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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "c9725f76-c622-4009-a2bd-541a62487d84",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-01-11T00:28:50.497605Z",
+ "iopub.status.busy": "2024-01-11T00:28:50.497179Z",
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+ "shell.execute_reply": "2024-01-11T00:28:50.500547Z",
+ "shell.execute_reply.started": "2024-01-11T00:28:50.497591Z"
+ }
+ },
+ "source": [
+ " \n",
+ "
\n",
+ "Explore Large Alerts
\n",
+ "Contact author: Brianna Smart
\n",
+ "Last verified to run: - 01/10/2024
\n",
+ "LSST Science Piplines version: - Weekly 2024_01 Large Container
\n",
+ "Container Size: Large
# Clean Notebook"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "585359b5-6dd0-43e1-9d77-652e5b3f14fe",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-01-22T21:36:38.543259Z",
+ "iopub.status.busy": "2024-01-22T21:36:38.543051Z",
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+ "shell.execute_reply.started": "2024-01-22T21:36:38.543243Z"
+ }
+ },
+ "source": [
+ "## Table of Contents\n",
+ "\n",
+ "1. [Introduction](#Introduction)\n",
+ "2. [Data](#Data)\n",
+ "3. [Conclusions](#Conclusions)\n",
+ "4. [Setup](#Setup)\n",
+ "5. [200 to 1000 pixel cutouts](#200-to-1000-pixel-cutouts)\n",
+ "6. [Cutout and Footprint Mismatch](#Cutout-and-Footprint-Mismatch)\n",
+ "7. [Explore Psf Dipole Flux Flags](#Explore-Psf-Dipole-Flux-Flags)\n",
+ "8. [Sources between 200 and 100 pixels](#Sources-between-200-and-100-pixels)\n",
+ "9. [Obvious cosmic rays](#Obvious-cosmic-rays)\n",
+ "10. [Sources between 100 and 50](#Sources-between-100-and-50-pixels)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "829356d6-1858-4609-bf2d-a07ada5592b9",
+ "metadata": {},
+ "source": [
+ "## Too large cutouts in alerts"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fe103691-788f-4899-9873-533f07f53374",
+ "metadata": {},
+ "source": [
+ "## Introduction\n",
+ "\n",
+ "This notebook inspects a number of alert packets which contain alerts with cutouts that are unreasonably large. The minimum cutout size for an alert is 30 x 30 pixels, but there is no set maximum. We are seeing several cutouts of 200x200 pixels, and some even largr than 800x800. While these are being filtered out and not making it into the alert stream, they should not be making it to the database in the first place.\n",
+ "\n",
+ "# Data\n",
+ "\n",
+ "The data is from a Weekly 2024_01 ap_verify run using schema 6.0. Any alert that is too large to go to the alert stream is filtered out and saved to disk. With the current setup, it also saves the whole alert packet to disk, and we investigate all the alerts contained in it, not just the overly large alerts.\n",
+ "\n",
+ "## Conclusions\n",
+ "\n",
+ "Upon initial inspection, all of the cutouts that are far too large are obviously bad sources. Most appear on over saturated stars, bad columns. The few that appear to have \"normal\" cutouts have incorrectly centered cutouts, where the dipole centroid the cutout is based on is far outside of the footprint. Upon inspecting the footprint itself, you can see that the source was detected on bad columns. \n",
+ "\n",
+ "In addition to the dipole centroids being far apart, all of these sources have the interpolated flag set. While this doesn't necessarily mean that the sources are bad, looking at a large subset of sources with the interpolated flag might be a good idea to see if any with the flag set are good. \n",
+ "\n",
+ "Other bad sources, such as source 25397859608167394, are abnormally long and straight. These are bad columns, but if we do additional filtering based on footprint size it's possible these would be filtered out. However, more discussion should be had about what it means to set a max footprint size. If a sources size is far beyond the footprint maximum, what does that mean for the alert itself? \n",
+ "\n",
+ "The current solution of not sending this alerts is not catching any legitimate images, and inspecting the large alerts after any major changing in filtering or algorithms will give us an idea of how well the pipeline is working.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3a850328-075e-4e3a-ba72-9d02bc1ded8e",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-01-16T23:47:07.912382Z",
+ "iopub.status.busy": "2024-01-16T23:47:07.911922Z",
+ "iopub.status.idle": "2024-01-16T23:47:07.914365Z",
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+ "shell.execute_reply.started": "2024-01-16T23:47:07.912368Z"
+ }
+ },
+ "source": [
+ "## Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "882524fe-93b1-469d-bf3b-2aab110cf485",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-02-02T18:39:12.445623Z",
+ "iopub.status.busy": "2024-02-02T18:39:12.445408Z",
+ "iopub.status.idle": "2024-02-02T18:39:17.054773Z",
+ "shell.execute_reply": "2024-02-02T18:39:17.054404Z",
+ "shell.execute_reply.started": "2024-02-02T18:39:12.445608Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import io\n",
+ "import gzip\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import importlib\n",
+ "from lsst.analysis.ap import apdb\n",
+ "from lsst.analysis.ap import legacyApdbUtils as utils\n",
+ "import fastavro\n",
+ "\n",
+ "import lsst.geom\n",
+ "import lsst.daf.butler as dafButler\n",
+ "from lsst.ap.association import UnpackApdbFlags, TransformDiaSourceCatalogConfig\n",
+ "from lsst.analysis.ap import legacyApdbUtils as utils\n",
+ "from lsst.analysis.ap import legacyPlotUtils as plac\n",
+ "from lsst.analysis.ap import apdb\n",
+ "\n",
+ "from astropy.time import Time\n",
+ "from astropy.io import fits\n",
+ "from astropy.nddata import CCDData\n",
+ "import lsst.afw.display as afwDisplay\n",
+ "from IPython.display import IFrame\n",
+ "import gc\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "a4012dfb-12ce-4eed-8637-d4d06fe8dbe7",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-02-02T18:39:17.062159Z",
+ "iopub.status.busy": "2024-02-02T18:39:17.061817Z",
+ "iopub.status.idle": "2024-02-02T18:39:17.067710Z",
+ "shell.execute_reply": "2024-02-02T18:39:17.067397Z",
+ "shell.execute_reply.started": "2024-02-02T18:39:17.062140Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def m_and_v_plots (exposure, matplot=None, display_num=None, record=None):\n",
+ " \"\"\"\n",
+ " Create an array of plots with their mask and variance\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " datasetRefs: a set of DataSetRefs\n",
+ " returned by the butler\n",
+ " matplot: Can be set to True\n",
+ " display_num: Integer setting number of images\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " None\n",
+ " \"\"\"\n",
+ " \n",
+ " if display_num == None:\n",
+ " display_num=1\n",
+ " else:\n",
+ " display_num=display_num\n",
+ " \n",
+ " if matplot != None:\n",
+ " afwDisplay.setDefaultBackend('matplotlib') \n",
+ " \n",
+ " print('Visit: ', exposure.visitInfo.getId(), ', Detector: ', exposure.detector.getId())\n",
+ " fig, ax = plt.subplots(1, 3, figsize=(14, 7))\n",
+ " plt.sca(ax[0]) # set the first axis as current\n",
+ " plt.xlabel('Image with Mask Overlay')\n",
+ " display1 = afwDisplay.Display(frame=fig)\n",
+ " display1.scale('asinh', -1, 30)\n",
+ " display1.mtv(exposure)\n",
+ " plt.sca(ax[1]) # set the second axis as current\n",
+ " display2 = afwDisplay.Display(frame=fig)\n",
+ " display2.mtv(exposure.mask)\n",
+ " plt.tight_layout()\n",
+ " plt.sca(ax[2]) \n",
+ " display3 = afwDisplay.Display(frame=fig)\n",
+ " display3.scale('asinh', 'zscale')\n",
+ " display3.mtv(exposure.variance)\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ " remove_figure(fig)\n",
+ "\n",
+ " else:\n",
+ " afwDisplay.setDefaultBackend('firefly')\n",
+ "\n",
+ " print('Visit: ', exposure.visitInfo.getId(), ', Detector: ',exposure.detector.getId())\n",
+ " display = afwDisplay.Display(frame=0)\n",
+ " display.scale('asinh', -1, 30)\n",
+ " display.mtv(exposure)\n",
+ " display = afwDisplay.Display(frame=1)\n",
+ " display.setMaskTransparency(90)\n",
+ " display.mtv(exposure.mask)\n",
+ " display = afwDisplay.Display(frame=2)\n",
+ " display.scale('asinh', 'zscale')\n",
+ " display.mtv(exposure.variance)\n",
+ "\n",
+ " if record is not None:\n",
+ " display1.dot('x', record.getX(),record.getY())\n",
+ " display2.dot('x', record.getX(),record.getY())\n",
+ " display3.dot('x', record.getX(),record.getY())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "c8f7c589-17e6-4195-a153-63f628e6e480",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-02-02T18:39:17.068633Z",
+ "iopub.status.busy": "2024-02-02T18:39:17.068507Z",
+ "iopub.status.idle": "2024-02-02T18:39:17.074246Z",
+ "shell.execute_reply": "2024-02-02T18:39:17.073934Z",
+ "shell.execute_reply.started": "2024-02-02T18:39:17.068621Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def remove_figure(fig):\n",
+ " \"\"\"\n",
+ " Remove a figure to reduce memory footprint.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " fig: matplotlib.figure.Figure\n",
+ " Figure to be removed.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " None\n",
+ " \"\"\"\n",
+ " # get the axes and clear their images\n",
+ " for ax in fig.get_axes():\n",
+ " for im in ax.get_images():\n",
+ " im.remove()\n",
+ " fig.clf() # clear the figure\n",
+ " plt.close(fig) # close the figure\n",
+ " gc.collect() # call the garbage collector"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "ecb03d04-c449-4d27-8613-12975e442139",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-02-02T18:39:17.074761Z",
