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"))))
select = Select(options=["Option 1", "Option 2", "Option 3"], css_classes=["foo"])
def cb(attr, old, new):
source.data['val'] = [old, new]
select.on_change('value', cb)
doc.add_root(column(select, plot))
def __init__(self, worker, **kwargs):
with log_errors():
self.worker = worker
names = ['nbytes', 'duration', 'bandwidth', 'count', 'type',
'inout-color', 'type-color', 'key', 'key-color', 'start',
'stop']
quantities = ['nbytes', 'duration', 'bandwidth', 'count',
'start', 'stop']
colors = ['inout-color', 'type-color', 'key-color']
# self.source = ColumnDataSource({name: [] for name in names})
self.source = ColumnDataSource({
'nbytes': [1, 2],
'duration': [0.01, 0.02],
'bandwidth': [0.01, 0.02],
'count': [1, 2],
'type': ['int', 'str'],
'inout-color': ['blue', 'red'],
'type-color': ['blue', 'red'],
'key': ['add', 'inc'],
'start': [1, 2],
'stop': [1, 2]
})
self.x = Select(title='X-Axis', value='nbytes', options=quantities)
self.x.on_change('value', self.update_figure)
self.y = Select(title='Y-Axis', value='bandwidth', options=quantities)
self.y.on_change('value', self.update_figure)
self.size = Select(title='Size', value='None',
options=['None'] + quantities)
self.size.on_change('value', self.update_figure)
self.color = Select(title='Color', value='inout-color',
options=['black'] + colors)
self.color.on_change('value', self.update_figure)
if 'sizing_mode' in kwargs:
kw = {'sizing_mode': kwargs['sizing_mode']}
else:
kw = {}
self.control = widgetbox([self.x, self.y, self.size, self.color],
width=200, **kw)
self.last_outgoing = 0
self.last_incoming = 0
self.kwargs = kwargs
self.layout = row(self.control, self.create_figure(**self.kwargs),
**kw)
self.root = self.layout
def plotDayOfWeekTimeline(fileName, initData, bokehPlaceholderId='bokehContent'):
source = ColumnDataSource(data=initData)
selectDOW = Select(title="Days:", value="Monday", options=["Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"])
selectUT = Select(title="User Type:", value="All", options=["All", "Subscriber", "Customer"])
model = dict(source=source, select_dow = selectDOW, select_ut = selectUT)
plot = Figure(plot_width=1200, plot_height=400, x_axis_type="datetime")
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
callback = CustomJS(args=model, code="""
var dayOfWeek = select_dow.get('value')
var userType = select_ut.get('value')
var xmlhttp;
xmlhttp = new XMLHttpRequest();
xmlhttp.onreadystatechange = function() {
if (xmlhttp.readyState == XMLHttpRequest.DONE ) {
if(xmlhttp.status == 200){
var data = source.get('data');
var result = JSON.parse(xmlhttp.responseText);
var temp=[];
for(var date in result.x) {
temp.push(new Date(result.x[date]));
}
data['x'] = temp;
data['y'] = result.y;
source.trigger('change');
}
else if(xmlhttp.status == 400) {
alert(400);
}
else {
alert(xmlhttp.status);
}
}
};
var params = {dow:dayOfWeek, ut:userType};
url = "/select?" + jQuery.param( params );
xmlhttp.open("GET", url, true);
xmlhttp.send();
""")
selectDOW.callback = callback
selectUT.callback = callback
layout = vform(selectDOW, selectUT, plot)
script, div = components(layout)
html = readHtmlFile(fileName)
html = insertScriptIntoHeader(html, script)
html = appendElementContent(html, div, "div", "bokehContent")
return html
def test_js_on_change_executes(self, bokeh_model_page):
select = Select(options=["Option 1", "Option 2", "Option 3"], css_classes=["foo"])
select.js_on_change('value', CustomJS(code=RECORD("value", "cb_obj.value")))
page = bokeh_model_page(select)
el = page.driver.find_element_by_css_selector('.foo select')
el.click()
el = page.driver.find_element_by_css_selector('.foo select option[value="Option 3"]')
el.click()
results = page.results
assert results['value'] == 'Option 3'
assert page.has_no_console_errors()
def 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='box_zoom,wheel_zoom,pan', x_range=self.x_range,
y_range=self.y_range)
self.fig.plot_height = 600
self.fig.plot_width = 1024
self.fig.axis.visible = True
# 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 ui components
axes_select = Select.create(name='Plot:',
options=self.model.axes)
axes_select.on_change('value', self.on_axes_change)
field_select = Select.create(name='Summary:', options=self.model.fields)
field_select.on_change('value', self.on_field_change)
aggregate_select = Select.create(name='Aggregation:',
options=self.model.aggregate_functions)
aggregate_select.on_change('value', self.on_aggregate_change)
transfer_select = Select.create(name='Scale:',
options=self.model.transfer_functions)
transfer_select.on_change('value', self.on_transfer_function_change)
controls = [axes_select, field_select, aggregate_select,
transfer_select]
self.controls = VBox(width=200, height=600, children=controls)
self.map_area = VBox(width=self.fig.plot_width, children=[self.fig])
self.layout = HBox(width=self.fig.plot_width, children=[self.controls, self.map_area])
def bball():
os.chdir('C:\\Users\\weinfz18\\Documents\\NBA_current') ## switch to correct directory
imgo = np.loadtxt('imgo.csv', delimiter=',')
imgd = np.loadtxt('imgd.csv', delimiter=',')
## court image
players = pd.read_csv('C:\\Users\\weinfz18\\Documents\\NBA_current\\all_players.csv', names=['players'],delimiter='$')
oplayer = "Whiteside, Hassan"
dplayer = "Whiteside, Hassan"
dplayername = ''.join([x for x in dplayer.lower() if x in 'abcdefghijklmnopqrstuvwxyz'])
oplayername = ''.join([x for x in oplayer.lower() if x in 'abcdefghijklmnopqrstuvwxyz'])
odata = pd.read_csv('C:\\Users\\weinfz18\\Documents\\NBA_current\\player_shots\\{}\\odata.csv'.format(oplayername))
ddata = pd.read_csv('C:\\Users\\weinfz18\\Documents\\NBA_current\\player_shots\\{}\\ddata.csv'.format(dplayername))
odata = odata[odata['dense']!=0]
ddata = ddata[ddata['dense']!=0]
ddata = ddata.reset_index(drop=True)
odata = odata.reset_index(drop=True)
odata = odata.to_dict("list")
ddata = ddata.to_dict("list")
oplayer_select = Select(value="Rubio, Ricky", title='Offensive player', options=sorted(players['players'].tolist()))
dplayer_select = Select(value="Rubio, Ricky", title='Defensive player', options=sorted(players['players'].tolist()))
oplayer_select.on_change('value', update_plot)
dplayer_select.on_change('value', update_plot)
op, odata_table, dp, ddata_table = Make_Plot(ddata,odata,imgo,imgd,oplayer,dplayer)
oscript, odiv = components(op)
otscript, otdiv = components(odata_table)
dscript, ddiv = components(dp)
dtscript, dtdiv = components(ddata_table)
script = [oscript, otscript, dscript, dtscript]
div = [odiv, otdiv, ddiv, dtdiv]
return render_template('index.html', script=script, div=div)
