def initUI(self):
# vispy
#canvas = vispy.app.Canvas()
print('start initUI')
if 1:
self.fig = vp.Fig(size=(800, 600), show=False)
data = np.random.rand(100, 2)
line1 = self.fig[0, 0].plot(data=data)
self.fig[1, 0].plot(data=data)
self.fig[2, 0].plot(data=data)
self.fig[3, 0].plot(data=data)
# qt
#w = QtWidgets.QMainWindow()
#widget = QtWidgets.QWidget()
#w.setCentralWidget(widget)
#widget.setLayout(QtWidgets.QHBoxLayout())
self.myHBoxLayout = QtWidgets.QHBoxLayout(self)
self.myHBoxLayout.addWidget(QtWidgets.QPushButton('A Button'))
self.myHBoxLayout.addWidget(self.fig.native)
#widget.layout().addWidget(QtWidgets.QPushButton('A Button'))
#widget.layout().addWidget(self.fig.native)
#widget.layout().addWidget(canvas.native)
#w.show()
#widget.show()
#vispy.app.run()
self.show()
data = np.random.rand(100, 2) + 100
line1.set_data(data)
def __init__(self):
super(bTest, self).__init__()
viewer = napari.Viewer(title='xxx')
self.initUI()
# vispy
#canvas = vispy.app.Canvas()
print('start __init__')
if 0:
self.fig = vp.Fig(size=(800, 600))
data = np.random.rand(100, 2)
self.fig[0, 0].plot(data=data)
self.fig[1, 0].plot(data=data)
self.fig[2, 0].plot(data=data)
self.fig[3, 0].plot(data=data)
# qt
#w = QtWidgets.QMainWindow()
widget = QtWidgets.QWidget()
#w.setCentralWidget(widget)
widget.setLayout(QtWidgets.QHBoxLayout())
widget.layout().addWidget(QtWidgets.QPushButton('A Button'))
widget.layout().addWidget(self.fig.native)
#widget.layout().addWidget(canvas.native)
#w.show()
#widget.show()
#vispy.app.run()
print('end __init__')
def visualise_translation_3D(world=None,
original_pressure_field=None,
sampler=None,
threshold=50,
colour_map='cubehelix'):
'''
This method visualises the effect of the (x-y) translation algorithm on the
amplitude and phase of the acoustic field.
Parameters
----------
world : handybeam_core.world
An instance of the handybeam.world class.
original_pressure_field : numpy array
A numpy array containing the propagated acoustic pressure field.
sampler : handybeam.sampler
An instance of one of the handybeam sampler classes.
filename : string
This string indicates the location in which the visualisation image should be stored.
threshold : float / int
This sets the threshold for which sampling points are visualised in the image. It can
be modified to ignore regions with pressure less than the threshold.
colour_map : string
This sets the colour map to be used in the visualisation. Please see
https://github.com/vispy/vispy/blob/master/vispy/color/colormap.py#L348-L441.
