def __init__(self, controller):
"""Initialize object.
First part of two-part initialization.
Put initialization code here that's very unlikely to fail.
"""
self.controller = controller
self.state = None
self.state_src = None
# State maop of the US.
self.stateusmap = figure(
title="""Electoral college votes by time and geography""",
x_axis_location=None,
y_axis_location=None,
x_axis_type="""linear""",
sizing_mode="""stretch_both""")
self.stateusmap.xgrid.visible = False
self.stateusmap.ygrid.visible = False
# The date for charting.
self.choosethedatefordisplay = DateSlider(
title="""Choose the date for display""",
start="""2018-11-13T20:20:39+00:00""",
end="""2025-11-13T20:20:39+00:00""",
step=24 * 60 * 60 * 1000,
value="""2018-11-13T20:20:39+00:00""",
sizing_mode="stretch_width")
# Layout the widgets
row1 = row(children=[
Spacer(width=10), self.choosethedatefordisplay,
Spacer(width=10)
],
sizing_mode='stretch_width')
self.layout = column(children=[
self.stateusmap, row1,
Spacer(height=75, sizing_mode='scale_width')
],
sizing_mode='stretch_both')
self.panel = Panel(child=self.layout, title='Forecast by geography')
disabled_slider = Slider(title="Disabled",
value=50,
start=0,
end=96,
step=5,
disabled=True)
range_slider = RangeSlider(title="Numerical range",
value=[30, 70],
start=0,
end=100,
step=0.5)
date_slider = DateSlider(title="Date",
value=date(2014, 1, 1),
start=date(2010, 1, 1),
end=date(2020, 1, 1),
step=1)
date_range_slider = DateRangeSlider(title="Date range",
value=(date(2014, 1,
1), date(2018, 12, 31)),
start=date(2010, 1, 1),
end=date(2020, 1, 1),
step=1)
only_value_slider = Slider(value=50, start=0, end=96, step=5)
no_title_slider = Slider(title=None, value=50, start=0, end=96, step=5)
# Set up data
#global:
param_choice = data['resolution2']
#
source = ColumnDataSource(data=dict(x=data['scene_lat'] , y=data['scene_lon'], c=param_choice ))
plot.circle('x', 'y', color='c', source=source, line_width=3, alpha=0.6)
# Set up widgets
text = TextInput(title="title", value='Daily Resolution')
print("You've made it this far, traveller!")
year_slider = Slider(title="Year", value=data['year'].min(), start=data['year'].min(), end=data['year'].max(), step=1)
month_slider = Slider(title="Month", value=data['yearmonth'].min(), start=data['yearmonth'].min(), end=data['yearmonth'].max(), step=1)
day_slider = DateSlider(title="Day", start=data['date'].min(), end=data['date'].max(), value=data['date'].min(), step=1)
#date = DateSlider(title="Day", start=data['date'].min(), end=data['date'].max(), value=data['date'].min(), step=1)
param_button_group = RadioButtonGroup(labels=["Resolution", "Potential Water", "Cloud Coverage", "Gelbstoff"], active=0)
time_button_group = RadioButtonGroup(labels=["Yearly", "Monthly", "Daily"], active=0)
# Set up callbacks
#global:
chosen_slider = year_slider
chosen_timeline = data['year']
inputs = widgetbox(chosen_slider, param_button_group, time_button_group) #(text, )
#
def update_title(attrname, old, new):
plot.title.text = text.value
# Initialize interactive widgets before calling first update
#############################
update()
date_selector = DateRangeSlider(title="Select Timeframe",
end=VARS['global_end'],
start=VARS['global_start'],
step=1,
value=(
VARS['time_range'][0],
VARS['time_range'][1],
))
single_day_selector = DateSlider(
title="Select Day",
end=VARS['global_end'],
start=VARS['global_start'],
step=1,
value=VARS['selected_day'],
)
date_selector.on_change('value', update_time_range)
single_day_selector.on_change('value', update_highlighted_day)
##################################################################
# Bokeh Plots
##################################################################
TOOLS = "pan,wheel_zoom,box_select,lasso_select,reset"
##########
# Stage Time Series
##########
