mutis.correlation

Complexity

Total Complexity

45

Size/Duplication

Total Lines	435
Duplicated Lines	0 %

Importance

Changes

0

8 Methods

Rating	Name	Duplication	Size	Complexity
A	Correlation.plot_times()	0	50	3
B	Correlation.plot_corr()	0	62	8
F	Correlation.gen_corr()	0	125	21
A	Correlation.__init__()	0	43	1
A	Correlation.gen_synth()	0	16	1
A	Correlation.gen_times()	0	38	5
A	Correlation.plot_signals()	0	27	2
A	Correlation.peak_find()	0	32	4

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

import logging

import matplotlib.pyplot as plt

Unable to import 'matplotlib.pyplot'

import numpy as np

Unable to import 'numpy'

import scipy as sp

Unable to import 'scipy'

from mutis.lib.correlation import *

get_grid was imported with wildcard, but is not used.

ndcf was imported with wildcard, but is not used.

welsh_ab_p was imported with wildcard, but is not used.

kroedel_ab_p was imported with wildcard, but is not used.

The usage of wildcard imports like mutis.lib.correlation should generally be avoided.

from mutis.lib.utils import interp_smooth_curve


__all__ = ["Correlation"]

log = logging.getLogger(__name__)


class Correlation:

Too many instance attributes (21/7)

    """Analysis of the correlation of two signals.

    Parameters
    ----------
    signal1 : :class:`~mutis.signal.Signal`
        Values of the time axis.
    signal2 : :class:`~mutis.signal.Signal`
        Values of the signal axis.
    fcorr : :py:class:`~str`
        Method used to correlate the signals.
    """

    def __init__(self, signal1, signal2, fcorr):
        self.signal1 = signal1
        self.signal2 = signal2
        self.fcorr = fcorr

        self.times = np.array([])
        self.dts = np.array([])
        self.nb = np.array([])

Attribute name "nb" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,|_[^\\WA-Z]*|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern)

        self.values = None

        # TODO: have a much smaller set of attributes

TODO and FIXME comments should generally be avoided.

        self.samples = None
        # storage of the significance limits of the correlation
        self.l1s = None
        self.l2s = None
        self.l3s = None
        # storage of the uncertainties of the correlation
        self.s1s = None
        self.s2s = None
        self.s3s = None

        # attributes indicating the ranges where the correlations are defined
        t1, t2 = self.signal1.times, self.signal2.times

Variable name "t2" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,|_[^\\WA-Z]*|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern)

Variable name "t1" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,|_[^\\WA-Z]*|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern)

        self.tmin_full = t2.min() - t1.max()
        self.tmax_full = t2.max() - t1.min()
        self.t0_full = (self.tmax_full + self.tmin_full) / 2
        self.tmin_same = -(np.max([t1.max() - t1.min(), t2.max() - t2.min()])) / 2 + self.t0_full
        self.tmax_same = (np.max([t1.max() - t1.min(), t2.max() - t2.min()])) / 2 + self.t0_full
        self.tmin_valid = (
            -(
                np.max([t1.max() - t1.min(), t2.max() - t2.min()])
                - np.min([t1.max() - t1.min(), t2.max() - t2.min()])
            )
            / 2
            + self.t0_full
        )
        self.tmax_valid = (
            +(
                np.max([t1.max() - t1.min(), t2.max() - t2.min()])
                - np.min([t1.max() - t1.min(), t2.max() - t2.min()])
            )
            / 2
            + self.t0_full
        )

    def peak_find(self, smooth=False, smooth_std=None, Ninterp=1000):

Argument name "Ninterp" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,|_[^\\WA-Z]*|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern)

This function exceeds the maximum number of variables (20/15).

        """Find the peaks of the correlation, optionally smoothing with a kernel of standard deviation `s`.

This line is too long as per the coding-style (107/100).

        Returns dict with peak positions and significances, ordered from closest to farthest from zero.

This line is too long as per the coding-style (103/100).

