/
plot.py
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/
plot.py
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"""
:mod:`plot` -- basic plotting routines for extremes
===================================================
.. module:: plot
:synopsis: Provide some basic plotting routines for extreme value
distribution modelling.
.. moduleauthor:: Craig Arthur <craig.arthur@ga.gov.au>
"""
from __future__ import division
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import genpareto
import statsmodels.api as sm
import logging
LOG = logging.getLogger(__name__)
from return_period import returnLevels, empiricalReturnPeriod, \
returnPeriodUncertainty
from distributions import fittedPDF
sns.set_context("poster")
sns.set_style("ticks")
def plotFit(data, mu, xi, sigma, title, figfile):
"""
Plot a fitted distribution, with approximate 90% confidence interval
and empirical return period values.
:param data: :class:`numpy.ndarray` of observed data values.
:param float mu: Selected threshold value.
:param float xi: Fitted shape parameter.
:param float sigma: Fitted scale parameter.
:param str title: Title string for the plot.
:param str figfile: Path to store the file (includes image format)
"""
LOG.info("Plotting fitted return period curve")
rp = np.array([1, 2, 5, 10, 20, 50, 100, 200,
500, 1000, 2000, 5000, 10000])
rate = float(len(data[data > mu])) / float(len(data))
rval = returnLevels(rp, mu, xi, sigma, rate)
emprp = empiricalReturnPeriod(data)
err = returnPeriodUncertainty(data, mu, xi, sigma, rp)
sortedmax = np.sort(data)
fig, ax1 = plt.subplots(1, 1, figsize=(12, 12))
ax1.semilogx(rp, rval, label="Fitted RP curve")
ax1.semilogx(rp, rval + 1.96 * err, label="90% CI",
linestyle='--', color='0.5')
ax1.semilogx(rp, rval - 1.96 * err, linestyle='--', color='0.5')
ax1.scatter(emprp[emprp > 1], sortedmax[emprp > 1], s=100,
color='r', label="Empirical RP")
title_str = (title + "\n" +
r"$\mu$ = {0:.2f}, $\xi$ = {1:.5f}, $\sigma$ = {2:.4f}".
format(mu, xi, sigma))
ax1.set_title(title_str)
ax1.legend(loc=2)
ax1.set_ylim((0, 100))
ax1.set_xlim((1, 10000))
ax1.set_ylabel('Wind speed (m/s)')
ax1.set_xlabel('Return period (years)')
ax1.grid(which='major')
ax1.grid(which='minor', linestyle='--', linewidth=1)
plt.savefig(figfile)
plt.close()
def plotDiagnostics(data, mu, xi, sigma, figfile):
"""
Create a 4-panel diagnostics plot of the fitted distribution.
:param data: :class:`numpy.ndarray` of observed data values (in units
of metres/second).
:param float mu: Selected threshold value.
:param float xi: Fitted shape parameter.
:param float sigma: Fitted scale parameter.
:param str figfile: Path to store the file (includes image format)
"""
LOG.info("Plotting diagnostics")
fig, ax = plt.subplots(2, 2)
axes = ax.flatten()
# Probability plots
sortedmax = np.sort(data[data > mu])
gpdf = fittedPDF(data, mu, xi, sigma)
pp_x = sm.ProbPlot(sortedmax)
pp_x.ppplot(xlabel="Empirical", ylabel="Model", ax=axes[0], line='45')
axes[0].set_title("Probability plot")
prplot = sm.ProbPlot(sortedmax, genpareto, distargs=(xi,),
loc=mu, scale=sigma)
prplot.qqplot(xlabel="Model", ylabel="Empirical", ax=axes[1], line='45')
axes[1].set_title("Quantile plot")
ax2 = axes[2]
rp = np.array([1, 2, 5, 10, 20, 50, 100, 200,
500, 1000, 2000, 5000, 10000])
rate = float(len(sortedmax)) / float(len(data))
rval = returnLevels(rp, mu, xi, sigma, rate)
emprp = empiricalReturnPeriod(np.sort(data))
ax2.semilogx(rp, rval, label="Fitted RP curve", color='r')
ax2.scatter(emprp[emprp > 1], np.sort(data)[emprp > 1],
color='b', label="Empirical RP", s=100)
ax2.legend(loc=2)
ax2.set_xlabel("Return period")
ax2.set_ylabel("Return level")
ax2.set_title("Return level plot")
ax2.grid(True)
maxbin = 4 * np.ceil(np.floor(data.max() / 4) + 1)
sns.distplot(sortedmax, bins=np.arange(mu, maxbin, 2),
hist=True, axlabel='Wind speed (m/s)',
ax=axes[3])
axes[3].plot(sortedmax, gpdf, color='r')
axes[3].set_title("Density plot")
plt.tight_layout()
plt.savefig(figfile)
plt.close()