def result_to_csv(self):
column_header = ['Class', 'Gauge #']
result_matrix = []
result_matrix.append(self.gauge_class_array)
result_matrix.append(self.gauge_number_array)
for percent in self.exceedance_percent:
for percentille in self.percentilles:
column_header.append('WIN_Tim_{}_{}'.format(
percent, percentille))
column_header.append('WIN_Dur_{}_{}'.format(
percent, percentille))
column_header.append('WIN_Fre_{}_{}'.format(
percent, percentille))
result_matrix.append(self.timing[percent][percentille])
result_matrix.append(self.duration[percent][percentille])
result_matrix.append(self.freq[percent][percentille])
result_matrix = sort_matrix(result_matrix, 0)
result_matrix = insert_column_header(result_matrix, column_header)
np.savetxt("post_processedFiles/winter_highflow_POR_result_matrix.csv",
result_matrix,
delimiter=",",
fmt="%s")
def result_to_csv(self):
column_header = ['Class', 'Gauge', 'SP_Tim_10', 'SP_Dur_10', 'SP_Mag_10', 'SP_ROC_10', 'SP_Tim_50', 'SP_Dur_50', 'SP_Mag_50', 'SP_ROC_50', 'SP_Tim_90', 'SP_Dur_90', 'SP_Mag_90', 'SP_ROC_90']
result_matrix = []
result_matrix.append(self.gauge_class_array)
result_matrix.append(self.gauge_number_array)
for percent in self.percentilles:
result_matrix.append(self.spring_timings[percent])
result_matrix.append(self.spring_durations[percent])
result_matrix.append(self.spring_magnitudes[percent])
result_matrix.append(self.spring_rocs[percent])
result_matrix = sort_matrix(result_matrix, 0)
result_matrix = insert_column_header(result_matrix, column_header)
np.savetxt("post_processedFiles/spring_transition_result_matrix.csv", result_matrix, delimiter=",", fmt="%s")
if self.plot:
smart_plot(result_matrix)
"""nonP plots"""
SP_Tim = []
SP_Mag = []
SP_Dur = []
SP_ROC = []
for class_id in range(1,10):
SP_Tim.append(self.metrics['SP_Tim'][class_id])
SP_Mag.append(self.metrics['SP_Mag'][class_id])
SP_Dur.append(self.metrics['SP_Dur'][class_id])
SP_ROC.append(self.metrics['SP_ROC'][class_id])
combined = {'SP_Tim': SP_Tim, 'SP_Mag': SP_Mag, 'SP_Dur': SP_Dur, 'SP_ROC': SP_ROC}
if self.plot:
nonP_box_plot(combined)
def result_to_csv(self):
column_header = [
'Class', 'Gauge', 'SU_Tim_10', 'SU_BFL_Mag_10_10',
'SU_BFL_Mag_50_10', 'SU_BFL_Dur_Flush_10', 'SU_BFL_Dur_Wet_10',
'SU_BFL_NoFlow_10', 'SU_Tim_50', 'SU_BFL_Mag_10_50',
'SU_BFL_Mag_50_50', 'SU_BFL_Dur_Flush_50', 'SU_BFL_Dur_Wet_50',
'SU_BFL_NoFlow_50', 'SU_Tim_90', 'SU_BFL_Mag_10_90',
'SU_BFL_Mag_50_90', 'SU_BFL_Dur_Flush_90', 'SU_BFL_Dur_Wet_90',
'SU_BFL_NoFlow_90'
]
result_matrix = []
result_matrix.append(self.gauge_class_array)
result_matrix.append(self.gauge_number_array)
for percentile in self.percentilles:
result_matrix.append(self.summer_timings[percentile])
result_matrix.append(self.summer_10_magnitudes[percentile])
result_matrix.append(self.summer_50_magnitudes[percentile])
result_matrix.append(self.summer_flush_durations[percentile])
result_matrix.append(self.summer_wet_durations[percentile])
result_matrix.append(self.summer_no_flow_counts[percentile])
result_matrix = sort_matrix(result_matrix, 0)
result_matrix = insert_column_header(result_matrix, column_header)
np.savetxt("post_processedFiles/summer_baseflow_result_matrix.csv",
result_matrix,
delimiter=",",
fmt="%s")
if self.plot:
smart_plot(result_matrix)
"""nonP plots"""
SU_BFL_Tim = []
SU_BFL_Mag_10 = []
SU_BFL_Mag_50 = []
SU_BFL_Dur_Fl = []
SU_BFL_Dur_Wet = []
SU_BFL_No_Flow = []
for class_id in range(1, 10):
SU_BFL_Tim.append(self.metrics['SU_BFL_Tim'][class_id])
SU_BFL_Mag_10.append(self.metrics['SU_BFL_Mag_10'][class_id])
SU_BFL_Mag_50.append(self.metrics['SU_BFL_Mag_50'][class_id])
SU_BFL_Dur_Fl.append(self.metrics['SU_BFL_Dur_Fl'][class_id])
SU_BFL_Dur_Wet.append(self.metrics['SU_BFL_Dur_Wet'][class_id])
SU_BFL_No_Flow.append(self.metrics['SU_BFL_No_Flow'][class_id])
combined = {
'SU_BFL_Tim': SU_BFL_Tim,
'SU_BFL_Mag_10': SU_BFL_Mag_10,
'SU_BFL_Mag_50': SU_BFL_Mag_50,
'SU_BFL_Dur_Fl, ': SU_BFL_Dur_Fl,
'SU_BFL_Dur_Wet': SU_BFL_Dur_Wet,
'SU_BFL_No_Flow': SU_BFL_No_Flow
}
if self.plot:
nonP_box_plot(combined)
def result_to_csv(self):
column_header = [
'Class', 'Gauge', 'FAFL_Tim_10%', 'FAFL_Mag_10%', 'FAFL_Dur_10%',
'FA_Tim_Wet_10%', 'FAFL_Tim_50%', 'FAFL_Mag_50%', 'FAFL_Dur_50%',
'FA_Tim_Wet_50%', 'FAFL_Tim_90%', 'FAFL_Mag_90%', 'FAFL_Dur_90%',
'FA_Tim_Wet_90%'
]
result_matrix = []
result_matrix.append(self.gauge_class_array)
result_matrix.append(self.gauge_number_array)
for percent in self.percentilles:
result_matrix.append(self.fall_timings[percent])
result_matrix.append(self.fall_magnitudes[percent])
result_matrix.append(self.fall_durations[percent])
result_matrix.append(self.fall_wet_timings[percent])
result_matrix = sort_matrix(result_matrix, 0)
result_matrix = insert_column_header(result_matrix, column_header)
np.savetxt("post_processedFiles/fall_flush_result_matrix.csv",
result_matrix,
delimiter=",",
fmt="%s")
if self.plot:
smart_plot(result_matrix)
"""nonP plots"""
FAFL_Tim = []
FAFL_Mag = []
FAFL_Tim_Wet = []
FAFL_Dur = []
for class_id in range(1, 10):
FAFL_Tim.append(self.metrics['FAFL_Tim'][class_id])
FAFL_Mag.append(self.metrics['FAFL_Mag'][class_id])
FAFL_Tim_Wet.append(self.metrics['FAFL_Tim_Wet'][class_id])
FAFL_Dur.append(self.metrics['FAFL_Dur'][class_id])
combined = {
'FAFL_Tim': FAFL_Tim,
'FAFL_Mag': FAFL_Mag,
'FAFL_Tim_Wet': FAFL_Tim_Wet,
