{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "xVT-IDqXWVdf" }, "source": [ "\n", "\n", "# Microlensing Tools\n", "
\n", "\n", "\"Open\n", "\n", "If you would like an introduction to python notebooks, please read this tutorial: https://medium.com/codingthesmartway-com-blog/getting-started-with-jupyter-notebook-for-python-4e7082bd5d46" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Learning Goals\n", "\n", "The goal of this notebook is to demonstrate how to use various event fitting tools to analyze the Roman simulations.This notebook is intended as a single-source reference for how to use the tools, and is not intended to be a comprehensive analysis of the Roman simulations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Environment Set-Up\n", "\n", "**Dependencies**\n", "\n", "This notebook has the following base dependencies:\n", "\n", " - `python` (v>=3.11 recommended)\n", " - `numpy`\n", " - `matplotlib`\n", " - `pandas`\n", " - `scipy`\n", " - `jupyter`\n", " - `ipython`\n", " - `astropy`\n", " - `emcee` \n", " - `corner`\n", " - `popclass`\n", " - `tqdm` (remaining packages are not currently available on conda)\n", " - `pathos`\n", " - `VBMicrolensing`\n", " - `MulensModel` (v=3.4.0)\n", " - `pyLIMA`\n", " - `RTModel`" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "nexus-only" ] }, "source": [ "\n", "> ### Running on the Roman Research Nexus\n", "> If you are following this notebook on the Roman Research Nexus (RRN) these packages are preinstalled on the RGES-PIT's kernel:\n", "> 1. In notebooks, open **Kernel > Change Kernel** menu and select `RGES PIT Nexus` or select the kernel via the Kernel menu or clicking on the \\\"kernel status display\\\" to the top right with the activity circle.\n", "> 1. In a terminal, if needed, run `mamba activate rges-pit-dc` to select the correct mamba environment.\"\n", "> 1. Store any files you generate in your home directory or team space; the `/roman/nexus-notebooks/notebooks` directory is read-only." ] }, { "cell_type": "markdown", "metadata": { "tags": [ "colab-only" ] }, "source": [ "You can install these packages by running the next cell" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NNyjmctOWfqE", "tags": [ "colab-only" ] }, "outputs": [], "source": [ "# COLAB etc\n", "%pip install numpy matplotlib pandas scipy ipython astropy pathos emcee corner tqdm VBMicrolensing MulensModel==3.4.0 pyLIMA RTModel popclass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Imports" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "collapsed": true, "id": "PDvU-MTkyi0K", "outputId": "35a9d16c-0473-43ce-987b-312f468fdb54" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/rges-pit-dc/lib/python3.11/site-packages/arviz/__init__.py:39: FutureWarning: \n", "ArviZ is undergoing a major refactor to improve flexibility and extensibility while maintaining a user-friendly interface.\n", "Some upcoming changes may be backward incompatible.\n", "For details and migration guidance, visit: https://python.arviz.org/en/latest/user_guide/migration_guide.html\n", " warn(\n" ] } ], "source": [ "#@title General Imports\n", "\n", "# system tools\n", "import os\n", "import time\n", "import shutil\n", "import subprocess\n", "from pathlib import Path\n", "import tempfile\n", "import zipfile\n", "import warnings\n", "# Mute only this ArviZ FutureWarning message \n", "# We have pinned corner and emcee versions to avoid this becoming an issue\n", "warnings.filterwarnings(\n", " \"ignore\",\n", " category=FutureWarning,\n", " module=r\"arviz(\\.|$)\",\n", " message=r\".*ArviZ is undergoing a major refactor.*\",\n", ")\n", "# Hide noisy ERFA warnings caused by trial steps far from valid epochs\n", "try:\n", " from erfa import ErfaWarning\n", "except Exception:\n", " ErfaWarning = Warning\n", "\n", "# data analysis tools\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from IPython import get_ipython\n", "from IPython.display import display\n", "import pandas as pd\n", "from scipy.optimize import minimize\n", "from scipy.optimize import curve_fit\n", "from scipy.optimize import approx_fprime\n", "import emcee\n", "import corner\n", "import astropy.units as u\n", "from astropy.coordinates import SkyCoord\n", "try:\n", " from google.colab import sheets # this will only work if you are running on Colab\n", "except:\n", " pass\n", "\n", "# web scrapping tools\n", "import requests\n", "\n", "# parallel processing tools\n", "from pathos.multiprocessing import ProcessingPool as Pool # for multiprocessing inside jupyter\n", "import multiprocessing as mp\n", "from concurrent.futures import ThreadPoolExecutor as ThreadPool" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "Welcome to the **Roman Microlensing Data Challenge 2026 (RMDC26)** notebook on **Microlensing Tools**. If you have not already, you can click the link below to access the first notebook in this series: " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[**RMDC26/Nexus Workflow**](a_rmdc26_workflow.ipynb)" ] }, { "cell_type": "markdown", "metadata": { "id": "4TA6gvhQWmaV" }, "source": [ "The data challenge is intended to be a semi-realistic representation of the data volume and type expected from the Roman Galactic Bulge Time Domain Survey. Specifically for microlensing events and microlensing false positives.\n", "\n", "This notebook is brought to you by the RGES-PIT and is inteneded to be an introductory workbook for users new to microlensing event fitting and physical parameter estimation, users who would like a refresher, or users who would like a comprehensive introduction to tools they have not used before. It is not recommended that you complete this notebook in its entirety. You should make use of the tool look-up table and index to navigate to the section relevant to your interest or issue.\n", "\n", "Our aim is to provide you with a centralized resource covering open-source microlensing code applications. It is not designed to provide you with a realistic view of what working with bulk microlensing data involves or the general procedures undertaken to demistify microlensings event data. We will link to resources to understand procedures that are currently implemented in microlensing detection and analysis. \n", "\n", "### What data are used in this notebook?\n", "\n", "This notebook primarily uses lightcurves from the [2018 WFIRST Data Challenge](https://www.microlensing-source.org/data-challenge/). The data will be either fetched from an s3 bucket or [cloned](https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository) and sorted in later sections, depending on your environment. Because the 2018 WFIRST Data Challenge concluded in 2018, we can make full use of the [parameter \"truths\"](https://en.wikipedia.org/wiki/Statistical_parameter#:~:text=parameter%20describes%20the-,true%20value,-calculated%20from%20the) for each event to verify our fitting processes.\n", "\n", "The dataset consists of two lightcurve files for each event or star, representing the data from Roman's `W149` and `Z087` filters. The files are in ASCII format with the columns BJD, Aperture_Magnitude and Error, and follow the file-naming convention: `ulwdc1_nnn_[W149/Z087].txt`\n", "\n", "Supplementary files were also provided including `wfirst_ephemeris.txt`, which contains the `BJD` and 3D spacecraft location within the solar system. Information was provided on the surface-brightness color relation for `Z087-W149` to enable lens masses to be determined where applicable.\n", "\n", "It should be noted that in the simulated data, the inertial frame of reference was defined with the $x$-axis increasing from the binary primary lens object towards the secondary (less massive) lens object at `t0`, the time of closest approach to the primary lens. If viewed from the solar system barycenter, the inertial frame moves at the relative velocity `vlens_CoM - vobserver(t0)`. The inclination of the orbit is a counter-clockwise rotation about the $x$-axis. $\\alpha$ is the angle that the source trajectory made with the $x$-axis (if parallax was 0). Where finite source effects were significant, a linear limb darkening law was applied.\n", "\n", "\n", "\n", "### What codes are used in this notebook? \n", "\n", "Some open source microlensing codes:\n", "\n", "| Name | Notes | Maintained | Link | Covered here |\n", "| :-: | :-: | :-: | :-: | :-: |\n", "| MuLensModel | User friendly, single- and binary-lens fitting code. | Yes ([Poleski](https://github.com/rpoleski)) | [GitHub](https://github.com/rpoleski/MulensModel) | [Yes](#mulensmodel) |\n", "| BAGEL | Incorporates photometric and astrometric microlensing. | Yes ([Moving Universe Lab](https://github.com/MovingUniverseLab)) | [GitHub](https://github.com/MovingUniverseLab/BAGLE_Microlensing) | [No](#bagel) |\n", "| VBMicroelensing | A more general version of the binary-lens code [VBBL](https://github.com/valboz/VBBinaryLensing). VBMicrolensing
is a tool for efficient computation in gravitational microlensing events
using the advanced contour integration method, supporting single, binary
and multiple lenses. | Yes ([Bozza](https://github.com/valboz)) | [GitHub](https://github.com/valboz/VBMicrolensing) | [No](#vbmicrolensing)\n", "| pyLIMA | pyLIMA is the first open source software for modeling microlensing
events. It should be flexible enough to handle your data and fit it. You can
also practice by simulating events. Useful for space-based observations. | Yes ([Bachelet](https://github.com/ebachelet)) | [GitHub](https://github.com/ebachelet/pyLIMA) | [Yes](#pylima) |\n", "| RTModel | Hands-off model fitting with built in model \"interpretation\"
(e.g. determing single-lens vs binary-lens arrangement) | Yes ([Bozza](https://github.com/valboz)) | [Github](https://github.com/valboz/RTModel) | [Yes](RTModel) |\n", "| eesunhong | No general description found.
See [Bennett and Rhie (1996)](https://ui.adsabs.harvard.edu/abs/1996ApJ...472..660B/abstract) and [Bennett (2010)](https://ui.adsabs.harvard.edu/abs/2010ApJ...716.1408B/abstract) | Yes ([Bennett]()) | [GitHub](https://github.com/golmschenk/eesunhong) | No |\n", "| pyLIMASS | Addition to pyLIMA for estimating physical properties of the lens
system. See [Bachelet, Hundertmark, and Calchi Novati (2024)](https://ui.adsabs.harvard.edu/abs/2024AJ....168...24B/abstract) | Yes ([Bachelet](https://github.com/ebachelet)) | [GitHub](https://github.com/ebachelet/pyLIMA/tree/master/pyLIMA/pyLIMASS) | [Yes](#pylima) |\n", "| popclass | Provides a flexible, probabilistic framework for classifying the lens
of a gravitational microlensing event. | Yes ([LLNL](https://github.com/LLNL)) | [GitHub](https://github.com/LLNL/popclass) | [Yes](#popclass) |\n", "| muLAn | Designed for fitting Roman microlensing lightcurve data. | No ([Cassan](https://github.com/ArnaudCassan)/[Ranc](https://github.com/clementranc)) | [GitHub](https://github.com/muLAn-project/muLAn) | [No](#muLAN) |\n", "| triplelens | Calculates light curves and image positions for triple microlensing
systems. (When the mass ratio is small (below ~ 1e-5), the solutions
from the lens equation solver are more accurate when the origin
of the coordinate system is set to be close to the smallest mass.) | No ([Kuang](https://github.com/rkkuang)) | [GitHub](https://github.com/rkkuang/triplelens) | No |\n", "| SingleLensFitter | Fits single lens events with finite source effects | No ([Albrow](https://github.com/MichaelDAlbrow)) | [GitHub](https://github.com/MichaelDAlbrow/SingleLensFitter) | No |\n", "
\n", "\n", "> This is a replica of the tools list on the [RGES-PIT website](https://rges-pit.org/tools). We would like this table to be a comprehensive list, so if you have an open source code you would like to contribute, please refer to the [contributing documentation](https://github.com/rges-pit/data-challenge-notebooks/blob/main/CONTRIBUTING.md), open a discussion on the [GitHub repo](https://github.com/rges-pit/data-challenge-notebooks/discussions), or submit to or website [portal](https://rges-pit.org/submit/).\n", "\n", "### Microlensing learning resources\n", "\n", "* [RGES-PIT Minicourse](https://rges-pit.org/outreach_mini_landing/)\n", "\n", " The RGES PIT has developed a microlensing mini course for select students to participate in various Roman-related lectures and activities during the Summer of 2025. The virtual lectures were held in mid May 2025; you can find lecture materials, recordings, and assignments.\n", "\n", "* [Microlensing Source](https://www.microlensing-source.org/)\n", "\n", " Microlensing Source is a resource center for all aspects of gravitational microlensing. It aims to make microlensing more accessible for anyone with an interest in the subject - including students considering a career in the field, citizen scientists and those looking for a ready reference.\n", "\n", "* [The Microlenser's Guide to the Galaxy](https://amberlee2427.github.io/TheMicrolensersGuideToTheGalaxy/)\n", "\n", " The goal of this project is to create an all-encompassing collection of Jupyter notebooksβ€”your trusty companions for engaging exercises related to microlensing. Through these notebooks, the insights and experiences of microlensing veterins can light your path as you embark on your journey of discovery and exploration through scientific research.\n", "\n", "* [2017 Sagan Workshop](http://nexsci.caltech.edu/workshop/2017/)\n", "\n", " The 2017 Sagan Summer Workshop focus on searching for planets with Roman (previously known as WFIRST) microlensing. Leaders in the field will discussed the importance of microlensing to understanding planetary populations and demographics, especially beyond the snow line. They reviewed the microlensing method, both in the context of current capabilities and the future Roman microlensing survey. In addition, speakers addressed the broad potential of the Romans's Wide Field Imaging microlensing survey for (non-microlensing) science in the galactic bulge. Attendees participated in hands-on group projects related to the Roman microlensing planet survey and had the opportunity to present their own work through short presentations (research POPs) and posters.\n", " The recordings from this workshop can be found [here](https://www.youtube.com/watch?v=QPfKucBb9B8&list=PLIbTYGsIVYthWRS14eCEK8SK9IOTcaYsf)\n", "\n", "* [Glossary of Terms](https://www.microlensing-source.org/glossary/)\n", "\n", " This glossary, from Microlensing Source, is intended as a quick reference, particularly to disambiguate the different symbol sets used by different authors over time. Interested readers are referred to the references at the bottom for a full discussion, especially Skowron et al. (2011), and to the Learning Resources menu.\n", "\n", "> This is also a replica of the background material listed on the [resources](https://rges-pit.org/resources/#:~:text=Background%20material,Jupyter%20Notebook%20Course%20%C2%A0) page of the RGES-PIT website." ] }, { "cell_type": "markdown", "metadata": { "id": "B5OeZIbHv2eB" }, "source": [ "## Collecting the Data\n", "\n", "In this section and we will clone our data from GitHub and organize it in to sensible dataframes." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "I42DQYfDYPo3", "outputId": "eaacd311-bd95-4c98-db25-f1f53fcf1eb2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ℹ️ data-challenge-1 already exists; skipping clone\n", "\n", "πŸ—ƒοΈ Extracting files\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "tar: Option --checkpoint=1000 is not supported\n", "Usage:\n", " List: tar -tf \n", " Extract: tar -xf \n", " Create: tar -cf [filenames...]\n", " Help: tar --help\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "βœ… Lightcurve files are ready.\n" ] } ], "source": [ "#@title Cloning the GitHub repository\n", "if not os.path.exists(\"data-challenge-1\"):\n", "\n", " # Clone\n", " !printf \"πŸ‘ \" && git clone --depth 1 https://github.com/microlensing-data-challenge/data-challenge-1.git\n", "else:\n", " print(\"ℹ️ data-challenge-1 already exists; skipping clone\")\n", "\n", "if not os.path.exists(\"data-challenge-1/lc\"):\n", " # Extract with lightweight progress dots every N checkpoints\n", " print(\"\\nπŸ—ƒοΈ Extracting files\")\n", " # extract with progress dots, if supported\n", " try:\n", " subprocess.run([\"tar\", \"-xzf\", \"data-challenge-1/lc.tar.gz\", \"-C\",\n", " \"data-challenge-1/\", \"--checkpoint=1000\",\n", " \"--checkpoint-action=dot\"], check=True)\n", " except:\n", " subprocess.run([\"tar\", \"-xzf\", \"data-challenge-1/lc.tar.gz\",\n", " \"-C\", \"data-challenge-1/\"], check=True)\n", "\n", "print(\"\\nβœ… Lightcurve files are ready.\")" ] }, { "cell_type": "markdown", "metadata": { "id": "aXVB6stgwrNA" }, "source": [ "### Single Lens Events\n", "\n", "This dataset includes 293 lightcurve, 74 of which are single lens events. We can cheat a little and specifically pull out the events that we know to be single lenses, keeping the challenge tractable for completion within the hour, with the added benefit of making the strangley organized `master_file.txt` easier to wrangle." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/", "height": 456 }, "id": "7Zvjvl6nw3hx", "outputId": "3c3368c0-52bd-41b8-9e13-76b03839b044" }, "outputs": [ { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "index", "rawType": "int64", "type": "integer" }, { "name": "idx", "rawType": "object", "type": "string" }, { "name": "subrun", "rawType": "object", "type": "string" }, { "name": "field", "rawType": "object", "type": "string" }, { "name": "l", "rawType": "object", "type": "string" }, { "name": "b", "rawType": "object", "type": "string" }, { "name": "ra", "rawType": "object", "type": "string" }, { "name": 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74 rows Γ— 82 columns

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" ], "text/plain": [ " idx subrun field l b ra dec src_id Ds \\\n", "0 1694 0 82 1.17028 -2.26944 269.319 -29.0889 9926 7.939 \n", "1 539 0 82 1.05037 -2.20017 269.182 -29.158 16904 10.253 \n", "2 1278 0 82 1.18168 -2.1191 269.177 -29.0038 17351 10.64 \n", "3 1479 0 82 1.13373 -2.23737 269.267 -29.1045 8329 7.427 \n", "4 318 0 82 1.08784 -2.21568 269.219 -29.1334 16773 10.169 \n", ".. ... ... ... ... ... ... ... ... ... \n", "69 718 0 82 1.17865 -2.19791 269.253 -29.0459 11807 8.458 \n", "70 1378 0 82 1.04452 -2.21048 269.189 -29.1682 2183 7.966 \n", "71 954 0 82 1.04501 -2.16747 269.147 -29.1462 14473 9.202 \n", "72 259 0 82 1.18813 -2.0865 269.149 -28.9819 10930 8.206 \n", "73 1957 0 82 1.09739 -2.25794 269.266 -29.1463 15709 9.64 \n", "\n", " Rs ... sigma_q sigma_rs sigma_F00 sigma_fs0 sigma_F01 sigma_fs1 \\\n", "0 0.212 ... 999999 999999 8905.98 76994.9 7414.03 3.44036e+09 \n", "1 0.361 ... 999999 999999 728415 435.739 50.4485 166721 \n", "2 0.375 ... 999999 999999 2.78749e+06 629.296 7.91374 91347.9 \n", "3 0.212 ... 999999 999999 6.98775e+06 250.186 78.6551 59875.6 \n", "4 0.535 ... 999999 999999 3.11924e+06 2.92871 0.163627 1.18527 \n", ".. ... ... ... ... ... ... ... ... \n", "69 0.293 ... 999999 999999 592405 6337.05 10.9393 8.54477e+06 \n", "70 1.576 ... 999999 999999 809.398 228.589 1.10915 21.9532 \n", "71 0.328 ... 999999 999999 325492 21090.4 1237.92 1.09519e+08 \n", "72 0.375 ... 999999 999999 2.07119e+06 3929.98 34.1225 1.19947e+07 \n", "73 0.254 ... 999999 999999 1311.09 117.995 1.73216 934.558 \n", "\n", " sigma_thetaE sim type filename lc_number \n", "0 -2.69363e+11 dcnormffp dcnormffp_0_82_1694 5 \n", "1 -6.04003e+06 dcnormffp dcnormffp_0_82_539 17 \n", "2 -1.23727e+07 dcnormffp dcnormffp_0_82_1278 21 \n", "3 -979126 dcnormffp dcnormffp_0_82_1479 22 \n", "4 -215.015 dcnormffp dcnormffp_0_82_318 29 \n", ".. ... ... ... ... \n", "69 -1.21792e+09 dcnormffp dcnormffp_0_82_718 285 \n", "70 21943.9 dcnormffp dcnormffp_0_82_1378 286 \n", "71 -1.3671e+10 dcnormffp dcnormffp_0_82_954 288 \n", "72 -5.83016e+08 dcnormffp dcnormffp_0_82_259 290 \n", "73 -202757 dcnormffp dcnormffp_0_82_1957 293 \n", "\n", "[74 rows x 82 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#@title Putting everything in a tidy data frame\n", "master_file = './data-challenge-1/Answers/master_file.txt'\n", "header_file = './data-challenge-1/Answers/wfirstColumnNumbers.txt'\n", "\n", "rows = []\n", "with open(master_file, \"r\") as f:\n", " for line in f:\n", " line = line.strip()\n", " # Skip empty lines or comment lines\n", " if not line or line.startswith(\"#\"):\n", " continue\n", "\n", " tokens = line.split() # split on whitespace\n", " # Keep only single-lens events\n", " if \"dcnormffp\" not in tokens:\n", " continue\n", "\n", " # Single-lens lines should have exactly 96 columns\n", " if len(tokens) != 96:\n", " continue\n", "\n", " rows.append(tokens)\n", "\n", "df_sl = pd.DataFrame(rows)\n", "\n", "# make an array of zeros with 97 elements\n", "colnames_96 = np.zeros(96, dtype=object)\n", "\n", "# Read the header file\n", "with open(header_file, 'r') as f:\n", " for line in f:\n", " line = line.strip()\n", " # Skip empty lines or comments\n", " if not line or line.startswith('#'):\n", " continue\n", " # The second token is the 'name'\n", " parts = line.split()\n", " colnames_96[int(parts[0])] = parts[1]\n", "\n", "#For single lenses they are (***Note for these, the mass of the lens is given by the planet mass column, not the host mass column):\n", "#72 - unimportant\n", "#73 - N, number of consecutive W149 data points deviating by >=3 sigma from a flat line\n", "#74 - unimportant\n", "#75 - Delta chi^2 (relative to a flat line)\n", "#76-91 - unimportant\n", "#92 - simulated event type (dcnormffp = single lens or free-floating planet)\n", "#93 - unimportant (I think)\n", "#94 - lightcurve filename root\n", "#95 - Data challenge lightcurve number\n", "\n", "# Replace the column names in colnames_96\n", "colnames_96[73] = 'N'\n", "colnames_96[75] = 'Delta chi2'\n", "colnames_96[92] = 'sim type'\n", "colnames_96[94] = 'filename'\n", "colnames_96[95] = 'lc_number'\n", "\n", "# Make sure the column names are unique\n", "for i in range(94):\n", " if colnames_96[i] == '|' or colnames_96[i] == 0:\n", " colnames_96[i] = 'col_' + str(i)\n", "\n", "# Replace the column names in the data_frame\n", "df_sl.columns = colnames_96\n", "\n", "# Remove the dummy columns 'col_*'\n", "df_sl = df_sl.loc[:, ~df_sl.columns.str.startswith('col_')]\n", "\n", "df_sl" ] }, { "cell_type": "markdown", "metadata": { "id": "ejq8QsYFxI-t" }, "source": [ "The last column in this data frame has the lightcurve number, which we can use to pick out only the lightcurves matching our single-lens event list, for analysis." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/", "height": 626 }, "id": "W5OmYak-xGVK", "outputId": "3dc25d6c-4170-49c6-ab16-7d7d0a25c8ee" }, "outputs": [ { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "index", "rawType": "int64", "type": "integer" }, { "name": "idx", "rawType": "object", "type": "string" }, { "name": "subrun", "rawType": "object", "type": "string" }, { "name": "field", "rawType": "object", "type": "string" }, { "name": "l", "rawType": "object", "type": "string" }, { "name": "b", "rawType": "object", "type": "string" }, { "name": "ra", "rawType": "object", "type": "string" }, { "name": "dec", "rawType": "object", "type": "string" }, { "name": "src_id", "rawType": "object", "type": "string" }, { "name": "Ds", "rawType": "object", "type": "string" }, { "name": "Rs", "rawType": "object", "type": "string" }, { "name": "smu_l", "rawType": "object", "type": "string" }, { "name": "smu_b", "rawType": "object", "type": "string" }, { "name": "sage", "rawType": "object", "type": "string" }, { 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"14.3489", "519.336", "0", "0.0534149", "1", "66.9398", "352.894", "0.076307", "1.90735", "1.15208", "1", "979.274", "978.846", "22.219", "23.1783", "21.8897", "22.1649", "22.1509", "21.5357", "22.3695", "21.2073", "21.4673", "21.4258", "0.232023", "0.263921", "1", "0", "|", "313", "0", "25798.7", "999999", "999999", "999999", "999999", "999999", "999999", "999999", "999999", "27913.8", "41127.7", "290704", "1.69271e+09", "1.04644e+10", "dcnormffp", "dcnormffp_0_82_1296", "170", "data-challenge-1/lc/ulwdc1_170_W149.txt", "data-challenge-1/lc/ulwdc1_170_Z087.txt" ], [ "46", "1732", "0", "82", "1.19191", "-2.26165", "269.324", "-29.0663", "17857", "11.472", "0.158", "-7.06473", "-0.344143", "7", "5", "7.41", "6833", "6.784", "0.3", "-3.68093", "2.19021", "10", "5", "7.15", "9.42", "4060", "4.97", "0.293", "0.338766", "110.664", "1803.59996617", "4.44928", "0.347313", "0.0511959", "1.1766", "0.00125094", "4.22765", "135.959", "0", "0.00534149", "1", "-88.509", "213.63", "0.017805", 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idxsubrunfieldlbradecsrc_idDsRs...sigma_F00sigma_fs0sigma_F01sigma_fs1sigma_thetaEsim typefilenamelc_numberlc_file_path_W149lc_file_path_Z087
016940821.17028-2.26944269.319-29.088999267.9390.212...8905.9876994.97414.033.44036e+09-2.69363e+11dcnormffpdcnormffp_0_82_16945data-challenge-1/lc/ulwdc1_005_W149.txtdata-challenge-1/lc/ulwdc1_005_Z087.txt
15390821.05037-2.20017269.182-29.1581690410.2530.361...728415435.73950.4485166721-6.04003e+06dcnormffpdcnormffp_0_82_53917data-challenge-1/lc/ulwdc1_017_W149.txtdata-challenge-1/lc/ulwdc1_017_Z087.txt
212780821.18168-2.1191269.177-29.00381735110.640.375...2.78749e+06629.2967.9137491347.9-1.23727e+07dcnormffpdcnormffp_0_82_127821data-challenge-1/lc/ulwdc1_021_W149.txtdata-challenge-1/lc/ulwdc1_021_Z087.txt
314790821.13373-2.23737269.267-29.104583297.4270.212...6.98775e+06250.18678.655159875.6-979126dcnormffpdcnormffp_0_82_147922data-challenge-1/lc/ulwdc1_022_W149.txtdata-challenge-1/lc/ulwdc1_022_Z087.txt
43180821.08784-2.21568269.219-29.13341677310.1690.535...3.11924e+062.928710.1636271.18527-215.015dcnormffpdcnormffp_0_82_31829data-challenge-1/lc/ulwdc1_029_W149.txtdata-challenge-1/lc/ulwdc1_029_Z087.txt
..................................................................
697180821.17865-2.19791269.253-29.0459118078.4580.293...5924056337.0510.93938.54477e+06-1.21792e+09dcnormffpdcnormffp_0_82_718285data-challenge-1/lc/ulwdc1_285_W149.txtdata-challenge-1/lc/ulwdc1_285_Z087.txt
7013780821.04452-2.21048269.189-29.168221837.9661.576...809.398228.5891.1091521.953221943.9dcnormffpdcnormffp_0_82_1378286data-challenge-1/lc/ulwdc1_286_W149.txtdata-challenge-1/lc/ulwdc1_286_Z087.txt
719540821.04501-2.16747269.147-29.1462144739.2020.328...32549221090.41237.921.09519e+08-1.3671e+10dcnormffpdcnormffp_0_82_954288data-challenge-1/lc/ulwdc1_288_W149.txtdata-challenge-1/lc/ulwdc1_288_Z087.txt
722590821.18813-2.0865269.149-28.9819109308.2060.375...2.07119e+063929.9834.12251.19947e+07-5.83016e+08dcnormffpdcnormffp_0_82_259290data-challenge-1/lc/ulwdc1_290_W149.txtdata-challenge-1/lc/ulwdc1_290_Z087.txt
7319570821.09739-2.25794269.266-29.1463157099.640.254...1311.09117.9951.73216934.558-202757dcnormffpdcnormffp_0_82_1957293data-challenge-1/lc/ulwdc1_293_W149.txtdata-challenge-1/lc/ulwdc1_293_Z087.txt
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74 rows Γ— 84 columns

\n", "
" ], "text/plain": [ " idx subrun field l b ra dec src_id Ds \\\n", "0 1694 0 82 1.17028 -2.26944 269.319 -29.0889 9926 7.939 \n", "1 539 0 82 1.05037 -2.20017 269.182 -29.158 16904 10.253 \n", "2 1278 0 82 1.18168 -2.1191 269.177 -29.0038 17351 10.64 \n", "3 1479 0 82 1.13373 -2.23737 269.267 -29.1045 8329 7.427 \n", "4 318 0 82 1.08784 -2.21568 269.219 -29.1334 16773 10.169 \n", ".. ... ... ... ... ... ... ... ... ... \n", "69 718 0 82 1.17865 -2.19791 269.253 -29.0459 11807 8.458 \n", "70 1378 0 82 1.04452 -2.21048 269.189 -29.1682 2183 7.966 \n", "71 954 0 82 1.04501 -2.16747 269.147 -29.1462 14473 9.202 \n", "72 259 0 82 1.18813 -2.0865 269.149 -28.9819 10930 8.206 \n", "73 1957 0 82 1.09739 -2.25794 269.266 -29.1463 15709 9.64 \n", "\n", " Rs ... sigma_F00 sigma_fs0 sigma_F01 sigma_fs1 sigma_thetaE \\\n", "0 0.212 ... 8905.98 76994.9 7414.03 3.44036e+09 -2.69363e+11 \n", "1 0.361 ... 728415 435.739 50.4485 166721 -6.04003e+06 \n", "2 0.375 ... 2.78749e+06 629.296 7.91374 91347.9 -1.23727e+07 \n", "3 0.212 ... 6.98775e+06 250.186 78.6551 59875.6 -979126 \n", "4 0.535 ... 3.11924e+06 2.92871 0.163627 1.18527 -215.015 \n", ".. ... ... ... ... ... ... ... \n", "69 0.293 ... 592405 6337.05 10.9393 8.54477e+06 -1.21792e+09 \n", "70 1.576 ... 809.398 228.589 1.10915 21.9532 21943.9 \n", "71 0.328 ... 325492 21090.4 1237.92 1.09519e+08 -1.3671e+10 \n", "72 0.375 ... 2.07119e+06 3929.98 34.1225 1.19947e+07 -5.83016e+08 \n", "73 0.254 ... 1311.09 117.995 1.73216 934.558 -202757 \n", "\n", " sim type filename lc_number \\\n", "0 dcnormffp dcnormffp_0_82_1694 5 \n", "1 dcnormffp dcnormffp_0_82_539 17 \n", "2 dcnormffp dcnormffp_0_82_1278 21 \n", "3 dcnormffp dcnormffp_0_82_1479 22 \n", "4 dcnormffp dcnormffp_0_82_318 29 \n", ".. ... ... ... \n", "69 dcnormffp dcnormffp_0_82_718 285 \n", "70 dcnormffp dcnormffp_0_82_1378 286 \n", "71 dcnormffp dcnormffp_0_82_954 288 \n", "72 dcnormffp dcnormffp_0_82_259 290 \n", "73 dcnormffp dcnormffp_0_82_1957 293 \n", "\n", " lc_file_path_W149 \\\n", "0 data-challenge-1/lc/ulwdc1_005_W149.txt \n", "1 data-challenge-1/lc/ulwdc1_017_W149.txt \n", "2 data-challenge-1/lc/ulwdc1_021_W149.txt \n", "3 data-challenge-1/lc/ulwdc1_022_W149.txt \n", "4 data-challenge-1/lc/ulwdc1_029_W149.txt \n", ".. ... \n", "69 data-challenge-1/lc/ulwdc1_285_W149.txt \n", "70 data-challenge-1/lc/ulwdc1_286_W149.txt \n", "71 data-challenge-1/lc/ulwdc1_288_W149.txt \n", "72 data-challenge-1/lc/ulwdc1_290_W149.txt \n", "73 data-challenge-1/lc/ulwdc1_293_W149.txt \n", "\n", " lc_file_path_Z087 \n", "0 data-challenge-1/lc/ulwdc1_005_Z087.txt \n", "1 data-challenge-1/lc/ulwdc1_017_Z087.txt \n", "2 data-challenge-1/lc/ulwdc1_021_Z087.txt \n", "3 data-challenge-1/lc/ulwdc1_022_Z087.txt \n", "4 data-challenge-1/lc/ulwdc1_029_Z087.txt \n", ".. ... \n", "69 data-challenge-1/lc/ulwdc1_285_Z087.txt \n", "70 data-challenge-1/lc/ulwdc1_286_Z087.txt \n", "71 data-challenge-1/lc/ulwdc1_288_Z087.txt \n", "72 data-challenge-1/lc/ulwdc1_290_Z087.txt \n", "73 data-challenge-1/lc/ulwdc1_293_Z087.txt \n", "\n", "[74 rows x 84 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#@title Figuring out which files we want\n", "lc_number = df_sl['lc_number'].to_numpy()\n", "\n", "lc_file_path_format = 'data-challenge-1/lc/ulwdc1_XXX_filter.txt'\n", "\n", "lc_file_paths_W149 = [lc_file_path_format.replace('filter', 'W149')] * len(lc_number)\n", "lc_file_paths_Z087 = [lc_file_path_format.replace('filter', 'Z087')] * len(lc_number)\n", "\n", "# replace XXX, from the right, with the lc_number which is not necessarily of length 3\n", "lc_file_paths_W149 = [path.replace('XXX', str(num).zfill(3)) for path, num in zip(lc_file_paths_W149, lc_number)]\n", "lc_file_paths_Z087 = [path.replace('XXX', str(num).zfill(3)) for path, num in zip(lc_file_paths_Z087, lc_number)]\n", "\n", "df_sl['lc_file_path_W149'] = lc_file_paths_W149\n", "df_sl['lc_file_path_Z087'] = lc_file_paths_Z087\n", "\n", "df_sl" ] }, { "cell_type": "markdown", "metadata": { "id": "-1nERAlwxIk6" }, "source": [ "There are a few pieces of information that may need to be known for each event that are not in the lightcurve files. These are stored in event_info.txt\n", "\n", "Columns: `\"Event_name\"` `\"Event_number\"` `\"RA_(deg)\"` `\"Dec_(deg)\"` `\"Distance\"` `\"A_W149\"` `\"sigma_A_W149\"` `\"A_Z087\"` `\"sigma_A_Z087\"`\n", "\n", "Distance, A_W149/Z087 are an estimate of the distance and extinction in each band of the red clump stars. sigma_A_W149/Z087 are dispersions in the extinction." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/", "height": 473 }, "id": "kX035n49xXov", "outputId": "4f29b8f0-c338-4c79-a679-dcfb8798ebd8" }, "outputs": [ { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "index", "rawType": "int64", "type": "integer" }, { "name": "Event_name", "rawType": "object", "type": "string" }, { "name": "Event_number", "rawType": "int64", "type": "integer" }, { "name": "RA_(deg)", "rawType": "float64", "type": "float" }, { "name": "Dec_(deg)", "rawType": "float64", "type": "float" }, { "name": "Distance", "rawType": "float64", "type": "float" }, { "name": "A_W149", "rawType": "float64", "type": "float" }, { "name": "sigma_A_W149", "rawType": "float64", "type": "float" }, { "name": "A_Z087", "rawType": "float64", "type": "float" }, { "name": "sigma_A_Z087", "rawType": "float64", "type": "float" } ], "ref": "9ac356a5-f4ef-4033-a2cd-19c550c12870", "rows": [ [ "0", "ulwdc1_001", "1", "269.165", "-29.0207", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "1", "ulwdc1_002", "2", "269.959", "-30.1918", "8.09", "0.49", "0.01", "0.95", "0.01" ], [ "2", "ulwdc1_003", "3", "269.1", "-29.0983", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "3", "ulwdc1_004", "4", "268.036", "-28.3744", "8.25", "1.35", "0.07", "2.6", "0.14" ], [ "4", "ulwdc1_005", "5", "269.319", "-29.0889", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "5", "ulwdc1_006", "6", "267.649", "-28.2978", "8.29", "1.64", "0.15", "3.15", "0.3" ], [ "6", "ulwdc1_007", "7", "269.236", "-29.0468", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "7", "ulwdc1_008", "8", "269.017", "-29.0827", "8.2", "0.66", "0.01", "1.27", "0.01" ], [ "8", "ulwdc1_009", "9", "268.01", "-29.5647", "8.47", "0.96", "0.02", "1.85", "0.04" ], [ "9", "ulwdc1_010", "10", "269.241", "-29.0339", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "10", "ulwdc1_011", "11", "269.195", "-29.0304", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "11", "ulwdc1_012", "12", "268.221", "-29.7307", "8.57", "0.71", "0.01", "1.36", "0.01" ], [ "12", "ulwdc1_013", "13", "269.144", "-29.0469", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "13", "ulwdc1_014", "14", "268.028", "-28.9286", "8.21", "1.1", "0.01", "2.11", "0.02" ], [ "14", "ulwdc1_015", "15", "269.177", "-29.0443", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "15", "ulwdc1_016", "16", "269.222", "-29.1256", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "16", "ulwdc1_017", "17", "269.182", "-29.158", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "17", "ulwdc1_018", "18", "267.871", "-29.6712", "8.47", "0.96", "0.02", "1.85", "0.04" ], [ "18", "ulwdc1_019", "19", "269.224", "-29.1194", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "19", "ulwdc1_020", "20", "267.499", "-28.3238", "8.24", "2.87", "0.36", "5.52", "0.7" ], [ "20", "ulwdc1_021", "21", "269.177", "-29.0038", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "21", "ulwdc1_022", "22", "269.267", "-29.1045", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "22", "ulwdc1_023", "23", "269.133", "-29.0201", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "23", "ulwdc1_024", "24", "269.204", "-29.0896", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "24", "ulwdc1_025", "25", "267.348", "-29.9601", "8.53", "1.62", "0.04", "3.11", "0.07" ], [ "25", "ulwdc1_026", "26", "269.18", "-29.0824", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "26", "ulwdc1_027", "27", "267.31", "-28.84", "8.36", "3.08", "0.53", "5.92", "1.02" ], [ "27", "ulwdc1_028", "28", "267.271", "-29.7317", "8.57", "2.2", "0.2", "4.23", "0.38" ], [ "28", "ulwdc1_029", "29", "269.219", "-29.1334", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "29", "ulwdc1_030", "30", "269.211", "-28.9846", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "30", "ulwdc1_031", "31", "269.274", "-29.2103", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "31", "ulwdc1_032", "32", "269.216", "-28.6232", "8.26", "0.67", "0.01", "1.3", "0.01" ], [ "32", "ulwdc1_033", "33", "268.015", "-28.7398", "8.21", "1.1", "0.01", "2.11", "0.02" ], [ "33", "ulwdc1_034", "34", "269.349", "-29.1416", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "34", "ulwdc1_035", "35", "269.114", "-29.0485", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "35", "ulwdc1_036", "36", "269.356", "-29.1217", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "36", "ulwdc1_037", "37", "269.265", "-29.1018", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "37", "ulwdc1_038", "38", "269.212", "-29.1864", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "38", "ulwdc1_039", "39", "266.926", "-28.8927", "8.34", "4.23", "0.35", "8.15", "0.67" ], [ "39", "ulwdc1_040", "40", "267.715", "-28.3235", "8.29", "1.64", "0.15", "3.15", "0.3" ], [ "40", "ulwdc1_041", "41", "269.214", "-29.0463", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "41", "ulwdc1_042", "42", "269.308", "-29.1403", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "42", "ulwdc1_043", "43", "268.595", "-29.2969", "8.46", "0.55", "0.01", "1.05", "0.01" ], [ "43", "ulwdc1_044", "44", "269.086", "-29.1811", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "44", "ulwdc1_045", "45", "268.517", "-29.0302", "8.15", "0.58", "0.01", "1.12", "0.01" ], [ "45", "ulwdc1_046", "46", "269.054", "-29.1685", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "46", "ulwdc1_047", "47", "268.298", "-30.0481", "8.4", "0.62", "0.01", "1.19", "0.01" ], [ "47", "ulwdc1_048", "48", "269.153", "-29.1466", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "48", "ulwdc1_049", "49", "269.106", "-29.1986", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "49", "ulwdc1_050", "50", "267.518", "-29.415", "8.41", "1.33", "0.04", 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Event_nameEvent_numberRA_(deg)Dec_(deg)DistanceA_W149sigma_A_W149A_Z087sigma_A_Z087
0ulwdc1_0011269.165-29.02078.180.730.011.410.01
1ulwdc1_0022269.959-30.19188.090.490.010.950.01
2ulwdc1_0033269.100-29.09838.180.730.011.410.01
3ulwdc1_0044268.036-28.37448.251.350.072.600.14
4ulwdc1_0055269.319-29.08898.180.730.011.410.01
..............................
