diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index b345ddba..20384420 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -62,22 +62,21 @@ jobs: use-mamba: true activate-environment: tjpcov miniforge-version: latest - miniforge-variant: Mambaforge - - name: Cache Date - id: get-date - run: echo "today=$(/bin/date -u '+%Y%m%d')" >> $GITHUB_OUTPUT - - - name: Check for cached environment - uses: actions/cache@v3 - id: cache - env: - # Increase this value to reset cache if etc/example-environment.yml has not changed - CACHE_NUMBER: 0 - with: - path: ${{env.CONDA}}/envs - key: - ${{runner.os}}.${{runner.arch}}.mamba-py.${{matrix.python-version}}.${{hashFiles('pyproject.toml')}}.${{hashFiles('environment.yml')}}.${{steps.get-date.outputs.today}}.${{env.CACHE_NUMBER}} + # - name: Cache Date + # id: get-date + # run: echo "today=$(/bin/date -u '+%Y%m%d')" >> $GITHUB_OUTPUT + # + # - name: Check for cached environment + # uses: actions/cache@v3 + # id: cache + # env: + # # Increase this value to reset cache if etc/example-environment.yml has not changed + # CACHE_NUMBER: 0 + # with: + # path: ${{env.CONDA}}/envs + # key: + # ${{runner.os}}.${{runner.arch}}.mamba-py.${{matrix.python-version}}.${{hashFiles('pyproject.toml')}}.${{hashFiles('environment.yml')}}.${{steps.get-date.outputs.today}}.${{env.CACHE_NUMBER}} - name: Install environment packages if: steps.cache.outputs.cache-hit != 'true' @@ -97,7 +96,7 @@ jobs: pip install -U mpi4py --no-cache-dir --no-deps - name: Run Tests - run: coverage run --source=tjpcov -m pytest -vv tests/ + run: coverage run --source=tjpcov -m pytest -vv tests && coverage lcov - name: Run MPI Tests (OpenMPI) run: | @@ -122,6 +121,7 @@ jobs: github-token: ${{ secrets.GITHUB_TOKEN }} flag-name: run-${{ join(matrix.*, '-') }} parallel: true + path-to-lcov: coverage.lcov finish: needs: tests diff --git a/Untitled.ipynb b/Untitled.ipynb deleted file mode 100644 index 5f2754f7..00000000 --- a/Untitled.ipynb +++ /dev/null @@ -1,145 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "id": "a6a12903", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline\n", - "\n", - "import numpy as np\n", - "import healpy as hp\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "fadde9f9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "786432\n" - ] - } - ], - "source": [ - "NSIDE=256\n", - "NPIX = hp.nside2npix(NSIDE)\n", - "print(NPIX)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f4b30ffe", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "m = np.arange(NPIX)\n", - "hp.mollview(m, title=\"Mollview image RING\")\n", - "hp.graticule()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "49e0d8b0", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "vec = hp.ang2vec(np.pi/2, 0.)