def Show3dImage(image, title = ''):
[max_f, max_y, max_x] = np.asarray(image.shape) - 1
def ShowRawImage(frame, y_range, x_range, my_gamma):
plt.figure(figsize=(15,10))
y_min = y_range[0]
y_max = y_range[1]+1
x_min = x_range[0]
x_max = x_range[1]+1
plt.imshow(image[frame,:, :], cmap = 'gray', norm=colors.PowerNorm(gamma=my_gamma))
# plt.imshow(image[frame,y_min:y_max, x_min:x_max], cmap = 'gray', norm=colors.PowerNorm(gamma=my_gamma))
plt.title(title)
plt.xlim([x_min, x_max])
plt.ylim([y_min, y_max])
plt.xlabel("x [Px]")
plt.ylabel("y [Px]")
# plt.colorbar()
plt.tight_layout()
frame_slider = IntSlider(min = 1, max = max_f, step = 1, description = "Frame")
y_range_slider = IntRangeSlider(value=[0, max_y], min=0, max=max_y, step = 5, description = "ROI - y")
x_range_slider = IntRangeSlider(value=[0, max_x], min=0, max=max_x, step = 5, description = "ROI - x")
gamma_slider = FloatSlider(min = 0.1, max = 2, step = 0.05, value = 0.5)
interact(ShowRawImage, frame = frame_slider, y_range = y_range_slider, x_range = x_range_slider, my_gamma = gamma_slider)
def Show2dImage(image, title = '', ShowSlider = True, gamma = 1):
[max_y, max_x] = np.asarray(image.shape) - 1
def ShowImage(y_range, x_range, my_gamma):
plt.figure(figsize=(15,10))
y_min = y_range[0]
y_max = y_range[1]+1
x_min = x_range[0]
x_max = x_range[1]+1
#plt.imshow(image[y_min:y_max, x_min:x_max], cmap = 'gray', norm=colors.PowerNorm(gamma=my_gamma))
plt.imshow(image, cmap = 'gray', norm=colors.PowerNorm(gamma=my_gamma))
plt.title(title)
plt.xlim([x_min, x_max])
plt.ylim([y_min, y_max])
plt.xlabel("x [Px]")
plt.ylabel("y [Px]")
plt.tight_layout()
y_range_slider = IntRangeSlider(value=[0, max_y], min=0, max=max_y, step = 5, description = "ROI - y")
x_range_slider = IntRangeSlider(value=[0, max_x], min=0, max=max_x, step = 5, description = "ROI - x")
gamma_slider = FloatSlider(min = 0.1, max = 2, step = 0.05, value = 0.5)
if ShowSlider == True:
interact(ShowImage, y_range = y_range_slider, x_range = x_range_slider, my_gamma = gamma_slider)
else:
ShowImage(y_range = [0, max_y], x_range = [0, max_x], my_gamma = gamma)
def plot_calibrate_time(self, *args, **kwargs):
"""Experimental viewer for time calibration in a jupyter notebook.
Args:
accepts the same arguments as `SpatialEncoder.calibrate_time` function
"""
scan_pos_fs, edge_pos_pix, fit_coeff = self.calibrate_time(
*args, **kwargs)
source_results = ColumnDataSource(
data=dict(x=scan_pos_fs, y=edge_pos_pix))
source_fit = ColumnDataSource(data=dict(
x=[scan_pos_fs[0], scan_pos_fs[-1]],
y=np.polyval(fit_coeff, [scan_pos_fs[0], scan_pos_fs[-1]]),
))
p_time = figure(
height=400,
width=800,
title='Time calibration',
x_axis_label='Stage position, fs',
y_axis_label='Edge position, pix',
)
p_time.scatter('x', 'y', source=source_results)
p_time.line('x', 'y', line_color='red', source=source_fit)
layout = gridplot([p_time], ncols=1, toolbar_options=dict(logo=None))
handle = show(layout, notebook_handle=True)
# Slider
def slider_callback(change):
left = change['new'][0]
right = change['new'][1]
fit_coeff = np.polyfit(scan_pos_fs[left:right + 1],
edge_pos_pix[left:right + 1], 1)
self.pix_per_fs = fit_coeff[0]
source_fit.data.update(
x=[scan_pos_fs[left], scan_pos_fs[right]],
y=np.polyval(fit_coeff,
[scan_pos_fs[left], scan_pos_fs[right]]),
)
push_notebook(handle=handle)
slider = IntRangeSlider(
min=0,
max=len(scan_pos_fs) - 1,
value=[0, len(scan_pos_fs) - 1],
step=1,
description="Fit range",
continuous_update=False,
layout=Layout(width='800px'),
)
slider.observe(slider_callback, names='value')
return slider
def ChooseROI(ApplyROI):
settings = nd.handle_data.ReadJson(ParameterJsonFile)
settings["ROI"]["Apply"] = ApplyROI
if ApplyROI == 0:
print("ROI not applied")
else:
[max_f, max_y, max_x] = np.asarray(rawframes_np.shape) - 1
def ShowImageROI(frame_range, y_range, x_range, my_gamma):
settings = nd.handle_data.ReadJson(ParameterJsonFile)
fig, axes = plt.subplots(2,1, sharex = True,figsize=(15,6))
frame_min = frame_range[0]
frame_max = frame_range[1]
y_min = y_range[0]
y_max = y_range[1]
x_min = x_range[0]
x_max = x_range[1]
settings["ROI"]["frame_min"] = frame_min
settings["ROI"]["frame_max"] = frame_max
settings["ROI"]["y_min"] = y_min
settings["ROI"]["y_max"] = y_max
settings["ROI"]["x_min"] = x_min
settings["ROI"]["x_max"] = x_max
nd.handle_data.WriteJson(ParameterJsonFile, settings)
axes[0].imshow(rawframes_np[frame_min,y_min:y_max+1, x_min:x_max+1], cmap = 'gray', norm=colors.PowerNorm(gamma=my_gamma))
axes[0].set_title("First Frame")
axes[0].set_xlabel("x [Px]")
axes[0].set_ylabel("y [Px]")
axes[1].imshow(rawframes_np[frame_max,y_min:y_max+1, x_min:x_max+1], cmap = 'gray', norm=colors.PowerNorm(gamma=my_gamma))
axes[1].set_title("Last Frame")
axes[1].set_xlabel("x [Px]")
axes[1].set_ylabel("y [Px]")
plt.tight_layout()
#insert the starting values from the json file
frames_range_slider = IntRangeSlider(value=[settings["ROI"]["frame_min"], settings["ROI"]["frame_max"]], min=0, max=max_f, step = 10, description = "ROI - frames")
y_range_slider = IntRangeSlider(value=[settings["ROI"]["y_min"], settings["ROI"]["y_max"]], min=0, max=max_y, step = 10, description = "ROI - y")
x_range_slider = IntRangeSlider(value=[settings["ROI"]["x_min"], settings["ROI"]["x_max"]], min=0, max=max_x, step = 10, description = "ROI - x")
gamma_slider = FloatSlider(min = 0.1, max = 2, step = 0.05, value = 0.5)
interact(ShowImageROI, frame_range = frames_range_slider, y_range = y_range_slider, x_range = x_range_slider, my_gamma = gamma_slider)
nd.handle_data.WriteJson(ParameterJsonFile, settings)
def __get_widgets(self, chromosomes, browser, frame=None):
if frame is None:
frame = HTML()
tracks = self.__get_tracks_name(browser)
widgets = OrderedDict([
("chromosomes_list", Dropdown(options=chromosomes)),
("left_button", Button(icon="arrow-left")),
("right_button", Button(icon="arrow-right")),
("zoom_out_button", Button(icon="search-minus")),
("zoom_in_button", Button(icon="search-plus")),
("range_textbox", Text(placeholder="genome range like: 'chr1:10000-20000'")),
("go_button", Button(description="Go")),
("range_slider", IntRangeSlider(continuous_update=False, readout=False, layout=Layout(width='90%'))),
("range_min_label", Label("", layout=Layout(width='2%'))),
("range_max_label", Label("", layout=Layout(width='20%'))),
("auto_check_box", Checkbox(value=True, description="Auto Range",
layout=Layout(width='120px'),
style={'description_width': 'initial'})),
("track_min_val_float_text",
FloatText(value=0.0001, description="Track's min value:", step=0.5, disabled=True,
layout=Layout(width='30%'),
style={'description_width': 'initial'})),
("track_max_val_float_text", FloatText(value=10, description="Track's max value:", step=0.5, disabled=True,
