{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "
\n", "\n", "**This is a fixed-text formatted version of a Jupyter notebook**\n", "\n", "- Try online[![Binder](https://static.mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.19?urlpath=lab/tree/tutorials/starting/analysis_1.ipynb)\n", "- You may download all the notebooks in the documentation as a\n", "[tar file](../../_downloads/notebooks-0.19.tar).\n", "- **Source files:**\n", "[analysis_1.ipynb](../../_static/notebooks/analysis_1.ipynb) |\n", "[analysis_1.py](../../_static/notebooks/analysis_1.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# High level interface\n", "\n", "## Prerequisites:\n", "\n", "- Understanding the gammapy data workflow, in particular what are DL3 events and instrument response functions (IRF).\n", "\n", "## Context\n", "\n", "This notebook is an introduction to gammapy analysis using the high level interface. \n", "\n", "Gammapy analysis consists in two main steps. \n", "\n", "The first one is data reduction: user selected observations are reduced to a geometry defined by the user. \n", "It can be 1D (spectrum from a given extraction region) or 3D (with a sky projection and an energy axis). \n", "The resulting reduced data and instrument response functions (IRF) are called datasets in Gammapy.\n", "\n", "The second step consists in setting a physical model on the datasets and fitting it to obtain relevant physical information.\n", "\n", "\n", "**Objective: Create a 3D dataset of the Crab using the H.E.S.S. DL3 data release 1 and perform a simple model fitting of the Crab nebula.**\n", "\n", "## Proposed approach:\n", "\n", "This notebook uses the high level `Analysis` class to orchestrate data reduction. In its current state, `Analysis` supports the standard analysis cases of joint or stacked 3D and 1D analyses. It is instantiated with an `AnalysisConfig` object that gives access to analysis parameters either directly or via a YAML config file. \n", "\n", "To see what is happening under-the-hood and to get an idea of the internal API, a second notebook performs the same analysis without using the `Analysis` class. \n", "\n", "In summary, we have to:\n", "\n", "- Create an `~gammapy.analysis.AnalysisConfig` object and edit it to define the analysis configuration:\n", " - Define what observations to use\n", " - Define the geometry of the dataset (data and IRFs)\n", " - Define the model we want to fit on the dataset.\n", "- Instantiate a `~gammapy.analysis.Analysis` from this configuration and run the different analysis steps\n", " - Observation selection\n", " - Data reduction\n", " - Model fitting\n", " - Estimating flux points\n", "\n", "Finally we will compare the results against a reference model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:10.245051Z", "iopub.status.busy": "2021-11-22T21:08:10.244151Z", "iopub.status.idle": "2021-11-22T21:08:10.415096Z", "shell.execute_reply": "2021-11-22T21:08:10.415342Z" } }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:10.417417Z", "iopub.status.busy": "2021-11-22T21:08:10.417108Z", "iopub.status.idle": "2021-11-22T21:08:10.980118Z", "shell.execute_reply": "2021-11-22T21:08:10.980291Z" } }, "outputs": [], "source": [ "from pathlib import Path\n", "from astropy import units as u\n", "from gammapy.analysis import Analysis, AnalysisConfig\n", "from gammapy.modeling.models import create_crab_spectral_model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analysis configuration\n", "\n", "For configuration of the analysis we use the [YAML](https://en.wikipedia.org/wiki/YAML) data format. YAML is a machine readable serialisation format, that is also friendly for humans to read. In this tutorial we will write the configuration file just using Python strings, but of course the file can be created and modified with any text editor of your choice.