x = [3, 4, 6, 12, 10, 1]
y = [7, 1, 3, 4, 1, 6]
c = ['blue', 'blue', 'blue', 'blue', 'blue', 'blue']
source = ColumnDataSource(data=dict(x=x, y=y, color=c))
plot_figure = figure(title='Dropdown',
plot_height=450,
plot_width=600,
tools="save,reset",
toolbar_location="below")
plot_figure.scatter('x', 'y', color='color', source=source, size=10)
menu = [("Red", "red"), ("Green", "green")]
dropdown = Dropdown(label="Choose color for plot", menu=menu)
def dropdown_click(attr, old, new):
active_dropdown = dropdown.value ##Getting dropdown value
if active_dropdown == 'red':
c = ['red', 'red', 'red', 'red', 'red', 'red']
elif active_dropdown == 'green':
c = ['green', 'green', 'green', 'green', 'green', 'green']
source.data = dict(x=x, y=y, color=c)
dropdown.on_change('value', dropdown_click)
layout = row(dropdown, plot_figure)
thread = Thread(target=blocking_task)
thread.start()
def make_plot():
#fig = make_facies_log_plotmake_fa(source.to_df(), facies_colors)
#fig.savefig("app/static/mpl_fig.png")
print(source.to_df().keys())
#print(source.to_df().color.unique(), source.to_df().cluster.unique())
#source.to_df().to_pickle("app/static/5_wells_p_30_no_pca_with_clusters.pkl")
run_button = Button(label="Run Dim-Red")
#This is our Cluster making hack
cluster_button = Button(label="Add Cluster")
menu = [(key, "_".join(key.lower().split(" "))) for key in df.keys()]
dropdown = Dropdown(label="Available Logs", button_type="warning", menu=menu)
def function_to_call(attr, old, new):
print(dropdown.value)
dropdown.on_change('value', function_to_call)
dropdown.on_click(function_to_call)
i = 1
def callback():
global i
i = i + 1
source.callback = CustomJS(args=dict(colors=colors, clusters=i), code="""
var inds = cb_obj.getv('selected')['1d'].indices;
var d1 = cb_obj.data;
if (inds.length == 0) { return; }
def update_data_on_dropdown():
print "In Update"
tuple_params = get_params()
data_src = pd.ExcelFile('Data2.xlsx').parse(tuple_params[0] + ' - ' +
tuple_params[1])
#data_src = pd.read_csv('Data1.csv')
bokeh_source = ColumnDataSource(data_src)
return ColumnDataSource(data_src)
#Adding the elements --- Interactions
cntry_list = [("Phillippines", "PH"), ("Vietnam", "VN"), ("Australia", "AU")]
cntry_selector = Dropdown(label="Country Selector",
button_type="primary",
menu=cntry_list)
scenario_list = [("A4", "A4"), ("B1", "B1"), ("B3", "B3")]
scenario_selector = Dropdown(label="Scenario Selector",
button_type="primary",
menu=scenario_list)
# Defining Data Control Functions
controls = [cntry_selector, scenario_selector]
for control in controls:
control.on_change('value',
lambda attr, old, new: update_data_on_dropdown())
#Build Data
def modify_doc(doc):
parameters = polymer.getparameter(ie)
bs = int(parameters['BS'])
try:
nblk = int(parameters['NBLK'])
except:
nblk = 1
ns = int(parameters['NS'])
try:
dw = parameters['DW'] * 1e-6 #dwell time is in [mu s]
except:
dw = 1
temperature = parameters['TEMP']
fid = pd.DataFrame(polymer.getdata(ie),
index=np.linspace(dw, dw * bs * nblk, bs * nblk),
columns=['real', 'im']) / ns
fid['magnitude'] = (
fid['real']**2 +
fid['im']**2)**0.5 # last two lines may represent a seperate method
tau = np.logspace(-3, np.log10(5 * parameters['T1MX']), nblk) #as a dummy
startpoint = int(0.05 * bs) - 1
endpoint = int(0.1 * bs)
phi = np.zeros(nblk)
for blk in range(nblk):
start = startpoint + blk * bs
end = endpoint + blk * bs
phi[blk] = fid['magnitude'].iloc[start:end].sum() / (endpoint -
startpoint)
df = pd.DataFrame(data=np.c_[tau, phi], columns=['tau', 'phi'])
source_fid = ColumnDataSource(data=ColumnDataSource.from_df(fid))
source_df = ColumnDataSource(data=ColumnDataSource.from_df(df))
p1 = figure(plot_width=300, plot_height=300)
p1.line('index', 'im', source=source_fid, color='blue')
p1.line('index', 'real', source=source_fid, color='green')
p1.line('index', 'magnitude', source=source_fid, color='red')
p2 = figure(plot_width=300, plot_height=300)
p2.circle_cross('tau', 'phi', source=source_df, color="navy")
menu = [("Experiment nr {:4d}".format(ie), "{:4d}".format(ie))
for ie in range(1, nr_experiments + 1)]
dropdown = Dropdown(label="choose experiment number", menu=menu)
def callback(attr, old, new):
ie = int(new)
parameters = polymer.getparameter(ie)
bs = int(parameters['BS'])
try:
nblk = int(parameters['NBLK'])
except:
nblk = 1
ns = int(parameters['NS'])
try:
dw = parameters['DW'] * 1e-6 #dwell time is in [mu s]
except:
dw = 1
temperature = parameters['TEMP']
fid = pd.DataFrame(polymer.getdata(ie),
index=np.linspace(dw, dw * bs * nblk, bs * nblk),
columns=['real', 'im']) / ns
fid['magnitude'] = (
fid['real']**2 + fid['im']**
2)**0.5 # last two lines may represent a seperate method
tau = np.logspace(-3, np.log10(5 * parameters['T1MX']),
nblk) #as a dummy
startpoint = int(0.05 * bs) - 1
endpoint = int(0.1 * bs)
phi = np.zeros(nblk)
for blk in range(nblk):
start = startpoint + blk * bs
end = endpoint + blk * bs
phi[blk] = fid['magnitude'].iloc[start:end].sum() / (endpoint -
startpoint)
df = pd.DataFrame(data=np.c_[tau, phi], columns=['tau', 'phi'])
source_fid.data = ColumnDataSource.from_df(fid)
source_df.data = ColumnDataSource.from_df(df)
dropdown.on_change('value', callback)
doc.add_root(column(dropdown, p1, p2))
#Ejemplo de un boton
boton = Button(label="Ejemplo-Boton", button_type="success")
#Ejemplo de un checkBox para multiples botones
multiBoton = CheckboxButtonGroup(labels=["Boton 1", "Boton 2", "Boton 3"],
active=[0, 1])
#Ejemplo de un Standard para un checkBox
checkboxBoton = CheckboxGroup(labels=["Opcion A", "Opcion B", "Opcion C"],
active=[0, 1])
#Ejemplo de un menu desplegable
menu = [("Opcion A", "item_1"), ("Opcion B", "item_2"), None,
("Opcion C", "item_3")]
desplegable = Dropdown(label="Boton desplegable",
button_type="warning",
menu=menu)
#Ejemplo de una canasta de seleccion
multiSeleccion = MultiSelect(title="Multiseleccionador de opciones:",
value=["foo", "quux"],
options=[("foo", "Opcion 1"), ("bar", "Opcion 2"),
("baz", "Opcion 3"),
("quux", "Opcion 4")])
#Ejemplo de un grupo de radioBotones
radioBotones = RadioGroup(labels=["Opcion 1", "Opcion 2", "Opcion 3"],
active=0)
#Ejemplo de una canasta de seleccion
seleccionador = Select(title="Opciones:",
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
# Set up widgets
text = TextInput(title="Custom function", value='Enter f(x)')
offset = Slider(title="Offset", value=0.0, start=-5.0, end=5.0, step=0.1)
amplitude = Slider(title="Amplitude",
value=1.0,
start=-3.0,
end=3.0,
step=0.01)
Speed = Slider(title="Speed", value=250, start=100, end=250)
Delay = Slider(title="Delay", value=1, start=1, end=100)
CurveList = [("Sin", "C1"), ("Poly", "C2"), ("Abs", "C3"), ("inv/inf", "C5"),
("Frq", "C6"), ("Custom", "C4")]
dropdown = Dropdown(label="Curve Lists", button_type="warning", menu=CurveList)
button = Button(label="Run ", button_type="success")
def update_title(attrname, old, new):
plot.title.text = text.value
x = np.linspace(-4 * np.pi, 4 * np.pi, N)
text.on_change('value', update_title)
div = Div(width=1000)
def change_output(attr, old, new):
def plot(self):
def update():
self.refresh_ticks += 1
self.query(self.selected_color, self.selected_year, self.selected_month)
self.hot_map_update()
self.trip_hour_update()
self.trip_distance_update()
self.trip_fare_update()
self.tasks_stat_update()
# self.resource_usage_update()
def on_select():
BOROUGHS_CODE = {v: k for k, v in NYCBorough.BOROUGHS.items()}
self.selected_color = 'green' if color.active == 1 else 'yellow'
pickup.label = 'Pickups' if pickup.active else 'Dropoffs'
self.selected_type = pickup.label
self.selected_borough = BOROUGHS_CODE[borough.value]
borough.label = borough.value
self.selected_year = int(year.value)
self.selected_month = int(month.value)
def on_submit():
self.logger.debug('submit (%s, %s, %s, %s, %s)' % \
(self.selected_type,
NYCBorough.BOROUGHS[self.selected_borough],
self.selected_color,
self.selected_year, self.selected_month))
self.tasks.create_tasks(
self.selected_color,
self.selected_year,
self.selected_month)
cwd = os.path.dirname(__file__)
desc = Div(text=open(
os.path.join(cwd, "description.html")).read(), width=1000)
# Create input controls
color = RadioButtonGroup(labels=['Yellow', 'Green'], active=1)
color.on_change('active', lambda attr, old, new: on_select())
pickup = Toggle(label='Pickups', button_type="primary", active=True)
pickup.on_change('active', lambda attr, old, new: on_select())
# BUG: Dropdown menu value cannot be integer, i.e., ('Mahattan', '1')
borough_menu = [('All Boroughs', 'All Boroughs'), None,
('Manhattan', 'Manhattan'), ('Bronx', 'Bronx'), ('Brooklyn', 'Brooklyn'),
('Queens', 'Queens'), ('Staten Island', 'Staten Island')]
# https://github.com/bokeh/bokeh/issues/4915
borough = Dropdown(label="Boroughs", button_type="warning",
menu=borough_menu, value='All Boroughs')
borough.on_change('value', lambda attr, old, new: on_select())
year = Select(title="Year:", value=str(self.selected_year),
options=[str(y) for y in range(MIN_DATE['green'].year, MAX_DATE['green'].year+1)])
year.on_change('value', lambda attr, old, new: on_select())
month = Select(title="Month:", value=str(self.selected_month),
options=[str(m) for m in range(1, 13)])
month.on_change('value', lambda attr, old, new: on_select())
submit = Button(label="Submit", button_type="success")
submit.on_click(on_submit)
controls = [color, pickup, borough, year, month, submit]
self.query(self.selected_color, self.selected_year, self.selected_month)
self.hot_map_init()
self.trip_hour_init()
self.trip_distance_init()
self.trip_fare_init()
self.tasks_stat_init()
self.resource_usage_init()
rightdown_row = row([self.trip_distance, self.trip_fare])
right_column = column([self.trip_hour, rightdown_row])
inputs = widgetbox(*controls, width=140, sizing_mode="fixed")
l = layout([
[desc],
[inputs, self.hot_map, right_column],
[self.tasks_stat, self.resource_usage],
], sizing_mode="fixed")
curdoc().add_root(l)
curdoc().add_periodic_callback(update, 5000)
curdoc().title = "NYC Taxi Data Explorer"
def expenses_tab(df, x_name, y_name, measure, t_count):
def make_dataset(category_list):
details = pd.DataFrame()
for cat in category_list:
subset = df[df[x_name] == cat]
details = details.append(subset)
by_category = details.groupby(x_name)[measure].agg(['sum', 'count']).reset_index()\
.rename(columns={'sum': y_name}).sort_values(by=y_name, ascending=False)
by_category[
'Percentage'] = by_category[y_name] / by_category[y_name].sum()
by_category['angle'] = by_category['Percentage'] * 2 * np.pi
by_category['Percentage'] = by_category['Percentage'] * 100
by_category['color'] = viridis(len(category_list))
return ColumnDataSource(by_category), ColumnDataSource(details)
def style(p):
# Title
p.title.align = 'center'
p.title.text_font_size = '20pt'
p.title.text_font = 'serif'
# Axis titles
p.xaxis.axis_label_text_font_size = '14pt'
p.xaxis.axis_label_text_font_style = 'bold'
p.yaxis.axis_label_text_font_size = '14pt'
p.yaxis.axis_label_text_font_style = 'bold'
# Tick labels
p.xaxis.major_label_text_font_size = '12pt'
p.yaxis.major_label_text_font_size = '12pt'
# Vertical Names
p.xaxis.major_label_orientation = np.pi / 4
return p
def make_plot(src):
# Create the blank plot
p = figure(x_range=src.data[x_name],
plot_width=1000,
plot_height=600,
title=x_name + ' vs ' + y_name,
x_axis_label=x_name,
y_axis_label=y_name)
# Setting the second y axis range name and range
p.extra_y_ranges = {
"Occurance": Range1d(start=0, end=src.data['count'].max())