layout=Layout(width='30%'),
style={'description_width': 'initial'})),
("track_dropdown", Dropdown(options=[ALL_BW_MARK] + tracks,
value=ALL_BW_MARK,
description="Select track",
disabled=True,
)),
("frame", frame)
])
return widgets
def widget_histograms(self):
return interact(
self.show_histograms,
size=FloatSlider(value=0.5, min=0, max=1, step=0.01, continuous_update=False, description="Size fraction"),
bin_width=IntSlider(value=5, min=1, max=32, description='Bin width'),
hist_range=IntRangeSlider(value=[-50, 50], min=-255, max=256, description='Range:', continuous_update=False),
width=IntSlider(value=15, min=5, max=50, step=1, continuous_update=False, description="Subplot width"),
height=IntSlider(value=10, min=5, max=50, step=1, continuous_update=False, description="Subplot height")
)
def show():
interact(
plot,
int_range=IntRangeSlider(
value=[1, 6],
min=events[0],
max=events[-1],
step=1,
description='$事象の範囲$：',
continuous_update=False))
def _init_gui(self):
close = Button(description=' Close', icon='trash', layout=_wlo)
def _close(b):
self.close()
close.on_click(_close)
frame = IntSlider(min=self._df.frame.astype(int).min(),
max=self._df.frame.astype(int).max(),
value=-1,
description='Frame',
layout=_wlo)
cbut = Button(description=' Columns', icon='barcode')
cols = self._df.columns.tolist()
cols = SelectMultiple(options=cols, value=cols)
def _cols(c):
self.columns = c.new
self._update_output()
cols.observe(_cols, names='value')
cfol = Folder(cbut, _ListDict([('cols', cols)]))
rbut = Button(description=' Rows', icon='bars')
rows = IntRangeSlider(min=self.indexes[0],
max=self.indexes[1],
value=[0, 50])
def _rows(c):
self.indexes = c.new
print(self.indexes)
self._update_output()
rows.observe(_rows, names='value')
rfol = Folder(rbut, _ListDict([('rows', rows)]))
return _ListDict([('close', close), ('frame', frame), ('cols', cfol),
('rows', rfol)])
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_amino_acid_on_protein(self,
assembly_name,
name,
sequence_id=0,
palette_name='Set1',
palette_size=2):
"""
Display visual controls for showing amino acid sequence on a protein of the scene
:param: assembly_name: Name of the assembly containing the protein
:param: name: Name of the protein
:param: sequence_id: ID of the protein sequence
:param: palette_name: Name of the color palette
:param: palette_size: Size of the color palette
"""
sequences = self._be.get_protein_amino_acid_sequences(
assembly_name, name)
if sequence_id >= len(sequences):
raise RuntimeError('Invalid sequence Id')
sequence_as_list = sequences[0].split(',')
value_range = [int(sequence_as_list[0]), int(sequence_as_list[1])]
irs = IntRangeSlider(value=[value_range[0], value_range[1]],
min=value_range[0],
max=value_range[1])
lbl = Label(value="AA sequence")
def update_slider(value):
self._be.set_protein_amino_acid_sequence_as_range(
assembly_name, name, value['new'])
self._be.set_protein_color_scheme(
assembly_name, name, self._be.COLOR_SCHEME_AMINO_ACID_SEQUENCE,
palette_name, palette_size)
lbl.value = sequence_as_list[2][value['new'][0] -
value_range[0]:value['new'][1] -
value_range[0]]
irs.observe(update_slider, 'value')
display(irs)
display(lbl)
def __init__(self):
self._top_n_terms: IntSlider = IntSlider(
description='#terms',
min=10,
max=1000,
value=100,
tooltip=
'The total number of most discriminating terms to return for each group',
)
self._max_n_terms: IntSlider = IntSlider(
description='#top',
min=1,
max=2000,
value=2000,
tooltip=
'Only consider terms whose document frequency is within the top # terms out of all terms',
)
self._period1: IntRangeSlider = IntRangeSlider(
description='Period',
min=1900,
max=2099,
value=(2001, 2002),
layout={'width': '250px'},
)
self._period2 = IntRangeSlider(
description='Period',
min=1900,
max=2099,
value=(2001, 2002),
layout={'width': '250px'},
)
self._compute = Button(description='Compute',
icon='',
button_style='Success',
layout={'width': '120px'})
self.compute_callback: Callable = None
self.done_callback: Callable = None
self.corpus: dtm.VectorizedCorpus = None
def interact(self):
"""
获取交互式控制专题图样式部件
"""
codomainslider = IntRangeSlider(
value=[self.codomain[0], self.codomain[1]],
min=self._codomainmin,
max=self._codomainmax,
step=1,
description='值域范围:',
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='d',
layout=Layout(width="350px"))
link((codomainslider, 'value'), (self, 'codomain'))
rslider = IntRangeSlider(value=[self.rrange[0], self.rrange[1]],
min=self._min_r,
max=self._max_r,
step=1,
description='半径范围:',
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='d',
layout=Layout(width="350px"))
link((rslider, 'value'), (self, 'rrange'))
color = ColorPicker(concise=False,
description='填充颜色：',
value=self.color,
disabled=False,
layout=Layout(width="350px"))
link((color, 'value'), (self, 'color'))
return VBox([codomainslider, rslider, color])
def _init_gui(self):
close = Button(description=' Close', icon='trash', layout=_wlo)
def _close(b): self.close()
close.on_click(_close)
frame = IntSlider(min=self._df.frame.astype(int).min(),
max=self._df.frame.astype(int).max(),
value=-1, description='Frame', layout=_wlo)
cbut = Button(description=' Columns', icon='barcode')
cols = self._df.columns.tolist()
cols = SelectMultiple(options=cols, value=cols)
def _cols(c):
self.columns = c.new
self._update_output()
cols.observe(_cols, names='value')
cfol = Folder(cbut, _ListDict([('cols', cols)]))
rbut = Button(description=' Rows', icon='bars')
rows = IntRangeSlider(min=self.indexes[0],
max=self.indexes[1],
value=[0, 50])
def _rows(c):
self.indexes = c.new
print(self.indexes)
self._update_output()
rows.observe(_rows, names='value')
rfol = Folder(rbut, _ListDict([('rows', rows)]))
return _ListDict([('close', close),
('frame', frame),
('cols', cfol),
('rows', rfol)])
def __init__(self):
self.run_bt = Button(description='Demo_deprec', button_style='info')
self.mod_add_bt = Button(description='Add model', button_style='info')
self.mod_remove_bt = Button(description='Remove model',
button_style='warning')
# self.dts_selector = Dropdown(options=SUPPORTED_DATASETS,
# value=SUPPORTED_DATASETS[0],
# description='Dataset:')
self.dts_selector = self.get_default_dts_selector()
self.size_selector = RadioButtons(options=[1000, 2000, 5000, 10000],
value=1000,
description='Size')
self.features_selector = IntRangeSlider(value=[30, 100],
min=30,
max=2000,
step=1,
layout=Layout(width='950px'))
self.model_name_column = VBox([Label(value='Model')])
self.sampler_name_column = VBox([Label(value='Sampler')])
self.box_type_column = VBox([Label(value='Box Type')])
self.n_estim_column = VBox([Label(value='Number Estimators')])
self.pca_column = VBox([Label(value='PCA?')])
