def modify_doc(doc):
source = ColumnDataSource(dict(x=[1, 2], y=[1, 1], val=["a", "b"]))
plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0)
plot.add_glyph(source, Circle(x='x', y='y', size=20))
plot.add_tools(CustomAction(callback=CustomJS(args=dict(s=source), code=RECORD("data", "s.data"))))
group = RadioButtonGroup(labels=LABELS, css_classes=["foo"])
def cb(active):
source.data['val'] = [active, "b"]
group.on_click(cb)
doc.add_root(column(group, plot))
def test_js_on_change_executes(self, bokeh_model_page):
group = RadioButtonGroup(labels=LABELS, css_classes=["foo"])
group.js_on_click(CustomJS(code=RECORD("active", "cb_obj.active")))
page = bokeh_model_page(group)
el = page.driver.find_element_by_css_selector('.foo .bk-btn:nth-child(3)')
el.click()
results = page.results
assert results['active'] == 2
el = page.driver.find_element_by_css_selector('.foo .bk-btn:nth-child(1)')
el.click()
results = page.results
assert results['active'] == 0
assert page.has_no_console_errors()
def home():
#Open file and create sources
dictionary = open_file_into_dictionary('SampleCSV2.csv')
keys = list(key.title() for key in dictionary.keys())
values = [value for value in dictionary.values()]
xy_source = ColumnDataSource(data=dict(xs=[values[0]], ys=[values[1]], labels = [keys[1]],
colors = ['red', 'green', 'blue', 'purple', 'brown', 'aqua']))
variables_source = ColumnDataSource(data = dict(keys = keys, values = values))
#Create general plot
plot = figure(plot_width=800, plot_height=600, toolbar_location = 'left')
plot.title.text_font= 'helvetica'
plot.title.text_font_size = '24pt'
plot.title.align = 'center'
plot.title.text_font_style = 'normal'
plot.multi_line(xs = 'xs', ys = 'ys', legend = 'labels', line_color = 'colors', source = xy_source)
#Define callbacks
x_axis_callback = CustomJS(args=dict(xy_source = xy_source, variables_source = variables_source,
axis = plot.xaxis[0]), code="""
var xy_data = xy_source.data;
var variables_data = variables_source.data;
var index = cb_obj.active;
var values = variables_data.values;
var y_length = xy_data['ys'].length;
if (y_length == 0){
y_length = 1}
var new_list = [];
for (i = 0; i < y_length; i++) {
new_list.push(values[index])}
xy_data['xs'] = new_list;
xy_source.change.emit();
var keys = variables_data.keys;
var label = keys[index];
axis.axis_label = label;
""")
y_axis_callback = CustomJS(args=dict(xy_source = xy_source, variables_source = variables_source,
axis = plot.yaxis[0]), code="""
var xy_data = xy_source.data;
var variables_data = variables_source.data;
var index_list = cb_obj.active;
var values = variables_data.values;
var index_length = index_list.length;
var keys = variables_data.keys;
var new_ys = [];
var new_labels = [];
for (i = 0; i < index_length; i++) {
new_ys.push(values[index_list[i]]);
new_labels.push(keys[index_list[i]])}
xy_data['labels'] = new_labels;
xy_data['ys'] = new_ys;
if (index_length > 0){
var x_variable = xy_data['xs'][0];
var new_x = [];
for (i = 0; i < index_length; i++) {
new_x.push(x_variable)}
xy_data['xs'] = new_x;}
xy_source.change.emit();
var y_axis_name = keys[[index_list[0]]];
for (i = 1; i < index_length; i++) {
y_axis_name += ", " + keys[[index_list[i]]];}
axis.axis_label = y_axis_name;
""")
title_callback = CustomJS(args= dict(title = plot.title), code="""
var title_text = cb_obj.value;
title.text = title_text;
""")
x_name_callback = CustomJS(args=dict(axis = plot.xaxis[0]), code="""
var label_text = cb_obj.value;
axis.axis_label = label_text;
""")
y_name_callback = CustomJS(args=dict(axis = plot.yaxis[0]), code="""
var label_text = cb_obj.value;
axis.axis_label = label_text;
""")
#Create toolbox
label_x = Div(text="""X-Axis""", width=200)
x_axis = RadioButtonGroup(labels=keys, active=0, callback = x_axis_callback)
label_y = Div(text="""Y-Axis""", width=200)
y_axis = CheckboxButtonGroup(labels=keys, active=[1], callback = y_axis_callback)
label_axes = Div(text="""<br />Modify Labels""", width=200)
title_name = TextInput(title="Title", value='Default Title')
plot.title.text = title_name.value
title_name.js_on_change('value', title_callback)
x_name = TextInput(title="X-Axis", value='Default X Label')
plot.xaxis.axis_label = keys[0]
x_name.js_on_change('value', x_name_callback)
y_name = TextInput(title="Y-Axis", value='Default Y Label')
plot.yaxis.axis_label = keys[1]
y_name.js_on_change('value', y_name_callback)
toolbox = widgetbox(label_x, x_axis, label_y, y_axis, label_axes, title_name, x_name, y_name)
#Integrate with html
parts = dict(toolbox = toolbox, plot = plot)
script, div = components(parts, INLINE)
return render_template('plotpage.html',
script=script,
toolbox_div=div['toolbox'],
plot_div=div['plot'],
js_resources=INLINE.render_js(),
css_resources=INLINE.render_css())
from bokeh.io import show
from bokeh.models import CustomJS, RadioButtonGroup
LABELS = ["Option 1", "Option 2", "Option 3"]
radio_button_group = RadioButtonGroup(labels=LABELS, active=0)
radio_button_group.js_on_event(
"button_click",
CustomJS(args=dict(btn=radio_button_group),
code="""
console.log('radio_button_group: active=' + radio_button_group.active, this.toString())
"""))
show(radio_button_group)
def button_group(level):
choice = RadioButtonGroup(labels=['None'],
visible=False,
active=None)
set_callback(choice, level)
return choice
def __init__(self, n_nodes):
self.i = 0
kps = [crypto_sign_keypair() for _ in range(n_nodes)]
stake = {kp[0]: 1 for kp in kps}
network = {}
self.nodes = [Node(kp, network, n_nodes, stake) for kp in kps]
for n in self.nodes:
network[n.pk] = n.ask_sync
self.ids = {kp[0]: i for i, kp in enumerate(kps)}
self.main_its = [n.main() for n in self.nodes]
for m in self.main_its:
next(m)
def toggle():
if play.label == '► Play':
play.label = '❚❚ Pause'
curdoc().add_periodic_callback(self.animate, 50)
else:
play.label = '► Play'
curdoc().remove_periodic_callback(self.animate)
play = Button(label='► Play', width=60)
play.on_click(toggle)
def sel_node(new):
self.active = new
node = self.nodes[new]
self.tbd = {}
self.tr_src.data, self.links_src.data = self.extract_data(
node, bfs((node.head, ), lambda u: node.hg[u].p), 0)
for u, j in tuple(self.tbd.items()):
self.tr_src.data['line_alpha'][j] = 1 if node.famous.get(
u) else 0
if u in node.idx:
self.tr_src.data['round_color'][j] = idx_color(node.idx[u])
self.tr_src.data['idx'][j] = node.idx.get(u)
if u in node.idx and u in node.famous:
del self.tbd[u]
print('updated')
self.tr_src.trigger('data', None, self.tr_src.data)
selector = RadioButtonGroup(
labels=['Node %i' % i for i in range(n_nodes)],
active=0,
name='Node to inspect')
selector.on_click(sel_node)
plot = figure(
plot_height=700,
plot_width=900,
y_range=(0, 30),
tools=[
PanTool(dimensions=['height']),
HoverTool(tooltips=[('round', '@round'), (
'hash',
'@hash'), ('timestamp',
'@time'), ('payload',
'@payload'), ('number', '@idx')])
])
plot.xgrid.grid_line_color = None
plot.xaxis.minor_tick_line_color = None
plot.ygrid.grid_line_color = None
plot.yaxis.minor_tick_line_color = None
self.links_src = ColumnDataSource(data={
'x0': [],
'y0': [],
'x1': [],
'y1': [],
'width': []
})
#self.links_rend = plot.add_layout(
# Arrow(end=NormalHead(fill_color='black'), x_start='x0', y_start='y0', x_end='x1',
# y_end='y1', source=self.links_src))
self.links_rend = plot.segment(color='#777777',
x0='x0',
y0='y0',
x1='x1',
y1='y1',
source=self.links_src,
line_width='width')
self.tr_src = ColumnDataSource(
data={
'x': [],
'y': [],
'round_color': [],
'idx': [],
'line_alpha': [],
'round': [],
'hash': [],
'payload': [],
'time': []
})
self.tr_rend = plot.circle(x='x',
y='y',
size=20,
color='round_color',
line_alpha='line_alpha',
source=self.tr_src,
line_width=5)
sel_node(0)
curdoc().add_root(
row([widgetbox(play, selector, width=300), plot],
sizing_mode='fixed'))
def wohngeld(plot_dict, data):
def make_dataset(sel_year, hh_size, wg_dict):
dataset = wg_dict[sel_year][hh_size]
heatmap_source = pd.DataFrame(dataset.stack(),
columns=["Wohngeld"]).reset_index()
heatmap_source.columns = ["Miete", "Einkommen", "Wohngeld"]
return ColumnDataSource(heatmap_source)
def update_plot(attr, old, new):
sel_year = [1992, 2001, 2009, 2016, 2020, 2021][year_selection.active]
hh_size = hh_size_selection.value
new_src = make_dataset(sel_year, hh_size, wg_dict)
src.data.update(new_src.data)
def setup_plot(src):
"""
Create the heatmap plot.
src: ColumnDataSource
"""
# Prepare a color pallete and color mapper
mapper = LinearColorMapper(
# force 0 to be mapped with white color
palette=tuple(
itertools.chain(["#FFFFFF"], tuple(reversed(Viridis256[1:])))),
low=0,
high=1000,
)
# Actual figure setup
p = figure(
plot_width=800,
plot_height=400,
x_range=(src.data["Miete"].min(), src.data["Miete"].max()),
y_range=(src.data["Einkommen"].min(), src.data["Einkommen"].max()),
tools="hover",
tooltips="Housing Benefit: @Wohngeld{0.0f}€",
)
p.rect(
x="Miete",
y="Einkommen",
width=25,
height=src.data["Einkommen"][1] - src.data["Einkommen"][0],
source=src,
line_color=transform("Wohngeld", mapper),
fill_color=transform("Wohngeld", mapper),
)
color_bar = ColorBar(
color_mapper=mapper,
location=(0, 0),
ticker=BasicTicker(desired_num_ticks=20),
formatter=NumeralTickFormatter(format="0€"),
label_standoff=12,
)
p.add_layout(color_bar, "right")
plot = plotstyle(p, plot_dict)
return plot
wg_dict = data
year_selection = RadioButtonGroup(
labels=[str(i) for i in [1992, 2001, 2009, 2016, 2020, 2021]],
active=5)
year_selection.on_change("active", update_plot)
hh_size_selection = Slider(start=1,
end=12,
value=4,
step=1,
title="Household Size")
hh_size_selection.on_change("value", update_plot)
src = make_dataset(2021, 4, wg_dict)
p = setup_plot(src)
description = Div(
text=plot_dict["description"],
width=1000,
)
year_label = Div(text="Year")
layout = column(description, year_label, year_selection, hh_size_selection,
p)
tab = Panel(child=layout, title="Housing benefits")
return tab
def change_range(attrname, old, new):
low, high = rangeslider.value
for i, lead in enumerate(StandardHeader):
leads[i].x_range.start = low * 100
leads[i].x_range.end = (high - rs_width) * 100
# Set widgets
rangeslider = RangeSlider(start=0,
end=10000,
step=1,
value=(0, rs_width + 30),
title="X range")
file_selector = Select(value=None, options=nix(None, files))
waveselector = RadioButtonGroup(labels=["P wave", "QRS wave", "T wave"],
active=0)
textboxnew = PreText(text="New points: \t[]")
retrievebutton = Button(label='Retrieve Segmentation')
storebutton = Button(label='Store Segmentation')
writebutton = Button(label='Write to File')
# Set callbacks
file_selector.on_change('value', file_change)
source.selected.on_change('indices', selection_change)
retrievebutton.on_click(retrieve_segmentation)
storebutton.on_click(save_segmentation)
writebutton.on_click(write_segmentation)
rangeslider.on_change('value', change_range)
waveselector.on_change('active', wave_change)
# set up layout
data_counties = pd.concat([data_counties, raw_df], axis=1)
# getting quantile values for categorising counties
top_qtl = raw_df.quantile(0.66)
middle_qtl = raw_df.quantile(0.33)
session = Session()
document = Document()
session.use_doc('python_project')
session.load_document(document)
source = ColumnDataSource(data=dict(x=[], y=[], c=[], i=[]))
inf_source = ColumnDataSource(data=dict(facs=[]))
# radio buttons intialization
radio = RadioButtonGroup(labels=['Cancer Deaths', 'Heart Disease Deaths', 'Respiratory Disease Deaths', 'Diabetes Deaths'], active=0)
radio.on_click(update)
# calling update function to intialize the plot
update(0)
if __name__ == '__main__':
link = session.object_link(document.context)
print("Please visit %s to see the plots" % link)
view(link)
session.poll_document(document)
def add_controllers(self):
titles = {'npeople': 'Number of people',
'sensorinterval': 'Interval',
'sensortpr': 'True positive rate',
'sensorexfp': 'Expected num of FP',
'sensorrange': 'Sensor range',
'sensorsnum': 'Fleet size',
'sensorspeed': 'Fleet speed'}
idx0 = 1
stacked = []
#Create a controller for the scaling
def on_scaling_changed(_, _old, _new):
self.scaling = _new
self.update_plot()
self.scalingwidget = Select(title='Scaling', value='linear',
options=['linear', 'log'])
self.scalingwidget.on_change('value', on_scaling_changed)
stacked.append(self.scalingwidget)
# Create a controller for each param
for k in titles.keys():
def on_radio_changed(attr, old, new, kk):
if self.blocked: return
newvalue = self.config[kk][new-1] if new != FIXEDIDX else -1
print('Changed ' + str(kk) + ' to ' + str(newvalue))
self.currparams[kk] = newvalue
if new == FIXEDIDX:
self.blocked = True
for param in self.contr.keys():
if param == kk or self.contr[param].active != FIXEDIDX:
continue
self.contr[param].active = 1
self.currparams[param] = self.config[param][0]
self.var = kk
varyingparam = False
for kkk, vvv in self.currparams.items():
if vvv == -1:
varyingparam = True
break
if not varyingparam: self.var = None
self.update_plot()
if self.blocked: self.blocked = False
my_radio_changed = partial(on_radio_changed, kk=k)
params = ['varying'] + list(map(str, self.config[k]))
buttonisactive = FIXEDIDX if self.var == k else idx0
self.contr[k] = RadioButtonGroup(labels=params, active=buttonisactive)
self.contr[k].on_change('active', my_radio_changed)
self.currparams[k] = params[idx0]
r = Row(widgetbox(Div(text='{}:'.format(titles[k]))),
self.contr[k])
stacked.append(r)
self.currparams[self.var] = -1
adjwidget = Column(*stacked)
self.guirow.children[0] = adjwidget
