From 9284c67342c75f9b45511534bb5e71aa3866610b Mon Sep 17 00:00:00 2001 From: galaxyumi Date: Fri, 19 Dec 2025 20:51:01 +0000 Subject: [PATCH 01/10] adding a new NB --- .../102_7_Masked_array_pitfalls.ipynb | 944 ++++++++++++++++++ 1 file changed, 944 insertions(+) create mode 100644 DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb diff --git a/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb b/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb new file mode 100644 index 0000000..d4f9177 --- /dev/null +++ b/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb @@ -0,0 +1,944 @@ +{ + "cells": [ + { + "attachments": { + "035de3f2-c616-497e-88ba-a74eef64312c.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "e032a51d-4ad8-4ff8-8844-4c1e052fbea5", + "metadata": {}, + "source": [ + "## original error report\n", + "\n", + "Inputs\n", + "\n", + "```\n", + "ra_cen = 6.128\n", + "dec_cen = -72.090\n", + "radius = 1.0 \n", + "query = \"\"\"\n", + " SELECT visitId, ra, dec, band, pixelScale, psfSigma, magLim\n", + " FROM dp1_v29.CcdVisit \n", + " WHERE CONTAINS(POINT('ICRS', ra, dec),CIRCLE('ICRS', {}, {}, {}))=1\n", + " ORDER BY visitId \n", + " \"\"\".format(ra_cen, dec_cen, radius)\n", + "job = service.submit_job(query)\n", + "ccdtab = job.fetch_result().to_table()\n", + "\n", + "# Compute and add a new column\n", + "ccdtab['psf_fwhm'] = 2*np.sqrt(2.0*np.log(2))*ccdtab['pixelScale'].value*ccdtab['psfSigma'].value\n", + "\n", + "# Print unique psfSigma values and a median value\n", + "print(np.unique(ccdtab[(ccdtab['psf_fwhm']< 0.6) & (ccdtab['psf_fwhm']>0.3) & (ccdtab['band'] == 'r')]['psfSigma']))\n", + "print(np.nanmedian(ccdtab[(ccdtab['psf_fwhm']< 0.6) & (ccdtab['psf_fwhm']>0.3) & (ccdtab['band'] == 'r')]['psfSigma']))\n", + "\n", + "# Print unique psf_fwhm values and a median value\n", + "print(np.unique(ccdtab[(ccdtab['psf_fwhm']< 0.6) & (ccdtab['psf_fwhm']>0.3) & (ccdtab['band'] == 'r')]['psf_fwhm']))\n", + "print(np.nanmedian(ccdtab[(ccdtab['psf_fwhm']< 0.6) & (ccdtab['psf_fwhm']>0.3) & (ccdtab['band'] == 'r')]['psf_fwhm']))\n", + "\n", + "```\n", + "\n", + "**RESULTS**\n", + "![screenshot_2025-06-04_at_1.33.35___pm.png](attachment:035de3f2-c616-497e-88ba-a74eef64312c.png)\n", + "\n", + "\n", + "Essentially, it seemed like null/nan were being \"converted\" to values and that was very worrying.\n", + "\n", + "Can't reproduce that in this NB but do want to explore masked array failure modes." + ] + }, + { + "cell_type": "markdown", + "id": "5ea726b9-6e17-4675-97ce-361e8243374f", + "metadata": {}, + "source": [ + "Starting point: Melissa's testing NB: https://github.com/lsst/cst-dev/blob/main/MLG_sandbox/DP1/issues/masked_array_nanmedian.ipynb." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0f6cbedb-ba6a-4d61-995f-a7c76a29e86e", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-18T20:28:01.250330Z", + "iopub.status.busy": "2025-12-18T20:28:01.249995Z", + "iopub.status.idle": "2025-12-18T20:28:01.736166Z", + "shell.execute_reply": "2025-12-18T20:28:01.735466Z", + "shell.execute_reply.started": "2025-12-18T20:28:01.250305Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from lsst.rsp import get_tap_service\n", + "import astropy\n", + "import scipy.stats.mstats as scistats" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f792a486-b06b-4523-b2f5-ece955f0e3b4", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-18T20:28:01.737563Z", + "iopub.status.busy": "2025-12-18T20:28:01.737170Z", + "iopub.status.idle": "2025-12-18T20:28:01.813164Z", + "shell.execute_reply": "2025-12-18T20:28:01.812408Z", + "shell.execute_reply.started": "2025-12-18T20:28:01.737542Z" + } + }, + "outputs": [], + "source": [ + "service = get_tap_service(\"tap\")\n", + "assert service is not None" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "34895f9f-41a2-46ad-8588-0d367d8f1cf2", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-18T20:59:39.660471Z", + "iopub.status.busy": "2025-12-18T20:59:39.660159Z", + "iopub.status.idle": "2025-12-18T20:59:39.665030Z", + "shell.execute_reply": "2025-12-18T20:59:39.664390Z", + "shell.execute_reply.started": "2025-12-18T20:59:39.660448Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.2.6'" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.__version__" + ] + }, + { + "cell_type": "markdown", + "id": "40e5458a-556e-443b-9ab3-b94a95af2c69", + "metadata": {}, + "source": [ + "## get the same data as YC originally used" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "580a8428-fe3c-459f-a8e6-ad30f05788d5", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-18T20:28:01.897316Z", + "iopub.status.busy": "2025-12-18T20:28:01.896994Z", + "iopub.status.idle": "2025-12-18T20:28:01.900717Z", + "shell.execute_reply": "2025-12-18T20:28:01.900092Z", + "shell.execute_reply.started": "2025-12-18T20:28:01.897291Z" + } + }, + "outputs": [], + "source": [ + "ra_cen = 6.128\n", + "dec_cen = -72.090\n", + "radius = 1.0 \n", + "query = \"\"\"\n", + " SELECT psfSigma\n", + " FROM dp1.CcdVisit \n", + " WHERE CONTAINS(POINT('ICRS', ra, dec),CIRCLE('ICRS', {}, {}, {}))=1\n", + " \"\"\".format(ra_cen, dec_cen, radius)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "dfa9699c-4138-4087-be47-dca4225df5f8", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-18T20:28:02.074417Z", + "iopub.status.busy": "2025-12-18T20:28:02.074080Z", + "iopub.status.idle": "2025-12-18T20:28:05.856180Z", + "shell.execute_reply": "2025-12-18T20:28:05.855265Z", + "shell.execute_reply.started": "2025-12-18T20:28:02.074390Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Job phase is COMPLETED\n" + ] + } + ], + "source": [ + "job = service.submit_job(query)\n", + "job.run()\n", + "job.wait(phases=['COMPLETED', 'ERROR'])\n", + "print('Job phase is', job.phase)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9f4f653d-e8aa-497f-bb4e-5b15b1208c54", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-18T20:28:05.857372Z", + "iopub.status.busy": "2025-12-18T20:28:05.857130Z", + "iopub.status.idle": "2025-12-18T20:28:05.943342Z", + "shell.execute_reply": "2025-12-18T20:28:05.942705Z", + "shell.execute_reply.started": "2025-12-18T20:28:05.857353Z" + } + }, + "outputs": [], + "source": [ + "results = job.fetch_result()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2630130b-e7f3-4e54-887e-8e6230ca6d55", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-18T20:34:30.514860Z", + "iopub.status.busy": "2025-12-18T20:34:30.514459Z", + "iopub.status.idle": "2025-12-18T20:34:30.518781Z", + "shell.execute_reply": "2025-12-18T20:34:30.518213Z", + "shell.execute_reply.started": "2025-12-18T20:34:30.514834Z" + } + }, + "outputs": [], + "source": [ + "ccd_visits = results.to_table()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5f35a9a2-b7e1-4452-b632-dc7091e85cbb", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-18T20:34:33.394988Z", + "iopub.status.busy": "2025-12-18T20:34:33.394650Z", + "iopub.status.idle": "2025-12-18T20:34:33.399379Z", + "shell.execute_reply": "2025-12-18T20:34:33.398911Z", + "shell.execute_reply.started": "2025-12-18T20:34:33.394965Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(dtype('float32'), dtype('float32'))" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results['psfSigma'].dtype, ccd_visits['psfSigma'].dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0b872e3a-5b75-49ee-bb36-4bfd3e1506d1", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-18T20:53:08.706740Z", + "iopub.status.busy": "2025-12-18T20:53:08.706354Z", + "iopub.status.idle": "2025-12-18T20:53:08.709720Z", + "shell.execute_reply": "2025-12-18T20:53:08.709129Z", + "shell.execute_reply.started": "2025-12-18T20:53:08.706712Z" + } + }, + "outputs": [], + "source": [ + "# ccd_visits['psfSigma']" + ] + }, + { + "cell_type": "markdown", + "id": "1b8062aa-425c-4cc4-950f-dca0902be288", + "metadata": {}, + "source": [ + "# With Numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "472dc142-8ac3-4e40-a40b-27c75054ae4c", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-18T20:53:47.820024Z", + "iopub.status.busy": "2025-12-18T20:53:47.819700Z", + "iopub.status.idle": "2025-12-18T20:53:47.823553Z", + "shell.execute_reply": "2025-12-18T20:53:47.822969Z", + "shell.execute_reply.started": "2025-12-18T20:53:47.820002Z" + } + }, + "outputs": [], + "source": [ + "# Define the data sources for clarity\n", + "data_sources = {\n", + " \"Raw Query\": results['psfSigma'],\n", + " \"Astropy Table\": ccd_visits['psfSigma']\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "663b24b4-ea60-4c7f-97c0-cd0d7e19ec43", + "metadata": {}, + "source": [ + "## 1. Mean\n", + "\n", + "Compare how standard, NaN-aware, and masked array reduction methods affect the calculated mean of psfSigma for both raw query results and Astropy tables." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c9a71065-95f0-4696-9469-586654694ce9", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-18T20:53:48.648602Z", + "iopub.status.busy": "2025-12-18T20:53:48.648262Z", + "iopub.status.idle": "2025-12-18T20:53:48.656966Z", + "shell.execute_reply": "2025-12-18T20:53:48.656471Z", + "shell.execute_reply.started": "2025-12-18T20:53:48.648578Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Source | Method | Result | dtype\n", + "------------------------------------------------------------------------------------------\n", + "Raw Query | np.mean | 2.65416754138675603514 | \n", + "Raw Query | np.nanmean | 2.65416765213012695312 | \n", + "Raw Query | np.ma.mean | 2.65416754138675603514 | \n", + "Raw Query | .compressed().mean() | 2.65416789054870605469 | \n", + "------------------------------------------------------------------------------------------\n", + "Astropy Table | np.mean | 2.65416754138675603514 | \n", + "Astropy Table | np.nanmean | 2.65416765213012695312 | \n", + "Astropy Table | np.ma.mean | 2.65416754138675603514 | \n", + "Astropy Table | .compressed().mean() | 2.65416789054870605469 | \n", + "------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "print(f\"{'Source':<15} | {'Method':<20} | {'Result':<22} | {'dtype'}\")\n", + "print(\"-\" * 90)\n", + "\n", + "for name, data in data_sources.items():\n", + " # 1. Standard Mean\n", + " print(f\"{name:<15} | np.mean | {np.mean(data):.20f} | {type(np.mean(data))}\")\n", + " \n", + " # 2. NaN Mean\n", + " print(f\"{name:<15} | np.nanmean | {np.nanmean(data):.20f} | {type(np.nanmean(data))}\")\n", + " \n", + " # 3. Masked Mean\n", + " print(f\"{name:<15} | np.ma.mean | {np.ma.mean(data):.20f} | {type(np.ma.mean(data))}\")\n", + " \n", + " # 4. Compressed Mean\n", + " # Note: .compressed() ensures we are acting on valid data only\n", + " print(f\"{name:<15} | .compressed().mean() | {np.mean(data.compressed()):.20f} | {type(np.mean(data.compressed()))}\")\n", + " print(\"-\" * 90)" + ] + }, + { + "cell_type": "markdown", + "id": "89b161af-19c0-4d8e-a62a-6cf877984967", + "metadata": {}, + "source": [ + "**Conclusion:** Computing the mean of a masked array works correctly across these methods, but the results differ slightly due to the `np.float32` data type of the input array. These discrepancies occur because different functions sum the data in different orders (e.g., contiguous vs. strided memory access). Since floating-point addition is non-associative (i.e., (A+B)+C $\\neq$ A+(B+C)), the accumulation order changes the final result at the limit of precision. The behavior is identical for both the \"Raw Query\" and the \"Astropy Table,\" confirming this is a fundamental NumPy interaction issue, not a container issue.\n", + "\n", + "**Recommendation for the users:** Let the users choose, based on the dtype of an input array!" + ] + }, + { + "cell_type": "markdown", + "id": "3d3718fb-fba7-4f5c-a9f0-ba8b1b4264c6", + "metadata": {}, + "source": [ + "## 2. Median\n", + "\n", + "Compare how standard, NaN-aware, and masked array reduction methods affect the calculated median of `psfSigma` for both raw query results and `Astropy` tables." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "3677db8c-3ffa-4bdf-8831-5bd008d47a0c", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-18T20:53:49.871123Z", + "iopub.status.busy": "2025-12-18T20:53:49.870816Z", + "iopub.status.idle": "2025-12-18T20:53:49.884542Z", + "shell.execute_reply": "2025-12-18T20:53:49.883878Z", + "shell.execute_reply.started": "2025-12-18T20:53:49.871101Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Source | Method | dtype | Result \n", + "------------------------------------------------------------------------------------------\n", + "Raw Query | np.median | | nan\n", + "Raw Query | np.nanmedian | | nan\n", + "Raw Query | np.ma.median | | 2.58667993545532226562\n", + "Raw Query | .compressed().median() | | 2.58667993545532226562\n", + "------------------------------------------------------------------------------------------\n", + "Astropy Table | np.median | | nan\n", + "Astropy Table | np.nanmedian | | nan\n", + "Astropy Table | np.ma.median | | 2.58667993545532226562\n", + "Astropy Table | .compressed().median() | | 2.58667993545532226562\n", + "------------------------------------------------------------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.1.0/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:868: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedArray.\n", + " a.partition(kth, axis=axis, kind=kind, order=order)\n", + "/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.1.0/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:868: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedColumn.\n", + " a.partition(kth, axis=axis, kind=kind, order=order)\n" + ] + } + ], + "source": [ + "print(f\"{'Source':<15} | {'Method':<22} | {'dtype':23} | {'Result':<22}\")\n", + "print(\"-\" * 90)\n", + "\n", + "for name, data in data_sources.items():\n", + " # 1. Standard Median\n", + " print(f\"{name:<15} | {'np.median':<22} | {type(np.median(data))} | {np.median(data):.20f}\")\n", + " \n", + " # 2. NaN Median\n", + " print(f\"{name:<15} | {'np.nanmedian':<22} | {type(np.nanmedian(data))} | {np.nanmedian(data):.20f}\")\n", + " \n", + " # 3. Masked Median\n", + " print(f\"{name:<15} | {'np.ma.median':<22} | {type(np.ma.median(data))} | {np.ma.median(data):.20f}\")\n", + " \n", + " # 4. Compressed Median\n", + " # Note: .compressed() ensures we are acting on valid data only\n", + " print(f\"{name:<15} | {'.compressed().median()':<22} | {type(np.median(data.compressed()))} | {np.median(data.compressed()):.20f}\")\n", + " print(\"-\" * 90)" + ] + }, + { + "cell_type": "markdown", + "id": "156442c3-9f90-4ce6-bd18-803d935b4d63", + "metadata": {}, + "source": [ + "**Conclusion:** Both `np.median` and `np.nanmedian` returned `nan`. This indicates that these functions stripped the mask, accessed the underlying \"bad\" data (likely `NaN` or invalid floating-point values), and allowed those values to propagate, destroying the result. Both `np.ma.median` and the `.compressed()` method correctly respected the mask, excluding invalid pixels/rows, and returned the correct result (precision is still something to be cautious about).\n", + "\n", + "**Recommendataion for the users:** Always trust the mask! Use either `np.ma.median(data)` or `np.median(data.compressed())`." + ] + }, + { + "cell_type": "markdown", + "id": "ade2b588-cdcc-49e5-9054-4b0c793b7066", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-05T18:49:45.187186Z", + "iopub.status.busy": "2025-12-05T18:49:45.186689Z", + "iopub.status.idle": "2025-12-05T18:49:45.190070Z", + "shell.execute_reply": "2025-12-05T18:49:45.189568Z", + "shell.execute_reply.started": "2025-12-05T18:49:45.187160Z" + } + }, + "source": [ + "## 3. Percentile & Quantile\n", + "\n", + "Compare how standard, NaN-aware, and masked array reduction methods affect the calculated percentile & quantile of `psfSigma` for both raw query results and `Astropy` tables." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ae471478-87c2-4ece-805f-f2c53a0454dd", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-08T21:13:45.924034Z", + "iopub.status.busy": "2025-12-08T21:13:45.923763Z", + "iopub.status.idle": "2025-12-08T21:13:45.942926Z", + "shell.execute_reply": "2025-12-08T21:13:45.942282Z", + "shell.execute_reply.started": "2025-12-08T21:13:45.924015Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Source | Method | dtype | Result \n", + "-------------------------------------------------------------------------------------------------\n", + "Raw Query | np.percentile | | nan\n", + "Raw Query | np.nanpercentile | | nan\n", + "Raw Query | np.ma.percentile | Not available | Not available \n", + "Raw Query | .compressed().percentile() | | 2.58667993545532226562\n", + "-------------------------------------------------------------------------------------------------\n", + "Astropy Table | np.percentile | | nan\n", + "Astropy Table | np.nanpercentile | | nan\n", + "Astropy Table | np.ma.percentile | Not available | Not available \n", + "Astropy Table | .compressed().percentile() | | 2.58667993545532226562\n", + "-------------------------------------------------------------------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.1.0/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py:4842: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedArray.\n", + " arr.partition(\n", + "/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.1.0/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py:4842: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedColumn.\n", + " arr.partition(\n" + ] + } + ], + "source": [ + "print(f\"{'Source':<15} | {'Method':<27} | {'dtype':23} | {'Result':<22}\")\n", + "print(\"-\" * 97)\n", + "\n", + "for name, data in data_sources.items():\n", + " # 1. Standard Percentile\n", + " print(f\"{name:<15} | {'np.percentile':<27} | {type(np.percentile(data, 50))} | {np.percentile(data, 50):.20f}\")\n", + " \n", + " # 2. NaN Percentile\n", + " print(f\"{name:<15} | {'np.nanpercentile':<27} | {type(np.nanpercentile(data, 50))} | {np.nanpercentile(data, 50):.20f}\")\n", + " \n", + " # 3. Masked Percentile\n", + " print(f\"{name:<15} | {'np.ma.percentile':<27} | {'Not available':<23} | {'Not available':<23}\")\n", + " \n", + " # 4. Compressed Percentile\n", + " # Note: .compressed() ensures we are acting on valid data only\n", + " print(f\"{name:<15} | {'.compressed().percentile()':<27} | {type(np.percentile(data.compressed(), 50))} | {np.percentile(data.compressed(), 50):.20f}\")\n", + " print(\"-\" * 97)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "fac36520-3537-4119-9fd2-5c3c60b4541c", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-08T21:13:45.943761Z", + "iopub.status.busy": "2025-12-08T21:13:45.943538Z", + "iopub.status.idle": "2025-12-08T21:13:45.967676Z", + "shell.execute_reply": "2025-12-08T21:13:45.967132Z", + "shell.execute_reply.started": "2025-12-08T21:13:45.943741Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Source | Method | dtype | Result \n", + "-------------------------------------------------------------------------------------------------\n", + "Raw Query | np.quantile | | nan\n", + "Raw Query | np.nanquantile | | nan\n", + "Raw Query | np.ma.quantile | Not available | Not available \n", + "Raw Query | .compressed().quantile() | | 2.58667993545532226562\n", + "-------------------------------------------------------------------------------------------------\n", + "Astropy Table | np.quantile | | nan\n", + "Astropy Table | np.nanquantile | | nan\n", + "Astropy Table | np.ma.quantile | Not available | Not available \n", + "Astropy Table | .compressed().quantile() | | 2.58667993545532226562\n", + "-------------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "print(f\"{'Source':<15} | {'Method':<27} | {'dtype':23} | {'Result':<22}\")\n", + "print(\"-\" * 97)\n", + "\n", + "for name, data in data_sources.items():\n", + " # 1. Standard Percentile\n", + " print(f\"{name:<15} | {'np.quantile':<27} | {type(np.quantile(data, 0.5))} | {np.quantile(data, 0.5):.20f}\")\n", + " \n", + " # 2. NaN Percentile\n", + " print(f\"{name:<15} | {'np.nanquantile':<27} | {type(np.nanquantile(data, 0.5))} | {np.nanquantile(data, 0.5):.20f}\")\n", + " \n", + " # 3. Masked Percentile\n", + " print(f\"{name:<15} | {'np.ma.quantile':<27} | {'Not available':<23} | {'Not available':<23}\")\n", + " \n", + " # 4. Compressed Percentile\n", + " # Note: .compressed() ensures we are acting on valid data only\n", + " print(f\"{name:<15} | {'.compressed().quantile()':<27} | {type(np.quantile(data.compressed(), 0.5))} | {np.quantile(data.compressed(), 0.5):.20f}\")\n", + " print(\"-\" * 97)" + ] + }, + { + "cell_type": "markdown", + "id": "ed3e332b-1726-4406-9ec4-2b88792934ee", + "metadata": {}, + "source": [ + "**Conclusion:** Just like the median test, `np.quantile` and `np.nanquantile` fail because they ignore the mask and ingest invalid underlying data (returning `NaN`). Unlike median, the `numpy.ma` module does not have a `quantile` (or `percentile`) function. Users looking for `np.ma.quantile` will find it doesn't exist. The **only** successful approach was performing the operation on compressed data.\n", + "\n", + "**Recommendation for the users:** Compress before doing `percentile` or `quantile`." + ] + }, + { + "cell_type": "markdown", + "id": "6269bb2d-bf3f-4650-ba21-43734d092098", + "metadata": {}, + "source": [ + "## 4. Min/Max" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "221903cb-bc48-4fbf-a86c-a685aafbb793", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-08T21:13:45.968545Z", + "iopub.status.busy": "2025-12-08T21:13:45.968345Z", + "iopub.status.idle": "2025-12-08T21:13:45.993183Z", + "shell.execute_reply": "2025-12-08T21:13:45.992668Z", + "shell.execute_reply.started": "2025-12-08T21:13:45.968529Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Source | Method | Result | dtype\n", + "------------------------------------------------------------------------------------------\n", + "Raw Query | np.min | 0.28867501020431518555 | \n", + "Raw Query | np.nanmin | 0.28867501020431518555 | \n", + "Raw Query | np.ma.min | 0.28867501020431518555 | \n", + "Raw Query | .compressed().min() | 0.28867501020431518555 | \n", + "------------------------------------------------------------------------------------------\n", + "Astropy Table | np.min | 0.28867501020431518555 | \n", + "Astropy Table | np.nanmin | 0.28867501020431518555 | \n", + "Astropy Table | np.ma.min | 0.28867501020431518555 | \n", + "Astropy Table | .compressed().min() | 0.28867501020431518555 | \n", + "------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "print(f\"{'Source':<15} | {'Method':<20} | {'Result':<22} | {'dtype'}\")\n", + "print(\"-\" * 90)\n", + "\n", + "for name, data in data_sources.items():\n", + " # 1. Standard Mean\n", + " print(f\"{name:<15} | np.min | {np.min(data):.20f} | {type(np.min(data))}\")\n", + " \n", + " # 2. NaN Mean\n", + " print(f\"{name:<15} | np.nanmin | {np.nanmin(data):.20f} | {type(np.nanmin(data))}\")\n", + " \n", + " # 3. Masked Mean\n", + " print(f\"{name:<15} | np.ma.min | {np.ma.min(data):.20f} | {type(np.ma.min(data))}\")\n", + " \n", + " # 4. Compressed Mean\n", + " # Note: .compressed() ensures we are acting on valid data only\n", + " print(f\"{name:<15} | .compressed().min() | {np.min(data.compressed()):.20f} | {type(np.min(data.compressed()))}\")\n", + " print(\"-\" * 90)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "c56b8a9b-0bea-477e-be63-ff097410c470", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-08T21:13:45.993922Z", + "iopub.status.busy": "2025-12-08T21:13:45.993723Z", + "iopub.status.idle": "2025-12-08T21:13:46.021080Z", + "shell.execute_reply": "2025-12-08T21:13:46.020416Z", + "shell.execute_reply.started": "2025-12-08T21:13:45.993905Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Source | Method | Result | dtype\n", + "------------------------------------------------------------------------------------------\n", + "Raw Query | np.max | 4.02725982666015625000 | \n", + "Raw Query | np.nanmax | 4.02725982666015625000 | \n", + "Raw Query | np.ma.max | 4.02725982666015625000 | \n", + "Raw Query | .compressed().max() | 4.02725982666015625000 | \n", + "------------------------------------------------------------------------------------------\n", + "Astropy Table | np.max | 4.02725982666015625000 | \n", + "Astropy Table | np.nanmax | 4.02725982666015625000 | \n", + "Astropy Table | np.ma.max | 4.02725982666015625000 | \n", + "Astropy Table | .compressed().max() | 4.02725982666015625000 | \n", + "------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "print(f\"{'Source':<15} | {'Method':<20} | {'Result':<22} | {'dtype'}\")\n", + "print(\"-\" * 90)\n", + "\n", + "for name, data in data_sources.items():\n", + " # 1. Standard Mean\n", + " print(f\"{name:<15} | np.max | {np.max(data):.20f} | {type(np.max(data))}\")\n", + " \n", + " # 2. NaN Mean\n", + " print(f\"{name:<15} | np.nanmax | {np.nanmax(data):.20f} | {type(np.nanmax(data))}\")\n", + " \n", + " # 3. Masked Mean\n", + " print(f\"{name:<15} | np.ma.max | {np.ma.max(data):.20f} | {type(np.ma.max(data))}\")\n", + " \n", + " # 4. Compressed Mean\n", + " # Note: .compressed() ensures we are acting on valid data only\n", + " print(f\"{name:<15} | .compressed().max() | {np.max(data.compressed()):.20f} | {type(np.max(data.compressed()))}\")\n", + " print(\"-\" * 90)" + ] + }, + { + "cell_type": "markdown", + "id": "80704492-0905-4e7d-a30d-a35ba974cd11", + "metadata": {}, + "source": [ + "## 5. Std/var" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "26d75cdd-5775-4a9c-adf6-024e8b4290d2", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-08T21:13:46.021956Z", + "iopub.status.busy": "2025-12-08T21:13:46.021720Z", + "iopub.status.idle": "2025-12-08T21:13:46.052510Z", + "shell.execute_reply": "2025-12-08T21:13:46.052019Z", + "shell.execute_reply.started": "2025-12-08T21:13:46.021937Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Source | Method | Result | dtype\n", + "------------------------------------------------------------------------------------------\n", + "Raw Query | np.std | 0.48106138027025396875 | \n", + "Raw Query | np.nanstd | 0.48106136918067932129 | \n", + "Raw Query | np.ma.std | 0.48106138027025396875 | \n", + "Raw Query | .compressed().std() | 0.48106136918067932129 | \n", + "------------------------------------------------------------------------------------------\n", + "Astropy Table | np.std | 0.48106138027025396875 | \n", + "Astropy Table | np.nanstd | 0.48106136918067932129 | \n", + "Astropy Table | np.ma.std | 0.48106138027025396875 | \n", + "Astropy Table | .compressed().std() | 0.48106136918067932129 | \n", + "------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "print(f\"{'Source':<15} | {'Method':<20} | {'Result':<22} | {'dtype'}\")\n", + "print(\"-\" * 90)\n", + "\n", + "for name, data in data_sources.items():\n", + " # 1. Standard std\n", + " print(f\"{name:<15} | np.std | {np.std(data):.20f} | {type(np.std(data))}\")\n", + " \n", + " # 2. NaN std\n", + " print(f\"{name:<15} | np.nanstd | {np.nanstd(data):.20f} | {type(np.nanstd(data))}\")\n", + " \n", + " # 3. Masked std\n", + " print(f\"{name:<15} | np.ma.std | {np.ma.std(data):.20f} | {type(np.ma.std(data))}\")\n", + " \n", + " # 4. Compressed std\n", + " # Note: .compressed() ensures we are acting on valid data only\n", + " print(f\"{name:<15} | .compressed().std() | {np.std(data.compressed()):.20f} | {type(np.std(data.compressed()))}\")\n", + " print(\"-\" * 90)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "4064c44e-4eae-4a08-8ceb-8dbc0275812c", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-08T21:13:46.053291Z", + "iopub.status.busy": "2025-12-08T21:13:46.053106Z", + "iopub.status.idle": "2025-12-08T21:13:46.077276Z", + "shell.execute_reply": "2025-12-08T21:13:46.076682Z", + "shell.execute_reply.started": "2025-12-08T21:13:46.053275Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Source | Method | Result | dtype\n", + "------------------------------------------------------------------------------------------\n", + "Raw Query | np.var | 0.23142005158752187999 | \n", + "Raw Query | np.nanvar | 0.23142005503177642822 | \n", + "Raw Query | np.ma.var | 0.23142005158752187999 | \n", + "Raw Query | .compressed().var() | 0.23142005503177642822 | \n", + "------------------------------------------------------------------------------------------\n", + "Astropy Table | np.var | 0.23142005158752187999 | \n", + "Astropy Table | np.nanvar | 0.23142005503177642822 | \n", + "Astropy Table | np.ma.var | 0.23142005158752187999 | \n", + "Astropy Table | .compressed().var() | 0.23142005503177642822 | \n", + "------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "print(f\"{'Source':<15} | {'Method':<20} | {'Result':<22} | {'dtype'}\")\n", + "print(\"-\" * 90)\n", + "\n", + "for name, data in data_sources.items():\n", + " # 1. Standard var\n", + " print(f\"{name:<15} | np.var | {np.var(data):.20f} | {type(np.var(data))}\")\n", + " \n", + " # 2. NaN var\n", + " print(f\"{name:<15} | np.nanvar | {np.nanvar(data):.20f} | {type(np.nanvar(data))}\")\n", + " \n", + " # 3. Masked var\n", + " print(f\"{name:<15} | np.ma.var | {np.ma.var(data):.20f} | {type(np.ma.var(data))}\")\n", + " \n", + " # 4. Compressed var\n", + " # Note: .compressed() ensures we are acting on valid data only\n", + " print(f\"{name:<15} | .compressed().var() | {np.var(data.compressed()):.20f} | {type(np.var(data.compressed()))}\")\n", + " print(\"-\" * 90)" + ] + }, + { + "cell_type": "markdown", + "id": "010aec2a-2e29-4a5c-879c-82bff7f8d9e3", + "metadata": {}, + "source": [ + "**Final Summary:** NumPy functions that delegate to the masked array's internal methods (e.g., np.min calling data.min()) respect the mask and work as expected. In contrast, functions that lack a corresponding internal method (e.g., np.quantile) implicitly convert the masked array to a raw array. This exposes the underlying invalid data to sorting or binning algorithms, resulting in errors or scientifically incorrect values.\n", + "\n", + "**Final recommendation with Numpy:** Compress the masked array before performing computations." + ] + }, + { + "cell_type": "markdown", + "id": "36deab32-b4b6-459b-bf44-90ede4c599d5", + "metadata": {}, + "source": [ + "# With Scipy.stats.mstats\n", + "\n", + "This module contains a large number of statistical functions that can be used with masked arrays." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "b85f5064-6697-40d8-b29b-9c128397b4b4", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-08T21:13:46.077996Z", + "iopub.status.busy": "2025-12-08T21:13:46.077821Z", + "iopub.status.idle": "2025-12-08T21:13:46.091017Z", + "shell.execute_reply": "2025-12-08T21:13:46.090519Z", + "shell.execute_reply.started": "2025-12-08T21:13:46.077980Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50th quantile using scipy: [2.58667994]\n" + ] + } + ], + "source": [ + "print(f\"50th quantile using scipy: {scistats.mquantiles(data, prob=[0.5])}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "9f9dc7e5-03ea-45b5-8dfe-4160da54a697", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-08T21:13:46.093677Z", + "iopub.status.busy": "2025-12-08T21:13:46.093478Z", + "iopub.status.idle": "2025-12-08T21:13:46.115331Z", + "shell.execute_reply": "2025-12-08T21:13:46.114864Z", + "shell.execute_reply.started": "2025-12-08T21:13:46.093660Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50th quantile using numpy with .compressed(): 2.5866799354553223\n" + ] + } + ], + "source": [ + "print(f\"50th quantile using numpy with .compressed(): {np.quantile(data.compressed(), 0.5)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "3c4a115c-6545-4257-aab6-33b71eef9b1f", + "metadata": {}, + "source": [ + "**Summary:** `mstats` functions always respect the mask. We don't need to manually compress data. Unlike `.compressed()`, which flattens everything into a 1D list, `mstats` operations preserve dimensions. This is crucial if you are stacking images and want a pixel-by-pixel map of the skewness or kurtosis. However, the naming convention is often slightly different (e.g., mquantiles instead of quantile), and it can be significantly slower than standard NumPy because of the overhead in handling the mask logic. Furthermore, this is for advanced stats, thus many simple ones are missing in the package. \n", + "\n", + "**Comparison with Numpy .compressed():** It is not always enough. When we have a stack of CCD images (2D array) and use .compressed(), we lose the spatial information by flattening. On the ther hand, `mstats` allows us to keep the image structure." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "083acba4-163e-455e-8c61-313cfb587e25", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "LSST", + "language": "python", + "name": "lsst" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 9a2e6263f3bf8e068e249e00f3ab541e2bfd0cf3 Mon Sep 17 00:00:00 2001 From: galaxyumi Date: Fri, 19 Dec 2025 22:59:01 +0000 Subject: [PATCH 02/10] drafting upto S3.1 --- .../102_7_Masked_array_pitfalls.ipynb | 604 ++++++++++-------- 1 file changed, 345 insertions(+), 259 deletions(-) diff --git a/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb b/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb index d4f9177..aa528f8 100644 --- a/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb +++ b/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb @@ -2,80 +2,97 @@ "cells": [ { "attachments": { - "035de3f2-c616-497e-88ba-a74eef64312c.png": { - "image/png": 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" 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ogUBYDHru/Hl35u25wOUnLTFIIpAyQFVz/ZLbF5Y/LQxmBx9aTTAam7uyIpbRsekZd2Rm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uvQQCJa7373u4fQtOyydXnggQdw2WWX4bOf/ayhbeNtHSsrK+PSsLTs2V0HLax1b+sRq33u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\nrkJggg==\n" } }, "cell_type": "markdown", - "id": "e032a51d-4ad8-4ff8-8844-4c1e052fbea5", + "id": "c0945603-ef76-4fb0-a1e5-3d031c966726", "metadata": {}, "source": [ - "## original error report\n", + "# 102.7 Masked array pitfalls\n", "\n", - "Inputs\n", + "
\n", "\n", - "```\n", - "ra_cen = 6.128\n", - "dec_cen = -72.090\n", - "radius = 1.0 \n", - "query = \"\"\"\n", - " SELECT visitId, ra, dec, band, pixelScale, psfSigma, magLim\n", - " FROM dp1_v29.CcdVisit \n", - " WHERE CONTAINS(POINT('ICRS', ra, dec),CIRCLE('ICRS', {}, {}, {}))=1\n", - " ORDER BY visitId \n", - " \"\"\".format(ra_cen, dec_cen, radius)\n", - "job = service.submit_job(query)\n", - "ccdtab = job.fetch_result().to_table()\n", + "![logo_for_header.png](attachment:5a0517c5-78d2-4cff-8581-f829d5ac5091.png)\n", "\n", - "# Compute and add a new column\n", - "ccdtab['psf_fwhm'] = 2*np.sqrt(2.0*np.log(2))*ccdtab['pixelScale'].value*ccdtab['psfSigma'].value\n", + "
\n", "\n", - "# Print unique psfSigma values and a median value\n", - "print(np.unique(ccdtab[(ccdtab['psf_fwhm']< 0.6) & (ccdtab['psf_fwhm']>0.3) & (ccdtab['band'] == 'r')]['psfSigma']))\n", - "print(np.nanmedian(ccdtab[(ccdtab['psf_fwhm']< 0.6) & (ccdtab['psf_fwhm']>0.3) & (ccdtab['band'] == 'r')]['psfSigma']))\n", - "\n", - "# Print unique psf_fwhm values and a median value\n", - "print(np.unique(ccdtab[(ccdtab['psf_fwhm']< 0.6) & (ccdtab['psf_fwhm']>0.3) & (ccdtab['band'] == 'r')]['psf_fwhm']))\n", - "print(np.nanmedian(ccdtab[(ccdtab['psf_fwhm']< 0.6) & (ccdtab['psf_fwhm']>0.3) & (ccdtab['band'] == 'r')]['psf_fwhm']))\n", - "\n", - "```\n", + "For the Rubin Science Platform at data.lsst.cloud.