'FAFL_Dur': FAFL_Dur
}
if self.plot:
nonP_box_plot(combined)
def result_to_csv(self):
column_header = [
'Class', 'Gauge', 'Avg_10%', 'Std_10%', 'CV_10%', 'Avg_50%',
'Std_50%', 'CV_50%', 'Avg_90%', 'Std_90%', 'CV_90%'
]
result_matrix = []
result_matrix.append(self.gauge_class_array)
result_matrix.append(self.gauge_number_array)
for percent in self.percentilles:
result_matrix.append(self.average_annual_flows[percent])
result_matrix.append(self.standard_deviations[percent])
result_matrix.append(self.coefficient_variations[percent])
result_matrix = sort_matrix(result_matrix, 0)
result_matrix = insert_column_header(result_matrix, column_header)
np.savetxt("post_processedFiles/all_year_result_matrix.csv",
result_matrix,
delimiter=",",
fmt="%s")
if self.plot:
smart_plot(result_matrix)
"""nonP plots"""
Avg = []
Std = []
CV = []
for class_id in range(1, 10):
Avg.append(self.metrics['Avg'][class_id])
Std.append(self.metrics['Std'][class_id])
CV.append(self.metrics['CV'][class_id])
combined = {'Avg': Avg, 'Std': Std, 'CV': CV}
if self.plot:
nonP_box_plot(combined)
def create_result_csv(self):
self.all_year()
self.start_of_summer()
self.fall_flush_timings_durations()
self.summer_baseflow_durations_magnitude()
self.winter_highflow_annual()
self.spring_transition_timing_magnitude()
self.spring_transition_duration()
self.spring_transition_roc()
self.fall_winter_baseflow()
"""Convert offset dates to non-offset dates"""
spring_timings = []
summer_timings = []
fall_timings = []
fall_wet_timings = []
for index, year in enumerate(self.year_ranges):
julian_start_date = datetime.strptime("{}/{}".format(self.start_date, year), "%m/%d/%Y").timetuple().tm_yday
spring_timings.append(remove_offset_from_julian_date(self.spring_timings[index], julian_start_date))
summer_timings.append(remove_offset_from_julian_date(self.summer_timings[index], julian_start_date))
fall_timings.append(remove_offset_from_julian_date(self.fall_timings[index], julian_start_date))
fall_wet_timings.append(remove_offset_from_julian_date(self.fall_wet_timings[index], julian_start_date))
winter_timings = {}
for percent in self.exceedance_percent:
winter_timings[percent] = []
for index, year in enumerate(self.year_ranges):
julian_start_date = datetime.strptime("{}/{}".format(self.start_date, year), "%m/%d/%Y").timetuple().tm_yday
winter_timings[percent].append(remove_offset_from_julian_date(self.winter_timings[percent][index], julian_start_date))
low_end = general_params['annual_result_low_Percentille_filter']
high_end = general_params['annual_result_high_Percentille_filter']
"""Filter data only to contain from low_end to high_end"""
self.average_annual_flows = [np.nan if ele < np.nanpercentile(self.average_annual_flows, low_end) or ele > np.nanpercentile(self.average_annual_flows, high_end) else ele for index, ele in enumerate(self.average_annual_flows)]
self.standard_deviations = [np.nan if ele < np.nanpercentile(self.standard_deviations, low_end) or ele > np.nanpercentile(self.standard_deviations, high_end) else ele for index, ele in enumerate(self.standard_deviations)]
self.coefficient_variations = [np.nan if ele < np.nanpercentile(self.coefficient_variations, low_end) or ele > np.nanpercentile(self.coefficient_variations, high_end) else ele for index, ele in enumerate(self.coefficient_variations)]
spring_timings = [np.nan if ele < np.nanpercentile(spring_timings, low_end) or ele > np.nanpercentile(spring_timings, high_end) else ele for index, ele in enumerate(spring_timings)]
self.spring_magnitudes = [np.nan if ele < np.nanpercentile(self.spring_magnitudes, low_end) or ele > np.nanpercentile(self.spring_magnitudes, high_end) else ele for index, ele in enumerate(self.spring_magnitudes)]
self.spring_durations = [np.nan if ele < np.nanpercentile(self.spring_durations, low_end) or ele > np.nanpercentile(self.spring_durations, high_end) else ele for index, ele in enumerate(self.spring_durations)]
self.spring_rocs = [np.nan if ele < np.nanpercentile(self.spring_rocs, low_end) or ele > np.nanpercentile(self.spring_rocs, high_end) else ele for index, ele in enumerate(self.spring_rocs)]
summer_timings = [np.nan if ele < np.nanpercentile(summer_timings, low_end) or ele > np.nanpercentile(summer_timings, high_end) else ele for index, ele in enumerate(summer_timings)]
self.summer_10_magnitudes = [np.nan if ele < np.nanpercentile(self.summer_10_magnitudes, low_end) or ele > np.nanpercentile(self.summer_10_magnitudes, high_end) else ele for index, ele in enumerate(self.summer_10_magnitudes)]
self.summer_50_magnitudes = [np.nan if ele < np.nanpercentile(self.summer_50_magnitudes, low_end) or ele > np.nanpercentile(self.summer_50_magnitudes, high_end) else ele for index, ele in enumerate(self.summer_50_magnitudes)]
self.summer_flush_durations = [np.nan if ele < np.nanpercentile(self.summer_flush_durations, low_end) or ele > np.nanpercentile(self.summer_flush_durations, high_end) else ele for index, ele in enumerate(self.summer_flush_durations)]
self.summer_wet_durations = [np.nan if ele < np.nanpercentile(self.summer_wet_durations, low_end) or ele > np.nanpercentile(self.summer_wet_durations, high_end) else ele for index, ele in enumerate(self.summer_wet_durations)]
self.summer_no_flow_counts = [np.nan if ele < np.nanpercentile(self.summer_no_flow_counts, low_end) or ele > np.nanpercentile(self.summer_no_flow_counts, high_end) else ele for index, ele in enumerate(self.summer_no_flow_counts)]
