diff --git a/CHANGES.rst b/CHANGES.rst index 386859a0..03290c8a 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,5 +1,7 @@ 3.4.1 (unreleased) ================== +- Finalized Point Source Catalog component and backgroud component https://github.com/galsci/pysm/pull/191 +- Configure verbosity easily with `set_verbosity()` - Updated `pixell` from 0.17.3 to 0.26.0 https://github.com/galsci/pysm/pull/183 - Initial implementation of a point source catalog component emission https://github.com/galsci/pysm/pull/187 - Switch the build system to Hatch https://github.com/galsci/pysm/pull/189 diff --git a/docs/index.rst b/docs/index.rst index 331418f7..d8d6ce47 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -130,7 +130,19 @@ Configure verbosity ------------------- PySM uses the `logging` module to configure its verbosity, -by default it will only print warnings and errors, to configure logging +by default it will only print warnings and errors. + +A log of useful information, for example intermediate results and timing can be +accessed enabling the `INFO` level of logging. +We provide a simplified function to configure this. + + pysm3.set_verbosity() + +By default this sets verbosity to `INFO`, otherwise you can specify a level: + + pysm3.set_verbosity(logging.DEBUG) + +To configure logging you can access the "pysm3" logger with:: import logging diff --git a/docs/preprocess-templates/catalog/background_create_websky_catalog_dask.ipynb b/docs/preprocess-templates/catalog/background_create_websky_catalog_dask.ipynb new file mode 100644 index 00000000..5533e72f --- /dev/null +++ b/docs/preprocess-templates/catalog/background_create_websky_catalog_dask.ipynb @@ -0,0 +1,1628 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "0ed31a46-d481-4958-af82-3889e2f6b80a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import h5pickle as h5py\n", + "import numpy as np\n", + "import healpy as hp\n", + "import matplotlib.pyplot as plt\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4d97b61d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "| Provider | Environment variable | Set? | Models |\n", + "|----------|----------------------|------|--------|\n", + "| `gemini` | `GOOGLE_API_KEY` | ✅ | |\n" + ], + "text/plain": [ + "gemini\n", + "Requires environment variable: GOOGLE_API_KEY (set)\n", + "* gemini:gemini-1.0-pro\n", + "* gemini:gemini-1.0-pro-001\n", + "* gemini:gemini-1.0-pro-latest\n", + "* gemini:gemini-1.0-pro-vision-latest\n", + "* gemini:gemini-pro\n", + "* gemini:gemini-pro-vision\n", + "\n" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%load_ext jupyter_ai\n", + "%ai list gemini" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6f9001d6", + "metadata": {}, + "outputs": [], + "source": [ + "#%%ai gemini:gemini-pro -f code\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0dcee1ff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import healpy as hp\n", + "hp.version" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "66420d6e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Created `%gm` as an alias for `%ai gemini:gemini-pro -f code`.\n", + "Created `%%gm` as an alias for `%%ai gemini:gemini-pro -f code`.\n" + ] + } + ], + "source": [ + "%alias_magic gm ai -p \"gemini:gemini-pro -f code\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "263a8761", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "num_threads = 128\n", + "os.environ[\"OMP_NUM_THREADS\"] = \"1\"" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5f8de1f5", + "metadata": {}, + "outputs": [], + "source": [ + "cutoff_flux = 10000" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "1e31f0a2", + "metadata": {}, + "outputs": [], + "source": [ + "output_filename = \"/pscratch/sd/z/zonca/websky_full_catalog.h5\"" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "82d11cad", + "metadata": {}, + "outputs": [], + "source": [ + "plot = False" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8e35f4b9-bc39-49e9-af61-67464526f4d9", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/global/cfs/cdirs/sobs/www/users/Radio_WebSky/matched_catalogs_2\n" + ] + } + ], + "source": [ + "cd /global/cfs/cdirs/sobs/www/users/Radio_WebSky/matched_catalogs_2" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "031d3e3d-b9d0-4c73-80bb-bbd804a825fe", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "catalog_100.0.h5 catalog_232.0.h5 catalog_353.0.h5 catalog_643.0.h5\r\n", + "catalog_111.0.h5 catalog_24.5.h5 catalog_375.0.h5 catalog_67.8.h5\r\n", + "catalog_129.0.h5 catalog_256.0.h5 catalog_409.0.h5 catalog_70.0.h5\r\n", + "catalog_143.0.h5 catalog_27.3.h5 catalog_41.7.h5 catalog_729.0.h5\r\n", + "catalog_153.0.h5 catalog_275.0.h5 catalog_44.0.h5 catalog_73.7.h5\r\n", + "catalog_164.0.h5 catalog_294.0.h5 catalog_467.0.h5 catalog_79.6.h5\r\n", + "catalog_18.7.h5 catalog_30.0.h5 catalog_47.4.h5 catalog_817.0.h5\r\n", + "catalog_189.0.h5 catalog_306.0.h5 catalog_525.0.h5 catalog_857.0.h5\r\n", + "catalog_21.6.h5 catalog_314.0.h5 catalog_545.0.h5 catalog_90.2.h5\r\n", + "catalog_210.0.h5 catalog_340.0.h5 catalog_584.0.h5 catalog_906.0.h5\r\n", + "catalog_217.0.h5 catalog_35.9.h5 catalog_63.9.h5 flux_coeff.h5\r\n" + ] + } + ], + "source": [ + "%ls" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ba71f7d1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "freqs = [\n", + " \"18.7\",\n", + " \"24.5\",\n", + " \"44.0\",\n", + " \"70.0\",\n", + " \"100.0\",\n", + " \"143.0\",\n", + " \"217.0\",\n", + " \"353.0\",\n", + " \"545.0\",\n", + " \"643.0\",\n", + " \"729.0\",\n", + " \"857.0\",\n", + " \"906.0\",\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ec6aeb18", + "metadata": {}, + "outputs": [], + "source": [ + "freqs_array = np.array(list(map(float, freqs)))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d0653ac2-3d67-4480-849d-bcca2727a143", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "cat = h5py.File(\"catalog_100.0.h5\", \"r\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "fd871a2b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.64009452, 1.64009452, 1.64009452, 1.69043016], dtype='>f8')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cat[\"theta\"][:4]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d06f8c9e", + "metadata": {}, + "outputs": [], + "source": [ + "#%%ai gemini:gemini-pro -f code\n", + " \n", + "#find the fields in a h5py File" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b02cba19", + "metadata": {}, + "outputs": [], + "source": [ + "import dask.array as da" + ] + }, + { + "cell_type": "markdown", + "id": "80b4c835", + "metadata": {}, + "source": [ + "There are no metadata in the file, I guess fluxes are in `Jy`" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "1b709764", + "metadata": {}, + "outputs": [], + "source": [ + "from dask.distributed import Client, LocalCluster\n", + "\n", + "cluster = LocalCluster(n_workers=num_threads, threads_per_worker=1, processes=True)\n", + "client = Client(cluster)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "886dbbaf-8890-449c-bab4-2f7eba722d31", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import xarray as xr" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "ec051ccf", + "metadata": {}, + "outputs": [], + "source": [ + "field = 'flux'" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "07789546", + "metadata": {}, + "outputs": [], + "source": [ + "arrays = [da.from_array(h5py.File(f\"catalog_{freq}.h5\", \"r\")[field], chunks=1000000) for freq in freqs]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "6bb7555a", + "metadata": {}, + "outputs": [], + "source": [ + "flux = da.stack(arrays, axis=0) " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "f1e3953e", + "metadata": {}, + "outputs": [], + "source": [ + "flux = flux.rechunk(chunks=(13, 1000000))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "1e17152a", + "metadata": {}, + 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" + ], + "text/plain": [ + "dask.array" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "flux" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "64e8f31e", + "metadata": {}, + "outputs": [], + "source": [ + "# Only keep sources below cutoff\n", + "cutoff_flux_Jy = 1e-3\n", + "# flux = flux[:, flux[4, :] < cutoff_flux_Jy]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "c442dd8e", + "metadata": {}, + "outputs": [], + "source": [ + "# flux.compute_chunk_sizes()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "a1ecd6d8", + "metadata": {}, + "outputs": [], + "source": [ + "from numba import njit\n", + "\n", + "@njit\n", + "def model(freq, a, b, c, d, e):\n", + " log_freq = np.log(freq)\n", + " return a * log_freq**4 + b * log_freq**3 + c * log_freq**2 + d * log_freq + e" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "3c08d830", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.optimize import curve_fit" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "c30eb71c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 5.37899652e-09, -1.29664725e-07, 1.20804354e-06, -5.22231671e-06,\n", + " 9.00274650e-06])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "curve_fit(model, freqs_array, flux[:,0])[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "efefaf03", + "metadata": {}, + "outputs": [], + "source": [ + "def run_curve_fit(flux):\n", + " return curve_fit(model, freqs_array, flux)[0]\n", + "\n", + "coeff = da.apply_along_axis(run_curve_fit, 0, flux)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "fd0813a7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 2min 2s, sys: 1min 28s, total: 3min 31s\n", + "Wall time: 5min 4s\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[ 5.37899652e-09, 6.42822511e-09, 3.31959319e-09,\n", + " 1.38862575e-08, 3.78195705e-09, 1.08966713e-08,\n", + " 8.68670067e-08, 3.64081373e-09, 3.66938830e-09,\n", + " 3.02356547e-09],\n", + " [-1.29664725e-07, -1.25727514e-07, -6.53823561e-08,\n", + " -2.67337421e-07, -8.86265294e-08, -2.11607996e-07,\n", + " -1.96845594e-06, -8.47114797e-08, -7.11052032e-08,\n", + " -6.49361314e-08],\n", + " [ 1.20804354e-06, 8.85551144e-07, 4.83957483e-07,\n", + " 1.85677682e-06, 8.11717509e-07, 1.47786561e-06,\n", + " 1.66899947e-05, 7.71488341e-07, 5.08938118e-07,\n", + " 5.45560777e-07],\n", + " [-5.22231671e-06, -2.61195734e-06, -1.68793603e-06,\n", + " -5.55460268e-06, -3.51684601e-06, -4.35226803e-06,\n", + " -6.28894194e-05, -3.33517575e-06, -1.66773519e-06,\n", + " -2.22563537e-06],\n", + " [ 9.00274650e-06, 2.94062995e-06, 2.71462806e-06,\n", + " 6.94587053e-06, 6.25448262e-06, 5.04064302e-06,\n", + " 8.91598585e-05, 5.95275131e-06, 2.49203039e-06,\n", + " 3.97960150e-06]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "\n", + "coeff[:,:10].compute()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "6769c4cb", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "b160bebd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(5, 281756376)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coeff.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "f5329aa7", + "metadata": {}, + "outputs": [], + "source": [ + "xr_flux = xr.DataArray(\n", + " data=coeff,\n", + " coords={\"power\": np.arange(4, -1, -1), \"index\": da.arange(coeff.shape[1])},\n", + " name=\"flux\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "6029553c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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  • `gemini:gemini-1.0-pro`
  • `gemini:gemini-1.0-pro-001`
  • `gemini:gemini-1.0-pro-latest`
  • `gemini:gemini-1.0-pro-vision-latest`
  • `gemini:gemini-pro`
  • `gemini:gemini-pro-vision`
|\n" + ], + "text/plain": [ + "gemini\n", + "Requires environment variable: GOOGLE_API_KEY (set)\n", + "* gemini:gemini-1.0-pro\n", + "* gemini:gemini-1.0-pro-001\n", + "* gemini:gemini-1.0-pro-latest\n", + "* gemini:gemini-1.0-pro-vision-latest\n", + "* gemini:gemini-pro\n", + "* gemini:gemini-pro-vision\n", + "\n" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%load_ext jupyter_ai\n", + "%ai list gemini" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6f9001d6", + "metadata": {}, + "outputs": [], + "source": [ + "#%%ai gemini:gemini-pro -f code\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0dcee1ff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import healpy as hp\n", + "hp.version" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "66420d6e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Created `%gm` as an alias for `%ai gemini:gemini-pro -f code`.\n", + "Created `%%gm` as an alias for `%%ai gemini:gemini-pro -f code`.\n" + ] + } + ], + "source": [ + "%alias_magic gm ai -p \"gemini:gemini-pro -f code\"" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "263a8761", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "num_threads = 128\n", + "os.environ[\"OMP_NUM_THREADS\"] = \"1\"" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "1b709764", + "metadata": {}, + "outputs": [], + "source": [ + "from dask.distributed import Client, LocalCluster\n", + "\n", + "cluster = LocalCluster(n_workers=num_threads, threads_per_worker=1, processes=True)\n", + "client = Client(cluster)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5f8de1f5", + "metadata": {}, + "outputs": [], + "source": [ + "cutoff_flux = 1e-3" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "82d11cad", + "metadata": {}, + "outputs": [], + "source": [ + "plot = False" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8e35f4b9-bc39-49e9-af61-67464526f4d9", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/global/cfs/cdirs/sobs/www/users/Radio_WebSky/matched_catalogs_2\n" + ] + } + ], + "source": [ + "cd /global/cfs/cdirs/sobs/www/users/Radio_WebSky/matched_catalogs_2" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d0653ac2-3d67-4480-849d-bcca2727a143", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "cat = h5py.File(\"catalog_100.0.h5\", \"r\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "d06f8c9e", + "metadata": {}, + "outputs": [], + "source": [ + "#%%ai gemini:gemini-pro -f code\n", + " \n", + "#find the fields in a h5py File" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "b02cba19", + "metadata": {}, + "outputs": [], + "source": [ + "import dask.array as da" + ] + }, + { + "cell_type": "markdown", + "id": "80b4c835", + "metadata": {}, + "source": [ + "There are no metadata in the file, I guess fluxes are in `Jy`" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "886dbbaf-8890-449c-bab4-2f7eba722d31", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import xarray as xr" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "ec051ccf", + "metadata": {}, + "outputs": [], + "source": [ + "field = 'flux'" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "efc4be5b", + "metadata": {}, + "outputs": [], + "source": [ + "chunk_size = int(1e6)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "988dd280", + "metadata": {}, + "outputs": [], + "source": [ + "cat_xr = xr.open_dataset(\"catalog_100.0.h5\", chunks=chunk_size)\n", + "cat_xr = cat_xr.rename({\"phony_dim_0\":\"index\"})" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "17200e51", + "metadata": {}, + "outputs": [], + "source": [ + "cutoff_mask = (cat_xr.flux < cutoff_flux).compute()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "fb1ce190", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The jupyter_ai extension is already loaded. To reload it, use:\n", + " %reload_ext jupyter_ai\n" + ] + }, + { + "data": { + "text/markdown": [ + "| Provider | Environment variable | Set? | Models |\n", + "|----------|----------------------|------|--------|\n", + "| `gemini` | `GOOGLE_API_KEY` | ✅ |
  • `gemini:gemini-1.0-pro`
  • `gemini:gemini-1.0-pro-001`
  • `gemini:gemini-1.0-pro-latest`
  • `gemini:gemini-1.0-pro-vision-latest`
  • `gemini:gemini-pro`
  • `gemini:gemini-pro-vision`
|\n" + ], + "text/plain": [ + "gemini\n", + "Requires environment variable: GOOGLE_API_KEY (set)\n", + "* gemini:gemini-1.0-pro\n", + "* gemini:gemini-1.0-pro-001\n", + "* gemini:gemini-1.0-pro-latest\n", + "* gemini:gemini-1.0-pro-vision-latest\n", + "* gemini:gemini-pro\n", + "* gemini:gemini-pro-vision\n", + "\n" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%load_ext jupyter_ai\n", + "%ai list gemini" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "878d216c", + "metadata": {}, + "outputs": [], + "source": [ + "#%%ai gemini:gemini-pro -f code" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "38e1a2ed", + "metadata": {}, + "outputs": [], + "source": [ + "pol_coeff = xr.open_dataarray(\n", + " \"/pscratch/sd/z/zonca/websky_full_catalog_polarized flux.h5\", chunks=chunk_size)[:, cutoff_mask]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "3eeaf786", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'flux' (power: 5, index: 281384121)>\n",
+       "dask.array<getitem, shape=(5, 281384121), dtype=float64, chunksize=(5, 999136), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * power    (power) int64 4 3 2 1 0\n",
+       "  * index    (index) int64 0 1 2 3 4 ... 281756372 281756373 281756374 281756375
