def plotDayOfWeekTimeline(fileName, initData, bokehPlaceholderId='bokehContent'):
source = ColumnDataSource(data=initData)
selectDOW = Select(title="Days:", value="Monday", options=["Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"])
selectUT = Select(title="User Type:", value="All", options=["All", "Subscriber", "Customer"])
model = dict(source=source, select_dow = selectDOW, select_ut = selectUT)
plot = Figure(plot_width=1200, plot_height=400, x_axis_type="datetime")
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
callback = CustomJS(args=model, code="""
var dayOfWeek = select_dow.get('value')
var userType = select_ut.get('value')
var xmlhttp;
xmlhttp = new XMLHttpRequest();
xmlhttp.onreadystatechange = function() {
if (xmlhttp.readyState == XMLHttpRequest.DONE ) {
if(xmlhttp.status == 200){
var data = source.get('data');
var result = JSON.parse(xmlhttp.responseText);
var temp=[];
for(var date in result.x) {
temp.push(new Date(result.x[date]));
}
data['x'] = temp;
data['y'] = result.y;
source.trigger('change');
}
else if(xmlhttp.status == 400) {
alert(400);
}
else {
alert(xmlhttp.status);
}
}
};
var params = {dow:dayOfWeek, ut:userType};
url = "/select?" + jQuery.param( params );
xmlhttp.open("GET", url, true);
xmlhttp.send();
""")
selectDOW.callback = callback
selectUT.callback = callback
layout = vform(selectDOW, selectUT, plot)
script, div = components(layout)
html = readHtmlFile(fileName)
html = insertScriptIntoHeader(html, script)
html = appendElementContent(html, div, "div", "bokehContent")
return html
def add_dropdown(self, df):
'''
Adds a dropdown to bokeh line chart
Inputs: df
(pd.DataFrame)
DataFrame that contains data for all the traces of the graph
'''
assert isinstance(df, pd.DataFrame)
data = df.to_dict(orient='list')
data['x'] = df.index.tolist()
source_available = ColumnDataSource(data=data)
select = Select(title="Country",
options=self.df.columns.tolist(),
value=self.country)
select.callback = CustomJS(args=dict(
source_visible=self.source_visible,
source_available=source_available),
code="""
var data_visible = source_visible.data;
var data_available = source_available.data;
var f = cb_obj.value;
data_visible.y = data_available[f];
source_visible.change.emit();
""")
return select
def test_callback_property_executes(self, bokeh_model_page):
select = Select(options=["Option 1", "Option 2", "Option 3"], css_classes=["foo"])
select.callback = CustomJS(code=RECORD("value", "cb_obj.value"))
page = bokeh_model_page(select)
el = page.driver.find_element_by_css_selector('.foo select')
el.click()
el = page.driver.find_element_by_css_selector('.foo select option[value="Option 3"]')
el.click()
results = page.results
assert results['value'] == 'Option 3'
assert page.has_no_console_errors()
def test_callback_property_executes(self, bokeh_model_page):
select = Select(options=["Option 1", "Option 2", "Option 3"], css_classes=["foo"])
select.callback = CustomJS(code=RECORD("value", "cb_obj.value"))
page = bokeh_model_page(select)
el = page.driver.find_element_by_css_selector('.foo select')
el.click()
el = page.driver.find_element_by_css_selector('.foo select option[value="Option 3"]')
el.click()
results = page.results
assert results['value'] == 'Option 3'
assert page.has_no_console_errors()
def popByGroup():
df1 = pd.read_csv('BokehApp/Data/ageGroupYear.csv', delimiter=',', index_col='Age Groups')
df1['color'] = viridis(len(df1.index))
source = ColumnDataSource(data=dict(x=df1.index, _2009=df1['2009'], _2010=df1['2010'], _2011=df1['2011'], _2012=df1['2012'], _2013=df1['2013'], _2014=df1['2014'], _2015=df1['2015'], _2016=df1['2016'], _2017=df1['2017'], _2018=df1['2018'], c=df1['color'], _2009a=df1['2009']))
