{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Superpose an observation point on a H2D plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:31:12.472864Z",
     "iopub.status.busy": "2025-01-13T11:31:12.472369Z",
     "iopub.status.idle": "2025-01-13T11:31:12.996081Z",
     "shell.execute_reply": "2025-01-13T11:31:12.995476Z"
    }
   },
   "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": "2025-01-13T11:31:12.998241Z",
     "iopub.status.busy": "2025-01-13T11:31:12.998033Z",
     "iopub.status.idle": "2025-01-13T11:31:13.113224Z",
     "shell.execute_reply": "2025-01-13T11:31:13.112728Z"
    }
   },
   "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": "2025-01-13T11:31:13.115273Z",
     "iopub.status.busy": "2025-01-13T11:31:13.115106Z",
     "iopub.status.idle": "2025-01-13T11:31:13.411925Z",
     "shell.execute_reply": "2025-01-13T11:31:13.411424Z"
    }
   },
   "outputs": [],
   "source": [
    "f = r.readfield('shortName:2r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:31:13.414077Z",
     "iopub.status.busy": "2025-01-13T11:31:13.413914Z",
     "iopub.status.idle": "2025-01-13T11:31:13.416868Z",
     "shell.execute_reply": "2025-01-13T11:31:13.416485Z"
    }
   },
   "outputs": [],
   "source": [
    "import cartopy.crs as ccrs\n",
    "lonlat_crs = ccrs.PlateCarree()\n",
    "station = (-52.6, 5.1)\n",
    "value = 60."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:31:13.418871Z",
     "iopub.status.busy": "2025-01-13T11:31:13.418580Z",
     "iopub.status.idle": "2025-01-13T11:31:14.166923Z",
     "shell.execute_reply": "2025-01-13T11:31:14.166484Z"
    }
   },
   "outputs": [],
   "source": [
    "fig, ax = f.cartoplot(colormap='viridis_r')\n",
    "x, y = ax.projection.transform_point(*station, lonlat_crs)\n",
    "ax.scatter(x, y, s=100, c='k')\n",
    "ax.text(x, y, ' Kourou', size=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now if we want the color of the obs point to match the colormap of the field:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:31:14.170174Z",
     "iopub.status.busy": "2025-01-13T11:31:14.169957Z",
     "iopub.status.idle": "2025-01-13T11:31:14.172699Z",
     "shell.execute_reply": "2025-01-13T11:31:14.172329Z"
    }
   },
   "outputs": [],
   "source": [
    "# for that we need arrays\n",
    "import numpy as np\n",
    "lons = np.array([station[0]])\n",
    "lats = np.array([station[1]])\n",
    "values = np.array([value])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:31:14.174558Z",
     "iopub.status.busy": "2025-01-13T11:31:14.174310Z",
     "iopub.status.idle": "2025-01-13T11:31:14.645015Z",
     "shell.execute_reply": "2025-01-13T11:31:14.644439Z"
    }
   },
   "outputs": [],
   "source": [
    "fig, ax = f.cartoplot(colormap='viridis_r')\n",
    "xyz = ax.projection.transform_points(lonlat_crs, lons, lats)\n",
    "x = xyz[..., 0]\n",
    "y = xyz[..., 1]\n",
    "ax.scatter(x, y, s=100, c=values, cmap='viridis_r',\n",
    "           vmin=f.data.min(), vmax=f.data.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## And now if the colormap is complex and needs a ColormapHelper to handle:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-13T11:31:14.647913Z",
     "iopub.status.busy": "2025-01-13T11:31:14.647713Z",
     "iopub.status.idle": "2025-01-13T11:31:15.253556Z",
     "shell.execute_reply": "2025-01-13T11:31:15.253060Z"
    }
   },
   "outputs": [],
   "source": [
    "f = r.readfield('parameterCategory:1,parameterNumber:19,typeOfStatisticalProcessing:0')\n",
    "values = np.array([12])  # the red color in colormap\n",
    "fig, ax = f.cartoplot(colormap='ptype')\n",
    "ch = epygram.colormapping.get_ColormapHelper('ptype')\n",
    "ax.scatter(x, y, s=100, c=values, **ch.kwargs_for_plot('scatter'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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