def _fast_threshold_preview(image: np.ndarray,
height: int = 15,
width: int = 15,
slider_width: int = 500,
scale: float = 0.25):
slider_width = str(slider_width) + 'px'
@widgets.interact(h=FloatRangeSlider(min=0.,
max=1.,
step=0.01,
readout_format='.2f',
layout={'width': slider_width},
continuous_update=False),
s=FloatRangeSlider(min=0.,
max=1.,
step=0.01,
readout_format='.2f',
layout={'width': slider_width},
continuous_update=False),
v=FloatRangeSlider(min=0.,
max=1.,
step=0.01,
readout_format='.2f',
layout={'width': slider_width},
continuous_update=False))
def interact_plot(h=(0.2, 0.4), s=(0.2, 0.4), v=(0.2, 0.4)):
out_shape = list(image.shape)
out_shape[0] = int(out_shape[0] * scale)
out_shape[1] = int(out_shape[1] * scale)
i = transform.resize(image, tuple(out_shape))
thresh = threshold_hsv_img(i, h=h, s=s, v=v)
@njit
def _do_(i, thresh):
x_d, y_d, _ = i.shape
for x in range(x_d):
for y in range(y_d):
if thresh[x, y]:
i[x, y] = (0, 1, 1)
return i
i = _do_(i, thresh)
plt.figure(figsize=(width, height))
plt.imshow(color.hsv2rgb(i))
return_string = "Selected Values\nHue: {0}\nSaturation: {1}\nValue: {2}\n".format(
h, s, v)
print(return_string)
def _perfect_threshold_preview(image: np.ndarray,
height: int = 15,
width: int = 15,
slider_width: int = 500):
slider_width = str(slider_width) + 'px'
@widgets.interact_manual(h=FloatRangeSlider(min=0.,
max=1.,
step=0.01,
readout_format='.2f',
layout={'width':
slider_width}),
s=FloatRangeSlider(min=0.,
max=1.,
step=0.01,
readout_format='.2f',
layout={'width':
slider_width}),
v=FloatRangeSlider(min=0.,
max=1.,
step=0.01,
readout_format='.2f',
layout={'width':
slider_width}))
def interact_plot(h=(0.2, 0.4), s=(0.2, 0.4), v=(0.2, 0.4)):
f = FloatProgress(min=0, max=100, step=1, description="Progress:")
display(f)
x = threshold_hsv_img(image, h=h, s=s, v=v)
f.value += 25
i = image.copy()
f.value += 25
i[x] = (0, 1, 1)
f.value += 25
plt.figure(figsize=(width, height))
plt.imshow(color.hsv2rgb(i))
f.value += 25
return_string = "Selected Values\nHue: {0}\nSaturation: {1}\nValue: {2}\n".format(
h, s, v)
print(return_string)
def __init__(self, src=None, auto=True, x=None, y=None, **kwargs):
super().__init__(**kwargs)
self._filters = {}
self.box = VBox()
self._links = []
self._group_filter = GroupUIFilter()
self._trigger = Trigger(self._group_filter, func=self._apply_filter)
self._filters = {}
self._auto = auto
self._auto_filter = None
self.show_measure = False
# setup controls
self._ctrls = HBox()
self._menu = widgets.Dropdown(
options=self.options,
description='Attribute:',
value=self.attr,
disabled=False,
)
self.x_slider = IntRangeSlider(min=0,
max=1,
value=(0, 1),
description='Points:')
self.y_slider = FloatRangeSlider(min=0,
max=1,
value=(0, 1),
description='Persistence:',
step=0.001)
self._ctrls = HBox([self._menu, self.y_slider, self.x_slider])
link((self, 'x_value'), (self.x_slider, 'value'))
link((self, 'y_value'), (self.y_slider, 'value'))
self._auto_filter = AttrFilter(attr=self._menu.value)
if self._auto:
self._group_filter.add(self._auto_filter, name='auto')
widgets.link((self, 'attr'), (self._menu, 'value'))
self.observe(self._auto_update, names=['attr'])
# setup view
self._links = [
widgets.link((self, 'x'), (self.x_slider, 'value')),
widgets.link((self, 'y'), (self.y_slider, 'value')),
]
self._update_children()
if src is not None:
self.src = src
if x is not None:
self.x = x
if y is not None:
self.y = y
def show():
step = 0.1
float_range = FloatRangeSlider(value=[0, 5.0],
min=xrange[0],
max=xrange[1],
step=step,
description='事象の範囲：',
continuous_update=False,
readout_format='.1f')
def enforce_gap(change):
new_min, new_max = change.new
old_min, old_max = change.old
if new_max - new_min < step:
if old_min == new_min:
new_max = new_min + step
else:
new_min = new_max - step
float_range.value = (new_min, new_max)
float_range.observe(enforce_gap, 'value')
interact(plot, float_range=float_range)
def __init__(self):
self.dx, self.dy = 0.005, 0.005
self.w_title = HTML("<h3>Область с заданной неравномерностью </h3>")
self.w_out5 = Output()
self.w_label_var = Label("Допустимая неравномерность:")
self.w_var = FloatSlider(1.1, min=1., max=1.5, step=0.005, continuous_update=False, readout_format='.3f')
self.w_label_xarea = Label('Границы допустимой области по горизонтали: ')
self.w_xarea = FloatRangeSlider(value=[0.2,0.7],
min=0,
max=1.0,
step=0.005,
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='.3f')
self.w_label_yarea = Label('Границы допустимой области по вертикали: ')
self.w_yarea = FloatRangeSlider(value=[0.45,0.55],
min=0,
max=1.0,
step=0.005,
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='.3f')
self.w_varn = Label()
self.btn_var = Button(description ='Пересчитать', button_style='primary', disabled = True) # доработать
self.btn_break = Button(description ='Прервать', disabled = True)
self.w_message = HTML("")
self.w_result = HTML("")
self.layout = VBox([self.w_title, self.w_label_var, self.w_var,
self.w_label_xarea, self.w_xarea,
self.w_label_yarea, self.w_yarea,
self.w_varn,
self.btn_var, self.w_message, self.w_result,
self.w_out5])
self.btn_var.on_click(self.button_var_onclick)
def __init__(self, tree=None, auto=True, **kwargs):
super().__init__(**kwargs)
self._filters = {}
self._treeview = None
self._links = []
self._group_filter = GroupUIFilter()
self._trigger = Trigger(self._group_filter, func=self._apply_filter)
self._filters = {}
self._auto = auto
self._auto_filter = None
# setup controls
self._ctrls = HBox()
self._menu = widgets.Dropdown(
options=self.attr_opts,
description='Attribute:',
value=self.attr,
disabled=False,
)
self._x = IntRangeSlider(min=0,
max=100,
value=(0, 100),
description='Points:')
self._y = FloatRangeSlider(min=0,
max=1,
value=(0, 1),
description='Persistence:',
step=0.001)
self._ctrls = HBox([self._menu, self._y, self._x])
self._auto_filter = AttrFilter(attr=self._menu.value)
if self._auto:
self._group_filter.add(self._auto_filter, name='auto')
widgets.link((self._menu, 'value'), (self, 'attr'))
self.observe(self._auto_update, names=['attr'])
# setup view
self._treeview = BaseTreeView(None, attr=self.attr)
self._treeview.show_attr = False
self._links = [
widgets.link((self, 'attr'), (self._treeview, 'attr')),
widgets.link((self._x, 'value'), (self._treeview, 'x')),
widgets.link((self._y, 'value'), (self._treeview, 'y'))
]
self._update_children()
if tree is not None:
self.tree = tree
from ipywidgets import FloatSlider, FloatRangeSlider, Dropdown, Select, Box, AppLayout, jslink, Layout, VBox, HBox
from ipygany import Scene, IsoColor, TetraMesh, Component, ColorBar, colormaps
mesh = TetraMesh.from_vtk(
'benchmarks/005-hemispherical-shell-point-load/hemisperical-shell-points-load.vtu',
show_edges=True)
u_min = 4.4e-6
u_max = 1.8e-1
# Colorize by height
colored_mesh = IsoColor(mesh, input=('u', 'X1'), min=u_min, max=u_max)
# Create a slider that will dynamically change the boundaries of the colormap
colormap_slider_range = FloatRangeSlider(value=[u_min, u_max],
min=u_min,
max=u_max,
step=(u_max - u_min) / 100.)
