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Tomographic 2pcf calculation with treecorr #297

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122bc97
added tomographic 2pcf implementation
LisaGoh Jul 22, 2026
a63afa8
ruff pass
LisaGoh Jul 22, 2026
6502508
edits after review
LisaGoh Jul 23, 2026
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70 changes: 57 additions & 13 deletions scripts/xip_xim.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,8 @@ def params_default():

params = {
"input_path": "shape_catalog_ngmix.fits",
"tomo_bin1": None,
"tomo_bin2": None,
"key_ra": "RA",
"key_dec": "DEC",
"key_e1": "e1",
Expand All @@ -30,20 +32,27 @@ def params_default():
"theta_max": 200,
"n_theta": 20,
"output_path": "./xip_xim.txt",
"key_tomo_bin_col": "tomo_bin_id",
}

short_options = {
"input_path": "-i",
"output_path": "-o",
"tomo_bin1": "-b1",
"tomo_bin2": "-b2",
}

types = {
"sign_e1": "int",
"sign_e2": "int",
"tomo_bin1": "int",
"tomo_bin2": "int",
}

help_strings = {
"input_path": "shear catalogue input path, default={}",
"tomo_bin1": "First tomographic bin, default={} (non-tomographic)",
"tomo_bin2": "Second tomographic bin, default={} (non-tomographic)",
"key_ra": "column name for right ascension, default={}",
"key_dec": "column name for declination, default={}",
"key_e1": "column name for ellipticity component 1, default={}",
Expand All @@ -54,6 +63,7 @@ def params_default():
"theta_max": "maximum angular scale [arcmin], default={}",
"n_theta": "number of angular scales, default={}",
"output_path": "output path, default={}",
"key_tomo_bin_col": "column name for tomography bin, default={}",
}

return params, short_options, types, help_strings
Expand Down Expand Up @@ -132,18 +142,52 @@ def main(argv=None):
"Signs for ellipticity components ="
+ f" ({params['sign_e1']:+d}, {params['sign_e2']:+d})"
)
g1 = data[params["key_e1"]] * params["sign_e1"]
g2 = data[params["key_e2"]] * params["sign_e2"]
cat = treecorr.Catalog(
ra=data[params["key_ra"]],
dec=data[params["key_dec"]],
g1=g1,
g2=g2,
w=data["w"],
ra_units=coord_units,
dec_units=coord_units,
)

tomographic = params["tomo_bin1"] is not None and params["tomo_bin2"] is not None
cat2 = None
if tomographic:
if params["verbose"]:
print(
f"Calculating 2PCF for tomographic bins {params['tomo_bin1']} and {params['tomo_bin2']}"
)
tomo_bin1_idx = data[params["key_tomo_bin_col"]] == params["tomo_bin1"]
tomo_bin2_idx = data[params["key_tomo_bin_col"]] == params["tomo_bin2"]

g1 = data[params["key_e1"]] * params["sign_e1"]
g2 = data[params["key_e2"]] * params["sign_e2"]

cat1 = treecorr.Catalog(
ra=data[params["key_ra"]][tomo_bin1_idx],
dec=data[params["key_dec"]][tomo_bin1_idx],
g1=g1[tomo_bin1_idx],
g2=g2[tomo_bin1_idx],
w=data["w"][tomo_bin1_idx],
ra_units=coord_units,
dec_units=coord_units,
)
if params["tomo_bin1"] != params["tomo_bin2"]:
cat2 = treecorr.Catalog(
ra=data[params["key_ra"]][tomo_bin2_idx],
dec=data[params["key_dec"]][tomo_bin2_idx],
g1=g1[tomo_bin2_idx],
g2=g2[tomo_bin2_idx],
w=data["w"][tomo_bin2_idx],
ra_units=coord_units,
dec_units=coord_units,
)
else:
if params["verbose"]:
print("Calculating non-tomographic 2PCF")
g1 = data[params["key_e1"]] * params["sign_e1"]
g2 = data[params["key_e2"]] * params["sign_e2"]
cat1 = treecorr.Catalog(
ra=data[params["key_ra"]],
dec=data[params["key_dec"]],
g1=g1,
g2=g2,
w=data["w"],
ra_units=coord_units,
dec_units=coord_units,
)
# Set treecorr config info for correlation
sep_units = "arcmin"
TreeCorrConfig = {
Expand All @@ -159,7 +203,7 @@ def main(argv=None):
# Compute correlation
if params["verbose"]:
print("Correlating...")
gg.process(cat, cat)
gg.process(cat1, cat2=cat2)

