From 6b390a338becc5213a4d8d470eb912dc1a46fa05 Mon Sep 17 00:00:00 2001 From: David Alonso Date: Wed, 9 Oct 2024 17:56:29 +0100 Subject: [PATCH] Anisotropic masks (#217) *Anisotropic weighting implemented --- doc/1BasicFunctionality.html | 3 +- doc/1BasicFunctionality.ipynb | 3 +- doc/2Spin.html | 3 +- doc/2Spin.ipynb | 3 +- doc/3Covariances.html | 3 +- doc/3Covariances.ipynb | 3 +- doc/4Catalogs.html | 3 +- doc/4Catalogs.ipynb | 3 +- doc/5AnisotropicWeighting.html | 7794 +++++++++++++++++++++ doc/5AnisotropicWeighting.ipynb | 270 + doc/conf.py | 3 +- doc/source/tutorials.rst | 1 + pymaster/covariance.py | 5 + pymaster/field.py | 109 +- pymaster/namaster.i | 49 +- pymaster/namaster_wrap.c | 613 ++ pymaster/nmtlib.py | 6 + pymaster/tests/test_master_anisotropic.py | 175 + pymaster/tests/test_utils.py | 2 +- pymaster/workspaces.py | 52 +- src/namaster.h | 16 + src/nmt_master.c | 232 + 22 files changed, 9327 insertions(+), 24 deletions(-) create mode 100644 doc/5AnisotropicWeighting.html create mode 100644 doc/5AnisotropicWeighting.ipynb create mode 100644 pymaster/tests/test_master_anisotropic.py diff --git a/doc/1BasicFunctionality.html b/doc/1BasicFunctionality.html index 74d3212f..4bb70ff8 100644 --- a/doc/1BasicFunctionality.html +++ b/doc/1BasicFunctionality.html @@ -7538,7 +7538,8 @@
  • A19. The original NaMaster paper (Alonso et al. 2019 https://arxiv.org/abs/1809.09603).
  • G19. Garcia-Garcia et al. 2019 (https://arxiv.org/abs/1906.11765), which introduces the basic approximation used by NaMaster to estimate covariance matrix.
  • N20. Nicola et al. 2020 (https://arxiv.org/abs/2010.09717), which refined these approximations and described in detail the procedure to estimate cosmic shear power spectra.
    • -
  • W24. Wolz et al. 2024 (TBD), which introduced the formalism for catalog-based $C_\ell$s.
    • +
  • W24. Wolz et al. 2024 (https://arxiv.org/abs/2407.21013), which introduced the formalism for catalog-based $C_\ell$s.
    • +
  • A24. Alonso 2024 (TBD), which generalised the estimator to anisotropic weights.
    • diff --git a/doc/1BasicFunctionality.ipynb b/doc/1BasicFunctionality.ipynb index c9e6010a..7f523493 100644 --- a/doc/1BasicFunctionality.ipynb +++ b/doc/1BasicFunctionality.ipynb @@ -22,7 +22,8 @@ "- **A19**. The original NaMaster paper (Alonso et al. 2019 https://arxiv.org/abs/1809.09603).\n", "- **G19**. Garcia-Garcia et al. 2019 (https://arxiv.org/abs/1906.11765), which introduces the basic approximation used by NaMaster to estimate covariance matrix.\n", "- **N20**. Nicola et al. 2020 (https://arxiv.org/abs/2010.09717), which refined these approximations and described in detail the procedure to estimate cosmic shear power spectra.\n", - "- **W24**. Wolz et al. 2024 (TBD), which introduced the formalism for catalog-based $C_\\ell$s." + "- **W24**. Wolz et al. 2024 (https://arxiv.org/abs/2407.21013), which introduced the formalism for catalog-based $C_\\ell$s.\n", + "- **A24**. Alonso 2024 (TBD), which generalised the estimator to anisotropic weights." ] }, { diff --git a/doc/2Spin.html b/doc/2Spin.html index 96df5b46..08483434 100644 --- a/doc/2Spin.html +++ b/doc/2Spin.html @@ -7538,7 +7538,8 @@
  • A19. The original NaMaster paper (Alonso et al. 2019 https://arxiv.org/abs/1809.09603).
  • G19. Garcia-Garcia et al. 2019 (https://arxiv.org/abs/1906.11765), which introduces the basic approximation used by NaMaster to estimate covariance matrix.
  • N20. Nicola et al. 2020 (https://arxiv.org/abs/2010.09717), which refined these approximations and described in detail the procedure to estimate cosmic shear power spectra.
    • -
  • W24. Wolz et al. 2024 (TBD), which introduced the formalism for catalog-based $C_\ell$s.
    • +
  • W24. Wolz et al. 2024 (https://arxiv.org/abs/2407.21013), which introduced the formalism for catalog-based $C_\ell$s.
    • +
  • A24. Alonso 2024 (TBD), which generalised the estimator to anisotropic weights.
    • diff --git a/doc/2Spin.ipynb b/doc/2Spin.ipynb index dd5d57bc..c009625c 100644 --- a/doc/2Spin.ipynb +++ b/doc/2Spin.ipynb @@ -22,7 +22,8 @@ "- **A19**. The original NaMaster paper (Alonso et al. 2019 https://arxiv.org/abs/1809.09603).\n", "- **G19**. Garcia-Garcia et al. 2019 (https://arxiv.org/abs/1906.11765), which introduces the basic approximation used by NaMaster to estimate covariance matrix.\n", "- **N20**. Nicola et al. 2020 (https://arxiv.org/abs/2010.09717), which refined these approximations and described in detail the procedure to estimate cosmic shear power spectra.\n", - "- **W24**. Wolz et al. 2024 (TBD), which introduced the formalism for catalog-based $C_\\ell$s." + "- **W24**. Wolz et al. 2024 (https://arxiv.org/abs/2407.21013), which introduced the formalism for catalog-based $C_\\ell$s.\n", + "- **A24**. Alonso 2024 (TBD), which generalised the estimator to anisotropic weights." ] }, { diff --git a/doc/3Covariances.html b/doc/3Covariances.html index 8604f957..e939da31 100644 --- a/doc/3Covariances.html +++ b/doc/3Covariances.html @@ -7540,7 +7540,8 @@
  • A19. The original NaMaster paper (Alonso et al. 2019 https://arxiv.org/abs/1809.09603).
  • G19. Garcia-Garcia et al. 2019 (https://arxiv.org/abs/1906.11765), which introduces the basic approximation used by NaMaster to estimate covariance matrix.
  • N20. Nicola et al. 2020 (https://arxiv.org/abs/2010.09717), which refined these approximations and described in detail the procedure to estimate cosmic shear power spectra.
    • -
  • W24. Wolz et al. 2024 (TBD), which introduced the formalism for catalog-based $C_\ell$s.
    • +
  • W24. Wolz et al. 2024 (https://arxiv.org/abs/2407.21013), which introduced the formalism for catalog-based $C_\ell$s.
    • +
  • A24. Alonso 2024 (TBD), which generalised the estimator to anisotropic weights.
    • diff --git a/doc/3Covariances.ipynb b/doc/3Covariances.ipynb index e8ebe74c..b6649ab2 100644 --- a/doc/3Covariances.ipynb +++ b/doc/3Covariances.ipynb @@ -24,7 +24,8 @@ "- **A19**. The original NaMaster paper (Alonso et al. 2019 https://arxiv.org/abs/1809.09603).\n", "- **G19**. Garcia-Garcia et al. 2019 (https://arxiv.org/abs/1906.11765), which introduces the basic approximation used by NaMaster to estimate covariance matrix.\n", "- **N20**. Nicola et al. 2020 (https://arxiv.org/abs/2010.09717), which refined these approximations and described in detail the procedure to estimate cosmic shear power spectra.\n", - "- **W24**. Wolz et al. 2024 (TBD), which introduced the formalism for catalog-based $C_\\ell$s." + "- **W24**. Wolz et al. 2024 (https://arxiv.org/abs/2407.21013), which introduced the formalism for catalog-based $C_\\ell$s.\n", + "- **A24**. Alonso 2024 (TBD), which generalised the estimator to anisotropic weights." ] }, { diff --git a/doc/4Catalogs.html b/doc/4Catalogs.html index f38a67ad..d325dce0 100644 --- a/doc/4Catalogs.html +++ b/doc/4Catalogs.html @@ -7542,7 +7542,8 @@
  • A19. The original NaMaster paper (Alonso et al. 2019 https://arxiv.org/abs/1809.09603).
  • G19. Garcia-Garcia et al. 2019 (https://arxiv.org/abs/1906.11765), which introduces the basic approximation used by NaMaster to estimate covariance matrix.
  • N20. Nicola et al. 2020 (https://arxiv.org/abs/2010.09717), which refined these approximations and described in detail the procedure to estimate cosmic shear power spectra.
    • -
  • W24. Wolz et al. 2024 (TBD), which introduced the formalism for catalog-based $C_\ell$s.
    • +
  • W24. Wolz et al. 2024 (https://arxiv.org/abs/2407.21013), which introduced the formalism for catalog-based $C_\ell$s.
    • +
  • A24. Alonso 2024 (TBD), which generalised the estimator to anisotropic weights.