fall_timings = [np.nan if ele < np.nanpercentile(fall_timings, low_end) or ele > np.nanpercentile(fall_timings, high_end) else ele for index, ele in enumerate(fall_timings)]
self.fall_magnitudes = [np.nan if ele < np.nanpercentile(self.fall_magnitudes, low_end) or ele > np.nanpercentile(self.fall_magnitudes, high_end) else ele for index, ele in enumerate(self.fall_magnitudes)]
fall_wet_timings = [np.nan if ele < np.nanpercentile(fall_wet_timings, low_end) or ele > np.nanpercentile(fall_wet_timings, high_end) else ele for index, ele in enumerate(fall_wet_timings)]
self.fall_durations = [np.nan if ele < np.nanpercentile(self.fall_durations, low_end) or ele > np.nanpercentile(self.fall_durations, high_end) else ele for index, ele in enumerate(self.fall_durations)]
self.wet_baseflows = [np.nan if ele < np.nanpercentile(self.wet_baseflows, low_end) or ele > np.nanpercentile(self.wet_baseflows, high_end) else ele for index, ele in enumerate(self.wet_baseflows)]
for percent in self.exceedance_percent:
winter_timings[percent] = [np.nan if ele < np.nanpercentile(winter_timings[percent], low_end) or ele > np.nanpercentile(winter_timings[percent], high_end) else ele for index, ele in enumerate(winter_timings[percent])]
self.winter_durations[percent] = [np.nan if ele < np.nanpercentile(self.winter_durations[percent], low_end) or ele > np.nanpercentile(self.winter_durations[percent], high_end) else ele for index, ele in enumerate(self.winter_durations[percent])]
self.winter_frequencys[percent] = [np.nan if ele < np.nanpercentile(self.winter_frequencys[percent], low_end) or ele > np.nanpercentile(self.winter_frequencys[percent], high_end) else ele for index, ele in enumerate(self.winter_frequencys[percent])]
self.winter_magnitudes[percent] = [np.nan if ele < np.nanpercentile(self.winter_magnitudes[percent], low_end) or ele > np.nanpercentile(self.winter_magnitudes[percent], high_end) else ele for index, ele in enumerate(self.winter_magnitudes[percent])]
"""result to CSV"""
result_matrix = []
result_matrix.append(self.year_ranges)
result_matrix.append(self.average_annual_flows)
result_matrix.append(self.standard_deviations)
result_matrix.append(self.coefficient_variations)
result_matrix.append(spring_timings)
result_matrix.append(self.spring_magnitudes)
result_matrix.append(self.spring_durations)
result_matrix.append(self.spring_rocs)
result_matrix.append(summer_timings)
result_matrix.append(self.summer_10_magnitudes)
result_matrix.append(self.summer_50_magnitudes)
result_matrix.append(self.summer_flush_durations)
result_matrix.append(self.summer_wet_durations)
result_matrix.append(self.summer_no_flow_counts)
result_matrix.append(fall_timings)
result_matrix.append(self.fall_magnitudes)
result_matrix.append(fall_wet_timings)
result_matrix.append(self.fall_durations)
result_matrix.append(self.wet_baseflows)
for percent in self.exceedance_percent:
result_matrix.append(winter_timings[percent])
result_matrix.append(self.winter_durations[percent])
result_matrix.append(self.winter_frequencys[percent])
result_matrix.append(self.winter_magnitudes[percent])
column_header = ['Year', 'Avg', 'Std', 'CV', 'SP_Tim', 'SP_Mag', 'SP_Dur', 'SP_ROC', 'SU_BFL_Tim', 'SU_BFL_Mag_10', 'SU_BFL_Mag_50', 'SU_BFL_Dur_Fl', 'SU_BFL_Dur_Wet', 'SU_BFL_No_Flow', 'FAFL_Tim', 'FAFL_Mag', 'FAFL_Tim_Wet', 'FAFL_Dur', 'Wet_BFL_Mag', 'WIN_Tim_2', 'WIN_Dur_2', 'WIN_Fre_2', 'WIN_Mag_2', 'WIN_Tim_5', 'WIN_Dur_5', 'WIN_Fre_5', 'WIN_Mag_5', 'WIN_Tim_10', 'WIN_Dur_10', 'WIN_Fre_10', 'WIN_Mag_10', 'WIN_Tim_20', 'WIN_Dur_20', 'WIN_Fre_20', 'WIN_Mag_20', 'WIN_Tim_50', 'WIN_Dur_50', 'WIN_Fre_50', 'WIN_Mag_50']
# column_header = ['Year', 'Avg', 'Std', 'CV', 'SP_Tim', 'SP_Mag', 'SP_Dur', 'SP_ROC', 'SU_Tim', 'SU_Mag_10', 'SU_Mag_50', 'SU_Dur_Fl', 'SU_Dur_Wet', 'SU_No_Flow', 'FA_Tim', 'FA_Mag', 'FA_Tim_Wet', 'FA_Dur', 'Wet_BFL_Mag', 'Tim_2', 'Dur_2', 'Fre_2', 'Mag_2', 'Tim_5', 'Dur_5', 'Fre_5', 'Mag_5','Tim_10', 'Dur_10', 'Fre_10', 'Mag_10', 'Tim_20', 'Dur_20', 'Fre_20', 'Mag_20', 'Tim_50', 'Dur_50', 'Fre_50', 'Mag_50']
new_result_matrix = []
for index, row in enumerate(result_matrix):
new_result_matrix.append(list(result_matrix[index]))
if len(new_result_matrix) == len(column_header):
new_result_matrix = insert_column_header(new_result_matrix, column_header)
else:
print('Column header does not have the same dimension as result matrix')
np.savetxt("post_processedFiles/{}_annual_result_matrix.csv".format(
int(self.gauge_number)), new_result_matrix, delimiter=",", fmt="%s")
def create_result_csv(self):
self.all_year()
self.start_of_summer()
self.fall_flush_timings_durations()
self.summer_baseflow_durations_magnitude()
self.winter_highflow_annual()
self.spring_transition_timing_magnitude()
self.spring_transition_duration()
self.spring_transition_roc()
self.fall_winter_baseflow()
"""Convert offset dates to non-offset dates"""
spring_timings_julian = []
summer_timings_julian = []
fall_timings_julian = []
fall_wet_timings_julian = []
for index, year in enumerate(self.year_ranges):
julian_start_date = datetime.strptime(
"{}/{}".format(self.start_date, year),
"%m/%d/%Y").timetuple().tm_yday
spring_timings_julian.append(
remove_offset_from_julian_date(self.spring_timings[index],
julian_start_date))
