def test_callback_property_executes(self, bokeh_model_page):
group = CheckboxGroup(labels=LABELS, css_classes=["foo"])
group.callback = CustomJS(code=RECORD("active", "cb_obj.active"))
page = bokeh_model_page(group)
el = page.driver.find_element_by_css_selector('.foo input[value="2"]')
el.click()
results = page.results
assert results['active'] == [2]
el = page.driver.find_element_by_css_selector('.foo input[value="0"]')
el.click()
results = page.results
assert results['active'] == [0, 2]
el = page.driver.find_element_by_css_selector('.foo input[value="2"]')
el.click()
results = page.results
assert results['active'] == [0]
assert page.has_no_console_errors()
def modify_doc(doc):
source = ColumnDataSource(dict(x=[1, 2], y=[1, 1], val=["a", "b"]))
plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0)
plot.add_glyph(source, Circle(x='x', y='y', size=20))
plot.add_tools(CustomAction(callback=CustomJS(args=dict(s=source), code=RECORD("data", "s.data"))))
group = CheckboxGroup(labels=LABELS, css_classes=["foo"])
def cb(active):
source.data['val'] = (active + [0, 0])[:2] # keep col length at 2, padded with zero
group.on_click(cb)
doc.add_root(column(group, plot))
def render_mask(img_path, document_objs, document_name='Default_Document', output_path='./outputs/', save_html=False):
img = load_img(img_path)
bokeh.plotting.reset_output()
hover = HoverTool(
tooltips=[
("Name", "@name"),
("(x,y)", "($x, $y)"),
]
)
fig = figure(tools=[hover,
bokeh.models.WheelZoomTool(),
bokeh.models.PanTool()])
fig.hover.point_policy = "follow_mouse"
fig.sizing_mode='scale_width'
fig.image_url(url=[os.path.basename(img_path)], x=0, y=img.shape[0], w=img.shape[1], h=img.shape[0])
script = "active = cb_obj.active;"
labels = list(document_objs.keys())
color_map = bp.magma(len(labels))
args = dict()
total_objs = 0
for key_id, key in enumerate(labels):
xs = []
ys = []
for box in document_objs[key]:
_ = np.array(box).reshape(-1,2).T
xs.append(_[0])
ys.append(_[1])
ys=doccoor2planecoor(ys, img.shape[0])
data_source = dict(x=xs, y=ys,
name=["%s %d"%(key, idx) for idx in range(len(xs))])
falpha = 0.5*int('table' not in key.lower())
lcolor = 'blue' if 'table' in key.lower() else 'red'
lwidth = 3 if 'table' in key.lower() else 1
total_objs += len(document_objs[key])
args[key] = fig.patches('x', 'y', source=data_source, fill_color=color_map[key_id],
fill_alpha=falpha, line_color=lcolor, line_width=lwidth,
legend=key+': %d'%len(document_objs[key]))
r = args[key]
script += "\n%s.visible = active.includes(%d);"%(key, key_id)
fig.patch([],[], fill_color='red', fill_alpha=0, line_color='white', legend='Total region: %d'%total_objs)
fig.legend.click_policy="hide"
fig.legend.location = "top_left"
checkbox = CheckboxGroup(labels=labels, active=[])#active=[*range(len(labels))])
checkbox.callback = CustomJS(args=args, code=script)
plt_obj = [fig]
html = file_html(plt_obj, CDN, title=document_name)
if save_html:
base = os.path.join(output_path, document_name)
if os.path.exists(base):
shutil.rmtree(base)
os.makedirs(base)
shutil.copy(img_path, base)
with open(os.path.join(base, 'main.html'),'w') as g:
g.write(html)
return html
if 3 in toggle_checkbox.active:
source.data = pdict['source.data']
else:
source.data = dict(
x=[],
y=[],
x1=[],
y1=[],
cx=[],
cy=[],
)
toggle_checkbox = CheckboxGroup(labels=[
"Toggle Start Points", "Toggle End Points", "Toggle Hexes", "Toggle Path"
],
active=[0, 1, 2, 3])
toggle_checkbox.on_change("active", toggle_checkbox_handler)
weekday_checkbox = CheckboxGroup(labels=[
'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday',
'Sunday'
],
active=[0, 1, 2, 3, 4, 5, 6])
cid_filter = CheckboxGroup(labels=["Filter Customers"], active=[])
#cid_filter.on_change('active',cid_filter_handler)
CID = list(set(df['customer_id'].astype('str')))
def main_gui_fnc(doc):
# Make widgets
plottype_list = ['Trends', '1D Distribution', '2D Distribution']
plottype_dropdown = Select(title='Plot Type',
value=plottype_list[0],
options=plottype_list)
trend_x_list = ['z', 't']
trend_y_list = [
'Beam Size', 'Bunch Length', 'Emittance (x,y)', 'Emittance (4D)',
'Slice emit. (x,y)', 'Slice emit. (4D)', 'Charge', 'Energy',
'Trajectory'
]
trend_x_dropdown = Select(title='X axis',
options=trend_x_list,
value=trend_x_list[0])
trend_y_dropdown = Select(title='Y axis',
options=trend_y_list,
value=trend_y_list[0])
dist_list = ['t', 'x', 'y', 'px', 'py', 'pz']
trend_slice_var_dropdown = Select(title='Slice variable',
options=dist_list,
value=dist_list[0])
trend_slice_nslices_text = make_text_input(text_input_params,
'Number of slices',
min=5,
max=500,
value=50,
step=1)
screen_z_dropdown = Select(title='Screen z (m)',
options=screen_z_str_list,
value=screen_z_str_list[0])
dist_x_1d_dropdown = Select(title='X axis',
options=dist_list,
value=dist_list[0])
dist_type_1d_list = [
'Charge Density', 'Emittance X', 'Emittance Y', 'Emittance 4D',
'Sigma X', 'Sigma Y'
]
dist_type_1d_dropdown = Select(title='Y axis',
options=dist_type_1d_list,
value=dist_type_1d_list[0])
nbin_1d_text = make_text_input(text_input_params,
'Histogram bins',
min=5,
max=500,
value=50,
step=1)
screen_z_dropdown_copy = Select(title='Screen z (m)',
options=screen_z_str_list,
value=screen_z_str_list[0])
screen_z_dropdown_copy.js_link('value', screen_z_dropdown, 'value')
screen_z_dropdown.js_link('value', screen_z_dropdown_copy, 'value')
dist_x_dropdown = Select(title='X axis',
options=dist_list,
value=dist_list[1])
dist_y_dropdown = Select(title='Y axis',
options=dist_list,
value=dist_list[2])
dist_2d_type_list = ['Scatter', 'Histogram']
dist2d_type_dropdown = Select(title='Plot method',
options=dist_2d_type_list,
value=dist_2d_type_list[1])
scatter_color_list = [
'density', 't', 'x', 'y', 'r', 'px', 'py', 'pz', 'pr'
]
scatter_color_dropdown = Select(title='Scatter color variable',
options=scatter_color_list,
value=scatter_color_list[0])
axis_equal_checkbox = CheckboxGroup(labels=['Enabled'], active=[])
nbin_x_text = make_text_input(text_input_params,
'Histogram bins, X',
min=5,
max=500,
value=50,
step=1)
nbin_y_text = make_text_input(text_input_params,
'Histogram bins, Y',
min=5,
max=500,
value=50,
step=1)
cyl_copies_checkbox = CheckboxGroup(labels=['Enabled'], active=[])
cyl_copies_text = make_text_input(text_input_params,
'Number of copies',
min=5,
max=500,
value=50,
step=1)
remove_correlation_checkbox = CheckboxGroup(labels=['Enabled'],
active=[])
remove_correlation_n_text = make_text_input(text_input_params,
'Max polynomial power',
min=0,
max=10,
value=1,
step=1)
remove_correlation_var1_dropdown = Select(title='Independent var (x)',
options=dist_list,
value=dist_list[1])
remove_correlation_var2_dropdown = Select(title='Dependent var (y)',
options=dist_list,
value=dist_list[3])
take_slice_checkbox = CheckboxGroup(labels=['Enabled'], active=[])
take_slice_var_dropdown = Select(title='Slice variable',
options=dist_list,
value=dist_list[0])
take_slice_nslices_text = make_text_input(text_input_params,
'Number of slices',
min=5,
max=500,
value=50,
step=1)
take_slice_index_text = make_text_input(
text_input_params,
'Slice index',
min=0,
max=int(take_slice_nslices_text.value) - 1,
value=0,
step=1)
trends_tab = column(add_label(trend_x_dropdown, widget_width),
add_label(trend_y_dropdown, widget_width),
add_label(trend_slice_var_dropdown, widget_width),
add_label(trend_slice_nslices_text, widget_width))
dist_1d_tab = column(add_label(screen_z_dropdown, widget_width),
add_label(dist_x_1d_dropdown, widget_width),
add_label(dist_type_1d_dropdown, widget_width),
add_label(nbin_1d_text, widget_width))
dist_2d_tab = column(
add_label(screen_z_dropdown_copy, widget_width),
add_label(dist2d_type_dropdown, widget_width),
add_label(scatter_color_dropdown, widget_width),
add_label(dist_x_dropdown, widget_width),
add_label(dist_y_dropdown, widget_width),
add_label(axis_equal_checkbox,
widget_width,
label='Equal scale axes'),
add_label(nbin_x_text, widget_width),
add_label(nbin_y_text, widget_width))
postprocess_tab = column(
add_label(cyl_copies_checkbox,
widget_width,
label='Cylindrical copies'),
add_label(cyl_copies_text, widget_width),
add_label(remove_correlation_checkbox,
widget_width,
label='Remove Correlation'),
add_label(remove_correlation_n_text, widget_width),
add_label(remove_correlation_var1_dropdown, widget_width),
add_label(remove_correlation_var2_dropdown, widget_width),
add_label(take_slice_checkbox,
widget_width,
label='Take slice of data'),
add_label(take_slice_var_dropdown, widget_width),
add_label(take_slice_index_text, widget_width),
add_label(take_slice_nslices_text, widget_width))
tab1 = Panel(child=trends_tab, title='Trends')
tab2 = Panel(child=dist_1d_tab, title='1D Dist.')
tab3 = Panel(child=dist_2d_tab, title='2D Dist.')
tab4 = Panel(child=postprocess_tab, title='Postprocess')
tabs = Tabs(tabs=[tab1, tab2, tab3, tab4])
main_panel = column(row(add_label(plottype_dropdown, widget_width)),
tabs)
# Main plotting function
def create_plot():
#Get current GUI settings
plottype = plottype_dropdown.value.lower()
trend_x = 'mean_' + trend_x_dropdown.value.lower()
trend_y = get_trend_vars(trend_y_dropdown.value.lower())
trend_slice_var = trend_slice_var_dropdown.value
trend_slice_nslices = int(
trend_slice_nslices_text.value
) #constrain_text_input(trend_slice_nslices_text, text_input_params)
screen_z = float(screen_z_dropdown.value)
dist_x_1d = dist_x_1d_dropdown.value
dist_y_1d = dist_type_1d_dropdown.value.lower()
nbins_1d = int(
nbin_1d_text.value
) # constrain_text_input(nbin_1d_text, text_input_params)
dist_x = dist_x_dropdown.value
dist_y = dist_y_dropdown.value
ptype = dist2d_type_dropdown.value.lower()
scatter_color_var = scatter_color_dropdown.value.lower()
is_trend = (plottype == 'trends')
is_slice_trend = any(['slice' in yy for yy in trend_y])
is_dist1d = (plottype == '1d distribution')
is_dist2d = (plottype == '2d distribution')
nbins = [int(nbin_x_text.value), int(nbin_y_text.value)]
axis_equal = (0 in axis_equal_checkbox.active)
cyl_copies_on = (0 in cyl_copies_checkbox.active) and (plottype !=
'trends')
cyl_copies = int(cyl_copies_text.value)
remove_correlation = (0 in remove_correlation_checkbox.active
) and (plottype != 'trends')
remove_correlation_n = int(remove_correlation_n_text.value)
remove_correlation_var1 = remove_correlation_var1_dropdown.value
remove_correlation_var2 = remove_correlation_var2_dropdown.value
take_slice = (0 in take_slice_checkbox.active) and (plottype !=
'trends')
take_slice_var = take_slice_var_dropdown.value
take_slice_nslices = int(take_slice_nslices_text.value)
text_input_params[
take_slice_index_text.id]['max'] = take_slice_nslices - 1
take_slice_index = int(take_slice_index_text.value)
# Disable widgets
trend_x_dropdown.disabled = not is_trend
trend_y_dropdown.disabled = not is_trend
trend_slice_var_dropdown.disabled = not is_slice_trend
trend_slice_nslices_text.disabled = not is_slice_trend
screen_z_dropdown.disabled = not is_dist1d
dist_x_1d_dropdown.disabled = not is_dist1d
dist_type_1d_dropdown.disabled = not is_dist1d
nbin_1d_text.disabled = not is_dist1d
screen_z_dropdown_copy.disabled = not is_dist2d
dist2d_type_dropdown.disabled = not is_dist2d
scatter_color_dropdown.disabled = not (is_dist2d
and ptype == 'scatter')
dist_x_dropdown.disabled = not is_dist2d
dist_y_dropdown.disabled = not is_dist2d
axis_equal_checkbox.disabled = not is_dist2d
nbin_x_text.disabled = not is_dist2d
nbin_y_text.disabled = not is_dist2d
if (is_trend):
params = {}
if (is_slice_trend):
params['slice_key'] = trend_slice_var
params['n_slices'] = trend_slice_nslices
p = gpt_plot(gpt_data,
trend_x,
trend_y,
show_plot=False,
format_input_data=False,
**params)
if (is_dist1d):
ptype_1d = get_dist_plot_type(dist_y_1d)
params = {}
if (cyl_copies_on):
params['cylindrical_copies'] = cyl_copies
if (remove_correlation):
params['remove_correlation'] = (remove_correlation_var1,
remove_correlation_var2,
remove_correlation_n)
if (take_slice):
params['take_slice'] = (take_slice_var, take_slice_index,
take_slice_nslices)
p = gpt_plot_dist1d(gpt_data,
dist_x_1d,
screen_z=screen_z,
plot_type=ptype_1d,
nbins=nbins_1d,
show_plot=False,
format_input_data=False,
**params)
if (is_dist2d):
params = {}
params['color_var'] = scatter_color_var
if (axis_equal):
params['axis'] = 'equal'
if (cyl_copies_on):
params['cylindrical_copies'] = cyl_copies
if (remove_correlation):
params['remove_correlation'] = (remove_correlation_var1,
remove_correlation_var2,
remove_correlation_n)
if (take_slice):
params['take_slice'] = (take_slice_var, take_slice_index,
take_slice_nslices)
p = gpt_plot_dist2d(gpt_data,
dist_x,
dist_y,
screen_z=screen_z,
plot_type=ptype,
nbins=nbins,
show_plot=False,
format_input_data=False,
**params)
p.width = plot_area_width
return p
gui = row(main_panel, create_plot())
doc.add_root(gui)
#callback functions
def just_redraw(attr, old, new):
gui.children[1] = create_plot()
def change_tab(attr, old, new):
new_index = plottype_list.index(new)
if (tabs.active < 3):
tabs.active = new_index
gui.children[1] = create_plot()
# Assign callbacks
plottype_dropdown.on_change('value', change_tab)
trend_x_dropdown.on_change('value', just_redraw)
trend_y_dropdown.on_change('value', just_redraw)
trend_slice_var_dropdown.on_change('value', just_redraw)
trend_slice_nslices_text.on_change('value', just_redraw)
screen_z_dropdown.on_change('value', just_redraw)
dist_x_1d_dropdown.on_change('value', just_redraw)
dist_type_1d_dropdown.on_change('value', just_redraw)
nbin_1d_text.on_change('value', just_redraw)
dist_x_dropdown.on_change('value', just_redraw)
dist_y_dropdown.on_change('value', just_redraw)
dist2d_type_dropdown.on_change('value', just_redraw)
scatter_color_dropdown.on_change('value', just_redraw)
nbin_x_text.on_change('value', just_redraw)
nbin_y_text.on_change('value', just_redraw)
axis_equal_checkbox.on_change('active', just_redraw)
cyl_copies_checkbox.on_change('active', just_redraw)
cyl_copies_text.on_change('value', just_redraw)
remove_correlation_checkbox.on_change('active', just_redraw)
remove_correlation_n_text.on_change('value', just_redraw)
remove_correlation_var1_dropdown.on_change('value', just_redraw)
remove_correlation_var2_dropdown.on_change('value', just_redraw)
take_slice_checkbox.on_change('active', just_redraw)
take_slice_var_dropdown.on_change('value', just_redraw)
take_slice_index_text.on_change('value', just_redraw)
take_slice_nslices_text.on_change('value', just_redraw)
source_1_hom520.data = dict(x=temp_range, y=SellarsTegart_1_hom520)
source_10_hom520.data = dict(x=temp_range, y=SellarsTegart_10_hom520)
# update the title
values_string = ('Homogenisation 520: ' + 'SigmaR = ' +
"{0:.3}".format(SigmaR_hom520) + ', SigmaP = ' +
"{0:.3}".format(SigmaP_hom520) + ', Q = ' +
"{0:.2E}".format(ST_values[2]) + ', A = ' +
"{0:.2E}".format(ST_values[3]) + ', n = ' +
"{0:.3}".format(ST_values[4]))
fig_hom520.title.text = values_string
# Set checkboxes
LABELS = ["SigmaR", "SigmaP", "Q", "A", "n"]
checkbox_group = CheckboxGroup(labels=LABELS)
checkbox_group.on_change('active', callback)
# insert sliders for the max and min values
SigmaR_range_slider = RangeSlider(start=0,
end=100,
value=(0, 50),
step=.1,
title="SigmaR")
SigmaP_range_slider = RangeSlider(start=0,
end=100,
value=(0, 50),
step=.1,
title="SigmaP")
Q_range_slider = RangeSlider(start=0.5e5,
end=3e5,
def _show_graph(self, data_frame):
#create the plot panels for the Agent_Tasks
panels = self._create_task_panels(data_frame)
top_left_x_range = panels[list(panels.keys())[0]]['panel1'].x_range
#Altitude over ground
pAltitude = figure(plot_width=800,
plot_height=300,
x_range=top_left_x_range)
alti_legend = []