}
# Adding the second axis to the plot.
p.add_layout(LinearAxis(y_range_name="Occurance"), 'right')
p.vbar(source=src,
x=x_name,
bottom=0,
top=y_name,
width=0.9,
fill_color='color',
line_color='black',
fill_alpha=0.75,
hover_fill_alpha=1.0,
hover_fill_color='grey')
p.line(x=x_name,
y=t_count,
source=src,
line_width=1.2,
line_color='black',
y_range_name="Occurance")
# Hover tool referring to our own data field using @ and
# a position on the graph using $
h = HoverTool(
tooltips=[(y_name,
'@' + y_name), ('Count of transactions',
'@' + t_count)])
# Add the hover tool to the graph
p.add_tools(h)
# Style the plot
p = style(p)
return p
def make_table(tbl_src):
columns = [
TableColumn(field=col, title=col)
for col in list(tbl_src.data.keys())[1:]
]
data_table = DataTable(source=tbl_src,
columns=columns,
width=600,
height=600)
return data_table
def make_pie_chart(src):
# Create the blank plot
pie = figure(plot_height=600,
title="Percentage per " + x_name,
toolbar_location=None,
tools="hover",
x_range=(-0.5, 1.0))
pie.wedge(x=0,
y=1,
radius=0.4,
start_angle=cumsum('angle', include_zero=True),
end_angle=cumsum('angle'),
legend=x_name,
source=src,
fill_color='color',
line_color='black',
fill_alpha=0.75,
hover_fill_alpha=1.0,
hover_fill_color='grey')
pie.axis.axis_label = None
pie.axis.visible = False
pie.grid.grid_line_color = None
h = HoverTool(tooltips=[(
'Percentage',
'@Percentage'), (y_name, '@' +
y_name), ('Count of transactions',
'@' + t_count)])
# Add the hover tool to the graph
pie.add_tools(h)
# Style the plot
pie = style(pie)
return pie
# def update(attr, old, new):
def update(event):
'''https://stackoverflow.com/questions/60715996/bokeh-2-0-dropdown-missing-value-attribute'''
categories_to_plot = event.item
new_src, new_tbl_src = make_dataset([categories_to_plot])
# src.data.update(new_src.data)
tbl_src.data.update(new_tbl_src.data)
# categories and colors
available_categories = list(set(df[x_name]))
print(available_categories)
dropdown = Dropdown(label="Category",
button_type="warning",
menu=available_categories)
# dropdown.on_change('value', update)
dropdown.on_click(update)
# Initial categories and data source
src, tbl_src = make_dataset(available_categories)
p = make_plot(src)
pie = make_pie_chart(src)
data_table = make_table(tbl_src)
# Put controls in a single element
controls = WidgetBox(dropdown)
# Create a row layout
# layout = column(p, controls, data_table)
# right = column(controls, data_table)
# left = column(pie, p)
right = column(p)
left = column(pie, controls, data_table)
layout = row(left, right)
# Make a tab with the layout
tab = Panel(child=layout, title='Expenses')
return tab
p.title.align = 'center'
p.title.text_color = Viridis256[50]
p.title.text_font = 'helvetica'
p.title.text_font_style = 'bold'
p.title.text_font_size = '16pt'
# x-axis
p.xaxis.axis_label = 'Weight'
p.xaxis.axis_label_text_color = Viridis256[150]
# y-axis
p.yaxis.axis_label = 'Horsepower'
p.yaxis.axis_label_text_color = Viridis256[150]
# setup widget
menu = [(b.capitalize(), b) for b in autompg.brand.unique()]
dropdown = Dropdown(label='Brand', menu=menu)
# callback to widgets
def callback(attr, old, new):
brand = dropdown.value
source.data = ColumnDataSource.from_df(autompg.loc[autompg.brand == brand])
dropdown.on_change('value', callback)
# arrange plots and widgets in layouts
layout = column(dropdown, p)
curdoc().add_root(layout)
functional_telescope_area = 0 functional_object_type = 0 functional_object_x = [] functional_object_y = [] functional_redshift = default_redshift ''' widget factory TODO: check for float values... I guess? ''' # radio button groups widget_telescope_sizes = RadioButtonGroup(labels=string_telescope_sizes, active=0) widget_object_types = RadioButtonGroup(labels=string_object_types, active=default_object_types) widget_galaxy_type = Dropdown(label=string_title[12],menu=string_galaxy_types) widget_mag_type = RadioButtonGroup(labels=string_magnitude_three,active=0) widget_grating_types = RadioButtonGroup(labels=string_grating_types, active=0) widget_moon_days = RadioButtonGroup(labels=string_moon_days, active=0) # TODO: need title for this, would be string_title[7] widget_binned_pixel_scale_mode = RadioButtonGroup(labels=string_binned_pixel_scale_modes, active=0) # dropdown menus widget_types_types = Dropdown(label=string_object_types_types[0],menu=string_star_types) # TODO: dynamically set these widget_filters = Dropdown(label='Filter',menu=string_filters_menu,width=100) # text input widget_mag_input = TextInput(value=default_magnitude_two,title=string_suffixes[0].title(),width=75) widget_redshift = TextInput(value=str(default_redshift), title=string_title[3]) widget_seeing = TextInput(value=default_seeing, title=string_title[5]) widget_slit_width = TextInput(value=default_slit_width, title=string_title[6]) widget_binned_pixel_scale = TextInput(value=default_binned_pixel_scale,title=string_binned_pixel_scale_manual) # TODO: hide on default # sliders widget_exposure_time = Slider(start=default_exposure_time_start, end=default_exposure_time_end,
tkRoot.withdraw() # Close the root window
fout = tkFileDialog.asksaveasfilename(initialdir=database_dir,
initialfile='stackplt-' +
PlotID + '.npy')
if fout:
imgdict
np.save(fout, imgdict)
Div_info.text = """<p>Stack-plot saved to <b>{}</b></p>.""".format(
fout)
else:
pass
menu_stackplt = [("Open", "Open"), ("Save As", "Save As")]
DropDn_stackplt = Dropdown(
label="File",
menu=menu_stackplt,
width=config_main['plot_config']['tab_MkPlot']['button_wdth_small'])
DropDn_stackplt.on_change('value', DropDn_stackplt_handler)
BUT_exit = Button(
label='Exit',
width=config_main['plot_config']['tab_MkPlot']['button_wdth'],
button_type='danger')
BUT_exit.on_click(exit_update)
# todo dump stackplt data
lout = row(column(p_img, p_imgprofile),
column(RadioButG_XYswitch, DropDn_stackplt, BUT_exit, Div_info))
curdoc().add_root(lout)
curdoc().title = "StackPlt"
def Gulmay_Output_Graph(conn):
############################################################################
#################### CREATE THE DATA FOR THE GRAPH #########################
output_file(
"Gulmay_Output_Graph.html"
) #????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????
# Use the connection passed to the function to read the data into a
# dataframe via an SQL query
df = pd.read_sql( 'select [gulmay session ID], [output], ' \
'[chamber and electrometer], [Dose rate],' \
'[energy], [T/P factor], [Temp], [Press]' \
'from [gulmay output]', conn )
# Going to want to turn the 'gulmay session id' into a date. Will do it at
# the top to speed up and simplify callbacks later
df.loc[:, 'gulmay session id'] = df.loc[:, 'gulmay session id'].str.slice(
stop=10)
df.loc[:,
'gulmay session id'] = pd.to_datetime(df.loc[:,
'gulmay session id'])
# Create a list of the fields using the dataframe
TableFields = (list(df.columns))
############################################################################
################## CREATE DROPDOWNS FOR CHANGING AXIS ######################
menu = [
] # ???????????? don't think this is actually needed ???????????????????????????????????????????????????????????????????????????????????????????????