+ "iopub.status.busy": "2024-02-02T18:39:17.074641Z",
+ "iopub.status.idle": "2024-02-02T18:39:17.078491Z",
+ "shell.execute_reply": "2024-02-02T18:39:17.078200Z",
+ "shell.execute_reply.started": "2024-02-02T18:39:17.074750Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def find_files(root_dir):\n",
+ " for dir_name, subdir_list, file_list in os.walk(root_dir, followlinks=True):\n",
+ " for fname in file_list:\n",
+ " if fname.endswith('.avro'):\n",
+ " yield dir_name+'/'+fname"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "a295f568-68ac-438f-91df-95da9150d4a1",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-02-02T18:39:17.079019Z",
+ "iopub.status.busy": "2024-02-02T18:39:17.078897Z",
+ "iopub.status.idle": "2024-02-02T18:39:17.086914Z",
+ "shell.execute_reply": "2024-02-02T18:39:17.086617Z",
+ "shell.execute_reply.started": "2024-02-02T18:39:17.079008Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def plot_stamps(filename, pixel_max=3200, pixel_min=200, sourceId = None, return_alert = False):\n",
+ " # Can take a single sourceId or look at all of them for a range of pixel dimensions. Can also return the packet if \n",
+ " with open(filename,'rb') as f:\n",
+ " try:\n",
+ " freader = fastavro.reader(f)\n",
+ " for packet in freader:\n",
+ " image_data = packet['cutoutDifference']\n",
+ " if sourceId is not None:\n",
+ " if packet['diaSource']['diaSourceId'] != sourceId:\n",
+ " continue\n",
+ " else:\n",
+ " print(\"source_found\")\n",
+ " if return_alert: \n",
+ " return packet\n",
+ " with io.BytesIO(image_data) as bytesIO:\n",
+ " cutoutFromBytes = CCDData.read(bytesIO, format=\"fits\")\n",
+ " bbox = cutoutFromBytes.shape\n",
+ " if bbox[0] >=pixel_min and bbox[1] >= pixel_min and bbox[0] <= pixel_max and bbox[1] <= pixel_max:\n",
+ " plt.figure()\n",
+ " source = apdbQuery.load_source(packet['diaSource']['diaSourceId'])\n",
+ " dataId ={'detector':source['detector'],'visit': source['visit']}\n",
+ " catalog = butler.get(f'goodSeeingDiff_diaSrc', dataId)\n",
+ " record = catalog.find(packet['diaSource']['diaSourceId'])\n",
+ " print('-----------------------------------------------')\n",
+ " print(filename)\n",
+ " print(\"DiaSource Id: \", packet['diaSource']['diaSourceId'])\n",
+ " print(\"Bounding Box: \", bbox)\n",
+ " print(\"Centroid: \", record.getX(), record.getY())\n",
+ " print(\"Interpolated Flag: \", record['base_PixelFlags_flag_interpolated'])\n",
+ " print(\"Saturated Flag: \", record['base_PixelFlags_flag_saturated'])\n",
+ " print(\"Trailed Source Flag: \", record['ext_trailedSources_Naive_flag'])\n",
+ " length_check = record['ext_trailedSources_Naive_length']\n",
+ " print(\"Trailed Source Length more than limit: \", )\n",
+ " print(\"Dipole Flux Flag: \", record['ip_diffim_PsfDipoleFlux_flag'])\n",
+ " print(\"Dipole Flux Centroid: \", record['ip_diffim_NaiveDipoleCentroid_x'],record['ip_diffim_NaiveDipoleCentroid_y'])\n",
+ " x_dipole_sep=record['ip_diffim_NaiveDipoleCentroid_pos_x']-record['ip_diffim_NaiveDipoleCentroid_neg_x']\n",
+ " y_dipole_sep=record['ip_diffim_NaiveDipoleCentroid_pos_y']-record['ip_diffim_NaiveDipoleCentroid_neg_y']\n",
+ " print(\"Seperation of x and y dipole coords: \", [x_dipole_sep,y_dipole_sep])\n",
+ " plt.imshow(cutoutFromBytes, vmin=-1, vmax=30)\n",
+ " except:\n",
+ " out=1\n",
+ " # print(filename + \" not a data packet\")\n",
+ " #print(\"\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "85b31d1d-6771-4f13-aea2-33d198d89419",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-02-02T18:39:17.087465Z",
+ "iopub.status.busy": "2024-02-02T18:39:17.087342Z",
+ "iopub.status.idle": "2024-02-02T18:39:17.092247Z",
+ "shell.execute_reply": "2024-02-02T18:39:17.091943Z",
+ "shell.execute_reply.started": "2024-02-02T18:39:17.087454Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def plot_footprint(source, dataSetType):\n",
+ " \n",
+ " dataId ={'detector':source['detector'],'visit': source['visit']}\n",
+ " calexp=butler.get(dataSetType, dataId)\n",
+ " catalog = butler.get(f'goodSeeingDiff_diaSrc', dataId)\n",
+ " record = catalog.find(source['diaSourceId'])\n",
+ " footprint = record.getFootprint()\n",
+ " cutout_calexp = calexp.subset(footprint.getBBox())\n",
+ " print(\"Footprint bounding box: \", footprint.getBBox())\n",
+ " print(\"Centroid from record = \", record.getX(), record.getY())\n",
+ " print(record['ip_diffim_PsfDipoleFlux_flag'])\n",
+ " m_and_v_plots(cutout_calexp, display_num=0, matplot=True, record=record)\n",
+ " print(record)\n",
+ " #print(footprint)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d7e7162f-60a4-433b-9344-7ee6e122f56e",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-01-16T23:52:19.467624Z",
+ "iopub.status.busy": "2024-01-16T23:52:19.467248Z",
+ "iopub.status.idle": "2024-01-16T23:52:19.470262Z",
+ "shell.execute_reply": "2024-01-16T23:52:19.469821Z",
+ "shell.execute_reply.started": "2024-01-16T23:52:19.467554Z"
+ }
+ },
+ "source": [
+ "## Set Up Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "a601b44b-2f1e-40b8-801b-0411d9509c48",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-02-02T18:39:17.092761Z",
+ "iopub.status.busy": "2024-02-02T18:39:17.092640Z",
+ "iopub.status.idle": "2024-02-02T18:39:17.689512Z",
+ "shell.execute_reply": "2024-02-02T18:39:17.689017Z",
+ "shell.execute_reply.started": "2024-02-02T18:39:17.092749Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "collection=\"ap_verify-output/20240110T180834Z\"\n",
+ "butler = lsst.daf.butler.Butler(\"/sdf/group/rubin/u/smart/code/workspacest/postage_stamp_investigation/repo/\", collections=collection)\n",
+ "\n",
+ "apdbQuery = apdb.ApdbSqliteQuery(\"/sdf/group/rubin/u/smart/code/workspacest/postage_stamp_investigation/association.db\", butler=butler, instrument=\"HSC\")\n",
+ "sources = apdbQuery.load_sources( limit=100000)\n",
+ "objects = apdbQuery.load_objects()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "120a2d22-5deb-46cc-8045-a0f5bd2cb5c8",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-02-02T18:39:17.690290Z",
+ "iopub.status.busy": "2024-02-02T18:39:17.690157Z",
+ "iopub.status.idle": "2024-02-02T18:39:17.692756Z",
+ "shell.execute_reply": "2024-02-02T18:39:17.692385Z",
+ "shell.execute_reply.started": "2024-02-02T18:39:17.690278Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "source = sources.loc[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "18f3c4ac-2314-4b74-8c6c-62881fdacf9f",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-02-02T18:39:17.694066Z",
+ "iopub.status.busy": "2024-02-02T18:39:17.693931Z",
+ "iopub.status.idle": "2024-02-02T18:39:18.156169Z",
+ "shell.execute_reply": "2024-02-02T18:39:18.155702Z",
+ "shell.execute_reply.started": "2024-02-02T18:39:17.694054Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "dataId ={'detector':source['detector'],'visit': source['visit']}\n",
+ "calexp=butler.get('calexp', dataId)\n",
+ "catalog = butler.get(f'goodSeeingDiff_diaSrc', dataId)\n",
+ "record = catalog.find(source['diaSourceId'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "1e18ba1e-a09c-478a-bc74-41b20923285a",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-02-02T18:39:18.156873Z",
+ "iopub.status.busy": "2024-02-02T18:39:18.156743Z",
+ "iopub.status.idle": "2024-02-02T18:39:18.159159Z",
+ "shell.execute_reply": "2024-02-02T18:39:18.158795Z",
+ "shell.execute_reply.started": "2024-02-02T18:39:18.156860Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "xx=catalog.schema['id']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "595cc9a2-32c8-4b2b-9acb-f87ed15115c2",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-02-02T18:39:19.131457Z",
+ "iopub.status.busy": "2024-02-02T18:39:19.131322Z",
+ "iopub.status.idle": "2024-02-02T18:39:19.135590Z",
+ "shell.execute_reply": "2024-02-02T18:39:19.135233Z",
+ "shell.execute_reply.started": "2024-02-02T18:39:19.131444Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "base_NaiveCentroid_flag True\n",
+ "base_NaiveCentroid_flag_resetToPeak True\n",
+ "base_SdssCentroid_flag True\n",
+ "base_SdssCentroid_flag_notAtMaximum True\n",
+ "base_SdssCentroid_flag_near_edge True\n",
+ "base_SdssShape_flag True\n",
+ "base_SdssShape_flag_unweightedBad True\n",
+ "ext_shapeHSM_HsmSourceMoments_flag True\n",
+ "base_CircularApertureFlux_9_0_flag_sincCoeffsTruncated True\n",
+ "base_CircularApertureFlux_25_0_flag True\n",
+ "base_CircularApertureFlux_25_0_flag_apertureTruncated True\n",
+ "base_CircularApertureFlux_35_0_flag True\n",
+ "base_CircularApertureFlux_35_0_flag_apertureTruncated True\n",
+ "base_CircularApertureFlux_50_0_flag True\n",
+ "base_CircularApertureFlux_50_0_flag_apertureTruncated True\n",
+ "base_CircularApertureFlux_70_0_flag True\n",
+ "base_CircularApertureFlux_70_0_flag_apertureTruncated True\n",
+ "base_GaussianFlux_flag True\n",
+ "base_PixelFlags_flag_edge True\n",
+ "base_PixelFlags_flag_interpolated True\n",
+ "base_PixelFlags_flag_bad True\n",
+ "base_PixelFlags_flag_interpolatedCenter True\n",
+ "base_PixelFlags_flag_badCenter True\n",
+ "base_PixelFlags_flag_interpolatedCenterAll True\n",
+ "base_PixelFlags_flag_badCenterAll True\n",
+ "ext_trailedSources_Naive_flag True\n",
+ "ip_diffim_DipoleFit_flag True\n"
+ ]
+ }
+ ],
+ "source": [
+ "for field in catalog.schema:\n",
+ " name = field.getField().getName()\n",
+ " if \"_flag\" in name and record[name]:\n",
+ " print(name, record[name])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "c5757ce5-3dcd-408a-9d87-ac87def900bd",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-02-02T18:39:19.544737Z",