jslink((colored_mesh, 'range'), (colormap_slider_range, 'value'))
# Create a colorbar widget
colorbar = ColorBar(colored_mesh)
# Colormap choice widget
colormap = Dropdown(options=colormaps, description='colormap:')
jslink((colored_mesh, 'colormap'), (colormap, 'index'))
AppLayout(
header=Scene([colored_mesh]),
left_sidebar=VBox((colormap, colormap_slider_range)),
def make_widg(self, trait, val):
return FloatRangeSlider(value=val,
min=trait.min,
max=trait.max,
step=(trait.max - trait.min) / 200)
def __init__(self):
items_layout = Layout (flex = '1 1 auto', width = 'auto')
self.w_title = HTML('<h3>Форма электродов</h3>')
self.w_l_x = Label('Границы изогнутой части электрода по горизонтали:')
self.w_x = FloatRangeSlider(value=[0.5,1.], min=0., max=1., step=0.02,
continuous_update=False,
readout_format='.3f', layout=items_layout)
self.w_l_y = Label('Координаты электрода по вертикали:')
self.w_l_y0 = Label('y0')
layouth = Layout(height='300px')
self.w_y0 = FloatSlider(value=0.6,
min=0.5,
max=1.0,
step=0.001,
description='y0',
disabled=False,
continuous_update=False,
orientation='vertical',
readout=True,
readout_format='.3f', layout=layouth
)
self.w_y1 = FloatSlider(value=0.6,
min=0.5,
max=1.0,
step=0.001,
description='y1',
disabled=False,
continuous_update=False,
orientation='vertical',
readout=True,
readout_format='.3f', layout=layouth
)
self.w_y2 = FloatSlider(value=0.6,
min=0.5,
max=1.0,
step=0.001,
description='y2',
disabled=False,
continuous_update=False,
orientation='vertical',
readout=True,
readout_format='.3f', layout=layouth
)
self.w_y3 = FloatSlider(value=0.6,
min=0.5,
max=1.0,
step=0.001,
description='y3',
disabled=False,
continuous_update=False,
orientation='vertical',
readout=True,
readout_format='.3f', layout=layouth
)
self.w_y4 = FloatSlider(value=0.6,
min=0.5,
max=1.0,
step=0.001,
description='y4',
disabled=False,
continuous_update=False,
orientation='vertical',
readout=True,
readout_format='.3f', layout=layouth
)
self.out_el = Output()
hbox1 = HBox([self.w_y0, self.w_y1, self.w_y2, self.w_y3, self.w_y4], layout=Layout(width='100%'))
vbox1 = VBox([self.w_l_x, self.w_x, self.w_l_y, hbox1], layout=Layout(width='50%', padding='5px'))
vbox2 = HBox([self.out_el], layout=Layout(width='50%', padding='5px'))
hbox = HBox ([vbox1, vbox2], layout=Layout(width='100%'))
self.layout = VBox([self.w_title, hbox])
self.interactive_el_form = interactive(self.el_form, x0xm=self.w_x, y0=self.w_y0, y1=self.w_y1,
y2=self.w_y2, y3=self.w_y3, y4=self.w_y4)
def __init__(self,
volume=None,
default_directory=os.getcwd(),
title='',
enhancement_steps=1000,
**kwargs):
def on_chosen_path_change(old_path, new_path):
self.dataset = FolderDataset(new_path)
# TODO: If the path doesn't contain images, display a warning
# A widget for changing the image folder
self.pathchooser = PathChooser(
chosen_path_desc='Image folder:',
default_directory=default_directory,
on_chosen_path_change=on_chosen_path_change,
)
self.pathchooser.layout.margin = '0 0 10px 0'
# The number of increments of the min/max slider
self.enhancement_steps = enhancement_steps
self.scales = {
'x': LinearScale(),
'y': LinearScale(),
}
# The currently displayed image will be in bytes at `self.image_plot.image.value`
self.image_plot = BQImage(
image=IPyImage(),
scales=self.scales,
)
self.figure = Figure(
marks=[self.image_plot],
padding_x=0,
padding_y=0,
animation_duration=1000,
fig_margin={
'top': 0,
'right': 0,
'bottom': 0,
'left': 0,
},
layout=Layout(
grid_area='figure',
margin='0',
width='320px',
height='320px',
),
)
# Custom toolbar
toolbar_width = '100%'
toolbar_margin = '0px 0 2px 0'
self.pan_zoom = PanZoom(scales={
'x': [self.scales['x']],
'y': [self.scales['y']],
}, )
self.save_button = Button(
description='Save Image',
tooltip='Save Image',
icon='save',
layout=Layout(
width=toolbar_width,
# flex='1 1 auto',
margin=toolbar_margin,
),
)
self.save_button.on_click(self.save_current_image)
self.hide_button = Button(
description='Hide Image',
tooltip='Hide Image',
icon='eye-slash',
layout=Layout(
width=toolbar_width,
# flex='1 1 auto',
margin=toolbar_margin,
))
self.hide_button.on_click(self.hide_current_image)
self.pan_zoom_toggle_button = ToggleButton(
description='Pan / Zoom',
tooltip='Pan/Zoom',
icon='arrows',
layout=Layout(
width=toolbar_width,
# flex='1 1 auto',
margin=toolbar_margin,
),
)
self.pan_zoom_toggle_button.observe(self.on_pan_zoom_toggle,
names='value')
self.reset_pan_zoom_button = Button(
description='Undo Zoom',
tooltip='Reset pan/zoom',
icon='refresh',
layout=Layout(
width=toolbar_width,
# flex='1 1 auto',
margin=toolbar_margin,
),
)
self.reset_pan_zoom_button.on_click(self.reset_pan_zoom)
self.reset_enhancements_button = Button(
description='Un-Enhance',
tooltip='Reset enhancements',
icon='ban',
layout=Layout(
width=toolbar_width,
# flex='1 1 auto',
margin=toolbar_margin,
),
)
self.reset_enhancements_button.on_click(self.reset_enhancements)
self.mini_map = IPyImage(layout=Layout(
grid_area='mini-map',
margin='0',
))
self.mini_map.width = 180
self.mini_map.height = 180
# PERFORMANCE CONCERN
# Ideally instead of four observations, this would observe 'scales' on `self.pan_zoom`
# However, it doesn't fire updates
# Ref: https://github.com/bloomberg/bqplot/issues/800
self.image_plot.scales['x'].observe(self.on_pan_zoom_change('x_min'),
names='min')
self.image_plot.scales['x'].observe(self.on_pan_zoom_change('x_max'),
names='max')
self.image_plot.scales['y'].observe(self.on_pan_zoom_change('y_min'),
names='min')
self.image_plot.scales['y'].observe(self.on_pan_zoom_change('y_max'),
names='max')
self.plane_toggle = ToggleButtons(
options=['yz', 'xz', 'xy'],
description='',
disabled=False,
button_style='',
tooltips=[
'Step in x direction', 'Step in y direction',
'Step in z direction'
],
layout=Layout(
width='200px',
# flex='1 1 auto',
margin='7px 0 auto auto',
),
)
self.plane_toggle.style.button_width = 'auto'
self.plane_toggle.observe(self.on_plane_change, names='value')
self.toolbar = VBox(
children=[
self.save_button,
self.hide_button,
self.pan_zoom_toggle_button,
self.reset_pan_zoom_button,
self.reset_enhancements_button,
],
layout=Layout(
grid_area='toolbar',
margin='0',
),
)
# Image enhancements
self.min_max_slider = FloatRangeSlider(
value=[0, 255],
min=0,
max=255,
step=255 / self.enhancement_steps,
description='Min/Max:',
orientation='horizontal',
readout=True,
readout_format='.1f',
continuous_update=True,
layout=Layout(
grid_area='min-max-slider',
margin='10px 0 10px -10px',
width='100%',
),
)
self.min_max_slider.observe(self.on_min_max_change, names='value')
self.index_slider = IntSlider(
value=0,
min=0,
max=1,
step=1,
description='Index:',
orientation='horizontal',
readout=True,
readout_format='d',
continuous_update=True,
layout=Layout(
grid_area='index-slider',
margin='8px -20px 10px -36px',
width='100%',
),
)
self.index_slider.observe(self.on_image_index_change, names='value')
# Animation
self.play = Play(
value=self.index_slider.value,
min=self.index_slider.min,
max=self.index_slider.max,
step=self.index_slider.step,
)
jslink((self.play, 'value'), (self.index_slider, 'value'))
# Keep 'max' in sync as well
self.index_slider.observe(self.on_index_slider_max_change, names='max')
self.bottom_bar = HBox(
children=[
self.play,
self.index_slider,
self.plane_toggle,
],
layout=Layout(
grid_area='bottom-bar',
margin=f'10px -20px 0 0',
# overflow='hidden',
))
# Layout
self.gridbox = GridBox(children=[
self.figure,
self.toolbar,
self.mini_map,
self.min_max_slider,
self.bottom_bar,
], )
# Initially hidden without data
self.gridbox.layout.display = 'none'
self._dataset = None
if volume is not None:
self.dataset = VolumeDataset(volume)
# Hide pathchooser when using a volume
self.pathchooser.layout.display = 'none'
# Call VBox super class __init__
super().__init__(
children=[
self.pathchooser,
self.gridbox,
],
layout=Layout(width='auto'),
**kwargs,
)
mesh = TetraMesh.from_vtk(
'benchmarks/004-eliptic-membrane/eliptic-membrane.vtu', show_edges=True)
sigmayy_min = -3.4e7
sigmayy_max = 9.3e7
# Colorize by height
colored_mesh = IsoColor(mesh,
input=('S', 'YY'),
min=sigmayy_min,
max=sigmayy_max)
# Create a slider that will dynamically change the boundaries of the colormap
colormap_slider_range = FloatRangeSlider(value=[sigmayy_min, sigmayy_max],
min=sigmayy_min,
max=sigmayy_max,
step=(sigmayy_max - sigmayy_min) /
100.)
