{ "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/api/maps.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", "[maps.ipynb](../../_static/notebooks/maps.ipynb) |\n", "[maps.py](../../_static/notebooks/maps.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Maps\n", "\n", "![Gammapy Maps Illustration](../images/gammapy_maps.png)\n", "\n", "## Introduction\n", "\n", "The `~gammapy.maps` submodule contains classes for representing pixilised data on the sky with an arbitrary number of non-spatial dimensions such as energy, time, event class or any possible user-defined dimension (illustrated in the image above). The main `Map` data structure features a uniform API for [WCS](https://fits.gsfc.nasa.gov/fits_wcs.html) as well as [HEALPix](https://en.wikipedia.org/wiki/HEALPix) based images. The API also generalizes simple image based operations such as smoothing, interpolation and reprojection to the arbitrary extra dimensions and makes working with (2 + N)-dimensional hypercubes as easy as working with a simple 2D image. Further information is also provided on the `~gammapy.maps` docs page.\n", "\n", "In the following introduction we will learn all the basics of working with WCS based maps. HEALPix based maps will be covered in a future tutorial. We will cover the following topics in order:\n", "\n", "1. [Creating WCS Maps](#Creating-WCS-Maps)\n", "1. [Reading and Writing](#Reading-and-Writing)\n", "1. [Accessing Data](#Accessing-Data)\n", "1. [Modifying Data](#Modifying-Data)\n", "1. [Maps operations](#Maps-operations)\n", "1. [Visualizing and Plotting](#Visualizing-and-Plotting)\n", "\n", "Make sure you have worked through the [Gammapy overview](../starting/overview.ipynb), because a solid knowledge about working with `SkyCoord` and `Quantity` objects as well as [Numpy](http://www.numpy.org/) is required for this tutorial.\n", "\n", "This notebook is rather lengthy, but getting to know the `Map` data structure in detail is essential for working with Gammapy and will allow you to fulfill complex analysis tasks with very few and simple code in future!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:43.294616Z", "iopub.status.busy": "2021-11-22T21:09:43.293735Z", "iopub.status.idle": "2021-11-22T21:09:43.463182Z", "shell.execute_reply": "2021-11-22T21:09:43.463366Z" } }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import os" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:43.465476Z", "iopub.status.busy": "2021-11-22T21:09:43.465155Z", "iopub.status.idle": "2021-11-22T21:09:43.955619Z", "shell.execute_reply": "2021-11-22T21:09:43.955791Z" } }, "outputs": [], "source": [ "from astropy import units as u\n", "from astropy.io import fits\n", "from astropy.table import Table\n", "from astropy.coordinates import SkyCoord\n", "from astropy.convolution import convolve\n", "from gammapy.maps import Map, MapAxis, WcsGeom, WcsNDMap\n", "from gammapy.data import EventList" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating WCS Maps\n", "\n", "### Using Factory Methods\n", "\n", "Maps are most easily created using the `~gammapy.maps.Map.create()` factory method:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:43.958204Z", "iopub.status.busy": "2021-11-22T21:09:43.957919Z", "iopub.status.idle": "2021-11-22T21:09:44.091581Z", "shell.execute_reply": "2021-11-22T21:09:44.091747Z" } }, "outputs": [], "source": [ "m_allsky = Map.create()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calling `Map.create()` without any further arguments creates by default an allsky WCS map using a CAR projection, ICRS coordinates and a pixel size of 1 deg. This can be easily checked by printing the `.geom` attribute of the map:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.094667Z", "iopub.status.busy": "2021-11-22T21:09:44.094311Z", "iopub.status.idle": "2021-11-22T21:09:44.096365Z", "shell.execute_reply": "2021-11-22T21:09:44.096564Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsGeom\n", "\n", "\taxes : ['lon', 'lat']\n", "\tshape : (3600, 1800)\n", "\tndim : 2\n", "\tframe : icrs\n", "\tprojection : CAR\n", "\tcenter : 0.0 deg, 0.0 deg\n", "\twidth : 360.0 deg x 180.0 deg\n", "\twcs ref : 0.0 deg, 0.0 deg\n", "\n" ] } ], "source": [ "print(m_allsky.geom)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `.geom` attribute is a `Geom` object, that defines the basic geometry of the map, such as size of the pixels, width and height of the image, coordinate system etc., but we will learn more about this object later.\n", "\n", "Besides the `.geom` attribute the map has also a `.data` attribute, which is just a plain `numpy.ndarray` and stores the data associated with this map:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.098841Z", "iopub.status.busy": "2021-11-22T21:09:44.098546Z", "iopub.status.idle": "2021-11-22T21:09:44.100569Z", "shell.execute_reply": "2021-11-22T21:09:44.100747Z" } }, "outputs": [ { "data": { "text/plain": [ "array([[0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " ...,\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m_allsky.data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default maps are filled with zeros.\n", "\n", "The `map_type` argument can be used to control the pixelization scheme (WCS or HPX)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.104728Z", "iopub.status.busy": "2021-11-22T21:09:44.104438Z", "iopub.status.idle": "2021-11-22T21:09:44.130066Z", "shell.execute_reply": "2021-11-22T21:09:44.130259Z" } }, "outputs": [], "source": [ "position = SkyCoord(0.0, 5.0, frame=\"galactic\", unit=\"deg\")\n", "\n", "# Create a WCS Map\n", "m_wcs = Map.create(binsz=0.1, map_type=\"wcs\", skydir=position, width=10.0)\n", "\n", "# Create a HPX Map\n", "m_hpx = Map.create(binsz=0.1, map_type=\"hpx\", skydir=position, width=10.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is an example that creates a WCS map centered on the Galactic center and now uses Galactic coordinates:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.133700Z", "iopub.status.busy": "2021-11-22T21:09:44.133104Z", "iopub.status.idle": "2021-11-22T21:09:44.134964Z", "shell.execute_reply": "2021-11-22T21:09:44.135144Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsGeom\n", "\n", "\taxes : ['lon', 'lat']\n", "\tshape : (500, 250)\n", "\tndim : 2\n", "\tframe : galactic\n", "\tprojection : TAN\n", "\tcenter : 0.0 deg, 0.0 deg\n", "\twidth : 10.0 deg x 5.0 deg\n", "\twcs ref : 0.0 deg, 0.0 deg\n", "\n" ] } ], "source": [ "skydir = SkyCoord(0, 0, frame=\"galactic\", unit=\"deg\")\n", "m_gc = Map.create(\n", " binsz=0.02, width=(10, 5), skydir=skydir, frame=\"galactic\", proj=\"TAN\"\n", ")\n", "print(m_gc.geom)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition we have defined a TAN projection, a pixel size of `0.02` deg and a width of the map of `10 deg x 5 deg`. The `width` argument also takes scalar value instead of a tuple, which is interpreted as both the width and height of the map, so that a quadratic map is created." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating from a Map Geometry\n", "\n", "As we have seen in the first examples, the `Map` object couples the data (stored as a `numpy.ndarray`) with a `Geom` object. The `Geom` object can be seen as a generalization of an `astropy.wcs.WCS` object, providing the information on how the data maps to physical coordinate systems. In some cases e.g. when creating many maps with the same WCS geometry it can be advantegeous to first create the map geometry independent of the map object itsself: " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.137469Z", "iopub.status.busy": "2021-11-22T21:09:44.137180Z", "iopub.status.idle": "2021-11-22T21:09:44.138281Z", "shell.execute_reply": "2021-11-22T21:09:44.138456Z" } }, "outputs": [], "source": [ "wcs_geom = WcsGeom.create(\n", " binsz=0.02, width=(10, 5), skydir=(0, 0), frame=\"galactic\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then create the map objects from the `wcs_geom` geometry specification:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.140346Z", "iopub.status.busy": "2021-11-22T21:09:44.140060Z", "iopub.status.idle": "2021-11-22T21:09:44.141523Z", "shell.execute_reply": "2021-11-22T21:09:44.141233Z" } }, "outputs": [], "source": [ "maps = {}\n", "\n", "for name in [\"counts\", \"background\"]:\n", " maps[name] = Map.from_geom(wcs_geom)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `Geom` object also has a few helpful methods. E.g. we can check whether a given position on the sky is contained in the map geometry:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.144208Z", "iopub.status.busy": "2021-11-22T21:09:44.143928Z", "iopub.status.idle": "2021-11-22T21:09:44.145221Z", "shell.execute_reply": "2021-11-22T21:09:44.145384Z" } }, "outputs": [ { "data": { "text/plain": [ "array([ True, False])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# define the position of the Galactic center and anti-center\n", "positions = SkyCoord([0, 180], [0, 0], frame=\"galactic\", unit=\"deg\")\n", "wcs_geom.contains(positions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or get the image center of the map:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.147793Z", "iopub.status.busy": "2021-11-22T21:09:44.147469Z", "iopub.status.idle": "2021-11-22T21:09:44.148964Z", "shell.execute_reply": "2021-11-22T21:09:44.149123Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wcs_geom.center_skydir" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or we can also retrieve the solid angle per pixel of the map:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.150769Z", "iopub.status.busy": "2021-11-22T21:09:44.150491Z", "iopub.status.idle": "2021-11-22T21:09:44.209811Z", "shell.execute_reply": "2021-11-22T21:09:44.210105Z" } }, "outputs": [ { "data": { "text/latex": [ "$$[[1.2173192 \\times 10^{-7},~1.2173192 \\times 10^{-7},~1.2173192 \\times 10^{-7},~\\dots,~1.2173192 \\times 10^{-7},~1.2173192 \\times 10^{-7},~1.2173192 \\times 10^{-7}],~\n", " [1.2173376 \\times 10^{-7},~1.2173376 \\times 10^{-7},~1.2173376 \\times 10^{-7},~\\dots,~1.2173376 \\times 10^{-7},~1.2173376 \\times 10^{-7},~1.2173376 \\times 10^{-7}],~\n", " [1.2173559 \\times 10^{-7},~1.2173559 \\times 10^{-7},~1.2173559 \\times 10^{-7},~\\dots,~1.2173559 \\times 10^{-7},~1.2173559 \\times 10^{-7},~1.2173559 \\times 10^{-7}],~\n", " \\dots,~\n", " [1.2173559 \\times 10^{-7},~1.2173559 \\times 10^{-7},~1.2173559 \\times 10^{-7},~\\dots,~1.2173559 \\times 10^{-7},~1.2173559 \\times 10^{-7},~1.2173559 \\times 10^{-7}],~\n", " [1.2173376 \\times 10^{-7},~1.2173376 \\times 10^{-7},~1.2173376 \\times 10^{-7},~\\dots,~1.2173376 \\times 10^{-7},~1.2173376 \\times 10^{-7},~1.2173376 \\times 10^{-7}],~\n", " [1.2173192 \\times 10^{-7},~1.2173192 \\times 10^{-7},~1.2173192 \\times 10^{-7},~\\dots,~1.2173192 \\times 10^{-7},~1.2173192 \\times 10^{-7},~1.2173192 \\times 10^{-7}]] \\; \\mathrm{sr}$$" ], "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wcs_geom.solid_angle()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adding Non-Spatial Axes\n", "\n", "In many analysis scenarios we would like to add extra dimension to the maps to study e.g. energy or time dependency of the data. Those non-spatial dimensions are handled with the `MapAxis` object. Let us first define an energy axis, with 4 bins:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.212489Z", "iopub.status.busy": "2021-11-22T21:09:44.212156Z", "iopub.status.idle": "2021-11-22T21:09:44.213323Z", "shell.execute_reply": "2021-11-22T21:09:44.213502Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MapAxis\n", "\n", "\tname : energy \n", "\tunit : 'TeV' \n", "\tnbins : 4 \n", "\tnode type : edges \n", "\tedges min : 1.0e+00 TeV\n", "\tedges max : 1.0e+02 TeV\n", "\tinterp : log \n", "\n" ] } ], "source": [ "energy_axis = MapAxis.from_bounds(\n", " 1, 100, nbin=4, unit=\"TeV\", name=\"energy\", interp=\"log\"\n", ")\n", "print(energy_axis)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Where `interp='log'` specifies that a logarithmic spacing is used between the bins, equivalent to `np.logspace(0, 2, 4)`. This `MapAxis` object we can now pass to `Map.create()` using the `axes=` argument:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.215701Z", "iopub.status.busy": "2021-11-22T21:09:44.215422Z", "iopub.status.idle": "2021-11-22T21:09:44.217518Z", "shell.execute_reply": "2021-11-22T21:09:44.217748Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsGeom\n", "\n", "\taxes : ['lon', 'lat', 'energy']\n", "\tshape : (500, 250, 4)\n", "\tndim : 3\n", "\tframe : galactic\n", "\tprojection : CAR\n", "\tcenter : 0.0 deg, 0.0 deg\n", "\twidth : 10.0 deg x 5.0 deg\n", "\twcs ref : 0.0 deg, 0.0 deg\n", "\n" ] } ], "source": [ "m_cube = Map.create(\n", " binsz=0.02, width=(10, 5), frame=\"galactic\", axes=[energy_axis]\n", ")\n", "print(m_cube.geom)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we see that besides `lon` and `lat` the map has an additional axes named `energy` with 4 bins. The total dimension of the map is now `ndim=3`.\n", "\n", "We can also add further axes by passing a list of `MapAxis` objects. To demonstrate this we create a time axis with\n", "linearly spaced bins and pass both axes to `Map.create()`:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.220189Z", "iopub.status.busy": "2021-11-22T21:09:44.219903Z", "iopub.status.idle": "2021-11-22T21:09:44.224495Z", "shell.execute_reply": "2021-11-22T21:09:44.224729Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsGeom\n", "\n", "\taxes : ['lon', 'lat', 'energy', 'time']\n", "\tshape : (500, 250, 4, 24)\n", "\tndim : 4\n", "\tframe : galactic\n", "\tprojection : CAR\n", "\tcenter : 0.0 deg, 0.0 deg\n", "\twidth : 10.0 deg x 5.0 deg\n", "\twcs ref : 0.0 deg, 0.0 deg\n", "\n" ] } ], "source": [ "time_axis = MapAxis.from_bounds(\n", " 0, 24, nbin=24, unit=\"hour\", name=\"time\", interp=\"lin\"\n", ")\n", "\n", "m_4d = Map.create(\n", " binsz=0.02, width=(10, 5), frame=\"galactic\", axes=[energy_axis, time_axis]\n", ")\n", "print(m_4d.geom)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `MapAxis` object internally stores the coordinates or \"position values\" associated with every map axis bin or \"node\". We distinguish between two node types: `edges` and `center`. The node type `edges`(which is also the default) specifies that the data associated with this axis is integrated between the edges of the bin (e.g. counts data). The node type `center` specifies that the data is given at the center of the bin (e.g. exposure or differential fluxes).