from PMG.COM.openbook import openHDF5
import PMG.COM.data as data
from PMG.COM.data import check_and_clean
import PMG.COM.plotstyle as style
import PMG.COM.table as tb
THOR = os.fspath('P:/AHEC/Data/THOR/')
chlist = ['11CHSTLEUPTHDSXB', '11CHSTRIUPTHDSXB', '11CHSTRILOTHDSXB', '11CHSTLELOTHDSXB']
time, fulldata = openHDF5(THOR, chlist)
for ch in chlist:
print(ch)
fulldata[ch] = check_and_clean(fulldata[ch],1)
table = tb.get('THOR')
table = table[table.TYPE.isin(['Frontale/Véhicule']) & table.VITESSE.isin([48,56])]
ok, slip = tb.tcns(tb.split(table, column='CBL_BELT', categories=['OK','SLIP']))
#%%
if 0:
cd = style.colordict(chlist)
titles = dict(zip(chlist, ['Upper Left', 'Upper Right', 'Lower Right', 'Lower Left']))
plt.close('all')
r, c = 2, 2
fig, axs = style.subplots(r, c, sharex='all', sharey='all')
ok_df = pd.DataFrame()
slip_df = pd.DataFrame()
for i, ch in enumerate(chlist):
ax=axs[i]
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 19 11:14:43 2018
@author: giguerf
"""
import matplotlib.pyplot as plt
from PMG.COM.openbook import openHDF5
from PMG.COM import table as tb
from matplotlib.animation import FuncAnimation
from matplotlib.widgets import Slider
import matplotlib.gridspec as gridspec
table = tb.get('SLED')
SLED = 'P:/SLED/Data/'
chlist = [
'12HEAD0000Y7ACXA', '12HEAD0000Y2ACXA', '12CHST0000Y7ACXC',
'12CHST0000Y2ACXC', '12PELV0000Y7ACXA', '12PELV0000Y2ACXA'
]
time, fulldata = openHDF5(SLED, chlist)
raw = fulldata['12PELV0000Y7ACXA'].iloc[:, 0]
#%% Animation
fig, ax = plt.subplots(figsize=(5, 3))
#ax.set(xlim=(-3, 3), ylim=(-1, 1))
ax.plot(raw, 'k')
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')
ax.plot(t, raw)
ax2 = plt.twinx(ax=ax)
ax2.plot(t, df, ':')
return clusters, df
### Run the main function unless imported
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,
import pandas as pd
import matplotlib.pyplot as plt
#from PMG.COM.openbook import openHDF5
import PMG.COM.plotstyle as style
import PMG.COM.table as tb
#THOR = os.fspath('P:/AHEC/Data/THOR/')
chlist = [
'11NECKLO00THFOXA', '11CHSTLEUPTHDSXB', '11PELV0000THACXA',
'11FEMRLE00THFOZB'
]
#chlist = ['11CHST0000THACXC', '11CHSTLEUPTHDSXB', '11CLAVLEINTHFOZA', '11CLAVRIINTHFOZA']
time, fulldata = openHDF5(THOR, chlist)
ok, slip = tb.split(tb.get('THOR'), 'CBL_BELT', ['OK', 'SLIP']).values()
slip = slip[~slip.T1.isnull() & ~slip.T1.isin(['?'])]
bin1 = slip[(0.060 <= slip.T1) & (slip.T1 < 0.075)]
#bin2 = slip[(0.075<=slip.T1) & (slip.T1<0.090)]
#bin3 = slip[(0.090<=slip.T1) & (slip.T1<0.115)]
#%%
ok, slip = tb.split(table, 'CBL_BELT', ['OK', 'SLIP']).values()
slip = slip[~slip.T1.isnull() & ~slip.T1.isin(['?'])]
bin1 = slip[(0.060 <= slip.T1) & (slip.T1 < 0.075)]
r, c = 2, 2
plt.close('all')
fig, axs = style.subplots(r, c, sharex='all', sharey='col', figsize=(6.5, 6.5))
ind = pd.concat([time, pd.Series(time.index)], axis=1).set_index('Time')
