def index():
streaming = True
source = AjaxDataSource(data_url="http://localhost:5000/source_data",
polling_interval=100)
source.data = get_data(time_slice)
payload = encode_utf8(get_html(source))
return payload
def index(request):
plot = figure(responsive=True, tools=[],
plot_width=500, plot_height=250,
x_range=(-180, 180), y_range=(-90, 90))
plot.toolbar_location = None
plot.axis.visible = None
source = AjaxDataSource(method='GET',
data_url='http://localhost:8000/view2d/data/',
polling_interval=1000)
source.data = dict(x=[], y=[]) # Workaround to initialize the plot
img = load_image("static/images/earth.png")
plot.image_rgba(image=[img], x=[-180], y=[-90], dw=[360], dh=[180])
plot.cross(source=source, x='x', y='y',
size=22, line_width=4, color='Orange') # CLU1
script, div = components(plot, INLINE)
js_resources = INLINE.render_js()
css_resources = INLINE.render_css()
context = {
'bokeh_script' : script,
'bokeh_div' : div,
'js_resources' : js_resources,
'css_resources' : css_resources
}
return render(request, 'view2d/index.html', context)
def makeTable(period, route):
source = AjaxDataSource(data_url=request.url_root + route,
polling_interval=period,
method='GET',
mode='replace')
source.data = dict(x=[], y=[])
colx = TableColumn(field="x", title="Time")
coly = TableColumn(field="y", title="Info")
table = DataTable(source=source, columns=[colx, coly], height=300)
script, div = components(table)
return script, div
def makePlot(period, route, title, line_color):
source = AjaxDataSource(data_url=request.url_root + route,
polling_interval=period,
method='GET',
mode='replace')
source.data = dict(x=[], y=[])
plot = figure(plot_height=200, plot_width=800, title=title)
plot.line('x', 'y', source=source, line_width=1, line_color=line_color)
script, div = components(plot)
return script, div
def simple():
source = AjaxDataSource(data_url="http://127.0.0.1:5000/data",
polling_interval=500,
mode='append',
max_size=30)
source.data = dict(x=[], y=[])
fig = figure(height=250, width=450)
fig.circle('x', 'y', source=source)
script, div = components(fig, INLINE)
return jsonify(script=script,
div=div,
js_resources=INLINE.render_js(),
css_resources=INLINE.render_css())
def bokeh_image_ajax_data(request, bokeh_id):
model = BokehVisual.objects.get(pk=bokeh_id)
x_title = ""
y_title = ""
with open(os.path.join(model.path,'vis.dat'),'r') as f_in:
titles = f_in.readline().split(',')
x_title = titles[0].replace(' ','')
y_title = titles[1].replace(' ','')
plot = figure(title= model.title,
x_axis_label= x_title,
y_axis_label= y_title,
plot_width = model.width,
plot_height = model.height)
source = AjaxDataSource(data_url="http://localhost:8000/bokeh/id/{}/data/".format(bokeh_id), content_type="json", method="GET", mode='replace')
source.data = dict(x=[], y=[])
if(model.img_type == 'line'):
im = plot.line('x', 'y', source=source)
if(model.img_type == 'circle'):
im = plot.circle('x', 'y', source=source)
callback = bokeh.models.CustomJS(args=dict(src=source,plt=plot), code="""
var source = src;
var plot = plt;
start_redrawing = Date.now();
for (var i = 0; i < 100000; i++) {
plot.change.emit();
}
stop_redrawing = Date.now();
statistic_redrawing();
""")
button = bokeh.models.Button(label="Start redrawing", callback=callback)
layout = bokeh.models.layouts.Column(button, plot)
response_dict = {'script':'', 'div':''}
response_dict['script'], response_dict['div'] = components(layout)
return JsonResponse({'get':response_dict})
def plot_conceptDrift():
# "append" mode is used to output the concatenated data
source = AjaxDataSource(data_url='http://127.0.0.1:5000/ConceptDrift/',
polling_interval=1000,
mode='append')
source.data = dict(t_stamp=[],
drift_num=[],
avg_loss=[],
loss_batch=[],
label_avg_loss=[],
label_loss_batch=[],
label_concept_drift=[])
plot = figure(plot_height=300,
plot_width=500,
x_range=(0, 2000),
y_range=(0, 1.1))
plot.line('t_stamp',
'avg_loss',
source=source,
line_color="red",
legend="label_avg_loss",
line_width=2)
plot.line('t_stamp',
'loss_batch',
source=source,
line_color="blue",
legend="label_loss_batch",
line_width=2)
plot.square('t_stamp',
'drift_num',
source=source,
color="orange",
legend="label_concept_drift",
size=5)
script, div = components(plot)
return script, div
def index():
global pollfreq
global range
post = False
if request.method == 'POST':
range = request.form['range']
post = True
# print("range:", range)
# Initialize figure data and data source
source = AjaxDataSource(data_url="/monitor/data",
max_size=5000,
polling_interval=pollfreq * 1000,
mode='append')
dates = []
tempfs = []
dates, tempfs = get_initial_data(range)
source.data = dict(x=dates, y=tempfs)
