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Add Control Loop Single and Double Exposure Test.
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‎notebooks/CLT/CLT-006/CLT-006-ComCam.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "dc42d337-8478-43d2-a3c9-562d6ccbe5b6",
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"metadata": {},
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"source": [
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"# CLT-006: Control Loop Single and Double Exposure Test\n",
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"\n",
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"Owner: **Bryce Kalmbach** <br>\n",
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"Last Verified to Run: **2025-04-03** <br>\n",
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"Software Version:\n",
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" - `ts_wep`: **14.1.1**\n",
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" - `donut_viz`: **1.6.2**\n",
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" - `lsst_distrib`: **w_2025_13**\n",
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"\n",
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"## Test Details:\n",
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"In this experiment we calculate the closed loop with 15 second exposure then reset the state back to the initial state and re-run the closed loop with 30 seconds.\n",
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"We then compare the residual AOS FWHM during the progression of the closed loop."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a5e0fbcf",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Times Square Parameters\n",
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"collection_name = 'u/brycek/aosRefitWcs_danish_singleBlends_80pxMinSep'\n",
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"day_obs = 20241118\n",
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"min_seq_num_15_sec = 31\n",
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"max_seq_num_15_sec = 53\n",
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"min_seq_num_30_sec = 54\n",
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"max_seq_num_30_sec = 75"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8f7a6e94-7e2f-4a68-a56a-0b6a6fe1e3e7",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from copy import copy\n",
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"from matplotlib import pyplot as plt\n",
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"from lsst.daf.butler import Butler\n",
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"\n",
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"# Can uncomment when running outside Times Square (Hopefully temporary)\n",
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"#from lsst.ts.wep.utils import getPsfGradPerZernike\n",
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"\n",
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"from astropy.table import Table\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "babf357a-875a-4766-ad88-8a3e61e0147a",
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"metadata": {},
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"outputs": [],
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"source": [
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"butler = Butler('/repo/main')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9878cab0-402f-4ac0-a6c9-c7852ac3ff0b",
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"metadata": {},
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"outputs": [],
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"source": [
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"camera = butler.get('camera', {'instrument': \"LSSTComCam\"}, collections=collection_name)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c1b56ecb-ce5c-4162-89d4-abf040b309aa",
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"metadata": {},
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"source": [
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"## Define Functions Needed (Temporary)\n",
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"This is hopefully temporary since this function lives in `ts_wep` which is currently unavailable from Times Square"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "35405e6c-5b07-49d2-85c9-2dfd4f3bd089",
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"metadata": {},
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"outputs": [],
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"source": [
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"import galsim\n",
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"\n",
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"def getPsfGradPerZernike(\n",
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" diameter: float = 8.36,\n",
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" obscuration: float = 0.612,\n",
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" jmin: int = 4,\n",
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" jmax: int = 22,\n",
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") -> np.ndarray:\n",
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" \"\"\"Get the gradient of the PSF FWHM with respect to each Zernike.\n",
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"\n",
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" This function takes no positional arguments. All parameters must be passed\n",
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" by name (see the list of parameters below).\n",
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"\n",
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" Parameters\n",
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" ----------\n",
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" diameter : float, optional\n",
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" The diameter of the telescope aperture, in meters.\n",
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" (the default, 8.36, corresponds to the LSST primary mirror)\n",
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" obscuration : float, optional\n",
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" Central obscuration of telescope aperture (i.e. R_outer / R_inner).\n",
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" (the default, 0.612, corresponds to the LSST primary mirror)\n",
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" jmin : int, optional\n",
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" The minimum Noll index, inclusive. Must be >= 0. (the default is 4)\n",
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" jmax : int, optional\n",
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" The max Zernike Noll index, inclusive. Must be >= jmin.\n",
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" (the default is 22.)\n",
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"\n",
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" Returns\n",
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" -------\n",
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" np.ndarray\n",
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" Gradient of the PSF FWHM with respect to the corresponding Zernike.\n",
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" Units are arcsec / micron.\n",
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"\n",
