def test_plot_seasonal(self):
daily_avg_df = hd.daily_average(df=self.merged_df)
daily_std_error = hd.daily_std_error(merged_data=self.merged_df)
# Creating test image array
hv.plot(merged_data_df=daily_avg_df,
title='Daily Average Streamflow (Standard Error)',
legend=('SFPT', 'GLOFAS'),
x_season=True,
labels=['Datetime', 'Streamflow (csm)'],
linestyles=['r-', 'k-'],
fig_size=(14, 8),
ebars=daily_std_error,
ecolor=('r', 'k'),
tight_xlim=True)
buf = BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
img_test = mpimg.imread(buf)
buf.close()
# Reading Original Image
img_original = mpimg.imread(
r'baseline_images/plot_tests/plot_seasonal.png')
# Comparing
self.assertTrue(np.all(np.isclose(img_test, img_original)))
def test_plot_full1(self):
# Creating Test Image
hv.plot(merged_data_df=self.merged_df,
title='Hydrograph of Entire Time Series',
linestyles=['r-', 'k-'],
legend=('SFPT', 'GLOFAS'),
labels=['Datetime', 'Streamflow (cfs)'],
metrics=['ME', 'NSE', 'SA'],
grid=True)
buf = BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
img_test = mpimg.imread(buf)
buf.close()
# Reading Original Image
img_original = mpimg.imread(r'baseline_images/plot_tests/plot_full1.png')
# Comparing
self.assertTrue(np.all(np.isclose(img_test, img_original)))
'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: ' +
str(comid),
labels=['Datetime', 'Volume (BCM)'],
linestyles=['b-', 'r-'],
fig_size=(15, 9))
plt.savefig(
path.join(plot_out_dir,
'{0}_{1}_hydrographs.png'.format(str(comid), name)))
hv.scatter(merged_data_df=merged_df,
grid=True,
title='Scatter Plot for ' + name + '\n River: ' + rio +
'. COMID: ' + str(comid),
labels=('Simulated', 'Observed'),
line45=True,
best_fit=True,
figsize=(15, 9))
metrics = []
# This list controls the axis labels:
labels=['Datetime', 'Streamflow (cms)']
# End of Dynamic Input. Do NOT Change the following -------------------------------------------
# Create a list of 18 stream modified Time Series
list_riv_part = []
for riv in list_riv_mouth:
riv_part = riv.loc[begin_date:end_date]
list_riv_part.append(riv_part)
for s, o, c1, t, s1 in zip(list_sim, list_obs, color1, range(18), series1):
merged_df = hd.merge_data(sim_df=list_riv_part[s], obs_df=list_obs_df[o])
filename = year + ': ' + list_titles[t]
hv.plot(merged_data_df=merged_df,
title=filename,
linestyles=[c1, 'k-'],
legend=(s1, series2),
labels=labels,
metrics = metrics,
grid=True)
plt.savefig('/home/chrisedwards/Documents/rapid_output/graphs/{}.png'.format(filename))
'k',
color='red',
label='Observed Volume')
plt.title('Volume Analysis for ' + str(id) + ' - ' + name + '\n River: ' +
rio + '. COMID: ' + str(comid))
plt.xlabel('Date')
plt.ylabel('Volume (Mm^3)')
plt.legend()
plt.grid()
plt.savefig(volume_analysis_out_dir + '/Volume Analysis for ' + str(id) +