    • diff --git a/doc/4Catalogs.ipynb b/doc/4Catalogs.ipynb index 69dc21f6..b63cee17 100644 --- a/doc/4Catalogs.ipynb +++ b/doc/4Catalogs.ipynb @@ -26,7 +26,8 @@ "- **A19**. The original NaMaster paper (Alonso et al. 2019 https://arxiv.org/abs/1809.09603).\n", "- **G19**. Garcia-Garcia et al. 2019 (https://arxiv.org/abs/1906.11765), which introduces the basic approximation used by NaMaster to estimate covariance matrix.\n", "- **N20**. Nicola et al. 2020 (https://arxiv.org/abs/2010.09717), which refined these approximations and described in detail the procedure to estimate cosmic shear power spectra.\n", - "- **W24**. Wolz et al. 2024 (TBD), which introduced the formalism for catalog-based $C_\\ell$s." + "- **W24**. Wolz et al. 2024 (https://arxiv.org/abs/2407.21013), which introduced the formalism for catalog-based $C_\\ell$s.\n", + "- **A24**. Alonso 2024 (TBD), which generalised the estimator to anisotropic weights." ] }, { diff --git a/doc/5AnisotropicWeighting.html b/doc/5AnisotropicWeighting.html new file mode 100644 index 00000000..60b0dfad --- /dev/null +++ b/doc/5AnisotropicWeighting.html @@ -0,0 +1,7794 @@ + + + + + +5AnisotropicWeighting + + + + + + + + + + + + +
    + + + + + + +
    + + diff --git a/doc/5AnisotropicWeighting.ipynb b/doc/5AnisotropicWeighting.ipynb new file mode 100644 index 00000000..6e7d41e3 --- /dev/null +++ b/doc/5AnisotropicWeighting.ipynb @@ -0,0 +1,270 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "e3b92a3a-8e40-4bef-9975-fd3fb830283b", + "metadata": {}, + "outputs": [], + "source": [ + "import pymaster as nmt\n", + "import numpy as np\n", + "import healpy as hp\n", + "import matplotlib.pyplot as plt\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "id": "4ec9c27e-7c2a-4d9c-b33b-91e019bc423a", + "metadata": {}, + "source": [ + "These notebooks aim to provide a comprehensive review of the functionality implemented in NaMaster. No rigorous mathematical derivations of any statements made will be provided, and users are referred to the references below for further details:\n", + "- **A19**. The original NaMaster paper (Alonso et al. 2019 https://arxiv.org/abs/1809.09603).\n", + "- **G19**. Garcia-Garcia et al. 2019 (https://arxiv.org/abs/1906.11765), which introduces the basic approximation used by NaMaster to estimate covariance matrix.\n", + "- **N20**. Nicola et al. 2020 (https://arxiv.org/abs/2010.09717), which refined these approximations and described in detail the procedure to estimate cosmic shear power spectra.\n", + "- **W24**. Wolz et al. 2024 (https://arxiv.org/abs/2407.21013), which introduced the formalism for catalog-based $C_\\ell$s.\n", + "- **A24**. Alonso 2024 (TBD), which generalised the estimator to anisotropic weights." + ] + }, + { + "cell_type": "markdown", + "id": "fde0cabb-9687-4316-b98a-ed9c50800996", + "metadata": {}, + "source": [ + "# 5 Anisotropic masks \n", + "This short tutorial shows how to use the functionality to account for anisotropic weighting of spin-$s$ fields. In short, anisotropic weighting applies different weights or masks to the different spin component of a field, and even mixes them up. This makes sense when the noise properties of your field are better characterised at every pixel by a covariance matrix that is not proportional to the identity (in which case, it may be appropriate to weight your field by the inverse of this matrix, instead of a simple scalar mask). The application of anisotropic weights causes additional mode coupling between different angular scales and $E/B$ modes, so the mode-coupling coefficients for the power spectra of these fields is also different.\n", + "\n", + "The code below describes one of the tests presented in [Alonso et al. 2024](TBD) to validate the implementation of anisotropic weights in NaMaster." + ] + }, + { + "cell_type": "markdown", + "id": "f65f0a8f-c7ba-4c05-a161-8f0776423aa2", + "metadata": {}, + "source": [ + "# Table of contents\n", + "\n", + "* [5.1 Simulating anisotropic weights](#Ss5.1)\n", + "* [5.2 Validation](#Ss5.2)" + ] + }, + { + "cell_type": "markdown", + "id": "69f172ee-1149-4b46-9a7c-617eca2f2b46", + "metadata": {}, + "source": [ + "## 5.1 Maps and masks \n", + "For a base mask $m$, we can use the following toy model to generate an anisotropic weighting matrix. We generate the diagonal elements, corresponding to the `11` and `22` weights as $w_{11}=(1+\\delta_m)m$ and $w_{22}=(1-\\delta_m)m$, where $\\delta_m$ is a parameter between -1 and 1. The off-diagonal element is given by $w_{12}=r_m\\sqrt{w_{11}w_{22}}$, where the correlation coefficient $r_m$ is defined in the same range. \n", + "\n", + "Below we construct an anisotropically-weighted field by generating a spin-2 field with power spectra $C_\\ell^{EE}=1/(10+\\ell)$ and $C_\\ell^{BB}=0.2/(10+\\ell)$, on which we will apply the mask above with parameters $\\delta_m=0.9$ and $r_m=0.5$. We use the same mask used in notebook 3 to construct the base mask here." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "13bbf000-1b94-4ca6-8048-342aa9339867", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nside = 256\n", + "delta_m = 0.9\n", + "r_m = 0.5\n", + "\n", + "fname_mask = 'selection_function_NSIDE64_G20.5_zsplit2bin0.fits'\n", + "if not os.path.isfile(fname_mask):\n", + " wget.download(\"https://zenodo.org/records/8098636/files/selection_function_NSIDE64_G20.5_zsplit2bin0.fits?download=1\")\n", + "mask = hp.ud_grade(hp.read_map(fname_mask), nside_out=256)\n", + "\n", + "mask_11 = (1+delta_m)*mask\n", + "mask_22 = (1-delta_m)*mask\n", + "mask_12 = r_m*np.sqrt(mask_11*mask_22)\n", + "\n", + "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n", + "plt.axes(axes[0][0])\n", + "hp.mollview(mask_11, title='$w_{11}$', hold=True, min=0, max=2)\n", + "plt.axes(axes[0][1])\n", + "hp.mollview(mask_22, title='$w_{22}$', hold=True, min=0, max=2)\n", + "plt.axes(axes[1][0])\n", + "hp.mollview(mask_12, title='$w_{12}$', hold=True, min=0, max=2)\n", + "plt.axes(axes[1][1])\n", + "hp.mollview(mask, title='$m$', hold=True, min=0, max=2)" + ] + }, + { + "cell_type": "markdown", + "id": "990fda0f-0111-4243-a392-e7a2b05597e2", + "metadata": {}, + "source": [ + "The syntax to generate anisotropically weighted fields is very simple (see below). Once it's been generated, estimating its power spectrum follows the same steps as in the standard case (see previous tutorials)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b5d0bc41-4c34-43f0-bbe7-296e5606d473", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "lmax = 3*nside-1\n", + "ls = np.arange(lmax+1)\n", + "cl_ee = 1/(10+ls)\n", + "cl_bb = 0.2/(1+ls)\n", + "cl_eb = 0*ls\n", + "\n", + "def get_sim():\n", + " almE, almB = hp.synalm([cl_ee, cl_bb, cl_eb], lmax=lmax, mmax=lmax, new=True)\n", + " mpQ, mpU = hp.alm2map_spin(np.array([almE, almB]), nside, spin=2, lmax=lmax, mmax=lmax)\n", + " f = nmt.NmtField(mask_11, [mpQ, mpU], n_iter=0, mask_12=mask_12, mask_22=mask_22)\n", + " return f\n", + "\n", + "f = get_sim()\n", + "b = nmt.NmtBin.from_nside_linear(nside, nlb=16)\n", + "w = nmt.NmtWorkspace.from_fields(f, f, b)\n", + "\n", + "leff = b.get_effective_ells()\n", + "cl_theory = w.decouple_cell(w.couple_cell([cl_ee, cl_eb, cl_eb, cl_bb]))\n", + "cl = w.decouple_cell(nmt.compute_coupled_cell(f, f))\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "ax = axes[0]\n", + "ax.plot(leff, cl_theory[0], 'k-', label='Input')\n", + "ax.plot(leff, cl[0], 'r.-', label='Simulation')\n", + "ax.set_yscale('log')\n", + "ax.set_xlabel(r'$\\ell$', fontsize=16)\n", + "ax.set_ylabel(r'$C^{EE}_\\ell$', fontsize=16)\n", + "ax.set_xlim([2, 2*nside])\n", + "ax.legend(fontsize=14, frameon=False, loc='upper right')\n", + "\n", + "ax = axes[1]\n", + "ax.plot(leff, cl_theory[3], 'k-', label='Input')\n", + "ax.plot(leff, cl[3], 'r.-', label='Simulation')\n", + "ax.set_yscale('log')\n", + "ax.set_xlabel(r'$\\ell$', fontsize=16)\n", + "ax.set_ylabel(r'$C^{BB}_\\ell$', fontsize=16)\n", + "ax.set_xlim([2, 2*nside]);" + ] + }, + { + "cell_type": "markdown", + "id": "da40c3bc-b52f-45d6-b123-c495955ca841", + "metadata": {}, + "source": [ + "## 5.2 Validation \n", + "To validate the estimator with anisotropic weights, we calculate the power spectrum for 100 simulations generated as described above, and verify that the their average is compatible with the spectrum used to generate the simulations." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bd54c01c-5dfe-4f3c-b3e1-24008791098c", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute spectra for 100 sims and then their mean and scatter\n", + "nsims = 100\n", + "cls = []\n", + "for i in range(nsims):\n", + " f = get_sim()\n", + " cl = w.decouple_cell(nmt.compute_coupled_cell(f, f))\n", + " cls.append(cl)\n", + "cls = np.array(cls)\n", + "clm = np.mean(cls, axis=0)\n", + "cle = np.std(cls, axis=0)/np.sqrt(nsims)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d8acd8ee-9c22-4135-b225-b06a2c999d51", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "plt.figure(figsize=(8, 4))\n", + "ax = plt.gca()\n", + "indices = [0, 1, 3]\n", + "names = [r'$C_\\ell^{EE}$', r'$C_\\ell^{EB}$', r'$C_\\ell^{BB}$']\n", + "cols = ['k', 'r', 'b']\n", + "\n", + "for ind, name, col in zip(indices, names, cols):\n", + " m = clm[ind]\n", + " e = cle[ind]\n", + " t = cl_theory[ind]\n", + " ax.errorbar(leff, (m-t)/e, yerr=np.ones_like(m),\n", + " fmt=col+'.', label=name)\n", + "ax.set_xlabel(r'$\\ell$', fontsize=16)\n", + "ax.set_ylabel(r'$(\\langle \\hat{C}_\\ell\\rangle-C_\\ell)/\\sigma(C_\\ell)$',\n", + " fontsize=16)\n", + "ax.set_xlim([2, 2*nside])\n", + "ax.set_ylim([-4, 4])\n", + "ax.legend(fontsize=14, loc='upper left', ncol=3)\n", + "ax.axhline(0, c='#AAAAAA', ls='--')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/conf.py b/doc/conf.py index 38716e15..b4f57148 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -155,7 +155,8 @@ def __getattr__(cls, name): html_extra_path = ['1BasicFunctionality.html', '2Spin.html', '3Covariances.html', - '4Catalogs.html'] + '4Catalogs.html', + '5AnisotropicWeighting.html'] # -- Options for HTMLHelp output ------------------------------------------ diff --git a/doc/source/tutorials.rst b/doc/source/tutorials.rst index b2666cd7..41bf9af8 100644 --- a/doc/source/tutorials.rst +++ b/doc/source/tutorials.rst @@ -8,3 +8,4 @@ Below is a list of tutorials, in the form of jupyter notebooks, that guide you t * `Fields with spin <../2Spin.html>`_ * `Covariances <../3Covariances.html>`_ * `Catalog-based fields <../4Catalogs.html>`_ +* `Anisotropic weights <../5AnisotropicWeighting.html>`_ diff --git a/pymaster/covariance.py b/pymaster/covariance.py index 91195b5a..49e9f9cc 100644 --- a/pymaster/covariance.py +++ b/pymaster/covariance.py @@ -207,6 +207,11 @@ def compute_coupling_coefficients(self, fla1, fla2, if flb2 is None: flb2 = fla2 + if np.any([fla1.anisotropic_mask, fla2.anisotropic_mask, + flb1.anisotropic_mask, flb2.anisotropic_mask]): + raise NotImplementedError("Covariance matrix estimation not " + "implemented for anisotropic weights.") + if (not (fla1.is_compatible(fla2) and fla1.is_compatible(flb1) and fla1.is_compatible(flb2))): diff --git a/pymaster/field.py b/pymaster/field.py index e934fc6b..9bc6398e 100644 --- a/pymaster/field.py +++ b/pymaster/field.py @@ -98,11 +98,21 @@ class NmtField(object): deprojection and purification, but you don't care about deprojection bias. This will significantly reduce the memory taken up by the resulting object. + mask_22 (`array`): Array containing the mask corresponding to the + ``22`` component of an anisotropic weight matrix. If not ``None`` + the ``mask`` argument will be interpreted as the ``11`` component + of the same matrix. The off-diagonal component of the weight + matrix must then be passed as ``mask_12`` (below). If ``mask_22`` + and ``mask_12`` are ``None``, isotropic weighting is assumed, + described by ``mask``. + mask_12 (`array`): Array containing the off-diagonal component of the + anisotropic weight matrix. See ``mask_22`` above. """ def __init__(self, mask, maps, *, spin=None, templates=None, beam=None, purify_e=False, purify_b=False, n_iter=None, n_iter_mask=None, tol_pinv=None, wcs=None, lmax=None, lmax_mask=None, - masked_on_input=False, lite=False): + masked_on_input=False, lite=False, + mask_22=None, mask_12=None): if n_iter_mask is None: n_iter_mask = ut.nmt_params.n_iter_mask_default if n_iter is None: @@ -133,14 +143,41 @@ def __init__(self, mask, maps, *, spin=None, templates=None, beam=None, self._Nf = 0 self._alpha = None self.is_catalog = False + # Anisotropic mask + self.anisotropic_mask = False + self.mask_a = None + self.alm_mask_a = None # 1. Store mask and beam - # This ensures the mask will have the right type - # and endianness (can cause issues when read from - # some FITS files). - mask = mask.astype(np.float64) + + # 1.1 Check for anisotropic masks + if (mask_22 is not None) or (mask_12 is not None): + # Check that all anisotropic components are provided + if (mask_22 is None) or (mask_12 is None): + raise ValueError("Both mask_22 and mask_12 must be passed if " + "either of them is passed.") + # Check that the noise covariance that the masks derive from + # is positive-definite + mask_scale = np.mean(mask+mask_22) + if np.any(mask*mask_22-mask_12**2 < -1E-5*mask_scale): + raise ValueError("The anisotropic mask does not seem to be " + "positive-definite.") + mask0 = (0.5*(mask + mask_22)).astype(np.float64) + mask1 = (0.5*(mask - mask_22)).astype(np.float64) + mask2 = mask_12.astype(np.float64) + mask = mask0 + self.anisotropic_mask = True + else: + # This ensures the mask will have the right type + # and endianness (can cause issues when read from + # some FITS files). + mask = mask.astype(np.float64) + + # 1.2 get all spatial info from the masks self.minfo = ut.NmtMapInfo(wcs, mask.shape) self.mask = self.minfo.reform_map(mask) + if self.anisotropic_mask: + self.mask_a = self.minfo.reform_map(np.array([mask1, mask2])) if lmax is None: lmax = self.minfo.get_lmax() if lmax_mask is None: @@ -166,6 +203,9 @@ def __init__(self, mask, maps, *, spin=None, templates=None, beam=None, if spin is None: raise ValueError("Please supply field spin") self.spin = spin + if (self.spin == 0) and self.anisotropic_mask: + raise ValueError("Anisotropic masks can't be used " + "with scalar fields.") self.nmaps = 2 if spin else 1 return @@ -195,6 +235,19 @@ def __init__(self, mask, maps, *, spin=None, templates=None, beam=None, if pure_any and self.spin != 2: raise ValueError("Purification only implemented for spin-2 fields") + # 2.1 Check that no bells nor whistles are requested for anisotropic + # masks, since these are not supported. + if self.anisotropic_mask: + if self.spin == 0: + raise ValueError("Anisotropic masks can't be used with " + "scalar fields.") + if pure_any: + raise NotImplementedError("Purification not implemented for " + "anisotropic masks") + if templates is not None: + raise NotImplementedError("Contaminant deprojection not " + "supported for anisotropic masks.") + # 3. Check templates if isinstance(templates, (list, tuple, np.ndarray)): templates = np.array(templates, dtype=np.float64) @@ -227,7 +280,14 @@ def __init__(self, mask, maps, *, spin=None, templates=None, beam=None, if w_temp: templates = np.array(templates) if not masked_on_input: - maps *= self.mask[None, :] + if self.anisotropic_mask: + maps = (maps*self.mask[None, :] + + np.array([maps[0]*self.mask_a[0] + + maps[1]*self.mask_a[1], + maps[0]*self.mask_a[1] - + maps[1]*self.mask_a[0]])) + else: + maps *= self.mask[None, :] if w_temp: templates *= self.mask[None, None, :] @@ -314,6 +374,17 @@ def get_mask(self): raise ValueError("Input mask unavailable for this field") return self.mask + def get_anisotropic_mask(self): + """ Get this field's anisotropic mask components. + + Returns: + (`array`): 2D array containing the field's anisotropic mask. + """ + if self.mask_a is None: + raise ValueError("Input anisotropic mask unavailable " + "for this field") + return self.mask_a + def get_mask_alms(self): """ Get the :math:`a_{\\ell m}` coefficients of this field's mask. Note that, in most cases, the mask :math:`a_{\\ell n}` s are not @@ -335,6 +406,26 @@ def get_mask_alms(self): amask = self.alm_mask return amask + def get_anisotropic_mask_alms(self): + """ Get the :math:`a_{\\ell m}` coefficients of this field's + anisotropic mask components. + + Returns: + (`array`): 2D array containing the mask :math:`a_{\\ell m}` s. + """ + if self.mask_a is None: + raise ValueError("This field does not have an anisotropic mask.") + + if self.alm_mask_a is None: + amask = ut.map2alm(self.mask_a, 2*self.spin, + self.minfo, self.ainfo_mask, + n_iter=self.n_iter_mask) + if not self.lite: # Store while we're at it + self.alm_mask_a = amask + else: + amask = self.alm_mask_a + return amask + def get_maps(self): """ Get this field's set of maps. The returned maps will be masked, contaminant-deprojected, and purified (if so required @@ -784,6 +875,9 @@ def __init__(self, positions, weights, field, lmax, lmax_mask=None, self.alm_temp = None self.minfo = None self.n_temp = 0 + self.anisotropic_mask = False + self.mask_a = None + self.alm_mask_a = None # Sanity checks on positions