jslink((colored_mesh, 'range'), (colormap_slider_range, 'value'))
# Create a colorbar widget
colorbar = ColorBar(colored_mesh)
# Colormap choice widget
colormap = Dropdown(options=colormaps, description='colormap:')
jslink((colored_mesh, 'colormap'), (colormap, 'index'))
AppLayout(
header=Scene([colored_mesh]),
plt.show()
if save_but:
fig.savefig('./plots/v1_{}.png'.format(df_num[col].name),
bbox_inches='tight')
# fig.savefig('./plots/v1_{}_{}.png'.format(i,df_num[col].name), bbox_inches='tight')
case1 = Checkbox(value=True, description='case1')
case2 = Checkbox(value=True, description='case2')
case3 = Checkbox(value=True, description='case3')
case4 = Checkbox(value=True, description='case4')
# xlimit = FloatRangeSlider(continuous_update=False,description='X_limit')
xlimit = FloatRangeSlider(
value=[df_num.iloc[:, 1].min(), df_num.iloc[:, 1].max()],
min=df_num.iloc[:, 1].min(),
max=df_num.iloc[:, 1].max(),
step=(df_num.iloc[:, 1].max() - df_num.iloc[:, 1].min()) / 100,
continuous_update=False,
description='X_limit')
version = Dropdown(options=[0, 1])
case = Dropdown(options=['CASE1', 'CASE2', 'CASE3', 'CASE4'])
save_but = ToggleButton(description='Save Figure')
col = Dropdown(options=df_num.columns)
out = interactive_output(
plot_num_and_save, {
'case1': case1,
'case2': case2,
'case3': case3,
'case4': case4,
'xlimit': xlimit,
def initialize_sliders(self, box_universe=None, box_name=None):
if box_universe is not None:
self._box_universe = box_universe
if box_name is not None:
self._box_name = box_name
logger.info(f"initialize_sliders( {self._box_name} )")
try:
self.joint_filters = pandas.DataFrame(
numpy.ones_like(self.joint_data, dtype=bool),
index=self.joint_data.index,
columns=self.joint_data.columns,
)
clusterdef = ChainedBox(self._box_universe, self._box_name)
self.sliders = []
self.outboxes = []
for i in self.joint_data.columns:
i_dtype = self.scope.get_dtype(i) or 'real'
if i_dtype == 'real':
current_setting = clusterdef.get(i, (None, None))
logger.info(f" initial setting {i} = {current_setting}")
current_min = self.joint_data[i].min(
) if current_setting[0] is None else current_setting[0]
current_max = self.joint_data[i].max(
) if current_setting[1] is None else current_setting[1]
controller = FloatRangeSlider(
value=[current_min, current_max],
min=self.joint_data[i].min(),
max=self.joint_data[i].max(),
step=(self.joint_data[i].max() -
self.joint_data[i].min()) / 20,
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='.2f',
description=i,
style=slider_style,
layout=slider_layout,
)
elif i_dtype == 'int':
current_setting = clusterdef.get(i, (None, None))
logger.info(f" initial setting {i} = {current_setting}")
current_min = self.joint_data[i].min(
) if current_setting[0] is None else current_setting[0]
current_max = self.joint_data[i].max(
) if current_setting[1] is None else current_setting[1]
controller = IntRangeSlider(
value=[current_min, current_max],
min=self.joint_data[i].min(),
max=self.joint_data[i].max(),
#step=(self.joint_data[i].max()-self.joint_data[i].min())/20,
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='d',
description=i,
style=slider_style,
layout=slider_layout,
)
elif i_dtype == 'cat':
cats = self.scope.get_cat_values(i)
controller = MultiToggleButtons(
description=i,
style=slider_style,
options=cats,
disabled=False,
button_style=
'', # 'success', 'info', 'warning', 'danger' or ''
layout=slider_layout,
)
controller.values = cats
elif i_dtype == 'bool':
cats = [False, True]
controller = MultiToggleButtons(
description=i,
style=slider_style,
options=cats,
disabled=False,
button_style=
'', # 'success', 'info', 'warning', 'danger' or ''
layout=slider_layout,
)
controller.values = cats
else: # buttons
raise NotImplementedError(f"filters for {i}:{i_dtype}")
controller = ToggleButtons(
description=i,
style=slider_style,
options=['Off', 'On', 'Both'],
value='Both',
disabled=False,
button_style=
'', # 'success', 'info', 'warning', 'danger' or ''
tooltips=['Definitely off', 'Definitely on', 'Maybe'],
layout=slider_layout,
)
self.sliders.append(controller)
self.outboxes.append(
Output(layout=Layout(
height='1in',
width='3in',
), ))
for s in range(len(self.sliders)):
self.sliders[s].observe(self.replot_many)
self.ui_risks = VBox([
HBox([s, ob]) for s, ob in zip(self.sliders, self.outboxes)
if s.description in self.data.all_risk_factors_
], )
self.ui_strategies = VBox([
HBox([s, ob]) for s, ob in zip(self.sliders, self.outboxes)
if s.description in self.data.all_strategy_names_
], )
self.ui_perform = VBox([
HBox([s, ob]) for s, ob in zip(self.sliders, self.outboxes)
if s.description in self.data.all_performance_measures_
], )
self.accordion = Accordion(
children=[self.ui_strategies, self.ui_risks, self.ui_perform])
self.accordion.set_title(0, 'Policy Levers')
self.accordion.set_title(1, 'Exogenous Uncertainties')
self.accordion.set_title(2, 'Performance Measures')
#self.footer = Output(layout={ 'border': '1px solid red', } )
self.stack = VBox([
self.header_area,
self.accordion,
self.footer,
])
self.set_header(clusterdef.names)
self.replot_many(None) # initial interactive plots
except:
logger.exception("error in initialize_sliders")
raise
mesh = TetraMesh.from_vtk(
'benchmarks/003-Hertz-contact/very-fine-quad-hex.vtu', show_edges=True)
stress_min = 0.0
stress_max = 934
# Colorize by height
colored_mesh = IsoColor(mesh,
input=('S', 'Mises'),
min=stress_min,
max=stress_max)
# Create a slider that will dynamically change the boundaries of the colormap
colormap_slider_range = FloatRangeSlider(value=[stress_min, stress_max],
min=stress_min,
max=stress_max,
step=(stress_max - stress_min) / 100.)
jslink((colored_mesh, 'range'), (colormap_slider_range, 'value'))
# Create a colorbar widget
colorbar = ColorBar(colored_mesh)
# Colormap choice widget
colormap = Dropdown(options=colormaps, description='colormap:')
jslink((colored_mesh, 'colormap'), (colormap, 'index'))
AppLayout(
header=Scene([colored_mesh]),
left_sidebar=VBox((colormap, colormap_slider_range)),
def __init__(self):
# размерность задачи
items_layout = Layout (flex = '1 1 auto', width = 'auto')
self.w_title = HTML('<h3>Параметры задачи</h3>')
self.w_l_dim = Label('Размерность задачи:')
self.w_dim = ToggleButtons(
options=['2', '3'],
value = '2',
#description=' ',
disabled=False,
button_style='', # 'success', 'info', 'warning', 'danger' or ''
tooltips=['Плоская двухмерная задача', 'Трехмерная осесимметричная задача'],
layout=items_layout
)
chg.r0=1e-6
self.w_l_r0 = Label('Радиус заряда:')
self.w_r0 =SelectionSlider(
options=['1e-6', '1e-5', '1e-4', '1e-3', '1e-2'],
value='1e-6',
#description=' ',
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True, layout=items_layout
)
chg.alpha = 0
self.w_l_alpha = Label(r"Параметр регуляризации ($\alpha$):")
self.w_alpha =SelectionSlider(
options=['0', '1e-6', '1e-4', '1e-2', '1e-1', '0.5', '1.0'],
value='0',
#description=' ',
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True, layout=items_layout
)
chg.n = 100
self.w_l_n = Label('Число разбиений области:')
self.w_n = SelectionSlider(
options=['20', '50', '100', '200', '400', '500', '1000'],
value='100',
#description=' ',
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True, layout=items_layout
)
chg.minmaxxy=(0., 0, 1.,1.)
