def plot_clusters(raw, t, Z, N, name, labels, plot_all, plot_path):
results = hierarchy.fcluster(Z, t=N, criterion='maxclust')
s = pd.DataFrame(results)
s.index = raw.columns
s.columns = ['name']
lbd = dict(zip(s.index, labels))
tcns = {}
for cluster in s['name'].unique():
tcns[cluster] = s[s['name'] == cluster].index.tolist()
if plot_all:
plot_dendrograms(Z, name, labels, plot_path)
r, c = sqfactors(N)
fig, axs = subplots(r,
c,
sharey='all',
sharex='all',
num='_'.join([name, 'cluster']),
figsize=[6 * c, 6 * r])
for i, cluster in enumerate(tcns.keys()):
ax = axs[i]
for tcn in tcns[cluster]:
ax.plot(t, raw.loc[:, tcn], label=lbd[tcn])
ax.legend(fontsize=8)
plt.tight_layout()
plt.savefig(plot_path + name + '_cluster.png')
plt.close('all')
return tcns
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]
peaks, times = data.find_peak(data.smooth(fulldata[ch]), time)
ok_df = pd.concat([ok_df, peaks.loc[ok].dropna().abs()], axis=1)
slip_df = pd.concat([slip_df, peaks.loc[slip].dropna().abs()], axis=1)
hist, bin_edges = np.histogram(np.array(peaks.loc[ok].dropna().abs()), bins=np.linspace(0,60,41), normed=True)
ax.bar(bin_edges[1:], hist, 60/40, label=titles[ch]+' ok', color=cd[ch], alpha=0.25)
hist, bin_edges = np.histogram(np.array(peaks.loc[slip].dropna().abs()), bins=np.linspace(0,60,41), normed=True)
ax.bar(bin_edges[1:], -hist, 60/40, label=titles[ch]+' slip', color=cd[ch], alpha=0.75)
ax.set_title(titles[ch])
ax.set_xlabel('Displacement')
def plotbook(subdir, tcns=None):
"""
Docsting for basic plotting routine
"""
import os
# import pandas as pd
import matplotlib.pyplot as plt
from PMG.COM import openbook as ob
from PMG.COM import plotstyle as style
readdir = os.fspath('P:/AHEC/SAI/')
savedir = os.fspath('P:/AHEC/Plots/')
time, slipdict, slidedict, okdict, singles, pairs = ob.thor(readdir, tcns)
colors = ['tab:blue', 'tab:green', 'tab:orange', 'tab:purple']
lines = {
'TC11-008': [0.016, 0.062, 0.080, 0.093],
'TC12-218': [0.030, 0.063, 0.082, 0.083],
'TC14-035': [0.042, 0.070, 0.081, 0.088],
'TC15-162': [0.013, 0.077, 0.088, 0.095],
'TC15-163': [0.000, 0.000, 0.000, 0.092],
'TC17-031': [0.030, 0.060, 0.076, 0.083],
'TC17-201': [0.030, 0.056, 0.076, 0.084],
'TC17-212': [0.031, 0.060, 0.080, 0.090]
}
#%% FIGURE 1 - all pairs plotted individually
xlim = (0, 0.15)
xlabel = 'Time [s]'
#
plt.close('all')
r, c = style.sqfactors(len(tcns))
fig, axs = style.subplots(r,
c,
sharex='all',
sharey='all',
visible=True,
num='SEBE-NECK',
figsize=(5 * c, 3.125 * r))
for i, tcn in enumerate(tcns):
ax = axs[i]
slip_cols = []
for channel in slipdict:
slip_cols.extend(slipdict[channel].columns)
slip_cols = list(set(slip_cols))
slip = tcn in slip_cols
ax.plot(time,
slipdict['11SEBE0000B3FO0D'][tcn]
if slip else okdict['11SEBE0000B3FO0D'][tcn],
color='tab:blue')
ax2 = plt.twinx(ax)
ax2.plot(time,
slipdict['11NECKLO00THFOXA'][tcn]
