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Commit 20b6f845 authored by Carsten Hinz's avatar Carsten Hinz
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fixes refactor of contributors endpoint

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%% Cell type:markdown id: tags:
### Get Dataset from request
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 id: tags:
``` python
from datetime import datetime as dt
from pathlib import Path
import pandas as pd
import numpy as np
from toargridding.grids import RegularGrid
from toargridding.toar_rest_client import (
AnalysisServiceDownload,
STATION_LAT,
STATION_LON,
)
from toargridding.metadata import Metadata, TimeSample, AnalysisRequestResult, Coordinates
from toargridding.variables import Coordinate
from toargridding.contributors import contributors_manager
from toargridding.contributors import contributions_manager_by_id
import logging
from toargridding.defaultLogging import toargridding_defaultLogging
#setup of logging
logger = toargridding_defaultLogging()
logger.addShellLogger(logging.DEBUG)
logger.logExceptions()
endpoint = "https://toar-data.fz-juelich.de/api/v2/analysis/statistics/"
#starts in directory [path/to/toargridding]/tests
#maybe adopt the toargridding_base_path for your machine.
toargridding_base_path = Path(".")
cache_dir = toargridding_base_path / "cache"
data_download_dir = toargridding_base_path / "results"
cache_dir.mkdir(exist_ok=True)
data_download_dir.mkdir(exist_ok=True)
analysis_service = AnalysisServiceDownload(endpoint, cache_dir, data_download_dir, use_downloaded=True)
my_grid = RegularGrid(1.9, 2.5)
time = TimeSample(dt(2016,1,1), dt(2016,2,28), "daily")
metadata = Metadata.construct("mole_fraction_of_ozone_in_air", time, "mean")
```
%% Cell type:markdown id: tags:
In the next step we want to download the data and store them to disc.
%% Cell type:code id: tags:
``` python
# this cell can runs longer than 30minutes
data = analysis_service.get_data(metadata)
# create contributors endpoint and write result to metadata
contrib = contributors_manager(metadata.get_id(), data_download_dir)
contrib = contributions_manager_by_id(metadata.get_id(), data_download_dir)
contrib.extract_contributors_from_data_frame(data.stations_data)
metadata.contributors_metadata_field = contrib.setup_contributors_endpoint_for_metadata()
ds = my_grid.as_xarray(data)
#store dataset
ds.to_netcdf(data_download_dir / f"{metadata.get_id()}_by_names_inline_{my_grid.get_id()}.nc")
```
%% Cell type:markdown id: tags:
### Visual inspection
We now clean the station metadata. Therefore we remove all stations which have invalid coordinates
%% Cell type:code id: tags:
``` python
#calculation of coordinates for plotting
#especially separation of coordinates with results and without results.
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
mean_data = ds["mean"]
clean_coords = data.stations_coords
all_na = data.stations_data.isna().all(axis=1)
clean_coords = all_na.to_frame().join(clean_coords)[["latitude", "longitude"]]
all_na_coords = clean_coords[all_na]
not_na_coords = clean_coords[~all_na]
```
%% Cell type:markdown id: tags:
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 id: tags:
``` python
import matplotlib as mpl
#definition of plotting function
def plot_cells(data, stations, na_stations, discrete=True, plot_stations=False):
fig = plt.figure(figsize=(9, 18))
ax = plt.axes(projection=ccrs.PlateCarree())
ax.coastlines()
gl = ax.gridlines(draw_labels=True)
gl.top_labels = False
gl.left_labels = False
gl.xlocator = mticker.FixedLocator(data.longitude.values)
gl.ylocator = mticker.FixedLocator(data.latitude.values)
cmap = mpl.cm.viridis
if discrete:
print(np.unique(data.values))
bounds = np.arange(8)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N, extend="both")
ticks = np.arange(bounds.size + 1)[:-1] + 0.5
ticklables = bounds
im = plt.pcolormesh(
data.longitude,
data.latitude,
data,
transform=ccrs.PlateCarree(),
cmap=cmap,
shading="nearest",
norm=norm,
)
cb = fig.colorbar(im, ax=ax, shrink=0.2, aspect=25)
cb.set_ticks(ticks)
cb.set_ticklabels(ticklables)
im = plt.pcolormesh(
data.longitude,
data.latitude,
data,
transform=ccrs.PlateCarree(),
cmap=cmap,
shading="nearest",
norm=norm,
)
else:
im = plt.pcolormesh(
data.longitude,
data.latitude,
data,
transform=ccrs.PlateCarree(),
cmap=cmap,
shading="nearest",
)
cb = fig.colorbar(im, ax=ax, shrink=0.2, aspect=25)
if plot_stations:
plt.scatter(na_stations["longitude"], na_stations["latitude"], s=1, c="k")
plt.scatter(stations["longitude"], stations["latitude"], s=1, c="r")
plt.tight_layout()
plt.title(f"global ozon at {data.time.values} {data.time.units}")
```
%% Cell type:markdown id: tags:
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 id: tags:
``` python
#example visualization for two time points
print(not_na_coords)
timestep = 2
time = ds.time[timestep]
data = ds.sel(time=time)
plot_cells(data["mean"], not_na_coords, all_na_coords, discrete=False, plot_stations=True)
plt.show()
plot_cells(data["n"], not_na_coords, all_na_coords, discrete=True)
plt.show()
n_observations = ds["n"].sum(["latitude", "longitude"])
plt.plot(ds.time, n_observations)
print(np.unique(ds["n"]))
```
%% Cell type:markdown id: tags:
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 id: tags:
``` python
print(data)
```
......
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