self.w_l_minmaxxy = Label('Область для расчета и отображения потенциала (x, y):')
self.w_minmaxx = FloatRangeSlider(value=[0.,1.],
min=0,
max=2.0,
#description='xmin, xmax',
step=0.01,
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='.2f', layout=items_layout)
self.w_minmaxy = FloatRangeSlider(value=[0.,1.],
min=0,
max=2.0,
#description='ymin, ymax',
step=0.02,
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='.2f', layout=items_layout)
chg.minmaxarea=(0.2, 0.2, 0.9,0.9)
self.w_l_minmaxarea = Label('Область для расчета напряженности (x, y):')
self.w_minmaxxarea = FloatRangeSlider(value=[0.2,0.9],
min=0,
max=1.0,
#description='xmin, xmax',
step=0.01,
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='.2f', layout=items_layout)
self.w_minmaxyarea = FloatRangeSlider(value=[0.2,0.9],
min=0,
max=1.2,
#description='ymin, ymax',
step=0.005,
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='.3f', layout=items_layout)
self.w_l_na = Label('Разбиений для аппроксимации электродов:')
chg.na = 50
self.w_na = IntSlider(
value=chg.na,
min=20,
max=250,
step=2,
#description=' ',
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='d', layout=items_layout
)
chg.c = None
self.l_c = Label('')
self.update_label('cond', chg.c, self.l_c)
chg.err = None
self.l_err = Label('')
self.update_label('err', chg.err, self.l_err)
chg.E = None
self.l_E = Label('')
self.update_label('E', chg.E, self.l_E)
chg.t = None
self.l_t = Label('')
self.update_label('t', chg.t, self.l_t)
self.l_msg=HTML('<b></b>')
self.l_var = Label('Вариант:')
self.w_dsc = Textarea(value='',
placeholder='Введите пояснения по варианту расчета',
description='',
disabled=False
)
self.btn0 = Button(description ='Пересчитать поле', button_style='primary')
self.btn01 = Button(description ='Emax/Emin', button_style='primary', disabled=True)
self.out0 = Output()
self.btn02 = Button(description='Сохранить', disabled=False)
vb01= VBox([self.w_l_dim, self.w_dim,
self.w_l_r0, self.w_r0,
self.w_l_alpha, self.w_alpha,
self.w_l_n, self.w_n,
self.w_l_minmaxxy, self.w_minmaxx, self.w_minmaxy,
self.w_l_minmaxarea, self.w_minmaxxarea, self.w_minmaxyarea],
layout=Layout(width='47%'))
vb02 = VBox([self.w_l_na, self.w_na, self.l_var, self.w_dsc,
self.l_c, self.l_err, self.l_E , self.l_t, self.l_msg ], layout=Layout(width='47%'))
hbox =HBox([vb01, vb02],
layout=Layout(justify_content='space-between'))
hbox2 = HBox([self.btn0, self.btn01, self.btn02])
self.layout = VBox([self.w_title, hbox, hbox2, self.out0], layout=Layout(width='100%'))
# инициализация
self.get_data()
def getNotebookBackend(actors2show, zoom, viewup):
vp = settings.plotter_instance
if zoom == 'tight':
zoom=1 # disable it
if isinstance(vp.shape, str) or sum(vp.shape) > 2:
colors.printc("Multirendering is not supported in jupyter.", c=1)
return
####################################################################################
# https://github.com/InsightSoftwareConsortium/itkwidgets
# /blob/master/itkwidgets/widget_viewer.py
if 'itk' in settings.notebookBackend:
from itkwidgets import view
settings.notebook_plotter = view(actors=actors2show,
cmap='jet', ui_collapsed=True,
gradient_opacity=False)
####################################################################################
elif settings.notebookBackend == 'k3d':
try:
import k3d # https://github.com/K3D-tools/K3D-jupyter
except:
print("Cannot find k3d, install with: pip install k3d")
return
actors2show2 = []
for ia in actors2show:
if not ia:
continue
if isinstance(ia, vtk.vtkAssembly): #unpack assemblies
acass = ia.unpack()
actors2show2 += acass
else:
actors2show2.append(ia)
# vbb, sizes, _, _ = addons.computeVisibleBounds()
# kgrid = vbb[0], vbb[2], vbb[4], vbb[1], vbb[3], vbb[5]
settings.notebook_plotter = k3d.plot(axes=[vp.xtitle, vp.ytitle, vp.ztitle],
menu_visibility=settings.k3dMenuVisibility,
height=settings.k3dPlotHeight,
antialias=settings.k3dAntialias,
)
# settings.notebook_plotter.grid = kgrid
settings.notebook_plotter.lighting = settings.k3dLighting
# set k3d camera
settings.notebook_plotter.camera_auto_fit = settings.k3dCameraAutoFit
settings.notebook_plotter.grid_auto_fit = settings.k3dGridAutoFit
settings.notebook_plotter.axes_helper = settings.k3dAxesHelper
if settings.plotter_instance and settings.plotter_instance.camera:
k3dc = utils.vtkCameraToK3D(settings.plotter_instance.camera)
if zoom:
k3dc[0] /= zoom
k3dc[1] /= zoom
k3dc[2] /= zoom
settings.notebook_plotter.camera = k3dc
# else:
# vsx, vsy, vsz = vbb[0]-vbb[1], vbb[2]-vbb[3], vbb[4]-vbb[5]
# vss = numpy.linalg.norm([vsx, vsy, vsz])
# if zoom:
# vss /= zoom
# vfp = (vbb[0]+vbb[1])/2, (vbb[2]+vbb[3])/2, (vbb[4]+vbb[5])/2 # camera target
# if viewup == 'z':
# vup = (0,0,1) # camera up vector
# vpos= vfp[0] + vss/1.9, vfp[1] + vss/1.9, vfp[2]+vss*0.01 # camera position
# elif viewup == 'x':
# vup = (1,0,0)
# vpos= vfp[0]+vss*0.01, vfp[1] + vss/1.5, vfp[2] # camera position
# else:
# vup = (0,1,0)
# vpos= vfp[0]+vss*0.01, vfp[1]+vss*0.01, vfp[2] + vss/1.5 # camera position
# settings.notebook_plotter.camera = [vpos[0], vpos[1], vpos[2],
# vfp[0], vfp[1], vfp[2],
# vup[0], vup[1], vup[2] ]
if not vp.axes:
settings.notebook_plotter.grid_visible = False
for ia in actors2show2:
if isinstance(ia, (vtk.vtkCornerAnnotation, vtk.vtkAssembly)):
continue
kobj = None
kcmap= None
name = None
if hasattr(ia, 'filename'):
if ia.filename:
name = os.path.basename(ia.filename)
if ia.name:
name = os.path.basename(ia.name)
#####################################################################scalars
# work out scalars first, Points Lines are also Mesh objs
if isinstance(ia, (Mesh, shapes.Line, Points)):
# print('scalars', ia.name, ia.N())
iap = ia.GetProperty()
if isinstance(ia, (shapes.Line, Points)):
iapoly = ia.polydata()
else:
iapoly = ia.clone().clean().triangulate().computeNormals().polydata()
vtkscals = None
color_attribute = None
if ia.mapper().GetScalarVisibility():
vtkdata = iapoly.GetPointData()
vtkscals = vtkdata.GetScalars()
if vtkscals is None:
vtkdata = iapoly.GetCellData()
vtkscals = vtkdata.GetScalars()
if vtkscals is not None:
c2p = vtk.vtkCellDataToPointData()
c2p.SetInputData(iapoly)
c2p.Update()
iapoly = c2p.GetOutput()
vtkdata = iapoly.GetPointData()
vtkscals = vtkdata.GetScalars()
if vtkscals is not None:
if not vtkscals.GetName():
vtkscals.SetName('scalars')
scals_min, scals_max = ia.mapper().GetScalarRange()
color_attribute = (vtkscals.GetName(), scals_min, scals_max)
lut = ia.mapper().GetLookupTable()
lut.Build()
kcmap=[]
nlut = lut.GetNumberOfTableValues()
for i in range(nlut):
r,g,b,a = lut.GetTableValue(i)
kcmap += [i/(nlut-1), r,g,b]
#####################################################################Volume
if isinstance(ia, Volume):
# print('Volume', ia.name, ia.dimensions())
kx, ky, kz = ia.dimensions()
arr = ia.pointdata[0]
kimage = arr.reshape(-1, ky, kx)
colorTransferFunction = ia.GetProperty().GetRGBTransferFunction()
kcmap=[]
for i in range(128):
r,g,b = colorTransferFunction.GetColor(i/127)
kcmap += [i/127, r,g,b]
kbounds = numpy.array(ia.imagedata().GetBounds()) \
+ numpy.repeat(numpy.array(ia.imagedata().GetSpacing()) / 2.0, 2)\
* numpy.array([-1,1] * 3)
kobj = k3d.volume(kimage.astype(numpy.float32),
color_map=kcmap,
#color_range=ia.imagedata().GetScalarRange(),
alpha_coef=10,
bounds=kbounds,
name=name,
)
settings.notebook_plotter += kobj
#####################################################################text
elif hasattr(ia, 'info') and 'formula' in ia.info.keys():
pos = (ia.GetPosition()[0],ia.GetPosition()[1])
kobj = k3d.text2d(ia.info['formula'], position=pos)
settings.notebook_plotter += kobj
#####################################################################Mesh
elif isinstance(ia, Mesh) and ia.N() and len(ia.faces()):
# print('Mesh', ia.name, ia.N(), len(ia.faces()))
kobj = k3d.vtk_poly_data(iapoly,
name=name,
# color=_rgb2int(iap.GetColor()),
color_attribute=color_attribute,
color_map=kcmap,
opacity=iap.GetOpacity(),
wireframe=(iap.GetRepresentation()==1))
if iap.GetInterpolation() == 0:
kobj.flat_shading = True
settings.notebook_plotter += kobj
#####################################################################Points
elif isinstance(ia, Points):
# print('Points', ia.name, ia.N())
kcols=[]
if color_attribute is not None:
scals = utils.vtk2numpy(vtkscals)
kcols = k3d.helpers.map_colors(scals, kcmap,
[scals_min,scals_max]).astype(numpy.uint32)
# sqsize = numpy.sqrt(numpy.dot(sizes, sizes))
kobj = k3d.points(ia.points().astype(numpy.float32),
color=_rgb2int(iap.GetColor()),
colors=kcols,
opacity=iap.GetOpacity(),
shader=settings.k3dPointShader,
point_size=iap.GetPointSize(),
name=name,
)
settings.notebook_plotter += kobj
#####################################################################Lines
elif ia.polydata(False).GetNumberOfLines():
# print('Line', ia.name, ia.N(), len(ia.faces()),
# ia.polydata(False).GetNumberOfLines(), len(ia.lines()),
# color_attribute, [vtkscals])
# kcols=[]
# if color_attribute is not None:
# scals = utils.vtk2numpy(vtkscals)
# kcols = k3d.helpers.map_colors(scals, kcmap,
# [scals_min,scals_max]).astype(numpy.uint32)
# sqsize = numpy.sqrt(numpy.dot(sizes, sizes))
for i, ln_idx in enumerate(ia.lines()):
if i>200:
print('WARNING: K3D nr of line segments is limited to 200.')
