def best_param(self, pm_temp):
rec, _ = split_dict(pm_temp, self.__rec_cols)
calidates = self.__params[self.__cond(rec)].iloc[:, self.__score_idx]
if calidates.empty:
return None, None
best = dict(self.__params.iloc[calidates.idxmax()].items())
meta, result, _ = split_dict(best, self.__meta_cols,
self.__result_cols)
#logging.info('best: %s, %s' % (meta, result))
return meta, result
def render_plot(ts, xs, *args, **kwargs):
'''
Renders a plot to the screen or to a file.
'''
figure = matplotlib.pyplot.figure()
axes = figure.gca()
opts, plot_args = split_dict(
('xlabel', 'ylabel', 'mod2pi', 'title', 'file_prefix'), kwargs)
title = opts.get('title', '$x(t)$')
file_prefix = opts.get('file_prefix')
modulo = opts.get('mod2pi', False)
axes.plot(ts, xs, *args, **plot_args)
axes.set_xlabel(opts.get('xlabel', 't'))
axes.set_ylabel(opts.get('ylabel', 'x'))
axes.set_title(title)
if modulo:
axes.set_xbound(0, 2 * numpy.pi)
axes.set_xticks(
(0, numpy.pi / 2, numpy.pi, 3 * numpy.pi / 2, 2 * numpy.pi))
axes.set_xticklabels(('0', r'$\frac{\pi}{2}$', r'$\pi$',
r'$\frac{3\pi}{2}$', r'$2\pi$'))
if file_prefix is None:
figure.show()
else:
figure.savefig('{0}.png'.format(file_prefix), dpi=220)
def make_phase_portrait(pfunc, *args, **kwargs):
'''
Constructs a phase portrait of the system.
'''
figure = matplotlib.pyplot.figure()
axes = figure.gca()
opts, plot_args = split_dict(('title', 'file_prefix'), kwargs)
file_prefix = opts.get('file_prefix')
title = opts.get('title')
for (theta, omega) in PHASE_POINTS:
_, xs = rungekutta.rk4(
pfunc, 0.0, numpy.array([theta, omega], dtype=numpy.float64),
0.005, 2000)
axes.plot(xs[0, :], xs[1, :], *args, **plot_args)
axes.set_xlabel(r'$\theta$')
axes.set_ylabel(r'$\omega$')
axes.set_xbound(-3 * numpy.pi / 2, 3 * numpy.pi / 2)
axes.set_xticks((-3 * numpy.pi / 2, -numpy.pi, -numpy.pi / 2, 0,
numpy.pi / 2, numpy.pi, 3 * numpy.pi / 2))
axes.set_xticklabels(
(r'$-\frac{3\pi}{2}$', r'$-\pi$', r'$-\frac{\pi}{2}$', '0',
r'$\frac{\pi}{2}$', r'$\pi$', r'$\frac{3\pi}{2}$'))
axes.set_title('Phase Portrait' if title is None else title)
if file_prefix is None:
figure.show()
else:
figure.savefig('{0}.png'.format(file_prefix), dpi=220)
def _fit(self, X, y, task_name):
# add cv info
pm_temp = {
'task_name': task_name,
'data_size': len(X),
'cv_fold': self.__cv_options['cv'],
'cv_scoring': self.__cv_options['scoring']
}
pm_temp.update(self.__cv_options['param_grid'])
params = self.__hyparams.gen_params(pm_temp)
if params:
cv_options = {
'verbose': 1,
'n_jobs': -1,
'return_train_score': True
}
cv_options.update(self.__cv_options)
cv_options['param_grid'] = params
cv = GridSearchCV(**cv_options)
cv.fit(X, y)
#save hyperparameters
res = cv.cv_results_
for pm, score, std in zip(res['params'], res['mean_test_score'],
res['std_test_score']):
pm = {**pm_temp, **pm}
pm['score'] = score
pm['std'] = std
self.__hyparams.add(pm)
self.__hyparams.save()
# trained model
cv_model = cv.best_estimator_
else:
cv_model = None
logging.debug('All possbiles hyperparameters are trained: \n%s' %
pformat(pm_temp))
# get best params from @pm_temp
best_pm, result = self.__hyparams.best_param(pm_temp)
logging.info('[MemoCV] CV(%s,%d), result: %s, Best parameter: \n%s' %
(self.__cv_options['scoring'], self.__cv_options['cv'],
result, pformat(best_pm)))
# return the model from best params
if cv_model is not None and split_dict(
cv_model.get_params(),
best_pm.keys())[0] == best_pm: # optimize for the lucky case
return cv_model
else:
logging.info('[MemoCV] Instaniate model from the best param')
model = clone(self.__cv_options['estimator'])
model.set_params(**best_pm)
model.fit(X, y)
return model
def skewed(x_start=1.0, y_start=0.5, size=1.0, iterations=13, **kwargs):
'''
Draws a fractal tree with tuneable parameters.
