import numpy as np
import scipy.stats
import pandas
import matplotlib
import matplotlib.pyplot as plt
c = {
"Afill": "#ee1111",
"Aedge": "#660000",
"Bfill": "#1111ee",
"Bedge": "#000066",
"Cfill": "#11bb11",
"Cedge": "#008800",
}
def align_series(A, B, names=None, start=None, end=None):
"""Align two series for plotting / comparison
Parameters
----------
A : `pandas.Series`
B : `pandas.Series`
names : list of strings
start : `pandas.Timestamp` or timestamp string
end : `pandas.Timestamp` or timestamp string
Example
-------
>>> A, B = align_series(A, B, ["Pywr", "Aquator"], start="1920-01-01", end="1929-12-31")
>>> plot_standard1(A, B)
"""
# join series B to series A
# TODO: better handling of heterogeneous frequencies
df = pandas.concat([A, B], join="inner", axis=1)
# apply names
if names is not None:
df.columns = names
else:
names = list(df.columns)
# clip start and end to user-specified dates
idx = [df.index[0], df.index[-1]]
if start is not None:
idx[0] = pandas.Timestamp(start)
if end is not None:
idx[1] = pandas.Timestamp(end)
if start or end:
df = df.loc[idx[0]:idx[-1],:]
A = df[names[0]]
B = df[names[1]]
return A, B
def plot_standard1(A, B):
fig, axarr = plt.subplots(3, figsize=(10, 12), facecolor="white")
plot_timeseries(A, B, axarr[0])
plot_QQ(A, B, axarr[1])
plot_percentiles(A, B, axarr[2])
return fig, axarr
def set_000formatter(axis):
axis.set_major_formatter(
matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))
def plot_timeseries(A, B, ax=None):
if ax is None:
ax = plt.gca()
B.plot(ax=ax, color=c["Bfill"], clip_on=False)
A.plot(ax=ax, color=c["Afill"], clip_on=False)
ax.grid(True)
ax.set_ylim(0, None)
set_000formatter(ax.get_yaxis())
ax.set_xlabel("")
ax.legend([B.name, A.name], loc="best")
return ax
def plot_QQ(A, B, ax=None):
if ax is None:
ax = plt.gca()
ax.scatter(B.values, A.values, color=c["Cfill"], edgecolor=c["Cedge"], clip_on=False)
xlim = ax.get_xlim()
ylim = ax.get_ylim()
limit = max(xlim[1], ylim[0])
ax.plot([0, limit], [0, limit], '-k')
ax.set_xlim(0, limit)
ax.set_ylim(0, limit)
ax.grid(True)
set_000formatter(ax.get_xaxis())
set_000formatter(ax.get_yaxis())
ax.set_xlabel(B.name)
ax.set_ylabel(A.name)
ax.legend(["Equality"], loc="best")
return ax
def plot_percentiles(A, B, ax=None):
if ax is None:
ax = plt.gca()
percentiles = np.linspace(0.001, 0.999, 1000) * 100
A_pct = scipy.stats.scoreatpercentile(A.values, percentiles)
B_pct = scipy.stats.scoreatpercentile(B.values, percentiles)
percentiles = percentiles / 100.0
ax.plot(percentiles, B_pct[::-1], color=c["Bfill"], clip_on=False, linewidth=2)
ax.plot(percentiles, A_pct[::-1], color=c["Afill"], clip_on=False, linewidth=2)
ax.set_xlabel("Cumulative frequency")
ax.grid(True)
ax.xaxis.grid(True, which="both")
set_000formatter(ax.get_yaxis())
ax.set_xscale("logit")
xticks = ax.get_xticks()
xticks_minr = ax.get_xticks(minor=True)
ax.set_xticklabels([], minor=True)
ax.set_xticks([0.01, 0.1, 0.5, 0.9, 0.99])
ax.set_xticklabels(["1", "10", "50", "90", "99"])
ax.set_xlim(0.001, 0.999)
ax.legend([B.name, A.name], loc="best")
return ax