if slip else okdict['11NECKLO00THFOXA'][tcn],
color='tab:green')
ax2.plot(time,
slipdict['11NECKLO00THFOYA'][tcn]
if slip else okdict['11NECKLO00THFOYA'][tcn],
color='tab:green')
ax3 = plt.twinx(ax)
ax3.plot(time,
slipdict['11CLAVLEINTHFOZA'][tcn]
if slip else okdict['11CLAVLEINTHFOZA'][tcn],
color='tab:orange')
ax3.plot(time,
slipdict['11CLAVRIOUTHFOZA'][tcn]
if slip else okdict['11CLAVRIOUTHFOZA'][tcn],
color='tab:purple')
for line, color in zip(lines.get(tcn, [-1] * 4), colors):
ax.axvline(line, color=color)
ax.set_xlim(*xlim)
ax.set_ylim(-6000, 6000)
ax2.set_ylim(-2000, 2000)
ax3.set_ylim(-1000, 1000)
title = tcn
ax.set_title(title)
ax.set_ylabel('Seat Belt Tension [N]')
ax2.set_ylabel('Neck Force [N]')
ax.set_xlabel(xlabel)
ax.legend(loc=4)
plt.tight_layout(rect=[0, 0, 1, 0.92])
plt.savefig(savedir + subdir + 'SEBE-NECK.png', dpi=200)
plt.close('all')
#%% FIGURE 2 - Vehicle groups and means
plt.close('all')
plt.figure('SEBE-NECK Groups', figsize=(20, 12.5))
#FIRST SUBPLOT - 'SLIP' Group full data set
ax = plt.subplot(2, 3, 1)
plt.plot(time,
slipdict['11SEBE0000B3FO0D'],
'.',
color='tab:blue',
markersize=0.5,
label='inner n = {}'.format(len(slipdict['11SEBE0000B3FO0D'])))
plt.xlim(*xlim)
ax.set_ylim(-6000, 6000)
ax.axvline(0.055)
ax.axvline(0.080)
ax.axvline(0.090)
plt.title('Slip SEBE')
plt.ylabel('Seat Belt Tension [N]')
plt.xlabel(xlabel)
style.legend(ax=plt.gca(), loc=4)
#FIRST SUBPLOT - 'SLIP' Group full data set
ax = plt.subplot(2, 3, 2)
plt.plot(time,
slipdict['11NECKLO00THFOXA'],
'.',
color='tab:orange',
markersize=0.5,
label='inner n = {}'.format(len(slipdict['11NECKLO00THFOXA'])))
plt.xlim(*xlim)
ax.set_ylim(-2000, 2000)
ax.axvline(0.055)
ax.axvline(0.080)
ax.axvline(0.090)
plt.title('Slip NECK X')
plt.ylabel('Neck Force [N]')
plt.xlabel(xlabel)
style.legend(ax=plt.gca(), loc=4)
#FIRST SUBPLOT - 'SLIP' Group full data set
ax = plt.subplot(2, 3, 3)
plt.plot(time,
slipdict['11NECKLO00THFOYA'],
'.',
color='tab:purple',
markersize=0.5,
label='inner n = {}'.format(len(slipdict['11NECKLO00THFOYA'])))
plt.xlim(*xlim)
ax.set_ylim(-2000, 2000)
ax.axvline(0.055)
ax.axvline(0.080)
ax.axvline(0.090)
plt.title('Slip NECK Y')
plt.ylabel('Neck Force [N]')
plt.xlabel(xlabel)
style.legend(ax=plt.gca(), loc=4)
#FIRST SUBPLOT - 'SLIP' Group full data set
ax = plt.subplot(2, 3, 4)
plt.plot(time,
okdict['11SEBE0000B3FO0D'],
'.',
color='tab:blue',
markersize=0.5,
label='inner n = {}'.format(len(okdict['11SEBE0000B3FO0D'])))
plt.xlim(*xlim)
ax.set_ylim(-6000, 6000)
ax.axvline(0.055)
ax.axvline(0.080)
ax.axvline(0.090)
plt.title('OK SEBE')
plt.ylabel('Seat Belt Tension [N]')
plt.xlabel(xlabel)
style.legend(ax=plt.gca(), loc=4)
#FIRST SUBPLOT - 'SLIP' Group full data set
ax = plt.subplot(2, 3, 5)
plt.plot(time,
okdict['11NECKLO00THFOXA'],
'.',
color='tab:orange',
markersize=0.5,
label='inner n = {}'.format(len(okdict['11NECKLO00THFOXA'])))