radio_button_group = RadioButtonGroup(labels=["Option 1", "Option 2", "Option 3"], active=0) text_input = TextInput(placeholder="Enter value ...") completions = ["aaa", "aab", "aac", "baa", "caa"] autocomplete_input = AutocompleteInput(placeholder="Enter value (auto-complete) ...", completions=completions) select = Select(options=["Option 1", "Option 2", "Option 3"]) multi_select = MultiSelect(options=["Option %d" % (i+1) for i in range(16)], size=6) slider = Slider(value=10, start=0, end=100, step=0.5) range_slider = RangeSlider(value=[10, 90], start=0, end=100, step=0.5) date_slider = DateSlider(value=date(2016, 1, 1), start=date(2015, 1, 1), end=date(2017, 12, 31)) date_range_slider = DateRangeSlider(value=(date(2016, 1, 1), date(2016, 12, 31)), start=date(2015, 1, 1), end=date(2017, 12, 31)) spinner = Spinner(value=100) color_picker = ColorPicker(color="red", title="Choose color:") date_picker = DatePicker(value=date(2017, 8, 1)) paragraph = Paragraph(text="some text") div = Div(text="some <b>text</b>") pre_text = PreText(text="some text")
'transform': mapper
},
line_color='white',
source=source)
p.add_layout(color_bar, 'right')
return p
palette.reverse()
plot = make_plot(LinearColorMapper(palette=palette, low=-2, high=15))
# Set up widgets
#text=TextInput(title="Title", value='my sine wave')
Date = DateSlider(title="Date",
start=start_date,
end=end_date,
value=start_date,
step=1)
# Set up callbacks
# =============================================================================
# def update_title(attrname, old, new):
# plot.title.text = text.value
#
# text.on_change('value', update_title)
# =============================================================================
# =============================================================================
# group=gdf.groupby('county')
# sample=group.agg({'cofiltered':'mean'}).reset_index(drop=False)
# lnd_bor.join(sample.set_index('county'),on='name')
#
var closest = stats[dates.indexOf(dates.reduce(function(prev, curr) {
return (Math.abs(curr - len) < Math.abs(prev - len) ? curr : prev);
}))]
plots[0].x_range.end=len;
plots[0].y_range.end=closest;
plots[0].y_range.start=0;
plots[1].x_range.end=len;
plots[1].y_range.end=closest;
plots[1].y_range.start=1;
''')
#changes x range to be (0,slider_value)
#changes y range to be (0 or 1, the closest stastics value to slider)
slider = DateSlider(start=min(dates),
end=max(dates),
value=max(dates),
step=1,
title='Time')
slider.js_on_change('value', callback)
loopchange = CustomJS(args=dict(slider=slider,
endlen=max(dates),
minlen=min(dates)),
code='''
function sleep(ms) {
return new Promise(resolve => setTimeout(resolve, ms));
}
async function run() {
var len = slider.value;
var loopcount=0;
while (cb_obj.active){
def bokeh_spacetimepop(
frm,
geometry,
title = '',
preamble = '',
varNames = None,
varNotes = dict(),
pw = 700,
ph = 700,
xZones = dict(),
):
import numpy as np
import pandas as pd
df = pd.DataFrame
idx = pd.IndexSlice
import geopandas as gpd
gdf = gpd.GeoDataFrame
from bokeh.models import ColumnDataSource, HoverTool, Legend, LegendItem, CDSView, IndexFilter
from bokeh.plotting import figure, show
from bokeh.io import output_notebook
# frm = frm.reset_index().pivot(index = frm.index.names[0], columns = frm.index.names[1])
frm = frm.copy()
frm = frm.sort_index()
# geometry = geometry.copy()
from bokeh.models import Div
title = f'<h1>{title}</h1>'
title = Div(
text = title,
width = pw,
)
preamble = Div(
text = preamble,
width = pw,
)
if varNames is None:
varNames = frm.columns.sort_values()
varMetaName = varNames.name
else:
varMetaName = 'variable'
varNames = list(varNames)
seriesNames = frm.index.levels[1].sort_values()
seriesMetaName = seriesNames.name
seriesNames = list(seriesNames)
dates = [str(int(round(i.to_numpy().astype(int) / 1e6))) for i in frm.index.levels[0]]
frm.index = frm.index.set_levels(dates, level = 0)
defaultVar = varNames[0]
defaultDate = dates[-1]
pivotFrm = frm.reset_index() \
.pivot(index = frm.index.names[0], columns = frm.index.names[1]) \
.sort_index()
defaultVar = varNames[0]
defaultDate = dates[-1]
for key in varNames:
if not key in varNotes:
varNotes[key] = ''
else:
varNotes[key] = f'<i>{varNotes[key]}</i>'
varNote = Div(
text = varNotes[defaultVar],
width = pw - 120,
)
lineSources = {
key: ColumnDataSource(pivotFrm[key])
for key in pivotFrm.columns.levels[0]
}
lineSource = ColumnDataSource(pivotFrm[defaultVar])
lineSource.name = defaultVar
barSources = dict()
for varName in varNames:
for index, date in zip(sorted(pivotFrm.index), dates):
series = pivotFrm.loc[index, varName]
subFrm = df(dict(
name = series.index,
value = series.values,
height = abs(series.values),
offset = series.values / 2.
