"""Implementations of various common operations.

Including `show()` for displaying an array or with matplotlib.
Most can handle a numpy array or `rasterio.Band()`.
Primarily supports `$ rio insp`.
"""

from __future__ import absolute_import

from collections import OrderedDict
import logging
import warnings

import numpy as np

import rasterio
from rasterio.io import DatasetReader
from rasterio.transform import guard_transform
from rasterio.compat import zip_longest

logger = logging.getLogger(__name__)


def get_plt():
    """import matplotlib.pyplot
    raise import error if matplotlib is not installed
    """
    try:
        import matplotlib.pyplot as plt
        return plt
    except (ImportError, RuntimeError):  # pragma: no cover
        msg = "Could not import matplotlib\n"
        msg += "matplotlib required for plotting functions"
        raise ImportError(msg)


def show(source, with_bounds=True, contour=False, contour_label_kws=None,
         ax=None, title=None, transform=None, adjust='linear', **kwargs):
    """Display a raster or raster band using matplotlib.

    Parameters
    ----------
    source : array or dataset object opened in 'r' mode or Band or tuple(dataset, bidx)
        If Band or tuple (dataset, bidx), display the selected band.
        If raster dataset display the rgb image
        as defined in the colorinterp metadata, or default to first band.
    with_bounds : bool (opt)
        Whether to change the image extent to the spatial bounds of the image,
        rather than pixel coordinates. Only works when source is
        (raster dataset, bidx) or raster dataset.
    contour : bool (opt)
        Whether to plot the raster data as contours
    contour_label_kws : dictionary (opt)
        Keyword arguments for labeling the contours,
        empty dictionary for no labels.
    ax : matplotlib axis (opt)
        Axis to plot on, otherwise uses current axis.
    title : str, optional
        Title for the figure.
    transform : Affine, optional
        Defines the affine transform if source is an array
    adjust : 'linear' | None
        If the plotted data is an RGB image, adjust the values of
        each band so that they fall between 0 and 1 before plotting. If
        'linear', values will be adjusted by the min / max of each band. If
        None, no adjustment will be applied.
    **kwargs : key, value pairings optional
        These will be passed to the matplotlib imshow or contour method
        depending on contour argument.
        See full lists at:
        http://matplotlib.org/api/axes_api.html?highlight=imshow#matplotlib.axes.Axes.imshow
        or
        http://matplotlib.org/api/axes_api.html?highlight=imshow#matplotlib.axes.Axes.contour

    Returns
    -------
    ax : matplotlib Axes
        Axes with plot.
    """
    plt = get_plt()

    if isinstance(source, tuple):
        arr = source[0].read(source[1])
        if len(arr.shape) >= 3:
            arr = reshape_as_image(arr)
        if with_bounds:
            kwargs['extent'] = plotting_extent(source[0])
    elif isinstance(source, DatasetReader):
        if with_bounds:
            kwargs['extent'] = plotting_extent(source)

        if source.count == 1:
            arr = source.read(1, masked=True)
        else:
            try:
                source_colorinterp = OrderedDict(zip(source.indexes, source.colorinterp))
                colorinterp = rasterio.enums.ColorInterp
                rgb_indexes = [source_colorinterp[ci] for ci in
                               (colorinterp.red, colorinterp.green, colorinterp.blue)]
                arr = source.read(rgb_indexes, masked=True)
                arr = reshape_as_image(arr)

            except KeyError:
                arr = source.read(1, masked=True)
    else:
        # The source is a numpy array reshape it to image if it has 3+ bands
        source = np.ma.squeeze(source)
        if len(source.shape) >= 3:
            arr = reshape_as_image(source)
        else:
            arr = source
        if transform and with_bounds:
            kwargs['extent'] = plotting_extent(arr, transform)
    if adjust is True and arr.ndim >= 3:
        # Adjust each band by the min/max so it will plot as RGB.
        arr = reshape_as_raster(arr)
        for ii, band in enumerate(arr):
            arr[ii] = adjust_band(band, kind='linear')
        arr = reshape_as_image(arr)

    show = False
    if not ax:
        show = True
        ax = plt.gca()

    if contour:
        if 'cmap' not in kwargs:
            kwargs['colors'] = kwargs.get('colors', 'red')
        kwargs['linewidths'] = kwargs.get('linewidths', 1.5)
        kwargs['alpha'] = kwargs.get('alpha', 0.8)

        C = ax.contour(arr, origin='upper', **kwargs)
        if contour_label_kws is None:
            # no explicit label kws passed use defaults
            contour_label_kws = dict(fontsize=8,
                                     inline=True)
        if contour_label_kws:
            ax.clabel(C, **contour_label_kws)
    else:
        ax.imshow(arr, **kwargs)
    if title:
        ax.set_title(title, fontweight='bold')

    if show:
        plt.show()

    return ax


def plotting_extent(source, transform=None):
    """Returns an extent in the format needed
     for matplotlib's imshow (left, right, bottom, top)
     instead of rasterio's bounds (left, bottom, top, right)

