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My goal is to have 16 time series from several location in one csv file, and several csv files for several parameters. The simple code below is not related to my goal.
| Code Block |
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import cdstoolbox as ct |
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variables = { |
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'Near-Surface Air Temperature': '2m_temperature', |
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'Eastward Near-Surface Wind': '10m_u_component_of_wind', |
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'Northward Near-Surface Wind': '10m_v_component_of_wind', |
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'Sea Level Pressure': 'mean_sea_level_pressure', |
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'Surface Pressure': 'surface_pressure', |
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} |
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@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.") |
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@ct.input.dropdown('variable', label='Variable', values=variables.keys()) |
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@ct.output.download() |
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@ct.output.download() |
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def application(variable): |
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# Retrieve the hourly 2m temperature over Europe for the selected year |
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data = ct.catalogue.retrieve( |
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'reanalysis-era5-single-levels', |
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{ |
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'variable': variables[variable], |
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'product_type': 'reanalysis', |
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'year': 1999, |
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'month': 1, |
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'day': 1, |
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'time': [ |
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'00:00', '01:00', '02:00', '03:00', '04:00', '05:00', |
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'06:00', '07:00', '08:00', '09:00', '10:00', '11:00', |
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'12:00', '13:00', '14:00', '15:00', '16:00', '17:00', |
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'18:00', '19:00', '20:00', '21:00', '22:00', '23:00' |
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], |
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'grid': [3, 3], |
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'area': [60., -11., 34., 35.], # retrieve data for Europe only |
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} |
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) |
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test = ct.geo.spatial_average(data) |
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print(test) |
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test2 = ct.cdm.to_csv(test) |
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#Create an empty list |
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empty_list = [] |
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# |
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temporary_list=test |
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empty_list.append(temporary_list) |
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empty_list.append(temporary_list-50) |
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test3 = ct.cdm.to_csv(empty_list) |
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print(test2) |
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print(test3) |
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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. This creates 16 csv for a single variable and a single period. Hopefully, there will be option and documentation to simplify the download to a single csv file.
| 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 |
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