"""
GUI progressbar decorator for iterators.
Includes a default `range` iterator printing to `stderr`.

Usage:
>>> from tqdm.gui import trange, tqdm
>>> for i in trange(10):
...     ...
"""
# future division is important to divide integers and get as
# a result precise floating numbers (instead of truncated int)
from __future__ import division, absolute_import
# import compatibility functions and utilities
from .utils import _range
# to inherit from the tqdm class
from .std import tqdm as std_tqdm
from .std import TqdmExperimentalWarning
from warnings import warn


__author__ = {"github.com/": ["casperdcl", "lrq3000"]}
__all__ = ['tqdm_gui', 'tgrange', 'tqdm', 'trange']


class tqdm_gui(std_tqdm):  # pragma: no cover
    """
    Experimental GUI version of tqdm!
    """

    # TODO: @classmethod: write() on GUI?

    def __init__(self, *args, **kwargs):
        import matplotlib as mpl
        import matplotlib.pyplot as plt
        from collections import deque
        kwargs['gui'] = True

        super(tqdm_gui, self).__init__(*args, **kwargs)

        # Initialize the GUI display
        if self.disable or not kwargs['gui']:
            return

        warn('GUI is experimental/alpha', TqdmExperimentalWarning, stacklevel=2)
        self.mpl = mpl
        self.plt = plt
        self.sp = None

        # Remember if external environment uses toolbars
        self.toolbar = self.mpl.rcParams['toolbar']
        self.mpl.rcParams['toolbar'] = 'None'

        self.mininterval = max(self.mininterval, 0.5)
        self.fig, ax = plt.subplots(figsize=(9, 2.2))
        # self.fig.subplots_adjust(bottom=0.2)
        total = self.__len__()  # avoids TypeError on None #971
        if total is not None:
            self.xdata = []
            self.ydata = []
            self.zdata = []
        else:
            self.xdata = deque([])
            self.ydata = deque([])
            self.zdata = deque([])
        self.line1, = ax.plot(self.xdata, self.ydata, color='b')
        self.line2, = ax.plot(self.xdata, self.zdata, color='k')
        ax.set_ylim(0, 0.001)
        if total is not None:
            ax.set_xlim(0, 100)
            ax.set_xlabel('percent')
            self.fig.legend((self.line1, self.line2), ('cur', 'est'),
                            loc='center right')
            # progressbar
            self.hspan = plt.axhspan(0, 0.001,
                                     xmin=0, xmax=0, color='g')
        else:
            # ax.set_xlim(-60, 0)
            ax.set_xlim(0, 60)
            ax.invert_xaxis()
            ax.set_xlabel('seconds')
            ax.legend(('cur', 'est'), loc='lower left')
        ax.grid()
        # ax.set_xlabel('seconds')
        ax.set_ylabel((self.unit if self.unit else 'it') + '/s')
        if self.unit_scale:
            plt.ticklabel_format(style='sci', axis='y',
                                 scilimits=(0, 0))
            ax.yaxis.get_offset_text().set_x(-0.15)

        # Remember if external environment is interactive
        self.wasion = plt.isinteractive()
        plt.ion()
        self.ax = ax

    def __iter__(self):
        # TODO: somehow allow the following:
        # if not self.gui:
        #   return super(tqdm_gui, self).__iter__()
        iterable = self.iterable
        if self.disable:
            for obj in iterable:
                yield obj
            return

        # ncols = self.ncols
        mininterval = self.mininterval
        maxinterval = self.maxinterval
        miniters = self.miniters
        dynamic_miniters = self.dynamic_miniters
        last_print_t = self.last_print_t
        last_print_n = self.last_print_n
        n = self.n
        # dynamic_ncols = self.dynamic_ncols
        smoothing = self.smoothing
        avg_time = self.avg_time
        time = self._time

        for obj in iterable:
            yield obj
            # Update and possibly print the progressbar.
            # Note: does not call self.update(1) for speed optimisation.
            n += 1
            # check counter first to avoid calls to time()
            if n - last_print_n >= self.miniters:
                miniters = self.miniters  # watch monitoring thread changes
                delta_t = time() - last_print_t
                if delta_t >= mininterval:
                    cur_t = time()
                    delta_it = n - last_print_n
                    # EMA (not just overall average)
                    if smoothing and delta_t and delta_it:
                        rate = delta_t / delta_it
                        avg_time = self.ema(rate, avg_time, smoothing)
                        self.avg_time = avg_time

                    self.n = n
                    self.display()

                    # If no `miniters` was specified, adjust automatically
                    # to the max iteration rate seen so far between 2 prints
                    if dynamic_miniters:
                        if maxinterval and delta_t >= maxinterval:
                            # Adjust miniters to time interval by rule of 3
                            if mininterval:
                                # Set miniters to correspond to mininterval
                                miniters = delta_it * mininterval / delta_t
                            else:
                                # Set miniters to correspond to maxinterval
                                miniters = delta_it * maxinterval / delta_t
                        elif smoothing:
                            # EMA-weight miniters to converge
                            # towards the timeframe of mininterval
                            rate = delta_it
                            if mininterval and delta_t:
                                rate *= mininterval / delta_t
                            miniters = self.ema(rate, miniters, smoothing)
                        else:
                            # Maximum nb of iterations between 2 prints
                            miniters = max(miniters, delta_it)