self.models_bar = HBox([
self.model_name_column,
self.sampler_name_column,
self.box_type_column,
self.n_estim_column,
self.pca_column,
],
layout=Layout(border='3px solid black'))
self.gui = VBox([
self.dts_selector,
self.size_selector,
self.mod_add_bt,
self.mod_remove_bt,
self.features_selector,
self.models_bar,
self.run_bt,
])
self.mod_add_bt.on_click(self.insert_model_bar)
self.mod_remove_bt.on_click(self.remove_model_bar)
self.insert_model_bar()
# self.box_type_changed()
self.sampler_changed()
super().__init__()
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
def slide_plot(self):
'''Interactive plot for use in jupyter notebook
Provides plot of UV spectrum with sliders to change
wavelength domain range and homogeneous broadening'''
@interact(domain=IntRangeSlider(value=[200, 900],
min=100,
max=1000,
step=25,
description='Spectrum Range: '),
width=FloatSlider(value=0.3,
min=0.1,
max=0.5,
step=0.05,
description='Broadening: '))
def _plot(domain, width):
x = np.linspace(1242 / domain[1], 1242 / domain[0], 1000)
self.gen_spectrum(X=x, width=width)
self.gen_plot('spec')
def _contour_folder(self, folder):
control = Button(description=' Contours', icon='dot-circle-o')
def _cshow(b):
for scn in self.active():
scn.cont_show = not scn.cont_show
control.on_click(_cshow)
content = _ListDict([
('fopts', folder['fopts']),
('axis', Dropdown(options=['x', 'y', 'z'], value='z')),
('num', IntSlider(description='N', min=5, max=20, value=10,
step=1)),
('lim',
IntRangeSlider(description='10**Limits',
min=-8,
max=0,
step=1,
value=[-7, -1])),
('val', FloatSlider(description='Value', min=-5, max=5, value=0))
])
def _cont_axis(c):
for scn in self.active():
scn.cont_axis = c.new
def _cont_num(c):
for scn in self.active():
scn.cont_num = c.new
def _cont_lim(c):
for scn in self.active():
scn.cont_lim = c.new
def _cont_val(c):
for scn in self.active():
scn.cont_val = c.new
content['axis'].observe(_cont_axis, names='value')
content['num'].observe(_cont_num, names='value')
content['lim'].observe(_cont_lim, names='value')
content['val'].observe(_cont_val, names='value')
contour = Folder(control, content)
folder.insert(2, 'contour', contour, active=True, update=True)
def __init__(self, storage, setup):
self.storage = storage
self.setup = setup
self.nans = None
self.play = Play()
self.step_slider = IntSlider()
self.fps_slider = IntSlider(min=100, max=1000, description="1000/fps")
self.product_select = Select()
self.plots_box = Box()
self.slider = {}
self.lines = {'x': [{}, {}], 'y': [{}, {}]}
for xy in ('x', 'y'):
self.slider[xy] = IntRangeSlider(min=0, max=1, description=f'spectrum_{xy}',
orientation='horizontal' if xy == 'x' else 'vertical')
self.reinit({})
def __init__(self, storage, setup):
self.storage = storage
self.setup = setup
self.play = Play(interval=1000)
self.step_slider = IntSlider(continuous_update=False,
description='t/dt:')
self.product_select = Dropdown()
self.plots_box = Box()
self.slider = {}
self.lines = {'X': [{}, {}], 'Z': [{}, {}]}
for xz in ('X', 'Z'):
self.slider[xz] = IntRangeSlider(
min=0,
max=1,
description=f'{xz}',
continuous_update=False,
orientation='horizontal' if xz == 'X' else 'vertical')
self.reinit({})
def _make_constrast_slider():
min_ = stack.min()
max_ = stack.max()
init_min = min_
init_max = max_
if (init_minval != None):
init_min = init_minval
if (init_maxval != None):
init_max = init_maxval
contrast_slider = IntRangeSlider(
value=[init_min, init_max],
min=min_,
max=max_,
step=1,
description='Contrast',
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='d',
)
return contrast_slider
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
def ChooseFindObjParameters(rawframes_pre, ParameterJsonFile):
#read in settings
settings = nd.handle_data.ReadJson(ParameterJsonFile)
# SEPARATION DISTANCE
# select if help is required
help_sep_distance = Dropdown(options=['0', 'auto'],
value=settings["Help"]["Separation"],
description='Help separation distance')
def ChooseSepDistance_Mode(mode):
settings["Help"]["Separation"] = mode
if mode == "auto":
Low_Diam_slider_start = settings["Help"]["GuessLowestDiameter_nm"]
Low_Diam_slider = IntSlider(min = 1, max = 100, step = 1, \
value = Low_Diam_slider_start,
description = "Guess lowest diameter [nm]")
def CalcSepDistance(Low_Diam):
Min_Separation, Max_displacement = \
nd.ParameterEstimation.FindMaxDisplacementTrackpy(ParameterJsonFile, GuessLowestDiameter_nm = Low_Diam)
settings["Find"]["Separation data"] = Min_Separation
settings["Link"]["Max displacement"] = Max_displacement
settings["Help"]["GuessLowestDiameter_nm"] = Low_Diam
nd.handle_data.WriteJson(ParameterJsonFile, settings)
interact(CalcSepDistance, Low_Diam=Low_Diam_slider)
else:
Min_Separation_slider = FloatSlider(min = 0, max = 100, step = 0.5, \
value = settings["Find"]["Separation data"],\
description = "Separation distance [Px]")
Max_displacement_slider = FloatSlider(min = 0, max = 50, step = 0.5, \
value = settings["Link"]["Max displacement"],\
description = "Maximal Displacement [Px]")
def ChooseSepDistance_Value(Min_Separation, Max_displacement):
settings["Find"]["Separation data"] = Min_Separation
settings["Link"]["Max displacement"] = Max_displacement
nd.handle_data.WriteJson(ParameterJsonFile, settings)
interact(ChooseSepDistance_Value, \
Min_Separation = Min_Separation_slider, Max_displacement = Max_displacement_slider)
nd.handle_data.WriteJson(ParameterJsonFile, settings)
interact(ChooseSepDistance_Mode, mode=help_sep_distance)
# BEAD DIAMETER
# select if help is required
help_diameter = Dropdown(options=['0', 'manual', 'auto'],
value=settings["Help"]["Bead size"],
description='Help bead diameter')
diameter_slider = IntSlider(min = 1, max = 31, step = 2, \
value = settings["Find"]["tp_diameter"],\
# value = 15,\
description = "Diameter of bead [Px]")
def OptimizeBeadDiameter(mode, diameter):
settings = nd.handle_data.ReadJson(ParameterJsonFile)
settings["Find"]["tp_diameter"] = diameter
settings["Help"]["Bead size"] = mode
if mode == "manual":
settings["Find"]["tp_diameter"] = \
nd.AdjustSettings.SpotSize_manual(rawframes_pre, settings, AutoIteration = False)
elif settings["Help"]["Bead size"] == "auto":
settings["Find"]["tp_diameter"] = nd.AdjustSettings.SpotSize_auto(
settings)
else:
print(
"Bead size not adjusted. Use 'manual' or 'auto' if you want to do it."