\n", "\n", "Here is what the configuration for our analysis looks like:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:10.985516Z", "iopub.status.busy": "2021-11-22T21:08:10.985152Z", "iopub.status.idle": "2021-11-22T21:08:10.986737Z", "shell.execute_reply": "2021-11-22T21:08:10.986914Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AnalysisConfig\n", "\n", " general:\n", " log: {level: info, filename: null, filemode: null, format: null, datefmt: null}\n", " outdir: .\n", " observations:\n", " datastore: $GAMMAPY_DATA/hess-dl3-dr1\n", " obs_ids: []\n", " obs_file: null\n", " obs_cone: {frame: null, lon: null, lat: null, radius: null}\n", " obs_time: {start: null, stop: null}\n", " required_irf: [aeff, edisp, psf, bkg]\n", " datasets:\n", " type: 1d\n", " stack: true\n", " geom:\n", " wcs:\n", " skydir: {frame: null, lon: null, lat: null}\n", " binsize: 0.02 deg\n", " width: {width: 5.0 deg, height: 5.0 deg}\n", " binsize_irf: 0.2 deg\n", " selection: {offset_max: 2.5 deg}\n", " axes:\n", " energy: {min: 1.0 TeV, max: 10.0 TeV, nbins: 5}\n", " energy_true: {min: 0.5 TeV, max: 20.0 TeV, nbins: 16}\n", " map_selection: [counts, exposure, background, psf, edisp]\n", " background:\n", " method: null\n", " exclusion: null\n", " parameters: {}\n", " safe_mask:\n", " methods: [aeff-default]\n", " parameters: {}\n", " on_region: {frame: null, lon: null, lat: null, radius: null}\n", " containment_correction: true\n", " fit:\n", " fit_range: {min: null, max: null}\n", " flux_points:\n", " energy: {min: null, max: null, nbins: null}\n", " source: source\n", " parameters: {selection_optional: all}\n", " excess_map:\n", " correlation_radius: 0.1 deg\n", " parameters: {}\n", " energy_edges: {min: null, max: null, nbins: null}\n", " light_curve:\n", " time_intervals: {start: null, stop: null}\n", " energy_edges: {min: null, max: null, nbins: null}\n", " source: source\n", " parameters: {selection_optional: all}\n", " \n" ] } ], "source": [ "config = AnalysisConfig()\n", "# the AnalysisConfig gives access to the various parameters used from logging to reduced dataset geometries\n", "print(config)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setting the data to use" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want to use Crab runs from the H.E.S.S. DL3-DR1. We define here the datastore and a cone search of observations pointing with 5 degrees of the Crab nebula. Parameters can be set directly or as a python dict.\n", "\n", "PS: do not forget to setup your environment variable _$GAMMAPY\\_DATA_ to your local directory containing the H.E.S.S. DL3-DR1 (see [here](../../getting-started/quickstart.rst#download-tutorials))." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:10.989313Z", "iopub.status.busy": "2021-11-22T21:08:10.989027Z", "iopub.status.idle": "2021-11-22T21:08:10.990145Z", "shell.execute_reply": "2021-11-22T21:08:10.990388Z" } }, "outputs": [], "source": [ "# We define the datastore containing the data\n", "config.observations.datastore = \"$GAMMAPY_DATA/hess-dl3-dr1\"\n", "\n", "# We define the cone search parameters\n", "config.observations.obs_cone.frame = \"icrs\"\n", "config.observations.obs_cone.lon = \"83.633 deg\"\n", "config.observations.obs_cone.lat = \"22.014 deg\"\n", "config.observations.obs_cone.radius = \"5 deg\"\n", "\n", "# Equivalently we could have set parameters with a python dict\n", "# config.observations.obs_cone = {\"frame\": \"icrs\", \"lon\": \"83.633 deg\", \"lat\": \"22.014 deg\", \"radius\": \"5 deg\"}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setting the reduced datasets geometry" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:10.993616Z", "iopub.status.busy": "2021-11-22T21:08:10.993339Z", "iopub.status.idle": "2021-11-22T21:08:10.994441Z", "shell.execute_reply": "2021-11-22T21:08:10.994672Z" } }, "outputs": [], "source": [ "# We want to perform a 3D analysis\n", "config.datasets.type = \"3d\"\n", "# We want to stack the data into a single reduced dataset\n", "config.datasets.stack = True\n", "\n", "# We fix the WCS geometry of the datasets\n", "config.datasets.geom.wcs.skydir = {\n", " \"lon\": \"83.633 deg\",\n", " \"lat\": \"22.014 