))
barSources[varName + '_' + date] = ColumnDataSource(subFrm)
barSource = ColumnDataSource(barSources[defaultVar + '_' + defaultDate].data)
barSource.name = ', '.join([str(defaultVar), str(defaultDate)])
bounds = geometry.bounds
minx = np.min(bounds['minx'])
maxx = np.max(bounds['maxx'])
miny = np.min(bounds['miny'])
maxy = np.max(bounds['maxy'])
aspect = (maxx - minx) / (maxy - miny)
from shapely.geometry import Polygon
import itertools
corners = list(itertools.product(geometry.total_bounds[::2], geometry.total_bounds[1::2]))
allPoly = Polygon([corners[0], corners[1], corners[3], corners[2]])
allPoly = allPoly.centroid.buffer(np.sqrt(allPoly.area) / 1e6)
for name in frm.index.levels[1]:
if not name in geometry.index:
geometry[name] = allPoly
geometry = geometry.simplify(np.sqrt(geometry.area).min() * 10. ** 3.5)
geoFrm = frm.reset_index().pivot(index = frm.index.names[1], columns = frm.index.names[0])
geoFrm.columns = geoFrm.columns.map('_'.join).str.strip('_')
geoFrm['geometry'] = geometry
geoFrm = gdf(geoFrm)
from bokeh.models import GeoJSONDataSource
geoJSON = geoFrm.reset_index().to_json()
geoSource = GeoJSONDataSource(geojson = geoJSON)
mins = {n: frm[n].min() for n in varNames}
maxs = {n: frm[n].max() for n in varNames}
xName = frm.index.names[0]
lineFig = figure(
x_axis_type = 'datetime',
y_range = (mins[defaultVar], maxs[defaultVar]),
plot_height = int((ph - 100) * 1. / 3.),
plot_width = pw,
toolbar_location = 'left',
tools = 'save, xpan, box_zoom, reset, xwheel_zoom',
active_scroll = 'auto',
# title = title,
)
barFig = figure(
x_range = seriesNames,
plot_height = int((ph - 100) * 1. / 2.),
plot_width = pw,
# title = "Scores on my birthday",
toolbar_location = None,
tools = ""
)
barFig.xgrid.grid_line_color = None
barFig.xaxis.major_label_orientation = 'vertical'
mapFig = figure(
plot_width = pw - 20,
plot_height = int(round((pw - 20) / aspect)),
toolbar_location = 'right',
tools = 'pan, wheel_zoom, reset',
background_fill_color = "lightgrey"
)
mapFig.xgrid.grid_line_color = None
mapFig.ygrid.grid_line_color = None
from matplotlib.pyplot import get_cmap
from matplotlib.colors import rgb2hex
cmap = get_cmap('nipy_spectral')
cs = [rgb2hex(cmap(i / len(seriesNames), alpha = 0.5)) for i in range(len(seriesNames))]
lines = []
for seriesName, colour in zip(seriesNames, cs):
line = lineFig.line(
xName,
seriesName,
source = lineSource,
color = colour,
alpha = 0.8,
muted_color = 'gray',
muted_alpha = 0.3,
muted = True,
line_width = 2,
# legend_label = seriesName,
)
from bokeh.models import HoverTool
lineFig.add_tools(HoverTool(
renderers = [
line,
],
tooltips = [
(seriesMetaName.capitalize(), seriesName),
(xName.capitalize(), f'@{xName}' + '{%Y-%m-%d}'),
('Value', f'@{{{seriesName}}}'),
],
formatters = {
f'@{xName}': 'datetime',
seriesName: 'numeral',
},
toggleable = False
))
lines.append(line)
bars = []
for i, (seriesName, colour) in enumerate(zip(seriesNames, cs)):
view = CDSView(source = barSource, filters = [IndexFilter([i,]),])
bar = barFig.rect(
source = barSource,
view = view,
x = 'name',
y = 'offset',
height = 'height',
width = 0.9,
color = colour,
muted_color = 'gray',
muted_alpha = 0.3,
muted = True,
)
bars.append(bar)
from bokeh.palettes import Viridis256
from bokeh.models import LinearColorMapper, ColorBar
palette = Viridis256
mapColourMapper = LinearColorMapper(
palette = palette,
low = frm.loc[idx[defaultDate, :], defaultVar].min(),
high = frm.loc[idx[defaultDate, :], defaultVar].max(),
)
mapColourBar = ColorBar(
color_mapper = mapColourMapper,
label_standoff = 8,
width = 30,
height = int(round(mapFig.plot_height * 0.9)),
border_line_color = None,
location = (0, 0),
orientation = 'vertical',
)
mapFig.add_layout(mapColourBar, 'left')
patches = []
for i, seriesName in enumerate(seriesNames):
view = CDSView(source = geoSource, filters = [IndexFilter([i,]),])
patch = mapFig.patches(
'xs',
'ys',
source = geoSource,
view = view,
fill_color = dict(
field = '_'.join([defaultVar, defaultDate]),
transform = mapColourMapper,
),
line_color = 'grey',
line_width = 0.25,
fill_alpha = 0.,
name = '_'.join([defaultVar, defaultDate])
)
patches.append(patch)
from bokeh.models import HoverTool
mapHover = HoverTool(
renderers = patches,
tooltips = [
(seriesMetaName.capitalize(), f'@{seriesMetaName}'),
('Value', '@$name'),
]
)
mapFig.add_tools(mapHover)
from bokeh.models import BoxAnnotation
from bokeh.models import Label
for name, zone in xZones.items():
convD = lambda x: int(round(pd.Timestamp(x).to_numpy().astype(int) / 1e6))