and weights positions = np.array(positions, dtype=np.float64) @@ -929,6 +1023,9 @@ def __init__(self, positions, weights, positions_rand, weights_rand, self.alm_temp = None self.minfo = None self.n_temp = 0 + self.anisotropic_mask = False + self.mask_a = None + self.alm_mask_a = None # Sanity checks on positions and weights def process_pos_w(pos, w, kind): diff --git a/pymaster/namaster.i b/pymaster/namaster.i index beaa7821..710d2086 100644 --- a/pymaster/namaster.i +++ b/pymaster/namaster.i @@ -21,10 +21,19 @@ %apply (double* ARGOUT_ARRAY1, int DIM1) {(double* dout, int ndout)}; %apply (double* ARGOUT_ARRAY1, long DIM1) {(double* ldout, long nldout)}; %apply (int DIM1,double *IN_ARRAY1) {(int npix_1,double *mask), - (int nell3,double *weights), + (int nell3,double *weights), (int nell4,double *f_ell), (int nlb1,double *beam1), - (int nlb2,double *beam2)}; + (int nlb2,double *beam2), + (int nl00,double *fl00), + (int nl0e,double *fl0e), + (int nl0b,double *fl0b), + (int nle0,double *fle0), + (int nlb0,double *flb0), + (int nlee,double *flee), + (int nleb,double *fleb), + (int nlbe,double *flbe), + (int nlbb,double *flbb)}; %apply (int DIM1,int *IN_ARRAY1) {(int nell1,int *bpws), (int nell2,int *ells), (int nfields,int *spin_arr)}; @@ -422,6 +431,42 @@ void synfast_new_flat(int nx,int ny,double lx,double ly, free(larr); } +nmt_workspace *comp_coupling_matrix_anisotropic(int spin1,int spin2, + int aniso1, int aniso2, + int lmax, int lmax_mask, + int nl00,double *fl00, + int nl0e,double *fl0e, + int nl0b,double *fl0b, + int nle0,double *fle0, + int nlb0,double *flb0, + int nlee,double *flee, + int nleb,double *fleb, + int nlbe,double *flbe, + int nlbb,double *flbb, + int nlb1,double *beam1, + int nlb2,double *beam2, + nmt_binning_scheme *bin, + int norm_type,double w2) +{ + asserting(nlb1==lmax+1); + asserting(nlb2==lmax+1); + asserting(nl00==lmax_mask+1); + asserting(nl0e==lmax_mask+1); + asserting(nl0b==lmax_mask+1); + asserting(nle0==lmax_mask+1); + asserting(nlb0==lmax_mask+1); + asserting(nlee==lmax_mask+1); + asserting(nleb==lmax_mask+1); + asserting(nlbe==lmax_mask+1); + asserting(nlbb==lmax_mask+1); + return nmt_compute_coupling_matrix_anisotropic(spin1,spin2,aniso1,aniso2, + lmax,lmax_mask, + fl00,fl0e,fl0b,fle0,flb0, + flee,fleb,flbe,flbb, + beam1,beam2,bin,norm_type,w2); +} + + nmt_workspace *comp_coupling_matrix(int spin1,int spin2, int lmax,int lmax_mask, int pure_e_1,int pure_b_1, diff --git a/pymaster/namaster_wrap.c b/pymaster/namaster_wrap.c index 3ece444e..8bfc6d70 100644 --- a/pymaster/namaster_wrap.c +++ b/pymaster/namaster_wrap.c @@ -3398,6 +3398,42 @@ void synfast_new_flat(int nx,int ny,double lx,double ly, free(larr); } +nmt_workspace *comp_coupling_matrix_anisotropic(int spin1,int spin2, + int aniso1, int aniso2, + int lmax, int lmax_mask, + int nl00,double *fl00, + int nl0e,double *fl0e, + int nl0b,double *fl0b, + int nle0,double *fle0, + int nlb0,double *flb0, + int nlee,double *flee, + int nleb,double *fleb, + int nlbe,double *flbe, + int nlbb,double *flbb, + int nlb1,double *beam1, + int nlb2,double *beam2, + nmt_binning_scheme *bin, + int norm_type,double w2) +{ + asserting(nlb1==lmax+1); + asserting(nlb2==lmax+1); + asserting(nl00==lmax_mask+1); + asserting(nl0e==lmax_mask+1); + asserting(nl0b==lmax_mask+1); + asserting(nle0==lmax_mask+1); + asserting(nlb0==lmax_mask+1); + asserting(nlee==lmax_mask+1); + asserting(nleb==lmax_mask+1); + asserting(nlbe==lmax_mask+1); + asserting(nlbb==lmax_mask+1); + return nmt_compute_coupling_matrix_anisotropic(spin1,spin2,aniso1,aniso2, + lmax,lmax_mask, + fl00,fl0e,fl0b,fle0,flb0, + flee,fleb,flbe,flbb, + beam1,beam2,bin,norm_type,w2); +} + + nmt_workspace *comp_coupling_matrix(int spin1,int spin2, int lmax,int lmax_mask, int pure_e_1,int pure_b_1, @@ -11418,6 +11454,180 @@ SWIGINTERN PyObject *_wrap_master_calculator_free(PyObject *SWIGUNUSEDPARM(self) } +SWIGINTERN PyObject *_wrap_compute_coupling_matrix_anisotropic(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + int arg1 ; + int arg2 ; + int arg3 ; + int arg4 ; + int arg5 ; + int arg6 ; + flouble *arg7 = (flouble *) 0 ; + flouble *arg8 = (flouble *) 0 ; + flouble *arg9 = (flouble *) 0 ; + flouble *arg10 = (flouble *) 0 ; + flouble *arg11 = (flouble *) 0 ; + flouble *arg12 = (flouble *) 0 ; + flouble *arg13 = (flouble *) 0 ; + flouble *arg14 = (flouble *) 0 ; + flouble *arg15 = (flouble *) 0 ; + flouble *arg16 = (flouble *) 0 ; + flouble *arg17 = (flouble *) 0 ; + nmt_binning_scheme *arg18 = (nmt_binning_scheme *) 0 ; + int arg19 ; + flouble arg20 ; + int val1 ; + int ecode1 = 0 ; + int val2 ; + int ecode2 = 0 ; + int val3 ; + int ecode3 = 0 ; + int val4 ; + int ecode4 = 0 ; + int val5 ; + int ecode5 = 0 ; + int val6 ; + int ecode6 = 0 ; + void *argp7 = 0 ; + int res7 = 0 ; + void *argp8 = 0 ; + int res8 = 0 ; + void *argp9 = 0 ; + int res9 = 0 ; + void *argp10 = 0 ; + int res10 = 0 ; + void *argp11 = 0 ; + int res11 = 0 ; + void *argp12 = 0 ; + int res12 = 0 ; + void *argp13 = 0 ; + int res13 = 0 ; + void *argp14 = 0 ; + int res14 = 0 ; + void *argp15 = 0 ; + int res15 = 0 ; + void *argp16 = 0 ; + int res16 = 0 ; + void *argp17 = 0 ; + int res17 = 0 ; + void *argp18 = 0 ; + int res18 = 0 ; + int val19 ; + int ecode19 = 0 ; + double val20 ; + int ecode20 = 0 ; + PyObject *swig_obj[20] ; + nmt_workspace *result = 0 ; + + if (!SWIG_Python_UnpackTuple(args, "compute_coupling_matrix_anisotropic", 20, 20, swig_obj)) SWIG_fail; + ecode1 = SWIG_AsVal_int(swig_obj[0], &val1); + if (!SWIG_IsOK(ecode1)) { + SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "compute_coupling_matrix_anisotropic" "', argument " "1"" of type '" "int""'"); + } + arg1 = (int)(val1); + ecode2 = SWIG_AsVal_int(swig_obj[1], &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "compute_coupling_matrix_anisotropic" "', argument " "2"" of type '" "int""'"); + } + arg2 = (int)(val2); + ecode3 = SWIG_AsVal_int(swig_obj[2], &val3); + if (!SWIG_IsOK(ecode3)) { + SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "compute_coupling_matrix_anisotropic" "', argument " "3"" of type '" "int""'"); + } + arg3 = (int)(val3); + ecode4 = SWIG_AsVal_int(swig_obj[3], &val4); + if (!SWIG_IsOK(ecode4)) { + SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "compute_coupling_matrix_anisotropic" "', argument " "4"" of type '" "int""'"); + } + arg4 = (int)(val4); + ecode5 = SWIG_AsVal_int(swig_obj[4], &val5); + if (!SWIG_IsOK(ecode5)) { + SWIG_exception_fail(SWIG_ArgError(ecode5), "in method '" "compute_coupling_matrix_anisotropic" "', argument " "5"" of type '" "int""'"); + } + arg5 = (int)(val5); + ecode6 = SWIG_AsVal_int(swig_obj[5], &val6); + if (!SWIG_IsOK(ecode6)) { + SWIG_exception_fail(SWIG_ArgError(ecode6), "in method '" "compute_coupling_matrix_anisotropic" "', argument " "6"" of type '" "int""'"); + } + arg6 = (int)(val6); + res7 = SWIG_ConvertPtr(swig_obj[6], &argp7,SWIGTYPE_p_double, 0 | 0 ); + if (!SWIG_IsOK(res7)) { + SWIG_exception_fail(SWIG_ArgError(res7), "in method '" "compute_coupling_matrix_anisotropic" "', argument " "7"" of type '" "flouble *""'"); + } + arg7 = (flouble *)(argp7); + res8 = SWIG_ConvertPtr(swig_obj[7], &argp8,SWIGTYPE_p_double, 0 | 0 ); + if (!SWIG_IsOK(res8)) { + SWIG_exception_fail(SWIG_ArgError(res8), "in method '" "compute_coupling_matrix_anisotropic" "', argument " "8"" of type '" "flouble *""'"); + } + arg8 = (flouble *)(argp8); + res9 = SWIG_ConvertPtr(swig_obj[8], &argp9,SWIGTYPE_p_double, 0 | 0 ); + if (!SWIG_IsOK(res9)) { + SWIG_exception_fail(SWIG_ArgError(res9), "in method '" "compute_coupling_matrix_anisotropic" "', argument " "9"" of type '" "flouble *""'"); + } + arg9 = (flouble *)(argp9); + res10 = SWIG_ConvertPtr(swig_obj[9], &argp10,SWIGTYPE_p_double, 0 | 0 ); + if (!SWIG_IsOK(res10)) { + SWIG_exception_fail(SWIG_ArgError(res10), "in method '" "compute_coupling_matrix_anisotropic" "', argument " "10"" of type '" "flouble *""'"); + } + arg10 = (flouble *)(argp10); + res11 = SWIG_ConvertPtr(swig_obj[10], &argp11,SWIGTYPE_p_double, 0 | 0 ); + if (!SWIG_IsOK(res11)) { + SWIG_exception_fail(SWIG_ArgError(res11), "in method '" "compute_coupling_matrix_anisotropic" "', argument " "11"" of type '" "flouble *""'"); + } + arg11 = (flouble *)(argp11); + res12 = SWIG_ConvertPtr(swig_obj[11], &argp12,SWIGTYPE_p_double, 0 | 0 ); + if (!SWIG_IsOK(res12)) { + SWIG_exception_fail(SWIG_ArgError(res12), "in method '" "compute_coupling_matrix_anisotropic" "', argument " "12"" of type '" "flouble *""'"); + } + arg12 = (flouble *)(argp12); + res13 = SWIG_ConvertPtr(swig_obj[12], &argp13,SWIGTYPE_p_double, 0 | 0 ); + if (!SWIG_IsOK(res13)) { + SWIG_exception_fail(SWIG_ArgError(res13), "in method '" "compute_coupling_matrix_anisotropic" "', argument " "13"" of type '" "flouble *""'"); + } + arg13 = (flouble *)(argp13); + res14 = SWIG_ConvertPtr(swig_obj[13], &argp14,SWIGTYPE_p_double, 0 | 0 ); + if (!SWIG_IsOK(res14)) { + SWIG_exception_fail(SWIG_ArgError(res14), "in method '" "compute_coupling_matrix_anisotropic" "', argument " "14"" of type '" "flouble *""'"); + } + arg14 = (flouble *)(argp14); + res15 = SWIG_ConvertPtr(swig_obj[14], &argp15,SWIGTYPE_p_double, 0 | 0 ); + if (!SWIG_IsOK(res15)) { + SWIG_exception_fail(SWIG_ArgError(res15), "in method '" "compute_coupling_matrix_anisotropic" "', argument " "15"" of type '" "flouble *""'"); + } + arg15 = (flouble *)(argp15); + res16 = SWIG_ConvertPtr(swig_obj[15], &argp16,SWIGTYPE_p_double, 0 | 0 ); + if (!SWIG_IsOK(res16)) { + SWIG_exception_fail(SWIG_ArgError(res16), "in method '" "compute_coupling_matrix_anisotropic" "', argument " "16"" of type '" "flouble *""'"); + } + arg16 = (flouble *)(argp16); + res17 = SWIG_ConvertPtr(swig_obj[16], &argp17,SWIGTYPE_p_double, 0 | 0 ); + if (!SWIG_IsOK(res17)) { + SWIG_exception_fail(SWIG_ArgError(res17), "in method '" "compute_coupling_matrix_anisotropic" "', argument " "17"" of type '" "flouble *""'"); + } + arg17 = (flouble *)(argp17); + res18 = SWIG_ConvertPtr(swig_obj[17], &argp18,SWIGTYPE_p_nmt_binning_scheme, 0 | 0 ); + if (!SWIG_IsOK(res18)) { + SWIG_exception_fail(SWIG_ArgError(res18), "in method '" "compute_coupling_matrix_anisotropic" "', argument " "18"" of type '" "nmt_binning_scheme *""'"); + } + arg18 = (nmt_binning_scheme *)(argp18); + ecode19 = SWIG_AsVal_int(swig_obj[18], &val19); + if (!SWIG_IsOK(ecode19)) { + SWIG_exception_fail(SWIG_ArgError(ecode19), "in method '" "compute_coupling_matrix_anisotropic" "', argument " "19"" of type '" "int""'"); + } + arg19 = (int)(val19); + ecode20 = SWIG_AsVal_double(swig_obj[19], &val20); + if (!SWIG_IsOK(ecode20)) { + SWIG_exception_fail(SWIG_ArgError(ecode20), "in method '" "compute_coupling_matrix_anisotropic" "', argument " "20"" of type '" "flouble""'"); + } + arg20 = (flouble)(val20); + result = (nmt_workspace *)nmt_compute_coupling_matrix_anisotropic(arg1,arg2,arg3,arg4,arg5,arg6,arg7,arg8,arg9,arg10,arg11,arg12,arg13,arg14,arg15,arg16,arg17,arg18,arg19,arg20); + resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_nmt_workspace, 0 | 0 ); + return resultobj; +fail: + return NULL; +} + + SWIGINTERN PyObject *_wrap_compute_coupling_matrix(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; int arg1 ; @@ -15920,6 +16130,407 @@ SWIGINTERN PyObject *_wrap_synfast_new_flat(PyObject *SWIGUNUSEDPARM(self), PyOb } +SWIGINTERN PyObject *_wrap_comp_coupling_matrix_anisotropic(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + int arg1 ; + int arg2 ; + int arg3 ; + int arg4 ; + int arg5 ; + int arg6 ; + int arg7 ; + double *arg8 = (double *) 0 ; + int arg9 ; + double *arg10 = (double *) 0 ; + int arg11 ; + double *arg12 = (double *) 0 ; + int arg13 ; + double *arg14 = (double *) 0 ; + int arg15 ; + double *arg16 = (double *) 0 ; + int arg17 ; + double *arg18 = (double *) 0 ; + int arg19 ; + double *arg20 = (double *) 0 ; + int arg21 ; + double *arg22 = (double *) 0 ; + int arg23 ; + double *arg24 = (double *) 0 ; + int arg25 ; + double *arg26 = (double *) 0 ; + int arg27 ; + double *arg28 = (double *) 0 ; + nmt_binning_scheme *arg29 = (nmt_binning_scheme *) 0 ; + int arg30 ; + double arg31 ; + int val1 ; + int ecode1 = 0 ; + int val2 ; + int ecode2 = 0 ; + int val3 ; + int ecode3 = 0 ; + int val4 ; + int ecode4 = 0 ; + int val5 ; + int ecode5 = 0 ; + int val6 ; + int ecode6 = 0 ; + PyArrayObject *array7 = NULL ; + int is_new_object7 = 0 ; + PyArrayObject *array9 = NULL ; + int is_new_object9 = 0 ; + PyArrayObject *array11 = NULL ; + int is_new_object11 = 0 ; + PyArrayObject *array13 = NULL ; + int is_new_object13 = 0 ; + PyArrayObject *array15 = NULL ; + int is_new_object15 = 0 ; + PyArrayObject *array17 = NULL ; + int is_new_object17 = 0 ; + PyArrayObject *array19 = NULL ; + int is_new_object19 = 0 ; + PyArrayObject *array21 = NULL ; + int is_new_object21 = 0 ; + PyArrayObject *array23 = NULL ; + int is_new_object23 = 0 ; + PyArrayObject *array25 = NULL ; + int is_new_object25 = 0 ; + PyArrayObject *array27 = NULL ; + int is_new_object27 = 0 ; + void *argp29 = 0 ; + int res29 = 0 ; + int val30 ; + int ecode30 = 0 ; + double val31 ; + int ecode31 = 0 ; + PyObject *swig_obj[20] ; + nmt_workspace *result = 0 ; + + if (!SWIG_Python_UnpackTuple(args, "comp_coupling_matrix_anisotropic", 20, 20, swig_obj)) SWIG_fail; + ecode1 = SWIG_AsVal_int(swig_obj[0], &val1); + if (!SWIG_IsOK(ecode1)) { + SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "comp_coupling_matrix_anisotropic" "', argument " "1"" of type '" "int""'"); + } + arg1 = (int)(val1); + ecode2 = SWIG_AsVal_int(swig_obj[1], &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "comp_coupling_matrix_anisotropic" "', argument " "2"" of type '" "int""'"); + } + arg2 = (int)(val2); + ecode3 = SWIG_AsVal_int(swig_obj[2], &val3); + if (!SWIG_IsOK(ecode3)) { + SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "comp_coupling_matrix_anisotropic" "', argument " "3"" of type '" "int""'"); + } + arg3 = (int)(val3); + ecode4 = SWIG_AsVal_int(swig_obj[3], &val4); + if (!SWIG_IsOK(ecode4)) { + SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "comp_coupling_matrix_anisotropic" "', argument " "4"" of type '" "int""'"); + } + arg4 = (int)(val4); + ecode5 = SWIG_AsVal_int(swig_obj[4], &val5); + if (!SWIG_IsOK(ecode5)) { + SWIG_exception_fail(SWIG_ArgError(ecode5), "in method '" "comp_coupling_matrix_anisotropic" "', argument " "5"" of type '" "int""'"); + } + arg5 = (int)(val5); + ecode6 = SWIG_AsVal_int(swig_obj[5], &val6); + if (!SWIG_IsOK(ecode6)) { + SWIG_exception_fail(SWIG_ArgError(ecode6), "in method '" "comp_coupling_matrix_anisotropic" "', argument " "6"" of type '" "int""'"); + } + arg6 = (int)(val6); + { + npy_intp size[1] = { + -1 + }; + array7 = obj_to_array_contiguous_allow_conversion(swig_obj[6], + NPY_DOUBLE, + &is_new_object7); + if (!array7 || !require_dimensions(array7, 1) || + !require_size(array7, size, 1)) SWIG_fail; + arg7 = (int) array_size(array7,0); + arg8 = (double*) array_data(array7); + } + { + npy_intp size[1] = { + -1 + }; + array9 = obj_to_array_contiguous_allow_conversion(swig_obj[7], + NPY_DOUBLE, + &is_new_object9); + if (!array9 || !require_dimensions(array9, 1) || + !require_size(array9, size, 1)) SWIG_fail; + arg9 = (int) array_size(array9,0); + arg10 = (double*) array_data(array9); + } + { + npy_intp size[1] = { + -1 + }; + array11 = obj_to_array_contiguous_allow_conversion(swig_obj[8], + NPY_DOUBLE, + &is_new_object11); + if (!array11 || !require_dimensions(array11, 1) || + !require_size(array11, size, 1)) SWIG_fail; + arg11 = (int) array_size(array11,0); + arg12 = (double*) array_data(array11); + } + { + npy_intp size[1] = { + -1 + }; + array13 = obj_to_array_contiguous_allow_conversion(swig_obj[9], + NPY_DOUBLE, + &is_new_object13); + if (!array13 || !require_dimensions(array13, 1) || + !require_size(array13, size, 1)) SWIG_fail; + arg13 = (int) array_size(array13,0); + arg14 = (double*) array_data(array13); + } + { + npy_intp size[1] = { + -1 + }; + array15 = obj_to_array_contiguous_allow_conversion(swig_obj[10], + NPY_DOUBLE, + &is_new_object15); + if (!array15 || !require_dimensions(array15, 1) || + !require_size(array15, size, 1)) SWIG_fail; + arg15 = (int) array_size(array15,0); + arg16 = (double*) array_data(array15); + } + { + npy_intp size[1] = { + -1 + }; + array17 = obj_to_array_contiguous_allow_conversion(swig_obj[11], + NPY_DOUBLE, + &is_new_object17); + if (!array17 || !require_dimensions(array17, 1) || + !require_size(array17, size, 1)) SWIG_fail; + arg17 = (int) array_size(array17,0); + arg18 = (double*) array_data(array17); + } + { + npy_intp size[1] = { + -1 + }; + array19 = obj_to_array_contiguous_allow_conversion(swig_obj[12], + NPY_DOUBLE, + &is_new_object19); + if (!array19 || !require_dimensions(array19, 1) || + !require_size(array19, size, 1)) SWIG_fail; + arg19 = (int) array_size(array19,0); + arg20 = (double*) array_data(array19); + } + { + npy_intp size[1] = { + -1 + }; + array21 = obj_to_array_contiguous_allow_conversion(swig_obj[13], + NPY_DOUBLE, + &is_new_object21); + if (!array21 || !require_dimensions(array21, 1) || + !require_size(array21, size, 1)) SWIG_fail; + arg21 = (int) array_size(array21,0); + arg22 = (double*) array_data(array21); + } + { + npy_intp size[1] = { + -1 + }; + array23 = obj_to_array_contiguous_allow_conversion(swig_obj[14], + NPY_DOUBLE, + &is_new_object23); + if (!array23 || !require_dimensions(array23, 1) || + !require_size(array23, size, 1)) SWIG_fail; + arg23 = (int) array_size(array23,0); + arg24 = (double*) array_data(array23); + } + { + npy_intp size[1] = { + -1 + }; + array25 = obj_to_array_contiguous_allow_conversion(swig_obj[15], + NPY_DOUBLE, + &is_new_object25); + if (!array25 || !require_dimensions(array25, 1) || + !require_size(array25, size, 1)) SWIG_fail; + arg25 = (int) array_size(array25,0); + arg26 = (double*) array_data(array25); + } + { + npy_intp size[1] = { + -1 + }; + array27 = obj_to_array_contiguous_allow_conversion(swig_obj[16], + NPY_DOUBLE, + &is_new_object27); + if (!array27 || !require_dimensions(array27, 1) || + !require_size(array27, size, 1)) SWIG_fail; + arg27 = (int) array_size(array27,0); + arg28 = (double*) array_data(array27); + } + res29 = SWIG_ConvertPtr(swig_obj[17], &argp29,SWIGTYPE_p_nmt_binning_scheme, 0 | 0 ); + if (!SWIG_IsOK(res29)) { + SWIG_exception_fail(SWIG_ArgError(res29), "in method '" "comp_coupling_matrix_anisotropic" "', argument " "29"" of type '" "nmt_binning_scheme *""'"); + } + arg29 = (nmt_binning_scheme *)(argp29); + ecode30 = SWIG_AsVal_int(swig_obj[18], &val30); + if (!SWIG_IsOK(ecode30)) { + SWIG_exception_fail(SWIG_ArgError(ecode30), "in method '" "comp_coupling_matrix_anisotropic" "', argument " "30"" of type '" "int""'"); + } + arg30 = (int)(val30); + ecode31 = SWIG_AsVal_double(swig_obj[19], &val31); + if (!SWIG_IsOK(ecode31)) { + SWIG_exception_fail(SWIG_ArgError(ecode31), "in method '" "comp_coupling_matrix_anisotropic" "', argument " "31"" of type '" "double""'"); + } + arg31 = (double)(val31); + { + try { + result = (nmt_workspace *)comp_coupling_matrix_anisotropic(arg1,arg2,arg3,arg4,arg5,arg6,arg7,arg8,arg9,arg10,arg11,arg12,arg13,arg14,arg15,arg16,arg17,arg18,arg19,arg20,arg21,arg22,arg23,arg24,arg25,arg26,arg27,arg28,arg29,arg30,arg31); + } + finally { + SWIG_exception(SWIG_RuntimeError,nmt_error_message); + } + } + resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_nmt_workspace, 0 | 0 ); + { + if (is_new_object7 && array7) + { + Py_DECREF(array7); + } + } + { + if (is_new_object9 && array9) + { + Py_DECREF(array9); + } + } + { + if (is_new_object11 && array11) + { + Py_DECREF(array11); + } + } + { + if (is_new_object13 && array13) + { + Py_DECREF(array13); + } + } + { + if (is_new_object15 && array15) + { + Py_DECREF(array15); + } + } + { + if (is_new_object17 && array17) + { + Py_DECREF(array17); + } + } + { + if (is_new_object19 && array19) + { + Py_DECREF(array19); + } + } + { + if (is_new_object21 && array21) + { + Py_DECREF(array21); + } + } + { + if (is_new_object23 && array23) + { + Py_DECREF(array23); + } + } + { + if (is_new_object25 && array25) + { + Py_DECREF(array25); + } + } + { + if (is_new_object27 && array27) + { + Py_DECREF(array27); + } + } + return resultobj; +fail: + { + if (is_new_object7 && array7) + { + Py_DECREF(array7); + } + } + { + if (is_new_object9 && array9) + { + Py_DECREF(array9); + } + } + { + if (is_new_object11 && array11) + { + Py_DECREF(array11); + } + } + { + if (is_new_object13 && array13) + { + Py_DECREF(array13); + } + } + { + if (is_new_object15 && array15) + { + Py_DECREF(array15); + } + } + { + if (is_new_object17 && array17) + { + Py_DECREF(array17); + } + } + { + if (is_new_object19 && array19) + { + Py_DECREF(array19); + } + } + { + if (is_new_object21 && array21) + { + Py_DECREF(array21); + } + } + { + if (is_new_object23 && array23) + { + Py_DECREF(array23); + } + } + { + if (is_new_object25 && array25) + { + Py_DECREF(array25); + } + } + { + if (is_new_object27 && array27) + { + Py_DECREF(array27); + } + } + return NULL; +} + + SWIGINTERN PyObject *_wrap_comp_coupling_matrix(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; int arg1 ; @@ -18744,6 +19355,7 @@ static PyMethodDef SwigMethods[] = { { "master_calculator_swiginit", master_calculator_swiginit, METH_VARARGS, NULL}, { "compute_master_coefficients", _wrap_compute_master_coefficients, METH_VARARGS, NULL}, { "master_calculator_free", _wrap_master_calculator_free, METH_O, NULL}, + { "compute_coupling_matrix_anisotropic", _wrap_compute_coupling_matrix_anisotropic, METH_VARARGS, NULL}, { "compute_coupling_matrix", _wrap_compute_coupling_matrix, METH_VARARGS, NULL}, { "update_coupling_matrix", _wrap_update_coupling_matrix, METH_VARARGS, NULL}, { "workspace_update_binning", _wrap_workspace_update_binning, METH_VARARGS, NULL}, @@ -18839,6 +19451,7 @@ static PyMethodDef SwigMethods[] = { { "apomask", _wrap_apomask, METH_VARARGS, NULL}, { "apomask_flat", _wrap_apomask_flat, METH_VARARGS, NULL}, { "synfast_new_flat", _wrap_synfast_new_flat, METH_VARARGS, NULL}, + { "comp_coupling_matrix_anisotropic", _wrap_comp_coupling_matrix_anisotropic, METH_VARARGS, NULL}, { "comp_coupling_matrix", _wrap_comp_coupling_matrix, METH_VARARGS, NULL}, { "comp_coupling_matrix_flat", _wrap_comp_coupling_matrix_flat, METH_VARARGS, NULL}, { "read_workspace", _wrap_read_workspace, METH_VARARGS, NULL}, diff --git a/pymaster/nmtlib.py b/pymaster/nmtlib.py index 3762a236..b1cd3ba3 100644 --- a/pymaster/nmtlib.py +++ b/pymaster/nmtlib.py @@ -343,6 +343,9 @@ def compute_master_coefficients(lmax, lmax_mask, npcl, pcl_masks, s1, s2, pure_e def master_calculator_free(c): return _nmtlib.master_calculator_free(c) +def compute_coupling_matrix_anisotropic(spin1, spin2, mask_aniso_1, mask_aniso_2, lmax, lmax_mask, pcl_masks_00, pcl_masks_0e, pcl_masks_e0, pcl_masks_0b, pcl_masks_b0, pcl_masks_ee, pcl_masks_eb, pcl_masks_be, pcl_masks_bb, beam1, beam2, bin, norm_type, w2): + return _nmtlib.compute_coupling_matrix_anisotropic(spin1, spin2, mask_aniso_1, mask_aniso_2, lmax, lmax_mask, pcl_masks_00, pcl_masks_0e, pcl_masks_e0, pcl_masks_0b, pcl_masks_b0, pcl_masks_ee, pcl_masks_eb, pcl_masks_be, pcl_masks_bb, beam1, beam2, bin, norm_type, w2) + def compute_coupling_matrix(spin1, spin2, lmax, lmax_mask, pure_e1, pure_b1, pure_e2, pure_b2, pcl_masks, beam1, beam2, bin, is_teb, l_toeplitz, l_exact, dl_band, norm_type, w2): return _nmtlib.compute_coupling_matrix(spin1, spin2, lmax, lmax_mask, pure_e1, pure_b1, pure_e2, pure_b2, pcl_masks, beam1, beam2, bin, is_teb, l_toeplitz, l_exact, dl_band, norm_type, w2) @@ -526,6 +529,9 @@ def apomask_flat(nx, ny, lx, ly, npix_1, dout, aposize, apotype): def synfast_new_flat(nx, ny, lx, ly, nfields, seed, ncl1, ncl2, dout): return _nmtlib.synfast_new_flat(nx, ny, lx, ly, nfields, seed, ncl1, ncl2, dout) +def comp_coupling_matrix_anisotropic(spin1, spin2, aniso1, aniso2, lmax, lmax_mask, nl00, nl0e, nl0b, nle0, nlb0, nlee, nleb, nlbe, nlbb, nlb1, nlb2, bin, norm_type, w2): + return _nmtlib.comp_coupling_matrix_anisotropic(spin1, spin2, aniso1, aniso2, lmax, lmax_mask, nl00, nl0e, nl0b, nle0, nlb0, nlee, nleb, nlbe, nlbb, nlb1, nlb2, bin, norm_type, w2) + def comp_coupling_matrix(spin1, spin2, lmax, lmax_mask, pure_e_1, pure_b_1, pure_e_2, pure_b_2, norm_type, w2, nlb1, nlb2, nell4, bin, is_teb, l_toeplitz, l_exact, dl_band): return _nmtlib.comp_coupling_matrix(spin1, spin2, lmax, lmax_mask, pure_e_1, pure_b_1, pure_e_2, pure_b_2, norm_type, w2, nlb1, nlb2, nell4, bin, is_teb, l_toeplitz, l_exact, dl_band) diff --git a/pymaster/tests/test_master_anisotropic.py b/pymaster/tests/test_master_anisotropic.py new file mode 100644 index 00000000..09acaf43 --- /dev/null +++ b/pymaster/tests/test_master_anisotropic.py @@ -0,0 +1,175 @@ +import numpy as np +import healpy as hp +import pymaster as nmt +import pytest + + +def test_anisotropic_weighting_smoke(): + nside = 64 + npix = hp.nside2npix(nside) + m = np.ones(npix) + + f = nmt.NmtField(m, [m, m], + mask_12=0.5*m, + mask_22=2.0*m) + msk_a = f.get_anisotropic_mask() + + assert np.allclose(msk_a[1], 0.5*m) + assert np.allclose(msk_a[0], -0.5*m) + + +def test_anisotropic_weighting_errors(): + nside = 64 + npix = hp.nside2npix(nside) + m = np.ones(npix) + + # Field-level errors + # Must pass full weight matrix + with pytest.raises(ValueError): + nmt.NmtField(m, [m, m], mask_12=m) + + # Non-positive-definite weight matrix + with pytest.raises(ValueError): + nmt.NmtField(m, [m, m], mask_22=0*m, mask_12=m) + + # No anisotropic matrix for scalar fields + with pytest.raises(ValueError): + nmt.NmtField(m, None, spin=0, mask_22=m, mask_12=m*0) + + # No anisotropic matrix for scalar fields (second check) + with pytest.raises(ValueError): + nmt.NmtField(m, [m], mask_22=m, mask_12=m*0) + + # No purification with anisotropic weights + with pytest.raises(NotImplementedError): + nmt.NmtField(m, [m, m], mask_12=0*m, mask_22=m, + purify_b=True) + + # No