)
nd.handle_data.WriteJson(ParameterJsonFile, settings)
interact(OptimizeBeadDiameter,
mode=help_diameter,
diameter=diameter_slider)
# MINMASS
# optimize minmass to identify particle
help_minmass = Dropdown(options=['0', 'manual', 'auto'],
value=settings["Help"]["Bead brightness"],
description='Help bead minmass')
# minmass_slider = FloatLogSlider(min = 1, max = 4, step = 0.1, \
minmass_slider = IntText(value = settings["Find"]["Minimal bead brightness"],\
description = "Minimal bead brightness", min = 1, step = 10)
[max_f, max_y, max_x] = np.asarray(rawframes_pre.shape) - 1
# frame_min = 1
# frame_max = settings["ROI"]["frame_max"] - settings["ROI"]["frame_min"]
#IntText
# frame_slider = IntText(value = 0,\
# description = "Frame", step = 1, \
# min = frame_min, max = frame_max)
frame_slider = IntSlider(min=1, max=max_f, step=1, description="Frame")
y_range_slider = IntRangeSlider(value=[0, max_y],
min=0,
max=max_y,
step=5,
description="y")
x_range_slider = IntRangeSlider(value=[0, max_x],
min=0,
max=max_x,
step=5,
description="x")
gamma_slider = FloatSlider(min=0.1, max=2, step=0.05, value=0.5)
def OptimizeMinmass(mode, minmass, frame, y_range, x_range, gamma):
settings = nd.handle_data.ReadJson(ParameterJsonFile)
settings["Find"]["Minimal bead brightness"] = minmass
settings["Help"]["Bead brightness"] = mode
y_min = y_range[0]
y_max = y_range[1] + 1
x_min = x_range[0]
x_max = x_range[1] + 1
nd.handle_data.WriteJson(ParameterJsonFile, settings)
if mode == "manual":
nd.AdjustSettings.FindSpot_manual(rawframes_pre[frame:frame+1, y_min:y_max, x_min:x_max], ParameterJsonFile, \
ExternalSlider = True, gamma = gamma)
elif settings["Help"]["Bead size"] == "auto":
minmass, num_particles_trackpy = nd.ParameterEstimation.MinmassMain(
rawframes_pre, settings)
settings["Find"]["Minimal bead brightness"] = minmass
else:
print(
"Bead size not adjusted. Use 'manual' or 'auto' if you want to do it."
)
nd.handle_data.WriteJson(ParameterJsonFile, settings)
interact(OptimizeMinmass, mode = help_minmass, minmass = minmass_slider, \
frame = frame_slider, y_range = y_range_slider, x_range = x_range_slider, gamma = gamma_slider)
def ChoosePreProcessingParameters(rawframes_np, ParameterJsonFile):
#read in settings
settings = nd.handle_data.ReadJson(ParameterJsonFile)
# LASER FLUCTUATIONS
# select if Laser Fluctuations are applied
process_laser_fluctuations = IntSlider(min = 0, max = 1, \
value = settings["PreProcessing"]["Remove_Laserfluctuation"],\
description = "Correct Laser fluctuations (0 - no, 1 - yes)")
def RemoveLaserFluctuations(process_laser_fluctuations):
settings = nd.handle_data.ReadJson(ParameterJsonFile)
settings["PreProcessing"][
"Remove_Laserfluctuation"] = process_laser_fluctuations
if process_laser_fluctuations == 0:
print("Laser Fluctuations not corrected")
else:
settings["Plot"]['Laserfluctuation_Show'] = True
nd.PreProcessing.RemoveLaserfluctuation(rawframes_np, settings)
nd.handle_data.WriteJson(ParameterJsonFile, settings)
interact(RemoveLaserFluctuations,
process_laser_fluctuations=process_laser_fluctuations)
# CAMERA OFFSET
process_camera_offset = IntSlider(min = 0, max = 1, \
value = settings["PreProcessing"]["Remove_CameraOffset"],\
description = "Correct camera offset (0 - no, 1 - yes)")
def RemoveCameraOffset(process_camera_offset):
settings = nd.handle_data.ReadJson(ParameterJsonFile)
settings["PreProcessing"][
"Remove_CameraOffset"] = process_camera_offset
if process_camera_offset == 0:
print("Camera Offset not corrected")
else:
nd.PreProcessing.SubtractCameraOffset(rawframes_np, settings)
nd.handle_data.WriteJson(ParameterJsonFile, settings)
interact(RemoveCameraOffset, process_camera_offset=process_camera_offset)
# # STATIC BACKGROUND
# process_static_background_slider = IntSlider(min = 0, max = 1, \
# value = settings["PreProcessing"]["Remove_StaticBackground"],\
# description = "Correct static background (0 - no, 1 - yes)")
#
# def RemoveStaticBackground(process_static_background):
# settings = nd.handle_data.ReadJson(ParameterJsonFile)
# settings["PreProcessing"]["Remove_CameraOffset"] = process_static_background
#
# if process_static_background == 0:
# print("Static Background not corrected")
# else:
# settings["Plot"]['Background_Show'] = True
# rawframes_np_no_bg, static_background = nd.PreProcessing.Remove_StaticBackground(rawframes_np, settings, ShowColorBar = False, ExternalSlider = True)
# Show2dImage(static_background, title = 'Background', ShowSlider = True)
#
# nd.handle_data.WriteJson(ParameterJsonFile, settings)
#
#
#
# interact(RemoveStaticBackground, process_static_background = process_static_background_slider)
#
#
# CALCULATE BACKGROUND AND ENHANCE SNR BY CONVOLVING WITH PSF
process_static_background_slider = IntSlider(min = 0, max = 1, \
value = settings["PreProcessing"]["Remove_StaticBackground"],\
description = "Correct static background (0 - no, 1 - yes)")
EnhanceSNR_Slider = IntSlider(min = 0, max = 1, \
value = settings["PreProcessing"]["EnhanceSNR"],\
description = "Enhance SNR by convolving with the PSF (0 - no, 1 - yes)")
KernelSize = settings["PreProcessing"]["KernelSize"]
if KernelSize == 'auto':
KernelSize = 0
KernelSize_Slider = FloatSlider(min = 0, max = 10, \
value = KernelSize,\
description = "Kernelsize (0 - auto)")
[max_f, max_y, max_x] = np.asarray(rawframes_np.shape) - 1
frame_slider = IntSlider(min=1, max=max_f, step=1, description="Frame")
y_range_slider = IntRangeSlider(value=[0, max_y],
min=0,
max=max_y,
step=5,
description="ROI - y")
x_range_slider = IntRangeSlider(value=[0, max_x],
min=0,
max=max_x,
step=5,
description="ROI - x")
gamma_slider = FloatSlider(min=0.1, max=2, step=0.05, value=0.5)
def ConvolveWithPSF(process_static_background, EnhanceSNR, KernelSize,
frame, y_range, x_range, my_gamma):
settings = nd.handle_data.ReadJson(ParameterJsonFile)
settings["PreProcessing"][
"Remove_CameraOffset"] = process_static_background
plt.figure(figsize=(15, 10))
y_min = y_range[0]
y_max = y_range[1] + 1
x_min = x_range[0]
x_max = x_range[1] + 1
image_roi_bg = rawframes_np[:, y_min:y_max, x_min:x_max]
image_roi = rawframes_np[frame, y_min:y_max, x_min:x_max]
# Here comes the background
if process_static_background == 0:
print("Static Background not corrected")
else:
settings["Plot"]['Background_Show'] = True
image_roi_no_bg, static_background = nd.PreProcessing.Remove_StaticBackground(
image_roi_bg,
settings,
ShowColorBar=False,
ExternalSlider=True)
Show2dImage(static_background,
title='Background',
ShowSlider=False)