+ "iopub.status.busy": "2024-02-02T18:39:19.544613Z",
+ "iopub.status.idle": "2024-02-02T18:39:19.547093Z",
+ "shell.execute_reply": "2024-02-02T18:39:19.546758Z",
+ "shell.execute_reply.started": "2024-02-02T18:39:19.544725Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(catalog.schema['id'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "665a3d71-3c07-4e68-960a-9576a1e20f68",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-02-02T18:39:19.547612Z",
+ "iopub.status.busy": "2024-02-02T18:39:19.547496Z",
+ "iopub.status.idle": "2024-02-02T18:39:19.552577Z",
+ "shell.execute_reply": "2024-02-02T18:39:19.552266Z",
+ "shell.execute_reply.started": "2024-02-02T18:39:19.547601Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Schema(\n",
+ " (Field['L'](name=\"id\", doc=\"unique ID\"), Key(offset=0, nElements=1)),\n",
+ " (Field['Angle'](name=\"coord_ra\", doc=\"position in ra/dec\"), Key(offset=8, nElements=1)),\n",
+ " (Field['Angle'](name=\"coord_dec\", doc=\"position in ra/dec\"), Key(offset=16, nElements=1)),\n",
+ " (Field['L'](name=\"parent\", doc=\"unique ID of parent source\"), Key(offset=24, nElements=1)),\n",
+ " (Field['F'](name=\"coord_raErr\", doc=\"1-sigma uncertainty on ra\", units=\"rad\"), Key(offset=32, nElements=1)),\n",
+ " (Field['F'](name=\"coord_decErr\", doc=\"1-sigma uncertainty on dec\", units=\"rad\"), Key(offset=36, nElements=1)),\n",
+ " (Field['F'](name=\"coord_ra_dec_Cov\", doc=\"uncertainty covariance between ra and dec\", units=\"rad rad\"), Key(offset=40, nElements=1)),\n",
+ " (Field['Flag'](name=\"flags_negative\", doc=\"set if source was detected as significantly negative\"), Key['Flag'](offset=48, bit=0)),\n",
+ " (Field['D'](name=\"base_NaiveCentroid_x\", doc=\"centroid from Naive Centroid algorithm\", units=\"pixel\"), Key(offset=56, nElements=1)),\n",
+ " (Field['D'](name=\"base_NaiveCentroid_y\", doc=\"centroid from Naive Centroid algorithm\", units=\"pixel\"), Key(offset=64, nElements=1)),\n",
+ " (Field['Flag'](name=\"base_NaiveCentroid_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=48, bit=1)),\n",
+ " (Field['Flag'](name=\"base_NaiveCentroid_flag_noCounts\", doc=\"Object to be centroided has no counts\"), Key['Flag'](offset=48, bit=2)),\n",
+ " (Field['Flag'](name=\"base_NaiveCentroid_flag_edge\", doc=\"Object too close to edge\"), Key['Flag'](offset=48, bit=3)),\n",
+ " (Field['Flag'](name=\"base_NaiveCentroid_flag_resetToPeak\", doc=\"set if CentroidChecker reset the centroid\"), Key['Flag'](offset=48, bit=4)),\n",
+ " (Field['D'](name=\"base_PeakCentroid_x\", doc=\"peak centroid\", units=\"pixel\"), Key(offset=72, nElements=1)),\n",
+ " (Field['D'](name=\"base_PeakCentroid_y\", doc=\"peak centroid\", units=\"pixel\"), Key(offset=80, nElements=1)),\n",
+ " (Field['Flag'](name=\"base_PeakCentroid_flag\", doc=\"Centroiding failed\"), Key['Flag'](offset=48, bit=5)),\n",
+ " (Field['D'](name=\"base_SdssCentroid_x\", doc=\"centroid from Sdss Centroid algorithm\", units=\"pixel\"), Key(offset=88, nElements=1)),\n",
+ " (Field['D'](name=\"base_SdssCentroid_y\", doc=\"centroid from Sdss Centroid algorithm\", units=\"pixel\"), Key(offset=96, nElements=1)),\n",
+ " (Field['F'](name=\"base_SdssCentroid_xErr\", doc=\"1-sigma uncertainty on x position\", units=\"pixel\"), Key(offset=104, nElements=1)),\n",
+ " (Field['F'](name=\"base_SdssCentroid_yErr\", doc=\"1-sigma uncertainty on y position\", units=\"pixel\"), Key(offset=108, nElements=1)),\n",
+ " (Field['Flag'](name=\"base_SdssCentroid_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=48, bit=6)),\n",
+ " (Field['Flag'](name=\"base_SdssCentroid_flag_edge\", doc=\"Object too close to edge; peak used.\"), Key['Flag'](offset=48, bit=7)),\n",
+ " (Field['Flag'](name=\"base_SdssCentroid_flag_noSecondDerivative\", doc=\"Vanishing second derivative\"), Key['Flag'](offset=48, bit=8)),\n",
+ " (Field['Flag'](name=\"base_SdssCentroid_flag_almostNoSecondDerivative\", doc=\"Almost vanishing second derivative\"), Key['Flag'](offset=48, bit=9)),\n",
+ " (Field['Flag'](name=\"base_SdssCentroid_flag_notAtMaximum\", doc=\"Object is not at a maximum\"), Key['Flag'](offset=48, bit=10)),\n",
+ " (Field['Flag'](name=\"base_SdssCentroid_flag_near_edge\", doc=\"Object close to edge; fallback kernel used.\"), Key['Flag'](offset=48, bit=11)),\n",
+ " (Field['Flag'](name=\"base_SdssCentroid_flag_resetToPeak\", doc=\"set if CentroidChecker reset the centroid\"), Key['Flag'](offset=48, bit=12)),\n",
+ " (Field['Flag'](name=\"base_SdssCentroid_flag_badError\", doc=\"Error on x and/or y position is NaN\"), Key['Flag'](offset=48, bit=13)),\n",
+ " (Field['Flag'](name=\"ip_diffim_NaiveDipoleCentroid_flag\", doc=\"general failure flag, set if anything went wrong\"), Key['Flag'](offset=48, bit=14)),\n",
+ " (Field['Flag'](name=\"ip_diffim_NaiveDipoleCentroid_pos_flag\", doc=\"failure flag for positive, set if anything went wrong\"), Key['Flag'](offset=48, bit=15)),\n",
+ " (Field['Flag'](name=\"ip_diffim_NaiveDipoleCentroid_neg_flag\", doc=\"failure flag for negative, set if anything went wrong\"), Key['Flag'](offset=48, bit=16)),\n",
+ " (Field['D'](name=\"ip_diffim_NaiveDipoleCentroid_x\", doc=\"unweighted first moment centroid: overall centroid\", units=\"pixel\"), Key(offset=112, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_NaiveDipoleCentroid_y\", doc=\"unweighted first moment centroid: overall centroid\", units=\"pixel\"), Key(offset=120, nElements=1)),\n",
+ " (Field['F'](name=\"ip_diffim_NaiveDipoleCentroid_xErr\", doc=\"1-sigma uncertainty on x position\", units=\"pixel\"), Key(offset=128, nElements=1)),\n",
+ " (Field['F'](name=\"ip_diffim_NaiveDipoleCentroid_yErr\", doc=\"1-sigma uncertainty on y position\", units=\"pixel\"), Key(offset=132, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_NaiveDipoleCentroid_pos_x\", doc=\"unweighted first moment centroid: positive lobe\", units=\"pixel\"), Key(offset=136, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_NaiveDipoleCentroid_pos_y\", doc=\"unweighted first moment centroid: positive lobe\", units=\"pixel\"), Key(offset=144, nElements=1)),\n",
+ " (Field['F'](name=\"ip_diffim_NaiveDipoleCentroid_pos_xErr\", doc=\"1-sigma uncertainty on x position\", units=\"pixel\"), Key(offset=152, nElements=1)),\n",
+ " (Field['F'](name=\"ip_diffim_NaiveDipoleCentroid_pos_yErr\", doc=\"1-sigma uncertainty on y position\", units=\"pixel\"), Key(offset=156, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_NaiveDipoleCentroid_neg_x\", doc=\"unweighted first moment centroid: negative lobe\", units=\"pixel\"), Key(offset=160, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_NaiveDipoleCentroid_neg_y\", doc=\"unweighted first moment centroid: negative lobe\", units=\"pixel\"), Key(offset=168, nElements=1)),\n",
+ " (Field['F'](name=\"ip_diffim_NaiveDipoleCentroid_neg_xErr\", doc=\"1-sigma uncertainty on x position\", units=\"pixel\"), Key(offset=176, nElements=1)),\n",
+ " (Field['F'](name=\"ip_diffim_NaiveDipoleCentroid_neg_yErr\", doc=\"1-sigma uncertainty on y position\", units=\"pixel\"), Key(offset=180, nElements=1)),\n",
+ " (Field['D'](name=\"base_SdssShape_xx\", doc=\"elliptical Gaussian adaptive moments\", units=\"pixel^2\"), Key(offset=184, nElements=1)),\n",
+ " (Field['D'](name=\"base_SdssShape_yy\", doc=\"elliptical Gaussian adaptive moments\", units=\"pixel^2\"), Key(offset=192, nElements=1)),\n",
+ " (Field['D'](name=\"base_SdssShape_xy\", doc=\"elliptical Gaussian adaptive moments\", units=\"pixel^2\"), Key(offset=200, nElements=1)),\n",
+ " (Field['F'](name=\"base_SdssShape_xxErr\", doc=\"Standard deviation of xx moment\", units=\"pixel^2\"), Key(offset=208, nElements=1)),\n",
+ " (Field['F'](name=\"base_SdssShape_yyErr\", doc=\"Standard deviation of yy moment\", units=\"pixel^2\"), Key(offset=212, nElements=1)),\n",
+ " (Field['F'](name=\"base_SdssShape_xyErr\", doc=\"Standard deviation of xy moment\", units=\"pixel^2\"), Key(offset=216, nElements=1)),\n",
+ " (Field['D'](name=\"base_SdssShape_x\", doc=\"elliptical Gaussian adaptive moments\", units=\"pixel\"), Key(offset=224, nElements=1)),\n",
+ " (Field['D'](name=\"base_SdssShape_y\", doc=\"elliptical Gaussian adaptive moments\", units=\"pixel\"), Key(offset=232, nElements=1)),\n",
+ " (Field['D'](name=\"base_SdssShape_instFlux\", doc=\"elliptical Gaussian adaptive moments\", units=\"count\"), Key(offset=240, nElements=1)),\n",
+ " (Field['D'](name=\"base_SdssShape_instFluxErr\", doc=\"1-sigma instFlux uncertainty\", units=\"count\"), Key(offset=248, nElements=1)),\n",
+ " (Field['D'](name=\"base_SdssShape_psf_xx\", doc=\"adaptive moments of the PSF model at the object position\", units=\"pixel^2\"), Key(offset=256, nElements=1)),\n",
+ " (Field['D'](name=\"base_SdssShape_psf_yy\", doc=\"adaptive moments of the PSF model at the object position\", units=\"pixel^2\"), Key(offset=264, nElements=1)),\n",
+ " (Field['D'](name=\"base_SdssShape_psf_xy\", doc=\"adaptive moments of the PSF model at the object position\", units=\"pixel^2\"), Key(offset=272, nElements=1)),\n",
+ " (Field['F'](name=\"base_SdssShape_instFlux_xx_Cov\", doc=\"uncertainty covariance between base_SdssShape_instFlux and base_SdssShape_xx\", units=\"count*pixel^2\"), Key(offset=280, nElements=1)),\n",
+ " (Field['F'](name=\"base_SdssShape_instFlux_yy_Cov\", doc=\"uncertainty covariance between base_SdssShape_instFlux and base_SdssShape_yy\", units=\"count*pixel^2\"), Key(offset=284, nElements=1)),\n",