'''
reshape_no = int(np.round(np.power(sampler.no_points, 1 / 3), 3))
sampler_pressure_field_reshaped = sampler.pressure_field.reshape(
(reshape_no, reshape_no, reshape_no))
original_pressure_field_reshaped = original_pressure_field.reshape(
(reshape_no, reshape_no, reshape_no))
fig = vp.Fig(bgcolor='white', size=(2000, 2000), show=False)
vol_data_before = np.abs(original_pressure_field_reshaped)
vol_data_before = np.flipud(np.rollaxis(vol_data_before, 1))
vol_data_before[vol_data_before < threshold] = 0
vol_pw_before = fig[0, 0]
vol_pw_before.volume(vol_data_before, cmap=colour_map)
vol_data_after = np.abs(sampler_pressure_field_reshaped)
vol_data_after = np.flipud(np.rollaxis(vol_data_after, 1))
vol_data_after[vol_data_after < threshold] = 0
vol_pw_after = fig[0, 1]
vol_pw_after.volume(vol_data_after, cmap=colour_map)
vol_pw_before.camera = scene.cameras.TurntableCamera(fov=45)
vol_pw_after.camera = scene.cameras.TurntableCamera(fov=45)
vol_pw_before.camera.link(vol_pw_after.camera)
fig.show(run=True)
def test_figure_creation():
"""Test creating a figure"""
with vp.Fig(show=False) as fig:
fig[0, 0:2]
fig[1:3, 0:2]
ax_right = fig[1:3, 2]
assert fig[1:3, 2] is ax_right
# collision
assert_raises(ValueError, fig.__getitem__, (slice(1, 3), 1))
def surface_plot(I, x=None, y=None, show=True, size=(800, 400)):
figure = vp.Fig(size=size, show=False)
if x is None or y is None:
p1 = figure[0, 0].surface(zdata=I)
else:
p1 = figure[0, 0].surface(zdata=I, x=x, y=x)
if show:
figure.show(run=True)
else:
return figure
def main():
points = np.random.randn(10, 2)
fig = vp.Fig(show=False)
fig[0, 0].plot(points,
width=0,
face_color=(.8, .4, .2, .5),
edge_color=None,
marker_size=100.)
fig.show(run=True)
def on_start(self):
widget = self.root
fig = vp.Fig(app='kivy_glir')
widget.link_vispy_canvas(fig)
plotwidget = fig[0, 0]
# fig.title = "bollu"
plotwidget.plot([(x, x**2) for x in range(0, 100)], title="y = x^2")
plotwidget.colorbar(position="top", cmap="autumn")
fig.show()
widget.vispy_canvas_backend.kivy_draw_trigger()
def __init__(self):
MainWindowBase.__init__(self)
self.ui = Ui_MainWindow()
self.ui.setupUi(self)
self.viewer = vp.Fig()
self.viewer.create_native()
self.ui.plotFrameLayout.addWidget(self.viewer.native)
x = np.linspace(0, 10, 100000)
y = np.cos(x)
self.viewer[0, 0].plot((x, y))
grid1 = vp.visuals.GridLines(color=(0, 0, 0, 0.5))
grid1.set_gl_state('translucent')
self.viewer[0, 0].view.add(grid1)
def visualise_3D(world=None,
sampler=None,
threshold=50,
colour_map='cubehelix'):
fig = vp.Fig(bgcolor='white', size=(2000, 2000), show=False)
reshape_no = int(np.round(np.power(sampler.no_points, 1 / 3), 3))
sampler_pressure_field_reshaped = sampler.pressure_field.reshape(
(reshape_no, reshape_no, reshape_no))
vol_data_after = np.abs(sampler_pressure_field_reshaped)
vol_data_after = np.flipud(np.rollaxis(vol_data_after, 1))
vol_data_after[vol_data_after < threshold] = 0
vol_pw_after = fig[0, 0]
vol_pw_after.volume(vol_data_after, cmap=colour_map)
vol_pw_after.camera = scene.cameras.TurntableCamera(fov=45)
fig.show(run=True)
def drawOutput(organism):
fname1 = 'teapot.obj'
fname2 = 'sphere.obj'
fig = plot.Fig(title=TITLE)
fig[0, 0].mesh(*io.read_mesh(fname1)[:2])
arr = io.read_mesh(fname2)[:2]
for dot in organism:
a = arr
for elem in a[0]:
elem[0] = elem[0] * 1 + dot[0]
elem[1] = elem[1] * 1 + dot[1]
elem[2] = elem[2] * 1 + dot[2]
#print('workin')
fig[0, 0].mesh(*arr, color=(1, 1, 0.1))
vispy.
