sciapy.regress._plot.plot_single_sample() - Code Metrics - Inspection of "regress: Document statistics interface" - st-bender/sciapy - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Branch — master (9c78f3)

by Stefan

created 2019-02-07 10:19 UTC

sciapy.regress._plot.plot_single_sample() A

↳ Parent: sciapy.regress._plot

Complexity

Conditions

Size

Total Lines	30
Code Lines	9

Duplication

Lines	30
Ratio	100 %

Code Coverage

Tests	1
CRAP Score	1.7023

Importance

Changes

Metric	Value
cc	1
eloc	9
nop	6
dl	30
loc	30
ccs	1
cts	9
cp	0.1111
crap	1.7023
rs	9.95
c	0
b	0
f	0

# -*- coding: utf-8 -*-
# vim:fileencoding=utf-8
#
# Copyright (c) 2017-2018 Stefan Bender
#
# This module is part of sciapy.
# sciapy is free software: you can redistribute it or modify
# it under the terms of the GNU General Public License as published
# by the Free Software Foundation, version 2.
# See accompanying LICENSE file or http://www.gnu.org/licenses/gpl-2.0.html.
"""SCIAMACHY regression tool plotting helpers

Plot (helper) functions for the regression command line tool.
"""

import logging

import numpy as np
import matplotlib as mpl
mpl.use("Agg")
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec


def plot_single_sample_and_residuals(model, times, data, errs,

		sample, filename):
	"""Plot one sample and residuals in one figure

	Parameters
	----------
	model: `celerite.Model`, `george.Model`, or `celerite.ModelSet`
		The Gaussian Process model to plot samples from.
	times: array_like
		Time axis values.
	data: array_like
		Data values.
	errs: array_like
		Data uncertainties for the errorbars.
	sample: array_like
		The (MCMC) sample of the parameter vector.
	filename: string
		Output filename of the figure file.
	"""
	# Set up the GP for this sample.
	logging.debug("sample values: %s", sample)
	model.set_parameter_vector(sample)
	log_lh = model.log_likelihood(data)
	logging.debug("sample log likelihood: %s", log_lh)
	logging.debug("half data variance: %s", 0.5 * np.var(data))
	t = np.sort(np.append(times,
			np.linspace(times.min(), times.max(), 1000)))
	# Plot
	fig = plt.figure()
	gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1])
	ax = plt.subplot(gs[0])
	ax2 = plt.subplot(gs[1])
	# Plot the data.
	ax.errorbar(times, data, yerr=2 * errs, fmt=".k", zorder=8)
	# Compute the prediction conditioned on the observations and plot it.
	mum = model.predict(data, t=times, return_cov=False)
	mup = model.mean.get_value(t)
	mum2 = model.mean.get_value(times)
	mu, cov = model.predict(data, t=t)
	try:
		cov[np.diag_indices_from(cov)] += model.kernel.jitter
	except AttributeError:
		pass
	std = np.sqrt(np.diagonal(cov))
	ax.plot(t, mup, alpha=0.75, color="C1", zorder=12,
			label="mean_model")
	ax.plot(t, mu, alpha=0.75, color="C0", zorder=10,
			label="logl: {0:.3f}".format(log_lh))
	ax.fill_between(t, mu - 2. * std, mu + 2. * std, color="C0", alpha=0.3, zorder=10)
	ax2.errorbar(times, data - mum, yerr=2 * errs, fmt=".k", zorder=12, alpha=0.5)
	ax2.errorbar(times, data - mum2, yerr=2 * errs, fmt=".C1", zorder=8, ms=4)
	ax2.axhline(y=0, color='k', alpha=0.5)
	ax.legend()
	fig.savefig(filename, transparent=True)


def plot_residual(model, times, data, errs, sample, scale, filename):