#switch the sliders on if they are required
if (EnhanceSNR == False) and (process_static_background == False):
KernelSize_Slider.layout.visibility = 'hidden'
frame_slider.layout.visibility = 'hidden'
y_range_slider.layout.visibility = 'hidden'
x_range_slider.layout.visibility = 'hidden'
gamma_slider.layout.visibility = 'hidden'
else:
KernelSize_Slider.layout.visibility = 'visible'
frame_slider.layout.visibility = 'visible'
y_range_slider.layout.visibility = 'visible'
x_range_slider.layout.visibility = 'visible'
gamma_slider.layout.visibility = 'visible'
# def ConvolveWithPSF(EnhanceSNR, KernelSize, frame, my_gamma):
# image_roi = rawframes_np[frame, :, :]
settings = nd.handle_data.ReadJson(ParameterJsonFile)
settings["PreProcessing"]["EnhanceSNR"] = EnhanceSNR
if KernelSize == 0:
KernelSize = 'auto'
settings["PreProcessing"]["KernelSize"] = KernelSize
if EnhanceSNR == 0:
print("SNR not enhanced by a convolution with the PSF")
else:
settings["Plot"]['Background_Show'] = True
if process_static_background == 0:
rawframes_filtered = nd.PreProcessing.ConvolveWithPSF(
image_roi,
settings,
ShowFirstFrame=True,
ShowColorBar=False,
ExternalSlider=True)
else:
rawframes_filtered = nd.PreProcessing.ConvolveWithPSF(
image_roi_no_bg[frame, :, :],
settings,
ShowFirstFrame=True,
ShowColorBar=False,
ExternalSlider=True)
Show2dImage(rawframes_filtered, ShowSlider=False, gamma=my_gamma)
nd.handle_data.WriteJson(ParameterJsonFile, settings)
# interact(ConvolveWithPSF, EnhanceSNR = EnhanceSNR_Slider, KernelSize = KernelSize_Slider\
# , frame = frame_slider, my_gamma = gamma_slider)
interact(ConvolveWithPSF,
process_static_background=process_static_background_slider,
EnhanceSNR=EnhanceSNR_Slider,
KernelSize=KernelSize_Slider,
frame=frame_slider,
y_range=y_range_slider,
x_range=x_range_slider,
my_gamma=gamma_slider)
# Rest is not implemented, yet
print("RollingPercentilFilter not inserted yet")
print("Clipping negative values not inserted yet. Clipping is bad")
print(
"Rotating the image is not inserted yet. Rotate your camera if that is a problem."
)
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 file_interface(fu, fworker, kpt_density):
try:
for filename in fu.keys():
file_str = list(fu.values())[0]["content"].decode("utf-8")
spl = filename.split(".")
if len(spl) == 1:
file_fmt = "poscar"
else:
file_fmt = spl[-1]
structure = Structure.from_str(input_string=file_str, fmt=file_fmt)
except UnicodeDecodeError:
print("Issue with geometry input file.")
return
except UnboundLocalError:
print("Please select an input file.")
return
except ValueError:
print("Incorrect format for input file.")
return
static = BulkStaticSet(
structure,
user_incar_settings=user_incar_settings,
user_kpoints_settings={"reciprocal_density": kpt_density})
nions = len(structure)
nelect = static.nelect
ispin = int(static.incar.get("ISPIN", 1))
if ispin == 1:
nbands = int(round(nelect / 2 + nions / 2))
elif ispin == 2:
nbands = int(nelect * 3 / 5 + nions)
else:
raise ValueError("ISPIN Value is not set to 1 or 2!")
cores_per_node = CLUSTER_DICT[fworker]["cores_per_node"]
nbands = (nbands // cores_per_node + 1) * cores_per_node
kpoints = static.kpoints
spg = SpacegroupAnalyzer(structure, symprec=1e-5)
max_kpts = len(spg.get_ir_reciprocal_mesh(kpoints.kpts))
selection = interactive(selection_interface,
structure=fixed(structure),
fworker=fixed(fworker),
kpt_density=fixed(kpt_density),
functional=Select(
options=['pbe', 'hse06'],
value='pbe',
rows=2,
description='Functional:',
),
nodes_list=Text(
value="1, 2, 4",
placeholder='Nodes (comma separated)',
description='Node List:',
),
nbands=BoundedIntText(value=nbands,
min=1,
max=10000,
step=1,
description='NBANDS:'),
max_kpt_range=IntRangeSlider(
value=[1, max_kpts],
min=1,
max=max_kpts + cores_per_node // 2,
step=1,
description='Max KPAR range:',
))
display(HBox(selection.children[:2]))
display(HBox(selection.children[2:]))
display(button)
return selection
def __init__(self):
self.output_dir = '.'
# self.output_dir = 'tmpdir'
self.figsize_width_substrate = 15.0 # allow extra for colormap
self.figsize_height_substrate = 12.5
self.figsize_width_svg = 12.0
self.figsize_height_svg = 12.0
# self.fig = plt.figure(figsize=(7.2,6)) # this strange figsize results in a ~square contour plot
self.first_time = True
self.modulo = 1
self.use_defaults = True
self.svg_delta_t = 1
self.substrate_delta_t = 1
self.svg_frame = 1
self.substrate_frame = 1
self.customized_output_freq = False
self.therapy_activation_time = 1000000
self.max_svg_frame_pre_therapy = 1000000
self.max_substrate_frame_pre_therapy = 1000000
self.svg_xmin = 0
# Probably don't want to hardwire these if we allow changing the domain size
# self.svg_xrange = 2000
# self.xmin = -1000.
# self.xmax = 1000.
# self.ymin = -1000.
# self.ymax = 1000.
# self.x_range = 2000.
# self.y_range = 2000.
self.show_nucleus = False
self.show_edge = True
# initial value
self.field_index = 4
# self.field_index = self.mcds_field.value + 4
# define dummy size of mesh (set in the tool's primary module)
self.numx = 0
self.numy = 0
self.title_str = ''
tab_height = '600px'
tab_height = '500px'
constWidth = '180px'
constWidth2 = '150px'
tab_layout = Layout(
width='900px', # border='2px solid black',
height=tab_height,
) #overflow_y='scroll')
max_frames = 1
# self.mcds_plot = interactive(self.plot_substrate, frame=(0, max_frames), continuous_update=False)
# self.i_plot = interactive(self.plot_plots, frame=(0, max_frames), continuous_update=False)
self.i_plot = interactive(self.plot_substrate,
frame=(0, max_frames),
continuous_update=False)
frame_slider = IntRangeSlider(
value=[5, 7],
min=0,
max=10,
step=1,
description='Test:',
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='d',
)
# "plot_size" controls the size of the tab height, not the plot (rf. figsize for that)
# NOTE: the Substrates Plot tab has an extra row of widgets at the top of it (cf. Cell Plots tab)
svg_plot_size = '700px'
svg_plot_size = '600px'
svg_plot_size = '700px'
svg_plot_size = '900px'
self.i_plot.layout.width = svg_plot_size
self.i_plot.layout.height = svg_plot_size
self.fontsize = 20
# description='# cell frames',
self.max_frames = BoundedIntText(
min=0,
max=99999,
value=max_frames,
description='# frames',
layout=Layout(width='160px'),
)
self.max_frames.observe(self.update_max_frames)
# self.field_min_max = {'dummy': [0., 1.]}
# NOTE: manually setting these for now (vs. parsing them out of data/initial.xml)
self.field_min_max = {
'director signal': [0., 1.],
'cargo signal': [0., 1.]