def create_layout():
year_select = Select(title="Year:", value="2010", options=years)
location_select = Select(title="Location:", value="World", options=locations)
year_select.on_change('value', on_year_change)
location_select.on_change('value', on_location_change)
controls = row(children=[year_select, location_select])
layout = column(children=[controls, pyramid(), population()])
return layout
def create_layout(self):
years = list(map(str, sorted(self.df.Year.unique())))
locations = sorted(self.df.Location.unique())
year_select = Select(title="Year:", value="2010", options=years)
location_select = Select(title="Location:", value="World", options=locations)
year_select.on_change('value', self.on_year_change)
location_select.on_change('value', self.on_location_change)
controls = HBox(year_select, location_select)
self.layout = VBox(controls, self.plot)
def create_layout():
size_select = Select(
value="Days Active", title="Marker Scaling:", options=["Days Active", "Days Remaining", "Power Output"]
)
size_select.on_change("value", on_size_change)
location_select = Select(title="Power Plant Name:", value=location, options=locations)
location_select.on_change("value", on_location_change)
controls = HBox(children=[size_select, location_select])
layout = VBox(children=[controls, map(), performance()])
return layout
def create_channel_widget(self):
self.w_channel = Select(title="Channel:", value="", options=[])
self.w_channel.on_change('value', self.on_channel_widget_change)
def create_telid_widget(self):
self.w_telid = Select(title="Telescope:", value="", options=[])
self.w_telid.on_change('value', self.on_telid_widget_change)
class BokehFileViewer(Tool):
name = "BokehFileViewer"
description = ("Interactively explore an event file using the bokeh "
"visualisation package")
port = Int(5006, help="Port to open bokeh server onto").tag(config=True)
disable_server = Bool(False, help="Do not start the bokeh server "
"(useful for testing)").tag(config=True)
aliases = Dict(dict(
port='BokehFileViewer.port',
disable_server='BokehFileViewer.disable_server',
r='EventSourceFactory.product',
f='EventSourceFactory.input_url',
max_events='EventSourceFactory.max_events',
ped='CameraR1CalibratorFactory.pedestal_path',
tf='CameraR1CalibratorFactory.tf_path',
pe='CameraR1CalibratorFactory.pe_path',
ff='CameraR1CalibratorFactory.ff_path',
extractor='ChargeExtractorFactory.product',
extractor_t0='ChargeExtractorFactory.t0',
extractor_window_width='ChargeExtractorFactory.window_width',
extractor_window_shift='ChargeExtractorFactory.window_shift',
extractor_sig_amp_cut_HG='ChargeExtractorFactory.sig_amp_cut_HG',
extractor_sig_amp_cut_LG='ChargeExtractorFactory.sig_amp_cut_LG',
extractor_lwt='ChargeExtractorFactory.lwt',
cleaner='WaveformCleanerFactory.product',
))
classes = List([
EventSourceFactory,
ChargeExtractorFactory,
CameraR1CalibratorFactory,
CameraDL1Calibrator,
WaveformCleanerFactory
])
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._event = None
self._event_index = None
self._event_id = None
self._telid = None
self._channel = None
self.w_next_event = None
self.w_previous_event = None
self.w_event_index = None
self.w_event_id = None
self.w_goto_event_index = None
self.w_goto_event_id = None
self.w_telid = None
self.w_channel = None
self.w_dl1_dict = None
self.wb_extractor = None
self.layout = None
self.reader = None
self.seeker = None
self.extractor = None
self.cleaner = None
self.r1 = None
self.dl0 = None
self.dl1 = None
self.viewer = None
self._updating_dl1 = False
def setup(self):
self.log_format = "%(levelname)s: %(message)s [%(name)s.%(funcName)s]"
kwargs = dict(config=self.config, tool=self)
default_url = get_dataset_path("gamma_test.simtel.gz")
EventSourceFactory.input_url.default_value = default_url
self.reader = EventSourceFactory.produce(**kwargs)
self.seeker = EventSeeker(self.reader, **kwargs)
self.extractor = ChargeExtractorFactory.produce(**kwargs)
self.cleaner = WaveformCleanerFactory.produce(**kwargs)
self.r1 = CameraR1CalibratorFactory.produce(
eventsource=self.reader,
**kwargs
)
self.dl0 = CameraDL0Reducer(**kwargs)
self.dl1 = CameraDL1Calibrator(
extractor=self.extractor,
cleaner=self.cleaner,
**kwargs
)
self.viewer = BokehEventViewer(**kwargs)
# Setup widgets
self.viewer.create()
self.viewer.enable_automatic_index_increment()
self.create_previous_event_widget()
self.create_next_event_widget()
self.create_event_index_widget()
self.create_goto_event_index_widget()
self.create_event_id_widget()
self.create_goto_event_id_widget()
self.create_telid_widget()
self.create_channel_widget()
self.create_dl1_widgets()
self.update_dl1_widget_values()
# Setup layout
self.layout = layout([
[self.viewer.layout],
[
self.w_previous_event,
self.w_next_event,
self.w_goto_event_index,
self.w_goto_event_id
],
[self.w_event_index, self.w_event_id],
[self.w_telid, self.w_channel],
[self.wb_extractor]
])
def start(self):
self.event_index = 0
def finish(self):
if not self.disable_server:
def modify_doc(doc):
doc.add_root(self.layout)
doc.title = self.name
directory = os.path.abspath(os.path.dirname(__file__))
theme_path = os.path.join(directory, "theme.yaml")
template_path = os.path.join(directory, "templates")
doc.theme = Theme(filename=theme_path)
env = jinja2.Environment(
loader=jinja2.FileSystemLoader(template_path)
)
doc.template = env.get_template('index.html')
self.log.info('Opening Bokeh application on '
'http://localhost:{}/'.format(self.port))
server = Server({'/': modify_doc}, num_procs=1, port=self.port)
server.start()
server.io_loop.add_callback(server.show, "/")
server.io_loop.start()
@property
def event_index(self):
return self._event_index
@event_index.setter
def event_index(self, val):
try:
self.event = self.seeker[val]
except IndexError:
self.log.warning("Event Index {} does not exist".format(val))
@property
def event_id(self):
return self._event_id
@event_id.setter
def event_id(self, val):
try:
self.event = self.seeker[str(val)]
except IndexError:
self.log.warning("Event ID {} does not exist".format(val))
@property
def telid(self):
return self._telid
@telid.setter
def telid(self, val):
self.channel = 0
tels = list(self.event.r0.tels_with_data)
if val not in tels:
val = tels[0]
self._telid = val
self.viewer.telid = val
self.update_telid_widget()
@property
def channel(self):
return self._channel
@channel.setter
def channel(self, val):
self._channel = val
self.viewer.channel = val
self.update_channel_widget()
@property
def event(self):
return self._event
@event.setter
def event(self, val):
# Calibrate
self.r1.calibrate(val)
self.dl0.reduce(val)
self.dl1.calibrate(val)
self._event = val
self.viewer.event = val