+ " (Field['F'](name=\"base_SdssShape_instFlux_xy_Cov\", doc=\"uncertainty covariance between base_SdssShape_instFlux and base_SdssShape_xy\", units=\"count*pixel^2\"), Key(offset=288, nElements=1)),\n",
+ " (Field['Flag'](name=\"base_SdssShape_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=48, bit=17)),\n",
+ " (Field['Flag'](name=\"base_SdssShape_flag_unweightedBad\", doc=\"Both weighted and unweighted moments were invalid\"), Key['Flag'](offset=48, bit=18)),\n",
+ " (Field['Flag'](name=\"base_SdssShape_flag_unweighted\", doc=\"Weighted moments converged to an invalid value; using unweighted moments\"), Key['Flag'](offset=48, bit=19)),\n",
+ " (Field['Flag'](name=\"base_SdssShape_flag_shift\", doc=\"centroid shifted by more than the maximum allowed amount\"), Key['Flag'](offset=48, bit=20)),\n",
+ " (Field['Flag'](name=\"base_SdssShape_flag_maxIter\", doc=\"Too many iterations in adaptive moments\"), Key['Flag'](offset=48, bit=21)),\n",
+ " (Field['Flag'](name=\"base_SdssShape_flag_psf\", doc=\"Failure in measuring PSF model shape\"), Key['Flag'](offset=48, bit=22)),\n",
+ " (Field['Flag'](name=\"ext_shapeHSM_HsmPsfMoments_flag\", doc=\"General failure flag, set if anything went wrong\"), Key['Flag'](offset=48, bit=23)),\n",
+ " (Field['Flag'](name=\"ext_shapeHSM_HsmPsfMoments_flag_no_pixels\", doc=\"No pixels to measure\"), Key['Flag'](offset=48, bit=24)),\n",
+ " (Field['Flag'](name=\"ext_shapeHSM_HsmPsfMoments_flag_not_contained\", doc=\"Center not contained in footprint bounding box\"), Key['Flag'](offset=48, bit=25)),\n",
+ " (Field['Flag'](name=\"ext_shapeHSM_HsmPsfMoments_flag_parent_source\", doc=\"Parent source, ignored\"), Key['Flag'](offset=48, bit=26)),\n",
+ " (Field['Flag'](name=\"ext_shapeHSM_HsmPsfMoments_flag_galsim\", doc=\"GalSim failure\"), Key['Flag'](offset=48, bit=27)),\n",
+ " (Field['Flag'](name=\"ext_shapeHSM_HsmPsfMoments_flag_invalid_param\", doc=\"Invalid combination of moments\"), Key['Flag'](offset=48, bit=28)),\n",
+ " (Field['Flag'](name=\"ext_shapeHSM_HsmPsfMoments_flag_edge\", doc=\"Variance undefined outside image edge\"), Key['Flag'](offset=48, bit=29)),\n",
+ " (Field['Flag'](name=\"ext_shapeHSM_HsmPsfMoments_flag_no_psf\", doc=\"Exposure lacks PSF\"), Key['Flag'](offset=48, bit=30)),\n",
+ " (Field['D'](name=\"ext_shapeHSM_HsmPsfMoments_x\", doc=\"Centroid of the PSF via the HSM shape algorithm\", units=\"pixel\"), Key(offset=296, nElements=1)),\n",
+ " (Field['D'](name=\"ext_shapeHSM_HsmPsfMoments_y\", doc=\"Centroid of the PSF via the HSM shape algorithm\", units=\"pixel\"), Key(offset=304, nElements=1)),\n",
+ " (Field['D'](name=\"ext_shapeHSM_HsmPsfMoments_xx\", doc=\"Adaptive moments of the PSF via the HSM shape algorithm\", units=\"pixel^2\"), Key(offset=312, nElements=1)),\n",
+ " (Field['D'](name=\"ext_shapeHSM_HsmPsfMoments_yy\", doc=\"Adaptive moments of the PSF via the HSM shape algorithm\", units=\"pixel^2\"), Key(offset=320, nElements=1)),\n",
+ " (Field['D'](name=\"ext_shapeHSM_HsmPsfMoments_xy\", doc=\"Adaptive moments of the PSF via the HSM shape algorithm\", units=\"pixel^2\"), Key(offset=328, nElements=1)),\n",
+ " (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMoments_flag\", doc=\"General failure flag, set if anything went wrong\"), Key['Flag'](offset=48, bit=31)),\n",
+ " (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMoments_flag_no_pixels\", doc=\"No pixels to measure\"), Key['Flag'](offset=48, bit=32)),\n",
+ " (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMoments_flag_not_contained\", doc=\"Center not contained in footprint bounding box\"), Key['Flag'](offset=48, bit=33)),\n",
+ " (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMoments_flag_parent_source\", doc=\"Parent source, ignored\"), Key['Flag'](offset=48, bit=34)),\n",
+ " (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMoments_flag_galsim\", doc=\"GalSim failure\"), Key['Flag'](offset=48, bit=35)),\n",
+ " (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMoments_flag_invalid_param\", doc=\"Invalid combination of moments\"), Key['Flag'](offset=48, bit=36)),\n",
+ " (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMoments_flag_edge\", doc=\"Variance undefined outside image edge\"), Key['Flag'](offset=48, bit=37)),\n",
+ " (Field['Flag'](name=\"ext_shapeHSM_HsmSourceMoments_flag_no_psf\", doc=\"Exposure lacks PSF\"), Key['Flag'](offset=48, bit=38)),\n",
+ " (Field['D'](name=\"ext_shapeHSM_HsmSourceMoments_x\", doc=\"Centroid of the source via the HSM shape algorithm\", units=\"pixel\"), Key(offset=336, nElements=1)),\n",
+ " (Field['D'](name=\"ext_shapeHSM_HsmSourceMoments_y\", doc=\"Centroid of the source via the HSM shape algorithm\", units=\"pixel\"), Key(offset=344, nElements=1)),\n",
+ " (Field['D'](name=\"ext_shapeHSM_HsmSourceMoments_xx\", doc=\"Adaptive moments of the source via the HSM shape algorithm\", units=\"pixel^2\"), Key(offset=352, nElements=1)),\n",
+ " (Field['D'](name=\"ext_shapeHSM_HsmSourceMoments_yy\", doc=\"Adaptive moments of the source via the HSM shape algorithm\", units=\"pixel^2\"), Key(offset=360, nElements=1)),\n",
+ " (Field['D'](name=\"ext_shapeHSM_HsmSourceMoments_xy\", doc=\"Adaptive moments of the source via the HSM shape algorithm\", units=\"pixel^2\"), Key(offset=368, nElements=1)),\n",
+ " (Field['D'](name=\"base_CircularApertureFlux_3_0_instFlux\", doc=\"instFlux within 3.000000-pixel aperture\", units=\"count\"), Key(offset=376, nElements=1)),\n",
+ " (Field['D'](name=\"base_CircularApertureFlux_3_0_instFluxErr\", doc=\"1-sigma instFlux uncertainty\", units=\"count\"), Key(offset=384, nElements=1)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_3_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=48, bit=39)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_3_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=48, bit=40)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_3_0_flag_sincCoeffsTruncated\", doc=\"full sinc coefficient image did not fit within measurement image\"), Key['Flag'](offset=48, bit=41)),\n",
+ " (Field['D'](name=\"base_CircularApertureFlux_4_5_instFlux\", doc=\"instFlux within 4.500000-pixel aperture\", units=\"count\"), Key(offset=392, nElements=1)),\n",
+ " (Field['D'](name=\"base_CircularApertureFlux_4_5_instFluxErr\", doc=\"1-sigma instFlux uncertainty\", units=\"count\"), Key(offset=400, nElements=1)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_4_5_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=48, bit=42)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_4_5_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=48, bit=43)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_4_5_flag_sincCoeffsTruncated\", doc=\"full sinc coefficient image did not fit within measurement image\"), Key['Flag'](offset=48, bit=44)),\n",
+ " (Field['D'](name=\"base_CircularApertureFlux_6_0_instFlux\", doc=\"instFlux within 6.000000-pixel aperture\", units=\"count\"), Key(offset=408, nElements=1)),\n",
+ " (Field['D'](name=\"base_CircularApertureFlux_6_0_instFluxErr\", doc=\"1-sigma instFlux uncertainty\", units=\"count\"), Key(offset=416, nElements=1)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_6_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=48, bit=45)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_6_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=48, bit=46)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_6_0_flag_sincCoeffsTruncated\", doc=\"full sinc coefficient image did not fit within measurement image\"), Key['Flag'](offset=48, bit=47)),\n",
+ " (Field['D'](name=\"base_CircularApertureFlux_9_0_instFlux\", doc=\"instFlux within 9.000000-pixel aperture\", units=\"count\"), Key(offset=424, nElements=1)),\n",
+ " (Field['D'](name=\"base_CircularApertureFlux_9_0_instFluxErr\", doc=\"1-sigma instFlux uncertainty\", units=\"count\"), Key(offset=432, nElements=1)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_9_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=48, bit=48)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_9_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=48, bit=49)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_9_0_flag_sincCoeffsTruncated\", doc=\"full sinc coefficient image did not fit within measurement image\"), Key['Flag'](offset=48, bit=50)),\n",
+ " (Field['D'](name=\"base_CircularApertureFlux_12_0_instFlux\", doc=\"instFlux within 12.000000-pixel aperture\", units=\"count\"), Key(offset=440, nElements=1)),\n",
+ " (Field['D'](name=\"base_CircularApertureFlux_12_0_instFluxErr\", doc=\"1-sigma instFlux uncertainty\", units=\"count\"), Key(offset=448, nElements=1)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_12_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=48, bit=51)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_12_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=48, bit=52)),\n",
+ " (Field['D'](name=\"base_CircularApertureFlux_17_0_instFlux\", doc=\"instFlux within 17.000000-pixel aperture\", units=\"count\"), Key(offset=456, nElements=1)),\n",
+ " (Field['D'](name=\"base_CircularApertureFlux_17_0_instFluxErr\", doc=\"1-sigma instFlux uncertainty\", units=\"count\"), Key(offset=464, nElements=1)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_17_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=48, bit=53)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_17_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=48, bit=54)),\n",
