def get_stressors(lat, lon):
"""
Example for looking up stressors at a particular location.
"""
# Note df is a flattened list of lat/lon values that only includes those over land
df = pvcz.get_pvcz_data()
# Point of interest specified by lat/lon coordinates.
lat_poi = float(lat)
lon_poi = float(lon)
# Find the closest location on land to a point of interest
closest_index = pvcz.arg_closest_point(lat_poi, lon_poi, df['lat'],
df['lon'])
first_table_keys = [
'lat', 'lon', 'T_equiv_rack_1p1eV', 'T_equiv_roof_1p1eV',
'specific_humidity_mean', 'T_velocity_rack', 'GHI_mean',
'T_ambient_min', 'T_ambient_max', 'T_ambient_mean', 'KG_zone',
'T_equiv_rack_1p1eV_zone', 'T_equiv_roof_1p1eV_zone',
'specific_humidity_mean_zone', 'pvcz', 'pvcz_labeled',
'ASCE 7-16 MRI 25-Year', 'wind_speed_max'
]
detail_table_keys = [
'T_equiv_rack_0p1eV', 'T_equiv_rack_0p3eV', 'T_equiv_rack_0p5eV',
'T_equiv_rack_0p7eV', 'T_equiv_rack_0p9eV', 'T_equiv_rack_1p1eV',
'T_equiv_rack_1p3eV', 'T_equiv_rack_1p5eV', 'T_equiv_rack_1p7eV',
'T_equiv_rack_1p9eV', 'T_equiv_rack_2p1eV', 'T_equiv_roof_0p1eV',
'T_equiv_roof_0p3eV', 'T_equiv_roof_0p5eV', 'T_equiv_roof_0p7eV',
'T_equiv_roof_0p9eV', 'T_equiv_roof_1p1eV', 'T_equiv_roof_1p3eV',
'T_equiv_roof_1p5eV', 'T_equiv_roof_1p7eV', 'T_equiv_roof_1p9eV',
'T_equiv_roof_2p1eV', 'T_velocity_rack', 'T_velocity_roof',
'wind_speed_rms', 'specific_humidity_rms'
]
# Get the stressor data from this location
# location_df = pd.DataFrame(data={'Parameter': [pvtoolslib.pvcz_legend_str[p] for p in df.keys()],
# 'Value': df.iloc[closest_index]})
location_df = pd.DataFrame(
data={
'Parameter':
[pvtoolslib.pvcz_legend_str[p] for p in first_table_keys],
'Value': df[first_table_keys].iloc[closest_index]
})
for p in [
'T_equiv_rack_1p1eV', 'T_equiv_roof_1p1eV',
'specific_humidity_mean', 'T_velocity_rack', 'GHI_mean',
'T_ambient_min', 'T_ambient_max', 'T_ambient_mean',
'wind_speed_max', 'ASCE 7-16 MRI 25-Year'
]:
location_df.loc[p, 'Value'] = '{:.2f}'.format(location_df['Value'][p])
detail_df = pd.DataFrame(
data={
'Parameter':
[pvtoolslib.pvcz_legend_str[p] for p in detail_table_keys],
'Value': df[detail_table_keys].iloc[closest_index]
})
for p in detail_df.index:
detail_df.loc[p, 'Value'] = '{:.2f}'.format(detail_df['Value'][p])
return html.Div([
dbc.Table.from_dataframe(
location_df,
striped=False,
bordered=True,
hover=True,
index=False,
size='sm',
# style={'font-size':'0.8rem'}
),
html.Details([
html.Summary("More stressors"),
html.Div(
[
dbc.Table.from_dataframe(
detail_df,
striped=False,
bordered=True,
hover=True,
index=False,
size='sm',
# style={'font-size':'0.8rem'}
),
],
style={'marginLeft': 50}),
]),
])
#
# if __name__ == '__main__':
# app.run_server(debug=True)
""" Example for looking up stressors at a particular location. """ import pvcz # Note df is a flattened list of lat/lon values that only includes those over land df = pvcz.get_pvcz_data() # Point of interest specified by lat/lon coordinates. lat_poi = 32 lon_poi = -95.23 # Find the closest location on land to a point of interest closest_index = pvcz.arg_closest_point(lat_poi, lon_poi, df['lat'], df['lon']) # Get the stressor data from this location location_data = df.iloc[closest_index]