\n", - "\n", - "fsky = 0.4\n", - "\n", - "radius = fsky* np.radians(180)\n", - "\n", - "mask = healpy.query_disc(NSIDE, vec = vec, radius=radius)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "82d0550b", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "setting the output map dtype to [dtype('float64')]\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "m = np.zeros(NPIX)\n", - "m[mask] = 1\n", - "hp.mollview(m, title=\"Mollview image RING\")\n", - "\n", - "hp.write_map(\"disk_fsky_04.fits\", m, overwrite=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b037a090", - "metadata": {}, - "outputs": [], - "source": [ - "import " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "forecasting", - "language": "python", - "name": "forecasting" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/disk_fsky_04.fits b/disk_fsky_04.fits deleted file mode 100644 index 27149f8a..00000000 Binary files a/disk_fsky_04.fits and /dev/null differ diff --git a/environment.yml b/environment.yml index 1e51b2d0..b909c26e 100644 --- a/environment.yml +++ b/environment.yml @@ -11,11 +11,12 @@ dependencies: - pytest-rerunfailures - coveralls - numpy - - scipy + # - scipy + - scipy<1.12 # <1.12 to avoid bug in CCL<3.1 (not in pypy) with simpson integration - pyyaml - pyccl>=3.0.0 - sacc>=0.12 - - namaster + - namaster>2 - camb - Jinja2 - healpy diff --git a/pyproject.toml b/pyproject.toml index cd3aa1ca..78f81c3a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,7 +23,7 @@ classifiers = [ "Topic :: Scientific/Engineering :: Physics" ] dependencies = [ - "scipy", + "scipy<1.12", # <1.12 to avoid bug in CCL<3.1 (not in pypy) with simpson integration "numpy", "Jinja2", "pyyaml", @@ -47,13 +47,13 @@ doc = [ "sphinx-book-theme", ] nmt = [ - "pymaster>=1.4.0" + "pymaster>=2" ] mpi = [ "mpi4py" ] full = [ - "pymaster>=1.4.0", + "pymaster>=2", "mpi4py", "sphinx", "sphinx-autoapi", diff --git a/tests/data/conf_covariance_calculator.yml b/tests/data/conf_covariance_calculator.yml index fc7e87ac..d5ab3f5b 100644 --- a/tests/data/conf_covariance_calculator.yml +++ b/tests/data/conf_covariance_calculator.yml @@ -41,19 +41,17 @@ NaMaster: module: pymaster f: n_iter: 0 - lmax_sht: -1 + lmax: null + lmax_mask: null w: - lmax_mask: -1 l_toeplitz: -1 l_exact: -1 dl_band: -1 - n_iter: 3 recompute: True cw: l_toeplitz: -1 l_exact: -1 dl_band: -1 - n_iter: 3 recompute: True cache: diff --git a/tests/data/conf_covariance_calculator_mpi.yml b/tests/data/conf_covariance_calculator_mpi.yml index 23438dec..b6d38485 100644 --- a/tests/data/conf_covariance_calculator_mpi.yml +++ b/tests/data/conf_covariance_calculator_mpi.yml @@ -42,19 +42,17 @@ NaMaster: module: pymaster f: n_iter: 0 - lmax_sht: -1 + lmax: null + lmax_mask: null w: - lmax_mask: -1 l_toeplitz: -1 l_exact: -1 dl_band: -1 - n_iter: 3 recompute: True cw: l_toeplitz: -1 l_exact: -1 dl_band: -1 - n_iter: 3 recompute: True cache: diff --git a/tests/data/conf_covariance_gaussian_fourier_nmt.yaml b/tests/data/conf_covariance_gaussian_fourier_nmt.yaml index d370cc76..35118288 100644 --- a/tests/data/conf_covariance_gaussian_fourier_nmt.yaml +++ b/tests/data/conf_covariance_gaussian_fourier_nmt.yaml @@ -40,19 +40,17 @@ NaMaster: module: pymaster f: n_iter: 0 - lmax_sht: -1 + lmax: null + lmax_mask: null w: - lmax_mask: -1 l_toeplitz: -1 l_exact: -1 dl_band: -1 - n_iter: 3 recompute: True cw: l_toeplitz: -1 l_exact: -1 dl_band: -1 - n_iter: 3 recompute: True parameters: diff --git a/tests/data/conf_covariance_gaussian_fourier_nmt_txpipe.yaml b/tests/data/conf_covariance_gaussian_fourier_nmt_txpipe.yaml