#!/usr/bin/env python
# -*- coding: utf-8 -*-

# Built-in imports
import warnings

# 3rd party imports
import xarray as xr
import matplotlib.pyplot as plt

# Local imports
from ..pyrf import histogram2d
from . import plot_spectr

__author__ = "<NAME>"
__email__ = "<EMAIL>"
__copyright__ = "Copyright 2020-2021"
__license__ = "MIT"
__version__ = "2.3.7"
__status__ = "Prototype"


def pl_scatter_matrix(inp1, inp2: xr.DataArray = None, pdf: bool = False,
                      cmap: str = "jet"):
    r"""Produces a scatter plot of each components of field inp1 with respect
    to every component of field inp2. If pdf is set to True, the scatter
    plot becomes a 2d histogram.

    Parameters
    ----------
    inp1 : xarray.DataArray
        First time series (x-axis).
    inp2 : xarray.DataArray
        Second time series (y-axis).
    pdf : bool, Optional
        Flag to plot the 2d histogram. If False the figure is a scatter plot.
        If True the figure is a 2d histogram.
    cmap : str, Optional
        Colormap. Default : "jet"

    Returns
    -------
    fig : matplotlib.pyplot.figure
        Figure with time series plots.
    axs : matplotlib.axes._subplots.AxesSubplot
        Axes.
    caxs : matplotlib.pyplot.colorbar
        Only if pdf is True

    """

    if inp2 is None:
        inp2 = inp1
        warnings.warn("inp2 is empty assuming that inp2=inp1", UserWarning)

    assert isinstance(inp1, xr.DataArray) and isinstance(inp2, xr.DataArray)

    if not pdf:
        fig, axs = plt.subplots(3, 3, sharex="all", sharey="all",
                                figsize=(16, 9))
        fig.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9,
                            hspace=0.05, wspace=0.05)

        for i in range(3):
            for j in range(3):
                axs[j, i].scatter(inp1[:, i].data, inp2[:, j].data, marker="+")

        out = (fig, axs)
    else:
        fig, axs = plt.subplots(3, 3, sharex="all", sharey="all",
                                figsize=(16, 9))
        fig.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9,
                            hspace=0.05, wspace=0.3)

        caxs = [[None]*3]*3

        for i in range(3):
            for j in range(3):
                hist_ = histogram2d(inp1[:, i], inp2[:, j])
                axs[j, i], caxs[j][i] = plot_spectr(axs[j, i], hist_,
                                                    cmap=cmap, cscale="log")
                axs[j, i].grid()

        out = (fig, axs, caxs)

    return out

#!/usr/bin/env python
# -*- coding: utf-8 -*-

# Built-in imports
import warnings

# 3rd party imports
import xarray as xr
import matplotlib.pyplot as plt

# Local imports
from ..pyrf import histogram2d
from . import plot_spectr

__author__ = "<NAME>"
__email__ = "<EMAIL>"
__copyright__ = "Copyright 2020-2021"
__license__ = "MIT"
__version__ = "2.3.7"
__status__ = "Prototype"


def pl_scatter_matrix(inp1, inp2: xr.DataArray = None, pdf: bool = False,
                      cmap: str = "jet"):
    r"""Produces a scatter plot of each components of field inp1 with respect
    to every component of field inp2. If pdf is set to True, the scatter
    plot becomes a 2d histogram.

    Parameters
    ----------
    inp1 : xarray.DataArray
        First time series (x-axis).
    inp2 : xarray.DataArray
        Second time series (y-axis).
    pdf : bool, Optional
        Flag to plot the 2d histogram. If False the figure is a scatter plot.
        If True the figure is a 2d histogram.
    cmap : str, Optional
        Colormap. Default : "jet"

    Returns
    -------
    fig : matplotlib.pyplot.figure
        Figure with time series plots.
    axs : matplotlib.axes._subplots.AxesSubplot
        Axes.
    caxs : matplotlib.pyplot.colorbar
        Only if pdf is True

    """

    if inp2 is None:
        inp2 = inp1
        warnings.warn("inp2 is empty assuming that inp2=inp1", UserWarning)

    assert isinstance(inp1, xr.DataArray) and isinstance(inp2, xr.DataArray)

    if not pdf:
        fig, axs = plt.subplots(3, 3, sharex="all", sharey="all",
                                figsize=(16, 9))
        fig.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9,
                            hspace=0.05, wspace=0.05)

        for i in range(3):
            for j in range(3):
                axs[j, i].scatter(inp1[:, i].data, inp2[:, j].data, marker="+")

        out = (fig, axs)
    else:
        fig, axs = plt.subplots(3, 3, sharex="all", sharey="all",
                                figsize=(16, 9))
        fig.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9,
                            hspace=0.05, wspace=0.3)

        caxs = [[None]*3]*3

        for i in range(3):
            for j in range(3):
                hist_ = histogram2d(inp1[:, i], inp2[:, j])
                axs[j, i], caxs[j][i] = plot_spectr(axs[j, i], hist_,
                                                    cmap=cmap, cscale="log")
                axs[j, i].grid()

        out = (fig, axs, caxs)

    return out

#!/usr/bin/env python # -*- coding: utf-8 -*- # Built-in imports # 3rd party imports # Local imports Produces a scatter plot of each components of field inp1 with respect     to every component of field inp2. If pdf is set to True, the scatter     plot becomes a 2d histogram.      Parameters     ----------     inp1 : xarray.DataArray         First time series (x-axis).     inp2 : xarray.DataArray         Second time series (y-axis).     pdf : bool, Optional         Flag to plot the 2d histogram. If False the figure is a scatter plot.         If True the figure is a 2d histogram.     cmap : str, Optional         Colormap. Default : "jet"      Returns     -------     fig : matplotlib.pyplot.figure         Figure with time series plots.     axs : matplotlib.axes._subplots.AxesSubplot         Axes.     caxs : matplotlib.pyplot.colorbar         Only if pdf is True