plt.xlim(*xlim)
ax.set_ylim(-2000, 2000)
ax.axvline(0.055)
ax.axvline(0.080)
ax.axvline(0.090)
plt.title('OK NECK X')
plt.ylabel('Neck Force [N]')
plt.xlabel(xlabel)
style.legend(ax=plt.gca(), loc=4)
#FIRST SUBPLOT - 'SLIP' Group full data set
ax = plt.subplot(2, 3, 6)
plt.plot(time,
okdict['11NECKLO00THFOYA'],
'.',
color='tab:purple',
markersize=0.5,
label='inner n = {}'.format(len(okdict['11NECKLO00THFOYA'])))
plt.xlim(*xlim)
ax.set_ylim(-2000, 2000)
ax.axvline(0.055)
ax.axvline(0.080)
ax.axvline(0.090)
plt.title('OK NECK Y')
plt.ylabel('Neck Force [N]')
plt.xlabel(xlabel)
style.legend(ax=plt.gca(), loc=4)
plt.subplots_adjust(top=0.893,
bottom=0.060,
left=0.048,
right=0.974,
hspace=0.222,
wspace=0.128)
plt.savefig(savedir + subdir + 'SEBE-NECK_stats.png', dpi=200)
plt.close('all')
#%% FIGURE 2 - Vehicle groups and means
plt.close('all')
plt.figure('SEBE-NECK Groups', figsize=(20, 12.5))
#FIRST SUBPLOT - 'SLIP' Group full data set
ax = plt.subplot(3, 2, 1)
plt.plot(time,
slipdict['11SEBE0000B3FO0D'],
'.',
color='tab:blue',
markersize=0.5,
label='inner n = {}'.format(len(slipdict['11SEBE0000B3FO0D'])))
plt.xlim(*xlim)
ax.set_ylim(-6000, 6000)
ax.axvline(0.055)
ax.axvline(0.080)
ax.axvline(0.090)
plt.title('Slip SEBE')
plt.ylabel('Seat Belt Tension [N]')
plt.xlabel(xlabel)
style.legend(ax=plt.gca(), loc=4)
#FIRST SUBPLOT - 'SLIP' Group full data set
ax = plt.subplot(3, 2, 3)
plt.plot(time,
slipdict['11NECKLO00THFOXA'],
'.',
color='tab:orange',
markersize=0.5,
label='inner n = {}'.format(len(slipdict['11NECKLO00THFOXA'])))
plt.xlim(*xlim)
ax.set_ylim(-2000, 2000)
ax.axvline(0.055)
ax.axvline(0.080)
ax.axvline(0.090)
plt.title('Slip NECK X')
plt.ylabel('Neck Force [N]')
plt.xlabel(xlabel)
style.legend(ax=plt.gca(), loc=4)
#FIRST SUBPLOT - 'SLIP' Group full data set
ax = plt.subplot(3, 2, 5)
plt.plot(time,
slipdict['11NECKLO00THFOYA'],
'.',
color='tab:purple',
markersize=0.5,
label='inner n = {}'.format(len(slipdict['11NECKLO00THFOYA'])))
plt.xlim(*xlim)
ax.set_ylim(-2000, 2000)
ax.axvline(0.055)
ax.axvline(0.080)
ax.axvline(0.090)
plt.title('Slip NECK Y')
plt.ylabel('Neck Force [N]')
plt.xlabel(xlabel)
style.legend(ax=plt.gca(), loc=4)
#FIRST SUBPLOT - 'SLIP' Group full data set
ax = plt.subplot(3, 2, 2)
plt.plot(time,
okdict['11SEBE0000B3FO0D'],
'.',
color='tab:blue',
markersize=0.5,
label='inner n = {}'.format(len(okdict['11SEBE0000B3FO0D'])))
plt.xlim(*xlim)
ax.set_ylim(-6000, 6000)
ax.axvline(0.055)
ax.axvline(0.080)
ax.axvline(0.090)
plt.title('OK SEBE')
plt.ylabel('Seat Belt Tension [N]')
plt.xlabel(xlabel)
style.legend(ax=plt.gca(), loc=4)
#FIRST SUBPLOT - 'SLIP' Group full data set
ax = plt.subplot(3, 2, 4)
plt.plot(time,
okdict['11NECKLO00THFOXA'],
'.',
color='tab:orange',
markersize=0.5,
label='inner n = {}'.format(len(okdict['11NECKLO00THFOXA'])))
plt.xlim(*xlim)
ax.set_ylim(-2000, 2000)
ax.axvline(0.055)
