def assemble_metric_comparison_table(self, daily_metrics, model_dropdown,
time_period_slider):
model = model_dropdown.value
date_start, date_end = time_period_slider.value_as_datetime
target_df = daily_metrics[model][date_start:date_end]
metric_data = self._calculate_metrics(target_df)
source = ColumnDataSource(data=metric_data)
columns = [
TableColumn(field="precision", title="Precision"),
TableColumn(field="recall", title="Recall"),
TableColumn(field="f1_score", title="F1 Score"),
TableColumn(field="sample_size", title="Sample Size"),
TableColumn(field="threshold", title="Threshold")
]
data_table = DataTable(source=source,
columns=columns,
width=600,
height=280)
title_div = Div()
title_div.text = "Precision, Recall, F1 Score, Sample Size and Threshold for <strong>{}</strong>".format(
model)
def update(attr, old, new):
model = model_dropdown.value
date_start, date_end = time_period_slider.value_as_datetime
title_div.text = "Precision, Recall, F1 Score, Sample Size and Threshold for <strong>{}</strong>".format(
model)
model_metrics = daily_metrics[model][date_start:date_end]
source.data = ColumnDataSource.from_df(
self._calculate_metrics(model_metrics))
model_dropdown.on_change('value', update)
time_period_slider.on_change('value', update)
return row(column(title_div, data_table))
def create_2d_molecule(data, title="Generated Molecules", position=-1, width=850, height=590,
css_classes=["img_outside"]):
img_fig = Div(text="", width=width, height=height)
img_fig.css_classes = css_classes
if position < 0:
position = len(data) - 1
smiles, score = extract_compounds(data, position)
mols = []
scores = []
for i, smile in enumerate(smiles):
mol = Chem.MolFromSmiles(smile)
if mol:
mols.append(mol)
scores.append(str(score[i]))
if len(mols) >= 6:
break
if len(mols) > 0:
img = Draw.MolsToGridImage(mols, molsPerRow=3, legends=scores, subImgSize=(250, 250), useSVG=True)
img = img.replace("FFFFFF", "EDEDED")
else:
img = ""
img_fig.text = '<h2>' + title + ' Step: ' + str(position) + '</h2>' + '<div class="img_inside">' + img + '</div>'
return img_fig
f_update()
def preview():
read_values()
c_update()
f_update()
c_controls = [ov_min_text, ov_min1_text]
for cc in c_controls:
cc.on_change("value", lambda attr, old, new: change_values())
preview_clust_button.on_click(preview)
reset_button.on_click(reset_values)
### Page layout
top_title = Div( text="<h3></h3>", name='top_title')
if trimmed:
top_title.text = "<h3>Caution: some regions had >50 hits, and have been truncated.</h3>"
cl_title = Div( text="<h3>Clustering parameters:</h3>" )
empty_vert = Div( text="<br><br>" )
goto_summary_button = Div(text='''<form name="Goto_summary" action="/'''+orf+'''" method="get">
<input type="hidden" name="prob" id="prob" value="'''+str(prob_def)+'''"/>
<input type="hidden" name="ovm" id="ovm" value="'''+str(om_def)+'''" />
<input type="hidden" name="ovm1" id="ovm1" value="'''+str(om_def1)+'''" />
<button type="submit">Go to summary view</button>
</form>''', name='goto_summary_button')
page = layout( [widgetbox(top_title)],
[widgetbox(threshold_text),
widgetbox(cl_title, ov_min_text, ov_min1_text),
widgetbox(empty_vert, preview_clust_button, reset_button),
widgetbox(goto_summary_button)],
[p],
sizing_mode='scale_width' )
year_input.on_change('value', season_handler)
# arrange layout
tab1 = Panel(child=plot1_wrap, title='Scores')
tab2 = Panel(child=plot2_wrap, title='Expected Wins')
tab3 = Panel(child=table_wrap, title='Summary')
figures = Tabs(tabs=[tab1, tab2, tab3], width=500)
compare_widgets = column(team1_dd, team2_dd, comp_button)
wid_spac1 = Spacer(height=30)
wid_spac2 = Spacer(height=30)
wid_spac3 = Spacer(height=30)
lg_id_message.text = '<b><p style="color: green;">League accessed successfully.</p></b>'
all_widgets = column(lg_id_input, lg_id_message, wid_spac1, compare_widgets,
wid_spac2, week_slider, wid_spac3, year_input)
page_title = Div(
text=
"""<strong><h1 style="font-size: 2.5em;">ESPN Fantasy Football League Explorer</h1></strong>""",
width=700,
height=50)
main_area = row(all_widgets, figures)