+ " (Field['D'](name=\"base_CircularApertureFlux_25_0_instFlux\", doc=\"instFlux within 25.000000-pixel aperture\", units=\"count\"), Key(offset=472, nElements=1)),\n",
+ " (Field['D'](name=\"base_CircularApertureFlux_25_0_instFluxErr\", doc=\"1-sigma instFlux uncertainty\", units=\"count\"), Key(offset=480, nElements=1)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_25_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=48, bit=55)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_25_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=48, bit=56)),\n",
+ " (Field['D'](name=\"base_CircularApertureFlux_35_0_instFlux\", doc=\"instFlux within 35.000000-pixel aperture\", units=\"count\"), Key(offset=488, nElements=1)),\n",
+ " (Field['D'](name=\"base_CircularApertureFlux_35_0_instFluxErr\", doc=\"1-sigma instFlux uncertainty\", units=\"count\"), Key(offset=496, nElements=1)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_35_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=48, bit=57)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_35_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=48, bit=58)),\n",
+ " (Field['D'](name=\"base_CircularApertureFlux_50_0_instFlux\", doc=\"instFlux within 50.000000-pixel aperture\", units=\"count\"), Key(offset=504, nElements=1)),\n",
+ " (Field['D'](name=\"base_CircularApertureFlux_50_0_instFluxErr\", doc=\"1-sigma instFlux uncertainty\", units=\"count\"), Key(offset=512, nElements=1)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_50_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=48, bit=59)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_50_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=48, bit=60)),\n",
+ " (Field['D'](name=\"base_CircularApertureFlux_70_0_instFlux\", doc=\"instFlux within 70.000000-pixel aperture\", units=\"count\"), Key(offset=520, nElements=1)),\n",
+ " (Field['D'](name=\"base_CircularApertureFlux_70_0_instFluxErr\", doc=\"1-sigma instFlux uncertainty\", units=\"count\"), Key(offset=528, nElements=1)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_70_0_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=48, bit=61)),\n",
+ " (Field['Flag'](name=\"base_CircularApertureFlux_70_0_flag_apertureTruncated\", doc=\"aperture did not fit within measurement image\"), Key['Flag'](offset=48, bit=62)),\n",
+ " (Field['D'](name=\"base_GaussianFlux_instFlux\", doc=\"instFlux from Gaussian Flux algorithm\", units=\"count\"), Key(offset=536, nElements=1)),\n",
+ " (Field['D'](name=\"base_GaussianFlux_instFluxErr\", doc=\"1-sigma instFlux uncertainty\", units=\"count\"), Key(offset=544, nElements=1)),\n",
+ " (Field['Flag'](name=\"base_GaussianFlux_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=48, bit=63)),\n",
+ " (Field['Flag'](name=\"base_LocalPhotoCalib_flag\", doc=\"Set for any fatal failure\"), Key['Flag'](offset=552, bit=0)),\n",
+ " (Field['D'](name=\"base_LocalPhotoCalib\", doc=\"Local approximation of the PhotoCalib calibration factor at the location of the src.\"), Key(offset=560, nElements=1)),\n",
+ " (Field['D'](name=\"base_LocalPhotoCalibErr\", doc=\"Error on the local approximation of the PhotoCalib calibration factor at the location of the src.\"), Key(offset=568, nElements=1)),\n",
+ " (Field['Flag'](name=\"base_LocalWcs_flag\", doc=\"Set for any fatal failure\"), Key['Flag'](offset=552, bit=1)),\n",
+ " (Field['D'](name=\"base_LocalWcs_CDMatrix_1_1\", doc=\"(1, 1) element of the CDMatrix for the linear approximation of the WCS at the src location. Gives units in radians.\"), Key(offset=576, nElements=1)),\n",
+ " (Field['D'](name=\"base_LocalWcs_CDMatrix_1_2\", doc=\"(1, 2) element of the CDMatrix for the linear approximation of the WCS at the src location. Gives units in radians.\"), Key(offset=584, nElements=1)),\n",
+ " (Field['D'](name=\"base_LocalWcs_CDMatrix_2_1\", doc=\"(2, 1) element of the CDMatrix for the linear approximation of the WCS at the src location. Gives units in radians.\"), Key(offset=592, nElements=1)),\n",
+ " (Field['D'](name=\"base_LocalWcs_CDMatrix_2_2\", doc=\"(2, 2) element of the CDMatrix for the linear approximation of the WCS at the src location. Gives units in radians.\"), Key(offset=600, nElements=1)),\n",
+ " (Field['D'](name=\"base_PeakLikelihoodFlux_instFlux\", doc=\"instFlux from PeakLikelihood Flux algorithm\", units=\"count\"), Key(offset=608, nElements=1)),\n",
+ " (Field['D'](name=\"base_PeakLikelihoodFlux_instFluxErr\", doc=\"1-sigma instFlux uncertainty\", units=\"count\"), Key(offset=616, nElements=1)),\n",
+ " (Field['Flag'](name=\"base_PeakLikelihoodFlux_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=552, bit=2)),\n",
+ " (Field['Flag'](name=\"base_PixelFlags_flag\", doc=\"General failure flag, set if anything went wrong\"), Key['Flag'](offset=552, bit=3)),\n",
+ " (Field['Flag'](name=\"base_PixelFlags_flag_offimage\", doc=\"Source center is off image\"), Key['Flag'](offset=552, bit=4)),\n",
+ " (Field['Flag'](name=\"base_PixelFlags_flag_edge\", doc=\"Source is outside usable exposure region (masked EDGE or NO_DATA, or centroid off image)\"), Key['Flag'](offset=552, bit=5)),\n",
+ " (Field['Flag'](name=\"base_PixelFlags_flag_interpolated\", doc=\"Interpolated pixel in the Source footprint\"), Key['Flag'](offset=552, bit=6)),\n",
+ " (Field['Flag'](name=\"base_PixelFlags_flag_saturated\", doc=\"Saturated pixel in the Source footprint\"), Key['Flag'](offset=552, bit=7)),\n",
+ " (Field['Flag'](name=\"base_PixelFlags_flag_cr\", doc=\"Cosmic ray in the Source footprint\"), Key['Flag'](offset=552, bit=8)),\n",
+ " (Field['Flag'](name=\"base_PixelFlags_flag_bad\", doc=\"Bad pixel in the Source footprint\"), Key['Flag'](offset=552, bit=9)),\n",
+ " (Field['Flag'](name=\"base_PixelFlags_flag_suspect\", doc=\"Source''s footprint includes suspect pixels\"), Key['Flag'](offset=552, bit=10)),\n",
+ " (Field['Flag'](name=\"base_PixelFlags_flag_interpolatedCenter\", doc=\"Interpolated pixel in the 3x3 region around the centroid.\"), Key['Flag'](offset=552, bit=11)),\n",
+ " (Field['Flag'](name=\"base_PixelFlags_flag_saturatedCenter\", doc=\"Saturated pixel in the 3x3 region around the centroid.\"), Key['Flag'](offset=552, bit=12)),\n",
+ " (Field['Flag'](name=\"base_PixelFlags_flag_crCenter\", doc=\"Cosmic ray in the 3x3 region around the centroid.\"), Key['Flag'](offset=552, bit=13)),\n",
+ " (Field['Flag'](name=\"base_PixelFlags_flag_badCenter\", doc=\"Bad pixel in the 3x3 region around the centroid\"), Key['Flag'](offset=552, bit=14)),\n",
+ " (Field['Flag'](name=\"base_PixelFlags_flag_suspectCenter\", doc=\"Suspect pixel in the 3x3 region around the centroid.\"), Key['Flag'](offset=552, bit=15)),\n",
+ " (Field['Flag'](name=\"base_PixelFlags_flag_interpolatedCenterAll\", doc=\"All pixels in the 3x3 region around the centroid are interpolated.\"), Key['Flag'](offset=552, bit=16)),\n",
+ " (Field['Flag'](name=\"base_PixelFlags_flag_saturatedCenterAll\", doc=\"All pixels in the 3x3 region around the centroid are saturated.\"), Key['Flag'](offset=552, bit=17)),\n",
+ " (Field['Flag'](name=\"base_PixelFlags_flag_crCenterAll\", doc=\"All pixels in the 3x3 region around the centroid have the cosmic ray mask bit.\"), Key['Flag'](offset=552, bit=18)),\n",
+ " (Field['Flag'](name=\"base_PixelFlags_flag_badCenterAll\", doc=\"All pixels in the 3x3 region around the centroid are bad.\"), Key['Flag'](offset=552, bit=19)),\n",
+ " (Field['Flag'](name=\"base_PixelFlags_flag_suspectCenterAll\", doc=\"All pixels in the 3x3 region around the centroid are suspect.\"), Key['Flag'](offset=552, bit=20)),\n",
+ " (Field['Flag'](name=\"base_PixelFlags_flag_streakCenter\", doc=\"3x3 region around the centroid has STREAK pixels\"), Key['Flag'](offset=552, bit=21)),\n",
+ " (Field['Flag'](name=\"base_PixelFlags_flag_streakCenterAll\", doc=\"All pixels in the 3x3 region around the source centroid are STREAK pixels\"), Key['Flag'](offset=552, bit=22)),\n",
+ " (Field['Flag'](name=\"base_PixelFlags_flag_streak\", doc=\"Source footprint includes STREAK pixels\"), Key['Flag'](offset=552, bit=23)),\n",
+ " (Field['D'](name=\"base_PsfFlux_instFlux\", doc=\"instFlux derived from linear least-squares fit of PSF model\", units=\"count\"), Key(offset=624, nElements=1)),\n",
+ " (Field['D'](name=\"base_PsfFlux_instFluxErr\", doc=\"1-sigma instFlux uncertainty\", units=\"count\"), Key(offset=632, nElements=1)),\n",
+ " (Field['F'](name=\"base_PsfFlux_area\", doc=\"effective area of PSF\", units=\"pixel\"), Key(offset=640, nElements=1)),\n",
+ " (Field['F'](name=\"base_PsfFlux_chi2\", doc=\"chi2 of the fitted PSF\"), Key(offset=644, nElements=1)),\n",
+ " (Field['I'](name=\"base_PsfFlux_npixels\", doc=\"number of pixels that were included in the PSF fit\", units=\"pixel\"), Key(offset=648, nElements=1)),\n",
+ " (Field['Flag'](name=\"base_PsfFlux_flag\", doc=\"General Failure Flag\"), Key['Flag'](offset=552, bit=24)),\n",
+ " (Field['Flag'](name=\"base_PsfFlux_flag_noGoodPixels\", doc=\"not enough non-rejected pixels in data to attempt the fit\"), Key['Flag'](offset=552, bit=25)),\n",
+ " (Field['Flag'](name=\"base_PsfFlux_flag_edge\", doc=\"object was too close to the edge of the image to use the full PSF model\"), Key['Flag'](offset=552, bit=26)),\n",
+ " (Field['Flag'](name=\"ip_diffim_NaiveDipoleFlux_flag\", doc=\"general failure flag, set if anything went wrong\"), Key['Flag'](offset=552, bit=27)),\n",
+ " (Field['Flag'](name=\"ip_diffim_NaiveDipoleFlux_pos_flag\", doc=\"failure flag for positive, set if anything went wrong\"), Key['Flag'](offset=552, bit=28)),\n",