'''
opts, plot_args = split_dict((
'title',
'filename',
'llength',
'rlength',
'ltheta',
'rtheta'
), kwargs)
llength = opts.get('llength', 0.6)
rlength = opts.get('rlength', 0.6)
render_color = opts.get('color', 'k')
ltheta = -numpy.deg2rad(opts.get('ltheta', 90.0))
rtheta = numpy.deg2rad(opts.get('rtheta', 90.0))
def _iterate(axes, p0, p1, iterations):
'''
Iterates a fancy fractal tree over the axes.
'''
if iterations == 0:
return
x0, y0 = p0
x1, y1 = p1
dv = numpy.array([x1 - x0, y1 - y0], numpy.float64)
lvec = llength * rotate(dv, ltheta)
rvec = rlength * rotate(dv, rtheta)
axes.plot((x1, x1+lvec[0]), (y1, y1+lvec[1]), color=render_color,
**plot_args)
_iterate(axes, p1, (x1+lvec[0], y1+lvec[1]), iterations-1)
axes.plot((x1, x1+rvec[0]), (y1, y1+rvec[1]), color=render_color,
**plot_args)
_iterate(axes, p1, (x1+rvec[0], y1+rvec[1]), iterations-1)
figure = matplotlib.pyplot.figure()
axes = figure.gca()
axes.plot(
(x_start, x_start),
(y_start - (size/2), y_start + (size/2)),
color=render_color,
**plot_args
)
_iterate(axes, (x_start, y_start-(size/2)), (x_start, y_start+(size/2)),
iterations)
axes.set_title(opts.get('title', 'Rotated Fractal Tree'))
if opts.get('filename') is not None:
figure.savefig(opts['filename'], dpi=220)
else:
figure.show()
def plot_time(ts, xs, **kwargs):
'''
Generates a 3D plot of the Henon map over time.
'''
figure = matplotlib.pyplot.figure()
axes = figure.add_subplot(111, projection='3d')
opts, plot_args = split_dict(('title', 'filename'), kwargs)
axes.plot(ts, xs[:,0], xs[:,1], '-', **plot_args)
figure.show()
def plot_time(ts, xs, **kwargs):
'''
Generates a 3D plot of the Henon map over time.
'''
figure = matplotlib.pyplot.figure()
axes = figure.add_subplot(111, projection='3d')
opts, plot_args = split_dict(('title', 'filename'), kwargs)
axes.plot(ts, xs[:, 0], xs[:, 1], '-', **plot_args)
figure.show()
def skewed(x_start=1.0, y_start=0.5, size=1.0, iterations=13, **kwargs):
'''
Draws a fractal tree with tuneable parameters.
'''
opts, plot_args = split_dict(
('title', 'filename', 'llength', 'rlength', 'ltheta', 'rtheta'),
kwargs)
llength = opts.get('llength', 0.6)
rlength = opts.get('rlength', 0.6)
render_color = opts.get('color', 'k')
ltheta = -numpy.deg2rad(opts.get('ltheta', 90.0))
rtheta = numpy.deg2rad(opts.get('rtheta', 90.0))
def _iterate(axes, p0, p1, iterations):
'''
Iterates a fancy fractal tree over the axes.
