From 155788e4f15d00a64fc6895cd2eff93c6268bfbf Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Fri, 3 Jan 2025 13:45:58 +0000 Subject: [PATCH 01/52] example notebook updates - update apparent mag notebook by running it with an install of numpy 2.0 to quit the errors in the online generation - update lightcurve notebook so that the url to add-ons shows (pandoc generation changed the parsing) - remove the confusing notebooks for developing scripts to run multiple runs --- docs/notebooks.rst | 3 - docs/notebooks/README.md | 8 - .../demo_ApparentMagnitudeValidation.ipynb | 857 +++++++++++++++++- docs/notebooks/demo_GenerateBashScripts.ipynb | 265 ------ .../notebooks/demo_GenerateSLURMScripts.ipynb | 326 ------- docs/notebooks/demo_Lightcurve.ipynb | 604 +++++++++++- docs/notebooks/example_file_structure.png | Bin 307066 -> 0 bytes 7 files changed, 1416 insertions(+), 647 deletions(-) delete mode 100644 docs/notebooks/demo_GenerateBashScripts.ipynb delete mode 100644 docs/notebooks/demo_GenerateSLURMScripts.ipynb delete mode 100644 docs/notebooks/example_file_structure.png diff --git a/docs/notebooks.rst b/docs/notebooks.rst index 9458fbfb..48437f3f 100644 --- a/docs/notebooks.rst +++ b/docs/notebooks.rst @@ -20,6 +20,3 @@ Demo Notebooks Lightcurve demo miniDifi Validation Sorcha End-to-End Verification - Example Bash Scripts for Multiple Runs - Example Slurm Scripts for Multiple Runs - diff --git a/docs/notebooks/README.md b/docs/notebooks/README.md index 0932b78d..31a50ebc 100644 --- a/docs/notebooks/README.md +++ b/docs/notebooks/README.md @@ -24,14 +24,6 @@ demo_FootprintFilter - **Demonstrates:** PPFootprintFilter - **Files:** detector_corners.csv, footprintFilterValidationObservations.csv, oneline_v2.0.db -demo_GenerateBashScripts -- **Demonstrates:** Generation of shell scripts for multiple runs of Sorcha -- **Files:** example_file_structure.png - -demo_GenerateSLURMScripts -- **Demonstrates:** Generation of SLURM scripts for multiple runs of Sorcha -- **Files:** example_file_structure.png - demo_Lightcurve - **Demonstrates:** lightcurve_registration (LC_METHODS, update_lc_subclasses), AbstractLightCurve class - **Files:** none diff --git a/docs/notebooks/demo_ApparentMagnitudeValidation.ipynb b/docs/notebooks/demo_ApparentMagnitudeValidation.ipynb index 9ca5c95e..bb0968c2 100644 --- a/docs/notebooks/demo_ApparentMagnitudeValidation.ipynb +++ b/docs/notebooks/demo_ApparentMagnitudeValidation.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "fc4ba06a", "metadata": {}, "outputs": [], @@ -35,10 +35,312 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "3e52682b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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targetnamedatetime_strdatetime_jdHGsolar_presencelunar_presenceVsurfbrightrr_ratedeltadelta_ratealpha_truePABLonPABLat
024 Themis (A853 GA)2021-Jan-01 00:002459215.57.240.19C13.3187.1123.2752411.8687674.212453-4.6413694.5918262.7496-0.4684
124 Themis (A853 GA)2021-Jan-02 00:002459216.57.240.19C13.3277.1223.2763191.8660164.209518-4.9225964.7832263.0067-0.4700
224 Themis (A853 GA)2021-Jan-03 00:002459217.57.240.19C13.3357.1323.2773961.8632524.206423-5.2037124.9741263.2635-0.4716
324 Themis (A853 GA)2021-Jan-04 00:002459218.57.240.19C13.3447.1423.2784711.8604764.203166-5.4847835.1645263.5200-0.4732
424 Themis (A853 GA)2021-Jan-05 00:002459219.57.240.19C13.3527.1523.2795451.8576874.199749-5.7658455.3543263.7760-0.4748
...................................................
72624 Themis (A853 GA)2022-Dec-28 00:002459941.57.240.19Cm13.4807.6263.407801-1.3375673.54859323.73819816.0868357.7423-0.3756
72724 Themis (A853 GA)2022-Dec-29 00:002459942.57.240.19Cm13.4857.6243.407028-1.3412883.56214023.63320416.0210357.9249-0.3731
72824 Themis (A853 GA)2022-Dec-30 00:002459943.57.240.19Cm13.4917.6213.406252-1.3450023.57562323.52387915.9522358.1090-0.3707
72924 Themis (A853 GA)2022-Dec-31 00:002459944.57.240.19Cm13.4967.6193.405474-1.3487083.58903923.41043315.8807358.2945-0.3683
73024 Themis (A853 GA)2023-Jan-01 00:002459945.57.240.19Cm13.5017.6163.404694-1.3524053.60238523.29303715.8063358.4816-0.3659
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13.335072 \n", + "3 6.287847e+08 5.1645 13.359831 13.354037 13.343783 \n", + "4 6.282735e+08 5.3543 13.368535 13.362378 13.352227 \n", + ".. ... ... ... ... ... \n", + "726 5.308620e+08 16.0868 13.481646 13.518557 13.522578 \n", + "727 5.328886e+08 16.0210 13.487347 13.523931 13.527875 \n", + "728 5.349056e+08 15.9522 13.492879 13.529119 13.532982 \n", + "729 5.369125e+08 15.8807 13.498251 13.534132 13.537910 \n", + "730 5.389091e+08 15.8063 13.503454 13.538962 13.542651 \n", + "\n", + "[731 rows x 13 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "observations_df" ] @@ -130,10 +902,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "a40763e1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(figsize=(10,8))\n", "ax.plot(observations_df[\"MJD\"] - 59000, observations_df[\"JPL_mag\"], linestyle=\"\", marker=\"x\", label=\"JPL\")\n", @@ -157,7 +940,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "c4828b4a", "metadata": {}, "outputs": [], @@ -183,7 +966,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "87743497", "metadata": {}, "outputs": [], @@ -201,7 +984,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "c097038b", "metadata": {}, "outputs": [], @@ -211,10 +994,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "358cfd19", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(figsize=(10,8))\n", "ax.plot(observations_df[\"MJD\"] - 59000, linear_mag_calc, linestyle=\"\", marker=\"x\", label=\"calculated\")\n", @@ -236,7 +1030,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "4ccabbea", "metadata": {}, "outputs": [], @@ -320,7 +1114,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "3dfdfd1f", "metadata": {}, "outputs": [], @@ -333,10 +1127,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "49d5a5fe", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.hist(observations_df['JPL_mag'] - observations_df['HG_mag'], bins=30, histtype='step', label='HG')\n", "plt.hist(observations_df['JPL_mag'] - observations_df['HG12_mag'], bins=30, histtype='step', label='HG12 (Penttila)')\n", @@ -383,7 +1198,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/docs/notebooks/demo_GenerateBashScripts.ipynb b/docs/notebooks/demo_GenerateBashScripts.ipynb deleted file mode 100644 index 73b390e2..00000000 --- a/docs/notebooks/demo_GenerateBashScripts.ipynb +++ /dev/null @@ -1,265 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "62e7dabd-d86e-420d-b76a-4b08f98f137a", - "metadata": {}, - "source": [ - "## Generating multiple Bash scripts" - ] - }, - { - "cell_type": "markdown", - "id": "448e076e-fd73-42f2-967d-a6e689fd91a5", - "metadata": {}, - "source": [ - "This notebook is an example of a notebook which generates either a single or multiple bash scripts to run Sorcha. If you want to use this, **you will need to heavily edit this to suit your own particular setup.**\n", - "\n", - "This notebook does assume that you have a local copy of the input files on the machine where you are running this notebook. If you don't, you'll have to edit this a bit more.\n", - "\n", - "In the case presented below, there are 16 folders each containing ~100 sets of input files for Sorcha. An example of a folder layout and file/folder naming system is shown below.\n", - "\n", - "![alternative text](example_file_structure.png)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9bc4271b-bd28-432c-8584-1f837b8c9fb9", - "metadata": {}, - "outputs": [], - "source": [ - "import glob\n", - "import os" - ] - }, - { - "cell_type": "markdown", - "id": "4ce350be-c598-4083-8df4-5cad01979771", - "metadata": {}, - "source": [ - "First get sorted lists of all of the orbit and physical parameters files." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e07dd90b-8ffc-4d1e-91e9-d9627f871073", - "metadata": {}, - "outputs": [], - "source": [ - "orbits_all = glob.glob('./*/*orbit.s3m')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8a6adbf6-edf1-4d8e-88a1-216e50b46d78", - "metadata": {}, - "outputs": [], - "source": [ - "orbits_all.sort()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3cfbe202-1564-4a34-b095-a55a4462203c", - "metadata": {}, - "outputs": [], - "source": [ - "len(orbits_all)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aafeda56-6690-44e8-9bf4-fee3edb3ea64", - "metadata": {}, - "outputs": [], - "source": [ - "params_all = glob.glob('./*/*physical.s3m')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b4776767-df45-4877-9962-88c46307fd31", - "metadata": {}, - "outputs": [], - "source": [ - "params_all.sort()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7c3b6111-ad70-41dc-97fc-473105af10a7", - "metadata": {}, - "outputs": [], - "source": [ - "len(params_all)" - ] - }, - { - "cell_type": "markdown", - "id": "cc41c50a-835d-4497-b75c-d6d21caaa337", - "metadata": {}, - "source": [ - "In this example, we are running on a workstation with 32 cores, so:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c6d7e713-45d2-4931-82cb-aea9d234714e", - "metadata": {}, - "outputs": [], - "source": [ - "1572/32" - ] - }, - { - "cell_type": "markdown", - "id": "d557d4f3-f027-49af-87f6-7ef1ea6554c4", - "metadata": {}, - "source": [ - "We need fifty runs across 32 cores. So we can set up a script to run on one core that contains fifty **consecutive** Sorcha runs, each overwriting the same ephemeris file. We can define our Sorcha input/outputs like so:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "051d93ec-beb8-48dc-ad8e-a637f38c07fb", - "metadata": {}, - "outputs": [], - "source": [ - "script_filepath = './sorcha_runscript.sh'\n", - "config_filepath = './config_file.ini'\n", - "pointing_filepath = './baseline_v3.0_10yrs.db'\n", - "ephemeris_filepath = './ephemeris_file_core_1.csv'\n", - "output_filepath = './outputs/'\n", - "cache_filepath = './sorcha_cache/'" - ] - }, - { - "cell_type": "markdown", - "id": "de7fda7b-38b4-461b-92b2-9c491c8a5266", - "metadata": {}, - "source": [ - "The below will create this single runscript." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b755ce57-2d6b-446b-9689-47b35f4bd54d", - "metadata": {}, - "outputs": [], - "source": [ - "with open(script_filepath, \"a\") as the_file:\n", - " the_file.write(\"#!/bin/bash\\n\")\n", - "\n", - "for orbit_file in orbits_all[:50]:\n", - "\n", - " stem_filepath = orbit_file.split('_')[0]\n", - " stem_identifier = orbit_file.split('_')[1]\n", - "\n", - " physical_file = '_'.join([stem_filepath, stem_identifier, 'physical.s3m'])\n", - "\n", - " output_file = '_'.join([stem_filepath.split('/')[-1], stem_identifier, 'output.csv'])\n", - " \n", - " sorcha_command = ('sorcha -c '\n", - " + config_filepath\n", - " + ' -p '\n", - " + physical_file\n", - " + ' -ob '\n", - " + orbit_file\n", - " + ' -e '\n", - " + pointing_filepath\n", - " + ' -o '\n", - " + output_filepath\n", - " + ' -t '\n", - " + output_file\n", - " + '-ar'\n", - " + cache_filepath)\n", - "\n", - " with open(script_filepath, \"a\") as the_file:\n", - " the_file.write(sorcha_command + \" \\n\")" - ] - }, - { - "cell_type": "markdown", - "id": "4eb030db-7083-49ed-8276-eea6a0f4b7bd", - "metadata": {}, - "source": [ - "To generate all 32 runscripts, one for each core, run this (making sure to change script_filepath)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e483cf31-ad2a-41ac-a0f1-6e4fda3f5d51", - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(0, 33):\n", - "\n", - " # CHANGE THESE IF YOU WANT THEM TO BE DIFFERENT. \n", - " # The other locations (pointing database, config filename) will be pulled from previous cell.\n", - " script_filepath = './sorcha_DP03_runscript_'+str(i)+'.sh'\n", - "\n", - " with open(script_filepath, \"a\") as the_file:\n", - " the_file.write(\"#!/bin/bash\\n\")\n", - " \n", - " for orbit_file in orbits_all[i*50:(i*50)+50]:\n", - " \n", - " stem_filepath = orbit_file.split('_')[0]\n", - " stem_identifier = orbit_file.split('_')[1]\n", - " \n", - " physical_file = '_'.join([stem_filepath, stem_identifier, 'physical.s3m'])\n", - " \n", - " output_file = '_'.join([stem_filepath.split('/')[-1], stem_identifier, 'output.csv'])\n", - " \n", - " sorcha_command = ('sorcha -c '\n", - " + config_filepath\n", - " + ' -p '\n", - " + physical_file\n", - " + ' -ob '\n", - " + orbit_file\n", - " + ' -e '\n", - " + pointing_filepath\n", - " + ' -o '\n", - " + output_filepath\n", - " + ' -t '\n", - " + output_file \n", - " + '-ar'\n", - " + cache_filepath)\n", - " \n", - " with open(script_filepath, \"a\") as the_file:\n", - " the_file.write(sorcha_command + \" \\n\")\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/notebooks/demo_GenerateSLURMScripts.ipynb b/docs/notebooks/demo_GenerateSLURMScripts.ipynb deleted file mode 100644 index e3f739f4..00000000 --- a/docs/notebooks/demo_GenerateSLURMScripts.ipynb +++ /dev/null @@ -1,326 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c659f068-3614-4753-896e-22e7df543aff", - "metadata": {}, - "source": [ - "## Generating multiple SLURM scripts" - ] - }, - { - "cell_type": "markdown", - "id": "09ecc02a-36da-4e23-8abb-80263025476f", - "metadata": {}, - "source": [ - "This notebook is an example of a notebook which generates either a single or multiple SLURM scripts for use on a supercomputer using the SLURM queue system. If you want to use this, **you will need to heavily edit this to suit your own particular setup.**\n", - "\n", - "This notebook does assume that you have a local copy of the input files on the machine where you are running this notebook. If you don't, you'll have to edit this a bit more.\n", - "\n", - "In the case presented below, there are 16 folders each containing ~100 sets of input files for Sorcha. An example of a folder layout and file/folder naming system is shown below.\n", - "\n", - "![alternative text](example_file_structure.png)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f6efef55-7d0f-456a-9415-54941bb01315", - "metadata": {}, - "outputs": [], - "source": [ - "import glob\n", - "import os\n", - "import re" - ] - }, - { - "cell_type": "markdown", - "id": "e6c4572f-eb5b-4149-9826-d8d08d1f4f83", - "metadata": {}, - "source": [ - "Below are defined a number of parameters, most of which go into the header of the SLURM scripts. You will likely need to edit these to match your own preferences.\n", - "\n", - "The top parameter controls the number of SLURM scripts you want to generate, corresponding to the number of input folders. It's perfectly fine to just put '1' here if you only need one." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "91284775-87c2-4ede-b77a-a48ea77f604b", - "metadata": {}, - "outputs": [], - "source": [ - "number_of_files = 16\n", - "filename = 'batch_script'\n", - "\n", - "job_name = 'Sorcha_batch'\n", - "ntasks = '100'\n", - "mem_per_cpu = '7G'\n", - "output_path = 'path/to/terminal/output'\n", - "time = '3:00:00'\n", - "partition = 'your_partition'" - ] - }, - { - "cell_type": "markdown", - "id": "23074160-c567-464d-8140-67755a541fe0", - "metadata": {}, - "source": [ - "The below should be the folder/pattern where your input files are **currently** located, i.e. on your local machine. The code uses this to get the list of input filenames. If you are running this notebook on the supercomputer, inputs_in is the same path as inputs_out below.\n", - "\n", - "In this example, the expectation is that the input folders are called './dp03_inputs_kelvin/kelvin_dp03_batch_1', './dp03_inputs_kelvin/kelvin_dp03_batch_2', './dp03_inputs_kelvin/kelvin_dp03_batch_3', etc, as shown in the above graphic." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5f71d1c3-7013-4b31-a75b-3cf235e2862e", - "metadata": {}, - "outputs": [], - "source": [ - "inputs_in = './dp03_inputs_kelvin/kelvin_dp03_batch_'" - ] - }, - { - "cell_type": "markdown", - "id": "9ddc1922-5564-42af-aa32-85b3a52af461", - "metadata": {}, - "source": [ - "The below parameters define where the inputs, configuration file, output folder, pointing database and SPICE files are located on the machine on which you will be running Sorcha. Edit these to where these files and folders will be located on your supercomputer." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "337d4ef0-742f-4156-bcca-9137f98174ff", - "metadata": {}, - "outputs": [], - "source": [ - "inputs_out = '/supercomputer_inputs_location/sorcha_batch_'\n", - "config = '/supercomputer_inputs_location/sorcha_config.ini'\n", - "outputs_out = '/supercomputer_outputs_location/sorcha_batch_'\n", - "pointing = 'supercomputer_outputs_location/baseline_v2.0_10yrs.db'\n", - "ar_data_path = '/supercomputer_cache_location/sorcha_cache_files'" - ] - }, - { - "cell_type": "markdown", - "id": "ce20b38e-d41c-418f-8e60-d37947d4bd7b", - "metadata": {}, - "source": [ - "The below function creates the header of the SLURM scripts, including any introductory commands such as loading Anaconda and activating the correct Conda environment. Once again, you will likely need to edit this heavily for your own setup." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "44bac1b6-2f69-4267-ab04-d4c3673dec10", - "metadata": {}, - "outputs": [], - "source": [ - "def print_header(filename, n, job_name, ntasks, mem_per_cpu, output_path, time, partition):\n", - "\n", - " with open(filename, \"a\") as the_file:\n", - " the_file.write(\"#!/bin/bash\\n\")\n", - " the_file.write(\"#SBATCH --job-name=\" + job_name + str(n) + \"\\n\")\n", - " the_file.write(\"#SBATCH --ntasks=\" + ntasks + \"\\n\")\n", - " the_file.write(\"#SBATCH --mem-per-cpu=\" + mem_per_cpu + \"\\n\")\n", - " the_file.write(\"#SBATCH --cpus-per-task=1\\n\")\n", - " the_file.write(\"#SBATCH --output=\" + output_path + job_name + str(n) + \".out\\n\")\n", - " the_file.write(\"#SBATCH --time=\" + time + \"\\n\")\n", - " the_file.write(\"#SBATCH --partition=\" + partition + \"\\n\")\n", - " the_file.write(\"#SBATCH --mail-user=YOUR EMAIL ADDRESS GOES HERE\\n\") # put your own email address in here!!\n", - " the_file.write(\"#SBATCH --mail-type=BEGIN,FAIL,END\\n\")\n", - " the_file.write(\"\\n\")\n", - " the_file.write(\"dt=$(date '+%d/%m/%Y %H:%M:%S');\\n\")\n", - " the_file.write(\"echo \\\"$dt Beginning Sorcha.\\\"\\n\")\n", - " the_file.write(\"\\n\")\n", - " the_file.write(\"module load apps/anaconda3/2022.10/bin\\n\")\n", - " the_file.write(\"\\n\")\n", - " the_file.write(\"source activate sorcha\\n\")\n", - " the_file.write(\"\\n\")\n", - " the_file.write(\"\\n\")" - ] - }, - { - "cell_type": "markdown", - "id": "7613da62-9dbd-45e6-a569-9744a515f45e", - "metadata": {}, - "source": [ - "The below prints a footer to the SLURM scripts, which you can also edit if you like." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "75a822dc-7256-4d1d-8c59-677dd1a4cb9d", - "metadata": {}, - "outputs": [], - "source": [ - "def print_footer(filename):\n", - " \n", - " with open(filename, \"a\") as the_file:\n", - " the_file.write(\"\\n\")\n", - " the_file.write(\"dt=$(date '+%d/%m/%Y %H:%M:%S');\\n\")\n", - " the_file.write(\"echo \\\"$dt Sorcha complete.\\\"\\n\")" - ] - }, - { - "cell_type": "markdown", - "id": "5124e484-437b-48cf-8251-3a6590246972", - "metadata": {}, - "source": [ - "The below function shouldn't need to be edited." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5f03b007-aae3-40a6-ac6a-ff291d62244d", - "metadata": {}, - "outputs": [], - "source": [ - "def get_sorted_list_of_files(filepath, stem):\n", - " \"\"\"Globs for a list of files using the suggested filepath and stem (which should\n", - " include wildcards) then sorts the list. If no files are found, the code exits.\n", - "\n", - " Parameters:\n", - " -----------\n", - " filepath (string): filepath of folder where files are located\n", - "\n", - " stem (string): string containing filename pattern to search for\n", - "\n", - " Returns:\n", - " -----------\n", - " globbed_list (list): sorted list of filename strings\n", - "\n", - " \"\"\"\n", - "\n", - " globbed_list = glob.glob(os.path.join(filepath, stem))\n", - " globbed_list.sort()\n", - "\n", - " if not globbed_list:\n", - " print(\"Could not find any files on given input path {} using stem {}.\".format(filepath, stem))\n", - "\n", - " return globbed_list" - ] - }, - { - "cell_type": "markdown", - "id": "edf60bd3-83ed-4f3d-a2c4-dce45b4b5b83", - "metadata": {}, - "source": [ - "The below function may need to be edited. It assumes that your input files take a specific format where the orbit files contain the pattern \\*orbit\\* and the physical parameters files contain the pattern \\*physical\\*. You may also wish to edit Sorcha's command line arguments here." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "06bf31d1-3b3b-46c7-bfaa-cf9f720f8b5a", - "metadata": {}, - "outputs": [], - "source": [ - "def add_SLURM_commands(filename, n, inputs_in, inputs_out, config, outputs_out, pointing):\n", - "\n", - " sorcha_base_command = \"srun --exclusive -N1 -n1 -c1 sorcha\" # you may want to edit this if you know what you're doing\n", - "\n", - " orbits = get_sorted_list_of_files(inputs_in+str(n), '*orbit*') # edit these two lines if your files have a different naming pattern\n", - " params = get_sorted_list_of_files(inputs_in+str(n), '*physical*')\n", - "\n", - " for i, orbits_fn in enumerate(orbits):\n", - "\n", - " root_fn = os.path.basename(os.path.splitext(orbits_fn)[0]).replace('_orbit', '')\n", - "\n", - " params_fn_new = os.path.join(inputs_out+str(n), os.path.basename(params[i]))\n", - " orbits_fn_new = os.path.join(inputs_out+str(n), os.path.basename(orbits_fn))\n", - "\n", - " output_folder = os.path.join(outputs_out+str(n), root_fn)\n", - " mkdir_command = \" \".join([\"mkdir\", output_folder])\n", - "\n", - " full_command = [\n", - " sorcha_base_command, # you may want to edit the command line arguments for Sorcha\n", - " \"-c\",\n", - " config,\n", - " \"-ob\",\n", - " orbits_fn_new,\n", - " \"-p\",\n", - " params_fn_new,\n", - " \"-pd\",\n", - " pointing,\n", - " \"-o\",\n", - " output_folder,\n", - " \"-t\",\n", - " \"_\".join(['SorchaOutput', root_fn]),\n", - " ]\n", - "\n", - " #ephem_out = os.path.join(output_folder, \"_\".join([\"ephem\", root_fn + \".txt\"]))\n", - " #full_command.extend([\"-ew\", ephem_out])\n", - "\n", - " full_command.extend([\"-ar\", ar_data_path])\n", - "\n", - " command_out = \" \".join(full_command)\n", - "\n", - " with open(filename, \"a\") as the_file:\n", - " the_file.write(mkdir_command + \"\\n\")\n", - " the_file.write(command_out + \" & \\n\")\n", - "\n", - " with open(filename, \"a\") as the_file:\n", - " the_file.write(\"wait\\n\")" - ] - }, - { - "cell_type": "markdown", - "id": "94652fe1-9f43-4cf1-8db3-e4ba7bf415d4", - "metadata": {}, - "source": [ - "Run the below cell to generate your SLURM scripts." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e97b6ead-1de8-4d14-aed1-dd1222661f5d", - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(1, number_of_files+1):\n", - "\n", - " script_filename = filename+str(i)+'.sh'\n", - " \n", - " print_header(script_filename, i, job_name, ntasks, mem_per_cpu, output_path, time, partition)\n", - " add_SLURM_commands(script_filename, i, inputs_in, inputs_out, config, outputs_out, pointing)\n", - " print_footer(script_filename)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a3057efd-cfb9-4207-aca7-19ee81a59cf3", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/notebooks/demo_Lightcurve.ipynb b/docs/notebooks/demo_Lightcurve.ipynb index 9ad05522..814bc1d3 100644 --- a/docs/notebooks/demo_Lightcurve.ipynb +++ b/docs/notebooks/demo_Lightcurve.ipynb @@ -16,7 +16,7 @@ "The goal of this notebook is to demonstrate the use of lightcurves within `sorcha`.\n", "\n", "This will be done in two different ways:\n", - "- We will use the community tools part of the [`sorcha-addons`](https://github.com/dirac-institute/sorcha-addons) package\n", + "- We will use the community tools part of the `sorcha-addons`(https://github.com/dirac-institute/sorcha-addons) package\n", "- We will implement a custom lightcurve, and use it inside the code\n", "\n", "The idea is that the user can, in principle, implement their own lightcurves, and incorporate them in their simulation. The goal of `sorcha-addons` is for both the development team, as well as for the community, to share their implementations of custom lightcurve models. " @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "fc4ba06a", "metadata": {}, "outputs": [], @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "46fc0914", "metadata": {}, "outputs": [], @@ -75,10 +75,227 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "99156011", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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[], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(figsize=(10, 8))\n", "ax.plot(observations_df[\"fieldMJD_TAI\"], observations_df[\"Simple_mag\"], linestyle=\"-\", label=\"No phase curve\")\n", @@ -163,10 +681,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "4e802cf1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'identity': , 'sinusoidal': }\n" + ] + } + ], "source": [ "from sorcha.lightcurves.lightcurve_registration import LC_METHODS, update_lc_subclasses\n", "\n", @@ -179,7 +705,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "072165e9", "metadata": {}, "outputs": [], @@ -191,7 +717,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "3e784192", "metadata": {}, "outputs": [], @@ -215,10 +741,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "993c1c58", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from matplotlib.lines import Line2D\n", "\n", @@ -327,7 +864,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "59390bba", "metadata": {}, "outputs": [], @@ -364,10 +901,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "eab8f417", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'identity': , 'sinusoidal': , 'doublesinusoidal': }\n" + ] + } + ], "source": [ "update_lc_subclasses()\n", "print(LC_METHODS)" @@ -375,7 +920,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "8889387a", "metadata": {}, "outputs": [], @@ -387,7 +932,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "4beeeaf7", "metadata": {}, "outputs": [], @@ -411,10 +956,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "6febb5ce", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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f08c8ce7e726e1dd2019b09e99ffef02ff3b464e Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Fri, 3 Jan 2025 13:55:20 +0000 Subject: [PATCH 02/52] add in instruction to install add-ons package to compile the docs add in instruction to install add-ons package to compile the docs --- docs/support.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/support.rst b/docs/support.rst index 538d7059..b0aff457 100644 --- a/docs/support.rst +++ b/docs/support.rst @@ -28,7 +28,7 @@ Contributing to the Documentation We are very happy to receive feedback on the online documentation through the `project's GitHub repository `_. Beyond pointing out typos and small changes through issues, we welcome pull requests on the `sphinx `_ documentation used here on the readthedocs. -You will need to install the development version of Sorcha from a clone of the Sorcha repository. See the our :ref:`dev_mode` instructions for further details. +You will need to install the development version of Sorcha from a clone of the Sorcha repository as well as the `sorcha-addons package `_. See the our :ref:`dev_mode` instructions for further details. If you move to the docs directory (cd sorcha/docs/), edit the .rst files, and run:: From 80e0435a51c4f49268cde790c0d972e8f6c59ffd Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Fri, 3 Jan 2025 14:54:49 +0000 Subject: [PATCH 03/52] doc updates address sqlite error when running multiple versions at the same time --- docs/troubleshooting.rst | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/troubleshooting.rst b/docs/troubleshooting.rst index 15642121..fd8a9406 100644 --- a/docs/troubleshooting.rst +++ b/docs/troubleshooting.rst @@ -54,6 +54,9 @@ ERROR: Unable to find ObjID column headings (OrbitAuxReader:....) -------------------------------------------------------------------- Check your input files and ensure that they have ObjID column as the first column. +in PPOutWriteSqlite3: sqlite3.OperationalError: index ObjID already existssqlite3.OperationalError: index ObjID already exists +--------------------------------------------------------------------------------------------------------------------------------------------- +This happens if you are outputting as sql databases and you have dueling sorcha processes running in the same directory with the same output file names running on the same input files using the -f flag to force overwriting of output files. One way to check this is to only allow for one sorcha run to be output to a directory and see if you've got two log files that are actively being written to/were created. Note if you're using CSV, text file, or pytables format you won't get this error when you hit this race condition. From 0894e7469106d822a19cd66b7be12cb110740f48 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Sat, 4 Jan 2025 20:16:56 +0000 Subject: [PATCH 04/52] documentation updates documentation updates --- docs/cite.rst | 11 +++++++---- docs/images/survey_simulator_flow_chart.png | Bin 255474 -> 402016 bytes docs/overview.rst | 18 ++++++++++-------- docs/uninstall.rst | 6 +++--- 4 files changed, 20 insertions(+), 15 deletions(-) diff --git a/docs/cite.rst b/docs/cite.rst index cbcdca86..69eda007 100644 --- a/docs/cite.rst +++ b/docs/cite.rst @@ -1,9 +1,14 @@ +.. _citethecode: + Citing the Software ========================== +``sorcha`` is described provided in joint Astromical Journal/JOSS software papers: Merritt et al. (submitted) and Holman et al.(submitted). We also ask that you reference in your software citations and acknowledgements the other packages that ``sorcha`` is built upon (see below). + + Built-In Citation Function ---------------------------- -If you use Sorcha in your research, please do include a citation in your published papers for sorcha and the software packages and resources that sorcha is based on. The simplest way to find this information is to use our built-in citation function. In an interactive Python session or a Jupyter notebook:: +If you use ``sorcha`` in your research, please do include a citation in your published papers for ``sorcha`` and the software packages and resources that sorcha is based on. The simplest way to find this information is to use our built-in citation function. In an interactive Python session or a Jupyter notebook:: import sorcha sorcha.cite() @@ -12,9 +17,7 @@ If you use Sorcha in your research, please do include a citation in your publish Additional Citation Details ---------------------------- -The main overview of Sorcha is provided in our AAS journal software papers and JOSS paper. (in prep) - -Please also cite the software and ancillary data files that helps power Sorcha: + Please also cite the software and ancillary data files that helps power ``sorcha``: * assist https://assist.readthedocs.io/en/latest/ * astropy https://www.astropy.org/acknowledging.html diff --git a/docs/images/survey_simulator_flow_chart.png b/docs/images/survey_simulator_flow_chart.png index 32cceedfdd22844b8ebb182da5eb0028a616bf88..0b387b13e57fc17c87c6c4102c6646a9db13b96e 100644 GIT binary patch literal 402016 zcmeFaWn5L+`#$U-AR&T+G=fS=Hwsb~(kR{Cl1fN=P(eXLkWNKPx|>5IjWiO6Mx?tr z|Fxa(%p7JMo%ua4pLsF!Va_>w@3q&uI35l;&WRHz@Fecux_{!tX~GjH zuo$s0!6$U2RA?to{K8;%^QOGTP4S!753KDJZ1fC_#0;(VjqZ!z;JkM2>WLGKuMKo{ z@84x%{HS+MSNCHZ^A#LB$NMi{yt=RJ(Ny)Zs)nYHrXfByTvKz5lwhp(1Ud)#dDk*b zMLhH{dog3Z=1C0AE2;HOqI>uRCJZD*AsieW#wP}wr|B?g9Pk5KgWlj7v!BTEeR2jp zx9G{h9KjZLHaXV*8C99Pf!u+IzkHmLLd%N~by!WjIe9#C?}4PLPUADh6P;$$_1_YNRS*EF*J*3H*(9;umkT6X@WtU%)@);GYvG(Bjd4`{XoX 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ztJ(Wc;}lVU)0{Y&?`Zh(1{tX1eN^y@*ZApAY5u&v{r0;ReEi_)kEHno2c|zUoDo0! zb`B=s9p1#B{?L|hyPy7~=?t$cra$@rM1weM46VLr-|rnJBY2wow0}w5bNb5v0Z;-d AvH$=8 diff --git a/docs/overview.rst b/docs/overview.rst index 2ad2dedd..6febf100 100644 --- a/docs/overview.rst +++ b/docs/overview.rst @@ -5,9 +5,9 @@ How Sorcha Works ------------------------------- In order to conduct detailed population studies on the orbital properties and physical characteristics of the various Solar System small body reservoirs, one must account for all the survey biases (the complex and often intertwined detection biases – brightness limits, -pointing, cadence, on-sky motion limits, software detection efficiencies) in one’s discovery survey (`see Lawler et al. 2018 `_ for a more detailed discussion). Sorcha is an open-source Python Solar System survey simulator designed for the `Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) `_. Sorcha works by the user inputting a synthetic Solar System small body population. The software forward models the input population to simulate what the survey should have detected using a series of :ref:`filters`. The filters can be switched on or off and customized as needed via a :ref:`configuration file`. In this way, a synthetic population can be compared to the real survey's discoveries. +pointing, cadence, on-sky motion limits, software detection efficiencies) in one’s discovery survey (`see Lawler et al. 2018 `_ for a more detailed discussion). ``sorcha`` is an open-source Python Solar System survey simulator designed for the `Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) `_. ``sorcha`` works by the user inputting a synthetic Solar System small body population. The software forward models the input population to simulate what the survey should have detected using a series of :ref:`filters`. The filters can be switched on or off and customized as needed via a :ref:`configuration file`. In this way, a synthetic population can be compared to the real survey's discoveries. -The :ref:`inputs` that Sorcha requires are shown in the figure below. The software requires input files that describe the small bodies to simulate (including a file for orbits, at least one file for physical parameters, and a SQLite database that describes the telescope survey's observation history). Sorcha outputs simulated detection data, including each time, position, and apparent magnitude at which a synthetic small body was detected based on various options set up in a configuration file. +The :ref:`inputs` that ``sorcha`` requires are shown in the figure below. The software requires input files that describe the small bodies to simulate (including a file for orbits, at least one file for physical parameters, and a SQLite database that describes the telescope survey's observation history). ``sorcha`` outputs simulated detection data, including each time, position, and apparent magnitude at which a synthetic small body was detected based on various options set up in a configuration file. .. image:: images/survey_simulator_flow_chart.png @@ -15,22 +15,24 @@ The :ref:`inputs` that Sorcha requires are shown in the figure below. Th :alt: An overview of the inputs and outputs for Sorcha -Sorcha by default uses its own :ref:`ephemeris generator` to propagate the orbits and translate them to on-sky locations and rates. Sorcha's ephemeris generator is powered by `ASSIST `_, a software package for ephemeris-quality integrations of test particles, and the `REBOUND `_ N-body integrator. If the user prefers to use a different generator, Sorcha is also to be configured to read in an external ephemeris file with pre-calculated ephemerides of the input synthetic orbital population. +``sorcha`` by default uses its own :ref:`ephemeris generator` to propagate the orbits and translate them to on-sky locations and rates. ``sorcha``'s ephemeris generator is powered by `ASSIST `_, a software package for ephemeris-quality integrations of test particles, and the `REBOUND `_ N-body integrator. If the user prefers to use a different generator, ``sorcha`` is also to be configured to read in an external ephemeris file with pre-calculated ephemerides of the input synthetic orbital population. .. warning:: - We have validated Sorcha with its internal :ref:`ephemeris generator`. If the user chooses to use a different ephemeris engine's calculations as input for Sorcha, the user has the responsibility to check the accuracy of this input. + We have validated ``sorcha`` with its internal :ref:`ephemeris generator`. If the user chooses to use a different ephemeris engine's calculations as input for ``sorcha``, the user has the responsibility to check the accuracy of this input. Design Philosophy ---------------------- -Sorcha has been designed in a modular way with each filter written as its own function, This makes it easy to add new filters in the future if required by users. We note for dealing with rotational light curve and activity effects, we have set up Sorcha such that the user can provide their own custom classes/functions and import them into Sorcha to use. Further details can be found on the :ref:`addons` page. Sorcha has been designed with LSST in mind, but many of the filters already developed will be applicable to other Solar System surveys. If you are interested in incorporating your survey into Sorcha do reach out. +``sorcha`` has been designed in a modular way with each filter written as its own function, This makes it easy to add new filters in the future if required by users. We note for dealing with rotational light curve and activity effects, we have set up ``sorcha`` such that the user can provide their own custom classes/functions and import them into ``sorcha`` to use. Further details can be found on the :ref:`addons` page. ``sorcha`` has been designed with LSST in mind, but many of the filters already developed will be applicable to other Solar System surveys. If you are interested in incorporating your survey into ``sorcha`` do reach out. .. warning:: - For a wide variety of use cases, the user should be able to use Sorcha straight out of the box. We have designed the software such that it should be straightforward to add in additional filters or rotational light curve/activity classes. As with any open-source package, **once the user has made modifications to the code, it is the responsibility of the user to confirm these changes provide an accurate result**. + For a wide variety of use cases, the user should be able to use ``sorcha`` straight out of the box. We have designed the software such that it should be straightforward to add in additional filters or rotational light curve/activity classes. As with any open-source package, **once the user has made modifications to the code, it is the responsibility of the user to confirm these changes provide an accurate result**. .. note:: - Contributions are very welcome. If there is a feature or functionality not yet available in Sorcha, we encourage you to propose the feature as an issue in the `main Sorcha repository `_ or share your code with the new enhancements. Further details can be found on our :ref:`reporting` page. + Contributions are very welcome. If there is a feature or functionality not yet available in ``sorcha``, we encourage you to propose the feature as an issue in the `main github repository `_ or share your code with the new enhancements. Further details can be found on our :ref:`reporting` page. - +Using Sorcha in Your Science +-------------------------------- +We made ``sorcha`` to be a tool for the small body planetary astronomer community. If ``sorcha`` enabled your science, please make sure to give the proper credit in your talks and papers by citing the relevant ``sorcha`` papers and the python packages that the software is built upon. Further details can be found :ref:`here`. diff --git a/docs/uninstall.rst b/docs/uninstall.rst index f382ec01..c786b98b 100644 --- a/docs/uninstall.rst +++ b/docs/uninstall.rst @@ -1,15 +1,15 @@ Uninstalling ================= -If you have installed Sorcha using conda, then you can uninstall the package with:: +If you have installed ``sorcha`` using conda, then you can uninstall the package with:: conda uninstall sorcha -If you have installed Sorcha using mamba, then you can uninstall the package with:: +If you have installed ``sorcha`` using mamba, then you can uninstall the package with:: mamba uninstall sorcha -If you have installed Sorcha using pip, then you can uninstall the package with:: +If you have installed ``sorcha`` using pip, then you can uninstall the package with:: pip uninstall sorcha From aa6268e3a54af664e1fc7ea2a4e664559e6d82cc Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Sat, 4 Jan 2025 23:16:14 +0000 Subject: [PATCH 05/52] documentation updates documentation updates --- docs/acknowledgements.rst | 13 ++++++++++--- docs/advanced.rst | 11 +++++++++++ docs/cite.rst | 8 ++++++-- docs/index.rst | 7 ++++--- docs/inputs.rst | 2 +- 5 files changed, 32 insertions(+), 9 deletions(-) create mode 100644 docs/advanced.rst diff --git a/docs/acknowledgements.rst b/docs/acknowledgements.rst index d8df1113..e40eb49f 100644 --- a/docs/acknowledgements.rst +++ b/docs/acknowledgements.rst @@ -28,9 +28,16 @@ This effort is a collaboration between Queen's University Belfast, the Universit :alt: LINCC Logo -Sorcha development was supported in part by: +``sorcha`` development was supported in part by: -- Science and Technology Facilities Council (STFC) grants ST/P000304/1 and ST/V000691/1 +- Science and Technology Facilities Council (STFC) grants ST/P000304/1, ST/V000691/1, ST/X001253/1, and ST/V506990/1 - Horizon 2020 Marie Skłodowska-Curie Postdoctoral Fellowship - Preparing for Astrophysics with LSST Program, funded by the Heising Simons Foundation through grant 2021-2975, and administered by Las Cumbres Observatory -- LINCC Frameworks is supported by Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt, as part of the Virtual Institute of Astrophysics (VIA) +- LSST Discovery Alliance LINCC Frameworks Incubator grant [2023-SFF-LFI-01-Schwamb]. Support was provided by Schmidt Sciences. +- University of Washington College of Arts and Sciences, Department of Astronomy, and the DiRAC (Data-intensive Research in Astrophysics and Cosmology) Institute. The DiRAC Institute is supported through generous gifts from the Charles and Lisa Simonyi Fund for Arts and Sciences and the Washington Research Foundation +- Washington Research Foundation Data Science Term Chair fund and the University of Washington Provost's Initiative in Data-Intensive Discovery. +- Department for the Economy (DfE) Northern Ireland postgraduate studentship scheme +- National Science Foundation through the following awards: Collaborative Research: SWIFT-SAT: Minimizing Science Impact on LSST and Observatories Worldwide through Accurate Predictions of Satellite Position and Optical Brightness NSF Award Number: 2332736 and Collaborative Research: Rubin Rocks: Enabling near-Earth asteroid science with LSST NSF Award Number: 2307570 +- Travel funding from the STFC for UK participation in LSST through STFC grant ST/S006206/1 + +Several functions within ``sorcha`` were adapted from code originally developed for `rubin_sim`_, We thank the Vera C. Rubin Observatory Data Management Team and Scheduler Team for making their software open-source. Development of ``rubin_sim` was supported in part by the National Science Foundation through Cooperative Agreement AST-1258333 and Cooperative Support Agreement AST1836783 managed by the Association of Universities for Research in Astronomy (AURA), and the Department of Energy under Contract No. DE-AC02-76SF00515 with the SLAC National Accelerator Laboratory managed by Stanford University. diff --git a/docs/advanced.rst b/docs/advanced.rst new file mode 100644 index 00000000..a1bbe656 --- /dev/null +++ b/docs/advanced.rst @@ -0,0 +1,11 @@ + +Advanced User Features +========================== + +.. warning:: + **If you're new to ``sorcha`` turn away from this section NOW! (we're only partially kidding)** This section provides information about features for advanced users of ``sorcha``. Changing or adjusting the parameters described in this section may produce unintended results. *With great power comes great responsibility. *Be very careful in applying the knowledge below.** Most users will not need to touch these parameters within ``sorcha``. + +Setting the Random Number Generator Seed +--------------------------------------------- + +``sorcha`` is described provided in diff --git a/docs/cite.rst b/docs/cite.rst index 69eda007..7e6bbde4 100644 --- a/docs/cite.rst +++ b/docs/cite.rst @@ -5,6 +5,10 @@ Citing the Software ``sorcha`` is described provided in joint Astromical Journal/JOSS software papers: Merritt et al. (submitted) and Holman et al.(submitted). We also ask that you reference in your software citations and acknowledgements the other packages that ``sorcha`` is built upon (see below). +.. tip:: + * Beyond citing the relevant papers, make sure to include details about your configuration for ``sorcha`` (e.g. which footprint filter you're using), details about your input population (e.g. orbital, H, color, and phase curve distribution), and information about the pointing database used. + +.. _citefunc: Built-In Citation Function ---------------------------- @@ -17,7 +21,7 @@ If you use ``sorcha`` in your research, please do include a citation in your pub Additional Citation Details ---------------------------- - Please also cite the software and ancillary data files that helps power ``sorcha``: +Please also cite the software and ancillary data files that helps power ``sorcha``. Our :ref:`citation function` described above will give the full details or you can manually find the acknowledgement information for each package: * assist https://assist.readthedocs.io/en/latest/ * astropy https://www.astropy.org/acknowledging.html @@ -38,5 +42,5 @@ Additional Citation Details * tqdm https://tqdm.github.io/ .. note:: - The same information is available from our built-in citation function. + The same information is available from our :ref:`built-on citation function`. diff --git a/docs/index.rst b/docs/index.rst index 7f270b0c..cdabfd20 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -54,11 +54,12 @@ works, tutorials, and demonstration notebooks that show how each of the various gettingstarted hpc whatsorchadoesnotdo - notebooks + cite troubleshooting support + uninstall + advanced + notebooks release contributors acknowledgements - cite - uninstall diff --git a/docs/inputs.rst b/docs/inputs.rst index 9c168093..6f4c6a23 100644 --- a/docs/inputs.rst +++ b/docs/inputs.rst @@ -210,7 +210,7 @@ We have implemented several phase curve parameterizations that can be specified | G, G1&G2, G12, S | Phase curve parameter(s) for all filters (either G12, G1 & G2, or β) (optional) | +------------------+----------------------------------------------------------------------------------+ -** note:: +.. note:: The Phase curve parameters(s) column will not be present if the phase curve function/calculation is set to None in the configuration file .. note:: From faa2d1878fc8338d4ab10cd7c2a3057675c6ccb3 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Sat, 4 Jan 2025 23:37:04 +0000 Subject: [PATCH 06/52] documentation updates documentation updates --- docs/advanced.rst | 30 +++++++++++++++++++++++++++++- docs/filters.rst | 27 --------------------------- docs/installation.rst | 32 ++++++++++++++++---------------- 3 files changed, 45 insertions(+), 44 deletions(-) diff --git a/docs/advanced.rst b/docs/advanced.rst index a1bbe656..2b47f40c 100644 --- a/docs/advanced.rst +++ b/docs/advanced.rst @@ -3,9 +3,37 @@ Advanced User Features ========================== .. warning:: - **If you're new to ``sorcha`` turn away from this section NOW! (we're only partially kidding)** This section provides information about features for advanced users of ``sorcha``. Changing or adjusting the parameters described in this section may produce unintended results. *With great power comes great responsibility. *Be very careful in applying the knowledge below.** Most users will not need to touch these parameters within ``sorcha``. + **If you're new to sorcha, turn away from this section NOW! (we're only partially kidding)** This section provides information about features for advanced users of ``sorcha``. Changing or adjusting the parameters described in this section may produce unintended results. *With great power comes great responsibility. **Be very careful in applying the knowledge below.** Most users will not need to touch these parameters within ``sorcha``. Setting the Random Number Generator Seed --------------------------------------------- ``sorcha`` is described provided in + + +SNR/Apparent Magnitude Filters +------------------------------------- + +.. warning:: + These filters are for the advanced user. If you only want to know what the survey will discover, you **DO NOT** need these filters on. + +These two mutually-exclusive filters serve to cut observations of faint objects. +The user may either implement the SNR limit, to remove all observations of objects +below a user-defined SNR threshold; or the magnitude limit, to remove all observations +of objects above a user-defined magnitude. + +To implement the SNR limit, include the following in the config file:: + + [EXPERT] + SNR_limit = 2.0 + +To implement the magnitude limit, include the following in the config file:: + + [EXPERT] + magnitude_limit = 22.0 + +.. attention:: + Only one of these filters may be implemented at once. + + + diff --git a/docs/filters.rst b/docs/filters.rst index 5fbe7a4b..a9149692 100644 --- a/docs/filters.rst +++ b/docs/filters.rst @@ -190,30 +190,3 @@ the observation is of a linked object or not. To enable this functionality, add drop_unlinked = False -Expert Filters ----------------------- - -SNR/Apparent Magnitude Cuts -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. warning:: - These filters are for the advanced user. If you only want to know what the survey will discover, you **DO NOT** need these filters on. - -These two mutually-exclusive filters serve to cut observations of faint objects. -The user may either implement the SNR limit, to remove all observations of objects -below a user-defined SNR threshold; or the magnitude limit, to remove all observations -of objects above a user-defined magnitude. - -To implement the SNR limit, include the following in the config file:: - - [EXPERT] - SNR_limit = 2.0 - -To implement the magnitude limit, include the following in the config file:: - - [EXPERT] - magnitude_limit = 22.0 - -.. attention:: - Only one of these filters may be implemented at once. - diff --git a/docs/installation.rst b/docs/installation.rst index d71957fa..06c2ee32 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -4,12 +4,12 @@ Installation ================= .. note:: - Sorcha is both conda/mamba and pip installable. We recommend installing via conda/mamba. + ``sorcha`` is both conda/mamba and pip installable. We recommend installing via conda/mamba. Requirements ----------------------------- -Sorcha has the following requirements that will be automatically installed using pip or conda when you install the sorcha package: +``sorcha`` has the following requirements that will be automatically installed using pip or conda when you install the sorcha package: * python 3.10 or later * assist @@ -29,7 +29,7 @@ Sorcha has the following requirements that will be automatically installed usin * tqdm .. tip:: - We also recommend installing h5py in your conda/mamba environment to ensure that the proper HD5 libraries are installed. + We also recommend installing h5py in your conda/mamba environment to ensure that the proper HDF5 libraries are installed. @@ -47,7 +47,7 @@ If using mamba:: mamba create -n sorcha -c conda-forge assist numpy numba pandas scipy astropy matplotlib sbpy pytables spiceypy healpy rebound pooch tqdm h5py importlib_resources python=3.10 .. tip:: - We recommend using python version 3.10 or higher with Sorcha. The conda command uses python 3.10. + We recommend using python version 3.10 or higher with ``sorcha``. The conda command uses python 3.10. **Step 2** Activate your conda/mamba environment @@ -62,7 +62,7 @@ On mamba:: Installing Sorcha ---------------------- -Unless you're editing the source code, you can use the version of Sorcha published on conda-forge. +Unless you're editing the source code, you can use the version of ``sorcha`` published on conda-forge. If using conda:: @@ -72,7 +72,7 @@ If using mamba:: mamba install -c conda-forge sorcha -You can install sorcha via from pypi using pip, but installation via conda/mamba is recommended. +You can install ``sorcha`` via from pypi using pip, but installation via conda/mamba is recommended. If using pip:: @@ -83,7 +83,7 @@ If using pip:: Downloading Required Supplemental Files ---------------------------------------- -To run Sorcha's built in :ref:`ephemeris generator`, you will need to download the auxiliary files required by assist and rebound for performing the N-body integrations. +To run ``sorcha``'s built in :ref:`ephemeris generator`, you will need to download the auxiliary files required by assist and rebound for performing the N-body integrations. To install the necessary `SPICE (Spacecraft, Planet, Instrument, C-matrix, Events) `_ auxiliary files and other required data files for ephemeris generation (774 MB total in size):: @@ -100,7 +100,7 @@ To install the necessary `SPICE (Spacecraft, Planet, Instrument, C-matrix, Event Testing Your Sorcha Installation ---------------------------------- -You can check that the Sorcha installation was successful, by obtaining the demo input files and running the demo command. +You can check that the ``sorcha`` installation was successful, by obtaining the demo input files and running the demo command. The demo input files and configuration file are installed with the socha package. You can run the following command on the command line to copy the files to the current directory (or a different location):: @@ -109,7 +109,7 @@ The demo input files and configuration file are installed with the socha package .. note:: The optional -p flag allows you to specify a specific location to copy the demo input files. If the files already exist, the -f flag can be used to force a fresh copy of the files to be generated. . -You can find the command to run the sorcha demo on the command line in two ways. First on the command line:: +You can find the command to run the ``sorcha`` demo on the command line in two ways. First on the command line:: sorcha demo howto @@ -140,25 +140,25 @@ The output will appear in a csv file (testrun_e2e.csv) in your current directory Installing Sorcha in Development Mode --------------------------------------------------------------------- -**This is the installation method for adding/edit Sorcha's codebase or for working on/updating Sorcha's documentation.** +**This is the installation method for adding/edit sorcha's codebase or for working on/updating sorcha's documentation.** -**Step 1** Create a directory to contain the Sorcha repos:: +**Step 1** Create a directory to contain the ``sorcha`` repos:: mkdir sorcha -**Step 2** Navigate to the directory you want to store the Sorcha source code in:: +**Step 2** Navigate to the directory you want to store the ``sorcha`` source code in:: cd sorcha -**Step 3** Download the Sorcha source code via:: +**Step 3** Download the ``sorcha`` source code via:: git clone https://github.com/dirac-institute/sorcha.git -**Step 4** Navigate to the sorcha repository directory:: +**Step 4** Navigate to the ``sorcha`` repository directory:: cd sorcha -**Step 5** Install an editable (in-place) development version of Sorcha. This will allow you to run the code from the source directory. +**Step 5** Install an editable (in-place) development version of ``sorcha``. This will allow you to run the code from the source directory. If you just want the source code installed so edits in the source code are automatically installed:: @@ -168,6 +168,6 @@ If you are going to be editing documentation or significantly modifying unit tes pip install -e '.[dev]' -**Step 6 (Optional unless working on documentation):** You will also install the pandoc package (either via conda/pip or `direct download `_ . +**Step 6 (Optional unless working on documentation):** You will need to install the pandoc package (either via conda/pip or `direct download `_ and a version of the `sorcha-addons package `_. From 610b44804e67c187987518d090ea547c118cc054 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Sun, 5 Jan 2025 10:08:40 +0000 Subject: [PATCH 07/52] documentation updates documentation updates --- docs/advanced.rst | 11 +++++++++-- docs/conf.py | 7 +++---- docs/inputs.rst | 30 +++++++++++++++--------------- 3 files changed, 27 insertions(+), 21 deletions(-) diff --git a/docs/advanced.rst b/docs/advanced.rst index 2b47f40c..dfb706a2 100644 --- a/docs/advanced.rst +++ b/docs/advanced.rst @@ -8,9 +8,16 @@ Advanced User Features Setting the Random Number Generator Seed --------------------------------------------- -``sorcha`` is described provided in +.. warning:: + For most science cases, you **DO NOT** want to set the same seed for each ``sorcha`` run, but if you need reproducability then you do want to see the seed as an environment variable before running ``sorcha`` - +The value used to seed the random number generator can be specified via the **SORCHA_SEED** environmental variable. This allows for ``sorcha`` to be fully reproducibly run with (if using a bash shell or Z-shell):: + + export SORCHA_SEED=42 + +.. tip:: + If you're trying to reproduce a crash or a certain behavior in ``sorcha``, you can find the value that you need to set the random seed to in the log file. + SNR/Apparent Magnitude Filters ------------------------------------- diff --git a/docs/conf.py b/docs/conf.py index f95f7392..9e0c94cc 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -6,7 +6,6 @@ import os import sys - import autoapi from importlib.metadata import version @@ -17,9 +16,9 @@ # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information -copyright = "2024" -project = "Sorcha" -author = "Sorcha development team" +copyright = "2025" +project = "sorcha" +author = "Sorcha Team" release = version("sorcha") # for example take major/minor version = ".".join(release.split(".")[:2]) diff --git a/docs/inputs.rst b/docs/inputs.rst index 6f4c6a23..4b3bfba8 100644 --- a/docs/inputs.rst +++ b/docs/inputs.rst @@ -3,7 +3,7 @@ Inputs ========== -"Sorcha requires two input files describing the synthetic solar system objects to simulate -- one for the orbital parameters and one for the physical parameters -- as well as survey pointing database. Optionally, the user can provide a pre-generated ephemeris with the positions of each object near the survey pointings and a complex physical parameter file for rotational light curves and cometary activity. Each of these files are described within this section and example files are shown. +``sorcha`` requires two input files describing the synthetic solar system objects to simulate -- one for the orbital parameters and one for the physical parameters -- as well as survey pointing database. Optionally, the user can provide a pre-generated ephemeris with the positions of each object near the survey pointings and a complex physical parameter file for rotational light curves and cometary activity. Each of these files are described within this section and example files are shown. .. image:: images/survey_simulator_flow_chart.png @@ -15,7 +15,7 @@ Inputs Each synthetic planetesimal has its own unique object identifier set by the user and must have entries in the orbits and physical parameters files, as well as the cometary activity file, if used. .. warning:: - Sorcha does not check whether or not a planetesimal ID has been repeated in another row of the input files. **It is up to the user to ensure their input files include only unique IDs**. + ``sorcha`` does not check whether or not a planetesimal ID has been repeated in another row of the input files. **It is up to the user to ensure their input files include only unique IDs**. .. _orbits: @@ -26,7 +26,7 @@ Orbit File This is a file which contains the orbital information of a set of synthetic objects. .. tip:: - * Sorcha is designed to handle heliocentric **Cometary (COM), Keplerian (KEP), and Cartesian (CART)** orbits, as well as their barycentric equivalents: **Barycentric Cometary (BCOM), Keplerian (BKEP) and Cartesian (BCART)** + * ``sorcha`` is designed to handle heliocentric **Cometary (COM), Keplerian (KEP), and Cartesian (CART)** orbits, as well as their barycentric equivalents: **Barycentric Cometary (BCOM), Keplerian (BKEP) and Cartesian (BCART)** * The orbit file **must** have a consistent format (i.e. Cometary or Keplerian or Cartesian) throughout * The first column must be ObjID, but the ordering of the remaining columns does not matter as long as the required columns exist and have entries * The first row in the orbit file **must** be a header listing the column names @@ -39,10 +39,10 @@ This is a file which contains the orbital information of a set of synthetic obje The orbit epoch is expected to be given in **TDB (Barycentric Dynamical Time)** .. tip:: - If using Sorcha's internal :ref:`ephemeris generator` (which is the default mode), **we recommend calculating/creating your input orbits with epochs close in time to the start of the first survey observation**. This will minimize the n-body integrations required to set up the ephemeris generation. + If using ``sorcha``'s internal :ref:`ephemeris generator` (which is the default mode), **we recommend calculating/creating your input orbits with epochs close in time to the start of the first survey observation**. This will minimize the n-body integrations required to set up the ephemeris generation. .. tip:: - Be careful about the way your input elements are defined! Using heliocentric elements as barycentric (or vice-versa) will lead to wrong outputs. Similarly, if using Cartesian elements, be careful about the orientation of the coordinate system! Sorcha assumes that Cartesian elements are Ecliptic-oriented. + Be careful about the way your input elements are defined! Using heliocentric elements as barycentric (or vice-versa) will lead to wrong outputs. Similarly, if using Cartesian elements, be careful about the orientation of the coordinate system! ``sorcha`` assumes that Cartesian elements are Ecliptic-oriented. .. note:: For readability we show examples of whitespace-separated files below. We show only the heliocentric versions of these inputs, as the barycentric column requirements are identical, changing only the `FORMAT` designation @@ -167,9 +167,9 @@ The input file for the physical parameters includes information about the object * The **correct capitalization of column names** is required * The physical parameters file can be either **whitespace-separated** or **comma-separated values (CSV)** * Each simulated object **must** have a unique string identifier - * You **must use the same phase curve prescription for all simulated objects**. If you want to use different phase curve prescriptions for different synthetic populations, you will need to run them in separate input files to Sorcha + * You **must use the same phase curve prescription for all simulated objects**. If you want to use different phase curve prescriptions for different synthetic populations, you will need to run them in separate input files to ``sorcha`` * If the phase curve function is set to NONE in the configuration value then no phase curve parameter values are required in the physical parameters files. - * In the config file you can decide which filters you want have Sorcha run on and specify which filter is the main filter that the absolute magnitude is defined for. You only need to provide colors for those filters specified in the config file. + * In the config file you can decide which filters you want have ``sorcha`` run on and specify which filter is the main filter that the absolute magnitude is defined for. You only need to provide colors for those filters specified in the config file. .. note:: For readability we show examples of whitespace-separated files below. @@ -196,7 +196,7 @@ An example of the physical parameters file where a HG prescription is specified Rubin Observatory will survey the sky in six broadband (optical filters), *u, g, r, i, z,* and *y* . In the physical parameters file, you will specify the object's absolute magnitude in the main filter (as specified in the config file. usually this is g or r band) and then provide the synthetic planetesimal's color in other filters relative to the main filter. -We have implemented several phase curve parameterizations that can be specified in the config file and then inputted through the physical parameters. **You can either specify one set of phase curve parameters for all filters or specify values for each filter examined by Sorcha.** We are using the `sbpy `_ phase function utilities. The supported options are: `HG `_, `HG1G2 `_, `HG12 `_, `linear `_ (specified by S in the header of the physical parameters file), and none (if no columns for phase curve are included in the physical parameters file then the synthetic object is considered to have a flat phase curve). Note that the HG12 model is the `Penttilä et al. (2016) `_ modified model, and not the original (IAU adopted) `Muinonen et al. (2010) `_ model. +We have implemented several phase curve parameterizations that can be specified in the config file and then inputted through the physical parameters. **You can either specify one set of phase curve parameters for all filters or specify values for each filter examined by ``sorcha``.** We are using the `sbpy `_ phase function utilities. The supported options are: `HG `_, `HG1G2 `_, `HG12 `_, `linear `_ (specified by S in the header of the physical parameters file), and none (if no columns for phase curve are included in the physical parameters file then the synthetic object is considered to have a flat phase curve). Note that the HG12 model is the `Penttilä et al. (2016) `_ modified model, and not the original (IAU adopted) `Muinonen et al. (2010) `_ model. +------------------+----------------------------------------------------------------------------------+ | Keyword | Description | @@ -214,7 +214,7 @@ We have implemented several phase curve parameterizations that can be specified The Phase curve parameters(s) column will not be present if the phase curve function/calculation is set to None in the configuration file .. note:: - In the config file you can decide which filters you want to have Sorcha run on and specify which filter is the main filter that the absolute magnitude is defined for. You only need to provide colors for those filters specified in the config file. + In the config file you can decide which filters you want to have ``sorcha`` run on and specify which filter is the main filter that the absolute magnitude is defined for. You only need to provide colors for those filters specified in the config file. .. _pointing: @@ -222,9 +222,9 @@ Survey Pointing Database ------------------------ .. note:: - Currently Sorcha is set up to run with the LSST cadence simulations pointing databases. + Currently ``sorcha`` is set up to run with the LSST cadence simulations pointing databases. -This database contains information about the LSST pointing history and observing conditions. We use observation mid-point time, right ascension, declination, rotation angle of the camera, 5-sigma limiting magnitude, filter, and seeing information in Sorcha to determine if a synthetic Solar System object is observable. +This database contains information about the LSST pointing history and observing conditions. We use observation mid-point time, right ascension, declination, rotation angle of the camera, 5-sigma limiting magnitude, filter, and seeing information in ``sorcha`` to determine if a synthetic Solar System object is observable. What we call the LSST pointing database (currently simulated since Rubin Observatory hasn’t started operations) is generated through the Rubin Observatory scheduler (since 2021 referred to as `rubin_sim `_ and previously known as OpSim). This software is currently under active development and is being used to run many simulated iterations of LSST scenarios, showing what the cadence would look like with differing survey strategies. A description of an early version of this Python software can be found in `Delgado et al.(2014) `_. The output of rubin_sim is a SQLlite database containing the pointing history and associated metadata of the simulated observation history of LSST. @@ -250,7 +250,7 @@ The latest version of rubin_sim cadence simulations can be found at https://s3df Setting Up the Correct LSST Pointing Database Query ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Sorcha's **ppsqldbquery** config file parameter contains the SQL query for obtaining this information from the pointing database. +``sorcha``'s **ppsqldbquery** config file parameter contains the SQL query for obtaining this information from the pointing database. From rubin_sim v2.0 simulations onward use the query:: SELECT observationId, observationStartMJD as observationStartMJD_TAI, visitTime, visitExposureTime, filter, seeingFwhmGeom as seeingFwhmGeom_arcsec, seeingFwhmEff as seeingFwhmEff_arcsec, fiveSigmaDepth as fieldFiveSigmaDepth_mag , fieldRA as fieldRA_deg, fieldDec as fieldDec_deg, rotSkyPos as fieldRotSkyPos_deg FROM observations order by observationId @@ -266,7 +266,7 @@ For past rubin_sim/OpSim simulations pre-v2.0 use the query:: Complex Physical Parameters File (Optional) --------------------------------------------------- -The complex physical parameters file is only needed if you're going to include your own rotational light curve class or cometary activity class to augment the calculated apparent magnitudes. Sorcha is set up such that any values required for this such as (light curve amplitude and period per simulated object) are included in a file, separate from the physical parameters file, that we refer to as the complex physical parameters file. What columns are required in the complex physical parameters file depends on the classes you are using. +The complex physical parameters file is only needed if you're going to include your own rotational light curve class or cometary activity class to augment the calculated apparent magnitudes. ``sorcha`` is set up such that any values required for this such as (light curve amplitude and period per simulated object) are included in a file, separate from the physical parameters file, that we refer to as the complex physical parameters file. What columns are required in the complex physical parameters file depends on the classes you are using. .. tip:: * The first column must be ObjID, but the ordering of the remaining columns does not matter as long as the required columns exist and have entries @@ -281,7 +281,7 @@ Ephemeris File (Optional) ----------------------------------------- .. note:: - Sorcha has an :ref:`ephemeris_gen` that we recommend using by default, but as an alternative Sorcha can read in an external file containing calculated ephemeris values for each simulated object within a reasonable search radius of a given survey field pointing and observation times as specified in the survey pointing database. This could be the output from a previous Sorcha run or provided from your own separate ephemeris generation method, + ``sorcha`` has an :ref:`ephemeris_gen` that we recommend using by default, but as an alternative ``sorcha`` can read in an external file containing calculated ephemeris values for each simulated object within a reasonable search radius of a given survey field pointing and observation times as specified in the survey pointing database. This could be the output from a previous ``sorcha`` run or provided from your own separate ephemeris generation method, .. tip:: @@ -362,7 +362,7 @@ If you are going to simulate the full camera architecture including CCD location The camera footprint file is a comma-separated text file with three columns describing the detector shapes, with the header “detector,x,y”. The first column indicates which detector a point belongs to, and should be an integer. Second and third columns specify where on the focal plane the corners are. Values are unitless, equal to tan( ra ), tan( dec ), where ra and dec are the vertical and horizontal angles of the points from the center of the sphere tangent to origin in the focal plane. Ordering does not matter, as the constructor sorts the points automatically. .. tip:: -Sorcha comes with a representation of the LSSTCam architecture already installed. Further details of how to use this built-in default file can be found in the description of the :ref:`Full Camera Footprint Filter`. +``sorcha`` comes with a representation of the LSSTCam architecture already installed. Further details of how to use this built-in default file can be found in the description of the :ref:`Full Camera Footprint Filter`. An example of an (optional) camera footprint file: From bcb7bed2a5d0979bbfab668b9ce7be4766ad5449 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Sun, 5 Jan 2025 11:57:30 +0000 Subject: [PATCH 08/52] documentation updates --- docs/advanced.rst | 48 +++++++++++++++++++++++++++++++++++++++++--- docs/apparentmag.rst | 10 --------- docs/filters.rst | 2 -- 3 files changed, 45 insertions(+), 15 deletions(-) diff --git a/docs/advanced.rst b/docs/advanced.rst index dfb706a2..35b3366a 100644 --- a/docs/advanced.rst +++ b/docs/advanced.rst @@ -3,7 +3,7 @@ Advanced User Features ========================== .. warning:: - **If you're new to sorcha, turn away from this section NOW! (we're only partially kidding)** This section provides information about features for advanced users of ``sorcha``. Changing or adjusting the parameters described in this section may produce unintended results. *With great power comes great responsibility. **Be very careful in applying the knowledge below.** Most users will not need to touch these parameters within ``sorcha``. + **If you're new to sorcha, turn away from this section NOW! (we're only partially kidding)** This section provides information about features for advanced users of ``sorcha``. Changing or adjusting the parameters described in this section may produce unintended results. **With great power comes great responsibility. Be very careful in applying the knowledge below.** Most users will not need to touch these parameters within ``sorcha``. Setting the Random Number Generator Seed --------------------------------------------- @@ -17,9 +17,51 @@ The value used to seed the random number generator can be specified via the **SO .. tip:: If you're trying to reproduce a crash or a certain behavior in ``sorcha``, you can find the value that you need to set the random seed to in the log file. - + + +Expert User Config File Options +----------------------------------- + +The following options can be optionally added to an expert section of the :ref:`configs`. The section will start with:: + + [EXPERT] + + +Turning Vignetting Off +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +By default, vignetting using LSSTCam parameters is applied. To turn vignetting off, add to the configuratuion file:: + + [EXPERT] + vignetting_on = False + +.. tip:: + Vignetting is a small effect for the LSSTCam, so you will see only a modest change in results if you turn this off for LSST simulations + + +Turning Off the Randomization of the Magnitude and Astrometry Values +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +There may be a reason that you want to turn off the randomization of the trailed source magnitude and PSF magnitude as well as the RA and Dec values:: + + [EXPERT] + randomization_on = False + + +Turning Off Trailing Losses +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The trailing losses filter is on by default, but it can be turned off by including the option in the configuration file:: + + [EXPERT] + trailing_losses_on = False + +.. warning:: + We **very strongly recommend** that the user never turn this off, but we provide + this option for debugging or for speed increases when the user is absolutely sure + they are only supplying slow-moving objects. + SNR/Apparent Magnitude Filters -------------------------------------- +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. warning:: These filters are for the advanced user. If you only want to know what the survey will discover, you **DO NOT** need these filters on. diff --git a/docs/apparentmag.rst b/docs/apparentmag.rst index 814613de..35d9f307 100644 --- a/docs/apparentmag.rst +++ b/docs/apparentmag.rst @@ -47,11 +47,6 @@ Applying Photometric and Astrometric Uncerainties Trailing Losses ----------------- -.. warning:: - We **very strongly recommend** that the user never turn this off, but we provide - this option for debugging or for speed increases when the user is absolutely sure - they are only supplying slow-moving objects. - If the observed object is fast-moving, the signal will form a trail, reducing the measured magnitude. This filter will recalculate the PSF magnitude of the observations, adjusting for trailing losses. @@ -60,8 +55,3 @@ This filter will recalculate the PSF magnitude of the observations, adjusting fo :alt: Sky image showing a short trailing source circled in red. :align: center -The trailing losses filter is on by default, but it can be turned off by including the option in the configuration file:: - - [EXPERT] - trailing_losses_on = False - diff --git a/docs/filters.rst b/docs/filters.rst index a9149692..ed5bece1 100644 --- a/docs/filters.rst +++ b/docs/filters.rst @@ -130,8 +130,6 @@ This filter applies a model of this from a built-in function tailored specifical `Araujo-Hauck et al. 2016 `_, with further discussion and below figure from `Veres and Chesley 2017 `_.) -Vignetting is applied by default and cannot be turned off by the user in the config file. - .. image:: images/vignetting.jpg :width: 500 :alt: Plot of the LSST camera footprint in Dec vs. RA, showing shaded dimming due to vignetting. From 4ef630fe55e00f58e96cad4b6295d24aa42d84a4 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Sun, 5 Jan 2025 22:25:43 +0000 Subject: [PATCH 09/52] doc updates doc updates --- docs/advanced.rst | 93 +++++++++++++++++++++++++++--- docs/configfiles.rst | 6 +- docs/ephemerisgen.rst | 37 ++++++------ docs/example_files/help_output.txt | 44 +++++++------- docs/index.rst | 2 +- docs/installation.rst | 8 +-- docs/overview.rst | 14 +++-- 7 files changed, 144 insertions(+), 60 deletions(-) diff --git a/docs/advanced.rst b/docs/advanced.rst index 35b3366a..a6821bca 100644 --- a/docs/advanced.rst +++ b/docs/advanced.rst @@ -13,7 +13,7 @@ Setting the Random Number Generator Seed The value used to seed the random number generator can be specified via the **SORCHA_SEED** environmental variable. This allows for ``sorcha`` to be fully reproducibly run with (if using a bash shell or Z-shell):: - export SORCHA_SEED=42 + export SORCHA_SEED=52 .. tip:: If you're trying to reproduce a crash or a certain behavior in ``sorcha``, you can find the value that you need to set the random seed to in the log file. @@ -22,15 +22,13 @@ The value used to seed the random number generator can be specified via the **SO Expert User Config File Options ----------------------------------- -The following options can be optionally added to an expert section of the :ref:`configs`. The section will start with:: - - [EXPERT] +The following options can be optionally added to an expert section ([EXPERT]) of the :ref:`configs`. Turning Vignetting Off ~~~~~~~~~~~~~~~~~~~~~~~~~~~ -By default, vignetting using LSSTCam parameters is applied. To turn vignetting off, add to the configuratuion file:: +By default, vignetting using LSSTCam parameters is applied. To turn vignetting off, add to the :ref:`configs`:: [EXPERT] vignetting_on = False @@ -38,9 +36,9 @@ By default, vignetting using LSSTCam parameters is applied. To turn vignetting o .. tip:: Vignetting is a small effect for the LSSTCam, so you will see only a modest change in results if you turn this off for LSST simulations - Turning Off the Randomization of the Magnitude and Astrometry Values ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + There may be a reason that you want to turn off the randomization of the trailed source magnitude and PSF magnitude as well as the RA and Dec values:: [EXPERT] @@ -50,7 +48,7 @@ There may be a reason that you want to turn off the randomization of the trailed Turning Off Trailing Losses ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The trailing losses filter is on by default, but it can be turned off by including the option in the configuration file:: +The trailing losses filter is on by default, but it can be turned off by including the option in the :ref:`configs`:: [EXPERT] trailing_losses_on = False @@ -60,6 +58,19 @@ The trailing losses filter is on by default, but it can be turned off by includi this option for debugging or for speed increases when the user is absolutely sure they are only supplying slow-moving objects. + +Turning Off the Camera Footprint Filter +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +In rare instances you may need to skip the footprint filter off. This can be done by setting the camera model to none in the field-of-view (FOV) section of the :ref:`configs`:: + + [FOV] + camera_model = none + +.. note:: + If you're using ``sorcha``'s bult-in :ref:`ephemeris generator`, the generator will apply a circular search region around each filed pointing when associating potential input population detections with the survey observations. + + SNR/Apparent Magnitude Filters ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -76,7 +87,7 @@ To implement the SNR limit, include the following in the config file:: [EXPERT] SNR_limit = 2.0 -To implement the magnitude limit, include the following in the config file:: +To implement the magnitude limit, include the following in the :ref:`configs`:: [EXPERT] magnitude_limit = 22.0 @@ -85,4 +96,70 @@ To implement the magnitude limit, include the following in the config file:: Only one of these filters may be implemented at once. +Specifying Alernative Versions of the Auxiliaryy Files Used in the Ephemeris Generator +----------------------------------------------------------------------------------------- + +For backwards compability and to enable new version of the files to be run as well, users can override the default filenames and download locations of the :ref:`auxiliary files` used by ``sorcha``'s bult-in :ref:`ephemeris generator`. These :ref:`configs`:: variables are added to a new auxiliary ( [AUXILIARY]) section:: + + + [AUXILIARY] + de440s = de440s.bsp + de440s_url = https://naif.jpl.nasa.gov/pub/naif/generic_kernels/spk/planets/de440s.bsp + + earth_predict = earth_200101_990827_predict.bpc + earth_predict_url = https://naif.jpl.nasa.gov/pub/naif/generic_kernels/pck/earth_200101_990827_predict.bpc + + earth_historical = earth_620120_240827.bpc + earth_historical_url = https://naif.jpl.nasa.gov/pub/naif/generic_kernels/pck/earth_620120_240827.bpc + + earth_high_precision = earth_latest_high_prec.bpc + earth_high_precision_url = https://naif.jpl.nasa.gov/pub/naif/generic_kernels/pck/earth_latest_high_prec.bpc + + jpl_planets = linux_p1550p2650.440 + jpl_planets_url = https://ssd.jpl.nasa.gov/ftp/eph/planets/Linux/de440/linux_p1550p2650.440 + + jpl_small_bodies = sb441-n16.bsp + jpl_small_bodies_url = https://ssd.jpl.nasa.gov/ftp/eph/small_bodies/asteroids_de441/sb441-n16.bsp + + leap_seconds = naif0012.tls + leap_seconds_url = https://naif.jpl.nasa.gov/pub/naif/generic_kernels/lsk/naif0012.tls + + meta_kernel = meta_kernel.txt + + observatory_codes = ObsCodes.json + observatory_codes_compressed = ObsCodes.json.gz + observatory_codes_compressed_url = https://minorplanetcenter.net/Extended_Files/obscodes_extended.json.gz + + orientation_constants = pck00010.pck + orientation_constants_url = https://naif.jpl.nasa.gov/pub/naif/generic_kernels/pck/pck00010.tpc + + +.. note:: + You can specify one or any number of the filenames or URLs. + +.. note:: + If you make changes to the filenames or the download urls, you'll likely need to first remove meta_kernel.txt from the auxiliary cache (the directory these files are stored in) or specify a different filename name for meta_kernel file in the config file so that it can be rebuilt with the appropriate names. + +.. note:: + ``sorcha`` checks if the :ref:`auxiliary files` exist in the cache directory first before attempting to download any missing files and copies them over into the default filenames. + +Advanced Output Options +----------------------------------- + +We recommend that you do not change the decimal place precision and instead leave ``sorcha`` to output the full value +to machine precision, but there may be reasons why you need to reduce the size of the output. + +In the [OUTPUT] section of the :ref:`configs`, you can set the decimal precision for the astrometry outputs:: + + [OUTPUT] + # Decimal places to which RA and Dec should be rounded to in output. + position_decimals = 7 + + +In the [OUTPUT] section of the :ref:`configs`, you can set the decimal precision for the magnitude outputs:: + + [OUTPUT] + # Decimal places to which all magnitudes should be rounded to in output. + magnitude_decimals = 3 + diff --git a/docs/configfiles.rst b/docs/configfiles.rst index fd8afad8..7a7f5e3f 100644 --- a/docs/configfiles.rst +++ b/docs/configfiles.rst @@ -3,14 +3,16 @@ Configuration File ===================== -Sorcha uses a configuration file to set the majority of the various required and optional parameters and well as providing the ability to turn on and off various filters applied to the simulated small body population. Details about the various settings and options available in the configuration files are described in the :ref:`inputs`, :ref:`filters`, :ref:`ephemeris_gen`, and :ref:`output` pages. +``sorcha`` uses a configuration file to set the majority of the various required and optional parameters and well as providing the ability to turn on and off various calculations and filters applied to the simulated small body population. Details about the various settings and options available in the configuration files are described in the :ref:`inputs`, :ref:`filters`, :ref:`ephemeris_gen`, and :ref:`output` pages. .. _example_configs: Example Configuration Files ------------------------------------ -The example configuration files are appropriate for setting up Sorcha to simulate what the LSST would discover. These examples come pre-installed with Sorcha. You use the **sorcha init** command on the terminal to copy these files to your working directory. +We provide example configuration files appropriate for setting up ``sorcha`` to simulate what the LSST would discover. These example config files come installed with ``sorcha`` and can be copied over to your working directory by typing on the command line:: + + sorcha init Rubin Full Footprint ~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/ephemerisgen.rst b/docs/ephemerisgen.rst index 8fa8674a..f6a17135 100644 --- a/docs/ephemerisgen.rst +++ b/docs/ephemerisgen.rst @@ -3,18 +3,18 @@ Ephemeris Generator ========================================================== -Sorcha's ephemeris generator is powered by `ASSIST `__, a software package for ephemeris-quality integrations of test particles, and the `REBOUND `__ N-body integration package. If the user prefers to use a different generator or provide the ephemeris output from a previous Sorcha run, they have the ability to point Sorcha to an external file to ingest instead. +``sorcha``'s ephemeris generator is powered by `ASSIST `__, a software package for ephemeris-quality integrations of test particles, and the `REBOUND `__ N-body integration package. If the user prefers to use a different generator or provide the ephemeris output from a previous ``sorcha`` run, they have the ability to point ``sorcha`` to an external file to ingest instead. .. tip:: - We recommend using Sorcha's ephemeris generator for all your survey simulations. + We recommend using ``sorcha``'s ephemeris generator for all your survey simulations. How It Works -------------------------------------------------------- -The Sorcha ephemeris generator determines which objects will appear in or near the camera field-of-view (FOV) for any given exposure. It uses spatial indexing to speed up these calculations. It runs through the survey visits and does on-the-fly checks of where every synthetic object is near the center of each night for which there are visits and organizes those positions using the `HEALPix (Hierarchical Equal Area isoLatitude Pixelation of a sphere) `_ tesselation of the sky. Given that information, it then steps through the visits for that night, doing precise calculations for just those objects that are near the camera FOV of each survey on-sky visit. Specifically, for each visit, the generator calculates the unit vector from the observatory's location to the RA/Dec location of the field center. Then it finds the set of HEALPix tiles that are overlapped by the survey visit's camera FOV (nside=64). The ephemeris generator then collects the IDs for the particles in the HEALPix tiles overlapped by the given survey visit FOV. It then does light-time-corrected ephemeris calculations for just those, outputting the right ascension, declination, rates, and relevant distances, and phase angle values for each of the particles. +``sorcha``'s ephemeris generator determines which objects will appear in or near the camera field-of-view (FOV) for any given exposure. It uses spatial indexing to speed up these calculations. It runs through the survey visits and does on-the-fly checks of where every synthetic object is near the center of each night for which there are visits and organizes those positions using the `HEALPix (Hierarchical Equal Area isoLatitude Pixelation of a sphere) `_ tesselation of the sky. Given that information, it then steps through the visits for that night, doing precise calculations for just those objects that are near the camera FOV of each survey on-sky visit. Specifically, for each visit, the generator calculates the unit vector from the observatory's location to the RA/Dec location of the field center. Then it finds the set of HEALPix tiles that are overlapped by the survey visit's camera FOV (nside=64). The ephemeris generator then collects the IDs for the particles in the HEALPix tiles overlapped by the given survey visit FOV. It then does light-time-corrected ephemeris calculations for just those, outputting the right ascension, declination, rates, and relevant distances, and phase angle values for each of the particles. -A cartoon schematic of ephemeris generation within Sorcha for a patch of sky and a single survey observation is shown below. Each box represents a healpixel in the HEALpix grid on the sky. The colored healpixels are where different Solar System objects is estimated to cover during some part of the night (based on their speed and velocity vector on sky they will be in one or more healpixels) based on the rough calculation from Sorcha. The midnight position and 2 other positions during each night are calculated for each simulated small body. Using interpolation, all the healpixels that the object passes through in the evening are identified. In the figure, each color represents a different moving object on a different orbit. Slower moving objects will cover less healpixels. The green circle represents an area slightly bigger than the survey's camera footprint. For the given observation time, any orbits with healpixels within the circle are integrated to calculate their exact positions at the time of the observation. Those orbits that land within the circle are then identified and the resulting ephemerides associated with those objects and the observation are saved. +A cartoon schematic of ephemeris generation within ``sorcha`` for a patch of sky and a single survey observation is shown below. Each box represents a healpixel in the HEALpix grid on the sky. The colored healpixels are where different Solar System objects is estimated to cover during some part of the night (based on their speed and velocity vector on sky they will be in one or more healpixels) based on the rough calculation from ``sorcha``. The midnight position and 2 other positions during each night are calculated for each simulated small body. Using interpolation, all the healpixels that the object passes through in the evening are identified. In the figure, each color represents a different moving object on a different orbit. Slower moving objects will cover less healpixels. The green circle represents an area slightly bigger than the survey's camera footprint. For the given observation time, any orbits with healpixels within the circle are integrated to calculate their exact positions at the time of the observation. Those orbits that land within the circle are then identified and the resulting ephemerides associated with those objects and the observation are saved. .. image:: images/ephemeris_generation.png @@ -24,10 +24,10 @@ A cartoon schematic of ephemeris generation within Sorcha for a patch of sky and -Because ASSIST uses REBOUND's `IAS15 integrator `_, which has an adaptive time step, Sorcha's ephemeris generator instantiates a REBOUND n-body simulation for each individual massless synthetic object including the effects of the Sun, planets, Moon, and 16 asteroids (see the :ref:`MAP` section). It also includes the J2, J3, and J4 gravitational harmonics of the Earth, the J2 gravitational harmonic of the Sun, and general relativistic correction terms for the Sun, using the Parameterized Post-Newtonian (PPN) formulation. The positions of the massive bodies come from the latest `DE441 `_ ephemeris, provided by NASA's `Navigation and Ancillary Information Facility (NAIF) `_. We note that the coordinate frame for ASSIST+REBOUND is the equatorial International Celestial Reference Frame (ICRF). The positions and velocities are barycentric within this frame, rather than heliocentric. The ephemeris generator translates the input barycentric or heliocentric orbits into x,y, z and velocities into the barycentric ICRF to be read into ASSIST. +Because ASSIST uses REBOUND's `IAS15 integrator `_, which has an adaptive time step, ``sorcha``'s ephemeris generator instantiates a REBOUND n-body simulation for each individual massless synthetic object including the effects of the Sun, planets, Moon, and 16 asteroids (see the :ref:`MAP` section). It also includes the J2, J3, and J4 gravitational harmonics of the Earth, the J2 gravitational harmonic of the Sun, and general relativistic correction terms for the Sun, using the Parameterized Post-Newtonian (PPN) formulation. The positions of the massive bodies come from the latest `DE441 `_ ephemeris, provided by NASA's `Navigation and Ancillary Information Facility (NAIF) `_. We note that the coordinate frame for ASSIST+REBOUND is the equatorial International Celestial Reference Frame (ICRF). The positions and velocities are barycentric within this frame, rather than heliocentric. The ephemeris generator translates the input barycentric or heliocentric orbits into x,y, z and velocities into the barycentric ICRF to be read into ASSIST. .. tip:: - If using Sorcha's internal ephemeris generation mode (which is the default mode), **we recommend calculating/creating your input orbits with epochs close in time to the start of the first survey observation**. This will minimize the REBOUND n-body integrations required to set up the ephemeris generation. + If using ``sorcha``'s internal ephemeris generation mode (which is the default mode), **we recommend calculating/creating your input orbits with epochs close in time to the start of the first survey observation**. This will minimize the REBOUND n-body integrations required to set up the ephemeris generation. .. tip:: For further details, we recommend you read the `ASSIST `__ and `REBOUND `__ papers. @@ -56,14 +56,14 @@ Here's the list of asteroid pertubers that are included in the ASSIST+REBOUND in - **(4) Vesta = A807 FA** .. warning:: - If you simulate the orbits of these select asteroids you will get **POOR results** with the internal Sorcha ephemeris generator because of how the n-body integration is set up. We recommend getting the positions of these asteroids from some other source and inputting them as an external ephemeris file. + If you simulate the orbits of these select asteroids you will get **POOR results** with the internal ``sorcha`` ephemeris generator because of how the n-body integration is set up. We recommend getting the positions of these asteroids from some other source and inputting them as an external ephemeris file. .. _tuneem: Tuning the Ephemeris Generator ----------------------------------- -There are several tunable options for the ephemeris generation which are described below that are set by the Sorcha :ref:`configs`. +There are several tunable options for the ephemeris generation which are described below that are set by the ``sorcha`` :ref:`configs`. - Minor Planet Center (MPC) observatory code for the provided telescope (**ar_obs_code** configuration parameter) - Field of view of our search field (in degrees) (**ar_ang_fov** configuration parameter) @@ -71,7 +71,7 @@ There are several tunable options for the ephemeris generation which are describ - Picket length (in days) (**ar_picket** configuration parameter) - Order of healpix used by healpy (*ar_healpix_order** configuration parameter) -To use Sorcha's internal ephemeris generation engine, the configuration file should contain:: +To use ``sorcha``'s internal ephemeris generation engine, the configuration file should contain:: [INPUT] ephemerides_type = ar @@ -86,26 +86,29 @@ To use Sorcha's internal ephemeris generation engine, the configuration file sho .. tip:: We recommend you use the above default values which we also use in our :ref:`example_configs`, as they are sufficient for most Solar System populations you'll want to simulate for LSST observations. For further details about these default values, we refer the reader to the :ref:`Footprint filter` discussion. +.. _auxfiles: + Required Auxiliary Files --------------------------- -A number of auxiliary files available from the `Minor Planet Center `_ and `NASA's Navigation and Ancillary Information Facility (NAIF) `_ are required for ephemeris generation: +A number of auxiliary files available from the `Minor Planet Center `_ `NASA's Navigation and Ancillary Information Facility (NAIF) `_ are required for ephemeris generation: - **naif0012.tls** is the leap second file. This changes whenever there is a new leap second. The last was in 2017. -- **earth_720101_070426.bpc** is the historical Earth orientation specification. This should not change, unless there is a new model. -- **earth_200101_990628_predict.bpc** is a prediction of the Earth's future orientation. Likewise, this should not change. +- **"earth_620120_240827.bpc** is the historical Earth orientation specification. This should not change, unless there is a new model. +- **earth_200101_990827_predict.bpc** is a prediction of the Earth's future orientation. Likewise, this should not change. - **pck00010.tpc** contains orientation information and physical constants for other bodies. This should only change rarely. - **de440s.bsp** gets used for getting the Earth's position for ephemerides. - **earth_latest_high_prec.bpc** is a regularly updated specification of the Earth's orientation, refined as new observations are incorporated. - **obscodes_extended.json** - observatory position information and Minor Planet Center (MPC) observatory codes. - +- **sb441-n16.bsp** - predictions of the locations of small bodies that will be used as perturbers in the ASSIST integrations +- **linux_p1550p2650.440** - predictions of the locations of planets that will be massive gravitational pertrubers in the ASSIST integrations .. tip:: See our :ref:`installation_aux` instructions to find out how to download and install these auxiliary files automatically using our download utility. Saving the Output From the Ephemeris Generator ------------------------------------------------ -If you want to use the same input orbits across multiple Sorcha runs, you can save time by outputting the output from the ephemeris generation stage using the command line flag **-ew** in combination with a stem filename (do not include the file extension). Then in subsequent runs you will need to use the **-er** flag to on the command line to specify the input ephemeris file to read in. You will also need to remove :ref:`the ephemeris generation parameters` from the configuration file and add the following:: +If you want to use the same input orbits across multiple ``sorcha`` runs, you can save time by outputting the output from the ephemeris generation stage using the command line flag **-ew** in combination with a stem filename (do not include the file extension). Then in subsequent runs you will need to use the **-er** flag to on the command line to specify the input ephemeris file to read in. You will also need to remove :ref:`the ephemeris generation parameters` from the configuration file and add the following:: [INPUT] ephemerides_type = external @@ -114,7 +117,7 @@ If you want to use the same input orbits across multiple Sorcha runs, you can sa **eph_format** is the format of the output ephemeris file. Options are **csv**, **whitespace**, and **hdf5**. .. attention:: - Currently the Sorcha-generated ephemeris is outputted in CSV, whitespace or HDF5 file format only. + Currently the ``sorcha``-generated ephemeris is outputted in CSV, whitespace or HDF5 file format only. Providing Your Own Ephemerides @@ -129,7 +132,7 @@ If you prefer to use a different method or software package for producing the ep **eph_format** is the format of the user provided ephemeris file. Options are **csv**, **whitespace**, and **hdf5**. .. tip:: - Use the **-er** flag on the command line to specify the external ephemeris file that Sorcha should use. + Use the **-er** flag on the command line to specify the external ephemeris file that ``sorcha`` should use. .. warning:: - We have validated and tested Sorcha and its internal ephemeris generator. If the user decides to use a different method to provide the required ephemerides for their science, it is up to the user to validate/check the output of the external ephemeris generator. + We have validated and tested ``sorcha`` and its internal ephemeris generator. If the user decides to use a different method to provide the required ephemerides for their science, it is up to the user to validate/check the output of the external ephemeris generator. diff --git a/docs/example_files/help_output.txt b/docs/example_files/help_output.txt index 71fed38b..6f839904 100644 --- a/docs/example_files/help_output.txt +++ b/docs/example_files/help_output.txt @@ -1,5 +1,6 @@ -usage: sorcha [-h] -c C -o O -ob OB -p P -pd PD [-er ER] [-ew EW] [-ar AR] - [-cp CP] [-f] [-s S] [-t T] [-v] +usage: sorcha run [-h] -c C -o O --ob OB -p P --pd PD [--er ER] [--ew EW] [--ar AR] [--cp CP] [-f] [-s S] [-t T] [-l] [--st ST] + +Run a simulation. options: -h, --help show this help message and exit @@ -7,29 +8,26 @@ options: Required arguments: -c C, --config C Input configuration file name (default: None) -o O, --outfile O Path to store output and logs. (default: None) - -ob OB, --orbit OB Orbit file name (default: None) - -p P, --params P Physical parameters file name (default: None) - -pd PD, --pointing_database PD + --ob OB, --orbits OB Orbit catalog file name (default: None) + -p P, --physical-parameters P + Catalog of object physical parameters (default: None) + --pd PD, --pointing-db PD Survey pointing information (default: None) Optional arguments: - -er ER, --ephem_read ER - Previously generated ephemeris simulation file name, - required if ephemerides_type in config file is - 'external'. (default: None) - -ew EW, --ephem_write EW - Output file name for newly generated ephemeris - simulation, required if ephemerides_type in config - file is not 'external'. (default: None) - -ar AR, --ar_data_path AR - Directory path where Assist+Rebound data files where - stored when running bootstrap_sorcha_data_files from - the command line. (default: None) - -cp CP, --complex_physical_parameters CP - Complex physical parameters file name (default: None) - -f, --force Force deletion/overwrite of existing output file(s). - Default False. (default: False) + --er ER, --ephem-read ER + Previously generated ephemeris simulation file name, required if ephemerides_type in config file is 'external'. (default: + None) + --ew EW, --ephem-write EW + Output file name for newly generated ephemeris simulation, required if ephemerides_type in config file is not 'external'. + (default: None) + --ar AR, --ar-data-path AR + Directory path where Assist+Rebound data files where stored when running bootstrap_sorcha_data_files from the command line. + (default: None) + --cp CP, --complex-physical-parameters CP + Catalog of object complex physical parameters (default: None) + -f, --force Force deletion/overwrite of existing output file(s). Default False. (default: False) -s S, --survey S Survey to simulate (default: rubin_sim) -t T, --stem T Output file name stem. (default: SSPPOutput) - -l, --log-level Verbosity. Default currently true; include to turn off - verbosity. (default: True) + -l, --log-level Print additional information to log while running (default: True) + --st ST, --stats ST Output summary statistics table to this stem filename. (default: None) diff --git a/docs/index.rst b/docs/index.rst index cdabfd20..10d0e637 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -46,10 +46,10 @@ works, tutorials, and demonstration notebooks that show how each of the various overview installation inputs + configfiles ephemerisgen apparentmag filters - configfiles outputs gettingstarted hpc diff --git a/docs/installation.rst b/docs/installation.rst index 06c2ee32..0824de84 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -11,7 +11,7 @@ Requirements ``sorcha`` has the following requirements that will be automatically installed using pip or conda when you install the sorcha package: -* python 3.10 or later +* python 3.11 or later * assist * astropy * healpy @@ -40,14 +40,14 @@ Setup Your Conda Environment If using conda:: - conda create -n sorcha -c conda-forge assist numpy numba pandas scipy astropy matplotlib sbpy pytables spiceypy healpy rebound pooch tqdm h5py importlib_resources python=3.10 + conda create -n sorcha -c conda-forge assist numpy numba pandas scipy astropy matplotlib sbpy pytables spiceypy healpy rebound pooch tqdm h5py importlib_resources python=3.11 If using mamba:: - mamba create -n sorcha -c conda-forge assist numpy numba pandas scipy astropy matplotlib sbpy pytables spiceypy healpy rebound pooch tqdm h5py importlib_resources python=3.10 + mamba create -n sorcha -c conda-forge assist numpy numba pandas scipy astropy matplotlib sbpy pytables spiceypy healpy rebound pooch tqdm h5py importlib_resources python=3.11 .. tip:: - We recommend using python version 3.10 or higher with ``sorcha``. The conda command uses python 3.10. + We recommend using python version 3.11 or higher with ``sorcha``. The conda/mamba install command uses python 3.11. **Step 2** Activate your conda/mamba environment diff --git a/docs/overview.rst b/docs/overview.rst index 6febf100..a675c492 100644 --- a/docs/overview.rst +++ b/docs/overview.rst @@ -26,13 +26,17 @@ Design Philosophy ---------------------- ``sorcha`` has been designed in a modular way with each filter written as its own function, This makes it easy to add new filters in the future if required by users. We note for dealing with rotational light curve and activity effects, we have set up ``sorcha`` such that the user can provide their own custom classes/functions and import them into ``sorcha`` to use. Further details can be found on the :ref:`addons` page. ``sorcha`` has been designed with LSST in mind, but many of the filters already developed will be applicable to other Solar System surveys. If you are interested in incorporating your survey into ``sorcha`` do reach out. -.. warning:: - For a wide variety of use cases, the user should be able to use ``sorcha`` straight out of the box. We have designed the software such that it should be straightforward to add in additional filters or rotational light curve/activity classes. As with any open-source package, **once the user has made modifications to the code, it is the responsibility of the user to confirm these changes provide an accurate result**. - - .. note:: Contributions are very welcome. If there is a feature or functionality not yet available in ``sorcha``, we encourage you to propose the feature as an issue in the `main github repository `_ or share your code with the new enhancements. Further details can be found on our :ref:`reporting` page. Using Sorcha in Your Science -------------------------------- -We made ``sorcha`` to be a tool for the small body planetary astronomer community. If ``sorcha`` enabled your science, please make sure to give the proper credit in your talks and papers by citing the relevant ``sorcha`` papers and the python packages that the software is built upon. Further details can be found :ref:`here`. +We made ``sorcha`` to be a tool for the small body planetary astronomer community. For a wide variety of use cases, the user should be able to use ``sorcha`` straight out of the box. + +.. note:: + If ``sorcha`` enabled your science, please make sure to give the proper credit in your talks and papers by citing the relevant ``sorcha`` papers and the python packages that the software is built upon. Further details can be found :ref:`here`. + +.. warning:: + We have designed ``sorcha`` such that it should be straightforward to add in additional filters or rotational light curve/activity classes. As with any open-source package, **once the user has made modifications to the code, it is the responsibility of the user to confirm these changes provide an accurate result**. + + From 22003a1249ef15051c6f75aa3698df9d4780eefb Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Sun, 5 Jan 2025 22:32:13 +0000 Subject: [PATCH 10/52] documentation updates documentation updates --- docs/.gettingstarted.rst.swp | Bin 0 -> 16384 bytes docs/gettingstarted.rst | 4 ++-- 2 files changed, 2 insertions(+), 2 deletions(-) create mode 100644 docs/.gettingstarted.rst.swp diff --git a/docs/.gettingstarted.rst.swp b/docs/.gettingstarted.rst.swp new file mode 100644 index 0000000000000000000000000000000000000000..95c47dac9f2762664469f4b2ba063c53f7851f13 GIT binary patch literal 16384 zcmeI3&yO5O6~{XP5=aO^NKEBy!_NK;nluaz;Xch=j!FRaMW-dN-C)B!rY6>1nmo zT~+UW-;a9lRn7S9GnbxJpG?mtd_I^Y&)xgw>5FQ1&);58lDW-ii)=oYN4NXrWue=& zGI>_r?zQB_e!|hy+Ul&$XQO86W~Lrh*1D1H+C#nEx8=O6GH2S6HN29=Kl}5ntoK{p znEw01{o^0|sCwu_*WSTc45AMR z3xn&E3gMh<$ey{R{E$UAO#wSN#9s`1jH+&*Q(tp8|3tyj=f}ivMzW)E~gGurS==ab| z(2Gz9{qun&`3LlO=r2$M8AwBKzCTI+1$_rP5B>Un#z9|!rqEN+lhCX8CCMw$GtlRt z^Uz12H{Zv2=ua^8N9cRdH=wUWUxQw|H%WdDeHVHGx)-_!`tv5?>6Rq1q_Rb`!5U7>e(RXWl)vw2hL{YhDAJDTaL**BeQ zI+wbeP9@2rtSZ&Crs#5=BupFbKd82=)y|H*&r44+|F){MJ7ty2ZfKRM%9tA}bIQ%M z%FTS9)dkDfJZh)iT-VO3iD~`ggM;;)2M4LTTpt^?t=zKVM#b)Kl02(bZ5C>l9Woir z$qKE0zAIZ@sIrE2<%LPrrHNXaP8DWRS0*cXEbs353mgn-j4ip*vD{YvqJ|nT)!ZDi z3bQ!l-;InF%$>`t3YOJWIh)dB&n1ub|1N|D7&eTzUV;dL|9bKNq{RM#jPj!{U~-7KpzgLw~&LbP<0 zwL#;lx-irE4KM6&t0%h$Wepk?cU?+TAHbw_ZI{bGL1B}w1;kbr+GS;BQ?$Qx;wO2! zt7`!>;Eh>gJXdEhmz#Q0PCYluY79ZNqJq^4cVo@6>cZYLVj=U)r3@77t&PKGWu6sW zu0zH$t2|a78zxj;(<_NI-*wM5u z9e(I-B{!961H`6lm0i}XNiTSx=;gjBvnR=k zW_}99))GxhE|;k#_tr%5!r3^p+6UUhN7J+Fi+g)lRZi>^Tij4V7Az(J*BAmG7#*Z< z;maqA@%bo*EP5r*pdQa$CLF;1ZQZocxFjBt&y3YU^>UT2H|h`tg1r;R!wVBr0k-J! zxE_4~@Ozba0^w=pApxVsjCFcHX8sV0#d>aa8yQ6ICVE!j{GkZM-uGhtiy7j??M#k8l=5L&%ivS*Z2!gUBK+p3v~ zLUOstD2p|^b)_b_$rg1y8u>`0GggfT6|Dwl?Od1`n}XqRhUN&J@Py!>5ndnD(cbZ0 z;q725IQEh8ve#3&5m=MSR&?TBql#F^$}WmGJDd5^Qr(+L>5#Q29D}>hH|r zO2TLGLndkZXpc4K+FA*u4BtRZc5yE>Non)}w2`+Z;an8&amgwVbkiHbdRzG~!q`cn z?e^a#7kb4DCjmH7_?4cIl`(o-#3Ey!ySLiYj{tgQ1t{jG)OlPi09g=Az3l*yfDKB0 zdMN%zXmEPRHkm|rEy8oxIHpWw>6MneCoZie0sttS7pVMrM)e%IWsV^q$yOzrSc!#k zdlogtgiG{AK1%SE3)UNXusSR=ucEE3K6#czxgp(#X_BN^421An&KuLZtag&uA$$n+ z8|!Q<34W4eNFs`aYDVtQ;!=NP>}d5=He$(HWrst=kZzKGg*2bJupHuH2(|<;#&eh3 zl~g^ef!_YGEtwUp1x(U;Q3R&OJ#tww%Y}7=RyKE1%u|BFp(ek^OQyv2ZayX-6kgUY z)KH9(84^pgimYL&#;C%3pCldF(RDLsh1TE=EAFZ?L~QX9O{)E@6gt9$#@u>u;)=OA zvFAc2iY5|M6H-`>5s+<$%0|2vZS?X*2NaW#-40Mh5tpL1^~&A|e}cZRLxL9YQ}`F_ z5Bgy1CuQBpGeV})>m*4&ebOiCQ;EHBVLp-i{{?F6yQ#aS{_oHCFFu$gPe5m(5%k6f zlH@ng>(DQtuR;?@Lk09DXhsiU3jK=O|Ci7=p_ifOp$580?f(SyDd>Lc{rjMQv#zXt zm#ja01_TBK1_TBK1_TBK1_b^m2#^Y2md2%_V}3llRd!}Nnr1O$rK0cKr|X6^X`Zh6 z^y9F6Gn0pZE3gq%8YUT%w$~$rRs6x8`ek}PeCYw4olNnL4}Ur zQ9a0iVIkkdqSft{OWkwG4@lKhdf^AG)zufvNaupSyrO*5DyH$Pmbvd>9%fZ1#k8E~ z#OWy&a)*(v6fxF$M!6@Yi+oqCblhSwW*$qMzEH<}tn{$5rm3X6?XAY^U~M{~YLPi* z!Z^+9bi$Wa#q|yxB&jL$($a_+b<}>8k#rs? ztPJHCZ~N07=~A42`uyl|ouBbm53d*|gIC>mYT>?JL`Tx@s&hM!os&}Zj{8UQi{Dsv z#bY#S{GpD0{isfJdl*{d(L}rgU_)BAu$d`d@Tym*Zy9z*p8F!3-koQ9<=xWm>l-SM zo%084zTosI_VdLc;ZNeM;a~y~r@ZT%XQlNgDSeq7n4^)mGBb60E499c=10`ifZjT0 zY&_*l&#A0^+zmK`lcpgpNHvvUw$K%)HS+GpWF8X~t>=wf2HARt@FTrgM>^zE!TO^X z#P+TyMuWA`Y-FX(*U4?s8#20(lcuiOta3%iO^eN(rsMCAF=*8KgUZuq&d9?_i^)d_ zI>#BBv`r%>N8VSVjUguoN4`&_rq(xgE`jVahfJ}_B5IdD1IITwpfB?MFV5kS)-5KB zXI=036*_TU4!y>lrKak+I9h}eKvwN=Z@z&~-zkw6i8FMXrAF@$4sZA~(@?cj^&j%< zct1;MHTk&GpWsf{-4*|%cR1Qzt?p0Kf=K;n_>+qh IUIVQE0Ir$?4gdfE literal 0 HcmV?d00001 diff --git a/docs/gettingstarted.rst b/docs/gettingstarted.rst index 4d93841a..916029bd 100644 --- a/docs/gettingstarted.rst +++ b/docs/gettingstarted.rst @@ -61,9 +61,9 @@ Running Sorcha We now have all the required input files. If you downloaded the Sorcha repository, start by moving into the sorcha directory or make a demo directory called **demo** and move/copy all the input files into there. For this example run, we assume that you have downloaded the required ephemeris generator's auxiliary files to ./ar_files. Check the :ref:`installation` instructions for further details. -Next, let's take a look at the command line arguments for sorcha. On the command line, typing:: +Next, let's take a look at the command line arguments for the ``sorcha run``. 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P. Kelley, Conor MacBride, Shannon Matthews, Steph Merritt, Joachim Moeyens, Joe Murtagh, Shantanu Naidu, Drew Oldag, Brian Rogers, Meg Schwamb, Colin Snodgrass, Max West, Dave Young +Pedro Bernardinelli, Aidan Berres, Ricardo Bánffy, Colin Orion Chandler Sam Cornwall, Siegfried Eggl, Grigori Fedorets, Matt Holman, Lynne Jones, Mario Jurić, Jeremy Kubica, Jake Kurlander, Michael S. P. Kelley, Conor MacBride, Shannon Matthews, Steph Merritt, Joachim Moeyens, Joe Murtagh, Shantanu Naidu, Drew Oldag, Brian Rogers, Meg Schwamb, Colin Snodgrass, Max West, and Dave Young diff --git a/docs/ephemerisgen.rst b/docs/ephemerisgen.rst index f6a17135..2d3af7ee 100644 --- a/docs/ephemerisgen.rst +++ b/docs/ephemerisgen.rst @@ -119,6 +119,9 @@ If you want to use the same input orbits across multiple ``sorcha`` runs, you ca .. attention:: Currently the ``sorcha``-generated ephemeris is outputted in CSV, whitespace or HDF5 file format only. +.. tip:: + Compared to the other outputs from ``sorcha``, the ephemeris output files are typicaly very large in size. The output will be slow to read in to ``sorcha``, but for some use cases reading in the ephemeris as a file can be faster than ephemeris generation on the fly. We recommend only outuputting the contents of the ephemeris stage if you need it to speed up future simulations. If possible, use the HDF5 file format to help with disk I/O speeds. + Providing Your Own Ephemerides --------------------------------- diff --git a/docs/gettingstarted.rst b/docs/gettingstarted.rst index 916029bd..c25865bd 100644 --- a/docs/gettingstarted.rst +++ b/docs/gettingstarted.rst @@ -1,10 +1,10 @@ Getting Started ===================== -In this tutorial, we will show you how to setup and run a basic simulation using Sorcha. +In this tutorial, we will show you how to setup and run a basic simulation using ``sorcha``. .. tip:: - In this tutorial, we demonstrate how to run a single instance of Sorcha. Sorcha is designed to allow multiple instances to be run in parallel in order to accommodate simulations with very large numbers of synthetic planetesimals by breaking up the job across multiple live processes. We recommend first starting with the examples below, before moving on to parallel processing. + In this tutorial, we demonstrate how to run a single instance of ``sorcha``. ``sorcha`` is designed to allow multiple instances to be run in parallel in order to accommodate simulations with very large numbers of synthetic planetesimals by breaking up the job across multiple live processes. We recommend first starting with the examples below, before moving on to parallel processing. .. important:: @@ -50,7 +50,7 @@ The key information about the simulation parameters are held in the configuratio :language: text .. note:: - For this tutorial, we have set up Sorcha to only find detections on g,r,i,z,u, or y filter observations, by what we have set the **observing_filters** parameter to. Since we specified the absolute magnitude and colors for our synthetic objects to r-band, the r filter starts the list of filters for **observing_filters**. + For this tutorial, we have set up ``sorcha`` to only find detections on g,r,i,z,u, or y filter observations, by what we have set the **observing_filters** parameter to. Since we specified the absolute magnitude and colors for our synthetic objects to r-band, the r filter starts the list of filters for **observing_filters**. .. note:: This config file sets the output to be in CSV format. @@ -59,7 +59,7 @@ The key information about the simulation parameters are held in the configuratio Running Sorcha ---------------------- -We now have all the required input files. If you downloaded the Sorcha repository, start by moving into the sorcha directory or make a demo directory called **demo** and move/copy all the input files into there. For this example run, we assume that you have downloaded the required ephemeris generator's auxiliary files to ./ar_files. Check the :ref:`installation` instructions for further details. +We now have all the required input files. If you downloaded the ``sorcha`` repository, start by moving into the ``sorcha`` directory or make a demo directory called **demo** and move/copy all the input files into there. For this example run, we assume that you have downloaded the required ephemeris generator's auxiliary files to ./ar_files. Check the :ref:`installation` instructions for further details. Next, let's take a look at the command line arguments for the ``sorcha run``. On the command line, typing:: @@ -70,7 +70,7 @@ will produce .. literalinclude:: ./example_files/help_output.txt :language: text -Now that you know how to provide the input files, let's go run a simulation: You can find the command to run the sorcha demo on the command line in two ways. First on the command line:: +Now that you know how to provide the input files, let's go run a simulation: You can find the command to run the ``sorcha`` demo on the command line in two ways. First on the command line:: sorcha demo howto @@ -83,7 +83,7 @@ Or you can in an interactive python session or jupyter notebook. You can run the .. tip:: - Sorcha outputs a log file (*.sorcha.log) and error file (*.sorcha.err) in the output directory. If all has gone well, the error file will be empty. The log file has the configuration parameters outputted to it as a record of the run setup. + ``sorcha`` outputs a log file (*.log) and error file (*.err) in the output directory. If all has gone well, the error file will be empty. The log file has the configuration parameters outputted to it as a record of the run setup. The output will appear in a csv file (testrun_e2e.csv) in your current directory. The first 51 lines of the csv file should look something like this: @@ -91,10 +91,10 @@ The output will appear in a csv file (testrun_e2e.csv) in your current directory :language: text :lines: 1-51 -.. note:: The values will not be exactly the same because of the different random number generator seed applied each time Sorcha runs. We use the random generator to adjust the calculated values to be within the measurement precision/uncertainty both in position (RA/Dec) and apparent magnitude. +.. note:: The values will not be exactly the same because of the different random number generator seed applied each time ``sorcha`` runs. We use the random generator to adjust the calculated values to be within the measurement precision/uncertainty both in position (RA/Dec) and apparent magnitude. .. tip:: If you want to run this command a second time you'll need to add a **-f** flag to the command line to force overwriting output files that already were exist in the output directory. Do note that the previous run's log and error log files will not be removed. New log files are generated at each run. .. warning:: - Only one instance of Sorcha should be run per output directory to ensure that distinct log and error files are created for each Sorcha run. Make sure to have different output pathways if you are running multiple instances on the same compute node. + Only one instance of ``sorcha`` should be run per output directory to ensure that distinct log and error files are created for each ``sorcha`` run. Make sure to have different output pathways if you are running multiple instances on the same compute node. diff --git a/docs/notebooks.rst b/docs/notebooks.rst index 48437f3f..19b5495c 100644 --- a/docs/notebooks.rst +++ b/docs/notebooks.rst @@ -1,6 +1,8 @@ Demo Notebooks ======================================================================================== +Below we provide jupyter notebooks that demonstrate and validate various functions and components of ``sorcha``. + .. toctree:: :maxdepth: 1 From 682d76c1d5b7b9db7c9fe3ad5ea1dd2d9ad61855 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Tue, 7 Jan 2025 09:56:46 +0000 Subject: [PATCH 12/52] update docs add in color notebook so it generates links - start comet activity notebook --- docs/notebooks.rst | 3 +- docs/notebooks/README.md | 4 + docs/notebooks/demo_Cometary_Activity.ipynb | 670 ++++++++++++++++++++ 3 files changed, 676 insertions(+), 1 deletion(-) create mode 100644 docs/notebooks/demo_Cometary_Activity.ipynb diff --git a/docs/notebooks.rst b/docs/notebooks.rst index 19b5495c..fb67f9ed 100644 --- a/docs/notebooks.rst +++ b/docs/notebooks.rst @@ -1,7 +1,7 @@ Demo Notebooks ======================================================================================== -Below we provide jupyter notebooks that demonstrate and validate various functions and components of ``sorcha``. +Below we provide Jupyter notebooks that demonstrate and validate various functions and components of ``sorcha``. .. toctree:: :maxdepth: 1 @@ -13,6 +13,7 @@ Below we provide jupyter notebooks that demonstrate and validate various functio LSST Camera Footprint Filter Coordinate Transformation Example Detection Efficiency Validation + Estimating Colors in LSST Filters From Optical/NIR Spectra SSP Linking Filter Magnitude and SNR Cuts Trailing Losses Validation diff --git a/docs/notebooks/README.md b/docs/notebooks/README.md index 31a50ebc..3131c3f0 100644 --- a/docs/notebooks/README.md +++ b/docs/notebooks/README.md @@ -24,6 +24,10 @@ demo_FootprintFilter - **Demonstrates:** PPFootprintFilter - **Files:** detector_corners.csv, footprintFilterValidationObservations.csv, oneline_v2.0.db +demo_CalculateLSSTColours +- **Demonstrates:** How to take an optical/near-infrared spectrum of a known Solar System object and convert it to predicted LSST filter colors +- **Files:** 2002PN34_highres.spec, + demo_Lightcurve - **Demonstrates:** lightcurve_registration (LC_METHODS, update_lc_subclasses), AbstractLightCurve class - **Files:** none diff --git a/docs/notebooks/demo_Cometary_Activity.ipynb b/docs/notebooks/demo_Cometary_Activity.ipynb new file mode 100644 index 00000000..c3356a38 --- /dev/null +++ b/docs/notebooks/demo_Cometary_Activity.ipynb @@ -0,0 +1,670 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c93503c5", + "metadata": {}, + "source": [ + "# Incorporating Cometary Activity" + ] + }, + { + "cell_type": "markdown", + "id": "c6a7190d", + "metadata": {}, + "source": [ + "The goal of this notebook is to demonstrate the use of lightcurves within `sorcha`.\n", + "\n", + "This will be done in two different ways:\n", + "- We will use the community tools part of the `sorcha-addons`(https://github.com/dirac-institute/sorcha-addons) package\n", + "- We will implement a custom lightcurve, and use it inside the code\n", + "\n", + "The idea is that the user can, in principle, implement their own lightcurves, and incorporate them in their simulation. The goal of `sorcha-addons` is for both the development team, as well as for the community, to share their implementations of custom lightcurve models. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fc4ba06a", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import astropy.units as u\n", + "from astroquery.jplhorizons import Horizons\n", + "from sorcha_addons.lightcurve.sinusoidal.sinusoidal_lightcurve import SinusoidalLightCurve\n", + "from sorcha.modules.PPCalculateApparentMagnitudeInFilter import PPCalculateApparentMagnitudeInFilter\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "2f79bca5", + "metadata": {}, + "source": [ + "This notebook will not use a realistic set of observations (as in the `demo_ApparentMagnitudeValidation` notebook), but rather create a toy scenario with a simple to understand and interpret set of results. The general structure of the notebook will be the same.\n", + "\n", + "We will create a dataframe for observations in a similar structure as in the `demo_ApparentMagnitudeValidation` notebook:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "46fc0914", + "metadata": {}, + "outputs": [], + "source": [ + "observations_df = pd.DataFrame(\n", + " {\n", + " \"fieldMJD_TAI\": np.linspace(\n", + " 0, 100, 1001\n", + " ), # time of observation - note these values are bogus, we only care about the Delta t for this demo\n", + " \"H_filter\": 10 * np.ones(1001),\n", + " \"Range_LTC_km\": 1.495978707e8 * np.linspace( 4, 30, 1001), # au\n", + " \"Obj_Sun_LTC_km\": 1.495978707e8 * np.linspace(5, 31, 1001), # au\n", + " \"phase_deg\": np.linspace(0, 10, 1001),\n", + " }\n", + ") # some phase angle variation so we can see the phase curve on top of the lightcurve" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "99156011", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " fieldMJD_TAI H_filter Range_LTC_km Obj_Sun_LTC_km phase_deg\n", + "0 0.0 10.0 5.983915e+08 7.479894e+08 0.00\n", + "1 0.1 10.0 6.022810e+08 7.518789e+08 0.01\n", + "2 0.2 10.0 6.061706e+08 7.557684e+08 0.02\n", + "3 0.3 10.0 6.100601e+08 7.596580e+08 0.03\n", + "4 0.4 10.0 6.139497e+08 7.635475e+08 0.04\n", + "... ... ... ... ... ...\n", + "996 99.6 10.0 4.472378e+09 4.621976e+09 9.96\n", + "997 99.7 10.0 4.476267e+09 4.625865e+09 9.97\n", + "998 99.8 10.0 4.480157e+09 4.629755e+09 9.98\n", + "999 99.9 10.0 4.484047e+09 4.633644e+09 9.99\n", + "1000 100.0 10.0 4.487936e+09 4.637534e+09 10.00\n", + "\n", + "[1001 rows x 5 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "observations_df" + ] + }, + { + "cell_type": "markdown", + "id": "191c5e0f", + "metadata": {}, + "source": [ + "Now we calculate the magnitude of the nuceleus assuming no phase curve model in PPCalculateApparentMagnitudeInFilter." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "69cc1794", + "metadata": {}, + "outputs": [], + "source": [ + "observations_df = PPCalculateApparentMagnitudeInFilter(observations_df.copy(), \"none\", \"r\", \"Simple_mag\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "89e840e0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " fieldMJD_TAI H_filter Range_LTC_km Obj_Sun_LTC_km phase_deg \\\n", + "0 0.0 10.0 5.983915e+08 7.479894e+08 0.00 \n", + "1 0.1 10.0 6.022810e+08 7.518789e+08 0.01 \n", + "2 0.2 10.0 6.061706e+08 7.557684e+08 0.02 \n", + "3 0.3 10.0 6.100601e+08 7.596580e+08 0.03 \n", + "4 0.4 10.0 6.139497e+08 7.635475e+08 0.04 \n", + "... ... ... ... ... ... \n", + "996 99.6 10.0 4.472378e+09 4.621976e+09 9.96 \n", + "997 99.7 10.0 4.476267e+09 4.625865e+09 9.97 \n", + "998 99.8 10.0 4.480157e+09 4.629755e+09 9.98 \n", + "999 99.9 10.0 4.484047e+09 4.633644e+09 9.99 \n", + "1000 100.0 10.0 4.487936e+09 4.637534e+09 10.00 \n", + "\n", + " Simple_mag \n", + "0 16.505150 \n", + "1 16.530481 \n", + "2 16.555664 \n", + "3 16.580700 \n", + "4 16.605590 \n", + "... ... \n", + "996 24.827577 \n", + "997 24.831291 \n", + "998 24.835002 \n", + "999 24.838710 \n", + "1000 24.842415 \n", + "\n", + "[1001 rows x 6 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "observations_df" + ] + }, + { + "cell_type": "markdown", + "id": "ba9e4dec", + "metadata": {}, + "source": [ + "Now we can plot the magnitudes and compare them." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "a40763e1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 8))\n", + "ax.plot(observations_df[\"fieldMJD_TAI\"], observations_df[\"Simple_mag\"], linestyle=\"-\", label=\"No phase curve\")\n", + "\n", + "ax.legend()\n", + "ax.set_xlabel(\"Time since first observation (days)\")\n", + "ax.set_ylabel(\"Apparent magnitude\")\n", + "plt.gca().invert_yaxis()\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "250e3f6f", + "metadata": {}, + "source": [ + "The effect of the lightcurve is to add an extra term to the apparent magnitude, that, in principle, can be a function of the characteristics of the observations, such as time of observation, phase angle or topocentric and heliocentric distances. The entire `observational_df` dataframe is exposed to the lightcurve, so any dependencies can be added. \n", + "\n", + "Let's use the basic sinusoidal lightcurve from `sorcha_addons`. We need the following columns in our dataframe:\n", + "\n", + " * ``LCA`` - lightcurve amplitude [magnitudes].\n", + " * ``Period`` - period of the sinusoidal oscillation [days]. Should be a positive value.\n", + " * ``Time0`` - phase for the light curve [days].\n", + "\n", + "Let's create a lightcurve with a period of 20 days, phased so that the first observation is at zero variation, and with 0.5 mag peak-to-peak amplitude." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4e802cf1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'identity': , 'sinusoidal': }\n" + ] + } + ], + "source": [ + "from sorcha.lightcurves.lightcurve_registration import LC_METHODS, update_lc_subclasses\n", + "\n", + "# LC_METHODS is the dictionary that contains all lightcurve implementations\n", + "# update_lc_subclasses adds newly defined classes to this dictionary\n", + "# this is run by default inside sorcha - we are just showing it here for completeness\n", + "update_lc_subclasses()\n", + "print(LC_METHODS)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "072165e9", + "metadata": {}, + "outputs": [], + "source": [ + "observations_df[\"LCA\"] = 0.25 # note peak-to-peak is 2LCA!\n", + "observations_df[\"Period\"] = 20.0\n", + "observations_df[\"Time0\"] = 0.0" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3e784192", + "metadata": {}, + "outputs": [], + "source": [ + "observations_df = PPCalculateApparentMagnitudeInFilter(\n", + " observations_df.copy(), \"none\", \"r\", \"LCA_mag\", \"sinusoidal\"\n", + ")\n", + "observations_df = PPCalculateApparentMagnitudeInFilter(\n", + " observations_df.copy(), \"HG\", \"r\", \"LCA_HG_mag\", \"sinusoidal\"\n", + ")\n", + "observations_df = PPCalculateApparentMagnitudeInFilter(\n", + " observations_df.copy(), \"HG12\", \"r\", \"LCA_HG12_mag\", \"sinusoidal\"\n", + ")\n", + "observations_df = PPCalculateApparentMagnitudeInFilter(\n", + " observations_df.copy(), \"HG1G2\", \"r\", \"LCA_HG1G2_mag\", \"sinusoidal\"\n", + ")\n", + "observations_df = PPCalculateApparentMagnitudeInFilter(\n", + " observations_df.copy(), \"linear\", \"r\", \"LCA_linear_mag\", \"sinusoidal\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "993c1c58", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib.lines import Line2D\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 8))\n", + "ax.plot(\n", + " observations_df[\"fieldMJD_TAI\"],\n", + " observations_df[\"Simple_mag\"],\n", + " linestyle=\"--\",\n", + " label=\"__none__\",\n", + " color=\"m\",\n", + ")\n", + "ax.plot(\n", + " observations_df[\"fieldMJD_TAI\"], observations_df[\"LCA_mag\"], linestyle=\"-\", label=\"__none__\", color=\"m\"\n", + ")\n", + "\n", + "ax.plot(\n", + " observations_df[\"fieldMJD_TAI\"],\n", + " observations_df[\"linear_mag\"],\n", + " linestyle=\"--\",\n", + " label=\"__none__\",\n", + " color=\"r\",\n", + ")\n", + "ax.plot(\n", + " observations_df[\"fieldMJD_TAI\"],\n", + " observations_df[\"LCA_linear_mag\"],\n", + " linestyle=\"-\",\n", + " label=\"__none__\",\n", + " color=\"r\",\n", + ")\n", + "\n", + "\n", + "ax.plot(\n", + " observations_df[\"fieldMJD_TAI\"],\n", + " observations_df[\"HG_mag\"],\n", + " linestyle=\"--\",\n", + " label=\"__none__\",\n", + " color=\"b\",\n", + ")\n", + "ax.plot(\n", + " observations_df[\"fieldMJD_TAI\"], observations_df[\"LCA_HG_mag\"], linestyle=\"-\", label=\"__none__\", color=\"b\"\n", + ")\n", + "\n", + "ax.plot(\n", + " observations_df[\"fieldMJD_TAI\"],\n", + " observations_df[\"HG12_mag\"],\n", + " linestyle=\"--\",\n", + " label=\"__none__\",\n", + " color=\"g\",\n", + ")\n", + "ax.plot(\n", + " observations_df[\"fieldMJD_TAI\"],\n", + " observations_df[\"LCA_HG12_mag\"],\n", + " linestyle=\"-\",\n", + " label=\"__none__\",\n", + " color=\"g\",\n", + ")\n", + "\n", + "ax.plot(\n", + " observations_df[\"fieldMJD_TAI\"],\n", + " observations_df[\"HG1G2_mag\"],\n", + " linestyle=\"--\",\n", + " label=\"__none__\",\n", + " color=\"c\",\n", + ")\n", + "ax.plot(\n", + " observations_df[\"fieldMJD_TAI\"],\n", + " observations_df[\"LCA_HG1G2_mag\"],\n", + " linestyle=\"-\",\n", + " label=\"__none__\",\n", + " color=\"c\",\n", + ")\n", + "\n", + "\n", + "custom_legend = [\n", + " Line2D([0], [0], color=\"m\", linestyle=\"-\"),\n", + " Line2D([0], [0], color=\"r\", linestyle=\"-\"),\n", + " Line2D([0], [0], color=\"b\", linestyle=\"-\"),\n", + " Line2D([0], [0], color=\"g\", linestyle=\"-\"),\n", + " Line2D([0], [0], color=\"c\", linestyle=\"-\"),\n", + " Line2D([0], [0], color=\"k\", linestyle=\"-\"),\n", + " Line2D([0], [0], color=\"k\", linestyle=\"--\"),\n", + "]\n", + "\n", + "ax.legend(\n", + " custom_legend,\n", + " [\"No phase curve\", \"Linear\", \"HG\", \"HG12\", \"HG1G2\", \"Lightcurve added\", \"No lightcurve\"],\n", + " ncol=2,\n", + ")\n", + "ax.set_xlabel(\"Time since first observation (days)\")\n", + "ax.set_ylabel(\"Apparent magnitude\")\n", + "ax.set_ylim(9.5, 11.5)\n", + "plt.gca().invert_yaxis()\n", + "plt.grid()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 10c043807d823efc509c1b3f8c9cec1ed61d46a4 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Tue, 7 Jan 2025 10:12:29 +0000 Subject: [PATCH 13/52] add a reminder about the colors demo notebook add a reminder about the colors demo notebook --- docs/inputs.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/inputs.rst b/docs/inputs.rst index 4b3bfba8..4484d27f 100644 --- a/docs/inputs.rst +++ b/docs/inputs.rst @@ -216,6 +216,8 @@ We have implemented several phase curve parameterizations that can be specified .. note:: In the config file you can decide which filters you want to have ``sorcha`` run on and specify which filter is the main filter that the absolute magnitude is defined for. You only need to provide colors for those filters specified in the config file. +.. tip:: + We have an `example Jupyter notebook `_ demonstrating how to take a representative optical/NIR spectra of your input population and using the `rubin_sim `_ package to estimate the expected colors in the LSST filter bandpasses. .. _pointing: Survey Pointing Database From 0b391cce295caac3f3c3391f2a50f9662d323f03 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Tue, 7 Jan 2025 12:48:32 +0000 Subject: [PATCH 14/52] Update demo_Cometary_Activity.ipynb Add complete demo for cometary activity --- docs/notebooks/demo_Cometary_Activity.ipynb | 732 +++++++++++++------- 1 file changed, 473 insertions(+), 259 deletions(-) diff --git a/docs/notebooks/demo_Cometary_Activity.ipynb b/docs/notebooks/demo_Cometary_Activity.ipynb index c3356a38..19192446 100644 --- a/docs/notebooks/demo_Cometary_Activity.ipynb +++ b/docs/notebooks/demo_Cometary_Activity.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 133, "id": "fc4ba06a", "metadata": {}, "outputs": [], @@ -32,10 +32,8 @@ "import pandas as pd\n", "import numpy as np\n", "import astropy.units as u\n", - "from astroquery.jplhorizons import Horizons\n", - "from sorcha_addons.lightcurve.sinusoidal.sinusoidal_lightcurve import SinusoidalLightCurve\n", - "from sorcha.modules.PPCalculateApparentMagnitudeInFilter import PPCalculateApparentMagnitudeInFilter\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "from sorcha.modules.PPCalculateApparentMagnitudeInFilter import PPCalculateApparentMagnitudeInFilter" ] }, { @@ -50,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 134, "id": "46fc0914", "metadata": {}, "outputs": [], @@ -60,17 +58,20 @@ " \"fieldMJD_TAI\": np.linspace(\n", " 0, 100, 1001\n", " ), # time of observation - note these values are bogus, we only care about the Delta t for this demo\n", - " \"H_filter\": 10 * np.ones(1001),\n", - " \"Range_LTC_km\": 1.495978707e8 * np.linspace( 4, 30, 1001), # au\n", - " \"Obj_Sun_LTC_km\": 1.495978707e8 * np.linspace(5, 31, 1001), # au\n", - " \"phase_deg\": np.linspace(0, 10, 1001),\n", + " \"H_filter\": 15 * np.ones(1001),\n", + " # starting at 30 au and coming inward to 5 au \n", + " \"Range_LTC_km\": 1.495978707e8 * np.flip(np.linspace( 4, 30, 1001)), # au\n", + " \"Obj_Sun_LTC_km\": 1.495978707e8 * np.flip(np.linspace(5, 30, 1001)), # au\n", + " \"phase_deg\": np.zeros(1001), \n", + " #keeping the same phase although this is unphysical so that we can look at just the effects of activity on the brightness of the object\n", + " \"optFilter\": np.full(1001,'r',dtype=str), \n", " }\n", - ") # some phase angle variation so we can see the phase curve on top of the lightcurve" + ") " ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 135, "id": "99156011", "metadata": {}, "outputs": [ @@ -100,48 +101,54 @@ " Range_LTC_km\n", " Obj_Sun_LTC_km\n", " phase_deg\n", + " optFilter\n", " \n", " \n", " \n", " \n", " 0\n", " 0.0\n", - " 10.0\n", - " 5.983915e+08\n", - " 7.479894e+08\n", - " 0.00\n", + " 15.0\n", + " 4.487936e+09\n", + " 4.487936e+09\n", + " 0.0\n", + " r\n", " \n", " \n", " 1\n", " 0.1\n", - " 10.0\n", - " 6.022810e+08\n", - " 7.518789e+08\n", - " 0.01\n", + " 15.0\n", + " 4.484047e+09\n", + " 4.484196e+09\n", + " 0.0\n", + " r\n", " \n", " \n", " 2\n", " 0.2\n", - " 10.0\n", - " 6.061706e+08\n", - " 7.557684e+08\n", - " 0.02\n", + " 15.0\n", + " 4.480157e+09\n", + " 4.480456e+09\n", + " 0.0\n", + " r\n", " \n", " \n", " 3\n", " 0.3\n", - " 10.0\n", - " 6.100601e+08\n", - " 7.596580e+08\n", - " 0.03\n", + " 15.0\n", + " 4.476267e+09\n", + " 4.476716e+09\n", + " 0.0\n", + " r\n", " \n", " \n", " 4\n", " 0.4\n", - " 10.0\n", - " 6.139497e+08\n", - " 7.635475e+08\n", - " 0.04\n", + " 15.0\n", + " 4.472378e+09\n", + " 4.472976e+09\n", + " 0.0\n", + " r\n", " \n", " \n", " ...\n", @@ -150,70 +157,89 @@ " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", " 996\n", " 99.6\n", - " 10.0\n", - " 4.472378e+09\n", - " 4.621976e+09\n", - " 9.96\n", + " 15.0\n", + " 6.139497e+08\n", + " 7.629491e+08\n", + " 0.0\n", + " r\n", " \n", " \n", " 997\n", " 99.7\n", - " 10.0\n", - " 4.476267e+09\n", - " 4.625865e+09\n", - " 9.97\n", + " 15.0\n", + " 6.100601e+08\n", + " 7.592092e+08\n", + " 0.0\n", + " r\n", " \n", " \n", " 998\n", " 99.8\n", - " 10.0\n", - " 4.480157e+09\n", - " 4.629755e+09\n", - " 9.98\n", + " 15.0\n", + " 6.061706e+08\n", + " 7.554692e+08\n", + " 0.0\n", + " r\n", " \n", " \n", " 999\n", " 99.9\n", - " 10.0\n", - " 4.484047e+09\n", - " 4.633644e+09\n", - " 9.99\n", + " 15.0\n", + " 6.022810e+08\n", + " 7.517293e+08\n", + " 0.0\n", + " r\n", " \n", " \n", " 1000\n", " 100.0\n", - " 10.0\n", - " 4.487936e+09\n", - " 4.637534e+09\n", - " 10.00\n", + " 15.0\n", + " 5.983915e+08\n", + " 7.479894e+08\n", + " 0.0\n", + " r\n", " \n", " \n", "\n", - "

1001 rows × 5 columns

\n", + "

1001 rows × 6 columns

\n", "" ], "text/plain": [ - " fieldMJD_TAI H_filter Range_LTC_km Obj_Sun_LTC_km phase_deg\n", - "0 0.0 10.0 5.983915e+08 7.479894e+08 0.00\n", - "1 0.1 10.0 6.022810e+08 7.518789e+08 0.01\n", - "2 0.2 10.0 6.061706e+08 7.557684e+08 0.02\n", - "3 0.3 10.0 6.100601e+08 7.596580e+08 0.03\n", - "4 0.4 10.0 6.139497e+08 7.635475e+08 0.04\n", - "... ... ... ... ... ...\n", - "996 99.6 10.0 4.472378e+09 4.621976e+09 9.96\n", - "997 99.7 10.0 4.476267e+09 4.625865e+09 9.97\n", - "998 99.8 10.0 4.480157e+09 4.629755e+09 9.98\n", - "999 99.9 10.0 4.484047e+09 4.633644e+09 9.99\n", - "1000 100.0 10.0 4.487936e+09 4.637534e+09 10.00\n", + " fieldMJD_TAI H_filter Range_LTC_km Obj_Sun_LTC_km phase_deg \\\n", + "0 0.0 15.0 4.487936e+09 4.487936e+09 0.0 \n", + "1 0.1 15.0 4.484047e+09 4.484196e+09 0.0 \n", + "2 0.2 15.0 4.480157e+09 4.480456e+09 0.0 \n", + "3 0.3 15.0 4.476267e+09 4.476716e+09 0.0 \n", + "4 0.4 15.0 4.472378e+09 4.472976e+09 0.0 \n", + "... ... ... ... ... ... \n", + "996 99.6 15.0 6.139497e+08 7.629491e+08 0.0 \n", + "997 99.7 15.0 6.100601e+08 7.592092e+08 0.0 \n", + "998 99.8 15.0 6.061706e+08 7.554692e+08 0.0 \n", + "999 99.9 15.0 6.022810e+08 7.517293e+08 0.0 \n", + "1000 100.0 15.0 5.983915e+08 7.479894e+08 0.0 \n", + "\n", + " optFilter \n", + "0 r \n", + "1 r \n", + "2 r \n", + "3 r \n", + "4 r \n", + "... ... \n", + "996 r \n", + "997 r \n", + "998 r \n", + "999 r \n", + "1000 r \n", "\n", - "[1001 rows x 5 columns]" + "[1001 rows x 6 columns]" ] }, - "execution_count": 11, + "execution_count": 135, "metadata": {}, "output_type": "execute_result" } @@ -232,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 136, "id": "69cc1794", "metadata": {}, "outputs": [], @@ -242,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 137, "id": "89e840e0", "metadata": {}, "outputs": [ @@ -272,6 +298,7 @@ " Range_LTC_km\n", " Obj_Sun_LTC_km\n", " phase_deg\n", + " optFilter\n", " Simple_mag\n", " \n", " \n", @@ -279,47 +306,52 @@ " \n", " 0\n", " 0.0\n", - " 10.0\n", - " 5.983915e+08\n", - " 7.479894e+08\n", - " 0.00\n", - " 16.505150\n", + " 15.0\n", + " 4.487936e+09\n", + " 4.487936e+09\n", + " 0.0\n", + " r\n", + " 29.771213\n", " \n", " \n", " 1\n", " 0.1\n", - " 10.0\n", - " 6.022810e+08\n", - " 7.518789e+08\n", - " 0.01\n", - " 16.530481\n", + " 15.0\n", + " 4.484047e+09\n", + " 4.484196e+09\n", + " 0.0\n", + " r\n", + " 29.767519\n", " \n", " \n", " 2\n", " 0.2\n", - " 10.0\n", - " 6.061706e+08\n", - " 7.557684e+08\n", - " 0.02\n", - " 16.555664\n", + " 15.0\n", + " 4.480157e+09\n", + " 4.480456e+09\n", + " 0.0\n", + " r\n", + " 29.763823\n", " \n", " \n", " 3\n", " 0.3\n", - " 10.0\n", - " 6.100601e+08\n", - " 7.596580e+08\n", - " 0.03\n", - " 16.580700\n", + " 15.0\n", + " 4.476267e+09\n", + " 4.476716e+09\n", + " 0.0\n", + " r\n", + " 29.760124\n", " \n", " \n", " 4\n", " 0.4\n", - " 10.0\n", - " 6.139497e+08\n", - " 7.635475e+08\n", - " 0.04\n", - " 16.605590\n", + " 15.0\n", + " 4.472378e+09\n", + " 4.472976e+09\n", + " 0.0\n", + " r\n", + " 29.756421\n", " \n", " \n", " ...\n", @@ -329,88 +361,94 @@ " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", " 996\n", " 99.6\n", - " 10.0\n", - " 4.472378e+09\n", - " 4.621976e+09\n", - " 9.96\n", - " 24.827577\n", + " 15.0\n", + " 6.139497e+08\n", + " 7.629491e+08\n", + " 0.0\n", + " r\n", + " 21.603888\n", " \n", " \n", " 997\n", " 99.7\n", - " 10.0\n", - " 4.476267e+09\n", - " 4.625865e+09\n", - " 9.97\n", - " 24.831291\n", + " 15.0\n", + " 6.100601e+08\n", + " 7.592092e+08\n", + " 0.0\n", + " r\n", + " 21.579416\n", " \n", " \n", " 998\n", " 99.8\n", - " 10.0\n", - " 4.480157e+09\n", - " 4.629755e+09\n", - " 9.98\n", - " 24.835002\n", + " 15.0\n", + " 6.061706e+08\n", + " 7.554692e+08\n", + " 0.0\n", + " r\n", + " 21.554804\n", " \n", " \n", " 999\n", " 99.9\n", - " 10.0\n", - " 4.484047e+09\n", - " 4.633644e+09\n", - " 9.99\n", - " 24.838710\n", + " 15.0\n", + " 6.022810e+08\n", + " 7.517293e+08\n", + " 0.0\n", + " r\n", + " 21.530049\n", " \n", " \n", " 1000\n", " 100.0\n", - " 10.0\n", - " 4.487936e+09\n", - " 4.637534e+09\n", - " 10.00\n", - " 24.842415\n", + " 15.0\n", + " 5.983915e+08\n", + " 7.479894e+08\n", + " 0.0\n", + " r\n", + " 21.505150\n", " \n", " \n", "\n", - "

1001 rows × 6 columns

\n", + "

1001 rows × 7 columns

\n", "" ], "text/plain": [ " fieldMJD_TAI H_filter Range_LTC_km Obj_Sun_LTC_km phase_deg \\\n", - "0 0.0 10.0 5.983915e+08 7.479894e+08 0.00 \n", - "1 0.1 10.0 6.022810e+08 7.518789e+08 0.01 \n", - "2 0.2 10.0 6.061706e+08 7.557684e+08 0.02 \n", - "3 0.3 10.0 6.100601e+08 7.596580e+08 0.03 \n", - "4 0.4 10.0 6.139497e+08 7.635475e+08 0.04 \n", + "0 0.0 15.0 4.487936e+09 4.487936e+09 0.0 \n", + "1 0.1 15.0 4.484047e+09 4.484196e+09 0.0 \n", + "2 0.2 15.0 4.480157e+09 4.480456e+09 0.0 \n", + "3 0.3 15.0 4.476267e+09 4.476716e+09 0.0 \n", + "4 0.4 15.0 4.472378e+09 4.472976e+09 0.0 \n", "... ... ... ... ... ... \n", - "996 99.6 10.0 4.472378e+09 4.621976e+09 9.96 \n", - "997 99.7 10.0 4.476267e+09 4.625865e+09 9.97 \n", - "998 99.8 10.0 4.480157e+09 4.629755e+09 9.98 \n", - "999 99.9 10.0 4.484047e+09 4.633644e+09 9.99 \n", - "1000 100.0 10.0 4.487936e+09 4.637534e+09 10.00 \n", + "996 99.6 15.0 6.139497e+08 7.629491e+08 0.0 \n", + "997 99.7 15.0 6.100601e+08 7.592092e+08 0.0 \n", + "998 99.8 15.0 6.061706e+08 7.554692e+08 0.0 \n", + "999 99.9 15.0 6.022810e+08 7.517293e+08 0.0 \n", + "1000 100.0 15.0 5.983915e+08 7.479894e+08 0.0 \n", "\n", - " Simple_mag \n", - "0 16.505150 \n", - "1 16.530481 \n", - "2 16.555664 \n", - "3 16.580700 \n", - "4 16.605590 \n", - "... ... \n", - "996 24.827577 \n", - "997 24.831291 \n", - "998 24.835002 \n", - "999 24.838710 \n", - "1000 24.842415 \n", + " optFilter Simple_mag \n", + "0 r 29.771213 \n", + "1 r 29.767519 \n", + "2 r 29.763823 \n", + "3 r 29.760124 \n", + "4 r 29.756421 \n", + "... ... ... \n", + "996 r 21.603888 \n", + "997 r 21.579416 \n", + "998 r 21.554804 \n", + "999 r 21.530049 \n", + "1000 r 21.505150 \n", "\n", - "[1001 rows x 6 columns]" + "[1001 rows x 7 columns]" ] }, - "execution_count": 13, + "execution_count": 137, "metadata": {}, "output_type": "execute_result" } @@ -429,13 +467,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 138, "id": "a40763e1", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -456,6 +494,35 @@ "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 139, + "id": "1051a6f1-732e-42fa-af23-2ef67b4170c1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 8))\n", + "ax.plot(observations_df[\"Obj_Sun_LTC_km\"]/1.495978707e8 , observations_df[\"Simple_mag\"], linestyle=\"-\", label=\"No phase curve\", color='purple')\n", + "\n", + "ax.legend()\n", + "ax.set_ylabel(\"Apparent magnitude\")\n", + "ax.set_xlabel(\"Heliocentric distance (au)\")\n", + "plt.gca().invert_yaxis()\n", + "plt.grid()\n", + "plt.show()" + ] + }, { "cell_type": "markdown", "id": "250e3f6f", @@ -474,73 +541,286 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 140, "id": "4e802cf1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'identity': , 'sinusoidal': }\n" - ] - } - ], + "outputs": [], "source": [ - "from sorcha.lightcurves.lightcurve_registration import LC_METHODS, update_lc_subclasses\n", + "from sorcha_addons.activity.lsst_comet.lsst_comet_activity import LSSTCometActivity\n", + "from sorcha.activity.activity_registration import update_activity_subclasses\n", "\n", - "# LC_METHODS is the dictionary that contains all lightcurve implementations\n", - "# update_lc_subclasses adds newly defined classes to this dictionary\n", - "# this is run by default inside sorcha - we are just showing it here for completeness\n", - "update_lc_subclasses()\n", - "print(LC_METHODS)" + "update_activity_subclasses()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 149, "id": "072165e9", "metadata": {}, "outputs": [], "source": [ - "observations_df[\"LCA\"] = 0.25 # note peak-to-peak is 2LCA!\n", - "observations_df[\"Period\"] = 20.0\n", - "observations_df[\"Time0\"] = 0.0" + "observations_df[\"afrho1\"] = 150\n", + "observations_df[\"k\"] =-0.5\n" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 150, "id": "3e784192", "metadata": {}, "outputs": [], "source": [ - "observations_df = PPCalculateApparentMagnitudeInFilter(\n", - " observations_df.copy(), \"none\", \"r\", \"LCA_mag\", \"sinusoidal\"\n", - ")\n", - "observations_df = PPCalculateApparentMagnitudeInFilter(\n", - " observations_df.copy(), \"HG\", \"r\", \"LCA_HG_mag\", \"sinusoidal\"\n", - ")\n", - "observations_df = PPCalculateApparentMagnitudeInFilter(\n", - " observations_df.copy(), \"HG12\", \"r\", \"LCA_HG12_mag\", \"sinusoidal\"\n", - ")\n", - "observations_df = PPCalculateApparentMagnitudeInFilter(\n", - " observations_df.copy(), \"HG1G2\", \"r\", \"LCA_HG1G2_mag\", \"sinusoidal\"\n", - ")\n", - "observations_df = PPCalculateApparentMagnitudeInFilter(\n", - " observations_df.copy(), \"linear\", \"r\", \"LCA_linear_mag\", \"sinusoidal\"\n", - ")" + "observations_df = PPCalculateApparentMagnitudeInFilter(observations_df.copy(), \"none\", \"r\",cometary_activity_choice=\"lsst_comet\")" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "id": "6a1b13ff-2ef2-41e8-8401-c513540ee9f3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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fieldMJD_TAIH_filterRange_LTC_kmObj_Sun_LTC_kmphase_degoptFilterSimple_magafrho1ktrailedSourceMagTruecoma_magnitude
00.015.04.487936e+094.487936e+090.0r29.771213150-0.528.03772728.283515
10.115.04.484047e+094.484196e+090.0r29.767519150-0.528.03442428.280311
20.215.04.480157e+094.480456e+090.0r29.763823150-0.528.03111728.277104
30.315.04.476267e+094.476716e+090.0r29.760124150-0.528.02780828.273894
40.415.04.472378e+094.472976e+090.0r29.756421150-0.528.02449728.270682
....................................