source1 = ColumnDataSource(data=dict(x=df1.index, _2016=df1['2016'],c=df1['color']))
p = figure(x_range=list(df1.index.values), plot_height=350, plot_width=550,title='Irish Population Breakdown by Age Group', tools='pan, wheel_zoom, box_zoom, reset')
p.vbar(x='x', top='_2009a', width=0.5, source=source, color='c')
#plot style
p.xaxis.major_label_orientation = 45
p.grid.grid_line_color=None
p.outline_line_color=None
p.axis.major_label_text_font_style = 'bold'
p.toolbar.autohide = True
hoverp = HoverTool()
hoverp.tooltips=[('Group Population', '@_2009a')]
p.add_tools(hoverp)
tick_labels_p = {'100':'100K','200':'200K','300':'300K','400':'400K'}
p.yaxis.major_label_overrides = tick_labels_p
select = Select(title="Year:", align='start', value='_2009a', width=80, height=30, options=['_2009','_2010', '_2011', '_2012','_2013','_2014','_2015','_2016','_2017','_2018'])
callback = CustomJS(args={'source':source},code="""
console.log(' changed selected option', cb_obj.value);
var data = source.data;
// allocate column
data['_2009a'] = data[cb_obj.value];
// register the change
source.change.emit();
""")
select.callback = callback
layout = row(select,p)
return layout
def create_figure(username):
MID_BLUE = "#91c8ff"
PALE_BLUE = "#add8e6"
ANGRY_RED = "#ef5252"
WHITE = "#FFFFFF"
HOUR = 60 * 60 * 1000 # 1hr in miliseconds
data = list(mongo.db.records.find({"user": username}).sort('date', 1))
df = pd.io.json.json_normalize(data).fillna(0)
df.set_index('date')
df_indices = list(df)
# TODO - dict of paired metrics to populate bar height and alpha
# Plot Setup
metric = Select(title='metric',
value='metrics.insomnia',
options=df_indices)
factor1 = Select(title='factor1', value='misc.carb', options=df_indices)
factor2 = Select(title='factor2', value='misc.coffee', options=df_indices)
jitter_slider = Slider(title="Jitter", start=0, end=1, value=0, step=0.1)
source_raw = ColumnDataSource(df.drop(['_id'], axis=1))
source = ColumnDataSource(
data=dict(date=df['date'],
metric=source_raw.data[metric.value],
factor1=source_raw.data[factor1.value],
factor2=source_raw.data[factor2.value]))
callback = CustomJS(args=dict(source=source, source_raw=source_raw),
code="""
var value = cb_obj.value;
var title = cb_obj.title;
source.data[title] = source_raw.data[value];
source.change.emit();
""")
metric.callback = callback
factor1.callback = callback
factor2.callback = callback
p_timeline = figure(
x_axis_type="datetime",
y_axis_label='Variable',
plot_height=300,
plot_width=1000,
tools='pan,box_zoom,reset',
)
p_factor1 = figure(x_axis_type="datetime",
x_range=p_timeline.x_range,
plot_height=100,
plot_width=1000,
tools='pan,box_zoom,hover,reset')
p_factor2 = figure(x_axis_type="datetime",
x_range=p_timeline.x_range,
plot_height=100,
plot_width=1000,
tools='pan,box_zoom,hover,reset')
pv = figure(toolbar_location=None,
plot_width=200,
plot_height=p_timeline.plot_height,
y_range=p_timeline.y_range,
min_border=10,
y_axis_location="right")
p3 = figure(plot_height=400,
plot_width=400,
tools='pan,box_zoom,hover,reset')
# Timeline
p_timeline.vbar(x=df['date'],
top=df['metrics.sleep_hours'],
alpha=(df['metrics.sleep_quality'] - 2) / 10,
color=PALE_BLUE,
width=20 * HOUR)
p_timeline.vbar(x='date',
top='metric',
alpha=0.8,
color=ANGRY_RED,
source=source,
width=20 * HOUR)
inc = df['metrics.afternoon'] >= df['metrics.morning']
dec = df['metrics.morning'] > df['metrics.afternoon']
p_timeline.line(df['date'],
df[['metrics.morning', 'metrics.afternoon']].mean(axis=1),
line_width=6,
color=PALE_BLUE)