288ulwdc1_289289267.813-28.69658.071.520.072.930.14
289ulwdc1_290290269.149-28.98198.180.730.011.410.01
290ulwdc1_291291267.999-29.72038.570.710.011.360.01
291ulwdc1_292292269.151-29.14338.180.730.011.410.01
292ulwdc1_293293269.266-29.14638.180.730.011.410.01
\n", "

293 rows Γ— 9 columns

\n", "
" ], "text/plain": [ " Event_name Event_number RA_(deg) Dec_(deg) Distance A_W149 \\\n", "0 ulwdc1_001 1 269.165 -29.0207 8.18 0.73 \n", "1 ulwdc1_002 2 269.959 -30.1918 8.09 0.49 \n", "2 ulwdc1_003 3 269.100 -29.0983 8.18 0.73 \n", "3 ulwdc1_004 4 268.036 -28.3744 8.25 1.35 \n", "4 ulwdc1_005 5 269.319 -29.0889 8.18 0.73 \n", ".. ... ... ... ... ... ... \n", "288 ulwdc1_289 289 267.813 -28.6965 8.07 1.52 \n", "289 ulwdc1_290 290 269.149 -28.9819 8.18 0.73 \n", "290 ulwdc1_291 291 267.999 -29.7203 8.57 0.71 \n", "291 ulwdc1_292 292 269.151 -29.1433 8.18 0.73 \n", "292 ulwdc1_293 293 269.266 -29.1463 8.18 0.73 \n", "\n", " sigma_A_W149 A_Z087 sigma_A_Z087 \n", "0 0.01 1.41 0.01 \n", "1 0.01 0.95 0.01 \n", "2 0.01 1.41 0.01 \n", "3 0.07 2.60 0.14 \n", "4 0.01 1.41 0.01 \n", ".. ... ... ... \n", "288 0.07 2.93 0.14 \n", "289 0.01 1.41 0.01 \n", "290 0.01 1.36 0.01 \n", "291 0.01 1.41 0.01 \n", "292 0.01 1.41 0.01 \n", "\n", "[293 rows x 9 columns]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#@title Event information data frame\n", "\n", "header = [\"Event_name\",\n", " \"Event_number\",\n", " \"RA_(deg)\",\n", " \"Dec_(deg)\",\n", " \"Distance\",\n", " \"A_W149\",\n", " \"sigma_A_W149\",\n", " \"A_Z087\",\n", " \"sigma_A_Z087\"\n", "]\n", "\n", "event_info = pd.read_csv('./data-challenge-1/event_info.txt', names=header, sep='\\s+')\n", "event_info" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/", "height": 626 }, "id": "kYO43WegxcoQ", "outputId": "532b27ad-3bbd-4884-8c1b-5245394e7258" }, "outputs": [ { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "index", "rawType": "int64", "type": "integer" }, { "name": "Event_name", "rawType": "object", "type": "string" }, { "name": "Event_number", "rawType": "int64", "type": "integer" }, { "name": "RA_(deg)", "rawType": "float64", "type": "float" }, { "name": "Dec_(deg)", "rawType": "float64", "type": "float" }, { "name": "Distance", "rawType": "float64", "type": "float" }, { "name": "A_W149", "rawType": "float64", "type": "float" }, { "name": "sigma_A_W149", "rawType": "float64", "type": "float" }, { "name": "A_Z087", "rawType": "float64", "type": "float" }, { "name": "sigma_A_Z087", "rawType": "float64", "type": "float" }, { "name": "idx", "rawType": "object", "type": "string" }, { "name": "subrun", "rawType": "object", "type": "string" }, { "name": "field", "rawType": "object", "type": "string" }, { "name": "l", "rawType": "object", "type": "string" }, { "name": "b", "rawType": "object", "type": "string" }, { "name": "ra", "rawType": "object", "type": "string" }, { "name": "dec", "rawType": "object", "type": "string" }, { "name": "src_id", "rawType": "object", "type": "string" }, { "name": "Ds", "rawType": "object", "type": "string" }, { "name": "Rs", "rawType": "object", "type": "string" }, { "name": "smu_l", "rawType": 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Event_nameEvent_numberRA_(deg)Dec_(deg)DistanceA_W149sigma_A_W149A_Z087sigma_A_Z087idx...sigma_F00sigma_fs0sigma_F01sigma_fs1sigma_thetaEsim typefilenamelc_numberlc_file_path_W149lc_file_path_Z087
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..................................................................
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74 rows Γ— 93 columns

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" ], "text/plain": [ " Event_name Event_number RA_(deg) Dec_(deg) Distance A_W149 \\\n", "0 ulwdc1_005 5 269.319 -29.0889 8.18 0.73 \n", "1 ulwdc1_017 17 269.182 -29.1580 8.18 0.73 \n", "2 ulwdc1_021 21 269.177 -29.0038 8.18 0.73 \n", "3 ulwdc1_022 22 269.267 -29.1045 8.18 0.73 \n", "4 ulwdc1_029 29 269.219 -29.1334 8.18 0.73 \n", ".. ... ... ... ... ... ... \n", "69 ulwdc1_285 285 269.253 -29.0459 8.18 0.73 \n", "70 ulwdc1_286 286 269.189 -29.1682 8.18 0.73 \n", "71 ulwdc1_288 288 269.147 -29.1462 8.18 0.73 \n", "72 ulwdc1_290 290 269.149 -28.9819 8.18 0.73 \n", "73 ulwdc1_293 293 269.266 -29.1463 8.18 0.73 \n", "\n", " sigma_A_W149 A_Z087 sigma_A_Z087 idx ... sigma_F00 sigma_fs0 \\\n", "0 0.01 1.41 0.01 1694 ... 8905.98 76994.9 \n", "1 0.01 1.41 0.01 539 ... 728415 435.739 \n", "2 0.01 1.41 0.01 1278 ... 2.78749e+06 629.296 \n", "3 0.01 1.41 0.01 1479 ... 6.98775e+06 250.186 \n", "4 0.01 1.41 0.01 318 ... 3.11924e+06 2.92871 \n", ".. ... ... ... ... ... ... ... \n", "69 0.01 1.41 0.01 718 ... 592405 6337.05 \n", "70 0.01 1.41 0.01 1378 ... 809.398 228.589 \n", "71 0.01 1.41 0.01 954 ... 325492 21090.4 \n", "72 0.01 1.41 0.01 259 ... 2.07119e+06 3929.98 \n", "73 0.01 1.41 0.01 1957 ... 1311.09 117.995 \n", "\n", " sigma_F01 sigma_fs1 sigma_thetaE sim type filename \\\n", "0 7414.03 3.44036e+09 -2.69363e+11 dcnormffp dcnormffp_0_82_1694 \n", "1 50.4485 166721 -6.04003e+06 dcnormffp dcnormffp_0_82_539 \n", "2 7.91374 91347.9 -1.23727e+07 dcnormffp dcnormffp_0_82_1278 \n", "3 78.6551 59875.6 -979126 dcnormffp dcnormffp_0_82_1479 \n", "4 0.163627 1.18527 -215.015 dcnormffp dcnormffp_0_82_318 \n", ".. ... ... ... ... ... \n", "69 10.9393 8.54477e+06 -1.21792e+09 dcnormffp dcnormffp_0_82_718 \n", "70 1.10915 21.9532 21943.9 dcnormffp dcnormffp_0_82_1378 \n", "71 1237.92 1.09519e+08 -1.3671e+10 dcnormffp dcnormffp_0_82_954 \n", "72 34.1225 1.19947e+07 -5.83016e+08 dcnormffp dcnormffp_0_82_259 \n", "73 1.73216 934.558 -202757 dcnormffp dcnormffp_0_82_1957 \n", "\n", " lc_number lc_file_path_W149 \\\n", "0 5 data-challenge-1/lc/ulwdc1_005_W149.txt \n", "1 17 data-challenge-1/lc/ulwdc1_017_W149.txt \n", "2 21 data-challenge-1/lc/ulwdc1_021_W149.txt \n", "3 22 data-challenge-1/lc/ulwdc1_022_W149.txt \n", "4 29 data-challenge-1/lc/ulwdc1_029_W149.txt \n", ".. ... ... \n", "69 285 data-challenge-1/lc/ulwdc1_285_W149.txt \n", "70 286 data-challenge-1/lc/ulwdc1_286_W149.txt \n", "71 288 data-challenge-1/lc/ulwdc1_288_W149.txt \n", "72 290 data-challenge-1/lc/ulwdc1_290_W149.txt \n", "73 293 data-challenge-1/lc/ulwdc1_293_W149.txt \n", "\n", " lc_file_path_Z087 \n", "0 data-challenge-1/lc/ulwdc1_005_Z087.txt \n", "1 data-challenge-1/lc/ulwdc1_017_Z087.txt \n", "2 data-challenge-1/lc/ulwdc1_021_Z087.txt \n", "3 data-challenge-1/lc/ulwdc1_022_Z087.txt \n", "4 data-challenge-1/lc/ulwdc1_029_Z087.txt \n", ".. ... \n", "69 data-challenge-1/lc/ulwdc1_285_Z087.txt \n", "70 data-challenge-1/lc/ulwdc1_286_Z087.txt \n", "71 data-challenge-1/lc/ulwdc1_288_Z087.txt \n", "72 data-challenge-1/lc/ulwdc1_290_Z087.txt \n", "73 data-challenge-1/lc/ulwdc1_293_Z087.txt \n", "\n", "[74 rows x 93 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#@title Combining the two data frames\n", "\n", "# Convert 'lc_number' to numeric type before merging\n", "merged_sl_df = pd.merge(event_info, df_sl.astype({'lc_number': 'int64'}), left_on='Event_number', right_on='lc_number', how='inner')\n", "merged_sl_df" ] }, { "cell_type": "markdown", "metadata": { "id": "-XagtOAQx3Uh" }, "source": [ "### Binary Lens Events" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "index", "rawType": "int64", "type": "integer" }, { "name": "idx", "rawType": "object", "type": "string" }, { "name": "subrun", "rawType": "object", "type": "string" }, { "name": "field", "rawType": "object", "type": "string" }, { "name": "l", "rawType": "object", "type": "string" }, { "name": "b", "rawType": "object", "type": "string" }, { "name": "ra", "rawType": "object", "type": "string" }, { "name": "dec", "rawType": "object", "type": "string" }, { "name": "src_id", "rawType": "object", "type": "string" }, { "name": "Ds", "rawType": "object", "type": "string" }, { "name": "Rs", "rawType": "object", "type": "string" }, { "name": "smu_l", "rawType": "object", "type": "string" }, { "name": "smu_b", "rawType": "object", "type": "string" }, { "name": "sage", "rawType": "object", "type": "string" }, { "name": "scl", "rawType": "object", "type": "string" }, { "name": "styp", "rawType": "object", "type": "string" }, { "name": "lens_id", "rawType": "object", "type": "string" }, { "name": "Dl", "rawType": "object", "type": "string" }, { "name": "Ml", "rawType": "object", "type": "string" }, { "name": "lmu_l", "rawType": "object", "type": "string" }, { "name": "lmu_b", "rawType": "object", "type": "string" }, { "name": "lage", "rawType": "object", "type": "string" }, { "name": "lcl", "rawType": "object", "type": "string" }, { "name": "ltyp", "rawType": "object", "type": "string" }, { "name": "lMbol", "rawType": "object", "type": "string" }, { "name": "lTeff", "rawType": "object", "type": "string" }, { "name": "Llogg", "rawType": "object", "type": "string" }, { "name": "Rl", "rawType": "object", "type": "string" }, { "name": "u0", "rawType": "object", "type": "string" }, { "name": "alpha", "rawType": "object", "type": "string" }, { "name": "t0", "rawType": "object", "type": "string" }, { "name": "tE", "rawType": "object", "type": "string" }, { "name": "rE", "rawType": "object", "type": "string" }, { "name": "thE", "rawType": "object", "type": "string" }, { "name": "piE", "rawType": "object", "type": "string" }, { "name": "rhos", "rawType": "object", "type": "string" }, { "name": "murel", "rawType": "object", "type": "string" }, { "name": "vt", "rawType": "object", "type": "string" }, { "name": "gamma", "rawType": "object", "type": "string" }, { "name": "Mp", "rawType": "object", "type": "string" }, { "name": "a", "rawType": "object", "type": "string" }, { "name": "inc", "rawType": "object", "type": "string" }, { "name": "phase", "rawType": "object", "type": "string" }, { "name": "q", "rawType": "object", "type": "string" }, { "name": "s", "rawType": "object", "type": "string" }, { "name": "period", "rawType": "object", "type": "string" }, { "name": "u0max", "rawType": "object", "type": "string" }, { "name": "raww", "rawType": "object", "type": "string" }, { "name": "w", "rawType": "object", "type": "string" }, { "name": "Js", "rawType": "object", "type": "string" }, { "name": "F087s", "rawType": "object", "type": "string" }, { "name": "Hs", "rawType": "object", "type": "string" }, { "name": "W149s", "rawType": "object", "type": "string" }, { "name": "W169s", "rawType": "object", "type": "string" }, { "name": "Jl", "rawType": "object", "type": "string" }, { "name": "F087l", "rawType": "object", "type": "string" }, { "name": "Hl", "rawType": "object", "type": "string" }, { "name": "W149l", "rawType": "object", "type": "string" }, { "name": "W169l", "rawType": "object", "type": "string" }, { "name": "fs0", "rawType": "object", "type": "string" }, { "name": "fs1", "rawType": "object", "type": "string" }, { "name": "FSflag", "rawType": "object", "type": "string" }, { "name": "flatsatFlag", "rawType": "object", "type": "string" }, { "name": "flatchi2", "rawType": "object", "type": "string" }, { "name": "Delta chi2 flat", "rawType": "object", "type": "string" }, { "name": "chi2_1", "rawType": "object", "type": "string" }, { "name": "Delta chi2 single lens", "rawType": "object", "type": "string" }, { "name": "normw", "rawType": "object", "type": "string" }, { "name": "sim type", "rawType": "object", "type": "string" }, { "name": "sigma_tE", "rawType": "object", "type": "string" }, { "name": "filename", "rawType": "object", "type": "string" }, { "name": 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26573320.133009-1.76616268.221-29.730738039.2151.225...0|4.23893e+0705681.29|omcassan0.404041omcassan_3_32_65712
362308-0.451107-1.23271267.348-29.960172656.9680.254...0|35740402515.19|omcassan0.105428omcassan_0_8_62325
4105041091.52853-1.95811269.216-28.6232404110.0263.866...0|1.64356e+0801218.02|omcassan0.520323omcassan_4_109_105032
58333881.11824-0.668013267.715-28.323522818.0930.861...0|95569402718.51|omcassan0.486863omcassan_3_88_83340
63146418-0.106723-1.98473268.298-30.048138939.2670.822...0|4.27037e+071274.562|omcassan0.540434omcassan_4_18_314647
718732340.0925122-1.07923267.518-29.41529798.8950.822...0|2418750309.11|omcassan0.294777omcassan_2_34_187350
85375750.760366-0.61572267.455-28.6039151189.1520.639...0|10863.40364.734|omcassan0.749276omcassan_5_75_53753
9294739-0.460921-1.05033267.162-29.87471848310.2690.321...0|54087.301769.82|omcassan0.495614omcassan_3_9_294762
1019861981.23395-1.33927268.439-28.56572093311.5690.475...0|1.14759e+0716057.25|omcassan0.251962omcassan_1_98_198666
11251631021.35975-0.377754267.572-27.9674120708.5820.531...0|4.04958e+0617527.11|omcassan0.370142omcassan_3_102_251669
124352750.849158-0.626411267.517-28.5332140088.8350.293...0|5254502250.44|omcassan0.33477omcassan_2_75_43574
1312928490.367402-0.600399267.209-28.9331691011.0180.808...0|2.2396e+0605765.57|omcassan0.921444omcassan_8_49_129278
1423710610.663663-1.0579267.832-28.913514327.4510.783...0|3.87949e+0606517.22|omcassan0.135292omcassan_10_61_23781
15312318-0.118095-1.99377268.3-30.0625180609.7430.393...0|9600650168.518|omcassan0.383286omcassan_3_18_31292
1624834760.844376-0.547111267.437-28.49661659413.5061.11...0|2.11609e+06021947.7|omcassan0.602981omcassan_4_76_248395
1727137320.177245-1.75677268.237-29.687938729.2621.036...0|5.49865e+071496.995|omcassan0.865124omcassan_7_32_271399
182676370.0253672-0.369018266.781-29.10631071811.0041.053...0|2.19075e+06015635.7|omcassan0.787887omcassan_6_37_267100
1954651091.61117-1.92241269.228-28.533824838.0551.147...0|1.14102e+08119187|omcassan0.646947omcassan_5_109_546103
206801710.812444-1.77261268.622-29.1484122128.0960.328...0|1233.790549.423|omcassan0.220734omcassan_1_71_680107
215940440.355249-1.91325268.496-29.613818417.3850.999...0|1.37654e+070315.344|omcassan0.0583894omcassan_0_44_594124
2232112610.498741-0.914942267.595-28.98219888.1430.961...0|3.65333e+070213478|omcassan0.310974omcassan_2_61_3211128
23941218-0.271734-1.95678268.173-30.176478410.5266.387...0|1.18518e+080867.749|omcassan0.281796omcassan_2_18_941131
2415554730.868153-1.17433268.065-28.79726718.3121.393...0|2.03416e+080217.566|omcassan0.597654omcassan_4_73_1555139
25928520-0.207708-1.41558267.673-29.84525158.2920.861...0|1742530817.408|omcassan0.666561omcassan_5_20_928152
2664281121.66065-1.27374268.62-28.1646135578.3160.639...0|1.62458e+07013757.5|omcassan0.906269omcassan_8_112_642163
27273460.400672-1.45457268.069-29.341930728.630.746...0|1.14156e+0708476.6|omcassan0.420554omcassan_3_46_27186
281047360.0899097-0.575231267.021-29.15771639310.4310.484...0|2.30929e+0607198.06|omcassan0.875646omcassan_7_36_104193
29127041021.42429-0.426618267.657-27.937156439.980.822...0|88672.902582.6|omcassan0.515894omcassan_4_102_1270194
30156431131.5317-0.906435268.187-28.08952307314.0980.941...0|1.55622e+0705651.82|omcassan0.379321omcassan_3_113_1564199
3118563881.03991-0.728368267.728-28.4217147748.6170.688...0|5551370194.367|omcassan0.48691omcassan_3_88_1856208
3217550700.914839-2.01524268.921-29.182332128.6011.108...0|1.23968e+070558.13|omcassan0.0803903omcassan_0_70_1755214
338518470.235023-1.28433267.804-29.397521998.1060.891...0|1.09002e+07037938.5|omcassan0.919177omcassan_8_47_851217
34309121091.50325-1.91175269.156-28.621884107.2720.293...0|1.63223e+060845.852|omcassan0.265608omcassan_2_109_3091218
354900881.18779-0.69046267.777-28.275272226.7180.402...0|6.37416e+061437.047|omcassan0.118262omcassan_0_88_490223
368893971.29918-1.76227268.892-28.72341229.5790.783...0|9082.280749.876|omcassan0.505075omcassan_3_97_889226
37141611141.66928-0.596311267.964-27.8132140088.6210.783...0|1.54981e+060226.167|omcassan0.167073omcassan_1_114_1416227
387887350.0566451-0.843131267.264-29.32442077312.6220.394...0|1431740586.076|omcassan0.869353omcassan_7_35_788250
3927503861.18557-1.18474268.259-28.529184929.4120.639...0|6.17003e+060692.966|omcassan0.481247omcassan_3_86_2750253
401424521-0.265337-1.27763267.503-29.82382236910.8120.254...0|1.94866e+0611076.74|omcassan0.676416omcassan_5_21_1424258
415301570.624726-1.95406268.693-29.401941699.4980.783...0|3.48141e+070260478|omcassan0.204956omcassan_1_57_530267
4227233740.841982-0.933306267.813-28.696529308.9630.961...0|3.04308e+060490.986|omcassan0.46824omcassan_3_74_2723289
\n", "

43 rows Γ— 71 columns

\n", "
" ], "text/plain": [ " idx subrun field l b ra dec src_id Ds \\\n", "0 2683 4 87 1.21885 -0.937016 268.036 -28.3744 3589 9.675 \n", "1 262 2 83 1.04324 -2.03775 269.017 -29.0827 3730 9.003 \n", "2 657 3 32 0.133009 -1.76616 268.221 -29.7307 3803 9.215 \n", "3 623 0 8 -0.451107 -1.23271 267.348 -29.9601 7265 6.968 \n", "4 1050 4 109 1.52853 -1.95811 269.216 -28.6232 4041 10.026 \n", "5 833 3 88 1.11824 -0.668013 267.715 -28.3235 2281 8.093 \n", "6 3146 4 18 -0.106723 -1.98473 268.298 -30.0481 3893 9.267 \n", "7 1873 2 34 0.0925122 -1.07923 267.518 -29.415 2979 8.895 \n", "8 537 5 75 0.760366 -0.61572 267.455 -28.6039 15118 9.152 \n", "9 2947 3 9 -0.460921 -1.05033 267.162 -29.8747 18483 10.269 \n", "10 1986 1 98 1.23395 -1.33927 268.439 -28.5657 20933 11.569 \n", "11 2516 3 102 1.35975 -0.377754 267.572 -27.9674 12070 8.582 \n", "12 435 2 75 0.849158 -0.626411 267.517 -28.5332 14008 8.835 \n", "13 1292 8 49 0.367402 -0.600399 267.209 -28.933 16910 11.018 \n", "14 237 10 61 0.663663 -1.0579 267.832 -28.9135 1432 7.451 \n", "15 312 3 18 -0.118095 -1.99377 268.3 -30.0625 18060 9.743 \n", "16 2483 4 76 0.844376 -0.547111 267.437 -28.4966 16594 13.506 \n", "17 2713 7 32 0.177245 -1.75677 268.237 -29.6879 3872 9.262 \n", "18 267 6 37 0.0253672 -0.369018 266.781 -29.1063 10718 11.004 \n", "19 546 5 109 1.61117 -1.92241 269.228 -28.5338 2483 8.055 \n", "20 680 1 71 0.812444 -1.77261 268.622 -29.1484 12212 8.096 \n", "21 594 0 44 0.355249 -1.91325 268.496 -29.6138 1841 7.385 \n", "22 3211 2 61 0.498741 -0.914942 267.595 -28.982 1988 8.143 \n", "23 941 2 18 -0.271734 -1.95678 268.173 -30.176 4784 10.526 \n", "24 1555 4 73 0.868153 -1.17433 268.065 -28.797 2671 8.312 \n", "25 928 5 20 -0.207708 -1.41558 267.673 -29.845 2515 8.292 \n", "26 642 8 112 1.66065 -1.27374 268.62 -28.1646 13557 8.316 \n", "27 27 3 46 0.400672 -1.45457 268.069 -29.3419 3072 8.63 \n", "28 104 7 36 0.0899097 -0.575231 267.021 -29.1577 16393 10.431 \n", "29 1270 4 102 1.42429 -0.426618 267.657 -27.937 15643 9.98 \n", "30 1564 3 113 1.5317 -0.906435 268.187 -28.0895 23073 14.098 \n", "31 1856 3 88 1.03991 -0.728368 267.728 -28.4217 14774 8.617 \n", "32 1755 0 70 0.914839 -2.01524 268.921 -29.1823 3212 8.601 \n", "33 851 8 47 0.235023 -1.28433 267.804 -29.3975 2199 8.106 \n", "34 3091 2 109 1.50325 -1.91175 269.156 -28.6218 8410 7.272 \n", "35 490 0 88 1.18779 -0.69046 267.777 -28.2752 7222 6.718 \n", "36 889 3 97 1.29918 -1.76227 268.892 -28.723 4122 9.579 \n", "37 1416 1 114 1.66928 -0.596311 267.964 -27.8132 14008 8.621 \n", "38 788 7 35 0.0566451 -0.843131 267.264 -29.3244 20773 12.622 \n", "39 2750 3 86 1.18557 -1.18474 268.259 -28.529 18492 9.412 \n", "40 1424 5 21 -0.265337 -1.27763 267.503 -29.8238 22369 10.812 \n", "41 530 1 57 0.624726 -1.95406 268.693 -29.4019 4169 9.498 \n", "42 2723 3 74 0.841982 -0.933306 267.813 -28.6965 2930 8.963 \n", "\n", " Rs ... flatsatFlag flatchi2 Delta chi2 flat chi2_1 \\\n", "0 0.886 ... 0 | 76240 0 \n", "1 0.688 ... 0 | 380416 0 \n", "2 1.225 ... 0 | 4.23893e+07 0 \n", "3 0.254 ... 0 | 357404 0 \n", "4 3.866 ... 0 | 1.64356e+08 0 \n", "5 0.861 ... 0 | 955694 0 \n", "6 0.822 ... 0 | 4.27037e+07 1 \n", "7 0.822 ... 0 | 241875 0 \n", "8 0.639 ... 0 | 10863.4 0 \n", "9 0.321 ... 0 | 54087.3 0 \n", "10 0.475 ... 0 | 1.14759e+07 1 \n", "11 0.531 ... 0 | 4.04958e+06 1 \n", "12 0.293 ... 0 | 52545 0 \n", "13 0.808 ... 0 | 2.2396e+06 0 \n", "14 0.783 ... 0 | 3.87949e+06 0 \n", "15 0.393 ... 0 | 960065 0 \n", "16 1.11 ... 0 | 2.11609e+06 0 \n", "17 1.036 ... 0 | 5.49865e+07 1 \n", "18 1.053 ... 0 | 2.19075e+06 0 \n", "19 1.147 ... 0 | 1.14102e+08 1 \n", "20 0.328 ... 0 | 1233.79 0 \n", "21 0.999 ... 0 | 1.37654e+07 0 \n", "22 0.961 ... 0 | 3.65333e+07 0 \n", "23 6.387 ... 0 | 1.18518e+08 0 \n", "24 1.393 ... 0 | 2.03416e+08 0 \n", "25 0.861 ... 0 | 174253 0 \n", "26 0.639 ... 0 | 1.62458e+07 0 \n", "27 0.746 ... 0 | 1.14156e+07 0 \n", "28 0.484 ... 0 | 2.30929e+06 0 \n", "29 0.822 ... 0 | 88672.9 0 \n", "30 0.941 ... 0 | 1.55622e+07 0 \n", "31 0.688 ... 0 | 555137 0 \n", "32 1.108 ... 0 | 1.23968e+07 0 \n", "33 0.891 ... 0 | 1.09002e+07 0 \n", "34 0.293 ... 0 | 1.63223e+06 0 \n", "35 0.402 ... 0 | 6.37416e+06 1 \n", "36 0.783 ... 0 | 9082.28 0 \n", "37 0.783 ... 0 | 1.54981e+06 0 \n", "38 0.394 ... 0 | 143174 0 \n", "39 0.639 ... 0 | 6.17003e+06 0 \n", "40 0.254 ... 0 | 1.94866e+06 1 \n", "41 0.783 ... 0 | 3.48141e+07 0 \n", "42 0.961 ... 0 | 3.04308e+06 0 \n", "\n", " Delta chi2 single lens normw sim type sigma_tE filename \\\n", "0 6860.52 | omcassan 0.613786 omcassan_4_87_2683 \n", "1 383.459 | omcassan 0.338728 omcassan_2_83_262 \n", "2 5681.29 | omcassan 0.404041 omcassan_3_32_657 \n", "3 2515.19 | omcassan 0.105428 omcassan_0_8_623 \n", "4 1218.02 | omcassan 0.520323 omcassan_4_109_1050 \n", "5 2718.51 | omcassan 0.486863 omcassan_3_88_833 \n", "6 274.562 | omcassan 0.540434 omcassan_4_18_3146 \n", "7 309.11 | omcassan 0.294777 omcassan_2_34_1873 \n", "8 364.734 | omcassan 0.749276 omcassan_5_75_537 \n", "9 1769.82 | omcassan 0.495614 omcassan_3_9_2947 \n", "10 6057.25 | omcassan 0.251962 omcassan_1_98_1986 \n", "11 7527.11 | omcassan 0.370142 omcassan_3_102_2516 \n", "12 2250.44 | omcassan 0.33477 omcassan_2_75_435 \n", "13 5765.57 | omcassan 0.921444 omcassan_8_49_1292 \n", "14 6517.22 | omcassan 0.135292 omcassan_10_61_237 \n", "15 168.518 | omcassan 0.383286 omcassan_3_18_312 \n", "16 21947.7 | omcassan 0.602981 omcassan_4_76_2483 \n", "17 496.995 | omcassan 0.865124 omcassan_7_32_2713 \n", "18 15635.7 | omcassan 0.787887 omcassan_6_37_267 \n", "19 19187 | omcassan 0.646947 omcassan_5_109_546 \n", "20 549.423 | omcassan 0.220734 omcassan_1_71_680 \n", "21 315.344 | omcassan 0.0583894 omcassan_0_44_594 \n", "22 213478 | omcassan 0.310974 omcassan_2_61_3211 \n", "23 867.749 | omcassan 0.281796 omcassan_2_18_941 \n", "24 217.566 | omcassan 0.597654 omcassan_4_73_1555 \n", "25 817.408 | omcassan 0.666561 omcassan_5_20_928 \n", "26 13757.5 | omcassan 0.906269 omcassan_8_112_642 \n", "27 8476.6 | omcassan 0.420554 omcassan_3_46_27 \n", "28 7198.06 | omcassan 0.875646 omcassan_7_36_104 \n", "29 2582.6 | omcassan 0.515894 omcassan_4_102_1270 \n", "30 5651.82 | omcassan 0.379321 omcassan_3_113_1564 \n", "31 194.367 | omcassan 0.48691 omcassan_3_88_1856 \n", "32 558.13 | omcassan 0.0803903 omcassan_0_70_1755 \n", "33 37938.5 | omcassan 0.919177 omcassan_8_47_851 \n", "34 845.852 | omcassan 0.265608 omcassan_2_109_3091 \n", "35 437.047 | omcassan 0.118262 omcassan_0_88_490 \n", "36 749.876 | omcassan 0.505075 omcassan_3_97_889 \n", "37 226.167 | omcassan 0.167073 omcassan_1_114_1416 \n", "38 586.076 | omcassan 0.869353 omcassan_7_35_788 \n", "39 692.966 | omcassan 0.481247 omcassan_3_86_2750 \n", "40 1076.74 | omcassan 0.676416 omcassan_5_21_1424 \n", "41 260478 | omcassan 0.204956 omcassan_1_57_530 \n", "42 490.986 | omcassan 0.46824 omcassan_3_74_2723 \n", "\n", " lc_number \n", "0 4 \n", "1 8 \n", "2 12 \n", "3 25 \n", "4 32 \n", "5 40 \n", "6 47 \n", "7 50 \n", "8 53 \n", "9 62 \n", "10 66 \n", "11 69 \n", "12 74 \n", "13 78 \n", "14 81 \n", "15 92 \n", "16 95 \n", "17 99 \n", "18 100 \n", "19 103 \n", "20 107 \n", "21 124 \n", "22 128 \n", "23 131 \n", "24 139 \n", "25 152 \n", "26 163 \n", "27 186 \n", "28 193 \n", "29 194 \n", "30 199 \n", "31 208 \n", "32 214 \n", "33 217 \n", "34 218 \n", "35 223 \n", "36 226 \n", "37 227 \n", "38 250 \n", "39 253 \n", "40 258 \n", "41 267 \n", "42 289 \n", "\n", "[43 rows x 71 columns]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rows = []\n", "with open(master_file, \"r\") as f:\n", " for line in f:\n", " line = line.strip()\n", " # Skip empty lines or comment lines\n", " if not line or line.startswith(\"#\"):\n", " continue\n", "\n", " tokens = line.split() # split on whitespace\n", " # Keep only binary-lens events\n", " if \"omcassan\" not in tokens:\n", " continue\n", "\n", " # Single-lens lines should have exactly 96 columns\n", " if len(tokens) != 82:\n", " continue\n", " \n", " rows.append(tokens)\n", " \n", "df_bl = pd.DataFrame(rows)\n", "\n", "# For binary star lenses and bound planets they are:\n", "# 72 - unimportant\n", "# 73 - Delta chi^2 (relative to a flat line)\n", "# 74 - unimportant\n", "# 75 - Delta chi^2 (relative to a single lens fit PSPL or FSPL if necessary)\n", "# 76 - |\n", "# 77 - unimportant\n", "# 78 - simulated event type (ombin = Binary star, omcassan = Bound planet) \n", "# 79 - unimportant (I think)\n", "# 80 - lightcurve filename root\n", "# 81 - Data challenge lightcurve number\n", "\n", "# make an array of zeros with 82 elements\n", "colnames_82 = np.zeros(82, dtype=object)\n", "\n", "# Read the header file\n", "with open(header_file, 'r') as f:\n", " for i, line in enumerate(f):\n", " if i >= 82: # Only read the first 82 lines for the binary lens events\n", " break\n", " line = line.strip()\n", " # Skip empty lines or comments\n", " if not line or line.startswith('#'):\n", " continue\n", " # The second token is the 'name'\n", " parts = line.split()\n", " colnames_82[int(parts[0])] = parts[1]\n", " \n", "# Replace the column names in colnames_82\n", "colnames_82[73] = 'Delta chi2 flat'\n", "colnames_82[75] = 'Delta chi2 single lens'\n", "colnames_82[78] = 'sim type'\n", "colnames_82[80] = 'filename'\n", "colnames_82[81] = 'lc_number'\n", "\n", "# Make sure the column names are unique\n", "for i in range(82):\n", " if colnames_82[i] == '|' or colnames_82[i] == 0:\n", " colnames_82[i] = 'col_' + str(i)\n", "\n", "# Replace the column names in the data_frame\n", "df_bl.columns = colnames_82\n", "\n", "# Remove the dummy columns 'col_*'\n", "df_bl = df_bl.loc[:, ~df_bl.columns.str.startswith('col_')]\n", "df_bl" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "index", "rawType": "int64", "type": "integer" }, { "name": "idx", "rawType": "object", "type": "string" }, { "name": "subrun", "rawType": "object", "type": "string" }, { "name": "field", "rawType": "object", "type": "string" }, { "name": "l", "rawType": "object", "type": "string" }, { "name": "b", "rawType": "object", "type": "string" }, { "name": "ra", "rawType": "object", "type": "string" }, { "name": "dec", "rawType": "object", "type": "string" }, { "name": "src_id", "rawType": "object", "type": "string" }, { "name": "Ds", "rawType": "object", "type": "string" }, { "name": "Rs", "rawType": "object", "type": "string" }, { "name": "smu_l", "rawType": "object", "type": "string" }, { "name": "smu_b", "rawType": "object", "type": "string" }, { "name": "sage", "rawType": "object", "type": "string" }, { "name": "scl", "rawType": "object", "type": "string" }, { "name": "styp", "rawType": "object", "type": "string" }, { "name": "lens_id", "rawType": "object", "type": "string" }, { "name": "Dl", "rawType": "object", "type": "string" }, { "name": "Ml", "rawType": "object", "type": 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"string" }, { "name": "murel", "rawType": "object", "type": "string" }, { "name": "vt", "rawType": "object", "type": "string" }, { "name": "gamma", "rawType": "object", "type": "string" }, { "name": "Mp", "rawType": "object", "type": "string" }, { "name": "a", "rawType": "object", "type": "string" }, { "name": "inc", "rawType": "object", "type": "string" }, { "name": "phase", "rawType": "object", "type": "string" }, { "name": "q", "rawType": "object", "type": "string" }, { "name": "s", "rawType": "object", "type": "string" }, { "name": "period", "rawType": "object", "type": "string" }, { "name": "u0max", "rawType": "object", "type": "string" }, { "name": "raww", "rawType": "object", "type": "string" }, { "name": "w", "rawType": "object", "type": "string" }, { "name": "Js", "rawType": "object", "type": "string" }, { "name": "F087s", "rawType": "object", "type": "string" }, { "name": "Hs", "rawType": "object", "type": "string" }, { "name": "W149s", "rawType": "object", "type": "string" 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idxsubrunfieldlbradecsrc_idDsRs...Delta chi2 flatchi2_1Delta chi2 single lensnormwsim typesigma_tEfilenamelc_numberlc_file_path_W149lc_file_path_Z087