' - ' + name + '. COMID - ' + str(comid) + '.png')
hv.plot(merged_df,
legend=('Simulated', 'Observed'),
grid=True,
title='Hydrograph for ' + str(id) + ' - ' + name + '\n River: ' +
rio + '. COMID: ' + str(comid),
labels=['Datetime', 'Streamflow (m$^3$/s)'],
linestyles=['b-', 'r-'],
fig_size=(15, 9))
plt.savefig(
path.join(plot_out_dir,
'{0}_{1}_hydrographs.png'.format(str(id), name)))
daily_avg = hd.daily_average(merged_df)
daily_std_error = hd.daily_std_error(merged_data=merged_df)
hv.plot(merged_data_df=daily_avg,
legend=('Simulated', 'Observed'),
grid=True,
x_season=True,
title='Daily Average Streamflow (Standard Error) for ' + str(id) +
' - ' + name + '\n River: ' + rio + '. COMID: ' + str(comid),
observed_monthly = pd.read_csv('/Users/student/Dropbox/PhD/2020 Winter/Dissertation_v9/Africa/Blue_Nile/Data/Historical/observed_data/Multiannual_Mean_Streamflow/{0}_{1}.csv'.format(comid, name), dtype={'Month': str})
observed_monthly.set_index('Month', inplace=True)
observed_monthly_avg = observed_monthly[['Mean Streamflow (m3/s)']]
observed_monthly_std_error = observed_monthly[['Standard Error']]
monthly_avg_obs_ERA5 = ERA5_monthly_avg.join(observed_monthly_avg)
monthly_std_error_ERA5 = ERA5_monthly_std_error.join(observed_monthly_std_error)
monthly_avg_obs_ERA_Interim = ERA_Interim_monthly_avg.join(observed_monthly_avg)
monthly_std_error_ERA_Interim = ERA_Interim_monthly_std_error.join(observed_monthly_std_error)
hv.plot(merged_data_df=monthly_avg_obs_ERA5, legend=('ERA-5', 'Observed'), grid=True, x_season=True,
# title='Monthly Average Streamflow (Standard Error) for ' + str(
# id) + ' - ' + name + '\n River: ' + rio + '. COMID: ' + str(comid),
title='Monthly Average Streamflow (Standard Error) for ' + name + '\n River: '
+ rio + '. COMID: ' + str(comid),
labels=['Datetime', 'Streamflow (m$^3$/s)'], linestyles=['b-', 'r-'], fig_size=(15, 9),
ebars=monthly_std_error_ERA5, ecolor=('b', 'r'), tight_xlim=False)
# plt.savefig(path.join(monthly_average_out_dir, '{0}_{1}_monthly_average.png'.format(str(id), name)))
plt.savefig(path.join(
'/Users/student/Dropbox/PhD/2020 Winter/Dissertation_v9/Africa/Blue_Nile/Data/Historical/observed_data/Multiannual_Mean_Streamflow',
'{0}_{1}_monthly_average_ERA5_Corrected.png'.format(str(comid), name)))
hv.plot(merged_data_df=monthly_avg_obs_ERA_Interim, legend=('ERA-5', 'Observed'), grid=True, x_season=True,
# title='Monthly Average Streamflow (Standard Error) for ' + str(
# id) + ' - ' + name + '\n River: ' + rio + '. COMID: ' + str(comid),
title='Monthly Average Streamflow (Standard Error) for ' + name + '\n River: '
+ rio + '. COMID: ' + str(comid),
labels=['Datetime', 'Streamflow (m$^3$/s)'], linestyles=['b-', 'r-'], fig_size=(15, 9),
ebars=monthly_std_error_ERA_Interim, ecolor=('b', 'r'), tight_xlim=False)