source1.change.emit(); // update the arrays
source2.change.emit(); // update phis
""")
# define widgets
modes_slider = Slider(
start=2,
end=maxMode,
value=100,
step=1,
title="Number of Modes, $N$",
callback=callback1,
# callback_policy="mouseup"
)
lock_toggle = RadioButtonGroup(labels=["Modelocking", "No Modelocking"],
active=0,
callback=callback2)
callback1.args["modes"] = modes_slider
callback1.args["locking"] = lock_toggle
callback2.args["modes"] = modes_slider
callback2.args["locking"] = lock_toggle
maxI = np.amax(y)
meanI = np.mean(y)
print(meanI)
div1 = Div(
text="""
<div> Peak intensity of the current plot: </div> <div id="maxInfo" class="valueDiv"></div>
"""
def show_cells_on_stack(data,
stack,
X='X',
Y='Y',
channelNames=None,
group='group',
hue='hue',
palette='Spectral11',
default=0,
alpha=.5,
plot_width=500,
plot_height=500,
vmin=0,
vmax=3,
znorm=True):
'''
stack: np. array of shape (nchannels,height,width)
'''
from bokeh.models import CheckboxGroup, RadioButtonGroup, Legend, LegendItem
if not channelNames:
channelNames = ['Channel ' + str(i) for i in range(len(stack))]
print(channelNames)
s1 = ColumnDataSource(data=data)
p1 = figure(plot_width=plot_width,
plot_height=plot_height,
tools="pan,wheel_zoom,reset")
channels = {}
for i, channel in enumerate(channelNames):
img = stack[i]
if znorm:
img = (img - img.mean()) / img.std()
channels[i] = p1.image(image=[img],
x=[0],
y=[0],
dw=[plot_width],
dh=[plot_height],
color_mapper=LogColorMapper(palette=palette,
low=vmin,
high=vmax),
global_alpha=alpha,
visible=(i == default))
plots = {}
#scaled_coordinates to fit in stack_dw,stack_dh
dh_ratio = plot_height / img.shape[0]
dw_ratio = plot_width / img.shape[1]
data['warped_X'] = data[X] * dw_ratio
data['warped_Y'] = data[Y] * dh_ratio
groups = list(data[group].unique())
for g_id in groups:
s2 = ColumnDataSource(data=data[data[group] == g_id])
scatter = p1.circle('warped_X',
'warped_Y',
source=s2,
alpha=1,
color='hue') #,legend_label = str(g_id))
scatter.visible = False
plots[g_id] = scatter
select_celltype = CheckboxGroup(labels=groups, active=[], width=100)
select_channel = RadioButtonGroup(labels=channelNames,
active=default,
width=100,
orientation='horizontal')
select_celltype.callback = CustomJS(args={
'plots': plots,
'groups': groups,
'msel': select_celltype,
'fig': p1
},
code="""
//fig.title.text = 'new Title'
for (var i =0; i<groups.length;i++){
plots[groups[i]].visible = msel.active.indexOf(i)>-1
}
""")
select_channel.callback = CustomJS(args={
'channels': channels,
'sel': select_channel,
'fig': p1
},
code="""
//fig.title.text = Object.keys(channels).length.toString()
for (var i =0; i<Object.keys(channels).length;i++){
channels[i].visible = false
}
var val = sel.active
channels[val].visible = true
/**
if (val==0){
fig.title.text = 'confirm upper'
chan1.visible = true
chan2.visible = false
//bg_channel = [z[0]]
}else if (val==1){
fig.title.text = 'confirm lower'
chan1.visible = false
chan2.visible = true
//bg_channel = [z[1]]
}
**/
""")
layout = column(p1, select_celltype, select_channel)
show(layout)
step=1,
title="% Female")
slider62.on_change('value', update)
# 7) Etnicity
slider7 = RangeSlider(start=0,
end=100,
value=(0, 100),
step=1,
title="Diversity Index")
slider7.on_change('value', update)
# 8)
#Rent
div81 = Div(text="<b> Housing Rent per Sqft </b>")
radio_button81 = RadioButtonGroup(
labels=["No preference", "< 20 $", "20-30$", ">30 $"], active=0)
radio_button81.on_change('active', update)
#Crime
div82 = Div(text="<b>Felonies per 100.000 </b>")
radio_button82 = RadioButtonGroup(
labels=["No preference", "< 30", "30-60", "> 60"], active=0)
radio_button82.on_change('active', update)
table, m, div0 = create_plot()
#Combine all controls to get in column
col1 = row(table, m, height=200, width=1180)
col2 = column(div1, select1, slider21, slider22, slider3, slider4, width=210)
col3 = column(div5,
click_button = Button(label="Button still has click event", button_type="success")
disabled_button = Button(label="Button (disabled) - still has click event", button_type="primary", disabled=True)
toggle = Toggle(label="Toggle button", button_type="success")
menu = [("Item 1", "item_1_value"), ("Item 2", "item_2_value"), None, ("Item 3", "item_3_value")]
dropdown = Dropdown(label="Dropdown button", button_type="warning", menu=menu)
dropdown_split = Dropdown(label="Split button", button_type="danger", menu=menu, split=True)
checkbox_group = CheckboxGroup(labels=["Option 1", "Option 2", "Option 3"], active=[0, 1])
radio_group = RadioGroup(labels=["Option 1", "Option 2", "Option 3"], active=0)
checkbox_button_group = CheckboxButtonGroup(labels=["Option 1", "Option 2", "Option 3"], active=[0, 1])
radio_button_group = RadioButtonGroup(labels=["Option 1", "Option 2", "Option 3"], active=0)
text_input = TextInput(placeholder="Enter value ...")
completions = ["aaa", "aab", "aac", "baa", "caa"]
autocomplete_input = AutocompleteInput(placeholder="Enter value (auto-complete) ...", completions=completions)
select = Select(options=["Option 1", "Option 2", "Option 3"])
multi_select = MultiSelect(options=["Option %d" % (i+1) for i in range(16)], size=6)
slider = Slider(value=10, start=0, end=100, step=0.5)
range_slider = RangeSlider(value=[10, 90], start=0, end=100, step=0.5)
date_slider = DateSlider(value=date(2016, 1, 1), start=date(2015, 1, 1), end=date(2017, 12, 31))
def load_data():
curdoc().clear()
button_1 = Button(label="Load data")
button_1.on_click(load_data)
button_2 = Button(label="Advanced options")
button_2.on_click(Advanced_options)
curdoc().add_root(button_1)
curdoc().add_root(button_2)
datapath=filedialog.askopenfilename()
global Dsource, Csource, Isource, Ssource, data_new_masked, data3, CX2, CY2, data, YY, XX,SelectedIon, mzlabs
mz=pd.read_csv(datapath,sep='\t',skiprows=(0,1,2),header=None, nrows=1)
mz=mz.drop(columns=[0,1,2])
#lastmz=mz.columns[-1]
#mz=mz.drop(columns=[lastmz-1,lastmz])
data = pd.read_csv(datapath,sep='\t',skiprows=(0,1,2,3),header=None)
Xpixels=data[1].tolist()
Ypixels=data[2].tolist()
last=data.columns[-1]
data = data.drop(data.columns[[0, 1, 2,last-1,last]], axis=1)
ScanNum=data.index.tolist()
TotalScan=len(ScanNum)
mzlabs=mz.loc[0].values.tolist()
data.columns=mzlabs
data = data.reindex(sorted(data.columns), axis=1)
mzlabs.sort()
peakNum=len(mzlabs)
# Work out pixel dimensions- need to do try/ catch here
a=Xpixels[1]
Ypix=Xpixels.count(a)
Xpix=np.round(TotalScan/Ypix)
print(Ypix)
print(Xpix)
# Make sure Ypix * Xpix = total pix
# Do sum normalisation.. this will have multiple options
# This is not the real sum normalisation
# data_new = data.div(data.sum(axis=1), axis=0)
# This is a crude method for clearing background pixels based on lipid vs non-lipid
#low1=int(peakNum/2)
#low2=int(peakNum/2)
#high2=int(peakNum)
low1=min(range(len(mzlabs)), key=lambda x:abs(mzlabs[x]-50))
high1=min(range(len(mzlabs)), key=lambda x:abs(mzlabs[x]-200))
low2=min(range(len(mzlabs)), key=lambda x:abs(mzlabs[x]-750))
high2=min(range(len(mzlabs)), key=lambda x:abs(mzlabs[x]-900))
D1=data.iloc[:,low1:high1]
D2=data.iloc[:,low2:high2]
D1s = D1.sum(axis=1)
D1s=D1s+1
D2s = D2.sum(axis=1)
Ratio=D2s/D1s
Ratio.tolist()
del D1,D1s,D2,D2s
# This may be possible to do with only one copy of the data
data2=data
data2.loc[Ratio<2,:]=0
data3=data2.loc[~(data2==0).all(axis=1)]
del data2
data_new_masked = data3.div(data3.sum(axis=1), axis=0)
data_new_masked=data_new_masked.fillna(0)
# Do PCA data reduction
#Data_reduced=PCA(n_components=10).fit_transform(data_new)
Data_reduced=PCA(n_components=10).fit_transform(data_new_masked)
# Perform the UMAP - these paramaters will be adjustable
reducer = umap.UMAP(n_neighbors=10,min_dist=0.1,n_components=2,metric='euclidean')
embedding = reducer.fit_transform(Data_reduced)
# This can be replaced using the Xpix Ypix from above
YY=int(Ypix)
XX=int(Xpix)
CX=[]
for y in range(YY):
for x in range(XX):
CX.append(x)
CY=[]
for y in range(YY):
for x in range(XX):
CY.append(y)
idx=data3.index
CX2 = [CX[i] for i in idx]
CY2 = [CY[i] for i in idx]
# CX2=reverse(CX2)
# CY2=reverse(CY2)
#CX2=CX2[::-1]
#CY2=CY2[::-1]
# This defines the UMAP output as columns for the plotting tools
x2=embedding[:, 0]
y2=embedding[:, 1]
x3=x2-np.min(x2)
y3=y2-np.min(y2)
scannum= np.arange(0,TotalScan).tolist()
spectra=scannum
spectra2 = [spectra[i] for i in idx]
ColX=(x3/np.max(x3))*255
ColY=(y3/np.max(y3))*255
CV1 = ["#%02x%02x%02x" % (int(r), int(g), 0) for r, g in zip(ColX, ColY)]
CV2 = ["#%02x%02x%02x" % (0, int(r), int(g)) for r, g in zip(ColX, ColY)]
CV3 = ["#%02x%02x%02x" % (int(r), 0, int(g)) for r, g in zip(ColX, ColY)]
# Create the data sources required
Mean1=np.mean(data3) #.iloc[1,:]
Blank=[0]*len(CX2)
BlankMap = ["#%02x%02x%02x" % (0, 0, 0) for r in(ColX)]
CompData=Mean1/Mean1
Ssource = ColumnDataSource(data=dict(x=mzlabs,y=Mean1))
Dsource = ColumnDataSource(data=dict(x=x2, y=y2, cordsX=CX2,cordsY=CY2,CV=CV1,spectra=spectra2))
Csource = ColumnDataSource(data=dict(x=mzlabs,Region1=Mean1,Region2=Mean1,y=CompData))
Isource = ColumnDataSource(data=dict(cordsX=CX2,cordsY=CY2,Region1=Blank,Region2=Blank,Map=Blank))
# Set up the plot region (need to define min and max for right plot)
TOOLS="lasso_select, box_select,pan,wheel_zoom,box_zoom,reset"
Right=figure(title="UMAP output",plot_width=500,plot_height=500,x_range=[-15,15], y_range=[-15,15],tools=TOOLS)
Left=figure(plot_width=500,plot_height=500,title=None,x_range=[0,XX], y_range=[0,YY],tools=TOOLS)
Left.axis.visible = False
Results=figure(plot_width=500,plot_height=400,title=None,x_range=[0,1200],tools="pan,wheel_zoom,box_zoom,reset,tap",x_axis_label='m/z',y_axis_label='log2 fold change')
Spectrum=figure(plot_width=500,plot_height=400,title=None,x_range=[0,1200],x_axis_label='m/z',y_axis_label='mean intensity')
SelectedIon=figure(plot_width=300,plot_height=300,title="Selected ion image",title_location = "below",x_range=[0,XX], y_range=[0,YY])
SelectedIon.axis.visible = False
Regions=figure(plot_width=200,plot_height=200,title=None,x_range=[0,Xpix], y_range=[0,Ypix],align="center")
Regions.axis.visible = False
Results.add_tools(HoverTool(
tooltips = [
("m/z", "@x"),
("fold change", "@y"),
],
mode='mouse',
point_policy='snap_to_data'
))
# Populate the initial plots
r=Right.scatter(x='x',y='y',fill_color='CV',line_color=None,source=Dsource,radius=0.1)
Left.square(x='cordsX',y='cordsY',fill_color='CV',line_color=None,alpha=1,size=5, source=Dsource)
#Spectrum.line(x='x',y='y',source=Ssource)
#Results.line(x='x',y='y',source=Csource)
Spectrum.vbar(x='x',top='y',source=Ssource,width=0.5)
Results.vbar(x='x',top='y',source=Csource,width=0.5)
Regions.square(x='cordsX',y='cordsY',fill_color='Map',line_color=None,alpha=1,size=5, source=Isource)
callback = CustomJS(args=dict(renderer=r), code="""
renderer.glyph.radius = cb_obj.value;
""")
slider1 = Slider(start=0.01, end=1, step=0.01, value=0.1,title='circle size')
slider1.js_on_change('value', callback)
#text = TextInput(title="title", value='Insert experiment name')
button_group = RadioButtonGroup(labels=["Red Green", "Red Blue", "Blue Green"], active=0)
select = Select(title="Option:", value="foo", options=["Sum normalisation", "No normalisation", "Mean normalisation", "Median normalisation"])
button_1 = Button(label="Load data")
button_2 = Button(label="Reset data")
button_3 = Button(label="Select region 1")
button_4 = Button(label="Select region 2")
button_5 = Button(label="Compare")
button_6 = Button(label="Output report")
# These are the list of actions possible
#text.on_change('value', update_title)
button_1.on_click(load_data)
button_3.on_click(Region_1)
button_4.on_click(Region_2)
button_5.on_click(Compare_data)
button_6.on_click(Output)
Dsource.selected.on_change('indices', update_data)
#taptool = Results.select(type=TapTool)
Csource.selected.on_change('indices',create_ion_map)
p = gridplot([[Left,Right,column(slider1,select, button_3,button_4,Regions,button_5,button_6)],[Spectrum,Results,SelectedIon]])
curdoc().add_root(p)
def __init__(self, n_nodes):
self.i = 0
kps = [crypto_sign_keypair() for _ in range(n_nodes)]
stake = {kp[0]: 1 for kp in kps}
network = {}
self.nodes = [Node(kp, network, n_nodes, stake) for kp in kps]
for n in self.nodes:
network[n.pk] = n.ask_sync
self.ids = {kp[0]: i for i, kp in enumerate(kps)}
self.main_its = [n.main() for n in self.nodes]
for m in self.main_its:
next(m)
def toggle():
if play.label == '► Play':
play.label = '❚❚ Pause'
curdoc().add_periodic_callback(self.animate, 50)
else:
play.label = '► Play'
curdoc().remove_periodic_callback(self.animate)
play = Button(label='► Play', width=60)
play.on_click(toggle)
def sel_node(new):
self.active = new
node = self.nodes[new]
self.tbd = {}
self.tr_src.data, self.links_src.data = self.extract_data(
node, bfs((node.head,), lambda u: node.hg[u].p), 0)
for u, j in tuple(self.tbd.items()):
self.tr_src.data['line_alpha'][j] = 1 if node.famous.get(u) else 0
if u in node.idx:
self.tr_src.data['round_color'][j] = idx_color(node.idx[u])
self.tr_src.data['idx'][j] = node.idx.get(u)
if u in node.idx and u in node.famous:
del self.tbd[u]
print('updated')
self.tr_src.trigger('data', None, self.tr_src.data)
selector = RadioButtonGroup(
labels=['Node %i' % i for i in range(n_nodes)], active=0,
name='Node to inspect')
selector.on_click(sel_node)
plot = figure(
plot_height=700, plot_width=900, y_range=(0, 30),
tools=[PanTool(dimensions=['height']),
HoverTool(tooltips=[
('round', '@round'), ('hash', '@hash'),