# Setting the second y axis range name and range
pAltitude.extra_y_ranges = {"speed": Range1d(50, 120)}
# Adding the second axis to the plot.
pAltitude.add_layout(
LinearAxis(y_range_name="speed", axis_label="IAS, TAS [Knots]"),
'right')
altitudeLine = pAltitude.line(data_frame.index * self.step_time,
data_frame['position_h_sl_ft'],
line_width=2,
color=Viridis4[2])
alti_legend.append(("Altitude [ftsl]", [altitudeLine]))
kiasLine = pAltitude.line(data_frame.index * self.step_time,
data_frame['velocities_vc_kts'],
line_width=2,
y_range_name="speed",
color=Viridis4[1])
alti_legend.append(("Indicated Airspeed [KIAS]", [kiasLine]))
tasLine = pAltitude.line(data_frame.index * self.step_time,
data_frame['velocities_vtrue_kts'],
line_width=2,
y_range_name="speed",
color=Viridis4[0])
alti_legend.append(("True Airspeed [KAS]", [tasLine]))
pAltitude.extra_y_ranges.renderers = [
kiasLine, tasLine
] #this does not quite work: https://stackoverflow.com/questions/48631530/bokeh-twin-axes-with-datarange1d-not-well-scaling
pAltitude.y_range.renderers = [altitudeLine]
lg_alti = Legend(items=alti_legend,
location=(0, 10),
glyph_width=25,
label_width=190)
lg_alti.click_policy = "hide"
pAltitude.add_layout(lg_alti, 'right')
tAlti = Title()
tAlti.text = 'Altitude and Speed [IAS, TAS] over Timesteps'
pAltitude.title = tAlti
pAltitude.xaxis.axis_label = 'timestep [s]'
pAltitude.yaxis[0].axis_label = 'Altitude [ftsl]'
pAltitude.legend.location = "center_right"
pSideslip = figure(plot_width=800,
plot_height=300,
x_range=top_left_x_range)
slip_legend = []
slip_skid_line = pSideslip.line(data_frame.index * self.step_time,
data_frame['aero_beta_deg'],
line_width=2,
color=Viridis4[2])
slip_legend.append(("Sideslip", [slip_skid_line]))
pSideslip.y_range.renderers = [slip_skid_line]
lg_slip = Legend(items=slip_legend,
location=(0, 10),
glyph_width=25,
label_width=190)
lg_slip.click_policy = "hide"
pSideslip.add_layout(lg_slip, 'right')
tSlip = Title()
tSlip.text = 'Sideslip'
pSideslip.title = tSlip
pSideslip.xaxis.axis_label = 'timestep [s]'
pSideslip.yaxis[0].axis_label = 'Sideslip [deg]'
pSideslip.legend.location = "center_right"
#activate the zooming on all plots
#this is not nice, but this not either: https://stackoverflow.com/questions/49282688/how-do-i-set-default-active-tools-for-a-bokeh-gridplot
pAltitude.toolbar.active_scroll = pAltitude.toolbar.tools[
1] #this selects the WheelZoomTool instance
pSideslip.toolbar.active_scroll = pSideslip.toolbar.tools[
1] #this selects the WheelZoomTool instance
reset_output()
# if self.env.meta_dict['model_type'] == 'trained':
# discriminator = self.env.meta_dict['model_base_name']+"_%+.2f" % (data_frame['reward'].sum())
# self.env.meta_dict['model_discriminator'] = discriminator
# else:
# discriminator = self.env.meta_dict['model_discriminator']
ts = time.time()
overshoot_frames_per_task = self._analyze_overshoot(data_frame)
overshoot_divs = [
Div(text=ovs_fr.round(3).to_html(), width=600)
for ovs_fr in overshoot_frames_per_task
]
print("Overshoot analysis done in %.2f sec" % (time.time() - ts))
ts = time.time()
settlement_times_per_task = self._analyze_settle_times(data_frame)
settlement_divs = [
Div(text=settle_fr.round(3).to_html(), width=600)
for settle_fr in settlement_times_per_task
]
print("Settlement analysis done in %.2f sec" % (time.time() - ts))
panel_grid = []
panel_grid.append([
Div(text='<h3>' + t.name + '</h3>', id='div_' + t.name)
for t in self.env.task_list
])
# to switch on and off the statistics panels, this is unfortuntely the best, I could achive
# https://stackoverflow.com/a/52416676/2682209
cols = []
checkbox = CheckboxGroup(
labels=["show stats"], active=[],
width=100) #checkbox is added to header_col later on
for i, t in enumerate(self.env.task_list):
# overshoot_stat = overshoot_divs[i]
c = column(Div()) #empty for the beginning
cols.append(c)
callback = CustomJS(args=dict(overshoot_divs=overshoot_divs,
settlement_divs=settlement_divs,
cols=cols,
checkbox=checkbox),
code="""
for (var j = 0; j < cols.length; j++) {
console.log('col', j)
const children = []
for (const i of checkbox.active) {
console.log('active', i)
children.push(overshoot_divs[j])
children.push(settlement_divs[j])
}
console.log('children', children)
cols[j].children = children
}
""")
checkbox.js_on_change('active', callback)
# show_stats_btn = [Div(text="""
# <button onclick="display_event(%s)">Try it</button>
# """ %t.name for t in self.env.task_list]
# for t, b in zip(self.env.task_list, show_stats_btn):
# b.tags = ['id', 'btn_'+t.name]
# panel_grid.append(show_stats_btn)
# [b.js_on_event(events.ButtonClick, display_event(b, t.name)) for t, b in zip(self.env.task_list, show_stats_btn)]
# panel_grid.append(chkbxs)
panel_grid.append(cols)
panel_grid_t = [[
panels[name]['panel1'], panels[name]['panel2'],
panels[name]['panel3']
] for name in self.task_names]
[panel_grid.append(fig) for fig in list(zip(*panel_grid_t))]
# add the additional plots
panel_grid.append([pAltitude, pSideslip])
panel_grid_plot = gridplot(panel_grid,
toolbar_location='right',
sizing_mode='stretch_width')
#for string formatting look here: https://pyformat.info/
titleString = ''
if 'experiment_name' in self.env.meta_dict:
titleString += "{}: ".format(self.env.meta_dict['experiment_name'])
titleString += "Run Date: {}; ".format(
datetime.datetime.now().strftime("%c"))
if 'train_step' in self.env.meta_dict:
titleString += "Training Step: {}; ".format(
self.env.meta_dict['train_step'])
if 'episode_number' in self.env.meta_dict:
titleString += "Episode: {}; ".format(
self.env.meta_dict['episode_number'])
if 'csv_line_nr' in self.env.meta_dict:
titleString += "Env in CSV line: {}; ".format(
self.env.meta_dict['csv_line_nr'])
# titleString += "Total Reward: {:.2f}; ".format(data_frame['reward'].sum())
# titleString += "Model Discriminator: {};".format(self.env.meta_dict['model_discriminator'])
header_col = column(
Div(text="<h1>" + self.env.unwrapped.spec.id +
(" - " + self.env.meta_dict['env_info']) if 'env_info' in
self.meta_dict else "" + "</h1>"),
row(Div(text="<h2>" + titleString + "</h2>", width=1200),
checkbox))
webpage = gridplot([[header_col], [panel_grid_plot]],
toolbar_location=None,
sizing_mode='stretch_width')
base_filename = 'Run_' + '_'.join([tsk.name for tsk in self.task_list])
html_output_name = os.path.join(self.save_path, 'plots',
base_filename + '_latest.html')
os.makedirs(os.path.dirname(html_output_name), exist_ok=True)
if self.showNextPlotFlag:
output_file(
html_output_name, mode='inline'
) #mode='absolute') #use mode='absolute' to make it work offline with the js and css installed in the bokeh package locally
if self.firstRun:
show(webpage) #opens up a new browser window
self.firstRun = False
else:
save(
webpage
) #just updates the HTML; Manual F5 in browser required :-(, (There must be a way to push...)
if self.exportNextPlotFlag and self.save_path:
#build the filename including the individual rewards
task_rewards = [
self.reward_variables[i].get_legal_name()
for i in range(len(self.task_list))
]
task_names_with_rewards = [
t.name + '_' + f'{data_frame[task_rewards[i]].sum():.2f}'
for i, t in enumerate(self.task_list)
]
name_with_rewards = 'Run_' + '_'.join(
task_names_with_rewards) + 'time_{}'.format(
datetime.datetime.now().strftime("%H-%M-%S"))
base_filename = os.path.join(
self.save_path, 'plots', name_with_rewards
) # 'glideAngle_Elevator_Reward_{:.2f}_time_{}'.format(data_frame['reward'].sum(), datetime.datetime.now().strftime("%H-%M-%S")))
if self.showNextPlotFlag:
#we keep the html as well for easy exploration
shutil.copyfile(html_output_name, base_filename + '.html')
def export(webpage):
png_filename = base_filename + '.png'
webpage.width = 1800 #set the width of the page instead of passing a width parameter to the export; https://stackoverflow.com/a/61563173/2682209
export_png(
webpage, filename=png_filename
) #TODO: the width parameter is ignored in bokeh/io/export.py get_layout_html() as webpage isn't a Plot
export(
gridplot([[header_col], [panel_grid_plot]],
toolbar_location=None))
self.showNextPlotFlag = False #only show the plot once and then reset
self.exportNextPlotFlag = False
print("Output Plot generated: " + titleString)
from bokeh.plotting import figure
x = [3, 4, 6, 12, 10, 1]
y = [7, 1, 3, 4, 1, 6]
source = ColumnDataSource(data=dict(x=x, y=y))
plot_figure = figure(title='Checkbox',
plot_height=450,
plot_width=600,
tools="save,reset",
toolbar_location="below")
plot_figure.scatter('x', 'y', source=source, size=10)
checkbox = CheckboxGroup(labels=['Show x-axis label', 'Show y-axis label'])
def checkbox_click(attr, old, new):
active_checkbox = checkbox.active ##Getting checkbox value in list
## Get first checkbox value and show x-axis label
if len(active_checkbox) != 0 and (0 in active_checkbox):
plot_figure.xaxis.axis_label = 'X-Axis'
else:
plot_figure.xaxis.axis_label = None
## Get second checkbox value and show y-axis label
if len(active_checkbox) != 0 and (1 in active_checkbox):
d3['y'].push(d2['y'][i])
d3['time'].push(d2['time'][i])
d3['ann'].push(d2['ann'][i])
d3['noise'].push(d2['noise'][i])
d3['change'].push(0)
}
}
}
s3.change.emit();
s4.change.emit();
""")
checkboxes = CheckboxGroup(name="Select beats to display",labels=['N','V','Q'], active=[0, 1,2],callback = callback3,height = 120)
checkboxes.active = []
checkboxes.js_on_click(callback3)
div = Div(text="""<b>Select annotations to change: </b>""",
width=200, height=0)
div2 = Div(text="""<b>Change annotations to : </b>""",
width=200, height=0)
div3 = Div(text="""<b>Select noise level : </b>""",
width=200, height=0)
def __init__(
self,
plot_height=894,
plot_width=854,
image_height=100,
image_width=100,
x_start=None,
x_end=None,
y_start=None,
y_end=None,
):
"""Initialize image view plot.
Args:
plot_height (int, optional): Height of plot area in screen pixels. Defaults to 894.
plot_width (int, optional): Width of plot area in screen pixels. Defaults to 854.
image_height (int, optional): Image height in pixels. Defaults to 100.
image_width (int, optional): Image width in pixels. Defaults to 100.
x_start (int, optional): Initial x-axis start value. If None, then equals to 0.
Defaults to None.
x_end (int, optional): Initial x-axis end value. If None, then equals to image_width.
Defaults to None.
y_start (int, optional): Initial y-axis start value. If None, then equals to 0.
Defaults to None.
y_end (int, optional): Initial y-axis end value. If None, then equals to image_height.
Defaults to None.
"""
if x_start is None:
x_start = 0
if x_end is None:
x_end = image_width
if y_start is None:
y_start = 0
if y_end is None:
y_end = image_height
self.zoom_views = []
plot = Plot(
x_range=Range1d(x_start, x_end, bounds=(0, image_width)),
y_range=Range1d(y_start, y_end, bounds=(0, image_height)),
plot_height=plot_height,
plot_width=plot_width,
toolbar_location="left",
)
self.plot = plot
# ---- tools
plot.toolbar.logo = None
hovertool = HoverTool(tooltips=[("intensity", "@image")],
names=["image_glyph"])
plot.add_tools(PanTool(), WheelZoomTool(maintain_focus=False),
SaveTool(), ResetTool(), hovertool)
plot.toolbar.active_scroll = plot.tools[1]
# ---- axes
plot.add_layout(LinearAxis(), place="above")
plot.add_layout(LinearAxis(major_label_orientation="vertical"),
place="right")
# ---- grid lines
plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
# ---- rgba image glyph
self._image_source = ColumnDataSource(
dict(
image=[np.zeros((1, 1), dtype="float32")],
x=[x_start],
y=[y_start],
dw=[x_end - x_start],
dh=[y_end - y_start],
))
self.image_glyph = Image(image="image", x="x", y="y", dw="dw", dh="dh")
image_renderer = plot.add_glyph(self._image_source,
self.image_glyph,
name="image_glyph")
# This avoids double update of image values on a client, see
# https://github.com/bokeh/bokeh/issues/7079
# https://github.com/bokeh/bokeh/issues/7299
image_renderer.view.source = ColumnDataSource()
# ---- pixel value text glyph
self._pvalue_source = ColumnDataSource(dict(x=[], y=[], text=[]))
plot.add_glyph(
self._pvalue_source,
Text(
x="x",
y="y",
text="text",
text_align="center",
text_baseline="middle",
text_color="white",
),
)
# ---- horizontal and vertical projection line glyphs
self._hproj_source = ColumnDataSource(dict(x=[], y=[]))
plot.add_glyph(self._hproj_source,
Line(x="x", y="y", line_color="greenyellow"))
self._vproj_source = ColumnDataSource(dict(x=[], y=[]))
plot.add_glyph(self._vproj_source,
Line(x="x", y="y", line_color="greenyellow"))
proj_toggle = CheckboxGroup(labels=["Inner Projections"],
default_size=145)
self.proj_toggle = proj_toggle
p.xaxis.major_label_text_font_size = '12pt'
p.yaxis.axis_label_text_font_size = '12pt'
p.yaxis.major_label_text_font_size = '12pt'
return p
def get_dataset(carrier_list):
subset = by_carrier[by_carrier['name'].isin(carrier_list)]
new_src = ColumnDataSource(subset)
return new_src
def update(attr, old, new):
carrier_list = [available_carriers[i] for i in carrier_select.active]
new_src = get_dataset(carrier_list)
src.data.update(new_src.data)
carrier_select = CheckboxGroup(labels=available_carriers,
active=[0])
carrier_select.on_change('active', update)
src = get_dataset([available_carriers[i] for i in carrier_select.active])
p = make_plot(src)
layout = row(carrier_select, p)
tab = Panel(child=layout, title='Histogram')
tabs = Tabs(tabs=[tab])
curdoc().add_root(tabs)
def show_cells_on_stack(data,
stack,
X='X',
Y='Y',
channelNames=None,
group='group',
hue='hue',
palette='Spectral11',
default=0,
alpha=.5,
plot_width=500,
plot_height=500,
vmin=0,
vmax=3,
znorm=True):
'''
stack: np. array of shape (nchannels,height,width)
'''
from bokeh.models import CheckboxGroup, RadioButtonGroup, Legend, LegendItem
if not channelNames:
channelNames = ['Channel ' + str(i) for i in range(len(stack))]
print(channelNames)
s1 = ColumnDataSource(data=data)
p1 = figure(plot_width=plot_width,
plot_height=plot_height,
tools="pan,wheel_zoom,reset")
channels = {}
for i, channel in enumerate(channelNames):
img = stack[i]
if znorm:
img = (img - img.mean()) / img.std()
channels[i] = p1.image(image=[img],
x=[0],
y=[0],
dw=[plot_width],
dh=[plot_height],
color_mapper=LogColorMapper(palette=palette,
low=vmin,
high=vmax),
global_alpha=alpha,
visible=(i == default))
plots = {}
#scaled_coordinates to fit in stack_dw,stack_dh
dh_ratio = plot_height / img.shape[0]
dw_ratio = plot_width / img.shape[1]
data['warped_X'] = data[X] * dw_ratio
data['warped_Y'] = data[Y] * dh_ratio
groups = list(data[group].unique())
for g_id in groups:
s2 = ColumnDataSource(data=data[data[group] == g_id])
scatter = p1.circle('warped_X',
'warped_Y',
source=s2,
alpha=1,
color='hue') #,legend_label = str(g_id))
scatter.visible = False
plots[g_id] = scatter
select_celltype = CheckboxGroup(labels=groups, active=[], width=100)
select_channel = RadioButtonGroup(labels=channelNames,
active=default,
width=100,
orientation='horizontal')
select_celltype.callback = CustomJS(args={
'plots': plots,
'groups': groups,
'msel': select_celltype,
'fig': p1
},
code="""
//fig.title.text = 'new Title'
for (var i =0; i<groups.length;i++){
plots[groups[i]].visible = msel.active.indexOf(i)>-1
}
""")
select_channel.callback = CustomJS(args={
'channels': channels,
'sel': select_channel,
'fig': p1
},
code="""
//fig.title.text = Object.keys(channels).length.toString()
for (var i =0; i<Object.keys(channels).length;i++){
channels[i].visible = false
}
var val = sel.active
channels[val].visible = true
/**
if (val==0){
fig.title.text = 'confirm upper'
chan1.visible = true
chan2.visible = false
//bg_channel = [z[0]]
}else if (val==1){
fig.title.text = 'confirm lower'
chan1.visible = false
chan2.visible = true
//bg_channel = [z[1]]
}
**/
""")
layout = column(p1, select_celltype, select_channel)
show(layout)
def create_layout(self):
# create figure
self.x_range = Range1d(start=self.model.map_extent[0],
end=self.model.map_extent[2],
bounds=None)
self.y_range = Range1d(start=self.model.map_extent[1],
end=self.model.map_extent[3],
bounds=None)
self.fig = Figure(tools='wheel_zoom,pan',
x_range=self.x_range,
lod_threshold=None,
plot_width=self.model.plot_width,
plot_height=self.model.plot_height,
y_range=self.y_range)
