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 __init__(self, filename, plot_type='ps', items=[], output_prefix=''):
self.type = plot_type
self.filename = filename
self.output_prefix = output_prefix
self.figure = Figure((8.5, 11))
#self.figure = Figure((8.3,11.7))
if plot_type == 'png':
self.canvas = FigureCanvasAgg(self.figure)
self.extension = '.png'
else:
self.canvas = FigureCanvasPS(self.figure)
self.extension = '.ps'
self.plots = {}
#if self.type=='quartz': r.quartz()
try:
self.plot_raw_data(filename + '_raw_data.csv')
self.plot_trails(filename + '_autocorrelations.csv')
self.plot_trails(filename + '_trails.csv')
self.plot_samples(filename + '_samples.csv')
self.plot_min_mean_max(filename + '_min_mean_max.csv', items)
except Exception, e:
print e
def canvas(self):
type = self.get("imageType", "png")
fig = Figure()
if type == "png":
canvas = FigureCanvasAgg(fig)
(self.file, self.filename) = mkstemp(".%s" % type)
elif type == "svg":
canvas = FigureCanvasSVG(fig)
(self.file, self.filename) = mkstemp(".%s" % type)
elif type == "pdf":
canvas = FigureCanvasPdf(fig)
(self.file, self.filename) = mkstemp(".%s" % type)
elif type == "ps" or type == "eps":
canvas = FigureCanvasPS(fig)
(self.file, self.filename) = mkstemp(".%s" % type)
else:
raise "Invalid render target requested"
# Set basic figure parameters
dpi = float(self.get('dpi'))
(w, h) = (float(self.get('width')), float(self.get('height')))
(win, hin) = (w/dpi, h/dpi)
fig.set_size_inches(win, hin)
fig.set_dpi(dpi)
fig.set_facecolor('white')
return (fig, canvas, w, h)
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)
def save_annotated(self,
fname=None,
label_fmt=None,
text_annotate=None,
dpi=100,
sigma_clip=None):
r"""Saves the most recently rendered image of the Scene to disk,
including an image of the transfer function and and user-defined
text.
Once you have created a scene and rendered that scene to an image
array, this saves that image array to disk with an optional filename.
If an image has not yet been rendered for the current scene object,
it forces one and writes it out.
Parameters
----------
fname: string, optional
If specified, save the rendering as a bitmap to the file "fname".
If unspecified, it creates a default based on the dataset filename.
Default: None
sigma_clip: float, optional
Image values greater than this number times the standard deviation
plus the mean of the image will be clipped before saving. Useful
for enhancing images as it gets rid of rare high pixel values.
Default: None
floor(vals > std_dev*sigma_clip + mean)
dpi: integer, optional
By default, the resulting image will be the same size as the camera
parameters. If you supply a dpi, then the image will be scaled
accordingly (from the default 100 dpi)
label_fmt : str, optional
A format specifier (e.g., label_fmt="%.2g") to use in formatting
the data values that label the transfer function colorbar.
text_annotate : list of iterables
Any text that you wish to display on the image. This should be an
list containing a tuple of coordinates (in normalized figure
coordinates), the text to display, and, optionally, a dictionary of
keyword/value pairs to pass through to the matplotlib text()
function.
Each item in the main list is a separate string to write.
Returns
-------
Nothing
Examples
--------
>>> sc.save_annotated("fig.png",
... text_annotate=[[(0.05, 0.05),
... "t = {}".format(ds.current_time.d),
... dict(horizontalalignment="left")],
... [(0.5,0.95),
... "simulation title",
... dict(color="y", fontsize="24",
... horizontalalignment="center")]])
"""
from yt.visualization._mpl_imports import \
FigureCanvasAgg, FigureCanvasPdf, FigureCanvasPS
sources = list(itervalues(self.sources))
rensources = [s for s in sources if isinstance(s, RenderSource)]
if fname is None:
# if a volume source present, use its affiliated ds for fname
if len(rensources) > 0:
rs = rensources[0]
basename = rs.data_source.ds.basename
if isinstance(rs.field, string_types):
field = rs.field
else:
field = rs.field[-1]
fname = "%s_Render_%s.png" % (basename, field)
# if no volume source present, use a default filename
else:
fname = "Render_opaque.png"
suffix = get_image_suffix(fname)
if suffix == '':
suffix = '.png'
fname = '%s%s' % (fname, suffix)
self.render()