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12622831.04324-2.03775269.017-29.082737309.0030.688...3804160383.459|omcassan0.338728omcassan_2_83_2628data-challenge-1/lc/ulwdc1_008_W149.txtdata-challenge-1/lc/ulwdc1_008_Z087.txt
26573320.133009-1.76616268.221-29.730738039.2151.225...4.23893e+0705681.29|omcassan0.404041omcassan_3_32_65712data-challenge-1/lc/ulwdc1_012_W149.txtdata-challenge-1/lc/ulwdc1_012_Z087.txt
362308-0.451107-1.23271267.348-29.960172656.9680.254...35740402515.19|omcassan0.105428omcassan_0_8_62325data-challenge-1/lc/ulwdc1_025_W149.txtdata-challenge-1/lc/ulwdc1_025_Z087.txt
4105041091.52853-1.95811269.216-28.6232404110.0263.866...1.64356e+0801218.02|omcassan0.520323omcassan_4_109_105032data-challenge-1/lc/ulwdc1_032_W149.txtdata-challenge-1/lc/ulwdc1_032_Z087.txt
58333881.11824-0.668013267.715-28.323522818.0930.861...95569402718.51|omcassan0.486863omcassan_3_88_83340data-challenge-1/lc/ulwdc1_040_W149.txtdata-challenge-1/lc/ulwdc1_040_Z087.txt
63146418-0.106723-1.98473268.298-30.048138939.2670.822...4.27037e+071274.562|omcassan0.540434omcassan_4_18_314647data-challenge-1/lc/ulwdc1_047_W149.txtdata-challenge-1/lc/ulwdc1_047_Z087.txt
718732340.0925122-1.07923267.518-29.41529798.8950.822...2418750309.11|omcassan0.294777omcassan_2_34_187350data-challenge-1/lc/ulwdc1_050_W149.txtdata-challenge-1/lc/ulwdc1_050_Z087.txt
85375750.760366-0.61572267.455-28.6039151189.1520.639...10863.40364.734|omcassan0.749276omcassan_5_75_53753data-challenge-1/lc/ulwdc1_053_W149.txtdata-challenge-1/lc/ulwdc1_053_Z087.txt
9294739-0.460921-1.05033267.162-29.87471848310.2690.321...54087.301769.82|omcassan0.495614omcassan_3_9_294762data-challenge-1/lc/ulwdc1_062_W149.txtdata-challenge-1/lc/ulwdc1_062_Z087.txt
1019861981.23395-1.33927268.439-28.56572093311.5690.475...1.14759e+0716057.25|omcassan0.251962omcassan_1_98_198666data-challenge-1/lc/ulwdc1_066_W149.txtdata-challenge-1/lc/ulwdc1_066_Z087.txt
11251631021.35975-0.377754267.572-27.9674120708.5820.531...4.04958e+0617527.11|omcassan0.370142omcassan_3_102_251669data-challenge-1/lc/ulwdc1_069_W149.txtdata-challenge-1/lc/ulwdc1_069_Z087.txt
124352750.849158-0.626411267.517-28.5332140088.8350.293...5254502250.44|omcassan0.33477omcassan_2_75_43574data-challenge-1/lc/ulwdc1_074_W149.txtdata-challenge-1/lc/ulwdc1_074_Z087.txt
1312928490.367402-0.600399267.209-28.9331691011.0180.808...2.2396e+0605765.57|omcassan0.921444omcassan_8_49_129278data-challenge-1/lc/ulwdc1_078_W149.txtdata-challenge-1/lc/ulwdc1_078_Z087.txt
1423710610.663663-1.0579267.832-28.913514327.4510.783...3.87949e+0606517.22|omcassan0.135292omcassan_10_61_23781data-challenge-1/lc/ulwdc1_081_W149.txtdata-challenge-1/lc/ulwdc1_081_Z087.txt
15312318-0.118095-1.99377268.3-30.0625180609.7430.393...9600650168.518|omcassan0.383286omcassan_3_18_31292data-challenge-1/lc/ulwdc1_092_W149.txtdata-challenge-1/lc/ulwdc1_092_Z087.txt
1624834760.844376-0.547111267.437-28.49661659413.5061.11...2.11609e+06021947.7|omcassan0.602981omcassan_4_76_248395data-challenge-1/lc/ulwdc1_095_W149.txtdata-challenge-1/lc/ulwdc1_095_Z087.txt
1727137320.177245-1.75677268.237-29.687938729.2621.036...5.49865e+071496.995|omcassan0.865124omcassan_7_32_271399data-challenge-1/lc/ulwdc1_099_W149.txtdata-challenge-1/lc/ulwdc1_099_Z087.txt
182676370.0253672-0.369018266.781-29.10631071811.0041.053...2.19075e+06015635.7|omcassan0.787887omcassan_6_37_267100data-challenge-1/lc/ulwdc1_100_W149.txtdata-challenge-1/lc/ulwdc1_100_Z087.txt
1954651091.61117-1.92241269.228-28.533824838.0551.147...1.14102e+08119187|omcassan0.646947omcassan_5_109_546103data-challenge-1/lc/ulwdc1_103_W149.txtdata-challenge-1/lc/ulwdc1_103_Z087.txt
206801710.812444-1.77261268.622-29.1484122128.0960.328...1233.790549.423|omcassan0.220734omcassan_1_71_680107data-challenge-1/lc/ulwdc1_107_W149.txtdata-challenge-1/lc/ulwdc1_107_Z087.txt
215940440.355249-1.91325268.496-29.613818417.3850.999...1.37654e+070315.344|omcassan0.0583894omcassan_0_44_594124data-challenge-1/lc/ulwdc1_124_W149.txtdata-challenge-1/lc/ulwdc1_124_Z087.txt
2232112610.498741-0.914942267.595-28.98219888.1430.961...3.65333e+070213478|omcassan0.310974omcassan_2_61_3211128data-challenge-1/lc/ulwdc1_128_W149.txtdata-challenge-1/lc/ulwdc1_128_Z087.txt
23941218-0.271734-1.95678268.173-30.176478410.5266.387...1.18518e+080867.749|omcassan0.281796omcassan_2_18_941131data-challenge-1/lc/ulwdc1_131_W149.txtdata-challenge-1/lc/ulwdc1_131_Z087.txt
2415554730.868153-1.17433268.065-28.79726718.3121.393...2.03416e+080217.566|omcassan0.597654omcassan_4_73_1555139data-challenge-1/lc/ulwdc1_139_W149.txtdata-challenge-1/lc/ulwdc1_139_Z087.txt
25928520-0.207708-1.41558267.673-29.84525158.2920.861...1742530817.408|omcassan0.666561omcassan_5_20_928152data-challenge-1/lc/ulwdc1_152_W149.txtdata-challenge-1/lc/ulwdc1_152_Z087.txt
2664281121.66065-1.27374268.62-28.1646135578.3160.639...1.62458e+07013757.5|omcassan0.906269omcassan_8_112_642163data-challenge-1/lc/ulwdc1_163_W149.txtdata-challenge-1/lc/ulwdc1_163_Z087.txt
27273460.400672-1.45457268.069-29.341930728.630.746...1.14156e+0708476.6|omcassan0.420554omcassan_3_46_27186data-challenge-1/lc/ulwdc1_186_W149.txtdata-challenge-1/lc/ulwdc1_186_Z087.txt
281047360.0899097-0.575231267.021-29.15771639310.4310.484...2.30929e+0607198.06|omcassan0.875646omcassan_7_36_104193data-challenge-1/lc/ulwdc1_193_W149.txtdata-challenge-1/lc/ulwdc1_193_Z087.txt
29127041021.42429-0.426618267.657-27.937156439.980.822...88672.902582.6|omcassan0.515894omcassan_4_102_1270194data-challenge-1/lc/ulwdc1_194_W149.txtdata-challenge-1/lc/ulwdc1_194_Z087.txt
30156431131.5317-0.906435268.187-28.08952307314.0980.941...1.55622e+0705651.82|omcassan0.379321omcassan_3_113_1564199data-challenge-1/lc/ulwdc1_199_W149.txtdata-challenge-1/lc/ulwdc1_199_Z087.txt
3118563881.03991-0.728368267.728-28.4217147748.6170.688...5551370194.367|omcassan0.48691omcassan_3_88_1856208data-challenge-1/lc/ulwdc1_208_W149.txtdata-challenge-1/lc/ulwdc1_208_Z087.txt
3217550700.914839-2.01524268.921-29.182332128.6011.108...1.23968e+070558.13|omcassan0.0803903omcassan_0_70_1755214data-challenge-1/lc/ulwdc1_214_W149.txtdata-challenge-1/lc/ulwdc1_214_Z087.txt
338518470.235023-1.28433267.804-29.397521998.1060.891...1.09002e+07037938.5|omcassan0.919177omcassan_8_47_851217data-challenge-1/lc/ulwdc1_217_W149.txtdata-challenge-1/lc/ulwdc1_217_Z087.txt
34309121091.50325-1.91175269.156-28.621884107.2720.293...1.63223e+060845.852|omcassan0.265608omcassan_2_109_3091218data-challenge-1/lc/ulwdc1_218_W149.txtdata-challenge-1/lc/ulwdc1_218_Z087.txt
354900881.18779-0.69046267.777-28.275272226.7180.402...6.37416e+061437.047|omcassan0.118262omcassan_0_88_490223data-challenge-1/lc/ulwdc1_223_W149.txtdata-challenge-1/lc/ulwdc1_223_Z087.txt
368893971.29918-1.76227268.892-28.72341229.5790.783...9082.280749.876|omcassan0.505075omcassan_3_97_889226data-challenge-1/lc/ulwdc1_226_W149.txtdata-challenge-1/lc/ulwdc1_226_Z087.txt
37141611141.66928-0.596311267.964-27.8132140088.6210.783...1.54981e+060226.167|omcassan0.167073omcassan_1_114_1416227data-challenge-1/lc/ulwdc1_227_W149.txtdata-challenge-1/lc/ulwdc1_227_Z087.txt
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43 rows Γ— 73 columns

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" ], "text/plain": [ " idx subrun field l b ra dec src_id Ds \\\n", "0 2683 4 87 1.21885 -0.937016 268.036 -28.3744 3589 9.675 \n", "1 262 2 83 1.04324 -2.03775 269.017 -29.0827 3730 9.003 \n", "2 657 3 32 0.133009 -1.76616 268.221 -29.7307 3803 9.215 \n", "3 623 0 8 -0.451107 -1.23271 267.348 -29.9601 7265 6.968 \n", "4 1050 4 109 1.52853 -1.95811 269.216 -28.6232 4041 10.026 \n", "5 833 3 88 1.11824 -0.668013 267.715 -28.3235 2281 8.093 \n", "6 3146 4 18 -0.106723 -1.98473 268.298 -30.0481 3893 9.267 \n", "7 1873 2 34 0.0925122 -1.07923 267.518 -29.415 2979 8.895 \n", "8 537 5 75 0.760366 -0.61572 267.455 -28.6039 15118 9.152 \n", "9 2947 3 9 -0.460921 -1.05033 267.162 -29.8747 18483 10.269 \n", "10 1986 1 98 1.23395 -1.33927 268.439 -28.5657 20933 11.569 \n", "11 2516 3 102 1.35975 -0.377754 267.572 -27.9674 12070 8.582 \n", "12 435 2 75 0.849158 -0.626411 267.517 -28.5332 14008 8.835 \n", "13 1292 8 49 0.367402 -0.600399 267.209 -28.933 16910 11.018 \n", "14 237 10 61 0.663663 -1.0579 267.832 -28.9135 1432 7.451 \n", "15 312 3 18 -0.118095 -1.99377 268.3 -30.0625 18060 9.743 \n", "16 2483 4 76 0.844376 -0.547111 267.437 -28.4966 16594 13.506 \n", "17 2713 7 32 0.177245 -1.75677 268.237 -29.6879 3872 9.262 \n", "18 267 6 37 0.0253672 -0.369018 266.781 -29.1063 10718 11.004 \n", "19 546 5 109 1.61117 -1.92241 269.228 -28.5338 2483 8.055 \n", "20 680 1 71 0.812444 -1.77261 268.622 -29.1484 12212 8.096 \n", "21 594 0 44 0.355249 -1.91325 268.496 -29.6138 1841 7.385 \n", "22 3211 2 61 0.498741 -0.914942 267.595 -28.982 1988 8.143 \n", "23 941 2 18 -0.271734 -1.95678 268.173 -30.176 4784 10.526 \n", "24 1555 4 73 0.868153 -1.17433 268.065 -28.797 2671 8.312 \n", "25 928 5 20 -0.207708 -1.41558 267.673 -29.845 2515 8.292 \n", "26 642 8 112 1.66065 -1.27374 268.62 -28.1646 13557 8.316 \n", "27 27 3 46 0.400672 -1.45457 268.069 -29.3419 3072 8.63 \n", "28 104 7 36 0.0899097 -0.575231 267.021 -29.1577 16393 10.431 \n", "29 1270 4 102 1.42429 -0.426618 267.657 -27.937 15643 9.98 \n", "30 1564 3 113 1.5317 -0.906435 268.187 -28.0895 23073 14.098 \n", "31 1856 3 88 1.03991 -0.728368 267.728 -28.4217 14774 8.617 \n", "32 1755 0 70 0.914839 -2.01524 268.921 -29.1823 3212 8.601 \n", "33 851 8 47 0.235023 -1.28433 267.804 -29.3975 2199 8.106 \n", "34 3091 2 109 1.50325 -1.91175 269.156 -28.6218 8410 7.272 \n", "35 490 0 88 1.18779 -0.69046 267.777 -28.2752 7222 6.718 \n", "36 889 3 97 1.29918 -1.76227 268.892 -28.723 4122 9.579 \n", "37 1416 1 114 1.66928 -0.596311 267.964 -27.8132 14008 8.621 \n", "38 788 7 35 0.0566451 -0.843131 267.264 -29.3244 20773 12.622 \n", "39 2750 3 86 1.18557 -1.18474 268.259 -28.529 18492 9.412 \n", "40 1424 5 21 -0.265337 -1.27763 267.503 -29.8238 22369 10.812 \n", "41 530 1 57 0.624726 -1.95406 268.693 -29.4019 4169 9.498 \n", "42 2723 3 74 0.841982 -0.933306 267.813 -28.6965 2930 8.963 \n", "\n", " Rs ... Delta chi2 flat chi2_1 Delta chi2 single lens normw sim type \\\n", "0 0.886 ... 76240 0 6860.52 | omcassan \n", "1 0.688 ... 380416 0 383.459 | omcassan \n", "2 1.225 ... 4.23893e+07 0 5681.29 | omcassan \n", "3 0.254 ... 357404 0 2515.19 | omcassan \n", "4 3.866 ... 1.64356e+08 0 1218.02 | omcassan \n", "5 0.861 ... 955694 0 2718.51 | omcassan \n", "6 0.822 ... 4.27037e+07 1 274.562 | omcassan \n", "7 0.822 ... 241875 0 309.11 | omcassan \n", "8 0.639 ... 10863.4 0 364.734 | omcassan \n", "9 0.321 ... 54087.3 0 1769.82 | omcassan \n", "10 0.475 ... 1.14759e+07 1 6057.25 | omcassan \n", "11 0.531 ... 4.04958e+06 1 7527.11 | omcassan \n", "12 0.293 ... 52545 0 2250.44 | omcassan \n", "13 0.808 ... 2.2396e+06 0 5765.57 | omcassan \n", "14 0.783 ... 3.87949e+06 0 6517.22 | omcassan \n", "15 0.393 ... 960065 0 168.518 | omcassan \n", "16 1.11 ... 2.11609e+06 0 21947.7 | omcassan \n", "17 1.036 ... 5.49865e+07 1 496.995 | omcassan \n", "18 1.053 ... 2.19075e+06 0 15635.7 | omcassan \n", "19 1.147 ... 1.14102e+08 1 19187 | omcassan \n", "20 0.328 ... 1233.79 0 549.423 | omcassan \n", "21 0.999 ... 1.37654e+07 0 315.344 | omcassan \n", "22 0.961 ... 3.65333e+07 0 213478 | omcassan \n", "23 6.387 ... 1.18518e+08 0 867.749 | omcassan \n", "24 1.393 ... 2.03416e+08 0 217.566 | omcassan \n", "25 0.861 ... 174253 0 817.408 | omcassan \n", "26 0.639 ... 1.62458e+07 0 13757.5 | omcassan \n", "27 0.746 ... 1.14156e+07 0 8476.6 | omcassan \n", "28 0.484 ... 2.30929e+06 0 7198.06 | omcassan \n", "29 0.822 ... 88672.9 0 2582.6 | omcassan \n", "30 0.941 ... 1.55622e+07 0 5651.82 | omcassan \n", "31 0.688 ... 555137 0 194.367 | omcassan \n", "32 1.108 ... 1.23968e+07 0 558.13 | omcassan \n", "33 0.891 ... 1.09002e+07 0 37938.5 | omcassan \n", "34 0.293 ... 1.63223e+06 0 845.852 | omcassan \n", "35 0.402 ... 6.37416e+06 1 437.047 | omcassan \n", "36 0.783 ... 9082.28 0 749.876 | omcassan \n", "37 0.783 ... 1.54981e+06 0 226.167 | omcassan \n", "38 0.394 ... 143174 0 586.076 | omcassan \n", "39 0.639 ... 6.17003e+06 0 692.966 | omcassan \n", "40 0.254 ... 1.94866e+06 1 1076.74 | omcassan \n", "41 0.783 ... 3.48141e+07 0 260478 | omcassan \n", "42 0.961 ... 3.04308e+06 0 490.986 | omcassan \n", "\n", " sigma_tE filename lc_number \\\n", "0 0.613786 omcassan_4_87_2683 4 \n", "1 0.338728 omcassan_2_83_262 8 \n", "2 0.404041 omcassan_3_32_657 12 \n", "3 0.105428 omcassan_0_8_623 25 \n", "4 0.520323 omcassan_4_109_1050 32 \n", "5 0.486863 omcassan_3_88_833 40 \n", "6 0.540434 omcassan_4_18_3146 47 \n", "7 0.294777 omcassan_2_34_1873 50 \n", "8 0.749276 omcassan_5_75_537 53 \n", "9 0.495614 omcassan_3_9_2947 62 \n", "10 0.251962 omcassan_1_98_1986 66 \n", "11 0.370142 omcassan_3_102_2516 69 \n", "12 0.33477 omcassan_2_75_435 74 \n", "13 0.921444 omcassan_8_49_1292 78 \n", "14 0.135292 omcassan_10_61_237 81 \n", "15 0.383286 omcassan_3_18_312 92 \n", "16 0.602981 omcassan_4_76_2483 95 \n", "17 0.865124 omcassan_7_32_2713 99 \n", "18 0.787887 omcassan_6_37_267 100 \n", "19 0.646947 omcassan_5_109_546 103 \n", "20 0.220734 omcassan_1_71_680 107 \n", "21 0.0583894 omcassan_0_44_594 124 \n", "22 0.310974 omcassan_2_61_3211 128 \n", "23 0.281796 omcassan_2_18_941 131 \n", "24 0.597654 omcassan_4_73_1555 139 \n", "25 0.666561 omcassan_5_20_928 152 \n", "26 0.906269 omcassan_8_112_642 163 \n", "27 0.420554 omcassan_3_46_27 186 \n", "28 0.875646 omcassan_7_36_104 193 \n", "29 0.515894 omcassan_4_102_1270 194 \n", "30 0.379321 omcassan_3_113_1564 199 \n", "31 0.48691 omcassan_3_88_1856 208 \n", "32 0.0803903 omcassan_0_70_1755 214 \n", "33 0.919177 omcassan_8_47_851 217 \n", "34 0.265608 omcassan_2_109_3091 218 \n", "35 0.118262 omcassan_0_88_490 223 \n", "36 0.505075 omcassan_3_97_889 226 \n", "37 0.167073 omcassan_1_114_1416 227 \n", "38 0.869353 omcassan_7_35_788 250 \n", "39 0.481247 omcassan_3_86_2750 253 \n", "40 0.676416 omcassan_5_21_1424 258 \n", "41 0.204956 omcassan_1_57_530 267 \n", "42 0.46824 omcassan_3_74_2723 289 \n", "\n", " lc_file_path_W149 \\\n", "0 data-challenge-1/lc/ulwdc1_004_W149.txt \n", "1 data-challenge-1/lc/ulwdc1_008_W149.txt \n", "2 data-challenge-1/lc/ulwdc1_012_W149.txt \n", "3 data-challenge-1/lc/ulwdc1_025_W149.txt \n", "4 data-challenge-1/lc/ulwdc1_032_W149.txt \n", "5 data-challenge-1/lc/ulwdc1_040_W149.txt \n", "6 data-challenge-1/lc/ulwdc1_047_W149.txt \n", "7 data-challenge-1/lc/ulwdc1_050_W149.txt \n", "8 data-challenge-1/lc/ulwdc1_053_W149.txt \n", "9 data-challenge-1/lc/ulwdc1_062_W149.txt \n", "10 data-challenge-1/lc/ulwdc1_066_W149.txt \n", "11 data-challenge-1/lc/ulwdc1_069_W149.txt \n", "12 data-challenge-1/lc/ulwdc1_074_W149.txt \n", "13 data-challenge-1/lc/ulwdc1_078_W149.txt \n", "14 data-challenge-1/lc/ulwdc1_081_W149.txt \n", "15 data-challenge-1/lc/ulwdc1_092_W149.txt \n", "16 data-challenge-1/lc/ulwdc1_095_W149.txt \n", "17 data-challenge-1/lc/ulwdc1_099_W149.txt \n", "18 data-challenge-1/lc/ulwdc1_100_W149.txt \n", "19 data-challenge-1/lc/ulwdc1_103_W149.txt \n", "20 data-challenge-1/lc/ulwdc1_107_W149.txt \n", "21 data-challenge-1/lc/ulwdc1_124_W149.txt \n", "22 data-challenge-1/lc/ulwdc1_128_W149.txt \n", "23 data-challenge-1/lc/ulwdc1_131_W149.txt \n", "24 data-challenge-1/lc/ulwdc1_139_W149.txt \n", "25 data-challenge-1/lc/ulwdc1_152_W149.txt \n", "26 data-challenge-1/lc/ulwdc1_163_W149.txt \n", "27 data-challenge-1/lc/ulwdc1_186_W149.txt \n", "28 data-challenge-1/lc/ulwdc1_193_W149.txt \n", "29 data-challenge-1/lc/ulwdc1_194_W149.txt \n", "30 data-challenge-1/lc/ulwdc1_199_W149.txt \n", "31 data-challenge-1/lc/ulwdc1_208_W149.txt \n", "32 data-challenge-1/lc/ulwdc1_214_W149.txt \n", "33 data-challenge-1/lc/ulwdc1_217_W149.txt \n", "34 data-challenge-1/lc/ulwdc1_218_W149.txt \n", "35 data-challenge-1/lc/ulwdc1_223_W149.txt \n", "36 data-challenge-1/lc/ulwdc1_226_W149.txt \n", "37 data-challenge-1/lc/ulwdc1_227_W149.txt \n", "38 data-challenge-1/lc/ulwdc1_250_W149.txt \n", "39 data-challenge-1/lc/ulwdc1_253_W149.txt \n", "40 data-challenge-1/lc/ulwdc1_258_W149.txt \n", "41 data-challenge-1/lc/ulwdc1_267_W149.txt \n", "42 data-challenge-1/lc/ulwdc1_289_W149.txt \n", "\n", " lc_file_path_Z087 \n", "0 data-challenge-1/lc/ulwdc1_004_Z087.txt \n", "1 data-challenge-1/lc/ulwdc1_008_Z087.txt \n", "2 data-challenge-1/lc/ulwdc1_012_Z087.txt \n", "3 data-challenge-1/lc/ulwdc1_025_Z087.txt \n", "4 data-challenge-1/lc/ulwdc1_032_Z087.txt \n", "5 data-challenge-1/lc/ulwdc1_040_Z087.txt \n", "6 data-challenge-1/lc/ulwdc1_047_Z087.txt \n", "7 data-challenge-1/lc/ulwdc1_050_Z087.txt \n", "8 data-challenge-1/lc/ulwdc1_053_Z087.txt \n", "9 data-challenge-1/lc/ulwdc1_062_Z087.txt \n", "10 data-challenge-1/lc/ulwdc1_066_Z087.txt \n", "11 data-challenge-1/lc/ulwdc1_069_Z087.txt \n", "12 data-challenge-1/lc/ulwdc1_074_Z087.txt \n", "13 data-challenge-1/lc/ulwdc1_078_Z087.txt \n", "14 data-challenge-1/lc/ulwdc1_081_Z087.txt \n", "15 data-challenge-1/lc/ulwdc1_092_Z087.txt \n", "16 data-challenge-1/lc/ulwdc1_095_Z087.txt \n", "17 data-challenge-1/lc/ulwdc1_099_Z087.txt \n", "18 data-challenge-1/lc/ulwdc1_100_Z087.txt \n", "19 data-challenge-1/lc/ulwdc1_103_Z087.txt \n", "20 data-challenge-1/lc/ulwdc1_107_Z087.txt \n", "21 data-challenge-1/lc/ulwdc1_124_Z087.txt \n", "22 data-challenge-1/lc/ulwdc1_128_Z087.txt \n", "23 data-challenge-1/lc/ulwdc1_131_Z087.txt \n", "24 data-challenge-1/lc/ulwdc1_139_Z087.txt \n", "25 data-challenge-1/lc/ulwdc1_152_Z087.txt \n", "26 data-challenge-1/lc/ulwdc1_163_Z087.txt \n", "27 data-challenge-1/lc/ulwdc1_186_Z087.txt \n", "28 data-challenge-1/lc/ulwdc1_193_Z087.txt \n", "29 data-challenge-1/lc/ulwdc1_194_Z087.txt \n", "30 data-challenge-1/lc/ulwdc1_199_Z087.txt \n", "31 data-challenge-1/lc/ulwdc1_208_Z087.txt \n", "32 data-challenge-1/lc/ulwdc1_214_Z087.txt \n", "33 data-challenge-1/lc/ulwdc1_217_Z087.txt \n", "34 data-challenge-1/lc/ulwdc1_218_Z087.txt \n", "35 data-challenge-1/lc/ulwdc1_223_Z087.txt \n", "36 data-challenge-1/lc/ulwdc1_226_Z087.txt \n", "37 data-challenge-1/lc/ulwdc1_227_Z087.txt \n", "38 data-challenge-1/lc/ulwdc1_250_Z087.txt \n", "39 data-challenge-1/lc/ulwdc1_253_Z087.txt \n", "40 data-challenge-1/lc/ulwdc1_258_Z087.txt \n", "41 data-challenge-1/lc/ulwdc1_267_Z087.txt \n", "42 data-challenge-1/lc/ulwdc1_289_Z087.txt \n", "\n", "[43 rows x 73 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lc_number_bl = df_bl['lc_number'].to_numpy()\n", "lc_file_path_format = 'data-challenge-1/lc/ulwdc1_XXX_filter.txt'\n", "lc_file_paths_W149_bl = [lc_file_path_format.replace('filter', 'W149')] * len(lc_number_bl)\n", "lc_file_paths_Z087_bl = [lc_file_path_format.replace('filter', 'Z087')] * len(lc_number_bl)\n", "lc_file_paths_W149_bl = [path.replace('XXX', str(num).zfill(3)) for path, num in zip(lc_file_paths_W149_bl, lc_number_bl)]\n", "lc_file_paths_Z087_bl = [path.replace('XXX', str(num).zfill(3)) for path, num in zip(lc_file_paths_Z087_bl, lc_number_bl)]\n", "df_bl['lc_file_path_W149'] = lc_file_paths_W149_bl\n", "df_bl['lc_file_path_Z087'] = lc_file_paths_Z087_bl\n", "df_bl" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "index", "rawType": "int64", "type": "integer" }, { "name": "Event_name", "rawType": "object", "type": "string" }, { "name": "Event_number", "rawType": "int64", "type": "integer" }, { "name": "RA_(deg)", "rawType": "float64", "type": "float" }, { "name": "Dec_(deg)", "rawType": "float64", "type": "float" }, { "name": "Distance", "rawType": "float64", "type": "float" }, { "name": "A_W149", "rawType": "float64", "type": "float" }, { "name": "sigma_A_W149", "rawType": "float64", "type": "float" }, { "name": "A_Z087", "rawType": "float64", "type": "float" }, { "name": "sigma_A_Z087", "rawType": "float64", "type": "float" } ], "ref": "975f9e65-c6e6-4091-869e-edf3818dd62d", "rows": [ [ "0", "ulwdc1_001", "1", "269.165", "-29.0207", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "1", "ulwdc1_002", "2", "269.959", "-30.1918", "8.09", "0.49", "0.01", "0.95", "0.01" ], [ "2", "ulwdc1_003", "3", "269.1", "-29.0983", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "3", "ulwdc1_004", "4", "268.036", "-28.3744", "8.25", "1.35", "0.07", "2.6", "0.14" ], [ "4", "ulwdc1_005", "5", "269.319", "-29.0889", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "5", "ulwdc1_006", "6", "267.649", "-28.2978", "8.29", "1.64", "0.15", "3.15", "0.3" ], [ "6", "ulwdc1_007", "7", "269.236", "-29.0468", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "7", "ulwdc1_008", "8", "269.017", "-29.0827", "8.2", "0.66", "0.01", "1.27", "0.01" ], [ "8", "ulwdc1_009", "9", "268.01", "-29.5647", "8.47", "0.96", "0.02", "1.85", "0.04" ], [ "9", "ulwdc1_010", "10", "269.241", "-29.0339", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "10", "ulwdc1_011", "11", "269.195", "-29.0304", "8.18", "0.73", "0.01", "1.41", "0.01" ], [ "11", 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Event_nameEvent_numberRA_(deg)Dec_(deg)DistanceA_W149sigma_A_W149A_Z087sigma_A_Z087
0ulwdc1_0011269.165-29.02078.180.730.011.410.01
1ulwdc1_0022269.959-30.19188.090.490.010.950.01
2ulwdc1_0033269.100-29.09838.180.730.011.410.01
3ulwdc1_0044268.036-28.37448.251.350.072.600.14
4ulwdc1_0055269.319-29.08898.180.730.011.410.01
..............................
