def plot_batch_profile(self, average=False, confidence=0.95,
xlim=None, ylim=None, title=None, show=False, save=False,
savename=None, imagetype='png'):
'''
Plots multiple profiles on same plot
'''
font = {'family': 'Arial', 'size': 20}
matplotlib.rc('font', **font)
coloridx = np.linspace(0,1,10) # for use with tab10 colormap
fig, ax = plt.subplots(figsize=(16,9), dpi=75)
if average:
data = [(self.lines[line]['x'], self.lines[line]['height']) \
for line in self.lines]
x, mean, std, lcl, ucl = utilities.batch_average_plot(data=data)
for line in self.lines:
ax.plot(self.lines[line]['x'], self.lines[line]['height'],
linewidth=3, color='k', alpha=0.2)
ax.plot(x, mean, linewidth=3, color='r', label='mean')
ax.plot(x, ucl, color='r', linewidth=3, linestyle='--',)
ax.plot(x, lcl, color='r', linewidth=3, linestyle='--',
label=str(confidence)[2:]+'% Confidence Interval (t-test)')
else:
rous, wavs, pa = [], [], []
for idx in self.lines:
rous.append(self.metrics[idx]['R']['Rq'])
wavs.append(self.metrics[idx]['W']['Wq'])
pa.append(self.metrics[idx]['P']['Pa'])
start = self.idxs[idx][0]
end = self.idxs[idx][1]
isolatedtrace = self.lines[idx]['x'][start:end]*0.001 - \
self.lines[idx]['x'][start]*0.001
ax.plot(
isolatedtrace,
self.lines[idx]['height'][start:end],
linewidth=2,
color=plt.cm.tab10(coloridx[int(idx[1:])-1]),
label=str(idx)
)
mean_r = np.mean(rous)
mean_w = np.mean(wavs)
mean_a = np.mean(pa)
stats = {
'r': r'$R_q = $'+'%.3f µm'%mean_r,
'w': r'$W_q = $'+'%.3f µm'%mean_w,
'a': r'$P_a = $'+'%.3f µm'%mean_a,
}
stats_text = stats['r']+'\n'+stats['w']+'\n'+stats['a']
x_tlim, y_tlim = ax.get_xlim(), ax.get_ylim()
x_tpos = 0.05*(x_tlim[1]-x_tlim[0]) + x_tlim[0]
y_tpos = 0.80*(y_tlim[1]-y_tlim[0]) + y_tlim[0]
ax.text(x_tpos, y_tpos, stats_text, bbox=dict(facecolor='w',
edgecolor='k'))
ax.set_xlabel('Horizontal Position [mm]')
ax.set_ylabel('Height [µm]')
ax.legend(loc='upper right')
ax.grid()
if xlim:
ax.set_xlim(xlim)
if ylim:
ax.set_ylim(ylim)
if title:
ax.set_title(title)
else:
ax.set_title(self.title)
if save:
if savename:
plt.savefig(savename + '.' + imagetype)
else:
plt.savefig('batch_' + self.title + '.' + imagetype)
if show:
plt.show()
plt.close(fig)
def plot_batch_vis_shearstress(self,
average=False,
confidence=None,
xlim=None,
ylim=None,
title=None,
show=False,
save=False,
savename=None,
imagetype='png'):
''' Plots multiple profiles on same plot
Possible choices for both x and y are
'step', 'point', 'time', 'viscosity', 'torque', 'speed',
'shearstress', 'shearrate', 'temperature', 'density', 'accuracy',
'''
font = {'family': 'Arial', 'size': 24}
matplotlib.rc('font', **font)
coloridx = np.linspace(0, 1, 10) # for use with tab10 colormap
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 10), dpi=100)
for yval, ax in zip(('viscosity', 'shearstress'), (ax1, ax2)):
if average:
for idx, inkname in enumerate(self.alldata):
data = [ (self.alldata[inkname][sample]['shearrate'], \
self.alldata[inkname][sample][yval]) \
for sample in self.alldata[inkname]
]
indep, mean, std, lcl, ucl = \
utilities.batch_average_plot(data=data)
mask = np.isfinite(mean)
if not confidence:
ax.errorbar(