\n", "\n", "The edges of the bins can be checked with `.edges` attribute:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.226769Z", "iopub.status.busy": "2021-11-22T21:09:44.226445Z", "iopub.status.idle": "2021-11-22T21:09:44.227838Z", "shell.execute_reply": "2021-11-22T21:09:44.228057Z" } }, "outputs": [ { "data": { "text/latex": [ "$$[1,~3.1622777,~10,~31.622777,~100] \\; \\mathrm{TeV}$$" ], "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "energy_axis.edges" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The numbers are given in the units we specified above, which can be checked again with:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.229873Z", "iopub.status.busy": "2021-11-22T21:09:44.229555Z", "iopub.status.idle": "2021-11-22T21:09:44.230910Z", "shell.execute_reply": "2021-11-22T21:09:44.231206Z" } }, "outputs": [ { "data": { "text/latex": [ "$$\\mathrm{TeV}$$" ], "text/plain": [ "Unit(\"TeV\")" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "energy_axis.unit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The centers of the axis bins can be checked with the `.center` attribute:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.233336Z", "iopub.status.busy": "2021-11-22T21:09:44.233030Z", "iopub.status.idle": "2021-11-22T21:09:44.234393Z", "shell.execute_reply": "2021-11-22T21:09:44.234579Z" } }, "outputs": [ { "data": { "text/latex": [ "$$[1.7782794,~5.6234133,~17.782794,~56.234133] \\; \\mathrm{TeV}$$" ], "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "energy_axis.center" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reading and Writing\n", "\n", "Gammapy `Map` objects are serialized using the Flexible Image Transport Format (FITS). Depending on the pixelisation scheme (HEALPix or WCS) and presence of non-spatial dimensions the actual convention to write the FITS file is different.\n", "By default Gammpy uses a generic convention named `gadf`, which will support WCS and HEALPix formats as well as an arbitrary number of non-spatial axes. The convention is documented in detail on the [Gamma Astro Data Formats](https://gamma-astro-data-formats.readthedocs.io/en/latest/skymaps/index.html) page.\n", "\n", "Other conventions required by specific software (e.g. the Fermi Science Tools) are supported as well. At the moment those are the following\n", "\n", "* `fgst-ccube`: Fermi counts cube format.\n", "* `fgst-ltcube`: Fermi livetime cube format.\n", "* `fgst-bexpcube`: Fermi exposure cube format\n", "* `fgst-template`: Fermi Galactic diffuse and source template format. \n", "* `fgst-srcmap` and `fgst-srcmap-sparse`: Fermi source map and sparse source map format.\n", "\n", "The conventions listed above only support an additional energy axis. \n", "\n", "### Reading Maps\n", "\n", "Reading FITS files is mainly exposed via the `Map.read()` method.Let us take a look at a first example: " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.236492Z", "iopub.status.busy": "2021-11-22T21:09:44.236199Z", "iopub.status.idle": "2021-11-22T21:09:44.242869Z", "shell.execute_reply": "2021-11-22T21:09:44.243095Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsNDMap\n", "\n", "\tgeom : WcsGeom \n", " \taxes : ['lon', 'lat']\n", "\tshape : (400, 200)\n", "\tndim : 2\n", "\tunit : \n", "\tdtype : >i8\n", "\n" ] } ], "source": [ "filename = \"$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-counts.fits.gz\"\n", "m_3fhl_gc = Map.read(filename)\n", "print(m_3fhl_gc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If `map_type` argument is not given when calling read a map object will be instantiated with the pixelization of the input HDU.\n", "\n", "By default `Map.read()` will try to find the first valid data hdu in the filename and read the data from there. If multiple HDUs are present in the FITS file, the desired one can be chosen with the additional `hdu=` argument:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.244925Z", "iopub.status.busy": "2021-11-22T21:09:44.244594Z", "iopub.status.idle": "2021-11-22T21:09:44.250044Z", "shell.execute_reply": "2021-11-22T21:09:44.250226Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsNDMap\n", "\n", "\tgeom : WcsGeom \n", " \taxes : ['lon', 'lat']\n", "\tshape : (400, 200)\n", "\tndim : 2\n", "\tunit : \n", "\tdtype : >i8\n", "\n" ] } ], "source": [ "m_3fhl_gc = Map.read(filename, hdu=\"PRIMARY\")\n", "print(m_3fhl_gc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In rare cases e.g. when the FITS file is not valid or meta data is missing from the header it can be necessary to modify the header of a certain HDU before creating the `Map` object. In this case we can use `astropy.io.fits` directly to read the FITS file:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.252015Z", "iopub.status.busy": "2021-11-22T21:09:44.251730Z", "iopub.status.idle": "2021-11-22T21:09:44.254338Z", "shell.execute_reply": "2021-11-22T21:09:44.254566Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Filename: /Users/adonath/github/gammapy/gammapy-data/fermi-3fhl-gc/fermi-3fhl-gc-exposure.fits.gz\n", "No. Name Ver Type Cards Dimensions Format\n", " 0 PRIMARY 1 PrimaryHDU 23 (400, 200) float32 \n" ] } ], "source": [ "filename = (\n", " os.environ[\"GAMMAPY_DATA\"]\n", " + \"/fermi-3fhl-gc/fermi-3fhl-gc-exposure.fits.gz\"\n", ")\n", "hdulist = fits.open(filename)\n", "hdulist.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then modify the header keyword and use `Map.from_hdulist()` to create the `Map` object after:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.256337Z", "iopub.status.busy": "2021-11-22T21:09:44.256057Z", "iopub.status.idle": "2021-11-22T21:09:44.259700Z", "shell.execute_reply": "2021-11-22T21:09:44.259855Z" } }, "outputs": [ { "data": { "text/plain": [ "WcsNDMap\n", "\n", "\tgeom : WcsGeom \n", " \taxes : ['lon', 'lat']\n", "\tshape : (400, 200)\n", "\tndim : 2\n", "\tunit : cm2 s\n", "\tdtype : >f4" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hdulist[\"PRIMARY\"].header[\"BUNIT\"] = \"cm2 s\"\n", "Map.from_hdulist(hdulist=hdulist)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Writing Maps\n", "\n", "Writing FITS files is mainoy exposure via the `Map.write()` method. Here is a first example:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.268412Z", "iopub.status.busy": "2021-11-22T21:09:44.268121Z", "iopub.status.idle": "2021-11-22T21:09:44.296193Z", "shell.execute_reply": "2021-11-22T21:09:44.296370Z" } }, "outputs": [], "source": [ "m_cube.write(\"example_cube.fits\", overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default Gammapy does not overwrite files. In this example we set `overwrite=True` in case the cell gets executed multiple times. Now we can read back