}
# hacky I know, but make a dict that's got (key,value) reversed from the dict in the Dropdown below
# self.field_dict = {0:'dummy'}
self.field_dict = {0: 'director signal', 1: 'cargo signal'}
self.mcds_field = Dropdown(
options={
'director signal': 0,
'cargo signal': 1
},
value=0,
# description='Field',
layout=Layout(width=constWidth))
# print("substrate __init__: self.mcds_field.value=",self.mcds_field.value)
# self.mcds_field.observe(self.mcds_field_cb)
self.mcds_field.observe(self.mcds_field_changed_cb)
# self.field_cmap = Text(
# value='viridis',
# description='Colormap',
# disabled=True,
# layout=Layout(width=constWidth),
# )
self.field_cmap = Dropdown(
options=['viridis', 'jet', 'YlOrRd'],
value='YlOrRd',
# description='Field',
layout=Layout(width=constWidth))
# self.field_cmap.observe(self.plot_substrate)
self.field_cmap.observe(self.mcds_field_cb)
self.cmap_fixed = Checkbox(
description='Fix',
disabled=False,
# layout=Layout(width=constWidth2),
)
self.save_min_max = Button(
description='Save', #style={'description_width': 'initial'},
button_style=
'success', # 'success', 'info', 'warning', 'danger' or ''
tooltip='Save min/max for this substrate',
disabled=True,
layout=Layout(width='90px'))
def save_min_max_cb(b):
# field_name = self.mcds_field.options[]
# field_name = next(key for key, value in self.mcds_field.options.items() if value == self.mcds_field.value)
field_name = self.field_dict[self.mcds_field.value]
# print(field_name)
# self.field_min_max = {'oxygen': [0., 30.], 'glucose': [0., 1.], 'H+ ions': [0., 1.], 'ECM': [0., 1.], 'NP1': [0., 1.], 'NP2': [0., 1.]}
self.field_min_max[field_name][0] = self.cmap_min.value
self.field_min_max[field_name][1] = self.cmap_max.value
# print(self.field_min_max)
self.save_min_max.on_click(save_min_max_cb)
self.cmap_min = FloatText(
description='Min',
value=0,
step=0.1,
disabled=True,
layout=Layout(width=constWidth2),
)
self.cmap_min.observe(self.mcds_field_cb)
self.cmap_max = FloatText(
description='Max',
value=38,
step=0.1,
disabled=True,
layout=Layout(width=constWidth2),
)
self.cmap_max.observe(self.mcds_field_cb)
def cmap_fixed_cb(b):
if (self.cmap_fixed.value):
self.cmap_min.disabled = False
self.cmap_max.disabled = False
self.save_min_max.disabled = False
else:
self.cmap_min.disabled = True
self.cmap_max.disabled = True
self.save_min_max.disabled = True
# self.mcds_field_cb()
self.cmap_fixed.observe(cmap_fixed_cb)
field_cmap_row2 = HBox([self.field_cmap, self.cmap_fixed])
# field_cmap_row3 = HBox([self.save_min_max, self.cmap_min, self.cmap_max])
items_auto = [
self.save_min_max, #layout=Layout(flex='3 1 auto', width='auto'),
self.cmap_min,
self.cmap_max,
]
box_layout = Layout(display='flex',
flex_flow='row',
align_items='stretch',
width='80%')
field_cmap_row3 = Box(children=items_auto, layout=box_layout)
#---------------------
self.cell_nucleus_toggle = Checkbox(
description='nuclei',
disabled=False,
value=self.show_nucleus,
# layout=Layout(width=constWidth2),
)
def cell_nucleus_toggle_cb(b):
# self.update()
if (self.cell_nucleus_toggle.value):
self.show_nucleus = True
else:
self.show_nucleus = False
self.i_plot.update()
self.cell_nucleus_toggle.observe(cell_nucleus_toggle_cb)
#----
self.cell_edges_toggle = Checkbox(
description='edges',
disabled=False,
value=self.show_edge,
# layout=Layout(width=constWidth2),
)
def cell_edges_toggle_cb(b):
# self.update()
if (self.cell_edges_toggle.value):
self.show_edge = True
else:
self.show_edge = False
self.i_plot.update()
self.cell_edges_toggle.observe(cell_edges_toggle_cb)
self.cells_toggle = Checkbox(
description='Cells',
disabled=False,
value=True,
# layout=Layout(width=constWidth2),
)
def cells_toggle_cb(b):
# self.update()
self.i_plot.update()
if (self.cells_toggle.value):
self.cell_edges_toggle.disabled = False
self.cell_nucleus_toggle.disabled = False
else:
self.cell_edges_toggle.disabled = True
self.cell_nucleus_toggle.disabled = True
self.cells_toggle.observe(cells_toggle_cb)
#---------------------
self.substrates_toggle = Checkbox(
description='Substrates',
disabled=False,
value=True,
# layout=Layout(width=constWidth2),
)
def substrates_toggle_cb(b):
if (self.substrates_toggle.value): # seems bass-ackwards
self.cmap_fixed.disabled = False
self.cmap_min.disabled = False
self.cmap_max.disabled = False
self.mcds_field.disabled = False
self.field_cmap.disabled = False
else:
self.cmap_fixed.disabled = True
self.cmap_min.disabled = True
self.cmap_max.disabled = True
self.mcds_field.disabled = True
self.field_cmap.disabled = True
self.substrates_toggle.observe(substrates_toggle_cb)
self.grid_toggle = Checkbox(
description='grid',
disabled=False,
value=True,
# layout=Layout(width=constWidth2),
)
def grid_toggle_cb(b):
# self.update()
self.i_plot.update()
self.grid_toggle.observe(grid_toggle_cb)
# field_cmap_row3 = Box([self.save_min_max, self.cmap_min, self.cmap_max])
# mcds_tab = widgets.VBox([mcds_dir, mcds_plot, mcds_play], layout=tab_layout)
# mcds_params = VBox([self.mcds_field, field_cmap_row2, field_cmap_row3, self.max_frames]) # mcds_dir
# mcds_params = VBox([self.mcds_field, field_cmap_row2, field_cmap_row3,]) # mcds_dir
# self.tab = HBox([mcds_params, self.mcds_plot], layout=tab_layout)
help_label = Label('select slider: drag or left/right arrows')
# row1 = Box([help_label, Box( [self.max_frames, self.mcds_field, self.field_cmap], layout=Layout(border='0px solid black',
row1a = Box([self.max_frames, self.mcds_field, self.field_cmap],
layout=Layout(border='1px solid black',
width='50%',
height='',
align_items='stretch',
flex_direction='row',
display='flex'))
row1b = Box([
self.cells_toggle, self.cell_nucleus_toggle, self.cell_edges_toggle
],
layout=Layout(border='1px solid black',
width='50%',
height='',
align_items='stretch',
flex_direction='row',
display='flex'))
row1 = HBox([row1a, Label('.....'), row1b])
row2a = Box([self.cmap_fixed, self.cmap_min, self.cmap_max],
layout=Layout(border='1px solid black',
width='50%',
height='',
align_items='stretch',
flex_direction='row',
display='flex'))
# row2b = Box( [self.substrates_toggle, self.grid_toggle], layout=Layout(border='1px solid black',
row2b = Box([
self.substrates_toggle,
],
layout=Layout(border='1px solid black',
width='50%',
height='',
align_items='stretch',
flex_direction='row',
display='flex'))
# row2 = HBox( [row2a, self.substrates_toggle, self.grid_toggle])
row2 = HBox([row2a, Label('.....'), row2b])
if (hublib_flag):
self.download_button = Download('mcds.zip',
style='warning',
icon='cloud-download',
tooltip='Download data',
cb=self.download_cb)
self.download_svg_button = Download(
'svg.zip',
style='warning',
icon='cloud-download',
tooltip='You need to allow pop-ups in your browser',
cb=self.download_svg_cb)
download_row = HBox([
self.download_button.w, self.download_svg_button.w,
Label("Download all cell plots (browser must allow pop-ups).")
