lat = coords[0]
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')
population_path = "/home/malcolm/uclan/tools/python/scripts/heat/input/populaiton"
output_path = "/home/malcolm/uclan/output/correlation"
year = 2018
hour = 1
grid = 'I'
# get weather, if not already present
download.weather_era5(output_path, year, hour, grid, 'weather', [
'2m_temperature', 'soil_temperature_level_4', '10m_u_component_of_wind',
'10m_v_component_of_wind', 'surface_pressure', 'total_cloud_cover',
'instantaneous_large_scale_surface_precipitation_fraction',
'large_scale_rain_rate', 'precipitation_type',
'surface_solar_radiation_downwards',
'surface_solar_radiation_downward_clear_sky'
], [
60,
-8,
48,
2,
])
# read weather
parameters = {
'air_temp': 't2m',
'soil_temp': 'stl4',
'wind_u': 'u10',
'wind_v': 'v10',
'pressure': 'sp',
'cloud': 'tcc',
lat = 53.7746
lon = -3.03647
site_location = pvlib.location.Location(lat,
lon,
tz=pytz.timezone('Europe/London'))
# 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)