left, right = [None if val is None else convD(val) for val in zone]
zone = BoxAnnotation(
left = left,
right = right,
fill_alpha = 0.1,
fill_color = 'gray',
)
zoneLabel = Label(
text = name + ' (end)' if left is None else name,
text_font_size = '8pt',
x = right if left is None else left,
y = 10,
x_units = 'data',
y_units = 'screen',
angle = -90 if left is None else 90,
angle_units = 'deg',
x_offset = -10 if left is None else 10,
y_offset = 5 * (len(name) + 6) if left is None else 0
)
lineFig.add_layout(zone)
lineFig.add_layout(zoneLabel)
from bokeh.models import Span
span = Span(
location = int(defaultDate),
dimension = 'height',
line_color = 'red',
# line_dash = 'dashed',
line_width = 1
)
lineFig.add_layout(span)
from bokeh.models.widgets import DateSlider
slider = DateSlider(
title = 'Date',
start = int(dates[0]),
end = int(dates[-1]),
step = int(8.64 * 1e7), # days
value = int(defaultDate),
width = pw - 60,
align = 'end'
)
from bokeh.models.widgets import Select
select = Select(
title = "Choose data:",
options = varNames,
value = defaultVar,
width = 100,
)
from bokeh.models import CheckboxGroup
checkboxes = CheckboxGroup(
labels = seriesNames,
active = [],
)
checkboxAll = CheckboxGroup(
labels = ['All',],
active = [],
)
from bokeh.models import CustomJS
callback = CustomJS(
args = dict(
y_range = lineFig.y_range,
lineSources = lineSources,
lineSource = lineSource,
barSources = barSources,
barSource = barSource,
bars = bars,
lines = lines,
patches = patches,
select = select,
slider = slider,
span = span,
checkboxes = checkboxes,
varNote = varNote,
varNotes = varNotes,
geoSource = geoSource,
mapColourMapper = mapColourMapper,
mins = mins,
maxs = maxs,
),
code = """
lineSource.data = lineSources[select.value].data
lineSource.name = select.value
lineSource.change.emit()
span.location = slider.value
span.change.emit()
y_range.setv({'start': mins[select.value], 'end': maxs[select.value]})
varNote.text = varNotes[select.value]
varNote.change.emit()
const barChoice = select.value + '_' + slider.value
barSource.data = barSources[barChoice].data
barSource.name = select.value.toString() + ', ' + slider.value.toString()
barSource.change.emit()
for (let i = 0; i < lines.length; i++){
let checked = checkboxes.active.includes(i)
lines[i].muted = !(checked)
bars[i].muted = !(checked)
var alpha = checked ? 1 : 0;
patches[i].glyph.fill_alpha = alpha
}
const newCol = select.value + '_' + slider.value
for (let i = 0; i < lines.length; i++){
patches[i].glyph.fill_color['field'] = newCol
patches[i].name = newCol
}
mapColourMapper.low = mins[select.value]
mapColourMapper.high = maxs[select.value]
geoSource.change.emit()
""",
)
allCheckCallback = CustomJS(
args = dict(
lines = lines,
checkboxes = checkboxes,
checkboxAll = checkboxAll,
callback = callback
),
code = """
checkboxes.active.length = 0
if (checkboxAll.active.length > 0) {
let arr = []
for (let i = 0; i < lines.length; i++){
arr.push(i)
}
checkboxes.active.push(...arr)
}
checkboxes.change.emit()
callback.execute()
"""
)
slider.js_on_change('value', callback)
select.js_on_change('value', callback)
checkboxes.js_on_change('active', callback)
checkboxAll.js_on_change('active', allCheckCallback)
from bokeh.layouts import column, row
layout = column(
title,
preamble,
row(select, varNote),
row(column(lineFig, slider, barFig), column(checkboxes, checkboxAll)),
mapFig
)
return layout
def make_dateMap(frm, name, title, size = 600, nonVisKeys = {}):
minx = np.min(frm.bounds['minx'])
maxx = np.max(frm.bounds['maxx'])
miny = np.min(frm.bounds['miny'])
maxy = np.max(frm.bounds['maxy'])
aspect = (maxx - minx) / (maxy - miny)
ts = sorted(set([n.split('_')[-1] for n in frm.columns]))
ts = [n for n in ts if n.isnumeric()]
assert len(ts)
ns = sorted(set([n.split('_')[0] for n in frm.columns]))
ns = [n for n in ns if not n in [*nonVisKeys, 'geometry']]
assert len(ns)
defaultCol = '_'.join([ns[0], ts[-1]])
indexName = frm.index.name
mins = {n: frm[['_'.join([n, t]) for t in ts]].min().min() for n in ns}
maxs = {n: frm[['_'.join([n, t]) for t in ts]].max().max() for n in ns}
from bokeh.models import GeoJSONDataSource
geoJSON = frm.reset_index().to_json()
source = GeoJSONDataSource(geojson = geoJSON)
from bokeh.io import output_file
outFilename = name + '.html'
outPath = os.path.join(dataDir, outFilename)
if os.path.isfile(outPath):
os.remove(outPath)
output_file(outPath)
from bokeh.plotting import figure
fig = figure(
title = title,
plot_height = size,