" + ], + "text/plain": [ + "\n", + "dask.array\n", + "Coordinates:\n", + " * power (power) int64 4 3 2 1 0\n", + " * index (index) int64 0 1 2 3 4 ... 281756372 281756373 281756374 281756375" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pol_coeff" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "07789546", + "metadata": {}, + "outputs": [], + "source": [ + "temp_coeff = xr.open_dataarray(\"/pscratch/sd/z/zonca/websky_full_catalog_flux.h5\", chunks=chunk_size)[:, cutoff_mask]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "40648c82", + "metadata": {}, + "outputs": [], + "source": [ + "output_catalog = xr.Dataset({\"logpolycoefpolflux\":pol_coeff,\"logpolycoefflux\":temp_coeff })" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "449d730c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "Dimensions:             (power: 5, index: 281384121)\n",
+       "Coordinates:\n",
+       "  * power               (power) int64 4 3 2 1 0\n",
+       "  * index               (index) int64 0 1 2 3 ... 281756373 281756374 281756375\n",
+       "Data variables:\n",
+       "    logpolycoefpolflux  (index, power) float64 dask.array<chunksize=(999136, 5), meta=np.ndarray>\n",
+       "    logpolycoefflux     (index, power) float64 dask.array<chunksize=(999136, 5), meta=np.ndarray>
" + ], + "text/plain": [ + "\n", + "Dimensions: (power: 5, index: 281384121)\n", + "Coordinates:\n", + " * power (power) int64 4 3 2 1 0\n", + " * index (index) int64 0 1 2 3 ... 281756373 281756374 281756375\n", + "Data variables:\n", + " logpolycoefpolflux (index, power) float64 dask.array\n", + " logpolycoefflux (index, power) float64 dask.array" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output_catalog" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "788118dd", + "metadata": {}, + "outputs": [], + "source": [ + "output_catalog = output_catalog.transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "b4fe03d8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(281384121, 5)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output_catalog[\"logpolycoefflux\"].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "64734e1b", + "metadata": {}, + "outputs": [], + "source": [ + "output_catalog.logpolycoefflux.attrs[\"units\"] = \"Jy\"\n", + "output_catalog.logpolycoefpolflux.attrs[\"units\"] = \"Jy\"" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "b7eaad17", + "metadata": {}, + "outputs": [], + "source": [ + "for coord in [\"theta\", \"phi\"]:\n", + " output_catalog = output_catalog.assign_coords(\n", + " **{coord:((\"index\"), cat_xr[coord][cutoff_mask].data)})" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "b5b3f316", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/distributed/client.py:3361: UserWarning: Sending large graph of size 1.05 GiB.\n", + "This may cause some slowdown.\n", + "Consider loading the data with Dask directly\n", + " or using futures or delayed objects to embed the data into the graph without repetition.\n", + "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", + " warnings.warn(\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/numpy/polynomial/polynomial.py:756: RuntimeWarning: overflow encountered in multiply\n", + " c0 = c[-i] + c0*x\n" + ] + } + ], + "source": [ + "output_catalog[\"flux_100\"] = np.polynomial.polynomial.polyval(\n", + " np.log(100), output_catalog[\"logpolycoefflux\"][:,::-1], tensor=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "945010ff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'flux_100' ()>\n",
+       "array(inf)
" + ], + "text/plain": [ + "\n", + "array(inf)" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output_catalog[\"flux_100\"].max()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "a3d49ed2", + "metadata": {}, + "outputs": [], + "source": [ + "output_catalog = output_catalog.sortby(\"flux_100\", ascending=False)\n", + "del output_catalog[\"flux_100\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "997cca47", + "metadata": {}, + "outputs": [], + "source": [ + "output_catalog.coords[\"theta\"].attrs[\"units\"] = \"rad\"\n", + "output_catalog.coords[\"phi\"].attrs[\"units\"] = \"rad\"" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "a04ad716", + "metadata": {}, + "outputs": [], + "source": [ + "output_catalog.attrs[\"notes\"] = \\\n", + "\"\"\"Catalog of sources where the flux in Jy at any frequency is calculated with a 5th order polynomial in the logarithm of the frequency in GHz, separately for temperature and polarization.\n", + "The catalog does not contain information about the polarization angle of a source.\n", + "The catalog sorted in descending order based on the source flux at 100 GHz\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "918171cd", + "metadata": {}, + "outputs": [], + "source": [ + "output_filename = f\"/pscratch/sd/z/zonca/websky_full_catalog_trasp.h5\"" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "6e73f4df", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/distributed/client.py:3361: UserWarning: Sending large graph of size 2.10 GiB.\n", + "This may cause some slowdown.\n", + "Consider loading the data with Dask directly\n", + " or using futures or delayed objects to embed the data into the graph without repetition.\n", + "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", + " warnings.warn(\n", + "2024-09-21 14:02:36,894 - distributed.worker.memory - WARNING - Worker is at 80% memory usage. Pausing worker. Process memory: 2.99 GiB -- Worker memory limit: 3.72 GiB\n", + "2024-09-21 14:02:36,934 - distributed.worker.memory - WARNING - Worker is at 79% memory usage. Resuming worker. Process memory: 2.95 GiB -- Worker memory limit: 3.72 GiB\n", + "2024-09-21 14:02:37,039 - distributed.worker.memory - WARNING - Worker is at 81% memory usage. Pausing worker. Process memory: 3.05 GiB -- Worker memory limit: 3.72 GiB\n", + "2024-09-21 14:02:37,159 - distributed.worker.memory - WARNING - Worker is at 42% memory usage. Resuming worker. Process memory: 1.58 GiB -- Worker memory limit: 3.72 GiB\n", + "2024-09-21 14:02:49,972 - distributed.worker.memory - WARNING - Worker is at 82% memory usage. Pausing worker. Process memory: 3.08 GiB -- Worker memory limit: 3.72 GiB\n", + "2024-09-21 14:02:50,525 - distributed.worker.memory - WARNING - Worker is at 78% memory usage. Resuming worker. Process memory: 2.92 GiB -- Worker memory limit: 3.72 GiB\n" + ] + } + ], + "source": [ + "output_catalog.to_netcdf(\n", + " output_filename, format=\"NETCDF4\") # requires netcdf4 package" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "c95cf867", + "metadata": {}, + "outputs": [], + "source": [ + "import h5py" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "b51507c3", + "metadata": {}, + "outputs": [], + "source": [ + "f = h5py.File(output_filename)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "750ab562", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "6a9e291f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 5.37899652e-09, -1.29664725e-07, 1.20804354e-06, -5.22231671e-06,\n", + " 9.00274650e-06])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f[\"logpolycoefflux\"][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f116bd47", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/bin/bash: benchmark-pixell-runner: command not found\r\n" + ] + } + ], + "source": [ + "!benchmark-pixell-runner" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1948f953", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " total used free shared buff/cache available\r\n", + "Mem: 515307 288278 237454 2284 3068 227028\r\n", + "Swap: 0 0 0\r\n" + ] + } + ], + "source": [ + "!free -m" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0166c39b", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "catalog = xr.open_dataset(\"/pscratch/sd/z/zonca/websky_full_catalog_trasp.h5\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "821355d7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:             (power: 5, index: 281384121)\n",
+       "Coordinates:\n",
+       "  * power               (power) int64 4 3 2 1 0\n",
+       "  * index               (index) int64 0 1 2 3 ... 281756373 281756374 281756375\n",
+       "    theta               (index) float64 ...\n",
+       "    phi                 (index) float64 ...\n",
+       "Data variables:\n",
+       "    logpolycoefpolflux  (index, power) float64 ...\n",
+       "    logpolycoefflux     (index, power) float64 ...\n",
+       "Attributes:\n",
+       "    notes:    Catalog of sources where the flux in Jy at any frequency is cal...
" + ], + "text/plain": [ + "\n", + "Dimensions: (power: 5, index: 281384121)\n", + "Coordinates:\n", + " * power (power) int64 4 3 2 1 0\n", + " * index (index) int64 0 1 2 3 ... 281756373 281756374 281756375\n", + " theta (index) float64 ...\n", + " phi (index) float64 ...\n", + "Data variables:\n", + " logpolycoefpolflux (index, power) float64 ...\n", + " logpolycoefflux (index, power) float64 ...\n", + "Attributes:\n", + " notes: Catalog of sources where the flux in Jy at any frequency is cal..." + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "catalog" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "69ade451", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(281384121, 5)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "catalog[\"logpolycoefflux\"].shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c123c12", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "@webio": { + "lastCommId": null, + "lastKernelId": null + }, + "kernelspec": { + "display_name": "pycmb", + "language": "python", + "name": "pycmb" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/preprocess-templates/catalog/compare_catalog_to_original_websky.ipynb b/docs/preprocess-templates/catalog/compare_catalog_to_original_websky.ipynb new file mode 100644 index 00000000..be70eeb9 --- /dev/null +++ b/docs/preprocess-templates/catalog/compare_catalog_to_original_websky.ipynb @@ -0,0 +1,537 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1700dd80", + "metadata": {}, + "source": [ + "# Compare catalog and original implementation of Websky radio sources\n", + "> `rg2` and `rg3` compared to `rg1`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2c8befd1", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# for jupyter.nersc.gov otherwise the notebook only uses 2 cores\n", + "\n", + "os.environ[\"OMP_NUM_THREADS\"] = \"128\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "06819e34", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import healpy as hp\n", + "import pysm3\n", + "from pysm3 import units as u\n", + "from pysm3.models import PointSourceCatalog\n", + "import matplotlib.pyplot as plt\n", + "import xarray as xr\n", + "import h5py\n", + "import gc\n", + "import sys" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ae0303d8", + "metadata": {}, + "outputs": [], + "source": [ + "pysm3.set_verbosity()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7270a601", + "metadata": {}, + "outputs": [], + "source": [ + "nside = 2048" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d61ceac1", + "metadata": {}, + "outputs": [], + "source": [ + "fwhm = {8192: 0.9 * u.arcmin, 4096: 2.6 * u.arcmin, 2048: 5.1 * u.arcmin} " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5dfdfada", + "metadata": {}, + "outputs": [], + "source": [ + "freq = [80, 100] * u.GHz" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9d66b9b5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-10-15 09:12:01,427 - pysm3 - INFO - Frequencies considered: [ 70. 100.]\n", + "2024-10-15 09:12:01,504 - pysm3 - INFO - Reading map websky/0.4/radio_catalog/{nside}/070.0.fits\n", + "2024-10-15 09:12:01,504 - pysm3 - INFO - Access data from /global/cfs/cdirs/cmb/www/pysm-data/websky/0.4/radio_catalog/2048/070.0.fits\n", + "2024-10-15 09:12:04,597 - pysm3 - INFO - Mean emission at 70.0 GHz in I: 0.9802 uK_RJ\n", + "2024-10-15 09:12:04,612 - pysm3 - INFO - Mean emission at 70.0 GHz in Q: 0.01168 uK_RJ\n", + "2024-10-15 09:12:04,626 - pysm3 - INFO - Mean emission at 70.0 GHz in U: 0.01168 uK_RJ\n", + "2024-10-15 09:12:04,744 - pysm3 - INFO - Reading map websky/0.4/radio_catalog/{nside}/100.0.fits\n", + "2024-10-15 09:12:04,745 - pysm3 - INFO - Access data from /global/cfs/cdirs/cmb/www/pysm-data/websky/0.4/radio_catalog/2048/100.0.fits\n", + "2024-10-15 09:12:07,162 - pysm3 - INFO - Mean emission at 100.0 GHz in I: 0.3938 uK_RJ\n", + "2024-10-15 09:12:07,175 - pysm3 - INFO - Mean emission at 100.0 GHz in Q: 0.004416 uK_RJ\n", + "2024-10-15 09:12:07,187 - pysm3 - INFO - Mean emission at 100.0 GHz in U: 0.004411 uK_RJ\n" + ] + } + ], + "source": [ + "background = pysm3.Sky(nside=nside, preset_strings=[\"rg3\"]).get_emission(freq)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "95be7209", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-10-15 09:12:14,594 - pysm3 - INFO - Access data from /global/cfs/cdirs/cmb/www/pysm-data/websky/0.4/radio_catalog/websky_high_flux_catalog_1mJy.h5\n" + ] + } + ], + "source": [ + "sky = pysm3.Sky(nside=nside, preset_strings=[\"rg2\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a126cf4c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-10-15 09:12:14,614 - pysm3 - INFO - HEALPix map resolution: 1.717743205908703 arcmin\n", + "2024-10-15 09:12:14,615 - pysm3 - INFO - CAR map resolution: 0.8588716029543515 arcmin\n", + "2024-10-15 09:12:14,616 - pysm3 - INFO - Rounded CAR map resolution: 0.8588469184890656 arcmin\n", + "2024-10-15 09:12:18,017 - pysm3 - INFO - CAR map shape (3, 12575, 25150)\n", + "2024-10-15 09:12:31,031 - pysm3 - INFO - Reprojecting to HEALPix\n", + "2024-10-15 09:12:39,522 - pysm3 - INFO - Catalog emission computed\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 4min 10s, sys: 18.7 s, total: 4min 29s\n", + "Wall time: 25 s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "bright = sky.get_emission(\n", + " freq,\n", + " fwhm=fwhm[nside],\n", + " return_car=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b1d4aa5a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-10-15 09:12:39,592 - pysm3 - INFO - Frequencies considered: [ 79.6 90.2 100. ]\n", + "2024-10-15 09:12:39,593 - pysm3 - INFO - Access data from /global/cfs/cdirs/cmb/www/pysm-data/websky/0.4/radio/radio_0079.6.fits\n", + "2024-10-15 09:12:39,594 - pysm3 - INFO - Reading map /global/cfs/cdirs/cmb/www/pysm-data/websky/0.4/radio/radio_0079.6.fits\n", + "2024-10-15 09:12:39,594 - pysm3 - INFO - Access data from /global/cfs/cdirs/cmb/www/pysm-data/websky/0.4/radio/radio_0079.6.fits\n", + "2024-10-15 09:12:41,715 - pysm3 - INFO - Access data from /global/cfs/cdirs/cmb/www/pysm-data/websky/0.4/radio/radio_0079.6.fits\n", + "2024-10-15 09:12:55,041 - pysm3 - INFO - Mean emission at 79.6 GHz in I: 1.951 uK_RJ\n", + "2024-10-15 09:12:55,043 - pysm3 - INFO - Access data from /global/cfs/cdirs/cmb/www/pysm-data/websky/0.4/radio/radio_0090.2.fits\n", + "2024-10-15 09:12:55,043 - pysm3 - INFO - Reading map /global/cfs/cdirs/cmb/www/pysm-data/websky/0.4/radio/radio_0090.2.fits\n", + "2024-10-15 09:12:55,044 - pysm3 - INFO - Access data from /global/cfs/cdirs/cmb/www/pysm-data/websky/0.4/radio/radio_0090.2.fits\n", + "2024-10-15 09:12:57,670 - pysm3 - INFO - Access data from /global/cfs/cdirs/cmb/www/pysm-data/websky/0.4/radio/radio_0090.2.fits\n", + "2024-10-15 09:13:10,942 - pysm3 - INFO - Mean emission at 90.2 GHz in I: 1.403 uK_RJ\n", + "2024-10-15 09:13:10,944 - pysm3 - INFO - Access data from /global/cfs/cdirs/cmb/www/pysm-data/websky/0.4/radio/radio_0100.0.fits\n", + "2024-10-15 09:13:10,944 - pysm3 - INFO - Reading map /global/cfs/cdirs/cmb/www/pysm-data/websky/0.4/radio/radio_0100.0.fits\n", + "2024-10-15 09:13:10,945 - pysm3 - INFO - Access data from /global/cfs/cdirs/cmb/www/pysm-data/websky/0.4/radio/radio_0100.0.fits\n", + "2024-10-15 09:13:13,637 - pysm3 - INFO - Access data from /global/cfs/cdirs/cmb/www/pysm-data/websky/0.4/radio/radio_0100.0.fits\n", + "2024-10-15 09:13:27,096 - pysm3 - INFO - Mean emission at 100.0 GHz in I: 1.07 uK_RJ\n" + ] + } + ], + "source": [ + "websky = pysm3.Sky(nside=nside, preset_strings=[\"rg1\"]).get_emission(freq)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5ef5491f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-10-15 09:13:28,018 - pysm3 - INFO - Setting lmax to 5120\n", + "2024-10-15 09:16:03,489 - pysm3 - WARNING - hp.map2alm_lsq did not converge in 10 iterations, residual relative error is 0.69\n", + "2024-10-15 09:16:03,490 - pysm3 - INFO - Smoothing with fwhm of 5.1 arcmin\n", + "2024-10-15 09:16:03,928 - pysm3 - INFO - Alm to map HEALPix\n" + ] + } + ], + "source": [ + "websky = pysm3.apply_smoothing_and_coord_transform(websky, fwhm=fwhm[nside])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "729599a0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$75931.164 \\; \\mathrm{\\mu K_{{RJ}}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "websky[0].max()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "92768b84", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$66457.062 \\; \\mathrm{\\mu K_{{RJ}}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bright[0].max()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5c1a7ead", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "eb7fabfa", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(20,10))\n", + "hp.gnomview(websky[0], rot=(1.5, .3), reso=.2, min=0, max=20, title=\"Websky\", fig=fig, sub=121)\n", + "hp.gnomview(bright[0], rot=(1.5, .3), reso=.2, min=0, max=20, title=\"Bright sources\", fig=fig, sub=122)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "cf103d2e", + "metadata": {}, + "outputs": [], + "source": [ + "lon, lat = hp.pix2ang(nside, bright[0].argmax(), lonlat=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "505233d9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(20,10))\n", + "hp.gnomview(websky[0], rot=(lon, lat), reso=.2, min=0, max=20, title=\"Websky\", fig=fig, sub=121)\n", + "hp.gnomview(bright[0], rot=(lon, lat), reso=.2, min=0, max=20, title=\"Bright