channels = ['11PCA00000THAV0B']
wherepeaks = np.array(['+tive'])
cutoff = range(1)
files1.sort()
files2.sort()
plotfigs = 0
savefigs = 0
writefiles = 0
bs_n_it = 5000
bs_alpha = 0.05
bs_nbin = [25, 25]
#%% read data
t, fulldata_1 = import_data(directory, channels, tcns=files1)
chdata_1 = arrange.test_ch_from_chdict(fulldata_1, cutoff)
t, fulldata_2 = import_data(directory, channels, tcns=files2)
chdata_2 = arrange.test_ch_from_chdict(fulldata_2, cutoff)
t = t.get_values()[cutoff]
#%% get values
def get_values(data):
return data[0]
chdata_1 = chdata_1.applymap(get_values)
chdata_2 = chdata_2.applymap(get_values)
print('No-slip:')
if __name__ == '__main__':
from PMG.COM import table as tb
path = os.fspath('P:/AHEC/DATA/Full Sample/48/')
project = 'AHEC'
plot_path = 'P:/AHEC/Plots/Clustering/THOR/'
table = tb.get(project)
tcns = table[table.SUBSET.isin(['HEV vs ICE'])
& table.VITESSE.isin([48])].CIBLE.tolist() + table[
table.SUBSET.isin(['HEV vs ICE'])
& table.VITESSE.isin([48])].BELIER.tolist()
# tcns = None
time, raw = import_data(path,
'10CVEHCG0000ACXD',
tcns,
sl=slice(100, 1600))
labels = make_labels(raw, project)
clusters, data = cluster(time,
raw,
labels,
plot_path,
mode='both',
N=4,
norm=True,
smooth=True,
plot_all=True,
plot_data=True,
matrix=False,
tag='new')
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 19 12:23:40 2018
@author: giguerf
"""
import matplotlib.pyplot as plt
import numpy as np
from PMG.COM import data, plotstyle as style, table as tb
THOR = 'P:/AHEC/Data/THOR/'
chlist = ['11NECKLO00THFOXA', '11CHSTLELOTHDSXB','11CHST0000THACXC','11CHSTRILOTHDSXB','11CHSTRIUPTHDSXB','11SPIN0100THACYC','11SEBE0000B3FO0D']
time, fulldata = data.import_data(THOR, chlist, check=False)
table = tb.get('THOR')
table = table[table.TYPE.isin(['Frontale/Véhicule'])]
slips = table[table.CBL_BELT.isin(['SLIP'])].CIBLE.tolist()
oks = table[table.CBL_BELT.isin(['OK'])].CIBLE.tolist()
#%%
my_color = 'tab:blue'
plt.close('all')
#plt.figure()
df = fulldata['11CHSTRILOTHDSXB'].loc[:,:].dropna(axis=1)
#for tcn in df.columns:
# plt.plot(time, df[tcn], '.', color='tab:blue', markersize=0.5, alpha=1)
fig, axs = plt.subplots(1,2,sharex=True,sharey=True, figsize=(12,6))
ax = axs[0]
ax.plot(time, df, color=my_color, lw=1, alpha=1)
# else:
# values = [cmap(n)[:3] for n in range(cmap.N)]
#
#
# values = [values[k%len(values)] for k in range(len(keys))]
# colors = dict(zip(keys, values))
#
# return colors
if __name__ == '__main__':
import PMG.COM.data as data
import PMG.COM.table as tb
import PMG.COM.plotstyle as style
THOR = 'P:/AHEC/Data/THOR/'
chlist = ['11NECKLO00THFOXA']
time, fulldata = data.import_data(THOR, chlist)
df = fulldata['11NECKLO00THFOXA'].dropna(axis=1)
table = tb.get('THOR')
table = table[table.TYPE.isin(['Frontale/Véhicule'])]
slips = table[table.CBL_BELT.isin(['SLIP'])].CIBLE.tolist()
oks = table[table.CBL_BELT.isin(['OK'])].CIBLE.tolist()
plt.close('all')
#%%
plt.figure()
colors = style.colordict(df, 'order', plt.cm.YlGnBu)
for tcn in df.columns[:]:
plt.plot(df.loc[:, tcn], color=colors[tcn])
#%%
df = df.loc[:, slips + oks]
colors = style.colordict(df, 'max', plt.cm.YlGnBu_r)
colors = style.colordict(df, 'max', ['tab:blue', 'tab:red'], 3)
def plotbook(savedir, chlist, tcns=None):
"""
Plot THOR-related basic overview plots. All pairs by channels and both
groups with means/intervals by channels.
