{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get Dataset from request\n",
"\n",
"This cell imports all required packages and sets up the logging as well as the required information for the requests to the TOAR-DB."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from datetime import datetime as dt\n",
"from pathlib import Path\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"from toargridding.grids import RegularGrid\n",
"from toargridding.toar_rest_client import (\n",
" AnalysisServiceDownload,\n",
" STATION_LAT,\n",
" STATION_LON,\n",
")\n",
"from toargridding.metadata import Metadata, TimeSample, AnalysisRequestResult, Coordinates\n",
"from toargridding.variables import Coordinate\n",
"\n",
"from toargridding.contributors import contributions_manager_by_id\n",
"\n",
"import logging\n",
"from toargridding.defaultLogging import toargridding_defaultLogging\n",
"#setup of logging\n",
"logger = toargridding_defaultLogging()\n",
"logger.addShellLogger(logging.DEBUG)\n",
"logger.logExceptions()\n",
"\n",
"endpoint = \"https://toar-data.fz-juelich.de/api/v2/analysis/statistics/\"\n",
"#starts in directory [path/to/toargridding]/tests\n",
"#maybe adopt the toargridding_base_path for your machine.\n",
"toargridding_base_path = Path(\".\")\n",
"cache_dir = toargridding_base_path / \"cache\"\n",
"data_download_dir = toargridding_base_path / \"results\"\n",
"\n",
"cache_dir.mkdir(exist_ok=True)\n",
"data_download_dir.mkdir(exist_ok=True)\n",
"analysis_service = AnalysisServiceDownload(endpoint, cache_dir, data_download_dir, use_downloaded=True)\n",
"my_grid = RegularGrid(1.9, 2.5)\n",
"\n",
"time = TimeSample(dt(2016,1,1), dt(2016,2,28), \"daily\")\n",
"metadata = Metadata.construct(\"mole_fraction_of_ozone_in_air\", time, \"mean\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the next step we want to download the data and store them to disc. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# this cell can runs longer than 30minutes\n",
"data = analysis_service.get_data(metadata)\n",
"\n",
"# create contributors endpoint and write result to metadata\n",
"contrib = contributions_manager_by_id(metadata.get_id(), data_download_dir)\n",
"contrib.extract_contributors_from_data_frame(data.stations_data)\n",
"metadata.contributors_metadata_field = contrib.setup_contributors_endpoint_for_metadata()\n",
"ds = my_grid.as_xarray(data)\n",
"#store dataset\n",
"ds.to_netcdf(data_download_dir / f\"{metadata.get_id()}_by_names_inline_{my_grid.get_id()}.nc\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visual inspection\n",
"We now clean the station metadata. Therefore we remove all stations which have invalid coordinates"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#calculation of coordinates for plotting\n",
"#especially separation of coordinates with results and without results.\n",
"\n",
"import cartopy.crs as ccrs\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.ticker as mticker\n",
"\n",
"mean_data = ds[\"mean\"]\n",
"clean_coords = data.stations_coords\n",
"all_na = data.stations_data.isna().all(axis=1)\n",
"clean_coords = all_na.to_frame().join(clean_coords)[[\"latitude\", \"longitude\"]]\n",
"all_na_coords = clean_coords[all_na]\n",
"not_na_coords = clean_coords[~all_na]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the next step we prepare a function for plotting the gridded data to a world map. The flag *discrete* influences the creation of the color bar. The *plot_stations* flag allows including the station positions into the map."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib as mpl\n",
"\n",
"#definition of plotting function\n",
"\n",
"def plot_cells(data, stations, na_stations, discrete=True, plot_stations=False):\n",
" fig = plt.figure(figsize=(9, 18))\n",
"\n",
" ax = plt.axes(projection=ccrs.PlateCarree())\n",
" ax.coastlines()\n",
" gl = ax.gridlines(draw_labels=True)\n",
" gl.top_labels = False\n",
" gl.left_labels = False\n",
" gl.xlocator = mticker.FixedLocator(data.longitude.values)\n",
" gl.ylocator = mticker.FixedLocator(data.latitude.values)\n",
"\n",
" cmap = mpl.cm.viridis\n",
"\n",
" if discrete:\n",
" print(np.unique(data.values))\n",
" bounds = np.arange(8)\n",
" norm = mpl.colors.BoundaryNorm(bounds, cmap.N, extend=\"both\")\n",
" ticks = np.arange(bounds.size + 1)[:-1] + 0.5\n",
" ticklables = bounds\n",
" \n",
" im = plt.pcolormesh(\n",
" data.longitude,\n",
" data.latitude,\n",
" data,\n",
" transform=ccrs.PlateCarree(),\n",
" cmap=cmap,\n",
" shading=\"nearest\",\n",
" norm=norm,\n",
" )\n",
" cb = fig.colorbar(im, ax=ax, shrink=0.2, aspect=25)\n",
" cb.set_ticks(ticks)\n",
" cb.set_ticklabels(ticklables)\n",
" im = plt.pcolormesh(\n",
" data.longitude,\n",
" data.latitude,\n",
" data,\n",
" transform=ccrs.PlateCarree(),\n",
" cmap=cmap,\n",
" shading=\"nearest\",\n",
" norm=norm,\n",
" )\n",
" else:\n",
" im = plt.pcolormesh(\n",
" data.longitude,\n",
" data.latitude,\n",
" data,\n",
" transform=ccrs.PlateCarree(),\n",
" cmap=cmap,\n",
" shading=\"nearest\",\n",
" )\n",
"\n",
" cb = fig.colorbar(im, ax=ax, shrink=0.2, aspect=25)\n",
" \n",
"\n",
" if plot_stations:\n",
" plt.scatter(na_stations[\"longitude\"], na_stations[\"latitude\"], s=1, c=\"k\")\n",
" plt.scatter(stations[\"longitude\"], stations[\"latitude\"], s=1, c=\"r\")\n",
"\n",
" plt.tight_layout()\n",
"\n",
" plt.title(f\"global ozon at {data.time.values} {data.time.units}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we do the actual plotting. We select a single time from the dataset. To obtain two maps: 1) the mean ozone concentration per grid point and second the number of stations contributing to a grid point."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#example visualization for two time points\n",
"print(not_na_coords)\n",
"timestep = 2\n",
"time = ds.time[timestep]\n",
"data = ds.sel(time=time)\n",
"\n",
"plot_cells(data[\"mean\"], not_na_coords, all_na_coords, discrete=False, plot_stations=True)\n",
"plt.show()\n",
"\n",
"plot_cells(data[\"n\"], not_na_coords, all_na_coords, discrete=True)\n",
"plt.show()\n",
"\n",
"n_observations = ds[\"n\"].sum([\"latitude\", \"longitude\"])\n",
"plt.plot(ds.time, n_observations)\n",
"print(np.unique(ds[\"n\"]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Last but not least: We print the data and metadata of the dataset. Especially a look into the metadata can be interesting."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(data)"
]
}
],
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