#%% Belt Score Distribution
plt.close('all')
from GitHub.COM import plotstyle as style
t = t.rename(columns = {'Toward':'Difference'})
groupA = t.loc[t.Group == 'A',['Group','LBS','Chest','Pelvis','Difference']].dropna(axis=0)
#groupB = t.loc[t.Group == 'B',['Group','LBS','Chest','Pelvis','Difference']].dropna(axis=0)
groupC = t.loc[t.Group == 'C',['Group','LBS','Chest','Pelvis','Difference']].dropna(axis=0)
groups = pd.concat([groupA,groupC],axis=0)
position = pd.DataFrame(list(zip([0,1],['A','C'])), columns=['X','Group'])
#plotdata = pd.merge(position, groups, on='Group', how='outer')
plotdata = groups
axs = style.subplots(1, 2, sharey='all', visible=False)
#ax1 = plotdata.plot.scatter('X','LBS')
#for column in ['LBS','Chest','Pelvis','Difference']:
#plotdata.boxplot(ax=ax, column=['Chest','Pelvis','Difference'], by='Group', grid=False)
plotdata[plotdata.Group == 'A'].boxplot(ax=axs[0], column=['Chest','Pelvis','Difference'], grid=False)
plotdata[plotdata.Group == 'C'].boxplot(ax=axs[1], column=['Chest','Pelvis','Difference'], grid=False)
axs[0].set_xlabel('Group \'A\'')
axs[1].set_xlabel('Group \'C\'')
axs[0].set_ylabel('Acceleration X [g]')
plt.subplots_adjust(wspace=.0)
#%%
groupA = t.loc[t.Group == 'A',['Group','LBS','Chest','Pelvis','Difference']]
#groupB = t.loc[t.Group == 'B',['Group','LBS','Chest','Pelvis','Difference']]
index_col=0,
skiprows=[1, 2])
#data = dict(zip(['TC18-011','TC18-018'],[MifoldData,TakataData]))
time = MifoldData['T_10000_0']
channels = ['12CHST0000Y7ACXC', '12PELV0000Y7ACXA']
colors = dict(zip(channels, ['tab:orange', 'tab:blue']))
name = dict(zip(channels, ['Chest', 'Pelvis']))
#%%
plt.close('all')
#plt.figure(figsize=(4.8,4.8))
#ax1 = plt.subplot(211)
#ax2 = plt.subplot(212)
axs = style.subplots(2,
1,
sharex='all',
sharey='all',
visible=True,
figsize=(4.8, 4.8))
ax1, ax2 = axs.flatten()
for ch in channels:
ax1.plot(time * 1000, MifoldData[ch], color=colors[ch], label=name[ch])
ax2.plot(time * 1000, TakataData[ch], color=colors[ch], label=name[ch])
for ax in [ax1, ax2]:
ax.set_xlabel('Time [ms]')
ax.set_ylabel('Acceleration X [g]')
ax.set_ylim((-80, 15))
ax.set_xlim((0, 200))
h, l = ax.get_legend_handles_labels()
savedir = os.fspath('P:/AHEC/Plots/')
subdir = 'test/'
tcns = None
#chlist = ['10SIMELE00INACXD', '10SIMERI00INACXD', '10CVEHCG0000ACXD']
time, data = ob.openbook(readdir)
left = data['10SIMELE00INACXD']
center = data['10CVEHCG0000ACXD']
right = data['10SIMERI00INACXD']
plt.close('all')
r, c = style.sqfactors(len(center.columns[:9]))
fig, axs = style.subplots(r, c, sharey='all', figsize=(10 * c, 6.25 * r))
ylim = style.ylim_no_outliers([center - right, center, right])
for i, tcn in enumerate(center.columns[:9]):
ax = axs[i]
try:
ax.plot(time, center.loc[:, tcn] - right.loc[:, tcn], color='k')
ax.plot(time, center.loc[:, tcn], color='tab:red')
ax.plot(time, right.loc[:, tcn], color='tab:blue')
ax.plot(time, left.loc[:, tcn], color='tab:purple')