"""
import pandas as pd
import matplotlib.pyplot as plt
from PMG.COM import plotstyle as style, data, table as tb
descriptions = pd.read_excel('P:/AHEC/Descriptions.xlsx', index_col=0)
description = descriptions.to_dict()[descriptions.columns[0]]
time, fulldata = data.import_data('P:/AHEC/DATA/THOR/',
chlist,
tcns,
check=False)
# for k,v in fulldata.items():
# fulldata[k] = data.check_and_clean(v, stage=2)
table = tb.get('THOR')
table = table[table.CIBLE.isin(tcns)]
slips = table[table.CBL_BELT.isin(['SLIP'])].CIBLE.tolist()
oks = table[table.CBL_BELT.isin(['OK'])].CIBLE.tolist()
table = table[table.CIBLE.isin(slips + oks)]
#%% FIGURE 1 - all pairs plotted individually
xlim = (0, 0.3)
xlabel = 'Time [s]'
plt.close('all')
for channel in chlist:
slipdf = fulldata[channel].loc[:, slips]
okdf = fulldata[channel].loc[:, oks]
plotdata = pd.concat([slipdf, okdf], axis=1)
if plotdata.empty:
continue
ylabel = style.ylabel(channel[12:14], channel[14:15])
r, c = style.sqfactors(len(plotdata.columns.tolist()))
fig, axs = style.subplots(r,
c,
sharex='all',
sharey='all',
visible=True,
num='All Pairs: ' + channel)
fig.suptitle('{ch} - {desc}'.format(ch=channel,
desc=description.get(
channel,
'Description Unavailable')))
for i, tcn in enumerate(table.CIBLE):
ax = axs[i]
ax.plot(time, plotdata.loc[:, tcn], color='tab:green', label=tcn)
ax.set_xlim(*xlim)
title = ' '.join(
[tcn, table[table.CIBLE == tcn].CBL_MODELE.tolist()[0]])
ax.set_title(title)
ax.set_ylabel(ylabel)
ax.set_xlabel(xlabel)
ax.legend(loc=4)
plt.tight_layout(rect=[0, 0, 1, 0.92])
plt.savefig(savedir + 'All_Pairs_' + channel + '.png', dpi=200)
plt.close('all')
#%% FIGURE 2 - Vehicle groups and means
plt.close('all')
for channel in chlist:
slipdf = fulldata[channel].loc[:, slips]
okdf = fulldata[channel].loc[:, oks]
ylabel = style.ylabel(channel[12:14], channel[14:15])
fig, axs = style.subplots(2,
2,
sharex='all',
sharey='all',
num='Belt: ' + channel,
figsize=(20, 12.5))
fig.suptitle('{ch} - {desc}'.format(ch=channel,
desc=description.get(
channel,
'Description Unavailable')))
#FIRST SUBPLOT - 'SLIP' Group full data set
ax = axs[0]
ax.plot(time,
slipdf,
lw=1,
color='tab:blue',
label='n = {}'.format(slipdf.shape[1]))
ax.set_title('Slipping Belts')
ax.set_ylabel(ylabel)
ax.set_xlabel(xlabel)
style.legend(ax, loc=4)
#THIRD SUBPLOT - 'OK' Group full data set
ax = axs[2]
ax.plot(time,
okdf,
lw=1,
color='tab:orange',
label='n = {}'.format(okdf.shape[1]))
ax.set_title('Ok Belts')
ax.set_ylabel(ylabel)
ax.set_xlabel(xlabel)
style.legend(ax, loc=4)
#SECOND SUBPLOT - 'CIBLE' vs 'BELIER' Groups (median and intervals)
ax = axs[1]
data.tolerance(ax, time, slipdf, 'tab:blue')
data.tolerance(ax, time, okdf, 'tab:orange')
ax.set_title('All Belts (Mean and Intervals)')
ax.set_ylabel(ylabel)
ax.set_xlabel(xlabel)
ax.legend(loc=4)
ax.set_xlim(*xlim)
#%%
plt.subplots_adjust(top=0.893,
bottom=0.060,
left=0.048,
right=0.974,
hspace=0.222,
wspace=0.128)
plt.savefig(savedir + 'Belt_' + channel + '.png', dpi=200)
plt.close('all')
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 6 12:55:37 2018
@author: giguerf
"""
import matplotlib.pyplot as plt
import numpy as np
import scipy
from PMG.COM import data as dat
time, data = dat.import_data('P:/AHEC/Data/THOR/',
'11PELV0000THACXA',
check=False)
#time, data = dat.import_data('P:/SLED/Data/', '12PELV0000Y7ACXA', check=False)
#sl = slice(100,1600)
sl = slice(100, 1100)
#%%
x = np.linspace((sl.start - 100) / 10000, (sl.stop - 100) / 10000,
sl.stop - sl.start)
A = 0.11
B = 0.08
C = -28
D = 1870
a = -2
b = 10000
c = 0.04
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import PMG.COM.data as dat
import PMG.COM.table as tb
import PMG.COM.plotstyle as style
THOR = 'P:/Data Analysis/Projects/AHEC/Data/THOR/'
chlist = []
chlist.extend(['11NECKLO00THFOXA','11NECKLO00THFOYA','11FEMRLE00THFOZB'])
chlist.extend(['11CHSTLEUPTHDSXB','11CHSTRIUPTHDSXB','11CHSTLELOTHDSXB','11CHSTRILOTHDSXB'])
chlist.extend(['11HEAD0000THACXA','11SPIN0100THACXC', '11CHST0000THACXC', '11SPIN1200THACXC', '11PELV0000THACXA'])
chlist.extend(['11HEAD0000THACYA','11SPIN0100THACYC', '11CHST0000THACYC', '11SPIN1200THACYC', '11PELV0000THACYA'])
chlist.extend(['11SPIN0100THACYC','11THSP0100THAVXA','11THSP0100THAVZA'])
chlist = list(set(chlist))
time, fulldata = dat.import_data(THOR, chlist, check=False)
table = tb.get('THOR')
#table = table[table.TYPE.isin(['Frontale/Véhicule'])]
#table = table[table.TYPE.isin(['Frontale/Mur'])]
slips = table[table.CBL_BELT.isin(['SLIP'])].CIBLE.tolist()
oks = table[table.CBL_BELT.isin(['OK'])].CIBLE.tolist()
plt.rcParams['font.size']= 10
#slip_color = 'tab:blue'
#ok_color = 'tab:red'
slip_color = '#5e3c99'
slide_color = '#fdb863'
ok_color = '#e66101'