def _theta_plot_default(self):
theta = self.theta
nclasses = theta.shape[0]
# create a plot data object and give it this data
plot_data = ArrayPlotData()
plot_data.set_data('classes', range(nclasses))
# create the plot
plot = Plot(plot_data)
# --- plot theta samples
if self.theta_samples is not None:
self._plot_samples(plot, plot_data)
# --- plot values of theta
plots = self._plot_theta_values(plot, plot_data)
# --- adjust plot appearance
plot.aspect_ratio = 1.6 if is_display_small() else 1.7
# adjust axis bounds
y_high = theta.max()
if self.theta_samples is not None:
y_high = max(y_high, self.theta_samples.max())
plot.range2d = DataRange2D(
low = (-0.2, 0.0),
high = (nclasses-1+0.2, y_high*1.1)
)
# create new horizontal axis
label_axis = self._create_increment_one_axis(
plot, 0., nclasses, 'bottom')
label_axis.title = 'True classes'
self._add_index_axis(plot, label_axis)
# label vertical axis
plot.value_axis.title = 'Probability'
# add legend
legend = Legend(component=plot, plots=plots,
align="ur", border_padding=10)
legend.tools.append(LegendTool(legend, drag_button="left"))
legend.padding_right = -100
plot.overlays.append(legend)
container = VPlotContainer(width=plot.width + 100, halign='left')
plot.padding_bottom = 50
plot.padding_top = 10
plot.padding_left = 0
container.add(plot)
container.bgcolor = 0xFFFFFF
self.decorate_plot(container, theta)
return container
def _theta_plot_default(self):
theta = self.theta
nclasses = theta.shape[0]
# create a plot data object and give it this data
plot_data = ArrayPlotData()
plot_data.set_data('classes', list(range(nclasses)))
# create the plot
plot = Plot(plot_data)
# --- plot theta samples
if self.theta_samples is not None:
self._plot_samples(plot, plot_data)
# --- plot values of theta
plots = self._plot_theta_values(plot, plot_data)
# --- adjust plot appearance
plot.aspect_ratio = 1.6 if is_display_small() else 1.7
# adjust axis bounds
y_high = theta.max()
if self.theta_samples is not None:
y_high = max(y_high, self.theta_samples.max())
plot.range2d = DataRange2D(low=(-0.2, 0.0),
high=(nclasses - 1 + 0.2, y_high * 1.1))
# create new horizontal axis
label_axis = self._create_increment_one_axis(plot, 0., nclasses,
'bottom')
label_axis.title = 'True classes'
self._add_index_axis(plot, label_axis)
# label vertical axis
plot.value_axis.title = 'Probability'
# add legend
legend = Legend(component=plot,
plots=plots,
align="ur",
border_padding=10)
legend.tools.append(LegendTool(legend, drag_button="left"))
legend.padding_right = -100
plot.overlays.append(legend)
container = VPlotContainer(width=plot.width + 100, halign='left')
plot.padding_bottom = 50
plot.padding_top = 10
plot.padding_left = 0
container.add(plot)
container.bgcolor = 0xFFFFFF
self.decorate_plot(container, theta)
return container
def monitor_to_plotdata(it):
''' Parse a mcstas monitor file and convert it to a chaco plotdata object '''
header, data = parse_monitor(it)
dtype = header['type']
if dtype.startswith('array_1d'):
# 1d
# populate with point data
plotdata = ArrayPlotData()
for i, name in enumerate(header['variables'].split()):
plotdata[name] = array(map(lambda line: line[i], data),
dtype='float64')
# compute error bar value_low and value_high using I_err
i, i_err = plotdata['I'], plotdata['I_err']
plotdata['value_error'] = i_err
plotdata['value_low'] = i - i_err
plotdata['value_high'] = i + i_err
return (header, plotdata)
if dtype.startswith('array_2d'):
# 2d
w, h = map(int, dtype[len('array_2d'):].strip('() ').split(','))
data = array(data[:h], dtype='float64')
plotdata = ArrayPlotData(imagedata=data,
imagedata_log=log(maximum(1, data)))
return (header, plotdata)
else:
print('Skipping unknown type:', header['type'])
return (header, None)
def test_selection_mask(self):
plot_data = ArrayPlotData()
plot = Plot(plot_data)
arr = np.array([-2, -1, 1, 2])
plot_data.set_data("x", arr)
plot_data.set_data("y", arr)
splot = plot.plot(('x', 'y'), type='scatter')[0]
tool = RectangularSelection(
component=splot,
selection_datasource=splot.index,
metadata_name='selections',
)
splot.tools.append(tool)
# Set the cursor start and stop positions to be such
# that the middle two points of the four possible are selected.
cursor_start = splot.map_screen([-1.5, -1.5])[0]
cursor_stop = splot.map_screen([1.5, 1.5])[0]
self.mouse_down(interactor=tool, x=cursor_start[0], y=cursor_start[1])
self.mouse_move(interactor=tool, x=cursor_stop[0], y=cursor_stop[1])
self.mouse_up(interactor=tool, x=cursor_stop[0], y=cursor_stop[1])
expected_mask = [False, True, True, False]
selection_mask = list(splot.index.metadata['selections'])
self.assertEqual(expected_mask, selection_mask)
def _matrix_plot_container_default(self):
matrix = np.nan_to_num(self.matrix)
width = matrix.shape[0]
# create a plot data object and give it this data
plot_data = ArrayPlotData()
plot_data.set_data("values", matrix)
# create the plot
plot = Plot(plot_data, origin=self.origin)
img_plot = plot.img_plot("values",
interpolation='nearest',
xbounds=(0, width),
ybounds=(0, width),
colormap=self._create_colormap())[0]
#### fix axes
self._remove_grid_and_axes(plot)
axis = self._create_increment_one_axis(plot, 0.5, width, 'bottom')
self._add_value_axis(plot, axis)
axis = self._create_increment_one_axis(
plot,
0.5,
width,
'left',
ticks=[str(i) for i in range(width - 1, -1, -1)])
self._add_index_axis(plot, axis)
#### tweak plot attributes
self._set_title(plot)
plot.aspect_ratio = 1.