99699.615.06.139497e+087.629491e+080.0r21.603888150-0.520.69674321.314036
99799.715.06.100601e+087.592092e+080.0r21.579416150-0.520.67466721.293797
99899.815.06.061706e+087.554692e+080.0r21.554804150-0.520.65246321.273449
99999.915.06.022810e+087.517293e+080.0r21.530049150-0.520.63013021.252989
1000100.015.05.983915e+087.479894e+080.0r21.505150150-0.520.60766721.232416
\n", + "

1001 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " fieldMJD_TAI H_filter Range_LTC_km Obj_Sun_LTC_km phase_deg \\\n", + "0 0.0 15.0 4.487936e+09 4.487936e+09 0.0 \n", + "1 0.1 15.0 4.484047e+09 4.484196e+09 0.0 \n", + "2 0.2 15.0 4.480157e+09 4.480456e+09 0.0 \n", + "3 0.3 15.0 4.476267e+09 4.476716e+09 0.0 \n", + "4 0.4 15.0 4.472378e+09 4.472976e+09 0.0 \n", + "... ... ... ... ... ... \n", + "996 99.6 15.0 6.139497e+08 7.629491e+08 0.0 \n", + "997 99.7 15.0 6.100601e+08 7.592092e+08 0.0 \n", + "998 99.8 15.0 6.061706e+08 7.554692e+08 0.0 \n", + "999 99.9 15.0 6.022810e+08 7.517293e+08 0.0 \n", + "1000 100.0 15.0 5.983915e+08 7.479894e+08 0.0 \n", + "\n", + " optFilter Simple_mag afrho1 k trailedSourceMagTrue coma_magnitude \n", + "0 r 29.771213 150 -0.5 28.037727 28.283515 \n", + "1 r 29.767519 150 -0.5 28.034424 28.280311 \n", + "2 r 29.763823 150 -0.5 28.031117 28.277104 \n", + "3 r 29.760124 150 -0.5 28.027808 28.273894 \n", + "4 r 29.756421 150 -0.5 28.024497 28.270682 \n", + "... ... ... ... ... ... ... \n", + "996 r 21.603888 150 -0.5 20.696743 21.314036 \n", + "997 r 21.579416 150 -0.5 20.674667 21.293797 \n", + "998 r 21.554804 150 -0.5 20.652463 21.273449 \n", + "999 r 21.530049 150 -0.5 20.630130 21.252989 \n", + "1000 r 21.505150 150 -0.5 20.607667 21.232416 \n", + "\n", + "[1001 rows x 11 columns]" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "observations_df" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 152, "id": "993c1c58", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -561,89 +841,23 @@ " color=\"m\",\n", ")\n", "ax.plot(\n", - " observations_df[\"fieldMJD_TAI\"], observations_df[\"LCA_mag\"], linestyle=\"-\", label=\"__none__\", color=\"m\"\n", - ")\n", - "\n", - "ax.plot(\n", - " observations_df[\"fieldMJD_TAI\"],\n", - " observations_df[\"linear_mag\"],\n", - " linestyle=\"--\",\n", - " label=\"__none__\",\n", - " color=\"r\",\n", - ")\n", - "ax.plot(\n", - " observations_df[\"fieldMJD_TAI\"],\n", - " observations_df[\"LCA_linear_mag\"],\n", - " linestyle=\"-\",\n", - " label=\"__none__\",\n", - " color=\"r\",\n", + " observations_df[\"fieldMJD_TAI\"], observations_df[\"trailedSourceMagTrue\"], linestyle=\"-\", label=\"__none__\", color=\"g\"\n", ")\n", "\n", - "\n", - "ax.plot(\n", - " observations_df[\"fieldMJD_TAI\"],\n", - " observations_df[\"HG_mag\"],\n", - " linestyle=\"--\",\n", - " label=\"__none__\",\n", - " color=\"b\",\n", - ")\n", - "ax.plot(\n", - " observations_df[\"fieldMJD_TAI\"], observations_df[\"LCA_HG_mag\"], linestyle=\"-\", label=\"__none__\", color=\"b\"\n", - ")\n", - "\n", - "ax.plot(\n", - " observations_df[\"fieldMJD_TAI\"],\n", - " observations_df[\"HG12_mag\"],\n", - " linestyle=\"--\",\n", - " label=\"__none__\",\n", - " color=\"g\",\n", - ")\n", - "ax.plot(\n", - " observations_df[\"fieldMJD_TAI\"],\n", - " observations_df[\"LCA_HG12_mag\"],\n", - " linestyle=\"-\",\n", - " label=\"__none__\",\n", - " color=\"g\",\n", - ")\n", - "\n", - "ax.plot(\n", - " observations_df[\"fieldMJD_TAI\"],\n", - " observations_df[\"HG1G2_mag\"],\n", - " linestyle=\"--\",\n", - " label=\"__none__\",\n", - " color=\"c\",\n", - ")\n", - "ax.plot(\n", - " observations_df[\"fieldMJD_TAI\"],\n", - " observations_df[\"LCA_HG1G2_mag\"],\n", - " linestyle=\"-\",\n", - " label=\"__none__\",\n", - " color=\"c\",\n", - ")\n", - "\n", - "\n", - "custom_legend = [\n", - " Line2D([0], [0], color=\"m\", linestyle=\"-\"),\n", - " Line2D([0], [0], color=\"r\", linestyle=\"-\"),\n", - " Line2D([0], [0], color=\"b\", linestyle=\"-\"),\n", - " Line2D([0], [0], color=\"g\", linestyle=\"-\"),\n", - " Line2D([0], [0], color=\"c\", linestyle=\"-\"),\n", - " Line2D([0], [0], color=\"k\", linestyle=\"-\"),\n", - " Line2D([0], [0], color=\"k\", linestyle=\"--\"),\n", - "]\n", - "\n", - "ax.legend(\n", - " custom_legend,\n", - " [\"No phase curve\", \"Linear\", \"HG\", \"HG12\", \"HG1G2\", \"Lightcurve added\", \"No lightcurve\"],\n", - " ncol=2,\n", - ")\n", "ax.set_xlabel(\"Time since first observation (days)\")\n", "ax.set_ylabel(\"Apparent magnitude\")\n", - "ax.set_ylim(9.5, 11.5)\n", "plt.gca().invert_yaxis()\n", "plt.grid()\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b3f5acd0-70f9-4690-8a2f-0d553747890a", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 96e00bb6082bfffc9bad9039f0f53e4b8504f4b2 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Tue, 7 Jan 2025 12:53:01 +0000 Subject: [PATCH 15/52] updated cometary activity notebook updated cometary activity notebook --- docs/notebooks/demo_Cometary_Activity.ipynb | 338 ++++++++++---------- 1 file changed, 169 insertions(+), 169 deletions(-) diff --git a/docs/notebooks/demo_Cometary_Activity.ipynb b/docs/notebooks/demo_Cometary_Activity.ipynb index 19192446..ac602e07 100644 --- a/docs/notebooks/demo_Cometary_Activity.ipynb +++ b/docs/notebooks/demo_Cometary_Activity.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 177, "id": "fc4ba06a", "metadata": {}, "outputs": [], @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 178, "id": "46fc0914", "metadata": {}, "outputs": [], @@ -60,8 +60,8 @@ " ), # time of observation - note these values are bogus, we only care about the Delta t for this demo\n", " \"H_filter\": 15 * np.ones(1001),\n", " # starting at 30 au and coming inward to 5 au \n", - " \"Range_LTC_km\": 1.495978707e8 * np.flip(np.linspace( 4, 30, 1001)), # au\n", - " \"Obj_Sun_LTC_km\": 1.495978707e8 * np.flip(np.linspace(5, 30, 1001)), # au\n", + " \"Range_LTC_km\": 1.495978707e8 * np.flip(np.linspace( 0.2, 30, 1001)), # au\n", + " \"Obj_Sun_LTC_km\": 1.495978707e8 * np.flip(np.linspace(1.2, 30, 1001)), # au\n", " \"phase_deg\": np.zeros(1001), \n", " #keeping the same phase although this is unphysical so that we can look at just the effects of activity on the brightness of the object\n", " \"optFilter\": np.full(1001,'r',dtype=str), \n", @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 179, "id": "99156011", "metadata": {}, "outputs": [ @@ -118,8 +118,8 @@ " 1\n", " 0.1\n", " 15.0\n", - " 4.484047e+09\n", - " 4.484196e+09\n", + " 4.483478e+09\n", + " 4.483628e+09\n", " 0.0\n", " r\n", " \n", @@ -127,8 +127,8 @@ " 2\n", " 0.2\n", " 15.0\n", - " 4.480157e+09\n", - " 4.480456e+09\n", + " 4.479020e+09\n", + " 4.479319e+09\n", " 0.0\n", " r\n", " \n", @@ -136,8 +136,8 @@ " 3\n", " 0.3\n", " 15.0\n", - " 4.476267e+09\n", - " 4.476716e+09\n", + " 4.474562e+09\n", + " 4.475011e+09\n", " 0.0\n", " r\n", " \n", @@ -145,8 +145,8 @@ " 4\n", " 0.4\n", " 15.0\n", - " 4.472378e+09\n", - " 4.472976e+09\n", + " 4.470104e+09\n", + " 4.470702e+09\n", " 0.0\n", " r\n", " \n", @@ -163,8 +163,8 @@ " 996\n", " 99.6\n", " 15.0\n", - " 6.139497e+08\n", - " 7.629491e+08\n", + " 4.775164e+07\n", + " 1.967511e+08\n", " 0.0\n", " r\n", " \n", @@ -172,8 +172,8 @@ " 997\n", " 99.7\n", " 15.0\n", - " 6.100601e+08\n", - " 7.592092e+08\n", + " 4.329362e+07\n", + " 1.924427e+08\n", " 0.0\n", " r\n", " \n", @@ -181,8 +181,8 @@ " 998\n", " 99.8\n", " 15.0\n", - " 6.061706e+08\n", - " 7.554692e+08\n", + " 3.883561e+07\n", + " 1.881343e+08\n", " 0.0\n", " r\n", " \n", @@ -190,8 +190,8 @@ " 999\n", " 99.9\n", " 15.0\n", - " 6.022810e+08\n", - " 7.517293e+08\n", + " 3.437759e+07\n", + " 1.838259e+08\n", " 0.0\n", " r\n", " \n", @@ -199,8 +199,8 @@ " 1000\n", " 100.0\n", " 15.0\n", - " 5.983915e+08\n", - " 7.479894e+08\n", + " 2.991957e+07\n", + " 1.795174e+08\n", " 0.0\n", " r\n", " \n", @@ -212,16 +212,16 @@ "text/plain": [ " fieldMJD_TAI H_filter Range_LTC_km Obj_Sun_LTC_km phase_deg \\\n", "0 0.0 15.0 4.487936e+09 4.487936e+09 0.0 \n", - "1 0.1 15.0 4.484047e+09 4.484196e+09 0.0 \n", - "2 0.2 15.0 4.480157e+09 4.480456e+09 0.0 \n", - "3 0.3 15.0 4.476267e+09 4.476716e+09 0.0 \n", - "4 0.4 15.0 4.472378e+09 4.472976e+09 0.0 \n", + "1 0.1 15.0 4.483478e+09 4.483628e+09 0.0 \n", + "2 0.2 15.0 4.479020e+09 4.479319e+09 0.0 \n", + "3 0.3 15.0 4.474562e+09 4.475011e+09 0.0 \n", + "4 0.4 15.0 4.470104e+09 4.470702e+09 0.0 \n", "... ... ... ... ... ... \n", - "996 99.6 15.0 6.139497e+08 7.629491e+08 0.0 \n", - "997 99.7 15.0 6.100601e+08 7.592092e+08 0.0 \n", - "998 99.8 15.0 6.061706e+08 7.554692e+08 0.0 \n", - "999 99.9 15.0 6.022810e+08 7.517293e+08 0.0 \n", - "1000 100.0 15.0 5.983915e+08 7.479894e+08 0.0 \n", + "996 99.6 15.0 4.775164e+07 1.967511e+08 0.0 \n", + "997 99.7 15.0 4.329362e+07 1.924427e+08 0.0 \n", + "998 99.8 15.0 3.883561e+07 1.881343e+08 0.0 \n", + "999 99.9 15.0 3.437759e+07 1.838259e+08 0.0 \n", + "1000 100.0 15.0 2.991957e+07 1.795174e+08 0.0 \n", "\n", " optFilter \n", "0 r \n", @@ -239,7 +239,7 @@ "[1001 rows x 6 columns]" ] }, - "execution_count": 135, + "execution_count": 179, "metadata": {}, "output_type": "execute_result" } @@ -258,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 180, "id": "69cc1794", "metadata": {}, "outputs": [], @@ -268,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 181, "id": "89e840e0", "metadata": {}, "outputs": [ @@ -317,41 +317,41 @@ " 1\n", " 0.1\n", " 15.0\n", - " 4.484047e+09\n", - " 4.484196e+09\n", + " 4.483478e+09\n", + " 4.483628e+09\n", " 0.0\n", " r\n", - " 29.767519\n", + " 29.766969\n", " \n", " \n", " 2\n", " 0.2\n", " 15.0\n", - " 4.480157e+09\n", - " 4.480456e+09\n", + " 4.479020e+09\n", + " 4.479319e+09\n", " 0.0\n", " r\n", - " 29.763823\n", + " 29.762721\n", " \n", " \n", " 3\n", " 0.3\n", " 15.0\n", - " 4.476267e+09\n", - " 4.476716e+09\n", + " 4.474562e+09\n", + " 4.475011e+09\n", " 0.0\n", " r\n", - " 29.760124\n", + " 29.758469\n", " \n", " \n", " 4\n", " 0.4\n", " 15.0\n", - " 4.472378e+09\n", - " 4.472976e+09\n", + " 4.470104e+09\n", + " 4.470702e+09\n", " 0.0\n", " r\n", - " 29.756421\n", + " 29.754213\n", " \n", " \n", " ...\n", @@ -367,51 +367,51 @@ " 996\n", " 99.6\n", " 15.0\n", - " 6.139497e+08\n", - " 7.629491e+08\n", + " 4.775164e+07\n", + " 1.967511e+08\n", " 0.0\n", " r\n", - " 21.603888\n", + " 13.115273\n", " \n", " \n", " 997\n", " 99.7\n", " 15.0\n", - " 6.100601e+08\n", - " 7.592092e+08\n", + " 4.329362e+07\n", + " 1.924427e+08\n", " 0.0\n", " r\n", - " 21.579416\n", + " 12.854373\n", " \n", " \n", " 998\n", " 99.8\n", " 15.0\n", - " 6.061706e+08\n", - " 7.554692e+08\n", + " 3.883561e+07\n", + " 1.881343e+08\n", " 0.0\n", " r\n", - " 21.554804\n", + " 12.569236\n", " \n", " \n", " 999\n", " 99.9\n", " 15.0\n", - " 6.022810e+08\n", - " 7.517293e+08\n", + " 3.437759e+07\n", + " 1.838259e+08\n", " 0.0\n", " r\n", - " 21.530049\n", + " 12.254156\n", " \n", " \n", " 1000\n", " 100.0\n", " 15.0\n", - " 5.983915e+08\n", - " 7.479894e+08\n", + " 2.991957e+07\n", + " 1.795174e+08\n", " 0.0\n", " r\n", - " 21.505150\n", + " 11.901056\n", " \n", " \n", "\n", @@ -421,34 +421,34 @@ "text/plain": [ " fieldMJD_TAI H_filter Range_LTC_km Obj_Sun_LTC_km phase_deg \\\n", "0 0.0 15.0 4.487936e+09 4.487936e+09 0.0 \n", - "1 0.1 15.0 4.484047e+09 4.484196e+09 0.0 \n", - "2 0.2 15.0 4.480157e+09 4.480456e+09 0.0 \n", - "3 0.3 15.0 4.476267e+09 4.476716e+09 0.0 \n", - "4 0.4 15.0 4.472378e+09 4.472976e+09 0.0 \n", + "1 0.1 15.0 4.483478e+09 4.483628e+09 0.0 \n", + "2 0.2 15.0 4.479020e+09 4.479319e+09 0.0 \n", + "3 0.3 15.0 4.474562e+09 4.475011e+09 0.0 \n", + "4 0.4 15.0 4.470104e+09 4.470702e+09 0.0 \n", "... ... ... ... ... ... \n", - "996 99.6 15.0 6.139497e+08 7.629491e+08 0.0 \n", - "997 99.7 15.0 6.100601e+08 7.592092e+08 0.0 \n", - "998 99.8 15.0 6.061706e+08 7.554692e+08 0.0 \n", - "999 99.9 15.0 6.022810e+08 7.517293e+08 0.0 \n", - "1000 100.0 15.0 5.983915e+08 7.479894e+08 0.0 \n", + "996 99.6 15.0 4.775164e+07 1.967511e+08 0.0 \n", + "997 99.7 15.0 4.329362e+07 1.924427e+08 0.0 \n", + "998 99.8 15.0 3.883561e+07 1.881343e+08 0.0 \n", + "999 99.9 15.0 3.437759e+07 1.838259e+08 0.0 \n", + "1000 100.0 15.0 2.991957e+07 1.795174e+08 0.0 \n", "\n", " optFilter Simple_mag \n", "0 r 29.771213 \n", - "1 r 29.767519 \n", - "2 r 29.763823 \n", - "3 r 29.760124 \n", - "4 r 29.756421 \n", + "1 r 29.766969 \n", + "2 r 29.762721 \n", + "3 r 29.758469 \n", + "4 r 29.754213 \n", "... ... ... \n", - "996 r 21.603888 \n", - "997 r 21.579416 \n", - "998 r 21.554804 \n", - "999 r 21.530049 \n", - "1000 r 21.505150 \n", + "996 r 13.115273 \n", + "997 r 12.854373 \n", + "998 r 12.569236 \n", + "999 r 12.254156 \n", + "1000 r 11.901056 \n", "\n", "[1001 rows x 7 columns]" ] }, - "execution_count": 137, + "execution_count": 181, "metadata": {}, "output_type": "execute_result" } @@ -467,13 +467,13 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 182, "id": "a40763e1", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -496,13 +496,13 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 183, "id": "1051a6f1-732e-42fa-af23-2ef67b4170c1", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -541,7 +541,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 184, "id": "4e802cf1", "metadata": {}, "outputs": [], @@ -554,18 +554,18 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 185, "id": "072165e9", "metadata": {}, "outputs": [], "source": [ "observations_df[\"afrho1\"] = 150\n", - "observations_df[\"k\"] =-0.5\n" + "observations_df[\"k\"] =-0.2\n" ] }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 186, "id": "3e784192", "metadata": {}, "outputs": [], @@ -575,7 +575,7 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 187, "id": "6a1b13ff-2ef2-41e8-8401-c513540ee9f3", "metadata": {}, "outputs": [ @@ -624,65 +624,65 @@ " r\n", " 29.771213\n", " 150\n", - " -0.5\n", - " 28.037727\n", - " 28.283515\n", + " -0.2\n", + " 27.080538\n", + " 27.175674\n", " \n", " \n", " 1\n", " 0.1\n", " 15.0\n", - " 4.484047e+09\n", - " 4.484196e+09\n", + " 4.483478e+09\n", + " 4.483628e+09\n", " 0.0\n", " r\n", - " 29.767519\n", + " 29.766969\n", " 150\n", - " -0.5\n", - " 28.034424\n", - " 28.280311\n", + " -0.2\n", + " 27.077092\n", + " 27.172301\n", " \n", " \n", " 2\n", " 0.2\n", " 15.0\n", - " 4.480157e+09\n", - " 4.480456e+09\n", + " 4.479020e+09\n", + " 4.479319e+09\n", " 0.0\n", " r\n", - " 29.763823\n", + " 29.762721\n", " 150\n", - " -0.5\n", - " 28.031117\n", - " 28.277104\n", + " -0.2\n", + " 27.073642\n", + " 27.168924\n", " \n", " \n", " 3\n", " 0.3\n", " 15.0\n", - " 4.476267e+09\n", - " 4.476716e+09\n", + " 4.474562e+09\n", + " 4.475011e+09\n", " 0.0\n", " r\n", - " 29.760124\n", + " 29.758469\n", " 150\n", - " -0.5\n", - " 28.027808\n", - " 28.273894\n", + " -0.2\n", + " 27.070189\n", + " 27.165544\n", " \n", " \n", " 4\n", " 0.4\n", " 15.0\n", - " 4.472378e+09\n", - " 4.472976e+09\n", + " 4.470104e+09\n", + " 4.470702e+09\n", " 0.0\n", " r\n", - " 29.756421\n", + " 29.754213\n", " 150\n", - " -0.5\n", - " 28.024497\n", - " 28.270682\n", + " -0.2\n", + " 27.066733\n", + " 27.162161\n", " \n", " \n", " ...\n", @@ -702,71 +702,71 @@ " 996\n", " 99.6\n", " 15.0\n", - " 6.139497e+08\n", - " 7.629491e+08\n", + " 4.775164e+07\n", + " 1.967511e+08\n", " 0.0\n", " r\n", - " 21.603888\n", + " 13.115273\n", " 150\n", - " -0.5\n", - " 20.696743\n", - " 21.314036\n", + " -0.2\n", + " 12.901903\n", + " 14.773316\n", " \n", " \n", " 997\n", " 99.7\n", " 15.0\n", - " 6.100601e+08\n", - " 7.592092e+08\n", + " 4.329362e+07\n", + " 1.924427e+08\n", " 0.0\n", " r\n", - " 21.579416\n", + " 12.854373\n", " 150\n", - " -0.5\n", - " 20.674667\n", - " 21.293797\n", + " -0.2\n", + " 12.658447\n", + " 14.614018\n", " \n", " \n", " 998\n", " 99.8\n", " 15.0\n", - " 6.061706e+08\n", - " 7.554692e+08\n", + " 3.883561e+07\n", + " 1.881343e+08\n", " 0.0\n", " r\n", - " 21.554804\n", + " 12.569236\n", " 150\n", - " -0.5\n", - " 20.652463\n", - " 21.273449\n", + " -0.2\n", + " 12.391186\n", + " 14.441950\n", " \n", " \n", " 999\n", " 99.9\n", " 15.0\n", - " 6.022810e+08\n", - " 7.517293e+08\n", + " 3.437759e+07\n", + " 1.838259e+08\n", " 0.0\n", " r\n", - " 21.530049\n", + " 12.254156\n", " 150\n", - " -0.5\n", - " 20.630130\n", - " 21.252989\n", + " -0.2\n", + " 12.094436\n", + " 14.254226\n", " \n", " \n", " 1000\n", " 100.0\n", " 15.0\n", - " 5.983915e+08\n", - " 7.479894e+08\n", + " 2.991957e+07\n", + " 1.795174e+08\n", " 0.0\n", " r\n", - " 21.505150\n", + " 11.901056\n", " 150\n", - " -0.5\n", - " 20.607667\n", - " 21.232416\n", + " -0.2\n", + " 11.760144\n", + " 14.046776\n", " \n", " \n", "\n", @@ -776,34 +776,34 @@ "text/plain": [ " fieldMJD_TAI H_filter Range_LTC_km Obj_Sun_LTC_km phase_deg \\\n", "0 0.0 15.0 4.487936e+09 4.487936e+09 0.0 \n", - "1 0.1 15.0 4.484047e+09 4.484196e+09 0.0 \n", - "2 0.2 15.0 4.480157e+09 4.480456e+09 0.0 \n", - "3 0.3 15.0 4.476267e+09 4.476716e+09 0.0 \n", - "4 0.4 15.0 4.472378e+09 4.472976e+09 0.0 \n", + "1 0.1 15.0 4.483478e+09 4.483628e+09 0.0 \n", + "2 0.2 15.0 4.479020e+09 4.479319e+09 0.0 \n", + "3 0.3 15.0 4.474562e+09 4.475011e+09 0.0 \n", + "4 0.4 15.0 4.470104e+09 4.470702e+09 0.0 \n", "... ... ... ... ... ... \n", - "996 99.6 15.0 6.139497e+08 7.629491e+08 0.0 \n", - "997 99.7 15.0 6.100601e+08 7.592092e+08 0.0 \n", - "998 99.8 15.0 6.061706e+08 7.554692e+08 0.0 \n", - "999 99.9 15.0 6.022810e+08 7.517293e+08 0.0 \n", - "1000 100.0 15.0 5.983915e+08 7.479894e+08 0.0 \n", + "996 99.6 15.0 4.775164e+07 1.967511e+08 0.0 \n", + "997 99.7 15.0 4.329362e+07 1.924427e+08 0.0 \n", + "998 99.8 15.0 3.883561e+07 1.881343e+08 0.0 \n", + "999 99.9 15.0 3.437759e+07 1.838259e+08 0.0 \n", + "1000 100.0 15.0 2.991957e+07 1.795174e+08 0.0 \n", "\n", " optFilter Simple_mag afrho1 k trailedSourceMagTrue coma_magnitude \n", - "0 r 29.771213 150 -0.5 28.037727 28.283515 \n", - "1 r 29.767519 150 -0.5 28.034424 28.280311 \n", - "2 r 29.763823 150 -0.5 28.031117 28.277104 \n", - "3 r 29.760124 150 -0.5 28.027808 28.273894 \n", - "4 r 29.756421 150 -0.5 28.024497 28.270682 \n", + "0 r 29.771213 150 -0.2 27.080538 27.175674 \n", + "1 r 29.766969 150 -0.2 27.077092 27.172301 \n", + "2 r 29.762721 150 -0.2 27.073642 27.168924 \n", + "3 r 29.758469 150 -0.2 27.070189 27.165544 \n", + "4 r 29.754213 150 -0.2 27.066733 27.162161 \n", "... ... ... ... ... ... ... \n", - "996 r 21.603888 150 -0.5 20.696743 21.314036 \n", - "997 r 21.579416 150 -0.5 20.674667 21.293797 \n", - "998 r 21.554804 150 -0.5 20.652463 21.273449 \n", - "999 r 21.530049 150 -0.5 20.630130 21.252989 \n", - "1000 r 21.505150 150 -0.5 20.607667 21.232416 \n", + "996 r 13.115273 150 -0.2 12.901903 14.773316 \n", + "997 r 12.854373 150 -0.2 12.658447 14.614018 \n", + "998 r 12.569236 150 -0.2 12.391186 14.441950 \n", + "999 r 12.254156 150 -0.2 12.094436 14.254226 \n", + "1000 r 11.901056 150 -0.2 11.760144 14.046776 \n", "\n", "[1001 rows x 11 columns]" ] }, - "execution_count": 151, + "execution_count": 187, "metadata": {}, "output_type": "execute_result" } @@ -814,13 +814,13 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 188, "id": "993c1c58", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] From 0d701f235062fc5861080b6de714a78cc40ccb37 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Tue, 7 Jan 2025 15:06:39 +0000 Subject: [PATCH 16/52] make cometary activity notebook make cometary activity notebook --- docs/notebooks/demo_Cometary_Activity.ipynb | 244 +++++++++++++++----- 1 file changed, 181 insertions(+), 63 deletions(-) diff --git a/docs/notebooks/demo_Cometary_Activity.ipynb b/docs/notebooks/demo_Cometary_Activity.ipynb index ac602e07..d4164891 100644 --- a/docs/notebooks/demo_Cometary_Activity.ipynb +++ b/docs/notebooks/demo_Cometary_Activity.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 177, + "execution_count": 225, "id": "fc4ba06a", "metadata": {}, "outputs": [], @@ -33,7 +33,8 @@ "import numpy as np\n", "import astropy.units as u\n", "import matplotlib.pyplot as plt\n", - "from sorcha.modules.PPCalculateApparentMagnitudeInFilter import PPCalculateApparentMagnitudeInFilter" + "from sorcha.modules.PPCalculateApparentMagnitudeInFilter import PPCalculateApparentMagnitudeInFilter\n", + "from matplotlib.lines import Line2D" ] }, { @@ -48,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 226, "id": "46fc0914", "metadata": {}, "outputs": [], @@ -71,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 227, "id": "99156011", "metadata": {}, "outputs": [ @@ -239,7 +240,7 @@ "[1001 rows x 6 columns]" ] }, - "execution_count": 179, + "execution_count": 227, "metadata": {}, "output_type": "execute_result" } @@ -258,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 180, + "execution_count": 228, "id": "69cc1794", "metadata": {}, "outputs": [], @@ -268,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": 181, + "execution_count": 229, "id": "89e840e0", "metadata": {}, "outputs": [ @@ -448,7 +449,7 @@ "[1001 rows x 7 columns]" ] }, - "execution_count": 181, + "execution_count": 229, "metadata": {}, "output_type": "execute_result" } @@ -467,7 +468,7 @@ }, { "cell_type": "code", - "execution_count": 182, + "execution_count": 230, "id": "a40763e1", "metadata": {}, "outputs": [ @@ -496,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 183, + "execution_count": 231, "id": "1051a6f1-732e-42fa-af23-2ef67b4170c1", "metadata": {}, "outputs": [ @@ -541,7 +542,7 @@ }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 232, "id": "4e802cf1", "metadata": {}, "outputs": [], @@ -554,18 +555,18 @@ }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 233, "id": "072165e9", "metadata": {}, "outputs": [], "source": [ "observations_df[\"afrho1\"] = 150\n", - "observations_df[\"k\"] =-0.2\n" + "observations_df[\"k\"] =-0.5\n" ] }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 234, "id": "3e784192", "metadata": {}, "outputs": [], @@ -575,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 187, + "execution_count": 235, "id": "6a1b13ff-2ef2-41e8-8401-c513540ee9f3", "metadata": {}, "outputs": [ @@ -624,9 +625,9 @@ " r\n", " 29.771213\n", " 150\n", - " -0.2\n", - " 27.080538\n", - " 27.175674\n", + " -0.5\n", + " 28.037727\n", + " 28.283515\n", " \n", " \n", " 1\n", @@ -638,9 +639,9 @@ " r\n", " 29.766969\n", " 150\n", - " -0.2\n", - " 27.077092\n", - " 27.172301\n", + " -0.5\n", + " 28.033928\n", + " 28.279829\n", " \n", " \n", " 2\n", @@ -652,9 +653,9 @@ " r\n", " 29.762721\n", " 150\n", - " -0.2\n", - " 27.073642\n", - " 27.168924\n", + " -0.5\n", + " 28.030125\n", + " 28.276139\n", " \n", " \n", " 3\n", @@ -666,9 +667,9 @@ " r\n", " 29.758469\n", " 150\n", - " -0.2\n", - " 27.070189\n", - " 27.165544\n", + " -0.5\n", + " 28.026318\n", + " 28.272446\n", " \n", " \n", " 4\n", @@ -680,9 +681,9 @@ " r\n", " 29.754213\n", " 150\n", - " -0.2\n", - " 27.066733\n", - " 27.162161\n", + " -0.5\n", + " 28.022508\n", + " 28.268749\n", " \n", " \n", " ...\n", @@ -708,9 +709,9 @@ " r\n", " 13.115273\n", " 150\n", - " -0.2\n", - " 12.901903\n", - " 14.773316\n", + " -0.5\n", + " 12.917297\n", + " 14.862560\n", " \n", " \n", " 997\n", @@ -722,9 +723,9 @@ " r\n", " 12.854373\n", " 150\n", - " -0.2\n", - " 12.658447\n", - " 14.614018\n", + " -0.5\n", + " 12.671571\n", + " 14.696050\n", " \n", " \n", " 998\n", @@ -736,9 +737,9 @@ " r\n", " 12.569236\n", " 150\n", - " -0.2\n", - " 12.391186\n", - " 14.441950\n", + " -0.5\n", + " 12.402153\n", + " 14.516606\n", " \n", " \n", " 999\n", @@ -750,9 +751,9 @@ " r\n", " 12.254156\n", " 150\n", - " -0.2\n", - " 12.094436\n", - " 14.254226\n", + " -0.5\n", + " 12.103375\n", + " 14.321336\n", " \n", " \n", " 1000\n", @@ -764,9 +765,9 @@ " r\n", " 11.901056\n", " 150\n", - " -0.2\n", - " 11.760144\n", - " 14.046776\n", + " -0.5\n", + " 11.767201\n", + " 14.106162\n", " \n", " \n", "\n", @@ -788,22 +789,22 @@ "1000 100.0 15.0 2.991957e+07 1.795174e+08 0.0 \n", "\n", " optFilter Simple_mag afrho1 k trailedSourceMagTrue coma_magnitude \n", - "0 r 29.771213 150 -0.2 27.080538 27.175674 \n", - "1 r 29.766969 150 -0.2 27.077092 27.172301 \n", - "2 r 29.762721 150 -0.2 27.073642 27.168924 \n", - "3 r 29.758469 150 -0.2 27.070189 27.165544 \n", - "4 r 29.754213 150 -0.2 27.066733 27.162161 \n", + "0 r 29.771213 150 -0.5 28.037727 28.283515 \n", + "1 r 29.766969 150 -0.5 28.033928 28.279829 \n", + "2 r 29.762721 150 -0.5 28.030125 28.276139 \n", + "3 r 29.758469 150 -0.5 28.026318 28.272446 \n", + "4 r 29.754213 150 -0.5 28.022508 28.268749 \n", "... ... ... ... ... ... ... \n", - "996 r 13.115273 150 -0.2 12.901903 14.773316 \n", - "997 r 12.854373 150 -0.2 12.658447 14.614018 \n", - "998 r 12.569236 150 -0.2 12.391186 14.441950 \n", - "999 r 12.254156 150 -0.2 12.094436 14.254226 \n", - "1000 r 11.901056 150 -0.2 11.760144 14.046776 \n", + "996 r 13.115273 150 -0.5 12.917297 14.862560 \n", + "997 r 12.854373 150 -0.5 12.671571 14.696050 \n", + "998 r 12.569236 150 -0.5 12.402153 14.516606 \n", + "999 r 12.254156 150 -0.5 12.103375 14.321336 \n", + "1000 r 11.901056 150 -0.5 11.767201 14.106162 \n", "\n", "[1001 rows x 11 columns]" ] }, - "execution_count": 187, + "execution_count": 235, "metadata": {}, "output_type": "execute_result" } @@ -814,13 +815,13 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 242, "id": "993c1c58", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -830,20 +831,20 @@ } ], "source": [ - "from matplotlib.lines import Line2D\n", "\n", "fig, ax = plt.subplots(figsize=(10, 8))\n", "ax.plot(\n", " observations_df[\"fieldMJD_TAI\"],\n", " observations_df[\"Simple_mag\"],\n", " linestyle=\"--\",\n", - " label=\"__none__\",\n", - " color=\"m\",\n", + " label=\"Apparent Magnitude of the Comet Nucleus\",\n", + " color=\"black\",\n", ")\n", "ax.plot(\n", - " observations_df[\"fieldMJD_TAI\"], observations_df[\"trailedSourceMagTrue\"], linestyle=\"-\", label=\"__none__\", color=\"g\"\n", + " observations_df[\"fieldMJD_TAI\"], observations_df[\"trailedSourceMagTrue\"], linestyle=\"-\", label=\"Apparent Magnitude Enhanced by Activity\", color=\"deeppink\"\n", ")\n", "\n", + "plt.legend()\n", "ax.set_xlabel(\"Time since first observation (days)\")\n", "ax.set_ylabel(\"Apparent magnitude\")\n", "plt.gca().invert_yaxis()\n", @@ -853,9 +854,126 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 243, + "id": "a9ce9b6a-c33e-4bc4-8100-dfe403d69189", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fig, ax = plt.subplots(figsize=(10, 8))\n", + "ax.plot(\n", + " observations_df[\"fieldMJD_TAI\"],\n", + " observations_df[\"Simple_mag\"],\n", + " linestyle=\"--\",\n", + " label=\"Apparent Magnitude of the Comet Nucleus\",\n", + " color=\"black\",\n", + ")\n", + "ax.plot(\n", + " observations_df[\"fieldMJD_TAI\"], observations_df[\"trailedSourceMagTrue\"], linestyle=\"-\", label=\"Apparent Magnitude Enhanced by Activity\", color=\"deeppink\"\n", + ")\n", + "\n", + "plt.legend()\n", + "ax.set_xlabel(\"Time since first observation (days)\")\n", + "ax.set_ylabel(\"Apparent magnitude\")\n", + "plt.gca().invert_yaxis()\n", + "plt.xlim(80,100)\n", + "plt.grid()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 245, "id": "b3f5acd0-70f9-4690-8a2f-0d553747890a", "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 8))\n", + "ax.plot(\n", + " observations_df[\"Obj_Sun_LTC_km\"]/1.495978707e8,\n", + " observations_df[\"Simple_mag\"],\n", + " linestyle=\"--\",\n", + " label=\"Apparent Magnitude of the Comet Nucleus\",\n", + " color=\"black\",\n", + ")\n", + "ax.plot(\n", + " observations_df[\"Obj_Sun_LTC_km\"]/1.495978707e8, observations_df[\"trailedSourceMagTrue\"], linestyle=\"-\", label=\"Apparent Magnitude Enhanced by Activity\", color=\"deeppink\"\n", + ")\n", + "\n", + "plt.legend()\n", + "ax.set_xlabel(\"Heliocentric distance (au)\")\n", + "ax.set_ylabel(\"Apparent magnitude\")\n", + "plt.gca().invert_yaxis()\n", + "plt.grid()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "id": "780a3880-b3b7-4d89-a272-654596aa70cd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 8))\n", + "ax.plot(\n", + " observations_df[\"Obj_Sun_LTC_km\"]/1.495978707e8,\n", + " observations_df[\"Simple_mag\"],\n", + " linestyle=\"--\",\n", + " label=\"Apparent Magnitude of the Comet Nucleus\",\n", + " color=\"black\",\n", + ")\n", + "ax.plot(\n", + " observations_df[\"Obj_Sun_LTC_km\"]/1.495978707e8, observations_df[\"trailedSourceMagTrue\"], linestyle=\"-\", label=\"Apparent Magnitude Enhanced by Activity\", color=\"deeppink\"\n", + ")\n", + "\n", + "plt.legend()\n", + "ax.set_xlabel(\"Heliocentric distance (au)\")\n", + "ax.set_ylabel(\"Apparent magnitude\")\n", + "plt.gca().invert_yaxis()\n", + "plt.xlim(0,5)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b67a631-b0d7-4150-bf48-1cbcc3c5cdbe", + "metadata": {}, "outputs": [], "source": [] } From 5ffb5c6664a04ee875bee2b88ce7db15202c26d1 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Tue, 7 Jan 2025 18:10:16 +0000 Subject: [PATCH 17/52] Update demo_Cometary_Activity.ipynb --- docs/notebooks/demo_Cometary_Activity.ipynb | 396 +++++++++++--------- 1 file changed, 224 insertions(+), 172 deletions(-) diff --git a/docs/notebooks/demo_Cometary_Activity.ipynb b/docs/notebooks/demo_Cometary_Activity.ipynb index d4164891..8acb9343 100644 --- a/docs/notebooks/demo_Cometary_Activity.ipynb +++ b/docs/notebooks/demo_Cometary_Activity.ipynb @@ -13,18 +13,16 @@ "id": "c6a7190d", "metadata": {}, "source": [ - "The goal of this notebook is to demonstrate the use of lightcurves within `sorcha`.\n", + "The goal of this notebook is to demonstrate the apply cometary activity within `Sorcha`.\n", "\n", - "This will be done in two different ways:\n", - "- We will use the community tools part of the `sorcha-addons`(https://github.com/dirac-institute/sorcha-addons) package\n", - "- We will implement a custom lightcurve, and use it inside the code\n", + "We will use the community tools part of the `Sorcha-addons`(https://github.com/dirac-institute/sorcha-addons) package\n", "\n", - "The idea is that the user can, in principle, implement their own lightcurves, and incorporate them in their simulation. The goal of `sorcha-addons` is for both the development team, as well as for the community, to share their implementations of custom lightcurve models. " + "The idea is that the user can, in principle, implement their own method for cometary activity, and incorporate them in their simulation. The goal of `Sorcha-addons` is for both the development team, as well as for the community, to share their implementations of custom coemtary activity models. " ] }, { "cell_type": "code", - "execution_count": 225, + "execution_count": 1, "id": "fc4ba06a", "metadata": {}, "outputs": [], @@ -42,14 +40,14 @@ "id": "2f79bca5", "metadata": {}, "source": [ - "This notebook will not use a realistic set of observations (as in the `demo_ApparentMagnitudeValidation` notebook), but rather create a toy scenario with a simple to understand and interpret set of results. The general structure of the notebook will be the same.\n", + "This notebook will not use a realistic set of observations (as in the `demo_ApparentMagnitudeValidation` notebook), but rather create a toy scenario with a simple to understand and interpret set of results. \n", "\n", "We will create a dataframe for observations in a similar structure as in the `demo_ApparentMagnitudeValidation` notebook:" ] }, { "cell_type": "code", - "execution_count": 226, + "execution_count": 2, "id": "46fc0914", "metadata": {}, "outputs": [], @@ -59,7 +57,7 @@ " \"fieldMJD_TAI\": np.linspace(\n", " 0, 100, 1001\n", " ), # time of observation - note these values are bogus, we only care about the Delta t for this demo\n", - " \"H_filter\": 15 * np.ones(1001),\n", + " \"H_filter\": 17 * np.ones(1001),\n", " # starting at 30 au and coming inward to 5 au \n", " \"Range_LTC_km\": 1.495978707e8 * np.flip(np.linspace( 0.2, 30, 1001)), # au\n", " \"Obj_Sun_LTC_km\": 1.495978707e8 * np.flip(np.linspace(1.2, 30, 1001)), # au\n", @@ -72,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 227, + "execution_count": 3, "id": "99156011", "metadata": {}, "outputs": [ @@ -109,7 +107,7 @@ " \n", " 0\n", " 0.0\n", - " 15.0\n", + " 17.0\n", " 4.487936e+09\n", " 4.487936e+09\n", " 0.0\n", @@ -118,7 +116,7 @@ " \n", " 1\n", " 0.1\n", - " 15.0\n", + " 17.0\n", " 4.483478e+09\n", " 4.483628e+09\n", " 0.0\n", @@ -127,7 +125,7 @@ " \n", " 2\n", " 0.2\n", - " 15.0\n", + " 17.0\n", " 4.479020e+09\n", " 4.479319e+09\n", " 0.0\n", @@ -136,7 +134,7 @@ " \n", " 3\n", " 0.3\n", - " 15.0\n", + " 17.0\n", " 4.474562e+09\n", " 4.475011e+09\n", " 0.0\n", @@ -145,7 +143,7 @@ " \n", " 4\n", " 0.4\n", - " 15.0\n", + " 17.0\n", " 4.470104e+09\n", " 4.470702e+09\n", " 0.0\n", @@ -163,7 +161,7 @@ " \n", " 996\n", " 99.6\n", - " 15.0\n", + " 17.0\n", " 4.775164e+07\n", " 1.967511e+08\n", " 0.0\n", @@ -172,7 +170,7 @@ " \n", " 997\n", " 99.7\n", - " 15.0\n", + " 17.0\n", " 4.329362e+07\n", " 1.924427e+08\n", " 0.0\n", @@ -181,7 +179,7 @@ " \n", " 998\n", " 99.8\n", - " 15.0\n", + " 17.0\n", " 3.883561e+07\n", " 1.881343e+08\n", " 0.0\n", @@ -190,7 +188,7 @@ " \n", " 999\n", " 99.9\n", - " 15.0\n", + " 17.0\n", " 3.437759e+07\n", " 1.838259e+08\n", " 0.0\n", @@ -199,7 +197,7 @@ " \n", " 1000\n", " 100.0\n", - " 15.0\n", + " 17.0\n", " 2.991957e+07\n", " 1.795174e+08\n", " 0.0\n", @@ -212,17 +210,17 @@ ], "text/plain": [ " fieldMJD_TAI H_filter Range_LTC_km Obj_Sun_LTC_km phase_deg \\\n", - "0 0.0 15.0 4.487936e+09 4.487936e+09 0.0 \n", - "1 0.1 15.0 4.483478e+09 4.483628e+09 0.0 \n", - "2 0.2 15.0 4.479020e+09 4.479319e+09 0.0 \n", - "3 0.3 15.0 4.474562e+09 4.475011e+09 0.0 \n", - "4 0.4 15.0 4.470104e+09 4.470702e+09 0.0 \n", + "0 0.0 17.0 4.487936e+09 4.487936e+09 0.0 \n", + "1 0.1 17.0 4.483478e+09 4.483628e+09 0.0 \n", + "2 0.2 17.0 4.479020e+09 4.479319e+09 0.0 \n", + "3 0.3 17.0 4.474562e+09 4.475011e+09 0.0 \n", + "4 0.4 17.0 4.470104e+09 4.470702e+09 0.0 \n", "... ... ... ... ... ... \n", - "996 99.6 15.0 4.775164e+07 1.967511e+08 0.0 \n", - "997 99.7 15.0 4.329362e+07 1.924427e+08 0.0 \n", - "998 99.8 15.0 3.883561e+07 1.881343e+08 0.0 \n", - "999 99.9 15.0 3.437759e+07 1.838259e+08 0.0 \n", - "1000 100.0 15.0 2.991957e+07 1.795174e+08 0.0 \n", + "996 99.6 17.0 4.775164e+07 1.967511e+08 0.0 \n", + "997 99.7 17.0 4.329362e+07 1.924427e+08 0.0 \n", + "998 99.8 17.0 3.883561e+07 1.881343e+08 0.0 \n", + "999 99.9 17.0 3.437759e+07 1.838259e+08 0.0 \n", + "1000 100.0 17.0 2.991957e+07 1.795174e+08 0.0 \n", "\n", " optFilter \n", "0 r \n", @@ -240,7 +238,7 @@ "[1001 rows x 6 columns]" ] }, - "execution_count": 227, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -259,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 228, + "execution_count": 4, "id": "69cc1794", "metadata": {}, "outputs": [], @@ -269,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 229, + "execution_count": 5, "id": "89e840e0", "metadata": {}, "outputs": [ @@ -307,52 +305,52 @@ " \n", " 0\n", " 0.0\n", - " 15.0\n", + " 17.0\n", " 4.487936e+09\n", " 4.487936e+09\n", " 0.0\n", " r\n", - " 29.771213\n", + " 31.771213\n", " \n", " \n", " 1\n", " 0.1\n", - " 15.0\n", + " 17.0\n", " 4.483478e+09\n", " 4.483628e+09\n", " 0.0\n", " r\n", - " 29.766969\n", + " 31.766969\n", " \n", " \n", " 2\n", " 0.2\n", - " 15.0\n", + " 17.0\n", " 4.479020e+09\n", " 4.479319e+09\n", " 0.0\n", " r\n", - " 29.762721\n", + " 31.762721\n", " \n", " \n", " 3\n", " 0.3\n", - " 15.0\n", + " 17.0\n", " 4.474562e+09\n", " 4.475011e+09\n", " 0.0\n", " r\n", - " 29.758469\n", + " 31.758469\n", " \n", " \n", " 4\n", " 0.4\n", - " 15.0\n", + " 17.0\n", " 4.470104e+09\n", " 4.470702e+09\n", " 0.0\n", " r\n", - " 29.754213\n", + " 31.754213\n", " \n", " \n", " ...\n", @@ -367,52 +365,52 @@ " \n", " 996\n", " 99.6\n", - " 15.0\n", + " 17.0\n", " 4.775164e+07\n", " 1.967511e+08\n", " 0.0\n", " r\n", - " 13.115273\n", + " 15.115273\n", " \n", " \n", " 997\n", " 99.7\n", - " 15.0\n", + " 17.0\n", " 4.329362e+07\n", " 1.924427e+08\n", " 0.0\n", " r\n", - " 12.854373\n", + " 14.854373\n", " \n", " \n", " 998\n", " 99.8\n", - " 15.0\n", + " 17.0\n", " 3.883561e+07\n", " 1.881343e+08\n", " 0.0\n", " r\n", - " 12.569236\n", + " 14.569236\n", " \n", " \n", " 999\n", " 99.9\n", - " 15.0\n", + " 17.0\n", " 3.437759e+07\n", " 1.838259e+08\n", " 0.0\n", " r\n", - " 12.254156\n", + " 14.254156\n", " \n", " \n", " 1000\n", " 100.0\n", - " 15.0\n", + " 17.0\n", " 2.991957e+07\n", " 1.795174e+08\n", " 0.0\n", " r\n", - " 11.901056\n", + " 13.901056\n", " \n", " \n", "\n", @@ -421,35 +419,35 @@ ], "text/plain": [ " fieldMJD_TAI H_filter Range_LTC_km Obj_Sun_LTC_km phase_deg \\\n", - "0 0.0 15.0 4.487936e+09 4.487936e+09 0.0 \n", - "1 0.1 15.0 4.483478e+09 4.483628e+09 0.0 \n", - "2 0.2 15.0 4.479020e+09 4.479319e+09 0.0 \n", - "3 0.3 15.0 4.474562e+09 4.475011e+09 0.0 \n", - "4 0.4 15.0 4.470104e+09 4.470702e+09 0.0 \n", + "0 0.0 17.0 4.487936e+09 4.487936e+09 0.0 \n", + "1 0.1 17.0 4.483478e+09 4.483628e+09 0.0 \n", + "2 0.2 17.0 4.479020e+09 4.479319e+09 0.0 \n", + "3 0.3 17.0 4.474562e+09 4.475011e+09 0.0 \n", + "4 0.4 17.0 4.470104e+09 4.470702e+09 0.0 \n", "... ... ... ... ... ... \n", - "996 99.6 15.0 4.775164e+07 1.967511e+08 0.0 \n", - "997 99.7 15.0 4.329362e+07 1.924427e+08 0.0 \n", - "998 99.8 15.0 3.883561e+07 1.881343e+08 0.0 \n", - "999 99.9 15.0 3.437759e+07 1.838259e+08 0.0 \n", - "1000 100.0 15.0 2.991957e+07 1.795174e+08 0.0 \n", + "996 99.6 17.0 4.775164e+07 1.967511e+08 0.0 \n", + "997 99.7 17.0 4.329362e+07 1.924427e+08 0.0 \n", + "998 99.8 17.0 3.883561e+07 1.881343e+08 0.0 \n", + "999 99.9 17.0 3.437759e+07 1.838259e+08 0.0 \n", + "1000 100.0 17.0 2.991957e+07 1.795174e+08 0.0 \n", "\n", " optFilter Simple_mag \n", - "0 r 29.771213 \n", - "1 r 29.766969 \n", - "2 r 29.762721 \n", - "3 r 29.758469 \n", - "4 r 29.754213 \n", + "0 r 31.771213 \n", + "1 r 31.766969 \n", + "2 r 31.762721 \n", + "3 r 31.758469 \n", + "4 r 31.754213 \n", "... ... ... \n", - "996 r 13.115273 \n", - "997 r 12.854373 \n", - "998 r 12.569236 \n", - "999 r 12.254156 \n", - "1000 r 11.901056 \n", + "996 r 15.115273 \n", + "997 r 14.854373 \n", + "998 r 14.569236 \n", + "999 r 14.254156 \n", + "1000 r 13.901056 \n", "\n", "[1001 rows x 7 columns]" ] }, - "execution_count": 229, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -463,18 +461,18 @@ "id": "ba9e4dec", "metadata": {}, "source": [ - "Now we can plot the magnitudes and compare them." + "Now we can plot the apparent magnitude of the inactive comet nucleus over time assuming no phase curve effects. Only the changing heliocentric and geocentric distances matter here. " ] }, { "cell_type": "code", - "execution_count": 230, + "execution_count": 6, "id": "a40763e1", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -497,13 +495,13 @@ }, { "cell_type": "code", - "execution_count": 231, + "execution_count": 7, "id": "1051a6f1-732e-42fa-af23-2ef67b4170c1", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -529,20 +527,26 @@ "id": "250e3f6f", "metadata": {}, "source": [ - "The effect of the lightcurve is to add an extra term to the apparent magnitude, that, in principle, can be a function of the characteristics of the observations, such as time of observation, phase angle or topocentric and heliocentric distances. The entire `observational_df` dataframe is exposed to the lightcurve, so any dependencies can be added. \n", + "The effect of the cometary activity class is compute the apparent magnitude of the active object from an input apparent magnitude of the nucleus. The entire `observational_df` dataframe is exposed to the cometary activty class, so any dependencies can be added. \n", "\n", - "Let's use the basic sinusoidal lightcurve from `sorcha_addons`. We need the following columns in our dataframe:\n", + "Let's use the LSSTCometActivity class from `sorcha_addons`. We need the following columns in our dataframe:\n", "\n", - " * ``LCA`` - lightcurve amplitude [magnitudes].\n", - " * ``Period`` - period of the sinusoidal oscillation [days]. Should be a positive value.\n", - " * ``Time0`` - phase for the light curve [days].\n", - "\n", - "Let's create a lightcurve with a period of 20 days, phased so that the first observation is at zero variation, and with 0.5 mag peak-to-peak amplitude." + " * ``afrho1\"`` = V-band Afρ value of the comet at 1 au\n", + " * ``k`` = power-law slope that describes how the activity varies with heliocentric distance\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "a2bd9dd4-7666-4311-bc9f-a09b0255ae1c", + "metadata": {}, + "source": [ + "Let's active the LSSTCometActivity class " ] }, { "cell_type": "code", - "execution_count": 232, + "execution_count": 8, "id": "4e802cf1", "metadata": {}, "outputs": [], @@ -553,20 +557,28 @@ "update_activity_subclasses()" ] }, + { + "cell_type": "markdown", + "id": "6f85de11-64dc-4413-afb6-b2354bc24df1", + "metadata": {}, + "source": [ + "Let's calculate the apparent magnitude assuming " + ] + }, { "cell_type": "code", - "execution_count": 233, + "execution_count": 9, "id": "072165e9", "metadata": {}, "outputs": [], "source": [ "observations_df[\"afrho1\"] = 150\n", - "observations_df[\"k\"] =-0.5\n" + "observations_df[\"k\"] =-0.3\n" ] }, { "cell_type": "code", - "execution_count": 234, + "execution_count": 10, "id": "3e784192", "metadata": {}, "outputs": [], @@ -576,7 +588,7 @@ }, { "cell_type": "code", - "execution_count": 235, + "execution_count": 11, "id": "6a1b13ff-2ef2-41e8-8401-c513540ee9f3", "metadata": {}, "outputs": [ @@ -618,72 +630,72 @@ " \n", " 0\n", " 0.0\n", - " 15.0\n", + " 17.0\n", " 4.487936e+09\n", " 4.487936e+09\n", " 0.0\n", " r\n", - " 29.771213\n", + " 31.771213\n", " 150\n", - " -0.5\n", - " 28.037727\n", - " 28.283515\n", + " -0.3\n", + " 27.523035\n", + " 27.544954\n", " \n", " \n", " 1\n", " 0.1\n", - " 15.0\n", + " 17.0\n", " 4.483478e+09\n", " 4.483628e+09\n", " 0.0\n", " r\n", - " 29.766969\n", + " 31.766969\n", " 150\n", - " -0.5\n", - " 28.033928\n", - " 28.279829\n", + " -0.3\n", + " 27.519542\n", + " 27.541477\n", " \n", " \n", " 2\n", " 0.2\n", - " 15.0\n", + " 17.0\n", " 4.479020e+09\n", " 4.479319e+09\n", " 0.0\n", " r\n", - " 29.762721\n", + " 31.762721\n", " 150\n", - " -0.5\n", - " 28.030125\n", - " 28.276139\n", + " -0.3\n", + " 27.516046\n", + " 27.537996\n", " \n", " \n", " 3\n", " 0.3\n", - " 15.0\n", + " 17.0\n", " 4.474562e+09\n", " 4.475011e+09\n", " 0.0\n", " r\n", - " 29.758469\n", + " 31.758469\n", " 150\n", - " -0.5\n", - " 28.026318\n", - " 28.272446\n", + " -0.3\n", + " 27.512546\n", + " 27.534512\n", " \n", " \n", " 4\n", " 0.4\n", - " 15.0\n", + " 17.0\n", " 4.470104e+09\n", " 4.470702e+09\n", " 0.0\n", " r\n", - " 29.754213\n", + " 31.754213\n", " 150\n", - " -0.5\n", - " 28.022508\n", - " 28.268749\n", + " -0.3\n", + " 27.509043\n", + " 27.531024\n", " \n", " \n", " ...\n", @@ -702,72 +714,72 @@ " \n", " 996\n", " 99.6\n", - " 15.0\n", + " 17.0\n", " 4.775164e+07\n", " 1.967511e+08\n", " 0.0\n", " r\n", - " 13.115273\n", + " 15.115273\n", " 150\n", - " -0.5\n", - " 12.917297\n", - " 14.862560\n", + " -0.3\n", + " 14.195410\n", + " 14.803064\n", " \n", " \n", " 997\n", " 99.7\n", - " 15.0\n", + " 17.0\n", " 4.329362e+07\n", " 1.924427e+08\n", " 0.0\n", " r\n", - " 12.854373\n", + " 14.854373\n", " 150\n", - " -0.5\n", - " 12.671571\n", - " 14.696050\n", + " -0.3\n", + " 13.990077\n", + " 14.641362\n", " \n", " \n", " 998\n", " 99.8\n", - " 15.0\n", + " 17.0\n", " 3.883561e+07\n", " 1.881343e+08\n", " 0.0\n", " r\n", - " 12.569236\n", + " 14.569236\n", " 150\n", - " -0.5\n", - " 12.402153\n", - " 14.516606\n", + " -0.3\n", + " 13.764254\n", + " 14.466835\n", " \n", " \n", " 999\n", " 99.9\n", - " 15.0\n", + " 17.0\n", " 3.437759e+07\n", " 1.838259e+08\n", " 0.0\n", " r\n", - " 12.254156\n", + " 14.254156\n", " 150\n", - " -0.5\n", - " 12.103375\n", - " 14.321336\n", + " -0.3\n", + " 13.512743\n", + " 14.276596\n", " \n", " \n", " 1000\n", " 100.0\n", - " 15.0\n", + " 17.0\n", " 2.991957e+07\n", " 1.795174e+08\n", " 0.0\n", " r\n", - " 11.901056\n", + " 13.901056\n", " 150\n", - " -0.5\n", - " 11.767201\n", - " 14.106162\n", + " -0.3\n", + " 13.228088\n", + " 14.066571\n", " \n", " \n", "\n", @@ -776,35 +788,35 @@ ], "text/plain": [ " fieldMJD_TAI H_filter Range_LTC_km Obj_Sun_LTC_km phase_deg \\\n", - "0 0.0 15.0 4.487936e+09 4.487936e+09 0.0 \n", - "1 0.1 15.0 4.483478e+09 4.483628e+09 0.0 \n", - "2 0.2 15.0 4.479020e+09 4.479319e+09 0.0 \n", - "3 0.3 15.0 4.474562e+09 4.475011e+09 0.0 \n", - "4 0.4 15.0 4.470104e+09 4.470702e+09 0.0 \n", + "0 0.0 17.0 4.487936e+09 4.487936e+09 0.0 \n", + "1 0.1 17.0 4.483478e+09 4.483628e+09 0.0 \n", + "2 0.2 17.0 4.479020e+09 4.479319e+09 0.0 \n", + "3 0.3 17.0 4.474562e+09 4.475011e+09 0.0 \n", + "4 0.4 17.0 4.470104e+09 4.470702e+09 0.0 \n", "... ... ... ... ... ... \n", - "996 99.6 15.0 4.775164e+07 1.967511e+08 0.0 \n", - "997 99.7 15.0 4.329362e+07 1.924427e+08 0.0 \n", - "998 99.8 15.0 3.883561e+07 1.881343e+08 0.0 \n", - "999 99.9 15.0 3.437759e+07 1.838259e+08 0.0 \n", - "1000 100.0 15.0 2.991957e+07 1.795174e+08 0.0 \n", + "996 99.6 17.0 4.775164e+07 1.967511e+08 0.0 \n", + "997 99.7 17.0 4.329362e+07 1.924427e+08 0.0 \n", + "998 99.8 17.0 3.883561e+07 1.881343e+08 0.0 \n", + "999 99.9 17.0 3.437759e+07 1.838259e+08 0.0 \n", + "1000 100.0 17.0 2.991957e+07 1.795174e+08 0.0 \n", "\n", " optFilter Simple_mag afrho1 k trailedSourceMagTrue coma_magnitude \n", - "0 r 29.771213 150 -0.5 28.037727 28.283515 \n", - "1 r 29.766969 150 -0.5 28.033928 28.279829 \n", - "2 r 29.762721 150 -0.5 28.030125 28.276139 \n", - "3 r 29.758469 150 -0.5 28.026318 28.272446 \n", - "4 r 29.754213 150 -0.5 28.022508 28.268749 \n", + "0 r 31.771213 150 -0.3 27.523035 27.544954 \n", + "1 r 31.766969 150 -0.3 27.519542 27.541477 \n", + "2 r 31.762721 150 -0.3 27.516046 27.537996 \n", + "3 r 31.758469 150 -0.3 27.512546 27.534512 \n", + "4 r 31.754213 150 -0.3 27.509043 27.531024 \n", "... ... ... ... ... ... ... \n", - "996 r 13.115273 150 -0.5 12.917297 14.862560 \n", - "997 r 12.854373 150 -0.5 12.671571 14.696050 \n", - "998 r 12.569236 150 -0.5 12.402153 14.516606 \n", - "999 r 12.254156 150 -0.5 12.103375 14.321336 \n", - "1000 r 11.901056 150 -0.5 11.767201 14.106162 \n", + "996 r 15.115273 150 -0.3 14.195410 14.803064 \n", + "997 r 14.854373 150 -0.3 13.990077 14.641362 \n", + "998 r 14.569236 150 -0.3 13.764254 14.466835 \n", + "999 r 14.254156 150 -0.3 13.512743 14.276596 \n", + "1000 r 13.901056 150 -0.3 13.228088 14.066571 \n", "\n", "[1001 rows x 11 columns]" ] }, - "execution_count": 235, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -813,15 +825,23 @@ "observations_df" ] }, + { + "cell_type": "markdown", + "id": "d489f095-17c7-4db6-8bb7-174967f658fa", + "metadata": {}, + "source": [ + "Let's plot by time" + ] + }, { "cell_type": "code", - "execution_count": 242, + "execution_count": 12, "id": "993c1c58", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -852,15 +872,23 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "bab58f32-755b-4b6a-acda-c9ef827f4ff4", + "metadata": {}, + "source": [ + "Let's plot by time and look closer to perihleion" + ] + }, { "cell_type": "code", - "execution_count": 243, + "execution_count": 13, "id": "a9ce9b6a-c33e-4bc4-8100-dfe403d69189", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -892,15 +920,23 @@ "plt.show()\n" ] }, + { + "cell_type": "markdown", + "id": "86f27d78-5082-4a24-a49c-7de00d0514ef", + "metadata": {}, + "source": [ + "Let's plot by heliocentric distance" + ] + }, { "cell_type": "code", - "execution_count": 245, + "execution_count": 14, "id": "b3f5acd0-70f9-4690-8a2f-0d553747890a", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -930,15 +966,23 @@ "plt.show()\n" ] }, + { + "cell_type": "markdown", + "id": "0fd07c75-2e6b-4d64-9d9f-e6b98fce5c73", + "metadata": {}, + "source": [ + "Let's plot by heliocentric distance and zoom in close to perihelion" + ] + }, { "cell_type": "code", - "execution_count": 239, + "execution_count": 15, "id": "780a3880-b3b7-4d89-a272-654596aa70cd", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -964,15 +1008,23 @@ "ax.set_xlabel(\"Heliocentric distance (au)\")\n", "ax.set_ylabel(\"Apparent magnitude\")\n", "plt.gca().invert_yaxis()\n", - "plt.xlim(0,5)\n", + "plt.xlim(1.0,2)\n", "plt.grid()\n", "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "e5d4ca57-b1f8-4aad-90c3-e11831fd6282", + "metadata": {}, + "source": [ + "At larger heliocentric distances the nucelus does not contribute much, the coma is the main contribution to the apparent magnitude and the comet is observed to much brighter than an inactive body at the same heliocentric distance. Closer to the Sun, the nucleus contirbution is more significant." + ] + }, { "cell_type": "code", "execution_count": null, - "id": "2b67a631-b0d7-4150-bf48-1cbcc3c5cdbe", + "id": "94d2363a-2985-46b4-b5de-85d64fb19baf", "metadata": {}, "outputs": [], "source": [] From 1bb5754d2f4c476aa1b8bc2fc282588a12fa8b1c Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Tue, 7 Jan 2025 18:30:50 +0000 Subject: [PATCH 18/52] add comet activity notebook --- docs/notebooks.rst | 1 + docs/notebooks/demo_Cometary_Activity.ipynb | 8 -------- 2 files changed, 1 insertion(+), 8 deletions(-) diff --git a/docs/notebooks.rst b/docs/notebooks.rst index fb67f9ed..6eb83d26 100644 --- a/docs/notebooks.rst +++ b/docs/notebooks.rst @@ -21,5 +21,6 @@ Below we provide Jupyter notebooks that demonstrate and validate various functio Uncertainties and Randomization Vignetting Demo Lightcurve demo + Cometary Activity demo miniDifi Validation Sorcha End-to-End Verification diff --git a/docs/notebooks/demo_Cometary_Activity.ipynb b/docs/notebooks/demo_Cometary_Activity.ipynb index 8acb9343..bf3ea549 100644 --- a/docs/notebooks/demo_Cometary_Activity.ipynb +++ b/docs/notebooks/demo_Cometary_Activity.ipynb @@ -1020,14 +1020,6 @@ "source": [ "At larger heliocentric distances the nucelus does not contribute much, the coma is the main contribution to the apparent magnitude and the comet is observed to much brighter than an inactive body at the same heliocentric distance. Closer to the Sun, the nucleus contirbution is more significant." ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "94d2363a-2985-46b4-b5de-85d64fb19baf", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 26361b209258dc32ff47da1dd71fdcf92a64daa5 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Tue, 7 Jan 2025 23:31:09 +0000 Subject: [PATCH 19/52] update docs update docs --- docs/apparentmag.rst | 5 +++++ docs/notebooks/demo_Cometary_Activity.ipynb | 15 ++++++++------- 2 files changed, 13 insertions(+), 7 deletions(-) diff --git a/docs/apparentmag.rst b/docs/apparentmag.rst index 35d9f307..ea54e47d 100644 --- a/docs/apparentmag.rst +++ b/docs/apparentmag.rst @@ -33,6 +33,11 @@ Cometary Activity or Simulating Other Active Objects :language: python +Through the ``Sorcha'' configuration file. + +lsst_comet + + Rotational Light Curve Effects ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The base lightcurve class is `AbstractLightCurve `_ (see below). Inside the `sorcha addons GitHub repository `_, we provide a simple example implementation where the apparent magnitude of the object (that is, the magnitude after all geometric effects have been taken into account), has a sinusoidal term added to it. To use this function, in the :ref:`CPP` file, the user must provide a light curve amplitude (`LCA`), corresponding to half the peak-to-peak amplitude for the magnitude changes, a period `Period`, and a reference time `Time0` where the light curve is at 0 - if these are not provided, the software will produce an error message. Despite being simple, that implementation shows all the class methods that need to be implemented for a custom light curve function. diff --git a/docs/notebooks/demo_Cometary_Activity.ipynb b/docs/notebooks/demo_Cometary_Activity.ipynb index bf3ea549..c4e51261 100644 --- a/docs/notebooks/demo_Cometary_Activity.ipynb +++ b/docs/notebooks/demo_Cometary_Activity.ipynb @@ -32,7 +32,8 @@ "import astropy.units as u\n", "import matplotlib.pyplot as plt\n", "from sorcha.modules.PPCalculateApparentMagnitudeInFilter import PPCalculateApparentMagnitudeInFilter\n", - "from matplotlib.lines import Line2D" + "from matplotlib.lines import Line2D\n", + "plt.rcParams.update({'font.size': 14})" ] }, { @@ -472,7 +473,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -501,7 +502,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -841,7 +842,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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" ] @@ -936,7 +937,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -982,7 +983,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] From 9321d5d020f503cd64fe1190b59a8a12d73fce1a Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Tue, 7 Jan 2025 23:40:17 +0000 Subject: [PATCH 20/52] Update notebooks.rst --- docs/notebooks.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/notebooks.rst b/docs/notebooks.rst index 6eb83d26..d64baea3 100644 --- a/docs/notebooks.rst +++ b/docs/notebooks.rst @@ -20,7 +20,7 @@ Below we provide Jupyter notebooks that demonstrate and validate various functio Trailed Source Magnitude Versus PSF Magnitude Uncertainties and Randomization Vignetting Demo - Lightcurve demo - Cometary Activity demo + Lightcurve Demo + Cometary Activity Demo miniDifi Validation Sorcha End-to-End Verification From 6f111a5d1df075df50abd323c256b9e258495311 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Wed, 8 Jan 2025 10:05:30 +0000 Subject: [PATCH 21/52] update presentation of sorcha in docs ``sorcha`` or sorcha/Sorcha --> ``Sorcha`` --- docs/acknowledgements.rst | 4 ++-- docs/advanced.rst | 16 ++++++++-------- docs/apparentmag.rst | 6 +++--- docs/cite.rst | 8 ++++---- docs/configfiles.rst | 10 +++++----- docs/contributors.rst | 2 +- docs/ephemerisgen.rst | 28 ++++++++++++++-------------- docs/filters.rst | 12 ++++++------ docs/gettingstarted.rst | 16 ++++++++-------- docs/hpc.rst | 3 ++- docs/index.rst | 16 ++++++++++------ docs/inputs.rst | 33 +++++++++++++++++---------------- docs/installation.rst | 26 +++++++++++++------------- docs/notebooks.rst | 2 +- docs/outputs.rst | 18 +++++++++--------- docs/overview.rst | 19 +++++++++---------- docs/release.rst | 4 ++-- docs/support.rst | 12 ++++++------ docs/troubleshooting.rst | 14 +++++++------- docs/uninstall.rst | 6 +++--- docs/whatsorchadoesnotdo.rst | 7 +++---- 21 files changed, 133 insertions(+), 129 deletions(-) diff --git a/docs/acknowledgements.rst b/docs/acknowledgements.rst index e40eb49f..f7697cb5 100644 --- a/docs/acknowledgements.rst +++ b/docs/acknowledgements.rst @@ -28,7 +28,7 @@ This effort is a collaboration between Queen's University Belfast, the Universit :alt: LINCC Logo -``sorcha`` development was supported in part by: +``Sorcha`` development was supported in part by: - Science and Technology Facilities Council (STFC) grants ST/P000304/1, ST/V000691/1, ST/X001253/1, and ST/V506990/1 - Horizon 2020 Marie Skłodowska-Curie Postdoctoral Fellowship @@ -40,4 +40,4 @@ This effort is a collaboration between Queen's University Belfast, the Universit - National Science Foundation through the following awards: Collaborative Research: SWIFT-SAT: Minimizing Science Impact on LSST and Observatories Worldwide through Accurate Predictions of Satellite Position and Optical Brightness NSF Award Number: 2332736 and Collaborative Research: Rubin Rocks: Enabling near-Earth asteroid science with LSST NSF Award Number: 2307570 - Travel funding from the STFC for UK participation in LSST through STFC grant ST/S006206/1 -Several functions within ``sorcha`` were adapted from code originally developed for `rubin_sim`_, We thank the Vera C. Rubin Observatory Data Management Team and Scheduler Team for making their software open-source. Development of ``rubin_sim` was supported in part by the National Science Foundation through Cooperative Agreement AST-1258333 and Cooperative Support Agreement AST1836783 managed by the Association of Universities for Research in Astronomy (AURA), and the Department of Energy under Contract No. DE-AC02-76SF00515 with the SLAC National Accelerator Laboratory managed by Stanford University. +Several functions within ``Sorcha`` were adapted from code originally developed for `rubin_sim`_, We thank the Vera C. Rubin Observatory Data Management Team and Scheduler Team for making their software open-source. Development of ``rubin_sim` was supported in part by the National Science Foundation through Cooperative Agreement AST-1258333 and Cooperative Support Agreement AST1836783 managed by the Association of Universities for Research in Astronomy (AURA), and the Department of Energy under Contract No. DE-AC02-76SF00515 with the SLAC National Accelerator Laboratory managed by Stanford University. diff --git a/docs/advanced.rst b/docs/advanced.rst index a6821bca..5a8d0e22 100644 --- a/docs/advanced.rst +++ b/docs/advanced.rst @@ -3,20 +3,20 @@ Advanced User Features ========================== .. warning:: - **If you're new to sorcha, turn away from this section NOW! (we're only partially kidding)** This section provides information about features for advanced users of ``sorcha``. Changing or adjusting the parameters described in this section may produce unintended results. **With great power comes great responsibility. Be very careful in applying the knowledge below.** Most users will not need to touch these parameters within ``sorcha``. + **If you're new to sorcha, turn away from this section NOW! (we're only partially kidding)** This section provides information about features for advanced users of ``Sorcha``. Changing or adjusting the parameters described in this section may produce unintended results. **With great power comes great responsibility. Be very careful in applying the knowledge below.** Most users will not need to touch these parameters within ``Sorcha``. Setting the Random Number Generator Seed --------------------------------------------- .. warning:: - For most science cases, you **DO NOT** want to set the same seed for each ``sorcha`` run, but if you need reproducability then you do want to see the seed as an environment variable before running ``sorcha`` + For most science cases, you **DO NOT** want to set the same seed for each ``Sorcha`` run, but if you need reproducability then you do want to see the seed as an environment variable before running ``Sorcha`` -The value used to seed the random number generator can be specified via the **SORCHA_SEED** environmental variable. This allows for ``sorcha`` to be fully reproducibly run with (if using a bash shell or Z-shell):: +The value used to seed the random number generator can be specified via the **SORCHA_SEED** environmental variable. This allows for ``Sorcha`` to be fully reproducibly run with (if using a bash shell or Z-shell):: export SORCHA_SEED=52 .. tip:: - If you're trying to reproduce a crash or a certain behavior in ``sorcha``, you can find the value that you need to set the random seed to in the log file. + If you're trying to reproduce a crash or a certain behavior in ``Sorcha``, you can find the value that you need to set the random seed to in the log file. Expert User Config File Options @@ -68,7 +68,7 @@ In rare instances you may need to skip the footprint filter off. This can be don camera_model = none .. note:: - If you're using ``sorcha``'s bult-in :ref:`ephemeris generator`, the generator will apply a circular search region around each filed pointing when associating potential input population detections with the survey observations. + If you're using ``Sorcha``'s bult-in :ref:`ephemeris generator`, the generator will apply a circular search region around each filed pointing when associating potential input population detections with the survey observations. SNR/Apparent Magnitude Filters @@ -99,7 +99,7 @@ To implement the magnitude limit, include the following in the :ref:`configs`:: Specifying Alernative Versions of the Auxiliaryy Files Used in the Ephemeris Generator ----------------------------------------------------------------------------------------- -For backwards compability and to enable new version of the files to be run as well, users can override the default filenames and download locations of the :ref:`auxiliary files` used by ``sorcha``'s bult-in :ref:`ephemeris generator`. These :ref:`configs`:: variables are added to a new auxiliary ( [AUXILIARY]) section:: +For backwards compability and to enable new version of the files to be run as well, users can override the default filenames and download locations of the :ref:`auxiliary files` used by ``Sorcha``'s bult-in :ref:`ephemeris generator`. These :ref:`configs`:: variables are added to a new auxiliary ( [AUXILIARY]) section:: [AUXILIARY] @@ -141,12 +141,12 @@ For backwards compability and to enable new version of the files to be run as we If you make changes to the filenames or the download urls, you'll likely need to first remove meta_kernel.txt from the auxiliary cache (the directory these files are stored in) or specify a different filename name for meta_kernel file in the config file so that it can be rebuilt with the appropriate names. .. note:: - ``sorcha`` checks if the :ref:`auxiliary files` exist in the cache directory first before attempting to download any missing files and copies them over into the default filenames. + ``Sorcha`` checks if the :ref:`auxiliary files` exist in the cache directory first before attempting to download any missing files and copies them over into the default filenames. Advanced Output Options ----------------------------------- -We recommend that you do not change the decimal place precision and instead leave ``sorcha`` to output the full value +We recommend that you do not change the decimal place precision and instead leave ``Sorcha`` to output the full value to machine precision, but there may be reasons why you need to reduce the size of the output. In the [OUTPUT] section of the :ref:`configs`, you can set the decimal precision for the astrometry outputs:: diff --git a/docs/apparentmag.rst b/docs/apparentmag.rst index ea54e47d..37416382 100644 --- a/docs/apparentmag.rst +++ b/docs/apparentmag.rst @@ -6,7 +6,7 @@ Apparent Magnitude Calculations Trailed Source Magnitude and PSF (Point Spread Function) Magnitude --------------------------------------------------------------------- -Sorcha calculates two apparent magnitudes that we will refer to as the **trailed source magnitude** and the **PSF magnitude**. +``Sorcha`` calculates two apparent magnitudes that we will refer to as the **trailed source magnitude** and the **PSF magnitude**. @@ -22,9 +22,9 @@ Phase Curves Incorporating Rotational Light Curves and Activity ------------------------------------------------------------ -Sorcha has the ability user provided functions though python classes that augment/change the apparent brightness calculations for the synthetic Solar System objects. Any values required as input for these calculations, must be provided in the separate :ref:`CPP` file as input. +``Sorcha`` has the ability user provided functions though python classes that augment/change the apparent brightness calculations for the synthetic Solar System objects. Any values required as input for these calculations, must be provided in the separate :ref:`CPP` file as input. -We have base example classes that the user can take and modify to whatever your need is. Within the Sorcha :ref:`configs`, the user would then specify when class would use and provide the required :ref:`CPP` file on the command line. We also have 2 pre-made example classes that can augment the calculated apparent magnitude of each synthetic object, One for handling cometary activity as a function of heliocentric distance and one that applies rotational light curves to the synthetic objects. In both cases, any derived class must inherit from the corresponding base class and follow its API, to ensure that sorcha knows how to find and use your class. +We have base example classes that the user can take and modify to whatever your need is. Within the ``Sorcha`` :ref:`configs`, the user would then specify when class would use and provide the required :ref:`CPP` file on the command line. We also have 2 pre-made example classes that can augment the calculated apparent magnitude of each synthetic object, One for handling cometary activity as a function of heliocentric distance and one that applies rotational light curves to the synthetic objects. In both cases, any derived class must inherit from the corresponding base class and follow its API, to ensure that sorcha knows how to find and use your class. Cometary Activity or Simulating Other Active Objects ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/cite.rst b/docs/cite.rst index 7e6bbde4..78f45d6f 100644 --- a/docs/cite.rst +++ b/docs/cite.rst @@ -3,16 +3,16 @@ Citing the Software ========================== -``sorcha`` is described provided in joint Astromical Journal/JOSS software papers: Merritt et al. (submitted) and Holman et al.(submitted). We also ask that you reference in your software citations and acknowledgements the other packages that ``sorcha`` is built upon (see below). +``Sorcha`` is described provided in joint Astromical Journal/JOSS software papers: Merritt et al. (submitted) and Holman et al.(submitted). We also ask that you reference in your software citations and acknowledgements the other packages that ``Sorcha`` is built upon (see below). .. tip:: - * Beyond citing the relevant papers, make sure to include details about your configuration for ``sorcha`` (e.g. which footprint filter you're using), details about your input population (e.g. orbital, H, color, and phase curve distribution), and information about the pointing database used. + * Beyond citing the relevant papers, make sure to include details about your configuration for ``Sorcha`` (e.g. which footprint filter you're using), details about your input population (e.g. orbital, H, color, and phase curve distribution), and information about the pointing database used. .. _citefunc: Built-In Citation Function ---------------------------- -If you use ``sorcha`` in your research, please do include a citation in your published papers for ``sorcha`` and the software packages and resources that sorcha is based on. The simplest way to find this information is to use our built-in citation function. In an interactive Python session or a Jupyter notebook:: +If you use ``Sorcha`` in your research, please do include a citation in your published papers for ``Sorcha`` and the software packages and resources that ``Sorcha'' is based on. The simplest way to find this information is to use our built-in citation function. In an interactive Python session or a Jupyter notebook:: import sorcha sorcha.cite() @@ -21,7 +21,7 @@ If you use ``sorcha`` in your research, please do include a citation in your pub Additional Citation Details ---------------------------- -Please also cite the software and ancillary data files that helps power ``sorcha``. Our :ref:`citation function` described above will give the full details or you can manually find the acknowledgement information for each package: +Please also cite the software and ancillary data files that helps power ``Sorcha``. Our :ref:`citation function` described above will give the full details or you can manually find the acknowledgement information for each package: * assist https://assist.readthedocs.io/en/latest/ * astropy https://www.astropy.org/acknowledging.html diff --git a/docs/configfiles.rst b/docs/configfiles.rst index 7a7f5e3f..2c42c992 100644 --- a/docs/configfiles.rst +++ b/docs/configfiles.rst @@ -3,21 +3,21 @@ Configuration File ===================== -``sorcha`` uses a configuration file to set the majority of the various required and optional parameters and well as providing the ability to turn on and off various calculations and filters applied to the simulated small body population. Details about the various settings and options available in the configuration files are described in the :ref:`inputs`, :ref:`filters`, :ref:`ephemeris_gen`, and :ref:`output` pages. +``Sorcha`` uses a configuration file to set the majority of the various required and optional parameters and well as providing the ability to turn on and off various calculations and filters applied to the simulated small body population. Details about the various settings and options available in the configuration files are described in the :ref:`inputs`, :ref:`filters`, :ref:`ephemeris_gen`, and :ref:`output` pages. .. _example_configs: Example Configuration Files ------------------------------------ -We provide example configuration files appropriate for setting up ``sorcha`` to simulate what the LSST would discover. These example config files come installed with ``sorcha`` and can be copied over to your working directory by typing on the command line:: +We provide example configuration files appropriate for setting up ``Sorcha`` to simulate what the LSST would discover. These example config files come installed with ``Sorcha`` and can be copied over to your working directory by typing on the command line:: sorcha init Rubin Full Footprint ~~~~~~~~~~~~~~~~~~~~~~ -This configuration file is appropriate for running ``sorcha`` using the Rubin +This configuration file is appropriate for running ``Sorcha`` using the Rubin full detector footprint. .. literalinclude:: ../src/sorcha/data/survey_setups/Rubin_full_footprint.ini @@ -27,7 +27,7 @@ full detector footprint. Rubin Circular Approximation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -This configuration file is appropriate for running ``sorcha`` using a circular +This configuration file is appropriate for running ``Sorcha`` using a circular approximation of the Rubin detector. .. literalinclude:: ../src/sorcha/data/survey_setups/Rubin_circular_approximation.ini @@ -36,7 +36,7 @@ approximation of the Rubin detector. Rubin Known Object Prediction ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -This configuration file is appropriate for running ``sorcha`` using the full camera footprint but with randomization, +This configuration file is appropriate for running ``Sorcha`` using the full camera footprint but with randomization, fading function, vignetting, SSP linking, saturation limit and trailing losses off. This will output all detections which lie on the CCD with unadulterated apparent magnitudes. This could thus be used to predict when and where known objects will appear in Rubin observations. diff --git a/docs/contributors.rst b/docs/contributors.rst index 698854ef..feb893ad 100644 --- a/docs/contributors.rst +++ b/docs/contributors.rst @@ -1,7 +1,7 @@ Contributors ============ -The people (listed alphabetically) who contributed to ``sorcha`` include: +The people (listed alphabetically) who contributed to ``Sorcha`` include: Pedro Bernardinelli, Aidan Berres, Ricardo Bánffy, Colin Orion Chandler Sam Cornwall, Siegfried Eggl, Grigori Fedorets, Matt Holman, Lynne Jones, Mario Jurić, Jeremy Kubica, Jake Kurlander, Michael S. P. Kelley, Conor MacBride, Shannon Matthews, Steph Merritt, Joachim Moeyens, Joe Murtagh, Shantanu Naidu, Drew Oldag, Brian Rogers, Meg Schwamb, Colin Snodgrass, Max West, and Dave Young diff --git a/docs/ephemerisgen.rst b/docs/ephemerisgen.rst index 2d3af7ee..2933c310 100644 --- a/docs/ephemerisgen.rst +++ b/docs/ephemerisgen.rst @@ -3,18 +3,18 @@ Ephemeris Generator ========================================================== -``sorcha``'s ephemeris generator is powered by `ASSIST `__, a software package for ephemeris-quality integrations of test particles, and the `REBOUND `__ N-body integration package. If the user prefers to use a different generator or provide the ephemeris output from a previous ``sorcha`` run, they have the ability to point ``sorcha`` to an external file to ingest instead. +``Sorcha``'s ephemeris generator is powered by `ASSIST `__, a software package for ephemeris-quality integrations of test particles, and the `REBOUND `__ N-body integration package. If the user prefers to use a different generator or provide the ephemeris output from a previous ``Sorcha`` run, they have the ability to point ``Sorcha`` to an external file to ingest instead. .. tip:: - We recommend using ``sorcha``'s ephemeris generator for all your survey simulations. + We recommend using ``Sorcha``'s ephemeris generator for all your survey simulations. How It Works -------------------------------------------------------- -``sorcha``'s ephemeris generator determines which objects will appear in or near the camera field-of-view (FOV) for any given exposure. It uses spatial indexing to speed up these calculations. It runs through the survey visits and does on-the-fly checks of where every synthetic object is near the center of each night for which there are visits and organizes those positions using the `HEALPix (Hierarchical Equal Area isoLatitude Pixelation of a sphere) `_ tesselation of the sky. Given that information, it then steps through the visits for that night, doing precise calculations for just those objects that are near the camera FOV of each survey on-sky visit. Specifically, for each visit, the generator calculates the unit vector from the observatory's location to the RA/Dec location of the field center. Then it finds the set of HEALPix tiles that are overlapped by the survey visit's camera FOV (nside=64). The ephemeris generator then collects the IDs for the particles in the HEALPix tiles overlapped by the given survey visit FOV. It then does light-time-corrected ephemeris calculations for just those, outputting the right ascension, declination, rates, and relevant distances, and phase angle values for each of the particles. +``Sorcha``'s ephemeris generator determines which objects will appear in or near the camera field-of-view (FOV) for any given exposure. It uses spatial indexing to speed up these calculations. It runs through the survey visits and does on-the-fly checks of where every synthetic object is near the center of each night for which there are visits and organizes those positions using the `HEALPix (Hierarchical Equal Area isoLatitude Pixelation of a sphere) `_ tesselation of the sky. Given that information, it then steps through the visits for that night, doing precise calculations for just those objects that are near the camera FOV of each survey on-sky visit. Specifically, for each visit, the generator calculates the unit vector from the observatory's location to the RA/Dec location of the field center. Then it finds the set of HEALPix tiles that are overlapped by the survey visit's camera FOV (nside=64). The ephemeris generator then collects the IDs for the particles in the HEALPix tiles overlapped by the given survey visit FOV. It then does light-time-corrected ephemeris calculations for just those, outputting the right ascension, declination, rates, and relevant distances, and phase angle values for each of the particles. -A cartoon schematic of ephemeris generation within ``sorcha`` for a patch of sky and a single survey observation is shown below. Each box represents a healpixel in the HEALpix grid on the sky. The colored healpixels are where different Solar System objects is estimated to cover during some part of the night (based on their speed and velocity vector on sky they will be in one or more healpixels) based on the rough calculation from ``sorcha``. The midnight position and 2 other positions during each night are calculated for each simulated small body. Using interpolation, all the healpixels that the object passes through in the evening are identified. In the figure, each color represents a different moving object on a different orbit. Slower moving objects will cover less healpixels. The green circle represents an area slightly bigger than the survey's camera footprint. For the given observation time, any orbits with healpixels within the circle are integrated to calculate their exact positions at the time of the observation. Those orbits that land within the circle are then identified and the resulting ephemerides associated with those objects and the observation are saved. +A cartoon schematic of ephemeris generation within ``Sorcha`` for a patch of sky and a single survey observation is shown below. Each box represents a healpixel in the HEALpix grid on the sky. The colored healpixels are where different Solar System objects is estimated to cover during some part of the night (based on their speed and velocity vector on sky they will be in one or more healpixels) based on the rough calculation from ``Sorcha``. The midnight position and 2 other positions during each night are calculated for each simulated small body. Using interpolation, all the healpixels that the object passes through in the evening are identified. In the figure, each color represents a different moving object on a different orbit. Slower moving objects will cover less healpixels. The green circle represents an area slightly bigger than the survey's camera footprint. For the given observation time, any orbits with healpixels within the circle are integrated to calculate their exact positions at the time of the observation. Those orbits that land within the circle are then identified and the resulting ephemerides associated with those objects and the observation are saved. .. image:: images/ephemeris_generation.png @@ -24,10 +24,10 @@ A cartoon schematic of ephemeris generation within ``sorcha`` for a patch of sky -Because ASSIST uses REBOUND's `IAS15 integrator `_, which has an adaptive time step, ``sorcha``'s ephemeris generator instantiates a REBOUND n-body simulation for each individual massless synthetic object including the effects of the Sun, planets, Moon, and 16 asteroids (see the :ref:`MAP` section). It also includes the J2, J3, and J4 gravitational harmonics of the Earth, the J2 gravitational harmonic of the Sun, and general relativistic correction terms for the Sun, using the Parameterized Post-Newtonian (PPN) formulation. The positions of the massive bodies come from the latest `DE441 `_ ephemeris, provided by NASA's `Navigation and Ancillary Information Facility (NAIF) `_. We note that the coordinate frame for ASSIST+REBOUND is the equatorial International Celestial Reference Frame (ICRF). The positions and velocities are barycentric within this frame, rather than heliocentric. The ephemeris generator translates the input barycentric or heliocentric orbits into x,y, z and velocities into the barycentric ICRF to be read into ASSIST. +Because ASSIST uses REBOUND's `IAS15 integrator `_, which has an adaptive time step, ``Sorcha``'s ephemeris generator instantiates a REBOUND n-body simulation for each individual massless synthetic object including the effects of the Sun, planets, Moon, and 16 asteroids (see the :ref:`MAP` section). It also includes the J2, J3, and J4 gravitational harmonics of the Earth, the J2 gravitational harmonic of the Sun, and general relativistic correction terms for the Sun, using the Parameterized Post-Newtonian (PPN) formulation. The positions of the massive bodies come from the latest `DE441 `_ ephemeris, provided by NASA's `Navigation and Ancillary Information Facility (NAIF) `_. We note that the coordinate frame for ASSIST+REBOUND is the equatorial International Celestial Reference Frame (ICRF). The positions and velocities are barycentric within this frame, rather than heliocentric. The ephemeris generator translates the input barycentric or heliocentric orbits into x,y, z and velocities into the barycentric ICRF to be read into ASSIST. .. tip:: - If using ``sorcha``'s internal ephemeris generation mode (which is the default mode), **we recommend calculating/creating your input orbits with epochs close in time to the start of the first survey observation**. This will minimize the REBOUND n-body integrations required to set up the ephemeris generation. + If using ``Sorcha``'s internal ephemeris generation mode (which is the default mode), **we recommend calculating/creating your input orbits with epochs close in time to the start of the first survey observation**. This will minimize the REBOUND n-body integrations required to set up the ephemeris generation. .. tip:: For further details, we recommend you read the `ASSIST `__ and `REBOUND `__ papers. @@ -56,14 +56,14 @@ Here's the list of asteroid pertubers that are included in the ASSIST+REBOUND in - **(4) Vesta = A807 FA** .. warning:: - If you simulate the orbits of these select asteroids you will get **POOR results** with the internal ``sorcha`` ephemeris generator because of how the n-body integration is set up. We recommend getting the positions of these asteroids from some other source and inputting them as an external ephemeris file. + If you simulate the orbits of these select asteroids you will get **POOR results** with the internal ``Sorcha`` ephemeris generator because of how the n-body integration is set up. We recommend getting the positions of these asteroids from some other source and inputting them as an external ephemeris file. .. _tuneem: Tuning the Ephemeris Generator ----------------------------------- -There are several tunable options for the ephemeris generation which are described below that are set by the ``sorcha`` :ref:`configs`. +There are several tunable options for the ephemeris generation which are described below that are set by the ``Sorcha`` :ref:`configs`. - Minor Planet Center (MPC) observatory code for the provided telescope (**ar_obs_code** configuration parameter) - Field of view of our search field (in degrees) (**ar_ang_fov** configuration parameter) @@ -71,7 +71,7 @@ There are several tunable options for the ephemeris generation which are describ - Picket length (in days) (**ar_picket** configuration parameter) - Order of healpix used by healpy (*ar_healpix_order** configuration parameter) -To use ``sorcha``'s internal ephemeris generation engine, the configuration file should contain:: +To use ``Sorcha``'s internal ephemeris generation engine, the configuration file should contain:: [INPUT] ephemerides_type = ar @@ -108,7 +108,7 @@ A number of auxiliary files available from the `Minor Planet Center ` from the configuration file and add the following:: +If you want to use the same input orbits across multiple ``Sorcha`` runs, you can save time by outputting the output from the ephemeris generation stage using the command line flag **-ew** in combination with a stem filename (do not include the file extension). Then in subsequent runs you will need to use the **-er** flag to on the command line to specify the input ephemeris file to read in. You will also need to remove :ref:`the ephemeris generation parameters` from the configuration file and add the following:: [INPUT] ephemerides_type = external @@ -117,10 +117,10 @@ If you want to use the same input orbits across multiple ``sorcha`` runs, you ca **eph_format** is the format of the output ephemeris file. Options are **csv**, **whitespace**, and **hdf5**. .. attention:: - Currently the ``sorcha``-generated ephemeris is outputted in CSV, whitespace or HDF5 file format only. + Currently the ``Sorcha``-generated ephemeris is outputted in CSV, whitespace or HDF5 file format only. .. tip:: - Compared to the other outputs from ``sorcha``, the ephemeris output files are typicaly very large in size. The output will be slow to read in to ``sorcha``, but for some use cases reading in the ephemeris as a file can be faster than ephemeris generation on the fly. We recommend only outuputting the contents of the ephemeris stage if you need it to speed up future simulations. If possible, use the HDF5 file format to help with disk I/O speeds. + Compared to the other outputs from ``Sorcha``, the ephemeris output files are typicaly very large in size. The output will be slow to read in to ``Sorcha``, but for some use cases reading in the ephemeris as a file can be faster than ephemeris generation on the fly. We recommend only outuputting the contents of the ephemeris stage if you need it to speed up future simulations. If possible, use the HDF5 file format to help with disk I/O speeds. Providing Your Own Ephemerides @@ -135,7 +135,7 @@ If you prefer to use a different method or software package for producing the ep **eph_format** is the format of the user provided ephemeris file. Options are **csv**, **whitespace**, and **hdf5**. .. tip:: - Use the **-er** flag on the command line to specify the external ephemeris file that ``sorcha`` should use. + Use the **-er** flag on the command line to specify the external ephemeris file that ``Sorcha`` should use. .. warning:: - We have validated and tested ``sorcha`` and its internal ephemeris generator. If the user decides to use a different method to provide the required ephemerides for their science, it is up to the user to validate/check the output of the external ephemeris generator. + We have validated and tested ``Sorcha`` and its internal ephemeris generator. If the user decides to use a different method to provide the required ephemerides for their science, it is up to the user to validate/check the output of the external ephemeris generator. diff --git a/docs/filters.rst b/docs/filters.rst index ed5bece1..48b5910f 100644 --- a/docs/filters.rst +++ b/docs/filters.rst @@ -3,7 +3,7 @@ Sorcha's Filter Options ======================================== -Below are the user-controlled filters applied by Sorcha with the relevant configuration +Below are the user-controlled filters applied by ``Sorcha`` with the relevant configuration file parameters and suggested/example values. .. tip:: @@ -17,7 +17,7 @@ The saturation limit filter removes all detections that are brighter than the sa of the survey. `Ivezić et al. (2019) `_ estimate that the saturation limit for the LSST will be ~16 in the r filter. -Sorcha includes functionality to specify either a single saturation limit, or a saturation limit in each filter. +``Sorcha`` includes functionality to specify either a single saturation limit, or a saturation limit in each filter. For the latter, limits must be given in a comma-separated list in the same order as the filters supplied for the observing_filters config file variable. @@ -35,7 +35,7 @@ Fading Function/Detection Efficiency ------------------------------------ This filter serves to remove observations of objects which are faint beyond the survey's capability -to detect them. Sorcha uses the fading function formulation of `Veres and Chesley (2017) `_: +to detect them. ``Sorcha`` uses the fading function formulation of `Veres and Chesley (2017) `_: see the below plot. This fading function is parameterised by the fading function width and peak efficiency. The default values are modelled on those from the aforementioned paper. @@ -106,7 +106,7 @@ To include this filter, the following options should be set in the configuration footprint_path = ./data/detectors_corners.csv .. tip:: - Sorcha comes with a representation of the LSSTCam footprint already installed. If you do not include the **footprint_path** in the configuration file, then Sorcha assumes you're using its internal LSSTCam footprint. + ``Sorcha`` comes with a representation of the LSSTCam footprint already installed. If you do not include the **footprint_path** in the configuration file, then ``Sorcha`` assumes you're using its internal LSSTCam footprint. .. warning:: Note that :ref:`ASSIST+REBOUND ephemeris generator` uses a circular radius for its search area. To get accurate results, the ASSIST+REBOUND radius must be set to be larger than the circle_radius. For simmulating the LSST, we rcommend setting **ar_ang_fov = 2.06** and **ar_fov_buffer = 0.2**. @@ -119,7 +119,7 @@ Additionally, the camera footprint model can account for the losses at the edge footprint_edge_threshold = 0.0001 .. tip:: - Sorcha comes with a representation of the LSSTCam footprint already installed. If you do not include the **footprint_path** in the configuration file, then Sorcha assumes you're using its internal LSSTCam footprint. + ``Sorcha`` comes with a representation of the LSSTCam footprint already installed. If you do not include the **footprint_path** in the configuration file, then ``Sorcha`` assumes you're using its internal LSSTCam footprint. Vignetting @@ -180,7 +180,7 @@ The defaults given below are those used by SSP and are explained in the comments # For the LSST, 12pm Chile Standard Time is 4pm UTC. SSP_night_start_utc = 16.0 -By default, when the linking filter is on, Sorcha will drop all observations of unlinked objects. If the user wishes to retain +By default, when the linking filter is on, ``Sorcha`` will drop all observations of unlinked objects. If the user wishes to retain these observations, this can be set in the configuration file. This will add an additional column to the output, **object_linked**, which states whether the observation is of a linked object or not. To enable this functionality, add the following to the configuration file:: diff --git a/docs/gettingstarted.rst b/docs/gettingstarted.rst index c25865bd..45f6f7b3 100644 --- a/docs/gettingstarted.rst +++ b/docs/gettingstarted.rst @@ -1,10 +1,10 @@ Getting Started ===================== -In this tutorial, we will show you how to setup and run a basic simulation using ``sorcha``. +In this tutorial, we will show you how to setup and run a basic simulation using ``Sorcha``. .. tip:: - In this tutorial, we demonstrate how to run a single instance of ``sorcha``. ``sorcha`` is designed to allow multiple instances to be run in parallel in order to accommodate simulations with very large numbers of synthetic planetesimals by breaking up the job across multiple live processes. We recommend first starting with the examples below, before moving on to parallel processing. + In this tutorial, we demonstrate how to run a single instance of ``Sorcha``. ``Sorcha`` is designed to allow multiple instances to be run in parallel in order to accommodate simulations with very large numbers of synthetic planetesimals by breaking up the job across multiple live processes. We recommend first starting with the examples below, before moving on to parallel processing. .. important:: @@ -50,7 +50,7 @@ The key information about the simulation parameters are held in the configuratio :language: text .. note:: - For this tutorial, we have set up ``sorcha`` to only find detections on g,r,i,z,u, or y filter observations, by what we have set the **observing_filters** parameter to. Since we specified the absolute magnitude and colors for our synthetic objects to r-band, the r filter starts the list of filters for **observing_filters**. + For this tutorial, we have set up ``Sorcha`` to only find detections on g,r,i,z,u, or y filter observations, by what we have set the **observing_filters** parameter to. Since we specified the absolute magnitude and colors for our synthetic objects to r-band, the r filter starts the list of filters for **observing_filters**. .. note:: This config file sets the output to be in CSV format. @@ -59,7 +59,7 @@ The key information about the simulation parameters are held in the configuratio Running Sorcha ---------------------- -We now have all the required input files. If you downloaded the ``sorcha`` repository, start by moving into the ``sorcha`` directory or make a demo directory called **demo** and move/copy all the input files into there. For this example run, we assume that you have downloaded the required ephemeris generator's auxiliary files to ./ar_files. Check the :ref:`installation` instructions for further details. +We now have all the required input files. If you downloaded the ``Sorcha`` repository, start by moving into the ``Sorcha`` directory or make a demo directory called **demo** and move/copy all the input files into there. For this example run, we assume that you have downloaded the required ephemeris generator's auxiliary files to ./ar_files. Check the :ref:`installation` instructions for further details. Next, let's take a look at the command line arguments for the ``sorcha run``. On the command line, typing:: @@ -70,7 +70,7 @@ will produce .. literalinclude:: ./example_files/help_output.txt :language: text -Now that you know how to provide the input files, let's go run a simulation: You can find the command to run the ``sorcha`` demo on the command line in two ways. First on the command line:: +Now that you know how to provide the input files, let's go run a simulation: You can find the command to run the ``Sorcha`` demo on the command line in two ways. First on the command line:: sorcha demo howto @@ -83,7 +83,7 @@ Or you can in an interactive python session or jupyter notebook. You can run the .. tip:: - ``sorcha`` outputs a log file (*.log) and error file (*.err) in the output directory. If all has gone well, the error file will be empty. The log file has the configuration parameters outputted to it as a record of the run setup. + ``Sorcha`` outputs a log file (*.log) and error file (*.err) in the output directory. If all has gone well, the error file will be empty. The log file has the configuration parameters outputted to it as a record of the run setup. The output will appear in a csv file (testrun_e2e.csv) in your current directory. The first 51 lines of the csv file should look something like this: @@ -91,10 +91,10 @@ The output will appear in a csv file (testrun_e2e.csv) in your current directory :language: text :lines: 1-51 -.. note:: The values will not be exactly the same because of the different random number generator seed applied each time ``sorcha`` runs. We use the random generator to adjust the calculated values to be within the measurement precision/uncertainty both in position (RA/Dec) and apparent magnitude. +.. note:: The values will not be exactly the same because of the different random number generator seed applied each time ``Sorcha`` runs. We use the random generator to adjust the calculated values to be within the measurement precision/uncertainty both in position (RA/Dec) and apparent magnitude. .. tip:: If you want to run this command a second time you'll need to add a **-f** flag to the command line to force overwriting output files that already were exist in the output directory. Do note that the previous run's log and error log files will not be removed. New log files are generated at each run. .. warning:: - Only one instance of ``sorcha`` should be run per output directory to ensure that distinct log and error files are created for each ``sorcha`` run. Make sure to have different output pathways if you are running multiple instances on the same compute node. + Only one instance of ``Sorcha`` should be run per output directory to ensure that distinct log and error files are created for each ``Sorcha`` run. Make sure to have different output pathways if you are running multiple instances on the same compute node. diff --git a/docs/hpc.rst b/docs/hpc.rst index 27195487..ebc3c8c1 100644 --- a/docs/hpc.rst +++ b/docs/hpc.rst @@ -1,7 +1,8 @@ Running on HPCs & Parallel Processing =============================================== -Testing Your Sorcha Installation +Testing Your Sorcha Installation +-------------------------------------------------- **Step 6** Install the necessary SPICE auxiliary files for ephemeris generation (774 MB total in size):: sorcha bootstrap [--cache ] diff --git a/docs/index.rst b/docs/index.rst index 10d0e637..fcb0199e 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -10,13 +10,16 @@ ========================================================================= +.. tip:: + We strongly recommend all new users read the ``Sorcha`` documentation before beginning any science-quality simulations. + What is Sorcha? ========================================================================= -Sorcha (pronounced "surk-ha") is an open-source Solar System survey simulator written in Python. -Sorcha means light or brightness in Irish and Scots Gaelic. Sorcha estimates the brightness of +``Sorcha`` (pronounced "surk-ha") is an open-source Solar System survey simulator written in Python. +``Sorcha`` means light or brightness in Irish and Scots Gaelic. Sorcha estimates the brightness of simulated Solar System small bodies and determines which ones the survey could detect in -each of the survey's observations based on user set criteria. Sorcha has been designed +each of the survey's observations based on user set criteria. ``Sorcha`` has been designed with the `Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) `_ in mind. The software has a modular design, and our code can be adapted to be used with any survey. @@ -30,13 +33,14 @@ used with any survey. Welcome to Sorcha's documentation! ========================================================================= -This documentation site contains an installation guide, an overview of how Sorcha -works, tutorials, and demonstration notebooks that show how each of the various key filters within Sorcha work. +This documentation site contains an installation guide, an overview of how ``Sorcha`` +works, tutorials, and demonstration notebooks that show how each of the various components within ``Sorcha`` work and can be customized. .. seealso:: A summary paper (currently in prep) provides a more detailed account of the software and Sorcha’s design methodology. - This documentation focuses on installation and examples of how to use Sorcha for LSST simulation. + This documentation focuses on installation and examples of how to use ``Sorcha`` for LSST simulation. + .. toctree:: :hidden: diff --git a/docs/inputs.rst b/docs/inputs.rst index 4484d27f..27cfe01a 100644 --- a/docs/inputs.rst +++ b/docs/inputs.rst @@ -3,7 +3,7 @@ Inputs ========== -``sorcha`` requires two input files describing the synthetic solar system objects to simulate -- one for the orbital parameters and one for the physical parameters -- as well as survey pointing database. Optionally, the user can provide a pre-generated ephemeris with the positions of each object near the survey pointings and a complex physical parameter file for rotational light curves and cometary activity. Each of these files are described within this section and example files are shown. +``Sorcha`` requires two input files describing the synthetic solar system objects to simulate -- one for the orbital parameters and one for the physical parameters -- as well as survey pointing database. Optionally, the user can provide a pre-generated ephemeris with the positions of each object near the survey pointings and a complex physical parameter file for rotational light curves and cometary activity. Each of these files are described within this section and example files are shown. .. image:: images/survey_simulator_flow_chart.png @@ -15,7 +15,7 @@ Inputs Each synthetic planetesimal has its own unique object identifier set by the user and must have entries in the orbits and physical parameters files, as well as the cometary activity file, if used. .. warning:: - ``sorcha`` does not check whether or not a planetesimal ID has been repeated in another row of the input files. **It is up to the user to ensure their input files include only unique IDs**. + ``Sorcha`` does not check whether or not a planetesimal ID has been repeated in another row of the input files. **It is up to the user to ensure their input files include only unique IDs**. .. _orbits: @@ -26,7 +26,7 @@ Orbit File This is a file which contains the orbital information of a set of synthetic objects. .. tip:: - * ``sorcha`` is designed to handle heliocentric **Cometary (COM), Keplerian (KEP), and Cartesian (CART)** orbits, as well as their barycentric equivalents: **Barycentric Cometary (BCOM), Keplerian (BKEP) and Cartesian (BCART)** + * ``Sorcha`` is designed to handle heliocentric **Cometary (COM), Keplerian (KEP), and Cartesian (CART)** orbits, as well as their barycentric equivalents: **Barycentric Cometary (BCOM), Keplerian (BKEP) and Cartesian (BCART)** * The orbit file **must** have a consistent format (i.e. Cometary or Keplerian or Cartesian) throughout * The first column must be ObjID, but the ordering of the remaining columns does not matter as long as the required columns exist and have entries * The first row in the orbit file **must** be a header listing the column names @@ -39,10 +39,10 @@ This is a file which contains the orbital information of a set of synthetic obje The orbit epoch is expected to be given in **TDB (Barycentric Dynamical Time)** .. tip:: - If using ``sorcha``'s internal :ref:`ephemeris generator` (which is the default mode), **we recommend calculating/creating your input orbits with epochs close in time to the start of the first survey observation**. This will minimize the n-body integrations required to set up the ephemeris generation. + If using ``Sorcha``'s internal :ref:`ephemeris generator` (which is the default mode), **we recommend calculating/creating your input orbits with epochs close in time to the start of the first survey observation**. This will minimize the n-body integrations required to set up the ephemeris generation. .. tip:: - Be careful about the way your input elements are defined! Using heliocentric elements as barycentric (or vice-versa) will lead to wrong outputs. Similarly, if using Cartesian elements, be careful about the orientation of the coordinate system! ``sorcha`` assumes that Cartesian elements are Ecliptic-oriented. + Be careful about the way your input elements are defined! Using heliocentric elements as barycentric (or vice-versa) will lead to wrong outputs. Similarly, if using Cartesian elements, be careful about the orientation of the coordinate system! ``Sorcha`` assumes that Cartesian elements are Ecliptic-oriented. .. note:: For readability we show examples of whitespace-separated files below. We show only the heliocentric versions of these inputs, as the barycentric column requirements are identical, changing only the `FORMAT` designation @@ -167,9 +167,9 @@ The input file for the physical parameters includes information about the object * The **correct capitalization of column names** is required * The physical parameters file can be either **whitespace-separated** or **comma-separated values (CSV)** * Each simulated object **must** have a unique string identifier - * You **must use the same phase curve prescription for all simulated objects**. If you want to use different phase curve prescriptions for different synthetic populations, you will need to run them in separate input files to ``sorcha`` + * You **must use the same phase curve prescription for all simulated objects**. If you want to use different phase curve prescriptions for different synthetic populations, you will need to run them in separate input files to ``Sorcha`` * If the phase curve function is set to NONE in the configuration value then no phase curve parameter values are required in the physical parameters files. - * In the config file you can decide which filters you want have ``sorcha`` run on and specify which filter is the main filter that the absolute magnitude is defined for. You only need to provide colors for those filters specified in the config file. + * In the config file you can decide which filters you want have ``Sorcha`` run on and specify which filter is the main filter that the absolute magnitude is defined for. You only need to provide colors for those filters specified in the config file. .. note:: For readability we show examples of whitespace-separated files below. @@ -196,7 +196,7 @@ An example of the physical parameters file where a HG prescription is specified Rubin Observatory will survey the sky in six broadband (optical filters), *u, g, r, i, z,* and *y* . In the physical parameters file, you will specify the object's absolute magnitude in the main filter (as specified in the config file. usually this is g or r band) and then provide the synthetic planetesimal's color in other filters relative to the main filter. -We have implemented several phase curve parameterizations that can be specified in the config file and then inputted through the physical parameters. **You can either specify one set of phase curve parameters for all filters or specify values for each filter examined by ``sorcha``.** We are using the `sbpy `_ phase function utilities. The supported options are: `HG `_, `HG1G2 `_, `HG12 `_, `linear `_ (specified by S in the header of the physical parameters file), and none (if no columns for phase curve are included in the physical parameters file then the synthetic object is considered to have a flat phase curve). Note that the HG12 model is the `Penttilä et al. (2016) `_ modified model, and not the original (IAU adopted) `Muinonen et al. (2010) `_ model. +We have implemented several phase curve parameterizations that can be specified in the config file and then inputted through the physical parameters. **You can either specify one set of phase curve parameters for all filters or specify values for each filter examined by ``Sorcha``.** We are using the `sbpy `_ phase function utilities. The supported options are: `HG `_, `HG1G2 `_, `HG12 `_, `linear `_ (specified by S in the header of the physical parameters file), and none (if no columns for phase curve are included in the physical parameters file then the synthetic object is considered to have a flat phase curve). Note that the HG12 model is the `Penttilä et al. (2016) `_ modified model, and not the original (IAU adopted) `Muinonen et al. (2010) `_ model. +------------------+----------------------------------------------------------------------------------+ | Keyword | Description | @@ -214,19 +214,20 @@ We have implemented several phase curve parameterizations that can be specified The Phase curve parameters(s) column will not be present if the phase curve function/calculation is set to None in the configuration file .. note:: - In the config file you can decide which filters you want to have ``sorcha`` run on and specify which filter is the main filter that the absolute magnitude is defined for. You only need to provide colors for those filters specified in the config file. + In the config file you can decide which filters you want to have ``Sorcha`` run on and specify which filter is the main filter that the absolute magnitude is defined for. You only need to provide colors for those filters specified in the config file. -.. tip:: +.. seealso:: We have an `example Jupyter notebook `_ demonstrating how to take a representative optical/NIR spectra of your input population and using the `rubin_sim `_ package to estimate the expected colors in the LSST filter bandpasses. + .. _pointing: Survey Pointing Database ------------------------ .. note:: - Currently ``sorcha`` is set up to run with the LSST cadence simulations pointing databases. + Currently ``Sorcha`` is set up to run with the LSST cadence simulations pointing databases. -This database contains information about the LSST pointing history and observing conditions. We use observation mid-point time, right ascension, declination, rotation angle of the camera, 5-sigma limiting magnitude, filter, and seeing information in ``sorcha`` to determine if a synthetic Solar System object is observable. +This database contains information about the LSST pointing history and observing conditions. We use observation mid-point time, right ascension, declination, rotation angle of the camera, 5-sigma limiting magnitude, filter, and seeing information in ``Sorcha`` to determine if a synthetic Solar System object is observable. What we call the LSST pointing database (currently simulated since Rubin Observatory hasn’t started operations) is generated through the Rubin Observatory scheduler (since 2021 referred to as `rubin_sim `_ and previously known as OpSim). This software is currently under active development and is being used to run many simulated iterations of LSST scenarios, showing what the cadence would look like with differing survey strategies. A description of an early version of this Python software can be found in `Delgado et al.(2014) `_. The output of rubin_sim is a SQLlite database containing the pointing history and associated metadata of the simulated observation history of LSST. @@ -252,7 +253,7 @@ The latest version of rubin_sim cadence simulations can be found at https://s3df Setting Up the Correct LSST Pointing Database Query ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -``sorcha``'s **ppsqldbquery** config file parameter contains the SQL query for obtaining this information from the pointing database. +``Sorcha``'s **ppsqldbquery** config file parameter contains the SQL query for obtaining this information from the pointing database. From rubin_sim v2.0 simulations onward use the query:: SELECT observationId, observationStartMJD as observationStartMJD_TAI, visitTime, visitExposureTime, filter, seeingFwhmGeom as seeingFwhmGeom_arcsec, seeingFwhmEff as seeingFwhmEff_arcsec, fiveSigmaDepth as fieldFiveSigmaDepth_mag , fieldRA as fieldRA_deg, fieldDec as fieldDec_deg, rotSkyPos as fieldRotSkyPos_deg FROM observations order by observationId @@ -268,7 +269,7 @@ For past rubin_sim/OpSim simulations pre-v2.0 use the query:: Complex Physical Parameters File (Optional) --------------------------------------------------- -The complex physical parameters file is only needed if you're going to include your own rotational light curve class or cometary activity class to augment the calculated apparent magnitudes. ``sorcha`` is set up such that any values required for this such as (light curve amplitude and period per simulated object) are included in a file, separate from the physical parameters file, that we refer to as the complex physical parameters file. What columns are required in the complex physical parameters file depends on the classes you are using. +The complex physical parameters file is only needed if you're going to include your own rotational light curve class or cometary activity class to augment the calculated apparent magnitudes. ``Sorcha`` is set up such that any values required for this such as (light curve amplitude and period per simulated object) are included in a file, separate from the physical parameters file, that we refer to as the complex physical parameters file. What columns are required in the complex physical parameters file depends on the classes you are using. .. tip:: * The first column must be ObjID, but the ordering of the remaining columns does not matter as long as the required columns exist and have entries @@ -283,7 +284,7 @@ Ephemeris File (Optional) ----------------------------------------- .. note:: - ``sorcha`` has an :ref:`ephemeris_gen` that we recommend using by default, but as an alternative ``sorcha`` can read in an external file containing calculated ephemeris values for each simulated object within a reasonable search radius of a given survey field pointing and observation times as specified in the survey pointing database. This could be the output from a previous ``sorcha`` run or provided from your own separate ephemeris generation method, + ``Sorcha`` has an :ref:`ephemeris_gen` that we recommend using by default, but as an alternative ``Sorcha`` can read in an external file containing calculated ephemeris values for each simulated object within a reasonable search radius of a given survey field pointing and observation times as specified in the survey pointing database. This could be the output from a previous ``Sorcha`` run or provided from your own separate ephemeris generation method, .. tip:: @@ -364,7 +365,7 @@ If you are going to simulate the full camera architecture including CCD location The camera footprint file is a comma-separated text file with three columns describing the detector shapes, with the header “detector,x,y”. The first column indicates which detector a point belongs to, and should be an integer. Second and third columns specify where on the focal plane the corners are. Values are unitless, equal to tan( ra ), tan( dec ), where ra and dec are the vertical and horizontal angles of the points from the center of the sphere tangent to origin in the focal plane. Ordering does not matter, as the constructor sorts the points automatically. .. tip:: -``sorcha`` comes with a representation of the LSSTCam architecture already installed. Further details of how to use this built-in default file can be found in the description of the :ref:`Full Camera Footprint Filter`. +``Sorcha`` comes with a representation of the LSSTCam architecture already installed. Further details of how to use this built-in default file can be found in the description of the :ref:`Full Camera Footprint Filter`. An example of an (optional) camera footprint file: diff --git a/docs/installation.rst b/docs/installation.rst index 0824de84..06e8eb0e 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -4,12 +4,12 @@ Installation ================= .. note:: - ``sorcha`` is both conda/mamba and pip installable. We recommend installing via conda/mamba. + ``Sorcha`` is both conda/mamba and pip installable. We recommend installing via conda/mamba. Requirements ----------------------------- -``sorcha`` has the following requirements that will be automatically installed using pip or conda when you install the sorcha package: +``Sorcha`` has the following requirements that will be automatically installed using pip or conda when you install the sorcha package: * python 3.11 or later * assist @@ -47,7 +47,7 @@ If using mamba:: mamba create -n sorcha -c conda-forge assist numpy numba pandas scipy astropy matplotlib sbpy pytables spiceypy healpy rebound pooch tqdm h5py importlib_resources python=3.11 .. tip:: - We recommend using python version 3.11 or higher with ``sorcha``. The conda/mamba install command uses python 3.11. + We recommend using python version 3.11 or higher with ``Sorcha``. The conda/mamba install command uses python 3.11. **Step 2** Activate your conda/mamba environment @@ -62,7 +62,7 @@ On mamba:: Installing Sorcha ---------------------- -Unless you're editing the source code, you can use the version of ``sorcha`` published on conda-forge. +Unless you're editing the source code, you can use the version of ``Sorcha`` published on conda-forge. If using conda:: @@ -72,7 +72,7 @@ If using mamba:: mamba install -c conda-forge sorcha -You can install ``sorcha`` via from pypi using pip, but installation via conda/mamba is recommended. +You can install ``Sorcha`` via from pypi using pip, but installation via conda/mamba is recommended. If using pip:: @@ -83,7 +83,7 @@ If using pip:: Downloading Required Supplemental Files ---------------------------------------- -To run ``sorcha``'s built in :ref:`ephemeris generator`, you will need to download the auxiliary files required by assist and rebound for performing the N-body integrations. +To run ``Sorcha``'s built in :ref:`ephemeris generator`, you will need to download the auxiliary files required by assist and rebound for performing the N-body integrations. To install the necessary `SPICE (Spacecraft, Planet, Instrument, C-matrix, Events) `_ auxiliary files and other required data files for ephemeris generation (774 MB total in size):: @@ -100,7 +100,7 @@ To install the necessary `SPICE (Spacecraft, Planet, Instrument, C-matrix, Event Testing Your Sorcha Installation ---------------------------------- -You can check that the ``sorcha`` installation was successful, by obtaining the demo input files and running the demo command. +You can check that the ``Sorcha`` installation was successful, by obtaining the demo input files and running the demo command. The demo input files and configuration file are installed with the socha package. You can run the following command on the command line to copy the files to the current directory (or a different location):: @@ -109,7 +109,7 @@ The demo input files and configuration file are installed with the socha package .. note:: The optional -p flag allows you to specify a specific location to copy the demo input files. If the files already exist, the -f flag can be used to force a fresh copy of the files to be generated. . -You can find the command to run the ``sorcha`` demo on the command line in two ways. First on the command line:: +You can find the command to run the ``Sorcha`` demo on the command line in two ways. First on the command line:: sorcha demo howto @@ -142,23 +142,23 @@ Installing Sorcha in Development Mode **This is the installation method for adding/edit sorcha's codebase or for working on/updating sorcha's documentation.