summer_timings_julian.append(
remove_offset_from_julian_date(self.summer_timings[index],
julian_start_date))
fall_timings_julian.append(
remove_offset_from_julian_date(self.fall_timings[index],
julian_start_date))
fall_wet_timings_julian.append(
remove_offset_from_julian_date(self.fall_wet_timings[index],
julian_start_date))
low_end = general_params['annual_result_low_Percentille_filter']
high_end = general_params['annual_result_high_Percentille_filter']
"""Filter data only to contain from low_end to high_end"""
self.average_annual_flows = [
np.nan
if ele < np.nanpercentile(self.average_annual_flows, low_end)
or ele > np.nanpercentile(self.average_annual_flows, high_end) else
ele for index, ele in enumerate(self.average_annual_flows)
]
self.standard_deviations = [
np.nan if ele < np.nanpercentile(self.standard_deviations, low_end)
or ele > np.nanpercentile(self.standard_deviations, high_end) else
ele for index, ele in enumerate(self.standard_deviations)
]
self.coefficient_variations = [
np.nan
if ele < np.nanpercentile(self.coefficient_variations, low_end)
or ele > np.nanpercentile(self.coefficient_variations, high_end)
else ele for index, ele in enumerate(self.coefficient_variations)
]
spring_timings = [
np.nan if ele < np.nanpercentile(self.spring_timings, low_end)
or ele > np.nanpercentile(self.spring_timings, high_end) else ele
for index, ele in enumerate(self.spring_timings)
]
spring_timings_julian = [
np.nan if ele < np.nanpercentile(spring_timings_julian, low_end)
or ele > np.nanpercentile(spring_timings_julian, high_end) else ele
for index, ele in enumerate(spring_timings_julian)
]
self.spring_magnitudes = [
np.nan if ele < np.nanpercentile(self.spring_magnitudes, low_end)
or ele > np.nanpercentile(self.spring_magnitudes, high_end) else
ele for index, ele in enumerate(self.spring_magnitudes)
]
self.spring_durations = [
np.nan if ele < np.nanpercentile(self.spring_durations, low_end)
or ele > np.nanpercentile(self.spring_durations, high_end) else ele
for index, ele in enumerate(self.spring_durations)
]
self.spring_rocs = [
np.nan if ele < np.nanpercentile(self.spring_rocs, low_end)
or ele > np.nanpercentile(self.spring_rocs, high_end) else ele
for index, ele in enumerate(self.spring_rocs)
]
summer_timings = [
np.nan if ele < np.nanpercentile(self.summer_timings, low_end)
or ele > np.nanpercentile(self.summer_timings, high_end) else ele
for index, ele in enumerate(self.summer_timings)
]
summer_timings_julian = [
np.nan if ele < np.nanpercentile(summer_timings_julian, low_end)
or ele > np.nanpercentile(summer_timings_julian, high_end) else ele
for index, ele in enumerate(summer_timings_julian)
]
self.summer_90_magnitudes = [
np.nan
if ele < np.nanpercentile(self.summer_90_magnitudes, low_end)
or ele > np.nanpercentile(self.summer_90_magnitudes, high_end) else
ele for index, ele in enumerate(self.summer_90_magnitudes)
]
self.summer_50_magnitudes = [
np.nan
if ele < np.nanpercentile(self.summer_50_magnitudes, low_end)
or ele > np.nanpercentile(self.summer_50_magnitudes, high_end) else
ele for index, ele in enumerate(self.summer_50_magnitudes)
]
self.summer_flush_durations = [
np.nan
if ele < np.nanpercentile(self.summer_flush_durations, low_end)
or ele > np.nanpercentile(self.summer_flush_durations, high_end)
else ele for index, ele in enumerate(self.summer_flush_durations)
]
self.summer_wet_durations = [
np.nan
if ele < np.nanpercentile(self.summer_wet_durations, low_end)
or ele > np.nanpercentile(self.summer_wet_durations, high_end) else
ele for index, ele in enumerate(self.summer_wet_durations)
]
self.summer_no_flow_counts = [
np.nan
if ele < np.nanpercentile(self.summer_no_flow_counts, low_end)
or ele > np.nanpercentile(self.summer_no_flow_counts, high_end)
else ele for index, ele in enumerate(self.summer_no_flow_counts)
]
fall_timings = [
np.nan if ele < np.nanpercentile(self.fall_timings, low_end)
or ele > np.nanpercentile(self.fall_timings, high_end) else ele
for index, ele in enumerate(self.fall_timings)
]
fall_timings_julian = [
np.nan if ele < np.nanpercentile(fall_timings_julian, low_end)
or ele > np.nanpercentile(fall_timings_julian, high_end) else ele
for index, ele in enumerate(fall_timings_julian)
]
self.fall_magnitudes = [
np.nan if ele < np.nanpercentile(self.fall_magnitudes, low_end)
or ele > np.nanpercentile(self.fall_magnitudes, high_end) else ele
for index, ele in enumerate(self.fall_magnitudes)
]
fall_wet_timings = [
np.nan if ele < np.nanpercentile(self.fall_wet_timings, low_end)
or ele > np.nanpercentile(self.fall_wet_timings, high_end) else ele
for index, ele in enumerate(self.fall_wet_timings)
]
fall_wet_timings_julian = [
np.nan if ele < np.nanpercentile(fall_wet_timings_julian, low_end)
or ele > np.nanpercentile(fall_wet_timings_julian, high_end) else
ele for index, ele in enumerate(fall_wet_timings_julian)
]
self.fall_durations = [
np.nan if ele < np.nanpercentile(self.fall_durations, low_end)
or ele > np.nanpercentile(self.fall_durations, high_end) else ele
for index, ele in enumerate(self.fall_durations)
]
self.wet_baseflows_10 = [
np.nan if ele < np.nanpercentile(self.wet_baseflows_10, low_end)
or ele > np.nanpercentile(self.wet_baseflows_10, high_end) else ele
for index, ele in enumerate(self.wet_baseflows_10)
]
self.wet_baseflows_50 = [
np.nan if ele < np.nanpercentile(self.wet_baseflows_50, low_end)