                    # Store old values for next call
                    self.n = self.last_print_n = last_print_n = n
                    self.last_print_t = last_print_t = cur_t
                    self.miniters = miniters

        # Closing the progress bar.
        # Update some internal variables for close().
        self.last_print_n = last_print_n
        self.n = n
        self.miniters = miniters
        self.close()

    def update(self, n=1):
        # if not self.gui:
        #   return super(tqdm_gui, self).close()
        if self.disable:
            return

        if n < 0:
            self.last_print_n += n  # for auto-refresh logic to work
        self.n += n

        # check counter first to reduce calls to time()
        if self.n - self.last_print_n >= self.miniters:
            delta_t = self._time() - self.last_print_t
            if delta_t >= self.mininterval:
                cur_t = self._time()
                delta_it = self.n - self.last_print_n  # >= n
                # elapsed = cur_t - self.start_t
                # EMA (not just overall average)
                if self.smoothing and delta_t and delta_it:
                    rate = delta_t / delta_it
                    self.avg_time = self.ema(
                        rate, self.avg_time, self.smoothing)

                self.display()

                # If no `miniters` was specified, adjust automatically to the
                # maximum iteration rate seen so far between two prints.
                # e.g.: After running `tqdm.update(5)`, subsequent
                # calls to `tqdm.update()` will only cause an update after
                # at least 5 more iterations.
                if self.dynamic_miniters:
                    if self.maxinterval and delta_t >= self.maxinterval:
                        if self.mininterval:
                            self.miniters = delta_it * self.mininterval \
                                / delta_t
                        else:
                            self.miniters = delta_it * self.maxinterval \
                                / delta_t
                    elif self.smoothing:
                        self.miniters = self.smoothing * delta_it * \
                            (self.mininterval / delta_t
                             if self.mininterval and delta_t
                             else 1) + \
                            (1 - self.smoothing) * self.miniters
                    else:
                        self.miniters = max(self.miniters, delta_it)

                # Store old values for next call
                self.last_print_n = self.n
                self.last_print_t = cur_t
                return True

    def close(self):
        # if not self.gui:
        #   return super(tqdm_gui, self).close()
        if self.disable:
            return

        self.disable = True

        with self.get_lock():
            self._instances.remove(self)

        # Restore toolbars
        self.mpl.rcParams['toolbar'] = self.toolbar
        # Return to non-interactive mode
        if not self.wasion:
            self.plt.ioff()
        if not self.leave:
            self.plt.close(self.fig)

    def display(self):
        n = self.n
        cur_t = self._time()
        elapsed = cur_t - self.start_t
        delta_it = n - self.last_print_n
        delta_t = cur_t - self.last_print_t

        # Inline due to multiple calls
        total = self.total
        xdata = self.xdata
        ydata = self.ydata
        zdata = self.zdata
        ax = self.ax
        line1 = self.line1
        line2 = self.line2
        # instantaneous rate
        y = delta_it / delta_t
        # overall rate
        z = n / elapsed
        # update line data
        xdata.append(n * 100.0 / total if total else cur_t)
        ydata.append(y)
        zdata.append(z)

        # Discard old values
        # xmin, xmax = ax.get_xlim()
        # if (not total) and elapsed > xmin * 1.1:
        if (not total) and elapsed > 66:
            xdata.popleft()
            ydata.popleft()
            zdata.popleft()

        ymin, ymax = ax.get_ylim()
        if y > ymax or z > ymax:
            ymax = 1.1 * y
            ax.set_ylim(ymin, ymax)
            ax.figure.canvas.draw()

        if total:
            line1.set_data(xdata, ydata)
            line2.set_data(xdata, zdata)
            try:
                poly_lims = self.hspan.get_xy()
            except AttributeError:
                self.hspan = self.plt.axhspan(
                    0, 0.001, xmin=0, xmax=0, color='g')
                poly_lims = self.hspan.get_xy()
            poly_lims[0, 1] = ymin
            poly_lims[1, 1] = ymax
            poly_lims[2] = [n / total, ymax]
            poly_lims[3] = [poly_lims[2, 0], ymin]
            if len(poly_lims) > 4:
                poly_lims[4, 1] = ymin
            self.hspan.set_xy(poly_lims)
        else:
            t_ago = [cur_t - i for i in xdata]
            line1.set_data(t_ago, ydata)
            line2.set_data(t_ago, zdata)

        ax.set_title(self.format_meter(
            n, total, elapsed, 0,
            self.desc, self.ascii, self.unit, self.unit_scale,
            1 / self.avg_time if self.avg_time else None, self.bar_format,
            self.postfix, self.unit_divisor),
            fontname="DejaVu Sans Mono", fontsize=11)
        self.plt.pause(1e-9)


def tgrange(*args, **kwargs):
    """
    A shortcut for `tqdm.gui.tqdm(xrange(*args), **kwargs)`.
    On Python3+, `range` is used instead of `xrange`.
    """
    return tqdm_gui(_range(*args), **kwargs)


# Aliases
tqdm = tqdm_gui
trange = tgrange