'''
if iterations == 0:
return
x0, y0 = p0
x1, y1 = p1
dv = numpy.array([x1 - x0, y1 - y0], numpy.float64)
lvec = llength * rotate(dv, ltheta)
rvec = rlength * rotate(dv, rtheta)
axes.plot((x1, x1 + lvec[0]), (y1, y1 + lvec[1]),
color=render_color,
**plot_args)
_iterate(axes, p1, (x1 + lvec[0], y1 + lvec[1]), iterations - 1)
axes.plot((x1, x1 + rvec[0]), (y1, y1 + rvec[1]),
color=render_color,
**plot_args)
_iterate(axes, p1, (x1 + rvec[0], y1 + rvec[1]), iterations - 1)
figure = matplotlib.pyplot.figure()
axes = figure.gca()
axes.plot((x_start, x_start), (y_start - (size / 2), y_start + (size / 2)),
color=render_color,
**plot_args)
_iterate(axes, (x_start, y_start - (size / 2)),
(x_start, y_start + (size / 2)), iterations)
axes.set_title(opts.get('title', 'Rotated Fractal Tree'))
if opts.get('filename') is not None:
figure.savefig(opts['filename'], dpi=220)
else:
figure.show()
def make_phase_portrait(pfunc, *args, **kwargs):
'''
Constructs a phase portrait of the system.
'''
figure = matplotlib.pyplot.figure()
axes = figure.gca()
opts, plot_args = split_dict(('title', 'file_prefix'), kwargs)
file_prefix = opts.get('file_prefix')
title = opts.get('title')
for (theta, omega) in PHASE_POINTS:
_, xs = rungekutta.rk4(
pfunc,
0.0,
numpy.array([theta, omega], dtype=numpy.float64),
0.005,
2000
)
axes.plot(xs[0,:], xs[1,:], *args, **plot_args)
axes.set_xlabel(r'$\theta$')
axes.set_ylabel(r'$\omega$')
axes.set_xbound(-3*numpy.pi/2, 3*numpy.pi/2)
axes.set_xticks((
-3*numpy.pi/2,
-numpy.pi,
-numpy.pi/2,
0,
numpy.pi/2,
numpy.pi,
3*numpy.pi/2
))
axes.set_xticklabels((
r'$-\frac{3\pi}{2}$',
r'$-\pi$',
r'$-\frac{\pi}{2}$',
'0',
r'$\frac{\pi}{2}$',
r'$\pi$',
r'$\frac{3\pi}{2}$'
))
axes.set_title('Phase Portrait' if title is None else title)
if file_prefix is None:
figure.show()
else:
figure.savefig('{0}.png'.format(file_prefix), dpi=220)
def render(x_start=1.0, y_start=0.5, size=1.0, iterations=13, **kwargs):
'''
Draws a neat fractal tree.
'''
opts, plot_args = split_dict(('title', 'filename', 'ratio'), kwargs)
render_color = opts.get('color', 'k')
ratio = opts.get('ratio', 0.6)
figure = matplotlib.pyplot.figure()
def _iterate(axes, x_center, y_center, horizontal, size, iterations):
'''
Iterates the fractal tree over the axes.
'''
if iterations == 0:
return
halfsize = size / 2
if horizontal:
(x1, x2) = (x_center - halfsize, x_center + halfsize)
(y1, y2) = (y_center, y_center)
else:
# Vertical
(x1, x2) = (x_center, x_center)
(y1, y2) = (y_center - halfsize, y_center + halfsize)
axes.plot((x1,x2), (y1,y2), color=render_color, **plot_args)
_iterate(axes, x1, y1, not horizontal, ratio*size, iterations-1)
_iterate(axes, x2, y2, not horizontal, ratio*size, iterations-1)
axes = figure.gca()
axes.plot(
(x_start, x_start),
(y_start - (size/2), y_start + (size/2)),
color=render_color,
**plot_args
)
_iterate(axes, x_start, y_start + (size/2), True, 0.6*size, iterations)
axes.set_xbound((0.4, 1.6))
axes.set_ybound((0.0, 1.4))
axes.set_title(opts.get('title', 'Self-similar Fractal Tree'))
if opts.get('filename') is not None:
figure.savefig(opts['filename'], dpi=220)
else:
figure.show()
def gen_params(self, pm_temp, by='nodup'):
# extract meta parameter from template
meta, rec, _ = split_dict(pm_temp, self.__meta_cols, self.__rec_cols)
def listify(pm):
for p in meta:
v = pm[p]
pm[p] = [v]
return pm
metas = []
for m in self.__gen(list(meta.items()), 0):
if not self.is_dup({**m, **rec}):
metas.append(listify(m))
return metas
'''
def plot_pfunc(pfunc, *args, **kwargs):
'''
Convenience function for experimenting with pendulum functions.