layout = column(page_title, main_area)
curdoc().add_root(layout)
def selection_tab(rtdap_data):
def make_base_map(tile_map=CARTODBPOSITRON_RETINA,
map_width=800,
map_height=500,
xaxis=None,
yaxis=None,
xrange=(-9990000, -9619944),
yrange=(5011119, 5310000),
plot_tools="pan,wheel_zoom,reset,save"):
p = figure(tools=plot_tools,
width=map_width,
height=map_height,
x_axis_location=xaxis,
y_axis_location=yaxis,
x_range=xrange,
y_range=yrange,
toolbar_location="above")
p.grid.grid_line_color = None
#p.background_fill_color = None
p.background_fill_alpha = 0.5
p.border_fill_color = None
p.add_tile(tile_map)
return p
def rtdap_avg(df, corr, value):
"""
returns mean for specificed attribute by highway corridor
Keyword arguments:
df -- dataframe to filter by corridor and calculate mean
corr -- corridor name
value -- dataframe column name to calculate mean
"""
df_corr = df.loc[df['corridor'] == corr]
mean_value = df_corr[value].mean()
return mean_value
def filter_selection(df, corr, date_s, date_e, weekday, tod):
"""
returns subset of data based on corridor and time selections
Keyword arguments:
df -- dataframe to filter by corridor and time selections
corr -- corridor name
date_s -- start date
date_e -- end date
weekday -- day of week (Monday - Friday)
tod -- time of day (8 tod time periods)
"""
tod_start = tod[0]
tod_end = tod[1]
date_start = datetime.strptime(date_s, '%Y-%m-%d')
date_end = datetime.strptime(date_e, '%Y-%m-%d')
if weekday == 'All':
weekday = df['dow'].drop_duplicates().values.tolist()
else:
weekday = [weekday]
select_df = df.loc[(df['corridor'] == corr) &\
(df['date']>=date_start) & (df['date']<=date_end) &\
(df['dow'].isin(weekday)) &\
(df['hour']>=tod_start) & (df['hour']<=tod_end)]
return select_df
def summarize_metrics(df, corr, group, avg, select, label, missing):
"""
return a summary of frequency, mean, mean difference, and count of missing values
Keyword arguments:
df -- dataframe to summarize
corr -- corridor name
group -- dataframe column name used to group and summarize data
avg -- mean value derived from rtdap_avg(), used calculate mean diff
select -- dateframe column name to calculate mean
label -- name for values being calculate (ie Speed, Volumne, Time etc)
missing -- dataframe column name of missing values
"""
df['freq'] = 1
df_groupby = df.groupby(group).agg({
'freq': 'count',
select: np.mean,
missing: sum
}).reset_index()
df_groupby.loc[:,
'Mean Diff'] = (avg -
df_groupby[select]) / df_groupby[select]
df_groupby.loc[:, group] = label
df_groupby.columns = [
corr, 'Frequency', 'Mean', 'Missing Values', 'Mean Diff'
]
df_groupby = df_groupby.set_index(corr)
return df_groupby[['Frequency', 'Mean', 'Mean Diff', 'Missing Values']]
def vbar_chart(full_df, df):
"""
returns bokeh horizontal barchart representing mean % diff
Keyword arguments:
df -- dataframe to derive content of barchart
col -- column name for values to diplay in graph
"""
df_avg = full_df.groupby('FieldDeviceID').agg({'avgSpeed': np.mean})
df_select = df.groupby('FieldDeviceID').agg({'avgSpeed': np.mean})
df_select.columns = ['speed']
diff = df_avg.merge(df_select,
how='left',
left_index=True,
right_index=True).fillna(0)
diff_no_zero = diff.loc[(diff['avgSpeed'] > 0) & (diff['speed'] > 0)]
diff_no_zero['speed_difference'] = diff_no_zero[
'avgSpeed'] - diff_no_zero['speed']
diff_no_zero = diff_no_zero.reset_index()
diff_no_zero['bins'] = pd.cut(diff_no_zero['speed_difference'],
bins=list(range(-20, 22)),
labels=list(range(-20, 22))[:-1])
source = ColumnDataSource(
data=diff_no_zero.groupby('bins').agg({
'speed_difference': 'count'
}).reset_index())
p = figure(plot_width=1000,
plot_height=150,
title="Speed Difference Distribution",
toolbar_location="above")
p.vbar(x='bins',
top='speed_difference',
width=1,
color='navy',
alpha=0.5,
source=source)
#p.yaxis.visible = False
#p.xaxis.formatter = NumeralTickFormatter(format="0.f%")
p.xgrid.visible = False
p.ygrid.visible = False
#p.background_fill_color = None
p.background_fill_alpha = 0.5
p.border_fill_color = None
return p
def hbar_chart(df, col):
"""