# The dropdown function needs an extra elelment of the form 'item_i' so this
# counter is to provide this
i = 1
for field in TableFields:
menu.append((field, 'item_' + str(i)))
i = i + 1
dropdown = Dropdown(label='Fields', button_type='warning', menu=menu)
# The select funtion will be used to create dropdown lists to change the
# x and y axis
# The select function doesn't need the extra element of the form item_i
# First make a select menu for changing the x_axis from the list of
# avialible fields
menu_x = []
for field in TableFields:
menu_x.append(field)
select_x = Select(title='Fields available:',
value='gulmay session id',
options=menu_x)
# Need to do the same for the y_axis so just copy the x_axis accross and
# make a select function (much quicker than running the loop again and
# could probably copy accross the select function but done this way for
# clarity of code)
menu_y = menu_x
select_y = Select(title='Fields available:',
value='output',
options=menu_y)
############################################################################
########################### CREATE THE PLOT ################################
# Create a sub dataframe to make things easier later. This sub_df contains
# an 'x', 'y' and a couple of other useful data-points
Sub_df = df[[
'gulmay session id', 'output', 'chamber and electrometer', 'energy'
]]
Sub_df.columns = ['x', 'y', 'ChamberCombination', 'Energy']
# When making the ColumnDataSource convert the dataframe to a dictionary.
# This seems to help with the callback functions.
# https://zduey.github.io/snippets/streaming-stock-data-with-bokeh/
# https://stackoverflow.com/questions/44829730/error-thrown-from-periodic-callback-valueerrormust-stream-updates-to-all-exis
src = ColumnDataSource(Sub_df.to_dict(orient='list'))
# Create the basics of the plot
p1 = figure(
title='Gulmay Output Results',
x_axis_label='Energy',
y_axis_label='Something (mm)',
plot_height=800,
plot_width=1600,
)
# Plot the actual data
p1.xaxis.formatter = DatetimeTickFormatter(days=['%d/%m', '%a%d'])
p1.scatter(source=src,
x='x',
y='y',
fill_alpha=0.4,
size=12,
legend_label='RecordDate')
# Create a hover tool and add it to the plot
hover = HoverTool(tooltips=[('Date', '@x{%F}'), ('(x,y)', '($x, $y)'),
('Chamber Comb.', '@ChamberCombination')],
formatters={'x': 'datetime'})
p1.add_tools(hover)
# Create a layout
layout = column([select_x, select_y, p1])
# Within the callback function 'old' is the previos value. (I.e. if the
# select function is changed from 'Output' to 'T/P Correction', then
# 'old' = 'Output' and 'new' = 'T/P Correction')
def callback_x(attr, old, new):
Sub_df_new = df[[
new, 'gulmay session id', 'chamber and electrometer', 'energy'
]]
Sub_df_new.columns = ['x', 'y', 'Chamber', 'Energy']
# When making the ColumnDataSource convert the dataframe to a dictionary.
# This seems to help with the callback functions.
# https://zduey.github.io/snippets/streaming-stock-data-with-bokeh/
# https://stackoverflow.com/questions/44829730/error-thrown-from-periodic-callback-valueerrormust-stream-updates-to-all-exis
src.data = Sub_df_new.to_dict(orient='list')
def callback_y(attr, old, new):
print(attr)
print(old)
print(new)
Sub_df_new = df[[old, new, 'chamber and electrometer', 'energy']]
Sub_df_new.columns = ['x', 'y', 'Chamber', 'Energy']
# When making the ColumnDataSource convert the dataframe to a dictionary.
# This seems to help with the callback functions.
# https://zduey.github.io/snippets/streaming-stock-data-with-bokeh/
# https://stackoverflow.com/questions/44829730/error-thrown-from-periodic-callback-valueerrormust-stream-updates-to-all-exis
src.data = Sub_df_new.to_dict(orient='list')
select_x.on_change('value', callback_x)
select_y.on_change('value', callback_y)
# Return a panel displaying the created plot and a title for the tab
return Panel(child=layout, title='PDD')
c_map = CategoricalColorMapper(
factors=["setosa","virginica","versicolor"],
palette=["blue","green","red"]
)
data = ColumnDataSource(data={
"x": df["sepal_length"],
"y": df["sepal_width"],
"x1": df["petal_length"],
"y1": df["petal_width"],
"target": df["target"]
})
p1 = figure(title="鳶尾花資料集-花萼")
p1.circle(x="x", y="y", source=data, size=15,
color={"field": "target", "transform": c_map},
legend="target")
p2 = figure(title="鳶尾花資料集-花瓣")
p2.circle(x="x1", y="y1", source=data, size=15,
color={"field": "target", "transform": c_map},
legend="target")
menu = [("setosa","1"),("virginica","2"),("versicolor","3")]
mnu1 = Dropdown(label="鳶尾花種類", menu=menu)
mnu2 = Dropdown(label="鳶尾花種類", menu=menu)
tab1 = Panel(child=column(mnu1,p1), title="花萼")
tab2 = Panel(child=column(mnu2,p2), title="花瓣")
tabs = Tabs(tabs=[tab1, tab2])
show(tabs)
if color_interval:
source_interval_patch = ColumnDataSource(data=dict(x_patch=[], y_patch=[]))
source_interval_bound = ColumnDataSource(
data=dict(x_min=[], x_max=[], y_minmax=[]))
#######################
# INTERACTIVE WIDGETS #
#######################
# text input window for function f(x,y) to be transformed
f_input = TextInput(value=sample_functions[sample_function_id][0],
title="x(t):")
#TODO: add Latex title for text inputs
# dropdown menu for selecting one of the sample functions
sample_fun_input_f = Dropdown(
label="choose a sample function x(t) or enter one above",
menu=sample_f_names,
width=200)
# text input window for T0 (Sampling interval length)
t0_input = TextInput(value='1', title=u"T\u2080 (Interval)")
# text input window for N (Number of sample points)
N_input = TextInput(value='2^6',
title=u"N (Number of sample points, max = 10\u2075)")
# button to show the solution
nyquist_label_default = "show sampling- and Nyquist-frequency"
nyquist_button = CheckboxButtonGroup(labels=[nyquist_label_default],
active=[],
sizing_mode="fixed",
width=240,
height=32)
###########
button = Button(label="Button (enabled) - has click event", button_type="primary")
button.js_on_click(CustomJS(code="console.log('button: click', this.toString())"))
button_disabled = Button(label="Button (disabled) - no click event", button_type="primary", disabled=True)
button_disabled.js_on_click(CustomJS(code="console.log('button_disabled: click', this.toString())"))
toggle_inactive = Toggle(label="Toggle button (initially inactive)", button_type="success")
toggle_inactive.js_on_click(CustomJS(code="console.log('toggle_inactive: ' + this.active, this.toString())"))
toggle_active = Toggle(label="Toggle button (initially active)", button_type="success", active=True)
toggle_active.js_on_click(CustomJS(code="console.log('toggle_active: ' + this.active, this.toString())"))
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.js_on_click(CustomJS(code="console.log('dropdown: ' + this.value, this.toString())"))
dropdown_disabled = Dropdown(label="Dropdown button (disabled)", button_type="warning", disabled=True, menu=menu)
dropdown_disabled.js_on_click(CustomJS(code="console.log('dropdown_disabled: ' + this.value, this.toString())"))
#dropdown_split = Dropdown(label="Split button", button_type="danger", menu=menu, default_value="default")
#dropdown_split.js_on_click(CustomJS(code="console.log('dropdown_split: ' + this.value, this.toString())"))
checkbox_group = CheckboxGroup(labels=["Option 1", "Option 2", "Option 3"], active=[0, 1])
checkbox_group.js_on_click(CustomJS(code="console.log('checkbox_group: ' + this.active, this.toString())"))
radio_group = RadioGroup(labels=["Option 1", "Option 2", "Option 3"], active=0)
radio_group.js_on_click(CustomJS(code="console.log('radio_group: ' + this.active, this.toString())"))
checkbox_button_group = CheckboxButtonGroup(labels=["Option 1", "Option 2", "Option 3"], active=[0, 1])
def _create_ui_components() -> (Figure, ColumnDataSource): # pylint: disable=too-many-statements
global asp_table_source, asp_filter_src, op_table_source, op_filter_src
global stats, aspects, tabs, lexicons_dropdown
stats = pd.DataFrame(columns=["Quantity", "Score"])
aspects = pd.Series([])
def new_col_data_src():
return ColumnDataSource({"file_contents": [], "file_name": []})
large_text = HTMLTemplateFormatter(template="""<div><%= value %></div>""")
def data_column(title):
return TableColumn(field=title,
title='<span class="header">' + title + "</span>",
formatter=large_text)
asp_table_columns = [
data_column("Term"),
data_column("Alias1"),
data_column("Alias2"),
data_column("Alias3"),
]
op_table_columns = [
data_column("Term"),
data_column("Score"),