# There are a total of 180 streams, or 180 keys in the dictionary: seas_avg_dict['az-7']
# list_streams_condensed = list of all the stream names, or names of the data frames.
# ***************************************************************************************************************
# ***************************************************************************************************************
az_9 = streamflow_dict['mo-7']
az_avg_9 = seas_avg_dict['mo-avg-7']
merged_df = hd.merge_data(sim_df=az_9,
obs_df=streamflow_dict['az-21'],
column_names=['9-calc', '21-calc'])
dailyavg2 = hd.daily_average(merged_df)
avg_calc = dailyavg2.drop(columns='21-calc')
az_avg_9.index = pd.date_range("2001-01-01", "2001-12-31").strftime("%m/%d")
group = [avg_calc, az_avg_9]
calc_vs_ncdf = pd.concat(group, axis=1)
calc_vs_ncdf.drop(index='02/29', inplace=True)
labels = ['Datetime', 'Streamflow (cms)']
hv.plot(merged_data_df=calc_vs_ncdf,
title="MO Daily Avg: Hydrostats vs Rapid ",
linestyles=['r-', 'k-'],
legend=('Hydrostats', 'Rapid NetCDF'),
labels=labels,
x_season=True,
grid=True)
plt.savefig(
'/home/chrisedwards/Documents/rapid_output/graphs/mo-dav_hd-vs-rapid.png')
# This list controls the axis labels:
labels=['Datetime', 'Streamflow (cms)']
# End of Dynamic Input. Do NOT Change the following -------------------------------------------
# Create a list of 18 stream modified Time Series
list_riv_part = []
for riv in list_riv_mouth:
riv_part = riv.loc[begin_date:end_date]
list_riv_part.append(riv_part)
for s, o, t, c1, c2, s1, s2 in zip(list_sim, list_obs, range(18), color1, color2, series1, series2):
merged_df = hd.merge_data(sim_df=list_riv_part[s], obs_df=list_riv_part[o])
davg_df = hd.daily_average(merged_df)
filename = list_titles[t]
hv.plot(merged_data_df=davg_df,
title=filename,
linestyles=[c1, c2],
legend=(s1, s2),
labels=labels,
metrics = metrics,
x_season=True,
grid=True)
plt.savefig('/home/chrisedwards/Documents/rapid_output/graphs/{}.png'.format(filename))
'AZ: Sim (str-60) vs Obs (gauge-09492400)',
'MO: Sim (str-50) vs Obs (gauge-07013000)',
'MO: Sim (str-51) vs Obs (gauge-07014000)',
'NY: Sim (str-47) vs Obs (gauge-01413408)',
'NY: Sim (str-46) vs Obs (gauge-01413398)',
'OR: Sim (str-58) vs Obs (gauge-14306400)',
'OR: Sim (str-47) vs Obs (gauge-14306100)'
]
# This list specifies which metrics to use:
metrics = []
# This list controls the axis labels:
labels = ['Datetime', 'Streamflow (cms)']
for s, o, t in zip(range(12), range(12), range(12)):
merged_df = hd.merge_data(sim_df=list_sim_df[s], obs_df=list_obs_df[o])
da_df = hd.daily_average(merged_df)
filename = list_titles[t] + ' Daily Average'
hv.plot(merged_data_df=da_df,
title=filename,
linestyles=['b-', 'k-'],
legend=('Sim', 'Obs'),
labels=labels,
metrics=metrics,
x_season=True,
grid=True)
plt.tight_layout()
plt.savefig(
'/home/chrisedwards/Documents/rapid_output/graphs/{}.png'.format(
filename))
list_obs_df = [az_obs_cms, id_obs_cms, mo_obs_cms, ny_obs_cms, or_obs_cms]
x_df = or_obs_full.drop(columns=['Flow-cms', 'Estimation'])
# ------------------------------------------------------------------------------------------------------
merged_df = hd.merge_data(sim_df=x_df,
obs_df=or_obs_cms,
column_names=['Delete', 'Observed'])
temp_da = hd.daily_average(merged_df)
avg_obs = temp_da.drop(columns='Delete')
or_lowres.index = pd.date_range("2001-01-01", "2001-12-31").strftime("%m/%d")
group = [or_lowres, avg_obs]
rapid_vs_obs = pd.concat(group, axis=1)
rapid_vs_obs.drop(index='02/29', inplace=True)
# print(rapid_vs_obs)
labels = ['Datetime', 'Streamflow (cms)']
hv.plot(merged_data_df=rapid_vs_obs,
title="OR Rapid Daily Avg: Low vs Obs",
linestyles=['r-', 'k-'],
legend=('Low Res', 'Obs'),
labels=labels,
x_season=True,
grid=True)
plt.savefig(
'/home/chrisedwards/Documents/rapid_output/graphs/or-dav_rapid-vs-obs.png')