** -**Step 1** Create a directory to contain the ``sorcha`` repos:: +**Step 1** Create a directory to contain the ``Sorcha`` repos:: mkdir sorcha -**Step 2** Navigate to the directory you want to store the ``sorcha`` source code in:: +**Step 2** Navigate to the directory you want to store the ``Sorcha`` source code in:: cd sorcha -**Step 3** Download the ``sorcha`` source code via:: +**Step 3** Download the ``Sorcha`` source code via:: git clone https://github.com/dirac-institute/sorcha.git -**Step 4** Navigate to the ``sorcha`` repository directory:: +**Step 4** Navigate to the ``Sorcha`` repository directory:: cd sorcha -**Step 5** Install an editable (in-place) development version of ``sorcha``. This will allow you to run the code from the source directory. +**Step 5** Install an editable (in-place) development version of ``Sorcha``. This will allow you to run the code from the source directory. If you just want the source code installed so edits in the source code are automatically installed:: diff --git a/docs/notebooks.rst b/docs/notebooks.rst index d64baea3..7f7dc055 100644 --- a/docs/notebooks.rst +++ b/docs/notebooks.rst @@ -1,7 +1,7 @@ Demo Notebooks ======================================================================================== -Below we provide Jupyter notebooks that demonstrate and validate various functions and components of ``sorcha``. +Below we provide Jupyter notebooks that demonstrate ``Sorcha``'s capabilities and validate various functions and components within `Sorcha``. .. toctree:: :maxdepth: 1 diff --git a/docs/outputs.rst b/docs/outputs.rst index ebfd7be6..8100c018 100644 --- a/docs/outputs.rst +++ b/docs/outputs.rst @@ -6,14 +6,14 @@ Outputs Sorcha Output ---------------------- -Sorcha produces an output file describing each predicted observation the survey will record of the input simulated objects, +``Sorcha`` produces an output file describing each predicted observation the survey will record of the input simulated objects, with a row for each predicted detection and a column for each parameter to be calculated. This output file can be in several formats set by the output_format configuration file keyword. Additionally, the output columns can be set to either "basic" or "all" settings (described below) using the output_columns configuration file keyword. Alternatively, you may specify the columns you wish to be output. -The format of any output from Sorcha will look something like:: +The format of any output from ``Sorcha`` will look something like:: ObjID,fieldMJD_TAI,fieldRA_deg,fieldDec_deg,RA_deg,Dec_deg,astrometricSigma_deg,optFilter,PSFMag,trailedSourceMag,PSFMagSigma,trailedSourceMagSigma,fiveSigmaDepth_mag,fiveSigmaDepthAtSource S1000000a,61769.320619,163.87542090842982,-18.84327137012991,164.03771300000017,-17.58257500000004,2.9880927198448093e-06,r,19.667095021023798,19.655534004675797,0.006775654132479691,0.006755926588113991,23.86356436464961,23.839403736057715 @@ -29,7 +29,7 @@ The format of any output from Sorcha will look something like:: Output Formats ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The configuration file keyword output_format allows Sorcha to output files in CSV, SQLite3 or HDF5 formats. For example:: +The configuration file keyword output_format allows ``Sorcha`` to output files in CSV, SQLite3 or HDF5 formats. For example:: [OUTPUT] # The options: csv, sqlite3, hdf5 @@ -258,7 +258,7 @@ Additional Outputs Ephemeris Output ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Optionally (with the **-ew** flag set at the command line), an ephemeris file of all detections near the -field can be generated to a separate file, which can then be provided back to Sorcha as an optional external ephemeris file with the **-er** flag. +field can be generated to a separate file, which can then be provided back to ``Sorcha`` as an optional external ephemeris file with the **-er** flag. More information can be found on this functionality, including the output columns, in the :ref:`Ephemeris Generation` section of the documentation. The format of the outputted ephemeris file is controlled by the **eph_format** configuration keyword in the Inputs section of the configuration file:: @@ -268,12 +268,12 @@ The format of the outputted ephemeris file is controlled by the **eph_format** c eph_format = csv .. attention:: - Users should note that output produced by reading in a previously-generated ephemeris file will be in a different order than the output produced when running the ephemeris generator within Sorcha. - This is simply a side-effect of how Sorcha reads in ephemeris files and does not affect the actual content of the output. + Users should note that output produced by reading in a previously-generated ephemeris file will be in a different order than the output produced when running the ephemeris generator within ``Sorcha``. + This is simply a side-effect of how ``Sorcha`` reads in ephemeris files and does not affect the actual content of the output. Statistics Output ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Sorcha can also output a statistics or "tally" file which contains an overview of the Sorcha output for each object and filter. Minimally, this +``Sorcha`` can also output a statistics or "tally" file which contains an overview of the ``Sorcha`` output for each object and filter. Minimally, this file lists the number of observations for each object in each filter, along with the minimum, maximum and median apparent magnitude and the minimum and maximum phase angle. If the :ref:`linking filter` is on, this file also contains information on whether and when the object was linked by SSP. @@ -304,5 +304,5 @@ The columns in the statistics file are as follows: +------------------------------------+--------------+----------------------------------------------------------------------------------------------------------+ .. note:: -Unless the user has specified **drop_unlinked = False** in the configuration file, the object_linked column will read TRUE for all objects. To see which objects were not linked by Sorcha, this -variable must be set to False. \ No newline at end of file +Unless the user has specified **drop_unlinked = False** in the configuration file, the object_linked column will read TRUE for all objects. To see which objects were not linked by ``Sorcha``, this +variable must be set to False. diff --git a/docs/overview.rst b/docs/overview.rst index a675c492..9bb86701 100644 --- a/docs/overview.rst +++ b/docs/overview.rst @@ -5,9 +5,9 @@ How Sorcha Works ------------------------------- In order to conduct detailed population studies on the orbital properties and physical characteristics of the various Solar System small body reservoirs, one must account for all the survey biases (the complex and often intertwined detection biases – brightness limits, -pointing, cadence, on-sky motion limits, software detection efficiencies) in one’s discovery survey (`see Lawler et al. 2018 `_ for a more detailed discussion). ``sorcha`` is an open-source Python Solar System survey simulator designed for the `Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) `_. ``sorcha`` works by the user inputting a synthetic Solar System small body population. The software forward models the input population to simulate what the survey should have detected using a series of :ref:`filters`. The filters can be switched on or off and customized as needed via a :ref:`configuration file`. In this way, a synthetic population can be compared to the real survey's discoveries. +pointing, cadence, on-sky motion limits, software detection efficiencies) in one’s discovery survey (`see Lawler et al. 2018 `_ for a more detailed discussion). ``Sorcha`` is an open-source Python Solar System survey simulator designed for the `Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) `_. ``Sorcha`` works by the user inputting a synthetic Solar System small body population. The software forward models the input population to simulate what the survey should have detected using a series of :ref:`filters`. The filters can be switched on or off and customized as needed via a :ref:`configuration file`. In this way, a synthetic population can be compared to the real survey's discoveries. -The :ref:`inputs` that ``sorcha`` requires are shown in the figure below. The software requires input files that describe the small bodies to simulate (including a file for orbits, at least one file for physical parameters, and a SQLite database that describes the telescope survey's observation history). ``sorcha`` outputs simulated detection data, including each time, position, and apparent magnitude at which a synthetic small body was detected based on various options set up in a configuration file. +The :ref:`inputs` that ``Sorcha`` requires are shown in the figure below. The software requires input files that describe the small bodies to simulate (including a file for orbits, at least one file for physical parameters, and a SQLite database that describes the telescope survey's observation history). ``Sorcha`` outputs simulated detection data, including each time, position, and apparent magnitude at which a synthetic small body was detected based on various options set up in a configuration file. .. image:: images/survey_simulator_flow_chart.png @@ -15,28 +15,27 @@ The :ref:`inputs` that ``sorcha`` requires are shown in the figure below :alt: An overview of the inputs and outputs for Sorcha -``sorcha`` by default uses its own :ref:`ephemeris generator` to propagate the orbits and translate them to on-sky locations and rates. ``sorcha``'s ephemeris generator is powered by `ASSIST `_, a software package for ephemeris-quality integrations of test particles, and the `REBOUND `_ N-body integrator. If the user prefers to use a different generator, ``sorcha`` is also to be configured to read in an external ephemeris file with pre-calculated ephemerides of the input synthetic orbital population. +``Sorcha`` by default uses its own :ref:`ephemeris generator` to propagate the orbits and translate them to on-sky locations and rates. ``Sorcha``'s ephemeris generator is powered by `ASSIST `_, a software package for ephemeris-quality integrations of test particles, and the `REBOUND `_ N-body integrator. If the user prefers to use a different generator, ``Sorcha`` is also to be configured to read in an external ephemeris file with pre-calculated ephemerides of the input synthetic orbital population. .. warning:: - We have validated ``sorcha`` with its internal :ref:`ephemeris generator`. If the user chooses to use a different ephemeris engine's calculations as input for ``sorcha``, the user has the responsibility to check the accuracy of this input. + We have validated ``Sorcha`` with its internal :ref:`ephemeris generator`. If the user chooses to use a different ephemeris engine's calculations as input for ``Sorcha``, the user has the responsibility to check the accuracy of this input. Design Philosophy ---------------------- -``sorcha`` has been designed in a modular way with each filter written as its own function, This makes it easy to add new filters in the future if required by users. We note for dealing with rotational light curve and activity effects, we have set up ``sorcha`` such that the user can provide their own custom classes/functions and import them into ``sorcha`` to use. Further details can be found on the :ref:`addons` page. ``sorcha`` has been designed with LSST in mind, but many of the filters already developed will be applicable to other Solar System surveys. If you are interested in incorporating your survey into ``sorcha`` do reach out. +``Sorcha`` has been designed in a modular way with each filter written as its own function, This makes it easy to add new filters in the future if required by users. We note for dealing with rotational light curve and activity effects, we have set up ``Sorcha`` such that the user can provide their own custom classes/functions and import them into ``Sorcha`` to use. Further details can be found on the :ref:`addons` page. ``Sorcha`` has been designed with LSST in mind, but many of the filters already developed will be applicable to other Solar System surveys. If you are interested in incorporating your survey into ``Sorcha`` do reach out. .. note:: - Contributions are very welcome. If there is a feature or functionality not yet available in ``sorcha``, we encourage you to propose the feature as an issue in the `main github repository `_ or share your code with the new enhancements. Further details can be found on our :ref:`reporting` page. + Contributions are very welcome. If there is a feature or functionality not yet available in ``Sorcha``, we encourage you to propose the feature as an issue in the `main github repository `_ or share your code with the new enhancements. Further details can be found on our :ref:`reporting` page. Using Sorcha in Your Science -------------------------------- -We made ``sorcha`` to be a tool for the small body planetary astronomer community. For a wide variety of use cases, the user should be able to use ``sorcha`` straight out of the box. +We made ``Sorcha`` to be a tool for the small body planetary astronomer community. For a wide variety of use cases, the user should be able to use ``Sorcha`` straight out of the box. .. note:: - If ``sorcha`` enabled your science, please make sure to give the proper credit in your talks and papers by citing the relevant ``sorcha`` papers and the python packages that the software is built upon. Further details can be found :ref:`here`. + If ``Sorcha`` enabled your science, please make sure to give the proper credit in your talks and papers by citing the relevant ``Sorcha`` papers and the python packages that the software is built upon. Further details can be found :ref:`here`. .. warning:: - We have designed ``sorcha`` such that it should be straightforward to add in additional filters or rotational light curve/activity classes. As with any open-source package, **once the user has made modifications to the code, it is the responsibility of the user to confirm these changes provide an accurate result**. - + We have designed ``Sorcha`` such that it should be straightforward to add in additional filters or rotational light curve/activity classes. As with any open-source package, **once the user has made modifications to the code, it is the responsibility of the user to confirm these changes provide an accurate result**. diff --git a/docs/release.rst b/docs/release.rst index 4c2322e6..479b990f 100644 --- a/docs/release.rst +++ b/docs/release.rst @@ -1,8 +1,8 @@ Release History ================= -See what's in the latest Sorcha release and the contents of past Sorcha releases `here `__. +See what's in the latest ``Sorcha`` release and the contents of past Sorcha releases `here `__. -See what's in each Sorcha addons release and in past releases `here `__. +See what's in each ``Sorcha addons`` release and in past releases `here `__. diff --git a/docs/support.rst b/docs/support.rst index b0aff457..ac538127 100644 --- a/docs/support.rst +++ b/docs/support.rst @@ -6,21 +6,21 @@ Reporting Issues, Proposing Changes, and Contributing .. tip:: Something not working? Have you checked the :ref:`troubleshooting` page to see if your problem is covered there? -Contributions are very welcome. If there is a feature or functionality not yet available in Sorcha, we encourage you to propose the feature or share your code with the new enhancements. +Contributions are very welcome. If there is a feature or functionality not yet available in ``Sorcha``, we encourage you to propose the feature or share your code with the new enhancements. Submitting a GitHub Issue --------------------------- -The best way to get in touch about a bug, suggest enhancements to Sorcha, or recommend changes to the documentation is raise an issue through the `project's GitHub repository `_. We have a small team working on the project, so please be patient while we get back to you. +The best way to get in touch about a bug, suggest enhancements to ``Sorcha``, or recommend changes to the documentation is raise an issue through the `project's GitHub repository `_. We have a small team working on the project, so please be patient while we get back to you. Contributing Code ----------------------------------- -We welcome upgrades/bug fixes to the code. This can be done by opening a pull request in the `main Sorcha GitHub repository `_. If you have new classes that provide enhanced light curve or activity estimations, we welcome pull requests to the `Sorcha Community Utils GitHub repository `_. +We welcome upgrades/bug fixes to the code. This can be done by opening a pull request in the main ``Sorcha`` `GitHub repository `_. If you have new classes that provide enhanced light curve or activity estimations, we welcome pull requests to the ``Sorcha Add-ons`` ` GitHub repository `_. -You will need to install sorcha from the source code via pip in editable mode as described in the :ref:`installation` page. +You will need to install ``Sorcha`` from the source code via pip in editable mode as described in the :ref:`installation` page. .. note:: - If you are planning to submit a pull request with enhancements, please raise a `GitHub issue in the main sorcha repository `_ first to discuss further with the Sorcha team. + If you are planning to submit a pull request with enhancements, please raise a `GitHub issue in the main sorcha repository `_ first to discuss further with the ``Sorcha`` team. Contributing to the Documentation @@ -28,7 +28,7 @@ Contributing to the Documentation We are very happy to receive feedback on the online documentation through the `project's GitHub repository `_. Beyond pointing out typos and small changes through issues, we welcome pull requests on the `sphinx `_ documentation used here on the readthedocs. -You will need to install the development version of Sorcha from a clone of the Sorcha repository as well as the `sorcha-addons package `_. See the our :ref:`dev_mode` instructions for further details. +You will need to install the development version of ``Sorcha`` from a clone of the ``Sorcha`` repository as well as the `sorcha-addons package `_. See the our :ref:`dev_mode` instructions for further details. If you move to the docs directory (cd sorcha/docs/), edit the .rst files, and run:: diff --git a/docs/troubleshooting.rst b/docs/troubleshooting.rst index fd8a9406..ddd45921 100644 --- a/docs/troubleshooting.rst +++ b/docs/troubleshooting.rst @@ -6,7 +6,7 @@ Troubleshooting Have You Checked the Error Log File? --------------------------------------------------------------- -If sorcha runs successfully the .err log file created will be empty. If the software exited gracefully with an error it will print error statements to the error log file. If sorcha looks like it completed but you're not getting the expected output, the .err log file is the first place to check. +If ``Sorcha`` runs successfully the .err log file created will be empty. If the software exited gracefully with an error it will print error statements to the error log file. If ``Sorcha'' looks like it completed but you're not getting the expected output, the .err log file is the first place to check. Using Relative File Paths --------------------------------------------------------------- @@ -15,9 +15,9 @@ If you're using relative paths (e.g. '../this_directory') and those do not seem Running Multiple Instances With the Same Output Directories --------------------------------------------------------------- -If your output looks mixed up or garbled, double check that you are not running more than one sorcha process with -the same output path. You **should only run one** instance of sorcha at the same time for a given output directory. -Otherwise, you run the risk of the output files being mixed up. If you want to run multiple versions of sorcha on +If your output looks mixed up or garbled, double check that you are not running more than one ``Sorcha`` process with +the same output path. You **should only run one** instance of ``Sorcha`` at the same time for a given output directory. +Otherwise, you run the risk of the output files being mixed up. If you want to run multiple versions of ``Sorcha`` on the same computer/compute node, make sure to update the output path in the config file or commandline arguments, as appropriate. We have developed tools and example Slurm scripts to help you run multiple instances safely. @@ -40,9 +40,9 @@ Mismatch in Inputs --------------------- There are several files associated with the synthetic small bodies which are passed into Sorcha. These are the orbit file, the physical parameter file and an optional complex parameters file and optional ephemeris -file (if not using the internal ephemeris generator within sorcha). Each provide specific information about the +file (if not using the internal ephemeris generator within ``Sorcha``). Each provide specific information about the synthetic population that is being analysed. Within these files, it is necessary to specify an entry for every -object. The sorcha code will run a check to ensure that all entries have an associated orbit and +object. The ``Sorcha`` code will run a check to ensure that all entries have an associated orbit and physical/complex physical parameter value, so if you get an error like:: ERROR: PPCheckOrbitAndColourMatching: input colour/cometary parameter and orbit files do not match. @@ -56,7 +56,7 @@ Check your input files and ensure that they have ObjID column as the first colum in PPOutWriteSqlite3: sqlite3.OperationalError: index ObjID already existssqlite3.OperationalError: index ObjID already exists --------------------------------------------------------------------------------------------------------------------------------------------- -This happens if you are outputting as sql databases and you have dueling sorcha processes running in the same directory with the same output file names running on the same input files using the -f flag to force overwriting of output files. One way to check this is to only allow for one sorcha run to be output to a directory and see if you've got two log files that are actively being written to/were created. Note if you're using CSV, text file, or pytables format you won't get this error when you hit this race condition. +This happens if you are outputting as sql databases and you have dueling ``Sorcha`` processes running in the same directory with the same output file names running on the same input files using the -f flag to force overwriting of output files. One way to check this is to only allow for one ``Sorcha`` run to be output to a directory and see if you've got two log files that are actively being written to/were created. Note if you're using CSV, text file, or pytables format you won't get this error when you hit this race condition. diff --git a/docs/uninstall.rst b/docs/uninstall.rst index c786b98b..f7d0c949 100644 --- a/docs/uninstall.rst +++ b/docs/uninstall.rst @@ -1,15 +1,15 @@ Uninstalling ================= -If you have installed ``sorcha`` using conda, then you can uninstall the package with:: +If you have installed ``Sorcha`` using conda, then you can uninstall the package with:: conda uninstall sorcha -If you have installed ``sorcha`` using mamba, then you can uninstall the package with:: +If you have installed ``Sorcha`` using mamba, then you can uninstall the package with:: mamba uninstall sorcha -If you have installed ``sorcha`` using pip, then you can uninstall the package with:: +If you have installed ``Sorcha`` using pip, then you can uninstall the package with:: pip uninstall sorcha diff --git a/docs/whatsorchadoesnotdo.rst b/docs/whatsorchadoesnotdo.rst index 169fdb8e..34e9e670 100644 --- a/docs/whatsorchadoesnotdo.rst +++ b/docs/whatsorchadoesnotdo.rst @@ -3,10 +3,10 @@ What Sorcha Does Not Handle Here we note the effects that are not currently captured within this survey simulator. With the modular nature of the package, it should be straightforward to develop functions to handle these -in the future. If you want to add any of these features into Sorcha, please check out our +in the future. If you want to add any of these features into ``Sorcha``, please check out our :ref:`reporting` page. -Here is a short summary of the key effects not accounted for in Sorcha: +Here is a short summary of the key effects not accounted for in ``Sorcha'': - Changing phase curves due to changing viewing angles (impacts some inner Solar System objects) - Stellar crowding as a function of galactic latitude @@ -20,5 +20,4 @@ Here is a short summary of the key effects not accounted for in Sorcha: We do have methods for users to easily develop their own functions for adjusting the apparent magnitude of the simulated objects due to cometary activity, rotational light curves, cometary outbursts, etc. We have some basic functionality already built for simple sinusoidal rotational - light curves and cometary activity. Further details can be found in the - `sorcha addons `_. + light curves and cometary activity. Further details can be found :ref:`here` From 331c75cf006c3b9daeca33de6e18c8da72de31ab Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Wed, 8 Jan 2025 16:56:16 +0000 Subject: [PATCH 22/52] doc updates doc updates --- docs/apparentmag.rst | 25 ++++++++++++++++++--- docs/configfiles.rst | 2 +- docs/inputs.rst | 3 +++ docs/notebooks/demo_Cometary_Activity.ipynb | 2 +- docs/overview.rst | 2 +- 5 files changed, 28 insertions(+), 6 deletions(-) diff --git a/docs/apparentmag.rst b/docs/apparentmag.rst index 37416382..66480cb0 100644 --- a/docs/apparentmag.rst +++ b/docs/apparentmag.rst @@ -1,6 +1,6 @@ .. _apparent_magnitudes -Apparent Magnitude Calculations +Post-Processing (Applying Survey Biases) ========================================================== Trailed Source Magnitude and PSF (Point Spread Function) Magnitude @@ -24,7 +24,14 @@ Incorporating Rotational Light Curves and Activity ------------------------------------------------------------ ``Sorcha`` has the ability user provided functions though python classes that augment/change the apparent brightness calculations for the synthetic Solar System objects. Any values required as input for these calculations, must be provided in the separate :ref:`CPP` file as input. -We have base example classes that the user can take and modify to whatever your need is. Within the ``Sorcha`` :ref:`configs`, the user would then specify when class would use and provide the required :ref:`CPP` file on the command line. We also have 2 pre-made example classes that can augment the calculated apparent magnitude of each synthetic object, One for handling cometary activity as a function of heliocentric distance and one that applies rotational light curves to the synthetic objects. In both cases, any derived class must inherit from the corresponding base class and follow its API, to ensure that sorcha knows how to find and use your class. +We have base example classes that the user can take and modify to whatever your need is. Within the ``Sorcha`` :ref:`configs`, the user would then specify which class they want to use and provide the required :ref:`CPP` file on the command line. + + +Once the Sorcha addons is installed, Sorcha will automatically detect the available plugins and make them available during processing. + +To use one of the plugins from the community utilities, simply add the unique name of the plugin to the configuration file provided to Sorcha, and provide the complex parameters file on the command line. + + We also have 2 pre-made example classes that can augment the calculated apparent magnitude of each synthetic object, One for handling cometary activity as a function of heliocentric distance and one that applies rotational light curves to the synthetic objects. In both cases, any derived class must inherit from the corresponding base class and follow its API, to ensure that Sorcha knows how to find and use your class. Cometary Activity or Simulating Other Active Objects ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -38,14 +45,26 @@ Through the ``Sorcha'' configuration file. lsst_comet +.. seealso:: + We have an `example Jupyter notebook `_ demonstrating the LSSTCometActivity class built into `Sorcha addons GitHub repository `_. + +You can also develop your own class to apply a different comentary activity and add it into a custom version of the``Sorcha addons`` package. Once the Sorcha-addons is installed, Sorcha will automatically detect the available plugins and make them available during processing. + + + + Rotational Light Curve Effects ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The base lightcurve class is `AbstractLightCurve `_ (see below). Inside the `sorcha addons GitHub repository `_, we provide a simple example implementation where the apparent magnitude of the object (that is, the magnitude after all geometric effects have been taken into account), has a sinusoidal term added to it. To use this function, in the :ref:`CPP` file, the user must provide a light curve amplitude (`LCA`), corresponding to half the peak-to-peak amplitude for the magnitude changes, a period `Period`, and a reference time `Time0` where the light curve is at 0 - if these are not provided, the software will produce an error message. Despite being simple, that implementation shows all the class methods that need to be implemented for a custom light curve function. +The base lightcurve class is `AbstractLightCurve `_ (see below). Inside the `Sorcha addons GitHub repository `_, we provide a simple example implementation where the apparent magnitude of the object (that is, the magnitude after all geometric effects have been taken into account), has a sinusoidal term added to it. To use this function, in the :ref:`CPP` file, the user must provide a light curve amplitude (`LCA`), corresponding to half the peak-to-peak amplitude for the magnitude changes, a period `Period`, and a reference time `Time0` where the light curve is at 0 - if these are not provided, the software will produce an error message. Despite being simple, that implementation shows all the class methods that need to be implemented for a custom light curve function. .. literalinclude:: ../src/sorcha/lightcurves/base_lightcurve.py :language: python +.. seealso:: + We have an `example Jupyter notebook `_ demonstrating the SinusoidalLightCurve class built into `Sorcha addons GitHub repository `_, + + Applying Photometric and Astrometric Uncerainties ------------------------------------------------------------ diff --git a/docs/configfiles.rst b/docs/configfiles.rst index 2c42c992..a1ac16c3 100644 --- a/docs/configfiles.rst +++ b/docs/configfiles.rst @@ -3,7 +3,7 @@ Configuration File ===================== -``Sorcha`` uses a configuration file to set the majority of the various required and optional parameters and well as providing the ability to turn on and off various calculations and filters applied to the simulated small body population. Details about the various settings and options available in the configuration files are described in the :ref:`inputs`, :ref:`filters`, :ref:`ephemeris_gen`, and :ref:`output` pages. +``Sorcha`` uses a configuration file to set the majority of the various required and optional parameters as well as providing the ability to turn on and off various calculations and filters applied to the input small body population. Details about the various settings and options available in the configuration files are described in the :ref:`inputs`, :ref:`filters`, :ref:`ephemeris_gen`, and :ref:`output` pages. .. _example_configs: diff --git a/docs/inputs.rst b/docs/inputs.rst index 27cfe01a..0d2dbc6c 100644 --- a/docs/inputs.rst +++ b/docs/inputs.rst @@ -278,6 +278,9 @@ The complex physical parameters file is only needed if you're going to include y * The complex physical parameters file can be either **whitespace-separated** or **comma-separated values (CSV)** * Each simulated object **must** have a unique string identifier +.. seealso:: + Further details about how to use ``sorcha addons`` to apply cometary activity and lightcurve effects can be found :ref:`here`. + .. _ephemf: Ephemeris File (Optional) diff --git a/docs/notebooks/demo_Cometary_Activity.ipynb b/docs/notebooks/demo_Cometary_Activity.ipynb index c4e51261..75f3c656 100644 --- a/docs/notebooks/demo_Cometary_Activity.ipynb +++ b/docs/notebooks/demo_Cometary_Activity.ipynb @@ -542,7 +542,7 @@ "id": "a2bd9dd4-7666-4311-bc9f-a09b0255ae1c", "metadata": {}, "source": [ - "Let's active the LSSTCometActivity class " + "Let's activate the LSSTCometActivity class " ] }, { diff --git a/docs/overview.rst b/docs/overview.rst index 9bb86701..73066b96 100644 --- a/docs/overview.rst +++ b/docs/overview.rst @@ -24,7 +24,7 @@ The :ref:`inputs` that ``Sorcha`` requires are shown in the figure below Design Philosophy ---------------------- -``Sorcha`` has been designed in a modular way with each filter written as its own function, This makes it easy to add new filters in the future if required by users. We note for dealing with rotational light curve and activity effects, we have set up ``Sorcha`` such that the user can provide their own custom classes/functions and import them into ``Sorcha`` to use. Further details can be found on the :ref:`addons` page. ``Sorcha`` has been designed with LSST in mind, but many of the filters already developed will be applicable to other Solar System surveys. If you are interested in incorporating your survey into ``Sorcha`` do reach out. +``Sorcha`` has been designed in a modular way with each filter written as its own function, This makes it easy to add new filters in the future if required by users. We note for dealing with rotational light curve and activity effects, we have set up ``Sorcha`` such that the user can provide their own custom classes/functions and import them into ``Sorcha`` to use. Further details can be found in the :ref:`addons` section. ``Sorcha`` has been designed with LSST in mind, but many of the filters already developed will be applicable to other Solar System surveys. If you are interested in incorporating your survey into ``Sorcha`` do reach out. .. note:: Contributions are very welcome. If there is a feature or functionality not yet available in ``Sorcha``, we encourage you to propose the feature as an issue in the `main github repository `_ or share your code with the new enhancements. Further details can be found on our :ref:`reporting` page. From 4a3eaa88fbac85618803ba99391e405b8e4421f5 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Thu, 9 Jan 2025 10:57:18 +0000 Subject: [PATCH 23/52] documentation udpates big overview of inputs in particular --- docs/acknowledgements.rst | 2 +- docs/advanced.rst | 9 +- docs/apparentmag.rst | 26 ++++ docs/configfiles.rst | 7 +- docs/ephemerisgen.rst | 4 +- docs/index.rst | 2 +- docs/inputs.rst | 270 ++++++++++++++++++++++++++++++----- docs/overview.rst | 2 +- docs/whatsorchadoesnotdo.rst | 2 +- 9 files changed, 274 insertions(+), 50 deletions(-) diff --git a/docs/acknowledgements.rst b/docs/acknowledgements.rst index f7697cb5..5da1a754 100644 --- a/docs/acknowledgements.rst +++ b/docs/acknowledgements.rst @@ -40,4 +40,4 @@ This effort is a collaboration between Queen's University Belfast, the Universit - National Science Foundation through the following awards: Collaborative Research: SWIFT-SAT: Minimizing Science Impact on LSST and Observatories Worldwide through Accurate Predictions of Satellite Position and Optical Brightness NSF Award Number: 2332736 and Collaborative Research: Rubin Rocks: Enabling near-Earth asteroid science with LSST NSF Award Number: 2307570 - Travel funding from the STFC for UK participation in LSST through STFC grant ST/S006206/1 -Several functions within ``Sorcha`` were adapted from code originally developed for `rubin_sim`_, We thank the Vera C. Rubin Observatory Data Management Team and Scheduler Team for making their software open-source. Development of ``rubin_sim` was supported in part by the National Science Foundation through Cooperative Agreement AST-1258333 and Cooperative Support Agreement AST1836783 managed by the Association of Universities for Research in Astronomy (AURA), and the Department of Energy under Contract No. DE-AC02-76SF00515 with the SLAC National Accelerator Laboratory managed by Stanford University. +Several functions within ``Sorcha`` were adapted from code originally developed for `rubin_sim`_, We thank the Vera C. Rubin Observatory Data Management Team and Scheduler Team for making their software open-source. Development of ``rubin_sim`` was supported in part by the National Science Foundation through Cooperative Agreement AST-1258333 and Cooperative Support Agreement AST1836783 managed by the Association of Universities for Research in Astronomy (AURA), and the Department of Energy under Contract No. DE-AC02-76SF00515 with the SLAC National Accelerator Laboratory managed by Stanford University. diff --git a/docs/advanced.rst b/docs/advanced.rst index 5a8d0e22..83fbb4e3 100644 --- a/docs/advanced.rst +++ b/docs/advanced.rst @@ -80,14 +80,15 @@ SNR/Apparent Magnitude Filters These two mutually-exclusive filters serve to cut observations of faint objects. The user may either implement the SNR limit, to remove all observations of objects below a user-defined SNR threshold; or the magnitude limit, to remove all observations -of objects above a user-defined magnitude. +of objects above a user-defined **trailed source magniitude** magnitude. +**These filters are applied before the detection efficiency (fading function) is applied in** ``Sorcha``. -To implement the SNR limit, include the following in the config file:: +The SNR filter which will remove syntheitc observations that are less than a user-supplied SNR limit, To implelment the SNR limit (in this example to keep synthetic observations of input objects with a SNR > =2) include the following in the config file:: [EXPERT] SNR_limit = 2.0 -To implement the magnitude limit, include the following in the :ref:`configs`:: +To implement the magnitude limit (remove detections of objects fainter than 22 mag in all survey observing bands), include the following in the :ref:`configs`:: [EXPERT] magnitude_limit = 22.0 @@ -99,7 +100,7 @@ To implement the magnitude limit, include the following in the :ref:`configs`:: Specifying Alernative Versions of the Auxiliaryy Files Used in the Ephemeris Generator ----------------------------------------------------------------------------------------- -For backwards compability and to enable new version of the files to be run as well, users can override the default filenames and download locations of the :ref:`auxiliary files` used by ``Sorcha``'s bult-in :ref:`ephemeris generator`. These :ref:`configs`:: variables are added to a new auxiliary ( [AUXILIARY]) section:: +For backwards compability and to enable new version of the files to be run as well, users can override the default filenames and download locations of the :ref:`auxiliary files` used by ``Sorcha``'s bult-in :ref:`ephemeris generator`. These :ref:`configs`:: variables are added to a new auxiliary ([AUXILIARY]) section:: [AUXILIARY] diff --git a/docs/apparentmag.rst b/docs/apparentmag.rst index 66480cb0..caf6c049 100644 --- a/docs/apparentmag.rst +++ b/docs/apparentmag.rst @@ -79,3 +79,29 @@ This filter will recalculate the PSF magnitude of the observations, adjusting fo :alt: Sky image showing a short trailing source circled in red. :align: center + + +Accounting for Saturation (Saturation/Bright Filter) +------------------------------------------------------------ + +The saturation limit filter removes all detections that are brighter than the saturation limit +of the survey. `Ivezić et al. (2019) `_ +estimate that the saturation limit for the LSST will be ~16 in the r filter. + +``Sorcha`` includes functionality to specify either a single saturation limit, or a saturation limit in each filter. +For the latter, limits must be given in a comma-separated list in the same order as the filters supplied +for the observing_filters config file variable. + +To include this filter, the configuration file should contain:: + + [SATURATION] + bright_limit = 16.0 + +Or:: + + [SATURATION] + bright_limit = 16.0, 16.1, 16.2 + + +.. note:: + The saturation filter is only applied if the :ref:`configuration file` has a SATURATION section. diff --git a/docs/configfiles.rst b/docs/configfiles.rst index a1ac16c3..6b4188f9 100644 --- a/docs/configfiles.rst +++ b/docs/configfiles.rst @@ -5,12 +5,17 @@ Configuration File ``Sorcha`` uses a configuration file to set the majority of the various required and optional parameters as well as providing the ability to turn on and off various calculations and filters applied to the input small body population. Details about the various settings and options available in the configuration files are described in the :ref:`inputs`, :ref:`filters`, :ref:`ephemeris_gen`, and :ref:`output` pages. + +The configuration file is using the Windowst INI file format. The configuration file is formatted into distinct sections with headers. The headers are enclosed in squarebrackets ([]). Below each header are the asosciated configuration variable key pair (e.g. configvariablename = value). Any lines started with '#' are considered comments and ignored when parsing the cofiguration file. + +The presence or absence of various variables in the configuration file will turn on/off or inializie diifferent functions and features witin``Sorcha``. + .. _example_configs: Example Configuration Files ------------------------------------ -We provide example configuration files appropriate for setting up ``Sorcha`` to simulate what the LSST would discover. These example config files come installed with ``Sorcha`` and can be copied over to your working directory by typing on the command line:: +We provide below example configuration files appropriate for setting up ``Sorcha`` to simulate what the LSST would discover. These example config files come installed with ``Sorcha`` and can be copied over to your working directory by typing on the command line:: sorcha init diff --git a/docs/ephemerisgen.rst b/docs/ephemerisgen.rst index 2933c310..eb880e49 100644 --- a/docs/ephemerisgen.rst +++ b/docs/ephemerisgen.rst @@ -108,7 +108,7 @@ A number of auxiliary files available from the `Minor Planet Center ` from the configuration file and add the following:: +If you want to use the same input orbits across multiple ``Sorcha`` runs, you can save time by outputting the output from the ephemeris generation stage using the command line flag **-ew** in combination with a stem filename (do not include the file extension). Then in subsequent runs you will need to use the **--er** flag to on the command line to specify the input ephemeris file to read in. You will also need to remove :ref:`the ephemeris generation parameters` from the configuration file and add the following:: [INPUT] ephemerides_type = external @@ -135,7 +135,7 @@ If you prefer to use a different method or software package for producing the ep **eph_format** is the format of the user provided ephemeris file. Options are **csv**, **whitespace**, and **hdf5**. .. tip:: - Use the **-er** flag on the command line to specify the external ephemeris file that ``Sorcha`` should use. + Use the **--er** flag on the command line to specify the external ephemeris file that ``Sorcha`` should use. .. warning:: We have validated and tested ``Sorcha`` and its internal ephemeris generator. If the user decides to use a different method to provide the required ephemerides for their science, it is up to the user to validate/check the output of the external ephemeris generator. diff --git a/docs/index.rst b/docs/index.rst index fcb0199e..30291134 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -49,8 +49,8 @@ works, tutorials, and demonstration notebooks that show how each of the various overview installation - inputs configfiles + inputs ephemerisgen apparentmag filters diff --git a/docs/inputs.rst b/docs/inputs.rst index 0d2dbc6c..9068ffc6 100644 --- a/docs/inputs.rst +++ b/docs/inputs.rst @@ -49,7 +49,11 @@ This is a file which contains the orbital information of a set of synthetic obje Cometary Orbit Format ~~~~~~~~~~~~~~~~~~~~~~~ -An example of an orbit file in Cometary format:: + +Example Orbit File in Cometary Format +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code-block:: ObjID FORMAT q e inc node argPeri t_p_MJD_TDB epochMJD_TDB S1000000a COM 3.01822 0.05208 22.56035 211.00286 335.42134 51575.94061 54800.00000 @@ -58,6 +62,9 @@ An example of an orbit file in Cometary format:: S1000003a COM 2.10917 0.13219 1.46615 266.54621 232.24412 54212.16304 54800.00000 S1000004a COM 2.17676 0.19949 12.92422 162.14580 192.22312 51895.46586 54800.00000 +Cometaryn Orbit Format Required Columns +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +-------------+----------------------------------------------------------------------------------+ | Keyword | Description | +=============+==================================================================================+ @@ -82,7 +89,11 @@ An example of an orbit file in Cometary format:: Keplerian Orbit Format ~~~~~~~~~~~~~~~~~~~~~~~~ -An example of an orbit file in Keplerian format:: + +Example Orbit File in Keplerian Format +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code-block:: ObjID FORMAT a e inc node argPeri ma epochMJD_TDB t1 KEP 47.9877 0.0585 11.3584 148.4661 140.4756 308.3244 53157.00 @@ -92,6 +103,8 @@ An example of an orbit file in Keplerian format:: t5 KEP 47.9356 0.2912 4.3621 306.0908 217.8116 18.7043 53157.00 t6 KEP 47.9786 0.2730 2.2425 147.9340 166.6578 327.8996 53157.00 +Keplerian Orbit Format Required Columns +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +-------------+----------------------------------------------------------------------------------+ | Keyword | Description | +=============+==================================================================================+ @@ -116,7 +129,11 @@ An example of an orbit file in Keplerian format:: Cartesian Orbit Format ~~~~~~~~~~~~~~~~~~~~~~~ -An example of an orbit file, in Cartesian format:: + +Example Orbit File in Cartesian format +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code-block:: ObjID FORMAT x y z xdot ydot zdot epochMJD_TDB STC001TFa CART 36.701800449281706 -8.770729364470023 -0.6261488665458296 0.0007155581026554 0.0026593939322716 7.344098975957749e-06 54466.0 36.54594860110992 0.04317 @@ -129,6 +146,9 @@ An example of an orbit file, in Cartesian format:: STC001TMa CART -35.205151144286006 -21.59643017634877 -6.399036148167812 0.0012861312376887 -0.0023168284708868 -0.0001863582741122 54466.0 41.6549967769547 0.05369 STC001TNa CART -33.79882997522472 -16.266135214977684 -5.221001391031022 0.0013485808895118 -0.0024033901851641 -0.0001051222283375 54466.0 36.890329257623286 0.06274 +Cartesian Orbit Format Required Columns +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +-------------+----------------------------------------------------------------------------------+ | Keyword | Description | +=============+==================================================================================+ @@ -154,6 +174,25 @@ An example of an orbit file, in Cartesian format:: .. note:: All positions and velocities are in respect to J2000 +Orbit File Configuration Parameters +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +``Sorcha`` is initialized for the format of the input orbit file through the :ref:`configuration file` INPUT sections: + +.. code-block:: + + [INPUT] + + # Sorcha chunk size: how many objects should be processed at once? + + size_serial_chunk = 20000 + + # Format for the orbit, physical parameters, and complex physical parameters input files. + # Options: csv or whitespace + + aux_format = csv + + .. _physical: Physical Parameters File @@ -169,13 +208,19 @@ The input file for the physical parameters includes information about the object * Each simulated object **must** have a unique string identifier * You **must use the same phase curve prescription for all simulated objects**. If you want to use different phase curve prescriptions for different synthetic populations, you will need to run them in separate input files to ``Sorcha`` * If the phase curve function is set to NONE in the configuration value then no phase curve parameter values are required in the physical parameters files. - * In the config file you can decide which filters you want have ``Sorcha`` run on and specify which filter is the main filter that the absolute magnitude is defined for. You only need to provide colors for those filters specified in the config file. + * In the :ref:`configuration file` you can decide which filters you want have ``Sorcha`` run on and specify which filter is the main filter that the absolute magnitude is defined for. You only need to provide colors for those filters specified in the :ref:`configuration file`. + +We have implemented several phase curve parameterizations that can be specified in the :ref:`configuration file` and then inputted through the physical parameters. **You can either specify one set of phase curve parameters for all filters or specify values for each filter examined by** ``Sorcha``. We are using the `sbpy `_ phase function utilities. The supported options are: `HG `_, `HG1G2 `_, `HG12 `_, `linear `_ (specified by S in the header of the physical parameters file), and none (if no columns for phase curve are included in the physical parameters file then the synthetic object is considered to have a flat phase curve). Note that the HG12 model is the `Penttilä et al. (2016) `_ modified model, and not the original (IAU adopted) `Muinonen et al. (2010) `_ model. + + + +Example Pphysical Parameters File (single linear slope phase curve parameter for all filters) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. note:: For readability we show examples of whitespace-separated files below. -An example of the physical parameters file where a single linear slope phase curve parameter is used for all filters:: - +.. code-block:: ObjID H u-r g-r i-r z-r y-r GS St500000a 5.63 2.55 0.92 -0.38 -0.59 -0.70 0.15 @@ -185,7 +230,13 @@ An example of the physical parameters file where a single linear slope phase cur St500004a 10.2 1.90 0.58 -0.21 -0.30 -0.39 0.15 -An example of the physical parameters file where a HG prescription is specified for each filter:: +Example Physical Parameters File (a HG value is specified for each filter) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. note:: + For readability we show examples of whitespace-separated files below. + +.. code-block:: ObjID H u-r g-r i-r z-r y-r Gr Gu Gg Gi Gz Gy St500000a 5.63 2.55 0.92 -0.38 -0.59 -0.70 0.15 0.17 0.14 0.19 0.18 0.20 @@ -194,9 +245,11 @@ An example of the physical parameters file where a HG prescription is specified St500003a 6.67 1.72 0.48 -0.11 -0.12 -0.12 0.15 0.16 0.12 0.20 0.15 0.19 St500004a 10.2 1.90 0.58 -0.21 -0.30 -0.39 0.15 0.15 0.16 0.15 0.14 0.16 -Rubin Observatory will survey the sky in six broadband (optical filters), *u, g, r, i, z,* and *y* . In the physical parameters file, you will specify the object's absolute magnitude in the main filter (as specified in the config file. usually this is g or r band) and then provide the synthetic planetesimal's color in other filters relative to the main filter. +Rubin Observatory will survey the sky in six broadband (optical filters), *u, g, r, i, z,* and *y* . In the physical parameters file, you will specify the object's absolute magnitude in the main filter (as specified in the :ref:`configuration file` (sually this is g or r band) and then provide the synthetic planetesimal's color in other filters relative to the main filter. -We have implemented several phase curve parameterizations that can be specified in the config file and then inputted through the physical parameters. **You can either specify one set of phase curve parameters for all filters or specify values for each filter examined by ``Sorcha``.** We are using the `sbpy `_ phase function utilities. The supported options are: `HG `_, `HG1G2 `_, `HG12 `_, `linear `_ (specified by S in the header of the physical parameters file), and none (if no columns for phase curve are included in the physical parameters file then the synthetic object is considered to have a flat phase curve). Note that the HG12 model is the `Penttilä et al. (2016) `_ modified model, and not the original (IAU adopted) `Muinonen et al. (2010) `_ model. + +Required Physical Parameters File Columns and Format +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +------------------+----------------------------------------------------------------------------------+ | Keyword | Description | @@ -211,14 +264,91 @@ We have implemented several phase curve parameterizations that can be specified +------------------+----------------------------------------------------------------------------------+ .. note:: - The Phase curve parameters(s) column will not be present if the phase curve function/calculation is set to None in the configuration file + The Phase curve parameters(s) column will not be present if the phase curve function/calculation is set to None in the :ref:`configuration file'. .. note:: - In the config file you can decide which filters you want to have ``Sorcha`` run on and specify which filter is the main filter that the absolute magnitude is defined for. You only need to provide colors for those filters specified in the config file. + In the :ref:`configuration file` you can decide which filters you want to have ``Sorcha`` run on and specify which filter is the main filter that the absolute magnitude is defined for. You only need to provide colors for those filters specified in the :ref:`configuration file`. .. seealso:: We have an `example Jupyter notebook `_ demonstrating how to take a representative optical/NIR spectra of your input population and using the `rubin_sim `_ package to estimate the expected colors in the LSST filter bandpasses. + +Physical Parameters File Configuration Parameters +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +``Sorcha`` is initialized for the format of the input physical parameters file through the :ref:`configuration file` INPUT, FILTERS. and PHASECURVES sections: + +.. code-block:: + + [INPUT] + + # Sorcha chunk size: how many objects should be processed at once? + + size_serial_chunk = 20000 + + # Format for the orbit, physical parameters, and complex physical parameters input files. + # Options: csv or whitespace + + aux_format = csv + + [FILTERS] + + # Filters of the observations you are interested in, comma-separated. + # Your physical parameters file must have H calculated in one of these filters + # and colour offset columns defined relative to that filter. + + observing_filters = r,g,i,z,u,y + + [PHASECURVES] + + # The phase function used to calculate apparent magnitude. The physical parameters input + # file must contain the columns needed to calculate the phase function. + # Options: HG, HG1G2, HG12, linear, none. + + phase_function = linear + +.. note:: + In the :ref:`configuration file` you can decide which filters you want to have ``Sorcha`` run on and specify which filter is the main filter that the absolute magnitude is defined for. You only need to provide colors for those filters specified in the :ref:`configuration file`. + + +.. _CPP: + +Complex Physical Parameters File (Optional) +--------------------------------------------------- + +The complex physical parameters file is only needed if you're going to include your own rotational light curve class or cometary activity class to augment the calculated apparent magnitudes. ``Sorcha`` is set up such that any values required for this such as (light curve amplitude and period per simulated object) are included in a file, separate from the physical parameters file, that we refer to as the complex physical parameters file. What columns are required in the complex physical parameters file depends on the classes you are using. + +.. tip:: + * The first column must be ObjID, but the ordering of the remaining columns does not matter as long as the required columns exist and have entries + * The first row in the complex physical parameters file **must** list the column names + * The **correct capitalization of column names** is required + * The complex physical parameters file can be either **whitespace-separated** or **comma-separated values (CSV)** + * Each simulated object **must** have a unique string identifier + +.. seealso:: + Further details about how to use ``sorcha addons`` to apply cometary activity and lightcurve effects can be found :ref:`here`. + + +Complex Parameters File Configuration Parameters +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +``Sorcha`` is initialized for the format of the complex physical parameters file through the :ref:`configuration file` INPUT sections: + +.. code-block:: + + [INPUT] + + # Sorcha chunk size: how many objects should be processed at once? + + size_serial_chunk = 20000 + + # Format for the orbit, physical parameters, and complex physical parameters input files. + # Options: csv or whitespace + + aux_format = csv + + + .. _pointing: Survey Pointing Database @@ -250,12 +380,14 @@ The latest version of rubin_sim cadence simulations can be found at https://s3df .. _database_query: + Setting Up the Correct LSST Pointing Database Query ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -``Sorcha``'s **ppsqldbquery** config file parameter contains the SQL query for obtaining this information from the pointing database. +``Sorcha``'s **ppsqldbquery** :ref:`configuration file` parameter contains the SQL query for obtaining this information from the pointing database. From rubin_sim v2.0 simulations onward use the query:: + SELECT observationId, observationStartMJD as observationStartMJD_TAI, visitTime, visitExposureTime, filter, seeingFwhmGeom as seeingFwhmGeom_arcsec, seeingFwhmEff as seeingFwhmEff_arcsec, fiveSigmaDepth as fieldFiveSigmaDepth_mag , fieldRA as fieldRA_deg, fieldDec as fieldDec_deg, rotSkyPos as fieldRotSkyPos_deg FROM observations order by observationId For past rubin_sim/OpSim simulations pre-v2.0 use the query:: @@ -264,22 +396,68 @@ For past rubin_sim/OpSim simulations pre-v2.0 use the query:: -.. _CPP: +Survey Pointing Database Configuration Parameters +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The survey pointing database query is set in the :ref:`configuration file` INPUT section: + + +.. code-block:: + + [INPUT] + + # SQL query for extracting data from the pointing database. + + pointing_sql_query = SELECT observationId, observationStartMJD as observationStartMJD_TAI, visitTime, visitExposureTime, filter, seeingFwhmGeom as seeingFwhmGeom_arcsec, seeingFwhmEff as seeingFwhmEff_arcsec, fiveSigmaDepth as fieldFiveSigmaDepth_mag , fieldRA as fieldRA_deg, fieldDec as fieldDec_deg, rotSkyPos as fieldRotSkyPos_deg FROM observations order by observationId -Complex Physical Parameters File (Optional) ---------------------------------------------------- -The complex physical parameters file is only needed if you're going to include your own rotational light curve class or cometary activity class to augment the calculated apparent magnitudes. ``Sorcha`` is set up such that any values required for this such as (light curve amplitude and period per simulated object) are included in a file, separate from the physical parameters file, that we refer to as the complex physical parameters file. What columns are required in the complex physical parameters file depends on the classes you are using. + +Camera Footprint File (Optional) +----------------------------------------- + +.. attention:: + The camera footprint file is only required if you are using the camera footprint + +If you are going to simulate the full camera architecture including CCD locations and chip gaps in the camera focal plane, you will need to provide a file that describes the layout of detectors on the camera focal plane. + +The camera footprint file is a comma-separated values (CSV) file with three columns describing the detector shapes, with the header “detector,x,y”. The first column indicates which detector a point belongs to, and should be an integer. Second and third columns specify where on the focal plane the corners are. Values are unitless, equal to tan( ra ), tan( dec ), where ra and dec are the vertical and horizontal angles of the points from the center of the sphere tangent to origin in the focal plane. Ordering does not matter, as the constructor sorts the points automatically. .. tip:: - * The first column must be ObjID, but the ordering of the remaining columns does not matter as long as the required columns exist and have entries - * The first row in the complex physical parameters file **must** list the column names - * The **correct capitalization of column names** is required - * The complex physical parameters file can be either **whitespace-separated** or **comma-separated values (CSV)** - * Each simulated object **must** have a unique string identifier + ``Sorcha`` comes with a representation of the LSSTCam architecture already installed. Further details of how to use this built-in default file can be found in the description of the :ref:`Full Camera Footprint Filter`. + +Example Camera Footprint File +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. literalinclude:: ../src/sorcha/modules/data/LSST_detector_corners_100123.csv + :language: text + :lines: 1-20 + +Camera Footprint File Configuration Parameters +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +You can set whether you're using a camera footprint file and the location of the file in the :ref:`configuration file` FOV section: + +.. code-block:: + + [FOV] + + # Choose between circular or actual camera footprint, including chip gaps. + # Options: circle, footprint. + + camera_model = footprint + + + # Path to camera footprint file. Uncomment to provide a path to the desired camera + # detector configuration file if not using the default built-in LSSTCam detector + # configuration for the actual camera footprint. + + footprint_path= ./data/detectors_corners.csv + +.. note:: + If camera_model is set to footprint and footprint_path config variable is not set, ``Sorcha`` will automatically read in its installed LSSTCam detector footprint file. + +.. tip:: + If using the cicle camera module, foot_print needs to be removed or commented out of the :ref:`configuration file` . -.. seealso:: - Further details about how to use ``sorcha addons`` to apply cometary activity and lightcurve effects can be found :ref:`here`. .. _ephemf: @@ -297,15 +475,23 @@ Ephemeris File (Optional) * The ephemeris file can be either **whitespace-separated** or **comma-separated values(CSV)** * Each simulated object **must** have a unique string identifier +.. hint:: + Use the **--er** flag on the command line to specify the external ephemeris file that ``Sorcha`` should use. + + +Example Ephemeris File +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + .. note:: For readability we show an example of a whitespace-separated file below. -An example of an (optional) ephemeris file: - .. literalinclude:: ../docs/example_files/assist_rebound.csv :language: text :lines: 1-20 +Required Ephemeris File Columns and Format +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +--------------------------+----------------------------------------------------------------------------------+ | Keyword | Description | +==========================+==================================================================================+ @@ -341,7 +527,7 @@ An example of an (optional) ephemeris file: +--------------------------+----------------------------------------------------------------------------------+ | Obs-Sun(J2000x)(km) | Cartesian X-component of observer's heliocentric distance (km) | +--------------------------+----------------------------------------------------------------------------------+ -| Obs-Sun(J2000y)(km) | Cartesian Y-component of the observer's heliocentric distance (km) | +| Obs-Sun(J2000y)(km) | Cartesian Y-component of the observer's heliocentric distance (km) | +--------------------------+----------------------------------------------------------------------------------+ | Obs-Sun(J2000z)(km) | Cartesian Z-component of the observer's heliocentric distance (km) | +--------------------------+----------------------------------------------------------------------------------+ @@ -355,23 +541,29 @@ An example of an (optional) ephemeris file: +--------------------------+----------------------------------------------------------------------------------+ .. note:: - All positions and velocities are in respect to J2000 + All positions and velocities are in respect to J2000 -Camera Footprint File (Optional) ------------------------------------------ -.. attention:: - The camera footprint file is only required if you are using the camera footprint +Ephemeris File Configuration Parameters +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -If you are going to simulate the full camera architecture including CCD locations and chip gaps in the camera focal plane, you will need to provide a file that describes the layout of detectors on the camera focal plane. +``Sorcha`` is initialized to use an external ephemeris file through the :ref:`configuration file` INPUT section: -The camera footprint file is a comma-separated text file with three columns describing the detector shapes, with the header “detector,x,y”. The first column indicates which detector a point belongs to, and should be an integer. Second and third columns specify where on the focal plane the corners are. Values are unitless, equal to tan( ra ), tan( dec ), where ra and dec are the vertical and horizontal angles of the points from the center of the sphere tangent to origin in the focal plane. Ordering does not matter, as the constructor sorts the points automatically. +.. code-block:: -.. tip:: -``Sorcha`` comes with a representation of the LSSTCam architecture already installed. Further details of how to use this built-in default file can be found in the description of the :ref:`Full Camera Footprint Filter`. + [INPUT] -An example of an (optional) camera footprint file: -.. literalinclude:: ../src/sorcha/modules/data/LSST_detector_corners_100123.csv - :language: text - :lines: 1-20 + # The simulation used for the ephemeris input. + # ar=ASSIST+REBOUND interal ephemeris generation + # external=providing an external input file from the command line + # Options: "ar", "external" + + ephemerides_type = external + + + # Format for ephemeris simulation input file if a file is specified at the command line. + # This is also the format to which ephemeris files will be written out, if specified. + # Options: csv, whitespace, hdf5 + + eph_format = csv diff --git a/docs/overview.rst b/docs/overview.rst index 73066b96..aeefdeab 100644 --- a/docs/overview.rst +++ b/docs/overview.rst @@ -7,7 +7,7 @@ How Sorcha Works In order to conduct detailed population studies on the orbital properties and physical characteristics of the various Solar System small body reservoirs, one must account for all the survey biases (the complex and often intertwined detection biases – brightness limits, pointing, cadence, on-sky motion limits, software detection efficiencies) in one’s discovery survey (`see Lawler et al. 2018 `_ for a more detailed discussion). ``Sorcha`` is an open-source Python Solar System survey simulator designed for the `Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) `_. ``Sorcha`` works by the user inputting a synthetic Solar System small body population. The software forward models the input population to simulate what the survey should have detected using a series of :ref:`filters`. The filters can be switched on or off and customized as needed via a :ref:`configuration file`. In this way, a synthetic population can be compared to the real survey's discoveries. -The :ref:`inputs` that ``Sorcha`` requires are shown in the figure below. The software requires input files that describe the small bodies to simulate (including a file for orbits, at least one file for physical parameters, and a SQLite database that describes the telescope survey's observation history). ``Sorcha`` outputs simulated detection data, including each time, position, and apparent magnitude at which a synthetic small body was detected based on various options set up in a configuration file. +The :ref:`inputs` that ``Sorcha`` requires are shown in the figure below. The software requires input files that describe the small bodies to simulate (including a file for orbits, at least one file for physical parameters, and a SQLite database that describes the telescope survey's observation history). ``Sorcha`` outputs simulated detection data, including each time, position, and apparent magnitude at which a synthetic small body was detected based on various options set up in a :ref:`configuration file`. .. image:: images/survey_simulator_flow_chart.png diff --git a/docs/whatsorchadoesnotdo.rst b/docs/whatsorchadoesnotdo.rst index 34e9e670..cd8b1802 100644 --- a/docs/whatsorchadoesnotdo.rst +++ b/docs/whatsorchadoesnotdo.rst @@ -20,4 +20,4 @@ Here is a short summary of the key effects not accounted for in ``Sorcha'': We do have methods for users to easily develop their own functions for adjusting the apparent magnitude of the simulated objects due to cometary activity, rotational light curves, cometary outbursts, etc. We have some basic functionality already built for simple sinusoidal rotational - light curves and cometary activity. Further details can be found :ref:`here` + light curves and cometary activity. Further details can be found :ref:`here`. From 174d3e65afb6647dd1af09a579d7dd8ecbaaefe3 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Thu, 9 Jan 2025 14:22:32 +0000 Subject: [PATCH 24/52] documentation updates --- docs/acknowledgements.rst | 2 +- docs/configfiles.rst | 4 ++++ docs/ephemerisgen.rst | 2 +- docs/inputs.rst | 21 ++++++++++++++++++--- docs/notebooks/demo_Cometary_Activity.ipynb | 18 +++++++++++++----- docs/notebooks/demo_Lightcurve.ipynb | 6 +++--- 6 files changed, 40 insertions(+), 13 deletions(-) diff --git a/docs/acknowledgements.rst b/docs/acknowledgements.rst index 5da1a754..ba68b806 100644 --- a/docs/acknowledgements.rst +++ b/docs/acknowledgements.rst @@ -40,4 +40,4 @@ This effort is a collaboration between Queen's University Belfast, the Universit - National Science Foundation through the following awards: Collaborative Research: SWIFT-SAT: Minimizing Science Impact on LSST and Observatories Worldwide through Accurate Predictions of Satellite Position and Optical Brightness NSF Award Number: 2332736 and Collaborative Research: Rubin Rocks: Enabling near-Earth asteroid science with LSST NSF Award Number: 2307570 - Travel funding from the STFC for UK participation in LSST through STFC grant ST/S006206/1 -Several functions within ``Sorcha`` were adapted from code originally developed for `rubin_sim`_, We thank the Vera C. Rubin Observatory Data Management Team and Scheduler Team for making their software open-source. Development of ``rubin_sim`` was supported in part by the National Science Foundation through Cooperative Agreement AST-1258333 and Cooperative Support Agreement AST1836783 managed by the Association of Universities for Research in Astronomy (AURA), and the Department of Energy under Contract No. DE-AC02-76SF00515 with the SLAC National Accelerator Laboratory managed by Stanford University. +Several functions within ``Sorcha`` were adapted from code originally developed for `rubin_sim `_, We thank the Vera C. Rubin Observatory Data Management Team and Scheduler Team for making their software open-source. Development of ``rubin_sim`` was supported in part by the National Science Foundation through Cooperative Agreement AST-1258333 and Cooperative Support Agreement AST1836783 managed by the Association of Universities for Research in Astronomy (AURA), and the Department of Energy under Contract No. DE-AC02-76SF00515 with the SLAC National Accelerator Laboratory managed by Stanford University. diff --git a/docs/configfiles.rst b/docs/configfiles.rst index 6b4188f9..fd3922c4 100644 --- a/docs/configfiles.rst +++ b/docs/configfiles.rst @@ -9,6 +9,10 @@ Configuration File The configuration file is using the Windowst INI file format. The configuration file is formatted into distinct sections with headers. The headers are enclosed in squarebrackets ([]). Below each header are the asosciated configuration variable key pair (e.g. configvariablename = value). Any lines started with '#' are considered comments and ignored when parsing the cofiguration file. The presence or absence of various variables in the configuration file will turn on/off or inializie diifferent functions and features witin``Sorcha``. + + +.. attention:: + Use the **-c** flag on the command line to specify the configuration file that ``Sorcha`` should use. .. _example_configs: diff --git a/docs/ephemerisgen.rst b/docs/ephemerisgen.rst index eb880e49..232964af 100644 --- a/docs/ephemerisgen.rst +++ b/docs/ephemerisgen.rst @@ -108,7 +108,7 @@ A number of auxiliary files available from the `Minor Planet Center ` from the configuration file and add the following:: +If you want to use the same input orbits across multiple ``Sorcha`` runs, you can save time by outputting the output from the ephemeris generation stage using the command line flag **--ew** in combination with a stem filename (do not include the file extension). Then in subsequent runs you will need to use the **--er** flag to on the command line to specify the input ephemeris file to read in. You will also need to remove :ref:`the ephemeris generation parameters` from the configuration file and add the following:: [INPUT] ephemerides_type = external diff --git a/docs/inputs.rst b/docs/inputs.rst index 9068ffc6..d62fa12d 100644 --- a/docs/inputs.rst +++ b/docs/inputs.rst @@ -44,6 +44,9 @@ This is a file which contains the orbital information of a set of synthetic obje .. tip:: Be careful about the way your input elements are defined! Using heliocentric elements as barycentric (or vice-versa) will lead to wrong outputs. Similarly, if using Cartesian elements, be careful about the orientation of the coordinate system! ``Sorcha`` assumes that Cartesian elements are Ecliptic-oriented. +.. attention:: + Use the **--ob** flag on the command line to specify the orbit file that ``Sorcha`` should use. + .. note:: For readability we show examples of whitespace-separated files below. We show only the heliocentric versions of these inputs, as the barycentric column requirements are identical, changing only the `FORMAT` designation @@ -62,7 +65,7 @@ Example Orbit File in Cometary Format S1000003a COM 2.10917 0.13219 1.46615 266.54621 232.24412 54212.16304 54800.00000 S1000004a COM 2.17676 0.19949 12.92422 162.14580 192.22312 51895.46586 54800.00000 -Cometaryn Orbit Format Required Columns +Cometary Orbit Format Required Columns ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +-------------+----------------------------------------------------------------------------------+ @@ -269,6 +272,10 @@ Required Physical Parameters File Columns and Format .. note:: In the :ref:`configuration file` you can decide which filters you want to have ``Sorcha`` run on and specify which filter is the main filter that the absolute magnitude is defined for. You only need to provide colors for those filters specified in the :ref:`configuration file`. + +.. attention:: + Use the **--p** flag on the command line to specify the pointing database that ``Sorcha`` should use. + .. seealso:: We have an `example Jupyter notebook `_ demonstrating how to take a representative optical/NIR spectra of your input population and using the `rubin_sim `_ package to estimate the expected colors in the LSST filter bandpasses. @@ -329,6 +336,9 @@ The complex physical parameters file is only needed if you're going to include y Further details about how to use ``sorcha addons`` to apply cometary activity and lightcurve effects can be found :ref:`here`. +.. attention:: + Use the **--cp** flag on the command line to specify the pointing database that ``Sorcha`` should use. + Complex Parameters File Configuration Parameters ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -348,7 +358,6 @@ Complex Parameters File Configuration Parameters aux_format = csv - .. _pointing: Survey Pointing Database @@ -367,6 +376,12 @@ What we call the LSST pointing database (currently simulated since Rubin Observa .. warning:: The pointing databases times are expected to be TAI (Temps Atomique International; French for International Atomic Time), + +.. attention:: + Use the **--pd** flag on the command line to specify the pointing database that ``Sorcha`` should use. + + + The latest version of rubin_sim cadence simulations can be found at https://s3df.slac.stanford.edu/data/rubin/sim-data/. An example rubin_sim simulation visualized on sky is shown in the plot below of the number of on-sky visits over the 10-year simulated baseline v3.2 survey (image credit: Lynne Jones): .. image:: images/Rubin_v3.2_baseline_visits.png @@ -475,7 +490,7 @@ Ephemeris File (Optional) * The ephemeris file can be either **whitespace-separated** or **comma-separated values(CSV)** * Each simulated object **must** have a unique string identifier -.. hint:: +.. attention:: Use the **--er** flag on the command line to specify the external ephemeris file that ``Sorcha`` should use. diff --git a/docs/notebooks/demo_Cometary_Activity.ipynb b/docs/notebooks/demo_Cometary_Activity.ipynb index 75f3c656..7012c6c9 100644 --- a/docs/notebooks/demo_Cometary_Activity.ipynb +++ b/docs/notebooks/demo_Cometary_Activity.ipynb @@ -17,7 +17,7 @@ "\n", "We will use the community tools part of the `Sorcha-addons`(https://github.com/dirac-institute/sorcha-addons) package\n", "\n", - "The idea is that the user can, in principle, implement their own method for cometary activity, and incorporate them in their simulation. The goal of `Sorcha-addons` is for both the development team, as well as for the community, to share their implementations of custom coemtary activity models. " + "The idea is that the user can, in principle, implement their own method for cometary activity, and incorporate it in their simulation. The goal of `Sorcha-addons` is for both the development team, as well as for the community, to share their implementations of custom cometary activity models. " ] }, { @@ -253,7 +253,7 @@ "id": "191c5e0f", "metadata": {}, "source": [ - "Now we calculate the magnitude of the nuceleus assuming no phase curve model in PPCalculateApparentMagnitudeInFilter." + "Now we calculate the magnitude of the nucleus assuming no phase curve model in PPCalculateApparentMagnitudeInFilter." ] }, { @@ -532,8 +532,8 @@ "\n", "Let's use the LSSTCometActivity class from `sorcha_addons`. We need the following columns in our dataframe:\n", "\n", - " * ``afrho1\"`` = V-band Afρ value of the comet at 1 au\n", - " * ``k`` = power-law slope that describes how the activity varies with heliocentric distance\n", + "* ``afrho1`` = V-band Afρ value of the comet at 1 au [cm]\n", + "* ``k`` = power-law slope that describes how the activity varies with heliocentric distance\n", " " ] }, @@ -1019,8 +1019,16 @@ "id": "e5d4ca57-b1f8-4aad-90c3-e11831fd6282", "metadata": {}, "source": [ - "At larger heliocentric distances the nucelus does not contribute much, the coma is the main contribution to the apparent magnitude and the comet is observed to much brighter than an inactive body at the same heliocentric distance. Closer to the Sun, the nucleus contirbution is more significant." + "At larger heliocentric distances the nucelus does not contribute much. The coma is the main contribution to the apparent magnitude at those distances, and the comet is observed to be much brighter than an inactive body at the same heliocentric distance. Closer to the Sun, the nucleus contribution to the apparent magntiude is more significant." ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8b36365-e4f8-4db7-9d77-2e16c9e0a7eb", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/docs/notebooks/demo_Lightcurve.ipynb b/docs/notebooks/demo_Lightcurve.ipynb index 814bc1d3..c358fdc7 100644 --- a/docs/notebooks/demo_Lightcurve.ipynb +++ b/docs/notebooks/demo_Lightcurve.ipynb @@ -672,9 +672,9 @@ "\n", "Let's use the basic sinusoidal lightcurve from `sorcha_addons`. We need the following columns in our dataframe:\n", "\n", - " * ``LCA`` - lightcurve amplitude [magnitudes].\n", - " * ``Period`` - period of the sinusoidal oscillation [days]. Should be a positive value.\n", - " * ``Time0`` - phase for the light curve [days].\n", + "* ``LCA`` - lightcurve amplitude [magnitudes].\n", + "* ``Period`` - period of the sinusoidal oscillation [days]. Should be a positive value.\n", + "* ``Time0`` - phase for the light curve [days].\n", "\n", "Let's create a lightcurve with a period of 20 days, phased so that the first observation is at zero variation, and with 0.5 mag peak-to-peak amplitude." ] From 7cf0e3f4504dbfa12e14d52b7f414bb0ec3f2e28 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Thu, 9 Jan 2025 17:46:07 +0000 Subject: [PATCH 25/52] update outputs section and advanced features --- docs/advanced.rst | 25 +++++++++-- docs/outputs.rst | 112 +++++++++++++++++++++++----------------------- 2 files changed, 78 insertions(+), 59 deletions(-) diff --git a/docs/advanced.rst b/docs/advanced.rst index 83fbb4e3..d1c0cbc6 100644 --- a/docs/advanced.rst +++ b/docs/advanced.rst @@ -97,7 +97,7 @@ To implement the magnitude limit (remove detections of objects fainter than 22 m Only one of these filters may be implemented at once. -Specifying Alernative Versions of the Auxiliaryy Files Used in the Ephemeris Generator +Specifying Alernative Versions of the Auxiliary Files Used in the Ephemeris Generator ----------------------------------------------------------------------------------------- For backwards compability and to enable new version of the files to be run as well, users can override the default filenames and download locations of the :ref:`auxiliary files` used by ``Sorcha``'s bult-in :ref:`ephemeris generator`. These :ref:`configs`:: variables are added to a new auxiliary ([AUXILIARY]) section:: @@ -147,8 +147,27 @@ For backwards compability and to enable new version of the files to be run as we Advanced Output Options ----------------------------------- -We recommend that you do not change the decimal place precision and instead leave ``Sorcha`` to output the full value -to machine precision, but there may be reasons why you need to reduce the size of the output. +Custom Outputs +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +By setting the value of the output_columns configuration file keyword to a comma-separated list of column names, you may +specify your own custom output, using this page as a reference for potential column names. + +For example, you could state this in your configuration file to get the object ID, position and magnitude only:: + + [OUTPUT] + output_columns = ObjID,RA_deg,Dec_deg,trailedSourceMag + +.. warning:: + If you are choosing to specify the column names in this way, please perform a quick test-run first to ensure your column names are correct before + embarking on any long runs. As we allow for user-written code and add-ons to add new column names, we do not error-handle the column names until + late in the code, upon output. + + +Specifying the Decimal Precision for the Photometric and Astromeitc Values +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +By default, no rounding is performed on any of the output values. We recommend that you do not change the decimal place precision and instead leave ``Sorcha`` to output the full value to machine precision, but there may be reasons why you need to reduce the size of the output. In the [OUTPUT] section of the :ref:`configs`, you can set the decimal precision for the astrometry outputs:: diff --git a/docs/outputs.rst b/docs/outputs.rst index 8100c018..18151da9 100644 --- a/docs/outputs.rst +++ b/docs/outputs.rst @@ -3,33 +3,17 @@ Outputs ================== -Sorcha Output ----------------------- +.. attention:: + Use the **-o** flag on the command line to specify where ``Sorcha`` should be saving any output and log files (the file path). -``Sorcha`` produces an output file describing each predicted observation the survey will record of the input simulated objects, -with a row for each predicted detection and a column for each parameter to be calculated. This output file can be in several formats -set by the output_format configuration file keyword. -Additionally, the output columns can be set to either "basic" or "all" settings (described below) using the output_columns configuration file keyword. -Alternatively, you may specify the columns you wish to be output. +.. attention:: + Use the **-t** flag on the command line to specify the filename stem for all the ``Sorcha`` output files and logs. -The format of any output from ``Sorcha`` will look something like:: - ObjID,fieldMJD_TAI,fieldRA_deg,fieldDec_deg,RA_deg,Dec_deg,astrometricSigma_deg,optFilter,PSFMag,trailedSourceMag,PSFMagSigma,trailedSourceMagSigma,fiveSigmaDepth_mag,fiveSigmaDepthAtSource - S1000000a,61769.320619,163.87542090842982,-18.84327137012991,164.03771300000017,-17.58257500000004,2.9880927198448093e-06,r,19.667095021023798,19.655534004675797,0.006775654132479691,0.006755926588113991,23.86356436464961,23.839403736057715 - S1000000a,61769.332335,163.87542090842982,-18.84327137012991,164.03840499999956,-17.583782000000177,3.0580983448792015e-06,i,19.654439857054346,19.651499866857677,0.008648382870172588,0.00861644095296432,23.50948086026021,23.485408367730255 - S1000000a,61773.283672,163.33185289781585,-17.478349047859123,164.25272700000096,-17.970833000000166,2.8628267283501646e-06,g,19.605094385361397,19.59913996244041,0.004573058990569846,0.004562676340629368,24.412081324532746,24.40274105573913 - S1000000a,61773.304607,163.33185289781585,-17.478349047859123,164.2535509999998,-17.972800999999485,2.8619239276501636e-06,r,19.60417845127433,19.610463241887746,0.005414938113316873,0.005396964439230442,24.142184414583568,24.132798535794453 - S1000000a,61780.286672,163.70205228035468,-18.10471138055092,164.4364500000006,-18.561287999999216,3.106487369364405e-06,i,19.50224387218658,19.49961057650898,0.00996299590797273,0.009945212307287087,23.1343489868631,23.13059981155987 - S1000000a,61780.310927,163.70205228035468,-18.10471138055092,164.4365160000002,-18.56311500000129,3.0899264531165437e-06,z,19.506070321795203,19.506622970072044,0.01126449135209172,0.011237007559280756,22.968207967454678,22.964441345175853 - S1000000a,61781.239134,163.95033588103914,-18.031113105727716,164.44201499999986,-18.63119400000105,3.2223774034283947e-06,i,19.50028114807821,19.494448387335947,0.01214406799779637,0.01212132996202541,22.85013563621249,22.84858482288965 - S1000000a,61781.263141,163.95033588103914,-18.031113105727716,164.4419770000004,-18.63294700000159,3.042088583360277e-06,z,19.486562767073988,19.47832341807803,0.011723502868190884,0.011688663662533069,22.899894717824814,22.898283896399494 - S1000000a,61789.27659,164.99043640246796,-19.09523631317997,164.29665099999988,-19.110176000000447,2.8895553381860802e-06,z,19.376978135088684,19.359651855968583,0.008079363622311368,0.00805998568672928,23.293210067462763,23.293123719813384 - - -Output Formats -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The configuration file keyword output_format allows ``Sorcha`` to output files in CSV, SQLite3 or HDF5 formats. For example:: +Output File Formats +---------------------------- +The :ref:`configuration file` keyword output_format in the OUTPUT section allows ``Sorcha`` to output files in CSV, SQLite3 or HDF5 formats. For example:: [OUTPUT] # The options: csv, sqlite3, hdf5 @@ -43,26 +27,27 @@ The configuration file keyword output_format allows ``Sorcha`` to output files i with a number (due to a limitation in PyTables). -Output Rounding -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -By default, no rounding is performed on any of the output values. If you wish to round -output values, this can be done separately for magnitude and position values using the following -configuration file keywords:: +Detections File +---------------------- - [OUTPUT] - position_decimals = 7 - magnitude_decimals = 3 +``Sorcha`` produces a detections file describing each predicted survey detection of the input small body populations, +with a row for each predicted detection and a column for each parameter calculated. +Additionally, the output columns of the detections file can be set to either "basic" or "all" settings (described below) using the output_columns :ref:`configuration file` keyword. + +.. _basic:: + Basic Output ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The "basic" output includes the columns most relevant to general photometry and detection purposes. This is declared -in the configuration file like so:: +in the :ref:`configuration file` like so:: [OUTPUT] output_columns = basic -The column names, formats, and descriptions are as follows: +Detections File: Basic Output Column Names, Formats, and Descriptions +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +------------------------------------+--------------+----------------------------------------------------------------------------------+ | Keyword | Format | Description | @@ -108,16 +93,40 @@ The column names, formats, and descriptions are as follows: The object_linked column only appears if the :ref:`linking filter` is on and the user has requested that observations of unlinked objects should not be dropped. -All Output +.. warning:: + If you are writing to a HDF5 file that you plan to access using the PyTables library, note that your object IDs cannot begin + with a number (due to a limitation in PyTables). + + +Example Detections File in Basic Format +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code-block:: + + ObjID,fieldMJD_TAI,fieldRA_deg,fieldDec_deg,RA_deg,Dec_deg,astrometricSigma_deg,optFilter,PSFMag,trailedSourceMag,PSFMagSigma,trailedSourceMagSigma,fiveSigmaDepth_mag,fiveSigmaDepthAtSource + S1000000a,61769.320619,163.87542090842982,-18.84327137012991,164.03771300000017,-17.58257500000004,2.9880927198448093e-06,r,19.667095021023798,19.655534004675797,0.006775654132479691,0.006755926588113991,23.86356436464961,23.839403736057715 + S1000000a,61769.332335,163.87542090842982,-18.84327137012991,164.03840499999956,-17.583782000000177,3.0580983448792015e-06,i,19.654439857054346,19.651499866857677,0.008648382870172588,0.00861644095296432,23.50948086026021,23.485408367730255 + S1000000a,61773.283672,163.33185289781585,-17.478349047859123,164.25272700000096,-17.970833000000166,2.8628267283501646e-06,g,19.605094385361397,19.59913996244041,0.004573058990569846,0.004562676340629368,24.412081324532746,24.40274105573913 + S1000000a,61773.304607,163.33185289781585,-17.478349047859123,164.2535509999998,-17.972800999999485,2.8619239276501636e-06,r,19.60417845127433,19.610463241887746,0.005414938113316873,0.005396964439230442,24.142184414583568,24.132798535794453 + S1000000a,61780.286672,163.70205228035468,-18.10471138055092,164.4364500000006,-18.561287999999216,3.106487369364405e-06,i,19.50224387218658,19.49961057650898,0.00996299590797273,0.009945212307287087,23.1343489868631,23.13059981155987 + S1000000a,61780.310927,163.70205228035468,-18.10471138055092,164.4365160000002,-18.56311500000129,3.0899264531165437e-06,z,19.506070321795203,19.506622970072044,0.01126449135209172,0.011237007559280756,22.968207967454678,22.964441345175853 + S1000000a,61781.239134,163.95033588103914,-18.031113105727716,164.44201499999986,-18.63119400000105,3.2223774034283947e-06,i,19.50028114807821,19.494448387335947,0.01214406799779637,0.01212132996202541,22.85013563621249,22.84858482288965 + S1000000a,61781.263141,163.95033588103914,-18.031113105727716,164.4419770000004,-18.63294700000159,3.042088583360277e-06,z,19.486562767073988,19.47832341807803,0.011723502868190884,0.011688663662533069,22.899894717824814,22.898283896399494 + S1000000a,61789.27659,164.99043640246796,-19.09523631317997,164.29665099999988,-19.110176000000447,2.8895553381860802e-06,z,19.376978135088684,19.359651855968583,0.008079363622311368,0.00805998568672928,23.293210067462763,23.293123719813384 + + +.. _full:: + +Full Output ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The 'all' output option includes all columns from the basic output, as well as those relevant to ephemeris generation for each -predicted detection, and some of the input orbital and physical parameters of each simulated object. This is declared -in the configuration file like so:: +predicted detection, and some of the input orbital and physical parameters of each simulated object. All columns within the pandas databframe at the end of the ``Sorcha`` run are written out. This is declared in the :ref:`configuration file` like so:: [OUTPUT] output_columns = all -The column names, formats, and descriptions are as follows +Detections File: Full Output Column Names, Formats, and Descriptions +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +------------------------------------+--------------+----------------------------------------------------------------------------------------------------------+ | Keyword | Format | Description | @@ -236,32 +245,24 @@ The column names, formats, and descriptions are as follows .. note:: All positions, positions, and velocities are in respect to J2000. -Custom Output -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -By setting the value of the output_columns configuration file keyword to a comma-separated list of column names, you may -specify your own custom output, using this page as a reference for potential column names. - -For example, you could state this in your configuration file to get the object ID, position and magnitude only:: +.. note:: + All columns in the comple physicalx parameters file will also be included in the full output. - [OUTPUT] - output_columns = ObjID,RA_deg,Dec_deg,trailedSourceMag .. warning:: - If you are choosing to specify the column names in this way, please perform a quick test-run first to ensure your column names are correct before - embarking on any long runs. As we allow for user-written code and add-ons to add new column names, we do not error-handle the column names until - late in the code, upon output. - + If you are writing to a HDF5 file that you plan to access using the PyTables library, note that your object IDs cannot begin + with a number (due to a limitation in PyTables). -Additional Outputs +Optional Outputs ---------------------- Ephemeris Output ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Optionally (with the **-ew** flag set at the command line), an ephemeris file of all detections near the +Optionally (with the **--ew** flag set at the command line), an ephemeris file of all detections near the field can be generated to a separate file, which can then be provided back to ``Sorcha`` as an optional external ephemeris file with the **-er** flag. More information can be found on this functionality, including the output columns, in the :ref:`Ephemeris Generation` section of the documentation. -The format of the outputted ephemeris file is controlled by the **eph_format** configuration keyword in the Inputs section of the configuration file:: +The format of the outputted ephemeris file is controlled by the **eph_format** configuration keyword in the Inputs section of the :ref:`configuration file`e:: [INPUT] ephemerides_type = external @@ -271,9 +272,9 @@ The format of the outputted ephemeris file is controlled by the **eph_format** c Users should note that output produced by reading in a previously-generated ephemeris file will be in a different order than the output produced when running the ephemeris generator within ``Sorcha``. This is simply a side-effect of how ``Sorcha`` reads in ephemeris files and does not affect the actual content of the output. -Statistics Output +Statistics (Tally) File ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -``Sorcha`` can also output a statistics or "tally" file which contains an overview of the ``Sorcha`` output for each object and filter. Minimally, this +``Sorcha`` can also output a statistics or "tally" file (if specified uisng the **--st flag) which contains an overview of the ``Sorcha`` output for each object and filter. Minimally, this file lists the number of observations for each object in each filter, along with the minimum, maximum and median apparent magnitude and the minimum and maximum phase angle. If the :ref:`linking filter` is on, this file also contains information on whether and when the object was linked by SSP. @@ -304,5 +305,4 @@ The columns in the statistics file are as follows: +------------------------------------+--------------+----------------------------------------------------------------------------------------------------------+ .. note:: -Unless the user has specified **drop_unlinked = False** in the configuration file, the object_linked column will read TRUE for all objects. To see which objects were not linked by ``Sorcha``, this -variable must be set to False. +Unless the user has specified **drop_unlinked = False** in the :ref:`configuration file`, the object_linked column will read TRUE for all objects. To see which objects were not linked by ``Sorcha``, this variable must be set to False. From 31253478fd523e3713247763d8e9553166233dd9 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Fri, 10 Jan 2025 10:45:50 +0000 Subject: [PATCH 26/52] remove comment from trailing losses notebook remove comment from trailing losses notebook --- docs/notebooks/demo_TrailingLossesValidation.ipynb | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/notebooks/demo_TrailingLossesValidation.ipynb b/docs/notebooks/demo_TrailingLossesValidation.ipynb index 45a83649..fbce3b77 100644 --- a/docs/notebooks/demo_TrailingLossesValidation.ipynb +++ b/docs/notebooks/demo_TrailingLossesValidation.ipynb @@ -33,7 +33,6 @@ "The trailing loss curves are calculated with different seeing values: 0.5, 0.7 and 1.0.\n", "\n", "2. The zoom-in of the difference between PSF trailing loss and combined PSF and detection trailing loss components.\n", - "At small velocities (less than 1 deg/day) the difference is negligible.\n", "\n", "3. Thresholds for typical on sky motions (formula 1 from Luu and Jewitt 1988, AJ 95 1256, \n", "https://ui.adsabs.harvard.edu/abs/1988AJ.....95.1256L/abstract ) \n", @@ -254,7 +253,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.12.7" } }, "nbformat": 4, From 46a0aa0d278928ff5c34a9fdf28269e905de25c7 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Fri, 10 Jan 2025 17:19:04 +0000 Subject: [PATCH 27/52] update docs --- docs/apparentmag.rst | 107 - docs/configfiles.rst | 2 +- docs/index.rst | 3 +- docs/notebooks/README.md | 4 - docs/notebooks/lsst-total-r.dat | 8508 ---------------------- docs/overview.rst | 2 + docs/{filters.rst => postprocessing.rst} | 166 +- 7 files changed, 128 insertions(+), 8664 deletions(-) delete mode 100644 docs/apparentmag.rst delete mode 100644 docs/notebooks/lsst-total-r.dat rename docs/{filters.rst => postprocessing.rst} (58%) diff --git a/docs/apparentmag.rst b/docs/apparentmag.rst deleted file mode 100644 index caf6c049..00000000 --- a/docs/apparentmag.rst +++ /dev/null @@ -1,107 +0,0 @@ -.. _apparent_magnitudes - -Post-Processing (Applying Survey Biases) -========================================================== - -Trailed Source Magnitude and PSF (Point Spread Function) Magnitude ---------------------------------------------------------------------- - -``Sorcha`` calculates two apparent magnitudes that we will refer to as the **trailed source magnitude** and the **PSF magnitude**. - - - -.. image:: images/trailed_source.png - :width: 500 - :alt: A cartoon explanation of trailed source mag and PSF mag - :align: center - -Phase Curves ------------------------------------------------------------- - -.. _addons: - -Incorporating Rotational Light Curves and Activity ------------------------------------------------------------- -``Sorcha`` has the ability user provided functions though python classes that augment/change the apparent brightness calculations for the synthetic Solar System objects. Any values required as input for these calculations, must be provided in the separate :ref:`CPP` file as input. - -We have base example classes that the user can take and modify to whatever your need is. Within the ``Sorcha`` :ref:`configs`, the user would then specify which class they want to use and provide the required :ref:`CPP` file on the command line. - - -Once the Sorcha addons is installed, Sorcha will automatically detect the available plugins and make them available during processing. - -To use one of the plugins from the community utilities, simply add the unique name of the plugin to the configuration file provided to Sorcha, and provide the complex parameters file on the command line. - - We also have 2 pre-made example classes that can augment the calculated apparent magnitude of each synthetic object, One for handling cometary activity as a function of heliocentric distance and one that applies rotational light curves to the synthetic objects. In both cases, any derived class must inherit from the corresponding base class and follow its API, to ensure that Sorcha knows how to find and use your class. - -Cometary Activity or Simulating Other Active Objects -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. literalinclude:: ../src/sorcha/activity/base_activity.py - :language: python - - -Through the ``Sorcha'' configuration file. - -lsst_comet - - -.. seealso:: - We have an `example Jupyter notebook `_ demonstrating the LSSTCometActivity class built into `Sorcha addons GitHub repository `_. - -You can also develop your own class to apply a different comentary activity and add it into a custom version of the``Sorcha addons`` package. Once the Sorcha-addons is installed, Sorcha will automatically detect the available plugins and make them available during processing. - - - - -Rotational Light Curve Effects -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The base lightcurve class is `AbstractLightCurve `_ (see below). Inside the `Sorcha addons GitHub repository `_, we provide a simple example implementation where the apparent magnitude of the object (that is, the magnitude after all geometric effects have been taken into account), has a sinusoidal term added to it. To use this function, in the :ref:`CPP` file, the user must provide a light curve amplitude (`LCA`), corresponding to half the peak-to-peak amplitude for the magnitude changes, a period `Period`, and a reference time `Time0` where the light curve is at 0 - if these are not provided, the software will produce an error message. Despite being simple, that implementation shows all the class methods that need to be implemented for a custom light curve function. - -.. literalinclude:: ../src/sorcha/lightcurves/base_lightcurve.py - :language: python - - -.. seealso:: - We have an `example Jupyter notebook `_ demonstrating the SinusoidalLightCurve class built into `Sorcha addons GitHub repository `_, - - -Applying Photometric and Astrometric Uncerainties ------------------------------------------------------------- - -Trailing Losses ------------------ - -If the observed object is fast-moving, the signal will form a trail, reducing the measured magnitude. -This filter will recalculate the PSF magnitude of the observations, adjusting for trailing losses. - -.. image:: images/Trail.png - :width: 400 - :alt: Sky image showing a short trailing source circled in red. - :align: center - - - -Accounting for Saturation (Saturation/Bright Filter) ------------------------------------------------------------- - -The saturation limit filter removes all detections that are brighter than the saturation limit -of the survey. `Ivezić et al. (2019) `_ -estimate that the saturation limit for the LSST will be ~16 in the r filter. - -``Sorcha`` includes functionality to specify either a single saturation limit, or a saturation limit in each filter. -For the latter, limits must be given in a comma-separated list in the same order as the filters supplied -for the observing_filters config file variable. - -To include this filter, the configuration file should contain:: - - [SATURATION] - bright_limit = 16.0 - -Or:: - - [SATURATION] - bright_limit = 16.0, 16.1, 16.2 - - -.. note:: - The saturation filter is only applied if the :ref:`configuration file` has a SATURATION section. diff --git a/docs/configfiles.rst b/docs/configfiles.rst index fd3922c4..81c4a7de 100644 --- a/docs/configfiles.rst +++ b/docs/configfiles.rst @@ -3,7 +3,7 @@ Configuration File ===================== -``Sorcha`` uses a configuration file to set the majority of the various required and optional parameters as well as providing the ability to turn on and off various calculations and filters applied to the input small body population. Details about the various settings and options available in the configuration files are described in the :ref:`inputs`, :ref:`filters`, :ref:`ephemeris_gen`, and :ref:`output` pages. +``Sorcha`` uses a configuration file to set the majority of the various required and optional parameters as well as providing the ability to turn on and off various calculations and filters applied to the input small body population. Details about the various settings and options available in the configuration files are described in the :ref:`inputs`,:ref:`ephemeris_gen`, :ref:`post_processing' and :ref:`output` pages. The configuration file is using the Windowst INI file format. The configuration file is formatted into distinct sections with headers. The headers are enclosed in squarebrackets ([]). Below each header are the asosciated configuration variable key pair (e.g. configvariablename = value). Any lines started with '#' are considered comments and ignored when parsing the cofiguration file. diff --git a/docs/index.rst b/docs/index.rst index 30291134..939be2e8 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -52,8 +52,7 @@ works, tutorials, and demonstration notebooks that show how each of the various configfiles inputs ephemerisgen - apparentmag - filters + postprocessing outputs gettingstarted hpc diff --git a/docs/notebooks/README.md b/docs/notebooks/README.md index 3131c3f0..31a50ebc 100644 --- a/docs/notebooks/README.md +++ b/docs/notebooks/README.md @@ -24,10 +24,6 @@ demo_FootprintFilter - **Demonstrates:** PPFootprintFilter - **Files:** detector_corners.csv, footprintFilterValidationObservations.csv, oneline_v2.0.db -demo_CalculateLSSTColours -- **Demonstrates:** How to take an optical/near-infrared spectrum of a known Solar System object and convert it to predicted LSST filter colors -- **Files:** 2002PN34_highres.spec, - demo_Lightcurve - **Demonstrates:** lightcurve_registration (LC_METHODS, update_lc_subclasses), AbstractLightCurve class - **Files:** none diff --git a/docs/notebooks/lsst-total-r.dat b/docs/notebooks/lsst-total-r.dat deleted file mode 100644 index d7fcb297..00000000 --- a/docs/notebooks/lsst-total-r.dat +++ /dev/null @@ -1,8508 +0,0 @@ -# LSST Throughputs files created from syseng_throughputs repo -# Version 1.1 -# sha1 acd2b1389ef7356e2125ef6f9dc40a5ffe2af05b -# Aerosols added to atmosphere -# Wavelen_cutoff_BLUE 536.90 -# Wavelen_cutoff_RED 706.00 -# Wavelength(nm) Throughput(0-1) -300.0 0.0 -300.1 0.0 -300.2 0.0 -300.3 0.0 -300.4 0.0 -300.5 0.0 -300.6 0.0 -300.7 0.0 -300.8 0.0 -300.9 0.0 -301.0 0.0 -301.1 0.0 -301.2 0.0 -301.3 0.0 -301.4 0.0 -301.5 0.0 -301.6 0.0 -301.7 0.0 -301.8 0.0 -301.9 0.0 -302.0 0.0 -302.1 0.0 -302.2 0.0 -302.3 0.0 -302.4 0.0 -302.5 0.0 -302.6 0.0 -302.7 0.0 -302.8 0.0 -302.9 0.0 -303.0 0.0 -303.1 0.0 -303.2 0.0 -303.3 0.0 -303.4 0.0 -303.5 0.0 -303.6 0.0 -303.7 0.0 -303.8 0.0 -303.9 0.0 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observational biases (such as detection effiency) in order to identify which objects in the input small body population would have been discovered by the discovery and the observations the objects would have been detected in. + ``Sorcha`` by default uses its own :ref:`ephemeris generator` to propagate the orbits and translate them to on-sky locations and rates. ``Sorcha``'s ephemeris generator is powered by `ASSIST `_, a software package for ephemeris-quality integrations of test particles, and the `REBOUND `_ N-body integrator. If the user prefers to use a different generator, ``Sorcha`` is also to be configured to read in an external ephemeris file with pre-calculated ephemerides of the input synthetic orbital population. diff --git a/docs/filters.rst b/docs/postprocessing.rst similarity index 58% rename from docs/filters.rst rename to docs/postprocessing.rst index 48b5910f..37a26347 100644 --- a/docs/filters.rst +++ b/docs/postprocessing.rst @@ -1,17 +1,111 @@ -.. _filters: +.. _post_processing: -Sorcha's Filter Options -======================================== +Post-Processing (Applying Survey Biases) +========================================================== -Below are the user-controlled filters applied by ``Sorcha`` with the relevant configuration -file parameters and suggested/example values. +How it Works +------------------------ -.. tip:: - For a more in-depth explanation of these filters and how they are implemented, - please see our upcoming paper (Merritt et al. in prep). +All aspects of post-processing can be adjusted + +Trailed Source Magnitude and PSF (Point Spread Function) Magnitude +--------------------------------------------------------------------- + +``Sorcha`` calculates two apparent magnitudes that we will refer to as the **trailed source magnitude** and the **PSF magnitude**. + + + +.. image:: images/trailed_source.png + :width: 500 + :alt: A cartoon explanation of trailed source mag and PSF mag + :align: center + +Phase Curves +------------------------------------------------------------ + +.. _addons: + +Incorporating Rotational Light Curves and Activity +------------------------------------------------------------ +``Sorcha`` has the ability user provided functions though python classes that augment/change the apparent brightness calculations for the synthetic Solar System objects. Any values required as input for these calculations, must be provided in the separate :ref:`CPP` file as input. + +We have base example classes that the user can take and modify to whatever your need is. Within the ``Sorcha`` :ref:`configs`, the user would then specify which class they want to use and provide the required :ref:`CPP` file on the command line. + + +Once the Sorcha addons is installed, Sorcha will automatically detect the available plugins and make them available during processing. + +To use one of the plugins from the community utilities, simply add the unique name of the plugin to the configuration file provided to Sorcha, and provide the complex parameters file on the command line. + + We also have 2 pre-made example classes that can augment the calculated apparent magnitude of each synthetic object, One for handling cometary activity as a function of heliocentric distance and one that applies rotational light curves to the synthetic objects. In both cases, any derived class must inherit from the corresponding base class and follow its API, to ensure that Sorcha knows how to find and use your class. + +Cometary Activity or Simulating Other Active Objects +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. literalinclude:: ../src/sorcha/activity/base_activity.py + :language: python -Brightness/Saturation Limit ---------------------------- + +Through the ``Sorcha'' configuration file. + +lsst_comet + + +.. seealso:: + We have an `example Jupyter notebook `_ demonstrating the LSSTCometActivity class built into `Sorcha addons GitHub repository `_. + +You can also develop your own class to apply a different comentary activity and add it into a custom version of the``Sorcha addons`` package. Once the Sorcha-addons is installed, Sorcha will automatically detect the available plugins and make them available during processing. + + + + +Rotational Light Curve Effects +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +The base lightcurve class is `AbstractLightCurve `_ (see below). Inside the `Sorcha addons GitHub repository `_, we provide a simple example implementation where the apparent magnitude of the object (that is, the magnitude after all geometric effects have been taken into account), has a sinusoidal term added to it. To use this function, in the :ref:`CPP` file, the user must provide a light curve amplitude (`LCA`), corresponding to half the peak-to-peak amplitude for the magnitude changes, a period `Period`, and a reference time `Time0` where the light curve is at 0 - if these are not provided, the software will produce an error message. Despite being simple, that implementation shows all the class methods that need to be implemented for a custom light curve function. + +.. literalinclude:: ../src/sorcha/lightcurves/base_lightcurve.py + :language: python + + +.. seealso:: + We have an `example Jupyter notebook `_ demonstrating the SinusoidalLightCurve class built into `Sorcha addons GitHub repository `_, + + +Applying Photometric and Astrometric Uncerainties +------------------------------------------------------------ + +Trailing Losses +----------------- + +If the observed object is fast-moving, the signal will form a trail, reducing the measured magnitude. +This filter will recalculate the PSF magnitude of the observations, adjusting for trailing losses. + +.. image:: images/Trail.png + :width: 400 + :alt: Sky image showing a short trailing source circled in red. + :align: center + + +.. seealso:: + We have a Jupyter notebook `_ demonstrating the LSSTCometActivity class built into `Sorcha addons GitHub repository `_. + + + +Vignetting +----------------- +Objects that are on the edges of the field of view are dimmer due to vignetting: the field-of-view is not +uniformly illuminated, and so the limiting magnitude for each detection will depend on its position within the FOV. +This filter applies a model of this from a built-in function tailored specifically for the LSST (see +`Araujo-Hauck et al. 2016 `_, with further +discussion and below figure from `Veres and Chesley 2017 `_.) + +.. image:: images/vignetting.jpg + :width: 500 + :alt: Plot of the LSST camera footprint in Dec vs. RA, showing shaded dimming due to vignetting. + :align: center + + +Accounting for Saturation (Saturation/Bright Filter) +------------------------------------------------------------ The saturation limit filter removes all detections that are brighter than the saturation limit of the survey. `Ivezić et al. (2019) `_ @@ -31,12 +125,17 @@ Or:: [SATURATION] bright_limit = 16.0, 16.1, 16.2 + +.. note:: + The saturation filter is only applied if the :ref:`configuration file` has a SATURATION section. + + Fading Function/Detection Efficiency ------------------------------------ This filter serves to remove observations of objects which are faint beyond the survey's capability to detect them. ``Sorcha`` uses the fading function formulation of `Veres and Chesley (2017) `_: -see the below plot. This fading function is parameterised by the fading function width and peak efficiency. +see the below plot. This fading function is parameterised by the fading function width and peak efficiency. The default values are modelled on those from the aforementioned paper. To include this filter, the following options should be set in the configuration file:: @@ -52,8 +151,6 @@ To include this filter, the following options should be set in the configuration :align: center -.. _the_camera_footprint: - Camera Footprint ----------------- @@ -61,15 +158,15 @@ Camera Footprint Applying some form of the camera footprint filter is mandatory. Due to the footprint of the LSST Camera (LSSTCam), see the figure below, it is possible that some object detections may be lost in -gaps between the chips. +gaps between the chips. .. image:: images/Footprint.png :width: 600 :alt: Plot of the LSST camera footprint where x and y are x and y distance from the pupil in degrees. The footprint also shows two overplotted circle radii of 1.75deg (corresponding to a 75% fill factor) and 2.06deg. :align: center -However, the full camera footprint is most relevant for slow-moving objects, where an object may move only a small amount per night and could thus in a -subsequent observation fall into a chip gap. This is less concerning for faster-moving objects such as asteroids and near-Earth objects. As a result, +However, the full camera footprint is most relevant for slow-moving objects, where an object may move only a small amount per night and could thus in a +subsequent observation fall into a chip gap. This is less concerning for faster-moving objects such as asteroids and near-Earth objects. As a result, we provide two methods of applying the camera footprint. Circle Radius (Simple Sensor Area) @@ -77,7 +174,7 @@ Circle Radius (Simple Sensor Area) Using this filter applies a very simple circular camera footprint. The radius of the circle (**circle_radius** key) should be given in degrees. The **fill_factor** key specifics what fraction of observations should be randomly removed to roughly mimic detector chip - gaps in this circular footprint approximation. The fraction of observations not removed is controlled by the config variable fill_factor. + gaps in this circular footprint approximation. The fraction of observations not removed is controlled by the config variable fill_factor. To include this filter, the following options should be set in the configuration file:: [FOV] @@ -86,7 +183,7 @@ To include this filter, the following options should be set in the configuration fill_factor = 0.9 .. warning:: - Note that :ref:`ASSIST+REBOUND ephemeris generator` also uses a circular radius for its search area. To get accurate results, the ASSIST+REBOUND radius must be set to be larger than the circle_radius. For simmulating the LSST, we rcommend setting **ar_ang_fov = 2.06** and **ar_fov_buffer = 0.2**. Setting the circle_radius to be larger than the radius used for ASSIST+REBOUND will have no effect. + Note that :ref:`ASSIST+REBOUND ephemeris generator` also uses a circular radius for its search area. To get accurate results, the ASSIST+REBOUND radius must be set to be larger than the circle_radius. For simmulating the LSST, we rcommend setting **ar_ang_fov = 2.06** and **ar_fov_buffer = 0.2**. Setting the circle_radius to be larger than the radius used for ASSIST+REBOUND will have no effect. .. tip:: For Rubin Observatory, the circle radius should be set to 1.75 degrees with a fill factor of 0.9 to approximate the detector area of LSSTCam. @@ -106,10 +203,10 @@ To include this filter, the following options should be set in the configuration footprint_path = ./data/detectors_corners.csv .. tip:: - ``Sorcha`` comes with a representation of the LSSTCam footprint already installed. If you do not include the **footprint_path** in the configuration file, then ``Sorcha`` assumes you're using its internal LSSTCam footprint. + ``Sorcha`` comes with a representation of the LSSTCam footprint already installed. If you do not include the **footprint_path** in the configuration file, then ``Sorcha`` assumes you're using its internal LSSTCam footprint. .. warning:: - Note that :ref:`ASSIST+REBOUND ephemeris generator` uses a circular radius for its search area. To get accurate results, the ASSIST+REBOUND radius must be set to be larger than the circle_radius. For simmulating the LSST, we rcommend setting **ar_ang_fov = 2.06** and **ar_fov_buffer = 0.2**. + Note that :ref:`ASSIST+REBOUND ephemeris generator` uses a circular radius for its search area. To get accurate results, the ASSIST+REBOUND radius must be set to be larger than the circle_radius. For simmulating the LSST, we rcommend setting **ar_ang_fov = 2.06** and **ar_fov_buffer = 0.2**. Additionally, the camera footprint model can account for the losses at the edge of the CCDs where the detection software will not be able to pick out sources close to the edge. You can add an exclusion zone around each CCD measured in arcseconds (on the focal plane) using the `footprint_edge_threshold` key to the configuraiton file. An example setup in the configuration file:: @@ -121,31 +218,16 @@ Additionally, the camera footprint model can account for the losses at the edge .. tip:: ``Sorcha`` comes with a representation of the LSSTCam footprint already installed. If you do not include the **footprint_path** in the configuration file, then ``Sorcha`` assumes you're using its internal LSSTCam footprint. - -Vignetting ------------------ -Objects that are on the edges of the field of view are dimmer due to vignetting: the field-of-view is not -uniformly illuminated, and so the limiting magnitude for each detection will depend on its position within the FOV. -This filter applies a model of this from a built-in function tailored specifically for the LSST (see -`Araujo-Hauck et al. 2016 `_, with further -discussion and below figure from `Veres and Chesley 2017 `_.) - -.. image:: images/vignetting.jpg - :width: 500 - :alt: Plot of the LSST camera footprint in Dec vs. RA, showing shaded dimming due to vignetting. - :align: center - - .. _linking: -Linking +Linking --------------------------- The linking filter simulates the behaviour of LSST's Solar System Processing (SSP, `Jurić et al. 2020 `_, -`Swinbank et al. 2020 `_), the automated software pipeline +`Swinbank et al. 2020 `_), the automated software pipeline dedicated to linking and cross-matching observations that belong to the same object. -Linking is performed by detecting multiple observations of an object in a single night: a 'tracklet'. +Linking is performed by detecting multiple observations of an object in a single night: a 'tracklet'. A number of these tracklets must then be detected in a specific time window to form a 'track'. @@ -154,18 +236,18 @@ The defaults given below are those used by SSP and are explained in the comments [LINKING] - # Not all objects will be linked by SSP: this variable controls the + # Not all objects will be linked by SSP: this variable controls the # fraction successfully linked. SSP_detection_efficiency = 0.95 # The number of observations required to form a valid tracklet. SSP_number_observations = 2 - # The minimum separation (in arcsec) between two observations of + # The minimum separation (in arcsec) between two observations of # an object required for the linking to distinguish them as separate. SSP_separation_threshold = 0.5 - # The maximum time separation (in days) between subsequent + # The maximum time separation (in days) between subsequent # observations in a tracklet. SSP_maximum_time = 0.0625 @@ -175,7 +257,7 @@ The defaults given below are those used by SSP and are explained in the comments # Tracklets must occur in <= this number of days to constitute a # complete track/detection. SSP_track_window = 15 - + # The time in UTC at which it is noon at the observatory location (in standard time). # For the LSST, 12pm Chile Standard Time is 4pm UTC. SSP_night_start_utc = 16.0 From 0f6787515e57f1523c8e1cfadc374b13b3122d7d Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Sat, 11 Jan 2025 09:13:52 +0000 Subject: [PATCH 28/52] documentation updates documentation updates --- docs/.notebooks.rst.swp | Bin 0 -> 12288 bytes docs/cite.rst | 6 +++++- docs/ephemerisgen.rst | 8 ++++++++ docs/inputs.rst | 1 + docs/outputs.rst | 13 +++++++++---- docs/support.rst | 2 +- 6 files changed, 24 insertions(+), 6 deletions(-) create mode 100644 docs/.notebooks.rst.swp diff --git a/docs/.notebooks.rst.swp b/docs/.notebooks.rst.swp new file mode 100644 index 0000000000000000000000000000000000000000..8a62ccbc1a6a24bcba787b99b265498cab220da6 GIT binary patch literal 12288 zcmeI2JC7ST5XWg3M*K?r=nNQOk|J(S@}sg~A8^*45IgsR)>f;dc1K#myX1o8N>+*l zsnQ1llF#5TkS-s<{tN-!8FrUCO@_OxmF^^~FyO+tAuu8&{%3~s(<}i&9Ng|b-sATh zw;7Hrj2)O?Mw@T4bHD8|mfA3$h;-nW){Rg`U>b=IMY2xDewqbrcEN%$Vcg87F4n4< zSnHZLH)ELgtxWSoIBl9%qeN6wKNV8-4P<)ud89+zRNBEn>*uy%tXrF%7Ky-VCvchF zyL+d2)W7}aYkvLOv(pZQ>L&t3fCvx)B0vO)01+Spr;32f&aj`c;&x}iMe`9 z6A>T+M1Tko0U|&IhyW2F0z`la5CJ0a84?f?V?Uf{>@}MI|9|%T|KG0|`xEUCwDV|R zp#AqHWAD-4p-s?kp}oD#*h{n+?JC;emlzAsZle8jk+I*=T4--CF!mSPuV~++T|)CY zU-`T=hyW2F0z`la5CI}U1U`lU#}O1)Ci#wvHk{tTiTA+Bp$vtSTJZ< zZZaPrl}c`-1Mi7MMytJMnz^L(ZR-@AFjKx2IyshZ%C|AeQqq~MKa*|pBK58u#V+)h zKv(*w;5%-UL;NGi<95w5aBZG164#S4*U2`q22_BifvY^O#2ZIJ?2 z+0oi%27B>CP;0>=*0wYuX0O*ij4@Gb*vWtlL5TdtdB9p{<4 z7N4ZN8i()Fo-k75<8$C*Y`5x6>w<5B2up?}vKxB1(UUM)+WujpUC}8bZEK_OIqV$t zu$PM?rj?2p5o9iI@jI;1;7()90km3-J%U6}_yl-n^cXL+^Cvl8PRI21k;z>w9QWs@ zve>BD97jjx-j0sGvpf`;7|2A{r^~oJL4l8}a)!Ao8nrxpLY-z>Ax_ID(#80E)X^G@ QJ^Vz26-V;^?AAQ{A16$COaK4? literal 0 HcmV?d00001 diff --git a/docs/cite.rst b/docs/cite.rst index 78f45d6f..c4d5b190 100644 --- a/docs/cite.rst +++ b/docs/cite.rst @@ -6,7 +6,11 @@ Citing the Software ``Sorcha`` is described provided in joint Astromical Journal/JOSS software papers: Merritt et al. (submitted) and Holman et al.