# padding [left, right, up, down]
plot.padding = [0, 0, 25, 25]
# create the colorbar, handing in the appropriate range and colormap
colormap = img_plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=img_plot,
orientation='v',
resizable='v',
width=20,
padding=[0, 20, 0, 0])
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
# create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
container.bgcolor = 0xFFFFFF
self.decorate_plot(container, self.matrix)
return container
def load(self):
data = ArrayPlotData()
p = Plot(data=data, padding=100)
X = linspace(0, 2 * pi)
data.set_data('x0', X)
data.set_data('y0', cos(X))
p.plot(('x0', 'y0'))
self.container.add(p)
def _matrix_plot_container_default(self):
matrix = np.nan_to_num(self.matrix)
width = matrix.shape[0]
# create a plot data object and give it this data
plot_data = ArrayPlotData()
plot_data.set_data("values", matrix)
# create the plot
plot = Plot(plot_data, origin=self.origin)
img_plot = plot.img_plot("values",
interpolation='nearest',
xbounds=(0, width),
ybounds=(0, width),
colormap=self._create_colormap())[0]
#### fix axes
self._remove_grid_and_axes(plot)
axis = self._create_increment_one_axis(plot, 0.5, width, 'bottom')
self._add_value_axis(plot, axis)
axis = self._create_increment_one_axis(
plot, 0.5, width, 'left',
ticks=[str(i) for i in range(width-1, -1, -1)])
self._add_index_axis(plot, axis)
#### tweak plot attributes
self._set_title(plot)
plot.aspect_ratio = 1.
# padding [left, right, up, down]
plot.padding = [0, 0, 25, 25]
# create the colorbar, handing in the appropriate range and colormap
colormap = img_plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=img_plot,
orientation='v',
resizable='v',
width=20,
padding=[0, 20, 0, 0])
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
# create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
container.bgcolor = 0xFFFFFF
self.decorate_plot(container, self.matrix)
return container
def cmap_plot(self, z):
from chaco.array_plot_data import ArrayPlotData
from chaco.plot import Plot
from chaco.default_colormaps import color_map_name_dict
pd = ArrayPlotData()
pd.set_data("cmapdata", z)
p = Plot(pd, padding=0)
p.img_plot("cmapdata", xbounds=(-25, 25), ybounds=(-25, 25), colormap=color_map_name_dict["hot"])
self.add(p)
return pd
def cmap_plot(self, z):
from chaco.array_plot_data import ArrayPlotData
from chaco.plot import Plot
from chaco.default_colormaps import color_map_name_dict
pd = ArrayPlotData()
pd.set_data('cmapdata', z)
p = Plot(pd, padding=0)
p.img_plot('cmapdata',
xbounds=(-25, 25),
ybounds=(-25, 25),
colormap=color_map_name_dict['hot'])
self.add(p)
return pd
def test_selection_no_warning(self):
plot_data = ArrayPlotData()
arr = np.arange(4)
plot_data.set_data("x", arr)
plot_data.set_data("y", arr)
plot = Plot(plot_data)
renderer = plot.plot(('x', 'y'))[0]
tool = RangeSelection(renderer)
with warnings.catch_warnings(record=True) as w:
tool.selection = np.array([2.0, 3.0])
self.assertEqual(w, [])
# Accept tuples and lists and None
tool.selection = (1.5, 3.5)
tool.selection = [1.0, 2.0]
tool.selection = None
def test_selecting_mouse_leave_clipping(self):
# Regression test for #216.
plot_data = ArrayPlotData()
arr = np.arange(4.0)
plot_data.set_data("x", arr)
plot_data.set_data("y", arr)
for origin in ('bottom left', 'top left', 'bottom right', 'top right'):
for orientation in ('h', 'v'):
for axis in ('index', 'value'):
plot = Plot(plot_data,
orientation=orientation,
origin='top right')
renderer = plot.plot(('x', 'y'))[0]
renderer.bounds = [10, 20]
tool = RangeSelection(
renderer,
left_button_selects=True,
axis=axis,
)
renderer.tools.append(tool)
low_x, low_y = plot.position
high_x = low_x + renderer.bounds[0] - 1
high_y = low_y + renderer.bounds[1] - 1
cx = 5
cy = 5
bounds = (
(low_x - 1, low_y),
(high_x + 1, low_y),
(low_x, low_y - 1),
(low_x, high_y + 1),
)
for x, y in bounds:
self.mouse_down(tool, x=cx, y=cy)
self.mouse_leave(tool, x=x, y=y)
selection = tool.selection
self.assertTrue(selection[0] <= selection[1])
self.mouse_up(tool, x=x, y=y)
def test_selection_no_warning(self):
plot_data = ArrayPlotData()
arr = np.arange(4)
plot_data.set_data("x", arr)
plot_data.set_data("y", arr)
plot = Plot(plot_data)
renderer = plot.plot(('x', 'y'))[0]
tool = RangeSelection(renderer)
with warnings.catch_warnings(record=True) as w:
# Ignore warnings coming from Traits
warnings.filterwarnings("ignore",
'elementwise == comparison failed')
tool.selection = np.array([2.0, 3.0])
self.assertEqual(w, [])
# Accept tuples and lists and None
tool.selection = (1.5, 3.5)
tool.selection = [1.0, 2.0]
tool.selection = None
def test_selecting_mouse_leave_clipping(self):