data_column("Polarity")
]
asp_table_source = empty_table("Term", "Alias1", "Alias2", "Alias3")
asp_filter_src = empty_table("Term", "Alias1", "Alias2", "Alias3")
asp_src = new_col_data_src()
op_table_source = empty_table("Term", "Score", "Polarity", "Polarity")
op_filter_src = empty_table("Term", "Score", "Polarity", "Polarity")
op_src = new_col_data_src()
asp_table = DataTable(
source=asp_table_source,
selectable="checkbox",
columns=asp_table_columns,
editable=True,
width=600,
height=500,
)
op_table = DataTable(
source=op_table_source,
selectable="checkbox",
columns=op_table_columns,
editable=True,
width=600,
height=500,
)
asp_examples_box = _create_examples_table()
op_examples_box = _create_examples_table()
asp_layout = layout([[asp_table, asp_examples_box]])
op_layout = layout([[op_table, op_examples_box]])
asp_tab = Panel(child=asp_layout, title="Aspect Lexicon")
op_tab = Panel(child=op_layout, title="Opinion Lexicon")
tabs = Tabs(tabs=[asp_tab, op_tab], width=700, css_classes=["mytab"])
lexicons_menu = [("Open", "open"), ("Save", "save")]
lexicons_dropdown = Dropdown(
label="Edit Lexicons",
button_type="success",
menu=lexicons_menu,
width=140,
height=31,
css_classes=["mybutton"],
)
train_menu = [("Parsed Data", "parsed"), ("Raw Data", "raw")]
train_dropdown = Dropdown(
label="Extract Lexicons",
button_type="success",
menu=train_menu,
width=162,
height=31,
css_classes=["mybutton"],
)
inference_menu = [("Parsed Data", "parsed"), ("Raw Data", "raw")]
inference_dropdown = Dropdown(
label="Classify",
button_type="success",
menu=inference_menu,
width=140,
height=31,
css_classes=["mybutton"],
)
text_status = TextInput(value="Select training data",
title="Train Run Status:",
css_classes=["statusText"])
text_status.visible = False
train_src = new_col_data_src()
infer_src = new_col_data_src()
with open(join(SOLUTION_DIR, "dropdown.js")) as f:
args = dict(
clicked=lexicons_dropdown,
asp_filter=asp_filter_src,
op_filter=op_filter_src,
asp_src=asp_src,
op_src=op_src,
tabs=tabs,
text_status=text_status,
train_src=train_src,
infer_src=infer_src,
train_clicked=train_dropdown,
infer_clicked=inference_dropdown,
opinion_lex_generic="",
)
code = f.read()
args["train_clicked"] = train_dropdown
train_dropdown.js_on_change("value", CustomJS(args=args, code=code))
args["train_clicked"] = inference_dropdown
inference_dropdown.js_on_change("value", CustomJS(args=args, code=code))
args["clicked"] = lexicons_dropdown
lexicons_dropdown.js_on_change("value", CustomJS(args=args, code=code))
def update_filter_source(table_source, filter_source):
df = table_source.to_df()
sel_inx = sorted(table_source.selected.indices)
df = df.iloc[sel_inx, 1:]
new_source = ColumnDataSource(df)
filter_source.data = new_source.data
def update_examples_box(data, examples_box, old, new):
examples_box.source.data = {"Examples": []}
unselected = list(set(old) - set(new))
selected = list(set(new) - set(old))
if len(selected) <= 1 and len(unselected) <= 1:
examples_box.source.data.update({
"Examples":
[str(data.iloc[unselected[0], i])
for i in range(4, 24)] if len(unselected) != 0 else
[str(data.iloc[selected[0], i]) for i in range(4, 24)]
})
def asp_selected_change(_, old, new):
global asp_filter_src, asp_table_source, aspects_data
update_filter_source(asp_table_source, asp_filter_src)
update_examples_box(aspects_data, asp_examples_box, old, new)
def op_selected_change(_, old, new):
global op_filter_src, op_table_source, opinions_data
update_filter_source(op_table_source, op_filter_src)
update_examples_box(opinions_data, op_examples_box, old, new)
def read_csv(file_src, headers=False, index_cols=False, readCSV=True):
if readCSV:
raw_contents = file_src.data["file_contents"][0]
if len(raw_contents.split(",")) == 1:
b64_contents = raw_contents
else:
# remove the prefix that JS adds
b64_contents = raw_contents.split(",", 1)[1]
file_contents = base64.b64decode(b64_contents)
return pd.read_csv(
io.BytesIO(file_contents),
encoding="ISO-8859-1",
keep_default_na=False,
na_values={None},
engine="python",
index_col=index_cols,
header=0 if headers else None,
)
return file_src
def read_parsed_files(file_content, file_name):
try:
# remove the prefix that JS adds
b64_contents = file_content.split(",", 1)[1]
file_content = base64.b64decode(b64_contents)
with open(SENTIMENT_OUT / file_name, "w") as json_file:
data_dict = json.loads(file_content.decode("utf-8"))
json.dump(data_dict, json_file)
except Exception as e:
print(str(e))
# pylint: disable=unused-argument
def train_file_callback(attr, old, new):
global train_data
SENTIMENT_OUT.mkdir(parents=True, exist_ok=True)
train = TrainSentiment(parse=True, rerank_model=None)
if len(train_src.data["file_contents"]) == 1:
train_data = read_csv(train_src, index_cols=0)
file_name = train_src.data["file_name"][0]
raw_data_path = SENTIMENT_OUT / file_name
train_data.to_csv(raw_data_path, header=False)
print("Running_SentimentTraining on data...")
train.run(data=raw_data_path)
else:
f_contents = train_src.data["file_contents"]
f_names = train_src.data["file_name"]
raw_data_path = SENTIMENT_OUT / train_src.data["file_name"][
0].split("/")[0]
if not os.path.exists(raw_data_path):
os.makedirs(raw_data_path)
for f_content, f_name in zip(f_contents, f_names):
read_parsed_files(f_content, f_name)
print("Running_SentimentTraining on data...")
train.run(parsed_data=raw_data_path)
text_status.value = "Lexicon extraction completed"
with io.open(AcquireTerms.acquired_aspect_terms_path, "r") as fp:
aspect_data_csv = fp.read()
file_data = base64.b64encode(str.encode(aspect_data_csv))
file_data = file_data.decode("utf-8")
asp_src.data = {
"file_contents": [file_data],
"file_name": ["nameFile.csv"]
}
out_path = LEXICONS_OUT / "generated_opinion_lex_reranked.csv"
with io.open(out_path, "r") as fp:
opinion_data_csv = fp.read()
file_data = base64.b64encode(str.encode(opinion_data_csv))
file_data = file_data.decode("utf-8")
op_src.data = {
"file_contents": [file_data],
"file_name": ["nameFile.csv"]
}
def show_analysis() -> None:
global stats, aspects, plot, source, tabs
plot, source = _create_plot()
events_table = _create_events_table()
# pylint: disable=unused-argument
def _events_handler(attr, old, new):
_update_events(events_table, events_type.active)
# Toggle display of in-domain / All aspect mentions
events_type = RadioButtonGroup(
labels=["All Events", "In-Domain Events"], active=0)
analysis_layout = layout([[plot], [events_table]])
# events_type display toggle disabled
# analysis_layout = layout([[plot],[events_type],[events_table]])
analysis_tab = Panel(child=analysis_layout, title="Analysis")
tabs.tabs.insert(2, analysis_tab)
tabs.active = 2
events_type.on_change("active", _events_handler)
source.selected.on_change("indices", _events_handler) # pylint: disable=no-member
# pylint: disable=unused-argument
def infer_file_callback(attr, old, new):
# run inference on input data and current aspect/opinion lexicons in view
global infer_data, stats, aspects
SENTIMENT_OUT.mkdir(parents=True, exist_ok=True)
df_aspect = pd.DataFrame.from_dict(asp_filter_src.data)
aspect_col_list = ["Term", "Alias1", "Alias2", "Alias3"]
df_aspect = df_aspect[aspect_col_list]
df_aspect.to_csv(SENTIMENT_OUT / "aspects.csv",
index=False,
na_rep="NaN")
df_opinion = pd.DataFrame.from_dict(op_filter_src.data)
opinion_col_list = ["Term", "Score", "Polarity", "isAcquired"]
df_opinion = df_opinion[opinion_col_list]
df_opinion.to_csv(SENTIMENT_OUT / "opinions.csv",
index=False,
na_rep="NaN")
solution = SentimentSolution()
if len(infer_src.data["file_contents"]) == 1:
infer_data = read_csv(infer_src, index_cols=0)
file_name = infer_src.data["file_name"][0]
raw_data_path = SENTIMENT_OUT / file_name
infer_data.to_csv(raw_data_path, header=False)
print("Running_SentimentInference on data...")
text_status.value = "Running classification on data..."
stats = solution.run(
data=raw_data_path,
aspect_lex=SENTIMENT_OUT / "aspects.csv",
opinion_lex=SENTIMENT_OUT / "opinions.csv",
)
else:
f_contents = infer_src.data["file_contents"]
f_names = infer_src.data["file_name"]
raw_data_path = SENTIMENT_OUT / infer_src.data["file_name"][
0].split("/")[0]
if not os.path.exists(raw_data_path):
os.makedirs(raw_data_path)
for f_content, f_name in zip(f_contents, f_names):
read_parsed_files(f_content, f_name)
print("Running_SentimentInference on data...")
text_status.value = "Running classification on data..."
