diff --git a/examples/estimation_examples/somocluSOM_cluster_qc_demo.ipynb b/examples/estimation_examples/somocluSOM_cluster_qc_demo.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "f48660fd",
+ "metadata": {},
+ "source": [
+ "# SOMocluSummarizer+Quality Control demo"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "db83f923",
+ "metadata": {},
+ "source": [
+ "Author: Ziang Yan
\n",
+ "Last successfully run: Nov 22, 2024
\n",
+ "\n",
+ "This notebook creats an end-to-end example for the SOM summarizer PLUS quality controld defined in https://arxiv.org/pdf/2007.15635. Including:\n",
+ "\n",
+ "1) create photometric realizations for a training and spectroscopic sample;\n",
+ "2) measuring BPZ for the training and spectroscopic samples;\n",
+ "3) make the same tomographic cut on the training and spec samples;\n",
+ "4) informing a `rail_som` model with the training sample and summarizing it with the spec sample;\n",
+ "5) performing two quality control (arXiv: 1909.09632);\n",
+ "6) summarizing the goodness of redshift calibration and compare between QCs;\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "b187a115",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib import cm\n",
+ "import pickle\n",
+ "import rail\n",
+ "import os\n",
+ "import qp\n",
+ "from rail.core.utils import RAILDIR\n",
+ "\n",
+ "import tables_io\n",
+ "from rail.core.data import TableHandle, ModelHandle\n",
+ "from rail.core.stage import RailStage\n",
+ "from rail.estimation.algos.somoclu_som import SOMocluInformer, SOMocluSummarizer\n",
+ "from rail.estimation.algos.somoclu_som import get_bmus, plot_som"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b9b1e4dc",
+ "metadata": {},
+ "source": [
+ "Next, let's set up the Data Store, so that our RAIL module will know where to fetch data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "b8cc9628",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "DS = RailStage.data_store\n",
+ "DS.__class__.allow_overwrite = True"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f0b8c14b",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "First, let's grab some data files. For the SOM, we will want to train on a fairly large, representative set that encompasses all of our expected data. We'll grab a larger data file than we typically use in our demos to ensure that we construct a meaningful SOM.\n",
+ "\n",
+ "## Run this command on the command line to get the larger data file to train the SOM:\n",
+ "`curl -O https://portal.nersc.gov/cfs/lsst/schmidt9/healpix_10326_bright_data.hdf5`\n",
+ "\n",
+ "and then move the resulting file to this directory, i.e. RAIL/examples/estimation. This data consists of ~150,000 galaxies from a single healpix pixel of the comsoDC2 truth catalog with mock 10-year magnitude errors added. It is cut at a relatively bright i<23.5 magnitudes in order to concentrate on galaxies with particularly high S/N rates."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "40276b32",
+ "metadata": {},
+ "source": [
+ "# First read the target and spec catalogue from a pre-trained pzflow stage."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "413b937a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "training_file = \"./healpix_10326_bright_data.hdf5\"\n",
+ "\n",
+ "if not os.path.exists(training_file):\n",
+ " os.system('curl -O https://portal.nersc.gov/cfs/lsst/PZ/healpix_10326_bright_data.hdf5')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "34082c32",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "training_data = DS.read_file(\"training_data\", TableHandle, training_file)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "470ee4c8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pmask = (training_data.data['photometry']['mag_i_lsst'] <23.5)\n",
+ "trim_test = {}\n",
+ "for key in training_data.data['photometry'].keys():\n",
+ " trim_test[key] = training_data.data['photometry'][key][pmask]\n",
+ "trim_dict = dict(photometry=trim_test)\n",
+ "target_data_all = DS.add_data(\"target_data_raw\", trim_dict, TableHandle)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "c988407f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from rail.utils.path_utils import find_rail_file\n",
+ "\n",
+ "specfile = find_rail_file(\"examples_data/testdata/test_dc2_validation_9816.hdf5\")\n",
+ "ref_data_raw = tables_io.read(specfile)['photometry']\n",
+ "smask = (ref_data_raw['mag_i_lsst'] <23.5)\n",