# Regression test for #216.
plot_data = ArrayPlotData()
arr = np.arange(4.0)
plot_data.set_data("x", arr)
plot_data.set_data("y", arr)
for origin in ('bottom left', 'top left', 'bottom right', 'top right'):
for orientation in ('h', 'v'):
for axis in ('index', 'value'):
plot = Plot(
plot_data, orientation=orientation, origin='top right'
)
renderer = plot.plot(('x', 'y'))[0]
renderer.bounds = [10, 20]
tool = RangeSelection(
renderer, left_button_selects=True, axis=axis,
)
renderer.tools.append(tool)
low_x, low_y = plot.position
high_x = low_x + renderer.bounds[0] - 1
high_y = low_y + renderer.bounds[1] - 1
cx = 5
cy = 5
bounds = (
(low_x - 1, low_y),
(high_x + 1, low_y),
(low_x, low_y - 1),
(low_x, high_y + 1),
)
for x, y in bounds:
self.mouse_down(tool, x=cx, y=cy)
self.mouse_leave(tool, x=x, y=y)
selection = tool.selection
self.assertTrue(selection[0] <= selection[1])
self.mouse_up(tool, x=x, y=y)
def test_selection_no_warning(self):
plot_data = ArrayPlotData()
arr = np.arange(4)
plot_data.set_data("x", arr)
plot_data.set_data("y", arr)
plot = Plot(plot_data)
renderer = plot.plot(('x', 'y'))[0]
tool = RangeSelection(renderer)
with warnings.catch_warnings(record=True) as w:
# Ignore warnings coming from any package other than Chaco
warnings.filterwarnings(
"ignore",
module="(?!chaco)",
)
tool.selection = np.array([2.0, 3.0])
self.assertEqual(w, [])
# Accept tuples and lists and None
tool.selection = (1.5, 3.5)
tool.selection = [1.0, 2.0]
tool.selection = None
def _load_image(self, path):
self.container = self._container_factory()
im = Image.open(path)
# oim = array(im)
im = im.convert('L')
odim = ndim = array(im)
pd = ArrayPlotData()
pd.set_data('img', odim)
plot = Plot(data=pd, padding=[30, 5, 5, 30], default_origin='top left')
img_plot = plot.img_plot('img',
colormap=color_map_name_dict[self.colormap_name_1]
)[0]
self.add_inspector(img_plot)
self.add_tools(img_plot)
self.oplot = plot
self.container.add(self.oplot)
# self.container.add(self.plot)
self.container.request_redraw()
def setup_images(self, n, wh):
self.container._components = []
self.plotdata = plotdata = ArrayPlotData()
for i in range(n):
plot = Plot(plotdata, padding=0, default_origin="top left")
name = 'imagedata{:03d}'.format(i)
plotdata.set_data(name, ones(wh))
plot.img_plot(name, colormap=hot)
self.container.add(plot)
self.container.request_redraw()
def test_restrict_to_data_with_empty_source(self):
# Regression test for #214.
plot_data = ArrayPlotData()
plot = Plot(plot_data)
arr = np.arange(4.0)
plot_data.set_data("x", arr)
plot_data.set_data("y", arr)
plot_data.set_data("z", np.array([], np.float64))
plot.plot(('x', 'y'))
plot.plot(('z', 'z'))
tool = PanTool(plot, restrict_to_data=True)
plot.tools.append(tool)
x_range = plot.x_mapper.range
y_range = plot.y_mapper.range
x_bounds = (x_range.low, x_range.high)
y_bounds = (y_range.low, y_range.high)
self.mouse_down(tool, 0.0, 0.0)
self.mouse_move(interactor=tool, x=1.0, y=1.0)
self.mouse_up(interactor=tool, x=1.0, y=1.0)
self.assertEqual((x_range.low, x_range.high), x_bounds)
self.assertEqual((y_range.low, y_range.high), y_bounds)
def load(self):
data = ArrayPlotData()
p = Plot(data=data, padding=100)
X = linspace(0, 2 * pi)
data.set_data('x0', X)
data.set_data('y0', cos(X))
p.plot(('x0', 'y0'))
self.container.add(p)
def plotFFT(widget):
""" Determine fft of signals in selected widget """
print("plotFFT")
model = ""
for (modelNumber, modelName, variableName), data in widget.getData():
# print "var:", var, "data: ", data
minVal = (0, float("inf"))
maxVal = (0, float("-inf"))
# for time, value in data:
# Get data from data array:
time = numpy.array(list((x for x, _ in data))) # data[0]
values = numpy.array(list((x for _, x in data)))
(Tmin, Tmax, N) = getFFTtimeRange(time)
(timeInRange, valuesInRange) = getValuesInRange(time, values, Tmin, Tmax)
# Compute fft: A=A(f)
(f, A) = fft(timeInRange, valuesInRange, N)
# Open new plot tab:
import plotWidget
window = widget.createNewWindow()
container = plotWidget.plotContainer(window)
plotWidget = plotWidget.PlotWidget(container)
container.setPlotWidget(plotWidget)
# Create the plot
plotdata = ArrayPlotData(x=f,
y=A,
border_visible=True,
overlay_border=True)
plot = Plot(plotdata, title="FFT") # Plot(plotdata, title="FFT")
barPlot = plot.plot(("x", "y"), type="bar", bar_width=0.1, color="blue")[0]
# scatterPlot = plot.plot(("x", "y"), type="scatter", color="blue")[0]
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
plot.overlays.append(ZoomTool(plot))
# Activate Plot:
plotWidget.setPlot(plot)
container.setPlotWidget(plotWidget)
layout = QtGui.QBoxLayout(QtGui.QBoxLayout.TopToBottom)
layout.addWidget(container)
window.setLayout(layout)
window.show()
def test_restrict_to_data_with_empty_source(self):