stats = solution.run(
parsed_data=raw_data_path,
aspect_lex=SENTIMENT_OUT / "aspects.csv",
opinion_lex=SENTIMENT_OUT / "opinions.csv",
)
aspects = pd.read_csv(SENTIMENT_OUT / "aspects.csv",
encoding="utf-8")["Term"]
text_status.value = "Classification completed"
show_analysis()
# pylint: disable=unused-argument
def asp_file_callback(attr, old, new):
global aspects_data, asp_table_source
aspects_data = read_csv(asp_src, headers=True)
# Replaces None values by empty string
aspects_data = aspects_data.fillna("")
new_source = ColumnDataSource(aspects_data)
asp_table_source.data = new_source.data
asp_table_source.selected.indices = list(range(len(aspects_data)))
# pylint: disable=unused-argument
def op_file_callback(attr, old, new):
global opinions_data, op_table_source, lexicons_dropdown, df_opinion_generic
df = read_csv(op_src, headers=True)
# Replaces None values by empty string
df = df.fillna("")
# Placeholder for generic opinion lexicons from the given csv file
df_opinion_generic = df[df["isAcquired"] == "N"]
# Update the argument value for the callback customJS
lexicons_dropdown.js_property_callbacks.get("change:value")[0].args[
"opinion_lex_generic"] = df_opinion_generic.to_dict(orient="list")
opinions_data = df[df["isAcquired"] == "Y"]
new_source = ColumnDataSource(opinions_data)
op_table_source.data = new_source.data
op_table_source.selected.indices = list(range(len(opinions_data)))
# pylint: disable=unused-argument
def txt_status_callback(attr, old, new):
print("Status: " + new)
text_status.on_change("value", txt_status_callback)
asp_src.on_change("data", asp_file_callback)
# pylint: disable=no-member
asp_table_source.selected.on_change("indices", asp_selected_change)
op_src.on_change("data", op_file_callback)
op_table_source.selected.on_change("indices", op_selected_change) # pylint: disable=no-member
train_src.on_change("data", train_file_callback)
infer_src.on_change("data", infer_file_callback)
return layout(
[[_create_header(train_dropdown, inference_dropdown, text_status)],
[tabs]])
# Plot corresponding tangent vector
quiver_constraint = my_bokeh_utils.Quiver(plot, fix_at_middle=False, line_width=2, color='red')
# Plot mark at position on constraint function
plot.cross(x='x', y='y', color='red', size=10, line_width=2, source=source_mark)
# object that detects, if a position in the plot is clicked on
interactor = my_bokeh_utils.Interactor(plot)
# adds callback function to interactor, if position in plot is clicked, call on_selection_change
interactor.on_click(on_selection_change)
# text input window for objective function f(x,y) to be optimized
f_input = TextInput(value=lagrange_settings.f_init, title="f(x,y):")
f_input.on_change('value', f_changed)
# dropdown menu for selecting one of the sample functions
sample_fun_input_f = Dropdown(label="choose a sample function f(x,y) or enter one below",
menu=lagrange_settings.sample_f_names)
sample_fun_input_f.on_click(sample_fun_input_f_changed)
# text input window for side condition g(x,y)=0
g_input = TextInput(value=lagrange_settings.g_init, title="g(x,y):")
g_input.on_change('value', g_changed)
# dropdown menu for selecting one of the sample functions
sample_fun_input_g = Dropdown(label="choose a sample function g(x,y) or enter one below",
menu=lagrange_settings.sample_g_names)
sample_fun_input_g.on_click(sample_fun_input_g_changed)
# calculate data
init_data()
# refresh quiver field all 100ms
var blob = new Blob([filetext], { type: 'text/csv;charset=utf-8;' });
if (navigator.msSaveBlob) { // IE 10+
navigator.msSaveBlob(blob, filename);
} else {
var link = document.createElement("a");
if (link.download !== undefined) { // feature detection
// Browsers that support HTML5 download attribute
var url = URL.createObjectURL(blob);
link.setAttribute("href", url);
link.setAttribute("download", filename);
link.style.visibility = 'hidden';
document.body.appendChild(link);
link.click();
document.body.removeChild(link);
}
}"""
button.callback = CustomJS(args=dict(source=source), code=js_download)
#Create dropdown button for auxilary data
menu = [("Temperature", "temperature"), ("Laser current", "laser_current")]
dropdown = Dropdown(label="Select data",
button_type="danger",
menu=menu,
value='temperature')
dropdown.on_change('value', update_plot)
#add figure to curdoc and configure callback
lay_out = layout([[radio_button_group, slider], [f_photo], [f_aux],
[dropdown, button]])
curdoc().add_root(lay_out)
curdoc().add_periodic_callback(update, 1000) #updates each 1000ms
button = Button(label="Button (enabled) - has click event",
button_type="primary")
button.on_click(button_handler)
button_disabled = Button(label="Button (disabled) - no click event",
button_type="primary",
disabled=True)
button_disabled.on_click(button_handler)
toggle = Toggle(label="Toggle button", button_type="success")
toggle.on_click(toggle_handler)
menu = [("Item 1", "item_1_value"), ("Item 2", "item_2_value"),
("Item 3", "item_3_value")]
dropdown = Dropdown(label="Dropdown button",
button_type="warning",
menu=menu,
default_value="item_1_value")
dropdown.on_click(dropdown_handler)
split_menu = [("Item 1", "item_1_value"), ("Item 2", "item_2_value"), None,
("Item 3", "item_3_value")]
split = Dropdown(label="Split button", button_type="danger", menu=split_menu)
split.on_click(split_handler)
checkbox_group = CheckboxGroup(labels=["Option 1", "Option 2", "Option 3"],
active=[0, 1])
checkbox_group.on_click(checkbox_group_handler)
radio_group = RadioGroup(labels=["Option 1", "Option 2", "Option 3"], active=0)
radio_group.on_click(radio_group_handler)
checkbox_group = CheckboxGroup(labels=["Option 1", "Option 2", "Option 3"],
active=[0, 1])
data = dict(
dates=[date(2014, 3, i + 1) for i in range(10)],
downloads=[randint(0, 100) for i in range(10)],
)
source = ColumnDataSource(data)
columns = [
TableColumn(field="dates", title="Date", formatter=DateFormatter()),
TableColumn(field="downloads", title="Downloads"),
]
data_table = DataTable(source=source, columns=columns, width=400, height=280)
menu = [("Item 1", "item_1"), ("Item 2", "item_2"), None, ("Item 3", "item_3")]
dropdown = Dropdown(label="Dropdown button", button_type="warning", menu=menu)
multi_select = MultiSelect(title="Option:",
value=["foo", "quux"],
options=[("foo", "Foo"), ("bar", "BAR"),
("baz", "bAz"), ("quux", "quux")])
radio_button_group = RadioButtonGroup(
labels=["Option 1", "Option 2", "Option 3"], active=0)
radio_group = RadioGroup(labels=["Option 1", "Option 2", "Option 3"], active=0)
select = Select(title="Option:",
value="foo",
options=["foo", "bar", "baz", "quux"])
"y": df["sepal_width"],
"x1": df["petal_length"],
"y1": df["petal_width"],
"target": df["target"]
})
p1 = figure(title="鳶尾花資料集-花萼")
p1.circle(x="x",
y="y",
source=data,
size=15,
color={
"field": "target",
"transform": c_map
},
legend="target")
p2 = figure(title="鳶尾花資料集-花瓣")
p2.circle(x="x1",
y="y1",
source=data,
size=15,
color={
"field": "target",
"transform": c_map
},
legend="target")
menu = [("setosa", "1"), ("virginica", "2"), ("versicolor", "3")]
mnu = Dropdown(label="鳶尾花種類", menu=menu)
layout = gridplot([mnu, None], [p1, p2])
show(layout)
# TAB1 population view ----------------------------------------------------------------------- TAB1 population view
# file loading and update
file_source_tree.on_change('data', file_callback_tree)
file_source_populations.on_change('data', file_callback_populations)
file_source_patient.on_change('data', file_callback_pat)
file_source_clinical.on_change('data', file_callback_clinical)
# upload tree files
menu_tree = [("Upload cluster coordinates", "coordinates"), ("Upload graph edges", "edges")]
tree_dropdown = Dropdown(label="Upload tree structure", button_type="warning", menu=menu_tree,
css_classes=['dropdowns'])
tree_dropdown.callback = CustomJS(args=dict(file_source=file_source_tree),
code=open(join(dirname(__file__), "static/js/upload.js")).read())
# upload patients data
upload_patients = Button(label="upload patients data")
upload_patients.js_on_click(CustomJS(args=dict(file_source=file_source_patient),
code=open(join(dirname(__file__), "static/js/upload_multiple.js")).read()))
# upload population list
upload_populations = Button(label="upload population list")
upload_populations.js_on_click(CustomJS(args=dict(file_source=file_source_populations),
code=open(join(dirname(__file__), "static/js/upload.js")).read()))
# select patient
patient = Select(title='Patient', value='None', options=['None'] + df_viz.columns.tolist())
def overview(states):
state_x = [(code, states[code]["lons"]) for code in states]
state_xsTest = sorted(state_x, key=lambda x: x[0])
state_xf = [i[1] for i in state_xsTest]
state_y = [(code, states[code]["lats"]) for code in states]
state_ysTest = sorted(state_y, key=lambda x: x[0])
state_yf = [i[1] for i in state_ysTest]
summ['plot'] = summ['percent'].copy()
summ['true'] = summ['percent'].copy()
colors = bokeh.palettes.Blues9[::-1]
mapper = LinearColorMapper(palette=colors,
nan_color='white',
low=0.,
high=100.)
ticker = FixedTicker(ticks=[0, 50, 100])
cbar = ColorBar(color_mapper=mapper,
ticker=ticker,
label_standoff=12,
border_line_color=None,
location=(0, 0))
source = ColumnDataSource(data=dict(x=state_xf,
y=state_yf,
plot=summ['plot'],
true=summ['true'],
user=summ['user'],
job=summ['job'],
JobPerUser=summ['jobPerUser'],
percent=summ['percent'],
state=summ['state']))
p = figure(tools="pan, zoom_in, zoom_out, hover, save,reset",
toolbar_location="right",
plot_width=800,
plot_height=450)
#title='Overview of the job market', p.title.text_font_size = '20pt'
p.patches('x',
'y',
source=source,
fill_color={
'field': 'plot',
'transform': mapper
},
fill_alpha=0.7,
line_color="grey",
line_width=2,
line_alpha=0.3)
p.add_layout(cbar, 'right')
##################call back
callback = CustomJS(args=dict(source=source),
code="""
var data = source.data;
var f = cb_obj.value