	"""Plot the residuals of one sample

	Parameters
	----------
	model: `celerite.Model`, `george.Model`, or `celerite.ModelSet`
		The Gaussian Process model to plot samples from.
	times: array_like
		Time axis values.
	data: array_like
		Data values.
	errs: array_like
		Data uncertainties for the errorbars.
	sample: array_like
		The (MCMC) sample of the parameter vector.
	scale: float
		The scale factor of the data to adjust the value axis.
	filename: string
		Output filename of the figure file.
	"""
	# Set up the GP for this sample.
	logging.debug("sample values: %s", sample)
	model.set_parameter_vector(sample)
	logging.debug("sample log likelihood: %s", model.log_likelihood(data))
	# Plot
	fig, ax = plt.subplots()
	# Plot the data.
	# Compute the prediction conditioned on the observations
	mu = model.predict(data, t=times, return_cov=False)
	# Plot the residuals with error bars
	ax.errorbar(times, data - mu, yerr=2 * errs, fmt=".k", zorder=8)
	ax.axhline(y=0, color='k', alpha=0.5)
	ax.set_xlabel("time [years]")
	ax.set_ylabel("number density residual [10$^{{{0:.0f}}}$ cm$^{{-3}}$]"
				.format(-np.log10(scale)))
	fig.savefig(filename, transparent=True)


def plot_single_sample(model, times, data, errs, sample, filename):

	"""Plot a sample and data

	Parameters
	----------
	model: `celerite.Model`, `george.Model`, or `celerite.ModelSet`
		The Gaussian Process model to plot samples from.
	times: array_like
		Time axis values.
	data: array_like
		Data values.
	errs: array_like
		Data uncertainties for the errorbars.
	sample: array_like
		The (MCMC) sample of the parameter vector.
	filename: string
		Output filename of the figure file.
	"""
	# Set up the GP for this sample.
	model.set_parameter_vector(sample)
	t = np.linspace(times.min(), times.max(), 1000)
	# Plot
	fig, ax = plt.subplots()
	# Plot the data.
	ax.errorbar(times, data, yerr=2 * errs, fmt="o", ms=4, zorder=4)
	# Compute the prediction conditioned on the observations and plot it.
	mu, cov = model.predict(data, t=t)
	_sample = np.random.multivariate_normal(mu, cov, 1)[0]
	ax.plot(t, _sample, alpha=0.75, zorder=2)
	fig.savefig(filename, transparent=True)


def plot_random_samples(model, times, data, errs,

		samples, scale, filename, size=8,
		extra_years=[0, 0]):
	"""Plot random samples and data

	Parameters
	----------
	model: `celerite.Model`, `george.Model`, or `celerite.ModelSet`
		The Gaussian Process model to plot samples from.
	times: array_like
		Time axis values.
	data: array_like
		Data values.
	errs: array_like
		Data uncertainties for the errorbars.
	samples: array_like
		The (MCMC) sample of the parameter vector.
	scale: float
		The scale factor of the data to adjust the value axis.
	filename: string
		Output filename of the figure file.
	size: int, optional
		Number of samples to plot.
	extra_years: list or tuple, optional
		Extend the prediction period extra_years[0] into the past
		and extra_years[1] into the future.
	"""
	t = np.linspace(times.min() - extra_years[0],
					times.max() + extra_years[1], 2000)
	fig, ax = plt.subplots()
	# Plot the data.
	ax.errorbar(times, data, yerr=2 * errs, fmt=".k", zorder=8)
	ax.set_xlim(np.min(t), np.max(t))
	ax.set_xlabel("time [years]")
	ax.set_ylabel("number density [10$^{{{0:.0f}}}$ cm$^{{-3}}$]"
				.format(-np.log10(scale)))
	for i in np.random.randint(len(samples), size=size):
		logging.info("plotting random sample %s.", i)
		logging.debug("sample values: %s", samples[i])
		model.set_parameter_vector(samples[i])
		logging.debug("sample log likelihood: %s", model.log_likelihood(data))
		# Compute the prediction conditioned on the observations and plot it.
		mu, cov = model.predict(data, t=t)
		try:
			cov[np.diag_indices_from(cov)] += model.kernel.jitter
		except AttributeError:
			pass
		sample = np.random.multivariate_normal(mu, cov, 1)[0]
		ax.plot(t, sample, color="C0", alpha=0.25, zorder=12)
	# fig.subplots_adjust(left=0.2, bottom=0.2, top=0.9, right=0.9)
	fig.savefig(filename, transparent=True)