indep[mask],
mean[mask],
yerr=std[mask],
color=plt.cm.tab10(coloridx[idx]),
marker='.',
markersize=8,
linewidth=3,
capsize=10,
elinewidth=3,
markeredgewidth=3,
label=self.labels[inkname],
)
else:
ax.plot(self.alldata[sample]['shearrate'],
self.alldata[sample][yval],
linewidth=3,
color='k',
alpha=0.2)
ax.plot(indep,
mean,
linewidth=3,
color='r',
label='mean')
ax.plot(
indep,
ucl,
color='r',
linewidth=3,
linestyle='--',
)
ax.plot(indep,
lcl,
color='r',
linewidth=3,
linestyle='--',
label=str(confidence)[2:] +
'% Confidence Interval (t-test)')
else:
idx = 0
for inkname in self.data[inkname]:
for sample in self.alldata[inkname]:
x_plot = np.array(
self.alldata[inkname][sample]['shearrate'])
y_plot = np.array(self.alldata[inkname][sample][yval])
if np.isnan(np.min(y_plot)):
x_plot = x_plot.astype(np.double)
y_plot = y_plot.astype(np.double)
mask = np.isfinite(y_plot)
ax.plot(x_plot[mask],
y_plot[mask],
linewidth=3,
color=plt.cm.tab10(coloridx[idx]),
linestyle='--')
ax.plot(x_plot,
y_plot,
linewidth=3,
color=plt.cm.tab10(coloridx[idx]),
marker='o',
markersize=8,
label=str(sample))
idx += 1
ax.set_xlabel(self.plotinfo['shearrate']['label'])
ax.set_ylabel(self.plotinfo[yval]['label'])
ax.legend()
# ax.grid()
if title:
plt.suptitle(title)
else:
plt.suptitle('batch_viscosity_shearstress_' + self.title)
plt.tight_layout()
if save:
if savename:
plt.savefig(savename + '.' + imagetype)
else:
plt.savefig('batch_viscosity_shearstress_' \
+ self.title + '.' + imagetype)
if show:
plt.show()
plt.close(fig)
def plot_DCIR_average(self,
confidence=0.95,
xlim=None,
ylim=None,
title=None,
show=False,
save=False,
savename=None,
imagetype='png'):
''' Plots average DCIR vs cycle number
Includes confidence interval '''
# Combines all sample data into list of tuples (x,y) by sample number (len = # of samples)
data = [(self.alldata[sample].DCIR['cycles'], self.alldata[sample].DCIR['average']) \
for sample in sorted(list(self.alldata.keys()))]
cycles, mean, std, lcl, ucl = utilities.batch_average_plot(
data, confidence=confidence)
font = {'family': 'Arial', 'size': 28}
matplotlib.rc('font', **font)
fig, ax = plt.subplots(figsize=(16, 9), dpi=75)
for sample in data:
ax.plot(sample[0], sample[1], color='b', linewidth=3, alpha=0.2)
ax.plot(cycles, mean, color='b', linewidth=3)
if confidence:
ax.plot(cycles,
lcl,
color='b',
linestyle='--',
linewidth=2,
label=str(confidence)[2:] +
'% Confidence Interval (t-test)')
ax.plot(cycles, ucl, color='b', linestyle='--', linewidth=2)
ax.fill_between(cycles, ucl, lcl, color='b', alpha=0.2)
else:
ax.plot(cycles,
mean + std,
color='b',
linestyle='--',
linewidth=2,
label='±1 ' + r'$\sigma$')
ax.plot(cycles, mean - std, color='b', linestyle='--', linewidth=2)
ax.fill_between(cycles,
mean + std,
mean - std,
color='b',
alpha=0.2)
ax.set_xlabel('Cycle Number')
ax.set_ylabel('DCIR [Ω]')
ax.legend()
ax.grid()
if xlim:
ax.set_xlim(xlim)
if ylim:
ax.set_ylim(ylim)
if title:
ax.set_title(title)
else:
ax.set_title(self.title)
if save:
if savename:
plt.savefig(savename + '.' + imagetype)
else:
plt.savefig('batch_' + self.title + '_DCIR_average' + '.' +
imagetype)
if show:
plt.show()
plt.close(fig)