or ele > np.nanpercentile(self.wet_baseflows_50, high_end) else ele
for index, ele in enumerate(self.wet_baseflows_50)
]
self.wet_bfl_durs = [
np.nan if ele < np.nanpercentile(self.wet_bfl_durs, low_end)
or ele > np.nanpercentile(self.wet_bfl_durs, high_end) else ele
for index, ele in enumerate(self.wet_bfl_durs)
]
all_exceedances = [2, 5, 10, 20, 50, 12, 15, 110, 120]
for percent in all_exceedances:
# self.winter_timings[percent] = [np.nan if ele < np.nanpercentile(self.winter_timings[percent], low_end) or ele > np.nanpercentile(
# self.winter_timings[percent], high_end) else ele for index, ele in enumerate(self.winter_timings[percent])]
self.winter_durations[percent] = [
np.nan if
ele < np.nanpercentile(self.winter_durations[percent], low_end)
or ele > np.nanpercentile(self.winter_durations[percent],
high_end) else ele
for index, ele in enumerate(self.winter_durations[percent])
]
self.winter_frequencys[percent] = [
np.nan if ele < np.nanpercentile(
self.winter_frequencys[percent], low_end)
or ele > np.nanpercentile(self.winter_frequencys[percent],
high_end) else ele
for index, ele in enumerate(self.winter_frequencys[percent])
]
self.winter_magnitudes[percent] = [
np.nan if ele < np.nanpercentile(
self.winter_magnitudes[percent], low_end)
or ele > np.nanpercentile(self.winter_magnitudes[percent],
high_end) else ele
for index, ele in enumerate(self.winter_magnitudes[percent])
]
"""result to CSV"""
result_matrix = []
self.year_ranges = [year + 1 for year in self.year_ranges]
result_matrix.append(self.year_ranges)
result_matrix.append(self.average_annual_flows)
result_matrix.append(self.standard_deviations)
result_matrix.append(self.coefficient_variations)
result_matrix.append(spring_timings)
# result_matrix.append(spring_timings_julian)
result_matrix.append(self.spring_magnitudes)
result_matrix.append(self.spring_durations)
result_matrix.append(self.spring_rocs)
result_matrix.append(summer_timings)
# result_matrix.append(summer_timings_julian)
result_matrix.append(self.summer_90_magnitudes)
result_matrix.append(self.summer_50_magnitudes)
# result_matrix.append(self.summer_flush_durations)
result_matrix.append(self.summer_wet_durations)
result_matrix.append(self.summer_no_flow_counts)
result_matrix.append(fall_timings)
# result_matrix.append(fall_timings_julian)
result_matrix.append(self.fall_magnitudes)
result_matrix.append(fall_wet_timings)
# result_matrix.append(fall_wet_timings_julian)
result_matrix.append(self.fall_durations)
result_matrix.append(self.wet_baseflows_10)
result_matrix.append(self.wet_baseflows_50)
result_matrix.append(self.wet_bfl_durs)
# all_exceedances = [2, 5, 10, 20, 50, 12, 15, 110, 120] # only ouput peak flows
all_exceedances = [2, 5, 10, 20, 50]
for percent in all_exceedances:
# result_matrix.append(self.winter_timings[percent])
result_matrix.append(self.winter_durations[percent])
result_matrix.append(self.winter_frequencys[percent])
result_matrix.append(self.winter_magnitudes[percent])
column_header = [
'Year', 'Avg', 'Std', 'CV', 'SP_Tim', 'SP_Mag', 'SP_Dur', 'SP_ROC',
'DS_Tim', 'DS_Mag_90', 'DS_Mag_50', 'DS_Dur_WS', 'DS_No_Flow',
'FA_Tim', 'FA_Mag', 'Wet_Tim', 'FA_Dur', 'Wet_BFL_Mag_10',
'Wet_BFL_Mag_50', 'Wet_BFL_Dur', 'Peak_Dur_2', 'Peak_Fre_2',
'Peak_2', 'Peak_Dur_5', 'Peak_Fre_5', 'Peak_5', 'Peak_Dur_10',
'Peak_Fre_10', 'Peak_10', 'Peak_Dur_20', 'Peak_Fre_20', 'Peak_20',
'Peak_Dur_50', 'Peak_Fre_50', 'Peak_50'
]
# OMG not me again....
# column_header = ['Year', 'Avg', 'Std', 'CV', 'SP_Tim', 'SP_Mag', 'SP_Dur', 'SP_ROC', 'SU_Tim', 'SU_Mag_10', 'SU_Mag_50', 'SU_Dur_Fl', 'SU_Dur_Wet', 'SU_No_Flow', 'FA_Tim', 'FA_Mag', 'FA_Tim_Wet', 'FA_Dur',
# 'Wet_BFL_Mag', 'Tim_2', 'Dur_2', 'Fre_2', 'Mag_2', 'Tim_5', 'Dur_5', 'Fre_5', 'Mag_5', 'Tim_10', 'Dur_10', 'Fre_10', 'Mag_10', 'Tim_20', 'Dur_20', 'Fre_20', 'Mag_20', 'Tim_50', 'Dur_50', 'Fre_50', 'Mag_50']
wateryear_type_matrix = create_wateryear_labels(result_matrix)
np.savetxt("post_processedFiles/Wateryear_Type/{}.csv".format(
int(self.gauge_number)),
wateryear_type_matrix,
delimiter=",",
fmt="%s")
new_result_matrix = []
for index, _ in enumerate(result_matrix):
new_result_matrix.append(list(result_matrix[index]))
if len(new_result_matrix) == len(column_header):
new_result_matrix = insert_column_header(new_result_matrix,
column_header)
else:
print(
'Column header does not have the same dimension as result matrix'
)
np.savetxt(
"post_processedFiles/Class-{}/{}_annual_result_matrix.csv".format(
int(self.class_number), int(self.gauge_number)),
new_result_matrix,
delimiter=",",
fmt="%s")
'''File format for FFC QA data input'''
def result_to_csv(self):
column_header = ['Class', 'Gauge #']
result_matrix = []
result_matrix.append(self.gauge_class_array)
result_matrix.append(self.gauge_number_array)
# only output percentiles 10, 20, 50
for percent in [50, 20, 10]:
for percentille in self.percentilles:
# Exceedance percentiles translated to recurrence intervals for output: exc_50 -> peak_2, exc_20 -> peak_5, exc_10 -> peak_10
if percent == 50:
name = 2
if percent == 20:
name = 5
if percent == 10:
name = 10
# column_header.append('Peak_Tim_{}_{}'.format(name, percentille))
column_header.append('Peak_Dur_{}_{}'.format(
name, percentille))
column_header.append('Peak_Fre_{}_{}'.format(
name, percentille))
column_header.append('Peak{}_{}'.format(name, percentille))