# Regression test for #214.
plot_data = ArrayPlotData()
plot = Plot(plot_data)
arr = np.arange(4.0)
plot_data.set_data("x", arr)
plot_data.set_data("y", arr)
plot_data.set_data("z", np.array([], np.float64))
plot.plot(('x', 'y'))
plot.plot(('z', 'z'))
tool = PanTool(plot, restrict_to_data=True)
plot.tools.append(tool)
x_range = plot.x_mapper.range
y_range = plot.y_mapper.range
x_bounds = (x_range.low, x_range.high)
y_bounds = (y_range.low, y_range.high)
self.mouse_down(tool, 0.0, 0.0)
self.mouse_move(interactor=tool, x=1.0, y=1.0)
self.mouse_up(interactor=tool, x=1.0, y=1.0)
self.assertEqual((x_range.low, x_range.high), x_bounds)
self.assertEqual((y_range.low, y_range.high), y_bounds)
def initialize_plot(self):
data = self.data_model
container = self.plot_container
self._series = []
self._plots = {}
index, rr = None, None
for i, (a, title) in enumerate((
('water_head', 'Head'),
('adjusted_water_head', 'Adj. Head'),
# ('temp', 'Temp.'),
# ('water_level_elevation', 'Elev.')
)):
plot = Plot(
data=ArrayPlotData(**{
'x': data.x,
a: getattr(data, a)
}),
padding=[70, 10, 10, 10],
# resizable='h',
# bounds=(1, 125)
)
if index is None:
index = plot.index_mapper
rr = plot.index_range
else:
plot.index_mapper = index
plot.index_range = rr
series = plot.plot(('x', a))[0]
plot.plot(('x', a), marker_size=1.5, type='scatter')
dt = DataTool(plot=series,
component=plot,
normalize_time=False,
use_date_str=True)
dto = DataToolOverlay(component=series, tool=dt)
series.tools.append(dt)
series.overlays.append(dto)
plot.y_axis.title = title
if i != 0:
plot.x_axis.visible = False
else:
zoom = ZoomTool(plot,
tool_mode="range",
axis='index',
color=(0, 1, 0, 0.5),
enable_wheel=False,
always_on=False)
plot.overlays.append(zoom)
tool = RangeSelection(series,
left_button_selects=True,
listeners=[self])
self._tool = tool
series.tools.append(tool)
# series.active_tool = tool
# plot.x_axis.title = 'Time'
bottom_axis = PlotAxis(
plot,
orientation="bottom", # mapper=xmapper,
tick_generator=ScalesTickGenerator(
scale=CalendarScaleSystem()))
plot.x_axis = bottom_axis
plot.padding_bottom = 50
series.overlays.append(RangeSelectionOverlay(component=series))
container.add(plot)
self._series.append(series)
self._plots[a] = plot
container.invalidate_and_redraw()
def load(self, path):
parser = ElementTree(file=open(path, 'r'))
circles = parser.find('circles')
outline = parser.find('outline')
bb = outline.find('bounding_box')
bs = bb.find('width'), bb.find('height')
w, h = [float(b.text) for b in bs]
use_label = parser.find('use_label')
if use_label is not None:
use_label = to_bool(use_label.text.strip())
else:
use_label = True
data = ArrayPlotData()
p = Plot(data=data, padding=100)
p.x_grid.visible = False
p.y_grid.visible = False
p.x_axis.visible = False
p.y_axis.visible = False
p.x_axis.title = 'X cm'
p.y_axis.title = 'Y cm'
# font = 'modern 22'
# p.x_axis.title_font = font
# p.x_axis.tick_label_font = font
# p.y_axis.title_font = font
# p.y_axis.tick_label_font = font
# p.x_axis_visible = False
# p.y_axis_visible = False
p.index_range.low_setting = -w / 2
p.index_range.high_setting = w / 2
p.value_range.low_setting = -h / 2
p.value_range.high_setting = h / 2
thetas = linspace(0, 2 * pi)
radius = circles.find('radius').text
radius = float(radius)
face_color = circles.find('face_color')
if face_color is not None:
face_color = face_color.text
else:
face_color = 'white'
labels = []
for i, pp in enumerate(circles.findall('point')):
x, y, l = pp.find('x').text, pp.find('y').text, pp.find('label').text
# print i, pp, x, y
# load hole specific attrs
r = pp.find('radius')
if r is None:
r = radius
else:
r = float(r.text)
fc = pp.find('face_color')
if fc is None:
fc = face_color
else:
fc = fc.text
x, y = list(map(float, (x, y)))
xs = x + r * sin(thetas)
ys = y + r * cos(thetas)
xn, yn = 'px{:03d}'.format(i), 'py{:03d}'.format(i)
data.set_data(xn, xs)
data.set_data(yn, ys)
plot = p.plot((xn, yn),
face_color=fc,
type='polygon')[0]
labels.append((x, y, l))
# if use_label:
# label = myDataLabel(component=plot,
# data_point=(x, y),
# label_text=l,
# bgcolor='transparent')
# plot.overlays.append(label)
if use_label:
p.overlays.append(LabelsOverlay(component=plot, labels=labels))
self.container.add(p)
self.container.invalidate_and_redraw()
def _theta_plot_default(self):
theta = self.theta
nannotators = theta.shape[0]
samples = self.theta_samples
# plot data object
plot_data = ArrayPlotData()
# create the plot
plot = Plot(plot_data)
# --- plot theta as vertical dashed lines
# add vertical lines extremes
plot_data.set_data('line_extr', [0., 1.])
