{ "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/v0.18.2?urlpath=lab/tree/maps.ipynb)\n", "- You can contribute with your own notebooks in this\n", "[GitHub repository](https://github.com/gammapy/gammapy/tree/master/docs/tutorials).\n", "- **Source files:**\n", "[maps.ipynb](../_static/notebooks/maps.ipynb) |\n", "[maps.py](../_static/notebooks/maps.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Gammapy Maps\n", "\n", "![Gammapy Maps Illustration](images/gammapy_maps.png)\n", "\n", "## Introduction\n", "\n", "The `~gammapy.maps` submodule contains classes for representing sky images 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. [Accessing and Modifying Data](#Accessing-and-Modifying-Data)\n", "1. [Reading and Writing](#Reading-and-Writing)\n", "1. [Visualizing and Plotting](#Visualizing-and-Plotting)\n", "1. [Interpolating and Miscellaneous](#Reprojecting,-Interpolating-and-Miscellaneous)\n", "\n", "Make sure you have worked through the [Gammapy overview](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": {}, "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": {}, "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 gammapy.maps import Map, MapAxis, WcsGeom" ] }, { "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": {}, "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": {}, "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", "\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": {}, "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", "Here is a second example that creates a WCS map centered on the Galactic center and now uses Galactic coordinates:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "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", "\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": 7, "metadata": {}, "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": 8, "metadata": {}, "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": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ True, False])" ] }, "execution_count": 9, "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": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "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": 11, "metadata": {}, "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": 11, "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": 12, "metadata": {}, "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": 13, "metadata": {}, "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", "\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": 14, "metadata": {}, "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", "\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": 15, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$$[1,~3.1622777,~10,~31.622777,~100] \\; \\mathrm{TeV}$$" ], "text/plain": [ "" ] }, "execution_count": 15, "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": 16, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$$\\mathrm{TeV}$$" ], "text/plain": [ "Unit(\"TeV\")" ] }, "execution_count": 16, "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": 17, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$$[1.7782794,~5.6234133,~17.782794,~56.234133] \\; \\mathrm{TeV}$$" ], "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "energy_axis.center" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Accessing and Modifying Data\n", "\n", "### Accessing Map 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": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.], dtype=float32)" ] }, "execution_count": 18, "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": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.], dtype=float32)" ] }, "execution_count": 19, "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": 20, "metadata": {}, "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", "\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": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0., nan])" ] }, "execution_count": 21, "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": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)" ] }, "execution_count": 22, "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": 23, "metadata": {}, "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": 23, "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": [ "### Modifying Map 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": 24, "metadata": {}, "outputs": [], "source": [ "m_cube.set_by_idx(idx=(10, 20, 3), vals=42)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([42.], dtype=float32)" ] }, "execution_count": 25, "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": 26, "metadata": {}, "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": 27, "metadata": {}, "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": [ "### 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": 28, "metadata": {}, "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 : float32\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": 29, "metadata": {}, "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 : float32\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": 30, "metadata": {}, "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": 31, "metadata": {}, "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", "\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": [ "## 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": 32, "metadata": {}, "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" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: FITSFixedWarning: 'datfix' made the change 'Set DATE-REF to '1858-11-17' from MJD-REF'. [astropy.wcs.wcs]\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": [ "By default `Map.read()` will try to find the first valid data hdu in the filename and read the data from there. If mutliple HDUs are present in the FITS file, the desired one can be chosen with the additional `hdu=` argument:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "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": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Filename: /Users/adonath/Desktop/gammapy-tutorials/datasets/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": 35, "metadata": {}, "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": 35, "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": 36, "metadata": {}, "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": 37, "metadata": {}, "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": 38, "metadata": {}, "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": 39, "metadata": {}, "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 32 (500, 250, 4, 24) float32 \n", " 1 PRIMARY_BANDS 1 BinTableHDU 33 96R x 7C ['I', 'E', 'E', 'E', 'E', 'E', 'E'] \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": 40, "metadata": {}, "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", "DATEREF = '1858-11-17' / ISO-8601 fiducial time \n", "MJDREFI = 0.0 / [d] MJD of fiducial time, integer part \n", "MJDREFF = 0.0 / [d] MJD of fiducial time, fractional part \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": 40, "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": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=96\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
CHANNELENERGYE_MINE_MAXTIMETIME_MINTIME_MAX
TeVTeVTeVhhh
int16float32float32float32float32float32float32
01.77827941.03.16227770.50.01.0
15.6234133.162277710.00.50.01.0
217.78279510.031.6227760.50.01.0
356.2341331.622776100.00.50.01.0
41.77827941.03.16227771.51.02.0
55.6234133.162277710.01.51.02.0
617.78279510.031.6227761.51.02.0
756.2341331.622776100.01.51.02.0
81.77827941.03.16227772.52.03.0
.....................
