# =============================================================================
# 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')
#
# pd.DataFrame(lnd_bor.name).join(sample.set_index('county'),on='name')
# =============================================================================
def update_data(attrname, old, new):
co_now = lnd_bor[['name', 'geometry', Date.value]]
co_now = co_now.rename(columns={Date.value: 'Ave_CO'})
gjson = co_now.to_json()
source.geojson = gjson
Date.on_change('value', update_data)
# Set up layouts and add to document
inputs = widgetbox(Date)
curdoc().add_root(row(inputs, plot))
curdoc().title = "London"
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
##########
hydrograph = figure(plot_width=1000,
plot_height=350,
tools=TOOLS + ',box_zoom,hover',
toolbar_location="above",
toolbar_sticky=False,
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)
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)
# Activate callback function with Bokeh Server
# (run the command below on Anaconda Prompt, from the corect directory)
# (it will open a new browser page)
# bokeh serve --show 02806_DateSliderMap.py
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)