returns bokeh horizontal barchart representing mean % diff
Keyword arguments:
df -- dataframe to derive content of barchart
col -- column name for values to diplay in graph
"""
df_src = df[[col]]
df_src['type'] = df_src.index
df_src['order'] = 0
df_src['order'] = np.where(df_src.index == 'Speed', 2, df_src['order'])
df_src['order'] = np.where(df_src.index == 'Occupancy', 1,
df_src['order'])
df_src['order'] = np.where(df_src.index == 'Volume', 0,
df_src['order'])
df_src['color'] = '#C0C0C0'
df_src['color'] = np.where(df_src['Mean Diff'] < -.05, '#FF0000',
df_src['color'])
df_src['color'] = np.where(df_src['Mean Diff'] > .05, '#008000',
df_src['color'])
source = ColumnDataSource(data=df_src.sort_values(by='order'))
hover = HoverTool(tooltips=[
("Corridor Attribute", "@type"),
("% Difference", "@{%s}" % (col) + '{%0.2f}'),
])
tools = ['reset', 'save', hover]
p = figure(plot_width=400,
plot_height=175,
toolbar_location="above",
title='Mean Difference',
tools=tools)
p.hbar(y='order',
height=0.5,
left=0,
fill_color='color',
line_color=None,
right=col,
color="navy",
source=source)
p.yaxis.visible = False
p.xaxis.formatter = NumeralTickFormatter(format="0.f%")
p.xgrid.visible = False
p.ygrid.visible = False
#p.background_fill_color = None
p.background_fill_alpha = 0.5
p.border_fill_color = None
return source, p
def scatter_plot(title_text):
rng = np.random.RandomState(0)
x = rng.randn(100)
y = rng.randn(100)
source = ColumnDataSource(data=dict(
x=rng.randn(100),
y=rng.randn(100),
desc=['A', 'b', 'C', 'd', 'E'] * 20,
))
hover = HoverTool(tooltips=[
("index", "$index"),
("(x,y)", "($x, $y)"),
("desc", "@desc"),
])
p = figure(plot_width=300,
plot_height=250,
tools=[hover, 'box_select'],
toolbar_location="above",
title=title_text)
p.circle('x', 'y', size=5, source=source)
#p.background_fill_color = None
p.background_fill_alpha = 0.5
p.border_fill_color = None
return p
"""
return selection tab contents
Keyword arguments:
rtdap_data - dataframe containing rtdap vds detail data
"""
#-----------------------------------------------------------------------------------------------------------------
#submit_selection -- Data Selection Update Function
def submit_selection():
"""
python callback to update table and visual content based on
user selections in the data review panel
"""
avgs_speed = rtdap_avg(rtdap_data, corridor_select.value, 'avgSpeed')
avgs_occ = rtdap_avg(rtdap_data, corridor_select.value, 'avgOccupancy')
avgs_volume = rtdap_avg(rtdap_data, corridor_select.value, 'avgVolume')
filtered_data = filter_selection(rtdap_data, corridor_select.value,
str(date_picker_start.value),
str(date_picker_end.value),
day_of_week.value, time_of_day.value)
speed = summarize_metrics(filtered_data, corridor_select.value,
'corridor', avgs_speed, 'avgSpeed', 'Speed',
'missing_speed')
occ = summarize_metrics(filtered_data, corridor_select.value,
'corridor', avgs_occ, 'avgOccupancy',
'Occupancy', 'missing_occ')
volume = summarize_metrics(filtered_data, corridor_select.value,
'corridor', avgs_volume, 'avgVolume',
'Volume', 'missing_vol')
summary_df = speed.append(occ)
summary_df = summary_df.append(volume)
summary_title.text = "<h1>" + corridor_select.value + " Summary</h1>"
summary_df_tbl = summary_df.copy()
summary_df_tbl['Mean Diff'] = summary_df_tbl['Mean Diff'] * 100
summary_df_tbl = summary_df_tbl.reset_index()
summary_table.text = str(
summary_df_tbl.fillna(0).to_html(
index=False,
formatters=[
str, '{:20,}'.format, '{:20,.1f}'.format,
'{:20,.1f}%'.format, '{:20,}'.format
],
classes=["w3-table", "w3-hoverable", "w3-small"]))
if len(summary_df) > 0:
new_df = summary_df.fillna(0)
bar_viz_new = hbar_chart(new_df, 'Mean Diff')[0]
bar_viz_src.data.update(bar_viz_new.data)
#-----------------------------------------------------------------------------------------------------------------
#Data Review Panel
panel_title = Div(
text="Data Review",
css_classes=["panel-heading", "text-center", "w3-text-white"])
panel_text = Div(
text=
"""Lorem Ipsum is simply dummy text of the printing and typesetting industry.