except KeyError:
pass
ax.set_title(tcn)
ax.set_xlim(-0.01, 0.3)
ax.set_ylim(*ylim)
ax.set_ylabel('Acceleration [g]')
def plotbook(subdir, chlist, tcns=None):
#if 1==1:
"""
Docsting for basic plotting routine
"""
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from GitHub.COM import openbook as ob
from GitHub.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]]
time, cibdict, beldict, pairs = ob.cibbel(readdir)
xlim = (0, 0.3)
xlabel = 'Time [s]'
colors = {'cib': '', 'bel': ''}
#%% FIGURE 1 - all pairs plotted individually
plt.close('all')
for channel in chlist:
if tcns is None:
cib = cibdict[channel]
bel = beldict[channel]
pairs = ob.lookup_pairs(cib.columns, project='AHEC')
# pairs = ob.lookup_pairs(cib.columns.tolist(), project='AHEC')
# pairs = ob.lookup_pairs(project='AHEC') #move out of if with tcns arg
else:
cib = cibdict[channel].loc[:, tcns].dropna(axis=1)
bel = beldict[channel].loc[:, tcns].dropna(axis=1)
pairs = ob.lookup_pairs(tcns, project='AHEC')
if cib.shape[0] == 0:
continue
plotframes = [cib, bel]
ylim = style.ylim_no_outliers(plotframes)
if (channel[2:6] == 'SIME') or (channel[2:6] == 'CVEH'
and channel[14:15] == 'X'):
ylim = (-60, 30)
ylabel = style.ylabel(channel[12:14], channel[14:15])
# pairs = ob.lookup_pairs(cib.columns.tolist(), project='AHEC')
# pairs = ob.lookup_pairs(tcns, project='AHEC')
# pairs = pairs.set_index('CIBLE')
r, c = style.sqfactors(pairs.shape[0])
fig, axs = style.subplots(r,
c,
sharex='all',
sharey='all',
visible=True,
num='All Pairs: ' + channel,
figsize=(5 * c, 3.125 * r))
fig.suptitle(
'{sbdir} - Frontal Offset Crash Test at {speed} km/h\n{ch} - {desc}'
.format(sbdir=subdir[:-4],
speed=subdir[-3:-1],
ch=channel,
desc=description.get(channel, 'Description Unavailable')))
# for i, (cib_tcn, bel_tcn) in enumerate(zip(cib.columns, bel.columns)):
for i, (cib_tcn, bel_tcn) in enumerate(zip(pairs.CIBLE, pairs.BELIER)):
ax = axs[i]
pair = pairs[pairs.CIBLE == cib_tcn]
# bool1 = pairs.loc[cib_tcn,'SUBSET'] == 'HEV vs ICE'
bool1 = pair.SUBSET.item() == 'HEV vs ICE'
# bool2 = not pairs.loc[cib_tcn,'SUBSET'] == 'General'
bool2 = not pair.SUBSET.item() == 'General'
colors['cib'] = (
'tab:blue' if bool1 else 'tab:purple') if bool2 else 'tab:pink'
colors['bel'] = ('tab:orange' if bool1 else
'tab:green') if bool2 else 'tab:grey'
# ax.plot(time, cib.loc[:, cib_tcn], color=colors['cib'], label=cib_tcn)
# ax.plot(time, bel.loc[:, bel_tcn], color=colors['bel'], label=bel_tcn)
ax.plot(time,
cib.get(cib_tcn, default=time * np.nan),
color=colors['cib'],
label=cib_tcn)
ax.plot(time,
bel.get(bel_tcn, default=time * np.nan),
color=colors['bel'],
label=bel_tcn)
ax.set_xlim(*xlim)
ax.set_ylim(*ylim)
title = '{} vs {}: {} vs {}'.format(
cib_tcn, pair.BELIER.to_string(index=False),
pair.CBL_MODELE.to_string(index=False),
pair.BLR_MODELE.to_string(index=False))
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_' + channel + '.png',