'''
opts, plot_args = split_dict(('tstep', 'theta0', 'omega0', 'nsteps'),
kwargs)
theta0 = opts.get('theta0', 3.0)
omega0 = opts.get('omega0', 0.1)
tstep = opts.get('tstep', 0.005)
nsteps = opts.get('nsteps', 2000)
_, xs = rungekutta.rk4(pfunc, 0.0,
numpy.array([theta0, omega0], dtype=numpy.float64),
tstep, nsteps)
fix_domain = lambda x: mod2pi(x) if kwargs.get('mod2pi', False) else x
xs[0, :] = numpy.array([fix_domain(theta) for theta in xs[0, :]],
dtype=numpy.float64)
render_plot(xs[0, :], xs[1, :], *args, **plot_args)
def plot_time(ts, xs, **kwargs):
'''
Plots the specified vector in the time domain.
'''
figure = matplotlib.pyplot.figure()
axes = figure.gca()
opts, plot_args = split_dict(['title', 'filename'], kwargs)
axes.plot(ts, xs, '.', **plot_args)
axes.set_ylim((0.0, 1.0))
axes.set_aspect(ts[-1] - ts[0])
axes.set_xlabel('n')
axes.set_ylabel(r'$x_n$')
axes.set_title(opts.get('title', r'$x_n$'))
if opts.get('filename') is not None:
figure.savefig(kwargs['filename'], dpi=220)
else:
figure.show()
def plot_bifdiag(ass, xs, **kwargs):
'''
Plots a bifurcation diagram of the Henon map.
'''
figure = matplotlib.pyplot.figure()
axes = figure.gca()
opts, plot_args = split_dict(('title', 'filename'), kwargs)
plot_args.update(markersize=0.2)
axes.plot(ass, xs, 'k.', **plot_args)
axes.set_xlabel('a')
axes.set_ylabel('$x_n$')
axes.set_title(opts.get('title', 'Bifurcation Diagram for the Henon Map'))
if opts.get('filename') is not None:
figure.savefig(kwargs['filename'], dpi=220)
else:
figure.show()
def plot_time(ts, xs, **kwargs):
'''
Plots the specified vector in the time domain.
'''
figure = matplotlib.pyplot.figure()
axes = figure.gca()
opts, plot_args = split_dict(['title', 'filename'], kwargs)
axes.plot(ts, xs, '.', **plot_args)
axes.set_ylim((0.0, 1.0))
axes.set_aspect(ts[-1] - ts[0])
axes.set_xlabel('n')
axes.set_ylabel(r'$x_n$')
axes.set_title(opts.get('title', r'$x_n$'))
if opts.get('filename') is not None:
figure.savefig(kwargs['filename'], dpi=220)
else:
figure.show()
def plot_bifdiag(ass, xs, **kwargs):
'''
Plots a bifurcation diagram of the Henon map.
'''
figure = matplotlib.pyplot.figure()
axes = figure.gca()
opts, plot_args = split_dict(('title', 'filename'), kwargs)
plot_args.update(markersize=0.2)
axes.plot(ass, xs, 'k.', **plot_args)
axes.set_xlabel('a')
axes.set_ylabel('$x_n$')
axes.set_title(opts.get('title', 'Bifurcation Diagram for the Henon Map'))
if opts.get('filename') is not None:
figure.savefig(kwargs['filename'], dpi=220)
else:
figure.show()
def render(x_start=1.0, y_start=0.5, size=1.0, iterations=13, **kwargs):
'''
Draws a neat fractal tree.
'''
opts, plot_args = split_dict(('title', 'filename', 'ratio'), kwargs)
render_color = opts.get('color', 'k')
ratio = opts.get('ratio', 0.6)
figure = matplotlib.pyplot.figure()
def _iterate(axes, x_center, y_center, horizontal, size, iterations):
'''
Iterates the fractal tree over the axes.