sources\", fig=fig, sub=122)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "36653082", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(20,10))\n", + "hp.gnomview(websky[0], rot=(1.5, .3), reso=.2, min=0, max=20, title=\"Websky\", fig=fig, sub=121)\n", + "hp.gnomview(bright[0], rot=(1.5, .3), reso=.2, min=0, max=20, title=\"Bright sources\", fig=fig, sub=122)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "b7d68f9e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(20,10))\n", + "hp.gnomview(websky[0], rot=(3.2, -.9), reso=.2, min=0, max=200, title=\"Websky\", fig=fig, sub=121)\n", + "hp.gnomview(bright[0], rot=(3.2, -.9), reso=.2, min=0, max=200, title=\"Bright sources\", fig=fig, sub=122)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "2b3ee16f", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "f = Path(\"/global/homes/z/zonca/prjcmb/www/pysm-data/websky/0.4/radio_catalog\") / str(nside)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "fe779d5b", + "metadata": {}, + "outputs": [], + "source": [ + "from pixell import enmap" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "45b617da", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(20,10))\n", + "hp.gnomview(websky[0], min=0, max=50, title=\"Websky\", fig=fig, sub=121)\n", + "hp.gnomview(bright[0], min=0, max=50, title=\"Bright sources\", fig=fig, sub=122)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "31b24b84", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(30,10))\n", + "hp.gnomview(websky[0], min=0, max=10, title=\"Websky\", fig=fig, sub=131)\n", + "hp.gnomview(background[0], min=0, max=10, title=\"Background\", fig=fig, sub=132)\n", + "hp.gnomview(bright[0]+background[0], min=0, max=10, title=\"Total\", fig=fig, sub=133)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "88b1d525", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 5))\n", + "plt.hist([websky[0].value, bright[0].value], density=True, bins=50, log=True,\n", + " label=[\"websky\", \"bright\"]);\n", + "plt.legend()\n", + "plt.title(\"Bright sources histogram\");" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "55fc0828", + "metadata": {}, + "outputs": [], + "source": [ + "bins = np.linspace(0, background.max().value, 50)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "de8d41bf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 5))\n", + "\n", + "plt.hist([websky[0].value, background[0].value, bright[0].value+background[0].value], density=True, bins=bins, log=True,\n", + " label=[\"websky\", \"background\", \"total\"]);\n", + "plt.legend()\n", + "plt.title(\"Background sources histogram\");" + ] + } + ], + "metadata": { + "@webio": { + "lastCommId": null, + "lastKernelId": null + }, + "kernelspec": { + "display_name": "pycmb", + "language": "python", + "name": "pycmb" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/preprocess-templates/catalog/create_catalog_background.py b/docs/preprocess-templates/catalog/create_catalog_background.py new file mode 100644 index 00000000..8c25a352 --- /dev/null +++ b/docs/preprocess-templates/catalog/create_catalog_background.py @@ -0,0 +1,113 @@ +#!/usr/bin/env python +# coding: utf-8 + +import os +from pixell import enmap, reproject + +# for jupyter.nersc.gov otherwise the code only uses 2 cores +os.environ["OMP_NUM_THREADS"] = "48" + +import numpy as np +import healpy as hp +import pysm3 +from pysm3 import units as u +from pysm3.models import PointSourceCatalog +import matplotlib.pyplot as plt +import xarray as xr +import h5py +import gc +import sys + +pysm3.set_verbosity() + +catalog_filename = sys.argv[1] + +nside = int(sys.argv[2]) + +output_path = f"/mnt/sdceph/users/azonca/pysm-data/websky/0.4/radio_catalog/background/{nside}/" + +car_map_resolution = None + +if nside == 8192: + car_map_resolution = hp.nside2resol(nside, arcmin=True) * u.arcmin / 1.3 + +freqs = ( + list( + map( + float, + [ + "5.0", + "18.7", + "24.5", + "44.0", + "70.0", + "100.0", + "143.0", + "217.0", + "353.0", + "545.0", + "643.0", + "729.0", + "857.0", + "906.0", + ], + ) + ) + * u.GHz +) + +freq = freqs[int(os.environ["SLURM_ARRAY_TASK_ID"])] + +out_filename = catalog_filename.replace( + ".h5", f"_nside_{nside}_map_{freq.value:04.1f}.h5" +) + + +if os.path.exists(out_filename.replace(".h5", "COMPLETED.txt")): + sys.exit(0) + + +catalog_size = len(h5py.File(catalog_filename)["theta"]) + +slice_size = int(2.82 * 1e6) + +fwhm = {8192: 0.9 * u.arcmin, 4096: 2.6 * u.arcmin, 2048: 5.1 * u.arcmin} + +for slice_start in range(0, catalog_size, slice_size): + gc.collect() + catalog = PointSourceCatalog( + catalog_filename, + catalog_slice=np.index_exp[slice_start : slice_start + slice_size], + nside=nside, + ) + if slice_start == 0: + m = catalog.get_emission( + freq, + fwhm=fwhm[nside], + car_map_resolution=car_map_resolution, + return_car=True, + ) + else: + m += catalog.get_emission( + freq, + fwhm=fwhm[nside], + car_map_resolution=car_map_resolution, + return_car=True, + ) + +enmap.write_map(out_filename, m, fmt="hdf") + +output_map = reproject.map2healpix( + m, + nside, + method="spline", +) +hp.write_map( + output_path + f"{freq.value:05.1f}.fits", + output_map, + column_units="uK_RJ", + coord="G", + overwrite=True, +) + +open(out_filename.replace(".h5", "_COMPLETED"), "a").close() diff --git a/docs/preprocess-templates/catalog/run_create_catalog_background.slurm b/docs/preprocess-templates/catalog/run_create_catalog_background.slurm new file mode 100644 index 00000000..2bee9686 --- /dev/null +++ b/docs/preprocess-templates/catalog/run_create_catalog_background.slurm @@ -0,0 +1,18 @@ +#!/bin/bash +#SBATCH --partition=genx +#SBATCH --nodes=1 +#SBATCH --constraint=cpu +#SBATCH --time=6:30:00 +#SBATCH --cpus-per-task=48 +#SBATCH --array=0-13 + +echo $SLURM_ARRAY_TASK_ID + +export OMP_NUM_THREADS=48 + +export PYTHONUNBUFFERED=1 + +for nside in 2048 4096 8192 +do +python create_catalog_background.py /mnt/sdceph/users/azonca/pysm-data/websky/0.4/radio_catalog/background/websky_full_catalog_trasp.h5 $nside +done diff --git a/docs/preprocess-templates/catalog/websky_sources_high_flux_catalog_out_1mJy.ipynb b/docs/preprocess-templates/catalog/websky_sources_high_flux_catalog_out_1mJy.ipynb new file mode 100644 index 00000000..80c06517 --- /dev/null +++ b/docs/preprocess-templates/catalog/websky_sources_high_flux_catalog_out_1mJy.ipynb @@ -0,0 +1,2175 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "0ed31a46-d481-4958-af82-3889e2f6b80a", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:03:05.236300Z", + "iopub.status.busy": "2024-06-27T11:03:05.236031Z", + "iopub.status.idle": "2024-06-27T11:03:09.246665Z", + "shell.execute_reply": "2024-06-27T11:03:09.246076Z" + }, + "papermill": { + "duration": 4.026483, + "end_time": "2024-06-27T11:03:09.248122", + "exception": false, + "start_time": "2024-06-27T11:03:05.221639", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import h5py\n", + "import numpy as np\n", + "import healpy as hp\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8e35f4b9-bc39-49e9-af61-67464526f4d9", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:03:09.262818Z", + "iopub.status.busy": "2024-06-27T11:03:09.262601Z", + "iopub.status.idle": "2024-06-27T11:03:09.373390Z", + "shell.execute_reply": "2024-06-27T11:03:09.372913Z" + }, + "papermill": { + "duration": 0.118578, + "end_time": "2024-06-27T11:03:09.374461", + "exception": false, + "start_time": "2024-06-27T11:03:09.255883", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/global/cfs/cdirs/sobs/www/users/Radio_WebSky/matched_catalogs_2\n" + ] + } + ], + "source": [ + "cd /global/cfs/cdirs/sobs/www/users/Radio_WebSky/matched_catalogs_2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "031d3e3d-b9d0-4c73-80bb-bbd804a825fe", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:03:09.388634Z", + "iopub.status.busy": "2024-06-27T11:03:09.388428Z", + "iopub.status.idle": "2024-06-27T11:03:09.862397Z", + "shell.execute_reply": "2024-06-27T11:03:09.861715Z" + }, + "papermill": { + "duration": 0.482588, + "end_time": "2024-06-27T11:03:09.863617", + "exception": false, + "start_time": "2024-06-27T11:03:09.381029", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "catalog_100.0.h5 catalog_232.0.h5 catalog_353.0.h5 catalog_643.0.h5\r\n", + "catalog_111.0.h5 catalog_24.5.h5 catalog_375.0.h5 catalog_67.8.h5\r\n", + "catalog_129.0.h5 catalog_256.0.h5 catalog_409.0.h5 catalog_70.0.h5\r\n", + "catalog_143.0.h5 catalog_27.3.h5 catalog_41.7.h5 catalog_729.0.h5\r\n", + "catalog_153.0.h5 catalog_275.0.h5 catalog_44.0.h5 catalog_73.7.h5\r\n", + "catalog_164.0.h5 catalog_294.0.h5 catalog_467.0.h5 catalog_79.6.h5\r\n", + "catalog_18.7.h5 catalog_30.0.h5 catalog_47.4.h5 catalog_817.0.h5\r\n", + "catalog_189.0.h5 catalog_306.0.h5 catalog_525.0.h5 catalog_857.0.h5\r\n", + "catalog_21.6.h5 catalog_314.0.h5 catalog_545.0.h5 catalog_90.2.h5\r\n", + "catalog_210.0.h5 catalog_340.0.h5 catalog_584.0.h5 catalog_906.0.h5\r\n", + "catalog_217.0.h5 catalog_35.9.h5 catalog_63.9.h5\r\n" + ] + } + ], + "source": [ + "%ls" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ba71f7d1", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:03:09.880107Z", + "iopub.status.busy": "2024-06-27T11:03:09.879867Z", + "iopub.status.idle": "2024-06-27T11:03:09.883207Z", + "shell.execute_reply": "2024-06-27T11:03:09.882752Z" + }, + "papermill": { + "duration": 0.012164, + "end_time": "2024-06-27T11:03:09.884216", + "exception": false, + "start_time": "2024-06-27T11:03:09.872052", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "freqs = [\n", + " \"18.7\",\n", + " \"24.5\",\n", + " \"44.0\",\n", + " \"70.0\",\n", + " \"100.0\",\n", + " \"143.0\",\n", + " \"217.0\",\n", + " \"353.0\",\n", + " \"545.0\",\n", + " \"643.0\",\n", + " \"729.0\",\n", + " \"857.0\",\n", + " \"906.0\",\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d0653ac2-3d67-4480-849d-bcca2727a143", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:03:09.898647Z", + "iopub.status.busy": "2024-06-27T11:03:09.898494Z", + "iopub.status.idle": "2024-06-27T11:03:09.968573Z", + "shell.execute_reply": "2024-06-27T11:03:09.968088Z" + }, + "papermill": { + "duration": 0.078639, + "end_time": "2024-06-27T11:03:09.969831", + "exception": false, + "start_time": "2024-06-27T11:03:09.891192", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "cat = h5py.File(\"catalog_100.0.h5\", \"r\")" + ] + }, + { + "cell_type": "markdown", + "id": "80b4c835", + "metadata": { + "papermill": { + "duration": 0.006384, + "end_time": "2024-06-27T11:03:09.983148", + "exception": false, + "start_time": "2024-06-27T11:03:09.976764", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "There are no metadata in the file, I guess fluxes are in `Jy`" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5f8de1f5", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:03:09.997293Z", + "iopub.status.busy": "2024-06-27T11:03:09.997033Z", + "iopub.status.idle": "2024-06-27T11:03:09.999593Z", + "shell.execute_reply": "2024-06-27T11:03:09.999170Z" + }, + "papermill": { + "duration": 0.010882, + "end_time": "2024-06-27T11:03:10.000575", + "exception": false, + "start_time": "2024-06-27T11:03:09.989693", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "cutoff_flux = 1e-3" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1ad4445f", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:03:10.014470Z", + "iopub.status.busy": "2024-06-27T11:03:10.014302Z", + "iopub.status.idle": "2024-06-27T11:03:11.957830Z", + "shell.execute_reply": "2024-06-27T11:03:11.957248Z" + }, + "papermill": { + "duration": 1.952123, + "end_time": "2024-06-27T11:03:11.959353", + "exception": false, + "start_time": "2024-06-27T11:03:10.007230", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "high_flux_sources_mask = cat[\"flux\"][:] > cutoff_flux" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e916cd08", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:03:12.002526Z", + "iopub.status.busy": "2024-06-27T11:03:12.002325Z", + "iopub.status.idle": "2024-06-27T11:03:12.077717Z", + "shell.execute_reply": "2024-06-27T11:03:12.077195Z" + }, + "papermill": { + "duration": 0.108826, + "end_time": "2024-06-27T11:03:12.078721", + "exception": false, + "start_time": "2024-06-27T11:03:11.969895", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "372255" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(high_flux_sources_mask).sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4483e313", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:03:12.093850Z", + "iopub.status.busy": "2024-06-27T11:03:12.093643Z", + "iopub.status.idle": "2024-06-27T11:03:12.212006Z", + "shell.execute_reply": "2024-06-27T11:03:12.211609Z" + }, + "papermill": { + "duration": 0.127315, + "end_time": "2024-06-27T11:03:12.212973", + "exception": false, + "start_time": "2024-06-27T11:03:12.085658", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.13211945911740433" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "high_flux_sources_mask.mean() * 100" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "06739d8b-fc8a-46d8-a430-c905f89fb37c", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:03:12.228796Z", + "iopub.status.busy": "2024-06-27T11:03:12.228637Z", + "iopub.status.idle": "2024-06-27T11:03:12.257469Z", + "shell.execute_reply": "2024-06-27T11:03:12.256943Z" + }, + "papermill": { + "duration": 0.037872, + "end_time": "2024-06-27T11:03:12.258531", + "exception": false, + "start_time": "2024-06-27T11:03:12.220659", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "flux [3.24291534e-07 3.16862867e-07 3.17171157e-07]\n", + "phi [3.22861886 3.22861886 3.22861886]\n", + "polarized flux [1.42910628e-09 1.99535624e-08 2.29563857e-09]\n", + "theta [1.64009452 1.64009452 1.64009452]\n" + ] + } + ], + "source": [ + "for k, v in cat.items():\n", + " print(k, v[:3])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "306159e9", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:03:12.273651Z", + "iopub.status.busy": "2024-06-27T11:03:12.273442Z", + "iopub.status.idle": "2024-06-27T11:03:12.319216Z", + "shell.execute_reply": "2024-06-27T11:03:12.318579Z" + }, + "papermill": { + "duration": 0.054896, + "end_time": "2024-06-27T11:03:12.320496", + "exception": false, + "start_time": "2024-06-27T11:03:12.265600", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "(all_indices,) = np.nonzero(high_flux_sources_mask)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "1b7ed1da-8a08-47a0-a513-538bfec1dd9b", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:03:12.335979Z", + "iopub.status.busy": "2024-06-27T11:03:12.335798Z", + "iopub.status.idle": "2024-06-27T11:03:12.339255Z", + "shell.execute_reply": "2024-06-27T11:03:12.338811Z" + }, + "papermill": { + "duration": 0.012085, + "end_time": "2024-06-27T11:03:12.340271", + "exception": false, + "start_time": "2024-06-27T11:03:12.328186", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "372255" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(all_indices)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "06ee9c0f-c6d6-4f86-ba98-5ec295e18f0b", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:03:12.354755Z", + "iopub.status.busy": "2024-06-27T11:03:12.354567Z", + "iopub.status.idle": "2024-06-27T11:03:12.404072Z", + "shell.execute_reply": "2024-06-27T11:03:12.403590Z" + }, + "papermill": { + "duration": 0.058266, + "end_time": "2024-06-27T11:03:12.405264", + "exception": false, + "start_time": "2024-06-27T11:03:12.346998", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "all_indices = np.array(sorted(all_indices))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "886dbbaf-8890-449c-bab4-2f7eba722d31", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:03:12.421611Z", + "iopub.status.busy": "2024-06-27T11:03:12.421415Z", + "iopub.status.idle": "2024-06-27T11:03:14.454325Z", + "shell.execute_reply": "2024-06-27T11:03:14.453747Z" + }, + "papermill": { + "duration": 2.042507, + "end_time": "2024-06-27T11:03:14.455704", + "exception": false, + "start_time": "2024-06-27T11:03:12.413197", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import xarray as xr" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b918e34e-b782-480b-8067-c3c31df9c436", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:03:14.482926Z", + "iopub.status.busy": "2024-06-27T11:03:14.482697Z", + "iopub.status.idle": "2024-06-27T11:03:14.485508Z", + "shell.execute_reply": "2024-06-27T11:03:14.485062Z" + }, + "papermill": { + "duration": 0.014321, + "end_time": "2024-06-27T11:03:14.486506", + "exception": false, + "start_time": "2024-06-27T11:03:14.472185", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "columns = [\"theta\", \"phi\", \"flux\", \"polarized flux\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "0851b3a4-7214-4182-9afb-c21768fd96e4", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:03:14.519543Z", + "iopub.status.busy": "2024-06-27T11:03:14.519337Z", + "iopub.status.idle": "2024-06-27T11:03:14.530055Z", + "shell.execute_reply": "2024-06-27T11:03:14.529558Z" + }, + "papermill": { + "duration": 0.026912, + "end_time": "2024-06-27T11:03:14.531157", + "exception": false, + "start_time": "2024-06-27T11:03:14.504245", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "flux = xr.DataArray(\n", + " data=np.zeros((len(all_indices), len(freqs)), dtype=np.float64),\n", + " coords={\"index\": all_indices, \"freq\": list(map(float, freqs))},\n", + " name=\"flux\",\n", + ")\n", + "fluxnorm = flux.