Lorem Ipsum has been the industry's standard dummy text ever since the 1500s,
when an unknown printer took a galley of type and scrambled it to make a type
specimen book.""",
css_classes=["panel-content", "w3-text-white"])
#Panel Buttons
corridor_select = Select(
options=rtdap_data['corridor'].drop_duplicates().values.tolist(),
title='Corridor:',
height=60,
value='Dan Ryan Express Lane',
css_classes=["panel-content"])
date_picker_start = DatePicker(min_date=date(2015, 1, 1),
max_date=date(2018, 12, 31),
css_classes=["panel-content"],
title="Start Date:",
height=60,
value=date(2015, 12, 31))
date_picker_end = DatePicker(min_date=date(2015, 1, 1),
max_date=date(2018, 12, 31),
css_classes=["panel-content"],
title="End Date:",
height=60,
value=date(2017, 12, 31))
time_of_day = RangeSlider(start=1,
end=8,
step=1,
value=(1, 2),
title="Time of Day:",
bar_color="black",
css_classes=["panel-content"])
tod_description = Div(
text="""Time of Day Categories:<br>
<ol>
<li>8pm-6am</li>
<li>6pm-7am</li>
<li>7am-8am</li>
<li>9am-10am</li>
<li>10am-2pm</li>
<li>2pm-4pm</li>
<li>4pm-6pm</li>
<li>6pm-8pm</li>
</ol>""",
css_classes=["panel-content", "caption", "w3-text-white"])
day_of_week = Select(options=['All'] +
rtdap_data['dow'].drop_duplicates().values.tolist(),
title="Day of Week:",
css_classes=["panel-content"],
height=60,
value="All")
select_data = Button(label="Select Subset",
css_classes=["panel-content"],
height=60)
select_data.on_click(submit_selection)
#-----------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------
#Create initial content
avgs_speed = rtdap_avg(rtdap_data, corridor_select.value, 'avgSpeed')
avgs_occ = rtdap_avg(rtdap_data, corridor_select.value, 'avgOccupancy')
avgs_volume = rtdap_avg(rtdap_data, corridor_select.value, 'avgVolume')
filtered_data = filter_selection(rtdap_data, corridor_select.value,
str(date_picker_start.value),
str(date_picker_end.value),
day_of_week.value, time_of_day.value)
speed = summarize_metrics(filtered_data, corridor_select.value, 'corridor',
avgs_speed, 'avgSpeed', 'Speed', 'missing_speed')
occ = summarize_metrics(filtered_data, corridor_select.value, 'corridor',
avgs_occ, 'avgOccupancy', 'Occupancy',
'missing_occ')
volume = summarize_metrics(filtered_data, corridor_select.value,
'corridor', avgs_volume, 'avgVolume', 'Volume',
'missing_vol')
summary_df = speed.append(occ)
summary_df = summary_df.append(volume)
summary_df_tbl = summary_df.copy()
summary_df_tbl['Mean Diff'] = summary_df_tbl['Mean Diff'] * 100
summary_df_tbl = summary_df_tbl.reset_index()
summary_title = Div(text="<h1>" + corridor_select.value + " Summary</h1>",
width=2000,
css_classes=["w3-panel", "w3-white"])
summary_table = Div(text="", width=550, height=150)
summary_table.text = str(
summary_df_tbl.fillna(0).to_html(
index=False,
formatters=[
str, '{:20,}'.format, '{:20,.1f}'.format, '{:20,.1f}%'.format,
'{:20,}'.format
],
classes=["w3-table", "w3-hoverable", "w3-small"]))
line = Div(text="<hr>", css_classes=["w3-container"], width=1000)
#-----------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------
#Create initial graphics
'''#horizontal bar chart
p = figure(plot_width=300, plot_height=100)
p.hbar(y=[1, 2, 3], height=0.5, left=0,
right=[1.2, 2.5, 3.7], color="navy")
p.yaxis.visible = False
p.xaxis.formatter = NumeralTickFormatter(format="0.0f%")'''
bar_viz = hbar_chart(summary_df.fillna(0), 'Mean Diff')
bar_viz_src = bar_viz[0]
bar_viz_chart = bar_viz[1]
volume_scatter = scatter_plot('Volumes')
time_scatter = scatter_plot('Time')
corr_df = rtdap_data.loc[rtdap_data['corridor'] == corridor_select.value]
speed_diff_vbar = (vbar_chart(corr_df, filtered_data))
occ_diff_vbar = (vbar_chart(corr_df, filtered_data))
volume_diff_vbar = (vbar_chart(corr_df, filtered_data))
base_map = make_base_map(map_width=450,
map_height=960,
xaxis=None,
yaxis=None,
xrange=(-9990000, -9619944),
yrange=(5011119, 5310000),
plot_tools="pan,wheel_zoom,reset,save")
select_content = row(
#PANEL
column(panel_title,
panel_text,
corridor_select,
date_picker_start,
date_picker_end,
day_of_week,
time_of_day,
tod_description,
select_data,
height=1000,
css_classes=["w3-sidebar", "w3-bar-block", "w3-darkgrey"]),
column(css_classes=["w3-col"], width=275),
#CONTENT
column(summary_title,
row(
Spacer(width=20),
column(
Spacer(height=10),
row(summary_table,
Spacer(width=50),
bar_viz_chart,
css_classes=["w3-panel", "w3-white", "w3-card-4"]),
Spacer(height=10),
row(volume_scatter,
Spacer(width=10),
time_scatter,
css_classes=["w3-panel", "w3-white", "w3-card-4"],
width=650),
Spacer(height=10),
row(column(speed_diff_vbar, occ_diff_vbar,
volume_diff_vbar),
css_classes=["w3-panel", "w3-white", "w3-card-4"],
width=1050),
),
row(
Spacer(width=20),
column(
Spacer(height=10),
column(base_map,
css_classes=[
"w3-panel", "w3-white", "w3-card-4"
],
width=500)))),
css_classes=["w3-container", "w3-row-padding"]),
css_classes=["w3-container", "w3-light-grey"],
width=2000,
height=1200)
return select_content
import pandas as pd
tsCache = qqqPath+'/qqqTs.pkl'
indxCache = qqqPath+'/qqqIndx.pkl'
if os.path.isfile(tsCache) and os.path.isfile(indxCache):