        """
        x, y = self.times, self.values

Variable name "x" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,|_[^\\WA-Z]*|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern)

Variable name "y" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,|_[^\\WA-Z]*|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern)

        if smooth_std is None:
            dt1 = np.mean(self.signal1.times[1:]-self.signal1.times[:-1])
            std1 = np.std(self.signal1.times[1:]-self.signal1.times[:-1])

The variable std1 seems to be unused.

            dt2 = np.mean(self.signal2.times[1:]-self.signal2.times[:-1])
            std2 = np.std(self.signal2.times[1:]-self.signal2.times[:-1])

The variable std2 seems to be unused.

            smooth_std = 1*np.max([dt1,dt2])

Exactly one space required after comma

Trailing whitespace

        if smooth:
            xs, ys = interp_smooth_curve(x, y, smooth_std, Ninterp)

Variable name "ys" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,|_[^\\WA-Z]*|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern)

Variable name "xs" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,|_[^\\WA-Z]*|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern)

        else:
            xs, ys = x, y

Variable name "ys" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,|_[^\\WA-Z]*|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern)

Variable name "xs" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,|_[^\\WA-Z]*|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern)

        idx, props = sp.signal.find_peaks(ys)

The variable props seems to be unused.

Trailing whitespace

        if smooth:
            s1s_x, s1s_y = interp_smooth_curve(x, self.l1s[1], smooth_std, Ninterp)

The variable s1s_x seems to be unused.

        else:
            s1s_x, s1s_y = x, self.l1s[1]

        peak_idx = idx[np.argsort(np.abs(xs[idx]))]
        peak_x = xs[peak_idx]
        peak_y = ys[peak_idx]
        peak_signf1s = ys[peak_idx]/s1s_y[peak_idx]

        return {'x':peak_x, 's':smooth_std, 'y':peak_y, 'signf1s':peak_signf1s}

    def gen_synth(self, samples):
        """Generates the synthetic light curves.

        Generates the specified number `samples` of synthetic light
        curves for each signal, to be used to compute the significance
        the correlation.

        Parameters
        ----------
        samples : :py:class:`~int`
            Number of synthetic light curves to be generated for each signal.
        """

        self.samples = samples
        self.signal1.gen_synth(samples)
        self.signal2.gen_synth(samples)

    def gen_corr(self, uncert=True, dsamples=500):
        """Generates the correlation of the signals.

        Generates the correlation of the signals, and computes their
        confidence level from the synthetic light curves, which must
        have been generated before.
        """

        if uncert and self.signal1.dvalues is None:
            log.error(
                "uncert is True but no uncertainties for Signal 1 were specified, setting uncert to False"

This line is too long as per the coding-style (106/100).

            )
            uncert = False
        if uncert and self.signal2.dvalues is None:
            log.error(
                "uncert is True but no uncertainties for Signal 2 were specified, setting uncert to False"

This line is too long as per the coding-style (106/100).

            )
            uncert = False

        if len(self.times) == 0 or len(self.dts) == 0:
            raise Exception(
                "You need to define the times on which to calculate the correlation."
                "Please use gen_times() or manually set them."
            )

        # TODO: refactor if/elif with a helper function

TODO and FIXME comments should generally be avoided.

        mc_corr = np.empty((self.samples, self.times.size))
        if uncert:
            mc_sig = np.empty((dsamples, self.times.size))