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "394e8f66-d9e7-446e-af75-6940184da4a7", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:03:14.568194Z", + "iopub.status.busy": "2024-06-27T11:03:14.567968Z", + "iopub.status.idle": "2024-06-27T11:03:14.576744Z", + "shell.execute_reply": "2024-06-27T11:03:14.576310Z" + }, + "papermill": { + "duration": 0.027027, + "end_time": "2024-06-27T11:03:14.577915", + "exception": false, + "start_time": "2024-06-27T11:03:14.550888", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "polarized_flux = flux.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "6dedf80b-4d9c-4a8b-b028-ca99f5f1393e", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:03:14.608989Z", + "iopub.status.busy": "2024-06-27T11:03:14.608782Z", + "iopub.status.idle": "2024-06-27T11:07:19.747753Z", + "shell.execute_reply": "2024-06-27T11:07:19.747130Z" + }, + "papermill": { + "duration": 245.149027, + "end_time": "2024-06-27T11:07:19.749412", + "exception": false, + "start_time": "2024-06-27T11:03:14.600385", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "18.7\n", + "24.5\n", + "44.0\n", + "70.0\n", + "100.0\n", + "143.0\n", + "217.0\n", + "353.0\n", + "545.0\n", + "643.0\n", + "729.0\n", + "857.0\n", + "906.0\n" + ] + } + ], + "source": [ + "sources_xr = xr.Dataset(\n", + " {\"flux\": flux, \"polarized_flux\": polarized_flux, \"fluxnorm\": fluxnorm}\n", + ")\n", + "for freq in freqs:\n", + " print(freq)\n", + " cat = h5py.File(f\"catalog_{freq}.h5\", \"r\")\n", + " for column in [\"flux\", \"polarized_flux\"]:\n", + " sources_xr[column].loc[dict(index=all_indices, freq=float(freq))] = cat[\n", + " column.replace(\"_\", \" \")\n", + " ][high_flux_sources_mask]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "8f1fa80e-f674-40c1-bb9d-901503650240", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:07:19.771124Z", + "iopub.status.busy": "2024-06-27T11:07:19.770934Z", + "iopub.status.idle": "2024-06-27T11:07:19.918759Z", + "shell.execute_reply": "2024-06-27T11:07:19.918192Z" + }, + "papermill": { + "duration": 0.157791, + "end_time": "2024-06-27T11:07:19.919914", + "exception": false, + "start_time": "2024-06-27T11:07:19.762123", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "sources_xr = sources_xr.sortby(sources_xr.flux.loc[dict(freq=float(freqs[0]))])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "074fa7ac-7aae-4513-af86-cbe2a24082b1", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:07:19.952685Z", + "iopub.status.busy": "2024-06-27T11:07:19.952477Z", + "iopub.status.idle": "2024-06-27T11:07:19.956953Z", + "shell.execute_reply": "2024-06-27T11:07:19.956512Z" + }, + "papermill": { + "duration": 0.015711, + "end_time": "2024-06-27T11:07:19.957995", + "exception": false, + "start_time": "2024-06-27T11:07:19.942284", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "sources_xr.coords[\"index\"] = np.arange(len(sources_xr.coords[\"index\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "e38b940b-9b60-4162-bb0f-392dcea5563f", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:07:19.974473Z", + "iopub.status.busy": "2024-06-27T11:07:19.974315Z", + "iopub.status.idle": "2024-06-27T11:12:25.379314Z", + "shell.execute_reply": "2024-06-27T11:12:25.378574Z" + }, + "papermill": { + "duration": 305.415048, + "end_time": "2024-06-27T11:12:25.380786", + "exception": false, + "start_time": "2024-06-27T11:07:19.965738", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "for s in range(len(all_indices)):\n", + " sources_xr[\"fluxnorm\"].loc[dict(index=s)] = sources_xr[\"flux\"].loc[\n", + " dict(index=s)\n", + " ] / sources_xr[\"flux\"].loc[dict(index=s)].sel(freq=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "8728e619-1716-4894-9ecb-94ba384a2266", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:12:25.401718Z", + "iopub.status.busy": "2024-06-27T11:12:25.401558Z", + "iopub.status.idle": "2024-06-27T11:12:25.404160Z", + "shell.execute_reply": "2024-06-27T11:12:25.403732Z" + }, + "papermill": { + "duration": 0.012806, + "end_time": "2024-06-27T11:12:25.405158", + "exception": false, + "start_time": "2024-06-27T11:12:25.392352", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "#print(sources_xr[\"fluxnorm\"].loc[dict(index=s)], sources_xr[\"flux\"].loc[dict(index=s)])" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "e6f77665-62a3-45d5-b1cd-73f19e4ba279", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:12:25.421863Z", + "iopub.status.busy": "2024-06-27T11:12:25.421646Z", + "iopub.status.idle": "2024-06-27T11:12:25.433854Z", + "shell.execute_reply": "2024-06-27T11:12:25.433425Z" + }, + "papermill": { + "duration": 0.021794, + "end_time": "2024-06-27T11:12:25.434940", + "exception": false, + "start_time": "2024-06-27T11:12:25.413146", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "#sources_xr.fluxnorm.plot(vmin=0, vmax=100)\n", + "#plt.figure()\n", + "#sources_xr.flux.plot(vmin=0, vmax=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "41cd2b38-0caf-41ae-a8f5-871d205238c3", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:12:25.451513Z", + "iopub.status.busy": "2024-06-27T11:12:25.451317Z", + "iopub.status.idle": "2024-06-27T11:12:25.463435Z", + "shell.execute_reply": "2024-06-27T11:12:25.462896Z" + }, + "papermill": { + "duration": 0.021755, + "end_time": "2024-06-27T11:12:25.464631", + "exception": false, + "start_time": "2024-06-27T11:12:25.442876", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "sources_xr[\"logpolycoefflux\"] = xr.DataArray(\n", + " np.zeros((len(all_indices), 5), dtype=np.float64),\n", + " dims=[\"index\", \"power\"],\n", + " coords={\"power\": np.arange(5)},\n", + ")\n", + "sources_xr[\"logpolycoefnorm\"] = sources_xr[\"logpolycoefflux\"].copy()\n", + "sources_xr[\"logpolycoefpolflux\"] = sources_xr[\"logpolycoefflux\"].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "6dbd89aa-fe34-401f-8d9d-43330c8057c9", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T11:12:25.481852Z", + "iopub.status.busy": "2024-06-27T11:12:25.481691Z", + "iopub.status.idle": "2024-06-27T23:53:19.807134Z", + "shell.execute_reply": "2024-06-27T23:53:19.806692Z" + }, + "papermill": { + "duration": 45654.335652, + "end_time": "2024-06-27T23:53:19.808563", + "exception": false, + "start_time": "2024-06-27T11:12:25.472911", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from scipy.optimize import curve_fit\n", + "\n", + "\n", + "def model(freq, a, b, c, d, e):\n", + " log_freq = np.log(freq)\n", + " return a + b * log_freq + c * log_freq**2 + d * log_freq**3 + e * log_freq**4\n", + "\n", + "\n", + "for s in range(len(all_indices)):\n", + " sources_xr[\"logpolycoefflux\"].loc[dict(index=s)], cov = curve_fit(\n", + " model, sources_xr.coords[\"freq\"], sources_xr.flux.sel(index=s)\n", + " )\n", + " sources_xr[\"logpolycoefpolflux\"].loc[dict(index=s)], cov = curve_fit(\n", + " model, sources_xr.coords[\"freq\"], sources_xr.polarized_flux.sel(index=s)\n", + " )\n", + " sources_xr[\"logpolycoefnorm\"].loc[dict(index=s)], cov = curve_fit(\n", + " model, sources_xr.coords[\"freq\"], sources_xr.fluxnorm.sel(index=s)\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "fbfa997a-54f3-43ab-bfbe-6b1ae107a368", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T23:53:19.841991Z", + "iopub.status.busy": "2024-06-27T23:53:19.841804Z", + "iopub.status.idle": "2024-06-27T23:53:19.844349Z", + "shell.execute_reply": "2024-06-27T23:53:19.843995Z" + }, + "papermill": { + "duration": 0.013058, + "end_time": "2024-06-27T23:53:19.845321", + "exception": false, + "start_time": "2024-06-27T23:53:19.832263", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# for s in range(len(all_indices)):\n", + "# plt.figure()\n", + "# sources_xr.flux.sel(index=s).plot(marker=\"o\", linestyle=\"none\") # , xscale=\"log\")\n", + "# sources_xr.fluxnorm.sel(index=s).plot(\n", + "# marker=\"o\", linestyle=\"none\"\n", + "# ) # , xscale=\"log\")\n", + "\n", + "# plt.loglog(\n", + "# sources_xr.coords[\"freq\"],\n", + "# model(sources_xr.coords[\"freq\"], *sources_xr.logpolycoefflux.sel(index=s)),\n", + "# )\n", + "# plt.loglog(\n", + "# sources_xr.coords[\"freq\"],\n", + "# model(sources_xr.coords[\"freq\"], *sources_xr.logpolycoefnorm.sel(index=s)),\n", + "# )\n", + "# plt.grid()\n", + "# break" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "31df666c-4e49-40f1-b884-9e3eec1da294", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T23:53:19.863934Z", + "iopub.status.busy": "2024-06-27T23:53:19.863636Z", + "iopub.status.idle": "2024-06-27T23:53:19.958182Z", + "shell.execute_reply": "2024-06-27T23:53:19.957806Z" + }, + "papermill": { + "duration": 0.105248, + "end_time": "2024-06-27T23:53:19.959160", + "exception": false, + "start_time": "2024-06-27T23:53:19.853912", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(\n", + " array(-17557.80288493),\n", + " \n", + " array(23993.59927165))" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sources_xr.logpolycoefflux.min(), sources_xr.logpolycoefflux.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "43f02e70-7a7e-408d-851e-17d4f6356f5e", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T23:53:19.976660Z", + "iopub.status.busy": "2024-06-27T23:53:19.976496Z", + "iopub.status.idle": "2024-06-27T23:53:19.978789Z", + "shell.execute_reply": "2024-06-27T23:53:19.978417Z" + }, + "papermill": { + "duration": 0.01214, + "end_time": "2024-06-27T23:53:19.979866", + "exception": false, + "start_time": "2024-06-27T23:53:19.967726", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# plt.figure(figsize=(12, 5))\n", + "# plt.subplot(121)\n", + "# sources_xr.logpolycoefflux.plot(vmax=50, vmin=-50)\n", + "# plt.subplot(122)\n", + "# sources_xr.logpolycoefnorm.plot(vmax=50, vmin=-50)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "a8be3926-00e6-4a14-a66a-060424b6d405", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T23:53:19.997861Z", + "iopub.status.busy": "2024-06-27T23:53:19.997553Z", + "iopub.status.idle": "2024-06-27T23:53:20.005002Z", + "shell.execute_reply": "2024-06-27T23:53:20.004624Z" + }, + "papermill": { + "duration": 0.017415, + "end_time": "2024-06-27T23:53:20.006092", + "exception": false, + "start_time": "2024-06-27T23:53:19.988677", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# plt.figure(figsize=(15, 8))\n", + "\n", + "# for power in range(5):\n", + "# plt.subplot(231 + power)\n", + "\n", + "# np.fabs(sources_xr.logpolycoefflux.loc[dict(power=power)]).plot.hist(\n", + "# bins=np.logspace(-0, 4, 20), density=False, lw=3, label=\"fluxes\"\n", + "# )\n", + "\n", + "# np.fabs(sources_xr.logpolycoefnorm.loc[dict(power=power)]).plot.hist(\n", + "# bins=np.logspace(-0, 4, 20),\n", + "# density=False,\n", + "# histtype=\"step\",\n", + "# lw=2,\n", + "# label=\"normalized fluxes\",\n", + "# linestyle=\"--\",\n", + "# )\n", + "# plt.grid()\n", + "# plt.title(f\"Power {power}\")\n", + "# plt.legend()\n", + "# plt.xscale(\"log\")\n", + "# plt.xlabel(None)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "f7f80d98", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T23:53:20.023356Z", + "iopub.status.busy": "2024-06-27T23:53:20.023125Z", + "iopub.status.idle": "2024-06-27T23:53:20.032373Z", + "shell.execute_reply": "2024-06-27T23:53:20.032010Z" + }, + "papermill": { + "duration": 0.019149, + "end_time": "2024-06-27T23:53:20.033476", + "exception": false, + "start_time": "2024-06-27T23:53:20.014327", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "output_catalog = sources_xr[[\"logpolycoefflux\",\"logpolycoefpolflux\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "af92c175", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T23:53:20.051763Z", + "iopub.status.busy": "2024-06-27T23:53:20.051482Z", + "iopub.status.idle": "2024-06-27T23:53:20.067522Z", + "shell.execute_reply": "2024-06-27T23:53:20.067159Z" + }, + "papermill": { + "duration": 0.025976, + "end_time": "2024-06-27T23:53:20.068642", + "exception": false, + "start_time": "2024-06-27T23:53:20.042666", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "output_catalog[\"index\"] = all_indices" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "e761dc39", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T23:53:20.086494Z", + "iopub.status.busy": "2024-06-27T23:53:20.086332Z", + "iopub.status.idle": "2024-06-27T23:53:20.090116Z", + "shell.execute_reply": "2024-06-27T23:53:20.089724Z" + }, + "papermill": { + "duration": 0.014046, + "end_time": "2024-06-27T23:53:20.091208", + "exception": false, + "start_time": "2024-06-27T23:53:20.077162", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "output_catalog.logpolycoefflux.attrs[\"units\"] = \"Jy\"\n", + "output_catalog.logpolycoefpolflux.attrs[\"units\"] = \"Jy\"" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "3606bcfa", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T23:53:20.108981Z", + "iopub.status.busy": "2024-06-27T23:53:20.108669Z", + "iopub.status.idle": "2024-06-27T23:53:35.212200Z", + "shell.execute_reply": "2024-06-27T23:53:35.211694Z" + }, + "papermill": { + "duration": 15.113927, + "end_time": "2024-06-27T23:53:35.213711", + "exception": false, + "start_time": "2024-06-27T23:53:20.099784", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "for coord in [\"theta\", \"phi\"]:\n", + " output_catalog = output_catalog.assign_coords(**{coord:((\"index\"), cat[coord][high_flux_sources_mask].astype(np.float64))})" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "bc3a3c86", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T23:53:35.235631Z", + "iopub.status.busy": "2024-06-27T23:53:35.235433Z", + "iopub.status.idle": "2024-06-27T23:53:35.273375Z", + "shell.execute_reply": "2024-06-27T23:53:35.272949Z" + }, + "papermill": { + "duration": 0.048451, + "end_time": "2024-06-27T23:53:35.274378", + "exception": false, + "start_time": "2024-06-27T23:53:35.225927", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:             (index: 372255, power: 5)\n",
+       "Coordinates:\n",
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+       "  * power               (power) int64 0 1 2 3 4\n",
+       "    theta               (index) float64 ...\n",
+       "    phi                 (index) float64 ...\n",
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"start_time": "2024-06-27T23:53:37.841086", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import h5py\n", + "f = h5py.File(output_filename, 'r')\n", + "f[\"logpolycoefflux\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "10bd3b7e", + "metadata": { + "execution": { + "iopub.execute_input": "2024-06-27T23:53:37.879404Z", + "iopub.status.busy": "2024-06-27T23:53:37.879218Z", + "iopub.status.idle": "2024-06-27T23:53:37.888190Z", + "shell.execute_reply": "2024-06-27T23:53:37.887780Z" + }, + "papermill": { + "duration": 0.020132, + "end_time": "2024-06-27T23:53:37.889159", + "exception": false, + "start_time": "2024-06-27T23:53:37.869027", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "b'Jy'" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f[\"logpolycoefflux\"].attrs[\"units\"]" + ] + } + ], + "metadata": { + "@webio": { + "lastCommId": null, + "lastKernelId": null + }, + "kernelspec": { + "display_name": "pycmb", + "language": "python", + "name": "pycmb" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + }, + "papermill": { + "default_parameters": {}, + "duration": 46239.220999, + "end_time": "2024-06-27T23:53:39.698232", + "environment_variables": {}, + "exception": true, + "input_path": "websky_sources_high_flux_catalog.ipynb", + "output_path": "websky_sources_high_flux_catalog_out_1mJy.ipynb", + "parameters": {}, + "start_time": "2024-06-27T11:03:00.477233", + "version": "2.4.