the cube from disk using `Map.read()`:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.298239Z", "iopub.status.busy": "2021-11-22T21:09:44.297934Z", "iopub.status.idle": "2021-11-22T21:09:44.347160Z", "shell.execute_reply": "2021-11-22T21:09:44.347398Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsNDMap\n", "\n", "\tgeom : WcsGeom \n", " \taxes : ['lon', 'lat', 'energy']\n", "\tshape : (500, 250, 4)\n", "\tndim : 3\n", "\tunit : \n", "\tdtype : >f4\n", "\n" ] } ], "source": [ "m_cube = Map.read(\"example_cube.fits\")\n", "print(m_cube)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also choose a different FITS convention to write the example cube in a format compatible to the Fermi Galactic diffuse background model:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.356099Z", "iopub.status.busy": "2021-11-22T21:09:44.355806Z", "iopub.status.idle": "2021-11-22T21:09:44.360682Z", "shell.execute_reply": "2021-11-22T21:09:44.360847Z" } }, "outputs": [], "source": [ "m_cube.write(\"example_cube_fgst.fits\", format=\"fgst-template\", overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To understand a little bit better the generic `gadf` convention we use `Map.to_hdulist()` to generate a list of FITS HDUs first: " ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.371059Z", "iopub.status.busy": "2021-11-22T21:09:44.370733Z", "iopub.status.idle": "2021-11-22T21:09:44.372150Z", "shell.execute_reply": "2021-11-22T21:09:44.372386Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Filename: (No file associated with this HDUList)\n", "No. Name Ver Type Cards Dimensions Format\n", " 0 PRIMARY 1 PrimaryHDU 30 (500, 250, 4, 24) float32 \n", " 1 PRIMARY_BANDS 1 BinTableHDU 33 96R x 7C ['K', 'D', 'D', 'D', 'D', 'D', 'D'] \n" ] } ], "source": [ "hdulist = m_4d.to_hdulist(format=\"gadf\")\n", "hdulist.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see the `HDUList` object contains to HDUs. The first one named `PRIMARY` contains the data array with shape corresponding to our data and the WCS information stored in the header:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.374519Z", "iopub.status.busy": "2021-11-22T21:09:44.374215Z", "iopub.status.idle": "2021-11-22T21:09:44.375776Z", "shell.execute_reply": "2021-11-22T21:09:44.375956Z" } }, "outputs": [ { "data": { "text/plain": [ "SIMPLE = T / conforms to FITS standard \n", "BITPIX = -32 / array data type \n", "NAXIS = 4 / number of array dimensions \n", "NAXIS1 = 500 \n", "NAXIS2 = 250 \n", "NAXIS3 = 4 \n", "NAXIS4 = 24 \n", "EXTEND = T \n", "WCSAXES = 2 / Number of coordinate axes \n", "CRPIX1 = 250.5 / Pixel coordinate of reference point \n", "CRPIX2 = 125.5 / Pixel coordinate of reference point \n", "CDELT1 = -0.02 / [deg] Coordinate increment at reference point \n", "CDELT2 = 0.02 / [deg] Coordinate increment at reference point \n", "CUNIT1 = 'deg' / Units of coordinate increment and value \n", "CUNIT2 = 'deg' / Units of coordinate increment and value \n", "CTYPE1 = 'GLON-CAR' / galactic longitude, plate caree projection \n", "CTYPE2 = 'GLAT-CAR' / galactic latitude, plate caree projection \n", "CRVAL1 = 0.0 / [deg] Coordinate value at reference point \n", "CRVAL2 = 0.0 / [deg] Coordinate value at reference point \n", "LONPOLE = 0.0 / [deg] Native longitude of celestial pole \n", "LATPOLE = 90.0 / [deg] Native latitude of celestial pole \n", "MJDREF = 0.0 / [d] MJD of fiducial time \n", "AXCOLS1 = 'E_MIN,E_MAX' \n", "INTERP1 = 'log ' \n", "AXCOLS2 = 'TIME_MIN,TIME_MAX' \n", "INTERP2 = 'lin ' \n", "WCSSHAPE= '(500,250,4,24)' \n", "BANDSHDU= 'PRIMARY_BANDS' \n", "META = '{} ' \n", "BUNIT = '' " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hdulist[\"PRIMARY\"].header" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The second HDU is a `BinTableHDU` named `PRIMARY_BANDS` contains the information on the non-spatial axes such as name, order, unit, min, max and center values of the axis bins. We use an `astropy.table.Table` to show the information:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.377665Z", "iopub.status.busy": "2021-11-22T21:09:44.377385Z", "iopub.status.idle": "2021-11-22T21:09:44.381125Z", "shell.execute_reply": "2021-11-22T21:09:44.381299Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "\n", "CHANNEL ENERGY E_MIN E_MAX TIME TIME_MIN TIME_MAX\n", " TeV TeV TeV h h h \n", " int64 float64 float64 float64 float64 float64 float64 \n", "------- ------------------ ------------------ ------------------ ------- -------- --------\n", " 0 1.778279410038923 1.0 3.1622776601683795 0.5 0.0 1.0\n", " 1 5.623413251903492 3.1622776601683795 10.000000000000002 0.5 0.0 1.0\n", " 2 17.782794100389236 10.000000000000002 31.622776601683803 0.5 0.0 1.0\n", " 3 56.234132519034915 31.622776601683803 100.00000000000004 0.5 0.0 1.0\n", " 4 1.778279410038923 1.0 3.1622776601683795 1.5 1.0 2.0\n", " 5 5.623413251903492 3.1622776601683795 10.000000000000002 1.5 1.0 2.0\n", " 6 17.782794100389236 10.000000000000002 31.622776601683803 1.5 1.0 2.0\n", " 7 56.234132519034915 31.622776601683803 100.00000000000004 1.5 1.0 2.0\n", " 8 1.778279410038923 1.0 3.1622776601683795 2.5 2.0 3.0\n", " 9 5.623413251903492 3.1622776601683795 10.000000000000002 2.5 2.0 3.0\n", " 10 17.782794100389236 10.000000000000002 31.622776601683803 2.5 2.0 3.0\n", " 11 56.234132519034915 31.622776601683803 100.00000000000004 2.5 2.0 3.0\n", " 12 1.778279410038923 1.0 3.1622776601683795 3.5 3.0 4.0\n", " 13 5.623413251903492 3.1622776601683795 10.000000000000002 3.5 3.0 4.0\n", " 14 17.782794100389236 10.000000000000002 31.622776601683803 3.5 3.0 4.0\n", " 15 56.234132519034915 31.622776601683803 100.00000000000004 3.5 3.0 4.0\n", " 16 1.778279410038923 1.0 3.1622776601683795 4.5 4.0 5.0\n", " 17 5.623413251903492 3.1622776601683795 10.000000000000002 4.5 4.0 5.0\n", " ... ... ... ... ... ... ...\n", " 77 5.623413251903492 3.1622776601683795 10.000000000000002 19.5 19.0 20.0\n", " 78 17.782794100389236 10.000000000000002 31.622776601683803 19.5 19.0 20.0\n", " 79 56.234132519034915 31.622776601683803 100.00000000000004 19.5 19.0 20.0\n", " 80 1.778279410038923 1.0 3.1622776601683795 20.5 20.0 21.0\n", " 81 5.623413251903492 3.1622776601683795 10.000000000000002 20.5 20.0 21.0\n", " 82 17.782794100389236 10.000000000000002 31.622776601683803 20.5 20.0 21.0\n", " 83 56.234132519034915 31.622776601683803 100.00000000000004 20.5 20.0 21.0\n", " 84 1.778279410038923 1.0 3.1622776601683795 21.5 21.0 22.0\n", " 85 5.623413251903492 3.1622776601683795 10.000000000000002 21.5 21.0 22.0\n", " 86 17.782794100389236 10.000000000000002 31.622776601683803 21.5 21.0 22.0\n", " 87 56.234132519034915 31.622776601683803 100.00000000000004 21.5 21.0 22.0\n", " 88 1.778279410038923 1.0 3.1622776601683795 22.5 22.0 23.0\n", " 89 5.623413251903492 3.1622776601683795 10.000000000000002 22.5 22.0 23.0\n", " 90 17.782794100389236 10.000000000000002 31.622776601683803 22.5 22.0 23.0\n", " 91 56.234132519034915 31.622776601683803 100.00000000000004 22.5 22.0 23.0\n", " 92 1.778279410038923 1.0 3.1622776601683795 23.5 23.0 24.0\n", " 93 5.623413251903492 3.1622776601683795 10.000000000000002 23.5 23.0 24.0\n", " 94 17.782794100389236 10.000000000000002 31.622776601683803 23.5 23.0 24.0\n", " 95 56.234132519034915 31.622776601683803 100.00000000000004 23.5 23.0 24.0" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Table.read(hdulist[\"PRIMARY_BANDS\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Maps can be serialized to a sparse data format by calling write with `sparse=True`. This will write all non-zero pixels in the map to a data table appropriate to the pixelization scheme." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.386326Z", "iopub.status.busy": "2021-11-22T21:09:44.385963Z", "iopub.status.idle": "2021-11-22T21:09:44.390465Z", "shell.execute_reply": "2021-11-22T21:09:44.390638Z" } }, "outputs": [], "source": [ "m = Map.create(binsz=0.1, map_type=\"wcs\", width=10.0)\n", "m.write(\"file.fits\", hdu=\"IMAGE\", sparse=True, overwrite=True)\n", "m = Map.read(\"file.fits\", hdu=\"IMAGE\", map_type=\"wcs\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Accessing Data\n", "\n", "### How to get data values\n", "\n", "All map objects have a set of accessor methods, which can be used to access or update the contents of the map irrespective of its underlying representation. Those accessor methods accept as their first argument a coordinate `tuple` containing scalars, `list`, or `numpy.ndarray` with one tuple element for each dimension. Some methods additionally accept a `dict` or `MapCoord` argument, of which both allow to assign coordinates by axis name.\n", "\n", "Let us first begin with the `.get_by_idx()` method, that accepts a tuple of indices. The order of the indices corresponds to the axis order of the map: " ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.392553Z", "iopub.status.busy": "2021-11-22T21:09:44.392269Z", "iopub.status.idle": "2021-11-22T21:09:44.393712Z", "shell.execute_reply": "2021-11-22T21:09:44.393894Z" } }, "outputs": [ { "data": { "text/plain": [ "array([0.], dtype=float32)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m_gc.get_by_idx((50, 30))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Important:** Gammapy uses a reversed index order in the map API with the longitude axes first. To achieve the same by directly indexing into the numpy array we have to call: " ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.395913Z", "iopub.status.busy": "2021-11-22T21:09:44.395622Z", "iopub.status.idle": "2021-11-22T21:09:44.396917Z", "shell.execute_reply": "2021-11-22T21:09:44.397089Z" } }, "outputs": [ { "data": { "text/plain": [ "array([0.], dtype=float32)" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m_gc.data[([30], [50])]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To check the order of the axes you can always print the `.geom` attribute:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.399419Z", "iopub.status.busy": "2021-11-22T21:09:44.399123Z", "iopub.status.idle": "2021-11-22T21:09:44.400431Z", "shell.execute_reply": "2021-11-22T21:09:44.400615Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsGeom\n", "\n", "\taxes : ['lon', 'lat']\n", "\tshape : (500, 250)\n", "\tndim : 2\n", "\tframe : galactic\n", "\tprojection : TAN\n", "\tcenter : 0.0 deg, 0.0 deg\n", "\twidth : 10.0 deg x 5.0 deg\n", "\twcs ref : 0.0 deg, 0.0 deg\n", "\n" ] } ], "source": [ "print(m_gc.geom)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To access values directly by sky coordinates we can use the `.get_by_coord()` method. This time we pass in a `dict`, specifying the axes names corresponding to the given coordinates:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.402386Z", "iopub.status.busy": "2021-11-22T21:09:44.402059Z", "iopub.status.idle": "2021-11-22T21:09:44.403916Z", "shell.execute_reply": "2021-11-22T21:09:44.404072Z" } }, "outputs": [ { "data": { "text/plain": [ "array([ 0., nan])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m_gc.get_by_coord({\"lon\": [0, 180], \"lat\": [0, 0]})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The units of the coordinates are assumed to be in degrees in the coordinate system used by the map. If the coordinates do not correspond to the exact pixel center, the value of the nearest pixel center will be returned. For positions outside the map geometry `np.nan` is returned.\n", "\n", "The coordinate or idx arrays follow normal [Numpy broadcasting rules](https://jakevdp.github.io/PythonDataScienceHandbook/02.05-computation-on-arrays-broadcasting.html). So the following works as expected:\n", "\n" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.405921Z", "iopub.status.busy": "2021-11-22T21:09:44.405573Z", "iopub.status.idle": "2021-11-22T21:09:44.407412Z", "shell.execute_reply": "2021-11-22T21:09:44.407583Z" } }, "outputs": [ { "data": { "text/plain": [ "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lons = np.linspace(-4, 4, 10)\n", "m_gc.get_by_coord({\"lon\": lons, \"lat\": 0})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or as an even more advanced example, we can provide `lats` as column vector and broadcasting to a 2D result array will be applied:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.410220Z", "iopub.status.busy": "2021-11-22T21:09:44.409935Z", "iopub.status.idle": "2021-11-22T21:09:44.411346Z", "shell.execute_reply": "2021-11-22T21:09:44.411520Z" } }, "outputs": [ { "data": { "text/plain": [ "array([[nan, nan, nan, nan, nan, nan, nan, nan],\n", " [nan, nan, nan, nan, nan, nan, nan, nan],\n", " [ 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [nan, nan, nan, nan, nan, nan, nan, nan],\n", " [nan, nan, nan, nan, nan, nan, nan, nan]])" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lons = np.linspace(-4, 4, 8)\n", "lats = np.linspace(-4, 4, 8).reshape(-1, 1)\n", "m_gc.get_by_coord({\"lon\": lons, \"lat\": lats})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Indexing and Slicing Sub-Maps\n", "\n", "When you have worked with Numpy arrays in the past you are probably familiar with the concept of indexing and slicing\n", "into data arrays. To support slicing of non-spatial axes of `Map` objects, the `Map` object has a `.slice_by_idx()` method, which allows to extract sub-maps from a larger map.\n", "\n", "The following example demonstrates how to get the map at the energy bin number 3: " ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.413448Z", "iopub.status.busy": "2021-11-22T21:09:44.413107Z", "iopub.status.idle": "2021-11-22T21:09:44.414520Z", "shell.execute_reply": "2021-11-22T21:09:44.414695Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsNDMap\n", "\n", "\tgeom : WcsGeom \n", " \taxes : ['lon', 'lat']\n", "\tshape : (500, 250)\n", "\tndim : 2\n", "\tunit : \n", "\tdtype : >f4\n", "\n" ] } ], "source": [ "m_sub = m_cube.slice_by_idx({\"energy\": 3})\n", "print(m_sub)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the returned object is again a `Map` with updated axes information. In this case, because we extracted only a single image, the energy axes is dropped from the map.