+ " (Field['Flag'](name=\"ip_diffim_NaiveDipoleFlux_neg_flag\", doc=\"failure flag for negative, set if anything went wrong\"), Key['Flag'](offset=552, bit=29)),\n",
+ " (Field['D'](name=\"ip_diffim_NaiveDipoleFlux_pos_instFlux\", doc=\"raw flux counts: positive lobe\", units=\"count\"), Key(offset=656, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_NaiveDipoleFlux_pos_instFluxErr\", doc=\"1-sigma instFlux uncertainty\", units=\"count\"), Key(offset=664, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_NaiveDipoleFlux_neg_instFlux\", doc=\"raw flux counts: negative lobe\", units=\"count\"), Key(offset=672, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_NaiveDipoleFlux_neg_instFluxErr\", doc=\"1-sigma instFlux uncertainty\", units=\"count\"), Key(offset=680, nElements=1)),\n",
+ " (Field['I'](name=\"ip_diffim_NaiveDipoleFlux_npos\", doc=\"number of positive pixels\", units=\"count\"), Key(offset=688, nElements=1)),\n",
+ " (Field['I'](name=\"ip_diffim_NaiveDipoleFlux_nneg\", doc=\"number of negative pixels\", units=\"count\"), Key(offset=692, nElements=1)),\n",
+ " (Field['Flag'](name=\"ip_diffim_PsfDipoleFlux_flag\", doc=\"general failure flag, set if anything went wrong\"), Key['Flag'](offset=552, bit=30)),\n",
+ " (Field['Flag'](name=\"ip_diffim_PsfDipoleFlux_pos_flag\", doc=\"failure flag for positive, set if anything went wrong\"), Key['Flag'](offset=552, bit=31)),\n",
+ " (Field['Flag'](name=\"ip_diffim_PsfDipoleFlux_neg_flag\", doc=\"failure flag for negative, set if anything went wrong\"), Key['Flag'](offset=552, bit=32)),\n",
+ " (Field['D'](name=\"ip_diffim_PsfDipoleFlux_pos_instFlux\", doc=\"jointly fitted psf flux counts: positive lobe\", units=\"count\"), Key(offset=696, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_PsfDipoleFlux_pos_instFluxErr\", doc=\"1-sigma instFlux uncertainty\", units=\"count\"), Key(offset=704, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_PsfDipoleFlux_neg_instFlux\", doc=\"jointly fitted psf flux counts: negative lobe\", units=\"count\"), Key(offset=712, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_PsfDipoleFlux_neg_instFluxErr\", doc=\"1-sigma instFlux uncertainty\", units=\"count\"), Key(offset=720, nElements=1)),\n",
+ " (Field['F'](name=\"ip_diffim_PsfDipoleFlux_chi2dof\", doc=\"chi2 per degree of freedom of fit\"), Key(offset=728, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_PsfDipoleFlux_pos_centroid_x\", doc=\"psf fitted center of positive lobe\", units=\"pixel\"), Key(offset=736, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_PsfDipoleFlux_pos_centroid_y\", doc=\"psf fitted center of positive lobe\", units=\"pixel\"), Key(offset=744, nElements=1)),\n",
+ " (Field['F'](name=\"ip_diffim_PsfDipoleFlux_pos_centroid_xErr\", doc=\"1-sigma uncertainty on x position\", units=\"pixel\"), Key(offset=752, nElements=1)),\n",
+ " (Field['F'](name=\"ip_diffim_PsfDipoleFlux_pos_centroid_yErr\", doc=\"1-sigma uncertainty on y position\", units=\"pixel\"), Key(offset=756, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_PsfDipoleFlux_neg_centroid_x\", doc=\"psf fitted center of negative lobe\", units=\"pixel\"), Key(offset=760, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_PsfDipoleFlux_neg_centroid_y\", doc=\"psf fitted center of negative lobe\", units=\"pixel\"), Key(offset=768, nElements=1)),\n",
+ " (Field['F'](name=\"ip_diffim_PsfDipoleFlux_neg_centroid_xErr\", doc=\"1-sigma uncertainty on x position\", units=\"pixel\"), Key(offset=776, nElements=1)),\n",
+ " (Field['F'](name=\"ip_diffim_PsfDipoleFlux_neg_centroid_yErr\", doc=\"1-sigma uncertainty on y position\", units=\"pixel\"), Key(offset=780, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_PsfDipoleFlux_centroid_x\", doc=\"average of negative and positive lobe positions\", units=\"pixel\"), Key(offset=784, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_PsfDipoleFlux_centroid_y\", doc=\"average of negative and positive lobe positions\", units=\"pixel\"), Key(offset=792, nElements=1)),\n",
+ " (Field['F'](name=\"ip_diffim_PsfDipoleFlux_centroid_xErr\", doc=\"1-sigma uncertainty on x position\", units=\"pixel\"), Key(offset=800, nElements=1)),\n",
+ " (Field['F'](name=\"ip_diffim_PsfDipoleFlux_centroid_yErr\", doc=\"1-sigma uncertainty on y position\", units=\"pixel\"), Key(offset=804, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_ClassificationDipole_value\", doc=\"Set to 1 for dipoles, else 0.\"), Key(offset=808, nElements=1)),\n",
+ " (Field['Flag'](name=\"ip_diffim_ClassificationDipole_flag\", doc=\"Set to 1 for any fatal failure.\"), Key['Flag'](offset=552, bit=33)),\n",
+ " (Field['D'](name=\"ext_trailedSources_Naive_ra\", doc=\"Trail centroid right ascension.\"), Key(offset=816, nElements=1)),\n",
+ " (Field['D'](name=\"ext_trailedSources_Naive_dec\", doc=\"Trail centroid declination.\"), Key(offset=824, nElements=1)),\n",
+ " (Field['D'](name=\"ext_trailedSources_Naive_x0\", doc=\"Trail head X coordinate.\", units=\"pixel\"), Key(offset=832, nElements=1)),\n",
+ " (Field['D'](name=\"ext_trailedSources_Naive_y0\", doc=\"Trail head Y coordinate.\", units=\"pixel\"), Key(offset=840, nElements=1)),\n",
+ " (Field['D'](name=\"ext_trailedSources_Naive_x1\", doc=\"Trail tail X coordinate.\", units=\"pixel\"), Key(offset=848, nElements=1)),\n",
+ " (Field['D'](name=\"ext_trailedSources_Naive_y1\", doc=\"Trail tail Y coordinate.\", units=\"pixel\"), Key(offset=856, nElements=1)),\n",
+ " (Field['D'](name=\"ext_trailedSources_Naive_flux\", doc=\"Trailed source flux.\", units=\"count\"), Key(offset=864, nElements=1)),\n",
+ " (Field['D'](name=\"ext_trailedSources_Naive_length\", doc=\"Trail length.\", units=\"pixel\"), Key(offset=872, nElements=1)),\n",
+ " (Field['D'](name=\"ext_trailedSources_Naive_angle\", doc=\"Angle measured from +x-axis.\"), Key(offset=880, nElements=1)),\n",
+ " (Field['D'](name=\"ext_trailedSources_Naive_x0Err\", doc=\"Trail head X coordinate error.\", units=\"pixel\"), Key(offset=888, nElements=1)),\n",
+ " (Field['D'](name=\"ext_trailedSources_Naive_y0Err\", doc=\"Trail head Y coordinate error.\", units=\"pixel\"), Key(offset=896, nElements=1)),\n",
+ " (Field['D'](name=\"ext_trailedSources_Naive_x1Err\", doc=\"Trail tail X coordinate error.\", units=\"pixel\"), Key(offset=904, nElements=1)),\n",
+ " (Field['D'](name=\"ext_trailedSources_Naive_y1Err\", doc=\"Trail tail Y coordinate error.\", units=\"pixel\"), Key(offset=912, nElements=1)),\n",
+ " (Field['D'](name=\"ext_trailedSources_Naive_fluxErr\", doc=\"Trail flux error.\", units=\"count\"), Key(offset=920, nElements=1)),\n",
+ " (Field['D'](name=\"ext_trailedSources_Naive_lengthErr\", doc=\"Trail length error.\", units=\"pixel\"), Key(offset=928, nElements=1)),\n",
+ " (Field['D'](name=\"ext_trailedSources_Naive_angleErr\", doc=\"Trail angle error.\"), Key(offset=936, nElements=1)),\n",
+ " (Field['Flag'](name=\"ext_trailedSources_Naive_flag\", doc=\"No trailed-source measured\"), Key['Flag'](offset=552, bit=34)),\n",
+ " (Field['Flag'](name=\"ext_trailedSources_Naive_flag_noFlux\", doc=\"No suitable prior flux measurement\"), Key['Flag'](offset=552, bit=35)),\n",
+ " (Field['Flag'](name=\"ext_trailedSources_Naive_flag_noConverge\", doc=\"The root finder did not converge\"), Key['Flag'](offset=552, bit=36)),\n",
+ " (Field['Flag'](name=\"ext_trailedSources_Naive_flag_noSigma\", doc=\"No PSF width (sigma)\"), Key['Flag'](offset=552, bit=37)),\n",
+ " (Field['Flag'](name=\"ext_trailedSources_Naive_flag_safeCentroid\", doc=\"Fell back to safe centroid extractor\"), Key['Flag'](offset=552, bit=38)),\n",
+ " (Field['Flag'](name=\"ext_trailedSources_Naive_flag_edge\", doc=\"Trail contains edge pixels or extends off chip\"), Key['Flag'](offset=552, bit=39)),\n",
+ " (Field['D'](name=\"ip_diffim_DipoleFit_pos_instFlux\", doc=\"Dipole pos lobe flux\", units=\"count\"), Key(offset=944, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_DipoleFit_pos_instFluxErr\", doc=\"1-sigma uncertainty for pos dipole flux\", units=\"count\"), Key(offset=952, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_DipoleFit_pos_centroid_x\", doc=\"Dipole pos lobe centroid\", units=\"pixel\"), Key(offset=960, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_DipoleFit_pos_centroid_y\", doc=\"Dipole pos lobe centroid\", units=\"pixel\"), Key(offset=968, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_DipoleFit_neg_instFlux\", doc=\"Dipole neg lobe flux\", units=\"count\"), Key(offset=976, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_DipoleFit_neg_instFluxErr\", doc=\"1-sigma uncertainty for neg dipole flux\", units=\"count\"), Key(offset=984, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_DipoleFit_neg_centroid_x\", doc=\"Dipole neg lobe centroid\", units=\"pixel\"), Key(offset=992, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_DipoleFit_neg_centroid_y\", doc=\"Dipole neg lobe centroid\", units=\"pixel\"), Key(offset=1000, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_DipoleFit_centroid_x\", doc=\"Dipole centroid\", units=\"pixel\"), Key(offset=1008, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_DipoleFit_centroid_y\", doc=\"Dipole centroid\", units=\"pixel\"), Key(offset=1016, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_DipoleFit_instFlux\", doc=\"Dipole overall flux\", units=\"count\"), Key(offset=1024, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_DipoleFit_orientation\", doc=\"Dipole orientation\", units=\"deg\"), Key(offset=1032, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_DipoleFit_separation\", doc=\"Pixel separation between positive and negative lobes of dipole\", units=\"pixel\"), Key(offset=1040, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_DipoleFit_chi2dof\", doc=\"Chi2 per degree of freedom (chi2/(nData-nVariables)) of dipole fit\"), Key(offset=1048, nElements=1)),\n",