+ "trim_spec = {}\n",
+ "for key in ref_data_raw.keys():\n",
+ " trim_spec[key] = ref_data_raw[key][smask]\n",
+ "trim_dict = dict(photometry=trim_spec)\n",
+ "ref_data_all = DS.add_data(\"ref_data_raw\", trim_dict, TableHandle)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "233e1c55",
+ "metadata": {},
+ "source": [
+ "# Now measure the photometric redshifts using the `bpz_lite`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "8fb36086",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst', 'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst']\n",
+ "['DC2LSST_u', 'DC2LSST_g', 'DC2LSST_r', 'DC2LSST_i', 'DC2LSST_z', 'DC2LSST_y']\n"
+ ]
+ }
+ ],
+ "source": [
+ "bands = [\"u\", \"g\", \"r\", \"i\", \"z\", \"y\"]\n",
+ "lsst_bands = []\n",
+ "lsst_errs = []\n",
+ "lsst_filts = []\n",
+ "for band in bands:\n",
+ " lsst_bands.append(f\"mag_{band}_lsst\")\n",
+ " lsst_errs.append(f\"mag_err_{band}_lsst\")\n",
+ " lsst_filts.append(f\"DC2LSST_{band}\")\n",
+ "print(lsst_bands)\n",
+ "print(lsst_filts)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "b2e264ee",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from rail.core.utils import RAILDIR\n",
+ "import os\n",
+ "from rail.core.utils import RAILDIR\n",
+ "from rail.estimation.algos.bpz_lite import BPZliteInformer, BPZliteEstimator\n",
+ "from rail.core.data import ModelHandle\n",
+ "custom_data_path = RAILDIR + '/rail/examples_data/estimation_data/data'\n",
+ "\n",
+ "hdfnfile = os.path.join(RAILDIR, \"rail/examples_data/estimation_data/data/CWW_HDFN_prior.pkl\")\n",
+ "sedfile = os.path.join(RAILDIR, \"rail/examples_data/estimation_data/data/SED/COSMOS_seds.list\")\n",
+ "\n",
+ "with open(hdfnfile, \"rb\") as f:\n",
+ " hdfnmodel = pickle.load(f)\n",
+ "\n",
+ "custom_dict_phot = dict(hdf5_groupname=\"photometry\",\n",
+ " output=\"bpz_results_phot_qc.hdf5\", \n",
+ " bands=lsst_bands, \n",
+ " err_bands=lsst_errs,\n",
+ " filter_list=lsst_filts,\n",
+ " prior_band='mag_i_lsst',spectra_file=sedfile,\n",
+ " data_path=custom_data_path,\n",
+ " no_prior=False)\n",
+ "\n",
+ "custom_dict_spec = dict(hdf5_groupname=\"photometry\",\n",
+ " output=\"bpz_results_spec_qc.hdf5\", \n",
+ " bands=lsst_bands, \n",
+ " err_bands=lsst_errs,\n",
+ " filter_list=lsst_filts,\n",
+ " prior_band='mag_i_lsst',spectra_file=sedfile,\n",
+ " data_path=custom_data_path,\n",
+ " no_prior=False)\n",
+ "\n",
+ "cosmospriorfile = os.path.join(RAILDIR, \"rail/examples_data/estimation_data/data/COSMOS31_HDFN_prior.pkl\")\n",
+ "cosmosprior = DS.read_file(\"cosmos_prior\", ModelHandle, cosmospriorfile)\n",
+ "\n",
+ "phot_run = BPZliteEstimator.make_stage(name=\"rerun_bpz_phot\", model=cosmosprior, **custom_dict_phot)\n",
+ "spec_run = BPZliteEstimator.make_stage(name=\"rerun_bpz_spec\", model=cosmosprior, **custom_dict_spec)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "ffde17c5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Process 0 running estimator on chunk 0 - 150818\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/net/home/fohlen14/yanza21/research/src/RAIL_new/rail_bpz/src/rail/estimation/algos/bpz_lite.py:478: RuntimeWarning: overflow encountered in cast\n",
+ " flux_err[unobserved] = 1e108\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Inserting handle into data store. output_rerun_bpz_phot: inprogress_bpz_results_phot_qc.hdf5, rerun_bpz_phot\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from collections import OrderedDict\n",
+ "phot_run.estimate(target_data_all)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "efb67aad",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Process 0 running estimator on chunk 0 - 5166\n",
+ "Inserting handle into data store. output_rerun_bpz_spec: inprogress_bpz_results_spec_qc.hdf5, rerun_bpz_spec\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "spec_run.estimate(ref_data_all)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "092295bf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "phot_bpz_file = 'bpz_results_phot_qc.hdf5'\n",
+ "bpz_phot_all = tables_io.read(phot_bpz_file)['ancil']['zmode']\n",
+ "\n",
+ "spec_bpz_file = 'bpz_results_spec_qc.hdf5'\n",
+ "bpz_spec_all = tables_io.read(spec_bpz_file)['ancil']['zmode']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "04ff0ad9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0, 0.5, '$Z_{\\\\mathrm{phot}}$')"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "