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wateryear summary added to plot_budget_summary #6

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wateryear summary added to plot_budget_summary #6

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74 changes: 65 additions & 9 deletions mfexport/listfile.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@
import os
import numpy as np
import pandas as pd
import re
import flopy
import matplotlib as mpl
import matplotlib.pyplot as plt
Expand Down Expand Up @@ -204,7 +205,11 @@ def plot_budget_summary(df, title_prefix='', title_suffix='', date_index_fmt='%Y
secondary_axis_units=None, xtick_stride=6,
plot_start_date=None, plot_end_date=None,
plot_pcts=False,
annual_sums=False
annual_sums=False,
wateryear_sums=False,
mindays=60,
cmap_in=None,
cmap_out=None
):
"""Plot a stacked bar chart summary of a MODFLOW listing file budget dataframe.

Expand Down Expand Up @@ -258,6 +263,20 @@ def plot_budget_summary(df, title_prefix='', title_suffix='', date_index_fmt='%Y
Option to summarize budget by year (e.g. using :py:meth:`pandas.DataFrame.groupby`).
Requires that ``df`` have a valid datetime index.
by default, False
wateryear_sums: bool
Option to summarize budget by wateryear (e.g. using :py:meth:`pandas.DataFrame.resample`).
Requires that ``df`` have a valid datetime index.
by default, False
mindays: int
minimum number of days for wateryear or annual summary, if number of days in
the interpolated year is less than mindays, that entry is dropped. Default=60.
cmap_in: str
Name of matplotlib colormap to use for _IN water budget components, if None
then it uses pandas default which is list('bgrcmyk'). Default is None.
cmap_out: str
Name of matplotlib colormap to use for _OUT water budget components, if None
then it uses pandas default which is list('bgrcmyk'). Default is None.


Returns
-------
Expand All @@ -276,15 +295,52 @@ def plot_budget_summary(df, title_prefix='', title_suffix='', date_index_fmt='%Y
# slice the dataframe to the specified time range (if any)
df = df.copy()
df = df.loc[slice(plot_start_date, plot_end_date)]
if annual_sums:

if wateryear_sums or annual_sums:
if isinstance(df.index, pd.DatetimeIndex):
dfa = df.groupby(df.index.year).mean()
dfa['kper'] = df.groupby(df.index.year).last()['kper']
dfa['kstp'] = df.groupby(df.index.year).last()['kstp']
df = dfa
interp = df.resample('D').last()
interp = interp.interpolate(method='time')
interp['days'] = 1
cols = interp.columns.to_list()
agg_type = dict()
for c in cols:
if re.match('kstp', c) or re.match('kper', c):
agg_type[c] = 'last'
else:
agg_type[c] = 'sum'
# resample to October 1 and set the label to the end of the interval
# this is consistent with budget output; the time printed is the end
# of the stress period. The agg_type dictionary is used so that
# kstp and kper are not interpolated, the last value is taken.
mass_WY = interp.resample('AS-OCT', label='right').agg(agg_type)

# resample to the end of the year, same aggregation types
mass_Y = interp.resample('Y', label='right').agg(agg_type)

# query operator, don't have to use .loc, etc.
mass_WY = mass_WY.query('days > {0}'.format(mindays))
mass_Y = mass_Y.query('days > {0}'.format(mindays))

# get the average rate in model units by dividing by the
# number of days in the period (might be partial year or leap year)
rate_WY = mass_WY[cols].divide(mass_WY['days'], axis='index')
rate_Y = mass_Y[cols].divide(mass_Y['days'], axis='index')

# kper got divided by days, need to remake it and set to integer
rate_WY['kper'] = rate_WY['kper']*mass_WY['days']
rate_WY['kper'] = rate_WY['kper'].astype(int)
rate_Y['kper'] = rate_Y['kper']*mass_Y['days']
rate_Y['kper'] = rate_Y['kper'].astype(int)

# set df to the desired frame
if wateryear_sums:
df = rate_WY
else:
df = rate_Y
else:
print('Skipping, annual_sums requires a datetime index.')
print('Skipping, annual or wateryear summaries require a datetime index.')
return

if len(df) < xtick_stride * 2:
xtick_stride = 1

Expand All @@ -293,10 +349,10 @@ def plot_budget_summary(df, title_prefix='', title_suffix='', date_index_fmt='%Y
out_cols = [c for c in df.columns if '_OUT' in c and 'TOTAL' not in c]
if not term_nets:
ax = df[in_cols].plot.bar(stacked=True, ax=ax,# width=20
)
cmap=cmap_in)
df[out_cols] *= -1
ax = (df[out_cols]).plot.bar(stacked=True, ax=ax,# width=20
)
cmap=cmap_out)
df_pcts = df.copy()
df_pcts[in_cols] = df[in_cols].div(df['TOTAL_IN'], axis=0)
df_pcts[out_cols] = df[out_cols].div(df['TOTAL_OUT'], axis=0)
Expand Down