Superpose an observation point on a H2D plot

[1]:

%matplotlib inline
# for figures in notebook

# import & initialize epygram
import epygram
epygram.init_env()

import os
INPUTS_DIR = os.path.join('..', 'inputs')

[2]:

r = epygram.formats.resource(os.path.join(INPUTS_DIR, 'grid.arome-forecast.guyane0025+0024:00.grib'), 'r')

[3]:

f = r.readfield('shortName:2r')

[4]:

import cartopy.crs as ccrs
lonlat_crs = ccrs.PlateCarree()
station = (-52.6, 5.1)
value = 60.

[5]:

fig, ax = f.cartoplot(colormap='viridis_r')
x, y = ax.projection.transform_point(*station, lonlat_crs)
ax.scatter(x, y, s=100, c='k')
ax.text(x, y, ' Kourou', size=20)

[5]:

Text(-119538.84270720901, 567864.880456437, ' Kourou')
../../_images/gallery_A.1-H2D_plots_11.superpose_obs_5_1.png

Now if we want the color of the obs point to match the colormap of the field:

[6]:

# for that we need arrays
import numpy as np
lons = np.array([station[0]])
lats = np.array([station[1]])
values = np.array([value])

[7]:

fig, ax = f.cartoplot(colormap='viridis_r')
xyz = ax.projection.transform_points(lonlat_crs, lons, lats)
x = xyz[..., 0]
y = xyz[..., 1]
ax.scatter(x, y, s=100, c=values, cmap='viridis_r',
           vmin=f.data.min(), vmax=f.data.max())

[7]:

<matplotlib.collections.PathCollection at 0x70a134d68670>
../../_images/gallery_A.1-H2D_plots_11.superpose_obs_8_1.png

And now if the colormap is complex and needs a ColormapHelper to handle:

[8]:

f = r.readfield('parameterCategory:1,parameterNumber:19,typeOfStatisticalProcessing:0')
values = np.array([12])  # the red color in colormap
fig, ax = f.cartoplot(colormap='ptype')
ch = epygram.colormapping.get_ColormapHelper('ptype')
ax.scatter(x, y, s=100, c=values, **ch.kwargs_for_plot('scatter'))

[8]:

<matplotlib.collections.PathCollection at 0x70a133d0cbe0>
../../_images/gallery_A.1-H2D_plots_11.superpose_obs_10_1.png 
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