visualisation/gallery/symbol_pictogram.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Simple picture plotting on Cartesian projection\n",
    "\n",
    "This is a one cell notebook with example how to plot external images as symbols using Magics.\n",
    "\n",
    "List of all **msymbol** parameters you can find [in Magics documentation](https://confluence.ecmwf.int/display/MAGP/Symbol \"Symbol parameters\").\n",
    "\n",
    "More symbol plotting examples can be found in [Simple symbol plotting](../tutorials/Symbol_simple.ipynb \"Symbol simple\") and [Advanced symbol plotting](../tutorials/Advanced_simple.ipynb \"Symbol advanced\") notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Installing Magics\n",
    "If you don't have Magics installed, run the next cell to install Magics using conda."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install Magics in the current Jupyter kernel\n",
    "import sys\n",
    "!conda install --yes --prefix {sys.prefix} Magics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import Magics.macro as magics\n",
    "import numpy as np\n",
    "\n",
    "#Setting the cartesian view\n",
    "cartesian = magics.mmap( \n",
    "    subpage_y_position     = 2., \n",
    "    subpage_map_projection = 'cartesian',\n",
    "    subpage_x_axis_type    = 'date',\n",
    "    subpage_y_axis_type    = 'regular',\n",
    "    subpage_x_date_min     = \"2018-11-11 12:00:00\",\n",
    "    subpage_x_date_max     = \"2018-11-17 12:00:00\",\n",
    "    subpage_y_min          = 25.,\n",
    "    subpage_y_max          = 75.,\n",
    "    page_id_line           = \"off\")\n",
    "\n",
    "#Vertical axis\n",
    "vertical = magics.maxis(\n",
    "    axis_orientation       = \"vertical\",\n",
    "    axis_type              = \"regular\",\n",
    "    axis_tick_label_height = 0.4,\n",
    "    axis_tick_label_colour = 'navy',\n",
    "    axis_grid              = \"on\",\n",
    "    axis_grid_colour       = \"grey\",\n",
    "    axis_grid_thickness    = 1,\n",
    "    axis_grid_line_style   = \"dot\")\n",
    "\n",
    "#Horizontal axis\n",
    "horizontal = magics.maxis(\n",
    "    axis_orientation         = \"horizontal\",\n",
    "    axis_type                = \"date\",\n",
    "    axis_days_label_height   = 0.40,\n",
    "    axis_months_label_height = 0.40,\n",
    "    axis_years_label_height  = 0.50,\n",
    "    axis_minor_tick          = \"on\",\n",
    "    axis_grid                = \"on\",\n",
    "    axis_grid_colour         = \"grey\",\n",
    "    axis_grid_thickness      = 1,\n",
    "    axis_grid_line_style     = \"dot\")\n",
    "\n",
    "#define  the  data \n",
    "x =  [\"2018-11-12 00:00:00\",\"2018-11-13 12:00:00\",\"2018-11-15 00:00:00\"]\n",
    "y = np.array([35.,45.,55.])\n",
    "\n",
    "input = magics.minput(\n",
    "    input_x_type        = 'date',\n",
    "    input_date_x_values = x,\n",
    "    input_y_values      = y)\n",
    "\n",
    "#Define the graph \n",
    "symbols = magics.msymb( \n",
    "    legend              = 'on',\n",
    "    symbol_type         = \"marker\",\n",
    "    symbol_marker_mode  = \"image\",\n",
    "    symbol_image_path   = 'D96.png',\n",
    "    symbol_image_format = 'png',\n",
    "    symbol_colour       = \"red\"\n",
    "    )\n",
    "\n",
    "legend = magics.mlegend(\n",
    "    legend                = \"on\",\n",
    "    legend_user_text      = \"<font colour='navy' size='0.7'> Pictograms </font>\",\n",
    "    legend_box_mode       = \"positional\",\n",
    "    legend_box_y_position = 16.5,\n",
    "    legend_box_x_position = 20.00,\n",
    "    legend_box_x_length   = 5.00,\n",
    "    legend_box_y_length   = 2.00)\n",
    "\n",
    "#To the plot\n",
    "magics.plot(cartesian, vertical, horizontal, input, symbols, legend)"
   ]
  }
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