{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# AMGeO 2.0: Crafting an API for Geospace Data Scientists\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Authors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Author1 = {\n", " \"name\": \"Willem Mirkovich\",\n", " \"affiliation\": \"University of Colorado Boulder, Smead Aerospace Engineering Sciences\",\n", " \"email\": \"willem.mirkovich@colorado.edu\",\n", " \"orcid\": \"0000-0003-0955-8281\"\n", "}\n", "\n", "Author2 = {\n", " \"name\": \"Tomoko Matsuo\",\n", " \"affiliation\": \"University of Colorado Boulder, Smead Aerospace Engineering Sciences\",\n", " \"email\": \"tomoko.matsuo@colorado.edu\",\n", " \"orcid\": \"0000-0002-2754-1224\" \n", "}\n", "\n", "Author3 = {\n", " \"name\": \"Liam Kilcommons\",\n", " \"affiliation\": \"University of Colorado Boulder, Smead Aerospace Engineering Sciences\",\n", " \"email\": \"Liam.Kilcommons@colorado.edu\",\n", " \"orcid\": \"0000-0002-4980-3045\"\n", "}" ] }, { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "

Table of Contents

\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Purpose\n", "\n", "The purpose of this notebook is to introduce Geospace scientists to the new AMGeO 2.0 API, as well as demonstrate functionality with other popular python packages." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Technical contributions\n", "\n", "- Introduces geospace researchers to AMGeO's new API\n", "- Showcases power of using Xarray to enable research of multidimensional datasets\n", "- Gives examples of using the API with various other Python packages, including\n", " - Numpy\n", " - Matplotlib\n", " - Apexpy\n", " - Cartopy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Methodology\n", "\n", "The Assimilative Mapping of Geospace Observations (AMGeO) is a data science tool for the geospace science community that automates labor-intensive data acquisition and processing, combining observations from various sensors into assimilative maps of the high-latitude ionosphere. While AMGeO offers a highly configurable toolset, it currently requires both domain expertise and familiarity with Python to use it effectively for scientific research. \n", "\n", "To remove hurdles for novice users and empower all AMGeO users, we have recently introduced a new Application Programming Interface (API) focused on enhanced user-experience, including better compatibility with Jupyter Notebooks, improved data manipulation with Xarray and more flexible data generation. This notebook will demonstrate the functionality offered by the new API and how to use AMGeO in conjunction with other popular Python research tools in order to accelerate geospace data science processes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Funding\n", "\n", "AMGeO is supported by the NSF EarthCube grants ICER 1928403 to the University of Colorado Boulder, ICER 1928327 to the Virginia Tech, and ICER 1928358 to the Johns Hopkins University Applied Physics Laboratory." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Keywords" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "keywords=[\"AMGeO\", \"Xarray\", \"Python\", \"API\", \"Geospace\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Citation\n", "\n", "Willem Mirkovich, Liam Kilcommons, Tomoko Matsuo, 2022. AMGeO, Xarray, Python, API, Geospace. Accessed 4/15/2022 at https://github.com/willemmirkovich/Earthcube-Meeting-2022\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Work in progress\n", "\n", "While this notebook is in a complete state, these are some notable additions that would be nice to add:\n", "\n", "- Apexpy example of converting to base vectors\n", "- Cartopy example of time-series \n", "- Cartopy example of vector plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Suggested next steps\n", "\n", "Please get started with AMGeO [here](https://amgeo.colorado.edu/), we would love any feedback to continue improving our software." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Acknowledgements\n", "\n", "We would like to thank our data providers:\n", "\n", "- [SuperMAG](https://supermag.jhuapl.edu/)\n", "- [SuperDARN](http://vt.superdarn.org/tiki-index.php)\n", "- [AMPERE](http://ampere.jhuapl.edu/)\n", "- [NASA SPDF](https://spdf.gsfc.nasa.gov/)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Setup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Library imports\n", "\n", "Below are the necessary libraries needed to run this notebook: \n", "\n", "- Python 3.8\n", "- AMGeO 2.0.2 (comes with Matplotlib, Numpy, Xarray)\n", "- Apexpy 1.0.1\n", "- Cartopy 0.20.2\n", "\n", "
\n", " NOTE: If AMGeO is installed locally, upon import, if you have not configured AMGeO with your API key, SuperMAG username or AMPERE username, this will be asked here (with instructions on how to get each). This notebook bypasses this step.\n", "
" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Ignore uunecessary warnings from AMGeO dependencies\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Solar wind data files will be saved to /home/jovyan/.local/share/nasaomnireader\n", "\n", "------------IMPORTANT----------------------------\n", "Unable to import spacepy. Will fall back to\n", "using Omni text files, which may have slightly\n", "different data and incomplete metadata\n", "-------------------------------------------------\n", "\n" ] } ], "source": [ "# AMGeO's API class\n", "from AMGeO.api import AMGeOApi\n", "\n", "# python's datetime module\n", "from datetime import datetime, date\n", "\n", "# matplotlib tools\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "# cartopy\n", "import cartopy.crs as crs\n", "\n", "# apexpy\n", "from apexpy import Apex" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Parameter definitions\n", "\n", "During the duration of this notebook, there are some commone placeholder variables that will be used.\n", "\n", "`api` will be used to store AMGeO's API instance" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AMGeO API instance\n", "current configuration: default\n", "current output directory: /workspaces/Earthcube-Meeting-2022/amgeo_out" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "api = AMGeOApi()\n", "api" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`controller` will be used to generate and load assimilative maps using AMGeO. This will be described in more detail in `Creating an AMGeO controller`" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Default AMGeO Controller\n", "Creates Assimilative Maps using SuperMAG and SuperDARN observations\n", "Does not predict magnetic potential nor field-aligned current" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "controller = api.get_controller()\n", "controller" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data import\n", "\n", "Since this notebook is a technical overview of AMGeO's new API, all Data used will be imported/generated during the course of the `Data Processing` section. \n", "To learn more about AMGeO and the data we generate, [please go to our website](https://amgeo.colorado.edu/)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data processing and analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## AMGeO's new API\n", "\n", "[AMGeO's 2.0 Release](https://amgeo.colorado.edu/) comes with a fleshed out Application Programming Interface (API) for easier\n", "generation/loading of Assimilative Maps of GeoSpace Observations (AMGeO).\n", "\n", "The development of this API had two main goals in mind:\n", "\n", "1. Simple *and* functional classes/objects to generate/manipulate/load AMGeO's main data product\n", "\n", "2. Interopability with various Python scientifict packages, such as \n", "Numpy, Xarray, Scipy, as well as popular geospatial packages like\n", "ApexPy, CartoPy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating an AMGeO API instance\n", "\n", "Here, we can create an AMGeO API instance, and get various details as to a default API instance" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "AMGeO API instance\n", "current configuration: default\n", "current output directory: /workspaces/Earthcube-Meeting-2022/amgeo_out" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "api = AMGeOApi()\n", "api" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Setting our output directory for AMGeO data\n", "\n", "When running AMGeO, a local file system directory will be selected to store your assimilative maps. By default, AMGeo will use ```~/amgeo_v2_ouptput```" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/workspaces/Earthcube-Meeting-2022/amgeo_out'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "api.get_output_dir()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But, if you want to specify another directory, you can set this in the API instance using ```set_output_dir```" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "api.set_output_dir('./amgeo_out')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/workspaces/Earthcube-Meeting-2022/amgeo_out'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "api.get_output_dir()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating an AMGeO controller\n", "\n", "To generate assimilative maps, you will have to load specific settings from AMGeO. Conveniently, AMGeO's new API allows for a simple way to load AMGeO's default settings using a ```controller```.