def retrieve_microwindow_averages(self, datetime):
'''
Calculates microwindow average features for the provided datetime
datetime: Pandas datetime object notation (can be a string)
Example: "2009-02-04 12:00:00"
Returns dict[CentralWaveNumber] -> average Brightess Temperature for microwindow
'''
BT_features = {}
for mw in self.microwindows:
slice_start = mw[0] - mw[1]
slice_end = mw[0] + mw[1]
truncated = self.data.loc[(self.data['wnum1'] >= slice_start)
& (self.data['wnum1'] <= slice_end) &
(self.data['time_offset'] == datetime)]
# small_frame = helpers.read_wavenumber_slice(self.data, (slice_start, slice_end))
# Convert radiance W to mW
truncated.mean_rad /= 1000
averaged = truncated['mean_rad'].mean()
BT_features[mw[0]] = helpers.brightness_temp(averaged, mw[0])
return BT_features
def avg_brightness_temp(df):
'''
Calculates the average brightness temperature at 10um wavelength,
averaged over each 7 minute measurement period
Adds the additional values as a new column titled 'avg_brightness_temp'
Also classifies period as sunny or not, in new column titled 'sunny'
'''
temp_df = df
temp_df['avg_brightness_temp'] = temp_df.apply(lambda row: helpers.brightness_temp(row.mean_rad, row.wnum1), axis=1)
temp_df['sunny'] = temp_df.apply(lambda row: row.avg_brightness_temp > 225, axis=1)
return temp_df