From 55a28e21466d609dc4d1aa0f91b0dfa8ff3850e5 Mon Sep 17 00:00:00 2001 From: Sandro Campos Date: Wed, 3 Jul 2024 15:18:49 -0400 Subject: [PATCH 01/12] Implement projected correlation methods --- src/corrgi/correlation/angular_correlation.py | 17 +++++- src/corrgi/correlation/correlation.py | 13 +++-- .../correlation/projected_correlation.py | 54 +++++++++++++++++-- .../correlation/redshift_correlation.py | 4 ++ src/corrgi/corrgi.py | 6 +-- src/corrgi/dask.py | 2 +- src/corrgi/estimators.py | 10 ++-- tests/corrgi/test_acf.py | 6 +-- tests/corrgi/test_counting.py | 6 +-- 9 files changed, 90 insertions(+), 28 deletions(-) diff --git a/src/corrgi/correlation/angular_correlation.py b/src/corrgi/correlation/angular_correlation.py index 70024fb..4afcb07 100644 --- a/src/corrgi/correlation/angular_correlation.py +++ b/src/corrgi/correlation/angular_correlation.py @@ -2,7 +2,9 @@ import gundam.cflibfor as cff import pandas as pd +from gundam import gundam from hipscat.catalog.catalog_info import CatalogInfo +from munch import Munch from corrgi.correlation.correlation import Correlation @@ -10,6 +12,17 @@ class AngularCorrelation(Correlation): """The angular correlation utilities.""" + def __init__(self, params: Munch, use_weights: bool = False): + super().__init__(params, use_weights) + self.sept = self.make_bins() + + def make_bins(self) -> list: + """Generate the angular separation bins""" + bins, _ = gundam.makebins( + self.params.nsept, self.params.septmin, self.params.dsept, self.params.logsept + ) + return bins + def _get_auto_method(self): return cff.mod.th_A_wg if self.use_weights else cff.mod.th_A_naiveway @@ -21,7 +34,7 @@ def _construct_auto_args(self, df: pd.DataFrame, catalog_info: CatalogInfo) -> l len(df), # number of particles *self.get_coords(df, catalog_info), # cartesian coordinates self.params.nsept, # number of angular separation bins - self.bins, # bins in angular separation [deg] + self.sept, # bins in angular separation [deg] ] def _construct_cross_args( @@ -37,5 +50,5 @@ def _construct_cross_args( len(right_df), # number of particles of the right partition *self.get_coords(right_df, right_catalog_info), # X,Y,Z coordinates of particles self.params.nsept, # number of angular separation bins - self.bins, # bins in angular separation [deg] + self.sept, # bins in angular separation [deg] ] diff --git a/src/corrgi/correlation/correlation.py b/src/corrgi/correlation/correlation.py index aba41fd..e92cc02 100644 --- a/src/corrgi/correlation/correlation.py +++ b/src/corrgi/correlation/correlation.py @@ -14,13 +14,7 @@ class Correlation(ABC): """Correlation base class.""" - def __init__( - self, - bins: np.ndarray, - params: Munch, - use_weights: bool = False, - ): - self.bins = bins + def __init__(self, params: Munch, use_weights: bool = False): self.params = params self.use_weights = use_weights @@ -40,6 +34,11 @@ def count_cross_pairs( args = self._construct_cross_args(left_df, right_df, left_catalog_info, right_catalog_info) return self._get_cross_method()(*args) + @abstractmethod + def make_bins(self): + """Generate bins for the correlation""" + raise NotImplementedError() + @abstractmethod def _get_auto_method(self) -> Callable: """Reference to Fortran routine to be called on auto pairing""" diff --git a/src/corrgi/correlation/projected_correlation.py b/src/corrgi/correlation/projected_correlation.py index 981863c..341d4ad 100644 --- a/src/corrgi/correlation/projected_correlation.py +++ b/src/corrgi/correlation/projected_correlation.py @@ -1,7 +1,11 @@ from typing import Callable +import gundam.cflibfor as cff import pandas as pd +from astropy.cosmology import LambdaCDM +from gundam import gundam from hipscat.catalog.catalog_info import CatalogInfo +from munch import Munch from corrgi.correlation.correlation import Correlation @@ -9,14 +13,42 @@ class ProjectedCorrelation(Correlation): """The projected correlation utilities.""" + def __init__(self, params: Munch, use_weights: bool = False): + super().__init__(params, use_weights) + self.sepp, self.sepv = self.make_bins() + + def make_bins(self) -> tuple[list]: + """Generate bins of projected separation and LOS for the correlation""" + sepp, _ = gundam.makebins( + self.params.nsepp, self.params.seppmin, self.params.dsepp, self.params.logsepp + ) + sepv, _ = gundam.makebins(self.params.nsepv, 0.0, self.params.dsepv, False) + return sepp, sepv + + def calculate_comoving_distances(self, df: pd.DataFrame): + """Calculate the comoving distances from the redshift of each particle""" + cosmo = LambdaCDM(H0=self.params.h0, Om0=self.params.omegam, Ode0=self.params.omegal) + return cosmo.comoving_distance(df["z"]).value + def _get_auto_method(self) -> Callable: - raise NotImplementedError() + return cff.mod.rppi_A_wg_naiveway if self.use_weights else cff.mod.rppi_A_naiveway def _construct_auto_args(self, df: pd.DataFrame, catalog_info: CatalogInfo) -> list: - raise NotImplementedError() + args = [ + len(df), + self.calculate_comoving_distances(df), + *self.get_coords(df, catalog_info), # cartesian coordinates + self.params.nsepp, # number of bins of projected separation rp + self.sepp, # Bins in projected separation rp + self.params.nsepv, # number of bins of LOS separation pi + self.sepv, # Bins in radial separation + ] + if self.use_weights: + args = [*args[:2], df["wei"].to_numpy(), *args[2:]] + return args def _get_cross_method(self) -> Callable: - raise NotImplementedError() + raise cff.mod.rppi_C_wg_naiveway if self.use_weights else cff.mod.rppi_C_naiveway def _construct_cross_args( self, @@ -25,4 +57,18 @@ def _construct_cross_args( left_catalog_info: CatalogInfo, right_catalog_info: CatalogInfo, ) -> list: - raise NotImplementedError() + args = [ + len(left_df), + self.calculate_comoving_distances(left_df), + *self.get_coords(left_df, left_catalog_info), + len(right_df), + self.calculate_comoving_distances(right_df), + *self.get_coords(right_df, left_catalog_info), + self.params.nsepp, + self.sepp, + self.params.nsepv, + self.sepv, + ] + if self.use_weights: + args = [*args[:2], left_df["wei"], *args[2:5], right_df["wei"], *args[5:]] + return args diff --git a/src/corrgi/correlation/redshift_correlation.py b/src/corrgi/correlation/redshift_correlation.py index a92a37f..6cf6b0d 100644 --- a/src/corrgi/correlation/redshift_correlation.py +++ b/src/corrgi/correlation/redshift_correlation.py @@ -9,6 +9,10 @@ class RedshiftCorrelation(Correlation): """The redshift correlation utilities.""" + def make_bins(self): + """Generate bins for the correlation""" + raise NotImplementedError() + def _get_auto_method(self) -> Callable: raise NotImplementedError() diff --git a/src/corrgi/corrgi.py b/src/corrgi/corrgi.py index 2dc62bd..8a42bb2 100644 --- a/src/corrgi/corrgi.py +++ b/src/corrgi/corrgi.py @@ -4,7 +4,7 @@ from corrgi.correlation.correlation import Correlation from corrgi.dask import compute_autocorrelation_counts -from corrgi.estimators import calculate_natural_estimate +from corrgi.estimators import calculate_tpcf def compute_autocorrelation( @@ -25,10 +25,10 @@ def compute_autocorrelation( Returns: A numpy array with the result of the auto-correlation, using the natural estimator. """ - num_galaxies = catalog.hc_structure.catalog_info.total_rows + num_particles = catalog.hc_structure.catalog_info.total_rows num_random = random.hc_structure.catalog_info.total_rows counts_dd, counts_rr = compute_autocorrelation_counts(corr_type, catalog, random, params) - return calculate_natural_estimate(counts_dd, counts_rr, num_galaxies, num_random) + return calculate_tpcf(counts_dd, counts_rr, num_particles, num_random) def compute_crosscorrelation(left: Catalog, right: Catalog, random: Catalog, params: Munch) -> np.ndarray: diff --git a/src/corrgi/dask.py b/src/corrgi/dask.py index bf87851..9ec35ed 100644 --- a/src/corrgi/dask.py +++ b/src/corrgi/dask.py @@ -31,7 +31,7 @@ def compute_autocorrelation_counts( # Calculate the angular separation bins bins, _ = gundam.makebins(params.nsept, params.septmin, params.dsept, params.logsept) # Create correlation with bins and params - correlation = corr_type(bins, params) + correlation = corr_type(params) # Generate the histograms with counts for each catalog counts_dd = perform_auto_counts(catalog, correlation) counts_rr = perform_auto_counts(random, correlation) diff --git a/src/corrgi/estimators.py b/src/corrgi/estimators.py index 7d03b38..938ed21 100644 --- a/src/corrgi/estimators.py +++ b/src/corrgi/estimators.py @@ -2,18 +2,20 @@ from gundam import tpcf -def calculate_natural_estimate( +def calculate_tpcf( counts_dd: np.ndarray, counts_rr: np.ndarray, - num_galaxies: int, + num_particles: int, num_random: int, ) -> np.ndarray: """Calculates the auto-correlation value for the natural estimator. + Evaluation given data (D), and random (R) samples. + Args: counts_dd (np.ndarray): The counts for the galaxy samples. counts_rr (np.ndarray): The counts for the random samples. - num_galaxies (int): The number of galaxy samples. + num_particles (int): The number of particles in data (D). num_random (int): The number of random samples. Returns: @@ -21,5 +23,5 @@ def calculate_natural_estimate( """ dr = 0 # We do not use DR counts for the natural estimator bdd = np.zeros([len(counts_dd), 0]) # We do not compute the bootstrap counts - wth, _ = tpcf(num_galaxies, num_random, counts_dd, bdd, counts_rr, dr, estimator="NAT") + wth, _ = tpcf(num_particles, num_random, counts_dd, bdd, counts_rr, dr, estimator="NAT") return wth diff --git a/tests/corrgi/test_acf.py b/tests/corrgi/test_acf.py index 8f4249c..6dba4db 100644 --- a/tests/corrgi/test_acf.py +++ b/tests/corrgi/test_acf.py @@ -7,7 +7,7 @@ from