{ "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://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy/v0.12?urlpath=lab/tree/intro_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", "[intro_maps.ipynb](../_static/notebooks/intro_maps.ipynb) |\n", "[intro_maps.py](../_static/notebooks/intro_maps.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Gammapy Maps\n", "\n", "![Gammapy Maps Illustration](images/gammapy_maps.png)\n", "\n", "## Introduction\n", "The [gammapy.maps](..\/maps/index.rst) 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 [gammpy.maps](..\/maps/index.rst) docs page.\n", "\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](#1.-Creating-WCS-Maps)\n", "1. [Accessing and Modifying Data](#2.-Accessing-and-Modifying-Data)\n", "1. [Reading and Writing](#3.-Reading-and-Writing)\n", "1. [Visualizing and Plotting](#4.-Visualizing-and-Plotting)\n", "1. [Reprojecting, Interpolating and Miscellaneous](#5.-Reprojecting,-Interpolating-and-Miscellaneous)\n", "\n", "\n", "Make sure you have worked through the [First Steps with Gammapy](first_steps.ipynb), because a solid knowledge about working with `SkyCoords` and `Quantity` objects as well as [Numpy](http://www.numpy.org/) is required for this tutorial.\n", "\n", "**Note:** 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": [ "## 0. 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": [ "## 1. Creating WCS Maps\n", "\n", "### 1.1 Using Factory Methods\n", "\n", "Maps are most easily created using the `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", "\tcoordsys : CEL\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 `MapGeom` 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", "\tcoordsys : GAL\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, coordsys=\"GAL\", 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": [ "### 1.2 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 `MapGeom` object. The `MapGeom` 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), coordsys=\"GAL\"\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 `MapGeom` 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": [ "### 1.3 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", "\tcoordsys : GAL\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), coordsys=\"GAL\", 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", "\tcoordsys : GAL\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), coordsys=\"GAL\", 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": [ "## 2. Accessing and Modifying Data\n", "\n", "### 2.1 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", "\tcoordsys : GAL\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": [ "### 2.2 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": [ "### 2.3 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", "\tcoordsys : GAL\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": [ "## 3. 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", "### 3.1 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/adonath/data/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": [ "### 3.2 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": [ "## 4. Visualizing and Plotting\n", "\n", "### 4.1 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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MQRRpcf1LVB4tHlVQ3u/I+Cycuh6NFeVqbbz3PvDe2Ojmv9Jr+Jn2Ko+zzdio76h9JgtKU8ZbrR85U150n9FejcQMPIMeOXjebw9/ZET4mtdn9n9U/82C874vYxf3cUSPwfDKep+RNlndCj3ZWe8I95wxcdtKe6sT3fWsuBRnlcYFYpqy8h7dEV3byILijO4Er/J4zvxEPN+mz+r4I356PIqu9x5XM1eOqvSMyEVVtitjGlnLo2Meqb+/j0Mg8tAyyxuVVTz6yCvVvnueQPZE14iOqjcfte2FtNyH0edtxDGuXuowojFqz/jV08y8O44+osMMXrvKNaVVUzlZxFv1/LyIMvKENRKdA5EX7/XHfY7wjnHYOs0ioOrTkb2+mEbtm//35MI7+ag4DrE+Qt9LeXr9RfLJssM4oxNr1bnfdfRxnzAcPagaA0+pLnByEo+x3vjMjE2l3JRdb2Ir4SrjnfPIjqjOSDrGoDoub6Eor3URR+kH774GBjVAPZqyNItX3zMgnlxFijOik9tESqqqdHu87UGUQtRr0XiiepkSZTyZE5GVa3stZ8gcOis/Qv4aBx6XJ9vRPUIMkWwz3vOCC2s4MqbNUZqqwHTR60TZXc9RnjSjieup55pFAZ73GS280ftCMvD66Cka72ZB5qd5dszvSIllizhTBJ6C70UivT4Nj/YdOR6enNlH3+Hh/QY2IykDbxw6557ynhOdqBPFEK21KDrq0eMdj+V+e/R76zWK2rwyz9hHdSt0VGTbizQz4+fR4vG3N+5t4MIajowRVaUZKQddqD2vrOK1jdxcBdQ9qTmh6px0Qy/aqERMukD5sRo9o+n1pYY7qq/tqp5ndk295swD9KLXBfovnzqmesq76P4D7pP56vEkivI8PNavOix6f0mkwHSes8jJK/OUa4/vo9GUZ5yideM5DhkNWm+uIe9FNlG9XcOFNRy7CNOMud7b5Eb72oYeb8KjiMhLBVT69zzaanQTlTGOqieYXa888sLD1TM8XM9ey1v1PKM++ZoXEXB7Vbzqgfb4WlWw2XXGYffQZJ6wtRn1vD2+KV+4T77juuLcZNGvN8aRtRsZJu7HM6JeW6NBIYs8tI2OqxdBVqOWXcCFNRzK1OrjDjyoPPJaofd+86xcac9C+SpNFQ+j55FEqRMt6y2YuZDdVZ+lH6NwX+tkipDrMc5ImfEcXsPJxb2QOp6nWok2lK4e3R4e5c01xHeia1sDvmHTU+rVPS0F716X6L+WZfXtpt6ek5TJVa9tD3oRpScr2eNiKuvuQL63HUMEF9ZwKOjiHV14PdD2PMGZB1tdmBz9RAouexRJBpFH1ROqavmuhVIXM6dmMu/bi5L4t6YgMlxehOelMLwIwr61jvdb6TTcerLHi46VjgjUWEXeejaP/HQFT945dZZFOxnsItUSrXmmmcuzk17ctrLmRiNDb/6zqKKiz67Kt0E1mq/ChTcc0YKsWugIMiXEYI/4jnBEKYCoH+9o8YKuGS1sHEfCcW8BGB71kKppit4YezQpZE9yrSzEKh3RgvX+Vxa5KfyKMmfgQwLWNtrD6J0WU7oMevyLnJ+K89CT8yzl0sOR1Wd+eQ+MjNZR5ORVDF5P/rw22QukIhzHOLm+t9FnuzwsA1xAw1EhuJKSGG2vgmpQCS0jJRT1o+DVUY82g4qX6xnbHmQGUNMcXttt56gC6qnbHEbjV29fvXWu70U0avh7dJrBPnY+3H+mbHtz67WL/nsGweubHaaKg1FxovR/9tZM5tMC/cMCEZ2ZUfLWWO/EpkdrlhasrPcoWurBrjMBDBfOcNwblEeWOYNIoXlQ8diYjop3MFdxelGW9Y3kvwejobdHh6cU2POOIghPIbKSrnjQFTqZhp7yOMZJenXuPbqisqw/pdvbN8scC1XqkYGJlD9f9+pm8pnddOvxxVsPzB9vfWVy45UreLKk5cdOPS97YWDPV4sg2yf0eGD4e48Byuakp7NOAy6c4TDwPNxjbE5c77n30bNtRj02VoLsBUX1M88j67vnKWaCGuHjOpp266UOMpp7z1vyeBUZXF5cWZhf5XnEp2qqxDMkWVlmADn3rDeUeWkvlfFjbD6FN+pnJM0RKSd9hUClvc5vVG/kjnEPp2ckIgcrm391SNi42G9L6XrKfvTYvf3v3feUzVnFidp19HEhDYenJD3vNlJec5Qs14k8rUg4R3AzZAvN/o9EWT18Gb+qXl7Ub9a3x1dVgpcQH6WN2ltb75W3WbSQGZlIQUULk/v2Frg+Rp4VqL4+VmXc85Ah9dV5UuNbPbJqys2b8+yEYWRUtX99g2ZmoBSnRggen73o7ph+exDhjRwcr585CnuOE8m0bLNWq3AhDUekuCOIlFYklJkSqEDPK64INnAyjPWgOm7PWFZosD4ypd4DXXQji8t4cA3ArUn9bLGoN5cZ+cypiJSg4mDQvj1Fb9dtnAyVI9J6ws/zuA3U+F5DLkPa3htvdqJK2+n9I9a/GtJjnNxr0j4iR4D7ZB5wPebZNWkT0c+GmnFkkDmElfUTrZdKdLnrKIOhZDhaa49vrX3s6veDW2sPO0WaypB5jfw7YqTnTZpQs6AYVN7eZ3gjLyRaWFlkNOeVmIpzTkQz2p/3n8uiSLGiuI5x8v0d6vVH3nN1ofLcVx9b05tnjSQyp0cNrD1Ej2GB9U1z7DFHURO3915v6q2NzHHyjnVmjpAaL90o1kjQyg6wqdD1PhFvvS3k4xlnz0mI1smhlGfzrX1GMleVd4/myEmJ1uCpGpDe43MBfD6A1wL4rdX/JwL4ufN8rPqi80jhqFw/0SORs8edR58eLYeDuBaoPwa6Ol44OKO2vcdFe/RqH7vg44LwHA7SpfMZjdmjU8s8uqL/I78rvGL6F/JbeR+Nwasfta3Qn8lrpX7leuW1Az36RubNk2fFb+v49kKfFXp6n12u/V0+Vr1iOO4E8AAAv0Zlbzzv93GooPbeyZExeM41T6i3nWQb04jCzRQHX+/V8+qP8KenhDLDWe3Lux71P6IQK2OsGvHTkIlsbiK59GSpp2x7/IsM7IisVIxJVU6z/jI5rhodxTPXCFXGz3W8ORuVoQMs1xyP9azfx3HPNE3vsT+ttfuviBuG1tpjW2s/31r7jdbam1pr/2BVftBae2Vr7WWttUUPzwInw8bo8QJRTnoBP1yG1ItC0zk31FRSQZXUFI+hl1M+lg+PGzg5bi8kjzbcgE3+ZvWOnH6Vlgi4rvYR3U/gpUDg1M3mWXkY0ZbR3aMja+vhUj4rD6/i5Aa7gbfn4a2Z6AnC3l4Al1XTe968Z3tOCiz3kcx5vNLxV1K5PPfal5cKU5p1j6qiA7LHtXvg1bFXFxvNc9LQKRQijhcC+AoAdwH4OAA/AuDrZoU3wAcBeMrq98NWY3sSgG8A8GQAnwTgCysRBxKLO+ohVr3POamXs/iMesHaZq5HXfUO544j8xArtPa8Xx0Hl42mMat8GPGstW7m0e5yHqI+DpO+tum/6smPRiJRxLENnRWZqsprpLdGZGxkDZ51xPECAL8P4I0A/h6AnwDwvxfanYBpmt4+TdPrVr/fBeA3ADwawA1Y3tt3L4CW4TCCe96q51X06nr/q1FGxav06mTg4fK89pGTG+x9RfcJRO0izy6iudI+quvhzv5H9xj0IiX7bR/zEK/hJI+A+DHiUcQS1atGMLwRrm1ss9obNz/srhLdZPNmXusRfHnjMg8Pv/RMafWimyiqjDbyexGHBxlPMpnx5oCva/0Md3RQItIzUX2VPY8n1bVXhvPaqwDwwQB+B0u5eDyAXwDwYwAeVt3jwIBlzur1IgnGF+Xqd+HN9GgcjQZ2TYN9RvZ3RvO1vfnIooCReZzDr2gsntenZdHGfuadqvc54j33opMRHldks8rfBfwxj8qrh+/AKY/6qIxRcWQ09vjZG2slmshkpYLvTDbHsYww3hB9tjQaCwC/CuDTRttmp6pGDUYmKKOLtLcY59BZEcoMf1U5VBbGXMMY9RPxOZrTXvgeKaMK73ryoPMwIndV3J6COsDJ8VcVoyc/fP32ZH6qY4mUdjamnhHqGTxPFnrGeIQvo2tN63uOpfGm4lxVP3Mch7MyHI9ffV64+vyl1ecbAHzlFkbjRgA/DeBLB9q8BMDdAO6+8cYb00mImDdX8Xl4Rg3WCK0jdEaKbYSWkf+jNM71tKp4e7hHI5cqr6KISxV9RVFk3noka5XyqB6wjn60j0yeNPLx6J4jS1XZ8fBU5TFzOioGbds1MFKnYjirH61/8803T1jp0dXnJTs3HKS0f6lSVjQADcD3APjmuQR7m+OREMxVSj3B36Uy3IUy3ba/yvXR6KVKV897VL5fTuY9o6+SxopwZeUj83SQ0DGHVx59ahSsHfMwui/GMyY9Gqv3WszhX2XeKvgqeqHatkrHiBxltGQ6aFSWzuM+jo+h/08HcOdMw/Exq0G8YYX3TgCfuAvDMSqU29b3BGFb70TxeLh7Xt0cIfeEtbeQvLaRJ1rlX8bDSt89fuyCLxX5qRjDaMxsAKL6mvqIlEjlptPqjamHVLenzOeePszmshoReLyP7jVRnDzGiK6qsq6syzk6J8PZM0xnbThuA/B6AG9dfe7E6kjteXzUcFQ2pbYR5FGPuKJwq4LXW0ij3suuBNTrX73aat+jXmDPWM+Zk8yTrhquSF48Wax6o5H8qXGJxqF9LuBHbFW52cZJiWQlk68MV9VJ68mL4hpdV9vwL0oXZu2ysUcfk5MzNRxkQC4B+AvnZTDs84jAcCyC/6OeY0UwIiGvCFF2Fp7p79E6opx7Y1T+jSgD+5i3lm0EVgQ+UwDMowPpP8LRU0BVvqh8ZTI1Ih+ZHFQMUdS2sofhXfOUWEWJ98Yb1dN5HVkD3ri9cVV4Fsm1/e+dDqwo/Co9I2t6RL53aTjujw601r5S/gMApmn6ml7b04D3rr4Xq2+7c1Ufopadqebz4Hpnpf3OzttfCsoj4PpHDt4Dott7iVBGH8MBNp92ugjq8TWt471JTfs3uqI79yF1gc3HohtE59K9fpVWnXfF4fHMw8u8V95q/0DtjP2IbER3sGcvnrJ5jsr5FcDROBW3d49EtAaUn9mceXUg5dan3vUetfXoX+DkU3o9PlXWhndPTu9u7kxmtF62hpUunjcPMl2087vFCSo3APIu/PsAPAvLezDOFYzxvZew9xSn1Tmg3wvkN5FFr4JkPHy99wgRE+5F8J0Ju6e8lS4Gxpktcqt3IN/ZTVWGX+vaTU2ZYdH+vMVroI+X4Q+Pwftt/+3GM1UIEQ6Plt51hZH6Wfk1bCoMNdCKg5WPjTvjL8uxJ89sjPi3GuRsHUU067tGquDJZSZzdlMhy6vXb0/eGafV93Adw+efx3te/z3dUeXXCC9LMJzbAh4I4Kevlz0O/VRCt5FQUNtWb2jrheVZ+21uzvLC4wzHLu49qfLT65fnpBfGZ2G9Nz9z6eul6xbYpI/bef1rWk3bV+bNa5PNofLD43NGQ5Xf2o+37+L14ZXNOVSQyYvybRHQV5GLHp+i/rfto7q+K32c9SNHFB4C4ENmtDt12IVV9TwQ4GQKwetLPQwu9zwXz2PkPrxrPe9nNGUSeSyRB+N5aaAyvpbRPvJyJfVmGS5h0/PV6E1xLZxvHXfvtbfeXFo7fZOdefsWHR9Lexub97BGj38aYbNXGo3Z+MPRhje3nnxGcqARq6WZjqQ8m1dI3ShN6qWvdB4Vt65F+3CqmPvWCEm/vYiB++WyQ2zSpZFFj3ZgPc+eHvGi8tNMS7lQiDD4DvI3AXgHgC+5HiKOiucceXjRJ7P0PS8067/3qJLME7P20TiyKKgy5pFP5tlFN4z1+BXdbZuNaWROR/hRwWvXD+Tbm7eeZ53xMpKJimerOLxTYh7dET18f8iInEXRUPXUWoRLZWbkfTfZzYwHOPk4cuVNdQ4q8tZrnx020Q9HU7oeFzj747iPp8+jAdz/vIwGG47RG4N04lWIRoxKVTB08ri+pTW8G7YihdO75vWzjeAy33Z1qmQbXkflnCaJFvqcvrJ5zORrzngyWiP5UIUX0eX1e9hpO8qfbDyRoxMZusy4zZE3jx+efFQNckWW+YSYZ0x685U5HBm9Ea//GO9cAAAgAElEQVSBszccJ25L98rO6nNzssdRtcxa32tT8WAWHRyR8FdpjHLmHh1ZXt6jzzOec3iYLZqM3soC9Wjr4e/NiUZFUb2ed1nlUaaYb5d+dC4j+iLl7EUpkeNSUawRjzwF3