# Anything vaguely portable from the ipython notebook is here in plaintext
import pandas
import stumpy
import numpy
import matplotlib.pyplot as plot
import matplotlib.dates as dates
from matplotlib.patches import Rectangle
import datetime as dt

## Process wrapper, time reporting
from functools import wraps
import time
def timeit(fn):
    def timed(*args, **kw):
        print (f'>>> {fn.__name__} >>>')

        ts = time.time()
        result = fn(*args, **kw)
        te = time.time()

        print (f'<<< {fn.__name__} <<< {(te-ts):.3f}s')
        return result
    return timed

@timeit
@wraps(stumpy.stump)
def timed_stump(dataset, w):
    l = len(dataset)
    ws = l - w + 1
    print(f'Processing a matrix profile over {ws} windows...')
    result = stumpy.stump(dataset, w)
    return result

# Basic feature extraction
def threshold_extraction(op, cmp, profile, motif_order, window_width, threshold):
    assert(0 < threshold < 1.0)
    closest = profile[motif_order[0], 0]
    cutoff = op(closest, (closest * threshold))
    rv = []

    for obs in motif_order:
        if cmp(profile[obs,0], cutoff):
            if not (any(map(lambda i: abs(i - obs) < window_width, rv))):
                rv.append(obs)
            else:
                # If two motifs overlap it's possible the window is too small?
                # Further criteria needed for that...
                pass
        else:
            break
    return rv

def get_motifs(profile, motif_order, window_width, threshold):
    return threshold_extraction(lambda a, b: a + b
                               ,lambda a, b: a < b
                               ,profile
                               ,motif_order
                               ,window_width
                               ,threshold)

def get_discords(profile, motif_order, window_width, threshold):
    return threshold_extraction(lambda a, b: a - b
                               ,lambda a, b: a > b
                               ,profile
                               ,numpy.flip(motif_order)
                               ,window_width
                               ,threshold)

def mark_discovered(motif_list, data_fig, profile_fig, window_width, fig_height):
    for motif in motif_list:
        rect = Rectangle((motif, 0), window_width, fig_height, facecolor='lightgrey')
        data_fig.add_patch(rect)
        profile_fig.axvline(x=motif, linestyle='dashed')

# Basic helper functions
def plot_matrix_profile(plot_, profile):
    plot_.set_xlabel('Time', fontsize='15')
    plot_.set_ylabel('Distance', fontsize='15')
    plot_.plot(profile[:, 0])

from string import capwords
plot.style.use("data/stumpy.mplstyle")
def plot_pandas_import (plot_, data, axis_name):
    plot_.set_ylabel(capwords(axis_name), fontsize='15')
    plot_.plot(data[axis_name].values)