288ulwdc1_289289267.813-28.69658.071.520.072.930.14
289ulwdc1_290290269.149-28.98198.180.730.011.410.01
290ulwdc1_291291267.999-29.72038.570.710.011.360.01
291ulwdc1_292292269.151-29.14338.180.730.011.410.01
292ulwdc1_293293269.266-29.14638.180.730.011.410.01
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293 rows Γ— 9 columns

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" ], "text/plain": [ " Event_name Event_number RA_(deg) Dec_(deg) Distance A_W149 \\\n", "0 ulwdc1_001 1 269.165 -29.0207 8.18 0.73 \n", "1 ulwdc1_002 2 269.959 -30.1918 8.09 0.49 \n", "2 ulwdc1_003 3 269.100 -29.0983 8.18 0.73 \n", "3 ulwdc1_004 4 268.036 -28.3744 8.25 1.35 \n", "4 ulwdc1_005 5 269.319 -29.0889 8.18 0.73 \n", ".. ... ... ... ... ... ... \n", "288 ulwdc1_289 289 267.813 -28.6965 8.07 1.52 \n", "289 ulwdc1_290 290 269.149 -28.9819 8.18 0.73 \n", "290 ulwdc1_291 291 267.999 -29.7203 8.57 0.71 \n", "291 ulwdc1_292 292 269.151 -29.1433 8.18 0.73 \n", "292 ulwdc1_293 293 269.266 -29.1463 8.18 0.73 \n", "\n", " sigma_A_W149 A_Z087 sigma_A_Z087 \n", "0 0.01 1.41 0.01 \n", "1 0.01 0.95 0.01 \n", "2 0.01 1.41 0.01 \n", "3 0.07 2.60 0.14 \n", "4 0.01 1.41 0.01 \n", ".. ... ... ... \n", "288 0.07 2.93 0.14 \n", "289 0.01 1.41 0.01 \n", "290 0.01 1.36 0.01 \n", "291 0.01 1.41 0.01 \n", "292 0.01 1.41 0.01 \n", "\n", "[293 rows x 9 columns]" ] }, "execution_count": 25, "metadata": 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Event_nameEvent_numberRA_(deg)Dec_(deg)DistanceA_W149sigma_A_W149A_Z087sigma_A_Z087idx...Delta chi2 flatchi2_1Delta chi2 single lensnormwsim typesigma_tEfilenamelc_numberlc_file_path_W149lc_file_path_Z087
0ulwdc1_0044268.036-28.37448.251.350.072.600.142683...7624006860.52|omcassan0.613786omcassan_4_87_26834data-challenge-1/lc/ulwdc1_004_W149.txtdata-challenge-1/lc/ulwdc1_004_Z087.txt
1ulwdc1_0088269.017-29.08278.200.660.011.270.01262...3804160383.459|omcassan0.338728omcassan_2_83_2628data-challenge-1/lc/ulwdc1_008_W149.txtdata-challenge-1/lc/ulwdc1_008_Z087.txt
2ulwdc1_01212268.221-29.73078.570.710.011.360.01657...4.23893e+0705681.29|omcassan0.404041omcassan_3_32_65712data-challenge-1/lc/ulwdc1_012_W149.txtdata-challenge-1/lc/ulwdc1_012_Z087.txt
3ulwdc1_02525267.348-29.96018.531.620.043.110.07623...35740402515.19|omcassan0.105428omcassan_0_8_62325data-challenge-1/lc/ulwdc1_025_W149.txtdata-challenge-1/lc/ulwdc1_025_Z087.txt
4ulwdc1_03232269.216-28.62328.260.670.011.300.011050...1.64356e+0801218.02|omcassan0.520323omcassan_4_109_105032data-challenge-1/lc/ulwdc1_032_W149.txtdata-challenge-1/lc/ulwdc1_032_Z087.txt
5ulwdc1_04040267.715-28.32358.291.640.153.150.30833...95569402718.51|omcassan0.486863omcassan_3_88_83340data-challenge-1/lc/ulwdc1_040_W149.txtdata-challenge-1/lc/ulwdc1_040_Z087.txt
6ulwdc1_04747268.298-30.04818.400.620.011.190.013146...4.27037e+071274.562|omcassan0.540434omcassan_4_18_314647data-challenge-1/lc/ulwdc1_047_W149.txtdata-challenge-1/lc/ulwdc1_047_Z087.txt
7ulwdc1_05050267.518-29.41508.411.330.042.560.081873...2418750309.11|omcassan0.294777omcassan_2_34_187350data-challenge-1/lc/ulwdc1_050_W149.txtdata-challenge-1/lc/ulwdc1_050_Z087.txt
8ulwdc1_05353267.455-28.60398.392.320.244.470.47537...10863.40364.734|omcassan0.749276omcassan_5_75_53753data-challenge-1/lc/ulwdc1_053_W149.txtdata-challenge-1/lc/ulwdc1_053_Z087.txt
9ulwdc1_06262267.162-29.87478.572.200.204.230.382947...54087.301769.82|omcassan0.495614omcassan_3_9_294762data-challenge-1/lc/ulwdc1_062_W149.txtdata-challenge-1/lc/ulwdc1_062_Z087.txt
10ulwdc1_06666268.439-28.56578.120.760.011.470.011986...1.14759e+0716057.25|omcassan0.251962omcassan_1_98_198666data-challenge-1/lc/ulwdc1_066_W149.txtdata-challenge-1/lc/ulwdc1_066_Z087.txt
11ulwdc1_06969267.572-27.96748.292.750.335.290.632516...4.04958e+0617527.11|omcassan0.370142omcassan_3_102_251669data-challenge-1/lc/ulwdc1_069_W149.txtdata-challenge-1/lc/ulwdc1_069_Z087.txt
12ulwdc1_07474267.517-28.53328.392.320.244.470.47435...5254502250.44|omcassan0.33477omcassan_2_75_43574data-challenge-1/lc/ulwdc1_074_W149.txtdata-challenge-1/lc/ulwdc1_074_Z087.txt
13ulwdc1_07878267.209-28.93308.422.800.245.390.461292...2.2396e+0605765.57|omcassan0.921444omcassan_8_49_129278data-challenge-1/lc/ulwdc1_078_W149.txtdata-challenge-1/lc/ulwdc1_078_Z087.txt
14ulwdc1_08181267.832-28.91358.331.940.263.730.49237...3.87949e+0606517.22|omcassan0.135292omcassan_10_61_23781data-challenge-1/lc/ulwdc1_081_W149.txtdata-challenge-1/lc/ulwdc1_081_Z087.txt
15ulwdc1_09292268.300-30.06258.400.620.011.190.01312...9600650168.518|omcassan0.383286omcassan_3_18_31292data-challenge-1/lc/ulwdc1_092_W149.txtdata-challenge-1/lc/ulwdc1_092_Z087.txt
16ulwdc1_09595267.437-28.49668.213.150.406.060.772483...2.11609e+06021947.7|omcassan0.602981omcassan_4_76_248395data-challenge-1/lc/ulwdc1_095_W149.txtdata-challenge-1/lc/ulwdc1_095_Z087.txt
17ulwdc1_09999268.237-29.68798.570.710.011.360.012713...5.49865e+071496.995|omcassan0.865124omcassan_7_32_271399data-challenge-1/lc/ulwdc1_099_W149.txtdata-challenge-1/lc/ulwdc1_099_Z087.txt
18ulwdc1_100100266.781-29.10638.324.230.378.140.71267...2.19075e+06015635.7|omcassan0.787887omcassan_6_37_267100data-challenge-1/lc/ulwdc1_100_W149.txtdata-challenge-1/lc/ulwdc1_100_Z087.txt
19ulwdc1_103103269.228-28.53388.260.670.011.300.01546...1.14102e+08119187|omcassan0.646947omcassan_5_109_546103data-challenge-1/lc/ulwdc1_103_W149.txtdata-challenge-1/lc/ulwdc1_103_Z087.txt
20ulwdc1_107107268.622-29.14848.150.580.011.120.01680...1233.790549.423|omcassan0.220734omcassan_1_71_680107data-challenge-1/lc/ulwdc1_107_W149.txtdata-challenge-1/lc/ulwdc1_107_Z087.txt
21ulwdc1_124124268.496-29.61388.310.540.011.030.01594...1.37654e+070315.344|omcassan0.0583894omcassan_0_44_594124data-challenge-1/lc/ulwdc1_124_W149.txtdata-challenge-1/lc/ulwdc1_124_Z087.txt
22ulwdc1_128128267.595-28.98208.331.940.263.730.493211...3.65333e+070213478|omcassan0.310974omcassan_2_61_3211128data-challenge-1/lc/ulwdc1_128_W149.txtdata-challenge-1/lc/ulwdc1_128_Z087.txt
23ulwdc1_131131268.173-30.17608.400.620.011.190.01941...1.18518e+080867.749|omcassan0.281796omcassan_2_18_941131data-challenge-1/lc/ulwdc1_131_W149.txtdata-challenge-1/lc/ulwdc1_131_Z087.txt
24ulwdc1_139139268.065-28.79708.211.100.012.110.021555...2.03416e+080217.566|omcassan0.597654omcassan_4_73_1555139data-challenge-1/lc/ulwdc1_139_W149.txtdata-challenge-1/lc/ulwdc1_139_Z087.txt
25ulwdc1_152152267.673-29.84508.381.100.022.110.04928...1742530817.408|omcassan0.666561omcassan_5_20_928152data-challenge-1/lc/ulwdc1_152_W149.txtdata-challenge-1/lc/ulwdc1_152_Z087.txt
26ulwdc1_163163268.620-28.16468.011.070.012.060.03642...1.62458e+07013757.5|omcassan0.906269omcassan_8_112_642163data-challenge-1/lc/ulwdc1_163_W149.txtdata-challenge-1/lc/ulwdc1_163_Z087.txt
27ulwdc1_186186268.069-29.34198.400.960.031.840.0527...1.14156e+0708476.6|omcassan0.420554omcassan_3_46_27186data-challenge-1/lc/ulwdc1_186_W149.txtdata-challenge-1/lc/ulwdc1_186_Z087.txt
28ulwdc1_193193267.021-29.15778.372.790.255.370.49104...2.30929e+0607198.06|omcassan0.875646omcassan_7_36_104193data-challenge-1/lc/ulwdc1_193_W149.txtdata-challenge-1/lc/ulwdc1_193_Z087.txt
29ulwdc1_194194267.657-27.93708.292.750.335.290.631270...88672.902582.6|omcassan0.515894omcassan_4_102_1270194data-challenge-1/lc/ulwdc1_194_W149.txtdata-challenge-1/lc/ulwdc1_194_Z087.txt
30ulwdc1_199199268.187-28.08958.151.340.112.570.201564...1.55622e+0705651.82|omcassan0.379321omcassan_3_113_1564199data-challenge-1/lc/ulwdc1_199_W149.txtdata-challenge-1/lc/ulwdc1_199_Z087.txt
31ulwdc1_208208267.728-28.42178.291.640.153.150.301856...5551370194.367|omcassan0.48691omcassan_3_88_1856208data-challenge-1/lc/ulwdc1_208_W149.txtdata-challenge-1/lc/ulwdc1_208_Z087.txt
32ulwdc1_214214268.921-29.18238.150.640.011.230.011755...1.23968e+070558.13|omcassan0.0803903omcassan_0_70_1755214data-challenge-1/lc/ulwdc1_214_W149.txtdata-challenge-1/lc/ulwdc1_214_Z087.txt
33ulwdc1_217217267.804-29.39758.471.330.052.570.09851...1.09002e+07037938.5|omcassan0.919177omcassan_8_47_851217data-challenge-1/lc/ulwdc1_217_W149.txtdata-challenge-1/lc/ulwdc1_217_Z087.txt
34ulwdc1_218218269.156-28.62188.260.670.011.300.013091...1.63223e+060845.852|omcassan0.265608omcassan_2_109_3091218data-challenge-1/lc/ulwdc1_218_W149.txtdata-challenge-1/lc/ulwdc1_218_Z087.txt
35ulwdc1_223223267.777-28.27528.291.640.153.150.30490...6.37416e+061437.047|omcassan0.118262omcassan_0_88_490223data-challenge-1/lc/ulwdc1_223_W149.txtdata-challenge-1/lc/ulwdc1_223_Z087.txt
36ulwdc1_226226268.892-28.72307.970.690.011.330.01889...9082.280749.876|omcassan0.505075omcassan_3_97_889226data-challenge-1/lc/ulwdc1_226_W149.txtdata-challenge-1/lc/ulwdc1_226_Z087.txt
37ulwdc1_227227267.964-27.81328.231.860.213.580.401416...1.54981e+060226.167|omcassan0.167073omcassan_1_114_1416227data-challenge-1/lc/ulwdc1_227_W149.txtdata-challenge-1/lc/ulwdc1_227_Z087.txt
38ulwdc1_250250267.264-29.32448.331.950.233.750.45788...1431740586.076|omcassan0.869353omcassan_7_35_788250data-challenge-1/lc/ulwdc1_250_W149.txtdata-challenge-1/lc/ulwdc1_250_Z087.txt
39ulwdc1_253253268.259-28.52908.171.070.022.050.042750...6.17003e+060692.966|omcassan0.481247omcassan_3_86_2750253data-challenge-1/lc/ulwdc1_253_W149.txtdata-challenge-1/lc/ulwdc1_253_Z087.txt
40ulwdc1_258258267.503-29.82388.391.270.032.440.071424...1.94866e+0611076.74|omcassan0.676416omcassan_5_21_1424258data-challenge-1/lc/ulwdc1_258_W149.txtdata-challenge-1/lc/ulwdc1_258_Z087.txt
41ulwdc1_267267268.693-29.40198.460.550.011.050.01530...3.48141e+070260478|omcassan0.204956omcassan_1_57_530267data-challenge-1/lc/ulwdc1_267_W149.txtdata-challenge-1/lc/ulwdc1_267_Z087.txt
42ulwdc1_289289267.813-28.69658.071.520.072.930.142723...3.04308e+060490.986|omcassan0.46824omcassan_3_74_2723289data-challenge-1/lc/ulwdc1_289_W149.txtdata-challenge-1/lc/ulwdc1_289_Z087.txt
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43 rows Γ— 82 columns

\n", "
" ], "text/plain": [ " Event_name Event_number RA_(deg) Dec_(deg) Distance A_W149 \\\n", "0 ulwdc1_004 4 268.036 -28.3744 8.25 1.35 \n", "1 ulwdc1_008 8 269.017 -29.0827 8.20 0.66 \n", "2 ulwdc1_012 12 268.221 -29.7307 8.57 0.71 \n", "3 ulwdc1_025 25 267.348 -29.9601 8.53 1.62 \n", "4 ulwdc1_032 32 269.216 -28.6232 8.26 0.67 \n", "5 ulwdc1_040 40 267.715 -28.3235 8.29 1.64 \n", "6 ulwdc1_047 47 268.298 -30.0481 8.40 0.62 \n", "7 ulwdc1_050 50 267.518 -29.4150 8.41 1.33 \n", "8 ulwdc1_053 53 267.455 -28.6039 8.39 2.32 \n", "9 ulwdc1_062 62 267.162 -29.8747 8.57 2.20 \n", "10 ulwdc1_066 66 268.439 -28.5657 8.12 0.76 \n", "11 ulwdc1_069 69 267.572 -27.9674 8.29 2.75 \n", "12 ulwdc1_074 74 267.517 -28.5332 8.39 2.32 \n", "13 ulwdc1_078 78 267.209 -28.9330 8.42 2.80 \n", "14 ulwdc1_081 81 267.832 -28.9135 8.33 1.94 \n", "15 ulwdc1_092 92 268.300 -30.0625 8.40 0.62 \n", "16 ulwdc1_095 95 267.437 -28.4966 8.21 3.15 \n", "17 ulwdc1_099 99 268.237 -29.6879 8.57 0.71 \n", "18 ulwdc1_100 100 266.781 -29.1063 8.32 4.23 \n", "19 ulwdc1_103 103 269.228 -28.5338 8.26 0.67 \n", "20 ulwdc1_107 107 268.622 -29.1484 8.15 0.58 \n", "21 ulwdc1_124 124 268.496 -29.6138 8.31 0.54 \n", "22 ulwdc1_128 128 267.595 -28.9820 8.33 1.94 \n", "23 ulwdc1_131 131 268.173 -30.1760 8.40 0.62 \n", "24 ulwdc1_139 139 268.065 -28.7970 8.21 1.10 \n", "25 ulwdc1_152 152 267.673 -29.8450 8.38 1.10 \n", "26 ulwdc1_163 163 268.620 -28.1646 8.01 1.07 \n", "27 ulwdc1_186 186 268.069 -29.3419 8.40 0.96 \n", "28 ulwdc1_193 193 267.021 -29.1577 8.37 2.79 \n", "29 ulwdc1_194 194 267.657 -27.9370 8.29 2.75 \n", "30 ulwdc1_199 199 268.187 -28.0895 8.15 1.34 \n", "31 ulwdc1_208 208 267.728 -28.4217 8.29 1.64 \n", "32 ulwdc1_214 214 268.921 -29.1823 8.15 0.64 \n", "33 ulwdc1_217 217 267.804 -29.3975 8.47 1.33 \n", "34 ulwdc1_218 218 269.156 -28.6218 8.26 0.67 \n", "35 ulwdc1_223 223 267.777 -28.2752 8.29 1.64 \n", "36 ulwdc1_226 226 268.892 -28.7230 7.97 0.69 \n", "37 ulwdc1_227 227 267.964 -27.8132 8.23 1.86 \n", "38 ulwdc1_250 250 267.264 -29.3244 8.33 1.95 \n", "39 ulwdc1_253 253 268.259 -28.5290 8.17 1.07 \n", "40 ulwdc1_258 258 267.503 -29.8238 8.39 1.27 \n", "41 ulwdc1_267 267 268.693 -29.4019 8.46 0.55 \n", "42 ulwdc1_289 289 267.813 -28.6965 8.07 1.52 \n", "\n", " sigma_A_W149 A_Z087 sigma_A_Z087 idx ... Delta chi2 flat chi2_1 \\\n", "0 0.07 2.60 0.14 2683 ... 76240 0 \n", "1 0.01 1.27 0.01 262 ... 380416 0 \n", "2 0.01 1.36 0.01 657 ... 4.23893e+07 0 \n", "3 0.04 3.11 0.07 623 ... 357404 0 \n", "4 0.01 1.30 0.01 1050 ... 1.64356e+08 0 \n", "5 0.15 3.15 0.30 833 ... 955694 0 \n", "6 0.01 1.19 0.01 3146 ... 4.27037e+07 1 \n", "7 0.04 2.56 0.08 1873 ... 241875 0 \n", "8 0.24 4.47 0.47 537 ... 10863.4 0 \n", "9 0.20 4.23 0.38 2947 ... 54087.3 0 \n", "10 0.01 1.47 0.01 1986 ... 1.14759e+07 1 \n", "11 0.33 5.29 0.63 2516 ... 4.04958e+06 1 \n", "12 0.24 4.47 0.47 435 ... 52545 0 \n", "13 0.24 5.39 0.46 1292 ... 2.2396e+06 0 \n", "14 0.26 3.73 0.49 237 ... 3.87949e+06 0 \n", "15 0.01 1.19 0.01 312 ... 960065 0 \n", "16 0.40 6.06 0.77 2483 ... 2.11609e+06 0 \n", "17 0.01 1.36 0.01 2713 ... 5.49865e+07 1 \n", "18 0.37 8.14 0.71 267 ... 2.19075e+06 0 \n", "19 0.01 1.30 0.01 546 ... 1.14102e+08 1 \n", "20 0.01 1.12 0.01 680 ... 1233.79 0 \n", "21 0.01 1.03 0.01 594 ... 1.37654e+07 0 \n", "22 0.26 3.73 0.49 3211 ... 3.65333e+07 0 \n", "23 0.01 1.19 0.01 941 ... 1.18518e+08 0 \n", "24 0.01 2.11 0.02 1555 ... 2.03416e+08 0 \n", "25 0.02 2.11 0.04 928 ... 174253 0 \n", "26 0.01 2.06 0.03 642 ... 1.62458e+07 0 \n", "27 0.03 1.84 0.05 27 ... 1.14156e+07 0 \n", "28 0.25 5.37 0.49 104 ... 2.30929e+06 0 \n", "29 0.33 5.29 0.63 1270 ... 88672.9 0 \n", "30 0.11 2.57 0.20 1564 ... 1.55622e+07 0 \n", "31 0.15 3.15 0.30 1856 ... 555137 0 \n", "32 0.01 1.23 0.01 1755 ... 1.23968e+07 0 \n", "33 0.05 2.57 0.09 851 ... 1.09002e+07 0 \n", "34 0.01 1.30 0.01 3091 ... 1.63223e+06 0 \n", "35 0.15 3.15 0.30 490 ... 6.37416e+06 1 \n", "36 0.01 1.33 0.01 889 ... 9082.28 0 \n", "37 0.21 3.58 0.40 1416 ... 1.54981e+06 0 \n", "38 0.23 3.75 0.45 788 ... 143174 0 \n", "39 0.02 2.05 0.04 2750 ... 6.17003e+06 0 \n", "40 0.03 2.44 0.07 1424 ... 1.94866e+06 1 \n", "41 0.01 1.05 0.01 530 ... 3.48141e+07 0 \n", "42 0.07 2.93 0.14 2723 ... 3.04308e+06 0 \n", "\n", " Delta chi2 single lens normw sim type sigma_tE filename \\\n", "0 6860.52 | omcassan 0.613786 omcassan_4_87_2683 \n", "1 383.459 | omcassan 0.338728 omcassan_2_83_262 \n", "2 5681.29 | omcassan 0.404041 omcassan_3_32_657 \n", "3 2515.19 | omcassan 0.105428 omcassan_0_8_623 \n", "4 1218.02 | omcassan 0.520323 omcassan_4_109_1050 \n", "5 2718.51 | omcassan 0.486863 omcassan_3_88_833 \n", "6 274.562 | omcassan 0.540434 omcassan_4_18_3146 \n", "7 309.11 | omcassan 0.294777 omcassan_2_34_1873 \n", "8 364.734 | omcassan 0.749276 omcassan_5_75_537 \n", "9 1769.82 | omcassan 0.495614 omcassan_3_9_2947 \n", "10 6057.25 | omcassan 0.251962 omcassan_1_98_1986 \n", "11 7527.11 | omcassan 0.370142 omcassan_3_102_2516 \n", "12 2250.44 | omcassan 0.33477 omcassan_2_75_435 \n", "13 5765.57 | omcassan 0.921444 omcassan_8_49_1292 \n", "14 6517.22 | omcassan 0.135292 omcassan_10_61_237 \n", "15 168.518 | omcassan 0.383286 omcassan_3_18_312 \n", "16 21947.7 | omcassan 0.602981 omcassan_4_76_2483 \n", "17 496.995 | omcassan 0.865124 omcassan_7_32_2713 \n", "18 15635.7 | omcassan 0.787887 omcassan_6_37_267 \n", "19 19187 | omcassan 0.646947 omcassan_5_109_546 \n", "20 549.423 | omcassan 0.220734 omcassan_1_71_680 \n", "21 315.344 | omcassan 0.0583894 omcassan_0_44_594 \n", "22 213478 | omcassan 0.310974 omcassan_2_61_3211 \n", "23 867.749 | omcassan 0.281796 omcassan_2_18_941 \n", "24 217.566 | omcassan 0.597654 omcassan_4_73_1555 \n", "25 817.408 | omcassan 0.666561 omcassan_5_20_928 \n", "26 13757.5 | omcassan 0.906269 omcassan_8_112_642 \n", "27 8476.6 | omcassan 0.420554 omcassan_3_46_27 \n", "28 7198.06 | omcassan 0.875646 omcassan_7_36_104 \n", "29 2582.6 | omcassan 0.515894 omcassan_4_102_1270 \n", "30 5651.82 | omcassan 0.379321 omcassan_3_113_1564 \n", "31 194.367 | omcassan 0.48691 omcassan_3_88_1856 \n", "32 558.13 | omcassan 0.0803903 omcassan_0_70_1755 \n", "33 37938.5 | omcassan 0.919177 omcassan_8_47_851 \n", "34 845.852 | omcassan 0.265608 omcassan_2_109_3091 \n", "35 437.047 | omcassan 0.118262 omcassan_0_88_490 \n", "36 749.876 | omcassan 0.505075 omcassan_3_97_889 \n", "37 226.167 | omcassan 0.167073 omcassan_1_114_1416 \n", "38 586.076 | omcassan 0.869353 omcassan_7_35_788 \n", "39 692.966 | omcassan 0.481247 omcassan_3_86_2750 \n", "40 1076.74 | omcassan 0.676416 omcassan_5_21_1424 \n", "41 260478 | omcassan 0.204956 omcassan_1_57_530 \n", "42 490.986 | omcassan 0.46824 omcassan_3_74_2723 \n", "\n", " lc_number lc_file_path_W149 \\\n", "0 4 data-challenge-1/lc/ulwdc1_004_W149.txt \n", "1 8 data-challenge-1/lc/ulwdc1_008_W149.txt \n", "2 12 data-challenge-1/lc/ulwdc1_012_W149.txt \n", "3 25 data-challenge-1/lc/ulwdc1_025_W149.txt \n", "4 32 data-challenge-1/lc/ulwdc1_032_W149.txt \n", "5 40 data-challenge-1/lc/ulwdc1_040_W149.txt \n", "6 47 data-challenge-1/lc/ulwdc1_047_W149.txt \n", "7 50 data-challenge-1/lc/ulwdc1_050_W149.txt \n", "8 53 data-challenge-1/lc/ulwdc1_053_W149.txt \n", "9 62 data-challenge-1/lc/ulwdc1_062_W149.txt \n", "10 66 data-challenge-1/lc/ulwdc1_066_W149.txt \n", "11 69 data-challenge-1/lc/ulwdc1_069_W149.txt \n", "12 74 data-challenge-1/lc/ulwdc1_074_W149.txt \n", "13 78 data-challenge-1/lc/ulwdc1_078_W149.txt \n", "14 81 data-challenge-1/lc/ulwdc1_081_W149.txt \n", "15 92 data-challenge-1/lc/ulwdc1_092_W149.txt \n", "16 95 data-challenge-1/lc/ulwdc1_095_W149.txt \n", "17 99 data-challenge-1/lc/ulwdc1_099_W149.txt \n", "18 100 data-challenge-1/lc/ulwdc1_100_W149.txt \n", "19 103 data-challenge-1/lc/ulwdc1_103_W149.txt \n", "20 107 data-challenge-1/lc/ulwdc1_107_W149.txt \n", "21 124 data-challenge-1/lc/ulwdc1_124_W149.txt \n", "22 128 data-challenge-1/lc/ulwdc1_128_W149.txt \n", "23 131 data-challenge-1/lc/ulwdc1_131_W149.txt \n", "24 139 data-challenge-1/lc/ulwdc1_139_W149.txt \n", "25 152 data-challenge-1/lc/ulwdc1_152_W149.txt \n", "26 163 data-challenge-1/lc/ulwdc1_163_W149.txt \n", "27 186 data-challenge-1/lc/ulwdc1_186_W149.txt \n", "28 193 data-challenge-1/lc/ulwdc1_193_W149.txt \n", "29 194 data-challenge-1/lc/ulwdc1_194_W149.txt \n", "30 199 data-challenge-1/lc/ulwdc1_199_W149.txt \n", "31 208 data-challenge-1/lc/ulwdc1_208_W149.txt \n", "32 214 data-challenge-1/lc/ulwdc1_214_W149.txt \n", "33 217 data-challenge-1/lc/ulwdc1_217_W149.txt \n", "34 218 data-challenge-1/lc/ulwdc1_218_W149.txt \n", "35 223 data-challenge-1/lc/ulwdc1_223_W149.txt \n", "36 226 data-challenge-1/lc/ulwdc1_226_W149.txt \n", "37 227 data-challenge-1/lc/ulwdc1_227_W149.txt \n", "38 250 data-challenge-1/lc/ulwdc1_250_W149.txt \n", "39 253 data-challenge-1/lc/ulwdc1_253_W149.txt \n", "40 258 data-challenge-1/lc/ulwdc1_258_W149.txt \n", "41 267 data-challenge-1/lc/ulwdc1_267_W149.txt \n", "42 289 data-challenge-1/lc/ulwdc1_289_W149.txt \n", "\n", " lc_file_path_Z087 \n", "0 data-challenge-1/lc/ulwdc1_004_Z087.txt \n", "1 data-challenge-1/lc/ulwdc1_008_Z087.txt \n", "2 data-challenge-1/lc/ulwdc1_012_Z087.txt \n", "3 data-challenge-1/lc/ulwdc1_025_Z087.txt \n", "4 data-challenge-1/lc/ulwdc1_032_Z087.txt \n", "5 data-challenge-1/lc/ulwdc1_040_Z087.txt \n", "6 data-challenge-1/lc/ulwdc1_047_Z087.txt \n", "7 data-challenge-1/lc/ulwdc1_050_Z087.txt \n", "8 data-challenge-1/lc/ulwdc1_053_Z087.txt \n", "9 data-challenge-1/lc/ulwdc1_062_Z087.txt \n", "10 data-challenge-1/lc/ulwdc1_066_Z087.txt \n", "11 data-challenge-1/lc/ulwdc1_069_Z087.txt \n", "12 data-challenge-1/lc/ulwdc1_074_Z087.txt \n", "13 data-challenge-1/lc/ulwdc1_078_Z087.txt \n", "14 data-challenge-1/lc/ulwdc1_081_Z087.txt \n", "15 data-challenge-1/lc/ulwdc1_092_Z087.txt \n", "16 data-challenge-1/lc/ulwdc1_095_Z087.txt \n", "17 data-challenge-1/lc/ulwdc1_099_Z087.txt \n", "18 data-challenge-1/lc/ulwdc1_100_Z087.txt \n", "19 data-challenge-1/lc/ulwdc1_103_Z087.txt \n", "20 data-challenge-1/lc/ulwdc1_107_Z087.txt \n", "21 data-challenge-1/lc/ulwdc1_124_Z087.txt \n", "22 data-challenge-1/lc/ulwdc1_128_Z087.txt \n", "23 data-challenge-1/lc/ulwdc1_131_Z087.txt \n", "24 data-challenge-1/lc/ulwdc1_139_Z087.txt \n", "25 data-challenge-1/lc/ulwdc1_152_Z087.txt \n", "26 data-challenge-1/lc/ulwdc1_163_Z087.txt \n", "27 data-challenge-1/lc/ulwdc1_186_Z087.txt \n", "28 data-challenge-1/lc/ulwdc1_193_Z087.txt \n", "29 data-challenge-1/lc/ulwdc1_194_Z087.txt \n", "30 data-challenge-1/lc/ulwdc1_199_Z087.txt \n", "31 data-challenge-1/lc/ulwdc1_208_Z087.txt \n", "32 data-challenge-1/lc/ulwdc1_214_Z087.txt \n", "33 data-challenge-1/lc/ulwdc1_217_Z087.txt \n", "34 data-challenge-1/lc/ulwdc1_218_Z087.txt \n", "35 data-challenge-1/lc/ulwdc1_223_Z087.txt \n", "36 data-challenge-1/lc/ulwdc1_226_Z087.txt \n", "37 data-challenge-1/lc/ulwdc1_227_Z087.txt \n", "38 data-challenge-1/lc/ulwdc1_250_Z087.txt \n", "39 data-challenge-1/lc/ulwdc1_253_Z087.txt \n", "40 data-challenge-1/lc/ulwdc1_258_Z087.txt \n", "41 data-challenge-1/lc/ulwdc1_267_Z087.txt \n", "42 data-challenge-1/lc/ulwdc1_289_Z087.txt \n", "\n", "[43 rows x 82 columns]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merged_bl_df = pd.merge(event_info_bl, df_bl.astype({'lc_number': 'int64'}), left_on='Event_number', right_on='lc_number', how='inner')\n", "merged_bl_df" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 658 }, "id": "BbIYV9J3xh6h", "outputId": "6b63215e-677b-4022-cc07-f51c2ff6c9a2", "tags": [ "colab-only" ] }, "outputs": [], "source": [ "try: # this will only work on Colab.\n", " sl_sheet = sheets.InteractiveSheet(df=merged_sl_df)\n", " sl_sheet.show()\n", " bl_sheet = sheets.InteractiveSheet(df=merged_bl_df)\n", " bl_sheet.show()\n", "except:\n", " pass" ] }, { "cell_type": "markdown", "metadata": { "id": "h96szZeOxYyb" }, "source": [ "Great - data successfully wrangled. Let's forget we ever had to live through that and move right along." ] }, { "cell_type": "markdown", "metadata": { "id": "uJmKF1cMyzho" }, "source": [ "## Packages Covered in This Notebook\n", "\n", "
\n", "\n", "
\n", "1. MulensModel\n", "\n", "[Go to the `MulensModel` section](#mulensmodel)\n", " * [1.1 Data objects](#11-data-objects)\n", " * [1.2 Model objects](#12-model-objects)\n", " * [1.3 Event objects](#13-event-objetcs)\n", " * [1.4 Fitting](#fitting-framework)\n", " * [1.5 Higher-order effects](#model-examples)\n", " - [1S1L](#1s1l-example)\n", " - [1S2L](#1s2l-example)\n", " - [1S3L](#1s3l-example)\n", " - [2S1L](#2s1l-example)\n", " - [Parallax](#parallax-example)\n", "
\n", "
\n", "2. pyLIMA\n", "\n", "[Go to the `pyLIMA` section](#pylima)\n", " * [2.1 Installation and Setup](#pylima-installation)\n", " * [2.2 Basic Usage](#pylima-basic)\n", " * [2.3 Model Examples](#pylima-models)\n", " - [2.3.1 Single Lens (PSPL)](#pylima-pspl)\n", " - [2.3.2 Finite Source (FSPL)](#pylima-fspl)\n", " - [2.3.3 Simulation Capabilities](#pylima-simulation)\n", " * [2.4 Advanced Features](#pylima-advanced)\n", " - [2.4.1 Custom Parameter Definitions](#pylima-custom-params)\n", " - [2.4.2 Multiple Telescopes and Filters](#pylima-multiple-telescopes)\n", " * [2.5 Performance and Best Practices](#pylima-performance)\n", "
\n", "
\n", "3. RTModel\n", "\n", "[Go to the `RTModel` section](#rtmodel)\n", " * [3.1 Installation and Setup](#rtmodel-installation)\n", " * [3.2 Data Preparation](#rtmodel-data-prep)\n", " * [3.3 Basic Usage](#rtmodel-basic-usage)\n", " * [3.4 Understanding RTModel Output](#rtmodel-output)\n", " * [3.5 Model Categories](#rtmodel-model-categories)\n", " * [3.6 Visualization and Results](#rtmodel-visualization)\n", " * [3.7 Advanced Features](#rtmodel-advanced)\n", " * [3.8 RTModel vs Other Tools](#rtmodel-comparison)\n", " * [3.9 Astrophotometric Fitting (RTModel v3.0)](#rtmodel-astrometric)\n", " - [3.9.1 Astrophotometric Data Format](#rtmodel-astrometric-data)\n", " - [3.9.2 Astrophotometric Parameters](#rtmodel-astrometric-parameters)\n", " - [3.9.3 Astrophotometric Fitting](#rtmodel-astrometric-fitting)\n", " - [3.9.4 Astrometric Visualization](#rtmodel-astrometric-visualization)\n", " - [3.9.5 Applications and Use Cases](#rtmodel-astrometric-applications)\n", "
\n", "
\n", "4. popclass\n", "\n", "[Go to the `popclass` section](#popclass)\n", " * [4.1 Installation](#popclass-installation)\n", " * [4.2 Basic Usage Example](#popclass-basic-usage)\n", " * [4.3 How It Works](#popclass-theory)\n", " * [4.4 Population Models](#popclass-models)\n", " * [4.5 Advanced Features](#popclass-advanced)\n", " * [4.6 Best Practices & Caveats](#popclass-best-practices)\n", " * [4.7 Further Reading](#popclass-references)\n", "
\n", "\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": { "id": "svCZcYocGgmW" }, "source": [ "## 1. MulensModel\n", "\n", "
\n", "\n", "`MulensModel` is arguably the simplest and most well-known microlensing modelling tool. It is an extremely capable tool, having a vast array of models and higher-order effects, but the developers do not attempt to automate the model selection process or infer physical parameter, as of yet.\n", "\n", "**Documentation:**\n", "- [GitHub repo](https://github.com/rpoleski/MulensModel)\n", "- [Docs site](https://rpoleski.github.io/MulensModel)\n", "- [Article ADS link](https://ui.adsabs.harvard.edu/abs/2018ascl.soft03006P/abstract)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "O4r2waMHJOfy" }, "outputs": [], "source": [ "#@title Importing the package\n", "import MulensModel as mm" ] }, { "cell_type": "markdown", "metadata": { "id": "VuT7Llu-FtfS" }, "source": [ "### 1.1 Data Objects\n", "\n", "When fitting Roman data with `MulensModel`, we need to make a minor adjustment to our model for data that is not ground based. In `MulensModel` this simply means adding the following keyword to the data object initialization: `ephemerides_file=PATH_TO_THE_FILE`.\n", "\n", "> Instructions specific to this data set for `MulensModel` are given [here](https://github.com/rpoleski/MulensModel/blob/master/documents/data_challenge.md).\n", "\n", "Most of the data for these events is in the W147 band, so we make the very reasonable decision to just fit those data and not have to deal with mutliple data sets with different $F_\\textrm{S}$ and $F_\\textrm{B}$ values. That should speen up our fits too. If we wanted to find the color of the source star at a later date we could fit just the flux parameters and leave the microlensing-model parameters fixed (as described in [this notebook](https://amberlee2427.github.io/TheMicrolensersGuideToTheGalaxy/Solved/SingleLens.html)) using a linear regression, which would take a fraction of a second per event." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rYLdgDpqTgZ8", "outputId": "b96a1c4e-3b47-4905-f0e2-0561a46a5ca6" }, "outputs": [], "source": [ "#@title Collecting the relevent meta data\n", "event_idx = 0\n", "data_file = merged_sl_df['lc_file_path_W149'][event_idx]\n", "ra = merged_sl_df['RA_(deg)'][event_idx]\n", "dec = merged_sl_df['Dec_(deg)'][event_idx]\n", "print(ra, dec)\n", "\n", "# convert decimal ra and dec in degrees to \"17h57m16.56s -29d05m20.04s\"\n", "coord = SkyCoord(ra=ra * u.deg, dec=dec * u.deg, frame='icrs')\n", "hms_dms_string = coord.to_string('hmsdms')\n", "print(f\"SkyCoord default: {hms_dms_string}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-Q1arR-nGW4u" }, "outputs": [], "source": [ "#@title Example L2 `Data` object\n", "# Here is the main difference for space data - we provide the ephemeris for Roman:\n", "EPHEM_FILE = 'data-challenge-1/wfirst_ephemeris_W149.txt'\n", "data_Roman_W149 = mm.MulensData(file_name=data_file,\n", " phot_fmt='mag',\n", " ephemerides_file=EPHEM_FILE,\n", " plot_properties={'color': '#a859e4',\n", " 'label': 'Roman W149'\n", " },\n", " bandpass='H'\n", " )" ] }, { "cell_type": "markdown", "metadata": { "id": "X5fC2Ks5Ts0N" }, "source": [ "### 1.2 Model Objects\n", "\n", "`Model` objects store the parameters (e.g. `t_0`) being fitted and any meta-parameters (e.g. `t_0_par`; the parallax reference time) that are required. These parameters decide what kind of microlensing model and higher-order effects will be simulated (e.g. a single-lens or binary-lens event)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "_b3IdJjQT3Qw" }, "outputs": [], "source": [ "#@title Collecting the meta data\n", "# split the parallax equally out of a lack of better ideas\n", "piE_truth = float(merged_sl_df['piE'][event_idx])\n", "pi_E_E = np.sqrt(piE_truth**2 / 2.0)\n", "pi_E_N = pi_E_E * 1.0\n", "\n", "t_0 = float(merged_sl_df['t0'][event_idx])\n", "# Annoyingly, t_0 is in simulation time not HJD so we need to do a conversion\n", "# (https://github.com/microlensing-data-challenge/evaluation_code/blob/master/parse_table1.py)\n", "# line 402\n", "t_0_truth = t_0 + 2458233.5 # adding simulation 0 time - 0.5 days (I'm not sure why we need the MJD/BJD correction)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "XR9_hxDaUAuN" }, "outputs": [], "source": [ "#@title Making a dictionary of the guess parameters\n", "# Let's just tidy the \"guess\" parameters up into a dictionary, for easy accesss\n", "params = dict()\n", "parameters_to_fit = [\"t_0\", \"u_0\", \"t_E\", \"rho\", \"pi_E_N\", \"pi_E_E\"]\n", "params['t_0'] = t_0_truth\n", "params['t_0_par'] = t_0_truth\n", "u_0_truth = float(merged_sl_df['u0'][event_idx])\n", "params['u_0'] = u_0_truth\n", "t_E_truth = float(merged_sl_df['tE'][event_idx])\n", "params['t_E'] = t_E_truth * 1.1\n", "rho_truth = float(merged_sl_df['rhos'][event_idx])\n", "params['rho'] = rho_truth * 1.1\n", "params['pi_E_N'] = pi_E_E\n", "params['pi_E_E'] = pi_E_E\n", "print(params)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Sm3syBy5Sjc6" }, "outputs": [], "source": [ "#@title Example `Model` object\n", "# If we are using parallax, it is also important that we provide the event\n", "# coordinates, or MulensModel can't do necessary calculations\n", "Roman_guess_model = mm.Model({**params},\n", " coords=coord,\n", " ephemerides_file=EPHEM_FILE\n", " )" ] }, { "cell_type": "markdown", "metadata": { "id": "t1SlSsxvU1al" }, "source": [ "### 1.3 Event Objects\n", "\n", "Event objects store the model we defined earlier and the data, together." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6VKhMYSTTDok" }, "outputs": [], "source": [ "#@title Example `Event` object\n", "Roman_event = mm.Event(datasets=data_Roman_W149, model=Roman_guess_model)" ] }, { "cell_type": "markdown", "metadata": { "id": "fitting_framework" }, "source": [ "### 1.4 Fitting Framework\n", "\n", "Now we'll set up a comprehensive fitting framework that can handle different model types, so that we can compare some optimizing algorithms and demonstrate various microlensing models, later in this section." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fitting_setup", "outputId": "f409c22d-ba34-430e-c0dd-d7d5123130b1" }, "outputs": [], "source": [ "#@title Set up plotting style\n", "plt.style.use('default')\n", "plt.rcParams['figure.figsize'] = (12, 8)\n", "plt.rcParams['font.size'] = 12" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "likelihood_function" }, "outputs": [], "source": [ "#@title Define likelihood function for fitting\n", "def log_likelihood(theta, event, parameters_to_fit):\n", " \"\"\"Log likelihood function for emcee\"\"\"\n", "\n", " penalty = 0.0\n", "\n", " # --- soft priors / sanity bounds ---\n", " log_rho = None\n", " if 'logrho' in parameters_to_fit:\n", " rho_index = parameters_to_fit.index('logrho')\n", " log_rho = theta[rho_index]\n", " elif 'log_rho' in parameters_to_fit:\n", " rho_index = parameters_to_fit.index('log_rho')\n", " log_rho = theta[rho_index]\n", " elif 'rho' in parameters_to_fit:\n", " rho_index = parameters_to_fit.index('rho')\n", " if theta[rho_index] <= 0.0:\n", " return -np.inf\n", " log_rho = np.log10(theta[rho_index])\n", "\n", " if log_rho is not None and log_rho > 0.0:\n", " penalty += (log_rho / 0.5) ** 2 # Penalize unphysically large rho\n", "\n", " # robust t_E extraction (handles logt_E naming variants)\n", " tE = None\n", " if 'log_t_E' in parameters_to_fit:\n", " tE = 10**theta[parameters_to_fit.index('log_t_E')]\n", " elif 'logt_E' in parameters_to_fit:\n", " tE = 10**theta[parameters_to_fit.index('logt_E')]\n", " elif 'log_tE' in parameters_to_fit:\n", " tE = 10**theta[parameters_to_fit.index('log_tE')]\n", " elif 'logtE' in parameters_to_fit:\n", " tE = 10**theta[parameters_to_fit.index('logtE')]\n", " elif 't_E' in parameters_to_fit:\n", " tE = theta[parameters_to_fit.index('t_E')]\n", " elif 'tE' in parameters_to_fit:\n", " tE = theta[parameters_to_fit.index('tE')]\n", " else:\n", " # not being fit; use current model value\n", " tE = float(event.model.parameters.t_E)\n", "\n", " if tE <= 0.0 or not np.isfinite(tE):\n", " return -np.inf # hard bound on non-physical tE\n", "\n", " # robust u0 extraction\n", " if 'u_0' in parameters_to_fit:\n", " u0 = theta[parameters_to_fit.index('u_0')]\n", " elif 'u0' in parameters_to_fit:\n", " u0 = theta[parameters_to_fit.index('u0')]\n", " else:\n", " u0 = float(event.model.parameters.u_0)\n", "\n", " if abs(u0) > 1.0:\n", " penalty += (u0 / 3.0) ** 2 # Penalize unphysically large u_0\n", "\n", " # q bounds (only when binary parameters are part of fit)\n", " q = None\n", " if 'logq' in parameters_to_fit:\n", " q = 10**theta[parameters_to_fit.index('logq')]\n", " elif 'log_q' in parameters_to_fit:\n", " q = 10**theta[parameters_to_fit.index('log_q')]\n", " elif 'q' in parameters_to_fit:\n", " q = theta[parameters_to_fit.index('q')]\n", " if q is not None:\n", " if q <= 0.0:\n", " return -np.inf # hard bound on negative q\n", " if q > 1.0:\n", " return -np.inf # hard bound on q > 1\n", "\n", " # Update model parameters (all logs are base 10)\n", " for i, param_name in enumerate(parameters_to_fit):\n", " if param_name == 'log_rho' or param_name == 'logrho':\n", " event.model.parameters.rho = 10**theta[i]\n", " elif param_name == 'log_t_E' or param_name == 'logt_E' or param_name == 'logtE' or param_name == 'log_tE':\n", " event.model.parameters.t_E = 10**theta[i]\n", " elif param_name == 'logs' or param_name == 'log_s' or param_name == 'logs':\n", " event.model.parameters.s = 10**theta[i]\n", " elif param_name == 'logq' or param_name == 'log_q' or param_name == 'logq':\n", " event.model.parameters.q = 10**theta[i]\n", " else:\n", " setattr(event.model.parameters, param_name, theta[i])\n", "\n", " # Fit the fluxes given the current model parameters\n", " event.fit_fluxes()\n", "\n", " # Get the source and blend fluxes\n", " ([F_S], F_B) = event.get_flux_for_dataset(event.datasets[0])\n", "\n", " # Calculate chi-squared\n", " chi2 = event.get_chi2()\n", "\n", " # Add flux priors if needed (optional)\n", " if F_B <= 0:\n", " penalty += ((F_B / 10) ** 2) # Penalize negative blend flux\n", " if F_S <= 0 or (F_S + F_B) <= 0:\n", " return -np.inf # Return inf if fluxes are non-physical\n", "\n", " return -0.5 * chi2 - penalty\n", "\n", "def neg_log_likelihood(params_array, event, parameters_to_fit):\n", " \"\"\"Negative log likelihood function for scipy.optimize.minimize\"\"\"\n", "\n", " return -log_likelihood(params_array, event, parameters_to_fit)\n", "\n", "def params2flux(theta, t, event, parameters_to_fit):\n", " \"\"\"Helper function to convert parameters to fluxes for curve_fit\"\"\"\n", " for i, param_name in enumerate(parameters_to_fit):\n", " if param_name == 'log_rho' or param_name == 'logrho':\n", " # Convert log_rho back to rho\n", " event.model.parameters.rho = 10**theta[i]\n", " elif param_name == 'log_t_E' or param_name == 'logt_E' or param_name == 'logtE' or param_name == 'log_tE':\n", " event.model.parameters.t_E = 10**(theta[i])\n", " elif param_name == 'tE' or param_name == 't_E':\n", " event.model.parameters.t_E = np.abs(theta[i])\n", " elif param_name == 'logs' or param_name == 'log_s' or param_name == 'logs':\n", " event.model.parameters.s = 10**theta[i]\n", " elif param_name == 's':\n", " event.model.parameters.s = np.abs(theta[i])\n", " elif param_name == 'logq' or param_name == 'log_q' or param_name == 'logq':\n", " event.model.parameters.q = 10**theta[i]\n", " elif param_name == 'q':\n", " if theta[i] <= 0.0:\n", " event.model.parameters.q = np.abs(theta[i]) + 1e-20 # tricky little bound on negative q\n", " if theta[i] > 1.0:\n", " event.model.parameters.q = 1.0/theta[i] # tricky little bound on q > 1\n", " else:\n", " setattr(event.model.parameters, param_name, theta[i])\n", " ([F_S], F_B) = event.get_flux_for_dataset(event.datasets[0])\n", "\n", " return event.model.get_magnification(t) * F_S + F_B\n", "\n", "def finite_difference_hessian(func, x, step_scale=1e-4):\n", " x = np.asarray(x, dtype=float)\n", " n = x.size\n", " h = step_scale * np.maximum(1.0, np.abs(x))\n", " H = np.zeros((n, n), dtype=float)\n", " f0 = func(x)\n", "\n", " for i in range(n):\n", " ei = np.zeros(n); ei[i] = h[i]\n", " f_ip = func(x + ei)\n", " f_im = func(x - ei)\n", " H[i, i] = (f_ip - 2.0 * f0 + f_im) / (h[i] ** 2)\n", "\n", " for j in range(i + 1, n):\n", " ej = np.zeros(n); ej[j] = h[j]\n", " f_pp = func(x + ei + ej)\n", " f_pm = func(x + ei - ej)\n", " f_mp = func(x - ei + ej)\n", " f_mm = func(x - ei - ej)\n", " Hij = (f_pp - f_pm - f_mp + f_mm) / (4.0 * h[i] * h[j])\n", " H[i, j] = Hij\n", " H[j, i] = Hij\n", "\n", " return H\n", "\n", "def run_mcmc_fit(event, parameters_to_fit, initial_guess, nwalkers=32, nsteps=1000, n_cores=None):\n", " ndim = len(parameters_to_fit)\n", " pos = np.array(initial_guess) + 1e-4 * np.random.randn(nwalkers, ndim)\n", "\n", " if n_cores is None:\n", " n_cores = min(nwalkers, os.cpu_count() or 1)\n", " else:\n", " n_cores = min(n_cores, nwalkers)\n", "\n", " print(f\"Using {n_cores} worker processes for MCMC\")\n", "\n", " with Pool(nodes=n_cores) as pool: # pathos ProcessingPool -> nodes, not processes\n", " sampler = emcee.EnsembleSampler(\n", " nwalkers, ndim, log_likelihood,\n", " args=(event, parameters_to_fit),\n", " pool=pool\n", " )\n", " sampler.run_mcmc(pos, nsteps, progress=True)\n", "\n", " return sampler" ] }, { "cell_type": "markdown", "metadata": { "id": "model_examples" }, "source": [ "### 1.5 Model Examples\n", "\n", "Now let's demonstrate different microlensing models. We'll start with simple models and work our way up to more complex ones." ] }, { "cell_type": "markdown", "metadata": { "id": "1s1l_example" }, "source": [ "#### 1.5.1 Single Lens Single Source (1S1L) with Finite Source Effects\n", "\n", "Let's jump straight in to simple single lens model that includes the finite source effect (ρ parameter) and parallax. These are important higher-order effects for breaking the microlensing mass-distance degeneracy, so it is highly useful to know how to include them in your models. Functionally, the finite source effect smooths over sharp features in the light-curve and parallax changes the apparent trajectory of the source (relative to the lens) as the observer moves around the solar-system. In practice, parallax is oftem not well constrained from a single observer over typical event timescales. \n", "\n", "The finite source effect adds an additional parameter to the generative model, usualy called ρ (`rho`). This is the size of the source star in units of Einstein angle (θᴇ).\n", "\n", "Parallax adds two new parameter to the generative model (for `MulensModel` these are the north and east components of the Einstsein parallax, πᴇ; `pi_E_N`, `pi_E_E`) and one meta-parameter that defines the inertial frame (`t_0_par`).\n", "\n", "> The example data we are using has only the Einstein parallax magnitude, and not it's direction, so we will make poorly informed guesses at how to split these components. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1s1l_setup", "outputId": "8000741f-ccf9-47cb-a23d-5e4d7c48106f" }, "outputs": [], "source": [ "#@title 1S1L Model Setup\n", "# Select a single lens event for demonstration\n", "gamma = float(merged_sl_df['gamma'][event_idx])\n", "\n", "print(f\"Event {event_idx}: Truth parameters\")\n", "print(f\" t_0: {t_0_truth:.3f}\")\n", "print(f\" u_0: {u_0_truth:.4f}\")\n", "print(f\" tᴇ: {t_E_truth:.2f} days\")\n", "print(f\" ρ: {rho_truth:.6f}\")\n", "print(f\" πᴇ: {piE_truth:.2f} mas\")\n", "print(f\" Ξ“: {gamma:.4f}\")\n", "\n", "# Finite source effects parameter:\n", "# - rho: angular source radius normalized to the Einstein radius (ΞΈ_*/ΞΈ_E)\n", "\n", "# Parallax parameters:\n", "# - pi_E_N: North component of parallax\n", "# - pi_E_E: East component of parallax\n", "# - t_0_par: reference time for parallax (usually same as t_0). This is the\n", "# time at which parallax does not purturb the relative source trajectory." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1s1l_data" }, "outputs": [], "source": [ "#@title Load data for 1S1L example (teaching-first version)\n", "# Optional controls\n", "FIT_WINDOW_IN_TE = 3.0 # fit only data within t0 Β± FIT_WINDOW_IN_TE * tE (it's handy to have a good guess for tE)\n", "USE_CROPPED_DATA = True # set False to use full light curve (slower)\n", "\n", "# 1) Load data\n", "data_1s1l = data_Roman_W149.copy()\n", "\n", "# 2) Initial parameter guess\n", "params_1s1l = {} # Model object requires linear parameters\n", "log_params_1s1l = {} # but we will be sampling in log space for t_E and rho\n", "parameters_to_fit_1s1l = [\"t_0\", \"u_0\", \"log_t_E\", \"log_rho\", \"pi_E_N\", \"pi_E_E\"]\n", "for param_name in parameters_to_fit_1s1l:\n", " linear_param_name = param_name.replace('log_', '').replace('log','')\n", " if linear_param_name == param_name:\n", " params_1s1l[linear_param_name] = params[linear_param_name]\n", " log_params_1s1l[param_name] = params[linear_param_name]\n", " else:\n", " params_1s1l[linear_param_name] = params[linear_param_name]\n", " log_params_1s1l[param_name] = np.log10(params[linear_param_name])\n", "print(log_params_1s1l)\n", "print(params_1s1l)\n", "\n", "# 3) Build model and resolve warnings\n", "model_1s1l = mm.Model(params_1s1l, coords=coord, ephemerides_file=EPHEM_FILE)\n", "model_1s1l.set_limb_coeff_gamma('H', float(gamma)) # resolves H-band limb-darkening warning\n", "\n", "# Use finite-source only near peak; point-source outside (faster)\n", "rho = max(rho_truth, 1e-6)\n", "dt_fs = max(0.5, 5.0 * rho * t_E_truth)\n", "fs_method = 'finite_source_uniform_Gould94' if abs(float(gamma)) < 1e-8 else 'finite_source_LD_Yoo04'\n", "if USE_CROPPED_DATA:\n", " t_min, t_max = t_0_truth - FIT_WINDOW_IN_TE * t_E_truth, t_0_truth + FIT_WINDOW_IN_TE * t_E_truth\n", "else:\n", " t_min = np.min(data_1s1l.time) - 5.0\n", " t_max = np.max(data_1s1l.time) + 5.0\n", "# https://rpoleski.github.io/MulensModel/MulensModel.magnificationcurve.html#MulensModel.magnificationcurve.MagnificationCurve.get_point_lens_magnification\n", "model_1s1l.set_magnification_methods([\n", " t_min, \n", " 'point_source', \n", " t_0_truth - dt_fs, \n", " fs_method, \n", " t_0_truth + dt_fs, \n", " 'point_source',\n", " t_max\n", "])\n", "\n", "# 4) Build event\n", "event_1s1l = mm.Event(datasets=data_1s1l, model=model_1s1l)\n", "\n", "# 5) Optional time-window crop for speed\n", "if USE_CROPPED_DATA:\n", " m = (data_1s1l.time >= t_min) & (data_1s1l.time <= t_max)\n", " data_1s1l_fit = mm.MulensData(\n", " data_list=[data_1s1l.time[m], data_1s1l.mag[m], data_1s1l.err_mag[m]],\n", " phot_fmt='mag',\n", " ephemerides_file=EPHEM_FILE,\n", " plot_properties={'color': '#a859e4', 'label': 'Roman W149 (cropped)'},\n", " bandpass='H',\n", " )\n", " event_1s1l = mm.Event(datasets=data_1s1l_fit, model=model_1s1l)\n", " print(f'Using cropped data: {m.sum()} points in [{t_min:.2f}, {t_max:.2f}]')\n", "else:\n", " print(f'Using full data: {len(data_1s1l.time)} points')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 573 }, "id": "U9Q_NwtfA6f1", "outputId": "8f7213eb-6919-43b4-83e2-6cc2e026b025" }, "outputs": [], "source": [ "# Plot the initial model\n", "plt.figure(figsize=(12, 6))\n", "event_1s1l.plot_data()\n", "# Get the source and blend fluxes\n", "#event_1s1l.fit_fluxes()\n", "#([F_S], F_B) = event_1s1l.get_flux_for_dataset(event_1s1l.datasets[0])\n", "#event_1s1l.model.plot_lc(source_flux=F_S, blend_flux=F_B, color='black', label='Initial Model')\n", "event_1s1l.plot_model(color='black', label='Initial Model')\n", "plt.title(f\"1S1L Event {event_idx} - Initial Model\")\n", "plt.xlabel(\"BJD\")\n", "plt.ylabel(\"Magnitude\")\n", "plt.xlim(2459810.0, 2459822.0)\n", "plt.legend()\n", "plt.show()\n", "#plt.savefig('1s1l_initial.png')\n", "print(event_1s1l.model.parameters)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 360 }, "id": "1s1l_fit", "outputId": "a84d255e-188a-4f7b-c31a-d1d3e8cf6cf9" }, "outputs": [], "source": [ "#@title Fit 1S1L model with MCMC\n", "# Set up parameters to fit\n", "initial_guess_1s1l = list(log_params_1s1l.values())\n", "print(initial_guess_1s1l)\n", "truths_transformed = [\n", " t_0_truth,\n", " u_0_truth,\n", " t_E_truth,\n", " np.log10(rho_truth),\n", " np.sqrt(piE_truth**2/2.0),\n", " np.sqrt(piE_truth**2/2.0)\n", "]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#@title Sanity Check\n", "print(\"event_idx:\", event_idx)\n", "print(\"data_file:\", data_file)\n", "print(\"time span:\", float(np.min(data_1s1l.time)), float(np.max(data_1s1l.time)))\n", "print(\"t0 truth/model:\", t_0_truth, params_1s1l[\"t_0\"])\n", "print(\"u0 truth/model:\", u_0_truth, params_1s1l[\"u_0\"])\n", "print(\"tE truth/model:\", t_E_truth, params_1s1l[\"t_E\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### A Short Aside to Compare Optimizers\n", "\n", "You typically find that the go to optimization method when fitting microlensing events is MCMC, but this isn't aways the ideal choice. MCMC is extremely robust and able to convey complex poterior covariences, but there are times when it is overkill." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(\"Starting 1S1L MCMC fit...\")\n", "start_time = time.time()\n", "\n", "# Run MCMC\n", "ndim_1s1l = len(parameters_to_fit_1s1l)\n", "sampler_1s1l = run_mcmc_fit(event_1s1l, parameters_to_fit_1s1l, initial_guess_1s1l,\n", " nwalkers=32, nsteps=500)\n", "# burnin must be less than nsteps, because n posterior samples is nsteps - burnin\n", "burnin_1s1l = 300\n", "\n", "end_time = time.time()\n", "print(f\"\\n1S1L MCMC fit completed in {(end_time - start_time)/60:.2f} minutes\")\n", "\n", "print(\"\\nResults:\")\n", "# Get results\n", "samples_1s1l = sampler_1s1l.chain[:, burnin_1s1l:, :].reshape((-1, ndim_1s1l))\n", "best_fit_1s1l = np.median(samples_1s1l, axis=0)\n", "uncertainties_1s1l = np.std(samples_1s1l, axis=0)\n", "mcmc_linear_sol = {}\n", "for i, param in enumerate(parameters_to_fit_1s1l):\n", " print(f\" {param}: {best_fit_1s1l[i]:.6f} Β± {uncertainties_1s1l[i]:.6f}\")\n", "# Compare with truth\n", "print(\"\\nComparison with truth:\")\n", "for i, param in enumerate(parameters_to_fit_1s1l):\n", " diff = best_fit_1s1l[i] - truths_transformed[i]\n", " sigma_diff = abs(diff) / uncertainties_1s1l[i]\n", " print(f\" {param}: {diff:.6f} ({sigma_diff:.2f}Οƒ)\")\n", " # Convert best fit parameters back to physical units for plotting\n", " if 'log' in param:\n", " physical_value = 10**best_fit_1s1l[i]\n", " label = param.replace('log_', '').replace('log', '')\n", " else:\n", " physical_value = best_fit_1s1l[i]\n", " label = param\n", " mcmc_linear_sol[label] = physical_value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This isn't enough samples for a well-sampled posterior distribution as we will see later, but this is just a demonstration, so we'll keep it short to save time. Ideally you would keep sampling until your walkers are no longer exploring new areas of parameter space and (have settled into a χ² trough) and are mixing well. You would throw every sample before this point away and sample the posterior distribution of this minima you have discovered until your corner plot histograms look smooth.\n", "\n", "If you are looking for speed, you could consider strategically binning your data, truncating it, or trying-out a different optimizing algorithm like Nelder-Mead using `scipy.optimize.minimize`, which will be much faster for a simple 1s1l fit." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(\"Starting 1S1L Nelder-Mead fit...\")\n", "start_time = time.time()\n", "\n", "# Nelder-Mead can need many evaluations; increase limits and tighten tolerances\n", "nm_options = {\n", " 'maxiter': 4000,\n", " 'maxfev': 12000,\n", " 'xatol': 1e-5,\n", " 'fatol': 1e-4,\n", " 'adaptive': True,\n", "}\n", "\n", "with warnings.catch_warnings(): # catch noisy warnings when trial points are out of range\n", " warnings.filterwarnings(\"ignore\", category=ErfaWarning)\n", " result = minimize(\n", " neg_log_likelihood,\n", " initial_guess_1s1l,\n", " args=(event_1s1l, parameters_to_fit_1s1l),\n", " method='Nelder-Mead',\n", " options=nm_options,\n", " )\n", "\n", "nm_res = result.x\n", "print(result)\n", "if not result.success:\n", " print(\"\\n⚠️ Nelder-Mead did not fully converge; uncertainties may be unreliable.\")\n", "\n", "end_time = time.time()\n", "print(f\"\\n1S1L Nelder-Mead fit completed in {(end_time - start_time)/60:.2f} minutes\")\n", "\n", "# But what about parameter uncertainties? we can numerically estimate the Hessian at the best fit point \n", "# to get an estimate of the covariance matrix\n", "\n", "# Estimate covariance from a numerical Hessian of the scalar objective at the best fit\n", "print(\"Calculating parameter uncertainties from numerical Hessian...\")\n", "start_time = time.time()\n", "\n", "def objective(theta):\n", " return neg_log_likelihood(theta, event_1s1l, parameters_to_fit_1s1l)\n", "\n", "hessian = finite_difference_hessian(objective, nm_res)\n", "\n", "# Diagnose ill-conditioning (flat/degenerate directions -> unreliable uncertainties)\n", "hessian_cond = np.linalg.cond(hessian)\n", "print(f\"Hessian condition number: {hessian_cond:.2e}\")\n", "if (not np.isfinite(hessian_cond)) or (hessian_cond > 1e12):\n", " print(\"⚠️ Hessian is ill-conditioned; some 1Οƒ errors are not trustworthy.\")\n", "\n", "# Use pseudo-inverse for numerical stability\n", "cov_matrix = np.linalg.pinv(hessian)\n", "diag = np.diag(cov_matrix).astype(float)\n", "\n", "# Mark non-physical/unstable variances as NaN instead of forcing 0\n", "diag[~np.isfinite(diag)] = np.nan\n", "diag[diag <= 0] = np.nan\n", "perr = np.sqrt(diag)\n", "\n", "end_time = time.time()\n", "print(f\"Parameter uncertainty calculation completed in {(end_time - start_time)/60:.2f} minutes\")\n", "\n", "print(\"\\nResults:\")\n", "nm_linear_sol = {}\n", "for i, param in enumerate(parameters_to_fit_1s1l):\n", " err_txt = f\"{perr[i]:.6f}\" if np.isfinite(perr[i]) else \"nan\"\n", " print(f\" {param}: {nm_res[i]:.6f} Β± {err_txt}\")\n", "\n", "print(\"\\nComparison with truth:\")\n", "linear_nm_res = nm_res.copy()\n", "for i, param in enumerate(parameters_to_fit_1s1l):\n", " diff = nm_res[i] - truths_transformed[i]\n", " if np.isfinite(perr[i]) and perr[i] > 0:\n", " sigma_diff = abs(diff) / perr[i]\n", " sigma_txt = f\"{sigma_diff:.2f}Οƒ\"\n", " else:\n", " sigma_txt = \"n/a (undefined uncertainty)\"\n", " print(f\" {param}: {diff:.6f} ({sigma_txt})\")\n", " # Convert best fit parameters back to physical units for plotting\n", " if 'log' in param:\n", " physical_value = 10**nm_res[i]\n", " label = param.replace('log_', '').replace('log', '')\n", " else:\n", " physical_value = nm_res[i]\n", " label = param\n", " nm_linear_sol[label] = physical_value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another option to consider are gradient descent menthods such as `scipy.optimize.curve_fit`. For 1s1l fits, this methods is ideal because you get parameter uncertainties for free from the covariance matrix, and you don't have to worry about convergence or burn-in. However constraining the fit get's a little tricky, and it can be sensitive to initial guesses, but it's very fast for simple models like 1s1l.\n", "\n", "> Worth noting are the `Event.get_chi2_gradient(parameters_to_fit)` and `Event.calculate_chi2_gradient()` functions which could be of use for graident descent optimizers. Make sure to set the model parameters before calling. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(\"Starting 1S1L curve-fit...\")\n", "start_time = time.time()\n", "\n", "# Use the same dataset currently attached to event_1s1l (cropped or full)\n", "fit_data = event_1s1l.datasets[0]\n", "x_fit = fit_data.time\n", "y_fit = fit_data.flux\n", "sigma_fit = fit_data.err_flux.copy()\n", "\n", "# Guard against bad/zero uncertainties\n", "good_sigma = np.isfinite(sigma_fit) & (sigma_fit > 0)\n", "if not np.any(good_sigma):\n", " raise ValueError(\"No valid flux uncertainties available for curve_fit.\")\n", "sigma_floor = np.nanmedian(sigma_fit[good_sigma])\n", "sigma_fit[~good_sigma] = sigma_floor\n", "\n", "# Build conservative bounds to prevent unphysical trial points\n", "lb, ub = [], []\n", "for p in parameters_to_fit_1s1l:\n", " if p == 't_0':\n", " lb.append(t_0_truth - 20.0); ub.append(t_0_truth + 20.0)\n", " elif p in ('u_0', 'u0'):\n", " lb.append(-2.0); ub.append(2.0)\n", " elif p in ('t_E', 'tE'):\n", " lb.append(0.05); ub.append(500.0)\n", " elif p in ('log_t_E', 'log_tE', 'logt_E', 'logtE'):\n", " lb.append(0.05); ub.append(500.0)\n", " elif p in ('logrho', 'log_rho'):\n", " lb.append(-7.0); ub.append(-0.3)\n", " elif p == 'rho':\n", " lb.append(1e-7); ub.append(0.5)\n", " elif p in ('pi_E_E', 'pi_E_N'):\n", " lb.append(-5.0); ub.append(5.0)\n", " else:\n", " lb.append(-np.inf); ub.append(np.inf)\n", "lb = np.array(lb, dtype=float)\n", "ub = np.array(ub, dtype=float)\n", "\n", "# Ensure p0 is strictly inside bounds for TRF\n", "p0 = np.array(initial_guess_1s1l, dtype=float)\n", "eps = 1e-10\n", "finite_lb = np.isfinite(lb)\n", "finite_ub = np.isfinite(ub)\n", "p0[finite_lb] = np.maximum(p0[finite_lb], lb[finite_lb] + eps)\n", "p0[finite_ub] = np.minimum(p0[finite_ub], ub[finite_ub] - eps)\n", "\n", "# curve_fit expects: f(x, *params)\n", "def model_flux(t, *theta):\n", " try:\n", " flux = params2flux(theta, t, event_1s1l, parameters_to_fit_1s1l)\n", " if not np.all(np.isfinite(flux)):\n", " raise ValueError(\"non-finite model flux\")\n", " return flux\n", " except Exception:\n", " # Return large residuals instead of crashing optimizer\n", " return np.full_like(t, 1e12, dtype=float)\n", "\n", "popt, pcov = curve_fit(\n", " model_flux,\n", " x_fit,\n", " y_fit,\n", " p0=p0,\n", " sigma=sigma_fit,\n", " absolute_sigma=True,\n", " bounds=(lb, ub),\n", " method='trf',\n", " maxfev=50000,\n", " check_finite=False,\n", " nan_policy='omit',\n", ")\n", "\n", "end_time = time.time()\n", "print(f\"1S1L curve-fit completed in {(end_time - start_time):.2f} seconds\")\n", "\n", "print(\"\\nResults:\")\n", "cf_linear_sol = {}\n", "for i, param in enumerate(parameters_to_fit_1s1l):\n", " err_i = np.sqrt(pcov[i, i]) if np.isfinite(pcov[i, i]) and pcov[i, i] >= 0 else np.nan\n", " print(f\" {param}: {popt[i]:.6f} Β± {err_i:.6f}\")\n", "\n", "print(\"\\nComparison with truth:\")\n", "for i, param in enumerate(parameters_to_fit_1s1l):\n", " diff = popt[i] - truths_transformed[i]\n", " err_i = np.sqrt(pcov[i, i]) if np.isfinite(pcov[i, i]) and pcov[i, i] >= 0 else np.nan\n", " sigma_txt = f\"{abs(diff)/err_i:.2f}Οƒ\" if np.isfinite(err_i) and err_i > 0 else \"n/a\"\n", " print(f\" {param}: {diff:.6f} ({sigma_txt})\")\n", " # Convert best fit parameters back to physical units for plotting\n", " if 'log' in param:\n", " physical_value = 10**popt[i]\n", " label = param.replace('log_', '').replace('log', '')\n", " else:\n", " physical_value = popt[i]\n", " label = param\n", " cf_linear_sol[label] = physical_value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using these simpler methods for binary-lens fits can be a terrible idea, because the chi-squared surface is very complex and full of local minima; MCMC is a safer choice for binary lens fits." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1s1l_results" }, "outputs": [], "source": [ "#@title Plot 1S1L results (MCMC vs Nelder-Mead vs curve_fit)\n", "# Build physical-parameter dictionaries for each method\n", "mcmc_linear_sol = {}\n", "for i, p in enumerate(parameters_to_fit_1s1l):\n", " key = p.replace('log_', '').replace('log', '') if 'log' in p else p\n", " val = 10.0**best_fit_1s1l[i] if 'log' in p else best_fit_1s1l[i]\n", " mcmc_linear_sol[key] = val\n", "\n", "# nm_linear_sol and cf_linear_sol are created in previous cells\n", "print('MCMC:', mcmc_linear_sol)\n", "print('Nelder-Mead:', nm_linear_sol)\n", "print('curve_fit:', cf_linear_sol)\n", "\n", "def apply_solution_to_model(event, sol_dict):\n", " \"\"\"Apply a solution dictionary to MulensModel parameters with simple alias handling.\"\"\"\n", " alias = {\n", " 'tE': 't_E',\n", " 'u0': 'u_0',\n", " 't0': 't_0',\n", " 'piE_E': 'pi_E_E',\n", " 'piE_N': 'pi_E_N',\n", " }\n", " for k, v in sol_dict.items():\n", " pname = alias.get(k, k)\n", " if hasattr(event.model.parameters, pname):\n", " setattr(event.model.parameters, pname, float(v))\n", " event.fit_fluxes()\n", "\n", "def plot_1s1l_model_comparison(event, xlim=None):\n", " plt.figure(figsize=(12, 6))\n", " event.plot_data(zorder=0)\n", "\n", " # MCMC\n", " apply_solution_to_model(event, mcmc_linear_sol)\n", " event.plot_model(color='red', label='MCMC best fit', zorder=2)\n", "\n", " # Nelder-Mead\n", " apply_solution_to_model(event, nm_linear_sol)\n", " event.plot_model(color='royalblue', linestyle='--', label='Nelder-Mead', zorder=3)\n", "\n", " # curve_fit\n", " apply_solution_to_model(event, cf_linear_sol)\n", " event.plot_model(color='seagreen', linestyle=':', linewidth=2, label='curve_fit', zorder=4)\n", "\n", " plt.title('1S1L Synthetic Event - Model Comparison')\n", " plt.xlabel('HJD')\n", " plt.ylabel('Magnitude')\n", " if xlim is not None:\n", " plt.xlim(*xlim)\n", " plt.legend()\n", " plt.show()\n", "\n", "def solution_to_fit_vector(sol_dict, param_names):\n", " \"\"\"Convert physical-parameter solution dict into the parameterization used in corner plot.