'''
if iterations == 0:
return
halfsize = size / 2
if horizontal:
(x1, x2) = (x_center - halfsize, x_center + halfsize)
(y1, y2) = (y_center, y_center)
else:
# Vertical
(x1, x2) = (x_center, x_center)
(y1, y2) = (y_center - halfsize, y_center + halfsize)
axes.plot((x1, x2), (y1, y2), color=render_color, **plot_args)
_iterate(axes, x1, y1, not horizontal, ratio * size, iterations - 1)
_iterate(axes, x2, y2, not horizontal, ratio * size, iterations - 1)
axes = figure.gca()
axes.plot((x_start, x_start), (y_start - (size / 2), y_start + (size / 2)),
color=render_color,
**plot_args)
_iterate(axes, x_start, y_start + (size / 2), True, 0.6 * size, iterations)
axes.set_xbound((0.4, 1.6))
axes.set_ybound((0.0, 1.4))
axes.set_title(opts.get('title', 'Self-similar Fractal Tree'))
if opts.get('filename') is not None:
figure.savefig(opts['filename'], dpi=220)
else:
figure.show()
def plot_ret1(xs, **kwargs):
'''
Plots the Henon map in the first return map space.
'''
figure = matplotlib.pyplot.figure()
axes = figure.gca()
opts, plot_args = split_dict(('title', 'filename'), kwargs)
plot_args.update(markersize=0.2)
axes.plot(xs[:,0], xs[:,1], '.', **plot_args)
axes.set_xlabel('$x_n$')
axes.set_ylabel('$y_n$')
axes.set_title(opts.get('title', 'Henon Map'))
if opts.get('filename') is not None:
figure.savefig(kwargs['filename'], dpi=220)
else:
figure.show()
def plot_time2(ts, xs1, xs2, **kwargs):
'''
Plots 2 vectors in the time domain.
'''
figure = matplotlib.pyplot.figure()
axes = figure.gca()
opts, plot_args = split_dict(['title', 'filename'], kwargs)
axes.plot(ts, xs1, 'b-o', **plot_args)
axes.plot(ts, xs2, 'r-o', **plot_args)
axes.set_ylim((0.0, 1.0))
axes.set_xlabel('n')
axes.set_ylabel(r'$x_n$')
axes.legend(('$x_0 = {0}$'.format(xs1[0]), '$x_0 = {0}$'.format(xs2[0])))
axes.set_title(opts.get('title', r'$x_n$'))
if opts.get('filename') is not None:
figure.savefig(kwargs['filename'], dpi=220)
else:
figure.show()
def plot_time2(ts, xs1, xs2, **kwargs):
'''
Plots 2 vectors in the time domain.
'''
figure = matplotlib.pyplot.figure()
axes = figure.gca()
opts, plot_args = split_dict(['title', 'filename'], kwargs)
axes.plot(ts, xs1, 'b-o', **plot_args)
axes.plot(ts, xs2, 'r-o', **plot_args)
axes.set_ylim((0.0, 1.0))
axes.set_xlabel('n')
axes.set_ylabel(r'$x_n$')
axes.legend(('$x_0 = {0}$'.format(xs1[0]), '$x_0 = {0}$'.format(xs2[0])))
axes.set_title(opts.get('title', r'$x_n$'))
if opts.get('filename') is not None:
figure.savefig(kwargs['filename'], dpi=220)
else:
figure.show()
def plot_ret1(xs, **kwargs):
'''
Plots the specified vector in the first return map space.
'''
figure = matplotlib.pyplot.figure()
axes = figure.gca()
opts, plot_args = split_dict(['title', 'filename'], kwargs)
axes.plot(xs[:-1], xs[1:], '.', **plot_args)
axes.set_ylim((0.0, 1.0))
axes.set_xlim((0.0, 1.0))
axes.set_aspect(1.0)
axes.set_xlabel(r'$x_n$')
axes.set_ylabel(r'$x_{n+1}$')
axes.set_title(opts.get('title', 'First Return Map Space'))
if opts.get('filename') is not None:
figure.savefig(kwargs['filename'], dpi=220)
else:
figure.show()
def plot_ret1(xs, **kwargs):
'''
Plots the specified vector in the first return map space.
'''
figure = matplotlib.pyplot.figure()
axes = figure.gca()
opts, plot_args = split_dict(['title', 'filename'], kwargs)
axes.plot(xs[:-1], xs[1:], '.', **plot_args)
axes.set_ylim((0.0, 1.0))
axes.set_xlim((0.0, 1.0))
axes.set_aspect(1.0)
axes.set_xlabel(r'$x_n$')
axes.set_ylabel(r'$x_{n+1}$')
axes.set_title(opts.get('title', 'First Return Map Space'))
if opts.get('filename') is not None:
figure.savefig(kwargs['filename'], dpi=220)
else:
figure.show()
def plot_ret1(xs, **kwargs):
'''
Plots the Henon map in the first return map space.