OP7iFz1jE92fXT992QwG09lL6wny5nsV2UxkzNuc9Z7HE/mP6sXOd1WaHcqcO/qe0Hf9js74ubl5DXfzHDklCkc4yQOrx/LcXu5yQw4Zx6BjcNO2nB5xhdvv2ER1PX65I/25+2bLLAGzbF7exhZ3ru3B9Q7kcW/o5y/njbT6wzKC66vJ7f4+h3wea37NDavhzj5GHLm21WqZ2O6hs058E4SMq32/9bVbz2F5fFB1wHjVrD20XXDt8DmvESnoxbwj6TqnGT7lpcDmrwTaNae59XTL4afy/UEH/Of36ke7TXBucbzoXJ2gPFbB8qQRBpfDuBdWN46cW31eReAPwTw9eedqsIWnoZ+Mi8rwqueWOSRebQtBI9Hg1c3G696HFn/Ub47402Fh71TMSP52oqX1esrm7fKHEfzknmQ3rwsnD49GkfSTZncK37uP5OjntxG/Iz2Y7w1ojRou4pMjvDHm4dojUTymo3dq+v1EeHpyd5Iqq7Hk7NOVZ2bkegZjpHF1BM+j+mRoGVGI5r87P/cxTHCh2wDt4Kr0l4FPaM74nO0ADN+KZ7M+EYLu0KbV9/bSFU8UcoxUlYer6O9OFXKniz3NqiVdxV+VddMNgfeHo1nSCLDVzWAyheVMY8OnocFzbOWV/nX0xc9I+bJSW9dqZyeSaqqtWbR6g+11p6in6jdWYCXDloU2i3om39rKBi9x9rCyej95gunzELahfy3cURpFf7NYaw3Bm2jRw2VZtBvC5kVn45fx+wdF9R5ydIWSovh07vLdYxe2M3j9eaF51fx8VwfUBmwTvlkaZqrOMmLBU4efb1K5VbHS5XYnd+cduwdDbZv4531Y2mK6Liu0cS0ZKkw7s+OQR+s6L+MTWB50pSYgd79r2uR+/TWkXc0mfFpPyznyjeue7jqy/jJx3i1XGnkOVd+6xgjGY1Sy1xf51TT4MrDXsp7GJJI4ztW3z/vfF55PUQcmSeYpUUOcPJhb0hwRf2xN5LV4365zPPSsr65XiW1xPUPE9yVMVZoY75H9T1PLcIZedBV+qPxRKd6emMb4R/3w/OvY+Insnr9qIz0vNEoHcb/vYhZj3JW5vAAyw3+Zwvuy9TuECdp1jShjvcAm0/ijWQmonEh7Tg9Gc2Vzpknz1nqzGsXyXRFbrM0aLYOMtk+61TVgyplZ/W5md4AqILsTXY2SSrUvQWrE6QCmgl3r7yyILyFEY0xUzaZ0KlwV5VmJsxR38p77/dIWk9xHAiOihGq8D6rmy1yU6z8MMheysHbp4hkUmmxuodO+97YMr7xt/JY2zLtaiA9Q9qbi0yWPfqjdJyXYlTnisevqSpvTjLZ1P6V5oh/TJuHx5ODCOdZn6r65WLZmcD9sBnqLeT6wvnN3wusw7kjnAwJFfTUlOHyHkaooatHm5figZTxt+GwtIDRz8+lysapdGkqgutbyH5V6iu9Oi6m37umYbqHg0N8PgmiJ76y+b6ETRzW1k4WafssheWlE3gs/PuS1DuAn+7kNIfx2js1xm2UXk6TRCds7M5lGz/LuaYCrR235XHw9wJrOWR8wMmTbvzAQksrHdF/a5OlULx14I3baGSe2/c1uW6gMn5I9HFbT150bNnTJLQ8ot/6sjV+DZt8Vto8XbOgNkrnriF8Om5r7VFY3vD34NbaR2D59j5gyf+HnBI9XbCn42q+3fKfrICyXK2VQeobvmuIJ4l/e30oTV7bjD7Gw4uGj1seUdtePtTgACcXeA9YYHmPh8HjrV7LFLDRxfNn+X7PmFp9badHbaP+mLaMRuDkfBq9C2zmvQ2yvRpV3rbQGQfPO+9tMUTjt/YePYaPx3CNyuz/EdZ7O1ewfowI444cJFZ0ZuRUdnlN6RxnYzvGch/FjC+wKSveEXAu43Erz1RWvD0r1Sk6nktSxnxlWfF0iv1n581zVtlRsf9mEG1M9s1yeBqQPVb9EwB8DoDHAPhGKn8XgK84JXq6YCGSMtOEkK9xGeiaKiP7zUKqdTPg/lSZRF7uqAfPi/BIcHi/PY9VvSxWYEpXFB1oBKBj8pQAcHK8/N/miBdEJPC8OPQeBVtAES+YDnMOrOzAqWNj4EeVq2JmukD1uH/GpfdaHNG1a9LGzsAbrbdjqcyvYVMBWqTFkZoqIkh9VthsRGx8rHiMJnNY7NtwHQC4i+rr+tIoQGmzzWg2Nsxv+74LvuPgRQZeZG08wIpulhcvimKaDa+uE5YjlUFPduDQxvrDeK+KXx3IBU4aTGtncuPpxp1BYY/jOee1n5Ftji+SvGIv76rtov8Rfq7n5Zyj/iO6s7FE9RW/9s//F1JH6YW007EvpI6OP+Jttpno0ZbVU/q86zYmbxy9vQSP5khe+Bpvblfm1JuLaK/NmyevHtPE8xHNs0eLbo57Y+D5jubEk0NPRqzOYYK3N95M3rw16PFb5+5QfnPfB067THd4c5HpIG//pCr/B/LbWxtn+iKnaZp+uLX2N7C8g/xBVH4uL3Ji4Jy2erDmtbBn1rO6jMcsOLD2trz0B9dZUL/ASc/A6quHxO3tN7c5cK4rzUy3HnM0HAtsprjUy+bjmREoDzhE9uqyF6bzpDQunHID7kM9Trtu7TgNZGP2Ik8IbQo8T5rasf6Zn4yDUxUefp6vI+ca88275vGS6bD+KhG3RlGanvLK1ZvW8THf7FsjHh4T709ccuoa3Rop85i81Ng1qaNRsMolp5Q0utZ1abRGGYpobfD+CPNJ58Cus3xE/emRYl0blczJCHQ3x1trLwLwGQC+BMt9jk/H8oGH5wqcXmBFoWWaKlEmqvBBrl3C2mh4G9uRMbp99c2bgrzg1ejwNy+yKN0D+Mra+jQ4IPotvWC02ALwjJIq8oV8PLojAfXSdvb7kOpp36rkdD/gGk7ylPHbmIF1SgVSX3nLysdLnTHPeUOTjbDVY34dY5NGLmeeMs+jF3ypMjcaOEVxhM10hdW5FZvKnL95zCb7kVHQjddL8g2s5Yv3JVTxqgNgZQu5zvVZJrz0DztKRq/995QzwyGWeylWbjezGR9YDnRNeGsG0t5LQZmh1EMhLL/eHPCa0DRlJDu7gsqpqqdP0/S3AfzxNE1fDeBpAB67YzqGwYSAJ40FlJUOGxMFzg+rx2sToMJq3wfYVAaGb4FlLpoFjfOWLIS8sC3vzXlY3fQ7lv8G0RhZ+JRuz2jyNYu0dKHqAjmk60yb9uOBKQalX5Us52nVePGYdVz2rblmu3ZI+IG1PLBnq/2oYWG54xvj2GkwMJ5cov8e8Pj1RjdWeCz7hpf7Z6NltBrPWQkeUJnSzGM+lHKWWVsvTLvuF3DEYuNk/Mb7A/qdRTUH0h44KR82DyonukdmezdXCO8VoVPXuleuDg/XVRnk6NkiBcbD4+K5s7nl7ArzwTOou4SK4fjT1fe7W2sHAP4MwBNOgZYhUAWrioSZdVXaQa6x9+15dzpJfN1+34rNjTFrxwuIF6UX9UQRBLApAKps2Ijaf4uUtJ4KtwqptynJghp5Pvb7MtGoRwqNLqPfvDkeN/fByoXnJ3rJjWegtIwNBLBWKObxaXpBabF+dYF6ylk3u1UJWhpG71TnsegmvgIrfZYzM5QGho+NBc/pEdU/pDqqAFkBm1NixtfqKa84GvFkXQ9HcHTce5IAp4VZ1oxOlXmeD+6T595k91b48sYOoKbcIP95vKozvDSpZjB4HYJo03EYTv32nIBdQHePA8ArWms3Afi/AbwOy82Wf3VK9HThfjjpDWrkoaAW/hLWnoQqSWCtdDVqADa9oFuxFlyrywtI/7O3ydEE08wKV/OtRqP3qIYDp46mE9iz4XEwHxD8P8bSKNxF/w2YP+atae7/2Kl7F11Xo+kpbx6HgqcMvDEaXcqLCPikSrZYNd1iZdaXGUwzFKYYNcWi8uPtUXCfbCBYllXGgE0jpTQbLUc4eQSb00ksT5pLZ5xMg42Hx5CN1a4zrZ7x0GiWDTKvaVWeVu5Ft6wD7oK/x2Lj49QvpL32cUj/r8o1o0Hni8dp6WYvtacRKbflOdwldCOOaZq+dpqmd07T9MNY7m3cCuDHd0xHGe6FHx14gqUKxybtLqxz/+whLajOHdjMB9tvtvhXsClQlwSfZxjYKzRB5/DUlAx7gyZkVs/6uUzt1Jh4aRT2Yo2OKPWgQn2AtbFlXAxR2oXx2Pdlqm/0qBLlfjiSND5E0YR3BJVxYtV/dGTRZMvqsHw9G+u55vnlfRdrzwrL5M6MutGkSs3jvQG3icau9HAa0fo2JcUyyGk1ljMzDtcIL/dta9HWk8cTltPL1PYSNteYjplxWXuTT07DGS5uy+3UoBi9No8HgkvhmOhko+gZIJNjo5XnTCMrxs/rlNekRxs7fvxfDbXycZcw9M7xaZrumabpTwD80CnQUgImmJVJFDl4qRJgzWzNz9tkL7Be6LdiUxmZwBmYEl5gqVjY8/fCc/WkOCVg9e6i/4c0DlNY1g8DK1RbyCzkBpyHZjpU2Wm0wqAK3uos5MOeI9NwBet0gm48qzfKht0iEVZW6vXZArJrisMiJ1Us6sHeIePCqh3/59QbsGkMb8cm31TZsadtcsBpJ6Pb86zZCBiNTIMpOS03UEeD62mKSh01VoIc7fL827Wr2NwnuEPGbXKuez66d2G8UwN9mWgE1sZLnS4e8yE203m8cb8QfDY+jo6V96BrBrfKdc/R8q7ZGBdYZzS4v0OqpxEOhG44NO4KhgwHQetXCRq29szW2n9qrb25tfaCVdmTW2uvbq29uLXWpcnbENNTMwa8ILLFZtfNULCBMWWkXodOzhGAl