p_timeline.segment(df.date[inc],
df['metrics.morning'][inc],
df.date[inc],
df['metrics.afternoon'][inc],
color=WHITE,
line_width=4,
line_cap='square')
p_timeline.segment(df.date[dec],
df['metrics.morning'][dec],
df.date[dec],
df['metrics.afternoon'][dec],
color=ANGRY_RED,
line_width=4,
line_cap='square')
# Histogram - metrics
# TODO - reflect selected metric; reflect values selected on scatter plot
vhist, vedges = np.histogram(df['metrics.sleep_quality'].fillna(0),
bins=10)
pv.ygrid.grid_line_color = None
pv.quad(left=0,
bottom=vedges[:-1] + 0.05,
top=vedges[1:] - 0.05,
right=vhist,
color=PALE_BLUE)
# Factor plots
p_factor1.vbar(x='date',
top='factor1',
color=WHITE,
source=source,
alpha=1,
width=20 * HOUR)
p_factor2.vbar(x='date',
top='factor2',
color=MID_BLUE,
source=source,
alpha=1,
width=20 * HOUR)
# Scatter plot
p3.circle(x=jitter('metric', width=0.5),
y='factor1',
color=WHITE,
size=8,
alpha=0.5,
source=source)
p3.circle(x=jitter('metric', width=0.5),
y='factor2',
color=MID_BLUE,
size=8,
alpha=0.5,
source=source)
p3.xaxis.axis_label = 'Selected Metric'
p3.yaxis.axis_label = 'Factor1 (white), Factor2 (blue)'
controls = widgetbox([metric, factor1, factor2, jitter_slider], width=200)
main_row = row(p_timeline, pv)
bottom_row = row(controls, Spacer(width=150, height=0), p3)
layout = column(main_row, p_factor1, p_factor2, bottom_row)
# Add theme to document
theme_path = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'theme.yaml')
theme = Theme(theme_path)
doc = curdoc()
doc.theme = theme
return layout
def plotDayOfWeekTimeline(fileName,
initData,
bokehPlaceholderId='bokehContent'):
source = ColumnDataSource(data=initData)
selectDOW = Select(title="Days:",
value="Monday",
options=[
"Sunday", "Monday", "Tuesday", "Wednesday",
"Thursday", "Friday", "Saturday"
])
selectUT = Select(title="User Type:",
value="All",
options=["All", "Subscriber", "Customer"])
model = dict(source=source, select_dow=selectDOW, select_ut=selectUT)
plot = Figure(plot_width=1200, plot_height=400, x_axis_type="datetime")
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
callback = CustomJS(args=model,
code="""
var dayOfWeek = select_dow.get('value')
var userType = select_ut.get('value')
var xmlhttp;
xmlhttp = new XMLHttpRequest();
xmlhttp.onreadystatechange = function() {
if (xmlhttp.readyState == XMLHttpRequest.DONE ) {
if(xmlhttp.status == 200){
var data = source.get('data');
var result = JSON.parse(xmlhttp.responseText);
var temp=[];
for(var date in result.x) {
temp.push(new Date(result.x[date]));
}
data['x'] = temp;
data['y'] = result.y;
source.trigger('change');
}
else if(xmlhttp.status == 400) {
alert(400);
}
else {
alert(xmlhttp.status);
}
}
};
var params = {dow:dayOfWeek, ut:userType};
url = "/select?" + jQuery.param( params );
xmlhttp.open("GET", url, true);
xmlhttp.send();
""")
selectDOW.callback = callback
selectUT.callback = callback
layout = vform(selectDOW, selectUT, plot)
script, div = components(layout)
html = readHtmlFile(fileName)
html = insertScriptIntoHeader(html, script)
html = appendElementContent(html, div, "div", "bokehContent")
return html
) # update selection in other plots and fill the table after using the box_select tool
fig_list[0].select_one(BoxSelectTool).callback = CustomJS(
args=dict(txt=select_text), code=box_select_code
) # make the BoxSelect tool update the 'txt' Div widget with the currently selected range of dates.
fig_list[1].select_one(BoxSelectTool).callback = CustomJS(
args=dict(txt=select_text), code=box_select_code
) # make the BoxSelect tool update the 'txt' Div widget with the currently selected range of dates.