app.run()
def test_plot_widget_axes():
"""Test that the axes domains are updated correctly when a figure is first drawn"""
fig = vp.Fig(size=(800, 800), show=False)
point = (0, 100)
fig[0, 0].plot((point, point))
# mocking the AxisVisual domain.setter
domain_setter = mock.Mock(wraps=AxisVisual.domain.fset)
mock_property = AxisVisual.domain.setter(domain_setter)
with mock.patch.object(AxisVisual, "domain", mock_property):
# note: fig.show() must be called for this test to work... otherwise
# Grid._update_child_widget_dim is not triggered and the axes aren't updated
fig.show(run=False)
# currently, the AxisWidget adds a buffer of 5% of the
# full range to either end of the axis domain
buffer = (point[1] - point[0]) * 0.05
expectation = [point[0] - buffer, point[1] + buffer]
for call in domain_setter.call_args_list:
assert [round(x, 2) for x in call[0][1]] == expectation
def initUI(self):
# vispy
data = np.random.rand(100, 2) # random data
fig = vp.Fig(size=(800, 600))
line1 = fig[0, 0].plot(data=data)
line2 = fig[1, 0].plot(data=data)
# PyQt (with vispy fig.native)
self.myHBoxLayout = QtWidgets.QHBoxLayout(self)
self.myHBoxLayout.addWidget(QtWidgets.QPushButton('My Button'))
self.myHBoxLayout.addWidget(fig.native)
self.show()
# update line1 plot with new data offset by 100
newData = np.random.rand(100, 2) + 100
# this call to set_data does not update the range of x/y axis
# If I zoom out with mouse-wheel I can verify it is plotted
line1.set_data(newData)
# Is there something I can add here ???
line1.update()
def visualise_3D(world=None,
sampler=None,
threshold=50,
colour_map='cubehelix'):
""" show a 3D volume visualisation using VisPy
:param world : handybeam.world.World
the world from which to take the data
:param sampler: handybeam.samplers.abstract_sampler
the sampler from which to take the data
:param threshold:
threshold of visibility for volume visualisation
:param colour_map:
colour map for volume visualisation
:return:
creates the figure, and shows it.
"""
fig = vp.Fig(bgcolor='white', size=(2000, 2000), show=False)
reshape_no = int(np.round(np.power(sampler.no_points, 1 / 3), 3))
sampler_pressure_field_reshaped = sampler.pressure_field.reshape(
(reshape_no, reshape_no, reshape_no))
vol_data_after = np.abs(sampler_pressure_field_reshaped)
vol_data_after = np.flipud(np.rollaxis(vol_data_after, 1))
vol_data_after[vol_data_after < threshold] = 0
vol_pw_after = fig[0, 0]
vol_pw_after.volume(vol_data_after, cmap=colour_map)
vol_pw_after.camera = scene.cameras.TurntableCamera(fov=45)
fig.show(run=True)
z = np.complex(x, y)
f = np.exp(1.0 / z)
return np.angle(f, deg=True)
# create a 2d grid whose elements are of exp_z_inv
def gen_image(width, height):
x_vals = np.linspace(-0.5, 0.5, width)
y_vals = np.linspace(-0.5, 0.5, height)
grid = np.meshgrid(x_vals, y_vals)
v_fn = np.vectorize(exp_z_inv)
return v_fn(*grid).astype(np.float32)
fig = vp.Fig(size=(800, 600), show=False)
plot = fig[0, 0]
plot.bgcolor = "#efefef"
img = gen_image(500, 500)
plot.image(img, cmap="hsl")
plot.camera.set_range((100, 400), (100, 400))
positions = ["top", "bottom", "left", "right"]
for position in positions:
plot.colorbar(position=position,
label="argument of e^(1/z)",
clim=("0°", "180°"),
cmap="hsl",
border_width=1,
np.random.seed(2324)
n = 100000
data = np.empty((n, 2))
lasti = 0
for i in range(1, 20):
nexti = lasti + (n - lasti) // 2
scale = np.abs(np.random.randn(2)) + 0.1
scale[1] = scale.mean()