self.fig.min_border_top = 0
self.fig.min_border_bottom = 10
self.fig.min_border_left = 0
self.fig.min_border_right = 0
self.fig.axis.visible = False
self.fig.xgrid.grid_line_color = None
self.fig.ygrid.grid_line_color = None
# add tiled basemap
self.tile_source = WMTSTileSource(url=self.model.basemap)
self.tile_renderer = TileRenderer(tile_source=self.tile_source)
self.fig.renderers.append(self.tile_renderer)
# add datashader layer
self.image_source = ImageSource(
url=self.model.service_url,
extra_url_vars=self.model.shader_url_vars)
self.image_renderer = DynamicImageRenderer(
image_source=self.image_source)
self.fig.renderers.append(self.image_renderer)
# add label layer
self.label_source = WMTSTileSource(url=self.model.labels_url)
self.label_renderer = TileRenderer(tile_source=self.label_source)
self.fig.renderers.append(self.label_renderer)
# Add a hover tool
hover_layer = HoverLayer()
hover_layer.field_name = self.model.field_title
hover_layer.is_categorical = self.model.field in self.model.categorical_fields
self.fig.renderers.append(hover_layer.renderer)
self.fig.add_tools(hover_layer.tool)
self.model.hover_layer = hover_layer
self.model.legend_side_vbox = VBox()
self.model.legend_bottom_vbox = VBox()
# add ui components
controls = []
axes_select = Select.create(name='Axes', options=self.model.axes)
axes_select.on_change('value', self.on_axes_change)
controls.append(axes_select)
self.field_select = Select.create(name='Field',
options=self.model.fields)
self.field_select.on_change('value', self.on_field_change)
controls.append(self.field_select)
self.aggregate_select = Select.create(
name='Aggregate', options=self.model.aggregate_functions)
self.aggregate_select.on_change('value', self.on_aggregate_change)
controls.append(self.aggregate_select)
transfer_select = Select.create(name='Transfer Function',
options=self.model.transfer_functions)
transfer_select.on_change('value', self.on_transfer_function_change)
controls.append(transfer_select)
color_ramp_select = Select.create(name='Color Ramp',
options=self.model.color_ramps)
color_ramp_select.on_change('value', self.on_color_ramp_change)
controls.append(color_ramp_select)
spread_size_slider = Slider(title="Spread Size (px)",
value=0,
start=0,
end=10,
step=1)
spread_size_slider.on_change('value', self.on_spread_size_change)
controls.append(spread_size_slider)
hover_size_slider = Slider(title="Hover Size (px)",
value=8,
start=4,
end=30,
step=1)
hover_size_slider.on_change('value', self.on_hover_size_change)
controls.append(hover_size_slider)
controls.append(self.model.legend_side_vbox)
# add map components
basemap_select = Select.create(name='Basemap',
value='Imagery',
options=self.model.basemaps)
basemap_select.on_change('value', self.on_basemap_change)
image_opacity_slider = Slider(title="Opacity",
value=100,
start=0,
end=100,
step=1)
image_opacity_slider.on_change('value',
self.on_image_opacity_slider_change)
basemap_opacity_slider = Slider(title="Basemap Opacity",
value=100,
start=0,
end=100,
step=1)
basemap_opacity_slider.on_change('value',
self.on_basemap_opacity_slider_change)
show_labels_chk = CheckboxGroup(labels=["Show Labels"], active=[0])
show_labels_chk.on_click(self.on_labels_change)
map_controls = [
basemap_select, basemap_opacity_slider, image_opacity_slider,
show_labels_chk
]
self.controls = VBox(width=200, height=600, children=controls)
self.map_controls = HBox(width=self.fig.plot_width,
children=map_controls)
self.map_area = VBox(width=self.fig.plot_width,
children=[
self.map_controls, self.fig,
self.model.legend_bottom_vbox
])
self.layout = HBox(width=1366, children=[self.controls, self.map_area])
sports_choice = [
'No preference', 'Dance and fitness', 'Outdoor', 'Water sports ',
'Martial arts ', 'Racquet sports', 'Ball team sports'
]
div51 = Div(text="<i> Choose Sport </i>")
select51 = Select(options=sports_choice, value=sports_choice[0])
select51.on_change('value', update)
elect_choice = ['No preference', 'Technology', 'Creativity', 'Society']
div52 = Div(text="<i> Choose Elective </i>")
select52 = Select(options=elect_choice, value=elect_choice[0])
select52.on_change('value', update)
div53 = Div(text="<i> Additional Choices</i>")
offer = ['Uniform', 'Extended day', 'Summer session', 'Weekend program']
checkbox52 = CheckboxGroup(labels=offer, active=[])
checkbox52.on_change('active', update)
# 6) Gender
div6 = Div(text="<b> Gender Rate </b>")
slider61 = RangeSlider(start=0,
end=100,
value=(0, 100),
step=1,
title="% Male")
slider61.on_change('value', update)
slider62 = RangeSlider(start=0,
end=100,
value=(0, 100),
step=1,
title="% Female")
x_range=FactorRange(factors=source_bar.data['x']),
plot_width=480, plot_height=400,
y_range=(0, 1)
)
#plot2.vbar(x=demography_age, top=list(demography_data.loc[demography_data['Location'] == state].values[0][1:-2]), width=0.7)
glyph = VBar(x='x', top='y', bottom=0, width=.8, fill_color="#6599ed")
plot2.add_glyph(source_bar, glyph)
print(list(demography_data.loc[demography_data['Location'] == state].values[0][1:-2]))
div_head = Div(text="""<h2>US vs states covid-19 spread plots</h2>""", width=500, height=50)
div_explain = Div(text="""<h3>US plots(thinner lines and smaller dots) are normalized according to the population of the compared state</h3>""", width=300, height=50)
div_us_pop = Div(text="""<br><br><br>Total population of US: """+f"{total_us_population:,d}", width=300, height=50)
div_state_pop = Div(text="""<br>Total population of """+state+" : "+f"{state_population_dict[state]:,d}", width=300, height=50)
checkbox_group = CheckboxGroup(labels=["positive", "recovered", "death", "positive increase", "death increase"], active=[0,1,2,3,4])
select_state = Select(title="Select State", value="Minnesota", options=states_fullname) #list(list(gp.groups.keys())))
def callbackselect_state(attr, old, new):
try:
div_state_pop.text = """<br>Total population of """+select_state.value+": "+f"{state_population_dict[select_state.value]:,d}"
except:
div_state_pop.text = """<br>Total population of """ + select_state.value + ": Not Available"
try:
#plot2.vbar(x=demography_age, top=list(demography_data.loc[demography_data['Location'] == select_state.value].values[0][1:-2]), width=0.7)
source_bar.data={
'x': demography_age,
'y': list(demography_data.loc[demography_data['Location'] == select_state.value].values[0][1:-2])
}
glyph = VBar(x='x', top='y', bottom=0, width=.8, fill_color="#6599ed")
plot2.add_glyph(source_bar, glyph)
def create_charts(cur, include_8888=False):
" Create chart on each tab for 5 regions + super DC "
tables = create_tables(cur, for_div=True, include_8888=include_8888)
tabs = []
for title, code, seats in regions:
# chart components
p = figure(x_axis_label='滾動日期',
y_axis_label='支持%' if include_8888 else '有效支持%',
x_axis_type="datetime")
candids = []
earliest_date, latest_date = None, None
for n in range(1, 30):
candid, redness, trend = get_trend(cur, code, n, include_8888)
if not trend: continue
earliest_date, latest_date = trend[0][0], trend[-1][0]
colour = getcolour(redness)
line = p.line([d for d, _ in trend], [p for _, p in trend],
color=colour,
line_width=2,
line_alpha=0.9)
label = p.text([trend[-1][0]], [trend[-1][1]],
text=[candid.rsplit(None, 1)[-1]],
text_align='right',
text_alpha=0.9,
text_baseline='bottom',
text_color=colour,
text_font_size="9pt",
y_offset=0.25)
candids.append([n, candid, colour, trend[-1][1], line, label])
if include_8888:
candid, redness, trend = get_trend(cur, code, 8888, True)
if not trend: continue
earliest_date, latest_date = trend[0][0], trend[-1][0]
line = p.line([d for d, _ in trend], [p for _, p in trend],
color="#707070",
line_width=2,
line_alpha=0.9)
label = p.text([trend[-1][0]], [trend[-1][1]],
text=["未決定"],
text_align='right',
text_alpha=0.9,
text_baseline='bottom',
text_color="#707070",
text_font_size="9pt",
y_offset=0.25)
candids.append([8888, "未決定", "#707070", trend[-1][1], line, label])
p.line([earliest_date, latest_date], [100.0 / seats, 100.0 / seats],
line_dash=[6, 3],
color="black",
line_width=2,
line_alpha=0.5)
# control
all_lines = [n for n, _ in enumerate(candids)]
top_5 = [
n for _, n in sorted([(c[3], n) for n, c in enumerate(candids)],
reverse=True)[:5]
]
for n in all_lines:
if n not in top_5:
candids[n][-1].visible = candids[n][-2].visible = False
customjs_params = [("line%d"%n, candid[-2]) for n,candid in enumerate(candids)] + \
[("label%d"%n, candid[-1]) for n,candid in enumerate(candids)]
jscode = '''
var lines = [%s];
var labels = [%s];
for (var n=0; n<lines.length; n++) {
lines[n].visible = (cb_obj.active.indexOf(n) >= 0);
labels[n].visible = (cb_obj.active.indexOf(n) >= 0);
};''' % (",".join("line%d" % n for n in all_lines), ",".join(
"label%d" % n for n in all_lines))
checkbox = CheckboxGroup(labels=[
str(800 + c[0] if code == 'SuperDC' else c[0]) + ' ' + c[1] + ' ' +
("%.2f%%" % c[3]) for c in candids
],
active=top_5)
checkbox.callback = CustomJS(args=dict(customjs_params), code=jscode)
# ranking table
div = Div(text=tables[code], width=600)
# layout and show
layout = row(checkbox, p, div)
tab = Panel(child=layout, title=title + "民調（" + str(seats) + '席）')
tabs.append(tab)
# build and return
return Tabs(tabs=tabs)
def create_layout(self):
# create figure
self.x_range = Range1d(start=self.model.map_extent[0],
end=self.model.map_extent[2], bounds=None)
self.y_range = Range1d(start=self.model.map_extent[1],
end=self.model.map_extent[3], bounds=None)
self.fig = Figure(tools='wheel_zoom,pan',
x_range=self.x_range,
lod_threshold=None,
plot_width=self.model.plot_width,
plot_height=self.model.plot_height,
y_range=self.y_range)
self.fig.min_border_top = 0
self.fig.min_border_bottom = 10
self.fig.min_border_left = 0
self.fig.min_border_right = 0
self.fig.axis.visible = False
self.fig.xgrid.grid_line_color = None
self.fig.ygrid.grid_line_color = None
# add tiled basemap
self.tile_source = WMTSTileSource(url=self.model.basemap)
self.tile_renderer = TileRenderer(tile_source=self.tile_source)
self.fig.renderers.append(self.tile_renderer)
# add datashader layer
self.image_source = ImageSource(url=self.model.service_url,
extra_url_vars=self.model.shader_url_vars)
self.image_renderer = DynamicImageRenderer(image_source=self.image_source)
self.fig.renderers.append(self.image_renderer)
# add label layer
self.label_source = WMTSTileSource(url=self.model.labels_url)
self.label_renderer = TileRenderer(tile_source=self.label_source)
self.fig.renderers.append(self.label_renderer)
# Add a hover tool
hover_layer = HoverLayer()
hover_layer.field_name = self.model.field_title
hover_layer.is_categorical = self.model.field in self.model.categorical_fields
self.fig.renderers.append(hover_layer.renderer)
self.fig.add_tools(hover_layer.tool)
self.model.hover_layer = hover_layer
self.model.legend_side_vbox = VBox()
self.model.legend_bottom_vbox = VBox()
# add ui components
controls = []
axes_select = Select.create(name='Axes',
options=self.model.axes)
axes_select.on_change('value', self.on_axes_change)
controls.append(axes_select)
self.field_select = Select.create(name='Field', options=self.model.fields)
self.field_select.on_change('value', self.on_field_change)
controls.append(self.field_select)
self.aggregate_select = Select.create(name='Aggregate',
options=self.model.aggregate_functions)
self.aggregate_select.on_change('value', self.on_aggregate_change)
controls.append(self.aggregate_select)
transfer_select = Select.create(name='Transfer Function',
options=self.model.transfer_functions)
transfer_select.on_change('value', self.on_transfer_function_change)
controls.append(transfer_select)
color_ramp_select = Select.create(name='Color Ramp', options=self.model.color_ramps)
color_ramp_select.on_change('value', self.on_color_ramp_change)
controls.append(color_ramp_select)
spread_size_slider = Slider(title="Spread Size (px)", value=0, start=0,
end=10, step=1)
spread_size_slider.on_change('value', self.on_spread_size_change)
controls.append(spread_size_slider)
hover_size_slider = Slider(title="Hover Size (px)", value=8, start=4,
end=30, step=1)
hover_size_slider.on_change('value', self.on_hover_size_change)
controls.append(hover_size_slider)
controls.append(self.model.legend_side_vbox)
# add map components
basemap_select = Select.create(name='Basemap', value='Imagery',
options=self.model.basemaps)
basemap_select.on_change('value', self.on_basemap_change)
image_opacity_slider = Slider(title="Opacity", value=100, start=0,
end=100, step=1)
image_opacity_slider.on_change('value', self.on_image_opacity_slider_change)
basemap_opacity_slider = Slider(title="Basemap Opacity", value=100, start=0,
end=100, step=1)
basemap_opacity_slider.on_change('value', self.on_basemap_opacity_slider_change)
show_labels_chk = CheckboxGroup(labels=["Show Labels"], active=[0])
show_labels_chk.on_click(self.on_labels_change)
map_controls = [basemap_select, basemap_opacity_slider,
image_opacity_slider, show_labels_chk]
self.controls = VBox(width=200, height=600, children=controls)
self.map_controls = HBox(width=self.fig.plot_width, children=map_controls)
self.map_area = VBox(width=self.fig.plot_width, children=[self.map_controls,
self.fig,
self.model.legend_bottom_vbox])
self.layout = HBox(width=1366, children=[self.controls, self.map_area])
class Scatter:
# X axis choices
AXIS_MAP = {
"Tempo": "tempo",
"Duration (sec)": "duration_s",
"Danceability": "danceability",
"Energy": "energy",
"Loudness": "loudness",
"Speechiness": "speechiness",
"Acousticness": "acousticness",
"Instrumentalness": "instrumentalness",
"Liveness": "liveness",
"Valence": "valence"
}
# Tooltips for circle glyphs
CIRC_TOOLTIPS = [("Track", "@track_name"), ("Artist", "@artist_name"),
("Times Played", "@count")]
def __init__(self, df: pd.DataFrame):
# Initialize data sources for scatter plot and regression line
self.backing_df = df
self.circ_source = ColumnDataSource({
'x': [],
'y': [],
'track_name': [],
'artist_name': [],
'count': [],
'circle_size': []
})
self.line_source = ColumnDataSource({'x': [], 'y_pred': []})
# Initialize widgets
self.x_axis = Select(title="X Axis",
options=list(self.AXIS_MAP.keys()),
value="Tempo")
self.y_axis = Select(title="Y Axis",
options=list(self.AXIS_MAP.keys()),
value="Duration (sec)")
time_start = datetime.datetime(1970, 1, 1, hour=0, minute=0, second=0)
time_end = datetime.datetime(1970, 1, 1, hour=23, minute=59, second=59)
start_date = min(self.backing_df['date_played'])
start_dt = datetime.datetime(year=start_date.year,
month=start_date.month,
day=start_date.day,
hour=0,
minute=0,
second=0)
end_date = max(self.backing_df['date_played'])
end_dt = datetime.datetime(year=end_date.year,
month=end_date.month,
day=end_date.day,
hour=23,
minute=59,
second=59)
date_step_size = 1000 * 60 * 60 * 24 # Step size of 1 day in ms
self.date_slider = DateRangeSlider(title="Date Range",
start=start_dt,
end=end_dt,
value=(start_dt, end_dt),
format="%d %b %Y",
step=date_step_size)
time_step_size = 1000 * 60 * 30 # 30 minues in ms
self.time_slider = DateRangeSlider(title="Time Range",
value=(time_start, time_end),
start=time_start,
end=time_end,
format="%X",
step=time_step_size)
self.track_name = TextInput(title="Song name includes")
self.artist_name = TextInput(title="Artist name includes")
self.reg_line_check = CheckboxGroup(labels=["Add Regression Line"],
active=[])
# Create the hover tools
self.points_hover = HoverTool(tooltips=self.CIRC_TOOLTIPS,
names=["circles"])
self.line_hover = HoverTool(tooltips=[], names=["reg_line"])
# Create the scatter plot and regression line
self.plot = figure(title="Scatter",
plot_height=450,
plot_width=800,
tools=[self.points_hover, self.line_hover])
self.plot.circle(x="x",
y="y",
source=self.circ_source,
size="circle_size",
fill_alpha=0.6,
name="circles")
self.reg_line = self.plot.line(x='x',
y='y_pred',
source=self.line_source,
color='#FFAF87',
name="reg_line")
self.layout = row(
column(self.x_axis, self.y_axis, self.date_slider,
self.time_slider, self.track_name, self.artist_name,
self.reg_line_check), self.plot)
# Fill data and create events for on change
self.update()
self.on_change()
def on_change(self):
"""
Creates on change events for all widgets in the scatter plot.