input_0.callback = CustomJS(
args=dict(
S_all=all_source,
S_main=main_source_list[-1],
s2=main_source_list[-2],
S_fill=main_source_list[-3],
S_save=save_source_list[-1],
mainy=fig_list[0].y_range,
main_laby=fig_list[0].yaxis[0],
fill_lab=fig_list[2].yaxis[0],
dt=data_table,
),
code=input_code + key_source_code.replace('cb_obj', 'S_main'))
input_1.callback = CustomJS(
args=dict(
S_all=all_source,
S_main=main_source_list[-2],
s2=main_source_list[-1],
S_fill=main_source_list[-3],
S_save=save_source_list[-1],
mainy=fig_list[1].y_range,
spark_timer = Slider(value=0, start=0, end=100, step=1, title="Title")
button_list = ["Start_Button", "Stop_Button", "Mass_of_m1", "Spark"]
button_call = Select(title="Button call",
options=sorted(button_list),
value="Start_Button",
width=200)
select_code = """
to_show = cb_obj.value;
options = cb_obj.options
for (i=0;i<options.length;i++) {
box = document.getElementsByClassName(options[i])[0];
if (!box.className.includes("hidden")) {box.className+=" hidden";}
if (box.className.includes(to_show)) {box.className=box.className.replace(" hidden","");}
}
"""
button_call.callback = CustomJS(code=select_code)
box_1 = widgetbox(start_button, css_classes=['Start_Button'])
box_2 = widgetbox(stop_button, css_classes=['Stop_Button', 'hidden'])
box_3 = widgetbox(m1_text, css_classes=['Mass_of_m1', 'hidden'])
box_4 = widgetbox(spark_timer, css_classes=['Spark', 'hidden'])
grid = gridplot([[button_call], [box_1, box_2, box_3, box_4]],
toolbar_location=None)
curdoc().add_root(grid)
select.callback = CustomJS(args=dict(source=fill_source,
source1=source1,
source2=source2),
code=codes)
select = Select(options=['source1', 'source2'], value='source1')
codes = """
var f = cb_obj.value;
var sdata = source.data;
var data1 = source1.data;
var data2 = source2.data;
for (key in data2) {
sdata[key] = [];
for (i=0;i<data2[key].length;i++){
sdata[key].push(data2[key][i]);
}
}
console.log("Hello world");
source.change.emit();
"""
select.on_change('value', newsource2)
select.callback = CustomJS(args=dict(source=fill_source,
source1=source1,
source2=source1),
code=codes)
curdoc().add_root(column(widgetbox(select), fig, width=1100))
def create(default_clf):
# Create ColumnDataSources to store the examples in
example_sources_train = create_sources(data.X_TRAIN, data.Y_TRAIN)
example_sources_test = create_sources(data.X_TEST, data.Y_TEST)
# Figure
# -------------------------------------------------------------------------
tools = [
HoverTool(tooltips=[("Index", "$index"), ("X", "@x"), ("Y", "@y")]),
"pan, wheel_zoom, box_select, lasso_select, reset, save"
]
fig = figure(tools=tools,
plot_width=1110,
plot_height=600,
x_range=data.get_padded_range(data.X_TRAIN_TEST[:, 0]),
y_range=data.get_padded_range(data.X_TRAIN_TEST[:, 1]),
toolbar_location="right",
background_fill_color="#fafafa",
active_drag="pan",
active_scroll="wheel_zoom",
logo=None)
fig.xaxis.visible = False
fig.yaxis.visible = False
fig.border_fill_color = None
# Decision region
# -------------------------------------------------------------------------
default_clf.train({
default_clf.slider1.title: default_clf.slider1.value,
default_clf.slider2.title: default_clf.slider2.value
})
d, x_min, y_min, dw, dh = default_clf.get_decision_boundary()
source_region = ColumnDataSource(data={'d': [d]})
fig.image(image='d',
x=x_min,
y=y_min,
dw=dw,
dh=dh,
source=source_region,
palette=['#DC9C76', '#D6655A'])
# Scatter plot
# -------------------------------------------------------------------------
colors = ["#D9CFB0", "#42282E"]
markers = ["circle", "triangle"]
scatters = {}
for i, (train_source, test_source, color, marker) in enumerate(
zip(example_sources_train, example_sources_test, colors, markers)):
scatters['train_scatter_class_%s' % i] = fig.scatter(
x='x',
y='y',
source=train_source,
size=10,
fill_color=color,
fill_alpha=.9,
line_color='#111111',
line_alpha=.9,
marker=marker)
scatters['test_scatter_class_%s' % i] = fig.scatter(
x='x',
y='y',
source=test_source,
size=10,
fill_color=color,
fill_alpha=.3,
line_color='#111111',
line_alpha=.3,
marker=marker)
# Checkboxes to show/hide data points
# -------------------------------------------------------------------------
checkbox_show_points = CheckboxGroup(
labels=["Show training data", "Show testing data"],
active=[0, 1],
callback=CustomJS(args=scatters,
code="""
var show_training_data = cb_obj.active.indexOf(0) !== -1;
var show_testing_data = cb_obj.active.indexOf(1) !== -1;
var max_classes_to_check = 20;
var source_name_train, source_name_test;
var source_train, source_test;
for (var class_nr=0; class_nr < max_classes_to_check; ++class_nr) {
try {
source_name_train = "train_scatter_class_"+class_nr.toString();
source_name_test = "test_scatter_class_"+class_nr.toString();
source_train = eval(source_name_train);
source_test = eval(source_name_test);
source_train.visible = show_training_data;
source_test.visible = show_testing_data;
} catch (e) {
// Do nothing.