        if self.fcorr == "welsh_ab":
            for idx in range(self.samples):
                mc_corr[idx] = welsh_ab(
                    self.signal1.times,
                    self.signal1.synth[idx],
                    self.signal2.times,
                    self.signal2.synth[idx],
                    self.times,
                    self.dts,
                )
            if uncert:
                for idx in range(dsamples):
                    mc_sig[idx] = welsh_ab(
                        self.signal1.times,
                        self.signal1.values
                        + self.signal1.dvalues * np.random.randn(self.signal1.values.size),
                        self.signal2.times,
                        self.signal2.values
                        + self.signal2.dvalues * np.random.randn(self.signal2.values.size),
                        self.times,
                        self.dts,
                    )
            self.values = welsh_ab(
                self.signal1.times,
                self.signal1.values,
                self.signal2.times,
                self.signal2.values,
                self.times,
                self.dts,
            )
        elif self.fcorr == "kroedel_ab":
            for idx in range(self.samples):
                mc_corr[idx] = kroedel_ab(
                    self.signal1.times,
                    self.signal1.synth[idx],
                    self.signal2.times,
                    self.signal2.synth[idx],
                    self.times,
                    self.dts,
                )
            if uncert:
                for idx in range(dsamples):
                    mc_sig[idx] = kroedel_ab(
                        self.signal1.times,
                        self.signal1.values
                        + self.signal1.dvalues * np.random.randn(self.signal1.values.size),
                        self.signal2.times,
                        self.signal2.values
                        + self.signal2.dvalues * np.random.randn(self.signal2.values.size),
                        self.times,
                        self.dts,
                    )
            self.values = kroedel_ab(
                self.signal1.times,
                self.signal1.values,
                self.signal2.times,
                self.signal2.values,
                self.times,
                self.dts,
            )
        elif self.fcorr == "numpy":
            for idx in range(self.samples):
                mc_corr[idx] = nindcf(
                    self.signal1.times,
                    self.signal1.synth[idx],
                    self.signal2.times,
                    self.signal2.synth[idx],
                )
            if uncert:
                for idx in range(dsamples):
                    mc_sig[idx] = nindcf(
                        self.signal1.times,
                        self.signal1.values
                        + self.signal1.dvalues * np.random.randn(self.signal1.values.size),
                        self.signal2.times,
                        self.signal2.values
                        + self.signal2.dvalues * np.random.randn(self.signal2.values.size),
                    )
            self.values = nindcf(
                self.signal1.times,
                self.signal1.values,
                self.signal2.times,
                self.signal2.values,
            )
        else:
            raise Exception("Unknown method " + self.fcorr + " for correlation.")

        self.l3s = np.percentile(mc_corr, [0.135, 99.865], axis=0)
        self.l2s = np.percentile(mc_corr, [2.28, 97.73], axis=0)
        self.l1s = np.percentile(mc_corr, [15.865, 84.135], axis=0)

        if uncert:
            self.s3s = np.percentile(mc_sig, [0.135, 99.865], axis=0)

The variable mc_sig does not seem to be defined in case uncert on line 155 is False. Are you sure this can never be the case?

            self.s2s = np.percentile(mc_sig, [2.28, 97.73], axis=0)
            self.s1s = np.percentile(mc_sig, [15.865, 84.135], axis=0)

    def gen_times(self, ftimes="canopy", *args, **kwargs):

Keyword argument before variable positional arguments list in the definition of gen_times function

        """Sets times and bins using the method defined by ftimes parameter.

        Parameters
        ----------
        ftimes : :py:class:`~str`
            Method used to bin the time interval of the correlation.
            Possible values are:
            - "canopy": Computes a binning as dense as possible, with
            variable bin width and (with a minimum and a maximum
            resolution) and a minimum statistic.
            - "rawab": Computes a binning with variable bin width,
            a given step, maximum bin size and a minimum statistic.
            - "uniform": Computes a binning with uniform bin width
            and a minimum statistic.
            - "numpy": Computes a binning suitable for method='numpy'.
        """
        if ftimes == "canopy":
            self.times, self.dts, self.nb = gen_times_canopy(
                self.signal1.times, self.signal2.times, *args, **kwargs
            )
        elif ftimes == "rawab":
            self.times, self.dts, self.nb = gen_times_rawab(
                self.signal1.times, self.signal2.times, *args, **kwargs
            )
        elif ftimes == "uniform":
            self.times, self.dts, self.nb = gen_times_uniform(
                self.signal1.times, self.signal2.times, *args, **kwargs
            )
        elif ftimes == "numpy":
            t1, t2 = self.signal1.times, self.signal1.times

Variable name "t2" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,|_[^\\WA-Z]*|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern)