(submitted). We also ask that you reference in your software citations and acknowledgements the other packages that ``Sorcha`` is built upon (see below). .. tip:: - * Beyond citing the relevant papers, make sure to include details about your configuration for ``Sorcha`` (e.g. which footprint filter you're using), details about your input population (e.g. orbital, H, color, and phase curve distribution), and information about the pointing database used. + Beyond citing the relevant papers, make sure to include details about your configuration for ``Sorcha`` (e.g. which footprint filter you're using), details about your input population (e.g. orbital, H, color, and phase curve distribution), and information about the pointing database used. + + +.. hint:: + You find out what version of ``Sorcha`` you're running by typing **sorcha --version** on the command line. .. _citefunc: diff --git a/docs/ephemerisgen.rst b/docs/ephemerisgen.rst index 232964af..48b9e9ae 100644 --- a/docs/ephemerisgen.rst +++ b/docs/ephemerisgen.rst @@ -122,6 +122,14 @@ If you want to use the same input orbits across multiple ``Sorcha`` runs, you ca .. tip:: Compared to the other outputs from ``Sorcha``, the ephemeris output files are typicaly very large in size. The output will be slow to read in to ``Sorcha``, but for some use cases reading in the ephemeris as a file can be faster than ephemeris generation on the fly. We recommend only outuputting the contents of the ephemeris stage if you need it to speed up future simulations. If possible, use the HDF5 file format to help with disk I/O speeds. +Validation +-------------------------- + +We have two Jupyter notebooks validationg different aspects of ``Sorcha``'s ephemeris generator: + +- `Coordinate Transformation `_ +- `Sorcha End-to-End Verification `_ + Providing Your Own Ephemerides --------------------------------- diff --git a/docs/inputs.rst b/docs/inputs.rst index d62fa12d..a0c4732a 100644 --- a/docs/inputs.rst +++ b/docs/inputs.rst @@ -50,6 +50,7 @@ This is a file which contains the orbital information of a set of synthetic obje .. note:: For readability we show examples of whitespace-separated files below. We show only the heliocentric versions of these inputs, as the barycentric column requirements are identical, changing only the `FORMAT` designation + Cometary Orbit Format ~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/outputs.rst b/docs/outputs.rst index 18151da9..290e8316 100644 --- a/docs/outputs.rst +++ b/docs/outputs.rst @@ -7,9 +7,6 @@ Outputs Use the **-o** flag on the command line to specify where ``Sorcha`` should be saving any output and log files (the file path). -.. attention:: - Use the **-t** flag on the command line to specify the filename stem for all the ``Sorcha`` output files and logs. - Output File Formats ---------------------------- @@ -26,6 +23,8 @@ The :ref:`configuration file` keyword output_format in the OUTPUT secti If you are writing to a HDF5 file that you plan to access using the PyTables library, note that your object IDs cannot begin with a number (due to a limitation in PyTables). +.. attention:: + Use the **-t** flag on the command line to specify the filename stem for all the ``Sorcha`` output files and logs. Detections File ---------------------- @@ -278,7 +277,13 @@ Statistics (Tally) File file lists the number of observations for each object in each filter, along with the minimum, maximum and median apparent magnitude and the minimum and maximum phase angle. If the :ref:`linking filter` is on, this file also contains information on whether and when the object was linked by SSP. -The columns in the statistics file are as follows: + +.. attention:: + Use the **-st** flag on the command line to initialize ``Sorcha`` to generate the statistics file and specify the file stem for the resulting file. + + +Statistics (Tally) File Column Names, Formats, and Descriptions +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +------------------------------------+--------------+----------------------------------------------------------------------------------------------------------+ | Keyword | Format | Description | diff --git a/docs/support.rst b/docs/support.rst index ac538127..22ade7d8 100644 --- a/docs/support.rst +++ b/docs/support.rst @@ -15,7 +15,7 @@ The best way to get in touch about a bug, suggest enhancements to ``Sorcha``, or Contributing Code ----------------------------------- -We welcome upgrades/bug fixes to the code. This can be done by opening a pull request in the main ``Sorcha`` `GitHub repository `_. If you have new classes that provide enhanced light curve or activity estimations, we welcome pull requests to the ``Sorcha Add-ons`` ` GitHub repository `_. +We welcome upgrades/bug fixes to the code. This can be done by opening a pull request in the main ``Sorcha`` `GitHub repository `_. If you have new classes that provide enhanced light curve or activity estimations, we welcome pull requests to the ``Sorcha Add-ons`` `GitHub repository `_. You will need to install ``Sorcha`` from the source code via pip in editable mode as described in the :ref:`installation` page. From a375d1963528c6fda9eb552aaffc770ac150d89a Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Sat, 11 Jan 2025 13:01:52 +0000 Subject: [PATCH 29/52] documentation updates documentation updates --- docs/.notebooks.rst.swp | Bin 12288 -> 0 bytes docs/advanced.rst | 6 ++- docs/images/lsst_ssp_linking.png | Bin 0 -> 324743 bytes docs/notebooks.rst | 2 +- docs/postprocessing.rst | 89 ++++++++++++++++++++----------- 5 files changed, 64 insertions(+), 33 deletions(-) delete mode 100644 docs/.notebooks.rst.swp create mode 100644 docs/images/lsst_ssp_linking.png diff --git a/docs/.notebooks.rst.swp b/docs/.notebooks.rst.swp deleted file mode 100644 index 8a62ccbc1a6a24bcba787b99b265498cab220da6..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 12288 zcmeI2JC7ST5XWg3M*K?r=nNQOk|J(S@}sg~A8^*45IgsR)>f;dc1K#myX1o8N>+*l zsnQ1llF#5TkS-s<{tN-!8FrUCO@_OxmF^^~FyO+tAuu8&{%3~s(<}i&9Ng|b-sATh zw;7Hrj2)O?Mw@T4bHD8|mfA3$h;-nW){Rg`U>b=IMY2xDewqbrcEN%$Vcg87F4n4< zSnHZLH)ELgtxWSoIBl9%qeN6wKNV8-4P<)ud89+zRNBEn>*uy%tXrF%7Ky-VCvchF zyL+d2)W7}aYkvLOv(pZQ>L&t3fCvx)B0vO)01+Spr;32f&aj`c;&x}iMe`9 z6A>T+M1Tko0U|&IhyW2F0z`la5CJ0a84?f?V?Uf{>@}MI|9|%T|KG0|`xEUCwDV|R zp#AqHWAD-4p-s?kp}oD#*h{n+?JC;emlzAsZle8jk+I*=T4--CF!mSPuV~++T|)CY zU-`T=hyW2F0z`la5CI}U1U`lU#}O1)Ci#wvHk{tTiTA+Bp$vtSTJZ< zZZaPrl}c`-1Mi7MMytJMnz^L(ZR-@AFjKx2IyshZ%C|AeQqq~MKa*|pBK58u#V+)h zKv(*w;5%-UL;NGi<95w5aBZG164#S4*U2`q22_BifvY^O#2ZIJ?2 z+0oi%27B>CP;0>=*0wYuX0O*ij4@Gb*vWtlL5TdtdB9p{<4 z7N4ZN8i()Fo-k75<8$C*Y`5x6>w<5B2up?}vKxB1(UUM)+WujpUC}8bZEK_OIqV$t zu$PM?rj?2p5o9iI@jI;1;7()90km3-J%U6}_yl-n^cXL+^Cvl8PRI21k;z>w9QWs@ zve>BD97jjx-j0sGvpf`;7|2A{r^~oJL4l8}a)!Ao8nrxpLY-z>Ax_ID(#80E)X^G@ QJ^Vz26-V;^?AAQ{A16$COaK4? diff --git a/docs/advanced.rst b/docs/advanced.rst index d1c0cbc6..229f0cf6 100644 --- a/docs/advanced.rst +++ b/docs/advanced.rst @@ -19,8 +19,8 @@ The value used to seed the random number generator can be specified via the **SO If you're trying to reproduce a crash or a certain behavior in ``Sorcha``, you can find the value that you need to set the random seed to in the log file. -Expert User Config File Options ------------------------------------ +Expert User Filters and Config File Options +----------------------------------------------- The following options can be optionally added to an expert section ([EXPERT]) of the :ref:`configs`. @@ -96,6 +96,8 @@ To implement the magnitude limit (remove detections of objects fainter than 22 m .. attention:: Only one of these filters may be implemented at once. +.. seealso:: + We have an `example Jupyter notebook `_ demonstrating how these filters work within ``Sorcha``. 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Filters From Optical/NIR Spectra SSP Linking Filter Magnitude and SNR Cuts diff --git a/docs/postprocessing.rst b/docs/postprocessing.rst index 37a26347..1d233e71 100644 --- a/docs/postprocessing.rst +++ b/docs/postprocessing.rst @@ -23,51 +23,68 @@ Trailed Source Magnitude and PSF (Point Spread Function) Magnitude Phase Curves ------------------------------------------------------------ + + .. _addons: Incorporating Rotational Light Curves and Activity ------------------------------------------------------------ -``Sorcha`` has the ability user provided functions though python classes that augment/change the apparent brightness calculations for the synthetic Solar System objects. Any values required as input for these calculations, must be provided in the separate :ref:`CPP` file as input. +``Sorcha`` has the ability user provided functions though python classes that augment/change the apparent brightness calculations for the synthetic Solar System objects. Any values required as input for these calculations, must be provided in the separate :ref:`CPP` file as input. Rather than forcing the user directly modify the ``Sorcha`` codebase every time they want to apply a different model for representing the effects of rotational lightcurves or cometary activity, we provide the ability to develop separate activity and lightcurve/brightness enhancement functions as plugins using our template classes and add them to the `Sorcha addons `_ package. In both cases, any derived class must inherit from the corresponding base class and follow its API, to ensure that Sorcha knows how to find and use your class. Once the Sorcha addons is installed, Sorcha will automatically detect the available plugins and make them available during post-processing. To use one of the plugins from the community utilities, simply add the unique name of the plugin to the :ref:`configs` provided to Sorcha, and provide the :ref:`CPP` file on the command line. We currently have 2 pre-made classes that can augment the calculated apparent magnitude of each synthetic object, One for handling cometary activity as a function of heliocentric distance and one that applies rotational light curves to the synthetic objects. -We have base example classes that the user can take and modify to whatever your need is. Within the ``Sorcha`` :ref:`configs`, the user would then specify which class they want to use and provide the required :ref:`CPP` file on the command line. +Cometary Activity or Simulating Other Active Objects +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Once the Sorcha addons is installed, Sorcha will automatically detect the available plugins and make them available during processing. +You can user cometary activity class provided in also your own class to apply a different comentary activity and add it into a custom version of the``Sorcha addons`` package. Once the Sorcha-addons is installed, Sorcha will automatically detect the available plugins and make them available during processing. -To use one of the plugins from the community utilities, simply add the unique name of the plugin to the configuration file provided to Sorcha, and provide the complex parameters file on the command line. - We also have 2 pre-made example classes that can augment the calculated apparent magnitude of each synthetic object, One for handling cometary activity as a function of heliocentric distance and one that applies rotational light curves to the synthetic objects. In both cases, any derived class must inherit from the corresponding base class and follow its API, to ensure that Sorcha knows how to find and use your class. +Cometary Activity Configuration Parameters +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Cometary Activity or Simulating Other Active Objects -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Set the **cometary_activity** :ref:`configuration file` file varialble to **none** if you do you want to apply any cometary activity brightness enhancements to ``Sorcha``'s apparent magnitude calculations. -.. literalinclude:: ../src/sorcha/activity/base_activity.py - :language: python +.. code-block:: + [ACTIVITY] -Through the ``Sorcha'' configuration file. + # The unique name of the actvity model to use. Defined in the ``name_id`` method + # of the subclasses of AbstractCometaryActivity. If not none, a complex physical parameters + # file must be specified at the command line. -lsst_comet + comet_activity = none + +Cometary Activity Template Class +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +.. literalinclude:: ../src/sorcha/activity/base_activity.py + :language: python + + +LSSTCometActivity Class +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. seealso:: - We have an `example Jupyter notebook `_ demonstrating the LSSTCometActivity class built into `Sorcha addons GitHub repository `_. + We have an `example Jupyter notebook `_ demonstrating the LSSTCometActivity class built into `Sorcha addons package `_. -You can also develop your own class to apply a different comentary activity and add it into a custom version of the``Sorcha addons`` package. Once the Sorcha-addons is installed, Sorcha will automatically detect the available plugins and make them available during processing. +lsst_comet -Rotational Light Curve Effects +Rotational Lightcurve Effects ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The base lightcurve class is `AbstractLightCurve `_ (see below). Inside the `Sorcha addons GitHub repository `_, we provide a simple example implementation where the apparent magnitude of the object (that is, the magnitude after all geometric effects have been taken into account), has a sinusoidal term added to it. To use this function, in the :ref:`CPP` file, the user must provide a light curve amplitude (`LCA`), corresponding to half the peak-to-peak amplitude for the magnitude changes, a period `Period`, and a reference time `Time0` where the light curve is at 0 - if these are not provided, the software will produce an error message. Despite being simple, that implementation shows all the class methods that need to be implemented for a custom light curve function. -.. literalinclude:: ../src/sorcha/lightcurves/base_lightcurve.py - :language: python - .. seealso:: - We have an `example Jupyter notebook `_ demonstrating the SinusoidalLightCurve class built into `Sorcha addons GitHub repository `_, + We have an `example Jupyter notebook `_ demonstrating the SinusoidalLightCurve class built into `Sorcha addons package `_, + + +Lightcurve Template Class +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. literalinclude:: ../src/sorcha/lightcurves/base_lightcurve.py + :language: python Applying Photometric and Astrometric Uncerainties @@ -115,7 +132,7 @@ estimate that the saturation limit for the LSST will be ~16 in the r filter. For the latter, limits must be given in a comma-separated list in the same order as the filters supplied for the observing_filters config file variable. -To include this filter, the configuration file should contain:: +To include this filter, the :ref:`configs` should contain:: [SATURATION] bright_limit = 16.0 @@ -138,7 +155,7 @@ to detect them. ``Sorcha`` uses the fading function formulation of `Veres and Ch see the below plot. This fading function is parameterised by the fading function width and peak efficiency. The default values are modelled on those from the aforementioned paper. -To include this filter, the following options should be set in the configuration file:: +To include this filter, the following options should be set in the :ref:`configs`:: [FADINGFUNCTION] fading_function_on = True @@ -151,6 +168,9 @@ To include this filter, the following options should be set in the configuration :align: center +.. seealso:: + We have a`Jupyter notebook `_ showing how ``Sorcha`` applies the survey detection efficiency (fading function). + Camera Footprint ----------------- @@ -175,7 +195,7 @@ Circle Radius (Simple Sensor Area) Using this filter applies a very simple circular camera footprint. The radius of the circle (**circle_radius** key) should be given in degrees. The **fill_factor** key specifics what fraction of observations should be randomly removed to roughly mimic detector chip gaps in this circular footprint approximation. The fraction of observations not removed is controlled by the config variable fill_factor. -To include this filter, the following options should be set in the configuration file:: +To include this filter, the following options should be set in the :ref:`configs`:: [FOV] camera_model = circle @@ -196,19 +216,17 @@ Full Camera Footprint Using this filter applies a full camera footprint, including chip gaps. This is the slowest and most accurate version of the footprint filter. -To include this filter, the following options should be set in the configuration file:: +To include this filter, the following options should be set in the :ref:`configs`:: [FOV] camera_model = footprint - footprint_path = ./data/detectors_corners.csv -.. tip:: - ``Sorcha`` comes with a representation of the LSSTCam footprint already installed. If you do not include the **footprint_path** in the configuration file, then ``Sorcha`` assumes you're using its internal LSSTCam footprint. +``Sorcha`` comes with a representation of the LSSTCam footprint already installed. If you do not include the **footprint_path** in the :ref:`configs`, then ``Sorcha`` assumes you're using its internal LSSTCam footprint. Further details about supplying your own camera footprint file can be found in the :ref:`inoputs` page. .. warning:: Note that :ref:`ASSIST+REBOUND ephemeris generator` uses a circular radius for its search area. To get accurate results, the ASSIST+REBOUND radius must be set to be larger than the circle_radius. For simmulating the LSST, we rcommend setting **ar_ang_fov = 2.06** and **ar_fov_buffer = 0.2**. -Additionally, the camera footprint model can account for the losses at the edge of the CCDs where the detection software will not be able to pick out sources close to the edge. You can add an exclusion zone around each CCD measured in arcseconds (on the focal plane) using the `footprint_edge_threshold` key to the configuraiton file. An example setup in the configuration file:: +Additionally, the camera footprint model can account for the losses at the edge of the CCDs where the detection software will not be able to pick out sources close to the edge. You can add an exclusion zone around each CCD measured in arcseconds (on the focal plane) using the `footprint_edge_threshold` key to the configuraiton file. An example setup in the :ref:`configs`:: [FOV] camera_model = footprint @@ -216,7 +234,7 @@ Additionally, the camera footprint model can account for the losses at the edge footprint_edge_threshold = 0.0001 .. tip:: - ``Sorcha`` comes with a representation of the LSSTCam footprint already installed. If you do not include the **footprint_path** in the configuration file, then ``Sorcha`` assumes you're using its internal LSSTCam footprint. + ``Sorcha`` comes with a representation of the LSSTCam footprint already installed. If you do not include the **footprint_path** in the :ref:`configs`, then ``Sorcha`` assumes you're using its internal LSSTCam footprint. .. _linking: @@ -231,7 +249,14 @@ Linking is performed by detecting multiple observations of an object in a single A number of these tracklets must then be detected in a specific time window to form a 'track'. -To use this filter, the user must specify all seven of the parameters in the configuration file. + +.. image:: images/lsst_ssp_linking.png + :width: 600 + :alt: Plot of the LSST camera footprint where x and y are x and y distance from the pupil in degrees. The footprint also shows two overplotted circle radii of 1.75deg (corresponding to a 75% fill factor) and 2.06deg. + :align: center + + +To use this filter, the user must specify all seven of the parameters in the :ref:`configs`. The defaults given below are those used by SSP and are explained in the comments:: [LINKING] @@ -263,10 +288,14 @@ The defaults given below are those used by SSP and are explained in the comments SSP_night_start_utc = 16.0 By default, when the linking filter is on, ``Sorcha`` will drop all observations of unlinked objects. If the user wishes to retain -these observations, this can be set in the configuration file. This will add an additional column to the output, **object_linked**, which states whether -the observation is of a linked object or not. To enable this functionality, add the following to the configuration file:: +these observations, this can be set in the :ref:`configs`. This will add an additional column to the output, **object_linked**, which states whether +the observation is of a linked object or not. To enable this functionality, add the following to the :ref:`configs`:: [LINKING] drop_unlinked = False +.. seealso:: + See our `Jupyter notebook `_ that validates the linking filter. + + From 1853c3fe7a5f68ec272c9df9f519a48e6074630b Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Sat, 11 Jan 2025 18:53:56 +0000 Subject: [PATCH 30/52] document updates --- docs/advanced.rst | 8 ++++ docs/example_files/multi_sorcha.py | 63 ++++++++++++++++++++++++++++++ docs/hpc.rst | 62 ++++++++++++++++++++++++----- docs/overview.rst | 2 +- docs/postprocessing.rst | 8 +++- 5 files changed, 131 insertions(+), 12 deletions(-) create mode 100644 docs/example_files/multi_sorcha.py diff --git a/docs/advanced.rst b/docs/advanced.rst index 229f0cf6..de956c09 100644 --- a/docs/advanced.rst +++ b/docs/advanced.rst @@ -99,6 +99,14 @@ To implement the magnitude limit (remove detections of objects fainter than 22 m .. seealso:: We have an `example Jupyter notebook `_ demonstrating how these filters work within ``Sorcha``. +Modifying the Ephemeris Generator Interpolation +-------------------------------------------------- + +A user can update the number of sub-intervals for the Lagrange ephemerides interpolation used within ``Sorcha``'s internal ephemeris generator. By default this value is set to **101**, but the user can update it to a different value. 101 works for most orbits, but it may be worth exploring using a different value if you're modeling Earth impactors and very close Near-Earth Objects (NEOs). To change the number of sub-intervals, **n_sub_intervals** variable is added to the ([SIMULATION]) section:: + + [SIMULATION] + n_sub_intervals = 122 + Specifying Alernative Versions of the Auxiliary Files Used in the Ephemeris Generator ----------------------------------------------------------------------------------------- diff --git a/docs/example_files/multi_sorcha.py b/docs/example_files/multi_sorcha.py new file mode 100644 index 00000000..8b6a7496 --- /dev/null +++ b/docs/example_files/multi_sorcha.py @@ -0,0 +1,63 @@ +import os +import astropy.table as tb +from multiprocessing import Pool +import pandas as pd +import sqlite3 + +def run_sorcha(i, args, path_inputs, pointings, instance, config): + print(f"sorcha run -c {config} -pd {pointings} -o {args.path}{instance}/ -t {instance}_{i} -ob {args.path}{instance}/orbits_{i}.csv -p {args.path}{instance}/physical_{i}.csv", flush=True) + os.system(f"sorcha run -c {config} -pd {pointings} -o {args.path}{instance}/ -t {instance}_{i} -ob {args.path}{instance}/orbits_{i}.csv -p {args.path}{instance}/physical_{i}.csv") + +if __name__ == '__main__': + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument('--input_orbits', type=str) + parser.add_argument('--input_physical', type=str) + parser.add_argument('--path', type=str) + parser.add_argument('--chunksize', type=int) + parser.add_argument('--norbits', type=int) + parser.add_argument('--cores', type=int) + parser.add_argument('--instance', type=int) + parser.add_argument('--cleanup', action='store_true') + parser.add_argument('--copy_inputs', action='store_true') + parser.add_argument('--pointings', type=str) + parser.add_argument('--config', type=str) + args = parser.parse_args() + chunk = args.chunksize + instance = args.instance + norbits = args.norbits + pointings = args.pointings + path = args.path + config = args.config + + orbits = tb.Table.read(args.input_orbits) + orbits = orbits[instance*chunk:(instance+1)*chunk] + physical = tb.Table.read(args.input_physical) + physical = physical[instance*chunk:(instance+1)*chunk] + + os.system(f'mkdir {instance}') + + + if args.copy_inputs: + os.system(f'cp {pointings} {instance}/') + path_inputs = f'{instance}' + + for i in range(args.cores): + sub_orb = orbits[i*norbits:(i+1)*norbits] + sub_phys = physical[i*norbits:(i+1)*norbits] + sub_orb.write(f"{args.path}{instance}/orbits_{i}.csv", overwrite=True) + sub_phys.write(f"{args.path}{instance}/physical_{i}.csv", overwrite=True) + + with Pool(processes=args.cores) as pool: + pool.starmap(run_sorcha, [(i, args, path_inputs, pointings, instance, config) for i in range(args.cores)]) + + data = [] + for i in range(args.cores): + data.append(pd.read_sql("select * from sorcha_results", sqlite3.connect(f"{args.path}{instance}/{instance}_{i}.db"))) + data = pd.concat(data) + data.to_sql("sorcha_results", sqlite3.connect(f"{args.path}output_{instance}.db")) + if args.cleanup: + os.system(f"rm {args.path}{instance}/*") + os.system(f"rmdir {args.path}{instance}") + diff --git a/docs/hpc.rst b/docs/hpc.rst index ebc3c8c1..c1a79e6e 100644 --- a/docs/hpc.rst +++ b/docs/hpc.rst @@ -1,17 +1,59 @@ -Running on HPCs & Parallel Processing +Sorcha Parallelization =============================================== -Testing Your Sorcha Installation --------------------------------------------------- -**Step 6** Install the necessary SPICE auxiliary files for ephemeris generation (774 MB total in size):: +Embarrassingly Parallel Problem +------------------------------------ - sorcha bootstrap [--cache ] +Sorcha’s design lends itself perfectly to parallelization – when it simulates a large number of solar system objects, each one is considered in turn independently of all other objects. If you have access to a large number of computing cores, you can run Sorcha much more quickly by dividing up the labor: giving a small part of your model population to each core. -.. tip:: - For the getting started tutorial we recommend installing these auxiliary files in ./ar_files +This involves two subtasks: breaking up your model population into an appropriate number of input files with unique names and organizing a large number of cores to simultaneously run Sorcha their own individually-named input files. Both of these tasks are easy in theory, but tricky enough in practice that we provide some guidance below. -.. note:: - These files are stored in your system's cache by default if the --cache flag is not provided. If the files already downloaded and want a fresh download, you need to use the -f flag. -.. warning:: These files can change/be updated with the revised positions of the planets every once in a while. So if you're running simulations for population statistics, we recommend downloading these files to a directory and having all Sorcha runs use these files for consistency. +SLURM +--------- +Slurm Workload Manager is a resource management utility commonly used by computing clusters. We provide starter code for running large parallel Sorcha batches using SLURM, though general guidance we provide is applicable to any system. Documentation for SLURM is available `here `_. Please note that your HPC facility’s SLURM setup may differ from those on which Sorcha was tested, and it is always a good idea to read any facility-specific documentation or speak to the HPC maintainers before you begin to run jobs. + +Quickstart +-------------- + +We provide as a starting point our example scripts for running Sorcha on HPC facilities using SLURM. Some modifications will be required to make them work for your facility. + +Below is a very simple SLURM script example designed to run the demo files three times on three cores in parallel. Here, one core has been assigned to each Sorcha run, with each core assigned 1Gb of memory. + +.. literalinclude:: ./example_files/multi_sorcha.sh + :language: text + +Please note that time taken to run and memory required will vary enormously based on the size of your input files, your input population, and the chunk size assigned in the Sorcha configuration file: we therefore recommend test runs before you commit to very large runs. The chunk size is an especially important parameter: too small and Sorcha will take a very long time to run, too large and the memory footprint may become prohibitive. We have found that chunk sizes of 1000 to 10,000 work best. + +Below is a more complex example of a SLURM script. Here, multi_sorcha.sh calls `multi_sorcha.py <.example_files/multi_sorcha.py>`_, which splits up an input file into a number of ‘chunks’ and runs Sorcha in parallel on a user-specified number of cores. + + +.. literalinclude:: ./example_files/multi_sorcha.sh + :language: text + + +You can run `multi_sorcha.py `_ in the demo/ on the command line as well:: + +python multi_sorcha.py --config sorcha_config_demo.ini --input_orbits mba_sample_1000_orbit.csv --input_physical mba_sample_1000_physical.csv --pointings baseline_v2.0_1yr.db --path ./ --chunksize 1000 --norbits 250 --cores 4 --instance 0 --cleanup --copy_inputs + +This will generate a single output file. It should work fine on a laptop, and be a bit, but not 4x, faster than the single-core equivalent due to overheads (time sorcha run -c sorcha_config_demo.ini -pd baseline_v2.0_1yr.db -o ./ -t 0_0 -ob mba_sample_1000_orbit.csv -p mba_sample_1000_physical.csv). + +This ratio improves as input file sizes grow. Make sure to experiment with different numbers of cores to find what’s fastest given your setup and file sizes. + +multi_sorcha.py: + +.. literalinclude:: ./example_files/multi_sorcha.py + :language: python + +multi_sorcha.sh requests many parallel Slurm jobs of multi_sorcha.py, feeding each a different --instance parameter. After changing ‘my_orbits.csv’, ‘my_colors.csv’, and ‘my_pointings.db’ to match the above, it could be run as sbatch --array=0-9 multi_sorcha.sh 25 4 to generate ten jobs, each with 4 cores running 25 orbits each. + + +Sorcha’s Helpful Utilities +--------------------------------- + +Sorcha comes with a tool designed to combine the results of multiple runs and the input files used to create them into tables on a SQL database. This can make exploring your results easier. To see the usage of this tool, On the command-line, run:: + + sorcha outputs create-sqlite –help + + diff --git a/docs/overview.rst b/docs/overview.rst index f471cdcc..2fee7909 100644 --- a/docs/overview.rst +++ b/docs/overview.rst @@ -5,7 +5,7 @@ How Sorcha Works ------------------------------- In order to conduct detailed population studies on the orbital properties and physical characteristics of the various Solar System small body reservoirs, one must account for all the survey biases (the complex and often intertwined detection biases – brightness limits, -pointing, cadence, on-sky motion limits, software detection efficiencies) in one’s discovery survey (`see Lawler et al. 2018 `_ for a more detailed discussion). ``Sorcha`` is an open-source Python Solar System survey simulator designed for the `Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) `_. ``Sorcha`` works by the user inputting a synthetic Solar System small body population. The software forward models the input population to simulate what the survey should have detected using a series of :ref:`filters`. The filters can be switched on or off and customized as needed via a :ref:`configuration file`. In this way, a synthetic population can be compared to the real survey's discoveries. +pointing, cadence, on-sky motion limits, software detection efficiencies) in one’s discovery survey (`see Lawler et al. 2018 `_ for a more detailed discussion). ``Sorcha`` is an open-source Python Solar System survey simulator designed for the `Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) `_. ``Sorcha`` works by the user inputting a synthetic Solar System small body population. The software forward models the input population to simulate what the survey should have detected using a series of calculations and filters. These can be switched on or off and customized as needed via a :ref:`configuration file`. In this way, a synthetic population can be compared to the real survey's discoveries. The :ref:`inputs` that ``Sorcha`` requires are shown in the figure below. The software requires input files that describe the small bodies to simulate (including a file for orbits, at least one file for physical parameters, and a SQLite database that describes the telescope survey's observation history). ``Sorcha`` outputs simulated detection data, including each time, position, and apparent magnitude at which a synthetic small body was detected based on various options set up in a :ref:`configuration file`. diff --git a/docs/postprocessing.rst b/docs/postprocessing.rst index 1d233e71..641a6220 100644 --- a/docs/postprocessing.rst +++ b/docs/postprocessing.rst @@ -20,6 +20,12 @@ Trailed Source Magnitude and PSF (Point Spread Function) Magnitude :alt: A cartoon explanation of trailed source mag and PSF mag :align: center + +.. seealso:: + See our `Jupyter notebook `_ that validates the apparent magnitude calulcation. + + + Phase Curves ------------------------------------------------------------ @@ -221,7 +227,7 @@ To include this filter, the following options should be set in the :ref:`configs [FOV] camera_model = footprint -``Sorcha`` comes with a representation of the LSSTCam footprint already installed. If you do not include the **footprint_path** in the :ref:`configs`, then ``Sorcha`` assumes you're using its internal LSSTCam footprint. Further details about supplying your own camera footprint file can be found in the :ref:`inoputs` page. +``Sorcha`` comes with a representation of the LSSTCam footprint already installed. If you do not include the **footprint_path** in the :ref:`configs`, then ``Sorcha`` assumes you're using its internal LSSTCam footprint. Further details about supplying your own camera footprint file can be found in the :ref:`inputs` page. .. warning:: Note that :ref:`ASSIST+REBOUND ephemeris generator` uses a circular radius for its search area. To get accurate results, the ASSIST+REBOUND radius must be set to be larger than the circle_radius. For simmulating the LSST, we rcommend setting **ar_ang_fov = 2.06** and **ar_fov_buffer = 0.2**. From 50e87c29bca95ae82070cebfa3bc3e4b7ef646dd Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Sat, 11 Jan 2025 22:52:42 +0000 Subject: [PATCH 31/52] Update hpc.rst --- docs/hpc.rst | 51 +++++++++++++++++++++++++++++++++++++++------------ 1 file changed, 39 insertions(+), 12 deletions(-) diff --git a/docs/hpc.rst b/docs/hpc.rst index c1a79e6e..f4f85e66 100644 --- a/docs/hpc.rst +++ b/docs/hpc.rst @@ -12,7 +12,7 @@ This involves two subtasks: breaking up your model population into an appropriat SLURM --------- -Slurm Workload Manager is a resource management utility commonly used by computing clusters. We provide starter code for running large parallel Sorcha batches using SLURM, though general guidance we provide is applicable to any system. Documentation for SLURM is available `here `_. Please note that your HPC facility’s SLURM setup may differ from those on which Sorcha was tested, and it is always a good idea to read any facility-specific documentation or speak to the HPC maintainers before you begin to run jobs. +Slurm Workload Manager is a resource management utility commonly used by computing clusters. We provide starter code for running large parallel Sorcha batches using SLURM, though general guidance we provide is applicable to any system. Documentation for SLURM is available `here `_. Please note that your HPC (High Performance Computing) facility’s SLURM setup may differ from those on which Sorcha was tested, and it is always a good idea to read any facility-specific documentation or speak to the HPC maintainers before you begin to run jobs. Quickstart -------------- @@ -26,34 +26,61 @@ Below is a very simple SLURM script example designed to run the demo files three Please note that time taken to run and memory required will vary enormously based on the size of your input files, your input population, and the chunk size assigned in the Sorcha configuration file: we therefore recommend test runs before you commit to very large runs. The chunk size is an especially important parameter: too small and Sorcha will take a very long time to run, too large and the memory footprint may become prohibitive. We have found that chunk sizes of 1000 to 10,000 work best. -Below is a more complex example of a SLURM script. Here, multi_sorcha.sh calls `multi_sorcha.py <.example_files/multi_sorcha.py>`_, which splits up an input file into a number of ‘chunks’ and runs Sorcha in parallel on a user-specified number of cores. +Below is a more complex example of a SLURM script. Here, multi_sorcha.sh calls multi_sorcha.py, which splits up an input file into a number of ‘chunks’ and runs Sorcha in parallel on a user-specified number of cores. +multi_sorcha.sh: .. literalinclude:: ./example_files/multi_sorcha.sh :language: text +multi_sorcha.py: -You can run `multi_sorcha.py `_ in the demo/ on the command line as well:: +.. literalinclude:: ./example_files/multi_sorcha.py + :language: python -python multi_sorcha.py --config sorcha_config_demo.ini --input_orbits mba_sample_1000_orbit.csv --input_physical mba_sample_1000_physical.csv --pointings baseline_v2.0_1yr.db --path ./ --chunksize 1000 --norbits 250 --cores 4 --instance 0 --cleanup --copy_inputs -This will generate a single output file. It should work fine on a laptop, and be a bit, but not 4x, faster than the single-core equivalent due to overheads (time sorcha run -c sorcha_config_demo.ini -pd baseline_v2.0_1yr.db -o ./ -t 0_0 -ob mba_sample_1000_orbit.csv -p mba_sample_1000_physical.csv). +multi_sorcha.sh requests many parallel Slurm jobs of multi_sorcha.py, feeding each a different --instance parameter. After changing ‘my_orbits.csv’, ‘my_colors.csv’, and ‘my_pointings.db’ to match the above, it could be run as sbatch --array=0-9 multi_sorcha.sh 25 4 to generate ten jobs, each with 4 cores running 25 orbits each. -This ratio improves as input file sizes grow. Make sure to experiment with different numbers of cores to find what’s fastest given your setup and file sizes. -multi_sorcha.py: +You can run multi_sorcha.py on the command line as well:: -.. literalinclude:: ./example_files/multi_sorcha.py - :language: python + python multi_sorcha.py --config sorcha_config_demo.ini --input_orbits mba_sample_1000_orbit.csv --input_physical mba_sample_1000_physical.csv --pointings baseline_v2.0_1yr.db --path ./ --chunksize 1000 --norbits 250 --cores 4 --instance 0 --cleanup --copy_inputs -multi_sorcha.sh requests many parallel Slurm jobs of multi_sorcha.py, feeding each a different --instance parameter. After changing ‘my_orbits.csv’, ‘my_colors.csv’, and ‘my_pointings.db’ to match the above, it could be run as sbatch --array=0-9 multi_sorcha.sh 25 4 to generate ten jobs, each with 4 cores running 25 orbits each. +This will generate a single output file. It should work fine on a laptop, and be a bit, but not 4x, faster than the single-core equivalent due to overheads (time sorcha run -c sorcha_config_demo.ini -pd baseline_v2.0_1yr.db -o ./ -t 0_0 -ob mba_sample_1000_orbit.csv -p mba_sample_1000_physical.csv). + +This ratio improves as input file sizes grow. Make sure to experiment with different numbers of cores to find what’s fastest given your setup and file sizes. Sorcha’s Helpful Utilities --------------------------------- -Sorcha comes with a tool designed to combine the results of multiple runs and the input files used to create them into tables on a SQL database. This can make exploring your results easier. To see the usage of this tool, On the command-line, run:: +Sorcha comes with a tool designed to combine the results of multiple runs and the input files used to create them into tables on a SQL database. This can make exploring your results easier. To see the usage of this tool, on the command line, run:: sorcha outputs create-sqlite –help - +Sorcha also has a tool designed to search for and check the logs of a large number of runs. This tool can make sure all of the runs completed successfully, and output to either the terminal or a .csv file the names of the runs which have not completed and the relevant error message, if applicable. To see the usage of this tool, on the command line run:: + + sorcha outputs check-logs –help + + +Best Practices/Tips and Tricks +------------------------------------- + +1. We strongly recommend that HPC users download the auxiliary files needed to run the ASSIST+REBOUND into a known, named directory, and use the -ar command line flag in their sorcha run call to point Sorcha to those files. You can download the auxiliary files using:: + + sorcha bootstrap --cache + + sorcha run … -ar /path/to/folder/ + + This is because Sorcha will otherwise attempt to download the files into the local cache, which may be on the HPC nodes rather than in your user directory, potentially triggering multiple slow downloads. + +2. We recommend that each Sorcha run be given its own individual output directory. If multiple parallel Sorcha runs are attempting to save to the same file in the same directory, this will cause confusing and unexpected results. + +3. Sorcha output files can be very large, and user directories on HPC facilities are usually space-limited. Please ensure that your Sorcha runs are directing the output to be saved in a location with sufficient space, like your HPC cluster’s scratch drive. + +4. Think about having useful, helpful file names for your outputs. It is often tempting to call them something like “sorcha_output_” or “sorcha_output_”, but hard-won experience has led us to instead recommend more explanatory names for when you come back to your output later. + + + + + From 2162419708b3bcba81cfcb93f6aa55c99cca60aa Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Sun, 12 Jan 2025 14:47:35 +0000 Subject: [PATCH 32/52] documentation updates documentation updates --- docs/configfiles.rst | 2 ++ docs/ephemerisgen.rst | 3 ++ docs/example_files/multi_sorcha.py | 10 +++--- docs/hpc.rst | 45 ++++++++++++++---------- docs/outputs.rst | 56 ++++++++++++++++++++---------- 5 files changed, 75 insertions(+), 41 deletions(-) diff --git a/docs/configfiles.rst b/docs/configfiles.rst index 81c4a7de..f7f2d0a6 100644 --- a/docs/configfiles.rst +++ b/docs/configfiles.rst @@ -43,6 +43,8 @@ approximation of the Rubin detector. :language: text :linenos: +.. _known_config: + Rubin Known Object Prediction ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This configuration file is appropriate for running ``Sorcha`` using the full camera footprint but with randomization, diff --git a/docs/ephemerisgen.rst b/docs/ephemerisgen.rst index 48b9e9ae..c5fdc2ef 100644 --- a/docs/ephemerisgen.rst +++ b/docs/ephemerisgen.rst @@ -122,6 +122,9 @@ If you want to use the same input orbits across multiple ``Sorcha`` runs, you ca .. tip:: Compared to the other outputs from ``Sorcha``, the ephemeris output files are typicaly very large in size. The output will be slow to read in to ``Sorcha``, but for some use cases reading in the ephemeris as a file can be faster than ephemeris generation on the fly. We recommend only outuputting the contents of the ephemeris stage if you need it to speed up future simulations. If possible, use the HDF5 file format to help with disk I/O speeds. +.. tip:: + If instead you want to know which of the input small body population lands in the survey observations with an estimate of their apparent magnitude wihtout applying any other cuts or filters on the detections (not including discovery efficiency and linking effects), you can use/adapt the :ref:`known_config` example :ref:`configs`. + Validation -------------------------- diff --git a/docs/example_files/multi_sorcha.py b/docs/example_files/multi_sorcha.py index 8b6a7496..d741e9c8 100644 --- a/docs/example_files/multi_sorcha.py +++ b/docs/example_files/multi_sorcha.py @@ -4,9 +4,9 @@ import pandas as pd import sqlite3 -def run_sorcha(i, args, path_inputs, pointings, instance, config): - print(f"sorcha run -c {config} -pd {pointings} -o {args.path}{instance}/ -t {instance}_{i} -ob {args.path}{instance}/orbits_{i}.csv -p {args.path}{instance}/physical_{i}.csv", flush=True) - os.system(f"sorcha run -c {config} -pd {pointings} -o {args.path}{instance}/ -t {instance}_{i} -ob {args.path}{instance}/orbits_{i}.csv -p {args.path}{instance}/physical_{i}.csv") +def run_sorcha(i, args, path_inputs, pointings, instance,stats, config): + print(f"sorcha run -c {config} --pd {pointings} -o {args.path}{instance}/ -t {instance}_{i} --ob {args.path}{instance}/orbits_{i}.csv -p {args.path}{instance}/physical_{i}.csv --st {stats}_{i}", flush=True) + os.system(f"sorcha run -c {config} --pd {pointings} -o {args.path}{instance}/ -t {instance}_{i} --ob {args.path}{instance}/orbits_{i}.csv -p {args.path}{instance}/physical_{i}.csv --st {stats}_{i}") if __name__ == '__main__': import argparse @@ -22,6 +22,7 @@ def run_sorcha(i, args, path_inputs, pointings, instance, config): parser.add_argument('--cleanup', action='store_true') parser.add_argument('--copy_inputs', action='store_true') parser.add_argument('--pointings', type=str) + parser.add_argument('--stats', type=str) parser.add_argument('--config', type=str) args = parser.parse_args() chunk = args.chunksize @@ -30,6 +31,7 @@ def run_sorcha(i, args, path_inputs, pointings, instance, config): pointings = args.pointings path = args.path config = args.config + stats=args.stats orbits = tb.Table.read(args.input_orbits) orbits = orbits[instance*chunk:(instance+1)*chunk] @@ -50,7 +52,7 @@ def run_sorcha(i, args, path_inputs, pointings, instance, config): sub_phys.write(f"{args.path}{instance}/physical_{i}.csv", overwrite=True) with Pool(processes=args.cores) as pool: - pool.starmap(run_sorcha, [(i, args, path_inputs, pointings, instance, config) for i in range(args.cores)]) + pool.starmap(run_sorcha, [(i, args, path_inputs, pointings, instance, config, stats) for i in range(args.cores)]) data = [] for i in range(args.cores): diff --git a/docs/hpc.rst b/docs/hpc.rst index f4f85e66..6b364599 100644 --- a/docs/hpc.rst +++ b/docs/hpc.rst @@ -1,32 +1,34 @@ -Sorcha Parallelization +.. _hpc: + + Parallelization =============================================== Embarrassingly Parallel Problem ------------------------------------ -Sorcha’s design lends itself perfectly to parallelization – when it simulates a large number of solar system objects, each one is considered in turn independently of all other objects. If you have access to a large number of computing cores, you can run Sorcha much more quickly by dividing up the labor: giving a small part of your model population to each core. +’s design lends itself perfectly to parallelization – when it simulates a large number of solar system objects, each one is considered in turn independently of all other objects. If you have access to a large number of computing cores, you can run ``Sorcha`` much more quickly by dividing up the labor: giving a small part of your model population to each core. -This involves two subtasks: breaking up your model population into an appropriate number of input files with unique names and organizing a large number of cores to simultaneously run Sorcha their own individually-named input files. Both of these tasks are easy in theory, but tricky enough in practice that we provide some guidance below. +This involves two subtasks: breaking up your model population into an appropriate number of input files with unique names and organizing a large number of cores to simultaneously run ``Sorcha`` on their own individually-named input files. Both of these tasks are easy in theory, but tricky enough in practice that we provide some guidance below. SLURM --------- -Slurm Workload Manager is a resource management utility commonly used by computing clusters. We provide starter code for running large parallel Sorcha batches using SLURM, though general guidance we provide is applicable to any system. Documentation for SLURM is available `here `_. Please note that your HPC (High Performance Computing) facility’s SLURM setup may differ from those on which Sorcha was tested, and it is always a good idea to read any facility-specific documentation or speak to the HPC maintainers before you begin to run jobs. +Slurm Workload Manager is a resource management utility commonly used by computing clusters. We provide starter code for running large parallel batches using SLURM, though general guidance we provide is applicable to any system. Documentation for SLURM is available `here `_. Please note that your HPC (High Performance Computing) facility’s SLURM setup may differ from those on which ``Sorcha`` was tested, and it is always a good idea to read any facility-specific documentation or speak to the HPC maintainers before you begin to run jobs. Quickstart -------------- -We provide as a starting point our example scripts for running Sorcha on HPC facilities using SLURM. Some modifications will be required to make them work for your facility. +We provide as a starting point our example scripts for running on HPC facilities using SLURM. Some modifications will be required to make them work for your facility. -Below is a very simple SLURM script example designed to run the demo files three times on three cores in parallel. Here, one core has been assigned to each Sorcha run, with each core assigned 1Gb of memory. +Below is a very simple SLURM script example designed to run the demo files three times on three cores in parallel. Here, one core has been assigned to each ``Sorcha`` run, with each core assigned 1Gb of memory. .. literalinclude:: ./example_files/multi_sorcha.sh :language: text -Please note that time taken to run and memory required will vary enormously based on the size of your input files, your input population, and the chunk size assigned in the Sorcha configuration file: we therefore recommend test runs before you commit to very large runs. The chunk size is an especially important parameter: too small and Sorcha will take a very long time to run, too large and the memory footprint may become prohibitive. We have found that chunk sizes of 1000 to 10,000 work best. +Please note that time taken to run and memory required will vary enormously based on the size of your input files, your input population, and the chunk size assigned in the ``Sorcha`` configuration file: we therefore recommend test runs before you commit to very large runs. The chunk size is an especially important parameter: too small and ``Sorcha`` will take a very long time to run, too large and the memory footprint may become prohibitive. We have found that chunk sizes of 1000 to 10,000 work best. -Below is a more complex example of a SLURM script. Here, multi_sorcha.sh calls multi_sorcha.py, which splits up an input file into a number of ‘chunks’ and runs Sorcha in parallel on a user-specified number of cores. +Below is a more complex example of a SLURM script. Here, multi_sorcha.sh calls multi_sorcha.py, which splits up an input file into a number of ‘chunks’ and runs ``Sorcha`` in parallel on a user-specified number of cores. multi_sorcha.sh: @@ -38,27 +40,30 @@ multi_sorcha.py: .. literalinclude:: ./example_files/multi_sorcha.py :language: python +.. note:: + We provide these here for you to copy, paste, and edit as needed. You might have to some some slight modifications to both the SLURM script and multi_sorcha.py depending if you're using ``Sorcha`` without calling the stats file. multi_sorcha.sh requests many parallel Slurm jobs of multi_sorcha.py, feeding each a different --instance parameter. After changing ‘my_orbits.csv’, ‘my_colors.csv’, and ‘my_pointings.db’ to match the above, it could be run as sbatch --array=0-9 multi_sorcha.sh 25 4 to generate ten jobs, each with 4 cores running 25 orbits each. You can run multi_sorcha.py on the command line as well:: - python multi_sorcha.py --config sorcha_config_demo.ini --input_orbits mba_sample_1000_orbit.csv --input_physical mba_sample_1000_physical.csv --pointings baseline_v2.0_1yr.db --path ./ --chunksize 1000 --norbits 250 --cores 4 --instance 0 --cleanup --copy_inputs + python multi_sorcha.py --config sorcha_config_demo.ini --input_orbits mba_sample_1000_orbit.csv --input_physical mba_sample_1000_physical.csv --pointings baseline_v2.0_1yr.db --path ./ --chunksize 1000 --norbits 250 --cores 4 --instance 0 --stats mbastats --cleanup --copy_inputs -This will generate a single output file. It should work fine on a laptop, and be a bit, but not 4x, faster than the single-core equivalent due to overheads (time sorcha run -c sorcha_config_demo.ini -pd baseline_v2.0_1yr.db -o ./ -t 0_0 -ob mba_sample_1000_orbit.csv -p mba_sample_1000_physical.csv). +This will generate a single output file. It should work fine on a laptop, and be a bit, but not 4x, faster than the single-core equivalent due to overheads (time sorcha run -c sorcha_config_demo.ini -pd baseline_v2.0_1yr.db -o ./ -t 0_0 --st mbatats_0 -ob mba_sample_1000_orbit.csv -p mba_sample_1000_physical.csv). -This ratio improves as input file sizes grow. Make sure to experiment with different numbers of cores to find what’s fastest given your setup and file sizes. +.. note:: + This ratio improves as input file sizes grow. Make sure to experiment with different numbers of cores to find what’s fastest given your setup and file sizes. Sorcha’s Helpful Utilities --------------------------------- -Sorcha comes with a tool designed to combine the results of multiple runs and the input files used to create them into tables on a SQL database. This can make exploring your results easier. To see the usage of this tool, on the command line, run:: +``Sorcha`` comes with a tool designed to combine the results of multiple runs and the input files used to create them into tables on a SQL database. This can make exploring your results easier. To see the usage of this tool, on the command line, run:: sorcha outputs create-sqlite –help -Sorcha also has a tool designed to search for and check the logs of a large number of runs. This tool can make sure all of the runs completed successfully, and output to either the terminal or a .csv file the names of the runs which have not completed and the relevant error message, if applicable. To see the usage of this tool, on the command line run:: +``Sorcha`` also has a tool designed to search for and check the logs of a large number of runs. This tool can make sure all of the runs completed successfully, and output to either the terminal or a .csv file the names of the runs which have not completed and the relevant error message, if applicable. To see the usage of this tool, on the command line run:: sorcha outputs check-logs –help @@ -66,21 +71,23 @@ Sorcha also has a tool designed to search for and check the logs of a large numb Best Practices/Tips and Tricks ------------------------------------- -1. We strongly recommend that HPC users download the auxiliary files needed to run the ASSIST+REBOUND into a known, named directory, and use the -ar command line flag in their sorcha run call to point Sorcha to those files. You can download the auxiliary files using:: +1. We strongly recommend that HPC users download the auxiliary files needed to run the ASSIST+REBOUND into a known, named directory, and use the -ar command line flag in their **sorcha run** call to point ``Sorcha`` to those files. You can download the auxiliary files using:: sorcha bootstrap --cache + And then run ``Sorcha`` via:: + sorcha run … -ar /path/to/folder/ - This is because Sorcha will otherwise attempt to download the files into the local cache, which may be on the HPC nodes rather than in your user directory, potentially triggering multiple slow downloads. + This is because ``Sorcha`` will otherwise attempt to download the files into the local cache, which may be on the HPC nodes rather than in your user directory, potentially triggering multiple slow downloads. -2. We recommend that each Sorcha run be given its own individual output directory. If multiple parallel Sorcha runs are attempting to save to the same file in the same directory, this will cause confusing and unexpected results. +2. We recommend that each ``Sorcha`` run be given its own individual output directory. If multiple parallel ``Sorcha`` runs are attempting to save to the same file in the same directory, this will cause confusing and unexpected results. -3. Sorcha output files can be very large, and user directories on HPC facilities are usually space-limited. Please ensure that your Sorcha runs are directing the output to be saved in a location with sufficient space, like your HPC cluster’s scratch drive. +3. ``Sorcha`` output files can be **very large**, and user directories on HPC facilities are usually space-limited. Please ensure that your ``Sorcha`` runs are directing the output to be saved in a location with sufficient space, like your HPC cluster’s scratch drive. 4. Think about having useful, helpful file names for your outputs. It is often tempting to call them something like “sorcha_output_” or “sorcha_output_”, but hard-won experience has led us to instead recommend more explanatory names for when you come back to your output later. - - +..tip:: + You can use the **sorcha init** command to copy ``Sorcha``'s :ref:`example configuration files ` into a directory of your choice. diff --git a/docs/outputs.rst b/docs/outputs.rst index 290e8316..582767b6 100644 --- a/docs/outputs.rst +++ b/docs/outputs.rst @@ -3,6 +3,17 @@ Outputs ================== +``Sorcha`` outputs: + * :ref:`Detections File ` (list of all the detections of the input popuation made by the simulated survey + * (Optioanal) :ref:`Statistics (Tally) File ` that provides a summary overview for the objects from the input population that were ''found'' in the simulated survey + * (Optional) :ref:`Ephemeris Output ` that provides the output from the :ref:`Ephemeris Generation` + +.. image:: images/survey_simulator_flow_chart.png + :width: 800 + :alt: An overview of the inputs and outputs of the Sorcha code. + :align: center + + .. attention:: Use the **-o** flag on the command line to specify where ``Sorcha`` should be saving any output and log files (the file path). @@ -26,6 +37,8 @@ The :ref:`configuration file` keyword output_format in the OUTPUT secti .. attention:: Use the **-t** flag on the command line to specify the filename stem for all the ``Sorcha`` output files and logs. +.. _detections: + Detections File ---------------------- @@ -35,7 +48,7 @@ with a row for each predicted detection and a column for each parameter calcula Additionally, the output columns of the detections file can be set to either "basic" or "all" settings (described below) using the output_columns :ref:`configuration file` keyword. -.. _basic:: +.. _basic: Basic Output ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -114,7 +127,7 @@ Example Detections File in Basic Format S1000000a,61789.27659,164.99043640246796,-19.09523631317997,164.29665099999988,-19.110176000000447,2.8895553381860802e-06,z,19.376978135088684,19.359651855968583,0.008079363622311368,0.00805998568672928,23.293210067462763,23.293123719813384 -.. _full:: +.. _full: Full Output ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -254,23 +267,9 @@ Detections File: Full Output Column Names, Formats, and Descriptions Optional Outputs ---------------------- - -Ephemeris Output -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Optionally (with the **--ew** flag set at the command line), an ephemeris file of all detections near the -field can be generated to a separate file, which can then be provided back to ``Sorcha`` as an optional external ephemeris file with the **-er** flag. -More information can be found on this functionality, including the output columns, in the :ref:`Ephemeris Generation` section of the documentation. - -The format of the outputted ephemeris file is controlled by the **eph_format** configuration keyword in the Inputs section of the :ref:`configuration file`e:: - - [INPUT] - ephemerides_type = external - eph_format = csv - -.. attention:: - Users should note that output produced by reading in a previously-generated ephemeris file will be in a different order than the output produced when running the ephemeris generator within ``Sorcha``. - This is simply a side-effect of how ``Sorcha`` reads in ephemeris files and does not affect the actual content of the output. +.. _stats: + Statistics (Tally) File ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ``Sorcha`` can also output a statistics or "tally" file (if specified uisng the **--st flag) which contains an overview of the ``Sorcha`` output for each object and filter. Minimally, this @@ -311,3 +310,24 @@ Statistics (Tally) File Column Names, Formats, and Descriptions .. note:: Unless the user has specified **drop_unlinked = False** in the :ref:`configuration file`, the object_linked column will read TRUE for all objects. To see which objects were not linked by ``Sorcha``, this variable must be set to False. + +.. _ephem_output: + +Ephemeris Output +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Optionally (with the **--ew** flag set at the command line), an ephemeris file of all detections near the +field can be generated to a separate file, which can then be provided back to ``Sorcha`` as an optional external ephemeris file with the **-er** flag. +More information can be found on this functionality, including the output columns, in the :ref:`Ephemeris Generation` section of the documentation. + +The format of the outputted ephemeris file is controlled by the **eph_format** configuration keyword in the Inputs section of the :ref:`configuration file`e:: + + [INPUT] + ephemerides_type = external + eph_format = csv + +.. attention:: + Users should note that output produced by reading in a previously-generated ephemeris file will be in a different order than the output produced when running the ephemeris generator within ``Sorcha``. This is simply a side-effect of how ``Sorcha`` reads in ephemeris files and does not affect the actual content of the output. + +.. tip:: + If instead you want to know which of the input small body population lands in the survey observations with an estimate of their apparent magnitude wihtout applying any other cuts or filters on the detections (not including discovery efficiency and linking effects), you can use/adapt the :ref:`known_config` example :ref:`configs`. + From 5a7c3f54c1804da7d7be8962bf5ee2b234c43aba Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Sun, 12 Jan 2025 16:31:21 +0000 Subject: [PATCH 33/52] hpc updates hpc updates --- docs/hpc.rst | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/hpc.rst b/docs/hpc.rst index 6b364599..abdf3334 100644 --- a/docs/hpc.rst +++ b/docs/hpc.rst @@ -1,34 +1,34 @@ .. _hpc: - Parallelization +Parallelization =============================================== Embarrassingly Parallel Problem ------------------------------------ -’s design lends itself perfectly to parallelization – when it simulates a large number of solar system objects, each one is considered in turn independently of all other objects. If you have access to a large number of computing cores, you can run ``Sorcha`` much more quickly by dividing up the labor: giving a small part of your model population to each core. +``Sorcha``’s design lends itself perfectly to parallelization – when it simulates a large number of solar system objects, each one is considered in turn independently of all other objects. If you have access to a large number of computing cores, you can run ``Sorcha`` much more quickly by dividing up the labor: giving a small part of your model population to each core. This involves two subtasks: breaking up your model population into an appropriate number of input files with unique names and organizing a large number of cores to simultaneously run ``Sorcha`` on their own individually-named input files. Both of these tasks are easy in theory, but tricky enough in practice that we provide some guidance below. -SLURM +Slurm --------- -Slurm Workload Manager is a resource management utility commonly used by computing clusters. We provide starter code for running large parallel batches using SLURM, though general guidance we provide is applicable to any system. Documentation for SLURM is available `here `_. Please note that your HPC (High Performance Computing) facility’s SLURM setup may differ from those on which ``Sorcha`` was tested, and it is always a good idea to read any facility-specific documentation or speak to the HPC maintainers before you begin to run jobs. +Slurm Workload Manager is a resource management utility commonly used by computing clusters. We provide starter code for running large parallel batches using slurm, though general guidance we provide is applicable to any system. Documentation for slurm is available `here `_. Please note that your HPC (High Performance Computing) facility’s slurm setup may differ from those on which ``Sorcha`` was tested, and it is always a good idea to read any facility-specific documentation or speak to the HPC maintainers before you begin to run jobs. Quickstart -------------- -We provide as a starting point our example scripts for running on HPC facilities using SLURM. Some modifications will be required to make them work for your facility. +We provide as a starting point our example scripts for running on HPC facilities using slurm. Some modifications will be required to make them work for your facility. -Below is a very simple SLURM script example designed to run the demo files three times on three cores in parallel. Here, one core has been assigned to each ``Sorcha`` run, with each core assigned 1Gb of memory. +Below is a very simple slurm script example designed to run the demo files three times on three cores in parallel. Here, one core has been assigned to each ``Sorcha`` run, with each core assigned 1Gb of memory. .. literalinclude:: ./example_files/multi_sorcha.sh :language: text Please note that time taken to run and memory required will vary enormously based on the size of your input files, your input population, and the chunk size assigned in the ``Sorcha`` configuration file: we therefore recommend test runs before you commit to very large runs. The chunk size is an especially important parameter: too small and ``Sorcha`` will take a very long time to run, too large and the memory footprint may become prohibitive. We have found that chunk sizes of 1000 to 10,000 work best. -Below is a more complex example of a SLURM script. Here, multi_sorcha.sh calls multi_sorcha.py, which splits up an input file into a number of ‘chunks’ and runs ``Sorcha`` in parallel on a user-specified number of cores. +Below is a more complex example of a slurm script. Here, multi_sorcha.sh calls multi_sorcha.py, which splits up an input file into a number of ‘chunks’ and runs ``Sorcha`` in parallel on a user-specified number of cores. multi_sorcha.sh: @@ -41,7 +41,7 @@ multi_sorcha.py: :language: python .. note:: - We provide these here for you to copy, paste, and edit as needed. You might have to some some slight modifications to both the SLURM script and multi_sorcha.py depending if you're using ``Sorcha`` without calling the stats file. + We provide these here for you to copy, paste, and edit as needed. You might have to some some slight modifications to both the slurm script and multi_sorcha.py depending if you're using ``Sorcha`` without calling the stats file. multi_sorcha.sh requests many parallel Slurm jobs of multi_sorcha.py, feeding each a different --instance parameter. After changing ‘my_orbits.csv’, ‘my_colors.csv’, and ‘my_pointings.db’ to match the above, it could be run as sbatch --array=0-9 multi_sorcha.sh 25 4 to generate ten jobs, each with 4 cores running 25 orbits each. From d8c1ff988a8bd9325752bfb5b74bcbd05dd6e683 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Sun, 12 Jan 2025 16:39:33 +0000 Subject: [PATCH 34/52] update hpc files update hpc files --- docs/hpc.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/hpc.rst b/docs/hpc.rst index abdf3334..4f216070 100644 --- a/docs/hpc.rst +++ b/docs/hpc.rst @@ -79,7 +79,7 @@ Best Practices/Tips and Tricks sorcha run … -ar /path/to/folder/ - This is because ``Sorcha`` will otherwise attempt to download the files into the local cache, which may be on the HPC nodes rather than in your user directory, potentially triggering multiple slow downloads. +This is because ``Sorcha`` will otherwise attempt to download the files into the local cache, which may be on the HPC nodes rather than in your user directory, potentially triggering multiple slow downloads. 2. We recommend that each ``Sorcha`` run be given its own individual output directory. If multiple parallel ``Sorcha`` runs are attempting to save to the same file in the same directory, this will cause confusing and unexpected results. @@ -88,6 +88,6 @@ Best Practices/Tips and Tricks 4. Think about having useful, helpful file names for your outputs. It is often tempting to call them something like “sorcha_output_” or “sorcha_output_”, but hard-won experience has led us to instead recommend more explanatory names for when you come back to your output later. -..tip:: +.. tip:: You can use the **sorcha init** command to copy ``Sorcha``'s :ref:`example configuration files ` into a directory of your choice. From d4c7552e51aebac5f206c3211208363fcc732bbf Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Sun, 12 Jan 2025 17:06:20 +0000 Subject: [PATCH 35/52] style updates --- docs/cite.rst | 5 +++++ docs/images/workflow.png | Bin 0 -> 475976 bytes docs/overview.rst | 5 +++++ 3 files changed, 10 insertions(+) create mode 100644 docs/images/workflow.png diff --git a/docs/cite.rst b/docs/cite.rst index c4d5b190..a5fd0b80 100644 --- a/docs/cite.rst +++ b/docs/cite.rst @@ -1,5 +1,10 @@ .. _citethecode: +.. image:: images/sorcha_logo.png + :width: 410 + :alt: Sorcha logo + :align: center + Citing the Software ========================== diff --git a/docs/images/workflow.png b/docs/images/workflow.png new file mode 100644 index 0000000000000000000000000000000000000000..fe0df0b161154e7b99e610ce552c009945b49cde GIT binary patch literal 475976 zcmeEuWmuKj_x4doQ4ujfMv)i;6(mMVX%j>Ql$1shkd|&33zZ=h5dPVDst&24!3t4 zhx@&E`)2qFdG0DB{IkPIV-O$6dX2UfS~QNbC7cx%7K-6gWpDPXRlEDT)b$<_MZ 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integrations of test particles, and the `REBOUND `_ N-body integrator. If the user prefers to use a different generator, ``Sorcha`` is also to be configured to read in an external ephemeris file with pre-calculated ephemerides of the input synthetic orbital population. +The default main steps, calculations, and filter within ``Sorcha`` that are used to estimate what the LSST would discover is shown below. + +.. image:: images/workflow.png + :width: 800 + :alt: An overview of the LSST workflow .. warning:: We have validated ``Sorcha`` with its internal :ref:`ephemeris generator`. If the user chooses to use a different ephemeris engine's calculations as input for ``Sorcha``, the user has the responsibility to check the accuracy of this input. From 8a7646a8b425a121d0cb4253b26cbce2503c981b Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Sun, 12 Jan 2025 17:41:42 +0000 Subject: [PATCH 36/52] minor clarifications in docs --- docs/advanced.rst | 1 + docs/ephemerisgen.rst | 3 +++ docs/index.rst | 35 ++++++++++++++++------------------- docs/postprocessing.rst | 2 +- 4 files changed, 21 insertions(+), 20 deletions(-) diff --git a/docs/advanced.rst b/docs/advanced.rst index de956c09..92e4f797 100644 --- a/docs/advanced.rst +++ b/docs/advanced.rst @@ -1,3 +1,4 @@ +.. _advanced: Advanced User Features ========================== diff --git a/docs/ephemerisgen.rst b/docs/ephemerisgen.rst index c5fdc2ef..01689b10 100644 --- a/docs/ephemerisgen.rst +++ b/docs/ephemerisgen.rst @@ -9,6 +9,9 @@ Ephemeris Generator We recommend using ``Sorcha``'s ephemeris generator for all your survey simulations. +.. seealso:: + For a more detailed description of ``Sorcha``'s ephemeris geneeration stage please see Holman et al (submitted). + How It Works -------------------------------------------------------- diff --git a/docs/index.rst b/docs/index.rst index 939be2e8..5ff996ae 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -13,8 +13,23 @@ .. tip:: We strongly recommend all new users read the ``Sorcha`` documentation before beginning any science-quality simulations. +Welcome to Sorcha's documentation! +------------------------------------------ + +This documentation site contains an installation guide, an overview of how ``Sorcha`` +works, tutorials, and demonstration notebooks that show how each of the various components within ``Sorcha`` work and can be customized. + +.. seealso:: + For a more detailed description of ``Sorcha`` and how it works, please see Merritt et al. (submiited) and Holman et al (submitted). + +.. warning:: + This documentation site and the software package it describes are under + review.. DO NOT USE this for science purposes just yet. Please wait until we release version 1.0. + + + What is Sorcha? -========================================================================= +------------------------------------------ ``Sorcha`` (pronounced "surk-ha") is an open-source Solar System survey simulator written in Python. ``Sorcha`` means light or brightness in Irish and Scots Gaelic. Sorcha estimates the brightness of @@ -24,24 +39,6 @@ with the `Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Date: Sun, 12 Jan 2025 19:41:39 +0000 Subject: [PATCH 37/52] updates to post-processing section updates to post-processing section --- docs/advanced.rst | 12 +- docs/autoapi/index.rst | 12 + .../activity/activity_registration/index.rst | 49 + .../sorcha/activity/base_activity/index.rst | 90 ++ .../activity/identity_activity/index.rst | 57 + docs/autoapi/sorcha/activity/index.rst | 172 +++ docs/autoapi/sorcha/ephemeris/index.rst | 417 ++++++ .../orbit_conversion_utilities/index.rst | 211 +++ .../sorcha/ephemeris/pixel_dict/index.rst | 232 ++++ .../ephemeris/simulation_constants/index.rst | 65 + .../ephemeris/simulation_data_files/index.rst | 33 + .../ephemeris/simulation_driver/index.rst | 180 +++ .../ephemeris/simulation_geometry/index.rst | 121 ++ .../ephemeris/simulation_parsing/index.rst | 97 ++ .../ephemeris/simulation_setup/index.rst | 74 + docs/autoapi/sorcha/index.rst | 54 + .../lightcurves/base_lightcurve/index.rst | 90 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docs/autoapi/sorcha/readers/index.rst | 21 + docs/autoapi/sorcha/sorcha/index.rst | 56 + .../utilities/check_output_logs/index.rst | 58 + .../sorcha/utilities/citation_text/index.rst | 27 + .../createResultsSQLDatabase/index.rst | 77 ++ .../utilities/dataUtilitiesForTests/index.rst | 33 + .../sorcha/utilities/diffTestUtils/index.rst | 69 + .../utilities/generateGoldens/index.rst | 19 + .../utilities/generate_meta_kernel/index.rst | 49 + docs/autoapi/sorcha/utilities/index.rst | 27 + .../retrieve_ephemeris_data_files/index.rst | 62 + .../utilities/sorchaArguments/index.rst | 130 ++ .../sorcha/utilities/sorchaConfigs/index.rst | 1232 +++++++++++++++++ .../utilities/sorcha_copy_configs/index.rst | 31 + .../sorcha_copy_demo_files/index.rst | 29 + .../utilities/sorcha_demo_command/index.rst | 42 + .../sorcha_cmdline/bootstrap/index.rst | 22 + docs/autoapi/sorcha_cmdline/demo/index.rst | 25 + docs/autoapi/sorcha_cmdline/index.rst | 21 + docs/autoapi/sorcha_cmdline/init/index.rst | 35 + docs/autoapi/sorcha_cmdline/main/index.rst | 25 + docs/autoapi/sorcha_cmdline/outputs/index.rst | 25 + docs/autoapi/sorcha_cmdline/run/index.rst | 22 + .../sorchaargumentparser/index.rst | 40 + docs/ephemerisgen.rst | 2 +- docs/images/fading_function.png | Bin 294157 -> 71888 bytes docs/images/vignetting.jpg | Bin 278581 -> 0 bytes docs/images/vignetting.png | Bin 0 -> 338310 bytes docs/index.rst | 2 +- docs/overview.rst | 3 + docs/postprocessing.rst | 82 +- docs/troubleshooting.rst | 3 + 88 files changed, 7694 insertions(+), 23 deletions(-) create mode 100644 docs/autoapi/index.rst create mode 100644 docs/autoapi/sorcha/activity/activity_registration/index.rst create mode 100644 docs/autoapi/sorcha/activity/base_activity/index.rst create mode 100644 docs/autoapi/sorcha/activity/identity_activity/index.rst create mode 100644 docs/autoapi/sorcha/activity/index.rst create mode 100644 docs/autoapi/sorcha/ephemeris/index.rst create mode 100644 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100644 docs/autoapi/sorcha_cmdline/bootstrap/index.rst create mode 100644 docs/autoapi/sorcha_cmdline/demo/index.rst create mode 100644 docs/autoapi/sorcha_cmdline/index.rst create mode 100644 docs/autoapi/sorcha_cmdline/init/index.rst create mode 100644 docs/autoapi/sorcha_cmdline/main/index.rst create mode 100644 docs/autoapi/sorcha_cmdline/outputs/index.rst create mode 100644 docs/autoapi/sorcha_cmdline/run/index.rst create mode 100644 docs/autoapi/sorcha_cmdline/sorchaargumentparser/index.rst delete mode 100644 docs/images/vignetting.jpg create mode 100644 docs/images/vignetting.png diff --git a/docs/advanced.rst b/docs/advanced.rst index 92e4f797..c90ed284 100644 --- a/docs/advanced.rst +++ b/docs/advanced.rst @@ -29,14 +29,17 @@ The following options can be optionally added to an expert section ([EXPERT]) of Turning Vignetting Off ~~~~~~~~~~~~~~~~~~~~~~~~~~~ -By default, vignetting using LSSTCam parameters is applied. To turn vignetting off, add to the :ref:`configs`:: +By default, :ref:`vignetting` using LSSTCam parameters is applied. To turn vignetting off, add to the :ref:`configs`:: [EXPERT] vignetting_on = False +If vigentting is turned off, then the 5σ Limiting Magnitude at the Source Location will be the limiting magnitude at the cetner of the FOV from the :ref:`pointing`. + .. tip:: Vignetting is a small effect for the LSSTCam, so you will see only a modest change in results if you turn this off for LSST simulations + Turning Off the Randomization of the Magnitude and Astrometry Values ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -59,6 +62,13 @@ The trailing losses filter is on by default, but it can be turned off by includi this option for debugging or for speed increases when the user is absolutely sure they are only supplying slow-moving objects. +Turning off Detection Efficiency/Applying the Fading Function +---------------------------------------------------------------- + +Applying the survey detection effieincy is on by default, but it can be turned off by including the option in the :ref:`configs`:: + + [FADINGFUNCTION] + fading_function_on = False Turning Off the Camera Footprint Filter ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/autoapi/index.rst b/docs/autoapi/index.rst new file mode 100644 index 00000000..90428d83 --- /dev/null +++ b/docs/autoapi/index.rst @@ -0,0 +1,12 @@ +API Reference +============= + +This page contains auto-generated API reference documentation [#f1]_. + +.. toctree:: + :titlesonly: + + /autoapi/sorcha/index + /autoapi/sorcha_cmdline/index + +.. [#f1] Created with `sphinx-autoapi `_ \ No newline at end of file diff --git a/docs/autoapi/sorcha/activity/activity_registration/index.rst b/docs/autoapi/sorcha/activity/activity_registration/index.rst new file mode 100644 index 00000000..99a7457d --- /dev/null +++ b/docs/autoapi/sorcha/activity/activity_registration/index.rst @@ -0,0 +1,49 @@ +sorcha.activity.activity_registration +===================================== + +.. py:module:: sorcha.activity.activity_registration + + +Attributes +---------- + +.. autoapisummary:: + + sorcha.activity.activity_registration.CA_METHODS + + +Functions +--------- + +.. autoapisummary:: + + sorcha.activity.activity_registration.register_activity_subclasses + sorcha.activity.activity_registration.update_activity_subclasses + + +Module Contents +--------------- + +.. py:function:: register_activity_subclasses() -> Dict[str, Callable] + + This method will identify all of the subclasses of ``AbstractCometaryActivity`` + and build a dictionary that maps ``name : subclass``. + + :returns: A dictionary of all of subclasses of ``AbstractCometaryActivity``. Where + the string returned from ``subclass.name_id()`` is the key, and the + subclass is the value. + :rtype: dict + + :raises ValueError: If a duplicate key is found, a ``ValueError`` is raised. This would + likely occur if a user copy/pasted an existing subclass but failed to + update the string returned from ``name_id()``. + + +.. py:function:: update_activity_subclasses() -> None + + This function is used to register newly created subclasses of the + `AbstractCometaryActivity`. + + +.. py:data:: CA_METHODS + diff --git a/docs/autoapi/sorcha/activity/base_activity/index.rst b/docs/autoapi/sorcha/activity/base_activity/index.rst new file mode 100644 index 00000000..a6a256d7 --- /dev/null +++ b/docs/autoapi/sorcha/activity/base_activity/index.rst @@ -0,0 +1,90 @@ +sorcha.activity.base_activity +============================= + +.. py:module:: sorcha.activity.base_activity + + +Attributes +---------- + +.. autoapisummary:: + + sorcha.activity.base_activity.logger + + +Classes +------- + +.. autoapisummary:: + + sorcha.activity.base_activity.AbstractCometaryActivity + + +Module Contents +--------------- + +.. py:data:: logger + +.. py:class:: AbstractCometaryActivity(required_column_names: List[str] = []) + + Bases: :py:obj:`abc.ABC` + + + Abstract base class for cometary activity models + + + .. py:attribute:: required_column_names + :value: [] + + + + .. py:method:: compute(df: pandas.DataFrame) -> numpy.array + :abstractmethod: + + + User implemented calculation based on the input provided by the + pandas dataframe ``df``. + + :param df: The ``observations`` dataframe provided by ``Sorcha``. + :type df: Pandas dataframe + + + + .. py:method:: _validate_column_names(df: pandas.DataFrame) -> None + + Private method that checks that the provided pandas dataframe contains + the required columns defined in ``self.required_column_names``. + + :param df: The ``observations`` dataframe provided by ``Sorcha``. + :type df: Pandas dataframe + + + + .. py:method:: _log_exception(exception: Exception) -> None + + Log an error message from an exception to the error log file + + :param exception: The exception with a value string to appended to the error log + :type exception: Exception + + + + .. py:method:: _log_error_message(error_msg: str) -> None + + Log a specific error string to the error log file + + :param error_msg: The string to be appended to the error log + :type error_msg: str + + + + .. py:method:: name_id() -> str + :staticmethod: + + :abstractmethod: + + + This method will return the unique name of the LightCurve Model + + + diff --git a/docs/autoapi/sorcha/activity/identity_activity/index.rst b/docs/autoapi/sorcha/activity/identity_activity/index.rst new file mode 100644 index 00000000..63b63fee --- /dev/null +++ b/docs/autoapi/sorcha/activity/identity_activity/index.rst @@ -0,0 +1,57 @@ +sorcha.activity.identity_activity +================================= + +.. py:module:: sorcha.activity.identity_activity + + +Classes +------- + +.. autoapisummary:: + + sorcha.activity.identity_activity.IdentityCometaryActivity + + +Module Contents +--------------- + +.. py:class:: IdentityCometaryActivity + + Bases: :py:obj:`sorcha.activity.base_activity.AbstractCometaryActivity` + + + !!! THIS SHOULD NEVER BE USED - FOR TESTING ONLY !!! + + Rudimentary cometary activity model that returns no change to the input ``observation`` + dataframe. + This class is explicitly created for testing purposes. + + + .. py:method:: compute(df: pandas.DataFrame) -> pandas.DataFrame + + Returns numpy array of 0's with shape equal to the input dataframe + time column. + + :param df: The ``observations`` dataframe provided by ``Sorcha``. + :type df: pd.DataFrame + + :returns: The original ``observations`` dataframe, unchanged. + :rtype: pd.DataFrame + + + + .. py:method:: name_id() -> str + :staticmethod: + + + Returns the string identifier for this cometary activity method. It + must be unique within all the subclasses of ``AbstractCometaryActivity``. + + We have chosen the name "identity" here because the input dataframe is + returned unchanged. + + :returns: Unique identifier for this cometary activity model + :rtype: str + + + diff --git a/docs/autoapi/sorcha/activity/index.rst b/docs/autoapi/sorcha/activity/index.rst new file mode 100644 index 00000000..ad9ed030 --- /dev/null +++ b/docs/autoapi/sorcha/activity/index.rst @@ -0,0 +1,172 @@ +sorcha.activity +=============== + +.. py:module:: sorcha.activity + + +Submodules +---------- + +.. toctree:: + :maxdepth: 1 + + /autoapi/sorcha/activity/activity_registration/index + /autoapi/sorcha/activity/base_activity/index + /autoapi/sorcha/activity/identity_activity/index + + +Attributes +---------- + +.. autoapisummary:: + + sorcha.activity.CA_METHODS + + +Classes +------- + +.. autoapisummary:: + + sorcha.activity.AbstractCometaryActivity + sorcha.activity.IdentityCometaryActivity + + +Functions +--------- + +.. autoapisummary:: + + sorcha.activity.register_activity_subclasses + sorcha.activity.update_activity_subclasses + + +Package Contents +---------------- + +.. py:class:: AbstractCometaryActivity(required_column_names: List[str] = []) + + Bases: :py:obj:`abc.ABC` + + + Abstract base class for cometary activity models + + + .. py:attribute:: required_column_names + :value: [] + + + + .. py:method:: compute(df: pandas.DataFrame) -> numpy.array + :abstractmethod: + + + User implemented calculation based on the input provided by the + pandas dataframe ``df``. + + :param df: The ``observations`` dataframe provided by ``Sorcha``. + :type df: Pandas dataframe + + + + .. py:method:: _validate_column_names(df: pandas.DataFrame) -> None + + Private method that checks that the provided pandas dataframe contains + the required columns defined in ``self.required_column_names``. + + :param df: The ``observations`` dataframe provided by ``Sorcha``. + :type df: Pandas dataframe + + + + .. py:method:: _log_exception(exception: Exception) -> None + + Log an error message from an exception to the error log file + + :param exception: The exception with a value string to appended to the error log + :type exception: Exception + + + + .. py:method:: _log_error_message(error_msg: str) -> None + + Log a specific error string to the error log file + + :param error_msg: The string to be appended to the error log + :type error_msg: str + + + + .. py:method:: name_id() -> str + :staticmethod: + + :abstractmethod: + + + This method will return the unique name of the LightCurve Model + + + +.. py:class:: IdentityCometaryActivity + + Bases: :py:obj:`sorcha.activity.base_activity.AbstractCometaryActivity` + + + !!! THIS SHOULD NEVER BE USED - FOR TESTING ONLY !!! + + Rudimentary cometary activity model that returns no change to the input ``observation`` + dataframe. + This class is explicitly created for testing purposes. + + + .. py:method:: compute(df: pandas.DataFrame) -> pandas.DataFrame + + Returns numpy array of 0's with shape equal to the input dataframe + time column. + + :param df: The ``observations`` dataframe provided by ``Sorcha``. + :type df: pd.DataFrame + + :returns: The original ``observations`` dataframe, unchanged. + :rtype: pd.DataFrame + + + + .. py:method:: name_id() -> str + :staticmethod: + + + Returns the string identifier for this cometary activity method. It + must be unique within all the subclasses of ``AbstractCometaryActivity``. + + We have chosen the name "identity" here because the input dataframe is + returned unchanged. + + :returns: Unique identifier for this cometary activity model + :rtype: str + + + +.. py:function:: register_activity_subclasses() -> Dict[str, Callable] + + This method will identify all of the subclasses of ``AbstractCometaryActivity`` + and build a dictionary that maps ``name : subclass``. + + :returns: A dictionary of all of subclasses of ``AbstractCometaryActivity``. Where + the string returned from ``subclass.name_id()`` is the key, and the + subclass is the value. + :rtype: dict + + :raises ValueError: If a duplicate key is found, a ``ValueError`` is raised. This would + likely occur if a user copy/pasted an existing subclass but failed to + update the string returned from ``name_id()``. + + +.. py:function:: update_activity_subclasses() -> None + + This function is used to register newly created subclasses of the + `AbstractCometaryActivity`. + + +.. py:data:: CA_METHODS + diff --git a/docs/autoapi/sorcha/ephemeris/index.rst b/docs/autoapi/sorcha/ephemeris/index.rst new file mode 100644 index 00000000..d959c025 --- /dev/null +++ b/docs/autoapi/sorcha/ephemeris/index.rst @@ -0,0 +1,417 @@ +sorcha.ephemeris +================ + +.. py:module:: sorcha.ephemeris + + +Submodules +---------- + +.. toctree:: + :maxdepth: 1 + + /autoapi/sorcha/ephemeris/orbit_conversion_utilities/index + /autoapi/sorcha/ephemeris/pixel_dict/index + /autoapi/sorcha/ephemeris/simulation_constants/index + /autoapi/sorcha/ephemeris/simulation_data_files/index + /autoapi/sorcha/ephemeris/simulation_driver/index + /autoapi/sorcha/ephemeris/simulation_geometry/index + /autoapi/sorcha/ephemeris/simulation_parsing/index + /autoapi/sorcha/ephemeris/simulation_setup/index + + +Attributes +---------- + +.. autoapisummary:: + + sorcha.ephemeris.AU_KM + sorcha.ephemeris.AU_M + sorcha.ephemeris.RADIUS_EARTH_KM + sorcha.ephemeris.SPEED_OF_LIGHT + sorcha.ephemeris.OBLIQUITY_ECLIPTIC + + +Classes +------- + +.. autoapisummary:: + + sorcha.ephemeris.Observatory + + +Functions +--------- + +.. autoapisummary:: + + sorcha.ephemeris.create_ecl_to_eq_rotation_matrix + sorcha.ephemeris.make_retriever + sorcha.ephemeris.barycentricObservatoryRates + sorcha.ephemeris.ecliptic_to_equatorial + sorcha.ephemeris.integrate_light_time + sorcha.ephemeris.ra_dec2vec + sorcha.ephemeris.mjd_tai_to_epoch + sorcha.ephemeris.parse_orbit_row + sorcha.ephemeris.create_assist_ephemeris + sorcha.ephemeris.furnish_spiceypy + sorcha.ephemeris.precompute_pointing_information + sorcha.ephemeris.create_ephemeris + sorcha.ephemeris.universal_cartesian + sorcha.ephemeris.universal_keplerian + + +Package Contents +---------------- + +.. py:data:: AU_KM + :value: 149597870.7 + + +.. py:data:: AU_M + :value: 149597870700 + + +.. py:data:: RADIUS_EARTH_KM + :value: 6378.137 + + +.. py:data:: SPEED_OF_LIGHT + :value: 173.1446326742403 + + +.. py:data:: OBLIQUITY_ECLIPTIC + +.. py:function:: create_ecl_to_eq_rotation_matrix(ecl) + + Creates a rotation matrix for transforming ecliptical coordinates + to equatorial coordinates. A rotation matrix based on the solar + system's ecliptic obliquity is already provided as + `ECL_TO_EQ_ROTATION_MATRIX`. + + :param ecl: The ecliptical obliquity. + :type ecl: float + + :returns: **rotmat** -- rotation matrix for transofmring ecliptical coordinates to equatorial coordinates. + Array has shape (3,3). + :rtype: numpy array/matrix of floats + + +.. py:function:: make_retriever(auxconfigs, directory_path: str = None) -> pooch.Pooch + + Helper function that will create a Pooch object to track and retrieve files. + + :param directory_path: The base directory to place all downloaded files. Default = None + :type directory_path: string, optional + :param registry: A dictionary of file names to SHA hashes. Generally we'll not use SHA=None + because the files we're tracking change frequently. Default = REGISTRY + :type registry: dictionary, optional + :param auxconfigs: Dataclass of auxiliary configuration file arguments. + :type auxconfigs: dataclass + + :returns: The instance of a Pooch object used to track and retrieve files. + :rtype: pooch + + +.. py:function:: barycentricObservatoryRates(et, obsCode, observatories, Rearth=RADIUS_EARTH_KM, delta_et=10) + + Computes the position and rate of motion for the observatory in barycentric coordinates + + :param et: JPL ephemeris time + :type et: float + :param obsCode: MPC observatory code + :type obsCode: str + :param observatories: Observatory object with spherical representations for the obsCode + :type observatories: Observatory + :param Rearth: Radius of the Earth (default is RADIUS_EARTH_KM) + :type Rearth: float + :param delta_et: Difference in ephemeris time (in days) to derive the rotation matrix from the fixed Earth equatorial frame to J2000 (default: 10) + :type delta_et: float + + :returns: * *array* -- Position of the observatory (baricentric) + * *array* -- Velocity of the observatory (baricentric) + + +.. py:function:: ecliptic_to_equatorial(v, rot_mat=ECL_TO_EQ_ROTATION_MATRIX) + + Converts an ecliptic-aligned vector to an equatorially-aligned vector + + :param v: vector + :type v: array (3 entries) + :param rot_mat: Rotation matrix. Default is the matrix that computes the ecliptic to equatorial conversion + :type rot_mat: 2D array (3x3 matrix) + + :returns: **v** -- Rotated vector + :rtype: array (3 entries) + + +.. py:function:: integrate_light_time(sim, ex, t, r_obs, lt0=0, iter=3, speed_of_light=SPEED_OF_LIGHT) + + Performs the light travel time correction between object and observatory iteratively for the object at a given reference time + + :param sim: Rebound simulation object + :type sim: simulation + :param ex: ASSIST simulation extras + :type ex: simulation extras + :param t: Target time + :type t: float + :param r_obs: Observatory position at time t + :type r_obs: array (3 entries) + :param lt0: First guess for light travel time + :type lt0: float + :param iter: Number of iterations + :type iter: int + :param speed_of_light: Speed of light for the calculation (default is SPEED_OF_LIGHT constant) + :type speed_of_light: float + + :returns: * **rho** (*array*) -- Object-observatory vector + * **rho_mag** (*float*) -- Magnitude of rho vector + * **lt** (*float*) -- Light travel time + * **target** (*array*) -- Object position vector at t-lt + * **vtarget** (*array*) -- Object velocity at t-lt + + +.. py:function:: ra_dec2vec(ra, dec) + + Converts a RA/Dec pair to a unit vector on the sphere + :param ra: Target RA + :type ra: float + :param dec: Target dec + :type dec: float + + :returns: Unit vector + :rtype: array + + +.. py:function:: mjd_tai_to_epoch(mjd_tai) + + Converts a MJD value in TAI to SPICE ephemeris time + + :param mjd_tai: Input mjd + :type mjd_tai: float + + :rtype: Ephemeris time + + +.. py:class:: Observatory(args, auxconfigs, oc_file=None) + + Class containing various utility tools related to the calculation of the observatory position + + + .. py:attribute:: observatoryPositionCache + + + .. py:attribute:: ObservatoryXYZ + + + .. py:method:: convert_to_geocentric(obs_location: dict) -> tuple + + Converts the observatory location to geocentric coordinates + + :param obs_location: Dictionary with Longitude and sin/cos of the observatory Latitude + :type obs_location: dict + + :returns: Geocentric position (x,y,z) + :rtype: tuple + + + + .. py:method:: barycentricObservatory(et, obsCode, Rearth=RADIUS_EARTH_KM) + + Computes the barycentric position of the observatory + + :param et: JPL internal ephemeris time + :type et: float + :param obsCode: MPC Observatory code + :type obsCode: str + :param Rearth: Radius of the Earth + :type Rearth: float + + :returns: Barycentric position of the observatory (x,y,z) + :rtype: array (3,) + + + +.. py:function:: parse_orbit_row(row, epochJD_TDB, ephem, sun_dict, gm_sun, gm_total) + + Parses the input orbit row, converting it to the format expected by + the ephemeris generation code later on + + :param row: Row of the input dataframe + :type row: Pandas dataframe row + :param epochJD_TDB: epoch of the elements, in JD TDB + :type epochJD_TDB: float + :param ephem: ASSIST ephemeris object + :type ephem: Ephem + :param sun_dict: Dictionary with the position of the Sun at each epoch + :type sun_dict: dict + :param gm_sun: Standard gravitational parameter GM for the Sun + :type gm_sun: float + :param gm_total: Standard gravitational parameter GM for the Solar System barycenter + :type gm_total: float + + :returns: State vector (position, velocity) + :rtype: tuple + + +.. py:function:: create_assist_ephemeris(args, auxconfigs) -> tuple + + Build the ASSIST ephemeris object + Parameter + --------- + auxconfigs: dataclass + Dataclass of auxiliary configuration file arguments. + :returns: * **Ephem** (*ASSIST ephemeris obejct*) -- The ASSIST ephemeris object + * **gm_sun** (*float*) -- value for the GM_SUN value + * **gm_total** (*float*) -- value for gm_total + + +.. py:function:: furnish_spiceypy(args, auxconfigs) + + Builds the SPICE kernel, downloading the required files if needed + :param auxconfigs: Dataclass of auxiliary configuration file arguments. + :type auxconfigs: dataclass + + +.. py:function:: precompute_pointing_information(pointings_df, args, sconfigs) + + This function is meant to be run once to prime the pointings dataframe + with additional information that Assist & Rebound needs for it's work. + + :param pointings_df: Contains the telescope pointing database. + :type pointings_df: pandas dataframe + :param args: Command line arguments needed for initialization. + :type args: dictionary + :param sconfigs: Dataclass of configuration file arguments. + :type sconfigs: dataclass + + :returns: **pointings_df** -- The original dataframe with several additional columns of precomputed values. + :rtype: pandas dataframe + + +.. py:function:: create_ephemeris(orbits_df, pointings_df, args, sconfigs) + + Generate a set of observations given a collection of orbits + and set of pointings. + + :param orbits_df: The dataframe containing the collection of orbits. + :type orbits_df: pandas dataframe + :param pointings_df: The dataframe containing the collection of telescope/camera pointings. + :type pointings_df: pandas dataframe + :param args: Various arguments necessary for the calculation + :param sconfigs: Dataclass of configuration file arguments. + Various configuration parameters necessary for the calculation + ang_fov : float + The angular size (deg) of the field of view + buffer : float + The angular size (deg) of the buffer around the field of view. + A buffer is required to allow for some motion between the time + of the observation and the time of the picket (t_picket) + picket_interval : float + The interval (days) between picket calculations. This is 1 day + by default. Current there is only one such interval, used for + all objects. It is currently possible for extremely fast-moving + objects to be missed. This will be remedied in future releases. + obsCode : string + The MPC code for the observatory. (This is current a configuration + parameter, but these should be included in the visit information, + to allow for multiple observatories. + nside : integer + The nside value used for the HEALPIx calculations. Must be a + power of 2 (1, 2, 4, ...) nside=64 is current default. + + :returns: **observations** -- The dataframe of observations needed for Sorcha to continue + :rtype: pandas dataframe + + .. rubric:: Notes + + This works by calculating and regularly updating the sky-plane + locations (unit vectors) of all the objects in the collection + of orbits. The HEALPix index for each of the locations is calculated. + A dictionary with pixel indices as keys and lists of ObjIDs for + those objects in each HEALPix tile as values is generated. An individual + one of these calculations is called a 'picket', as one element of a long + picket fence. Typically, the interval between pickets is one day. + + Given a specific pointing, the set of HEALPix tiles that are overlapped + by the pointing (and a buffer region) is computed. Then the precise + locations of just those objects within that set of HEALPix tiles are + computed. Details for those that actually do land within the field + of view are passed along. + + +.. py:function:: universal_cartesian(mu, q, e, incl, longnode, argperi, tp, epochMJD_TDB) + + Converts from a series of orbital elements into state vectors + using the universal variable formulation + + The output vector will be oriented in the same system as + the positional angles (i, Omega, omega) + + Note that mu, q, tp and epochMJD_TDB must have compatible units + As an example, if q is in au and tp/epoch are in days, mu must + be in (au^3)/days^2 + + :param mu: Standard gravitational parameter GM (see note above about units) + :type mu: float + :param q: Perihelion (see note above about units) + :type q: float + :param e: Eccentricity + :type e: float + :param incl: Inclination (radians) + :type incl: float + :param longnode: Longitude of ascending node (radians) + :type longnode: float + :param argperi: Argument of perihelion (radians) + :type argperi: float + :param tp: Time of perihelion passage in TDB scale (see note above about units) + :type tp: float + :param epochMJD_TDB: Epoch (in TDB) when the elements are defined (see note above about units) + :type epochMJD_TDB: float + + :returns: * *float* -- x coordinate + * *float* -- y coordinate + * *float* -- z coordinate + * *float* -- x velocity + * *float* -- y velocity + * *float* -- z velocity + + +.. py:function:: universal_keplerian(mu, x, y, z, vx, vy, vz, epochMJD_TDB) + + Converts from a state vectors into orbital elements + using the universal variable formulation + + The input vector will determine the orientation + of the positional angles (i, Omega, omega) + + + Note that mu and the state vectors must have compatible units + As an example, if x is in au and vx are in au/days, mu must + be in (au^3)/days^2 + + + :param mu: Standard gravitational parameter GM (see note above about units) + :type mu: float + :param x: x coordinate + :type x: float + :param y: y coordinate + :type y: float + :param z: z coordinate + :type z: float + :param vx: x velocity + :type vx: float + :param vy: y velocity + :type vy: float + :param vz: z velocity + :type vz: float + :param epochMJD_TDB (float): Epoch (in TDB) when the elements are defined (see note above about units) + + :returns: * *float* -- Perihelion (see note above about units) + * *float* -- Eccentricity + * *float* -- Inclination (radians) + * *float* -- Longitude of ascending node (radians) + * *float* -- Argument of perihelion (radians) + * *float* -- Time of perihelion passage in TDB scale (see note above about units) + + diff --git a/docs/autoapi/sorcha/ephemeris/orbit_conversion_utilities/index.rst b/docs/autoapi/sorcha/ephemeris/orbit_conversion_utilities/index.rst new file mode 100644 index 00000000..d11b4eec --- /dev/null +++ b/docs/autoapi/sorcha/ephemeris/orbit_conversion_utilities/index.rst @@ -0,0 +1,211 @@ +sorcha.ephemeris.orbit_conversion_utilities +=========================================== + +.. py:module:: sorcha.ephemeris.orbit_conversion_utilities + + +Classes +------- + +.. autoapisummary:: + + sorcha.ephemeris.orbit_conversion_utilities.halley_result + + +Functions +--------- + +.. autoapisummary:: + + sorcha.ephemeris.orbit_conversion_utilities.stumpff + sorcha.ephemeris.orbit_conversion_utilities.root_function + sorcha.ephemeris.orbit_conversion_utilities.halley_safe + sorcha.ephemeris.orbit_conversion_utilities.universal_cartesian + sorcha.ephemeris.orbit_conversion_utilities.principal_value + sorcha.ephemeris.orbit_conversion_utilities.universal_keplerian + + +Module Contents +--------------- + +.. py:class:: halley_result + + Bases: :py:obj:`tuple` + + + .. py:attribute:: root + + + .. py:attribute:: iterations + + + .. py:attribute:: function_calls + + + .. py:attribute:: converged + + + .. py:attribute:: flag + + + .. py:attribute:: f + + + .. py:attribute:: fp + + + .. py:attribute:: fpp + + +.. py:function:: stumpff(x) + + Computes the Stumpff function c_k(x) for k = 0, 1, 2, 3 + + :param x: Argument of the Stumpff function + :type x: float + + :returns: * **c_0(x)** (*float*) + * **c_1(x)** (*float*) + * **c_2(x)** (*float*) + * **c_3(x)** (*float*) + + +.. py:function:: root_function(s, mu, alpha, r0, r0dot, t) + + Root function used in the Halley minimizer + Computes the zeroth, first, second, and third derivatives + of the universal Kepler equation f + + :param s: Eccentric anomaly + :type s: float + :param mu: Standard gravitational parameter GM + :type mu: float + :param alpha: Total energy + :type alpha: float + :param r0: Initial position + :type r0: float + :param r0dot: Initial velocity + :type r0dot: float + :param t: Time + :type t: float + + :returns: * **f** (*float*) -- universal Kepler equation) + * **fp** (*float*) -- (first derivative of f + * **fpp** (*float*) -- second derivative of f + * **fppp** (*float*) -- third derivative of f + + +.. py:function:: halley_safe(x1, x2, mu, alpha, r0, r0dot, t, xacc=1e-14, maxit=100) + + Applies the Halley root finding algorithm on the universal Kepler equation + + :param x1: Previous guess used in minimization + :type x1: float + :param x2: Current guess for minimization + :type x2: float + :param mu: Standard gravitational parameter GM + :type mu: float + :param alpha: Total energy + :type alpha: float + :param r0: Initial position + :type r0: float + :param r0dot: Initial velocity + :type r0dot: float + :param t: Time + :type t: float + :param xacc: Accuracy in x before algorithm declares convergence + :type xacc: float + :param maxit: Maximum number of iterations + :type maxit: int + + :returns: * *boolean* -- True if minimization converged, False otherwise + * *float* -- Solution + * *float* -- First derivative of solution + + +.. py:function:: universal_cartesian(mu, q, e, incl, longnode, argperi, tp, epochMJD_TDB) + + Converts from a series of orbital elements into state vectors + using the universal variable formulation + + The output vector will be oriented in the same system as + the positional angles (i, Omega, omega) + + Note that mu, q, tp and epochMJD_TDB must have compatible units + As an example, if q is in au and tp/epoch are in days, mu must + be in (au^3)/days^2 + + :param mu: Standard gravitational parameter GM (see note above about units) + :type mu: float + :param q: Perihelion (see note above about units) + :type q: float + :param e: Eccentricity + :type e: float + :param incl: Inclination (radians) + :type incl: float + :param longnode: Longitude of ascending node (radians) + :type longnode: float + :param argperi: Argument of perihelion (radians) + :type argperi: float + :param tp: Time of perihelion passage in TDB scale (see note above about units) + :type tp: float + :param epochMJD_TDB: Epoch (in TDB) when the elements are defined (see note above about units) + :type epochMJD_TDB: float + + :returns: * *float* -- x coordinate + * *float* -- y coordinate + * *float* -- z coordinate + * *float* -- x velocity + * *float* -- y velocity + * *float* -- z velocity + + +.. py:function:: principal_value(theta) + + Computes the principal value of an angle + + :param theta: Angle + :type theta: float + + :returns: Principal value of angle + :rtype: float + + +.. py:function:: universal_keplerian(mu, x, y, z, vx, vy, vz, epochMJD_TDB) + + Converts from a state vectors into orbital elements + using the universal variable formulation + + The input vector will determine the orientation + of the positional angles (i, Omega, omega) + + + Note that mu and the state vectors must have compatible units + As an example, if x is in au and vx are in au/days, mu must + be in (au^3)/days^2 + + + :param mu: Standard gravitational parameter GM (see note above about units) + :type mu: float + :param x: x coordinate + :type x: float + :param y: y coordinate + :type y: float + :param z: z coordinate + :type z: float + :param vx: x velocity + :type vx: float + :param vy: y velocity + :type vy: float + :param vz: z velocity + :type vz: float + :param epochMJD_TDB (float): Epoch (in TDB) when the elements are defined (see note above about units) + + :returns: * *float* -- Perihelion (see note above about units) + * *float* -- Eccentricity + * *float* -- Inclination (radians) + * *float* -- Longitude of ascending node (radians) + * *float* -- Argument of perihelion (radians) + * *float* -- Time of perihelion passage in TDB scale (see note above about units) + + diff --git a/docs/autoapi/sorcha/ephemeris/pixel_dict/index.rst b/docs/autoapi/sorcha/ephemeris/pixel_dict/index.rst new file mode 100644 index 00000000..4f600a97 --- /dev/null +++ b/docs/autoapi/sorcha/ephemeris/pixel_dict/index.rst @@ -0,0 +1,232 @@ +sorcha.ephemeris.pixel_dict +=========================== + +.. py:module:: sorcha.ephemeris.pixel_dict + + +Classes +------- + +.. autoapisummary:: + + sorcha.ephemeris.pixel_dict.PixelDict + + +Functions +--------- + +.. autoapisummary:: + + sorcha.ephemeris.pixel_dict.lagrange3 + + +Module Contents +--------------- + +.. py:function:: lagrange3(t0, t1, t2, t) + + Calculate the coefficients for + second-order Lagrange interpolation + for measured points at times t0, t1, + and t2 and for an array of times t. + + These coefficients can be reused for + any number of input vectors. + + :param t0: Time t0 + :type t0: float + :param t1: Time t1 + :type t1: float + :param t2: Time t2 + :type t2: float + :param t: Times for the interpolation + :type t: 1D array + + :returns: * **L0** (*1D array*) -- interpolation coefficient at t0 + * **L1** (*1D array*) -- interpolation coefficient at t1 + * **L2** (*1D array*) -- interpolation coefficient at t2 + + +.. py:class:: PixelDict(jd_tdb, sim_dict, ephem, obsCode, observatory, picket_interval=1.0, nside=128, nested=True, n_sub_intervals=101) + + Class with methods needed during the ephemerides generation + Interfaces directly with the ASSIST+Rebound simulation objects as well as healpix + + + .. py:attribute:: nside + :value: 128 + + + + .. py:attribute:: picket_interval + :value: 1.0 + + + + .. py:attribute:: n_sub_intervals + :value: 101 + + + + .. py:attribute:: obsCode + + + .. py:attribute:: nested + :value: True + + + + .. py:attribute:: sim_dict + + + .. py:attribute:: ephem + + + .. py:attribute:: observatory + + + .. py:attribute:: t0 + + + .. py:attribute:: r_obs_0 + + + .. py:attribute:: tp + + + .. py:attribute:: r_obs_p + + + .. py:attribute:: tm + + + .. py:attribute:: r_obs_m + + + .. py:attribute:: pixel_dict + + + .. py:attribute:: rho_hat_m_dict + + + .. py:attribute:: rho_hat_0_dict + + + .. py:attribute:: rho_hat_p_dict + + + .. py:method:: get_observatory_position(t) + + Computes the barycentric position of the observatory (in au) + + :param t: Epoch for the position vector + :type t: float + + :returns: Barycentric position of the observatory (x,y,z) + :rtype: array (3,) + + + + .. py:method:: get_object_unit_vectors(desigs, r_obs, t, lt0=0.01) + + Computes the unit vector (in the equatorial sphere) that point towards the object - observatory vector + for a list of objects, at a given time + + :param desigs: List of designations (consistent with the simulation dictionary) + :type desigs: list + :param r_obs: Observatory location + :type r_obs: array (3 entries) + :param t: Time of the observation + :type t: float + :param lt0: Initial guess (in days) for light-time correction (default: 0.01 days) + :type lt0: float + + :returns: **rho_hat_dict** -- Dictionary of unit vectors + :rtype: dict + + + + .. py:method:: get_all_object_unit_vectors(r_obs, t, lt0=0.01) + + Computes the unit vector (in the equatorial sphere) that point towards the object - observatory vector + for *all* objects, at a given time + + :param r_obs: Observatory location + :type r_obs: array (3 entries) + :param t: Time of the observation + :type t: float + :param lt0: Initial guess (in days) for light-time correction (default: 0.01 days) + :type lt0: float + + :returns: **rho_hat_dict** -- Dictionary of unit vectors + :rtype: dict + + + + .. py:method:: get_interp_factors(tm, t0, tp, n_sub_intervals) + + Computes the Lagrange interpolation factors at a set of 3 times for an + equally spaced grid of points with a chosen number of sub-intervals + :param tm: First reference time + :type tm: float + :param t0: Second reference time + :type t0: float + :param tp: Third reference time + :type tp: float + :param n_sub_intervals: Number of sub-intervals for the Lagrange interpolation (default: 101) + :type n_sub_intervals: int + + :returns: * **Lm** (*2D array*) -- Lagrange coefficients at tm + * **L0** (*2D array*) -- Lagrange coefficients at t0 + * **Lp** (*2D array*) -- Lagrange coefficient at tp + + + + .. py:method:: interpolate_unit_vectors(desigs, jd_tdb) + + Interpolates the unit vectors for a list of designations towards the new target time + + :param desigs: List of designations (consistent with the simulation dictionary) + :type desigs: list + :param jd_tdb: Target time + :type jd_tdb: float + + :returns: **unit_vector_dict** -- Dictionary of unit vectors + :rtype: dict + + + + .. py:method:: compute_pixel_traversed() + + Computes the healpix pixels traversed by all the objects during between times tm and tp + + + + .. py:method:: update_pickets(jd_tdb) + + Updates the picket interpolation vectors for the new reference time + + :param jd_tdb: Target time + :type jd_tdb: float + + + + .. py:method:: get_designations(jd_tdb, ra, dec, ang_fov) + + Get the object designations that are within an angular radius of a topocentric unit vector at a + given time. + + :param jd_tdb: Target time + :type jd_tdb: float + :param ra: right ascension (degrees) + :type ra: float + :param dec: declination (degrees) + :type dec: float + :param ang_fov: Field of view radius + :type ang_fov: float + + :returns: **desigs** -- List of designations + :rtype: list + + + diff --git a/docs/autoapi/sorcha/ephemeris/simulation_constants/index.rst b/docs/autoapi/sorcha/ephemeris/simulation_constants/index.rst new file mode 100644 index 00000000..74c6b2ce --- /dev/null +++ b/docs/autoapi/sorcha/ephemeris/simulation_constants/index.rst @@ -0,0 +1,65 @@ +sorcha.ephemeris.simulation_constants +===================================== + +.. py:module:: sorcha.ephemeris.simulation_constants + + +Attributes +---------- + +.. autoapisummary:: + + sorcha.ephemeris.simulation_constants.RADIUS_EARTH_KM + sorcha.ephemeris.simulation_constants.AU_M + sorcha.ephemeris.simulation_constants.AU_KM + sorcha.ephemeris.simulation_constants.SPEED_OF_LIGHT + sorcha.ephemeris.simulation_constants.OBLIQUITY_ECLIPTIC + sorcha.ephemeris.simulation_constants.ECL_TO_EQ_ROTATION_MATRIX + + +Functions +--------- + +.. autoapisummary:: + + sorcha.ephemeris.simulation_constants.create_ecl_to_eq_rotation_matrix + + +Module Contents +--------------- + +.. py:data:: RADIUS_EARTH_KM + :value: 6378.137 + + +.. py:data:: AU_M + :value: 149597870700 + + +.. py:data:: AU_KM + :value: 149597870.7 + + +.. py:data:: SPEED_OF_LIGHT + :value: 173.1446326742403 + + +.. py:data:: OBLIQUITY_ECLIPTIC + +.. py:function:: create_ecl_to_eq_rotation_matrix(ecl) + + Creates a rotation matrix for transforming ecliptical coordinates + to equatorial coordinates. A rotation matrix based on the solar + system's ecliptic obliquity is already provided as + `ECL_TO_EQ_ROTATION_MATRIX`. + + :param ecl: The ecliptical obliquity. + :type ecl: float + + :returns: **rotmat** -- rotation matrix for transofmring ecliptical coordinates to equatorial coordinates. + Array has shape (3,3). + :rtype: numpy array/matrix of floats + + +.. py:data:: ECL_TO_EQ_ROTATION_MATRIX + diff --git a/docs/autoapi/sorcha/ephemeris/simulation_data_files/index.rst b/docs/autoapi/sorcha/ephemeris/simulation_data_files/index.rst new file mode 100644 index 00000000..83d47b91 --- /dev/null +++ b/docs/autoapi/sorcha/ephemeris/simulation_data_files/index.rst @@ -0,0 +1,33 @@ +sorcha.ephemeris.simulation_data_files +====================================== + +.. py:module:: sorcha.ephemeris.simulation_data_files + + +Functions +--------- + +.. autoapisummary:: + + sorcha.ephemeris.simulation_data_files.make_retriever + + +Module Contents +--------------- + +.. py:function:: make_retriever(auxconfigs, directory_path: str = None) -> pooch.Pooch + + Helper function that will create a Pooch object to track and retrieve files. + + :param directory_path: The base directory to place all downloaded files. Default = None + :type directory_path: string, optional + :param registry: A dictionary of file names to SHA hashes. Generally we'll not use SHA=None + because the files we're tracking change frequently. Default = REGISTRY + :type registry: dictionary, optional + :param auxconfigs: Dataclass of auxiliary configuration file arguments. + :type auxconfigs: dataclass + + :returns: The instance of a Pooch object used to track and retrieve files. + :rtype: pooch + + diff --git a/docs/autoapi/sorcha/ephemeris/simulation_driver/index.rst b/docs/autoapi/sorcha/ephemeris/simulation_driver/index.rst new file mode 100644 index 00000000..e2982f1a --- /dev/null +++ b/docs/autoapi/sorcha/ephemeris/simulation_driver/index.rst @@ -0,0 +1,180 @@ +sorcha.ephemeris.simulation_driver +================================== + +.. py:module:: sorcha.ephemeris.simulation_driver + + +Classes +------- + +.. autoapisummary:: + + sorcha.ephemeris.simulation_driver.EphemerisGeometryParameters + + +Functions +--------- + +.. autoapisummary:: + + sorcha.ephemeris.simulation_driver.get_vec + sorcha.ephemeris.simulation_driver.create_ephemeris + sorcha.ephemeris.simulation_driver.get_residual_vectors + sorcha.ephemeris.simulation_driver.calculate_rates_and_geometry + sorcha.ephemeris.simulation_driver.write_out_ephemeris_file + + +Module Contents +--------------- + +.. py:class:: EphemerisGeometryParameters + + Data class for holding parameters related to ephemeris geometry + + + .. py:attribute:: obj_id + :type: str + :value: None + + + + .. py:attribute:: mjd_tai + :type: float + :value: None + + + + .. py:attribute:: rho + :type: float + :value: None + + + + .. py:attribute:: rho_hat + :type: float + :value: None + + + + .. py:attribute:: rho_mag + :type: float + :value: None + + + + .. py:attribute:: r_ast + :type: float + :value: None + + + + .. py:attribute:: v_ast + :type: float + :value: None + + + +.. py:function:: get_vec(row, vecname) + + Extracts a vector from a Pandas dataframe row + :param row: + :type row: row from the dataframe + :param vecname: + :type vecname: name of the vector + + :rtype: 3D numpy array + + +.. py:function:: create_ephemeris(orbits_df, pointings_df, args, sconfigs) + + Generate a set of observations given a collection of orbits + and set of pointings. + + :param orbits_df: The dataframe containing the collection of orbits. + :type orbits_df: pandas dataframe + :param pointings_df: The dataframe containing the collection of telescope/camera pointings. + :type pointings_df: pandas dataframe + :param args: Various arguments necessary for the calculation + :param sconfigs: Dataclass of configuration file arguments. + Various configuration parameters necessary for the calculation + ang_fov : float + The angular size (deg) of the field of view + buffer : float + The angular size (deg) of the buffer around the field of view. + A buffer is required to allow for some motion between the time + of the observation and the time of the picket (t_picket) + picket_interval : float + The interval (days) between picket calculations. This is 1 day + by default. Current there is only one such interval, used for + all objects. It is currently possible for extremely fast-moving + objects to be missed. This will be remedied in future releases. + obsCode : string + The MPC code for the observatory. (This is current a configuration + parameter, but these should be included in the visit information, + to allow for multiple observatories. + nside : integer + The nside value used for the HEALPIx calculations. Must be a + power of 2 (1, 2, 4, ...) nside=64 is current default. + + :returns: **observations** -- The dataframe of observations needed for Sorcha to continue + :rtype: pandas dataframe + + .. rubric:: Notes + + This works by calculating and regularly updating the sky-plane + locations (unit vectors) of all the objects in the collection + of orbits. The HEALPix index for each of the locations is calculated. + A dictionary with pixel indices as keys and lists of ObjIDs for + those objects in each HEALPix tile as values is generated. An individual + one of these calculations is called a 'picket', as one element of a long + picket fence. Typically, the interval between pickets is one day. + + Given a specific pointing, the set of HEALPix tiles that are overlapped + by the pointing (and a buffer region) is computed. Then the precise + locations of just those objects within that set of HEALPix tiles are + computed. Details for those that actually do land within the field + of view are passed along. + + +.. py:function:: get_residual_vectors(v1) + + Decomposes the vector into two unit vectors to facilitate computation of on-sky angles + The decomposition is such that A = (-sin (RA), cos(RA), 0) is in the direction of increasing RA, + and D = (-sin(dec)cos (RA), -sin(dec) sin(RA), cos(dec)) is in the direction of increasing Dec + The triplet (A,D,v1) forms an orthonormal basis of the 3D vector space + :param v1: The vector to be decomposed + :type v1: array, shape = (3,)) + + :returns: * **A** (*array, shape = (3,))*) -- A vector + * **D** (*array, shape = (3,))*) -- D vector + + +.. py:function:: calculate_rates_and_geometry(pointing: pandas.DataFrame, ephem_geom_params: EphemerisGeometryParameters) + + Calculate rates and geometry for objects within the field of view + + :param pointing: The dataframe containing the pointing database. + :type pointing: pandas dataframe + :param ephem_geom_params: Various parameters necessary to calculate the ephemeris + :type ephem_geom_params: EphemerisGeometryParameters + + :returns: Tuple containing the ephemeris parameters needed for Sorcha post processing. + :rtype: tuple + + +.. py:function:: write_out_ephemeris_file(ephemeris_df, ephemeris_csv_filename, args, sconfigs) + + Writes the ephemeris out to an external file. + + :param ephemeris_df: The data frame of ephemeris information to be written out. + :type ephemeris_df: Pandas DataFrame + :param ephemeris_csv_filename: The filepath (without extension) to write the ephemeris file to. + :type ephemeris_csv_filename: string + :param args: Command-line arguments from Sorcha. + :type args: sorchaArguments object or similar + :param sconfigs: Dataclass of configuration file arguments. + :type sconfigs: dataclass + + :rtype: None. + + diff --git a/docs/autoapi/sorcha/ephemeris/simulation_geometry/index.rst b/docs/autoapi/sorcha/ephemeris/simulation_geometry/index.rst new file mode 100644 index 00000000..7be49eb5 --- /dev/null +++ b/docs/autoapi/sorcha/ephemeris/simulation_geometry/index.rst @@ -0,0 +1,121 @@ +sorcha.ephemeris.simulation_geometry +==================================== + +.. py:module:: sorcha.ephemeris.simulation_geometry + + +Functions +--------- + +.. autoapisummary:: + + sorcha.ephemeris.simulation_geometry.ecliptic_to_equatorial + sorcha.ephemeris.simulation_geometry.integrate_light_time + sorcha.ephemeris.simulation_geometry.get_hp_neighbors + sorcha.ephemeris.simulation_geometry.ra_dec2vec + sorcha.ephemeris.simulation_geometry.vec2ra_dec + sorcha.ephemeris.simulation_geometry.barycentricObservatoryRates + + +Module Contents +--------------- + +.. py:function:: ecliptic_to_equatorial(v, rot_mat=ECL_TO_EQ_ROTATION_MATRIX) + + Converts an ecliptic-aligned vector to an equatorially-aligned vector + + :param v: vector + :type v: array (3 entries) + :param rot_mat: Rotation matrix. Default is the matrix that computes the ecliptic to equatorial conversion + :type rot_mat: 2D array (3x3 matrix) + + :returns: **v** -- Rotated vector + :rtype: array (3 entries) + + +.. py:function:: integrate_light_time(sim, ex, t, r_obs, lt0=0, iter=3, speed_of_light=SPEED_OF_LIGHT) + + Performs the light travel time correction between object and observatory iteratively for the object at a given reference time + + :param sim: Rebound simulation object + :type sim: simulation + :param ex: ASSIST simulation extras + :type ex: simulation extras + :param t: Target time + :type t: float + :param r_obs: Observatory position at time t + :type r_obs: array (3 entries) + :param lt0: First guess for light travel time + :type lt0: float + :param iter: Number of iterations + :type iter: int + :param speed_of_light: Speed of light for the calculation (default is SPEED_OF_LIGHT constant) + :type speed_of_light: float + + :returns: * **rho** (*array*) -- Object-observatory vector + * **rho_mag** (*float*) -- Magnitude of rho vector + * **lt** (*float*) -- Light travel time + * **target** (*array*) -- Object position vector at t-lt + * **vtarget** (*array*) -- Object velocity at t-lt + + +.. py:function:: get_hp_neighbors(ra_c, dec_c, search_radius, nside=32, nested=True) + + Queries the healpix grid for pixels near the given RA/Dec with a given search radius + + :param ra_c: Target RA + :type ra_c: float + :param dec_c: Target dec + :type dec_c: float + :param search_radius: Radius for the query + :type search_radius: float + :param nside: healpix nside + :type nside: int + :param nested: Defines the ordering scheme for the healpix ordering. True (default) means a NESTED ordering + :type nested: boolean + + :returns: **res** -- List of healpix pixels + :rtype: list + + +.. py:function:: ra_dec2vec(ra, dec) + + Converts a RA/Dec pair to a unit vector on the sphere + :param ra: Target RA + :type ra: float + :param dec: Target dec + :type dec: float + + :returns: Unit vector + :rtype: array + + +.. py:function:: vec2ra_dec(vec) + + Decomposes a unit vector on the sphere into a RA/Dec pair + :param vec: Unit vector + :type vec: array + + :returns: * **ra** (*float*) -- Target RA + * **dec** (*float*) -- Target dec + + +.. py:function:: barycentricObservatoryRates(et, obsCode, observatories, Rearth=RADIUS_EARTH_KM, delta_et=10) + + Computes the position and rate of motion for the observatory in barycentric coordinates + + :param et: JPL ephemeris time + :type et: float + :param obsCode: MPC observatory code + :type obsCode: str + :param observatories: Observatory object with spherical representations for the obsCode + :type observatories: Observatory + :param Rearth: Radius of the Earth (default is RADIUS_EARTH_KM) + :type Rearth: float + :param delta_et: Difference in ephemeris time (in days) to derive the rotation matrix from the fixed Earth equatorial frame to J2000 (default: 10) + :type delta_et: float + + :returns: * *array* -- Position of the observatory (baricentric) + * *array* -- Velocity of the observatory (baricentric) + + diff --git a/docs/autoapi/sorcha/ephemeris/simulation_parsing/index.rst b/docs/autoapi/sorcha/ephemeris/simulation_parsing/index.rst new file mode 100644 index 00000000..4124be88 --- /dev/null +++ b/docs/autoapi/sorcha/ephemeris/simulation_parsing/index.rst @@ -0,0 +1,97 @@ +sorcha.ephemeris.simulation_parsing +=================================== + +.. py:module:: sorcha.ephemeris.simulation_parsing + + +Classes +------- + +.. autoapisummary:: + + sorcha.ephemeris.simulation_parsing.Observatory + + +Functions +--------- + +.. autoapisummary:: + + sorcha.ephemeris.simulation_parsing.mjd_tai_to_epoch + sorcha.ephemeris.simulation_parsing.parse_orbit_row + + +Module Contents +--------------- + +.. py:function:: mjd_tai_to_epoch(mjd_tai) + + Converts a MJD value in TAI to SPICE ephemeris time + + :param mjd_tai: Input mjd + :type mjd_tai: float + + :rtype: Ephemeris time + + +.. py:function:: parse_orbit_row(row, epochJD_TDB, ephem, sun_dict, gm_sun, gm_total) + + Parses the input orbit row, converting it to the format expected by + the ephemeris generation code later on + + :param row: Row of the input dataframe + :type row: Pandas dataframe row + :param epochJD_TDB: epoch of the elements, in JD TDB + :type epochJD_TDB: float + :param ephem: ASSIST ephemeris object + :type ephem: Ephem + :param sun_dict: Dictionary with the position of the Sun at each epoch + :type sun_dict: dict + :param gm_sun: Standard gravitational parameter GM for the Sun + :type gm_sun: float + :param gm_total: Standard gravitational parameter GM for the Solar System barycenter + :type gm_total: float + + :returns: State vector (position, velocity) + :rtype: tuple + + +.. py:class:: Observatory(args, auxconfigs, oc_file=None) + + Class containing various utility tools related to the calculation of the observatory position + + + .. py:attribute:: observatoryPositionCache + + + .. py:attribute:: ObservatoryXYZ + + + .. py:method:: convert_to_geocentric(obs_location: dict) -> tuple + + Converts the observatory location to geocentric coordinates + + :param obs_location: Dictionary with Longitude and sin/cos of the observatory Latitude + :type obs_location: dict + + :returns: Geocentric position (x,y,z) + :rtype: tuple + + + + .. py:method:: barycentricObservatory(et, obsCode, Rearth=RADIUS_EARTH_KM) + + Computes the barycentric position of the observatory + + :param et: JPL internal ephemeris time + :type et: float + :param obsCode: MPC Observatory code + :type obsCode: str + :param Rearth: Radius of the Earth + :type Rearth: float + + :returns: Barycentric position of the observatory (x,y,z) + :rtype: array (3,) + + + diff --git a/docs/autoapi/sorcha/ephemeris/simulation_setup/index.rst b/docs/autoapi/sorcha/ephemeris/simulation_setup/index.rst new file mode 100644 index 00000000..27c80764 --- /dev/null +++ b/docs/autoapi/sorcha/ephemeris/simulation_setup/index.rst @@ -0,0 +1,74 @@ +sorcha.ephemeris.simulation_setup +================================= + +.. py:module:: sorcha.ephemeris.simulation_setup + + +Functions +--------- + +.. autoapisummary:: + + sorcha.ephemeris.simulation_setup.create_assist_ephemeris + sorcha.ephemeris.simulation_setup.furnish_spiceypy + sorcha.ephemeris.simulation_setup.generate_simulations + sorcha.ephemeris.simulation_setup.precompute_pointing_information + + +Module Contents +--------------- + +.. py:function:: create_assist_ephemeris(args, auxconfigs) -> tuple + + Build the ASSIST ephemeris object + Parameter + --------- + auxconfigs: dataclass + Dataclass of auxiliary configuration file arguments. + :returns: * **Ephem** (*ASSIST ephemeris obejct*) -- The ASSIST ephemeris object + * **gm_sun** (*float*) -- value for the GM_SUN value + * **gm_total** (*float*) -- value for gm_total + + +.. py:function:: furnish_spiceypy(args, auxconfigs) + + Builds the SPICE kernel, downloading the required files if needed + :param auxconfigs: Dataclass of auxiliary configuration file arguments. + :type auxconfigs: dataclass + + +.. py:function:: generate_simulations(ephem, gm_sun, gm_total, orbits_df, args) + + Creates the dictionary of ASSIST simulations for the ephemeris generation + + :param ephem: The ASSIST ephemeris object + :type ephem: Ephem + :param gm_sun: Standard gravitational parameter GM for the Sun + :type gm_sun: float + :param gm_total: Standard gravitational parameter GM for the Solar System barycenter + :type gm_total: float + :param orbits_df: Pandas dataframe with the input orbits + :type orbits_df: dataframe + :param args: dictionary of command-line arguments. + :type args: dictionary or `sorchaArguments` object + + :returns: **sim_dict** -- Dictionary of ASSIST simulations + :rtype: dict + + +.. py:function:: precompute_pointing_information(pointings_df, args, sconfigs) + + This function is meant to be run once to prime the pointings dataframe + with additional information that Assist & Rebound needs for it's work. + + :param pointings_df: Contains the telescope pointing database. + :type pointings_df: pandas dataframe + :param args: Command line arguments needed for initialization. + :type args: dictionary + :param sconfigs: Dataclass of configuration file arguments. + :type sconfigs: dataclass + + :returns: **pointings_df** -- The original dataframe with several additional columns of precomputed values. + :rtype: pandas dataframe + + diff --git a/docs/autoapi/sorcha/index.rst b/docs/autoapi/sorcha/index.rst new file mode 100644 index 00000000..2b3daaa8 --- /dev/null +++ b/docs/autoapi/sorcha/index.rst @@ -0,0 +1,54 @@ +sorcha +====== + +.. py:module:: sorcha + + +Submodules +---------- + +.. toctree:: + :maxdepth: 1 + + /autoapi/sorcha/activity/index + /autoapi/sorcha/ephemeris/index + /autoapi/sorcha/lightcurves/index + /autoapi/sorcha/modules/index + /autoapi/sorcha/readers/index + /autoapi/sorcha/sorcha/index + /autoapi/sorcha/utilities/index + + +Attributes +---------- + +.. autoapisummary:: + + sorcha.