plot_width = int(round(size * aspect)) + 50,
toolbar_location = 'right',
tools = 'pan, zoom_in, zoom_out, wheel_zoom, reset',
background_fill_color = "lightgrey"
)
fig.xgrid.grid_line_color = None
fig.ygrid.grid_line_color = None
from bokeh.palettes import Viridis256
from bokeh.models import LinearColorMapper, ColorBar
palette = Viridis256
colourMapper = LinearColorMapper(
palette = palette,
low = mins[ns[0]],
high = maxs[ns[0]],
)
colourBar = ColorBar(
color_mapper = colourMapper,
label_standoff = 8,
width = 30,
height = int(round(fig.plot_height * 0.9)),
border_line_color = None,
location = (0, 0),
orientation = 'vertical',
)
fig.add_layout(colourBar, 'left')
patches = fig.patches(
'xs',
'ys',
source = source,
fill_color = dict(
field = defaultCol,
transform = colourMapper,
),
line_color = 'grey',
line_width = 0.25,
fill_alpha = 1,
name = defaultCol
)
from bokeh.models.widgets import DateSlider as Slider
slider = Slider(
title = 'Date',
start = int(ts[0]),
end = int(ts[-1]),
step = int(8.64 * 1e7), # days
value = int(ts[-1]),
width = fig.plot_width - 70
)
from bokeh.models.widgets import Select
select = Select(
title = "Dataset",
options = ns,
value = defaultCol.split('_')[0],
width = 60
)
from bokeh.models import CustomJS
callback = CustomJS(
args = dict(
patches = patches,
source = source,
slider = slider,
# key = 'stay', # <--- TESTING
select = select,
colourMapper = colourMapper,
mins = mins,
maxs = maxs,
),
code = """
const newCol = select.value + '_' + slider.value
patches.glyph.fill_color['field'] = newCol
patches.name = newCol
colourMapper.low = mins[select.value]
colourMapper.high = maxs[select.value]
source.change.emit()
""",
)
from bokeh.models import HoverTool
tooltips = [
('Index', '@' + indexName),
('Value', '@$name')
]
tooltips.extend([(k.capitalize(), '@' + k) for k in nonVisKeys])
hover = HoverTool(
renderers = [patches],
tooltips = tooltips
)
fig.add_tools(hover)
slider.js_on_change('value', callback)
select.js_on_change('value', callback)
from bokeh.layouts import column, row
layout = column(fig, row(select, slider))
from bokeh.io import show
show(layout)
return {
'selected': ColumnDataSource(data=selected),
'unselected': ColumnDataSource(unselected)
}
def update_plot():
src = get_datasets(df)
selected_data_source.data.update(src['selected'].data)
unselected_data_source.data.update(src['unselected'].data)
df0 = pd.read_pickle(join(dirname(__file__), 'data', 'tremors.pkl'))
df = df0.sample(frac=0.1)
date_slider = DateSlider(start=df['date'].min(),
end=df['date'].max(),
step=100,
value=df['date'].min())
date_slider.on_change('value', lambda attr, old, new: update_plot())
slider_box = widgetbox(children=[date_slider], width=600)
datasets_init = get_datasets(df) # get bokeh ColumnDataSource
selected_data_source = datasets_init['selected']
unselected_data_source = datasets_init['unselected']
p = make_plot(selected_data_source, unselected_data_source)
layout = layout(children=[[p], [slider_box]], sizing_mode='fixed')
curdoc().add_root(layout)
# Define the callback function: update_plot
def update_plot(attr, old, new):
day = datetime.utcfromtimestamp(date_slider.value //
1000.0) #date_slider.value #slider.value
#geosource = GeoJSONDataSource(geojson = json_data(day))
new_data = json_data(day)
geosource.geojson = new_data
p.title.text = 'CovIndex, {:%d %b %Y}'.format(day) #'CovIndex, %d' %day
date_slider = DateSlider(
title="Date Range: ",
start=date(2020, 3, 25),
end=date(2020, 5, 14),
value=date(2020, 3, 25),
step=86400000
) # https://discourse.bokeh.org/t/bokeh-dateslider-still-broken/2466
date_slider.on_change('value', update_plot)
# Make a column layout of widgetbox(slider) and plot, and add it to the current document
layout = column(p, column(date_slider))
curdoc().add_root(layout)
#Display plot inline in Jupyter notebook
output_notebook()
#Display plot
show(layout)
def scatter(df):
#global df1
def make_dataset(df):
return ColumnDataSource(df)
def style(p):
# Title
p.title.align = 'center'
p.title.text_font_size = '20pt'
p.title.text_font = 'serif'
# Axis titles
p.xaxis.axis_label_text_font_size = '14pt'
p.xaxis.axis_label_text_font_style = 'bold'
p.yaxis.axis_label_text_font_size = '14pt'
p.yaxis.axis_label_text_font_style = 'bold'
# Tick labels
p.xaxis.major_label_text_font_size = '12pt'
p.yaxis.major_label_text_font_size = '12pt'
return p
def make_plot(src):
# Blank plot with correct labels
p = figure(plot_width = 1000, plot_height = 600, title = 'Confirmed Cases vs Recovered Cases (Size of glyph = Confirmed Death)',