Variable name "t1" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,|_[^\\WA-Z]*|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern)

            dt = np.max([(t1.max() - t1.min()) / t1.size, (t2.max() - t2.min()) / t2.size])

Variable name "dt" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,|_[^\\WA-Z]*|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern)

            n1 = int(np.ptp(t1) / dt * 10.0)

Variable name "n1" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,|_[^\\WA-Z]*|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern)

            n2 = int(np.ptp(t1) / dt * 10.0)

Variable name "n2" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,|_[^\\WA-Z]*|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern)

            self.times = np.linspace(self.tmin_full, self.tmax_full, n1 + n2 - 1)
            self.dts = np.full(self.times.size, (self.tmax_full - self.tmin_full) / (n1 + n2))
        else:
            raise Exception("Unknown method " + ftimes + ", please indicate how to generate times.")

    def plot_corr(self, uncert=True, ax=None, legend=False):

Argument name "ax" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,|_[^\\WA-Z]*|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern)

        """Plots the correlation of the signals.

        Plots the correlation of the signal, and the confidence limits
        computed from the synthetic curves.

        Parameters
        ----------
        ax : :class:`matplotlib.axes.Axes`
            Axes to be used (default None, it creates a new axes).
        legend : :py:class:`~bool`
            Whether to add a legend indicating the confidence levels.
        """

        # TODO: develop a plotting object for plots

TODO and FIXME comments should generally be avoided.

        #       this will considerably shorten the
        #       number of attributes of this class

        if uncert and self.signal1.dvalues is None:
            log.error(
                "uncert is True but no uncertainties for Signal 1 were specified, setting uncert to False"

This line is too long as per the coding-style (106/100).

            )
            uncert = False
        if uncert and self.signal2.dvalues is None:
            log.error(
                "uncert is True but no uncertainties for Signal 2 were specified, setting uncert to False"

This line is too long as per the coding-style (106/100).

            )
            uncert = False

        # plt.figure()
        if ax is None:
            ax = plt.gca()

        ax.plot(self.times, self.l1s[0], "c-.")
        ax.plot(self.times, self.l1s[1], "c-.", label=r"$1\sigma$")
        ax.plot(self.times, self.l2s[0], "k--")
        ax.plot(self.times, self.l2s[1], "k--", label=r"$2\sigma$")
        ax.plot(self.times, self.l3s[0], "r-")
        ax.plot(self.times, self.l3s[1], "r-", label=r"$3\sigma$")
        ax.plot(self.times, self.values, "b.--", lw=1)

        # full limit
        ax.axvline(x=self.tmin_full, ymin=-1, ymax=+1, color="red", linewidth=4, alpha=0.5)
        ax.axvline(x=self.tmax_full, ymin=-1, ymax=+1, color="red", linewidth=4, alpha=0.5)
        # same limit
        ax.axvline(x=self.tmin_same, ymin=-1, ymax=+1, color="black", linewidth=2, alpha=0.5)
        ax.axvline(x=self.tmax_same, ymin=-1, ymax=+1, color="black", linewidth=2, alpha=0.5)
        # valid limit
        ax.axvline(x=self.tmin_valid, ymin=-1, ymax=+1, color="cyan", linewidth=1, alpha=0.5)
        ax.axvline(x=self.tmax_valid, ymin=-1, ymax=+1, color="cyan", linewidth=1, alpha=0.5)

        if uncert:
            ax.fill_between(x=self.times, y1=self.s1s[0], y2=self.s1s[1], color="b", alpha=0.5)
            ax.fill_between(x=self.times, y1=self.s2s[0], y2=self.s2s[1], color="b", alpha=0.3)
            ax.fill_between(x=self.times, y1=self.s3s[0], y2=self.s3s[1], color="b", alpha=0.1)
            

Trailing whitespace

Trailing whitespace

        if legend:
            ax.legend()

        # plt.show()
        return ax

    def plot_times(self, rug=False):
        """Plots the time binning generated previously.

        Plots the number of total bins, their distribution and the
        number of points in each bin for the generated time binning,
        previously generated with Correlation().gen_times(...).

