class radiobutton():
def __init__(self, widget_lst, default_value, label, inline=True):
self.widget_lst = widget_lst
self.radiobutton = None
self.initialize(default_value, label, inline)
def initialize(self, default_value, label, inline):
self.radiobutton = RadioGroup(active=default_value,
labels=label,
inline=inline)
self.widget_lst.append(self.radiobutton)
def add_callback(self, callback):
self.radiobutton.on_click(callback)
def file_callback(attr, old, new):
global raw_df, hist_df
loaded_file_div.text = '<font color=#006699>' + "Loaded file" + ': </font>' + \
file_source.data['file_name'][0]
raw_contents = file_source.data['file_contents'][0]
# remove the prefix that JS adds
prefix, b64_contents = raw_contents.split(",", 1)
file_contents = base64.b64decode(b64_contents).decode()
# read in new rawdata
raw_df = esp8266_aux.preprocess_csv_string(file_contents)
MAC_list = esp8266_aux.get_MAC_list(raw_df)
# replace old MAC list by a new list.
mac_radio_group_column.children.pop()
mac_radio_group = RadioGroup(labels=MAC_list, active=0)
mac_radio_group.on_click(mac_radio_group_callback)
mac_radio_group_column.children.append(mac_radio_group)
# calculate histogram for the first MAC
hist_df = time_series_histogram.histogram_of_time(
raw_df, MAC_list[0], time_window_length=time_window)
histogram_line.data_source.data = ColumnDataSource(hist_df).data
class radiobutton():
def __init__(self, widget_lst, default_value, label, inline=True):
self.label = label
self.default_value = default_value
self.inline = inline
self.radiobutton = None
self.callback = None
# self.initialize(default_value, label, inline)
widget_lst.append(self)
def initialize(self, widget_lst):
self.radiobutton = RadioGroup(active=self.default_value,
labels=self.label,
inline=self.inline)
widget_lst.append(self.radiobutton)
if self.callback is not None:
self.radiobutton.on_click(self.callback)
def add_callback(self, callback):
self.callback = callback
if self.radiobutton is not None:
self.radiobutton.on_click(self.callback)
for i in range(100):
line3.append(
p.line(x, [obj**2 + np.random.normal() for obj in x], name='line3'))
line1.glyph.line_alpha = 0
line2.glyph.line_alpha = 0
for line in line3:
line.glyph.line_alpha = 0
def my_radio_handler(new):
line1.glyph.line_alpha = 0
line2.glyph.line_alpha = 0
for line in line3:
line.glyph.line_alpha = 0
if new == 0:
line1.glyph.line_alpha = 100
elif new == 1:
line2.glyph.line_alpha = 100
elif new == 2:
for line in line3:
time.sleep(0.1)
line.glyph.line_alpha = 1
else:
print('Error')
radio_group = RadioGroup(labels=["Option 1", "Option 2", "Option 3"], active=0)
radio_group.on_click(my_radio_handler)
curdoc().add_root(column(widgetbox(radio_group), p))
'''
Created on 29 May 2017
@author: jermz
'''
from bokeh.plotting import Figure
from bokeh.models.layouts import VBox
from bokeh.models.widgets import RadioGroup
from bokeh.io import curdoc
fig1 = Figure()
fig1.circle(x=[1,2], y=[3,4])
fig2 = Figure()
fig2.circle(x=[100,200], y=[200, 1000])
def switch_plots(selected_plot):
main_box.children = layouts[selected_plot]
layout_picker = RadioGroup(labels=['Layout1', 'Layout2'])
layout_picker.on_click(switch_plots)
layout1 = VBox(children=[fig1])
layout2 = VBox(children=[fig2])
layouts = [[layout1], [layout2]]
main_box = VBox(children=[layout1])
curdoc().add_root(main_box)
curdoc().add_root(layout_picker)
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())"))
radio_group = RadioGroup(labels=["Option 1", "Option 2", "Option 3"], active=0)
radio_group.on_click(lambda value: print('radio_group: %s' % value))
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])
checkbox_button_group.on_click(lambda value: print('checkbox_button_group: %s' % value))
checkbox_button_group.js_on_click(CustomJS(code="console.log('checkbox_button_group: ' + this.active, this.toString())"))
radio_button_group = RadioButtonGroup(labels=["Option 1", "Option 2", "Option 3"], active=0)
radio_button_group.on_click(lambda value: print('radio_button_group: %s' % value))
radio_button_group.js_on_click(CustomJS(code="console.log('radio_button_group: ' + this.active, this.toString())"))
widgetBox = WidgetBox(children=[
button, button_disabled,
toggle_inactive, toggle_active,
dropdown, dropdown_disabled, #dropdown_split,
checkbox_group, radio_group,
t_selected = bool(active)
update_conf_levels(0, 0, 0)
################################################
update_text_button_sample_CLT(0, 0, 0)
update_text_button_sample_population(0, 0, 0)
update_text_button_sample_bootstrap(0, 0, 0)
update_distribution_type(0, 0, 0)
################################################