8617.78279510.031.62277621.521.022.0
8756.2341331.622776100.021.521.022.0
881.77827941.03.162277722.522.023.0
895.6234133.162277710.022.522.023.0
9017.78279510.031.62277622.522.023.0
9156.2341331.622776100.022.522.023.0
921.77827941.03.162277723.523.024.0
935.6234133.162277710.023.523.024.0
9417.78279510.031.62277623.523.024.0
9556.2341331.622776100.023.523.024.0
" ], "text/plain": [ "\n", "CHANNEL ENERGY E_MIN E_MAX TIME TIME_MIN TIME_MAX\n", " TeV TeV TeV h h h \n", " int16 float32 float32 float32 float32 float32 float32 \n", "------- --------- --------- --------- ------- -------- --------\n", " 0 1.7782794 1.0 3.1622777 0.5 0.0 1.0\n", " 1 5.623413 3.1622777 10.0 0.5 0.0 1.0\n", " 2 17.782795 10.0 31.622776 0.5 0.0 1.0\n", " 3 56.23413 31.622776 100.0 0.5 0.0 1.0\n", " 4 1.7782794 1.0 3.1622777 1.5 1.0 2.0\n", " 5 5.623413 3.1622777 10.0 1.5 1.0 2.0\n", " 6 17.782795 10.0 31.622776 1.5 1.0 2.0\n", " 7 56.23413 31.622776 100.0 1.5 1.0 2.0\n", " 8 1.7782794 1.0 3.1622777 2.5 2.0 3.0\n", " ... ... ... ... ... ... ...\n", " 86 17.782795 10.0 31.622776 21.5 21.0 22.0\n", " 87 56.23413 31.622776 100.0 21.5 21.0 22.0\n", " 88 1.7782794 1.0 3.1622777 22.5 22.0 23.0\n", " 89 5.623413 3.1622777 10.0 22.5 22.0 23.0\n", " 90 17.782795 10.0 31.622776 22.5 22.0 23.0\n", " 91 56.23413 31.622776 100.0 22.5 22.0 23.0\n", " 92 1.7782794 1.0 3.1622777 23.5 23.0 24.0\n", " 93 5.623413 3.1622777 10.0 23.5 23.0 24.0\n", " 94 17.782795 10.0 31.622776 23.5 23.0 24.0\n", " 95 56.23413 31.622776 100.0 23.5 23.0 24.0" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Table.read(hdulist[\"PRIMARY_BANDS\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing and Plotting\n", "\n", "### Plotting \n", "\n", "For debugging and inspecting the map data it is useful to plot ot visualize the images planes contained in the map. " ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "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": 43, "metadata": {}, "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": 44, "metadata": {}, "outputs": [ { "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": 45, "metadata": {}, "outputs": [ { "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": [ "### Interactive 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": 46, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "filename = \"$GAMMAPY_DATA/fermi-3fhl-gc/gll_iem_v06_gc.fits.gz\"\n", "m_iem_gc = Map.read(filename)\n", "\n", "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 diplayed 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." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interpolating and Miscellaneous\n", "\n", "### Interpolating Map Values\n", "\n", "While for the reprojection example above we used `.get_image_by_coord()` to extract the closest image to `~10 GeV`, we can use the more general method `.interp_by_coord()` to interpolate in the energy axis as well. For this we first define again the target map geometry:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "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", ")" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "m_iem_10GeV = Map.from_geom(wcs_geom_cel)\n", "coords = m_iem_10GeV.geom.get_coord()\n", "\n", "m_iem_10GeV.data = m_iem_gc.interp_by_coord(\n", " {\"skycoord\": coords.skycoord, \"energy_true\": 10 * u.GeV},\n", " interp=\"linear\",\n", " fill_value=np.nan,\n", ")\n", "m_iem_10GeV.plot(add_cbar=True, vmin=0, vmax=2.5e-9);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Making 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": 49, "metadata": {}, "outputs": [ { "data": { "image/png": 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