('timestamp', '@time'), ('payload', '@payload'),
('number', '@idx')])])
plot.xgrid.grid_line_color = None
plot.xaxis.minor_tick_line_color = None
plot.ygrid.grid_line_color = None
plot.yaxis.minor_tick_line_color = None
self.links_src = ColumnDataSource(data={'x0': [], 'y0': [], 'x1': [],
'y1': [], 'width': []})
#self.links_rend = plot.add_layout(
# Arrow(end=NormalHead(fill_color='black'), x_start='x0', y_start='y0', x_end='x1',
# y_end='y1', source=self.links_src))
self.links_rend = plot.segment(color='#777777',
x0='x0', y0='y0', x1='x1',
y1='y1', source=self.links_src, line_width='width')
self.tr_src = ColumnDataSource(
data={'x': [], 'y': [], 'round_color': [], 'idx': [],
'line_alpha': [], 'round': [], 'hash': [], 'payload': [],
'time': []})
self.tr_rend = plot.circle(x='x', y='y', size=20, color='round_color',
line_alpha='line_alpha', source=self.tr_src, line_width=5)
sel_node(0)
curdoc().add_root(row([widgetbox(play, selector, width=300), plot], sizing_mode='fixed'))
def setup_widgets(self):
# Generation of selectable items
# Dict contains all inspectable runs (maps display strings to timestamps)
# The dict structure allows to get the timestamp from the display string
# in O(1)
self.runs_dict = helper.gen_runs_selection(self.metadata)
# Dict maps display strings to column names for the different factors
# (var, backend, test)
self.factors_dict = {
"Variables": "variable",
"Backends": "vfc_backend",
"Tests": "test"
}
# Run selection
# Contains all options strings
runs_display = list(self.runs_dict.keys())
# Will be used when updating plots (contains actual number)
self.current_run = self.runs_dict[runs_display[-1]]
# Contains the selected option string, used to update current_n_runs
current_run_display = runs_display[-1]
# This contains only entries matching the run
self.run_data = self.data[self.data["timestamp"] == self.current_run]
change_run_callback_js = "updateRunMetadata(cb_obj.value, \"\");"
self.widgets["select_run"] = Select(name="select_run",
title="Run :",
value=current_run_display,
options=runs_display)
self.doc.add_root(self.widgets["select_run"])
self.widgets["select_run"].on_change("value", self.update_run)
self.widgets["select_run"].js_on_change(
"value",
CustomJS(
code=change_run_callback_js,
args=(dict(metadata=helper.metadata_to_dict(
helper.get_metadata(self.metadata, self.current_run))))))
# Factors selection
# "Group by" radio
self.widgets["groupby_radio"] = RadioButtonGroup(
name="groupby_radio",
labels=list(self.factors_dict.keys()),
active=0)
self.doc.add_root(self.widgets["groupby_radio"])
# The functions are defined inside the template to avoid writing too
# much JS server side
self.widgets["groupby_radio"].on_change("active", self.update_groupby)
# "Filter by" radio
# Get all possible factors, and remove the one selected in "Group by"
filterby_list = list(self.factors_dict.keys())
del filterby_list[self.widgets["groupby_radio"].active]
self.widgets["filterby_radio"] = RadioButtonGroup(
name="filterby_radio", labels=filterby_list, active=0)
self.doc.add_root(self.widgets["filterby_radio"])
# The functions are defined inside the template to avoid writing too
# much JS server side
self.widgets["filterby_radio"].on_change("active",
self.update_filterby)
# Filter selector
filterby = self.widgets["filterby_radio"].labels[
self.widgets["filterby_radio"].active]
filterby = self.factors_dict[filterby]
if not self.run_data.empty:
options = self.run_data.index\
.get_level_values(filterby).drop_duplicates().tolist()
else:
options = ["None"]
self.widgets["select_filter"] = Select(
# We need a different name to avoid collision in the template with
# the runs comparison's widget
name="select_filter",
title="Select a filter :",
value=options[0],
options=options)
self.doc.add_root(self.widgets["select_filter"])
self.widgets["select_filter"]\
.on_change("value", self.update_filter)
# Toggle for outliers filtering
self.widgets["outliers_filtering_inspect"] = CheckboxGroup(
name="outliers_filtering_inspect",
labels=["Filter outliers"],
active=[])
self.doc.add_root(self.widgets["outliers_filtering_inspect"])
self.widgets["outliers_filtering_inspect"]\
.on_change("active", self.update_outliers_filtering)
'Orange',
]
df = pd.DataFrame({'x': x, 'y': y, 'label': label})
source = ColumnDataSource(data=dict(x=df.x, y=df.y, label=df.label))
plot_figure = figure(title='Radio Button Group',
plot_height=450,
plot_width=600,
tools="save,reset",
toolbar_location="below")
plot_figure.scatter('x', 'y', color='label', source=source, size=10)
radio_button_group = RadioButtonGroup(labels=["Red", "Orange"])
def radiogroup_click(attr, old, new):
active_radio = radio_button_group.active ##Getting radio button value
# filter the dataframe with value in radio-button
if active_radio == 0:
selected_df = df[df['label'] == 'Red']
elif active_radio == 1:
selected_df = df[df['label'] == "Orange"]
source.data = dict(x=selected_df.x,
y=selected_df.y,
label=selected_df.label)
value=selected_dataset)
measure_1 = Select(title="Measure",
options=[
('cosine_similarity', 'Cosine Similarity'),
('cosine', 'Cosine Distance'),
('euclidean', 'Euclidean'),
('dot_product', 'Dot product'),
('correlation', 'Correlation'),
],
value='cosine_similarity')
x_axis = TextInput(title="X Axis Formula", placeholder="formula")
y_axis = TextInput(title="Y Axis Formula", placeholder="formula")
explicit_tab = Panel(child=column(measure_1, x_axis, y_axis), title="Explicit")
filtering_before_after_2 = RadioButtonGroup(
labels=["Filter before projection", "Filter after projection"], active=0)
pca_tab = Panel(child=filtering_before_after_2, title="PCA")
measure_3 = Select(title="Measure",
options=[
('cosine', 'Cosine Distance'),
('euclidean', 'Euclidean'),
('dot_product', 'Dot product'),
('correlation', 'Correlation'),
],
value='cosine')
filtering_before_after_3 = RadioButtonGroup(
labels=["Filter before projection", "Filter after projection"], active=0)
tsne_tab = Panel(child=column(measure_3, filtering_before_after_3),
title="t-SNE")
def build_lockdown_tab():
# Get data for lockdown tab
lockdown_data = get_lockdown_data()
lockdown_data = prep_lockdown_data(lockdown_data)
source_gantt = ColumnDataSource(lockdown_data.dropna())
source_points = ColumnDataSource(lockdown_data)
# Create lockdown figure
lockdown_fig = figure(
y_range=FactorRange(factors=lockdown_data.Country.unique().tolist()),
x_axis_type='datetime',
title="Lockdown Status by Nation",
x_range=(lockdown_data['start_date'].min() - timedelta(days=3),
lockdown_data['end_date'].max() + timedelta(days=3)),
outline_line_color=None,
width=1000,
height=650)
# Adding hbar glyph of lockdown dates
gantt_plot = lockdown_fig.hbar(y="Country",
left='start_date',
right='end_date',
height=0.4,
source=source_gantt,
color='color')
# Adding start point circle glyph
start_point = lockdown_fig.circle(x='start_date',
y='Country',
source=source_points,
size=5,
line_color='blue',
fill_color='white')
# Adding end point circle glyph
end_point = lockdown_fig.circle(x='end_date',
y='Country',
source=source_points,
size=5,
line_color='blue',
fill_color='white')
# Formatting x-axis
lockdown_fig.xaxis.axis_label = "Date"
lockdown_fig.xaxis.formatter = DatetimeTickFormatter(days='%d/%m/%Y')
lockdown_fig.xaxis.ticker = DaysTicker(days=np.arange(5, 365, 7))
lockdown_fig.xaxis.major_label_orientation = math.pi / 6
# Formatting y-axis
lockdown_fig.yaxis.axis_label = "Country"
lockdown_fig.yaxis.major_label_orientation = math.pi / 12
lockdown_fig.yaxis.major_label_text_font_size = "7pt"
lockdown_fig.yaxis.major_label_text_font_style = "bold"
lockdown_fig.ygrid.grid_line_color = None
lockdown_fig.y_range.range_padding = 0.05
# Align grid and axis tickers
lockdown_fig.xgrid[0].ticker = lockdown_fig.xaxis[0].ticker
# Adding hover tools
gantt_hover = HoverTool(renderers=[gantt_plot],
tooltips=[('Country', '@Country'),
('Start Date', '@start_date{%d/%m/%Y}'),
('End Date', '@end_date{%d/%m/%Y}'),
('Length', '@length{%d days}')],
formatters={
'@start_date': 'datetime',
'@end_date': 'datetime',
'@length': 'printf'
})
start_hover = HoverTool(renderers=[start_point],
tooltips=[('Country', '@Country'),
('Start Date', '@start_date{%d/%m/%Y}')],
formatters={'@start_date': 'datetime'})
end_hover = HoverTool(renderers=[end_point],
tooltips=[('Country', '@Country'),
('End Date', '@end_date{%d/%m/%Y}')],
formatters={'@end_date': 'datetime'})
lockdown_fig.add_tools(gantt_hover, start_hover, end_hover)
# Adding vertical span for today's date
today_date_span = Span(location=datetime.today(),
dimension='height',
line_color='blue',
line_width=3,
line_dash=[6, 6])
lockdown_fig.add_layout(today_date_span)
# Labelling span
span_label = Label(x=datetime.today() + timedelta(hours=12),
y=-1.2,
y_units='screen',
text='Current Date',
text_font_size='12pt')
lockdown_fig.add_layout(span_label)
# Adding CheckboxButtonGroup for continents
continents = lockdown_data['continent'].unique().tolist()
continent_rbg = RadioButtonGroup(labels=continents, active=None, width=500)
def continent_rbg_callback(attr, old, new):
if continent_rbg.active != None:
selected_continent = continent_rbg.labels[continent_rbg.active]
filtered_df = lockdown_data.loc[lockdown_data.continent ==
selected_continent]
gantt_cds = ColumnDataSource(filtered_df.dropna())
points_cds = ColumnDataSource(filtered_df)
source_gantt.data.update(gantt_cds.data)
source_points.data.update(points_cds.data)
lockdown_fig.y_range.factors = filtered_df.Country.unique().tolist(
)
# Synchronise country filter
country_multiselect.options = filtered_df['Country'].unique(
).tolist()
country_multiselect.value = filtered_df['Country'].unique().tolist(
)
continent_rbg.on_change('active', continent_rbg_callback)
# Adding MultiSelect Widget for filtering by country
countries = lockdown_data['Country'].unique().tolist()
country_multiselect = MultiSelect(title='Countries:',
options=countries,
value=countries,
height=500)
def country_multiselect_callback(attr, old, new):
selected_countries = country_multiselect.value
filter_condition = lockdown_data.Country.isin(selected_countries)
filtered_df = lockdown_data.loc[filter_condition]
gantt_cds = ColumnDataSource(filtered_df.dropna())
points_cds = ColumnDataSource(filtered_df)
source_gantt.data.update(gantt_cds.data)
source_points.data.update(points_cds.data)
lockdown_fig.y_range.factors = filtered_df.Country.unique().tolist()
# Synchronise continent filter
continent_rbg.active = None
country_multiselect.on_change('value', country_multiselect_callback)
# Creating Clear All, Select All Buttons
def clear_button_callback(event):
country_multiselect.options = countries
country_multiselect.value = []
continent_rbg.active = None
lockdown_fig.y_range.factors = lockdown_data.Country.unique().tolist()
def select_all_button_callback(event):
country_multiselect.options = countries
country_multiselect.value = countries
continent_rbg.active = None
lockdown_fig.y_range.factors = lockdown_data.Country.unique().tolist()
clear_button = Button(label="Clear Selection", button_type="success")
clear_button.on_click(clear_button_callback)
select_all_button = Button(label="Select All", button_type="success")
select_all_button.on_click(select_all_button_callback)
# Add the plot to the current document and add a title
layout = row(
widgetbox(clear_button, select_all_button, continent_rbg,
country_multiselect), lockdown_fig)
layout.sizing_mode = 'scale_width'
# Creating lockdown tab
lockdown_tab = Panel(child=layout, title='Lockdown')
return (lockdown_tab)
def create():
config = pyzebra.AnatricConfig()
def _load_config_file(file):
config.load_from_file(file)
logfile_textinput.value = config.logfile
logfile_verbosity_select.value = config.logfile_verbosity
filelist_type.value = config.filelist_type
filelist_format_textinput.value = config.filelist_format
filelist_datapath_textinput.value = config.filelist_datapath
filelist_ranges_textareainput.value = "\n".join(
map(str, config.filelist_ranges))
crystal_sample_textinput.value = config.crystal_sample
lambda_textinput.value = config.crystal_lambda
zeroOM_textinput.value = config.crystal_zeroOM
zeroSTT_textinput.value = config.crystal_zeroSTT
zeroCHI_textinput.value = config.crystal_zeroCHI
ub_textareainput.value = config.crystal_UB
dataFactory_implementation_select.value = config.dataFactory_implementation
dataFactory_dist1_textinput.value = config.dataFactory_dist1
reflectionPrinter_format_select.value = config.reflectionPrinter_format
set_active_widgets(config.algorithm)
if config.algorithm == "adaptivemaxcog":
threshold_textinput.value = config.threshold
shell_textinput.value = config.shell
steepness_textinput.value = config.steepness
duplicateDistance_textinput.value = config.duplicateDistance
maxequal_textinput.value = config.maxequal
aps_window_textinput.value = str(
tuple(map(int, config.aps_window.values())))
elif config.algorithm == "adaptivedynamic":
adm_window_textinput.value = str(
tuple(map(int, config.adm_window.values())))
border_textinput.value = str(
tuple(map(int, config.border.values())))
minWindow_textinput.value = str(
tuple(map(int, config.minWindow.values())))
reflectionFile_textinput.value = config.reflectionFile
targetMonitor_textinput.value = config.targetMonitor
smoothSize_textinput.value = config.smoothSize
loop_textinput.value = config.loop
minPeakCount_textinput.value = config.minPeakCount
displacementCurve_textinput.value = "\n".join(
map(str, config.displacementCurve))
else:
raise ValueError("Unknown processing mode.")