])
# box_layout = Layout(border='0px solid')
controls_box = VBox([row1,
row2]) # ,width='50%', layout=box_layout)
self.tab = VBox([controls_box, self.i_plot, download_row])
else:
# self.tab = VBox([row1, row2])
self.tab = VBox([row1, row2, self.i_plot])
# <h1><center>Teoría de Perturbaciones</center></h1>
# Consiste en resolver un sistema perturbado(se conoce la solución al no perturbado), y donde el interés es conocer la contribución de la parte perturbada $H'$ al nuevo sistema total.
# $$ H = H^{0}+H'$$
# La resolución adecuada del problema, depende en gran parte, de una correcta elección de $H'$.
# In[151]:
form_item_layout = Layout(display='flex',flex_flow='row',justify_content='space-between')
PType=Dropdown(options=['Particle in a one-dimensional box', 'Harmonic oscilator', 'Hydrogen atom (Helium correction)'])
Pert=Text()
Rang=IntRangeSlider(min=0, max=20, step=1, disabled=False, continuous_update=False, orientation='horizontal', readout=True, readout_format='d')
M=Text()
Correc=Dropdown(options=['1', '2'])
hbarra=Dropdown(options=[1, 1.0545718e-34])
form_items = [
Box([Label(value='Problem'),PType], layout=form_item_layout),
Box([Label(value='Perturbation'),Pert], layout=form_item_layout),
Box([Label(value='Correction order'),Correc], layout=form_item_layout),
Box([Label(value='n Range'),Rang], layout=form_item_layout),
Box([Label(value='Mass'),M], layout=form_item_layout),
Box([Label(value='Hbar'),hbarra], layout=form_item_layout),]
form = Box(form_items, layout=Layout(display='flex',flex_flow='column',border='solid 2px',align_items='stretch',width='40%'))
form
def resetSpGCheckBoxes(b):
for k in spgSelector:
if type(k) == int:
spgSelector[k].value = False
spgSelector[1].value = True
spgSelector['reset'].on_click(resetSpGCheckBoxes)
spgSelectorLayout = HBox([
VBox([spgSelector[i] for i in range(1, 6)]),
VBox([spgSelector[i] for i in range(6, 11)]),
VBox([spgSelector[i] for i in [11, 21, 31, 'which', 'reset']])
])
bottLine = HBox([AOGenus, ATspecies, spgSelectorLayout])
startEnd = IntRangeSlider(value=[1, 10],
min=1,
max=10,
step=1,
description='Ranks')
aggWidg = VBox([topRowGroup, midLine, bottLine, startEnd])
kind = Tab()
kind.children = [aggWidg, unaggWidg]
for i, name in enumerate(['Aggregated', 'Unaggregated']):
kind.set_title(i, name)
def imgs_grid(path):
def show_imgs():
print(f"Crop name: {crop_name}, Area: {area:.2f} sqm")
def multi_bands_imgs(bands, fname):
df = raster_utils.create_df(ci_path, pid, ci_band.value)
rows = round((df.shape[0] / columns) + 0.5)
fig = plt.figure(figsize=(16, 4 * rows))
for i, row in df.iterrows():
fig.add_subplot(rows, columns, i + 1)
str_date = str(row['date'].date()).replace('-', '')
img_png = normpath(join(ci_path, f'{fname}_{str_date}.png'))
# Create color image if it does not exist
# Merge bands (images path, export image path, bands list)
if not isfile(img_png):
imgs_path = normpath(join(ci_path, row['imgs']))
raster_utils.merge_bands(imgs_path, img_png, bands)
with rasterio.open(img_png, format='PNG') as img:
overlay_date(img, row['date'].date()) # Add date overlay.
ax = plt.gca()
if show_parcel.value:
ax.add_patch(overlay_parcel(img, info_data))
plt.axis('off') # Turn of axis.
pA, pB = np.percentile(img.read(1),
tuple(ci_percent.value))
# Strech image to A - B percentile.
stack = [
exposure.rescale_intensity(img.read()[i, :, :],
in_range=(pA, pB))
for i in range(len(bands))
]
rgb_enhanced = np.dstack(stack)
show(np.uint16(rgb_enhanced.transpose(2, 0, 1) / 300),
ax=ax,
transform=img.transform)
return plt.show()
def ndvi_imgs(bands, fname):
df = raster_utils.create_df(ci_path, pid, ci_band.value)
rows = round((df.shape[0] / columns) + 0.5)
fig = plt.figure(figsize=(16, 4 * rows))
for i, row in df.iterrows():
fig.add_subplot(rows, columns, i + 1)
str_date = str(row['date'].date()).replace('-', '')
img_png = normpath(join(ci_path, f'{fname}_{str_date}.png'))
imgs_path = normpath(join(ci_path, row['imgs']))
b4f = f"{imgs_path}.B04.tif"
b4 = rasterio.open(b4f, format='GTiff')
ndvi = raster_utils.calc_ndvi(imgs_path, img_png, bands)
overlay_date(b4, row['date'].date()) # Add date overlay.
ax = plt.gca()
if show_parcel.value:
ax.add_patch(overlay_parcel(b4, info_data))
plt.axis('off') # Turn of axis.
