# Read in inverse routing output
#===============================================================#
# A dictionary; keys: 'lat_lon'; element: Series of total runoff
dict_s_total_runoff = my_functions.read_inverse_route_output(\
cfg['INPUT']['inv_ro_basin_output_dir'], \
smooth_window=cfg['PARAM']['smooth_window'], \
n_runs=cfg['PARAM']['n_runs'], \
skip_steps=cfg['PARAM']['skip_steps'], \
start_date_data=start_date, \
time_step=cfg['PARAM']['time_step'], \
ksteps=cfg['PARAM']['ksteps'], \
latlon_precision=cfg['PARAM']['latlon_precision'])
# Select full water years
start_date_WY, end_date_WY = my_functions.find_full_water_years_within_a_range(\
dict_s_total_runoff[dict_s_total_runoff.keys()[0]].index[0],\
dict_s_total_runoff[dict_s_total_runoff.keys()[0]].index[-1])
for lat_lon in dict_s_total_runoff.keys():
s_total_runoff = dict_s_total_runoff[lat_lon]
dict_s_total_runoff[lat_lon] = my_functions.select_time_range(\
s_total_runoff, start_date_WY, end_date_WY)
##===============================================================#
## 1) Adjust negative runoff to zero;
## 2) Rescale each month so that water is balanced within the month
##===============================================================#
#for lat_lon in dict_s_total_runoff.keys():
# print 'Adjusting negative runoff and rescaling {}...'.format(lat_lon)
# s_total_runoff = dict_s_total_runoff[lat_lon]
# # Original sum of water (with negative runoff)
print 'All RBM output values are missing!'
exit()
if s_usgs.notnull().sum()==0: # if all missing
print 'All USGS data are missing!'
exit()
#========================================================
# Determine plot starting and ending date (always plot full water years)
#========================================================
# determine the common range of available data of both data sets
data_avai_start_date, data_avai_end_date = my_functions.find_data_common_range(s_rbm, s_usgs)
if (data_avai_start_date-data_avai_end_date).days>=0: # if no common time range
print "No common range data available!"
exit()
# find the full water years with available data for both data sets
plot_start_date, plot_end_date = my_functions.find_full_water_years_within_a_range(data_avai_start_date, data_avai_end_date)
# determine time locator
if plot_end_date.year-plot_start_date.year < 5: # if less than 5 years
time_locator = ('year', 1) # time locator on the plot; 'year' for year; 'month' for month. e.g., ('month', 3) for plot one tick every 3 months
else: # if at least 5 years
time_locator = ('year', (plot_end_date.year-plot_start_date.year)/5) # time locator on the plot; 'year' for year; 'month' for month. e.g., ('month', 3) for plot one tick every 3 months
#========================================================
# Select data to be plotted
#========================================================
s_rbm_to_plot = my_functions.select_time_range(s_rbm, plot_start_date, plot_end_date)
s_usgs_to_plot = my_functions.select_time_range(s_usgs, plot_start_date, plot_end_date)
#========================================================
# plot
#========================================================
if s_usgs.notnull().sum() == 0: # if all missing
print 'All USGS data are missing!'
exit()
#========================================================
# Determine plot starting and ending date (always plot full water years)
#========================================================
# determine the common range of available data of both data sets
data_avai_start_date, data_avai_end_date = my_functions.find_data_common_range(
s_rbm, s_usgs)
if (data_avai_start_date -
data_avai_end_date).days >= 0: # if no common time range
print "No common range data available!"
exit()
# find the full water years with available data for both data sets
plot_start_date, plot_end_date = my_functions.find_full_water_years_within_a_range(
data_avai_start_date, data_avai_end_date)
# determine time locator
if plot_end_date.year - plot_start_date.year < 5: # if less than 5 years
time_locator = (
'year', 1
) # time locator on the plot; 'year' for year; 'month' for month. e.g., ('month', 3) for plot one tick every 3 months
else: # if at least 5 years
time_locator = (
'year', (plot_end_date.year - plot_start_date.year) / 5
) # time locator on the plot; 'year' for year; 'month' for month. e.g., ('month', 3) for plot one tick every 3 months
#========================================================
# Select data to be plotted
#========================================================
s_rbm_to_plot = my_functions.select_time_range(s_rbm, plot_start_date,
plot_end_date)
dict_path[line_split[0]] = [line_split[1], line_split[2]]
else:
print 'Error: unsupported observation data format!'
exit()
f.close()
# Read in routed streamflow from inverted runoff
dict_Lohmann_routed = {} # {station_name: pd.Series of daily data} [unit: cfs]
for stn in dict_path:
# Load data
s_Lohmann_routed = my_functions.read_Lohmann_route_daily_output(
dict_path[stn][1])
dict_Lohmann_routed[stn] = s_Lohmann_routed
# Select full water years
start_date_WY, end_date_WY = my_functions.find_full_water_years_within_a_range(\
dict_Lohmann_routed[stn].index[0], \
dict_Lohmann_routed[stn].index[-1])
dict_Lohmann_routed[stn] = my_functions.select_time_range(dict_Lohmann_routed[stn], \
start_date_WY, \
end_date_WY)
# Read in original station obs rmat
dict_obs = {} # {station_name: pd.Series of daily data} [unit: cfs]
for stn in dict_path:
# Load data
filename = dict_path[stn][0]
if cfg['INPUT']['obs_format'] == 'USGS':
column = dict_path[stn][2]
dict_obs[stn] = my_functions.read_USGS_data(filename, [column],
['Discharge'])
elif cfg['INPUT']['obs_format'] == 'Lohmann':