ts = pd.read_pickle(tsCache)
indx = pd.read_pickle(indxCache)
else:
ts,indx=qqq_ret(getRetDF())
ts.to_pickle(tsCache)
indx.to_pickle(indxCache)
if not ts.empty and not indx.empty:
try:
lineSrc = ColumnDataSource(data={'t':ts,'i':indx})
retPlot.line(x='t',y='i',line_color='tan',source=lineSrc)
except Exception as e:
errBox.text = 'ERROR: '+str(e)
if log.DEBUG>=log.getLogger().getEffectiveLevel():
traceback.print_exc()
else:
errBox.text = 'ERROR: QQQ timeseries is emtpy'
except Exception as e:
errBox.text = 'ERROR: '+str(e)
if log.DEBUG>=log.getLogger().getEffectiveLevel():
traceback.print_exc()
# assign actions
submit1.on_click(searchNews1)
submit2.on_click(searchNews2)
selBox1.on_change('value', populateQuery1)
selBox2.on_change('value', populateQuery2)
assemble_page()
notificationDiv = Div(text="", width=800)
gateways = []
if redisconn.get("gateways"):
gateways = json.loads(redisconn.get("gateways"))
else:
try:
cursor = db.gateways.find( {}, {"id":1, "name":1} )
gateways = []
gateways.append((None, "--- Choose a Gateway ---"))
for d in cursor:
gateways.append((d["id"], d["name"]))
redisconn.set("gateways", json.dumps(gateways))
except errors.ServerSelectionTimeoutError as e:
print ("MongoDB Server timed out: %s" % e)
notificationDiv.text = "MongoDB Server timed out: %s" % e
sys.exit(-1)
gatewayControl = Select( title="Choose a Gateway", options=gateways)
deviceControl = Select( title="Choose a Device")
indicatorControl = Select( title="Choose an indicator")
submitButton = Button(label="Submit", button_type="primary")
timemachine = Slider(title="How many minutes back would you like to travel", start=1, end=30, value=1, step=1,
callback_policy="mouseup")
controls = [gatewayControl, deviceControl, indicatorControl, timemachine, submitButton, notificationDiv]
doc = curdoc()
source = ColumnDataSource(data=dict(last_mod_date=[None], date=[None], v=[None]))
# source = ColumnDataSource(data=dict(last_mod_date=[None], date=[None], v=[None]))
# hover = HoverTool(tooltips=[
def bokeh_plot(import_df):
import pandas as pd
import numpy as np
from bokeh.plotting import figure, show
from bokeh.layouts import layout, widgetbox, row, column, gridplot
from bokeh.models import ColumnDataSource, HoverTool, BoxZoomTool, ResetTool, PanTool, CustomJS, PrintfTickFormatter, WheelZoomTool, SaveTool, LassoSelectTool, NumeralTickFormatter
from bokeh.models.widgets import Slider, Select, TextInput, Div, Tabs, Panel, DataTable, DateFormatter, TableColumn, PreText, NumberFormatter, RangeSlider
from bokeh.io import curdoc
from functools import lru_cache
from bokeh.transform import dodge
from os.path import dirname, join
from bokeh.core.properties import value
#load plotting data here
@lru_cache()
def load_data():
df = import_df
df.dropna(how='all', axis=0)
#Northest=['3229','3277','3276','3230','3259','All_Stores_NE']
df.location_reference_id = df.location_reference_id.astype(str)
#df['region'] = ['Northeast' if x in Northest else 'Midwest' for x in df['location_reference_id']]
df['date'] = pd.to_datetime(df['date'])
df[[
'BOH_gt_Shelf_Capacity', 'OTL_gt_Shelf_Capacity',
'Ideal_BOH_gt_Shelf_Capacity', 'BOH_lt_Ideal', 'BOH_eq_Ideal',
'BOH_gt_Ideal', 'Demand_Fulfilled', 'Fill_Rate', 'Backroom_OH',
'Total_OH', 'Prop_OH_in_Backroom', 'Never_Q98_gt_POG',
'Never_Ideal_BOH_gt_POG', 'Sometimes_OTL_Casepack_1_gt_POG',
'Always_OTL_Casepack_1_le_POG', 'Non_POG'
]] = df[[
'BOH > Shelf Capacity', 'OTL > Shelf Capacity',
'Ideal BOH > Shelf Capacity', 'BOH < Ideal', 'BOH = Ideal',
'BOH > Ideal', 'Demand Fulfilled', 'Fill Rate', 'Backroom_OH',
'Total OH', 'Prop OH in Backroom', 'Never: Q98 > POG',
'Never: Ideal BOH > POG', 'Sometimes: OTL+Casepack-1 > POG',
'Always: OTL+Casepack-1 <= POG', 'Non-POG'
]]
df['date_bar'] = df['date']
df['date_bar'] = df['date_bar'].astype(str)
return df
#Filter data source for "All" stores OR data agrregation on DC level
df_agg = load_data().groupby(['location_reference_id'],
as_index=False).sum()
source1 = ColumnDataSource(data=df_agg)
sdate = min(load_data()['date'])
edate = max(load_data()['date'])
nodes = len(list(load_data().location_reference_id.unique()))
days = len(list(load_data().date.unique()))
policy = "Prod"
#list of dates for vbar charts
x_range_list = list(load_data().date_bar.unique())
#direct access to number of location_reference_idand region
all_locations1 = list(load_data().location_reference_id.unique())
#agg_value=['All']
#all location_reference_idfrom csv file along with an option for agg data "All"
#all_locations=all_locations1+agg_value
#all_regions = ['Northeast', 'Midwest']
all_regions = list(load_data().region.unique())
desc = Div(text="All locations", width=230)
pre = Div(text="_", width=230)
location = Select(title="Location",
options=all_locations1,
value="All_Stores_NE")
region = Select(title="Region", options=all_regions, value="NE")
text_input = TextInput(value="default", title="Search Location:")
#full data set from load_data(df=df_import)
source = ColumnDataSource(data=load_data())
original_source = ColumnDataSource(data=load_data())
#plotting starts........... here are total 8 graphs for each Metric.
#Back room on hand
hover = HoverTool(
tooltips=[("Location", "@location_reference_id"), (
"Date", "@date_bar"), ("Backroom_OH", "@Backroom_OH{0,0.00}")])
TOOLS = [
hover,
BoxZoomTool(),
LassoSelectTool(),
WheelZoomTool(),
PanTool(),
ResetTool(),
SaveTool()
]
p = figure(x_range=x_range_list,
plot_width=1000,
plot_height=525,
title="Backroom On hand by store",
tools=TOOLS,
toolbar_location='above',
x_axis_label="Date",
y_axis_label="Backroom OH")
p.background_fill_color = "#e6e9ed"
p.background_fill_alpha = 0.5
p.vbar(x=dodge('date_bar', -0.25, range=p.x_range),
top='Backroom_OH',
hover_alpha=0.5,
hover_line_color='black',
width=0.8,
source=source,
color="#718dbf")
p.xaxis.major_label_orientation = 1
p.legend.border_line_width = 3
p.legend.border_line_color = None
p.legend.border_line_alpha = 0.5
p.title.text_color = "olive"
#inbound outbound
hover_m = HoverTool(
tooltips=[("Location", "@location_reference_id"), (
"Date", "@date_bar"), (
"Inbound",
"@Inbound{0,0.00}"), ("Outbound", "@Outbound{0,0.00}")])
TOOLS_m = [
hover_m,
BoxZoomTool(),
LassoSelectTool(),
WheelZoomTool(),
PanTool(),
ResetTool(),
SaveTool()
]
m = figure(plot_height=525,
plot_width=1000,
x_range=x_range_list,
title="Inbound/Outbound by store",
tools=TOOLS_m,
toolbar_location='above',
x_axis_label="Date",
y_axis_label="Units")
m.background_fill_color = "#e6e9ed"
m.background_fill_alpha = 0.5
m.vbar(x=dodge('date_bar', -0.25, range=m.x_range),
top='Inbound',
hover_alpha=0.5,
hover_line_color='black',
width=0.4,
source=source,
color="#718dbf",
legend=value("Inbound"))
m.vbar(x=dodge('date_bar', 0.25, range=m.x_range),
top='Outbound',
hover_alpha=0.5,
hover_line_color='black',
width=0.4,
source=source,
color="#e84d60",
legend=value("Outbound"))
m.xaxis.major_label_orientation = 1
m.legend.border_line_width = 3
m.legend.border_line_color = None
m.legend.border_line_alpha = 0.5
m.title.text_color = "olive"
#Stockout
hover_s = HoverTool(
tooltips=[("Location", "@location_reference_id"), (
"Date", "@date_bar"), (
"BOH_OOS",
"@BOH_OOS{0,0.000}"), ("EOH_OOS", "@EOH_OOS{0,0.000}")])
TOOLS_s = [
hover_s,
BoxZoomTool(),
LassoSelectTool(),
WheelZoomTool(),
PanTool(),
ResetTool(),
SaveTool()
]
s = figure(plot_height=525,
plot_width=1000,
title="Stockouts by store",
x_axis_type="datetime",
toolbar_location='above',
tools=TOOLS_s,
x_axis_label="Date",
y_axis_label="Prop Stockout")
s.background_fill_color = "#e6e9ed"
s.background_fill_alpha = 0.5
s.circle(x='date',
y='EOH_OOS',
source=source,
fill_color=None,
line_color="#4375c6")
s.line(x='date',
y='EOH_OOS',
source=source,
hover_alpha=0.5,
hover_line_color='black',
line_width=2,
line_color='navy',
legend=value("EOH OOS"))
s.circle(x='date',
y='BOH_OOS',
source=source,
fill_color=None,
line_color="#4375c6")
s.line(x='date',
y='BOH_OOS',
source=source,
hover_alpha=0.5,
hover_line_color='black',
line_width=2,
line_color='red',
legend=value("BOH OOS"))
s.legend.border_line_width = 3
s.legend.border_line_color = None
s.legend.border_line_alpha = 0.5
s.title.text_color = "olive"
#Fill rate
hover_t = HoverTool(
tooltips=[("Location", "@location_reference_id"), (
"Date", "@date_bar"), ("Fill Rate", "@Fill_Rate{0,0.00}")])
TOOLS_t = [
hover_t,
BoxZoomTool(),
LassoSelectTool(),
WheelZoomTool(),
PanTool(),
ResetTool(),
SaveTool()
]
t = figure(plot_height=525,
x_range=x_range_list,
plot_width=1000,
title="Fill rates by store",