deg\",\n", " \"frame\": \"icrs\",\n", "}\n", "config.datasets.geom.wcs.width = {\"width\": \"2 deg\", \"height\": \"2 deg\"}\n", "config.datasets.geom.wcs.binsize = \"0.02 deg\"\n", "\n", "# We now fix the energy axis for the counts map\n", "config.datasets.geom.axes.energy.min = \"1 TeV\"\n", "config.datasets.geom.axes.energy.max = \"10 TeV\"\n", "config.datasets.geom.axes.energy.nbins = 4\n", "\n", "# We now fix the energy axis for the IRF maps (exposure, etc)\n", "config.datasets.geom.axes.energy_true.min = \"0.5 TeV\"\n", "config.datasets.geom.axes.energy_true.max = \"20 TeV\"\n", "config.datasets.geom.axes.energy.nbins = 10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setting the background normalization maker" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:10.996443Z", "iopub.status.busy": "2021-11-22T21:08:10.996154Z", "iopub.status.idle": "2021-11-22T21:08:10.997506Z", "shell.execute_reply": "2021-11-22T21:08:10.997318Z" } }, "outputs": [], "source": [ "config.datasets.background.method = \"fov_background\"\n", "config.datasets.background.parameters = {\"method\": \"scale\"}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setting the exclusion mask" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to properly adjust the background normalisation on regions without gamma-ray signal, one needs to define an exclusion mask for the background normalisation.\n", "For this tutorial, we use the following one ``$GAMMAPY_DATA/joint-crab/exclusion/exclusion_mask_crab.fits.gz``" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:10.999218Z", "iopub.status.busy": "2021-11-22T21:08:10.998921Z", "iopub.status.idle": "2021-11-22T21:08:11.000133Z", "shell.execute_reply": "2021-11-22T21:08:11.000293Z" } }, "outputs": [], "source": [ "config.datasets.background.exclusion = (\n", " \"$GAMMAPY_DATA/joint-crab/exclusion/exclusion_mask_crab.fits.gz\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setting modeling and fitting parameters\n", "`Analysis` can perform a few modeling and fitting tasks besides data reduction. Parameters have then to be passed to the configuration object.\n", "\n", "Here we define the energy range on which to perform the fit. We also set the energy edges used for flux point computation as well as the correlation radius to compute excess and significance maps. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:11.002493Z", "iopub.status.busy": "2021-11-22T21:08:11.002188Z", "iopub.status.idle": "2021-11-22T21:08:11.003327Z", "shell.execute_reply": "2021-11-22T21:08:11.003615Z" } }, "outputs": [], "source": [ "config.fit.fit_range.min = 1 * u.TeV\n", "config.fit.fit_range.max = 10 * u.TeV\n", "config.flux_points.energy = {\"min\": \"1 TeV\", \"max\": \"10 TeV\", \"nbins\": 3}\n", "config.excess_map.correlation_radius = 0.1 * u.deg" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're all set. \n", "But before we go on let's see how to save or import `AnalysisConfig` objects though YAML files." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using YAML configuration files\n", "\n", "One can export/import the `AnalysisConfig` to/from a YAML file." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:11.008881Z", "iopub.status.busy": "2021-11-22T21:08:11.008595Z", "iopub.status.idle": "2021-11-22T21:08:11.009928Z", "shell.execute_reply": "2021-11-22T21:08:11.010104Z" } }, "outputs": [], "source": [ "config.write(\"config.yaml\", overwrite=True)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:11.011658Z", "iopub.status.busy": "2021-11-22T21:08:11.011364Z", "iopub.status.idle": "2021-11-22T21:08:11.020890Z", "shell.execute_reply": "2021-11-22T21:08:11.021107Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AnalysisConfig\n", "\n", " general:\n", " log: {level: info, filename: null, filemode: null, format: null, datefmt: null}\n", " outdir: .