x_axis_label = 'Confirmed Cases', y_axis_label = 'Recovered Cases')
p.circle('confirmed_cases', 'recovered_cases', source=src, fill_alpha=0.7, size='death1',
hover_fill_color = 'purple', hover_fill_alpha = 0.7, color='color', legend_field = 'continent')
hover = HoverTool(tooltips=[('As at', '@Date{%F}'),
('Country', '@Country'),
('Confirmed', '@confirm'),
('Recovered', '@recovered'),
('Death', '@death')],
formatters={'@Date': 'datetime'})
p.add_tools(hover)
p.legend.location = "center_right"
p.legend.click_policy = 'hide'
# Styling
p = style(p)
return p
# Callback function
def update(attr, old, new):
continent_to_plot = [continent_selection.labels[i] for i in
continent_selection.active]
df1 = df.set_index(['continent'])
df1 = df1.loc[continent_to_plot]
a = day_slider.value_as_date
date = pd.to_datetime(a)
d = df1[df1['Date'] == date]
new_src = make_dataset(d)
src.data.update(new_src.data)
def animate_update():
day = day_slider.value_as_date + timedelta(days=1)
if day>df['Date'].max():
day = df['Date'].min()
day_slider.value = day
def animate():
global callback_id
if button.label == '► Play':
button.label = '❚❚ Pause'
callback_id = curdoc().add_periodic_callback(animate_update, 200)
else:
button.label = '► Play'
curdoc().remove_periodic_callback(callback_id)
callback_id = None
button = Button(label='► Play', width=60)
button.on_click(animate)
value = list(df['continent'].unique())
continent_selection = CheckboxGroup(labels=value, active = [0, 1])
continent_selection.on_change('active', update)
day_slider = DateSlider(title="Date: ", start=df['Date'].min(), end=df['Date'].max(),
value=df['Date'].max(), step=1)
day_slider.on_change('value', update)
controls = row(continent_selection, day_slider, button)
initial = [continent_selection.labels[i] for i in continent_selection.active]
df1 = df.set_index(['continent'])
df1 = df1.loc[initial]
dat = df1['Date'].max()
d = df1[df1['Date'] == dat]
src = make_dataset(d)
p = make_plot(src)
layout = column([controls, p])
tab = Panel(child = layout, title = 'Progression')
return tab
def third_tab_create(filterData):
#def arima(date = dummy_date, house = dummy_house , data = dummy_data,trainDays = dummy_trainDays):
def arima(date, state, data, trainDays):
houseData = filterData.groupby(
filterData[filterData['state'] == state]['time']).sum()
houseData['time'] = houseData.index
#houseData['time'] = pd.to_datetime(houseData['time'])
#houseData = houseData[['car1','grid','solar','time','load']]
houseData = houseData[['time', data]]
#houseData = filterData[filterData['dataid'] == house][['time',data]]
#houseData = houseData.sort_values('time', ascending = True)
houseData = houseData.sort_index()
#houseData.index = houseData['time']
startDate = pd.to_datetime(date) + pd.DateOffset(days=-trainDays)
endDate = pd.to_datetime(date) + pd.DateOffset(days=1)
daterange = [startDate, endDate]
houseData = houseData.loc[daterange[0]:daterange[1], :]
houseData[data] = houseData[data] * 60 * 15 / 3600 # kWh
#houseData[data] = ( houseData[data] > .01 ) * houseData[data]
weekdays = houseData[houseData.index.dayofweek < 5]
weekends = houseData[houseData.index.dayofweek > 4]
weekdayProfile = weekdays.groupby(weekdays['time'].dt.time).mean()
weekendProfile = weekends.groupby(weekends['time'].dt.time).mean()
houseData['detrend'] = houseData[data]
for i in range(0, len(houseData)):
if houseData['time'][i].dayofweek > 4:
houseData['detrend'][i] = houseData['detrend'][
i] - weekendProfile[weekendProfile.index ==
houseData['time'].dt.time[i]][data]
else:
houseData['detrend'][i] = houseData['detrend'][
i] - weekdayProfile[weekdayProfile.index ==
houseData['time'].dt.time[i]][data]
trainData = houseData['detrend']
stepwise_model = auto_arima(trainData,
start_p=1,
start_q=1,
max_p=3,
max_q=3,
m=7,
start_P=0,
seasonal=True,
d=1,
D=1,
trace=True,
error_action='ignore',
suppress_warnings=True,
stepwise=True)
train = houseData[data].loc[startDate:date]
test = pd.DataFrame(data=houseData.loc[date:endDate])
test = test.drop(columns='detrend')
future_forecast = stepwise_model.predict(n_periods=len(test))
test['arima'] = future_forecast
if pd.to_datetime(date).dayofweek > 4:
aveProfile = weekendProfile
else:
aveProfile = weekdayProfile
for i in range(0, len(test)):
test['arima'][i] = test['arima'][i] + aveProfile[
aveProfile.index == test['time'].dt.time[i]][data]
test['error'] = abs(test[data] - test['arima'])
#test['error'] = ( test['error'] > .03 ) * test['error']