for k in range(nannotators):
name = self._theta_name(k)
plot_data.set_data(name, [theta[k], theta[k]])
plots = {}
for k in range(nannotators):
name = self._theta_name(k)
line_plot = plot.plot(
(name, 'line_extr'),
line_width = 2.,
color = get_annotator_color(k),
line_style = 'dash',
name = name
)
plots[name] = line_plot
# --- plot samples as distributions
if samples is not None:
bins = np.linspace(0., 1., 100)
max_hist = 0.
for k in range(nannotators):
name = self._theta_name(k) + '_distr_'
hist, x = np.histogram(samples[:,k], bins=bins)
hist = hist / float(hist.sum())
max_hist = max(max_hist, hist.max())
# make "bars" out of histogram values
y = np.concatenate(([0], np.repeat(hist, 2), [0]))
plot_data.set_data(name+'x', np.repeat(x, 2))
plot_data.set_data(name+'y', y)
for k in range(nannotators):
name = self._theta_name(k) + '_distr_'
plot.plot((name+'x', name+'y'),
line_width = 2.,
color = get_annotator_color(k)
)
# --- adjust plot appearance
plot.aspect_ratio = 1.6 if is_display_small() else 1.7
plot.padding = [20,0,10,40]
# adjust axis bounds
x_low, x_high = theta.min(), theta.max()
y_low, y_high = 0., 1.
if samples is not None:
x_high = max(x_high, samples.max())
x_low = min(x_low, samples.min())
y_high = max_hist
plot.range2d = DataRange2D(
low = (max(x_low-0.05, 0.), y_low),
high = (min(x_high*1.1, 1.), min(y_high*1.1, 1.))
)
# label axes
plot.value_axis.title = 'Probability'
plot.index_axis.title = 'Theta'
# add legend
legend = Legend(component=plot, plots=plots,
align="ul", padding=5)
legend.tools.append(LegendTool(legend, drag_button="left"))
plot.overlays.append(legend)
container = VPlotContainer()
container.add(plot)
container.bgcolor = 0xFFFFFF
self.decorate_plot(container, theta)
self._set_title(plot)
return container
def _plot_container_default(self):
data = self.posterior
nannotations, nclasses = data.shape
# create a plot data object
plot_data = ArrayPlotData()
plot_data.set_data("values", data)
# create the plot
plot = Plot(plot_data, origin=self.origin)
img_plot = plot.img_plot("values",
interpolation='nearest',
xbounds=(0, nclasses),
ybounds=(0, nannotations),
colormap=self._create_colormap())[0]
ndisp = 55
img_plot.y_mapper.range.high = ndisp
img_plot.y_mapper.domain_limits=((0, nannotations))
self._set_title(plot)
plot.padding_top = 80
# create x axis for labels
label_axis = self._create_increment_one_axis(plot, 0.5, nclasses, 'top')
label_axis.title = 'classes'
self._add_index_axis(plot, label_axis)
plot.y_axis.title = 'items'
# tweak plot aspect
goal_aspect_ratio = 2.0
plot_width = (goal_aspect_ratio * self.plot_height
* nclasses / ndisp)
self.plot_width = min(max(plot_width, 200), 400)
plot.aspect_ratio = self.plot_width / self.plot_height
# add colorbar
colormap = img_plot.color_mapper
colorbar = ColorBar(index_mapper = LinearMapper(range=colormap.range),
color_mapper = colormap,
plot = img_plot,
orientation = 'v',
resizable = '',
width = 15,
height = 250)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = int(self.plot_height - colorbar.height -
plot.padding_top)
colorbar.padding_left = 0
colorbar.padding_right = 30
# create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
container.bgcolor = 0xFFFFFF # light gray: 0xEEEEEE
# add pan tools
img_plot.tools.append(PanTool(img_plot, constrain=True,
constrain_direction="y", speed=7.))