index 2e85a600..752af664 100644 --- a/tests/data/conf_covariance_gaussian_fourier_nmt_txpipe.yaml +++ b/tests/data/conf_covariance_gaussian_fourier_nmt_txpipe.yaml @@ -26,19 +26,17 @@ NaMaster: module: pymaster f: n_iter: 0 - lmax_sht: -1 + lmax: null + lmax_mask: null w: - lmax_mask: -1 l_toeplitz: -1 l_exact: -1 dl_band: -1 - n_iter: 3 recompute: True cw: l_toeplitz: -1 l_exact: -1 dl_band: -1 - n_iter: 3 recompute: True parameters: diff --git a/tests/data/conf_covariance_gaussian_fourier_nmt_txpipe_mpi.yaml b/tests/data/conf_covariance_gaussian_fourier_nmt_txpipe_mpi.yaml index fbef6b7f..c560f504 100644 --- a/tests/data/conf_covariance_gaussian_fourier_nmt_txpipe_mpi.yaml +++ b/tests/data/conf_covariance_gaussian_fourier_nmt_txpipe_mpi.yaml @@ -26,19 +26,17 @@ NaMaster: module: pymaster f: n_iter: 0 - lmax_sht: -1 + lmax: null + lmax_mask: null w: - lmax_mask: -1 l_toeplitz: -1 l_exact: -1 dl_band: -1 - n_iter: 3 recompute: True cw: l_toeplitz: -1 l_exact: -1 dl_band: -1 - n_iter: 3 recompute: True parameters: diff --git a/tests/test_covariance_fourier_gaussian_nmt.py b/tests/test_covariance_fourier_gaussian_nmt.py index 9bb593cb..8e3016d9 100644 --- a/tests/test_covariance_fourier_gaussian_nmt.py +++ b/tests/test_covariance_fourier_gaussian_nmt.py @@ -389,16 +389,18 @@ def test_get_cl_for_cov(cov_fg_nmt): with pytest.raises(ValueError): cov_fg_nmt.get_cl_for_cov( - cl_fid_Sh, cl["nl_cp"], m, m, w, nl_is_cp=True + cl_fid_Sh["cl"], cl["nl_cp"], m, m, w, nl_is_cp=True ) with pytest.raises(ValueError): # Uncoupled binned noise - cov_fg_nmt.get_cl_for_cov(cl_fid, cl["nl"], m, m, w, nl_is_cp=True) + cov_fg_nmt.get_cl_for_cov( + cl_fid["cl"], cl["nl"], m, m, w, nl_is_cp=True + ) with pytest.raises(ValueError): cov_fg_nmt.get_cl_for_cov( - cl_fid, cl["nl_cp"], m, m, wSh, nl_is_cp=True + cl_fid["cl"], cl["nl_cp"], m, m, wSh, nl_is_cp=True ) @@ -662,7 +664,7 @@ def test_get_covariance_block_cache(cov_fg_nmt, tracer_comb1, tracer_comb2): @pytest.mark.parametrize( "kwargs", - [{}, {"l_toeplitz": 10, "l_exact": 10, "dl_band": 10, "n_iter": 0}], + [{}, {"l_toeplitz": 10, "l_exact": 10, "dl_band": 10}], ) def test_get_covariance_workspace(cov_fg_nmt, kwargs): m1 = get_mask_from_dtype("galaxy_clustering") @@ -715,7 +717,7 @@ def test_get_covariance_workspace(cov_fg_nmt, kwargs): # Check only the first is written/computed created & that cw is correct for fields, masks_names in zip(combinations, combinations_names): - spins = [fi.fl.spin for fi in fields] + spins = [fi.spin for fi in fields] cw_code = cov_fg_nmt.get_covariance_workspace( *fields, *masks_names, **kwargs ) @@ -967,7 +969,7 @@ def get_data_types(self): @pytest.mark.parametrize( "kwargs", - [{}, {"l_toeplitz": 10, "l_exact": 10, "dl_band": 10, "n_iter": 0}], + [{}, {"l_toeplitz": 10, "l_exact": 10, "dl_band": 10}], ) def test_get_workspace(cov_fg_nmt, kwargs): # Compute NmtBins @@ -1030,7 +1032,7 @@ def test_get_workspace(cov_fg_nmt, kwargs): @pytest.mark.parametrize( "kwargs", - [{}, {"l_toeplitz": 10, "l_exact": 10, "dl_band": 10, "n_iter": 0}], + [{}, {"l_toeplitz": 10, "l_exact": 10, "dl_band": 10}], ) def