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/preprocess-templates/websky_sources_high_flux_catalog.ipynb b/docs/preprocess-templates/websky_sources_high_flux_catalog.ipynb new file mode 100644 index 00000000..3047bcae --- /dev/null +++ b/docs/preprocess-templates/websky_sources_high_flux_catalog.ipynb @@ -0,0 +1,2856 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "0ed31a46-d481-4958-af82-3889e2f6b80a", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n" + ] + } + ], + "source": [ + "import h5py\n", + "import numpy as np\n", + "import healpy as hp\n", + "import matplotlib.pyplot as plt\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "263a8761", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# for jupyter.nersc.gov otherwise the notebook only uses 2 cores\n", + "num_threads = 128\n", + "os.environ[\"OMP_NUM_THREADS\"] = \"1\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5f8de1f5", + "metadata": {}, + "outputs": [], + "source": [ + "cutoff_flux = 1e-3" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1e31f0a2", + "metadata": {}, + "outputs": [], + "source": [ + "output_filename = \"websky_high_flux_catalog_1mJy.h5\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "82d11cad", + "metadata": {}, + "outputs": [], + "source": [ + "plot = False" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8e35f4b9-bc39-49e9-af61-67464526f4d9", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/global/cfs/cdirs/sobs/www/users/Radio_WebSky/matched_catalogs_2\n" + ] + } + ], + "source": [ + "cd /global/cfs/cdirs/sobs/www/users/Radio_WebSky/matched_catalogs_2" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "031d3e3d-b9d0-4c73-80bb-bbd804a825fe", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "catalog_100.0.h5 catalog_232.0.h5 catalog_353.0.h5 catalog_643.0.h5\r\n", + "catalog_111.0.h5 catalog_24.5.h5 catalog_375.0.h5 catalog_67.8.h5\r\n", + "catalog_129.0.h5 catalog_256.0.h5 catalog_409.0.h5 catalog_70.0.h5\r\n", + "catalog_143.0.h5 catalog_27.3.h5 catalog_41.7.h5 catalog_729.0.h5\r\n", + "catalog_153.0.h5 catalog_275.0.h5 catalog_44.0.h5 catalog_73.7.h5\r\n", + "catalog_164.0.h5 catalog_294.0.h5 catalog_467.0.h5 catalog_79.6.h5\r\n", + "catalog_18.7.h5 catalog_30.0.h5 catalog_47.4.h5 catalog_817.0.h5\r\n", + "catalog_189.0.h5 catalog_306.0.h5 catalog_525.0.h5 catalog_857.0.h5\r\n", + "catalog_21.6.h5 catalog_314.0.h5 catalog_545.0.h5 catalog_90.2.h5\r\n", + "catalog_210.0.h5 catalog_340.0.h5 catalog_584.0.h5 catalog_906.0.h5\r\n", + "catalog_217.0.h5 catalog_35.9.h5 catalog_63.9.h5\r\n" + ] + } + ], + "source": [ + "%ls" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ba71f7d1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "freqs = [\n", + " \"18.7\",\n", + " \"24.5\",\n", + " \"44.0\",\n", + " \"70.0\",\n", + " \"100.0\",\n", + " \"143.0\",\n", + " \"217.0\",\n", + " \"353.0\",\n", + " \"545.0\",\n", + " \"643.0\",\n", + " \"729.0\",\n", + " \"857.0\",\n", + " \"906.0\",\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d0653ac2-3d67-4480-849d-bcca2727a143", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "cat = h5py.File(\"catalog_100.0.h5\", \"r\")" + ] + }, + { + "cell_type": "markdown", + "id": "80b4c835", + "metadata": {}, + "source": [ + "There are no metadata in the file, I guess fluxes are in `Jy`" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1ad4445f", + "metadata": {}, + "outputs": [], + "source": [ + "high_flux_sources_mask = cat[\"flux\"][:] > cutoff_flux" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "63276683", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.42910628e-09, 1.99535624e-08, 2.29563857e-09], dtype='>f8')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cat[\"polarized flux\"][:3]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e916cd08", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "372255" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(high_flux_sources_mask).sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4483e313", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.13211945911740433" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "high_flux_sources_mask.mean() * 100" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "06739d8b-fc8a-46d8-a430-c905f89fb37c", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "flux [3.24291534e-07 3.16862867e-07 3.17171157e-07]\n", + "phi [3.22861886 3.22861886 3.22861886]\n", + "polarized flux [1.42910628e-09 1.99535624e-08 2.29563857e-09]\n", + "theta [1.64009452 1.64009452 1.64009452]\n" + ] + } + ], + "source": [ + "for k, v in cat.items():\n", + " print(k, v[:3])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "306159e9", + "metadata": {}, + "outputs": [], + "source": [ + "(all_indices,) = np.nonzero(high_flux_sources_mask)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "1b7ed1da-8a08-47a0-a513-538bfec1dd9b", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "372255" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(all_indices)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "06ee9c0f-c6d6-4f86-ba98-5ec295e18f0b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#all_indices = np.array(sorted(all_indices))\n", + "all_indices = np.array(all_indices)" + ] + }, + { + "cell_type": "markdown", + "id": "68206975", + "metadata": {}, + "source": [ + "Generate 1 source only\n", + "\n", + "```\n", + "argmax = np.array(cat[\"flux\"]).argmax()\n", + "all_indices = np.array([argmax])\n", + "high_flux_sources_mask = high_flux_sources_mask * 0\n", + "high_flux_sources_mask[argmax] = 1\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "886dbbaf-8890-449c-bab4-2f7eba722d31", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import xarray as xr" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "b918e34e-b782-480b-8067-c3c31df9c436", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "columns = [\"theta\", \"phi\", \"flux\", \"polarized flux\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "0851b3a4-7214-4182-9afb-c21768fd96e4", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "flux = xr.DataArray(\n", + " data=np.zeros((len(all_indices), len(freqs)), dtype=np.float64),\n", + " coords={\"index\": all_indices, \"freq\": list(map(float, freqs))},\n", + " name=\"flux\",\n", + ")\n", + "fluxnorm = flux.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "394e8f66-d9e7-446e-af75-6940184da4a7", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "polarized_flux = flux.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "6dedf80b-4d9c-4a8b-b028-ca99f5f1393e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 13/13 [00:20<00:00, 1.54s/it]\n" + ] + } + ], + "source": [ + "sources_xr = xr.Dataset(\n", + " {\"flux\": flux, \"polarized_flux\": polarized_flux, \"fluxnorm\": fluxnorm}\n", + ")\n", + "for freq in tqdm(freqs):\n", + " cat = h5py.File(f\"catalog_{freq}.h5\", \"r\")\n", + " for column in [\"flux\", \"polarized_flux\"]:\n", + " sources_xr[column].loc[dict(index=all_indices, freq=float(freq))] = cat[\n", + " column.replace(\"_\", \" \")\n", + " ][high_flux_sources_mask]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "8f1fa80e-f674-40c1-bb9d-901503650240", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# sources_xr = sources_xr.sortby(sources_xr.flux.loc[dict(freq=float(freqs[0]))])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "074fa7ac-7aae-4513-af86-cbe2a24082b1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "sources_xr.coords[\"index\"] = np.arange(len(sources_xr.coords[\"index\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "e38b940b-9b60-4162-bb0f-392dcea5563f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "if plot:\n", + " for s in tqdm(range(len(all_indices))):\n", + " sources_xr[\"fluxnorm\"].loc[dict(index=s)] = sources_xr[\"flux\"].loc[\n", + " dict(index=s)\n", + " ] / sources_xr[\"flux\"].loc[dict(index=s)].sel(freq=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "8728e619-1716-4894-9ecb-94ba384a2266", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#print(sources_xr[\"fluxnorm\"].loc[dict(index=s)], sources_xr[\"flux\"].loc[dict(index=s)])" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "e6f77665-62a3-45d5-b1cd-73f19e4ba279", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "if plot:\n", + " #sources_xr.fluxnorm.plot(vmin=0, vmax=100)\n", + "\n", + " plt.figure()\n", + " sources_xr.flux.plot(vmin=0, vmax=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "41cd2b38-0caf-41ae-a8f5-871d205238c3", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "sources_xr[\"logpolycoefflux\"] = xr.DataArray(\n", + " np.zeros((len(all_indices), 5), dtype=np.float64),\n", + " dims=[\"index\", \"power\"],\n", + " coords={\"power\": np.arange(5)[::-1]},\n", + ")\n", + "sources_xr[\"logpolycoefpolflux\"] = sources_xr[\"logpolycoefflux\"].copy()\n", + "\n", + "if plot:\n", + " sources_xr[\"logpolycoefnorm\"] = sources_xr[\"logpolycoefflux\"].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "a1ecd6d8", + "metadata": {}, + "outputs": [], + "source": [ + "from numba import njit\n", + "\n", + "@njit\n", + "def model(freq, a, b, c, d, e):\n", + " log_freq = np.log(freq)\n", + " return a * log_freq**4 + b * log_freq**3 + c * log_freq**2 + d * log_freq + e" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "3c08d830", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.optimize import curve_fit" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "1b709764", + "metadata": {}, + "outputs": [], + "source": [ + "from dask.distributed import Client, LocalCluster\n", + "\n", + "cluster = LocalCluster(n_workers=32, threads_per_worker=2, processes=True)\n", + "client = Client(cluster)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "1e06b450", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "print(client)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "6e73f4df", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.02853885, 0.01962042, 0.0104193 , 0.00548068, 0.00337772,\n", + " 0.00218903, 0.0014772 , 0.00095432, 0.00064614, 0.000557 ,\n", + " 0.00049761, 0.00043035, 0.00040939])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s=0\n", + "sources_xr[\"flux\"].sel(index=s).data" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "16faa0af", + "metadata": {}, + "outputs": [], + "source": [ + "def run_curve_fit_factory(field):\n", + " def run_curve_fit(s):\n", + " return curve_fit(\n", + " model, sources_xr.coords[\"freq\"].data, sources_xr[field].sel(index=s).data\n", + " )[0]\n", + " return run_curve_fit" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "8a9a654c", + "metadata": {}, + "outputs": [], + "source": [ + "from dask.diagnostics import ProgressBar" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "094e45f8", + "metadata": {}, + "outputs": [], + "source": [ + "import dask.bag as db" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "10df0e53", + "metadata": {}, + "outputs": [], + "source": [ + "from dask.distributed import progress\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "ea5a3ea2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/distributed/client.py:3361: UserWarning: Sending large graph of size 142.02 MiB.\n", + "This may cause some slowdown.\n", + "Consider loading the data with Dask directly\n", + " or using futures or delayed objects to embed the data into the graph without repetition.\n", + "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", + " warnings.warn(\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n", + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.26.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 18.7 s, sys: 11.5 s, total: 30.2 s\n", + "Wall time: 39 s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "sources_xr[\"logpolycoefflux\"].data = db.range(len(all_indices), npartitions=num_threads).map(run_curve_fit_factory(\"flux\")).compute()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "ba2f0e67", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/global/common/software/cmb/zonca/conda/pycmb/lib/python3.10/site-packages/distributed/client.py:3361: UserWarning: Sending large graph of size 142.02 MiB.\n", + "This may cause some slowdown.\n", + "Consider loading the data with Dask directly\n", + " or using futures or delayed objects to embed the data into the graph without repetition.\n", + "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 16.2 s, sys: 10.1 s, total: 26.3 s\n", + "Wall time: 33.3 s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "sources_xr[\"logpolycoefpolflux\"].data = db.range(len(all_indices), npartitions=num_threads).map(run_curve_fit_factory(\"polarized_flux\")).compute()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "cec940c0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 3 µs, sys: 3 µs, total: 6 µs\n", + "Wall time: 11 µs\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "if plot:\n", + " sources_xr[\"logpolycoefnorm\"].data = db.range(len(all_indices), npartitions=num_threads).map(run_curve_fit_factory(\"fluxnorm\")).compute()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "6dbd89aa-fe34-401f-8d9d-43330c8057c9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# for s in range(len(all_indices)):\n", + "# sources_xr[\"logpolycoefflux\"].loc[dict(index=s)], cov = curve_fit(\n", + "# model, sources_xr.coords[\"freq\"], sources_xr.flux.sel(index=s)\n", + "# )\n", + "# sources_xr[\"logpolycoefpolflux\"].loc[dict(index=s)], cov = curve_fit(\n", + "# model, sources_xr.coords[\"freq\"], sources_xr.polarized_flux.sel(index=s)\n", + "# )\n", + "# if plot:\n", + "# sources_xr[\"logpolycoefnorm\"].loc[dict(index=s)], cov = curve_fit(\n", + "# model, sources_xr.coords[\"freq\"], sources_xr.fluxnorm.sel(index=s)\n", + "# )" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "fbfa997a-54f3-43ab-bfbe-6b1ae107a368", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "if plot:\n", + " for s in range(len(all_indices)):\n", + " plt.figure()\n", + " sources_xr.flux.sel(index=s).plot(marker=\"o\", linestyle=\"none\") # , xscale=\"log\")\n", + " sources_xr.fluxnorm.sel(index=s).plot(\n", + " marker=\"o\", linestyle=\"none\"\n", + " ) # , xscale=\"log\")\n", + "\n", + " plt.loglog(\n", + " sources_xr.coords[\"freq\"],\n", + " model(sources_xr.coords[\"freq\"], *sources_xr.logpolycoefflux.sel(index=s)),\n", + " )\n", + " plt.loglog(\n", + " sources_xr.coords[\"freq\"],\n", + " model(sources_xr.coords[\"freq\"], *sources_xr.logpolycoefnorm.sel(index=s)),\n", + " )\n", + " plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "31df666c-4e49-40f1-b884-9e3eec1da294", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(\n", + " array(-17557.7742453),\n", + " \n", + " array(23993.56673443))" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sources_xr.logpolycoefflux.min(), sources_xr.logpolycoefflux.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "43f02e70-7a7e-408d-851e-17d4f6356f5e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "if plot:\n", + " plt.figure(figsize=(12, 5))\n", + " plt.subplot(121)\n", + " sources_xr.logpolycoefflux.plot(vmax=50, vmin=-50)\n", + " plt.subplot(122)\n", + " sources_xr.logpolycoefnorm.plot(vmax=50, vmin=-50)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "a8be3926-00e6-4a14-a66a-060424b6d405", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "if plot:\n", + " plt.figure(figsize=(15, 8))\n", + "\n", + " for power in range(5):\n", + " plt.subplot(231 + power)\n", + "\n", + " np.fabs(sources_xr.logpolycoefflux.loc[dict(power=power)]).plot.hist(\n", + " bins=np.logspace(-0, 4, 20), density=False, lw=3, label=\"fluxes\"\n", + " )\n", + "\n", + " np.fabs(sources_xr.logpolycoefnorm.loc[dict(power=power)]).plot.hist(\n", + " bins=np.logspace(-0, 4, 20),\n", + " density=False,\n", + " histtype=\"step\",\n", + " lw=2,\n", + " label=\"normalized fluxes\",\n", + " linestyle=\"--\",\n", + " )\n", + " plt.grid()\n", + " plt.title(f\"Power {power}\")\n", + " plt.legend()\n", + " plt.xscale(\"log\")\n", + " plt.xlabel(None)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "f7f80d98", + "metadata": {}, + "outputs": [], + "source": [ + "output_catalog = sources_xr[[\"logpolycoefflux\",\"logpolycoefpolflux\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "af92c175", + "metadata": {}, + "outputs": [], + "source": [ + "output_catalog[\"index\"] = all_indices" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "e761dc39", + "metadata": {}, + "outputs": [], + "source": [ + "output_catalog.logpolycoefflux.attrs[\"units\"] = \"Jy\"\n", + "output_catalog.logpolycoefpolflux.attrs[\"units\"] = \"Jy\"" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "3606bcfa", + "metadata": {}, + "outputs": [], + "source": [ + "for coord in [\"theta\", \"phi\"]:\n", + " output_catalog = output_catalog.assign_coords(\n", + " **{coord:((\"index\"), cat[coord][high_flux_sources_mask].astype(np.float64))})" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "bc3a3c86", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "\n", + "array(41.02290808)" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output_catalog[\"flux_100\"].max()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "907b7a1a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(372255, 5)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array(output_catalog[\"logpolycoefflux\"]).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "a6ba3897", + "metadata": {}, + "outputs": [], + "source": [ + "output_catalog = output_catalog.sortby(\"flux_100\", ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "55eab2fb", + "metadata": {}, + "outputs": [], + "source": [ + "del output_catalog[\"flux_100\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "87e06bf2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.003478966281996676" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.polyval(output_catalog.logpolycoefflux[0], np.log(100))" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "8e981f2c", + "metadata": {}, + "outputs": [], + "source": [ + "output_catalog.attrs[\"notes\"] = \\\n", + "\"\"\"Catalog of sources where the flux in Jy at any frequency is calculated with a 5th order polynomial in the logarithm of the frequency in GHz, separately for temperature and polarization.\n", + "The catalog does not contain information about the polarization angle of a source.\n", + "The catalog sorted in descending order based on the source flux at 100 GHz\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "26c1e7a7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "Dimensions:             (index: 372255, power: 5)\n",
+       "Coordinates:\n",
+       "  * index               (index) int64 11253 16428 24110 ... 281755430 281755795\n",
+       "  * power               (power) int64 4 3 2 1 0\n",
+       "    theta               (index) float64 2.655 2.659 2.596 ... 1.38 1.377 1.426\n",
+       "    phi                 (index) float64 4.177 3.977 4.17 ... 0.4638 0.5054\n",
+       "Data variables:\n",
+       "    logpolycoefflux     (index, power) float64 0.0002901 -0.006796 ... 0.01476\n",
+       "    logpolycoefpolflux  (index, power) float64 5.318e-05 -0.001129 ... -0.005808\n",
+       "Attributes:\n",
+       "    notes:    Catalog of sources where the flux in Jy at any frequency is cal...