// We end up in here if eval() name is undefined.
}
}
"""))
# Sliders
# -------------------------------------------------------------------------
slider1, slider2 = default_clf.slider1, default_clf.slider2
slider1.width, slider2.width = 800, 800
# Classifier select box
# -------------------------------------------------------------------------
select_clf = Select(title='Classifier',
value=default_clf.name,
options=Classifiers.get_classifier_names())
# Classifier select box callback
# -------------------------------------------------------------------------
select_clf.callback = CustomJS(args=dict(slider1=slider1, slider2=slider2),
code="""
var selected_clf = cb_obj.value;
jQuery.ajax({
type: 'POST',
url: '/get_classifier_info',
data: {'newly_selected_clf': selected_clf},
dataType: 'json',
success: function (json_from_server) {
// Sliders have 'title' attribute but changing it
// has no effect. Thus, change the label through jQuery.
var slider1_label = $("label[for='" + slider1.id + "']");
var slider2_label = $("label[for='" + slider2.id + "']");
slider1_label.html(json_from_server.slider1.title + ':');
slider2_label.html(json_from_server.slider2.title + ':');
slider1.start = json_from_server.slider1.start;
slider1.end = json_from_server.slider1.end;
slider1.step = json_from_server.slider1.step;
slider1.value = json_from_server.slider1.value;
slider2.start = json_from_server.slider2.start;
slider2.end = json_from_server.slider2.end;
slider2.step = json_from_server.slider2.step;
slider2.value = json_from_server.slider2.value;
},
error: function() {
alert("Oh no, something went wrong.");
}
});
""")
# Slider callback
# -------------------------------------------------------------------------
general_slider_callback = CustomJS(args=dict(select_clf=select_clf,
slider1=slider1,
slider2=slider2,
source_region=source_region),
code="""
var clf_name = select_clf.value;
// Bokeh automatically adds ':' and whitespace to Slider label.
// Let's remove them.
var param1_name = $("label[for='" + slider1.id + "']").html().replace(
":", "").trim();
var param2_name = $("label[for='" + slider2.id + "']").html().replace(
":", "").trim();
var param1_val = slider1.value;
var param2_val = slider2.value;
jQuery.ajax({
type: 'POST',
url: '/get_new_decision_boundary',
data: {'clf_name': clf_name,
'param1_name': param1_name, 'param1_val': param1_val,
'param2_name': param2_name, 'param2_val': param2_val},
dataType: 'json',
success: function (json_from_server) {
source_region.data.d = [json_from_server.new_dec_bound];
source_region.trigger('change');
accuracy_text.text = "<h2 class='accuracy'>" +
"Train accuracy: " + json_from_server.train_acc + "<br>" +
"Test accuracy: " + json_from_server.test_acc + "</h2>";
},
error: function() {
alert("Oh no, something went wrong.");
}
});
""")
slider1.callback = general_slider_callback
slider2.callback = general_slider_callback
# Accuracy textbox
# -------------------------------------------------------------------------
accuracy_text = Div(text="", width=600, height=100)
general_slider_callback.args["accuracy_text"] = accuracy_text
# Generate layout, HTML, CSS and JS
# -------------------------------------------------------------------------
layout = column(row(select_clf, slider1), row(checkbox_show_points,
slider2), row(fig),
row(accuracy_text))
script, div = components(layout)
return script, div, INLINE.render_js(), INLINE.render_css()
def popOverall():
df1 = pd.read_csv('BokehApp/Data/ageGroupYear.csv', delimiter=',')
df1['color'] = viridis(len(df1.index))
df1['2009.'] = df1['2009'].values