\n", + "Data Release: Data Preview 1
\n", + "Container Size: large
\n", + "LSST Science Pipelines version: r29.2.0
\n", + "Last verified to run: 2025-12-19
\n", + "Repository: github.com/lsst/tutorial-notebooks
\n", + "DOI: 10.11578/rubin/dc.20250909.20
" + ] + }, + { + "cell_type": "markdown", + "id": "e321c3df-719c-4e52-b9ba-cec9924abc82", + "metadata": {}, + "source": [ + "**Learning objective:** How to correctly compute statistics on masked arrays by identifying and avoiding common `numpy` pitfalls.\n", "\n", - "**RESULTS**\n", - "![screenshot_2025-06-04_at_1.33.35___pm.png](attachment:035de3f2-c616-497e-88ba-a74eef64312c.png)\n", + "**LSST data products:** `CcdVisit` table\n", "\n", + "**Packages:** `lsst.rsp.get_tap_service`\n", "\n", - "Essentially, it seemed like null/nan were being \"converted\" to values and that was very worrying.\n", + "**Credit:** Originally developed by the Rubin Community Science team.\n", + "Please consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks.\n", "\n", - "Can't reproduce that in this NB but do want to explore masked array failure modes." + "**Get Support:**\n", + "Everyone is encouraged to ask questions or raise issues in the \n", + "Support Category \n", + "of the Rubin Community Forum.\n", + "Rubin staff will respond to all questions posted there." ] }, { "cell_type": "markdown", - "id": "5ea726b9-6e17-4675-97ce-361e8243374f", + "id": "e0246b7e-3916-41b1-8f90-bc5c854429ff", "metadata": {}, "source": [ - "Starting point: Melissa's testing NB: https://github.com/lsst/cst-dev/blob/main/MLG_sandbox/DP1/issues/masked_array_nanmedian.ipynb." + "## 1. Introduction\n", + "\n", + "This tutorial demonstrates how to correctly handle masked arrays, which may have missing or invalid entries, to avoid silent scientific errors. Use the specific failure modes presented in this tutorial as a guide to to the fundamental disconnect between standard `numpy` functions and the masked array structure. \n", + "\n", + "While this tutorial investigates discrepancies in common operations like `numpy.median` and `numpy.quantile`, these risks extend to any function that ignores the internal mask mechanism. Develop a rigorous habit of verifying whether your statistical tools respect or discard the mask. Adopt robust statistical strategies, such as data compression using `.compressed()` or specialized modules/libraries like `numpy.ma` and `scipy.stats.mstats`, to guarantee your scientific results remain robust against invalid underlying data.\n", + "\n", + "### 1.1. Import packages\n", + "\n", + "Import python packages `numpy`, and `scipy`. From the `lsst` package, import the module for accessing the Table Access Protocol (TAP) service." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 9, "id": "0f6cbedb-ba6a-4d61-995f-a7c76a29e86e", "metadata": { "execution": { - "iopub.execute_input": "2025-12-18T20:28:01.250330Z", - "iopub.status.busy": "2025-12-18T20:28:01.249995Z", - "iopub.status.idle": "2025-12-18T20:28:01.736166Z", - "shell.execute_reply": "2025-12-18T20:28:01.735466Z", - "shell.execute_reply.started": "2025-12-18T20:28:01.250305Z" + "iopub.execute_input": "2025-12-19T21:28:47.112751Z", + "iopub.status.busy": "2025-12-19T21:28:47.112349Z", + "iopub.status.idle": "2025-12-19T21:28:47.115987Z", + "shell.execute_reply": "2025-12-19T21:28:47.115401Z", + "shell.execute_reply.started": "2025-12-19T21:28:47.112723Z" } }, "outputs": [], "source": [ "import numpy as np\n", - "from lsst.rsp import get_tap_service\n", - "import astropy\n", - "import scipy.stats.mstats as scistats" + "import scipy.stats.mstats as scistats\n", + "\n", + "from lsst.rsp import get_tap_service" + ] + }, + { + "cell_type": "markdown", + "id": "8c84ba59-8f2e-49ca-b69c-c3e9fd5b5e9f", + "metadata": {}, + "source": [ + "### 1.2. Define parameters\n", + "\n", + "Instantiate the TAP service." ] }, { @@ -84,11 +101,11 @@ "id": "f792a486-b06b-4523-b2f5-ece955f0e3b4", "metadata": { "execution": { - "iopub.execute_input": "2025-12-18T20:28:01.737563Z", - "iopub.status.busy": "2025-12-18T20:28:01.737170Z", - "iopub.status.idle": "2025-12-18T20:28:01.813164Z", - "shell.execute_reply": "2025-12-18T20:28:01.812408Z", - "shell.execute_reply.started": "2025-12-18T20:28:01.737542Z" + "iopub.execute_input": "2025-12-19T21:21:51.967917Z", + "iopub.status.busy": "2025-12-19T21:21:51.967507Z", + "iopub.status.idle": "2025-12-19T21:21:52.016483Z", + "shell.execute_reply": "2025-12-19T21:21:52.015931Z", + "shell.execute_reply.started": "2025-12-19T21:21:51.967897Z" } }, "outputs": [], @@ -97,33 +114,38 @@ "assert service is not None" ] }, + { + "cell_type": "markdown", + "id": "064bfd9d-5a96-44b5-af15-18fc66a03b37", + "metadata": {}, + "source": [ + "Option to check the `numpy` version." + ] + }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 11, "id": "34895f9f-41a2-46ad-8588-0d367d8f1cf2", "metadata": { "execution": { - "iopub.execute_input": "2025-12-18T20:59:39.660471Z", - "iopub.status.busy": "2025-12-18T20:59:39.660159Z", - "iopub.status.idle": "2025-12-18T20:59:39.665030Z", - "shell.execute_reply": "2025-12-18T20:59:39.664390Z", - "shell.execute_reply.started": "2025-12-18T20:59:39.660448Z" + "iopub.execute_input": "2025-12-19T21:30:18.972198Z", + "iopub.status.busy": "2025-12-19T21:30:18.971847Z", + "iopub.status.idle": "2025-12-19T21:30:18.975703Z", + "shell.execute_reply": "2025-12-19T21:30:18.975165Z", + "shell.execute_reply.started": "2025-12-19T21:30:18.972174Z" } }, "outputs": [ { - "data": { - "text/plain": [ - "'2.2.6'" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "2.2.6\n" + ] } ], "source": [ - "np.__version__" + "# print(np.__version__)" ] }, { @@ -131,20 +153,22 @@ "id": "40e5458a-556e-443b-9ab3-b94a95af2c69", "metadata": {}, "source": [ - "## get the same data as YC originally used" + "## 2. Retrieve data\n", + "\n", + "Start with a simple query executing a cone search on the `CcdVisit` table for CCD metadata within 1 degrees of the center of the 47 Tuc field, RA, Dec = 6.128, -72.090 degrees." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 48, "id": "580a8428-fe3c-459f-a8e6-ad30f05788d5", "metadata": { "execution": { - "iopub.execute_input": "2025-12-18T20:28:01.897316Z", - "iopub.status.busy": "2025-12-18T20:28:01.896994Z", - "iopub.status.idle": "2025-12-18T20:28:01.900717Z", - "shell.execute_reply": "2025-12-18T20:28:01.900092Z", - "shell.execute_reply.started": "2025-12-18T20:28:01.897291Z" + "iopub.execute_input": "2025-12-19T22:00:15.040208Z", + "iopub.status.busy": "2025-12-19T22:00:15.039840Z", + "iopub.status.idle": "2025-12-19T22:00:15.043649Z", + "shell.execute_reply": "2025-12-19T22:00:15.043082Z", + "shell.execute_reply.started": "2025-12-19T22:00:15.040181Z" } }, "outputs": [], @@ -152,24 +176,34 @@ "ra_cen = 6.128\n", "dec_cen = -72.090\n", "radius = 1.0 \n", + "\n", "query = \"\"\"\n", - " SELECT psfSigma\n", + " SELECT ra, psfSigma, ccdVisitId\n", " FROM dp1.CcdVisit \n", - " WHERE CONTAINS(POINT('ICRS', ra, dec),CIRCLE('ICRS', {}, {}, {}))=1\n", + " WHERE CONTAINS(POINT('ICRS', ra, dec),\n", + " CIRCLE('ICRS', {}, {}, {}))=1\n", " \"\"\".format(ra_cen, dec_cen, radius)" ] }, + { + "cell_type": "markdown", + "id": "a811e299-73cc-412b-98f2-00976ef4658e", + "metadata": {}, + "source": [ + "Submit the query to the TAP service." + ] + }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 49, "id": "dfa9699c-4138-4087-be47-dca4225df5f8", "metadata": { "execution": { - "iopub.execute_input": "2025-12-18T20:28:02.074417Z", - "iopub.status.busy": "2025-12-18T20:28:02.074080Z", - "iopub.status.idle": "2025-12-18T20:28:05.856180Z", - "shell.execute_reply": "2025-12-18T20:28:05.855265Z", - "shell.execute_reply.started": "2025-12-18T20:28:02.074390Z" + "iopub.execute_input": "2025-12-19T22:00:16.099641Z", + "iopub.status.busy": "2025-12-19T22:00:16.099259Z", + "iopub.status.idle": "2025-12-19T22:00:20.113313Z", + "shell.execute_reply": "2025-12-19T22:00:20.112762Z", + "shell.execute_reply.started": "2025-12-19T22:00:16.099608Z" } }, "outputs": [ @@ -185,90 +219,167 @@ "job = service.submit_job(query)\n", "job.run()\n", "job.wait(phases=['COMPLETED', 'ERROR'])\n", - "print('Job phase is', job.phase)" + "print('Job phase is', job.phase)\n", + "if job.phase == 'ERROR':\n", + " job.raise_if_error()" + ] + }, + { + "cell_type": "markdown", + "id": "81bb4229-d452-4f53-9807-27e83402fd5d", + "metadata": {}, + "source": [ + "Fetch the results and store them as an `Astropy` table." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 50, "id": "9f4f653d-e8aa-497f-bb4e-5b15b1208c54", "metadata": { "execution": { - "iopub.execute_input": "2025-12-18T20:28:05.857372Z", - "iopub.status.busy": "2025-12-18T20:28:05.857130Z", - "iopub.status.idle": "2025-12-18T20:28:05.943342Z", - "shell.execute_reply": "2025-12-18T20:28:05.942705Z", - "shell.execute_reply.started": "2025-12-18T20:28:05.857353Z" + "iopub.execute_input": "2025-12-19T22:00:29.637922Z", + "iopub.status.busy": "2025-12-19T22:00:29.637468Z", + "iopub.status.idle": "2025-12-19T22:00:29.758608Z", + "shell.execute_reply": "2025-12-19T22:00:29.757781Z", + "shell.execute_reply.started": "2025-12-19T22:00:29.637889Z" } }, "outputs": [], "source": [ - "results = job.fetch_result()" + "results = job.fetch_result().to_table()" ] }, { - "cell_type": "code", - "execution_count": 6, - "id": "2630130b-e7f3-4e54-887e-8e6230ca6d55", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-18T20:34:30.514860Z", - "iopub.status.busy": "2025-12-18T20:34:30.514459Z", - "iopub.status.idle": "2025-12-18T20:34:30.518781Z", - "shell.execute_reply": "2025-12-18T20:34:30.518213Z", - "shell.execute_reply.started": "2025-12-18T20:34:30.514834Z" - } - }, - "outputs": [], + "cell_type": "markdown", + "id": "26903dce-2aec-44c9-b4ad-74bdaa78cabd", + "metadata": {}, "source": [ - "ccd_visits = results.to_table()" + "### 2.1. Identify columns with actual masked values\n", + "\n", + "Check if the table has masked columns. " ] }, { "cell_type": "code", - "execution_count": 7, - "id": "5f35a9a2-b7e1-4452-b632-dc7091e85cbb", + "execution_count": 61, + "id": "0b872e3a-5b75-49ee-bb36-4bfd3e1506d1", "metadata": { "execution": { - "iopub.execute_input": "2025-12-18T20:34:33.394988Z", - "iopub.status.busy": "2025-12-18T20:34:33.394650Z", - "iopub.status.idle": "2025-12-18T20:34:33.399379Z", - "shell.execute_reply": "2025-12-18T20:34:33.398911Z", - "shell.execute_reply.started": "2025-12-18T20:34:33.394965Z" + "iopub.execute_input": "2025-12-19T22:09:31.400827Z", + "iopub.status.busy": "2025-12-19T22:09:31.400473Z", + "iopub.status.idle": "2025-12-19T22:09:31.405173Z", + "shell.execute_reply": "2025-12-19T22:09:31.404548Z", + "shell.execute_reply.started": "2025-12-19T22:09:31.400804Z" } }, "outputs": [ { "data": { "text/plain": [ - "(dtype('float32'), dtype('float32'))" + "True" ] }, - "execution_count": 7, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "results['psfSigma'].dtype, ccd_visits['psfSigma'].dtype" + "results.has_masked_columns" + ] + }, + { + "cell_type": "markdown", + "id": "c9605069-1434-4284-8369-92607dd15448", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-19T22:18:58.380184Z", + "iopub.status.busy": "2025-12-19T22:18:58.379815Z", + "iopub.status.idle": "2025-12-19T22:18:58.385359Z", + "shell.execute_reply": "2025-12-19T22:18:58.384511Z", + "shell.execute_reply.started": "2025-12-19T22:18:58.380162Z" + } + }, + "source": [ + "Find out which columns are `MaskedColumn`." ] }, { "cell_type": "code", - "execution_count": 12, - "id": "0b872e3a-5b75-49ee-bb36-4bfd3e1506d1", + "execution_count": 65, + "id": "f207de21-0a29-4d9c-9368-595e1b5fd627", "metadata": { "execution": { - "iopub.execute_input": "2025-12-18T20:53:08.706740Z", - "iopub.status.busy": "2025-12-18T20:53:08.706354Z", - "iopub.status.idle": "2025-12-18T20:53:08.709720Z", - "shell.execute_reply": "2025-12-18T20:53:08.709129Z", - "shell.execute_reply.started": "2025-12-18T20:53:08.706712Z" + "iopub.execute_input": "2025-12-19T22:19:23.515282Z", + "iopub.status.busy": "2025-12-19T22:19:23.514974Z", + "iopub.status.idle": "2025-12-19T22:19:23.519358Z", + "shell.execute_reply": "2025-12-19T22:19:23.518733Z", + "shell.execute_reply.started": "2025-12-19T22:19:23.515262Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Columns with MaskedColumn type: ['ra', 'psfSigma', 'ccdVisitId']\n" + ] + } + ], "source": [ - "# ccd_visits['psfSigma']" + "masked_type_cols = [\n", + " name for name in results.colnames \n", + " if hasattr(results[name], 'mask')\n", + "]\n", + "\n", + "print(f\"Columns with MaskedColumn type: {masked_type_cols}\")" + ] + }, + { + "cell_type": "markdown", + "id": "b1969961-3e29-437d-bb9b-fe91e8720a67", + "metadata": {}, + "source": [ + "The instantiation of a `MaskedColumn` object does not imply the presence of invalid data; the column may still consist entirely of valid entries. Check which columns actually contain masked values. " + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "5eac0db9-b5bf-4b42-aa10-5aac76c79b3f", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-19T22:36:16.300724Z", + "iopub.status.busy": "2025-12-19T22:36:16.300388Z", + "iopub.status.idle": "2025-12-19T22:36:16.305292Z", + "shell.execute_reply": "2025-12-19T22:36:16.304649Z", + "shell.execute_reply.started": "2025-12-19T22:36:16.300703Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Columns with bad data (Masked): ['psfSigma']\n", + "Columns with good data (Clean): ['ra', 'ccdVisitId']\n" + ] + } + ], + "source": [ + "bad_data_cols = [\n", + " name for name in results.colnames \n", + " if hasattr(results[name], 'mask') and np.any(results[name].mask)\n", + "]\n", + "\n", + "good_data_cols = [\n", + " name for name in results.colnames \n", + " if name not in bad_data_cols\n", + "]\n", + "\n", + "print(f\"Columns with bad data (Masked): {bad_data_cols}\")\n", + "print(f\"Columns with good data (Clean): {good_data_cols}\")" ] }, { @@ -276,28 +387,31 @@ "id": "1b8062aa-425c-4cc4-950f-dca0902be288", "metadata": {}, "source": [ - "# With Numpy" + "## 3. Compute statistics with `Numpy`.\n", + "\n", + "Compute statistics for both masked columns (those containing flagged/bad entries) and clean columns (those with only valid entries) to investigate how different methods handle valid and invalid entries.\n", + "\n", + "Define a `data_sources` dictionary containing one example column from the `good_data_cols` list and one from the `bad_data_cols` list to streamline our tests." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 91, "id": "472dc142-8ac3-4e40-a40b-27c75054ae4c", "metadata": { "execution": { - "iopub.execute_input": "2025-12-18T20:53:47.820024Z", - "iopub.status.busy": "2025-12-18T20:53:47.819700Z", - "iopub.status.idle": "2025-12-18T20:53:47.823553Z", - "shell.execute_reply": "2025-12-18T20:53:47.822969Z", - "shell.execute_reply.started": "2025-12-18T20:53:47.820002Z" + "iopub.execute_input": "2025-12-19T22:50:13.002393Z", + "iopub.status.busy": "2025-12-19T22:50:13.002076Z", + "iopub.status.idle": "2025-12-19T22:50:13.005696Z", + "shell.execute_reply": "2025-12-19T22:50:13.005033Z", + "shell.execute_reply.started": "2025-12-19T22:50:13.002372Z" } }, "outputs": [], "source": [ - "# Define the data sources for clarity\n", "data_sources = {\n", - " \"Raw Query\": results['psfSigma'],\n", - " \"Astropy Table\": ccd_visits['psfSigma']\n", + " \"Clean column\": results[good_data_cols[0]],\n", + " \"Masked column\": results[bad_data_cols[0]]\n", "}" ] }, @@ -306,22 +420,22 @@ "id": "663b24b4-ea60-4c7f-97c0-cd0d7e19ec43", "metadata": {}, "source": [ - "## 1. Mean\n", + "### 3.1. Mean\n", "\n", - "Compare how standard, NaN-aware, and masked array reduction methods affect the calculated mean of psfSigma for both raw query results and Astropy tables." + "Iterate through the selected columns and display the output of standard `Numpy` functions (`np.mean`and `np.nanmean`) versus masked-aware methods (`np.ma` and `.compressed()`) to reveal differences in resultant statistics and precision." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 104, "id": "c9a71065-95f0-4696-9469-586654694ce9", "metadata": { "execution": { - "iopub.execute_input": "2025-12-18T20:53:48.648602Z", - "iopub.status.busy": "2025-12-18T20:53:48.648262Z", - "iopub.status.idle": "2025-12-18T20:53:48.656966Z", - "shell.execute_reply": "2025-12-18T20:53:48.656471Z", - "shell.execute_reply.started": "2025-12-18T20:53:48.648578Z" + "iopub.execute_input": "2025-12-19T22:58:31.401831Z", + "iopub.status.busy": "2025-12-19T22:58:31.401498Z", + "iopub.status.idle": "2025-12-19T22:58:31.410155Z", + "shell.execute_reply": "2025-12-19T22:58:31.409549Z", + "shell.execute_reply.started": "2025-12-19T22:58:31.401811Z" } }, "outputs": [ @@ -329,39 +443,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Source | Method | Result | dtype\n", - "------------------------------------------------------------------------------------------\n", - "Raw Query | np.mean | 2.65416754138675603514 | \n", - "Raw Query | np.nanmean | 2.65416765213012695312 | \n", - "Raw Query | np.ma.mean | 2.65416754138675603514 | \n", - "Raw Query | .compressed().mean() | 2.65416789054870605469 | \n", - "------------------------------------------------------------------------------------------\n", - "Astropy Table | np.mean | 2.65416754138675603514 | \n", - "Astropy Table | np.nanmean | 2.65416765213012695312 | \n", - "Astropy Table | np.ma.mean | 2.65416754138675603514 | \n", - "Astropy Table | .compressed().mean() | 2.65416789054870605469 | \n", - "------------------------------------------------------------------------------------------\n" + "Source | Method | Result | dtype\n", + "--------------------------------------------------------------------------------\n", + "Clean column | np.mean | 6.06579679440014096770 | \n", + "Clean column | np.nanmean | 6.06579679440014096770 | \n", + "Clean column | np.ma.mean | 6.06579679440014096770 | \n", + "Clean column | .compressed() | 6.06579679440014096770 | \n", + "--------------------------------------------------------------------------------\n", + "Masked column | np.mean | 2.65416754138675603514 | \n", + "Masked column | np.nanmean | 2.65416765213012695312 | \n", + "Masked column | np.ma.mean | 2.65416754138675603514 | \n", + "Masked column | .compressed() | 2.65416789054870605469 | \n", + "--------------------------------------------------------------------------------\n" ] } ], "source": [ - "print(f\"{'Source':<15} | {'Method':<20} | {'Result':<22} | {'dtype'}\")\n", - "print(\"-\" * 90)\n", + "print(f\"{'Source':<13} | {'Method':<13} | {'Result':<22} | {'dtype'}\")\n", + "print(\"-\" * 80)\n", "\n", "for name, data in data_sources.items():\n", - " # 1. Standard Mean\n", - " print(f\"{name:<15} | np.mean | {np.mean(data):.20f} | {type(np.mean(data))}\")\n", - " \n", - " # 2. NaN Mean\n", - " print(f\"{name:<15} | np.nanmean | {np.nanmean(data):.20f} | {type(np.nanmean(data))}\")\n", - " \n", - " # 3. Masked Mean\n", - " print(f\"{name:<15} | np.ma.mean | {np.ma.mean(data):.20f} | {type(np.ma.mean(data))}\")\n", - " \n", - " # 4. Compressed Mean\n", - " # Note: .compressed() ensures we are acting on valid data only\n", - " print(f\"{name:<15} | .compressed().mean() | {np.mean(data.compressed()):.20f} | {type(np.mean(data.compressed()))}\")\n", - " print(\"-\" * 90)" + " print(f\"{name:<13} | np.mean | {np.mean(data):.20f} | {type(np.mean(data))}\")\n", + " print(f\"{name:<13} | np.nanmean | {np.nanmean(data):.20f} | {type(np.nanmean(data))}\")\n", + " print(f\"{name:<13} | np.ma.mean | {np.ma.mean(data):.20f} | {type(np.ma.mean(data))}\")\n", + " print(f\"{name:<13} | .compressed() | {np.mean(data.compressed()):.20f} | {type(np.mean(data.compressed()))}\")\n", + " print(\"-\" * 80)" ] }, { @@ -379,22 +485,22 @@ "id": "3d3718fb-fba7-4f5c-a9f0-ba8b1b4264c6", "metadata": {}, "source": [ - "## 2. Median\n", + "### 3.2. Median\n", "\n", "Compare how standard, NaN-aware, and masked array reduction methods affect the calculated median of `psfSigma` for both raw query results and `Astropy` tables." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 74, "id": "3677db8c-3ffa-4bdf-8831-5bd008d47a0c", "metadata": { "execution": { - "iopub.execute_input": "2025-12-18T20:53:49.871123Z", - "iopub.status.busy": "2025-12-18T20:53:49.870816Z", - "iopub.status.idle": "2025-12-18T20:53:49.884542Z", - "shell.execute_reply": "2025-12-18T20:53:49.883878Z", - "shell.execute_reply.started": "2025-12-18T20:53:49.871101Z" + "iopub.execute_input": "2025-12-19T22:29:51.109256Z", + "iopub.status.busy": "2025-12-19T22:29:51.108952Z", + "iopub.status.idle": "2025-12-19T22:29:51.124200Z", + "shell.execute_reply": "2025-12-19T22:29:51.123571Z", + "shell.execute_reply.started": "2025-12-19T22:29:51.109236Z" } }, "outputs": [ @@ -404,27 +510,17 @@ "text": [ "Source | Method | dtype | Result \n", "------------------------------------------------------------------------------------------\n", - "Raw Query | np.median | | nan\n", - "Raw Query | np.nanmedian | | nan\n", - "Raw Query | np.ma.median | | 2.58667993545532226562\n", - "Raw Query | .compressed().median() | | 2.58667993545532226562\n", + "Column only with good data | np.median | | 6.06872133075949093950\n", + "Column only with good data | np.nanmedian | | 6.06872133075949093950\n", + "Column only with good data | np.ma.median | | 6.06872133075949093950\n", + "Column only with good data | .compressed().median() | | 6.06872133075949093950\n", "------------------------------------------------------------------------------------------\n", - "Astropy Table | np.median | | nan\n", - "Astropy Table | np.nanmedian | | nan\n", - "Astropy Table | np.ma.median | | 2.58667993545532226562\n", - "Astropy Table | .compressed().median() | | 2.58667993545532226562\n", + "Column with bad data | np.median | | nan\n", + "Column with bad data | np.nanmedian | | nan\n", + "Column with bad data | np.ma.median | | 2.58667993545532226562\n", + "Column with bad data | .compressed().median() | | 2.58667993545532226562\n", "------------------------------------------------------------------------------------------\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.1.0/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:868: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedArray.\n", - " a.partition(kth, axis=axis, kind=kind, order=order)\n", - "/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.1.0/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:868: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedColumn.\n", - " a.partition(kth, axis=axis, kind=kind, order=order)\n" - ] } ], "source": [ @@ -470,22 +566,22 @@ } }, "source": [ - "## 3. Percentile & Quantile\n", + "### 3.3. Percentile & Quantile\n", "\n", "Compare how standard, NaN-aware, and masked array reduction methods affect the calculated percentile & quantile of `psfSigma` for both raw query results and `Astropy` tables." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 78, "id": "ae471478-87c2-4ece-805f-f2c53a0454dd", "metadata": { "execution": { - "iopub.execute_input": "2025-12-08T21:13:45.924034Z", - "iopub.status.busy": "2025-12-08T21:13:45.923763Z", - "iopub.status.idle": "2025-12-08T21:13:45.942926Z", - "shell.execute_reply": "2025-12-08T21:13:45.942282Z", - "shell.execute_reply.started": "2025-12-08T21:13:45.924015Z" + "iopub.execute_input": "2025-12-19T22:31:48.056753Z", + "iopub.status.busy": "2025-12-19T22:31:48.056409Z", + "iopub.status.idle": "2025-12-19T22:31:48.068067Z", + "shell.execute_reply": "2025-12-19T22:31:48.067454Z", + "shell.execute_reply.started": "2025-12-19T22:31:48.056732Z" } }, "outputs": [ @@ -495,27 +591,17 @@ "text": [ "Source | Method | dtype | Result \n", "-------------------------------------------------------------------------------------------------\n", - "Raw Query | np.percentile | | nan\n", - "Raw Query | np.nanpercentile | | nan\n", - "Raw Query | np.ma.percentile | Not available | Not available \n", - "Raw Query | .compressed().percentile() | | 2.58667993545532226562\n", + "Column only with good data | np.percentile | | 6.06872133075949093950\n", + "Column only with good data | np.nanpercentile | | 6.06872133075949093950\n", + "Column only with good data | np.ma.percentile | Not available | Not available \n", + "Column only with good data | .compressed().percentile() | | 6.06872133075949093950\n", "-------------------------------------------------------------------------------------------------\n", - "Astropy Table | np.percentile | | nan\n", - "Astropy Table | np.nanpercentile | | nan\n", - "Astropy Table | np.ma.percentile | Not available | Not available \n", - "Astropy Table | .compressed().percentile() | | 2.58667993545532226562\n", + "Column with bad data | np.percentile | | nan\n", + "Column with bad data | np.nanpercentile | | nan\n", + "Column with bad data | np.ma.percentile | Not available | Not available \n", + "Column with bad data | .compressed().percentile() | | 2.58667993545532226562\n", "-------------------------------------------------------------------------------------------------\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.1.0/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py:4842: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedArray.\n", - " arr.partition(\n", - "/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.1.0/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py:4842: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedColumn.\n", - " arr.partition(\n" - ] } ], "source": [ @@ -540,15 +626,15 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 77, "id": "fac36520-3537-4119-9fd2-5c3c60b4541c", "metadata": { "execution": { - "iopub.execute_input": "2025-12-08T21:13:45.943761Z", - "iopub.status.busy": "2025-12-08T21:13:45.943538Z", - "iopub.status.idle": "2025-12-08T21:13:45.967676Z", - "shell.execute_reply": "2025-12-08T21:13:45.967132Z", - "shell.execute_reply.started": "2025-12-08T21:13:45.943741Z" + "iopub.execute_input": "2025-12-19T22:31:37.842145Z", + "iopub.status.busy": "2025-12-19T22:31:37.841834Z", + "iopub.status.idle": "2025-12-19T22:31:37.853415Z", + "shell.execute_reply": "2025-12-19T22:31:37.852801Z", + "shell.execute_reply.started": "2025-12-19T22:31:37.842125Z" } }, "outputs": [ @@ -558,15 +644,15 @@ "text": [ "Source | Method | dtype | Result \n", "-------------------------------------------------------------------------------------------------\n", - "Raw Query | np.quantile | | nan\n", - "Raw Query | np.nanquantile | | nan\n", - "Raw Query | np.ma.quantile | Not available | Not available \n", - "Raw Query | .compressed().quantile() | | 2.58667993545532226562\n", + "Column only with good data | np.quantile | | 6.06872133075949093950\n", + "Column only with good data | np.nanquantile | | 6.06872133075949093950\n", + "Column only with good data | np.ma.quantile | Not available | Not available \n", + "Column only with good data | .compressed().quantile() | | 6.06872133075949093950\n", "-------------------------------------------------------------------------------------------------\n", - "Astropy Table | np.quantile | | nan\n", - "Astropy Table | np.nanquantile | | nan\n", - "Astropy Table | np.ma.quantile | Not available | Not available \n", - "Astropy Table | .compressed().quantile() | | 2.58667993545532226562\n", + "Column with bad data | np.quantile | | nan\n", + "Column with bad data | np.nanquantile | | nan\n", + "Column with bad data | np.ma.quantile | Not available | Not available \n", + "Column with bad data | .compressed().quantile() | | 2.58667993545532226562\n", "-------------------------------------------------------------------------------------------------\n" ] } @@ -606,7 +692,7 @@ "id": "6269bb2d-bf3f-4650-ba21-43734d092098", "metadata": {}, "source": [ - "## 4. Min/Max" + "### 3.4. Min/Max" ] }, { @@ -720,20 +806,20 @@ "id": "80704492-0905-4e7d-a30d-a35ba974cd11", "metadata": {}, "source": [ - "## 5. Std/var" + "### 3.5. Std/var" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 79, "id": "26d75cdd-5775-4a9c-adf6-024e8b4290d2", "metadata": { "execution": { - "iopub.execute_input": "2025-12-08T21:13:46.021956Z", - "iopub.status.busy": "2025-12-08T21:13:46.021720Z", - "iopub.status.idle": "2025-12-08T21:13:46.052510Z", - "shell.execute_reply": "2025-12-08T21:13:46.052019Z", - "shell.execute_reply.started": "2025-12-08T21:13:46.021937Z" + "iopub.execute_input": "2025-12-19T22:31:56.092590Z", + "iopub.status.busy": "2025-12-19T22:31:56.092254Z", + "iopub.status.idle": "2025-12-19T22:31:56.108801Z", + "shell.execute_reply": "2025-12-19T22:31:56.108146Z", + "shell.execute_reply.started": "2025-12-19T22:31:56.092568Z" } }, "outputs": [ @@ -743,15 +829,15 @@ "text": [ "Source | Method | Result | dtype\n", "------------------------------------------------------------------------------------------\n", - "Raw Query | np.std | 0.48106138027025396875 | \n", - "Raw Query | np.nanstd | 0.48106136918067932129 | \n", - "Raw Query | np.ma.std | 0.48106138027025396875 | \n", - "Raw Query | .compressed().std() | 0.48106136918067932129 | \n", + "Column only with good data | np.std | 0.70406182027289054837 | \n", + "Column only with good data | np.nanstd | 0.70406182027289054837 | \n", + "Column only with good data | np.ma.std | 0.70406182027289054837 | \n", + "Column only with good data | .compressed().std() | 0.70406182027289054837 | \n", "------------------------------------------------------------------------------------------\n", - "Astropy Table | np.std | 0.48106138027025396875 | \n", - "Astropy Table | np.nanstd | 0.48106136918067932129 | \n", - "Astropy Table | np.ma.std | 0.48106138027025396875 | \n", - "Astropy Table | .compressed().std() | 0.48106136918067932129 | \n", + "Column with bad data | np.std | 0.48106138027025396875 | \n", + "Column with bad data | np.nanstd | 0.48106136918067932129 | \n", + "Column with bad data | np.ma.std | 0.48106138027025396875 | \n", + "Column with bad data | .compressed().std() | 0.48106136918067932129 | \n", "------------------------------------------------------------------------------------------\n" ] } @@ -778,15 +864,15 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 80, "id": "4064c44e-4eae-4a08-8ceb-8dbc0275812c", "metadata": { "execution": { - "iopub.execute_input": "2025-12-08T21:13:46.053291Z", - "iopub.status.busy": "2025-12-08T21:13:46.053106Z", - "iopub.status.idle": "2025-12-08T21:13:46.077276Z", - "shell.execute_reply": "2025-12-08T21:13:46.076682Z", - "shell.execute_reply.started": "2025-12-08T21:13:46.053275Z" + "iopub.execute_input": "2025-12-19T22:31:59.413189Z", + "iopub.status.busy": "2025-12-19T22:31:59.412896Z", + "iopub.status.idle": "2025-12-19T22:31:59.429774Z", + "shell.execute_reply": "2025-12-19T22:31:59.429180Z", + "shell.execute_reply.started": "2025-12-19T22:31:59.413170Z" } }, "outputs": [ @@ -796,15 +882,15 @@ "text": [ "Source | Method | Result | dtype\n", "------------------------------------------------------------------------------------------\n", - "Raw Query | np.var | 0.23142005158752187999 | \n", - "Raw Query | np.nanvar | 0.23142005503177642822 | \n", - "Raw Query | np.ma.var | 0.23142005158752187999 | \n", - "Raw Query | .compressed().var() | 0.23142005503177642822 | \n", + "Column only with good data | np.var | 0.49570304676597604088 | \n", + "Column only with good data | np.nanvar | 0.49570304676597604088 | \n", + "Column only with good data | np.ma.var | 0.49570304676597604088 | \n", + "Column only with good data | .compressed().var() | 0.49570304676597604088 | \n", "------------------------------------------------------------------------------------------\n", - "Astropy Table | np.var | 0.23142005158752187999 | \n", - "Astropy Table | np.nanvar | 0.23142005503177642822 | \n", - "Astropy Table | np.ma.var | 0.23142005158752187999 | \n", - "Astropy Table | .compressed().var() | 0.23142005503177642822 | \n", + "Column with bad data | np.var | 0.23142005158752187999 | \n", + "Column with bad data | np.nanvar | 0.23142005503177642822 | \n", + "Column with bad data | np.ma.var | 0.23142005158752187999 | \n", + "Column with bad data | .compressed().var() | 0.23142005503177642822 | \n", "------------------------------------------------------------------------------------------\n" ] } @@ -844,7 +930,7 @@ "id": "36deab32-b4b6-459b-bf44-90ede4c599d5", "metadata": {}, "source": [ - "# With Scipy.stats.mstats\n", + "## 4. Compute statistics with `Scipy.stats.mstats`\n", "\n", "This module contains a large number of statistical functions that can be used with masked arrays." ] From 482435a020089dc8db57c447c900c24c606c6a43 Mon Sep 17 00:00:00 2001 From: galaxyumi Date: Sat, 20 Dec 2025 02:09:37 +0000 Subject: [PATCH 03/10] streamline examples --- .../102_7_Masked_array_pitfalls.ipynb | 611 ++++++------------ 1 file changed, 208 insertions(+), 403 deletions(-) diff --git a/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb b/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb index aa528f8..5b34ba1 100644 --- a/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb +++ b/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb @@ -55,7 +55,7 @@ "source": [ "## 1. Introduction\n", "\n", - "This tutorial demonstrates how to correctly handle masked arrays, which may have missing or invalid entries, to avoid silent scientific errors. Use the specific failure modes presented in this tutorial as a guide to to the fundamental disconnect between standard `numpy` functions and the masked array structure. \n", + "This tutorial demonstrates how to correctly handle masked arrays, which may have missing or invalid entries, to avoid silent scientific errors. Use the specific failure modes presented in this tutorial as a guide to the fundamental disconnect between standard `numpy` functions and the masked array structure. \n", "\n", "While this tutorial investigates discrepancies in common operations like `numpy.median` and `numpy.quantile`, these risks extend to any function that ignores the internal mask mechanism. Develop a rigorous habit of verifying whether your statistical tools respect or discard the mask. Adopt robust statistical strategies, such as data compression using `.compressed()` or specialized modules/libraries like `numpy.ma` and `scipy.stats.mstats`, to guarantee your scientific results remain robust against invalid underlying data.\n", "\n", @@ -66,15 +66,15 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "id": "0f6cbedb-ba6a-4d61-995f-a7c76a29e86e", "metadata": { "execution": { - "iopub.execute_input": "2025-12-19T21:28:47.112751Z", - "iopub.status.busy": "2025-12-19T21:28:47.112349Z", - "iopub.status.idle": "2025-12-19T21:28:47.115987Z", - "shell.execute_reply": "2025-12-19T21:28:47.115401Z", - "shell.execute_reply.started": "2025-12-19T21:28:47.112723Z" + "iopub.execute_input": "2025-12-20T01:18:40.544521Z", + "iopub.status.busy": "2025-12-20T01:18:40.544347Z", + "iopub.status.idle": "2025-12-20T01:18:41.226583Z", + "shell.execute_reply": "2025-12-20T01:18:41.225668Z", + "shell.execute_reply.started": "2025-12-20T01:18:40.544504Z" } }, "outputs": [], @@ -101,11 +101,11 @@ "id": "f792a486-b06b-4523-b2f5-ece955f0e3b4", "metadata": { "execution": { - "iopub.execute_input": "2025-12-19T21:21:51.967917Z", - "iopub.status.busy": "2025-12-19T21:21:51.967507Z", - "iopub.status.idle": "2025-12-19T21:21:52.016483Z", - "shell.execute_reply": "2025-12-19T21:21:52.015931Z", - "shell.execute_reply.started": "2025-12-19T21:21:51.967897Z" + "iopub.execute_input": "2025-12-20T01:18:41.229011Z", + "iopub.status.busy": "2025-12-20T01:18:41.228830Z", + "iopub.status.idle": "2025-12-20T01:18:41.277422Z", + "shell.execute_reply": "2025-12-20T01:18:41.276715Z", + "shell.execute_reply.started": "2025-12-20T01:18:41.228993Z" } }, "outputs": [], @@ -124,26 +124,18 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "id": "34895f9f-41a2-46ad-8588-0d367d8f1cf2", "metadata": { "execution": { - "iopub.execute_input": "2025-12-19T21:30:18.972198Z", - "iopub.status.busy": "2025-12-19T21:30:18.971847Z", - "iopub.status.idle": "2025-12-19T21:30:18.975703Z", - "shell.execute_reply": "2025-12-19T21:30:18.975165Z", - "shell.execute_reply.started": "2025-12-19T21:30:18.972174Z" + "iopub.execute_input": "2025-12-20T01:18:41.279900Z", + "iopub.status.busy": "2025-12-20T01:18:41.279692Z", + "iopub.status.idle": "2025-12-20T01:18:41.282856Z", + "shell.execute_reply": "2025-12-20T01:18:41.282250Z", + "shell.execute_reply.started": "2025-12-20T01:18:41.279881Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.2.6\n" - ] - } - ], + "outputs": [], "source": [ "# print(np.__version__)" ] @@ -160,15 +152,15 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 4, "id": "580a8428-fe3c-459f-a8e6-ad30f05788d5", "metadata": { "execution": { - "iopub.execute_input": "2025-12-19T22:00:15.040208Z", - "iopub.status.busy": "2025-12-19T22:00:15.039840Z", - "iopub.status.idle": "2025-12-19T22:00:15.043649Z", - "shell.execute_reply": "2025-12-19T22:00:15.043082Z", - "shell.execute_reply.started": "2025-12-19T22:00:15.040181Z" + "iopub.execute_input": "2025-12-20T01:18:41.285061Z", + "iopub.status.busy": "2025-12-20T01:18:41.284877Z", + "iopub.status.idle": "2025-12-20T01:18:41.310038Z", + "shell.execute_reply": "2025-12-20T01:18:41.309227Z", + "shell.execute_reply.started": "2025-12-20T01:18:41.285044Z" } }, "outputs": [], @@ -195,15 +187,15 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 5, "id": "dfa9699c-4138-4087-be47-dca4225df5f8", "metadata": { "execution": { - "iopub.execute_input": "2025-12-19T22:00:16.099641Z", - "iopub.status.busy": "2025-12-19T22:00:16.099259Z", - "iopub.status.idle": "2025-12-19T22:00:20.113313Z", - "shell.execute_reply": "2025-12-19T22:00:20.112762Z", - "shell.execute_reply.started": "2025-12-19T22:00:16.099608Z" + "iopub.execute_input": "2025-12-20T01:18:41.312402Z", + "iopub.status.busy": "2025-12-20T01:18:41.312213Z", + "iopub.status.idle": "2025-12-20T01:18:44.718136Z", + "shell.execute_reply": "2025-12-20T01:18:44.717590Z", + "shell.execute_reply.started": "2025-12-20T01:18:41.312386Z" } }, "outputs": [ @@ -234,15 +226,15 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 6, "id": "9f4f653d-e8aa-497f-bb4e-5b15b1208c54", "metadata": { "execution": { - "iopub.execute_input": "2025-12-19T22:00:29.637922Z", - "iopub.status.busy": "2025-12-19T22:00:29.637468Z", - "iopub.status.idle": "2025-12-19T22:00:29.758608Z", - "shell.execute_reply": "2025-12-19T22:00:29.757781Z", - "shell.execute_reply.started": "2025-12-19T22:00:29.637889Z" + "iopub.execute_input": "2025-12-20T01:18:44.718985Z", + "iopub.status.busy": "2025-12-20T01:18:44.718771Z", + "iopub.status.idle": "2025-12-20T01:18:44.810791Z", + "shell.execute_reply": "2025-12-20T01:18:44.810155Z", + "shell.execute_reply.started": "2025-12-20T01:18:44.718967Z" } }, "outputs": [], @@ -262,15 +254,15 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 7, "id": "0b872e3a-5b75-49ee-bb36-4bfd3e1506d1", "metadata": { "execution": { - "iopub.execute_input": "2025-12-19T22:09:31.400827Z", - "iopub.status.busy": "2025-12-19T22:09:31.400473Z", - "iopub.status.idle": "2025-12-19T22:09:31.405173Z", - "shell.execute_reply": "2025-12-19T22:09:31.404548Z", - "shell.execute_reply.started": "2025-12-19T22:09:31.400804Z" + "iopub.execute_input": "2025-12-20T01:18:44.811622Z", + "iopub.status.busy": "2025-12-20T01:18:44.811378Z", + "iopub.status.idle": "2025-12-20T01:18:44.815127Z", + "shell.execute_reply": "2025-12-20T01:18:44.814645Z", + "shell.execute_reply.started": "2025-12-20T01:18:44.811603Z" } }, "outputs": [ @@ -280,7 +272,7 @@ "True" ] }, - "execution_count": 61, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -307,15 +299,15 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 8, "id": "f207de21-0a29-4d9c-9368-595e1b5fd627", "metadata": { "execution": { - "iopub.execute_input": "2025-12-19T22:19:23.515282Z", - "iopub.status.busy": "2025-12-19T22:19:23.514974Z", - "iopub.status.idle": "2025-12-19T22:19:23.519358Z", - "shell.execute_reply": "2025-12-19T22:19:23.518733Z", - "shell.execute_reply.started": "2025-12-19T22:19:23.515262Z" + "iopub.execute_input": "2025-12-20T01:18:44.815832Z", + "iopub.status.busy": "2025-12-20T01:18:44.815654Z", + "iopub.status.idle": "2025-12-20T01:18:44.835550Z", + "shell.execute_reply": "2025-12-20T01:18:44.835014Z", + "shell.execute_reply.started": "2025-12-20T01:18:44.815817Z" } }, "outputs": [ @@ -346,15 +338,15 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 9, "id": "5eac0db9-b5bf-4b42-aa10-5aac76c79b3f", "metadata": { "execution": { - "iopub.execute_input": "2025-12-19T22:36:16.300724Z", - "iopub.status.busy": "2025-12-19T22:36:16.300388Z", - "iopub.status.idle": "2025-12-19T22:36:16.305292Z", - "shell.execute_reply": "2025-12-19T22:36:16.304649Z", - "shell.execute_reply.started": "2025-12-19T22:36:16.300703Z" + "iopub.execute_input": "2025-12-20T01:18:44.836226Z", + "iopub.status.busy": "2025-12-20T01:18:44.836055Z", + "iopub.status.idle": "2025-12-20T01:18:44.862383Z", + "shell.execute_reply": "2025-12-20T01:18:44.861909Z", + "shell.execute_reply.started": "2025-12-20T01:18:44.836212Z" } }, "outputs": [ @@ -396,15 +388,15 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 10, "id": "472dc142-8ac3-4e40-a40b-27c75054ae4c", "metadata": { "execution": { - "iopub.execute_input": "2025-12-19T22:50:13.002393Z", - "iopub.status.busy": "2025-12-19T22:50:13.002076Z", - "iopub.status.idle": "2025-12-19T22:50:13.005696Z", - "shell.execute_reply": "2025-12-19T22:50:13.005033Z", - "shell.execute_reply.started": "2025-12-19T22:50:13.002372Z" + "iopub.execute_input": "2025-12-20T01:18:44.863109Z", + "iopub.status.busy": "2025-12-20T01:18:44.862929Z", + "iopub.status.idle": "2025-12-20T01:18:44.881977Z", + "shell.execute_reply": "2025-12-20T01:18:44.881390Z", + "shell.execute_reply.started": "2025-12-20T01:18:44.863094Z" } }, "outputs": [], @@ -422,20 +414,20 @@ "source": [ "### 3.1. Mean\n", "\n", - "Iterate through the selected columns and display the output of standard `Numpy` functions (`np.mean`and `np.nanmean`) versus masked-aware methods (`np.ma` and `.compressed()`) to reveal differences in resultant statistics and precision." + "Iterate through the selected columns and display the output of standard `numpy` functions (`np.mean`, `np.nanmean`) against explicit mask-handling techniques (via `np.ma` or `.compressed()`) to reveal differences in resultant statistics and precision." ] }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 52, "id": "c9a71065-95f0-4696-9469-586654694ce9", "metadata": { "execution": { - "iopub.execute_input": "2025-12-19T22:58:31.401831Z", - "iopub.status.busy": "2025-12-19T22:58:31.401498Z", - "iopub.status.idle": "2025-12-19T22:58:31.410155Z", - "shell.execute_reply": "2025-12-19T22:58:31.409549Z", - "shell.execute_reply.started": "2025-12-19T22:58:31.401811Z" + "iopub.execute_input": "2025-12-20T01:45:34.785229Z", + "iopub.status.busy": "2025-12-20T01:45:34.784931Z", + "iopub.status.idle": "2025-12-20T01:45:34.793786Z", + "shell.execute_reply": "2025-12-20T01:45:34.793297Z", + "shell.execute_reply.started": "2025-12-20T01:45:34.785209Z" } }, "outputs": [ @@ -443,31 +435,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Source | Method | Result | dtype\n", - "--------------------------------------------------------------------------------\n", - "Clean column | np.mean | 6.06579679440014096770 | \n", - "Clean column | np.nanmean | 6.06579679440014096770 | \n", - "Clean column | np.ma.mean | 6.06579679440014096770 | \n", - "Clean column | .compressed() | 6.06579679440014096770 | \n", - "--------------------------------------------------------------------------------\n", - "Masked column | np.mean | 2.65416754138675603514 | \n", - "Masked column | np.nanmean | 2.65416765213012695312 | \n", - "Masked column | np.ma.mean | 2.65416754138675603514 | \n", - "Masked column | .compressed() | 2.65416789054870605469 | \n", - "--------------------------------------------------------------------------------\n" + "Source | dtype | Method | Result \n", + "-------------------------------------------------------------\n", + "Clean column | float64 | np.mean | 6.06579679440014097\n", + "Clean column | float64 | np.nanmean | 6.06579679440014097\n", + "Clean column | float64 | np.ma.mean | 6.06579679440014097\n", + "Clean column | float64 | .compressed() | 6.06579679440014097\n", + "-------------------------------------------------------------\n", + "Masked column | float64 | np.mean | 2.65416754138675604\n", + "Masked column | float32 | np.nanmean | 2.65416765213012695\n", + "Masked column | float64 | np.ma.mean | 2.65416754138675604\n", + "Masked column | float32 | .compressed() | 2.65416789054870605\n", + "-------------------------------------------------------------\n" ] } ], "source": [ - "print(f\"{'Source':<13} | {'Method':<13} | {'Result':<22} | {'dtype'}\")\n", - "print(\"-\" * 80)\n", + "print(f\"{'Source':<13} | {'dtype':<7} | {'Method':<13} | {'Result':<22}\")\n", + "print(\"-\" * 61)\n", "\n", "for name, data in data_sources.items():\n", - " print(f\"{name:<13} | np.mean | {np.mean(data):.20f} | {type(np.mean(data))}\")\n", - " print(f\"{name:<13} | np.nanmean | {np.nanmean(data):.20f} | {type(np.nanmean(data))}\")\n", - " print(f\"{name:<13} | np.ma.mean | {np.ma.mean(data):.20f} | {type(np.ma.mean(data))}\")\n", - " print(f\"{name:<13} | .compressed() | {np.mean(data.compressed()):.20f} | {type(np.mean(data.compressed()))}\")\n", - " print(\"-\" * 80)" + " print(f\"{name:<13} | {np.mean(data).dtype} | {'np.mean':<13} | {np.mean(data):.17f}\")\n", + " print(f\"{name:<13} | {np.nanmean(data).dtype} | {'np.nanmean':<13} | {np.nanmean(data):.17f}\")\n", + " print(f\"{name:<13} | {np.ma.mean(data).dtype} | {'np.ma.mean':<13} | {np.ma.mean(data):.17f}\")\n", + " print(f\"{name:<13} | {np.mean(data.compressed()).dtype} | {'.compressed()':<13} | {np.mean(data.compressed()):.17f}\") \n", + " print(\"-\" * 61)" ] }, { @@ -492,15 +484,15 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 53, "id": "3677db8c-3ffa-4bdf-8831-5bd008d47a0c", "metadata": { "execution": { - "iopub.execute_input": "2025-12-19T22:29:51.109256Z", - "iopub.status.busy": "2025-12-19T22:29:51.108952Z", - "iopub.status.idle": "2025-12-19T22:29:51.124200Z", - "shell.execute_reply": "2025-12-19T22:29:51.123571Z", - "shell.execute_reply.started": "2025-12-19T22:29:51.109236Z" + "iopub.execute_input": "2025-12-20T01:45:59.758689Z", + "iopub.status.busy": "2025-12-20T01:45:59.758295Z", + "iopub.status.idle": "2025-12-20T01:45:59.772914Z", + "shell.execute_reply": "2025-12-20T01:45:59.772361Z", + "shell.execute_reply.started": "2025-12-20T01:45:59.758658Z" } }, "outputs": [ @@ -508,39 +500,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Source | Method | dtype | Result \n", - "------------------------------------------------------------------------------------------\n", - "Column only with good data | np.median | | 6.06872133075949093950\n", - "Column only with good data | np.nanmedian | | 6.06872133075949093950\n", - "Column only with good data | np.ma.median | | 6.06872133075949093950\n", - "Column only with good data | .compressed().median() | | 6.06872133075949093950\n", - "------------------------------------------------------------------------------------------\n", - "Column with bad data | np.median | | nan\n", - "Column with bad data | np.nanmedian | | nan\n", - "Column with bad data | np.ma.median | | 2.58667993545532226562\n", - "Column with bad data | .compressed().median() | | 2.58667993545532226562\n", - "------------------------------------------------------------------------------------------\n" + "Source | dtype | Method | Result \n", + "-------------------------------------------------------------\n", + "Clean column | float64 | np.median | 6.06872133075949094\n", + "Clean column | float64 | np.nanmedian | 6.06872133075949094\n", + "Clean column | float64 | np.ma.median | 6.06872133075949094\n", + "Clean column | float64 | .compressed() | 6.06872133075949094\n", + "-------------------------------------------------------------\n", + "Masked column | float32 | np.median | nan\n", + "Masked column | float32 | np.nanmedian | nan\n", + "Masked column | float32 | np.ma.median | 2.58667993545532227\n", + "Masked column | float32 | .compressed() | 2.58667993545532227\n", + "-------------------------------------------------------------\n" ] } ], "source": [ - "print(f\"{'Source':<15} | {'Method':<22} | {'dtype':23} | {'Result':<22}\")\n", - "print(\"-\" * 90)\n", + "print(f\"{'Source':<13} | {'dtype':<7} | {'Method':<13} | {'Result':<22}\")\n", + "print(\"-\" * 61)\n", "\n", "for name, data in data_sources.items():\n", - " # 1. Standard Median\n", - " print(f\"{name:<15} | {'np.median':<22} | {type(np.median(data))} | {np.median(data):.20f}\")\n", - " \n", - " # 2. NaN Median\n", - " print(f\"{name:<15} | {'np.nanmedian':<22} | {type(np.nanmedian(data))} | {np.nanmedian(data):.20f}\")\n", - " \n", - " # 3. Masked Median\n", - " print(f\"{name:<15} | {'np.ma.median':<22} | {type(np.ma.median(data))} | {np.ma.median(data):.20f}\")\n", - " \n", - " # 4. Compressed Median\n", - " # Note: .compressed() ensures we are acting on valid data only\n", - " print(f\"{name:<15} | {'.compressed().median()':<22} | {type(np.median(data.compressed()))} | {np.median(data.compressed()):.20f}\")\n", - " print(\"-\" * 90)" + " print(f\"{name:<13} | {np.median(data).dtype} | {'np.median':<13} | {np.median(data):.17f}\")\n", + " print(f\"{name:<13} | {np.nanmedian(data).dtype} | {'np.nanmedian':<13} | {np.nanmedian(data):.17f}\")\n", + " print(f\"{name:<13} | {np.ma.median(data).dtype} | {'np.ma.median':<13} | {np.ma.median(data):.17f}\")\n", + " print(f\"{name:<13} | {np.median(data.compressed()).dtype} | {'.compressed()':<13} | {np.median(data.compressed()):.17f}\") \n", + " print(\"-\" * 61)" ] }, { @@ -566,75 +550,22 @@ } }, "source": [ - "### 3.3. Percentile & Quantile\n", + "### 3.3. Quantile/Percentile\n", "\n", - "Compare how standard, NaN-aware, and masked array reduction methods affect the calculated percentile & quantile of `psfSigma` for both raw query results and `Astropy` tables." + "Compare how standard, NaN-aware, and masked array reduction methods affect the calculated quantile of `psfSigma` for both raw query results and `Astropy` tables. The percentile calculation behaves identically to the quantile calculation. " ] }, { "cell_type": "code", - "execution_count": 78, - "id": "ae471478-87c2-4ece-805f-f2c53a0454dd", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-19T22:31:48.056753Z", - "iopub.status.busy": "2025-12-19T22:31:48.056409Z", - "iopub.status.idle": "2025-12-19T22:31:48.068067Z", - "shell.execute_reply": "2025-12-19T22:31:48.067454Z", - "shell.execute_reply.started": "2025-12-19T22:31:48.056732Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Source | Method | dtype | Result \n", - "-------------------------------------------------------------------------------------------------\n", - "Column only with good data | np.percentile | | 6.06872133075949093950\n", - "Column only with good data | np.nanpercentile | | 6.06872133075949093950\n", - "Column only with good data | np.ma.percentile | Not available | Not available \n", - "Column only with good data | .compressed().percentile() | | 6.06872133075949093950\n", - "-------------------------------------------------------------------------------------------------\n", - "Column with bad data | np.percentile | | nan\n", - "Column with bad data | np.nanpercentile | | nan\n", - "Column with bad data | np.ma.percentile | Not available | Not available \n", - "Column with bad data | .compressed().percentile() | | 2.58667993545532226562\n", - "-------------------------------------------------------------------------------------------------\n" - ] - } - ], - "source": [ - "print(f\"{'Source':<15} | {'Method':<27} | {'dtype':23} | {'Result':<22}\")\n", - "print(\"-\" * 97)\n", - "\n", - "for name, data in data_sources.items():\n", - " # 1. Standard Percentile\n", - " print(f\"{name:<15} | {'np.percentile':<27} | {type(np.percentile(data, 50))} | {np.percentile(data, 50):.20f}\")\n", - " \n", - " # 2. NaN Percentile\n", - " print(f\"{name:<15} | {'np.nanpercentile':<27} | {type(np.nanpercentile(data, 50))} | {np.nanpercentile(data, 50):.20f}\")\n", - " \n", - " # 3. Masked Percentile\n", - " print(f\"{name:<15} | {'np.ma.percentile':<27} | {'Not available':<23} | {'Not available':<23}\")\n", - " \n", - " # 4. Compressed Percentile\n", - " # Note: .compressed() ensures we are acting on valid data only\n", - " print(f\"{name:<15} | {'.compressed().percentile()':<27} | {type(np.percentile(data.compressed(), 50))} | {np.percentile(data.compressed(), 50):.20f}\")\n", - " print(\"-\" * 97)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, + "execution_count": 