break
pts = ia.points()[ln_idx]
kobj = k3d.line(pts.astype(numpy.float32),
color=_rgb2int(iap.GetColor()),
opacity=iap.GetOpacity(),
shader=settings.k3dLineShader,
# width=iap.GetLineWidth()*sqsize/1000,
name=name,
)
settings.notebook_plotter += kobj
####################################################################################
elif settings.notebookBackend == 'panel' and hasattr(vp, 'window') and vp.window:
import panel # https://panel.pyviz.org/reference/panes/VTK.html
vp.renderer.ResetCamera()
settings.notebook_plotter = panel.pane.VTK(vp.window,
width=int(vp.size[0]/1.5),
height=int(vp.size[1]/2))
####################################################################################
elif 'ipyvtk' in settings.notebookBackend and hasattr(vp, 'window') and vp.window:
from ipyvtklink.viewer import ViewInteractiveWidget
vp.renderer.ResetCamera()
settings.notebook_plotter = ViewInteractiveWidget(vp.window)
####################################################################################
elif 'ipygany' in settings.notebookBackend:
from ipygany import PolyMesh, Scene, IsoColor, RGB, Component
from ipygany import Alpha, ColorBar, colormaps, PointCloud
from ipywidgets import FloatRangeSlider, Dropdown, VBox, AppLayout, jslink
bgcol = colors.rgb2hex(colors.getColor(vp.backgrcol))
actors2show2 = []
for ia in actors2show:
if not ia:
continue
if isinstance(ia, vedo.Assembly): #unpack assemblies
assacts = ia.unpack()
for ja in assacts:
if isinstance(ja, vedo.Assembly):
actors2show2 += ja.unpack()
else:
actors2show2.append(ja)
else:
actors2show2.append(ia)
pmeshes = []
colorbar = None
for obj in actors2show2:
# print("ipygany processing:", [obj], obj.name)
if isinstance(obj, vedo.shapes.Line):
lg = obj.diagonalSize()/1000 * obj.GetProperty().GetLineWidth()
vmesh = vedo.shapes.Tube(obj.points(), r=lg, res=4).triangulate()
vmesh.c(obj.c())
faces = vmesh.faces()
# todo: Lines
elif isinstance(obj, Mesh):
vmesh = obj.triangulate()
faces = vmesh.faces()
elif isinstance(obj, Points):
vmesh = obj
faces = []
elif isinstance(obj, Volume):
vmesh = obj.isosurface()
faces = vmesh.faces()
elif isinstance(obj, vedo.TetMesh):
vmesh = obj.tomesh(fill=False)
faces = vmesh.faces()
else:
print("ipygany backend: cannot process object type", [obj])
continue
vertices = vmesh.points()
scals = vmesh.inputdata().GetPointData().GetScalars()
if scals and not colorbar: # there is an active array, only pick the first
aname = scals.GetName()
arr = vmesh.pointdata[aname]
parr = Component(name=aname, array=arr)
if len(faces):
pmesh = PolyMesh(vertices=vertices, triangle_indices=faces, data={aname: [parr]})
else:
pmesh = PointCloud(vertices=vertices, data={aname: [parr]})
rng = scals.GetRange()
colored_pmesh = IsoColor(pmesh, input=aname, min=rng[0], max=rng[1])
if obj.scalarbar:
colorbar = ColorBar(colored_pmesh)
colormap_slider_range = FloatRangeSlider(value=rng,
min=rng[0], max=rng[1],
step=(rng[1] - rng[0]) / 100.)
jslink((colored_pmesh, 'range'), (colormap_slider_range, 'value'))
colormap = Dropdown(
options=colormaps,
description='Colormap:'
)
jslink((colored_pmesh, 'colormap'), (colormap, 'index'))
else:
if len(faces):
pmesh = PolyMesh(vertices=vertices, triangle_indices=faces)
else:
pmesh = PointCloud(vertices=vertices)
if vmesh.alpha() < 1:
colored_pmesh = Alpha(RGB(pmesh, input=tuple(vmesh.color())), input=vmesh.alpha())
else:
colored_pmesh = RGB(pmesh, input=tuple(vmesh.color()))
pmeshes.append(colored_pmesh)
if colorbar:
scene = AppLayout(
left_sidebar=Scene(pmeshes, background_color=bgcol),
right_sidebar=VBox((colormap_slider_range, #not working
colorbar,
colormap)),
pane_widths=[2, 0, 1],
)
else:
scene = Scene(pmeshes, background_color=bgcol)
settings.notebook_plotter = scene
####################################################################################
elif '2d' in settings.notebookBackend.lower() and hasattr(vp, 'window') and vp.window:
import PIL.Image
try:
import IPython
except ImportError:
raise Exception('IPython not available.')
from vedo.io import screenshot
settings.screeshotLargeImage = True
nn = screenshot(returnNumpy=True, scale=settings.screeshotScale+2)
pil_img = PIL.Image.fromarray(nn)
settings.notebook_plotter = IPython.display.display(pil_img)
return settings.notebook_plotter
def interactive_numerical_plot(df_num, df_Y):
"""Plot Interactive KDE graph. Allow user to choose variable, set xlim and save figure. df_Y only allow binary class
Parameters
----------
df_num : DataFrame
df_Y : Series
Returns
-------
None
"""
from ipywidgets import HBox, Checkbox, FloatRangeSlider, VBox, ToggleButton, interactive_output, Dropdown
from IPython.display import display
def plot_num_and_save(xlimit, save_but, col, clip_box, clip_limit):
nonlocal df_num, df_Y
plt.close('all')
if clip_box:
clip_df_num = df_num.copy()
clip_df_num.loc[clip_df_num[col] > clip_limit[1], col] = np.nan
clip_df_num.loc[clip_df_num[col] < clip_limit[0], col] = np.nan
else:
clip_df_num = df_num
# for i,col in zip(range(clip_df_num[col].shape[1]),clip_df_num[col]):
fig, ax = plt.subplots(1, 1, figsize=(10, 5))
sns.kdeplot(clip_df_num[col][df_Y == 0],
label='label0').set_title(clip_df_num[col].name)
sns.kdeplot(clip_df_num[col][df_Y == 1], label='label1')
ax.set_xlim(xlimit[0], xlimit[1])
plt.show()
if save_but:
fig.savefig('./plots/{}.png'.format(clip_df_num[col].name),
bbox_inches='tight')
xlimit = FloatRangeSlider(
value=[df_num.iloc[:, 1].min(), df_num.iloc[:, 1].max()],
min=df_num.iloc[:, 1].min(),
max=df_num.iloc[:, 1].max(),
step=(df_num.iloc[:, 1].max() - df_num.iloc[:, 1].min()) / 100,
continuous_update=False,
description='X_limit')
save_but = ToggleButton(description='Save Figure')
col = Dropdown(options=df_num.columns.tolist())
clip_box = Checkbox(value=False, description='Clip ?')
clip_limit = FloatRangeSlider(
value=[df_num.iloc[:, 1].min(), df_num.iloc[:, 1].max()],
min=df_num.iloc[:, 1].min(),
max=df_num.iloc[:, 1].max(),
step=(df_num.iloc[:, 1].max() - df_num.iloc[:, 1].min()) / 100,
continuous_update=False,
description='X_limit')
out = interactive_output(
plot_num_and_save, {
'xlimit': xlimit,
'save_but': save_but,
'col': col,
'clip_box': clip_box,
'clip_limit': clip_limit
})
# save_but = Button(description='Save Fig')
vbox1 = VBox([xlimit, save_but, col, clip_box, clip_limit])
ui = HBox([vbox1, out])
display(ui)
def on_click(change):
change['owner'].value = False
def on_click_case(change):
try:
xlimit.min = df_num[change['new']].min()
xlimit.max = df_num[change['new']].max()
clip_limit.min = df_num[change['new']].min()
clip_limit.max = df_num[change['new']].max()
except:
xlimit.max = df_num[change['new']].max()
xlimit.min = df_num[change['new']].min()
clip_limit.max = df_num[change['new']].max()
clip_limit.min = df_num[change['new']].min()
xlimit.step = (df_num[change['new']].max() -
df_num[change['new']].min()) / 100
xlimit.value = [
df_num[change['new']].min(), df_num[change['new']].max()
]
clip_limit.step = (df_num[change['new']].max() -
df_num[change['new']].min()) / 100
clip_limit.value = [
df_num[change['new']].min(), df_num[change['new']].max()
]
save_but.observe(on_click, 'value')
col.observe(on_click_case, 'value')
def _default_plotter(self, clim=None, mask_background=False, **kwargs):
"""
Plot three orthogonal views.