tools=TOOLS_t,
toolbar_location='above',
x_axis_label="Date",
y_axis_label="Fill rate")
t.background_fill_color = "#e6e9ed"
t.background_fill_alpha = 0.5
t.vbar(x=dodge('date_bar', -0.25, range=t.x_range),
top='Fill Rate',
hover_alpha=0.5,
hover_line_color='black',
width=0.8,
source=source,
color="#718dbf")
t.xaxis.major_label_orientation = 1
t.legend.border_line_width = 3
t.legend.border_line_color = None
t.legend.border_line_alpha = 0.5
t.title.text_color = "olive"
# % Backroom spillover
hover_w = HoverTool(
tooltips=[("Location", "@location_reference_id"), ("Date",
"@date_bar"),
("Prop OH in Backroom", "@Prop_OH_in_Backroom{0,0.00}")])
TOOLS_w = [
hover_w,
BoxZoomTool(),
LassoSelectTool(),
WheelZoomTool(),
PanTool(),
ResetTool(),
SaveTool()
]
w = figure(plot_height=525,
plot_width=1000,
title="Prop OH in Backroom by store",
x_axis_type="datetime",
tools=TOOLS_w,
toolbar_location='above',
x_axis_label="Date",
y_axis_label=" % Backroom spillover")
w.background_fill_color = "#e6e9ed"
w.background_fill_alpha = 0.5
w.circle(x='date',
y='Prop OH in Backroom',
source=source,
fill_color=None,
line_color="#4375c6")
w.line(x='date',
y='Prop OH in Backroom',
source=source,
hover_alpha=0.5,
hover_line_color='black',
line_width=2,
line_color='navy')
w.title.text_font_style = "bold"
w.title.text_color = "olive"
w.legend.click_policy = "hide"
w.yaxis[0].formatter = NumeralTickFormatter(format="0.0%")
#BOH vs Ideal
hover_f = HoverTool(
tooltips=[("Location", "@location_reference_id"), (
"Date",
"@date_bar"), ('BOH < Ideal', "@BOH_lt_Ideal{0,0.00}"
), ('BOH > Ideal', "@BOH_gt_Ideal{0,0.00}"
), ('BOH = Ideal', "@BOH_eq_Ideal{0,0.00}")])
TOOLS_f = [
hover_f,
BoxZoomTool(),
LassoSelectTool(),
WheelZoomTool(),
PanTool(),
ResetTool(),
SaveTool()
]
colors = ["#c9d9d3", "#718dbf", "#e84d60"]
BOH_vs_ideal = ['BOH < Ideal', 'BOH > Ideal', 'BOH = Ideal']
f = figure(x_range=x_range_list,
plot_height=525,
plot_width=1000,
title="BOH vs Ideal by store",
toolbar_location='above',
x_axis_label="Date",
y_axis_label="Prop",
tools=TOOLS_f)
f.vbar_stack(BOH_vs_ideal,
x='date_bar',
width=0.9,
color=colors,
source=source,
legend=[value(x) for x in BOH_vs_ideal],
name=BOH_vs_ideal)
f.xaxis.major_label_orientation = 1
f.legend.border_line_width = 3
f.legend.border_line_color = None
f.legend.border_line_alpha = 0.5
f.title.text_color = "olive"
#Pog Fit
hover_g = HoverTool(
tooltips=[("Location", "@location_reference_id"), (
"Date",
"@date_bar"), ('Never: Q98 > POG', "@Never_Q98_gt_POG{0,0.00}"),
("Never: Ideal BOH > POG",
"@Never_Ideal_BOH_gt_POG{0,0.00}"),
("Sometimes: OTL+Casepack-1 > POG",
"@Sometimes_OTL_Casepack_1_gt_POG{0,0.00}"),
("Always: OTL+Casepack-1 <= POG",
"@Always_OTL_Casepack_1_le_POG{0,0.00}"
), ("Non-POG'", "@Non_POG{0,0.00}")])
TOOLS_g = [
hover_g,
BoxZoomTool(),
LassoSelectTool(),
WheelZoomTool(),
PanTool(),
ResetTool(),
SaveTool()
]
colors2 = ['#79D151', "#718dbf", '#29788E', '#fc8d59', '#d53e4f']
pog_fit = [
'Never: Q98 > POG', 'Never: Ideal BOH > POG',
'Sometimes: OTL+Casepack-1 > POG', 'Always: OTL+Casepack-1 <= POG',
'Non-POG'
]
g = figure(x_range=x_range_list,
plot_height=525,
plot_width=1200,
title="Pog Fit by store",
toolbar_location='above',
x_axis_label="Date",
y_axis_label="Counts",
tools=TOOLS_g)
g.vbar_stack(pog_fit,
x='date_bar',
width=0.9,
color=colors2,
source=source,
legend=[value(x) for x in pog_fit],
name=pog_fit)