self._event_index = val.count
self._event_id = val.r0.event_id
self.update_event_index_widget()
self.update_event_id_widget()
self._telid = self.viewer.telid
self.update_telid_widget()
self._channel = self.viewer.channel
self.update_channel_widget()
def update_dl1_calibrator(self, extractor=None, cleaner=None):
"""
Recreate the dl1 calibrator with the specified extractor and cleaner
Parameters
----------
extractor : ctapipe.image.charge_extractors.ChargeExtractor
cleaner : ctapipe.image.waveform_cleaning.WaveformCleaner
"""
if extractor is None:
extractor = self.dl1.extractor
if cleaner is None:
cleaner = self.dl1.cleaner
self.extractor = extractor
self.cleaner = cleaner
kwargs = dict(config=self.config, tool=self)
self.dl1 = CameraDL1Calibrator(
extractor=self.extractor,
cleaner=self.cleaner,
**kwargs
)
self.dl1.calibrate(self.event)
self.viewer.refresh()
def create_next_event_widget(self):
self.w_next_event = Button(label=">", button_type="default", width=50)
self.w_next_event.on_click(self.on_next_event_widget_click)
def on_next_event_widget_click(self):
self.event_index += 1
def create_previous_event_widget(self):
self.w_previous_event = Button(
label="<",
button_type="default",
width=50
)
self.w_previous_event.on_click(self.on_previous_event_widget_click)
def on_previous_event_widget_click(self):
self.event_index -= 1
def create_event_index_widget(self):
self.w_event_index = TextInput(title="Event Index:", value='')
def update_event_index_widget(self):
if self.w_event_index:
self.w_event_index.value = str(self.event_index)
def create_event_id_widget(self):
self.w_event_id = TextInput(title="Event ID:", value='')
def update_event_id_widget(self):
if self.w_event_id:
self.w_event_id.value = str(self.event_id)
def create_goto_event_index_widget(self):
self.w_goto_event_index = Button(
label="GOTO Index",
button_type="default",
width=100
)
self.w_goto_event_index.on_click(self.on_goto_event_index_widget_click)
def on_goto_event_index_widget_click(self):
self.event_index = int(self.w_event_index.value)
def create_goto_event_id_widget(self):
self.w_goto_event_id = Button(
label="GOTO ID",
button_type="default",
width=70
)
self.w_goto_event_id.on_click(self.on_goto_event_id_widget_click)
def on_goto_event_id_widget_click(self):
self.event_id = int(self.w_event_id.value)
def create_telid_widget(self):
self.w_telid = Select(title="Telescope:", value="", options=[])
self.w_telid.on_change('value', self.on_telid_widget_change)
def update_telid_widget(self):
if self.w_telid:
tels = [str(t) for t in self.event.r0.tels_with_data]
self.w_telid.options = tels
self.w_telid.value = str(self.telid)
def on_telid_widget_change(self, _, __, ___):
if self.telid != int(self.w_telid.value):
self.telid = int(self.w_telid.value)
def create_channel_widget(self):
self.w_channel = Select(title="Channel:", value="", options=[])
self.w_channel.on_change('value', self.on_channel_widget_change)
def update_channel_widget(self):
if self.w_channel:
try:
n_chan = self.event.r0.tel[self.telid].waveform.shape[0]
except AttributeError:
n_chan = 1
channels = [str(c) for c in range(n_chan)]
self.w_channel.options = channels
self.w_channel.value = str(self.channel)
def on_channel_widget_change(self, _, __, ___):
if self.channel != int(self.w_channel.value):
self.channel = int(self.w_channel.value)
def create_dl1_widgets(self):
self.w_dl1_dict = dict(
cleaner=Select(title="Cleaner:", value='', width=5,
options=WaveformCleanerFactory.subclass_names),
extractor=Select(title="Extractor:", value='', width=5,
options=ChargeExtractorFactory.subclass_names),
extractor_t0=TextInput(title="T0:", value=''),
extractor_window_width=TextInput(title="Window Width:", value=''),
extractor_window_shift=TextInput(title="Window Shift:", value=''),
extractor_sig_amp_cut_HG=TextInput(title="Significant Amplitude "
"Cut (HG):", value=''),
extractor_sig_amp_cut_LG=TextInput(title="Significant Amplitude "
"Cut (LG):", value=''),
extractor_lwt=TextInput(title="Local Pixel Weight:", value=''))
for val in self.w_dl1_dict.values():
val.on_change('value', self.on_dl1_widget_change)
self.wb_extractor = widgetbox(
PreText(text="Charge Extractor Configuration"),
self.w_dl1_dict['cleaner'],
self.w_dl1_dict['extractor'],
self.w_dl1_dict['extractor_t0'],
self.w_dl1_dict['extractor_window_width'],
self.w_dl1_dict['extractor_window_shift'],
self.w_dl1_dict['extractor_sig_amp_cut_HG'],
self.w_dl1_dict['extractor_sig_amp_cut_LG'],
self.w_dl1_dict['extractor_lwt'])
def update_dl1_widget_values(self):
if self.w_dl1_dict:
for key, val in self.w_dl1_dict.items():
if 'extractor' in key:
if key == 'extractor':
val.value = self.extractor.__class__.__name__
else:
key = key.replace("extractor_", "")
try:
val.value = str(getattr(self.extractor, key))
except AttributeError:
val.value = ''
elif 'cleaner' in key:
if key == 'cleaner':
val.value = self.cleaner.__class__.__name__
else:
key = key.replace("cleaner_", "")
try:
val.value = str(getattr(self.cleaner, key))
except AttributeError:
val.value = ''
def on_dl1_widget_change(self, _, __, ___):
if self.event:
if not self._updating_dl1:
self._updating_dl1 = True
cmdline = []
for key, val in self.w_dl1_dict.items():
if val.value:
cmdline.append('--{}'.format(key))
cmdline.append(val.value)
self.parse_command_line(cmdline)
kwargs = dict(config=self.config, tool=self)
extractor = ChargeExtractorFactory.produce(**kwargs)
cleaner = WaveformCleanerFactory.produce(**kwargs)
self.update_dl1_calibrator(extractor, cleaner)
self.update_dl1_widget_values()
self._updating_dl1 = False
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])
source=source2,
level='overlay')
p2.select_one(HoverTool).tooltips = [("poisonous", "@poisonous"),
('prediction', '@pred{0.00}')]
# set up widgets
ml_models = [
'Logistic Regression', 'KNN (N=1)', 'KNN (N=20)',
'Decision Tree (max_depth=5)', 'Decision Tree (full depth)',
'Random Forest', 'SVM (linear kernel)', 'SVM (rbf kernel)',
'Gaussian Naive Bayes', 'MLP (3 hidden layers, relu activation)'
]
model_select = Select(value='Logistic Regression',
title='Select model:',
width=200,
options=ml_models)
test_split_button = Button(label="New Train/Test Split", width=200)
prediction_types = ['Probability', 'Decision']
prediction_select = Select(value='Probability',
title='Select boundary:',
width=200,
options=prediction_types)
inputs = column(widgetbox(test_split_button, model_select, prediction_select))
def update_predictions(attrname, old, new):
class BokehEventViewerCamera(CameraDisplay):
def __init__(self, event_viewer, fig=None):
"""
A `ctapipe.visualization.bokeh.CameraDisplay` modified to utilise a
`ctapipe.core.container.DataContainer` directly.