71, "id": "fac36520-3537-4119-9fd2-5c3c60b4541c", "metadata": { "execution": { - "iopub.execute_input": "2025-12-19T22:31:37.842145Z", - "iopub.status.busy": "2025-12-19T22:31:37.841834Z", - "iopub.status.idle": "2025-12-19T22:31:37.853415Z", - "shell.execute_reply": "2025-12-19T22:31:37.852801Z", - "shell.execute_reply.started": "2025-12-19T22:31:37.842125Z" + "iopub.execute_input": "2025-12-20T01:52:09.186488Z", + "iopub.status.busy": "2025-12-20T01:52:09.186202Z", + "iopub.status.idle": "2025-12-20T01:52:09.197005Z", + "shell.execute_reply": "2025-12-20T01:52:09.196450Z", + "shell.execute_reply.started": "2025-12-20T01:52:09.186467Z" } }, "outputs": [ @@ -642,39 +573,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Source | Method | dtype | Result \n", - "-------------------------------------------------------------------------------------------------\n", - "Column only with good data | np.quantile | | 6.06872133075949093950\n", - "Column only with good data | np.nanquantile | | 6.06872133075949093950\n", - "Column only with good data | np.ma.quantile | Not available | Not available \n", - "Column only with good data | .compressed().quantile() | | 6.06872133075949093950\n", - "-------------------------------------------------------------------------------------------------\n", - "Column with bad data | np.quantile | | nan\n", - "Column with bad data | np.nanquantile | | nan\n", - "Column with bad data | np.ma.quantile | Not available | Not available \n", - "Column with bad data | .compressed().quantile() | | 2.58667993545532226562\n", - "-------------------------------------------------------------------------------------------------\n" + "Source | dtype | Method | Result \n", + "--------------------------------------------------------------\n", + "Clean column | float64 | np.quantile | 6.06872133075949094\n", + "Clean column | float64 | np.nanquantile | 6.06872133075949094\n", + "Clean column | N/A | np.ma.quantile | Not available \n", + "Clean column | float64 | .compressed() | 6.06872133075949094\n", + "--------------------------------------------------------------\n", + "Masked column | float32 | np.quantile | nan\n", + "Masked column | float32 | np.nanquantile | nan\n", + "Masked column | N/A | np.ma.quantile | Not available \n", + "Masked column | float32 | .compressed() | 2.58667993545532227\n", + "--------------------------------------------------------------\n" ] } ], "source": [ - "print(f\"{'Source':<15} | {'Method':<27} | {'dtype':23} | {'Result':<22}\")\n", - "print(\"-\" * 97)\n", + "print(f\"{'Source':<13} | {'dtype':<7} | {'Method':<14} | {'Result':<22}\")\n", + "print(\"-\" * 62)\n", "\n", "for name, data in data_sources.items():\n", - " # 1. Standard Percentile\n", - " print(f\"{name:<15} | {'np.quantile':<27} | {type(np.quantile(data, 0.5))} | {np.quantile(data, 0.5):.20f}\")\n", - " \n", - " # 2. NaN Percentile\n", - " print(f\"{name:<15} | {'np.nanquantile':<27} | {type(np.nanquantile(data, 0.5))} | {np.nanquantile(data, 0.5):.20f}\")\n", - " \n", - " # 3. Masked Percentile\n", - " print(f\"{name:<15} | {'np.ma.quantile':<27} | {'Not available':<23} | {'Not available':<23}\")\n", - " \n", - " # 4. Compressed Percentile\n", - " # Note: .compressed() ensures we are acting on valid data only\n", - " print(f\"{name:<15} | {'.compressed().quantile()':<27} | {type(np.quantile(data.compressed(), 0.5))} | {np.quantile(data.compressed(), 0.5):.20f}\")\n", - " print(\"-\" * 97)" + " print(f\"{name:<13} | {np.quantile(data, 0.5).dtype} | {'np.quantile':<14} | {np.quantile(data, 0.5):.17f}\")\n", + " print(f\"{name:<13} | {np.nanquantile(data, 0.5).dtype} | {'np.nanquantile':<14} | {np.nanquantile(data, 0.5):.17f}\")\n", + " print(f\"{name:<13} | {'N/A':<7} | {'np.ma.quantile':<13} | {'Not available':<14}\")\n", + " print(f\"{name:<13} | {np.quantile(data.compressed(), 0.5).dtype} | {'.compressed()':<14} | {np.quantile(data.compressed(), 0.5):.17f}\") \n", + " print(\"-\" * 62)" ] }, { @@ -692,73 +615,22 @@ "id": "6269bb2d-bf3f-4650-ba21-43734d092098", "metadata": {}, "source": [ - "### 3.4. Min/Max" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "221903cb-bc48-4fbf-a86c-a685aafbb793", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-08T21:13:45.968545Z", - "iopub.status.busy": "2025-12-08T21:13:45.968345Z", - "iopub.status.idle": "2025-12-08T21:13:45.993183Z", - "shell.execute_reply": "2025-12-08T21:13:45.992668Z", - "shell.execute_reply.started": "2025-12-08T21:13:45.968529Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Source | Method | Result | dtype\n", - "------------------------------------------------------------------------------------------\n", - "Raw Query | np.min | 0.28867501020431518555 | \n", - "Raw Query | np.nanmin | 0.28867501020431518555 | \n", - "Raw Query | np.ma.min | 0.28867501020431518555 | \n", - "Raw Query | .compressed().min() | 0.28867501020431518555 | \n", - "------------------------------------------------------------------------------------------\n", - "Astropy Table | np.min | 0.28867501020431518555 | \n", - "Astropy Table | np.nanmin | 0.28867501020431518555 | \n", - "Astropy Table | np.ma.min | 0.28867501020431518555 | \n", - "Astropy Table | .compressed().min() | 0.28867501020431518555 | \n", - "------------------------------------------------------------------------------------------\n" - ] - } - ], - "source": [ - "print(f\"{'Source':<15} | {'Method':<20} | {'Result':<22} | {'dtype'}\")\n", - "print(\"-\" * 90)\n", + "### 3.4. Min/Max\n", "\n", - "for name, data in data_sources.items():\n", - " # 1. Standard Mean\n", - " print(f\"{name:<15} | np.min | {np.min(data):.20f} | {type(np.min(data))}\")\n", - " \n", - " # 2. NaN Mean\n", - " print(f\"{name:<15} | np.nanmin | {np.nanmin(data):.20f} | {type(np.nanmin(data))}\")\n", - " \n", - " # 3. Masked Mean\n", - " print(f\"{name:<15} | np.ma.min | {np.ma.min(data):.20f} | {type(np.ma.min(data))}\")\n", - " \n", - " # 4. Compressed Mean\n", - " # Note: .compressed() ensures we are acting on valid data only\n", - " print(f\"{name:<15} | .compressed().min() | {np.min(data.compressed()):.20f} | {type(np.min(data.compressed()))}\")\n", - " print(\"-\" * 90)" + "The maximum calculation behaves identically to the minimum calculation." ] }, { "cell_type": "code", - "execution_count": 14, - "id": "c56b8a9b-0bea-477e-be63-ff097410c470", + "execution_count": 72, + "id": "221903cb-bc48-4fbf-a86c-a685aafbb793", "metadata": { "execution": { - "iopub.execute_input": "2025-12-08T21:13:45.993922Z", - "iopub.status.busy": "2025-12-08T21:13:45.993723Z", - "iopub.status.idle": "2025-12-08T21:13:46.021080Z", - "shell.execute_reply": "2025-12-08T21:13:46.020416Z", - "shell.execute_reply.started": "2025-12-08T21:13:45.993905Z" + "iopub.execute_input": "2025-12-20T01:54:52.757067Z", + "iopub.status.busy": "2025-12-20T01:54:52.756733Z", + "iopub.status.idle": "2025-12-20T01:54:52.765406Z", + "shell.execute_reply": "2025-12-20T01:54:52.764863Z", + "shell.execute_reply.started": "2025-12-20T01:54:52.757045Z" } }, "outputs": [ @@ -766,39 +638,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Source | Method | Result | dtype\n", - "------------------------------------------------------------------------------------------\n", - "Raw Query | np.max | 4.02725982666015625000 | \n", - "Raw Query | np.nanmax | 4.02725982666015625000 | \n", - "Raw Query | np.ma.max | 4.02725982666015625000 | \n", - "Raw Query | .compressed().max() | 4.02725982666015625000 | \n", - "------------------------------------------------------------------------------------------\n", - "Astropy Table | np.max | 4.02725982666015625000 | \n", - "Astropy Table | np.nanmax | 4.02725982666015625000 | \n", - "Astropy Table | np.ma.max | 4.02725982666015625000 | \n", - "Astropy Table | .compressed().max() | 4.02725982666015625000 | \n", - "------------------------------------------------------------------------------------------\n" + "Source | dtype | Method | Result \n", + "-------------------------------------------------------------\n", + "Clean column | float64 | np.min | 4.46371685993863654\n", + "Clean column | float64 | np.nanmin | 4.46371685993863654\n", + "Clean column | float64 | np.ma.min | 4.46371685993863654\n", + "Clean column | float64 | .compressed() | 4.46371685993863654\n", + "-------------------------------------------------------------\n", + "Masked column | float32 | np.min | 0.28867501020431519\n", + "Masked column | float32 | np.nanmin | 0.28867501020431519\n", + "Masked column | float32 | np.ma.min | 0.28867501020431519\n", + "Masked column | float32 | .compressed() | 0.28867501020431519\n", + "-------------------------------------------------------------\n" ] } ], "source": [ - "print(f\"{'Source':<15} | {'Method':<20} | {'Result':<22} | {'dtype'}\")\n", - "print(\"-\" * 90)\n", + "print(f\"{'Source':<13} | {'dtype':<7} | {'Method':<13} | {'Result':<22}\")\n", + "print(\"-\" * 61)\n", "\n", "for name, data in data_sources.items():\n", - " # 1. Standard Mean\n", - " print(f\"{name:<15} | np.max | {np.max(data):.20f} | {type(np.max(data))}\")\n", - " \n", - " # 2. NaN Mean\n", - " print(f\"{name:<15} | np.nanmax | {np.nanmax(data):.20f} | {type(np.nanmax(data))}\")\n", - " \n", - " # 3. Masked Mean\n", - " print(f\"{name:<15} | np.ma.max | {np.ma.max(data):.20f} | {type(np.ma.max(data))}\")\n", - " \n", - " # 4. Compressed Mean\n", - " # Note: .compressed() ensures we are acting on valid data only\n", - " print(f\"{name:<15} | .compressed().max() | {np.max(data.compressed()):.20f} | {type(np.max(data.compressed()))}\")\n", - " print(\"-\" * 90)" + " print(f\"{name:<13} | {np.min(data).dtype} | {'np.min':<13} | {np.min(data):.17f}\")\n", + " print(f\"{name:<13} | {np.nanmin(data).dtype} | {'np.nanmin':<13} | {np.nanmin(data):.17f}\")\n", + " print(f\"{name:<13} | {np.ma.min(data).dtype} | {'np.ma.min':<13} | {np.ma.min(data):.17f}\")\n", + " print(f\"{name:<13} | {np.min(data.compressed()).dtype} | {'.compressed()':<13} | {np.min(data.compressed()):.17f}\") \n", + " print(\"-\" * 61)" ] }, { @@ -806,73 +670,22 @@ "id": "80704492-0905-4e7d-a30d-a35ba974cd11", "metadata": {}, "source": [ - "### 3.5. Std/var" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "26d75cdd-5775-4a9c-adf6-024e8b4290d2", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-19T22:31:56.092590Z", - "iopub.status.busy": "2025-12-19T22:31:56.092254Z", - "iopub.status.idle": "2025-12-19T22:31:56.108801Z", - "shell.execute_reply": "2025-12-19T22:31:56.108146Z", - "shell.execute_reply.started": "2025-12-19T22:31:56.092568Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Source | Method | Result | dtype\n", - "------------------------------------------------------------------------------------------\n", - "Column only with good data | np.std | 0.70406182027289054837 | \n", - "Column only with good data | np.nanstd | 0.70406182027289054837 | \n", - "Column only with good data | np.ma.std | 0.70406182027289054837 | \n", - "Column only with good data | .compressed().std() | 0.70406182027289054837 | \n", - "------------------------------------------------------------------------------------------\n", - "Column with bad data | np.std | 0.48106138027025396875 | \n", - "Column with bad data | np.nanstd | 0.48106136918067932129 | \n", - "Column with bad data | np.ma.std | 0.48106138027025396875 | \n", - "Column with bad data | .compressed().std() | 0.48106136918067932129 | \n", - "------------------------------------------------------------------------------------------\n" - ] - } - ], - "source": [ - "print(f\"{'Source':<15} | {'Method':<20} | {'Result':<22} | {'dtype'}\")\n", - "print(\"-\" * 90)\n", + "### 3.5. Std/var\n", "\n", - "for name, data in data_sources.items():\n", - " # 1. Standard std\n", - " print(f\"{name:<15} | np.std | {np.std(data):.20f} | {type(np.std(data))}\")\n", - " \n", - " # 2. NaN std\n", - " print(f\"{name:<15} | np.nanstd | {np.nanstd(data):.20f} | {type(np.nanstd(data))}\")\n", - " \n", - " # 3. Masked std\n", - " print(f\"{name:<15} | np.ma.std | {np.ma.std(data):.20f} | {type(np.ma.std(data))}\")\n", - " \n", - " # 4. Compressed std\n", - " # Note: .compressed() ensures we are acting on valid data only\n", - " print(f\"{name:<15} | .compressed().std() | {np.std(data.compressed()):.20f} | {type(np.std(data.compressed()))}\")\n", - " print(\"-\" * 90)" + "The variation calculation behaves identically to the standard deviation calculation." ] }, { "cell_type": "code", - "execution_count": 80, - "id": "4064c44e-4eae-4a08-8ceb-8dbc0275812c", + "execution_count": 73, + "id": "26d75cdd-5775-4a9c-adf6-024e8b4290d2", "metadata": { "execution": { - "iopub.execute_input": "2025-12-19T22:31:59.413189Z", - "iopub.status.busy": "2025-12-19T22:31:59.412896Z", - "iopub.status.idle": "2025-12-19T22:31:59.429774Z", - "shell.execute_reply": "2025-12-19T22:31:59.429180Z", - "shell.execute_reply.started": "2025-12-19T22:31:59.413170Z" + "iopub.execute_input": "2025-12-20T02:09:01.147089Z", + "iopub.status.busy": "2025-12-20T02:09:01.146752Z", + "iopub.status.idle": "2025-12-20T02:09:01.167116Z", + "shell.execute_reply": "2025-12-20T02:09:01.166271Z", + "shell.execute_reply.started": "2025-12-20T02:09:01.147062Z" } }, "outputs": [ @@ -880,39 +693,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Source | Method | Result | dtype\n", - "------------------------------------------------------------------------------------------\n", - "Column only with good data | np.var | 0.49570304676597604088 | \n", - "Column only with good data | np.nanvar | 0.49570304676597604088 | \n", - "Column only with good data | np.ma.var | 0.49570304676597604088 | \n", - "Column only with good data | .compressed().var() | 0.49570304676597604088 | \n", - "------------------------------------------------------------------------------------------\n", - "Column with bad data | np.var | 0.23142005158752187999 | \n", - "Column with bad data | np.nanvar | 0.23142005503177642822 | \n", - "Column with bad data | np.ma.var | 0.23142005158752187999 | \n", - "Column with bad data | .compressed().var() | 0.23142005503177642822 | \n", - "------------------------------------------------------------------------------------------\n" + "Source | dtype | Method | Result \n", + "-------------------------------------------------------------\n", + "Clean column | float64 | np.std | 0.70406182027289055\n", + "Clean column | float64 | np.nanstd | 0.70406182027289055\n", + "Clean column | float64 | np.ma.std | 0.70406182027289055\n", + "Clean column | float64 | .compressed() | 0.70406182027289055\n", + "-------------------------------------------------------------\n", + "Masked column | float64 | np.std | 0.48106138027025397\n", + "Masked column | float32 | np.nanstd | 0.48106136918067932\n", + "Masked column | float64 | np.ma.std | 0.48106138027025397\n", + "Masked column | float32 | .compressed() | 0.48106136918067932\n", + "-------------------------------------------------------------\n" ] } ], "source": [ - "print(f\"{'Source':<15} | {'Method':<20} | {'Result':<22} | {'dtype'}\")\n", - "print(\"-\" * 90)\n", + "print(f\"{'Source':<13} | {'dtype':<7} | {'Method':<13} | {'Result':<22}\")\n", + "print(\"-\" * 61)\n", "\n", "for name, data in data_sources.items():\n", - " # 1. Standard var\n", - " print(f\"{name:<15} | np.var | {np.var(data):.20f} | {type(np.var(data))}\")\n", - " \n", - " # 2. NaN var\n", - " print(f\"{name:<15} | np.nanvar | {np.nanvar(data):.20f} | {type(np.nanvar(data))}\")\n", - " \n", - " # 3. Masked var\n", - " print(f\"{name:<15} | np.ma.var | {np.ma.var(data):.20f} | {type(np.ma.var(data))}\")\n", - " \n", - " # 4. Compressed var\n", - " # Note: .compressed() ensures we are acting on valid data only\n", - " print(f\"{name:<15} | .compressed().var() | {np.var(data.compressed()):.20f} | {type(np.var(data.compressed()))}\")\n", - " print(\"-\" * 90)" + " print(f\"{name:<13} | {np.std(data).dtype} | {'np.std':<13} | {np.std(data):.17f}\")\n", + " print(f\"{name:<13} | {np.nanstd(data).dtype} | {'np.nanstd':<13} | {np.nanstd(data):.17f}\")\n", + " print(f\"{name:<13} | {np.ma.std(data).dtype} | {'np.ma.std':<13} | {np.ma.std(data):.17f}\")\n", + " print(f\"{name:<13} | {np.std(data.compressed()).dtype} | {'.compressed()':<13} | {np.std(data.compressed()):.17f}\") \n", + " print(\"-\" * 61)" ] }, { @@ -937,15 +742,15 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "id": "b85f5064-6697-40d8-b29b-9c128397b4b4", "metadata": { "execution": { - "iopub.execute_input": "2025-12-08T21:13:46.077996Z", - "iopub.status.busy": "2025-12-08T21:13:46.077821Z", - "iopub.status.idle": "2025-12-08T21:13:46.091017Z", - "shell.execute_reply": "2025-12-08T21:13:46.090519Z", - "shell.execute_reply.started": "2025-12-08T21:13:46.077980Z" + "iopub.execute_input": "2025-12-20T01:18:45.106666Z", + "iopub.status.busy": "2025-12-20T01:18:45.106462Z", + "iopub.status.idle": "2025-12-20T01:18:45.124801Z", + "shell.execute_reply": "2025-12-20T01:18:45.124235Z", + "shell.execute_reply.started": "2025-12-20T01:18:45.106650Z" } }, "outputs": [ @@ -963,15 +768,15 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "id": "9f9dc7e5-03ea-45b5-8dfe-4160da54a697", "metadata": { "execution": { - "iopub.execute_input": "2025-12-08T21:13:46.093677Z", - "iopub.status.busy": "2025-12-08T21:13:46.093478Z", - "iopub.status.idle": "2025-12-08T21:13:46.115331Z", - "shell.execute_reply": "2025-12-08T21:13:46.114864Z", - "shell.execute_reply.started": "2025-12-08T21:13:46.093660Z" + "iopub.execute_input": "2025-12-20T01:18:45.125599Z", + "iopub.status.busy": "2025-12-20T01:18:45.125382Z", + "iopub.status.idle": "2025-12-20T01:18:45.151106Z", + "shell.execute_reply": "2025-12-20T01:18:45.150638Z", + "shell.execute_reply.started": "2025-12-20T01:18:45.125582Z" } }, "outputs": [ From 69d2e02ca19b55ee41543febb2ec36534907700e Mon Sep 17 00:00:00 2001 From: galaxyumi Date: Tue, 23 Dec 2025 00:23:21 +0000 Subject: [PATCH 04/10] finish section 3 --- .../102_7_Masked_array_pitfalls.ipynb | 258 ++++++++++-------- 1 file changed, 138 insertions(+), 120 deletions(-) diff --git a/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb b/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb index 5b34ba1..c4e8e47 100644 --- a/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb +++ b/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb @@ -22,7 +22,7 @@ "Data Release: Data Preview 1
\n", "Container Size: large
\n", "LSST Science Pipelines version: r29.2.0
\n", - "Last verified to run: 2025-12-19
\n", + "Last verified to run: 2025-12-22
\n", "Repository: github.com/lsst/tutorial-notebooks
\n", "DOI: 10.11578/rubin/dc.20250909.20
" ] @@ -57,7 +57,7 @@ "\n", "This tutorial demonstrates how to correctly handle masked arrays, which may have missing or invalid entries, to avoid silent scientific errors. Use the specific failure modes presented in this tutorial as a guide to the fundamental disconnect between standard `numpy` functions and the masked array structure. \n", "\n", - "While this tutorial investigates discrepancies in common operations like `numpy.median` and `numpy.quantile`, these risks extend to any function that ignores the internal mask mechanism. Develop a rigorous habit of verifying whether your statistical tools respect or discard the mask. Adopt robust statistical strategies, such as data compression using `.compressed()` or specialized modules/libraries like `numpy.ma` and `scipy.stats.mstats`, to guarantee your scientific results remain robust against invalid underlying data.\n", + "While this tutorial investigates common operations like `numpy.median` and `numpy.quantile`, these risks extend to any function that ignores the internal mask mechanism. Develop a rigorous habit of verifying whether your statistical tools respect the mask. Adopt robust statistical strategies, such as data compression using `.compressed()` or specialized modules/libraries like `numpy.ma` and `scipy.stats.mstats`, to guarantee your scientific results remain robust against invalid underlying data.\n", "\n", "### 1.1. Import packages\n", "\n", @@ -70,11 +70,11 @@ "id": "0f6cbedb-ba6a-4d61-995f-a7c76a29e86e", "metadata": { "execution": { - "iopub.execute_input": "2025-12-20T01:18:40.544521Z", - "iopub.status.busy": "2025-12-20T01:18:40.544347Z", - "iopub.status.idle": "2025-12-20T01:18:41.226583Z", - "shell.execute_reply": "2025-12-20T01:18:41.225668Z", - "shell.execute_reply.started": "2025-12-20T01:18:40.544504Z" + "iopub.execute_input": "2025-12-22T23:46:58.636443Z", + "iopub.status.busy": "2025-12-22T23:46:58.636265Z", + "iopub.status.idle": "2025-12-22T23:46:59.175652Z", + "shell.execute_reply": "2025-12-22T23:46:59.175098Z", + "shell.execute_reply.started": "2025-12-22T23:46:58.636426Z" } }, "outputs": [], @@ -101,11 +101,11 @@ "id": "f792a486-b06b-4523-b2f5-ece955f0e3b4", "metadata": { "execution": { - "iopub.execute_input": "2025-12-20T01:18:41.229011Z", - "iopub.status.busy": "2025-12-20T01:18:41.228830Z", - "iopub.status.idle": "2025-12-20T01:18:41.277422Z", - "shell.execute_reply": "2025-12-20T01:18:41.276715Z", - "shell.execute_reply.started": "2025-12-20T01:18:41.228993Z" + "iopub.execute_input": "2025-12-22T23:46:59.176646Z", + "iopub.status.busy": "2025-12-22T23:46:59.176287Z", + "iopub.status.idle": "2025-12-22T23:46:59.233490Z", + "shell.execute_reply": "2025-12-22T23:46:59.232908Z", + "shell.execute_reply.started": "2025-12-22T23:46:59.176627Z" } }, "outputs": [], @@ -128,11 +128,11 @@ "id": "34895f9f-41a2-46ad-8588-0d367d8f1cf2", "metadata": { "execution": { - "iopub.execute_input": "2025-12-20T01:18:41.279900Z", - "iopub.status.busy": "2025-12-20T01:18:41.279692Z", - "iopub.status.idle": "2025-12-20T01:18:41.282856Z", - "shell.execute_reply": "2025-12-20T01:18:41.282250Z", - "shell.execute_reply.started": "2025-12-20T01:18:41.279881Z" + "iopub.execute_input": "2025-12-22T23:46:59.234306Z", + "iopub.status.busy": "2025-12-22T23:46:59.234087Z", + "iopub.status.idle": "2025-12-22T23:46:59.236780Z", + "shell.execute_reply": "2025-12-22T23:46:59.236275Z", + "shell.execute_reply.started": "2025-12-22T23:46:59.234287Z" } }, "outputs": [], @@ -156,11 +156,11 @@ "id": "580a8428-fe3c-459f-a8e6-ad30f05788d5", "metadata": { "execution": { - "iopub.execute_input": "2025-12-20T01:18:41.285061Z", - "iopub.status.busy": "2025-12-20T01:18:41.284877Z", - "iopub.status.idle": "2025-12-20T01:18:41.310038Z", - "shell.execute_reply": "2025-12-20T01:18:41.309227Z", - "shell.execute_reply.started": "2025-12-20T01:18:41.285044Z" + "iopub.execute_input": "2025-12-22T23:46:59.237524Z", + "iopub.status.busy": "2025-12-22T23:46:59.237334Z", + "iopub.status.idle": "2025-12-22T23:46:59.264061Z", + "shell.execute_reply": "2025-12-22T23:46:59.263552Z", + "shell.execute_reply.started": "2025-12-22T23:46:59.237508Z" } }, "outputs": [], @@ -191,11 +191,11 @@ "id": "dfa9699c-4138-4087-be47-dca4225df5f8", "metadata": { "execution": { - "iopub.execute_input": "2025-12-20T01:18:41.312402Z", - "iopub.status.busy": "2025-12-20T01:18:41.312213Z", - "iopub.status.idle": "2025-12-20T01:18:44.718136Z", - "shell.execute_reply": "2025-12-20T01:18:44.717590Z", - "shell.execute_reply.started": "2025-12-20T01:18:41.312386Z" + "iopub.execute_input": "2025-12-22T23:46:59.264816Z", + "iopub.status.busy": "2025-12-22T23:46:59.264608Z", + "iopub.status.idle": "2025-12-22T23:47:02.649582Z", + "shell.execute_reply": "2025-12-22T23:47:02.648994Z", + "shell.execute_reply.started": "2025-12-22T23:46:59.264792Z" } }, "outputs": [ @@ -230,11 +230,11 @@ "id": "9f4f653d-e8aa-497f-bb4e-5b15b1208c54", "metadata": { "execution": { - "iopub.execute_input": "2025-12-20T01:18:44.718985Z", - "iopub.status.busy": "2025-12-20T01:18:44.718771Z", - "iopub.status.idle": "2025-12-20T01:18:44.810791Z", - "shell.execute_reply": "2025-12-20T01:18:44.810155Z", - "shell.execute_reply.started": "2025-12-20T01:18:44.718967Z" + "iopub.execute_input": "2025-12-22T23:47:02.650441Z", + "iopub.status.busy": "2025-12-22T23:47:02.650230Z", + "iopub.status.idle": "2025-12-22T23:47:02.749048Z", + "shell.execute_reply": "2025-12-22T23:47:02.748498Z", + "shell.execute_reply.started": "2025-12-22T23:47:02.650423Z" } }, "outputs": [], @@ -247,7 +247,7 @@ "id": "26903dce-2aec-44c9-b4ad-74bdaa78cabd", "metadata": {}, "source": [ - "### 2.1. Identify columns with actual masked values\n", + "### 2.1. Identify masked columns\n", "\n", "Check if the table has masked columns. " ] @@ -258,11 +258,11 @@ "id": "0b872e3a-5b75-49ee-bb36-4bfd3e1506d1", "metadata": { "execution": { - "iopub.execute_input": "2025-12-20T01:18:44.811622Z", - "iopub.status.busy": "2025-12-20T01:18:44.811378Z", - "iopub.status.idle": "2025-12-20T01:18:44.815127Z", - "shell.execute_reply": "2025-12-20T01:18:44.814645Z", - "shell.execute_reply.started": "2025-12-20T01:18:44.811603Z" + "iopub.execute_input": "2025-12-22T23:47:02.749863Z", + "iopub.status.busy": "2025-12-22T23:47:02.749650Z", + "iopub.status.idle": "2025-12-22T23:47:02.753181Z", + "shell.execute_reply": "2025-12-22T23:47:02.752713Z", + "shell.execute_reply.started": "2025-12-22T23:47:02.749846Z" } }, "outputs": [ @@ -303,11 +303,11 @@ "id": "f207de21-0a29-4d9c-9368-595e1b5fd627", "metadata": { "execution": { - "iopub.execute_input": "2025-12-20T01:18:44.815832Z", - "iopub.status.busy": "2025-12-20T01:18:44.815654Z", - "iopub.status.idle": "2025-12-20T01:18:44.835550Z", - "shell.execute_reply": "2025-12-20T01:18:44.835014Z", - "shell.execute_reply.started": "2025-12-20T01:18:44.815817Z" + "iopub.execute_input": "2025-12-22T23:47:02.753850Z", + "iopub.status.busy": "2025-12-22T23:47:02.753660Z", + "iopub.status.idle": "2025-12-22T23:47:02.780271Z", + "shell.execute_reply": "2025-12-22T23:47:02.779759Z", + "shell.execute_reply.started": "2025-12-22T23:47:02.753834Z" } }, "outputs": [ @@ -333,6 +333,8 @@ "id": "b1969961-3e29-437d-bb9b-fe91e8720a67", "metadata": {}, "source": [ + "### 2.2. Columns with actual masked values\n", + "\n", "The instantiation of a `MaskedColumn` object does not imply the presence of invalid data; the column may still consist entirely of valid entries. Check which columns actually contain masked values. " ] }, @@ -342,11 +344,11 @@ "id": "5eac0db9-b5bf-4b42-aa10-5aac76c79b3f", "metadata": { "execution": { - "iopub.execute_input": "2025-12-20T01:18:44.836226Z", - "iopub.status.busy": "2025-12-20T01:18:44.836055Z", - "iopub.status.idle": "2025-12-20T01:18:44.862383Z", - "shell.execute_reply": "2025-12-20T01:18:44.861909Z", - "shell.execute_reply.started": "2025-12-20T01:18:44.836212Z" + "iopub.execute_input": "2025-12-22T23:47:02.781051Z", + "iopub.status.busy": "2025-12-22T23:47:02.780842Z", + "iopub.status.idle": "2025-12-22T23:47:02.806581Z", + "shell.execute_reply": "2025-12-22T23:47:02.806066Z", + "shell.execute_reply.started": "2025-12-22T23:47:02.781034Z" } }, "outputs": [ @@ -354,24 +356,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "Columns with bad data (Masked): ['psfSigma']\n", + "Columns with flagged/invalid data (Masked): ['psfSigma']\n", "Columns with good data (Clean): ['ra', 'ccdVisitId']\n" ] } ], "source": [ - "bad_data_cols = [\n", + "masked_data_cols = [\n", " name for name in results.colnames \n", " if hasattr(results[name], 'mask') and np.any(results[name].mask)\n", "]\n", "\n", - "good_data_cols = [\n", + "clean_data_cols = [\n", " name for name in results.colnames \n", - " if name not in bad_data_cols\n", + " if name not in masked_data_cols\n", "]\n", "\n", - "print(f\"Columns with bad data (Masked): {bad_data_cols}\")\n", - "print(f\"Columns with good data (Clean): {good_data_cols}\")" + "print(f\"Columns with flagged/invalid data (Masked): {masked_data_cols}\")\n", + "print(f\"Columns with good data (Clean): {clean_data_cols}\")" ] }, { @@ -383,7 +385,7 @@ "\n", "Compute statistics for both masked columns (those containing flagged/bad entries) and clean columns (those with only valid entries) to investigate how different methods handle valid and invalid entries.\n", "\n", - "Define a `data_sources` dictionary containing one example column from the `good_data_cols` list and one from the `bad_data_cols` list to streamline our tests." + "Define a `data_sources` dictionary containing one example column from the `clean_data_cols` list and one from the `masked_data_cols` list to streamline our tests." ] }, { @@ -392,18 +394,18 @@ "id": "472dc142-8ac3-4e40-a40b-27c75054ae4c", "metadata": { "execution": { - "iopub.execute_input": "2025-12-20T01:18:44.863109Z", - "iopub.status.busy": "2025-12-20T01:18:44.862929Z", - "iopub.status.idle": "2025-12-20T01:18:44.881977Z", - "shell.execute_reply": "2025-12-20T01:18:44.881390Z", - "shell.execute_reply.started": "2025-12-20T01:18:44.863094Z" + "iopub.execute_input": "2025-12-22T23:47:02.807412Z", + "iopub.status.busy": "2025-12-22T23:47:02.807178Z", + "iopub.status.idle": "2025-12-22T23:47:02.830992Z", + "shell.execute_reply": "2025-12-22T23:47:02.830431Z", + "shell.execute_reply.started": "2025-12-22T23:47:02.807396Z" } }, "outputs": [], "source": [ "data_sources = {\n", - " \"Clean column\": results[good_data_cols[0]],\n", - " \"Masked column\": results[bad_data_cols[0]]\n", + " \"Clean column\": results[clean_data_cols[0]],\n", + " \"Masked column\": results[masked_data_cols[0]]\n", "}" ] }, @@ -419,15 +421,15 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 11, "id": "c9a71065-95f0-4696-9469-586654694ce9", "metadata": { "execution": { - "iopub.execute_input": "2025-12-20T01:45:34.785229Z", - "iopub.status.busy": "2025-12-20T01:45:34.784931Z", - "iopub.status.idle": "2025-12-20T01:45:34.793786Z", - "shell.execute_reply": "2025-12-20T01:45:34.793297Z", - "shell.execute_reply.started": "2025-12-20T01:45:34.785209Z" + "iopub.execute_input": "2025-12-22T23:47:02.831734Z", + "iopub.status.busy": "2025-12-22T23:47:02.831543Z", + "iopub.status.idle": "2025-12-22T23:47:02.861748Z", + "shell.execute_reply": "2025-12-22T23:47:02.861219Z", + "shell.execute_reply.started": "2025-12-22T23:47:02.831718Z" } }, "outputs": [ @@ -467,9 +469,7 @@ "id": "89b161af-19c0-4d8e-a62a-6cf877984967", "metadata": {}, "source": [ - "**Conclusion:** Computing the mean of a masked array works correctly across these methods, but the results differ slightly due to the `np.float32` data type of the input array. These discrepancies occur because different functions sum the data in different orders (e.g., contiguous vs. strided memory access). Since floating-point addition is non-associative (i.e., (A+B)+C $\\neq$ A+(B+C)), the accumulation order changes the final result at the limit of precision. The behavior is identical for both the \"Raw Query\" and the \"Astropy Table,\" confirming this is a fundamental NumPy interaction issue, not a container issue.\n", - "\n", - "**Recommendation for the users:** Let the users choose, based on the dtype of an input array!" + "**Conclusion:** Note that the clean array yields consistent results because its input data type is `np.float64`, whereas the `np.float32` masked array produces slight variations across methods. Attribute these discrepancies to the non-associativity of floating-point addition (i.e., (A+B)+C $\\neq$ A+(B+C)); different functions utilize different summation orders (e.g., contiguous vs. strided memory access), altering the result at the limit of precision. Account for the fixed input data type provided by the pipeline by selecting your reduction functions carefully, ensuring you are aware of the return precision each method yields for your specific input." ] }, { @@ -479,20 +479,22 @@ "source": [ "### 3.2. Median\n", "\n", - "Compare how standard, NaN-aware, and masked array reduction methods affect the calculated median of `psfSigma` for both raw query results and `Astropy` tables." + "Repeat the same analysis for the median. \n", + "\n", + "> **Warning:** When you first run this notebook tutorial, the following cell produces a warning stating that \"'partition' will ignore the 'mask' of the `MaskedColumn`\". Disregard this alert; the code intentionally triggers this behavior to demonstrate how `np.median` and `np.nanmedian` fail to respect `MaskedColumn` masks." ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 19, "id": "3677db8c-3ffa-4bdf-8831-5bd008d47a0c", "metadata": { "execution": { - "iopub.execute_input": "2025-12-20T01:45:59.758689Z", - "iopub.status.busy": "2025-12-20T01:45:59.758295Z", - "iopub.status.idle": "2025-12-20T01:45:59.772914Z", - "shell.execute_reply": "2025-12-20T01:45:59.772361Z", - "shell.execute_reply.started": "2025-12-20T01:45:59.758658Z" + "iopub.execute_input": "2025-12-22T23:55:19.180503Z", + "iopub.status.busy": "2025-12-22T23:55:19.180161Z", + "iopub.status.idle": "2025-12-22T23:55:19.194315Z", + "shell.execute_reply": "2025-12-22T23:55:19.193800Z", + "shell.execute_reply.started": "2025-12-22T23:55:19.180483Z" } }, "outputs": [ @@ -532,9 +534,7 @@ "id": "156442c3-9f90-4ce6-bd18-803d935b4d63", "metadata": {}, "source": [ - "**Conclusion:** Both `np.median` and `np.nanmedian` returned `nan`. This indicates that these functions stripped the mask, accessed the underlying \"bad\" data (likely `NaN` or invalid floating-point values), and allowed those values to propagate, destroying the result. Both `np.ma.median` and the `.compressed()` method correctly respected the mask, excluding invalid pixels/rows, and returned the correct result (precision is still something to be cautious about).\n", - "\n", - "**Recommendataion for the users:** Always trust the mask! Use either `np.ma.median(data)` or `np.median(data.compressed())`." + "**Conclusion:** Both `np.median` and `np.nanmedian` returned `nan` for the masked array. This indicates that these functions ignored the mask, accessing the underlying invalid data (likely `NaN` or other bad values), which propagated to corrupt the final result. In contrast, both `np.ma.median` and the `.compressed()` method correctly respected the mask, excluded the invalid entries and produced the correct result. However, the users should remain cautious regarding the precision match between the input array and returned value. Always use functions/methods that explicitly respect the mask when computing the median." ] }, { @@ -552,20 +552,22 @@ "source": [ "### 3.3. Quantile/Percentile\n", "\n", - "Compare how standard, NaN-aware, and masked array reduction methods affect the calculated quantile of `psfSigma` for both raw query results and `Astropy` tables. The percentile calculation behaves identically to the quantile calculation. " + "Repeat the same analysis for the quantile. \n", + "\n", + "> **Warning:** When you first run this notebook tutorial, the following cell produces a warning stating that \"'partition' will ignore the 'mask' of the `MaskedColumn`\". Disregard this alert; the code intentionally triggers this behavior to demonstrate how `np.quantile` and `np.nanquantile` fail to respect `MaskedColumn` masks." ] }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 20, "id": "fac36520-3537-4119-9fd2-5c3c60b4541c", "metadata": { "execution": { - "iopub.execute_input": "2025-12-20T01:52:09.186488Z", - "iopub.status.busy": "2025-12-20T01:52:09.186202Z", - "iopub.status.idle": "2025-12-20T01:52:09.197005Z", - "shell.execute_reply": "2025-12-20T01:52:09.196450Z", - "shell.execute_reply.started": "2025-12-20T01:52:09.186467Z" + "iopub.execute_input": "2025-12-23T00:04:11.218544Z", + "iopub.status.busy": "2025-12-23T00:04:11.218189Z", + "iopub.status.idle": "2025-12-23T00:04:11.229289Z", + "shell.execute_reply": "2025-12-23T00:04:11.228752Z", + "shell.execute_reply.started": "2025-12-23T00:04:11.218521Z" } }, "outputs": [ @@ -605,9 +607,7 @@ "id": "ed3e332b-1726-4406-9ec4-2b88792934ee", "metadata": {}, "source": [ - "**Conclusion:** Just like the median test, `np.quantile` and `np.nanquantile` fail because they ignore the mask and ingest invalid underlying data (returning `NaN`). Unlike median, the `numpy.ma` module does not have a `quantile` (or `percentile`) function. Users looking for `np.ma.quantile` will find it doesn't exist. The **only** successful approach was performing the operation on compressed data.\n", - "\n", - "**Recommendation for the users:** Compress before doing `percentile` or `quantile`." + "**Conclusion:** Similar to the median functions, `np.quantile` and `np.nanquantile` fail on masked arrays because they ignore the mask. Since the current `numpy.ma` lacks a direct `np.quantile` equivalent, the only robust approach is to perform these operations on compressed data. Always compress your `MaskedColumn` before calculating quantiles, by explicitly calling `.compressed()` to remove masked values. This conclusion also applies to the percentile functions." ] }, { @@ -617,20 +617,20 @@ "source": [ "### 3.4. Min/Max\n", "\n", - "The maximum calculation behaves identically to the minimum calculation." + "Repeat the same analysis for the mininum." ] }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 14, "id": "221903cb-bc48-4fbf-a86c-a685aafbb793", "metadata": { "execution": { - "iopub.execute_input": "2025-12-20T01:54:52.757067Z", - "iopub.status.busy": "2025-12-20T01:54:52.756733Z", - "iopub.status.idle": "2025-12-20T01:54:52.765406Z", - "shell.execute_reply": "2025-12-20T01:54:52.764863Z", - "shell.execute_reply.started": "2025-12-20T01:54:52.757045Z" + "iopub.execute_input": "2025-12-22T23:47:02.910133Z", + "iopub.status.busy": "2025-12-22T23:47:02.909948Z", + "iopub.status.idle": "2025-12-22T23:47:02.928050Z", + "shell.execute_reply": "2025-12-22T23:47:02.927552Z", + "shell.execute_reply.started": "2025-12-22T23:47:02.910117Z" } }, "outputs": [ @@ -665,27 +665,35 @@ " print(\"-\" * 61)" ] }, + { + "cell_type": "markdown", + "id": "90459637-7963-46bf-be82-66f66d4ecca7", + "metadata": {}, + "source": [ + "**Conclusion:** Both the clean and masked arrays yield consistent results. This conclusion also applies to the maximum functions. " + ] + }, { "cell_type": "markdown", "id": "80704492-0905-4e7d-a30d-a35ba974cd11", "metadata": {}, "source": [ - "### 3.5. Std/var\n", + "### 3.5. Std/Var\n", "\n", - "The variation calculation behaves identically to the standard deviation calculation." + "Repeat the same analysis for the standard deviation." ] }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 15, "id": "26d75cdd-5775-4a9c-adf6-024e8b4290d2", "metadata": { "execution": { - "iopub.execute_input": "2025-12-20T02:09:01.147089Z", - "iopub.status.busy": "2025-12-20T02:09:01.146752Z", - "iopub.status.idle": "2025-12-20T02:09:01.167116Z", - "shell.execute_reply": "2025-12-20T02:09:01.166271Z", - "shell.execute_reply.started": "2025-12-20T02:09:01.147062Z" + "iopub.execute_input": "2025-12-22T23:47:02.928810Z", + "iopub.status.busy": "2025-12-22T23:47:02.928601Z", + "iopub.status.idle": "2025-12-22T23:47:02.959998Z", + "shell.execute_reply": "2025-12-22T23:47:02.959457Z", + "shell.execute_reply.started": "2025-12-22T23:47:02.928786Z" } }, "outputs": [ @@ -720,14 +728,24 @@ " print(\"-\" * 61)" ] }, + { + "cell_type": "markdown", + "id": "b4fea740-bbcc-4758-9472-a5c70642dd22", + "metadata": {}, + "source": [ + "**Conclusion:** Both the clean and masked arrays yield consistent results. This conclusion also applies to the variance functions. " + ] + }, { "cell_type": "markdown", "id": "010aec2a-2e29-4a5c-879c-82bff7f8d9e3", "metadata": {}, "source": [ - "**Final Summary:** NumPy functions that delegate to the masked array's internal methods (e.g., np.min calling data.min()) respect the mask and work as expected. In contrast, functions that lack a corresponding internal method (e.g., np.quantile) implicitly convert the masked array to a raw array. This exposes the underlying invalid data to sorting or binning algorithms, resulting in errors or scientifically incorrect values.\n", + "### 3.6. Summary\n", + "\n", + "`numpy` functions that delegate to the masked array's internal methods (e.g., `np.min` calling `data.min()`) respect the mask and work as expected. In contrast, functions that lack a corresponding internal method (e.g., `np.quantile`) implicitly convert the masked array to a raw array. This exposes the underlying invalid data to sorting or binning algorithms, resulting in errors or scientifically incorrect values.\n", "\n", - "**Final recommendation with Numpy:** Compress the masked array before performing computations." + "A safe recommendation when using `numpy` for masked arrays is to compress them before performing computations, or to use the `numpy.ma` module when the desired functions are available." ] }, { @@ -742,15 +760,15 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 16, "id": "b85f5064-6697-40d8-b29b-9c128397b4b4", "metadata": { "execution": { - "iopub.execute_input": "2025-12-20T01:18:45.106666Z", - "iopub.status.busy": "2025-12-20T01:18:45.106462Z", - "iopub.status.idle": "2025-12-20T01:18:45.124801Z", - "shell.execute_reply": "2025-12-20T01:18:45.124235Z", - "shell.execute_reply.started": "2025-12-20T01:18:45.106650Z" + "iopub.execute_input": "2025-12-22T23:47:02.960747Z", + "iopub.status.busy": "2025-12-22T23:47:02.960560Z", + "iopub.status.idle": "2025-12-22T23:47:02.975265Z", + "shell.execute_reply": "2025-12-22T23:47:02.974725Z", + "shell.execute_reply.started": "2025-12-22T23:47:02.960731Z" } }, "outputs": [ @@ -768,15 +786,15 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 17, "id": "9f9dc7e5-03ea-45b5-8dfe-4160da54a697", "metadata": { "execution": { - "iopub.execute_input": "2025-12-20T01:18:45.125599Z", - "iopub.status.busy": "2025-12-20T01:18:45.125382Z", - "iopub.status.idle": "2025-12-20T01:18:45.151106Z", - "shell.execute_reply": "2025-12-20T01:18:45.150638Z", - "shell.execute_reply.started": "2025-12-20T01:18:45.125582Z" + "iopub.execute_input": "2025-12-22T23:47:02.976063Z", + "iopub.status.busy": "2025-12-22T23:47:02.975870Z", + "iopub.status.idle": "2025-12-22T23:47:02.997679Z", + "shell.execute_reply": "2025-12-22T23:47:02.997205Z", + "shell.execute_reply.started": "2025-12-22T23:47:02.976046Z" } }, "outputs": [ From e2189e1f0dcfef6596033aeb82f5496529790ada Mon Sep 17 00:00:00 2001 From: galaxyumi Date: Tue, 23 Dec 2025 00:30:22 +0000 Subject: [PATCH 05/10] improving section 4 --- .../102_7_Masked_array_pitfalls.ipynb | 29 ++++++++++++++++++- 1 file changed, 28 insertions(+), 1 deletion(-) diff --git a/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb b/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb index c4e8e47..4430967 100644 --- a/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb +++ b/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb @@ -820,10 +820,37 @@ "**Comparison with Numpy .compressed():** It is not always enough. When we have a stack of CCD images (2D array) and use .compressed(), we lose the spatial information by flattening. On the ther hand, `mstats` allows us to keep the image structure." ] }, + { + "cell_type": "markdown", + "id": "007bc6d4-efb4-4f30-a0c8-0ff942663dee", + "metadata": {}, + "source": [ + "Option to list all the functions available in a `scipy.stats.mstats` module." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "083acba4-163e-455e-8c61-313cfb587e25", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-23T00:30:00.827079Z", + "iopub.status.busy": "2025-12-23T00:30:00.826730Z", + "iopub.status.idle": "2025-12-23T00:30:00.829949Z", + "shell.execute_reply": "2025-12-23T00:30:00.829266Z", + "shell.execute_reply.started": "2025-12-23T00:30:00.827056Z" + } + }, + "outputs": [], + "source": [ + "# fns = [fn for fn in dir(scistats) if not fn.startswith('_')]\n", + "# print(fns)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1fa29e0b-ea7d-4b87-b9eb-674dfe751433", "metadata": {}, "outputs": [], "source": [] From 2d649e5d19cf2b0932c31596b8928da19d51db43 Mon Sep 17 00:00:00 2001 From: galaxyumi Date: Tue, 23 Dec 2025 04:12:13 +0000 Subject: [PATCH 06/10] finish the draft --- .../102_7_Masked_array_pitfalls.ipynb | 260 +++++++++--------- 1 file changed, 137 insertions(+), 123 deletions(-) diff --git a/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb b/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb index 4430967..1e1511f 100644 --- a/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb +++ b/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb @@ -70,11 +70,11 @@ "id": "0f6cbedb-ba6a-4d61-995f-a7c76a29e86e", "metadata": { "execution": { - "iopub.execute_input": "2025-12-22T23:46:58.636443Z", - "iopub.status.busy": "2025-12-22T23:46:58.636265Z", - "iopub.status.idle": "2025-12-22T23:46:59.175652Z", - "shell.execute_reply": "2025-12-22T23:46:59.175098Z", - "shell.execute_reply.started": "2025-12-22T23:46:58.636426Z" + "iopub.execute_input": "2025-12-23T04:10:54.648636Z", + "iopub.status.busy": "2025-12-23T04:10:54.648465Z", + "iopub.status.idle": "2025-12-23T04:10:55.189883Z", + "shell.execute_reply": "2025-12-23T04:10:55.189307Z", + "shell.execute_reply.started": "2025-12-23T04:10:54.648620Z" } }, "outputs": [], @@ -101,11 +101,11 @@ "id": "f792a486-b06b-4523-b2f5-ece955f0e3b4", "metadata": { "execution": { - "iopub.execute_input": "2025-12-22T23:46:59.176646Z", - "iopub.status.busy": "2025-12-22T23:46:59.176287Z", - "iopub.status.idle": "2025-12-22T23:46:59.233490Z", - "shell.execute_reply": "2025-12-22T23:46:59.232908Z", - "shell.execute_reply.started": "2025-12-22T23:46:59.176627Z" + "iopub.execute_input": "2025-12-23T04:10:55.192068Z", + "iopub.status.busy": "2025-12-23T04:10:55.191889Z", + "iopub.status.idle": "2025-12-23T04:10:55.238200Z", + "shell.execute_reply": "2025-12-23T04:10:55.237669Z", + "shell.execute_reply.started": "2025-12-23T04:10:55.192052Z" } }, "outputs": [], @@ -128,11 +128,11 @@ "id": "34895f9f-41a2-46ad-8588-0d367d8f1cf2", "metadata": { "execution": { - "iopub.execute_input": "2025-12-22T23:46:59.234306Z", - "iopub.status.busy": "2025-12-22T23:46:59.234087Z", - "iopub.status.idle": "2025-12-22T23:46:59.236780Z", - "shell.execute_reply": "2025-12-22T23:46:59.236275Z", - "shell.execute_reply.started": "2025-12-22T23:46:59.234287Z" + "iopub.execute_input": "2025-12-23T04:10:55.240136Z", + "iopub.status.busy": "2025-12-23T04:10:55.239957Z", + "iopub.status.idle": "2025-12-23T04:10:55.242406Z", + "shell.execute_reply": "2025-12-23T04:10:55.241980Z", + "shell.execute_reply.started": "2025-12-23T04:10:55.240120Z" } }, "outputs": [], @@ -156,11 +156,11 @@ "id": "580a8428-fe3c-459f-a8e6-ad30f05788d5", "metadata": { "execution": { - "iopub.execute_input": "2025-12-22T23:46:59.237524Z", - "iopub.status.busy": "2025-12-22T23:46:59.237334Z", - "iopub.status.idle": "2025-12-22T23:46:59.264061Z", - "shell.execute_reply": "2025-12-22T23:46:59.263552Z", - "shell.execute_reply.started": "2025-12-22T23:46:59.237508Z" + "iopub.execute_input": "2025-12-23T04:10:55.243076Z", + "iopub.status.busy": "2025-12-23T04:10:55.242903Z", + "iopub.status.idle": "2025-12-23T04:10:55.263408Z", + "shell.execute_reply": "2025-12-23T04:10:55.262933Z", + "shell.execute_reply.started": "2025-12-23T04:10:55.243061Z" } }, "outputs": [], @@ -191,11 +191,11 @@ "id": "dfa9699c-4138-4087-be47-dca4225df5f8", "metadata": { "execution": { - "iopub.execute_input": "2025-12-22T23:46:59.264816Z", - "iopub.status.busy": "2025-12-22T23:46:59.264608Z", - "iopub.status.idle": "2025-12-22T23:47:02.649582Z", - "shell.execute_reply": "2025-12-22T23:47:02.648994Z", - "shell.execute_reply.started": "2025-12-22T23:46:59.264792Z" + "iopub.execute_input": "2025-12-23T04:10:55.264136Z", + "iopub.status.busy": "2025-12-23T04:10:55.263944Z", + "iopub.status.idle": "2025-12-23T04:10:58.652452Z", + "shell.execute_reply": "2025-12-23T04:10:58.651883Z", + "shell.execute_reply.started": "2025-12-23T04:10:55.264120Z" } }, "outputs": [ @@ -230,11 +230,11 @@ "id": "9f4f653d-e8aa-497f-bb4e-5b15b1208c54", "metadata": { "execution": { - "iopub.execute_input": "2025-12-22T23:47:02.650441Z", - "iopub.status.busy": "2025-12-22T23:47:02.650230Z", - "iopub.status.idle": "2025-12-22T23:47:02.749048Z", - "shell.execute_reply": "2025-12-22T23:47:02.748498Z", - "shell.execute_reply.started": "2025-12-22T23:47:02.650423Z" + "iopub.execute_input": "2025-12-23T04:10:58.653177Z", + "iopub.status.busy": "2025-12-23T04:10:58.652987Z", + "iopub.status.idle": "2025-12-23T04:10:58.752832Z", + "shell.execute_reply": "2025-12-23T04:10:58.752267Z", + "shell.execute_reply.started": "2025-12-23T04:10:58.653162Z" } }, "outputs": [], @@ -258,11 +258,11 @@ "id": "0b872e3a-5b75-49ee-bb36-4bfd3e1506d1", "metadata": { "execution": { - "iopub.execute_input": "2025-12-22T23:47:02.749863Z", - "iopub.status.busy": "2025-12-22T23:47:02.749650Z", - "iopub.status.idle": "2025-12-22T23:47:02.753181Z", - "shell.execute_reply": "2025-12-22T23:47:02.752713Z", - "shell.execute_reply.started": "2025-12-22T23:47:02.749846Z" + "iopub.execute_input": "2025-12-23T04:10:58.753582Z", + "iopub.status.busy": "2025-12-23T04:10:58.753380Z", + "iopub.status.idle": "2025-12-23T04:10:58.756856Z", + "shell.execute_reply": "2025-12-23T04:10:58.756410Z", + "shell.execute_reply.started": "2025-12-23T04:10:58.753566Z" } }, "outputs": [ @@ -303,11 +303,11 @@ "id": "f207de21-0a29-4d9c-9368-595e1b5fd627", "metadata": { "execution": { - "iopub.execute_input": "2025-12-22T23:47:02.753850Z", - "iopub.status.busy": "2025-12-22T23:47:02.753660Z", - "iopub.status.idle": "2025-12-22T23:47:02.780271Z", - "shell.execute_reply": "2025-12-22T23:47:02.779759Z", - "shell.execute_reply.started": "2025-12-22T23:47:02.753834Z" + "iopub.execute_input": "2025-12-23T04:10:58.757508Z", + "iopub.status.busy": "2025-12-23T04:10:58.757324Z", + "iopub.status.idle": "2025-12-23T04:10:58.783895Z", + "shell.execute_reply": "2025-12-23T04:10:58.783423Z", + "shell.execute_reply.started": "2025-12-23T04:10:58.757483Z" } }, "outputs": [ @@ -344,11 +344,11 @@ "id": "5eac0db9-b5bf-4b42-aa10-5aac76c79b3f", "metadata": { "execution": { - "iopub.execute_input": "2025-12-22T23:47:02.781051Z", - "iopub.status.busy": "2025-12-22T23:47:02.780842Z", - "iopub.status.idle": "2025-12-22T23:47:02.806581Z", - "shell.execute_reply": "2025-12-22T23:47:02.806066Z", - "shell.execute_reply.started": "2025-12-22T23:47:02.781034Z" + "iopub.execute_input": "2025-12-23T04:10:58.784569Z", + "iopub.status.busy": "2025-12-23T04:10:58.784373Z", + "iopub.status.idle": "2025-12-23T04:10:58.808564Z", + "shell.execute_reply": "2025-12-23T04:10:58.808112Z", + "shell.execute_reply.started": "2025-12-23T04:10:58.784553Z" } }, "outputs": [ @@ -394,11 +394,11 @@ "id": "472dc142-8ac3-4e40-a40b-27c75054ae4c", "metadata": { "execution": { - "iopub.execute_input": "2025-12-22T23:47:02.807412Z", - "iopub.status.busy": "2025-12-22T23:47:02.807178Z", - "iopub.status.idle": "2025-12-22T23:47:02.830992Z", - "shell.execute_reply": "2025-12-22T23:47:02.830431Z", - "shell.execute_reply.started": "2025-12-22T23:47:02.807396Z" + "iopub.execute_input": "2025-12-23T04:10:58.809256Z", + "iopub.status.busy": "2025-12-23T04:10:58.809079Z", + "iopub.status.idle": "2025-12-23T04:10:58.833809Z", + "shell.execute_reply": "2025-12-23T04:10:58.833306Z", + "shell.execute_reply.started": "2025-12-23T04:10:58.809242Z" } }, "outputs": [], @@ -425,11 +425,11 @@ "id": "c9a71065-95f0-4696-9469-586654694ce9", "metadata": { "execution": { - "iopub.execute_input": "2025-12-22T23:47:02.831734Z", - "iopub.status.busy": "2025-12-22T23:47:02.831543Z", - "iopub.status.idle": "2025-12-22T23:47:02.861748Z", - "shell.execute_reply": "2025-12-22T23:47:02.861219Z", - "shell.execute_reply.started": "2025-12-22T23:47:02.831718Z" + "iopub.execute_input": "2025-12-23T04:10:58.834485Z", + "iopub.status.busy": "2025-12-23T04:10:58.834307Z", + "iopub.status.idle": "2025-12-23T04:10:58.858630Z", + "shell.execute_reply": "2025-12-23T04:10:58.858163Z", + "shell.execute_reply.started": "2025-12-23T04:10:58.834469Z" } }, "outputs": [ @@ -486,15 +486,15 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 12, "id": "3677db8c-3ffa-4bdf-8831-5bd008d47a0c", "metadata": { "execution": { - "iopub.execute_input": "2025-12-22T23:55:19.180503Z", - "iopub.status.busy": "2025-12-22T23:55:19.180161Z", - "iopub.status.idle": "2025-12-22T23:55:19.194315Z", - "shell.execute_reply": "2025-12-22T23:55:19.193800Z", - "shell.execute_reply.started": "2025-12-22T23:55:19.180483Z" + "iopub.execute_input": "2025-12-23T04:10:58.859313Z", + "iopub.status.busy": "2025-12-23T04:10:58.859128Z", + "iopub.status.idle": "2025-12-23T04:10:58.890960Z", + "shell.execute_reply": "2025-12-23T04:10:58.890465Z", + "shell.execute_reply.started": "2025-12-23T04:10:58.859298Z" } }, "outputs": [ @@ -515,6 +515,14 @@ "Masked column | float32 | .compressed() | 2.58667993545532227\n", "-------------------------------------------------------------\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.1.0/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:868: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedColumn.\n", + " a.partition(kth, axis=axis, kind=kind, order=order)\n" + ] } ], "source": [ @@ -559,15 +567,15 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 13, "id": "fac36520-3537-4119-9fd2-5c3c60b4541c", "metadata": { "execution": { - "iopub.execute_input": "2025-12-23T00:04:11.218544Z", - "iopub.status.busy": "2025-12-23T00:04:11.218189Z", - "iopub.status.idle": "2025-12-23T00:04:11.229289Z", - "shell.execute_reply": "2025-12-23T00:04:11.228752Z", - "shell.execute_reply.started": "2025-12-23T00:04:11.218521Z" + "iopub.execute_input": "2025-12-23T04:10:58.891640Z", + "iopub.status.busy": "2025-12-23T04:10:58.891462Z", + "iopub.status.idle": "2025-12-23T04:10:58.915140Z", + "shell.execute_reply": "2025-12-23T04:10:58.914649Z", + "shell.execute_reply.started": "2025-12-23T04:10:58.891625Z" } }, "outputs": [ @@ -588,6 +596,14 @@ "Masked column | float32 | .compressed() | 2.58667993545532227\n", "--------------------------------------------------------------\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.1.0/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py:4842: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedColumn.\n", + " arr.partition(\n" + ] } ], "source": [ @@ -626,11 +642,11 @@ "id": "221903cb-bc48-4fbf-a86c-a685aafbb793", "metadata": { "execution": { - "iopub.execute_input": "2025-12-22T23:47:02.910133Z", - "iopub.status.busy": "2025-12-22T23:47:02.909948Z", - "iopub.status.idle": "2025-12-22T23:47:02.928050Z", - "shell.execute_reply": "2025-12-22T23:47:02.927552Z", - "shell.execute_reply.started": "2025-12-22T23:47:02.910117Z" + "iopub.execute_input": "2025-12-23T04:10:58.915873Z", + "iopub.status.busy": "2025-12-23T04:10:58.915676Z", + "iopub.status.idle": "2025-12-23T04:10:58.937428Z", + "shell.execute_reply": "2025-12-23T04:10:58.936895Z", + "shell.execute_reply.started": "2025-12-23T04:10:58.915857Z" } }, "outputs": [ @@ -689,11 +705,11 @@ "id": "26d75cdd-5775-4a9c-adf6-024e8b4290d2", "metadata": { "execution": { - "iopub.execute_input": "2025-12-22T23:47:02.928810Z", - "iopub.status.busy": "2025-12-22T23:47:02.928601Z", - "iopub.status.idle": "2025-12-22T23:47:02.959998Z", - "shell.execute_reply": "2025-12-22T23:47:02.959457Z", - "shell.execute_reply.started": "2025-12-22T23:47:02.928786Z" + "iopub.execute_input": "2025-12-23T04:10:58.938172Z", + "iopub.status.busy": "2025-12-23T04:10:58.937982Z", + "iopub.status.idle": "2025-12-23T04:10:58.964122Z", + "shell.execute_reply": "2025-12-23T04:10:58.963593Z", + "shell.execute_reply.started": "2025-12-23T04:10:58.938156Z" } }, "outputs": [ @@ -753,9 +769,13 @@ "id": "36deab32-b4b6-459b-bf44-90ede4c599d5", "metadata": {}, "source": [ - "## 4. Compute statistics with `Scipy.stats.mstats`\n", + "## 4. Compute statistics with `scipy.stats.mstats`\n", + "\n", + "`scipy.stats.mstats` is a specialized sub-module within `scipy` dedicated to statistical functions for masked data. It serves as the masked-array equivalent of the standard `scipy.stats` module. While `numpy.ma` provides mainly basic reductions (mean, median), `mstats` offers a broader suite of statistical tools that automatically respect the input mask, including correlation functions such as the Spearman rank-order correlation and statistical tests like Pearson's chi-squared and the Kolmogorov-Smirnov test. This eliminates the need to manually compress arrays before performing advanced statistical analysis.\n", + "\n", + "### 4.1. Compare with `numpy`\n", "\n", - "This module contains a large number of statistical functions that can be used with masked arrays." + "Compare quantile computations using `mstats` versus `numpy` with `.compressed()`." ] }, { @@ -764,11 +784,11 @@ "id": "b85f5064-6697-40d8-b29b-9c128397b4b4", "metadata": { "execution": { - "iopub.execute_input": "2025-12-22T23:47:02.960747Z", - "iopub.status.busy": "2025-12-22T23:47:02.960560Z", - "iopub.status.idle": "2025-12-22T23:47:02.975265Z", - "shell.execute_reply": "2025-12-22T23:47:02.974725Z", - "shell.execute_reply.started": "2025-12-22T23:47:02.960731Z" + "iopub.execute_input": "2025-12-23T04:10:58.964829Z", + "iopub.status.busy": "2025-12-23T04:10:58.964633Z", + "iopub.status.idle": "2025-12-23T04:10:58.983110Z", + "shell.execute_reply": "2025-12-23T04:10:58.982598Z", + "shell.execute_reply.started": "2025-12-23T04:10:58.964813Z" } }, "outputs": [ @@ -776,48 +796,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "50th quantile using scipy: [2.58667994]\n" + "Method | dtype | Result\n", + "--------------------------------------------------------\n", + "mstats | float64 | 2.82579901218414298\n", + "numpy with .compressed() | float32 | 2.82483005523681641\n" ] } ], "source": [ - "print(f\"50th quantile using scipy: {scistats.mquantiles(data, prob=[0.5])}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "9f9dc7e5-03ea-45b5-8dfe-4160da54a697", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-22T23:47:02.976063Z", - "iopub.status.busy": "2025-12-22T23:47:02.975870Z", - "iopub.status.idle": "2025-12-22T23:47:02.997679Z", - "shell.execute_reply": "2025-12-22T23:47:02.997205Z", - "shell.execute_reply.started": "2025-12-22T23:47:02.976046Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "50th quantile using numpy with .compressed(): 2.5866799354553223\n" - ] - } - ], - "source": [ - "print(f\"50th quantile using numpy with .compressed(): {np.quantile(data.compressed(), 0.5)}\")" + "prob = 0.75\n", + "\n", + "data = results[masked_data_cols[0]]\n", + "scipy_val = scistats.mquantiles(data, prob=[prob])[0]\n", + "numpy_val = np.quantile(data.compressed(), prob)\n", + "\n", + "print(f\"{'Method':<24} | {'dtype':<7} | {'Result'}\")\n", + "print(\"-\" * 56)\n", + "print(f\"{'mstats':<24} | {scipy_val.dtype} | {scipy_val:.17f}\")\n", + "print(f\"{'numpy with .compressed()':<24} | {numpy_val.dtype} | {numpy_val:.17f}\")" ] }, { "cell_type": "markdown", - "id": "3c4a115c-6545-4257-aab6-33b71eef9b1f", + "id": "087f5c35-771f-4247-9444-14c0cef5ff1d", "metadata": {}, "source": [ - "**Summary:** `mstats` functions always respect the mask. We don't need to manually compress data. Unlike `.compressed()`, which flattens everything into a 1D list, `mstats` operations preserve dimensions. This is crucial if you are stacking images and want a pixel-by-pixel map of the skewness or kurtosis. However, the naming convention is often slightly different (e.g., mquantiles instead of quantile), and it can be significantly slower than standard NumPy because of the overhead in handling the mask logic. Furthermore, this is for advanced stats, thus many simple ones are missing in the package. \n", - "\n", - "**Comparison with Numpy .compressed():** It is not always enough. When we have a stack of CCD images (2D array) and use .compressed(), we lose the spatial information by flattening. On the ther hand, `mstats` allows us to keep the image structure." + "**Conclusion:** They returned essentially the same results. The only difference is the precision of the returned values." ] }, { @@ -830,15 +834,15 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 17, "id": "083acba4-163e-455e-8c61-313cfb587e25", "metadata": { "execution": { - "iopub.execute_input": "2025-12-23T00:30:00.827079Z", - "iopub.status.busy": "2025-12-23T00:30:00.826730Z", - "iopub.status.idle": "2025-12-23T00:30:00.829949Z", - "shell.execute_reply": "2025-12-23T00:30:00.829266Z", - "shell.execute_reply.started": "2025-12-23T00:30:00.827056Z" + "iopub.execute_input": "2025-12-23T04:10:58.983878Z", + "iopub.status.busy": "2025-12-23T04:10:58.983676Z", + "iopub.status.idle": "2025-12-23T04:10:59.007895Z", + "shell.execute_reply": "2025-12-23T04:10:59.007333Z", + "shell.execute_reply.started": "2025-12-23T04:10:58.983862Z" } }, "outputs": [], @@ -847,10 +851,20 @@ "# print(fns)" ] }, + { + "cell_type": "markdown", + "id": "cca5d83f-a250-4aa5-b840-594ff4ca681f", + "metadata": {}, + "source": [ + "## 5. Exercise for the learner\n", + "\n", + "Retrieve a table you use frequently and check the data types of the columns most relevant to your research. Then, repeat the analysis presented in this tutorial." + ] + }, { "cell_type": "code", "execution_count": null, - "id": "1fa29e0b-ea7d-4b87-b9eb-674dfe751433", + "id": "ba53c515-21e7-486d-a0cc-b0abdabd7743", "metadata": {}, "outputs": [], "source": [] From 0d835f64fd9d56c2a724389551247bc34ffd6acc Mon Sep 17 00:00:00 2001 From: galaxyumi Date: Tue, 23 Dec 2025 04:23:09 +0000 Subject: [PATCH 07/10] addressing flake8 errors --- .../102_7_Masked_array_pitfalls.ipynb | 370 +++++++++--------- 1 file changed, 185 insertions(+), 185 deletions(-) diff --git a/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb b/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb index 1e1511f..5487609 100644 --- a/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb +++ b/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb @@ -70,11 +70,11 @@ "id": "0f6cbedb-ba6a-4d61-995f-a7c76a29e86e", "metadata": { "execution": { - "iopub.execute_input": "2025-12-23T04:10:54.648636Z", - "iopub.status.busy": "2025-12-23T04:10:54.648465Z", - "iopub.status.idle": "2025-12-23T04:10:55.189883Z", - "shell.execute_reply": "2025-12-23T04:10:55.189307Z", - "shell.execute_reply.started": "2025-12-23T04:10:54.648620Z" + "iopub.execute_input": "2025-12-23T04:22:02.776294Z", + "iopub.status.busy": "2025-12-23T04:22:02.776110Z", + "iopub.status.idle": "2025-12-23T04:22:03.319538Z", + "shell.execute_reply": "2025-12-23T04:22:03.318959Z", + "shell.execute_reply.started": "2025-12-23T04:22:02.776277Z" } }, "outputs": [], @@ -101,11 +101,11 @@ "id": "f792a486-b06b-4523-b2f5-ece955f0e3b4", "metadata": { "execution": { - "iopub.execute_input": "2025-12-23T04:10:55.192068Z", - "iopub.status.busy": "2025-12-23T04:10:55.191889Z", - "iopub.status.idle": "2025-12-23T04:10:55.238200Z", - "shell.execute_reply": "2025-12-23T04:10:55.237669Z", - "shell.execute_reply.started": "2025-12-23T04:10:55.192052Z" + "iopub.execute_input": "2025-12-23T04:22:03.321972Z", + "iopub.status.busy": "2025-12-23T04:22:03.321796Z", + "iopub.status.idle": "2025-12-23T04:22:03.367714Z", + "shell.execute_reply": "2025-12-23T04:22:03.367172Z", + "shell.execute_reply.started": "2025-12-23T04:22:03.321955Z" } }, "outputs": [], @@ -128,11 +128,11 @@ "id": "34895f9f-41a2-46ad-8588-0d367d8f1cf2", "metadata": { "execution": { - "iopub.execute_input": "2025-12-23T04:10:55.240136Z", - "iopub.status.busy": "2025-12-23T04:10:55.239957Z", - "iopub.status.idle": "2025-12-23T04:10:55.242406Z", - "shell.execute_reply": "2025-12-23T04:10:55.241980Z", - "shell.execute_reply.started": "2025-12-23T04:10:55.240120Z" + "iopub.execute_input": "2025-12-23T04:22:03.370103Z", + "iopub.status.busy": "2025-12-23T04:22:03.369920Z", + "iopub.status.idle": "2025-12-23T04:22:03.372720Z", + "shell.execute_reply": "2025-12-23T04:22:03.372204Z", + "shell.execute_reply.started": "2025-12-23T04:22:03.370086Z" } }, "outputs": [], @@ -156,22 +156,22 @@ "id": "580a8428-fe3c-459f-a8e6-ad30f05788d5", "metadata": { "execution": { - "iopub.execute_input": "2025-12-23T04:10:55.243076Z", - "iopub.status.busy": "2025-12-23T04:10:55.242903Z", - "iopub.status.idle": "2025-12-23T04:10:55.263408Z", - "shell.execute_reply": "2025-12-23T04:10:55.262933Z", - "shell.execute_reply.started": "2025-12-23T04:10:55.243061Z" + "iopub.execute_input": "2025-12-23T04:22:03.373395Z", + "iopub.status.busy": "2025-12-23T04:22:03.373225Z", + "iopub.status.idle": "2025-12-23T04:22:03.397632Z", + "shell.execute_reply": "2025-12-23T04:22:03.397170Z", + "shell.execute_reply.started": "2025-12-23T04:22:03.373380Z" } }, "outputs": [], "source": [ "ra_cen = 6.128\n", "dec_cen = -72.090\n", - "radius = 1.0 \n", + "radius = 1.0\n", "\n", "query = \"\"\"\n", " SELECT ra, psfSigma, ccdVisitId\n", - " FROM dp1.CcdVisit \n", + " FROM dp1.CcdVisit\n", " WHERE CONTAINS(POINT('ICRS', ra, dec),\n", " CIRCLE('ICRS', {}, {}, {}))=1\n", " \"\"\".format(ra_cen, dec_cen, radius)" @@ -191,11 +191,11 @@ "id": "dfa9699c-4138-4087-be47-dca4225df5f8", "metadata": { "execution": { - "iopub.execute_input": "2025-12-23T04:10:55.264136Z", - "iopub.status.busy": "2025-12-23T04:10:55.263944Z", - "iopub.status.idle": "2025-12-23T04:10:58.652452Z", - "shell.execute_reply": "2025-12-23T04:10:58.651883Z", - "shell.execute_reply.started": "2025-12-23T04:10:55.264120Z" + "iopub.execute_input": "2025-12-23T04:22:03.398334Z", + "iopub.status.busy": "2025-12-23T04:22:03.398158Z", + "iopub.status.idle": "2025-12-23T04:22:06.794074Z", + "shell.execute_reply": "2025-12-23T04:22:06.793420Z", + "shell.execute_reply.started": "2025-12-23T04:22:03.398318Z" } }, "outputs": [ @@ -230,11 +230,11 @@ "id": "9f4f653d-e8aa-497f-bb4e-5b15b1208c54", "metadata": { "execution": { - "iopub.execute_input": "2025-12-23T04:10:58.653177Z", - "iopub.status.busy": "2025-12-23T04:10:58.652987Z", - "iopub.status.idle": "2025-12-23T04:10:58.752832Z", - "shell.execute_reply": "2025-12-23T04:10:58.752267Z", - "shell.execute_reply.started": "2025-12-23T04:10:58.653162Z" + "iopub.execute_input": "2025-12-23T04:22:06.794868Z", + "iopub.status.busy": "2025-12-23T04:22:06.794645Z", + "iopub.status.idle": "2025-12-23T04:22:06.883060Z", + "shell.execute_reply": "2025-12-23T04:22:06.882515Z", + "shell.execute_reply.started": "2025-12-23T04:22:06.794851Z" } }, "outputs": [], @@ -258,11 +258,11 @@ "id": "0b872e3a-5b75-49ee-bb36-4bfd3e1506d1", "metadata": { "execution": { - "iopub.execute_input": "2025-12-23T04:10:58.753582Z", - "iopub.status.busy": "2025-12-23T04:10:58.753380Z", - "iopub.status.idle": "2025-12-23T04:10:58.756856Z", - "shell.execute_reply": "2025-12-23T04:10:58.756410Z", - "shell.execute_reply.started": "2025-12-23T04:10:58.753566Z" + "iopub.execute_input": "2025-12-23T04:22:06.883786Z", + "iopub.status.busy": "2025-12-23T04:22:06.883583Z", + "iopub.status.idle": "2025-12-23T04:22:06.887955Z", + "shell.execute_reply": "2025-12-23T04:22:06.887243Z", + "shell.execute_reply.started": "2025-12-23T04:22:06.883756Z" } }, "outputs": [ @@ -303,11 +303,11 @@ "id": "f207de21-0a29-4d9c-9368-595e1b5fd627", "metadata": { "execution": { - "iopub.execute_input": "2025-12-23T04:10:58.757508Z", - "iopub.status.busy": "2025-12-23T04:10:58.757324Z", - "iopub.status.idle": "2025-12-23T04:10:58.783895Z", - "shell.execute_reply": "2025-12-23T04:10:58.783423Z", - "shell.execute_reply.started": "2025-12-23T04:10:58.757483Z" + "iopub.execute_input": "2025-12-23T04:22:06.888643Z", + "iopub.status.busy": "2025-12-23T04:22:06.888471Z", + "iopub.status.idle": "2025-12-23T04:22:06.914059Z", + "shell.execute_reply": "2025-12-23T04:22:06.913487Z", + "shell.execute_reply.started": "2025-12-23T04:22:06.888626Z" } }, "outputs": [ @@ -321,7 +321,7 @@ ], "source": [ "masked_type_cols = [\n", - " name for name in results.colnames \n", + " name for name in results.colnames\n", " if hasattr(results[name], 'mask')\n", "]\n", "\n", @@ -344,11 +344,11 @@ "id": "5eac0db9-b5bf-4b42-aa10-5aac76c79b3f", "metadata": { "execution": { - "iopub.execute_input": "2025-12-23T04:10:58.784569Z", - "iopub.status.busy": "2025-12-23T04:10:58.784373Z", - "iopub.status.idle": "2025-12-23T04:10:58.808564Z", - "shell.execute_reply": "2025-12-23T04:10:58.808112Z", - "shell.execute_reply.started": "2025-12-23T04:10:58.784553Z" + "iopub.execute_input": "2025-12-23T04:22:06.914712Z", + "iopub.status.busy": "2025-12-23T04:22:06.914542Z", + "iopub.status.idle": "2025-12-23T04:22:06.941295Z", + "shell.execute_reply": "2025-12-23T04:22:06.940706Z", + "shell.execute_reply.started": "2025-12-23T04:22:06.914697Z" } }, "outputs": [ @@ -363,12 +363,12 @@ ], "source": [ "masked_data_cols = [\n", - " name for name in results.colnames \n", + " name for name in results.colnames\n", " if hasattr(results[name], 'mask') and np.any(results[name].mask)\n", "]\n", "\n", "clean_data_cols = [\n", - " name for name in results.colnames \n", + " name for name in results.colnames\n", " if name not in masked_data_cols\n", "]\n", "\n", @@ -394,11 +394,11 @@ "id": "472dc142-8ac3-4e40-a40b-27c75054ae4c", "metadata": { "execution": { - "iopub.execute_input": "2025-12-23T04:10:58.809256Z", - "iopub.status.busy": "2025-12-23T04:10:58.809079Z", - "iopub.status.idle": "2025-12-23T04:10:58.833809Z", - "shell.execute_reply": "2025-12-23T04:10:58.833306Z", - "shell.execute_reply.started": "2025-12-23T04:10:58.809242Z" + "iopub.execute_input": "2025-12-23T04:22:06.941951Z", + "iopub.status.busy": "2025-12-23T04:22:06.941780Z", + "iopub.status.idle": "2025-12-23T04:22:06.966699Z", + "shell.execute_reply": "2025-12-23T04:22:06.966205Z", + "shell.execute_reply.started": "2025-12-23T04:22:06.941936Z" } }, "outputs": [], @@ -425,11 +425,11 @@ "id": "c9a71065-95f0-4696-9469-586654694ce9", "metadata": { "execution": { - "iopub.execute_input": "2025-12-23T04:10:58.834485Z", - "iopub.status.busy": "2025-12-23T04:10:58.834307Z", - "iopub.status.idle": "2025-12-23T04:10:58.858630Z", - "shell.execute_reply": "2025-12-23T04:10:58.858163Z", - "shell.execute_reply.started": "2025-12-23T04:10:58.834469Z" + "iopub.execute_input": "2025-12-23T04:22:06.967437Z", + "iopub.status.busy": "2025-12-23T04:22:06.967243Z", + "iopub.status.idle": "2025-12-23T04:22:06.991258Z", + "shell.execute_reply": "2025-12-23T04:22:06.990713Z", + "shell.execute_reply.started": "2025-12-23T04:22:06.967421Z" } }, "outputs": [ @@ -438,30 +438,30 @@ "output_type": "stream", "text": [ "Source | dtype | Method | Result \n", - "-------------------------------------------------------------\n", - "Clean column | float64 | np.mean | 6.06579679440014097\n", - "Clean column | float64 | np.nanmean | 6.06579679440014097\n", - "Clean column | float64 | np.ma.mean | 6.06579679440014097\n", - "Clean column | float64 | .compressed() | 6.06579679440014097\n", - "-------------------------------------------------------------\n", - "Masked column | float64 | np.mean | 2.65416754138675604\n", - "Masked column | float32 | np.nanmean | 2.65416765213012695\n", - "Masked column | float64 | np.ma.mean | 2.65416754138675604\n", - "Masked column | float32 | .compressed() | 2.65416789054870605\n", - "-------------------------------------------------------------\n" + "--------------------------------------------------------------\n", + "Clean column | float64 | np.mean | 6.06579679440014097\n", + "Clean column | float64 | np.nanmean | 6.06579679440014097\n", + "Clean column | float64 | np.ma.mean | 6.06579679440014097\n", + "Clean column | float64 | .compressed() | 6.06579679440014097\n", + "--------------------------------------------------------------\n", + "Masked column | float64 | np.mean | 2.65416754138675604\n", + "Masked column | float32 | np.nanmean | 2.65416765213012695\n", + "Masked column | float64 | np.ma.mean | 2.65416754138675604\n", + "Masked column | float32 | .compressed() | 2.65416789054870605\n", + "--------------------------------------------------------------\n" ] } ], "source": [ - "print(f\"{'Source':<13} | {'dtype':<7} | {'Method':<13} | {'Result':<22}\")\n", - "print(\"-\" * 61)\n", + "print(f\"{'Source': <13} | {'dtype': <7} | {'Method': <13} | {'Result': <22}\")\n", + "print(\"-\" * 62)\n", "\n", "for name, data in data_sources.items():\n", - " print(f\"{name:<13} | {np.mean(data).dtype} | {'np.mean':<13} | {np.mean(data):.17f}\")\n", - " print(f\"{name:<13} | {np.nanmean(data).dtype} | {'np.nanmean':<13} | {np.nanmean(data):.17f}\")\n", - " print(f\"{name:<13} | {np.ma.mean(data).dtype} | {'np.ma.mean':<13} | {np.ma.mean(data):.17f}\")\n", - " print(f\"{name:<13} | {np.mean(data.compressed()).dtype} | {'.compressed()':<13} | {np.mean(data.compressed()):.17f}\") \n", - " print(\"-\" * 61)" + " print(f\"{name: <13} | {np.mean(data).dtype} | {'np.mean': <13} | {np.mean(data): .17f}\")\n", + " print(f\"{name: <13} | {np.nanmean(data).dtype} | {'np.nanmean': <13} | {np.nanmean(data): .17f}\")\n", + " print(f\"{name: <13} | {np.ma.mean(data).dtype} | {'np.ma.mean': <13} | {np.ma.mean(data): .17f}\")\n", + " print(f\"{name: <13} | {np.mean(data.compressed()).dtype} | {'.compressed()': <13} | {np.mean(data.compressed()): .17f}\")\n", + " print(\"-\" * 62)" ] }, { @@ -490,11 +490,11 @@ "id": "3677db8c-3ffa-4bdf-8831-5bd008d47a0c", "metadata": { "execution": { - "iopub.execute_input": "2025-12-23T04:10:58.859313Z", - "iopub.status.busy": "2025-12-23T04:10:58.859128Z", - "iopub.status.idle": "2025-12-23T04:10:58.890960Z", - "shell.execute_reply": "2025-12-23T04:10:58.890465Z", - "shell.execute_reply.started": "2025-12-23T04:10:58.859298Z" + "iopub.execute_input": "2025-12-23T04:22:06.991999Z", + "iopub.status.busy": "2025-12-23T04:22:06.991820Z", + "iopub.status.idle": "2025-12-23T04:22:07.023966Z", + "shell.execute_reply": "2025-12-23T04:22:07.023331Z", + "shell.execute_reply.started": "2025-12-23T04:22:06.991983Z" } }, "outputs": [ @@ -503,17 +503,17 @@ "output_type": "stream", "text": [ "Source | dtype | Method | Result \n", - "-------------------------------------------------------------\n", - "Clean column | float64 | np.median | 6.06872133075949094\n", - "Clean column | float64 | np.nanmedian | 6.06872133075949094\n", - "Clean column | float64 | np.ma.median | 6.06872133075949094\n", - "Clean column | float64 | .compressed() | 6.06872133075949094\n", - "-------------------------------------------------------------\n", - "Masked column | float32 | np.median | nan\n", - "Masked column | float32 | np.nanmedian | nan\n", - "Masked column | float32 | np.ma.median | 2.58667993545532227\n", - "Masked column | float32 | .compressed() | 2.58667993545532227\n", - "-------------------------------------------------------------\n" + "--------------------------------------------------------------\n", + "Clean column | float64 | np.median | 6.06872133075949094\n", + "Clean column | float64 | np.nanmedian | 6.06872133075949094\n", + "Clean column | float64 | np.ma.median | 6.06872133075949094\n", + "Clean column | float64 | .compressed() | 6.06872133075949094\n", + "--------------------------------------------------------------\n", + "Masked column | float32 | np.median | nan\n", + "Masked column | float32 | np.nanmedian | nan\n", + "Masked column | float32 | np.ma.median | 2.58667993545532227\n", + "Masked column | float32 | .compressed() | 2.58667993545532227\n", + "--------------------------------------------------------------\n" ] }, { @@ -526,15 +526,15 @@ } ], "source": [ - "print(f\"{'Source':<13} | {'dtype':<7} | {'Method':<13} | {'Result':<22}\")\n", - "print(\"-\" * 61)\n", + "print(f\"{'Source': <13} | {'dtype': <7} | {'Method': <13} | {'Result': <22}\")\n", + "print(\"-\" * 62)\n", "\n", "for name, data in data_sources.items():\n", - " print(f\"{name:<13} | {np.median(data).dtype} | {'np.median':<13} | {np.median(data):.17f}\")\n", - " print(f\"{name:<13} | {np.nanmedian(data).dtype} | {'np.nanmedian':<13} | {np.nanmedian(data):.17f}\")\n", - " print(f\"{name:<13} | {np.ma.median(data).dtype} | {'np.ma.median':<13} | {np.ma.median(data):.17f}\")\n", - " print(f\"{name:<13} | {np.median(data.compressed()).dtype} | {'.compressed()':<13} | {np.median(data.compressed()):.17f}\") \n", - " print(\"-\" * 61)" + " print(f\"{name: <13} | {np.median(data).dtype} | {'np.median': <13} | {np.median(data): .17f}\")\n", + " print(f\"{name: <13} | {np.nanmedian(data).dtype} | {'np.nanmedian': <13} | {np.nanmedian(data): .17f}\")\n", + " print(f\"{name: <13} | {np.ma.median(data).dtype} | {'np.ma.median': <13} | {np.ma.median(data): .17f}\")\n", + " print(f\"{name: <13} | {np.median(data.compressed()).dtype} | {'.compressed()': <13} | {np.median(data.compressed()): .17f}\")\n", + " print(\"-\" * 62)" ] }, { @@ -571,11 +571,11 @@ "id": "fac36520-3537-4119-9fd2-5c3c60b4541c", "metadata": { "execution": { - "iopub.execute_input": "2025-12-23T04:10:58.891640Z", - "iopub.status.busy": "2025-12-23T04:10:58.891462Z", - "iopub.status.idle": "2025-12-23T04:10:58.915140Z", - "shell.execute_reply": "2025-12-23T04:10:58.914649Z", - "shell.execute_reply.started": "2025-12-23T04:10:58.891625Z" + "iopub.execute_input": "2025-12-23T04:22:07.024655Z", + "iopub.status.busy": "2025-12-23T04:22:07.024478Z", + "iopub.status.idle": "2025-12-23T04:22:07.047215Z", + "shell.execute_reply": "2025-12-23T04:22:07.046575Z", + "shell.execute_reply.started": "2025-12-23T04:22:07.024640Z" } }, "outputs": [ @@ -584,17 +584,17 @@ "output_type": "stream", "text": [ "Source | dtype | Method | Result \n", - "--------------------------------------------------------------\n", - "Clean column | float64 | np.quantile | 6.06872133075949094\n", - "Clean column | float64 | np.nanquantile | 6.06872133075949094\n", + "---------------------------------------------------------------\n", + "Clean column | float64 | np.quantile | 6.06872133075949094\n", + "Clean column | float64 | np.nanquantile | 6.06872133075949094\n", "Clean column | N/A | np.ma.quantile | Not available \n", - "Clean column | float64 | .compressed() | 6.06872133075949094\n", - "--------------------------------------------------------------\n", - "Masked column | float32 | np.quantile | nan\n", - "Masked column | float32 | np.nanquantile | nan\n", + "Clean column | float64 | .compressed() | 6.06872133075949094\n", + "---------------------------------------------------------------\n", + "Masked column | float32 | np.quantile | nan\n", + "Masked column | float32 | np.nanquantile | nan\n", "Masked column | N/A | np.ma.quantile | Not available \n", - "Masked column | float32 | .compressed() | 2.58667993545532227\n", - "--------------------------------------------------------------\n" + "Masked column | float32 | .compressed() | 2.58667993545532227\n", + "---------------------------------------------------------------\n" ] }, { @@ -607,15 +607,15 @@ } ], "source": [ - "print(f\"{'Source':<13} | {'dtype':<7} | {'Method':<14} | {'Result':<22}\")\n", - "print(\"-\" * 62)\n", + "print(f\"{'Source': <13} | {'dtype': <7} | {'Method': <14} | {'Result': <22}\")\n", + "print(\"-\" * 63)\n", "\n", "for name, data in data_sources.items():\n", - " print(f\"{name:<13} | {np.quantile(data, 0.5).dtype} | {'np.quantile':<14} | {np.quantile(data, 0.5):.17f}\")\n", - " print(f\"{name:<13} | {np.nanquantile(data, 0.5).dtype} | {'np.nanquantile':<14} | {np.nanquantile(data, 0.5):.17f}\")\n", - " print(f\"{name:<13} | {'N/A':<7} | {'np.ma.quantile':<13} | {'Not available':<14}\")\n", - " print(f\"{name:<13} | {np.quantile(data.compressed(), 0.5).dtype} | {'.compressed()':<14} | {np.quantile(data.compressed(), 0.5):.17f}\") \n", - " print(\"-\" * 62)" + " print(f\"{name: <13} | {np.quantile(data, 0.5).dtype} | {'np.quantile': <14} | {np.quantile(data, 0.5): .17f}\")\n", + " print(f\"{name: <13} | {np.nanquantile(data, 0.5).dtype} | {'np.nanquantile': <14} | {np.nanquantile(data, 