contaminant deprojection with anisotropic weights + with pytest.raises(NotImplementedError): + nmt.NmtField(m, [m, m], mask_12=0*m, mask_22=m, + templates=[[m, m]]) + + # Workspace-level errors + f = nmt.NmtField(m, [m, m], + mask_12=0.5*m, + mask_22=2.0*m) + b = nmt.NmtBin.from_nside_linear(nside, nlb=4) + with pytest.raises(NotImplementedError): + nmt.NmtWorkspace.from_fields(f, f, b, l_toeplitz=3) + + # Covariance-level errors + with pytest.raises(NotImplementedError): + nmt.NmtCovarianceWorkspace.from_fields(f, f, f, f) + + +def test_anisotropic_weighting(): + # Test parameters + nside = 64 + spin = 2 + nlb = 4 + nsims = 100 + + # Create anisotropic weights + mask = hp.read_map("test/benchmarks/msk.fits") + mask = hp.ud_grade(mask, nside_out=nside) + mask = hp.smoothing(mask, sigma=np.radians(1.0)) + mask[mask > 1] = 1 + mask[mask < 0.001] = 0 + + delta_m = 0.9 + r_m = 0.5 + w11 = (1+delta_m)*mask + w22 = (1-delta_m)*mask + w12 = r_m*np.sqrt(w11*w22) + + # Input power spectra + ls = np.arange(3*nside) + cl_temp = 1/(ls+10) + cl_tt = 1.5*cl_temp + cl_te = 0.6*cl_temp + cl_tb = 0.3*cl_temp + cl_ee = 1.0*cl_temp + cl_eb = 0.2*cl_temp + cl_bb = 0.4*cl_temp + + # Workspaces + b = nmt.NmtBin.from_nside_linear(nside, nlb=nlb) + leff = b.get_effective_ells() + f0 = nmt.NmtField(mask, None, spin=0, n_iter=0) + fs = nmt.NmtField(mask, None, spin=spin, n_iter=0) + fsa = nmt.NmtField(w11, None, spin=spin, n_iter=0, + mask_12=w12, mask_22=w22) + w0sa = nmt.NmtWorkspace.from_fields(f0, fsa, b) + wsa0 = nmt.NmtWorkspace.from_fields(fsa, f0, b) + wsasa = nmt.NmtWorkspace.from_fields(fsa, fsa, b) + wssa = nmt.NmtWorkspace.from_fields(fs, fsa, b) + wsas = nmt.NmtWorkspace.from_fields(fsa, fs, b) + + # Run simulations + cl0sa_s = [] + clsa0_s = [] + clsasa_s = [] + clssa_s = [] + clsas_s = [] + for i in range(nsims): + almt, alme, almb = hp.synalm([cl_tt, cl_ee, cl_bb, + cl_te, cl_eb, cl_tb], new=True) + map_t = hp.alm2map(almt, nside, lmax=3*nside-1) + map_q, map_u = hp.alm2map_spin([alme, almb], nside, spin, + lmax=3*nside-1, mmax=3*nside-1) + + f0 = nmt.NmtField(mask, [map_t], n_iter=0) + fs = nmt.NmtField(mask, [map_q, map_u], spin=spin, n_iter=0) + fsa = nmt.NmtField(w11, [map_q, map_u], spin=spin, n_iter=0, + mask_12=w12, mask_22=w22) + + cl0sa_s.append(w0sa.decouple_cell(nmt.compute_coupled_cell(f0, fsa))) + clsa0_s.append(wsa0.decouple_cell(nmt.compute_coupled_cell(fsa, f0))) + clsasa_s.append(wsasa.decouple_cell(nmt.compute_coupled_cell(fsa, + fsa))) + clssa_s.append(wssa.decouple_cell(nmt.compute_coupled_cell(fs, fsa))) + clsas_s.append(wsas.decouple_cell(nmt.compute_coupled_cell(fsa, fs))) + cl0sa_s = np.array(cl0sa_s) + clsa0_s = np.array(clsa0_s) + clsasa_s = np.array(clsasa_s) + clssa_s = np.array(clssa_s) + clsas_s = np.array(clsas_s) + # Mean + cl0sa_m = np.mean(cl0sa_s, axis=0) + clsa0_m = np.mean(clsa0_s, axis=0) + clsasa_m = np.mean(clsasa_s, axis=0) + clssa_m = np.mean(clssa_s, axis=0) + clsas_m = np.mean(clsas_s, axis=0) + # STD + cl0sa_e = np.std(cl0sa_s, axis=0) + clsa0_e = np.std(clsa0_s, axis=0) + clsasa_e = np.std(clsasa_s, axis=0) + clssa_e = np.std(clssa_s, axis=0) + clsas_e = np.std(clsas_s, axis=0) + # Truth + cl0sa_t = w0sa.decouple_cell(w0sa.couple_cell([cl_te, cl_tb])) + clsa0_t = wsa0.decouple_cell(wsa0.couple_cell([cl_te, cl_tb])) + clsasa_t = wsasa.decouple_cell(wsasa.couple_cell([cl_ee, cl_eb, + cl_eb, cl_bb])) + clssa_t = wssa.decouple_cell(wssa.couple_cell([cl_ee, cl_eb, + cl_eb, cl_bb])) + clsas_t = wsas.decouple_cell(wsas.couple_cell([cl_ee, cl_eb, + cl_eb, cl_bb])) + + # Compare all power spectra and check for > 6sigma deviations + def comp_cl(clm, cle, clt, nsigma=6): + lgood = leff < 2*nside + for m, e, t in zip(clm, cle, clt): + r = ((m-t)*np.sqrt(nsims)/e)[lgood] + if np.any(np.fabs(r) > nsigma): + return False + return True + + test_0sa = comp_cl(cl0sa_m, cl0sa_e, cl0sa_t) + test_sa0 = comp_cl(clsa0_m, clsa0_e, clsa0_t) + test_sasa = comp_cl(clsasa_m, clsasa_e, clsasa_t) + test_ssa = comp_cl(clssa_m, clssa_e, clssa_t) + test_sas = comp_cl(clsas_m, clsas_e, clsas_t) + + assert np.all([test_0sa, test_sa0, test_sasa, test_ssa, test_sas]) diff --git a/pymaster/tests/test_utils.py b/pymaster/tests/test_utils.py index 5c39396e..45009ef2 100644 --- a/pymaster/tests/test_utils.py +++ b/pymaster/tests/test_utils.py @@ -64,7 +64,7 @@ def test_moore_penrose_pinv(): # Should only have two non-zero eigvals assert np.sum(np.fabs(w) < 1E-15) == 2 # Check e is parallel to v - assert np.isclose(np.dot(e, v), + assert np.isclose(np.fabs(np.dot(e, v)), np.sqrt(np.dot(e, e))) # For invertible matrix, we just get diff --git a/pymaster/workspaces.py b/pymaster/workspaces.py index e0b9c703..447feed5 100644 --- a/pymaster/workspaces.py +++ b/pymaster/workspaces.py @@ -246,6 +246,10 @@ def compute_coupling_matrix(self, fl1, fl2, bins, is_teb=False, lib.workspace_free(self.wsp) self.wsp = None + anisotropic_mask_any = fl1.anisotropic_mask or fl2.anisotropic_mask + if anisotropic_mask_any and (l_toeplitz >= 0): + raise NotImplementedError("Toeplitz approximation not " + "implemented for anisotropic masks.") ut._toeplitz_sanity(l_toeplitz, l_exact, dl_band, bins.bin.ell_max, fl1, fl2) @@ -258,6 +262,30 @@ def compute_coupling_matrix(self, fl1, fl2, bins, is_teb=False, else: alm2 = fl2.get_mask_alms() pcl_mask = hp.alm2cl(alm1, alm2, lmax=fl1.ainfo_mask.lmax) + if anisotropic_mask_any: + pcl0 = pcl_mask * 0 + pclm_00 = pcl_mask + pclm_0e = pclm_0b = pclm_e0 = pclm_b0 = pcl0 + pclm_ee = pclm_eb = pclm_be = pclm_bb = pcl0 + if fl1.anisotropic_mask: + alm1a = fl1.get_anisotropic_mask_alms() + if fl2.anisotropic_mask: + alm2a = fl2.get_anisotropic_mask_alms() + if fl2.anisotropic_mask: + pclm_0e = hp.alm2cl(alm1, alm2a[0], lmax=fl1.ainfo_mask.lmax) + pclm_0b = hp.alm2cl(alm1, alm2a[1], lmax=fl1.ainfo_mask.lmax) + if fl1.anisotropic_mask: + pclm_e0 = hp.alm2cl(alm1a[0], alm2, lmax=fl1.ainfo_mask.lmax) + pclm_b0 = hp.alm2cl(alm1a[1], alm2, lmax=fl1.ainfo_mask.lmax) + if fl2.anisotropic_mask: + pclm_ee = hp.alm2cl(alm1a[0], alm2a[0], + lmax=fl1.ainfo_mask.lmax) + pclm_eb = hp.alm2cl(alm1a[0], alm2a[1], + lmax=fl1.ainfo_mask.lmax) + pclm_be = hp.alm2cl(alm1a[1], alm2a[0], + lmax=fl1.ainfo_mask.lmax) + pclm_bb = hp.alm2cl(alm1a[1], alm2a[1], + lmax=fl1.ainfo_mask.lmax) if normalization == 'MASTER': norm_type = 0 @@ -281,12 +309,24 @@ def compute_coupling_matrix(self, fl1, fl2, bins, is_teb=False, msk2 = fl2.get_mask() wawb = fl1.minfo.si.dot_map(msk1, msk2)/(4*np.pi) - self.wsp = lib.comp_coupling_matrix( - int(fl1.spin), int(fl2.spin), - int(fl1.ainfo.lmax), int(fl1.ainfo_mask.lmax), - int(fl1.pure_e), int(fl1.pure_b), int(fl2.pure_e), int(fl2.pure_b), - int(norm_type), wawb, fl1.beam, fl2.beam, pcl_mask.flatten()-Nw, - bins.bin, int(is_teb), l_toeplitz, l_exact, dl_band) + if anisotropic_mask_any: + self.wsp = lib.comp_coupling_matrix_anisotropic( + int(fl1.spin), int(fl2.spin), + int(fl1.anisotropic_mask), int(fl2.anisotropic_mask), + int(fl1.ainfo.lmax), int(fl1.ainfo_mask.lmax), + pclm_00, pclm_0e, pclm_0b, pclm_e0, pclm_b0, + pclm_ee, pclm_eb, pclm_be, pclm_bb, + fl1.beam, fl2.beam, bins.bin, + int(norm_type), wawb) + else: + self.wsp = lib.comp_coupling_matrix( + int(fl1.spin), int(fl2.spin), + int(fl1.ainfo.lmax), int(fl1.ainfo_mask.lmax), + int(fl1.pure_e), int(fl1.pure_b), + int(fl2.pure_e), int(fl2.pure_b), + int(norm_type), wawb, + fl1.beam, fl2.beam, pcl_mask.flatten()-Nw, + bins.bin, int(is_teb), l_toeplitz, l_exact, dl_band) self.has_unbinned = True def write_to(self, fname): diff --git a/src/namaster.h b/src/namaster.h index 6dcefc54..a7c9ff6c 100644 --- a/src/namaster.h +++ b/src/namaster.h @@ -740,6 +740,22 @@ nmt_master_calculator *nmt_compute_master_coefficients(int lmax, int lmax_mask, int l_exact, int dl_band); void nmt_master_calculator_free(nmt_master_calculator *c); +nmt_workspace *nmt_compute_coupling_matrix_anisotropic(int spin1, int spin2, + int mask_aniso_1, int mask_aniso_2, + int lmax, int lmax_mask, + flouble *pcl_masks_00, + flouble *pcl_masks_0e, + flouble *pcl_masks_e0, + flouble *pcl_masks_0b, + flouble *pcl_masks_b0, + flouble *pcl_masks_ee, + flouble *pcl_masks_eb, + flouble *pcl_masks_be, + flouble *pcl_masks_bb, + flouble *beam1,flouble *beam2, + nmt_binning_scheme *bin, + int norm_type,flouble w2); + nmt_workspace *nmt_compute_coupling_matrix(int spin1,int spin2, int lmax, int lmax_mask, int pure_e1,int pure_b1, diff --git a/src/nmt_master.c b/src/nmt_master.c index 300c6d55..a8ca0dc1 100644 --- a/src/nmt_master.c +++ b/src/nmt_master.c @@ -418,6 +418,238 @@ static void populate_toeplitz(nmt_master_calculator *c, flouble **pcl_masks, int } } +nmt_workspace *nmt_compute_coupling_matrix_anisotropic(int spin1, int spin2, + int mask_aniso_1, int mask_aniso_2, + int lmax, int lmax_mask, + flouble *pcl_masks_00, + flouble *pcl_masks_0e, + flouble *pcl_masks_e0, + flouble *pcl_masks_0b, + flouble *pcl_masks_b0, + flouble *pcl_masks_ee, + flouble *pcl_masks_eb, + flouble *pcl_masks_be, + flouble *pcl_masks_bb, + flouble *beam1,flouble *beam2, + nmt_binning_scheme *bin, + int norm_type,flouble w2) +{ + int l2,lmax_large,lmax_fields; + int n_cl,nmaps1=1,nmaps2=1; + nmt_workspace *w; + if(spin1) nmaps1=2; + if(spin2) nmaps2=2; + n_cl=nmaps1*nmaps2; + + if(bin->ell_max>lmax) + report_error(NMT_ERROR_CONSISTENT_RESO, + "Requesting bandpowers for too high a " + "multipole given map resolution\n"); + lmax_fields=lmax; // ell_max for the maps + lmax_large=lmax_mask; // ell_max for the masks + + + w=nmt_workspace_new(n_cl,bin,0, + lmax_fields,lmax_large,norm_type,w2); + + for(l2=0;l2<=w->lmax_fields;l2++) + w->beam_prod[l2]=beam1[l2]*beam2[l2]; + + for(l2=0;l2<=w->lmax_mask;l2++) + w->pcl_masks[l2]=pcl_masks_00[l2]*(2*l2+1.)