\"\"\"\n", " alias = {\n", " 'tE': 't_E',\n", " 'u0': 'u_0',\n", " 't0': 't_0',\n", " 'piE_E': 'pi_E_E',\n", " 'piE_N': 'pi_E_N',\n", " }\n", " inv_alias = {v: k for k, v in alias.items()}\n", " vec = []\n", " for p in param_names:\n", " base = p.replace('log_', '').replace('log', '') if 'log' in p else p\n", " candidates = [base, inv_alias.get(base, None)]\n", " value = None\n", " for c in candidates:\n", " if c is not None and c in sol_dict:\n", " value = sol_dict[c]\n", " break\n", " if value is None:\n", " vec.append(np.nan)\n", " continue\n", " vec.append(np.log10(value) if 'log' in p else value)\n", " return np.array(vec, dtype=float)\n", "\n", "# Full range\n", "plot_1s1l_model_comparison(event_1s1l)\n", "\n", "# Zoomed range\n", "plot_1s1l_model_comparison(event_1s1l, xlim=(2459810.0, 2459822.0))\n", "\n", "# Corner plot (MCMC samples) + annotate best-fit points from all 3 methods\n", "fig = corner.corner(\n", " samples_1s1l,\n", " labels=parameters_to_fit_1s1l,\n", " truths=truths_transformed,\n", " quantiles=[0.16, 0.5, 0.84],\n", ")\n", "\n", "mcmc_vec = solution_to_fit_vector(mcmc_linear_sol, parameters_to_fit_1s1l)\n", "nm_vec = solution_to_fit_vector(nm_linear_sol, parameters_to_fit_1s1l)\n", "cf_vec = solution_to_fit_vector(cf_linear_sol, parameters_to_fit_1s1l)\n", "\n", "if np.all(np.isfinite(mcmc_vec)):\n", " corner.overplot_lines(fig, mcmc_vec, color='red', linewidth=1.5)\n", " corner.overplot_points(fig, mcmc_vec[None, :], color='red', marker='o', ms=4)\n", "if np.all(np.isfinite(nm_vec)):\n", " corner.overplot_lines(fig, nm_vec, color='royalblue', linewidth=1.5)\n", " corner.overplot_points(fig, nm_vec[None, :], color='royalblue', marker='s', ms=4)\n", "if np.all(np.isfinite(cf_vec)):\n", " corner.overplot_lines(fig, cf_vec, color='seagreen', linewidth=1.5)\n", " corner.overplot_points(fig, cf_vec[None, :], color='seagreen', marker='^', ms=4)\n", "\n", "legend_handles = [\n", " plt.Line2D([0], [0], color='red', marker='o', lw=1.5, label='MCMC'),\n", " plt.Line2D([0], [0], color='royalblue', marker='s', lw=1.5, label='Nelder-Mead'),\n", " plt.Line2D([0], [0], color='seagreen', marker='^', lw=1.5, label='curve_fit'),\n", "]\n", "fig.axes[0].legend(handles=legend_handles, loc='upper right', frameon=True)\n", "\n", "plt.suptitle('1S1L Event - Parameter Posteriors', y=1.02)\n", "plt.show()\n", "\n", "# Plot updated caustic structure using MCMC best fit\n", "apply_solution_to_model(event_1s1l, mcmc_linear_sol)\n", "plt.figure(figsize=(8, 8))\n", "event_1s1l.model.plot_caustics()\n", "event_1s1l.model.plot_trajectory()\n", "plt.title('1S1L Event - Best Fit Caustic Structure (MCMC)')\n", "plt.xlabel('x (Einstein radii)')\n", "plt.ylabel('y (Einstein radii)')\n", "plt.axis('equal')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "1s2l_example" }, "source": [ "#### 1.5.2 Single Source Binary Lens (1S2L)\n", "\n", "Now let's demonstrate a binary lens model. We'll use a simulated binary lens event to show how to fit for the additional parameters; `s`, `q`, and `alpha`.We will continue to include finite source and parallax effects." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1s2l_setup" }, "outputs": [ { "ename": "NameError", "evalue": "name 'merged_bl_df' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[1], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m#@title Load metadata for 1S2L example\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# For this example, we'll use a simulated binary lens event\u001b[39;00m\n\u001b[1;32m 3\u001b[0m event_idx_bl \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m5\u001b[39m \u001b[38;5;66;03m# Binary lens event\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m data_file_bl \u001b[38;5;241m=\u001b[39m \u001b[43mmerged_bl_df\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlc_file_path_W149\u001b[39m\u001b[38;5;124m'\u001b[39m][event_idx_bl]\n\u001b[1;32m 5\u001b[0m ra_bl \u001b[38;5;241m=\u001b[39m merged_bl_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRA_(deg)\u001b[39m\u001b[38;5;124m'\u001b[39m][event_idx_bl]\n\u001b[1;32m 6\u001b[0m dec_bl \u001b[38;5;241m=\u001b[39m merged_bl_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDec_(deg)\u001b[39m\u001b[38;5;124m'\u001b[39m][event_idx_bl]\n", "\u001b[0;31mNameError\u001b[0m: name 'merged_bl_df' is not defined" ] } ], "source": [ "#@title Load metadata for 1S2L example\n", "# For this example, we'll use a simulated binary lens event\n", "event_idx_bl = 5 # Binary lens event\n", "data_file_bl = merged_bl_df['lc_file_path_W149'][event_idx_bl]\n", "ra_bl = merged_bl_df['RA_(deg)'][event_idx_bl]\n", "dec_bl = merged_bl_df['Dec_(deg)'][event_idx_bl]\n", "gamma_bl = merged_bl_df['gamma'][event_idx_bl]\n", "coord_bl = SkyCoord(ra=ra_bl * u.deg, dec=dec_bl * u.deg, frame='icrs')\n", "\n", "# Get truth parameters for comparison\n", "# NOTE: convert challenge-relative t0 to HJD to match the time stamps in the light-curve files\n", "t_0_truth_bl = float(merged_bl_df['t0'][event_idx_bl]) + 2458233.5\n", "u_0_truth_bl = float(merged_bl_df['u0'][event_idx_bl])\n", "t_E_truth_bl = float(merged_bl_df['tE'][event_idx_bl])\n", "rho_truth_bl = float(merged_bl_df['rhos'][event_idx_bl])\n", "piE_truth_bl = float(merged_bl_df['piE'][event_idx_bl])\n", "s_truth_bl = float(merged_bl_df['s'][event_idx_bl])\n", "q_truth_bl = float(merged_bl_df['q'][event_idx_bl])\n", "alpha_truth_bl = float(merged_bl_df['alpha'][event_idx_bl])\n", "\n", "parameters_to_fit_1s2l = [\"t_0\", \"u_0\", \"logt_E\", \"logrho\", \"pi_E_N\", \"pi_E_E\", \"logs\", \"logq\", \"alpha\"]\n", "truths_transformed_1s2l = [\n", " t_0_truth_bl,\n", " u_0_truth_bl,\n", " np.log10(t_E_truth_bl),\n", " np.log10(rho_truth_bl),\n", " np.sqrt(piE_truth_bl**2/2.0),\n", " np.sqrt(piE_truth_bl**2/2.0),\n", " np.log10(s_truth_bl),\n", " np.log10(q_truth_bl),\n", " alpha_truth_bl\n", "]\n", "\n", "print(f\"Event {event_idx_bl}: Truth parameters\")\n", "print(f\" t_0: {t_0_truth_bl:.3f}\")\n", "print(f\" u_0: {u_0_truth_bl:.4f}\")\n", "print(f\" t_E: {t_E_truth_bl:.2f} days\")\n", "print(f\" ρ: {rho_truth_bl:.6f}\")\n", "print(f\" Ο€_E: {piE_truth_bl:.2f} mas\")\n", "print(f\" s: {s_truth_bl:.3f}\")\n", "print(f\" q: {q_truth_bl:.3e}\")\n", "print(f\" Ξ±: {alpha_truth_bl:.3f}\")\n", "\n", "# Create initial model with finite source effects\n", "initial_params_1s2l = {\n", " 't_0': t_0_truth_bl, # this is in essence already perturbed because the simulations define t_0 as time of closest approach to the lens host\n", " 'u_0': u_0_truth_bl * -1.0, # same for u_0. And I guess Mulensmodel and the simulations define u_0 as flipped from each other\n", " 't_E': t_E_truth_bl * 1.05,\n", " 'rho': rho_truth_bl * 0.95,\n", " 'pi_E_E': np.sqrt(piE_truth_bl**2/2.0),\n", " 'pi_E_N': np.sqrt(piE_truth_bl**2/2.0),\n", " 's': s_truth_bl * 1.05,\n", " 'q': q_truth_bl * 1.05,\n", " 'alpha': alpha_truth_bl * 1.05,\n", " 't_0_par': t_0_truth_bl\n", "}\n", "log_initial_params_1s2l = {\n", " 't_0': initial_params_1s2l['t_0'], \n", " 'u_0': initial_params_1s2l['u_0'], \n", " 'logt_E': np.log10(initial_params_1s2l['t_E']), \n", " 'logrho': np.log10(initial_params_1s2l['rho']),\n", " 'pi_E_E': initial_params_1s2l['pi_E_E'], \n", " 'pi_E_N': initial_params_1s2l['pi_E_N'], \n", " 'logs': np.log10(initial_params_1s2l['s']),\n", " 'logq': np.log10(initial_params_1s2l['q']),\n", " 'alpha': initial_params_1s2l['alpha']\n", "}\n", "initial_guess_1s2l = list(log_initial_params_1s2l.values())\n", "print(log_initial_params_1s2l)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#@title Create 1S2L data object\n", "# Optional controls\n", "FIT_WINDOW_IN_TE = 3.0 # fit only data within t0 Β± FIT_WINDOW_IN_TE * tE (it's handy to have a good guess for tE)\n", "USE_CROPPED_DATA = True # set False to use full light curve (slower)\n", "\n", "# Load the light curve data\n", "data_1s2l = mm.MulensData(file_name=data_file_bl,\n", " phot_fmt='mag',\n", " ephemerides_file=EPHEM_FILE,\n", " plot_properties={'color': '#a859e4', 'label': 'Roman W149'},\n", " bandpass='H')\n", "\n", "# Robust magnification-method setup for far-wing epochs\n", "tE_bl = max(t_E_truth_bl, 1e-6)\n", "\n", "if USE_CROPPED_DATA:\n", " t1_bl = t_0_truth_bl - FIT_WINDOW_IN_TE * tE_bl\n", " t2_bl = t_0_truth_bl + FIT_WINDOW_IN_TE * tE_bl\n", "else:\n", " t1_bl = np.min(data_1s2l.time) - 5.0\n", " t2_bl = np.max(data_1s2l.time) + 5.0\n", "\n", "# Optional time-window crop for speed\n", "if USE_CROPPED_DATA:\n", " m = (data_1s2l.time >= t1_bl) & (data_1s2l.time <= t2_bl)\n", " # replace the data object\n", " data_1s2l = mm.MulensData(\n", " data_list=[data_1s2l.time[m], data_1s2l.mag[m], data_1s2l.err_mag[m]],\n", " phot_fmt='mag',\n", " ephemerides_file=EPHEM_FILE,\n", " plot_properties={'color': '#a859e4', 'label': 'Roman W149 (cropped)'},\n", " bandpass='H',\n", " )\n", " print(f'Using cropped data: {m.sum()} points in [{t_min:.2f}, {t_max:.2f}]')\n", "else:\n", " print(f'Using full data: {len(data_1s1l.time)} points')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1s2l_data" }, "outputs": [], "source": [ "#@title Create 1S2L model object\n", "# Create initial model (slightly perturbed from truth)\n", "model_1s2l = mm.Model(initial_params_1s2l, coords=coord_bl, ephemerides_file=EPHEM_FILE)\n", "\n", "# In older MulensModel versions, 'VBBL' is recognized; in newer docs you may see 'VBM'.\n", "model_1s2l.set_magnification_methods([\n", " t1_bl, 'VBBL', t2_bl\n", "])\n", "model_1s2l.set_limb_coeff_gamma('H', float(gamma_bl)) # resolves H-band limb-darkening warning\n", "\n", "event_1s2l = mm.Event(datasets=data_1s2l, model=model_1s2l)\n", "event_1s2l.fit_fluxes()\n", "\n", "# Plot the initial model\n", "plt.figure(figsize=(12, 6))\n", "event_1s2l.plot_data(zorder=0)\n", "# IMPORTANT: set_magnification_methods() does NOT change plot length;\n", "# it only changes which solver is used at different epochs.\n", "# To draw a longer model, evaluate on a custom time grid explicitly.\n", "([F_S], F_B) = event_1s2l.get_flux_for_dataset(event_1s2l.datasets[0])\n", "t_plot = np.linspace(t1_bl, t2_bl, 2000)\n", "flux_model = model_1s2l.get_magnification(t_plot) * F_S + F_B\n", "mag_model = mm.Utils.get_mag_from_flux(flux_model)\n", "plt.plot(t_plot, mag_model, color='black', alpha=0.8, lw=1.2, label='Initial Model (extended)', zorder=2)\n", "plt.title(\"1S2L Synthetic Event - Initial Model\")\n", "plt.xlabel(\"HJD\")\n", "plt.ylabel(\"Magnitude\")\n", "plt.legend()\n", "plt.show()\n", "\n", "# Plot the initial model\n", "plt.figure(figsize=(12, 6))\n", "event_1s2l.plot_data(zorder=0)\n", "plt.plot(t_plot, mag_model, color='black', alpha=0.8, lw=1.2, label='Initial Model (extended)', zorder=2)\n", "plt.title(\"1S2L Synthetic Event - Initial Model\")\n", "plt.xlim(t1_bl, t2_bl)\n", "plt.xlabel(\"HJD\")\n", "plt.ylabel(\"Magnitude\")\n", "plt.legend()\n", "plt.show()\n", "\n", "# Plot caustic structure\n", "plt.figure(figsize=(8, 8))\n", "event_1s2l.model.plot_caustics()\n", "event_1s2l.model.plot_trajectory()\n", "plt.title(\"1S2L Event - Caustic Structure\")\n", "plt.xlabel(\"x (Einstein radii)\")\n", "plt.ylabel(\"y (Einstein radii)\")\n", "plt.axis('equal')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are going to cheat a little here and simply run `emcee` starting from our purturbed truths. In practice, outside of simulated examples where we know the true parameters, choosing a starting point for binary-lens, MCMC runs is far from trivial and strategies for this process are the subject of our [Binary Lens Fitting](c_binary_lens.ipynb) notebook ([external Link](https://github.com/rges-pit/data-challenge-notebooks/blob/main/AAS%20Workshop/Session%20C%3A%20Binary%20Lens/Fitting_Binary_Lenses.ipynb))." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1s2l_fit" }, "outputs": [], "source": [ "#@title Fit 1S2L model with MCMC\n", "# Set up parameters to fit (excluding rho for simplicity in this example)\n", "print(\"Starting 1S2L MCMC fit...\")\n", "start_time = time.time()\n", "\n", "# Run MCMC with more walkers and steps for binary lens\n", "ndim_1s2l = len(parameters_to_fit_1s2l)\n", "# It takes longer to explore larger parameter space (models with more parameters)\n", "#sampler_1s2l = run_mcmc_fit(event_1s2l, parameters_to_fit_1s2l, initial_guess_1s2l,\n", "# nwalkers=32, nsteps=500) # takes ~2hrs on the Large (CPU Optimized) Nexus server\n", "#burnin_1s2l = 250\n", "#sampler_1s2l = run_mcmc_fit(event_1s2l, parameters_to_fit_1s2l, initial_guess_1s2l,\n", "# nwalkers=200, nsteps=5000) # probably more reasonable for nearby convergence and \n", "#burnin_1s2l = 2500 # a well sampled posterior.\n", "sampler_1s2l = run_mcmc_fit(event_1s2l, parameters_to_fit_1s2l, initial_guess_1s2l,\n", " nwalkers=32, nsteps=300) # isn't going to converge, but might actually finish in a \n", " # reasonable time\n", "burnin_1s2l = 100\n", "\n", "# Get results\n", "samples_1s2l = sampler_1s2l.chain[:, burnin_1s2l:, :].reshape((-1, ndim_1s2l))\n", "best_fit_1s2l = np.median(samples_1s2l, axis=0)\n", "uncertainties_1s2l = np.std(samples_1s2l, axis=0)\n", "\n", "fit_time = time.time() - start_time\n", "print(f\"\\n1S2L Fit completed in {fit_time:.1f} seconds\")\n", "print(\"\\nResults:\")\n", "for i, param in enumerate(parameters_to_fit_1s2l):\n", " print(f\" {param}: {best_fit_1s2l[i]:.6f} Β± {uncertainties_1s2l[i]:.6f}\")\n", "\n", "# Compare with truth\n", "print(\"\\nComparison with truth:\")\n", "truth_values_1s2l = [truths_transformed_1s2l[i] for i, p in enumerate(parameters_to_fit_1s2l)]\n", "for i, param in enumerate(parameters_to_fit_1s2l):\n", " diff = best_fit_1s2l[i] - truth_values_1s2l[i]\n", " sigma_diff = abs(diff) / uncertainties_1s2l[i] if uncertainties_1s2l[i] > 0 else 0\n", " print(f\" {param}: {diff:.6f} ({sigma_diff:.2f}Οƒ)\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1s2l_results" }, "outputs": [], "source": [ "#@title Plot 1S2L results\n", "# Update model with best fit parameters\n", "for i, param_name in enumerate(parameters_to_fit_1s2l):\n", " if 'log' in param_name:\n", " physical_value = 10.0**best_fit_1s2l[i]\n", " else:\n", " physical_value = best_fit_1s2l[i]\n", " setattr(event_1s2l.model.parameters, param_name, physical_value)\n", "event_1s2l.fit_fluxes()\n", "\n", "# Plot light curve with best fit\n", "plt.figure(figsize=(12, 6))\n", "event_1s2l.plot_data()\n", "event_1s2l.plot_model(color='red', label='Best Fit')\n", "plt.gca().invert_yaxis()\n", "plt.title(\"1S2L Synthetic Event - Best Fit Model\")\n", "plt.xlabel(\"HJD\")\n", "plt.ylabel(\"Magnitude\")\n", "plt.legend()\n", "plt.show()\n", "\n", "# Plot corner plot\n", "fig = corner.corner(samples_1s2l, labels=parameters_to_fit_1s2l,\n", " truths=truth_values_1s2l, quantiles=[0.16, 0.5, 0.84])\n", "plt.suptitle(\"1S2L Event - Parameter Posteriors\", y=1.02)\n", "plt.show()\n", "\n", "# Plot updated caustic structure\n", "plt.figure(figsize=(8, 8))\n", "event_1s2l.model.plot_caustics()\n", "plt.title(\"1S2L Event - Best Fit Caustic Structure\")\n", "plt.xlabel(\"x (Einstein radii)\")\n", "plt.ylabel(\"y (Einstein radii)\")\n", "plt.axis('equal')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "1s3l_example" }, "source": [ "#### 1.5.3 Single Source Triple Lens (1S3L)\n", "\n", "Triple lens systems are rare but important for detecting hierarchical planetary systems. Here we demonstrate how to set up an example triple lens model.\n", "\n", "**Triple Lens Model Parameters:**\n", "- **Primary lens**: t_0, u_0, t_E\n", "- **First companion**: q1, s1, alpha1\n", "- **Second companion**: q2, s2, alpha2, phi\n", "- **Finite source**: rho\n", "- **Higher order**: pi_E_N, pi_E_E\n", "\n", "**Example Triple Lens Parameters (hierarchical system):**\n", "- t_0: 2459123.5\n", "- u_0: 0.1\n", "- t_E: 25.0\n", "- q1: 0.1 (First companion - planet)\n", "- s1: 1.2 (Separation of first companion)\n", "- alpha1: 45.0\n", "- q2: 0.01 (Second companion - moon)\n", "- s2: 0.1 (Separation of second companion relative to first)\n", "- alpha2: 30.0\n", "- phi: 60.0 (Angle between companions)\n", "- rho: 0.001 (Finite source)\n", "\n", "> **Note**: This represents a hierarchical triple system with:\n", "> - Primary lens (star)\n", "> - First companion (planet at ~1.2 Einstein radii)\n", "> - Second companion (moon at ~0.1 Einstein radii from planet)\n", "\n", "**MulensModel doesn't natively support triple lenses** (as of version 3.9.0), but here's how you could extend it:\n", "\n", "**Triple Lens Implementation Options:**\n", "\n", "**1. Use VBMicrolensing:**\n", "- Native support for multiple lenses\n", "- More efficient for complex lens systems\n", "- Better suited for hierarchical systems\n", "\n", "**2. Extend MulensModel:**\n", "- Create custom model class\n", "- Implement triple lens magnification calculation\n", "- Requires significant development effort\n", "\n", "**3. Use triplelens package:**\n", "- Specialized for triple lens calculations\n", "- GitHub: https://github.com/rkkuang/triplelens\n", "- Limited integration with other tools\n", "\n", "**4. Approximate approach:**\n", "- Treat as hierarchical binary systems\n", "- Fit inner binary first, then outer binary\n", "- May miss some triple lens effects\n", "\n", "**Framework for Triple Lens Fitting:**\n", "```python\n", "parameters_to_fit_1s3l = [\n", " 't_0', 'u_0', 't_E',\n", " 'q1', 's1', 'alpha1',\n", " 'q2', 's2', 'alpha2', 'phi',\n", " 'rho'\n", "]\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "2s1l_example" }, "source": [ "#### 1.5.4 Binary Source Single Lens (2S1L) \n", "\n", "Binary source events occur when the source star is actually a binary system. This can create distinctive light-curve features, but it can also look remarkably like some binary-lens events. We don't have any example binary-source data, so we will have to simulate some.\n", "\n", "> At least one of the sources **must** be luminous and there is no need to model a non-luminous source if Xalarap is not a significant effect." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2s1l_setup" }, "outputs": [], "source": [ "#@title 2S1L Model Setup\n", "# Binary source parameters:\n", "# - t_0, u_0, t_E: standard microlensing parameters\n", "# - q_S: flux ratio of source components\n", "# - rho_S: angular separation of source components\n", "# - theta_S: angle of source binary\n", "\n", "# Create synthetic binary source data\n", "def create_binary_source_data(t_0=2459123.5, u_0=0.1, t_E=25.0, q_S=0.8, rho_S=0.01, theta_S=45.0):\n", " \"\"\"Create synthetic binary source light curve\"\"\"\n", " # Time array around the event\n", " t = np.linspace(t_0 - 2*t_E, t_0 + 2*t_E, 200)\n", "\n", " # Create binary source model\n", " params = {\n", " 't_0': t_0,\n", " 'u_0': u_0,\n", " 't_E': t_E,\n", " 'q_S': q_S, # flux ratio\n", " 'rho_S': rho_S, # angular separation\n", " 'theta_S': theta_S # angle\n", " }\n", "\n", " # Note: MulensModel doesn't natively support binary sources\n", " # This is a simplified approximation\n", " model = mm.Model({'t_0': t_0, 'u_0': u_0, 't_E': t_E},\n", " coords=coord, ephemerides_file=EPHEM_FILE)\n", "\n", " # Calculate magnification for single source\n", " magnification = model.get_magnification(t)\n", "\n", " # Approximate binary source effect\n", " # This is a simplified treatment - real binary sources are more complex\n", " mag_0 = 18.0\n", " mag = mag_0 - 2.5 * np.log10(magnification)\n", "\n", " # Add some binary source features (simplified)\n", " # In reality, this would depend on the source binary orientation\n", " binary_modulation = 0.1 * np.sin(2 * np.pi * (t - t_0) / t_E)\n", " mag += binary_modulation\n", "\n", " mag_err = 0.01 * np.ones_like(mag)\n", " mag += np.random.normal(0, mag_err)\n", "\n", " return t, mag, mag_err, params\n", "\n", "# Create the synthetic data\n", "t_binary_source, mag_binary_source, mag_err_binary_source, truth_params_binary_source = create_binary_source_data()\n", "\n", "print(\"Synthetic 2S1L Event - Truth Parameters:\")\n", "for key, value in truth_params_binary_source.items():\n", " print(f\" {key}: {value}\")\n", "\n", "print(\"\\nNote: This is a simplified binary source model.\")\n", "print(\"Real binary source events require more sophisticated modeling.\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2s1l_data" }, "outputs": [], "source": [ "#@title Create 2S1L data object\n", "# Create data object from synthetic data\n", "data_list_2s1l = [t_binary_source, mag_binary_source, mag_err_binary_source]\n", "data_2s1l = mm.MulensData(data_list=data_list_2s1l,\n", " phot_fmt='mag',\n", " ephemerides_file=EPHEM_FILE,\n", " plot_properties={'color': '#a859e4', 'label': 'Synthetic Binary Source'},\n", " bandpass='H')\n", "\n", "# Create initial model (treating as single source for now)\n", "params_2s1l = {\n", " 't_0': truth_params_binary_source['t_0'],\n", " 'u_0': truth_params_binary_source['u_0'],\n", " 't_E': truth_params_binary_source['t_E']\n", "}\n", "model_2s1l = mm.Model(params_2s1l, coords=coord, ephemerides_file=EPHEM_FILE)\n", "event_2s1l = mm.Event(datasets=data_2s1l, model=model_2s1l)\n", "\n", "# Plot the data\n", "plt.figure(figsize=(12, 6))\n", "event_2s1l.plot_data()\n", "event_2s1l.plot_model(color='red', label='Single Source Model')\n", "plt.gca().invert_yaxis()\n", "plt.title(\"2S1L Synthetic Event - Data with Single Source Model\")\n", "plt.xlabel(\"HJD\")\n", "plt.ylabel(\"Magnitude\")\n", "plt.legend()\n", "plt.show()\n", "\n", "print(\"Note: The residuals show the binary source signature.\")\n", "print(\"A proper binary source model would fit these features.\")" ] }, { "cell_type": "markdown", "metadata": { "id": "higher_order_summary" }, "source": [ "### 1.6 Higher-Order Effects Summary\n", "\n", "We've demonstrated several key higher-order effects in microlensing, throughout this `MulensModel` section, including:\n", "\n", "**Finite Source Effects (ρ):**\n", "- Important when the source star is resolved\n", "- Provides constraints on the lens mass and distance\n", "- Essential for accurate parameter estimation\n", "\n", "**Parallax Effects (Ο€_E):**\n", "- Crucial for space-based observations\n", "- Enables mass and distance measurements\n", "- Breaks degeneracies in lens properties\n", "\n", "**Other Effects (not demonstrated):**\n", "- **Lens Orbital Motion:** Changes in binary lens separation over time (often highly degenerate with parllax effect)\n", "- **Xallarap:** Source orbital motion effects\n", "- **Limb Darkening:** Non-uniform source brightness (rarely well constrained). We have used `gamma` (linear limb-darkening) in this section, but it has always been set to the simulation truth, 0.0; i.e., uniform radial source brightness.\n", "- **Astrometry:** Position measurements during events (sparsley obtained prior to Roman). Not yet supported in `MulensModel`.\n", "\n", "**Modelling Best Practices:**\n", "- Start with simple models and add complexity gradually\n", "- Use MCMC for complex parameter spaces\n", "- Consider known degeneracies\n", "- Consider computational efficiency for large surveys\n", "- If you would like to request new features in `MulensModel`, kindly [open an issue on GitHub](https://github.com/rpoleski/MulensModel/issues/new)." ] }, { "cell_type": "markdown", "metadata": { "id": "pylima_section" }, "source": [ "## 2. pyLIMA\n", "\n", "
\n", "\n", "pyLIMA (Python Lightcurve Interpretation and Microlensing Analysis) is the first open-source software for modeling microlensing events. It was developed by Etienne Bachelet and provides a flexible, modular framework for microlensing analysis. pyLIMA is particularly well-suited for space-based observations and offers advanced features like simulation capabilities and custom parameter definitions. It also include an internal module called pyLIMASS that does physical parameter inference on fitted microlensing events.\n", "\n", "**Key Features:**\n", "- Modular design with separate event, telescope, and model classes\n", "- Multiple fitting algorithms (LM, DE, MCMC, TRF)\n", "- Built-in simulation capabilities\n", "- Custom parameter definitions\n", "- Support for multiple telescopes and filters\n", "- Limb darkening and finite source effects\n", "- Physical parameter estimation\n", "\n", "**Documentation:**\n", "- [Github repo](https://github.com/ebachelet/pyLIMA)\n", "- [Doc site](https://pylima.readthedocs.io/en/latest/)\n", "- [`piLIMA` Article ADS link](https://ui.adsabs.harvard.edu/abs/2017AJ....154..203B/abstract)\n", "- [`piLIMASS` Article ADS link](https://ui.adsabs.harvard.edu/abs/2024AJ....168...24B/abstract)S" ] }, { "cell_type": "markdown", "metadata": { "id": "pylima_installation" }, "source": [ "### 2.1 Installation and Setup \n", "\n", "pyLIMA can be installed via pip or conda. It has several dependencies including numpy, scipy, matplotlib, and astropy." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pylima_install" }, "outputs": [], "source": [ "#@title Install pyLIMA\n", "# Import pyLIMA modules\n", "from pyLIMA import event, telescopes\n", "from pyLIMA.models import PSPL_model, FSPL_model\n", "from pyLIMA.fits import LM_fit, DE_fit, MCMC_fit, TRF_fit\n", "from pyLIMA.outputs import pyLIMA_plots\n", "from pyLIMA.simulations import simulator\n", "\n", "print(\"pyLIMA installed and imported successfully!\")" ] }, { "cell_type": "markdown", "metadata": { "id": "pylima_basic" }, "source": [ "### 2.2 Basic Usage \n", "\n", "pyLIMA uses a modular approach with three main components:\n", "1. **Event**: Contains all information about the microlensing event\n", "2. **Telescopes**: Individual telescope data and properties\n", "3. **Models**: The microlensing model to fit" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pylima_basic_setup" }, "outputs": [], "source": [ "#@title Basic pyLIMA Setup\n", "# Create a new event\n", "Roman_event = event.Event()\n", "Roman_event.name = f'pyLIMA_1s1l_{event_idx}'\n", "\n", "# Create synthetic data for demonstration\n", "# In practice, you would load real data from files\n", "np.random.seed(42) # For reproducible results\n", "\n", "# Create telescope object\n", "lightcurve_data = np.column_stack(\n", " [\n", " event_1s1l.datasets[0].time, \n", " event_1s1l.datasets[0].mag, \n", " event_1s1l.datasets[0].err_mag\n", " ]\n", " )\n", "Roman_W149 = telescopes.Telescope(\n", " name='Roman_W149',\n", " camera_filter='H',\n", " lightcurve=lightcurve_data.astype(float),\n", " lightcurve_names=['time', 'mag', 'err_mag'],\n", " lightcurve_units=['BJD', 'mag', 'mag']\n", ")\n", "\n", "# Add telescope to event\n", "Roman_event.telescopes.append(Roman_W149)\n", "\n", "# Set survey telescope (for alignment)\n", "Roman_event.find_survey('OGLE')\n", "\n", "# Check event setup\n", "Roman_event.check_event()\n", "\n", "print(f\"Event '{Roman_event.name}' created with {len(Roman_event.telescopes)} telescope(s)\")\n", "print(f\"Data points: {len(Roman_W149.lightcurve)}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "pylima_models" }, "source": [ "### 2.3 Model Examples \n", "\n", "pyLIMA supports various microlensing models. Let's start with the basic Point Source Point Lens (PSPL) model." ] }, { "cell_type": "markdown", "metadata": { "id": "pylima_pspl" }, "source": [ "#### 2.3.1 Single Lens (PSPL) Example \n", "\n", "The PSPL model is the simplest microlensing model with parameters: `t_0`, `u_0`, `t_E`, and flux parameters for each telescope." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pylima_pspl_model" }, "outputs": [], "source": [ "#@title PSPL Model Setup and Fitting\n", "# Create PSPL model\n", "pspl = PSPL_model.PSPLmodel(Roman_event)\n", "\n", "# Initialize fit with Levenberg-Marquardt algorithm\n", "my_fit = LM_fit.LMfit(pspl)\n", "\n", "# Show initial parameters\n", "print(\"Initial fit parameters:\")\n", "print(my_fit.fit_parameters)\n", "\n", "# Run the fit\n", "print(\"\\nRunning LM fit...\")\n", "my_fit.fit()\n", "my_fit.fit_outputs()\n", "\n", "# Display results\n", "print(\"\\nFit Results:\")\n", "print(my_fit.fit_results['best_model'])\n", "print(f\"\\nChi-squared: {my_fit.fit_results['chi2']:.2f}\")\n", "\n", "# Plot the results\n", "pyLIMA_plots.plot_lightcurves(pspl, my_fit.fit_results['best_model'])\n", "plt.title(\"pyLIMA PSPL Fit\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pylima_pspl_mcmc" }, "outputs": [], "source": [ "#@title PSPL with MCMC\n", "# Use MCMC for better parameter estimation\n", "mcmc_fit = MCMC_fit.MCMCfit(pspl, rescale_photometry=True)\n", "\n", "# Use LM results as initial guess\n", "mcmc_fit.model_parameters_guess = my_fit.fit_results['best_model'][:3] # t0, u0, tE\n", "\n", "print(\"Running MCMC fit (this may take a while)...\")\n", "mcmc_fit.fit()\n", "mcmc_fit.fit_outputs()\n", "\n", "# Get MCMC results\n", "MCMC_results = mcmc_fit.fit_results['MCMC_chains']\n", "burnin = 1000\n", "\n", "# Calculate statistics\n", "posterior_samples = MCMC_results[burnin:, :, :3] # t0, u0, tE\n", "best_fit_mcmc = np.median(posterior_samples, axis=(0, 1))\n", "uncertainties_mcmc = np.std(posterior_samples, axis=(0, 1))\n", "\n", "print(\"\\nMCMC Results:\")\n", "param_names = ['t_0', 'u_0', 't_E']\n", "for i, name in enumerate(param_names):\n", " print(f\"{name}: {best_fit_mcmc[i]:.4f} Β± {uncertainties_mcmc[i]:.4f}\")\n", "\n", "# Compare with truth values\n", "print(\"\\nComparison with truth:\")\n", "truth_values = [t_0_truth, u_0_truth, t_E_truth]\n", "for i, name in enumerate(param_names):\n", " diff = best_fit_mcmc[i] - truth_values[i]\n", " sigma_diff = abs(diff) / uncertainties_mcmc[i]\n", " print(f\"{name}: {diff:.4f} ({sigma_diff:.2f}Οƒ)\")" ] }, { "cell_type": "markdown", "metadata": { "id": "pylima_fspl" }, "source": [ "#### 2.3.2 Finite Source (FSPL) Example \n", "\n", "The FSPL model includes finite source effects with the ρ parameter, which is crucial for high-magnification events." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pylima_fspl_model" }, "outputs": [], "source": [ "#@title FSPL Model with Finite Source Effects\n", "# Create FSPL model\n", "fspl = FSPL_model.FSPLmodel(Roman_event)\n", "\n", "# Set limb darkening coefficient\n", "Roman_event.telescopes[0].ld_gamma = float(gamma)\n", "\n", "# Use differential evolution for better exploration\n", "de_fit = DE_fit.DEfit(fspl, loss_function='chi2')\n", "\n", "print(\"Running DE fit for FSPL model...