def set_active_widgets(implementation):
if implementation == "adaptivemaxcog":
mode_radio_button_group.active = 0
disable_adaptivemaxcog = False
disable_adaptivedynamic = True
elif implementation == "adaptivedynamic":
mode_radio_button_group.active = 1
disable_adaptivemaxcog = True
disable_adaptivedynamic = False
else:
raise ValueError(
"Implementation can be either 'adaptivemaxcog' or 'adaptivedynamic'"
)
threshold_textinput.disabled = disable_adaptivemaxcog
shell_textinput.disabled = disable_adaptivemaxcog
steepness_textinput.disabled = disable_adaptivemaxcog
duplicateDistance_textinput.disabled = disable_adaptivemaxcog
maxequal_textinput.disabled = disable_adaptivemaxcog
aps_window_textinput.disabled = disable_adaptivemaxcog
adm_window_textinput.disabled = disable_adaptivedynamic
border_textinput.disabled = disable_adaptivedynamic
minWindow_textinput.disabled = disable_adaptivedynamic
reflectionFile_textinput.disabled = disable_adaptivedynamic
targetMonitor_textinput.disabled = disable_adaptivedynamic
smoothSize_textinput.disabled = disable_adaptivedynamic
loop_textinput.disabled = disable_adaptivedynamic
minPeakCount_textinput.disabled = disable_adaptivedynamic
displacementCurve_textinput.disabled = disable_adaptivedynamic
upload_div = Div(text="Open XML configuration file:")
def upload_button_callback(_attr, _old, new):
with io.BytesIO(base64.b64decode(new)) as file:
_load_config_file(file)
upload_button = FileInput(accept=".xml")
upload_button.on_change("value", upload_button_callback)
# General parameters
# ---- logfile
def logfile_textinput_callback(_attr, _old, new):
config.logfile = new
logfile_textinput = TextInput(title="Logfile:",
value="logfile.log",
width=520)
logfile_textinput.on_change("value", logfile_textinput_callback)
def logfile_verbosity_select_callback(_attr, _old, new):
config.logfile_verbosity = new
logfile_verbosity_select = Select(title="verbosity:",
options=["0", "5", "10", "15", "30"],
width=70)
logfile_verbosity_select.on_change("value",
logfile_verbosity_select_callback)
# ---- FileList
def filelist_type_callback(_attr, _old, new):
config.filelist_type = new
filelist_type = Select(title="File List:",
options=["TRICS", "SINQ"],
width=100)
filelist_type.on_change("value", filelist_type_callback)
def filelist_format_textinput_callback(_attr, _old, new):
config.filelist_format = new
filelist_format_textinput = TextInput(title="format:", width=490)
filelist_format_textinput.on_change("value",
filelist_format_textinput_callback)
def filelist_datapath_textinput_callback(_attr, _old, new):
config.filelist_datapath = new
filelist_datapath_textinput = TextInput(title="datapath:")
filelist_datapath_textinput.on_change(
"value", filelist_datapath_textinput_callback)
def filelist_ranges_textareainput_callback(_attr, _old, new):
ranges = []
for line in new.splitlines():
ranges.append(re.findall(r"\b\d+\b", line))
config.filelist_ranges = ranges
filelist_ranges_textareainput = TextAreaInput(title="ranges:", height=100)
filelist_ranges_textareainput.on_change(
"value", filelist_ranges_textareainput_callback)
# ---- crystal
def crystal_sample_textinput_callback(_attr, _old, new):
config.crystal_sample = new
crystal_sample_textinput = TextInput(title="Sample Name:")
crystal_sample_textinput.on_change("value",
crystal_sample_textinput_callback)
def lambda_textinput_callback(_attr, _old, new):
config.crystal_lambda = new
lambda_textinput = TextInput(title="lambda:", width=140)
lambda_textinput.on_change("value", lambda_textinput_callback)
def ub_textareainput_callback(_attr, _old, new):
config.crystal_UB = new
ub_textareainput = TextAreaInput(title="UB matrix:", height=100)
ub_textareainput.on_change("value", ub_textareainput_callback)
def zeroOM_textinput_callback(_attr, _old, new):
config.crystal_zeroOM = new
zeroOM_textinput = TextInput(title="zeroOM:", width=140)
zeroOM_textinput.on_change("value", zeroOM_textinput_callback)
def zeroSTT_textinput_callback(_attr, _old, new):
config.crystal_zeroSTT = new
zeroSTT_textinput = TextInput(title="zeroSTT:", width=140)
zeroSTT_textinput.on_change("value", zeroSTT_textinput_callback)
def zeroCHI_textinput_callback(_attr, _old, new):
config.crystal_zeroCHI = new
zeroCHI_textinput = TextInput(title="zeroCHI:", width=140)
zeroCHI_textinput.on_change("value", zeroCHI_textinput_callback)
# ---- DataFactory
def dataFactory_implementation_select_callback(_attr, _old, new):
config.dataFactory_implementation = new
dataFactory_implementation_select = Select(
title="DataFactory implementation:",
options=DATA_FACTORY_IMPLEMENTATION,
width=300,
)
dataFactory_implementation_select.on_change(
"value", dataFactory_implementation_select_callback)
def dataFactory_dist1_textinput_callback(_attr, _old, new):
config.dataFactory_dist1 = new
dataFactory_dist1_textinput = TextInput(title="dist1:", width=290)
dataFactory_dist1_textinput.on_change(
"value", dataFactory_dist1_textinput_callback)
# ---- BackgroundProcessor
# ---- DetectorEfficency
# ---- ReflectionPrinter
def reflectionPrinter_format_select_callback(_attr, _old, new):
config.reflectionPrinter_format = new
reflectionPrinter_format_select = Select(
title="ReflectionPrinter format:",
options=REFLECTION_PRINTER_FORMATS,
width=300,
)
reflectionPrinter_format_select.on_change(
"value", reflectionPrinter_format_select_callback)
# Adaptive Peak Detection (adaptivemaxcog)
# ---- threshold
def threshold_textinput_callback(_attr, _old, new):
config.threshold = new
threshold_textinput = TextInput(title="Threshold:")
threshold_textinput.on_change("value", threshold_textinput_callback)
# ---- shell
def shell_textinput_callback(_attr, _old, new):
config.shell = new
shell_textinput = TextInput(title="Shell:")
shell_textinput.on_change("value", shell_textinput_callback)
# ---- steepness
def steepness_textinput_callback(_attr, _old, new):
config.steepness = new
steepness_textinput = TextInput(title="Steepness:")
steepness_textinput.on_change("value", steepness_textinput_callback)
# ---- duplicateDistance
def duplicateDistance_textinput_callback(_attr, _old, new):
config.duplicateDistance = new
duplicateDistance_textinput = TextInput(title="Duplicate Distance:")
duplicateDistance_textinput.on_change(
"value", duplicateDistance_textinput_callback)
# ---- maxequal
def maxequal_textinput_callback(_attr, _old, new):
config.maxequal = new
maxequal_textinput = TextInput(title="Max Equal:")
maxequal_textinput.on_change("value", maxequal_textinput_callback)
# ---- window
def aps_window_textinput_callback(_attr, _old, new):
config.aps_window = dict(
zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
aps_window_textinput = TextInput(title="Window (x, y, z):")
aps_window_textinput.on_change("value", aps_window_textinput_callback)
# Adaptive Dynamic Mask Integration (adaptivedynamic)
# ---- window
def adm_window_textinput_callback(_attr, _old, new):
config.adm_window = dict(
zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
adm_window_textinput = TextInput(title="Window (x, y, z):")
adm_window_textinput.on_change("value", adm_window_textinput_callback)
# ---- border
def border_textinput_callback(_attr, _old, new):
config.border = dict(zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
border_textinput = TextInput(title="Border (x, y, z):")
border_textinput.on_change("value", border_textinput_callback)
# ---- minWindow
def minWindow_textinput_callback(_attr, _old, new):
config.minWindow = dict(
zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
minWindow_textinput = TextInput(title="Min Window (x, y, z):")
minWindow_textinput.on_change("value", minWindow_textinput_callback)
# ---- reflectionFile
def reflectionFile_textinput_callback(_attr, _old, new):
config.reflectionFile = new
reflectionFile_textinput = TextInput(title="Reflection File:")
reflectionFile_textinput.on_change("value",
reflectionFile_textinput_callback)
# ---- targetMonitor
def targetMonitor_textinput_callback(_attr, _old, new):
config.targetMonitor = new
targetMonitor_textinput = TextInput(title="Target Monitor:")
targetMonitor_textinput.on_change("value",
targetMonitor_textinput_callback)
# ---- smoothSize
def smoothSize_textinput_callback(_attr, _old, new):
config.smoothSize = new
smoothSize_textinput = TextInput(title="Smooth Size:")
smoothSize_textinput.on_change("value", smoothSize_textinput_callback)
# ---- loop
def loop_textinput_callback(_attr, _old, new):
config.loop = new
loop_textinput = TextInput(title="Loop:")
loop_textinput.on_change("value", loop_textinput_callback)
# ---- minPeakCount
def minPeakCount_textinput_callback(_attr, _old, new):
config.minPeakCount = new
minPeakCount_textinput = TextInput(title="Min Peak Count:")
minPeakCount_textinput.on_change("value", minPeakCount_textinput_callback)
# ---- displacementCurve
def displacementCurve_textinput_callback(_attr, _old, new):
maps = []
for line in new.splitlines():
maps.append(re.findall(r"\d+(?:\.\d+)?", line))
config.displacementCurve = maps
displacementCurve_textinput = TextAreaInput(
title="Displacement Curve (twotheta, x, y):", height=100)
displacementCurve_textinput.on_change(
"value", displacementCurve_textinput_callback)
def mode_radio_button_group_callback(active):
if active == 0:
config.algorithm = "adaptivemaxcog"
set_active_widgets("adaptivemaxcog")
else:
config.algorithm = "adaptivedynamic"
set_active_widgets("adaptivedynamic")
mode_radio_button_group = RadioButtonGroup(
labels=["Adaptive Peak Detection", "Adaptive Dynamic Integration"],
active=0)
mode_radio_button_group.on_click(mode_radio_button_group_callback)
set_active_widgets("adaptivemaxcog")
def process_button_callback():
with tempfile.TemporaryDirectory() as temp_dir:
temp_file = temp_dir + "/temp.xml"
config.save_as(temp_file)
pyzebra.anatric(temp_file)
with open(config.logfile) as f_log:
output_log.value = f_log.read()
process_button = Button(label="Process", button_type="primary")
process_button.on_click(process_button_callback)
output_log = TextAreaInput(title="Logfile output:",
height=700,
disabled=True)
output_config = TextAreaInput(title="Current config:",
height=700,
width=400,
disabled=True)
tab_layout = row(
column(
upload_div,
upload_button,
row(logfile_textinput, logfile_verbosity_select),
row(filelist_type, filelist_format_textinput),
filelist_datapath_textinput,
filelist_ranges_textareainput,
crystal_sample_textinput,
row(lambda_textinput, zeroOM_textinput, zeroSTT_textinput,
zeroCHI_textinput),
ub_textareainput,
row(dataFactory_implementation_select,
dataFactory_dist1_textinput),
reflectionPrinter_format_select,
process_button,
),
column(
mode_radio_button_group,
row(
column(
threshold_textinput,
shell_textinput,
steepness_textinput,
duplicateDistance_textinput,
maxequal_textinput,
aps_window_textinput,
),
column(
adm_window_textinput,
border_textinput,
minWindow_textinput,
reflectionFile_textinput,
targetMonitor_textinput,
smoothSize_textinput,
loop_textinput,
minPeakCount_textinput,
displacementCurve_textinput,
),
),
),
output_config,
output_log,
)
async def update_config():
config.save_as("debug.xml")
with open("debug.xml") as f_config:
output_config.value = f_config.read()
curdoc().add_periodic_callback(update_config, 1000)
return Panel(child=tab_layout, title="Anatric")
Button(label="Primary Button 2", button_type="primary"),
Button(label="Success Button 3", button_type="success"),
Toggle(label="Default Toggle 1", button_type="default"),
Toggle(label="Primary Toggle 2", button_type="primary"),
Toggle(label="Success Toggle 3", button_type="success"),
