def gen_select_state_county(state_name, county_name): state_list = list(us_counties["state"].unique()) state_list.sort() counties_in_states = list(us_counties.loc[us_counties["state"] == state_name]['county'].unique()) counties_in_states.sort() select_state = Select(title="State:", value="foo", options=state_list) select_county = Select(title="County:", value="foo", options=counties_in_states) select_state.value = state_name select_county.value = county_name return select_state, select_county
def get_selections():
gates = [
"IN - what to add on", "NOT IMPORTANT - what to drop off",
"IMPORTANT - where to focus on"
]
select_gate = Select(title="Gate",
value="IN - what to add on",
options=gates)
if select_gate.value == "IN - what to add on":
select_gate.value = "input_gate"
elif select_gate.value == "NOT IMPORTANT - what to drop off":
select_gate.value = "forget_gate"
elif select_gate.value == "IMPORTANT - where to focus on":
select_gate.value = "output_gate"
return select_gate
apply_thresholds(a,
threshold=threshold_slider.value,
cluster_size=clustersize_slider.value)
update_guide()
plot()
def apply_thresholds_calback(attr, old, new):
apply_thresholds(a,
threshold=threshold_slider.value,
cluster_size=clustersize_slider.value)
update_guide()
plot()
def set_slice_callback(attr, old, new):
plot()
subject_select.on_change('value', select_subject_callback)
select_subject_callback('', '', '')
slice_slider.on_change('value', set_slice_callback)
threshold_slider.on_change('value', apply_thresholds_calback)
clustersize_slider.on_change('value', apply_thresholds_calback)
transparency_slider.on_change('value', set_slice_callback)
# In[19]:
subject_select.value = sorted_xs[0]
ss_select_ed = Select(
title="ss",
value="59",
options=['{:02d}'.format(ll) for ll in range(0, 60)],
width=config_main['plot_config']['tab_aiaBrowser']['ss_select_wdth'])
Text_PlotID = TextInput(
value='',
title="Plot ID:",
width=config_main['plot_config']['tab_aiaBrowser']['button_wdth'])
print database_dir + 'Trange_args.json'
try:
with open(database_dir + 'Trange_args.json', 'r') as fp:
trcfg = json.load(fp)
print trcfg['aiaBrowser']['tst']
YY_select_st.value, MM_select_st.value = trcfg['aiaBrowser']['tst'][
0:4], trcfg['aiaBrowser']['tst'][5:7]
DD_select_st.value, hh_select_st.value = trcfg['aiaBrowser']['tst'][
8:10], trcfg['aiaBrowser']['tst'][11:13]
mm_select_st.value, ss_select_st.value = trcfg['aiaBrowser']['tst'][
14:16], trcfg['aiaBrowser']['tst'][17:19]
YY_select_ed.value, MM_select_ed.value = trcfg['aiaBrowser']['ted'][
0:4], trcfg['aiaBrowser']['ted'][5:7]
DD_select_ed.value, hh_select_ed.value = trcfg['aiaBrowser']['ted'][
8:10], trcfg['aiaBrowser']['ted'][11:13]
mm_select_ed.value, ss_select_ed.value = trcfg['aiaBrowser']['ted'][
14:16], trcfg['aiaBrowser']['ted'][17:19]
except:
trcfg = {}
def YY_select_st_select_update(attrname, old, new):
def buildPlot():
#####################Setup
# Grab graph colors, pop undesireable ones
colors = SEABORN_PALETTES['bright']
#Grab and sort the FQs
quals = fruit_df.reset_index()
quals = quals['FruitQuality'].unique().tolist()
for idx, i in enumerate(list(quals)):
if type(i) == type(0.5):
quals.pop(idx)
unique_FQs = quals
#a little math to get the epoch time to set the initial x range
minDate = ts_to_epoch(fruit_df['Date'].min())
maxDate = ts_to_epoch(fruit_df['Date'].max())
###########Create and format the plot
plot = figure(
x_axis_type="datetime",
plot_width=600,
plot_height=400,
tools=[PanTool(),
WheelZoomTool(),
SaveTool(),
BoxZoomTool()],
x_range=DataRange1d(
start=minDate, end=maxDate