select_distribution.on_change('value', update_distribution_type)
button_sample_population.on_click(take_single_sample_population)
button_sample_bootstrap.on_click(take_bootstrap_samples)
button_sample_CLT.on_click(take_many_samples_for_CLT)
z_vs_t_group.on_click(update_zt)
slider_bandwidth.on_change('value', draw_kde)
slider_CL_CLT.on_change('value', update_conf_levels)
slider_CL_bootstrap.on_change('value', update_conf_levels_boot)
slider_sample_size_population.on_change('value',
update_text_button_sample_population)
slider_sample_size_population.on_change('value', update_text_button_sample_CLT)
slider_repetitions_CLT.on_change('value', update_text_button_sample_CLT)
slider_sample_size_bootstrap.on_change('value',
update_text_button_sample_bootstrap)
for slider in [slider_param1, slider_param2]:
slider.on_change('value', update_distribution_type)
################################################
p_comparison = figure(title="Normal vs t-distribution",
"Whole year", "January", "February", "March", "April", "May", "June",
"July", "August", "September", "October", "November", "December"
],
active=0)
def monthSel(active):
if active == 0:
p2.x_range.start = MonthTimestamps[0]
p2.x_range.end = MonthTimestamps[12]
else:
p2.x_range.start = MonthTimestamps[active - 1]
p2.x_range.end = MonthTimestamps[active]
radio_group.on_click(monthSel)
dPickWidget = widgetbox(button)
blank = widgetbox(radio_group, width=400)
p1Widget = widgetbox(checkbox1)
p2Widget = widgetbox(checkbox2)
l = layout([[
column(slider,
row(datePickStart, datePickEnd, height=210),
dPickWidget,
width=400), p1, p1Widget
], [blank, p2, p2Widget]])
legend1 = Legend(legends=[("Mon", [l_mon]), ("Tue", [l_tue]), ("Wed", [l_wed]),
("Thu", [l_thu]), ("Fri", [l_fri]), ("Sat", [l_sat]),
("Sun", [l_sun]), ("Deviation", [l_std])],
location=(0, -5))
# allow for playing and pausing duing the animations and pre animation
if play_button.label == '► Play':
play_button.label = '❚❚ Pause'
curdoc().add_periodic_callback(
play_update, play_speed_vals[play_speed_radiogroup.active])
else:
play_button.label = '► Play'
curdoc().remove_periodic_callback(play_update)
# add widgets
time_gran_radio_title = Div(text="""<b>Time Granularity:</b>""")
time_gran_radio = RadioGroup(labels=['Per Email', 'Per Hour', 'Per Day'],
active=0,
width=100) # MUST START AT 0 FOR GLBL
time_gran_radio.on_click(change_time_gran)
slider_titles = ['Chronological Email Number:', 'Hour:', 'Day:']
time_slider = Slider(start=1,
end=20,
value=1,
step=1,
title=slider_titles[time_gran_radio.active],
width=500)
time_slider.on_change('value', update_arrows)
play_speeds_radio_title = Div(text="""<b>Play Speed:</b>""")
play_speed_vals = [750, 500, 250]
play_speeds = [
'Slow ({}ms delay)'.format(play_speed_vals[0]),
'Medium ({}ms delay)'.format(play_speed_vals[1]),
def tab_learning():
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
select_data = Select(title="Data:", value="", options=[])
select_model = Select(title="Model script:", value="", options=[])
data_descriptor = Paragraph(text=""" Data descriptor """, width=250, height=250)
model_descriptor = Paragraph(text=""" Model descriptor """, width=250, height=250)
select_grid = gridplot([[select_data, select_model], [data_descriptor, model_descriptor]])
problem_type = RadioGroup(labels=["Classification", "Regression"], active=0)
def problem_handler(new):
if(new == 0):
select_data.options = glob.glob('./np/Classification/*.npy')
select_model.options = list(filter(lambda x: 'model_' in x, dir(classification)))
elif(new == 1):
select_data.options = glob.glob('./np/Regression/*.npy')
select_model.options = list(filter(lambda x: 'model_' in x, dir(regression)))
problem_type.on_click(problem_handler)
learning_rate = TextInput(value="0.01", title="Learning rate")
epoch_size = Slider(start=2, end=200, value=5, step=1, title="Epoch")
batch_size = Slider(start=16, end=256, value=64, step=1, title="Batch")
model_insert = TextInput(value="model", title="Model name")
opeimizer = Select(title="Optimizer:", value="", options=["SGD", "ADAM", "RMS"])
hyper_param = gridplot([[learning_rate], [epoch_size], [batch_size], [opeimizer], [model_insert]])
xs = [[1], [1]]
ys = [[1], [1]]
label = [['Train loss'], ['Validation loss']]
color = [['blue'], ['green']]
total_loss_src = ColumnDataSource(data=dict(xs=xs, ys=ys, label=label, color=color))
plot2 = Figure(plot_width=500, plot_height=300)
plot2.multi_line('xs', 'ys', color='color', source=total_loss_src, line_width=3, line_alpha=0.6)
TOOLTIPS = [("loss type", "@label"), ("loss value", "$y")]
plot2.add_tools(HoverTool(tooltips=TOOLTIPS))
t = Title()
t.text = 'Loss'
plot2.title = t
