sciapy.regress._plot.plot_random_samples() - Code Metrics - Inspection of "ci: Upload coverage to scrutinizer" - st-bender/sciapy - Measure and Improve Code Quality continuously with Scrutinizer

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by Stefan

created 2022-10-19 14:40 UTC

sciapy.regress._plot.plot_random_samples() A

↳ Parent: sciapy.regress._plot

Complexity

Conditions

Size

Total Lines	51
Code Lines	24

Duplication

Lines	0
Ratio	0 %

Code Coverage

Tests	18
CRAP Score	3.009

Importance

Changes

Metric	Value
cc	3
eloc	24
nop	9
dl	0
loc	51
ccs	18
cts	20
cp	0.9
crap	3.009
rs	9.304
c	0
b	0
f	0

How to fix Long Method Many Parameters

# -*- 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	def plot_single_sample_and_residuals(model, times, data, errs,
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	1	logging.debug("sample values: %s", sample)
46	1	model.set_parameter_vector(sample)
47	1	log_lh = model.log_likelihood(data)
48	1	logging.debug("sample log likelihood: %s", log_lh)
49	1	logging.debug("half data variance: %s", 0.5 * np.var(data))
50	1	t = np.sort(np.append(times,
51		np.linspace(times.min(), times.max(), 1000)))
52		# Plot
53	1	fig = plt.figure()
54	1	gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1])
55	1	ax = plt.subplot(gs[0])
56	1	ax2 = plt.subplot(gs[1])
57		# Plot the data.
58	1	ax.errorbar(times, data, yerr=2 * errs, fmt=".k", zorder=8)
59		# Compute the prediction conditioned on the observations and plot it.
60	1	mum = model.predict(data, t=times, return_cov=False)
61	1	mup = model.mean.get_value(t)
62	1	mum2 = model.mean.get_value(times)
63	1	mu, cov = model.predict(data, t=t)
64	1	try:
65	1	cov[np.diag_indices_from(cov)] += model.kernel.jitter
66		except AttributeError:
67		pass
68	1	std = np.sqrt(np.diagonal(cov))
69	1	ax.plot(t, mup, alpha=0.75, color="C1", zorder=12,
70		label="mean_model")
71	1	ax.plot(t, mu, alpha=0.75, color="C0", zorder=10,
72		label="logl: {0:.3f}".format(log_lh))
73	1	ax.fill_between(t, mu - 2. * std, mu + 2. * std, color="C0", alpha=0.3, zorder=10)
74	1	ax2.errorbar(times, data - mum, yerr=2 * errs, fmt=".k", zorder=12, alpha=0.5)
75	1	ax2.errorbar(times, data - mum2, yerr=2 * errs, fmt=".C1", zorder=8, ms=4)
76	1	ax2.axhline(y=0, color='k', alpha=0.5)
77	1	ax.legend()
78	1	fig.savefig(filename, transparent=True)
79
80
81	1	def plot_residual(model, times, data, errs, sample, scale, filename):
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	1	logging.debug("sample values: %s", sample)
103	1	model.set_parameter_vector(sample)
104	1	logging.debug("sample log likelihood: %s", model.log_likelihood(data))
105		# Plot
106	1	fig, ax = plt.subplots()
107		# Plot the data.
108		# Compute the prediction conditioned on the observations
109	1	mu = model.predict(data, t=times, return_cov=False)
110		# Plot the residuals with error bars
111	1	ax.errorbar(times, data - mu, yerr=2 * errs, fmt=".k", zorder=8)
112	1	ax.axhline(y=0, color='k', alpha=0.5)
113	1	ax.set_xlabel("time [years]")
114	1	ax.set_ylabel("number density residual [10$^{{{0:.0f}}}$ cm$^{{-3}}$]"
115		.format(-np.log10(scale)))
116	1	fig.savefig(filename, transparent=True)
117
118
119	1	def plot_single_sample(model, times, data, errs, sample, filename):
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	def plot_random_samples(model, times, data, errs,
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	1	t = np.linspace(times.min() - extra_years[0],
179		times.max() + extra_years[1], 2000)
180	1	fig, ax = plt.subplots()
181		# Plot the data.
182	1	ax.errorbar(times, data, yerr=2 * errs, fmt=".k", zorder=8)
183	1	ax.set_xlim(np.min(t), np.max(t))
184	1	ax.set_xlabel("time [years]")
185	1	ax.set_ylabel("number density [10$^{{{0:.0f}}}$ cm$^{{-3}}$]"
186		.format(-np.log10(scale)))
187	1	for i in np.random.randint(len(samples), size=size):
188	1	logging.info("plotting random sample %s.", i)
189	1	logging.debug("sample values: %s", samples[i])
190	1	model.set_parameter_vector(samples[i])
191	1	logging.debug("sample log likelihood: %s", model.log_likelihood(data))
192		# Compute the prediction conditioned on the observations and plot it.
193	1	mu, cov = model.predict(data, t=t)
194	1	try:
195	1	cov[np.diag_indices_from(cov)] += model.kernel.jitter
196		except AttributeError:
197		pass
198	1	sample = np.random.multivariate_normal(mu, cov, 1)[0]
199	1	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

Push — master ( d1ac5b...af9f8c )

sciapy.regress._plot.plot_random_samples() A

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Duplication

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How to fix Long Method Many Parameters

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