), #sets the initial date range to the limits of the data
y_range=DataRange1d(start=0, end=1),
name='the_plot',
toolbar_location='above')
#some styling
plot.title.text = "Historical Volatility"
plot.xaxis.axis_label = "Trade Date"
plot.yaxis.axis_label = "Vol"
plot.background_fill_color = '#EAEBF0'
plot.xgrid.grid_line_color = 'white'
plot.ygrid.grid_line_color = 'white'
plot.xaxis.axis_line_color = 'white'
plot.xaxis.major_tick_line_color = 'white'
plot.xaxis.minor_tick_line_color = 'white'
plot.yaxis.axis_line_color = 'white'
plot.yaxis.major_tick_line_color = 'white'
plot.yaxis.minor_tick_line_color = 'white'
plot.toolbar.logo = None
#a list for all of the lines to reside in
lines = []
legends = []
##############Create the widgets
#a console style window to show debug messages TODO: add on/off functionality
debug = PreText(text="", width=1200, height=500)
#echos the debug in a place more visiable for the user
user_message = Paragraph(text='')
#Asset_Class, Product, and From dropdown boxes. Sets dropdown's initial value.
asCls = Select(title="Asset Class",
options=ddOpts['Asset_Class'].unique().tolist())
asCls.value = asCls.options[0]
prod = Select(title="Products",
options=ddOpts[ddOpts['Asset_Class'] == asCls.value]
['Product'].unique().tolist())
prod.value = prod.options[0]
whereFrom = Select(title="From",
options=ddOpts[(ddOpts['Asset_Class'] == asCls.value)
& (ddOpts['Product'] == prod.value)]
['From'].unique().tolist())
whereFrom.value = whereFrom.options[0]
FQslider = Slider(title='Fruit Quality',
start=min(unique_FQs),
end=max(unique_FQs),
step=1)
#the amount of days back to look for the data
days_back = TextInput(title='Days ago', value='365')
days_back_buttons = RadioButtonGroup(
labels=['10', '30', '90', '180', '365', '730'], active=4)
#the date to linear fit to
fixed_date_buttons = RadioButtonGroup(
labels=['30', '60', '90', '120', '180', '365'], active=2)
fixed_date = TextInput(title='Days to Exp', value='90')
#the amount of days with which to calculate the rolling mean
rolling_days_buttons = RadioButtonGroup(labels=['1', '2', '5', '10'],
active=0)
rolling_days = TextInput(title='Rolling Mean Days', value='1')
#a dynamically resizing checkbox group that allows for the changing of the visablity of any line on the plot
line_onOff = CheckboxGroup(width=400, name='line_onOff')
#the associated colors to act as a legend for line_onOff
legendDiv = Div(width=50)
#button to add a line
addLine = Button(label="Add Line")
#an html rendered visualization of the data for each line
descriptions = Div(text='', width=500)
#resizes the plot
rszButton = Button(label='resize')
##########Define functions associated with the widgets
#concats any dubug call to the end of the current debug text, and changes the user message
def updateDebug(inString):
inString = str(inString)
user_message.text = inString
oldText = debug.text
newText = ("*- " + str(datetime.now()) + " : " + inString)
debug.text = oldText + '\n' + newText
#changes the potential products and contract categories to match the user selected asset class
def asClsChange(attrname, old, new):
prod.options = ddOpts[ddOpts['Asset_Class'] ==
asCls.value]['Product'].unique().tolist()
prod.value = prod.options[0]
#changes the potential contract categories to match the user selected product
def prodChange(attrname, old, new):
whereFrom.options = ddOpts[(ddOpts['Asset_Class'] == asCls.value) & (
ddOpts['Product'] == prod.value)]['From'].unique().tolist()
whereFrom.value = whereFrom.options[0]
#links the days back button and text box
def days_back_buttonChange(attrname, old, new):