acc_src = ColumnDataSource(data=dict(x=[1], y=[1], label=['R^2 score']))
plot_acc = Figure(plot_width=500, plot_height=300, title="Accuracy")
plot_acc.line('x', 'y', source=acc_src, line_width=3, line_alpha=0.7, color='red')
TOOLTIPS = [("Type ", "@label"), ("Accuracy value", "$y")]
plot_acc.add_tools(HoverTool(tooltips=TOOLTIPS))
acc_list = []
notifier = Paragraph(text=""" Notification """, width=200, height=100)
def learning_handler():
print("Start learning")
del acc_list[:]
tf.reset_default_graph()
K.clear_session()
data = np.load(select_data.value)
data = data.item()
print("data load complete")
time_window = data.get('x').shape[-2]
model_name = model_insert.value
model_name = '(' + str(time_window) + ')' + model_name
if (problem_type.active == 0):
sub_path = 'Classification/'
elif (problem_type.active == 1):
sub_path = 'Regression/'
model_save_dir = './model/' + sub_path + model_name + '/'
if not os.path.exists(model_save_dir):
os.makedirs(model_save_dir)
x_shape = list(data.get('x').shape)
print("Optimizer: " + str(opeimizer.value))
print(select_model.value)
if (problem_type.active == 0):
target_model = getattr(classification, select_model.value)
model = target_model(x_shape[-3], x_shape[-2], float(learning_rate.value), str(opeimizer.value),
data.get('y').shape[-1])
elif (problem_type.active == 1):
target_model = getattr(regression, select_model.value)
model = target_model(x_shape[-3], x_shape[-2], float(learning_rate.value), str(opeimizer.value),
data.get('y').shape[-1])
notifier.text = """ get model """
training_epochs = int(epoch_size.value)
batch = int(batch_size.value)
loss_train = []
loss_val = []
train_ratio = 0.8
train_x = data.get('x')
train_y = data.get('y')
length = train_x.shape[0]
print(train_x.shape)
data_descriptor.text = "Data shape: " + str(train_x.shape)
# model_descriptor.text = "Model layer: " + str(model.model.summary())
val_x = train_x[int(length * train_ratio):]
if(val_x.shape[-1] == 1 and not 'cnn' in select_model.value):
val_x = np.squeeze(val_x, -1)
val_y = train_y[int(length * train_ratio):]
train_x = train_x[:int(length * train_ratio)]
if (train_x.shape[-1] == 1 and not 'cnn' in select_model.value):
train_x = np.squeeze(train_x, -1)
train_y = train_y[:int(length * train_ratio)]
print(train_x.shape)
if('model_dl' in select_model.value):
for epoch in range(training_epochs):
notifier.text = """ learning -- epoch: """ + str(epoch)
hist = model.fit(train_x,
train_y,
epochs=1,
batch_size=batch,
validation_data=(val_x, val_y),
verbose=1)
print("%d epoch's cost: %f" % (epoch, hist.history['loss'][0]))
loss_train.append(hist.history['loss'][0])
loss_val.append(hist.history['val_loss'][0])
xs_temp = []
xs_temp.append([i for i in range(epoch + 1)])
xs_temp.append([i for i in range(epoch + 1)])
ys_temp = []
ys_temp.append(loss_train)
ys_temp.append(loss_val)
total_loss_src.data['xs'] = xs_temp
total_loss_src.data['ys'] = ys_temp
if (problem_type.active == 0):
r2 = hist.history['val_acc'][0]
label_str = 'Class accuracy'
elif (problem_type.active == 1):
pred_y = model.predict(val_x)
r2 = r2_score(val_y, pred_y)
label_str = 'R^2 score'
print("%d epoch's acc: %f" % (epoch, r2))
acc_list.append(np.max([r2, 0]))
acc_src.data['x'] = [i for i in range(epoch+1)]
acc_src.data['y'] = acc_list
acc_src.data['label'] = [label_str for _ in range(epoch + 1)]
print(acc_src.data)
notifier.text = """ learning complete """
model.save(model_save_dir + model_name + '.h5')
notifier.text = """ model save complete """
K.clear_session()
elif('model_ml' in select_model.value):
notifier.text = """ Machine learning model """
if(train_x.shape[-2] != 1):
notifier.text = """ Data include more then one time-frame. \n\n Data will automatically be flatten"""
train_x = train_x.reshape([train_x.shape[0], -1])
val_x = val_x.reshape([val_x.shape[0], -1])
##### shit
if (problem_type.active == 0):
train_y = np.argmax(train_y, axis=-1).astype(float)
print(train_x.shape)
print(train_y.shape)
model.fit(train_x, train_y)
notifier.text = """ Training done """
pred_y = model.predict(val_x)
print(pred_y)
pickle.dump(model, open(model_save_dir + model_name + '.sav', 'wb'))
notifier.text = """ Machine learning model saved """
button_learning = Button(label="Run model")
button_learning.on_click(learning_handler)
learning_grid = gridplot(
[[problem_type],
[select_grid, hyper_param, button_learning, notifier],
[plot2, plot_acc]])
tab = Panel(child=learning_grid, title='Learning')
return tab
def make_document(doc):
doc.title = "Hello, world!"
df = pd.read_csv(fileName_csv_source) # return dataframe
if False:
df = df.set_index('date')
''' make a copy of df. therefor changing the source will not affect df.