Dropdown(label="Default Dropdown 1", button_type="default", menu=menu),
Dropdown(label="Primary Dropdown 2", button_type="primary", menu=menu),
Dropdown(label="Success Dropdown 3", button_type="success", menu=menu),
CheckboxButtonGroup(labels=["Checkbox Option 1", "Checkbox Option 2", "Checkbox Option 3"], button_type="default", active=[0, 1]),
CheckboxButtonGroup(labels=["Checkbox Option 4", "Checkbox Option 5", "Checkbox Option 6"], button_type="primary", active=[1, 2]),
CheckboxButtonGroup(labels=["Checkbox Option 7", "Checkbox Option 8", "Checkbox Option 9"], button_type="success", active=[0, 2]),
RadioButtonGroup(labels=["Radio Option 1", "Radio Option 2", "Radio Option 3"], button_type="default", active=0),
RadioButtonGroup(labels=["Radio Option 4", "Radio Option 5", "Radio Option 6"], button_type="primary", active=1),
RadioButtonGroup(labels=["Radio Option 7", "Radio Option 8", "Radio Option 9"], button_type="success", active=2),
TextInput(placeholder="TextInput 1"),
TextInput(placeholder="TextInput 2"),
TextInput(placeholder="TextInput 3"),
AutocompleteInput(placeholder="AutocompleteInput 1 ...", completions=["aaa", "aab", "aac", "baa", "caa"]),
AutocompleteInput(placeholder="AutocompleteInput 2 ...", completions=["AAA", "AAB", "AAC", "BAA", "CAA"]),
AutocompleteInput(placeholder="AutocompleteInput 3 ...", completions=["000", "001", "002", "100", "200"]),
DatePicker(value=date(2018, 9, 1)),
DatePicker(value=date(2018, 9, 2)),
DatePicker(value=date(2018, 9, 3)),
)
map_plot_glyphs[i].glyph.line_color = "#8f8f8f"
elevation_graph_glyphs[i].glyph.line_alpha = 0.2
gradient_graph_glyphs[i].glyph.line_alpha = 0.2
map_plot_glyphs[i].glyph.line_alpha = 0.3
gradient_histogram_glyphs[i].visible = False
###############################
# Route selection pane (left) #
###############################
origin_input = TextInput(width=400, title="Origin")
destination_input = TextInput(width=400, title="Destination")
type_input = RadioButtonGroup(
labels=["Bicycle", "Driving", "Walking"],
width=400,
active=0
)
go_button = Button(width=400, label="Search", button_type="success")
go_button.on_click(find_routes)
alert_holder = PreText(text="", css_classes=['hidden'], visible=False)
alert = CustomJS(args={}, code='alert(cb_obj.text);')
alert_holder.js_on_change("text", alert)
result_picker = RadioGroup(labels=[], width=400)
result_picker.on_change("active", display_results)
route_selection_pane = column(
Div(text="<h2>Search</h2>"),
origin_input,
width=5,
height=1,
color=DARK_GRAY)
# create components --------------
title_div = Div(
text=
'Revenue Impact of a 4% Surtax on Taxpayers with Adjusted Gross Income over $5 Million',
height=30)
radio_group_text = Div(text="Include Additional Capital Gains Behavior.")
radio_group = RadioGroup(labels=["Without", "With"], active=0)
sources['radio_group'] = radio_group
elasticity_text = Div(text="Elasticity of Taxable Income")
elasticity_option = RadioButtonGroup(
labels=sources['capital_gains_no'].data['labels'], active=0)
sources['elasticity_option'] = elasticity_option
# create callbacks ---------------
radio_group.callback = CustomJS(args=sources,
code="""
var capitalGainsType = radio_group.active === 0 ? capital_gains_no : capital_gains_yes;
elasticity_option.set('labels', capitalGainsType.data.labels);
while (elasticity_option.active > capitalGainsType.data.source_ids.length - 1) {
elasticity_option.active = elasticity_option.active - 1;
}
ref.data = eval(capitalGainsType.data.source_ids[elasticity_option.active]).data;
ref.trigger('change');
""")
def barchart(df):
def make_dataset(df, usageType):
df_verwerkt = df[df['attributeName'] == usageType]
df_verwerkt = df_verwerkt.sort_values('time')
df_verwerkt = df_verwerkt.replace(0, np.NaN)
df_verwerkt = df_verwerkt.fillna(method='ffill')
df_verwerkt = df_verwerkt.fillna(
method='bfill') # nodig om eerste rij te fixen.
df_verwerkt['time'] = pd.to_datetime(df_verwerkt['time'],
unit='ms',
utc=True)
df_verwerkt['time'] = df_verwerkt['time'].apply(
lambda x: x.astimezone(timezone('Europe/Amsterdam')))
df_verwerkt['hour'] = df_verwerkt['time'].dt.hour
df_verwerkt['minutes'] = (df_verwerkt['time'].dt.hour *
60) + df_verwerkt['time'].dt.minute
df_verwerkt['dayofweek'] = df_verwerkt['time'].dt.dayofweek
df_verwerkt['difference'] = df_verwerkt['value'].diff()
df_verwerkt['difference'] = df_verwerkt['difference'].fillna(0)
df_verwerkt[df_verwerkt['difference'] >
1000] = 0 # sprong naar Nederhoven uitfilteren
df_verwerkt = df_verwerkt[df_verwerkt['difference'] > 0]
df_verwerkt = remove_outlier(df_verwerkt, 'difference')
groupby = df_verwerkt.groupby('minutes').sum()
return ColumnDataSource(data=groupby)
def make_plot(src):
# Deze regel maakt een dict met de vorm {0: '00:00', 1: '00:01', ... t/m 1440 voor alle minuten van de dag.
# In feite zet deze regel alle minuten van 0 t/m 1440 om in een string met tijd voor de x-as.
d = {
i: f"{floor(i/60):02d}" + ":" + f"{int(i%60):02d}"
for i in range(1440)
}
p = figure(title='Bar chart',
x_range=(0, 1440),
tools=TOOLS,
background_fill_color="#fafafa")
p.sizing_mode = 'stretch_both' # https://docs.bokeh.org/en/latest/docs/user_guide/layout.html
p.vbar(x='minutes',
bottom=0,
top='difference',
source=src,
width=0.9,
color={
'field': 'difference',
'transform': color_mapper
})
p.y_range.start = 0
p.xaxis.axis_label = 'Tijd'
p.xaxis.major_label_overrides = d
p.yaxis.axis_label = 'Verbruik'
p.grid.grid_line_color = "white"
return p
def update(attr, old, new):
# Get the selected items for the graph
# ...
selected = radio_button_group.active
usageType = 'gastotalusage' # standaardwaarde
# Koppel de selectie aan de juiste gegevens uit het DataFrame
if selected == 0:
usageType = 'gastotalusage'
p.title.text = 'Gasverbruik per minuut'
elif selected == 1:
usageType = 'tariff1totalusage'
p.title.text = 'Stroomtarief 1 verbruik'
elif selected == 2:
usageType = 'tariff2totalusage'
p.title.text = 'Stroomtarief 2 verbruik'
print('Update usageType: ' + str(usageType))
# update data
new_src = make_dataset(df, usageType)
src.data.update(new_src.data)
radio_button_group = RadioButtonGroup(
labels=["Gas", "Tarief 1", "Tarief 2"], active=0)
radio_button_group.on_change('active', update)
# initial execution
src = make_dataset(df, 'gastotalusage')
p = make_plot(src)
# make a grid
grid = gridplot([[p], [radio_button_group]])
grid.sizing_mode = 'scale_width'
tab = Panel(child=grid, title='Bar chart')
print("barchart() uitgevoerd")
return tab
def __init__(self):
### Methods
self.args = Settings()
self.index = None
self.data_getter = None
self.filter = None
self._data = None
self._model_type = None
self._model_dir = self.args.models_path + 'unique_object/'
self.controls = {}
self.scene_plotter = ScenePlotter(self._set_head_selection)
### initialization of the interface
## Model type selector
def update_select_net():
if self._model_dir is not None:
file_list = [fn for fn in os.listdir(self._model_dir) if os.path.isfile(os.path.join(self._model_dir, fn))]
file_list.sort(key=lambda fn: os.stat(os.path.join(self._model_dir, fn)).st_mtime, reverse=True)
file_list = [os.path.splitext(fn)[0] for fn in file_list]
self.controls['net'].options = file_list
# print(self._model_dir)
# print('file_list')
# print(file_list)
if len(file_list) > 0:
self.controls['net'].value = file_list[0]
else:
self.controls['net'].value = None
def update_model_type():
if self.controls['multi_mono_object'].active == 0:
self._model_type = 'mono'
self._model_dir = self.args.models_path + 'unique_object/'
elif self.controls['multi_mono_object'].active == 1:
self._model_type = 'multi_obj'
self._model_dir = self.args.models_path + 'multi_objects/'
elif self.controls['multi_mono_object'].active == 2:
self._model_type = 'multi_pred'
self._model_dir = self.args.models_path + 'multi_pred/'
model_types = ['CV', 'CA', 'Bicycle', 'CV_LSTM', 'CA_LSTM', 'Bicycle_LSTM', 'nn_attention']
existing_types = [type for type in model_types if os.path.isdir(self._model_dir + type)]
self.controls['model_sub_type'].options = existing_types
print('existing types')
print(existing_types)
if len(existing_types) > 0 and not self.controls['model_sub_type'].value in existing_types:
self.controls['model_sub_type'].value = existing_types[0]
return
set_model_sub_type()
update_select_net()
def set_model_sub_type():
if self.controls['model_sub_type'].value is not None:
self._model_dir = self._model_dir + self.controls['model_sub_type'].value + '/'
self.args.model_type = self.controls['model_sub_type'].value
else:
self._model_dir = None
def update_multi_mono_object(attr, old, new):
update_model_type()
print(self._model_dir)
self._set_data_getter()
print('___')
multi_mono_object = RadioButtonGroup(labels=["Mono-object", "Multi-objects", "Multi-pred"], active=1)
self.controls['multi_mono_object'] = multi_mono_object
multi_mono_object.on_change('active', update_multi_mono_object)
## Model sub type selector
model_types = ['CV', 'CA', 'Bicycle', 'CV_LSTM', 'CA_LSTM', 'Bicycle_LSTM', 'nn_attention']
model_sub_type = Select(title='Select model type:', value=model_types[3], options=model_types)
self.controls['model_sub_type'] = model_sub_type
model_sub_type.on_change('value', lambda att, old, new: update_model_type())
## Model selector
select = Select(title="Select parameter file:", value=None, options=[])
self.controls['net'] = select
select.on_change('value', lambda att, old, new: self._set_data_getter())
## Select dataset to use
dataset_list = ['Argoverse', 'Fusion', 'NGSIM']
select = Select(title='Dataset:', value=dataset_list[0], options=dataset_list)
self.controls['dataset'] = select
select.on_change('value', lambda att, old, new: self._set_data_getter(change_index=True))
## Set what to draw
checkbox_group = CheckboxGroup(
labels=['Draw lanes', 'Draw history', 'Draw true future', 'Draw forecast', 'Draw forecast covariance'],
active=[0, 1, 2, 3, 4])
self.controls['check_box'] = checkbox_group
checkbox_group.on_change('active',
lambda att, old, new: (self._update_cov(), self._update_lanes(), self._update_path()))
## Set the number of pred
n_pred = Slider(start=1, end=6, step=1, value=1, title='Number of prediction hypothesis')
self.controls['n_pred'] = n_pred
n_pred.on_change('value', lambda att, old, new: (self._update_cov(), self._update_path()))
## Sequence ID input
text_input = TextInput(title="Sequence ID to plot:", value="Random")
self.controls['sequence_id'] = text_input
## Head selection input
multi_select_head = MultiSelect(title='Attention head multiple selection:',
value=[], options=[])
self.controls['Head_selection'] = multi_select_head
multi_select_head.on_change('value', self.scene_plotter.set_active_heads)
## Refresh all sample
button = Button(label="Refresh", button_type="success")
self.controls['refresh'] = button
button.on_click(
lambda event: (self._set_index(), self._set_data()))
# button.js_on_click(CustomJS(args=dict(p=self.image), code="""p.reset.emit()"""))
update_multi_mono_object(None, None, None)
## Set the interface layout
inputs = column(*(self.controls.values()), width=320, height=1000)
inputs.sizing_mode = "fixed"
lay = layout([[inputs, self.scene_plotter.get_image()]], sizing_mode="scale_both")
curdoc().add_root(lay)
self.scene_plotter._tap_on_veh('selected', [], [0])
data = data.cpu().detach().numpy()
p = figure(plot_height=int(880*0.8), plot_width=int(1680*0.8))
p.image(image='image', x=0, y=0, dw=168, dh=88, palette="Viridis256", source = source)
energy_slider = Slider(title="Energy", value = data[0][8], start = 0., end = 2000., step = 10.)