0.5): .17f}\")\n", + " print(f\"{name: <13} | {'N/A': <7} | {'np.ma.quantile': <13} | {'Not available': <14}\")\n", + " print(f\"{name: <13} | {np.quantile(data.compressed(), 0.5).dtype} | {'.compressed()': <14} | {np.quantile(data.compressed(), 0.5): .17f}\")\n", + " print(\"-\" * 63)" ] }, { @@ -642,11 +642,11 @@ "id": "221903cb-bc48-4fbf-a86c-a685aafbb793", "metadata": { "execution": { - "iopub.execute_input": "2025-12-23T04:10:58.915873Z", - "iopub.status.busy": "2025-12-23T04:10:58.915676Z", - "iopub.status.idle": "2025-12-23T04:10:58.937428Z", - "shell.execute_reply": "2025-12-23T04:10:58.936895Z", - "shell.execute_reply.started": "2025-12-23T04:10:58.915857Z" + "iopub.execute_input": "2025-12-23T04:22:07.047961Z", + "iopub.status.busy": "2025-12-23T04:22:07.047765Z", + "iopub.status.idle": "2025-12-23T04:22:07.068207Z", + "shell.execute_reply": "2025-12-23T04:22:07.067681Z", + "shell.execute_reply.started": "2025-12-23T04:22:07.047945Z" } }, "outputs": [ @@ -655,30 +655,30 @@ "output_type": "stream", "text": [ "Source | dtype | Method | Result \n", - "-------------------------------------------------------------\n", - "Clean column | float64 | np.min | 4.46371685993863654\n", - "Clean column | float64 | np.nanmin | 4.46371685993863654\n", - "Clean column | float64 | np.ma.min | 4.46371685993863654\n", - "Clean column | float64 | .compressed() | 4.46371685993863654\n", - "-------------------------------------------------------------\n", - "Masked column | float32 | np.min | 0.28867501020431519\n", - "Masked column | float32 | np.nanmin | 0.28867501020431519\n", - "Masked column | float32 | np.ma.min | 0.28867501020431519\n", - "Masked column | float32 | .compressed() | 0.28867501020431519\n", - "-------------------------------------------------------------\n" + "--------------------------------------------------------------\n", + "Clean column | float64 | np.min | 4.46371685993863654\n", + "Clean column | float64 | np.nanmin | 4.46371685993863654\n", + "Clean column | float64 | np.ma.min | 4.46371685993863654\n", + "Clean column | float64 | .compressed() | 4.46371685993863654\n", + "--------------------------------------------------------------\n", + "Masked column | float32 | np.min | 0.28867501020431519\n", + "Masked column | float32 | np.nanmin | 0.28867501020431519\n", + "Masked column | float32 | np.ma.min | 0.28867501020431519\n", + "Masked column | float32 | .compressed() | 0.28867501020431519\n", + "--------------------------------------------------------------\n" ] } ], "source": [ - "print(f\"{'Source':<13} | {'dtype':<7} | {'Method':<13} | {'Result':<22}\")\n", - "print(\"-\" * 61)\n", + "print(f\"{'Source': <13} | {'dtype': <7} | {'Method': <13} | {'Result': <22}\")\n", + "print(\"-\" * 62)\n", "\n", "for name, data in data_sources.items():\n", - " print(f\"{name:<13} | {np.min(data).dtype} | {'np.min':<13} | {np.min(data):.17f}\")\n", - " print(f\"{name:<13} | {np.nanmin(data).dtype} | {'np.nanmin':<13} | {np.nanmin(data):.17f}\")\n", - " print(f\"{name:<13} | {np.ma.min(data).dtype} | {'np.ma.min':<13} | {np.ma.min(data):.17f}\")\n", - " print(f\"{name:<13} | {np.min(data.compressed()).dtype} | {'.compressed()':<13} | {np.min(data.compressed()):.17f}\") \n", - " print(\"-\" * 61)" + " print(f\"{name: <13} | {np.min(data).dtype} | {'np.min': <13} | {np.min(data): .17f}\")\n", + " print(f\"{name: <13} | {np.nanmin(data).dtype} | {'np.nanmin': <13} | {np.nanmin(data): .17f}\")\n", + " print(f\"{name: <13} | {np.ma.min(data).dtype} | {'np.ma.min': <13} | {np.ma.min(data): .17f}\")\n", + " print(f\"{name: <13} | {np.min(data.compressed()).dtype} | {'.compressed()': <13} | {np.min(data.compressed()): .17f}\")\n", + " print(\"-\" * 62)" ] }, { @@ -705,11 +705,11 @@ "id": "26d75cdd-5775-4a9c-adf6-024e8b4290d2", "metadata": { "execution": { - "iopub.execute_input": "2025-12-23T04:10:58.938172Z", - "iopub.status.busy": "2025-12-23T04:10:58.937982Z", - "iopub.status.idle": "2025-12-23T04:10:58.964122Z", - "shell.execute_reply": "2025-12-23T04:10:58.963593Z", - "shell.execute_reply.started": "2025-12-23T04:10:58.938156Z" + "iopub.execute_input": "2025-12-23T04:22:07.068923Z", + "iopub.status.busy": "2025-12-23T04:22:07.068717Z", + "iopub.status.idle": "2025-12-23T04:22:07.099223Z", + "shell.execute_reply": "2025-12-23T04:22:07.098644Z", + "shell.execute_reply.started": "2025-12-23T04:22:07.068906Z" } }, "outputs": [ @@ -718,30 +718,30 @@ "output_type": "stream", "text": [ "Source | dtype | Method | Result \n", - "-------------------------------------------------------------\n", - "Clean column | float64 | np.std | 0.70406182027289055\n", - "Clean column | float64 | np.nanstd | 0.70406182027289055\n", - "Clean column | float64 | np.ma.std | 0.70406182027289055\n", - "Clean column | float64 | .compressed() | 0.70406182027289055\n", - "-------------------------------------------------------------\n", - "Masked column | float64 | np.std | 0.48106138027025397\n", - "Masked column | float32 | np.nanstd | 0.48106136918067932\n", - "Masked column | float64 | np.ma.std | 0.48106138027025397\n", - "Masked column | float32 | .compressed() | 0.48106136918067932\n", - "-------------------------------------------------------------\n" + "--------------------------------------------------------------\n", + "Clean column | float64 | np.std | 0.70406182027289055\n", + "Clean column | float64 | np.nanstd | 0.70406182027289055\n", + "Clean column | float64 | np.ma.std | 0.70406182027289055\n", + "Clean column | float64 | .compressed() | 0.70406182027289055\n", + "--------------------------------------------------------------\n", + "Masked column | float64 | np.std | 0.48106138027025397\n", + "Masked column | float32 | np.nanstd | 0.48106136918067932\n", + "Masked column | float64 | np.ma.std | 0.48106138027025397\n", + "Masked column | float32 | .compressed() | 0.48106136918067932\n", + "--------------------------------------------------------------\n" ] } ], "source": [ - "print(f\"{'Source':<13} | {'dtype':<7} | {'Method':<13} | {'Result':<22}\")\n", - "print(\"-\" * 61)\n", + "print(f\"{'Source': <13} | {'dtype': <7} | {'Method': <13} | {'Result': <22}\")\n", + "print(\"-\" * 62)\n", "\n", "for name, data in data_sources.items():\n", - " print(f\"{name:<13} | {np.std(data).dtype} | {'np.std':<13} | {np.std(data):.17f}\")\n", - " print(f\"{name:<13} | {np.nanstd(data).dtype} | {'np.nanstd':<13} | {np.nanstd(data):.17f}\")\n", - " print(f\"{name:<13} | {np.ma.std(data).dtype} | {'np.ma.std':<13} | {np.ma.std(data):.17f}\")\n", - " print(f\"{name:<13} | {np.std(data.compressed()).dtype} | {'.compressed()':<13} | {np.std(data.compressed()):.17f}\") \n", - " print(\"-\" * 61)" + " print(f\"{name: <13} | {np.std(data).dtype} | {'np.std': <13} | {np.std(data): .17f}\")\n", + " print(f\"{name: <13} | {np.nanstd(data).dtype} | {'np.nanstd': <13} | {np.nanstd(data): .17f}\")\n", + " print(f\"{name: <13} | {np.ma.std(data).dtype} | {'np.ma.std': <13} | {np.ma.std(data): .17f}\")\n", + " print(f\"{name: <13} | {np.std(data.compressed()).dtype} | {'.compressed()': <13} | {np.std(data.compressed()): .17f}\")\n", + " print(\"-\" * 62)" ] }, { @@ -784,11 +784,11 @@ "id": "b85f5064-6697-40d8-b29b-9c128397b4b4", "metadata": { "execution": { - "iopub.execute_input": "2025-12-23T04:10:58.964829Z", - "iopub.status.busy": "2025-12-23T04:10:58.964633Z", - "iopub.status.idle": "2025-12-23T04:10:58.983110Z", - "shell.execute_reply": "2025-12-23T04:10:58.982598Z", - "shell.execute_reply.started": "2025-12-23T04:10:58.964813Z" + "iopub.execute_input": "2025-12-23T04:22:07.099944Z", + "iopub.status.busy": "2025-12-23T04:22:07.099751Z", + "iopub.status.idle": "2025-12-23T04:22:07.115904Z", + "shell.execute_reply": "2025-12-23T04:22:07.115365Z", + "shell.execute_reply.started": "2025-12-23T04:22:07.099928Z" } }, "outputs": [ @@ -797,9 +797,9 @@ "output_type": "stream", "text": [ "Method | dtype | Result\n", - "--------------------------------------------------------\n", - "mstats | float64 | 2.82579901218414298\n", - "numpy with .compressed() | float32 | 2.82483005523681641\n" + "---------------------------------------------------------\n", + "mstats | float64 | 2.82579901218414298\n", + "numpy with .compressed() | float32 | 2.82483005523681641\n" ] } ], @@ -810,10 +810,10 @@ "scipy_val = scistats.mquantiles(data, prob=[prob])[0]\n", "numpy_val = np.quantile(data.compressed(), prob)\n", "\n", - "print(f\"{'Method':<24} | {'dtype':<7} | {'Result'}\")\n", - "print(\"-\" * 56)\n", - "print(f\"{'mstats':<24} | {scipy_val.dtype} | {scipy_val:.17f}\")\n", - "print(f\"{'numpy with .compressed()':<24} | {numpy_val.dtype} | {numpy_val:.17f}\")" + "print(f\"{'Method': <24} | {'dtype': <7} | {'Result'}\")\n", + "print(\"-\" * 57)\n", + "print(f\"{'mstats': <24} | {scipy_val.dtype} | {scipy_val: .17f}\")\n", + "print(f\"{'numpy with .compressed()': <24} | {numpy_val.dtype} | {numpy_val: .17f}\")" ] }, { @@ -838,11 +838,11 @@ "id": "083acba4-163e-455e-8c61-313cfb587e25", "metadata": { "execution": { - "iopub.execute_input": "2025-12-23T04:10:58.983878Z", - "iopub.status.busy": "2025-12-23T04:10:58.983676Z", - "iopub.status.idle": "2025-12-23T04:10:59.007895Z", - "shell.execute_reply": "2025-12-23T04:10:59.007333Z", - "shell.execute_reply.started": "2025-12-23T04:10:58.983862Z" + "iopub.execute_input": "2025-12-23T04:22:07.116557Z", + "iopub.status.busy": "2025-12-23T04:22:07.116388Z", + "iopub.status.idle": "2025-12-23T04:22:07.134660Z", + "shell.execute_reply": "2025-12-23T04:22:07.134163Z", + "shell.execute_reply.started": "2025-12-23T04:22:07.116541Z" } }, "outputs": [], From bbff20e8fe4593ecb10dd54b559f0f23ba06c3ad Mon Sep 17 00:00:00 2001 From: galaxyumi Date: Tue, 23 Dec 2025 04:23:43 +0000 Subject: [PATCH 08/10] clear outputs --- .../102_7_Masked_array_pitfalls.ipynb | 382 ++---------------- 1 file changed, 44 insertions(+), 338 deletions(-) diff --git a/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb b/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb index 5487609..cff298d 100644 --- a/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb +++ b/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb @@ -66,17 +66,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "0f6cbedb-ba6a-4d61-995f-a7c76a29e86e", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-23T04:22:02.776294Z", - "iopub.status.busy": "2025-12-23T04:22:02.776110Z", - "iopub.status.idle": "2025-12-23T04:22:03.319538Z", - "shell.execute_reply": "2025-12-23T04:22:03.318959Z", - "shell.execute_reply.started": "2025-12-23T04:22:02.776277Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -97,17 +89,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "f792a486-b06b-4523-b2f5-ece955f0e3b4", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-23T04:22:03.321972Z", - "iopub.status.busy": "2025-12-23T04:22:03.321796Z", - "iopub.status.idle": "2025-12-23T04:22:03.367714Z", - "shell.execute_reply": "2025-12-23T04:22:03.367172Z", - "shell.execute_reply.started": "2025-12-23T04:22:03.321955Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "service = get_tap_service(\"tap\")\n", @@ -124,17 +108,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "34895f9f-41a2-46ad-8588-0d367d8f1cf2", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-23T04:22:03.370103Z", - "iopub.status.busy": "2025-12-23T04:22:03.369920Z", - "iopub.status.idle": "2025-12-23T04:22:03.372720Z", - "shell.execute_reply": "2025-12-23T04:22:03.372204Z", - "shell.execute_reply.started": "2025-12-23T04:22:03.370086Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "# print(np.__version__)" @@ -152,17 +128,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "580a8428-fe3c-459f-a8e6-ad30f05788d5", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-23T04:22:03.373395Z", - "iopub.status.busy": "2025-12-23T04:22:03.373225Z", - "iopub.status.idle": "2025-12-23T04:22:03.397632Z", - "shell.execute_reply": "2025-12-23T04:22:03.397170Z", - "shell.execute_reply.started": "2025-12-23T04:22:03.373380Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "ra_cen = 6.128\n", @@ -187,26 +155,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "dfa9699c-4138-4087-be47-dca4225df5f8", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-23T04:22:03.398334Z", - "iopub.status.busy": "2025-12-23T04:22:03.398158Z", - "iopub.status.idle": "2025-12-23T04:22:06.794074Z", - "shell.execute_reply": "2025-12-23T04:22:06.793420Z", - "shell.execute_reply.started": "2025-12-23T04:22:03.398318Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Job phase is COMPLETED\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "job = service.submit_job(query)\n", "job.run()\n", @@ -226,17 +178,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "9f4f653d-e8aa-497f-bb4e-5b15b1208c54", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-23T04:22:06.794868Z", - "iopub.status.busy": "2025-12-23T04:22:06.794645Z", - "iopub.status.idle": "2025-12-23T04:22:06.883060Z", - "shell.execute_reply": "2025-12-23T04:22:06.882515Z", - "shell.execute_reply.started": "2025-12-23T04:22:06.794851Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "results = job.fetch_result().to_table()" @@ -254,29 +198,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "0b872e3a-5b75-49ee-bb36-4bfd3e1506d1", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-23T04:22:06.883786Z", - "iopub.status.busy": "2025-12-23T04:22:06.883583Z", - "iopub.status.idle": "2025-12-23T04:22:06.887955Z", - "shell.execute_reply": "2025-12-23T04:22:06.887243Z", - "shell.execute_reply.started": "2025-12-23T04:22:06.883756Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "results.has_masked_columns" ] @@ -299,26 +224,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "f207de21-0a29-4d9c-9368-595e1b5fd627", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-23T04:22:06.888643Z", - "iopub.status.busy": "2025-12-23T04:22:06.888471Z", - "iopub.status.idle": "2025-12-23T04:22:06.914059Z", - "shell.execute_reply": "2025-12-23T04:22:06.913487Z", - "shell.execute_reply.started": "2025-12-23T04:22:06.888626Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Columns with MaskedColumn type: ['ra', 'psfSigma', 'ccdVisitId']\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "masked_type_cols = [\n", " name for name in results.colnames\n", @@ -340,27 +249,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "5eac0db9-b5bf-4b42-aa10-5aac76c79b3f", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-23T04:22:06.914712Z", - "iopub.status.busy": "2025-12-23T04:22:06.914542Z", - "iopub.status.idle": "2025-12-23T04:22:06.941295Z", - "shell.execute_reply": "2025-12-23T04:22:06.940706Z", - "shell.execute_reply.started": "2025-12-23T04:22:06.914697Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Columns with flagged/invalid data (Masked): ['psfSigma']\n", - "Columns with good data (Clean): ['ra', 'ccdVisitId']\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "masked_data_cols = [\n", " name for name in results.colnames\n", @@ -390,17 +282,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "472dc142-8ac3-4e40-a40b-27c75054ae4c", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-23T04:22:06.941951Z", - "iopub.status.busy": "2025-12-23T04:22:06.941780Z", - "iopub.status.idle": "2025-12-23T04:22:06.966699Z", - "shell.execute_reply": "2025-12-23T04:22:06.966205Z", - "shell.execute_reply.started": "2025-12-23T04:22:06.941936Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "data_sources = {\n", @@ -421,37 +305,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "c9a71065-95f0-4696-9469-586654694ce9", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-23T04:22:06.967437Z", - "iopub.status.busy": "2025-12-23T04:22:06.967243Z", - "iopub.status.idle": "2025-12-23T04:22:06.991258Z", - "shell.execute_reply": "2025-12-23T04:22:06.990713Z", - "shell.execute_reply.started": "2025-12-23T04:22:06.967421Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Source | dtype | Method | Result \n", - "--------------------------------------------------------------\n", - "Clean column | float64 | np.mean | 6.06579679440014097\n", - "Clean column | float64 | np.nanmean | 6.06579679440014097\n", - "Clean column | float64 | np.ma.mean | 6.06579679440014097\n", - "Clean column | float64 | .compressed() | 6.06579679440014097\n", - "--------------------------------------------------------------\n", - "Masked column | float64 | np.mean | 2.65416754138675604\n", - "Masked column | float32 | np.nanmean | 2.65416765213012695\n", - "Masked column | float64 | np.ma.mean | 2.65416754138675604\n", - "Masked column | float32 | .compressed() | 2.65416789054870605\n", - "--------------------------------------------------------------\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "print(f\"{'Source': <13} | {'dtype': <7} | {'Method': <13} | {'Result': <22}\")\n", "print(\"-\" * 62)\n", @@ -486,45 +343,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "3677db8c-3ffa-4bdf-8831-5bd008d47a0c", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-23T04:22:06.991999Z", - "iopub.status.busy": "2025-12-23T04:22:06.991820Z", - "iopub.status.idle": "2025-12-23T04:22:07.023966Z", - "shell.execute_reply": "2025-12-23T04:22:07.023331Z", - "shell.execute_reply.started": "2025-12-23T04:22:06.991983Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Source | dtype | Method | Result \n", - "--------------------------------------------------------------\n", - "Clean column | float64 | np.median | 6.06872133075949094\n", - "Clean column | float64 | np.nanmedian | 6.06872133075949094\n", - "Clean column | float64 | np.ma.median | 6.06872133075949094\n", - "Clean column | float64 | .compressed() | 6.06872133075949094\n", - "--------------------------------------------------------------\n", - "Masked column | float32 | np.median | nan\n", - "Masked column | float32 | np.nanmedian | nan\n", - "Masked column | float32 | np.ma.median | 2.58667993545532227\n", - "Masked column | float32 | .compressed() | 2.58667993545532227\n", - "--------------------------------------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.1.0/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:868: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedColumn.\n", - " a.partition(kth, axis=axis, kind=kind, order=order)\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "print(f\"{'Source': <13} | {'dtype': <7} | {'Method': <13} | {'Result': <22}\")\n", "print(\"-\" * 62)\n", @@ -567,45 +389,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "fac36520-3537-4119-9fd2-5c3c60b4541c", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-23T04:22:07.024655Z", - "iopub.status.busy": "2025-12-23T04:22:07.024478Z", - "iopub.status.idle": "2025-12-23T04:22:07.047215Z", - "shell.execute_reply": "2025-12-23T04:22:07.046575Z", - "shell.execute_reply.started": "2025-12-23T04:22:07.024640Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Source | dtype | Method | Result \n", - "---------------------------------------------------------------\n", - "Clean column | float64 | np.quantile | 6.06872133075949094\n", - "Clean column | float64 | np.nanquantile | 6.06872133075949094\n", - "Clean column | N/A | np.ma.quantile | Not available \n", - "Clean column | float64 | .compressed() | 6.06872133075949094\n", - "---------------------------------------------------------------\n", - "Masked column | float32 | np.quantile | nan\n", - "Masked column | float32 | np.nanquantile | nan\n", - "Masked column | N/A | np.ma.quantile | Not available \n", - "Masked column | float32 | .compressed() | 2.58667993545532227\n", - "---------------------------------------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/lsst/software/stack/conda/envs/lsst-scipipe-10.1.0/lib/python3.12/site-packages/numpy/lib/_function_base_impl.py:4842: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedColumn.\n", - " arr.partition(\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "print(f\"{'Source': <13} | {'dtype': <7} | {'Method': <14} | {'Result': <22}\")\n", "print(\"-\" * 63)\n", @@ -638,37 +425,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "221903cb-bc48-4fbf-a86c-a685aafbb793", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-23T04:22:07.047961Z", - "iopub.status.busy": "2025-12-23T04:22:07.047765Z", - "iopub.status.idle": "2025-12-23T04:22:07.068207Z", - "shell.execute_reply": "2025-12-23T04:22:07.067681Z", - "shell.execute_reply.started": "2025-12-23T04:22:07.047945Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Source | dtype | Method | Result \n", - "--------------------------------------------------------------\n", - "Clean column | float64 | np.min | 4.46371685993863654\n", - "Clean column | float64 | np.nanmin | 4.46371685993863654\n", - "Clean column | float64 | np.ma.min | 4.46371685993863654\n", - "Clean column | float64 | .compressed() | 4.46371685993863654\n", - "--------------------------------------------------------------\n", - "Masked column | float32 | np.min | 0.28867501020431519\n", - "Masked column | float32 | np.nanmin | 0.28867501020431519\n", - "Masked column | float32 | np.ma.min | 0.28867501020431519\n", - "Masked column | float32 | .compressed() | 0.28867501020431519\n", - "--------------------------------------------------------------\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "print(f\"{'Source': <13} | {'dtype': <7} | {'Method': <13} | {'Result': <22}\")\n", "print(\"-\" * 62)\n", @@ -701,37 +461,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "26d75cdd-5775-4a9c-adf6-024e8b4290d2", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-23T04:22:07.068923Z", - "iopub.status.busy": "2025-12-23T04:22:07.068717Z", - "iopub.status.idle": "2025-12-23T04:22:07.099223Z", - "shell.execute_reply": "2025-12-23T04:22:07.098644Z", - "shell.execute_reply.started": "2025-12-23T04:22:07.068906Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Source | dtype | Method | Result \n", - "--------------------------------------------------------------\n", - "Clean column | float64 | np.std | 0.70406182027289055\n", - "Clean column | float64 | np.nanstd | 0.70406182027289055\n", - "Clean column | float64 | np.ma.std | 0.70406182027289055\n", - "Clean column | float64 | .compressed() | 0.70406182027289055\n", - "--------------------------------------------------------------\n", - "Masked column | float64 | np.std | 0.48106138027025397\n", - "Masked column | float32 | np.nanstd | 0.48106136918067932\n", - "Masked column | float64 | np.ma.std | 0.48106138027025397\n", - "Masked column | float32 | .compressed() | 0.48106136918067932\n", - "--------------------------------------------------------------\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "print(f\"{'Source': <13} | {'dtype': <7} | {'Method': <13} | {'Result': <22}\")\n", "print(\"-\" * 62)\n", @@ -780,29 +513,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "b85f5064-6697-40d8-b29b-9c128397b4b4", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-23T04:22:07.099944Z", - "iopub.status.busy": "2025-12-23T04:22:07.099751Z", - "iopub.status.idle": "2025-12-23T04:22:07.115904Z", - "shell.execute_reply": "2025-12-23T04:22:07.115365Z", - "shell.execute_reply.started": "2025-12-23T04:22:07.099928Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Method | dtype | Result\n", - "---------------------------------------------------------\n", - "mstats | float64 | 2.82579901218414298\n", - "numpy with .compressed() | float32 | 2.82483005523681641\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "prob = 0.75\n", "\n", @@ -834,17 +548,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "083acba4-163e-455e-8c61-313cfb587e25", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-23T04:22:07.116557Z", - "iopub.status.busy": "2025-12-23T04:22:07.116388Z", - "iopub.status.idle": "2025-12-23T04:22:07.134660Z", - "shell.execute_reply": "2025-12-23T04:22:07.134163Z", - "shell.execute_reply.started": "2025-12-23T04:22:07.116541Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "# fns = [fn for fn in dir(scistats) if not fn.startswith('_')]\n", From e1b7ab0e0cecdfd85249cbe900045d79492f84d0 Mon Sep 17 00:00:00 2001 From: galaxyumi Date: Tue, 23 Dec 2025 04:29:42 +0000 Subject: [PATCH 09/10] run pre-commit --- .../102_7_Masked_array_pitfalls.ipynb | 20 ++----------------- 1 file changed, 2 insertions(+), 18 deletions(-) diff --git a/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb b/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb index cff298d..9f1f7bb 100644 --- a/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb +++ b/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb @@ -209,15 +209,7 @@ { "cell_type": "markdown", "id": "c9605069-1434-4284-8369-92607dd15448", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-19T22:18:58.380184Z", - "iopub.status.busy": "2025-12-19T22:18:58.379815Z", - "iopub.status.idle": "2025-12-19T22:18:58.385359Z", - "shell.execute_reply": "2025-12-19T22:18:58.384511Z", - "shell.execute_reply.started": "2025-12-19T22:18:58.380162Z" - } - }, + "metadata": {}, "source": [ "Find out which columns are `MaskedColumn`." ] @@ -370,15 +362,7 @@ { "cell_type": "markdown", "id": "ade2b588-cdcc-49e5-9054-4b0c793b7066", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-05T18:49:45.187186Z", - "iopub.status.busy": "2025-12-05T18:49:45.186689Z", - "iopub.status.idle": "2025-12-05T18:49:45.190070Z", - "shell.execute_reply": "2025-12-05T18:49:45.189568Z", - "shell.execute_reply.started": "2025-12-05T18:49:45.187160Z" - } - }, + "metadata": {}, "source": [ "### 3.3. Quantile/Percentile\n", "\n", From d7db3bdaf99323dee733c579bb4846813f737425 Mon Sep 17 00:00:00 2001 From: galaxyumi Date: Fri, 2 Jan 2026 22:52:30 +0000 Subject: [PATCH 10/10] addressing Christinas' feedback --- .../102_7_Masked_array_pitfalls.ipynb | 220 ++++++++++-------- 1 file changed, 120 insertions(+), 100 deletions(-) diff --git a/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb b/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb index 9f1f7bb..6456adb 100644 --- a/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb +++ b/DP1/100_How_to_Use_RSP_Tools/102_Catalog_access/102_7_Masked_array_pitfalls.ipynb @@ -22,7 +22,7 @@ "Data Release: Data Preview 1
\n", "Container Size: large
\n", "LSST Science Pipelines version: r29.2.0
\n", - "Last verified to run: 2025-12-22
\n", + "Last verified to run: 2026-01-02
\n", "Repository: github.com/lsst/tutorial-notebooks