corrgi.correlation.angular_correlation import AngularCorrelation from corrgi.corrgi import compute_autocorrelation from corrgi.dask import compute_autocorrelation_counts -from corrgi.estimators import calculate_natural_estimate +from corrgi.estimators import calculate_tpcf def test_acf_bins_are_correct( @@ -53,9 +53,7 @@ def test_acf_natural_estimate_is_correct( random_hc_catalog = hipscat.read_from_hipscat(rand_catalog_dir) num_galaxies = galaxy_hc_catalog.catalog_info.total_rows num_random = random_hc_catalog.catalog_info.total_rows - estimate = calculate_natural_estimate( - acf_dd_counts, acf_rr_counts, num_galaxies, num_random - ) + estimate = calculate_tpcf(acf_dd_counts, acf_rr_counts, num_galaxies, num_random) npt.assert_allclose(acf_nat_estimate, estimate, rtol=2e-3) diff --git a/tests/corrgi/test_counting.py b/tests/corrgi/test_counting.py index fc5d842..82a2ef1 100644 --- a/tests/corrgi/test_counting.py +++ b/tests/corrgi/test_counting.py @@ -10,7 +10,7 @@ def test_count_auto_pairs( autocorr_params, ): data_catalog = hipscat.read_from_hipscat(data_catalog_dir) - partial = AngularCorrelation( - corr_bins, autocorr_params, use_weights=False - ).count_auto_pairs(single_data_partition, data_catalog.catalog_info) + partial = AngularCorrelation(autocorr_params).count_auto_pairs( + single_data_partition, data_catalog.catalog_info + ) assert len(partial) == len(corr_bins) - 1 From a777664ce58552bb263613b7fcfb6ca8ec7e10b6 Mon Sep 17 00:00:00 2001 From: Sandro Campos Date: Wed, 3 Jul 2024 17:24:09 -0400 Subject: [PATCH 02/12] Add test for auto-correlation pcf --- .../correlation/projected_correlation.py | 9 ++-- src/corrgi/dask.py | 5 +- tests/corrgi/conftest.py | 43 ++++++++++++------ tests/corrgi/test_acf.py | 20 ++++---- tests/corrgi/test_counting.py | 8 ++-- tests/corrgi/test_pcf.py | 23 ++++++++++ tests/data/expected_results/pcf/dd_pcf.npy | Bin 0 -> 352 bytes tests/data/expected_results/pcf/rr_pcf.npy | Bin 0 -> 352 bytes 8 files changed, 72 insertions(+), 36 deletions(-) create mode 100644 tests/corrgi/test_pcf.py create mode 100644 tests/data/expected_results/pcf/dd_pcf.npy create mode 100644 tests/data/expected_results/pcf/rr_pcf.npy diff --git a/src/corrgi/correlation/projected_correlation.py b/src/corrgi/correlation/projected_correlation.py index 341d4ad..b6510f3 100644 --- a/src/corrgi/correlation/projected_correlation.py +++ b/src/corrgi/correlation/projected_correlation.py @@ -1,6 +1,7 @@ from typing import Callable import gundam.cflibfor as cff +import numpy as np import pandas as pd from astropy.cosmology import LambdaCDM from gundam import gundam @@ -15,6 +16,7 @@ class ProjectedCorrelation(Correlation): def __init__(self, params: Munch, use_weights: bool = False): super().__init__(params, use_weights) + self.cosmo = LambdaCDM(H0=params.h0, Om0=params.omegam, Ode0=params.omegal) self.sepp, self.sepv = self.make_bins() def make_bins(self) -> tuple[list]: @@ -25,10 +27,9 @@ def make_bins(self) -> tuple[list]: sepv, _ = gundam.makebins(self.params.nsepv, 0.0, self.params.dsepv, False) return sepp, sepv - def calculate_comoving_distances(self, df: pd.DataFrame): + def calculate_comoving_distances(self, df: pd.DataFrame) -> np.ndarray: """Calculate the comoving distances from the redshift of each particle""" - cosmo = LambdaCDM(H0=self.params.h0, Om0=self.params.omegam, Ode0=self.params.omegal) - return cosmo.comoving_distance(df["z"]).value + return self.cosmo.comoving_distance(df["z"].to_numpy()).value def _get_auto_method(self) -> Callable: return cff.mod.rppi_A_wg_naiveway if self.use_weights else cff.mod.rppi_A_naiveway @@ -48,7 +49,7 @@ def _construct_auto_args(self, df: pd.DataFrame, catalog_info: CatalogInfo) -> l return args def _get_cross_method(self) -> Callable: - raise cff.mod.rppi_C_wg_naiveway if self.use_weights else cff.mod.rppi_C_naiveway + return cff.mod.rppi_C_wg_naiveway if self.use_weights else cff.mod.rppi_C_naiveway def _construct_cross_args( self, diff --git a/src/corrgi/dask.py b/src/corrgi/dask.py index 9ec35ed..54ecc16 100644 --- a/src/corrgi/dask.py +++ b/src/corrgi/dask.py @@ -2,7 +2,6 @@ import numpy as np import pandas as pd from dask.distributed import print as dask_print -from gundam import gundam from hipscat.catalog.catalog_info import CatalogInfo from hipscat.pixel_math import HealpixPixel from lsdb import Catalog @@ -28,9 +27,7 @@ def compute_autocorrelation_counts( Returns: The histogram counts to calculate the auto-correlation. """ - # Calculate the angular separation bins - bins, _ = gundam.makebins(params.nsept, params.septmin, params.dsept, params.logsept) - # Create correlation with bins and params + # Create correlation with params correlation = corr_type(params) # Generate the histograms with counts for each catalog counts_dd = perform_auto_counts(catalog, correlation) diff --git a/tests/corrgi/conftest.py b/tests/corrgi/conftest.py index 7df4758..f2cb8f6 100644 --- a/tests/corrgi/conftest.py +++ b/tests/corrgi/conftest.py @@ -30,6 +30,11 @@ def acf_expected_results(test_data_dir): return test_data_dir / "expected_results" / "acf" +@pytest.fixture +def pcf_expected_results(test_data_dir): + return test_data_dir / "expected_results" / "pcf" + + @pytest.fixture def data_catalog_dir(hipscat_catalogs_dir): return hipscat_catalogs_dir / "DATA" @@ -70,6 +75,16 @@ def acf_rr_counts(acf_expected_results): return np.load(acf_expected_results / "rr_acf.npy") +@pytest.fixture +def pcf_dd_counts(pcf_expected_results): + return np.load(pcf_expected_results / "dd_pcf.npy") + + +@pytest.fixture +def pcf_rr_counts(pcf_expected_results): + return np.load(pcf_expected_results / "rr_pcf.npy") + + @pytest.fixture def acf_nat_estimate(acf_expected_results): return np.load(acf_expected_results / "w_acf_nat.npy") @@ -81,27 +96,29 @@ def single_data_partition(data_catalog_dir): @pytest.fixture -def corr_bins(): +def acf_corr_bins(): bins, _ = gundam.makebins(33, 0.01, 0.1, 1) return bins @pytest.fixture -def autocorr_params(): - params = gundam.packpars(kind="acf", write=False) - +def acf_params(): + params = gundam.packpars(kind="acf") params.dsept = 0.1 params.nsept = 33 params.septmin = 0.01 + return params - params.kind = "thA" - params.cntid = "DD" - params.logf = "DD_log" - # Disable grid and fill some mock parameters - params.grid = 0 - params.sbound = [1, 2, 1, 2] - params.mxh1 = 2 - params.mxh2 = 2 - params.sk1 = [[1, 2], [1, 2]] +@pytest.fixture +def pcf_params(): + params = gundam.packpars(kind="pcf") + params.nsepp = 28 # Number of bins of projected separation rp + params.seppmin = 0.02 # Minimum rp in Mpc/h + params.dsepp = 0.12 # Bin size of rp (in log space) + params.nsepv = 1 # Number of bins of LOS separation pi + params.dsepv = 40.0 # Bin size of pi (in linear space) + params.omegam = 0.25 # Omega matter + params.omegal = 0.75 # Omega lambda + params.h0 = 100 # Hubble constant [km/s/Mpc] return params diff --git a/tests/corrgi/test_acf.py b/tests/corrgi/test_acf.py index 6dba4db..657b54a 100644 --- a/tests/corrgi/test_acf.py +++ b/tests/corrgi/test_acf.py @@ -10,14 +10,12 @@ from corrgi.estimators import calculate_tpcf -def test_acf_bins_are_correct( - acf_bins_left_edges, acf_bins_right_edges, autocorr_params -): +def test_acf_bins_are_correct(acf_bins_left_edges, acf_bins_right_edges, acf_params): bins, _ = gundam.makebins( - autocorr_params.nsept, - autocorr_params.septmin, - autocorr_params.dsept, - autocorr_params.logsept, + acf_params.nsept, + acf_params.septmin, + acf_params.dsept, + acf_params.logsept, ) all_bins = np.append(acf_bins_left_edges, acf_bins_right_edges[-1]) assert np.array_equal(bins, all_bins) @@ -29,14 +27,14 @@ def test_acf_counts_are_correct( rand_catalog_dir, acf_dd_counts, acf_rr_counts, - autocorr_params, + acf_params, ): galaxy_catalog = lsdb.read_hipscat(data_catalog_dir) random_catalog = lsdb.read_hipscat(rand_catalog_dir) assert isinstance(galaxy_catalog, lsdb.Catalog) assert isinstance(random_catalog, lsdb.Catalog) counts_dd, counts_rr = compute_autocorrelation_counts( - AngularCorrelation, galaxy_catalog, random_catalog, autocorr_params + AngularCorrelation, galaxy_catalog, random_catalog, acf_params ) npt.assert_allclose(counts_dd, acf_dd_counts, rtol=1e-3) npt.assert_allclose(counts_rr, acf_rr_counts, rtol=2e-3) @@ -58,13 +56,13 @@ def test_acf_natural_estimate_is_correct( def test_acf_e2e( - dask_client, data_catalog_dir, rand_catalog_dir, acf_nat_estimate, autocorr_params + dask_client, data_catalog_dir, rand_catalog_dir, acf_nat_estimate, acf_params ): galaxy_catalog = lsdb.read_hipscat(data_catalog_dir) random_catalog = lsdb.read_hipscat(rand_catalog_dir) assert isinstance(galaxy_catalog, lsdb.Catalog) assert isinstance(random_catalog, lsdb.Catalog) estimate = compute_autocorrelation( - AngularCorrelation, galaxy_catalog, random_catalog, autocorr_params + AngularCorrelation, galaxy_catalog, random_catalog, acf_params ) npt.assert_allclose(estimate, acf_nat_estimate, rtol=1e-7) diff --git a/tests/corrgi/test_counting.py b/tests/corrgi/test_counting.py index 82a2ef1..9f9ab96 100644 --- a/tests/corrgi/test_counting.py +++ b/tests/corrgi/test_counting.py @@ -6,11 +6,11 @@ def test_count_auto_pairs( single_data_partition, data_catalog_dir, - corr_bins, - autocorr_params, + acf_corr_bins, + acf_params, ): data_catalog = hipscat.read_from_hipscat(data_catalog_dir) - partial = AngularCorrelation(autocorr_params).count_auto_pairs( + partial = AngularCorrelation(acf_params).count_auto_pairs( single_data_partition, data_catalog.catalog_info ) - assert len(partial) == len(corr_bins) - 1 + assert len(partial) == len(acf_corr_bins) - 1 diff --git a/tests/corrgi/test_pcf.py b/tests/corrgi/test_pcf.py new file mode 100644 index 0000000..9969ec3 --- /dev/null +++ b/tests/corrgi/test_pcf.py @@ -0,0 +1,23 @@ +import lsdb +from corrgi.correlation.projected_correlation import ProjectedCorrelation +from corrgi.dask import compute_autocorrelation_counts +import numpy.testing as npt + + +def test_pcf_counts_are_correct( + dask_client, + data_catalog_dir, + rand_catalog_dir, + pcf_dd_counts, + pcf_rr_counts, + pcf_params, +): + galaxy_catalog = lsdb.read_hipscat(data_catalog_dir) + random_catalog = lsdb.read_hipscat(rand_catalog_dir) + assert isinstance(galaxy_catalog, lsdb.Catalog) + assert isinstance(random_catalog, lsdb.Catalog) + counts_dd, counts_rr = compute_autocorrelation_counts( + ProjectedCorrelation, galaxy_catalog, random_catalog, pcf_params + ) + npt.assert_allclose(counts_dd, pcf_dd_counts, rtol=1e-3) + npt.assert_allclose(counts_rr, pcf_rr_counts, rtol=2e-3) diff --git a/tests/data/expected_results/pcf/dd_pcf.npy b/tests/data/expected_results/pcf/dd_pcf.npy new file mode 100644 index 0000000000000000000000000000000000000000..0bec37275f628dcf689937a91be1eb609bf33b68 GIT binary patch literal 352 zcmbR27wQ`j$;eQ~P_3SlTAW;@Zl$1ZlV+i=qoAIaUsO_*m=~X4l#&V(cT3DEP6dh= zXCxM+0{I$77CH)snmP)#3giMV1_*F~P%2Pb4@#RuX-6pS51|i)Ky1_ zfN}`!