phi_slider = Slider(title="Azimuthal angle", value = np.arctan2(data[0][7], data[0][5]), start = -np.pi, end = np.pi, step = np.pi/10.)
coseta_slider = Slider(title="Cosine of zenith angle", value = data[0][6], start = -0.999, end = 0.999, step = 1/20.)
posx_slider = Slider(title="Position x", value = data[0][2], start = -250., end = 250., step = 500./20)
posy_slider = Slider(title="Position y", value = data[0][3], start = -250., end = 250., step = 500./20)
posz_slider = Slider(title="Position z", value = data[0][4], start = -250., end = 250., step = 500./20)
PID_button = RadioButtonGroup(labels=["gamma", "electron", "muon"], active=0)
inputs = row(column(PID_button,energy_slider), column(phi_slider,coseta_slider), column(posx_slider, posy_slider, posz_slider))
def update_data(attrname, old, new) :
energy = energy_slider.value
phi = phi_slider.value
coseta = coseta_slider.value
dx = np.cos(phi)
dy = coseta
dz = np.sin(phi)
r = (1-dy**2)**0.5
if r :
menu = [("Item 1", "item_1_value"), ("Item 2", "item_2_value"), None,
("Item 3", "item_3_value")]
dropdown = Dropdown(label="Dropdown button", button_type="warning", menu=menu)
dropdown_split = Dropdown(label="Split button",
button_type="danger",
menu=menu,
split=True)
checkbox_group = CheckboxGroup(labels=["Option 1", "Option 2", "Option 3"],
active=[0, 1])
radio_group = RadioGroup(labels=["Option 1", "Option 2", "Option 3"], active=0)
checkbox_button_group = CheckboxButtonGroup(
labels=["Option 1", "Option 2", "Option 3"], active=[0, 1])
radio_button_group = RadioButtonGroup(
labels=["Option 1", "Option 2", "Option 3"], active=0)
checkbox_button_group_vertical = CheckboxButtonGroup(
labels=["Option 1", "Option 2", "Option 3"],
active=[0, 1],
orientation="vertical")
radio_button_group_vertical = RadioButtonGroup(
labels=["Option 1", "Option 2", "Option 3"],
active=0,
orientation="vertical")
text_input = TextInput(placeholder="Enter value ...")
completions = ["aaa", "aab", "aac", "baa", "caa"]
autocomplete_input = AutocompleteInput(
placeholder="Enter value (auto-complete) ...", completions=completions)
def main():
#path variables
#in_path = '/mnt/data04/Conduit/afib/new_files/1hour/'
#af_outpath = '/mnt/data04/Conduit/afib/AF_annotations/af_ann4/'
in_path = join(dirname(__file__), "data_in/")
af_outpath = join(dirname(__file__), "data_out/")
#Miscellaneous variables
colours = {
'': 'black',
'O': 'blue',
'N': 'green',
'~': 'purple',
'A': 'red'
}
#wf_classes = ['AF','Normal','Other','Noise','No Signal']
wf_classes = ['AF', 'Not AF']
#global variables remove if you can
newECG = pd.DataFrame()
df_ann = pd.DataFrame(columns=[0])
#widgets to be used as necessary
rdo_btn_wave_lbls = RadioButtonGroup(labels=wf_classes, active=2)
date_range_slider = Slider(title="Date Range", step=10)
table_source, table_columns = load_file_source(in_path, af_outpath)
file_table = DataTable(source=table_source,
columns=table_columns,
width=800,
height=600)
pgph_file_loaded = Div(text="Select file from table.",
width=1000,
height=50)
pgph_file_loaded.style = {
"font-size": '1.2em',
'font-weight': 'bold',
'color': 'SteelBlue'
}
txt_processing = Div(
text=
"Use slider to select segments, label by selecting the wave type and pressing 'Label'. Use the save to save when finished all annotations.",
width=1000,
height=30)
txt_processing.style = {
"font-size": '1.2em',
'font-weight': 'bold',
'color': 'SteelBlue',
'white-space': 'pre'
}
btn_lbl = Button(label='Label', button_type='success')
btn_save = Button(label='Save', button_type='danger')
btn_load_annotated_graph = Button(label='Load Annotated Graph',
button_type='warning')
#----------------------------- Functions ---------------------------------#
''' Callback function for the data table ColumnDataSource (table_source).
Load the hd5 file based on the selected row in the data_table.
Generate the output file path.
Update the date range slider according to the new file (date_range_slider).
Generate a plot of the hd5 ECG lead II data (wave_graph).
Then combine all into a layout (graph_layout)
Update the Tab Pane with this new layout.
Returns: Nothing'''
def callback_select_file_table(attr, old, new):
global newECG, out_file
#clear tabs graphs to reduce overhead
output_tab.child.children = []
sel_id = new
txt_processing.text = "Use slider to select segments, label by selecting the wave type and pressing 'Label'. Use the save button to when done."
txt_processing.style = {"font-size": '1.2em', 'color': 'SteelBlue'}
wave_file_path = load_selected_file(table_source, sel_id)
out_file = load_annotation_file(
table_source, sel_id, af_outpath) #to be used for saving the file
pgph_file_loaded.text = "Processing..."
wave_hdfs, hdfs_keys = load_hd5_file(wave_file_path)
print(hdfs_keys)
newECG = (wave_hdfs.select(
key='Waveforms').II).to_frame() #get lead II from waveforms)
newECG.reset_index(
inplace=True) #move datetime index to column and reset the index
newECG.columns = ['date', 'II']
wave_hdfs.close()
#enable the buttons if disabled
btn_save.disabled = False
btn_lbl.disabled = False
btn_load_annotated_graph.disabled = True
#check if the file has already been annotated
annotated = check_if_annotated(table_source, sel_id)
if annotated == 'Yes':
txt_processing.text = 'This file has already been annotated. If you save this file again you will overwrite previous data.'
txt_processing.style = {"font-size": '1.2em', 'color': 'Red'}
btn_load_annotated_graph.disabled = False
#create figure
print("*********************Creating Figure (in Callback File Select)")
get_next_graph(0, newECG)
pgph_file_loaded.text = "File loaded, navigate Label Data Tab to annotate lead II."
''' Using the AF annotation file, create a Bokeh figure with annotated data.
Update the output_tab to show this figure.
Disable some buttons to keep users on track. '''
def load_output_graph():
global newECG
btn_save.disabled = True
btn_lbl.disabled = True
btn_load_annotated_graph.disabled = True
txt_processing.text = 'Loading Plot...'
df_AF = pd.read_hdf(out_file)
noise, normal, other, af, nosig, notAF = load_annotations(
0, df_AF.shape[0] - 1, newECG, df_AF)
output_graph = get_graph_annotated(noise, normal, other, af, nosig,
notAF)
output_tab.child.children = [output_graph]
txt_processing.text = 'Plot loaded, navigate to "Final Annotated Graph" tab to view. 
 If you save this file again you will overwrite previous data.'
btn_save.disabled = False
btn_lbl.disabled = False
def get_next_graph(sind, df):
length = 20000 #7200
eind = sind + length
if eind <= list(df.index)[-1] and sind < list(
df.index
)[-1]: #as long as the end and beginning are less than the end of the dataframe
sub_df = df.iloc[sind:eind]
sub_df = sub_df.set_index('date')
del sub_df.index.name
source = ColumnDataSource(sub_df)
wave_graph = get_graph(source)
start_span, end_span = add_span(source)
wave_graph.add_layout(start_span)
wave_graph.add_layout(end_span)
start_rng = start_span.location
end_rng = end_span.location
elif eind < list(df.index)[-1] and sind < list(
df.index
)[-1]: #if the start is before but the end is after then just use the end of the dataframe
sub_df = df.iloc[sind:list(df.index)[-1]]
sub_df = sub_df.set_index('date')
del sub_df.index.name
source = ColumnDataSource(sub_df)
wave_graph = get_graph(source)
start_span, end_span = add_span(source)
wave_graph.add_layout(start_span)
wave_graph.add_layout(end_span)
start_rng = start_span.location
end_rng = end_span.location
else:
txt_processing.text = 'You have finished annotating this file.'
return
end_span.location = start_span.location
#slider to change date range
date_range_slider.start = start_rng
date_range_slider.end = end_rng
date_range_slider.value = start_rng
date_range_slider.on_change('value', callback_date_time_slider)
graph_layout = column(
widgetbox([txt_processing, btn_load_annotated_graph, btn_save],
width=250),
widgetbox(Div(text="""<hr/>""",
style={
'display': 'block',
'height': '1px',
'border': '0',
'border-top': '1px solid #css',
'margin': '1em 0',
'padding': '0'
}),
width=1400),
widgetbox([rdo_btn_wave_lbls, btn_lbl], width=300),
widgetbox(date_range_slider, width=1350),
widgetbox(Div(text="""<hr/>""",
style={
'display': 'block',
'height': '1px',
'border': '0',
'border-top': '1px solid #css',
'margin': '1em 0',
'padding': '0'
}),
width=1400), wave_graph)
wf_tab.child.children = [graph_layout]
''' Use a ColumnDataSource to plot the ECG lead II waveform data in a line plot.
Parameters: source a ColumnDataSource
Returns: p a Bokeh figure '''
def get_graph(source):
p = figure(plot_width=1400,
plot_height=500,
x_axis_type='datetime',
tools=['zoom_in', 'zoom_out', 'xpan', 'ypan'])
date_range = source.data['II'][0:20000]
p.y_range = Range1d(start=min(date_range) - 1, end=max(date_range) + 1)
dt_axis_format = ["%d-%m-%Y %H:%M"]
wf_x_axis = DatetimeTickFormatter(
hours=dt_axis_format,
days=dt_axis_format,
months=dt_axis_format,
years=dt_axis_format,
)
p.xaxis.formatter = wf_x_axis
p.line(x='index',
y='II',
source=source,
line_color='black',
line_width=1)
return p
''' Get the first and last time points from the ColumnDataSource (source).
Utlizes tzlocal to add an offset which modifies the data.
Returns integer timetuple value for the dates found: start, end '''
def get_time(source):
start = pd.to_datetime(min(source.data['index'])).timestamp() * 1000
end = pd.to_datetime(max(source.data['index'])).timestamp() * 1000
return start, end
''' Generate two Bokeh Spans based on the ColumnDataSource given (source).
Spans are at the first and last datetimes in the source data.
Parameters: source a ColumnDataSource
Returns: Span, Span '''
def add_span(source):
start, end = get_time(source)
# Start span represents the start of the area of interest
start_span = Span(location=start,
dimension='height',
line_color='green',
line_dash='dashed',
line_width=3)
# End span represents the end of the area of interest
end_span = Span(location=end,
dimension='height',
line_color='red',
line_dash='dashed',
line_width=3)
return start_span, end_span
''' Callback function for Bokeh Slider, move the Spans on the specified graph (generated in callback_file_table) to the location specified by the Span.
Returns: Nothing '''
def callback_date_time_slider(attr, old, new):
inds = get_spans()
wf_tab.child.children[0].children[5].renderers[inds[1]].location = new
''' Navigate through the widgets on the wave_graph to find the spans
Returns the widget indexes of the spans'''
def get_spans():
inds = []
for x in range(len(wf_tab.child.children[0].children[5].renderers)):
if isinstance(wf_tab.child.children[0].children[5].renderers[x],
Span):
inds.append(x)
return inds
''' Callback function for btn_lbl.
Get the location of the spans from the wave_graph, then call segment_and_label
Return: Nothing '''
def callback_btn_lbl():
inds = get_spans()
active = rdo_btn_wave_lbls.labels[rdo_btn_wave_lbls.active]
start_span = wf_tab.child.children[0].children[5].renderers[inds[0]]
end_span = wf_tab.child.children[0].children[5].renderers[inds[1]]
segment_and_label(active, start_span.location, end_span.location)
''' Function to get ECG data between two Spans (after modifying the timetuple to timestamp).
Call apply_annotations using start and end indexes found.
Modify the global df_ann variable.
Update slider position (start to end), (end to start).
Parameters: label a string (AF, Normal, Noise, Other), start a timpletuple integer, end a timetuple integer
Return: nothing '''
def segment_and_label(label, start, end):
global newECG
print("*********************Segmenting and Labelling")
try:
txt_processing.text = "Use slider to select segments, label by selecting the wave type and pressing 'Label'. Use the save button to when done."
start_dt = pd.Timestamp(start / 1000, unit='s')
end_dt = pd.Timestamp(end / 1000, unit='s')
mask = (newECG['date'] > start_dt) & (newECG['date'] <= end_dt)
df_sub = newECG.loc[mask] #apply mask
indexes = list(df_sub.index) #get indexes
s_ind = indexes[0] #get first index
e_ind = indexes[-1] #get last index
apply_annotations(label, s_ind, e_ind,
df_ann) #concatenate dataframe of annotations
get_next_graph(e_ind, newECG)
except IndexError:
txt_processing.text = 'Indexing error. Advance slider.'
''' Function to apply annotations to a dataframe structured like that of Computing in Cardiology AF algorithm.