"""
widgets = [
self.x_axis, self.y_axis, self.date_slider, self.time_slider,
self.track_name, self.artist_name
]
for control in widgets:
control.on_change("value", lambda attr, old, new: self.update())
self.reg_line_check.on_change("active",
lambda attr, old, new: self.update())
def update(self):
"""
Updates the data source and regression line based on current values of all widgets.
"""
new_df = self.get_selected()
# Get number of individual plays and then remove duplicate tracks for plotting
num_plays = len(new_df)
new_df.drop_duplicates(subset='track_id', inplace=True)
# Choose the x and y axis
x_name = self.AXIS_MAP[self.x_axis.value]
y_name = self.AXIS_MAP[self.y_axis.value]
self.plot.xaxis.axis_label = self.x_axis.value
self.plot.yaxis.axis_label = self.y_axis.value
# Calculate correlation coefficient between x and y axis
corr = np.corrcoef(new_df[x_name],
new_df[y_name])[0, 1] if not new_df.empty else 0
self.plot.title.text = f"{num_plays} track plays selected, correlation: {round(corr, 2)}"
# Provide the new selected data to the Data Source
data_dict = {
'x': new_df[x_name],
'y': new_df[y_name],
'track_name': new_df['song_name'],
'artist_name': new_df['artist_name'],
'count': new_df['counts'],
'circle_size': new_df['circle_size']
}
self.circ_source.data = data_dict
# Update the regression line if more than one track is selected
if len(new_df) <= 1:
self.reg_line.visible = False
else:
x = sm.add_constant(new_df[x_name])
reg_model = sm.OLS(new_df[y_name], x)
results = reg_model.fit()
y_pred = list(
map(
lambda x: results.params.iloc[1] * x + results.params.iloc[
0], new_df[x_name]))
reg_data_dict = {'x': new_df[x_name], 'y_pred': y_pred}
self.line_source.data = reg_data_dict
# Update hover tool for regression line
self.line_hover.tooltips = [(
"Y=",
f"{round(results.params.iloc[1], 2)}x + {round(results.params.iloc[0], 2)}"
), ("R\u00b2", str(round(results.rsquared, 2)))]
self.reg_line.visible = (len(self.reg_line_check.active) > 0)
def get_selected(self):
"""
Filter data based on widget values. Returns filtered DataFrame
"""
df = self.backing_df
if not self.track_name.value.isspace():
df = df[df['song_name'].str.lower().str.contains(
self.track_name.value.strip().lower())]
if not self.artist_name.value.isspace():
df = df[df['artist_name'].str.lower().str.contains(
self.artist_name.value.strip().lower())]
# Filter by date played
date_begin = pd.to_datetime(self.date_slider.value[0], unit='ms')
date_end = pd.to_datetime(self.date_slider.value[1], unit='ms')
df = df[(date_begin <= df['date_played'])
& (df['date_played'] <= date_end)]
# Filter by time played
time_begin = pd.to_datetime(self.time_slider.value[0],
unit='ms').time()
time_end = pd.to_datetime(self.time_slider.value[1], unit='ms').time()
df = df[(time_begin <= df['time_played'])
& (df['time_played'] <= time_end)]
# Join the counts and circle size columns to the df
df = self.get_selected_counts(df)
return df
def get_selected_counts(self, df):
"""
If no tracks are selected, simply join empty columns for counts and circle_size.
Otherwise, compute the counts and circle sizes, and join those columns to the df.
Arguemnts:
-df : filtered DataFrame
Returns filtered DataFrame with additional columns for counts and circle_size.
"""
if df.empty:
df['counts'] = pd.Series([])
df['circle_size'] = pd.Series([])
return df
df_counts = df.groupby(['song_name', 'artist_name'
]).size().reset_index(name='counts')
df_counts = df_counts.apply(self.apply_circle_sizes, axis=1)
return pd.merge(df,
df_counts,
on=['song_name', 'artist_name'],
how='left')
def apply_circle_sizes(self, row):
"""
Determines the size of each circle based on the number of times that track has been played.
"""
if row['counts'] == 1:
row['circle_size'] = 5
elif 1 < row['counts'] <= 5:
row['circle_size'] = 7
elif row['counts'] > 5:
row['circle_size'] = 10
return row
disabled_button = Button(label="Button (disabled) - still has click event",
button_type="primary",
disabled=True)
toggle = Toggle(label="Toggle button", button_type="success")
menu = [("Item 1", "item_1_value"), ("Item 2", "item_2_value"), None,
("Item 3", "item_3_value")]
dropdown = Dropdown(label="Dropdown button", button_type="warning", menu=menu)
dropdown_split = Dropdown(label="Split button",
button_type="danger",
menu=menu,
split=True)
checkbox_group = CheckboxGroup(labels=["Option 1", "Option 2", "Option 3"],
active=[0, 1])
radio_group = RadioGroup(labels=["Option 1", "Option 2", "Option 3"], active=0)
checkbox_button_group = CheckboxButtonGroup(
labels=["Option 1", "Option 2", "Option 3"], active=[0, 1])
radio_button_group = RadioButtonGroup(
labels=["Option 1", "Option 2", "Option 3"], active=0)
checkbox_button_group_vertical = CheckboxButtonGroup(
labels=["Option 1", "Option 2", "Option 3"],
active=[0, 1],
orientation="vertical")
radio_button_group_vertical = RadioButtonGroup(
labels=["Option 1", "Option 2", "Option 3"],
active=0,
orientation="vertical")
def create_layout(self):
# create figure
self.x_range = Range1d(start=self.model.map_extent[0],
end=self.model.map_extent[2],
bounds=None)
self.y_range = Range1d(start=self.model.map_extent[1],
end=self.model.map_extent[3],
bounds=None)
self.fig = Figure(tools='wheel_zoom,pan',
x_range=self.x_range,
y_range=self.y_range)
self.fig.plot_height = 560
self.fig.plot_width = 800
self.fig.axis.visible = False
# add tiled basemap
self.tile_source = WMTSTileSource(url=self.model.basemap)
self.tile_renderer = TileRenderer(tile_source=self.tile_source)
self.fig.renderers.append(self.tile_renderer)
# add datashader layer
self.image_source = ImageSource(
url=self.model.service_url,
extra_url_vars=self.model.shader_url_vars)
self.image_renderer = DynamicImageRenderer(
image_source=self.image_source)
self.fig.renderers.append(self.image_renderer)
# add label layer
self.label_source = WMTSTileSource(url=self.model.labels_url)
self.label_renderer = TileRenderer(tile_source=self.label_source)
self.fig.renderers.append(self.label_renderer)
# add ui components
axes_select = Select.create(name='Axes', options=self.model.axes)
axes_select.on_change('value', self.on_axes_change)
field_select = Select.create(name='Field', options=self.model.fields)
field_select.on_change('value', self.on_field_change)
aggregate_select = Select.create(
name='Aggregate', options=self.model.aggregate_functions)
aggregate_select.on_change('value', self.on_aggregate_change)
transfer_select = Select.create(name='Transfer Function',
options=self.model.transfer_functions)
transfer_select.on_change('value', self.on_transfer_function_change)
basemap_select = Select.create(name='Basemap',
value='Toner',
options=self.model.basemaps)
basemap_select.on_change('value', self.on_basemap_change)
image_opacity_slider = Slider(title="Opacity",
value=100,
start=0,
end=100,
step=1)
image_opacity_slider.on_change('value',
self.on_image_opacity_slider_change)
basemap_opacity_slider = Slider(title="Basemap Opacity",
value=100,
start=0,
end=100,
step=1)
basemap_opacity_slider.on_change('value',
self.on_basemap_opacity_slider_change)
show_labels_chk = CheckboxGroup(labels=["Show Labels"], active=[0])
show_labels_chk.on_click(self.on_labels_change)
controls = [
axes_select, field_select, aggregate_select, transfer_select
]
map_controls = [
basemap_select, basemap_opacity_slider, image_opacity_slider,
show_labels_chk
]
self.controls = VBox(width=200, height=600, children=controls)
self.map_controls = HBox(width=self.fig.plot_width,
children=map_controls)
self.map_area = VBox(width=self.fig.plot_width,
children=[self.map_controls, self.fig])
self.layout = HBox(width=1024, children=[self.controls, self.map_area])
line_width=3)
layout.children[0] = p
kjonnselect = Select(title="Kjønn",
value="Begge kjønn",
options=sorted(set(df['kjonn'])))
fullfselect = Select(title="Fullføringsgrad",
value="I alt",
options=sorted(set(df['fullforingsgrad'])))
studretnselect = Select(title="Studieretning",
value="Alle studieretninger/utdanningsprogram",
options=sorted(
set(df['studieretning_utdanningsprogram'])))
prosent = CheckboxGroup(labels=["Vis prosent"], active=[])
kjonnselect.on_change('value', callback)
fullfselect.on_change('value', callback)
studretnselect.on_change('value', callback)
prosent.on_change('active', callback)
p = figure(plot_height=400,
plot_width=950,
tooltips=TOOLTIPS,
sizing_mode='scale_width')
p.xaxis.axis_label = "Skoleår"
p.yaxis.axis_label = andelprosent_label(prosent)
# Apply list on grouped dataframe returns list of lists - must be run on each variable.
df2 = df.loc[(df['kjonn'] == kjonnselect.value)
def create_layout(self):
# create figure
self.x_range = Range1d(start=self.model.map_extent[0], end=self.model.map_extent[2], bounds=None)
self.y_range = Range1d(start=self.model.map_extent[1], end=self.model.map_extent[3], bounds=None)
self.fig = Figure(
tools="wheel_zoom,pan",
x_range=self.x_range,
lod_threshold=None,
plot_width=self.model.plot_width,
plot_height=self.model.plot_height,
background_fill_color="black",
y_range=self.y_range,
)
self.fig.min_border_top = 0
self.fig.min_border_bottom = 10
self.fig.min_border_left = 0
self.fig.min_border_right = 0
self.fig.axis.visible = False
self.fig.xgrid.grid_line_color = None
self.fig.ygrid.grid_line_color = None
# add tiled basemap
self.tile_source = WMTSTileSource(url=self.model.basemap)
self.tile_renderer = TileRenderer(tile_source=self.tile_source)
self.fig.renderers.append(self.tile_renderer)
# add datashader layer
self.image_source = ImageSource(url=self.model.service_url, extra_url_vars=self.model.shader_url_vars)
self.image_renderer = DynamicImageRenderer(image_source=self.image_source)
self.fig.renderers.append(self.image_renderer)
# add label layer
self.label_source = WMTSTileSource(url=self.model.labels_url)
self.label_renderer = TileRenderer(tile_source=self.label_source)
self.fig.renderers.append(self.label_renderer)
# Add placeholder for legends (temporarily disabled)
# self.model.legend_side_vbox = VBox()
# self.model.legend_bottom_vbox = VBox()
# add ui components
controls = []
axes_select = Select.create(name="Axes", options=self.model.axes)
axes_select.on_change("value", self.on_axes_change)
controls.append(axes_select)
self.field_select = Select.create(name="Field", options=self.model.fields)
self.field_select.on_change("value", self.on_field_change)
controls.append(self.field_select)
self.aggregate_select = Select.create(name="Aggregate", options=self.model.aggregate_functions)
self.aggregate_select.on_change("value", self.on_aggregate_change)
controls.append(self.aggregate_select)
transfer_select = Select.create(name="Transfer Function", options=self.model.transfer_functions)
transfer_select.on_change("value", self.on_transfer_function_change)
controls.append(transfer_select)
color_ramp_select = Select.create(name="Color Ramp", options=self.model.color_ramps)
color_ramp_select.on_change("value", self.on_color_ramp_change)
controls.append(color_ramp_select)
spread_size_slider = Slider(title="Spread Size (px)", value=0, start=0, end=10, step=1)
spread_size_slider.on_change("value", self.on_spread_size_change)
controls.append(spread_size_slider)
hover_size_slider = Slider(title="Hover Size (px)", value=8, start=4, end=30, step=1)
hover_size_slider.on_change("value", self.on_hover_size_change)
controls.append(hover_size_slider)
# legends (temporarily disabled)
# controls.append(self.model.legend_side_vbox)
# add map components
basemap_select = Select.create(name="Basemap", value="Imagery", options=self.model.basemaps)
basemap_select.on_change("value", self.on_basemap_change)
image_opacity_slider = Slider(title="Opacity", value=100, start=0, end=100, step=1)
image_opacity_slider.on_change("value", self.on_image_opacity_slider_change)
basemap_opacity_slider = Slider(title="Basemap Opacity", value=100, start=0, end=100, step=1)
basemap_opacity_slider.on_change("value", self.on_basemap_opacity_slider_change)
show_labels_chk = CheckboxGroup(labels=["Show Labels"], active=[0])
show_labels_chk.on_click(self.on_labels_change)
map_controls = [basemap_select, basemap_opacity_slider, image_opacity_slider, show_labels_chk]
self.controls = VBox(height=600, children=controls)
self.map_controls = HBox(width=self.fig.plot_width, children=map_controls)
# legends (temporarily disabled)
self.map_area = VBox(width=900, height=600, children=[self.map_controls, self.fig])
self.layout = HBox(width=1300, height=600, children=[self.controls, self.map_area])
self.model.fig = self.fig
self.model.update_hover()
def __init__(self, df: pd.DataFrame):
# Initialize data sources for scatter plot and regression line
self.backing_df = df
self.circ_source = ColumnDataSource({
'x': [],
'y': [],
'track_name': [],
'artist_name': [],
'count': [],
'circle_size': []
})
self.line_source = ColumnDataSource({'x': [], 'y_pred': []})
# Initialize widgets
self.x_axis = Select(title="X Axis",
options=list(self.AXIS_MAP.keys()),
value="Tempo")
self.y_axis = Select(title="Y Axis",
options=list(self.AXIS_MAP.keys()),
value="Duration (sec)")
time_start = datetime.datetime(1970, 1, 1, hour=0, minute=0, second=0)
time_end = datetime.datetime(1970, 1, 1, hour=23, minute=59, second=59)
start_date = min(self.backing_df['date_played'])
start_dt = datetime.datetime(year=start_date.year,
month=start_date.month,
day=start_date.day,
hour=0,
minute=0,
second=0)
end_date = max(self.backing_df['date_played'])
end_dt = datetime.datetime(year=end_date.year,
month=end_date.month,
day=end_date.day,
hour=23,
minute=59,
second=59)
date_step_size = 1000 * 60 * 60 * 24 # Step size of 1 day in ms
self.date_slider = DateRangeSlider(title="Date Range",
start=start_dt,
end=end_dt,
value=(start_dt, end_dt),
format="%d %b %Y",
step=date_step_size)
time_step_size = 1000 * 60 * 30 # 30 minues in ms
self.time_slider = DateRangeSlider(title="Time Range",
value=(time_start, time_end),
start=time_start,
end=time_end,
format="%X",
step=time_step_size)
self.track_name = TextInput(title="Song name includes")
self.artist_name = TextInput(title="Artist name includes")
self.reg_line_check = CheckboxGroup(labels=["Add Regression Line"],
active=[])
# Create the hover tools
self.points_hover = HoverTool(tooltips=self.CIRC_TOOLTIPS,
names=["circles"])
self.line_hover = HoverTool(tooltips=[], names=["reg_line"])
# Create the scatter plot and regression line
self.plot = figure(title="Scatter",
plot_height=450,
plot_width=800,
tools=[self.points_hover, self.line_hover])
self.plot.circle(x="x",
y="y",
source=self.circ_source,
size="circle_size",
fill_alpha=0.6,
name="circles")
self.reg_line = self.plot.line(x='x',
y='y_pred',
source=self.line_source,
color='#FFAF87',
name="reg_line")
self.layout = row(
column(self.x_axis, self.y_axis, self.date_slider,
self.time_slider, self.track_name, self.artist_name,
self.reg_line_check), self.plot)
# Fill data and create events for on change
self.update()
self.on_change()
def __init__(
self,
options,
name,
data,
data_types,
type_categories,
):
"""
Configuration options:
- default/X Axis: name
- default/Y Axis: name
- default/Group by: name
- default/Exclude: name1;name2;...;nameN
- default/discard_axis_x: name1;name2;...;nameN
- default/discard_axis_y: name1;name2;...;nameN
- default/discard_group_by: name1;name2;...;nameN
- default/discard_exclude_max_values: number. the maximum number of excluded values for a category to be shown. If an
exclusion category has more values, no values added for this category
- default/number_of_columns: int
Args:
options:
name:
data:
data_types:
type_categories:
"""
self.discard_group_by = safe_lookup(options.config, name, 'default', 'discard_group_by', default='').split(';')
self.discard_axis_x = safe_lookup(options.config, name, 'default', 'discard_axis_x', default='').split(';')
self.discard_axis_y = safe_lookup(options.config, name, 'default', 'discard_axis_y', default='').split(';')
self.discard_exclude_max_values = int(safe_lookup(options.config, name, 'default', 'discard_exclude_max_values', default='15'))
self.group_by = CheckboxGroup()
self.y_axis = CheckboxGroup()
self.x_axis = RadioGroup()
self.exclude = CheckboxGroup()
controls_list = [
PreText(text='Group by:'),
self.group_by,
PreText(text='Y Axis:'),
self.y_axis,
PreText(text='X Axis:'),
self.x_axis,
PreText(text='Exclude:'),
self.exclude,
]
controls = column(controls_list, name='PanelDataGraph_controls')
controls.sizing_mode = "fixed"
self.update_controls(options, name, data, data_types, type_categories)
self.options = options
self.layout = row(controls, sizing_mode='stretch_both')
self.last_data = data
self.last_data_types = data_types
self.last_type_categories = type_categories
self.last_figs_by_group = {}
self.number_of_columns = int(safe_lookup(options.config, name, 'default', 'number_of_columns', default='2'))
for control in controls_list:
if hasattr(control, 'active'):
control.on_change('active', lambda attr, old, new: self._update_and_clear_plots())
self._update()
super().__init__(ui=Panel(child=self.layout, title=name))
# Import CheckboxGroup, RadioGroup, Toggle from bokeh.models from bokeh.models import CheckboxGroup, RadioGroup, Toggle from bokeh.io import curdoc from bokeh.plotting import figure from bokeh.layouts import widgetbox # Add a Toggle: toggle toggle = Toggle(label='Toggle button', button_type='success') # Add a CheckboxGroup: checkbox checkbox = CheckboxGroup(labels=['Option 1', 'Option 2', 'Option 3']) # Add a RadioGroup: radio radio = RadioGroup(labels=['Option 1', 'Option 2', 'Option 3']) # Add widgetbox(toggle, checkbox, radio) to the current document curdoc().add_root(widgetbox(toggle, checkbox, radio))
def candlestick_plot():
def obv_indicator(data):
res = talib.OBV(data.close.values, data.volume.values)
return res
def rsi_indicator(data):
res = talib.RSI(data.close.values, timeperiod=14)
return res
def cci_indicator(data):
res = talib.CCI(data.high.values, data.low.values, data.close.values, timeperiod=14)
return res
def technical_indicator(data, indicator):
if indicator == 'CCI':
data['tech'] = cci_indicator(data)
elif indicator == 'RSI':
data['tech'] = rsi_indicator(data)
else:
data['tech'] = obv_indicator(data)
return data
def load_data(obid, start, end, freq='1d'):
print('running....')