var max_val = 0
var min_val = 0
cb_obj['attributes']['label'] = f;
for (i = 0; i < data['x'].length; i++) {
data['plot'][i] = data[f][i]
}
for (i=0;i < data['x'].length;i++){
if(data['plot'][i] > max_val){
max_val = data['plot'][i];
}
}
for (i=0;i < data['x'].length;i++){
data['true'][i] = data['plot'][i]
data['plot'][i] = 100. * (data['plot'][i])/ (max_val)
}
source.change.emit();
""")
###################
menu = [('User Number', 'user'), ('Job Number', 'job'),
('Jobs Per User', 'JobPerUser'),
('Percent: job application habit', 'percent')]
dropdown = Dropdown(label='Select', button_type="warning", menu=menu)
dropdown.js_on_change('value', callback)
hover = p.select_one(HoverTool)
hover.point_policy = 'follow_mouse'
hover.tooltips = [("State", '@state'), ('value', '@true')]
layout1 = row(p, dropdown)
df1 = df[:8]
data = dict(UserID=df1['UserID'],
Title=df1['Title'],
Description=df1['Description'],
Requirements=df1['Requirements'],
City=df1['City'],
State=df1['State'])
source = ColumnDataSource(data)
columns = [
TableColumn(field="UserID", title="UserID"),
TableColumn(field="Title", title="Title"),
TableColumn(field="City", title="City"),
TableColumn(field="State", title="State"),
TableColumn(field="Description", title="Description"),
TableColumn(field="Requirements", title="Requirements")
]
data_table = DataTable(source=source,
columns=columns,
width=1000,
height=230)
layout2 = row(data_table)
script, divs = components({'plot': layout1, 'table': layout2})
return render_template('index.html', script=script, div=divs)
def scatter_tab(population):
def make_dataset(gender_list=list(['Male']),
age_start=2,
age_end=26,
weight_start=10,
weight_end=218,
height_start=80,
height_end=200,
bmi_start=11.2,
bmi_end=67.3,
bin_width=25,
x_axis='SESSION_IQR',
y_axis='SESSION_STD'):
gender_list = sorted(gender_list)
plot_population = pd.DataFrame(
columns=[x_axis, y_axis, 'gender', 'color'])
if len(gender_list) == 0:
plot_population = pd.DataFrame(
columns=[x_axis, y_axis, 'gender', 'color'])
else:
for i, gender_name in enumerate(gender_list):
if gender_name == 'All':
subset = population
else:
subset = population[population['GENDER2'] == gender_name]
subset = subset[(subset.AGE > age_start)
& (subset.AGE < age_end)
& (subset.WEIGHT > weight_start)
& (subset.WEIGHT < weight_end)
& (subset.HEIGHT > height_start)
& (subset.HEIGHT < height_end)
& (subset.BMI > bmi_start)
& (subset.BMI < bmi_end)]
arr_df = pd.DataFrame({
'SEQN': subset['SEQN'],
x_axis: subset[x_axis],
y_axis: subset[y_axis]
})
arr_df['gender'] = gender_name
arr_df['color'] = Category20_16[i]
arr_df = arr_df[['SEQN', x_axis, y_axis, 'gender', 'color']]
if gender_name == "Male":
arr_df1 = arr_df
elif gender_name == "Female":
arr_df2 = arr_df
else:
arr_df3 = arr_df
if len(gender_list) == 1:
plot_population = arr_df
plot_population = plot_population.sort_values(['gender', 'SEQN'])
plot_population = plot_population[[
'SEQN', x_axis, y_axis, 'gender', 'color'
]]
plot_population = plot_population.reset_index(drop=True)
elif len(gender_list) == 2:
if gender_list[0] == "Female":
plot_population = arr_df1.append(arr_df2, ignore_index=True)
plot_population = plot_population.sort_values(
['gender', 'SEQN'])
plot_population = plot_population.reset_index(drop=True)
elif gender_list[1] == "Female":
plot_population = arr_df3.append(arr_df2, ignore_index=True)
plot_population = plot_population.sort_values(
['gender', 'SEQN'])
plot_population = plot_population.reset_index(drop=True)
else:
plot_population = arr_df1.append(arr_df3, ignore_index=True)
plot_population = plot_population.sort_values(
['gender', 'SEQN'])
plot_population = plot_population.reset_index(drop=True)
elif len(gender_list) == 3:
plot_population = arr_df1.append(arr_df2, ignore_index=True)
plot_population = plot_population.append(arr_df3,
ignore_index=True)
plot_population = plot_population.sort_values(['gender', 'SEQN'])
plot_population = plot_population.reset_index(drop=True)
return ColumnDataSource(plot_population)
def make_plot(src):
p = figure()
menu = [(
'Raw Curve Sequence Number',
'RAW_CURVE',
), ('Force Vital Capacity (ml)', 'FVC_MAX'),
('Force Vital Capacity (ml) at 1sec', 'FEV1'),
('Force Vital Capacity (ml) at 3sec', 'FEV3'),
('Force Vital Capacity (ml) at 6sec', 'FEV6'),
('Peak expiratory flow (ml)', 'PEAK_EXPIRATORY'),
('Max-Mid expiratory flow (ml)', 'MAX_MID_EXPIRATORY'),
('Pseudo PSU', 'PSEUDO_PSU'), ('Age (years)', 'AGE'),
('Gender', 'GENDER2'), ('Height (cm)', 'HEIGHT'),
('Weight (kg)', 'WEIGHT'), ('Body mass index (kg/m2)', 'BMI'),
('Session best FVC', 'SESSION_BEST'),
('Session mean FVC', 'SESSION_MEAN'),
('Session std FVC', 'SESSION_STD'),
('Session median FVC', 'SESSION_MEDIAN'),
('Session iqr FVC', 'SESSION_IQR'),
('Session minimum FVC', 'SESSION_MINIMUM'),
('Session maximum FVC', 'SESSION_MAXIMUM'),
('Session max distance FVC', 'SESSION_MAX_DISTANCE'),
('Session median distance FVC', 'SESSION_MEDIAN_DISTANCE')]
if len(src.column_names) == 5:
for i in range(22):
if menu[i][1] == src.column_names[1]:
titulo_x = menu[i][0]
titulo_y = menu[i][0]
break
xs = src.column_names[1]
ys = src.column_names[1]
gs = src.column_names[2]
zs = src.column_names[3]
x_title = titulo_x.title()
y_title = titulo_y.title()
kw = dict()
kw['title'] = "%s vs %s" % (x_title, y_title)
# Blank plot with correct labels
p = figure(plot_width=700,
plot_height=700,
background_fill_color="#2E3332",
**kw)
p.xaxis.axis_label = x_title
p.yaxis.axis_label = y_title
# Quad glyphs to create a histogram
p.circle(source=src,
x=xs,
y=ys,
size=7,
color=zs,
legend=gs,
fill_alpha=0.7)
#
if len(src.column_names) == 6:
for i in range(22):
if menu[i][1] == src.column_names[1]:
titulo_x = menu[i][0]
if menu[i][1] == src.column_names[2]:
titulo_y = menu[i][0]
xs = src.column_names[1]
ys = src.column_names[2]
gs = src.column_names[3]
zs = src.column_names[4]
x_title = titulo_x.title()
y_title = titulo_y.title()
kw = dict()
kw['title'] = "%s vs %s" % (x_title, y_title)
# Blank plot with correct labels
p = figure(plot_width=700,
plot_height=700,
background_fill_color="#2E3332",
**kw)
p.xaxis.axis_label = x_title
p.yaxis.axis_label = y_title
# Quad glyphs to create a histogram
p.circle(source=src,
x=xs,
y=ys,
size=7,
color=zs,
legend=gs,
fill_alpha=0.7)
return p
def update():
genders_to_plot = [
select_gender.labels[i] for i in select_gender.active
]
new_src = make_dataset(genders_to_plot,
age_start=age_select.value[0],
age_end=age_select.value[1],
weight_start=weight_select.value[0],
weight_end=weight_select.value[1],
height_start=height_select.value[0],
height_end=height_select.value[1],
bmi_start=bmi_select.value[0],
bmi_end=bmi_select.value[1],
x_axis=select_x.value,
y_axis=select_y.value)
layout.children[1] = make_plot(new_src)
#gender selection
available_gender = list(['Male', 'Female', 'All'])
available_gender.sort()
gender_colors = Category20_16
gender_colors.sort()
select_gender = CheckboxGroup(labels=list(['Male', 'Female', 'All']),
active=[0, 1, 2])
select_gender.on_change('active', lambda attr, old, new: update())
#age range select
age_select = RangeSlider(start=1,
end=25,
value=(1, 28),
step=1,
title='Range of Age')
age_select.on_change('value', lambda attr, old, new: update())
#weight range
weight_select = RangeSlider(start=10,
end=200,
value=(10, 200),
step=1,
title='Range of Weight')
weight_select.on_change('value', lambda attr, old, new: update())
#height range select
height_select = RangeSlider(start=80,
end=200,
value=(80, 200),
step=1,
title='Range of Height')
height_select.on_change('value', lambda attr, old, new: update())
#BMI range select
bmi_select = RangeSlider(start=10,
end=60,
value=(10, 60),
step=0.5,
title='Range of Body Mass Index')
bmi_select.on_change('value', lambda attr, old, new: update())
menu = [(
'Raw Curve Sequence Number',
'RAW_CURVE',
), ('Force Vital Capacity (ml)', 'FVC_MAX'),
('Force Vital Capacity (ml) at 1sec', 'FEV1'),
('Force Vital Capacity (ml) at 3sec', 'FEV3'),
('Force Vital Capacity (ml) at 6sec', 'FEV6'),
('Peak expiratory flow (ml)', 'PEAK_EXPIRATORY'),
('Max-Mid expiratory flow (ml)', 'MAX_MID_EXPIRATORY'),
('Pseudo PSU', 'PSEUDO_PSU'), ('Age (years)', 'AGE'),
('Gender', 'GENDER2'), ('Height (cm)', 'HEIGHT'),
('Weight (kg)', 'WEIGHT'), ('Body mass index (kg/m2)', 'BMI'),
('Session best FVC', 'SESSION_BEST'),
('Session mean FVC', 'SESSION_MEAN'),
('Session std FVC', 'SESSION_STD'),
('Session median FVC', 'SESSION_MEDIAN'),
('Session iqr FVC', 'SESSION_IQR'),
('Session minimum FVC', 'SESSION_MINIMUM'),
('Session maximum FVC', 'SESSION_MAXIMUM'),
('Session max distance FVC', 'SESSION_MAX_DISTANCE'),
('Session median distance FVC', 'SESSION_MEDIAN_DISTANCE')]
#select x axis
select_x = Dropdown(label='X Axis',
button_type="warning",
value='SESSION_IQR',
menu=menu)
select_x.on_change('value', lambda attr, old, new: update())
#select y axis
select_y = Dropdown(label='Y Axis',
button_type="warning",
value='SESSION_STD',
menu=menu)
select_y.on_change('value', lambda attr, old, new: update())
# Initial carriers and data source
initial_gender = [select_gender.labels[i] for i in select_gender.active]
src = make_dataset(initial_gender,
age_start=age_select.value[0],
age_end=age_select.value[1],
weight_start=weight_select.value[0],
weight_end=weight_select.value[1],
height_start=height_select.value[0],
height_end=height_select.value[1],
bmi_start=bmi_select.value[0],
bmi_end=bmi_select.value[1],
x_axis=select_x.value,
y_axis=select_y.value)
# p = make_plot(src)
# Put controls in a single element
controls = WidgetBox(select_gender, age_select, weight_select, bmi_select,
height_select, select_x, select_y)
# Create a row layout
layout = row(controls, make_plot(src))
# Make a tab with the layout
tab2 = Panel(child=layout, title='Scatter')
update()
return tab2
#build all widget objects and aggregate them in a grid plot for each panel (according to job board origin).
