using-python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Try PyGMT online"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Jupyter lab environment has the latest PyGMT installed. You can try PyGMT with no extra install required."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE:** Any changes will be lost when the Jupyter lab is closed. Select \"File\"->\"Download\" to save notebooks to your computer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To execute the code cells, select it and type `Shift + Enter` or click on the \"Run\" ► button in the menu bar. \n",
    "\n",
    "For tutorials and more examples, visit the official website: https://www.pygmt.org"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Note for Google Colab users\n",
    "\n",
    "Google Colab doesn't have PyGMT and its dependencies installed. You need to run the following commands to install them once.\n",
    "\n",
    "Be patient! The installation may take a few minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
    "!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
    "!bash ./Miniconda3-latest-Linux-x86_64.sh -bfp /usr/local\n",
    "!conda update conda -y -q\n",
    "!conda config --prepend channels conda-forge\n",
    "!conda install -q -y --prefix /usr/local python=3.7 pygmt\n",
    "\n",
    "import sys\n",
    "import os\n",
    "sys.path.append('/usr/local/lib/python3.7/site-packages')\n",
    "os.environ[\"GMT_LIBRARY_PATH\"]=\"/usr/local/lib\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the PyGMT package\n",
    "\n",
    "You can load PyGMT by importing the `pygmt` Python package. Most GMT processing modules will be avialable as functions in this package. The plotting modules are methods of the `pygmt.Figure` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the PyGMT package. This only needs to be run once\n",
    "import pygmt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check the installed environment\n",
    "\n",
    "Before starting, let's first check the current environment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pygmt.show_versions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All figure generation in PyGMT is handled by the `pygmt.Figure` class. \n",
    "It has methods to add layers to your figure, like a basemap, coastlines, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start a new figure by creating an instance of `pygmt.Figure`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = pygmt.Figure()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We add elements to the figure using its methods. For example, lets add the global coastlines to a 15 cm wide map using the Hammer projection (**H**). \n",
    "Our figure will also have a nice frame with automatic ticks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.coast(region='g', projection='H15c', frame=True, land='lightbrown', water='lightblue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see a preview of the figure directly in the Jupyter notebook using `fig.show()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also save your figure in several graphics formats (e.g., png, pdf, jpg) using `fig.savefig()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(\"global.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = pygmt.Figure()\n",
    "fig.basemap(region='g', projection='G200/30/12c', frame=True)\n",
    "fig.grdimage('@earth_relief_20m', cmap='geo', shading=True)\n",
    "fig.coast(resolution='c', shorelines=True, area_thresh=1000)\n",
    "fig.plot(data='@hotspots.txt', style='c0.2c', color='red')\n",
    "fig.colorbar()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment for yourself"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try running different things in the cells below or edit and rerun the cells above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = pygmt.Figure()\n",
    "# Any Python codes here\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Print dependencies**\n",
    "\n",
    "Dependences are fundamental to record the **computational environment**.\n",
    "\n",
    "- Use watermark to print version of python, ipython, and packages, and characteristics of the computer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.8.5\n",
      "IPython version      : 7.28.0\n",
      "\n",
      "wget         : not installed\n",
      "pandas       : 1.3.3\n",
      "numpy        : 1.20.1\n",
      "geopy        : 2.2.0\n",
      "altair       : not installed\n",
      "vega_datasets: not installed\n",
      "watermark    : 2.3.0\n",
      "\n",
      "Compiler    : MSC v.1916 64 bit (AMD64)\n",
      "OS          : Windows\n",
      "Release     : 10\n",
      "Machine     : AMD64\n",
      "Processor   : Intel64 Family 6 Model 42 Stepping 7, GenuineIntel\n",
      "CPU cores   : 1\n",
      "Architecture: 64bit\n",
      "\n",
      " \n",
      "Last updated: Fri Feb 04 2022 15:58:04Hora estándar central (México)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "\n",
    "# python, ipython, packages, and machine characteristics\n",
    "%watermark -v -m -p wget,pandas,numpy,geopy,altair,vega_datasets,watermark\n",
    "\n",
    "#date\n",
    "print(\" \")\n",
    "%watermark -u -n -t -z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}