days_back.value = days_back_buttons.labels[days_back_buttons.active]
#checks that the users input is an int
def days_backChange(attrname, old, new):
try:
days_back.value = str(int(days_back.value))
except ValueError:
days_back.value = '0'
updateDebug('please type an integer')
#links the fixed date button and text box
def fixed_date_buttonChange(attrname, old, new):
fixed_date.value = fixed_date_buttons.labels[fixed_date_buttons.active]
#checks that the users input is an int
def fixed_dateChange(attrname, old, new):
try:
fixed_date.value = str(int(fixed_date.value))
except ValueError:
fixed_date.value = '0'
updateDebug('please type an integer')
#links the rolling days button and text box
def rolling_days_buttonsChange(attrname, old, new):
rolling_days.value = rolling_days_buttons.labels[
rolling_days_buttons.active]
#checks that the users input is an int
def rolling_daysChange(attrname, old, new):
try:
rolling_days.value = str(int(rolling_days.value))
except ValueError:
rolling_days.value = '0'
updateDebug('please type an integer')
#fits the plot to the currently visiable lines
def resize():
if len(line_onOff.active) == 0 or len(line_onOff.labels) == 0:
plot.x_range.start = ts_to_epoch(fruit_df['Date'].min())
plot.x_range.end = ts_to_epoch(fruit_df['Date'].max())
plot.y_range.start = 0
plot.y_range.end = 100
else:
xmin, xmax, ymin, ymax = calc_range(lines)
plot.x_range.start = xmin
plot.x_range.end = xmax
plot.y_range.start = ymin
plot.y_range.end = ymax
#turn lines on or off
def line_onOffChange(attrname, old, new):
for i in range(len(line_onOff.labels)):
if i in line_onOff.active:
lines[i].glyph.visible = True
else:
lines[i].glyph.visible = False
legendDiv.text = '<div>'
for line in lines:
legendDiv.text += '<br><div style="background-color: %s; float:up; padding: 4px 4px 4px 4px"></div><br>' % line.glyph.line_color
legendDiv.text += '</div>'
resize()
#adds a line to the graph
def grphUpdt():
#adds some debug messages, grabs the current time as to later show the total time taken to calculate
updateDebug("Starting")
updateDebug("total dataframe size: " + str(fruit_df.shape))
stTime = datetime.now()
#the value to linear fit to
fit_to = int(fixed_date.value)
#instiantiate an empty dataframe that will eventually contain the graphs data
graphData = pd.DataFrame({
'Date': [],
'PriceVolatility': [],
'Days_to_Exp': []
})
#grab the appropriate subset of the whole dataframe based on the users input into the widgets
updateDebug("querying the data..")
try:
workingDf = fruit_df.loc[asCls.value, prod.value, whereFrom.value]
except KeyError:
updateDebug(
'no data with that combination of Asset Class, Product, From')
return
try:
workingDf = workingDf[[
'Date', 'PriceVolatility', 'Days_to_Exp'
]][(workingDf['Date'] >
(date.today() - timedelta(days=int(days_back.value))))]
except KeyError:
updateDebug(
'no data with that combination of Asset Class, Product, From, and days back'
)
return
updateDebug("done breaking down df")
#a hook in the case that the users inputs resulted in an empty dataframe
if (workingDf.empty):
updateDebug(
'no data with that combination of Asset Class, Product, From, and days back'
)
return
#widdle down the database to only contain the user specified FQ
try:
graphData = workingDf.loc[int(FQslider.value)].copy()
except KeyError:
updateDebug('no data with that FQ')
#another empty graph hook
if (graphData.empty):
updateDebug(
'no data with that combination of Asset Class, Product, Contract Category, FQ, and days back'
)
return
updateDebug('grabed correct FQs')
#calculate linear fit on the current subset
updateDebug('calculating linear fit...')