# Write to file
if params["verbose"]:
Expand Down
92 changes: 58 additions & 34 deletions src/sp_validation/cosmo_val/cosebis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,6 +29,7 @@ def calculate_cosebis(
cov_path=None,
scale_cuts=None,
evaluate_all_scale_cuts=False,
compute_tomography=False,
min_sep=None,
max_sep=None,
nbins=None,
Expand Down Expand Up @@ -68,6 +69,8 @@ def calculate_cosebis(
If True, evaluates COSEBIs for all possible scale cut combinations
using the reporting binning parameters. Ignored when scale_cuts is
provided. Defaults to False.
compute_tomography : bool, optional
If True, computes COSEBIs for all tomographic bin combinations. Defaults to False.s
min_sep : float, optional
Minimum separation for reporting binning (only used when
evaluate_all_scale_cuts=True). Defaults to self.treecorr_config["min_sep"].
Expand Down Expand Up @@ -99,44 +102,63 @@ def calculate_cosebis(
f"Computing fine-binned 2PCF with {nbins_int} bins from {min_sep_int} to "
f"{max_sep_int} arcmin"
)
gg = self.calculate_2pcf(version, npatch=npatch, **treecorr_config)

if scale_cuts is not None:
# Explicit scale cuts provided
print(f"Evaluating {len(scale_cuts)} explicit scale cuts")
results = calculate_cosebis(
gg=gg, nmodes=nmodes, scale_cuts=scale_cuts, cov_path=cov_path
)
elif evaluate_all_scale_cuts:
# Use reporting binning parameters or inherit from class config
binning = self._binning(min_sep, max_sep, nbins)
min_sep, max_sep, nbins = (
binning["min_sep"],
binning["max_sep"],
binning["nbins"],
)
ggs = self.calculate_2pcf(
version,
npatch=npatch,
compute_tomography=compute_tomography,
**treecorr_config,
)
results = {
bin_key: None for bin_key in ggs.keys()
} # Initialize results dictionary

# Generate scale cuts using np.geomspace (no TreeCorr needed)
bin_edges = np.geomspace(min_sep, max_sep, nbins + 1)
generated_cuts = [
(bin_edges[start], bin_edges[stop])
for start in range(nbins)
for stop in range(start + 1, nbins + 1)
]
for bin_key in ggs.keys():
# LG TO-DO: account for tomographic scale-cuts
if scale_cuts is not None:
# Explicit scale cuts provided
print(f"Evaluating {len(scale_cuts)} explicit scale cuts")
results[bin_key] = calculate_cosebis(
gg=ggs[bin_key],
nmodes=nmodes,
scale_cuts=scale_cuts,
cov_path=cov_path,
)
elif evaluate_all_scale_cuts:
# Use reporting binning parameters or inherit from class config
binning = self._binning(min_sep, max_sep, nbins)
min_sep, max_sep, nbins = (
binning["min_sep"],
binning["max_sep"],
binning["nbins"],
)

print(f"Evaluating {len(generated_cuts)} scale cut combinations")
# Generate scale cuts using np.geomspace (no TreeCorr needed)
bin_edges = np.geomspace(min_sep, max_sep, nbins + 1)
generated_cuts = [
(bin_edges[start], bin_edges[stop])
for start in range(nbins)
for stop in range(start + 1, nbins + 1)
]

# Call b_modes function with scale cuts list
results = calculate_cosebis(
gg=gg, nmodes=nmodes, scale_cuts=generated_cuts, cov_path=cov_path
)
else:
# Single scale cut behavior: use full range
results = calculate_cosebis(
gg=gg, nmodes=nmodes, scale_cuts=None, cov_path=cov_path
)
# Extract single results dict from scale_cuts dictionary
results = next(iter(results.values()))
print(f"Evaluating {len(generated_cuts)} scale cut combinations")