test = test.rename(columns={data: 'data'})
test = test.drop(columns='time')
#mape = 100 * sum( abs( test['error'] / test['data'] ) ) / len (test)
mape = 100 * sum(abs(test['error'] / test['data'].max())) / len(test)
print(stepwise_model.summary())
return ColumnDataSource(test), mape
def plot1_plot(src, mape):
plot1 = figure(
title='PV Generation + Battery(Discharge) forecasting of NY',
x_axis_label='Time',
y_axis_label='Generation [kWh]',
x_axis_type="datetime")
a = plot1.line('time', 'data', source=src, color='blue')
b = plot1.line('time', 'arima', source=src, color='green')
c = plot1.line('time', 'error', source=src, color='red')
plot1.plot_width = 1300
legend = Legend(items=[
LegendItem(label="Raw Data", renderers=[a], index=0),
LegendItem(label="Forecast", renderers=[b], index=1),
LegendItem(label="Error", renderers=[c], index=2),
])
plot1.add_layout(legend)
plot1.legend.title = f'Abs Error = {round(mape1,3)}%'
return plot1
def update(attr, old, new):
button.update(button_type='warning', label='Loading, please wait')
def calculate():
global home_to_plot, state_dict
data_selector = data_type_selector.labels[
data_type_selector.active]
if data_selector == 'Net Load':
data_to_plot = 'grid'
plot1.yaxis.axis_label = 'Net Load [kWh]'
if data_selector == 'Load + Battery(Charging)':
data_to_plot = 'Load_+_Battery(Charging)'
plot1.yaxis.axis_label = 'Load [kWh]'
if data_selector == "Electric Vehicle Consumption":
data_to_plot = 'car1'
plot1.yaxis.axis_label = 'Consumption [kWh]'
if data_selector == "PV Generation + Battery(Discharge)":
data_to_plot = 'PV_+_Battery(Discharge)'
plot1.yaxis.axis_label = 'Generation [kWh]'
trainDays_to_plot = int(trainDays_input.value)
new_home_to_plot = state_dict[community_selector.active]
#new_home_to_plot = int(home_id_selector.value) ###
plot1.title.text = f'{data_selector} forecasting of {new_home_to_plot} for date {date_slider.value}'
if new_home_to_plot != home_to_plot:
startDate = filterData[
filterData['state'] ==
new_home_to_plot]['time'].iloc[0].date() ##change
endDate = filterData[filterData['state'] ==
new_home_to_plot]['time'].iloc[-1].date()
middle = startDate + (endDate - startDate) / 1.5
date_slider.start = startDate
date_slider.end = endDate
date_slider.value = middle
date_to_plot = str(middle)
print(startDate, endDate, middle)
#daterange_raw = list(date_slider.value_as_datetime)
#daterange_to_plot = [daterange_raw[0].strftime("%Y-%m-%d"), daterange_raw[1].strftime("%Y-%m-%d")]
date_to_plot = date_slider.value
new_src1, new_mape1 = arima(date=date_to_plot,
state=new_home_to_plot,
data=data_to_plot,
trainDays=trainDays_to_plot)
src1.data.update(new_src1.data)
plot1.legend.title = f'Abs Error = {round(new_mape1,3)}%'
button.update(button_type='success', label='Done')
home_to_plot = new_home_to_plot
curdoc().add_next_tick_callback(calculate)
## Initialize src and plot
src1, mape1 = arima(date='2019-07-20',
state='NY',
data='PV_+_Battery(Discharge)',
trainDays=2)
plot1 = plot1_plot(src1, mape1)
## Date Slider
date_slider = DateSlider(title="Date: ",
start=date(2019, 5, 1),
end=date(2019, 8, 20),
value=date(2019, 7, 20),
step=1,
callback_policy='mouseup',
width=1300)
date_slider.on_change("value_throttled", update)
## Text input
trainDays_input = TextInput(value='2',
title='Training Days',
max_width=200,
max_height=50)
trainDays_input.on_change('value', update)
## Data Options
data_type_selector = RadioGroup(labels=[
"PV Generation + Battery(Discharge)", "Load + Battery(Charging)",
"Net Load", "Electric Vehicle Consumption"
],
active=0)
data_type_selector.on_change('active', update)
## Loading Status
button = Button(label="Done", button_type="success")
## Home Selector
#home_ids_available = np.unique(filterData['dataid'])
#home_ids_available = list(map(str, home_ids_available))
#home_id_selector = Dropdown(label="Home ID", button_type="warning", menu=home_ids_available, value="5679", max_width = 200)
#home_id_selector.on_change('value',update)
## Agg house selection
community_selector = RadioGroup(labels=list(np.unique(
filterData['state'])),
active=6,
max_width=200)
community_selector.on_change('active', update)
row1 = row(
plot1,
column(data_type_selector,
trainDays_input,
community_selector,
button,
sizing_mode="scale_width"))
row2 = row(date_slider)
## Layout
layout = column(row1, row2)
tab = Panel(child=layout, title='Forecasting')
return tab
class ForecastByGeography():
"""Shows US map and electoral college votes by state."""