using df in update() will ~reset the source to original values '''
source = ColumnDataSource(data=df) # dict())
columns = [
TableColumn(field="name", title="Employee Name"),
TableColumn(field="salary", title="Income", formatter=NumberFormatter(format="$0,0.00")),
TableColumn(field="years_experience", title="Experience (years)")
]
data_table = DataTable(source=source, columns=columns, width=800) # ,row_headers=None)
table = widgetbox(data_table, width=880)
# def slider_table_update(attr, old, new):
# print(attr)
# print(old)
# print(new)
def slider_table_update():
print ("slider update")
# adjustment by https://groups.google.com/a/continuum.io/forum/#!topic/bokeh/fPAoHTyMcuQ
current = df[df['salary'] <= slider.value].dropna() # df ##
source.data = {
'name' : list(current.name),
'salary' : list(current.salary),
'years_experience' : list(current.years_experience)
}
return None
slider = Slider(title="values range", start=0, end=100000, value=21000, step=1, width=800)
# slider.on_change('value', lambda attr, old, new: slider_table_update(attr, old, new))
slider.on_change('value', lambda attr, old, new: slider_table_update())
# fig1 = figure(title='Line plot!') #, sizing_mode='scale_width')
# fig1.line(x=[1, 2, 3], y=[1, 4, 9])
#, sizing_mode='scale_width') ) # , y_range=(00000, 100000),
# fig2.scatter(x=source.data['years_experience'], y=source.data['salary'])
# title="scatter example") #, xlabel="xlable", ylabel="ylabel")
# plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
selected_tools = 'pan,wheel_zoom,xbox_select,reset'
fig2 = figure(title='salary - vs years scatter plot', width=500, height=400, tools='pan, wheel_zoom')
fig2.scatter(x='years_experience', y='salary', source=source)
# https://stackoverflow.com/questions/34646270/how-do-i-work-with-images-in-bokeh-python
# img_path = 'https://bokeh.pydata.org/en/latest/_static/images/logo.png'
img_path = join(rel_DATA_DIR,'logoScrnSht.png')
# img_path = r'C:\Users\Ran_the_User\Documents\GitHub\TAILOR\bokeh\bokeh_pages\static\logoScrnSht.png'
my_print(img_path)
x_range = (-20,10) # could be anything - e.g.(0,1)
y_range = (20,30)
factor = 1.2
figImg = figure(x_range=x_range, y_range=y_range)
# figImg = figure(x_range=x_range, y_range=y_range, width=500, height=400)
print (img_path)
figImg.image_url(url=[img_path], x=x_range[0], y=y_range[1], w=x_range[1]-x_range[0], h=y_range[1]-y_range[0])
# figImg.image_url(url=[img_path], x=x_range[0]/factor, y=(y_range[0]+y_range[1])/2, w=(x_range[1]-x_range[0])/factor, h=(y_range[1]-y_range[0])/factor, anchor="bottom_left")
toggle_callback = CustomJS(args=dict(source=source), code="""
var data = source.data;
var A = a.value;
var k = b.value;
var phi = c.value;
var B = d.value;
var x = data['years_experience']
var y = data['salary']
for (var i = 0; i < x.length; i++) {
y[i] =i*2.;
}
source.change.emit();
""")
def isToggleActive(status):
print("toggle case")
print(status)
def on_chkbx_clicked(list_of_checked_options):
print("chkbx case")
print(list_of_checked_options)
def on_radio_clicked(checked_option_ndx):
print("cradio case")
print(checked_option_ndx)
def on_text_input_change(attr, old, new):
print("Previous label: " + old)
print("attribute : ", attr)
print("Updated label: " + new)
# check if new is path. if sow - get files list
# print(base_path_text_Input.value, file_attr_text_Input.value)
ret = fH.list_files_in_path(base_path=base_path_text_Input.value, filter=file_attr_text_Input.value)
col1 = [itm[0] for itm in ret]
col2 = [itm[1] for itm in ret]
col3 = [itm[2] for itm in ret]
sourceFilteredFiles.data ={
'name' : list(col1),
'path' : list(col2),
'mod date' : list(col3)
}
return None
def fTable_clicked(attr, old, new):
# print("fTAble clicked", attr)
# print(old)
# print(new)
# https://groups.google.com/a/continuum.io/forum/#!topic/bokeh/jGHbTWVqH6U
# https: // github.com / bokeh / bokeh / wiki / Filterable - Data - Source
try:
selected_rows_indeces = sourceFilteredFiles.selected["1d"]["indices"]
print("selected_rows_indeces",selected_rows_indeces)
# print(sourceFilteredFiles.data)
for ndx in selected_rows_indeces:
selected_files_str = sourceFilteredFiles.data['mod date'][ndx]
print (selected_files_str)
except IndexError:
print("index error")
pass
# relevant output is : selected_rows_indeces
template = """<span href="#" data-toggle="tooltip" title="<%= value %>"><%= value %></span>"""
sourceFilteredFiles = ColumnDataSource(data=pd.DataFrame()) # dict())
files_table_columns = [
TableColumn(field="name", title="Name"),
TableColumn(field="path", title="Path"),
TableColumn(field="mod date", title="modification date", formatter=HTMLTemplateFormatter(template=template))
]
# edit dataTable: https://stackoverflow.com/questions/32321841/how-to-add-a-callback-to-bokeh-datatable
# https://bokeh.pydata.org/en/latest/docs/reference/model.html#bokeh.model.Model
# https: // bokeh.pydata.org / en / latest / docs / reference / models / widgets.tables.html
files_table = DataTable(source=sourceFilteredFiles, columns=files_table_columns, width=900, fit_columns=True)
# files_table.on_click(fTable_clicked)
print(sourceFilteredFiles.to_json(include_defaults=True))
print(files_table.to_json(include_defaults=True))
fTable = widgetbox(files_table, width=980)
sourceFilteredFiles.on_change('selected', fTable_clicked)
# toggle = Toggle(label='Some on/off', button_type='success')
toggle = Button(label='change table by source', button_type='success', callback=toggle_callback)
# toggleLayout = layout([toggle])
# base_path_str = os.path.curdir # relative path
base_path_str = os.path.abspath(os.path.curdir) # absolute format
base_path_text_Input = TextInput(value=base_path_str, title="base path for files:")
base_path_text_Input.on_change("value", on_text_input_change)
file_attr_str = '.csv'
file_attr_text_Input = TextInput(value=file_attr_str, title="files attribute for search")
file_attr_text_Input.on_change("value", on_text_input_change)
checkbox = CheckboxGroup(labels=['foo', 'bar', 'baz'])
radio = RadioGroup(labels=['2000', '2010', '2020'])
# toggle.on_click(isToggleActive)
checkbox.on_click(on_chkbx_clicked)
radio.on_click(on_chkbx_clicked)
checkbox_button_group = CheckboxButtonGroup(
labels=["Option 1", "Option 2", "Option 3"], active=[0, 1])
def set_vbar():
fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']
fig3 = figure(x_range=fruits, plot_height=250, title="Fruit Counts", toolbar_location=None, tools="")
fig3.vbar(x=fruits, top=[5, 3, 4, 2, 4, 6], width=0.9)
fig3.xgrid.grid_line_color = None
fig3.y_range.start = 0
return fig3
def event_chart():
factors = ["a","b","c,","d"]
x = [24.3, -22.3, -25, 6]
LineColor=["green" if a>=0 else "red" for a in x]
Tooltips = [
("index", "$index"),
("(x,y)", "($x, $y)")
# ("radius", "@radius"),
# ("fill color", "$color[hex, swatch]:fill_color"),
# ("x", "@x"),
# ("bar", "@bar"),
]
dots_fig = figure(title="exapmple", y_range = factors, x_range = [-30,30], toolbar_location="below", toolbar_sticky=False, \
tools='lasso_select, poly_select, undo, redo, reset')
# , \
# tooltips=Tooltips)
dots_fig.segment(0, factors, x, factors, line_width=2, line_color=LineColor)
c1 = dots_fig.circle(x, factors, size=15, fill_color="orange", line_width=3, line_color=LineColor)
# tool = BoxEditTool(renderers=[c1]) # ERROR:bokeh.core.validation.check:E-1014 (INCOMPATIBLE_BOX_EDIT_RENDERER): BoxEditTool renderers may only reference Rect glyph models: Circle glyph type(s) found.