+ " (Field['L'](name=\"ip_diffim_DipoleFit_nData\", doc=\"Number of data points in the dipole fit\"), Key(offset=1056, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_DipoleFit_signalToNoise\", doc=\"Estimated signal-to-noise of dipole fit\"), Key(offset=1064, nElements=1)),\n",
+ " (Field['Flag'](name=\"ip_diffim_DipoleFit_flag_classification\", doc=\"Flag indicating diaSource is classified as a dipole\"), Key['Flag'](offset=552, bit=40)),\n",
+ " (Field['Flag'](name=\"ip_diffim_DipoleFit_flag_classificationAttempted\", doc=\"Flag indicating diaSource was attempted to be classified as a dipole\"), Key['Flag'](offset=552, bit=41)),\n",
+ " (Field['Flag'](name=\"ip_diffim_DipoleFit_flag\", doc=\"General failure flag for dipole fit\"), Key['Flag'](offset=552, bit=42)),\n",
+ " (Field['Flag'](name=\"ip_diffim_DipoleFit_flag_edge\", doc=\"Flag set when dipole is too close to edge of image\"), Key['Flag'](offset=552, bit=43)),\n",
+ " (Field['D'](name=\"base_GaussianFlux_apCorr\", doc=\"aperture correction applied to base_GaussianFlux\"), Key(offset=1072, nElements=1)),\n",
+ " (Field['D'](name=\"base_GaussianFlux_apCorrErr\", doc=\"standard deviation of aperture correction applied to base_GaussianFlux\"), Key(offset=1080, nElements=1)),\n",
+ " (Field['Flag'](name=\"base_GaussianFlux_flag_apCorr\", doc=\"set if unable to aperture correct base_GaussianFlux\"), Key['Flag'](offset=552, bit=44)),\n",
+ " (Field['D'](name=\"base_PsfFlux_apCorr\", doc=\"aperture correction applied to base_PsfFlux\"), Key(offset=1088, nElements=1)),\n",
+ " (Field['D'](name=\"base_PsfFlux_apCorrErr\", doc=\"standard deviation of aperture correction applied to base_PsfFlux\"), Key(offset=1096, nElements=1)),\n",
+ " (Field['Flag'](name=\"base_PsfFlux_flag_apCorr\", doc=\"set if unable to aperture correct base_PsfFlux\"), Key['Flag'](offset=552, bit=45)),\n",
+ " (Field['D'](name=\"ip_diffim_forced_PsfFlux_instFlux\", doc=\"Forced PSF flux measured on the direct image.\", units=\"count\"), Key(offset=1104, nElements=1)),\n",
+ " (Field['D'](name=\"ip_diffim_forced_PsfFlux_instFluxErr\", doc=\"Forced PSF flux error measured on the direct image.\", units=\"count\"), Key(offset=1112, nElements=1)),\n",
+ " (Field['F'](name=\"ip_diffim_forced_PsfFlux_area\", doc=\"Forced PSF flux effective area of PSF.\", units=\"pixel\"), Key(offset=1120, nElements=1)),\n",
+ " (Field['Flag'](name=\"ip_diffim_forced_PsfFlux_flag\", doc=\"Forced PSF flux general failure flag.\"), Key['Flag'](offset=552, bit=46)),\n",
+ " (Field['Flag'](name=\"ip_diffim_forced_PsfFlux_flag_noGoodPixels\", doc=\"Forced PSF flux not enough non-rejected pixels in data to attempt the fit.\"), Key['Flag'](offset=552, bit=47)),\n",
+ " (Field['Flag'](name=\"ip_diffim_forced_PsfFlux_flag_edge\", doc=\"Forced PSF flux object was too close to the edge of the image to use the full PSF model.\"), Key['Flag'](offset=552, bit=48)),\n",
+ " (Field['L'](name=\"refMatchId\", doc=\"unique id of reference catalog match\"), Key(offset=1128, nElements=1)),\n",
+ " (Field['L'](name=\"srcMatchId\", doc=\"unique id of source match\"), Key(offset=1136, nElements=1)),\n",
+ " (Field['Flag'](name=\"sky_source\", doc=\"Sky objects.\"), Key['Flag'](offset=552, bit=49)),\n",
+ " 'base_CircularApertureFlux_flag_badCentroid'->'ip_diffim_NaiveDipoleCentroid_flag'\n",
+ " 'base_GaussianFlux_flag_badCentroid'->'ip_diffim_NaiveDipoleCentroid_flag'\n",
+ " 'base_GaussianFlux_flag_badShape'->'ext_shapeHSM_HsmSourceMoments_flag'\n",
+ " 'base_NaiveCentroid_flag_badInitialCentroid'->'ip_diffim_NaiveDipoleCentroid_flag'\n",
+ " 'base_PeakLikelihoodFlux_flag_badCentroid'->'ip_diffim_NaiveDipoleCentroid_flag'\n",
+ " 'base_PsfFlux_flag_badCentroid'->'ip_diffim_NaiveDipoleCentroid_flag'\n",
+ " 'base_SdssCentroid_flag_badInitialCentroid'->'ip_diffim_NaiveDipoleCentroid_flag'\n",
+ " 'base_SdssShape_flag_badCentroid'->'ip_diffim_NaiveDipoleCentroid_flag'\n",
+ " 'ext_shapeHSM_HsmPsfMoments_flag_badCentroid'->'ip_diffim_NaiveDipoleCentroid_flag'\n",
+ " 'ext_shapeHSM_HsmSourceMoments_flag_badCentroid'->'ip_diffim_NaiveDipoleCentroid_flag'\n",
+ " 'ext_trailedSources_Naive_flag_badCentroid'->'ip_diffim_NaiveDipoleCentroid_flag'\n",
+ " 'slot_ApFlux'->'base_CircularApertureFlux_12_0'\n",
+ " 'slot_Centroid'->'ip_diffim_NaiveDipoleCentroid'\n",
+ " 'slot_PsfFlux'->'base_PsfFlux'\n",
+ " 'slot_PsfShape'->'ext_shapeHSM_HsmPsfMoments'\n",
+ " 'slot_Shape'->'ext_shapeHSM_HsmSourceMoments'\n",
+ ")"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "catalog.schema"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a479d9f9-1abd-452c-9427-a83eaf98ec1b",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-01-16T23:57:43.308949Z",
+ "iopub.status.busy": "2024-01-16T23:57:43.308763Z",
+ "iopub.status.idle": "2024-01-16T23:57:43.311220Z",
+ "shell.execute_reply": "2024-01-16T23:57:43.310847Z",
+ "shell.execute_reply.started": "2024-01-16T23:57:43.308935Z"
+ }
+ },
+ "source": [
+ "## 200 to 1000 pixel cutouts"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2b55b4c3-8063-4846-a147-753216c663c8",
+ "metadata": {},
+ "source": [
+ "These are all the alerts with a cutout larger than 200 by 200 pixels. We see that every one of these is a bad source, and identified a few sources we wanted to look at closer, such as source 25409118864933814.\n",
+ "\n",
+ "Future changes to the pipeline will likely remove many of these sources from the catalog so they do not reach the alerts."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "82f045f3-ef84-4cb5-8550-8c1768b6c20b",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-02-02T18:40:13.348121Z",
+ "iopub.status.busy": "2024-02-02T18:40:13.347613Z",
+ "iopub.status.idle": "2024-02-02T18:40:29.611980Z",
+ "shell.execute_reply": "2024-02-02T18:40:29.611655Z",
+ "shell.execute_reply.started": "2024-02-02T18:40:13.348106Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11826804.avro\n",
+ "DiaSource Id: 25397868198101636\n",
+ "Bounding Box: (286, 286)\n",
+ "Centroid: 503.0114873520232 179.02175299198856\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: True\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: True\n",
+ "Dipole Flux Centroid: 503.0114873520232 179.02175299198856\n",
+ "Seperation of x and y dipole coords: [49.962738037109375, -103.3310775756836]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11826804.avro\n",
+ "DiaSource Id: 25397868198101650\n",
+ "Bounding Box: (230, 230)\n",
+ "Centroid: 1113.3542779400866 490.2660928419115\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: True\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: True\n",
+ "Dipole Flux Centroid: 1113.3542779400866 490.2660928419115\n",
+ "Seperation of x and y dipole coords: [-8.26318359375, -55.006103515625]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11826804.avro\n",
+ "DiaSource Id: 25397868198101671\n",
+ "Bounding Box: (222, 222)\n",
+ "Centroid: 15.437384788256317 1167.877847676624\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: True\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: False\n",
+ "Dipole Flux Centroid: 15.437384788256317 1167.877847676624\n",
+ "Seperation of x and y dipole coords: [6.183385848999023, -83.93798828125]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11832043.avro\n",
+ "DiaSource Id: 25409118864933676\n",
+ "Bounding Box: (282, 282)\n",
+ "Centroid: 1108.8429959602133 513.5927193128958\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: True\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: True\n",
+ "Dipole Flux Centroid: 1108.8429959602133 513.5927193128958\n",
+ "Seperation of x and y dipole coords: [-19.100830078125, 24.083648681640625]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11832043.avro\n",
+ "DiaSource Id: 25409118864933814\n",
+ "Bounding Box: (214, 214)\n",
+ "Centroid: 1600.6063086766635 1941.9561393177312\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: False\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: True\n",
+ "Dipole Flux Centroid: 1600.6063086766635 1941.9561393177312\n",
+ "Seperation of x and y dipole coords: [-1008.978515625, 90.1041259765625]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11832043.avro\n",
+ "DiaSource Id: 25409118864933909\n",
+ "Bounding Box: (206, 206)\n",
+ "Centroid: 999.1591809812496 2913.9272076450065\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: True\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: True\n",
+ "Dipole Flux Centroid: 999.1591809812496 2913.9272076450065\n",
+ "Seperation of x and y dipole coords: [69.07806396484375, -41.231201171875]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11832043.avro\n",
+ "DiaSource Id: 25409118864933953\n",
+ "Bounding Box: (230, 230)\n",
+ "Centroid: 570.1474984056201 3094.9826353527683\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: True\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: True\n",
+ "Dipole Flux Centroid: 570.1474984056201 3094.9826353527683\n",
+ "Seperation of x and y dipole coords: [29.01397705078125, -0.019287109375]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11832043.avro\n",
+ "DiaSource Id: 25409118864933970\n",
+ "Bounding Box: (269, 269)\n",