dpi=200)
plt.close('all')
#%% FIGURE 3 - Vehicle groups
pairs = ob.lookup_pairs(tcns, project='AHEC')
popdict = ob.populations(readdir, pairs)
NULLDF = pd.DataFrame(columns=tcns)
NULL = {'CIBLE': NULLDF, 'BELIER': NULLDF}
plt.close('all')
for channel in chlist:
HEV = popdict[channel].get('HEV vs ICE', NULL)['CIBLE']
ICE = popdict[channel].get('HEV vs ICE', NULL)['BELIER']
OLD = popdict[channel].get('Old vs New', NULL)['CIBLE']
NEW = popdict[channel].get('Old vs New', NULL)['BELIER']
CIB = popdict[channel].get('General', NULL)['CIBLE']
BEL = popdict[channel].get('General', NULL)['BELIER']
if tcns is not None:
HEV = pd.DataFrame(HEV, columns=tcns).dropna(axis=1)
ICE = pd.DataFrame(ICE, columns=tcns).dropna(axis=1)
OLD = pd.DataFrame(OLD, columns=tcns).dropna(axis=1)
NEW = pd.DataFrame(NEW, columns=tcns).dropna(axis=1)
CIB = pd.DataFrame(CIB, columns=tcns).dropna(axis=1)
BEL = pd.DataFrame(BEL, columns=tcns).dropna(axis=1)
# HEVstats = popdict['HEV vs ICE']['CIBLE'][channel+'_stats']
# ICEstats = popdict['HEV vs ICE']['BELIER'][channel+'_stats']
# OLDstats = popdict['Old vs New']['CIBLE'][channel+'_stats']
# NEWstats = popdict['Old vs New']['BELIER'][channel+'_stats']
# CIBstats = popdict['General']['CIBLE'][channel+'_stats']
# BELstats = popdict['General']['BELIER'][channel+'_stats']
fig = plt.figure('Groups: ' + channel, figsize=(20, 12.5))
fig.suptitle(
'{sbdir} - Frontal Offset Crash Test at {speed} km/h\n{ch} - {desc}'
.format(sbdir=subdir[:-4],
speed=subdir[-3:-1],
ch=channel,
desc=description.get(channel, 'Description Unavailable')))
plotframes = [CIB, BEL, HEV, ICE, OLD, NEW]
ylim = style.ylim_no_outliers(plotframes)
ylabel = style.ylabel(channel[12:14], channel[14:15])
if not CIB.empty:
#FIRST SUBPLOT - 'CIBLE' Group full data set
ax = plt.subplot(2, 3, 1)
plt.plot(time,
CIB,
'.',
color='tab:pink',
markersize=0.5,
label='n = {}'.format(CIB.shape[1]))
plt.xlim(*xlim)
plt.ylim(*ylim)
plt.title('Cible Vehicle Data')
plt.ylabel(ylabel)
plt.xlabel(xlabel)
style.legend(ax=plt.gca(), loc=4)
if not BEL.empty:
#SECOND SUBPLOT - 'BELIER' Group full data set
plt.subplot(2, 3, 4, sharey=ax)
plt.plot(time,
BEL,
'.',
color='tab:gray',
markersize=0.5,
label='n = {}'.format(BEL.shape[1]))
plt.xlim(*xlim)
plt.ylim(*ylim)
plt.title('Belier Vehicle Data')
plt.ylabel(ylabel)
plt.xlabel(xlabel)
style.legend(ax=plt.gca(), loc=4)
if not HEV.empty:
# HEV
plt.subplot(2, 3, 2, sharey=ax)
plt.plot(time,
HEV,
'.',
color='tab:blue',
markersize=0.5,
label='n = {}'.format(HEV.shape[1]))
plt.xlim(*xlim)
plt.ylim(*ylim)
plt.title('HEV Vehicle Data')
plt.ylabel(ylabel)
plt.xlabel(xlabel)
style.legend(ax=plt.gca(), loc=4)
if not ICE.empty:
# ICE
plt.subplot(2, 3, 5, sharey=ax)
plt.plot(time,
ICE,
'.',
color='tab:orange',
markersize=0.5,
label='n = {}'.format(ICE.shape[1]))
plt.xlim(*xlim)
plt.ylim(*ylim)
plt.title('ICE Vehicle Data')
plt.ylabel(ylabel)
plt.xlabel(xlabel)
style.legend(ax=plt.gca(), loc=4)
