def run(self, output_dir):
model = models_index[self.model]('pt_BR')
g = model.callback(**self.kwargs)
t, y = self.nmethod(g, self.sample_time, self.total_time)
fig = Figure(figsize=(8, 6), dpi=300)
ax = fig.add_subplot(111, xlabel='Tempo (seg)', ylabel='Amplitude')
ax.plot(t, y, label='Resposta ao Degrau')
# generate the tuning line
ident = Smith(t, y)
t1, y1 = ident.tuning_line
ax.plot(t1, y1, label='Reta de Sintonia')
ax.annotate(
'%.1f%% (p1)' % ident.point1, xy=(t1[1], y1[1]), xycoords='data',
xytext=(t1[1]+(self.total_time/15.0), y1[1]),
arrowprops=dict(facecolor='black', shrink=0.05),
)
ax.annotate(
'%.1f%% (p2)' % ident.point2, xy=(t1[2], y1[2]), xycoords='data',
xytext=(t1[2]+(self.total_time/15.0), y1[2]),
arrowprops=dict(facecolor='black', shrink=0.05),
)
legend = []
for arg in model.args:
if arg in self.blacklist_args:
continue
legend.append('%s=%s' % (arg, self.kwargs[arg]))
#ax.legend(loc='best', prop={'size': 'x-small'}, title='; '.join(legend))
canvas = FigureCanvas(fig)
filename = 'cap3_model%i_%i.eps' % (self.model, self.plot_id)
canvas.print_eps(os.path.join(output_dir, filename))
def run(self, output_dir):
model = models_index[self.model]('pt_BR')
g = model.callback(**self.kwargs)
t, y = self.nmethod(g, self.sample_time, self.total_time)
fig = Figure(figsize=(8, 6), dpi=300)
ax = fig.add_subplot(111, xlabel='Tempo (seg)', ylabel='Amplitude')
ax.plot(t, y, label='Resposta ao Degrau')
# generate the tuning line
ident = Smith(t, y)
t1, y1 = ident.tuning_line
ax.plot(t1, y1, label='Reta de Sintonia')
ax.annotate(
'%.1f%% (p1)' % ident.point1,
xy=(t1[1], y1[1]),
xycoords='data',
xytext=(t1[1] + (self.total_time / 15.0), y1[1]),
arrowprops=dict(facecolor='black', shrink=0.05),
)
ax.annotate(
'%.1f%% (p2)' % ident.point2,
xy=(t1[2], y1[2]),
xycoords='data',
xytext=(t1[2] + (self.total_time / 15.0), y1[2]),
arrowprops=dict(facecolor='black', shrink=0.05),
)
legend = []
for arg in model.args:
if arg in self.blacklist_args:
continue
legend.append('%s=%s' % (arg, self.kwargs[arg]))
#ax.legend(loc='best', prop={'size': 'x-small'}, title='; '.join(legend))
canvas = FigureCanvas(fig)
filename = 'cap3_model%i_%i.eps' % (self.model, self.plot_id)
canvas.print_eps(os.path.join(output_dir, filename))
def run(self, output_dir):
model = models_index[self.model]('pt_BR')
g = model.callback(**self.kwargs)
t, y = self.nmethod(g, self.sample_time, self.total_time)
fig = Figure(figsize=(8, 6), dpi=300)
ax = fig.add_subplot(111, xlabel='Tempo (seg)', ylabel='Amplitude')
ax.plot(t, y, label='Resposta ao Degrau')
if self.kp is None or self.ki is None or self.kd is None:
kp, ki, kd = ZieglerNichols(t, y, Smith).gains
else:
kp, ki, kd = self.kp, self.ki, self.kd
if self.simulate:
# transfer function of the PID controller
g_ = tf([kd, kp, ki], [1, 0])
my_g = (g_ * g).feedback_unit()
# discretize the controlled system
t1, y1 = self.nmethod(my_g, self.sample_time, self.total_time)
ax.plot(t1, y1, label='Resposta ao Degrau Controlada')
else:
# generate the tuning line
ident = Smith(t, y)
t1, y1 = ident.tuning_line
ax.plot(t1, y1, label='Reta de Sintonia')
ax.annotate(
'%.1f%%' % ident.point1,
xy=(t1[1], y1[1]),
xycoords='data',
xytext=(t1[1] + (self.total_time / 15.0), y1[1]),
arrowprops=dict(facecolor='black', shrink=0.05),
)
ax.annotate(
'%.1f%%' % ident.point2,
xy=(t1[2], y1[2]),
xycoords='data',
xytext=(t1[2] + (self.total_time / 15.0), y1[2]),
arrowprops=dict(facecolor='black', shrink=0.05),
)
legend = []
for arg in model.args:
if arg in self.blacklist_args:
continue
legend.append('%s=%s' % (arg, self.kwargs[arg]))
ax.legend(
loc='best',
prop={'size': 'x-small'},
title='kp=%.1f; ki=%.1f; kd=%.1f;' % (kp, ki, kd),
)
canvas = FigureCanvas(fig)
filename = 'cap4_model%i_%i.eps' % (self.model, self.plot_id)
canvas.print_eps(os.path.join(output_dir, filename))
def run(self, output_dir):
model = models_index[self.model]('pt_BR')
g = model.callback(**self.kwargs)
t, y = self.nmethod(g, self.sample_time, self.total_time)
fig = Figure(figsize=(8, 6), dpi=300)
ax = fig.add_subplot(111, xlabel='Tempo (seg)', ylabel='Amplitude')
ax.plot(t, y, label='Resposta ao Degrau')
legend = []
for arg in model.args:
if arg in self.blacklist_args:
continue
legend.append('%s=%s' % (arg, self.kwargs[arg]))
ax.legend(loc='best', prop={'size': 'x-small'}, title='; '.join(legend))
canvas = FigureCanvas(fig)
filename = 'cap2_model%i_%i.eps' % (self.model, self.plot_id)
canvas.print_eps(os.path.join(output_dir, filename))
def save_as_eps(x, y, xlim, figsize, orientation):
"""Create and save an EPS file from plot data.
:param x: (numpy ndarray)
:param y: (numpy ndarray)
:param xlim: (float, float) a tuple of
(max chemical shift, min chemical shift)
:param figsize: (float, float) a tuple of (plot width, plot height) in
inches.
:param orientation: 'landscape' or 'portrait'"""
figure = _create_figure(x, y, xlim, figsize)
backend = FigureCanvasPS(figure)
filename = asksaveasfilename()
if filename:
if filename[-4:] != '.eps':
filename += '.eps'
backend.print_eps(filename, orientation=orientation)