graphData = mu.linearFit(fit_to=fit_to,
group_on_column='Date',
df=graphData,
fit_column='Days_to_Exp',
on_columns=['PriceVolatility'])
updateDebug('finished with linear fit')
# a few more debug messages
updateDebug(
"working df qry: Asset_Class = %s and Product = %s and From = %s and Date > %s "
% (asCls.value, prod.value, whereFrom.value,
str(date.today() - timedelta(days=int(days_back.value)))))
updateDebug("graph data shape: " + str(workingDf.shape))
#makes sure graph data has at least 5 rows, so that rolling mean can be calculated
if graphData.shape[0] > int(rolling_days.value):
#make the graph legend, based on if there's a denominator specified or not
this_legend = '%s - %s FQ: %s Days to Exp: %s From: %s Rolling Days: %s' % (
prod.value, whereFrom.value, int(
FQslider.value), fixed_date.value,
str(date.today() - timedelta(days=int(days_back.value))),
rolling_days.value)
#add a new line to the graph, and add the accosiated GlyphRenderer created by adding the line to the lines list.
#Set the legend to the previously calculated legend, and set the color to the next color in the current theme (if there are more lines than colors, there will be multiple lines with the same color)
#Calculates a 5 day rolling mean on the y values. Maybe add a slider/text box/other widget so the user can set the rolling mean themselves
updateDebug('adding line to plot')
lines.append(
plot.line(graphData.index.values[int(rolling_days.value) - 1:],
graphData['PriceVolatility'].rolling(
window=int(rolling_days.value)).mean()
[int(rolling_days.value) - 1:],
line_width=3,
color=colors[len(lines) % len(colors)]))
legends.append(this_legend)
updateDebug("updated graph")
global descDf
#either creates, or adds to, a dataframe containing statistics about the data. stats come from pandas DataFrame.describe.
if descDf is None:
graphData[this_legend] = graphData['PriceVolatility']
descDf = graphData[[
this_legend
]].rolling(window=int(rolling_days.value)).mean(
)[int(rolling_days.value) -
1:].describe(percentiles=[]).transpose().copy()
else:
graphData[this_legend] = graphData['PriceVolatility']
descDf = pd.concat([
descDf, graphData[[
this_legend
]].rolling(window=int(rolling_days.value)).mean()
[int(rolling_days.value) -
1:].describe(percentiles=[]).transpose().copy()
])
descDf = descDf.round(1)
descriptions.text = descDf.to_html().replace('\\n', '')
graphData.drop(this_legend, 1, inplace=True)
#add the name of the line to the checkbox so that it can be turned off and o
line_onOff.labels.append(this_legend)
line_onOff.active.append(len(line_onOff.labels) - 1)
legendDiv.text = '<div>'
for line in lines:
legendDiv.text += '<br><div style="background-color: %s; float:up; padding: 4px 4px 4px 4px"></div><br>' % line.glyph.line_color
legendDiv.text += '</div>'
##leaving this in case we get around to figuring out the hover tool
##formats the date values for the hover tool, currently commented out until we, or bokeh, fix the hover tool for multiple lines
#formDates= pd.to_datetime(graphData['Date'] ,format="%m-%d-%Y")
#lines[-1].data_source.data['formDates'] = formDates.apply(lambda x: x.strftime('%m-%d-%Y'))
##Displays the amout of time it took to draw the line, as well as the number of points in the graph
updateDebug("updated y vals, with rolling mean calculated")
updateDebug(
str(datetime.now() - stTime) + " FOR " +
str(len(lines[-1].data_source.data['x'])) + " points")
else:
updateDebug("There's no data to display")
del graphData
del workingDf
#######Link widgets to their associated functions
asCls.on_change('value', asClsChange)
prod.on_change('value', prodChange)
days_back_buttons.on_change('active', days_back_buttonChange)
days_back.on_change('value', days_backChange)
fixed_date_buttons.on_change('active', fixed_date_buttonChange)
fixed_date.on_change('value', fixed_dateChange)
rolling_days_buttons.on_change('active', rolling_days_buttonsChange)
rolling_days.on_change('value', rolling_daysChange)
line_onOff.on_change('active', line_onOffChange)
addLine.on_click(grphUpdt)
rszButton.on_click(resize)
#Formatting
fixed_date_box = WidgetBox(fixed_date, fixed_date_buttons)
days_back_box = WidgetBox(days_back, days_back_buttons)
rolling_days_box = WidgetBox(rolling_days, rolling_days_buttons)
widgets = [
asCls, prod, whereFrom, FQslider, days_back_box, fixed_date_box,
rolling_days_box, addLine, rszButton, user_message
]
plot_w_description = VBox(plot, descriptions, width=700)
pwd_w_leg = HBox(plot_w_description,
VBox(legendDiv),
VBox(line_onOff),
width=plot_w_description.width + line_onOff.width + 100,
name='div_to_save')
input_box = VBox(*widgets, width=400, height=1200)
total_box = HBox(VBox(input_box),
VBox(pwd_w_leg),
width=input_box.width + pwd_w_leg.width + 100,
height=1200)
tot_w_debug = VBox(total_box, VBox(HBox(debug)))
resize()
return tot_w_debug
def create_layout():
"""Inclui o gráficos e os controles no curdoc do Bokeh Server."""