__version__ + + +Functions +--------- + +.. autoapisummary:: + + sorcha.cite + + +Package Contents +---------------- + +.. py:function:: cite() + + Providing the bibtex, AAS Journals software latex command, and acknowledgement + statements for Sorcha and the associated packages that power it. + + :param None: + + :rtype: None + + +.. py:data:: __version__ + :value: 'unknown version' + + diff --git a/docs/autoapi/sorcha/lightcurves/base_lightcurve/index.rst b/docs/autoapi/sorcha/lightcurves/base_lightcurve/index.rst new file mode 100644 index 00000000..988b89cd --- /dev/null +++ b/docs/autoapi/sorcha/lightcurves/base_lightcurve/index.rst @@ -0,0 +1,90 @@ +sorcha.lightcurves.base_lightcurve +================================== + +.. py:module:: sorcha.lightcurves.base_lightcurve + + +Attributes +---------- + +.. autoapisummary:: + + sorcha.lightcurves.base_lightcurve.logger + + +Classes +------- + +.. autoapisummary:: + + sorcha.lightcurves.base_lightcurve.AbstractLightCurve + + +Module Contents +--------------- + +.. py:data:: logger + +.. py:class:: AbstractLightCurve(required_column_names: List[str] = []) + + Bases: :py:obj:`abc.ABC` + + + Abstract base class for lightcurve models + + + .. py:attribute:: required_column_names + :value: [] + + + + .. py:method:: compute(df: pandas.DataFrame) -> numpy.array + :abstractmethod: + + + User implemented calculation based on the input provided by the + pandas dataframe ``df``. + + :param df: The ``observations`` dataframe provided by ``Sorcha``. + :type df: Pandas dataframe + + + + .. py:method:: _validate_column_names(df: pandas.DataFrame) -> None + + Private method that checks that the provided pandas dataframe contains + the required columns defined in ``self.required_column_names``. + + :param df: The ``observations`` dataframe provided by ``Sorcha``. + :type df: Pandas dataframe + + + + .. py:method:: _log_exception(exception: Exception) -> None + + Log an error message from an exception to the error log file + + :param exception: The exception with a string to appended to the error log + :type exception: Exception + + + + .. py:method:: _log_error_message(error_msg: str) -> None + + Log a specific error string to the error log file + + :param error_msg: The string to be appended to the error log + :type error_msg: string + + + + .. py:method:: name_id() -> str + :staticmethod: + + :abstractmethod: + + + This method will return the unique name of the LightCurve Model + + + diff --git a/docs/autoapi/sorcha/lightcurves/identity_lightcurve/index.rst b/docs/autoapi/sorcha/lightcurves/identity_lightcurve/index.rst new file mode 100644 index 00000000..bb45235b --- /dev/null +++ b/docs/autoapi/sorcha/lightcurves/identity_lightcurve/index.rst @@ -0,0 +1,56 @@ +sorcha.lightcurves.identity_lightcurve +====================================== + +.. py:module:: sorcha.lightcurves.identity_lightcurve + + +Classes +------- + +.. autoapisummary:: + + sorcha.lightcurves.identity_lightcurve.IdentityLightCurve + + +Module Contents +--------------- + +.. py:class:: IdentityLightCurve(required_column_names: List[str] = ['fieldMJD_TAI']) + + Bases: :py:obj:`sorcha.lightcurves.base_lightcurve.AbstractLightCurve` + + + !!! THIS SHOULD NEVER BE USED - FOR TESTING ONLY !!! + + Rudimentary lightcurve model that returns no shift. This class is explicitly + created for testing purposes. + + + .. py:method:: compute(df: pandas.DataFrame) -> numpy.array + + Returns numpy array of 0's with shape equal to the input dataframe + time column. + + :param df: The ``observations`` dataframe provided by ``Sorcha``. + :type df: Pandas dataframe + + :returns: Numpy array of 0's with shape equal to the input dataframe time column. + :rtype: np.array + + + + .. py:method:: name_id() -> str + :staticmethod: + + + Returns the string identifier for this light curve method. It must be + unique within all the subclasses of ``AbstractLightCurve``. + + We have chosen the name "identity" here because the input brightness will + equal the output brightness if this model is applied. + + :returns: Unique identifier for this light curve calculator + :rtype: string + + + diff --git a/docs/autoapi/sorcha/lightcurves/index.rst b/docs/autoapi/sorcha/lightcurves/index.rst new file mode 100644 index 00000000..086a1814 --- /dev/null +++ b/docs/autoapi/sorcha/lightcurves/index.rst @@ -0,0 +1,171 @@ +sorcha.lightcurves +================== + +.. py:module:: sorcha.lightcurves + + +Submodules +---------- + +.. toctree:: + :maxdepth: 1 + + /autoapi/sorcha/lightcurves/base_lightcurve/index + /autoapi/sorcha/lightcurves/identity_lightcurve/index + /autoapi/sorcha/lightcurves/lightcurve_registration/index + + +Attributes +---------- + +.. autoapisummary:: + + sorcha.lightcurves.LC_METHODS + + +Classes +------- + +.. autoapisummary:: + + sorcha.lightcurves.AbstractLightCurve + sorcha.lightcurves.IdentityLightCurve + + +Functions +--------- + +.. autoapisummary:: + + sorcha.lightcurves.register_lc_subclasses + sorcha.lightcurves.update_lc_subclasses + + +Package Contents +---------------- + +.. py:class:: AbstractLightCurve(required_column_names: List[str] = []) + + Bases: :py:obj:`abc.ABC` + + + Abstract base class for lightcurve models + + + .. py:attribute:: required_column_names + :value: [] + + + + .. py:method:: compute(df: pandas.DataFrame) -> numpy.array + :abstractmethod: + + + User implemented calculation based on the input provided by the + pandas dataframe ``df``. + + :param df: The ``observations`` dataframe provided by ``Sorcha``. + :type df: Pandas dataframe + + + + .. py:method:: _validate_column_names(df: pandas.DataFrame) -> None + + Private method that checks that the provided pandas dataframe contains + the required columns defined in ``self.required_column_names``. + + :param df: The ``observations`` dataframe provided by ``Sorcha``. + :type df: Pandas dataframe + + + + .. py:method:: _log_exception(exception: Exception) -> None + + Log an error message from an exception to the error log file + + :param exception: The exception with a string to appended to the error log + :type exception: Exception + + + + .. py:method:: _log_error_message(error_msg: str) -> None + + Log a specific error string to the error log file + + :param error_msg: The string to be appended to the error log + :type error_msg: string + + + + .. py:method:: name_id() -> str + :staticmethod: + + :abstractmethod: + + + This method will return the unique name of the LightCurve Model + + + +.. py:class:: IdentityLightCurve(required_column_names: List[str] = ['fieldMJD_TAI']) + + Bases: :py:obj:`sorcha.lightcurves.base_lightcurve.AbstractLightCurve` + + + !!! THIS SHOULD NEVER BE USED - FOR TESTING ONLY !!! + + Rudimentary lightcurve model that returns no shift. This class is explicitly + created for testing purposes. + + + .. py:method:: compute(df: pandas.DataFrame) -> numpy.array + + Returns numpy array of 0's with shape equal to the input dataframe + time column. + + :param df: The ``observations`` dataframe provided by ``Sorcha``. + :type df: Pandas dataframe + + :returns: Numpy array of 0's with shape equal to the input dataframe time column. + :rtype: np.array + + + + .. py:method:: name_id() -> str + :staticmethod: + + + Returns the string identifier for this light curve method. It must be + unique within all the subclasses of ``AbstractLightCurve``. + + We have chosen the name "identity" here because the input brightness will + equal the output brightness if this model is applied. + + :returns: Unique identifier for this light curve calculator + :rtype: string + + + +.. py:function:: register_lc_subclasses() -> Dict[str, Callable] + + This method will identify all of the subclasses of ``AbstractLightCurve`` + and build a dictionary that maps ``name : subclass``. + + :returns: A dictionary of all of subclasses of ``AbstractLightCurve``. Where + the string returned from ``subclass.name_id()`` is the key, and the + subclass is the value. + :rtype: dict + + :raises ValueError: If a duplicate key is found, a ``ValueError`` is raised. This would + likely occur if a user copy/pasted an existing subclass but failed to + update the string returned from ``name_id()``. + + +.. py:function:: update_lc_subclasses() -> None + + This function is used to register newly created subclasses of the + `AbstractLightCurve`. + + +.. py:data:: LC_METHODS + diff --git a/docs/autoapi/sorcha/lightcurves/lightcurve_registration/index.rst b/docs/autoapi/sorcha/lightcurves/lightcurve_registration/index.rst new file mode 100644 index 00000000..edadb7af --- /dev/null +++ b/docs/autoapi/sorcha/lightcurves/lightcurve_registration/index.rst @@ -0,0 +1,49 @@ +sorcha.lightcurves.lightcurve_registration +========================================== + +.. py:module:: sorcha.lightcurves.lightcurve_registration + + +Attributes +---------- + +.. autoapisummary:: + + sorcha.lightcurves.lightcurve_registration.LC_METHODS + + +Functions +--------- + +.. autoapisummary:: + + sorcha.lightcurves.lightcurve_registration.register_lc_subclasses + sorcha.lightcurves.lightcurve_registration.update_lc_subclasses + + +Module Contents +--------------- + +.. py:function:: register_lc_subclasses() -> Dict[str, Callable] + + This method will identify all of the subclasses of ``AbstractLightCurve`` + and build a dictionary that maps ``name : subclass``. + + :returns: A dictionary of all of subclasses of ``AbstractLightCurve``. Where + the string returned from ``subclass.name_id()`` is the key, and the + subclass is the value. + :rtype: dict + + :raises ValueError: If a duplicate key is found, a ``ValueError`` is raised. This would + likely occur if a user copy/pasted an existing subclass but failed to + update the string returned from ``name_id()``. + + +.. py:function:: update_lc_subclasses() -> None + + This function is used to register newly created subclasses of the + `AbstractLightCurve`. + + +.. py:data:: LC_METHODS + diff --git a/docs/autoapi/sorcha/modules/PPAddUncertainties/index.rst b/docs/autoapi/sorcha/modules/PPAddUncertainties/index.rst new file mode 100644 index 00000000..082abe4e --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPAddUncertainties/index.rst @@ -0,0 +1,217 @@ +sorcha.modules.PPAddUncertainties +================================= + +.. py:module:: sorcha.modules.PPAddUncertainties + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPAddUncertainties.degCos + sorcha.modules.PPAddUncertainties.degSin + sorcha.modules.PPAddUncertainties.addUncertainties + sorcha.modules.PPAddUncertainties.uncertainties + sorcha.modules.PPAddUncertainties.calcAstrometricUncertainty + sorcha.modules.PPAddUncertainties.calcRandomAstrometricErrorPerCoord + sorcha.modules.PPAddUncertainties.calcPhotometricUncertainty + + +Module Contents +--------------- + +.. py:function:: degCos(x) + + Calculate cosine of an angle in degrees. + + :param x: angle in degrees. + :type x: float + + :returns: The cosine of x. + :rtype: float + + +.. py:function:: degSin(x) + + Calculate sine of an angle in degrees. + + :param x: angle in degrees. + :type x: float + + :returns: The sine of x. + :rtype: float + + +.. py:function:: addUncertainties(detDF, sconfigs, module_rngs, verbose=True) + + Generates astrometric and photometric uncertainties, and SNR. Uses uncertainties + to randomize the photometry. Accounts for trailing losses. + + Adds the following columns to the observations dataframe: + + - astrometricSigma_deg + - trailedSourceMagSigma + - PSFMagSigma + - SNR + - trailedSourceMag + - PSFMag + + :param detDF: Dataframe of observations. + :type detDF: Pandas dataframe) + :param sconfigs: Dataclass of configuration file arguments. + :type sconfigs: dataclass + :param module_rngs: A collection of random number generators (per module). + :type module_rngs: PerModuleRNG + :param verbose: + :type verbose: Boolean, optional + :param Verbose Logging Flag. Default = True: + + :returns: **detDF** -- dataframe of observations, with new columns for observed + magnitudes, SNR, and astrometric/photometric uncertainties. + :rtype: Pandas dataframe + + +.. py:function:: uncertainties(detDF, sconfigs, limMagName='fiveSigmaDepth_mag', seeingName='seeingFwhmGeom_arcsec', filterMagName='trailedSourceMagTrue', dra_name='RARateCosDec_deg_day', ddec_name='DecRate_deg_day', dec_name='Dec_deg', visit_time_name='visitExposureTime') + + Add astrometric and photometric uncertainties to observations. + + :param detDF: dataframe containing observations. + :type detDF: Pandas dataframe + :param sconfigs: Dataclass of configuration file arguments. + :type sconfigs: dataclass + :param limMagName: pandas dataframe column name of the limiting magnitude. + Default = "fiveSigmaDepth_mag" + :type limMagName: string, optional + :param seeingName: pandas dataframe column name of the seeing + Default = "seeingFwhmGeom_arcsec" + :type seeingName: string, optional + :param filterMagName: pandas dataframe column name of the object magnitude + Default = "trailedSourceMagTrue" + :type filterMagName: string, optional + :param dra_name: pandas dataframe column name of the object RA rate + Default = "RARateCosDec_deg_day" + :type dra_name: string, optional + :param ddec_name: pandas dataframe column name of the object declination rate + Default = "DecRate_deg_day" + :type ddec_name: string, optional + :param dec_name: pandas dataframe column name of the object declination + Default = "Dec_deg" + :type dec_name: string, optional + :param visit_time_name: pandas dataframe column name for exposure length + Default = "visitExposureTime" + :type visit_time_name: string, optional + + :returns: * **astrSigDeg** (*numpy array*) -- astrometric uncertainties in degrees. + * **photometric_sigma** (*numpy array*) -- photometric uncertainties in magnitude. + * **SNR** (*numpy array*) -- signal-to-noise ratio. + + +.. py:function:: calcAstrometricUncertainty(mag, m5, nvisit=1, FWHMeff=700.0, error_sys=10.0, astErrCoeff=0.6, output_units='mas') + + Calculate the astrometric uncertainty, for object catalog purposes. + + + :param mag: magnitude of the observation. + :type mag: float or array of floats) + :param m5: 5-sigma limiting magnitude. + :type m5: float or array of floats + :param nvisit: number of visits to consider. + Default = 1 + :type nvisit: int, optional + :param FWHMeff: effective Full Width at Half Maximum of Point Spread Function [mas]. + Default = 700.0 + :type FWHMeff: float, optional + :param error_sys: systematic error [mas]. + Default = 10.0 + :type error_sys: float, optional + :param astErrCoeff: Astrometric error coefficient + (see calcRandomAstrometricErrorPerCoord description). + Default = 0.60 + :type astErrCoeff: float, optional + :param output_units: + Default: "mas" (milliarcseconds) + other options: "arcsec" (arcseconds) + :type output_units: string, optional + + :returns: * **astrom_error** (*float or array of floats)*) -- astrometric error. + * **SNR** (*float or array of floats)*) -- signal to noise ratio. + * **error_rand** (*float or array of floats*) -- random error. + + .. rubric:: Notes + + The effective FWHMeff MUST BE given in miliarcsec (NOT arcsec!). + Systematic error, error_sys, must be given in miliarcsec. + The result corresponds to a single-coordinate uncertainty. + Note that the total astrometric uncertainty (e.g. relevant when + matching two catalogs) will be sqrt(2) times larger. + Default values for parameters are based on estimates for LSST. + + The astrometric error can be applied to parallax or proper motion (for nvisit>1). + If applying to proper motion, should also divide by the # of years of the survey. + This is also referenced in the LSST overview paper (arXiv:0805.2366, ls.st/lop) + + - assumes sqrt(Nvisit) scaling, which is the best-case scenario + - calcRandomAstrometricError assumes maxiumm likelihood solution, + which is also the best-case scenario + - the systematic error, error_sys = 10 mas, corresponds to the + design spec from the LSST Science Requirements Document (ls.st/srd) + + +.. py:function:: calcRandomAstrometricErrorPerCoord(FWHMeff, SNR, AstromErrCoeff=0.6) + + Calculate the random astrometric uncertainty, as a function of + effective FWHMeff and signal-to-noise ratio SNR and return + the astrometric uncertainty in the same units as FWHM. + + This error corresponds to a single-coordinate error + the total astrometric uncertainty (e.g. relevant when matching + two catalogs) will be sqrt(2) times larger. + + :param FWHMeff: Effective Full Width at Half Maximum of Point Spread Function [mas]. + :type FWHMeff: float or array of floats + :param SNR: Signal-to-noise ratio. + :type SNR: float or array of floats + :param AstromErrCoeff: Astrometric error coefficient (see description below). + Default =0.60 + :type AstromErrCoeff: float, optional + + :returns: * **RandomAstrometricErrorPerCoord** (*float or array of floats*) -- random astrometric uncertainty per coordinate. + * *Returns astrometric uncertainty in the same units as FWHMeff.* + + .. rubric:: Notes + + The coefficient AstromErrCoeff for Maximum Likelihood + solution is given by + + AstromErrCoeff = / <|dP/dx|^2> * 1/FWHMeff + + where P is the point spread function, P(x,y). + + For a single-Gaussian PSF, AstromErrCoeff = 0.60 + For a double-Gaussian approximation to Kolmogorov + seeing, AstromErrCoeff = 0.55; however, given the + same core seeing (FWHMgeom) as for a single-Gaussian + PSF, the resulting error will be 36% larger because + FWHMeff is 1.22 times larger and SNR is 1.22 times + smaller, compared to error for single-Gaussian PSF. + Although Kolmogorov seeing is a much better approximation + of the free atmospheric seeing than single Gaussian seeing, + the default value of AstromErrCoeff is set to the + more conservative value. + + Note also that AstromErrCoeff = 1.0 is often used in + practice to empirically account for other error sources. + + +.. py:function:: calcPhotometricUncertainty(snr) + + Convert flux signal to noise ratio to an uncertainty in magnitude. + + :param snr: The signal-to-noise-ratio in flux. + :type snr: float or array of floats + + :returns: **magerr** -- The resulting uncertainty in magnitude. + :rtype: float or rray of floats + + diff --git a/docs/autoapi/sorcha/modules/PPApplyColourOffsets/index.rst b/docs/autoapi/sorcha/modules/PPApplyColourOffsets/index.rst new file mode 100644 index 00000000..d5a74c4d --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPApplyColourOffsets/index.rst @@ -0,0 +1,51 @@ +sorcha.modules.PPApplyColourOffsets +=================================== + +.. py:module:: sorcha.modules.PPApplyColourOffsets + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPApplyColourOffsets.PPApplyColourOffsets + + +Module Contents +--------------- + +.. py:function:: PPApplyColourOffsets(observations, function, othercolours, observing_filters, mainfilter) + + Adds the correct colour offset to H based on the filter of each observation, + then checks to make sure the appropriate columns exist for each phase function model. + If phase model variables exist for each colour, this function also selects the + correct variables for each observation based on filter. + + Adds the following columns to the observations dataframe: + + - H_filter + + Removes the following columns from the observations dataframe: + + - Colour offset columns (i.e. u-r, g-r) + - Colour-specific phase curve variables (if extant): the correct filter-specific value + for each observation is located and stored instead. i.e. GS_r and GS_g columns will be deleted + and replaced with a GS column containing either GS_r or GS_g depending on observation filter. + + :param observations: dataframe of observations. + :type observations: Pandas dataframe + :param function: string of desired phase function model. Options are HG, HG12, HG1G2, linear, H. + :type function: string + :param othercolours: list of colour offsets present in input files. + :type othercolours: list of strings + :param observing_filters: list of observation filters of interest. + :type observing_filters: list of strings + :param mainfilter: the main filter in which H is given and all colour offsets are calculated against. + :type mainfilter: string + + :returns: **observations** -- observations dataframe modified with H calculated in relevant filter (H_filter) + The dataframe has also been modified to have the appropriate phase curve filter specific values/columns. + :rtype: Pandas dataframe + + diff --git a/docs/autoapi/sorcha/modules/PPApplyFOVFilter/index.rst b/docs/autoapi/sorcha/modules/PPApplyFOVFilter/index.rst new file mode 100644 index 00000000..3180b350 --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPApplyFOVFilter/index.rst @@ -0,0 +1,100 @@ +sorcha.modules.PPApplyFOVFilter +=============================== + +.. py:module:: sorcha.modules.PPApplyFOVFilter + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPApplyFOVFilter.PPApplyFOVFilter + sorcha.modules.PPApplyFOVFilter.PPGetSeparation + sorcha.modules.PPApplyFOVFilter.PPCircleFootprint + sorcha.modules.PPApplyFOVFilter.PPSimpleSensorArea + + +Module Contents +--------------- + +.. py:function:: PPApplyFOVFilter(observations, sconfigs, module_rngs, footprint=None, verbose=False) + + Wrapper function for PPFootprintFilter and PPFilterDetectionEfficiency that checks to see + whether a camera footprint filter should be applied or if a simple fraction of the + circular footprint should be used, then applies the required filter where rows are + are removed from the inputted pandas dataframevfor moving objects that land outside of + their associated observation's footprint. + + Adds the following columns to the observations dataframe: + + - detectorId (if full camera footprint is used) + + :param observations: + :type observations: Pandas dataframe + :param dataframe of observations.: + :param sconfigs: Dataclass of configuration file arguments. + :type sconfigs: dataclass + :param module_rngs: A collection of random number generators (per module). + :type module_rngs: PerModuleRNG + :param footprint: A Footprint class object that represents the boundaries of the detector(s). + Default: None. + :type footprint: Footprint + :param verbose: Controls whether logging in verbose mode is on or off. + Default: False + :type verbose: boolean + + :returns: **observations** -- dataframe of observations updated after field-of-view filters have been applied. + :rtype: Pandas dataframe + + +.. py:function:: PPGetSeparation(obj_RA, obj_Dec, cen_RA, cen_Dec) + + Function to calculate the distance of an object from the field centre. + + :param obj_RA: RA of object in decimal degrees. + :type obj_RA: float + :param obj_Dec: Dec of object in decimal degrees. + :type obj_Dec: float + :param cen_RA: RA of field centre in decimal degrees. + :type cen_RA: float + :param cen_Dec: Dec of field centre in decimal degrees. + :type cen_Dec: float + + :returns: **sep_degree** -- The separation of the object from the centre of the field, in decimal + degrees. + :rtype: float + + +.. py:function:: PPCircleFootprint(observations, circle_radius) + + Simple function which removes objects which lay outside of a circle + of given radius centred on the field centre. + + :param observations: dataframe of observations. + :type observations: Pandas dataframe + :param circle_radius: radius of circle footprint in degrees. + :type circle_radius: float + + :returns: **new_observations** -- dataframe of observations with all lying beyond the circle radius dropped. + :rtype: Pandas dataframe + + +.. py:function:: PPSimpleSensorArea(ephemsdf, module_rngs, fillfactor=0.9) + + Randomly removes a number of observations proportional to the + fraction of the field not covered by the detector. + + :param ephemsdf: Dataframe containing observations. + :type ephemsdf: Pandas dataframe + :param module_rngs: A collection of random number generators (per module). + :type module_rngs: PerModuleRNG + :param fillfactor: fraction of FOV covered by the sensor. + Default = 0.9 + :type fillfactor: float + + :returns: **ephemsOut** -- Dataframe of observations with 1- fillfactor fraction of objects + removed per on-sky observation pointing. + :rtype: Pandas dataframe + + diff --git a/docs/autoapi/sorcha/modules/PPBrightLimit/index.rst b/docs/autoapi/sorcha/modules/PPBrightLimit/index.rst new file mode 100644 index 00000000..131561a7 --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPBrightLimit/index.rst @@ -0,0 +1,36 @@ +sorcha.modules.PPBrightLimit +============================ + +.. py:module:: sorcha.modules.PPBrightLimit + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPBrightLimit.PPBrightLimit + + +Module Contents +--------------- + +.. py:function:: PPBrightLimit(observations, observing_filters, bright_limit) + + Drops observations brighter than the user-defined saturation + limit. Can take either a single saturation limit for a straight cut, or + filter-specific saturation limits. + + :param observations: Dataframe of observations. + :type observations: Pandas dataframe + :param observing_filters: Observing filters present in the data. + :type observing_filters: list of strings + :param bright_limit: Saturation limits: either single value applied to all filters or a list of values for each filter. + :type bright_limit: float or list of floats + + :returns: **observations_out** -- observations dataframe modified with rows dropped for apparent + magnitudes brigher than the bright_limit for the given observation's + filter + :rtype: Pandas dataframe + + diff --git a/docs/autoapi/sorcha/modules/PPCalculateApparentMagnitude/index.rst b/docs/autoapi/sorcha/modules/PPCalculateApparentMagnitude/index.rst new file mode 100644 index 00000000..c74b77d6 --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPCalculateApparentMagnitude/index.rst @@ -0,0 +1,63 @@ +sorcha.modules.PPCalculateApparentMagnitude +=========================================== + +.. py:module:: sorcha.modules.PPCalculateApparentMagnitude + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPCalculateApparentMagnitude.PPCalculateApparentMagnitude + + +Module Contents +--------------- + +.. py:function:: PPCalculateApparentMagnitude(observations, phasefunction, mainfilter, othercolours, observing_filters, cometary_activity_choice=None, lightcurve_choice=None, verbose=False) + + This function applies the correct colour offset to H for the relevant filter, checks to make sure + the correct columns are included (with additional functionality for colour-specific phase curves), + then calculates the trailed source apparent magnitude including optional adjustments for + cometary activity and rotational light curves. + + Adds the following columns to the observations dataframe: + + - H_filter + - trailedSourceMagTrue + - any columns created by the optional light curve and cometary activity models + + Removes the following columns from the observations dataframe: + + - Colour offset columns (i.e. u-r) + - Colour-specific phase curve variables (if extant): the correct filter-specific value + for each observation is located and stored instead. i.e. GS_r and GS_g columns will be deleted + and replaced with a GS column containing either GS_r or GS_g depending on observation filter. + + :param observations: dataframe of observations. + :type observations: Pandas dataframe + :param phasefunction: Desired phase function model. Options are HG, HG12, HG1G2, linear, none + :type phasefunction: string + :param mainfilter: The main filter in which H is given and all colour offsets are calculated against. + :type mainfilter: string + :param othercolours: List of colour offsets present in input files. + :type othercolours: list of strings + :param observing_filters: List of observation filters of interest. + :type observing_filters: list of strings + :param cometary_activity_choice: Choice of cometary activity model. + Default = None + :type cometary_activity_choice: string + :param lc_choice: Choice of lightcurve model. Default = None + :type lc_choice: string + :param verbose: Flag for turning on verbose logging. Default = False + :type verbose: boolean + + :returns: **observations** -- Modified observations pandas dataframe with calculated trailed source + apparent magnitude column, H calculated in relevant filter (H_filter), + renames the column for H in the main filter as H_original and + adds a column for the light curve contribution to the trailed source + apparent magnitude (if included) + :rtype: Pandas dataframe + + diff --git a/docs/autoapi/sorcha/modules/PPCalculateApparentMagnitudeInFilter/index.rst b/docs/autoapi/sorcha/modules/PPCalculateApparentMagnitudeInFilter/index.rst new file mode 100644 index 00000000..332ece2e --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPCalculateApparentMagnitudeInFilter/index.rst @@ -0,0 +1,57 @@ +sorcha.modules.PPCalculateApparentMagnitudeInFilter +=================================================== + +.. py:module:: sorcha.modules.PPCalculateApparentMagnitudeInFilter + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPCalculateApparentMagnitudeInFilter.PPCalculateApparentMagnitudeInFilter + + +Module Contents +--------------- + +.. py:function:: PPCalculateApparentMagnitudeInFilter(padain, function, observing_filters, colname='trailedSourceMagTrue', lightcurve_choice=None, cometary_activity_choice=None) + + The trailed source apparent magnitude is calculated in the filter for given H, + phase function, light curve, and cometary activity parameters. + + Adds the following columns to the observations dataframe: + + - trailedSourceMagTrue + - any columns created by the optional light curve and cometary activity models + + .. rubric:: Notes + + PPApplyColourOffsets should be run beforehand to apply any needed colour offset to H and ensure correct + variables are present. + + The phase function model options utlized are the sbpy package's implementation: + - HG: Bowell et al. (1989) Asteroids II book. + - HG1G2: Muinonen et al. (2010) Icarus 209 542. + - HG12: Penttilä et al. (2016) PSS 123 117. + - linear: (as implemented in sbpy) + - none : No model is applied + + :param padain: Dataframe of observations. + :type padain: Pandas dataframe + :param function: Desired phase function model. Options are "HG", "HG12", "HG1G2", "linear", "none". + :type function: string + :param colname: Column name in which to store calculated magnitude to the padain dataframe. + Default = "TrailedSourceMag" + :type colname: string + :param lightcurve_choice: Choice of light curve model. Default = None + :type lightcurve_choice: stringm optional + :param cometary_activity_choice: Choice of cometary activity model. Default = None + :type cometary_activity_choice: string, optional + + :returns: **padain** -- Dataframe of observations (padain) modified with calculated trailed + source apparent magnitude column and any optional cometary actvity or + light curve added columns based on the models used. + :rtype: Pandas dataframe + + diff --git a/docs/autoapi/sorcha/modules/PPCalculateSimpleCometaryMagnitude/index.rst b/docs/autoapi/sorcha/modules/PPCalculateSimpleCometaryMagnitude/index.rst new file mode 100644 index 00000000..1156a5db --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPCalculateSimpleCometaryMagnitude/index.rst @@ -0,0 +1,41 @@ +sorcha.modules.PPCalculateSimpleCometaryMagnitude +================================================= + +.. py:module:: sorcha.modules.PPCalculateSimpleCometaryMagnitude + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPCalculateSimpleCometaryMagnitude.PPCalculateSimpleCometaryMagnitude + + +Module Contents +--------------- + +.. py:function:: PPCalculateSimpleCometaryMagnitude(padain: pandas.DataFrame, observing_filters: List[str], rho: List[float], delta: List[float], alpha: List[float], activity_choice: str = None) -> pandas.DataFrame + + Adjusts the observations' trailed source apparent magnitude for cometary activity + using the model specified by `activity_choice` added by the user + + :param padain: The input ``observations`` dataframe + :type padain: pd.DataFrame + :param observing_filters: The photometric filters the observation is taken in (the filter + requested that the coma magnitude be calculated for) + :type observing_filters: List[str] + :param rho: Heliocentric distance [units au] + :type rho: List[float] + :param delta: Distance to Earth [units au] + :type delta: List[float] + :param alpha: Phase angle [units degrees] + :type alpha: List[float] + :param activity_choice: The activity model to use, by default None + :type activity_choice: string, optional + + :returns: The ``observations`` dataframe with updated trailed + source apparent magnitude values. + :rtype: pd.DataFrame + + diff --git a/docs/autoapi/sorcha/modules/PPCommandLineParser/index.rst b/docs/autoapi/sorcha/modules/PPCommandLineParser/index.rst new file mode 100644 index 00000000..488b7f65 --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPCommandLineParser/index.rst @@ -0,0 +1,48 @@ +sorcha.modules.PPCommandLineParser +================================== + +.. py:module:: sorcha.modules.PPCommandLineParser + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPCommandLineParser.warn_or_remove_file + sorcha.modules.PPCommandLineParser.PPCommandLineParser + + +Module Contents +--------------- + +.. py:function:: warn_or_remove_file(filepath, force_remove, pplogger) + + Given a path to a file(s), first determine if the file exists. If it does not + exist, pass through. + + If the file does exist check if the user has set `--force` on the command line. + If the user set --force, log that the existing file will be removed. + Otherwise, warn the user that the file exists and exit the program. + + :param filepath: The full file path to a given file. i.e. /home/data/output.csv + :type filepath: string + :param force_remove: Whether to remove the file if it exists. + :type force_remove: boolean + :param pplogger: Used to log the output. + :type pplogger: Logger + + +.. py:function:: PPCommandLineParser(args) + + Parses the command line arguments, error-handles them, then stores them in a single dict. + + Will only look for the comet parameters file if it's actually given at the command line. + + :param args: argparse object of command line arguments + :type args: ArgumentParser object + + :returns: **cmd_args_dict** -- dictionary of variables taken from command line arguments + :rtype: dictionary + + diff --git a/docs/autoapi/sorcha/modules/PPConfigParser/index.rst b/docs/autoapi/sorcha/modules/PPConfigParser/index.rst new file mode 100644 index 00000000..1cdb18c9 --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPConfigParser/index.rst @@ -0,0 +1,204 @@ +sorcha.modules.PPConfigParser +============================= + +.. py:module:: sorcha.modules.PPConfigParser + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPConfigParser.log_error_and_exit + sorcha.modules.PPConfigParser.PPGetOrExit + sorcha.modules.PPConfigParser.PPGetFloatOrExit + sorcha.modules.PPConfigParser.PPGetIntOrExit + sorcha.modules.PPConfigParser.PPGetBoolOrExit + sorcha.modules.PPConfigParser.PPGetValueAndFlag + sorcha.modules.PPConfigParser.PPFindFileOrExit + sorcha.modules.PPConfigParser.PPFindDirectoryOrExit + sorcha.modules.PPConfigParser.PPCheckFiltersForSurvey + sorcha.modules.PPConfigParser.PPConfigFileParser + sorcha.modules.PPConfigParser.PPPrintConfigsToLog + + +Module Contents +--------------- + +.. py:function:: log_error_and_exit(message: str) -> None + + Log a message to the error output file and terminal, then exit. + + :param message: The error message to be logged to the error output file. + :type message: string + + :rtype: None + + +.. py:function:: PPGetOrExit(config, section, key, message) + + Checks to see if the config file parser has a key. If it does not, this + function errors out and the code stops. + + :param config: ConfigParser object containing configs. + :type config: ConfigParser + :param section: Section of the key being checked. + :type section: string + :param key: The key being checked. + :type key: string) + :param message: The message to log and display if the key is not found. + :type message: string + + :rtype: None. + + +.. py:function:: PPGetFloatOrExit(config, section, key, message) + + Checks to see if a key in the config parser is present and can be read as a + float. If it cannot, this function errors out and the code stops. + + :param config: ConfigParser object containing configs. + :type config: ConfigParser + :param section: section of the key being checked. + :type section: string + :param key: The key being checked. + :type key: string + :param message: The message to log and display if the key is not found. + :type message: string + + :rtype: None. + + +.. py:function:: PPGetIntOrExit(config, section, key, message) + + Checks to see if a key in the config parser is present and can be read as an + int. If it cannot, this function errors out and the code stops. + + :param config: ConfigParser object containing configs. + :type config: ConfigParser + :param section: Section of the key being checked. + :type section: string + :param key: The key being checked. + :type key: string + :param message: The message to log and display if the key is not found. + :type message: string + + :rtype: None. + + +.. py:function:: PPGetBoolOrExit(config, section, key, message) + + Checks to see if a key in the config parser is present and can be read as a + Boolean. If it cannot, this function errors out and the code stops. + + :param config: ConfigParser object containing configs. + :type config: ConfigParser object + :param section: Section of the key being checked. + :type section: string + :param key: The key being checked. + :type key: string + :param message: The message to log and display if the key is not found. + :type message: string + + :rtype: None. + + +.. py:function:: PPGetValueAndFlag(config, section, key, type_wanted) + + Obtains a value from the config flag, forcing it to be the specified + type and error-handling if it can't be forced. If the value is not present + in the config fie, the flag is set to False; if it is, the flag is True. + + :param config: ConfigParser object containing configs. + :type config: ConfigParser + :param section: Section of the key being checked. + :type section: string + :param key: The key being checked. + :type key: string + :param type_wanted: The type the value should be forced to. + Accepts int, float, none (for no type-forcing). + :type type_wanted: string + + :returns: * **value** (*any type*) -- The value of the key, with type dependent on type_wanted. + Will be None if the key is not present. + * **flag** (*boolean*) -- Will be False if the key is not present in the config file + and True if it is. + + +.. py:function:: PPFindFileOrExit(arg_fn, argname) + + Checks to see if a file given by a filename exists. If it doesn't, + this fails gracefully and exits to the command line. + + :param arg_fn: The filepath/name of the file to be checked. + :type arg_fn: string + :param argname: The name of the argument being checked. Used for error message. + :type argname: string + + :returns: **arg_fn** -- The filepath/name of the file to be checked. + :rtype: string + + +.. py:function:: PPFindDirectoryOrExit(arg_fn, argname) + + Checks to see if a directory given by a filepath exists. If it doesn't, + this fails gracefully and exits to the command line. + + :param arg_fn: The filepath of the directory to be checked. + :type arg_fn: string + :param argname: The name of the argument being checked. Used for error message. + :type argname: string + + :returns: **arg_fn** -- The filepath of the directory to be checked. + :rtype: string + + +.. py:function:: PPCheckFiltersForSurvey(survey_name, observing_filters) + + When given a list of filters, this function checks to make sure they exist in the + user-selected survey, and if the filters given in the config file do not match the + survey filters, the function exits the program with an error. + + :param survey_name: Survey name. Currently only "LSST", "lsst" accepted. + :type survey_name: string + :param observing_filters: Observation filters of interest. + :type observing_filters: list of strings + + :rtype: None. + + .. rubric:: Notes + + Currently only has options for LSST, but can be expanded upon later. + + +.. py:function:: PPConfigFileParser(configfile, survey_name) + + Parses the config file, error-handles, then assigns the values into a single + dictionary, which is passed out. + + :param configfile: Filepath/name of config file. + :type configfile: string + :param survey_name: Survey name. Currently only "LSST", "lsst" accepted. + :type survey_name: string + + :returns: **config_dict** -- Dictionary of config file variables. + :rtype: dictionary + + .. rubric:: Notes + + We chose not to use the original ConfigParser object for readability: it's a dict of + dicts, so calling the various values can become quite unwieldy. + + +.. py:function:: PPPrintConfigsToLog(configs, cmd_args) + + Prints all the values from the config file and command line to the log. + + :param configs: Dictionary of config file variables. + :type configs: dictionary + :param cmd_args: Dictionary of command line arguments. + :type cmd_args: dictionary + + :rtype: None. + + diff --git a/docs/autoapi/sorcha/modules/PPDetectionEfficiency/index.rst b/docs/autoapi/sorcha/modules/PPDetectionEfficiency/index.rst new file mode 100644 index 00000000..04d01846 --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPDetectionEfficiency/index.rst @@ -0,0 +1,34 @@ +sorcha.modules.PPDetectionEfficiency +==================================== + +.. py:module:: sorcha.modules.PPDetectionEfficiency + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPDetectionEfficiency.PPDetectionEfficiency + + +Module Contents +--------------- + +.. py:function:: PPDetectionEfficiency(padain, threshold, module_rngs) + + Applies a random cut to the observations dataframe based on an efficiency + threshold: if the threshold is 0.95, for example, 5% of observations will be + randomly dropped. Used by PPLinkingFilter. + + :param padain: Dataframe of observations. + :type padain: Pandas dataframe + :param threshold: Fraction between 0 and 1 of detections retained in the dataframe. + :type threshold: float + :param module_rngs: A collection of random number generators (per module). + :type module_rngs: PerModuleRNG + + :returns: Dataframe of observations with a fraction equal to 1-threshold randomly dropped. + :rtype: Pandas dataframe + + diff --git a/docs/autoapi/sorcha/modules/PPDetectionProbability/index.rst b/docs/autoapi/sorcha/modules/PPDetectionProbability/index.rst new file mode 100644 index 00000000..f4043595 --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPDetectionProbability/index.rst @@ -0,0 +1,67 @@ +sorcha.modules.PPDetectionProbability +===================================== + +.. py:module:: sorcha.modules.PPDetectionProbability + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPDetectionProbability.calcDetectionProbability + sorcha.modules.PPDetectionProbability.PPDetectionProbability + + +Module Contents +--------------- + +.. py:function:: calcDetectionProbability(mag, limmag, fillFactor=1.0, w=0.1) + + Find the probability of a detection given a visual magnitude, + limiting magnitude, and fill factor, determined by the fading function + from Veres & Chesley (2017). + + :param mag: Magnitude of object in filter used for that field. + :type mag: float or array of floats + :param limmag: Limiting magnitude of the field. + :type limmag: float or array of floats + :param fillFactor: Fraction of FOV covered by the camera sensor. Default = 1.0 + :type fillFactor: float), optional + :param w: Distribution parameter. Default = 0.1 + :type w: float + + :returns: **P** -- Probability of detection. + :rtype: float or array of floats + + +.. py:function:: PPDetectionProbability(eph_df, trailing_losses=False, trailing_loss_name='dmagDetect', magnitude_name='PSFMag', limiting_magnitude_name='fiveSigmaDepth_mag', field_id_name='FieldID', fillFactor=1.0, w=0.1) + + Find probability of observations being observable for objectInField output. + Wrapper for calcDetectionProbability which takes into account column names + and trailing losses. Used by PPFadingFunctionFilter. + + :param eph_df: Dataframe of observations. + :type eph_df: Pandas dataframe + :param trailing_losses: Are trailing losses being applied?, Default = False + :type trailing_losses: Boolean, optional + :param trailing_loss_name: eph_df column name for trailing losses, Default = dmagDetect + :type trailing_loss_name: string, optional + :param magnitude_name: eph_df column name for observation limiting magnitude + Default = PSFMag + :type magnitude_name: string, optional + :param limiting_magnitude_name: eph_df column used for observation limiting magnitude. + Default = fiveSigmaDepth_mag + :type limiting_magnitude_name: string, optional + :param field ID: eph_df column name for observation field_id + Default = FieldID + :type field ID: string, optional + :param fillFactor: Fraction of FOV covered by the camera sensor. Default = 1.0 + :type fillFactor: float, optional + :param w: Distribution parameter. Default =0.1 + :type w: float + + :returns: Probability of detection. + :rtype: float or array of floats + + diff --git a/docs/autoapi/sorcha/modules/PPDropObservations/index.rst b/docs/autoapi/sorcha/modules/PPDropObservations/index.rst new file mode 100644 index 00000000..ee809482 --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPDropObservations/index.rst @@ -0,0 +1,33 @@ +sorcha.modules.PPDropObservations +================================= + +.. py:module:: sorcha.modules.PPDropObservations + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPDropObservations.PPDropObservations + + +Module Contents +--------------- + +.. py:function:: PPDropObservations(observations, module_rngs, probability='detection probability') + + Drops rows where the probabilty of detection is less than sample drawn + from a uniform distribution. Used by PPFadingFunctionFilter. + + :param observations: Dataframe of observations with a column containing the probability of detection. + :type observations: Pandas dataframe + :param module_rngs: A collection of random number generators (per module). + :type module_rngs: PerModuleRNG + :param probability: Name of column containing detection probability. + :type probability: string + + :returns: **out** -- New dataframe of 'observations' modified to remove observations that could not be observed. + :rtype: Pandas dataframe + + diff --git a/docs/autoapi/sorcha/modules/PPFadingFunctionFilter/index.rst b/docs/autoapi/sorcha/modules/PPFadingFunctionFilter/index.rst new file mode 100644 index 00000000..2b055e7a --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPFadingFunctionFilter/index.rst @@ -0,0 +1,37 @@ +sorcha.modules.PPFadingFunctionFilter +===================================== + +.. py:module:: sorcha.modules.PPFadingFunctionFilter + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPFadingFunctionFilter.PPFadingFunctionFilter + + +Module Contents +--------------- + +.. py:function:: PPFadingFunctionFilter(observations, fillfactor, width, module_rngs, verbose=False) + + Wrapper function for PPDetectionProbability and PPDropObservations. + + Calculates detection probability based on a fading function, then drops rows where the + probabilty of detection is less than sample drawn from a uniform distribution. + + :param observations: Dataframe of observations with a column containing the probability of detection. + :type observations: Pandas dataframe + :param fillFactor: Fraction of camera field-of-view covered by detectors + :type fillFactor: float + :param module_rngs: A collection of random number generators (per module). + :type module_rngs: PerModuleRNG + :param verbose: Verbose logging flag. Default = False + :type verbose: boolean, optional + + :returns: **observations_drop** -- Modified 'observations' dataframe without observations that could not be observed. + :rtype: Pandas dataframe) + + diff --git a/docs/autoapi/sorcha/modules/PPFootprintFilter/index.rst b/docs/autoapi/sorcha/modules/PPFootprintFilter/index.rst new file mode 100644 index 00000000..56bef97a --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPFootprintFilter/index.rst @@ -0,0 +1,311 @@ +sorcha.modules.PPFootprintFilter +================================ + +.. py:module:: sorcha.modules.PPFootprintFilter + + +Attributes +---------- + +.. autoapisummary:: + + sorcha.modules.PPFootprintFilter.deg2rad + sorcha.modules.PPFootprintFilter.sin + sorcha.modules.PPFootprintFilter.cos + sorcha.modules.PPFootprintFilter.logger + + +Classes +------- + +.. autoapisummary:: + + sorcha.modules.PPFootprintFilter.Detector + sorcha.modules.PPFootprintFilter.Footprint + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPFootprintFilter.distToSegment + sorcha.modules.PPFootprintFilter.radec_to_tangent_plane + sorcha.modules.PPFootprintFilter.radec_to_focal_plane + + +Module Contents +--------------- + +.. py:data:: deg2rad + +.. py:data:: sin + +.. py:data:: cos + +.. py:data:: logger + +.. py:function:: distToSegment(points, x0, y0, x1, y1) + + Compute the distance from each point to the line segment defined by + the points (x0, y0) and (x1, y1). Returns the distance in the same + units as the points are specified in (radians, degrees, etc.). Uses planar + geometry for the calculations (assuming small angular distances). + + :param points: Array of shape (2, n) describing the corners of the sensor. + :type points: array + :param x0: The x coordinate of the first end of the segment. + :type x0: float + :param y0: The y coordinate of the first end of the segment. + :type y0: float + :param x1: The x coordinate of the second end of the segment. + :type x1: float + :param y1: The y coordinate of the second end of the segment. + :type y1: float + + :returns: **dist** -- Array of length n storing the distances. + :rtype: array + + +.. py:function:: radec_to_tangent_plane(ra, dec, field_ra, field_dec) + + Converts ra and dec to xy on the plane tangent to image center, in the 2-d coordinate system where y is aligned with the meridian. + + Parameters: + ----------- + ra (float/array of floats): observation Right Ascension, radians. + + dec (float/array of floats): observation Declination, radians. + + fieldra (float/array of floats): field pointing Right Ascension, radians. + + fielddec (float/array of floats): field pointing Declination, radians. + + fieldID (float/array of floats): Field ID, optional. + + Returns: + ---------- + x, y (float/array of floats): Coordinates on the focal plane, radians projected + to the plane tangent to the unit sphere. + + + +.. py:function:: radec_to_focal_plane(ra, dec, field_ra, field_dec, field_rot) + +.. py:class:: Detector(points, ID=0, units='radians') + + Detector class + + + .. py:attribute:: ID + :value: 0 + + + + .. py:attribute:: ra + + + .. py:attribute:: dec + + + .. py:attribute:: units + :value: 'radians' + + + + .. py:attribute:: x + + + .. py:attribute:: y + + + .. py:attribute:: centerx + + + .. py:attribute:: centery + + + .. py:method:: ison(point, ε=10.0**(-11), edge_thresh=None, plot=False) + + Determines whether a point (or array of points) falls on the + detector. + + :param point: Array of shape (2, n) for n points. + :type point: array + :param ϵ: Threshold for whether point is on detector. Default: 10.0 ** (-11) + :type ϵ: float, optional + :param edge_thresh: The focal plane distance (in arcseconds) from the detector's edge + for a point to be counted. Removes points that are too + close to the edge for source detection. Default = None + :type edge_thresh: float, optional + :param plot: Flag for whether to plot the detector and the point. Default = False + :type plot: Boolean, optional + + :returns: **selectedidx** -- Indices of points in point array that fall on the sensor. + :rtype: array + + + + .. py:method:: trueArea() + + Returns the area of the detector. Uses the same method as + segmentedArea, but the test point is the mean of the corner coordinates. + Will probably fail if the sensor is not convex. + + :param None.: + + :returns: **area** -- The area of the detector. + :rtype: float + + + + .. py:method:: segmentedArea(point) + + Returns the area of the detector by calculating the area of each + triangle segment defined by each pair of adjacent corners and a point + inside the sensor. + Fails if the point is not inside the sensor or if the sensor is not + convex. + + :param point: Array of shape (2, n) for n points. + :type point: array + + :returns: **area** -- The area of the detector. + :rtype: float + + + + .. py:method:: sortCorners() + + Sorts the corners to be counterclockwise by angle from center of + the detector. Modifies self. + + :param None.: + + :rtype: None. + + + + .. py:method:: rotateDetector(theta) + + Rotates a sensor around the origin of the coordinate system its + corner locations are provided in. + + :param theta: Angle to rotate by, in radians. + :type theta: float + + :returns: **Detector** -- New Detector instance. + :rtype: Detector + + + + .. py:method:: rad2deg() + + Converts corners from radians to degrees. + + :param None.: + + :rtype: None. + + + + .. py:method:: deg2rad() + + Converts corners from degrees to radians. + + :param None.: + + :rtype: None. + + + + .. py:method:: plot(theta=0.0, color='gray', units='rad', annotate=False) + + Plots the footprint for an individual sensor. Currently not on the + focal plane, just the sky coordinates. Relatively minor difference + (width of footprint for LSST is <2.1 degrees), so should be fine for + internal demonstration purposes, but not for confirming algorithms or + for offical plots. + + :param theta: Aangle to rotate footprint by, radians or degrees. Default =0.0 + :type theta: float, optional + :param color: Line color. Default = "gray" + :type color: string, optional + :param units: Units. Units is provided in ("deg" or "rad"). Default = 'rad'. + :type units: string, optional + :param annotate: Flag whether to annotate each sensor with its index in self.detectors. + Default = False + :type annotate: Boolean + + :rtype: None. + + + +.. py:class:: Footprint(path=None, detectorName='detector') + + Camera footprint class + + + .. py:attribute:: detectors + + + .. py:attribute:: N + + + .. py:method:: plot(theta=0.0, color='gray', units='rad', annotate=False) + + Plots the footprint. Currently not on the focal plane, just the sky + coordinates. Relatively minor difference (width of footprint for LSST + is <2.1 degrees), so should be fine for internal demonstration + purposes, but not for confirming algorithms or for offical plots. + + :param theta: Angle to rotate footprint by, radians or degrees. Default = 0.0 + :type theta: float, optional + :param color: Line color. Default = "gray" + :type color: string, optional + :param units: Units theta is provided in ("deg" or "rad"). Default = "rad" + :type units: string, optional + :param annotate: Whether to annotate each sensor with its index in + self.detectors. Default = False + :type annotate: boolean, optional + + :rtype: None. + + + + .. py:method:: applyFootprint(field_df, ra_name='RA_deg', dec_name='Dec_deg', field_name='FieldID', ra_name_field='fieldRA_deg', dec_name_field='fieldDec_deg', rot_name_field='fieldRotSkyPos_deg', edge_thresh=None) + + Determine whether detections fall on the sensors defined by the + footprint. Also returns the an ID for the sensor a detection is made + on. + + :param field_df: Dataframe containing detection information with pointings. + :type field_df: Pandas dataframe + :param ra_name: + "field_df" dataframe's column name for object's RA + for the given observation. Default = "RA_deg" [units: degrees] + :type ra_name: string, optional + :param dec_name: + "field_df" dataframe's column name for object's declination + for the given observation. Default = "Dec_deg" [units: dgrees] + :type dec_name: string, optional + :param ra_name_field: + "field_df" dataframe's column name for the observation field's RA + Default = "fieldRA_deg" [units: degrees] + :type ra_name_field: string, optional + :param dec_name_field: + "field_df" dataframe's column name for the observation field's declination + Default = "fieldDec_deg" [Units: degrees] + :type dec_name_field: string, optional + :param rot_name_field: "field_df" dataframe's column name for the observation field's rotation angle + Default = "fieldRotSkyPos_deg" [Units: degrees] + :type rot_name_field: string, optional + :param edge_thresh: An angular threshold in arcseconds for dropping pixels too close to the edge. + Default = None + :type edge_thresh: float, optional + + :returns: * **detected** (*array*) -- Indices of rows in field_df which fall on the sensor(s). + * **detectorID** (*array*) -- Index corresponding to a detector in self.detectors for each entry in detected. + + + diff --git a/docs/autoapi/sorcha/modules/PPGetLogger/index.rst b/docs/autoapi/sorcha/modules/PPGetLogger/index.rst new file mode 100644 index 00000000..1eb8af32 --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPGetLogger/index.rst @@ -0,0 +1,39 @@ +sorcha.modules.PPGetLogger +========================== + +.. py:module:: sorcha.modules.PPGetLogger + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPGetLogger.PPGetLogger + + +Module Contents +--------------- + +.. py:function:: PPGetLogger(log_location, log_stem, log_format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s ', log_name='', log_file_info='sorcha.log', log_file_error='sorcha.err') + + Initialises log and error files. + + :param log_location: Filepath to directory in which to save logs. + :type log_location: string + :param log_stem: String output stem used to prefix all Sorcha outputs. + :type log_stem: string + :param log_format: Format for log filename. + Default = "%(asctime)s %(name)-12s %(levelname)-8s %(message)s " + :type log_format: string, optional + :param log_name: Name of log. Default = "" + :type log_name: string, optional + :param log_file_info: Suffix and extension with which to save info log. Default = "sorcha.log" + :type log_file_info: string, optional + :param log_file_error: Suffix and extension with which to save error log. Default = "sorcha.err" + :type log_file_error: string, optional + + :returns: **log** -- Log object. + :rtype: logging object + + diff --git a/docs/autoapi/sorcha/modules/PPGetMainFilterAndColourOffsets/index.rst b/docs/autoapi/sorcha/modules/PPGetMainFilterAndColourOffsets/index.rst new file mode 100644 index 00000000..1cd730fe --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPGetMainFilterAndColourOffsets/index.rst @@ -0,0 +1,41 @@ +sorcha.modules.PPGetMainFilterAndColourOffsets +============================================== + +.. py:module:: sorcha.modules.PPGetMainFilterAndColourOffsets + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPGetMainFilterAndColourOffsets.PPGetMainFilterAndColourOffsets + + +Module Contents +--------------- + +.. py:function:: PPGetMainFilterAndColourOffsets(filename, observing_filters, filesep) + + Function to obtain the main filter (i.e. the filter in which H is + defined) from the header of the physical parameters file and then generate + the expected colour offsets. Also makes sure that columns exist for all + the expected colour offsets in the physical parameters file. + + :param filename: The filename of the physical parameters file. + :type filename: string + :param observing_filters: The observation filters requested in the configuration file. + :type observing_filters: list of strings + :param filesep: The format of the physical parameters file. Should be "csv"/"comma" + or "whitespace". + :type filesep: string + + :returns: * **mainfilter** (*string*) -- The main filter in which H is defined. + * **colour_offsets** (*list of strings*) -- A list of the colour offsets present in the physical parameters file. + + .. rubric:: Notes + + The main filter should be found as a column heading of H_[mainfilter]. If + this format isn NOT followed, this function will error out. + + diff --git a/docs/autoapi/sorcha/modules/PPLinkingFilter/index.rst b/docs/autoapi/sorcha/modules/PPLinkingFilter/index.rst new file mode 100644 index 00000000..c8b920a4 --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPLinkingFilter/index.rst @@ -0,0 +1,55 @@ +sorcha.modules.PPLinkingFilter +============================== + +.. py:module:: sorcha.modules.PPLinkingFilter + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPLinkingFilter.PPLinkingFilter + + +Module Contents +--------------- + +.. py:function:: PPLinkingFilter(observations, detection_efficiency, min_observations, min_tracklets, tracklet_interval, minimum_separation, maximum_time, night_start_utc, survey_name='rubin_sim', drop_unlinked=True) + + A function which mimics the effects of the SSP linking process by looking + for valid tracklets within valid tracks and only outputting observations + which would be thus successfully "linked" by SSP. + + Parameters: + ----------- + detection_efficiency (float): the fractional percentage of successfully linked + detections. + + min_observations (int): the minimum number of observations in a night required + to form a tracklet. + + min_tracklets (int): the minimum number of tracklets required to form a valid track. + + tracklet_interval (int): the time window (in days) in which the minimum number of + tracklets must occur to form a valid track. + + minimum_separation (float): the minimum separation inside a tracklet for it + to be recognised as motion between images (in arcseconds). + + maximum_time (float): # Maximum time separation (in days) between subsequent observations in a tracklet. + + rng (numpy Generator object): numpy random number generator object. + + survey_name (str): a string with the survey name. used for time-zone purposes. + Currently only accepts "rubin_sim", "RUBIN_SIM", "lsst", "LSST". + + drop_unlinked (boolean): rejects all observations that are considered to not be linked. Default is True + + Returns: + ----------- + observations_out (pandas dataframe): a pandas dataframe containing observations + of linked objects only. + + + diff --git a/docs/autoapi/sorcha/modules/PPMagnitudeLimit/index.rst b/docs/autoapi/sorcha/modules/PPMagnitudeLimit/index.rst new file mode 100644 index 00000000..afc32d74 --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPMagnitudeLimit/index.rst @@ -0,0 +1,32 @@ +sorcha.modules.PPMagnitudeLimit +=============================== + +.. py:module:: sorcha.modules.PPMagnitudeLimit + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPMagnitudeLimit.PPMagnitudeLimit + + +Module Contents +--------------- + +.. py:function:: PPMagnitudeLimit(observations, mag_limit) + + Filter that performs a straight cut on apparent PSF magnitude + based on a defined threshold. + + :param observations: Dataframe of observations. Must have "observedPSFMag" column. + :type observations: pandas dataframe + :param mag_limit: Limit for apparent magnitude cut. + :type mag_limit: float + + :returns: **observations** -- "observations" dataframe modified with apparent PSF mag greater than + or equal to the limit removed. + :rtype: pandas dataframe + + diff --git a/docs/autoapi/sorcha/modules/PPMatchPointingToObservations/index.rst b/docs/autoapi/sorcha/modules/PPMatchPointingToObservations/index.rst new file mode 100644 index 00000000..1cacb158 --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPMatchPointingToObservations/index.rst @@ -0,0 +1,46 @@ +sorcha.modules.PPMatchPointingToObservations +============================================ + +.. py:module:: sorcha.modules.PPMatchPointingToObservations + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPMatchPointingToObservations.PPMatchPointingToObservations + + +Module Contents +--------------- + +.. py:function:: PPMatchPointingToObservations(padain, pointfildb) + + Merges all relevant columns of each observation from the pointing + database onto the observations dataframe, then drops all observations which are not + in one of the requested filters and any duplicate columns. + + Adds the following columns to the dataframe of observations: + + - visitTime + - visitExposureTime + - optFilter + - seeingFwhmGeom_arcsec + - seeingFwhmEff_arcsec + - fieldFiveSigmaDepth_mag + - fieldRA_deg + - fieldDec_deg + - fieldRotSkyPos_deg + - observationMidpointMJD_TAI + + :param padain: Dataframe of observations. + :type padain: pandas dataframe + :param pointfildb: Dataframe of the pointing database. + :type pointfildb: pandas dataframe + + :returns: **res_df** -- Merged dataframe of observations ("padain") with pointing + database ("pointfildb"), with all superfluous observations dropped. + :rtype: Pandas dataframe + + diff --git a/docs/autoapi/sorcha/modules/PPMiniDifi/index.rst b/docs/autoapi/sorcha/modules/PPMiniDifi/index.rst new file mode 100644 index 00000000..d0d6b144 --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPMiniDifi/index.rst @@ -0,0 +1,193 @@ +sorcha.modules.PPMiniDifi +========================= + +.. py:module:: sorcha.modules.PPMiniDifi + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPMiniDifi.haversine_np + sorcha.modules.PPMiniDifi.hasTracklet + sorcha.modules.PPMiniDifi.trackletsInNights + sorcha.modules.PPMiniDifi.discoveryOpportunities + sorcha.modules.PPMiniDifi.linkObject + sorcha.modules.PPMiniDifi.linkObservations + + +Module Contents +--------------- + +.. py:function:: haversine_np(lon1, lat1, lon2, lat2) + + Calculate the great circle distance between two points + on the earth (specified in decimal degrees) + + :param lon1: longitude of point 1 + :type lon1: float or array of floats + :param lat1: latitude of point 1 + :type lat1: float or array of floats + :param lon2: longitude of point 2 + :type lon2: float or array of floats + :param lat1: latitude of point 1 + :type lat1: float or array of floats + + :returns: * *float or array of floats* + * **Great distance between the two points [Units** (*Decimal degrees]*) + + .. rubric:: Notes + + All args must be of equal length. + + Because SkyCoord is slow AF. + + +.. py:function:: hasTracklet(mjd, ra, dec, maxdt_minutes, minlen_arcsec) + + Given a set of observations in one night, calculate it has + at least onedetectable tracklet. + + :param mjd: Modified Julian date time + :type mjd: float or array of floats + :param ra: Object's RA at given mjd [Units: degrees] + :type ra: float or array of floats + :param dec: Object's dec at given mjd [Units: degrees] + :type dec: float or array of floats + :param maxdt_minutes: Maximum allowable time between observations [Units: minutes] + :type maxdt_minutes: float + :param minlen_arcsec: Minimum allowable distance separation between observations [Units: arcsec] + :type minlen_arcsec: float + + :returns: * *boolean* + * *True if tracklet can be made else False* + + +.. py:function:: trackletsInNights(night, mjd, ra, dec, maxdt_minutes, minlen_arcsec) + + Calculate, for a given set of observations sorted by observation time, + whether or not it has at least one discoverable tracklet in each night. + + :param night: Array of the integer night corresponding to each observation + :type night: float or array of floats + :param mjd: Modified Julian date time + :type mjd: float or array of floats + :param ra: Object's RA at given mjd [Units: degrees] + :type ra: float or array of floats + :param dec: Object's dec at given mjd [Units: degrees] + :type dec: float or array of floats + :param maxdt_minutes: Maximum allowable time between observations [Units: minutes] + :type maxdt_minutes: float + :param minlen_arcsec: Minimum allowable distance separation between observations [Units: arcsec] + :type minlen_arcsec: float + + :returns: * **nights** (*float or array of floats*) -- Numpy array of the unique nights in the set of observations + * **hasTrk** (*boolean or array of booleans*) -- Array denoting if each night has a discoverable tracklet + + +.. py:function:: discoveryOpportunities(nights, nightHasTracklets, window, nlink, p, rng) + + Find all nights where a trailing window of nights (including the + current night) has at least tracklets to constitute a discovery. + + :param nights: Array of the integer night corresponding to each observation + :type nights: float or array of floats + :param nightHasTracklets: List of nights that have tracklets within them + :type nightHasTracklets: list of booleans + :param window: Number of tracklets required with <= this window to complete a detection + :type window: float + :param nlink: Number of tracklets required to form detection + :type nlink: float + :param p: SSP detection efficiency, or what fraction of objects are successfuly linked + :type p: float + :param rng: PGC64 generator object to determine which objects to drop + :type rng: numpy RNG generator object + + :returns: * **discIdx** (*float*) -- The index of where in the observation array the object is reported as discovered + * **disc** (*list of floats*) -- List of MJD dates where the object is discoverable + + +.. py:function:: linkObject(obsv, seed, maxdt_minutes, minlen_arcsec, window, nlink, p, night_start_utc_days) + + For a set of observations of a single object, calculate if there are any tracklets, + if there are enough tracklets to form a discovery window, and then report back all of + those successful discoveries. + + :param obsv: Array of observations for one object, of the format: + ssObjectId : str + Unique ID for the Solar System object + diaSourceId : float + Unique ID for the observation + midPointTai : float + Time for the observation midpoint (MJD) + ra : float + RA of the object (J2000) + decl : float + Declination of the object (J2000) + :type obsv: numpy array + :param seed: Initial seed per object to keep observations deterministic for multithreading + :type seed: float + :param maxdt_minutes: Maximum allowable time between observations [Units: minutes] + :type maxdt_minutes: float + :param minlen_arcsec: Minimum allowable distance separation between observations [Units: arcsec] + :type minlen_arcsec: float + :param window: Number of tracklets required with <= this window to complete a detection + :type window: float + :param nlink: Number of tracklets required to form detection + :type nlink: float + :param p: SSP detection efficiency, or what fraction of objects are successfuly linked + :type p: float + :param night_start_utc_days: The UTC time of local noon at the observatory + :type night_start_utc_days: float + + :returns: * **discoveryObservationId** (*float*) -- The ID of the observation that triggered the successful linking + * **discoverySubmissionDate** (*float*) -- The night at which the discovery is first submitted + * **discoveryChances** (*float*) -- The number of chances for discovery of the object + + +.. py:function:: linkObservations(obsv, seed, objectId='ssObjectId', sourceId='diaSourceId', mjdTime='midPointTai', ra='ra', dec='decl', **config) + + Ingesting a set of observations for one or more objects, determine if each object + would be discovered by the SSP pipeline based on tracklet forming and linking. + + :param obsv: Array of observations for each object, of the format: + ssObjectId : str + Unique ID for the Solar System object + diaSourceId : float + Unique ID for the observation + midPointTai : float + Time for the observation midpoint (MJD) + ra : float + RA of the object (J2000) + decl : float + Declination of the object (J2000) + :type obsv: numpy array + :param seed: Initial seed per object to keep observations deterministic for multithreading + :type seed: float + :param objectId: Column name for object ID's in observations dataframe + :type objectId: string + :param sourceId: Column name for observation ID's in observations dataframe + :type sourceId: string + :param mjdTime: Column name for MJD's in observations dataframe + :type mjdTime: string + :param ra: Column name for object RA's in observations dataframe + :type ra: string + :param dec: Column name for object Dec's in observations dataframe + :type dec: string + :param \*\*config: Dictionary containing configuration file variables + + :returns: **obj** -- + + Array with one row per detected object, of the format: + ssObjectId : str + Unique ID for the Solar System object + discoveryObservationId : float + Unique ID for the observation + discoverySubmissionDate : float + The night at which the discovery is first submitted + discoveryChances : float + The number of chances for discovery of the object + :rtype: numpy array + + diff --git a/docs/autoapi/sorcha/modules/PPModuleRNG/index.rst b/docs/autoapi/sorcha/modules/PPModuleRNG/index.rst new file mode 100644 index 00000000..19806494 --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPModuleRNG/index.rst @@ -0,0 +1,46 @@ +sorcha.modules.PPModuleRNG +========================== + +.. py:module:: sorcha.modules.PPModuleRNG + + +Classes +------- + +.. autoapisummary:: + + sorcha.modules.PPModuleRNG.PerModuleRNG + + +Module Contents +--------------- + +.. py:class:: PerModuleRNG(base_seed, pplogger=None) + + A collection of per-module random number generators. + + + .. py:attribute:: _base_seed + + + .. py:attribute:: _rngs + + + .. py:attribute:: pplogger + :value: None + + + + .. py:method:: getModuleRNG(module_name) + + Return a random number generator that is based on a base seed + and the current module name. + + :param module_name: The name of the module + :type module_name: string + + :returns: **rng** -- The random number generator. + :rtype: numpy Generator + + + diff --git a/docs/autoapi/sorcha/modules/PPOutput/index.rst b/docs/autoapi/sorcha/modules/PPOutput/index.rst new file mode 100644 index 00000000..39a869db --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPOutput/index.rst @@ -0,0 +1,95 @@ +sorcha.modules.PPOutput +======================= + +.. py:module:: sorcha.modules.PPOutput + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPOutput.PPOutWriteCSV + sorcha.modules.PPOutput.PPOutWriteHDF5 + sorcha.modules.PPOutput.PPOutWriteSqlite3 + sorcha.modules.PPOutput.PPIndexSQLDatabase + sorcha.modules.PPOutput.PPWriteOutput + + +Module Contents +--------------- + +.. py:function:: PPOutWriteCSV(padain, outf, separator=',') + + Writes a pandas dataframe out to a CSV file at a location given by the user. + + :param padain: Dataframe of output. + :type padain: pandas dataframe + :param outf: Location to which file should be written. + :type outf: string + :param separator: String of CSV separator. Default is ','. + :type separator: string of length 1 + + :rtype: None. + + +.. py:function:: PPOutWriteHDF5(pp_results, outf, keyname='sorcha_results') + + Writes a pandas dataframe out to a HDF5 file at a location given by the user. + + :param padain: Dataframe of output. + :type padain: pandas dataframe + :param outf: Location to which file should be written. + :type outf: string + :param keyin: Key at which data will be located. + :type keyin: string + + :rtype: None. + + +.. py:function:: PPOutWriteSqlite3(pp_results, outf, tablename='sorcha_results') + + Writes a pandas dataframe out to a CSV file at a location given by the user. + + :param pp_results: Dataframe of output. + :type pp_results: pandas dataframe + :param outf: Location to which file should be written. + :type outf: string + :param tablename: String of the table within the database to be indexed. + :type tablename: string + + :rtype: None. + + +.. py:function:: PPIndexSQLDatabase(outf, tablename='sorcha_results') + + Indexes a SQLite database of Sorcha output. + + :param outf: Location of SQLite database to be indexed. + :type outf: string + :param tablename: String of the table within the database to be indexed. + :type tablename: string + + :rtype: None. + + +.. py:function:: PPWriteOutput(cmd_args, sconfigs, observations_in, verbose=False) + + Writes the output in the format specified in the config file to a location + specified by the user. + + :param cmd_args: Dictonary of command line arguments. + :type cmd_args: dictionary + :param sconfigs: Dataclass of configuration file arguments. + :type sconfigs: dataclass + :param observations_in: Dataframe of output. + :type observations_in: Pandas dataframe + :param endChunk: Integer of last object in chunk. Used only for HDF5 output key. + Default = 0 + :type endChunk: integer, optional + :param verbose: Verbose logging mode on or off. Default = False + :type verbose: boolean, optional + + :rtype: None. + + diff --git a/docs/autoapi/sorcha/modules/PPRandomizeMeasurements/index.rst b/docs/autoapi/sorcha/modules/PPRandomizeMeasurements/index.rst new file mode 100644 index 00000000..ff3ee2e6 --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPRandomizeMeasurements/index.rst @@ -0,0 +1,213 @@ +sorcha.modules.PPRandomizeMeasurements +====================================== + +.. py:module:: sorcha.modules.PPRandomizeMeasurements + + +Attributes +---------- + +.. autoapisummary:: + + sorcha.modules.PPRandomizeMeasurements.logger + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPRandomizeMeasurements.randomizeAstrometryAndPhotometry + sorcha.modules.PPRandomizeMeasurements.randomizeAstrometry + sorcha.modules.PPRandomizeMeasurements.sampleNormalFOV + sorcha.modules.PPRandomizeMeasurements.randomizePhotometry + sorcha.modules.PPRandomizeMeasurements.flux2mag + sorcha.modules.PPRandomizeMeasurements.mag2flux + sorcha.modules.PPRandomizeMeasurements.icrf2radec + sorcha.modules.PPRandomizeMeasurements.radec2icrf + + +Module Contents +--------------- + +.. py:data:: logger + +.. py:function:: randomizeAstrometryAndPhotometry(observations, sconfigs, module_rngs, verbose=False) + + Wrapper function to perform randomisation of astrometry and photometry around + their uncertainties. Calls randomizePhotometry() and randomizeAstrometry(). + + Adds the following columns to the dataframe: + - trailedSourceMag + - PSFMag + - AstRATrue(deg) + - AstDecTrue(deg) + + :param observations: Dataframe containing observations. + :type observations: pandas dataframe + :param sconfigs: Dataclass of configuration file arguments. + :type sconfigs: dataclass + :param module_rngs: A collection of random number generators (per module). + :type module_rngs: PerModuleRNG + :param verbose: Verbosity on or off. Default False. + :type verbose: bool + + :returns: **observations** -- Original input dataframe with RA and Dec columns and trailedSourceMag and PSFMag + columns randomized around astrometric and photometric sigma. Original RA and Dec/magnitudes + stored in separate columns. + :rtype: pandas dataframe + + +.. py:function:: randomizeAstrometry(df, module_rngs, raName='RA_deg', decName='Dec_deg', raOrigName='RATrue_deg', decOrigName='DecTrue_deg', sigName='AstSig(deg)', radecUnits='deg', sigUnits='mas') + + Randomize astrometry with a normal distribution around the actual RADEC pointing. + The randomized values replace the original astrometry, with the original values + stored in separate columns. + + Adds the following columns to the observations dataframe: + + - AstRATrue(deg) + - AstDecTrue(deg) + + :param df: Dataframe containing astrometry and sigma. + :type df: pandas dataframe + :param module_rngs: A collection of random number generators (per module). + :type module_rngs: PerModuleRNG + :param ra_Name: "df" dataframe column name for the right ascension. + Default = "RA_deg" + :type ra_Name: string, optional + :param dec_Name: "df" dataframe column name for the declination. Default = "Dec_deg" + :type dec_Name: string, optional + :param raOrigName: "df" dataframe column name for where to store original right + ascension. Default = "RATrue_deg" + :type raOrigName: string, optional + :param decOrigName: "df" dataframe column name for where to store original declination. + Default = "DecTrue_deg" + :type decOrigName: string, optional + :param sigName: "df" dataframe column name for the standard deviation, uncertainty in the + astrometric position. + Default = "AstSig(deg)" + :type sigName: string, optional + :param radecUnits: Units for RA and Dec ('deg'/'rad'/'mas'). Default = "deg" + :type radecUnits: string + :param sigUnits: Units for standard deviation ('deg'/'rad'/'mas'). Default = "mas" + :type sigUnits: string + + :returns: **df** -- original input dataframe with RA and Dec columns randomized around + astrometric sigma and original RA and Dec stored in separate columns + :rtype: pandas dataframe + + .. rubric:: Notes + + Covariances in RADEC are currently not supported. The routine calculates + a normal distribution on the unit sphere, so as to allow for a correct modeling of + the poles. Distributions close to the poles may look odd in RADEC. + + +.. py:function:: sampleNormalFOV(center, sigma, module_rngs, ndim=3) + + Sample n points randomly (normal distribution) on a region on the unit (hyper-)sphere. + + :param center: Center of hpyer-sphere: can be an [n, ndim] dimensional array, + but only if n == npoints. + :type center: float + :param sigma: 1 sigma distance on unit sphere [radians]x + :type sigma: n-dimensional array + :param module_rngs: A collection of random number generators (per module). + :type module_rngs: PerModuleRNG + :param ndim: Dimension of hyper-sphere. Default = 3 + :type ndim: integer, optional + + :returns: **vec** -- Size [npoints, ndim] + :rtype: numpy array + + +.. py:function:: randomizePhotometry(df, module_rngs, magName='Filtermag', magRndName='FiltermagRnd', sigName='FiltermagSig') + + Randomize photometry with normal distribution around magName value. + + :param df: Dataframe containing astrometry and sigma. + :type df: pandas dataframe + :param module_rngs: A collection of random number generators (per module). + :type module_rngs: PerModuleRNG + :param magName: 'df' column name of apparent magnitude. Default = "Filtermag" + :type magName: string, optional + :param magRndName: 'df' column name for storing randomized apparent magnitude, Default = "FiltermagRnd" + :type magRndName: string, optional + :param sigName: 'df' column name for magnitude standard deviation. Default = "FiltermagSig" + :type sigName: float, optional + + :returns: randomized magnitudes for each row in 'df' + :rtype: array of floats + + .. rubric:: Notes + + The normal distribution here is in magnitudes while it should be in flux. This will fail for large sigmas. + Should be fixed at some point. + + We assume that apparent magnitudes are stored within 'df' and that 'magName' + corresponds to the corresponding column within 'df' + + 'df' is also modified with added column magRndNam to store the randomize apparent magnitude + + +.. py:function:: flux2mag(f, f0=3631) + + AB ugriz system (f0 = 3631 Jy) to magnitude conversion. + + :param f: flux. [Units : Jy]. + :type f: float or array of floats + :param f0: Zero point flux. Default = 3631 + :type f0: float, optional + + :returns: **mag** -- pogson magnitude. [Units: mag] + :rtype: float or array of floats + + +.. py:function:: mag2flux(mag, f0=3631) + + AB ugriz system (f0 = 3631 Jy) magnitude to flux conversion. + + :param mag: Pogson magnitude. [Units: mag] + :type mag: float or rray of floats + :param f0: Zero point flux. Default = 3631 + :type f0: float, optional + + :returns: **f (float/array of floats)** + :rtype: flux [Units: Jy]. + + +.. py:function:: icrf2radec(x, y, z, deg=True) + + Convert ICRF xyz to Right Ascension and Declination. + Geometric states on unit sphere, no light travel time/aberration correction. + + :param x: 3D vector of unit length (ICRF) + :type x: floats/arrays of floats + :param y: 3D vector of unit length (ICRF) + :type y: floats/arrays of floats + :param z: 3D vector of unit length (ICRF) + :type z: floats/arrays of floats + :param de: True for angles in degrees, False for angles in radians. Default = True + :type de: boolean, optional + + :returns: * **ra** (*float or array of floats*) -- Right Ascension. [Units: deg] + * **dec** (*float or array of floats*) -- Declination. [Units: deg] + + +.. py:function:: radec2icrf(ra, dec, deg=True) + + Convert Right Ascension and Declination to ICRF xyz unit vector. + Geometric states on unit sphere, no light travel time/aberration correction. + + :param ra: Right Ascension. [Units: deg] + :type ra: float or array of floats + :param dec: Declination. [Units deg] + :type dec: float or array of floats + :param deg: True for angles in degrees, False for angles in radians. Default = True + :type deg: boolean, optional + + :returns: **array([x, y, z])** -- 3D vector of unit length (ICRF) + :rtype: arrays/matrix of floats + + diff --git a/docs/autoapi/sorcha/modules/PPReadPointingDatabase/index.rst b/docs/autoapi/sorcha/modules/PPReadPointingDatabase/index.rst new file mode 100644 index 00000000..bb9ab25d --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPReadPointingDatabase/index.rst @@ -0,0 +1,32 @@ +sorcha.modules.PPReadPointingDatabase +===================================== + +.. py:module:: sorcha.modules.PPReadPointingDatabase + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPReadPointingDatabase.PPReadPointingDatabase + + +Module Contents +--------------- + +.. py:function:: PPReadPointingDatabase(bsdbname, observing_filters, dbquery, surveyname) + + Reads in the pointing database as a Pandas dataframe. + + :param bsdbname: File location of pointing database. + :type bsdbname: string + :param observing_filters: List of observation filters of interest. + :type observing_filters: list of strings + :param dbquery: Databse query to perform on pointing database. + :type dbquery: string + + :returns: **dfo** -- Dataframe of pointing database. + :rtype: pandas dataframe + + diff --git a/docs/autoapi/sorcha/modules/PPSNRLimit/index.rst b/docs/autoapi/sorcha/modules/PPSNRLimit/index.rst new file mode 100644 index 00000000..40ec59db --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPSNRLimit/index.rst @@ -0,0 +1,31 @@ +sorcha.modules.PPSNRLimit +========================= + +.. py:module:: sorcha.modules.PPSNRLimit + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPSNRLimit.PPSNRLimit + + +Module Contents +--------------- + +.. py:function:: PPSNRLimit(observations, sigma_limit=2.0) + + Filter that performs a straight SNR cut based on a limit, removing + observations that are less than a SNR limit + + :param observations: Dataframe of observations. Must have "SNR" column. + :type observations: pandas dataframe + :param sigma_limit: Limit for SNR cut. + :type sigma_limit: float, optional. + + :returns: **observations** -- "observations" dataframed modified with entries with SNR < the limit removed. + :rtype: pandas dataframe + + diff --git a/docs/autoapi/sorcha/modules/PPStats/index.rst b/docs/autoapi/sorcha/modules/PPStats/index.rst new file mode 100644 index 00000000..c262c12e --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPStats/index.rst @@ -0,0 +1,34 @@ +sorcha.modules.PPStats +====================== + +.. py:module:: sorcha.modules.PPStats + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPStats.stats + + +Module Contents +--------------- + +.. py:function:: stats(observations, statsfilename, outpath, sconfigs) + + Write a summary statistics file including whether each object was linked + or not within miniDifi, their number of observations, min/max phase angles, + min/max trailed source magnitudes, and median trailed source magnitudes + per filter + + :param observations: Pandas dataframe of observations + :type observations: Pandas dataframe + :param statsfilename: Stem filename to write summary stats file to + :type statsfilename: string + :param sconfigs: Dataclass of configuration file arguments. + :type sconfigs: dataclass + + :rtype: None. + + diff --git a/docs/autoapi/sorcha/modules/PPTrailingLoss/index.rst b/docs/autoapi/sorcha/modules/PPTrailingLoss/index.rst new file mode 100644 index 00000000..f188e56f --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPTrailingLoss/index.rst @@ -0,0 +1,85 @@ +sorcha.modules.PPTrailingLoss +============================= + +.. py:module:: sorcha.modules.PPTrailingLoss + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPTrailingLoss.calcTrailingLoss + sorcha.modules.PPTrailingLoss.PPTrailingLoss + + +Module Contents +--------------- + +.. py:function:: calcTrailingLoss(dRaCosDec, dDec, seeing, texp=30.0, model='circularPSF', a_trail=0.761, b_trail=1.162, a_det=0.42, b_det=0.003) + + Find the trailing loss from trailing and detection (Veres & Chesley 2017) + + :param dRa: on sky velocity component in RA*Cos(Dec). [Units: deg/day] + :type dRa: float or array of floats + :param dDec: on sky velocity component in Dec. [Units: deg/day] + :type dDec: float/array of floats + :param seeing: FWHM of the seeing disk. [Units: arcseconds] + :type seeing: float or array of floats + :param texp: Exposure length. [Units: seconds] Default = 30 + :type texp: float or array of floats, optional + :param model: Options: 'circularPSF' or trailedSource' + 'circularPSF': Trailing loss due to the DM detection algorithm. Limit SNR: + 5 sigma in a PSF-convolved image with a circular PSF (no trail fitting). Peak + fluxes will be lower due to motion of the object. + 'trailedSource': Unavoidable trailing loss due to spreading the PSF + over more pixels lowering the SNR in each pixel. + See https://github.com/rhiannonlynne/318-proceedings/blob/master/Trailing%20Losses.ipynb for details. + Default = "circularPSF" + :type model: string, optional + :param a_trail: a fit parameters for trailedSource model. Default parameters from Veres & Chesley (2017). + Default = 0.761 + :type a_trail: float, optional + :param b_trail: b fit parameters for trailedSource model. Default parameters from Veres & Chesley (2017). + Default = 1.162 + :type b_trail: float, optional + :param a_det: a fit parameters for circularPSF model. Default parameters from Veres & Chesley (2017). + Default = 0.420 + :type a_det: float, optional + :param b_det: b fit parameters for circularPSF model. Default parameters from Veres & Chesley (2017). + Default = 0.003 + :type b_det: float, optional + + :returns: **dmag** -- Loss in detection magnitude due to trailing. + :rtype: float or array of floats + + +.. py:function:: PPTrailingLoss(eph_df, model='circularPSF', dra_cosdec_name='RARateCosDec_deg_day', ddec_name='DecRate_deg_day', dec_name='Dec_deg', seeing_name_survey='seeingFwhmEff_arcsec', visit_time_name='visitExposureTime') + + Calculates detection trailing losses. Wrapper for calcTrailingLoss. + + :param eph_df: Dataframe of observations for which to calculate trailing losses. + :type eph_df: pandas dataframe + :param model: Photometric model. Either 'circularPSF' or 'trailedSource': see docstring for + calcTrailingLoss for details. Default = "circularPSF" + :type model: string, optional + :param dra_name: "eph_df" column name for object RA rate. Default = "RARateCosDec_deg_day" + Assumes cos(dec) normalization has already been applied + :type dra_name: string, optional + :param ddec_name: "eph_df" column name for object dec rate. Default = "DecRate_deg_day" + :type ddec_name: string, optional + :param dec_name: "eph_df" column name for object declination. Default = "Dec_deg" + :type dec_name: string, default + :param seeing_name_survey: "eph_df" column name for seeing. Default = "seeingFwhmEff_arcsec" + :type seeing_name_survey: string, optional + :param visit_time_name: "eph_df" column name for exposure length. Default = "visitExposureTime" + :type visit_time_name: string, optional + + :returns: **dmag** -- Loss in detection magnitude due to trailing losses. + :rtype: float or array of floats + + .. rubric:: Notes + + Assumes 'eph_df" has RA and Dec stored in deg/dayrates and the seeing in arcseconds + + diff --git a/docs/autoapi/sorcha/modules/PPVignetting/index.rst b/docs/autoapi/sorcha/modules/PPVignetting/index.rst new file mode 100644 index 00000000..d4a17af2 --- /dev/null +++ b/docs/autoapi/sorcha/modules/PPVignetting/index.rst @@ -0,0 +1,116 @@ +sorcha.modules.PPVignetting +=========================== + +.. py:module:: sorcha.modules.PPVignetting + + +Attributes +---------- + +.. autoapisummary:: + + sorcha.modules.PPVignetting.deg2rad + sorcha.modules.PPVignetting.rad2deg + sorcha.modules.PPVignetting.sin + sorcha.modules.PPVignetting.cos + + +Functions +--------- + +.. autoapisummary:: + + sorcha.modules.PPVignetting.vignettingEffects + sorcha.modules.PPVignetting.calcVignettingLosses + sorcha.modules.PPVignetting.haversine + sorcha.modules.PPVignetting.vignetFunc + + +Module Contents +--------------- + +.. py:data:: deg2rad + +.. py:data:: rad2deg + +.. py:data:: sin + +.. py:data:: cos + +.. py:function:: vignettingEffects(df, raName='RA_deg', decName='Dec_deg', fieldName='FieldID', raNameSurvey='fieldRA_deg', decNameSurvey='fieldDec_deg') + + Calculates effective limiting magnitude at source, taking vignetting into account. + Wrapper for calcVignettingLosses(). + + :param df: dataframe of observations. + :type df: pandas dataframe + :param raName: 'df' column name of object RA. Default = "RA_deg" + :type raName: string, optional + :param decName: 'df' column name of object declination. Default = "Dec_deg" + :type decName: string, optional + :param fieldName: 'df' column name for observation pointing field ID. Default = "FieldID" + :type fieldName: string, optional + :param raNameSurvey: 'df' column name for observation pointing RA. Default = "fieldRA_deg" + + decNameSurvey : string, optional + 'df' column name for observation pointing declination. Default = "fieldDec_deg" + :type raNameSurvey: string, optional + + :returns: Five sigma limiting magnitude at object location adjusted for vignetting for each + row in 'df' dataframe. + :rtype: list of floats + + +.. py:function:: calcVignettingLosses(ra, dec, fieldra, fielddec) + + Calculates magnitude loss due to vignetting for a point with the telescope + centered on fieldra, fielddec. + + :param ra: RA of object(s). + :type ra: float or aarray of floats + :param dec: Dec of object(s). + :type dec: float or array of floats + :param fieldra: RA of field(s). + :type fieldra: float or array of floats + :param fielddec: Dec of field(s). + :type fielddec: float or array of floats + + :returns: Magnitude loss due to vignetting at object position. + :rtype: floats or array of floats + + +.. py:function:: haversine(ra1, dec1, ra2, dec2) + + Calculates angular distance between two points. Can produce floating point + errors for antipodal points, which are not intended to be encountered within + the scope of this module. + + :param ra1: RA of first point. + :type ra1: float or array of floats + :param dec1 or float or array of floats: Dec of first point. + :param ra2: RA of second point. + :type ra2: float or array of floats + :param dec2: Dec of second point. + :type dec2: float/array of floats + + :returns: Angular distance between two points. + :rtype: float or array of floats + + +.. py:function:: vignetFunc(x) + + Returns the magnitude of dimming caused by the vignetting relative to the + center of the field. + + :param x: Angular separation of point from field centre. + :type x: float or array of floats + + :returns: Magnitude of dimming due to vignetting at object position. + :rtype: float or array of floats + + .. rubric:: Notes + + Grabbed from sims_selfcal. From VignettingFunc_v3.3.TXT. r is in degrees, + frac is fraction of rays which were not vignetted. + + diff --git a/docs/autoapi/sorcha/modules/index.rst b/docs/autoapi/sorcha/modules/index.rst new file mode 100644 index 00000000..28d4cd5e --- /dev/null +++ b/docs/autoapi/sorcha/modules/index.rst @@ -0,0 +1,42 @@ +sorcha.modules +============== + +.. py:module:: sorcha.modules + + +Submodules +---------- + +.. toctree:: + :maxdepth: 1 + + /autoapi/sorcha/modules/PPAddUncertainties/index + /autoapi/sorcha/modules/PPApplyColourOffsets/index + /autoapi/sorcha/modules/PPApplyFOVFilter/index + /autoapi/sorcha/modules/PPBrightLimit/index + /autoapi/sorcha/modules/PPCalculateApparentMagnitude/index + /autoapi/sorcha/modules/PPCalculateApparentMagnitudeInFilter/index + /autoapi/sorcha/modules/PPCalculateSimpleCometaryMagnitude/index + /autoapi/sorcha/modules/PPCommandLineParser/index + /autoapi/sorcha/modules/PPConfigParser/index + /autoapi/sorcha/modules/PPDetectionEfficiency/index + /autoapi/sorcha/modules/PPDetectionProbability/index + /autoapi/sorcha/modules/PPDropObservations/index + /autoapi/sorcha/modules/PPFadingFunctionFilter/index + /autoapi/sorcha/modules/PPFootprintFilter/index + /autoapi/sorcha/modules/PPGetLogger/index + /autoapi/sorcha/modules/PPGetMainFilterAndColourOffsets/index + /autoapi/sorcha/modules/PPLinkingFilter/index + /autoapi/sorcha/modules/PPMagnitudeLimit/index + /autoapi/sorcha/modules/PPMatchPointingToObservations/index + /autoapi/sorcha/modules/PPMiniDifi/index + /autoapi/sorcha/modules/PPModuleRNG/index + /autoapi/sorcha/modules/PPOutput/index + /autoapi/sorcha/modules/PPRandomizeMeasurements/index + /autoapi/sorcha/modules/PPReadPointingDatabase/index + /autoapi/sorcha/modules/PPSNRLimit/index + /autoapi/sorcha/modules/PPStats/index + /autoapi/sorcha/modules/PPTrailingLoss/index + /autoapi/sorcha/modules/PPVignetting/index + + diff --git a/docs/autoapi/sorcha/readers/CSVReader/index.rst b/docs/autoapi/sorcha/readers/CSVReader/index.rst new file mode 100644 index 00000000..b3105d88 --- /dev/null +++ b/docs/autoapi/sorcha/readers/CSVReader/index.rst @@ -0,0 +1,140 @@ +sorcha.readers.CSVReader +======================== + +.. py:module:: sorcha.readers.CSVReader + + +Classes +------- + +.. autoapisummary:: + + sorcha.readers.CSVReader.CSVDataReader + + +Module Contents +--------------- + +.. py:class:: CSVDataReader(filename, sep='csv', header=-1, **kwargs) + + Bases: :py:obj:`sorcha.readers.ObjectDataReader.ObjectDataReader` + + + A class to read in object data files stored as CSV or whitespace + separated values. + + Requires that the file's first column is ObjID. + + + .. py:attribute:: filename + + + .. py:attribute:: sep + :value: 'csv' + + + + .. py:attribute:: header_row + + + .. py:attribute:: obj_id_table + :value: None + + + + .. py:method:: get_reader_info() + + Return a string identifying the current reader name + and input information (for logging and output). + + :returns: **name** -- The reader information. + :rtype: string + + + + .. py:method:: _find_and_validate_header_line(header=-1) + + Read and validate the header line. If no line number is provided, use + a heuristic match to find the header line. This is used in cases + where the header is not the first line and we want to skip down. + + :param header: The row number of the header. If not provided, does an automatic search. + Default = -1 + :type header: integer, optional + + :returns: The line index of the header. + :rtype: integer + + + + .. py:method:: _check_header_line(header_line) + + Check that a given header line is valid and exit if it is invalid. + + :param header_line: The proposed header line. + :type header_line: str + + + + .. py:method:: _read_rows_internal(block_start=0, block_size=None, **kwargs) + + Reads in a set number of rows from the input. + + :param block_start: The 0-indexed row number from which + to start reading the data. For example in a CSV file + block_start=2 would skip the first two lines after the header + and return data starting on row=2. Default =0 + :type block_start: integer, optional + :param block_size: The number of rows to read in. + Use block_size=None to read in all available data. + default =None + :type block_size: integer, optional, default=None + :param \*\*kwargs: Extra arguments + :type \*\*kwargs: dictionary, optional + + :returns: **res_df** -- Dataframe of the object data. + :rtype: pandas dataframe + + + + .. py:method:: _build_id_map() + + Builds a table of just the object IDs + + + + .. py:method:: _read_objects_internal(obj_ids, **kwargs) + + Read in a chunk of data for given object IDs. + + :param obj_ids: A list of object IDs to use. + :type obj_ids: list + :param \*\*kwargs: Extra arguments + :type \*\*kwargs: dictionary, optional + + :returns: **res_df** -- The dataframe for the object data. + :rtype: pandas dataframe + + + + .. py:method:: _process_and_validate_input_table(input_table, **kwargs) + + Perform any input-specific processing and validation on the input table. + Modifies the input dataframe in place. + + .. rubric:: Notes + + The base implementation includes filtering that is common to most + input types. Subclasses should call super.process_and_validate() + to ensure that the ancestor’s validation is also applied. + + :param input_table: A loaded table. + :type input_table: Pandas dataframe + :param \*\*kwargs: Extra arguments + :type \*\*kwargs: dictionary, optional + + :returns: **input_table** -- Returns the input dataframe modified in-place. + :rtype: pandas dataframe + + + diff --git a/docs/autoapi/sorcha/readers/CombinedDataReader/index.rst b/docs/autoapi/sorcha/readers/CombinedDataReader/index.rst new file mode 100644 index 00000000..72059631 --- /dev/null +++ b/docs/autoapi/sorcha/readers/CombinedDataReader/index.rst @@ -0,0 +1,127 @@ +sorcha.readers.CombinedDataReader +================================= + +.. py:module:: sorcha.readers.CombinedDataReader + +.. autoapi-nested-parse:: + + The CombinedDataReader class supports loading the entire input data + for the simulator post processing by using individuals reader classes + to read individual input files and combining the data into a single table. + + The CombinedDataReader object reads the data in blocks to limit memory usage. + For each blocks, it uses two stages: + 1) It reads a range of individual rows from the ``primary_reader``. By default this + reader is the first auxiliary data reader, but can be set to the ephemeris reader. + This reader is used to extract a list of object IDs for this block. + 2) For each of the readers (ephemeris and auxiliary data) load in all the rows + corresponding to the object IDs extracted in stage 1. + + For example, if the ephemeris file is used as the primary reader, the algorithm + will load data in blocks of the ephemeris rows and join in the auxiliary data + for just the object IDs on those rows. It is not guaranteed to include all + rows for the current objects. + + + +Classes +------- + +.. autoapisummary:: + + sorcha.readers.CombinedDataReader.CombinedDataReader + + +Module Contents +--------------- + +.. py:class:: CombinedDataReader(ephem_primary=False, **kwargs) + + .. py:attribute:: ephem_reader + :value: None + + + + .. py:attribute:: aux_data_readers + :value: [] + + + + .. py:attribute:: block_start + :value: 0 + + + + .. py:attribute:: ephem_primary + :value: False + + + + .. py:method:: add_ephem_reader(new_reader) + + Add a new reader for ephemeris data. + + :param new_reader: The reader for a specific input file. + :type new_reader: ObjectDataReader + + + + .. py:method:: add_aux_data_reader(new_reader) + + Add a new object reader that corresponds to an auxiliary input data type.. + + :param new_reader: The reader for a specific input file. + :type new_reader: ObjectDataReader + + + + .. py:method:: check_aux_object_ids() + + Checks the ObjIDs in all of the auxiliary data readers to make sure + both files contain exactly the same ObjIDs. + + + + .. py:method:: read_block(block_size=None, verbose=False, **kwargs) + + Reads in a set number of rows from the input, performs + post-processing and validation, and returns a data frame. + + :param block_size: the number of rows to read in. + Use block_size=None to read in all available data. + Default = None + :type block_size: integer, optional + :param verbose: Use verbose logging. + Default = False + :type verbose: boolean, optional + :param \*\*kwargs: Extra arguments + :type \*\*kwargs: dictionary, optional + + :returns: **res_df** -- dataframe of the combined object data. + :rtype: pandas dataframe + + + + .. py:method:: read_aux_block(block_size=None, verbose=False, **kwargs) + + Reads in a set number of rows from the input, performs + post-processing and validation, and returns a data frame. + + This function DOES NOT include the ephemeris data in the returned data frame. + It is to be used when generating the ephemeris during the execution of Sorcha. + + :param block_size: the number of rows to read in. + Use block_size=None to read in all available data. + Default = None + :type block_size: integer, optional + :param verbose: use verbose logging. + Default = False + :type verbose: boolean, optional + :param \*\*kwargs: Extra arguments + :type \*\*kwargs: dictionary, optional + + :returns: **res_df** -- dataframe of the combined object data, excluding any ephemeris data. + :rtype: pandas dataframe + + + diff --git a/docs/autoapi/sorcha/readers/DatabaseReader/index.rst b/docs/autoapi/sorcha/readers/DatabaseReader/index.rst new file mode 100644 index 00000000..864821ef --- /dev/null +++ b/docs/autoapi/sorcha/readers/DatabaseReader/index.rst @@ -0,0 +1,99 @@ +sorcha.readers.DatabaseReader +============================= + +.. py:module:: sorcha.readers.DatabaseReader + + +Classes +------- + +.. autoapisummary:: + + sorcha.readers.DatabaseReader.DatabaseReader + + +Module Contents +--------------- + +.. py:class:: DatabaseReader(intermdb, **kwargs) + + Bases: :py:obj:`sorcha.readers.ObjectDataReader.ObjectDataReader` + + + A class to read in object data stored in a sqlite database. + + + .. py:attribute:: intermdb + + + .. py:method:: get_reader_info() + + Return a string identifying the current reader name + and input information (for logging and output). + + :returns: **name** -- The reader information. + :rtype: string + + + + .. py:method:: _read_rows_internal(block_start=0, block_size=None, **kwargs) + + Reads in a set number of rows from the input. + + :param block_start: The 0-indexed row number from which + to start reading the data. For example in a CSV file + block_start=2 would skip the first two lines after the header + and return data starting on row=2. Default=0 + :type block_start: integer, optional + :param block_size: the number of rows to read in. + Use block_size=None to read in all available data. + A non-None block size must be provided if block_start > 0. + Default = None + :type block_size: int, optional + :param \*\*kwargs: Extra arguments + :type \*\*kwargs: dictionary, optional + + :returns: **res_df** -- dataframe of the object data. + :rtype: pandas dataframe + + .. rubric:: Notes + + A non-None block size must be provided if block_start > 0. + + + + .. py:method:: _read_objects_internal(obj_ids, **kwargs) + + Read in a chunk of data for given object IDs. + + :param obj_ids: A list of object IDs to use. + :type obj_ids: list + :param \*\*kwargs: Extra arguments + :type \*\*kwargs: dictionary, optional + + :returns: **res_df** -- The dataframe for the object data. + :rtype: pandas dataframe + + + + .. py:method:: _process_and_validate_input_table(input_table, **kwargs) + + Perform any input-specific processing and validation on the input table. + Modifies the input dataframe in place. + + .. rubric:: Notes + + The base implementation includes filtering that is common to most + input types. Subclasses should call super.process_and_validate() + to ensure that the ancestor’s validation is also applied. + + :param input_table: A loaded table. + :type input_table: pandas dataframe + :param \*\*kwargs: Extra arguments + :type \*\*kwargs: dictionary, optional + + :returns: **input_table** -- Returns the input dataframe modified in-place. + :rtype: pandas dataframe + + + diff --git a/docs/autoapi/sorcha/readers/EphemerisReader/index.rst b/docs/autoapi/sorcha/readers/EphemerisReader/index.rst new file mode 100644 index 00000000..aa876945 --- /dev/null +++ b/docs/autoapi/sorcha/readers/EphemerisReader/index.rst @@ -0,0 +1,124 @@ +sorcha.readers.EphemerisReader +============================== + +.. py:module:: sorcha.readers.EphemerisReader + + +Classes +------- + +.. autoapisummary:: + + sorcha.readers.EphemerisReader.EphemerisDataReader + + +Functions +--------- + +.. autoapisummary:: + + sorcha.readers.EphemerisReader.read_full_ephemeris_table + + +Module Contents +--------------- + +.. py:class:: EphemerisDataReader(filename, inputformat, **kwargs) + + Bases: :py:obj:`sorcha.readers.ObjectDataReader.ObjectDataReader` + + + A class to read in ephemeris from an external ephemeris file. + + Instead of subclassing the various readers (CSV, HDF5, etc.) individually, this class instantiates + one of those classes in an internal ``reader`` attribute. As such all reading, validation, etc. is + passed off to the ``reader`` object this object owns. While this adds a level of indirection, it + allows us to support a cross product of N file types from M ephemeris generators with M + N readers + instead of M * N. + + + .. py:attribute:: reader + :value: None + + + + .. py:method:: get_reader_info() + + Return a string identifying the current reader name + and input information (for logging and output). + + :returns: The reader information. + :rtype: string + + + + .. py:method:: _read_rows_internal(block_start=0, block_size=None, **kwargs) + + Reads in a set number of rows from the input. + + :param block_start: The 0-indexed row number from which + to start reading the data. For example in a CSV file + block_start=2 would skip the first two lines after the header + and return data starting on row=2. Default =0 + :type block_start: int, optional + :param block_size: the number of rows to read in. + Use block_size=None to read in all available data. + Default = None + :type block_size: int, optional + :param \*\*kwargs: Extra arguments + :type \*\*kwargs: dictionary, optional + + :returns: **res_df** -- dataframe of the object data. + :rtype: Pandas dataframe + + + + .. py:method:: _read_objects_internal(obj_ids, **kwargs) + + Read in a chunk of data corresponding to all rows for + a given set of object IDs. + + :param obj_ids: A list of object IDs to use. + :type obj_ids: list + :param \*\*kwargs: Extra arguments + :type \*\*kwargs: dictionary, optional + + :returns: **res_df** -- The dataframe for the object data. + :rtype: pandas dataframe + + + + .. py:method:: _process_and_validate_input_table(input_table, **kwargs) + + Perform any input-specific processing and validation on the input table. + Modifies the input dataframe in place. + + :param input_table: A loaded table. + :type input_table: Pandas dataframe + :param \*\*kwargs: Extra arguments + :type \*\*kwargs: dictionary, optional + + :returns: **input_table** -- Returns the input dataframe modified in-place. + :rtype: Pandas dataframe + + .. rubric:: Notes + + The base implementation includes filtering that is common to most + input types. Subclasses should call super.process_and_validate() + to ensure that the ancestor’s validation is also applied. + + + +.. py:function:: read_full_ephemeris_table(filename, inputformat) + + A helper function for testing that reads and returns an entire ephemeris table. + + :param filename: location/name of the data file. + :type filename: string + :param inputformat: format of input file ("whitespace"/"comma"/"csv"/"h5"/"hdf5"). + :type inputformat: string + + :returns: **res_df** -- dataframe of the object data. + :rtype: pandas dataframe + + diff --git a/docs/autoapi/sorcha/readers/HDF5Reader/index.rst b/docs/autoapi/sorcha/readers/HDF5Reader/index.rst new file mode 100644 index 00000000..9016120c --- /dev/null +++ b/docs/autoapi/sorcha/readers/HDF5Reader/index.rst @@ -0,0 +1,105 @@ +sorcha.readers.HDF5Reader +========================= + +.. py:module:: sorcha.readers.HDF5Reader + + +Classes +------- + +.. autoapisummary:: + + sorcha.readers.HDF5Reader.HDF5DataReader + + +Module Contents +--------------- + +.. py:class:: HDF5DataReader(filename, **kwargs) + + Bases: :py:obj:`sorcha.readers.ObjectDataReader.ObjectDataReader` + + + A class to read in object data files stored as HDF5 files. + + + .. py:attribute:: filename + + + .. py:attribute:: obj_id_table + :value: None + + + + .. py:method:: get_reader_info() + + Return a string identifying the current reader name + and input information (for logging and output). + + :returns: **name** -- The reader information. + :rtype: string + + + + .. py:method:: _read_rows_internal(block_start=0, block_size=None, **kwargs) + + Reads in a set number of rows from the input. + + :param block_start: The 0-indexed row number from which + to start reading the data. For example in a CSV file + block_start=2 would skip the first two lines after the header + and return data starting on row=2. Default=0 + :type block_start: integer, optional + :param block_size: the number of rows to read in. + Use block_size=None to read in all available data. + Default = None + :type block_size: integer, optional + :param \*\*kwargs: Extra arguments + :type \*\*kwargs: dictionary, optional + + :returns: **res_df** -- Dataframe of the object data. + :rtype: pandas dataframe + + + + .. py:method:: _build_id_map() + + Builds a table of just the object IDs + + + + .. py:method:: _read_objects_internal(obj_ids, **kwargs) + + Read in a chunk of data for given object IDs. + + :param obj_ids: A list of object IDs to use. + :type obj_ids: list + :param \*\*kwargs: Extra arguments + :type \*\*kwargs: dictionary, optional + + :returns: **res_df** -- The dataframe for the object data. + :rtype: Pandas dataframe + + + + .. py:method:: _process_and_validate_input_table(input_table, **kwargs) + + Perform any input-specific processing and validation on the input table. + Modifies the input dataframe in place. + + .. rubric:: Notes + + The base implementation includes filtering that is common to most + input types. Subclasses should call super.process_and_validate() + to ensure that the ancestor’s validation is also applied. + + :param input_table: A loaded table. + :type input_table: pandas dataframe + :param \*\*kwargs: Extra arguments + :type \*\*kwargs: dictionary, optional + + :returns: **input_table** -- Returns the input dataframe modified in-place. + :rtype: pandas dataframe + + + diff --git a/docs/autoapi/sorcha/readers/ObjectDataReader/index.rst b/docs/autoapi/sorcha/readers/ObjectDataReader/index.rst new file mode 100644 index 00000000..9684a245 --- /dev/null +++ b/docs/autoapi/sorcha/readers/ObjectDataReader/index.rst @@ -0,0 +1,156 @@ +sorcha.readers.ObjectDataReader +=============================== + +.. py:module:: sorcha.readers.ObjectDataReader + +.. autoapi-nested-parse:: + + Base class for reading object-related data from a variety of sources + and returning a pandas data frame. + + Each subclass of ObjectDataReader must implement at least the functions + _read_rows_internal and _read_objects_internal, both of which return a + pandas data frame. Each data source needs to have a column ObjID that + identifies the object and can be used for joining and filtering. + + Caching is implemented in the base class. This