data[lasti:nexti] = np.random.normal(size=(nexti - lasti, 2),
loc=np.random.randn(2),
scale=scale / i)
lasti = nexti
data = data[:lasti]
color = (0.3, 0.5, 0.8)
n_bins = 100
fig = vp.Fig(show=False)
line = fig[0:4, 0:4].plot(data,
symbol='o',
width=0,
face_color=color + (0.02, ),
edge_color=None,
marker_size=4)
line.set_gl_state(depth_test=False)
fig[4, 0:4].histogram(data[:, 0], bins=n_bins, color=color, orientation='h')
fig[0:4, 4].histogram(data[:, 1], bins=n_bins, color=color, orientation='v')
if __name__ == '__main__':
fig.show(run=True)
def main(argv):
START = 0
STOP = 64
STEP = 2
# colors for plotting
colz = [
"#C900E5", "#C603E1", "#C306DD", "#C009DA", "#BD0CD6", "#BA0FD2",
"#B812CF", "#B515CB", "#B218C7", "#AF1BC4", "#AC1EC0", "#A921BD",
"#A724B9", "#A427B5", "#A12AB2", "#9E2DAE", "#9B30AA", "#9833A7",
"#9636A3", "#93399F", "#903C9C", "#8D3F98", "#8A4295", "#884591",
"#85488D", "#824B8A", "#7F4E86", "#7C5182", "#79547F", "#77577B",
"#745A77", "#715D74", "#6E6170", "#6B646D", "#686769", "#666A65",
"#636D62", "#60705E", "#5D735A", "#5A7657", "#577953", "#557C4F",
"#527F4C", "#4F8248", "#4C8545", "#498841", "#478B3D", "#448E3A",
"#419136", "#3E9432", "#3B972F", "#389A2B", "#369D27", "#33A024",
"#30A320", "#2DA61D", "#2AA919", "#27AC15", "#25AF12", "#22B20E",
"#1FB50A", "#1CB807", "#19BB03", "#17BF00"
]
# go through list of arguments and check for existing files and dirs
files = arg2files(argv)
# open filedialog for files
if len(argv) == 0:
Tk().withdraw()
files = filedialog.askopenfilenames(title='Choose file/s to load')
fs = 100.
N = 360000
t = np.arange(N) / float(fs)
figs = []
for ind, _file in enumerate(files):
print(_file)
figs.append(vp.Fig(size=(1600, 1000), show=False))
fig = figs[-1]
plt_even = fig[0, 0]
plt_odd = fig[1, 0]
plt_even._configure_2d()
plt_odd._configure_2d()
plt_even.xlabel.text = 'Time (s)'
plt_odd.xlabel.text = 'Time (s)'
plt_even.title.text = os.path.basename(_file) + " even CH"
plt_odd.title.text = os.path.basename(_file) + " odd CH"
this_data = get_data(_file)
if N > get_data_len(_file):
N = get_data_len(_file)
t = np.arange(N) / float(fs)
print(this_data.shape)
diff_data = np.zeros(t.shape)
sum_signal = np.zeros(t.shape)
thr = 200
for ch in range(START, STOP, STEP):
if ch % 16 == 0:
print(ch)
""" This one does the magic """
KSIZE = 21 ##501
filtered_signal = sg.medfilt(this_data[ch + 1], kernel_size=KSIZE)
filtered_signal = np.abs(
filtered_signal -
filtered_signal[0]) # positive changes from baseline
thr_signal = filtered_signal > thr
sum_signal += thr_signal
#plt_even.plot(np.array((t, filtered_signal)).T, marker_size=0, color=colz[ch])
plt_even.plot(np.array((t, this_data[ch] + 1000 * ch)).T,
marker_size=0,
color=colz[ch])
#plt_even.plot(np.array((t[thr_signal==1], 1000*thr_signal[thr_signal==1])).T, marker_size=0, color='r')
#plt_odd.plot(np.array((t, this_data[ch+1]+1000*ch)).T, marker_size=0, color=colz[ch])
#plt_odd.spectrogram(this_data[ch], fs=fs)
#plt_odd.plot(np.array((t, filtered_signal)).T, marker_size=0, color=colz[ch])
plt_odd.plot(np.array((t, this_data[ch + 1] + 1000 * ch)).T,
marker_size=0,
color=colz[ch])
ch_thr = 24
thr_sum_signal = sum_signal > ch_thr
if np.count_nonzero(thr_sum_signal) > 0:
print(np.count_nonzero(thr_sum_signal),
consecutive_one(thr_sum_signal))
if (consecutive_one(thr_sum_signal) > 500):
print("Noise detected at",
(np.nonzero(thr_sum_signal)[0])[0] / fs, "secs")
else:
print("No noise detected.")
else:
print("No noise detected.")