data = get_price(obid, start, end, freqency=freq).reset_index()
data['pct_change'] = data['close'].pct_change()
# data.dropna(inplace=True)
data['pct_change'] = data['pct_change'].apply(lambda x: str(round(x * 100, 2)) + '%')
data['index'] = list(np.arange(len(data)))
data['date'] = data['date'].apply(lambda x: x.strftime("%Y%m%d"))
return data
def moving_average(data, selection):
selection_mapping = {k: int(k.split('_')[-1]) for k in selection}
for k, v in selection_mapping.items():
data[k] = data['close'].rolling(window=v).mean()
return data
def update_lines(attr, old, new):
line_0.visible = 0 in average_selection.active
line_1.visible = 1 in average_selection.active
line_2.visible = 2 in average_selection.active
line_3.visible = 3 in average_selection.active
line_4.visible = 4 in average_selection.active
line_5.visible = 5 in average_selection.active
def update_plot(attr, old, new):
indicator = indicator_selection.value
new_data = technical_indicator(data, indicator)
new_source = ColumnDataSource(new_data)
source.data.update(new_source.data)
def update_data():
# global obid, start, end
obid = order_book_id.value
start = start_date.value
end = end_date.value
# 提取数据，均线根据选取与否进行添加
new_data = load_data(obid, start, end)
new_data_1 = moving_average(new_data, average_labels)
new_data_2 = technical_indicator(new_data, indicator_selection.value)
new_source = ColumnDataSource(new_data_2)
new_source_1 = ColumnDataSource(new_data_1)
source.data.update(new_source.data)
source_1.data.update(new_source_1.data)
inc = new_data.close >= new_data.open
dec = new_data.close < new_data.open
inc_source.data = inc_source.from_df(new_data_2.loc[inc])
dec_source.data = dec_source.from_df(new_data_2.loc[dec])
p.title.text = instruments(obid).symbol
p.x_range.end = len(new_data) + 1
p2.xaxis.major_label_overrides = {i: date for i, date in enumerate(new_data['date'])}
today = datetime.now().date()
average_labels = ["MA_5", "MA_10", "MA_20", 'MA_30', 'MA_60', 'MA_120']
average_selection = CheckboxGroup(labels=average_labels, active=[0, 1, 2, 3, 4, 5, 6])
indicator_selection = Select(title='TechnicalIndicator', value='RSI', options=['OBV', 'RSI', 'CCI'])
order_book_id = TextInput(title='StockCode', value='002916.XSHE')
symbol = instruments(order_book_id.value).symbol
start_date = DatePicker(title="StartDate", value='2018-01-01', min_date='2015-01-01', max_date=today)
end_date = DatePicker(title="EndDate", value=today, min_date=start_date.value, max_date=today)
# labels = [average_selection.labels[i] for i in average_selection.active]
data = load_data(order_book_id.value, start_date.value, end_date.value)
# 均线计算
data_1 = moving_average(data, average_labels) # 计算各种长度的均线
# 技术指标计算
data_2 = technical_indicator(data, indicator_selection.value)
source = ColumnDataSource(data_2)
source_1 = ColumnDataSource(data_1)
inc = data.close >= data.open
dec = data.open > data.close
inc_source = ColumnDataSource(data_2.loc[inc])
dec_source = ColumnDataSource(data_2.loc[dec])
TOOLS = 'save, pan, box_zoom, reset, wheel_zoom'
hover = HoverTool(tooltips=[('date', '@date'),
('open', '@open'),
('high', '@high'),
('low', '@low'),
('close', '@close'),
('pct_change', "@pct_change")
]
)
length = len(data)
p = figure(plot_width=1000, plot_height=500, title='{}'.format(symbol), tools=TOOLS, x_range=(0, length + 1))
p.xaxis.visible = False # 隐藏x-axis
p.min_border_bottom = 0
# 均线图
line_0 = p.line(x='index', y='MA_5', source=source_1, color=Spectral6[5])
line_1 = p.line(x='index', y='MA_10', source=source_1, color=Spectral6[4])
line_2 = p.line(x='index', y='MA_20', source=source_1, color=Spectral6[3])
line_3 = p.line(x='index', y='MA_30', source=source_1, color=Spectral6[2])
line_4 = p.line(x='index', y='MA_60', source=source_1, color=Spectral6[1])
line_5 = p.line(x='index', y='MA_120', source=source_1, color=Spectral6[0])
p.segment(x0='index', y0='high', x1='index', y1='low', color='red', source=inc_source)
p.segment(x0='index', y0='high', x1='index', y1='low', color='green', source=dec_source)
p.vbar('index', 0.5, 'open', 'close', fill_color='red', line_color='red', source=inc_source, hover_fill_alpha=0.5)
p.vbar('index', 0.5, 'open', 'close', fill_color='green', line_color='green', source=dec_source,
hover_fill_alpha=0.5)
p.add_tools(hover)
p1 = figure(plot_width=p.plot_width, plot_height=200, x_range=p.x_range, toolbar_location=None)
p1.vbar('index', 0.5, 0, 'volume', color='red', source=inc_source)
p1.vbar('index', 0.5, 0, 'volume', color='green', source=dec_source)
p1.xaxis.visible = False
p2 = figure(plot_width=p.plot_width, plot_height=p1.plot_height, x_range=p.x_range, toolbar_location=None)
p2.line(x='index', y='tech', source=source)
p2.xaxis.major_label_overrides = {i: date for i, date in enumerate(data['date'])}
p2.xaxis.major_label_orientation = pi / 4
p2.min_border_bottom = 0
button = Button(label="ClickToChange", button_type="success")
button.on_click(update_data)
average_selection.inline = True
average_selection.width = 500
average_selection.on_change('active', update_lines)
indicator_selection.on_change('value', update_plot)
widgets = column(row(order_book_id, start_date, end_date, button), row(indicator_selection, average_selection))
layouts = column(widgets, p, p1, p2)
# doc.add_root(pp)
# make a layout
tab = Panel(child=layouts, title='StockPrice')
return tab
def create_layout(self):
# create figure
self.x_range = Range1d(start=self.model.map_extent[0],
end=self.model.map_extent[2],
bounds=None)
self.y_range = Range1d(start=self.model.map_extent[1],
end=self.model.map_extent[3],
bounds=None)
self.fig = Figure(tools='wheel_zoom,pan',
x_range=self.x_range,
lod_threshold=None,
plot_width=self.model.plot_width,
plot_height=self.model.plot_height,
background_fill_color='black',
y_range=self.y_range)
self.fig.min_border_top = 0
self.fig.min_border_bottom = 10
self.fig.min_border_left = 0
self.fig.min_border_right = 0
self.fig.axis.visible = False
self.fig.xgrid.grid_line_color = None
self.fig.ygrid.grid_line_color = None
# add tiled basemap
self.tile_source = WMTSTileSource(url=self.model.basemap)
self.tile_renderer = TileRenderer(tile_source=self.tile_source)
self.fig.renderers.append(self.tile_renderer)
# add datashader layer
self.image_source = ImageSource(
url=self.model.service_url,
extra_url_vars=self.model.shader_url_vars)
self.image_renderer = DynamicImageRenderer(
image_source=self.image_source)
self.fig.renderers.append(self.image_renderer)
# add label layer
self.label_source = WMTSTileSource(url=self.model.labels_url)
self.label_renderer = TileRenderer(tile_source=self.label_source)
self.fig.renderers.append(self.label_renderer)
# Add placeholder for legends (temporarily disabled)
# self.model.legend_side_vbox = Column()
# self.model.legend_bottom_vbox = Column()
# add ui components
controls = []
axes_select = Select(name='Axes', options=list(self.model.axes.keys()))
axes_select.on_change('value', self.on_axes_change)
controls.append(axes_select)
self.field_select = Select(name='Field',
options=list(self.model.fields.keys()))
self.field_select.on_change('value', self.on_field_change)
controls.append(self.field_select)
self.aggregate_select = Select(
name='Aggregate',
options=list(self.model.aggregate_functions.keys()))
self.aggregate_select.on_change('value', self.on_aggregate_change)
controls.append(self.aggregate_select)
transfer_select = Select(name='Transfer Function',
options=list(
self.model.transfer_functions.keys()))
transfer_select.on_change('value', self.on_transfer_function_change)
controls.append(transfer_select)
color_ramp_select = Select(name='Color Ramp',
options=list(self.model.color_ramps.keys()))
color_ramp_select.on_change('value', self.on_color_ramp_change)
controls.append(color_ramp_select)
spread_size_slider = Slider(title="Spread Size (px)",
value=0,
start=0,
end=10,
step=1)
spread_size_slider.on_change('value', self.on_spread_size_change)
controls.append(spread_size_slider)
# hover (temporarily disabled)
#hover_size_slider = Slider(title="Hover Size (px)", value=8, start=4,
# end=30, step=1)
#hover_size_slider.on_change('value', self.on_hover_size_change)
#controls.append(hover_size_slider)
# legends (temporarily disabled)
# controls.append(self.model.legend_side_vbox)
# add map components
basemap_select = Select(name='Basemap',
value='Imagery',
options=list(self.model.basemaps.keys()))
basemap_select.on_change('value', self.on_basemap_change)
image_opacity_slider = Slider(title="Opacity",
value=100,
start=0,
end=100,
step=1)
image_opacity_slider.on_change('value',
self.on_image_opacity_slider_change)
basemap_opacity_slider = Slider(title="Basemap Opacity",
value=100,
start=0,
end=100,
step=1)
basemap_opacity_slider.on_change('value',
self.on_basemap_opacity_slider_change)
show_labels_chk = CheckboxGroup(labels=["Show Labels"], active=[0])
show_labels_chk.on_click(self.on_labels_change)
map_controls = [
basemap_select, basemap_opacity_slider, image_opacity_slider,
show_labels_chk
]
self.controls = Column(height=600, children=controls)
self.map_controls = Row(width=self.fig.plot_width,
children=map_controls)
# legends (temporarily disabled)
self.map_area = Column(width=900,
height=600,
children=[self.map_controls, self.fig])
self.layout = Row(width=1300,
height=600,
children=[self.controls, self.map_area])
self.model.fig = self.fig
self.model.update_hover()
def create_layout(self):
# create figure
self.x_range = Range1d(start=self.model.map_extent[0],
end=self.model.map_extent[2], bounds=None)
self.y_range = Range1d(start=self.model.map_extent[1],
end=self.model.map_extent[3], bounds=None)
self.fig = Figure(tools='wheel_zoom,pan',
x_range=self.x_range,
lod_threshold=None,
plot_width=self.model.plot_width,
plot_height=self.model.plot_height,
y_range=self.y_range)
self.fig.min_border_top = 0
self.fig.min_border_bottom = 10
self.fig.min_border_left = 0
self.fig.min_border_right = 0
self.fig.axis.visible = False
self.fig.xgrid.grid_line_color = None
self.fig.ygrid.grid_line_color = None
# add tiled basemap
self.tile_source = WMTSTileSource(url=self.model.basemap)
self.tile_renderer = TileRenderer(tile_source=self.tile_source)
self.fig.renderers.append(self.tile_renderer)
# add datashader layer
self.image_source = ImageSource(url=self.model.service_url,
extra_url_vars=self.model.shader_url_vars)
self.image_renderer = DynamicImageRenderer(image_source=self.image_source)
self.fig.renderers.append(self.image_renderer)
# add label layer
self.label_source = WMTSTileSource(url=self.model.labels_url)
self.label_renderer = TileRenderer(tile_source=self.label_source)
self.fig.renderers.append(self.label_renderer)
# Add a hover tool
self.invisible_square = Square(x='x',
y='y',
fill_color=None,
line_color=None,
size=self.model.hover_size)
self.visible_square = Square(x='x',
y='y',
fill_color='#79DCDE',
fill_alpha=.5,
line_color='#79DCDE',
line_alpha=1,
size=self.model.hover_size)
cr = self.fig.add_glyph(self.model.hover_source,
self.invisible_square,
selection_glyph=self.visible_square,
nonselection_glyph=self.invisible_square)
code = "source.set('selected', cb_data['index']);"
callback = CustomJS(args={'source': self.model.hover_source}, code=code)
self.model.hover_tool = HoverTool(tooltips=[(self.model.fields.keys()[0], "@value")],
callback=callback,
renderers=[cr],
mode='mouse')
self.fig.add_tools(self.model.hover_tool)
self.model.legend_side_vbox = VBox()
self.model.legend_bottom_vbox = VBox()
# add ui components
controls = []
axes_select = Select.create(name='Axes',
options=self.model.axes)
axes_select.on_change('value', self.on_axes_change)
controls.append(axes_select)
self.field_select = Select.create(name='Field', options=self.model.fields)
self.field_select.on_change('value', self.on_field_change)
controls.append(self.field_select)
self.aggregate_select = Select.create(name='Aggregate',
options=self.model.aggregate_functions)
self.aggregate_select.on_change('value', self.on_aggregate_change)
controls.append(self.aggregate_select)
transfer_select = Select.create(name='Transfer Function',
options=self.model.transfer_functions)
transfer_select.on_change('value', self.on_transfer_function_change)
controls.append(transfer_select)
color_ramp_select = Select.create(name='Color Ramp', options=self.model.color_ramps)
color_ramp_select.on_change('value', self.on_color_ramp_change)
controls.append(color_ramp_select)
spread_size_slider = Slider(title="Spread Size (px)", value=0, start=0,
end=10, step=1)
spread_size_slider.on_change('value', self.on_spread_size_change)
controls.append(spread_size_slider)
hover_size_slider = Slider(title="Hover Size (px)", value=8, start=4,
end=30, step=1)
hover_size_slider.on_change('value', self.on_hover_size_change)
controls.append(hover_size_slider)
controls.append(self.model.legend_side_vbox)
# add map components
basemap_select = Select.create(name='Basemap', value='Imagery',
options=self.model.basemaps)
basemap_select.on_change('value', self.on_basemap_change)
image_opacity_slider = Slider(title="Opacity", value=100, start=0,
end=100, step=1)
image_opacity_slider.on_change('value', self.on_image_opacity_slider_change)
basemap_opacity_slider = Slider(title="Basemap Opacity", value=100, start=0,
end=100, step=1)
basemap_opacity_slider.on_change('value', self.on_basemap_opacity_slider_change)
show_labels_chk = CheckboxGroup(labels=["Show Labels"], active=[0])
show_labels_chk.on_click(self.on_labels_change)
map_controls = [basemap_select, basemap_opacity_slider,
image_opacity_slider, show_labels_chk]
self.controls = VBox(width=200, height=600, children=controls)
self.map_controls = HBox(width=self.fig.plot_width, children=map_controls)
self.map_area = VBox(width=self.fig.plot_width, children=[self.map_controls,
self.fig,
self.model.legend_bottom_vbox])
self.layout = HBox(width=1366, children=[self.controls, self.map_area])
import numpy as np
from bokeh.io import curdoc
from bokeh.layouts import row
from bokeh.palettes import Viridis3
from bokeh.plotting import figure
from bokeh.models import CheckboxGroup
p = figure()
props = dict(line_width=4, line_alpha=0.7)
x = np.linspace(0, 4 * np.pi, 100)
l0 = p.line(x, np.sin(x), color=Viridis3[0], legend="Line 0", **props)
l1 = p.line(x, 4 * np.cos(x), color=Viridis3[1], legend="Line 1", **props)
l2 = p.line(x, np.tan(x), color=Viridis3[2], legend="Line 2", **props)
checkbox = CheckboxGroup(labels=["Line 0", "Line 1", "Line 2"],
active=[0, 1, 2],
width=100)
def update(attr, old, new):
l0.visible = 0 in checkbox.active
l1.visible = 1 in checkbox.active
l2.visible = 2 in checkbox.active
checkbox.on_change('active', update)
layout = row(checkbox, p)
curdoc().add_root(layout)
def setup_widgets(self):
# Generation of selectable items
# Dict contains all inspectable runs (maps display strings to timestamps)
# The dict structure allows to get the timestamp from the display string
# in O(1)
self.runs_dict = helper.gen_runs_selection(self.metadata)
# Dict maps display strings to column names for the different factors
# (var, backend, test)
self.factors_dict = {
"Variables": "variable",
"Backends": "vfc_backend",
"Tests": "test"
}
# Run selection
# Contains all options strings
runs_display = list(self.runs_dict.keys())
# Will be used when updating plots (contains actual number)
self.current_run = self.runs_dict[runs_display[-1]]
# Contains the selected option string, used to update current_n_runs
current_run_display = runs_display[-1]
# This contains only entries matching the run
self.run_data = self.data[self.data["timestamp"] == self.current_run]
change_run_callback_js = "updateRunMetadata(cb_obj.value, \"\");"
self.widgets["select_run"] = Select(name="select_run",
title="Run :",
value=current_run_display,
options=runs_display)
self.doc.add_root(self.widgets["select_run"])
self.widgets["select_run"].on_change("value", self.update_run)
self.widgets["select_run"].js_on_change(
"value",
CustomJS(
code=change_run_callback_js,
args=(dict(metadata=helper.metadata_to_dict(
helper.get_metadata(self.metadata, self.current_run))))))
# Factors selection
# "Group by" radio
self.widgets["groupby_radio"] = RadioButtonGroup(
name="groupby_radio",
labels=list(self.factors_dict.keys()),
active=0)
self.doc.add_root(self.widgets["groupby_radio"])
# The functions are defined inside the template to avoid writing too
# much JS server side
self.widgets["groupby_radio"].on_change("active", self.update_groupby)
# "Filter by" radio
# Get all possible factors, and remove the one selected in "Group by"
filterby_list = list(self.factors_dict.keys())
del filterby_list[self.widgets["groupby_radio"].active]
self.widgets["filterby_radio"] = RadioButtonGroup(
name="filterby_radio", labels=filterby_list, active=0)
self.doc.add_root(self.widgets["filterby_radio"])
# The functions are defined inside the template to avoid writing too
# much JS server side
self.widgets["filterby_radio"].on_change("active",
self.update_filterby)
# Filter selector
filterby = self.widgets["filterby_radio"].labels[
self.widgets["filterby_radio"].active]
filterby = self.factors_dict[filterby]
if not self.run_data.empty:
options = self.run_data.index\
.get_level_values(filterby).drop_duplicates().tolist()
else:
options = ["None"]
self.widgets["select_filter"] = Select(
# We need a different name to avoid collision in the template with
# the runs comparison's widget
name="select_filter",
title="Select a filter :",
value=options[0],
options=options)
self.doc.add_root(self.widgets["select_filter"])
self.widgets["select_filter"]\
.on_change("value", self.update_filter)
# Toggle for outliers filtering
self.widgets["outliers_filtering_inspect"] = CheckboxGroup(
name="outliers_filtering_inspect",
labels=["Filter outliers"],
active=[])
self.doc.add_root(self.widgets["outliers_filtering_inspect"])
self.widgets["outliers_filtering_inspect"]\
.on_change("active", self.update_outliers_filtering)
def __init__(self, type):
self.test = False
self.report_type = type
self.utc = TimezoneInfo()
self.kp_zone = TimezoneInfo(utc_offset=-7*u.hour)
self.zones = [self.utc, self.kp_zone]
self.datefmt = DateFormatter(format="%m/%d/%Y %H:%M:%S")
self.inst_style = {'font-size':'150%'}
self.subt_style = {'font-size':'200%','font-style':'bold'}
self.title_style = {'font-size':'250%','font-style':'bold'}
self.alert_style = {'font-size':'150%','color':'red'}
self.nl_file = None
self.intro_subtitle = Div(text="Connect to Night Log",css_classes=['subt-style'])
self.time_note = Div(text="<b> Note: </b> Enter all times as HHMM (1818 = 18:18 = 6:18pm) in Kitt Peak local time. Either enter the time or hit the <b> Now </b> button if it just occured.", css_classes=['inst-style'])
hostname = socket.gethostname()
ip_address = socket.gethostbyname(hostname)
if 'desi' in hostname:
self.location = 'kpno'
#self.conn = psycopg2.connect(host="desi-db", port="5442", database="desi_dev", user="******", password="******")
elif 'app' in hostname: #this is not true. Needs to change.