for idx, origin in enumerate(origin_source):
cv_input[origin] = FileInput(accept='.txt')
location_select[origin] = RadioButtonGroup(
labels=location_choice,
active=0,
css_classes=['custom_group_button_bokeh'])
text = template_div.format(site_name=origin,
number=total_job_numbers[idx],
total=total_job_numbers[idx])
div_job_number[origin] = Div(text=text, height=50)
select_1[origin] = Dropdown(value=age_max,
label='Publication date',
css_classes=['custom_button_bokeh'],
menu=[("All", age_max),
("Less than 1 day", "1"),
("Less than 3 days", "3"),
("Less than 7 days", "7"),
("Less than 14 days", "14"),
("Less than 30 days", "30")])
# WARNING for Dropdown button, use value param. to set default value (and not default_value param. !!!)
select_2[origin] = CheckboxGroup(labels=job_cat_labels,
active=list(range(len(job_cat_labels))))
select_3[origin] = RangeSlider(title="Salary(k€)",
start=min_salary[idx],
end=max_salary[idx],
step=5,
value=(min_salary[idx], max_salary[idx]))
def table_tab(df):
source = ColumnDataSource(data=dict())
def update():
current = df
if select_gender.value == "Male":
current = df[(df['GENDER2'] == 'Male')]
elif select_gender.value == "Female":
current = df[(df['GENDER2'] == 'Female')]
elif select_gender.value == "All":
current = df
current = current[(current['HEIGHT'] >= height_select.value[0])
& (current['HEIGHT'] <= height_select.value[1])
& (current['WEIGHT'] >= weight_select.value[0]) &
(current['WEIGHT'] <= weight_select.value[1])
& (current['AGE'] >= age_select.value[0]) &
(current['AGE'] <= age_select.value[1])
& (current['BMI'] >= bmi_select.value[0]) &
(current['BMI'] <= bmi_select.value[1])].dropna()
source.data = {
'SEQN': current.SEQN,
'RAW_CURVE': current.RAW_CURVE,
'FVC_MAX': current.FVC_MAX,
'FEV1': current.FEV1,
'FEV3': current.FEV3,
'FEV6': current.FEV6,
'PEAK_EXPIRATORY': current.PEAK_EXPIRATORY,
'MAX_MID_EXPIRATORY': current.MAX_MID_EXPIRATORY,
'PSEUDO_PSU': current.PSEUDO_PSU,
'AGE': current.AGE,
'GENDER2': current.GENDER2,
'GENDER': current.GENDER,
'HEIGHT': current.HEIGHT,
'WEIGHT': current.WEIGHT,
'BMI': current.BMI,
'SESSION_BEST': current.SESSION_BEST,
'SESSION_MEAN': current.SESSION_MEAN,
'SESSION_STD': current.SESSION_STD,
'SESSION_MEDIAN': current.SESSION_MEDIAN,
'SESSION_IQR': current.SESSION_IQR,
'SESSION_MINIMUM': current.SESSION_MINIMUM,
'SESSION_MAXIMUM': current.SESSION_MAXIMUM,
'SESSION_MAX_DISTANCE': current.SESSION_MAX_DISTANCE,
'SESSION_MEDIAN_DISTANCE': current.SESSION_MEDIAN_DISTANCE
}
menu = [("All", "All"), ("Male", "Male"), ("Female", "Female")]
select_gender = Dropdown(label="Gender Selection",
button_type="warning",
menu=menu)
select_gender.on_change('value', lambda attr, old, new: update())
#height range select
height_select = RangeSlider(title="Range of Height",
start=80,
end=200,
value=(80, 200),
step=0.5)
height_select.on_change('value', lambda attr, old, new: update())
#height range select
age_select = RangeSlider(title="Range of Age",
start=1,
end=28,
value=(1, 28),
step=1)
age_select.on_change('value', lambda attr, old, new: update())
#weight range select
weight_select = RangeSlider(title="Range of Weight",
start=10,
end=200,
value=(10, 200),
step=1)
weight_select.on_change('value', lambda attr, old, new: update())
#height range select
bmi_select = RangeSlider(title="Range of Body Mass Index",
start=10,
end=60,
value=(10, 60),
step=0.5)
bmi_select.on_change('value', lambda attr, old, new: update())
button = Button(label="Download", button_type="success")
button.callback = CustomJS(args=dict(source=source),
code=open(join(dirname(__file__),
"download.js")).read())
columns = [
TableColumn(field='SEQN', title='SEQN'),
TableColumn(field='FVC_MAX', title='FVC (ml)'),
TableColumn(field='AGE', title='AGE (years)'),
TableColumn(field='GENDER2', title='GENDER'),
TableColumn(field='HEIGHT', title='HEIGHT (cm)'),
TableColumn(field='BMI', title='BMI'),
TableColumn(field="SESSION_BEST", title='SESSION MAXIMUM (ml)')
]
data_table = DataTable(source=source, columns=columns, width=1000)
# Put controls in a single element
controls = WidgetBox(select_gender, age_select, weight_select, bmi_select,
height_select, button)
# Create a row layout
layout = row(controls, data_table)
# Make a tab with the layout
tab = Panel(child=layout, title='Summary Table')
update()
return tab
plot4.line('x', 'y', source=source4, line_width=3, line_alpha=0.6)
plot5.line('x', 'y', source=source5, line_width=3, line_alpha=0.6)
a = 1.7 / 3.0
theta = np.arange(0, 2 * np.pi + 2 * np.pi * 0.01, 2 * np.pi * 0.01)
x = np.arcsin(0.7)
R = 1.7 + a * np.cos(theta + x * np.sin(theta))
Z = 1.8 * a * np.sin(theta)
source3 = ColumnDataSource(data=dict(x=R, y=Z))
plot3.line('x', 'y', source=source3, line_width=3, line_alpha=0.6)
tokamaks = [("DIII_D", "d3d"), ("JET", "JET"), ("ITER steady-state", "ITERSS"),
("ARC", "ARC"), ("CAT-DEMO", "CAT-DEMO"), ("ARIES ACT 2", "ACT2")]
starting_tok = Dropdown(label="Starting Tokamak",
button_type="warning",
menu=tokamaks)
majorRadius = Slider(title="Major Radius",
value=1.7,
start=0.5,
end=10.0,
step=0.1,
callback_policy='mouseup')
Bt = Slider(title="Bt",
value=2.2,
start=1.0,
end=12.0,
step=0.1,
callback_policy='mouseup')
delta = Slider(title="triangularity",
return price, column(price, row(volume, stochastic, macd))
# %%
search_symbol = AutocompleteInput(title="Query Symbol",
completions=symbol_list)
plot, plot_layout = plot_stock(stock_code)
stock_symbol = TextInput(title="Stock Symbol:", value="AGL.AX")
porfolio_list = [("AGL.AX", "AGL.AX"), ("AIZ.AX", "AIZ.AX"),
("IFN.AX", "IFN.AX"), ("NAB.AX", "NAB.AX")]
porfolio = Dropdown(label="Select from porfolio",
button_type="primary",
menu=porfolio_list)
def update_day(attrname, old, new):
offset = int(day_offset.value)
new_stock = stock.iloc[-offset:]
new_source = ColumnDataSource(new_stock)
source.data = new_source.data
def update_stock(attrname, old, new):
global stock
new_stock_symbol = stock_symbol.value
plot.title.text = new_stock_symbol
stock = get_stock(new_stock_symbol)
button = Button(label="Button (enabled) - has click event", button_type="primary")
button.js_on_click(CustomJS(code="console.log('button: click ', this.toString())"))
button_disabled = Button(label="Button (disabled) - no click event", button_type="primary", disabled=True)
button_disabled.js_on_click(CustomJS(code="console.log('button(disabled): click ', this.toString())"))
toggle_inactive = Toggle(label="Toggle button (initially inactive)", button_type="success")
toggle_inactive.js_on_click(CustomJS(code="console.log('toggle(inactive): active=' + this.active, this.toString())"))
toggle_active = Toggle(label="Toggle button (initially active)", button_type="success", active=True)
toggle_active.js_on_click(CustomJS(code="console.log('toggle(active): active=' + this.active, this.toString())"))
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.js_on_click(CustomJS(code="console.log('dropdown: click ' + this.toString())"))
dropdown.js_on_event("menu_item_click", CustomJS(code="console.log('dropdown: ' + this.item, this.toString())"))
dropdown_disabled = Dropdown(label="Dropdown button (disabled)", button_type="warning", disabled=True, menu=menu)
dropdown_disabled.js_on_click(CustomJS(code="console.log('dropdown(disabled): click ' + this.toString())"))
dropdown_disabled.js_on_event("menu_item_click", CustomJS(code="console.log('dropdown(disabled): ' + this.item, this.toString())"))
dropdown_split = Dropdown(label="Split button", split=True, button_type="danger", menu=menu)
dropdown_split.js_on_click(CustomJS(code="console.log('dropdown(split): click ' + this.toString())"))
dropdown_split.js_on_event("menu_item_click", CustomJS(code="console.log('dropdown(split): ' + this.item, this.toString())"))
checkbox_group = CheckboxGroup(labels=["Option 1", "Option 2", "Option 3"], active=[0, 1])
checkbox_group.js_on_click(CustomJS(code="console.log('checkbox_group: active=' + this.active, this.toString())"))
radio_group = RadioGroup(labels=["Option 1", "Option 2", "Option 3"], active=0)
NumberEditor,
SelectEditor,
)
from bokeh.plotting import figure
from bokeh.sampledata.iris import flowers
from bokeh.sampledata.autompg2 import autompg2 as mpg
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 ...")
button_disabled = Button(label="Button (disabled) - no click event", button_type="primary", disabled=True)
button_disabled.on_click(lambda: print('button_disabled: click'))
button_disabled.js_on_click(CustomJS(code="console.log('button_disabled: click', this.toString())"))
toggle_inactive = Toggle(label="Toggle button (initially inactive)", button_type="success")
toggle_inactive.on_click(lambda value: print('toggle_inactive: %s' % value))
toggle_inactive.js_on_click(CustomJS(code="console.log('toggle_inactive: ' + this.active, this.toString())"))
toggle_active = Toggle(label="Toggle button (initially active)", button_type="success", active=True)
toggle_active.on_click(lambda value: print('toggle_active: %s' % value))
toggle_active.js_on_click(CustomJS(code="console.log('toggle_active: ' + this.active, this.toString())"))
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.on_click(lambda value: print('dropdown: %s' % value))
dropdown.js_on_click(CustomJS(code="console.log('dropdown: ' + this.value, this.toString())"))
dropdown_disabled = Dropdown(label="Dropdown button (disabled)", button_type="warning", menu=menu)
dropdown_disabled.on_click(lambda value: print('dropdown_disabled: %s' % value))
dropdown_disabled.js_on_click(CustomJS(code="console.log('dropdown_disabled: ' + this.value, this.toString())"))
#dropdown_split = Dropdown(label="Split button", button_type="danger", menu=menu, default_value="default")
#dropdown_split.on_click(lambda value: print('dropdown_split: %s' % value))
#dropdown_split.js_on_click(CustomJS(code="console.log('dropdown_split: ' + this.value, this.toString())"))
checkbox_group = CheckboxGroup(labels=["Option 1", "Option 2", "Option 3"], active=[0, 1])
checkbox_group.on_click(lambda value: print('checkbox_group: %s' % value))
checkbox_group.js_on_click(CustomJS(code="console.log('checkbox_group: ' + this.active, this.toString())"))
def __init__(self, sources, categories, dvhs, rad_bio, roi_viewer,
time_series, correlation, regression, mlc_analyzer,
custom_title, data_tables):
self.sources = sources
self.selector_categories = categories.selector
self.range_categories = categories.range
self.correlation_variables = categories.correlation_variables
self.dvhs = dvhs
self.rad_bio = rad_bio
self.roi_viewer = roi_viewer
self.time_series = time_series
self.correlation = correlation
self.regression = regression
self.mlc_analyzer = mlc_analyzer
self.uids = {n: [] for n in GROUP_LABELS}
self.allow_source_update = True
self.current_dvh = []
self.anon_id_map = []
self.colors = itertools.cycle(palette)
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!