plot_html = ColumnDataSource(dict(file_html=["Title"], html=[""]))
plot = figure(
title="Title",
sizing_mode="stretch_both",
tools="wheel_zoom,box_zoom,pan,,crosshair,reset,save",
# tooltips=[("y", "$y"), ("x", "$x")],
active_scroll="wheel_zoom",
toolbar_location="above",
output_backend="webgl",
)
plot.xaxis.axis_label = "X"
plot.yaxis.axis_label = "Y"
plot.add_layout(Legend())
plot.legend.click_policy = "hide"
plot.legend.background_fill_alpha = 0.6
table_source = ColumnDataSource()
side_controls = column(width=400, height=200)
series_source_controls = row(sizing_mode="scale_width")
tabs = Tabs()
models.COLORS = cycle(Category10_10)
models.Series.labels = []
def upload_callback(attr, old, new):
"""Função que atualiza os dados do arquivo aberto."""
file_contents = base64.b64decode(new)
file_contents_bytes = io.BytesIO(file_contents)
table_source.data = pd.read_csv(file_contents_bytes).to_dict("list")
colmmns = list(table_source.data.keys())
for widget in list(values_selectors.values()):
widget.options = colmmns
datatable.columns = [TableColumn(field=_, title=_) for _ in colmmns]
def update_plot_html(event):
title = plot.title.text
file_name = title + ".html"
# output_file(file_name, title=title, mode="inline")
# save(plot)
html = file_html(plot, INLINE, title)
plot_html.data = dict(file_name=[file_name], html=[html])
# Widgets (controls) ======================================================
# Plot controls
plot_title = TextInput(title="Plot title", value="Title")
x_title = TextInput(title="X title", value="X")
y_title = TextInput(title="Y title", value="Y")
positions = [_.replace("_", "-") for _ in list(LegendLocation)]
legend_position = Select(title="Legend position", options=positions)
download_button = Button(label="Download saved",
button_type="success",
align="end")
save_button = Button(label="Save", align="end")
save_button.on_click(update_plot_html)
with open("bokeh_chart_maker/download.js") as f:
callback = CustomJS(args=dict(source=plot_html), code=f.read())
download_button.js_on_click(callback)
plot_controls = row(plot_title, x_title, y_title, legend_position,
save_button, download_button)
def update_plot(attr, old, new):
plot.title.text = plot_title.value
plot.xaxis.axis_label = x_title.value
plot.yaxis.axis_label = y_title.value
plot.legend.location = legend_position.value.replace("-", "_")
for widget in plot_controls.children:
if hasattr(widget, "value"):
widget.on_change("value", update_plot)
legend_position.value = "top_left"
# Series controls
upload_button = FileInput(accept=".csv")
upload_button.on_change("value", upload_callback)
columns = [
TableColumn(field=_, title=_, width=10)
for _ in table_source.data.keys()
]
datatable = DataTable(source=table_source,
columns=columns,
width=390,
height=200)
values_selectors = {}
glyph_type = Select(title="Glyph type",
options=["line", "scatter"],
width=100)
def update_series_source_controls(attr, old, new):
colmmns = list(table_source.data.keys())
kw = dict(options=colmmns, width=80)
values_selectors["x"] = Select(title=f"x-values", **kw)
values_selectors["y"] = Select(title=f"y-values", **kw)
series_source_controls.children = list(values_selectors.values())