\n", " observations:\n", " datastore: $GAMMAPY_DATA/hess-dl3-dr1\n", " obs_ids: []\n", " obs_file: null\n", " obs_cone: {frame: icrs, lon: 83.633 deg, lat: 22.014 deg, radius: 5.0 deg}\n", " obs_time: {start: null, stop: null}\n", " required_irf: [aeff, edisp, psf, bkg]\n", " datasets:\n", " type: 3d\n", " stack: true\n", " geom:\n", " wcs:\n", " skydir: {frame: icrs, lon: 83.633 deg, lat: 22.014 deg}\n", " binsize: 0.02 deg\n", " width: {width: 2.0 deg, height: 2.0 deg}\n", " binsize_irf: 0.2 deg\n", " selection: {offset_max: 2.5 deg}\n", " axes:\n", " energy: {min: 1.0 TeV, max: 10.0 TeV, nbins: 10}\n", " energy_true: {min: 0.5 TeV, max: 20.0 TeV, nbins: 16}\n", " map_selection: [counts, exposure, background, psf, edisp]\n", " background:\n", " method: fov_background\n", " exclusion: $GAMMAPY_DATA/joint-crab/exclusion/exclusion_mask_crab.fits.gz\n", " parameters: {method: scale}\n", " safe_mask:\n", " methods: [aeff-default]\n", " parameters: {}\n", " on_region: {frame: null, lon: null, lat: null, radius: null}\n", " containment_correction: true\n", " fit:\n", " fit_range: {min: 1.0 TeV, max: 10.0 TeV}\n", " flux_points:\n", " energy: {min: 1.0 TeV, max: 10.0 TeV, nbins: 3}\n", " source: source\n", " parameters: {selection_optional: all}\n", " excess_map:\n", " correlation_radius: 0.1 deg\n", " parameters: {}\n", " energy_edges: {min: null, max: null, nbins: null}\n", " light_curve:\n", " time_intervals: {start: null, stop: null}\n", " energy_edges: {min: null, max: null, nbins: null}\n", " source: source\n", " parameters: {selection_optional: all}\n", " \n" ] } ], "source": [ "config = AnalysisConfig.read(\"config.yaml\")\n", "print(config)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running the analysis\n", "\n", "We first create an `~gammapy.analysis.Analysis` object from our configuration." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:11.022945Z", "iopub.status.busy": "2021-11-22T21:08:11.022661Z", "iopub.status.idle": "2021-11-22T21:08:11.023986Z", "shell.execute_reply": "2021-11-22T21:08:11.024155Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Setting logging config: {'level': 'INFO', 'filename': None, 'filemode': None, 'format': None, 'datefmt': None}\n" ] } ], "source": [ "analysis = Analysis(config)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Observation selection\n", "\n", "We can directly select and load the observations from disk using `~gammapy.analysis.Analysis.get_observations()`:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:11.025894Z", "iopub.status.busy": "2021-11-22T21:08:11.025613Z", "iopub.status.idle": "2021-11-22T21:08:11.046770Z", "shell.execute_reply": "2021-11-22T21:08:11.046998Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Fetching observations.\n", "No HDU found matching: OBS_ID = 23523, HDU_TYPE = rad_max, HDU_CLASS = None\n", "No HDU found matching: OBS_ID = 23526, HDU_TYPE = rad_max, HDU_CLASS = None\n", "No HDU found matching: OBS_ID = 23559, HDU_TYPE = rad_max, HDU_CLASS = None\n", "No HDU found matching: OBS_ID = 23592, HDU_TYPE = rad_max, HDU_CLASS = None\n", "Number of selected observations: 4\n" ] } ], "source": [ "analysis.get_observations()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The observations are now available on the `Analysis` object. The selection corresponds to the following ids:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:11.049628Z", "iopub.status.busy": "2021-11-22T21:08:11.049264Z", "iopub.status.idle": "2021-11-22T21:08:11.051072Z", "shell.execute_reply": "2021-11-22T21:08:11.051244Z" } }, "outputs": [ { "data": { "text/plain": [ "['23523', '23526', '23559', '23592']" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "analysis.observations.ids" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To see how to explore observations, please refer to the following notebook: [CTA with Gammapy](../data/cta.ipynb) or [HESS with Gammapy](../data/hess.ipynb) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data reduction\n", "\n", "Now we proceed to the data reduction. In the config file we have chosen a WCS map geometry, energy axis and decided to stack the maps. We can run the reduction using `.get_datasets()`:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:11.053109Z", "iopub.status.busy": "2021-11-22T21:08:11.052832Z", "iopub.status.idle": "2021-11-22T21:08:12.890602Z", "shell.execute_reply": "2021-11-22T21:08:12.890840Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Creating reference dataset and makers.