# result_matrix.append(self.timing[percent][percentille])
result_matrix.append(self.duration[percent][percentille])
result_matrix.append(self.freq[percent][percentille])
result_matrix.append(self.mag[percent][percentille])
result_matrix = sort_matrix(result_matrix, 0)
result_matrix = insert_column_header(result_matrix, column_header)
np.savetxt("post_processedFiles/winter_highflow_result_matrix.csv",
result_matrix,
delimiter=",",
fmt="%s")
if self.plot:
smart_plot(result_matrix)
"""nonP plots"""
WIN_Tim_2 = []
WIN_Dur_2 = []
WIN_Fre_2 = []
WIN_Mag_2 = []
WIN_Tim_5 = []
WIN_Dur_5 = []
WIN_Fre_5 = []
WIN_Mag_5 = []
WIN_Tim_10 = []
WIN_Dur_10 = []
WIN_Fre_10 = []
WIN_Mag_10 = []
WIN_Tim_20 = []
WIN_Dur_20 = []
WIN_Fre_20 = []
WIN_Mag_20 = []
WIN_Tim_50 = []
WIN_Dur_50 = []
WIN_Fre_50 = []
WIN_Mag_50 = []
for class_id in range(1, 10):
WIN_Tim_2.append(self.metrics['WIN_Tim_2'][class_id])
WIN_Dur_2.append(self.metrics['WIN_Dur_2'][class_id])
WIN_Fre_2.append(self.metrics['WIN_Fre_2'][class_id])
WIN_Fre_2.append(self.metrics['WIN_Mag_2'][class_id])
WIN_Tim_5.append(self.metrics['WIN_Tim_5'][class_id])
WIN_Dur_5.append(self.metrics['WIN_Dur_5'][class_id])
WIN_Fre_5.append(self.metrics['WIN_Fre_5'][class_id])
WIN_Fre_5.append(self.metrics['WIN_Mag_5'][class_id])
WIN_Tim_10.append(self.metrics['WIN_Tim_10'][class_id])
WIN_Dur_10.append(self.metrics['WIN_Dur_10'][class_id])
WIN_Fre_10.append(self.metrics['WIN_Fre_10'][class_id])
WIN_Fre_10.append(self.metrics['WIN_Mag_10'][class_id])
WIN_Tim_20.append(self.metrics['WIN_Tim_20'][class_id])
WIN_Dur_20.append(self.metrics['WIN_Dur_20'][class_id])
WIN_Fre_20.append(self.metrics['WIN_Fre_20'][class_id])
WIN_Fre_20.append(self.metrics['WIN_Mag_20'][class_id])
WIN_Tim_50.append(self.metrics['WIN_Tim_50'][class_id])
WIN_Dur_50.append(self.metrics['WIN_Dur_50'][class_id])
WIN_Fre_50.append(self.metrics['WIN_Fre_50'][class_id])
WIN_Fre_50.append(self.metrics['WIN_Mag_50'][class_id])
combined = {
'WIN_Tim_2': WIN_Tim_2,
'WIN_Dur_2': WIN_Dur_2,
'WIN_Fre_2': WIN_Fre_2,
'WIN_Mag_2': WIN_Mag_2,
'WIN_Tim_5': WIN_Tim_5,
'WIN_Dur_5': WIN_Dur_5,
'WIN_Fre_5': WIN_Fre_5,
'WIN_Mag_5': WIN_Mag_5,
'WIN_Tim_10': WIN_Tim_10,
'WIN_Dur_10': WIN_Dur_10,
'WIN_Fre_10': WIN_Fre_10,
'WIN_Mag_10': WIN_Mag_10,
'WIN_Tim_20': WIN_Tim_20,
'WIN_Dur_20': WIN_Dur_20,
'WIN_Fre_20': WIN_Fre_20,
'WIN_Mag_20': WIN_Mag_20,
'WIN_Tim_50': WIN_Tim_50,
'WIN_Dur_50': WIN_Dur_50,
'WIN_Fre_50': WIN_Fre_50,
'WIN_Mag_50': WIN_Mag_50
}
if self.plot:
nonP_box_plot(combined)
def create_result_csv(self):
self.all_year()
self.start_of_summer()
self.fall_flush_timings_durations()
self.summer_baseflow_durations_magnitude()
self.winter_highflow_annual()
self.spring_transition_timing_magnitude()
self.spring_transition_duration()
self.spring_transition_roc()
self.fall_winter_baseflow()
"""Convert offset dates to non-offset dates"""
spring_timings = []
summer_timings = []
fall_timings = []
fall_wet_timings = []
for index, year in enumerate(self.year_ranges):
julian_start_date = datetime.strptime(
"{}/{}".format(self.start_date, year),
"%m/%d/%Y").timetuple().tm_yday
spring_timings.append(
remove_offset_from_julian_date(self.spring_timings[index],
julian_start_date))
summer_timings.append(
remove_offset_from_julian_date(self.summer_timings[index],
julian_start_date))
fall_timings.append(
remove_offset_from_julian_date(self.fall_timings[index],
julian_start_date))
fall_wet_timings.append(
remove_offset_from_julian_date(self.fall_wet_timings[index],
julian_start_date))
winter_timings = {}
for percent in self.exceedance_percent:
winter_timings[percent] = []
for index, year in enumerate(self.year_ranges):
julian_start_date = datetime.strptime(
"{}/{}".format(self.start_date, year),
"%m/%d/%Y").timetuple().tm_yday
winter_timings[percent].append(
remove_offset_from_julian_date(
self.winter_timings[percent][index],
julian_start_date))
low_end = general_params['annual_result_low_Percentille_filter']
high_end = general_params['annual_result_high_Percentille_filter']
#sigma = summer_params['sigma']
"""Filter data only to contain from low_end to high_end"""
self.average_annual_flows = [
np.nan
if ele < np.nanpercentile(self.average_annual_flows, low_end)
or ele > np.nanpercentile(self.average_annual_flows, high_end) else
ele for index, ele in enumerate(self.average_annual_flows)
]
self.standard_deviations = [
np.nan if ele < np.nanpercentile(self.standard_deviations, low_end)
or ele > np.nanpercentile(self.standard_deviations, high_end) else
ele for index, ele in enumerate(self.standard_deviations)
]
self.coefficient_variations = [
np.nan
if ele < np.nanpercentile(self.coefficient_variations, low_end)
or ele > np.nanpercentile(self.coefficient_variations, high_end)
else ele for index, ele in enumerate(self.coefficient_variations)
]
spring_timings = [
np.nan if ele < np.nanpercentile(spring_timings, low_end)
or ele > np.nanpercentile(spring_timings, high_end) else ele
for index, ele in enumerate(spring_timings)
]
self.spring_magnitudes = [
np.nan if ele < np.nanpercentile(self.spring_magnitudes, low_end)
or ele > np.nanpercentile(self.spring_magnitudes, high_end) else
ele for index, ele in enumerate(self.spring_magnitudes)
]
self.spring_durations = [
np.nan if ele < np.nanpercentile(self.spring_durations, low_end)