glyph_type.on_change("value", update_series_source_controls)
glyph_type.value = "line"
add_button = Button(label="Add glyph",
button_type="success",
width=50,
align="end")
side_controls.children = [
Div(text="<h3>Load file</h3>", height=35),
upload_button,
datatable,
row(glyph_type, series_source_controls, add_button, width=390),
Div(text="<h3>Glyphs</h3>", height=35),
tabs,
]
def add_series(event):
any_col = list(table_source.data.keys())[0]
source = ColumnDataSource()
for key, selector in values_selectors.items():
column_name = selector.value or any_col
source.data[key] = table_source.data[column_name]
vars_map = dict((k, str(k)) for k in values_selectors.keys())
if glyph_type.value == "line":
series = models.line_series(plot, source=source, **vars_map)
elif glyph_type.value == "scatter":
series = models.scatter_series(plot, source=source, **vars_map)
def delete_series(event):
plot.renderers = [
r for r in plot.renderers if r.glyph != series.glyph
]
legend_items = list(plot.legend.items)
plot.legend.items = [
item for item in legend_items
if series.glyph != item.renderers[0].glyph
]
tabs.tabs = [panel for panel in tabs.tabs if panel != series.panel]
series.delete_button.on_click(delete_series)
tabs.tabs.append(series.panel)
add_button.on_click(add_series)
return [
side_controls,
column(plot_controls, plot, sizing_mode="stretch_width")
]
def create_module(user):
# start controller
controller = Controller(user)
# hover
hover1 = create_hover(1)
# module
fig1 = figure(plot_width=600,
plot_height=300,
css_classes=['monitoring_fig1'],
tools='pan,box_zoom,reset',
name='fig1',
title='Cantidad de Clientes x día de Lectura')
fig1.toolbar.logo = None
fig1.toolbar_location = 'above'
fig1.vbar(x=[1, 2, 3],
width=1000 * 3600 * 24,
bottom=0,
top=[0, 0, 0],
color='red',
name='vbar2',
alpha=0.2)
line1 = fig1.line(x=[0, 1],
y=[0, 1],
line_color="darkgray",
line_width=2,
alpha=0.6,
legend='Planificación',
name='line1')
line2 = fig1.line(x=[0, 1],
y=[0, 1],
line_color="blue",
line_width=2,
alpha=0.8,
legend='Real',
name='line1.2')
fig1.legend.click_policy = "hide"
fig1.legend.location = "top_left"
fig1.legend.background_fill_color = "white"
fig1.legend.background_fill_alpha = 0.5
fig1.legend.label_text_color = "#505050"
fig1.legend.orientation = "vertical"
fig1.xaxis.axis_label = 'Días del mes'
hover1.renderers = [line1, line2]
fig1.add_tools(hover1)
fig1.xaxis.formatter = DatetimeTickFormatter(days=["%d"])
fig1.yaxis[0].formatter = NumeralTickFormatter(format="0.0a")
fig2 = figure(plot_width=600,
plot_height=300,
css_classes=['monitoring_fig2'],
tools='pan,box_zoom,reset',
name='fig2',
title='Cantidad de Itinerarios x día de Lectura')
fig2.toolbar.logo = None
fig2.toolbar_location = 'above'
fig2.vbar(x=[1, 2, 3],
width=1000 * 3600 * 24,
bottom=0,
top=[0, 0, 0],
color='red',
name='vbar3',
alpha=0.2)
line3 = fig2.line(x=[0, 1],
y=[0, 1],
line_color="darkgray",
line_width=2,
alpha=0.6,
legend='Planificación',
name='line2')
line4 = fig2.line(x=[0, 1],
y=[0, 1],
line_color="blue",
line_width=2,
alpha=0.8,
legend='Real',
name='line2.2')
fig2.legend.click_policy = "hide"
fig2.legend.location = "top_left"
fig2.legend.background_fill_color = "white"