self.location = 'nersc'
else:
self.location = 'other'
nw_dirs = {'nersc':'/global/cfs/cdirs/desi/spectro/nightwatch/nersc/', 'kpno':'/exposures/nightwatch/', 'other':None}
self.nw_dir = nw_dirs[self.location]
self.nl_dir = os.environ['NL_DIR']
self.your_name = TextInput(title ='Your Name', placeholder = 'John Smith')
self.os_name_1 = TextInput(title ='Observing Scientist 1', placeholder = 'Ruth Bader Ginsberg')
self.os_name_2 = TextInput(title ='Observing Scientist 2', placeholder = "Sandra Day O'Connor")
self.lo_names = ['None ','Liz Buckley-Geer','Ann Elliott','Parker Fagrelius','Satya Gontcho A Gontcho','James Lasker','Martin Landriau','Claire Poppett','Michael Schubnell','Luke Tyas','Other ']
self.oa_names = ['None ','Karen Butler','Amy Robertson','Anthony Paat','Dave Summers','Doug Williams','Other ']
self.intro_txt = Div(text=' ')
self.comment_txt = Div(text=" ", css_classes=['inst-style'], width=1000)
self.date_init = Select(title="Existing Night Logs")
self.time_title = Paragraph(text='Time* (Kitt Peak local time)', align='center')
self.now_btn = Button(label='Now', css_classes=['now_button'], width=50)
days = [d for d in os.listdir(self.nl_dir) if os.path.isdir(os.path.join(self.nl_dir, d))]
init_nl_list = np.sort([day for day in days if 'nightlog_meta.json' in os.listdir(os.path.join(self.nl_dir,day))])[::-1][0:10]
self.date_init.options = list(init_nl_list)
self.date_init.value = init_nl_list[0]
self.connect_txt = Div(text=' ', css_classes=['alert-style'])
self.connect_bt = Button(label="Connect to Existing Night Log", css_classes=['connect_button'])
self.exp_info = Div(text="Fill In Only Relevant Data. Mandatory fields have an asterisk*.", css_classes=['inst-style'],width=500)
self.exp_comment = TextAreaInput(title ='Comment/Remark', placeholder = 'Humidity high for calibration lamps',value=None,rows=10, cols=5,width=800,max_length=5000)
self.exp_time = TextInput(placeholder = '20:07',value=None, width=100) #title ='Time in Kitt Peak local time*',
self.exp_btn = Button(label='Add', css_classes=['add_button'])
self.exp_type = Select(title="Exposure Type", value = None, options=['None','Zero','Focus','Dark','Arc','FVC','DESI'])
self.exp_alert = Div(text=' ', css_classes=['alert-style'])
self.exp_exposure_start = TextInput(title='Exposure Number: First', placeholder='12345', value=None)
self.exp_exposure_finish = TextInput(title='Exposure Number: Last', placeholder='12346', value=None)
self.nl_subtitle = Div(text="Current DESI Night Log: {}".format(self.nl_file), css_classes=['subt-style'])
self.nl_btn = Button(label='Get Current DESI Night Log', css_classes=['connect_button'])
self.nl_text = Div(text=" ", css_classes=['inst-style'], width=1000)
self.nl_alert = Div(text='You must be connected to a Night Log', css_classes=['alert-style'], width=500)
self.nl_info = Div(text="Night Log Info:", css_classes=['inst-style'], width=500)
self.exptable_alert = Div(text=" ",css_classes=['alert-style'], width=500)
self.checklist = CheckboxGroup(labels=[])
self.check_time = TextInput(placeholder = '20:07', value=None) #title ='Time in Kitt Peak local time*',
self.check_alert = Div(text=" ", css_classes=['alert-style'])
self.check_btn = Button(label='Submit', css_classes=['add_button'])
self.check_comment = TextAreaInput(title='Comment', placeholder='comment if necessary', rows=3, cols=3)
self.prob_subtitle = Div(text="Problems", css_classes=['subt-style'])
self.prob_inst = Div(text="Describe problems as they come up and at what time they occur. If there is an Alarm ID associated with the problem, include it, but leave blank if not. If possible, include a description of the remedy.", css_classes=['inst-style'], width=1000)
self.prob_time = TextInput(placeholder = '20:07', value=None, width=100) #title ='Time in Kitt Peak local time*',
self.prob_input = TextAreaInput(placeholder="NightWatch not plotting raw data", rows=10, cols=3, title="Problem Description*:")
self.prob_alarm = TextInput(title='Alarm ID', placeholder='12', value=None, width=100)
self.prob_action = TextAreaInput(title='Resolution/Action',placeholder='description',rows=10, cols=3)
self.prob_btn = Button(label='Add', css_classes=['add_button'])
self.prob_alert = Div(text=' ', css_classes=['alert-style'])
self.img_subtitle = Div(text="Images", css_classes=['subt-style'])
self.img_upinst = Div(text="Include images in the Night Log by uploading a png image from your local computer. Select file, write a comment and click Add", css_classes=['inst-style'], width=1000)
self.img_upinst2 = Div(text=" Choose image to include with comment: ", css_classes=['inst-style'])
self.img_upload = FileInput(accept=".png")
self.img_upload.on_change('value', self.upload_image)
self.img_upload_comments = FileInput(accept=".png")
self.img_upload_comments.on_change('value', self.upload_image_comments)
self.img_upload_comments_os = FileInput(accept=".png")
self.img_upload_comments_os.on_change('value', self.upload_image_comments_os)
self.img_upload_problems = FileInput(accept=".png")
self.img_upload_problems.on_change('value', self.upload_image_problems)
self.img_inst = Div(text="Include images in the Night Log by entering the location of the images on the desi cluster", css_classes=['inst-style'], width=1000)
self.img_input = TextInput(title='image file location on desi cluster', placeholder='/n/home/desiobserver/image.png',value=None)
self.img_comment = TextAreaInput(placeholder='comment about image', rows=8, cols=3, title='Image caption')
self.img_btn = Button(label='Add', css_classes=['add_button'])
self.img_alert = Div(text=" ",width=1000)
self.plot_subtitle = Div(text="Telemetry Plots", css_classes=['subt-style'])
self.DESI_Log = None
self.save_telem_plots = False
#plot.title.text = "The Shanghai city has been evaluated by %d blocks for tessllation" % (length*width)
slider = Slider(title="size", start=5, end=20, step=1, value=10)
factors = [
"FAR (Floor Area Ratio)", "Building Type Mix Diversity",
"Heating/Cooling Degree Days", "Urban Heating/Cooling Loads",
"Electricy Tariff", "Population and Urban Compactness", "GDP per Capita",
"Ratio of Street Surface to Building Area", "Vegetation Coverage",
"Building Shading and Urban Context", "Roof Color and Albedo",
"Building Materials", "Vehicles and Transportation",
"Percentage of Water Body"
]
checkbox = CheckboxGroup(labels=factors, active=[])
source = get_dataset()
plot = make_plot(source)
p = Div(
text=
"""<h><b>The Shanghai city is now being evaluated by __ districts for tessellation</b></h>""",
width=600,
height=10)
img = Div(
text=
"""<img src="myapp/static/images/icon.png" alt="Engie Lab China" height="50" width="85">""",
width=85,
height=50)
slider.on_change("value", update_plot)
checkbox.on_change("active", update_plot)
def make_document(doc):
frame_ratio = 320 / 180
# Frames from Pipelines
raw_image_win = init_image_win("Raw Image")
motion_mask_win = init_image_win("Motion Mask")
cv_mask_win = init_image_win("CV Mask")
new_alert_win = init_image_win("New Alert Snapshot")
pipeline_win_height = 225
pipeline_grid_plot = gridplot(
[
raw_image_win,
motion_mask_win,
cv_mask_win,
new_alert_win,
],
ncols=1,
plot_width=int(pipeline_win_height * frame_ratio),
plot_height=pipeline_win_height,
toolbar_location=None,
)
pipeline_col = column(pipeline_grid_plot)
# Frames from Motion
h264_win = init_image_win("H.264")
fd_win = init_image_win("FD")
mog2_win = init_image_win("MOG2")
amf_win = init_image_win("AMF")
display_detection_win = init_image_win("Motion Detection")
motion_win_height = 225
motion_grid_plot = gridplot(
children=[
[h264_win, fd_win],
[mog2_win, amf_win],
],
plot_width=int(motion_win_height * frame_ratio),
plot_height=motion_win_height,
toolbar_location=None,
)
motion_col = column(display_detection_win, motion_grid_plot)
# Motion Control Panel
motion_checkbox_group = CheckboxGroup(
labels=["H.264", "FD", "MOG2", "AMF", "Show Motion Detections"])
motion_select = Select(title="Motion Filter Type",
value="h264",
options=["h264", "fd", "mog2", "amf"])
motion_interval_slider = Slider(start=0.05,
end=30,
value=1.5,
step=0.01,
title="Motion Interval")
motion_threshold_slider = Slider(start=0,
end=200,
value=50,
step=1,
title="Motion Threshold(H.264 only)")
motion_bbx_area_threshold_slider = Slider(
start=25,
end=200,
value=200,
step=1,
title="Motion BBX Area Threshold(OpenCV only)",
)
# AMF only configs
variance_threshold_slider = Slider(start=10,
end=500,
value=500,
step=1,
title="Variance Threshold(AMF only)")
amf_checkbox_group = CheckboxGroup(
labels=["History Variance(AMF only)", "Frame Reduction(AMF only)"],
active=[1])
# Config Control Panel
show_configs_checkbox_group = CheckboxGroup(
labels=["Show CV Config", "Show Event Config"])
override_cv_config_input = TextInput(value=json.dumps({}),
title="Override CV Config")
override_event_config_input = TextInput(value=json.dumps({}),
title="Override Event Config")
env_select = Select(title="Environment",
value="staging",
options=["staging", "rc", "production"])
jumbo_id_input = TextInput(value="", title="Jumbo ID")
api_token_input = TextInput(value="", title="API Token")
panel_col = column(
Div(text="<h4> Config Control Panel </h4>"),
show_configs_checkbox_group,
override_cv_config_input,
override_event_config_input,
env_select,
jumbo_id_input,
api_token_input,
Div(text="<h4> Motion Control Panel </h4>"),
motion_checkbox_group,
motion_select,
motion_interval_slider,
motion_threshold_slider,
motion_bbx_area_threshold_slider,
variance_threshold_slider,
amf_checkbox_group,
sizing_mode="scale_width",
)
main_row = row(
pipeline_col,
panel_col,
motion_col,
)
doc.title = "StreamDebugger"
doc.add_root(main_row)
def plot_benchmarks_as_lines(title,
*bm,
transform=None,
line_title_transform=None,
logx=True,
logy=True):
import bokeh
from bokeh.plotting import figure, output_file, show
from bokeh.palettes import Dark2_5 as palette
from bokeh.layouts import row, column
from bokeh.models import (Legend, LegendItem, CheckboxGroup, CustomJS, Div,
RadioGroup, Toggle, ColumnDataSource, DataTable,
TableColumn)
from bokeh.models.markers import marker_types
#
ids = entry_ids(*bm, transform=transform)
colors = itertools.cycle(palette)
markers = itertools.cycle(marker_types)
p = figure(
title=title,
x_axis_type="log" if logx else "linear",
y_axis_type="log" if logy else "linear",
#background_fill_color="#fafafa",
plot_width=1000,
x_axis_label="Number of pixels",
y_axis_label="Throughput (MB/s)",
)
p.toolbar.autohide = True
#p.toolbar.active_inspect = [hover_tool, crosshair_tool]
p.toolbar.active_drag = "auto"
p.toolbar.active_scroll = "auto"
#
def dft(v):
return v if v else (lambda n: n)
tr = dft(transform)
lttr = dft(line_title_transform)
#
for results in bm:
x = [ids[name] for name in results.names]
y = [bps / 1e6 for bps in results.bytes_per_second]
c = next(colors)
marker = next(markers)
next(markers) # advance two
line_name = lttr(results.first)
#legends.append(LegendItem(name=c, label=line_name))
p.scatter(x, y, marker=marker, size=8, color=c, legend_label=line_name)
p.line(
x,
y,
color=c,
alpha=0.9,
#muted_color=c, muted_alpha=0.05,
legend_label=line_name)
p.legend.click_policy = "hide"
p.legend.label_text_font_size = "10px"
#
def input_title(title):
return Div(text=f"<h3>{title}</h3>")
inputs = []
first = bm[0].first.meta
for k, g in first.checkbox_groups().items():
cb = CheckboxGroup(labels=[str(v) for v in g],
active=[i for i in range(len(g))],
inline=True)
inputs.append(input_title(k))
inputs.append(cb)
#
# https://github.com/bokeh/bokeh/blob/branch-2.3/examples/app/export_csv/main.py
x_axis_values = [f"{m.num_pixels}px" for m in bm[0].meta]
table_sources = []
for i, px in enumerate(x_axis_values):
c = ColumnDataSource(
data={
'name': [nth(results.filtered_names, i) for results in bm],
'bytes_per_second':
[nth(results.bytes_per_second, i) for results in bm],
'items_per_second':
[nth(results.items_per_second, i) for results in bm],
'cpu_time': [nth(results.real_time, i) for results in bm],
'real_time': [nth(results.real_time, i) for results in bm],
'iterations': [nth(results.iterations, i) for results in bm],
'threads': [nth(results.threads, i) for results in bm],
})
table_sources.append(c)
selected_x_index = 8 # FIXME (currently 2000 pixels)
table_source = copy.deepcopy(table_sources[selected_x_index])
relvalues = Toggle(label="Table: Relative values")
px_title = input_title("Table: number of pixels")
px_radiogroup = RadioGroup(labels=x_axis_values, active=selected_x_index)
table_inputs = [relvalues, px_title, px_radiogroup]
#
table_cols = [
TableColumn(field='name', title='Name'),
TableColumn(field='bytes_per_second', title='Bytes/second'),
TableColumn(field='items_per_second', title='Items/second'),
TableColumn(field='cpu_time', title='CPU time'),
TableColumn(field='real_time', title='Real time'),
TableColumn(field='iterations', title='Iterations'),
TableColumn(field='threads', title='Threads'),
]
data_table = DataTable(source=table_source, columns=table_cols, width=1200)
callback = CustomJS(args=dict(radiogroup=px_radiogroup,
source=table_source,
table=table_sources),
code="""
console.log(`active=${radiogroup.active}`);
/*source.data=table[radiogroup.active];*/
var nrows = source.data['name'].length;
var ts = table[radiogroup.active].data;
var names = ["name", "bytes_per_second", "items_per_second", "cpu_time", "real_time", "iterations", "threads"];
var ncols = names.length;
console.log(`names=${names} nrows=${nrows} ncols=${ncols}`);
for(var i = 0; i < nrows; i++) {
for(var j = 0; j < ncols; ++j) {
var name = names[j];
/*console.log(`i=${i} j=${j} name=${name}`);*/
source.data[name][i] = ts[name][i];
}
}
source.change.emit();
""")
px_radiogroup.js_on_change('active', callback)
# lambda attr, old, new: log(f"attr={attr} old={old} new={new} active={px_radiogroup.active}"))
#
layout = column(row(column(*inputs), p),
row(column(*table_inputs), data_table))
show(layout)
"""
import numpy as np
from bokeh.io import output_file, show
from bokeh.layouts import row
from bokeh.palettes import Viridis3
from bokeh.plotting import figure
from bokeh.models import CheckboxGroup, CustomJS
output_file("line_on_off.html", title="line_on_off.py example")
p = figure()
props = dict(line_width=4, line_alpha=0.7)
x = np.linspace(0, 4 * np.pi, 100)
l0 = p.line(x, np.sin(x), color=Viridis3[0], legend="Line 0", **props)
l1 = p.line(x, 4 * np.cos(x), color=Viridis3[1], legend="Line 1", **props)
l2 = p.line(x, np.tan(x), color=Viridis3[2], legend="Line 2", **props)
checkbox = CheckboxGroup(labels=["Line 0", "Line 1", "Line 2"],
active=[0, 1, 2], width=100)
checkbox.callback = CustomJS(args=dict(l0=l0, l1=l1, l2=l2, checkbox=checkbox),
lang="coffeescript", code="""
l0.visible = 0 in checkbox.active;
l1.visible = 1 in checkbox.active;
l2.visible = 2 in checkbox.active;
""")
layout = row(checkbox, p)
show(layout)
def bkapp(doc):
def createDataSet():
'''
Charge les données d'entrées dans un dataFrame geopandas
Données d'entrées :
- shapefile contenant le traçé des communes au format geojson
- fichier texte contenant les données aui nous interesse au format csv
Tâches réalisées :
- chargement des fichiers
- reprojection de wgs84 vers webmercator
- tri des données inutiles
- calcul de données à partir des données existantes
- assemblage des données dans un geodataframe
- tri des NaN/inf
Sortie :
- un geoDataFrame
'''
# Fichier contenant le tracé des communes (format geojson)
city_shapefile = "DATA/communes-20190101.json"
# Fichiers de données
taxe_hab = "DATA/taux_taxe_habitation.xlsx"
taxe_fon = "DATA/taux_taxe_fonciere.xlsx"
########## Gestion de la géométrie des communes ############
# import de la geometrie des communes
df_shape = gpd.read_file(city_shapefile)
# Suppression des colonnes "wiki" et "surface", inutiles
df_shape.drop(columns=["wikipedia", "surf_ha"], inplace=True)
# reprojection en webmercator
df_shape["geometry"] = df_shape["geometry"].to_crs("EPSG:3857")
df_shape.crs = "EPSG:3857"
########## Gestion des stats sur les communes ############
# Taxe habitation
# Import des taux d'imposition par commune dans la dataframe
dfTH = pd.read_excel(taxe_hab,
sheet_name="COM",
header=2,
usecols="A:B,E:G",
converters={
'Code commune': str,
'Code DEP': str
})
# Mise en forme des libelles des colonnes
dfTH.columns = dfTH.columns.str.replace(' ', '_')
dfTH.columns = dfTH.columns.str.replace('Taux_communal_TH*',
'TauxTH').str.replace(
'Taux_communal_voté_TH*',
'TauxTH')