or ele > np.nanpercentile(self.spring_durations, high_end) else ele
for index, ele in enumerate(self.spring_durations)
]
self.spring_rocs = [
np.nan if ele < np.nanpercentile(self.spring_rocs, low_end)
or ele > np.nanpercentile(self.spring_rocs, high_end) else ele
for index, ele in enumerate(self.spring_rocs)
]
summer_timings = [
np.nan if ele < np.nanpercentile(summer_timings, low_end)
or ele > np.nanpercentile(summer_timings, high_end) else ele
for index, ele in enumerate(summer_timings)
]
self.summer_10_magnitudes = [
np.nan
if ele < np.nanpercentile(self.summer_10_magnitudes, low_end)
or ele > np.nanpercentile(self.summer_10_magnitudes, high_end) else
ele for index, ele in enumerate(self.summer_10_magnitudes)
]
self.summer_50_magnitudes = [
np.nan
if ele < np.nanpercentile(self.summer_50_magnitudes, low_end)
or ele > np.nanpercentile(self.summer_50_magnitudes, high_end) else
ele for index, ele in enumerate(self.summer_50_magnitudes)
]
self.summer_flush_durations = [
np.nan
if ele < np.nanpercentile(self.summer_flush_durations, low_end)
or ele > np.nanpercentile(self.summer_flush_durations, high_end)
else ele for index, ele in enumerate(self.summer_flush_durations)
]
self.summer_wet_durations = [
np.nan
if ele < np.nanpercentile(self.summer_wet_durations, low_end)
or ele > np.nanpercentile(self.summer_wet_durations, high_end) else
ele for index, ele in enumerate(self.summer_wet_durations)
]
self.summer_no_flow_counts = [
np.nan
if ele < np.nanpercentile(self.summer_no_flow_counts, low_end)
or ele > np.nanpercentile(self.summer_no_flow_counts, high_end)
else ele for index, ele in enumerate(self.summer_no_flow_counts)
]
fall_timings = [
np.nan if ele < np.nanpercentile(fall_timings, low_end)
or ele > np.nanpercentile(fall_timings, high_end) else ele
for index, ele in enumerate(fall_timings)
]
self.fall_magnitudes = [
np.nan if ele < np.nanpercentile(self.fall_magnitudes, low_end)
or ele > np.nanpercentile(self.fall_magnitudes, high_end) else ele
for index, ele in enumerate(self.fall_magnitudes)
]
fall_wet_timings = [
np.nan if ele < np.nanpercentile(fall_wet_timings, low_end)
or ele > np.nanpercentile(fall_wet_timings, high_end) else ele
for index, ele in enumerate(fall_wet_timings)
]
self.fall_durations = [
np.nan if ele < np.nanpercentile(self.fall_durations, low_end)
or ele > np.nanpercentile(self.fall_durations, high_end) else ele
for index, ele in enumerate(self.fall_durations)
]
self.wet_baseflows = [
np.nan if ele < np.nanpercentile(self.wet_baseflows, low_end)
or ele > np.nanpercentile(self.wet_baseflows, high_end) else ele
for index, ele in enumerate(self.wet_baseflows)
]
for percent in self.exceedance_percent:
winter_timings[percent] = [
np.nan
if ele < np.nanpercentile(winter_timings[percent], low_end)
or ele > np.nanpercentile(winter_timings[percent], high_end)
else ele for index, ele in enumerate(winter_timings[percent])
]
self.winter_durations[percent] = [
np.nan if
ele < np.nanpercentile(self.winter_durations[percent], low_end)
or ele > np.nanpercentile(self.winter_durations[percent],
high_end) else ele
for index, ele in enumerate(self.winter_durations[percent])
]
self.winter_frequencys[percent] = [
np.nan if ele < np.nanpercentile(
self.winter_frequencys[percent], low_end)
or ele > np.nanpercentile(self.winter_frequencys[percent],
high_end) else ele
for index, ele in enumerate(self.winter_frequencys[percent])
]
self.winter_magnitudes[percent] = [
np.nan if ele < np.nanpercentile(
self.winter_magnitudes[percent], low_end)
or ele > np.nanpercentile(self.winter_magnitudes[percent],
high_end) else ele
for index, ele in enumerate(self.winter_magnitudes[percent])
]
"""result to CSV"""
result_matrix = []
self.year_ranges = [year + 1 for year in self.year_ranges]
result_matrix.append(self.year_ranges)
result_matrix.append(self.average_annual_flows)
result_matrix.append(self.standard_deviations)
result_matrix.append(self.coefficient_variations)
result_matrix.append(spring_timings)
result_matrix.append(self.spring_magnitudes)
result_matrix.append(self.spring_durations)
result_matrix.append(self.spring_rocs)
result_matrix.append(summer_timings)
result_matrix.append(self.summer_10_magnitudes)
result_matrix.append(self.summer_50_magnitudes)
result_matrix.append(self.summer_flush_durations)
result_matrix.append(self.summer_wet_durations)
result_matrix.append(self.summer_no_flow_counts)
result_matrix.append(fall_timings)
result_matrix.append(self.fall_magnitudes)
result_matrix.append(fall_wet_timings)
result_matrix.append(self.fall_durations)
result_matrix.append(self.wet_baseflows)
for percent in self.exceedance_percent:
result_matrix.append(winter_timings[percent])
result_matrix.append(self.winter_durations[percent])
result_matrix.append(self.winter_frequencys[percent])
result_matrix.append(self.winter_magnitudes[percent])
column_header = [
'Year', 'Avg', 'Std', 'CV', 'SP_Tim', 'SP_Mag', 'SP_Dur', 'SP_ROC',
'DS_Tim', 'DS_Mag_10', 'DS_Mag_50', 'DS_Dur_WSI', 'DS_Dur_WS',
'DS_No_Flow', 'WSI_Tim', 'WSI_Mag', 'Wet_Tim', 'WSI_Dur',
'Wet_BFL_Mag', 'Peak_Tim_2', 'Peak_Dur_2', 'Peak_Fre_2',
'Peak_Mag_2', 'Peak_Tim_5', 'Peak_Dur_5', 'Peak_Fre_5',
'Peak_Mag_5', 'Peak_Tim_10', 'Peak_Dur_10', 'Peak_Fre_10',
'Peak_Mag_10', 'Peak_Tim_20', 'Peak_Dur_20', 'Peak_Fre_20',
'Peak_Mag_20', 'Peak_Tim_50', 'Peak_Dur_50', 'Peak_Fre_50',
'Peak_Mag_50'
]