Parameters
----------
event_viewer : BokehEventViewer
The BokehEventViewer this object belongs to
fig : bokeh.plotting.figure
Figure to store the bokeh plot onto (optional)
"""
self._event = None
self._view = 'r0'
self._telid = None
self._channel = 0
self._time = 0
super().__init__(fig=fig)
self._view_options = {
'r0': lambda e, t, c, time: e.r0.tel[t].waveform[c, :, time],
'r1': lambda e, t, c, time: e.r1.tel[t].waveform[c, :, time],
'dl0': lambda e, t, c, time: e.dl0.tel[t].waveform[c, :, time],
'dl1': lambda e, t, c, time: e.dl1.tel[t].image[c, :],
'peakpos': lambda e, t, c, time: e.dl1.tel[t].peakpos[c, :],
'cleaned': lambda e, t, c, time: e.dl1.tel[t].cleaned[c, :, time],
}
self.w_view = None
self._geom_tel = None
self.event_viewer = event_viewer
def _reset(self):
self.reset_pixels()
self.event_viewer.change_time(0)
def _set_image(self):
e = self.event
v = self.view
t = self.telid
c = self.channel
time = self.time
if not e:
self.event_viewer.log.warning("No event has been provided")
return
tels = list(e.r0.tels_with_data)
if t is None:
t = tels[0]
if t not in tels:
raise KeyError(f"Telescope {t} has no data")
try:
self.image = self._view_options[v](e, t, c, time)
self.fig.title.text = f'{v} (T = {time})'
except TypeError:
self.image = None
def _update_geometry(self):
e = self.event
t = self.telid
if e:
# Check if geom actually needs to be changed
if not t == self._geom_tel:
self.geom = e.inst.subarray.tel[t].camera
self._geom_tel = t
else:
self.event_viewer.log.warning("No event has been provided")
def refresh(self):
self._set_image()
@property
def event(self):
return self._event
@event.setter
def event(self, val):
self._event = val
self._update_geometry()
self._set_image()
def change_event(self, event, telid):
if self.event: # Only reset when an event exists
self._reset()
self._telid = telid
self.event = event
@property
def view(self):
return self._view
@view.setter
def view(self, val):
if val not in list(self._view_options.keys()):
raise ValueError(f"View is not valid: {val}")
self._view = val
self._set_image()
@property
def telid(self):
return self._telid
@telid.setter
def telid(self, val):
if self.event: # Only reset when an event exists
self._reset()
self._telid = val
self._update_geometry()
self._set_image()
@property
def channel(self):
return self._channel
@channel.setter
def channel(self, val):
self._channel = val
self._set_image()
@property
def time(self):
return self._time
@time.setter
def time(self, val):
self._time = int(val)
self._set_image()
def _on_pixel_click(self, pix_id):
super()._on_pixel_click(pix_id)
ai = self.active_index
self.event_viewer.waveforms[ai].pixel = pix_id
def create_view_widget(self):
self.w_view = Select(title="View:", value="", options=[], width=5)
self.w_view.on_change('value', self.on_view_widget_change)
self.layout = column([self.w_view, self.layout])
def update_view_widget(self):
self.w_view.options = list(self._view_options.keys())
self.w_view.value = self.view
def on_view_widget_change(self, _, __, ___):
if self.view != self.w_view.value:
self.view = self.w_view.value
class BokehEventViewerWaveform(WaveformDisplay):
def __init__(self, event_viewer, fig=None):
"""
A `ctapipe.visualization.bokeh.WaveformDisplay` modified to utilise a
`ctapipe.core.container.DataContainer` directly.
