{ "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/v0.17?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/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" ] } ], "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/terrier/Code/gammapy-dev/gammapy-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\", conv=\"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 29 (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(conv=\"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", "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", 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" ] }, "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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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "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))\n", "\n", "rc_params = {\n", " \"figure.figsize\": (12, 5.4),\n", " \"font.size\": 12,\n", " \"axes.formatter.limits\": (2, -2),\n", "}\n", "m_iem_cutout.plot_interactive(\n", " add_cbar=True, rc_params=rc_params, stretch=\"linear\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The returned object is again a `Map` object with udpated 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": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0" }, "nbsphinx": { "orphan": true }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "18cadad77ffb42f680e72178fd67e7d7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "SelectionSliderModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "SelectionSliderModel", "_options_labels": [ "5.85e+01 MeV MeV", "8.00e+01 MeV MeV", "1.09e+02 MeV MeV", "1.50e+02 MeV MeV", "2.05e+02 MeV MeV", "2.80e+02 MeV MeV", "3.83e+02 MeV 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