will lazy load the full + table into memory from the chosen data source, so it should only be + used with smaller data sets. Both ``read_rows`` and ``read_objects`` + will check for a cached table before reading the files, allowing them + to perform direct pandas operations if the data is already in memory. + + + +Classes +------- + +.. autoapisummary:: + + sorcha.readers.ObjectDataReader.ObjectDataReader + + +Module Contents +--------------- + +.. py:class:: ObjectDataReader(cache_table=False, **kwargs) + + Bases: :py:obj:`abc.ABC` + + + The base class for reading in the object data. + + + .. py:attribute:: _cache_table + :value: False + + + + .. py:attribute:: _table + :value: None + + + + .. py:method:: get_reader_info() + :abstractmethod: + + + Return a string identifying the current reader name + and input information (for logging and output). + + :returns: **name** -- The reader information. + :rtype: str + + + + .. py:method:: read_rows(block_start=0, block_size=None, **kwargs) + + Reads in a set number of rows from the input, performs + post-processing and validation, and returns a data frame. + + :param block_start: The 0-indexed row number from which + to start reading the data. For example in a CSV file + block_start=2 would skip the first two lines after the header + and return data starting on row=2. Default=0 + :type block_start: int (optional) + :param block_size: the number of rows to read in. + Use block_size=None to read in all available data. + Default = None + :type block_size: int (optional) + :param \*\*kwargs: Extra arguments + :type \*\*kwargs: dictionary, optional + + :returns: **res_df** -- dataframe of the object data. + :rtype: Pandas dataframe + + + + .. py:method:: _read_rows_internal(block_start=0, block_size=None, **kwargs) + :abstractmethod: + + + Function to do the actual source-specific reading. + + + + .. py:method:: read_objects(obj_ids, **kwargs) + + Read in a chunk of data corresponding to all rows for + a given set of object IDs. + + :param obj_ids: A list of object IDs to use. + :type obj_ids: list + :param \*\*kwargs: Extra arguments + :type \*\*kwargs: dictionary, optional + + :returns: **res_df** -- The dataframe for the object data. + :rtype: Pandas dataframe + + + + .. py:method:: _read_objects_internal(obj_ids, **kwargs) + :abstractmethod: + + + Function to do the actual source-specific reading. + + + + .. py:method:: _validate_object_id_column(input_table) + + Checks that the object ID column exists and converts it to a string. + This is the common validity check for all object data tables. + + :param input_table: A loaded table. + :type input_table: Pandas dataframe + + :returns: **input_table** -- Returns the input dataframe modified in-place. + :rtype: Pandas dataframe + + + + .. py:method:: _process_and_validate_input_table(input_table, **kwargs) + + Perform any input-specific processing and validation on the input table. + Modifies the input dataframe in place. + + :param input_table: A loaded table. + :type input_table: Pandas dataframe + :param \*\*kwargs: Extra arguments + :type \*\*kwargs: dictionary, optional + + :returns: **input_table** -- Returns the input dataframe modified in-place. + :rtype: Pandas dataframe + + .. rubric:: Notes + + The base implementation includes filtering that is common to most + input types. Subclasses should call super.process_and_validate() + to ensure that the ancestor’s validation is also applied. + + Additional arguments to use: + + disallow_nan : boolean + if True then checks the data for NaNs or nulls. + + + diff --git a/docs/autoapi/sorcha/readers/OrbitAuxReader/index.rst b/docs/autoapi/sorcha/readers/OrbitAuxReader/index.rst new file mode 100644 index 00000000..fd05cd9d --- /dev/null +++ b/docs/autoapi/sorcha/readers/OrbitAuxReader/index.rst @@ -0,0 +1,56 @@ +sorcha.readers.OrbitAuxReader +============================= + +.. py:module:: sorcha.readers.OrbitAuxReader + + +Classes +------- + +.. autoapisummary:: + + sorcha.readers.OrbitAuxReader.OrbitAuxReader + + +Module Contents +--------------- + +.. py:class:: OrbitAuxReader(filename, sep='csv', header=-1, **kwargs) + + Bases: :py:obj:`sorcha.readers.CSVReader.CSVDataReader` + + + A class to read in the auxiliary orbit data files. + + + .. py:method:: get_reader_info() + + Return a string identifying the current reader name + and input information (for logging and output). + + :returns: The reader information. + :rtype: string + + + + .. py:method:: _process_and_validate_input_table(input_table, **kwargs) + + Perform any input-specific processing and validation on the input table. + Modifies the input dataframe in place. + + :param input_table: A loaded table. + :type input_table: pandas dataframe + :param \*\*kwargs: + :type \*\*kwargs: dictionary, optional + + :returns: **res_df** -- Returns the input dataframe modified in-place. + :rtype: pandas dataframe + + .. rubric:: Notes + + The base implementation includes filtering that is common to most + input types. Subclasses should call super.process_and_validate() + to ensure that the ancestor’s validation is also applied. + + + diff --git a/docs/autoapi/sorcha/readers/index.rst b/docs/autoapi/sorcha/readers/index.rst new file mode 100644 index 00000000..964bd5a6 --- /dev/null +++ b/docs/autoapi/sorcha/readers/index.rst @@ -0,0 +1,21 @@ +sorcha.readers +============== + +.. py:module:: sorcha.readers + + +Submodules +---------- + +.. toctree:: + :maxdepth: 1 + + /autoapi/sorcha/readers/CSVReader/index + /autoapi/sorcha/readers/CombinedDataReader/index + /autoapi/sorcha/readers/DatabaseReader/index + /autoapi/sorcha/readers/EphemerisReader/index + /autoapi/sorcha/readers/HDF5Reader/index + /autoapi/sorcha/readers/ObjectDataReader/index + /autoapi/sorcha/readers/OrbitAuxReader/index + + diff --git a/docs/autoapi/sorcha/sorcha/index.rst b/docs/autoapi/sorcha/sorcha/index.rst new file mode 100644 index 00000000..688d1726 --- /dev/null +++ b/docs/autoapi/sorcha/sorcha/index.rst @@ -0,0 +1,56 @@ +sorcha.sorcha +============= + +.. py:module:: sorcha.sorcha + + +Functions +--------- + +.. autoapisummary:: + + sorcha.sorcha.cite + sorcha.sorcha.mem + sorcha.sorcha.runLSSTSimulation + + +Module Contents +--------------- + +.. py:function:: cite() + + Providing the bibtex, AAS Journals software latex command, and acknowledgement + statements for Sorcha and the associated packages that power it. + + :param None: + + :rtype: None + + +.. py:function:: mem(df) + + Memory utility function that returns back how much memory the inputted pandas dataframe is using + :param df: + :type df: pandas dataframe + + :returns: **usage** + :rtype: int + + +.. py:function:: runLSSTSimulation(args, sconfigs) + + Runs the post processing survey simulator functions that apply a series of + filters to bias a model Solar System small body population to what the + Vera C. Rubin Observatory Legacy Survey of Space and Time would observe. + + :param args: dictionary of command-line arguments. + :type args: dictionary or `sorchaArguments` object + :param pplogger: The logger to use in this function. If None creates a new one. + Default = None + :type pplogger: logging.Logger, optional + :param sconfigs: Dataclass of configuration file arguments. + :type sconfigs: dataclass + + :rtype: None. + + diff --git a/docs/autoapi/sorcha/utilities/check_output_logs/index.rst b/docs/autoapi/sorcha/utilities/check_output_logs/index.rst new file mode 100644 index 00000000..a81214af --- /dev/null +++ b/docs/autoapi/sorcha/utilities/check_output_logs/index.rst @@ -0,0 +1,58 @@ +sorcha.utilities.check_output_logs +================================== + +.. py:module:: sorcha.utilities.check_output_logs + + +Functions +--------- + +.. autoapisummary:: + + sorcha.utilities.check_output_logs.find_all_log_files + sorcha.utilities.check_output_logs.check_all_logs + sorcha.utilities.check_output_logs.check_output_logs + + +Module Contents +--------------- + +.. py:function:: find_all_log_files(filepath) + + Looks for all Sorcha log files in the given filepath and subdirectories + recursively. Specifically searches for files ending *sorcha.log. + + :param filepath: Filepath of top-level directory within which to search for Sorcha log files. + :type filepath: str + + :returns: **log_files** -- A list of the discovered log files (absolute paths) + :rtype: list + + +.. py:function:: check_all_logs(log_files) + + Checks the last line of all the log files supplied and checks to see + if the Sorcha run completed successfully, saving the last line of the log + in question if it did not. + + :param log_files: A list of filepaths pointing to Sorcha log files. + :type log_files: list + + :returns: * **good_log** (*list of Booleans*) -- A list of whether each log file was deemed to be successful or not + * **last_lines** (*list of str*) -- A list of the last lines of unsuccessful Sorcha runs. + + +.. py:function:: check_output_logs(filepath, output=False) + + Searches directories recursively for Sorcha log files, classifies them as + belonging to successful or unsuccessful Sorcha runs, and provides this information + to the user. This is helpful in cases where several runs of Sorcha are being + performed simultaneously (i.e. on a supercomputer). Can output either a .csv + file or straight to the terminal. + + :param filepath: Filepath of top-level directory within which to search for Sorcha log files. + :type filepath: str + :param output: Either the filepath/name in which to save output, or False to print output to terminal. Default=False. + :type output: str or bool + + diff --git a/docs/autoapi/sorcha/utilities/citation_text/index.rst b/docs/autoapi/sorcha/utilities/citation_text/index.rst new file mode 100644 index 00000000..eb5d3660 --- /dev/null +++ b/docs/autoapi/sorcha/utilities/citation_text/index.rst @@ -0,0 +1,27 @@ +sorcha.utilities.citation_text +============================== + +.. py:module:: sorcha.utilities.citation_text + + +Functions +--------- + +.. autoapisummary:: + + sorcha.utilities.citation_text.cite_sorcha + + +Module Contents +--------------- + +.. py:function:: cite_sorcha() + + Providing the bibtex, AAS Journals software latex command, and acknowledgement + statements for Sorcha and the associated packages that power it. + + :param None: + + :rtype: None + + diff --git a/docs/autoapi/sorcha/utilities/createResultsSQLDatabase/index.rst b/docs/autoapi/sorcha/utilities/createResultsSQLDatabase/index.rst new file mode 100644 index 00000000..a7f8b6cd --- /dev/null +++ b/docs/autoapi/sorcha/utilities/createResultsSQLDatabase/index.rst @@ -0,0 +1,77 @@ +sorcha.utilities.createResultsSQLDatabase +========================================= + +.. py:module:: sorcha.utilities.createResultsSQLDatabase + + +Functions +--------- + +.. autoapisummary:: + + sorcha.utilities.createResultsSQLDatabase.create_results_table + sorcha.utilities.createResultsSQLDatabase.create_inputs_table + sorcha.utilities.createResultsSQLDatabase.create_results_database + sorcha.utilities.createResultsSQLDatabase.get_column_names + + +Module Contents +--------------- + +.. py:function:: create_results_table(cnx_out, filename, output_path, output_stem, table_name='sorcha_results') + + Creates a table in a SQLite database from SSPP results. + + :param cnx_out: Connection to sqlite3 database. + :type cnx_out: sqlite3 connection + :param filename: filepath/name of sqlite3 database. + :type filename: string + :param output_path: filepath of directory containing SSPP output folders. + :type output_path: string + :param output_stem: stem filename for SSPP outputs. + :type output_stem: string + :param table_name: name of table of for storing sorcha results. Default ="sorcha_results" + :type table_name: string, optional + + :rtype: None + + +.. py:function:: create_inputs_table(cnx_out, input_path, table_type) + + Creates a table in a SQLite database from the input files (i.e. orbits, + physical parameters, etc). + + :param cnx_out: Connection to sqlite3 database. + :type cnx_out: sqlite3 connection + :param input_path: Filepath of directory containing input files. + :type input_path: string + :param table_type: Type of file. Should be "orbits"/"params"/"complex". + :type table_type: string + + :rtype: None + + +.. py:function:: create_results_database(args) + + Creates a SQLite database with tables of SSPP results and all orbit/physical + parameters/comet files. + + :param args: argparse ArgumentParser object; command line arguments. + :type args: ArgumentParser + + :rtype: None + + +.. py:function:: get_column_names(filename, table_name='sorcha_results') + + Obtains column names from a table in a SQLite database. + + :param filename: Filepath/name of sqlite3 database. + :type filename: string + :param table_name: Name of table. Default = "sorcha_results" + :type table_name: string, optional + + :returns: **col_names (list)** + :rtype: list of column names. + + diff --git a/docs/autoapi/sorcha/utilities/dataUtilitiesForTests/index.rst b/docs/autoapi/sorcha/utilities/dataUtilitiesForTests/index.rst new file mode 100644 index 00000000..85179bd8 --- /dev/null +++ b/docs/autoapi/sorcha/utilities/dataUtilitiesForTests/index.rst @@ -0,0 +1,33 @@ +sorcha.utilities.dataUtilitiesForTests +====================================== + +.. py:module:: sorcha.utilities.dataUtilitiesForTests + +.. autoapi-nested-parse:: + + This package contains all of sorcha's test data. + + + +Functions +--------- + +.. autoapisummary:: + + sorcha.utilities.dataUtilitiesForTests.get_test_filepath + + +Module Contents +--------------- + +.. py:function:: get_test_filepath(filename) + + Return the full path to a test file in the ``.../tests/data`` directory. + + :param filename: The name of the file inside the ``tests/data`` directory. + :type filename: string + + :returns: The full path to the file. + :rtype: string + + diff --git a/docs/autoapi/sorcha/utilities/diffTestUtils/index.rst b/docs/autoapi/sorcha/utilities/diffTestUtils/index.rst new file mode 100644 index 00000000..3f152961 --- /dev/null +++ b/docs/autoapi/sorcha/utilities/diffTestUtils/index.rst @@ -0,0 +1,69 @@ +sorcha.utilities.diffTestUtils +============================== + +.. py:module:: sorcha.utilities.diffTestUtils + + +Attributes +---------- + +.. autoapisummary:: + + sorcha.utilities.diffTestUtils.BASELINE_ARGS + sorcha.utilities.diffTestUtils.WITH_EPHEMERIS_ARGS + sorcha.utilities.diffTestUtils.CHUNKED_ARGS + sorcha.utilities.diffTestUtils.UNCHUNKED_ARGS + sorcha.utilities.diffTestUtils.VERIFICATION_TRUTH + + +Functions +--------- + +.. autoapisummary:: + + sorcha.utilities.diffTestUtils.compare_result_files + sorcha.utilities.diffTestUtils.override_seed_and_run + + +Module Contents +--------------- + +.. py:function:: compare_result_files(test_output, golden_output) + + Compare the results in test_output to those in golden_output. + + :param test_output: The path and file name of the test results. + :type test_output: string + :param golden_output: The path and file name of the golden set results. + :type golden_output: string + + :returns: Indicates whether the results are the same. + :rtype: bool + + +.. py:data:: BASELINE_ARGS + +.. py:data:: WITH_EPHEMERIS_ARGS + +.. py:data:: CHUNKED_ARGS + +.. py:data:: UNCHUNKED_ARGS + +.. py:data:: VERIFICATION_TRUTH + +.. py:function:: override_seed_and_run(outpath, arg_set='baseline') + + Run the full Rubin sim on the demo data and a fixed seed. + + WARNING: Never use a fixed seed for scientific analysis. This is + for testing purposes only. + + :param outpath: The path for the output files. + :type outpath: string + :param arg_set: set of arguments for setting up the run. Options: "baseline" or "with_ephemeris". + "baseline"" run does not ephemeris generation. "with_ephemeeris" is a full end to end run + of all main components of sorcha. + Default = "baseline" + :type arg_set: string, optional + + diff --git a/docs/autoapi/sorcha/utilities/generateGoldens/index.rst b/docs/autoapi/sorcha/utilities/generateGoldens/index.rst new file mode 100644 index 00000000..fd15dfe0 --- /dev/null +++ b/docs/autoapi/sorcha/utilities/generateGoldens/index.rst @@ -0,0 +1,19 @@ +sorcha.utilities.generateGoldens +================================ + +.. py:module:: sorcha.utilities.generateGoldens + + +Attributes +---------- + +.. autoapisummary:: + + sorcha.utilities.generateGoldens.golden_dir + + +Module Contents +--------------- + +.. py:data:: golden_dir + diff --git a/docs/autoapi/sorcha/utilities/generate_meta_kernel/index.rst b/docs/autoapi/sorcha/utilities/generate_meta_kernel/index.rst new file mode 100644 index 00000000..abd0b20d --- /dev/null +++ b/docs/autoapi/sorcha/utilities/generate_meta_kernel/index.rst @@ -0,0 +1,49 @@ +sorcha.utilities.generate_meta_kernel +===================================== + +.. py:module:: sorcha.utilities.generate_meta_kernel + + +Functions +--------- + +.. autoapisummary:: + + sorcha.utilities.generate_meta_kernel.build_meta_kernel_file + sorcha.utilities.generate_meta_kernel._build_file_name + + +Module Contents +--------------- + +.. py:function:: build_meta_kernel_file(auxconfigs, retriever: pooch.Pooch) -> None + + Builds a specific text file that will be fed into `spiceypy` that defines + the list of spice kernel to load, as well as the order to load them. + + :param retriever: Pooch object that maintains the registry of files to download + :type retriever: pooch + :param auxconfigs: Dataclass of auxiliary configuration file arguments. + :type auxconfigs: dataclass + + :rtype: None + + +.. py:function:: _build_file_name(cache_dir: str, file_path: str) -> str + + Given a string defining the cache directory, and a string defining the full + path to a given file. This function will strip out the cache directory from + the file path and replace it with the required meta_kernel directory + substitution character. + + :param cache_dir: The full path to the cache directory used when retrieving files for Assist + and Rebound. + :type cache_dir: string + :param file_path: The full file path for a given file that will have the cache directory + segment replace. + :type file_path: string + + :returns: Shortened file path, appropriate for use in kernel_meta files. + :rtype: string + + diff --git a/docs/autoapi/sorcha/utilities/index.rst b/docs/autoapi/sorcha/utilities/index.rst new file mode 100644 index 00000000..92b373a1 --- /dev/null +++ b/docs/autoapi/sorcha/utilities/index.rst @@ -0,0 +1,27 @@ +sorcha.utilities +================ + +.. py:module:: sorcha.utilities + + +Submodules +---------- + +.. toctree:: + :maxdepth: 1 + + /autoapi/sorcha/utilities/check_output_logs/index + /autoapi/sorcha/utilities/citation_text/index + /autoapi/sorcha/utilities/createResultsSQLDatabase/index + /autoapi/sorcha/utilities/dataUtilitiesForTests/index + /autoapi/sorcha/utilities/diffTestUtils/index + /autoapi/sorcha/utilities/generateGoldens/index + /autoapi/sorcha/utilities/generate_meta_kernel/index + /autoapi/sorcha/utilities/retrieve_ephemeris_data_files/index + /autoapi/sorcha/utilities/sorchaArguments/index + /autoapi/sorcha/utilities/sorchaConfigs/index + /autoapi/sorcha/utilities/sorcha_copy_configs/index + /autoapi/sorcha/utilities/sorcha_copy_demo_files/index + /autoapi/sorcha/utilities/sorcha_demo_command/index + + diff --git a/docs/autoapi/sorcha/utilities/retrieve_ephemeris_data_files/index.rst b/docs/autoapi/sorcha/utilities/retrieve_ephemeris_data_files/index.rst new file mode 100644 index 00000000..be51ef33 --- /dev/null +++ b/docs/autoapi/sorcha/utilities/retrieve_ephemeris_data_files/index.rst @@ -0,0 +1,62 @@ +sorcha.utilities.retrieve_ephemeris_data_files +============================================== + +.. py:module:: sorcha.utilities.retrieve_ephemeris_data_files + + +Functions +--------- + +.. autoapisummary:: + + sorcha.utilities.retrieve_ephemeris_data_files._decompress + sorcha.utilities.retrieve_ephemeris_data_files._remove_files + sorcha.utilities.retrieve_ephemeris_data_files._check_for_existing_files + + +Module Contents +--------------- + +.. py:function:: _decompress(fname, action, pup) + + Override the functionality of Pooch's `Decompress` class so that the resulting + decompressed file uses the original file name without the compression extension. + For instance `filename.json.bz` will be decompressed and saved as `filename.json`. + + :param fname: Original filename + :type fname: string + :param action: One of []"download", "update", "fetch"] + :type action: string + :param pup: The Pooch object that defines the location of the file. + :type pup: pooch + + :rtype: None + + +.. py:function:: _remove_files(auxconfigs, retriever: pooch.Pooch) -> None + + Utility to remove all the files tracked by the pooch retriever. This includes + the decompressed ObservatoryCodes.json file as well as the META_KERNEL file + that are created after downloading the files in the DATA_FILES_TO_DOWNLOAD + list. + + :param retriever: Pooch object that maintains the registry of files to download. + :type retriever: pooch + :param auxconfigs: Dataclass of auxiliary configuration file arguments. + :type auxconfigs: dataclass + + +.. py:function:: _check_for_existing_files(retriever: pooch.Pooch, file_list: list[str]) -> bool + + Will check for existing local files, any file not found will be printed + to the terminal. + + :param retriever: Pooch object that maintains the registry of files to download. + :type retriever: pooch + :param file_list: A list of file names look for in the local cache. + :type file_list: list of strings + + :returns: Returns True if all files are found in the local cache, False otherwise. + :rtype: bool + + diff --git a/docs/autoapi/sorcha/utilities/sorchaArguments/index.rst b/docs/autoapi/sorcha/utilities/sorchaArguments/index.rst new file mode 100644 index 00000000..5184dc70 --- /dev/null +++ b/docs/autoapi/sorcha/utilities/sorchaArguments/index.rst @@ -0,0 +1,130 @@ +sorcha.utilities.sorchaArguments +================================ + +.. py:module:: sorcha.utilities.sorchaArguments + + +Classes +------- + +.. autoapisummary:: + + sorcha.utilities.sorchaArguments.sorchaArguments + + +Module Contents +--------------- + +.. py:class:: sorchaArguments(cmd_args_dict=None) + + Data class for holding runtime arguments + + + .. py:attribute:: paramsinput + :type: str + :value: '' + + + path to file with input objects + + + .. py:attribute:: orbinfile + :type: str + :value: '' + + + path to file with input object orbits + + + .. py:attribute:: input_ephemeris_file + :type: str + :value: '' + + + path the ephemeris input file + + + .. py:attribute:: configfile + :type: str + :value: '' + + + path to the config.ini file + + + .. py:attribute:: outpath + :type: str + :value: '' + + + path where data should be output + + + .. py:attribute:: outfilestem + :type: str + :value: '' + + + file system for output + + + .. py:attribute:: loglevel + :type: bool + :value: False + + + logger verbosity + + + .. py:attribute:: surveyname + :type: str + :value: '' + + + name of the survey (`rubin_sim` is only one implemented currently) + + + .. py:attribute:: complex_parameters + :type: str + :value: '' + + + optional, extra complex physical parameter input files + + + .. py:attribute:: linking + :type: bool + :value: True + + + Turns on or off the rejection of unlinked sources + + + .. py:attribute:: _rngs + :value: None + + + A collection of per-module random number generators + + + .. py:attribute:: pplogger + :value: None + + + The Python logger instance + + + .. py:method:: read_from_dict(args) + + set the parameters from a cmd_args dict. + + :param aguments: dictionary of configuration parameters + :type aguments: dictionary + + :rtype: None + + + + .. py:method:: validate_arguments() + + diff --git a/docs/autoapi/sorcha/utilities/sorchaConfigs/index.rst b/docs/autoapi/sorcha/utilities/sorchaConfigs/index.rst new file mode 100644 index 00000000..99e0ef0a --- /dev/null +++ b/docs/autoapi/sorcha/utilities/sorchaConfigs/index.rst @@ -0,0 +1,1232 @@ +sorcha.utilities.sorchaConfigs +============================== + +.. py:module:: sorcha.utilities.sorchaConfigs + + +Classes +------- + +.. autoapisummary:: + + sorcha.utilities.sorchaConfigs.inputConfigs + sorcha.utilities.sorchaConfigs.simulationConfigs + sorcha.utilities.sorchaConfigs.filtersConfigs + sorcha.utilities.sorchaConfigs.saturationConfigs + sorcha.utilities.sorchaConfigs.phasecurvesConfigs + sorcha.utilities.sorchaConfigs.fovConfigs + sorcha.utilities.sorchaConfigs.fadingfunctionConfigs + sorcha.utilities.sorchaConfigs.linkingfilterConfigs + sorcha.utilities.sorchaConfigs.outputConfigs + sorcha.utilities.sorchaConfigs.lightcurveConfigs + sorcha.utilities.sorchaConfigs.activityConfigs + sorcha.utilities.sorchaConfigs.expertConfigs + sorcha.utilities.sorchaConfigs.auxiliaryConfigs + sorcha.utilities.sorchaConfigs.sorchaConfigs + + +Functions +--------- + +.. autoapisummary:: + + sorcha.utilities.sorchaConfigs.check_key_exists + sorcha.utilities.sorchaConfigs.check_key_doesnt_exist + sorcha.utilities.sorchaConfigs.cast_as_int + sorcha.utilities.sorchaConfigs.cast_as_float + sorcha.utilities.sorchaConfigs.cast_as_bool + sorcha.utilities.sorchaConfigs.check_value_in_list + sorcha.utilities.sorchaConfigs.PPFindFileOrExit + sorcha.utilities.sorchaConfigs.cast_as_bool_or_set_default + sorcha.utilities.sorchaConfigs.PrintConfigsToLog + + +Module Contents +--------------- + +.. py:class:: inputConfigs + + Data class for holding INPUTS section configuration file keys and validating them. + + + .. py:attribute:: ephemerides_type + :type: str + :value: None + + + Simulation used for ephemeris input. + + + .. py:attribute:: eph_format + :type: str + :value: None + + + Format for ephemeris simulation input file. + + + .. py:attribute:: size_serial_chunk + :type: int + :value: None + + + Sorcha chunk size. + + + .. py:attribute:: aux_format + :type: str + :value: None + + + Format for the auxiliary input files. + + + .. py:attribute:: pointing_sql_query + :type: str + :value: None + + + SQL query for extracting data from pointing database. + + + .. py:method:: __post_init__() + + Automagically validates the input configs after initialisation. + + + + .. py:method:: _validate_input_configs() + + Validates the input config attributes after initialisation. + + :param None.: + + :rtype: None + + + +.. py:class:: simulationConfigs + + Data class for holding SIMULATION section configuration file keys and validating them + + + .. py:attribute:: ar_ang_fov + :type: float + :value: None + + + the field of view of our search field, in degrees + + + .. py:attribute:: ar_fov_buffer + :type: float + :value: None + + + the buffer zone around the field of view we want to include, in degrees + + + .. py:attribute:: ar_picket + :type: float + :value: None + + + imprecise discretization of time that allows us to move progress our simulations forward without getting too granular when we don't have to. the unit is number of days. + + + .. py:attribute:: ar_obs_code + :type: str + :value: None + + + the obscode is the MPC observatory code for the provided telescope. + + + .. py:attribute:: ar_healpix_order + :type: int + :value: None + + + the order of healpix which we will use for the healpy portions of the code. + + + .. py:attribute:: _ephemerides_type + :type: str + :value: None + + + Simulation used for ephemeris input. + + + .. py:method:: __post_init__() + + Automagically validates the simulation configs after initialisation. + + + + .. py:method:: _validate_simulation_configs() + + Validates the simulation config attributes after initialisation. + + :param None.: + + :rtype: None + + + +.. py:class:: filtersConfigs + + Data class for holding FILTERS section configuration file keys and validating them + + + .. py:attribute:: observing_filters + :type: str + :value: None + + + Filters of the observations you are interested in, comma-separated. + + + .. py:attribute:: survey_name + :type: str + :value: None + + + survey name to be used for checking filters are correct + + + .. py:attribute:: mainfilter + :type: str + :value: None + + + main filter chosen in physical parameter file + + + .. py:attribute:: othercolours + :type: str + :value: None + + + other filters given alongside main filter + + + .. py:method:: __post_init__() + + Automagically validates the filters configs after initialisation. + + + + .. py:method:: _validate_filters_configs() + + Validates the filters config attributes after initialisation. + + :param None.: + + :rtype: None + + + + .. py:method:: _check_for_correct_filters() + + Checks the filters selected are used by the chosen survey. + + :param None.: + + :rtype: None + + + +.. py:class:: saturationConfigs + + Data class for holding SATURATION section configuration file keys and validating them + + + .. py:attribute:: bright_limit_on + :type: bool + :value: None + + + + .. py:attribute:: bright_limit + :type: float + :value: None + + + Upper magnitude limit on sources that will overfill the detector pixels/have counts above the non-linearity regime of the pixels where one can’t do photometry. Objects brighter than this limit (in magnitude) will be cut. + + + .. py:attribute:: _observing_filters + :type: list + :value: None + + + Filters of the observations you are interested in, comma-separated. + + + .. py:method:: __post_init__() + + Automagically validates the saturation configs after initialisation. + + + + .. py:method:: _validate_saturation_configs() + + Validates the saturation config attributes after initialisation. + + :param None.: + + :rtype: None + + + +.. py:class:: phasecurvesConfigs + + Data class for holding PHASECURVES section configuration file keys and validating them + + + .. py:attribute:: phase_function + :type: str + :value: None + + + The phase function used to calculate apparent magnitude. The physical parameters input + + + .. py:method:: __post_init__() + + Automagically validates the phasecurve configs after initialisation. + + + + .. py:method:: _validate_phasecurve_configs() + + Validates the phasecurve config attributes after initialisation. + + :param None.: + + :rtype: None + + + +.. py:class:: fovConfigs + + Data class for holding FOV section configuration file keys and validating them + + + .. py:attribute:: camera_model + :type: str + :value: None + + + Choose between circular or actual camera footprint, including chip gaps. + + + .. py:attribute:: footprint_path + :type: str + :value: None + + + Path to camera footprint file. Uncomment to provide a path to the desired camera detector configuration file if not using the default built-in LSSTCam detector configuration for the actual camera footprint. + + + .. py:attribute:: fill_factor + :type: str + :value: None + + + Fraction of detector surface area which contains CCD -- simulates chip gaps for OIF output. Comment out if using camera footprint. + + + .. py:attribute:: circle_radius + :type: float + :value: None + + + Radius of the circle for a circular footprint (in degrees). Float. Comment out or do not include if using footprint camera model. + + + .. py:attribute:: footprint_edge_threshold + :type: float + :value: None + + + The distance from the edge of a detector (in arcseconds on the focal plane) at which we will not correctly extract an object. By default this is 10px or 2 arcseconds. Comment out or do not include if not using footprint camera model. + + + .. py:attribute:: survey_name + :type: str + :value: None + + + name of survey + + + .. py:method:: __post_init__() + + Automagically validates the fov configs after initialisation. + + + + .. py:method:: _validate_fov_configs() + + Validates the fov config attributes after initialisation. + + :param None.: + + :rtype: None + + + + .. py:method:: _camera_footprint() + + Validates the fov config attributes for a footprint camera model. + + :param None.: + + :rtype: None + + + + .. py:method:: _camera_circle() + + Validates the fov config attributes for a circle camera model. + + :param None.: + + :rtype: None + + + +.. py:class:: fadingfunctionConfigs + + Data class for holding FADINGFUNCTION section configuration file keys and validating them + + + .. py:attribute:: fading_function_on + :type: bool + :value: None + + + Detection efficiency fading function on or off. + + + .. py:attribute:: fading_function_width + :type: float + :value: None + + + Width parameter for fading function. Should be greater than zero and less than 0.5. + + + .. py:attribute:: fading_function_peak_efficiency + :type: float + :value: None + + + Peak efficiency for the fading function, called the 'fill factor' in Chesley and Veres (2017). + + + .. py:method:: __post_init__() + + Automagically validates the fading function configs after initialisation. + + + + .. py:method:: _validate_fadingfunction_configs() + + Validates the fadindfunction config attributes after initialisation. + + :param None.: + + :rtype: None + + + +.. py:class:: linkingfilterConfigs + + Data class for holding LINKINGFILTER section configuration file keys and validating them. + + + .. py:attribute:: ssp_linking_on + :type: bool + :value: None + + + flag to see if model should run ssp linking filter + + + .. py:attribute:: drop_unlinked + :type: bool + :value: None + + + Decides if unlinked objects will be dropped. + + + .. py:attribute:: ssp_detection_efficiency + :type: float + :value: None + + + ssp detection efficiency. Which fraction of the observations of an object will the automated solar system processing pipeline successfully link? Float. + + + .. py:attribute:: ssp_number_observations + :type: int + :value: None + + + Length of tracklets. How many observations of an object during one night are required to produce a valid tracklet? + + + .. py:attribute:: ssp_separation_threshold + :type: float + :value: None + + + Minimum separation (in arcsec) between two observations of an object required for the linking software to distinguish them as separate and therefore as a valid tracklet. + + + .. py:attribute:: ssp_maximum_time + :type: float + :value: None + + + Maximum time separation (in days) between subsequent observations in a tracklet. Default is 0.0625 days (90mins). + + + .. py:attribute:: ssp_number_tracklets + :type: int + :value: None + + + Number of tracklets for detection. How many tracklets are required to classify an object as detected? + + + .. py:attribute:: ssp_track_window + :type: int + :value: None + + + The number of tracklets defined above must occur in <= this number of days to constitute a complete track/detection. + + + .. py:attribute:: ssp_night_start_utc + :type: float + :value: None + + + The time in UTC at which it is noon at the observatory location (in standard time). For the LSST, 12pm Chile Standard Time is 4pm UTC. + + + .. py:method:: __post_init__() + + Automagically validates the linking filter configs after initialisation. + + + + .. py:method:: _validate_linkingfilter_configs() + + Validates the linkingfilter config attributes after initialisation. + + :param None.: + + :rtype: None + + + +.. py:class:: outputConfigs + + Data class for holding OUTPUT section configuration file keys and validating them. + + + .. py:attribute:: output_format + :type: str + :value: None + + + Output format of the output file[s] + + + .. py:attribute:: output_columns + :type: str + :value: None + + + Controls which columns are in the output files. + + + .. py:attribute:: position_decimals + :type: float + :value: None + + + position decimal places + + + .. py:attribute:: magnitude_decimals + :type: float + :value: None + + + magnitude decimal places + + + .. py:method:: __post_init__() + + Automagically validates the output configs after initialisation. + + + + .. py:method:: _validate_output_configs() + + Validates the output config attributes after initialisation. + + :param None.: + + :rtype: None + + + + .. py:method:: _validate_decimals() + + Validates the decimal output config attributes after initialisation. + + :param None.: + + :rtype: None + + + +.. py:class:: lightcurveConfigs + + Data class for holding LIGHTCURVE section configuration file keys and validating them. + + + .. py:attribute:: lc_model + :type: str + :value: None + + + The unique name of the lightcurve model to use. Defined in the ``name_id`` method of the subclasses of AbstractLightCurve. If not none, the complex physical parameters file must be specified at the command line.lc_model = none + + + .. py:method:: __post_init__() + + Automagically validates the lightcurve configs after initialisation. + + + + .. py:method:: _validate_lightcurve_configs() + + Validates the lightcurve config attributes after initialisation. + + :param None.: + + :rtype: None + + + +.. py:class:: activityConfigs + + Data class for holding Activity section configuration file keys and validating them. + + + .. py:attribute:: comet_activity + :type: str + :value: None + + + The unique name of the actvity model to use. Defined in the ``name_id`` method of the subclasses of AbstractCometaryActivity. If not none, a complex physical parameters file must be specified at the command line. + + + .. py:method:: __post_init__() + + Automagically validates the activity configs after initialisation. + + + + .. py:method:: _validate_activity_configs() + + Validates the activity config attributes after initialisation. + + :param None.: + + :rtype: None + + + +.. py:class:: expertConfigs + + Data class for holding expert section configuration file keys and validating them. + + + .. py:attribute:: SNR_limit + :type: float + :value: None + + + Drops observations with signal to noise ratio less than limit given + + + .. py:attribute:: SNR_limit_on + :type: bool + :value: None + + + flag for when an SNR limit is given + + + .. py:attribute:: mag_limit + :type: float + :value: None + + + Drops observations with magnitude less than limit given + + + .. py:attribute:: mag_limit_on + :type: bool + :value: None + + + flag for when a magnitude limit is given + + + .. py:attribute:: trailing_losses_on + :type: bool + :value: None + + + flag for trailing losses + + + .. py:attribute:: default_SNR_cut + :type: bool + :value: None + + + flag for default SNR + + + .. py:attribute:: randomization_on + :type: bool + :value: None + + + flag for randomizing astrometry and photometry + + + .. py:attribute:: vignetting_on + :type: bool + :value: None + + + flag for calculating effects of vignetting on limiting magnitude + + + .. py:method:: __post_init__() + + Automagically validates the expert configs after initialisation. + + + + .. py:method:: _validate_expert_configs() + + Validates the expert config attributes after initialisation. + + :param None.: + + :rtype: None + + + +.. py:class:: auxiliaryConfigs + + .. py:attribute:: de440s + :type: str + :value: 'de440s.bsp' + + + filename of de440s + + + .. py:attribute:: de440s_url + :type: str + :value: 'https://naif.jpl.nasa.gov/pub/naif/generic_kernels/spk/planets/de440s.bsp' + + + url for de4440s + + + .. py:attribute:: earth_predict + :type: str + :value: 'earth_200101_990827_predict.bpc' + + + filename of earth_predict + + + .. py:attribute:: earth_predict_url + :type: str + :value: 'https://naif.jpl.nasa.gov/pub/naif/generic_kernels/pck/earth_200101_990827_predict.bpc' + + + url for earth_predict + + + .. py:attribute:: earth_historical + :type: str + :value: 'earth_620120_240827.bpc' + + + filename of earth_histoical + + + .. py:attribute:: earth_historical_url + :type: str + :value: 'https://naif.jpl.nasa.gov/pub/naif/generic_kernels/pck/earth_620120_240827.bpc' + + + url for earth_historical + + + .. py:attribute:: earth_high_precision + :type: str + :value: 'earth_latest_high_prec.bpc' + + + filename of earth_high_precision + + + .. py:attribute:: earth_high_precision_url + :type: str + :value: 'https://naif.jpl.nasa.gov/pub/naif/generic_kernels/pck/earth_latest_high_prec.bpc' + + + url of earth_high_precision + + + .. py:attribute:: jpl_planets + :type: str + :value: 'linux_p1550p2650.440' + + + filename of jpl_planets + + + .. py:attribute:: jpl_planets_url + :type: str + :value: 'https://ssd.jpl.nasa.gov/ftp/eph/planets/Linux/de440/linux_p1550p2650.440' + + + url of jpl_planets + + + .. py:attribute:: jpl_small_bodies + :type: str + :value: 'sb441-n16.bsp' + + + filename of jpl_small_bodies + + + .. py:attribute:: jpl_small_bodies_url + :type: str + :value: 'https://ssd.jpl.nasa.gov/ftp/eph/small_bodies/asteroids_de441/sb441-n16.bsp' + + + url of jpl_small_bodies + + + .. py:attribute:: leap_seconds + :type: str + :value: 'naif0012.tls' + + + filename of leap_seconds + + + .. py:attribute:: leap_seconds_url + :type: str + :value: 'https://naif.jpl.nasa.gov/pub/naif/generic_kernels/lsk/naif0012.tls' + + + url of leap_seconds + + + .. py:attribute:: meta_kernel + :type: str + :value: 'meta_kernel.txt' + + + filename of meta_kernal + + + .. py:attribute:: observatory_codes + :type: str + :value: 'ObsCodes.json' + + + filename of observatory_codes + + + .. py:attribute:: observatory_codes_compressed + :type: str + :value: 'ObsCodes.json.gz' + + + filename of observatory_codes_compressed + + + .. py:attribute:: observatory_codes_compressed_url + :type: str + :value: 'https://minorplanetcenter.net/Extended_Files/obscodes_extended.json.gz' + + + url of observatory_codes_compressed + + + .. py:attribute:: orientation_constants + :type: str + :value: 'pck00010.pck' + + + filename of observatory_constants + + + .. py:attribute:: orientation_constants_url + :type: str + :value: 'https://naif.jpl.nasa.gov/pub/naif/generic_kernels/pck/pck00010.tpc' + + + url of observatory_constants + + + .. py:attribute:: data_file_list + :type: list + :value: None + + + convenience list of all the file names + + + .. py:attribute:: urls + :type: dict + :value: None + + + url + + :type: dictionary of filename + + + .. py:attribute:: data_files_to_download + :type: list + :value: None + + + list of files that need to be downloaded + + + .. py:attribute:: ordered_kernel_files + :type: list + :value: None + + + list of kernels ordered from least to most precise - used to assemble meta_kernel file + + + .. py:attribute:: registry + :type: list + :value: None + + + Default Pooch registry to define which files will be tracked and retrievable + + + .. py:property:: default_url + + returns a dictionary of the default urls used in this version of sorcha + + + .. py:property:: default_filenames + + returns a dictionary of the default filenames used in this version + + + .. py:method:: __post_init__() + + Automagically validates the auxiliary configs after initialisation. + + + + .. py:method:: _validate_auxiliary_configs() + + validates the auxililary config attributes after initialisation. + + + + .. py:method:: _create_lists_auxiliary_configs() + + creates lists of the auxililary config attributes after initialisation. + + :param None.: + + :rtype: None + + + +.. py:class:: sorchaConfigs(config_file_location=None, survey_name=None) + + Dataclass which stores configuration file keywords in dataclasses. + + + .. py:attribute:: input + :type: inputConfigs + :value: None + + + inputConfigs dataclass which stores the keywords from the INPUT section of the config file. + + + .. py:attribute:: simulation + :type: simulationConfigs + :value: None + + + simulationConfigs dataclass which stores the keywords from the SIMULATION section of the config file. + + + .. py:attribute:: filters + :type: filtersConfigs + :value: None + + + filtersConfigs dataclass which stores the keywords from the FILTERS section of the config file. + + + .. py:attribute:: saturation + :type: saturationConfigs + :value: None + + + saturationConfigs dataclass which stores the keywords from the SATURATION section of the config file. + + + .. py:attribute:: phasecurves + :type: phasecurvesConfigs + :value: None + + + phasecurveConfigs dataclass which stores the keywords from the PHASECURVES section of the config file. + + + .. py:attribute:: fov + :type: fovConfigs + :value: None + + + fovConfigs dataclass which stores the keywords from the FOV section of the config file. + + + .. py:attribute:: fadingfunction + :type: fadingfunctionConfigs + :value: None + + + fadingfunctionConfigs dataclass which stores the keywords from the FADINGFUNCTION section of the config file. + + + .. py:attribute:: linkingfilter + :type: linkingfilterConfigs + :value: None + + + linkingfilterConfigs dataclass which stores the keywords from the LINKINGFILTER section of the config file. + + + .. py:attribute:: output + :type: outputConfigs + :value: None + + + outputConfigs dataclass which stores the keywords from the OUTPUT section of the config file. + + + .. py:attribute:: lightcurve + :type: lightcurveConfigs + :value: None + + + lightcurveConfigs dataclass which stores the keywords from the LIGHTCURVE section of the config file. + + + .. py:attribute:: activity + :type: activityConfigs + :value: None + + + activityConfigs dataclass which stores the keywords from the ACTIVITY section of the config file. + + + .. py:attribute:: expert + :type: expertConfigs + :value: None + + + expertConfigs dataclass which stores the keywords from the EXPERT section of the config file. + + + .. py:attribute:: auxiliary + :type: auxiliaryConfigs + :value: None + + + auxiliaryConfigs dataclass which stores the keywords from the AUXILIARY section of the config file. + + + .. py:attribute:: pplogger + :type: None + :value: None + + + The Python logger instance + + + .. py:attribute:: survey_name + :type: str + :value: '' + + + The name of the survey. + + + .. py:method:: _read_configs_from_object(config_object) + + function that populates the class attributes + + :param config_object: ConfigParser object that has the config file read into it + :type config_object: ConfigParser object + + :rtype: None + + + +.. py:function:: check_key_exists(value, key_name) + + Checks to confirm that a mandatory config file value is present and has been read into the dataclass as truthy. Returns an error if value is falsy + + :param value: value of the config file attribute + :type value: object attribute + :param key_name: The key being checked. + :type key_name: string + + :rtype: None. + + +.. py:function:: check_key_doesnt_exist(value, key_name, reason) + + Checks to confirm that a config file value is not present and has been read into the dataclass as falsy. Returns an error if value is truthy + + :param value: value of the config file attribute + :type value: object attribute + :param key_name: The key being checked. + :type key_name: string + :param reason: reason given in the error message on why this value shouldn't be in the config file + :type reason: string + + :rtype: None. + + +.. py:function:: cast_as_int(value, key) + + Checks to see if value can be cast as an interger. + + :param value: value of the config file attribute + :type value: object attribute + :param key: The key being checked. + :type key: string + + :rtype: value as an integer + + +.. py:function:: cast_as_float(value, key) + + Checks to see if value can be cast as a float. + + :param value: value of the config file attribute + :type value: object attribute + :param key: The key being checked. + :type key: string + + :rtype: value as a float + + +.. py:function:: cast_as_bool(value, key) + + Checks to see if value can be cast as a boolen. + + :param value: value of the config file attribute + :type value: object attribute + :param key: The key being checked. + :type key: string + + :rtype: value as a boolen + + +.. py:function:: check_value_in_list(value, valuelist, key) + + Checks to see if a config variable is in a list of permissible variables. + + :param value: value of the config file value + :type value: object attribute + :param valuelist: list of permissible values for attribute + :type valuelist: list + :param key: The key being checked. + :type key: string + + :rtype: None. + + +.. py:function:: PPFindFileOrExit(arg_fn, argname) + + Checks to see if a file given by a filename exists. If it doesn't, + this fails gracefully and exits to the command line. + + :param arg_fn: The filepath/name of the file to be checked. + :type arg_fn: string + :param argname: The name of the argument being checked. Used for error message. + :type argname: string + + :returns: **arg_fn** -- The filepath/name of the file to be checked. + :rtype: string + + +.. py:function:: cast_as_bool_or_set_default(value, key, default) + + Checks to see if value can be cast as a boolen and if not set (equals None) gives default bool. + + :param value: value of the config file attribute + :type value: object attribute + :param key: The key being checked. + :type key: string + :param default: default bool if value is None + :type default: bool + + :rtype: value as a boolen + + +.. py:function:: PrintConfigsToLog(sconfigs, cmd_args) + + Prints all the values from the config file and command line to the log. + + :param sconfigs: Dataclass of config file variables. + :type sconfigs: dataclass + :param cmd_args: Dictionary of command line arguments. + :type cmd_args: dictionary + + :rtype: None. + + diff --git a/docs/autoapi/sorcha/utilities/sorcha_copy_configs/index.rst b/docs/autoapi/sorcha/utilities/sorcha_copy_configs/index.rst new file mode 100644 index 00000000..e8e8c013 --- /dev/null +++ b/docs/autoapi/sorcha/utilities/sorcha_copy_configs/index.rst @@ -0,0 +1,31 @@ +sorcha.utilities.sorcha_copy_configs +==================================== + +.. py:module:: sorcha.utilities.sorcha_copy_configs + + +Functions +--------- + +.. autoapisummary:: + + sorcha.utilities.sorcha_copy_configs.copy_demo_configs + + +Module Contents +--------------- + +.. py:function:: copy_demo_configs(copy_location, which_configs, force_overwrite) + + Copies the example Sorcha configuration files to a user-specified location. + + :param copy_location: String containing the filepath of the location to which the configuration files should be copied. + :type copy_location: string + :param which_configs: String indicating which configuration files to retrieve. Should be "rubin", "demo" or "all". + :type which_configs: string + :param force_overwrite: Flag for determining whether existing files should be overwritten. + :type force_overwrite: boolean + + :rtype: None + + diff --git a/docs/autoapi/sorcha/utilities/sorcha_copy_demo_files/index.rst b/docs/autoapi/sorcha/utilities/sorcha_copy_demo_files/index.rst new file mode 100644 index 00000000..99a90e11 --- /dev/null +++ b/docs/autoapi/sorcha/utilities/sorcha_copy_demo_files/index.rst @@ -0,0 +1,29 @@ +sorcha.utilities.sorcha_copy_demo_files +======================================= + +.. py:module:: sorcha.utilities.sorcha_copy_demo_files + + +Functions +--------- + +.. autoapisummary:: + + sorcha.utilities.sorcha_copy_demo_files.copy_demo_files + + +Module Contents +--------------- + +.. py:function:: copy_demo_files(copy_location, force_overwrite) + + Copies the files needed to run the Sorcha demo to a user-specified location. + + :param copy_location: String containing the filepath of the location to which the configuration files should be copied. + :type copy_location: string + :param force_overwrite: Flag for determining whether existing files should be overwritten. + :type force_overwrite: boolean + + :rtype: None + + diff --git a/docs/autoapi/sorcha/utilities/sorcha_demo_command/index.rst b/docs/autoapi/sorcha/utilities/sorcha_demo_command/index.rst new file mode 100644 index 00000000..6a8e703e --- /dev/null +++ b/docs/autoapi/sorcha/utilities/sorcha_demo_command/index.rst @@ -0,0 +1,42 @@ +sorcha.utilities.sorcha_demo_command +==================================== + +.. py:module:: sorcha.utilities.sorcha_demo_command + + +Functions +--------- + +.. autoapisummary:: + + sorcha.utilities.sorcha_demo_command.get_demo_command + sorcha.utilities.sorcha_demo_command.print_demo_command + + +Module Contents +--------------- + +.. py:function:: get_demo_command() + + Returns the current working version of the Sorcha demo command as a string. + If the Sorcha run command changes, updating this function will ensure + associated unit tests pass. + + :param None.: + + :returns: working sorcha demo command + :rtype: string + + +.. py:function:: print_demo_command(printall=True) + + Prints the current working version of the Sorcha demo command to the terminal, with + optional functionality to also tell the user how to copy the demo files. + + :param printall: When True, prints the demo command plus the instructions for copying the demo files. + When False, prints the demo command only. + :type printall: boolean + + :rtype: None. + + diff --git a/docs/autoapi/sorcha_cmdline/bootstrap/index.rst b/docs/autoapi/sorcha_cmdline/bootstrap/index.rst new file mode 100644 index 00000000..7e275715 --- /dev/null +++ b/docs/autoapi/sorcha_cmdline/bootstrap/index.rst @@ -0,0 +1,22 @@ +sorcha_cmdline.bootstrap +======================== + +.. py:module:: sorcha_cmdline.bootstrap + + +Functions +--------- + +.. autoapisummary:: + + sorcha_cmdline.bootstrap.main + sorcha_cmdline.bootstrap.execute + + +Module Contents +--------------- + +.. py:function:: main() + +.. py:function:: execute(args) + diff --git a/docs/autoapi/sorcha_cmdline/demo/index.rst b/docs/autoapi/sorcha_cmdline/demo/index.rst new file mode 100644 index 00000000..e7ef4c8d --- /dev/null +++ b/docs/autoapi/sorcha_cmdline/demo/index.rst @@ -0,0 +1,25 @@ +sorcha_cmdline.demo +=================== + +.. py:module:: sorcha_cmdline.demo + + +Functions +--------- + +.. autoapisummary:: + + sorcha_cmdline.demo.cmd_demo_prepare + sorcha_cmdline.demo.cmd_demo_howto + sorcha_cmdline.demo.main + + +Module Contents +--------------- + +.. py:function:: cmd_demo_prepare(args) + +.. py:function:: cmd_demo_howto(args) + +.. py:function:: main() + diff --git a/docs/autoapi/sorcha_cmdline/index.rst b/docs/autoapi/sorcha_cmdline/index.rst new file mode 100644 index 00000000..196da700 --- /dev/null +++ b/docs/autoapi/sorcha_cmdline/index.rst @@ -0,0 +1,21 @@ +sorcha_cmdline +============== + +.. py:module:: sorcha_cmdline + + +Submodules +---------- + +.. toctree:: + :maxdepth: 1 + + /autoapi/sorcha_cmdline/bootstrap/index + /autoapi/sorcha_cmdline/demo/index + /autoapi/sorcha_cmdline/init/index + /autoapi/sorcha_cmdline/main/index + /autoapi/sorcha_cmdline/outputs/index + /autoapi/sorcha_cmdline/run/index + /autoapi/sorcha_cmdline/sorchaargumentparser/index + + diff --git a/docs/autoapi/sorcha_cmdline/init/index.rst b/docs/autoapi/sorcha_cmdline/init/index.rst new file mode 100644 index 00000000..61dbdc97 --- /dev/null +++ b/docs/autoapi/sorcha_cmdline/init/index.rst @@ -0,0 +1,35 @@ +sorcha_cmdline.init +=================== + +.. py:module:: sorcha_cmdline.init + + +Functions +--------- + +.. autoapisummary:: + + sorcha_cmdline.init.parse_file_selection + sorcha_cmdline.init.execute + sorcha_cmdline.init.main + + +Module Contents +--------------- + +.. py:function:: parse_file_selection(file_select) + + Turns the number entered by the user at the command line into a string + prompt. Also performs error handling. + + :param file_select: Integer entered by the user at command line. + :type file_select: int + + :returns: **which_configs** -- String indicating which configuration files to retrieve. Should be "rubin", "demo" or "all". + :rtype: string + + +.. py:function:: execute(args) + +.. py:function:: main() + diff --git a/docs/autoapi/sorcha_cmdline/main/index.rst b/docs/autoapi/sorcha_cmdline/main/index.rst new file mode 100644 index 00000000..8a6febe6 --- /dev/null +++ b/docs/autoapi/sorcha_cmdline/main/index.rst @@ -0,0 +1,25 @@ +sorcha_cmdline.main +=================== + +.. py:module:: sorcha_cmdline.main + + +Functions +--------- + +.. autoapisummary:: + + sorcha_cmdline.main.find_sorcha_verbs + sorcha_cmdline.main.main + + +Module Contents +--------------- + +.. py:function:: find_sorcha_verbs() + + Find available sorcha commands in the system's PATH. + + +.. py:function:: main() + diff --git a/docs/autoapi/sorcha_cmdline/outputs/index.rst b/docs/autoapi/sorcha_cmdline/outputs/index.rst new file mode 100644 index 00000000..f1bfcaf3 --- /dev/null +++ b/docs/autoapi/sorcha_cmdline/outputs/index.rst @@ -0,0 +1,25 @@ +sorcha_cmdline.outputs +====================== + +.. py:module:: sorcha_cmdline.outputs + + +Functions +--------- + +.. autoapisummary:: + + sorcha_cmdline.outputs.cmd_outputs_create_sqlite + sorcha_cmdline.outputs.cmd_outputs_check_logs + sorcha_cmdline.outputs.main + + +Module Contents +--------------- + +.. py:function:: cmd_outputs_create_sqlite(args) + +.. py:function:: cmd_outputs_check_logs(args) + +.. py:function:: main() + diff --git a/docs/autoapi/sorcha_cmdline/run/index.rst b/docs/autoapi/sorcha_cmdline/run/index.rst new file mode 100644 index 00000000..8d6ea857 --- /dev/null +++ b/docs/autoapi/sorcha_cmdline/run/index.rst @@ -0,0 +1,22 @@ +sorcha_cmdline.run +================== + +.. py:module:: sorcha_cmdline.run + + +Functions +--------- + +.. autoapisummary:: + + sorcha_cmdline.run.main + sorcha_cmdline.run.execute + + +Module Contents +--------------- + +.. py:function:: main() + +.. py:function:: execute(args) + diff --git a/docs/autoapi/sorcha_cmdline/sorchaargumentparser/index.rst b/docs/autoapi/sorcha_cmdline/sorchaargumentparser/index.rst new file mode 100644 index 00000000..947c51ee --- /dev/null +++ b/docs/autoapi/sorcha_cmdline/sorchaargumentparser/index.rst @@ -0,0 +1,40 @@ +sorcha_cmdline.sorchaargumentparser +=================================== + +.. py:module:: sorcha_cmdline.sorchaargumentparser + + +Classes +------- + +.. autoapisummary:: + + sorcha_cmdline.sorchaargumentparser.SorchaArgumentParser + + +Module Contents +--------------- + +.. py:class:: SorchaArgumentParser(*args, **kwargs) + + Bases: :py:obj:`argparse.ArgumentParser` + + + A subclass of the argparse.ArgumentParser that adds in a print statement + to make it clearer how to get detailed help for new users who may not be + as familiar with linux/unix + + + .. py:method:: print_usage(file=None) + + Print a brief description of how the ArgumentParser should be invoked + on the command line. If file is None, sys.stdout is assumed. + + + :param file: Variable length argument list. + :type file: str or None + + :rtype: None. + + + diff --git a/docs/ephemerisgen.rst b/docs/ephemerisgen.rst index 01689b10..1f873e89 100644 --- a/docs/ephemerisgen.rst +++ b/docs/ephemerisgen.rst @@ -10,7 +10,7 @@ Ephemeris Generator .. seealso:: - For a more detailed description of ``Sorcha``'s ephemeris geneeration stage please see Holman et al (submitted). + For a more detailed description of ``Sorcha``'s ephemeris geneeration stage please see Holman et al. (submitted). 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zNizEL4-5!@gWec4KGaLA)D8yP1TH%9c2o;$tWB$bA%O1_{3?_!^R*9mWBupCrvd~p zA=H`cUx@!VE>$S$vJ%ZCe}S&S%>Ea;O@)6jNo|+J&_Piuq%y?%I{;r)LecATgV8^j zC>m1(Fm$8w(9r(@iHd+8=K5-}08IE788%#0KQ!sd-v!ULUX-8!A2}&yNvODS;Qs-+ CSSOnR literal 0 HcmV?d00001 diff --git a/docs/index.rst b/docs/index.rst index 5ff996ae..6e239bce 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -20,7 +20,7 @@ This documentation site contains an installation guide, an overview of how ``Sor works, tutorials, and demonstration notebooks that show how each of the various components within ``Sorcha`` work and can be customized. .. seealso:: - For a more detailed description of ``Sorcha`` and how it works, please see Merritt et al. (submiited) and Holman et al (submitted). + For a more detailed description of ``Sorcha`` and how it works, please see Merritt et al. (submiited) and Holman et al. (submitted). .. warning:: This documentation site and the software package it describes are under diff --git a/docs/overview.rst b/docs/overview.rst index 9dbd4baa..cc4cfa8a 100644 --- a/docs/overview.rst +++ b/docs/overview.rst @@ -1,6 +1,9 @@ Overview ================= +.. seealso:: + For a more detailed description of ``Sorcha`` and how it works, please see Merritt et al. (submiited) and Holman et al. (submitted). + How Sorcha Works ------------------------------- diff --git a/docs/postprocessing.rst b/docs/postprocessing.rst index 01452fe3..ed540dea 100644 --- a/docs/postprocessing.rst +++ b/docs/postprocessing.rst @@ -3,10 +3,20 @@ Post-Processing (Applying Survey Biases) ========================================================== + +.. seealso:: + For a more detailed description of ``Sorcha``'s post-processing stage please see Merritt et al. (submitted). + How it Works ------------------------ -All aspects of post-processing can be adjusted +Once the ephemerides have been generated or read in from an external file, Sorcha moves on to486 +the second phase, which we call post-processing. For each of the input objects, Sorcha goes through487 +the potential observations identified in the ephemeris generation step and performs a series of cal-488 +culations and assessments in the post-processing stage to determine whether the objects would have489 +been detectable as a source in the survey images and would have later been identified as a moving490 +solar system object. All aspects of post-processing can be adjusted or turned on/off via ``Sorcha``'s :ref:`configs`. + Trailed Source Magnitude and PSF (Point Spread Function) Magnitude --------------------------------------------------------------------- @@ -30,7 +40,6 @@ Phase Curves ------------------------------------------------------------ - .. _addons: Incorporating Rotational Light Curves and Activity @@ -75,8 +84,6 @@ LSSTCometActivity Class lsst_comet - - Rotational Lightcurve Effects ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The base lightcurve class is `AbstractLightCurve `_ (see below). Inside the `Sorcha addons GitHub repository `_, we provide a simple example implementation where the apparent magnitude of the object (that is, the magnitude after all geometric effects have been taken into account), has a sinusoidal term added to it. To use this function, in the :ref:`CPP` file, the user must provide a light curve amplitude (`LCA`), corresponding to half the peak-to-peak amplitude for the magnitude changes, a period `Period`, and a reference time `Time0` where the light curve is at 0 - if these are not provided, the software will produce an error message. Despite being simple, that implementation shows all the class methods that need to be implemented for a custom light curve function. @@ -111,32 +118,44 @@ This filter will recalculate the PSF magnitude of the observations, adjusting fo .. seealso:: We have a Jupyter notebook `_ demonstrating the LSSTCometActivity class built into `Sorcha addons GitHub repository `_. +.. _vignettting: +Calculating the 5σ Limiting Magnitude at the Source Location and Vignetting +---------------------------------------------------------------------------------------------------- -Vignetting ------------------ Objects that are on the edges of the field of view are dimmer due to vignetting: the field-of-view is not -uniformly illuminated, and so the limiting magnitude for each detection will depend on its position within the FOV. -This filter applies a model of this from a built-in function tailored specifically for the LSST (see -`Araujo-Hauck et al. 2016 `_, with further -discussion and below figure from `Veres and Chesley 2017 `_.) +uniformly illuminated, and so the limiting magnitude for each detection will depend on its position within the FOV (field-of-view). +The effect of this is to decrease the 5σ limiting magnitude – the apparent magnitude where a detected point source has exactly a +50% probability of detection – at the edges of the LSSTCam FOV. Sorcha accommodates this by +calculating the effects of vignetting at the source’s location on the focal plane and adjusting the +5σ limiting magnitude accordingly for each potential detection. This modified limiting magnitude +will be used when applying the survey detection efficiency. We this value the **5σ Limiting Magnitude at the Source Location** -.. image:: images/vignetting.jpg - :width: 500 + +``Sorcha`` applies a vignetting model from a built-in function tailored specifically for the LSST (see +`Araujo-Hauck et al. 2016 `_). The image below shows the +effects of vignetting on the 5σ limiting magnitude for a randomized series of points on a +circular FOV in the LSSTCam focal plane. The LSSTCam detector footprint is also plotted. Locations +further from the center of the FOV have shallower depths. + + +.. image:: images/vignetting.png + :width: 600 :alt: Plot of the LSST camera footprint in Dec vs. RA, showing shaded dimming due to vignetting. :align: center +.. note:: + The :ref:`pointing` provides the 5σ limiting magnitude at the center of the exposure's FOV. -Accounting for Saturation (Saturation/Bright Filter) +Accounting for Saturation (Saturation/Bright Limit Filter) ------------------------------------------------------------ -The saturation limit filter removes all detections that are brighter than the saturation limit +The saturation/bright limit filter removes all detections that are brighter than the saturation limit of the survey. `Ivezić et al. (2019) `_ estimate that the saturation limit for the LSST will be ~16 in the r filter. ``Sorcha`` includes functionality to specify either a single saturation limit, or a saturation limit in each filter. -For the latter, limits must be given in a comma-separated list in the same order as the filters supplied -for the observing_filters config file variable. +For the latter, limits must be given in a comma-separated list in the same order as the :ref`optical filters set in the configuration file `.. To include this filter, the :ref:`configs` should contain:: @@ -149,7 +168,7 @@ Or:: bright_limit = 16.0, 16.1, 16.2 -.. note:: +.. tip:: The saturation filter is only applied if the :ref:`configuration file` has a SATURATION section. @@ -164,7 +183,6 @@ The default values are modelled on those from the aforementioned paper. To include this filter, the following options should be set in the :ref:`configs`:: [FADINGFUNCTION] - fading_function_on = True fading_function_width = 0.1 fading_function_peak_efficiency = 1. @@ -247,7 +265,7 @@ Additionally, the camera footprint model can account for the losses at the edge Linking --------------------------- -The linking filter simulates the behaviour of LSST's Solar System Processing (SSP, `Jurić et al. 2020 `_, +The linking filter simulates the behavior of LSST's Solar System Processing (SSP, `Jurić et al. 2020 `_, `Swinbank et al. 2020 `_), the automated software pipeline dedicated to linking and cross-matching observations that belong to the same object. @@ -300,8 +318,32 @@ the observation is of a linked object or not. To enable this functionality, add [LINKING] drop_unlinked = False - .. seealso:: See our `Jupyter notebook `_ that validates the linking filter. +.. tip:: + The linking filter is only applied if the :ref:`configuration file` has a LINKING section. + + +.. _whatobs: + +What Observations to Include +------------------------------------- + +The user sets what observations from the survey :ref:`pointing` will be used by setting the **observing_filters** :ref:`configs` variable:: + + + [FILTERS] + + # Filters of the observations you are interested in, comma-separated. + # Your physical parameters file must have H calculated in one of these filters + # and colour offset columns defined relative to that filter. + + observing_filters = r,g,i,z,u,y + +If the user wants to use a subset of the observations, such as only include observations from the first year of the survey or are part of a database, they can either modify the :ref:`pointing` or modify the :ref:`pointing` query in the :ref:`configs`. + +Expert Advanced Post-Processing Features +--------------------------------------------------- +Once a user is familar with ``Sorcha`` and how it works, there are additional :ref:`advanced post-processing tunable features and parameters ` available for the expert user. diff --git a/docs/troubleshooting.rst b/docs/troubleshooting.rst index ddd45921..4592fb48 100644 --- a/docs/troubleshooting.rst +++ b/docs/troubleshooting.rst @@ -8,6 +8,9 @@ Have You Checked the Error Log File? --------------------------------------------------------------- If ``Sorcha`` runs successfully the .err log file created will be empty. If the software exited gracefully with an error it will print error statements to the error log file. If ``Sorcha'' looks like it completed but you're not getting the expected output, the .err log file is the first place to check. +.. tip:: + You cna also usee the **-l** flag to set get more detailed and informative messages in the log file produced by **sorcha run**. + Using Relative File Paths --------------------------------------------------------------- From 2272b28fd8b531d0ce99e423e7bbd1dfc30f71b2 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Sun, 12 Jan 2025 19:42:45 +0000 Subject: [PATCH 38/52] documentation updates documentation updates --- docs/autoapi/index.rst | 12 - .../activity/activity_registration/index.rst | 49 - .../sorcha/activity/base_activity/index.rst | 90 -- .../activity/identity_activity/index.rst | 57 - docs/autoapi/sorcha/activity/index.rst | 172 --- docs/autoapi/sorcha/ephemeris/index.rst | 417 ------ .../orbit_conversion_utilities/index.rst | 211 --- .../sorcha/ephemeris/pixel_dict/index.rst | 232 ---- .../ephemeris/simulation_constants/index.rst | 65 - .../ephemeris/simulation_data_files/index.rst | 33 - .../ephemeris/simulation_driver/index.rst | 180 --- .../ephemeris/simulation_geometry/index.rst | 121 -- .../ephemeris/simulation_parsing/index.rst | 97 -- .../ephemeris/simulation_setup/index.rst | 74 - docs/autoapi/sorcha/index.rst | 54 - .../lightcurves/base_lightcurve/index.rst | 90 -- .../lightcurves/identity_lightcurve/index.rst | 56 - docs/autoapi/sorcha/lightcurves/index.rst | 171 --- .../lightcurve_registration/index.rst | 49 - .../modules/PPAddUncertainties/index.rst | 217 --- .../modules/PPApplyColourOffsets/index.rst | 51 - .../sorcha/modules/PPApplyFOVFilter/index.rst | 100 -- .../sorcha/modules/PPBrightLimit/index.rst | 36 - .../PPCalculateApparentMagnitude/index.rst | 63 - .../index.rst | 57 - .../index.rst | 41 - .../modules/PPCommandLineParser/index.rst | 48 - .../sorcha/modules/PPConfigParser/index.rst | 204 --- .../modules/PPDetectionEfficiency/index.rst | 34 - .../modules/PPDetectionProbability/index.rst | 67 - .../modules/PPDropObservations/index.rst | 33 - .../modules/PPFadingFunctionFilter/index.rst | 37 - .../modules/PPFootprintFilter/index.rst | 311 ----- .../sorcha/modules/PPGetLogger/index.rst | 39 - .../PPGetMainFilterAndColourOffsets/index.rst | 41 - .../sorcha/modules/PPLinkingFilter/index.rst | 55 - .../sorcha/modules/PPMagnitudeLimit/index.rst | 32 - .../PPMatchPointingToObservations/index.rst | 46 - .../sorcha/modules/PPMiniDifi/index.rst | 193 --- .../sorcha/modules/PPModuleRNG/index.rst | 46 - .../autoapi/sorcha/modules/PPOutput/index.rst | 95 -- .../modules/PPRandomizeMeasurements/index.rst | 213 --- .../modules/PPReadPointingDatabase/index.rst | 32 - .../sorcha/modules/PPSNRLimit/index.rst | 31 - docs/autoapi/sorcha/modules/PPStats/index.rst | 34 - .../sorcha/modules/PPTrailingLoss/index.rst | 85 -- .../sorcha/modules/PPVignetting/index.rst | 116 -- docs/autoapi/sorcha/modules/index.rst | 42 - .../sorcha/readers/CSVReader/index.rst | 140 -- 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[#f1] Created with `sphinx-autoapi `_ \ No newline at end of file diff --git a/docs/autoapi/sorcha/activity/activity_registration/index.rst b/docs/autoapi/sorcha/activity/activity_registration/index.rst deleted file mode 100644 index 99a7457d..00000000 --- a/docs/autoapi/sorcha/activity/activity_registration/index.rst +++ /dev/null @@ -1,49 +0,0 @@ -sorcha.activity.activity_registration -===================================== - -.. py:module:: sorcha.activity.activity_registration - - -Attributes ----------- - -.. autoapisummary:: - - sorcha.activity.activity_registration.CA_METHODS - - -Functions ---------- - -.. autoapisummary:: - - sorcha.activity.activity_registration.register_activity_subclasses - sorcha.activity.activity_registration.update_activity_subclasses - - -Module Contents ---------------- - -.. py:function:: register_activity_subclasses() -> Dict[str, Callable] - - This method will identify all of the subclasses of ``AbstractCometaryActivity`` - and build a dictionary that maps ``name : subclass``. - - :returns: A dictionary of all of subclasses of ``AbstractCometaryActivity``. Where - the string returned from ``subclass.name_id()`` is the key, and the - subclass is the value. - :rtype: dict - - :raises ValueError: If a duplicate key is found, a ``ValueError`` is raised. This would - likely occur if a user copy/pasted an existing subclass but failed to - update the string returned from ``name_id()``. - - -.. py:function:: update_activity_subclasses() -> None - - This function is used to register newly created subclasses of the - `AbstractCometaryActivity`. - - -.. py:data:: CA_METHODS - diff --git a/docs/autoapi/sorcha/activity/base_activity/index.rst b/docs/autoapi/sorcha/activity/base_activity/index.rst deleted file mode 100644 index a6a256d7..00000000 --- a/docs/autoapi/sorcha/activity/base_activity/index.rst +++ /dev/null @@ -1,90 +0,0 @@ -sorcha.activity.base_activity -============================= - -.. py:module:: sorcha.activity.base_activity - - -Attributes ----------- - -.. autoapisummary:: - - sorcha.activity.base_activity.logger - - -Classes -------- - -.. autoapisummary:: - - sorcha.activity.base_activity.AbstractCometaryActivity - - -Module Contents ---------------- - -.. py:data:: logger - -.. py:class:: AbstractCometaryActivity(required_column_names: List[str] = []) - - Bases: :py:obj:`abc.ABC` - - - Abstract base class for cometary activity models - - - .. py:attribute:: required_column_names - :value: [] - - - - .. py:method:: compute(df: pandas.DataFrame) -> numpy.array - :abstractmethod: - - - User implemented calculation based on the input provided by the - pandas dataframe ``df``. - - :param df: The ``observations`` dataframe provided by ``Sorcha``. - :type df: Pandas dataframe - - - - .. py:method:: _validate_column_names(df: pandas.DataFrame) -> None - - Private method that checks that the provided pandas dataframe contains - the required columns defined in ``self.required_column_names``. - - :param df: The ``observations`` dataframe provided by ``Sorcha``. - :type df: Pandas dataframe - - - - .. py:method:: _log_exception(exception: Exception) -> None - - Log an error message from an exception to the error log file - - :param exception: The exception with a value string to appended to the error log - :type exception: Exception - - - - .. py:method:: _log_error_message(error_msg: str) -> None - - Log a specific error string to the error log file - - :param error_msg: The string to be appended to the error log - :type error_msg: str - - - - .. py:method:: name_id() -> str - :staticmethod: - - :abstractmethod: - - - This method will return the unique name of the LightCurve Model - - - diff --git a/docs/autoapi/sorcha/activity/identity_activity/index.rst b/docs/autoapi/sorcha/activity/identity_activity/index.rst deleted file mode 100644 index 63b63fee..00000000 --- a/docs/autoapi/sorcha/activity/identity_activity/index.rst +++ /dev/null @@ -1,57 +0,0 @@ -sorcha.activity.identity_activity -================================= - -.. py:module:: sorcha.activity.identity_activity - - -Classes -------- - -.. autoapisummary:: - - sorcha.activity.identity_activity.IdentityCometaryActivity - - -Module Contents ---------------- - -.. py:class:: IdentityCometaryActivity - - Bases: :py:obj:`sorcha.activity.base_activity.AbstractCometaryActivity` - - - !!! THIS SHOULD NEVER BE USED - FOR TESTING ONLY !!! - - Rudimentary cometary activity model that returns no change to the input ``observation`` - dataframe. - This class is explicitly created for testing purposes. - - - .. py:method:: compute(df: pandas.DataFrame) -> pandas.DataFrame - - Returns numpy array of 0's with shape equal to the input dataframe - time column. - - :param df: The ``observations`` dataframe provided by ``Sorcha``. - :type df: pd.DataFrame - - :returns: The original ``observations`` dataframe, unchanged. - :rtype: pd.DataFrame - - - - .. py:method:: name_id() -> str - :staticmethod: - - - Returns the string identifier for this cometary activity method. It - must be unique within all the subclasses of ``AbstractCometaryActivity``. - - We have chosen the name "identity" here because the input dataframe is - returned unchanged. - - :returns: Unique identifier for this cometary activity model - :rtype: str - - - diff --git a/docs/autoapi/sorcha/activity/index.rst b/docs/autoapi/sorcha/activity/index.rst deleted file mode 100644 index ad9ed030..00000000 --- a/docs/autoapi/sorcha/activity/index.rst +++ /dev/null @@ -1,172 +0,0 @@ -sorcha.activity -=============== - -.. py:module:: sorcha.activity - - -Submodules ----------- - -.. toctree:: - :maxdepth: 1 - - /autoapi/sorcha/activity/activity_registration/index - /autoapi/sorcha/activity/base_activity/index - /autoapi/sorcha/activity/identity_activity/index - - -Attributes ----------- - -.. autoapisummary:: - - sorcha.activity.CA_METHODS - - -Classes -------- - -.. autoapisummary:: - - sorcha.activity.AbstractCometaryActivity - sorcha.activity.IdentityCometaryActivity - - -Functions ---------- - -.. autoapisummary:: - - sorcha.activity.register_activity_subclasses - sorcha.activity.update_activity_subclasses - - -Package Contents ----------------- - -.. py:class:: AbstractCometaryActivity(required_column_names: List[str] = []) - - Bases: :py:obj:`abc.ABC` - - - Abstract base class for cometary activity models - - - .. py:attribute:: required_column_names - :value: [] - - - - .. py:method:: compute(df: pandas.DataFrame) -> numpy.array - :abstractmethod: - - - User implemented calculation based on the input provided by the - pandas dataframe ``df``. - - :param df: The ``observations`` dataframe provided by ``Sorcha``. - :type df: Pandas dataframe - - - - .. py:method:: _validate_column_names(df: pandas.DataFrame) -> None - - Private method that checks that the provided pandas dataframe contains - the required columns defined in ``self.required_column_names``. - - :param df: The ``observations`` dataframe provided by ``Sorcha``. - :type df: Pandas dataframe - - - - .. py:method:: _log_exception(exception: Exception) -> None - - Log an error message from an exception to the error log file - - :param exception: The exception with a value string to appended to the error log - :type exception: Exception - - - - .. py:method:: _log_error_message(error_msg: str) -> None - - Log a specific error string to the error log file - - :param error_msg: The string to be appended to the error log - :type error_msg: str - - - - .. py:method:: name_id() -> str - :staticmethod: - - :abstractmethod: - - - This method will return the unique name of the LightCurve Model - - - -.. py:class:: IdentityCometaryActivity - - Bases: :py:obj:`sorcha.activity.base_activity.AbstractCometaryActivity` - - - !!! THIS SHOULD NEVER BE USED - FOR TESTING ONLY !!! - - Rudimentary cometary activity model that returns no change to the input ``observation`` - dataframe. - This class is explicitly created for testing purposes. - - - .. py:method:: compute(df: pandas.DataFrame) -> pandas.DataFrame - - Returns numpy array of 0's with shape equal to the input dataframe - time column. - - :param df: The ``observations`` dataframe provided by ``Sorcha``. - :type df: pd.DataFrame - - :returns: The original ``observations`` dataframe, unchanged. - :rtype: pd.DataFrame - - - - .. py:method:: name_id() -> str - :staticmethod: - - - Returns the string identifier for this cometary activity method. It - must be unique within all the subclasses of ``AbstractCometaryActivity``. - - We have chosen the name "identity" here because the input dataframe is - returned unchanged. - - :returns: Unique identifier for this cometary activity model - :rtype: str - - - -.. py:function:: register_activity_subclasses() -> Dict[str, Callable] - - This method will identify all of the subclasses of ``AbstractCometaryActivity`` - and build a dictionary that maps ``name : subclass``. - - :returns: A dictionary of all of subclasses of ``AbstractCometaryActivity``. Where - the string returned from ``subclass.name_id()`` is the key, and the - subclass is the value. - :rtype: dict - - :raises ValueError: If a duplicate key is found, a ``ValueError`` is raised. This would - likely occur if a user copy/pasted an existing subclass but failed to - update the string returned from ``name_id()``. - - -.. py:function:: update_activity_subclasses() -> None - - This function is used to register newly created subclasses of the - `AbstractCometaryActivity`. - - -.. py:data:: CA_METHODS - diff --git a/docs/autoapi/sorcha/ephemeris/index.rst b/docs/autoapi/sorcha/ephemeris/index.rst deleted file mode 100644 index d959c025..00000000 --- a/docs/autoapi/sorcha/ephemeris/index.rst +++ /dev/null @@ -1,417 +0,0 @@ -sorcha.ephemeris -================ - -.. py:module:: sorcha.ephemeris - - -Submodules ----------- - -.. toctree:: - :maxdepth: 1 - - /autoapi/sorcha/ephemeris/orbit_conversion_utilities/index - /autoapi/sorcha/ephemeris/pixel_dict/index - /autoapi/sorcha/ephemeris/simulation_constants/index - /autoapi/sorcha/ephemeris/simulation_data_files/index - /autoapi/sorcha/ephemeris/simulation_driver/index - /autoapi/sorcha/ephemeris/simulation_geometry/index - /autoapi/sorcha/ephemeris/simulation_parsing/index - /autoapi/sorcha/ephemeris/simulation_setup/index - - -Attributes ----------- - -.. autoapisummary:: - - sorcha.ephemeris.AU_KM - sorcha.ephemeris.AU_M - sorcha.ephemeris.RADIUS_EARTH_KM - sorcha.ephemeris.SPEED_OF_LIGHT - sorcha.ephemeris.OBLIQUITY_ECLIPTIC - - -Classes -------- - -.. autoapisummary:: - - sorcha.ephemeris.Observatory - - -Functions ---------- - -.. autoapisummary:: - - sorcha.ephemeris.create_ecl_to_eq_rotation_matrix - sorcha.ephemeris.make_retriever - sorcha.ephemeris.barycentricObservatoryRates - sorcha.ephemeris.ecliptic_to_equatorial - sorcha.ephemeris.integrate_light_time - sorcha.ephemeris.ra_dec2vec - sorcha.ephemeris.mjd_tai_to_epoch - sorcha.ephemeris.parse_orbit_row - sorcha.ephemeris.create_assist_ephemeris - sorcha.ephemeris.furnish_spiceypy - sorcha.ephemeris.precompute_pointing_information - sorcha.ephemeris.create_ephemeris - sorcha.ephemeris.universal_cartesian - sorcha.ephemeris.universal_keplerian - - -Package Contents ----------------- - -.. py:data:: AU_KM - :value: 149597870.7 - - -.. py:data:: AU_M - :value: 149597870700 - - -.. py:data:: RADIUS_EARTH_KM - :value: 6378.137 - - -.. py:data:: SPEED_OF_LIGHT - :value: 173.1446326742403 - - -.. py:data:: OBLIQUITY_ECLIPTIC - -.. py:function:: create_ecl_to_eq_rotation_matrix(ecl) - - Creates a rotation matrix for transforming ecliptical coordinates - to equatorial coordinates. A rotation matrix based on the solar - system's ecliptic obliquity is already provided as - `ECL_TO_EQ_ROTATION_MATRIX`. - - :param ecl: The ecliptical obliquity. - :type ecl: float - - :returns: **rotmat** -- rotation matrix for transofmring ecliptical coordinates to equatorial coordinates. - Array has shape (3,3). - :rtype: numpy array/matrix of floats - - -.. py:function:: make_retriever(auxconfigs, directory_path: str = None) -> pooch.Pooch - - Helper function that will create a Pooch object to track and retrieve files. - - :param directory_path: The base directory to place all downloaded files. Default = None - :type directory_path: string, optional - :param registry: A dictionary of file names to SHA hashes. Generally we'll not use SHA=None - because the files we're tracking change frequently. Default = REGISTRY - :type registry: dictionary, optional - :param auxconfigs: Dataclass of auxiliary configuration file arguments. - :type auxconfigs: dataclass - - :returns: The instance of a Pooch object used to track and retrieve files. - :rtype: pooch - - -.. py:function:: barycentricObservatoryRates(et, obsCode, observatories, Rearth=RADIUS_EARTH_KM, delta_et=10) - - Computes the position and rate of motion for the observatory in barycentric coordinates - - :param et: JPL ephemeris time - :type et: float - :param obsCode: MPC observatory code - :type obsCode: str - :param observatories: Observatory object with spherical representations for the obsCode - :type observatories: Observatory - :param Rearth: Radius of the Earth (default is RADIUS_EARTH_KM) - :type Rearth: float - :param delta_et: Difference in ephemeris time (in days) to derive the rotation matrix from the fixed Earth equatorial frame to J2000 (default: 10) - :type delta_et: float - - :returns: * *array* -- Position of the observatory (baricentric) - * *array* -- Velocity of the observatory (baricentric) - - -.. py:function:: ecliptic_to_equatorial(v, rot_mat=ECL_TO_EQ_ROTATION_MATRIX) - - Converts an ecliptic-aligned vector to an equatorially-aligned vector - - :param v: vector - :type v: array (3 entries) - :param rot_mat: Rotation matrix. Default is the matrix that computes the ecliptic to equatorial conversion - :type rot_mat: 2D array (3x3 matrix) - - :returns: **v** -- Rotated vector - :rtype: array (3 entries) - - -.. py:function:: integrate_light_time(sim, ex, t, r_obs, lt0=0, iter=3, speed_of_light=SPEED_OF_LIGHT) - - Performs the light travel time correction between object and observatory iteratively for the object at a given reference time - - :param sim: Rebound simulation object - :type sim: simulation - :param ex: ASSIST simulation extras - :type ex: simulation extras - :param t: Target time - :type t: float - :param r_obs: Observatory position at time t - :type r_obs: array (3 entries) - :param lt0: First guess for light travel time - :type lt0: float - :param iter: Number of iterations - :type iter: int - :param speed_of_light: Speed of light for the calculation (default is SPEED_OF_LIGHT constant) - :type speed_of_light: float - - :returns: * **rho** (*array*) -- Object-observatory vector - * **rho_mag** (*float*) -- Magnitude of rho vector - * **lt** (*float*) -- Light travel time - * **target** (*array*) -- Object position vector at t-lt - * **vtarget** (*array*) -- Object velocity at t-lt - - -.. py:function:: ra_dec2vec(ra, dec) - - Converts a RA/Dec pair to a unit vector on the sphere - :param ra: Target RA - :type ra: float - :param dec: Target dec - :type dec: float - - :returns: Unit vector - :rtype: array - - -.. py:function:: mjd_tai_to_epoch(mjd_tai) - - Converts a MJD value in TAI to SPICE ephemeris time - - :param mjd_tai: Input mjd - :type mjd_tai: float - - :rtype: Ephemeris time - - -.. py:class:: Observatory(args, auxconfigs, oc_file=None) - - Class containing various utility tools related to the calculation of the observatory position - - - .. py:attribute:: observatoryPositionCache - - - .. py:attribute:: ObservatoryXYZ - - - .. py:method:: convert_to_geocentric(obs_location: dict) -> tuple - - Converts the observatory location to geocentric coordinates - - :param obs_location: Dictionary with Longitude and sin/cos of the observatory Latitude - :type obs_location: dict - - :returns: Geocentric position (x,y,z) - :rtype: tuple - - - - .. py:method:: barycentricObservatory(et, obsCode, Rearth=RADIUS_EARTH_KM) - - Computes the barycentric position of the observatory - - :param et: JPL internal ephemeris time - :type et: float - :param obsCode: MPC Observatory code - :type obsCode: str - :param Rearth: Radius of the Earth - :type Rearth: float - - :returns: Barycentric position of the observatory (x,y,z) - :rtype: array (3,) - - - -.. py:function:: parse_orbit_row(row, epochJD_TDB, ephem, sun_dict, gm_sun, gm_total) - - Parses the input orbit row, converting it to the format expected by - the ephemeris generation code later on - - :param row: Row of the input dataframe - :type row: Pandas dataframe row - :param epochJD_TDB: epoch of the elements, in JD TDB - :type epochJD_TDB: float - :param ephem: ASSIST ephemeris object - :type ephem: Ephem - :param sun_dict: Dictionary with the position of the Sun at each epoch - :type sun_dict: dict - :param gm_sun: Standard gravitational parameter GM for the Sun - :type gm_sun: float - :param gm_total: Standard gravitational parameter GM for the Solar System barycenter - :type gm_total: float - - :returns: State vector (position, velocity) - :rtype: tuple - - -.. py:function:: create_assist_ephemeris(args, auxconfigs) -> tuple - - Build the ASSIST ephemeris object - Parameter - --------- - auxconfigs: dataclass - Dataclass of auxiliary configuration file arguments. - :returns: * **Ephem** (*ASSIST ephemeris obejct*) -- The ASSIST ephemeris object - * **gm_sun** (*float*) -- value for the GM_SUN value - * **gm_total** (*float*) -- value for gm_total - - -.. py:function:: furnish_spiceypy(args, auxconfigs) - - Builds the SPICE kernel, downloading the required files if needed - :param auxconfigs: Dataclass of auxiliary configuration file arguments. - :type auxconfigs: dataclass - - -.. py:function:: precompute_pointing_information(pointings_df, args, sconfigs) - - This function is meant to be run once to prime the pointings dataframe - with additional information that Assist & Rebound needs for it's work. - - :param pointings_df: Contains the telescope pointing database. - :type pointings_df: pandas dataframe - :param args: Command line arguments needed for initialization. - :type args: dictionary - :param sconfigs: Dataclass of configuration file arguments. - :type sconfigs: dataclass - - :returns: **pointings_df** -- The original dataframe with several additional columns of precomputed values. - :rtype: pandas dataframe - - -.. py:function:: create_ephemeris(orbits_df, pointings_df, args, sconfigs) - - Generate a set of observations given a collection of orbits - and set of pointings. - - :param orbits_df: The dataframe containing the collection of orbits. - :type orbits_df: pandas dataframe - :param pointings_df: The dataframe containing the collection of telescope/camera pointings. - :type pointings_df: pandas dataframe - :param args: Various arguments necessary for the calculation - :param sconfigs: Dataclass of configuration file arguments. - Various configuration parameters necessary for the calculation - ang_fov : float - The angular size (deg) of the field of view - buffer : float - The angular size (deg) of the buffer around the field of view. - A buffer is required to allow for some motion between the time - of the observation and the time of the picket (t_picket) - picket_interval : float - The interval (days) between picket calculations. This is 1 day - by default. Current there is only one such interval, used for - all objects. It is currently possible for extremely fast-moving - objects to be missed. This will be remedied in future releases. - obsCode : string - The MPC code for the observatory. (This is current a configuration - parameter, but these should be included in the visit information, - to allow for multiple observatories. - nside : integer - The nside value used for the HEALPIx calculations. Must be a - power of 2 (1, 2, 4, ...) nside=64 is current default. - - :returns: **observations** -- The dataframe of observations needed for Sorcha to continue - :rtype: pandas dataframe - - .. rubric:: Notes - - This works by calculating and regularly updating the sky-plane - locations (unit vectors) of all the objects in the collection - of orbits. The HEALPix index for each of the locations is calculated. - A dictionary with pixel indices as keys and lists of ObjIDs for - those objects in each HEALPix tile as values is generated. An individual - one of these calculations is called a 'picket', as one element of a long - picket fence. Typically, the interval between pickets is one day. - - Given a specific pointing, the set of HEALPix tiles that are overlapped - by the pointing (and a buffer region) is computed. Then the precise - locations of just those objects within that set of HEALPix tiles are - computed. Details for those that actually do land within the field - of view are passed along. - - -.. py:function:: universal_cartesian(mu, q, e, incl, longnode, argperi, tp, epochMJD_TDB) - - Converts from a series of orbital elements into state vectors - using the universal variable formulation - - The output vector will be oriented in the same system as - the positional angles (i, Omega, omega) - - Note that mu, q, tp and epochMJD_TDB must have compatible units - As an example, if q is in au and tp/epoch are in days, mu must - be in (au^3)/days^2 - - :param mu: Standard gravitational parameter GM (see note above about units) - :type mu: float - :param q: Perihelion (see note above about units) - :type q: float - :param e: Eccentricity - :type e: float - :param incl: Inclination (radians) - :type incl: float - :param longnode: Longitude of ascending node (radians) - :type longnode: float - :param argperi: Argument of perihelion (radians) - :type argperi: float - :param tp: Time of perihelion passage in TDB scale (see note above about units) - :type tp: float - :param epochMJD_TDB: Epoch (in TDB) when the elements are defined (see note above about units) - :type epochMJD_TDB: float - - :returns: * *float* -- x coordinate - * *float* -- y coordinate - * *float* -- z coordinate - * *float* -- x velocity - * *float* -- y velocity - * *float* -- z velocity - - -.. py:function:: universal_keplerian(mu, x, y, z, vx, vy, vz, epochMJD_TDB) - - Converts from a state vectors into orbital elements - using the universal variable formulation - - The input vector will determine the orientation - of the positional angles (i, Omega, omega) - - - Note that mu and the state vectors must have compatible units - As an example, if x is in au and vx are in au/days, mu must - be in (au^3)/days^2 - - - :param mu: Standard gravitational parameter GM (see note above about units) - :type mu: float - :param x: x coordinate - :type x: float - :param y: y coordinate - :type y: float - :param z: z coordinate - :type z: float - :param vx: x velocity - :type vx: float - :param vy: y velocity - :type vy: float - :param vz: z velocity - :type vz: float - :param epochMJD_TDB (float): Epoch (in TDB) when the elements are defined (see note above about units) - - :returns: * *float* -- Perihelion (see note above about units) - * *float* -- Eccentricity - * *float* -- Inclination (radians) - * *float* -- Longitude of ascending node (radians) - * *float* -- Argument of perihelion (radians) - * *float* -- Time of perihelion passage in TDB scale (see note above about units) - - diff --git a/docs/autoapi/sorcha/ephemeris/orbit_conversion_utilities/index.rst b/docs/autoapi/sorcha/ephemeris/orbit_conversion_utilities/index.rst deleted file mode 100644 index d11b4eec..00000000 --- a/docs/autoapi/sorcha/ephemeris/orbit_conversion_utilities/index.rst +++ /dev/null @@ -1,211 +0,0 @@ -sorcha.ephemeris.orbit_conversion_utilities -=========================================== - -.. py:module:: sorcha.ephemeris.orbit_conversion_utilities - - -Classes -------- - -.. autoapisummary:: - - sorcha.ephemeris.orbit_conversion_utilities.halley_result - - -Functions ---------- - -.. autoapisummary:: - - sorcha.ephemeris.orbit_conversion_utilities.stumpff - sorcha.ephemeris.orbit_conversion_utilities.root_function - sorcha.ephemeris.orbit_conversion_utilities.halley_safe - sorcha.ephemeris.orbit_conversion_utilities.universal_cartesian - sorcha.ephemeris.orbit_conversion_utilities.principal_value - sorcha.ephemeris.orbit_conversion_utilities.universal_keplerian - - -Module Contents ---------------- - -.. py:class:: halley_result - - Bases: :py:obj:`tuple` - - - .. py:attribute:: root - - - .. py:attribute:: iterations - - - .. py:attribute:: function_calls - - - .. py:attribute:: converged - - - .. py:attribute:: flag - - - .. py:attribute:: f - - - .. py:attribute:: fp - - - .. py:attribute:: fpp - - -.. py:function:: stumpff(x) - - Computes the Stumpff function c_k(x) for k = 0, 1, 2, 3 - - :param x: Argument of the Stumpff function - :type x: float - - :returns: * **c_0(x)** (*float*) - * **c_1(x)** (*float*) - * **c_2(x)** (*float*) - * **c_3(x)** (*float*) - - -.. py:function:: root_function(s, mu, alpha, r0, r0dot, t) - - Root function used in the Halley minimizer - Computes the zeroth, first, second, and third derivatives - of the universal Kepler equation f - - :param s: Eccentric anomaly - :type s: float - :param mu: Standard gravitational parameter GM - :type mu: float - :param alpha: Total energy - :type alpha: float - :param r0: Initial position - :type r0: float - :param r0dot: Initial velocity - :type r0dot: float - :param t: Time - :type t: float - - :returns: * **f** (*float*) -- universal Kepler equation) - * **fp** (*float*) -- (first derivative of f - * **fpp** (*float*) -- second derivative of f - * **fppp** (*float*) -- third derivative of f - - -.. py:function:: halley_safe(x1, x2, mu, alpha, r0, r0dot, t, xacc=1e-14, maxit=100) - - Applies the Halley root finding algorithm on the universal Kepler equation - - :param x1: Previous guess used in minimization - :type x1: float - :param x2: Current guess for minimization - :type x2: float - :param mu: Standard gravitational parameter GM - :type mu: float - :param alpha: Total energy - :type alpha: float - :param r0: Initial position - :type r0: float - :param r0dot: Initial velocity - :type r0dot: float - :param t: Time - :type t: float - :param xacc: Accuracy in x before algorithm declares convergence - :type xacc: float - :param maxit: Maximum number of iterations - :type maxit: int - - :returns: * *boolean* -- True if minimization converged, False otherwise - * *float* -- Solution - * *float* -- First derivative of solution - - -.. py:function:: universal_cartesian(mu, q, e, incl, longnode, argperi, tp, epochMJD_TDB) - - Converts from a series of orbital elements into state vectors - using the universal variable formulation - - The output vector will be oriented in the same system as - the positional angles (i, Omega, omega) - - Note that mu, q, tp and epochMJD_TDB must have compatible units - As an example, if q is in au and tp/epoch are in days, mu must - be in (au^3)/days^2 - - :param mu: Standard gravitational parameter GM (see note above about units) - :type mu: float - :param q: Perihelion (see note above about units) - :type q: float - :param e: Eccentricity - :type e: float - :param incl: Inclination (radians) - :type incl: float - :param longnode: Longitude of ascending node (radians) - :type longnode: float - :param argperi: Argument of perihelion (radians) - :type argperi: float - :param tp: Time of perihelion passage in TDB scale (see note above about units) - :type tp: float - :param epochMJD_TDB: Epoch (in TDB) when the elements are defined (see note above about units) - :type epochMJD_TDB: float - - :returns: * *float* -- x coordinate - * *float* -- y coordinate - * *float* -- z coordinate - * *float* -- x velocity - * *float* -- y velocity - * *float* -- z velocity - - -.. py:function:: principal_value(theta) - - Computes the principal value of an angle - - :param theta: Angle - :type theta: float - - :returns: Principal value of angle - :rtype: float - - -.. py:function:: universal_keplerian(mu, x, y, z, vx, vy, vz, epochMJD_TDB) - - Converts from a state vectors into orbital elements - using the universal variable formulation - - The input vector will determine the orientation - of the positional angles (i, Omega, omega) - - - Note that mu and the state vectors must have compatible units - As an example, if x is in au and vx are in au/days, mu must - be in (au^3)/days^2 - - - :param mu: Standard gravitational parameter GM (see note above about units) - :type mu: float - :param x: x coordinate - :type x: float - :param y: y coordinate - :type y: float - :param z: z coordinate - :type z: float - :param vx: x velocity - :type vx: float - :param vy: y velocity - :type vy: float - :param vz: z velocity - :type vz: float - :param epochMJD_TDB (float): Epoch (in TDB) when the elements are defined (see note above about units) - - :returns: * *float* -- Perihelion (see note above about units) - * *float* -- Eccentricity - * *float* -- Inclination (radians) - * *float* -- Longitude of ascending node (radians) - * *float* -- Argument of perihelion (radians) - * *float* -- Time of perihelion passage in TDB scale (see note above about units) - - diff --git a/docs/autoapi/sorcha/ephemeris/pixel_dict/index.rst b/docs/autoapi/sorcha/ephemeris/pixel_dict/index.rst deleted file mode 100644 index 4f600a97..00000000 --- a/docs/autoapi/sorcha/ephemeris/pixel_dict/index.rst +++ /dev/null @@ -1,232 +0,0 @@ -sorcha.ephemeris.pixel_dict -=========================== - -.. py:module:: sorcha.ephemeris.pixel_dict - - -Classes -------- - -.. autoapisummary:: - - sorcha.ephemeris.pixel_dict.PixelDict - - -Functions ---------- - -.. autoapisummary:: - - sorcha.ephemeris.pixel_dict.lagrange3 - - -Module Contents ---------------- - -.. py:function:: lagrange3(t0, t1, t2, t) - - Calculate the coefficients for - second-order Lagrange interpolation - for measured points at times t0, t1, - and t2 and for an array of times t. - - These coefficients can be reused for - any number of input vectors. - - :param t0: Time t0 - :type t0: float - :param t1: Time t1 - :type t1: float - :param t2: Time t2 - :type t2: float - :param t: Times for the interpolation - :type t: 1D array - - :returns: * **L0** (*1D array*) -- interpolation coefficient at t0 - * **L1** (*1D array*) -- interpolation coefficient at t1 - * **L2** (*1D array*) -- interpolation coefficient at t2 - - -.. py:class:: PixelDict(jd_tdb, sim_dict, ephem, obsCode, observatory, picket_interval=1.0, nside=128, nested=True, n_sub_intervals=101) - - Class with methods needed during the ephemerides generation - Interfaces directly with the ASSIST+Rebound simulation objects as well as healpix - - - .. py:attribute:: nside - :value: 128 - - - - .. py:attribute:: picket_interval - :value: 1.0 - - - - .. py:attribute:: n_sub_intervals - :value: 101 - - - - .. py:attribute:: obsCode - - - .. py:attribute:: nested - :value: True - - - - .. py:attribute:: sim_dict - - - .. py:attribute:: ephem - - - .. py:attribute:: observatory - - - .. py:attribute:: t0 - - - .. py:attribute:: r_obs_0 - - - .. py:attribute:: tp - - - .. py:attribute:: r_obs_p - - - .. py:attribute:: tm - - - .. py:attribute:: r_obs_m - - - .. py:attribute:: pixel_dict - - - .. py:attribute:: rho_hat_m_dict - - - .. py:attribute:: rho_hat_0_dict - - - .. py:attribute:: rho_hat_p_dict - - - .. py:method:: get_observatory_position(t) - - Computes the barycentric position of the observatory (in au) - - :param t: Epoch for the position vector - :type t: float - - :returns: Barycentric position of the observatory (x,y,z) - :rtype: array (3,) - - - - .. py:method:: get_object_unit_vectors(desigs, r_obs, t, lt0=0.01) - - Computes the unit vector (in the equatorial sphere) that point towards the object - observatory vector - for a list of objects, at a given time - - :param desigs: List of designations (consistent with the simulation dictionary) - :type desigs: list - :param r_obs: Observatory location - :type r_obs: array (3 entries) - :param t: Time of the observation - :type t: float - :param lt0: Initial guess (in days) for light-time correction (default: 0.01 days) - :type lt0: float - - :returns: **rho_hat_dict** -- Dictionary of unit vectors - :rtype: dict - - - - .. py:method:: get_all_object_unit_vectors(r_obs, t, lt0=0.01) - - Computes the unit vector (in the equatorial sphere) that point towards the object - observatory vector - for *all* objects, at a given time - - :param r_obs: Observatory location - :type r_obs: array (3 entries) - :param t: Time of the observation - :type t: float - :param lt0: Initial guess (in days) for light-time correction (default: 0.01 days) - :type lt0: float - - :returns: **rho_hat_dict** -- Dictionary of unit vectors - :rtype: dict - - - - .. py:method:: get_interp_factors(tm, t0, tp, n_sub_intervals) - - Computes the Lagrange interpolation factors at a set of 3 times for an - equally spaced grid of points with a chosen number of sub-intervals - :param tm: First reference time - :type tm: float - :param t0: Second reference time - :type t0: float - :param tp: Third reference time - :type tp: float - :param n_sub_intervals: Number of sub-intervals for the Lagrange interpolation (default: 101) - :type n_sub_intervals: int - - :returns: * **Lm** (*2D array*) -- Lagrange coefficients at tm - * **L0** (*2D array*) -- Lagrange coefficients at t0 - * **Lp** (*2D array*) -- Lagrange coefficient at tp - - - - .. py:method:: interpolate_unit_vectors(desigs, jd_tdb) - - Interpolates the unit vectors for a list of designations towards the new target time - - :param desigs: List of designations (consistent with the simulation dictionary) - :type desigs: list - :param jd_tdb: Target time - :type jd_tdb: float - - :returns: **unit_vector_dict** -- Dictionary of unit vectors - :rtype: dict - - - - .. py:method:: compute_pixel_traversed() - - Computes the healpix pixels traversed by all the objects during between times tm and tp - - - - .. py:method:: update_pickets(jd_tdb) - - Updates the picket interpolation vectors for the new reference time - - :param jd_tdb: Target time - :type jd_tdb: float - - - - .. py:method:: get_designations(jd_tdb, ra, dec, ang_fov) - - Get the object designations that are within an angular radius of a topocentric unit vector at a - given time. - - :param jd_tdb: Target time - :type jd_tdb: float - :param ra: right ascension (degrees) - :type ra: float - :param dec: declination (degrees) - :type dec: float - :param ang_fov: Field of view radius - :type ang_fov: float - - :returns: **desigs** -- List of designations - :rtype: list - - - diff --git a/docs/autoapi/sorcha/ephemeris/simulation_constants/index.rst b/docs/autoapi/sorcha/ephemeris/simulation_constants/index.rst deleted file mode 100644 index 74c6b2ce..00000000 --- a/docs/autoapi/sorcha/ephemeris/simulation_constants/index.rst +++ /dev/null @@ -1,65 +0,0 @@ -sorcha.ephemeris.simulation_constants -===================================== - -.. py:module:: sorcha.ephemeris.simulation_constants - - -Attributes ----------- - -.. autoapisummary:: - - sorcha.ephemeris.simulation_constants.RADIUS_EARTH_KM - sorcha.ephemeris.simulation_constants.AU_M - sorcha.ephemeris.simulation_constants.AU_KM - sorcha.ephemeris.simulation_constants.SPEED_OF_LIGHT - sorcha.ephemeris.simulation_constants.OBLIQUITY_ECLIPTIC - sorcha.ephemeris.simulation_constants.ECL_TO_EQ_ROTATION_MATRIX - - -Functions ---------- - -.. autoapisummary:: - - sorcha.ephemeris.simulation_constants.create_ecl_to_eq_rotation_matrix - - -Module Contents ---------------- - -.. py:data:: RADIUS_EARTH_KM - :value: 6378.137 - - -.. py:data:: AU_M - :value: 149597870700 - - -.. py:data:: AU_KM - :value: 149597870.7 - - -.. py:data:: SPEED_OF_LIGHT - :value: 173.1446326742403 - - -.. py:data:: OBLIQUITY_ECLIPTIC - -.. py:function:: create_ecl_to_eq_rotation_matrix(ecl) - - Creates a rotation matrix for transforming ecliptical coordinates - to equatorial coordinates. A rotation matrix based on the solar - system's ecliptic obliquity is already provided as - `ECL_TO_EQ_ROTATION_MATRIX`. - - :param ecl: The ecliptical obliquity. - :type ecl: float - - :returns: **rotmat** -- rotation matrix for transofmring ecliptical coordinates to equatorial coordinates. - Array has shape (3,3). - :rtype: numpy array/matrix of floats - - -.. py:data:: ECL_TO_EQ_ROTATION_MATRIX - diff --git a/docs/autoapi/sorcha/ephemeris/simulation_data_files/index.rst b/docs/autoapi/sorcha/ephemeris/simulation_data_files/index.rst deleted file mode 100644 index 83d47b91..00000000 --- a/docs/autoapi/sorcha/ephemeris/simulation_data_files/index.rst +++ /dev/null @@ -1,33 +0,0 @@ -sorcha.ephemeris.simulation_data_files -====================================== - -.. py:module:: sorcha.ephemeris.simulation_data_files - - -Functions ---------- - -.. autoapisummary:: - - sorcha.ephemeris.simulation_data_files.make_retriever - - -Module Contents ---------------- - -.. py:function:: make_retriever(auxconfigs, directory_path: str = None) -> pooch.Pooch - - Helper function that will create a Pooch object to track and retrieve files. - - :param directory_path: The base directory to place all downloaded files. Default = None - :type directory_path: string, optional - :param registry: A dictionary of file names to SHA hashes. Generally we'll not use SHA=None - because the files we're tracking change frequently. Default = REGISTRY - :type registry: dictionary, optional - :param auxconfigs: Dataclass of auxiliary configuration file arguments. - :type auxconfigs: dataclass - - :returns: The instance of a Pooch object used to track and retrieve files. - :rtype: pooch - - diff --git a/docs/autoapi/sorcha/ephemeris/simulation_driver/index.rst b/docs/autoapi/sorcha/ephemeris/simulation_driver/index.rst deleted file mode 100644 index e2982f1a..00000000 --- a/docs/autoapi/sorcha/ephemeris/simulation_driver/index.rst +++ /dev/null @@ -1,180 +0,0 @@ -sorcha.ephemeris.simulation_driver -================================== - -.. py:module:: sorcha.ephemeris.simulation_driver - - -Classes -------- - -.. autoapisummary:: - - sorcha.ephemeris.simulation_driver.EphemerisGeometryParameters - - -Functions ---------- - -.. autoapisummary:: - - sorcha.ephemeris.simulation_driver.get_vec - sorcha.ephemeris.simulation_driver.create_ephemeris - sorcha.ephemeris.simulation_driver.get_residual_vectors - sorcha.ephemeris.simulation_driver.calculate_rates_and_geometry - sorcha.ephemeris.simulation_driver.write_out_ephemeris_file - - -Module Contents ---------------- - -.. py:class:: EphemerisGeometryParameters - - Data class for holding parameters related to ephemeris geometry - - - .. py:attribute:: obj_id - :type: str - :value: None - - - - .. py:attribute:: mjd_tai - :type: float - :value: None - - - - .. py:attribute:: rho - :type: float - :value: None - - - - .. py:attribute:: rho_hat - :type: float - :value: None - - - - .. py:attribute:: rho_mag - :type: float - :value: None - - - - .. py:attribute:: r_ast - :type: float - :value: None - - - - .. py:attribute:: v_ast - :type: float - :value: None - - - -.. py:function:: get_vec(row, vecname) - - Extracts a vector from a Pandas dataframe row - :param row: - :type row: row from the dataframe - :param vecname: - :type vecname: name of the vector - - :rtype: 3D numpy array - - -.. py:function:: create_ephemeris(orbits_df, pointings_df, args, sconfigs) - - Generate a set of observations given a collection of orbits - and set of pointings. - - :param orbits_df: The dataframe containing the collection of orbits. - :type orbits_df: pandas dataframe - :param pointings_df: The dataframe containing the collection of telescope/camera pointings. - :type pointings_df: pandas dataframe - :param args: Various arguments necessary for the calculation - :param sconfigs: Dataclass of configuration file arguments. - Various configuration parameters necessary for the calculation - ang_fov : float - The angular size (deg) of the field of view - buffer : float - The angular size (deg) of the buffer around the field of view. - A buffer is required to allow for some motion between the time - of the observation and the time of the picket (t_picket) - picket_interval : float - The interval (days) between picket calculations. This is 1 day - by default. Current there is only one such interval, used for - all objects. It is currently possible for extremely fast-moving - objects to be missed. This will be remedied in future releases. - obsCode : string - The MPC code for the observatory. (This is current a configuration - parameter, but these should be included in the visit information, - to allow for multiple observatories. - nside : integer - The nside value used for the HEALPIx calculations. Must be a - power of 2 (1, 2, 4, ...) nside=64 is current default. - - :returns: **observations** -- The dataframe of observations needed for Sorcha to continue - :rtype: pandas dataframe - - .. rubric:: Notes - - This works by calculating and regularly updating the sky-plane - locations (unit vectors) of all the objects in the collection - of orbits. The HEALPix index for each of the locations is calculated. - A dictionary with pixel indices as keys and lists of ObjIDs for - those objects in each HEALPix tile as values is generated. An individual - one of these calculations is called a 'picket', as one element of a long - picket fence. Typically, the interval between pickets is one day. - - Given a specific pointing, the set of HEALPix tiles that are overlapped - by the pointing (and a buffer region) is computed. Then the precise - locations of just those objects within that set of HEALPix tiles are - computed. Details for those that actually do land within the field - of view are passed along. - - -.. py:function:: get_residual_vectors(v1) - - Decomposes the vector into two unit vectors to facilitate computation of on-sky angles - The decomposition is such that A = (-sin (RA), cos(RA), 0) is in the direction of increasing RA, - and D = (-sin(dec)cos (RA), -sin(dec) sin(RA), cos(dec)) is in the direction of increasing Dec - The triplet (A,D,v1) forms an orthonormal basis of the 3D vector space - :param v1: The vector to be decomposed - :type v1: array, shape = (3,)) - - :returns: * **A** (*array, shape = (3,))*) -- A vector - * **D** (*array, shape = (3,))*) -- D vector - - -.. py:function:: calculate_rates_and_geometry(pointing: pandas.DataFrame, ephem_geom_params: EphemerisGeometryParameters) - - Calculate rates and geometry for objects within the field of view - - :param pointing: The dataframe containing the pointing database. - :type pointing: pandas dataframe - :param ephem_geom_params: Various parameters necessary to calculate the ephemeris - :type ephem_geom_params: EphemerisGeometryParameters - - :returns: Tuple containing the ephemeris parameters needed for Sorcha post processing. - :rtype: tuple - - -.. py:function:: write_out_ephemeris_file(ephemeris_df, ephemeris_csv_filename, args, sconfigs) - - Writes the ephemeris out to an external file. - - :param ephemeris_df: The data frame of ephemeris information to be written out. - :type ephemeris_df: Pandas DataFrame - :param ephemeris_csv_filename: The filepath (without extension) to write the ephemeris file to. - :type ephemeris_csv_filename: string - :param args: Command-line arguments from Sorcha. - :type args: sorchaArguments object or similar - :param sconfigs: Dataclass of configuration file arguments. - :type sconfigs: dataclass - - :rtype: None. - - diff --git a/docs/autoapi/sorcha/ephemeris/simulation_geometry/index.rst b/docs/autoapi/sorcha/ephemeris/simulation_geometry/index.rst deleted file mode 100644 index 7be49eb5..00000000 --- a/docs/autoapi/sorcha/ephemeris/simulation_geometry/index.rst +++ /dev/null @@ -1,121 +0,0 @@ -sorcha.ephemeris.simulation_geometry -==================================== - -.. py:module:: sorcha.ephemeris.simulation_geometry - - -Functions ---------- - -.. autoapisummary:: - - sorcha.ephemeris.simulation_geometry.ecliptic_to_equatorial - sorcha.ephemeris.simulation_geometry.integrate_light_time - sorcha.ephemeris.simulation_geometry.get_hp_neighbors - sorcha.ephemeris.simulation_geometry.ra_dec2vec - sorcha.ephemeris.simulation_geometry.vec2ra_dec - sorcha.ephemeris.simulation_geometry.barycentricObservatoryRates - - -Module Contents ---------------- - -.. py:function:: ecliptic_to_equatorial(v, rot_mat=ECL_TO_EQ_ROTATION_MATRIX) - - Converts an ecliptic-aligned vector to an equatorially-aligned vector - - :param v: vector - :type v: array (3 entries) - :param rot_mat: Rotation matrix. Default is the matrix that computes the ecliptic to equatorial conversion - :type rot_mat: 2D array (3x3 matrix) - - :returns: **v** -- Rotated vector - :rtype: array (3 entries) - - -.. py:function:: integrate_light_time(sim, ex, t, r_obs, lt0=0, iter=3, speed_of_light=SPEED_OF_LIGHT) - - Performs the light travel time correction between object and observatory iteratively for the object at a given reference time - - :param sim: Rebound simulation object - :type sim: simulation - :param ex: ASSIST simulation extras - :type ex: simulation extras - :param t: Target time - :type t: float - :param r_obs: Observatory position at time t - :type r_obs: array (3 entries) - :param lt0: First guess for light travel time - :type lt0: float - :param iter: Number of iterations - :type iter: int - :param speed_of_light: Speed of light for the calculation (default is SPEED_OF_LIGHT constant) - :type speed_of_light: float - - :returns: * **rho** (*array*) -- Object-observatory vector - * **rho_mag** (*float*) -- Magnitude of rho vector - * **lt** (*float*) -- Light travel time - * **target** (*array*) -- Object position vector at t-lt - * **vtarget** (*array*) -- Object velocity at t-lt - - -.. py:function:: get_hp_neighbors(ra_c, dec_c, search_radius, nside=32, nested=True) - - Queries the healpix grid for pixels near the given RA/Dec with a given search radius - - :param ra_c: Target RA - :type ra_c: float - :param dec_c: Target dec - :type dec_c: float - :param search_radius: Radius for the query - :type search_radius: float - :param nside: healpix nside - :type nside: int - :param nested: Defines the ordering scheme for the healpix ordering. True (default) means a NESTED ordering - :type nested: boolean - - :returns: **res** -- List of healpix pixels - :rtype: list - - -.. py:function:: ra_dec2vec(ra, dec) - - Converts a RA/Dec pair to a unit vector on the sphere - :param ra: Target RA - :type ra: float - :param dec: Target dec - :type dec: float - - :returns: Unit vector - :rtype: array - - -.. py:function:: vec2ra_dec(vec) - - Decomposes a unit vector on the sphere into a RA/Dec pair - :param vec: Unit vector - :type vec: array - - :returns: * **ra** (*float*) -- Target RA - * **dec** (*float*) -- Target dec - - -.. py:function:: barycentricObservatoryRates(et, obsCode, observatories, Rearth=RADIUS_EARTH_KM, delta_et=10) - - Computes the position and rate of motion for the observatory in barycentric coordinates - - :param et: JPL ephemeris time - :type et: float - :param obsCode: MPC observatory code - :type obsCode: str - :param observatories: Observatory object with spherical representations for the obsCode - :type observatories: Observatory - :param Rearth: Radius of the Earth (default is RADIUS_EARTH_KM) - :type Rearth: float - :param delta_et: Difference in ephemeris time (in days) to derive the rotation matrix from the fixed Earth equatorial frame to J2000 (default: 10) - :type delta_et: float - - :returns: * *array* -- Position of the observatory (baricentric) - * *array* -- Velocity of the observatory (baricentric) - - diff --git a/docs/autoapi/sorcha/ephemeris/simulation_parsing/index.rst b/docs/autoapi/sorcha/ephemeris/simulation_parsing/index.rst deleted file mode 100644 index 4124be88..00000000 --- a/docs/autoapi/sorcha/ephemeris/simulation_parsing/index.rst +++ /dev/null @@ -1,97 +0,0 @@ -sorcha.ephemeris.simulation_parsing -=================================== - -.. py:module:: sorcha.ephemeris.simulation_parsing - - -Classes -------- - -.. autoapisummary:: - - sorcha.ephemeris.simulation_parsing.Observatory - - -Functions ---------- - -.. autoapisummary:: - - sorcha.ephemeris.simulation_parsing.mjd_tai_to_epoch - sorcha.ephemeris.simulation_parsing.parse_orbit_row - - -Module Contents ---------------- - -.. py:function:: mjd_tai_to_epoch(mjd_tai) - - Converts a MJD value in TAI to SPICE ephemeris time - - :param mjd_tai: Input mjd - :type mjd_tai: float - - :rtype: Ephemeris time - - -.. py:function:: parse_orbit_row(row, epochJD_TDB, ephem, sun_dict, gm_sun, gm_total) - - Parses the input orbit row, converting it to the format expected by - the ephemeris generation code later on - - :param row: Row of the input dataframe - :type row: Pandas dataframe row - :param epochJD_TDB: epoch of the elements, in JD TDB - :type epochJD_TDB: float - :param ephem: ASSIST ephemeris object - :type ephem: Ephem - :param sun_dict: Dictionary with the position of the Sun at each epoch - :type sun_dict: dict - :param gm_sun: Standard gravitational parameter GM for the Sun - :type gm_sun: float - :param gm_total: Standard gravitational parameter GM for the Solar System barycenter - :type gm_total: float - - :returns: State vector (position, velocity) - :rtype: tuple - - -.. py:class:: Observatory(args, auxconfigs, oc_file=None) - - Class containing various utility tools related to the calculation of the observatory position - - - .. py:attribute:: observatoryPositionCache - - - .. py:attribute:: ObservatoryXYZ - - - .. py:method:: convert_to_geocentric(obs_location: dict) -> tuple - - Converts the observatory location to geocentric coordinates - - :param obs_location: Dictionary with Longitude and sin/cos of the observatory Latitude - :type obs_location: dict - - :returns: Geocentric position (x,y,z) - :rtype: tuple - - - - .. py:method:: barycentricObservatory(et, obsCode, Rearth=RADIUS_EARTH_KM) - - Computes the barycentric position of the observatory - - :param et: JPL internal ephemeris time - :type et: float - :param obsCode: MPC Observatory code - :type obsCode: str - :param Rearth: Radius of the Earth - :type Rearth: float - - :returns: Barycentric position of the observatory (x,y,z) - :rtype: array (3,) - - - diff --git a/docs/autoapi/sorcha/ephemeris/simulation_setup/index.rst b/docs/autoapi/sorcha/ephemeris/simulation_setup/index.rst deleted file mode 100644 index 27c80764..00000000 --- a/docs/autoapi/sorcha/ephemeris/simulation_setup/index.rst +++ /dev/null @@ -1,74 +0,0 @@ -sorcha.ephemeris.simulation_setup -================================= - -.. py:module:: sorcha.ephemeris.simulation_setup - - -Functions ---------- - -.. autoapisummary:: - - sorcha.ephemeris.simulation_setup.create_assist_ephemeris - sorcha.ephemeris.simulation_setup.furnish_spiceypy - sorcha.ephemeris.simulation_setup.generate_simulations - sorcha.ephemeris.simulation_setup.precompute_pointing_information - - -Module Contents ---------------- - -.. py:function:: create_assist_ephemeris(args, auxconfigs) -> tuple - - Build the ASSIST ephemeris object - Parameter - --------- - auxconfigs: dataclass - Dataclass of auxiliary configuration file arguments. - :returns: * **Ephem** (*ASSIST ephemeris obejct*) -- The ASSIST ephemeris object - * **gm_sun** (*float*) -- value for the GM_SUN value - * **gm_total** (*float*) -- value for gm_total - - -.. py:function:: furnish_spiceypy(args, auxconfigs) - - Builds the SPICE kernel, downloading the required files if needed - :param auxconfigs: Dataclass of auxiliary configuration file arguments. - :type auxconfigs: dataclass - - -.. py:function:: generate_simulations(ephem, gm_sun, gm_total, orbits_df, args) - - Creates the dictionary of ASSIST simulations for the ephemeris generation - - :param ephem: The ASSIST ephemeris object - :type ephem: Ephem - :param gm_sun: Standard gravitational parameter GM for the Sun - :type gm_sun: float - :param gm_total: Standard gravitational parameter GM for the Solar System barycenter - :type gm_total: float - :param orbits_df: Pandas dataframe with the input orbits - :type orbits_df: dataframe - :param args: dictionary of command-line arguments. - :type args: dictionary or `sorchaArguments` object - - :returns: **sim_dict** -- Dictionary of ASSIST simulations - :rtype: dict - - -.. py:function:: precompute_pointing_information(pointings_df, args, sconfigs) - - This function is meant to be run once to prime the pointings dataframe - with additional information that Assist & Rebound needs for it's work. - - :param pointings_df: Contains the telescope pointing database. - :type pointings_df: pandas dataframe - :param args: Command line arguments needed for initialization. - :type args: dictionary - :param sconfigs: Dataclass of configuration file arguments. - :type sconfigs: dataclass - - :returns: **pointings_df** -- The original dataframe with several additional columns of precomputed values. - :rtype: pandas dataframe - - diff --git a/docs/autoapi/sorcha/index.rst b/docs/autoapi/sorcha/index.rst deleted file mode 100644 index 2b3daaa8..00000000 --- a/docs/autoapi/sorcha/index.rst +++ /dev/null @@ -1,54 +0,0 @@ -sorcha -====== - -.. py:module:: sorcha - - -Submodules ----------- - -.. toctree:: - :maxdepth: 1 - - /autoapi/sorcha/activity/index - /autoapi/sorcha/ephemeris/index - /autoapi/sorcha/lightcurves/index - /autoapi/sorcha/modules/index - /autoapi/sorcha/readers/index - /autoapi/sorcha/sorcha/index - /autoapi/sorcha/utilities/index - - -Attributes ----------- - -.. autoapisummary:: - - sorcha.