if not NEW.empty:
# NEW
plt.subplot(2, 3, 3, sharey=ax)
plt.plot(time,
NEW,
'.',
color='tab:green',
markersize=0.5,
label='n = {}'.format(NEW.shape[1]))
plt.xlim(*xlim)
plt.ylim(*ylim)
plt.title('New Vehicle Data')
plt.ylabel(ylabel)
plt.xlabel(xlabel)
style.legend(ax=plt.gca(), loc=4)
if not OLD.empty:
# OLD
plt.subplot(2, 3, 6, sharey=ax)
plt.plot(time,
OLD,
'.',
color='tab:purple',
markersize=0.5,
label='n = {}'.format(OLD.shape[1]))
plt.xlim(*xlim)
plt.ylim(*ylim)
plt.title('Old Vehicle Data')
plt.ylabel(ylabel)
plt.xlabel(xlabel)
style.legend(ax=plt.gca(), loc=4)
plt.tight_layout(rect=[0, 0, 1, 0.92])
plt.savefig(savedir + subdir + 'Groups_' + channel + '.png', dpi=200)
plt.close('all')
#%% FIGURE 4 - all pairs plotted individually
plt.close('all')
table = pd.read_excel('P:/AHEC/ahectable.xlsx')
table = table.dropna(axis=0, how='all').dropna(axis=1, thresh=2)
for channel in chlist:
if tcns is None:
cib = cibdict[channel]
bel = beldict[channel]
table2 = table[table.loc[:, ['CIBLE', 'BELIER']].isin(
cib.columns).any(axis=1)]
else:
cib = cibdict[channel].loc[:, tcns].dropna(axis=1)
bel = beldict[channel].loc[:, tcns].dropna(axis=1)
table2 = table[table.loc[:, ['CIBLE', 'BELIER']].isin(tcns).any(
axis=1)]
pairs = table2.loc[:, [
'CIBLE', 'BELIER', 'VITESSE', 'CBL_MODELE', 'BLR_MODELE', 'SUBSET',
'CBL_POIDS', 'BLR_POIDS'
]]
pairs['Mass Gap'] = pairs['CBL_POIDS'] - pairs['BLR_POIDS']
pairs = pairs.sort_values(by=['Mass Gap'])
if cib.shape[0] == 0:
continue
plotframes = [cib, bel]
ylim = style.ylim_no_outliers(plotframes)
if (channel[2:6] == 'SIME') or (channel[2:6] == 'CVEH'
and channel[14:15] == 'X'):
ylim = (-60, 30)
ylabel = style.ylabel(channel[12:14], channel[14:15])
r, c = style.sqfactors(pairs.shape[0])
fig, axs = style.subplots(r,
c,
sharex='all',
sharey='all',
visible=True,
num='Mass: ' + channel,
figsize=(5 * c, 3.125 * r))
fig.suptitle(
'{sbdir} - Frontal Offset Crash Test at {speed} km/h\n{ch} - {desc}'
.format(sbdir=subdir[:-4],
speed=subdir[-3:-1],
ch=channel,
desc=description.get(channel, 'Description Unavailable')))
for i, (cib_tcn, bel_tcn) in enumerate(zip(pairs.CIBLE, pairs.BELIER)):
ax = axs[i]
pair = pairs[pairs.CIBLE == cib_tcn]
bool1 = pair.SUBSET.item() == 'HEV vs ICE'
bool2 = not pair.SUBSET.item() == 'General'
colors['cib'] = (
'tab:blue' if bool1 else 'tab:purple') if bool2 else 'tab:pink'
colors['bel'] = ('tab:orange' if bool1 else
'tab:green') if bool2 else 'tab:grey'
ax.plot(time,
cib.get(cib_tcn, default=time * np.nan),
color=colors['cib'],
label=cib_tcn)
ax.plot(time,
bel.get(bel_tcn, default=time * np.nan),
color=colors['bel'],
label=bel_tcn)
ax.set_xlim(*xlim)
ax.set_ylim(*ylim)
title = '{} vs {}: {} vs {}\n({} lbs)'.format(
cib_tcn, pair.BELIER.to_string(index=False),
pair.CBL_MODELE.to_string(index=False),
pair.BLR_MODELE.to_string(index=False),
pair['Mass Gap'].to_string(index=False))