" + ], + "text/plain": [ + "\n", + "Dimensions: (index: 372255, power: 5)\n", + "Coordinates:\n", + " * index (index) int64 11253 16428 24110 ... 281755430 281755795\n", + " * power (power) int64 4 3 2 1 0\n", + " theta (index) float64 2.655 2.659 2.596 ... 1.38 1.377 1.426\n", + " phi (index) float64 4.177 3.977 4.17 ... 0.4638 0.5054\n", + "Data variables:\n", + " logpolycoefflux (index, power) float64 0.0002901 -0.006796 ... 0.01476\n", + " logpolycoefpolflux (index, power) float64 5.318e-05 -0.001129 ... -0.005808\n", + "Attributes:\n", + " notes: Catalog of sources where the flux in Jy at any frequency is cal..." + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output_catalog" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "6cc1bd05", + "metadata": {}, + "outputs": [], + "source": [ + "output_catalog.to_netcdf(output_filename, format=\"NETCDF4\") # requires netcdf4 package" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "e106b83e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-rw-rw---- 1 zonca sobs 37M Sep 19 11:15 websky_high_flux_catalog_1mJy.h5\r\n" + ] + } + ], + "source": [ + "%ls -lah $output_filename" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "f38b5686", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "7226bf84", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:             (index: 372255, power: 5)\n",
+       "Coordinates:\n",
+       "  * index               (index) int64 11253 16428 24110 ... 281755430 281755795\n",
+       "  * power               (power) int64 4 3 2 1 0\n",
+       "    theta               (index) float64 ...\n",
+       "    phi                 (index) float64 ...\n",
+       "Data variables:\n",
+       "    logpolycoefflux     (index, power) float64 ...\n",
+       "    logpolycoefpolflux  (index, power) float64 ...\n",
+       "Attributes:\n",
+       "    notes:    Catalog of sources where the flux in Jy at any frequency is cal...
" + ], + "text/plain": [ + "\n", + "Dimensions: (index: 372255, power: 5)\n", + "Coordinates:\n", + " * index (index) int64 11253 16428 24110 ... 281755430 281755795\n", + " * power (power) int64 4 3 2 1 0\n", + " theta (index) float64 ...\n", + " phi (index) float64 ...\n", + "Data variables:\n", + " logpolycoefflux (index, power) float64 ...\n", + " logpolycoefpolflux (index, power) float64 ...\n", + "Attributes:\n", + " notes: Catalog of sources where the flux in Jy at any frequency is cal..." + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xarray.open_dataset(output_filename)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "505d4b7c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import h5py\n", + "f = h5py.File(output_filename, 'r')\n", + "f[\"logpolycoefflux\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "10bd3b7e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "b'Jy'" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f[\"logpolycoefflux\"].attrs[\"units\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "a0b98440", + "metadata": {}, + "outputs": [], + "source": [ + "!mv $output_filename ~/p/issues/202405_pysm_catalog_pixell/" + ] + } + ], + "metadata": { + "@webio": { + "lastCommId": null, + "lastKernelId": null + }, + "kernelspec": { + "display_name": "pycmb", + "language": "python", + "name": "pycmb" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/sharp.py b/sharp.py new file mode 100644 index 00000000..465d49d5 --- /dev/null +++ b/sharp.py @@ -0,0 +1,10 @@ +import libsharp +import numpy as np +from mpi4py import MPI + +lmax = 10 +mpi_comm = MPI.COMM_WORLD +local_m_indices = np.arange(mpi_comm.rank, lmax + 1, mpi_comm.size, dtype=np.int32) +order = libsharp.packed_real_order(lmax, ms=local_m_indices) +print(MPI.COMM_WORLD.rank, order.mval(), order.mvstart(), order.local_size()) + diff --git a/src/pysm3/__init__.py b/src/pysm3/__init__.py index d0784180..b4b3158e 100644 --- a/src/pysm3/__init__.py +++ b/src/pysm3/__init__.py @@ -11,4 +11,11 @@ from . import units from .distribution import MapDistribution from .mpi import mpi_smoothing -from .utils import normalize_weights, bandpass_unit_conversion, check_freq_input +from .utils import ( + normalize_weights, + bandpass_unit_conversion, + check_freq_input, + set_verbosity, + apply_smoothing_and_coord_transform, + map2alm, +) diff --git a/src/pysm3/data/presets.cfg b/src/pysm3/data/presets.cfg index 4433d39b..183099a7 100644 --- a/src/pysm3/data/presets.cfg +++ b/src/pysm3/data/presets.cfg @@ -365,6 +365,20 @@ input_units = "Jy / sr" interpolation_kind = "linear" apply_SPT_correction = false max_nside = 4096 +[rg2] +class = "PointSourceCatalog" +catalog_filename = "websky/0.4/radio_catalog/websky_high_flux_catalog_1mJy.h5" +max_nside = 8192 +[rg3] +class = "InterpolatingComponent" +input_units = "uK_RJ" +freqs = [5.0, 18.7, 24.5, 44.0, 70.0, 100.0, 143.0, 217.0, 353.0, 545.0, 643.0, 729.0, 857.0, 906.0] +path = "websky/0.4/radio_catalog/{nside}" +interpolation_kind = "linear" +available_nside = [2048, 4096, 8192] +pre_applied_beam = {2048=5.1, 4096=2.6, 8192=0.9} +pre_applied_beam_units = "arcmin" +max_nside = 8192 [ksz1] class = "WebSkySZ" version = "0.4" diff --git a/src/pysm3/models/__init__.py b/src/pysm3/models/__init__.py index e775c7da..aabac066 100644 --- a/src/pysm3/models/__init__.py +++ b/src/pysm3/models/__init__.py @@ -3,7 +3,7 @@ from .dust import ModifiedBlackBody, DecorrelatedModifiedBlackBody from .hd2017 import HensleyDraine2017 from .power_law import PowerLaw, CurvedPowerLaw -from .template import Model, read_map, apply_smoothing_and_coord_transform +from .template import Model, read_map from .spdust import SpDust, SpDustPol from .interpolating import InterpolatingComponent from .cmb import CMBMap, CMBLensed diff --git a/src/pysm3/models/catalog.py b/src/pysm3/models/catalog.py index ed353e76..178fe3e2 100644 --- a/src/pysm3/models/catalog.py +++ b/src/pysm3/models/catalog.py @@ -3,6 +3,10 @@ import healpy as hp import numpy as np +import logging + +log = logging.getLogger("pysm3") + try: from numpy import trapezoid except ImportError: @@ -17,12 +21,31 @@ @njit +def aggregate(index, array, values): + """Sums values by index + + Example: + m = np.zeros(3) + m[2, 2] += np.ones(2) + gives: + m + [0, 0, 1] + instead + aggregate([2,2], m, np.ones(2)) + gives + m + [0, 0, 2] + """ + for i, v in zip(index, values): + array[i] += v + + def fwhm2sigma(fwhm): """Converts the Full Width Half Maximum of a Gaussian beam to its standard deviation""" return fwhm / (2.0 * np.sqrt(2.0 * np.log(2.0))) -@njit +# njit fails with the np.clip function def flux2amp(flux, fwhm): """Converts the total flux of a radio source to the peak amplitude of its Gaussian beam representation, taking into account the width of the beam as specified @@ -40,7 +63,11 @@ def flux2amp(flux, fwhm): amp: float Peak amplitude of the Gaussian beam representation of the radio source""" sigma = fwhm2sigma(fwhm) - return flux / (2 * np.pi * sigma**2) + amp = flux / (2 * np.pi * sigma**2) + # sim_objects fails if amp is zero + amp[np.logical_and(amp < 1e-5, amp > 0)] = 1e-5 + amp[np.logical_and(amp > -1e-5, amp < 0)] = -1e-5 + return amp @njit @@ -55,6 +82,7 @@ def evaluate_poly(p, x): N = len(p) for i in range(N): out += p[i] * x ** (N - 1 - i) + out = max(0, out) return out @@ -121,12 +149,16 @@ def __init__( catalog_filename, nside=None, target_wcs=None, + catalog_slice=None, + max_nside=None, map_dist=None, ): - self.catalog_filename = catalog_filename - self.nside = nside - self.shape = (3, hp.nside2npix(nside)) + super().__init__(nside=nside, max_nside=max_nside, map_dist=map_dist) + self.catalog_filename = utils.RemoteData().get(catalog_filename) self.wcs = target_wcs + if catalog_slice is None: + catalog_slice = np.index_exp[:] + self.catalog_slice = catalog_slice with h5py.File(self.catalog_filename) as f: assert f["theta"].attrs["units"].decode("UTF-8") == "rad" @@ -138,9 +170,12 @@ def __init__( def get_fluxes(self, freqs: u.GHz, coeff="logpolycoefflux", weights=None): """Get catalog fluxes in Jy integrated over a bandpass""" - weights /= trapezoid(weights, x=freqs.to_value(u.GHz)) + freqs = utils.check_freq_input(freqs) + weights = utils.normalize_weights(freqs, weights) with h5py.File(self.catalog_filename) as f: - flux = evaluate_model(freqs.to_value(u.GHz), weights, np.array(f[coeff])) + flux = evaluate_model( + freqs, weights, np.array(f[coeff][self.catalog_slice]) + ) return flux * u.Jy @u.quantity_input @@ -150,6 +185,7 @@ def get_emission( fwhm: Optional[u.Quantity[u.arcmin]] = None, weights=None, output_units=u.uK_RJ, + coord=None, car_map_resolution: Optional[u.Quantity[u.arcmin]] = None, return_car=False, ): @@ -171,6 +207,9 @@ def get_emission( car_map_resolution: float Resolution of the CAR map used by pixell to generate the map, if None, it is set to half of the resolution of the HEALPix map given by `self.nside` + coord: tuple of str + coordinate rotation, it uses the healpy convention, "Q" for Equatorial, + "G" for Galactic. return_car: bool If True return a CAR map, if False return a HEALPix map @@ -178,28 +217,33 @@ def get_emission( ------- output_map: np.array Output HEALPix or CAR map""" - with h5py.File(self.catalog_filename) as f: - pix = hp.ang2pix(self.nside, f["theta"], f["phi"]) + + convolve_beam = fwhm is not None scaling_factor = utils.bandpass_unit_conversion( freqs, weights, output_unit=output_units, input_unit=u.Jy / u.sr ) + log.info( + "HEALPix map resolution: %s arcmin", + hp.nside2resol(self.nside, arcmin=True), + ) pix_size = hp.nside2pixarea(self.nside) * u.sr - if car_map_resolution is None: - car_map_resolution = (hp.nside2resol(self.nside) * u.rad) / 2 - - # Make sure the resolution evenly divides the map vertically - if (car_map_resolution.to_value(u.rad) % np.pi) > 1e-8: - car_map_resolution = ( - np.pi / np.round(np.pi / car_map_resolution.to_value(u.rad)) - ) * u.rad - fluxes_I = self.get_fluxes(freqs, weights=weights, coeff="logpolycoefflux") - if fwhm is None: - output_map = np.zeros(self.shape, dtype=np.float32) * output_units - # sum, what if we have 2 sources on the same pixel? - output_map[0, pix] += fluxes_I / pix_size * scaling_factor - else: + if convolve_beam: + if car_map_resolution is None: + car_map_resolution = (hp.nside2resol(self.nside) * u.rad) / 2 + log.info("CAR map resolution: %s", car_map_resolution.to(u.arcmin)) + + # Make sure the resolution evenly divides the map vertically + if (car_map_resolution.to_value(u.rad) % np.pi) > 1e-8: + car_map_resolution = ( + np.pi / np.round(np.pi / car_map_resolution.to_value(u.rad)) + ) * u.rad + log.info( + "Rounded CAR map resolution: %s", car_map_resolution.to(u.arcmin) + ) + fluxes_I = self.get_fluxes(freqs, weights=weights, coeff="logpolycoefflux") + if convolve_beam: from pixell import ( enmap, pointsrcs, @@ -210,22 +254,40 @@ def get_emission( dims=(3,), variant="fejer1", ) + log.info("CAR map shape %s", shape) output_map = enmap.enmap(np.zeros(shape, dtype=np.float32), wcs) r, p = pointsrcs.expand_beam(fwhm2sigma(fwhm.to_value(u.rad))) with h5py.File(self.catalog_filename) as f: - pointing = np.column_stack( - (np.pi / 2 - np.array(f["theta"]), np.array(f["phi"])) + pointing = np.vstack( + ( + np.pi / 2 - np.array(f["theta"][self.catalog_slice]), + np.array(f["phi"][self.catalog_slice]), + ) ) + + amps = flux2amp( + fluxes_I.to_value(u.Jy) * scaling_factor.value, + fwhm.to_value(u.rad), + ) # to peak amplitude and to output units output_map[0] = pointsrcs.sim_objects( - shape, - wcs, - pointing, - flux2amp( - fluxes_I.to_value(u.Jy) * scaling_factor.value, - fwhm.to_value(u.rad), - ), # to peak amplitude and to output units - ((r, p)), + shape=shape, + wcs=wcs, + poss=pointing, + amps=amps, + profile=((r, p)), ) + else: + with h5py.File(self.catalog_filename) as f: + pix = hp.ang2pix( + self.nside, + f["theta"][self.catalog_slice], + f["phi"][self.catalog_slice], + ) + output_map = ( + np.zeros((3, hp.nside2npix(self.nside)), dtype=np.float32) + * output_units + ) + aggregate(pix, output_map[0], fluxes_I / pix_size * scaling_factor) del fluxes_I fluxes_P = self.get_fluxes(freqs, weights=weights, coeff="logpolycoefpolflux") @@ -235,14 +297,7 @@ def get_emission( psirand = np.random.uniform( low=-np.pi / 2.0, high=np.pi / 2.0, size=len(fluxes_P) ) - if fwhm is None: - output_map[1, pix] += ( - fluxes_P / pix_size * scaling_factor * np.cos(2 * psirand) - ) - output_map[2, pix] += ( - fluxes_P / pix_size * scaling_factor * np.sin(2 * psirand) - ) - else: + if convolve_beam: pols = [(1, np.cos)] pols.append((2, np.sin)) for i_pol, sincos in pols: @@ -258,8 +313,35 @@ def get_emission( ), ((r, p)), ) - if return_car: - return output_map - from pixell import reproject + if return_car: + assert ( + coord is None + ), "Coord rotation for CAR not implemented yet, open issue if you need it" + else: + from pixell import reproject + + frames_dict = {"Q": "equ", "C": "equ", "G": "gal"} + if coord is not None: + coord = [frames_dict[frame] for frame in coord] + + log.info("Reprojecting to HEALPix") + output_map = ( + reproject.map2healpix( + output_map, self.nside, rot=coord, method="spline" + ) + * output_units + ) - return reproject.map2healpix(output_map, self.nside) + else: + aggregate( + pix, + output_map[1], + fluxes_P / pix_size * scaling_factor * np.cos(2 * psirand), + ) + aggregate( + pix, + output_map[2], + fluxes_P / pix_size * scaling_factor * np.sin(2 * psirand), + ) + log.info("Catalog emission computed") + return output_map diff --git a/src/pysm3/models/interpolating.py b/src/pysm3/models/interpolating.py index 629c15de..cef751ea 100644 --- a/src/pysm3/models/interpolating.py +++ b/src/pysm3/models/interpolating.py @@ -1,5 +1,6 @@ import logging import os +from typing import Optional import warnings import healpy as hp @@ -9,7 +10,7 @@ from .. import units as u from .. import utils -from ..utils import trapz_step_inplace +from ..utils import trapz_step_inplace, map2alm from .template import Model log = logging.getLogger("pysm3") @@ -24,6 +25,10 @@ def __init__( max_nside=None, interpolation_kind="linear", map_dist=None, + freqs=None, + available_nside=None, + pre_applied_beam=None, + pre_applied_beam_units=None, verbose=False, ): """PySM component interpolating between precomputed maps @@ -56,17 +61,29 @@ def __init__( Control amount of output """ - super().