test_get_workspace_dict(cov_fg_nmt, kwargs): tracers = get_tracers_dict_for_cov() diff --git a/tjpcov/covariance_fourier_cNG_fsky.py b/tjpcov/covariance_fourier_cNG_fsky.py deleted file mode 100644 index 07945d90..00000000 --- a/tjpcov/covariance_fourier_cNG_fsky.py +++ /dev/null @@ -1,235 +0,0 @@ -import os - -import healpy as hp -import numpy as np -import pyccl as ccl - -from .covariance_builder import CovarianceFourier - - -class FouriercNGHaloModelFsky(CovarianceFourier): - """Class to compute the CellxCell Halo Model Super Sample Covariance. - - The SSC is computed in CCL with the "linear bias" approximation using - :func:`pyccl.halos.halo_model.halomod_Tk3D_SSC_linear_bias`. - """ - - cov_type = "cNG" - - def __init__(self, config): - """Initialize the class with a config file or dictionary. - - Args: - config (dict or str): If dict, it returns the configuration - dictionary directly. If string, it asumes a YAML file and - parses it. - """ - super().__init__(config) - - self.ssc_conf = self.config.get("cNG", {}) - self.fsky = self.config["GaussianFsky"].get("fsky", None) - if self.fsky is None: - raise ValueError("You need to set fsky for FouriercNGHaloModelFsky") - def get_binning_info(self, binning, lmax, lmin): - """Get the ells for bins given the sacc object. - - Args: - binning (str): Binning type. - - Returns: - tuple: - - ell (array): All the ells covered - - ell_eff (array): The effective ells - - ell_edges (array): The bandpower edges - """ - # TODO: This should be obtained from the sacc file or the input - # configuration. Check how it is done in TXPipe: - # https://github.com/LSSTDESC/TXPipe/blob/a9dfdb7809ac7ed6c162fd3930c643a67afcd881/txpipe/covariance.py#L23 - ell_eff = self.get_ell_eff() - #nbpw = ell_eff.size - ncl = len(ell_eff) - #ellb_min, ellb_max = ell_eff.min(), ell_eff.max() - #print(ell_eff) - #print(binning, lmax, lmin) - if binning == "linear": - print('linear binning!') - linspacing = np.linspace(lmin, lmax, 2*ncl+1) - ell_edges = linspacing[0::2] - ell = linspacing[1::2] - - elif binning == "log":#(np.log10(ellb_max)-np.log10(ellb_min))/len(ell_eff)==np.log10(ell_eff[1])-np.log10(ell_eff[0]): - print('log binning!') - logspacing = np.logspace(np.log10(lmin), np.log10(lmax), num=2*ncl+1, endpoint=True) - ell_edges = logspacing[0::2] - ell = logspacing[1::2] - - elif binning=="sqrt":#(np.sqrt(ellb_max)-np.sqrt(ellb_min))/len(ell_eff)==np.sqrt(ell_eff[1])-np.sqrt(ell_eff[0]): - print('sqrt binning!') - spacing = (np.sqrt(lmax) - np.sqrt(lmin))/(2*ncl) - temp = [lmin] - for i in range(2*ncl): temp.append((spacing + np.sqrt(temp[-1]))**2) - temp=np.array(temp) - ell_edges = temp[0::2] - ell = temp[1::2] - - else: - raise NotImplementedError(f"Binning {binning} not implemented yet") - - return ell, ell_eff, ell_edges - def get_covariance_block( - self, - tracer_comb1, - tracer_comb2, - integration_method=None, - include_b_modes=True, - ): - """Compute a single SSC covariance matrix for a given pair of C_ell. - - If outdir is set, it will save the covariance to a file called - ssc_tr1_tr2_tr3_tr4.npz. This file will be read and its output returned - if found. - - Blocks of the B-modes are