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" Raises\n",
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" ------\n",
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" ValueError\n",
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" If jmin is negative or jmax is less than jmin\n",
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" \"\"\"\n",
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" # Check jmin and jmax\n",
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" if jmin < 0:\n",
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" raise ValueError(\"jmin cannot be negative.\")\n",
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" if jmax < jmin:\n",
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" raise ValueError(\"jmax must be greater than jmin.\")\n",
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"\n",
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" # Calculate the conversion factors\n",
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" conversion_factors = np.zeros(jmax + 1)\n",
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" for i in range(jmin, jmax + 1):\n",
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" # Set coefficients for this Noll index: coefs = [0, 0, ..., 1]\n",
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" # Note the first coefficient is Noll index 0, which does not exist and\n",
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" # is therefore always ignored by galsim\n",
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" coefs = [0] * i + [1]\n",
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"\n",
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" # Create the Zernike polynomial with these coefficients\n",
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" R_outer = diameter / 2\n",
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" R_inner = R_outer * obscuration\n",
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" Z = galsim.zernike.Zernike(coefs, R_outer=R_outer, R_inner=R_inner)\n",
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"\n",
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" # We can calculate the size of the PSF from the RMS of the gradient of\n",
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" # the wavefront. The gradient of the wavefront perturbs photon paths.\n",
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" # The RMS quantifies the size of the collective perturbation.\n",
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" # If we expand the wavefront gradient in another series of Zernike\n",
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" # polynomials, we can exploit the orthonormality of the Zernikes to\n",
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" # calculate the RMS from the Zernike coefficients.\n",
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" rms_tilt = np.sqrt(np.sum(Z.gradX.coef**2 + Z.gradY.coef**2) / 2)\n",
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"\n",
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" # Convert to arcsec per micron\n",
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" rms_tilt = np.rad2deg(rms_tilt * 1e-6) * 3600\n",
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"\n",
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" # Convert rms -> fwhm\n",
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" fwhm_tilt = 2 * np.sqrt(2 * np.log(2)) * rms_tilt\n",
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"\n",
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" # Save this conversion factor\n",
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" conversion_factors[i] = fwhm_tilt\n",
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"\n",
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" return conversion_factors[jmin:]\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "15c10091-61d9-4e79-acbf-39aa16e68174",
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"metadata": {},
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"source": [
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"## Gather Visit Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "efed4f97-5e8a-4c93-93a0-d5124368a658",
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"metadata": {},
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"outputs": [],
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"source": [
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"visit_tables_15_sec = butler.query_datasets('aggregateAOSVisitTableAvg', \n",
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" collections=collection_name,\n",
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" where=f\"exposure.day_obs = {day_obs} and exposure.seq_num >= {min_seq_num_15_sec} and exposure.seq_num <= {max_seq_num_15_sec} and instrument = 'LSSTComCam'\")\n",
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"visit_tables_30_sec = butler.query_datasets('aggregateAOSVisitTableAvg', \n",
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" collections=collection_name,\n",
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" where=f\"exposure.day_obs = {day_obs} and exposure.seq_num >= {min_seq_num_30_sec} and exposure.seq_num <= {max_seq_num_30_sec} and instrument = 'LSSTComCam'\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d32a7b53-e841-49d7-a6d0-5aa016e9f87e",
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"metadata": {},
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"outputs": [],
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"source": [
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"visit_dict_15_sec = dict()\n",
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"for visit_ref in visit_tables_15_sec:\n",
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" visit_table = butler.get(visit_ref)\n",
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" visit_dict_15_sec[visit_table.meta['visit']] = visit_table\n",
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"visit_dict_15_sec = dict(sorted(visit_dict_15_sec.items()))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ff407e43-7b1c-43b3-8ed5-f152380209e7",
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"metadata": {},
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"outputs": [],
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"source": [
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"visit_dict_30_sec = dict()\n",
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"for visit_ref in visit_tables_30_sec:\n",
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" visit_table = butler.get(visit_ref)\n",
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" visit_dict_30_sec[visit_table.meta['visit']] = visit_table\n",
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"visit_dict_30_sec = dict(sorted(visit_dict_30_sec.items()))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "500b20a7-4371-4136-9063-66655a6636a9",
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"metadata": {},
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"outputs": [],
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"source": [
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"noll_idx = visit_table.meta['nollIndices']\n",
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"noll_min = np.min(noll_idx)\n",
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"noll_max = np.max(noll_idx)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "848ade3f-9c12-47b1-96be-a218c4b107d7",