tool2 = BoxSelectTool(dimensions="width") # To make a multiple selection, press the SHIFT key. To clear the selection, press the ESC key
# dots_fig.add_tools(tool) # disappears the points..
dots_fig.add_tools(tool2)
dots_fig.add_tools(CrosshairTool(dimensions='height'))
return dots_fig
# fig2.axis.visible = False
# fig2.image
phase1 = column(table, slider)
phase2 = row(phase1, fig2)
phase3 = row(event_chart(), set_vbar())
phase4 = column(phase2, phase3 , checkbox, radio)
# phase5 = bk_example.bk_example()
# doc.add_root(phase5)
# doc.add_root(phase4)
tab1 = Panel(child=phase4, title="phase4 part")
# doc.add_root(toggle)
# doc.add_root(figImg)
# secPanelLy = column(toggle, figImg, checkbox_button_group, base_path_text_Input, file_attr_text_Input, fTable)
secPanelLy = column(base_path_text_Input, file_attr_text_Input, fTable)
tab2 = Panel(child=secPanelLy, title="other parts", closable=False)
tabs = Tabs(tabs=[ tab2, tab1 ])
doc_add_root(doc, tabs)
# if __name__!='__main__':
# print("doc : ", str(doc))
# print("name : ",__name__)
# doc().add_root(tabs)
# else:
# print("doc : ", str(doc))
# doc.add_root(tabs)
toggle_callback.args['a']=slider
toggle_callback.args['b']=toggle
toggle_callback.args['c']=checkbox
toggle_callback.args['d']=radio
slider_table_update()
],
active=5)
def callback_radio(new):
vars = ['Age', 'Height']
ps = [
'Extra-Lightweight', 'Half-Middleweight', 'Lightweight', 'Heavyweight',
'Half-Heavyweight', 'Middleweight', 'Half-Lightweight'
]
var = vars[radio_var.active]
p = ps[radio_poid.active]
data1.data.update(dataset_graphe1(df_judo, var, p))
radio_var.on_click(callback_radio)
radio_poid.on_click(callback_radio)
# Création du jeu de données pour le graphe
df_graphe1 = dataset_graphe1(df_judo, "Age", "Middleweight")
data1 = ColumnDataSource(data=df_graphe1)
# Graphique 1
p1 = figure(title="Fréquence", y_axis_label="Count", x_axis_label="Variable")
p1.title.text_color = "Blue"
p1.title.border_line_color = "green"
l0 = p1.line(x='Var', y='Density', source=data1, color='black')
l1 = p1.line(x='Var', y='Density_H', source=data1, color='blue')
l2 = p1.line(x='Var', y='Density_F', source=data1, color='red')
print(new)
# Get a distribution plot together for distances
distances = figure(plot_height=300,
plot_width=800,
title="Distribution of Distances for All Hikes")
distance_counts, distance_bins = ufunc.update_distances(
list(trail_stats.find()))
dist_source = ColumnDataSource(
data=dict(bins=distance_bins, counts=distance_counts))
distances.vbar(x='bins', bottom=0, top='counts', width=0.5, source=dist_source)
# Set up the radio buttons for switching between data sets
buttons = RadioGroup(labels=['All', 'Solo', 'Group', 'After Work'], active=0)
buttons.on_click(change_data_set)
# Read trail stats into pandas dataframe for text description
ts_frame = pd.DataFrame(list(trail_stats.find()))
stats_box = PreText(name="Summary",
text=ts_frame.describe().to_string(),
width=1200)
# Data source for all line_graphs
lg_source = ColumnDataSource(data=dict(
distances=[trail['Distance'] for trail in list(trail_stats.find())],
dates=sorted([
datetime.strptime(trail['Date'], '%Y/%m/%d %H:%M')
for trail in list(trail_stats.find())
]),
energy=[trail['Energy'] for trail in list(trail_stats.find())],
select_chrg_type = Select(title="Charge Type", value="Charge", options=["Charge", "Discharge", "Cycle", "Re-Peak", "AutoCharge", "Balance Charge", "Fast Charge", "Storage"])
def select_chrg_type_handler(attr, old, new):
settings['chrg_type'] = new
select_chrg_type.on_change('value', select_chrg_type_handler)
maxmah_slider = Slider(start=50, end=24000, value=1, step=50, title="Bat. Specified Capacity, mah")
def maxmah_handler(attr, old, new):
settings['nominal_mah'] = new
maxmah_slider.on_change('value', maxmah_handler)
DC_radio_group = RadioGroup(labels=["Discharge/Charge", "Charge/Discharge"], active=0)
def DC_radio_handler(new):
settings['DC'] = new
DC_radio_group.on_click(DC_radio_handler)
select_cells = Select(title = "No. of Cells", value ="4", options = [str(i) for i in range(1,13)])
def select_cells_handler(attr, old, new):
settings['cells'] = new
select_cells.on_change('value', select_cells_handler)
#imax discharge/charge # of cycles; imax limit is 5
#no way to read this from imax only shows up in set up packet
select_cycles = Select(title = "Cycles", value ="1", options = [str(i) for i in range(1,6)])
def select_cycles_handler(attr, old, new):
settings['cycles'] = new
select_cycles.on_change('value', select_cycles_handler)
text_input = TextInput(value="Enter run information", title="Run Info::")
def text_input_handler(attr, old, new):