__init__(nside=nside, max_nside=max_nside, map_dist=map_dist) - self.maps = {} - self.maps = self.get_filenames(path) - + super().__init__( + nside=nside, + available_nside=available_nside, + max_nside=max_nside, + map_dist=map_dist, + ) + if freqs is None: + self.maps = {} + self.maps = self.get_filenames(path) + self.freqs = np.array(list(self.maps.keys())) + self.freqs.sort() + else: + self.freqs = np.array(freqs) + self.maps = {freq: path + f"/{freq:05.1f}.fits" for freq in freqs} + + self.pre_applied_beam = pre_applied_beam + if pre_applied_beam_units is not None: + self.pre_applied_beam_units = u.Unit(pre_applied_beam_units) # use a numba typed Dict so we can used in JIT compiled code self.cached_maps = Dict.empty( key_type=types.float64, value_type=types.float32[:, :] ) - self.freqs = np.array(list(self.maps.keys())) - self.freqs.sort() self.input_units = input_units self.interpolation_kind = interpolation_kind self.verbose = verbose @@ -82,60 +99,93 @@ def get_filenames(self, path): @u.quantity_input def get_emission( - self, freqs: u.Quantity[u.GHz], weights=None + self, + freqs: u.Quantity[u.GHz], + weights=None, + fwhm: Optional[u.Quantity[u.arcmin]] = None, + output_nside: Optional[int] = None, + lmax: Optional[int] = None, ) -> u.Quantity[u.uK_RJ]: nu = utils.check_freq_input(freqs) weights = utils.normalize_weights(nu, weights) - if len(nu) == 1: - - # special case: we request only 1 frequency and that is among the ones - # available as input - check_isclose = np.isclose(self.freqs, nu[0]) - if np.any(check_isclose): - - freq = self.freqs[check_isclose][0] - out = self.read_map_by_frequency(freq) - if out.ndim == 1 or out.shape[0] == 1: - zeros = np.zeros_like(out) - out = np.array([out, zeros, zeros]) - return out << u.uK_RJ - - npix = hp.nside2npix(self.nside) - if nu[0] < self.freqs[0]: - warnings.warn( - f"Frequency not supported, requested {nu[0]} Ghz < lower bound {self.freqs[0]} GHz" + # special case: we request only 1 frequency and that is among the ones + # available as input + check_isclose = np.isclose(self.freqs, nu[0]) + if len(nu) == 1 and np.any(check_isclose): + freq = self.freqs[check_isclose][0] + log.info("Selecting single frequency map: %s", str(freq)) + out = self.read_map_by_frequency(freq) + if out.ndim == 1 or out.shape[0] == 1: + zeros = np.zeros_like(out) + out = np.array([out, zeros, zeros]) + else: + + npix = hp.nside2npix(self.nside) + if nu[0] < self.freqs[0]: + warnings.warn( + f"Frequency not supported, requested {nu[0]} Ghz < lower bound {self.freqs[0]} GHz" + ) + return np.zeros((3, npix)) << u.uK_RJ + if nu[-1] > self.freqs[-1]: + warnings.warn( + f"Frequency not supported, requested {nu[-1]} Ghz > upper bound {self.freqs[-1]} GHz" + ) + return np.zeros((3, npix)) << u.uK_RJ + + freq_range = utils.get_relevant_frequencies(self.freqs, nu[0], nu[-1]) + log.info("Frequencies considered: %s", str(freq_range)) + + for freq in freq_range: + if freq not in self.cached_maps: + m = self.read_map_by_frequency(freq) + if m.shape[0] != 3: + m = m.reshape((1, -1)) + self.cached_maps[freq] = m.astype(np.float32) + for i_pol, pol in enumerate("IQU" if m.shape[0] == 3 else "I"): + log.info( + f"Mean emission at {freq} GHz in {pol}: {self.cached_maps[freq][i_pol].mean():.4g} uK_RJ" + ) + + out = compute_interpolated_emission_numba( + nu, weights, freq_range, self.cached_maps ) - return np.zeros((3, npix)) << u.uK_RJ - if nu[-1] > self.freqs[-1]: - warnings.warn( - f"Frequency not supported, requested {nu[-1]} Ghz > upper bound {self.freqs[-1]} GHz" + + if out.ndim == 1 or out.shape[0] == 1: + if out.ndim == 2: + out = out[0] + zeros = np.zeros_like(out) + out = np.array([out, zeros, zeros]) + + if (self.pre_applied_beam is not None) and (fwhm is not None): + assert lmax is not None, "lmax must be provided when applying a beam" + if output_nside is None: + output_nside = self.nside + pre_beam = ( + self.pre_applied_beam.get( + self.nside, self.pre_applied_beam[self.available_nside[0]] + ) + * self.pre_applied_beam_units ) - return np.zeros((3, npix)) << u.uK_RJ - - freq_range = utils.get_relevant_frequencies(self.freqs, nu[0], nu[-1]) - log.info("Frequencies considered: %s", str(freq_range)) - - for freq in freq_range: - if freq not in self.cached_maps: - m = self.read_map_by_frequency(freq) - if m.shape[0] != 3: - m = m.reshape((1, -1)) - self.cached_maps[freq] = m.astype(np.float32) - for i_pol, pol in enumerate("IQU" if m.shape[0] == 3 else "I"): - log.info( - f"Mean emission at {freq} GHz in {pol}: {self.cached_maps[freq][i_pol].mean():.4g} uK_RJ" - ) - - out = compute_interpolated_emission_numba( - nu, weights, freq_range, self.cached_maps - ) + if pre_beam != fwhm: + log.info( + "Applying the differential beam between: %s %s", + str(pre_beam), + str(fwhm), + ) + alm = map2alm(out, self.nside, lmax) + + beam = hp.gauss_beam( + fwhm.to_value(u.radian), lmax=lmax, pol=True + ) / hp.gauss_beam( + pre_beam.to_value(u.radian), + lmax=lmax, + pol=True, + ) + for each_alm, each_beam in zip(alm, beam.T): + hp.almxfl(each_alm, each_beam, mmax=lmax, inplace=True) + out = hp.alm2map(alm, nside=output_nside, pixwin=False) - if out.ndim == 1 or out.shape[0] == 1: - if out.ndim == 2: - out = out[0] - zeros = np.zeros_like(out) - out = np.array([out, zeros, zeros]) # the output of out is always 2D, (IQU, npix) return out << u.uK_RJ diff --git a/src/pysm3/models/template.py b/src/pysm3/models/template.py index dc08b1f1..117a4769 100644 --- a/src/pysm3/models/template.py +++ b/src/pysm3/models/template.py @@ -47,7 +47,7 @@ class Model: If libsharp is not available, pixels are distributed uniformly across processes, see :py:func:`pysm.mpi.distribute_pixels_uniformly`""" - def __init__(self, nside, max_nside=None, map_dist=None): + def __init__(self, nside, available_nside=None, max_nside=None, map_dist=None): """ Parameters ---------- @@ -55,6 +55,8 @@ def __init__(self, nside, max_nside=None, map_dist=None): MPI communicator object (optional, default=None). nside: int Resolution parameter at which this model is to be calculated. + available_nside: list[int] + Which Nsides the template maps are available at max_nside: int Keeps track of the the maximum Nside this model is available at by default 512 like PySM 2 models @@ -62,6 +64,7 @@ def __init__(self, nside, max_nside=None, map_dist=None): :math:`\\ell_{max}` for the smoothing step, by default :math:`2*N_{side}` """ self.nside = nside + self.available_nside = available_nside assert nside is not None self.max_nside = 512 if max_nside is None else max_nside self.map_dist = map_dist @@ -126,165 +129,6 @@ def get_emission( return outputs << u.uK_RJ -def apply_smoothing_and_coord_transform( - input_map, - fwhm=None, - rot=None, - lmax=None, - output_nside=None, - output_car_resol=None, - return_healpix=True, - return_car=False, - input_alm=False, - map2alm_lsq_maxiter=10, - map_dist=None, -): - """Apply smoothing and coordinate rotation to an input map - - it applies the `healpy.smoothing` Gaussian smoothing kernel if `map_dist` - is None, otherwise applies distributed smoothing with `libsharp`. - In the distributed case, no rotation is supported. - - Parameters - ---------- - input_map : ndarray - Input map, of shape `(3, npix)` - This is assumed to have no beam at this point, as the - simulated small scale template on which the simulations are based - have no beam. - fwhm : astropy.units.Quantity - Full width at half-maximum, defining the - Gaussian kernels to be applied. - rot: hp.Rotator - Apply a coordinate rotation give a healpy `Rotator`, e.g. if the - inputs are in Galactic, `hp.Rotator(coord=("G", "C"))` rotates - to Equatorial - output_nside : int - HEALPix output map Nside, if None, use the same as the input - lmax : int - lmax for the map2alm step, if None, it is set to 2.5 * nside - if output_nside is equal or higher than nside. - It is set to 1.5 * nside if output_nside is lower than nside - output_car_resol : astropy.Quantity - CAR output map resolution, generally in arcmin - return_healpix : bool - Whether to return the HEALPix map - return_car : bool - Whether to return the CAR map - input_alm : bool - Instead of starting from a map, `input_map` is a set of Alm - map2alm_lsq_maxiter : int - Number of iteration for the least squares map to Alm transform, - setting it to 0 uses the standard map2alm, the default of 10 - makes the transform slow if the input map is not band limited, - for example if has point sources or sharp features. - If ell_max is <= 1.5 nside, this setting is ignored - and `map2alm` with pixel weights is used. - - Returns - ------- - smoothed_map : np.ndarray or tuple of np.ndarray - Array containing the smoothed sky or tuple of HEALPix and CAR maps - """ - - if not input_alm: - nside = hp.get_nside(input_map) - if output_nside is None: - output_nside = nside - - unit = input_map.unit if hasattr(input_map, "unit") else 1 - - if lmax is None: - if nside == output_nside: - lmax = int(2.5 * output_nside) - elif output_nside > nside: - lmax = int(2.5 * nside) - elif output_nside < nside: - lmax = int(1.5 * nside) - log.info("Setting lmax to %d", lmax) - - output_maps = [] - - if map_dist is None: - if input_alm: - alm = input_map.copy() - else: - if lmax <= 1.5 * nside: - log.info("Using map2alm with pixel weights") - alm = hp.map2alm( - input_map, - lmax=lmax, - use_pixel_weights=True if nside > 16 else False, - ) - elif map2alm_lsq_maxiter == 0: - alm = hp.map2alm(input_map, lmax=lmax, iter=0) - log.info("Using map2alm with no weights and no iterations") - else: - alm, error, n_iter = hp.map2alm_lsq( - input_map, - lmax=lmax, - mmax=lmax, - tol=1e-7, - maxiter=map2alm_lsq_maxiter, - ) - if n_iter == map2alm_lsq_maxiter: - log.warning( - "hp.map2alm_lsq did not converge in %d iterations," - + " residual relative error is %.2g", - n_iter, - error, - ) - else: - log.info( - "Used map2alm_lsq, converged in %d iterations," - + "residual relative error %.2g", - n_iter, - error, - ) - if fwhm is not None: - log.info("Smoothing with fwhm of %s", str(fwhm)) - hp.smoothalm(alm, fwhm=fwhm.to_value(u.rad), inplace=True, pol=True) - if rot is not None: - log.info("Rotate Alm") - rot.rotate_alm(alm, inplace=True) - if return_healpix: - log.info("Alm to map HEALPix") - if input_alm: - assert ( - output_nside is not None - ), "If inputting Alms, specify output_nside" - output_maps.append( - u.Quantity( - hp.alm2map(alm, nside=output_nside, pixwin=False), unit, copy=False - ) - ) - if return_car: - log.info("Alm to map CAR") - shape, wcs = pixell.enmap.fullsky_geometry( - output_car_resol.to_value(u.radian), - dims=(3,), - variant="fejer1", - ) - ainfo = pixell.curvedsky.alm_info(lmax=lmax) - output_maps.append( - u.Quantity( - pixell.curvedsky.alm2map( - alm, pixell.enmap.empty(shape, wcs), ainfo=ainfo - ), - unit, - copy=False, - ) - ) - else: - assert (rot is None) or ( - rot.coordin == rot.coordout - ), "No rotation supported in distributed smoothing" - output_maps.append(mpi.mpi_smoothing(input_map, fwhm, map_dist)) - assert not return_car, "No CAR output supported in Libsharp smoothing" - - return output_maps[0] if len(output_maps) == 1 else tuple(output_maps) - - def apply_normalization(freqs, weights): """Function to apply a normalization constraing to a set of weights. This imposes the requirement that the integral of the weights over the diff --git a/src/pysm3/sky.py b/src/pysm3/sky.py index 59568363..1e231506 100644 --- a/src/pysm3/sky.py +++ b/src/pysm3/sky.py @@ -171,11 +171,11 @@ def __init__( def add_component(self, component): self.components.append(component) - def get_emission(self, freq, weights=None): + def get_emission(self, freq, weights=None, **kwargs): """This function returns the emission at a frequency, set of frequencies, or over a bandpass. """ - output = self.components[0].get_emission(freq, weights=weights) + output = self.components[0].get_emission(freq, weights=weights, **kwargs) for comp in self.components[1:]: - output += comp.get_emission(freq, weights=weights) + output += comp.get_emission(freq, weights=weights, **kwargs) return output * bandpass_unit_conversion(freq, weights, self.output_unit) diff --git a/src/pysm3/utils/__init__.py b/src/pysm3/utils/__init__.py index 73c4443f..96ddac78 100644 --- a/src/pysm3/utils/__init__.py +++ b/src/pysm3/utils/__init__.py @@ -16,10 +16,22 @@ healpix_aperture_photometry, # noqa: F401 ) from .small_scales import sigmoid # noqa: F401 +from .spherical_harmonics import apply_smoothing_and_coord_transform, map2alm log = logging.getLogger("pysm3") +def set_verbosity(level=logging.INFO): + logger = logging.getLogger("pysm3") + logger.setLevel(level) + if not logger.hasHandlers(): + handler = logging.StreamHandler() + handler.setFormatter( + logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") + ) + logger.addHandler(handler) + + def get_relevant_frequencies(freqs, low, high): """Get the frequencies necessary for interpolation in the input list between a low and a high limit @@ -153,9 +165,7 @@ def bandpass_unit_conversion( weights /= np.trapz(weights, freqs) if weights.min() < cut: good = np.logical_not(weights < cut) - log.info( - f"Removing {good.sum()}/{len(good)} points below {cut}" - ) + log.info(f"Removing {(good==0).sum()}/{len(good)} points below {cut}") weights = weights[good] freqs = freqs[good] weights /= np.trapz(weights, freqs) diff --git a/src/pysm3/utils/spherical_harmonics.py b/src/pysm3/utils/spherical_harmonics.py new file mode 100644 index 00000000..25a8b938 --- /dev/null +++ b/src/pysm3/utils/spherical_harmonics.py @@ -0,0 +1,209 @@ +import healpy as hp +import numpy as np +import logging + +from astropy import units as u +import pixell.enmap, pixell.curvedsky + +from .. import mpi, utils + +log = logging.getLogger("pysm3") + + +def apply_smoothing_and_coord_transform( + input_map, + fwhm=None, + beam_window=None, + rot=None, + lmax=None, + output_nside=None, + output_car_resol=None, + return_healpix=True, + return_car=False, + input_alm=False, + map2alm_lsq_maxiter=None, + map_dist=None, +): + """Apply smoothing and coordinate rotation to an input map + + it applies the `healpy.smoothing` Gaussian smoothing kernel if `map_dist` + is None, otherwise applies distributed smoothing with `libsharp`. + In the distributed case, no rotation is supported. + + Parameters + ---------- + input_map : ndarray + Input map, of shape `(3, npix)` + This is assumed to have no beam at this point, as the + simulated small scale template on which the simulations are based + have no beam. + fwhm : astropy.units.Quantity + Full width at half-maximum, defining the + Gaussian kernels to be applied. + beam_window: array, optional + Custom beam window function (:math:`B_\ell`) + rot: hp.Rotator + Apply a coordinate rotation give a healpy `Rotator`, e.g. if the + inputs are in Galactic, `hp.Rotator(coord=("G", "C"))` rotates + to Equatorial + output_nside : int + HEALPix output map Nside, if None, use the same as the input + lmax : int + lmax for the map2alm step, if None, it is set to 2.5 * nside + if output_nside is equal or higher than nside. + It is set to 1.5 * nside if output_nside is lower than nside + output_car_resol : astropy.Quantity + CAR output map resolution, generally in arcmin + return_healpix : bool + Whether to return the HEALPix map + return_car : bool + Whether to return the CAR map + input_alm : bool + Instead of starting from a map, `input_map` is a set of Alm + map2alm_lsq_maxiter : int + Number of iteration for the least squares map to Alm transform, + setting it to 0 uses the standard map2alm, the default of 10 + makes the transform slow if the input map is not band limited, + for example if has point sources or sharp features. + If ell_max is <= 1.5 nside, this setting is ignored + and `map2alm` with pixel weights is used. + + Returns + ------- + smoothed_map : np.ndarray or tuple of np.ndarray + Array containing the smoothed sky or tuple of HEALPix and CAR maps + """ + + if not input_alm: + nside = hp.get_nside(input_map) + if output_nside is None: + output_nside = nside + + unit = input_map.unit if hasattr(input_map, "unit") else 1 + + if lmax is None: + if nside == output_nside: + lmax = int(2.5 * output_nside) + elif output_nside > nside: + lmax = int(2.5 * nside) + elif output_nside < nside: + lmax = int(1.5 * nside) + log.info("Setting lmax to %d", lmax) + + output_maps = [] + + if map_dist is None: + if input_alm: + alm = input_map.copy() + else: + alm = map2alm(input_map, nside, lmax, map2alm_lsq_maxiter) + if fwhm is not None: + assert beam_window is None, "Either FWHM or beam_window" + log.info("Smoothing with fwhm of %s", str(fwhm)) + hp.smoothalm(alm, fwhm=fwhm.to_value(u.rad), inplace=True, pol=True) + if beam_window is not None: + assert fwhm is None, "Either FWHM or beam_window" + log.info("Smoothing with a custom isotropic beam") + # smoothalm does not support polarized beam + for i in range(3): + try: + beam_window_i = beam_window[:, i] + log.info("Using polarized beam") + except IndexError: + beam_window_i = beam_window + log.info("Using the same beam for all components") + hp.smoothalm(alm[i], beam_window=beam_window_i, inplace=True) + if rot is not None: + log.info("Rotate Alm") + rot.rotate_alm(alm, inplace=True) + if return_healpix: + log.info("Alm to map HEALPix") + if input_alm: + assert ( + output_nside is not None + ), "If inputting Alms, specify output_nside" + output_maps.append( + u.Quantity( + hp.alm2map(alm, nside=output_nside, pixwin=False), unit, copy=False + ) + ) + if return_car: + log.info("Alm to map CAR") + shape, wcs = pixell.enmap.fullsky_geometry( + output_car_resol.to_value(u.radian), + dims=(3,), + variant="fejer1", + ) + ainfo = pixell.curvedsky.alm_info(lmax=lmax) + output_maps.append( + pixell.curvedsky.alm2map( + alm, pixell.enmap.empty(shape, wcs), ainfo=ainfo + ) + ) + else: + assert (rot is None) or ( + rot.coordin == rot.coordout + ), "No rotation supported in distributed smoothing" + output_maps.append(mpi.mpi_smoothing(input_map, fwhm, map_dist)) + assert not return_car, "No CAR output supported in Libsharp smoothing" + + return output_maps[0] if len(output_maps) == 1 else tuple(output_maps) + + +def map2alm(input_map, nside, lmax, map2alm_lsq_maxiter=None): + """Compute alm from a map using healpy. + + Automatically selects the most appropriate method based on + the target lmax + + Parameters + ---------- + input_map : np.ndarray + Input HEALPix map + nside : int + Resolution parameter of the input map + lmax : int + Maximum multipole of the alm + map2alm_lsq_maxiter : int, optional + Maximum number of iterations for map2alm_lsq, by default 10 + + Returns + ------- + alm: np.ndarray + alm array""" + if map2alm_lsq_maxiter is None: + map2alm_lsq_maxiter = 10 + nside = hp.get_nside(input_map) + if lmax <= 1.5 * nside: + log.info("Using map2alm with pixel weights") + alm = hp.map2alm( + input_map, + lmax=lmax, + use_pixel_weights=True if nside > 16 else False, + ) + elif map2alm_lsq_maxiter == 0: + alm = hp.map2alm(input_map, lmax=lmax, iter=0) + log.info("Using map2alm with no weights and no iterations") + else: + alm, error, n_iter = hp.map2alm_lsq( + input_map, + lmax=lmax, + mmax=lmax, + tol=1e-7, + maxiter=map2alm_lsq_maxiter, + ) + if n_iter == map2alm_lsq_maxiter: + log.warning( + "hp.map2alm_lsq did not converge in %d iterations," + + " residual relative error is %.2g", + n_iter, + error, + ) + else: + log.info( + "Used map2alm_lsq, converged in %d iterations," + + "residual relative error %.2g", + n_iter, + error, + ) + return alm diff --git a/tests/test_catalog.py b/tests/test_catalog.py index 175443e9..f4b85ac7 100644 --- a/tests/test_catalog.py +++ b/tests/test_catalog.py @@ -16,10 +16,21 @@ from pysm3 import units as u from pysm3 import utils -from pysm3.models.catalog import PointSourceCatalog, evaluate_model, evaluate_poly +from pysm3.models.catalog import ( + PointSourceCatalog, + evaluate_model, + evaluate_poly, + aggregate, +) from pysm3.utils import car_aperture_photometry, healpix_aperture_photometry +def test_aggregate(): + m = np.zeros(3) + aggregate(np.array([2, 2]), m, np.ones(2)) + assert_allclose(m, np.array([0, 0, 2])) + + def test_evaluate_poly(): np.random.seed(100) for N in [4, 5, 6]: @@ -97,11 +108,13 @@ def test_catalog_class_fluxes(test_catalog): catalog = PointSourceCatalog(test_catalog, nside=nside) freqs = np.exp(np.array([3, 4])) * u.GHz # ~ 20 and ~ 55 GHz weights = np.array([1, 1], dtype=np.float64) - weights /= trapezoid(weights, x=freqs.to_value(u.GHz)) + normalized_weights = utils.normalize_weights(utils.check_freq_input(freqs), weights) flux = catalog.get_fluxes(freqs, weights=weights) assert_allclose(flux[0], 3.7 * u.Jy) assert ( - flux[1] == trapezoid(weights * np.array([6, 8]), x=freqs.to_value(u.GHz)) * u.Jy + flux[1] + == trapezoid(normalized_weights * np.array([6, 8]), x=freqs.to_value(u.GHz)) + * u.Jy ) @@ -214,7 +227,7 @@ def test_catalog_class_map_healpix(test_catalog): assert output_map.argmax() == pix[1] flux = healpix_aperture_photometry( - (output_map[0] * scaling_factor.value), + (output_map[0].value * scaling_factor.value), aperture_radius=2 * fwhm.to_value(u.rad), theta=theta[1], phi=phi[1], diff --git a/tests/test_interpolating.py b/tests/test_interpolating.py index 0c51db3f..eec1bd45 100644 --- a/tests/test_interpolating.py +++ b/tests/test_interpolating.py @@ -5,17 +5,24 @@ from pysm3 import InterpolatingComponent from pysm3 import units as u -nside = 4 +nside = 64 shape = (3, hp.nside2npix(nside)) @pytest.fixture -def interp(tmp_path): - """Setup the interpolating component""" +def create_maps(tmp_path): hp.write_map(tmp_path / "10.fits", np.ones(shape, dtype=np.float32)) hp.write_map(tmp_path / "20.fits", 2 * np.ones(shape, dtype=np.float32)) + return tmp_path + + +@pytest.fixture +def interp(create_maps): + """Setup the interpolating component""" - return InterpolatingComponent(tmp_path, "uK_RJ", nside, interpolation_kind="linear") + return InterpolatingComponent( + create_maps, "uK_RJ", nside, interpolation_kind="linear" + ) def test_interpolating(interp): @@ -44,3 +51,55 @@ def test_interpolating_bandpass_boundary_below(interp): np.testing.assert_allclose( 1.118 * np.ones(shape) * u.uK_RJ, interpolated_map, rtol=1e-2 ) + + +@pytest.fixture +def interp_pre_smoothed(tmp_path): + """Setup the interpolating component""" + m = np.zeros(shape, dtype=np.float32) + m[0] += 10 + + m[0, hp.ang2pix(nside, np.pi / 2, 0)] = 100 + hp.write_map(tmp_path / "10.fits", m) + + return m, InterpolatingComponent( + tmp_path, + "uK_RJ", + nside, + interpolation_kind="linear", + available_nside=[nside], + pre_applied_beam={nside: 5}, + pre_applied_beam_units="deg", + ) + + +def test_presmoothed_null(interp_pre_smoothed): + input_map, interp_pre_smoothed = interp_pre_smoothed + output_map = interp_pre_smoothed.get_emission( + 10 * u.GHz, + fwhm=5 * u.deg, + lmax=1.5 * nside, + ) + np.testing.assert_allclose(input_map, output_map.value) + + +def test_presmoothed(tmp_path): + """Setup the interpolating component""" + m = np.ones(shape, dtype=np.float32) * 10 + m[0, hp.ang2pix(nside, np.pi / 2, 0)] = 100 + m_smoothed = hp.smoothing(m, fwhm=np.radians(3)) + hp.write_map(tmp_path / "10.fits", m_smoothed) + + c = InterpolatingComponent( + tmp_path, + "uK_RJ", + nside, + interpolation_kind="linear", + available_nside=[nside], + pre_applied_beam={nside: 3}, + pre_applied_beam_units="deg", + ) + + input_map = hp.smoothing(m, fwhm=np.radians(5)) + output_map = c.get_emission(10 * u.GHz, fwhm=5 * u.deg, lmax=1.5 * nside) + np.testing.assert_allclose(input_map, output_map.value, rtol=1e-3, atol=1e-4) diff --git a/tests/test_smoothing.py b/tests/test_smoothing.py index d94779c3..01f966ac 100644 --- a/tests/test_smoothing.py +++ b/tests/test_smoothing.py @@ -13,8 +13,9 @@ from astropy.tests.helper import assert_quantity_allclose import pysm3.units as u -from pysm3.models import apply_smoothing_and_coord_transform +from pysm3 import apply_smoothing_and_coord_transform +INITIAL_FWHM = (1 * u.deg).to_value(u.radian) FWHM = (5 * u.deg).to_value(u.radian) NSIDE = 128 CAR_RESOL = 12 * u.arcmin @@ -25,15 +26,30 @@ scope="module" ) # scope makes the fixture just run once per execution of module def input_map(): - beam_window = hp.gauss_beam(fwhm=FWHM, lmax=LMAX) ** 2 + beam_window = hp.gauss_beam(fwhm=INITIAL_FWHM, lmax=LMAX) ** 2 cl = np.zeros((6, len(beam_window))) cl[0:3] = beam_window np.random.seed(7) return hp.synfast(cl, NSIDE, lmax=LMAX, new=True) * u.uK_RJ -def test_smoothing_healpix(input_map): +def test_smoothing_healpix_beamwindow(input_map): + beam_window = hp.gauss_beam(fwhm=FWHM, lmax=LMAX, pol=True) + smoothed_map = apply_smoothing_and_coord_transform( + input_map, lmax=LMAX, beam_window=beam_window + ) + assert input_map.shape[0] == 3 + assert smoothed_map.shape == input_map.shape + assert_quantity_allclose( + actual=smoothed_map, + desired=hp.smoothing(input_map, fwhm=FWHM, lmax=LMAX, use_pixel_weights=True) + * input_map.unit, + rtol=1e-7, + ) + + +def test_smoothing_healpix(input_map): smoothed_map = apply_smoothing_and_coord_transform( input_map, lmax=LMAX, fwhm=FWHM * u.radian ) @@ -47,7 +63,6 @@ def test_smoothing_healpix(input_map): def test_car_nosmoothing(input_map): - # `enmap_from_healpix` has no iteration or weights # so for test purpose we reproduce it here alm = ( @@ -71,13 +86,11 @@ def test_car_nosmoothing(input_map): pixell.reproject.enmap_from_healpix( input_map, shape, wcs, lmax=LMAX, rot=None, ncomp=3 ) - * input_map.unit ) assert_quantity_allclose(actual=car_map, desired=map_rep) def test_healpix_output_nside(input_map): - output_nside = 64 output_map = apply_smoothing_and_coord_transform( input_map, fwhm=None, output_nside=output_nside, lmax=LMAX