source1 = ColumnDataSource(data=dict(df1))
p1 = figure(x_range=list(df1['Age Groups'].values), plot_height=300, plot_width=500,title='Irish Population Breakdown by Age Group',
tools='pan, wheel_zoom, box_zoom, reset', toolbar_location='right')
p1.vbar(x='Age Groups', top='2009.', width=0.5, source=source1, color='color')
#plot style
p1.xaxis.major_label_orientation = 45
#p1.grid.grid_line_color=None
p1.outline_line_color=None
p1.axis.major_label_text_font_style = 'bold'
#p1.toolbar.autohide = True
p1.grid.grid_line_alpha = 0.6
p1.grid.grid_line_dash = 'dotted'
p1.grid.grid_line_dash_offset = 5
p1.grid.grid_line_width = 2
hoverp1 = HoverTool()
hoverp1.tooltips=[('Group Population', '@2009')]
p1.add_tools(hoverp1)
tick_labels_p1 = {'100':'100K','200':'200K','300':'300K','400':'400K'}
p1.yaxis.major_label_overrides = tick_labels_p1
df = pd.read_csv('BokehApp/Data/OverAll.csv', delimiter=',', index_col='Year')
df['color'] = viridis(len(df.index))
xrange = (2009,2018)
xrange
yrange = (df['Population'].min(), df['Population'].max())
source = ColumnDataSource(df)
p = figure(plot_height=300, plot_width=400,title='Irish Population Growth by Year',
y_range=Range1d(*yrange),tools='pan, wheel_zoom, box_zoom, reset', toolbar_location='above')
p.vbar(x='Year', top='Population', source=source, width=0.5, color='color')
#plot style
#p.xaxis.major_label_orientation = 45
#p.grid.grid_line_color=None
#p.x_range.start = 2009
p.x_range.end = 2018.5
p.grid.grid_line_alpha = 0.6
p.y_range.start = 4500
p.y_range.end = df['Population'].max()*1.003
p.outline_line_color=None
p.axis.major_label_text_font_style = 'bold'
p.toolbar.autohide = True
p.grid.grid_line_dash = 'dotted'
p.grid.grid_line_dash_offset = 5
p.grid.grid_line_width = 2
p.toolbar.autohide=True
#p.yaxis.axis_line_color = None
hoverp = HoverTool()
hoverp.tooltips=[('Year','@Year'),('Population', '@Population{int}')]
p.add_tools(hoverp)
tick_labels_p = {'4500':'450M','4550':'455M','4600':'460M','4650':'465M','4700':'470M','4750':'475M', '4800':'480M','4850':'485M'}
p.yaxis.major_label_overrides = tick_labels_p
select = Select(title="Year:", align='center', value='2009.', width=60, height=25, options=['2009','2010', '2011', '2012','2013','2014','2015','2016','2017','2018'])
p1.title.text = 'Irish Population by Age Group ' + str(select.value)
callback = CustomJS(args={'source':source1, 'title':p1.title},code="""
console.log(' changed selected option', cb_obj.value);
var data = source.data;
title.text = 'Irish Population by Age Group ' + cb_obj.value
// allocate column
data['2009.'] = data[cb_obj.value];
// register the change
source.change.emit()""")
select.callback = callback
layout = row(p,select, p1, margin=5)
return layout
}
source_sf_tag.trigger('change');
source_abq_tag.trigger('change');
source_det_tag.trigger('change');
source_sf_price.trigger('change');
source_abq_price.trigger('change');
source_det_price.trigger('change');
source_sf_rate.trigger('change');
source_abq_rate.trigger('change');
source_det_rate.trigger('change');
source_sf_area.trigger('change');
source_abq_area.trigger('change');
source_det_area.trigger('change');
""")
select_city.callback = callback
select_var.callback = callback
plot.grid.grid_line_width = 2
plot.xaxis.major_label_text_font_size="12pt"
plot.xaxis.axis_label = 'Variable'
plot.yaxis.axis_label = 'Count'
layout_bar1 = column(row(select_city, select_var), plot)
#####!!!!prepare the data for bar plot2
from collections import Counter
from filter_dataframe import *
def var_by_tag(df, tag, var='price'):
df_tag = find_by_tag(df, d[tag])