\n", "Creating the background Maker.\n", "Start the data reduction loop.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.75 s, sys: 88.9 ms, total: 1.84 s\n", "Wall time: 1.84 s\n" ] } ], "source": [ "%%time\n", "analysis.get_datasets()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we have chosen to stack the data, there is finally one dataset contained which we can print:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:12.892937Z", "iopub.status.busy": "2021-11-22T21:08:12.892652Z", "iopub.status.idle": "2021-11-22T21:08:12.896424Z", "shell.execute_reply": "2021-11-22T21:08:12.896596Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MapDataset\n", "----------\n", "\n", " Name : stacked \n", "\n", " Total counts : 2485 \n", " Total background counts : 1997.49\n", " Total excess counts : 487.51\n", "\n", " Predicted counts : 1997.49\n", " Predicted background counts : 1997.49\n", " Predicted excess counts : nan\n", "\n", " Exposure min : 2.97e+08 m2 s\n", " Exposure max : 3.51e+09 m2 s\n", "\n", " Number of total bins : 100000 \n", " Number of fit bins : 100000 \n", "\n", " Fit statistic type : cash\n", " Fit statistic value (-2 log(L)) : nan\n", "\n", " Number of models : 0 \n", " Number of parameters : 0\n", " Number of free parameters : 0\n", "\n", "\n" ] } ], "source": [ "print(analysis.datasets[\"stacked\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see the dataset comes with a predefined background model out of the data reduction, but no source model has been set yet.\n", "\n", "The counts, exposure and background model maps are directly available on the dataset and can be printed and plotted:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:12.898579Z", "iopub.status.busy": "2021-11-22T21:08:12.898252Z", "iopub.status.idle": "2021-11-22T21:08:13.145489Z", "shell.execute_reply": "2021-11-22T21:08:13.145866Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "15c2da5e55eb4e10a867715f429277a6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(SelectionSlider(continuous_update=False, description='Select energy:', layout=Layout(wid…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "counts = analysis.datasets[\"stacked\"].counts\n", "counts.smooth(\"0.05 deg\").plot_interactive()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also compute the map of the sqrt_ts (significance) of the excess counts above the background. The correlation radius to sum counts is defined in the config file." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:13.148477Z", "iopub.status.busy": "2021-11-22T21:08:13.148145Z", "iopub.status.idle": "2021-11-22T21:08:13.273243Z", "shell.execute_reply": "2021-11-22T21:08:13.273437Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Computing excess maps.\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "analysis.get_excess_map()\n", "analysis.excess_map[\"sqrt_ts\"].plot(add_cbar=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Save dataset to disk\n", "\n", "It is common to run the preparation step independent of the likelihood fit, because often the preparation of maps, PSF and energy dispersion is slow if you have a lot of data. We first create a folder:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:13.275238Z", "iopub.status.busy": "2021-11-22T21:08:13.274523Z", "iopub.status.idle": "2021-11-22T21:08:13.276445Z", "shell.execute_reply": "2021-11-22T21:08:13.276632Z" } }, "outputs": [], "source": [ "path = Path(\"analysis_1\")\n", "path.mkdir(exist_ok=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then write the maps and IRFs to disk by calling the dedicated `write()` method:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:13.315323Z", "iopub.status.busy": "2021-11-22T21:08:13.278802Z", "iopub.status.idle": "2021-11-22T21:08:13.438756Z", "shell.execute_reply": "2021-11-22T21:08:13.438999Z" } }, "outputs": [], "source": [ "filename = path / \"crab-stacked-dataset.fits.gz\"\n", "analysis.datasets[0].write(filename, overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model fitting\n", "\n", "Now we define a model to be fitted to the dataset. Here we use its YAML definition to load it:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:13.441159Z", "iopub.status.busy": "2021-11-22T21:08:13.440808Z", "iopub.status.idle": "2021-11-22T21:08:13.442043Z", "shell.execute_reply": "2021-11-22T21:08:13.442234Z" } }, "outputs": [], "source": [ "model_config = \"\"\"\n", "components:\n", "- name: crab\n", " type: SkyModel\n", " spatial:\n", " type: PointSpatialModel\n", " frame: icrs\n", " parameters:\n", " - name: lon_0\n", " value: 83.63\n", " unit: deg\n", " - name: lat_0 \n", " value: 22.014 \n", " unit: deg\n", " spectral:\n", " type: PowerLawSpectralModel\n", " parameters:\n", " - name: amplitude \n", " value: 1.0e-12\n", " unit: cm-2 s-1 TeV-1\n", " - name: index\n", " value: 2.0\n", " unit: ''\n", " - name: reference\n", " value: 1.0\n", " unit: TeV\n", " frozen: true\n", "\"\"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we set the model on the analysis object:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:13.444738Z", "iopub.status.busy": "2021-11-22T21:08:13.443931Z", "iopub.status.idle": "2021-11-22T21:08:13.455169Z", "shell.execute_reply": "2021-11-22T21:08:13.455398Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Reading model.\n", "Models\n", "\n", "Component 0: SkyModel\n", "\n", " Name : crab\n", " Datasets names : None\n", " Spectral model type : PowerLawSpectralModel\n", " Spatial model type : PointSpatialModel\n", " Temporal model type : \n", " Parameters:\n", " index : 2.000 +/- 0.00 \n", " amplitude : 1.00e-12 +/- 0.0e+00 1 / (cm2 s TeV)\n", " reference (frozen) : 1.000 TeV \n", " lon_0 : 83.630 +/- 0.00 deg \n", " lat_0 : 22.014 +/- 0.00 deg \n", "\n", "Component 1: FoVBackgroundModel\n", "\n", " Name : stacked-bkg\n", " Datasets names : ['stacked']\n", " Spectral model type : PowerLawNormSpectralModel\n", " Parameters:\n", " norm : 1.000 +/- 0.00 \n", " tilt (frozen) : 0.000 \n", " reference (frozen) : 1.000 TeV \n", "\n", "\n" ] } ], "source": [ "analysis.set_models(model_config)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we run the fit:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:13.457380Z", "iopub.status.busy": "2021-11-22T21:08:13.457025Z", "iopub.status.idle": "2021-11-22T21:08:19.885858Z", "shell.execute_reply": "2021-11-22T21:08:19.886091Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Fitting datasets.\n", "OptimizeResult\n", "\n", "\tbackend : minuit\n", "\tmethod : migrad\n", "\tsuccess : True\n", "\tmessage : Optimization terminated successfully.\n", "\tnfev : 271\n", "\ttotal stat : 19992.17\n", "\n", "OptimizeResult\n", "\n", "\tbackend : minuit\n", "\tmethod : migrad\n", "\tsuccess : True\n", "\tmessage : Optimization terminated successfully.\n", "\tnfev : 271\n", "\ttotal stat : 19992.17\n", "\n", "\n" ] } ], "source": [ "analysis.run_fit()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:19.887897Z", "iopub.status.busy": "2021-11-22T21:08:19.887574Z", "iopub.status.idle": "2021-11-22T21:08:19.888969Z", "shell.execute_reply": "2021-11-22T21:08:19.889189Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OptimizeResult\n", "\n", "\tbackend : minuit\n", "\tmethod : migrad\n", "\tsuccess : True\n", "\tmessage : Optimization terminated successfully.\n", "\tnfev : 271\n", "\ttotal stat : 19992.17\n", "\n", "OptimizeResult\n", "\n", "\tbackend : minuit\n", "\tmethod : migrad\n", "\tsuccess : True\n", "\tmessage : Optimization terminated successfully.