fig2.legend.background_fill_alpha = 0.5
fig2.legend.label_text_color = "#505050"
fig2.legend.orientation = "vertical"
fig2.xaxis.axis_label = 'Días del mes'
hover2 = create_hover(1)
hover2.renderers = [line3, line4]
fig2.add_tools(hover2)
fig2.xaxis.formatter = DatetimeTickFormatter(days=["%d"])
map_options = GMapOptions(lat=10.032663,
lng=-74.042470,
map_type="roadmap",
zoom=7)
fig3 = GMapPlot(x_range=Range1d(),
y_range=Range1d(),
map_options=map_options,
plot_width=600,
plot_height=450,
css_classes=['monitoring_fig3'],
name='fig3')
fig3.toolbar.logo = None
fig3.toolbar_location = 'above'
fig3.add_tools(PanTool(), WheelZoomTool())
fig3.title.text = 'Dispersión Geográfica de Itinerarios'
fig3.api_key = 'AIzaSyATl81v4Wnm4udDvlNTcgw4oWMzWJndkfQ'
x = np.linspace(-2, 2, 10)
source = ColumnDataSource(data=dict(
lat=x,
lon=x**2,
sizes=np.linspace(10, 20, 10),
colors=controller.day_colors[0:10],
))
circle = Circle(x="lon",
y="lat",
size='sizes',
fill_color='colors',
fill_alpha=0.6,
line_color='black')
fig3.add_glyph(source, circle, name='circles1')
fig3.add_tools(create_hover(2))
menu1 = Select(title="Periodo:",
value="opt1",
name='menu1',
options=["opt1", "opt2", "opt3", "opt4"],
width=150,
css_classes=['monitoring_menu1'])
menu1.options = controller.periodos_str
menu1.value = controller.periodos_str[controller.now.month - 1]
menu2 = Select(title="Delegación:",
value='TODOS',
name='menu2',
options=['TODOS'],
width=200,
css_classes=['monitoring_menu2'])
menu3 = Select(title="Unicom:",
value="TODOS",
name='menu3',
options=["TODOS"],
width=150,
css_classes=['monitoring_menu3'])
menu4 = Select(title="Día:",
value="TODOS",
name='menu4',
options=["TODOS"],
width=150,
css_classes=['monitoring_menu4'])
menu5 = Select(title="Municipio:",
value="TODOS",
name='menu5',
options=["TODOS"],
width=200,
css_classes=['monitoring_menu5'])
menu6 = Select(title="Tipología:",
value="TODOS",
name='menu6',
options=["TODOS"],
width=150,
css_classes=['monitoring_menu6'])
fig4 = figure(plot_width=600,
plot_height=300,
css_classes=['monitoring_fig4'],
tools='pan,box_zoom,reset',
name='fig4',
title='Promedio de Días Facturados')
fig4.toolbar.logo = None
fig4.toolbar_location = 'above'
fig4.line(x=[0, 1],
y=[0, 1],
line_color="darkgray",
line_width=2,
alpha=0.6,
legend='Planificación',
name='line4')
fig4.line(x=[0, 1],
y=[0, 1],
line_color="blue",
line_width=2,
alpha=0.8,
legend='Real',
name='line4.2')
fig4.legend.click_policy = "hide"
fig4.legend.location = "top_left"
fig4.legend.background_fill_color = "white"
fig4.legend.background_fill_alpha = 0.5
fig4.legend.label_text_color = "#505050"
fig4.legend.orientation = "vertical"
fig4.xaxis.axis_label = 'Mes del Año'
fig4.add_tools(create_hover(4))
fig4.yaxis[0].formatter = NumeralTickFormatter(format="0.0a")
# TODO: visualizar el promedio y el total dias facturados al año
fig5 = figure(plot_width=600,
plot_height=300,
css_classes=['monitoring_fig5'],
tools='pan,box_zoom,reset',
name='fig5',
title='Histograma de Días Facturados')
fig5.toolbar.logo = None
fig5.toolbar_location = 'above'
fig5.vbar(x=[1, 2, 3],
width=0.5,
bottom=0,
top=[1.2, 2.5, 3.7],
color="darkcyan",
fill_alpha=0.6,
line_color='black',
name='vbar1',
legend='Planificación')