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 + 'Mass_' + channel + '.png', dpi=200)
plt.close('all')
plt.close('all')
for vitesse in [40, 48, 56]:
tcnlist = list(table[table['Vitesse'] == vitesse].index)
peakdata = {}
lbs = {}
for channel in fulldata:
peakdata[channel] = pd.DataFrame(columns=['t0', 'tp', 'start', 'peak'])
for tcn in fulldata[channel].columns.intersection(tcnlist):
peakdata[channel].loc[tcn] = gp.get_peak(time,
fulldata[channel][tcn])
mask = scoretable.Modele == table.loc[tcn].Modele
lbs[tcn] = scoretable[mask].iloc[0, -4:]
axs = style.subplots(3,
4,
num='Lap Belt Scores %s' % vitesse,
sharex=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 11, 12],
sharey=[1, 1, 3, 3, 5, 5, 3, 3, 9, 10, 11, 12])
xlabel = 'Lap Belt Score'
for channel, subloc in subplot_peak.items():
ax = axs[subloc - 1]
for tcn in peakdata[channel].index:
ax.plot(lbs[tcn]['D3'],
peakdata[channel].loc[tcn, 'peak'],
'.',
color='tab:orange',
label='C3')
ax.plot(lbs[tcn]['D4'],
table = pd.concat([table, table2], axis=0)
sets = set(table[column].sort_values())
sub = {}
for set_item in sets:
sub[set_item] = table[table[column] == set_item]['CIBLE'].tolist()
for channel in chlist:
ch = channel[0] if isinstance(channel, list) else channel
ylabel = style.ylabel(ch[12:14], ch[14:15])
r, c = style.sqfactors(len(sub))
fig, axs = style.subplots(r,
c,
sharex='all',
sharey='all',
num='All Pairs: ' + ch,
figsize=(5 * c, 3.125 * r))
fig.suptitle('{ch} - {desc}'.format(ch=ch,
desc=description.get(
ch,
'Description Unavailable')))
chdata = pd.concat([data[ch] for ch in channel], axis=1) if isinstance(
channel, list) else data[channel]
ylim = style.ylim_no_outliers(chdata)
for i, set_item in enumerate(sub):
ax = axs[i]
try:
df = chdata.loc[:, sub[set_item]]
#for group in groupdict:
# for channel in groupdict[group]:
# temp = style.explode(groupdict[group][channel],[])
# dvdf = pd.concat(temp, axis=1)
# groupdict[group][channel] = dvdf
#groupA = groupdict['A']
#groupC = groupdict['C']
chm = [channel+'m' for channel in channels]
colors = dict(zip(channels+chm, ['tab:orange','tab:blue','tab:red','tab:blue']))
name = dict(zip(channels, ['Chest','Pelvis']))
al = dict(zip(channels, [0.25,0.25]))
#%% Group A and C for Offset, Wall
plt.close('all')
axs = style.subplots(2,2,sharex='all',sharey='all',visible=True,figsize=(9,6))
for i, config in enumerate(['offset','mur']):
for ch in channels:
axs[i].plot(time*1000, groupdict['A'][ch][config], color = colors[ch], alpha=al[ch], label = name[ch])
axs[i+2].plot(time*1000, groupdict['C'][ch][config], color = colors[ch], alpha=al[ch], label = name[ch])
axs[i].plot(time*1000, groupdict['A'][ch][config].mean(axis=1), color = colors[ch+'m'], label = name[ch]+' Mean')
axs[i+2].plot(time*1000, groupdict['C'][ch][config].mean(axis=1), color = colors[ch+'m'], label = name[ch]+' Mean')
for group in ['A','C']:
ax = axs[i] if group == 'A' else axs[i+2]
ax.set_xlabel('Time [ms]')
ax.set_ylabel('Acceleration X [g]')