This is called by nifti_plotter, you shouldn't call it directly.
"""
data_array = self.data
if not ((data_array.ndim == 3) or (data_array.ndim == 4)):
raise ValueError('Input image should be 3D or 4D')
# mask the background
if mask_background:
# TODO: add the ability to pass 'mne' to use a default brain mask
# TODO: split this out into a different function
if data_array.ndim == 3:
labels, n_labels = scipy.ndimage.measurements.label(
(np.round(data_array) == 0))
else: # 4D
labels, n_labels = scipy.ndimage.measurements.label(
(np.round(data_array).max(axis=3) == 0))
mask_labels = [
lab for lab in range(1, n_labels + 1)
if (np.any(labels[[0, -1], :, :] == lab)
| np.any(labels[:, [0, -1], :] == lab)
| np.any(labels[:, :, [0, -1]] == lab))
]
if data_array.ndim == 3:
data_array = np.ma.masked_where(np.isin(labels, mask_labels),
data_array)
else:
data_array = np.ma.masked_where(
np.broadcast_to(
np.isin(labels, mask_labels)[:, :, :, np.newaxis],
data_array.shape), data_array)
# init sliders for the various dimensions
for dim, label in enumerate(['x', 'y', 'z']):
if label not in kwargs.keys():
kwargs[label] = IntSlider(value=(data_array.shape[dim] - 1) /
2,
min=0,
max=data_array.shape[dim] - 1,
description=self.directions[dim],
continuous_update=True)
if (data_array.ndim == 3) or (data_array.shape[3] == 1):
kwargs['t'] = fixed(0) # time is fixed
else: # 4D
if self.plottype == 'image':
desc = 'time'
elif self.plottype == 'projection':
desc = 'Azim_angle'
else:
desc = 't'
kwargs['t'] = IntSlider(value=data_array.shape[3] // 2,
min=0,
max=data_array.shape[3] - 1,
description=desc,
continuous_update=True)
if clim is None:
clim = (0, data_array.max())
kwargs['clim'] = FloatRangeSlider(
value=clim,
min=0,
max=clim[1],
step=0.05,
description='Contrast',
disabled=False,
continuous_update=True,
orientation='horizontal',
readout=True,
readout_format='.1f',
)
interact(self._plot_slices, data=fixed(data_array), **kwargs)
class SliceViewer(VBox):
def __init__(self,
volume=None,
default_directory=os.getcwd(),
title='',
enhancement_steps=1000,
**kwargs):
def on_chosen_path_change(old_path, new_path):
self.dataset = FolderDataset(new_path)
# TODO: If the path doesn't contain images, display a warning
# A widget for changing the image folder
self.pathchooser = PathChooser(
chosen_path_desc='Image folder:',
default_directory=default_directory,
on_chosen_path_change=on_chosen_path_change,
)
self.pathchooser.layout.margin = '0 0 10px 0'
# The number of increments of the min/max slider
self.enhancement_steps = enhancement_steps
self.scales = {
'x': LinearScale(),
'y': LinearScale(),
}
# The currently displayed image will be in bytes at `self.image_plot.image.value`
self.image_plot = BQImage(
image=IPyImage(),
scales=self.scales,
)
self.figure = Figure(
marks=[self.image_plot],
padding_x=0,
padding_y=0,
animation_duration=1000,
fig_margin={
'top': 0,
'right': 0,
'bottom': 0,
'left': 0,
},
layout=Layout(
grid_area='figure',
margin='0',
width='320px',
height='320px',
),
)
# Custom toolbar
toolbar_width = '100%'
toolbar_margin = '0px 0 2px 0'
self.pan_zoom = PanZoom(scales={
'x': [self.scales['x']],
'y': [self.scales['y']],
}, )
self.save_button = Button(
description='Save Image',
tooltip='Save Image',
icon='save',
layout=Layout(
width=toolbar_width,
# flex='1 1 auto',
margin=toolbar_margin,
),
)
self.save_button.on_click(self.save_current_image)
self.hide_button = Button(
description='Hide Image',
tooltip='Hide Image',
icon='eye-slash',
layout=Layout(
width=toolbar_width,
# flex='1 1 auto',
margin=toolbar_margin,
))
self.hide_button.on_click(self.hide_current_image)
self.pan_zoom_toggle_button = ToggleButton(
description='Pan / Zoom',
tooltip='Pan/Zoom',
icon='arrows',
layout=Layout(
width=toolbar_width,
# flex='1 1 auto',
margin=toolbar_margin,
),
)
self.pan_zoom_toggle_button.observe(self.on_pan_zoom_toggle,
names='value')
self.reset_pan_zoom_button = Button(
description='Undo Zoom',
tooltip='Reset pan/zoom',
icon='refresh',
layout=Layout(
width=toolbar_width,
# flex='1 1 auto',
margin=toolbar_margin,
),
)
self.reset_pan_zoom_button.on_click(self.reset_pan_zoom)
self.reset_enhancements_button = Button(
description='Un-Enhance',
tooltip='Reset enhancements',
icon='ban',
layout=Layout(
width=toolbar_width,
# flex='1 1 auto',
margin=toolbar_margin,
),
)
self.reset_enhancements_button.on_click(self.reset_enhancements)
self.mini_map = IPyImage(layout=Layout(
grid_area='mini-map',
margin='0',
))
self.mini_map.width = 180
self.mini_map.height = 180
# PERFORMANCE CONCERN
# Ideally instead of four observations, this would observe 'scales' on `self.pan_zoom`
# However, it doesn't fire updates
# Ref: https://github.com/bloomberg/bqplot/issues/800
self.image_plot.scales['x'].observe(self.on_pan_zoom_change('x_min'),
names='min')
self.image_plot.scales['x'].observe(self.on_pan_zoom_change('x_max'),
names='max')
self.image_plot.scales['y'].observe(self.on_pan_zoom_change('y_min'),
names='min')
self.image_plot.scales['y'].observe(self.on_pan_zoom_change('y_max'),
names='max')
self.plane_toggle = ToggleButtons(
options=['yz', 'xz', 'xy'],
description='',
disabled=False,
button_style='',
tooltips=[
'Step in x direction', 'Step in y direction',
'Step in z direction'
],
layout=Layout(
width='200px',
# flex='1 1 auto',
margin='7px 0 auto auto',
),
)
self.plane_toggle.style.button_width = 'auto'
self.plane_toggle.observe(self.on_plane_change, names='value')
self.toolbar = VBox(
children=[
self.save_button,
self.hide_button,
self.pan_zoom_toggle_button,
self.reset_pan_zoom_button,
self.reset_enhancements_button,
],
layout=Layout(
grid_area='toolbar',
margin='0',
),
)
# Image enhancements
self.min_max_slider = FloatRangeSlider(
value=[0, 255],
min=0,
max=255,
step=255 / self.enhancement_steps,
description='Min/Max:',
orientation='horizontal',
readout=True,
readout_format='.1f',
continuous_update=True,
layout=Layout(
grid_area='min-max-slider',
margin='10px 0 10px -10px',
width='100%',
),
)
self.min_max_slider.observe(self.on_min_max_change, names='value')
self.index_slider = IntSlider(
value=0,
min=0,
max=1,
step=1,
description='Index:',
orientation='horizontal',
readout=True,
readout_format='d',
continuous_update=True,
layout=Layout(
grid_area='index-slider',
margin='8px -20px 10px -36px',
width='100%',
),
)
self.index_slider.observe(self.on_image_index_change, names='value')
# Animation
self.play = Play(
value=self.index_slider.value,
min=self.index_slider.min,
max=self.index_slider.max,
step=self.index_slider.step,
)
jslink((self.play, 'value'), (self.index_slider, 'value'))
# Keep 'max' in sync as well
self.index_slider.observe(self.on_index_slider_max_change, names='max')
self.bottom_bar = HBox(
children=[
self.play,
self.index_slider,
self.plane_toggle,
],
layout=Layout(
grid_area='bottom-bar',
margin=f'10px -20px 0 0',
# overflow='hidden',
))
# Layout
self.gridbox = GridBox(children=[
self.figure,
self.toolbar,
self.mini_map,
self.min_max_slider,
self.bottom_bar,
], )
# Initially hidden without data
self.gridbox.layout.display = 'none'
self._dataset = None
if volume is not None:
self.dataset = VolumeDataset(volume)
# Hide pathchooser when using a volume
self.pathchooser.layout.display = 'none'
# Call VBox super class __init__
super().__init__(
children=[
self.pathchooser,
self.gridbox,
],
layout=Layout(width='auto'),
**kwargs,
)
@property
def dataset(self):
"""
Get the dataset that the SliceViewer is displaying.
