def statistics_tables(corrected: pd.DataFrame, simulated: pd.DataFrame, observed: pd.DataFrame) -> pd.DataFrame:
# merge the datasets together
merged_sim_obs = hd.merge_data(sim_df=simulated, obs_df=observed)
merged_cor_obs = hd.merge_data(sim_df=corrected, obs_df=observed)
metrics = ['ME', 'RMSE', 'NRMSE (Mean)', 'MAPE', 'NSE', 'KGE (2009)', 'KGE (2012)']
# Merge Data
table1 = hs.make_table(merged_dataframe=merged_sim_obs, metrics=metrics)
table2 = hs.make_table(merged_dataframe=merged_cor_obs, metrics=metrics)
table2 = table2.rename(index={'Full Time Series': 'Corrected Full Time Series'})
table1 = table1.rename(index={'Full Time Series': 'Original Full Time Series'})
table1 = table1.transpose()
table2 = table2.transpose()
return pd.merge(table1, table2, right_index=True, left_index=True)
def statistics_tables(corrected: pd.DataFrame,
simulated: pd.DataFrame,
observed: pd.DataFrame,
merged_sim_obs: pd.DataFrame = False,
merged_cor_obs: pd.DataFrame = False,
metrics: list = None) -> str:
"""
Makes an html table of various statistical metrics for corrected vs observed data alongside the same metrics for
the simulated vs observed data as a way to see the improvement made by the bias correction. This function uses
hydrostats.data.merge_data on the 3 inputs. If you have already computed these because you are doing a full
comparison of bias correction, you can provide them to save time
Args:
corrected: A dataframe with a datetime index and a single column of streamflow values
simulated: A dataframe with a datetime index and a single column of streamflow values
observed: A dataframe with a datetime index and a single column of streamflow values
merged_sim_obs: (optional) if you have already computed it, hydrostats.data.merge_data(simulated, observed)
merged_cor_obs: (optional) if you have already computed it, hydrostats.data.merge_data(corrected, observed)
metrics: A list of abbreviated statistic names. See the documentation for HydroErr
"""
if corrected is False and simulated is False and observed is False:
if merged_sim_obs is not False and merged_cor_obs is not False:
pass # if you provided the merged dataframes already, we use those
else:
# merge the datasets together
merged_sim_obs = hd.merge_data(sim_df=simulated, obs_df=observed)
merged_cor_obs = hd.merge_data(sim_df=corrected, obs_df=observed)
if metrics is None:
metrics = [
'ME', 'RMSE', 'NRMSE (Mean)', 'MAPE', 'NSE', 'KGE (2009)',
'KGE (2012)'
]
# Merge Data
table1 = hs.make_table(merged_dataframe=merged_sim_obs, metrics=metrics)
table2 = hs.make_table(merged_dataframe=merged_cor_obs, metrics=metrics)
table2 = table2.rename(
index={'Full Time Series': 'Corrected Full Time Series'})
table1 = table1.rename(
index={'Full Time Series': 'Original Full Time Series'})
table1 = table1.transpose()
table2 = table2.transpose()
table_final = pd.merge(table1, table2, right_index=True, left_index=True)
return table_final.to_html()
'datetime': dates_sim,
'simulated volume (BCM)': sim_df.iloc[:, 0].values
})
simData.set_index(['datetime'], inplace=True)
#Merging the Data
merged_df = hd.merge_data(sim_df=simData,
obs_df=obsData,
column_names=('Simulated', 'Observed'))
'''Tables and Plots'''
# Appending the table to the final table
table = hs.make_table(merged_df,
metrics=[
'ME', 'MAE', 'MAPE', 'RMSE', 'NRMSE (Mean)',
'NSE', 'KGE (2009)', 'KGE (2012)', 'R (Pearson)',
'R (Spearman)', 'r2'
],
location=name,
remove_neg=False,
remove_zero=False)
all_station_table = all_station_table.append(table)
#Making plots for all the stations
sim_array = merged_df.iloc[:, 0].values
obs_array = merged_df.iloc[:, 1].values
hv.plot(merged_df,
legend=('Simulated', 'Observed'),
grid=True,
title='Hydrograph for ' + name + '\n River: ' + rio + '. COMID: ' +
def make_table_ajax(request):
get_data = request.GET
global simulated_df
global observed_df
global corrected_df
try:
# Indexing the metrics to get the abbreviations
selected_metric_abbr = get_data.getlist("metrics[]", None)
# print(selected_metric_abbr)
# Retrive additional parameters if they exist
# Retrieving the extra optional parameters
extra_param_dict = {}
