import argparse
import math

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.metrics.pairwise import euclidean_distances
from scipy.spatial import distance_matrix
from scipy.spatial.distance import euclidean, pdist, squareform
import csv


parser = argparse.ArgumentParser()
parser.add_argument('-a', "--anonymous", action='store_true', default=False, help='Hide axes')
parser.add_argument('-t', "--truck", type=str, default=None, help='PLot one truck ID')
parser.add_argument('-f', "--filename", type=str, default="Multi_P1FWM_turbo_failure_deviation_ano.csv",
                    help='CSV filename')
parser.add_argument('-n', "--normalise", action='store_true', default=False, help='Normalise')
parser.add_argument("--max_pics", type=int, default=16, help='Max histograms on a plot')
parser.add_argument("--cols", type=int, default=4, help='Number of histogram columns in plot')
parser.add_argument("--rows", type=int, default=None, help='Number of rows to read from CSV file')
parser.add_argument("--start", type=int, default=0, help='Start histogram')
parser.add_argument("--trucks", type=str, default=None, help='Multiple chassis IDs, comma seperated')
args = parser.parse_args()
df = pd.read_csv("vehiclethree.csv", parse_dates=['Date'], header=0, index_col='Date')
df.head()
# df = pd.read_csv("vehiclethree.csv",parse_dates=[0])
# print(df.shape)
df = pd.DataFrame(df)
# print(type(df))
# print(df)
# df['Date'] = pd.to_datetime(df['Date'])
# df.set_index('Date')
# title= df.iloc[:, 'Date']
# title= pd.to_datetime(title)
# title = title.dt.date
# title = np.array(title)

two_week = df.resample('2W').mean()
# print(two_week)
# print(two_week.shape)

# title=two_week.loc[:, 'Date']
# title= pd.to_datetime(title)
# title = title.dt.date
# title = np.array(title)

interpolated = two_week.interpolate(method='linear')
# print(interpolated)

b = interpolated.iloc[:, 1:]

# print(b)
# print(b.shape)
d = b.diff(axis=0, periods=1)
std_devi_diff = d.std(axis=1)
print(std_devi_diff.shape)
print(std_devi_diff)
mean_diff = d.mean(axis=1)
print(mean_diff)
print(mean_diff.shape)
print(mean_diff.values)
fig = plt.figure()
plt.plot(std_devi_diff.values)
plt.tick_params(axis='both', which='major', labelsize=10)
plt.tick_params(axis='both', which='minor', labelsize=8)
plt.xlabel('Vehicle-3_biweekly',fontsize=18)
plt.ylabel('standard deviation ',fontsize=16)
fig.suptitle('standard deviation_rows',fontsize=20)
plt.plot(mean_diff.values)

num_pics = d.shape[0]
fig, axes = plt.subplots(nrows=1, ncols=d.shape[0])
fig.set_figheight(20)
fig.set_figwidth(20)
cols = args.cols
rows = int(num_pics/cols)+ 1*((num_pics % cols)!=0)
sp=1
pc=0
i = 0
fig = plt.figure(figsize=(cols*3, rows*3)) #plt.figure(figsize=(sz,sz))
plt.subplots_adjust( hspace=0.7, wspace=0.5 )

for index, row in d.iterrows():
    ax = fig.add_subplot(rows, cols, sp)
    # print(type(row))
    xx = row.iloc[:19]
    yy = row.iloc[19:]

    drop_reshape1 = row.values.reshape(20, 20)
    im = drop_reshape1
    print(im)
    # im = im.astype(int)
    # im = drop_reshape1.astype(int)
    im = np.flipud(im)  # rot90(im) #flipud(im) #rot90()
    # print(im)
    # print("checking...")

    if args.normalise:
        _min = 0
        _max = 1
        im += -(np.min(im))
        im /= np.max(im) / (_max - _min)
    # ax.append( fig.add_subplot(20, 20, index+1) )

    im_masked = np.ma.masked_where(im == 0, im)
    plt.imshow(im_masked, interpolation='none')

    ax.set_aspect('equal')
    ax.set_title('title')
    ax.get_xaxis().set_ticks([0, 19])
    ax.get_yaxis().set_ticks([])
    #
    ax.set_xticks(np.arange(-.5, 19, 1), minor=True);
    ax.set_yticks(np.arange(-.5, 19, 1), minor=True);
    ax.grid(which='minor', color='w', linestyle='-', linewidth=1)
    #
    if not args.anonymous:
        # ax.title.set_text(title[i])
        # i += 1
        ax.set_xlabel("engine speed")
        ax.set_ylabel("engine torque")
        # ax.title.set_text(B_new.SEND_DATETIME[row])
        plt.colorbar(orientation='vertical', ax=ax, format='%.1f', fraction=0.0408, pad=0.04)
        # plt.clim(0, 10);
    else:
        plt.colorbar(orientation='vertical', ax=ax, ticks=[])
    sp += 1
    if not args.anonymous:
        fig.suptitle('VEHICLE_ID' + ": " + str('SEND_DATETIME') + ' P1FWM')  # Training data '+str(st)+" +"+str(nn) )
    # fn = "Sorted_title_" + str('SEND_DATETIME') + "_" + str(index) + "+" + str(pc)
    # if args.normalise:
    #     fn += "_N"
    # fn += ".png"
    # print("Saving", fn)
    # if os.path.exists(fn):
    #     os.remove(fn)
    # fig.savefig(fn, dpi=288)


plt.show()






plt.show()
#



ff= d.iloc[1]