1			# -- coding: utf-8 --
2			# vim:fileencoding=utf-8
3			#
4			# Copyright (c) 2017-2018 Stefan Bender
5			#
6			# This module is part of sciapy.
7			# sciapy is free software: you can redistribute it or modify
8			# it under the terms of the GNU General Public License as published
9			# by the Free Software Foundation, version 2.
10			# See accompanying LICENSE file or http://www.gnu.org/licenses/gpl-2.0.html.
11	1		"""SCIAMACHY regression tool plotting helpers
12
13			Plot (helper) functions for the regression command line tool.
14			"""
15
16	1		import logging
17
18	1		import numpy as np
19	1		import matplotlib as mpl
20	1		mpl.use("Agg")
21	1		import matplotlib.pyplot as plt
22	1		import matplotlib.gridspec as gridspec
23
24
25	1	View Code Duplication	def plot_single_sample_and_residuals(model, times, data, errs,
			0 ignored issues – show Duplication introduced 2018-08-03 14:32 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
26			sample, filename):
27			"""Plot one sample and residuals in one figure
28
29			Parameters
30			----------
31			model: `celerite.Model`, `george.Model`, or `celerite.ModelSet`
32			The Gaussian Process model to plot samples from.
33			times: array_like
34			Time axis values.
35			data: array_like
36			Data values.
37			errs: array_like
38			Data uncertainties for the errorbars.
39			sample: array_like
40			The (MCMC) sample of the parameter vector.
41			filename: string
42			Output filename of the figure file.
43			"""
44			# Set up the GP for this sample.
45			logging.debug("sample values: %s", sample)
46			model.set_parameter_vector(sample)
47			log_lh = model.log_likelihood(data)
48			logging.debug("sample log likelihood: %s", log_lh)
49			logging.debug("half data variance: %s", 0.5 * np.var(data))
50			t = np.sort(np.append(times,
51			np.linspace(times.min(), times.max(), 1000)))
52			# Plot
53			fig = plt.figure()
54			gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1])
55			ax = plt.subplot(gs[0])
56			ax2 = plt.subplot(gs[1])
57			# Plot the data.
58			ax.errorbar(times, data, yerr=2 * errs, fmt=".k", zorder=8)
59			# Compute the prediction conditioned on the observations and plot it.
60			mum = model.predict(data, t=times, return_cov=False)
61			mup = model.mean.get_value(t)
62			mum2 = model.mean.get_value(times)
63			mu, cov = model.predict(data, t=t)
64			try:
65			cov[np.diag_indices_from(cov)] += model.kernel.jitter
66			except AttributeError:
67			pass
68			std = np.sqrt(np.diagonal(cov))
69			ax.plot(t, mup, alpha=0.75, color="C1", zorder=12,
70			label="mean_model")
71			ax.plot(t, mu, alpha=0.75, color="C0", zorder=10,
72			label="logl: {0:.3f}".format(log_lh))
73			ax.fill_between(t, mu - 2. * std, mu + 2. * std, color="C0", alpha=0.3, zorder=10)
74			ax2.errorbar(times, data - mum, yerr=2 * errs, fmt=".k", zorder=12, alpha=0.5)
75			ax2.errorbar(times, data - mum2, yerr=2 * errs, fmt=".C1", zorder=8, ms=4)
76			ax2.axhline(y=0, color='k', alpha=0.5)
77			ax.legend()
78			fig.savefig(filename, transparent=True)
79
80
81	1	View Code Duplication	def plot_residual(model, times, data, errs, sample, scale, filename):
			0 ignored issues – show Duplication introduced 2018-08-03 14:32 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
82			"""Plot the residuals of one sample
83
84			Parameters
85			----------
86			model: `celerite.Model`, `george.Model`, or `celerite.ModelSet`
87			The Gaussian Process model to plot samples from.
88			times: array_like
89			Time axis values.
90			data: array_like
91			Data values.
92			errs: array_like
93			Data uncertainties for the errorbars.
94			sample: array_like
95			The (MCMC) sample of the parameter vector.
96			scale: float
97			The scale factor of the data to adjust the value axis.
98			filename: string
99			Output filename of the figure file.
100			"""
101			# Set up the GP for this sample.
102			logging.debug("sample values: %s", sample)
103			model.set_parameter_vector(sample)
104			logging.debug("sample log likelihood: %s", model.log_likelihood(data))
105			# Plot
106			fig, ax = plt.subplots()
107			# Plot the data.