ax.axvline(0.080)
ax.axvline(0.090)
plt.title('OK NECK X')
plt.ylabel('Neck Force [N]')
plt.xlabel(xlabel)
style.legend(ax=plt.gca(), loc=4)
#FIRST SUBPLOT - 'SLIP' Group full data set
ax = plt.subplot(3, 2, 6)
plt.plot(time,
okdict['11NECKLO00THFOYA'],
'.',
color='tab:purple',
markersize=0.5,
label='inner n = {}'.format(len(okdict['11NECKLO00THFOYA'])))
plt.xlim(*xlim)
ax.set_ylim(-2000, 2000)
ax.axvline(0.055)
ax.axvline(0.080)
ax.axvline(0.090)
plt.title('OK NECK Y')
plt.ylabel('Neck Force [N]')
plt.xlabel(xlabel)
style.legend(ax=plt.gca(), loc=4)
plt.subplots_adjust(top=0.893,
bottom=0.060,
left=0.048,
right=0.974,
hspace=0.222,
wspace=0.128)
plt.savefig(savedir + subdir + 'SEBE-NECK_stats2.png', dpi=200)
plt.close('all')
for ch in chlist:
data[ch] = pair_data.loc[:, ch + 'ICE'] - pair_data.loc[:, ch + 'EV']
data2 = {}
for ch in th_chs:
data2[ch] = pair_data_2.loc[:, ch + 'ICE'] - pair_data_2.loc[:, ch + 'EV']
#%% Figure 1 - Boxplot with scatter/jitter
H3_color = '#1b9e77'
TH_color = '#d95f02'
TH2_color = '#7570b3'
plt.close('all')
fig, axs = style.subplots(2,
3,
sharex='col',
sharey='all',
figsize=(6.5, 5),
visible=False)
axs = list(axs)
ax2 = axs[2].twinx() #secondary axis for neck force
for i, (h3, th, name) in enumerate(zip(h3_chs, th_chs, chname)):
if i == 2:
ax = ax2
else:
ax = axs[i]
# H3_array = np.arange(-5,1,1)
# TH_array = np.arange(5,11,1)
# TH2_array = np.arange(-3,8,1)
H3_array = data[h3].dropna().values
#%%
table = table[table.BACK.isin(['HB', 'LB']) & table.DATA.isin(['YES'])]
colors = style.colordict(
['HBold_accel', 'LBold_accel', 'HBnew_accel', 'LBnew_accel'])
labels = dict(
zip(['HBold_accel', 'LBold_accel', 'HBnew_accel', 'LBnew_accel'],
['Highback Old', 'Lowback Old', 'Highback New', 'Lowback New']))
channels = ['12HEAD0000Y7ACXA', '12CHST0000Y7ACXC', '12PELV0000Y7ACXA']
chname = dict(zip(channels, ['Head', 'Chest', 'Pelvis']))
plt.close('all')
for ch in channels:
fig, axs = style.subplots(2, 2, sharex='all', sharey='all')
for i, (back, sled, ax) in enumerate(
zip(['HB', 'HB', 'LB', 'LB'],
['old_accel', 'new_accel', 'old_accel', 'new_accel'], axs)):
SEs = table[table.BACK.isin([back])
& table.SLED.isin([sled])].SE.tolist()
data = fulldata[ch].loc[:, SEs].rolling(20,
0,
center=True,
win_type='triang').mean()
ax.plot(time,
data,
color=colors[back + sled],
label=labels[back + sled])
style.legend(ax, loc='lower right')
ax.set_xlim(0, 0.12)
def plotbook(subdir, tcns=None):
"""
Docsting for basic plotting routine
"""
import os
import pandas as pd
import matplotlib.pyplot as plt
from PMG.COM import openbook as ob
from PMG.COM import plotstyle as style
readdir = os.fspath('P:/AHEC/SAI/')
savedir = os.fspath('P:/AHEC/Plots/')
descriptions = pd.read_excel('P:/AHEC/Descriptions.xlsx', index_col = 0)
description = descriptions.to_dict()[descriptions.columns[0]]