Parameters
----------
event_viewer : BokehEventViewer
The BokehEventViewer this object belongs to
fig : bokeh.plotting.figure
Figure to store the bokeh plot onto (optional)
"""
self._event = None
self._view = 'r0'
self._telid = None
self._channel = 0
self._pixel = 0
super().__init__(fig=fig)
self._draw_integration_window()
self._view_options = {
'r0': lambda e, t, c, p: e.r0.tel[t].waveform[c, p],
'r1': lambda e, t, c, p: e.r1.tel[t].waveform[c, p],
'dl0': lambda e, t, c, p: e.dl0.tel[t].waveform[c, p],
'cleaned': lambda e, t, c, p: e.dl1.tel[t].cleaned[c, p],
}
self.w_view = None
self.event_viewer = event_viewer
def _reset(self):
for wav in self.event_viewer.waveforms:
wav.pixel = 0
def _set_waveform(self):
e = self.event
v = self.view
t = self.telid
c = self.channel
p = self.pixel
if not e:
self.event_viewer.log.warning("No event has been provided")
return
tels = list(e.r0.tels_with_data)
if t is None:
t = tels[0]
if t not in tels:
raise KeyError(f"Telescope {t} has no data")
try:
self.waveform = self._view_options[v](e, t, c, p)
self.fig.title.text = f'{v} (Pixel = {p})'
except TypeError:
self.waveform = None
def _draw_integration_window(self):
self.intwin1 = Span(location=0, dimension='height',
line_color='green', line_dash='dotted')
self.intwin2 = Span(location=0, dimension='height',
line_color='green', line_dash='dotted')
self.fig.add_layout(self.intwin1)
self.fig.add_layout(self.intwin2)
def _set_integration_window(self):
e = self.event
t = self.telid
c = self.channel
p = self.pixel
if e:
if e.dl1.tel[t].extracted_samples is not None:
# Get Windows
windows = e.dl1.tel[t].extracted_samples[c, p]
length = np.sum(windows)
start = np.argmax(windows)
end = start + length - 1
self.intwin1.location = start
self.intwin2.location = end
else:
self.event_viewer.log.warning("No event has been provided")
def refresh(self):
self._set_waveform()
self._set_integration_window()
@property
def event(self):
return self._event
@event.setter
def event(self, val):
self._event = val
self._set_waveform()
self._set_integration_window()
def change_event(self, event, telid):
if self.event: # Only reset when an event exists
self._reset()
self._telid = telid
self.event = event
@property
def view(self):
return self._view
@view.setter
def view(self, val):
if val not in list(self._view_options.keys()):
raise ValueError(f"View is not valid: {val}")
self._view = val
self._set_waveform()
self._set_integration_window()
@property
def telid(self):
return self._telid
@telid.setter
def telid(self, val):
if self.event: # Only reset when an event exists
self._reset()
self._telid = val
self._set_waveform()
self._set_integration_window()
@property
def channel(self):
return self._channel
@channel.setter
def channel(self, val):
self._channel = val
self._set_waveform()
self._set_integration_window()
@property
def pixel(self):
return self._pixel
@pixel.setter
def pixel(self, val):
self._pixel = val
self._set_waveform()
self._set_integration_window()
def _on_waveform_click(self, time):
super()._on_waveform_click(time)
self.event_viewer.change_time(time)
def create_view_widget(self):
self.w_view = Select(title="View:", value="", options=[], width=5)
self.w_view.on_change('value', self.on_view_widget_change)
self.layout = column([self.w_view, self.layout])
def update_view_widget(self):
self.w_view.options = list(self._view_options.keys())
self.w_view.value = self.view
def on_view_widget_change(self, _, __, ___):
if self.view != self.w_view.value:
self.view = self.w_view.value
cities = {
'Austin': {
'airport': 'AUS',
'title': 'Austin, TX',
},
'Boston': {
'airport': 'BOS',
'title': 'Boston, MA',
},
'Seattle': {
'airport': 'SEA',
'title': 'Seattle, WA',
}
}
city_select = Select(value=city, title='City', options=sorted(cities.keys()))
distribution_select = Select(value=distribution, title='Distribution', options=['Discrete', 'Smooth'])
df = pd.read_csv(join(dirname(__file__), 'data/2015_weather.csv'))
source = get_dataset(df, cities[city]['airport'], distribution)
plot = make_plot(source, cities[city]['title'])
city_select.on_change('value', update_plot)
distribution_select.on_change('value', update_plot)
controls = VBox(city_select, distribution_select)
# add to document
curdoc().add_root(HBox(controls, plot))
def modify_doc_dummy(self, symbol):
print("cmoing in modify_doc_dummy")
df = self.optionUtility.getProcessedOptionChainData(symbol)
self.latestData[symbol] = df
allSources = {}
def update(attr, old, new):
selected_expiryDate = expiry_date_selector.value
# selectedStrikePrice = allUniqueStrikePrice[strikePrice_selector.active];
activeStrikePriceIndexes = strikePrice_selector.active
# will take top of selected if number is greater
activeStrikePriceIndexes.sort()
# starting one will be selected
index = 0
selectedStrikeNumber = activeStrikePriceIndexes.__sizeof__()
processed = []
# print(activeStrikePriceIndexes)
for source in allWorkingSources:
if (index > (selectedStrikeNumber - 1)):
break
# in case less number of strike is selected don't through error
strikeIndex = activeStrikePriceIndexes[index]
selectedStrikePrice = allUniqueStrikePrice[strikeIndex]
new_src = 0
new_src = self.dashboard_tool.make_dataset(
int(selectedStrikePrice), selected_expiryDate, symbol,
self.isForTodayOnly)
#
# if((not (selectedStrikePrice in allSources.keys()))) :
# new_src = dashboard_tool.make_dataset(int(selectedStrikePrice), selected_expiryDate, symbol,
# isForTodayOnly)
# allSources[selectedStrikePrice] = new_src; #save it once processed data base call is saved.. which also requires computing
# else :
# new_src = allSources[selectedStrikePrice]
source.data.update(new_src.data)
index = index + 1
processed.append(selectedStrikePrice)
# print(selectedStrikePrice) # shown prices are these..
# print(processed, activeStrikePriceIndexes);
allUniqueStrikePrice = self.latestData[symbol]['strikePrice'].apply(
str).unique().tolist()
ATMStrikeindex = int(np.floor(len(allUniqueStrikePrice) / 2))
expiry_date_selector = Select(value=self.nearestExpiryDate,
options=self.expiryDates)
strikePrice_selector = CheckboxButtonGroup(
labels=allUniqueStrikePrice,
active=[
ATMStrikeindex - 2, ATMStrikeindex - 1, ATMStrikeindex,
ATMStrikeindex + 1, ATMStrikeindex + 2
]) # in case multiple to be shown at once#
# strikePrice_selector = RadioButtonGroup(
# labels=allUniqueStrikePrice, active=ATMStrikeindex);
#
strikePrice_selector.on_change('active', update)
expiry_date_selector.on_change('value', update)
selected_expiryDate = expiry_date_selector.value
# selectedStrikePrice = allUniqueStrikePrice[strikePrice_selector.active];
activeStrikePriceIndexes = strikePrice_selector.active
allplots = []
allWorkingSources = []
for oneindex in activeStrikePriceIndexes:
selectedStrikePrice = allUniqueStrikePrice[oneindex]
src = self.dashboard_tool.make_dataset(int(selectedStrikePrice),
selected_expiryDate, symbol,
self.isForTodayOnly)
allSources[selectedStrikePrice] = src
allWorkingSources.append(src)
p = self.dashboard_tool.make_plot(src, selectedStrikePrice)
allplots.append(p)
allplotslayout = column(allplots)
layout = column(row(expiry_date_selector, strikePrice_selector),
allplotslayout)
tab = Panel(child=layout, title=symbol)
return tab
("atomic mass", "@mass"),
("electronic configuration", "@electronic"),
]
######### CREATES CROSSFILTER ##########################
# decide if all columns or crossfilter down to sub properties
# The crossfilter widgets
# first select code this crossfilters the available options to
# available exchanges and elements
code = Select(title='Code', value='vasp', options=codes)
code.on_change('value', update)
# second select exchange
element = Select(title='Element', value='Cu', options=_elements)
element.on_change('value', update)
exchange = Select()
# The plotter widgets
x = Select(title='X-Axis', value='k-point', options=plottables)
x.on_change('value', update)
plot_data = ColumnDataSource(dict(time_mid=[0], price_mean=[0], time_left=[0], time_right=[0], price_min=[0], price_max=[0]))
# create a plot and style its properties
fig = figure(width=plot_width, height=plot_height, x_axis_type="datetime", toolbar_location='above', tools="pan,box_zoom,xwheel_zoom,reset,save");
fig.quad(top='price_max', bottom='price_min', left='time_left', right='time_right', source=plot_data, color=Blues3[1])
fig.line(x='time_mid', y='price_mean', source=plot_data, color=Blues3[0]);
# add widgets
title=Div(text="<h1>Stock Price</h1>")
description=Paragraph(text='Type in a symbol or select one from Dow 30 \
and Nasdaq 100, and its price on 5/6/2010 will be plotted shortly. Due to its \
high frequency, the series plotted is downsampled, with the shaded area denoting \
the min/max prices at every time point. Details can be revealed by zoom in with \
tools above the plot.')