\n", "\n", "To extract a sub-cube with a sliced energy axes we can use a normal `slice()` object:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.416852Z", "iopub.status.busy": "2021-11-22T21:09:44.416558Z", "iopub.status.idle": "2021-11-22T21:09:44.417680Z", "shell.execute_reply": "2021-11-22T21:09:44.417848Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsNDMap\n", "\n", "\tgeom : WcsGeom \n", " \taxes : ['lon', 'lat', 'energy']\n", "\tshape : (500, 250, 2)\n", "\tndim : 3\n", "\tunit : \n", "\tdtype : >f4\n", "\n" ] } ], "source": [ "m_sub = m_cube.slice_by_idx({\"energy\": slice(1, 3)})\n", "print(m_sub)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the returned object is also a `Map` object, but this time with updated energy axis specification.\n", "\n", "Slicing of multiple dimensions is supported by adding further entries to the dict passed to `.slice_by_idx()`" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.419782Z", "iopub.status.busy": "2021-11-22T21:09:44.419437Z", "iopub.status.idle": "2021-11-22T21:09:44.421170Z", "shell.execute_reply": "2021-11-22T21:09:44.421341Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsNDMap\n", "\n", "\tgeom : WcsGeom \n", " \taxes : ['lon', 'lat', 'energy', 'time']\n", "\tshape : (500, 250, 2, 6)\n", "\tndim : 4\n", "\tunit : \n", "\tdtype : float32\n", "\n" ] } ], "source": [ "m_sub = m_4d.slice_by_idx({\"energy\": slice(1, 3), \"time\": slice(4, 10)})\n", "print(m_sub)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For convenience there is also a `.get_image_by_coord()` method which allows to access image planes at given non-spatial physical coordinates. This method also supports `Quantity` objects:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.424082Z", "iopub.status.busy": "2021-11-22T21:09:44.423761Z", "iopub.status.idle": "2021-11-22T21:09:44.425259Z", "shell.execute_reply": "2021-11-22T21:09:44.425427Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsGeom\n", "\n", "\taxes : ['lon', 'lat']\n", "\tshape : (500, 250)\n", "\tndim : 2\n", "\tframe : galactic\n", "\tprojection : CAR\n", "\tcenter : 0.0 deg, 0.0 deg\n", "\twidth : 10.0 deg x 5.0 deg\n", "\twcs ref : 0.0 deg, 0.0 deg\n", "\n" ] } ], "source": [ "image = m_4d.get_image_by_coord({\"energy\": 4 * u.TeV, \"time\": 5 * u.h})\n", "print(image.geom)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Iterating by image\n", "\n", "For maps with non-spatial dimensions the `~Map.iter_by_image` method can be used\n", "to loop over image slices. The image plane index ``idx`` is returned in data order,\n", "so that the data array can be indexed directly. Here is an example for an in-place\n", "convolution of an image using `astropy.convolution.convolve` to interpolate NaN\n", "values:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.428931Z", "iopub.status.busy": "2021-11-22T21:09:44.428564Z", "iopub.status.idle": "2021-11-22T21:09:44.430240Z", "shell.execute_reply": "2021-11-22T21:09:44.430421Z" } }, "outputs": [], "source": [ "axis1 = MapAxis([1, 10, 100], interp=\"log\", name=\"energy\")\n", "axis2 = MapAxis([1, 2, 3], interp=\"lin\", name=\"time\")\n", "m = Map.create(width=(5, 3), axes=[axis1, axis2], binsz=0.1)\n", "m.data[:, :, 15:18, 20:25] = np.nan\n", "\n", "for img, idx in m.iter_by_image():\n", " kernel = np.ones((5, 5))\n", " m.data[idx] = convolve(img, kernel)\n", "\n", "assert not np.isnan(m.data).any()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modifying Data\n", "\n", "### How to set data values\n", "\n", "To modify and set map data values the `Map` object features as well a `.set_by_idx()` method: \n" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.432163Z", "iopub.status.busy": "2021-11-22T21:09:44.431875Z", "iopub.status.idle": "2021-11-22T21:09:44.432944Z", "shell.execute_reply": "2021-11-22T21:09:44.433121Z" } }, "outputs": [], "source": [ "m_cube.set_by_idx(idx=(10, 20, 3), vals=42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "here we check that data have been updated:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.435050Z", "iopub.status.busy": "2021-11-22T21:09:44.434738Z", "iopub.status.idle": "2021-11-22T21:09:44.436254Z", "shell.execute_reply": "2021-11-22T21:09:44.436408Z" } }, "outputs": [ { "data": { "text/plain": [ "array([42.], dtype=float32)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m_cube.get_by_idx((10, 20, 3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of course there is also a `.set_by_coord()` method, which allows to set map data values in physical coordinates. " ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.438365Z", "iopub.status.busy": "2021-11-22T21:09:44.438085Z", "iopub.status.idle": "2021-11-22T21:09:44.439375Z", "shell.execute_reply": "2021-11-22T21:09:44.439153Z" } }, "outputs": [], "source": [ "m_cube.set_by_coord({\"lon\": 0, \"lat\": 0, \"energy\": 2 * u.TeV}, vals=42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again the `lon` and `lat` values are assumed to be given in degrees in the coordinate system used by the map. For the energy axis, the unit is the one specified on the axis (use `m_cube.geom.axes[0].unit` to check if needed...).\n", "\n", "All `.xxx_by_coord()` methods accept `SkyCoord` objects as well. In this case we have to use the `skycoord` keyword instead of `lon` and `lat`:" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.441799Z", "iopub.status.busy": "2021-11-22T21:09:44.441527Z", "iopub.status.idle": "2021-11-22T21:09:44.442671Z", "shell.execute_reply": "2021-11-22T21:09:44.442841Z" } }, "outputs": [], "source": [ "skycoords = SkyCoord([1.2, 3.4], [-0.5, 1.1], frame=\"galactic\", unit=\"deg\")\n", "m_cube.set_by_coord({\"skycoord\": skycoords, \"energy\": 2 * u.TeV}, vals=42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Filling maps from event lists\n", "\n", "This example shows how to fill a counts cube from an event list:\n" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.445734Z", "iopub.status.busy": "2021-11-22T21:09:44.445434Z", "iopub.status.idle": "2021-11-22T21:09:44.522286Z", "shell.execute_reply": "2021-11-22T21:09:44.522473Z" }, "run_control": { "marked": false } }, "outputs": [], "source": [ "energy_axis = MapAxis.from_bounds(\n", " 10.0, 2e3, 12, interp=\"log\", name=\"energy\", unit=\"GeV\"\n", ")\n", "counts_3d = WcsNDMap.create(\n", " binsz=0.1, width=10.0, skydir=(0, 0), frame=\"galactic\", axes=[energy_axis]\n", ")\n", "\n", "events = EventList.read(\n", " \"$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-events.fits.gz\"\n", ")\n", "\n", "counts_3d.fill_by_coord({\"skycoord\": events.radec, \"energy\": events.energy})\n", "counts_3d.write(\"ccube.fits\", format=\"fgst-ccube\", overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively you can use the `fill_events` method:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.524876Z", "iopub.status.busy": "2021-11-22T21:09:44.524560Z", "iopub.status.idle": "2021-11-22T21:09:44.536282Z", "shell.execute_reply": "2021-11-22T21:09:44.536450Z" } }, "outputs": [], "source": [ "counts_3d = WcsNDMap.create(\n", " binsz=0.1, width=10.0, skydir=(0, 0), frame=\"galactic\", axes=[energy_axis]\n", ")\n", "\n", "counts_3d.fill_events(events)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you have a given map already, and want to make a counts image\n", "with the same geometry (not using the pixel data from the original map), you can also use the `fill_events` method.