diff --git a/CAL/CalibrationProcessing/lib/processing/averaging.py b/CAL/CalibrationProcessing/lib/processing/averaging.py
index bf296f73588a0b73aa4e292baa77b4616e3bad4e..c761ec8b9dfc0dd090541c5cbbc9b26c312ce491 100644
--- a/CAL/CalibrationProcessing/lib/processing/averaging.py
+++ b/CAL/CalibrationProcessing/lib/processing/averaging.py
@@ -65,6 +65,17 @@ def average_values_by_sample(data_array, window_size, field_name=None):
return result
+def is_datatable_completely_flagged(data_table: numpy.ndarray):
+ """
+ Checks if the datatable is entirely flagged
+ :param data_table: input data
+ :return: if the data is completely flagged
+ :rtype: bool
+ """
+ flags = numpy.array(data_table)['flag']
+ return numpy.product(flags)
+
+
def _average_datatable_with_function(data_table_in, parameter, expected_array_size, function):
field_names_mean = []
formats_mean = []
@@ -100,8 +111,7 @@ def _average_datatable_with_function(data_table_in, parameter, expected_array_si
fields_to_average = list(data_table_in.dtype.names)
fields_to_average.remove('flag')
- flags = numpy.array(data_table_in)['flag']
- is_flagged = numpy.product(flags) == True
+ is_flagged = is_datatable_completely_flagged(data_table_in)
for field_name in fields_to_average:
datatable_values = numpy.array(data_table_in)
@@ -166,7 +176,6 @@ def average_datatable(data_table_in):
return result
-
def average_datatable_by_time_interval(data_table_in, time_interval):
"""
Average the datatable with a given sample time interval window
@@ -344,7 +353,6 @@ def average_data(input_data_table):
return out_data
-
def _compute_size_of_new_array(array_size, window_size):
"""
Round up the array size given a window size.
@@ -364,7 +372,7 @@ def _compute_size_of_new_array(array_size, window_size):
return new_array_size
-def _weigthed_average_data_per_polarization(input_dataset, output_dataset,
+def _weighted_average_data_per_polarization(input_dataset, output_dataset,
real_weights, imag_weights, polarization):
mask = input_dataset['flag'] == False
try:
@@ -379,6 +387,60 @@ def _weigthed_average_data_per_polarization(input_dataset, output_dataset,
output_dataset['flag'] = True
+def __prepare_input_to_weight_dataset(stations, frequency, beam,
+ input_data_table):
+ """
+ Prepare the input data for the weighted average
+ :param stations: list of stations to iterate on
+ :type stations: (str)
+ :param frequency: frequency ID
+ :type frequency: str
+ :param beam: beam ID
+ :type beam: str
+ :param input_data_table: data to iterate over
+ :return: the average values,
+ the standard deviation of the real part,
+ the standard deviation of the imaginary part
+ :rtype: list(numpy.ndarray, numpy.ndarray, numpy.ndarray)
+ """
+
+ n_stations = len(stations)
+
+ average_per_beam_station = numpy.zeros(n_stations, dtype=HDS_data_sample_type)
+ std_real_per_beam_station = numpy.zeros(n_stations, dtype=HDS_data_sample_type)
+ std_imag_per_beam_station = numpy.zeros(n_stations, dtype=HDS_data_sample_type)
+
+ for i, station in enumerate(stations):
+ data_per_station_frequency_beam = \
+ input_data_table[station][str(frequency)][beam]
+ average_per_beam_station['XX'][i] = data_per_station_frequency_beam['mean']['XX']
+ average_per_beam_station['XY'][i] = data_per_station_frequency_beam['mean']['XY']
+ average_per_beam_station['YX'][i] = data_per_station_frequency_beam['mean']['YX']
+ average_per_beam_station['YY'][i] = data_per_station_frequency_beam['mean']['YY']
+
+ average_per_beam_station['l'][i] = data_per_station_frequency_beam['mean']['l']