+ "Centroid: 944.5436637125295 3271.8201244344195\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: True\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: True\n",
+ "Dipole Flux Centroid: 944.5436637125295 3271.8201244344195\n",
+ "Seperation of x and y dipole coords: [100.76007080078125, -0.858642578125]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11832051.avro\n",
+ "DiaSource Id: 25409136044802431\n",
+ "Bounding Box: (212, 212)\n",
+ "Centroid: 681.311806027289 931.6233594435865\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: True\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: True\n",
+ "Dipole Flux Centroid: 681.311806027289 931.6233594435865\n",
+ "Seperation of x and y dipole coords: [20.7529296875, 9.89447021484375]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11832051.avro\n",
+ "DiaSource Id: 25409136044802552\n",
+ "Bounding Box: (284, 284)\n",
+ "Centroid: 779.4421166331397 3592.8105554219524\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: True\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: True\n",
+ "Dipole Flux Centroid: 779.4421166331397 3592.8105554219524\n",
+ "Seperation of x and y dipole coords: [-49.09857177734375, -24.035400390625]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11832051.avro\n",
+ "DiaSource Id: 25409136044802568\n",
+ "Bounding Box: (214, 214)\n",
+ "Centroid: 1495.3591579199806 3955.13428890723\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: True\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: True\n",
+ "Dipole Flux Centroid: 1495.3591579199806 3955.13428890723\n",
+ "Seperation of x and y dipole coords: [-1.40380859375, 19.03662109375]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11828405.avro\n",
+ "DiaSource Id: 25401306319421670\n",
+ "Bounding Box: (334, 334)\n",
+ "Centroid: 1497.2463463232564 221.79083136009615\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: True\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: True\n",
+ "Dipole Flux Centroid: 1497.2463463232564 221.79083136009615\n",
+ "Seperation of x and y dipole coords: [-18.16064453125, -90.76304626464844]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11828405.avro\n",
+ "DiaSource Id: 25401306319421687\n",
+ "Bounding Box: (226, 226)\n",
+ "Centroid: 2031.9264521089888 768.149329395472\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: False\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: False\n",
+ "Dipole Flux Centroid: 2031.9264521089888 768.149329395472\n",
+ "Seperation of x and y dipole coords: [-3.2706298828125, 17.1558837890625]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11826805.avro\n",
+ "DiaSource Id: 25397870345584999\n",
+ "Bounding Box: (223, 223)\n",
+ "Centroid: 149.79180321906924 628.3949888886841\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: True\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: True\n",
+ "Dipole Flux Centroid: 149.79180321906924 628.3949888886841\n",
+ "Seperation of x and y dipole coords: [65.64706420898438, -61.98297119140625]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11826805.avro\n",
+ "DiaSource Id: 25397870345585039\n",
+ "Bounding Box: (373, 373)\n",
+ "Centroid: 529.7818210142283 1143.0687072357898\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: True\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: True\n",
+ "Dipole Flux Centroid: 529.7818210142283 1143.0687072357898\n",
+ "Seperation of x and y dipole coords: [-123.99295043945312, -33.8897705078125]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11826805.avro\n",
+ "DiaSource Id: 25397870345585094\n",
+ "Bounding Box: (785, 785)\n",
+ "Centroid: 2012.97383013992 1543.2999442361568\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: False\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: True\n",
+ "Dipole Flux Centroid: 2012.97383013992 1543.2999442361568\n",
+ "Seperation of x and y dipole coords: [17.82958984375, -84.08544921875]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11826805.avro\n",
+ "DiaSource Id: 25397870345585105\n",
+ "Bounding Box: (299, 299)\n",
+ "Centroid: 1162.791192237474 1754.2101065120323\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: True\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: True\n",
+ "Dipole Flux Centroid: 1162.791192237474 1754.2101065120323\n",
+ "Seperation of x and y dipole coords: [77.9449462890625, -42.869384765625]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11828411.avro\n",
+ "DiaSource Id: 25401319204324010\n",
+ "Bounding Box: (230, 230)\n",
+ "Centroid: 919.7697022084532 54.43380931284447\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: True\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: True\n",
+ "Dipole Flux Centroid: 919.7697022084532 54.43380931284447\n",
+ "Seperation of x and y dipole coords: [6.8289794921875, -16.826400756835938]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11828411.avro\n",
+ "DiaSource Id: 25401319204324351\n",
+ "Bounding Box: (201, 201)\n",
+ "Centroid: 130.01235961914062 4161.18701171875\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: False\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: False\n",
+ "Dipole Flux Centroid: 130.01235961914062 4161.18701171875\n",
+ "Seperation of x and y dipole coords: [nan, nan]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11830050.avro\n",
+ "DiaSource Id: 25404838930022928\n",
+ "Bounding Box: (404, 404)\n",
+ "Centroid: 386.60670420783487 728.5655109462267\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: True\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: True\n",
+ "Dipole Flux Centroid: 386.60670420783487 728.5655109462267\n",
+ "Seperation of x and y dipole coords: [-14.22406005859375, -17.1529541015625]\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_5797/754947270.py:19: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n",
+ " plt.figure()\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11830058.avro\n",
+ "DiaSource Id: 25404856109892044\n",
+ "Bounding Box: (223, 223)\n",
+ "Centroid: 2016.9906793474295 1268.8077341043213\n",
+ "Interpolated Flag: True\n",
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+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: False\n",
+ "Dipole Flux Centroid: 2016.9906793474295 1268.8077341043213\n",
+ "Seperation of x and y dipole coords: [0.0013427734375, -14.111083984375]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11830058.avro\n",
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+ "Interpolated Flag: True\n",
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+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: False\n",
+ "Dipole Flux Centroid: 1141.796630859375 2706.879150390625\n",
+ "Seperation of x and y dipole coords: [nan, nan]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11830058.avro\n",
+ "DiaSource Id: 25404856109892175\n",
+ "Bounding Box: (203, 203)\n",
+ "Centroid: 14.655866255847176 3781.4835648142544\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: False\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: False\n",
+ "Dipole Flux Centroid: 14.655866255847176 3781.4835648142544\n",
+ "Seperation of x and y dipole coords: [4.117061614990234, -30.021240234375]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11828400.avro\n",
+ "DiaSource Id: 25401295582004093\n",
+ "Bounding Box: (587, 587)\n",
+ "Centroid: 739.5726795570472 203.36758809689476\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: False\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: True\n",
+ "Dipole Flux Centroid: 739.5726795570472 203.36758809689476\n",
+ "Seperation of x and y dipole coords: [-385.9924621582031, 69.68991088867188]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11828400.avro\n",
+ "DiaSource Id: 25401295582004203\n",
+ "Bounding Box: (212, 212)\n",
+ "Centroid: 619.9697875976562 1056.9822998046875\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: False\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: False\n",
+ "Dipole Flux Centroid: 619.9697875976562 1056.9822998046875\n",
+ "Seperation of x and y dipole coords: [nan, nan]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11828400.avro\n",
+ "DiaSource Id: 25401295582004274\n",
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+ "Centroid: 842.5304280956508 1481.3866601703196\n",
+ "Interpolated Flag: True\n",
+ "Saturated Flag: False\n",
+ "Trailed Source Flag: True\n",
+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: True\n",
+ "Dipole Flux Centroid: 842.5304280956508 1481.3866601703196\n",
+ "Seperation of x and y dipole coords: [473.02734375, 1.9158935546875]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11828400.avro\n",
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+ "Bounding Box: (903, 903)\n",
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+ "Interpolated Flag: True\n",
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+ "Trailed Source Length more than limit: \n",
+ "Dipole Flux Flag: True\n",
+ "Dipole Flux Centroid: 581.5710693184451 1937.9515803658846\n",
+ "Seperation of x and y dipole coords: [-131.87142944335938, -6.119873046875]\n",
+ "-----------------------------------------------\n",
+ "/home/s/smart/u/code/workspacest/postage_stamp_investigation/alerts/11828400.avro\n",
+ "DiaSource Id: 25401295582004379\n",
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+ "Interpolated Flag: True\n",
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",
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+ "