Parameters: label a string (AF, Normal, Noise, Other), s_ind the index of the start datetime,
e_ind the index of the end datetime, df a pandas database to be appended to, columns= 'AF'
Return: Nothing '''
def apply_annotations(label, s_ind, e_ind, df):
#data frame structure like this
if label == 'AF':
df.loc[s_ind, 0] = 'A'
elif label == 'Not AF':
df.loc[s_ind, 0] = 'nAF'
elif label == 'Noise':
df.loc[s_ind, 0] = '~'
elif label == 'Normal':
df.loc[s_ind, 0] = 'N'
elif label == 'Other':
df.loc[s_ind, 0] = 'O'
elif label == 'No Signal':
df.loc[s_ind, 0] = '-'
''' Stream update to ColumnDataSource that the file has been annotated'''
def mark_as_done(file_path):
print("*********************Marking as Done")
table_source = file_table.source
name = os.path.splitext(os.path.basename(file_path))[0] #get file name
ind = list(table_source.data['name']).index(
name) #get index within the source
patches = {
'annotated': [(ind, 'Yes')]
} #new data to update ColumnDataSource with
table_source.patch(patches) #update - ***** THROWING ERROR - CHECK!!
''' Callback function for btn_save.
Utilizes the out_file global variable for the path.
Appends dataframe to output file. '''
def callback_save_annotations():
print("*********************Saving Annotations")
txt_processing.style = {"font-size": '1.2em', 'color': 'Red'}
print("Writting: ", out_file)
txt_processing.text = 'Saving Annotations...'
df_ann.to_hdf(out_file, key='AF', format='t')
print("success!")
btn_save.disabled = True
btn_lbl.disabled = True
mark_as_done(out_file)
nrows = df_ann.shape[0]
df_ann.drop(df_ann.index[:nrows],
inplace=True) #clear dataframe for a new file to be loaded
btn_load_annotated_graph.disabled = False
txt_processing.style = {"font-size": '1.2em', 'color': 'SteelBlue'}
txt_processing.text = '''Done. Click 'Load Annotated Graph' to view annotations or to "File Management" to select new file to anntoate. You will need to reload this file to make changes.'''
''' Load annotations from AF formatted hdf file. '''
def load_annotations(start, end, df, df_AF):
# Initialize empty dataframes
noise = pd.DataFrame()
normal = pd.DataFrame()
other = pd.DataFrame()
af = pd.DataFrame()
nosig = pd.DataFrame()
notAF = pd.DataFrame()
# Read annotations
for n in range(start, end):
df_temp = df.iloc[df_AF.index[n]:df_AF.index[n + 1]]
df_temp.index = list(df_temp['date'])
df_temp.drop(columns='date')
value = df_AF.iloc[n, 0]
if value == '~':
noise = noise.append(df_temp)
elif value == 'N':
normal = normal.append(df_temp)
elif value == 'O':
other = other.append(df_temp)
elif value == 'A':
af = af.append(df_temp)
elif value == '-':
nosig = nosig.append(df_temp)
elif value == 'nAF':
notAF = notAF.append(df_temp)
#add the last labelled section (end+1) to end of df
df_temp = df.iloc[df_AF.index[end]:df.index[-1]]
df_temp.index = list(df_temp['date'])
df_temp.drop(columns='date')
value = df_AF.iloc[end, 0]
if value == '~':
noise = noise.append(df_temp)
elif value == 'N':
normal = normal.append(df_temp)
elif value == 'O':
other = other.append(df_temp)
elif value == 'A':
af = af.append(df_temp)
elif value == '-':
nosig = nosig.append(df_temp)
elif value == 'nAF':
notAF = notAF.append(df_temp)
return noise, normal, other, af, nosig, notAF
''' Create Bokeh figure from dataframes af, normal, other and noise. '''
def get_graph_annotated(noise, normal, other, af, nosig, notAF):
p = figure(
plot_width=1400,
plot_height=500,
x_axis_type='datetime',
tools=['box_zoom', 'wheel_zoom', 'pan', 'reset', 'crosshair'])
# plot color coded waves (if they exist)
if noise.empty is False:
p.line(x='index',
y='II',
source=noise,
color='blue',
legend='Noise')
if normal.empty is False:
p.line(x='index',
y='II',
source=normal,
color='green',
legend='Normal')
if other.empty is False:
p.line(x='index',
y='II',
source=other,
color='purple',
legend='Other')
if af.empty is False:
p.line(x='index', y='II', source=af, color='red', legend='AF')
if nosig.empty is False:
p.line(x='index',
y='II',
source=nosig,
color='black',
legend='No Signal')
if notAF.empty is False:
p.line(x='index',
y='II',
source=notAF,
color='grey',
legend='No Signal')
dt_axis_format = ["%d-%m-%Y %H:%M"]
wf_x_axis = DatetimeTickFormatter(
hours=dt_axis_format,
days=dt_axis_format,
months=dt_axis_format,
years=dt_axis_format,
)
p.xaxis.formatter = wf_x_axis
return p
############################## Assign Callbacks ##########################################
#table_source.on_change('selected', callback_select_file_table) #assign callback
table_source.selected.on_change('indices', callback_select_file_table)
btn_lbl.on_click(callback_btn_lbl)
btn_save.on_click(callback_save_annotations)
btn_load_annotated_graph.on_click(load_output_graph)
################################## Load Document ##########################################
####layouts####
file_layout = column(widgetbox(pgph_file_loaded, file_table, width=1000))
col_waveforms = column(name="figures", sizing_mode='scale_width')
col_output = column(name="output", sizing_mode='scale_width')
###tabs###
wf_tab = Panel(child=col_waveforms, title='Label Data')
file_tab = Panel(child=file_layout, title='File Management')
output_tab = Panel(child=col_output, title='Final Annotated Graph')
tab_pane = Tabs(tabs=[file_tab, wf_tab, output_tab], width=1000)
###combine into document####
curdoc().add_root(column(tab_pane))
curdoc().title = "AF Annotator 4"
("Daily New Cases per Capita", "@dnc"),
],
renderers=[cantons])
p1.add_tools(hover)
# T2.3 Add circles at the locations of capital cities for each canton, and the sizes are proportional to daily new cases per capita
sites = p1.circle('long',
'lat',
source=geosource,
color='violet',
size='size',
alpha=0.33)
# T2.4 Create a radio button group with labels 'Density', and 'BedsPerCapita'
buttons = RadioButtonGroup(labels=['Density', 'BedPerCapita'], active=0)
# Define a function to update color mapper used in both patches and colorbar
def update_bar(new):
for i, d in enumerate(labels):
if i == new:
color_bar.color_mapper = mappers[d]["transform"]
color_bar.title = d
cantons.glyph.fill_color = mappers[d]
buttons.on_click(update_bar)
# T2.5 Add a dateslider to control which per capita daily new cases information to display
def prepare_server(doc,
input_data,
cell_stack,
cell_markers=None,
default_cell_marker=None):
@lru_cache()
def image_markers(lower=False, mapping=False):
if mapping:
return {
y: j
for j, y in sorted(
((i, x) for i, x in enumerate(
image_markers(lower=lower, mapping=False))),
key=lambda x: x[1].lower(),
)
}
if lower:
return [
x.lower() for x in image_markers(lower=False, mapping=False)
]
return (cell_markers if cell_markers is not None else
[f"Marker {i + 1}" for i in range(cell_stack.shape[1])])
# Data sources
###########################################################################
def prepare_data(input_data):
data = input_data.copy()
if "contour" in data and not all(x in data
for x in ["contour_x", "contour_y"]):
contour = parse_contour(data["contour"])
data["contour_x"] = contour[0]
data["contour_y"] = contour[1]
if "marked" not in data:
data["marked"] = np.full(data.shape[0], "")
source.data = data
source = ColumnDataSource(data={})
prepare_data(input_data)
image_source = ColumnDataSource(
data=dict(image=[], dw=[], dh=[], contour_x=[], contour_y=[]))
# Cell picture plot
###########################################################################
def add_outline():
data = source.data
if all(x in data for x in ["contour_x", "contour_y"]):
cell_outline = cell_figure.patches(
xs="contour_x",
ys="contour_y",
fill_color=None,
line_color="red",
name="cell_outline",
source=image_source,
)
cell_outline.level = "overlay"
else:
cell_outline = cell_figure.select(name="cell_outline")
for x in cell_outline:
cell_figure.renderers.remove(x)
default_cell_marker = (0 if default_cell_marker is None else image_markers(
mapping=True)[default_cell_marker])
cell_markers_select = Select(
value=str(default_cell_marker),
options=list(
(str(i), x) for x, i in image_markers(mapping=True).items()),
title="Marker cell image",
)
cell_marker_input = AutocompleteInput(
completions=list(image_markers()) + list(image_markers(lower=True)),
min_characters=1,
placeholder="Search for marker",
)
cell_slider = RangeSlider(start=0,
end=1,
value=(0, 1),
orientation="vertical",
direction="rtl")
metric_select = RadioButtonGroup(active=0, labels=CELL_IMAGE_METRICS[0])
stats = PreText(text="", width=100)
cell_mapper = bokeh.models.mappers.LinearColorMapper(viridis(20),
low=0,
high=1000,
high_color=None)
cell_color_bar = ColorBar(color_mapper=cell_mapper,
width=12,
location=(0, 0))
cell_figure = figure(
plot_width=450,
plot_height=350,
tools="pan,wheel_zoom,reset",
toolbar_location="left",
)
cell_image = cell_figure.image(
image="image",
color_mapper=cell_mapper,
x=0,
y=0,
dw="dw",
dh="dh",
source=image_source,
)
add_outline()
cell_figure.add_layout(cell_color_bar, "right")
# Edit data of selected cells
###########################################################################
marker_edit_container = column()
marker_edit_instances = []
def add_marker_edit_callback():
editor = ColumnEditor(
source,
marker_edit_container,
log_widget=edit_selecton_log,
editor_delete_callback=delete_marker_edit_callback,
external_edit_callback=edit_selection_callback,
)
marker_edit_instances.append(editor)
def delete_marker_edit_callback(editor):
idx = next(i for i, x in enumerate(marker_edit_instances)
if x is editor)
del marker_edit_instances[idx]
file_name_text = Div()
add_marker_edit_button = Button(label="+",
button_type="success",
align=("start", "end"),
width=50)
add_marker_edit_button.on_click(add_marker_edit_callback)
edit_selection_submit = Button(label="Submit change",
button_type="primary",
align=("start", "end"))
download_button = Button(label="Download edited data",
button_type="success",
align=("start", "end"))
download_button.js_on_click(
CustomJS(args=dict(source=source), code=DOWNLOAD_JS))
edit_selecton_log = TextAreaInput(value="",
disabled=True,
css_classes=["edit_log"],
cols=30,
rows=10)
upload_file_input = FileInput(accept="text/csv", align=("end", "end"))
# Cell table
###########################################################################
default_data_table_cols = [
TableColumn(field="marked", title="Seen", width=20)
]
data_table = DataTable(source=source,
columns=default_data_table_cols,
width=800)
# Callbacks for buttons and widgets
###########################################################################
def cell_slider_change(attrname, old, new):
cell_mapper.low = new[0]
cell_mapper.high = new[1]
def selection_change(attrname, old, new):
selected = source.selected.indices
data = source.data
if not selected:
return
mean_image = CELL_IMAGE_METRICS[1][metric_select.active](
cell_stack[selected,
int(cell_markers_select.value), :, :], axis=0)
image_data = {
"image": [mean_image],
"dw": [cell_stack.shape[2]],
"dh": [cell_stack.shape[3]],
}
for coord in ["contour_x", "contour_y"]:
try:
image_data[coord] = list(data[coord][selected])
except KeyError:
pass
image_source.data = image_data
image_extr = round_signif(mean_image.min()), round_signif(
mean_image.max())
cell_slider.start = image_extr[0]
cell_slider.end = image_extr[1]
cell_slider.step = round_signif((image_extr[1] - image_extr[0]) / 50)
cell_slider.value = image_extr
stats.text = "n cells: " + str(len(selected))
def autocomplete_cell_change(attrname, old, new):
try:
idx = image_markers(mapping=True)[new]
except KeyError:
try:
idx = image_markers(lower=True, mapping=True)[new]
except KeyError:
return
cell_markers_select.value = str(idx)
cell_marker_input.value = None
def data_change(attrname, old, new):
new_keys = [n for n in new.keys() if n not in set(old.keys())]
for n in new_keys:
data_table.columns.append(TableColumn(field=n, title=n))
def edit_selection_submit_click():
for x in marker_edit_instances:
x.edit_callback()
def edit_selection_callback():
idx = source.selected.indices
try:
if len(idx) == 1 and all(
source.data[x.widgets["input_col"].value][idx] != "NA"
for x in marker_edit_instances):
source.selected.indices = [idx[0] + 1]
except KeyError:
pass
def upload_file_callback(attrname, old, new):
try:
data_text = b64decode(new)
data = pd.read_csv(BytesIO(data_text))
except Exception:
file_name_text.text = f"Error loading file {upload_file_input.filename}"
return
file_name_text.text = f"Editing file {upload_file_input.filename}"
# Have to regenerate contours
try:
del data["contour_x"]
del data["contour_y"]
except KeyError:
pass
data_table.columns = default_data_table_cols
prepare_data(data)
add_outline()
source.selected.on_change("indices", selection_change)
source.on_change("data", data_change)
cell_slider.on_change("value", cell_slider_change)
metric_select.on_change("active", selection_change)