\n", "\tnfev : 271\n", "\ttotal stat : 19992.17\n", "\n", "\n" ] } ], "source": [ "print(analysis.fit_result)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is how we can write the model back to file again:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:19.890997Z", "iopub.status.busy": "2021-11-22T21:08:19.890690Z", "iopub.status.idle": "2021-11-22T21:08:19.900935Z", "shell.execute_reply": "2021-11-22T21:08:19.901094Z" } }, "outputs": [], "source": [ "filename = path / \"model-best-fit.yaml\"\n", "analysis.models.write(filename, overwrite=True)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:19.902860Z", "iopub.status.busy": "2021-11-22T21:08:19.902557Z", "iopub.status.idle": "2021-11-22T21:08:20.019904Z", "shell.execute_reply": "2021-11-22T21:08:20.020282Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "components:\r\n", "- name: crab\r\n", " type: SkyModel\r\n", " spectral:\r\n", " type: PowerLawSpectralModel\r\n", " parameters:\r\n", " - name: index\r\n", " value: 2.5538019288289857\r\n", " error: 0.10302531554727902\r\n", " - name: amplitude\r\n", " value: 4.529912786057623e-11\r\n", " unit: cm-2 s-1 TeV-1\r\n", " error: 3.7040387851201704e-12\r\n", " - name: reference\r\n", " value: 1.0\r\n", " unit: TeV\r\n", " frozen: true\r\n", " spatial:\r\n", " type: PointSpatialModel\r\n", " frame: icrs\r\n", " parameters:\r\n", " - name: lon_0\r\n", " value: 83.61981157931403\r\n", " unit: deg\r\n", " error: 0.003136890354033938\r\n", " - name: lat_0\r\n", " value: 22.024556120192997\r\n", " unit: deg\r\n", " error: 0.002956430444973766\r\n", "- type: FoVBackgroundModel\r\n", " datasets_names:\r\n", " - stacked\r\n", " spectral:\r\n", " type: PowerLawNormSpectralModel\r\n", " parameters:\r\n", " - name: norm\r\n", " value: 0.9864855339268036\r\n", " error: 0.023473292266494795\r\n", " - name: tilt\r\n", " value: 0.0\r\n", " frozen: true\r\n", " - name: reference\r\n", " value: 1.0\r\n", " unit: TeV\r\n", " frozen: true\r\n", "covariance: model-best-fit_covariance.dat\r\n" ] } ], "source": [ "!cat analysis_1/model-best-fit.yaml" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Flux points" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:20.025692Z", "iopub.status.busy": "2021-11-22T21:08:20.025120Z", "iopub.status.idle": "2021-11-22T21:08:22.711570Z", "shell.execute_reply": "2021-11-22T21:08:22.711772Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Calculating flux points.\n", "Reoptimize = False ignored for iminuit backend\n", "Reoptimize = False ignored for iminuit backend\n", "Reoptimize = False ignored for iminuit backend\n", "Reoptimize = False ignored for iminuit backend\n", "Reoptimize = False ignored for iminuit backend\n", "Reoptimize = False ignored for iminuit backend\n", "\n", " e_ref dnde dnde_ul dnde_err sqrt_ts \n", " TeV 1 / (cm2 s TeV) 1 / (cm2 s TeV) 1 / (cm2 s TeV) \n", "------------------ ---------------------- ---------------------- ---------------------- ------------------\n", "1.4125375446227544 1.8458165524256397e-11 2.1070498239636086e-11 1.2545944678579331e-12 29.19604570871725\n", "3.1622776601683795 2.4761998180771976e-12 2.9210092422785443e-12 2.1186999239487364e-13 24.242265531875965\n", " 7.079457843841381 2.9304711864719196e-13 4.1958743058008545e-13 5.643875691976319e-14 11.764723730021181\n" ] } ], "source": [ "analysis.config.flux_points.source = \"crab\"\n", "analysis.get_flux_points()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:22.768102Z", "iopub.status.busy": "2021-11-22T21:08:22.767770Z", "iopub.status.idle": "2021-11-22T21:08:22.981613Z", "shell.execute_reply": "2021-11-22T21:08:22.981852Z" }, "nbsphinx-thumbnail": { "tooltip": "Introduction to Gammapy analysis using the high level interface." } }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax_sed, ax_residuals = analysis.flux_points.plot_fit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The flux points can be exported to a fits table following the format defined [here](https://gamma-astro-data-formats.readthedocs.io/en/latest/spectra/flux_points/index.html) " ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:22.986690Z", "iopub.status.busy": "2021-11-22T21:08:22.986384Z", "iopub.status.idle": "2021-11-22T21:08:23.029478Z", "shell.execute_reply": "2021-11-22T21:08:23.029691Z" } }, "outputs": [], "source": [ "filename = path / \"flux-points.fits\"\n", "analysis.flux_points.write(filename, overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To check the fit is correct, we compute the map of the sqrt_ts of the excess counts above the current model." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:08:23.031770Z", "iopub.status.busy": "2021-11-22T21:08:23.031443Z", "iopub.status.idle": "2021-11-22T21:08:23.165447Z", "shell.execute_reply": "2021-11-22T21:08:23.165764Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Computing excess maps.\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "analysis.get_excess_map()\n", "analysis.excess_map[\"sqrt_ts\"].plot(add_cbar=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What's next\n", "\n", "You can look at the same analysis without the high level interface in [analysis_2](analysis_2.ipynb)\n", "\n", "You can see how to perform a 1D spectral analysis of the same data in [spectrum analysis](../analysis/1D/spectral_analysis.ipynb)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.9.0" }, "nbsphinx": { "orphan": true }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "15c2da5e55eb4e10a867715f429277a6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "VBoxModel", "state": { "_dom_classes": [ "widget-interact" ], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "VBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "VBoxView", "box_style": "", "children": [ "IPY_MODEL_7ae5feb108f24655b2c57128c17c3033", "IPY_MODEL_6bc610e221bd4f4e83e2cefb8f5a1d50", "IPY_MODEL_bf86dc90a12f477193558f65d1a383d2" ], "layout": "IPY_MODEL_af323a967d874bb29876eeda96bcf4c9" } }, "23911f8567f04e5d8ed064d1c063bbe2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "initial" } }, "54ace72e3a9d47e5adbf09858f010bb8": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": "50%" } }, "6bc610e221bd4f4e83e2cefb8f5a1d50": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "RadioButtonsModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "RadioButtonsModel", "_options_labels": [ "linear", "sqrt", "log" ], "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "RadioButtonsView", "description": "Select stretch:", "description_tooltip": null, "disabled": false, "index": 1, "layout": "IPY_MODEL_d64ed8a04aca46179bcb8a65504b5c7f", "style": "IPY_MODEL_e53a51b03d6a4da19daf7a4b25bd854a" } }, "7ae5feb108f24655b2c57128c17c3033": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "SelectionSliderModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "SelectionSliderModel", "_options_labels": [ "1.00e+00 TeV - 1.26e+00 TeV", "1.26e+00 TeV - 1.58e+00 TeV", "1.58e+00 TeV - 2.00e+00 TeV", "2.00e+00 TeV - 2.51e+00 TeV", "2.51e+00 TeV - 3.16e+00 TeV", "3.16e+00 TeV - 3.98e+00 TeV", "3.98e+00 TeV - 5.01e+00 TeV", "5.01e+00 TeV - 6.31e+00 TeV", "6.31e+00 TeV - 7.94e+00 TeV", "7.94e+00 TeV - 1.00e+01 TeV" ], "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "SelectionSliderView", "continuous_update": false, "description": "Select energy:", "description_tooltip": null, "disabled": false, "index": 0, "layout": "IPY_MODEL_54ace72e3a9d47e5adbf09858f010bb8", "orientation": "horizontal", "readout": true, "style": "IPY_MODEL_23911f8567f04e5d8ed064d1c063bbe2" } }, "af323a967d874bb29876eeda96bcf4c9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "bf86dc90a12f477193558f65d1a383d2": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_cc31c6180c184abe8ae717dd1173343e", "msg_id": "", "outputs": [ { "data": { "image/png": "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\n", "text/plain": "
" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ] } }, "cc31c6180c184abe8ae717dd1173343e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "d64ed8a04aca46179bcb8a65504b5c7f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "e53a51b03d6a4da19daf7a4b25bd854a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "initial" } } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 }