lon = coords[1]
bl,x,y = bil.bilinear(lat, lon, df)
wind = bl['t_interp']
wind_hourly = wind.rename('wind_speed')
wind_locations[key] = wind_hourly
else:
# ERA5 weather
print('Using weather data from ERA5')
parameters = {
'wind_u': 'u10',
'wind_v': 'v10'
}
download.weather_era5(input_path, year, 1, 'I', 'wind', ['10m_u_component_of_wind', '10m_v_component_of_wind'], [ 60, -8, 50, 2, ])
df_u = read.weather_era5(input_path, year, 1, 'I', 'wind','u10')
df_v = read.weather_era5(input_path, year, 1, 'I', 'wind','v10')
print(df_v)
# for each location ...
for key, generator in generators.items():
# blinearly interpolate wind from weather grid
coords = generator['coords']
lat = coords[0]
lon = coords[1]
bl_u,x,y = bil.bilinear(lat, lon, df_u)
bl_v,x,y = bil.bilinear(lat, lon, df_v)
wind_u = bl_u['t_interp']
wind_v = bl_v['t_interp']
wind_squared = wind_u.pow(2) + wind_u.pow(2)
wind_hourly = wind_squared.pow(1/2).rename('wind_speed')
wind_locations[key] = wind_hourly
# read weather
parameters = {
'air_temp': 't2m',
'soil_temp': 'stl4',
'wind_u': 'u10',
'wind_v': 'v10',
'pressure': 'sp',
'cloud': 'tcc',
'precipitation': 'ilspf',
'rain': 'lsrr',
# 'precipitation_type': 'ptype',
'irradiance': 'ssrd',
'irradiance_cs': 'ssrdc'
}
weather = pd.concat([
read.weather_era5(output_path, year, hour, grid, 'weather', parameter)
for parameter in parameters.values()
],
keys=parameters.keys(),
names=['parameter', 'latitude', 'longitude'],
axis=1)
print(weather)
weather.to_pickle("/home/malcolm/uclan/output/correlation/weather/test.pickle")
# map population to the weather grid
population_filename = os.path.join(output_path, "population")
if os.path.isfile(population_filename):
s = pd.read_pickle(population_filename)
print("mapped population read from file")
else:
# read ERA5
input_path = "/home/malcolm/uclan/input/"
interim_path = "/home/malcolm/uclan/interim/"
year = '2018'
download.weather_era5(input_path, year, 1, 'I', 'pv', [
'surface_solar_radiation_downwards',
'surface_solar_radiation_downward_clear_sky'
], [
60,
-8,
50,
2,
])
df_ir = read.weather_era5(input_path, year, 1, 'I', 'pv', 'ssrd')
df_ir, x, y = bil.bilinear(lat, lon, df_ir)
era5_ir = df_ir['t_interp']
df_cs = read.weather_era5(input_path, year, 1, 'I', 'pv', 'ssrdc')
df_cs, x, y = bil.bilinear(lat, lon, df_cs)
era5_cs = df_cs['t_interp']
# convert from J/m2 to wh/m2
era5_ir = era5_ir * 0.000277778
era5_cs = era5_cs * 0.000277778
# print(era5_ir)
# print(era5_cs)
# read midas
midas_file = "/home/malcolm/uclan/data/midas/midas-open_uk-radiation-obs_dv-201908_cornwall_01395_camborne_qcv-1_2018.csv"
pv_hourly = read_midas_irradiance(
midas_file, ['glbl_irad_amt', 'difu_irad_amt', 'direct_irad'])