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 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 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)