\n", "\n", "To create a ```controller``` instance, you can call ```get_controller``` on an API instance" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Default AMGeO Controller\n", "Creates Assimilative Maps using SuperMAG and SuperDARN observations\n", "Does not predict magnetic potential nor field-aligned current" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "controller = api.get_controller()\n", "controller" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As observed, calling ```get_controller``` returns an ```Default AMGeO Controller```, that is able to both create and load assimilative maps using AMGeO's default settings. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generating AMGeO maps\n", "\n", "\n", "\n", "Now that we have a ```controller``` instance, we can create assimilative maps by calling the ```generate``` method on our ```controller```. \n", "This method takes both a hemisphere and a date argument. \n", "\n", "```controller.generate``` supports multiple different ways of generating maps based on dates/datetimes:\n", "\n", "1. A single datetime\n", "\n", " Will generate data for a specific date and time \n", " ```python\n", " controller.generate(datetime(YYYY, MM, DD, hh, mm, ss), 'N' | 'S')\n", " ```\n", " \n", "2. A single date\n", "\n", " Will generate data for 5 min slices over the entire date provided\n", " Ex: date(2013, 5, 5) => datetime(2013, 5, 5, 0, 2, 30), datetime(2013, 5, 5, 0, 7, 30), ...\n", " ```python\n", " controller.generate(date(YYYY, MMMM, DD), 'N' | 'S')\n", " ```\n", "3. A list of dates/datetimes\n", "\n", " This will handle each element within the list on a case by case basis, in a bulk job\n", " ```python\n", " controller.generate([\n", " datetime(YYYY, MM, DD, hh, mm, ss),\n", " date(YYYY, MM, DD),\n", " ...\n", " ], 'N' | 'S')\n", " ```\n", "\n", "For example, if we wanted to generate maps for dates:\n", "\n", "- January 6th, 2013 16:30:00\n", "- January 6th, 2013 17:30:00\n", "- February 6th, 2013 12:30:00\n", "- February 6th, 2013 13:30:00\n", "\n", "and on the Northern hemisphere, we can call ```generate``` with ..." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "dts = [\n", " datetime(2013, 1, 6, 16, 30, 0), # January 6th, 2013 16:30:00\n", " datetime(2013, 1, 6, 17, 30, 0), # January 6th, 2013 17:30:00\n", " datetime(2013, 2, 6, 12, 30, 0), # February 6th, 2013 12:30:00\n", " datetime(2013, 2, 6, 13, 30, 0) # February 6th, 2013 13:30:00\n", "]\n", "# hemisphere\n", "h = 'N'" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data requested for 2 day(s)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ab7e57bcc77b4fb09d6735f99f326329", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/2 [00:00\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
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       "  * lat             (lat) float64 88.33 86.67 85.0 83.33 ... 53.33 51.67 50.0\n",
       "  * lon             (lon) float64 0.0 10.0 20.0 30.0 ... 330.0 340.0 350.0 360.0\n",
       "Data variables:\n",
       "    E_ph            (time, lat, lon) float64 -0.01019 -0.009465 ... 3.771e-07\n",
       "    E_th            (time, lat, lon) float64 0.027 0.02508 ... -0.0003171\n",
       "    cond_hall       (time, lat, lon) float64 4.0 4.0 4.0 4.0 ... 4.0 4.0 4.0 4.0\n",
       "    cond_ped        (time, lat, lon) float64 4.0 4.0 4.0 4.0 ... 4.0 4.0 4.0 4.0\n",
       "    epot            (time, lat, lon) float64 -6.755e+03 -6.42e+03 ... 363.5\n",
       "    int_joule_heat  (time) float64 9.623 27.86 5.876 6.996\n",
       "    joule_heat      (time, lat, lon) float64 3.331 2.874 ... 0.0003407 0.0004021\n",
       "    v_ph            (time, lat, lon) float64 459.4 426.8 ... -0.6923 -6.492\n",
       "    v_th            (time, lat, lon) float64 173.4 161.0 ... -5.936 -0.007721\n",
       "Attributes:\n",
       "    description:  AMGeO Assimilative Maps\n",
       "    version:      v2_beta\n",
       "    hemisphere:   N
" ], "text/plain": [ "\n", "Dimensions: (time: 4, lat: 24, lon: 37)\n", "Coordinates:\n", " * time (time) datetime64[ns] 2013-01-06T16:30:00 ... 2013-02-06T...\n", " * lat (lat) float64 88.33 86.67 85.0 83.33 ... 53.33 51.67 50.0\n", " * lon (lon) float64 0.0 10.0 20.0 30.0 ... 330.0 340.0 350.0 360.0\n", "Data variables:\n", " E_ph (time, lat, lon) float64 -0.01019 -0.009465 ... 3.771e-07\n", " E_th (time, lat, lon) float64 0.027 0.02508 ... -0.0003171\n", " cond_hall (time, lat, lon) float64 4.0 4.0 4.0 4.0 ... 4.0 4.0 4.0 4.0\n", " cond_ped (time, lat, lon) float64 4.0 4.0 4.0 4.0 ... 4.0 4.0 4.0 4.0\n", " epot (time, lat, lon) float64 -6.755e+03 -6.42e+03 ... 363.5\n", " int_joule_heat (time) float64 9.623 27.86 5.876 6.996\n", " joule_heat (time, lat, lon) float64 3.331 2.874 ... 