# image_stack = hv.DynamicMap(load_image, kdims=['time', 'p'], streams=[Param()])
#plot = regrid(image_stack)# * gf.coastline(plot=dict(scale='10m')) #.redim.range(time=(0,3))
gf.coastline.set_param("extents", (90, -18, 154, 30))
coastline = gf.coastline(plot=dict(scale='50m'))
#regridded_map = regrid(load_image(0, params[0]))
data_map = hv.DynamicMap(load_image, streams=[Param()])
regridded_map = regrid(data_map)
print("coastline: %s, regridded_map %s" % (coastline, regridded_map))
plot = regridded_map * coastline
print("plot: %s" % (plot))
print(plot.id)
def select_param(param=params[0]):
data_map.event(p=param)
def select_param_handler(index):
select_param(params[index])
radio_group = RadioGroup(labels=params, active=0)
radio_group.on_click(select_param_handler)
if not is_notebook:
hvplot = renderer.get_plot(plot)
curdoc().add_root(column(hvplot.state, radio_group))
plot
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)
radio_button_group = RadioButtonGroup(labels=["Option 1", "Option 2", "Option 3"], active=0)
radio_button_group.on_click(radio_button_group_handler)
vbox = VBox(children=[button, toggle, dropdown, split, checkbox_group, radio_group, checkbox_button_group, radio_button_group])
document.add(vbox)
session.store_document(document)
if __name__ == "__main__":
link = session.object_link(document.context)
print("Please visit %s to see the plots" % link)
def make_document(doc):
doc.title = "Hello, world!"
df = pd.read_csv(fileName) # return dataframe
if False:
df = df.set_index('date')
''' make a copy of df. therefor changing the source will not affect df.
using df in update() will ~reset the source to original values '''
source = ColumnDataSource(data=df) # dict())
columns = [
TableColumn(field="name", title="Employee Name"),
TableColumn(field="salary", title="Income", formatter=NumberFormatter(format="$0,0.00")),
TableColumn(field="years_experience", title="Experience (years)")
]
data_table = DataTable(source=source, columns=columns, width=800) # ,row_headers=None)
table = widgetbox(data_table, width=880)
def slider_table_update(attr, old, new):
print ("slider update")
print(attr)
print(old)
print(new)
current = df[df['salary'] <= slider.value].dropna() # df ##
source.data = {
'name' : current.name,
'salary' : current.salary,
'years_experience' : current.years_experience
}
return None
slider = Slider(title="values range", start=0, end=100000, value=21000, step=1, width=800)
slider.on_change('value', lambda attr, old, new: slider_table_update(attr, old, new))
# fig1 = figure(title='Line plot!') #, sizing_mode='scale_width')
# fig1.line(x=[1, 2, 3], y=[1, 4, 9])
#, sizing_mode='scale_width') ) # , y_range=(00000, 100000),
# fig2.scatter(x=source.data['years_experience'], y=source.data['salary'])
# title="scatter example") #, xlabel="xlable", ylabel="ylabel")
# plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
selected_tools = 'pan,wheel_zoom,xbox_select,reset'
fig2 = figure(title='salary - vs years scatter plot', width=500, height=400, tools='pan, wheel_zoom')
fig2.scatter(x='years_experience', y='salary', source=source)
# https://stackoverflow.com/questions/34646270/how-do-i-work-with-images-in-bokeh-python
# img_path = 'https://bokeh.pydata.org/en/latest/_static/images/logo.png'
img_path = join(DATA_DIR,'logoScrnSht.png')
# img_path = r'C:\Users\Ran_the_User\Documents\GitHub\TAILOR\bokeh\bokeh_pages\static\logoScrnSht.png'
x_range = (-20,10) # could be anything - e.g.(0,1)
y_range = (20,30)
factor = 1.2
figImg = figure(x_range=x_range, y_range=y_range)
# figImg = figure(x_range=x_range, y_range=y_range, width=500, height=400)
print (img_path)
figImg.image_url(url=[img_path], x=x_range[0], y=y_range[1], w=x_range[1]-x_range[0], h=y_range[1]-y_range[0])
# figImg.image_url(url=[img_path], x=x_range[0]/factor, y=(y_range[0]+y_range[1])/2, w=(x_range[1]-x_range[0])/factor, h=(y_range[1]-y_range[0])/factor, anchor="bottom_left")
toggle_callback = CustomJS(args=dict(source=source), code="""
var data = source.data;
var A = a.value;
var k = b.value;
var phi = c.value;
var B = d.value;
var x = data['years_experience']
var y = data['salary']
for (var i = 0; i < x.length; i++) {
y[i] =i*2.;
}
source.change.emit();
""")
def isToggleActive(status):
print("toggle case")
print(status)
def on_chkbx_clicked(list_of_checked_options):
print("chkbx case")
print(list_of_checked_options)
def on_radio_clicked(checked_option_ndx):
print("cradio case")
print(checked_option_ndx)
# toggle = Toggle(label='Some on/off', button_type='success')
toggle = Button(label='change table by source', button_type='success', callback=toggle_callback)