+OkwfCAJ9QE1r5vX/V1GX6YaWk0VjScL79K1zmHy2kwXiQLbG6Cg+rygmZPiCMG4KSRYYHWdMsBfMXA/zkS8CI3YDMvb/Vs/EyD5oX5RjJTPlexucfAXrf2bSmdy1h70CYzlwifOgrsKarHavKkR7UjL5WVhsdPzvnbWFhBHmPTSTjEek65/mVqw33rKSp21pi3pqRZHtT7NyXOc83OF/PD5svgLsLJqRxV2nrai+fA6Gbjw3J4Vb45ojiQujaeK9iUQd0XYf1ha1PXqvHAi+QvY3O/lKNBzljYmu1lA0ZhruGY5jRqrd0A4J8DeBaAJwH4m621JwH4Uiyd6CsAPj7DcT/4G028UNnDsAnicFm/gSVjWSCBJfM/CWuPySbTFh1707z4rW/2pj3gRcGboHes+rxCeEzgNPLRcdumHhsTYK0sL2GTLvvmvDawVsLGQ97YNw9M+W38UqNieDmFxx4vG7Rjqa8nqSKFoFGLLvzLUr6QNuzpsrd4IL/ZUD0RSz7YvJjCuQtro29tWNmwMWBFyulFlo3LVMZzaP3xPofKHHvp1gePVRUnnyI8xlIGD7GpBI1Gi8T10AiP+Qgn01G3YnNtHtPH8BzipLE0fvF65G8bv+ewXZO65nSwTIKusQ4xo2rjY0NifbLBBXwamPfskF3F5jrm/xrx2Rq8g/phvcPtjf8sA7uC7H0cP9Zae7nz+TEAHzCzv48C8OZpmn57mqb3APh+AJ8M4AYsjdG96EQz75X/kRFQL+CK/NffZlwOsVYGhwBehbUX+UqcVGTqxbHgqpAwbVxHoyJbfBblAEsBYMUC6pvPmtt1U7rHTn32JJU2w8O0cwTFUZvnDd2NTcFl3LwgebGxp2b9q1E1HDzfpuwu4aTCMHzmENj883h1E509ZB7bITY9N1OWr6L+DwB8yk3LuXjGqu4TiWaOBo0260MjYJUb86qBk2M0YMWlaR2ua+khjijZMF4iXFZmfGEesFE/IDzqgBj9PC6NClT52Zq4FvwH1vtPvDdjwLLHPFYnj9cer7nL9NvgilPGDo/RZxmLY6zv5VrI9Uv0W6MP47XhV3lXvvG4IL+PhA+7hOxU1T+ZeS2DRwP4Xfr/NgAfDeBbsNxw/00AL6ogYmsKbAoFh2rqvR3i5A15xuBXEm7DZfVfjs1JNJy68DR3DGyGnOz92ULTBXMrNnPwJkisdM2oXMVaEfCC4XSMGtU7Vh8O8Q+pPRsa5oEaCQ6nre4rsWngbJFoSsnrj/GyUed6zANQ33DqcPpB95WY50qTXWcDzPRrWvQZWMrHtWvAZ30s8O3/bllmjoYZ9rtWdU0Jcd6clSrz9BLVZcfA0mo2NjNmJjuGl9MfzEfOnx9ic6+BI3qdG+ML85bX4q1ST5Ub08rrSB0cjiR5HgyMh2w0eE0yGG3qmAAnZZWNAP+/jPUpK43KmUeHWDspRiOIVtYLRrOuqyhK0EyLwTXn90L+K0+2hbZ6HtWZQGvt0wF8wjRNn7f6/zwAHzVN05d02r0EwKcBwANvvPEhH/BnfwbgZO40ArbwJjQW/huwZ3IXgP8ZSwv3q4TjCtbeq002TxqnhmxhXsNasbBXAWx6fewpmpI+wDpdZfitHYge5oP9P0KsUJVXHL4bf1SJesbR/tuiNyOlKSzuj40i0waqy4rd+62Ljw0YLxCdc7t+rfB9K5YRAwD8GI0TxJtbsZyDL1zF7f/H+14G4Bl443Mehh/9UeBF956k16I9m2Prkx0FYFOWWDmZ/DFe5s8BTvLQ5tVwHkgZ08b9ahRndJgjo/NsYDJo8sNrxehXAxXNrY5ToyyjQR1Cu8Zr364ZjUfYNErefqLXrxo87RPw17iBGoDL2Ew9Kc3aJ685G7ddZxrUkb355pvxR3/0R+8mtC+dpul5zpC7MHePYy68DZt3nT8GhfTbNE3Pm6bpodM0PfTBD33oCQ9QPVQvlwhsLn5TyMzYq1gqi0sAfgXA27HpVSyweW+HKQ/DzaG/7QEcw1/oGiYDmwrwLqxTZjYOzYPaOEygTGnfIXzwwlkTMsN7RHXZ22Zgr834bULLUYf1cYhNxWB8McN0mcbN4TovKvVYj6WeKXG7Zv+vYnPMwNqY21zduqr/DKlvc/qq1cecjAXWp3hMXr7sJuDyZeCV9wL42RcCWOBPri3/83weEn9UYRpPDLcqMOMNR5WWTmF6rR9ToKy41ZDY3F+jzzF9s2dt88R7QkdOPftmx8Dqm0K84pRZW5Zj6/NWbI6fwYwgOys2Lo1EmD4zXteoLfPQdMrtRKP2f4j1+jrESaPBxtL4ajqDo0VgrR9AdXn/S9NNZvDYyTPecerL8BiuJzzhCTA9uvrMMhrA2RuO1wJ4YmvtCa21BwD4TCyd8SE4oA+wZiILnwk2ez4GrKR0g/LHqN4x1ht7NoGH2ARLE3EYqkJmi92EWvu0OkYrh8gGnK+3tMJlbB45NXoWgo8XFej/Ef1mowC6DrpuoHsqBizEisvqc6rJxqEpKN5XMbo5otH0yyFdu4N+q0ywt2YK/CrhNjx3Yb2YTRFbavCK9PmadwJHR8C3fhrwtc/8JfzixzX8/CrnaR42GzKWTZsPMxrGI1YuLAc8p3dgLROmjI0+NeLXsM63q1xekjJW1MZjlu0D+Tb6WRkab43vHKF40QMbIJVjvuHUDB7P61Vpb+M1GtU5s0iDaWdDYk7FApsGGHTdyliRGxgdtkeqYzWaTH7Y0bpMdVlX2BiPBY+N3/pnfrID4q3FbeFMU1UA0Fr7RADfjOWG+HdN0/R1I+0PHv7w6e3vfKebygBOhsBeSMzhPLC2zKYkbLE/EUtD8gys0z9W3/Dwb1BfB1Rfw0mmzfBdpjq8d2HKwGg+wqbxYoXEhoYXEqdFjAe20JU3oLqcF+f+DFjIr+Hk4lDDwjxhHNa3Rl5Mk9ZTPmp/x04bxs20mjHmebT9CDXoOk7u05T4Zfo23vNdyGxMGCyNpwae+WC8iKJXyLUsJaTr5ppTbrjYIeAowWSWaeA0LffJ+NgZYO9Zx+fVhfxWZ4XHzHMKhyb7jiIGYHMNqCEyMDy8pnW8umelsmjAcu2l2bz/KgO6Bo8B3Hbbbbhy5crsWykYKk/H/dnVI0fs/8Nbaz89t8Npmn5imqbDaZr+m1GjAQD3wBcss7I6YWz5bdJM4HXvwDw3YBPPXXKNhYOVNntCnMJhj8DSEcBaIE2BA5vK4xlYRyomYGaEbsPaIzFcvFHPkdbCuW59ste7oLqGw/rm9CCwDtU948DRIEcPvEj1+mXCobSqx2ZgdXhfiRWdwULq23Ub6yuxyTfeZLYIg1Nl1tacCePzVSyPI1ukam1NdgyPlet4mL8sMyy3HqjHydGVOhP2bTxlGWJlyvU5glU6rR2XXZHrplBViQJr757rKtjaYnnlNBVHzkYLyydH3pz2YYPGXr214Y15NhjMO4sMgE1dwOlglvVrWDuCngxYPabJ6qjsquxwpKhGJJKduVBJVT1imqZ32p9pmv4YwCN3TMcwGCNZMZuALui35rk5clDPyNJS5m1+HzYnjIXJaOAJsZCehcvA6vEd4ebpmpI7xNpo2Fn3V1J9E7bbsTx+dpXaepGULSJ+mKDRxguXjYf1w0qD0xDWHysrjbrUQ7fFxIuPaQfVZ5zMX+XnITYNL7BOb1hko2kVDtk5vaEL1ZwFVSzWn+G+lfAZPa/C5j0u1j8rEANNyVn/HJ1qGsm+dd6tH0up8fpQZWvtrEz3+vib+2f+Gz9YWVmdQ8GnuPTQgsGC6jNexq9pl1vlv/XBPNR59PqzjAPzR+n2ogOWVfvP64v7MxlnR5DXiOK1vtnZ4vlhvcc0G+8uS1+7hMpDDu9trT1umqbfAYDW2uMx8wbAXcC9q29Nu1yVcmBTsajXYCdZdFFZ+ys46bVx+MeTyN70pQCf1eF65nnYOCwKsv6uYDOPz57Mgsq+8H7AV967ToewAjZDqGkdYFPgDqgu08+pJTXSmg7wjJHxzcbLC9IWNwgXcHKRcx9X5ZqXEjvC5h6BRzPLjzfOQ6KXUyHX6L/Rfwmbx5utnnrs7JlyOQPTzaeczJgZXRxVgPrkCIxBUyjseXM0q9GDpkpYoR3AP/hhY2X5YGOhBhPS3sbsXdMy7oP/829WzrzPZPVYdr21bm15XliW4LTVtWK/eVwqhwuqw79t7o1fbBS1rY3V9JuV7dpwVCKO/w3AL7bWXrI6FvsLAL58x3QMg+bwj7GplGwhAJuLw4A9WlVUV+k6h8Dc/sipa3jZgLBnx2BGzQyF4biEZbRzjPWNZIdOW944s2OfvBBtXEp/FB5bCG34WTEysLHRBcveG4/5CCefhKueKnt/nldnfSgtngG3Pq9iM/LgCEPTfAxXsT6ZZjKgqRCVG00dGHhKkvvksT6b/lvUwg9GZGXPd2NrWo/HZ/SyQ8L8ZIOm3jiwuUYO5Bo7JMD6bnl2Tpgm65sVr0YELKNZNKIpJ+OD/WeecD+ek8B7D/bR+WZQQ8Z1VK4Nl0aIPD/H0uYY67Sr1Te+qS7w5MqMh+qkXULl6bg/BeApAH4AwA8CuG2aptl7HLuGBTYn6zLWE2ELgwWQldORlFkai4X/MjajB0+xGVwmfKY0jTYTAPvNnpkJkgkGKw32EC38PKTrLOyaduAxK6/01I314QmxtT12/nugC58jCRuv4bqV6nJYzilHnWNgc854MXEdLb9KbTTqUrC216QeOyt2lzh70SofbHTsNzslrAT1ugIfVjCjaPUvUz0vp8+woGssq+zpX6Y+TBFfpXqcauHow5M9NnwqN+o8sOfMPDdH0H5z9M38430Gw8P9cKqYr9t6Yr4tpA47PaqwuQ92utjoHFCZlls7w3OFrml0zHOqGQ0bi33rOtsVZI8cuXX1/RQAj1vR8V8APG5Vdi5gBDPjebFeoTJQXc8bB9aLzxaeLUoTsCvYFGIWKO7/KjY3vaKUAStmzseaMB1RHY6ezHPmjVz2LngBXcLmI0oipQts8olp8hS2KTk2fgz8n4XdDDFHWFb/Lml7GZuLAVg/QJANi7Ux3nheLkeFPE6bTzNQds0MMgN7s/afFQDvQbGR4fnn9AboGhtRa3+AzQMUrCRtHGx0bPz8WA82umw82HAvBAenxkxxHRBOdTTYm1VZ9zxj9rS9cjW6jM/bLLb/9m281TWn3rb1d4hNQ2m8YXnnNaUOk8qJjYHTYkzrETYdT+M/O5twfjPYvHDWgHWBOl4MV7BbCI/jtta+Y5qmL2it/bxzeZqm6RlO+anDwx/+8OmR73yn610YMOPZYts1T/FxTpbBFikrYVsoehc0qA7Twf3xwj/EyUd5cN6YjdCzqZzbsPeq+ysakvP4vd/Wv25eel65pje0ruJjYNysrNV79CDqx4uSrJx5YbzSb1McrEyZRvaaPaXBSk37NNxqEHmcxiedS1X8t2F5YyrPLc8Xj195eRmbexLKS+1TN8yBkzLM5aD6qnw1l++tDeVjT3ZUjk2R8hwq3dpG17XXlsenUR/jZf6pE8J8YN6p42fXNRK0+obXo0+dCu4bOKPjuNM0fcHq57Omafpr/AHwibvofA7cD2sP07xX9mpZODzmeULBilw9U1ug7EVw2Mt4o4WrtNhv9Zg0vcAKhD3bv4lNoVJ+8GIw74rpYjDeefRrffYII2PNdbgel7N3d4STeFlRMrCHZXR7/Vs78yqZVt4ENZ4fUpn2zZELiF7uV8s0hcLGgOfJZErTSRzhMt1HWD9Y0ei16OR2nJx3a2dza0aDQdMexhfe3GfgedKowkCVM6dMbHyKjyNHjZa4XRQ5G02Z0TDgvS4+VMCyau0WTnvri+eX+wc25Tqj2wwFGxM2GuyweGM2Oq0P3mONIpedwDRN6QfA6yplZ/W5+aabJixPdf35Z7H68O8FMB1QGVb/D+l/1FZ/Q/Awbu+a0tHr57LgW6zoVNwH1PZ2Kr/s4FRcB9QH82UhZd61aEw9/nt1dUyXnX6i/rVdhNfmpzfPxuMD+LzQebc32hUAACAASURBVGYeWtvLgsfjgY7B2ps82u+F4H+24DnAplxoG+1LZZR5oW2UR4eCx+Op0sE4LjvtdO1x+x69mSx5/I3+c1/cH9Prjc3jUyTzGW88fBmdPBfaTnFH9Q3XbbfdNu1KD2d7HI9qrd0G4MGttY9orT1l9bkdwEOidmcFngVf0G8N9aw+e4fszahX4UUlnie9kLaa2mFvKfLK+dw/06n9LwjPHVi/7InftWF11ENRr5t5ZePR9IOmBZi37Kl6Y1OPj8fA/OLNyktUL4sQFzh5osfKmRbPY2YabaxeXtzqae6ePcoF1vtS9ttoVx4oLTxH1p4jhiNsepqXsPkEAk7jsJdp/S+wGW1wJMVj1/Jj6ofxqFwv5JtlBFhHVPbbxql4+LqmWExeTLa9tgucpEWv82+OqHRNWGTk8SuKgg14zrkO98FpKS+qUToNLBLTNRWliTXCO+tTVZ+A5ePTHwPgn9Ln+QC+Ysd0lOFexJOoCkY31VgwmfGcWuAJtW9bpDrB1qcXFutmripPXhTcloHrcDht5ZqD5ty0ngXn8XjKX+vp9Sjs7eW6vdDa+8/j0cXm0aGLz4BxqHLU+pGjwI4F5LfSosevtU+mi5UEP1PsEGs+8qb5FWymRK44tEL61k1lPRSiiu0Ia7lnQ8b14ZQrbw7ktycT6jRw2QKbeEBj4fWnp6SO5VvBDKFXDqxPah3JNT5KDhqPrW37rTrjAGsHzksRRzJo88IGgvWGyYHKlR3b5TGa/HD/O09bFVJVzzmvtJT3uemmm9xUkX6i0BFOeHdAnyyM5DaXpf3CwWvXlB5NS0W/K7R4Y/H60/EtJKTV/r0xZ3xUPBleLyXA9b00U4UeL32Y8VfbeKG/zpWXlqnwayEfnQuvf00xZn1GfDhw+vTkz+OxNw/6yWTRo8FrG6WDRmTRm/9ozrN15q1Rw+2l2jgNx2W6Dj1amb9e/R69Pf5r2Zmkqghuc55V9X8V2p0acJjOZQspY+8AyNNK5qFGlnmBTfz8UDw9UcPhrm7WZeG254mzx800LGRshlc3+LitebPmgWg6zPPgIWX8HXl7OlYvfGcvyepwaM20exGdB7phr2298bFHr6fJ7LrV58iPwebFiyI9Oi7Jt+GwDXSbW+abRSWa8lKZMDw6Jo1aGaJoiuU6SsExb6J7BTh1wmknBpV1jQiYBuuX6bVx6zoFlXlrUMfE887t7XDCMTZfQ21tLHKztkaLjpVpZv5ymtM7sqz9KXhj8Mp2BoWI49eup83xm2hz3PNc+NqBY7G1reddQNr1opvo43kyWZ/m1WTtEeDUcXobv4dOe+aVeqoeL7z/kQeU4enNSdTvodNOx5vR6/WR0RXV13mLZIfn2z7ewYfbBdehtI9o8aIkby14v7mvbB148pvxrUpjFZ/yI+J1xPtePS8C7vGhIlOGW8evMtqTxUWAx6Nf5Y3x7jLiqBiONwB4IP1/MIA3nZfhuHmVqsoEJyrrKcdskWXKpGdgvBREta9MoHr09NpFCyJbaJ6A9ujN+sj44NWvGvFIeUR4esYkUm49GYrmx9rqIvcUO8uXp0B0PF5qy8PrORXePEeyEvHQS8F4/IlOWfXWUCQnWUovGlNkEKO5zGTUO7GpfI5o4bk+dK731nBlLZ614fgyAL8I4HMB/N3V7y87T8PBDLnsCECmSKJFENWr7HtkwhTl8zMcPQXWE5KMpqhvT/GMKulqeW8evPHqfGS4MiXa43tm/CPlFeWqK/xYSBv9vcDmce3MUERz7NEWGSo1ZN589ObNk+XRqD2jLzNs0XqrKF5P9itKn8ujvY1D+Y746ZWrMcnm2otmbAxnuscxTdMLAXwdgP8OwJMBfO2q7FzgXmzm+67gZL6S9zu8kw0LbOZCvdy3/Y6eT6X5Vf7Ndb1TNtkJEG3PexQG3uM17JvrMR8018n1jM4F/dYyDxS3gle+kG+jk3ns3SBmbaITXIbH6uhY7LrSovRE+K8m13jfg0/qKW5PBq29Xdc9qktYn6ZaYPPRLdz/gurbGtB9P31USATe8U2TSz59ZXR6+y4eTmBzzyKTLe+xHdETIIDNuVfQfS5vneuelnc8PerbymwfaYGTJ9U8fHbNeGIf4ynTafsrSscxTsqSyQmo7Mz3OK63z02SqlLrn+VCgdiiA5seg/eJvLwe/pF6Sk+GR8ce1WVvaIQvPdqzsojHUb7b43O134gfPb5HXpv3P+q35zVGcxThswiaPV2PZxF/lQYvevDq9iKzbK+qR4cXKVTmz2uTrdFMxvS7ss/AvO9Fdb3xeJFINLfeHPbkxpMjrXumEUdr7amttde21o5ba+9prb2vtZY5LKcOeirCYIGTHkXUPsKRDYxPbeg9FOZlKD6OeJQu9rIXUu79NlCPSU8v8bj491WnDrDp6WTg0big72P57fGYT5JEtOj9NgzeiR6lUfmnnjf3z6deMny9mwm1vXdah2nTE1UGdn+HPr+o1z6SL73RzTsVZf2xDC/kw3PC9wzoPOt4eCxe5K7yY98HQRsbkwfeiSiPXxAc3rwfSz2OBDUjwGPw1gPjYX7xKTSNUBiHN788NwxGzxXEDzndBVSO434blo9H+k0sN8Y/D8A/2zEdQ6DpG54kPRq7oDa8MAD/mJs3eZA6fI2ve3dyHgVtFbeniI1u7V8VX0Qf11eDEtVhUEPoKXpNgRl4fR0617UO44sMWbQIIiXOBlPxaCrCM4B8LTJoGX2eYVZZAdbP1uJ2Jj+WejLQZ0llfXrG2YCNJzsXrHB1ji1tEs0h9+MZKr3utc/SpF761gOWN51nlTPPGOg10x8HVMb6ho9Qq0x765TTn2rADZ8abdBvT94Zn/eQyp1BYXP8yur7DVT2y+eZqoITUmLGZxH8notDP3pEtNJuEXyy02AZLqtf3eTPcHopJK03l4/Ks7k879E3clghomdkjDyHSofS0tvY9WjwrnOf3tHfKBW4gJ9SieSylzKqyGZlLqKxZuUjc6x4mE9Zn9k4ejKj/OvxFM61iFeRvjnrGwDf3Vp7AIA7W2svbK09H8BDC+3OBCIv00DTFJHHmEUDI+V83bxFTb2oR8IeFD/OgMfGnkjkPSkd3FY9yRHQjUcvQvDGFUHmKR4F173DBcrzhVy339lcM889GpkeLxW5oI8HPI9cx7xNTidwSopxerIZRVAGRuuRXFMPViNavmE1ikxZrlh2da1ZG48/GsVquY7HoymSNS+C4G/FrVEPRxP62BaPJ5kMKX8ZvBQivx9EadXoJOIV30CbyeY2UDEczwNwA4AvBnA3gMcCeM4p0FIGj6nRde/uV6CfCskEnhe25iC9yWTB5bBVBeIYJ42C0tobb5QT9Ra1B5qzNfp6BmHEIGWK2nAp73hBqUKNlJDV91JEcMq4T8Z1LPXgXPOg55zYKRuj8RL99lIoqjAiGWY5shM7l6R+JlORodI6rOj4fSNKc+QkKQ8jB4+Vd0+GuR2faOJvw1lZD3qazuAYJ1OHwGZKnOchMpCZQY0gmnP7Vnk9l1TV9fa56aabSiFydK0XUnp1Km0iHNGJluikxAJ+ymKkXy9c7eHzrns4Rvm3zVhGaK3SGPE8G7eVVU+3VPg0V8Yi3BE+/c5SdZdp7JeT+Yz4eFpzyH1lp7+8/95NhnNo92ias6aiNhGNlZskdc1HtJ3JDYAA3ojlXePu57wMh/c+jsqEVIUnyzGOCIlOYpQrrwhS76hkRQDnjmWBTTo9fJV9iZ5QR7wZXXwj4x/lzQiuqoLI5E0fG+Mp/UyRc5lHXwVXZGB7MjJiaDPZy/qtzEkFB9OczUPGjx5NkSGvykOkD0Zk+awMx+Ozz3BHwGdhbXh+GcCH0bXPBPA6AP+wEnFUlGq00LLJrAh01frvwsvRMc5VdBVhHzFOI7T0lHlFEYy0z3hgvy932oyMpVcnauPxu2ooR2VxJEL3FFuGI2rPay96FIfW740pG3ePJz2d4dERGZ4R57JivDMjHPU36lABZ/zIkZ11BDwdwMNXv58F4DV07Uex3Ef5fgCLnuHIBLAnVHONRYS3pxgWzicTvh6NFeVVXUBzyuYoz13Tkc13Jb3GCi3rI0ozespRceiJuophrs5tRoMnaxHPdEw9g947mZelwXrjqihH7r9Ha4WHEV+jVOVcnCN0ZQY0ax8ZPP6c9bOqngrgtVjusbwHwPsAXNvSiDwcwH+h/y/D0nB8H4CHZW0f4RzHHWX66OKsCrz28WwSdk+hjXj5WT9zFO7IghgV4rP6zKErM8xzFHfvf3UeKgZyxGnIjm974x2NKEb5n423Ygw8foxGyVX6RnCNGK1M7no4Rvge0XDWx3G/Dbu/AfBzAfwk/X8pljc7Xpmm6V1Zw3tX34vVt3diRo+IahmfsmJco7DAyRM5wPrdCd4ziJgm7w5Xrw+ty+X6xrAKMK3es7QU9AYoLeMxMSjOET5X6mYnjjw67LqeXGEZiU67ePSofDEvvFN2h9ROT+3xqZuF1APViWjSNvyUg+hOfO8Ejofb/ut89k5faV9eW56T3jFqYPO4Oo/L2h6eaHGSPo9vPB/VU0hGs/Klug4ZT8YrLetdZ4jW5tZQiA52egMggL8G4DcAfMCc9l6qalee07aexuGq/0Mso403fBqmb75p+Tvroxo19LyZirfjtck8p6oHWaXXS/9UPekKHzwZ2AVfIh5VaO/tMRwmdSo0q/edjSfCVfFio/maM4ce/orcaZ/R/wh/dayjMqeyMsLnivx68hjJgv42fGedqvoFAA8A8D0AXojlO8dfXzQSXwTgztXnAMB/D+C3ABwOGpuXYHkPyd033nhjeVIqkz1HWCsKfIHly3kOsbk5uO3x1Cw87911mtE+YjSr/3uLcS4No3yag987KZY9EntU9no0Mn7v7u+M5xV5ZjnMNoujsspc8v5OJPdz53yOocr6m2P0vPVXcYZG1kXkrIyM1drcfPPNE1Z6dPV5yWkajscDeBCWUeJXAfhGAH9xRqTxOABvBvD0bSyd9yKns/xUFIRNlBqNqndXubYLIzBSd9c8ryraOQq5x+tt8FfmLvPOe7RFm+kRvh7fIjnp4VDDaYagNxbuT/dZtrm3aM79I1UnrWfAs3pVOayW92Rk7roBLu6pqu8E8MdYRyBX5uDh47hV6xt5ZJXJGvWaI3zVTeio7lyvr4dnxIOZu5Fubed4xh5/KrREHvNc4z3C2wr9PTpH5KzH34znUV+e4ejNb4/GkTf+RTwdMfTZtZGDNJU5GjXgkRxX8WROR/Q5q/s4PhnAF9H/1wD47dXnuWdlcDzDUV2IIwLpTcIcb2HU6IwavpEx9hbC3PtDrM2I8p7zqYx72z52heOsxtS7NucGskgZ9RypSL68Ps6CRxXHpGo4KzRV+BXRVXFmdc62dWzOynD8EoDH0v87AXwAlimnn7seDEd1QY0I3YiA9PrOhHREOWz7n4VvtC8Px2ko2l0u4Oq8ZWWV6KFyfXSeVbGMKrisrpbPuekuUshVmaq0Hxlfj09syCrOTm8Mcw1iFlmMzPk2zvJZHcd9wDRNv0v/f3Gapj+cpul3cJ08HXdBv3vHMBm8o376cMAFlXvHNrW9B9FTN7ntgfPb6w9UVn3goNLYe62mB4rDew2l1qnwxuN/r47WM15kxxMVx4F828PuevR4tGTj9B5Wt5Bv/e093NB7aGXUPqJH5cj+e6+5BdZHWvUYc8RvlmFvDSlkD6WMQPmkayV6OKAeN/bebeHR4L0/Y+G09+jjMqOTj0czfuapN+cKxw5tFVp2DknE8ebk2m+dZ8RR8dxHHrOg5ZU6eqdm1ofnSUb9jHoTFbqreCqe9jb0RrT2PNBtNlWjcY609/YPRng0QieX9U5TRfzrzWV1zrx5qUQTc2XY20Dv9TsSfVTmY47X77Ublbm5slLRIVZ2VhHHa1prn6+FrbW/B+BXknanDr2bYQDfu9b66sXob/Xw+Ea73nsLrB6oziWc7Ie9kshbWyRlES8qHr1ez26A47KIb0D+VrqIx1lEBuTvH4n6Yn55nh7gj8fDr4+VH43Sor688TLuo6A8ojfzvpUH2qe+nZFl0Rv7gXONb86LPHkPOCqI1m3UxqMtij4YPPo8bz7KToDKj+HzCnKd++bvCmj7g6AMUua13wkkUcUjsXwY4c8D+Kerzx0AXg3gA6+3PQ4kFvc0rP2cdtl59qj9YrBNRu8uPJ9t+FDdjJ9D4y4iAqOv97TfXczFnPFEXvZcL5evR1Gdt5Z6uCtzMRLlRNcrEVGPRu/+pl4EsYtxZPM1EkXoWLI+ziTimKbpHdM0PR3A1wJ46+rzNdM0PW2apt+L2p0H9DyCkbYeVPLdvXYLjHlUiiPyHqM23vVjuRZ5PhHuEQ9J+wVOvtQmwlfd74jajOTOOdrrvaNZI88KzIlOPBye96j7NdomilK8ceib56J9iCzy4ba8h+RBtN8XRQvRGL2++X8U/bFM6rrQ/QbtI6LBo6Mny70+o369PkZ1y1ZwXpHD3A/fALiLJ1ai6BXtMmqpejHVCGFbD71ybdSDij52lv+0I5+e1+bREP0fmYee1+jViWQj6zN7gusiuD4qNxmd2/BhZA6rEcvIPlRFvitj6PUxwr/RuZjT9qwfcnhdwb3YjYVljzfzGrw66gFkDzXz2lf2G8z7qni3Pa+oR0+GLyubE9Ed4eReTxUnz9lIBKJli851/e/NwxyvUfeRsjy90sjj5uiIr/G+G1+PoLcHoPWqMqBjHI1UPX56Dy9kvLwPtZDfHn0qRxm9WQTvQaQzPJr0dzW6iKLArO0u4cIZDoOeAqkolmghjCh5wH+yaCV0zmi8inlP7+2FuT1lERlBG1PEm57AM3hPFI7qKy+zTUiFyiIc4a23WKu0jBreESPmzcmIslCDzEYOiNeaN3ZVftq2wm+PdjsokNGnR3Ttd6SQdYNZ+x/RA56cRm2z+ao6gZVU3mnChTUcu7aynmL06hju3qRmC99w9Wisvmw+U/Yj0cgxao/gVnwj/WSekteOo5Mq3hH8Iwrdq5udqZ/r1FTa7NKLVIPsecqqnJmGSA4Yt7apQC+qY5wahSmdx3LNZB1Szte574rDp208XmXgRUIKXvkCuTN2GnBhDQeQK9/RNMqokomU2ajSXOCkYFbac1nl6PEI3qrXysrmEJuLO2szcs17Z4ka7ooR9ozbiIGI2kU3hBmezEvPZHcXhsLrmw1AJE9ev6Z8o5tIdWNbxxDxIuufx5wd82b67LrecMcQrZerznXPmGZRCv/v3SwYjaPqwGbr7bQNyIU2HBUl5AmPFzl4aZho0iMDUKVJyyppgay9B96YeydcKjiitkcDeLR8xPhqv/o7q+9FUHNeuqOyFXmKnrHyjJc6DxwBwKkP51vrZbSbYo2i4hGcds2716a3Nkai4cpLz7zrc+Y4cyjtehaBZPohcxyszqXgupVljscuI9EMLrThUPC89yxtZL8rHmuGT3FWQT1AxbeN1+Apyt5b3jwcHk5eBFUaR7yraEHNecNhhheoh/jVCJTrZopTPfRIrrS+Ko1MFiPnyaN1AX+PwCvjdhXIZFmduIr3HCntqI/sEI1nuHvgRSBRCq/nUHLfVidbp9laiByN04g+7lOGo5piidpF5RrWz5kMz0NUD3AET6XeiGdWBVVeVW9/Ltg47G79Cnj9RamZrE0v1eKVV50KvadF+9N2o0Zf8UTRRM8Qea881nrKA2998Fryyj08Xp2szWjEa23UcLMhyJwkvhadYoucB32qhPXpRZQ2RubdNfmv+zK9yGRbuHCGwyPYs9pzIPNYoknJcFUWgZbNVWCVfjx8vRRED0a8tJFyvj5iWBmYNp3DLNXTo8Xa8BHRSjQUKZ7KAtdIb26EG6WH+DE6IxFbdJIpSovyPHrGfE5qKIKel6+4VF4YR5a5YAeQ8WTrWvdRtEznyTIGx/Lfrnv4gLq+GIULZzjudcqihedZb/728MxJWUTQ86Z6iyFa5D06ezT2Uiz2O7rG/9Wj2lZIdyHkPQ+Ur2UpGG9sXP+I6rHS7RnJCn0ePfaMqdF22r9X1zvBp+kXTwaiVF+kyLROJQXWk8OewfX4zvNfmQ/PWVB6Mgegsj56tHI/mbPc+78LuHCGA/A9hsyTz8LxisfjXR9Rzl5bnfCekvM8EIURhRT143mQmXBmXvMcI9CLyHoQ9Vk1tJ4iM75EY+Q0QmaIuY/Ms2dQD7OCX+nIcCs9vK70aHamkKJ1kSl2j/7K+quuG72eGfTqeub7SbRt5b4rry/DzQ+f1Hpem7kGahdwIQ1HRWAZokVa8d5GwvYqDp70SgieRQIVmkZ403tnx9yIqwqjit/LmXu8zRZipjjsWuRZW39XsfmOBbtm/3ueZCSfimch5ZHCzfB5cCwfLs/we/zQpz1HfWeKr1e3Kg+9+nYtklV910iEk2VE63hOos4lcDJK4/nVJxozLq/P04gyGC6k4QDGwv7IOutCj4A9Su+afW/rYc+d7DnRD1+rhOiKayS6GVnMFaOYGbosevAWr7UZMbpRGT8kURW8196jNYpe7H+WttDyjF6PHvtdMd6RQeFDDCPyHOGL6Ijq9TaprX3vmn3rvoNn+LMoKHIQovGqE2PXvbSegvGl4kxvCxfWcBh4C0cZFnkNKuCZIusd28wU8Eio7tXpGZdq1LVLGFEKkSdWSRGM9h3hzOa2N/+ZIrB2lXpcrvPKCiW7qdB7TEbP+Gf9RfTNVT7eG/ZGwWufOQRctxLFZHU8+amMoZci4sjD6ytzNHr9RLTPXV8VuPCGA/AXbeSV9gSr5wVnXmwPMgH2lNGoctXFXllsEW0Rzl2Bjqu38LReD3evLHpM9xylF8lUpoCsTban5PWfGRWvn6icTz1lEdGciFiV40jbiJ5KqiriWbS2vJSatvGUfQaZ85iNqxLJR+vZW/ec4joNuPCGo6fwKvXnLI4eeFFQJgwVj7gKmbGJ0kZRyiNKm8wFz3h6j6vI2nhlo15hxTP2xlx5ErK1rfDMS7XNVU7RdY6IvBRM1L4653McFW6bzV0U/USGxKur9HEWwTPSnj7oOQeZvon61zZZ6rxyI+axfIDTe0fHhTMcRnBFUDzILHBF2Ee80MggeQK3TSRT6d+gojCq9YF6WsjrIxLuivc3h1/c1vOmK6kOfeGRlxoYdWYYT+TpWllk2KvzMFeuKnNaSYV5bavtNLXjQdUB07I5fPHWtOcYVWWLXyqlwHsc2cMTPUN1GnDhDIfdx5EpjkwIKu8pMIi8hTneLsMuvPeRhTMC2SIeSQ8o9DzLCLJ0whzIFEWWZojae3IY8bBqULWcZU8dpWwjWg2lByrf0aMs5kS9c6BnRJTv0Z3oFSO97XUvjRXxOjIakSOb0dBz/rYxhlW4cIaDofcYCQ96Kawo3dAzTN6LZubQUYFsIatXNpLC8XBl/70+tZ4+WqEKRrunGEfztnMM1lwDXElPRhB5kiNGTmnpKRG97h0CqToH3I6/e+m9TP6yflk+uE/Fq7+PnXLtN4PefOj1zBhHd+tHKSnPmTstJzKDMzccrbWPbK29r7X2XCp7fmvtda21zxjBld0QNRe81FcGKgDb0rItDk3FjKRw5kAvKhnJsWoKL6I9en5SBNumIea2y1JuDBo9bAtZdOzV3SalFSll7lvTeyP9ZNd7WYdj+e2lJ1V/VB3A7P3o3hrwMhdKR7aWqhGuXjstOFPD0Vq7AcA/BvDTVLYA8JEAPgrA3+rhiJ5VtWuoGKTMo4smrfdMIw19tb8MdyWP6nku26SBjN7sUdDAySPRqrDU0I0ahJH0QnStAtkjJyr9Ayfp3uUC99InPVoiA6OG3IOKoRxNnYzMh9KXOV5epKFG70jaRLRkz/VSWnSOq+2yyEj/H6B+cGMXcNYRx5cA+GEA76CytvqeKgh6z6qa67FnC2xUgWd9b/Mu6J4HGS3i0dCawVPwHvS8ZR73MX0iGqJFEymD3hgyr7Cn0Liu98iJiDaGqA99QVE21sqDFCv8MDmKlJFGfb11lPGgGvF6//mNfNXIqCLXLFvKr8zB8PBUIiI15F673tsCI6NicBXrNegdmNh19HFmhqO19mgAnwrgRVw+TdO7ALwRwBUAP7BtP1WFyBAt1m1CwDnGpgI9D9L+R4I/2n9PwVdxzhm3l27bNu006v1mdT0lyYqnonyye4xUIVdSWZU+PUOqqZ0qbBu5ZvTyG/kiJe21rzypoGoUs368a140UwFvbs1gRlF4pLfm3Lk/DNM0nckHwA8BeOrq93cDeO5A25cAuBvA3TfeeOOEZXRy4rMIyrNPpc0cvNt+Fp3+59JUbVetd7BDOs6Dz3M+B8B0SPTO5cGccUd1FwU8C6fuYkX/ooN/Tp9z1pb3f1RmszajMtYbX5XvI3W2oT+7fvPNN09Y6dHV5yWz9fkpG4svAnDn6vMWAG9dfY6xTFd9yijOm266aWcTXxHc0T56SiTDH7Xt0XRQrLcNn7TeQdBmF8r/PAzliKx4SnZEwe16Lk5r/JnzMkcBV9fJXGOU4Z/Dh54c7lK2tqkTyaPWue2226ad6fazijjEoHw3BiIOz3Bs6+n0BFg9gLnCES24nnCelfe9rbGZ4wGdhRE47LTZlu5to4yqnERyw5+5Dscu+b2tJ79red9F1FGlsaK4R2gcodvD4dGxa8Nx4e7jMIJHTxZFkL0as7qfwG0URk6aLKisMgalpVq/R/MIZJvOc/Evgt8je1b8kiUPerh613e13wD4Dy5UyK5HtFT3Zrxruo4yuc/6ivrT/aDqfkOP9tE1NII7otHbYM90B9NY6V/rZ+NcSJ05+1YVOBfDMU3T50zT9G/ntPVOVTHMZZAnaN4E9zYwR0EV4wiOnnDqEdhthaiiLCoKZERJjiokr01vQVbhGPn7GSI6eC5UeSzgn7SL5Co7jcPg0Vblv9aJHghZhWhzN3OiFCq04O6fYwAADptJREFUR9f0CHXmTOi60TbRWEDlvfFCrntKftQh5fIIzy7hwkUcVdgFwzzGZ3hHPLSoj22UunpwvacCV3AxeB5PFZ+19/gy6mlm+DzQedGFqvgzmMNTnovsNNMc6L1ruipbmWLuvQsiihAVtpHtyskirw+OPrV+FIFHTyCeG/3qf1bs/Km0t3Y6t71oZtdwnzUc2URUPVAv5OxNSDRx6sFlQjjH6EVCOCed1VNmlTTeZalf7asSRVQjkgiP1+fclFpPwXjlI/2N1PWckMy4jhiVqkGo0DrijPXwR7LjrUM1QFmk48l4xEtNHXk4KvfgaF9Zn0rfaaWkIrjPGg4DLxzshZKR0FWgMnE9D65iSDLPvaegomfncN+eN9YD7utq0I8K+y5D6apCqqTcKhAZ0CyNwN/Z2Eejquzx973UDJxvpaEacVRAI9fTSKl4Y5vjcFQ8eY8/Op7KS+Oy9nPrVvsbhQtvOLb1Vqo5a40YPDoqaZfRNA/3Xc179ryP3nvFIxyZIlae2J2sldQAl0XprAy81JOHO6JhpA/93WuTQdV5qES50VsWo/pRf1XDns1hD9Qg2dzNeWgp46zIMPdf6Yvpq9CgfTPw+HqG+LhTN4pybC16Mr9LuJCGoxpiM1SUaDXcixSveh6Rxz3q4faE7XAGzjmQhc2Vkz291ID+jqKgDKJ0oxqXLOXhgZfiyPq1NpW0VRUiY9eLcDyvuaLAKrgrdFRw2rvKIxxeRN3D2es/6yvCnTkNPSer8t5wxuPNXxQFcrk+4fg0dMOFNBxzGNHzNEe9yQp4p2Uq6Qn93xuvPpgtg7njy9Jbu+yHoRdBRJ6r1+YYfYVhkM1DhGPkUe+ZAcvoiRRTT4ZH+sr6jyDy6Kt5/czxMHwj82dt5q5tnUvlZVX5jzo6vYgr69uT+zmpuSpcSMPhQUW4vfPyXnpnhMnV9FZWFl3bFR0Zzmo771TQXG91FwaFlW9kYCMvtRcFjPI9ShUxrii1kNGhOLTMk12v/jYe+kiEpbirj4lfIFbqc/pnGvh3trbZyHlvpJwjs1Gkwt9cxyISldVsPkcciF3CfcZwVITrNN6/uwsvbiQK6dWv9DvqiVjeNLuukC02T5mOQjV1xEqjmtrh9ll9u1ZJr3BU1INeyoMhe4e89zuDkbnoOQW9ufW8eI8/VQ+7B72x9XTDXIeL2/cMvOGNZNuTX62r0aw6V7uCC2s4IkYc0vXRBTOqwLcBi4Dsd1ZvTlm0cL3FqUbMW7wj3mNGRy/srxpUxpGVVw1AFa9BxsMMx2gU1qPDe0lQBJEDMBo1W5vemEfkeltnqNeHJw8jjouX/qvw0uujx29ro2u1ws9dGdoeXFjDEVnr3qMmrF4PF9eLwsVtjUl0EmZE0LSNergIrjH0Uj49iJRFNe0X8XnbaC7qf+RMfSVqsv9aN3qUiM7XqBxF8tuLVCIHoKd0IogiKM9TjlI129LAbSopnEj2GapOQMWZqq6BqI2Xusq+4dQ9DbiwhgNYM6biuTOMKtTIcxxJKVQn8RJOejO9FIcXBuv16BrTNkfIuX0EmTHQ0Hrbvip4ogWvBqUStUZe7SLpxzOuIx54pEyiPhS4vjfmCmSy5BnVaqomgkokmjkwWubR6bXtlWdt9dqhU6cC2Vgq6/40oK2eVnth4FGPetT06Ec/eqPsIQDe7dTl8qiOXUNyPWpzmvWrcAuA3z/lPhhOsw8eyygtSteu+TKKY3Qsu4TKeujVHcU90ld1vY3QNmeO57YB1uPIdEp1DqJyLmN5mitbrTVcuXKl9WsWcF00w/GgBz1o+tAP/dDzJuM+A295y1vwhCc84bzJuM/Anp+7gz0vdwtvfOMbcc8997x/Go7W2t3TND30vOm4r8Cen7uFPT93B3te7hZ2yc8Lvcexhz3sYQ97OHvYG4497GEPe9jDEFxEw/HS8ybgPgZ7fu4W9vzcHex5uVvYGT8v3B7HHvawhz3s4XzhIkYce9jDHvawh3OEveHYwx72sIc9DMF1aThaa9/VWntHa+3Xqezm1trPttZ+c/X98FX5/Vpr39Na++XW2pPPj+qLAa21t7bW3thau7O1dmVVdtBae2Vr7WWttdN8UsF9Dlprz2yt/afW2ptbay9YlT25tfbq1tqLW2vX5Ro7L2itPai19iuttde31t7UWvvqVfl3t9bespLLO1trH74qv7219idU/pWE6zNba69rrf