def mon_boxplot(ts,
col='all',
dtype='flow',
fun='mean',
min_yrs=10,
export_path='',
export_name='flow_boxplot.png'):
"""
"""
from os import path
from calendar import month_abbr
import seaborn as sns
import matplotlib.pyplot as plt
from core.ts import w_resample
from core.ts.sw import flow_stats
from pandas import Series
ylabel_dict = {
'flow': 'Mean Flow $(m^{3}/s)$',
'precip': 'Precipitation $(mm/month)$',
'PET': 'Potential Evapotranspiration $(mm/month)$',
'diff': 'Precipitation - PET $(mm/month)$'
}
sns.set_style("whitegrid")
sns.set_context('poster')
col_dict = {
'flow': sns.color_palette("pastel")[0],
'precip': sns.color_palette("pastel")[0],
'PET': sns.color_palette("pastel")[1],
'diff': sns.light_palette("blue")[0]
}
color = col_dict[dtype]
if type(ts) is Series:
ts2 = ts
else:
stats1 = flow_stats(ts)
index1 = stats1.loc['Tot data yrs', :] >= min_yrs
sites = ts.columns[index1]
ts2 = ts[sites]
tsm = w_resample(ts2, period='month', fun=fun)
tsm['month_num'] = tsm.index.month
tsm['month'] = tsm['month_num'].apply(lambda x: month_abbr[x])
if type(ts) is Series:
fig, ax = plt.subplots(figsize=(15, 10))
sns.boxplot(x='month', y=0, data=tsm, color=color, showfliers=False)
plt.ylabel(ylabel_dict[dtype])
plt.xlabel('Month')
plt.tight_layout()
plot2 = ax.get_figure()
plot2.savefig(path.join(export_path, export_name))
elif col is 'all':
for i in sites:
fig, ax = plt.subplots(figsize=(15, 10))
sns.boxplot(x='month',
y=i,
data=tsm,
color=color,
showfliers=False)
plt.ylabel(ylabel_dict[dtype])
plt.xlabel('Month')
plt.tight_layout()
plot2 = ax.get_figure()
plot2.savefig(path.join(export_path, str(i) + '_' + export_name))
elif type(col) is list:
for i in col:
fig, ax = plt.subplots(figsize=(15, 10))
sns.boxplot(x='month', y=i, color=color, data=tsm)
plt.ylabel(ylabel_dict[dtype])
plt.xlabel('Month')
plt.tight_layout()
plot2 = ax.get_figure()
plot2.savefig(path.join(export_path, str(i) + '_' + export_name))
#### Plot
data = concat([g_flow[69650], r_flow], axis=1).dropna()
data.columns = data.columns.astype('str')
sns.regplot(x=log(data[r_sites]), y=log(data[g_sites]))
lm = sns.regplot(x='69618', y='69650', data=data, truncate=True)
lm.axes.set_ylim(0, 20)
lm.axes.set_xlim(0, 20)
###############################
#### Export results
predam1 = new_ts.loc[new_ts[69650].first_valid_index():, 69650]
predam2 = predam1.copy()
predam2[predam2 > (reg1.loc[69650].max_y * 1.5)] = nan
stats1 = flow_stats(predam1)
stats2 = flow_stats(predam2)
malf1 = malf7d(predam1, intervals=[10, 20, 30])
stats_both = concat(
[concat([stats1, malf1], axis=1),
concat([stats2, malf1], axis=1)])
stats_both.index = ['all_flows', 'constrained_flows']
stats_both.to_csv(stats_export2, header=True)
predam1.to_csv(ts_export2, header=True)
#plt1 = hydrograph_plot(predam1)
plt2 = reg_plot(r_flow[69618],
g_flow[69650],
def dual_mon_boxplot(ts1,
ts2,
col='all',
dtype='flow',
fun='mean',
min_yrs=10,
export_path='',
export_name='flow_dual_boxplot.png'):
"""
"""
from calendar import month_abbr
from os import path
from pandas import concat, melt, DatetimeIndex
import seaborn as sns
import matplotlib.pyplot as plt
from core.ts import w_resample
from core.ts.sw import flow_stats
### Set base parameters
ylabel_dict = {
'flow': 'Mean Flow $(m^{3}/s)$',
'precip': 'Precipitation $(mm/month)$',
'ET': 'Evapotranspiration $(mm/month)$'
}
sns.set_style("whitegrid")
sns.set_context('poster')
cat = ['Recorded', 'Naturalised']
### Process data
ts1 = ts1.dropna(axis=1, how='all')
stats1 = flow_stats(ts1)
index1 = stats1.loc[:, 'Tot data yrs'] >= min_yrs
sites = ts1.columns[index1.values]
ts1_sel = ts1[sites]
ts2_sel = ts2[sites]
ts1_selm = w_resample(ts1_sel, period='month', fun=fun)
ts2_selm = w_resample(ts2_sel, period='month', fun=fun)
ts_all = concat([ts1_selm, ts2_selm], axis=1, join='inner')
### Make the figures
if col is 'all':
for i in sites:
ts_site = ts_all[i]
ts_site.columns = cat
ts_site2 = melt(ts_site.reset_index(),
id_vars='Time',
value_vars=cat,
var_name='type')
ts_site2['month_num'] = DatetimeIndex(ts_site2.Time).month
ts_site2['month'] = ts_site2['month_num'].apply(
lambda x: month_abbr[x])
ts_site2 = ts_site2.sort_values(['month_num', 'type'],
ascending=[True, False],
axis='index')
fig, ax = plt.subplots(figsize=(15, 10))
sns.boxplot(x='month',
y='value',
hue='type',
data=ts_site2,
showfliers=False)
plt.ylabel(ylabel_dict[dtype])
plt.xlabel('Month')
plt.legend()
plt.tight_layout()
plot2 = ax.get_figure()
plot2.savefig(path.join(export_path, str(i) + '_' + export_name))
elif type(col) is list:
for i in col:
ts_site = ts_all[i]
ts_site.columns = cat
ts_site2 = melt(ts_site.reset_index(),
id_vars='Time',
value_vars=cat,
var_name='type')
ts_site2['month_num'] = DatetimeIndex(ts_site2.Time).month
ts_site2['month'] = ts_site2['month_num'].apply(
lambda x: month_abbr[x])
ts_site2 = ts_site2.sort_values(['month_num', 'type'],
ascending=[True, False],
axis='index')
fig, ax = plt.subplots(figsize=(15, 10))
sns.boxplot(x='month',
y='value',
hue='type',
data=ts_site2,
showfliers=False)
plt.ylabel(ylabel_dict[dtype])
plt.xlabel('Month')
plt.legend()
plt.tight_layout()
plot2 = ax.get_figure()
plot2.savefig(path.join(export_path, str(i) + '_' + export_name))