mutis.signal.Signal.__init__() - Code Metrics - Inspection of "rename some var names and remove lc_gen_psd" - IAA-CSIC/MUTIS - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — main (#11)

unknown

created 2021-05-19 17:52 UTC

mutis.signal.Signal.init() A

↳ Parent: mutis.signal

Complexity

Conditions

Size

Total Lines	10
Code Lines	8

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	8
dl	0
loc	10
rs	10
c	0
b	0
f	0
cc	1
nop	4

# Licensed under a 3-clause BSD style license - see LICENSE
"""Synthetic generation of light curves."""

import logging

import matplotlib.pyplot as plt

import numpy as np

import scipy.optimize as scipy_optimize

import scipy.special as scipy_special

import scipy.stats as scipy_stats

from matplotlib.offsetbox import AnchoredText


from mutis.lib.signal import *


__all__ = ["Signal"]

log = logging.getLogger(__name__)



class Signal:
    """Analysis and generation of a signal.

    Description goes here.

    Parameters
    ----------
    times : :class:`numpy.ndarray` or :class:`pandas.Series`
        Values of the time axis.
    values : :class:`numpy.ndarray` or :class:`pandas.Series`
        Values of the signal axis.
    fgen : :class:`str`
        Method used to generate the synthetic signal.
    """

    def __init__(self, times, values, fgen):
        self.times = np.array(times)
        self.values = np.array(values)
        self.fgen = fgen
        self.synth = None

        # TODO make attributes below specific of OU method / not the entire class

        self.theta = None
        self.mu = None

        self.sigma = None

    def gen_synth(self, samples):
        """Description goes here."""

        self.synth = np.empty((samples, self.times.size))
        for n in range(samples):

            if self.fgen == "lc_gen_samp":
                self.synth[n] = lc_gen_samp(self.values)
            elif self.fgen == "lc_gen_psd_nft":
                self.synth[n] = lc_gen_psd_nft(self.times, self.values)
            elif self.fgen == "lc_gen_psd_lombscargle":
                self.synth[n] = lc_gen_psd_lombscargle(self.times, self.values)
            elif self.fgen == "lc_gen_psd_fft":
                self.synth[n] = lc_gen_psd_fft(self.values)
            elif self.fgen == "lc_gen_ou":
                if self.theta is None or self.mu is None or self.sigma is None:
                    raise Exception("You need to set the parameters for the signal")
                self.synth[n] = lc_gen_ou(self.theta, self.mu, self.sigma, self.times)
            else:
                raise Exception(f"Unknown fgen method {self.fgen}")

    def OU_fit(self, bins=None, rang=None, a=1e-5, b=100):

        """Description goes here."""

        # TODO: make a generic fit method

        res = dict()

        # estimate sigma
        try:
            # dy = np.diff(self.values)
            # dt = np.diff(self.times)
            sigma_est = (np.nanmean(np.diff(self.values) ** 2 / self.values[:-1] ** 2 / np.diff(self.times))) ** 0.5

            res["sigma_est"] = sigma_est
        except Exception as e:

            raise Exception(f"Could not estimate sigma: {e}")

        # plot histogram
        if bins is None:
            bins = np.int(self.values.size ** 0.5 / 1.5)  # bins='auto'
        if rang is None:
            rang = (np.percentile(self.values, 0), np.percentile(self.values, 99))
        p, x = np.histogram(self.values, density=True, bins=bins, range=rang)  # bins='sqrt')

        x = (x + np.roll(x, -1))[:-1] / 2.0


        plt.subplots()
        plt.hist(self.values, density=True, alpha=0.75, bins=bins, range=rang)
        plt.plot(x, p, "r-", alpha=0.5)
        anchored_text = AnchoredText(
            f"mean    {np.mean(self.values):.2g} \n "
            "median  {np.median(self.values):.2g} \n "
            "mode    {scipy_stats.mode(self.values)[0][0]:.2g} \n "
            "std     {np.std(self.values):.2g} \n "
            "var     {np.var(self.values):.2g}",
            loc="upper right",
        )
        plt.gca().add_artist(anchored_text)

        # curve_fit
        try:
            popt, pcov = scipy_optimize.curve_fit(f=self.pdf, xdata=x, ydata=p)
            # print('curve_fit: (l, mu)')
            # print('popt: ')
            # print(popt)
            # print('pcov: ')
            # print(np.sqrt(np.diag(pcov)))
            x_c = np.linspace(1e-5, 1.1 * np.max(x), 1000)
            plt.plot(x_c, self.pdf(x_c, *popt), "k-", label="curve_fit", alpha=0.8)
            res["curve_fit"] = (popt, np.sqrt(np.diag(pcov)))
        except Exception as e:

            raise Exception(f"Some error fitting with curve_fit {e}")

        # TODO: place outside as a helper class

        #       and understand how it is used
        #       is it mu really needed ?
        #       mu here is different than self.mu
        # fit pdf with MLE
        class OU(scipy_stats.rv_continuous):

            def _pdf(self, x, l, mu):
class Foo:
    def some_method(self, x, y):
        return x + y;
                return (l * mu) ** (1 + l) / scipy_special.gamma(1 + l) * np.exp(-l * mu / x) / x ** (l + 2)


        try:
            fit = OU(a=a, b=np.percentile(self.values, b)).fit(self.values, 1, 1, floc=0, fscale=1)
            # print('MLE fit: (l, mu)')
            # print(fit)
            x_c = np.linspace(0, 1.1 * np.max(x), 1000)
            plt.plot(x_c, self.pdf(x_c, fit[0], fit[1]), "k-.", label="MLE", alpha=0.8)
            res["MLE_fit"] = fit[:-2]
        except Exception as e:

            raise Exception(f"Some error fitting with MLE {e}")
        plt.legend(loc="lower right")
        plt.show()

        # estimate theta
        res["th_est1"] = fit[0] * sigma_est ** 2 / 2
        res["th_est2"] = popt[0] * sigma_est ** 2 / 2

        return res

    def OU_check_gen(self, theta, mu, sigma):

        """Description goes here."""