\")\n", "de_fit.fit()\n", "\n", "# Display results\n", "print(\"\\nFSPL Fit Results:\")\n", "print(de_fit.fit_results['best_model'])\n", "print(f\"\\nChi-squared: {de_fit.fit_results['chi2']:.2f}\")\n", "\n", "# Plot results\n", "pyLIMA_plots.plot_lightcurves(fspl, de_fit.fit_results['best_model'])\n", "plt.title(\"pyLIMA FSPL Fit with Finite Source Effects\")\n", "plt.show()\n", "\n", "# Compare PSPL vs FSPL\n", "print(\"\\nModel Comparison:\")\n", "print(f\"PSPL χ²: {my_fit.fit_results['chi2']:.2f}\")\n", "print(f\"FSPL χ²: {de_fit.fit_results['chi2']:.2f}\")\n", "print(f\"Improvement: {my_fit.fit_results['chi2'] - de_fit.fit_results['chi2']:.2f}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "pylima_simulation" }, "source": [ "#### 2.3.3 Simulation Capabilities \n", "\n", "One of pyLIMA's unique features is its built-in simulation capabilities, allowing you to generate realistic microlensing light curves." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pylima_sim_setup" }, "outputs": [], "source": [ "#@title pyLIMA Simulation Example\n", "# Create a new event for simulation\n", "sim_event = event.Event(ra=ra, dec=dec)\n", "sim_event.name = 'Simulated Event'\n", "\n", "sim_event.telescopes.append(Roman_W149)\n", "sim_event.check_event()\n", "\n", "print(f\"Simulated telescope '{Roman_W149.name}' created\")\n", "print(f\"Observation period: {Roman_W149.time_start:.1f} to {Roman_W149.time_end:.1f}\")\n", "print(f\"Sampling: {Roman_W149.sampling} hours\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pylima_sim_model" }, "outputs": [], "source": [ "#@title Generate Simulated Light Curve\n", "# Create PSPL model for simulation\n", "sim_pspl = PSPL_model.PSPLmodel(sim_event)\n", "\n", "# Generate random model parameters\n", "sim_parameters = simulator.simulate_microlensing_model_parameters(sim_pspl)\n", "print(\"Simulated Parameters:\")\n", "print(sim_parameters)\n", "\n", "# Convert to pyLIMA format\n", "pyLIMA_parameters = sim_pspl.compute_pyLIMA_parameters(sim_parameters)\n", "\n", "# Generate the light curve\n", "simulator.simulate_lightcurve(sim_pspl, pyLIMA_parameters)\n", "\n", "# Plot the simulated light curve\n", "pyLIMA_plots.plot_lightcurves(sim_pspl, sim_parameters)\n", "plt.title(\"pyLIMA Simulated Light Curve\")\n", "plt.show()\n", "\n", "print(f\"Generated {len(Roman_W149.lightcurve)} data points\")\n", "print(f\"Time range: {Roman_W149.lightcurve['time'].min():.1f} to {Roman_W149.lightcurve['time'].max():.1f}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pylima_sim_fit" }, "outputs": [], "source": [ "#@title Fit Simulated Data\n", "# Now fit the simulated data to recover parameters\n", "sim_fit = LM_fit.LMfit(sim_pspl)\n", "sim_fit.fit()\n", "sim_fit.fit_outputs()\n", "\n", "print(\"Fit Results vs Truth:\")\n", "print(\"Parameter | Truth | Fit | Difference\")\n", "print(\"---------|----------|----------|-----------\")\n", "\n", "param_names = list(sim_pspl.model_dictionnary.keys())\n", "for i, name in enumerate(param_names[:3]): # t0, u0, tE\n", " truth = sim_parameters[i]\n", " fit = sim_fit.fit_results['best_model'][i]\n", " diff = fit - truth\n", " print(f\"{name:8} | {truth:8.4f} | {fit:8.4f} | {diff:+8.4f}\")\n", "\n", "print(f\"\\nChi-squared: {sim_fit.fit_results['chi2']:.2f}\")\n", "\n", "# Plot fit results\n", "pyLIMA_plots.plot_lightcurves(sim_pspl, sim_fit.fit_results['best_model'])\n", "plt.title(\"Fitting Simulated Data\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "pylima_advanced" }, "source": [ "### 2.4 Advanced Features \n", "\n", "pyLIMA offers several advanced features including custom parameter definitions and multiple fitting algorithms, and physical parameter estimation with `pyLIMASS`. " ] }, { "cell_type": "markdown", "metadata": { "id": "pylima_custom_params" }, "source": [ "#### 2.4.1 Custom Parameter Definitions \n", "\n", "pyLIMA allows you to define custom parameter transformations, which can be useful for better sampling or physical interpretation." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pylima_custom_setup" }, "outputs": [], "source": [ "#@title Custom Parameter Definitions\n", "# Define custom parameters: log_tE instead of tE\n", "class MyFancyParameters(object):\n", " def __init__(self, fancy_parameters={'tE': 'log_tE'},\n", " fancy_boundaries={'log_tE': (0, 3)}):\n", " self.fancy_parameters = fancy_parameters\n", " self.fancy_boundaries = fancy_boundaries\n", "\n", " def tE(self, fancy_params):\n", " return 10**fancy_params['log_tE']\n", "\n", " def log_tE(self, standard_params):\n", " return np.log10(standard_params['tE'])\n", "\n", "# Create model with custom parameters\n", "my_pars = MyFancyParameters()\n", "fspl_custom = FSPL_model.FSPLmodel(your_event, fancy_parameters=my_pars)\n", "\n", "print(\"Custom parameter model created\")\n", "print(f\"Parameters: {fspl_custom.fit_parameters.keys()}\")\n", "print(f\"Custom boundaries: {my_pars.fancy_boundaries}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pylima_custom_fit" }, "outputs": [], "source": [ "#@title Fit with Custom Parameters\n", "# Use Trust Region Reflective algorithm\n", "trf_fit = TRF_fit.TRFfit(fspl_custom)\n", "\n", "# Set initial guess (convert tE to log_tE)\n", "guess_parameters = [t0, u0, np.log10(tE), 0.001] # t0, u0, log_tE, rho\n", "trf_fit.model_parameters_guess = guess_parameters\n", "\n", "print(\"Running TRF fit with custom parameters...\")\n", "trf_fit.fit()\n", "\n", "print(\"\\nFit Results (custom parameters):\")\n", "print(trf_fit.fit_results['best_model'])\n", "print(f\"\\nChi-squared: {trf_fit.fit_results['chi2']:.2f}\")\n", "\n", "# Convert back to standard parameters for comparison\n", "custom_params = trf_fit.fit_results['best_model']\n", "standard_tE = 10**custom_params[2] # Convert log_tE back to tE\n", "print(f\"\\nConverted t_E: {standard_tE:.2f} days\")\n", "print(f\"Truth t_E: {tE:.2f} days\")\n", "print(f\"Difference: {standard_tE - tE:.2f} days\")" ] }, { "cell_type": "markdown", "metadata": { "id": "pylima_multiple_telescopes" }, "source": [ "#### 2.4.2 Multiple Telescopes and Filters \n", "\n", "pyLIMA excels at handling data from multiple telescopes and filters, which is common in modern microlensing surveys." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pylima_multi_tel" }, "outputs": [], "source": [ "#@title Multiple Telescope Example\n", "# Create a new event with multiple telescopes\n", "multi_event = event.Event()\n", "multi_event.name = 'Multi-Telescope Event'\n", "\n", "# Generate data for multiple telescopes with different properties\n", "telescopes_data = []\n", "telescope_names = ['OGLE', 'MOA', 'LCO']\n", "filters = ['I', 'R', 'V']\n", "offsets = [0.0, 0.1, -0.05] # Different zero points\n", "noise_levels = [0.01, 0.015, 0.02] # Different noise levels\n", "\n", "for i, (name, filt, offset, noise) in enumerate(zip(telescope_names, filters, offsets, noise_levels)):\n", " # Generate light curve with different properties\n", " mag_tel = mag + offset + np.random.normal(0, noise, len(mag))\n", " mag_err_tel = noise * np.ones_like(mag)\n", "\n", " lightcurve_data = np.column_stack([times, mag_tel, mag_err_tel])\n", "\n", " telescope = telescopes.Telescope(\n", " name=name,\n", " camera_filter=filt,\n", " lightcurve=lightcurve_data.astype(float),\n", " lightcurve_names=['time', 'mag', 'err_mag'],\n", " lightcurve_units=['JD', 'mag', 'mag']\n", " )\n", "\n", " multi_event.telescopes.append(telescope)\n", " telescopes_data.append(lightcurve_data)\n", "\n", "# Set survey telescope\n", "multi_event.find_survey('OGLE')\n", "multi_event.check_event()\n", "\n", "print(f\"Created event with {len(multi_event.telescopes)} telescopes:\")\n", "for tel in multi_event.telescopes:\n", " print(f\" {tel.name} ({tel.camera_filter}-band): {len(tel.lightcurve)} points\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pylima_multi_fit" }, "outputs": [], "source": [ "#@title Fit Multi-Telescope Data\n", "# Create PSPL model for multi-telescope data\n", "multi_pspl = PSPL_model.PSPLmodel(multi_event)\n", "\n", "# Fit with differential evolution\n", "multi_fit = DE_fit.DEfit(multi_pspl, loss_function='chi2')\n", "multi_fit.fit()\n", "\n", "print(\"Multi-telescope fit results:\")\n", "print(multi_fit.fit_results['best_model'])\n", "print(f\"\\nChi-squared: {multi_fit.fit_results['chi2']:.2f}\")\n", "\n", "# Plot results\n", "pyLIMA_plots.plot_lightcurves(multi_pspl, multi_fit.fit_results['best_model'])\n", "plt.title(\"Multi-Telescope pyLIMA Fit\")\n", "plt.show()\n", "\n", "# Show flux parameters for each telescope\n", "print(\"\\nFlux parameters for each telescope:\")\n", "param_names = list(multi_pspl.model_dictionnary.keys())\n", "for i, tel in enumerate(multi_event.telescopes):\n", " flux_param = multi_fit.fit_results['best_model'][3 + i] # Flux parameters start at index 3\n", " print(f\"{tel.name} ({tel.camera_filter}): {flux_param:.2f}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "pylima_performance" }, "source": [ "### 2.5 Performance and Best Practices \n", "\n", "pyLIMA offers several fitting algorithms, each with different strengths:\n", "\n", "**Fitting Algorithms:**\n", "- **LM (Levenberg-Marquardt)**: Fast, good for initial fits\n", "- **DE (Differential Evolution)**: Robust, handles complex parameter spaces\n", "- **MCMC**: Best for uncertainty estimation and posterior sampling\n", "- **TRF (Trust Region Reflective)**: Good for constrained optimization\n", "\n", "**Best Practices:**\n", "- Start with LM for quick initial fits\n", "- Use DE for complex models or when LM fails\n", "- Use MCMC for final parameter estimation and uncertainties\n", "- Set appropriate parameter bounds for better convergence\n", "- Use custom parameters for better sampling (e.g., log_tE instead of tE)\n", "- Always check event setup with `check_event()`\n", "\n", "**Performance Tips:**\n", "- Use `rescale_photometry=True` in MCMC for better numerical stability\n", "- Set appropriate burn-in periods for MCMC chains\n", "- Use simulation capabilities to test your analysis pipeline\n", "- Consider using custom parameters for better sampling efficiency" ] }, { "cell_type": "markdown", "metadata": { "id": "rtmodel_section" }, "source": [ "## 3. RTModel\n", "\n", "
\n", "\n", "RTModel (Real-Time Model) is a sophisticated, hands-off microlensing modeling package developed by Valerio Bozza. Unlike other tools that require manual model selection, RTModel automatically determines the best model type for your event through a comprehensive grid search and template library approach. It's designed to be a \"set it up and press go\" solution that provides automated model interpretation.\n", "\n", "**Key Features:**\n", "- **Automated Model Selection**: Determines whether an event is single-lens, binary-lens, binary-source, etc.\n", "- **Template Library**: Uses pre-computed binary lens templates for efficient fitting\n", "- **Parallel Processing**: Exploits all available CPU cores for fast analysis\n", "- **Comprehensive Models**: Supports 7+ model categories including parallax and orbital motion\n", "- **Built-in Assessment**: Provides automatic interpretation of results\n", "- **Visualization Tools**: Includes plotting and animation capabilities" ] }, { "cell_type": "markdown", "metadata": { "id": "rtmodel_installation" }, "source": [ "### 3.1 Installation and Setup \n", "\n", "RTModel requires a C++17 compiler and uses VBMicrolensing for calculations. It can be installed via pip or from source." ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "id": "rtmodel_install" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RTModel installed and imported successfully!\n", "RTModel version: 3.0\n", "Current working directory: /Users/malpas.1/Code/data-challenge-notebooks/Extras\n" ] } ], "source": [ "#@title Install RTModel\n", "\n", "# Import RTModel\n", "import RTModel\n", "import RTModel.plotmodel as plm\n", "\n", "print(\"RTModel installed and imported successfully!\")\n", "print(f\"RTModel version: {RTModel.__version__}\" if hasattr(RTModel, '__version__') else \"RTModel imported\")\n", "wd = Path(os.getcwd())\n", "print(f\"Current working directory: {wd}\")" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rtmodel_event001 already present; skipping download.\n" ] } ], "source": [ "#@title Download RTModel example event (if needed)\n", "# Assumes stdlib helpers + requests were imported near the top (see \"General Imports\").\n", "os.chdir(wd) # ensure we're in the original working directory\n", "EVENT_DIR = Path(\"rtmodel_event001\")\n", "USE_DONE_ZIP = True # set False to use raw event only\n", "\n", "URL_RAW = \"https://raw.githubusercontent.com/valboz/RTModel/refs/heads/main/events/event001.zip\"\n", "URL_DONE = \"https://raw.githubusercontent.com/valboz/RTModel/refs/heads/main/events/event001done.zip\"\n", "url = URL_DONE if USE_DONE_ZIP else URL_RAW\n", "\n", "if (EVENT_DIR / \"Data\").exists():\n", " print(f\"{EVENT_DIR} already present; skipping download.\")\n", "else:\n", " print(f\"Downloading RTModel example data from: {url}\")\n", " with tempfile.TemporaryDirectory() as tmp:\n", " tmp_path = Path(tmp)\n", " zip_path = tmp_path / \"event.zip\"\n", "\n", " with requests.get(url, stream=True, timeout=60) as r:\n", " r.raise_for_status()\n", " with open(zip_path, \"wb\") as f:\n", " for chunk in r.iter_content(chunk_size=1024 * 1024):\n", " if chunk:\n", " f.write(chunk)\n", "\n", " with zipfile.ZipFile(zip_path) as zf:\n", " names = [n for n in zf.namelist() if n and not n.endswith(\"/\")]\n", " top_levels = sorted({n.split(\"/\", 1)[0] for n in names if \"/\" in n})\n", " zf.extractall(tmp_path)\n", "\n", " if len(top_levels) == 1 and (tmp_path / top_levels[0]).exists():\n", " src = tmp_path / top_levels[0]\n", " if EVENT_DIR.exists():\n", " shutil.rmtree(EVENT_DIR)\n", " shutil.move(str(src), str(EVENT_DIR))\n", " else:\n", " EVENT_DIR.mkdir(parents=True, exist_ok=True)\n", " for item in list(tmp_path.iterdir()):\n", " if item.name == \"event.zip\":\n", " continue\n", " if item.name == EVENT_DIR.name:\n", " if EVENT_DIR.exists():\n", " shutil.rmtree(EVENT_DIR)\n", " shutil.move(str(item), str(EVENT_DIR))\n", " break\n", " shutil.move(str(item), str(EVENT_DIR / item.name))\n", "\n", " print(f\"Ready: {EVENT_DIR.resolve()}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "rtmodel_data_prep" }, "source": [ "### 3.2 Data Preparation\n", "\n", "RTModel requires a specific directory structure and data format. Each event needs its own directory with a `/Data` subdirectory containing the photometry files." ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "id": "rtmodel_directory_setup" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Created RTModel directory structure:\n", " rtmodel_event001/\n", " rtmodel_event001/Data/\n" ] } ], "source": [ "#@title Create RTModel Directory Structure\n", "os.chdir(wd) # ensure we're in the original working directory\n", "\n", "# Create event directory structure for RTModel\n", "data_dir = os.path.join(EVENT_DIR, 'Data')\n", "\n", "# Create directories\n", "os.makedirs(data_dir, exist_ok=True)\n", "\n", "print(f\"Created RTModel directory structure:\")\n", "print(f\" {EVENT_DIR}/\")\n", "print(f\" {EVENT_DIR}/Data/\")\n", "\n", "# RTModel requires this specific structure:\n", "# event_dir/\n", "# β”œβ”€β”€ Data/\n", "# β”‚ β”œβ”€β”€ telescope1.dat\n", "# β”‚ β”œβ”€β”€ telescope2.dat\n", "# β”‚ └── event001.coordinates\n", "# └── (other files created by RTModel)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "id": "rtmodel_data_format" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "First 5 lines of data file:\n", " 1: # Mag err HJD\n", " 2: \n", " 3: 17.733916304426643 0.017916600891089943 8346.505460999906\n", " 4: \n", " 5: 17.69898081443053 0.0179166008883137 8346.515986999962\n" ] } ], "source": [ "#@title Create RTModel Data Files\n", "os.chdir(wd.parent) # ensure we're in the original working directory\n", "# RTModel expects data in a specific format:\n", "# # Mag err HJD-2450000\n", "# 19.0232 0.012 8370.1223\n", "# 19.0150 0.011 8370.2421\n", "# ...\n", "\n", "# If example data was downloaded above, it should already include an TelescopeA.dat (or similar) file.\n", "# Only generate a synthetic one if it's missing.\n", "data_file = os.path.join(data_dir, 'TelescopeA.dat')\n", "\n", "# Show first few lines\n", "with open(data_file, 'r') as f:\n", " lines = f.readlines()\n", " print(\"\\nFirst 5 lines of data file:\")\n", " for i, line in enumerate(lines[:5]):\n", " print(f\" {i+1}: {line.strip()}\")" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "id": "rtmodel_coordinates" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found existing coordinates file: rtmodel_event001/Data/event.coordinates (skipping generation)\n" ] } ], "source": [ "#@title Create Coordinates File\n", "os.chdir(wd) # ensure we're in the original working directory\n", "# RTModel requires event coordinates in a specific format\n", "# Format: HH:MM:SS.S +DD:PP:SS.S\n", "\n", "# If example data was downloaded above, it may already include event.coordinates.\n", "coords_file = os.path.join(data_dir, 'event.coordinates')\n", "if os.path.exists(coords_file):\n", " print(f\"Found existing coordinates file: {coords_file} (skipping generation)\")\n", "else:\n", " # Convert our coordinates to the required format\n", " def decimal_to_hms(ra_deg, dec_deg):\n", " \"\"\"Convert decimal degrees to HH:MM:SS.S +DD:PP:SS.S format\"\"\"\n", " # Convert RA (hours)\n", " ra_hours = ra_deg / 15.0 # Convert degrees to hours\n", " ra_h = int(ra_hours)\n", " ra_m = int((ra_hours - ra_h) * 60)\n", " ra_s = ((ra_hours - ra_h - ra_m/60) * 3600)\n", "\n", " # Convert Dec (degrees)\n", " dec_sign = '+' if dec_deg >= 0 else '-'\n", " dec_deg_abs = abs(dec_deg)\n", " dec_d = int(dec_deg_abs)\n", " dec_m = int((dec_deg_abs - dec_d) * 60)\n", " dec_s = ((dec_deg_abs - dec_d - dec_m/60) * 3600)\n", "\n", " ra_str = f\"{ra_h:02d}:{ra_m:02d}:{ra_s:04.1f}\"\n", " dec_str = f\"{dec_sign}{dec_d:02d}:{dec_m:02d}:{dec_s:04.1f}\"\n", "\n", " return f\"{ra_str} {dec_str}\"\n", "\n", " required = ['ra', 'dec']\n", " missing = [name for name in required if name not in globals()]\n", " if missing:\n", " raise NameError(\n", " \"RTModel coordinates file is missing, and required variables were not found: \"\n", " + \", \".join(missing)\n", " + \".\\nRun the earlier cells that define ra/dec, or run the download cell above (USE_DONE_ZIP=True).\"\n", " )\n", "\n", " coords_str = decimal_to_hms(ra, dec)\n", " with open(coords_file, 'w') as f:\n", " f.write(coords_str)\n", "\n", " print(f\"Created coordinates file: {coords_file}\")\n", " print(f\"Coordinates: {coords_str}\")\n", " print(f\"Original coordinates: RA={ra:.6f}Β°, Dec={dec:.6f}Β°\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Satelite Observers\n", "\n", "Satellites data are identified through their photometry filename, which **must** end with a number; e.g., `Spitzer1.dat`. Each satellite is identified by a unique number, which **must match** the number that ephemerides filename ends with; e.g., `satellite1.txt`. All other files ending by anything that is not a number are considered as taken from a ground telescope.\n", "\n", "You can use the [JPL Horizons Tool](http://ssd.jpl.nasa.gov/horizons.cgi) to generate ephemerides files. The table settings should be:\n", "1. Check Astrometric RA & Dec\n", "2. Check Observer range & range-rate\n", "3. Date/time format should be Julian Day calendar\n", "4. Angle format should be decimal degrees\n", "5. Range units should be astronomical units.\n", "\n", "These tables can also be generated using astroquery and the horiszons API." ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found existing ephemerides file: /Users/malpas.1/Code/data-challenge-notebooks/Extras/satelite1.txt (skipping generation)\n", "Renamed rtmodel_event001/Data/TelescopeB.dat to rtmodel_event001/Data/Telescope1.dat for mock parallax fitting\n" ] } ], "source": [ "from astroquery.jplhorizons import Horizons\n", "os.chdir(wd) # ensure we're in the original working directory\n", "\n", "def generate_ephemerides_file(filename, start_time, end_time, step, location, id='@0'):\n", " \"\"\"Generate ephemerides file using astroquery and JPL Horizons API\"\"\"\n", " path = Path(filename)\n", " path.parent.mkdir(parents=True, exist_ok=True) # <-- make sure directory exists\n", "\n", " obj = Horizons(id=id, location=location, epochs={'start': start_time, 'stop': end_time, 'step': step})\n", " eph = obj.ephemerides()\n", "\n", " with path.open('w') as f:\n", " f.write(\"# Ephemerides for RTModel\\n\")\n", " f.write(\"# Columns: JD RA(deg) Dec(deg) Range(AU) Range-rate(km/s)\\n\")\n", " for row in eph:\n", " jd = row['datetime_jd']\n", " ra_deg = row['RA']\n", " dec_deg = row['DEC']\n", " range_au = row['delta']\n", " range_rate_kms = row['delta_rate'] * 1731.456\n", " f.write(f\"{jd:.5f} {ra_deg:.6f} {dec_deg:.6f} {range_au:.6f} {range_rate_kms:.3f}\\n\")\n", "\n", "\n", "ephem_file = wd / \"satelite1.txt\"\n", "\n", "if os.path.exists(ephem_file):\n", " print(f\"Found existing ephemerides file: {ephem_file} (skipping generation)\")\n", "else:\n", " # generate and L2 ephemerides file for Jan 1, 2027 to Dec 31, 2027 with daily cadence\n", " start_time = '2027-01-01'\n", " end_time = '2027-12-31'\n", " location = '500' # L2 point\n", " id = '@-170' # JPL Horizons ID for L2 point\n", " generate_ephemerides_file(ephem_file, \n", " start_time=start_time, \n", " end_time=end_time, \n", " step='1d', \n", " location=location, id=id)\n", " print(f\"Generated ephemerides file: {ephem_file}\")\n", " \n", "# rename Data/TelescopeB.dat as Data/Telescope1.dat for mock parallax fitting\n", "telescope_b_file = os.path.join(data_dir, 'TelescopeB.dat')\n", "telescope_1_file = os.path.join(data_dir, 'Telescope1.dat')\n", "if os.path.exists(telescope_1_file):\n", " print(f\"Found existing {telescope_1_file} (skipping rename)\")\n", "elif os.path.exists(telescope_b_file):\n", " os.rename(telescope_b_file, telescope_1_file)\n", " print(f\"Renamed {telescope_b_file} to {telescope_1_file} for mock parallax fitting\")" ] }, { "cell_type": "markdown", "metadata": { "id": "rtmodel_basic_usage" }, "source": [ "### 3.3 Basic Usage\n", "\n", "RTModel is designed to be simple to use - just set up your data and run the analysis. The tool automatically determines the best model type." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "rtmodel_initialization" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "*********************\n", "**** RTModel ****\n", "*********************\n", "Event name: /Users/malpas.1/Code/data-challenge-notebooks/Extras/rtmodel_event001\n", "Number of processors: 8\n", "RTModel initialized for event: rtmodel_event001\n", "Available processors: 8\n" ] } ], "source": [ "#@title Initialize RTModel\n", "os.chdir(wd) # ensure we're in the original working directory\n", "\n", "# Create RTModel instance\n", "rtm = RTModel.RTModel(EVENT_DIR)\n", "\n", "# Check number of processors (RTModel uses all available by default)\n", "print(f\"RTModel initialized for event: {EVENT_DIR}\")\n", "print(f\"Available processors: {rtm.nprocessors}\")\n", "\n", "# You can limit the number of processors if needed\n", "# rtm.set_processors(4) # Use only 4 processors" ] }, { "cell_type": "markdown", "metadata": { "id": "g-VGrCG-zjfz" }, "source": [ "RTModel will automatically:\n", " 1. Pre-process the data\n", " 2. Generate initial conditions\n", " 3. Fit all model categories\n", " 4. Select best models\n", " 5. Provide final assessment" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "id": "rtmodel_run" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting RTModel analysis...\n", "This may take several minutes depending on your machine.\n", "RTModel will fit 7 different model categories:\n", " PS: Single-lens-single-source\n", " PX: Single-lens-single-source with parallax\n", " BS: Single-lens-binary-source\n", " BO: Single-lens-binary-source with xallarap\n", " LS: Binary-lens-single-source\n", " LX: Binary-lens-single-source with parallax\n", " LO: Binary-lens-single-source with orbital motion\n", "o Mon Feb 16 23:16:17 2026\n", "- Analyzing event: /Users/malpas.1/Code/data-challenge-notebooks/Extras/rtmodel_event001\n", "- Launching: Reader\n", " Pre-processing data...\n", " OK\n", "o Mon Feb 16 23:16:17 2026\n", "- Launching: InitCond\n", " Setting initial conditions...\n", "Peaks: 8550.1520 8555.3180 \n", " OK\n", "o Mon Feb 16 23:16:18 2026\n", "- Single-lens-Single-source fits\n", "Fits completed: 100%|\u001b[32mβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ\u001b[0m| 120/120 [00:03<00:00, 38.71it/s]\n", " OK\n", "o Mon Feb 16 23:16:21 2026\n", "- Selecting models for Single-lens-Single-source fits\n", " OK\n", "o Mon Feb 16 23:16:21 2026\n", "- Single-lens-Single-source fits with parallax\n", "Fits completed: 100%|\u001b[32mβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ\u001b[0m| 8/8 [00:00<00:00, 13.33it/s]\n", " OK\n", "o Mon Feb 16 23:16:21 2026\n", "- Selecting models for Single-lens-Single-source fits with parallax\n", " OK\n", "o Mon Feb 16 23:16:21 2026\n", "- Single-lens-Binary-source fits\n", "Fits completed: 100%|\u001b[32mβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ\u001b[0m| 135/135 [00:05<00:00, 26.60it/s]\n", " OK\n", "o Mon Feb 16 23:16:26 2026\n", "- Selecting models for Single-lens-Binary-source fits\n", " OK\n", "o Mon Feb 16 23:16:28 2026\n", "- Single-lens-Binary-source fits with xallarap\n", "Fits completed: 100%|\u001b[32mβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ\u001b[0m| 8/8 [00:01<00:00, 7.33it/s]\n", " OK\n", "o Mon Feb 16 23:16:29 2026\n", "- Selecting models for Single-lens-Binary-source fits with xallarap\n", " OK\n", "o Mon Feb 16 23:16:29 2026\n", "- Binary-lens-Single-source fits\n", "Fits completed: 100%|\u001b[32mβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ\u001b[0m| 250/250 [53:35<00:00, 12.86s/it]\n", "timed out fits: 18\n", " OK\n", "o Tue Feb 17 00:10:05 2026\n", "- Selecting models for Binary-lens-Single-source fits\n", " OK\n", "o Tue Feb 17 00:10:05 2026\n", "- Binary-lens-Single-source fits with parallax\n", "Fits completed: 100%|\u001b[32mβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ\u001b[0m| 6/6 [00:32<00:00, 5.44s/it]\n", " OK\n", "o Tue Feb 17 00:10:38 2026\n", "- Selecting models for Binary-lens-Single-source fits with parallax\n", " OK\n", "o Tue Feb 17 00:10:38 2026\n", "- Binary-lens-Single-source fits with orbital motion\n", "Fits completed: 100%|\u001b[32mβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ\u001b[0m| 6/6 [00:16<00:00, 2.67s/it]\n", " OK\n", "o Tue Feb 17 00:10:54 2026\n", "- Selecting models for Binary-lens-Single-source fits with orbital motion\n", " OK\n", "o Tue Feb 17 00:10:54 2026\n", "- Launching: Finalizer\n", " Making final assessment for this event\n", " PS: 969627.358389\n", " PX: 951366.286448\n", " BS: 554847.751431\n", " BO: 546298.188225\n", " LS: 6225.255535\n", " LX: 5864.781521\n", " LO: 962642.253701\n", " \n", " BestPlanetary: N/A\n", " BestBinary: 5864.781521\n", " \n", " Successful: Binary lens with parallax\n", " ----\n", " Number of alternative models: 2\n", " \n", " chisquare model\n", " 5864.781521 LX0000-2.txt\n", " 5892.035351 LX0001-2.txt\n", " OK\n", "o Tue Feb 17 00:10:54 2026\n", "- Cleaning up preliminary models\n", "- Analysis of /Users/malpas.1/Code/data-challenge-notebooks/Extras/rtmodel_event001 successfully completed!\n", "o Tue Feb 17 00:10:54 2026\n" ] } ], "source": [ "#@title Run RTModel Analysis\n", "os.chdir(wd) # ensure we're in the original working directory\n", "\n", "# This is the main command - RTModel does everything automatically!\n", "print(\"Starting RTModel analysis...\")\n", "print(\"This may take several minutes depending on your machine.\")\n", "print(\"RTModel will fit 7 different model categories:\")\n", "print(\" PS: Single-lens-single-source\")\n", "print(\" PX: Single-lens-single-source with parallax\")\n", "print(\" BS: Single-lens-binary-source\")\n", "print(\" BO: Single-lens-binary-source with xallarap\")\n", "print(\" LS: Binary-lens-single-source\")\n", "print(\" LX: Binary-lens-single-source with parallax\")\n", "print(\" LO: Binary-lens-single-source with orbital motion\")\n", "\n", "# Set RUN to True to actually run RTModel\n", "RUN = False\n", "if RUN:\n", " rtm.run()\n", "else:\n", " print(\"\\nNote: RTModel run skipped out to avoid long execution.\")\n", " print(\"Set RUN to True to perform the actual analysis.\")" ] }, { "cell_type": "markdown", "metadata": { "id": "rtmodel_output" }, "source": [ "### 3.4 Understanding RTModel Output\n", "\n", "After running RTModel, you'll get several output files and directories. Let's explore what each contains.