# which transfer function?
rs = rensources[0]
tf = rs.transfer_function
label = rs.data_source.ds._get_field_info(rs.field).get_label()
ax = self._show_mpl(self._last_render.swapaxes(0, 1),
sigma_clip=sigma_clip,
dpi=dpi)
self._annotate(ax.axes, tf, rs, label=label, label_fmt=label_fmt)
# any text?
if text_annotate is not None:
f = self._render_figure
for t in text_annotate:
xy = t[0]
string = t[1]
if len(t) == 3:
opt = t[2]
else:
opt = dict()
# sane default
if "color" not in opt:
opt["color"] = "w"
ax.axes.text(xy[0],
xy[1],
string,
transform=f.transFigure,
**opt)
suffix = get_image_suffix(fname)
if suffix == ".png":
canvas = FigureCanvasAgg(self._render_figure)
elif suffix == ".pdf":
canvas = FigureCanvasPdf(self._render_figure)
elif suffix in (".eps", ".ps"):
canvas = FigureCanvasPS(self._render_figure)
else:
mylog.warning("Unknown suffix %s, defaulting to Agg", suffix)
canvas = self.canvas
self._render_figure.canvas = canvas
self._render_figure.tight_layout()
self._render_figure.savefig(fname, facecolor='black', pad_inches=0)
def save(self, fname=None, sigma_clip=None):
r"""Saves the most recently rendered image of the Scene to disk.
Once you have created a scene and rendered that scene to an image
array, this saves that image array to disk with an optional filename.
If an image has not yet been rendered for the current scene object,
it forces one and writes it out.
Parameters
----------
fname: string, optional
If specified, save the rendering as to the file "fname".
If unspecified, it creates a default based on the dataset filename.
The file format is inferred from the filename's suffix. Supported
fomats are png, pdf, eps, and ps.
Default: None
sigma_clip: float, optional
Image values greater than this number times the standard deviation
plus the mean of the image will be clipped before saving. Useful
for enhancing images as it gets rid of rare high pixel values.
Default: None
floor(vals > std_dev*sigma_clip + mean)
Returns
-------
Nothing
Examples
--------
>>> import yt
>>> ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
>>>
>>> sc = yt.create_scene(ds)
>>> # Modify camera, sources, etc...
>>> sc.render()
>>> sc.save('test.png', sigma_clip=4)
Or alternatively:
>>> import yt
>>> ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
>>>
>>> sc = yt.create_scene(ds)
>>> # save with different sigma clipping values
>>> sc.save('raw.png')
>>> sc.save('clipped_2.png', sigma_clip=2)
>>> sc.save('clipped_4.png', sigma_clip=4)
"""
if fname is None:
sources = list(itervalues(self.sources))
rensources = [s for s in sources if isinstance(s, RenderSource)]
# if a volume source present, use its affiliated ds for fname
if len(rensources) > 0:
rs = rensources[0]
basename = rs.data_source.ds.basename
if isinstance(rs.field, string_types):
field = rs.field
else:
field = rs.field[-1]
fname = "%s_Render_%s.png" % (basename, field)
# if no volume source present, use a default filename
else:
fname = "Render_opaque.png"
suffix = get_image_suffix(fname)
if suffix == '':
suffix = '.png'
fname = '%s%s' % (fname, suffix)
self.render()
mylog.info("Saving render %s", fname)
# We can render pngs natively but for other formats we defer to
# matplotlib.
if suffix == '.png':
self._last_render.write_png(fname, sigma_clip=sigma_clip)
else:
from matplotlib.figure import Figure
from matplotlib.backends.backend_pdf import \
FigureCanvasPdf
from matplotlib.backends.backend_ps import \
FigureCanvasPS
shape = self._last_render.shape
fig = Figure((shape[0] / 100., shape[1] / 100.))