# OMG not me again....
# column_header = ['Year', 'Avg', 'Std', 'CV', 'SP_Tim', 'SP_Mag', 'SP_Dur', 'SP_ROC', 'SU_Tim', 'SU_Mag_10', 'SU_Mag_50', 'SU_Dur_Fl', 'SU_Dur_Wet', 'SU_No_Flow', 'FA_Tim', 'FA_Mag', 'FA_Tim_Wet', 'FA_Dur',
# 'Wet_BFL_Mag', 'Tim_2', 'Dur_2', 'Fre_2', 'Mag_2', 'Tim_5', 'Dur_5', 'Fre_5', 'Mag_5', 'Tim_10', 'Dur_10', 'Fre_10', 'Mag_10', 'Tim_20', 'Dur_20', 'Fre_20', 'Mag_20', 'Tim_50', 'Dur_50', 'Fre_50', 'Mag_50']
wateryear_type_matrix = create_wateryear_labels(result_matrix)
np.savetxt("post_processedFiles/Wateryear_Type/{}.csv".format(
int(self.gauge_number)),
wateryear_type_matrix,
delimiter=",",
fmt="%s")
new_result_matrix = []
for index, _ in enumerate(result_matrix):
new_result_matrix.append(list(result_matrix[index]))
if len(new_result_matrix) == len(column_header):
new_result_matrix = insert_column_header(new_result_matrix,
column_header)
else:
print(
'Column header does not have the same dimension as result matrix'
)
# remove 50 percentile values from final matrix. Will need to update this code if variables are added or deleted.
if len(new_result_matrix) > 35:
new_result_matrix = new_result_matrix[:-4]
np.savetxt(
"post_processedFiles/{}_sigma{}_annual_result_matrix.csv".format(
int(self.gauge_number), int(sigmaa)),
new_result_matrix,
delimiter=",",
fmt="%s")
#1/29/2019 adding sigma to the name in the two lines above
'''File format for FFC QA data input'''
def result_to_csv(self):
column_header = ['Class', 'Gauge #']
result_matrix = []
result_matrix.append(self.gauge_class_array)
result_matrix.append(self.gauge_number_array)
for percent in self.exceedance_percent:
for percentille in self.percentilles:
column_header.append('WIN_Tim_{}_{}'.format(
percent, percentille))
column_header.append('WIN_Dur_{}_{}'.format(
percent, percentille))
column_header.append('WIN_Fre_{}_{}'.format(
percent, percentille))
column_header.append('WIN_Mag_{}_{}'.format(
percent, percentille))
result_matrix.append(self.timing[percent][percentille])
result_matrix.append(self.duration[percent][percentille])
result_matrix.append(self.freq[percent][percentille])
result_matrix.append(self.mag[percent][percentille])
result_matrix = sort_matrix(result_matrix, 0)
result_matrix = insert_column_header(result_matrix, column_header)
np.savetxt("post_processedFiles/winter_highflow_result_matrix.csv",
result_matrix,
delimiter=",",
fmt="%s")
if self.plot:
smart_plot(result_matrix)
"""nonP plots"""
WIN_Tim_2 = []
WIN_Dur_2 = []
WIN_Fre_2 = []
WIN_Mag_2 = []
WIN_Tim_5 = []
WIN_Dur_5 = []
WIN_Fre_5 = []
WIN_Mag_5 = []
WIN_Tim_10 = []
WIN_Dur_10 = []
WIN_Fre_10 = []
WIN_Mag_10 = []
WIN_Tim_20 = []
WIN_Dur_20 = []
WIN_Fre_20 = []
WIN_Mag_20 = []
WIN_Tim_50 = []
WIN_Dur_50 = []
WIN_Fre_50 = []
WIN_Mag_50 = []
for class_id in range(1, 10):
WIN_Tim_2.append(self.metrics['WIN_Tim_2'][class_id])
WIN_Dur_2.append(self.metrics['WIN_Dur_2'][class_id])
WIN_Fre_2.append(self.metrics['WIN_Fre_2'][class_id])
WIN_Fre_2.append(self.metrics['WIN_Mag_2'][class_id])
WIN_Tim_5.append(self.metrics['WIN_Tim_5'][class_id])
WIN_Dur_5.append(self.metrics['WIN_Dur_5'][class_id])
WIN_Fre_5.append(self.metrics['WIN_Fre_5'][class_id])
WIN_Fre_5.append(self.metrics['WIN_Mag_5'][class_id])
WIN_Tim_10.append(self.metrics['WIN_Tim_10'][class_id])
WIN_Dur_10.append(self.metrics['WIN_Dur_10'][class_id])
WIN_Fre_10.append(self.metrics['WIN_Fre_10'][class_id])
WIN_Fre_10.append(self.metrics['WIN_Mag_10'][class_id])
WIN_Tim_20.append(self.metrics['WIN_Tim_20'][class_id])
WIN_Dur_20.append(self.metrics['WIN_Dur_20'][class_id])
WIN_Fre_20.append(self.metrics['WIN_Fre_20'][class_id])
WIN_Fre_20.append(self.metrics['WIN_Mag_20'][class_id])
WIN_Tim_50.append(self.metrics['WIN_Tim_50'][class_id])
WIN_Dur_50.append(self.metrics['WIN_Dur_50'][class_id])
WIN_Fre_50.append(self.metrics['WIN_Fre_50'][class_id])
WIN_Fre_50.append(self.metrics['WIN_Mag_50'][class_id])
combined = {
'WIN_Tim_2': WIN_Tim_2,
'WIN_Dur_2': WIN_Dur_2,
'WIN_Fre_2': WIN_Fre_2,
'WIN_Mag_2': WIN_Mag_2,
'WIN_Tim_5': WIN_Tim_5,
'WIN_Dur_5': WIN_Dur_5,
'WIN_Fre_5': WIN_Fre_5,
'WIN_Mag_5': WIN_Mag_5,
'WIN_Tim_10': WIN_Tim_10,
'WIN_Dur_10': WIN_Dur_10,
'WIN_Fre_10': WIN_Fre_10,
'WIN_Mag_10': WIN_Mag_10,
'WIN_Tim_20': WIN_Tim_20,
'WIN_Dur_20': WIN_Dur_20,
'WIN_Fre_20': WIN_Fre_20,
'WIN_Mag_20': WIN_Mag_20,
'WIN_Tim_50': WIN_Tim_50,
'WIN_Dur_50': WIN_Dur_50,
'WIN_Fre_50': WIN_Fre_50,
'WIN_Mag_50': WIN_Mag_50
}
if self.plot:
nonP_box_plot(combined)