\n", "\n", "**RTModel creates the following output structure:**\n", "\n", "```\n", "/\n", "β”œβ”€β”€ Data/ # Original input data\n", "β”œβ”€β”€ ini/ # Configuration files\n", "β”œβ”€β”€ InitCond/ # Initial conditions for fitting\n", "β”œβ”€β”€ Models/ # Selected models for each category\n", "β”œβ”€β”€ FinalModels/ # Best models from final assessment\n", "β”œβ”€β”€ LCToFit.txt # Pre-processed data\n", "β”œβ”€β”€ FilterToData.txt # Dataset mapping\n", "β”œβ”€β”€ spline.txt # Spline approximation points\n", "└── nature.txt # Final assessment and best models\n", "```\n", "\n", "**Key output files:**\n", " `nature.txt`: Contains the final assessment and list of best models\n", " `FinalModels/`: Contains the best model files with parameters and uncertainties\n", " `Models/`: Contains selected models for each category\n", "\n", "#### Understanding the nature.txt File\n", "\n", "The `nature.txt` file contains RTModel's assessment of the event\n", "\n", "**The nature.txt file contains:**\n", "\n", "1. Best chi-square for each model category:\n", " - `PS`: Single-lens-single-source\n", " - `PX`: Single-lens-single-source with parallax\n", " - `BS`: Single-lens-binary-source\n", " - `BO`: Single-lens-binary-source with xallarap\n", " - `LS`: Binary-lens-single-source\n", " - `LX`: Binary-lens-single-source with parallax\n", " - `LO`: Binary-lens-single-source with orbital motion\n", "\n", "2. Final assessment of the event nature\n", "\n", "3. List of proposed best models\n", "\n", "4. Model comparison and interpretation\n", "\n", "**Example `nature.txt` content:**\n", "\n", "```nature.txt\n", "*********************\n", "**** RTModel ****\n", "*********************\n", "\n", "Best chi-square for each category:\n", "PS: 1234.56 (Single-lens-single-source)\n", "PX: 1230.45 (Single-lens-single-source with parallax)\n", "BS: 1235.67 (Single-lens-binary-source)\n", "BO: 1232.34 (Single-lens-binary-source with xallarap)\n", "LS: 1238.90 (Binary-lens-single-source)\n", "LX: 1235.12 (Binary-lens-single-source with parallax)\n", "LO: 1236.78 (Binary-lens-single-source with orbital motion)\n", "\n", "Final Assessment:\n", "This appears to be a single-lens event with parallax effects.\n", "The best model is PX with chi-square = 1230.45\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "rtmodel_model_categories" }, "source": [ "### 3.5 Model Categories\n", "\n", "RTModel fits 7 different model categories by default. Each has a specific label and parameter set.\n", "\n", "#### RTModel Model Categories\n", "\n", "| Label | Model | Parameters | Description |\n", "|-------|-------|------------|-------------|\n", "| `PS` | Single-lens-single-source | 4 | Basic microlensing (`t0`, `u0`, `tE`, `rho`) |\n", "| `PX` | Single-lens-single-source with parallax | 6 | Basic + parallax effects |\n", "| `BS` | Single-lens-binary-source | 7 | Two source stars, one lens |\n", "| `BO` | Single-lens-binary-source with xallarap | 10 | Binary source + orbital motion |\n", "| `LS` | Binary-lens-single-source | 7 | Two lens components (planetary) |\n", "| `LX` | Binary-lens-single-source with parallax | 9 | Binary lens + parallax |\n", "| `LO` | Binary-lens-single-source with orbital motion | 12 | Binary lens + orbital motion |\n", "\n", "#### Additional categories available:\n", "- `LK`: Binary-lens-single-source with eccentric orbital motion (14 parameters)\n", "\n", "**Parameter details for each model category:**\n", "\n", "| Parameter | PS | PX | BS | BO | LS | LX | LO | Notes |\n", "|-----------|----|----|----|----|----|----|----|-------|\n", "| `u0` | βœ…* | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | Impact parameter |\n", "| `tE` | βœ…* | βœ…* | βœ…* | βœ…* | βœ…* | βœ…* | βœ…* | Einstein time in days |\n", "| `t0` | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | Closest approach time in HJD |\n", "| `rho` | βœ…* | βœ…* | βœ…* | βœ…* | βœ…* | βœ…* | βœ…* | Source radius |\n", "| `piN` | ❌ | βœ… | ❌ | ❌ | ❌ | βœ… | ❌ | Parallax component along North |\n", "| `piE` | ❌ | βœ… | ❌ | ❌ | ❌ | βœ… | ❌ | Parallax component along East |\n", "| `s` | ❌ | ❌ | ❌ | ❌ | βœ…* | βœ…* | βœ…* | Separation between lenses |\n", "| `q` | ❌ | ❌ | ❌ | ❌ | βœ…* | βœ…* | βœ…* | Mass ratio |\n", "| `alpha` | ❌ | ❌ | ❌ | ❌ | βœ… | βœ… | βœ… | Angle of source trajectory |\n", "| `q_S` | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | ❌ | Source flux ratio |\n", "| `rho_S` | ❌ | ❌ | βœ…* | βœ…* | ❌ | ❌ | ❌ | Source separation |\n", "| `theta_S` | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | ❌ | Source angle |\n", "\n", "> key: `*` - ln scale" ] }, { "cell_type": "markdown", "metadata": { "id": "rtmodel_visualization" }, "source": [ "### 3.6 Visualization and Results \n", "\n", "RTModel includes built-in visualization tools through the `plotmodel` subpackage.\n", "\n", "1. Light curve plotting:\n", "\n", " ```python\n", " plm.plotmodel(eventname, modelfile) # Plot light curve with model\n", " plm.plotmodel(eventname, modelfile, show_caustics=True) # Show caustics\n", " ```\n", "2. Animation capabilities:\n", "\n", " ```python\n", " plm.animate_model(eventname, modelfile) # Create animated GIF\n", " ```\n", "3. Astrometric plots:\n", "\n", " ```python\n", " plm.plot_astrometry(eventname, modelfile) # Plot astrometric trajectory\n", " ```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "rtmodel_plotting" }, "outputs": [], "source": [ "#@title RTModel Plotting\n", "os.chdir(wd) # ensure we're in the original working directory\n", "\n", "# Example usage:\n", "\n", "# After running RTModel and getting results, check the model \n", "# file names in the 'FinalModels' directory created by RTModel, \n", "# and update the modelfile argument accordingly.\n", "\n", "if RUN:\n", " print(\"\\nPlotting RTModel results...\")\n", " plm.plotmodel(\n", " eventname='rtmodel_event001', \n", " modelfile='FinalModels/LX0001-1.txt'\n", " )\n", " plm.plotmodel(\n", " eventname='rtmodel_event001', \n", " modelfile='FinalModels/LX0001-2.txt', \n", " show_caustics=True\n", " )" ] }, { "cell_type": "markdown", "metadata": { "id": "HUsKSsM6zjf0" }, "source": [ "#### How to analyze RTModel results:\n", "\n", "1. Check nature.txt for the final assessment\n", "\n", " - Best chi-square for each model category\n", " - RTModel's interpretation of the event\n", " - List of proposed best models\n", "\n", "2. Examine FinalModels/ directory\n", "\n", " - Each file contains parameters and uncertainties\n", " - First line: parameters + fluxes + chi-square\n", " - Second line: parameter uncertainties\n", " - Remaining lines: covariance matrix\n", "\n", "3. Compare models using chi-square\n", "\n", " - Lower chi-square = better fit\n", " - Consider degrees of freedom (more parameters = higher expected chi-square)\n", " - Use F-test or AIC/BIC for model comparison\n", "\n", "4. Validate results\n", "\n", " - Check parameter uncertainties\n", " - Examine residuals\n", " - Look for systematic effects\n", " - Consider physical plausibility" ] }, { "cell_type": "markdown", "metadata": { "id": "rtmodel_advanced" }, "source": [ "### 3.7 Advanced Features\n", "\n", "RTModel offers several advanced features for customization and control." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6gSrs-LPzjf0" }, "outputs": [], "source": [ "#@title: 1. Model Category Selection:\n", "\n", "# Fit only specific model categories\n", "rtm.set_model_categories(['PS', 'LS']) # Only single-lens and binary-lens" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PQ-lVXpBzjf0" }, "outputs": [], "source": [ "#@title: 2. Fitting Options:\n", "\n", "# Configure Levenberg-Marquardt fitting\n", "rtm.config_LevMar(nfits=10, timelimit=1200.0, maxsteps=100)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vPcvIE_fzjf0" }, "outputs": [], "source": [ "#@title: 3. Parameter Constraints:\n", "\n", "# Set gaussian constraints on parameters\n", "rtm.set_constraints('tE', 25.0, 2.0) # tE = 25 Β± 2 days" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "OiklV_Dgzjf0" }, "outputs": [], "source": [ "#@title: 4. Parallel Processing:\n", "\n", "# Control number of processors\n", "rtm.set_processors(8) # Use 8 processors" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "MxJrzHdHzjf0" }, "outputs": [], "source": [ "#@title: 5. Step-by-step Execution:\n", "\n", "# Run individual steps manually\n", "rtm.Reader() # Data pre-processing\n", "rtm.InitCond() # Generate initial conditions\n", "rtm.launch_fits('PS') # Fit specific category\n", "rtm.ModelSelection() # Select best models\n", "rtm.FinalAssessment() # Final assessment" ] }, { "cell_type": "markdown", "metadata": { "id": "rtmodel_best_practices" }, "source": [ "### 3.8 RTModel Best Practices\n", "\n", "**1. Data Quality:**\n", "- Ensure data is properly formatted\n", "- Check for systematic errors\n", "- Verify coordinate accuracy\n", "\n", "**2. Computational Resources:**\n", "- Use all available processors for speed\n", "- Allow sufficient time (1-3 hours typical)\n", "- Ensure adequate disk space\n", "\n", "**3. Model Interpretation:**\n", "- Don't rely solely on chi-square\n", "- Consider physical plausibility\n", "- Check parameter uncertainties\n", "- Validate against known physics\n", "\n", "**4. Troubleshooting:**\n", "- Check ini/ directory for configuration\n", "- Examine error messages in output\n", "- Verify data format compliance\n", "- Use step-by-step execution for debugging\n", "\n", "**5. Results Validation:**\n", "- Compare with other fitting codes\n", "- Check for systematic residuals\n", "- Verify parameter correlations\n", "- Consider alternative models" ] }, { "cell_type": "markdown", "metadata": { "id": "rtmodel_comparison" }, "source": [ "### 3.9 RTModel vs Other Tools\n", "\n", "RTModel offers a unique approach compared to other microlensing fitting tools." ] }, { "cell_type": "markdown", "metadata": { "id": "rtmodel_comparison_table" }, "source": [ "#### Comparison with Other Tools \n", "\n", "**RTModel vs Other Microlensing Tools:**\n", "\n", "| Feature | RTModel | MulensModel | pyLIMA |\n", "|---------|---------|-------------|--------|\n", "| Model Selection | Automatic | Manual | Manual |\n", "| Ease of Use | Moderate set up
Very easy to run | Moderate | Moderate |\n", "| Speed | Fast (parallel) | Variable | Variable |\n", "| Automation | High | Low | Low |\n", "| Customization | Moderate | High | High |\n", "| Visualization | Built-in | Basic | Good |\n", "| Parallel Processing | Yes | Manual | Manual |\n", "\n", "**RTModel Advantages:**\n", "- Hands-off operation - just set up data and run\n", "- Automatic model selection and interpretation\n", "- Template library for efficient binary lens fitting\n", "- Built-in assessment and visualization\n", "- Parallel processing for speed\n", "\n", "**RTModel Limitations:**\n", "- Less customization than manual tools\n", "- Requires specific data format\n", "- Limited to supported model categories\n", "- May miss subtle effects in complex events" ] }, { "cell_type": "markdown", "metadata": { "id": "rtmodel_astrometric" }, "source": [ "### 3.10 Astrophotometric Fitting (RTModel v3.0)\n", "\n", "**NEW IN RTModel v3.0!** RTModel now supports combined photometric and astrometric fitting, making it the first tool to offer comprehensive astrophotometric microlensing analysis. This capability is crucial for space-based observations like Roman, where milliarcsecond astrometric precision enables measurement of the centroid trajectory during microlensing events.\n", "\n", "**Key Astrometric Features:**\n", "- **Combined Fitting**: Simultaneous photometric and astrometric parameter estimation\n", "- **Centroid Trajectory**: Models the astrometric shift of images during events\n", "- **Proper Motion**: Measures source and lens proper motions\n", "- **Einstein Angle**: Direct measurement of ΞΈ_E from astrometry\n", "- **Source Parallax**: Geometric parallax of the source star\n", "- **Visualization**: Built-in astrometric plotting and trajectory visualization" ] }, { "cell_type": "markdown", "metadata": { "id": "rtmodel_astrometric_data" }, "source": [ "#### 3.10.1 Astrophotometric Data Format\n", "\n", "Astrophotometric datasets include both photometric and astrometric information in a single file.\n", "\n", "Astrophotometric datasets have 7 columns: `Mag`, `err`, `HJD-2450000`, `Dec`, `errDec`, `RA`, `errRA`\n", "\n", "**Example astrophotometric data:**\n", "\n", "```\n", "# Mag err HJD-2450000 Dec errDec RA errRA\n", "19.0232 0.012 8370.1223 1.2534 1.0 0.0165 1.0\n", "19.0150 0.011 8370.2421 1.7510 1.1 -0.5422 1.1\n", "19.0034 0.011 8370.3697 1.1190 1.1 -0.1981 1.1\n", "18.9712 0.010 8370.4911 1.4281 1.0 0.2119 1.0\n", "18.9592 0.011 8370.6114 1.3005 0.9 0.3982 1.0\n", "18.9109 0.009 8370.8234 1.6233 1.0 0.5121 1.0\n", "18.8798 0.009 8371.0092 2.0223 1.2 0.9411 1.1\n", "```\n", "\n", "**Column descriptions:**\n", "\n", " - `Mag`: Magnitude\n", " - `err`: Photometric error\n", " - `HJD-2450000`: Heliocentric Julian Date - 2450000\n", " - `Dec`: Declination offset in milliarcseconds\n", " - `errDec`: Declination error in milliarcseconds\n", " - `RA`: Right Ascension offset in milliarcseconds\n", " - `errRA`: Right Ascension error in milliarcseconds\n", "\n", "> Note: Dec and RA are angular displacements from a fixed reference point\n", "in the North and East directions respectively." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "rtmodel_astrometric_create" }, "outputs": [], "source": [ "#@title Create Astrophotometric Data\n", "# Create sample astrophotometric data\n", "def create_astrophotometric_data(filename, times, mags, mag_errs):\n", " \"\"\"Create RTModel-compatible astrophotometric data file\"\"\"\n", " with open(filename, 'w') as f:\n", " f.write(\"# Mag err HJD-2450000 Dec errDec RA errRA\\n\")\n", " for t, m, e in zip(times, mags, mag_errs):\n", " # Convert to HJD-2450000 format\n", " hjd_offset = t - 2450000\n", "\n", " # Generate synthetic astrometric data\n", " # Simulate centroid motion during microlensing event\n", " t0 = 2459123.5 # Peak time\n", " tE = 25.0 # Einstein time\n", "\n", " # Simple astrometric model (centroid shift)\n", " u = abs(t - t0) / tE\n", " if u < 0.1: # Near peak\n", " dec_offset = 2.0 * np.exp(-u**2) # Peak shift\n", " ra_offset = 1.5 * np.exp(-u**2) # Peak shift\n", " else:\n", " dec_offset = 0.5 * np.exp(-u**2) # Background\n", " ra_offset = 0.3 * np.exp(-u**2) # Background\n", "\n", " # Add noise\n", " dec_offset += np.random.normal(0, 0.1)\n", " ra_offset += np.random.normal(0, 0.1)\n", "\n", " # Astrometric errors (typically 0.5-1.0 mas)\n", " dec_err = 0.8 + 0.2 * np.random.random()\n", " ra_err = 0.8 + 0.2 * np.random.random()\n", "\n", " f.write(f\"{m:.4f} {e:.3f} {hjd_offset:.4f} {dec_offset:.4f} {dec_err:.1f} {ra_offset:.4f} {ra_err:.1f}\\n\")\n", "\n", "# Create astrophotometric data file\n", "astro_data_file = os.path.join(data_dir, 'roman_astrometric.dat')\n", "create_astrophotometric_data(astro_data_file, times, mag, mag_err)\n", "\n", "print(f\"Created astrophotometric data file: {astro_data_file}\")\n", "print(f\"Data points: {len(times)}\")\n", "\n", "# Show first few lines\n", "with open(astro_data_file, 'r') as f:\n", " lines = f.readlines()\n", " print(\"\\nFirst 5 lines of astrophotometric data:\")\n", " for i, line in enumerate(lines[:5]):\n", " print(f\" {i+1}: {line.strip()}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "rtmodel_astrometric_parameters" }, "source": [ "#### 3.10.2 Astrophotometric Parameters\n", "\n", "When RTModel detects astrophotometric data, it automatically adds 4 additional parameters to the fit:\n", "\n", "| Parameter | Meaning | Physical Significance |\n", "|-----------|---------|---------------------|\n", "| `muS_Dec` | Source proper motion component in North direction (mas/yr) | Source motion perpendicular to line of sight |\n", "| `muS_RA` | Source proper motion component in East direction (mas/yr) | Source motion perpendicular to line of sight |\n", "| `piS` | Geometric parallax of the source (mas) | Distance to source star |\n", "| `thetaE` | Einstein angle (mas) | Lens mass and distance (ΞΈ_E = √(4GM/cΒ²D_L)) |\n", "\n", "**Model Categories for Astrophotometric Fits:**\n", "* `PX`: Single-lens-single-source with parallax (10 parameters)\n", "* `BO`: Single-lens-binary-source with xallarap (14 parameters)\n", "* `LX`: Binary-lens-single-source with parallax (13 parameters)\n", "* `LO`: Binary-lens-single-source with orbital motion (16 parameters)\n", "* `LK`: Binary-lens-single-source with eccentric orbital motion (18 parameters)\n", "\n", "> Note: No static models are fitted in astrophotometric mode because parallax is always included to model the centroid trajectory." ] }, { "cell_type": "markdown", "metadata": { "id": "rtmodel_astrometric_fitting" }, "source": [ "#### 3.10.3 Astrophotometric Fitting\n", "\n", "RTModel automatically detects astrophotometric datasets and switches to combined fitting mode.\n", "\n", "**RTModel Astrophotometric Fitting:**\n", "\n", "1. Automatic Detection:\n", "\n", " RTModel automatically detects astrophotometric datasets by checking for 7-column format (vs 3-column for photometric)\n", "\n", "2. Combined Chi-Square:\n", "\n", " * $\\chi^2_{total} = \\chi^2_{photometric} + \\chi^2_{astrometric}$\n", " * Both contributions are weighted appropriately\n", "\n", "3. Enhanced Parameter Space:\n", "\n", " - 4 additional astrometric parameters\n", " - Parallax always included (no static models)\n", " - More complex model selection\n", "\n", "4. Example Usage:\n", "\n", " ```python\n", " # RTModel automatically detects astrophotometric data\n", " rtm = RTModel.RTModel('astro_event')\n", " rtm.run() # Automatically uses astrophotometric fitting\n", " ```\n", "\n", "5. Output Structure:\n", "\n", " * Model files contain: `nps + 4*ntel + 1` parameters\n", " - `nps`: model parameters + 4 astrometric parameters\n", " - `4*ntel`: fluxes and centroid positions for each telescope\n", " - `+1`: total chi-square" ] }, { "cell_type": "markdown", "metadata": { "id": "rtmodel_astrometric_visualization" }, "source": [ "#### 3.10.4 Astrometric Visualization\n", "\n", "RTModel includes specialized plotting functions for astrometric data visualization.\n", "\n", "**RTModel Astrometric Plotting Functions:**\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NbOKvS0Qzjf0" }, "outputs": [], "source": [ "#@title: 1. Combined Light Curve and Astrometry\n", "# Shows both photometric light curve and astrometric parameters\n", "\n", "if RUN:\n", " myplot = plm.plotmodel(\n", " 'rtmodel_event001', \n", " 'LX0001',\n", " modelfile='FinalModels/LX0001-1.txt'\n", " )\n", " myplot.show_lightcurve()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "CKpxmm6Jzjf0" }, "outputs": [], "source": [ "#@title: 2. Centroid Trajectory in the Sky:\")\n", "# Shows the centroid trajectory as a function of time\n", "# Displays RA vs Dec with error ellipses\n", "\n", "if RUN:\n", " myplot.showastrometry()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "mc3OGZL3zjf0" }, "outputs": [], "source": [ "#@title: 3. Individual Astrometric Components:\")\n", "\n", "if RUN:\n", " myplot.showastrometryRA() # RA vs time\n", " myplot.showastrometryDec() # Dec vs time" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qC21jJ1szjf0" }, "outputs": [], "source": [ "#@title: 4. Multiple Telescope Support:\")\n", "# Different telescopes may have different blending fractions\n", "\n", "if RUN:\n", " myplot.showastrometry(1) # Show astrometry for telescope 1" ] }, { "cell_type": "markdown", "metadata": { "id": "MezgDm8tzjf1" }, "source": [ "Key Features:\n", "\n", " - Automatic error ellipse plotting\n", " - Time evolution of centroid position\n", " - Comparison with observed astrometric data\n", " - Proper motion vector visualization" ] }, { "cell_type": "markdown", "metadata": { "id": "oOvUtqTpzjf1" }, "source": [ "#### 3.10.5 Physical Interpretation of Astrophotometric Parameters\n", "\n", "1. Einstein Angle ($\\theta_E$)\n", "> $\\theta_E = \\sqrt{(4GM/c^2D_L)}$\n", "- Direct measurement of lens mass and distance\n", "- Breaks the mass-distance degeneracy\n", "- Typical values: 0.1-1.0 mas for stellar lenses\n", "\n", "2. Source Proper Motion ($\\mu_S$)\n", "> $\\mu_S = v_S / D_S$\n", "- Tangential velocity of source star\n", "- Helps constrain source distance and kinematics\n", "- Typical values: 1-10 mas/yr for Galactic sources\n", "\n", "3. Source Parallax ($\\pi_S$)\n", "$\\pi_S = 1/D_S$\n", "- Geometric parallax of source star\n", "- Provides independent distance measurement\n", "- Typical values: $0.1-1.0 \\,\\text{mas}$ for Galactic sources\n", "\n", "4. Centroid Trajectory\n", "- Shows the motion of the light centroid during the event\n", "- Amplitude depends on lens mass and source-lens separation\n", "- Shape reveals lens geometry (single vs binary)\n", "- Duration related to Einstein time\n", "\n", "5. Advantages of Astrophotometric Fitting\n", "- Breaks degeneracies in lens mass and distance\n", "- Provides independent constraints on source properties\n", "- Enables direct measurement of Einstein angle\n", "- Improves parameter precision and accuracy\n", "- Essential for space-based microlensing surveys" ] }, { "cell_type": "markdown", "metadata": { "id": "PxRwY_WMzjf1" }, "source": [ "## 4. popclass: Probabilistic Classification of Microlensing Lenses\n", "\n", "### What is popclass?\n", "\n", "**popclass** is a Python package developed by LLNL (lead: Peter McGill) for *probabilistic classification* of the lens in a gravitational microlensing event. It bridges the gap between event posteriors (from light curve modeling) and population synthesis simulations of the Milky Way, allowing you to infer the probability that a given event was caused by a star, white dwarf, neutron star, or black hole.\n", "\n", "- **Key use:** Given posterior samples (e.g., from MulensModel, pyLIMA, or any Bayesian fit) and a Galactic population model, popclass computes the probability that the lens belongs to each class.\n", "- **Why use it?** It enables population-level inference, e.g., identifying likely black hole events, and is designed for the era of large surveys (Roman, Rubin)." ] }, { "cell_type": "markdown", "metadata": { "id": "Pi7iJf5Dzjf1" }, "source": [ "### 4.1 Installation \n", "\n", "```bash\n", "pip install popclass\n", "```\n", "or via conda:\n", "```bash\n", "conda install -c conda-forge popclass\n", "```\n", "\n", "### 4.2 Basic Usage Example \n", "\n", "Suppose you have posterior samples for an event in log10(tE) and log10(piE):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ledRhbJczjf1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'black_hole': 0.09671087911996425, 'neutron_star': 0.0014161261827535846, 'star': 0.7049543986944241, 'white_dwarf': 0.19691859600285797}\n" ] } ], "source": [ "from popclass.posterior import Posterior\n", "from popclass.model import PopulationModel\n", "from popclass.classify import classify\n", "import popclass.model as pm\n", "\n", "#copy_arrays is not supported in our version of asdf (5.1.0) but popclass \n", "# expects it, so we monkey-patch it here to avoid errors.\n", "_asdf_open = pm.asdf.open\n", "def _open_compat(*args, **kwargs):\n", " kwargs.pop(\"copy_arrays\", None)\n", " return _asdf_open(*args, **kwargs)\n", "\n", "pm.asdf.open = _open_compat\n", "\n", "# Mock posterior samples\n", "NUM_POSTERIOR_SAMPLES = 10000\n", "logtE = np.random.normal(loc=2, scale=0.1, size=NUM_POSTERIOR_SAMPLES)\n", "logpiE = np.random.normal(loc=-1, scale=0.5, size=NUM_POSTERIOR_SAMPLES)\n", "posterior_samples = np.vstack((logtE, logpiE)).T\n", "prior_density = 0.028 * np.ones(NUM_POSTERIOR_SAMPLES) # uniform prior\n", "\n", "# Wrap in popclass objects\n", "posterior = Posterior(samples=posterior_samples, parameter_labels=['log10tE', 'log10piE'])\n", "inference_data = posterior.to_inference_data(prior_density)\n", "\n", "# Load a pre-built population model (e.g., PopSyCLE)\n", "popsycle = PopulationModel.from_library('popsycle_singles_sukhboldn20')\n", "\n", "# Classify!\n", "classification = classify(population_model=popsycle, inference_data=inference_data, parameters=['log10tE', 'log10piE'])\n", "print(classification)" ] }, { "cell_type": "markdown", "metadata": { "id": "0UOACuOuzjf1" }, "source": [ "You should get something like:\n", "\n", "**Output:**\n", "`{'black_hole': 0.09, 'neutron_star': 0.001, 'star': 0.71, 'white_dwarf': 0.20}`\n", "\n", "These are the relative probabilities of our made up lens belonging to each of the classified groups.\n", "\n", "If you have run an MCMC fit with parallax before this step in the notebook, consider running this classifier on your posterior sample as an exercise. Visit the [`popclass` documentation site](https://popclass.readthedocs.io/en/latest/), if you get stuck.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "os268jhrzjf1" }, "source": [ "### 4.3 How It Works\n", "\n", "- **Inputs:** Posterior samples for event parameters (e.g., `tE`, `piE`), their prior densities, and a Galactic population model (e.g., from PopSyCLE).\n", "- **Method:** Uses Bayesian importance sampling to compare the event's posterior to simulated populations, computing the probability for each lens class.\n", "- **Output:** A dictionary of class probabilities (star, white dwarf, neutron star, black hole).\n", "\n", "**Mathematical summary:**\n", "\n", "$$\n", "p(\\text{class}_L| \\boldsymbol{d}, \\mathcal{G}) = \\frac{p(\\text{class}_L| \\mathcal{G})}{p(\\boldsymbol{d}| \\mathcal{G})}\n", " \\times \\frac{1}{S} \\sum _{c=0}^{S} \\frac{p(\\theta _c | \\text{class}_L, \\mathcal{G})}{\\pi(\\theta _{c})}\n", "$$\n", "\n", "where:\n", "- $ \\boldsymbol{d} $: event data\n", "- $ \\mathcal{G} $: Galactic model\n", "- $ \\theta_c $: posterior samples\n", "- $ \\pi(\\theta_c) $: prior density\n", "\n", "### 4.4 Population Models\n", "\n", "popclass comes with several pre-built population models (from PopSyCLE), e.g.:\n", "- `popsycle_singles_sukhboldn20`\n", "- `popsycle_singles_raithel18`\n", "- `popsycle_singles_spera15`\n", "\n", "You can also load your own models in ASDF format.\n", "\n", "### 4.5 Advanced Features\n", "\n", "- **Flexible parameter spaces:** Use any set of event parameters (e.g., `tE`, `piE`, `thetaE`, `blend_fraction`).\n", "- **Custom population models:** Supply your own simulation data in the required format.\n", "- **Uncertainty quantification:** Includes a \"None\" class to flag events not well explained by the model.\n", "- **ArviZ and PyMultiNest integration:** For handling posteriors from common Bayesian tools.\n", "- **Plotting:** Built-in tools for visualizing classification results.\n", "\n", "### 4.6 Best Practices & Caveats\n", "\n", "- **Parameter consistency:** Ensure your posterior and population model use the same parameterization (e.g., both in `log10(tE)`, not `tE`).\n", "- **Prior density:** Must match the parameter space of the population model (apply Jacobian if transforming variables).\n", "- **Model completeness:** If your event is outside the simulated parameter space, the \"None\" class will help flag this.\n", "- **Interpretation:** Probabilities are only as good as the population model and the event posterior.\n", "\n", "### 4.7 Further Reading \n", "\n", "- [popclass documentation](https://popclass.readthedocs.io)\n", "- [PopSyCLE population models](https://github.com/jluastro/PopSyCLE)\n", "- Perkins et al. (2024), Kaczmarek et al. (2024) β€” theoretical background\n", "\n", "> **popclass** is a powerful tool for population-level inference in microlensing, enabling robust, probabilistic classification of lens types for large event samples." ] }, { "cell_type": "markdown", "metadata": { "id": "_sVQQPtGzjf1", "tags": [ "colab-only" ] }, "source": [ "## Contributing\n", "\n", "We welcome contributions to improve this tutorial! Whether you want to add new tools, enhance existing sections, or fix issues, please check out our contributing guidelines below.\n", "\n", "
\n", " \n", " \"GitHub\"\n", " Contributing to This Tutorial\n", " \n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## About this Notebook\n", "\n", "**Author(s):** Amber Malpas, Ali Crisp\n", "**Maintainers:** RGES-PIT Working Group 9 \n", "**Last Updated:** 20 Nov 2025 \n", "**Contact:** malpas.1@osu.edu\n", "\n", "## Citations\n", "\n", "If you use `MulensModel`, `pyLIMA`, `RTModel`, `popClass`, or this notebook for published research, please cite the\n", "authors. Follow these links for more information about citing:\n", "\n", "* [Citing `MulensModel`](https://github.com/rpoleski/MulensModel/blob/master/CITATION.cff)\n", "* [Citing `pyLIMA`](https://github.com/ebachelet/pyLIMA?tab=readme-ov-file#citations)\n", "* [Citing `RTModel`](https://github.com/valboz/RTModel#attribution)\n", "* [Citing `popClass`](https://github.com/LLNL/popclass/blob/main/CITATION.cff)\n", "* [Citing **Roman Microlensing Data Challenge 2026 Notebooks**](https://github.com/rges-pit/data-challenge-notebooks/blob/main/zenodo.txt)\n", "\n", "### Citing Roman Microlensing Data Challenge 2026 Notebooks\n", "\n", "If you use our notebooks in your project, please cite:\n", "\n", "```\n", "Malpas, A., Murlidhar, A., Vandorou, K., KruszyΕ„ska, K., Crisp, A., & Vyas, M. (2026). Roman Microlensing Data Challenge 2026 Notebooks (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.18262183\n", "```\n", "\n", "**BibTeX:**\n", "```bibtex\n", "@software{malpas_2025_17806271,\n", " author = {Malpas, Amber and Murlidhar, Arjun and Vyas, Meet and Vandorou, Katie and KruszyΕ„ska, Katarzyna and Crisp, Ali},\n", " title = {Roman Microlensing Data Challenge 2026 Notebooks},\n", " month = dec,\n", " year = 2026,\n", " publisher = {Zenodo},\n", " version = {v1.0.0},\n", " doi = {10.5281/zenodo.18262183},\n", " url = {https://doi.org/10.5281/zenodo.18262183}\n", "}\n", "```\n", "\n", "\n", "\n", "" ] } ], "metadata": { "colab": { "collapsed_sections": [ "B5OeZIbHv2eB" ], "provenance": [] }, "kernelspec": { "display_name": "rges-pit-dc", "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": "3.11.14" } }, "nbformat": 4, "nbformat_minor": 0 }