symbol_box=TextInput(value='', title='Type Symbol', width=100)
market_select = Select(value='', title='Select ', width=100, options=['', 'Dow 30', 'Nasdaq 100'])
symbol_select = Select(value='', title='Symbol ', width=100, options=[])
button = Button(label="GO", button_type='success', width=100)
progress_bar=PreText()
# add callbacks and interactions
button.callback=CustomJS(code=dims_jscode, args=dict(plot=fig, dims=dims))
button.on_click(button_click)
fig.x_range.on_change('start', update_plot)
#fig.x_range.on_change('end', update_plot)
fig.x_range.callback = CustomJS(code=dims_jscode, args=dict(plot=fig, dims=dims))
market_select.on_change('value', update_symbol_select)
symbol_select.on_change('value', update_symbol_box)
# put the button and plot in a layout and add to the document
doc=curdoc()
color = to_color(main_scatter_df)
r=main_scatter_plot.circle('x','y', source=main_scatter_source, radius=0.25, fill_alpha=0.8, color=color)
# calculate context
context_df = df.groupby(['attr_value']).agg({'id':'count'}).rename(columns={'id':'n'})
# setup attribute table
table_df = df[df.attr_name==selected_attribute].groupby(['attr_value']).agg({'id':'count'})
table_df = table_df.sort_values(by='id', ascending=False).rename(columns={'id':'n'})
joined_df = calculate_ratio(table_df)
table_source = ColumnDataSource(joined_df)
table_source_column = [TableColumn(field="attr_value", title="Attribute Value"),TableColumn(field="n", title="Counts"),TableColumn(field="ratio", title="Ratio"),]
table_data_table = DataTable(source=table_source, columns=table_source_column, width=400, height=800)
# setup dropdowns
main_dropdown = Select(title="Chart Attributes", options=attributes_name, value=selected_attribute)
table_dropdown = Select(title="Histogram Attributes", options=attributes_name, value=selected_attribute)
# setup text input
threshold_input = TextInput(value=str(threshold), title="Threshold:")
# setup layout
layout_left = VBox(main_scatter_plot, main_dropdown)
layout_right = VBox(HBox(table_dropdown,threshold_input), table_data_table)
layout = HBox(layout_left,layout_right)
def update_threshold_callback(attr_name, old, new):
global threshold
threshold=int(new)
update_table()
# dividing data into gneeral discretes and continuous columns = sorted(df_obs.columns) #+ sorted(df.columns) discrete = [x for x in columns if df_obs[x].dtype == object] continuous = [x for x in columns if x not in discrete] #################################################################### ##divide data into plottables and non plottables (aggregates or 3D plottables) , #keep it to 2D plottables for now, this is known from the column names themselves plottables = ['k-point', 'value', 'perc_precisions'] x_select = Select(title='X-Axis', value='k-point', options=plottables) y_select = Select(title='Y-Axis', value='value', options=plottables) non_plottables = [ x for x in columns if x not in plottables ] # for aggregates structures = list(np.unique(df_obs['structure'])) _elements = list(np.unique(df_obs['element'])) #print (_elements) exchanges = list(np.unique(df_obs['exchange'])) properties = list(np.unique(df_obs['property'])) codes = list(np.unique(df_obs['code'])) # which sets of k-point and value to string together ? any unit transformations on the dataset values or k-point ## have another dataframe (mongo collection) for the reference standards to compute the accuracy (uniquely identified by the element SAME standard should apply to all codes/exchanges/elements.
# Create a new plot: plot
plot = figure()
# Add circles to the plot
plot.circle('x', 'y', source=source)
# Define a callback function: update_plot
def update_plot(attr, old, new):
# If the new Selection is 'female_literacy', update 'y' to female_literacy
if new == 'female_literacy':
source.data = {
'x' : fertility,
'y' : female_literacy
}
# Else, update 'y' to population
else:
source.data = {
'x' : fertility,
'y' : population
}
# Create a dropdown Select widget: select
select = Select(title="distribution", options=['female_literacy', 'population'], value='female_literacy')
# Attach the update_plot callback to the 'value' property of select
select.on_change('value', update_plot)
# Create layout and add to current document
layout = row(select, plot)
curdoc().add_root(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])
new_data['image'] = [img.data]
new_data['x'] = [x_range[0]]
new_data['y'] = [y_range[0]]
new_data['dh'] = [y_range[1] - y_range[0]]
new_data['dw'] = [x_range[1] - x_range[0]]
image_source.stream(new_data, 1)
time_select_options = OrderedDict()
time_select_options['1 Hour'] = 60
time_select_options['30 Minutes'] = 30
time_select_options['15 Minutes'] = 15
time_period = list(time_select_options.values())[0]
time_select = Select.create(name="Time Period", options=time_select_options)
time_select.on_change('value', on_time_select_change)
time_text = Paragraph(text='Time Period')
# load nyc taxi data
path = './data/nyc_taxi.csv'
datetime_field = 'tpep_dropoff_datetime'
cols = ['dropoff_x', 'dropoff_y', 'trip_distance', datetime_field]
df = pd.read_csv(path, usecols=cols, parse_dates=[datetime_field]).dropna(axis=0)
times = pd.DatetimeIndex(df[datetime_field])
group_count = grouped = groups = None
bin_data()
# manage client-side dimensions
def create_view_widget(self):
self.w_view = Select(title="View:", value="", options=[], width=5)
self.w_view.on_change('value', self.on_view_widget_change)
self.layout = column([self.w_view, self.layout])
},
line_color='black',
line_width=0.35,
fill_alpha=1,
hover_fill_color="#fec44f")
def handleSelectorChange(attrname, old, new):
print(attrname, old, new)
patch = generatePatchBasedOnSelect(selector.value)
plot.add_tools(
HoverTool(tooltips=[('Country', '@country'),
('Total Cases', '@total_cases_formatted'),
('Total Deaths', '@total_deaths_formatted')],
renderers=[patch]))
plot.title.text = '%s Plot' % (selector.value)
selectOptions = [
'Total Cases', 'Total Deaths', 'Total Cases Per Million',
'Total Deaths Per Million'
]
