...
import cdstoolbox as ct
variables = {
'Near-Surface Air Temperature': '2m_temperature',
'Eastward Near-Surface Wind': '10m_u_component_of_wind',
'Northward Near-Surface Wind': '10m_v_component_of_wind',
'Sea Level Pressure': 'mean_sea_level_pressure',
'Surface Pressure': 'surface_pressure',
}@ct.application(title="AWS Matching data acquisation", description="Download point data matching AWS location for TNB Project. Main purpose is to compare AWS observation with ERA5 and ERA5-Land. Data downloaded will be further processed in R.")
@ct.input.dropdown('variable', label='Variable', values=variables.keys())
@ct.output.download()
@ct.output.download()
def application(variable):
# Retrieve the hourly 2m temperature over Europe for the selected year
data = ct.catalogue.retrieve(
'reanalysis-era5-single-levels',
{
'variable': variables[variable],
'product_type': 'reanalysis',
'year': 1999,
'month': 1,
'day': 1,
'time': [
'00:00', '01:00', '02:00', '03:00', '04:00', '05:00',
'06:00', '07:00', '08:00', '09:00', '10:00', '11:00',
'12:00', '13:00', '14:00', '15:00', '16:00', '17:00',
'18:00', '19:00', '20:00', '21:00', '22:00', '23:00'
],
'grid': [3, 3],
'area': [60., -11., 34., 35.], # retrieve data for Europe only
}
)
test = ct.geo.spatial_average(data)
print(test)
test2 = ct.cdm.to_csv(test)
#Create an empty list
empty_list = []
#
temporary_list=test
empty_list.append(temporary_list)
empty_list.append(temporary_list-50)
test3 = ct.cdm.to_csv(empty_list)
print(test2)
print(test3)
return test2, test3
Yunus.
Edit 6 January 2022. After trying various methods based on the documentation and API. I gave up. This is my approach now.
| Code Block |
|---|
import cdstoolbox as ct
variables = {
'Near-Surface Air Temperature': '2m_temperature',
'Eastward Near-Surface Wind': '10m_u_component_of_wind',
'Northward Near-Surface Wind': '10m_v_component_of_wind',
'Sea Level Pressure': 'mean_sea_level_pressure',
'Surface Pressure': 'surface_pressure',
}
Case = {
'Case 1 1993':0,
'Case 2 1994':1,
}
Case_Year = {
'Case 1 1993':1993,
'Case 2 1994':1994,
}
Case_Month = {
'Case 1 1993':2,
'Case 2 1994':5,
}
Case_Day = {
'Case 1 1993':[21,27],
'Case 2 1994':[20,27],
}
@ct.application(title="AWS Matching data acquisation", description="Download point data matching AWS location for TNB Project. Main purpose is to compare AWS observation with ERA5 and ERA5-Land. Data downloaded will be further processed in R.")
@ct.input.dropdown('variable', label='Variable', values=variables.keys())
@ct.input.dropdown('Case', label='Case Study', values=Case.keys())
@ct.output.figure()
@ct.output.download()
@ct.output.download()
@ct.output.download()
@ct.output.download()
@ct.output.download()
@ct.output.download()
@ct.output.download()
@ct.output.download()
@ct.output.download()
@ct.output.download()
@ct.output.download()
@ct.output.download()
@ct.output.download()
@ct.output.download()
@ct.output.download()
@ct.output.download()
def application(variable,Case):
# Retrieve the hourly 2m temperature over Europe for the selected year
data = ct.catalogue.retrieve(
'reanalysis-era5-single-levels',
{
'variable': variables[variable],
'product_type': 'reanalysis',
'year': Case_Year[Case],
'month': Case_Month[Case],
'day': Case_Day[Case],
'time': [
'00:00', '01:00', '02:00', '03:00', '04:00', '05:00',
'06:00', '07:00', '08:00', '09:00', '10:00', '11:00',
'12:00', '13:00', '14:00', '15:00', '16:00', '17:00',
'18:00', '19:00', '20:00', '21:00', '22:00', '23:00'
],
'grid': [3, 3],
'area': [-70., 140.,-80. , 170.], # retrieve data for Europe only #NESW
}
)
# Compute the daily mean temperature over Europe
temperature_daily_mean = ct.cube.resample(data, freq='day', how='mean')
fig = ct.map.plot(temperature_daily_mean)
#Creating a simple figure is just to make sure the server side application is working. If a figure isn't plotted and a bunch of errors in the console page, give it up, something wrong with the server.
data_point_01 = ct.geo.extract_point(data,lon=166.6211,lat=-73.5861)
data_point_02 = ct.geo.extract_point(data,lon=162.9700,lat=-76.7150)
data_point_03 = ct.geo.extract_point(data,lon=164.0922,lat=-74.6958)
data_point_04 = ct.geo.extract_point(data,lon=145.8589,lat=-75.5361)
data_point_05 = ct.geo.extract_point(data,lon=148.6556,lat=-71.6525)
data_point_06 = ct.geo.extract_point(data,lon=164.1177,lat=-74.6944)
data_point_07 = ct.geo.extract_point(data,lon=163.4306,lat=-74.1350)
data_point_08 = ct.geo.extract_point(data,lon=161.7720,lat=-74.9506)
data_point_09 = ct.geo.extract_point(data,lon=160.6456,lat=-74.6392)
data_point_10 = ct.geo.extract_point(data,lon=159.1933,lat=-72.8292)
data_point_11 = ct.geo.extract_point(data,lon=164.0331,lat=-74.7250)
data_point_12 = ct.geo.extract_point(data,lon=169.6000,lat=-73.0500)
data_point_13 = ct.geo.extract_point(data,lon=163.2333,lat=-74.8167)
data_point_14 = ct.geo.extract_point(data,lon=158.5906,lat=-75.6166)
data_point_15 = ct.geo.extract_point(data,lon=162.8956,lat=-74.1766)
data_point_16 = ct.geo.extract_point(data,lon=164.2205,lat=-74.6159)
#print(data_point_1)
download_point_01=ct.cdm.to_csv(data=data_point_01)
download_point_02=ct.cdm.to_csv(data=data_point_02)
download_point_03=ct.cdm.to_csv(data=data_point_03)
download_point_04=ct.cdm.to_csv(data=data_point_04)
download_point_05=ct.cdm.to_csv(data=data_point_05)
download_point_06=ct.cdm.to_csv(data=data_point_06)
download_point_07=ct.cdm.to_csv(data=data_point_07)
download_point_08=ct.cdm.to_csv(data=data_point_08)
download_point_09=ct.cdm.to_csv(data=data_point_09)
download_point_10=ct.cdm.to_csv(data=data_point_10)
download_point_11=ct.cdm.to_csv(data=data_point_11)
download_point_12=ct.cdm.to_csv(data=data_point_12)
download_point_13=ct.cdm.to_csv(data=data_point_13)
download_point_14=ct.cdm.to_csv(data=data_point_14)
download_point_15=ct.cdm.to_csv(data=data_point_15)
download_point_16=ct.cdm.to_csv(data=data_point_16)
return fig, download_point_01, download_point_02, download_point_03, download_point_04, download_point_05, download_point_06, download_point_07, download_point_08, download_point_09, download_point_10, download_point_11, download_point_12, download_point_13, download_point_14, download_point_15, download_point_16#test2, test3 |