    Parameters
    ----------
    source : array or dataset object opened in 'r' mode
        input data
    transform: Affine, required if source is array
        Defines the affine transform if source is an array

    Returns
    -------
    tuple of float
        left, right, bottom, top
    """
    if hasattr(source, 'bounds'):
        extent = (source.bounds.left, source.bounds.right,
                  source.bounds.bottom, source.bounds.top)
    elif not transform:
        raise ValueError(
            "transform is required if source is an array")
    else:
        transform = guard_transform(transform)
        rows, cols = source.shape[0:2]
        left, top = transform * (0, 0)
        right, bottom = transform * (cols, rows)
        extent = (left, right, bottom, top)

    return extent


def reshape_as_image(arr):
    """Returns the source array reshaped into the order
    expected by image processing and visualization software
    (matplotlib, scikit-image, etc)
    by swapping the axes order from (bands, rows, columns)
    to (rows, columns, bands)

    Parameters
    ----------
    arr : array-like of shape (bands, rows, columns)
        image to reshape
    """
    # swap the axes order from (bands, rows, columns) to (rows, columns, bands)
    im = np.ma.transpose(arr, [1, 2, 0])
    return im


def reshape_as_raster(arr):
    """Returns the array in a raster order
    by swapping the axes order from (rows, columns, bands)
    to (bands, rows, columns)

    Parameters
    ----------
    arr : array-like in the image form of (rows, columns, bands)
        image to reshape
    """
    # swap the axes order from (rows, columns, bands) to (bands, rows, columns)
    im = np.transpose(arr, [2, 0, 1])
    return im


def show_hist(source, bins=10, masked=True, title='Histogram', ax=None, **kwargs):
    """Easily display a histogram with matplotlib.

    Parameters
    ----------
    source : array or dataset object opened in 'r' mode or Band or tuple(dataset, bidx)
        Input data to display.
        The first three arrays in multi-dimensional
        arrays are plotted as red, green, and blue.
    bins : int, optional
        Compute histogram across N bins.
    masked : bool, optional
        When working with a `rasterio.Band()` object, specifies if the data
        should be masked on read.
    title : str, optional
        Title for the figure.
    ax : matplotlib axes (opt)
        The raster will be added to this axes if passed.
    **kwargs : optional keyword arguments
        These will be passed to the matplotlib hist method. See full list at:
        http://matplotlib.org/api/axes_api.html?highlight=imshow#matplotlib.axes.Axes.hist
    """
    plt = get_plt()

    if isinstance(source, DatasetReader):
        arr = source.read(masked=masked)
    elif isinstance(source, (tuple, rasterio.Band)):
        arr = source[0].read(source[1], masked=masked)
    else:
        arr = source

    # The histogram is computed individually for each 'band' in the array
    # so we need the overall min/max to constrain the plot
    rng = np.nanmin(arr), np.nanmax(arr)

    if len(arr.shape) is 2:
        arr = np.expand_dims(arr.flatten(), 0).T
        colors = ['gold']
    else:
        arr = arr.reshape(arr.shape[0], -1).T
        colors = ['red', 'green', 'blue', 'violet', 'gold', 'saddlebrown']

    # The goal is to provide a curated set of colors for working with
    # smaller datasets and let matplotlib define additional colors when
    # working with larger datasets.
    if arr.shape[-1] > len(colors):
        n = arr.shape[-1] - len(colors)
        colors.extend(np.ndarray.tolist(plt.get_cmap('Accent')(np.linspace(0, 1, n))))
    else:
        colors = colors[:arr.shape[-1]]

    # If a rasterio.Band() is given make sure the proper index is displayed
    # in the legend.
    if isinstance(source, (tuple, rasterio.Band)):
        labels = [str(source[1])]
    else:
        labels = (str(i + 1) for i in range(len(arr)))

    if ax:
        show = False
    else:
        show = True
        ax = plt.gca()

    fig = ax.get_figure()

    ax.hist(arr,
            bins=bins,
            color=colors,
            label=labels,
            range=rng,
            **kwargs)

    ax.legend(loc="upper right")
    ax.set_title(title, fontweight='bold')
    ax.grid(True)
    ax.set_xlabel('DN')
    ax.set_ylabel('Frequency')
    if show:
        plt.show()


def adjust_band(band, kind='linear'):
    """Adjust a band to be between 0 and 1.

    Parameters
    ----------
    band : array, shape (height, width)
        A band of a raster object.
    kind : 'linear'
        The kind of normalization to apply. For now, there
        is only one option ('linear').

    Returns
    -------
    band_normed : array, shape (height, width)
        An adjusted version of the input band.
    """
    band_normed = np.zeros_like(band)
    if kind == 'linear':
        # Including this `if` statement in case future norms are added.
        imin = np.nanmin(band)
        imax = np.nanmax(band)
        band_normed = (band - imin) / (imax - imin)
    return band_normed