selector = Select(value=selectOptions[0], options=selectOptions)
selector.on_change('value', handleSelectorChange)
# initialize the plot
init()
layout = column(selector, plot)
curdoc().add_root(layout)
'SpectralClustering',
'Ward',
'AgglomerativeClustering',
'DBSCAN',
'Birch'
]
datasets_names = [
'Noisy Circles',
'Noisy Moons',
'Blobs',
'No Structure'
]
algorithm_select = Select(value='MiniBatchKMeans',
title='Select algorithm:',
options=clustering_algorithms)
dataset_select = Select(value='Noisy Circles',
title='Select dataset:',
options=datasets_names)
samples_slider = Slider(title="Number of samples",
value=1500.0,
start=1000.0,
end=3000.0,
step=100)
clusters_slider = Slider(title="Number of clusters",
value=2.0,
start=2.0,
class CrossFilter(DashboardComponent):
def __init__(self, worker, **kwargs):
with log_errors():
self.worker = worker
quantities = ['nbytes', 'duration', 'bandwidth', 'count',
'start', 'stop']
colors = ['inout-color', 'type-color', 'key-color']
# self.source = ColumnDataSource({name: [] for name in names})
self.source = ColumnDataSource({
'nbytes': [1, 2],
'duration': [0.01, 0.02],
'bandwidth': [0.01, 0.02],
'count': [1, 2],
'type': ['int', 'str'],
'inout-color': ['blue', 'red'],
'type-color': ['blue', 'red'],
'key': ['add', 'inc'],
'start': [1, 2],
'stop': [1, 2]
})
self.x = Select(title='X-Axis', value='nbytes', options=quantities)
self.x.on_change('value', self.update_figure)
self.y = Select(title='Y-Axis', value='bandwidth', options=quantities)
self.y.on_change('value', self.update_figure)
self.size = Select(title='Size', value='None',
options=['None'] + quantities)
self.size.on_change('value', self.update_figure)
self.color = Select(title='Color', value='inout-color',
options=['black'] + colors)
self.color.on_change('value', self.update_figure)
if 'sizing_mode' in kwargs:
kw = {'sizing_mode': kwargs['sizing_mode']}
else:
kw = {}
self.control = widgetbox([self.x, self.y, self.size, self.color],
width=200, **kw)
self.last_outgoing = 0
self.last_incoming = 0
self.kwargs = kwargs
self.layout = row(self.control, self.create_figure(**self.kwargs),
**kw)
self.root = self.layout
@without_property_validation
def update(self):
with log_errors():
outgoing = self.worker.outgoing_transfer_log
n = self.worker.outgoing_count - self.last_outgoing
n = min(n, 1000)
outgoing = [outgoing[-i].copy() for i in range(1, n)]
self.last_outgoing = self.worker.outgoing_count
incoming = self.worker.incoming_transfer_log
n = self.worker.incoming_count - self.last_incoming
n = min(n, 1000)
incoming = [incoming[-i].copy() for i in range(1, n)]
self.last_incoming = self.worker.incoming_count
out = []
for msg in incoming:
if msg['keys']:
d = self.process_msg(msg)
d['inout-color'] = 'red'
out.append(d)
for msg in outgoing:
if msg['keys']:
d = self.process_msg(msg)
d['inout-color'] = 'blue'
out.append(d)
if out:
out = transpose(out)
if (len(self.source.data['stop']) and
min(out['start']) > self.source.data['stop'][-1] + 10):
self.source.data.update(out)
else:
self.source.stream(out, rollover=1000)
def create_figure(self, **kwargs):
with log_errors():
fig = figure(title='', tools='', **kwargs)
size = self.size.value
if size == 'None':
size = 1
fig.circle(source=self.source, x=self.x.value, y=self.y.value,
color=self.color.value, size=10, alpha=0.5,
hover_alpha=1)
fig.xaxis.axis_label = self.x.value
fig.yaxis.axis_label = self.y.value
fig.add_tools(
# self.hover,
ResetTool(),
PanTool(),
WheelZoomTool(),
BoxZoomTool(),
)
return fig
@without_property_validation
def update_figure(self, attr, old, new):
with log_errors():
fig = self.create_figure(**self.kwargs)
self.layout.children[1] = fig
def process_msg(self, msg):
try:
def func(k):
return msg['keys'].get(k, 0)
main_key = max(msg['keys'], key=func)
typ = self.worker.types.get(main_key, object).__name__
keyname = key_split(main_key)
d = {
'nbytes': msg['total'],
'duration': msg['duration'],
'bandwidth': msg['bandwidth'],
'count': len(msg['keys']),
'type': typ,
'type-color': color_of(typ),
'key': keyname,
'key-color': color_of(keyname),
'start': msg['start'],
'stop': msg['stop']
}
return d
except Exception as e:
logger.exception(e)
raise
fs, f = 100, 5
offset, amplitude = 0, 2
phase = 0
# initialize bokeh figure objects for 'trig' data and 'psd' data
props = {'toolbar_location': 'right', 'tools': 'xbox_zoom, undo, redo, reset', 'logo': 'grey', 'width': 500, 'height': 250}
trig = figure(x_range=(0, 1), y_range=(-10, 10), x_axis_label='time, sec', **props)
psd = figure(title='power spectral density', y_axis_type='log', x_axis_label='frequency, Hz', **props)
# initialize glyphs on trig and psd figures as empty data sources
trig.circle('x', 'y', source=ColumnDataSource(data={'x': [], 'y': []}), size=1)
trig.line('x', 'y', source=ColumnDataSource(data={'x': [], 'y': []}), line_width=2)
psd.line('x', 'y', source=ColumnDataSource(data={'x': [], 'y': []}), line_width=3)
# create a Select widget controlling the trig function selection, start a list of widgets
select = Select(title='Trig Function', options=['sin', 'cos'])
select.on_change('value', callback_select)
widgets = [widgetbox(select, width=465)]
# initialize lists of sliders with inc / dec buttons to control trig attributes
controls = [
[Button(label='-'), Slider(title='Sample Rate', value=fs, start=2, end=400, step=1), Button(label='+')],
[Button(label='-'), Slider(title='Frequency', value=f, start=1, end=100, step=1), Button(label='+')],
[Button(label='-'), Slider(title='Offset', value=offset, start=-10, end=10, step=1), Button(label='+')],
[Button(label='-'), Slider(title='Amplitude', value=amplitude, start=0, end=10, step=1), Button(label='+')],
[Button(label='-'), Slider(title='Phase', value=phase, start=0, end=6, step=1), Button(label='+')]]
# setup callbacks for controls, update the list of widgets
for control in controls:
tmp = []
for c in control:
def render_crossfilter(itmdt: Intermediate, plot_width: int, plot_height: int) -> column:
"""
Render crossfilter scatter plot with a regression line.