/(4*M_PI); + + int i_00=0, i_0e=1, i_0b=2, i_e0=3, i_b0=4, i_ee=5, i_eb=6, i_be=7, i_bb=8; + flouble **pcl_masks=my_malloc(9*sizeof(flouble *)); + for(l2=0;l2<9;l2++) + pcl_masks[l2]=my_calloc(lmax_mask+1,sizeof(flouble)); + memcpy(pcl_masks[i_00], pcl_masks_00, (lmax_mask+1)*sizeof(flouble)); + if(mask_aniso_2) { + memcpy(pcl_masks[i_0e], pcl_masks_0e, (lmax_mask+1)*sizeof(flouble)); + memcpy(pcl_masks[i_0b], pcl_masks_0b, (lmax_mask+1)*sizeof(flouble)); + } + if(mask_aniso_1) { + memcpy(pcl_masks[i_e0], pcl_masks_e0, (lmax_mask+1)*sizeof(flouble)); + memcpy(pcl_masks[i_b0], pcl_masks_b0, (lmax_mask+1)*sizeof(flouble)); + if(mask_aniso_2) { + memcpy(pcl_masks[i_ee], pcl_masks_ee, (lmax_mask+1)*sizeof(flouble)); + memcpy(pcl_masks[i_eb], pcl_masks_eb, (lmax_mask+1)*sizeof(flouble)); + memcpy(pcl_masks[i_be], pcl_masks_be, (lmax_mask+1)*sizeof(flouble)); + memcpy(pcl_masks[i_bb], pcl_masks_bb, (lmax_mask+1)*sizeof(flouble)); + } + } + + int sign_00 = 1; + int sign_0P = (spin2 & 1) ? -1 : 1; + int sign_P0 = (spin1 & 1) ? -1 : 1; + int sign_PP = ((spin1+spin2) & 1) ? -1 : 1; + for(l2=0;l2<=lmax_mask;l2++) { + flouble fac=(2*l2+1)/(4*M_PI); + pcl_masks[i_00][l2] *= sign_00*fac; + pcl_masks[i_0e][l2] *= sign_0P*fac; + pcl_masks[i_0b][l2] *= sign_0P*fac; + pcl_masks[i_e0][l2] *= sign_P0*fac; + pcl_masks[i_b0][l2] *= sign_P0*fac; + pcl_masks[i_ee][l2] *= sign_PP*fac; + pcl_masks[i_eb][l2] *= sign_PP*fac; + pcl_masks[i_be][l2] *= sign_PP*fac; + pcl_masks[i_bb][l2] *= sign_PP*fac; + } + + int max_spin=NMT_MAX(spin1, spin2); + int lstart=max_spin; + int reuse_ss1=(spin1==spin2); + int is_0s=(spin1==0) && (spin2!=0); + int is_s0=(spin1!=0) && (spin2==0); + int is_ss=(spin1!=0) && (spin2!=0); + +#pragma omp parallel default(none) \ + shared(pcl_masks, lstart, reuse_ss1, lmax, lmax_mask) \ + shared(mask_aniso_1, mask_aniso_2, is_0s, is_s0, is_ss) \ + shared(spin1, spin2, i_00, i_0e, i_0b, i_e0, i_b0) \ + shared(i_ee, i_eb, i_be, i_bb, w) + { + int ll2,ll3; + double *wigner_ss1=NULL, *wigner_ss2=NULL, *wigner_ss1a=NULL, *wigner_ss2a=NULL; + // s -s 0 terms + wigner_ss1=my_malloc(2*(lmax_mask+1)*sizeof(double)); + if(reuse_ss1) + wigner_ss2=wigner_ss1; + else + wigner_ss2=my_malloc(2*(lmax_mask+1)*sizeof(double)); + + // s s -2s terms + if(mask_aniso_1) + wigner_ss1a=my_malloc(2*(lmax_mask+1)*sizeof(double)); + if(mask_aniso_2) { + if((!reuse_ss1) || (mask_aniso_1 == 0)) + wigner_ss2a=my_malloc(2*(lmax_mask+1)*sizeof(double)); + else + wigner_ss2a=wigner_ss1a; + } + +#pragma omp for schedule(dynamic) + for(ll2=lstart;ll2<=lmax;ll2++) { + for(ll3=lstart;ll3<=lmax;ll3++) { //TODO: investigate if there are ell-ell' symmetries to exploit + int l1,lmin_here,lmax_here; + int l3fac=2*ll3+1; + int lmin_ss1=0,lmax_ss1=2*(lmax_mask+1)+1; + int lmin_ss2=0,lmax_ss2=2*(lmax_mask+1)+1; + int lmin_ss1a=0,lmax_ss1a=2*(lmax_mask+1)+1; + int lmin_ss2a=0,lmax_ss2a=2*(lmax_mask+1)+1; + lmin_here=abs(ll2-ll3); + lmax_here=ll2+ll3; + + // s -s 0 terms + drc3jj(ll2,ll3,spin1,-spin1,&lmin_ss1,&lmax_ss1,wigner_ss1,2*(lmax_mask+1)); + if(reuse_ss1) { + lmin_ss2=lmin_ss1; + lmax_ss2=lmax_ss1; + } + else + drc3jj(ll2,ll3,spin2,-spin2,&lmin_ss2,&lmax_ss2,wigner_ss2,2*(lmax_mask+1)); + + // s s -2s terms + if(mask_aniso_1) + drc3jj(ll2,ll3,spin1,spin1,&lmin_ss1a,&lmax_ss1a,wigner_ss1a,2*(lmax_mask+1)); + if(mask_aniso_2) { + if((!reuse_ss1) || (mask_aniso_1 == 0)) + drc3jj(ll2,ll3,spin2,spin2,&lmin_ss2a,&lmax_ss2a,wigner_ss2a,2*(lmax_mask+1)); + else { + lmin_ss2a=lmin_ss1a; + lmax_ss2a=lmax_ss1a; + } + } + + for(l1=lmin_here;l1<=lmax_here;l1++) { + if(l1<=lmax_mask) { + flouble m00=0,m0e=0,m0b=0,me0=0,mb0=0,mee=0,meb=0,mbe=0,mbb=0; + int suml=l1+ll2+ll3; + flouble wss1=0,wss2=0,wss1a=0,wss2a=0; + int jss1=l1-lmin_ss1; + int jss2=l1-lmin_ss2; + int jss1a=l1-lmin_ss1a; + int jss2a=l1-lmin_ss2a; + int splus= (suml & 1) ? 0 : 1; + int sminus= (suml & 1) ? 1 : 0; + wss1=jss1 < 0 ? 0 : wigner_ss1[jss1]; + wss2=jss2 < 0 ? 0 : wigner_ss2[jss2]; + if(mask_aniso_1) + wss1a=jss1a < 0 ? 0 : wigner_ss1a[jss1a]; + if(mask_aniso_2) + wss2a=jss2a < 0 ? 0 : wigner_ss2a[jss2a]; + + m00 = wss1*wss2*pcl_masks[i_00][l1]; + if(mask_aniso_2) { + m0e = wss1*wss2a*pcl_masks[i_0e][l1]; + m0b = wss1*wss2a*pcl_masks[i_0b][l1]; + } + if(mask_aniso_1) { + me0 = wss1a*wss2*pcl_masks[i_e0][l1]; + mb0 = wss1a*wss2*pcl_masks[i_b0][l1]; + if(mask_aniso_2) { + mee = wss1a*wss2a*pcl_masks[i_ee][l1]; + meb = wss1a*wss2a*pcl_masks[i_eb][l1]; + mbe = wss1a*wss2a*pcl_masks[i_be][l1]; + mbb = wss1a*wss2a*pcl_masks[i_bb][l1]; + } + } + if(is_0s) { + w->coupling_matrix_unbinned[2*ll2+0][2*ll3+0] += l3fac*(m00-m0e); //0E,0E + w->coupling_matrix_unbinned[2*ll2+0][2*ll3+1] += l3fac*(-m0b); //0E,0B + w->coupling_matrix_unbinned[2*ll2+1][2*ll3+0] += l3fac*(-m0b); //0B,0E + w->coupling_matrix_unbinned[2*ll2+1][2*ll3+1] += l3fac*(m00+m0e); //0B,0B + } + if(is_s0) { + w->coupling_matrix_unbinned[2*ll2+0][2*ll3+0] += l3fac*(m00-me0); //E0,E0 + w->coupling_matrix_unbinned[2*ll2+0][2*ll3+1] += l3fac*(-mb0); //E0,B0 + w->coupling_matrix_unbinned[2*ll2+1][2*ll3+0] += l3fac*(-mb0); //B0,E0 + w->coupling_matrix_unbinned[2*ll2+1][2*ll3+1] += l3fac*(m00+me0); //B0,B0 + } + if(is_ss) { + w->coupling_matrix_unbinned[4*ll2+0][4*ll3+0] += l3fac*(splus * (m00-m0e-me0+mee) + sminus * mbb); //EE,EE + w->coupling_matrix_unbinned[4*ll2+0][4*ll3+1] += l3fac*(splus * (-m0b+meb) + sminus * (-mb0-mbe)); //EE,EB + w->coupling_matrix_unbinned[4*ll2+0][4*ll3+2] += l3fac*(splus * (-mb0+mbe) + sminus * (-m0b-meb)); //EE,BE + w->coupling_matrix_unbinned[4*ll2+0][4*ll3+3] += l3fac*(splus * mbb + sminus * (m00+m0e+me0+mee)); //EE,BB + w->coupling_matrix_unbinned[4*ll2+1][4*ll3+0] += l3fac*(splus * (-m0b+meb) + sminus * (mb0-mbe)); //EB,EE + w->coupling_matrix_unbinned[4*ll2+1][4*ll3+1] += l3fac*(splus * (m00+m0e-me0-mee) + sminus * (-mbb)); //EB,EB + w->coupling_matrix_unbinned[4*ll2+1][4*ll3+2] += l3fac*(splus * mbb + sminus * (-m00+m0e-me0+mee)); //EB,BE + w->coupling_matrix_unbinned[4*ll2+1][4*ll3+3] += l3fac*(splus * (-mb0-mbe) + sminus * (m0b+meb)); //EB,BB + w->coupling_matrix_unbinned[4*ll2+2][4*ll3+0] += l3fac*(splus * (-mb0+mbe) + sminus * (m0b-meb)); //BE,EE + w->coupling_matrix_unbinned[4*ll2+2][4*ll3+1] += l3fac*(splus * mbb + sminus * (-m00-m0e+me0+mee)); //BE,EB + w->coupling_matrix_unbinned[4*ll2+2][4*ll3+2] += l3fac*(splus * (m00-m0e+me0-mee) + sminus * (-mbb)); //BE,BE + w->coupling_matrix_unbinned[4*ll2+2][4*ll3+3] += l3fac*(splus * (-m0b-meb) + sminus * (mb0+mbe)); //BE,BB + w->coupling_matrix_unbinned[4*ll2+3][4*ll3+0] += l3fac*(splus * mbb + sminus * (m00-m0e-me0+mee)); //BB,EE + w->coupling_matrix_unbinned[4*ll2+3][4*ll3+1] += l3fac*(splus * (-mb0-mbe) + sminus * (-m0b+meb)); //BB,EB + w->coupling_matrix_unbinned[4*ll2+3][4*ll3+2] += l3fac*(splus * (-m0b-meb) + sminus * (-mb0+mbe)); //BB,BE + w->coupling_matrix_unbinned[4*ll2+3][4*ll3+3] += l3fac*(splus * (m00+m0e+me0+mee) + sminus * mbb); //BB,BB + } + } + } + } + } //end omp for + + // s -s 0 terms + free(wigner_ss1); + if(!reuse_ss1) + free(wigner_ss2); + // s s -2s terms + if(mask_aniso_1) + free(wigner_ss1a); + if(mask_aniso_2) { + if((!reuse_ss1) || (mask_aniso_1 == 0)) + free(wigner_ss2a); + } + } //end omp parallel + + for(l2=0;l2<9;l2++) + free(pcl_masks[l2]); + free(pcl_masks); + + bin_coupling_matrix(w); + + return w; +} + nmt_master_calculator *nmt_compute_master_coefficients(int lmax, int lmax_mask, int npcl, flouble **pcl_masks, int s1, int s2,