Mathijs's addition of user-chosen sn_thr and bic_thr

.ipynb_checkpoints/testing_shadow_star-checkpoint.ipynb

@@ -0,0 +1,260 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5dc84dc9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import star_shadow as sts\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "28b06dcd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-10-30 18:23:40 - INFO - Start of analysis\n",
+      "2023-10-30 18:23:40 - INFO - Frequency extraction done. Total time elapsed: 0.3s.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded analysis file with identifier: sim_002_lc_analysis_1, created on 2023-10-30 17:14:14.619250. \n",
+      "data_id: 2. Description: Frequency extraction results. \n",
+      "\n",
+      "Loaded analysis file with identifier: sim_002_lc_analysis_2, created on 2023-10-30 17:14:38.836586. \n",
+      "data_id: 2. Description: Multi-sinusoid NL-LS optimisation results. \n",
+      "\n",
+      "Loaded analysis file with identifier: sim_002_lc_analysis_2, created on 2023-10-30 17:14:38.836586. \n",
+      "data_id: 2. Description: Multi-sinusoid NL-LS optimisation results. \n",
+      "\n",
+      "Loaded analysis file with identifier: sim_002_lc_analysis_3, created on 2023-10-30 17:16:13.270897. \n",
+      "data_id: 2. Description: Harmonic frequencies coupled. \n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-10-30 18:23:41 - INFO - Harmonic analysis done. Total time elapsed: 0.9s.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded analysis file with identifier: sim_002_lc_analysis_4, created on 2023-10-30 17:16:17.110115. \n",
+      "data_id: 2. Description: Additional non-harmonic extraction. \n",
+      "\n",
+      "Loaded analysis file with identifier: sim_002_lc_analysis_5, created on 2023-10-30 17:16:40.121536. \n",
+      "data_id: 2. Description: Multi-sine NL-LS optimisation results with coupled harmonics. \n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-10-30 18:23:42 - INFO - End of analysis. Total time elapsed: 3.3s.\n"
+     ]
+    }
+   ],
+   "source": [
+    "location = '/STER/mathijsv/star_shadow/data/'\n",
+    "file = 'sim_002_lc.dat'\n",
+    "sts.analyse_lc_from_file(location + file, p_orb=0, i_sectors=None, stage='harmonics', method='fitter', data_id='2', \n",
+    "                         save_dir=None, overwrite=False, verbose=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8fdfec26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function analyse_lc_from_file in module star_shadow.main_functions:\n",
+      "\n",
+      "analyse_lc_from_file(file_name, p_orb=0, i_sectors=None, stage='all', method='fitter', data_id='none', save_dir=None, overwrite=False, verbose=False)\n",
+      "    Do all steps of the analysis for a given light curve file\n",
+      "    \n",
+      "    Parameters\n",
+      "    ----------\n",
+      "    file_name: str\n",
+      "        Path to a file containing the light curve data, with\n",
+      "        timestamps, normalised flux, error values as the\n",
+      "        first three columns, respectively.\n",
+      "    p_orb: float\n",
+      "        Orbital period of the eclipsing binary in days\n",
+      "    i_sectors: numpy.ndarray[int]\n",
+      "        Pair(s) of indices indicating the separately handled timespans\n",
+      "        in the piecewise-linear curve. These can indicate the TESS\n",
+      "        observation sectors, but taking half the sectors is recommended.\n",
+      "        If only a single curve is wanted, set\n",
+      "        i_sectors = np.array([[0, len(times)]]).\n",
+      "    stage: str\n",
+      "        Which analysis stages to do: 'all'/'a' for everything\n",
+      "        'frequencies'/'freq'/'f' for just the iterative prewhitening\n",
+      "        'harmonics'/'harm'/'h' for up to and including the harmonic coupling only\n",
+      "        'timings'/'t' for up to and including finding the eclipse timings\n",
+      "    method: str\n",
+      "        Method of EB light curve model optimisation. Can be 'sampler' or 'fitter'.\n",
+      "        Sampler gives extra error estimates on the eclipse parameters\n",
+      "        Fitter is much faster and still accurate\n",
+      "    data_id: int, str\n",
+      "        User defined identification for the dataset used\n",
+      "    save_dir: str\n",
+      "        Path to a directory for saving the results. Also used to load\n",
+      "        previous analysis results.\n",
+      "    overwrite: bool\n",
+      "        If set to True, overwrite old results in the same directory as\n",
+      "        save_dir, or (if False) to continue from the last save-point.\n",
+      "    verbose: bool\n",
+      "        If set to True, this function will print some information\n",
+      "    \n",
+      "    Returns\n",
+      "    -------\n",
+      "    None\n",
+      "    \n",
+      "    Notes\n",
+      "    -----\n",
+      "    If save_dir is not given, results are saved in the same directory as\n",
+      "    the given light curve file (will create a subfolder)\n",
+      "    \n",
+      "    The input text files are expected to have three columns with in order:\n",
+      "    times (bjd), signal (flux), signal_err (flux error)\n",
+      "    And the timestamps should be in ascending order.\n",
+      "    The expected text file format is space separated.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(sts.analyse_lc_from_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01de6e98",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b0c02e31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2855a23b",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Dataset(s) incompatible with Pandas data types, not table, or no datasets found in HDF5 file.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_141129/2602489043.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_hdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/STER/mathijsv/star_shadow/data/sim_002_lc_analysis/sim_002_lc_analysis_3.hdf5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/anaconda3/envs/python37/lib/python3.7/site-packages/pandas/io/pytables.py\u001b[0m in \u001b[0;36mread_hdf\u001b[0;34m(path_or_buf, key, mode, errors, where, start, stop, columns, iterator, chunksize, **kwargs)\u001b[0m\n\u001b[1;32m    437\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroups\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m                 raise ValueError(\n\u001b[0;32m--> 439\u001b[0;31m                     \u001b[0;34m\"Dataset(s) incompatible with Pandas data types, \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    440\u001b[0m                     \u001b[0;34m\"not table, or no datasets found in HDF5 file.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m                 )\n",
+      "\u001b[0;31mValueError\u001b[0m: Dataset(s) incompatible with Pandas data types, not table, or no datasets found in HDF5 file."
+     ]
+    }
+   ],
+   "source": [
+    "pd.read_hdf('/STER/mathijsv/star_shadow/data/sim_002_lc_analysis/sim_002_lc_analysis_3.hdf5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8aaeb9b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.read_csv('/STER/mathijsv/star_shadow/data/sim_002_lc_analysis/sim_002_lc_analysis_6_ecl_indices.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5dac03e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.read_hdf('/STER/mathijsv/star_shadow/data/sim_002_lc_analysis/sim_002_lc_analysis_7_dists.hdf5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02d6b063",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.read_csv('/STER/mathijsv/star_shadow/data/sim_002_lc_analysis/sim_002_lc_analysis_summary.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed40bc2a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b3754e28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.__version__"
+   ]
+  }
+ ],
+ "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": "3.7.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

MANIFEST.in

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+include *.txt
+include data/*
+include LICENCE.md
+include README.md

build/lib/star_shadow/__init__.py

@@ -0,0 +1,13 @@
+"""STAR SHADOW __init__ file
+
+Code written by: Luc IJspeert
+"""
+
+from . import main_functions
+from .main_functions import *
+from . import timeseries_functions as tsf
+from . import timeseries_fitting as tsfit
+from . import analysis_functions as af
+from . import mcmc_functions as mcf
+from . import utility as ut
+from . import visualisation as vis