{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add and use a user-defined colormap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Look at the Colormaps page in **epygram** doc, and try out some of these to see their characteristics - basically, 3 kinds:\n",
    "   * continuous (default)\n",
    "   * normalized (e.g. _rr1h_), i.e. each value interval occupy a normalized size in colormap\n",
    "   * and normalized-centered (e.g. _ptype_), i.e. normalized, with interval centered on defined values\n",
    "2. Once you found the one which characteristics you would like to imitate, copy its `.json` file from `EPYGRAM_INSTALL_DIR/epygram/data/colormaps/` to wherever you want (say, `/path/to/my_cmap.json`)\n",
    "3. Modify the name of the new colormap, its colors (RGB, optional transparency) and colorsteps in the json file.\n",
    "4. Register your colormap in your `HOME/.epygram/userconfig.py`:\n",
    "   `usercolormaps = {'my_cmap':'/path/to/my_cmap.json'}`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An example below of a normalized-centered colormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:31:09.025564Z",
     "iopub.status.busy": "2025-01-13T11:31:09.025094Z",
     "iopub.status.idle": "2025-01-13T11:31:09.557258Z",
     "shell.execute_reply": "2025-01-13T11:31:09.556642Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# for figures in notebook\n",
    "\n",
    "# import & initialize epygram\n",
    "import epygram\n",
    "epygram.init_env()\n",
    "\n",
    "import os\n",
    "INPUTS_DIR = os.path.join('..', 'inputs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:31:09.559440Z",
     "iopub.status.busy": "2025-01-13T11:31:09.559239Z",
     "iopub.status.idle": "2025-01-13T11:31:09.676897Z",
     "shell.execute_reply": "2025-01-13T11:31:09.676394Z"
    }
   },
   "outputs": [],
   "source": [
    "r = epygram.open(os.path.join(INPUTS_DIR, 'grid.arome-forecast.guyane0025+0024:00.grib'), 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:31:09.678939Z",
     "iopub.status.busy": "2025-01-13T11:31:09.678778Z",
     "iopub.status.idle": "2025-01-13T11:31:09.883509Z",
     "shell.execute_reply": "2025-01-13T11:31:09.883015Z"
    }
   },
   "outputs": [],
   "source": [
    "f = r.readfield('parameterCategory:1,parameterNumber:19,typeOfStatisticalProcessing:0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:31:09.885660Z",
     "iopub.status.busy": "2025-01-13T11:31:09.885452Z",
     "iopub.status.idle": "2025-01-13T11:31:10.628590Z",
     "shell.execute_reply": "2025-01-13T11:31:10.628185Z"
    }
   },
   "outputs": [],
   "source": [
    "fig, ax = f.cartoplot(colormap='ptype')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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