fig5.line(x=[0, 1],
y=[0, 1],
line_color="blue",
line_width=2,
alpha=0.8,
legend='Real',
name='line5.2')
fig5.legend.click_policy = "hide"
fig5.legend.location = "top_left"
fig5.legend.background_fill_color = "white"
fig5.legend.background_fill_alpha = 0.5
fig5.legend.label_text_color = "#505050"
fig5.legend.orientation = "vertical"
fig5.xaxis.axis_label = 'Días Facturados'
fig5.add_tools(create_hover(3))
fig5.yaxis[0].formatter = NumeralTickFormatter(format="0.0a")
# TODO: agregar curva de suma acumulativa
fig6 = figure(plot_width=600,
plot_height=300,
css_classes=['monitoring_fig6'],
tools='pan,box_zoom,reset',
name='fig6',
title='Traslados')
fig6.toolbar.logo = None
fig6.toolbar_location = 'above'
fig6.line(x=[0, 1],
y=[0, 1],
line_color="darkgray",
line_width=2,
alpha=0.6,
legend='Planificación',
name='line6')
fig6.line(x=[0, 1],
y=[0, 1],
line_color="blue",
line_width=2,
alpha=0.8,
legend='Real',
name='line6.2')
fig6.legend.click_policy = "hide"
fig6.legend.location = "top_left"
fig6.legend.background_fill_color = "white"
fig6.legend.background_fill_alpha = 0.5
fig6.legend.label_text_color = "#505050"
fig6.legend.orientation = "vertical"
fig6.xaxis.axis_label = 'Mes del Año'
fig6.add_tools(create_hover(4))
fig6.yaxis[0].formatter = NumeralTickFormatter(format="0.0a")
button_group1 = RadioButtonGroup(
labels=["Itinerarios", "Energía", "Importe"],
active=0,
name='button_group1',
css_classes=['monitoring_button_group1'])
button_group1.on_change('active', controller.on_change_menus)
widget1 = layout([[fig1], [fig2]], sizing_mode='fixed')
widget2 = layout([
[fig3],
[menu1, menu2, menu3],
[menu4, menu5, menu6],
],
sizing_mode='fixed')
widget3 = layout([[fig6], [button_group1]], sizing_mode='fixed')
dashboard = layout([
[widget1, widget2],
[fig4, fig5],
[widget3],
],
sizing_mode='fixed')
dashboard.name = 'monitoring'
# ini module data
curdoc().add_root(dashboard)
controller.get_user_data()
controller.populate_menus(controller.info_itin, controller.fechas_itin)
menu1.on_change('value', controller.on_change_menus)
menu2.on_change('value', controller.on_change_menus)
menu3.on_change('value', controller.on_change_menus)
menu4.on_change('value', controller.on_change_menus)
menu5.on_change('value', controller.on_change_menus)
menu6.on_change('value', controller.on_change_menus)
controller.on_change_menus(None, None, None)
print(e_i)
for m_i, mention in enumerate(entity):
print('\t', mention)
if m_i == 0:
entities[e_i] = mention['text']
if mention['isRepresentative']:
entities[e_i] = mention['text']
print(entities)
entities = [str(k) + ': ' + v for k, v in entities.items()]
entity_select.options = entities
selected_entity = 0
entity_select.value = entities[selected_entity]
def update_selected_entity(attrname, old, new):
print(new)
div.update(text="")
parts = new.split(':')
index = parts[0]
#entity_type = ' '.join(parts[1:])
selected_entity = int(index)
update_div(selected_entity)
"""
event_select.on_change('value', update_selected_event)
doc_select.on_change('value', update_selected_document)
#ner_select.on_change('value', update_selected_entity_type)
#entity_select.on_change('value', update_selected_entity)
doc_select.value = '0'
