import os.path

from matplotlib import pyplot as plt
import numpy as np
import matplotlib
import matplotlib.patches as patches
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from datetime import datetime
from enstools.feature.util.data_utils import pb_str_to_datetime

# plots the wave state (all wavetroughs given specific timestep in a set) ts: pb2.Timestep
def plot_wavetroughs(ts, fig_name, cv=None):
    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(11, 4), subplot_kw=dict(projection=ccrs.PlateCarree()))

    x_ticks = [-100, -95, -85, -75, -65, -55, -45, -35, -25, -15, -5, 5, 15, 25, 35]
    y_ticks = [0, 10, 20, 30]
    extent = [-100, -45, -10, 35]

    if cv is not None:
        levelfc = np.asarray([0, 0.5, 1, 2, 3]) * 1e-5
        cv.plot.contourf(levels=levelfc, vmin=0, extend='max', cmap='Blues')

    # generate plot per pressure level, per time step

    # colors per time step
    # min_time = wave_thr_list[0].time.astype('float64')
    # max_time = wave_thr_list[-1].time.astype('float64')
    # cmap = matplotlib.cm.get_cmap('rainbow')
    # color_wgts = np.linspace(0.0, 1.0, len(wave_thr_list))
    # colors = ['red', 'yellow', 'green', 'blue', 'purple']

    vt = ts.valid_time
    for obj_idx, obj in enumerate(ts.objects):
        # time64 = wave.time.astype('float64')
        # time_weight = (time64 - min_time) / (max_time - min_time) if max_time > min_time else 1.0

        line_pts = obj.properties.line_pts
        line = patches.Path([[p.lon, p.lat] for p in line_pts])
        patch = patches.PathPatch(line, linewidth=2, facecolor='none', edgecolor='red') # cmap(time_weight)
        ax.add_patch(patch)

    ax.coastlines()
    ax.add_feature(cfeature.BORDERS.with_scale('50m'))
    ax.set_extent(extent, crs=ccrs.PlateCarree())
    yt1 = ax.set_yticks(y_ticks, crs=ccrs.PlateCarree())
    xt1 = ax.set_xticks(x_ticks, crs=ccrs.PlateCarree())

    figure_name = fig_name.replace(':', '_') + '_aew_troughs.png'
    plt.savefig(figure_name, format='png')

    plt.figure().clear()
    plt.close()
    plt.cla()
    plt.clf()

    return figure_name


def plot_timesteps_from_desc(object_desc, cv=None):
    # plot for each set for each timestep everything detected.
    from enstools.feature.util.data_utils import get_subset_by_description

    for set_idx, od_set in enumerate(object_desc.sets):
        fn = "ts_set_" + str(set_idx)

        cv_set = get_subset_by_description(cv, od_set, '2d')

        for ts in od_set.timesteps:
            cv_st = cv_set.sel(time=ts.valid_time).cv
            fnt = fn + "_" + ts.valid_time
            print(fnt)
            # ts.validTime / .objects
            fout_name = plot_wavetroughs(ts, fnt, cv=cv_st)
            print("Plot to " + fout_name)

    return None

def plot_track_from_graph(track_desc, fig_name_prefix, cv=None):

    nodes = [node.parent for node in track_desc.nodes]
    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(11, 4), subplot_kw=dict(projection=ccrs.PlateCarree()))

    x_ticks = [-100, -95, -85, -75, -65, -55, -45, -35, -25, -15, -5, 5, 15, 25, 35]
    y_ticks = [0, 10, 20, 30]
    extent = [-100, -45, -10, 35]

    if cv is not None:
        cv = cv.isel(time=0)
        levelfc = np.asarray([0, 0.5, 1, 2, 3]) * 1e-5
        cv.plot.contourf(levels=levelfc, vmin=0, extend='max', cmap='Blues')

    # generate plot per pressure level, per time step

    # colors per time step

    min_time = pb_str_to_datetime(nodes[0].time).timestamp()
    max_time = pb_str_to_datetime(nodes[-1].time).timestamp()
    cmap = matplotlib.cm.get_cmap('rainbow')
    color_wgts = np.linspace(0.0, 1.0, len(nodes))
    colors = ['red', 'yellow', 'green', 'blue', 'purple']

    for node_idx, node in enumerate(nodes):
        obj = node.object
        time_d = pb_str_to_datetime(node.time).timestamp()
        time_weight = (time_d - min_time) / (max_time - min_time) if max_time > min_time else 1.0

        line_pts = obj.properties.line_pts
        line = patches.Path([[p.lon, p.lat] for p in line_pts])
        patch = patches.PathPatch(line, linewidth=2, facecolor='none', edgecolor=cmap(time_weight))
        ax.add_patch(patch)

    ax.coastlines()
    ax.add_feature(cfeature.BORDERS.with_scale('50m'))
    ax.set_extent(extent, crs=ccrs.PlateCarree())
    yt1 = ax.set_yticks(y_ticks, crs=ccrs.PlateCarree())
    xt1 = ax.set_xticks(x_ticks, crs=ccrs.PlateCarree())

    figure_name = fig_name_prefix.replace(':', '_') + '_troughs.png'
    plt.title(nodes[0].time + " - " + nodes[-1].time)
    print("Plot to " + str(figure_name))
    plt.savefig(figure_name, format='png')

    plt.figure().clear()
    plt.close()
    plt.cla()
    plt.clf()

    return figure_name



def plot_tracks_from_graph(graph_desc, ds=None):

    from enstools.feature.util.data_utils import get_subset_by_description

    for set_idx, od_set in enumerate(graph_desc.sets):

        # cv_set = get_subset_by_description(ds, od_set, '2d')
        for set_tr, track in enumerate(od_set.tracks):
            fn = os.path.expanduser('~') + "/phd/data/aew/plots/set_" + str(set_idx) + "_track_" + str(set_tr)
            plot_track_from_graph(track, fn, cv=None)

    pass