# On crée le code INSEE en concatenant le code departement et commune
# Le code Insee sera la clé commune entre les dataframe de géométrie et de data.
# Création du code Insee dans une nouvelle colonne de la df
dfTH["insee"] = dfTH["Code_DEP"] + dfTH["Code_commune"]
# Suppression de la colonne code commune qui ne sert plus à rien
dfTH.drop(columns=["Code_commune"], inplace=True)
# On converti les valeurs non numériques de la colonnes TauxTH en NaN pour les filtrer
dfTH["TauxTH_2018"] = pd.to_numeric(dfTH["TauxTH_2018"],
errors='coerce')
dfTH["TauxTH_2017"] = pd.to_numeric(dfTH["TauxTH_2017"],
errors='coerce')
# Taxe foncière
dfTF = pd.read_excel(taxe_fon,
sheet_name="COM",
header=2,
usecols="A:B,D:F",
converters={
'Code commune': str,
'Code DEP': str
})
dfTF.columns = dfTF.columns.str.replace(' ', '_')
dfTF.columns = dfTF.columns.str.replace('Taux_communal_TFB*',
'TauxTF').str.replace(
'Taux_communal_voté_TFB*',
'TauxTF')
dfTF["insee"] = dfTF["Code_DEP"] + dfTF["Code_commune"]
dfTF.drop(columns=["Code_commune"], inplace=True)
dfTF.drop(columns=["Code_DEP"], inplace=True)
# On converti les valeurs non numériques de la colonnes TauxTH en NaN pour les filtrer
dfTF["TauxTF_2018"] = pd.to_numeric(dfTF["TauxTF_2018"],
errors='coerce')
dfTF["TauxTF_2017"] = pd.to_numeric(dfTF["TauxTF_2017"],
errors='coerce')
# Assemblage de la géométrie et des taux d'imposition.
dataCities = pd.merge(df_shape,
dfTH,
left_on="insee",
right_on="insee",
how='left')
dataCities = pd.merge(dataCities,
dfTF,
left_on="insee",
right_on="insee",
how='left')
return dataCities
### Fonctions de traitement ###
def select_data(df, ogCity, dist):
"""
Fonction qui permet de sélectionner les données à afficher
Sélectionnées en fonction de la distance autour de la ville :
On prend toutes les villes dont le contour est intersecté
par le contour de la ville originale augmenté de dist
Entrées :
- df : dataframe qui contient toutes les données
- ogCity : extract de la commune sélectionnée
- dist : distance à l'origine (l'unité dépend du CRS, le EPSG:3857 est en m)
Sortie :
- dataFrame ne contenant que les données retenues
"""
# La fonction renvoie les communes qui sont intersectées par le cercle de centre ogCity
# et de rayon dist*1000 (le rayon est entré en km)
return df[df.intersects(other=ogCity.geometry.buffer(dist * 1000))]
def create_choropleth(displaySet, displayParam, palette, ogCity):
"""
Fonction qui met à jour la coloration des communes affichées
Entrées :
- displaySet : dataFrame contenant les données affichées
- displayParam : paramètres que l'on souhaite aficher
- palette : liste de couleurs (identique à celle de la choroplèthe)
- ogCity : extract de la commune sélectionnée
Sorties :
- Figure contenant la carte.
"""
# On récupère les limites géographiques pour initialiser la carte
displayBounds = displaySet.total_bounds
# conversion du tracé des communes en json pour interpretation par Bokeh
geosource = GeoJSONDataSource(geojson=displaySet.to_json())
# Creation de la figure de la carte
choroPlot = figure(title='Taux ' + select_imp.value + " " +
str(slider_yr.value),
x_range=(displayBounds[0], displayBounds[2]),
y_range=(displayBounds[1], displayBounds[3]),
x_axis_type="mercator",
y_axis_type="mercator",
plot_height=500,
plot_width=850,
sizing_mode="scale_width",
toolbar_location='below',
tools="pan, wheel_zoom, box_zoom, reset",
x_axis_location=None,
y_axis_location=None)
choroPlot.xgrid.grid_line_color = None
choroPlot.ygrid.grid_line_color = None
# Ajout d'un évèmenent de type clic, pour sélectionnr la commune de référence
choroPlot.on_event(Tap, update_loc)
#outil de zoom molette activé par défaut
choroPlot.toolbar.active_scroll = choroPlot.select_one(WheelZoomTool)
# ajout du fond de carte
tile_provider = get_provider(Vendors.CARTODBPOSITRON)
choroPlot.add_tile(tile_provider)
# On détermine les vals min et max du jeu de test pour la gestion des couleurs
mini = displaySet[displayParam].min()
maxi = displaySet[displayParam].max()
# Création d'une échelle de couleur évoulant linéairement avec le paramètre à afficher
color_mapper = LinearColorMapper(palette=defaultPalette,
low=mini,
high=maxi,
nan_color='#808080')
# Ajout du tracé des communes sur la carte
citiesPatch = choroPlot.patches('xs',
'ys',
source=geosource,
line_color='gray',
line_width=0.25,
fill_alpha=0.5,
fill_color={
'field': displayParam,
'transform': color_mapper
})
# création de la legende #
color_bar = ColorBar(color_mapper=color_mapper,
label_standoff=8,
location=(0, 0),
orientation='vertical')
choroPlot.add_layout(color_bar, 'right')
# ajout d'une flèche sur la commune de reférence
start = ogCity.geometry.centroid
pin_point = Arrow(end=VeeHead(size=15),
line_color="red",
x_start=start.x,
y_start=start.y,
x_end=start.x,
y_end=start.y - 0.001)
choroPlot.add_layout(pin_point)
# Ajout d'un tooltip au survol de la carte
choroHover = HoverTool(renderers=[citiesPatch],
tooltips=[('Commune', '@nom'),
(displayParam, '@' + displayParam)])
toolTip = choroPlot.add_tools(choroHover)
return choroPlot
# Fonction de création de l'histogramme
def createHisto(displaySet, displayParam, palette, ogCity):
"""
L'histogramme permet de visualiser la répartition des taux des communes affichées
Entrées :
- displaySet : dataFrame contenant les données affichées
- displayParam : paramètres que l'on souhaite aficher
- palette : liste de couleurs (identique à celle de la choroplèthe)
- ogCity : extract de la commune sélectionnée
Sorties :
- figure contenant l'histogramme.
"""
# On crée autant de regroupement que de couleurs passées à la fct°
nBins = len(palette)
# Calcul de l'histogramme
hist, edges = np.histogram(displaySet[displayParam].dropna(),
bins=nBins)
# Nombre de lignes dans displaySet (vectorisé pour passage à datasource)
total = displaySet[displayParam].size * np.ones(nBins, np.int8)
# Normalisation de l'histogramme (affichage en % du total d'éléments)
hist_pct = 100 * hist / total[0]
# Calcul de l'étendue l'échelle verticale
hmax = max(hist_pct) * 1.1
hmin = -0.1 * hmax
# Calcul de la moyenne et médiane de l'échantillon
mean = displaySet[displayParam].mean()
med = displaySet[displayParam].quantile(0.5)
# Création de la figure contenant l'histogramme
histoPlot = figure(title='Répartition du taux de ' + select_imp.value +
" " + str(slider_yr.value),
y_range=(hmin, hmax),
plot_height=300,
plot_width=400,
sizing_mode="scale_width",
y_axis_location='right',
toolbar_location=None)
histoPlot.xgrid.grid_line_color = None
histoPlot.xaxis.axis_label = displayParam
histoPlot.xaxis.axis_line_color = None
histoPlot.ygrid.grid_line_color = "white"
histoPlot.yaxis.axis_label = '% de l\'échantillon'
histoPlot.yaxis.axis_line_color = None
# Source de données
data = dict(right=edges[1:],
left=edges[:-1],
top=hist_pct,
nb=hist,
total=total,
color=palette)
histoSource = ColumnDataSource(data=data)
# Tracé de l'histogramme
histoDraw = histoPlot.quad(bottom=0,
left="left",
right="right",
top="top",
fill_color="color",
line_color=None,
source=histoSource)
# Ajout d'un tooltip au survol de la carte
histoHover = HoverTool(renderers=[histoDraw],
mode="vline",
tooltips=[
('Taille', '@nb'),
('Fourchette', '@left - ' + '@right'),
])
histoTooltip = histoPlot.add_tools(histoHover)
# Ajout d'un repère vertical pour la commune sélectionnée
if ~np.isnan(ogCity[displayParam]):
ogCityDraw = histoPlot.quad(bottom=hmin,
top=hmax,
left=ogCity[displayParam] - 0.05,
right=ogCity[displayParam] + 0.05,
fill_color="pink",
line_color=None,
legend_label=ogCity["nom"] + ' (' +
ogCity["Code_DEP"] + ')')
# Ajout d'un tooltip au survol de la commune d'orginie
displayName = ogCity["nom"] + " (" + ogCity["Code_DEP"] + ")"
ogCityHover = HoverTool(renderers=[ogCityDraw],
mode="vline",
tooltips=[
('Commune sélectionnée', displayName),
(displayParam,
str(ogCity[displayParam])),
])
histoTooltip = histoPlot.add_tools(ogCityHover)
# Ajout d'un repère vertical pour la moyenne de l'échantillon
ogCityDraw = histoPlot.quad(bottom=hmin,
top=hmax,
left=mean - 0.05,
right=mean + 0.05,
fill_color="blue",
line_color=None,
legend_label="Moyenne ")
# Ajout d'un repère vertical pour la mediane de l'échantillon
ogCityDraw = histoPlot.quad(bottom=hmin,
top=hmax,
left=med - 0.05,
right=med + 0.05,
fill_color="purple",
line_color=None,
legend_label="Mediane ")
# On rend la légende interactive
histoPlot.legend.click_policy = "hide"
# On oriente horizontalement la légende
histoPlot.legend.orientation = "vertical"
# Réduction de la police
histoPlot.legend.label_text_font_size = "8px"
# On place la légende hors de la zone de tracé
histoLegend = histoPlot.legend[0]
histoPlot.legend[0] = None
histoPlot.add_layout(histoLegend, 'right')
return histoPlot
def create_info(displaySet, displayParam, ogCity):
"""
Affiche un panneau textuel contenant des infomations sur le jeu de données
affiché et la commune sélectionnée.
Entrées :
- displaySet : dataFrame contenant les données affichées
- displayParam : paramètres que l'on souhaite aficher
- ogCity : extract de la commune sélectionnée
Sorties :
- figure contenant le texte à afficher.
"""
stats = displaySet[displayParam].dropna().describe(
percentiles=[0.5]).round(decimals=2)
stats = stats[stats.index !=
'count'] #On supprime la variable "count" deja affichée
# Modification de l'intitulé des colonnes
stats.columns = ["Taux " + str(elt) for elt in data_yr]
# Creation du texte
infoText = [
f"<b>Communes affichées</b> : {len(displaySet)}",
f"<b>Commune sélectionnée</b> : {ogCity['nom']} ({ogCity['Code_DEP']})",
"</br><b>Statistiques</b> : " + select_imp.value
]
return [Div(text="</br>".join(infoText)), PreText(text=str(stats))]
def update_layout(displaySet, displayParam, ogCity, palette):
"""
Fonction permettant de mettre à jour toutes les figures du layout
Entrées :
- displaySet : dataFrame contenant les données affichées
- displayParam : paramètres que l'on souhaite aficher
- ogCity : extract de la commune sélectionnée
- palette : liste de couleurs
Sorties :
- rien
"""
# Mise à jour de la chroplèthe
appLayout.children[0].children[0] = create_choropleth(
displaySet, displayParam, palette, ogCity)
# Mise à jour de l'histogramme
appLayout.children[1].children[0] = createHisto(
displaySet, displayParam, palette, ogCity)
# Mise à jour des infos
appLayout.children[1].children[1:] = create_info(
displaySet, infoParam, ogCity)
def create_displayParam(impot='TauxTH_', year=2018):
"""
Fonction qui retourne le paramètre à afficher dans la dataframe, à partir de l'impôt
et de l'année désirée.
Entrées :
- impot : l'impot que l'ont souhaite afficher (str)
- année (int) : l'année que l'on souhait afficher (int)
Sortie :
- displayParam : le paramètre d'affichge (str)
"""
return impot + str(year)
### Fonction callback ###
# Callback fonction (met à jour le graphique)
def update_yr(attr, old, new):
"""
Fonction callback appelée au changement du slider des années.
Permet de modifier l'année du taux affiché.