# Call b_modes function with scale cuts list
results = calculate_cosebis(

@cailmdaley cailmdaley Jul 24, 2026

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This rebinding clobbers the accumulator dict initialized above (results = {bin_key: None …}), and then line 161's results[bin_key] = results self-references — even the plain non-tomo single-scale-cut case returns a mangled dict. A differently-named inner variable (res = calculate_cosebis(...); results[bin_key] = res) should fix it — same in the else branch below.

gg=ggs[bin_key],
nmodes=nmodes,
scale_cuts=generated_cuts,
cov_path=cov_path,
)
else:
# Single scale cut behavior: use full range
results = calculate_cosebis(
gg=ggs[bin_key], nmodes=nmodes, scale_cuts=None, cov_path=cov_path
)
# Extract single results dict from scale_cuts dictionary
results = next(iter(results.values()))

results[bin_key] = results # Store results for this bin_key

return results

Expand All @@ -152,6 +174,7 @@ def plot_cosebis(
cov_path=None,
scale_cuts=None, # Explicit scale cuts
evaluate_all_scale_cuts=False, # Grid-based scale cuts
compute_tomography=False,
min_sep=None,
max_sep=None,
nbins=None, # Reporting binning
Expand Down Expand Up @@ -228,6 +251,7 @@ def plot_cosebis(
cov_path=cov_path,
scale_cuts=scale_cuts,
evaluate_all_scale_cuts=evaluate_all_scale_cuts,
compute_tomography=False, # LG: Hardcoded to False for now

@cailmdaley cailmdaley Jul 24, 2026

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Fine to leave for the future PR — just noting plot_cosebis also still assumes the old un-binned return shape of calculate_cosebis (results are now one level deeper, even non-tomographically), so COSEBIs plotting is broken until that pass; worth a TODO so it's not a surprise.

min_sep=min_sep,
max_sep=max_sep,
nbins=nbins,
Expand Down
41 changes: 30 additions & 11 deletions src/sp_validation/cosmo_val/pure_eb.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,6 +29,7 @@ def calculate_pure_eb(
max_sep_int=300,
nbins_int=100,
npatch=256,
compute_tomography=False,
var_method="jackknife",
cov_path_int=None,
cosmo_cov=None,
Expand Down Expand Up @@ -61,6 +62,8 @@ def calculate_pure_eb(
npatch : int, optional
Number of patches for the jackknife or bootstrap resampling. Defaults to
the value in self.npatch if not provided.
compute_tomography : bool, optional
Whether to compute tomographic (cross-bin) pure E/B modes. Defaults to False.
var_method : str, optional
Variance estimation method. Defaults to "jackknife".
cov_path_int : str, optional
Expand Down Expand Up @@ -109,26 +112,42 @@ def calculate_pure_eb(
treecorr_config_int = self._binning(min_sep_int, max_sep_int, nbins_int)

# Calculate correlation functions
gg = self.calculate_2pcf(version, npatch=npatch, **treecorr_config)
gg_int = self.calculate_2pcf(version, npatch=npatch, **treecorr_config_int)
ggs = self.calculate_2pcf(
version,
npatch=npatch,
compute_tomography=compute_tomography,
**treecorr_config,
)
ggs_int = self.calculate_2pcf(
version,
npatch=npatch,
compute_tomography=compute_tomography,
**treecorr_config_int,
)

# Get redshift distribution if using analytic covariance
# LG TO-DO: check tomographic redshift distribution handling
z_dist = (
np.column_stack(self.get_redshift(version))
if cov_path_int is not None
else None
)

# Delegate to b_modes module
results = calculate_pure_eb_correlation(
gg=gg,
gg_int=gg_int,
var_method=var_method,
cov_path_int=cov_path_int,
cosmo_cov=cosmo_cov,
n_samples=n_samples,
z_dist=z_dist,
)
results = {
bin_key: None for bin_key in ggs.keys()
} # Initialize results dictionary

for bin_key in ggs.keys():
results[bin_key] = calculate_pure_eb_correlation(
gg=ggs[bin_key],
gg_int=ggs_int[bin_key],
var_method=var_method,
cov_path_int=cov_path_int,
cosmo_cov=cosmo_cov,
n_samples=n_samples,
z_dist=z_dist,
)

return results

Expand Down
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