0.0003407 0.0004021\n", " v_ph (time, lat, lon) float64 459.4 426.8 ... -0.6923 -6.492\n", " v_th (time, lat, lon) float64 173.4 161.0 ... -5.936 -0.007721\n", "Attributes:\n", " description: AMGeO Assimilative Maps\n", " version: v2_beta\n", " hemisphere: N" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hemi = 'N'\n", "dates = controller.browse(hemi)\n", "controller.load(dates, hemi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## AMGeO's xarray datasets\n", "\n", "As stated, AMGeO uses Xarray to load Assimilative Maps into a sensible data structure for \n", "data wrangling and processing. \n", "\n", "This section is to go over some of the neat features that come with interacting with AMGeO maps with Xarray datasets.\n", "\n", "Below is a diagram of the organization of an Xarray dataset with AMGeO maps\n", "\n", "![AMGeO Xarray dataset](./static/AMGeOXarrayDataset.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets load all of our data generated so far into a Dataset for us to experiment with" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:         (time: 4, lat: 24, lon: 37)\n",
       "Coordinates:\n",
       "  * time            (time) datetime64[ns] 2013-01-06T16:30:00 ... 2013-02-06T...\n",
       "  * lat             (lat) float64 88.33 86.67 85.0 83.33 ... 53.33 51.67 50.0\n",
       "  * lon             (lon) float64 0.0 10.0 20.0 30.0 ... 330.0 340.0 350.0 360.0\n",
       "Data variables:\n",
       "    E_ph            (time, lat, lon) float64 -0.01019 -0.009465 ... 3.771e-07\n",
       "    E_th            (time, lat, lon) float64 0.027 0.02508 ... -0.0003171\n",
       "    cond_hall       (time, lat, lon) float64 4.0 4.0 4.0 4.0 ... 4.0 4.0 4.0 4.0\n",
       "    cond_ped        (time, lat, lon) float64 4.0 4.0 4.0 4.0 ... 4.0 4.0 4.0 4.0\n",
       "    epot            (time, lat, lon) float64 -6.755e+03 -6.42e+03 ... 363.5\n",
       "    int_joule_heat  (time) float64 9.623 27.86 5.876 6.996\n",
       "    joule_heat      (time, lat, lon) float64 3.331 2.874 ... 0.0003407 0.0004021\n",
       "    v_ph            (time, lat, lon) float64 459.4 426.8 ... -0.6923 -6.492\n",
       "    v_th            (time, lat, lon) float64 173.4 161.0 ... -5.936 -0.007721\n",
       "Attributes:\n",
       "    description:  AMGeO Assimilative Maps\n",
       "    version:      v2_beta\n",
       "    hemisphere:   N
" ], "text/plain": [ "\n", "Dimensions: (time: 4, lat: 24, lon: 37)\n", "Coordinates:\n", " * time (time) datetime64[ns] 2013-01-06T16:30:00 ... 2013-02-06T...\n", " * lat (lat) float64 88.33 86.67 85.0 83.33 ... 53.33 51.67 50.0\n", " * lon (lon) float64 0.0 10.0 20.0 30.0 ... 330.0 340.0 350.0 360.0\n", "Data variables:\n", " E_ph (time, lat, lon) float64 -0.01019 -0.009465 ... 3.771e-07\n", " E_th (time, lat, lon) float64 0.027 0.02508 ... -0.0003171\n", " cond_hall (time, lat, lon) float64 4.0 4.0 4.0 4.0 ... 4.0 4.0 4.0 4.0\n", " cond_ped (time, lat, lon) float64 4.0 4.0 4.0 4.0 ... 4.0 4.0 4.0 4.0\n", " epot (time, lat, lon) float64 -6.755e+03 -6.42e+03 ... 363.5\n", " int_joule_heat (time) float64 9.623 27.86 5.876 6.996\n", " joule_heat (time, lat, lon) float64 3.331 2.874 ... 0.0003407 0.0004021\n", " v_ph (time, lat, lon) float64 459.4 426.8 ... -0.6923 -6.492\n", " v_th (time, lat, lon) float64 173.4 161.0 ... -5.936 -0.007721\n", "Attributes:\n", " description: AMGeO Assimilative Maps\n", " version: v2_beta\n", " hemisphere: N" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = controller.load(controller.browse('N'), 'N')\n", "ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you have loaded a dataset, you are able to access various ```data variables``` that an AMGeO map generates. These are just ways to differentiate between different kinds of data you might be interested in. When accessing a ```data variable```, you will get a ```DataArray``` in return.\n", "\n", "We can see all of the data variables that are accessible in an AMGeO Dataset by doing the following: " ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Data variables:\n", " E_ph (time, lat, lon) float64 -0.01019 -0.009465 ... 3.771e-07\n", " E_th (time, lat, lon) float64 0.027 0.02508 ... -0.0003171\n", " cond_hall (time, lat, lon) float64 4.0 4.0 4.0 4.0 ... 4.0 4.0 4.0 4.0\n", " cond_ped (time, lat, lon) float64 4.0 4.0 4.0 4.0 ... 4.0 4.0 4.0 4.0\n", " epot (time, lat, lon) float64 -6.755e+03 -6.42e+03 ... 363.5\n", " int_joule_heat (time) float64 9.623 27.86 5.876 6.996\n", " joule_heat (time, lat, lon) float64 3.331 2.874 ... 0.0003407 0.0004021\n", " v_ph (time, lat, lon) float64 459.4 426.8 ... -0.6923 -6.492\n", " v_th (time, lat, lon) float64 173.4 161.0 ... -5.936 -0.007721" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds.data_vars" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get a `DataArray` of the data variable in question, simple access it like you would in a normal python `dict` option" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'cond_hall' (time: 4, lat: 24, lon: 37)>\n",
       "array([[[4., 4., 4., ..., 4., 4., 4.],\n",