self.decorate_plot(container, self.posterior)
self.plot_posterior = plot
return container
def load(self, path):
parser = ElementTree(file=open(path, 'r'))
circles = parser.find('circles')
outline = parser.find('outline')
bb = outline.find('bounding_box')
bs = bb.find('width'), bb.find('height')
w, h = map(lambda b: float(b.text), bs)
use_label = parser.find('use_label')
if use_label is not None:
use_label = to_bool(use_label.text.strip())
else:
use_label = True
data = ArrayPlotData()
p = Plot(data=data, padding=100)
p.x_grid.visible = False
p.y_grid.visible = False
p.x_axis.title = 'X cm'
p.y_axis.title = 'Y cm'
font = 'modern 22'
p.x_axis.title_font = font
p.x_axis.tick_label_font = font
p.y_axis.title_font = font
p.y_axis.tick_label_font = font
# p.x_axis_visible = False
# p.y_axis_visible = False
p.index_range.low_setting = -w / 2
p.index_range.high_setting = w / 2
p.value_range.low_setting = -h / 2
p.value_range.high_setting = h / 2
thetas = linspace(0, 2 * pi)
radius = circles.find('radius').text
radius = float(radius)
face_color = circles.find('face_color')
if face_color is not None:
face_color = face_color.text
else:
face_color = 'white'
for i, pp in enumerate(circles.findall('point')):
x, y, l = pp.find('x').text, pp.find('y').text, pp.find(
'label').text
# print i, pp, x, y
# load hole specific attrs
r = pp.find('radius')
if r is None:
r = radius
else:
r = float(r.text)
fc = pp.find('face_color')
if fc is None:
fc = face_color
else:
fc = fc.text
x, y = map(float, (x, y))
xs = x + r * sin(thetas)
ys = y + r * cos(thetas)
xn, yn = 'px{:03n}'.format(i), 'py{:03n}'.format(i)
data.set_data(xn, xs)
data.set_data(yn, ys)
plot = p.plot(
(xn, yn),
face_color=fc,
type='polygon',
)[0]
if use_label:
label = myDataLabel(
component=plot,
data_point=(x, y),
label_text=l,
bgcolor='transparent',
)
plot.overlays.append(label)
self.container.add(p)
def load(self, path):
parser = ElementTree(file=open(path, 'r'))
circles = parser.find('circles')
outline = parser.find('outline')
bb = outline.find('bounding_box')
bs = bb.find('width'), bb.find('height')
w, h = map(lambda b:float(b.text), bs)
data = ArrayPlotData()
p = Plot(data=data)
p.x_grid.visible = False
p.y_grid.visible = False
p.index_range.low_setting = -w / 2
p.index_range.high_setting = w / 2
p.value_range.low_setting = -h / 2
p.value_range.high_setting = h / 2
thetas = linspace(0, 2 * pi)
radius = circles.find('radius').text
radius = float(radius)
face_color = circles.find('face_color')
if face_color is not None:
face_color = face_color.text
else:
face_color = 'white'
for i, pp in enumerate(circles.findall('point')):
x, y, l = pp.find('x').text, pp.find('y').text, pp.find('label').text
# load hole specific attrs
r = pp.find('radius')
if r is None:
r = radius
else:
r = float(r.text)
fc = pp.find('face_color')
if fc is None:
fc = face_color
else:
fc = fc.text
x, y = map(float, (x, y))
xs = x + r * sin(thetas)
ys = y + r * cos(thetas)
xn, yn = 'px{:03n}'.format(i), 'py{:03n}'.format(i)
data.set_data(xn, xs)
data.set_data(yn, ys)
plot = p.plot((xn, yn),
face_color=fc,
type='polygon',
)[0]
label = myDataLabel(component=plot,
data_point=(x, y),
label_text=l,
bgcolor=fc
)
plot.overlays.append(label)
self.container.add(p)
def _theta_plot_default(self):
theta = self.theta
nannotators = theta.shape[0]
samples = self.theta_samples
# plot data object
plot_data = ArrayPlotData()
# create the plot
plot = Plot(plot_data)
# --- plot theta as vertical dashed lines
# add vertical lines extremes
plot_data.set_data('line_extr', [0., 1.])
for k in range(nannotators):
name = self._theta_name(k)
plot_data.set_data(name, [theta[k], theta[k]])
plots = {}
for k in range(nannotators):
name = self._theta_name(k)
line_plot = plot.plot(
(name, 'line_extr'),
line_width = 2.,
color = get_annotator_color(k),
line_style = 'dash',
name = name
)
plots[name] = line_plot
# --- plot samples as distributions
if samples is not None:
bins = np.linspace(0., 1., 100)
max_hist = 0.
for k in range(nannotators):
name = self._theta_name(k) + '_distr_'
hist, x = np.histogram(samples[:,k], bins=bins)
hist = hist / float(hist.sum())
max_hist = max(max_hist, hist.max())
# make "bars" out of histogram values
y = np.concatenate(([0], np.repeat(hist, 2), [0]))
plot_data.set_data(name+'x', np.repeat(x, 2))
plot_data.set_data(name+'y', y)
for k in range(nannotators):
name = self._theta_name(k) + '_distr_'
plot.plot((name+'x', name+'y'),
line_width = 2.,
color = get_annotator_color(k)
)
# --- adjust plot appearance
plot.aspect_ratio = 1.6 if is_display_small() else 1.7
plot.padding = [20,0,10,40]
# adjust axis bounds
x_low, x_high = theta.min(), theta.max()
y_low, y_high = 0., 1.
if samples is not None:
x_high = max(x_high, samples.max())
x_low = min(x_low, samples.min())
y_high = max_hist
plot.range2d = DataRange2D(
low = (max(x_low-0.05, 0.), y_low),
high = (min(x_high*1.1, 1.), min(y_high*1.1, 1.))