'''
figure = matplotlib.pyplot.figure()
axes = figure.gca()
opts, plot_args = split_dict(('title', 'filename'), kwargs)
plot_args.update(markersize=0.2)
axes.plot(xs[:, 0], xs[:, 1], '.', **plot_args)
axes.set_xlabel('$x_n$')
axes.set_ylabel('$y_n$')
axes.set_title(opts.get('title', 'Henon Map'))
if opts.get('filename') is not None:
figure.savefig(kwargs['filename'], dpi=220)
else:
figure.show()
def plot_bifdiag(rs, xs, **kwargs):
'''
Plots a bifurcation diagram given a data set of rs, xs.
'''
figure = matplotlib.pyplot.figure()
axes = figure.gca()
opts, plot_args = split_dict(('title', 'filename'), kwargs)
plot_args.update(markersize=0.2)
axes.plot(rs, xs, 'k.', **plot_args)
axes.set_xlabel('R')
axes.set_ylabel('$x_n$')
axes.set_title(opts.get('title', 'Bifurcation Diagram for the Logistic Map'))
# Check opts.get('filename') is not None instead of 'filename' in opts.
# Expect callers to pass filename=None and show the plot to the screen.
if opts.get('filename') is not None:
figure.savefig(kwargs['filename'], dpi=220)
else:
figure.show()
def render_plot(ts, xs, *args, **kwargs):
'''
Renders a plot to the screen or to a file.
'''
figure = matplotlib.pyplot.figure()
axes = figure.gca()
opts, plot_args = split_dict((
'xlabel', 'ylabel', 'mod2pi', 'title', 'file_prefix'
), kwargs)
title = opts.get('title', '$x(t)$')
file_prefix = opts.get('file_prefix')
modulo = opts.get('mod2pi', False)
axes.plot(ts, xs, *args, **plot_args)
axes.set_xlabel(opts.get('xlabel', 't'))
axes.set_ylabel(opts.get('ylabel', 'x'))
axes.set_title(title)
if modulo:
axes.set_xbound(0, 2*numpy.pi)
axes.set_xticks((
0,
numpy.pi/2,
numpy.pi,
3*numpy.pi/2,
2*numpy.pi
))
axes.set_xticklabels((
'0',
r'$\frac{\pi}{2}$',
r'$\pi$',
r'$\frac{3\pi}{2}$',
r'$2\pi$'
))
if file_prefix is None:
figure.show()
else:
figure.savefig('{0}.png'.format(file_prefix), dpi=220)
def plot_pfunc(pfunc, *args, **kwargs):
'''
Convenience function for experimenting with pendulum functions.
'''
opts, plot_args = split_dict(('tstep', 'theta0', 'omega0', 'nsteps'), kwargs)
theta0 = opts.get('theta0', 3.0)
omega0 = opts.get('omega0', 0.1)
tstep = opts.get('tstep', 0.005)
nsteps = opts.get('nsteps', 2000)
_, xs = rungekutta.rk4(
pfunc,
0.0,
numpy.array([theta0, omega0], dtype=numpy.float64),
tstep,
nsteps
)
fix_domain = lambda x: mod2pi(x) if kwargs.get('mod2pi', False) else x
xs[0,:] = numpy.array([
fix_domain(theta) for theta in xs[0,:]
], dtype=numpy.float64)
render_plot(xs[0,:], xs[1,:], *args, **plot_args)
def plot3d(*args, **kwargs):
'''
Renders a 3D plot.