# %%
def __init__(self, controller):
"""Initialize object.
First part of two-part initialization.
Put initialization code here that's very unlikely to fail.
"""
self.controller = controller
self.state = None
self.state_src = None
# State maop of the US.
self.stateusmap = figure(
title="""Electoral college votes by time and geography""",
x_axis_location=None,
y_axis_location=None,
x_axis_type="""linear""",
sizing_mode="""stretch_both""")
self.stateusmap.xgrid.visible = False
self.stateusmap.ygrid.visible = False
# The date for charting.
self.choosethedatefordisplay = DateSlider(
title="""Choose the date for display""",
start="""2018-11-13T20:20:39+00:00""",
end="""2025-11-13T20:20:39+00:00""",
step=24 * 60 * 60 * 1000,
value="""2018-11-13T20:20:39+00:00""",
sizing_mode="stretch_width")
# Layout the widgets
row1 = row(children=[
Spacer(width=10), self.choosethedatefordisplay,
Spacer(width=10)
],
sizing_mode='stretch_width')
self.layout = column(children=[
self.stateusmap, row1,
Spacer(height=75, sizing_mode='scale_width')
],
sizing_mode='stretch_both')
self.panel = Panel(child=self.layout, title='Forecast by geography')
# %%
def setup(self):
"""Set up object.
Second part of two-part initialization.
Place initialization code here that's more likely to fail.
"""
# Load the files containing the state outlines and the Alaska/Hawaii
# dividing lines
_folder = os.path.dirname(os.path.realpath(__file__))
_state_j = json.load(
open(os.path.join(_folder, MAP_FOLDER, "state.json"), 'r'))
_state = pd.DataFrame(_state_j['data'])
_state = _state.sort_values('State abbreviation')
_state['color'] = random.choices(brewer['RdBu'][11], k=_state.shape[0])
_state['Democratic percentage'] = np.random.rand(_state.shape[0])
_state['Republican percentage'] = np.random.rand(_state.shape[0])
_frame_j = json.load(
open(os.path.join(_folder, MAP_FOLDER, "frame.json"), 'r'))
# Set up the sources
self.state_src = ColumnDataSource(_state)
frame_src = ColumnDataSource(
data=dict(x=_frame_j['data']['x'], y=_frame_j['data']['y']))
# Draw the states and the lines
states = self.stateusmap.patches(xs='x',
ys='y',
source=self.state_src,
fill_alpha=0.5,
fill_color='color',
line_color="gray",
line_width=0.5)
# The frame that separates AK, HI from the rest of the US
self.stateusmap.multi_line(xs='x',
ys='y',
source=frame_src,
line_color="gray",
line_width=1.0)
# Now set up the hover tool - so the state name is given
hover = HoverTool(point_policy="follow_mouse",
renderers=[states],
tooltips=[
("State name", "@{State name}"),
("State abbreviation", "@{State abbreviation}"),
("Democratic",
"@{Democratic percentage}{%0.1f}"),
("Republican", "@{Republican percentage}{%0.1f}")
])
self.stateusmap.add_tools(hover)
# Setup the callbacks.
self.choosethedatefordisplay.on_change(
"value", self.callback_choosethedatefordisplay)
# %%
def update(self, state):
"""Update view object."""
# Make a copy of the state data and change the copy
self.state = state.copy()
self.state['color index'] = self.state['Spread D-R'] * 100
self.state['color index'] = pd.cut(
self.state['color index'],
[-100, -10, -5, -2, -1, -0.5, 0.5, 1, 2, 5, 10, 100],
labels=[10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0])
self.state['color'] =\
self.state['color index'].map(
{k: v for k, v in enumerate(brewer['RdBu'][11])})
self.choosethedatefordisplay.start = self.state['Date'].min()
self.choosethedatefordisplay.value = self.state['Date'].max()
self.choosethedatefordisplay.end = self.state['Date'].max()
self._update_chart(self.choosethedatefordisplay.value_as_datetime)
# %%
def _update_chart(self, date):
"""Update chart based on date."""
_slice = self.state[self.state['Date'] == date]
self.state_src.data['color'] = \
_slice[['State abbreviation',
'color']].sort_values(
'State abbreviation')['color'].to_list()
self.state_src.data['Democratic percentage'] = \
_slice[['State abbreviation',
'Democratic proportion']].sort_values(
'State abbreviation')[
'Democratic proportion'].to_list()
self.state_src.data['Republican percentage'] = \
_slice[['State abbreviation',
'Republican proportion']].sort_values(
'State abbreviation')[
'Republican proportion'].to_list()
# %%
def callback_choosethedatefordisplay(self, attrname, old, new):
"""Execute callback method for self.choosethedatefordisplay."""
# pylint: disable=W0613
self._update_chart(self.choosethedatefordisplay.value_as_datetime)