)
# label axes
plot.value_axis.title = 'Probability'
plot.index_axis.title = 'Theta'
# add legend
legend = Legend(component=plot, plots=plots,
align="ul", padding=5)
legend.tools.append(LegendTool(legend, drag_button="left"))
plot.overlays.append(legend)
container = VPlotContainer()
container.add(plot)
container.bgcolor = 0xFFFFFF
self.decorate_plot(container, theta)
self._set_title(plot)
return container
def _theta_plot_default(self):
"""Create plot of theta parameters."""
# We plot both the thetas and the samples from the posterior; if the
# latter are not defined, the corresponding ArrayPlotData names
# should be set to an empty list, so that they are not displayed
theta = self.model.theta
theta_len = theta.shape[0]
# create the plot data
if not self.theta_plot_data:
self.theta_plot_data = ArrayPlotData()
self._update_plot_data()
# create the plot
theta_plot = Plot(self.theta_plot_data)
for idx in range(theta_len):
# candle plot summarizing samples over the posterior
theta_plot.candle_plot((_w_idx('index', idx),
_w_idx('min', idx),
_w_idx('barmin', idx),
_w_idx('avg', idx),
_w_idx('barmax', idx),
_w_idx('max', idx)),
color = get_annotator_color(idx),
bar_line_color = "black",
stem_color = "blue",
center_color = "red",
center_width = 2)
# plot of raw samples
theta_plot.plot((_w_idx('ysamples', idx),
_w_idx('xsamples', idx)),
type='scatter',
color='black',
marker='dot',
line_width=0.5,
marker_size=1)
# plot current parameters
theta_plot.plot((_w_idx('y', idx), _w_idx('x', idx)),
type='scatter',
color=get_annotator_color(idx),
marker='plus',
marker_size=8,
line_width=2)
# adjust axis bounds
theta_plot.range2d = self._compute_range2d()
# remove horizontal grid and axis
theta_plot.underlays = [theta_plot.x_grid, theta_plot.y_axis]
# create new horizontal axis
label_list = [str(i) for i in range(theta_len)]
label_axis = LabelAxis(
theta_plot,
orientation = 'bottom',
positions = list(range(1, theta_len+1)),
labels = label_list,
label_rotation = 0
)
# use a FixedScale tick generator with a resolution of 1
label_axis.tick_generator = ScalesTickGenerator(scale=FixedScale(1.))
theta_plot.index_axis = label_axis
theta_plot.underlays.append(label_axis)
theta_plot.padding = 25
theta_plot.padding_left = 40
theta_plot.aspect_ratio = 1.0
container = VPlotContainer()
container.add(theta_plot)
container.bgcolor = 0xFFFFFF
self.decorate_plot(container, theta)
self._set_title(theta_plot)
return container
def _plot_container_default(self):
data = self.posterior
nannotations, nclasses = data.shape
# create a plot data object
plot_data = ArrayPlotData()
plot_data.set_data("values", data)
# create the plot
plot = Plot(plot_data, origin=self.origin)
img_plot = plot.img_plot("values",
interpolation='nearest',
xbounds=(0, nclasses),
ybounds=(0, nannotations),
colormap=self._create_colormap())[0]
ndisp = 55
img_plot.y_mapper.range.high = ndisp
img_plot.y_mapper.domain_limits = ((0, nannotations))
self._set_title(plot)
plot.padding_top = 80
# create x axis for labels
label_axis = self._create_increment_one_axis(plot, 0.5, nclasses,
'top')
label_axis.title = 'classes'
self._add_index_axis(plot, label_axis)
plot.y_axis.title = 'items'
# tweak plot aspect
goal_aspect_ratio = 2.0
plot_width = (goal_aspect_ratio * self.plot_height * nclasses / ndisp)
self.plot_width = min(max(plot_width, 200), 400)
plot.aspect_ratio = self.plot_width / self.plot_height
# add colorbar
colormap = img_plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=img_plot,
orientation='v',
resizable='',
width=15,
height=250)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = int(self.plot_height - colorbar.height -
plot.padding_top)
colorbar.padding_left = 0
colorbar.padding_right = 30
# create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
container.bgcolor = 0xFFFFFF # light gray: 0xEEEEEE
# add pan tools
img_plot.tools.append(
PanTool(img_plot,
constrain=True,
constrain_direction="y",
speed=7.))
self.decorate_plot(container, self.posterior)
self.plot_posterior = plot
return container
def plotEigenvalues(model, gui):
''' This function calculates the linearization of model as well as additional information
(eigenvalues, damping ...) if this was not done before.