       "        [4., 4., 4., ..., 4., 4., 4.],\n",
       "        [4., 4., 4., ..., 4., 4., 4.],\n",
       "        ...,\n",
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       "        [4., 4., 4., ..., 4., 4., 4.],\n",
       "        [4., 4., 4., ..., 4., 4., 4.]],\n",
       "\n",
       "       [[4., 4., 4., ..., 4., 4., 4.],\n",
       "        [4., 4., 4., ..., 4., 4., 4.],\n",
       "        [4., 4., 4., ..., 4., 4., 4.],\n",
       "        ...,\n",
       "        [4., 4., 4., ..., 4., 4., 4.],\n",
       "        [4., 4., 4., ..., 4., 4., 4.],\n",
       "        [4., 4., 4., ..., 4., 4., 4.]],\n",
       "\n",
       "       [[4., 4., 4., ..., 4., 4., 4.],\n",
       "        [4., 4., 4., ..., 4., 4., 4.],\n",
       "        [4., 4., 4., ..., 4., 4., 4.],\n",
       "        ...,\n",
       "        [4., 4., 4., ..., 4., 4., 4.],\n",
       "        [4., 4., 4., ..., 4., 4., 4.],\n",
       "        [4., 4., 4., ..., 4., 4., 4.]],\n",
       "\n",
       "       [[4., 4., 4., ..., 4., 4., 4.],\n",
       "        [4., 4., 4., ..., 4., 4., 4.],\n",
       "        [4., 4., 4., ..., 4., 4., 4.],\n",
       "        ...,\n",
       "        [4., 4., 4., ..., 4., 4., 4.],\n",
       "        [4., 4., 4., ..., 4., 4., 4.],\n",
       "        [4., 4., 4., ..., 4., 4., 4.]]])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2013-01-06T16:30:00 ... 2013-02-06T13:30:00\n",
       "  * lat      (lat) float64 88.33 86.67 85.0 83.33 ... 55.0 53.33 51.67 50.0\n",
       "  * lon      (lon) float64 0.0 10.0 20.0 30.0 40.0 ... 330.0 340.0 350.0 360.0\n",
       "Attributes:\n",
       "    description:  cond_hall\n",
       "    longname:     Ovation Pyme Hall Conductance\n",
       "    shortname:    cond_hall\n",
       "    units:        mho
" ], "text/plain": [ "\n", "array([[[4., 4., 4., ..., 4., 4., 4.],\n", " [4., 4., 4., ..., 4., 4., 4.],\n", " [4., 4., 4., ..., 4., 4., 4.],\n", " ...,\n", " [4., 4., 4., ..., 4., 4., 4.],\n", " [4., 4., 4., ..., 4., 4., 4.],\n", " [4., 4., 4., ..., 4., 4., 4.]],\n", "\n", " [[4., 4., 4., ..., 4., 4., 4.],\n", " [4., 4., 4., ..., 4., 4., 4.],\n", " [4., 4., 4., ..., 4., 4., 4.],\n", " ...,\n", " [4., 4., 4., ..., 4., 4., 4.],\n", " [4., 4., 4., ..., 4., 4., 4.],\n", " [4., 4., 4., ..., 4., 4., 4.]],\n", "\n", " [[4., 4., 4., ..., 4., 4., 4.],\n", " [4., 4., 4., ..., 4., 4., 4.],\n", " [4., 4., 4., ..., 4., 4., 4.],\n", " ...,\n", " [4., 4., 4., ..., 4., 4., 4.],\n", " [4., 4., 4., ..., 4., 4., 4.],\n", " [4., 4., 4., ..., 4., 4., 4.]],\n", "\n", " [[4., 4., 4., ..., 4., 4., 4.],\n", " [4., 4., 4., ..., 4., 4., 4.],\n", " [4., 4., 4., ..., 4., 4., 4.],\n", " ...,\n", " [4., 4., 4., ..., 4., 4., 4.],\n", " [4., 4., 4., ..., 4., 4., 4.],\n", " [4., 4., 4., ..., 4., 4., 4.]]])\n", "Coordinates:\n", " * time (time) datetime64[ns] 2013-01-06T16:30:00 ... 2013-02-06T13:30:00\n", " * lat (lat) float64 88.33 86.67 85.0 83.33 ... 55.0 53.33 51.67 50.0\n", " * lon (lon) float64 0.0 10.0 20.0 30.0 40.0 ... 330.0 340.0 350.0 360.0\n", "Attributes:\n", " description: cond_hall\n", " longname: Ovation Pyme Hall Conductance\n", " shortname: cond_hall\n", " units: mho" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get Hall Conductance\n", "ds['cond_hall']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Coordinates/Dimensions\n", "\n", "One of the main advantages to using Xarray for managing scientific data is how it is able to manage multi-dimensional datasets. Traditionally, accessing information regarding the dimensions of your arrays must be made explicit in comments or stored in auxillary data structures. With ```DataArray```, it is all in one place" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('time', 'lat', 'lon')" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "epots = ds['epot']\n", "epots.dims" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As seen above, the first dimension of the 3 dimensional array ```epots``` is ```time```, followed by ```latitude``` and ```longitude``` respectively. \n", "\n", "`lat` and `lon` have shapes `24` and `37` respecively, to create a 24x37 grid of the N/S hemisphere.\n", "\n", "Another cool part of Xarray is ```coords```, that act as coordinates to your dimensions for specific elements" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Coordinates:\n", " * time (time) datetime64[ns] 2013-01-06T16:30:00 ... 2013-02-06T13:30:00\n", " * lat (lat) float64 88.33 86.67 85.0 83.33 ... 55.0 53.33 51.67 50.0\n", " * lon (lon) float64 0.0 10.0 20.0 30.0 40.0 ... 330.0 340.0 350.0 360.0" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "epots.coords" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By accessing the ```coords``` property, we can see the types and data stored in the coordinates for a specific DataArray. \n", "These are also accessible on a per element basis, with the ability to see a specific coordinate" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'time' ()>\n",
