_map.get_root.assert_not_called() + mock_map.save.assert_not_called() + + +def test_trajectory_on_map_marks_apogee_between_launch_and_landing( + flight_calisto_robust, +): + """The apogee marker should sit between the launch and landing markers.""" + folium = mocked_folium() + + with patch_folium(folium): + flight_calisto_robust.plots.trajectory_on_map() + + labels = [call.kwargs["tooltip"] for call in folium.Marker.call_args_list] + assert labels[0] == "Launch" + assert labels[1].startswith("Apogee") + assert labels[-1] == "Landing" + + +def test_trajectory_on_map_skips_apogee_when_not_detected(flight_calisto_robust): + """A flight without a detected apogee should only get two markers.""" + folium = mocked_folium() + + with patch_folium(folium): + with patch.object(flight_calisto_robust, "apogee_time", 0): + flight_calisto_robust.plots.trajectory_on_map() + + labels = [call.kwargs["tooltip"] for call in folium.Marker.call_args_list] + assert labels == ["Launch", "Landing"] + + +def test_trajectory_on_map_draws_safety_radii(flight_calisto_robust): + """safety_radii= should draw one circle per radius on the launch site.""" + folium = mocked_folium() + + with patch_folium(folium): + flight_calisto_robust.plots.trajectory_on_map(safety_radii=[2500, 5000]) + + assert folium.Circle.call_count == 2 + radii = [call.kwargs["radius"] for call in folium.Circle.call_args_list] + assert radii == [2500.0, 5000.0] + folium.FeatureGroup.assert_called_once_with(name="Safety radii") + + +def test_trajectory_on_map_frames_safety_radii(flight_calisto_robust): + """The initial viewport should widen to contain the requested circles.""" + folium = mocked_folium() + mock_map = folium.Map.return_value + + with patch_folium(folium): + flight_calisto_robust.plots.trajectory_on_map() + (track_bounds,) = mock_map.fit_bounds.call_args.args + + folium = mocked_folium() + mock_map = folium.Map.return_value + with patch_folium(folium): + flight_calisto_robust.plots.trajectory_on_map(safety_radii=[5000]) + (widened,) = mock_map.fit_bounds.call_args.args + + (track_sw, track_ne), (wide_sw, wide_ne) = track_bounds, widened + assert wide_sw[0] < track_sw[0] and wide_sw[1] < track_sw[1] + assert wide_ne[0] > track_ne[0] and wide_ne[1] > track_ne[1] + # 5 km is roughly 0.045 degrees of latitude. + assert 0.08 < (wide_ne[0] - wide_sw[0]) < 0.12 + + +def test_trajectory_on_map_escapes_title(flight_calisto_robust): + """A title should be injected as HTML with its markup escaped.""" + folium = mocked_folium() + + with patch_folium(folium): + flight_calisto_robust.plots.trajectory_on_map(title="") + + folium.Element.assert_called_once() + title_html = folium.Element.call_args.args[0] + assert "