__version__ - - -Functions ---------- - -.. autoapisummary:: - - sorcha.cite - - -Package Contents ----------------- - -.. py:function:: cite() - - Providing the bibtex, AAS Journals software latex command, and acknowledgement - statements for Sorcha and the associated packages that power it. - - :param None: - - :rtype: None - - -.. py:data:: __version__ - :value: 'unknown version' - - diff --git a/docs/autoapi/sorcha/lightcurves/base_lightcurve/index.rst b/docs/autoapi/sorcha/lightcurves/base_lightcurve/index.rst deleted file mode 100644 index 988b89cd..00000000 --- a/docs/autoapi/sorcha/lightcurves/base_lightcurve/index.rst +++ /dev/null @@ -1,90 +0,0 @@ -sorcha.lightcurves.base_lightcurve -================================== - -.. py:module:: sorcha.lightcurves.base_lightcurve - - -Attributes ----------- - -.. autoapisummary:: - - sorcha.lightcurves.base_lightcurve.logger - - -Classes -------- - -.. autoapisummary:: - - sorcha.lightcurves.base_lightcurve.AbstractLightCurve - - -Module Contents ---------------- - -.. py:data:: logger - -.. py:class:: AbstractLightCurve(required_column_names: List[str] = []) - - Bases: :py:obj:`abc.ABC` - - - Abstract base class for lightcurve models - - - .. py:attribute:: required_column_names - :value: [] - - - - .. py:method:: compute(df: pandas.DataFrame) -> numpy.array - :abstractmethod: - - - User implemented calculation based on the input provided by the - pandas dataframe ``df``. - - :param df: The ``observations`` dataframe provided by ``Sorcha``. - :type df: Pandas dataframe - - - - .. py:method:: _validate_column_names(df: pandas.DataFrame) -> None - - Private method that checks that the provided pandas dataframe contains - the required columns defined in ``self.required_column_names``. - - :param df: The ``observations`` dataframe provided by ``Sorcha``. - :type df: Pandas dataframe - - - - .. py:method:: _log_exception(exception: Exception) -> None - - Log an error message from an exception to the error log file - - :param exception: The exception with a string to appended to the error log - :type exception: Exception - - - - .. py:method:: _log_error_message(error_msg: str) -> None - - Log a specific error string to the error log file - - :param error_msg: The string to be appended to the error log - :type error_msg: string - - - - .. py:method:: name_id() -> str - :staticmethod: - - :abstractmethod: - - - This method will return the unique name of the LightCurve Model - - - diff --git a/docs/autoapi/sorcha/lightcurves/identity_lightcurve/index.rst b/docs/autoapi/sorcha/lightcurves/identity_lightcurve/index.rst deleted file mode 100644 index bb45235b..00000000 --- a/docs/autoapi/sorcha/lightcurves/identity_lightcurve/index.rst +++ /dev/null @@ -1,56 +0,0 @@ -sorcha.lightcurves.identity_lightcurve -====================================== - -.. py:module:: sorcha.lightcurves.identity_lightcurve - - -Classes -------- - -.. autoapisummary:: - - sorcha.lightcurves.identity_lightcurve.IdentityLightCurve - - -Module Contents ---------------- - -.. py:class:: IdentityLightCurve(required_column_names: List[str] = ['fieldMJD_TAI']) - - Bases: :py:obj:`sorcha.lightcurves.base_lightcurve.AbstractLightCurve` - - - !!! THIS SHOULD NEVER BE USED - FOR TESTING ONLY !!! - - Rudimentary lightcurve model that returns no shift. This class is explicitly - created for testing purposes. - - - .. py:method:: compute(df: pandas.DataFrame) -> numpy.array - - Returns numpy array of 0's with shape equal to the input dataframe - time column. - - :param df: The ``observations`` dataframe provided by ``Sorcha``. - :type df: Pandas dataframe - - :returns: Numpy array of 0's with shape equal to the input dataframe time column. - :rtype: np.array - - - - .. py:method:: name_id() -> str - :staticmethod: - - - Returns the string identifier for this light curve method. It must be - unique within all the subclasses of ``AbstractLightCurve``. - - We have chosen the name "identity" here because the input brightness will - equal the output brightness if this model is applied. - - :returns: Unique identifier for this light curve calculator - :rtype: string - - - diff --git a/docs/autoapi/sorcha/lightcurves/index.rst b/docs/autoapi/sorcha/lightcurves/index.rst deleted file mode 100644 index 086a1814..00000000 --- a/docs/autoapi/sorcha/lightcurves/index.rst +++ /dev/null @@ -1,171 +0,0 @@ -sorcha.lightcurves -================== - -.. py:module:: sorcha.lightcurves - - -Submodules ----------- - -.. toctree:: - :maxdepth: 1 - - /autoapi/sorcha/lightcurves/base_lightcurve/index - /autoapi/sorcha/lightcurves/identity_lightcurve/index - /autoapi/sorcha/lightcurves/lightcurve_registration/index - - -Attributes ----------- - -.. autoapisummary:: - - sorcha.lightcurves.LC_METHODS - - -Classes -------- - -.. autoapisummary:: - - sorcha.lightcurves.AbstractLightCurve - sorcha.lightcurves.IdentityLightCurve - - -Functions ---------- - -.. autoapisummary:: - - sorcha.lightcurves.register_lc_subclasses - sorcha.lightcurves.update_lc_subclasses - - -Package Contents ----------------- - -.. py:class:: AbstractLightCurve(required_column_names: List[str] = []) - - Bases: :py:obj:`abc.ABC` - - - Abstract base class for lightcurve models - - - .. py:attribute:: required_column_names - :value: [] - - - - .. py:method:: compute(df: pandas.DataFrame) -> numpy.array - :abstractmethod: - - - User implemented calculation based on the input provided by the - pandas dataframe ``df``. - - :param df: The ``observations`` dataframe provided by ``Sorcha``. - :type df: Pandas dataframe - - - - .. py:method:: _validate_column_names(df: pandas.DataFrame) -> None - - Private method that checks that the provided pandas dataframe contains - the required columns defined in ``self.required_column_names``. - - :param df: The ``observations`` dataframe provided by ``Sorcha``. - :type df: Pandas dataframe - - - - .. py:method:: _log_exception(exception: Exception) -> None - - Log an error message from an exception to the error log file - - :param exception: The exception with a string to appended to the error log - :type exception: Exception - - - - .. py:method:: _log_error_message(error_msg: str) -> None - - Log a specific error string to the error log file - - :param error_msg: The string to be appended to the error log - :type error_msg: string - - - - .. py:method:: name_id() -> str - :staticmethod: - - :abstractmethod: - - - This method will return the unique name of the LightCurve Model - - - -.. py:class:: IdentityLightCurve(required_column_names: List[str] = ['fieldMJD_TAI']) - - Bases: :py:obj:`sorcha.lightcurves.base_lightcurve.AbstractLightCurve` - - - !!! THIS SHOULD NEVER BE USED - FOR TESTING ONLY !!! - - Rudimentary lightcurve model that returns no shift. This class is explicitly - created for testing purposes. - - - .. py:method:: compute(df: pandas.DataFrame) -> numpy.array - - Returns numpy array of 0's with shape equal to the input dataframe - time column. - - :param df: The ``observations`` dataframe provided by ``Sorcha``. - :type df: Pandas dataframe - - :returns: Numpy array of 0's with shape equal to the input dataframe time column. - :rtype: np.array - - - - .. py:method:: name_id() -> str - :staticmethod: - - - Returns the string identifier for this light curve method. It must be - unique within all the subclasses of ``AbstractLightCurve``. - - We have chosen the name "identity" here because the input brightness will - equal the output brightness if this model is applied. - - :returns: Unique identifier for this light curve calculator - :rtype: string - - - -.. py:function:: register_lc_subclasses() -> Dict[str, Callable] - - This method will identify all of the subclasses of ``AbstractLightCurve`` - and build a dictionary that maps ``name : subclass``. - - :returns: A dictionary of all of subclasses of ``AbstractLightCurve``. Where - the string returned from ``subclass.name_id()`` is the key, and the - subclass is the value. - :rtype: dict - - :raises ValueError: If a duplicate key is found, a ``ValueError`` is raised. This would - likely occur if a user copy/pasted an existing subclass but failed to - update the string returned from ``name_id()``. - - -.. py:function:: update_lc_subclasses() -> None - - This function is used to register newly created subclasses of the - `AbstractLightCurve`. - - -.. py:data:: LC_METHODS - diff --git a/docs/autoapi/sorcha/lightcurves/lightcurve_registration/index.rst b/docs/autoapi/sorcha/lightcurves/lightcurve_registration/index.rst deleted file mode 100644 index edadb7af..00000000 --- a/docs/autoapi/sorcha/lightcurves/lightcurve_registration/index.rst +++ /dev/null @@ -1,49 +0,0 @@ -sorcha.lightcurves.lightcurve_registration -========================================== - -.. py:module:: sorcha.lightcurves.lightcurve_registration - - -Attributes ----------- - -.. autoapisummary:: - - sorcha.lightcurves.lightcurve_registration.LC_METHODS - - -Functions ---------- - -.. autoapisummary:: - - sorcha.lightcurves.lightcurve_registration.register_lc_subclasses - sorcha.lightcurves.lightcurve_registration.update_lc_subclasses - - -Module Contents ---------------- - -.. py:function:: register_lc_subclasses() -> Dict[str, Callable] - - This method will identify all of the subclasses of ``AbstractLightCurve`` - and build a dictionary that maps ``name : subclass``. - - :returns: A dictionary of all of subclasses of ``AbstractLightCurve``. Where - the string returned from ``subclass.name_id()`` is the key, and the - subclass is the value. - :rtype: dict - - :raises ValueError: If a duplicate key is found, a ``ValueError`` is raised. This would - likely occur if a user copy/pasted an existing subclass but failed to - update the string returned from ``name_id()``. - - -.. py:function:: update_lc_subclasses() -> None - - This function is used to register newly created subclasses of the - `AbstractLightCurve`. - - -.. py:data:: LC_METHODS - diff --git a/docs/autoapi/sorcha/modules/PPAddUncertainties/index.rst b/docs/autoapi/sorcha/modules/PPAddUncertainties/index.rst deleted file mode 100644 index 082abe4e..00000000 --- a/docs/autoapi/sorcha/modules/PPAddUncertainties/index.rst +++ /dev/null @@ -1,217 +0,0 @@ -sorcha.modules.PPAddUncertainties -================================= - -.. py:module:: sorcha.modules.PPAddUncertainties - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPAddUncertainties.degCos - sorcha.modules.PPAddUncertainties.degSin - sorcha.modules.PPAddUncertainties.addUncertainties - sorcha.modules.PPAddUncertainties.uncertainties - sorcha.modules.PPAddUncertainties.calcAstrometricUncertainty - sorcha.modules.PPAddUncertainties.calcRandomAstrometricErrorPerCoord - sorcha.modules.PPAddUncertainties.calcPhotometricUncertainty - - -Module Contents ---------------- - -.. py:function:: degCos(x) - - Calculate cosine of an angle in degrees. - - :param x: angle in degrees. - :type x: float - - :returns: The cosine of x. - :rtype: float - - -.. py:function:: degSin(x) - - Calculate sine of an angle in degrees. - - :param x: angle in degrees. - :type x: float - - :returns: The sine of x. - :rtype: float - - -.. py:function:: addUncertainties(detDF, sconfigs, module_rngs, verbose=True) - - Generates astrometric and photometric uncertainties, and SNR. Uses uncertainties - to randomize the photometry. Accounts for trailing losses. - - Adds the following columns to the observations dataframe: - - - astrometricSigma_deg - - trailedSourceMagSigma - - PSFMagSigma - - SNR - - trailedSourceMag - - PSFMag - - :param detDF: Dataframe of observations. - :type detDF: Pandas dataframe) - :param sconfigs: Dataclass of configuration file arguments. - :type sconfigs: dataclass - :param module_rngs: A collection of random number generators (per module). - :type module_rngs: PerModuleRNG - :param verbose: - :type verbose: Boolean, optional - :param Verbose Logging Flag. Default = True: - - :returns: **detDF** -- dataframe of observations, with new columns for observed - magnitudes, SNR, and astrometric/photometric uncertainties. - :rtype: Pandas dataframe - - -.. py:function:: uncertainties(detDF, sconfigs, limMagName='fiveSigmaDepth_mag', seeingName='seeingFwhmGeom_arcsec', filterMagName='trailedSourceMagTrue', dra_name='RARateCosDec_deg_day', ddec_name='DecRate_deg_day', dec_name='Dec_deg', visit_time_name='visitExposureTime') - - Add astrometric and photometric uncertainties to observations. - - :param detDF: dataframe containing observations. - :type detDF: Pandas dataframe - :param sconfigs: Dataclass of configuration file arguments. - :type sconfigs: dataclass - :param limMagName: pandas dataframe column name of the limiting magnitude. - Default = "fiveSigmaDepth_mag" - :type limMagName: string, optional - :param seeingName: pandas dataframe column name of the seeing - Default = "seeingFwhmGeom_arcsec" - :type seeingName: string, optional - :param filterMagName: pandas dataframe column name of the object magnitude - Default = "trailedSourceMagTrue" - :type filterMagName: string, optional - :param dra_name: pandas dataframe column name of the object RA rate - Default = "RARateCosDec_deg_day" - :type dra_name: string, optional - :param ddec_name: pandas dataframe column name of the object declination rate - Default = "DecRate_deg_day" - :type ddec_name: string, optional - :param dec_name: pandas dataframe column name of the object declination - Default = "Dec_deg" - :type dec_name: string, optional - :param visit_time_name: pandas dataframe column name for exposure length - Default = "visitExposureTime" - :type visit_time_name: string, optional - - :returns: * **astrSigDeg** (*numpy array*) -- astrometric uncertainties in degrees. - * **photometric_sigma** (*numpy array*) -- photometric uncertainties in magnitude. - * **SNR** (*numpy array*) -- signal-to-noise ratio. - - -.. py:function:: calcAstrometricUncertainty(mag, m5, nvisit=1, FWHMeff=700.0, error_sys=10.0, astErrCoeff=0.6, output_units='mas') - - Calculate the astrometric uncertainty, for object catalog purposes. - - - :param mag: magnitude of the observation. - :type mag: float or array of floats) - :param m5: 5-sigma limiting magnitude. - :type m5: float or array of floats - :param nvisit: number of visits to consider. - Default = 1 - :type nvisit: int, optional - :param FWHMeff: effective Full Width at Half Maximum of Point Spread Function [mas]. - Default = 700.0 - :type FWHMeff: float, optional - :param error_sys: systematic error [mas]. - Default = 10.0 - :type error_sys: float, optional - :param astErrCoeff: Astrometric error coefficient - (see calcRandomAstrometricErrorPerCoord description). - Default = 0.60 - :type astErrCoeff: float, optional - :param output_units: - Default: "mas" (milliarcseconds) - other options: "arcsec" (arcseconds) - :type output_units: string, optional - - :returns: * **astrom_error** (*float or array of floats)*) -- astrometric error. - * **SNR** (*float or array of floats)*) -- signal to noise ratio. - * **error_rand** (*float or array of floats*) -- random error. - - .. rubric:: Notes - - The effective FWHMeff MUST BE given in miliarcsec (NOT arcsec!). - Systematic error, error_sys, must be given in miliarcsec. - The result corresponds to a single-coordinate uncertainty. - Note that the total astrometric uncertainty (e.g. relevant when - matching two catalogs) will be sqrt(2) times larger. - Default values for parameters are based on estimates for LSST. - - The astrometric error can be applied to parallax or proper motion (for nvisit>1). - If applying to proper motion, should also divide by the # of years of the survey. - This is also referenced in the LSST overview paper (arXiv:0805.2366, ls.st/lop) - - - assumes sqrt(Nvisit) scaling, which is the best-case scenario - - calcRandomAstrometricError assumes maxiumm likelihood solution, - which is also the best-case scenario - - the systematic error, error_sys = 10 mas, corresponds to the - design spec from the LSST Science Requirements Document (ls.st/srd) - - -.. py:function:: calcRandomAstrometricErrorPerCoord(FWHMeff, SNR, AstromErrCoeff=0.6) - - Calculate the random astrometric uncertainty, as a function of - effective FWHMeff and signal-to-noise ratio SNR and return - the astrometric uncertainty in the same units as FWHM. - - This error corresponds to a single-coordinate error - the total astrometric uncertainty (e.g. relevant when matching - two catalogs) will be sqrt(2) times larger. - - :param FWHMeff: Effective Full Width at Half Maximum of Point Spread Function [mas]. - :type FWHMeff: float or array of floats - :param SNR: Signal-to-noise ratio. - :type SNR: float or array of floats - :param AstromErrCoeff: Astrometric error coefficient (see description below). - Default =0.60 - :type AstromErrCoeff: float, optional - - :returns: * **RandomAstrometricErrorPerCoord** (*float or array of floats*) -- random astrometric uncertainty per coordinate. - * *Returns astrometric uncertainty in the same units as FWHMeff.* - - .. rubric:: Notes - - The coefficient AstromErrCoeff for Maximum Likelihood - solution is given by - - AstromErrCoeff = / <|dP/dx|^2> * 1/FWHMeff - - where P is the point spread function, P(x,y). - - For a single-Gaussian PSF, AstromErrCoeff = 0.60 - For a double-Gaussian approximation to Kolmogorov - seeing, AstromErrCoeff = 0.55; however, given the - same core seeing (FWHMgeom) as for a single-Gaussian - PSF, the resulting error will be 36% larger because - FWHMeff is 1.22 times larger and SNR is 1.22 times - smaller, compared to error for single-Gaussian PSF. - Although Kolmogorov seeing is a much better approximation - of the free atmospheric seeing than single Gaussian seeing, - the default value of AstromErrCoeff is set to the - more conservative value. - - Note also that AstromErrCoeff = 1.0 is often used in - practice to empirically account for other error sources. - - -.. py:function:: calcPhotometricUncertainty(snr) - - Convert flux signal to noise ratio to an uncertainty in magnitude. - - :param snr: The signal-to-noise-ratio in flux. - :type snr: float or array of floats - - :returns: **magerr** -- The resulting uncertainty in magnitude. - :rtype: float or rray of floats - - diff --git a/docs/autoapi/sorcha/modules/PPApplyColourOffsets/index.rst b/docs/autoapi/sorcha/modules/PPApplyColourOffsets/index.rst deleted file mode 100644 index d5a74c4d..00000000 --- a/docs/autoapi/sorcha/modules/PPApplyColourOffsets/index.rst +++ /dev/null @@ -1,51 +0,0 @@ -sorcha.modules.PPApplyColourOffsets -=================================== - -.. py:module:: sorcha.modules.PPApplyColourOffsets - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPApplyColourOffsets.PPApplyColourOffsets - - -Module Contents ---------------- - -.. py:function:: PPApplyColourOffsets(observations, function, othercolours, observing_filters, mainfilter) - - Adds the correct colour offset to H based on the filter of each observation, - then checks to make sure the appropriate columns exist for each phase function model. - If phase model variables exist for each colour, this function also selects the - correct variables for each observation based on filter. - - Adds the following columns to the observations dataframe: - - - H_filter - - Removes the following columns from the observations dataframe: - - - Colour offset columns (i.e. u-r, g-r) - - Colour-specific phase curve variables (if extant): the correct filter-specific value - for each observation is located and stored instead. i.e. GS_r and GS_g columns will be deleted - and replaced with a GS column containing either GS_r or GS_g depending on observation filter. - - :param observations: dataframe of observations. - :type observations: Pandas dataframe - :param function: string of desired phase function model. Options are HG, HG12, HG1G2, linear, H. - :type function: string - :param othercolours: list of colour offsets present in input files. - :type othercolours: list of strings - :param observing_filters: list of observation filters of interest. - :type observing_filters: list of strings - :param mainfilter: the main filter in which H is given and all colour offsets are calculated against. - :type mainfilter: string - - :returns: **observations** -- observations dataframe modified with H calculated in relevant filter (H_filter) - The dataframe has also been modified to have the appropriate phase curve filter specific values/columns. - :rtype: Pandas dataframe - - diff --git a/docs/autoapi/sorcha/modules/PPApplyFOVFilter/index.rst b/docs/autoapi/sorcha/modules/PPApplyFOVFilter/index.rst deleted file mode 100644 index 3180b350..00000000 --- a/docs/autoapi/sorcha/modules/PPApplyFOVFilter/index.rst +++ /dev/null @@ -1,100 +0,0 @@ -sorcha.modules.PPApplyFOVFilter -=============================== - -.. py:module:: sorcha.modules.PPApplyFOVFilter - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPApplyFOVFilter.PPApplyFOVFilter - sorcha.modules.PPApplyFOVFilter.PPGetSeparation - sorcha.modules.PPApplyFOVFilter.PPCircleFootprint - sorcha.modules.PPApplyFOVFilter.PPSimpleSensorArea - - -Module Contents ---------------- - -.. py:function:: PPApplyFOVFilter(observations, sconfigs, module_rngs, footprint=None, verbose=False) - - Wrapper function for PPFootprintFilter and PPFilterDetectionEfficiency that checks to see - whether a camera footprint filter should be applied or if a simple fraction of the - circular footprint should be used, then applies the required filter where rows are - are removed from the inputted pandas dataframevfor moving objects that land outside of - their associated observation's footprint. - - Adds the following columns to the observations dataframe: - - - detectorId (if full camera footprint is used) - - :param observations: - :type observations: Pandas dataframe - :param dataframe of observations.: - :param sconfigs: Dataclass of configuration file arguments. - :type sconfigs: dataclass - :param module_rngs: A collection of random number generators (per module). - :type module_rngs: PerModuleRNG - :param footprint: A Footprint class object that represents the boundaries of the detector(s). - Default: None. - :type footprint: Footprint - :param verbose: Controls whether logging in verbose mode is on or off. - Default: False - :type verbose: boolean - - :returns: **observations** -- dataframe of observations updated after field-of-view filters have been applied. - :rtype: Pandas dataframe - - -.. py:function:: PPGetSeparation(obj_RA, obj_Dec, cen_RA, cen_Dec) - - Function to calculate the distance of an object from the field centre. - - :param obj_RA: RA of object in decimal degrees. - :type obj_RA: float - :param obj_Dec: Dec of object in decimal degrees. - :type obj_Dec: float - :param cen_RA: RA of field centre in decimal degrees. - :type cen_RA: float - :param cen_Dec: Dec of field centre in decimal degrees. - :type cen_Dec: float - - :returns: **sep_degree** -- The separation of the object from the centre of the field, in decimal - degrees. - :rtype: float - - -.. py:function:: PPCircleFootprint(observations, circle_radius) - - Simple function which removes objects which lay outside of a circle - of given radius centred on the field centre. - - :param observations: dataframe of observations. - :type observations: Pandas dataframe - :param circle_radius: radius of circle footprint in degrees. - :type circle_radius: float - - :returns: **new_observations** -- dataframe of observations with all lying beyond the circle radius dropped. - :rtype: Pandas dataframe - - -.. py:function:: PPSimpleSensorArea(ephemsdf, module_rngs, fillfactor=0.9) - - Randomly removes a number of observations proportional to the - fraction of the field not covered by the detector. - - :param ephemsdf: Dataframe containing observations. - :type ephemsdf: Pandas dataframe - :param module_rngs: A collection of random number generators (per module). - :type module_rngs: PerModuleRNG - :param fillfactor: fraction of FOV covered by the sensor. - Default = 0.9 - :type fillfactor: float - - :returns: **ephemsOut** -- Dataframe of observations with 1- fillfactor fraction of objects - removed per on-sky observation pointing. - :rtype: Pandas dataframe - - diff --git a/docs/autoapi/sorcha/modules/PPBrightLimit/index.rst b/docs/autoapi/sorcha/modules/PPBrightLimit/index.rst deleted file mode 100644 index 131561a7..00000000 --- a/docs/autoapi/sorcha/modules/PPBrightLimit/index.rst +++ /dev/null @@ -1,36 +0,0 @@ -sorcha.modules.PPBrightLimit -============================ - -.. py:module:: sorcha.modules.PPBrightLimit - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPBrightLimit.PPBrightLimit - - -Module Contents ---------------- - -.. py:function:: PPBrightLimit(observations, observing_filters, bright_limit) - - Drops observations brighter than the user-defined saturation - limit. Can take either a single saturation limit for a straight cut, or - filter-specific saturation limits. - - :param observations: Dataframe of observations. - :type observations: Pandas dataframe - :param observing_filters: Observing filters present in the data. - :type observing_filters: list of strings - :param bright_limit: Saturation limits: either single value applied to all filters or a list of values for each filter. - :type bright_limit: float or list of floats - - :returns: **observations_out** -- observations dataframe modified with rows dropped for apparent - magnitudes brigher than the bright_limit for the given observation's - filter - :rtype: Pandas dataframe - - diff --git a/docs/autoapi/sorcha/modules/PPCalculateApparentMagnitude/index.rst b/docs/autoapi/sorcha/modules/PPCalculateApparentMagnitude/index.rst deleted file mode 100644 index c74b77d6..00000000 --- a/docs/autoapi/sorcha/modules/PPCalculateApparentMagnitude/index.rst +++ /dev/null @@ -1,63 +0,0 @@ -sorcha.modules.PPCalculateApparentMagnitude -=========================================== - -.. py:module:: sorcha.modules.PPCalculateApparentMagnitude - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPCalculateApparentMagnitude.PPCalculateApparentMagnitude - - -Module Contents ---------------- - -.. py:function:: PPCalculateApparentMagnitude(observations, phasefunction, mainfilter, othercolours, observing_filters, cometary_activity_choice=None, lightcurve_choice=None, verbose=False) - - This function applies the correct colour offset to H for the relevant filter, checks to make sure - the correct columns are included (with additional functionality for colour-specific phase curves), - then calculates the trailed source apparent magnitude including optional adjustments for - cometary activity and rotational light curves. - - Adds the following columns to the observations dataframe: - - - H_filter - - trailedSourceMagTrue - - any columns created by the optional light curve and cometary activity models - - Removes the following columns from the observations dataframe: - - - Colour offset columns (i.e. u-r) - - Colour-specific phase curve variables (if extant): the correct filter-specific value - for each observation is located and stored instead. i.e. GS_r and GS_g columns will be deleted - and replaced with a GS column containing either GS_r or GS_g depending on observation filter. - - :param observations: dataframe of observations. - :type observations: Pandas dataframe - :param phasefunction: Desired phase function model. Options are HG, HG12, HG1G2, linear, none - :type phasefunction: string - :param mainfilter: The main filter in which H is given and all colour offsets are calculated against. - :type mainfilter: string - :param othercolours: List of colour offsets present in input files. - :type othercolours: list of strings - :param observing_filters: List of observation filters of interest. - :type observing_filters: list of strings - :param cometary_activity_choice: Choice of cometary activity model. - Default = None - :type cometary_activity_choice: string - :param lc_choice: Choice of lightcurve model. Default = None - :type lc_choice: string - :param verbose: Flag for turning on verbose logging. Default = False - :type verbose: boolean - - :returns: **observations** -- Modified observations pandas dataframe with calculated trailed source - apparent magnitude column, H calculated in relevant filter (H_filter), - renames the column for H in the main filter as H_original and - adds a column for the light curve contribution to the trailed source - apparent magnitude (if included) - :rtype: Pandas dataframe - - diff --git a/docs/autoapi/sorcha/modules/PPCalculateApparentMagnitudeInFilter/index.rst b/docs/autoapi/sorcha/modules/PPCalculateApparentMagnitudeInFilter/index.rst deleted file mode 100644 index 332ece2e..00000000 --- a/docs/autoapi/sorcha/modules/PPCalculateApparentMagnitudeInFilter/index.rst +++ /dev/null @@ -1,57 +0,0 @@ -sorcha.modules.PPCalculateApparentMagnitudeInFilter -=================================================== - -.. py:module:: sorcha.modules.PPCalculateApparentMagnitudeInFilter - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPCalculateApparentMagnitudeInFilter.PPCalculateApparentMagnitudeInFilter - - -Module Contents ---------------- - -.. py:function:: PPCalculateApparentMagnitudeInFilter(padain, function, observing_filters, colname='trailedSourceMagTrue', lightcurve_choice=None, cometary_activity_choice=None) - - The trailed source apparent magnitude is calculated in the filter for given H, - phase function, light curve, and cometary activity parameters. - - Adds the following columns to the observations dataframe: - - - trailedSourceMagTrue - - any columns created by the optional light curve and cometary activity models - - .. rubric:: Notes - - PPApplyColourOffsets should be run beforehand to apply any needed colour offset to H and ensure correct - variables are present. - - The phase function model options utlized are the sbpy package's implementation: - - HG: Bowell et al. (1989) Asteroids II book. - - HG1G2: Muinonen et al. (2010) Icarus 209 542. - - HG12: Penttilä et al. (2016) PSS 123 117. - - linear: (as implemented in sbpy) - - none : No model is applied - - :param padain: Dataframe of observations. - :type padain: Pandas dataframe - :param function: Desired phase function model. Options are "HG", "HG12", "HG1G2", "linear", "none". - :type function: string - :param colname: Column name in which to store calculated magnitude to the padain dataframe. - Default = "TrailedSourceMag" - :type colname: string - :param lightcurve_choice: Choice of light curve model. Default = None - :type lightcurve_choice: stringm optional - :param cometary_activity_choice: Choice of cometary activity model. Default = None - :type cometary_activity_choice: string, optional - - :returns: **padain** -- Dataframe of observations (padain) modified with calculated trailed - source apparent magnitude column and any optional cometary actvity or - light curve added columns based on the models used. - :rtype: Pandas dataframe - - diff --git a/docs/autoapi/sorcha/modules/PPCalculateSimpleCometaryMagnitude/index.rst b/docs/autoapi/sorcha/modules/PPCalculateSimpleCometaryMagnitude/index.rst deleted file mode 100644 index 1156a5db..00000000 --- a/docs/autoapi/sorcha/modules/PPCalculateSimpleCometaryMagnitude/index.rst +++ /dev/null @@ -1,41 +0,0 @@ -sorcha.modules.PPCalculateSimpleCometaryMagnitude -================================================= - -.. py:module:: sorcha.modules.PPCalculateSimpleCometaryMagnitude - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPCalculateSimpleCometaryMagnitude.PPCalculateSimpleCometaryMagnitude - - -Module Contents ---------------- - -.. py:function:: PPCalculateSimpleCometaryMagnitude(padain: pandas.DataFrame, observing_filters: List[str], rho: List[float], delta: List[float], alpha: List[float], activity_choice: str = None) -> pandas.DataFrame - - Adjusts the observations' trailed source apparent magnitude for cometary activity - using the model specified by `activity_choice` added by the user - - :param padain: The input ``observations`` dataframe - :type padain: pd.DataFrame - :param observing_filters: The photometric filters the observation is taken in (the filter - requested that the coma magnitude be calculated for) - :type observing_filters: List[str] - :param rho: Heliocentric distance [units au] - :type rho: List[float] - :param delta: Distance to Earth [units au] - :type delta: List[float] - :param alpha: Phase angle [units degrees] - :type alpha: List[float] - :param activity_choice: The activity model to use, by default None - :type activity_choice: string, optional - - :returns: The ``observations`` dataframe with updated trailed - source apparent magnitude values. - :rtype: pd.DataFrame - - diff --git a/docs/autoapi/sorcha/modules/PPCommandLineParser/index.rst b/docs/autoapi/sorcha/modules/PPCommandLineParser/index.rst deleted file mode 100644 index 488b7f65..00000000 --- a/docs/autoapi/sorcha/modules/PPCommandLineParser/index.rst +++ /dev/null @@ -1,48 +0,0 @@ -sorcha.modules.PPCommandLineParser -================================== - -.. py:module:: sorcha.modules.PPCommandLineParser - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPCommandLineParser.warn_or_remove_file - sorcha.modules.PPCommandLineParser.PPCommandLineParser - - -Module Contents ---------------- - -.. py:function:: warn_or_remove_file(filepath, force_remove, pplogger) - - Given a path to a file(s), first determine if the file exists. If it does not - exist, pass through. - - If the file does exist check if the user has set `--force` on the command line. - If the user set --force, log that the existing file will be removed. - Otherwise, warn the user that the file exists and exit the program. - - :param filepath: The full file path to a given file. i.e. /home/data/output.csv - :type filepath: string - :param force_remove: Whether to remove the file if it exists. - :type force_remove: boolean - :param pplogger: Used to log the output. - :type pplogger: Logger - - -.. py:function:: PPCommandLineParser(args) - - Parses the command line arguments, error-handles them, then stores them in a single dict. - - Will only look for the comet parameters file if it's actually given at the command line. - - :param args: argparse object of command line arguments - :type args: ArgumentParser object - - :returns: **cmd_args_dict** -- dictionary of variables taken from command line arguments - :rtype: dictionary - - diff --git a/docs/autoapi/sorcha/modules/PPConfigParser/index.rst b/docs/autoapi/sorcha/modules/PPConfigParser/index.rst deleted file mode 100644 index 1cdb18c9..00000000 --- a/docs/autoapi/sorcha/modules/PPConfigParser/index.rst +++ /dev/null @@ -1,204 +0,0 @@ -sorcha.modules.PPConfigParser -============================= - -.. py:module:: sorcha.modules.PPConfigParser - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPConfigParser.log_error_and_exit - sorcha.modules.PPConfigParser.PPGetOrExit - sorcha.modules.PPConfigParser.PPGetFloatOrExit - sorcha.modules.PPConfigParser.PPGetIntOrExit - sorcha.modules.PPConfigParser.PPGetBoolOrExit - sorcha.modules.PPConfigParser.PPGetValueAndFlag - sorcha.modules.PPConfigParser.PPFindFileOrExit - sorcha.modules.PPConfigParser.PPFindDirectoryOrExit - sorcha.modules.PPConfigParser.PPCheckFiltersForSurvey - sorcha.modules.PPConfigParser.PPConfigFileParser - sorcha.modules.PPConfigParser.PPPrintConfigsToLog - - -Module Contents ---------------- - -.. py:function:: log_error_and_exit(message: str) -> None - - Log a message to the error output file and terminal, then exit. - - :param message: The error message to be logged to the error output file. - :type message: string - - :rtype: None - - -.. py:function:: PPGetOrExit(config, section, key, message) - - Checks to see if the config file parser has a key. If it does not, this - function errors out and the code stops. - - :param config: ConfigParser object containing configs. - :type config: ConfigParser - :param section: Section of the key being checked. - :type section: string - :param key: The key being checked. - :type key: string) - :param message: The message to log and display if the key is not found. - :type message: string - - :rtype: None. - - -.. py:function:: PPGetFloatOrExit(config, section, key, message) - - Checks to see if a key in the config parser is present and can be read as a - float. If it cannot, this function errors out and the code stops. - - :param config: ConfigParser object containing configs. - :type config: ConfigParser - :param section: section of the key being checked. - :type section: string - :param key: The key being checked. - :type key: string - :param message: The message to log and display if the key is not found. - :type message: string - - :rtype: None. - - -.. py:function:: PPGetIntOrExit(config, section, key, message) - - Checks to see if a key in the config parser is present and can be read as an - int. If it cannot, this function errors out and the code stops. - - :param config: ConfigParser object containing configs. - :type config: ConfigParser - :param section: Section of the key being checked. - :type section: string - :param key: The key being checked. - :type key: string - :param message: The message to log and display if the key is not found. - :type message: string - - :rtype: None. - - -.. py:function:: PPGetBoolOrExit(config, section, key, message) - - Checks to see if a key in the config parser is present and can be read as a - Boolean. If it cannot, this function errors out and the code stops. - - :param config: ConfigParser object containing configs. - :type config: ConfigParser object - :param section: Section of the key being checked. - :type section: string - :param key: The key being checked. - :type key: string - :param message: The message to log and display if the key is not found. - :type message: string - - :rtype: None. - - -.. py:function:: PPGetValueAndFlag(config, section, key, type_wanted) - - Obtains a value from the config flag, forcing it to be the specified - type and error-handling if it can't be forced. If the value is not present - in the config fie, the flag is set to False; if it is, the flag is True. - - :param config: ConfigParser object containing configs. - :type config: ConfigParser - :param section: Section of the key being checked. - :type section: string - :param key: The key being checked. - :type key: string - :param type_wanted: The type the value should be forced to. - Accepts int, float, none (for no type-forcing). - :type type_wanted: string - - :returns: * **value** (*any type*) -- The value of the key, with type dependent on type_wanted. - Will be None if the key is not present. - * **flag** (*boolean*) -- Will be False if the key is not present in the config file - and True if it is. - - -.. py:function:: PPFindFileOrExit(arg_fn, argname) - - Checks to see if a file given by a filename exists. If it doesn't, - this fails gracefully and exits to the command line. - - :param arg_fn: The filepath/name of the file to be checked. - :type arg_fn: string - :param argname: The name of the argument being checked. Used for error message. - :type argname: string - - :returns: **arg_fn** -- The filepath/name of the file to be checked. - :rtype: string - - -.. py:function:: PPFindDirectoryOrExit(arg_fn, argname) - - Checks to see if a directory given by a filepath exists. If it doesn't, - this fails gracefully and exits to the command line. - - :param arg_fn: The filepath of the directory to be checked. - :type arg_fn: string - :param argname: The name of the argument being checked. Used for error message. - :type argname: string - - :returns: **arg_fn** -- The filepath of the directory to be checked. - :rtype: string - - -.. py:function:: PPCheckFiltersForSurvey(survey_name, observing_filters) - - When given a list of filters, this function checks to make sure they exist in the - user-selected survey, and if the filters given in the config file do not match the - survey filters, the function exits the program with an error. - - :param survey_name: Survey name. Currently only "LSST", "lsst" accepted. - :type survey_name: string - :param observing_filters: Observation filters of interest. - :type observing_filters: list of strings - - :rtype: None. - - .. rubric:: Notes - - Currently only has options for LSST, but can be expanded upon later. - - -.. py:function:: PPConfigFileParser(configfile, survey_name) - - Parses the config file, error-handles, then assigns the values into a single - dictionary, which is passed out. - - :param configfile: Filepath/name of config file. - :type configfile: string - :param survey_name: Survey name. Currently only "LSST", "lsst" accepted. - :type survey_name: string - - :returns: **config_dict** -- Dictionary of config file variables. - :rtype: dictionary - - .. rubric:: Notes - - We chose not to use the original ConfigParser object for readability: it's a dict of - dicts, so calling the various values can become quite unwieldy. - - -.. py:function:: PPPrintConfigsToLog(configs, cmd_args) - - Prints all the values from the config file and command line to the log. - - :param configs: Dictionary of config file variables. - :type configs: dictionary - :param cmd_args: Dictionary of command line arguments. - :type cmd_args: dictionary - - :rtype: None. - - diff --git a/docs/autoapi/sorcha/modules/PPDetectionEfficiency/index.rst b/docs/autoapi/sorcha/modules/PPDetectionEfficiency/index.rst deleted file mode 100644 index 04d01846..00000000 --- a/docs/autoapi/sorcha/modules/PPDetectionEfficiency/index.rst +++ /dev/null @@ -1,34 +0,0 @@ -sorcha.modules.PPDetectionEfficiency -==================================== - -.. py:module:: sorcha.modules.PPDetectionEfficiency - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPDetectionEfficiency.PPDetectionEfficiency - - -Module Contents ---------------- - -.. py:function:: PPDetectionEfficiency(padain, threshold, module_rngs) - - Applies a random cut to the observations dataframe based on an efficiency - threshold: if the threshold is 0.95, for example, 5% of observations will be - randomly dropped. Used by PPLinkingFilter. - - :param padain: Dataframe of observations. - :type padain: Pandas dataframe - :param threshold: Fraction between 0 and 1 of detections retained in the dataframe. - :type threshold: float - :param module_rngs: A collection of random number generators (per module). - :type module_rngs: PerModuleRNG - - :returns: Dataframe of observations with a fraction equal to 1-threshold randomly dropped. - :rtype: Pandas dataframe - - diff --git a/docs/autoapi/sorcha/modules/PPDetectionProbability/index.rst b/docs/autoapi/sorcha/modules/PPDetectionProbability/index.rst deleted file mode 100644 index f4043595..00000000 --- a/docs/autoapi/sorcha/modules/PPDetectionProbability/index.rst +++ /dev/null @@ -1,67 +0,0 @@ -sorcha.modules.PPDetectionProbability -===================================== - -.. py:module:: sorcha.modules.PPDetectionProbability - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPDetectionProbability.calcDetectionProbability - sorcha.modules.PPDetectionProbability.PPDetectionProbability - - -Module Contents ---------------- - -.. py:function:: calcDetectionProbability(mag, limmag, fillFactor=1.0, w=0.1) - - Find the probability of a detection given a visual magnitude, - limiting magnitude, and fill factor, determined by the fading function - from Veres & Chesley (2017). - - :param mag: Magnitude of object in filter used for that field. - :type mag: float or array of floats - :param limmag: Limiting magnitude of the field. - :type limmag: float or array of floats - :param fillFactor: Fraction of FOV covered by the camera sensor. Default = 1.0 - :type fillFactor: float), optional - :param w: Distribution parameter. Default = 0.1 - :type w: float - - :returns: **P** -- Probability of detection. - :rtype: float or array of floats - - -.. py:function:: PPDetectionProbability(eph_df, trailing_losses=False, trailing_loss_name='dmagDetect', magnitude_name='PSFMag', limiting_magnitude_name='fiveSigmaDepth_mag', field_id_name='FieldID', fillFactor=1.0, w=0.1) - - Find probability of observations being observable for objectInField output. - Wrapper for calcDetectionProbability which takes into account column names - and trailing losses. Used by PPFadingFunctionFilter. - - :param eph_df: Dataframe of observations. - :type eph_df: Pandas dataframe - :param trailing_losses: Are trailing losses being applied?, Default = False - :type trailing_losses: Boolean, optional - :param trailing_loss_name: eph_df column name for trailing losses, Default = dmagDetect - :type trailing_loss_name: string, optional - :param magnitude_name: eph_df column name for observation limiting magnitude - Default = PSFMag - :type magnitude_name: string, optional - :param limiting_magnitude_name: eph_df column used for observation limiting magnitude. - Default = fiveSigmaDepth_mag - :type limiting_magnitude_name: string, optional - :param field ID: eph_df column name for observation field_id - Default = FieldID - :type field ID: string, optional - :param fillFactor: Fraction of FOV covered by the camera sensor. Default = 1.0 - :type fillFactor: float, optional - :param w: Distribution parameter. Default =0.1 - :type w: float - - :returns: Probability of detection. - :rtype: float or array of floats - - diff --git a/docs/autoapi/sorcha/modules/PPDropObservations/index.rst b/docs/autoapi/sorcha/modules/PPDropObservations/index.rst deleted file mode 100644 index ee809482..00000000 --- a/docs/autoapi/sorcha/modules/PPDropObservations/index.rst +++ /dev/null @@ -1,33 +0,0 @@ -sorcha.modules.PPDropObservations -================================= - -.. py:module:: sorcha.modules.PPDropObservations - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPDropObservations.PPDropObservations - - -Module Contents ---------------- - -.. py:function:: PPDropObservations(observations, module_rngs, probability='detection probability') - - Drops rows where the probabilty of detection is less than sample drawn - from a uniform distribution. Used by PPFadingFunctionFilter. - - :param observations: Dataframe of observations with a column containing the probability of detection. - :type observations: Pandas dataframe - :param module_rngs: A collection of random number generators (per module). - :type module_rngs: PerModuleRNG - :param probability: Name of column containing detection probability. - :type probability: string - - :returns: **out** -- New dataframe of 'observations' modified to remove observations that could not be observed. - :rtype: Pandas dataframe - - diff --git a/docs/autoapi/sorcha/modules/PPFadingFunctionFilter/index.rst b/docs/autoapi/sorcha/modules/PPFadingFunctionFilter/index.rst deleted file mode 100644 index 2b055e7a..00000000 --- a/docs/autoapi/sorcha/modules/PPFadingFunctionFilter/index.rst +++ /dev/null @@ -1,37 +0,0 @@ -sorcha.modules.PPFadingFunctionFilter -===================================== - -.. py:module:: sorcha.modules.PPFadingFunctionFilter - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPFadingFunctionFilter.PPFadingFunctionFilter - - -Module Contents ---------------- - -.. py:function:: PPFadingFunctionFilter(observations, fillfactor, width, module_rngs, verbose=False) - - Wrapper function for PPDetectionProbability and PPDropObservations. - - Calculates detection probability based on a fading function, then drops rows where the - probabilty of detection is less than sample drawn from a uniform distribution. - - :param observations: Dataframe of observations with a column containing the probability of detection. - :type observations: Pandas dataframe - :param fillFactor: Fraction of camera field-of-view covered by detectors - :type fillFactor: float - :param module_rngs: A collection of random number generators (per module). - :type module_rngs: PerModuleRNG - :param verbose: Verbose logging flag. Default = False - :type verbose: boolean, optional - - :returns: **observations_drop** -- Modified 'observations' dataframe without observations that could not be observed. - :rtype: Pandas dataframe) - - diff --git a/docs/autoapi/sorcha/modules/PPFootprintFilter/index.rst b/docs/autoapi/sorcha/modules/PPFootprintFilter/index.rst deleted file mode 100644 index 56bef97a..00000000 --- a/docs/autoapi/sorcha/modules/PPFootprintFilter/index.rst +++ /dev/null @@ -1,311 +0,0 @@ -sorcha.modules.PPFootprintFilter -================================ - -.. py:module:: sorcha.modules.PPFootprintFilter - - -Attributes ----------- - -.. autoapisummary:: - - sorcha.modules.PPFootprintFilter.deg2rad - sorcha.modules.PPFootprintFilter.sin - sorcha.modules.PPFootprintFilter.cos - sorcha.modules.PPFootprintFilter.logger - - -Classes -------- - -.. autoapisummary:: - - sorcha.modules.PPFootprintFilter.Detector - sorcha.modules.PPFootprintFilter.Footprint - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPFootprintFilter.distToSegment - sorcha.modules.PPFootprintFilter.radec_to_tangent_plane - sorcha.modules.PPFootprintFilter.radec_to_focal_plane - - -Module Contents ---------------- - -.. py:data:: deg2rad - -.. py:data:: sin - -.. py:data:: cos - -.. py:data:: logger - -.. py:function:: distToSegment(points, x0, y0, x1, y1) - - Compute the distance from each point to the line segment defined by - the points (x0, y0) and (x1, y1). Returns the distance in the same - units as the points are specified in (radians, degrees, etc.). Uses planar - geometry for the calculations (assuming small angular distances). - - :param points: Array of shape (2, n) describing the corners of the sensor. - :type points: array - :param x0: The x coordinate of the first end of the segment. - :type x0: float - :param y0: The y coordinate of the first end of the segment. - :type y0: float - :param x1: The x coordinate of the second end of the segment. - :type x1: float - :param y1: The y coordinate of the second end of the segment. - :type y1: float - - :returns: **dist** -- Array of length n storing the distances. - :rtype: array - - -.. py:function:: radec_to_tangent_plane(ra, dec, field_ra, field_dec) - - Converts ra and dec to xy on the plane tangent to image center, in the 2-d coordinate system where y is aligned with the meridian. - - Parameters: - ----------- - ra (float/array of floats): observation Right Ascension, radians. - - dec (float/array of floats): observation Declination, radians. - - fieldra (float/array of floats): field pointing Right Ascension, radians. - - fielddec (float/array of floats): field pointing Declination, radians. - - fieldID (float/array of floats): Field ID, optional. - - Returns: - ---------- - x, y (float/array of floats): Coordinates on the focal plane, radians projected - to the plane tangent to the unit sphere. - - - -.. py:function:: radec_to_focal_plane(ra, dec, field_ra, field_dec, field_rot) - -.. py:class:: Detector(points, ID=0, units='radians') - - Detector class - - - .. py:attribute:: ID - :value: 0 - - - - .. py:attribute:: ra - - - .. py:attribute:: dec - - - .. py:attribute:: units - :value: 'radians' - - - - .. py:attribute:: x - - - .. py:attribute:: y - - - .. py:attribute:: centerx - - - .. py:attribute:: centery - - - .. py:method:: ison(point, ε=10.0**(-11), edge_thresh=None, plot=False) - - Determines whether a point (or array of points) falls on the - detector. - - :param point: Array of shape (2, n) for n points. - :type point: array - :param ϵ: Threshold for whether point is on detector. Default: 10.0 ** (-11) - :type ϵ: float, optional - :param edge_thresh: The focal plane distance (in arcseconds) from the detector's edge - for a point to be counted. Removes points that are too - close to the edge for source detection. Default = None - :type edge_thresh: float, optional - :param plot: Flag for whether to plot the detector and the point. Default = False - :type plot: Boolean, optional - - :returns: **selectedidx** -- Indices of points in point array that fall on the sensor. - :rtype: array - - - - .. py:method:: trueArea() - - Returns the area of the detector. Uses the same method as - segmentedArea, but the test point is the mean of the corner coordinates. - Will probably fail if the sensor is not convex. - - :param None.: - - :returns: **area** -- The area of the detector. - :rtype: float - - - - .. py:method:: segmentedArea(point) - - Returns the area of the detector by calculating the area of each - triangle segment defined by each pair of adjacent corners and a point - inside the sensor. - Fails if the point is not inside the sensor or if the sensor is not - convex. - - :param point: Array of shape (2, n) for n points. - :type point: array - - :returns: **area** -- The area of the detector. - :rtype: float - - - - .. py:method:: sortCorners() - - Sorts the corners to be counterclockwise by angle from center of - the detector. Modifies self. - - :param None.: - - :rtype: None. - - - - .. py:method:: rotateDetector(theta) - - Rotates a sensor around the origin of the coordinate system its - corner locations are provided in. - - :param theta: Angle to rotate by, in radians. - :type theta: float - - :returns: **Detector** -- New Detector instance. - :rtype: Detector - - - - .. py:method:: rad2deg() - - Converts corners from radians to degrees. - - :param None.: - - :rtype: None. - - - - .. py:method:: deg2rad() - - Converts corners from degrees to radians. - - :param None.: - - :rtype: None. - - - - .. py:method:: plot(theta=0.0, color='gray', units='rad', annotate=False) - - Plots the footprint for an individual sensor. Currently not on the - focal plane, just the sky coordinates. Relatively minor difference - (width of footprint for LSST is <2.1 degrees), so should be fine for - internal demonstration purposes, but not for confirming algorithms or - for offical plots. - - :param theta: Aangle to rotate footprint by, radians or degrees. Default =0.0 - :type theta: float, optional - :param color: Line color. Default = "gray" - :type color: string, optional - :param units: Units. Units is provided in ("deg" or "rad"). Default = 'rad'. - :type units: string, optional - :param annotate: Flag whether to annotate each sensor with its index in self.detectors. - Default = False - :type annotate: Boolean - - :rtype: None. - - - -.. py:class:: Footprint(path=None, detectorName='detector') - - Camera footprint class - - - .. py:attribute:: detectors - - - .. py:attribute:: N - - - .. py:method:: plot(theta=0.0, color='gray', units='rad', annotate=False) - - Plots the footprint. Currently not on the focal plane, just the sky - coordinates. Relatively minor difference (width of footprint for LSST - is <2.1 degrees), so should be fine for internal demonstration - purposes, but not for confirming algorithms or for offical plots. - - :param theta: Angle to rotate footprint by, radians or degrees. Default = 0.0 - :type theta: float, optional - :param color: Line color. Default = "gray" - :type color: string, optional - :param units: Units theta is provided in ("deg" or "rad"). Default = "rad" - :type units: string, optional - :param annotate: Whether to annotate each sensor with its index in - self.detectors. Default = False - :type annotate: boolean, optional - - :rtype: None. - - - - .. py:method:: applyFootprint(field_df, ra_name='RA_deg', dec_name='Dec_deg', field_name='FieldID', ra_name_field='fieldRA_deg', dec_name_field='fieldDec_deg', rot_name_field='fieldRotSkyPos_deg', edge_thresh=None) - - Determine whether detections fall on the sensors defined by the - footprint. Also returns the an ID for the sensor a detection is made - on. - - :param field_df: Dataframe containing detection information with pointings. - :type field_df: Pandas dataframe - :param ra_name: - "field_df" dataframe's column name for object's RA - for the given observation. Default = "RA_deg" [units: degrees] - :type ra_name: string, optional - :param dec_name: - "field_df" dataframe's column name for object's declination - for the given observation. Default = "Dec_deg" [units: dgrees] - :type dec_name: string, optional - :param ra_name_field: - "field_df" dataframe's column name for the observation field's RA - Default = "fieldRA_deg" [units: degrees] - :type ra_name_field: string, optional - :param dec_name_field: - "field_df" dataframe's column name for the observation field's declination - Default = "fieldDec_deg" [Units: degrees] - :type dec_name_field: string, optional - :param rot_name_field: "field_df" dataframe's column name for the observation field's rotation angle - Default = "fieldRotSkyPos_deg" [Units: degrees] - :type rot_name_field: string, optional - :param edge_thresh: An angular threshold in arcseconds for dropping pixels too close to the edge. - Default = None - :type edge_thresh: float, optional - - :returns: * **detected** (*array*) -- Indices of rows in field_df which fall on the sensor(s). - * **detectorID** (*array*) -- Index corresponding to a detector in self.detectors for each entry in detected. - - - diff --git a/docs/autoapi/sorcha/modules/PPGetLogger/index.rst b/docs/autoapi/sorcha/modules/PPGetLogger/index.rst deleted file mode 100644 index 1eb8af32..00000000 --- a/docs/autoapi/sorcha/modules/PPGetLogger/index.rst +++ /dev/null @@ -1,39 +0,0 @@ -sorcha.modules.PPGetLogger -========================== - -.. py:module:: sorcha.modules.PPGetLogger - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPGetLogger.PPGetLogger - - -Module Contents ---------------- - -.. py:function:: PPGetLogger(log_location, log_stem, log_format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s ', log_name='', log_file_info='sorcha.log', log_file_error='sorcha.err') - - Initialises log and error files. - - :param log_location: Filepath to directory in which to save logs. - :type log_location: string - :param log_stem: String output stem used to prefix all Sorcha outputs. - :type log_stem: string - :param log_format: Format for log filename. - Default = "%(asctime)s %(name)-12s %(levelname)-8s %(message)s " - :type log_format: string, optional - :param log_name: Name of log. Default = "" - :type log_name: string, optional - :param log_file_info: Suffix and extension with which to save info log. Default = "sorcha.log" - :type log_file_info: string, optional - :param log_file_error: Suffix and extension with which to save error log. Default = "sorcha.err" - :type log_file_error: string, optional - - :returns: **log** -- Log object. - :rtype: logging object - - diff --git a/docs/autoapi/sorcha/modules/PPGetMainFilterAndColourOffsets/index.rst b/docs/autoapi/sorcha/modules/PPGetMainFilterAndColourOffsets/index.rst deleted file mode 100644 index 1cd730fe..00000000 --- a/docs/autoapi/sorcha/modules/PPGetMainFilterAndColourOffsets/index.rst +++ /dev/null @@ -1,41 +0,0 @@ -sorcha.modules.PPGetMainFilterAndColourOffsets -============================================== - -.. py:module:: sorcha.modules.PPGetMainFilterAndColourOffsets - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPGetMainFilterAndColourOffsets.PPGetMainFilterAndColourOffsets - - -Module Contents ---------------- - -.. py:function:: PPGetMainFilterAndColourOffsets(filename, observing_filters, filesep) - - Function to obtain the main filter (i.e. the filter in which H is - defined) from the header of the physical parameters file and then generate - the expected colour offsets. Also makes sure that columns exist for all - the expected colour offsets in the physical parameters file. - - :param filename: The filename of the physical parameters file. - :type filename: string - :param observing_filters: The observation filters requested in the configuration file. - :type observing_filters: list of strings - :param filesep: The format of the physical parameters file. Should be "csv"/"comma" - or "whitespace". - :type filesep: string - - :returns: * **mainfilter** (*string*) -- The main filter in which H is defined. - * **colour_offsets** (*list of strings*) -- A list of the colour offsets present in the physical parameters file. - - .. rubric:: Notes - - The main filter should be found as a column heading of H_[mainfilter]. If - this format isn NOT followed, this function will error out. - - diff --git a/docs/autoapi/sorcha/modules/PPLinkingFilter/index.rst b/docs/autoapi/sorcha/modules/PPLinkingFilter/index.rst deleted file mode 100644 index c8b920a4..00000000 --- a/docs/autoapi/sorcha/modules/PPLinkingFilter/index.rst +++ /dev/null @@ -1,55 +0,0 @@ -sorcha.modules.PPLinkingFilter -============================== - -.. py:module:: sorcha.modules.PPLinkingFilter - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPLinkingFilter.PPLinkingFilter - - -Module Contents ---------------- - -.. py:function:: PPLinkingFilter(observations, detection_efficiency, min_observations, min_tracklets, tracklet_interval, minimum_separation, maximum_time, night_start_utc, survey_name='rubin_sim', drop_unlinked=True) - - A function which mimics the effects of the SSP linking process by looking - for valid tracklets within valid tracks and only outputting observations - which would be thus successfully "linked" by SSP. - - Parameters: - ----------- - detection_efficiency (float): the fractional percentage of successfully linked - detections. - - min_observations (int): the minimum number of observations in a night required - to form a tracklet. - - min_tracklets (int): the minimum number of tracklets required to form a valid track. - - tracklet_interval (int): the time window (in days) in which the minimum number of - tracklets must occur to form a valid track. - - minimum_separation (float): the minimum separation inside a tracklet for it - to be recognised as motion between images (in arcseconds). - - maximum_time (float): # Maximum time separation (in days) between subsequent observations in a tracklet. - - rng (numpy Generator object): numpy random number generator object. - - survey_name (str): a string with the survey name. used for time-zone purposes. - Currently only accepts "rubin_sim", "RUBIN_SIM", "lsst", "LSST". - - drop_unlinked (boolean): rejects all observations that are considered to not be linked. Default is True - - Returns: - ----------- - observations_out (pandas dataframe): a pandas dataframe containing observations - of linked objects only. - - - diff --git a/docs/autoapi/sorcha/modules/PPMagnitudeLimit/index.rst b/docs/autoapi/sorcha/modules/PPMagnitudeLimit/index.rst deleted file mode 100644 index afc32d74..00000000 --- a/docs/autoapi/sorcha/modules/PPMagnitudeLimit/index.rst +++ /dev/null @@ -1,32 +0,0 @@ -sorcha.modules.PPMagnitudeLimit -=============================== - -.. py:module:: sorcha.modules.PPMagnitudeLimit - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPMagnitudeLimit.PPMagnitudeLimit - - -Module Contents ---------------- - -.. py:function:: PPMagnitudeLimit(observations, mag_limit) - - Filter that performs a straight cut on apparent PSF magnitude - based on a defined threshold. - - :param observations: Dataframe of observations. Must have "observedPSFMag" column. - :type observations: pandas dataframe - :param mag_limit: Limit for apparent magnitude cut. - :type mag_limit: float - - :returns: **observations** -- "observations" dataframe modified with apparent PSF mag greater than - or equal to the limit removed. - :rtype: pandas dataframe - - diff --git a/docs/autoapi/sorcha/modules/PPMatchPointingToObservations/index.rst b/docs/autoapi/sorcha/modules/PPMatchPointingToObservations/index.rst deleted file mode 100644 index 1cacb158..00000000 --- a/docs/autoapi/sorcha/modules/PPMatchPointingToObservations/index.rst +++ /dev/null @@ -1,46 +0,0 @@ -sorcha.modules.PPMatchPointingToObservations -============================================ - -.. py:module:: sorcha.modules.PPMatchPointingToObservations - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPMatchPointingToObservations.PPMatchPointingToObservations - - -Module Contents ---------------- - -.. py:function:: PPMatchPointingToObservations(padain, pointfildb) - - Merges all relevant columns of each observation from the pointing - database onto the observations dataframe, then drops all observations which are not - in one of the requested filters and any duplicate columns. - - Adds the following columns to the dataframe of observations: - - - visitTime - - visitExposureTime - - optFilter - - seeingFwhmGeom_arcsec - - seeingFwhmEff_arcsec - - fieldFiveSigmaDepth_mag - - fieldRA_deg - - fieldDec_deg - - fieldRotSkyPos_deg - - observationMidpointMJD_TAI - - :param padain: Dataframe of observations. - :type padain: pandas dataframe - :param pointfildb: Dataframe of the pointing database. - :type pointfildb: pandas dataframe - - :returns: **res_df** -- Merged dataframe of observations ("padain") with pointing - database ("pointfildb"), with all superfluous observations dropped. - :rtype: Pandas dataframe - - diff --git a/docs/autoapi/sorcha/modules/PPMiniDifi/index.rst b/docs/autoapi/sorcha/modules/PPMiniDifi/index.rst deleted file mode 100644 index d0d6b144..00000000 --- a/docs/autoapi/sorcha/modules/PPMiniDifi/index.rst +++ /dev/null @@ -1,193 +0,0 @@ -sorcha.modules.PPMiniDifi -========================= - -.. py:module:: sorcha.modules.PPMiniDifi - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPMiniDifi.haversine_np - sorcha.modules.PPMiniDifi.hasTracklet - sorcha.modules.PPMiniDifi.trackletsInNights - sorcha.modules.PPMiniDifi.discoveryOpportunities - sorcha.modules.PPMiniDifi.linkObject - sorcha.modules.PPMiniDifi.linkObservations - - -Module Contents ---------------- - -.. py:function:: haversine_np(lon1, lat1, lon2, lat2) - - Calculate the great circle distance between two points - on the earth (specified in decimal degrees) - - :param lon1: longitude of point 1 - :type lon1: float or array of floats - :param lat1: latitude of point 1 - :type lat1: float or array of floats - :param lon2: longitude of point 2 - :type lon2: float or array of floats - :param lat1: latitude of point 1 - :type lat1: float or array of floats - - :returns: * *float or array of floats* - * **Great distance between the two points [Units** (*Decimal degrees]*) - - .. rubric:: Notes - - All args must be of equal length. - - Because SkyCoord is slow AF. - - -.. py:function:: hasTracklet(mjd, ra, dec, maxdt_minutes, minlen_arcsec) - - Given a set of observations in one night, calculate it has - at least onedetectable tracklet. - - :param mjd: Modified Julian date time - :type mjd: float or array of floats - :param ra: Object's RA at given mjd [Units: degrees] - :type ra: float or array of floats - :param dec: Object's dec at given mjd [Units: degrees] - :type dec: float or array of floats - :param maxdt_minutes: Maximum allowable time between observations [Units: minutes] - :type maxdt_minutes: float - :param minlen_arcsec: Minimum allowable distance separation between observations [Units: arcsec] - :type minlen_arcsec: float - - :returns: * *boolean* - * *True if tracklet can be made else False* - - -.. py:function:: trackletsInNights(night, mjd, ra, dec, maxdt_minutes, minlen_arcsec) - - Calculate, for a given set of observations sorted by observation time, - whether or not it has at least one discoverable tracklet in each night. - - :param night: Array of the integer night corresponding to each observation - :type night: float or array of floats - :param mjd: Modified Julian date time - :type mjd: float or array of floats - :param ra: Object's RA at given mjd [Units: degrees] - :type ra: float or array of floats - :param dec: Object's dec at given mjd [Units: degrees] - :type dec: float or array of floats - :param maxdt_minutes: Maximum allowable time between observations [Units: minutes] - :type maxdt_minutes: float - :param minlen_arcsec: Minimum allowable distance separation between observations [Units: arcsec] - :type minlen_arcsec: float - - :returns: * **nights** (*float or array of floats*) -- Numpy array of the unique nights in the set of observations - * **hasTrk** (*boolean or array of booleans*) -- Array denoting if each night has a discoverable tracklet - - -.. py:function:: discoveryOpportunities(nights, nightHasTracklets, window, nlink, p, rng) - - Find all nights where a trailing window of nights (including the - current night) has at least tracklets to constitute a discovery. - - :param nights: Array of the integer night corresponding to each observation - :type nights: float or array of floats - :param nightHasTracklets: List of nights that have tracklets within them - :type nightHasTracklets: list of booleans - :param window: Number of tracklets required with <= this window to complete a detection - :type window: float - :param nlink: Number of tracklets required to form detection - :type nlink: float - :param p: SSP detection efficiency, or what fraction of objects are successfuly linked - :type p: float - :param rng: PGC64 generator object to determine which objects to drop - :type rng: numpy RNG generator object - - :returns: * **discIdx** (*float*) -- The index of where in the observation array the object is reported as discovered - * **disc** (*list of floats*) -- List of MJD dates where the object is discoverable - - -.. py:function:: linkObject(obsv, seed, maxdt_minutes, minlen_arcsec, window, nlink, p, night_start_utc_days) - - For a set of observations of a single object, calculate if there are any tracklets, - if there are enough tracklets to form a discovery window, and then report back all of - those successful discoveries. - - :param obsv: Array of observations for one object, of the format: - ssObjectId : str - Unique ID for the Solar System object - diaSourceId : float - Unique ID for the observation - midPointTai : float - Time for the observation midpoint (MJD) - ra : float - RA of the object (J2000) - decl : float - Declination of the object (J2000) - :type obsv: numpy array - :param seed: Initial seed per object to keep observations deterministic for multithreading - :type seed: float - :param maxdt_minutes: Maximum allowable time between observations [Units: minutes] - :type maxdt_minutes: float - :param minlen_arcsec: Minimum allowable distance separation between observations [Units: arcsec] - :type minlen_arcsec: float - :param window: Number of tracklets required with <= this window to complete a detection - :type window: float - :param nlink: Number of tracklets required to form detection - :type nlink: float - :param p: SSP detection efficiency, or what fraction of objects are successfuly linked - :type p: float - :param night_start_utc_days: The UTC time of local noon at the observatory - :type night_start_utc_days: float - - :returns: * **discoveryObservationId** (*float*) -- The ID of the observation that triggered the successful linking - * **discoverySubmissionDate** (*float*) -- The night at which the discovery is first submitted - * **discoveryChances** (*float*) -- The number of chances for discovery of the object - - -.. py:function:: linkObservations(obsv, seed, objectId='ssObjectId', sourceId='diaSourceId', mjdTime='midPointTai', ra='ra', dec='decl', **config) - - Ingesting a set of observations for one or more objects, determine if each object - would be discovered by the SSP pipeline based on tracklet forming and linking. - - :param obsv: Array of observations for each object, of the format: - ssObjectId : str - Unique ID for the Solar System object - diaSourceId : float - Unique ID for the observation - midPointTai : float - Time for the observation midpoint (MJD) - ra : float - RA of the object (J2000) - decl : float - Declination of the object (J2000) - :type obsv: numpy array - :param seed: Initial seed per object to keep observations deterministic for multithreading - :type seed: float - :param objectId: Column name for object ID's in observations dataframe - :type objectId: string - :param sourceId: Column name for observation ID's in observations dataframe - :type sourceId: string - :param mjdTime: Column name for MJD's in observations dataframe - :type mjdTime: string - :param ra: Column name for object RA's in observations dataframe - :type ra: string - :param dec: Column name for object Dec's in observations dataframe - :type dec: string - :param \*\*config: Dictionary containing configuration file variables - - :returns: **obj** -- - - Array with one row per detected object, of the format: - ssObjectId : str - Unique ID for the Solar System object - discoveryObservationId : float - Unique ID for the observation - discoverySubmissionDate : float - The night at which the discovery is first submitted - discoveryChances : float - The number of chances for discovery of the object - :rtype: numpy array - - diff --git a/docs/autoapi/sorcha/modules/PPModuleRNG/index.rst b/docs/autoapi/sorcha/modules/PPModuleRNG/index.rst deleted file mode 100644 index 19806494..00000000 --- a/docs/autoapi/sorcha/modules/PPModuleRNG/index.rst +++ /dev/null @@ -1,46 +0,0 @@ -sorcha.modules.PPModuleRNG -========================== - -.. py:module:: sorcha.modules.PPModuleRNG - - -Classes -------- - -.. autoapisummary:: - - sorcha.modules.PPModuleRNG.PerModuleRNG - - -Module Contents ---------------- - -.. py:class:: PerModuleRNG(base_seed, pplogger=None) - - A collection of per-module random number generators. - - - .. py:attribute:: _base_seed - - - .. py:attribute:: _rngs - - - .. py:attribute:: pplogger - :value: None - - - - .. py:method:: getModuleRNG(module_name) - - Return a random number generator that is based on a base seed - and the current module name. - - :param module_name: The name of the module - :type module_name: string - - :returns: **rng** -- The random number generator. - :rtype: numpy Generator - - - diff --git a/docs/autoapi/sorcha/modules/PPOutput/index.rst b/docs/autoapi/sorcha/modules/PPOutput/index.rst deleted file mode 100644 index 39a869db..00000000 --- a/docs/autoapi/sorcha/modules/PPOutput/index.rst +++ /dev/null @@ -1,95 +0,0 @@ -sorcha.modules.PPOutput -======================= - -.. py:module:: sorcha.modules.PPOutput - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPOutput.PPOutWriteCSV - sorcha.modules.PPOutput.PPOutWriteHDF5 - sorcha.modules.PPOutput.PPOutWriteSqlite3 - sorcha.modules.PPOutput.PPIndexSQLDatabase - sorcha.modules.PPOutput.PPWriteOutput - - -Module Contents ---------------- - -.. py:function:: PPOutWriteCSV(padain, outf, separator=',') - - Writes a pandas dataframe out to a CSV file at a location given by the user. - - :param padain: Dataframe of output. - :type padain: pandas dataframe - :param outf: Location to which file should be written. - :type outf: string - :param separator: String of CSV separator. Default is ','. - :type separator: string of length 1 - - :rtype: None. - - -.. py:function:: PPOutWriteHDF5(pp_results, outf, keyname='sorcha_results') - - Writes a pandas dataframe out to a HDF5 file at a location given by the user. - - :param padain: Dataframe of output. - :type padain: pandas dataframe - :param outf: Location to which file should be written. - :type outf: string - :param keyin: Key at which data will be located. - :type keyin: string - - :rtype: None. - - -.. py:function:: PPOutWriteSqlite3(pp_results, outf, tablename='sorcha_results') - - Writes a pandas dataframe out to a CSV file at a location given by the user. - - :param pp_results: Dataframe of output. - :type pp_results: pandas dataframe - :param outf: Location to which file should be written. - :type outf: string - :param tablename: String of the table within the database to be indexed. - :type tablename: string - - :rtype: None. - - -.. py:function:: PPIndexSQLDatabase(outf, tablename='sorcha_results') - - Indexes a SQLite database of Sorcha output. - - :param outf: Location of SQLite database to be indexed. - :type outf: string - :param tablename: String of the table within the database to be indexed. - :type tablename: string - - :rtype: None. - - -.. py:function:: PPWriteOutput(cmd_args, sconfigs, observations_in, verbose=False) - - Writes the output in the format specified in the config file to a location - specified by the user. - - :param cmd_args: Dictonary of command line arguments. - :type cmd_args: dictionary - :param sconfigs: Dataclass of configuration file arguments. - :type sconfigs: dataclass - :param observations_in: Dataframe of output. - :type observations_in: Pandas dataframe - :param endChunk: Integer of last object in chunk. Used only for HDF5 output key. - Default = 0 - :type endChunk: integer, optional - :param verbose: Verbose logging mode on or off. Default = False - :type verbose: boolean, optional - - :rtype: None. - - diff --git a/docs/autoapi/sorcha/modules/PPRandomizeMeasurements/index.rst b/docs/autoapi/sorcha/modules/PPRandomizeMeasurements/index.rst deleted file mode 100644 index ff3ee2e6..00000000 --- a/docs/autoapi/sorcha/modules/PPRandomizeMeasurements/index.rst +++ /dev/null @@ -1,213 +0,0 @@ -sorcha.modules.PPRandomizeMeasurements -====================================== - -.. py:module:: sorcha.modules.PPRandomizeMeasurements - - -Attributes ----------- - -.. autoapisummary:: - - sorcha.modules.PPRandomizeMeasurements.logger - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPRandomizeMeasurements.randomizeAstrometryAndPhotometry - sorcha.modules.PPRandomizeMeasurements.randomizeAstrometry - sorcha.modules.PPRandomizeMeasurements.sampleNormalFOV - sorcha.modules.PPRandomizeMeasurements.randomizePhotometry - sorcha.modules.PPRandomizeMeasurements.flux2mag - sorcha.modules.PPRandomizeMeasurements.mag2flux - sorcha.modules.PPRandomizeMeasurements.icrf2radec - sorcha.modules.PPRandomizeMeasurements.radec2icrf - - -Module Contents ---------------- - -.. py:data:: logger - -.. py:function:: randomizeAstrometryAndPhotometry(observations, sconfigs, module_rngs, verbose=False) - - Wrapper function to perform randomisation of astrometry and photometry around - their uncertainties. Calls randomizePhotometry() and randomizeAstrometry(). - - Adds the following columns to the dataframe: - - trailedSourceMag - - PSFMag - - AstRATrue(deg) - - AstDecTrue(deg) - - :param observations: Dataframe containing observations. - :type observations: pandas dataframe - :param sconfigs: Dataclass of configuration file arguments. - :type sconfigs: dataclass - :param module_rngs: A collection of random number generators (per module). - :type module_rngs: PerModuleRNG - :param verbose: Verbosity on or off. Default False. - :type verbose: bool - - :returns: **observations** -- Original input dataframe with RA and Dec columns and trailedSourceMag and PSFMag - columns randomized around astrometric and photometric sigma. Original RA and Dec/magnitudes - stored in separate columns. - :rtype: pandas dataframe - - -.. py:function:: randomizeAstrometry(df, module_rngs, raName='RA_deg', decName='Dec_deg', raOrigName='RATrue_deg', decOrigName='DecTrue_deg', sigName='AstSig(deg)', radecUnits='deg', sigUnits='mas') - - Randomize astrometry with a normal distribution around the actual RADEC pointing. - The randomized values replace the original astrometry, with the original values - stored in separate columns. - - Adds the following columns to the observations dataframe: - - - AstRATrue(deg) - - AstDecTrue(deg) - - :param df: Dataframe containing astrometry and sigma. - :type df: pandas dataframe - :param module_rngs: A collection of random number generators (per module). - :type module_rngs: PerModuleRNG - :param ra_Name: "df" dataframe column name for the right ascension. - Default = "RA_deg" - :type ra_Name: string, optional - :param dec_Name: "df" dataframe column name for the declination. Default = "Dec_deg" - :type dec_Name: string, optional - :param raOrigName: "df" dataframe column name for where to store original right - ascension. Default = "RATrue_deg" - :type raOrigName: string, optional - :param decOrigName: "df" dataframe column name for where to store original declination. - Default = "DecTrue_deg" - :type decOrigName: string, optional - :param sigName: "df" dataframe column name for the standard deviation, uncertainty in the - astrometric position. - Default = "AstSig(deg)" - :type sigName: string, optional - :param radecUnits: Units for RA and Dec ('deg'/'rad'/'mas'). Default = "deg" - :type radecUnits: string - :param sigUnits: Units for standard deviation ('deg'/'rad'/'mas'). Default = "mas" - :type sigUnits: string - - :returns: **df** -- original input dataframe with RA and Dec columns randomized around - astrometric sigma and original RA and Dec stored in separate columns - :rtype: pandas dataframe - - .. rubric:: Notes - - Covariances in RADEC are currently not supported. The routine calculates - a normal distribution on the unit sphere, so as to allow for a correct modeling of - the poles. Distributions close to the poles may look odd in RADEC. - - -.. py:function:: sampleNormalFOV(center, sigma, module_rngs, ndim=3) - - Sample n points randomly (normal distribution) on a region on the unit (hyper-)sphere. - - :param center: Center of hpyer-sphere: can be an [n, ndim] dimensional array, - but only if n == npoints. - :type center: float - :param sigma: 1 sigma distance on unit sphere [radians]x - :type sigma: n-dimensional array - :param module_rngs: A collection of random number generators (per module). - :type module_rngs: PerModuleRNG - :param ndim: Dimension of hyper-sphere. Default = 3 - :type ndim: integer, optional - - :returns: **vec** -- Size [npoints, ndim] - :rtype: numpy array - - -.. py:function:: randomizePhotometry(df, module_rngs, magName='Filtermag', magRndName='FiltermagRnd', sigName='FiltermagSig') - - Randomize photometry with normal distribution around magName value. - - :param df: Dataframe containing astrometry and sigma. - :type df: pandas dataframe - :param module_rngs: A collection of random number generators (per module). - :type module_rngs: PerModuleRNG - :param magName: 'df' column name of apparent magnitude. Default = "Filtermag" - :type magName: string, optional - :param magRndName: 'df' column name for storing randomized apparent magnitude, Default = "FiltermagRnd" - :type magRndName: string, optional - :param sigName: 'df' column name for magnitude standard deviation. Default = "FiltermagSig" - :type sigName: float, optional - - :returns: randomized magnitudes for each row in 'df' - :rtype: array of floats - - .. rubric:: Notes - - The normal distribution here is in magnitudes while it should be in flux. This will fail for large sigmas. - Should be fixed at some point. - - We assume that apparent magnitudes are stored within 'df' and that 'magName' - corresponds to the corresponding column within 'df' - - 'df' is also modified with added column magRndNam to store the randomize apparent magnitude - - -.. py:function:: flux2mag(f, f0=3631) - - AB ugriz system (f0 = 3631 Jy) to magnitude conversion. - - :param f: flux. [Units : Jy]. - :type f: float or array of floats - :param f0: Zero point flux. Default = 3631 - :type f0: float, optional - - :returns: **mag** -- pogson magnitude. [Units: mag] - :rtype: float or array of floats - - -.. py:function:: mag2flux(mag, f0=3631) - - AB ugriz system (f0 = 3631 Jy) magnitude to flux conversion. - - :param mag: Pogson magnitude. [Units: mag] - :type mag: float or rray of floats - :param f0: Zero point flux. Default = 3631 - :type f0: float, optional - - :returns: **f (float/array of floats)** - :rtype: flux [Units: Jy]. - - -.. py:function:: icrf2radec(x, y, z, deg=True) - - Convert ICRF xyz to Right Ascension and Declination. - Geometric states on unit sphere, no light travel time/aberration correction. - - :param x: 3D vector of unit length (ICRF) - :type x: floats/arrays of floats - :param y: 3D vector of unit length (ICRF) - :type y: floats/arrays of floats - :param z: 3D vector of unit length (ICRF) - :type z: floats/arrays of floats - :param de: True for angles in degrees, False for angles in radians. Default = True - :type de: boolean, optional - - :returns: * **ra** (*float or array of floats*) -- Right Ascension. [Units: deg] - * **dec** (*float or array of floats*) -- Declination. [Units: deg] - - -.. py:function:: radec2icrf(ra, dec, deg=True) - - Convert Right Ascension and Declination to ICRF xyz unit vector. - Geometric states on unit sphere, no light travel time/aberration correction. - - :param ra: Right Ascension. [Units: deg] - :type ra: float or array of floats - :param dec: Declination. [Units deg] - :type dec: float or array of floats - :param deg: True for angles in degrees, False for angles in radians. Default = True - :type deg: boolean, optional - - :returns: **array([x, y, z])** -- 3D vector of unit length (ICRF) - :rtype: arrays/matrix of floats - - diff --git a/docs/autoapi/sorcha/modules/PPReadPointingDatabase/index.rst b/docs/autoapi/sorcha/modules/PPReadPointingDatabase/index.rst deleted file mode 100644 index bb9ab25d..00000000 --- a/docs/autoapi/sorcha/modules/PPReadPointingDatabase/index.rst +++ /dev/null @@ -1,32 +0,0 @@ -sorcha.modules.PPReadPointingDatabase -===================================== - -.. py:module:: sorcha.modules.PPReadPointingDatabase - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPReadPointingDatabase.PPReadPointingDatabase - - -Module Contents ---------------- - -.. py:function:: PPReadPointingDatabase(bsdbname, observing_filters, dbquery, surveyname) - - Reads in the pointing database as a Pandas dataframe. - - :param bsdbname: File location of pointing database. - :type bsdbname: string - :param observing_filters: List of observation filters of interest. - :type observing_filters: list of strings - :param dbquery: Databse query to perform on pointing database. - :type dbquery: string - - :returns: **dfo** -- Dataframe of pointing database. - :rtype: pandas dataframe - - diff --git a/docs/autoapi/sorcha/modules/PPSNRLimit/index.rst b/docs/autoapi/sorcha/modules/PPSNRLimit/index.rst deleted file mode 100644 index 40ec59db..00000000 --- a/docs/autoapi/sorcha/modules/PPSNRLimit/index.rst +++ /dev/null @@ -1,31 +0,0 @@ -sorcha.modules.PPSNRLimit -========================= - -.. py:module:: sorcha.modules.PPSNRLimit - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPSNRLimit.PPSNRLimit - - -Module Contents ---------------- - -.. py:function:: PPSNRLimit(observations, sigma_limit=2.0) - - Filter that performs a straight SNR cut based on a limit, removing - observations that are less than a SNR limit - - :param observations: Dataframe of observations. Must have "SNR" column. - :type observations: pandas dataframe - :param sigma_limit: Limit for SNR cut. - :type sigma_limit: float, optional. - - :returns: **observations** -- "observations" dataframed modified with entries with SNR < the limit removed. - :rtype: pandas dataframe - - diff --git a/docs/autoapi/sorcha/modules/PPStats/index.rst b/docs/autoapi/sorcha/modules/PPStats/index.rst deleted file mode 100644 index c262c12e..00000000 --- a/docs/autoapi/sorcha/modules/PPStats/index.rst +++ /dev/null @@ -1,34 +0,0 @@ -sorcha.modules.PPStats -====================== - -.. py:module:: sorcha.modules.PPStats - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPStats.stats - - -Module Contents ---------------- - -.. py:function:: stats(observations, statsfilename, outpath, sconfigs) - - Write a summary statistics file including whether each object was linked - or not within miniDifi, their number of observations, min/max phase angles, - min/max trailed source magnitudes, and median trailed source magnitudes - per filter - - :param observations: Pandas dataframe of observations - :type observations: Pandas dataframe - :param statsfilename: Stem filename to write summary stats file to - :type statsfilename: string - :param sconfigs: Dataclass of configuration file arguments. - :type sconfigs: dataclass - - :rtype: None. - - diff --git a/docs/autoapi/sorcha/modules/PPTrailingLoss/index.rst b/docs/autoapi/sorcha/modules/PPTrailingLoss/index.rst deleted file mode 100644 index f188e56f..00000000 --- a/docs/autoapi/sorcha/modules/PPTrailingLoss/index.rst +++ /dev/null @@ -1,85 +0,0 @@ -sorcha.modules.PPTrailingLoss -============================= - -.. py:module:: sorcha.modules.PPTrailingLoss - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPTrailingLoss.calcTrailingLoss - sorcha.modules.PPTrailingLoss.PPTrailingLoss - - -Module Contents ---------------- - -.. py:function:: calcTrailingLoss(dRaCosDec, dDec, seeing, texp=30.0, model='circularPSF', a_trail=0.761, b_trail=1.162, a_det=0.42, b_det=0.003) - - Find the trailing loss from trailing and detection (Veres & Chesley 2017) - - :param dRa: on sky velocity component in RA*Cos(Dec). [Units: deg/day] - :type dRa: float or array of floats - :param dDec: on sky velocity component in Dec. [Units: deg/day] - :type dDec: float/array of floats - :param seeing: FWHM of the seeing disk. [Units: arcseconds] - :type seeing: float or array of floats - :param texp: Exposure length. [Units: seconds] Default = 30 - :type texp: float or array of floats, optional - :param model: Options: 'circularPSF' or trailedSource' - 'circularPSF': Trailing loss due to the DM detection algorithm. Limit SNR: - 5 sigma in a PSF-convolved image with a circular PSF (no trail fitting). Peak - fluxes will be lower due to motion of the object. - 'trailedSource': Unavoidable trailing loss due to spreading the PSF - over more pixels lowering the SNR in each pixel. - See https://github.com/rhiannonlynne/318-proceedings/blob/master/Trailing%20Losses.ipynb for details. - Default = "circularPSF" - :type model: string, optional - :param a_trail: a fit parameters for trailedSource model. Default parameters from Veres & Chesley (2017). - Default = 0.761 - :type a_trail: float, optional - :param b_trail: b fit parameters for trailedSource model. Default parameters from Veres & Chesley (2017). - Default = 1.162 - :type b_trail: float, optional - :param a_det: a fit parameters for circularPSF model. Default parameters from Veres & Chesley (2017). - Default = 0.420 - :type a_det: float, optional - :param b_det: b fit parameters for circularPSF model. Default parameters from Veres & Chesley (2017). - Default = 0.003 - :type b_det: float, optional - - :returns: **dmag** -- Loss in detection magnitude due to trailing. - :rtype: float or array of floats - - -.. py:function:: PPTrailingLoss(eph_df, model='circularPSF', dra_cosdec_name='RARateCosDec_deg_day', ddec_name='DecRate_deg_day', dec_name='Dec_deg', seeing_name_survey='seeingFwhmEff_arcsec', visit_time_name='visitExposureTime') - - Calculates detection trailing losses. Wrapper for calcTrailingLoss. - - :param eph_df: Dataframe of observations for which to calculate trailing losses. - :type eph_df: pandas dataframe - :param model: Photometric model. Either 'circularPSF' or 'trailedSource': see docstring for - calcTrailingLoss for details. Default = "circularPSF" - :type model: string, optional - :param dra_name: "eph_df" column name for object RA rate. Default = "RARateCosDec_deg_day" - Assumes cos(dec) normalization has already been applied - :type dra_name: string, optional - :param ddec_name: "eph_df" column name for object dec rate. Default = "DecRate_deg_day" - :type ddec_name: string, optional - :param dec_name: "eph_df" column name for object declination. Default = "Dec_deg" - :type dec_name: string, default - :param seeing_name_survey: "eph_df" column name for seeing. Default = "seeingFwhmEff_arcsec" - :type seeing_name_survey: string, optional - :param visit_time_name: "eph_df" column name for exposure length. Default = "visitExposureTime" - :type visit_time_name: string, optional - - :returns: **dmag** -- Loss in detection magnitude due to trailing losses. - :rtype: float or array of floats - - .. rubric:: Notes - - Assumes 'eph_df" has RA and Dec stored in deg/dayrates and the seeing in arcseconds - - diff --git a/docs/autoapi/sorcha/modules/PPVignetting/index.rst b/docs/autoapi/sorcha/modules/PPVignetting/index.rst deleted file mode 100644 index d4a17af2..00000000 --- a/docs/autoapi/sorcha/modules/PPVignetting/index.rst +++ /dev/null @@ -1,116 +0,0 @@ -sorcha.modules.PPVignetting -=========================== - -.. py:module:: sorcha.modules.PPVignetting - - -Attributes ----------- - -.. autoapisummary:: - - sorcha.modules.PPVignetting.deg2rad - sorcha.modules.PPVignetting.rad2deg - sorcha.modules.PPVignetting.sin - sorcha.modules.PPVignetting.cos - - -Functions ---------- - -.. autoapisummary:: - - sorcha.modules.PPVignetting.vignettingEffects - sorcha.modules.PPVignetting.calcVignettingLosses - sorcha.modules.PPVignetting.haversine - sorcha.modules.PPVignetting.vignetFunc - - -Module Contents ---------------- - -.. py:data:: deg2rad - -.. py:data:: rad2deg - -.. py:data:: sin - -.. py:data:: cos - -.. py:function:: vignettingEffects(df, raName='RA_deg', decName='Dec_deg', fieldName='FieldID', raNameSurvey='fieldRA_deg', decNameSurvey='fieldDec_deg') - - Calculates effective limiting magnitude at source, taking vignetting into account. - Wrapper for calcVignettingLosses(). - - :param df: dataframe of observations. - :type df: pandas dataframe - :param raName: 'df' column name of object RA. Default = "RA_deg" - :type raName: string, optional - :param decName: 'df' column name of object declination. Default = "Dec_deg" - :type decName: string, optional - :param fieldName: 'df' column name for observation pointing field ID. Default = "FieldID" - :type fieldName: string, optional - :param raNameSurvey: 'df' column name for observation pointing RA. Default = "fieldRA_deg" - - decNameSurvey : string, optional - 'df' column name for observation pointing declination. Default = "fieldDec_deg" - :type raNameSurvey: string, optional - - :returns: Five sigma limiting magnitude at object location adjusted for vignetting for each - row in 'df' dataframe. - :rtype: list of floats - - -.. py:function:: calcVignettingLosses(ra, dec, fieldra, fielddec) - - Calculates magnitude loss due to vignetting for a point with the telescope - centered on fieldra, fielddec. - - :param ra: RA of object(s). - :type ra: float or aarray of floats - :param dec: Dec of object(s). - :type dec: float or array of floats - :param fieldra: RA of field(s). - :type fieldra: float or array of floats - :param fielddec: Dec of field(s). - :type fielddec: float or array of floats - - :returns: Magnitude loss due to vignetting at object position. - :rtype: floats or array of floats - - -.. py:function:: haversine(ra1, dec1, ra2, dec2) - - Calculates angular distance between two points. Can produce floating point - errors for antipodal points, which are not intended to be encountered within - the scope of this module. - - :param ra1: RA of first point. - :type ra1: float or array of floats - :param dec1 or float or array of floats: Dec of first point. - :param ra2: RA of second point. - :type ra2: float or array of floats - :param dec2: Dec of second point. - :type dec2: float/array of floats - - :returns: Angular distance between two points. - :rtype: float or array of floats - - -.. py:function:: vignetFunc(x) - - Returns the magnitude of dimming caused by the vignetting relative to the - center of the field. - - :param x: Angular separation of point from field centre. - :type x: float or array of floats - - :returns: Magnitude of dimming due to vignetting at object position. - :rtype: float or array of floats - - .. rubric:: Notes - - Grabbed from sims_selfcal. From VignettingFunc_v3.3.TXT. r is in degrees, - frac is fraction of rays which were not vignetted. - - diff --git a/docs/autoapi/sorcha/modules/index.rst b/docs/autoapi/sorcha/modules/index.rst deleted file mode 100644 index 28d4cd5e..00000000 --- a/docs/autoapi/sorcha/modules/index.rst +++ /dev/null @@ -1,42 +0,0 @@ -sorcha.modules -============== - -.. py:module:: sorcha.modules - - -Submodules ----------- - -.. toctree:: - :maxdepth: 1 - - /autoapi/sorcha/modules/PPAddUncertainties/index - /autoapi/sorcha/modules/PPApplyColourOffsets/index - /autoapi/sorcha/modules/PPApplyFOVFilter/index - /autoapi/sorcha/modules/PPBrightLimit/index - /autoapi/sorcha/modules/PPCalculateApparentMagnitude/index - /autoapi/sorcha/modules/PPCalculateApparentMagnitudeInFilter/index - /autoapi/sorcha/modules/PPCalculateSimpleCometaryMagnitude/index - /autoapi/sorcha/modules/PPCommandLineParser/index - /autoapi/sorcha/modules/PPConfigParser/index - /autoapi/sorcha/modules/PPDetectionEfficiency/index - /autoapi/sorcha/modules/PPDetectionProbability/index - /autoapi/sorcha/modules/PPDropObservations/index - /autoapi/sorcha/modules/PPFadingFunctionFilter/index - /autoapi/sorcha/modules/PPFootprintFilter/index - /autoapi/sorcha/modules/PPGetLogger/index - /autoapi/sorcha/modules/PPGetMainFilterAndColourOffsets/index - /autoapi/sorcha/modules/PPLinkingFilter/index - /autoapi/sorcha/modules/PPMagnitudeLimit/index - /autoapi/sorcha/modules/PPMatchPointingToObservations/index - /autoapi/sorcha/modules/PPMiniDifi/index - /autoapi/sorcha/modules/PPModuleRNG/index - /autoapi/sorcha/modules/PPOutput/index - /autoapi/sorcha/modules/PPRandomizeMeasurements/index - /autoapi/sorcha/modules/PPReadPointingDatabase/index - /autoapi/sorcha/modules/PPSNRLimit/index - /autoapi/sorcha/modules/PPStats/index - /autoapi/sorcha/modules/PPTrailingLoss/index - /autoapi/sorcha/modules/PPVignetting/index - - diff --git a/docs/autoapi/sorcha/readers/CSVReader/index.rst b/docs/autoapi/sorcha/readers/CSVReader/index.rst deleted file mode 100644 index b3105d88..00000000 --- a/docs/autoapi/sorcha/readers/CSVReader/index.rst +++ /dev/null @@ -1,140 +0,0 @@ -sorcha.readers.CSVReader -======================== - -.. py:module:: sorcha.readers.CSVReader - - -Classes -------- - -.. autoapisummary:: - - sorcha.readers.CSVReader.CSVDataReader - - -Module Contents ---------------- - -.. py:class:: CSVDataReader(filename, sep='csv', header=-1, **kwargs) - - Bases: :py:obj:`sorcha.readers.ObjectDataReader.ObjectDataReader` - - - A class to read in object data files stored as CSV or whitespace - separated values. - - Requires that the file's first column is ObjID. - - - .. py:attribute:: filename - - - .. py:attribute:: sep - :value: 'csv' - - - - .. py:attribute:: header_row - - - .. py:attribute:: obj_id_table - :value: None - - - - .. py:method:: get_reader_info() - - Return a string identifying the current reader name - and input information (for logging and output). - - :returns: **name** -- The reader information. - :rtype: string - - - - .. py:method:: _find_and_validate_header_line(header=-1) - - Read and validate the header line. If no line number is provided, use - a heuristic match to find the header line. This is used in cases - where the header is not the first line and we want to skip down. - - :param header: The row number of the header. If not provided, does an automatic search. - Default = -1 - :type header: integer, optional - - :returns: The line index of the header. - :rtype: integer - - - - .. py:method:: _check_header_line(header_line) - - Check that a given header line is valid and exit if it is invalid. - - :param header_line: The proposed header line. - :type header_line: str - - - - .. py:method:: _read_rows_internal(block_start=0, block_size=None, **kwargs) - - Reads in a set number of rows from the input. - - :param block_start: The 0-indexed row number from which - to start reading the data. For example in a CSV file - block_start=2 would skip the first two lines after the header - and return data starting on row=2. Default =0 - :type block_start: integer, optional - :param block_size: The number of rows to read in. - Use block_size=None to read in all available data. - default =None - :type block_size: integer, optional, default=None - :param \*\*kwargs: Extra arguments - :type \*\*kwargs: dictionary, optional - - :returns: **res_df** -- Dataframe of the object data. - :rtype: pandas dataframe - - - - .. py:method:: _build_id_map() - - Builds a table of just the object IDs - - - - .. py:method:: _read_objects_internal(obj_ids, **kwargs) - - Read in a chunk of data for given object IDs. - - :param obj_ids: A list of object IDs to use. - :type obj_ids: list - :param \*\*kwargs: Extra arguments - :type \*\*kwargs: dictionary, optional - - :returns: **res_df** -- The dataframe for the object data. - :rtype: pandas dataframe - - - - .. py:method:: _process_and_validate_input_table(input_table, **kwargs) - - Perform any input-specific processing and validation on the input table. - Modifies the input dataframe in place. - - .. rubric:: Notes - - The base implementation includes filtering that is common to most - input types. Subclasses should call super.process_and_validate() - to ensure that the ancestor’s validation is also applied. - - :param input_table: A loaded table. - :type input_table: Pandas dataframe - :param \*\*kwargs: Extra arguments - :type \*\*kwargs: dictionary, optional - - :returns: **input_table** -- Returns the input dataframe modified in-place. - :rtype: pandas dataframe - - - diff --git a/docs/autoapi/sorcha/readers/CombinedDataReader/index.rst b/docs/autoapi/sorcha/readers/CombinedDataReader/index.rst deleted file mode 100644 index 72059631..00000000 --- a/docs/autoapi/sorcha/readers/CombinedDataReader/index.rst +++ /dev/null @@ -1,127 +0,0 @@ -sorcha.readers.CombinedDataReader -================================= - -.. py:module:: sorcha.readers.CombinedDataReader - -.. autoapi-nested-parse:: - - The CombinedDataReader class supports loading the entire input data - for the simulator post processing by using individuals reader classes - to read individual input files and combining the data into a single table. - - The CombinedDataReader object reads the data in blocks to limit memory usage. - For each blocks, it uses two stages: - 1) It reads a range of individual rows from the ``primary_reader``. By default this - reader is the first auxiliary data reader, but can be set to the ephemeris reader. - This reader is used to extract a list of object IDs for this block. - 2) For each of the readers (ephemeris and auxiliary data) load in all the rows - corresponding to the object IDs extracted in stage 1. - - For example, if the ephemeris file is used as the primary reader, the algorithm - will load data in blocks of the ephemeris rows and join in the auxiliary data - for just the object IDs on those rows. It is not guaranteed to include all - rows for the current objects. - - - -Classes -------- - -.. autoapisummary:: - - sorcha.readers.CombinedDataReader.CombinedDataReader - - -Module Contents ---------------- - -.. py:class:: CombinedDataReader(ephem_primary=False, **kwargs) - - .. py:attribute:: ephem_reader - :value: None - - - - .. py:attribute:: aux_data_readers - :value: [] - - - - .. py:attribute:: block_start - :value: 0 - - - - .. py:attribute:: ephem_primary - :value: False - - - - .. py:method:: add_ephem_reader(new_reader) - - Add a new reader for ephemeris data. - - :param new_reader: The reader for a specific input file. - :type new_reader: ObjectDataReader - - - - .. py:method:: add_aux_data_reader(new_reader) - - Add a new object reader that corresponds to an auxiliary input data type.. - - :param new_reader: The reader for a specific input file. - :type new_reader: ObjectDataReader - - - - .. py:method:: check_aux_object_ids() - - Checks the ObjIDs in all of the auxiliary data readers to make sure - both files contain exactly the same ObjIDs. - - - - .. py:method:: read_block(block_size=None, verbose=False, **kwargs) - - Reads in a set number of rows from the input, performs - post-processing and validation, and returns a data frame. - - :param block_size: the number of rows to read in. - Use block_size=None to read in all available data. - Default = None - :type block_size: integer, optional - :param verbose: Use verbose logging. - Default = False - :type verbose: boolean, optional - :param \*\*kwargs: Extra arguments - :type \*\*kwargs: dictionary, optional - - :returns: **res_df** -- dataframe of the combined object data. - :rtype: pandas dataframe - - - - .. py:method:: read_aux_block(block_size=None, verbose=False, **kwargs) - - Reads in a set number of rows from the input, performs - post-processing and validation, and returns a data frame. - - This function DOES NOT include the ephemeris data in the returned data frame. - It is to be used when generating the ephemeris during the execution of Sorcha. - - :param block_size: the number of rows to read in. - Use block_size=None to read in all available data. - Default = None - :type block_size: integer, optional - :param verbose: use verbose logging. - Default = False - :type verbose: boolean, optional - :param \*\*kwargs: Extra arguments - :type \*\*kwargs: dictionary, optional - - :returns: **res_df** -- dataframe of the combined object data, excluding any ephemeris data. - :rtype: pandas dataframe - - - diff --git a/docs/autoapi/sorcha/readers/DatabaseReader/index.rst b/docs/autoapi/sorcha/readers/DatabaseReader/index.rst deleted file mode 100644 index 864821ef..00000000 --- a/docs/autoapi/sorcha/readers/DatabaseReader/index.rst +++ /dev/null @@ -1,99 +0,0 @@ -sorcha.readers.DatabaseReader -============================= - -.. py:module:: sorcha.readers.DatabaseReader - - -Classes -------- - -.. autoapisummary:: - - sorcha.readers.DatabaseReader.DatabaseReader - - -Module Contents ---------------- - -.. py:class:: DatabaseReader(intermdb, **kwargs) - - Bases: :py:obj:`sorcha.readers.ObjectDataReader.ObjectDataReader` - - - A class to read in object data stored in a sqlite database. - - - .. py:attribute:: intermdb - - - .. py:method:: get_reader_info() - - Return a string identifying the current reader name - and input information (for logging and output). - - :returns: **name** -- The reader information. - :rtype: string - - - - .. py:method:: _read_rows_internal(block_start=0, block_size=None, **kwargs) - - Reads in a set number of rows from the input. - - :param block_start: The 0-indexed row number from which - to start reading the data. For example in a CSV file - block_start=2 would skip the first two lines after the header - and return data starting on row=2. Default=0 - :type block_start: integer, optional - :param block_size: the number of rows to read in. - Use block_size=None to read in all available data. - A non-None block size must be provided if block_start > 0. - Default = None - :type block_size: int, optional - :param \*\*kwargs: Extra arguments - :type \*\*kwargs: dictionary, optional - - :returns: **res_df** -- dataframe of the object data. - :rtype: pandas dataframe - - .. rubric:: Notes - - A non-None block size must be provided if block_start > 0. - - - - .. py:method:: _read_objects_internal(obj_ids, **kwargs) - - Read in a chunk of data for given object IDs. - - :param obj_ids: A list of object IDs to use. - :type obj_ids: list - :param \*\*kwargs: Extra arguments - :type \*\*kwargs: dictionary, optional - - :returns: **res_df** -- The dataframe for the object data. - :rtype: pandas dataframe - - - - .. py:method:: _process_and_validate_input_table(input_table, **kwargs) - - Perform any input-specific processing and validation on the input table. - Modifies the input dataframe in place. - - .. rubric:: Notes - - The base implementation includes filtering that is common to most - input types. Subclasses should call super.process_and_validate() - to ensure that the ancestor’s validation is also applied. - - :param input_table: A loaded table. - :type input_table: pandas dataframe - :param \*\*kwargs: Extra arguments - :type \*\*kwargs: dictionary, optional - - :returns: **input_table** -- Returns the input dataframe modified in-place. - :rtype: pandas dataframe - - - diff --git a/docs/autoapi/sorcha/readers/EphemerisReader/index.rst b/docs/autoapi/sorcha/readers/EphemerisReader/index.rst deleted file mode 100644 index aa876945..00000000 --- a/docs/autoapi/sorcha/readers/EphemerisReader/index.rst +++ /dev/null @@ -1,124 +0,0 @@ -sorcha.readers.EphemerisReader -============================== - -.. py:module:: sorcha.readers.EphemerisReader - - -Classes -------- - -.. autoapisummary:: - - sorcha.readers.EphemerisReader.EphemerisDataReader - - -Functions ---------- - -.. autoapisummary:: - - sorcha.readers.EphemerisReader.read_full_ephemeris_table - - -Module Contents ---------------- - -.. py:class:: EphemerisDataReader(filename, inputformat, **kwargs) - - Bases: :py:obj:`sorcha.readers.ObjectDataReader.ObjectDataReader` - - - A class to read in ephemeris from an external ephemeris file. - - Instead of subclassing the various readers (CSV, HDF5, etc.) individually, this class instantiates - one of those classes in an internal ``reader`` attribute. As such all reading, validation, etc. is - passed off to the ``reader`` object this object owns. While this adds a level of indirection, it - allows us to support a cross product of N file types from M ephemeris generators with M + N readers - instead of M * N. - - - .. py:attribute:: reader - :value: None - - - - .. py:method:: get_reader_info() - - Return a string identifying the current reader name - and input information (for logging and output). - - :returns: The reader information. - :rtype: string - - - - .. py:method:: _read_rows_internal(block_start=0, block_size=None, **kwargs) - - Reads in a set number of rows from the input. - - :param block_start: The 0-indexed row number from which - to start reading the data. For example in a CSV file - block_start=2 would skip the first two lines after the header - and return data starting on row=2. Default =0 - :type block_start: int, optional - :param block_size: the number of rows to read in. - Use block_size=None to read in all available data. - Default = None - :type block_size: int, optional - :param \*\*kwargs: Extra arguments - :type \*\*kwargs: dictionary, optional - - :returns: **res_df** -- dataframe of the object data. - :rtype: Pandas dataframe - - - - .. py:method:: _read_objects_internal(obj_ids, **kwargs) - - Read in a chunk of data corresponding to all rows for - a given set of object IDs. - - :param obj_ids: A list of object IDs to use. - :type obj_ids: list - :param \*\*kwargs: Extra arguments - :type \*\*kwargs: dictionary, optional - - :returns: **res_df** -- The dataframe for the object data. - :rtype: pandas dataframe - - - - .. py:method:: _process_and_validate_input_table(input_table, **kwargs) - - Perform any input-specific processing and validation on the input table. - Modifies the input dataframe in place. - - :param input_table: A loaded table. - :type input_table: Pandas dataframe - :param \*\*kwargs: Extra arguments - :type \*\*kwargs: dictionary, optional - - :returns: **input_table** -- Returns the input dataframe modified in-place. - :rtype: Pandas dataframe - - .. rubric:: Notes - - The base implementation includes filtering that is common to most - input types. Subclasses should call super.process_and_validate() - to ensure that the ancestor’s validation is also applied. - - - -.. py:function:: read_full_ephemeris_table(filename, inputformat) - - A helper function for testing that reads and returns an entire ephemeris table. - - :param filename: location/name of the data file. - :type filename: string - :param inputformat: format of input file ("whitespace"/"comma"/"csv"/"h5"/"hdf5"). - :type inputformat: string - - :returns: **res_df** -- dataframe of the object data. - :rtype: pandas dataframe - - diff --git a/docs/autoapi/sorcha/readers/HDF5Reader/index.rst b/docs/autoapi/sorcha/readers/HDF5Reader/index.rst deleted file mode 100644 index 9016120c..00000000 --- a/docs/autoapi/sorcha/readers/HDF5Reader/index.rst +++ /dev/null @@ -1,105 +0,0 @@ -sorcha.readers.HDF5Reader -========================= - -.. py:module:: sorcha.readers.HDF5Reader - - -Classes -------- - -.. autoapisummary:: - - sorcha.readers.HDF5Reader.HDF5DataReader - - -Module Contents ---------------- - -.. py:class:: HDF5DataReader(filename, **kwargs) - - Bases: :py:obj:`sorcha.readers.ObjectDataReader.ObjectDataReader` - - - A class to read in object data files stored as HDF5 files. - - - .. py:attribute:: filename - - - .. py:attribute:: obj_id_table - :value: None - - - - .. py:method:: get_reader_info() - - Return a string identifying the current reader name - and input information (for logging and output). - - :returns: **name** -- The reader information. - :rtype: string - - - - .. py:method:: _read_rows_internal(block_start=0, block_size=None, **kwargs) - - Reads in a set number of rows from the input. - - :param block_start: The 0-indexed row number from which - to start reading the data. For example in a CSV file - block_start=2 would skip the first two lines after the header - and return data starting on row=2. Default=0 - :type block_start: integer, optional - :param block_size: the number of rows to read in. - Use block_size=None to read in all available data. - Default = None - :type block_size: integer, optional - :param \*\*kwargs: Extra arguments - :type \*\*kwargs: dictionary, optional - - :returns: **res_df** -- Dataframe of the object data. - :rtype: pandas dataframe - - - - .. py:method:: _build_id_map() - - Builds a table of just the object IDs - - - - .. py:method:: _read_objects_internal(obj_ids, **kwargs) - - Read in a chunk of data for given object IDs. - - :param obj_ids: A list of object IDs to use. - :type obj_ids: list - :param \*\*kwargs: Extra arguments - :type \*\*kwargs: dictionary, optional - - :returns: **res_df** -- The dataframe for the object data. - :rtype: Pandas dataframe - - - - .. py:method:: _process_and_validate_input_table(input_table, **kwargs) - - Perform any input-specific processing and validation on the input table. - Modifies the input dataframe in place. - - .. rubric:: Notes - - The base implementation includes filtering that is common to most - input types. Subclasses should call super.process_and_validate() - to ensure that the ancestor’s validation is also applied. - - :param input_table: A loaded table. - :type input_table: pandas dataframe - :param \*\*kwargs: Extra arguments - :type \*\*kwargs: dictionary, optional - - :returns: **input_table** -- Returns the input dataframe modified in-place. - :rtype: pandas dataframe - - - diff --git a/docs/autoapi/sorcha/readers/ObjectDataReader/index.rst b/docs/autoapi/sorcha/readers/ObjectDataReader/index.rst deleted file mode 100644 index 9684a245..00000000 --- a/docs/autoapi/sorcha/readers/ObjectDataReader/index.rst +++ /dev/null @@ -1,156 +0,0 @@ -sorcha.readers.ObjectDataReader -=============================== - -.. py:module:: sorcha.readers.ObjectDataReader - -.. autoapi-nested-parse:: - - Base class for reading object-related data from a variety of sources - and returning a pandas data frame. - - Each subclass of ObjectDataReader must implement at least the functions - _read_rows_internal and _read_objects_internal, both of which return a - pandas data frame. Each data source needs to have a column ObjID that - identifies the object and can be used for joining and filtering. - - Caching is implemented in the base class. This will lazy load the full - table into memory from the chosen data source, so it should only be - used with smaller data sets. Both ``read_rows`` and ``read_objects`` - will check for a cached table before reading the files, allowing them - to perform direct pandas operations if the data is already in memory. - - - -Classes -------- - -.. autoapisummary:: - - sorcha.readers.ObjectDataReader.ObjectDataReader - - -Module Contents ---------------- - -.. py:class:: ObjectDataReader(cache_table=False, **kwargs) - - Bases: :py:obj:`abc.ABC` - - - The base class for reading in the object data. - - - .. py:attribute:: _cache_table - :value: False - - - - .. py:attribute:: _table - :value: None - - - - .. py:method:: get_reader_info() - :abstractmethod: - - - Return a string identifying the current reader name - and input information (for logging and output). - - :returns: **name** -- The reader information. - :rtype: str - - - - .. py:method:: read_rows(block_start=0, block_size=None, **kwargs) - - Reads in a set number of rows from the input, performs - post-processing and validation, and returns a data frame. - - :param block_start: The 0-indexed row number from which - to start reading the data. For example in a CSV file - block_start=2 would skip the first two lines after the header - and return data starting on row=2. Default=0 - :type block_start: int (optional) - :param block_size: the number of rows to read in. - Use block_size=None to read in all available data. - Default = None - :type block_size: int (optional) - :param \*\*kwargs: Extra arguments - :type \*\*kwargs: dictionary, optional - - :returns: **res_df** -- dataframe of the object data. - :rtype: Pandas dataframe - - - - .. py:method:: _read_rows_internal(block_start=0, block_size=None, **kwargs) - :abstractmethod: - - - Function to do the actual source-specific reading. - - - - .. py:method:: read_objects(obj_ids, **kwargs) - - Read in a chunk of data corresponding to all rows for - a given set of object IDs. - - :param obj_ids: A list of object IDs to use. - :type obj_ids: list - :param \*\*kwargs: Extra arguments - :type \*\*kwargs: dictionary, optional - - :returns: **res_df** -- The dataframe for the object data. - :rtype: Pandas dataframe - - - - .. py:method:: _read_objects_internal(obj_ids, **kwargs) - :abstractmethod: - - - Function to do the actual source-specific reading. - - - - .. py:method:: _validate_object_id_column(input_table) - - Checks that the object ID column exists and converts it to a string. - This is the common validity check for all object data tables. - - :param input_table: A loaded table. - :type input_table: Pandas dataframe - - :returns: **input_table** -- Returns the input dataframe modified in-place. - :rtype: Pandas dataframe - - - - .. py:method:: _process_and_validate_input_table(input_table, **kwargs) - - Perform any input-specific processing and validation on the input table. - Modifies the input dataframe in place. - - :param input_table: A loaded table. - :type input_table: Pandas dataframe - :param \*\*kwargs: Extra arguments - :type \*\*kwargs: dictionary, optional - - :returns: **input_table** -- Returns the input dataframe modified in-place. - :rtype: Pandas dataframe - - .. rubric:: Notes - - The base implementation includes filtering that is common to most - input types. Subclasses should call super.process_and_validate() - to ensure that the ancestor’s validation is also applied. - - Additional arguments to use: - - disallow_nan : boolean - if True then checks the data for NaNs or nulls. - - - diff --git a/docs/autoapi/sorcha/readers/OrbitAuxReader/index.rst b/docs/autoapi/sorcha/readers/OrbitAuxReader/index.rst deleted file mode 100644 index fd05cd9d..00000000 --- a/docs/autoapi/sorcha/readers/OrbitAuxReader/index.rst +++ /dev/null @@ -1,56 +0,0 @@ -sorcha.readers.OrbitAuxReader -============================= - -.. py:module:: sorcha.readers.OrbitAuxReader - - -Classes -------- - -.. autoapisummary:: - - sorcha.readers.OrbitAuxReader.OrbitAuxReader - - -Module Contents ---------------- - -.. py:class:: OrbitAuxReader(filename, sep='csv', header=-1, **kwargs) - - Bases: :py:obj:`sorcha.readers.CSVReader.CSVDataReader` - - - A class to read in the auxiliary orbit data files. - - - .. py:method:: get_reader_info() - - Return a string identifying the current reader name - and input information (for logging and output). - - :returns: The reader information. - :rtype: string - - - - .. py:method:: _process_and_validate_input_table(input_table, **kwargs) - - Perform any input-specific processing and validation on the input table. - Modifies the input dataframe in place. - - :param input_table: A loaded table. - :type input_table: pandas dataframe - :param \*\*kwargs: - :type \*\*kwargs: dictionary, optional - - :returns: **res_df** -- Returns the input dataframe modified in-place. - :rtype: pandas dataframe - - .. rubric:: Notes - - The base implementation includes filtering that is common to most - input types. Subclasses should call super.process_and_validate() - to ensure that the ancestor’s validation is also applied. - - - diff --git a/docs/autoapi/sorcha/readers/index.rst