\n" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.538462Z", "iopub.status.busy": "2021-11-22T21:09:44.538175Z", "iopub.status.idle": "2021-11-22T21:09:44.608119Z", "shell.execute_reply": "2021-11-22T21:09:44.608291Z" } }, "outputs": [], "source": [ "events = EventList.read(\n", " \"$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-events.fits.gz\"\n", ")\n", "reference_map = Map.read(\n", " \"$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-counts.fits.gz\"\n", ")\n", "counts = Map.from_geom(reference_map.geom)\n", "counts.fill_events(events)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It works for IACT and Fermi-LAT events, for WCS or HEALPix map geometries,\n", "and also for extra axes. Especially energy axes are automatically handled correctly.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Filling maps from interpolation\n", "\n", "Maps support interpolation via the `~Map.interp_by_coord` and\n", "`~Map.interp_by_pix` methods. Currently the following interpolation methods are\n", "supported:\n", "\n", "* ``nearest`` : Return value of nearest pixel (no interpolation).\n", "* ``linear`` : Interpolation with first order polynomial. This is the\n", " only interpolation method that is supported for all map types.\n", "* ``quadratic`` : Interpolation with second order polynomial.\n", "* ``cubic`` : Interpolation with third order polynomial.\n", "\n", "Note that ``quadratic`` and ``cubic`` interpolation are currently only supported\n", "for WCS-based maps with regular geometry (e.g. 2D or ND with the same geometry\n", "in every image plane). ``linear`` and higher order interpolation by pixel\n", "coordinates is only supported for WCS-based maps.\n", "\n", "In the following example we create a new map and fill it by interpolating another map:\n" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.611012Z", "iopub.status.busy": "2021-11-22T21:09:44.610686Z", "iopub.status.idle": "2021-11-22T21:09:44.634171Z", "shell.execute_reply": "2021-11-22T21:09:44.634374Z" } }, "outputs": [], "source": [ "# read map\n", "filename = \"$GAMMAPY_DATA/fermi-3fhl-gc/gll_iem_v06_gc.fits.gz\"\n", "m_iem_gc = Map.read(filename)\n", "\n", "# create new geometry\n", "skydir = SkyCoord(266.4, -28.9, frame=\"icrs\", unit=\"deg\")\n", "wcs_geom_cel = WcsGeom.create(\n", " skydir=skydir, binsz=0.1, frame=\"icrs\", width=(8, 4)\n", ")\n", "\n", "# create new empty map from geometry\n", "m_iem_10GeV = Map.from_geom(wcs_geom_cel)\n", "coords = m_iem_10GeV.geom.get_coord()\n", "\n", "# fill new map using interpolation\n", "m_iem_10GeV.data = m_iem_gc.interp_by_coord(\n", " {\"skycoord\": coords.skycoord, \"energy_true\": 10 * u.GeV},\n", " method=\"linear\",\n", " fill_value=np.nan,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Interpolating onto a different geometry" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For 3d geometries this operation can be performed directly using the `.interp_to_geom()` method. This is very useful, ex: while using map arithmetic." ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.637775Z", "iopub.status.busy": "2021-11-22T21:09:44.637487Z", "iopub.status.idle": "2021-11-22T21:09:44.650150Z", "shell.execute_reply": "2021-11-22T21:09:44.650385Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsNDMap\n", "\n", "\tgeom : WcsGeom \n", " \taxes : ['lon', 'lat', 'energy_true']\n", "\tshape : (80, 40, 6)\n", "\tndim : 3\n", "\tunit : 1 / (cm2 MeV s sr)\n", "\tdtype : float64\n", "\n" ] } ], "source": [ "# create new geometry\n", "energy_axis = MapAxis.from_bounds(\n", " 10.0, 2e3, 6, interp=\"log\", name=\"energy_true\", unit=\"GeV\"\n", ")\n", "skydir = SkyCoord(266.4, -28.9, frame=\"icrs\", unit=\"deg\")\n", "wcs_geom_3d = WcsGeom.create(\n", " skydir=skydir, binsz=0.1, frame=\"icrs\", width=(8, 4), axes=[energy_axis]\n", ")\n", "\n", "# create the interpolated map\n", "m_iem_interp = m_iem_gc.interp_to_geom(\n", " wcs_geom_3d, preserve_counts=False, method=\"linear\", fill_value=np.nan\n", ")\n", "print(m_iem_interp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that `preserve_counts` option should be true if the map is an integral quantity (e.g. counts) and false if the map is a differential quantity (e.g. intensity)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Maps operations\n", "\n", "### Basic operators\n", "\n", "One can perform simple arithmetic on maps using the `+`, `-`, `*`, `/` operators, this works only for maps with the same geometry:" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.652760Z", "iopub.status.busy": "2021-11-22T21:09:44.652440Z", "iopub.status.idle": "2021-11-22T21:09:44.653558Z", "shell.execute_reply": "2021-11-22T21:09:44.653803Z" } }, "outputs": [], "source": [ "iem_plus_iem = m_iem_10GeV + m_iem_10GeV\n", "\n", "iem_minus_iem = m_iem_10GeV - m_iem_10GeV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These operations can be applied between a Map and a scalar in that specific order:" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.655676Z", "iopub.status.busy": "2021-11-22T21:09:44.655387Z", "iopub.status.idle": "2021-11-22T21:09:44.656610Z", "shell.execute_reply": "2021-11-22T21:09:44.656901Z" } }, "outputs": [], "source": [ "iem_times_two = m_iem_10GeV * 2\n", "# iem_times_two = 2 * m_iem_10GeV # this won't work" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The logic operators can also by applied on maps (the result is a map of boolean type):" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.659148Z", "iopub.status.busy": "2021-11-22T21:09:44.658858Z", "iopub.status.idle": "2021-11-22T21:09:44.659998Z", "shell.execute_reply": "2021-11-22T21:09:44.660170Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsNDMap\n", "\n", "\tgeom : WcsGeom \n", " \taxes : ['lon', 'lat']\n", "\tshape : (80, 40)\n", "\tndim : 2\n", "\tunit : \n", "\tdtype : bool\n", "\n" ] } ], "source": [ "is_null = iem_minus_iem == 0\n", "print(is_null)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we check that the result is `True` for all the well-defiend pixels (not `NaN`):" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.662270Z", "iopub.status.busy": "2021-11-22T21:09:44.661953Z", "iopub.status.idle": "2021-11-22T21:09:44.663272Z", "shell.execute_reply": "2021-11-22T21:09:44.663491Z" } }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.all(is_null.data[~np.isnan(iem_minus_iem)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cutouts\n", "\n", "The `WCSNDMap` objects features a `.cutout()` method, which allows you to cut out a smaller part of a larger map. This can be useful, e.g. when working with allsky diffuse maps. Here is an example: " ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.667729Z", "iopub.status.busy": "2021-11-22T21:09:44.667436Z", "iopub.status.idle": "2021-11-22T21:09:44.668649Z", "shell.execute_reply": "2021-11-22T21:09:44.668866Z" } }, "outputs": [], "source": [ "position = SkyCoord(0, 0, frame=\"galactic\", unit=\"deg\")\n", "m_iem_cutout = m_iem_gc.cutout(position=position, width=(4 * u.deg, 2 * u.deg))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The returned object is again a `Map` object with updated WCS information and data size. As one can see the cutout is automatically applied to all the non-spatial axes as well. The cutout width is given in the order of `(lon, lat)` and can be specified with units that will be handled correctly. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing and Plotting\n", "\n", "\n", "All map objects provide a ``plot`` method for generating a visualization of a\n", "map. This method returns figure, axes, and image objects that can be used to\n", "further tweak/customize the image. The `plot` method should be used with 2D maps, while 3D maps can be displayed with the `.plot_interative()` or `.plot_grid()` methods.\n", "\n", "\n", "### Image Plotting \n", "\n", "For debugging and inspecting the map data it is useful to plot or visualize the images planes contained in the map. " ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.670640Z", "iopub.status.busy": "2021-11-22T21:09:44.670361Z", "iopub.status.idle": "2021-11-22T21:09:44.676123Z", "shell.execute_reply": "2021-11-22T21:09:44.676284Z" } }, "outputs": [], "source": [ "filename = \"$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-counts.fits.gz\"\n", "m_3fhl_gc = Map.read(filename)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After reading the map we can now plot it on the screen by calling the `.plot()` method:" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.678035Z", "iopub.status.busy": "2021-11-22T21:09:44.677751Z", "iopub.status.idle": "2021-11-22T21:09:44.891680Z", "shell.execute_reply": "2021-11-22T21:09:44.891879Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "m_3fhl_gc.plot();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can easily improve the plot by calling `Map.smooth()` first and providing additional arguments to `.plot()`. Most of them are passed further to [plt.imshow()](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.imshow.html):" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:44.894200Z", "iopub.status.busy": "2021-11-22T21:09:44.893904Z", "iopub.status.idle": "2021-11-22T21:09:45.070992Z", "shell.execute_reply": "2021-11-22T21:09:45.071175Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adonath/software/mambaforge/envs/gammapy-dev/lib/python3.9/site-packages/astropy/visualization/wcsaxes/core.py:211: MatplotlibDeprecationWarning: Passing parameters norm and vmin/vmax simultaneously is deprecated since 3.3 and will become an error two minor releases later. Please pass vmin/vmax directly to the norm when creating it.\n", " return super().imshow(X, *args, origin=origin, **kwargs)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "smoothed = m_3fhl_gc.smooth(width=0.2 * u.deg, kernel=\"gauss\")\n", "smoothed.plot(stretch=\"sqrt\", add_cbar=True, vmax=4, cmap=\"inferno\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use the [plt.rc_context()](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.rc_context.html) context manager to further tweak the plot by adapting the figure and font size:" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:45.073897Z", "iopub.status.busy": "2021-11-22T21:09:45.073577Z", "iopub.status.idle": "2021-11-22T21:09:45.272080Z", "shell.execute_reply": "2021-11-22T21:09:45.272274Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adonath/software/mambaforge/envs/gammapy-dev/lib/python3.9/site-packages/astropy/visualization/wcsaxes/core.py:211: MatplotlibDeprecationWarning: Passing parameters norm and vmin/vmax simultaneously is deprecated since 3.3 and will become an error two minor releases later. Please pass vmin/vmax directly to the norm when creating it.\n", " return super().imshow(X, *args, origin=origin, **kwargs)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "rc_params = {\"figure.figsize\": (12, 5.4), \"font.size\": 12}\n", "with plt.rc_context(rc=rc_params):\n", " smoothed = m_3fhl_gc.smooth(width=0.2 * u.deg, kernel=\"gauss\")\n", " smoothed.plot(stretch=\"sqrt\", add_cbar=True, vmax=4);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cube plotting \n", "\n", "For maps with non-spatial dimensions the `Map` object features an interactive plotting method, that works in jupyter notebooks only (Note: it requires the package `ipywidgets` to be installed). We first read a small example cutout from the Fermi Galactic diffuse model and display the data cube by calling `.plot_interactive()`:" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:45.275384Z", "iopub.status.busy": "2021-11-22T21:09:45.275085Z", "iopub.status.idle": "2021-11-22T21:09:45.389935Z", "shell.execute_reply": "2021-11-22T21:09:45.390131Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c2a5657f9b54435097de39aebdd74cff", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(SelectionSlider(continuous_update=False, description='Select energy_true:', layout=Layou…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rc_params = {\n", " \"figure.figsize\": (12, 5.4),\n", " \"font.size\": 12,\n", " \"axes.formatter.limits\": (2, -2),\n", "}\n", "m_iem_gc.plot_interactive(add_cbar=True, stretch=\"sqrt\", rc_params=rc_params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you can use the interactive slider to select an energy range and the corresponding image is displayed on the screen. You can also use the radio buttons to select your preferred image stretching. We have passed additional keywords using the `rc_params` argument to improve the figure and font size. Those keywords are directly passed to the [plt.rc_context()](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.rc_context.html) context manager.\n", "\n", "Additionally all the slices of a 3D `Map` can be displayed using the `.plot_grid()` method. By default the colorbars bounds of the subplots are not the same, we can make them consistent using the `vmin` and `vmax` options:" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:09:45.476814Z", "iopub.status.busy": "2021-11-22T21:09:45.421022Z", "iopub.status.idle": "2021-11-22T21:09:45.910217Z", "shell.execute_reply": "2021-11-22T21:09:45.910508Z" }, "nbsphinx-thumbnail": { "tooltip": "A thorough tutorial to work with WCS maps." } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adonath/software/mambaforge/envs/gammapy-dev/lib/python3.9/site-packages/astropy/visualization/wcsaxes/core.py:211: MatplotlibDeprecationWarning: Passing parameters norm and vmin/vmax simultaneously is deprecated since 3.3 and will become an error two minor releases later. Please pass vmin/vmax directly to the norm when creating it.\n", " return super().imshow(X, *args, origin=origin, **kwargs)\n" ] }, { "data": { "image/png": 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\n", 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\n", 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