# Selection Filter UI objects
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!
category_options = list(self.selector_categories)
category_options.sort()
# Add Current row to source
self.add_selector_row_button = Button(label="Add Selection Filter",
button_type="primary",
width=200)
self.add_selector_row_button.on_click(self.add_selector_row)
# Row
self.selector_row = Select(value='1',
options=['1'],
width=50,
title="Row")
self.selector_row.on_change('value', self.selector_row_ticker)
# Category 1
self.select_category1 = Select(value="ROI Institutional Category",
options=category_options,
width=300,
title="Category 1")
self.select_category1.on_change('value', self.select_category1_ticker)
# Category 2
cat_2_sql_table = self.selector_categories[
self.select_category1.value]['table']
cat_2_var_name = self.selector_categories[
self.select_category1.value]['var_name']
self.category2_values = DVH_SQL().get_unique_values(
cat_2_sql_table, cat_2_var_name)
self.select_category2 = Select(value=self.category2_values[0],
options=self.category2_values,
width=300,
title="Category 2")
self.select_category2.on_change('value', self.select_category2_ticker)
# Misc
self.delete_selector_row_button = Button(label="Delete",
button_type="warning",
width=100)
self.delete_selector_row_button.on_click(self.delete_selector_row)
self.group_selector = CheckboxButtonGroup(
labels=["Group 1", "Group 2"], active=[0], width=180)
self.group_selector.on_change('active',
self.ensure_selector_group_is_assigned)
self.selector_not_operator_checkbox = CheckboxGroup(labels=['Not'],
active=[])
self.selector_not_operator_checkbox.on_change(
'active', self.selector_not_operator_ticker)
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!
# Range Filter UI objects
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!
category_options = list(self.range_categories)
category_options.sort()
# Add Current row to source
self.add_range_row_button = Button(label="Add Range Filter",
button_type="primary",
width=200)
self.add_range_row_button.on_click(self.add_range_row)
# Row
self.range_row = Select(value='', options=[''], width=50, title="Row")
self.range_row.on_change('value', self.range_row_ticker)
# Category
self.select_category = Select(value=options.SELECT_CATEGORY_DEFAULT,
options=category_options,
width=240,
title="Category")
self.select_category.on_change('value', self.select_category_ticker)
# Min and max
self.text_min = TextInput(value='', title='Min: ', width=150)
self.text_min.on_change('value', self.min_text_ticker)
self.text_max = TextInput(value='', title='Max: ', width=150)
self.text_max.on_change('value', self.max_text_ticker)
# Misc
self.delete_range_row_button = Button(label="Delete",
button_type="warning",
width=100)
self.delete_range_row_button.on_click(self.delete_range_row)
self.group_range = CheckboxButtonGroup(labels=["Group 1", "Group 2"],
active=[0],
width=180)
self.group_range.on_change('active',
self.ensure_range_group_is_assigned)
self.range_not_operator_checkbox = CheckboxGroup(labels=['Not'],
active=[])
self.range_not_operator_checkbox.on_change(
'active', self.range_not_operator_ticker)
self.query_button = Button(label="Query",
button_type="success",
width=100)
self.query_button.on_click(self.update_data)
# define Download button and call download.js on click
menu = [("All Data", "all"), ("Lite", "lite"), ("Only DVHs", "dvhs"),
("Anonymized DVHs", "anon_dvhs")]
self.download_dropdown = Dropdown(label="Download",
button_type="default",
menu=menu,
width=100)
self.download_dropdown.callback = CustomJS(
args=dict(source=sources.dvhs,
source_rxs=sources.rxs,
source_plans=sources.plans,
source_beams=sources.beams),
code=open(join(dirname(__file__), "download.js")).read())
self.layout = column(
Div(text="<b>DVH Analytics v%s</b>" % options.VERSION),
row(custom_title['1']['query'], Spacer(width=50),
custom_title['2']['query'], Spacer(width=50),
self.query_button, Spacer(width=50), self.download_dropdown),
Div(text="<b>Query by Categorical Data</b>",
width=1000), self.add_selector_row_button,
row(self.selector_row, Spacer(width=10), self.select_category1,
self.select_category2,
self.group_selector, self.delete_selector_row_button,
Spacer(width=10), self.selector_not_operator_checkbox),
data_tables.selection_filter, Div(text="<hr>", width=1050),
Div(text="<b>Query by Numerical Data</b>",
width=1000), self.add_range_row_button,
row(self.range_row,
Spacer(width=10), self.select_category, self.text_min,
Spacer(width=30), self.text_max, Spacer(width=30),
self.group_range, self.delete_range_row_button,
Spacer(width=10), self.range_not_operator_checkbox),
data_tables.range_filter)
'show arc length parametrization'],
active=[0, 1])
parametrization_input.on_click(parametrization_change)
# slider controlling the current parameter t
t_value_input = Slider(title="parameter t", name='parameter t', value=arc_settings.t_value_init,
start=arc_settings.t_value_min, end=arc_settings.t_value_max,
step=arc_settings.t_value_step)
t_value_input.on_change('value', t_value_change)
# text input for the x component of the curve
x_component_input = TextInput(value=arc_settings.x_component_input_msg, title="curve x")
x_component_input.on_change('value', curve_change)
# text input for the y component of the curve
y_component_input = TextInput(value=arc_settings.y_component_input_msg, title="curve y")
y_component_input.on_change('value', curve_change)
# dropdown menu for selecting one of the sample curves
sample_curve_input = Dropdown(label="choose a sample function pair or enter one below",
menu=arc_settings.sample_curve_names)
sample_curve_input.on_click(sample_curve_change)
# initialize plot
toolset = "crosshair,pan,reset,resize,save,wheel_zoom"
# Generate a figure container
plot = Figure(plot_height=400, plot_width=400, tools=toolset,
title="Arc length parametrization",
x_range=[arc_settings.x_min_view, arc_settings.x_max_view],
y_range=[arc_settings.y_min_view, arc_settings.y_max_view])
# Plot the line by the x,y values in the source property
plot.line('x', 'y', source=source_curve, line_width=3, line_alpha=1, color='black', legend='curve')
# quiver related to normal length parametrization
quiver = 2*[None]
# setup tooltips
tooltips = [('index', '@index'), ('Sepal Length', '@sepal_length'),
('Sepal Width', '@sepal_width'), ('Species', '@species')]
# setup plot
p = figure(plot_width=400, plot_height=400, tooltips=tooltips)
p.circle('sepal_length',
'sepal_width',
size=10,
color='blue',
alpha=0.5,
source=source)
# setup widget
menu = [(s.capitalize(), s) for s in flowers.species.unique()]
dropdown = Dropdown(label='Species', menu=menu)
# callback to widgets
def callback(attr, old, new):
species = dropdown.value
source.data = ColumnDataSource.from_df(
flowers.loc[flowers.species == species])
dropdown.on_change('value', callback)
# arrange plots and widgets in layouts
layout = column(dropdown, p)
curdoc().add_root(layout)
def checkbox_button_group_handler(active):
print("checkbox_button_group_handler: %s" % active)
session.store_document(document)
def radio_button_group_handler(active):
print("radio_button_group_handler: %s" % active)
session.store_document(document)
button = Button(label="Push button", icon=Icon(name="check"), type="primary")
button.on_click(button_handler)
toggle = Toggle(label="Toggle button", type="success")
toggle.on_click(toggle_handler)
menu = [("Item 1", "item_1"), ("Item 2", "item_2"), None, ("Item 3", "item_3")]
dropdown = Dropdown(label="Dropdown button", type="warning", menu=menu)
dropdown.on_click(dropdown_handler)
menu = [("Item 1", "foo"), ("Item 2", "bar"), None, ("Item 3", "baz")]
split = Dropdown(label="Split button", type="danger", menu=menu, default_action="baz")
split.on_click(split_handler)
checkbox_group = CheckboxGroup(labels=["Option 1", "Option 2", "Option 3"], active=[0, 1])
checkbox_group.on_click(checkbox_group_handler)
radio_group = RadioGroup(labels=["Option 1", "Option 2", "Option 3"], active=0)
radio_group.on_click(radio_group_handler)
checkbox_button_group = CheckboxButtonGroup(labels=["Option 1", "Option 2", "Option 3"], active=[0, 1])
checkbox_button_group.on_click(checkbox_button_group_handler)
p2.segment('x', 'sdplus', 'x', 'sdminus', source = s2, line_width = 2)
p2.circle('x', 'y', color = 'color', source = s2, size = 12, alpha = 0.65)
p3 = figure(width = 600, height = 400, title = 'Plot of Area vs. Area',
x_axis_label = 'Area (um^2)',
y_axis_label = 'Area (um^2)')
p3.segment('x', 'ysdplus', 'x', 'ysdminus', source = s3, line_width = 2)
p3.segment('xsdplus', 'y', 'xsdminus', 'y', source = s3, line_width = 2)
p3.circle('x', 'y', color = 'color', source = s3, size = 12, alpha = 0.65)
# Set up widgets
kneighb = Slider(title = 'Number of nearest neighbors to average', value = 1, start = 1, end = (k - 1), step = 1)
radio_button_group1 = RadioButtonGroup(labels=["NN Distance", "NN Index"], active = 0)
radio_button_group2 = RadioButtonGroup(labels=["Average", "NN Difference", "NN Difference Index"], active=0)
menu = [('Area', 'Area'), ('Coherency', 'Coherency'), ('Orientaiton', 'Orientation')]
dropdown = Dropdown(label = 'Lower Right Parameter', menu = menu, value = 'Area')
menu2 = [('Area', 'Area'), ('Coherency', 'Coherency'), ('Orientaiton', 'Orientation'), ('Distance', 'Distance')]
x3drop = Dropdown(label = 'Lower Left X-axis', menu = menu2, value = 'Area')
y3drop = Dropdown(label = 'Lower Left Y-axis', menu = menu2, value = 'Area')
pTXT = Paragraph(text = """Welcome to the interactive Nearest Neighbor data explorer. The first toggle controls
the top right plot, selecting average NN distance in microns, or NN Index (normalized by the number of domains).
The next menus control the lower two plots: select which parameter to be plotted with the dropdown menus, and
use the toggle to pick average value, average NN difference, or NN difference index. Green points are constant
flow series, Pink are constant volume series. Error bars are 1/4 standard deviation.""")
w = widgetbox(pTXT, radio_button_group1, dropdown, x3drop, y3drop, radio_button_group2)
#Set up callbacks
def update_data(attrname, old, new):
ks = kneighb.value