(Et_ifY2fnA#~3y2yL?jLaS|n(BF4}=?2{+5dN(T5dO8>U^?N|Gcf(&$0sno dpy)rCUh$ICkpT!QyhXuuf}SFne#Ecs2mo+>KVkp? literal 0 HcmV?d00001 diff --git a/tests/data/expected_results/pcf/rr_pcf.npy b/tests/data/expected_results/pcf/rr_pcf.npy new file mode 100644 index 0000000000000000000000000000000000000000..22a406a4948ae28af1cac907ec49bcebf2c17181 GIT binary patch literal 352 zcmbR27wQ`j$;eQ~P_3SlTAW;@Zl$1ZlV+i=qoAIaUsO_*m=~X4l#&V(cT3DEP6dh= zXCxM+0{I$77CH)snmP)#3giMV1~B+w522MDAhZsYHh|LRP}&(nHv~Xvg#-xwAO}KE zsDO&ML+Aq&q5K69`qC;0owglJH*7fq;Xl3zrVs462jOpi1*RWxeTVS(GCMK=K?AEG kn7*PR3#R#QXn^VclBQsK{Z>a1-5`?d3!)D!7Km^J0J+IPG5`Po literal 0 HcmV?d00001 From a1cd22ab4ec7c588ed15213303ad78efe437705e Mon Sep 17 00:00:00 2001 From: Sandro Campos Date: Wed, 3 Jul 2024 18:30:11 -0400 Subject: [PATCH 03/12] Upload correct counts for pcf tests --- src/corrgi/correlation/projected_correlation.py | 2 +- tests/corrgi/test_pcf.py | 4 ++-- tests/data/expected_results/pcf/dd_pcf.npy | Bin 352 -> 352 bytes tests/data/expected_results/pcf/rr_pcf.npy | Bin 352 -> 352 bytes 4 files changed, 3 insertions(+), 3 deletions(-) diff --git a/src/corrgi/correlation/projected_correlation.py b/src/corrgi/correlation/projected_correlation.py index b6510f3..fea3027 100644 --- a/src/corrgi/correlation/projected_correlation.py +++ b/src/corrgi/correlation/projected_correlation.py @@ -64,7 +64,7 @@ def _construct_cross_args( *self.get_coords(left_df, left_catalog_info), len(right_df), self.calculate_comoving_distances(right_df), - *self.get_coords(right_df, left_catalog_info), + *self.get_coords(right_df, right_catalog_info), self.params.nsepp, self.sepp, self.params.nsepv, diff --git a/tests/corrgi/test_pcf.py b/tests/corrgi/test_pcf.py index 9969ec3..fff01b6 100644 --- a/tests/corrgi/test_pcf.py +++ b/tests/corrgi/test_pcf.py @@ -19,5 +19,5 @@ def test_pcf_counts_are_correct( counts_dd, counts_rr = compute_autocorrelation_counts( ProjectedCorrelation, galaxy_catalog, random_catalog, pcf_params ) - npt.assert_allclose(counts_dd, pcf_dd_counts, rtol=1e-3) - npt.assert_allclose(counts_rr, pcf_rr_counts, rtol=2e-3) + npt.assert_allclose(counts_dd.transpose([1, 0]), pcf_dd_counts, rtol=1e-3) + npt.assert_allclose(counts_rr.transpose([1, 0]), pcf_rr_counts, rtol=2e-3) diff --git a/tests/data/expected_results/pcf/dd_pcf.npy b/tests/data/expected_results/pcf/dd_pcf.npy index 0bec37275f628dcf689937a91be1eb609bf33b68..a7c41dac16335ebb80a4678c6dfb7db5ccac6a8c 100644 GIT binary patch literal 352 zcmbR27wQ`j$;eQ~P_3SlTAW;@Zl$1ZlV+i=qoAIaUsO_*m=~X4l#&V(cT3DEP6dh= zXCxM+0{I$77CH)snmP)#3giMV1`yEbZ~)UC6Ct$CObC5#5rlrV8bYt%0-^8igU~yU zf$0XOi(vY|;hSJuA@MPocDVZnBCr1yOb00Z2h%gm*&P{x;La{VFs;)f4W@U!RRz;# zFAc%;y1_ zfN}`!(Et_ifY2fnA#~3y2yL?jLaS|n(BF4}=?2{+5dN(T5dO8>U^?N|Gcf(&$0sno dpy)rCUh$ICkpT!QyhXuuf}SFne#Ecs2mo+>KVkp? diff --git a/tests/data/expected_results/pcf/rr_pcf.npy b/tests/data/expected_results/pcf/rr_pcf.npy index 22a406a4948ae28af1cac907ec49bcebf2c17181..06611cfe3844770dd768b15cbb6ccfa276e51f23 100644 GIT binary patch literal 352 zcmbR27wQ`j$;eQ~P_3SlTAW;@Zl$1ZlV+i=qoAIaUsO_*m=~X4l#&V(cT3DEP6dh= zXCxM+0{I$77CH)snmP)#3giMV1`u#4bO6&AY9O>mH-x@1146G_0-0H43{GV5Ii{}2Bwn^D}(9#s)k_t=L%a8-LQI@Czxi_ z3IovzJEfCB^oJuQ1t5CC0*@LHt>JCi0ir`XS4{!Y?`E|v1ksNsR;~rnW&3J&0O^K< JBL5FN0s!?FPJI9X literal 352 zcmbR27wQ`j$;eQ~P_3SlTAW;@Zl$1ZlV+i=qoAIaUsO_*m=~X4l#&V(cT3DEP6dh= zXCxM+0{I$77CH)snmP)#3giMV1~B+w522MDAhZsYHh|LRP}&(nHv~Xvg#-xwAO}KE zsDO&ML+Aq&q5K69`qC;0owglJH*7fq;Xl3zrVs462jOpi1*RWxeTVS(GCMK=K?AEG kn7*PR3#R#QXn^VclBQsK{Z>a1-5`?d3!)D!7Km^J0J+IPG5`Po From cc36fcfcb12056de85b4e71ccd0177e03a3c076f Mon Sep 17 00:00:00 2001 From: Sandro Campos Date: Mon, 8 Jul 2024 11:49:04 -0400 Subject: [PATCH 04/12] Revert some naming changes --- src/corrgi/corrgi.py | 6 +++--- src/corrgi/estimators.py | 10 ++++------ tests/corrgi/test_acf.py | 6 ++++-- 3 files changed, 11 insertions(+), 11 deletions(-) diff --git a/src/corrgi/corrgi.py b/src/corrgi/corrgi.py index 8a42bb2..2dc62bd 100644 --- a/src/corrgi/corrgi.py +++ b/src/corrgi/corrgi.py @@ -4,7 +4,7 @@ from corrgi.correlation.correlation import Correlation from corrgi.dask import compute_autocorrelation_counts -from corrgi.estimators import calculate_tpcf +from corrgi.estimators import calculate_natural_estimate def compute_autocorrelation( @@ -25,10 +25,10 @@ def compute_autocorrelation( Returns: A numpy array with the result of the auto-correlation, using the natural estimator. """ - num_particles = catalog.hc_structure.catalog_info.total_rows + num_galaxies = catalog.hc_structure.catalog_info.total_rows num_random = random.hc_structure.catalog_info.total_rows counts_dd, counts_rr = compute_autocorrelation_counts(corr_type, catalog, random, params) - return calculate_tpcf(counts_dd, counts_rr, num_particles, num_random) + return calculate_natural_estimate(counts_dd, counts_rr, num_galaxies, num_random) def compute_crosscorrelation(left: Catalog, right: Catalog, random: Catalog, params: Munch) -> np.ndarray: diff --git a/src/corrgi/estimators.py b/src/corrgi/estimators.py index 938ed21..7d03b38 100644 --- a/src/corrgi/estimators.py +++ b/src/corrgi/estimators.py @@ -2,20 +2,18 @@ from gundam import tpcf -def calculate_tpcf( +def calculate_natural_estimate( counts_dd: np.ndarray, counts_rr: np.ndarray, - num_particles: int, + num_galaxies: int, num_random: int, ) -> np.ndarray: """Calculates the auto-correlation value for the natural estimator. - Evaluation given data (D), and random (R) samples. - Args: counts_dd (np.ndarray): The counts for the galaxy samples. counts_rr (np.ndarray): The counts for the random samples. - num_particles (int): The number of particles in data (D). + num_galaxies (int): The number of galaxy samples. num_random (int): The number of random samples. Returns: @@ -23,5 +21,5 @@ def calculate_tpcf( """ dr = 0 # We do not use DR counts for the natural estimator bdd = np.zeros([len(counts_dd), 0]) # We do not compute the bootstrap counts - wth, _ = tpcf(num_particles, num_random, counts_dd, bdd, counts_rr, dr, estimator="NAT") + wth, _ = tpcf(num_galaxies, num_random, counts_dd, bdd, counts_rr, dr, estimator="NAT") return wth diff --git a/tests/corrgi/test_acf.py b/tests/corrgi/test_acf.py index 657b54a..cf4a04b 100644 --- a/tests/corrgi/test_acf.py +++ b/tests/corrgi/test_acf.py @@ -7,7 +7,7 @@ from corrgi.correlation.angular_correlation import AngularCorrelation from corrgi.corrgi import compute_autocorrelation from corrgi.dask import compute_autocorrelation_counts -from corrgi.estimators import calculate_tpcf +from corrgi.estimators import calculate_natural_estimate def test_acf_bins_are_correct(acf_bins_left_edges, acf_bins_right_edges, acf_params): @@ -51,7 +51,9 @@ def test_acf_natural_estimate_is_correct( random_hc_catalog = hipscat.read_from_hipscat(rand_catalog_dir) num_galaxies = galaxy_hc_catalog.catalog_info.total_rows num_random = random_hc_catalog.catalog_info.total_rows - estimate = calculate_tpcf(acf_dd_counts, acf_rr_counts, num_galaxies, num_random) + estimate = calculate_natural_estimate( + acf_dd_counts, acf_rr_counts, num_galaxies, num_random + ) npt.assert_allclose(acf_nat_estimate, estimate, rtol=2e-3) From 61f7f245fc1603301bc2e7a2b18e3369c4305b76 Mon Sep 17 00:00:00 2001 From: Sandro Campos Date: Mon, 8 Jul 2024 12:25:19 -0400 Subject: [PATCH 05/12] Fix cross args for the projected function with weights --- src/corrgi/correlation/projected_correlation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/corrgi/correlation/projected_correlation.py b/src/corrgi/correlation/projected_correlation.py index fea3027..ab46a76 100644 --- a/src/corrgi/correlation/projected_correlation.py +++ b/src/corrgi/correlation/projected_correlation.py @@ -71,5 +71,5 @@ def _construct_cross_args( self.sepv, ] if self.use_weights: - args = [*args[:2], left_df["wei"], *args[2:5], right_df["wei"], *args[5:]] + args = [*args[:2], left_df["wei"].to_numpy(), *args[2:7], right_df["wei"].to_numpy(), *args[7:]] return args From bd3c8abae761299749561329a228d1f43e7ad21b Mon Sep 17 00:00:00 2001 From: Sandro Campos Date: Mon, 8 Jul 2024 16:22:34 -0400 Subject: [PATCH 06/12] Resolve conflicts with main --- src/corrgi/correlation/angular_correlation.py | 26 ++++++++---- src/corrgi/correlation/correlation.py | 8 +++- .../correlation/projected_correlation.py | 4 +- src/corrgi/corrgi.py | 10 ++++- src/corrgi/dask.py | 11 ++++- tests/corrgi/conftest.py | 20 +++++++++ tests/corrgi/test_acf.py | 39 ++++++++++++++++++ .../expected_results/acf/dd_acf_weight.npy | Bin 0 -> 392 bytes .../expected_results/acf/rr_acf_weight.npy | Bin 0 -> 392 bytes tests/data/generate_data.ipynb | 12 +++++- .../Norder=0/Dir=0/Npix=1.parquet | Bin 0 -> 68531 bytes .../Norder=0/Dir=0/Npix=10.parquet 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-> 3434 bytes tests/data/hipscat/acf_rans_weight/_metadata | Bin 0 -> 9162 bytes .../hipscat/acf_rans_weight/catalog_info.json | 8 ++++ .../acf_rans_weight/partition_info.csv | 8 ++++ .../acf_rans_weight/provenance_info.json | 25 +++++++++++ tests/data/raw/acf_gals_weight.fits | Bin 0 -> 135360 bytes tests/data/raw/acf_rans_weight.fits | Bin 0 -> 360000 bytes 35 files changed, 196 insertions(+), 15 deletions(-) create mode 100644 tests/data/expected_results/acf/dd_acf_weight.npy create mode 100644 tests/data/expected_results/acf/rr_acf_weight.npy create mode 100644 tests/data/hipscat/acf_gals_weight/Norder=0/Dir=0/Npix=1.parquet create mode 100644 tests/data/hipscat/acf_gals_weight/Norder=0/Dir=0/Npix=10.parquet create mode 100644 tests/data/hipscat/acf_gals_weight/Norder=0/Dir=0/Npix=2.parquet create mode 100644 tests/data/hipscat/acf_gals_weight/Norder=0/Dir=0/Npix=5.parquet create mode 100644 tests/data/hipscat/acf_gals_weight/Norder=0/Dir=0/Npix=6.parquet create mode 100644 tests/data/hipscat/acf_gals_weight/Norder=0/Dir=0/Npix=7.parquet create mode 100644 tests/data/hipscat/acf_gals_weight/_common_metadata create mode 100644 tests/data/hipscat/acf_gals_weight/_metadata create mode 100644 tests/data/hipscat/acf_gals_weight/catalog_info.json create mode 100644 tests/data/hipscat/acf_gals_weight/partition_info.csv create mode 100644 tests/data/hipscat/acf_gals_weight/provenance_info.json create mode 100644 tests/data/hipscat/acf_rans_weight/Norder=0/Dir=0/Npix=1.parquet create mode 100644 tests/data/hipscat/acf_rans_weight/Norder=0/Dir=0/Npix=10.parquet create mode 100644 tests/data/hipscat/acf_rans_weight/Norder=0/Dir=0/Npix=2.parquet create mode 100644 tests/data/hipscat/acf_rans_weight/Norder=0/Dir=0/Npix=5.parquet create mode 100644 tests/data/hipscat/acf_rans_weight/Norder=0/Dir=0/Npix=6.parquet create mode 100644 tests/data/hipscat/acf_rans_weight/Norder=0/Dir=0/Npix=7.parquet create mode 100644 tests/data/hipscat/acf_rans_weight/Norder=0/Dir=0/Npix=9.parquet create mode 100644 tests/data/hipscat/acf_rans_weight/_common_metadata create mode 100644 tests/data/hipscat/acf_rans_weight/_metadata create mode 100644 tests/data/hipscat/acf_rans_weight/catalog_info.json create mode 100644 tests/data/hipscat/acf_rans_weight/partition_info.csv create mode 100644 tests/data/hipscat/acf_rans_weight/provenance_info.json create mode 100644 tests/data/raw/acf_gals_weight.fits create mode 100644 tests/data/raw/acf_rans_weight.fits diff --git a/src/corrgi/correlation/angular_correlation.py b/src/corrgi/correlation/angular_correlation.py index 4afcb07..a79c052 100644 --- a/src/corrgi/correlation/angular_correlation.py +++ b/src/corrgi/correlation/angular_correlation.py @@ -12,8 +12,8 @@ class AngularCorrelation(Correlation): """The angular correlation utilities.""" - def __init__(self, params: Munch, use_weights: bool = False): - super().__init__(params, use_weights) + def __init__(self, params: Munch, weight_column: str = "wei", use_weights: bool = False): + super().__init__(params, weight_column, use_weights) self.sept = self.make_bins() def make_bins(self) -> list: @@ -24,18 +24,21 @@ def make_bins(self) -> list: return bins def _get_auto_method(self): - return cff.mod.th_A_wg if self.use_weights else cff.mod.th_A_naiveway + return cff.mod.th_A_wg_naiveway if self.use_weights else cff.mod.th_A_naiveway def _get_cross_method(self) -> Callable: - return cff.mod.th_C_wg if self.use_weights else cff.mod.th_C_naiveway + return cff.mod.th_C_wg_naiveway if self.use_weights else cff.mod.th_C_naiveway def _construct_auto_args(self, df: pd.DataFrame, catalog_info: CatalogInfo) -> list: - return [ - len(df), # number of particles + args = [ + len(df), *self.get_coords(df, catalog_info), # cartesian coordinates self.params.nsept, # number of angular separation bins self.sept, # bins in angular separation [deg] ] + if self.use_weights: + args = [args[0], df[self.weight_column].to_numpy(), *args[1:]] + return args def _construct_cross_args( self, @@ -44,7 +47,7 @@ def _construct_cross_args( left_catalog_info: CatalogInfo, right_catalog_info: CatalogInfo, ) -> list: - return [ + args = [ len(left_df), # number of particles of the left partition *self.get_coords(left_df, left_catalog_info), # X,Y,Z coordinates of particles len(right_df), # number of particles of the right partition @@ -52,3 +55,12 @@ def _construct_cross_args( self.params.nsept, # number of angular separation bins self.sept, # bins in angular separation [deg] ] + if self.use_weights: + args = [ + args[0], + left_df[self.weight_column].to_numpy(), + *args[1:5], + right_df[self.weight_column].to_numpy(), + *args[5:], + ] + return args diff --git a/src/corrgi/correlation/correlation.py b/src/corrgi/correlation/correlation.py index e92cc02..0d5cd4f 100644 --- a/src/corrgi/correlation/correlation.py +++ b/src/corrgi/correlation/correlation.py @@ -14,8 +14,14 @@ class Correlation(ABC): """Correlation base class.""" - def __init__(self, params: Munch, use_weights: bool = False): + def __init__( + self, + params: Munch, + weight_column: str = "wei", + use_weights: bool = False, + ): self.params = params + self.weight_column = weight_column self.use_weights = use_weights def count_auto_pairs(self, df: pd.DataFrame, catalog_info: CatalogInfo) -> np.ndarray: diff --git a/src/corrgi/correlation/projected_correlation.py b/src/corrgi/correlation/projected_correlation.py index ab46a76..cc575d3 100644 --- a/src/corrgi/correlation/projected_correlation.py +++ b/src/corrgi/correlation/projected_correlation.py @@ -14,8 +14,8 @@ class ProjectedCorrelation(Correlation): """The projected correlation utilities.""" - def __init__(self, params: Munch, use_weights: bool = False): - super().__init__(params, use_weights) + def __init__(self, params: Munch, weight_column: str = "wei", use_weights: bool = False): + super().__init__(params, weight_column, use_weights) self.cosmo = LambdaCDM(H0=params.h0, Om0=params.omegam, Ode0=params.omegal) self.sepp, self.sepv = self.make_bins() diff --git a/src/corrgi/corrgi.py b/src/corrgi/corrgi.py index 2dc62bd..a063d29 100644 --- a/src/corrgi/corrgi.py +++ b/src/corrgi/corrgi.py @@ -12,6 +12,8 @@ def compute_autocorrelation( catalog: Catalog, random: Catalog, params: Munch, + weight_column: str = "wei", + use_weights: bool = False, ) -> np.ndarray: """Calculates the auto-correlation for a catalog. @@ -21,13 +23,19 @@ def compute_autocorrelation( catalog (Catalog): The catalog. random (Catalog): A random samples catalog. params (Munch): The parameters dictionary to run gundam with. + weight_column (str): The weights column name. Defaults to "wei". + use_weights (bool): Whether to use weights or not. Defaults to False. Returns: A numpy array with the result of the auto-correlation, using the natural estimator. """ + if use_weights and weight_column not in catalog.columns: + raise ValueError(f"Weight column {weight_column} does not exist") num_galaxies = catalog.hc_structure.catalog_info.total_rows num_random = random.hc_structure.catalog_info.total_rows - counts_dd, counts_rr = compute_autocorrelation_counts(corr_type, catalog, random, params) + counts_dd, counts_rr = compute_autocorrelation_counts( + corr_type, catalog, random, params, weight_column, use_weights + ) return calculate_natural_estimate(counts_dd, counts_rr, num_galaxies, num_random) diff --git a/src/corrgi/dask.py b/src/corrgi/dask.py index 54ecc16..9826055 100644 --- a/src/corrgi/dask.py +++ b/src/corrgi/dask.py @@ -14,7 +14,12 @@ def compute_autocorrelation_counts( - corr_type: type[Correlation], catalog: Catalog, random: Catalog, params: Munch + corr_type: type[Correlation], + catalog: Catalog, + random: Catalog, + params: Munch, + weight_column: str = "wei", + use_weights: bool = False, ) -> np.ndarray: """Computes the auto-correlation counts for a catalog. @@ -23,12 +28,14 @@ def compute_autocorrelation_counts( catalog (Catalog): The catalog with galaxy samples. random (Catalog): The catalog with random samples. params (dict): The gundam parameters for the Fortran subroutine. + weight_column (str): The weights column name. Defaults to "wei". + use_weights (bool): Whether to use weights or not. Defaults to False. Returns: The histogram counts to calculate the auto-correlation. """ # Create correlation with params - correlation = corr_type(params) + correlation = corr_type(params, weight_column, use_weights) # Generate the histograms with counts for each catalog counts_dd = perform_auto_counts(catalog, correlation) counts_rr = perform_auto_counts(random, correlation) diff --git a/tests/corrgi/conftest.py b/tests/corrgi/conftest.py index f2cb8f6..a876679 100644 --- a/tests/corrgi/conftest.py +++ b/tests/corrgi/conftest.py @@ -55,6 +55,16 @@ def rand_catalog_dir(hipscat_catalogs_dir): return hipscat_catalogs_dir / "RAND" +@pytest.fixture +def acf_gals_weight_dir(hipscat_catalogs_dir): + return hipscat_catalogs_dir / "acf_gals_weight" + + +@pytest.fixture +def acf_rans_weight_dir(hipscat_catalogs_dir): + return hipscat_catalogs_dir / "acf_rans_weight" + + @pytest.fixture def acf_bins_left_edges(acf_expected_results): return np.load(acf_expected_results / "l_binedges_acf.npy") @@ -75,6 +85,16 @@ def acf_rr_counts(acf_expected_results): return np.load(acf_expected_results / "rr_acf.npy") +@pytest.fixture +def acf_dd_counts_with_weights(acf_expected_results): + return np.load(acf_expected_results / "dd_acf_weight.npy") + + +@pytest.fixture +def acf_rr_counts_with_weights(acf_expected_results): + return np.load(acf_expected_results / "rr_acf_weight.npy") + + @pytest.fixture def pcf_dd_counts(pcf_expected_results): return np.load(pcf_expected_results / "dd_pcf.npy") diff --git a/tests/corrgi/test_acf.py b/tests/corrgi/test_acf.py index cf4a04b..a083715 100644 --- a/tests/corrgi/test_acf.py +++ b/tests/corrgi/test_acf.py @@ -2,6 +2,7 @@ import lsdb import numpy as np import numpy.testing as npt +import pytest from gundam import gundam from corrgi.correlation.angular_correlation import AngularCorrelation @@ -68,3 +69,41 @@ def test_acf_e2e( AngularCorrelation, galaxy_catalog, random_catalog, acf_params ) npt.assert_allclose(estimate, acf_nat_estimate, rtol=1e-7) + + +def test_acf_counts_with_weights_are_correct( + dask_client, + acf_gals_weight_dir, + acf_rans_weight_dir, + acf_dd_counts_with_weights, + acf_rr_counts_with_weights, + acf_params, +): + galaxy_catalog = lsdb.read_hipscat(acf_gals_weight_dir) + random_catalog = lsdb.read_hipscat(acf_rans_weight_dir) + assert isinstance(galaxy_catalog, lsdb.Catalog) + assert isinstance(random_catalog, lsdb.Catalog) + counts_dd, counts_rr = compute_autocorrelation_counts( + AngularCorrelation, + galaxy_catalog, + random_catalog, + acf_params, + use_weights=True, + ) + npt.assert_allclose(counts_dd, acf_dd_counts_with_weights, rtol=1e-3) + npt.assert_allclose(counts_rr, acf_rr_counts_with_weights, rtol=2e-3) + + +def test_acf_weights_not_provided(data_catalog_dir, rand_catalog_dir, acf_params): + galaxy_catalog = lsdb.read_hipscat(data_catalog_dir) + random_catalog = lsdb.read_hipscat(rand_catalog_dir) + assert isinstance(galaxy_catalog, lsdb.Catalog) + assert isinstance(random_catalog, lsdb.Catalog) + with pytest.raises(ValueError, match="does not exist"): + compute_autocorrelation( + AngularCorrelation, + galaxy_catalog, + random_catalog, + acf_params, + use_weights=True, + ) diff --git a/tests/data/expected_results/acf/dd_acf_weight.npy b/tests/data/expected_results/acf/dd_acf_weight.npy new file mode 100644 index 0000000000000000000000000000000000000000..60150377e7712973dcff4d69e65f479d77afffc1 GIT binary patch literal 392 zcmbR27wQ`j$;eQ~P_3SlTAW;@Zl$1ZlV+i=qoAIaUsO_*m=~X4l#&V(cT3DEP6dh= zXCxM+0{I%o#yXlh3bhL411<&zh7hiEzwCjuL+0NTAiA4(mZ$@e*5;NF0@2l$`jSAp zp+o7sI*5+G$F2*c4@~^=!xF^j)O4@~(h9kq-p)YULFKkj0FeH`-S;*ONKcSmu_gf| ze(|St8j#*_-J~fWNXOW#mXrZ$g-S>LMj&m|??15vNSodi)|v#Q1K+S8nB~CGaNBOC z&=Mfcktfcv-htsj$lpmzwgc%+diLChfHdQy^S@6!Fc`$0G}XT9z;GnG?HJDk2Zjyr zYf}1OI54~l`{{K2lLLd^zuH@6e;gRXnc8zbSREPmD(a`_*&1 literal 0 HcmV?d00001 diff --git a/tests/data/expected_results/acf/rr_acf_weight.npy b/tests/data/expected_results/acf/rr_acf_weight.npy new file mode 100644 index 0000000000000000000000000000000000000000..466e80297b3cca30f76bf1bfd2c17b1dd1ea8ed0 GIT binary patch literal 392 zcmbR27wQ`j$;eQ~P_3SlTAW;@Zl$1ZlV+i=qoAIaUsO_*m=~X4l#&V(cT3DEP6dh= zXCxM+0{I%o#yXlh3bhL411<&zh5}KafA&CnhVs8}AX+C)Ouzw1zcO1Q4WeZpzEl9x z4lmBm)dJE9yN-S_1=0rZyPDj9w1nHX=K(-^!+P$L2q3-V)j_#rAkFHmI-vkaf9lzt zU+%!rz~UD7ya~wP^kkh}ACUjdU;5T`2ZjTUhtK_31mv@N@}64@a zjtqB;w)ymMJ2IHCGAeh9Iwt((Qe3iL!SO(*QOXvUGC<0EUNv*8l(j literal 0 HcmV?d00001 diff --git a/tests/data/generate_data.ipynb b/tests/data/generate_data.ipynb index e59165d..0cd1441 100644 --- a/tests/data/generate_data.ipynb +++ b/tests/data/generate_data.ipynb @@ -47,8 +47,12 @@ "metadata": {}, "outputs": [], "source": [ + "# Without weights\n", "generate_catalog(\"DATA\")\n", - "generate_catalog(\"DR7-lrg\")" + "generate_catalog(\"DR7-lrg\")\n", + "\n", + "# With weights\n", + "generate_catalog(\"acf_gals_weight\")" ] }, { @@ -64,8 +68,12 @@ "metadata": {}, "outputs": [], "source": [ + "# Without weights\n", 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zTa?(^%aKpwYC)ZT=7IV9A>=I+>~rzW_$CKnHA$Rs#^;l^uu)4dHxB#KUd)utElBmv z$Ro!TPJ+YbTEfm=7JJ&Y{}qzi&5-*D?S-)p9nc1Lp}^3!N=_l)-oBb^74m|MY-! zOZVR%gwU|XG(BmQ)YZ+Xd6YX07S_nWe2;y`hF^o4JKbRx_A@6so*|8=b>>d$oCT|f z%o+c10%>?++q?g2JL?O%$5eN7aGtcXX_r$LNL1hDNK~O;Gov(|464Gc!8T^MD1WgX?n(Y$RvaZEKgshj~v&Lw(n0?IXPlKy0s~1chvxC z%pR>@sc!^RzCB^aZc3y>Eq$ncia+Q@)Y5iSd_Gt1EG_y~0YZt{O!;FdX;yTYDIT^6 zyj#TRo`Y88v$)i(R-SNhiAbT1YsN{7UCb9rm9^ldj{dcP08+PSy%LmYL-3DmHgCT! z;l9@M6oynoWO6Yxq9Re_rU%7BHqcZa+wkcjWoKJG*%P`}yw){PzU@djkJG Sf&ZSse^21QC-DF53H%@b=Io;Y literal 0 HcmV?d00001 From c3e944a969844c92d1e498cd465efd3028cdaeb9 Mon Sep 17 00:00:00 2001 From: Sandro Campos Date: Tue, 9 Jul 2024 10:05:47 -0400 Subject: [PATCH 07/12] Validate correlation args/data --- src/corrgi/correlation/correlation.py | 13 +++++ .../correlation/projected_correlation.py | 30 +++++++++-- .../correlation/redshift_correlation.py | 4 ++ src/corrgi/corrgi.py | 23 +++------ src/corrgi/dask.py | 17 +------ tests/corrgi/conftest.py | 33 +++++++++--- tests/corrgi/test_acf.py | 51 ++++++------------- tests/corrgi/test_pcf.py | 20 +++----- 8 files changed, 99 insertions(+), 92 deletions(-) diff --git a/src/corrgi/correlation/correlation.py b/src/corrgi/correlation/correlation.py index 0d5cd4f..452e0a8 100644 --- a/src/corrgi/correlation/correlation.py +++ b/src/corrgi/correlation/correlation.py @@ -8,6 +8,8 @@ from hipscat.catalog.catalog_info import CatalogInfo from munch import Munch +from lsdb import Catalog + from corrgi.utils import project_coordinates @@ -24,6 +26,17 @@ def __init__( self.weight_column = weight_column self.use_weights = use_weights + def validate(self, catalogs: List[Catalog]): + """Validate that the correlation args/data are valid""" + if not self.use_weights: + return + for catalog in catalogs: + if self.weight_column not in catalog.columns: + raise ValueError( + f"Weight column '{self.weight_column}' does not exist" + + f" in {catalog.hc_structure.catalog_info.catalog_name}" + ) + def count_auto_pairs(self, df: pd.DataFrame, catalog_info: CatalogInfo) -> np.ndarray: """Computes the counts for pairs of the same partition""" args = self._construct_auto_args(df, catalog_info) diff --git a/src/corrgi/correlation/projected_correlation.py b/src/corrgi/correlation/projected_correlation.py index cc575d3..b265cc6 100644 --- a/src/corrgi/correlation/projected_correlation.py +++ b/src/corrgi/correlation/projected_correlation.py @@ -7,6 +7,7 @@ from gundam import gundam from hipscat.catalog.catalog_info import CatalogInfo from munch import Munch +from lsdb import Catalog from corrgi.correlation.correlation import Correlation @@ -14,10 +15,23 @@ class ProjectedCorrelation(Correlation): """The projected correlation utilities.""" - def __init__(self, params: Munch, weight_column: str = "wei", use_weights: bool = False): + def __init__( + self, + params: Munch, + weight_column: str = "wei", + redshift_column: str = "z", + use_weights: bool = False, + ): super().__init__(params, weight_column, use_weights) - self.cosmo = LambdaCDM(H0=params.h0, Om0=params.omegam, Ode0=params.omegal) + self.redshift_column = redshift_column self.sepp, self.sepv = self.make_bins() + self.cosmo = LambdaCDM(H0=params.h0, Om0=params.omegam, Ode0=params.omegal) + + def validate(self, catalogs: list[Catalog]): + super().validate_cross_correlation(catalogs) + for catalog in catalogs: + if self.redshift_column not in catalog.columns: + raise ValueError(f"Redshift column {self.redshift_column} not found in {catalog}") def make_bins(self) -> tuple[list]: """Generate bins of projected separation and LOS for the correlation""" @@ -29,7 +43,7 @@ def make_bins(self) -> tuple[list]: def calculate_comoving_distances(self, df: pd.DataFrame) -> np.ndarray: """Calculate the comoving distances from the redshift of each particle""" - return self.cosmo.comoving_distance(df["z"].to_numpy()).value + return self.cosmo.comoving_distance(df[self.redshift_column].to_numpy()).value def _get_auto_method(self) -> Callable: return cff.mod.rppi_A_wg_naiveway if self.use_weights else cff.mod.rppi_A_naiveway @@ -45,7 +59,7 @@ def _construct_auto_args(self, df: pd.DataFrame, catalog_info: CatalogInfo) -> l self.sepv, # Bins in radial separation ] if self.use_weights: - args = [*args[:2], df["wei"].to_numpy(), *args[2:]] + args = [*args[:2], df[self.weight_column].to_numpy(), *args[2:]] return args def _get_cross_method(self) -> Callable: @@ -71,5 +85,11 @@ def _construct_cross_args( self.sepv, ] if self.use_weights: - args = [*args[:2], left_df["wei"].to_numpy(), *args[2:7], right_df["wei"].to_numpy(), *args[7:]] + args = [ + *args[:2], + left_df[self.weight_column].to_numpy(), + *args[2:7], + right_df[self.weight_column].to_numpy(), + *args[7:], + ] return args diff --git a/src/corrgi/correlation/redshift_correlation.py b/src/corrgi/correlation/redshift_correlation.py index 6cf6b0d..3e6396d 100644 --- a/src/corrgi/correlation/redshift_correlation.py +++ b/src/corrgi/correlation/redshift_correlation.py @@ -4,11 +4,15 @@ from hipscat.catalog.catalog_info import CatalogInfo from corrgi.correlation.correlation import Correlation +from lsdb import Catalog class RedshiftCorrelation(Correlation): """The redshift correlation utilities.""" + def validate(self, catalogs: list[Catalog]): + super().validate(catalogs) + def make_bins(self): """Generate bins for the correlation""" raise NotImplementedError() diff --git a/src/corrgi/corrgi.py b/src/corrgi/corrgi.py index a063d29..3e272ac 100644 --- a/src/corrgi/corrgi.py +++ b/src/corrgi/corrgi.py @@ -8,34 +8,25 @@ def compute_autocorrelation( - corr_type: type[Correlation], - catalog: Catalog, - random: Catalog, - params: Munch, - weight_column: str = "wei", - use_weights: bool = False, + catalog: Catalog, random: Catalog, corr_type: type[Correlation], **kwargs ) -> np.ndarray: """Calculates the auto-correlation for a catalog. Args: - corr_type (type[Correlation]): The corrgi class corresponding to the type of - correlation (AngularCorrelation, RedshiftCorrelation, or ProjectedCorrelation). catalog (Catalog): The catalog. random (Catalog): A random samples catalog. - params (Munch): The parameters dictionary to run gundam with. - weight_column (str): The weights column name. Defaults to "wei". - use_weights (bool): Whether to use weights or not. Defaults to False. + corr_type (type[Correlation]): The corrgi class corresponding to the type of + correlation (AngularCorrelation, RedshiftCorrelation, or ProjectedCorrelation). + **kwargs (dict): The arguments for the creation of the correlation instance. Returns: A numpy array with the result of the auto-correlation, using the natural estimator. """ - if use_weights and weight_column not in catalog.columns: - raise ValueError(f"Weight column {weight_column} does not exist") + correlation = corr_type(**kwargs) + correlation.validate([catalog, random]) + counts_dd, counts_rr = compute_autocorrelation_counts(catalog, random, correlation) num_galaxies = catalog.hc_structure.catalog_info.total_rows num_random = random.hc_structure.catalog_info.total_rows - counts_dd, counts_rr = compute_autocorrelation_counts( - corr_type, catalog, random, params, weight_column, use_weights - ) return calculate_natural_estimate(counts_dd, counts_rr, num_galaxies, num_random) diff --git a/src/corrgi/dask.py b/src/corrgi/dask.py index 9826055..d720cba 100644 --- a/src/corrgi/dask.py +++ b/src/corrgi/dask.py @@ -13,30 +13,17 @@ from corrgi.utils import join_count_histograms -def compute_autocorrelation_counts( - corr_type: type[Correlation], - catalog: Catalog, - random: Catalog, - params: Munch, - weight_column: str = "wei", - use_weights: bool = False, -) -> np.ndarray: +def compute_autocorrelation_counts(catalog: Catalog, random: Catalog, correlation: Correlation) -> np.ndarray: """Computes the auto-correlation counts for a catalog. Args: - corr_type (type[Correlation]): The correlation class. catalog (Catalog): The catalog with galaxy samples. random (Catalog): The catalog with random samples. - params (dict): The gundam parameters for the Fortran subroutine. - weight_column (str): The weights column name. Defaults to "wei". - use_weights (bool): Whether to use weights or not. Defaults to False. + correlation (Correlation): The correlation instance. Returns: The histogram counts to calculate the auto-correlation. """ - # Create correlation with params - correlation = corr_type(params, weight_column, use_weights) - # Generate the histograms with counts for each catalog counts_dd = perform_auto_counts(catalog, correlation) counts_rr = perform_auto_counts(random, correlation) return dask.compute(*[counts_dd, counts_rr]) diff --git a/tests/corrgi/conftest.py b/tests/corrgi/conftest.py index a876679..3680a28 100644 --- a/tests/corrgi/conftest.py +++ b/tests/corrgi/conftest.py @@ -1,6 +1,7 @@ from pathlib import Path import gundam +import lsdb import numpy as np import pandas as pd import pytest @@ -40,29 +41,49 @@ def data_catalog_dir(hipscat_catalogs_dir): return hipscat_catalogs_dir / "DATA" +@pytest.fixture +def data_catalog(data_catalog_dir): + return lsdb.read_hipscat(data_catalog_dir) + + +@pytest.fixture +def rand_catalog_dir(hipscat_catalogs_dir): + return hipscat_catalogs_dir / "RAND" + + +@pytest.fixture +def rand_catalog(rand_catalog_dir): + return lsdb.read_hipscat(rand_catalog_dir) + + @pytest.fixture def dr7_lrg_catalog_dir(hipscat_catalogs_dir): return hipscat_catalogs_dir / "DR7-lrg" +@pytest.fixture +def dr7_lrg_catalog(dr7_lrg_catalog_dir): + return lsdb.read_hipscat(dr7_lrg_catalog_dir) + + @pytest.fixture def dr7_lrg_rand_catalog_dir(hipscat_catalogs_dir): return hipscat_catalogs_dir / "DR7-lrg-rand" @pytest.fixture -def rand_catalog_dir(hipscat_catalogs_dir): - return hipscat_catalogs_dir / "RAND" +def dr7_lrg_rand_catalog(dr7_lrg_rand_catalog_dir): + return lsdb.read_hipscat(dr7_lrg_rand_catalog_dir) @pytest.fixture -def acf_gals_weight_dir(hipscat_catalogs_dir): - return hipscat_catalogs_dir / "acf_gals_weight" +def acf_gals_weight_catalog(hipscat_catalogs_dir): + return lsdb.read_hipscat(hipscat_catalogs_dir / "acf_gals_weight") @pytest.fixture -def acf_rans_weight_dir(hipscat_catalogs_dir): - return hipscat_catalogs_dir / "acf_rans_weight" +def acf_rans_weight_catalog(hipscat_catalogs_dir): + return lsdb.read_hipscat(hipscat_catalogs_dir / "acf_rans_weight") @pytest.fixture diff --git a/tests/corrgi/test_acf.py b/tests/corrgi/test_acf.py index a083715..f7ccd73 100644 --- a/tests/corrgi/test_acf.py +++ b/tests/corrgi/test_acf.py @@ -1,9 +1,8 @@ -import hipscat -import lsdb import numpy as np import numpy.testing as npt import pytest from gundam import gundam +import hipscat from corrgi.correlation.angular_correlation import AngularCorrelation from corrgi.corrgi import compute_autocorrelation @@ -24,18 +23,15 @@ def test_acf_bins_are_correct(acf_bins_left_edges, acf_bins_right_edges, acf_par def test_acf_counts_are_correct( dask_client, - data_catalog_dir, - rand_catalog_dir, + data_catalog, + rand_catalog, acf_dd_counts, acf_rr_counts, acf_params, ): - galaxy_catalog = lsdb.read_hipscat(data_catalog_dir) - random_catalog = lsdb.read_hipscat(rand_catalog_dir) - assert isinstance(galaxy_catalog, lsdb.Catalog) - assert isinstance(random_catalog, lsdb.Catalog) + ang_corr = AngularCorrelation(params=acf_params) counts_dd, counts_rr = compute_autocorrelation_counts( - AngularCorrelation, galaxy_catalog, random_catalog, acf_params + data_catalog, rand_catalog, ang_corr ) npt.assert_allclose(counts_dd, acf_dd_counts, rtol=1e-3) npt.assert_allclose(counts_rr, acf_rr_counts, rtol=2e-3) @@ -58,52 +54,35 @@ def test_acf_natural_estimate_is_correct( npt.assert_allclose(acf_nat_estimate, estimate, rtol=2e-3) -def test_acf_e2e( - dask_client, data_catalog_dir, rand_catalog_dir, acf_nat_estimate, acf_params -): - galaxy_catalog = lsdb.read_hipscat(data_catalog_dir) - random_catalog = lsdb.read_hipscat(rand_catalog_dir) - assert isinstance(galaxy_catalog, lsdb.Catalog) - assert isinstance(random_catalog, lsdb.Catalog) +def test_acf_e2e(dask_client, data_catalog, rand_catalog, acf_nat_estimate, acf_params): estimate = compute_autocorrelation( - AngularCorrelation, galaxy_catalog, random_catalog, acf_params + data_catalog, rand_catalog, AngularCorrelation, params=acf_params ) npt.assert_allclose(estimate, acf_nat_estimate, rtol=1e-7) def test_acf_counts_with_weights_are_correct( dask_client, - acf_gals_weight_dir, - acf_rans_weight_dir, + acf_gals_weight_catalog, + acf_rans_weight_catalog, acf_dd_counts_with_weights, acf_rr_counts_with_weights, acf_params, ): - galaxy_catalog = lsdb.read_hipscat(acf_gals_weight_dir) - random_catalog = lsdb.read_hipscat(acf_rans_weight_dir) - assert isinstance(galaxy_catalog, lsdb.Catalog) - assert isinstance(random_catalog, lsdb.Catalog) + ang_corr = AngularCorrelation(params=acf_params, use_weights=True) counts_dd, counts_rr = compute_autocorrelation_counts( - AngularCorrelation, - galaxy_catalog, - random_catalog, - acf_params, - use_weights=True, + acf_gals_weight_catalog, acf_rans_weight_catalog, ang_corr ) npt.assert_allclose(counts_dd, acf_dd_counts_with_weights, rtol=1e-3) npt.assert_allclose(counts_rr, acf_rr_counts_with_weights, rtol=2e-3) -def test_acf_weights_not_provided(data_catalog_dir, rand_catalog_dir, acf_params): - galaxy_catalog = lsdb.read_hipscat(data_catalog_dir) - random_catalog = lsdb.read_hipscat(rand_catalog_dir) - assert isinstance(galaxy_catalog, lsdb.Catalog) - assert isinstance(random_catalog, lsdb.Catalog) +def test_acf_weights_not_provided(data_catalog, rand_catalog, acf_params): with pytest.raises(ValueError, match="does not exist"): compute_autocorrelation( + data_catalog, + rand_catalog, AngularCorrelation, - galaxy_catalog, - random_catalog, - acf_params, + params=acf_params, use_weights=True, ) diff --git a/tests/corrgi/test_pcf.py b/tests/corrgi/test_pcf.py index fff01b6..cc9517e 100644 --- a/tests/corrgi/test_pcf.py +++ b/tests/corrgi/test_pcf.py @@ -1,23 +1,15 @@ -import lsdb from corrgi.correlation.projected_correlation import ProjectedCorrelation from corrgi.dask import compute_autocorrelation_counts import numpy.testing as npt def test_pcf_counts_are_correct( - dask_client, - data_catalog_dir, - rand_catalog_dir, - pcf_dd_counts, - pcf_rr_counts, - pcf_params, + dask_client, data_catalog, rand_catalog, pcf_dd_counts, pcf_rr_counts, pcf_params ): - galaxy_catalog = lsdb.read_hipscat(data_catalog_dir) - random_catalog = lsdb.read_hipscat(rand_catalog_dir) - assert isinstance(galaxy_catalog, lsdb.Catalog) - assert isinstance(random_catalog, lsdb.Catalog) + proj_corr = ProjectedCorrelation(params=pcf_params) counts_dd, counts_rr = compute_autocorrelation_counts( - ProjectedCorrelation, galaxy_catalog, random_catalog, pcf_params + data_catalog, rand_catalog, proj_corr ) - npt.assert_allclose(counts_dd.transpose([1, 0]), pcf_dd_counts, rtol=1e-3) - npt.assert_allclose(counts_rr.transpose([1, 0]), pcf_rr_counts, rtol=2e-3) + expected_dd, expected_rr = counts_dd.transpose([1, 0]), counts_rr.transpose([1, 0]) + npt.assert_allclose(expected_dd, pcf_dd_counts, rtol=1e-3) + npt.assert_allclose(expected_rr, pcf_rr_counts, rtol=2e-3) From f05e5c1c4d7b310162383175e5cb88b3ec4e56b7 Mon Sep 17 00:00:00 2001 From: Sandro Campos Date: Tue, 9 Jul 2024 10:16:42 -0400 Subject: [PATCH 08/12] Add validation unit test for pcf --- src/corrgi/correlation/correlation.py | 5 ++--- src/corrgi/correlation/projected_correlation.py | 5 +++-- src/corrgi/correlation/redshift_correlation.py | 3 ++- src/corrgi/dask.py | 1 - tests/corrgi/test_pcf.py | 13 +++++++++++++ 5 files changed, 20 insertions(+), 7 deletions(-) diff --git a/src/corrgi/correlation/correlation.py b/src/corrgi/correlation/correlation.py index 452e0a8..ba8a1d9 100644 --- a/src/corrgi/correlation/correlation.py +++ b/src/corrgi/correlation/correlation.py @@ -6,9 +6,8 @@ import numpy as np import pandas as pd from hipscat.catalog.catalog_info import CatalogInfo -from munch import Munch - from lsdb import Catalog +from munch import Munch from corrgi.utils import project_coordinates @@ -26,7 +25,7 @@ def __init__( self.weight_column = weight_column self.use_weights = use_weights - def validate(self, catalogs: List[Catalog]): + def validate(self, catalogs: list[Catalog]): """Validate that the correlation args/data are valid""" if not self.use_weights: return diff --git a/src/corrgi/correlation/projected_correlation.py b/src/corrgi/correlation/projected_correlation.py index b265cc6..d8ff591 100644 --- a/src/corrgi/correlation/projected_correlation.py +++ b/src/corrgi/correlation/projected_correlation.py @@ -6,8 +6,8 @@ from astropy.cosmology import LambdaCDM from gundam import gundam from hipscat.catalog.catalog_info import CatalogInfo -from munch import Munch from lsdb import Catalog +from munch import Munch from corrgi.correlation.correlation import Correlation @@ -28,7 +28,8 @@ def __init__( self.cosmo = LambdaCDM(H0=params.h0, Om0=params.omegam, Ode0=params.omegal) def validate(self, catalogs: list[Catalog]): - super().validate_cross_correlation(catalogs) + """Validate that the correlation args/data are valid""" + super().validate(catalogs) for catalog in catalogs: if self.redshift_column not in catalog.columns: raise ValueError(f"Redshift column {self.redshift_column} not found in {catalog}") diff --git a/src/corrgi/correlation/redshift_correlation.py b/src/corrgi/correlation/redshift_correlation.py index 3e6396d..80a0bf9 100644 --- a/src/corrgi/correlation/redshift_correlation.py +++ b/src/corrgi/correlation/redshift_correlation.py @@ -2,15 +2,16 @@ import pandas as pd from hipscat.catalog.catalog_info import CatalogInfo +from lsdb import Catalog from corrgi.correlation.correlation import Correlation -from lsdb import Catalog class RedshiftCorrelation(Correlation): """The redshift correlation utilities.""" def validate(self, catalogs: list[Catalog]): + """Validate that the correlation args/data are valid""" super().validate(catalogs) def make_bins(self): diff --git a/src/corrgi/dask.py b/src/corrgi/dask.py index d720cba..1457ea0 100644 --- a/src/corrgi/dask.py +++ b/src/corrgi/dask.py @@ -6,7 +6,6 @@ from hipscat.pixel_math import HealpixPixel from lsdb import Catalog from lsdb.dask.merge_catalog_functions import align_and_apply, get_healpix_pixels_from_alignment -from munch import Munch from corrgi.alignment import autocorrelation_alignment, crosscorrelation_alignment from corrgi.correlation.correlation import Correlation diff --git a/tests/corrgi/test_pcf.py b/tests/corrgi/test_pcf.py index cc9517e..3743859 100644 --- a/tests/corrgi/test_pcf.py +++ b/tests/corrgi/test_pcf.py @@ -1,4 +1,6 @@ +import pytest from corrgi.correlation.projected_correlation import ProjectedCorrelation +from corrgi.corrgi import compute_autocorrelation from corrgi.dask import compute_autocorrelation_counts import numpy.testing as npt @@ -13,3 +15,14 @@ def test_pcf_counts_are_correct( expected_dd, expected_rr = counts_dd.transpose([1, 0]), counts_rr.transpose([1, 0]) npt.assert_allclose(expected_dd, pcf_dd_counts, rtol=1e-3) npt.assert_allclose(expected_rr, pcf_rr_counts, rtol=2e-3) + + +def test_pcf_catalog_has_no_redshift(data_catalog, rand_catalog, pcf_params): + with pytest.raises(ValueError, match="ph_z not found"): + compute_autocorrelation( + data_catalog, + rand_catalog, + ProjectedCorrelation, + params=pcf_params, + redshift_column="ph_z", + ) From 6e8e4375eca1432547c04ea5dc14ea6238b75da8 Mon Sep 17 00:00:00 2001 From: Sandro Campos Date: Tue, 9 Jul 2024 11:21:33 -0400 Subject: [PATCH 09/12] Implement projected cross validation --- src/corrgi/corrgi.py | 21 +++++++--- src/corrgi/dask.py | 19 +++++++++ src/corrgi/estimators.py | 36 ++++++++++++++---- tests/corrgi/conftest.py | 30 +++++++++++++++ tests/corrgi/test_acf.py | 6 +-- .../corrgi/{test_pcf.py => test_projected.py} | 23 ++++++++++- .../expected_results/pccf/cd_pccf_weight.npy | Bin 0 -> 352 bytes .../expected_results/pccf/cr_pccf_weight.npy | Bin 0 -> 352 bytes tests/data/generate_data.ipynb | 9 +++-- .../Norder=0/Dir=0/Npix=1.parquet | Bin 0 -> 378547 bytes .../Norder=0/Dir=0/Npix=10.parquet | Bin 0 -> 6888 bytes .../Norder=0/Dir=0/Npix=2.parquet | Bin 0 -> 511363 bytes .../Norder=0/Dir=0/Npix=5.parquet | Bin 0 -> 13889 bytes .../Norder=0/Dir=0/Npix=6.parquet | Bin 0 -> 340709 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8 ++++ .../pcf_gals_weight/partition_info.csv | 8 ++++ .../pcf_gals_weight/provenance_info.json | 25 ++++++++++++ .../Norder=0/Dir=0/Npix=1.parquet | Bin 0 -> 2769862 bytes .../Norder=0/Dir=0/Npix=10.parquet | Bin 0 -> 16191 bytes .../Norder=0/Dir=0/Npix=2.parquet | Bin 0 -> 3105717 bytes .../Norder=0/Dir=0/Npix=5.parquet | Bin 0 -> 64490 bytes .../Norder=0/Dir=0/Npix=6.parquet | Bin 0 -> 2269665 bytes .../Norder=0/Dir=0/Npix=7.parquet | Bin 0 -> 327345 bytes .../Norder=0/Dir=0/Npix=9.parquet | Bin 0 -> 6181 bytes .../hipscat/pcf_rans_weight/_common_metadata | Bin 0 -> 3767 bytes tests/data/hipscat/pcf_rans_weight/_metadata | Bin 0 -> 10451 bytes .../hipscat/pcf_rans_weight/catalog_info.json | 8 ++++ .../pcf_rans_weight/partition_info.csv | 8 ++++ .../pcf_rans_weight/provenance_info.json | 25 ++++++++++++ tests/data/raw/pcf_gals1_weight.fits | Bin 0 -> 725760 bytes tests/data/raw/pcf_gals_weight.fits | Bin 0 -> 1693440 bytes tests/data/raw/pcf_rans_weight.fits | Bin 0 -> 4553280 bytes 48 files changed, 245 insertions(+), 22 deletions(-) rename tests/corrgi/{test_pcf.py => test_projected.py} (57%) create mode 100644 tests/data/expected_results/pccf/cd_pccf_weight.npy create mode 100644 tests/data/expected_results/pccf/cr_pccf_weight.npy create mode 100644 tests/data/hipscat/pcf_gals1_weight/Norder=0/Dir=0/Npix=1.parquet create mode 100644 tests/data/hipscat/pcf_gals1_weight/Norder=0/Dir=0/Npix=10.parquet create mode 100644 tests/data/hipscat/pcf_gals1_weight/Norder=0/Dir=0/Npix=2.parquet create mode 100644 tests/data/hipscat/pcf_gals1_weight/Norder=0/Dir=0/Npix=5.parquet create mode 100644 tests/data/hipscat/pcf_gals1_weight/Norder=0/Dir=0/Npix=6.parquet create mode 100644 tests/data/hipscat/pcf_gals1_weight/Norder=0/Dir=0/Npix=7.parquet create mode 100644 tests/data/hipscat/pcf_gals1_weight/Norder=0/Dir=0/Npix=9.parquet create mode 100644 tests/data/hipscat/pcf_gals1_weight/_common_metadata create mode 100644 tests/data/hipscat/pcf_gals1_weight/_metadata create mode 100644 tests/data/hipscat/pcf_gals1_weight/catalog_info.json create mode 100644 tests/data/hipscat/pcf_gals1_weight/partition_info.csv create mode 100644 tests/data/hipscat/pcf_gals1_weight/provenance_info.json create mode 100644 tests/data/hipscat/pcf_gals_weight/Norder=0/Dir=0/Npix=1.parquet create mode 100644 tests/data/hipscat/pcf_gals_weight/Norder=0/Dir=0/Npix=10.parquet create mode 100644 tests/data/hipscat/pcf_gals_weight/Norder=0/Dir=0/Npix=2.parquet create mode 100644 tests/data/hipscat/pcf_gals_weight/Norder=0/Dir=0/Npix=5.parquet create mode 100644 tests/data/hipscat/pcf_gals_weight/Norder=0/Dir=0/Npix=6.parquet create mode 100644 tests/data/hipscat/pcf_gals_weight/Norder=0/Dir=0/Npix=7.parquet create mode 100644 tests/data/hipscat/pcf_gals_weight/Norder=0/Dir=0/Npix=9.parquet create mode 100644 tests/data/hipscat/pcf_gals_weight/_common_metadata create mode 100644 tests/data/hipscat/pcf_gals_weight/_metadata create mode 100644 tests/data/hipscat/pcf_gals_weight/catalog_info.json create mode 100644 tests/data/hipscat/pcf_gals_weight/partition_info.csv create mode 100644 tests/data/hipscat/pcf_gals_weight/provenance_info.json create mode 100644 tests/data/hipscat/pcf_rans_weight/Norder=0/Dir=0/Npix=1.parquet create mode 100644 tests/data/hipscat/pcf_rans_weight/Norder=0/Dir=0/Npix=10.parquet create mode 100644 tests/data/hipscat/pcf_rans_weight/Norder=0/Dir=0/Npix=2.parquet create mode 100644 tests/data/hipscat/pcf_rans_weight/Norder=0/Dir=0/Npix=5.parquet create mode 100644 tests/data/hipscat/pcf_rans_weight/Norder=0/Dir=0/Npix=6.parquet create mode 100644 tests/data/hipscat/pcf_rans_weight/Norder=0/Dir=0/Npix=7.parquet create mode 100644 tests/data/hipscat/pcf_rans_weight/Norder=0/Dir=0/Npix=9.parquet create mode 100644 tests/data/hipscat/pcf_rans_weight/_common_metadata create mode 100644 tests/data/hipscat/pcf_rans_weight/_metadata create mode 100644 tests/data/hipscat/pcf_rans_weight/catalog_info.json create mode 100644 tests/data/hipscat/pcf_rans_weight/partition_info.csv create mode 100644 tests/data/hipscat/pcf_rans_weight/provenance_info.json create mode 100644 tests/data/raw/pcf_gals1_weight.fits create mode 100644 tests/data/raw/pcf_gals_weight.fits create mode 100644 tests/data/raw/pcf_rans_weight.fits diff --git a/src/corrgi/corrgi.py b/src/corrgi/corrgi.py index 3e272ac..b80d714 100644 --- a/src/corrgi/corrgi.py +++ b/src/corrgi/corrgi.py @@ -4,7 +4,7 @@ from corrgi.correlation.correlation import Correlation from corrgi.dask import compute_autocorrelation_counts -from corrgi.estimators import calculate_natural_estimate +from corrgi.estimators import calculate_tpcf, calculate_tpccf def compute_autocorrelation( @@ -25,21 +25,30 @@ def compute_autocorrelation( correlation = corr_type(**kwargs) correlation.validate([catalog, random]) counts_dd, counts_rr = compute_autocorrelation_counts(catalog, random, correlation) - num_galaxies = catalog.hc_structure.catalog_info.total_rows + num_particles = catalog.hc_structure.catalog_info.total_rows num_random = random.hc_structure.catalog_info.total_rows - return calculate_natural_estimate(counts_dd, counts_rr, num_galaxies, num_random) + return calculate_tpcf(counts_dd, counts_rr, num_particles, num_random) -def compute_crosscorrelation(left: Catalog, right: Catalog, random: Catalog, params: Munch) -> np.ndarray: +def compute_crosscorrelation( + left: Catalog, right: Catalog, random: Catalog, corr_type: type[Correlation], **kwargs +) -> np.ndarray: """Computes the cross-correlation between two catalogs. Args: left (Catalog): Left catalog for the cross-correlation. right (Catalog): Right catalog for the cross-correlation. random (Catalog): A random samples catalog. - params (Munch): The parameters dictionary to run gundam with. + corr_type (type[Correlation]): The corrgi class corresponding to the type of + correlation (AngularCorrelation, RedshiftCorrelation, or ProjectedCorrelation). + **kwargs (dict): The arguments for the creation of the correlation instance. Returns: A numpy array with the result of the cross-correlation, using the natural estimator. """ - raise NotImplementedError() + correlation = corr_type(**kwargs) + correlation.validate([catalog, random]) + counts_cd, counts_cr = compute_crosscorrelation_counts(left, right, random, correlation) + num_particles = left.hc_structure.catalog_info.total_rows + num_random = random.hc_structure.catalog_info.total_rows + return calculate_tpccf(counts_cd, counts_cr, num_particles, num_random) diff --git a/src/corrgi/dask.py b/src/corrgi/dask.py index 1457ea0..ff08bc6 100644 --- a/src/corrgi/dask.py +++ b/src/corrgi/dask.py @@ -53,6 +53,25 @@ def perform_auto_counts(catalog: Catalog, *args) -> np.ndarray: return join_count_histograms(all_partials) +def compute_crosscorrelation_counts( + left: Catalog, right: Catalog, random: Catalog, correlation: Correlation +) -> np.ndarray: + """Computes the cross-correlation counts for a catalog. + + Args: + left (Catalog): The left catalog with galaxy samples. + right (Catalog): The right catalog with galaxy samples. + random (Catalog): The catalog with random samples. + correlation (Correlation): The correlation instance. + + Returns: + The histogram counts to calculate the cross-correlation. + """ + counts_cd = perform_cross_counts(right, left, correlation) + counts_cr = perform_cross_counts(right, random, correlation) + return dask.compute(*[counts_cd, counts_cr]) + + def perform_cross_counts(left: Catalog, right: Catalog, *args) -> np.ndarray: """Aligns the pixel of two catalogs and performs the pairs counting. diff --git a/src/corrgi/estimators.py b/src/corrgi/estimators.py index 7d03b38..9eaf5df 100644 --- a/src/corrgi/estimators.py +++ b/src/corrgi/estimators.py @@ -1,19 +1,18 @@ import numpy as np -from gundam import tpcf +from gundam import tpcf, tpccf -def calculate_natural_estimate( - counts_dd: np.ndarray, - counts_rr: np.ndarray, - num_galaxies: int, - num_random: int, +def calculate_tpcf( + counts_dd: np.ndarray, counts_rr: np.ndarray, num_particles: int, num_random: int ) -> np.ndarray: """Calculates the auto-correlation value for the natural estimator. + Evaluation given data (D), and random (R) samples. + Args: counts_dd (np.ndarray): The counts for the galaxy samples. counts_rr (np.ndarray): The counts for the random samples. - num_galaxies (int): The number of galaxy samples. + num_particles (int): The number of particles in data (D). num_random (int): The number of random samples. Returns: @@ -21,5 +20,26 @@ def calculate_natural_estimate( """ dr = 0 # We do not use DR counts for the natural estimator bdd = np.zeros([len(counts_dd), 0]) # We do not compute the bootstrap counts - wth, _ = tpcf(num_galaxies, num_random, counts_dd, bdd, counts_rr, dr, estimator="NAT") + wth, _ = tpcf(num_particles, num_random, counts_dd, bdd, counts_rr, dr, estimator="NAT") + return wth + + +def calculate_tpccf( + counts_cd: np.ndarray, counts_cr: np.ndarray, num_particles: int, num_random: int +) -> np.ndarray: + """Calculates the cross-correlation value for the natural estimator. + + Evaluation given data (D), random (R) and cross (C) samples. + + Args: + counts_cd (np.ndarray): The counts for data-cross. + counts_cr (np.ndarray): The counts for cross-random. + num_particles (int): The number of particles in data (D). + num_random (int): The number of particles in random samples (R). + + Returns: + The natural correlation function estimate. + """ + bcd = np.zeros([len(counts_cd), 0]) # We do not compute the bootstrap counts + wth, _ = tpccf(num_particles, num_random, counts_cd, bcd, counts_cr, estimator="NAT") return wth diff --git a/tests/corrgi/conftest.py b/tests/corrgi/conftest.py index 3680a28..178dc24 100644 --- a/tests/corrgi/conftest.py +++ b/tests/corrgi/conftest.py @@ -36,6 +36,11 @@ def pcf_expected_results(test_data_dir): return test_data_dir / "expected_results" / "pcf" +@pytest.fixture +def pccf_expected_results(test_data_dir): + return test_data_dir / "expected_results" / "pccf" + + @pytest.fixture def data_catalog_dir(hipscat_catalogs_dir): return hipscat_catalogs_dir / "DATA" @@ -116,6 +121,21 @@ def acf_rr_counts_with_weights(acf_expected_results): return np.load(acf_expected_results / "rr_acf_weight.npy") +@pytest.fixture +def pcf_gals_weight_catalog(hipscat_catalogs_dir): + return lsdb.read_hipscat(hipscat_catalogs_dir / "pcf_gals_weight") + + +@pytest.fixture +def pcf_gals1_weight_catalog(hipscat_catalogs_dir): + return lsdb.read_hipscat(hipscat_catalogs_dir / "pcf_gals1_weight") + + +@pytest.fixture +def pcf_rans_weight_catalog(hipscat_catalogs_dir): + return lsdb.read_hipscat(hipscat_catalogs_dir / "pcf_rans_weight") + + @pytest.fixture def pcf_dd_counts(pcf_expected_results): return np.load(pcf_expected_results / "dd_pcf.npy") @@ -126,6 +146,16 @@ def pcf_rr_counts(pcf_expected_results): return np.load(pcf_expected_results / "rr_pcf.npy") +@pytest.fixture +def pccf_cd_counts(pccf_expected_results): + return np.load(pccf_expected_results / "cd_pccf_weight.npy") + + +@pytest.fixture +def pccf_cr_counts(pccf_expected_results): + return np.load(pccf_expected_results / "cr_pccf_weight.npy") + + @pytest.fixture def acf_nat_estimate(acf_expected_results): return np.load(acf_expected_results / "w_acf_nat.npy") diff --git a/tests/corrgi/test_acf.py b/tests/corrgi/test_acf.py index f7ccd73..82d84aa 100644 --- a/tests/corrgi/test_acf.py +++ b/tests/corrgi/test_acf.py @@ -7,7 +7,7 @@ from corrgi.correlation.angular_correlation import AngularCorrelation from corrgi.corrgi import compute_autocorrelation from corrgi.dask import compute_autocorrelation_counts -from corrgi.estimators import calculate_natural_estimate +from corrgi.estimators import calculate_tpcf def test_acf_bins_are_correct(acf_bins_left_edges, acf_bins_right_edges, acf_params): @@ -48,9 +48,7 @@ def test_acf_natural_estimate_is_correct( random_hc_catalog = hipscat.read_from_hipscat(rand_catalog_dir) num_galaxies = galaxy_hc_catalog.catalog_info.total_rows num_random = random_hc_catalog.catalog_info.total_rows - estimate = calculate_natural_estimate( - acf_dd_counts, acf_rr_counts, num_galaxies, num_random - ) + estimate = calculate_tpcf(acf_dd_counts, acf_rr_counts, num_galaxies, num_random) npt.assert_allclose(acf_nat_estimate, estimate, rtol=2e-3) diff --git a/tests/corrgi/test_pcf.py b/tests/corrgi/test_projected.py similarity index 57% rename from tests/corrgi/test_pcf.py rename to tests/corrgi/test_projected.py index 3743859..253f3e6 100644 --- a/tests/corrgi/test_pcf.py +++ b/tests/corrgi/test_projected.py @@ -1,7 +1,7 @@ import pytest from corrgi.correlation.projected_correlation import ProjectedCorrelation from corrgi.corrgi import compute_autocorrelation -from corrgi.dask import compute_autocorrelation_counts +from corrgi.dask import compute_autocorrelation_counts, compute_crosscorrelation_counts import numpy.testing as npt @@ -26,3 +26,24 @@ def test_pcf_catalog_has_no_redshift(data_catalog, rand_catalog, pcf_params): params=pcf_params, redshift_column="ph_z", ) + + +def test_pccf_with_weights( + dask_client, + pcf_gals_weight_catalog, + pcf_gals1_weight_catalog, + pcf_rans_weight_catalog, + pccf_cd_counts, + pccf_cr_counts, + pcf_params, +): + proj_corr = ProjectedCorrelation(params=pcf_params, use_weights=True) + counts_cd, counts_cr = compute_crosscorrelation_counts( + pcf_gals_weight_catalog, + pcf_gals1_weight_catalog, + pcf_rans_weight_catalog, + proj_corr, + ) + expected_cd, expected_cr = counts_cd.transpose([1, 0]), counts_cr.transpose([1, 0]) + npt.assert_allclose(expected_cd, pccf_cd_counts, rtol=1e-3) + npt.assert_allclose(expected_cr, pccf_cr_counts, rtol=2e-3) diff --git a/tests/data/expected_results/pccf/cd_pccf_weight.npy b/tests/data/expected_results/pccf/cd_pccf_weight.npy new file mode 100644 index 0000000000000000000000000000000000000000..366a41e101a2c3814a7c1f43d362bc347e389048 GIT binary patch literal 352 zcmbR27wQ`j$;eQ~P_3SlTAW;@Zl$1ZlV+i=qoAIaUsO_*m=~X4l#&V(cT3DEP6dh= zXCxM+0{I$77CH)snmP)#3giMV28IB?g6Fah3=AgcRvlMV4(r)MmDOJSaF}fL#rkRktE1#0ZIRe|AxCLZsrh7!+`QiTM(>?8NA<|mS5v1KIRXGgU4SA0 literal 0 HcmV?d00001 diff --git a/tests/data/expected_results/pccf/cr_pccf_weight.npy b/tests/data/expected_results/pccf/cr_pccf_weight.npy new file mode 100644 index 0000000000000000000000000000000000000000..99b7dd6cdbe98096e6e810272b1e9e55bd3c0972 GIT binary patch literal 352 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