cell_markers_select.on_change("value", selection_change)
cell_marker_input.on_change("value", autocomplete_cell_change)
edit_selection_submit.on_click(edit_selection_submit_click)
upload_file_input.on_change("value", upload_file_callback)
style_div = Div(text=CUSTOM_CSS)
# set up layout
layout = column(
row(
column(data_table),
column(
cell_markers_select,
cell_marker_input,
metric_select,
row(cell_figure, cell_slider),
stats,
),
),
file_name_text,
marker_edit_container,
add_marker_edit_button,
row(edit_selection_submit, download_button, upload_file_input),
edit_selecton_log,
style_div,
)
doc.add_root(layout)
doc.title = "Cell classifier"
# get all osm files
select_file = Select(title="Select OSM File:", value='', options=osm_list)
def on_file_change(obj, attr, old, new):
print "OSM File:", new
global osmfile, osm_list
osmfile = new
#osm_list.append(osmfile)
select_file.on_change('value', on_file_change)
##############
##############
# radio group gmap
radio_group_gmap = RadioButtonGroup(labels=["GMap", "Blank"],
#title='Background Map',
active=0, name='Select Plot Options')
def radio_group_handler_gmap(active):
# need to update skipnodes too
global gmap_background
# detault to empty datasources
if active == 0:
gmap_background = True
elif active == 1:
gmap_background = False
radio_group_gmap.on_click(radio_group_handler_gmap)
##############
#############
def plot(location=None):
(
source_df, labels, colors,
location_labels, xs_df, ys_df, scales_dict
) = load_database(location=location)
print(location_labels)
if location is None:
location = location_labels[0]
print(location)
source = ColumnDataSource(source_df)
xs = ColumnDataSource(xs_df)
ys = ColumnDataSource(ys_df)
scales = ColumnDataSource(scales_dict)
bfig = bokeh_plot(source)
labels_group = RadioGroup(
labels=labels, active=None)
active = location_labels.index(location)
locations_group = RadioButtonGroup(
labels=location_labels, active=active, name='locations_group'
)
x_scale, y_scale = scales_dict[location]
x_slider = Slider(
start=0.05, end=20, value=x_scale, step=0.05,
title='x scale'
)
y_slider = Slider(
start=0.001, end=0.2, value=y_scale, step=0.001,
title='y scale'
)
with open('bokeh_js/bokeh_plot_onclick.js', 'r') as f:
code = f.read()
source.selected.js_on_change(
'indices',
CustomJS(
args=dict(
s=source,
ls=labels,
r=labels_group,
),
code=code
)
)
with open('bokeh_js/bokeh_label_onchange.js', 'r') as f:
code = f.read()
labels_group.js_on_change(
'active',
CustomJS(
args=dict(
s=source,
r=labels_group,
ls=labels,
cs=colors),
code=code
)
)
with open('bokeh_js/bokeh_locations_onchange.js', 'r') as f:
code = f.read()
locations_group.js_on_change(
'active',
CustomJS(
args=dict(
s=source,
r=locations_group,
location_labels=location_labels,
xs=xs,
ys=ys,
scales=scales,
x_slider=x_slider,
y_slider=y_slider,
),
code=code
)
)
with open('bokeh_js/bokeh_x_slider_onchange.js', 'r') as f:
code = f.read()
x_slider.js_on_change(
'value',
CustomJS(
args=dict(
s=source,
r=locations_group,
location_labels=location_labels,
xs=xs,
ys=ys,
scales=scales,
x_slider=x_slider,
y_slider=y_slider,
),
code=code
)
)
with open('bokeh_js/bokeh_y_slider_onchange.js', 'r') as f:
code = f.read()
y_slider.js_on_change(
'value',
CustomJS(
args=dict(
s=source,
r=locations_group,
location_labels=location_labels,
xs=xs,
ys=ys,
scales=scales,
x_slider=x_slider,
y_slider=y_slider,
),
code=code
)
)
return components(
column(
row(
locations_group,
),
row(
x_slider,
y_slider,
),
row(
bfig,
column(
labels_group,
)
)
)
)
# initialize controls
# slider for going though time
time_slider = Slider(title="time", name='time', value=pde_settings.t_init, start=pde_settings.t_min, end=pde_settings.t_max,
step=pde_settings.t_step)
time_slider.on_change('value', time_change)
# slider controlling spatial stepsize of the solver
h_slider = Slider(title="spatial meshwidth", name='spatial meshwidth', value=pde_settings.h_init, start=pde_settings.h_min,
end=pde_settings.h_max, step=pde_settings.h_step)
h_slider.on_change('value', mesh_change)
# slider controlling spatial stepsize of the solver
k_slider = Slider(title="temporal meshwidth", name='temporal meshwidth', value=pde_settings.k_init, start=pde_settings.k_min,
end=pde_settings.k_max, step=pde_settings.k_step)
k_slider.on_change('value', mesh_change)
# radiobuttons controlling pde type
pde_type = RadioButtonGroup(labels=['Heat', 'Wave'], active=0)
pde_type.on_change('active', pde_type_change)
# radiobuttons controlling solver type
solver_type = RadioButtonGroup(labels=['Explicit', 'Implicit'], active=0)
solver_type.on_change('active', mesh_change)
# text input for IC
initial_condition = TextInput(value=pde_settings.IC_init, title="initial condition")
initial_condition.on_change('value', initial_condition_change)
# initialize plot
toolset = "crosshair,pan,reset,resize,wheel_zoom,box_zoom"
# Generate a figure container
plot = Figure(plot_height=400,
plot_width=400,
tools=toolset,
title="Time dependent PDEs",
class BokehGainMatching(Tool):
name = "BokehGainMatching"
description = "Interactively explore the steps in obtaining charge vs hv"
input_path = Unicode('',
help='Path to the numpy array containing the '
'gain and hv').tag(config=True)
aliases = Dict(dict(f='BokehGainMatching.input_path'))
classes = List([])
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._active_pixel = None
self.dead = Dead()
self.charge = None
self.charge_error = None
self.hv = None
self.n_hv = None
self.n_pixels = None
self.n_tmpix = 64
self.modules = None
self.tmpix = None
self.n_tm = None
self.m_pix = None
self.c_pix = None
self.m_tm = None
self.c_tm = None
self.m_tm2048 = None
self.c_tm2048 = None
self.p_camera_pix = None
self.p_plotter_pix = None
self.p_camera_tm = None
self.p_plotter_tm = None
self.w_view_radio = None
self.layout = None
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
arrays = np.load(self.input_path)
self.charge = self.dead.mask2d(arrays['charge'])
self.charge = np.ma.masked_where(self.charge <= 0, self.charge)
self.charge_error = np.ma.array(arrays['charge_error'],
mask=self.charge.mask)
self.hv = arrays['rundesc']
self.n_hv, self.n_pixels = self.charge.shape
assert (self.n_hv == self.hv.size)
geom = CameraGeometry.guess(*checm_pixel_pos * u.m,
optical_foclen * u.m)
self.modules = np.arange(self.n_pixels) // self.n_tmpix
self.tmpix = np.arange(self.n_pixels) % self.n_tmpix
self.n_tm = np.unique(self.modules).size
# Init Plots
self.p_camera_pix = Camera(self, "Gain Matching Pixels", geom)
self.p_camera_tm = Camera(self, "Gain Matching TMs", geom)
self.p_plotter_pix = Plotter(**kwargs)
self.p_plotter_tm = Plotter(**kwargs)
def start(self):
# Overcomplicated method instead of just reshaping...
# gain_modules = np.zeros((self.n_hv, self.n_tm, self.n_tmpix))
# hv_r = np.arange(self.n_hv, dtype=np.int)[:, None]
# hv_z = np.zeros(self.n_hv, dtype=np.int)[:, None]
# tm_r = np.arange(self.n_tm, dtype=np.int)[None, :]
# tm_z = np.zeros(self.n_tm, dtype=np.int)[None, :]
# tmpix_r = np.arange(self.n_tmpix, dtype=np.int)[None, :]
# tmpix_z = np.zeros(self.n_tmpix, dtype=np.int)[None, :]
# hv_i = (hv_r + tm_z)[..., None] + tmpix_z
# tm_i = (hv_z + tm_r)[..., None] + tmpix_z
# tmpix_i = (hv_z + tm_z)[..., None] + tmpix_r
# gain_rs = np.reshape(self.charge, (self.n_hv, self.n_tm, self.n_tmpix))
# modules_rs = np.reshape(self.modules, (self.n_tm, self.n_tmpix))
# tmpix_rs = np.reshape(self.tmpix, (self.n_tm, self.n_tmpix))
# tm_j = hv_z[..., None] + modules_rs[None, ...]
# tmpix_j = hv_z[..., None] + tmpix_rs[None, ...]
# gain_modules[hv_i, tm_i, tmpix_i] = gain_rs[hv_i, tm_j, tmpix_j]
# gain_modules_mean = np.mean(gain_modules, axis=2)
shape = (self.n_hv, self.n_tm, self.n_tmpix)
gain_tm = np.reshape(self.charge, shape)
gain_error_tm = np.reshape(self.charge_error, shape)
gain_tm_mean = np.mean(gain_tm, axis=2)
gain_error_tm_mean = np.sqrt(np.sum(gain_error_tm**2, axis=2))
self.m_pix = np.ma.zeros(self.n_pixels, fill_value=0)
self.c_pix = np.ma.zeros(self.n_pixels, fill_value=0)
self.m_tm = np.ma.zeros(self.n_tm, fill_value=0)
self.c_tm = np.ma.zeros(self.n_tm, fill_value=0)
p0 = [0, 5]
bounds = (-np.inf, np.inf) # ([-2000, -10], [2000, 10])
for pix in range(self.n_pixels):
x = self.hv[~self.charge.mask[:, pix]]
y = self.charge[:, pix][~self.charge.mask[:, pix]]
if x.size == 0:
continue
try:
coeff, _ = curve_fit(
gain_func,
x,
y,
p0=p0,
bounds=bounds,
# sigma=y_err[:, pix],
# absolute_sigma=True
)
self.c_pix[pix], self.m_pix[pix] = coeff
except RuntimeError:
self.log.warning("Unable to fit pixel: {}".format(pix))
for tm in range(self.n_tm):
x = self.hv
y = gain_tm_mean[:, tm]
try:
coeff, _ = curve_fit(
gain_func,
x,
y,
p0=p0,
bounds=bounds,
# sigma=y_err_tm[:, tm],
# absolute_sigma=True
)
self.c_tm[tm], self.m_tm[tm] = coeff
except RuntimeError:
self.log.warning("Unable to fit tm: {}".format(tm))
self.m_tm2048 = self.m_tm[:, None] * np.ones((self.n_tm, self.n_tmpix))
self.c_tm2048 = self.c_tm[:, None] * np.ones((self.n_tm, self.n_tmpix))
self.m_pix = self.dead.mask1d(self.m_pix)
self.c_pix = self.dead.mask1d(self.c_pix)
self.m_tm2048 = self.dead.mask1d(self.m_tm2048.ravel())
self.c_tm2048 = self.dead.mask1d(self.c_tm2048.ravel())
# Setup Plots
self.p_camera_pix.enable_pixel_picker()
self.p_camera_pix.add_colorbar()
self.p_camera_tm.enable_pixel_picker()
self.p_camera_tm.add_colorbar()
self.p_plotter_pix.create(self.hv, self.charge, self.charge_error,
self.m_pix, self.c_pix)
self.p_plotter_tm.create(self.hv, gain_tm_mean, gain_error_tm_mean,
self.m_tm, self.c_tm)
# Setup widgets
self.create_view_radio_widget()
self.set_camera_image()
self.active_pixel = 0
# Get bokeh layouts
l_camera_pix = self.p_camera_pix.layout
l_camera_tm = self.p_camera_tm.layout
l_plotter_pix = self.p_plotter_pix.layout
l_plotter_tm = self.p_plotter_tm.layout
# Setup layout
self.layout = layout([[self.w_view_radio],
[l_camera_pix, l_plotter_pix],
[l_camera_tm, l_plotter_tm]])
def finish(self):
curdoc().add_root(self.layout)
curdoc().title = "Charge Vs HV"
output_dir = dirname(self.input_path)
output_path = join(output_dir, 'gain_matching_coeff.npz')
np.savez(output_path,
alpha_pix=np.ma.filled(self.m_pix),
C_pix=np.ma.filled(self.c_pix),
alpha_tm=np.ma.filled(self.m_tm),
C_tm=np.ma.filled(self.c_tm))
self.log.info("Numpy array saved to: {}".format(output_path))
@property
def active_pixel(self):
return self._active_pixel
@active_pixel.setter
def active_pixel(self, val):
if not self._active_pixel == val:
self._active_pixel = val
self.p_camera_pix.active_pixel = val
self.p_camera_tm.active_pixel = val
self.p_plotter_pix.active_pixel = val
self.p_plotter_pix.fig.title.text = 'Pixel {}'.format(val)
module = self.modules[val]
self.p_plotter_tm.active_pixel = module
self.p_plotter_tm.fig.title.text = 'TM {}'.format(module)
def set_camera_image(self):
if self.w_view_radio.active == 0:
self.p_camera_pix.image = self.m_pix
self.p_camera_tm.image = self.m_tm2048
self.p_camera_pix.fig.title.text = 'Gain Matching Pixels (gradient)'
self.p_camera_tm.fig.title.text = 'Gain Matching TMs (gradient)'
elif self.w_view_radio.active == 1:
self.p_camera_pix.image = self.c_pix
self.p_camera_tm.image = self.c_tm2048
self.p_camera_pix.fig.title.text = 'Gain Matching Pixels (intercept)'
self.p_camera_tm.fig.title.text = 'Gain Matching TMs (intercept)'
def create_view_radio_widget(self):
self.w_view_radio = RadioButtonGroup(labels=["gradient", "intercept"],
active=0)
self.w_view_radio.on_click(self.on_view_radio_widget_click)
def on_view_radio_widget_click(self, active):
self.set_camera_image()
def create_view_radio_widget(self):
self.w_view_radio = RadioButtonGroup(labels=["gradient", "intercept"],
active=0)
self.w_view_radio.on_click(self.on_view_radio_widget_click)
source=sources['static'],
angle=pi/2,
width=5,
height=1,
color=DARK_GRAY)
# create components --------------
title_div = Div(text='Revenue Impact of a 4% Surtax on Taxpayers with Adjusted Gross Income over $5 Million', height=30)
radio_group_text = Div(text="Include Additional Capital Gains Behavior.")
radio_group = RadioGroup(labels=["Without", "With"], active=0)
sources['radio_group'] = radio_group
elasticity_text = Div(text="Elasticity of Taxable Income")
elasticity_option = RadioButtonGroup(labels=sources['capital_gains_no'].data['labels'], active=0)
sources['elasticity_option'] = elasticity_option
# create callbacks ---------------
radio_group.callback = CustomJS(args=sources, code="""
var capitalGainsType = radio_group.active === 0 ? capital_gains_no : capital_gains_yes;
elasticity_option.set('labels', capitalGainsType.data.labels);
while (elasticity_option.active > capitalGainsType.data.source_ids.length - 1) {
elasticity_option.active = elasticity_option.active - 1;
}
ref.data = eval(capitalGainsType.data.source_ids[elasticity_option.active]).data;
ref.trigger('change');
""")