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"metadata": {},
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"outputs": [],
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"source": [
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"conv_array = getPsfGradPerZernike(jmin=noll_min, jmax=noll_max)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3cda025b-94fb-473d-bc73-d53a4cada57f",
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"metadata": {},
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"outputs": [],
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"source": [
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"zk_table_15_sec = Table()\n",
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"zk_table_15_sec = Table(names=['visit', 'full_array', 'fwhm_combined'], dtype=[int, (float, 25), float])\n",
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"for visit_id, visit_results in visit_dict_15_sec.items():\n",
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" zk_array = np.zeros((noll_max - noll_min + 1))\n",
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" for det_name in camera.getNameIter():\n",
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" zk_det_array = np.zeros((noll_max - noll_min + 1))\n",
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" zk_det_array[noll_idx - noll_min - 1] = visit_results[visit_results['detector'] == det_name]['zk_CCS']\n",
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" zk_array += zk_det_array * conv_array\n",
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" zk_array /= len(camera)\n",
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" zk_table_15_sec.add_row(vals=[visit_id, zk_array, np.sqrt(np.sum(zk_array**2))])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2d8f07c2-d467-4610-881b-bbb9c046b75b",
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"metadata": {},
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"outputs": [],
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"source": [
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"zk_table_30_sec = Table()\n",
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"zk_table_30_sec = Table(names=['visit', 'full_array', 'fwhm_combined'], dtype=[int, (float, 25), float])\n",
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"for visit_id, visit_results in visit_dict_30_sec.items():\n",
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" zk_array = np.zeros((noll_max - noll_min + 1))\n",
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" for det_name in camera.getNameIter():\n",
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" zk_det_array = np.zeros((noll_max - noll_min + 1))\n",
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" zk_det_array[noll_idx - noll_min - 1] = visit_results[visit_results['detector'] == det_name]['zk_CCS']\n",
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" zk_array += zk_det_array * conv_array\n",
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" zk_array /= len(camera)\n",
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" zk_table_30_sec.add_row(vals=[visit_id, zk_array, np.sqrt(np.sum(zk_array**2))])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c71d3a42-98f8-46ee-b2b5-13be0c569bf0",
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.plot((zk_table_15_sec['visit'] - zk_table_15_sec['visit'][0])/3, zk_table_15_sec['fwhm_combined'], label='15 Second Exposures')\n",
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"plt.plot((zk_table_30_sec['visit'] - zk_table_30_sec['visit'][0])/3, zk_table_30_sec['fwhm_combined'], label='30 Second Exposures')\n",
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"plt.xlabel('Closed Loop Iteration')\n",
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"plt.ylabel('FWHM AOS Residual (arcsec)')\n",
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"plt.legend()\n",
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"band = visit_table.meta['band']\n",
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"plt.title(\n",
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" 'Closed Loop Convergence: 15 sec vs 30 sec.\\n ' +\n",
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" f'day_obs: {day_obs} seq_num: {min_seq_num_15_sec} - {max_seq_num_15_sec}, {min_seq_num_30_sec} - {max_seq_num_30_sec}, band: {band}'\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "120b73b3-5cdf-42ff-a745-fe3ff8fe5e21",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "LSST",
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"language": "python",
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"name": "lsst"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎notebooks/CLT/CLT-006/CLT-006-ComCam.yaml‎

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title: "CLT-006-ComCam: 15 vs 30 second exposures"
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description: Validate the closed loop works for 15 second exposures just as well as for 30 seconds.
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authors:
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- name: Bryce Kalmbach
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slack: brycek
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tags:
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- comcam
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- CLT
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parameters:
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collection_name:
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type: string
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description: Butler collection.
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default: 'u/brycek/aosRefitWcs_danish_singleBlends_80pxMinSep'
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day_obs:
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type: integer
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description: Exposure day_obs value from visitInfo.
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default: 20241118
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min_seq_num_15_sec:
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type: integer
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description: Starting Sequence Number for 15 second closed loop run.
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default: 31
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max_seq_num_15_sec:
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type: integer
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description: Ending Sequence Number for 15 second closed loop run.
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default: 53
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min_seq_num_30_sec:
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type: integer
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description: Starting Sequence Number for 30 second closed loop run.
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default: 54
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max_seq_num_30_sec:
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type: integer
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description: Ending Sequence Number for 30 second closed loop run.
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default: 75

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