# toggleLayout = layout([toggle])
checkbox = CheckboxGroup(labels=['foo', 'bar', 'baz'])
radio = RadioGroup(labels=['2000', '2010', '2020'])
# toggle.on_click(isToggleActive)
checkbox.on_click(on_chkbx_clicked)
radio.on_click(on_chkbx_clicked)
checkbox_button_group = CheckboxButtonGroup(
labels=["Option 1", "Option 2", "Option 3"], active=[0, 1])
def set_vbar():
fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']
fig3 = figure(x_range=fruits, plot_height=250, title="Fruit Counts", toolbar_location=None, tools="")
fig3.vbar(x=fruits, top=[5, 3, 4, 2, 4, 6], width=0.9)
fig3.xgrid.grid_line_color = None
fig3.y_range.start = 0
return fig3
def event_chart():
factors = ["a","b","c,","d"]
x = [24.3, -22.3, -25, 6]
LineColor=["green" if a>=0 else "red" for a in x]
Tooltips = [
("index", "$index"),
("(x,y)", "($x, $y)")
# ("radius", "@radius"),
# ("fill color", "$color[hex, swatch]:fill_color"),
# ("x", "@x"),
# ("bar", "@bar"),
]
dots_fig = figure(title="exapmple", y_range = factors, x_range = [-30,30], toolbar_location="below", toolbar_sticky=False, \
tools='lasso_select, poly_select, undo, redo, reset')
# , \
# tooltips=Tooltips)
dots_fig.segment(0, factors, x, factors, line_width=2, line_color=LineColor)
c1 = dots_fig.circle(x, factors, size=15, fill_color="orange", line_width=3, line_color=LineColor)
# tool = BoxEditTool(renderers=[c1])
tool2 = BoxSelectTool(dimensions="width") # To make a multiple selection, press the SHIFT key. To clear the selection, press the ESC key
# dots_fig.add_tools(tool) # disappears the points..
dots_fig.add_tools(tool2)
dots_fig.add_tools(CrosshairTool(dimensions='height'))
return dots_fig
# fig2.axis.visible = False
# fig2.image
phase1 = column(table, slider)
phase2 = row(phase1, fig2)
phase3 = row(event_chart(), set_vbar())
phase4 = column(phase2, phase3 , checkbox, radio)
# phase5 = bk_example.bk_example()
# doc.add_root(phase5)
# doc.add_root(phase4)
tab1 = Panel(child=phase4, title="phase4 part")
# doc.add_root(toggle)
# doc.add_root(figImg)
secPanelLy = column(toggle, figImg, checkbox_button_group)
tab2 = Panel(child=secPanelLy, title="other parts", closable=False)
tabs = Tabs(tabs=[ tab1, tab2 ])
if __name__!='__main__':
print("doc : ", doc)
print("name : ",__name__)
doc().add_root(tabs)
else:
doc.add_root(tabs)
# doc.add_root(event_chart_example())
toggle_callback.args['a']=slider
toggle_callback.args['b']=toggle
toggle_callback.args['c']=checkbox
toggle_callback.args['d']=radio
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_value="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)
radio_button_group = RadioButtonGroup(
labels=["Option 1", "Option 2", "Option 3"], active=0)
radio_button_group.on_click(radio_button_group_handler)
vbox = VBox(children=[
button, toggle, dropdown, split, checkbox_group, radio_group,
checkbox_button_group, radio_button_group
])
document = Document()
} else {
alert('FileReader is not supported in this browser');
}
}
input.click();
"""
load_file_button = Button(label="Load Data From CSV File",
button_type='success',
callback=CustomJS(args=dict(file_source=file_source),
code=load_file_js))
loaded_file_div = Div(text="Loaded file: None")
mac_radio_group = RadioGroup(labels=MAC_list, active=0)
mac_radio_group.on_click(mac_radio_group_callback)
time_window_text_input = TextInput(title="Time Window", value=str(time_window))
time_window_text_input.on_change('value', time_window_text_input_callback)
# --- Layouts
mac_radio_group_column = column(mac_radio_group)
input_widgets = column(load_file_button, loaded_file_div,
time_window_text_input)
figure_widgets = column(histogram)
column1 = row(figure_widgets, mac_radio_group_column, input_widgets)
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())"))
radio_group = RadioGroup(labels=["Option 1", "Option 2", "Option 3"], active=0)
radio_group.on_click(lambda value: print('radio_group: %s' % value))
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])
checkbox_button_group.on_click(
lambda value: print('checkbox_button_group: %s' % value))
checkbox_button_group.js_on_click(
CustomJS(
code=
"console.log('checkbox_button_group: ' + this.active, this.toString())"
))
radio_button_group = RadioButtonGroup(
def main():
name = ''
sex = ''
years = [i for i in range(1880, 2018)]
if "HI" in us_states.keys():
del us_states["HI"]
if "AK" in us_states.keys():
del us_states["AK"]
state_xs = [us_states[code]["lons"] for code in us_states]
state_ys = [us_states[code]["lats"] for code in us_states]
states = [state for state in us_states.keys()]
"""Begin formatting data for popularity trend model.