b/docs/autoapi/sorcha/readers/index.rst deleted file mode 100644 index 964bd5a6..00000000 --- a/docs/autoapi/sorcha/readers/index.rst +++ /dev/null @@ -1,21 +0,0 @@ -sorcha.readers -============== - -.. py:module:: sorcha.readers - - -Submodules ----------- - -.. toctree:: - :maxdepth: 1 - - /autoapi/sorcha/readers/CSVReader/index - /autoapi/sorcha/readers/CombinedDataReader/index - /autoapi/sorcha/readers/DatabaseReader/index - /autoapi/sorcha/readers/EphemerisReader/index - /autoapi/sorcha/readers/HDF5Reader/index - /autoapi/sorcha/readers/ObjectDataReader/index - /autoapi/sorcha/readers/OrbitAuxReader/index - - diff --git a/docs/autoapi/sorcha/sorcha/index.rst b/docs/autoapi/sorcha/sorcha/index.rst deleted file mode 100644 index 688d1726..00000000 --- a/docs/autoapi/sorcha/sorcha/index.rst +++ /dev/null @@ -1,56 +0,0 @@ -sorcha.sorcha -============= - -.. py:module:: sorcha.sorcha - - -Functions ---------- - -.. autoapisummary:: - - sorcha.sorcha.cite - sorcha.sorcha.mem - sorcha.sorcha.runLSSTSimulation - - -Module Contents ---------------- - -.. py:function:: cite() - - Providing the bibtex, AAS Journals software latex command, and acknowledgement - statements for Sorcha and the associated packages that power it. - - :param None: - - :rtype: None - - -.. py:function:: mem(df) - - Memory utility function that returns back how much memory the inputted pandas dataframe is using - :param df: - :type df: pandas dataframe - - :returns: **usage** - :rtype: int - - -.. py:function:: runLSSTSimulation(args, sconfigs) - - Runs the post processing survey simulator functions that apply a series of - filters to bias a model Solar System small body population to what the - Vera C. Rubin Observatory Legacy Survey of Space and Time would observe. - - :param args: dictionary of command-line arguments. - :type args: dictionary or `sorchaArguments` object - :param pplogger: The logger to use in this function. If None creates a new one. - Default = None - :type pplogger: logging.Logger, optional - :param sconfigs: Dataclass of configuration file arguments. - :type sconfigs: dataclass - - :rtype: None. - - diff --git a/docs/autoapi/sorcha/utilities/check_output_logs/index.rst b/docs/autoapi/sorcha/utilities/check_output_logs/index.rst deleted file mode 100644 index a81214af..00000000 --- a/docs/autoapi/sorcha/utilities/check_output_logs/index.rst +++ /dev/null @@ -1,58 +0,0 @@ -sorcha.utilities.check_output_logs -================================== - -.. py:module:: sorcha.utilities.check_output_logs - - -Functions ---------- - -.. autoapisummary:: - - sorcha.utilities.check_output_logs.find_all_log_files - sorcha.utilities.check_output_logs.check_all_logs - sorcha.utilities.check_output_logs.check_output_logs - - -Module Contents ---------------- - -.. py:function:: find_all_log_files(filepath) - - Looks for all Sorcha log files in the given filepath and subdirectories - recursively. Specifically searches for files ending *sorcha.log. - - :param filepath: Filepath of top-level directory within which to search for Sorcha log files. - :type filepath: str - - :returns: **log_files** -- A list of the discovered log files (absolute paths) - :rtype: list - - -.. py:function:: check_all_logs(log_files) - - Checks the last line of all the log files supplied and checks to see - if the Sorcha run completed successfully, saving the last line of the log - in question if it did not. - - :param log_files: A list of filepaths pointing to Sorcha log files. - :type log_files: list - - :returns: * **good_log** (*list of Booleans*) -- A list of whether each log file was deemed to be successful or not - * **last_lines** (*list of str*) -- A list of the last lines of unsuccessful Sorcha runs. - - -.. py:function:: check_output_logs(filepath, output=False) - - Searches directories recursively for Sorcha log files, classifies them as - belonging to successful or unsuccessful Sorcha runs, and provides this information - to the user. This is helpful in cases where several runs of Sorcha are being - performed simultaneously (i.e. on a supercomputer). Can output either a .csv - file or straight to the terminal. - - :param filepath: Filepath of top-level directory within which to search for Sorcha log files. - :type filepath: str - :param output: Either the filepath/name in which to save output, or False to print output to terminal. Default=False. - :type output: str or bool - - diff --git a/docs/autoapi/sorcha/utilities/citation_text/index.rst b/docs/autoapi/sorcha/utilities/citation_text/index.rst deleted file mode 100644 index eb5d3660..00000000 --- a/docs/autoapi/sorcha/utilities/citation_text/index.rst +++ /dev/null @@ -1,27 +0,0 @@ -sorcha.utilities.citation_text -============================== - -.. py:module:: sorcha.utilities.citation_text - - -Functions ---------- - -.. autoapisummary:: - - sorcha.utilities.citation_text.cite_sorcha - - -Module Contents ---------------- - -.. py:function:: cite_sorcha() - - Providing the bibtex, AAS Journals software latex command, and acknowledgement - statements for Sorcha and the associated packages that power it. - - :param None: - - :rtype: None - - diff --git a/docs/autoapi/sorcha/utilities/createResultsSQLDatabase/index.rst b/docs/autoapi/sorcha/utilities/createResultsSQLDatabase/index.rst deleted file mode 100644 index a7f8b6cd..00000000 --- a/docs/autoapi/sorcha/utilities/createResultsSQLDatabase/index.rst +++ /dev/null @@ -1,77 +0,0 @@ -sorcha.utilities.createResultsSQLDatabase -========================================= - -.. py:module:: sorcha.utilities.createResultsSQLDatabase - - -Functions ---------- - -.. autoapisummary:: - - sorcha.utilities.createResultsSQLDatabase.create_results_table - sorcha.utilities.createResultsSQLDatabase.create_inputs_table - sorcha.utilities.createResultsSQLDatabase.create_results_database - sorcha.utilities.createResultsSQLDatabase.get_column_names - - -Module Contents ---------------- - -.. py:function:: create_results_table(cnx_out, filename, output_path, output_stem, table_name='sorcha_results') - - Creates a table in a SQLite database from SSPP results. - - :param cnx_out: Connection to sqlite3 database. - :type cnx_out: sqlite3 connection - :param filename: filepath/name of sqlite3 database. - :type filename: string - :param output_path: filepath of directory containing SSPP output folders. - :type output_path: string - :param output_stem: stem filename for SSPP outputs. - :type output_stem: string - :param table_name: name of table of for storing sorcha results. Default ="sorcha_results" - :type table_name: string, optional - - :rtype: None - - -.. py:function:: create_inputs_table(cnx_out, input_path, table_type) - - Creates a table in a SQLite database from the input files (i.e. orbits, - physical parameters, etc). - - :param cnx_out: Connection to sqlite3 database. - :type cnx_out: sqlite3 connection - :param input_path: Filepath of directory containing input files. - :type input_path: string - :param table_type: Type of file. Should be "orbits"/"params"/"complex". - :type table_type: string - - :rtype: None - - -.. py:function:: create_results_database(args) - - Creates a SQLite database with tables of SSPP results and all orbit/physical - parameters/comet files. - - :param args: argparse ArgumentParser object; command line arguments. - :type args: ArgumentParser - - :rtype: None - - -.. py:function:: get_column_names(filename, table_name='sorcha_results') - - Obtains column names from a table in a SQLite database. - - :param filename: Filepath/name of sqlite3 database. - :type filename: string - :param table_name: Name of table. Default = "sorcha_results" - :type table_name: string, optional - - :returns: **col_names (list)** - :rtype: list of column names. - - diff --git a/docs/autoapi/sorcha/utilities/dataUtilitiesForTests/index.rst b/docs/autoapi/sorcha/utilities/dataUtilitiesForTests/index.rst deleted file mode 100644 index 85179bd8..00000000 --- a/docs/autoapi/sorcha/utilities/dataUtilitiesForTests/index.rst +++ /dev/null @@ -1,33 +0,0 @@ -sorcha.utilities.dataUtilitiesForTests -====================================== - -.. py:module:: sorcha.utilities.dataUtilitiesForTests - -.. autoapi-nested-parse:: - - This package contains all of sorcha's test data. - - - -Functions ---------- - -.. autoapisummary:: - - sorcha.utilities.dataUtilitiesForTests.get_test_filepath - - -Module Contents ---------------- - -.. py:function:: get_test_filepath(filename) - - Return the full path to a test file in the ``.../tests/data`` directory. - - :param filename: The name of the file inside the ``tests/data`` directory. - :type filename: string - - :returns: The full path to the file. - :rtype: string - - diff --git a/docs/autoapi/sorcha/utilities/diffTestUtils/index.rst b/docs/autoapi/sorcha/utilities/diffTestUtils/index.rst deleted file mode 100644 index 3f152961..00000000 --- a/docs/autoapi/sorcha/utilities/diffTestUtils/index.rst +++ /dev/null @@ -1,69 +0,0 @@ -sorcha.utilities.diffTestUtils -============================== - -.. py:module:: sorcha.utilities.diffTestUtils - - -Attributes ----------- - -.. autoapisummary:: - - sorcha.utilities.diffTestUtils.BASELINE_ARGS - sorcha.utilities.diffTestUtils.WITH_EPHEMERIS_ARGS - sorcha.utilities.diffTestUtils.CHUNKED_ARGS - sorcha.utilities.diffTestUtils.UNCHUNKED_ARGS - sorcha.utilities.diffTestUtils.VERIFICATION_TRUTH - - -Functions ---------- - -.. autoapisummary:: - - sorcha.utilities.diffTestUtils.compare_result_files - sorcha.utilities.diffTestUtils.override_seed_and_run - - -Module Contents ---------------- - -.. py:function:: compare_result_files(test_output, golden_output) - - Compare the results in test_output to those in golden_output. - - :param test_output: The path and file name of the test results. - :type test_output: string - :param golden_output: The path and file name of the golden set results. - :type golden_output: string - - :returns: Indicates whether the results are the same. - :rtype: bool - - -.. py:data:: BASELINE_ARGS - -.. py:data:: WITH_EPHEMERIS_ARGS - -.. py:data:: CHUNKED_ARGS - -.. py:data:: UNCHUNKED_ARGS - -.. py:data:: VERIFICATION_TRUTH - -.. py:function:: override_seed_and_run(outpath, arg_set='baseline') - - Run the full Rubin sim on the demo data and a fixed seed. - - WARNING: Never use a fixed seed for scientific analysis. This is - for testing purposes only. - - :param outpath: The path for the output files. - :type outpath: string - :param arg_set: set of arguments for setting up the run. Options: "baseline" or "with_ephemeris". - "baseline"" run does not ephemeris generation. "with_ephemeeris" is a full end to end run - of all main components of sorcha. - Default = "baseline" - :type arg_set: string, optional - - diff --git a/docs/autoapi/sorcha/utilities/generateGoldens/index.rst b/docs/autoapi/sorcha/utilities/generateGoldens/index.rst deleted file mode 100644 index fd15dfe0..00000000 --- a/docs/autoapi/sorcha/utilities/generateGoldens/index.rst +++ /dev/null @@ -1,19 +0,0 @@ -sorcha.utilities.generateGoldens -================================ - -.. py:module:: sorcha.utilities.generateGoldens - - -Attributes ----------- - -.. autoapisummary:: - - sorcha.utilities.generateGoldens.golden_dir - - -Module Contents ---------------- - -.. py:data:: golden_dir - diff --git a/docs/autoapi/sorcha/utilities/generate_meta_kernel/index.rst b/docs/autoapi/sorcha/utilities/generate_meta_kernel/index.rst deleted file mode 100644 index abd0b20d..00000000 --- a/docs/autoapi/sorcha/utilities/generate_meta_kernel/index.rst +++ /dev/null @@ -1,49 +0,0 @@ -sorcha.utilities.generate_meta_kernel -===================================== - -.. py:module:: sorcha.utilities.generate_meta_kernel - - -Functions ---------- - -.. autoapisummary:: - - sorcha.utilities.generate_meta_kernel.build_meta_kernel_file - sorcha.utilities.generate_meta_kernel._build_file_name - - -Module Contents ---------------- - -.. py:function:: build_meta_kernel_file(auxconfigs, retriever: pooch.Pooch) -> None - - Builds a specific text file that will be fed into `spiceypy` that defines - the list of spice kernel to load, as well as the order to load them. - - :param retriever: Pooch object that maintains the registry of files to download - :type retriever: pooch - :param auxconfigs: Dataclass of auxiliary configuration file arguments. - :type auxconfigs: dataclass - - :rtype: None - - -.. py:function:: _build_file_name(cache_dir: str, file_path: str) -> str - - Given a string defining the cache directory, and a string defining the full - path to a given file. This function will strip out the cache directory from - the file path and replace it with the required meta_kernel directory - substitution character. - - :param cache_dir: The full path to the cache directory used when retrieving files for Assist - and Rebound. - :type cache_dir: string - :param file_path: The full file path for a given file that will have the cache directory - segment replace. - :type file_path: string - - :returns: Shortened file path, appropriate for use in kernel_meta files. - :rtype: string - - diff --git a/docs/autoapi/sorcha/utilities/index.rst b/docs/autoapi/sorcha/utilities/index.rst deleted file mode 100644 index 92b373a1..00000000 --- a/docs/autoapi/sorcha/utilities/index.rst +++ /dev/null @@ -1,27 +0,0 @@ -sorcha.utilities -================ - -.. py:module:: sorcha.utilities - - -Submodules ----------- - -.. toctree:: - :maxdepth: 1 - - /autoapi/sorcha/utilities/check_output_logs/index - /autoapi/sorcha/utilities/citation_text/index - /autoapi/sorcha/utilities/createResultsSQLDatabase/index - /autoapi/sorcha/utilities/dataUtilitiesForTests/index - /autoapi/sorcha/utilities/diffTestUtils/index - /autoapi/sorcha/utilities/generateGoldens/index - /autoapi/sorcha/utilities/generate_meta_kernel/index - /autoapi/sorcha/utilities/retrieve_ephemeris_data_files/index - /autoapi/sorcha/utilities/sorchaArguments/index - /autoapi/sorcha/utilities/sorchaConfigs/index - /autoapi/sorcha/utilities/sorcha_copy_configs/index - /autoapi/sorcha/utilities/sorcha_copy_demo_files/index - /autoapi/sorcha/utilities/sorcha_demo_command/index - - diff --git a/docs/autoapi/sorcha/utilities/retrieve_ephemeris_data_files/index.rst b/docs/autoapi/sorcha/utilities/retrieve_ephemeris_data_files/index.rst deleted file mode 100644 index be51ef33..00000000 --- a/docs/autoapi/sorcha/utilities/retrieve_ephemeris_data_files/index.rst +++ /dev/null @@ -1,62 +0,0 @@ -sorcha.utilities.retrieve_ephemeris_data_files -============================================== - -.. py:module:: sorcha.utilities.retrieve_ephemeris_data_files - - -Functions ---------- - -.. autoapisummary:: - - sorcha.utilities.retrieve_ephemeris_data_files._decompress - sorcha.utilities.retrieve_ephemeris_data_files._remove_files - sorcha.utilities.retrieve_ephemeris_data_files._check_for_existing_files - - -Module Contents ---------------- - -.. py:function:: _decompress(fname, action, pup) - - Override the functionality of Pooch's `Decompress` class so that the resulting - decompressed file uses the original file name without the compression extension. - For instance `filename.json.bz` will be decompressed and saved as `filename.json`. - - :param fname: Original filename - :type fname: string - :param action: One of []"download", "update", "fetch"] - :type action: string - :param pup: The Pooch object that defines the location of the file. - :type pup: pooch - - :rtype: None - - -.. py:function:: _remove_files(auxconfigs, retriever: pooch.Pooch) -> None - - Utility to remove all the files tracked by the pooch retriever. This includes - the decompressed ObservatoryCodes.json file as well as the META_KERNEL file - that are created after downloading the files in the DATA_FILES_TO_DOWNLOAD - list. - - :param retriever: Pooch object that maintains the registry of files to download. - :type retriever: pooch - :param auxconfigs: Dataclass of auxiliary configuration file arguments. - :type auxconfigs: dataclass - - -.. py:function:: _check_for_existing_files(retriever: pooch.Pooch, file_list: list[str]) -> bool - - Will check for existing local files, any file not found will be printed - to the terminal. - - :param retriever: Pooch object that maintains the registry of files to download. - :type retriever: pooch - :param file_list: A list of file names look for in the local cache. - :type file_list: list of strings - - :returns: Returns True if all files are found in the local cache, False otherwise. - :rtype: bool - - diff --git a/docs/autoapi/sorcha/utilities/sorchaArguments/index.rst b/docs/autoapi/sorcha/utilities/sorchaArguments/index.rst deleted file mode 100644 index 5184dc70..00000000 --- a/docs/autoapi/sorcha/utilities/sorchaArguments/index.rst +++ /dev/null @@ -1,130 +0,0 @@ -sorcha.utilities.sorchaArguments -================================ - -.. py:module:: sorcha.utilities.sorchaArguments - - -Classes -------- - -.. autoapisummary:: - - sorcha.utilities.sorchaArguments.sorchaArguments - - -Module Contents ---------------- - -.. py:class:: sorchaArguments(cmd_args_dict=None) - - Data class for holding runtime arguments - - - .. py:attribute:: paramsinput - :type: str - :value: '' - - - path to file with input objects - - - .. py:attribute:: orbinfile - :type: str - :value: '' - - - path to file with input object orbits - - - .. py:attribute:: input_ephemeris_file - :type: str - :value: '' - - - path the ephemeris input file - - - .. py:attribute:: configfile - :type: str - :value: '' - - - path to the config.ini file - - - .. py:attribute:: outpath - :type: str - :value: '' - - - path where data should be output - - - .. py:attribute:: outfilestem - :type: str - :value: '' - - - file system for output - - - .. py:attribute:: loglevel - :type: bool - :value: False - - - logger verbosity - - - .. py:attribute:: surveyname - :type: str - :value: '' - - - name of the survey (`rubin_sim` is only one implemented currently) - - - .. py:attribute:: complex_parameters - :type: str - :value: '' - - - optional, extra complex physical parameter input files - - - .. py:attribute:: linking - :type: bool - :value: True - - - Turns on or off the rejection of unlinked sources - - - .. py:attribute:: _rngs - :value: None - - - A collection of per-module random number generators - - - .. py:attribute:: pplogger - :value: None - - - The Python logger instance - - - .. py:method:: read_from_dict(args) - - set the parameters from a cmd_args dict. - - :param aguments: dictionary of configuration parameters - :type aguments: dictionary - - :rtype: None - - - - .. py:method:: validate_arguments() - - diff --git a/docs/autoapi/sorcha/utilities/sorchaConfigs/index.rst b/docs/autoapi/sorcha/utilities/sorchaConfigs/index.rst deleted file mode 100644 index 99e0ef0a..00000000 --- a/docs/autoapi/sorcha/utilities/sorchaConfigs/index.rst +++ /dev/null @@ -1,1232 +0,0 @@ -sorcha.utilities.sorchaConfigs -============================== - -.. py:module:: sorcha.utilities.sorchaConfigs - - -Classes -------- - -.. autoapisummary:: - - sorcha.utilities.sorchaConfigs.inputConfigs - sorcha.utilities.sorchaConfigs.simulationConfigs - sorcha.utilities.sorchaConfigs.filtersConfigs - sorcha.utilities.sorchaConfigs.saturationConfigs - sorcha.utilities.sorchaConfigs.phasecurvesConfigs - sorcha.utilities.sorchaConfigs.fovConfigs - sorcha.utilities.sorchaConfigs.fadingfunctionConfigs - sorcha.utilities.sorchaConfigs.linkingfilterConfigs - sorcha.utilities.sorchaConfigs.outputConfigs - sorcha.utilities.sorchaConfigs.lightcurveConfigs - sorcha.utilities.sorchaConfigs.activityConfigs - sorcha.utilities.sorchaConfigs.expertConfigs - sorcha.utilities.sorchaConfigs.auxiliaryConfigs - sorcha.utilities.sorchaConfigs.sorchaConfigs - - -Functions ---------- - -.. autoapisummary:: - - sorcha.utilities.sorchaConfigs.check_key_exists - sorcha.utilities.sorchaConfigs.check_key_doesnt_exist - sorcha.utilities.sorchaConfigs.cast_as_int - sorcha.utilities.sorchaConfigs.cast_as_float - sorcha.utilities.sorchaConfigs.cast_as_bool - sorcha.utilities.sorchaConfigs.check_value_in_list - sorcha.utilities.sorchaConfigs.PPFindFileOrExit - sorcha.utilities.sorchaConfigs.cast_as_bool_or_set_default - sorcha.utilities.sorchaConfigs.PrintConfigsToLog - - -Module Contents ---------------- - -.. py:class:: inputConfigs - - Data class for holding INPUTS section configuration file keys and validating them. - - - .. py:attribute:: ephemerides_type - :type: str - :value: None - - - Simulation used for ephemeris input. - - - .. py:attribute:: eph_format - :type: str - :value: None - - - Format for ephemeris simulation input file. - - - .. py:attribute:: size_serial_chunk - :type: int - :value: None - - - Sorcha chunk size. - - - .. py:attribute:: aux_format - :type: str - :value: None - - - Format for the auxiliary input files. - - - .. py:attribute:: pointing_sql_query - :type: str - :value: None - - - SQL query for extracting data from pointing database. - - - .. py:method:: __post_init__() - - Automagically validates the input configs after initialisation. - - - - .. py:method:: _validate_input_configs() - - Validates the input config attributes after initialisation. - - :param None.: - - :rtype: None - - - -.. py:class:: simulationConfigs - - Data class for holding SIMULATION section configuration file keys and validating them - - - .. py:attribute:: ar_ang_fov - :type: float - :value: None - - - the field of view of our search field, in degrees - - - .. py:attribute:: ar_fov_buffer - :type: float - :value: None - - - the buffer zone around the field of view we want to include, in degrees - - - .. py:attribute:: ar_picket - :type: float - :value: None - - - imprecise discretization of time that allows us to move progress our simulations forward without getting too granular when we don't have to. the unit is number of days. - - - .. py:attribute:: ar_obs_code - :type: str - :value: None - - - the obscode is the MPC observatory code for the provided telescope. - - - .. py:attribute:: ar_healpix_order - :type: int - :value: None - - - the order of healpix which we will use for the healpy portions of the code. - - - .. py:attribute:: _ephemerides_type - :type: str - :value: None - - - Simulation used for ephemeris input. - - - .. py:method:: __post_init__() - - Automagically validates the simulation configs after initialisation. - - - - .. py:method:: _validate_simulation_configs() - - Validates the simulation config attributes after initialisation. - - :param None.: - - :rtype: None - - - -.. py:class:: filtersConfigs - - Data class for holding FILTERS section configuration file keys and validating them - - - .. py:attribute:: observing_filters - :type: str - :value: None - - - Filters of the observations you are interested in, comma-separated. - - - .. py:attribute:: survey_name - :type: str - :value: None - - - survey name to be used for checking filters are correct - - - .. py:attribute:: mainfilter - :type: str - :value: None - - - main filter chosen in physical parameter file - - - .. py:attribute:: othercolours - :type: str - :value: None - - - other filters given alongside main filter - - - .. py:method:: __post_init__() - - Automagically validates the filters configs after initialisation. - - - - .. py:method:: _validate_filters_configs() - - Validates the filters config attributes after initialisation. - - :param None.: - - :rtype: None - - - - .. py:method:: _check_for_correct_filters() - - Checks the filters selected are used by the chosen survey. - - :param None.: - - :rtype: None - - - -.. py:class:: saturationConfigs - - Data class for holding SATURATION section configuration file keys and validating them - - - .. py:attribute:: bright_limit_on - :type: bool - :value: None - - - - .. py:attribute:: bright_limit - :type: float - :value: None - - - Upper magnitude limit on sources that will overfill the detector pixels/have counts above the non-linearity regime of the pixels where one can’t do photometry. Objects brighter than this limit (in magnitude) will be cut. - - - .. py:attribute:: _observing_filters - :type: list - :value: None - - - Filters of the observations you are interested in, comma-separated. - - - .. py:method:: __post_init__() - - Automagically validates the saturation configs after initialisation. - - - - .. py:method:: _validate_saturation_configs() - - Validates the saturation config attributes after initialisation. - - :param None.: - - :rtype: None - - - -.. py:class:: phasecurvesConfigs - - Data class for holding PHASECURVES section configuration file keys and validating them - - - .. py:attribute:: phase_function - :type: str - :value: None - - - The phase function used to calculate apparent magnitude. The physical parameters input - - - .. py:method:: __post_init__() - - Automagically validates the phasecurve configs after initialisation. - - - - .. py:method:: _validate_phasecurve_configs() - - Validates the phasecurve config attributes after initialisation. - - :param None.: - - :rtype: None - - - -.. py:class:: fovConfigs - - Data class for holding FOV section configuration file keys and validating them - - - .. py:attribute:: camera_model - :type: str - :value: None - - - Choose between circular or actual camera footprint, including chip gaps. - - - .. py:attribute:: footprint_path - :type: str - :value: None - - - Path to camera footprint file. Uncomment to provide a path to the desired camera detector configuration file if not using the default built-in LSSTCam detector configuration for the actual camera footprint. - - - .. py:attribute:: fill_factor - :type: str - :value: None - - - Fraction of detector surface area which contains CCD -- simulates chip gaps for OIF output. Comment out if using camera footprint. - - - .. py:attribute:: circle_radius - :type: float - :value: None - - - Radius of the circle for a circular footprint (in degrees). Float. Comment out or do not include if using footprint camera model. - - - .. py:attribute:: footprint_edge_threshold - :type: float - :value: None - - - The distance from the edge of a detector (in arcseconds on the focal plane) at which we will not correctly extract an object. By default this is 10px or 2 arcseconds. Comment out or do not include if not using footprint camera model. - - - .. py:attribute:: survey_name - :type: str - :value: None - - - name of survey - - - .. py:method:: __post_init__() - - Automagically validates the fov configs after initialisation. - - - - .. py:method:: _validate_fov_configs() - - Validates the fov config attributes after initialisation. - - :param None.: - - :rtype: None - - - - .. py:method:: _camera_footprint() - - Validates the fov config attributes for a footprint camera model. - - :param None.: - - :rtype: None - - - - .. py:method:: _camera_circle() - - Validates the fov config attributes for a circle camera model. - - :param None.: - - :rtype: None - - - -.. py:class:: fadingfunctionConfigs - - Data class for holding FADINGFUNCTION section configuration file keys and validating them - - - .. py:attribute:: fading_function_on - :type: bool - :value: None - - - Detection efficiency fading function on or off. - - - .. py:attribute:: fading_function_width - :type: float - :value: None - - - Width parameter for fading function. Should be greater than zero and less than 0.5. - - - .. py:attribute:: fading_function_peak_efficiency - :type: float - :value: None - - - Peak efficiency for the fading function, called the 'fill factor' in Chesley and Veres (2017). - - - .. py:method:: __post_init__() - - Automagically validates the fading function configs after initialisation. - - - - .. py:method:: _validate_fadingfunction_configs() - - Validates the fadindfunction config attributes after initialisation. - - :param None.: - - :rtype: None - - - -.. py:class:: linkingfilterConfigs - - Data class for holding LINKINGFILTER section configuration file keys and validating them. - - - .. py:attribute:: ssp_linking_on - :type: bool - :value: None - - - flag to see if model should run ssp linking filter - - - .. py:attribute:: drop_unlinked - :type: bool - :value: None - - - Decides if unlinked objects will be dropped. - - - .. py:attribute:: ssp_detection_efficiency - :type: float - :value: None - - - ssp detection efficiency. Which fraction of the observations of an object will the automated solar system processing pipeline successfully link? Float. - - - .. py:attribute:: ssp_number_observations - :type: int - :value: None - - - Length of tracklets. How many observations of an object during one night are required to produce a valid tracklet? - - - .. py:attribute:: ssp_separation_threshold - :type: float - :value: None - - - Minimum separation (in arcsec) between two observations of an object required for the linking software to distinguish them as separate and therefore as a valid tracklet. - - - .. py:attribute:: ssp_maximum_time - :type: float - :value: None - - - Maximum time separation (in days) between subsequent observations in a tracklet. Default is 0.0625 days (90mins). - - - .. py:attribute:: ssp_number_tracklets - :type: int - :value: None - - - Number of tracklets for detection. How many tracklets are required to classify an object as detected? - - - .. py:attribute:: ssp_track_window - :type: int - :value: None - - - The number of tracklets defined above must occur in <= this number of days to constitute a complete track/detection. - - - .. py:attribute:: ssp_night_start_utc - :type: float - :value: None - - - The time in UTC at which it is noon at the observatory location (in standard time). For the LSST, 12pm Chile Standard Time is 4pm UTC. - - - .. py:method:: __post_init__() - - Automagically validates the linking filter configs after initialisation. - - - - .. py:method:: _validate_linkingfilter_configs() - - Validates the linkingfilter config attributes after initialisation. - - :param None.: - - :rtype: None - - - -.. py:class:: outputConfigs - - Data class for holding OUTPUT section configuration file keys and validating them. - - - .. py:attribute:: output_format - :type: str - :value: None - - - Output format of the output file[s] - - - .. py:attribute:: output_columns - :type: str - :value: None - - - Controls which columns are in the output files. - - - .. py:attribute:: position_decimals - :type: float - :value: None - - - position decimal places - - - .. py:attribute:: magnitude_decimals - :type: float - :value: None - - - magnitude decimal places - - - .. py:method:: __post_init__() - - Automagically validates the output configs after initialisation. - - - - .. py:method:: _validate_output_configs() - - Validates the output config attributes after initialisation. - - :param None.: - - :rtype: None - - - - .. py:method:: _validate_decimals() - - Validates the decimal output config attributes after initialisation. - - :param None.: - - :rtype: None - - - -.. py:class:: lightcurveConfigs - - Data class for holding LIGHTCURVE section configuration file keys and validating them. - - - .. py:attribute:: lc_model - :type: str - :value: None - - - The unique name of the lightcurve model to use. Defined in the ``name_id`` method of the subclasses of AbstractLightCurve. If not none, the complex physical parameters file must be specified at the command line.lc_model = none - - - .. py:method:: __post_init__() - - Automagically validates the lightcurve configs after initialisation. - - - - .. py:method:: _validate_lightcurve_configs() - - Validates the lightcurve config attributes after initialisation. - - :param None.: - - :rtype: None - - - -.. py:class:: activityConfigs - - Data class for holding Activity section configuration file keys and validating them. - - - .. py:attribute:: comet_activity - :type: str - :value: None - - - The unique name of the actvity model to use. Defined in the ``name_id`` method of the subclasses of AbstractCometaryActivity. If not none, a complex physical parameters file must be specified at the command line. - - - .. py:method:: __post_init__() - - Automagically validates the activity configs after initialisation. - - - - .. py:method:: _validate_activity_configs() - - Validates the activity config attributes after initialisation. - - :param None.: - - :rtype: None - - - -.. py:class:: expertConfigs - - Data class for holding expert section configuration file keys and validating them. - - - .. py:attribute:: SNR_limit - :type: float - :value: None - - - Drops observations with signal to noise ratio less than limit given - - - .. py:attribute:: SNR_limit_on - :type: bool - :value: None - - - flag for when an SNR limit is given - - - .. py:attribute:: mag_limit - :type: float - :value: None - - - Drops observations with magnitude less than limit given - - - .. py:attribute:: mag_limit_on - :type: bool - :value: None - - - flag for when a magnitude limit is given - - - .. py:attribute:: trailing_losses_on - :type: bool - :value: None - - - flag for trailing losses - - - .. py:attribute:: default_SNR_cut - :type: bool - :value: None - - - flag for default SNR - - - .. py:attribute:: randomization_on - :type: bool - :value: None - - - flag for randomizing astrometry and photometry - - - .. py:attribute:: vignetting_on - :type: bool - :value: None - - - flag for calculating effects of vignetting on limiting magnitude - - - .. py:method:: __post_init__() - - Automagically validates the expert configs after initialisation. - - - - .. py:method:: _validate_expert_configs() - - Validates the expert config attributes after initialisation. - - :param None.: - - :rtype: None - - - -.. py:class:: auxiliaryConfigs - - .. py:attribute:: de440s - :type: str - :value: 'de440s.bsp' - - - filename of de440s - - - .. py:attribute:: de440s_url - :type: str - :value: 'https://naif.jpl.nasa.gov/pub/naif/generic_kernels/spk/planets/de440s.bsp' - - - url for de4440s - - - .. py:attribute:: earth_predict - :type: str - :value: 'earth_200101_990827_predict.bpc' - - - filename of earth_predict - - - .. py:attribute:: earth_predict_url - :type: str - :value: 'https://naif.jpl.nasa.gov/pub/naif/generic_kernels/pck/earth_200101_990827_predict.bpc' - - - url for earth_predict - - - .. py:attribute:: earth_historical - :type: str - :value: 'earth_620120_240827.bpc' - - - filename of earth_histoical - - - .. py:attribute:: earth_historical_url - :type: str - :value: 'https://naif.jpl.nasa.gov/pub/naif/generic_kernels/pck/earth_620120_240827.bpc' - - - url for earth_historical - - - .. py:attribute:: earth_high_precision - :type: str - :value: 'earth_latest_high_prec.bpc' - - - filename of earth_high_precision - - - .. py:attribute:: earth_high_precision_url - :type: str - :value: 'https://naif.jpl.nasa.gov/pub/naif/generic_kernels/pck/earth_latest_high_prec.bpc' - - - url of earth_high_precision - - - .. py:attribute:: jpl_planets - :type: str - :value: 'linux_p1550p2650.440' - - - filename of jpl_planets - - - .. py:attribute:: jpl_planets_url - :type: str - :value: 'https://ssd.jpl.nasa.gov/ftp/eph/planets/Linux/de440/linux_p1550p2650.440' - - - url of jpl_planets - - - .. py:attribute:: jpl_small_bodies - :type: str - :value: 'sb441-n16.bsp' - - - filename of jpl_small_bodies - - - .. py:attribute:: jpl_small_bodies_url - :type: str - :value: 'https://ssd.jpl.nasa.gov/ftp/eph/small_bodies/asteroids_de441/sb441-n16.bsp' - - - url of jpl_small_bodies - - - .. py:attribute:: leap_seconds - :type: str - :value: 'naif0012.tls' - - - filename of leap_seconds - - - .. py:attribute:: leap_seconds_url - :type: str - :value: 'https://naif.jpl.nasa.gov/pub/naif/generic_kernels/lsk/naif0012.tls' - - - url of leap_seconds - - - .. py:attribute:: meta_kernel - :type: str - :value: 'meta_kernel.txt' - - - filename of meta_kernal - - - .. py:attribute:: observatory_codes - :type: str - :value: 'ObsCodes.json' - - - filename of observatory_codes - - - .. py:attribute:: observatory_codes_compressed - :type: str - :value: 'ObsCodes.json.gz' - - - filename of observatory_codes_compressed - - - .. py:attribute:: observatory_codes_compressed_url - :type: str - :value: 'https://minorplanetcenter.net/Extended_Files/obscodes_extended.json.gz' - - - url of observatory_codes_compressed - - - .. py:attribute:: orientation_constants - :type: str - :value: 'pck00010.pck' - - - filename of observatory_constants - - - .. py:attribute:: orientation_constants_url - :type: str - :value: 'https://naif.jpl.nasa.gov/pub/naif/generic_kernels/pck/pck00010.tpc' - - - url of observatory_constants - - - .. py:attribute:: data_file_list - :type: list - :value: None - - - convenience list of all the file names - - - .. py:attribute:: urls - :type: dict - :value: None - - - url - - :type: dictionary of filename - - - .. py:attribute:: data_files_to_download - :type: list - :value: None - - - list of files that need to be downloaded - - - .. py:attribute:: ordered_kernel_files - :type: list - :value: None - - - list of kernels ordered from least to most precise - used to assemble meta_kernel file - - - .. py:attribute:: registry - :type: list - :value: None - - - Default Pooch registry to define which files will be tracked and retrievable - - - .. py:property:: default_url - - returns a dictionary of the default urls used in this version of sorcha - - - .. py:property:: default_filenames - - returns a dictionary of the default filenames used in this version - - - .. py:method:: __post_init__() - - Automagically validates the auxiliary configs after initialisation. - - - - .. py:method:: _validate_auxiliary_configs() - - validates the auxililary config attributes after initialisation. - - - - .. py:method:: _create_lists_auxiliary_configs() - - creates lists of the auxililary config attributes after initialisation. - - :param None.: - - :rtype: None - - - -.. py:class:: sorchaConfigs(config_file_location=None, survey_name=None) - - Dataclass which stores configuration file keywords in dataclasses. - - - .. py:attribute:: input - :type: inputConfigs - :value: None - - - inputConfigs dataclass which stores the keywords from the INPUT section of the config file. - - - .. py:attribute:: simulation - :type: simulationConfigs - :value: None - - - simulationConfigs dataclass which stores the keywords from the SIMULATION section of the config file. - - - .. py:attribute:: filters - :type: filtersConfigs - :value: None - - - filtersConfigs dataclass which stores the keywords from the FILTERS section of the config file. - - - .. py:attribute:: saturation - :type: saturationConfigs - :value: None - - - saturationConfigs dataclass which stores the keywords from the SATURATION section of the config file. - - - .. py:attribute:: phasecurves - :type: phasecurvesConfigs - :value: None - - - phasecurveConfigs dataclass which stores the keywords from the PHASECURVES section of the config file. - - - .. py:attribute:: fov - :type: fovConfigs - :value: None - - - fovConfigs dataclass which stores the keywords from the FOV section of the config file. - - - .. py:attribute:: fadingfunction - :type: fadingfunctionConfigs - :value: None - - - fadingfunctionConfigs dataclass which stores the keywords from the FADINGFUNCTION section of the config file. - - - .. py:attribute:: linkingfilter - :type: linkingfilterConfigs - :value: None - - - linkingfilterConfigs dataclass which stores the keywords from the LINKINGFILTER section of the config file. - - - .. py:attribute:: output - :type: outputConfigs - :value: None - - - outputConfigs dataclass which stores the keywords from the OUTPUT section of the config file. - - - .. py:attribute:: lightcurve - :type: lightcurveConfigs - :value: None - - - lightcurveConfigs dataclass which stores the keywords from the LIGHTCURVE section of the config file. - - - .. py:attribute:: activity - :type: activityConfigs - :value: None - - - activityConfigs dataclass which stores the keywords from the ACTIVITY section of the config file. - - - .. py:attribute:: expert - :type: expertConfigs - :value: None - - - expertConfigs dataclass which stores the keywords from the EXPERT section of the config file. - - - .. py:attribute:: auxiliary - :type: auxiliaryConfigs - :value: None - - - auxiliaryConfigs dataclass which stores the keywords from the AUXILIARY section of the config file. - - - .. py:attribute:: pplogger - :type: None - :value: None - - - The Python logger instance - - - .. py:attribute:: survey_name - :type: str - :value: '' - - - The name of the survey. - - - .. py:method:: _read_configs_from_object(config_object) - - function that populates the class attributes - - :param config_object: ConfigParser object that has the config file read into it - :type config_object: ConfigParser object - - :rtype: None - - - -.. py:function:: check_key_exists(value, key_name) - - Checks to confirm that a mandatory config file value is present and has been read into the dataclass as truthy. Returns an error if value is falsy - - :param value: value of the config file attribute - :type value: object attribute - :param key_name: The key being checked. - :type key_name: string - - :rtype: None. - - -.. py:function:: check_key_doesnt_exist(value, key_name, reason) - - Checks to confirm that a config file value is not present and has been read into the dataclass as falsy. Returns an error if value is truthy - - :param value: value of the config file attribute - :type value: object attribute - :param key_name: The key being checked. - :type key_name: string - :param reason: reason given in the error message on why this value shouldn't be in the config file - :type reason: string - - :rtype: None. - - -.. py:function:: cast_as_int(value, key) - - Checks to see if value can be cast as an interger. - - :param value: value of the config file attribute - :type value: object attribute - :param key: The key being checked. - :type key: string - - :rtype: value as an integer - - -.. py:function:: cast_as_float(value, key) - - Checks to see if value can be cast as a float. - - :param value: value of the config file attribute - :type value: object attribute - :param key: The key being checked. - :type key: string - - :rtype: value as a float - - -.. py:function:: cast_as_bool(value, key) - - Checks to see if value can be cast as a boolen. - - :param value: value of the config file attribute - :type value: object attribute - :param key: The key being checked. - :type key: string - - :rtype: value as a boolen - - -.. py:function:: check_value_in_list(value, valuelist, key) - - Checks to see if a config variable is in a list of permissible variables. - - :param value: value of the config file value - :type value: object attribute - :param valuelist: list of permissible values for attribute - :type valuelist: list - :param key: The key being checked. - :type key: string - - :rtype: None. - - -.. py:function:: PPFindFileOrExit(arg_fn, argname) - - Checks to see if a file given by a filename exists. If it doesn't, - this fails gracefully and exits to the command line. - - :param arg_fn: The filepath/name of the file to be checked. - :type arg_fn: string - :param argname: The name of the argument being checked. Used for error message. - :type argname: string - - :returns: **arg_fn** -- The filepath/name of the file to be checked. - :rtype: string - - -.. py:function:: cast_as_bool_or_set_default(value, key, default) - - Checks to see if value can be cast as a boolen and if not set (equals None) gives default bool. - - :param value: value of the config file attribute - :type value: object attribute - :param key: The key being checked. - :type key: string - :param default: default bool if value is None - :type default: bool - - :rtype: value as a boolen - - -.. py:function:: PrintConfigsToLog(sconfigs, cmd_args) - - Prints all the values from the config file and command line to the log. - - :param sconfigs: Dataclass of config file variables. - :type sconfigs: dataclass - :param cmd_args: Dictionary of command line arguments. - :type cmd_args: dictionary - - :rtype: None. - - diff --git a/docs/autoapi/sorcha/utilities/sorcha_copy_configs/index.rst b/docs/autoapi/sorcha/utilities/sorcha_copy_configs/index.rst deleted file mode 100644 index e8e8c013..00000000 --- a/docs/autoapi/sorcha/utilities/sorcha_copy_configs/index.rst +++ /dev/null @@ -1,31 +0,0 @@ -sorcha.utilities.sorcha_copy_configs -==================================== - -.. py:module:: sorcha.utilities.sorcha_copy_configs - - -Functions ---------- - -.. autoapisummary:: - - sorcha.utilities.sorcha_copy_configs.copy_demo_configs - - -Module Contents ---------------- - -.. py:function:: copy_demo_configs(copy_location, which_configs, force_overwrite) - - Copies the example Sorcha configuration files to a user-specified location. - - :param copy_location: String containing the filepath of the location to which the configuration files should be copied. - :type copy_location: string - :param which_configs: String indicating which configuration files to retrieve. Should be "rubin", "demo" or "all". - :type which_configs: string - :param force_overwrite: Flag for determining whether existing files should be overwritten. - :type force_overwrite: boolean - - :rtype: None - - diff --git a/docs/autoapi/sorcha/utilities/sorcha_copy_demo_files/index.rst b/docs/autoapi/sorcha/utilities/sorcha_copy_demo_files/index.rst deleted file mode 100644 index 99a90e11..00000000 --- a/docs/autoapi/sorcha/utilities/sorcha_copy_demo_files/index.rst +++ /dev/null @@ -1,29 +0,0 @@ -sorcha.utilities.sorcha_copy_demo_files -======================================= - -.. py:module:: sorcha.utilities.sorcha_copy_demo_files - - -Functions ---------- - -.. autoapisummary:: - - sorcha.utilities.sorcha_copy_demo_files.copy_demo_files - - -Module Contents ---------------- - -.. py:function:: copy_demo_files(copy_location, force_overwrite) - - Copies the files needed to run the Sorcha demo to a user-specified location. - - :param copy_location: String containing the filepath of the location to which the configuration files should be copied. - :type copy_location: string - :param force_overwrite: Flag for determining whether existing files should be overwritten. - :type force_overwrite: boolean - - :rtype: None - - diff --git a/docs/autoapi/sorcha/utilities/sorcha_demo_command/index.rst b/docs/autoapi/sorcha/utilities/sorcha_demo_command/index.rst deleted file mode 100644 index 6a8e703e..00000000 --- a/docs/autoapi/sorcha/utilities/sorcha_demo_command/index.rst +++ /dev/null @@ -1,42 +0,0 @@ -sorcha.utilities.sorcha_demo_command -==================================== - -.. py:module:: sorcha.utilities.sorcha_demo_command - - -Functions ---------- - -.. autoapisummary:: - - sorcha.utilities.sorcha_demo_command.get_demo_command - sorcha.utilities.sorcha_demo_command.print_demo_command - - -Module Contents ---------------- - -.. py:function:: get_demo_command() - - Returns the current working version of the Sorcha demo command as a string. - If the Sorcha run command changes, updating this function will ensure - associated unit tests pass. - - :param None.: - - :returns: working sorcha demo command - :rtype: string - - -.. py:function:: print_demo_command(printall=True) - - Prints the current working version of the Sorcha demo command to the terminal, with - optional functionality to also tell the user how to copy the demo files. - - :param printall: When True, prints the demo command plus the instructions for copying the demo files. - When False, prints the demo command only. - :type printall: boolean - - :rtype: None. - - diff --git a/docs/autoapi/sorcha_cmdline/bootstrap/index.rst b/docs/autoapi/sorcha_cmdline/bootstrap/index.rst deleted file mode 100644 index 7e275715..00000000 --- a/docs/autoapi/sorcha_cmdline/bootstrap/index.rst +++ /dev/null @@ -1,22 +0,0 @@ -sorcha_cmdline.bootstrap -======================== - -.. py:module:: sorcha_cmdline.bootstrap - - -Functions ---------- - -.. autoapisummary:: - - sorcha_cmdline.bootstrap.main - sorcha_cmdline.bootstrap.execute - - -Module Contents ---------------- - -.. py:function:: main() - -.. py:function:: execute(args) - diff --git a/docs/autoapi/sorcha_cmdline/demo/index.rst b/docs/autoapi/sorcha_cmdline/demo/index.rst deleted file mode 100644 index e7ef4c8d..00000000 --- a/docs/autoapi/sorcha_cmdline/demo/index.rst +++ /dev/null @@ -1,25 +0,0 @@ -sorcha_cmdline.demo -=================== - -.. py:module:: sorcha_cmdline.demo - - -Functions ---------- - -.. autoapisummary:: - - sorcha_cmdline.demo.cmd_demo_prepare - sorcha_cmdline.demo.cmd_demo_howto - sorcha_cmdline.demo.main - - -Module Contents ---------------- - -.. py:function:: cmd_demo_prepare(args) - -.. py:function:: cmd_demo_howto(args) - -.. py:function:: main() - diff --git a/docs/autoapi/sorcha_cmdline/index.rst b/docs/autoapi/sorcha_cmdline/index.rst deleted file mode 100644 index 196da700..00000000 --- a/docs/autoapi/sorcha_cmdline/index.rst +++ /dev/null @@ -1,21 +0,0 @@ -sorcha_cmdline -============== - -.. py:module:: sorcha_cmdline - - -Submodules ----------- - -.. toctree:: - :maxdepth: 1 - - /autoapi/sorcha_cmdline/bootstrap/index - /autoapi/sorcha_cmdline/demo/index - /autoapi/sorcha_cmdline/init/index - /autoapi/sorcha_cmdline/main/index - /autoapi/sorcha_cmdline/outputs/index - /autoapi/sorcha_cmdline/run/index - /autoapi/sorcha_cmdline/sorchaargumentparser/index - - diff --git a/docs/autoapi/sorcha_cmdline/init/index.rst b/docs/autoapi/sorcha_cmdline/init/index.rst deleted file mode 100644 index 61dbdc97..00000000 --- a/docs/autoapi/sorcha_cmdline/init/index.rst +++ /dev/null @@ -1,35 +0,0 @@ -sorcha_cmdline.init -=================== - -.. py:module:: sorcha_cmdline.init - - -Functions ---------- - -.. autoapisummary:: - - sorcha_cmdline.init.parse_file_selection - sorcha_cmdline.init.execute - sorcha_cmdline.init.main - - -Module Contents ---------------- - -.. py:function:: parse_file_selection(file_select) - - Turns the number entered by the user at the command line into a string - prompt. Also performs error handling. - - :param file_select: Integer entered by the user at command line. - :type file_select: int - - :returns: **which_configs** -- String indicating which configuration files to retrieve. Should be "rubin", "demo" or "all". - :rtype: string - - -.. py:function:: execute(args) - -.. py:function:: main() - diff --git a/docs/autoapi/sorcha_cmdline/main/index.rst b/docs/autoapi/sorcha_cmdline/main/index.rst deleted file mode 100644 index 8a6febe6..00000000 --- a/docs/autoapi/sorcha_cmdline/main/index.rst +++ /dev/null @@ -1,25 +0,0 @@ -sorcha_cmdline.main -=================== - -.. py:module:: sorcha_cmdline.main - - -Functions ---------- - -.. autoapisummary:: - - sorcha_cmdline.main.find_sorcha_verbs - sorcha_cmdline.main.main - - -Module Contents ---------------- - -.. py:function:: find_sorcha_verbs() - - Find available sorcha commands in the system's PATH. - - -.. py:function:: main() - diff --git a/docs/autoapi/sorcha_cmdline/outputs/index.rst b/docs/autoapi/sorcha_cmdline/outputs/index.rst deleted file mode 100644 index f1bfcaf3..00000000 --- a/docs/autoapi/sorcha_cmdline/outputs/index.rst +++ /dev/null @@ -1,25 +0,0 @@ -sorcha_cmdline.outputs -====================== - -.. py:module:: sorcha_cmdline.outputs - - -Functions ---------- - -.. autoapisummary:: - - sorcha_cmdline.outputs.cmd_outputs_create_sqlite - sorcha_cmdline.outputs.cmd_outputs_check_logs - sorcha_cmdline.outputs.main - - -Module Contents ---------------- - -.. py:function:: cmd_outputs_create_sqlite(args) - -.. py:function:: cmd_outputs_check_logs(args) - -.. py:function:: main() - diff --git a/docs/autoapi/sorcha_cmdline/run/index.rst b/docs/autoapi/sorcha_cmdline/run/index.rst deleted file mode 100644 index 8d6ea857..00000000 --- a/docs/autoapi/sorcha_cmdline/run/index.rst +++ /dev/null @@ -1,22 +0,0 @@ -sorcha_cmdline.run -================== - -.. py:module:: sorcha_cmdline.run - - -Functions ---------- - -.. autoapisummary:: - - sorcha_cmdline.run.main - sorcha_cmdline.run.execute - - -Module Contents ---------------- - -.. py:function:: main() - -.. py:function:: execute(args) - diff --git a/docs/autoapi/sorcha_cmdline/sorchaargumentparser/index.rst b/docs/autoapi/sorcha_cmdline/sorchaargumentparser/index.rst deleted file mode 100644 index 947c51ee..00000000 --- a/docs/autoapi/sorcha_cmdline/sorchaargumentparser/index.rst +++ /dev/null @@ -1,40 +0,0 @@ -sorcha_cmdline.sorchaargumentparser -=================================== - -.. py:module:: sorcha_cmdline.sorchaargumentparser - - -Classes -------- - -.. autoapisummary:: - - sorcha_cmdline.sorchaargumentparser.SorchaArgumentParser - - -Module Contents ---------------- - -.. py:class:: SorchaArgumentParser(*args, **kwargs) - - Bases: :py:obj:`argparse.ArgumentParser` - - - A subclass of the argparse.ArgumentParser that adds in a print statement - to make it clearer how to get detailed help for new users who may not be - as familiar with linux/unix - - - .. py:method:: print_usage(file=None) - - Print a brief description of how the ArgumentParser should be invoked - on the command line. If file is None, sys.stdout is assumed. - - - :param file: Variable length argument list. - :type file: str or None - - :rtype: None. - - - From 9c8e65312b70c35fa45c2c5c143d156c5dbc170a Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Sun, 12 Jan 2025 20:05:58 +0000 Subject: [PATCH 39/52] postprocessing updates --- docs/postprocessing.rst | 26 ++++++++++++++------------ 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/docs/postprocessing.rst b/docs/postprocessing.rst index ed540dea..8895d9e7 100644 --- a/docs/postprocessing.rst +++ b/docs/postprocessing.rst @@ -10,16 +10,17 @@ Post-Processing (Applying Survey Biases) How it Works ------------------------ -Once the ephemerides have been generated or read in from an external file, Sorcha moves on to486 -the second phase, which we call post-processing. For each of the input objects, Sorcha goes through487 -the potential observations identified in the ephemeris generation step and performs a series of cal-488 -culations and assessments in the post-processing stage to determine whether the objects would have489 -been detectable as a source in the survey images and would have later been identified as a moving490 +Once the ephemerides have been generated or read in from an external file, Sorcha moves on to +the second phase, which we call post-processing. For each of the input objects, Sorcha goes through +the potential observations identified in the ephemeris generation step and performs a series of +calculations and assessments in the post-processing stage to determine whether the objects would have +been detectable as a source in the survey images and would have later been identified as a moving solar system object. All aspects of post-processing can be adjusted or turned on/off via ``Sorcha``'s :ref:`configs`. +.. _mags:: -Trailed Source Magnitude and PSF (Point Spread Function) Magnitude ---------------------------------------------------------------------- +Calculating the Trailed Source Magnitude and PSF (Point Spread Function) Magnitude +------------------------------------------------------------------------------------- ``Sorcha`` calculates two apparent magnitudes that we will refer to as the **trailed source magnitude** and the **PSF magnitude**. @@ -37,8 +38,7 @@ Trailed Source Magnitude and PSF (Point Spread Function) Magnitude Phase Curves ------------------------------------------------------------- - +~~~~~~~~~~~~~~~~~~~~~ .. _addons: @@ -175,12 +175,12 @@ Or:: Fading Function/Detection Efficiency ------------------------------------ -This filter serves to remove observations of objects which are faint beyond the survey's capability -to detect them. ``Sorcha`` uses the fading function formulation of `Veres and Chesley (2017) `_: +This filter serves to remove potential detections of the input small bodies which are too faint to be detected in the each survey observation. + ``Sorcha`` uses the fading function formulation of `Veres and Chesley (2017) `_: see the below plot. This fading function is parameterised by the fading function width and peak efficiency. The default values are modelled on those from the aforementioned paper. -To include this filter, the following options should be set in the :ref:`configs`:: +To configure the fading function, the following variabless should be set in the :ref:`configs`:: [FADINGFUNCTION] fading_function_width = 0.1 @@ -191,6 +191,8 @@ To include this filter, the following options should be set in the :ref:`configs :alt: Graph showing the fading function. Detection probability is plotted against magnitude - limiting magnitude, showing three smoothed step-functions centred on 0.0 on the x axis for three different widths. :align: center +.. note:: + The fading function uses the :ref:`PSF magnitude ` to evaluate detectability on the relevant survey images. .. seealso:: We have a`Jupyter notebook `_ showing how ``Sorcha`` applies the survey detection efficiency (fading function). From 6552045a4d61c4d0e33649aa3804e9cd9d366fd5 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Sun, 12 Jan 2025 20:32:39 +0000 Subject: [PATCH 40/52] documentation updates --- docs/postprocessing.rst | 79 +++++++++++++++++++++++----------------- docs/troubleshooting.rst | 2 +- 2 files changed, 46 insertions(+), 35 deletions(-) diff --git a/docs/postprocessing.rst b/docs/postprocessing.rst index 8895d9e7..daf3e88a 100644 --- a/docs/postprocessing.rst +++ b/docs/postprocessing.rst @@ -24,7 +24,9 @@ Calculating the Trailed Source Magnitude and PSF (Point Spread Function) Magnitu ``Sorcha`` calculates two apparent magnitudes that we will refer to as the **trailed source magnitude** and the **PSF magnitude**. - +Below is a cartoon schematic depicting the difference between how the trailed source magnitude and the +PSF magnitude for a moving solar system object observed on an LSST image are estimated by the Rubin +data management pipelines (including Solar System Processing [SSP]). .. image:: images/trailed_source.png :width: 500 @@ -115,11 +117,33 @@ This filter will recalculate the PSF magnitude of the observations, adjusting fo :align: center -.. seealso:: - We have a Jupyter notebook `_ demonstrating the LSSTCometActivity class built into `Sorcha addons GitHub repository `_. - .. _vignettting: +Accounting for Saturation (Saturation/Bright Limit Filter) +------------------------------------------------------------ + +The saturation/bright limit filter removes all detections that are brighter than the saturation limit +of the survey. `Ivezić et al. (2019) `_ +estimate that the saturation limit for the LSST will be ~16 in the r filter. + +``Sorcha`` includes functionality to specify either a single saturation limit, or a saturation limit in each filter. +For the latter, limits must be given in a comma-separated list in the same order as the :ref:`optical filters set in the configuration file ` + +To include this filter, the :ref:`configs` should contain:: + + [SATURATION] + bright_limit = 16.0 + +Or:: + + [SATURATION] + bright_limit = 16.0, 16.1, 16.2 + + +.. tip:: + The saturation filter is only applied if the :ref:`configuration file` has a SATURATION section. + + Calculating the 5σ Limiting Magnitude at the Source Location and Vignetting ---------------------------------------------------------------------------------------------------- @@ -147,30 +171,8 @@ further from the center of the FOV have shallower depths. .. note:: The :ref:`pointing` provides the 5σ limiting magnitude at the center of the exposure's FOV. -Accounting for Saturation (Saturation/Bright Limit Filter) ------------------------------------------------------------- - -The saturation/bright limit filter removes all detections that are brighter than the saturation limit -of the survey. `Ivezić et al. (2019) `_ -estimate that the saturation limit for the LSST will be ~16 in the r filter. - -``Sorcha`` includes functionality to specify either a single saturation limit, or a saturation limit in each filter. -For the latter, limits must be given in a comma-separated list in the same order as the :ref`optical filters set in the configuration file `.. - -To include this filter, the :ref:`configs` should contain:: - - [SATURATION] - bright_limit = 16.0 - -Or:: - - [SATURATION] - bright_limit = 16.0, 16.1, 16.2 - - -.. tip:: - The saturation filter is only applied if the :ref:`configuration file` has a SATURATION section. - +.. seealso:: + We have a `Jupyter notebook `_ demonstrating ``Sorcha``'s vignetting calculation. Fading Function/Detection Efficiency ------------------------------------ @@ -187,22 +189,27 @@ To configure the fading function, the following variabless should be set in the fading_function_peak_efficiency = 1. .. image:: images/fading_function.png - :width: 400 + :width: 600 :alt: Graph showing the fading function. Detection probability is plotted against magnitude - limiting magnitude, showing three smoothed step-functions centred on 0.0 on the x axis for three different widths. :align: center +The figure above shows the fading function and how ``Sorcha`` appliels it. The top plot shows the fading function representing the fraction of detected point +sources as a function of magnitude. The different lines represent the effect of the variation of the peak +detection efficiency and the width parameter on the shape of the function. The 5σ limiting magnitude +at the source location is marked in gray (m5σ=24.5). The bottom plot show histogram showing detection probability +of 10,000 point sources passed through Sorcha’s fading function filter, with the actual calculated detection +probability from Equation 10 overplotted as a solid line. Here, detection efficiency = 1.0, width parameter = 0.1, and m5σ=24.5 and the +binsize is 0.04 mag. + .. note:: The fading function uses the :ref:`PSF magnitude ` to evaluate detectability on the relevant survey images. .. seealso:: - We have a`Jupyter notebook `_ showing how ``Sorcha`` applies the survey detection efficiency (fading function). + We have a `Jupyter notebook `_ showing how ``Sorcha`` applies the survey detection efficiency (fading function). Camera Footprint ----------------- -.. attention:: - Applying some form of the camera footprint filter is mandatory if you are trying to preform a science quality simulation, but we do have the ability to turn it off for other types of modeling cases. See the :ref:`advamced`:: page. - Due to the footprint of the LSST Camera (LSSTCam), see the figure below, it is possible that some object detections may be lost in gaps between the chips. @@ -215,6 +222,10 @@ However, the full camera footprint is most relevant for slow-moving objects, whe subsequent observation fall into a chip gap. This is less concerning for faster-moving objects such as asteroids and near-Earth objects. As a result, we provide two methods of applying the camera footprint. + +.. attention:: + Applying some form of the camera footprint filter is mandatory if you are trying to preform a science quality simulation, but we do have the ability to turn it off for other types of modeling cases. See the :ref:`advanced post-processing tunable features and parameters `. + Circle Radius (Simple Sensor Area) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -332,7 +343,7 @@ the observation is of a linked object or not. To enable this functionality, add What Observations to Include ------------------------------------- -The user sets what observations from the survey :ref:`pointing` will be used by setting the **observing_filters** :ref:`configs` variable:: +The user sets what observations from the survey :ref:`pointing` will be used by setting the **observing_filters** :ref:`configs` variable in the [FILTERS] section:: [FILTERS] diff --git a/docs/troubleshooting.rst b/docs/troubleshooting.rst index 4592fb48..b5195660 100644 --- a/docs/troubleshooting.rst +++ b/docs/troubleshooting.rst @@ -9,7 +9,7 @@ Have You Checked the Error Log File? If ``Sorcha`` runs successfully the .err log file created will be empty. If the software exited gracefully with an error it will print error statements to the error log file. If ``Sorcha'' looks like it completed but you're not getting the expected output, the .err log file is the first place to check. .. tip:: - You cna also usee the **-l** flag to set get more detailed and informative messages in the log file produced by **sorcha run**. + You can also usee the **-l** flag to set get more detailed and informative messages in the log file produced by **sorcha run**. Using Relative File Paths --------------------------------------------------------------- From 1625770466373832f0b5aafe9ab1ac90ee18361c Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Sun, 12 Jan 2025 22:26:34 +0000 Subject: [PATCH 41/52] post-processing updates post-processing updates --- docs/inputs.rst | 8 ++-- docs/overview.rst | 2 +- docs/postprocessing.rst | 96 +++++++++++++++++++++++++++++------------ 3 files changed, 74 insertions(+), 32 deletions(-) diff --git a/docs/inputs.rst b/docs/inputs.rst index a0c4732a..05dcae30 100644 --- a/docs/inputs.rst +++ b/docs/inputs.rst @@ -212,13 +212,13 @@ The input file for the physical parameters includes information about the object * Each simulated object **must** have a unique string identifier * You **must use the same phase curve prescription for all simulated objects**. If you want to use different phase curve prescriptions for different synthetic populations, you will need to run them in separate input files to ``Sorcha`` * If the phase curve function is set to NONE in the configuration value then no phase curve parameter values are required in the physical parameters files. - * In the :ref:`configuration file` you can decide which filters you want have ``Sorcha`` run on and specify which filter is the main filter that the absolute magnitude is defined for. You only need to provide colors for those filters specified in the :ref:`configuration file`. + * In the :ref:`configuration file` you can decide which observing filters (e.g *r*-band,*g*-band,etc) you want have ``Sorcha`` run on and specify which observing filter is the main filter that the absolute magnitude is defined for. You only need to provide colors for those filters specified in the :ref:`configuration file`. -We have implemented several phase curve parameterizations that can be specified in the :ref:`configuration file` and then inputted through the physical parameters. **You can either specify one set of phase curve parameters for all filters or specify values for each filter examined by** ``Sorcha``. We are using the `sbpy `_ phase function utilities. The supported options are: `HG `_, `HG1G2 `_, `HG12 `_, `linear `_ (specified by S in the header of the physical parameters file), and none (if no columns for phase curve are included in the physical parameters file then the synthetic object is considered to have a flat phase curve). Note that the HG12 model is the `Penttilä et al. (2016) `_ modified model, and not the original (IAU adopted) `Muinonen et al. (2010) `_ model. +We have implemented several phase curve parameterizations that can be specified in the :ref:`configuration file` and then inputted through the physical parameters. **You can either specify one set of phase curve parameters for all observing filters or specify values for each filter examined by** ``Sorcha``. We are using the `sbpy `_ phase function utilities. The supported options are: `HG `_, `HG1G2 `_, `HG12 `_, `linear `_ (specified by S in the header of the physical parameters file), and none (if no columns for phase curve are included in the physical parameters file then the synthetic object is considered to have a flat phase curve). Note that the HG12 model is the `Penttilä et al. (2016) `_ modified model, and not the original (IAU adopted) `Muinonen et al. (2010) `_ model. -Example Pphysical Parameters File (single linear slope phase curve parameter for all filters) +Example Physical Parameters File (single linear slope phase curve parameter for all observing filters) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. note:: @@ -234,7 +234,7 @@ Example Pphysical Parameters File (single linear slope phase curve parameter for St500004a 10.2 1.90 0.58 -0.21 -0.30 -0.39 0.15 -Example Physical Parameters File (a HG value is specified for each filter) +Example Physical Parameters File (a HG value is specified for each observing filter) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. note:: diff --git a/docs/overview.rst b/docs/overview.rst index cc4cfa8a..5f7c2350 100644 --- a/docs/overview.rst +++ b/docs/overview.rst @@ -22,7 +22,7 @@ The :ref:`inputs` that ``Sorcha`` requires are shown in the figure below ``Sorcha`` by default uses its own :ref:`ephemeris generator` to propagate the orbits and translate them to on-sky locations and rates. ``Sorcha``'s ephemeris generator is powered by `ASSIST `_, a software package for ephemeris-quality integrations of test particles, and the `REBOUND `_ N-body integrator. If the user prefers to use a different generator, ``Sorcha`` is also to be configured to read in an external ephemeris file with pre-calculated ephemerides of the input synthetic orbital population. -The default main steps, calculations, and filter within ``Sorcha`` that are used to estimate what the LSST would discover is shown below. +The default main steps, calculations, and filter within ``Sorcha`` that are used to estimate what the LSST would discover are shown below. .. image:: images/workflow.png :width: 800 diff --git a/docs/postprocessing.rst b/docs/postprocessing.rst index daf3e88a..6b45e569 100644 --- a/docs/postprocessing.rst +++ b/docs/postprocessing.rst @@ -3,20 +3,26 @@ Post-Processing (Applying Survey Biases) ========================================================== - -.. seealso:: - For a more detailed description of ``Sorcha``'s post-processing stage please see Merritt et al. (submitted). - How it Works ------------------------ -Once the ephemerides have been generated or read in from an external file, Sorcha moves on to -the second phase, which we call post-processing. For each of the input objects, Sorcha goes through +Once the ephemerides have been generated or read in from an external file, `Sorcha`` moves on to +the second phase, which we call post-processing. For each of the input objects, ``Sorcha`` goes through the potential observations identified in the ephemeris generation step and performs a series of calculations and assessments in the post-processing stage to determine whether the objects would have been detectable as a source in the survey images and would have later been identified as a moving solar system object. All aspects of post-processing can be adjusted or turned on/off via ``Sorcha``'s :ref:`configs`. +.. seealso:: + For a more detailed description of ``Sorcha``'s post-processing stage please see Merritt et al. (submitted). + + +The steps within ``Sorcha``'s post-processing stage that are used to estimate what the LSST would discover are shown below. + +.. image:: images/workflow.png + :width: 800 + :alt: An overview of the LSST workflow + .. _mags:: Calculating the Trailed Source Magnitude and PSF (Point Spread Function) Magnitude @@ -39,20 +45,65 @@ data management pipelines (including Solar System Processing [SSP]). -Phase Curves +Colors and Phase Curves ~~~~~~~~~~~~~~~~~~~~~ +For each potential detection of an object from the input population, the trailed source magnitude is calculated for the relevant observing filter using the colors specificed in the :ref:`physical`. The trailed source magnitude is also adjusted for phase curve effects. We have implemented several phase curve parameterizations that can be specified in the :ref:`configuration file` and then inputted through the :ref:`physical`. **You can either specify one set of phase curve parameters for all observing filters or specify values for each observing filter examined by** ``Sorcha``. We are using the `sbpy `_ phase function utilities. The supported options are: + + +* `HG `_ +* `HG1G2 `_ +* `HG12 `_ +* `linear `_ (specified by S in the header of the :ref:`physical`) +* none (if no columns for phase curve are included in the physical parameters file then the synthetic object is considered to have a flat phase curve). + +.. note:: + The HG12 model is the `Penttilä et al. (2016) `_ modified model, and not the original (IAU adopted) `Muinonen et al. (2010) `_ model. + +The phase curve function to apply is set via the [PHASECURVES] section of:ref:`configs`:: + + [PHASECURVES] + + # The phase function used to calculate apparent magnitude. The physical parameters input + # file must contain the columns needed to calculate the phase function. + # Options: HG, HG1G2, HG12, linear, none. + .. _addons: + +Calculating Trailing Losses and Calculating the PSF Magnigtude +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +If the observed object is fast-moving, the signal will form a trail, reducing the measured magnitude. +This filter will recalculate the PSF magnitude of the observations, adjusting for trailing losses. + +.. image:: images/Trail.png + :width: 400 + :alt: Sky image showing a short trailing source circled in red. + :align: center + + +Applying Photometric and Astrometric Uncerainties +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Real astronomical surveys measure photometry and astrometry that have uncertainities. To better compare to what the survey detected, ``Sorcha`` applies photometric and astrometric errors that modify the ca;culated value for the right acension, declination, trailed source magnitude, and PSF masgnitude for each potential detection. The models for these uncertainties are primarily driven by the signal-to-noise ratio (SNR) for a particular input object in an image, following the methods in `(Ivezić et al. 2019) `_ + +.. note:: + As a compromise between low-probability detections and unrealistic magnitude uncertainties producing “fake detections”, by default ``Sorcha`` removes all observations with SNR less than 2 after calculating the astronometric and photometric uncertainties. + +.. warning:: + Right now ``Sorcha`` only has functions to compute the photometric and astrometric uncertainties and SNR estimations specifically for Rubin Observatory. + Incorporating Rotational Light Curves and Activity ------------------------------------------------------------ -``Sorcha`` has the ability user provided functions though python classes that augment/change the apparent brightness calculations for the synthetic Solar System objects. Any values required as input for these calculations, must be provided in the separate :ref:`CPP` file as input. Rather than forcing the user directly modify the ``Sorcha`` codebase every time they want to apply a different model for representing the effects of rotational lightcurves or cometary activity, we provide the ability to develop separate activity and lightcurve/brightness enhancement functions as plugins using our template classes and add them to the `Sorcha addons `_ package. In both cases, any derived class must inherit from the corresponding base class and follow its API, to ensure that Sorcha knows how to find and use your class. Once the Sorcha addons is installed, Sorcha will automatically detect the available plugins and make them available during post-processing. To use one of the plugins from the community utilities, simply add the unique name of the plugin to the :ref:`configs` provided to Sorcha, and provide the :ref:`CPP` file on the command line. We currently have 2 pre-made classes that can augment the calculated apparent magnitude of each synthetic object, One for handling cometary activity as a function of heliocentric distance and one that applies rotational light curves to the synthetic objects. +``Sorcha`` has the ability user provided functions though python classes that augment/change the apparent brightness calculations for the synthetic Solar System objects. Any values required as input for these calculations, must be provided in the separate :ref:`CPP` file as input. Rather than forcing the user directly modify the ``Sorcha`` codebase every time they want to apply a different model for representing the effects of rotational lightcurves or cometary activity, we provide the ability to develop separate activity and lightcurve/brightness enhancement functions as plugins using our template classes and add them to the `Sorcha addons `_ package. In both cases, any derived class must inherit from the corresponding base class and follow its API, to ensure that ``Sorcha`` knows how to find and use your class. Once the ``Sorcha addons`` is installed, ``Sorcha`` will automatically detect the available plugins and make them available during post-processing. To use one of the plugins from the community utilities, simply add the unique name of the plugin to the :ref:`configs` provided to ``Sorcha``, and provide the :ref:`CPP` file on the command line. We currently have 2 pre-made classes that can augment the calculated apparent magnitude of each synthetic object, One for handling cometary activity as a function of heliocentric distance and one that applies rotational light curves to the synthetic objects. Cometary Activity or Simulating Other Active Objects ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -You can user cometary activity class provided in also your own class to apply a different comentary activity and add it into a custom version of the``Sorcha addons`` package. Once the Sorcha-addons is installed, Sorcha will automatically detect the available plugins and make them available during processing. +You can user cometary activity class provided in also your own class to apply a different comentary activity and add it into a custom version of the``Sorcha addons`` package. Once the ``Sorcha-addons`` package is installed, ``Sorcha`` will automatically detect the available plugins and make them available during processing. Cometary Activity Configuration Parameters @@ -102,21 +153,6 @@ Lightcurve Template Class :language: python -Applying Photometric and Astrometric Uncerainties ------------------------------------------------------------- - -Trailing Losses ------------------ - -If the observed object is fast-moving, the signal will form a trail, reducing the measured magnitude. -This filter will recalculate the PSF magnitude of the observations, adjusting for trailing losses. - -.. image:: images/Trail.png - :width: 400 - :alt: Sky image showing a short trailing source circled in red. - :align: center - - .. _vignettting: Accounting for Saturation (Saturation/Bright Limit Filter) @@ -127,7 +163,7 @@ of the survey. `Ivezić et al. (2019) ` +For the latter, limits must be given in a comma-separated list in the same order as the :ref:`observing filters set in the configuration file ` To include this filter, the :ref:`configs` should contain:: @@ -150,7 +186,7 @@ Calculating the 5σ Limiting Magnitude at the Source Location and Vignetting Objects that are on the edges of the field of view are dimmer due to vignetting: the field-of-view is not uniformly illuminated, and so the limiting magnitude for each detection will depend on its position within the FOV (field-of-view). The effect of this is to decrease the 5σ limiting magnitude – the apparent magnitude where a detected point source has exactly a -50% probability of detection – at the edges of the LSSTCam FOV. Sorcha accommodates this by +50% probability of detection – at the edges of the LSSTCam FOV. ``Sorcha`` accommodates this by calculating the effects of vignetting at the source’s location on the focal plane and adjusting the 5σ limiting magnitude accordingly for each potential detection. This modified limiting magnitude will be used when applying the survey detection efficiency. We this value the **5σ Limiting Magnitude at the Source Location** @@ -171,6 +207,9 @@ further from the center of the FOV have shallower depths. .. note:: The :ref:`pointing` provides the 5σ limiting magnitude at the center of the exposure's FOV. +.. note:: +``Sorcha`` currently only has a vignetting model for the LSSTCam. + .. seealso:: We have a `Jupyter notebook `_ demonstrating ``Sorcha``'s vignetting calculation. @@ -197,7 +236,7 @@ The figure above shows the fading function and how ``Sorcha`` appliels it. The t sources as a function of magnitude. The different lines represent the effect of the variation of the peak detection efficiency and the width parameter on the shape of the function. The 5σ limiting magnitude at the source location is marked in gray (m5σ=24.5). The bottom plot show histogram showing detection probability -of 10,000 point sources passed through Sorcha’s fading function filter, with the actual calculated detection +of 10,000 point sources passed through ``Sorcha``’s fading function filter, with the actual calculated detection probability from Equation 10 overplotted as a solid line. Here, detection efficiency = 1.0, width parameter = 0.1, and m5σ=24.5 and the binsize is 0.04 mag. @@ -354,6 +393,9 @@ The user sets what observations from the survey :ref:`pointing` will be used by observing_filters = r,g,i,z,u,y +The first observing filters in the list are separated by a comma. The first observing filter listed should is the main filter that the absolute magnitude is defined for. +The :ref:`physical` must have colors relative to the main filter specified for the iput small body population. + If the user wants to use a subset of the observations, such as only include observations from the first year of the survey or are part of a database, they can either modify the :ref:`pointing` or modify the :ref:`pointing` query in the :ref:`configs`. Expert Advanced Post-Processing Features From 426e16d5899457c15a2d8ed10c4c6d264964b5cb Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Sun, 12 Jan 2025 22:37:06 +0000 Subject: [PATCH 42/52] Update postprocessing.rst --- docs/postprocessing.rst | 20 ++++++++++++-------- 1 file changed, 12 insertions(+), 8 deletions(-) diff --git a/docs/postprocessing.rst b/docs/postprocessing.rst index 6b45e569..c03ca984 100644 --- a/docs/postprocessing.rst +++ b/docs/postprocessing.rst @@ -40,11 +40,6 @@ data management pipelines (including Solar System Processing [SSP]). :align: center -.. seealso:: - See our `Jupyter notebook `_ that validates the apparent magnitude calulcation. - - - Colors and Phase Curves ~~~~~~~~~~~~~~~~~~~~~ @@ -60,7 +55,7 @@ For each potential detection of an object from the input population, the trailed .. note:: The HG12 model is the `Penttilä et al. (2016) `_ modified model, and not the original (IAU adopted) `Muinonen et al. (2010) `_ model. -The phase curve function to apply is set via the [PHASECURVES] section of:ref:`configs`:: +The phase curve function to apply is set via the [PHASECURVES] section of the :ref:`configs` :: [PHASECURVES] @@ -68,10 +63,12 @@ The phase curve function to apply is set via the [PHASECURVES] section of:ref:`c # file must contain the columns needed to calculate the phase function. # Options: HG, HG1G2, HG12, linear, none. + phase_function = HG12 + .. _addons: -Calculating Trailing Losses and Calculating the PSF Magnigtude +Applying Trailing Losses and Calculating the PSF Magnigtude ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -84,7 +81,7 @@ This filter will recalculate the PSF magnitude of the observations, adjusting fo :align: center -Applying Photometric and Astrometric Uncerainties +Applying Photometric and Astrometric Uncertainities ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Real astronomical surveys measure photometry and astrometry that have uncertainities. To better compare to what the survey detected, ``Sorcha`` applies photometric and astrometric errors that modify the ca;culated value for the right acension, declination, trailed source magnitude, and PSF masgnitude for each potential detection. The models for these uncertainties are primarily driven by the signal-to-noise ratio (SNR) for a particular input object in an image, following the methods in `(Ivezić et al. 2019) `_ @@ -95,6 +92,13 @@ Real astronomical surveys measure photometry and astrometry that have uncertaini .. warning:: Right now ``Sorcha`` only has functions to compute the photometric and astrometric uncertainties and SNR estimations specifically for Rubin Observatory. +Validating Sorcha's Trailed Source Magnitude Calculations +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. seealso:: + See our `Jupyter notebook `_ that validates the apparent magnitude calulcation. + + Incorporating Rotational Light Curves and Activity ------------------------------------------------------------ ``Sorcha`` has the ability user provided functions though python classes that augment/change the apparent brightness calculations for the synthetic Solar System objects. Any values required as input for these calculations, must be provided in the separate :ref:`CPP` file as input. Rather than forcing the user directly modify the ``Sorcha`` codebase every time they want to apply a different model for representing the effects of rotational lightcurves or cometary activity, we provide the ability to develop separate activity and lightcurve/brightness enhancement functions as plugins using our template classes and add them to the `Sorcha addons `_ package. In both cases, any derived class must inherit from the corresponding base class and follow its API, to ensure that ``Sorcha`` knows how to find and use your class. Once the ``Sorcha addons`` is installed, ``Sorcha`` will automatically detect the available plugins and make them available during post-processing. To use one of the plugins from the community utilities, simply add the unique name of the plugin to the :ref:`configs` provided to ``Sorcha``, and provide the :ref:`CPP` file on the command line. We currently have 2 pre-made classes that can augment the calculated apparent magnitude of each synthetic object, One for handling cometary activity as a function of heliocentric distance and one that applies rotational light curves to the synthetic objects. From 1d88cc618b08aac792177757d3739ae3af534541 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Sun, 12 Jan 2025 23:15:45 +0000 Subject: [PATCH 43/52] documentation updates --- docs/advanced.rst | 11 ++++++----- docs/postprocessing.rst | 32 ++++++++++++++++++-------------- 2 files changed, 24 insertions(+), 19 deletions(-) diff --git a/docs/advanced.rst b/docs/advanced.rst index c90ed284..041dae0c 100644 --- a/docs/advanced.rst +++ b/docs/advanced.rst @@ -34,7 +34,8 @@ By default, :ref:`vignetting` using LSSTCam parameters is applied. T [EXPERT] vignetting_on = False -If vigentting is turned off, then the 5σ Limiting Magnitude at the Source Location will be the limiting magnitude at the cetner of the FOV from the :ref:`pointing`. +.. note:: + If vigentting is turned off, then the 5σ Limiting Magnitude at the Source Location will be the limiting magnitude at the cetner of the FOV from the :ref:`pointing`. .. tip:: Vignetting is a small effect for the LSSTCam, so you will see only a modest change in results if you turn this off for LSST simulations @@ -43,7 +44,7 @@ If vigentting is turned off, then the 5σ Limiting Magnitude at the Source Locat Turning Off the Randomization of the Magnitude and Astrometry Values ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -There may be a reason that you want to turn off the randomization of the trailed source magnitude and PSF magnitude as well as the RA and Dec values:: +There may be a reason that you want to turn off the :ref:`randomization` of the trailed source magnitude and PSF magnitude as well as the RA and Dec values:: [EXPERT] randomization_on = False @@ -52,7 +53,7 @@ There may be a reason that you want to turn off the randomization of the trailed Turning Off Trailing Losses ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The trailing losses filter is on by default, but it can be turned off by including the option in the :ref:`configs`:: +Applying :ref:`trailing losses` is on by default, but it can be turned off by including the option in the :ref:`configs`:: [EXPERT] trailing_losses_on = False @@ -65,7 +66,7 @@ The trailing losses filter is on by default, but it can be turned off by includi Turning off Detection Efficiency/Applying the Fading Function ---------------------------------------------------------------- -Applying the survey detection effieincy is on by default, but it can be turned off by including the option in the :ref:`configs`:: +Applying the :ref:`survey detection efficiency` is on by default, but it can be turned off by including the option in the :ref:`configs`:: [FADINGFUNCTION] fading_function_on = False @@ -73,7 +74,7 @@ Applying the survey detection effieincy is on by default, but it can be turned o Turning Off the Camera Footprint Filter ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -In rare instances you may need to skip the footprint filter off. This can be done by setting the camera model to none in the field-of-view (FOV) section of the :ref:`configs`:: +In rare instances you may need to skip the :ref:`camera footprint filter` and turn it off. This can be done by setting the camera model to none in the field-of-view (FOV) section of the :ref:`configs`:: [FOV] camera_model = none diff --git a/docs/postprocessing.rst b/docs/postprocessing.rst index c03ca984..69a7f204 100644 --- a/docs/postprocessing.rst +++ b/docs/postprocessing.rst @@ -6,9 +6,9 @@ Post-Processing (Applying Survey Biases) How it Works ------------------------ -Once the ephemerides have been generated or read in from an external file, `Sorcha`` moves on to +Once the ephemerides have been generated or read in from an external file, ``Sorcha`` moves on to the second phase, which we call post-processing. For each of the input objects, ``Sorcha`` goes through -the potential observations identified in the ephemeris generation step and performs a series of +the potential detections identified in the ephemeris generation step and performs a series of calculations and assessments in the post-processing stage to determine whether the objects would have been detectable as a source in the survey images and would have later been identified as a moving solar system object. All aspects of post-processing can be adjusted or turned on/off via ``Sorcha``'s :ref:`configs`. @@ -41,7 +41,7 @@ data management pipelines (including Solar System Processing [SSP]). Colors and Phase Curves -~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ For each potential detection of an object from the input population, the trailed source magnitude is calculated for the relevant observing filter using the colors specificed in the :ref:`physical`. The trailed source magnitude is also adjusted for phase curve effects. We have implemented several phase curve parameterizations that can be specified in the :ref:`configuration file` and then inputted through the :ref:`physical`. **You can either specify one set of phase curve parameters for all observing filters or specify values for each observing filter examined by** ``Sorcha``. We are using the `sbpy `_ phase function utilities. The supported options are: @@ -65,13 +65,11 @@ The phase curve function to apply is set via the [PHASECURVES] section of the :r phase_function = HG12 -.. _addons: - +.. _trailing: Applying Trailing Losses and Calculating the PSF Magnigtude ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - If the observed object is fast-moving, the signal will form a trail, reducing the measured magnitude. This filter will recalculate the PSF magnitude of the observations, adjusting for trailing losses. @@ -81,6 +79,8 @@ This filter will recalculate the PSF magnitude of the observations, adjusting fo :align: center +.. _randomization: + Applying Photometric and Astrometric Uncertainities ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -99,6 +99,8 @@ Validating Sorcha's Trailed Source Magnitude Calculations See our `Jupyter notebook `_ that validates the apparent magnitude calulcation. +.. _addons: + Incorporating Rotational Light Curves and Activity ------------------------------------------------------------ ``Sorcha`` has the ability user provided functions though python classes that augment/change the apparent brightness calculations for the synthetic Solar System objects. Any values required as input for these calculations, must be provided in the separate :ref:`CPP` file as input. Rather than forcing the user directly modify the ``Sorcha`` codebase every time they want to apply a different model for representing the effects of rotational lightcurves or cometary activity, we provide the ability to develop separate activity and lightcurve/brightness enhancement functions as plugins using our template classes and add them to the `Sorcha addons `_ package. In both cases, any derived class must inherit from the corresponding base class and follow its API, to ensure that ``Sorcha`` knows how to find and use your class. Once the ``Sorcha addons`` is installed, ``Sorcha`` will automatically detect the available plugins and make them available during post-processing. To use one of the plugins from the community utilities, simply add the unique name of the plugin to the :ref:`configs` provided to ``Sorcha``, and provide the :ref:`CPP` file on the command line. We currently have 2 pre-made classes that can augment the calculated apparent magnitude of each synthetic object, One for handling cometary activity as a function of heliocentric distance and one that applies rotational light curves to the synthetic objects. @@ -123,9 +125,10 @@ Set the **cometary_activity** :ref:`configuration file` file varialble # of the subclasses of AbstractCometaryActivity. If not none, a complex physical parameters # file must be specified at the command line. - comet_activity = none - + comet_activity = lsst_comet +.. tip:: + To not include an cometary activity effects on the apparent magnitude calculations, set **comet_activity** to none. Cometary Activity Template Class ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -135,11 +138,8 @@ Cometary Activity Template Class LSSTCometActivity Class ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. seealso:: - We have an `example Jupyter notebook `_ demonstrating the LSSTCometActivity class built into `Sorcha addons package `_. - -lsst_comet +We have an `example Jupyter notebook `_ demonstrating the LSSTCometActivity class built into `Sorcha addons package `_. To **comet_activity** :ref:`configuration file` should be set to lsst_comet to use this cometary activity parameterixation. Rotational Lightcurve Effects ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -183,6 +183,7 @@ Or:: .. tip:: The saturation filter is only applied if the :ref:`configuration file` has a SATURATION section. +.. _vignetting: Calculating the 5σ Limiting Magnitude at the Source Location and Vignetting ---------------------------------------------------------------------------------------------------- @@ -217,8 +218,10 @@ further from the center of the FOV have shallower depths. .. seealso:: We have a `Jupyter notebook `_ demonstrating ``Sorcha``'s vignetting calculation. -Fading Function/Detection Efficiency ------------------------------------- +.. _fading: + +Applying the Survey Detection Efficiency (Fading Function) +----------------------------------------------------------------- This filter serves to remove potential detections of the input small bodies which are too faint to be detected in the each survey observation. ``Sorcha`` uses the fading function formulation of `Veres and Chesley (2017) `_: @@ -249,6 +252,7 @@ binsize is 0.04 mag. .. seealso:: We have a `Jupyter notebook `_ showing how ``Sorcha`` applies the survey detection efficiency (fading function). +.. _footprint: Camera Footprint ----------------- From 60d06521e4b74970f94ed7b32dfe9765eff47090 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Mon, 13 Jan 2025 00:10:15 +0000 Subject: [PATCH 44/52] update post-processing documentation update post-processing documentation --- docs/advanced.rst | 5 +++- docs/images/full_footprint_filter.png | Bin 0 -> 296780 bytes docs/postprocessing.rst | 39 +++++++++++++++++--------- 3 files changed, 30 insertions(+), 14 deletions(-) create mode 100644 docs/images/full_footprint_filter.png diff --git a/docs/advanced.rst b/docs/advanced.rst index 041dae0c..6bb3b89b 100644 --- a/docs/advanced.rst +++ b/docs/advanced.rst @@ -63,8 +63,11 @@ Applying :ref:`trailing losses` is on by default, but it can be turned this option for debugging or for speed increases when the user is absolutely sure they are only supplying slow-moving objects. +.. note:: + If trailing losses are not applied, then for each potential input population detection ``Sorcha`` will set the PSF magnitude equal to the calculated trailed source magnitude. + Turning off Detection Efficiency/Applying the Fading Function ----------------------------------------------------------------- +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Applying the :ref:`survey detection efficiency` is on by default, but it can be turned off by including the option in the :ref:`configs`:: diff --git a/docs/images/full_footprint_filter.png b/docs/images/full_footprint_filter.png new file mode 100644 index 0000000000000000000000000000000000000000..ee6a3ba6131114a94fe99560f769c57e8aeb8a67 GIT binary patch literal 296780 zcmeFZ^M57Vw*{I`I!-!HI=0n8$F^;&W83Q3wr$&HM?1D{b0;t7i+k=pf5Ln3hx+W= zYgf%xHEXRg$DC`_4waJ;g@?g{0RaJl7Z(#!009A=1_Aj}0|oKf;w!13`T2$1Oi)nH 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z^~8v=tU80ZEEoJ=%dJgyt>B@|mH%=~N%5iGvTDx8QN^bJTGp0ZIT7RI-9`LwXVhi& zpXzs~^Sl0Q`TsxR{~6l#xN{{^*(MUwyk diff --git a/docs/postprocessing.rst b/docs/postprocessing.rst index d842205a..58a33c46 100644 --- a/docs/postprocessing.rst +++ b/docs/postprocessing.rst @@ -83,15 +83,29 @@ Applying Trailing Losses and Calculating the PSF Magnigtude Once ``Sorcha`` calculates the trailed source magnitude for all potential detections, it then calculates the PSF magnitude for each potential detection accoutning for trailing losses (the effect that the simulated moving object does not have a perfect point-source PSF but is instead elongated due the object's on-sky motion). simulated moving object is moving fast enough in the potential detection's observation, the flux wouldl form a trail (elongated source on the image in the direction of the object's motion), changing the apparent magnitude that the survey's source deteciton software will measure as well as decrease the SNR of the trailed soruce magnitude compared to a point source. ``Sorcha``'s trailing loss functions calculates these trailing losses to be used by the rest of the post-processing stage. .. image:: images/Trail.png - :width: 400 + :width: 800 :alt: Sky image showing a short trailing source circled in red. :align: center +Magnitude losses due to trailing, shown as a function of the object’s on-sky velocity (v). Left: +the trailing loss components for different values of the seeing θ. The dashed lines represent magnitude +losses due to the PSF trailing loss component only. The solid lines represent trailing losses due to both +the PSF and detection trailing loss components. Right: a single trailing loss model at low v, with vertical +lines representing the thresholds for typical on-sky motions of a TNO (Trans-Neptunian object), a Jupiter +Trojan, and inner and outer MBAs (main-belt asteroids) `(Luu & Jewitt 1988, Equation 1) ` + +.. warning:: + Right now ``Sorcha`` only has functions to compute the trailing losses for the LSST. +.. warning + When analyzing the detections and discoveries output from a ``Sorcha`` simulation, we caution the + user **to only use the trailed source magnitude**. Using the PSF magnitude will give incorrect results + because it is missing some of the object’s flux. The PSF magnitude is only used to assess detectability/apply the + survey detection efficiency. .. _randomization: -Applying Photometric and Astrometric Uncertainities -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Applying Photometric and Astrometric Uncertainitie and Randomization +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Real astronomical surveys measure photometry and astrometry that have uncertainities. To better compare to what the survey detected, ``Sorcha`` applies photometric and astrometric errors that modify the ca;culated value for the right acension, declination, trailed source magnitude, and PSF masgnitude for each potential detection. The models for these uncertainties are primarily driven by the signal-to-noise ratio (SNR) for a particular input object in an image, following the methods in `(Ivezić et al. 2019) `_ @@ -101,16 +115,20 @@ Real astronomical surveys measure photometry and astrometry that have uncertaini .. warning:: Right now ``Sorcha`` only has functions to compute the photometric and astrometric uncertainties and SNR estimations specifically for Rubin Observatory. +.. seealso:: + We have a `Jupyter notebook `_ demonstrating the application of the uncertainities and radnomization of the photometric and astrometric values within ``Sorcha``. + + Validating Sorcha's Trailed Source Magnitude Calculations ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. seealso:: - See our `Jupyter notebook `_ that validates the apparent magnitude calulcation. + See our `Jupyter notebook `_ that validates the apparent magnitude calculation. .. _addons: -Incorporating Rotational Light Curves and Activity +Incorporating Rotational Lightcurves and Cometary Activity ------------------------------------------------------------ ``Sorcha`` has the ability user provided functions though python classes that augment/change the apparent brightness calculations for the synthetic Solar System objects. Any values required as input for these calculations, must be provided in the separate :ref:`CPP` file as input. Rather than forcing the user directly modify the ``Sorcha`` codebase every time they want to apply a different model for representing the effects of rotational lightcurves or cometary activity, we provide the ability to develop separate activity and lightcurve/brightness enhancement functions as plugins using our template classes and add them to the `Sorcha addons `_ package. In both cases, any derived class must inherit from the corresponding base class and follow its API, to ensure that ``Sorcha`` knows how to find and use your class. Once the ``Sorcha addons`` is installed, ``Sorcha`` will automatically detect the available plugins and make them available during post-processing. To use one of the plugins from the community utilities, simply add the unique name of the plugin to the :ref:`configs` provided to ``Sorcha``, and provide the :ref:`CPP` file on the command line. We currently have 2 pre-made classes that can augment the calculated apparent magnitude of each synthetic object, One for handling cometary activity as a function of heliocentric distance and one that applies rotational light curves to the synthetic objects. @@ -233,9 +251,9 @@ Applying the Survey Detection Efficiency (Fading Function) Filter -------------------------------------------------------------------- This filter serves to remove potential detections of the input small bodies which are too faint to be detected in the each survey observation. - ``Sorcha`` uses the fading function formulation of `Veres and Chesley (2017) `_: -see the below plot. This fading function is parameterised by the fading function width and peak efficiency. -The default values are modelled on those from the aforementioned paper. +``Sorcha`` uses the fading function formulation of `Veres and Chesley (2017) `_. +This fading function is parameterised by the fading function width and peak efficiency. +The default values are modeled on those from the aforementioned paper. To configure the fading function, the following variabless should be set in the :ref:`configs`:: @@ -305,6 +323,8 @@ To include this filter, the following options should be set in the :ref:`configs For Rubin Observatory, the circle radius should be set to 1.75 degrees with a fill factor of 0.9 to approximate the detector area of LSSTCam. +.. seealso:: + We have a `Jupyter notebook `_ demonstrating ``Sorcha``'s circle radius (simple sensor area) filter. .. _full_camera_footprint: @@ -342,6 +362,10 @@ Additionally, the camera footprint model can account for the losses at the edge .. note:: If **footprint_edge_threshold** is not includeed, then ``Sorcha`` will assume all of the CCD detector area should be considered. + +.. seealso:: + We have a `Jupyter notebook `_ demonstrating ``Sorcha``'s full camera footprint filter. + .. _linking: The Linking Filter From 209210195c4a419d27248ec193557575e54f6097 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Mon, 13 Jan 2025 09:32:31 +0000 Subject: [PATCH 50/52] fixing grammar --- docs/installation.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/installation.rst b/docs/installation.rst index 06e8eb0e..df8c57d0 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -113,7 +113,7 @@ You can find the command to run the ``Sorcha`` demo on the command line in two w sorcha demo howto -Or you can in an interactive python session or jupyter notebook. You can run the following +Or in an interactive python session or jupyter notebook, you can run the following .. exec:: From ab41e6ba9d3474e9c8529931125870a4bbba4d39 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Mon, 13 Jan 2025 10:00:04 +0000 Subject: [PATCH 51/52] typo fixes and recording --- docs/troubleshooting.rst | 20 +++++++++----------- 1 file changed, 9 insertions(+), 11 deletions(-) diff --git a/docs/troubleshooting.rst b/docs/troubleshooting.rst index b5195660..74bc074a 100644 --- a/docs/troubleshooting.rst +++ b/docs/troubleshooting.rst @@ -24,8 +24,13 @@ Otherwise, you run the risk of the output files being mixed up. If you want to r the same computer/compute node, make sure to update the output path in the config file or commandline arguments, as appropriate. We have developed tools and example Slurm scripts to help you run multiple instances safely. -Pointing Database ---------------------- +sqlite3.OperationalError: index ObjID already exists/ sqlite3.OperationalError: index ObjID already exists +--------------------------------------------------------------------------------------------------------------------------------------------- +This happens if you are outputting as sql databases and you have dueling ``Sorcha`` processes running in the same directory with the same output file names running on the same input files using the -f flag to force overwriting of output files. One way to check this is to only allow for one ``Sorcha`` run to be output to a directory and see if you've got two log files that are actively being written to/were created. Note if you're using CSV, text file, or pytables format you won't get this error when you hit this race condition. + + +Pointing Database Issues +---------------------------- If you are having issues with reading the LSST pointing database such as getting an error like:: @@ -41,9 +46,9 @@ it might be your computer setup. SQLite uses a temporary store to hold temporary Mismatch in Inputs --------------------- -There are several files associated with the synthetic small bodies which are passed into Sorcha. These are +There are several files associated with the synthetic small bodies which are passed into ``Sorcha``. These are the orbit file, the physical parameter file and an optional complex parameters file and optional ephemeris -file (if not using the internal ephemeris generator within ``Sorcha``). Each provide specific information about the +file (if not using the :ref:`the internal ephemeris generator ` buit within ``Sorcha``). Each provide specific information about the synthetic population that is being analysed. Within these files, it is necessary to specify an entry for every object. The ``Sorcha`` code will run a check to ensure that all entries have an associated orbit and physical/complex physical parameter value, so if you get an error like:: @@ -56,10 +61,3 @@ then make sure to check that you have entries in all the input files for each ob ERROR: Unable to find ObjID column headings (OrbitAuxReader:....) -------------------------------------------------------------------- Check your input files and ensure that they have ObjID column as the first column. - -in PPOutWriteSqlite3: sqlite3.OperationalError: index ObjID already existssqlite3.OperationalError: index ObjID already exists ---------------------------------------------------------------------------------------------------------------------------------------------- -This happens if you are outputting as sql databases and you have dueling ``Sorcha`` processes running in the same directory with the same output file names running on the same input files using the -f flag to force overwriting of output files. One way to check this is to only allow for one ``Sorcha`` run to be output to a directory and see if you've got two log files that are actively being written to/were created. Note if you're using CSV, text file, or pytables format you won't get this error when you hit this race condition. - - - From 7936d106569044677a313812cb8249a9de58a808 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Mon, 13 Jan 2025 10:57:02 +0000 Subject: [PATCH 52/52] Update contributors.rst --- docs/contributors.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/contributors.rst b/docs/contributors.rst index feb893ad..9ec884b1 100644 --- a/docs/contributors.rst +++ b/docs/contributors.rst @@ -3,5 +3,5 @@ Contributors The people (listed alphabetically) who contributed to ``Sorcha`` include: -Pedro Bernardinelli, Aidan Berres, Ricardo Bánffy, Colin Orion Chandler Sam Cornwall, Siegfried Eggl, Grigori Fedorets, Matt Holman, Lynne Jones, Mario Jurić, Jeremy Kubica, Jake Kurlander, Michael S. P. Kelley, Conor MacBride, Shannon Matthews, Steph Merritt, Joachim Moeyens, Joe Murtagh, Shantanu Naidu, Drew Oldag, Brian Rogers, Meg Schwamb, Colin Snodgrass, Max West, and Dave Young +Pedro Bernardinelli, Aidan Berres, Ricardo Bánffy, Colin Orion Chandler Sam Cornwall, Siegfried Eggl, Grigori Fedorets, Matt Holman, Lynne Jones, Mario Jurić, Jeremy Kubica, Jake Kurlander, Michael S. P. Kelley, Ryan Lyttle, Conor MacBride, Shannon Matthews, Steph Merritt, Joachim Moeyens, Joe Murtagh, Shantanu Naidu, Drew Oldag, Brian Rogers, Meg Schwamb, Colin Snodgrass, Max West, and Dave Young

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zN9GJ8D`-!MPFK)Gn^Bej=OseqCs{Qb?WidF%$2E literal 0 HcmV?d00001 diff --git a/docs/postprocessing.rst b/docs/postprocessing.rst index 69a7f204..4b3aec0d 100644 --- a/docs/postprocessing.rst +++ b/docs/postprocessing.rst @@ -254,8 +254,8 @@ binsize is 0.04 mag. We have a `Jupyter notebook `_ showing how ``Sorcha`` applies the survey detection efficiency (fading function). .. _footprint: -Camera Footprint ------------------ +Applying the Camera Footprint Filter +----------------------------------------- Due to the footprint of the LSST Camera (LSSTCam), see the figure below, it is possible that some object detections may be lost in gaps between the chips. @@ -278,7 +278,7 @@ Circle Radius (Simple Sensor Area) Using this filter applies a very simple circular camera footprint. The radius of the circle (**circle_radius** key) should be given in degrees. The **fill_factor** key specifics what fraction of observations should be randomly removed to roughly mimic detector chip - gaps in this circular footprint approximation. The fraction of observations not removed is controlled by the config variable fill_factor. +gaps in this circular footprint approximation. The fraction of observations not removed is controlled by the config variable fill_factor. To include this filter, the following options should be set in the :ref:`configs`:: [FOV] @@ -289,18 +289,32 @@ To include this filter, the following options should be set in the :ref:`configs .. warning:: Note that :ref:`ASSIST+REBOUND ephemeris generator` also uses a circular radius for its search area. To get accurate results, the ASSIST+REBOUND radius must be set to be larger than the circle_radius. For simmulating the LSST, we rcommend setting **ar_ang_fov = 2.06** and **ar_fov_buffer = 0.2**. Setting the circle_radius to be larger than the radius used for ASSIST+REBOUND will have no effect. +.. tip:: + Applying the fill factor in the circle radius camera filter is option. If the **fill_factor** is not present in the :ref:`configs` file then ``Sorcha`` includes all potential detections that land within the circular area. + .. tip:: For Rubin Observatory, the circle radius should be set to 1.75 degrees with a fill factor of 0.9 to approximate the detector area of LSSTCam. + .. _full_camera_footprint: Full Camera Footprint ~~~~~~~~~~~~~~~~~~~~~~~ -Using this filter applies a full camera footprint, including chip gaps. This is the slowest and most accurate version of the footprint filter. +Using this filter applies a full camera footprint, including chip gaps. The full camera footprint filter figures out which of the possible input population detections (as idenitifed by the ephemeris generation stage/input) for each survey observations land within on the survey camera's detectors. This is the slowest and most accurate version of the footprint filter. The image below shows the full camera footprint filter for the default LSSTCam architecture. -To include this filter, the following options should be set in the :ref:`configs`:: + +.. image:: images/full_footprint_filter.png + :width: 800 + :alt: Example of how the full camera footprint filter for LSSTCam. Left plot is a full circle of detections, and on the right shows those detections in the sahpe of the LSSTCam detectors where detector gaps can be seen. + :align: center +The effect of the full camera footprint filter on a selection of 100,000 random synthetic sources. +Left: original sources, distributed over a circular FOV (field-of-view) of radius 2.1 degrees. Right: the same sources after running +Sorcha’s full camera footprint filter. The shape of the LSSTCam detector footprint can be seen with the +loss of detections in the raft and chip gaps. + +To use the full camera footprint filter, the following option should be set in the :ref:`configs`:: [FOV] camera_model = footprint @@ -310,20 +324,19 @@ To include this filter, the following options should be set in the :ref:`configs .. warning:: Note that :ref:`ASSIST+REBOUND ephemeris generator` uses a circular radius for its search area. To get accurate results, the ASSIST+REBOUND radius must be set to be larger than the circle_radius. For simmulating the LSST, we rcommend setting **ar_ang_fov = 2.06** and **ar_fov_buffer = 0.2**. -Additionally, the camera footprint model can account for the losses at the edge of the CCDs where the detection software will not be able to pick out sources close to the edge. You can add an exclusion zone around each CCD measured in arcseconds (on the focal plane) using the `footprint_edge_threshold` key to the configuraiton file. An example setup in the :ref:`configs`:: +Additionally, the camera footprint model can account for the losses at the edge of the CCDs where the detection software will not be able to pick out sources close to the edge. You can add an exclusion zone around each CCD measured in arcseconds (on the focal plane) using the **footprint_edge_threshold** key to the configuraiton file. An example setup in the :ref:`configs`:: [FOV] camera_model = footprint - footprint_path = ./data/detectors_corners.csv footprint_edge_threshold = 0.0001 -.. tip:: - ``Sorcha`` comes with a representation of the LSSTCam footprint already installed. If you do not include the **footprint_path** in the :ref:`configs`, then ``Sorcha`` assumes you're using its internal LSSTCam footprint. +.. note:: + If **footprint_edge_threshold** is not includeed, then ``Sorcha`` will assume all of the CCD detector area should be considered. .. _linking: -Linking ---------------------------- +Applying the Linking Filter +------------------------------- The linking filter simulates the behavior of LSST's Solar System Processing (SSP, `Jurić et al. 2020 `_, `Swinbank et al. 2020 `_), the automated software pipeline @@ -387,8 +400,8 @@ the observation is of a linked object or not. To enable this functionality, add .. _whatobs: -What Observations to Include -------------------------------------- +Specifying What Observations to Include +------------------------------------------ The user sets what observations from the survey :ref:`pointing` will be used by setting the **observing_filters** :ref:`configs` variable in the [FILTERS] section:: From a03d4ebf724bf47cabfd5bb3a7a199ce6c4edf09 Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Mon, 13 Jan 2025 00:35:58 +0000 Subject: [PATCH 45/52] Update postprocessing.rst --- docs/postprocessing.rst | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/docs/postprocessing.rst b/docs/postprocessing.rst index 4b3aec0d..47b8dea2 100644 --- a/docs/postprocessing.rst +++ b/docs/postprocessing.rst @@ -39,6 +39,11 @@ data management pipelines (including Solar System Processing [SSP]). :alt: A cartoon explanation of trailed source mag and PSF mag :align: center +.. warning + When analyzing the detections and discoveries output from a ``Sorcha`` simulation, we caution the + user **to only use the trailed source magnitude**. Using the PSF magnitude will give incorrect results + because it is missing some of the object’s flux. The PSF magnitude is only used to assess detectability/apply the + survey detection efficiency. Colors and Phase Curves ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -67,11 +72,15 @@ The phase curve function to apply is set via the [PHASECURVES] section of the :r .. _trailing: +Accounting for Cometary Activity and Rotational Lightcurves +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +``Sorcha`` has the capability of accounting for the rotational lightcurve and cometary activity effects on the calculated trailed source magnitude. Further details are available in this :ref:`addons` section. + Applying Trailing Losses and Calculating the PSF Magnigtude ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -If the observed object is fast-moving, the signal will form a trail, reducing the measured magnitude. -This filter will recalculate the PSF magnitude of the observations, adjusting for trailing losses. +Once ``Sorcha`` calculates the trailed source magnitude for all potential detections, it then calculates the PSF magnitude for each potential detection accoutning for trailing losses (the effect that the simulated moving object does not have a perfect point-source PSF but is instead elongated due the object's on-sky motion). simulated moving object is moving fast enough in the potential detection's observation, the flux wouldl form a trail (elongated source on the image in the direction of the object's motion), changing the apparent magnitude that the survey's source deteciton software will measure as well as decrease the SNR of the trailed soruce magnitude compared to a point source. ``Sorcha``'s trailing loss functions calculates these trailing losses to be used by the rest of the post-processing stage. .. image:: images/Trail.png :width: 400 From 188d44c87ad66fa99c2fc0cef0f3fc7e7451355a Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Mon, 13 Jan 2025 00:37:00 +0000 Subject: [PATCH 46/52] Update postprocessing.rst --- docs/postprocessing.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/postprocessing.rst b/docs/postprocessing.rst index 47b8dea2..2821ff61 100644 --- a/docs/postprocessing.rst +++ b/docs/postprocessing.rst @@ -75,7 +75,7 @@ The phase curve function to apply is set via the [PHASECURVES] section of the :r Accounting for Cometary Activity and Rotational Lightcurves ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -``Sorcha`` has the capability of accounting for the rotational lightcurve and cometary activity effects on the calculated trailed source magnitude. Further details are available in this :ref:`addons` section. +``Sorcha`` has the capability of accounting for the rotational lightcurve and cometary activity effects on the calculated trailed source magnitude. Further details are available in the :ref:`addons` section. Applying Trailing Losses and Calculating the PSF Magnigtude ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ From e9d8caeff11441f4353f321d843b2830d29adcca Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Mon, 13 Jan 2025 00:38:02 +0000 Subject: [PATCH 47/52] Update postprocessing.rst --- docs/postprocessing.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/postprocessing.rst b/docs/postprocessing.rst index 2821ff61..f0dde473 100644 --- a/docs/postprocessing.rst +++ b/docs/postprocessing.rst @@ -229,8 +229,8 @@ further from the center of the FOV have shallower depths. .. _fading: -Applying the Survey Detection Efficiency (Fading Function) ------------------------------------------------------------------ +Applying the Survey Detection Efficiency (Fading Function) Filter +-------------------------------------------------------------------- This filter serves to remove potential detections of the input small bodies which are too faint to be detected in the each survey observation. ``Sorcha`` uses the fading function formulation of `Veres and Chesley (2017) `_: From 7f8dc8312193699c834432001f100e32b29b116b Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Mon, 13 Jan 2025 00:40:10 +0000 Subject: [PATCH 48/52] Update postprocessing.rst --- docs/postprocessing.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/postprocessing.rst b/docs/postprocessing.rst index f0dde473..d842205a 100644 --- a/docs/postprocessing.rst +++ b/docs/postprocessing.rst @@ -344,7 +344,7 @@ Additionally, the camera footprint model can account for the losses at the edge .. _linking: -Applying the Linking Filter +The Linking Filter ------------------------------- The linking filter simulates the behavior of LSST's Solar System Processing (SSP, `Jurić et al. 2020 `_, From 6ed0406a9f8cdd7922d57f7931bbb66bf3bece2a Mon Sep 17 00:00:00 2001 From: Meg Schwamb Date: Mon, 13 Jan 2025 01:07:36 +0000 Subject: [PATCH 49/52] 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