+ average_per_beam_station['m'][i] = data_per_station_frequency_beam['mean']['m']
+
+ std_real_per_beam_station['XX'][i] = \
+ data_per_station_frequency_beam['std']['XX_real']
+ std_real_per_beam_station['XY'][i] = \
+ data_per_station_frequency_beam['std']['XY_real']
+ std_real_per_beam_station['YX'][i] = \
+ data_per_station_frequency_beam['std']['YX_real']
+ std_real_per_beam_station['YY'][i] = \
+ data_per_station_frequency_beam['std']['YY_real']
+
+ std_imag_per_beam_station['XX'][i] = \
+ data_per_station_frequency_beam['std']['XX_imag']
+ std_imag_per_beam_station['XY'][i] = \
+ data_per_station_frequency_beam['std']['XY_imag']
+ std_imag_per_beam_station['YX'][i] = \
+ data_per_station_frequency_beam['std']['YX_imag']
+ std_imag_per_beam_station['YY'][i] = \
+ data_per_station_frequency_beam['std']['YY_imag']
+ return average_per_beam_station, std_real_per_beam_station, std_imag_per_beam_station
+
+
def weighted_average_dataset_per_station(dataset, input_data_table):
"""
Perform a weighted average of the dataset using the std deviations in the input data
@@ -389,7 +451,7 @@ def weighted_average_dataset_per_station(dataset, input_data_table):
:return:
"""
stations = dataset.reference_stations
- n_stations = len(stations)
+
frequencies = dataset.frequencies
beams = dataset.beamlets
@@ -403,41 +465,11 @@ def weighted_average_dataset_per_station(dataset, input_data_table):
beam_str = str(beam)
result_per_beam = numpy.zeros(1, dtype=HDS_data_sample_type)
- average_per_beam_station = numpy.zeros(n_stations, dtype=HDS_data_sample_type)
- std_real_per_beam_station = numpy.zeros(n_stations, dtype=HDS_data_sample_type)
- std_imag_per_beam_station = numpy.zeros(n_stations, dtype=HDS_data_sample_type)
-
- for i, station in enumerate(stations):
- data_per_station_frequency_beam = \
- input_data_table[station][str(frequency)][beam_str]
- average_per_beam_station['XX'][i] = data_per_station_frequency_beam['mean']['XX']
- average_per_beam_station['XY'][i] = data_per_station_frequency_beam['mean']['XY']
- average_per_beam_station['YX'][i] = data_per_station_frequency_beam['mean']['YX']
- average_per_beam_station['YY'][i] = data_per_station_frequency_beam['mean']['YY']
-
- average_per_beam_station['l'][i] = data_per_station_frequency_beam['mean']['l']
- average_per_beam_station['m'][i] = data_per_station_frequency_beam['mean']['m']
-
- std_real_per_beam_station['XX'][i] = \
- data_per_station_frequency_beam['std']['XX_real']
- std_real_per_beam_station['XY'][i] = \
- data_per_station_frequency_beam['std']['XY_real']
- std_real_per_beam_station['YX'][i] = \
- data_per_station_frequency_beam['std']['YX_real']
- std_real_per_beam_station['YY'][i] = \
- data_per_station_frequency_beam['std']['YY_real']
-
- std_imag_per_beam_station['XX'][i] = \
- data_per_station_frequency_beam['std']['XX_imag']
- std_imag_per_beam_station['XY'][i] = \
- data_per_station_frequency_beam['std']['XY_imag']
- std_imag_per_beam_station['YX'][i] = \
- data_per_station_frequency_beam['std']['YX_imag']
- std_imag_per_beam_station['YY'][i] = \
- data_per_station_frequency_beam['std']['YY_imag']
+ average_per_beam_station, std_real_per_beam_station, std_imag_per_beam_station = \
+ __prepare_input_to_weight_dataset(stations, frequency, beam_str, input_data_table)
for polarization in ['XX', 'XY', 'YX', 'YY']:
- _weigthed_average_data_per_polarization(average_per_beam_station,
+ _weighted_average_data_per_polarization(average_per_beam_station,
result_per_beam,
std_real_per_beam_station,
std_imag_per_beam_station,