tcns = ['TC09-027', 'TC13-007', 'TC17-201', 'TC17-212', 'TC15-163', 'TC11-008', 'TC14-035', 'TC12-003', 'TC15-162', 'TC17-209', 'TC14-220', 'TC17-211', 'TC17-025', 'TC12-217', 'TC12-501', 'TC14-139', 'TC16-013', 'TC14-180', 'TC16-129', 'TC17-208']
# REPLACE THE BELOW WITH HDF5 VERSION
# time, slipdict, slidedict, okdict, singles, pairs = ob.thor(readdir, tcns)
#%% FIGURE 1 - all pairs plotted individually
xlim = (0, 0.3)
xlabel = 'Time [s]'
#
plt.close('all')
for outer, inner in [['11CLAVRIOUTHFOXA', '11CLAVRIINTHFOXA'],
['11CLAVRIOUTHFOZA', '11CLAVRIINTHFOZA'],
['11CLAVLEOUTHFOXA', '11CLAVLEINTHFOXA'],
['11CLAVLEOUTHFOZA', '11CLAVLEINTHFOZA']]:
slipinner = slipdict[inner]
slipouter = slipdict[outer]
okinner = okdict[inner]
okouter = okdict[outer]
plotinner = pd.concat([slipinner, okinner], axis=1).dropna(axis=1)
plotouter = pd.concat([slipouter, okouter], axis=1).dropna(axis=1)
plotinner = plotinner.loc[:,tcns] if tcns is not None else plotinner
plotouter = plotouter.loc[:,tcns] if tcns is not None else plotouter
if plotinner.shape[0] == 0:
continue
ylim = style.ylim_no_outliers(pd.concat([plotinner, plotouter], axis=1))
ylabel = style.ylabel(inner[12:14], inner[14:15])
r, c = style.sqfactors(len(plotinner.columns.tolist()))
fig, axs = style.subplots(r, c, sharex='all', sharey='all', visible=True,
num='All Pairs: '+inner, figsize=(5*c, 3.125*r))
fig.suptitle('{ch} - {desc}'.format(ch = inner,
desc = description.get(inner, 'Description Unavailable')))
for i, tcn in enumerate(plotinner.columns.tolist()):
ax = axs[i]
bool1 = tcn in slipinner.columns
ax.plot(time, plotinner.loc[:, tcn], color = 'tab:blue' if bool1 else 'tab:orange', label = 'inner')
ax.plot(time, plotouter.loc[:, tcn], color = 'tab:green' if bool1 else 'tab:purple', label = 'outer')
ax.set_xlim(*xlim)
ax.set_ylim(*ylim)
title = tcn
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+subdir+'All_Pairs_'+inner+'_combo.png', dpi=200)
plt.close('all')
#%% FIGURE 2 - Vehicle groups and means
plt.close('all')
for outer, inner in [['11CLAVRIOUTHFOXA', '11CLAVRIINTHFOXA'],
['11CLAVRIOUTHFOZA', '11CLAVRIINTHFOZA'],
['11CLAVLEOUTHFOXA', '11CLAVLEINTHFOXA'],
['11CLAVLEOUTHFOZA', '11CLAVLEINTHFOZA']]:
slipinner = slipdict[inner]
slipouter = slipdict[outer]
okinner = okdict[inner]
okouter = okdict[outer]
plotinner = pd.concat([slipinner, okinner], axis=1).dropna(axis=1)
plotouter = pd.concat([slipouter, okouter], axis=1).dropna(axis=1)
plotinner = plotinner.loc[:,tcns] if tcns is not None else plotinner
plotouter = plotouter.loc[:,tcns] if tcns is not None else plotouter
ylim = style.ylim_no_outliers(pd.concat([plotinner, plotouter], axis=1))
ylabel = style.ylabel(inner[12:14], inner[14:15])
fig = plt.figure('Groups: '+inner, figsize=(20, 12.5))
fig.suptitle('{ch} - {desc}'.format(ch = inner,
desc = description.get(inner, 'Description Unavailable')))
#FIRST SUBPLOT - 'SLIP' Group full data set
ax = plt.subplot(2,1,1)