       "array('2013-01-06T16:30:00.000000000', dtype='datetime64[ns]')\n",
       "Coordinates:\n",
       "    time     datetime64[ns] 2013-01-06T16:30:00
" ], "text/plain": [ "\n", "array('2013-01-06T16:30:00.000000000', dtype='datetime64[ns]')\n", "Coordinates:\n", " time datetime64[ns] 2013-01-06T16:30:00" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get time coordinate on first element on epot DataArray\n", "epots[0].time" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'lat' ()>\n",
       "array(68.33319991)\n",
       "Coordinates:\n",
       "    time     datetime64[ns] 2013-01-06T16:30:00\n",
       "    lat      float64 68.33\n",
       "    lon      float64 160.0\n",
       "Attributes:\n",
       "    longname:   Modified Magnetic Apex Latitudes\n",
       "    shortname:  lats\n",
       "    units:      degrees
" ], "text/plain": [ "\n", "array(68.33319991)\n", "Coordinates:\n", " time datetime64[ns] 2013-01-06T16:30:00\n", " lat float64 68.33\n", " lon float64 160.0\n", "Attributes:\n", " longname: Modified Magnetic Apex Latitudes\n", " shortname: lats\n", " units: degrees" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# dims => [time, lat, lon]\n", "potential = epots[0][12][16]\n", "potential.lat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dataset metadata\n", "\n", "In addition to robust dimension/coordinate information, you can also attach metadata to your DataArrays. AMGeO does this on multiple properties, and they are accessible by calling the ```attrs``` property" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "{'description': 'AMGeO Assimilative Maps',\n", " 'version': 'v2_beta',\n", " 'hemisphere': 'N'}" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get DataSet metadata\n", "ds.attrs" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'description': 'joule_heat',\n", " 'longname': 'Joule Heating (E-field^2*Pedersen)',\n", " 'shortname': 'joule_heat',\n", " 'units': 'mW/m^2'}" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get DataVariable metadata\n", "ds['joule_heat'].attrs" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'longname': 'Modified Magnetic Apex Latitudes',\n", " 'shortname': 'lats',\n", " 'units': 'degrees'}" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get latitude attributes\n", "ds['joule_heat'][0].lat.attrs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compatability with matplotlib\n", "\n", "Xarray DataArrays also make quick plotting much easier, with coordinates and metadata accessible from the same data structure" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\n", "def _polar2dial(ax):\n", " \"\"\"\n", " Turns a matplotlib axes polar plot into a dial plot\n", " \"\"\"\n", " #Rotate the plot so that noon is at the top and midnight\n", " #is at the bottom, and fix the labels so radial direction\n", " #is latitude and azimuthal direction is local time in hours\n", " ax.set_theta_zero_location('S')\n", " theta_label_values = np.array([0.,3.,6.,9.,12.,15.,18.,21.])*180./12\n", " theta_labels = ['%d' % (int(th/180.*12)) for th in theta_label_values.flatten().tolist()]\n", " ax.set_thetagrids(theta_label_values,labels=theta_labels)\n", "\n", " r_label_values = 90.-np.array([80.,70.,60.,50.,40.])\n", " r_labels = [r'$%d^{o}$' % (int(90.-rv)) for rv in r_label_values.flatten().tolist()]\n", " ax.set_rgrids(r_label_values,labels=r_labels)\n", " ax.set_rlim([0.,40.])\n", " \n", "def plot_epot_map(fig, lats, lons, epot):\n", " ax = fig.add_subplot(111, projection='polar')\n", " _polar2dial(ax)\n", " \n", " # plotting\n", " r = 90.-lats\n", " th = np.radians(lons)\n", " v = 30000\n", " levels=np.linspace(-v,v,30)\n", " cb = ax.contourf(th,r,epot,levels=levels,cmap='RdBu_r', vmin=-v, vmax=v,extend='both')\n", " \n", " # metadata attributes accessible on a DataArray\n", " units = epot.attrs['units']\n", " description = epot.attrs['longname']\n", " \n", " fig.colorbar(cb,label=f'{description} [{units}]')\n", " return ax\n", "\n", "fig = plt.figure(figsize=(4, 4))\n", "\n", "# grab 24x37 grid of Electo-static potentials\n", "epot = ds['epot'][1]\n", "# grab the time for this grid\n", "t = epot.time.values\n", "# plot epots with correct lat/lon coords\n", "ax = plot_epot_map(fig, epot.lat, epot.lon, epot)\n", "ax.set_title('Electric Potential map for %s' % t, pad=15, size=20)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compatability with numpy\n", "\n", "Xarray has native support for quite a few Numpy compatable operations\n", "\n", "Look [here](http://xarray.pydata.org/en/stable/user-guide/duckarrays.html) for a full list of support for Numpy\n", "\n", "It is also possible to convert a `DataArray` to a `ndarray` with the `values` property" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "numpy.ndarray" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# convert from DataArray to Numpy Array\n", "arr = ds['joule_heat'].values\n", "type(arr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compatability with apexpy\n", "\n", "[ApexPy](https://pypi.org/project/apexpy/) is a fully fleshed out library for geospatial coordinate systems conversions such as Geodetic, Magnetic Local Time, and Apex. \n", "\n", "AMGeO's latitude and longitude are in `Magnetic Apex Latitude` and `Magnetic Local Time in Degrees`, respectively, which is simple enough to transform to other coordinate systems using this package. " ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "datetime.date(2013, 1, 6)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# first, create an apex_out instance\n", "\n", "# for mlt, need specific date, so lets load up a dataset with a specific date in mine\n", "dates = controller.browse('N')\n", "dates[0]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = dates[0]\n", "ds = controller.load(d, 'N')\n", "\n", "# from here, we can create an apex_out instance for coordinate conversion\n", "apex_out = Apex(date=d)\n", "apex_out" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### AMGeO to geodetic\n", "\n", "For plots using the common mercator projection, Geodetic is what we will be looking for" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((24, 37), (24, 37))" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# need to create 24x37 grid of lat & lons for conversion based on 24x37 lat lon grid\n", "\n", "amgeo_lats, amgeo_lons = np.zeros((24, 37)), np.zeros((24, 37))\n", "for i in range(24):\n", " for j in range(37):\n", " amgeo_lats[i][j] = ds.lat.values[i]\n", " amgeo_lons[i][j] = ds.lon.values[j]\n", "amgeo_lats, amgeo_lons = np.array(amgeo_lats), np.array(amgeo_lons)\n", "amgeo_lats.shape, amgeo_lons.shape" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "# NOTE: AMGeO lons are mlt in degrees, so simple transform to move to apex mlt\n", "mlt = amgeo_lons / 180 * 12" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((24, 37), (24, 37))" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get specific reference date for conversion (needed for mlt conversion)\n", "dt = controller.browse(d, 'N')[0]\n", "\n", "# now, can convert to geodetic, AMGeO uses a reference height of 110km (the nominal F layer)\n", "geo_lat, geo_lon = apex_out.convert(amgeo_lats, mlt, 'mlt', 'geo', datetime=dt, height=110)\n", "geo_lat.shape, geo_lon.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compatability with cartopy\n", "\n", "[CartoPy](https://scitools.org.uk/cartopy/docs/latest/index.html) is a popular python package that allows for plotting onto Geographic \n", "projections of the world.\n", "\n", "AMGeO's API lends itself well to mapping its data easily to these plotting tools." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting AMGeO electostatic potential on orthographic projection\n", "\n", "Using the convenient `geo_lat` and `geo_lon` values we just generated, we can use Cartopy's `PlatteCarree` transform to map these values onto an `Orthographic` projection. \n", "\n", "The `Orhtographic` projection will suit nicely for visualzing an AMGeO grid of electostatic potentials on the northern hemisphere, which will spread from the magnetic north pole." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=[10, 5])\n", "\n", "idx = 1\n", "\n", "# Northern Hemisphere\n", "ax = fig.add_subplot(1, 2, 1, projection=crs.Orthographic(0, 90))\n", "\n", "ax.coastlines(zorder=3)\n", "ax.stock_img()\n", "ax.gridlines()\n", "\n", "ax.scatter(geo_lon, geo_lat, c=ds.epot[idx], cmap='coolwarm', transform=crs.PlateCarree())\n", "\n", "ax.set_title(f'Epots for {ds.time.values[idx]}')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pandas compatability\n", "\n", "While not necessarily easier, since AMGeO works with multi-dimensional data, Xarray provides compatability with pandas. A more comprehensive overview can be found [here](http://xarray.pydata.org/en/stable/user-guide/pandas.html)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/html": [ "
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E_phE_thcond_hallcond_pedepotint_joule_heatjoule_heatv_phv_th
timelatlon
2013-01-06 16:30:0088.3333230.0-0.0101930.0269994.04.0-6755.3282599.6226103.331331459.399493173.436070
10.0-0.0094650.0250804.04.0-6419.5149089.6226102.874432426.756367161.045790
20.0-0.0079840.0232454.04.0-6113.7215539.6226102.416306395.527306135.848700
30.0-0.0057800.0216674.04.0-5862.9003049.6226102.011470368.67312398.356825
40.0-0.0030630.0205314.04.0-5688.0337049.6226101.723594349.34327452.117439
....................................