'''
figure = matplotlib.pyplot.figure()
axes = figure.add_subplot(111, projection='3d')
opt_actions = (
('title', axes.set_title),
('xlabel', axes.set_xlabel),
('ylabel', axes.set_ylabel),
('zlabel', axes.set_zlabel),
('xticks', axes.set_xticks),
('yticks', axes.set_yticks),
('zticks', axes.set_zticks),
('xticklabels', axes.set_xticklabels),
('yticklabels', axes.set_yticklabels),
('zticklabels', axes.set_zticklabels),
('aspect', axes.set_aspect),
('xbound', lambda (x1, x2): axes.set_xbound(x1, x2)),
('ybound', lambda (y1, y2): axes.set_ybound(y1, y2)),
('zbound', lambda (z1, z2): axes.set_zbound(z1, z2)),
('file_prefix', _noop),
('ax_callback', _noop),
)
opts, plot_args = utils.split_dict([name for (name, _) in opt_actions],
kwargs)
if 'ax_callback' in opts:
opts['ax_callback'](axes)
else:
axes.plot(*args, **plot_args)
for (name, action) in opt_actions:
if name in opts:
action(opts[name])
_render_to_output(figure, opts.get('file_prefix'))
def plot(*args, **kwargs):
'''
Renders a 2D plot.
'''
figure = matplotlib.pyplot.figure()
axes = figure.gca()
opt_actions = (
('title', axes.set_title),
('xlabel', axes.set_xlabel),
('ylabel', axes.set_ylabel),
('xticks', axes.set_xticks),
('yticks', axes.set_yticks),
('xticklabels', axes.set_xticklabels),
('yticklabels', axes.set_yticklabels),
('aspect', axes.set_aspect),
('xbound', lambda (x1, x2): axes.set_xbound(x1, x2)),
('ybound', lambda (y1, y2): axes.set_ybound(y1, y2)),
('legend', axes.legend),
('file_prefix', _noop),
('ax_callback', _noop),
)
opts, plot_args = utils.split_dict([name for (name, _) in opt_actions],
kwargs)
if 'ax_callback' in opts:
opts['ax_callback'](axes)
else:
axes.plot(*args, **plot_args)
# Use tuples instead of a dict because order matters. Want to set xticks
# before xlabels, etc.
for (name, action) in opt_actions:
if name in opts:
action(opts[name])
_render_to_output(figure, opts.get('file_prefix'))
def plot3d(*args, **kwargs):
'''
Renders a 3D plot.
'''
figure = matplotlib.pyplot.figure()
axes = figure.add_subplot(111, projection='3d')
opt_actions = (
('title', axes.set_title),
('xlabel', axes.set_xlabel),
('ylabel', axes.set_ylabel),
('zlabel', axes.set_zlabel),
('xticks', axes.set_xticks),
('yticks', axes.set_yticks),
('zticks', axes.set_zticks),
('xticklabels', axes.set_xticklabels),
('yticklabels', axes.set_yticklabels),
('zticklabels', axes.set_zticklabels),
('aspect', axes.set_aspect),
('xbound', lambda (x1, x2): axes.set_xbound(x1, x2)),
('ybound', lambda (y1, y2): axes.set_ybound(y1, y2)),
('zbound', lambda (z1, z2): axes.set_zbound(z1, z2)),
('file_prefix', _noop),
('ax_callback', _noop),
)
opts, plot_args = utils.split_dict([name for (name, _) in opt_actions], kwargs)
if 'ax_callback' in opts:
opts['ax_callback'](axes)
else:
axes.plot(*args, **plot_args)
for (name, action) in opt_actions:
if name in opts:
action(opts[name])
_render_to_output(figure, opts.get('file_prefix'))
def plot(*args, **kwargs):
'''
Renders a 2D plot.
'''
figure = matplotlib.pyplot.figure()
axes = figure.gca()
opt_actions = (
('title', axes.set_title),
('xlabel', axes.set_xlabel),
('ylabel', axes.set_ylabel),
('xticks', axes.set_xticks),
('yticks', axes.set_yticks),
('xticklabels', axes.set_xticklabels),
('yticklabels', axes.set_yticklabels),
('aspect', axes.set_aspect),
('xbound', lambda (x1, x2): axes.set_xbound(x1, x2)),
('ybound', lambda (y1, y2): axes.set_ybound(y1, y2)),
('legend', axes.legend),
('file_prefix', _noop),
('ax_callback', _noop),
)
opts, plot_args = utils.split_dict([name for (name, _) in opt_actions], kwargs)
if 'ax_callback' in opts:
opts['ax_callback'](axes)
else:
axes.plot(*args, **plot_args)
# Use tuples instead of a dict because order matters. Want to set xticks
# before xlabels, etc.
for (name, action) in opt_actions:
if name in opts:
action(opts[name])
_render_to_output(figure, opts.get('file_prefix'))