plt.plot(time, slipinner, '.', color = 'tab:blue', markersize=0.5, label = 'inner n = {}'.format(slipinner.shape[1]))
plt.plot(time, slipouter, '.', color = 'tab:green', markersize=0.5, label = 'outer n = {}'.format(slipouter.shape[1]))
plt.xlim(*xlim)
ax.set_ylim(*ylim)
plt.title('Slipping Belts')
plt.ylabel(ylabel)
plt.xlabel(xlabel)
style.legend(ax=plt.gca(), loc=4)
#THIRD SUBPLOT - 'OK' Group full data set
plt.subplot(2,1,2, sharey = ax)
plt.plot(time, okinner, '.', color = 'tab:orange', markersize=0.5, label = 'inner n = {}'.format(okinner.shape[1]))
plt.plot(time, okouter, '.', color = 'tab:purple', markersize=0.5, label = 'outer n = {}'.format(okouter.shape[1]))
plt.xlim(*xlim)
ax.set_ylim(*ylim)
plt.title('Ok Belts')
plt.ylabel(ylabel)
plt.xlabel(xlabel)
style.legend(ax=plt.gca(), loc=4)
plt.subplots_adjust(top=0.893, bottom=0.060, left=0.048, right=0.974, hspace=0.222, wspace=0.128)
plt.savefig(savedir+subdir+'Belt_'+inner+'_combo.png', dpi=200)
plt.close('all')
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')
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')
chname = dict(zip(chlist, ['Lower Neck $F_x$', 'Upper Left Chest $D_x$']))
alltcns = bin1.CIBLE.tolist() + ok.CIBLE.tolist()
for c, (ch, group) in enumerate(
zip([
'11NECKLO00THFOXA', '11CHSTLEUPTHDSXB', '11NECKLO00THFOXA',
'11CHSTLEUPTHDSXB'
], [bin1, bin1, ok, ok])):
ax = axs[c]
tcns = group.CIBLE.tolist()
df = fulldata[ch].loc[:, tcns]
if c in [0, 2]:
xfmt.set_powerlimits((-3,4))
#xloc = matplotlib.ticker.MaxNLocator(nbins='auto', steps=[1, 1.5, 2, 2.5, 3, 4, 5, 10],
# prune=None, min_n_ticks=4)
if 1:
plt.close('all')
chlist = ['11NECKLO00THFOXA','11NECKLO00THFOYA','11CHSTLEUPTHDSXB','11CHSTRIUPTHDSXB','11CHSTLELOTHDSXB','11CHSTRILOTHDSXB']
labels = ['Lower Neck $\mathregular{F_x}$ [N]',
'Lower Neck $\mathregular{F_y}$ [N]',
'Upper Left Chest $\mathregular{D_x}$ [mm]',
'Upper Right Chest $\mathregular{D_x}$ [mm]',
'Lower Left Chest $\mathregular{D_x}$ [mm]',
'Lower Right Chest $\mathregular{D_x}$ [mm]']
ylabel = dict(zip(chlist, labels))
fig, axs = style.subplots(3, 2, sharex='all', sharey=[1,2,3,3,3,3], figsize=(7,6.5))
for i, channel in enumerate(chlist):
df = fulldata[channel].dropna(axis=0).dropna(axis=1)
# df = df.loc[:,slips+oks]
df.reindex(slips+oks, axis=1)
df_slip = df.reindex(slips, axis=1)
df_ok = df.reindex(oks, axis=1)
window = 100
alpha = 0.10
slip_median = df_slip.median(axis=1).rolling(window,0,center=True,win_type='triang').mean()
ok_median = df_ok.median(axis=1).rolling(window,0,center=True,win_type='triang').mean()
slip_high = df_slip.quantile(1-alpha/2, axis=1).rolling(window,0,center=True,win_type='triang').mean()
slip_low = df_slip.quantile(alpha/2, axis=1).rolling(window,0,center=True,win_type='triang').mean()
ok_high = df_ok.quantile(1-alpha/2, axis=1).rolling(window,0,center=True,win_type='triang').mean()