#-------------------------------------------------
#plot_start_date = dt.datetime(1950, 10, 1) # start date shown on the plot (should be complete water years)
#plot_end_date = dt.datetime(1987, 9, 30) # end date shown on the plot
#time_locator = ('year', 5) # time locator on the plot; 'year' for year; 'month' for month. e.g., ('month', 3) for plot one tick every 3 months
#-------------------------------------------------
#========================================================
# Load data
#========================================================
# RBM output
rbm_data = np.loadtxt(rbm_output_formatted_path, skiprows=1) # year; month; day; flow(cfs); T_stream(degC)
rbm_date = my_functions.convert_YYYYMMDD_to_datetime(rbm_data[:,0], rbm_data[:,1], rbm_data[:,2])
df_rbm = my_functions.convert_time_series_to_df(rbm_date, rbm_data[:,4], ['streamT']) # convert to pd.DataFrame
s_rbm = df_rbm.ix[:,0] # convert df to Series
# USGS stream T
if ave_flag==0: # if only one needed data column
df_usgs = my_functions.read_USGS_data(usgs_data_path, columns=[usgs_streamT_col], names=['streamT']) # [degC]
s_usgs= df_usgs.ix[:,0] # convert df to Series
else: # if more than one data column needed, take average
usgs_streamT_col_split = usgs_streamT_col.split('&')
names=[]
for i in range(len(usgs_streamT_col_split)):
usgs_streamT_col_split[i] = int(usgs_streamT_col_split[i])
names.append('streamT%d' %i)
df_usgs = my_functions.read_USGS_data(usgs_data_path, columns=usgs_streamT_col_split, names=names) # read in data
s_usgs = df_usgs.mean(axis=1, skipna=False) # if either column is missing, return NaN
#time_locator = ('year', 5) # time locator on the plot; 'year' for year; 'month' for month. e.g., ('month', 3) for plot one tick every 3 months
#-------------------------------------------------
#========================================================
# Load data
#========================================================
# RBM output
rbm_data = np.loadtxt(
rbm_output_formatted_path,
skiprows=1) # year; month; day; flow(cfs); T_stream(degC)
rbm_date = my_functions.convert_YYYYMMDD_to_datetime(rbm_data[:, 0],
rbm_data[:, 1],
rbm_data[:, 2])
df_rbm = my_functions.convert_time_series_to_df(
rbm_date, rbm_data[:, 4], ['streamT']) # convert to pd.DataFrame
s_rbm = df_rbm.ix[:, 0] # convert df to Series
# USGS stream T
if ave_flag == 0: # if only one needed data column
df_usgs = my_functions.read_USGS_data(usgs_data_path,
columns=[usgs_streamT_col],
names=['streamT']) # [degC]
s_usgs = df_usgs.ix[:, 0] # convert df to Series
else: # if more than one data column needed, take average
usgs_streamT_col_split = usgs_streamT_col.split('&')
names = []
for i in range(len(usgs_streamT_col_split)):
usgs_streamT_col_split[i] = int(usgs_streamT_col_split[i])
names.append('streamT%d' % i)
df_usgs = my_functions.read_USGS_data(usgs_data_path,
#-------------------------------------------------
#plot_start_date = dt.datetime(1950, 10, 1) # start date shown on the plot (should be complete water years)
#plot_end_date = dt.datetime(1987, 9, 30) # end date shown on the plot
#time_locator = ('year', 5) # time locator on the plot; 'year' for year; 'month' for month. e.g., ('month', 3) for plot one tick every 3 months
#-------------------------------------------------
#========================================================
# Load data
#========================================================
# RBM output
rbm_data = np.loadtxt(rbm_output_formatted_path, skiprows=1) # year; month; day; flow(cfs); T_stream(deg)
rbm_date = my_functions.convert_YYYYMMDD_to_datetime(rbm_data[:,0], rbm_data[:,1], rbm_data[:,2])
df_rbm = my_functions.convert_time_series_to_df(rbm_date, rbm_data[:,3], ['flow']) # convert to pd.DataFrame
s_rbm = df_rbm.ix[:,0] # convert df to Series
# USGS flow
if ave_flag==0: # if only one needed data column
df_usgs = my_functions.read_USGS_data(usgs_data_path, columns=[usgs_flow_col], names=['flow']) # [degC]
s_usgs= df_usgs.ix[:,0] # convert df to Series
else: # if more than one data column needed, take average
usgs_flow_col_split = usgs_flow_col.split('&')
names=[]
for i in range(len(usgs_flow_col_split)):
usgs_flow_col_split[i] = int(usgs_flow_col_split[i])
names.append('flow%d' %i)
df_usgs = my_functions.read_USGS_data(usgs_data_path, columns=usgs_flow_col_split, names=names) # read in data
s_usgs = df_usgs.mean(axis=1, skipna=False) # if either column is missing, return NaN