        Parameters
        ----------
        rug : :py:class:`~bool`
            Whether to make a rug plot just below the binning, to make
            it easier to visually understand the density and distribution
            of the generated bins.

        """

        # TODO: develop a plotting object for plots

TODO and FIXME comments should generally be avoided.

        #       this will considerably shorten the
        #       number of attributes of this class

        fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True)

Variable name "ax" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,|_[^\\WA-Z]*|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern)

        tab, dtab, nab = self.times, self.dts, self.nb

        fig.suptitle("Total bins: {:d}".format(self.times.size))
        ax[0].plot(tab, nab, "b.")
        ax[0].errorbar(x=tab, y=nab, xerr=dtab / 2, fmt="none")
        ax[0].set_ylabel("$n_i$")
        ax[0].grid()
        ax[0].axvline(x=self.tmin_full, ymin=-1, ymax=+1, color="red", linewidth=4, alpha=0.5)
        ax[0].axvline(x=self.tmax_full, ymin=-1, ymax=+1, color="red", linewidth=4, alpha=0.5)
        ax[0].axvline(x=self.tmin_same, ymin=-1, ymax=+1, color="black", linewidth=2, alpha=0.5)
        ax[0].axvline(x=self.tmax_same, ymin=-1, ymax=+1, color="black", linewidth=2, alpha=0.5)
        ax[0].axvline(x=self.tmin_valid, ymin=-1, ymax=+1, color="cyan", linewidth=1, alpha=0.5)
        ax[0].axvline(x=self.tmax_valid, ymin=-1, ymax=+1, color="cyan", linewidth=1, alpha=0.5)
        ax[1].plot(tab, dtab, "b.")
        ax[1].set_ylabel("$dt_i$")
        # ax[1].grid()
        ax[1].axvline(x=self.tmin_full, ymin=-1, ymax=+1, color="red", linewidth=4, alpha=0.5)
        ax[1].axvline(x=self.tmax_full, ymin=-1, ymax=+1, color="red", linewidth=4, alpha=0.5)
        ax[1].axvline(x=self.tmin_same, ymin=-1, ymax=+1, color="black", linewidth=2, alpha=0.5)
        ax[1].axvline(x=self.tmax_same, ymin=-1, ymax=+1, color="black", linewidth=2, alpha=0.5)
        ax[1].axvline(x=self.tmin_valid, ymin=-1, ymax=+1, color="cyan", linewidth=1, alpha=0.5)
        ax[1].axvline(x=self.tmax_valid, ymin=-1, ymax=+1, color="cyan", linewidth=1, alpha=0.5)

        if rug:
            for time in self.times:
                ax[1].axvline(x=time, ymin=0, ymax=0.2, color="black", linewidth=0.8, alpha=1.0)
            # ax[1].plot(self.t, ax[1].get_ylim()[0]+np.zeros(self.t.size), 'k|', alpha=0.8, lw=1)

        ax[1].grid()
        # fig.show()

    def plot_signals(self, ax=None):

Argument name "ax" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,|_[^\\WA-Z]*|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern)

        """Plots the signals involved in this correlation.

        Plots the signals involved in this correlation, in the same window
        but with different twin y-axes and different colors.

        Parameters
        ----------
        ax : :py:class:`~matplotlib.axes.Axes`
            Axes to be used for plotting.
        """

        # TODO: develop a plotting object for plots

TODO and FIXME comments should generally be avoided.

        #       this will considerably shorten the
        #       number of attributes of this class

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

        ax.plot(self.signal1.times, self.signal1.values, "b.-", lw=1, alpha=0.4)
        ax.tick_params(axis="y", labelcolor="b")
        ax.set_ylabel("sig 1", color="b")

        ax2 = ax.twinx()
        ax2.plot(self.signal2.times, self.signal2.values, "r.-", lw=1, alpha=0.4)
        ax2.tick_params(axis="y", labelcolor="r")
        ax2.set_ylabel("sig 2", color="r")