"""
return self._dataset
@dataset.setter
def dataset(self, dataset):
"""
Set the dataset that the SliceViewer is displaying.
"""
if dataset:
# TODO: set initial pan_zoom_scales
# image_as_array = dataset[0]
# width, height = image_as_array.shape
# self.get_initial_pan_zoom_scales()
self.index_slider.max = len(dataset) - 1
# self.play.max = self.index_slider.max
# Could be simpler with 0 margins, but for now is written generically
self.plane_toggle.disabled = not isinstance(dataset, VolumeDataset)
self.gridbox.layout = Layout(
width='auto',
# height='500px',
grid_gap='0px 10px',
# grid_template_columns='auto auto auto',
grid_template_columns=f'{self.figure_size[0]}px 180px',
# grid_template_rows=f'134px {self.figure_size[1] - 110}px 52px 52px',
grid_template_rows=f'140px 180px 36px 60px',
grid_template_areas='''
"figure toolbar"
"figure mini-map"
"min-max-slider min-max-slider"
"bottom-bar bottom-bar"
''',
)
self.gridbox.layout.display = None
else:
self.gridbox.layout.display = 'none'
self._dataset = dataset
# Crucially, this also calls self.redraw
self.reset_enhancements()
@property
def figure_size(self):
"""
Get the figure layout width and height as integers.
"""
width = int(self.figure.layout.width[:-2])
height = int(self.figure.layout.height[:-2])
return [width, height]
@figure_size.setter
def figure_size(self, size):
"""
Set the figure layout width and height with the provided list.
"""
width, height = size
self.figure.layout.width = f'{width}px'
self.figure.layout.height = f'{height}px'
@property
def current_image(self):
"""
Get the current image from backing `self.dataset` according to `self.index_slider`.
It should be a normalized numpy array.
"""
return self.dataset[self.index_slider.value]
@property
def value_range(self):
"""
Get the value ranges of the unormalized dataset.
"""
low = getattr(self.dataset, 'min', 0)
high = getattr(self.dataset, 'max', 255)
return [low, high]
@output.capture()
def get_current_image_name(self):
"""
Return the name of the current image selected according to `self.index_slider`.
"""
if not self.dataset:
return ''
else:
index = self.index_slider.value
image_names = getattr(self.dataset, 'image_names', [])
if image_names:
return image_names[index]
else:
return f'sliceviewer-image-{index}.jpg'
@output.capture()
def get_current_scales(self):
"""
Get the current image_plot scales in a plain dictionary.
"""
scales = self.image_plot.scales
plain_scales = {
'x': [scales['x'].min, scales['x'].max],
'y': [scales['y'].min, scales['y'].max],
}
# Coerce None to 0
plain_scales['x'] = [x if x else 0 for x in plain_scales['x']]
plain_scales['y'] = [y if y else 0 for y in plain_scales['y']]
return plain_scales
@output.capture()
def redraw(self, image_as_array=None):
"""
Redraw main image and mini-map. Defaults to enhanced current image.
"""
# Main image
if image_as_array is None:
image_as_array = self.enhance_image(self.current_image)
image = PIL_to_bytes(numpy_to_PIL(image_as_array))
self.image_plot.image = IPyImage(value=image)
# Mini-map
self.redraw_mini_map(image_as_array=image_as_array)
@output.capture()
def redraw_mini_map(self, image_as_array=None, scales=None):
"""
Redraw the mini-map. Defaults to enhanced current image.
"""
if image_as_array is None:
image_as_array = self.enhance_image(self.current_image)
if scales is None:
scales = self.get_current_scales()
mini_map = self.draw_mini_map(image_as_array, scales)
self.mini_map.value = PIL_to_bytes(numpy_to_PIL(mini_map))
@output.capture()
def on_image_index_change(self, change):
"""
Load and display the new image.
"""
enhanced = self.enhance_image(self.current_image)
self.redraw(image_as_array=enhanced)
@output.capture()
def on_index_slider_max_change(self, change):
"""
Sync play.max with index_slider.max and reset index slider.
"""
self.play.max = change.new
@output.capture()
def get_initial_pan_zoom_scales(self):
"""
Calculate the necessary pan_zoom scales to fill-in the main viewport
when the input image is not square.
"""
raise NotImplementedError
@output.capture()
def on_pan_zoom_toggle(self, change):
"""
Update the `self.figure` interaction.
"""
if change.new:
self.figure.interaction = self.pan_zoom
else:
self.figure.interaction = None
@output.capture()
def reset_pan_zoom(self, button):
"""
Reset figure/plot scales.
"""
self.image_plot.scales['x'].min = None
self.image_plot.scales['x'].max = None
self.image_plot.scales['y'].min = None
self.image_plot.scales['y'].max = None
self.redraw_mini_map()
@output.capture()
def draw_mini_map(self, image_as_array, scales):
"""
Draw a mini version of image_as_array with a rectangle indicating pan/zoom location.
"""
# Commented code is preparation for non-square image
# canvas_as_array = draw_checkerboard_canvas(
# height=int(self.mini_map.height),
# width=int(self.mini_map.width),
# )
# offsets, image_as_array = get_offsets_and_resize_for_canvas(
# image_as_array,
# canvas_as_array,
# )
shape = image_as_array.shape
# Convert grayscale to RGB
mini_map = to_rgb(image_as_array)
# Draw a red square indicating the zoom location
xs = [int(x * shape[1]) for x in scales['x']]
ys = [int((1.0 - y) * shape[0]) for y in scales['y']]
# Make sure values are in range
def clamp(values, low, high):
temp = [v if v > low else low for v in values]
return [v if v < high else high for v in temp]
# This will give a border width of 2 pixels
xs.extend([x + 1 for x in xs])
ys.extend([y - 1 for y in ys])
xs = clamp(xs, 0, shape[1] - 2)
ys = clamp(ys, 0, shape[0] - 2)
for x in xs + [x + 1 for x in xs]:
for y in range(ys[1], ys[0] + 1):
# Color these locations full-on red
mini_map[y][x][0] = 1.0
for y in ys + [y + 1 for y in ys]:
for x in range(xs[0], xs[1] + 1):
# Color these locations full-on red
mini_map[y][x][0] = 1.0
return mini_map
# Commented code is preparation for non-square image
# canvas_as_array = to_rgb(canvas_as_array)
# canvas_as_array[offsets[0]:, offsets[1]:] = mini_map
# return canvas_as_array
@output.capture()
def on_pan_zoom_change(self, change_type):
"""
Return a function that produces new scales when the user pans or zooms.
:param change_type: One of ['x_min', 'x_max', 'y_min', 'y_max']
"""
def handle_case(change):
if change.new == None:
return
scales = self.get_current_scales()
cases = {
'x_min': {
'x': [change.new, scales['x'][1]],
'y': scales['y'],
},
'x_max': {
'x': [scales['x'][0], change.new],
'y': scales['y'],
},
'y_min': {
'x': scales['x'],
'y': [change.new, scales['y'][1]],
},
'y_max': {
'x': scales['x'],
'y': [scales['y'][0], change.new],
},
}
new_scales = cases[change_type]
self.redraw_mini_map(scales=new_scales)
return handle_case
@output.capture()
def reset_enhancements(self, button=None):
"""
Reset all of the image enhancement sliders.
"""
[low, high] = self.value_range
self.min_max_slider.min = low
self.min_max_slider.max = high
self.min_max_slider.value = [low, high]
self.min_max_slider.step = (high - low) / self.enhancement_steps
self.redraw()
@output.capture()
def save_current_image(self, button):
"""
Save the current image with any processing applied.
"""
directory = getattr(self.dataset, 'directory', os.getcwd())
processed_directory = os.path.join(directory, 'ipysliceviewer')
if not os.path.exists(processed_directory):
os.makedirs(processed_directory)
filepath = os.path.join(processed_directory,
self.get_current_image_name())
with open(filepath, 'wb') as f:
f.write(self.image_plot.image.value)
@output.capture()
def hide_current_image(self, button):
"""
Hide the current image and remember this as a setting.
This is like a soft form of deleting the image.