"""
# pylint: disable=too-many-locals, too-many-function-args
df = itmdt["data"]
df["__x__"] = df[df.columns[0]]
df["__y__"] = df[df.columns[0]]
source_scatter = ColumnDataSource(df)
source_xy_value = ColumnDataSource({"x": [df.columns[0]], "y": [df.columns[0]]})
var_list = list(df.columns[:-2])
xcol = source_xy_value.data["x"][0]
ycol = source_xy_value.data["y"][0]
tooltips = [("X-Axis: ", "@__x__"), ("Y-Axis: ", "@__y__")]
fig = Figure(
plot_width=plot_width,
plot_height=plot_height,
toolbar_location=None,
title=Title(text="Scatter Plot", align="center"),
tools=[],
x_axis_label=xcol,
y_axis_label=ycol,
)
scatter = fig.scatter("__x__", "__y__", source=source_scatter)
hover = HoverTool(tooltips=tooltips, renderers=[scatter])
fig.add_tools(hover)
x_select = Select(title="X-Axis", value=xcol, options=var_list, width=150)
y_select = Select(title="Y-Axis", value=ycol, options=var_list, width=150)
x_select.js_on_change(
"value",
CustomJS(
args=dict(
scatter=source_scatter,
xy_value=source_xy_value,
x_axis=fig.xaxis[0],
),
code="""
let currentSelect = this.value;
let xyValueData = xy_value.data;
let scatterData = scatter.data;
xyValueData['x'][0] = currentSelect;
scatterData['__x__'] = scatterData[currentSelect];
x_axis.axis_label = currentSelect;
scatter.change.emit();
xy_value.change.emit();
""",
),
)
y_select.js_on_change(
"value",
CustomJS(
args=dict(
scatter=source_scatter,
xy_value=source_xy_value,
y_axis=fig.yaxis[0],
),
code="""
let currentSelect = this.value;
let xyValueData = xy_value.data;
let scatterData = scatter.data;
xyValueData['y'][0] = currentSelect;
scatterData['__y__'] = scatterData[currentSelect];
y_axis.axis_label = currentSelect;
scatter.change.emit();
xy_value.change.emit();
""",
),
)
fig = column(row(x_select, y_select, align="center"), fig, sizing_mode="stretch_width")
return fig
x_axis_label="Year",y_axis_label="Population")
population.line("x", "y", color="violet", line_width=2, source=known, legend="known")
population.line("x", "y", color="violet", line_width=2, line_dash="dashed", source=predicted, legend="predicted")
population.xaxis.major_label_orientation = pi/4
population.xgrid.grid_line_color = None
population.legend.location = "center_right"
population.x_range.end = 2150
population.yaxis.minor_tick_line_color = None
population.yaxis[0].formatter = FuncTickFormatter(code="""
return (Math.abs(tick) / 1e9) + " B"
""")
# Controls and callbacks
year = Select(title="Year:", value="2010", options=years)
location = Select(title="Location:", value="World", options=locations)
def update():
age = df[(df.Location == location.value) & (df.Year == int(year.value))]
ages.data = ColumnDataSource.from_df(age)
pop = df[df.Location == location.value].groupby(df.Year).Value.sum()
new_known = pop[pop.index <= 2010]
new_predicted = pop[pop.index >= 2010]
known.data = dict(x=new_known.index.map(str), y=new_known.values)
predicted.data = dict(x=new_predicted.index.map(str), y=new_predicted.values)
year.on_change('value', lambda attr, old, new: update())
location.on_change('value', lambda attr, old, new: update())
#############
# Road Type
#menu_rt = [("Motorway", "motorway"), ("Trunk", "trunk"),
# ("Primary", "primary"), ("Secondary", "secondary"),
# ("Tertiary", "tertiary")]
#dropdown_min_road_type = Dropdown(label="Min Road Type", type="warning",
# menu=menu_rt, default_action="secondary")
#def dropdown_handler_rt(action):
# print("rt dropdown_handler: %s" % action)
# global min_road_type
# #min_road_type =
# #session.store_document(document)
#dropdown_min_road_type.on_click(dropdown_handler_rt)
select_rt = Select(title="Min Road Type:", value=min_road_type,
options=['Motorway', 'Trunk', 'Primary', 'Secondary',
'Tertiary'])
def on_rt_change(obj, attr, old, new):
print "Min Road:", new
global min_road_type
min_road_type = new
select_rt.on_change('value', on_rt_change)
##############
##############
# set max distance from gdelt to osm node
#select_dist = Select(title="Max Node Distance (km):",
# value=str(max_node_dist_km),
# options=['0','1','2','3','4','5','6','7','8','9','10'])
def create_figure():
label = "%s vs %s" % (x.value.title(), y.value.title())
kdims = [x.value, y.value]
opts, style = {}, {}
opts['color_index'] = color.value if color.value != 'None' else None
if size.value != 'None':
opts['size_index'] = size.value
opts['scaling_factor'] = (1./df[size.value].max())*200
points = hv.Points(df, kdims=kdims, label=label).opts(plot=opts, style=style)
return renderer.get_plot(points).state
def update(attr, old, new):
layout.children[1] = create_figure()
x = Select(title='X-Axis', value='mpg', options=quantileable)
x.on_change('value', update)
y = Select(title='Y-Axis', value='hp', options=quantileable)
y.on_change('value', update)
size = Select(title='Size', value='None', options=['None'] + quantileable)
size.on_change('value', update)
color = Select(title='Color', value='None', options=['None'] + quantileable)
color.on_change('value', update)
controls = widgetbox([x, y, color, size], width=200)
layout = row(controls, create_figure())
curdoc().add_root(layout)
transformer_class_list.remove("TransformerMixin")
transformer_dict = {}
transformers_name = []
for transformer_class in transformer_class_list:
# Get class
transformer = getattr(transformers, transformer_class)
# Get an instance to have the name
transformer_instance = transformer()
transformers_name.append(transformer_instance.name)
# Put the class object in dict
transformer_dict[transformer_instance.name] = transformer
# Add widgets
select_processing = Select(title="Preprocessing:",
value=transformers_name[0],
options=transformers_name)
button_add = Button(label="Add", button_type="success")
button_remove = Button(label="Remove")
button_apply = Button(label="Apply")
multi_select = MultiSelect(title="Processing list:", value=[], options=[])
slider_window = Slider(start=3,
end=66,
value=7,
step=2,
title="Window Size Savitsky-Golay")
slider_polynomial = Slider(start=1,
end=4,
value=1,
step=1,
title="Polynomial Order Savitsky-Golay")