        # TODO: make a generic check_gen method


        t, y = self.times, self.values

        y2 = lc_gen_ou(theta, mu, sigma, self.times, scale=np.std(self.values), loc=np.mean(self.values))


        # plot the two signals
        fig, ax = plt.subplots()

        ax.plot(t, y, "b-", label="orig", lw=0.5, alpha=0.8)
        ax2 = ax.twinx()
        ax2.plot(t, y2, "r-", label="gen", lw=0.5, alpha=0.8)
        plt.show()

        # plot their histogram
        fig, ax = plt.subplots()

        bins = "auto"  # bins = np.int(y.size**0.5/1.5) #
        rang = (np.percentile(y, 0), np.percentile(y, 99))
        ax.hist(y, density=True, color="b", alpha=0.4, bins=bins, range=rang)
        ax2 = ax.twinx()
        bins = "auto"  # bins = np.int(y.size**0.5/1.5) #
        rang = (np.percentile(y2, 0), np.percentile(y2, 99))
        ax2.hist(y2, density=True, color="r", alpha=0.4, bins=bins, range=rang)
        plt.show()

        # plot their PSD
        fig, ax = plt.subplots()

        ax.psd(y, color="b", lw=1, alpha=0.5)
        ax2 = ax.twinx()
        ax2.psd(y2, color="r", lw=1, alpha=0.5)
        plt.show()

    def PSD_check_gen(self, fgen=None, ax=None):

        """Description goes here."""

        # TODO: make a generic check_gen method


        if fgen is None:
            fgen = self.fgen
        if ax is None:
            ax = plt.gca()

        if fgen == "lc_gen_psd_nft":
            s2 = lc_gen_psd_nft(self.times, self.values)

        elif fgen == "lc_gen_psd_lombscargle":
            s2 = lc_gen_psd_lombscargle(self.times, self.values)

        elif fgen == "lc_gen_psd_fft":
            s2 = lc_gen_psd_fft(self.values)

        else:
            raise Exception("No valid fgen specified")

        ax.plot(self.times, self.values, "b-", label="orig", lw=0.5, alpha=0.8)
        ax.plot(self.times, s2, "r-", label="gen", lw=0.5, alpha=0.8)
        ax.legend()

    @staticmethod
    def pdf(xx, ll, mu):

        """Fit pdf as a curve."""
        return (ll * mu) ** (1 + ll) / scipy_special.gamma(1 + ll) * np.exp(-ll * mu / xx) / xx ** (ll + 2)



1			# Licensed under a 3-clause BSD style license - see LICENSE
2			"""Synthetic generation of light curves."""
3
4			import logging
5
6			import matplotlib.pyplot as plt
			0 ignored issues – show introduced 2021-05-18 22:21 UTC by Report Bug Copy Issue Report Unable to import 'matplotlib.pyplot' Loading history...
7			import numpy as np
			0 ignored issues – show introduced 2021-05-18 22:21 UTC by Report Bug Copy Issue Report Unable to import 'numpy' Loading history...
8			import scipy.optimize as scipy_optimize
			0 ignored issues – show introduced 2021-05-18 22:21 UTC by Report Bug Copy Issue Report Unable to import 'scipy.optimize' Loading history...
9			import scipy.special as scipy_special
			0 ignored issues – show introduced 2021-05-18 22:21 UTC by Report Bug Copy Issue Report Unable to import 'scipy.special' Loading history...
10			import scipy.stats as scipy_stats
			0 ignored issues – show introduced 2021-05-18 22:21 UTC by Report Bug Copy Issue Report Unable to import 'scipy.stats' Loading history...
11			from matplotlib.offsetbox import AnchoredText
			0 ignored issues – show introduced 2021-05-18 22:21 UTC by Report Bug Copy Issue Report Unable to import 'matplotlib.offsetbox' Loading history...
12
13			from mutis.lib.signal import *
			0 ignored issues – show Coding Style introduced 2021-05-18 22:21 UTC by Report Bug Copy Issue Report The usage of wildcard imports like `mutis.lib.signal` should generally be avoided. Loading history... Unused Code introduced 2021-05-18 22:21 UTC by Report Bug Copy Issue Report `log` was imported with wildcard, but is not used. Loading history... Unused Code introduced 2021-05-18 22:21 UTC by Report Bug Copy Issue Report `nfft` was imported with wildcard, but is not used. Loading history... Unused Code introduced 2021-05-18 22:21 UTC by Report Bug Copy Issue Report `scipy_signal` was imported with wildcard, but is not used. Loading history... Unused Code introduced 2021-05-18 22:21 UTC by Report Bug Copy Issue Report `lc_gen_psd_c` was imported with wildcard, but is not used. Loading history...
14
15			__all__ = ["Signal"]
16
17			log = logging.getLogger(__name__)
18
19
20
21			class Signal:
22			"""Analysis and generation of a signal.
23
24			Description goes here.
25
26			Parameters
27			----------
28			times : :class:`numpy.ndarray` or :class:`pandas.Series`
29			Values of the time axis.
30			values : :class:`numpy.ndarray` or :class:`pandas.Series`
31			Values of the signal axis.
32			fgen : :class:`str`
33			Method used to generate the synthetic signal.

IAA-CSIC / MUTIS

Pull Request — main (#11)

mutis.signal.Signal.__init__() A

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mutis.signal.Signal.init() A