assumed 0 so far. - - Args: - tracer_comb1 (list): List of the pair of tracer names of C_ell^1 - tracer_comb2 (list): List of the pair of tracer names of C_ell^2 - integration_method (str, optional): integration method to be - used for the Limber integrals. Possibilities: 'qag_quad' (GSL's - qag method backed up by quad when it fails) and 'spline' - (the integrand is splined and then integrated analytically). If - given, it will take priority over the specified in the - configuration file through config['SSC']['integration_method']. - Elsewise, it will use 'qag_quad'. - include_b_modes (bool, optional): If True, return the full SSC with - zeros in for B-modes (if any). If False, return the non-zero - block. This option cannot be modified through the configuration - file to avoid breaking the compatibility with the NaMaster - covariance. Defaults to True. - - Returns: - array: Super sample covariance matrix for a pair of C_ell. - """ - fname = "cNG_{}_{}_{}_{}.npz".format(*tracer_comb1, *tracer_comb2) - fname = os.path.join(self.io.outdir, fname) - if os.path.isfile(fname): - cf = np.load(fname) - return cf["cov" if include_b_modes else "cov_nob"] - - if integration_method is None: - integration_method = self.ssc_conf.get( - "integration_method", "qag_quad" - ) - - tr = {} - tr[1], tr[2] = tracer_comb1 - tr[3], tr[4] = tracer_comb2 - - cosmo = self.get_cosmology() - mass_def = ccl.halos.MassDef200m - hmf = ccl.halos.MassFuncTinker08(mass_def=mass_def) - hbf = ccl.halos.HaloBiasTinker10(mass_def=mass_def) - cM = ccl.halos.ConcentrationDuffy08(mass_def=mass_def) - nfw = ccl.halos.HaloProfileNFW( - mass_def=mass_def, concentration=cM, fourier_analytic=True - ) - hmc = ccl.halos.HMCalculator( - mass_function=hmf, halo_bias=hbf, mass_def=mass_def - ) - - # Get range of redshifts. z_min = 0 for compatibility with the limber - # integrals - sacc_file = self.io.get_sacc_file() - z_max = [] - for i in range(4): - tr_sacc = sacc_file.tracers[tr[i + 1]] - z = tr_sacc.z - # z, nz = tr_sacc.z, tr_sacc.nz - # z_min.append(z[np.where(nz > 0)[0][0]]) - # z_max.append(z[np.where(np.cumsum(nz)/np.sum(nz) > 0.999)[0][0]]) - z_max.append(z.max()) - - z_max = np.min(z_max) - - # Array of a. - # Use the a's in the pk spline - na = ccl.ccllib.get_pk_spline_na(cosmo.cosmo) - a, _ = ccl.ccllib.get_pk_spline_a(cosmo.cosmo, na, 0) - # Cut the array for efficiency - sel = 1 / a < z_max + 1 - # Include the next node so that z_max is in the range - sel[np.sum(~sel) - 1] = True - a = a[sel] - - bias1 = self.bias_lens.get(tr[1], 1) - bias2 = self.bias_lens.get(tr[2], 1) - bias3 = self.bias_lens.get(tr[3], 1) - bias4 = self.bias_lens.get(tr[4], 1) - - ccl_tracers, _ = self.get_tracer_info() - - s = self.io.get_sacc_file() - isnc = {} - for i in range(1, 5): - isnc[i] = (s.tracers[tr[i]].quantity == "galaxy_density") or ( - "lens" in tr[i] - ) - - tk3D = ccl.halos.halomod_Tk3D_SSC_linear_bias( - cosmo=cosmo, - hmc=hmc, - prof=nfw, - bias1=bias1, - bias2=bias2, - bias3=bias3, - bias4=bias4, - is_number_counts1=isnc[1], - is_number_counts2=isnc[2], - is_number_counts3=isnc[3], - is_number_counts4=isnc[4], - ) - - #masks = self.get_masks_dict(tr, {}) - # TODO: Optimize this, avoid computing the mask_wl for all blocks. - # Note that this is correct for same footprint cross-correlations. In - # case of multisurvey analyses this approximation might break. - #m12 = masks[1] * masks[2] - #m34 = masks[3] * masks[4] - #area = hp.nside2pixarea(hp.npix2nside(m12.size)) - - #alm = hp.map2alm(m12) - #blm = hp.map2alm(m34) - - #mask_wl = hp.alm2cl(alm, blm) - #mask_wl *= 2 * np.arange(mask_wl.size) + 1 - #mask_wl /= np.sum(m12) * np.sum(m34) * area**2 - - # Allow using fsky - sigma2_B = ccl.sigma2_B_disc(cosmo, a_arr=a, fsky=self.fsky) - ell, ell_bins, ell_edges = self.get_binning_info(self.binning_scheme, self.lmax, self.lmin) - #ell = self.get_ell_eff() - cov_ssc = ccl.covariances.angular_cl_cov_SSC( - cosmo, - tracer1=ccl_tracers[tr[1]], - tracer2=ccl_tracers[tr[2]], - tracer3=ccl_tracers[tr[3]], - tracer4=ccl_tracers[tr[4]], - ell=ell, - t_of_kk_a=tk3D, - sigma2_B=(a, sigma2_B), - integration_method=integration_method, - ) - - nbpw = ell.size - ncell1 = self.get_tracer_comb_ncell(tracer_comb1) - ncell2 = self.get_tracer_comb_ncell(tracer_comb2) - cov_full = np.zeros((nbpw, ncell1, nbpw, ncell2)) - cov_full[:, 0, :, 0] = cov_ssc - cov_full = cov_full.reshape((nbpw * ncell1, nbpw * ncell2)) - - np.savez_compressed(fname, cov=cov_full, cov_nob=cov_ssc) - - if not include_b_modes: - return cov_ssc - - return cov_full diff --git a/tjpcov/covariance_fourier_gaussian_nmt.py b/tjpcov/covariance_fourier_gaussian_nmt.py index 57a589f4..945048fb 100644 --- a/tjpcov/covariance_fourier_gaussian_nmt.py +++ b/tjpcov/covariance_fourier_gaussian_nmt.py @@ -319,7 +319,6 @@ def get_covariance_block( mn[2], mn[3], mn[4], - lmax=int(ell[-1]), **self.nmt_conf["cw"], ) @@ -385,10 +384,10 @@ def get_covariance_workspace( """ outdir = self.io.outdir spins = { - m1: f1.fl.spin, - m2: f2.fl.spin, - m3: f3.fl.spin, - m4: f4.fl.spin, + m1: f1.spin, + m2: f2.spin, + m3: f3.spin, + m4: f4.spin, } # Any other symmetry? @@ -673,7 +672,7 @@ def get_workspace(self, f1, f2, m1, m2, bins, **kwargs): ) outdir = self.io.outdir - s1, s2 = f1.fl.spin, f2.fl.spin + s1, s2 = f1.spin, f2.spin # Currently, outdir will be always not None. If not specified, it # will be the current directory. I leave this for now since we might @@ -763,7 +762,7 @@ def get_workspaces_dict( if key in cache: w[i] = cache[key] else: - s1, s2 = fields[i1].fl.spin, fields[i2].fl.spin + s1, s2 = fields[i1].spin, fields[i2].spin # In this case you have to check for m1 x m2 and m2 x m1 k = (mask_names[i1], mask_names[i2]) sk = "".join(sorted(f"{s1}{s2}")) diff --git a/tjpcov/covariance_gaussian_fsky.py b/tjpcov/covariance_gaussian_fsky.py index 972252e7..80fcc363 100644 --- a/tjpcov/covariance_gaussian_fsky.py +++ b/tjpcov/covariance_gaussian_fsky.py @@ -43,7 +43,7 @@ def get_binning_info(self, binning="linear"): # TODO: This should be obtained from the sacc file or the input # configuration. Check how it is done in TXPipe: # https://github.com/LSSTDESC/TXPipe/blob/a9dfdb7809ac7ed6c162fd3930c643a67a - out = self.get_binning_info() + out = super().get_binning_info() if out is not None: ell = out[0] ell_eff = out[1]