108			# Compute the prediction conditioned on the observations
109			mu = model.predict(data, t=times, return_cov=False)
110			# Plot the residuals with error bars
111			ax.errorbar(times, data - mu, yerr=2 * errs, fmt=".k", zorder=8)
112			ax.axhline(y=0, color='k', alpha=0.5)
113			ax.set_xlabel("time [years]")
114			ax.set_ylabel("number density residual [10$^{{{0:.0f}}}$ cm$^{{-3}}$]"
115			.format(-np.log10(scale)))
116			fig.savefig(filename, transparent=True)
117
118
119	1	View Code Duplication	def plot_single_sample(model, times, data, errs, sample, filename):
			0 ignored issues – show Duplication introduced 2018-07-02 09:55 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
120			"""Plot a sample and data
121
122			Parameters
123			----------
124			model: `celerite.Model`, `george.Model`, or `celerite.ModelSet`
125			The Gaussian Process model to plot samples from.
126			times: array_like
127			Time axis values.
128			data: array_like
129			Data values.
130			errs: array_like
131			Data uncertainties for the errorbars.
132			sample: array_like
133			The (MCMC) sample of the parameter vector.
134			filename: string
135			Output filename of the figure file.
136			"""
137			# Set up the GP for this sample.
138			model.set_parameter_vector(sample)
139			t = np.linspace(times.min(), times.max(), 1000)
140			# Plot
141			fig, ax = plt.subplots()
142			# Plot the data.
143			ax.errorbar(times, data, yerr=2 * errs, fmt="o", ms=4, zorder=4)
144			# Compute the prediction conditioned on the observations and plot it.
145			mu, cov = model.predict(data, t=t)
146			_sample = np.random.multivariate_normal(mu, cov, 1)[0]
147			ax.plot(t, _sample, alpha=0.75, zorder=2)
148			fig.savefig(filename, transparent=True)
149
150
151	1	View Code Duplication	def plot_random_samples(model, times, data, errs,
			0 ignored issues – show Duplication introduced 2018-07-02 09:55 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
152			samples, scale, filename, size=8,
153			extra_years=[0, 0]):
154			"""Plot random samples and data
155
156			Parameters
157			----------
158			model: `celerite.Model`, `george.Model`, or `celerite.ModelSet`
159			The Gaussian Process model to plot samples from.
160			times: array_like
161			Time axis values.
162			data: array_like
163			Data values.
164			errs: array_like
165			Data uncertainties for the errorbars.
166			samples: array_like
167			The (MCMC) sample of the parameter vector.
168			scale: float
169			The scale factor of the data to adjust the value axis.
170			filename: string
171			Output filename of the figure file.
172			size: int, optional
173			Number of samples to plot.
174			extra_years: list or tuple, optional
175			Extend the prediction period extra_years[0] into the past
176			and extra_years[1] into the future.
177			"""
178			t = np.linspace(times.min() - extra_years[0],
179			times.max() + extra_years[1], 2000)
180			fig, ax = plt.subplots()
181			# Plot the data.
182			ax.errorbar(times, data, yerr=2 * errs, fmt=".k", zorder=8)
183			ax.set_xlim(np.min(t), np.max(t))
184			ax.set_xlabel("time [years]")
185			ax.set_ylabel("number density [10$^{{{0:.0f}}}$ cm$^{{-3}}$]"
186			.format(-np.log10(scale)))
187			for i in np.random.randint(len(samples), size=size):
188			logging.info("plotting random sample %s.", i)
189			logging.debug("sample values: %s", samples[i])
190			model.set_parameter_vector(samples[i])
191			logging.debug("sample log likelihood: %s", model.log_likelihood(data))
192			# Compute the prediction conditioned on the observations and plot it.
193			mu, cov = model.predict(data, t=t)
194			try:
195			cov[np.diag_indices_from(cov)] += model.kernel.jitter
196			except AttributeError:
197			pass
198			sample = np.random.multivariate_normal(mu, cov, 1)[0]
199			ax.plot(t, sample, color="C0", alpha=0.25, zorder=12)
200			# fig.subplots_adjust(left=0.2, bottom=0.2, top=0.9, right=0.9)
201			fig.savefig(filename, transparent=True)
202

st-bender / sciapy

Branch — master (9c78f3)

sciapy.regress._plot.plot_single_sample() A

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