"""
# Need more thought on how this should be remembered across restarts for the current dataset
# Rough idea: text file or similar containing SliceViewer settings, just need to figure out
# a good naming scheme for datasets - possibly can take name from Experimenter or SliceViewer's own text box
raise NotImplementedError
@output.capture()
def enhance_image(self, image_as_array):
"""
Apply enhancement sliders to image_as_array and return as a numpy array
"""
# These values are not normalized even though the image above is
# (this allows the user to work with the input range)
new_min, new_max = self.min_max_slider.value
# So, we'll convert them before using them to scale the normalized image
[low, high] = self.value_range
def rescale(x, low, high):
return (x - low) / (high - low)
new_min = rescale(new_min, low, high)
new_max = rescale(new_max, low, high)
processed_image = rescale(to_rgb(image_as_array), new_min, new_max)
processed_image = np.clip(processed_image, 0, 1)
return processed_image
@output.capture()
def on_min_max_change(self, change):
"""
Handle changes to the min/max slider.
"""
self.redraw()
@output.capture()
def on_plane_change(self, change):
"""
Called when the slice plane is toggled.
"""
if hasattr(self.dataset, 'plane'):
self.dataset.plane = change.new
old_max = self.index_slider.max
new_max = len(self.dataset) - 1
self.index_slider.max = new_max
self.index_slider.value = min(self.index_slider.value, new_max)
# Guarantee the image updates even if index does not change
self.redraw()
import pyvista as pv
from ipywidgets import FloatSlider, FloatRangeSlider, Dropdown, Select, Box, AppLayout, jslink, Layout, VBox, HBox
from ipygany import Scene, IsoColor, TetraMesh, Component, ColorBar, colormaps
mesh = TetraMesh.from_vtk('benchmarks/002-cook-membrane/elmer.vtu',
show_edges=True)
dispy_min = 0.0
dispy_max = 32.5
# Colorize by height
colored_mesh = IsoColor(mesh, input=('U', 'D2'), min=dispy_min, max=dispy_max)
# Create a slider that will dynamically change the boundaries of the colormap
colormap_slider_range = FloatRangeSlider(value=[dispy_min, dispy_max],
min=dispy_min,
max=dispy_max,
step=(dispy_max - dispy_min) / 100.)
jslink((colored_mesh, 'range'), (colormap_slider_range, 'value'))
# Create a colorbar widget
colorbar = ColorBar(colored_mesh)
# Colormap choice widget
colormap = Dropdown(options=colormaps, description='colormap:')
jslink((colored_mesh, 'colormap'), (colormap, 'index'))
AppLayout(
header=Scene([colored_mesh]),
left_sidebar=VBox((colormap, colormap_slider_range)),
def create_parameter_widgets(self, augmentation_name):
"""
augmentation_name - name of the augmentation you want to test
"""
function = partial(self.test_aug, augmentation_name=augmentation_name)
ui_s = []
widget_dict = {}
for param_name, values in self.aug_dict[augmentation_name].items():
if values[-2] == "width":
lower = values[1]
upper = self.shape[1]
elif values[-2] == "height":
lower = values[1]
upper = self.shape[0]
elif values[-2] == "channels-1":
lower = values[1]
upper = self.channels - 1
elif values[-1] == "max":
lower = values[1]
upper = max(self.shape)
else:
lower = values[1]
upper = values[2]
if values[-1] == "odd" and values[0] in ["int" "int_range"]:
step = 2
elif values[-1] != "odd" and (values[0] == "int"
or values[0] == "int_range"):
step = 1
elif values[0] == "float" or values[0] == "float_range":
step = 0.01
if values[0] == "int":
widget = IntSlider(
min=lower,
max=upper,
step=step,
orientation="horizontal",
# description=f'{param_name}:',
continuous_update=False,
)
widget_ = IntText(
description="",
continuous_update=False,
readout=True,
readout_format="d",
)
widgets.link((widget, "value"), (widget_, "value"))
widgets_linked = VBox([widget, widget_])
widgets_linked_with_description = HBox(
[Label(param_name), widgets_linked])
ui_s.append(widgets_linked_with_description)
setattr(widgets_linked_with_description, "value", widget.value)
elif values[0] == "int_range":
widget = IntRangeSlider(
value=[1, 1] if values[-1] == "odd" else [0, 0],
min=lower,
max=upper,
step=step,
# description=f'{param_name}:',
disabled=False,
continuous_update=False,
orientation="horizontal",
readout=True,
readout_format="d",
)
widget_with_description = HBox([Label(param_name), widget])
setattr(widget_with_description, "value", widget.value)
ui_s.append(widget_with_description)
elif values[0] == "float":
widget = FloatSlider(
value=0,
min=lower,
max=upper,
step=step,
# description=f'{param_name}:',
disabled=False,
continuous_update=False,
orientation="horizontal",
readout=True,
readout_format=".2f",
)
widget_ = FloatText(
description="",
continuous_update=False,
readout=True,
readout_format=".2f",
)
widgets.link((widget, "value"), (widget_, "value"))
widgets_linked = VBox([widget, widget_])
widgets_linked_with_description = HBox(
[Label(param_name), widgets_linked])
ui_s.append(widgets_linked_with_description)
setattr(widgets_linked_with_description, "value", widget.value)
elif values[0] == "float_range":
widget = FloatRangeSlider(
value=[0, 0],
min=lower,
max=upper,
step=step,
disabled=False,
continuous_update=False,
orientation="horizontal",
readout=True,
readout_format=".2f",
)
widget_with_description = HBox([Label(param_name), widget])
ui_s.append(widget_with_description)
setattr(widget_with_description, "value", widget.value)
elif values[0] == "str" or values[0] == "bool":
widget = RadioButtons(options=[*values[1]],
value=values[1][1],
disabled=False)
widget_with_description = HBox([Label(param_name), widget])
setattr(widget_with_description, "value", widget.value)
ui_s.append(widget_with_description)
# ui_s.append(widget)
widget_dict[f"{param_name}"] = widget
out = interactive_output(function, widget_dict)
display(*ui_s, out)
def single_run(data_dir="", id=""):
if not os.path.exists(data_dir):
raise FileNotFoundError("No directory found named: " + data_dir)
run_summary = RunSummary(data_dir=data_dir, id=id)
sedov_solution = SedovSolution(E_0,
run_summary.overview.background_density,
run_summary.overview.metallicity)
#### PASS TO PLOTTER ####
num_checkpoints = len(run_summary.filenames)
# log_R_max = round(np.log10(run_summary.df["Radius"].max()), 2)
# log_R_min = max(log_R_max-4,
# round(np.log10(run_summary.df["Radius"].min()), 2)+1)
R_min = run_summary.df["Radius"].min()
R_min = 0
R_max = run_summary.df["Radius"].max()
if type(single_run.previous_widget) is widgets.Box:
single_run.previous_widget.close()
w = interactive(
plotter,
run_summary=fixed(run_summary),
sedov_solution=fixed(sedov_solution),
xlim=FloatRangeSlider(
min=R_min,
max=R_max,
step=0.01 * (R_max - R_min),
value=(R_min, R_max),
),
checkpoint_index=IntSlider(min=0,
max=num_checkpoints - 1,
step=0,
value=num_checkpoints - 1),
y_axis_variable=Dropdown(options=cols_plot, value="Density"),
x_axis_variable=RadioButtons(options=["Radius", "M_int", "zones"]),
label=fixed("numeric"),
density_in_mH_cm3=fixed(False),
verbose=fixed(True),
)
w1 = widgets.Button(description=u"\u23EA")
def show_first(b):
checkpoint_slider = w.children[5]
checkpoint_slider.value = checkpoint_slider.min
w1.on_click(show_first)
wl = widgets.Button(description=u"\u276E")
def show_prev(b):
checkpoint_slider = w.children[5]
if checkpoint_slider.value > checkpoint_slider.min:
checkpoint_slider.value -= 1
wl.on_click(show_prev)
w2 = widgets.Button(description=u"\u25BA")
# w2.stop = False
def play(b):
checkpoint_slider = w.children[5]
for i in range(checkpoint_slider.value + 1, checkpoint_slider.max + 1):
plt.gcf()
checkpoint_slider.value = i
# plt.show()
# time.sleep(.1)
# if b.stop:
# break
w2.on_click(play)
# wp = widgets.Button(description=u"p")
# def pause(b):
# w2.stop=True
# wp.on_click(pause)
wr = widgets.Button(description=u"\u276F")
def show_next(b):
checkpoint_slider = w.children[5]
if checkpoint_slider.value < checkpoint_slider.max:
checkpoint_slider.value += 1
wr.on_click(show_next)
w3 = widgets.Button(description=u"\u23E9")
def show_last(b):
checkpoint_slider = w.children[5]
checkpoint_slider.value = checkpoint_slider.max
w3.on_click(show_last)
w_buttons = widgets.HBox(children=[w1, wl, w2, wr, w3])
w.children = tuple([_w for _w in w.children] + [w_buttons])
single_run.previous_widget = w
display(w)
# display(w_buttons)
return run_summary
