mutis.signal.Signal.check_gen()

Complexity

Conditions

5

Size

Total Lines	76
Code Lines	32

Duplication

Lines	0
Ratio	0 %

Importance

Changes

0

# Licensed under a 3-clause BSD style license - see LICENSE
    def check_gen(self, fgen=None, fgen_params=None, fpsd="lombscargle", **axes):

This function exceeds the maximum number of variables (16/15).

        """Check the generation of synthetic signals.

        This function checks the generation of a synthetic light curve.
        

Trailing whitespace

        Parameters
        ----------
        fgen: :str:
            A valid fgen method name.
        fgen_params: :dict:
            Parameters for fgen (e.g. for fgen='lc_gen_ou', a dict containing
            values for theta, mu and sigma).

        Returns
        -------
        axes: :tuple:
            Tuple of three axes, on which:
            - The first plot show both the original signal and the synthetic one.
            - The second plot shows the histogram of the values taken by both signals.
            - The third plot shows their PSD.
        """
        

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        t, y = self.times, self.values

Variable name "t" 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)

        y2 = fgen_wrapper(fgen=fgen, t=t, y=y, fgen_params=fgen_params)

Variable name "y2" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,|_[^\\WA-Z]*|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern)

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        print(f"Original vs Synthethic:",

Using an f-string that does not have any interpolated variables

              f"mean: {np.mean(y)} / {np.mean(y2)}",
              f"std: {np.std(y)} / {np.std(y2)}", sep='\n')
        

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        if ("ax1" not in axes) or ("ax2" not in axes) or ("ax3" not in axes):
            fig, (axes["ax1"], axes["ax2"], axes["ax3"]) = plt.subplots(

The variable fig seems to be unused.

                nrows=1, ncols=3, figsize=(20, 4)
            )

        # plot the two signals
        axes["ax1"].plot(t, y, "b-", label="orig", lw=0.5, alpha=0.8)

        # ax1p = ax1.twinx()
        axes["ax1"].plot(t, y2, "r-", label="gen", lw=0.5, alpha=0.8)
        axes["ax1"].set_title("light curves")

        # plot their histogram
        bins = "auto"  # bins = int(y.size**0.5/1.5) #
        rang = (np.percentile(y, 0), np.percentile(y, 99))
        axes["ax2"].hist(y, density=True, color="b", alpha=0.4, bins=bins, range=rang)

        # ax2p = ax2.twinx()
        bins = "auto"  # bins = int(y.size**0.5/1.5) #
        rang = (np.percentile(y2, 0), np.percentile(y2, 99))
        axes["ax2"].hist(y2, density=True, color="r", alpha=0.4, bins=bins, range=rang)

        axes["ax2"].set_title("pdf")

        # plot their PSD
        if fpsd == "lombscargle":
            k = np.linspace(1e-3, self.values.size / 2, self.values.size // 2)
            freqs = k / 2 / np.pi

            pxx = scipy_signal.lombscargle(t, y, freqs, normalize=True)
            axes["ax3"].plot(freqs, pxx, "b-", lw=1, alpha=0.5)

            pxx2 = scipy_signal.lombscargle(t, y2, freqs, normalize=True)
            axes["ax3"].plot(freqs, pxx2, "r-", lw=1, alpha=0.5)

            axes["ax3"].set_xscale("log")
            # ax3.set_yscale('log')
        else:
            axes["ax3"].psd(y, color="b", lw=1, alpha=0.5)

            ax3p = axes["ax3"].twinx()
            ax3p.psd(y2, color="r", lw=1, alpha=0.5)

        axes["ax3"].set_title("PSD")

        return axes