\n", "DOI: 10.11578/rubin/dc.20250909.20
" ] @@ -55,9 +55,11 @@ "source": [ "## 1. Introduction\n", "\n", - "This tutorial demonstrates how to correctly handle masked arrays, which may have missing or invalid entries, to avoid silent scientific errors. Use the specific failure modes presented in this tutorial as a guide to the fundamental disconnect between standard `numpy` functions and the masked array structure. \n", + "[A masked array](https://numpy.org/doc/stable/reference/maskedarray.generic.html#what-is-a-masked-array) pairs a raw data array with a boolean mask that flags invalid entries. Crucially, this mask layer exists even if all data is valid. Scientific errors could occur when standard functions ignore the mask and compute results using the underlying raw data. This tutorial demonstrates how to correctly handle masked arrays, which may have missing or invalid entries, to avoid silent scientific errors. Use the specific failure modes presented in this tutorial as a guide to the fundamental disconnect between standard `numpy` functions and the masked array structure. \n", "\n", - "While this tutorial investigates common operations like `numpy.median` and `numpy.quantile`, these risks extend to any function that ignores the internal mask mechanism. Develop a rigorous habit of verifying whether your statistical tools respect the mask. Adopt robust statistical strategies, such as data compression using `.compressed()` or specialized modules/libraries like `numpy.ma` and `scipy.stats.mstats`, to guarantee your scientific results remain robust against invalid underlying data.\n", + "While this tutorial investigates common operations like `numpy.median`, these risks extend to any function that ignores the internal mask mechanism. Develop a rigorous habit of verifying whether your statistical tools respect the mask. Adopt robust statistical strategies, such as data compression using [`.compressed()`](https://numpy.org/doc/2.1/reference/generated/numpy.ma.compressed.html), which flattens the input array and discards masked values, or specialized modules/libraries for handling masked arrays like [`numpy.ma`](https://numpy.org/doc/stable/reference/maskedarray.generic.html) and [`scipy.stats.mstats`](https://docs.scipy.org/doc/scipy/reference/stats.mstats.html), to guarantee your scientific results remain robust against invalid underlying data.\n", + "\n", + "**Note:** The examples demonstrated herein are based on `numpy 2.2.6`, the default version on the RSP as of January 2, 2026. Future updates to the library may alter how these functions handle masked input, potentially rendering some examples obsolete.\n", "\n", "### 1.1. Import packages\n", "\n", @@ -103,7 +105,7 @@ "id": "064bfd9d-5a96-44b5-af15-18fc66a03b37", "metadata": {}, "source": [ - "Option to check the `numpy` version." + "Option to check the `numpy` version. " ] }, { @@ -123,7 +125,7 @@ "source": [ "## 2. Retrieve data\n", "\n", - "Start with a simple query executing a cone search on the `CcdVisit` table for CCD metadata within 1 degrees of the center of the 47 Tuc field, RA, Dec = 6.128, -72.090 degrees." + "Start with a simple query executing a cone search on the `CcdVisit` table for CCD metadata within 1 degree of the center of the 47 Tuc field, RA, Dec = 6.128, -72.090 degrees." ] }, { @@ -170,16 +172,16 @@ }, { "cell_type": "markdown", - "id": "81bb4229-d452-4f53-9807-27e83402fd5d", + "id": "413866ed-b2d0-4c66-8d9e-e300e2f91b43", "metadata": {}, "source": [ - "Fetch the results and store them as an `Astropy` table." + "Fetch the results, store them as an `Astropy` table." ] }, { "cell_type": "code", "execution_count": null, - "id": "9f4f653d-e8aa-497f-bb4e-5b15b1208c54", + "id": "9c741781-65d5-4156-b858-4de2c66d01e6", "metadata": {}, "outputs": [], "source": [ @@ -191,27 +193,9 @@ "id": "26903dce-2aec-44c9-b4ad-74bdaa78cabd", "metadata": {}, "source": [ - "### 2.1. Identify masked columns\n", + "### 2.1. Verify masked column types\n", "\n", - "Check if the table has masked columns. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0b872e3a-5b75-49ee-bb36-4bfd3e1506d1", - "metadata": {}, - "outputs": [], - "source": [ - "results.has_masked_columns" - ] - }, - { - "cell_type": "markdown", - "id": "c9605069-1434-4284-8369-92607dd15448", - "metadata": {}, - "source": [ - "Find out which columns are `MaskedColumn`." + "Verify that all columns in the `results` table are `MaskedColumn` objects. This is a default safety feature of the [VOTable](https://www.ivoa.net/documents/VOTable/20230913/WD-VOTable-1.5-20230913.html#tth_sEc1.1) reader, even if the underlying database columns contain no missing values." ] }, { @@ -226,7 +210,12 @@ " if hasattr(results[name], 'mask')\n", "]\n", "\n", - "print(f\"Columns with MaskedColumn type: {masked_type_cols}\")" + "all_masked = len(masked_type_cols) == len(results.colnames)\n", + "\n", + "if all_masked == True:\n", + " print(f\"All {len(results.colnames)} columns MaskedColumn objects.\")\n", + "else:\n", + " print(\"Some columns are standard arrays (not masked).\")" ] }, { @@ -234,30 +223,37 @@ "id": "b1969961-3e29-437d-bb9b-fe91e8720a67", "metadata": {}, "source": [ - "### 2.2. Columns with actual masked values\n", + "### 2.2. Identify active masks\n", + "\n", + "While it is confirmed that every column is technically a `MaskedColumn` object, this does not mean every column contains bad data. Many columns (like IDs or coordinates) may be fully populated. \n", "\n", - "The instantiation of a `MaskedColumn` object does not imply the presence of invalid data; the column may still consist entirely of valid entries. Check which columns actually contain masked values. " + "Inspect each column's boolean mask using `np.any(results[name].mask)` to check if at least one entry is flagged as `True` (invalid). Based on this check, separate the columns into two categories:\n", + "- Active Masks: Columns that contain at least one invalid/flagged entry.\n", + "- Fully Valid: Columns that are entirely clean (all mask values are `False`)." ] }, { "cell_type": "code", "execution_count": null, - "id": "5eac0db9-b5bf-4b42-aa10-5aac76c79b3f", + "id": "27c80cdb-1401-42d7-a442-619dce5053de", "metadata": {}, "outputs": [], "source": [ - "masked_data_cols = [\n", - " name for name in results.colnames\n", - " if hasattr(results[name], 'mask') and np.any(results[name].mask)\n", + "active_mask_cols = [\n", + " name for name in results.colnames \n", + " if np.any(results[name].mask)\n", "]\n", "\n", - "clean_data_cols = [\n", - " name for name in results.colnames\n", - " if name not in masked_data_cols\n", + "fully_valid_cols = [\n", + " name for name in results.colnames \n", + " if name not in active_mask_cols\n", "]\n", "\n", - "print(f\"Columns with flagged/invalid data (Masked): {masked_data_cols}\")\n", - "print(f\"Columns with good data (Clean): {clean_data_cols}\")" + "print(f\"Columns with missing/invalid data:\")\n", + "print(f\" -> {active_mask_cols}\\n\")\n", + "\n", + "print(f\"Columns that are fully valid:\")\n", + "print(f\" -> {fully_valid_cols}\")" ] }, { @@ -267,9 +263,9 @@ "source": [ "## 3. Compute statistics with `Numpy`.\n", "\n", - "Compute statistics for both masked columns (those containing flagged/bad entries) and clean columns (those with only valid entries) to investigate how different methods handle valid and invalid entries.\n", + "Compute statistics for both \"active mask\" columns (those containing invalid entries) and \"fully valid\" columns (those with only valid entries) to investigate how different functions/methods handle valid and invalid entries.\n", "\n", - "Define a `data_sources` dictionary containing one example column from the `clean_data_cols` list and one from the `masked_data_cols` list to streamline our tests." + "Define a `data_sources` dictionary containing one sample column from each category (`fully_valid_cols` and `active_mask_cols`) to streamline the upcoming tests." ] }, { @@ -280,8 +276,8 @@ "outputs": [], "source": [ "data_sources = {\n", - " \"Clean column\": results[clean_data_cols[0]],\n", - " \"Masked column\": results[masked_data_cols[0]]\n", + " \"A fully valid column\": results[fully_valid_cols[0]],\n", + " \"An active mask column\": results[active_mask_cols[0]]\n", "}" ] }, @@ -302,15 +298,20 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"{'Source': <13} | {'dtype': <7} | {'Method': <13} | {'Result': <22}\")\n", - "print(\"-\" * 62)\n", + "print(f\"{'Source': <21} | {'dtype_in'} | {'dtype_out'} | {'Method': <13} | {'Result'}\")\n", + "print(\"-\" * 83)\n", "\n", "for name, data in data_sources.items():\n", - " print(f\"{name: <13} | {np.mean(data).dtype} | {'np.mean': <13} | {np.mean(data): .17f}\")\n", - " print(f\"{name: <13} | {np.nanmean(data).dtype} | {'np.nanmean': <13} | {np.nanmean(data): .17f}\")\n", - " print(f\"{name: <13} | {np.ma.mean(data).dtype} | {'np.ma.mean': <13} | {np.ma.mean(data): .17f}\")\n", - " print(f\"{name: <13} | {np.mean(data.compressed()).dtype} | {'.compressed()': <13} | {np.mean(data.compressed()): .17f}\")\n", - " print(\"-\" * 62)" + " input_dtype = str(data.dtype)\n", + " out_dtype_mean = str(np.mean(data).dtype)\n", + " out_dtype_nanmean = str(np.nanmean(data).dtype)\n", + " out_dtype_mamean = str(np.ma.mean(data).dtype)\n", + " out_dtype_compmean = str(np.mean(data.compressed()).dtype)\n", + " print(f\"{name: <21} | {input_dtype: <8} | {out_dtype_mean: <9} | {'np.mean': <13} | {np.mean(data): .17f}\")\n", + " print(f\"{name: <21} | {input_dtype: <8} | {out_dtype_nanmean: <9} | {'np.nanmean': <13} | {np.nanmean(data): .17f}\")\n", + " print(f\"{name: <21} | {input_dtype: <8} | {out_dtype_mamean: <9} | {'np.ma.mean': <13} | {np.ma.mean(data): .17f}\")\n", + " print(f\"{name: <21} | {input_dtype: <8} | {out_dtype_compmean: <9} | {'.compressed()': <13} | {np.mean(data.compressed()): .17f}\")\n", + " print(\"-\" * 83)" ] }, { @@ -318,7 +319,7 @@ "id": "89b161af-19c0-4d8e-a62a-6cf877984967", "metadata": {}, "source": [ - "**Conclusion:** Note that the clean array yields consistent results because its input data type is `np.float64`, whereas the `np.float32` masked array produces slight variations across methods. Attribute these discrepancies to the non-associativity of floating-point addition (i.e., (A+B)+C $\\neq$ A+(B+C)); different functions utilize different summation orders (e.g., contiguous vs. strided memory access), altering the result at the limit of precision. Account for the fixed input data type provided by the pipeline by selecting your reduction functions carefully, ensuring you are aware of the return precision each method yields for your specific input." + "**Conclusion:** Calculating the mean generally works well across all four methods for both \"fully valid\" and \"active mask\" columns. However, notice that while the \"fully valid\" column (with input dtype of `np.float64`) yields identical results, the \"active mask\" column (with input dtype of `np.float32`) shows slight variations. This discrepancy arises because different reduction functions handle accumulator precision differently: some promote `np.float32` inputs to `np.float64` for the internal calculation (yielding high precision), while others compute or return results in the original `np.float32`. Account for the fixed input data types provided by the LSST Science Pipelines by verifying the return type of your chosen reduction functions." ] }, { @@ -340,15 +341,20 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"{'Source': <13} | {'dtype': <7} | {'Method': <13} | {'Result': <22}\")\n", - "print(\"-\" * 62)\n", + "print(f\"{'Source': <21} | {'dtype_in'} | {'dtype_out'} | {'Method': <13} | {'Result'}\")\n", + "print(\"-\" * 83)\n", "\n", "for name, data in data_sources.items():\n", - " print(f\"{name: <13} | {np.median(data).dtype} | {'np.median': <13} | {np.median(data): .17f}\")\n", - " print(f\"{name: <13} | {np.nanmedian(data).dtype} | {'np.nanmedian': <13} | {np.nanmedian(data): .17f}\")\n", - " print(f\"{name: <13} | {np.ma.median(data).dtype} | {'np.ma.median': <13} | {np.ma.median(data): .17f}\")\n", - " print(f\"{name: <13} | {np.median(data.compressed()).dtype} | {'.compressed()': <13} | {np.median(data.compressed()): .17f}\")\n", - " print(\"-\" * 62)" + " input_dtype = str(data.dtype)\n", + " out_dtype_median = str(np.median(data).dtype)\n", + " out_dtype_nanmedian = str(np.nanmedian(data).dtype)\n", + " out_dtype_mamedian = str(np.ma.median(data).dtype)\n", + " out_dtype_compmedian = str(np.median(data.compressed()).dtype)\n", + " print(f\"{name: <21} | {input_dtype: <8} | {out_dtype_median: <9} | {'np.median': <13} | {np.median(data): .17f}\")\n", + " print(f\"{name: <21} | {input_dtype: <8} | {out_dtype_nanmedian: <9} | {'np.nanmedian': <13} | {np.nanmedian(data): .17f}\")\n", + " print(f\"{name: <21} | {input_dtype: <8} | {out_dtype_mamedian: <9} | {'np.ma.median': <13} | {np.ma.median(data): .17f}\")\n", + " print(f\"{name: <21} | {input_dtype: <8} | {out_dtype_compmedian: <9} | {'.compressed()': <13} | {np.median(data.compressed()): .17f}\")\n", + " print(\"-\" * 83)" ] }, { @@ -356,7 +362,7 @@ "id": "156442c3-9f90-4ce6-bd18-803d935b4d63", "metadata": {}, "source": [ - "**Conclusion:** Both `np.median` and `np.nanmedian` returned `nan` for the masked array. This indicates that these functions ignored the mask, accessing the underlying invalid data (likely `NaN` or other bad values), which propagated to corrupt the final result. In contrast, both `np.ma.median` and the `.compressed()` method correctly respected the mask, excluded the invalid entries and produced the correct result. However, the users should remain cautious regarding the precision match between the input array and returned value. Always use functions/methods that explicitly respect the mask when computing the median." + "**Conclusion:** Both `np.median` and `np.nanmedian` returned `nan` for the \"active mask\" array. This indicates that these functions ignored the mask, accessing the underlying invalid data (likely `NaN` or other bad values), which propagated to corrupt the final result. In contrast, both `np.ma.median` and the `.compressed()` method handled the mask array correctly, excluded the invalid entries and produced the correct result. However, the users should remain cautious regarding the precision match between the input array and returned value. Always use functions/methods that correctly handle the mask array when computing the median." ] }, { @@ -366,7 +372,7 @@ "source": [ "### 3.3. Quantile/Percentile\n", "\n", - "Repeat the same analysis for the quantile. \n", + "Repeat the same analysis for the quantile. Percentile calculations share the exact same behavior regarding masked data and precision types, so the findings here apply to both.\n", "\n", "> **Warning:** When you first run this notebook tutorial, the following cell produces a warning stating that \"'partition' will ignore the 'mask' of the `MaskedColumn`\". Disregard this alert; the code intentionally triggers this behavior to demonstrate how `np.quantile` and `np.nanquantile` fail to respect `MaskedColumn` masks." ] @@ -378,15 +384,19 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"{'Source': <13} | {'dtype': <7} | {'Method': <14} | {'Result': <22}\")\n", - "print(\"-\" * 63)\n", + "print(f\"{'Source': <21} | {'dtype_in'} | {'dtype_out'} | {'Method': <14} | {'Result'}\")\n", + "print(\"-\" * 84)\n", "\n", "for name, data in data_sources.items():\n", - " print(f\"{name: <13} | {np.quantile(data, 0.5).dtype} | {'np.quantile': <14} | {np.quantile(data, 0.5): .17f}\")\n", - " print(f\"{name: <13} | {np.nanquantile(data, 0.5).dtype} | {'np.nanquantile': <14} | {np.nanquantile(data, 0.5): .17f}\")\n", - " print(f\"{name: <13} | {'N/A': <7} | {'np.ma.quantile': <13} | {'Not available': <14}\")\n", - " print(f\"{name: <13} | {np.quantile(data.compressed(), 0.5).dtype} | {'.compressed()': <14} | {np.quantile(data.compressed(), 0.5): .17f}\")\n", - " print(\"-\" * 63)" + " input_dtype = str(data.dtype)\n", + " out_dtype_quantile = str(np.quantile(data, 0.5).dtype)\n", + " out_dtype_nanquantile = str(np.nanquantile(data, 0.5).dtype)\n", + " out_dtype_compquantile = str(np.quantile(data.compressed(), 0.5).dtype)\n", + " print(f\"{name: <21} | {input_dtype: <8} | {out_dtype_quantile: <9} | {'np.quantile': <14} | {np.quantile(data, 0.5): .17f}\")\n", + " print(f\"{name: <21} | {input_dtype: <8} | {out_dtype_nanquantile: <9} | {'np.nanquantile': <14} | {np.nanquantile(data, 0.5): .17f}\")\n", + " print(f\"{name: <21} | {input_dtype: <8} | {'N/A': <9} | {'np.ma.quantile': <14} | {' Not available'}\")\n", + " print(f\"{name: <21} | {input_dtype: <8} | {out_dtype_compquantile: <9} | {'.compressed()': <14} | {np.quantile(data.compressed(), 0.5): .17f}\")\n", + " print(\"-\" * 84)" ] }, { @@ -394,7 +404,7 @@ "id": "ed3e332b-1726-4406-9ec4-2b88792934ee", "metadata": {}, "source": [ - "**Conclusion:** Similar to the median functions, `np.quantile` and `np.nanquantile` fail on masked arrays because they ignore the mask. Since the current `numpy.ma` lacks a direct `np.quantile` equivalent, the only robust approach is to perform these operations on compressed data. Always compress your `MaskedColumn` before calculating quantiles, by explicitly calling `.compressed()` to remove masked values. This conclusion also applies to the percentile functions." + "**Conclusion:** Similar to the median functions, `np.quantile` and `np.nanquantile` fail on the \"active mask\" array because they do not properly handle the mask. Since the current `numpy.ma` lacks a direct `np.quantile` equivalent, the only robust approach is to perform these operations on compressed data. Always compress your `MaskedColumn` before calculating quantiles, by explicitly calling `.compressed()` to remove invalid entries. This conclusion also applies to the percentile calculations." ] }, { @@ -404,7 +414,7 @@ "source": [ "### 3.4. Min/Max\n", "\n", - "Repeat the same analysis for the mininum." + "Repeat the same analysis for the mininum. Maximum calculations share the exact same behavior regarding masked data and precision types, so the findings here apply to both." ] }, { @@ -414,15 +424,20 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"{'Source': <13} | {'dtype': <7} | {'Method': <13} | {'Result': <22}\")\n", - "print(\"-\" * 62)\n", + "print(f\"{'Source': <21} | {'dtype_in'} | {'dtype_out'} | {'Method': <13} | {'Result'}\")\n", + "print(\"-\" * 83)\n", "\n", "for name, data in data_sources.items():\n", - " print(f\"{name: <13} | {np.min(data).dtype} | {'np.min': <13} | {np.min(data): .17f}\")\n", - " print(f\"{name: <13} | {np.nanmin(data).dtype} | {'np.nanmin': <13} | {np.nanmin(data): .17f}\")\n", - " print(f\"{name: <13} | {np.ma.min(data).dtype} | {'np.ma.min': <13} | {np.ma.min(data): .17f}\")\n", - " print(f\"{name: <13} | {np.min(data.compressed()).dtype} | {'.compressed()': <13} | {np.min(data.compressed()): .17f}\")\n", - " print(\"-\" * 62)" + " input_dtype = str(data.dtype)\n", + " out_dtype_min = str(np.min(data).dtype)\n", + " out_dtype_nanmin = str(np.nanmin(data).dtype)\n", + " out_dtype_mamin = str(np.ma.min(data).dtype)\n", + " out_dtype_compmin = str(np.min(data.compressed()).dtype)\n", + " print(f\"{name: <21} | {input_dtype: <8} | {out_dtype_min: <9} | {'np.min': <13} | {np.min(data): .17f}\")\n", + " print(f\"{name: <21} | {input_dtype: <8} | {out_dtype_nanmin: <9} | {'np.nanmin': <13} | {np.nanmin(data): .17f}\")\n", + " print(f\"{name: <21} | {input_dtype: <8} | {out_dtype_mamin: <9} | {'np.ma.min': <13} | {np.ma.min(data): .17f}\")\n", + " print(f\"{name: <21} | {input_dtype: <8} | {out_dtype_compmin: <9} | {'.compressed()': <13} | {np.min(data.compressed()): .17f}\")\n", + " print(\"-\" * 83)" ] }, { @@ -430,7 +445,7 @@ "id": "90459637-7963-46bf-be82-66f66d4ecca7", "metadata": {}, "source": [ - "**Conclusion:** Both the clean and masked arrays yield consistent results. This conclusion also applies to the maximum functions. " + "**Conclusion:** Both the \"fully valid\" and \"active mask\" columns yield consistent results across all four methods. This conclusion also applies to the maximum calculations. " ] }, { @@ -438,9 +453,9 @@ "id": "80704492-0905-4e7d-a30d-a35ba974cd11", "metadata": {}, "source": [ - "### 3.5. Std/Var\n", + "### 3.5. Standard deviation and variance\n", "\n", - "Repeat the same analysis for the standard deviation." + "Repeat the same analysis for the stdstandard deviation. Variance calculations share the exact same behavior regarding masked data and precision types, so the findings here apply to both." ] }, { @@ -450,15 +465,20 @@ "metadata": {}, "outputs": [], "source": [ - "print(f\"{'Source': <13} | {'dtype': <7} | {'Method': <13} | {'Result': <22}\")\n", - "print(\"-\" * 62)\n", + "print(f\"{'Source': <21} | {'dtype_in'} | {'dtype_out'} | {'Method': <13} | {'Result'}\")\n", + "print(\"-\" * 83)\n", "\n", "for name, data in data_sources.items():\n", - " print(f\"{name: <13} | {np.std(data).dtype} | {'np.std': <13} | {np.std(data): .17f}\")\n", - " print(f\"{name: <13} | {np.nanstd(data).dtype} | {'np.nanstd': <13} | {np.nanstd(data): .17f}\")\n", - " print(f\"{name: <13} | {np.ma.std(data).dtype} | {'np.ma.std': <13} | {np.ma.std(data): .17f}\")\n", - " print(f\"{name: <13} | {np.std(data.compressed()).dtype} | {'.compressed()': <13} | {np.std(data.compressed()): .17f}\")\n", - " print(\"-\" * 62)" + " input_dtype = str(data.dtype)\n", + " out_dtype_std = str(np.std(data).dtype)\n", + " out_dtype_nanstd = str(np.nanstd(data).dtype)\n", + " out_dtype_mastd = str(np.ma.std(data).dtype)\n", + " out_dtype_compstd = str(np.std(data.compressed()).dtype)\n", + " print(f\"{name: <21} | {input_dtype: <8} | {out_dtype_std: <9} | {'np.std': <13} | {np.std(data): .17f}\")\n", + " print(f\"{name: <21} | {input_dtype: <8} | {out_dtype_nanstd: <9} | {'np.nanstd': <13} | {np.nanstd(data): .17f}\")\n", + " print(f\"{name: <21} | {input_dtype: <8} | {out_dtype_mastd: <9} | {'np.ma.std': <13} | {np.ma.std(data): .17f}\")\n", + " print(f\"{name: <21} | {input_dtype: <8} | {out_dtype_compstd: <9} | {'.compressed()': <13} | {np.std(data.compressed()): .17f}\")\n", + " print(\"-\" * 83)" ] }, { @@ -466,7 +486,7 @@ "id": "b4fea740-bbcc-4758-9472-a5c70642dd22", "metadata": {}, "source": [ - "**Conclusion:** Both the clean and masked arrays yield consistent results. This conclusion also applies to the variance functions. " + "**Conclusion:** Both the \"fully valid\" and \"active mask\" columns yield consistent results across all four methods. This conclusion also applies to the variance calculations. " ] }, { @@ -476,9 +496,9 @@ "source": [ "### 3.6. Summary\n", "\n", - "`numpy` functions that delegate to the masked array's internal methods (e.g., `np.min` calling `data.min()`) respect the mask and work as expected. In contrast, functions that lack a corresponding internal method (e.g., `np.quantile`) implicitly convert the masked array to a raw array. This exposes the underlying invalid data to sorting or binning algorithms, resulting in errors or scientifically incorrect values.\n", + "While functions like `np.mean` correctly handles `MaskedColumn` objects, others (e.g., `np.median`) do not respect the mask layer and implicitly convert the input to a raw array. This exposes invalid data to the calculation, resulting in errors or scientifically incorrect values.\n", "\n", - "A safe recommendation when using `numpy` for masked arrays is to compress them before performing computations, or to use the `numpy.ma` module when the desired functions are available." + "A safe recommendation when using `numpy` for `MaskedColumn` objects is to compress them before performing computations, or to use the `numpy.ma` module when the desired functions are available." ] }, { @@ -488,11 +508,11 @@ "source": [ "## 4. Compute statistics with `scipy.stats.mstats`\n", "\n", - "`scipy.stats.mstats` is a specialized sub-module within `scipy` dedicated to statistical functions for masked data. It serves as the masked-array equivalent of the standard `scipy.stats` module. While `numpy.ma` provides mainly basic reductions (mean, median), `mstats` offers a broader suite of statistical tools that automatically respect the input mask, including correlation functions such as the Spearman rank-order correlation and statistical tests like Pearson's chi-squared and the Kolmogorov-Smirnov test. This eliminates the need to manually compress arrays before performing advanced statistical analysis.\n", + "`scipy.stats.mstats` is a specialized sub-module within `scipy` dedicated to statistical functions for `MaskedColumn` objects. It serves as the masked-array equivalent of the standard `scipy.stats` module. While `numpy.ma` provides mainly basic reductions (like mean, median), `mstats` offers a broader suite of statistical tools that automatically respect the input mask, including correlation functions such as the Spearman rank-order correlation and statistical tests like Pearson's chi-squared and the Kolmogorov-Smirnov test. This eliminates the need to manually compress arrays before performing advanced statistical analysis.\n", "\n", "### 4.1. Compare with `numpy`\n", "\n", - "Compare quantile computations using `mstats` versus `numpy` with `.compressed()`." + "Compare the 75th quantile results for the \"active mask\" column using `mstats` versus `numpy` with `.compressed()`." ] }, { @@ -502,16 +522,16 @@ "metadata": {}, "outputs": [], "source": [ - "prob = 0.75\n", + "target_percentile = 0.75\n", "\n", - "data = results[masked_data_cols[0]]\n", - "scipy_val = scistats.mquantiles(data, prob=[prob])[0]\n", - "numpy_val = np.quantile(data.compressed(), prob)\n", + "data = data_sources['An active mask column']\n", + "scipy_val = scistats.mquantiles(data, prob=[target_percentile])[0]\n", + "numpy_val = np.quantile(data.compressed(), target_percentile)\n", "\n", - "print(f\"{'Method': <24} | {'dtype': <7} | {'Result'}\")\n", - "print(\"-\" * 57)\n", - "print(f\"{'mstats': <24} | {scipy_val.dtype} | {scipy_val: .17f}\")\n", - "print(f\"{'numpy with .compressed()': <24} | {numpy_val.dtype} | {numpy_val: .17f}\")" + "print(f\"{'Method': <24} | {'dtype_out': <9} | {'Result'}\")\n", + "print(\"-\" * 59)\n", + "print(f\"{'mstats': <24} | {str(scipy_val.dtype): <9} | {scipy_val: .17f}\")\n", + "print(f\"{'numpy with .compressed()': <24} | {str(numpy_val.dtype): <9} | {numpy_val: .17f}\")" ] }, {