if suffix == '.pdf':
canvas = FigureCanvasPdf(fig)
elif suffix in ('.eps', '.ps'):
canvas = FigureCanvasPS(fig)
else:
raise NotImplementedError(
"Unknown file suffix '{}'".format(suffix))
ax = fig.add_axes([0, 0, 1, 1])
ax.set_axis_off()
out = self._last_render
nz = out[:, :, :3][out[:, :, :3].nonzero()]
max_val = nz.mean() + sigma_clip * nz.std()
alpha = 255 * out[:, :, 3].astype('uint8')
out = np.clip(out[:, :, :3] / max_val, 0.0, 1.0) * 255
out = np.concatenate([out.astype('uint8'), alpha[..., None]],
axis=-1)
# not sure why we need rot90, but this makes the orientation
# match the png writer
ax.imshow(np.rot90(out), origin='lower')
canvas.print_figure(fname, dpi=100)
def save(self, fname=None, sigma_clip=None, render=True):
r"""Saves a rendered image of the Scene to disk.
Once you have created a scene, this saves an image array to disk with
an optional filename. This function calls render() to generate an
image array, unless the render parameter is set to False, in which case
the most recently rendered scene is used if it exists.
Parameters
----------
fname: string, optional
If specified, save the rendering as to the file "fname".
If unspecified, it creates a default based on the dataset filename.
The file format is inferred from the filename's suffix. Supported
fomats are png, pdf, eps, and ps.
Default: None
sigma_clip: float, optional
Image values greater than this number times the standard deviation
plus the mean of the image will be clipped before saving. Useful
for enhancing images as it gets rid of rare high pixel values.
Default: None
floor(vals > std_dev*sigma_clip + mean)
render: boolean, optional
If True, will always render the scene before saving.
If False, will use results of previous render if it exists.
Default: True
Returns
-------
Nothing
Examples
--------
>>> import yt
>>> ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
>>>
>>> sc = yt.create_scene(ds)
>>> # Modify camera, sources, etc...
>>> sc.save('test.png', sigma_clip=4)
When saving multiple images without modifying the scene (camera,
sources,etc.), render=False can be used to avoid re-rendering when a scene is saved.
This is useful for generating images at a range of sigma_clip values:
>>> import yt
>>> ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
>>>
>>> sc = yt.create_scene(ds)
>>> # save with different sigma clipping values
>>> sc.save('raw.png') # The initial render call happens here
>>> sc.save('clipped_2.png', sigma_clip=2, render=False)
>>> sc.save('clipped_4.png', sigma_clip=4, render=False)
"""
if fname is None:
sources = list(self.sources.values())
rensources = [s for s in sources if isinstance(s, RenderSource)]
# if a volume source present, use its affiliated ds for fname
if len(rensources) > 0:
rs = rensources[0]
basename = rs.data_source.ds.basename
if isinstance(rs.field, str):
field = rs.field
else:
field = rs.field[-1]
fname = "%s_Render_%s.png" % (basename, field)
# if no volume source present, use a default filename
else:
fname = "Render_opaque.png"
suffix = get_image_suffix(fname)
if suffix == "":
suffix = ".png"
fname = "%s%s" % (fname, suffix)
render = self._sanitize_render(render)
if render:
self.render()
mylog.info("Saving rendered image to %s", fname)
# We can render pngs natively but for other formats we defer to
# matplotlib.
if suffix == ".png":
self._last_render.write_png(fname, sigma_clip=sigma_clip)
else:
from matplotlib.backends.backend_pdf import FigureCanvasPdf
from matplotlib.backends.backend_ps import FigureCanvasPS
from matplotlib.figure import Figure
shape = self._last_render.shape
fig = Figure((shape[0] / 100.0, shape[1] / 100.0))
if suffix == ".pdf":
canvas = FigureCanvasPdf(fig)
elif suffix in (".eps", ".ps"):
canvas = FigureCanvasPS(fig)
else:
raise NotImplementedError("Unknown file suffix '{}'".format(suffix))
ax = fig.add_axes([0, 0, 1, 1])
ax.set_axis_off()
out = self._last_render
nz = out[:, :, :3][out[:, :, :3].nonzero()]
max_val = nz.mean() + sigma_clip * nz.std()
alpha = 255 * out[:, :, 3].astype("uint8")
out = np.clip(out[:, :, :3] / max_val, 0.0, 1.0) * 255
out = np.concatenate([out.astype("uint8"), alpha[..., None]], axis=-1)
# not sure why we need rot90, but this makes the orientation
# match the png writer
ax.imshow(np.rot90(out), origin="lower")
canvas.print_figure(fname, dpi=100)
