def Plot_on_Segments(Scalar_Field,
Variable_Name,
year,
month,
day,
hour,
forecast_period,
num_cells,
filename='plot'):
"""
Plot some field associated with the different segments.
"""
#Load a single instance of the data set so that we can get the latitude and longitude coordinates:
f = mogreps.download_data('mogreps-uk',
mogreps.make_data_object_name(
'mogreps-uk', year, Month_Map(month), day,
hour, 0, forecast_period),
data_folder=Path('.'))
data_set = netCDF4.Dataset(f)
rotation = data_set['rotated_latitude_longitude']
transform = ccrs.RotatedPole(
pole_longitude=rotation.grid_north_pole_longitude,
pole_latitude=rotation.grid_north_pole_latitude)
projection = transform
fig = plt.figure(figsize=(20, 10))
#create an axis instance:
ax = fig.add_subplot(111, projection=projection)
pcm = ax.pcolormesh(data_set['grid_longitude'],
data_set['grid_latitude'],
Scalar_Field,
transform=transform,
cmap='jet')
ax.coastlines(resolution='10m')
#ax.colorbar()
colbar = fig.colorbar(pcm)
#ax.imshow(mark_boundaries(image, segments))
#colbar.set_label("VAR(" + str(variable_name) + ")")
plt.title(Variable_Name + " " + str(year) + "_" + month + "_" + str(day) +
"_" + str(hour) + "_forcast_period=" + str(forecast_period))
plt.savefig(filename + "_" + str(year) + "_" + month + "_" + str(day) +
"_h_" + str(hour) + "_fp_" + str(forecast_period) + "_cells_" +
str(num_cells),
bbox_inches='tight')
plt.show()
import numpy, scipy, matplotlib, cartopy, netCDF4, seaborn, mogreps
from pathlib import Path
forecast_3 = mogreps.download_data('mogreps-uk',
mogreps.make_data_object_name(
'mogreps-uk', 2015, 12, 5, 15, 0, 3),
data_folder=Path('.'))
forecast_9 = mogreps.download_data('mogreps-uk',
mogreps.make_data_object_name(
'mogreps-uk', 2015, 12, 5, 9, 0, 9),
data_folder=Path('.'))
forecast_15 = mogreps.download_data('mogreps-uk',
mogreps.make_data_object_name(
'mogreps-uk', 2015, 12, 5, 3, 0, 15),
data_folder=Path('.'))
forecast_21 = mogreps.download_data('mogreps-uk',
mogreps.make_data_object_name(
'mogreps-uk', 2015, 12, 4, 21, 0, 21),
data_folder=Path('.'))
forecast_27 = mogreps.download_data('mogreps-uk',
mogreps.make_data_object_name(
'mogreps-uk', 2015, 12, 4, 15, 0, 27),
data_folder=Path('.'))
forecast_33 = mogreps.download_data('mogreps-uk',
mogreps.make_data_object_name(
'mogreps-uk', 2015, 12, 4, 9, 0, 33),
data_folder=Path('.'))
DS_forecast_3 = netCDF4.Dataset(forecast_3)
DS_forecast_9 = netCDF4.Dataset(forecast_9)
def Get_Super_Pixel_STDs(variable, segments, year, month, day, hour,
forecast_period):
"""
Given a segmentation into superpixels we compute the variance
in a given variable across ensemble members for each superpixel.
We then return the variance across the different ensemble members.
Parameters
------------
variable : str
String specifying the property we are interested in analyisng
"""
#variable = 'air_temperature'
STD_Vals = []
Variance_Vals = []
CV_Vals = []
for PIXEL in range(np.max(segments)):
#Loop over ensembles within a superpixel:
num_ensemble_members = 12
Averages_in_this_pixel = []
print("Pixel = " + str(PIXEL))
for i in range(num_ensemble_members):
f = mogreps.download_data('mogreps-uk',
mogreps.make_data_object_name(
'mogreps-uk', year, Month_Map(month),
day, hour, i, forecast_period),
data_folder=Path('.'))
data_set = netCDF4.Dataset(f)
#Try except fudge to account for the fact that different arrays are at different depths:
try:
average_for_this_ensemble_member_in_this_super_pixel = np.mean(
data_set.variables[variable][0].data[segments == PIXEL])
except:
average_for_this_ensemble_member_in_this_super_pixel = np.mean(
data_set.variables[variable][0][0].data[segments == PIXEL])
Averages_in_this_pixel.append(
average_for_this_ensemble_member_in_this_super_pixel)
#compute the varaiance for this superpixel:
Variance_for_this_pixel = np.var(Averages_in_this_pixel)
STD_for_this_pixel = np.std(Averages_in_this_pixel)
STD_Vals.append(STD_for_this_pixel)
Variance_Vals.append(Variance_for_this_pixel)
CV_Vals.append(
np.std(Averages_in_this_pixel) / np.mean(Averages_in_this_pixel))
return STD_Vals, Variance_Vals, CV_Vals
p_array = output_data.variables['pressure_error']
T_array = output_data.variables['temperature_error']
r_array = output_data.variables['rainfall_error']
# Lead time of 3hrs
lead_time = 3
for year in years:
for month in months:
for day in days:
for time in times:
forecast_name = download_data('mogreps-uk',
make_data('mogreps-uk',
year,
month,
day - 1,
time,
0,
lead_time),
data_folder=Path('.'))
reference_name = download_data('mogreps-uk',
make_data('mogreps-uk',
year,
month,
day,
time,
0,
lead_time),
data_folder=Path('.'))