2013-01-06 17:30:0049.999754320.00.000019-0.0000304.04.0313.32527927.8638840.000005-0.614055-0.385016
330.0-0.000059-0.0000134.04.0313.29619527.8638840.000015-0.2634581.207273
340.00.0000090.0000954.04.0338.20758027.8638840.0000361.939544-0.191665
350.00.000146-0.0000184.04.0267.73195227.8638840.000087-0.369148-2.998625
360.0-0.000003-0.0001664.04.0196.51456327.8638840.000111-3.4039630.068048
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1776 rows × 9 columns

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" ], "text/plain": [ " E_ph E_th cond_hall cond_ped \\\n", "time lat lon \n", "2013-01-06 16:30:00 88.333323 0.0 -0.010193 0.026999 4.0 4.0 \n", " 10.0 -0.009465 0.025080 4.0 4.0 \n", " 20.0 -0.007984 0.023245 4.0 4.0 \n", " 30.0 -0.005780 0.021667 4.0 4.0 \n", " 40.0 -0.003063 0.020531 4.0 4.0 \n", "... ... ... ... ... \n", "2013-01-06 17:30:00 49.999754 320.0 0.000019 -0.000030 4.0 4.0 \n", " 330.0 -0.000059 -0.000013 4.0 4.0 \n", " 340.0 0.000009 0.000095 4.0 4.0 \n", " 350.0 0.000146 -0.000018 4.0 4.0 \n", " 360.0 -0.000003 -0.000166 4.0 4.0 \n", "\n", " epot int_joule_heat joule_heat \\\n", "time lat lon \n", "2013-01-06 16:30:00 88.333323 0.0 -6755.328259 9.622610 3.331331 \n", " 10.0 -6419.514908 9.622610 2.874432 \n", " 20.0 -6113.721553 9.622610 2.416306 \n", " 30.0 -5862.900304 9.622610 2.011470 \n", " 40.0 -5688.033704 9.622610 1.723594 \n", "... ... ... ... \n", "2013-01-06 17:30:00 49.999754 320.0 313.325279 27.863884 0.000005 \n", " 330.0 313.296195 27.863884 0.000015 \n", " 340.0 338.207580 27.863884 0.000036 \n", " 350.0 267.731952 27.863884 0.000087 \n", " 360.0 196.514563 27.863884 0.000111 \n", "\n", " v_ph v_th \n", "time lat lon \n", "2013-01-06 16:30:00 88.333323 0.0 459.399493 173.436070 \n", " 10.0 426.756367 161.045790 \n", " 20.0 395.527306 135.848700 \n", " 30.0 368.673123 98.356825 \n", " 40.0 349.343274 52.117439 \n", "... ... ... \n", "2013-01-06 17:30:00 49.999754 320.0 -0.614055 -0.385016 \n", " 330.0 -0.263458 1.207273 \n", " 340.0 1.939544 -0.191665 \n", " 350.0 -0.369148 -2.998625 \n", " 360.0 -3.403963 0.068048 \n", "\n", "[1776 rows x 9 columns]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# convert AMGeO Xarray DataSet to Pandas DataFrame\n", "df = ds.to_dataframe()\n", "print(type(df))\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "This notebook showcases the standalone utility of AMGeO's new API, as well as the various ways it can be used in tandem with other popular python packages. \n", "\n", "Please get started with AMGeO [here](https://amgeo.colorado.edu/), we would love any feedback to continue improving our software." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# References\n", "\n", "AMGeO Collaboration (2019), A Collaborative Data Science Platform for the Geospace Community: Assimilative Mapping of Geospace Observations (AMGeO) v1.0.0, http://doi.org/10.5281/zenodo.3564914." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": true, "toc_position": {}, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 4 }