pA, pB = np.percentile(ndvi, tuple(ci_percent.value))
show(ndvi,
ax=ax,
transform=b4.transform,
cmap=ci_cmaps.value,
vmin=pA,
vmax=pB)
b4.close()
return plt.show()
def single_band(band):
df = raster_utils.create_df(ci_path, pid, ci_band.value)
rows = round((df.shape[0] / columns) + 0.5)
fig = plt.figure(figsize=(16, 4 * rows))
for i, row in df.iterrows():
img_gtif = normpath(
join(ci_path, f"{row['imgs']}.{ci_band.value[0]}.tif"))
with rasterio.open(img_gtif, format='GTiff') as img:
fig.add_subplot(rows, columns, i + 1)
overlay_date(img, row['date'].date())
plt.axis('off')
ax = plt.gca()
if show_parcel.value:
ax.add_patch(overlay_parcel(img, info_data))
img_read = img.read(1)
pA, pB = np.percentile(img_read, tuple(ci_percent.value))
show(img.read(1),
ax=ax,
transform=img.transform,
cmap=data_options.cmaps(ci_band.value[0]),
vmin=pA,
vmax=pB)
return plt.show()
if len(ci_band.value) == 1:
single_band(ci_band.value[0])
elif ci_band.value == ['B04', 'B08']:
ndvi_imgs(ci_band.value, 'NDVI')
else:
multi_bands_imgs(ci_band.value, ('').join(ci_band.value))
def overlay_date(img, date):
date_text = plt.text(
img.bounds.left + ((img.bounds.right - img.bounds.left) / 6.5),
img.bounds.top - ((img.bounds.top - img.bounds.bottom) / 6.5),
date,
color='yellow',
weight='bold',
size=12,
bbox=dict(boxstyle="round", ec='yellow', fc='black', alpha=0.2))
return date_text
def overlay_parcel(img, geom):
with open(file_info, 'r') as f:
info_data = json.loads(f.read())
img_epsg = img.crs.to_epsg()
geo_json = spatial_utils.transform_geometry(info_data, img_epsg)
patche = [
PolygonPatch(feature,
edgecolor="yellow",
facecolor="none",
linewidth=2) for feature in [geo_json['geom'][0]]
]
return patche[0]
# Images options.
file_info = normpath(join(path, 'info.json'))
with open(file_info, 'r') as f:
info_data = json.loads(f.read())
# print(info_data)
pid = info_data['pid'][0]
crop_name = info_data['cropname'][0]
area = info_data['area'][0]
ci_path = normpath(join(path, 'chip_images'))
columns = 4
available_options = raster_utils.available_options(path, pid)
ci_band = Dropdown(
options=available_options,
description='Select band:',
disabled=False,
)
ci_cmaps = Dropdown(options=data_options.color_maps(),
value='RdYlGn_r',
description='Color map:',
disabled=False,
layout=Layout(width='15%'))
ci_percent = IntRangeSlider(
value=[2, 98],
min=0,
max=100,
step=1,
description='%:',
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='d',
)
show_parcel = Checkbox(value=True,
description='Show parcel',
disabled=False,
indent=False,
layout=Layout(width='100px'))
ci_cloud = Checkbox(value=False,
description='Cloud free',
disabled=True,
indent=False,
layout=Layout(width='140px'))
btn_ci = Button(value=False,
description='Show images',
disabled=False,
button_style='info',
tooltip='Refresh output',
icon='')
ci_out = Output()
@btn_ci.on_click
def btn_ci_on_click(b):
btn_ci.description = 'Refresh'
btn_ci.icon = 'refresh'
with ci_out:
ci_out.clear_output()
show_imgs()
wbox_ci_cloud = HBox([])
if len([val for key, val in available_options if 'SCL' in val]) > 0:
wbox_ci_cloud = HBox([ci_cloud])
wbox_ci = HBox([btn_ci, ci_band, show_parcel, ci_percent, wbox_ci_cloud])
def ci_band_change(change):
if len(ci_band.value) == 1:
if ci_band.value[0] in ['B02', 'B03', 'B04', 'B08']:
wbox_ci.children = [btn_ci, ci_band, show_parcel, ci_percent]
show_parcel.value = True
else:
wbox_ci.children = [btn_ci, ci_band, ci_percent]
show_parcel.value = False
elif ci_band.value == ['B04', 'B08']:
wbox_ci.children = [
btn_ci, ci_band, show_parcel, ci_cmaps, ci_percent,
wbox_ci_cloud
]
show_parcel.value = True
else:
wbox_ci.children = [
btn_ci, ci_band, show_parcel, ci_percent, wbox_ci_cloud
]
show_parcel.value = True
ci_band.observe(ci_band_change, 'value')
wbox = VBox([wbox_ci, ci_out])
return wbox
def load_UI(self):
'''Setting up the interactive visualization tool'''
# UI elements
range_slider = IntRangeSlider(value=[1, self.time_window],
min=1,
max=self.time_window,
description="Range: ",
continuous_update=False)
view_ftr_i = IntSlider(min=1,
max=self.num_features,
default_value=2,
description="View Feature: ",
continuous_update=False)
self.modify_ftr_i = IntSlider(min=1,
max=self.num_features,
default_value=2,
description="Mod Feature: ",
continuous_update=False)
uniform_slider = FloatSlider(value=0,
min=-1,
max=1,
step=0.05,
description='Value:',
continuous_update=False)
radio_button_uni = RadioButtons(options=[('Positive Weights', 1),
('Negative Weights', -1)],
description='Affect:')
select_target = BoundedFloatText(
value=(self.min_target + self.max_target) / 2,
min=self.min_target,
max=self.max_target,
layout={'width': '150px'})
radio_button_xyz7 = RadioButtons(options=[
('Present (' + str(self.forecast_window) + '-last values)', 0),
('Future (' + str(self.forecast_window) + '-next values)', 1)
],
description='Affect:')
enable_iPCA = Checkbox(value=False, description='Enable iPCA')
iml_method = ToggleButtons(options=['LioNets', 'Lime'])
self.forecast = Dropdown(options=[('Neural', 6), ('Static', 7),
('N-Beats', 8)],
description="Forecast: ")
mod = Dropdown(options=[('Original', 0), ('Uniform', 1),
('Mean (Local)', 2), ('Mean (Global)', 3),
('Zeros', 4), ('Noise', 5),
('Forecast (Neural)', 6),
('Forecast (Static)', 7),
('Forecast (N-Beats)', 8),
('Forecast (XYZ7)', 9)],
description="Mods: ")
jsdlink((self.modify_ftr_i, 'value'), (view_ftr_i, 'value'))
# UI layout
interpretable_settings = HBox(
[Label('Interpretation method:'), iml_method, enable_iPCA])
enable_iPCA.layout.margin = '0 0 0 -50px'
interpretable_settings.layout.margin = '20px 0 20px 0'
standard_settings = VBox([self.modify_ftr_i, view_ftr_i])
xyz7_settings = VBox([
HBox([Label('Desired Target:'), select_target]), radio_button_xyz7
])
xyz7_settings.layout.margin = '0 0 0 30px'
self.opt1_settings = VBox([mod])
self.opt2_settings = VBox([mod, range_slider])
self.opt3_settings = HBox([
VBox([mod, range_slider]),
VBox([uniform_slider, radio_button_uni])
])
self.opt4_settings = HBox([VBox([mod, self.forecast]), xyz7_settings])
self.mod_settings = VBox([])
ui = VBox([
interpretable_settings,
HBox([standard_settings, self.mod_settings])
])
# Starting the interactive tool
inter = interactive_output(
self.plot_feature, {
'ftr_i': view_ftr_i,
'mod_ftr_i': self.modify_ftr_i,
'mod': mod,
'rng_sldr': range_slider,
'uni_sldr': uniform_slider,
'rd_btn_uni': radio_button_uni,
'select_target': select_target,
'rd_btn_xyz7': radio_button_xyz7,
'forecast_optns': self.forecast,
'iml_method': iml_method,
'enable_ipca': enable_iPCA
})
display(ui, inter)