3D8xrPecMMfrbW2reu5PUNrbWnEK7nr/j5Gd2Op2m67j4A/gqApwD4dSp7IYAXrH6/AMA/Xv1+JoAvAvCBAL7rvGm/3j8A3grgEVL2DQCeDOCTAHzhedN4UT4AbgDwWwA+BMADALwewJMA/D9Y3uD7JQCeed50Xk8fAA3AYvX7RgCvAfBUAN8N4LlO/dsBvCLA9aOrOfh+w/n+9pnBz08E8JOrdk8F8JpV+QLA9wK4P4CX9fq9Lr2haZp+AcAfSfEnA3jx6veLAXzK6vcNAO5dfXZyV+T7Iex5OA8+CsCbp2n67Wma3oOlAvtkLPk5Yc/PEzAtwR6jdOPqM/eEjvF2wvspn2fw85MBfM+q3X8AcFNr7YOwycsuXJeGI4APnKbp7QCw+n7kqvynAfxVAC8H8I3nRNtFggnAz7TWfrW19gWrsm8D8C8BfCGAf3NulF08eDSA36X/b1uVfQuAHwfwNAA/cw50XdfQWruhtXYngHcA+Nlpml6zuvR1q/TJN7XWHkhNnrZKxfykpKNfCuAKgCvTNL3rjMi/7mCQn67Mrvj3Riz5+QO9Pu+/O/LPB6Zpei+AzzxvOi4Q/OVpmq621h4J4Gdba3etIry/ct6EXUDwvNxpmqZfA/DRZ03MRYFpmt4H4MNbazcB+JHW2ocC+HIA/xXLlN93APhHAL4GwOsAPH6apuPW2idimZ564grPi7HOQrzfwiA/XZld4fl6AF9f6fMiRRy/twqpsPp+xznTcyFhmqarq+93APgRLNMte5gHbwPwWPr/GJzOiybvkzBN0zsB3IHlPtDbV+mTewD8a6zkcpqma5aKmabpJwDc2Fp7xHnRfD1DhZ/YkcxeJMPxcgCfvfr92QBedo60XEhorT20tfYw+w3g4wH8et5qDwm8FsATW2tPaK09AMvI9+XnTNN1Da21W1aeMVprDwbwsQDuIqewYbl/+eur/49alaG19lFY6qw/PA/ar0cY5SeW8vm3V6erngrgT2wLYASuy1RVa+37sDxN8YjW2tsAfBWWJ39+sLX2uQB+B8Cnnx+FFxY+EMtQFljO/fdO0/RT50vSxYVpmt7bWvtiLPfZbsDyVN+bzpms6x0+CMCLW2s3YGkEfnCaplesjoPfgmUq5U4s99sA4LkA/n5r7b0A/hTAZ06rY0B7ADDOz5/A8mTVm7F83cffmdPp/pEje9jDHvawhyG4SKmqPexhD3vYw3UAe8Oxhz3sYQ97GIK94djDHvawhz0Mwd5w7GEPe9jDHoZgbzj2sIc97GEPQ7A3HHvYwx72sIch2BuOPVyX0Fr7wNba97bWfnv1XK1Xt9Y+tdPmgxs9in+wv89prR3Q/+9srT2p2Pb21tor5vRbhdbaL6++P7i19rdmtP+c1tq37Z6yPbw/wt5w7OG6g9Xdrj8K4BemafqQaZpuw/Ku7MecYrefA+DPDcc0TZ83TdN/PMX+hmCapqevfn4wgGHDsYc97BL2hmMP1yM8A8B7pml6kRVM0/Sfp2n6Z8Cfe93/fvXSmde11p6uCLI6rbUva8uXWb2+tfYNrbXnArgM4P9dvfTmwa21O1prl1f1n7nC8frW2s9VB9Fa++uttV9b9fVd9oTStnyZ1levcL6xtXbrqvyWtnxJ2etaa/+ytfaf7blMrTV7dPY3APgfVnQ+XyOJ1torWmu3r37/ndbaUWvtVQD+MtW5pbX2w621164+f35tD3uowN5w7OF6hCdj+VTUCN4B4OOmaXoKgM8A8K3VOq21Z2H57J6PnqbpwwC8cJqmf4vl46Q/a5qmD5+m6U8NyeqxDf8KwHNW9UuPummtPQjLl+l8xjRNfwnLR7z8faryByva/gWA/3VV9lUAXrkq/xEAj3NQvwDAv1/R+U1J/x8E4KuxNBgfh+ULpgy+BcA3TdP0kQCeA+A7K2Pawx4MrstnVe1hDwyttX8O4GOwjEI+EsuX1XxbW74O830ADp1mUZ2PBfCvp2l6NwBM06QvDFN4KpYps7cU6xv8twDeMk3T0er/i7F8U+U3r/6/dPX9qwA+bfX7YwB86qqfn2qt/XGxLw8+GsAd0zT9PgC01n4Amzx40uqZZQBwqbX2sPfnd1rsYQz2hmMP1yO8CUtPGAAwTdMXrVI2V1ZFzwfwewA+DMuo+f9zcER1GsbeODdan9tlcM/q+31Yr8M5b7F7LzYzBw+i3xHd9wPwNI6s9rCHEdinqvZwPcIrATyotcapnYfQ778A4O3TNN0L4HlYPplWIarzMwD+bmvtIQDQWrt5Vf4uAA9z8LwawF9trT1B6vfgLgAf3Fr7i6v/zwPwqk6bXwTwP636+XgAD3fqKJ1vxfIlPvdrrT0W6/cuvAbA7a21D2it3YjNFNvPAPhi+7OKyvawhzLsDccerjtYPTb7U7BU2G9prf0Klqmef7Sq8u0APru19h+wTL/c7aBx66weI/9yAFfa8nWbtr/w3QBeZJvjRMvvA/gCAC9trb0e8Ws1/3pr7W32AfARWD6y+odaa2/E8v3jLwraGnw1gI9vrb0OwLMAvB1LQ8HwBgDvXW3UPx/ALwF4C5av/fwnWO0Nrd6x8H9iafj+HTb3jP4XAJfb8rWi/xHrR27vYQ8l2D9WfQ97uE5gderqfav3fDwNwL+YpmkfDezhuoP9Hsce9nD9wOOwfFnZ/QC8B8DnnzM9e9iDC/uIYw972MMe9jAE+z2OPexhD3vYwxDsDcce9rCHPexhCPaGYw972MMe9jAEe8Oxhz3sYQ97GIK94djDHvawhz0Mwf8Pa+qbP7HtbtoAAAAASUVORK5CYII=\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": [ "### 4.2 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": [ "## 5. Reprojecting, Interpolating and Miscellaneous\n", "\n", "### 5.1 Reprojecting to Different Map Geometries\n", "\n", "The example map `m_iem_gc` is given in Galactic coordinates:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsGeom\n", "\n", "\taxes : lon, lat, energy\n", "\tshape : (120, 64, 30)\n", "\tndim : 3\n", "\tcoordsys : GAL\n", "\tprojection : CAR\n", "\tcenter : 0.0 deg, -0.1 deg\n", "\twidth : 15.0 deg x 8.0 deg\n", "\n" ] } ], "source": [ "print(m_iem_gc.geom)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an example we will now extract the image at `~10 GeV` and reproject it to ICRS coordinates. For this we first define the target map WCS geometry. As `.reproject()` only applies to the spatial axes, we do not have to specify any additional non-spatial axes:" ] }, { "cell_type": "code", "execution_count": 48, "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, coordsys=\"CEL\", width=(8, 4)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we extract the image at `~10 GeV`, reproject to the target geometry and plot the result:" ] }, { "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": [ "m_iem = m_iem_gc.get_image_by_coord({\"energy\": 10 * u.GeV})\n", "m_iem_cel = m_iem.reproject(wcs_geom_cel)\n", "m_iem_cel.plot(add_cbar=True, vmin=0, vmax=2.5e-9);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5.2 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": 50, "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\": 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": [ "### 5.3 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": 51, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "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": 52, "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": "3.6.0" }, "nbsphinx": { "orphan": true } }, "nbformat": 4, "nbformat_minor": 2 }