if request.GET.get('mase_m', None) is not None:
mase_m = float(request.GET.get('mase_m', None))
extra_param_dict['mase_m'] = mase_m
else:
mase_m = 1
extra_param_dict['mase_m'] = mase_m
if request.GET.get('dmod_j', None) is not None:
dmod_j = float(request.GET.get('dmod_j', None))
extra_param_dict['dmod_j'] = dmod_j
else:
dmod_j = 1
extra_param_dict['dmod_j'] = dmod_j
if request.GET.get('nse_mod_j', None) is not None:
nse_mod_j = float(request.GET.get('nse_mod_j', None))
extra_param_dict['nse_mod_j'] = nse_mod_j
else:
nse_mod_j = 1
extra_param_dict['nse_mod_j'] = nse_mod_j
if request.GET.get('h6_k_MHE', None) is not None:
h6_mhe_k = float(request.GET.get('h6_k_MHE', None))
extra_param_dict['h6_mhe_k'] = h6_mhe_k
else:
h6_mhe_k = 1
extra_param_dict['h6_mhe_k'] = h6_mhe_k
if request.GET.get('h6_k_AHE', None) is not None:
h6_ahe_k = float(request.GET.get('h6_k_AHE', None))
extra_param_dict['h6_ahe_k'] = h6_ahe_k
else:
h6_ahe_k = 1
extra_param_dict['h6_ahe_k'] = h6_ahe_k
if request.GET.get('h6_k_RMSHE', None) is not None:
h6_rmshe_k = float(request.GET.get('h6_k_RMSHE', None))
extra_param_dict['h6_rmshe_k'] = h6_rmshe_k
else:
h6_rmshe_k = 1
extra_param_dict['h6_rmshe_k'] = h6_rmshe_k
if float(request.GET.get('lm_x_bar', None)) != 1:
lm_x_bar_p = float(request.GET.get('lm_x_bar', None))
extra_param_dict['lm_x_bar_p'] = lm_x_bar_p
else:
lm_x_bar_p = None
extra_param_dict['lm_x_bar_p'] = lm_x_bar_p
if float(request.GET.get('d1_p_x_bar', None)) != 1:
d1_p_x_bar_p = float(request.GET.get('d1_p_x_bar', None))
extra_param_dict['d1_p_x_bar_p'] = d1_p_x_bar_p
else:
d1_p_x_bar_p = None
extra_param_dict['d1_p_x_bar_p'] = d1_p_x_bar_p
'''Merge Data'''
merged_df = hd.merge_data(sim_df=simulated_df, obs_df=observed_df)
merged_df2 = hd.merge_data(sim_df=corrected_df, obs_df=observed_df)
'''Plotting Data'''
# Creating the Table Based on User Input
table = hs.make_table(
merged_dataframe=merged_df,
metrics=selected_metric_abbr,
# remove_neg=remove_neg,
# remove_zero=remove_zero,
mase_m=extra_param_dict['mase_m'],
dmod_j=extra_param_dict['dmod_j'],
nse_mod_j=extra_param_dict['nse_mod_j'],
h6_mhe_k=extra_param_dict['h6_mhe_k'],
h6_ahe_k=extra_param_dict['h6_ahe_k'],
h6_rmshe_k=extra_param_dict['h6_rmshe_k'],
d1_p_obs_bar_p=extra_param_dict['d1_p_x_bar_p'],
lm_x_obs_bar_p=extra_param_dict['lm_x_bar_p'],
# seasonal_periods=all_date_range_list
)
table = table.round(decimals=2)
table_html = table.transpose()
table_html = table_html.to_html(
classes="table table-hover table-striped").replace(
'border="1"', 'border="0"')
# Creating the Table Based on User Input
table2 = hs.make_table(
merged_dataframe=merged_df2,
metrics=selected_metric_abbr,
# remove_neg=remove_neg,
# remove_zero=remove_zero,
mase_m=extra_param_dict['mase_m'],
dmod_j=extra_param_dict['dmod_j'],
nse_mod_j=extra_param_dict['nse_mod_j'],
h6_mhe_k=extra_param_dict['h6_mhe_k'],
h6_ahe_k=extra_param_dict['h6_ahe_k'],
h6_rmshe_k=extra_param_dict['h6_rmshe_k'],
d1_p_obs_bar_p=extra_param_dict['d1_p_x_bar_p'],
lm_x_obs_bar_p=extra_param_dict['lm_x_bar_p'],
# seasonal_periods=all_date_range_list
)
table2 = table2.round(decimals=2)
table_html2 = table.transpose()
table_html2 = table_html2.to_html(
classes="table table-hover table-striped").replace(
'border="1"', 'border="0"')
table2 = table2.rename(
index={'Full Time Series': 'Corrected Full Time Series'})
table = table.rename(
index={'Full Time Series': 'Original Full Time Series'})
table_html2 = table2.transpose()
table_html1 = table.transpose()
table_final = pd.merge(table_html1,
table_html2,
right_index=True,
left_index=True)
table_html2 = table_html2.to_html(
classes="table table-hover table-striped",
table_id="corrected_1").replace('border="1"', 'border="0"')
table_final_html = table_final.to_html(
classes="table table-hover table-striped",
table_id="corrected_1").replace('border="1"', 'border="0"')
return HttpResponse(table_final_html)
except Exception:
traceback.print_exc()
return JsonResponse(
{'error': 'No data found for the selected station.'})