Once data is formatted, build the model and add labels"""
data_sources = create_dataset(name, sex, years, states, state_xs, state_ys)
#adding layout tools and color pallete
name_field = TextInput(value="", title="Name")
sex_selection = RadioGroup(labels=["Male", "Female"], active=0)
sub_button = Button(label="Submit", button_type="success", disabled=False)
directions = Paragraph(
text="""Type in any name and select the sex you would like to query.
Then press Submit to display your results.""",
width=250,
height=60)
palette.reverse()
color_mapper = LogColorMapper(palette=palette)
#create popularity trend model
pop_plt = figure(title="Popularity Trend",
x_axis_label='Year',
y_axis_label="Quantity")
pop_plt.line('x',
'y',
source=data_sources['line_source'],
legend="Qty",
line_width=2)
pop_plt.circle('year',
'qty',
source=data_sources['circ_source'],
size=14,
color="navy",
alpha=0.5)
labels = LabelSet(x='year',
y='qty',
text='value',
level='glyph',
x_offset=5,
y_offset=5,
source=data_sources['label_source'])
pop_plt.add_layout(labels)
#create the choropleth model
TOOLS = "pan,wheel_zoom,reset,hover,save"
choro_plt = figure(title="US Name Distribution",
tools=TOOLS,
x_axis_location=None,
plot_width=850,
y_axis_location=None,
plot_height=565,
tooltips=[("State", "@state_names"),
("Quantity", "@rate")])
choro_plt.grid.grid_line_color = None
choro_plt.hover.point_policy = "follow_mouse"
choro_plt.patches('x',
'y',
source=data_sources['choropleth_source'],
fill_color={
'field': 'rate',
'transform': color_mapper
},
line_color="white",
fill_alpha=.8,
line_width=0.5)
year_slider = Slider(start=1880,
end=2017,
value=1880,
step=1,
title="Year")
color_bar = ColorBar(color_mapper=color_mapper,
ticker=LogTicker(),
label_standoff=12,
border_line_color=None,
location=(0, 0))
choro_plt.add_layout(color_bar, 'right')
#handlers and callbacks
def slider_callback(attr, old, new):
"""Called by year_slider to cause interactive changes
on the popularity trend and choropleth models"""
yr = str(year_slider.value)
#x is a pandas series
xlist = data_sources['line_source'].data['x'].tolist()
# x~year y~qty
if year_slider.value in xlist:
new_circ_data = dict(
year=[
data_sources['line_source'].data['x'][xlist.index(
year_slider.value)]
],
qty=[
data_sources['line_source'].data['y'][xlist.index(
year_slider.value)]
])
new_label_data = dict(year=new_circ_data['year'],
qty=new_circ_data['qty'],
value=new_circ_data['qty'])
data_sources['label_source'].data = new_label_data
data_sources['circ_source'].data = new_circ_data
choro_data = dict(x=state_xs,
y=state_ys,
state_names=states,
rate=data_sources['matrix'][str(yr)])
data_sources['choropleth_source'].data = choro_data
def text_input_handler(attr, old, new):
new_name = new.strip()
try:
name_validation(new_name)
try:
new_name = new_name.capitalize()
check_tables(new_name)
new_query_data = dict(
name=[new_name],
sex=[data_sources["query_source"].data["sex"][0]])
query_data = ColumnDataSource(new_query_data)
data_sources['query_source'] = query_data
except ValueError:
name_field.value = f"Sorry, {new_name} doesn't exist in the database"
except ValueError:
name_field.value = "Enter a name with valid letters only."
def radio_handler(new):
#value based on radio button index: 0-Male 1-Female
if new == 0:
sex = 'M'
else:
sex = 'F'
new_query_data = dict(
name=[data_sources["query_source"].data["name"][0]], sex=[sex])
query_data = ColumnDataSource(new_query_data)
data_sources['query_source'] = query_data
def replot():
new_name = str(name_field.value).strip()
new_name = new_name.capitalize()
if sex_selection.active == 0:
new_sex = 'M'
else:
new_sex = 'F'
data_dict = get_data(new_name, new_sex, years, states)
new_query_data = dict(name=[new_name], sex=[new_sex])
data_sources['query_source'].data = new_query_data
new_line_data = dict(x=data_dict['pop_df']['Year'],
y=data_dict['pop_df']['Qty'])
data_sources['line_source'].data = new_line_data
xlist = data_sources['line_source'].data['x'].tolist()
if year_slider.value not in xlist:
new_circ_data = dict(
year=[data_sources['line_source'].data['x'][0]],
qty=[data_sources['line_source'].data['y'][0]])
else:
new_circ_data = dict(
year=[
data_sources['line_source'].data['x'][xlist.index(
year_slider.value)]
],
qty=[
data_sources['line_source'].data['y'][xlist.index(
year_slider.value)]
])
new_label_data = dict(year=new_circ_data['year'],
qty=new_circ_data['qty'],
value=new_circ_data['qty'])
new_choropleth_data = dict(x=state_xs,
y=state_ys,
state_names=states,
rate=data_dict['matrix'][str(
year_slider.value)])
data_sources['circ_source'].data = new_circ_data
data_sources['label_source'].data = new_label_data
data_sources['choropleth_source'].data = new_choropleth_data
data_sources['matrix'] = data_dict['matrix']
year_slider.on_change('value', slider_callback)
sex_selection.on_click(radio_handler)
name_field.on_change('value', text_input_handler)
sub_button.on_click(replot)
layout = row(column(directions, name_field, sex_selection, sub_button),
row(pop_plt, column(choro_plt, widgetbox(year_slider))))
curdoc().add_root(layout)