"""
# Création du paramètre à afficher en fonction de l'année sélectionnée :
displayParam = create_displayParam(impot, slider_yr.value)
# Mise à jour du layout
update_layout(displaySet, displayParam, ogCity, defaultPalette)
def update_dst(attr, old, new):
"""
Fonction callback appelée au changement de la distance d'affichage
Modifie le jeu de données afiché et recalcule les couleurs de nouveau jeu
"""
# Utilisation de variables globales (nécessaire car utilisée par plusieurs callback)
global displaySet
# Création du paramètre à afficher en fonction de l'année sélectionnée :
displayParam = create_displayParam(impot, slider_yr.value)
# Mise à jour du jeu d'affichage
displaySet = select_data(dataCities, ogCity, slider_dst.value)
# Mise à jour du layout
update_layout(displaySet, displayParam, ogCity, defaultPalette)
def update_loc(event):
"""
Fonction callback activé au clic sur la map
Permet de changer la commune sélectionnée
Maj la carte avec la nouvelle commune de référence
"""
# Utilisation de variables globales (nécessaire car utilisée par plusieurs callback)
global ogCity
global displaySet
### Identification de la commune sous le point cliqué ###
# Création d'un objet shapely de type Point aux coords du clic :
clicPoint = gpd.points_from_xy([event.x], [event.y])
# Creation d'une geoserie contenant le point :
# Rem : utilisation de iloc[0] pour ne pas avoir d'index
# cf issue https://github.com/geopandas/geopandas/issues/317
pointSerie = gpd.GeoSeries(crs='epsg:3857', data=clicPoint).iloc[0]
# On recherche la commune qui contient le point :
clicCity = dataCities[dataCities.contains(other=pointSerie)]
# On vérifie avant maj que le clic a bien retourné une géométrie
if not clicCity.empty:
ogCity = clicCity.iloc[0]
### Mise à jour de la carte avec la commune cliquée pour référence ###
# Calcul du nouveau jeu de données à afficher
displaySet = select_data(dataCities, ogCity, slider_dst.value)
# Création du paramètre à afficher en fonction de l'année sélectionnée :
displayParam = create_displayParam(impot, slider_yr.value)
# Mise à jour du layout
update_layout(displaySet, displayParam, ogCity, defaultPalette)
def update_colormap(attr, old, new):
"""
Change la palette de couleurs utilisée à l'action sur le toggle idoine
"""
global defaultPalette
# Création du paramètre à afficher en fonction de l'année sélectionnée :
displayParam = create_displayParam(impot, slider_yr.value)
if len(new) > 0:
print('Mode Daltonien')
defaultPalette = Colorblind[7]
else:
print('Mode Normal')
defaultPalette = brewer['RdYlGn'][7]
# Mise à jour du layout
update_layout(displaySet, displayParam, ogCity, defaultPalette)
def update_impot(attr, old, new):
global impot
dict_imp = {"Taxe d'habitation": "TauxTH_", "Taxe foncière": "TauxTF_"}
impot = dict_imp[new]
# Création du paramètre à afficher en fonction de l'année sélectionnée :
displayParam = create_displayParam(impot, slider_yr.value)
# Mise à jour du layout
update_layout(displaySet, displayParam, ogCity, defaultPalette)
#%%
# Chargement du jeu de test
try:
dataCities = gpd.read_file("DATA/dataCities.json")
except:
print("fichier dataCities.json non trouvé, génération en cours")
dataCities = createDataSet()
# Sauvegarde du dataSet
dataCities.to_file("DATA/dataCities.json", driver='GeoJSON')
# %%
### Main Code ####
### Constrution de la carte et légende ####
global displaySet
global impot
global defaultPalette
global ogCity
# Paramètres par défaut
ogCity = dataCities[dataCities["nom"] == 'Paris'].iloc[
0] # Paris sélectionnée par défaut
# paramètre affiché par défaut = taxe d'habitation la plus récente
defaultParam = impot + str(data_yr[-1])
infoParam = [impot + str(elt) for elt in data_yr]
# Création du set de donnée à afficher
displaySet = select_data(dataCities, ogCity, dist)
### Construction du front-end ###
# Ajout d'un slider pour choisir l'année
slider_yr = Slider(title='Année',
start=data_yr[0],
end=data_yr[-1],
step=1,
value=data_yr[-1],
default_size=250)
slider_yr.on_change('value', update_yr)
# Ajout d'un slider pour choisir la distance d'affichage
slider_dst = Slider(title='Distance d\'affichage (km)',
start=0,
end=100,
step=5,
value=dist,
default_size=250)
slider_dst.on_change('value', update_dst)
# Ajout d'un sélecteur pour choisir l'impot à afficher
select_imp = Select(title="Impôt:",
value="Taxe d'habitation",
options=["Taxe d'habitation", "Taxe foncière"])
select_imp.on_change('value', update_impot)
# Ajout d'un mode daltonien
checkbox_dalto = CheckboxGroup(labels=["Mode Daltonien"])
checkbox_dalto.on_change('active', update_colormap)
# Creation de la choropleth
choroPlot = create_choropleth(displaySet, defaultParam, defaultPalette,
ogCity)
# Creation de l'historamme
histoPlot = createHisto(displaySet, defaultParam, defaultPalette, ogCity)
# Creation des figures infos
infoTitle, infoDisplaySet = create_info(displaySet, infoParam, ogCity)
# Organisation colones/lignes
Col1 = column(slider_yr, slider_dst)
Col2 = column(select_imp, checkbox_dalto)
row_wgt = row(Col1, Col2)
Col3 = column(choroPlot, row_wgt)
Col4 = column(histoPlot, infoTitle, infoDisplaySet)
appLayout = row(Col3, Col4)
doc.add_root(appLayout)
doc.title = "VizImpôts"
doc.theme = Theme(filename="theme.yaml")
""" Example demonstrating turning lines on and off - with bokeh server
"""
import numpy as np
from bokeh.io import curdoc
from bokeh.layouts import row
from bokeh.palettes import Viridis3
from bokeh.plotting import figure
from bokeh.models import CheckboxGroup
p = figure()
props = dict(line_width=4, line_alpha=0.7)
x = np.linspace(0, 4 * np.pi, 100)
l0 = p.line(x, np.sin(x), color=Viridis3[0], legend="Line 0", **props)
l1 = p.line(x, 4 * np.cos(x), color=Viridis3[1], legend="Line 1", **props)
l2 = p.line(x, np.tan(x), color=Viridis3[2], legend="Line 2", **props)
checkbox = CheckboxGroup(labels=["Line 0", "Line 1", "Line 2"], active=[0, 1, 2], width=100)
def update(attr, old, new):
l0.visible = 0 in checkbox.active
l1.visible = 1 in checkbox.active
l2.visible = 2 in checkbox.active
checkbox.on_change('active', update)
layout = row(checkbox, p)
curdoc().add_root(layout)
def plot(tables, output_filename, xmin, xmax, ymin, ymax, superterm):
'''
This is the plot function that uses Bokeh functions and widgets to make an interactive hexagon plot.
This function recieves:
- tables: dictionary with tables used to create arrays of repeated x, y coordinates (depending on the counts) for the hexagon plot.
- output_filename: filename of .html output in the plots folder
The coordinate arrays are used to create a pandas dataframe with Bokeh functions. This dataframe contains the q, r coordinates and counts used to plot the
hexagons. To this dataframe, extra information is added (e.g. most common chemicals), which is displayed in the hover tooltip.
Gaussian blur is added to copies of this dataframe and given as input to the Bokeh slider widget.
Other widgets are added as well, for saturation, normalization etc. Bokeh allows to customize these widges with javascript code.
The hexagon plot is saved as a .html file and also shown in the browser.
'''
file_name = 'plots/' + str(output_filename) + '.html'
output_file(file_name)
# Blur and saturation values
BLUR_MAX = 4
BLUR_STEP_SIZE = 0.25
SATURATION_MAX = 5
SATURATION_STEP_SIZE = 0.25
# Hexagon plot properties
SIZE_HEXAGONS = 10
orientation = 'flattop' #bokeh alows 2 different hexagon orientations which also influences hexagon size calculations, but we currently have only calculated blur distances for this orientation
ratio = ((ymax - ymin) / (xmax - xmin))
size = SIZE_HEXAGONS / ratio
hexagon_height = sqrt(3) * size
hexagon_height = hexagon_height * ratio
# make figure
p = figure(x_range=[xmin, xmax],
y_range=[ymin - (hexagon_height / 2), ymax],
tools="wheel_zoom,reset,save",
background_fill_color='#440154')
p.grid.visible = False
p.xaxis.axis_label = "log(P)"
p.yaxis.axis_label = "mass in Da"
p.xaxis.axis_label_text_font_style = 'normal'
p.yaxis.axis_label_text_font_style = 'normal'
# term_to_source, term_to_metadata, options = make_plot_sources(tables, size, ratio, orientation, BLUR_MAX, BLUR_STEP_SIZE)
# source for widgets
term_to_source = dict()
term_to_class = dict()
term_to_metadata = dict()
options = []
for term in tables.keys():
options.append((term, term))
table = tables[term]['table']
if superterm:
source = create_class_source(table, term, size, ratio, orientation,
superterm)
term_to_class[term] = {}
term_to_class[term]['show_class'] = True
term_to_class[term]['source'] = source
else:
term_to_class[term] = {'show_class': False}
source, title = create_data_source(table, term, size, ratio,
orientation, BLUR_MAX,
BLUR_STEP_SIZE)
metadata = return_html(tables[term]['metadata'])
term_to_source[term] = {'source': source, 'title': title}
term_to_metadata[term] = metadata
# hex = p.hex_tile(q='q', r="r", size=size, line_color=None, source=source, aspect_scale=ratio,orientation=orientation,
# fill_color='pink' )
# show(p)
# make default souce for plot, this is the first source shown in the plot, and also works like a container. Old data is thrown out and new data is thrown in.
default_term = list(tables.keys())[0] # pick the first one
metadata = tables[default_term]['metadata']
metadata = return_html(metadata)
table = tables[default_term]['table']
source, title = create_data_source(table, default_term, size, ratio,
orientation, BLUR_MAX, BLUR_STEP_SIZE)
p.title.text = title
# color mapper
mapper = linear_cmap('scaling', 'Viridis256', 0,
max(source.data['scaling']))
# plot
hex = p.hex_tile(q="q",
r="r",
size=size,
line_color=None,
source=source,
aspect_scale=ratio,
orientation=orientation,
fill_color=mapper)
if superterm:
source_class = term_to_class[default_term]['source']
class_hex = p.hex_tile(q='q',
r="r",
size=size,
line_color=None,
source=source_class,
aspect_scale=ratio,
orientation=orientation,
fill_color='pink',
fill_alpha=0.7)
class_hex.visible = False
# HOVER
TOOLTIPS = return_JS_code('tooltips')
TOOLTIPS_tfidf = return_JS_code('tooltips_tfidf')
code_callback_hover = return_JS_code('hover')
callback_hover = CustomJS(code=code_callback_hover)
hover = HoverTool(tooltips=TOOLTIPS,
callback=callback_hover,
show_arrow=False)
p.add_tools(hover)
# WIDGETS
slider1 = Slider(start=1,
end=SATURATION_MAX,
value=1,
step=SATURATION_STEP_SIZE,
title="Saturation",
width=100)
slider2 = Slider(start=0,
end=BLUR_MAX,
value=0,
step=BLUR_STEP_SIZE,
title="Blur",
width=100)
checkbox = CheckboxGroup(labels=["TFIDF"], active=[])
radio_button_group = RadioGroup(labels=["Viridis256", "Greys256"],
active=0)
button = Button(label="Metadata", button_type="default", width=100)
multi_select = MultiSelect(title=output_filename,
value=[default_term],
options=options,
width=100,
height=300)
if superterm:
label = "Show " + str(superterm)
checkbox_class = CheckboxGroup(labels=[label], active=[])
# WIDGETS CODE FOR CALLBACK
code_callback_slider1 = return_JS_code('slider1')
code_callback_slider2 = return_JS_code('slider2')
code_callback_checkbox = return_JS_code('checkbox')
code_callback_rbg = return_JS_code('rbg')
code_callback_button = return_JS_code('button')
code_callback_ms = return_JS_code('multi_select')
if superterm:
code_callback_class = return_JS_code('class')
# WIDGETS CALLBACK
callback_slider1 = CustomJS(args={
'source': source,
'mapper': mapper,
'slider2': slider2,
'checkbox': checkbox
},
code=code_callback_slider1)
callback_slider2 = CustomJS(args={
'source': source,
'mapper': mapper,
'slider1': slider1,
'checkbox': checkbox
},
code=code_callback_slider2)
callback_checkbox = CustomJS(args={
'source': source,
'slider1': slider1,
'slider2': slider2,
'mapper': mapper,
'hover': hover,
'tooltips': TOOLTIPS,
'tooltips_tfidf': TOOLTIPS_tfidf
},
code=code_callback_checkbox)
callback_radio_button_group = CustomJS(args={
'p': p,
'multi_select': multi_select,
'mapper': mapper,
'term_to_class': term_to_class,
'Viridis256': Viridis256,
'Greys256': Greys256
},
code=code_callback_rbg)
callback_button = CustomJS(args={
'term_to_metadata': term_to_metadata,
'multi_select': multi_select
},
code=code_callback_button)
callback_ms = CustomJS(args={
'source': source,
'term_to_source': term_to_source,
'term_to_class': term_to_class,
'checkbox': checkbox,
'slider2': slider2,
'slider1': slider1,
'p': p,
'mapper': mapper
},
code=code_callback_ms)
if superterm:
callback_radio_button_group = CustomJS(args={
'p': p,
'multi_select': multi_select,
'class_hex': class_hex,
'term_to_class': term_to_class,
'mapper': mapper,
'Viridis256': Viridis256,
'Greys256': Greys256
},
code=code_callback_rbg)
callback_class = CustomJS(args={
'multi_select': multi_select,
'term_to_class': term_to_class,
'class_hex': class_hex
},
code=code_callback_class)
callback_ms = CustomJS(args={
'source': source,
'term_to_source': term_to_source,
'checkbox': checkbox,
'slider2': slider2,
'slider1': slider1,
'p': p,
'mapper': mapper,
'checkbox_class': checkbox_class,
'class_hex': class_hex,
'term_to_class': term_to_class
},
code=code_callback_ms)
# WIDGETS INTERACTION
slider1.js_on_change('value', callback_slider1)
slider2.js_on_change('value', callback_slider2)
checkbox.js_on_change('active', callback_checkbox)
radio_button_group.js_on_change('active', callback_radio_button_group)
button.js_on_event(events.ButtonClick, callback_button)
multi_select.js_on_change("value", callback_ms)
if superterm:
checkbox_class.js_on_change('active', callback_class)
# LAYOUT
if superterm:
layout = row(
multi_select, p,
column(slider1, slider2, checkbox, checkbox_class,
radio_button_group, button))
else:
layout = row(
multi_select, p,
column(slider1, slider2, checkbox, radio_button_group, button))
show(layout)
#sdate_range_slider = DateRangeSlider(title="Date Range: ", start=datetime.datetime(2017, 1, 1,1), end=datetime.datetime(2017, 2, 7,2), value=(datetime.datetime(2017, 9, 7,1), datetime.datetime(2017, 9, 7,2)),step=1)
#sdate_range_slider.on_change("value", my_slider_handler)
#sdate_range_slider = DateSlider(title="Date Range: ", start=datetime.datetime(2017, 1, 1,1), end=datetime.datetime(2019, 9, 7,2), value=(datetime.datetime(2017, 9, 7,1), datetime.datetime(2017, 9, 7,2)), step=1)
date_range_radio = RadioButtonGroup(
name='Date Range Filter',
labels=["Date Range Filter On", "Date Range Filter Off"],
active=0)
idle_range_radio = RadioButtonGroup(
name='Idle Range Filter',
labels=["Idle Range Filte On", "Idle Range Filte Off"],
active=1)
checkbox_group = CheckboxGroup(
labels=["Cumulative Date Filter", "Fine Date Filter"], active=[0])
hour_range_slider = RangeSlider(start=0,
end=360,
value=(0, 150),
step=1,
title="Idle time for a vehicle (hours)")
alpha_range_slider = Slider(start=0,
end=1,
value=0.4,
step=.1,
title="Spot Transparency")
size_range_slider = Slider(start=4, end=50, value=4, step=1, title="Spot Size")
def create_layout(self):
# create figure
self.x_range = Range1d(start=self.model.map_extent[0],
end=self.model.map_extent[2], bounds=None)
self.y_range = Range1d(start=self.model.map_extent[1],
end=self.model.map_extent[3], bounds=None)
self.fig = Figure(tools='wheel_zoom,pan', x_range=self.x_range,
y_range=self.y_range)
self.fig.plot_height = 560
self.fig.plot_width = 800
self.fig.axis.visible = False
# add tiled basemap
self.tile_source = WMTSTileSource(url=self.model.basemap)
self.tile_renderer = TileRenderer(tile_source=self.tile_source)
self.fig.renderers.append(self.tile_renderer)
# add datashader layer
self.image_source = ImageSource(url=self.model.service_url,
extra_url_vars=self.model.shader_url_vars)
self.image_renderer = DynamicImageRenderer(image_source=self.image_source)
self.fig.renderers.append(self.image_renderer)
# add label layer
self.label_source = WMTSTileSource(url=self.model.labels_url)
self.label_renderer = TileRenderer(tile_source=self.label_source)
self.fig.renderers.append(self.label_renderer)
# add ui components
axes_select = Select.create(name='Axes',
options=self.model.axes)
axes_select.on_change('value', self.on_axes_change)
field_select = Select.create(name='Field', options=self.model.fields)
field_select.on_change('value', self.on_field_change)
aggregate_select = Select.create(name='Aggregate',
options=self.model.aggregate_functions)
aggregate_select.on_change('value', self.on_aggregate_change)
transfer_select = Select.create(name='Transfer Function',
options=self.model.transfer_functions)
transfer_select.on_change('value', self.on_transfer_function_change)
basemap_select = Select.create(name='Basemap', value='Toner',
options=self.model.basemaps)
basemap_select.on_change('value', self.on_basemap_change)
image_opacity_slider = Slider(title="Opacity", value=100, start=0,
end=100, step=1)
image_opacity_slider.on_change('value', self.on_image_opacity_slider_change)
basemap_opacity_slider = Slider(title="Basemap Opacity", value=100, start=0,
end=100, step=1)
basemap_opacity_slider.on_change('value', self.on_basemap_opacity_slider_change)
show_labels_chk = CheckboxGroup(labels=["Show Labels"], active=[0])
show_labels_chk.on_click(self.on_labels_change)
controls = [axes_select, field_select, aggregate_select,
transfer_select]
map_controls = [basemap_select, basemap_opacity_slider,
image_opacity_slider, show_labels_chk]
self.controls = VBox(width=200, height=600, children=controls)
self.map_controls = HBox(width=self.fig.plot_width, children=map_controls)
self.map_area = VBox(width=self.fig.plot_width, children=[self.map_controls, self.fig])
self.layout = HBox(width=1024, children=[self.controls, self.map_area])