It opens a new plotting tab and displays this information
'''
if model is None:
print("No model selected!")
return
# Check if already linearized, do so otherwise:
try:
data = model.pluginData["EigenvalueAnalysis"]
except:
print "Performing linearization"
data = EigenvalueAnalysis()
data._performLinearization(model)
model.pluginData["EigenvalueAnalysis"] = data
# Open new plotting tab:
parent = QtGui.QApplication.activeWindow()
widgetContainer = parent._newPlotContainer()
widget = widgetContainer.activeWidget
# Plot the data:
x = numpy.real(data.eigenvalues[:])
y = numpy.imag(data.eigenvalues[:])
plotdata = ArrayPlotData(x=x,
y=y,
border_visible=True,
overlay_border=True)
plot = Plot(plotdata, title="Eigenvalues of %s" % data.modelName)
scatter = plot.plot(("x", "y"), type="scatter", color="blue")[0]
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
plot.overlays.append(ZoomTool(plot))
# Add axis titles:
x_axis = PlotAxis(orientation="bottom")
x_axis.mapper = plot.index_mapper
x_axis.title = "real part"
plot.underlays.append(x_axis)
y_axis = PlotAxis(orientation="left")
y_axis.mapper = plot.value_mapper
y_axis.title = "imag. part"
plot.underlays.append(y_axis)
# Attach the inspector and its overlay
scatter.tools.append(ScatterInspector(scatter))
overlay = myScatterInspectorOverlay(scatter,
hover_color="red",
hover_marker_size=6,
selection_marker_size=6,
selection_color="yellow",
selection_outline_color="purple",
selection_line_width=3,
stateNames=data.StateNames,
eigenVectors=data.eigenvectors,
frequencies=data.frequencies,
damping=data.damping,
observability=data.observability,
controllability=data.controllability)
scatter.overlays.append(overlay)
# Activate Plot:
widget.setPlot(plot)
widgetContainer.activeWidget = widget
def plotFFTPlusTHD(widget):
""" Determine fft of signals in selected widget and
calculate Total harmonic disturbance"""
print("plotFFT and Total Harmonic Disturbance")
model = ""
for (modelNumber, modelName, variableName), data in widget.getData():
# print "var:", var, "data: ", data
minVal = (0, float("inf"))
maxVal = (0, float("-inf"))
# for time, value in data:
# Get data from data array:
time = numpy.array(list((x for x, _ in data))) # data[0]
values = numpy.array(list((x for _, x in data)))
unit = ""
(Tmin, Tmax, N) = getFFTtimeRange(time)
(timeInRange, valuesInRange) = getValuesInRange(time, values, Tmin, Tmax)
# Compute fft: A=A(f)
(f, A) = fft(timeInRange, valuesInRange, N)
#******* START THD CALCULATION *************
# Estimate fundamental frequency:
maxindex = A.argmax()
estimation = f[maxindex]
def getExFreq(estimation):
return estimation
"""
Inquire im measured fundamental frequency is correct:
"""
'''
import guidata
guidata.qapplication()
import guidata.dataset.dataitems as di
import guidata.dataset.datatypes as dt
class Processing(dt.DataSet):
""" Fundamental Frequency """
correctedFreq = di.FloatItem("fundamental frequency [Hz]", default=estimation)
param = Processing()
okPressed = param.edit()
if okPressed:
return param.correctedFreq
else: # Cancel button pressed
return estimation
'''
# Ask for a better fundamental frequency
exFreq = max(0, min(f.max(), getExFreq(estimation)))
# Check if we have at least one harmonic:
if exFreq > 0.5 * f.max():
print "THD calculation not possible, extend frequency window to at least 2*fundamental frequency"
THD = 999
else:
# Get 5% window around fundamental frequency and calculate power:
mask = (f > exFreq * 0.975) & (f < exFreq * 1.025)
print "Calculating fundamental energy from points: frequency=%s, Amplitude=%s" % (
f[mask], A[mask])
P1 = numpy.vdot(A[mask], A[mask]) # squared amplitude
PH = 0
# Sum up the Power of all harmonic frequencies in spectrum:
noHarmonics = numpy.int(numpy.floor(f.max() / exFreq))
for i in range(noHarmonics - 1):
mask = (f > (i + 2) * exFreq * 0.975) & (f <
(i + 2) * exFreq * 1.025)
PH = PH + (numpy.vdot(A[mask], A[mask])) # squared amplitude
THD = PH / P1 * 100
#******* END THD CALCULATION *************
# Open new plot tab:
import plotWidget
window = widget.createNewWindow()
container = plotWidget.plotContainer(window)
plotWidget = plotWidget.PlotWidget(container)
container.setPlotWidget(plotWidget)
# Plot data
plotdata = ArrayPlotData(x=f,
y=A,
border_visible=True,
overlay_border=True)
plot = Plot(plotdata, title="FFT") # Plot(plotdata, title="FFT")
barPlot = plot.plot(("x", "y"), type="bar", bar_width=0.3, color="blue")[0]
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
plot.overlays.append(ZoomTool(plot))
# Activate Plot:
plotWidget.setPlot(plot)
if THD != 999:
thdLabel = DataLabel(
component=plotWidget.plot,
data_point=(f[A.argmax()], A.max()),
label_position="bottom right",
padding_bottom=20,
marker_color="transparent",
marker_size=8,
marker="circle",
arrow_visible=False,
label_format=str(
'THD = %.4g percent based on %d harmonics of the %.4g Hz frequency'
% (THD, noHarmonics, exFreq)))
plotWidget.plot.overlays.append(thdLabel)
container.setPlotWidget(plotWidget)
layout = QtGui.QBoxLayout(QtGui.QBoxLayout.TopToBottom)
layout.addWidget(container)
window.setLayout(layout)
window.show()