#plt_even.plot(np.array((t, (1000/32)*sum_signal)).T, marker_size=0, width=1, color='b')
plt_even.plot(np.array((t, (1000) * thr_sum_signal - 1000)).T,
marker_size=0,
width=2,
color='r')
plt_odd.plot(np.array((t, (1000) * thr_sum_signal - 1000)).T,
marker_size=0,
width=2,
color='r')
for fig in figs:
fig.show(run=True)
# if no files are given
if len(files) == 0:
print("WARNING: No valid files specified.")
# -*- coding: utf-8 -*-
# Copyright (c) Vispy Development Team. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
"""
Show use of colorbars in plot
"""
import vispy.plot as vp
fig = vp.Fig(size=(600, 500), show=False)
plotwidget = fig[0, 0]
fig.title = "bollu"
plotwidget.plot([(x, x**2) for x in range(0, 100)], title="y = x^2")
plotwidget.colorbar(position="top", cmap="autumn")
if __name__ == '__main__':
fig.show(run=True)
# -*- coding: utf-8 -*-
# Copyright (c) 2015, Vispy Development Team.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# vispy: gallery 1
"""
Plot various views of a structural MRI.
"""
import numpy as np
from vispy import io, plot as vp
fig = vp.Fig(bgcolor='k', size=(800, 800), show=False)
vol_data = np.load(io.load_data_file('brain/mri.npz'))['data']
vol_data = np.flipud(np.rollaxis(vol_data, 1))
clim = [32, 192]
vol_pw = fig[0, 0]
vol_pw.volume(vol_data, clim=clim)
vol_pw.view.camera.elevation = 30
vol_pw.view.camera.azimuth = 30
vol_pw.view.camera.scale_factor /= 1.5
shape = vol_data.shape
fig[1, 0].image(vol_data[:, :, shape[2] // 2],
cmap='grays',
clim=clim,
fg_color=(0.5, 0.5, 0.5, 1))
fig[0, 1].image(vol_data[:, shape[1] // 2, :],
cmap='grays',
import numpy as np
import vispy.plot as vp
from vispy.color import get_colormap
# load example data
from vispy.io import load_data_file
data = np.load(load_data_file('electrophys/iv_curve.npz'))['arr_0']
data *= 1000 # V -> mV
dt = 1e-4 # this data is sampled at 10 kHz
# create figure with plot
fig = vp.Fig()
plt = fig[0, 0]
plt._configure_2d()
plt.title.text = 'Current Clamp Recording'
plt.ylabel.text = 'Membrane Potential (mV)'
plt.xlabel.text = 'Time (ms)'
selected = None
# plot data
cmap = get_colormap('hsl', value=0.5)
colors = cmap.map(np.linspace(0.1, 0.9, data.shape[0]))
t = np.arange(data.shape[1]) * (dt * 1000)
for i, y in enumerate(data):
line = plt.plot((t, y), color=colors[i])
line.interactive = True
line.unfreeze() # make it so we can add a new property to the instance
line.data_index = i
line.freeze()
"""
fragment = """
void main() {
gl_FragColor = vec4(1.0, 1.0, 1.0, 1.0);
}
"""
program = gloo.Program(vertex, fragment)
program['a_position'] = np.c_[
np.random.uniform(-0.5, +0.5, 1000).astype(np.float32),
np.random.uniform(-0.5, +0.5, 1000).astype(np.float32)]
''''''
fname1 = 'teapot.obj'
fname2 = 'sphere.obj'
fig = plot.Fig()
fig[0, 0].mesh(*io.read_mesh(fname1)[:2])
fig[0, 0].mesh(*io.read_mesh(fname2)[:2])
''''''
@c.connect
def on_resize(event):
gloo.set_viewport(0, 0, *event.size)
@c.connect
def on_draw(event):
gloo.clear((0, 0, 0, 1))
program.draw('points')
# -*- coding: utf-8 -*-
# Copyright (c) Vispy Development Team. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
"""
Demonstrates rendering a canvas to an image at higher resolution than the
original display.