# Create the plot
TOOLS = "pan,wheel_zoom,box_zoom,reset,save"
fig = figure(title='DS18B20 Sensor',
x_axis_type='datetime',
tools=TOOLS,
plot_height=280,
sizing_mode='scale_width')
hover = HoverTool(
tooltips=[
('temp', '@y{0.00}'),
('time', '@x{%F %T}'),
],
formatters={
'x': 'datetime', # use 'datetime' formatter for 'x' field
},
mode='vline')
fig.add_tools(hover)
fig.xgrid.grid_line_color = None
fig.ygrid.grid_line_alpha = 0.8
fig.xaxis.axis_label = 'Time'
fig.yaxis.axis_label = 'Temperature'
fig.y_range = Range1d(60, 90)
fig.line('x', 'y', source=source, line_width=2)
fig.add_layout(BoxAnnotation(top=70, fill_alpha=0.1, fill_color='blue'))
fig.add_layout(
BoxAnnotation(bottom=70,
top=80,
fill_alpha=0.1,
line_color='green',
fill_color='green'))
fig.add_layout(BoxAnnotation(bottom=80, fill_alpha=0.1, fill_color='red'))
plot_script, plot_div = components(fig)
if post:
return (jsonify(plotscript=plot_script, plotdiv=plot_div))
else:
return render_template(
"monitor/index.html",
plot_script=plot_script,
plot_div=plot_div,
pollfreq=pollfreq,
)
def make_ajax_plot():
source = AjaxDataSource(data_url=request.url_root + 'data/',
polling_interval=86400000,
mode='append',
max_size=300)
source.data = dict(time=[], karma=[], daily_inc=[], ma_week=[])
global df, t
for i in xrange(len(df.karma)):
t += 1
source.data['karma'].append(df.karma[i])
# t2 = re.split(' .*', str(df.date[i]))[0]
source.data['time'].append(t)
if i == 0:
source.data['daily_inc'].append(0)
source.data['ma_week'].append(0)
else:
source.data['daily_inc'].append(df.karma[i] - df.karma[i - 1])
source.data['ma_week'].append(_moving_avg(list(df.karma[:i])))
p = figure(plot_height=500,
tools="xpan,xwheel_zoom,xbox_zoom,reset",
x_axis_type=None,
y_axis_location="right")
p.x_range.follow = "end"
p.x_range.follow_interval = 100
p.x_range.range_padding_units = 'absolute'
p.x_range.range_padding = 10
p.line(x='time',
y='karma',
alpha=0.2,
line_width=3,
color='orange',
source=source,
legend='Karma Trend')
p.legend.location = "top_left"
p2 = figure(plot_height=250,
x_range=p.x_range,
tools="xpan,xwheel_zoom,xbox_zoom,reset",
y_axis_location="right")
p2.segment(x0='time',
y0=0,
x1='time',
y1='daily_inc',
line_width=6,
color='black',
alpha=0.5,
source=source,
legend='Daily Increase')
p2.line(x='time',
y='ma_week',
color='red',
source=source,
legend='Weekly Moving Average')
p2.legend.location = "top_left"
final_plot = gridplot([[p], [p2]],
toolbar_location="left",
plot_width=1000)
script, div = components(final_plot)
return script, div
def plot_dataset():
'''data_directory = "/Users/Jingwei/PycharmProjects/distributed_use/venv/TestDataset/UCR_TS_Archive_2015"
datasetName = "/FordA/FordA_TRAIN"
list_timeseries = util.load_dataset(data_directory+datasetName)'''
plot_window = figure(plot_height=150,
plot_width=500,
title='New Incoming TS micro-batch')
plot_all = figure(plot_height=150,
plot_width=500,
title='All historical TS')
plot_window.axis.visible = False
plot_all.axis.visible = False
source = AjaxDataSource(data_url='http://127.0.0.1:5000/ConceptDrift/',
polling_interval=1000,
mode='replace')
source.data = dict(inputTSBatch=[], TS_set=[])
def deserialize(data_string):
data_set = data_string.split(';')
data_list = []
print("data_set is: " + str(data_set))
for data in data_set:
print("data is: " + data)
data_set.append([int(i) for i in data[1:-1].split(',')])
return data_list
if len(source.data['inputTSBatch']) != 0:
inputTSBatch = deserialize(source.data['inputTSBatch'][0])
TS_set = deserialize(source.data['TS_set'][0])
for ts_w in inputTSBatch:
x = range(len(ts_w.timeseries))
y = ts_w.timeseries
plot_window.line(x, y, line_width=1)
for ts_history in TS_set:
x = range(len(ts_history.timeseries))
y = ts_history.timeseries
plot_all.line(x, y, line_width=1)
'''global datasetName
list_timeseries = util.load_dataset(datasetName)
name_dataset = {k: v for ds in list_timeseries for k, v in ds.items()}
dataset_list = list(name_dataset.values())
#plot_window: input_TSBatch
#plot_all: TS_set in "ISETS_Web_backend", how to extract the value? refer to a function which returns an object
plot_window = figure(plot_height=150, plot_width=500, title='New Incoming TS micro-batch')
plot_all = figure(plot_height=150, plot_width=500, title='All historical TS')
plot_window.axis.visible = False
plot_all.axis.visible = False
#get the window size
for ts in dataset_list[:2]:
x = range(len(ts.timeseries))
y = ts.timeseries
plot_window.line(x, y, line_width=1)
for ts in dataset_list[:50]:
x = range(len(ts.timeseries))
y = ts.timeseries
plot_all.line(x, y, line_width=1)'''
plot = gridplot([[plot_window], [plot_all]])
script, div = components(plot)
return script, div