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Paolo Veglio
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import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import numpy as np
import netCDF4 as nc
import sys
import os
from scene import test_scene
def main(scene_flag):
scn = test_scene()
lat = scn['lat']
lon = scn['lon']
plt.figure(figsize=[15, 10])
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent([np.min(lon)-15, np.max(lon)+15,
np.min(lat)-15, np.max(lat)+15], crs=ccrs.PlateCarree())
ax.coastlines(resolution='50m', color='black', linewidth=1)
plt.pcolormesh(lon, lat, scn[scene_flag],
transform=ccrs.PlateCarree())
plt.show()
def plot_tests(numpy_file='test_confidence.npz'):
data = np.load(numpy_file)
lat = data['lat']
lon = data['lon']
# confidence = data['confidence']
conf_test = data['conf_test']
confidence = data['confidence']
cm = np.zeros(confidence.shape)
cm[confidence > 0.66] = 1
cm[confidence > 0.95] = 2
cm[confidence > 0.99] = 3
for i in range(9):
print(f'Making plot {i}\n')
plt.figure(figsize=[15, 10])
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent([np.min(lon)-5, np.max(lon)+5,
np.min(lat)-5, np.max(lat)+5], crs=ccrs.PlateCarree())
ax.coastlines(resolution='50m', color='black', linewidth=1)
plt.pcolormesh(lon, lat, conf_test[i, :, :], vmin=0, vmax=1,
transform=ccrs.PlateCarree())
plt.title(f'Confidence {i}')
plt.colorbar()
plt.savefig(f'figures/confplot_{i}.png')
plt.figure(figsize=[15, 10])
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent([np.min(lon)-5, np.max(lon)+5,
np.min(lat)-5, np.max(lat)+5], crs=ccrs.PlateCarree())
ax.coastlines(resolution='50m', color='black', linewidth=1)
plt.pcolormesh(lon, lat, cm, vmin=0, vmax=3,
transform=ccrs.PlateCarree())
plt.title('Cloud Mask')
# plt.colorbar()
cb = plt.colorbar(ticks=range(4))
cb.set_ticklabels(['confident cloudy', 'probably cloudy', 'probably clear', 'confident clear'])
plt.savefig('figures/total_cm.png')
def plot_confidence(numpy_file='test_confidence.npz'):
data = np.load(numpy_file)
lat = data['lat']
lon = data['lon']
# confidence = data['confidence']
confidence = data['confidence']
plt.figure(figsize=[15, 10])
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent([np.min(lon)-5, np.max(lon)+5,
np.min(lat)-5, np.max(lat)+5], crs=ccrs.PlateCarree())
ax.coastlines(resolution='50m', color='black', linewidth=1)
plt.pcolormesh(lon, lat, confidence, vmin=0, vmax=1,
transform=ccrs.PlateCarree())
plt.title('Confidence')
plt.colorbar()
plt.savefig('figures/total_confidence.png')
def plot_cloud_mask():
f = nc.Dataset('/ships19/hercules/pveglio/mvcm_viirs_hires/CLDMSK_L2_VIIRS_SNPP.A2022173.1454.001.2022174035130.nc')
lat = f['geolocation_data/latitude'][:]
lon = f['geolocation_data/longitude'][:]
# icm = f['geophysical_data/Integer_Cloud_Mask'][:]
cm = f['geophysical_data/Cloud_Mask'][:]
qa = f['geophysical_data/Quality_Assurance'][:]
f.close()
# cm_flag = np.array((np.array(cm[0, :, :], np.byte) &
# (pow(2, 1) + pow(2, 2)))/pow(2, 1))
cm_flag = read_bits(qa[:, :, 2], 2) & ~read_bits(cm[2, :, :], 2)
plt.figure(figsize=[15, 10])
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent([np.min(lon)-5, np.max(lon)+5,
np.min(lat)-5, np.max(lat)+5], crs=ccrs.PlateCarree())
ax.coastlines(resolution='50m', color='black', linewidth=1)
plt.pcolormesh(lon, lat, cm_flag, vmin=0, vmax=1,
transform=ccrs.PlateCarree())
# cb = plt.colorbar(ticks=range(4))
# cb.set_ticklabels(['confident cloudy', 'probably cloudy', 'probably clear', 'confident clear'])
cb = plt.colorbar(ticks=range(2))
cb.set_ticklabels(['No', 'Yes'])
# plt.savefig('figures/ref_cloudmask.png')
plt.savefig('figures/high_cloud_flag_11_12.png')
def read_bits(byte, bits):
# orig_shape = byte.shape
# if byte.ndim > 1:
# byte = byte.reshape((byte.shape[0]*byte.shape[1],))
if type(bits) is int:
flag = np.array((np.array(byte, np.byte) & pow(2, bits))/pow(2, bits),
dtype='bool')
else:
flag = (np.array(byte, np.byte) & pow(2, bits[0]) +
pow(2, bits[1]))/pow(2, bits[0])
flag = np.array(np.trunc(1.0-flag/2.0), dtype='bool')
# flag = flag.reshape((orig_shape))
return flag
if __name__ == "__main__":
# main(sys.argv[1])
plot_tests(sys.argv[1])
# plot_confidence(sys.argv[1])
# plot_cloud_mask()