"""
import vispy.plot as vp
# Create a canvas showing plot data
fig = vp.Fig()
fig[0, 0].plot([1, 6, 2, 4, 3, 8, 5, 7, 6, 3])
# Render the canvas scene to a numpy array image with higher resolution
# than the original canvas
scale = 4
image = fig.render(size=(fig.size[0] * scale, fig.size[1] * scale))
# Display the data in the array, sub-sampled down to the original canvas
# resolution
fig_2 = vp.Fig()
fig_2[0, 0].image(image[::-scale, ::scale])
# By default, the view adds some padding when setting its range.
# We'll remove that padding so the image looks exactly like the original
# canvas:
fig_2[0, 0].camera.set_range(margin=0)
if __name__ == '__main__':
fig.app.run()
import numpy as np import vispy.plot as vplt fname = '../data/seismic_volume.npy' data = np.load(fname) fig = vplt.Fig() scene = fig[0, 0] scene.volume(data) fig.show(run=True)
def __init__(self, parent=None, name=None):
super(VispyCanvas, self).__init__(parent=parent)
self.parent = parent
self.main = self.parent.parent.parent.main
self.treebase = self.main.treebase
self.database = self.main.database
self.dpState = {}
self.opts = None
self.hasState = False
self._name = name
self.canvas = vpp.Fig(show=False)
self.canvas00 = self.canvas[0, 0]
self.canvas.unfreeze()
self.canvas.visuals = []
self.canvas.pick_mode = False
self.canvas.sigPickedPoint = self.sigPickedPoint
self.canvas.freeze()
self.toggle_pick_mode(False)
self.sigPickedPoint.connect(self.print_coord)
vbox = QVBoxLayout()
vbox.addWidget(self.canvas.native)
self.setLayout(vbox)
@self.canvas.connect
def on_mouse_press(event):
if not self.canvas.pick_mode:
return
if len(self.canvas.visuals) == 0:
return # canvas is empty
#if event.handled or event.button != 1:
# return
if event.button == 2:
queried = None
for v in self.canvas.visuals_at(event.pos):
if isinstance(v, vp.scene.widgets.viewbox.ViewBox):
continue
queried = v
break
if queried is not None:
logger.info("Object name {}".format(queried.name))
return
# emit signal of coordinate of picked point
vis = self.canvas.visuals[0]
tr = self.canvas.scene.node_transform(vis)
pos = tr.map(event.pos)
x, y, z = pos[:3]
xy = np.array([x, y])
survey = self.treebase.survey
if survey is None:
ilno, xlno = (0, 0)
else:
ilno, xlno = xy2ln(xy, survey=survey)
self.canvas.sigPickedPoint.emit((ilno, xlno, x, y, z))
selected = None
for v in self.canvas.visuals_at(event.pos):
if isinstance(v, vp.scene.widgets.viewbox.ViewBox):
continue
selected = v
break
if selected is not None:
display_selection(self.canvas, selected, event.pos)
def get_figure(size=(800, 400), show=False):
return vp.Fig(size=size, show=False)
#!/usr/bin/env ipython -i ipython_fig_playground.py # -*- coding: utf-8 -*- # Copyright (c) Vispy Development Team. All Rights Reserved. # Distributed under the (new) BSD License. See LICENSE.txt for more info. """ Bare bones plotting region that can be used with the python interpreter as a playground. Run with python -i ipython_fig_playground.py """ from vispy import plot as vp # noqa import numpy as np # noqa fig = vp.Fig(size=(600, 500)) # noqa plotwidget = fig[0, 0]