g.xaxis.major_label_orientation = 1
g.legend.border_line_width = 3
g.legend.border_line_color = None
g.legend.border_line_alpha = 0.5
g.title.text_color = "olive"
g.legend.location = "top_right"
# BOH vs Pog
colors3 = ["#c9d9d3", "#718dbf", "#e84d60"]
shelf = [
'BOH > Shelf Capacity', 'OTL > Shelf Capacity',
'Ideal BOH > Shelf Capacity'
]
hover_h = HoverTool(
tooltips=[("Location", "@location_reference_id"), ("Date",
"@date_bar"),
("OTL > Shelf Capacity", "@OTL_gt_Shelf_Capacity{0,0.00}"
), ("BOH > Shelf Capacity",
"@BOH_gt_Shelf_Capacity{0,0.00}"),
("Ideal BOH > Shelf Capacity",
"@Ideal_BOH_gt_Shelf_Capacity{0,0.00}")])
TOOLS_h = [
hover_h,
BoxZoomTool(),
LassoSelectTool(),
WheelZoomTool(),
PanTool(),
ResetTool(),
SaveTool()
]
h = figure(plot_height=525,
plot_width=1000,
title="BOH vs Pog by store",
x_axis_type="datetime",
toolbar_location='above',
tools=TOOLS_h,
x_axis_label="Date",
y_axis_label="Prop")
h.background_fill_color = "#e6e9ed"
h.background_fill_alpha = 0.5
h.circle(x='date',
y='BOH > Shelf Capacity',
source=source,
fill_color=None,
line_color="#4375c6")
h.line(x='date',
y='BOH > Shelf Capacity',
source=source,
hover_alpha=0.5,
hover_line_color='black',
line_width=2,
line_color='navy',
legend=value("BOH > Shelf Capacity"))
h.circle(x='date',
y='OTL > Shelf Capacity',
source=source,
fill_color=None,
line_color="#4375c6")
h.line(x='date',
y='OTL > Shelf Capacity',
source=source,
hover_alpha=0.5,
hover_line_color='black',
line_width=2,
line_color="green",
legend=value("OTL > Shelf Capacity"))
h.circle(x='date',
y='Ideal BOH > Shelf Capacity',
source=source,
fill_color=None,
line_color="#4375c6")
h.line(x='date',
y='Ideal BOH > Shelf Capacity',
source=source,
hover_alpha=0.5,
hover_line_color='black',
line_width=2,
line_color="#e84d60",
legend=value("Ideal BOH > Shelf Capacity"))
h.legend.border_line_width = 3
h.legend.border_line_color = None
h.legend.border_line_alpha = 0.5
h.title.text_color = "olive"
h.legend.click_policy = "mute"
# Inventory
hover_j = HoverTool(
tooltips=[("Location", "@location_reference_id"), (
"Date", "@date_bar"), ("DFE_Q98", "@DFE_Q98{0,0.00}"),
("OTL",
"@OTL{0,0.00}"), ("EOH",
"@EOH{0,0.00}"), ("BOH", "@BOH{0,0.00}")])
TOOLS_j = [
hover_j,
BoxZoomTool(),
LassoSelectTool(),
WheelZoomTool(),
PanTool(),
ResetTool(),
SaveTool()
]
j = figure(plot_height=525,
plot_width=1200,
x_range=x_range_list,
title="Inbound/Outbound by store",
tools=TOOLS_j,
toolbar_location='above',
x_axis_label="Date",
y_axis_label="Units")
j.background_fill_color = "#e6e9ed"
j.background_fill_alpha = 0.5
j.vbar(x=dodge('date_bar', -0.40, range=j.x_range),
top='DFE_Q98',
hover_alpha=0.3,
hover_line_color='black',
width=0.2,
source=source,
color="#FBA40A",
legend=value("DFE_Q98"))
j.vbar(x=dodge('date_bar', -0.20, range=j.x_range),
top='OTL',
hover_alpha=0.3,
hover_line_color='black',
width=0.2,
source=source,
color="#4292c6",
legend=value("OTL"))
j.vbar(x=dodge('date_bar', 0.00, range=j.x_range),
top='EOH',
hover_alpha=0.3,
hover_line_color='black',
width=0.2,
source=source,
color='#a1dab4',
legend=value("EOH"))
j.vbar(x=dodge('date_bar', 0.20, range=j.x_range),
top='BOH',
hover_alpha=0.3,
hover_line_color='black',
width=0.2,
source=source,
color="#DC5039",
legend=value("BOH"))
j.xaxis.major_label_orientation = 1
j.legend.border_line_width = 3
j.legend.border_line_color = None
j.legend.border_line_alpha = 0.5
j.title.text_color = "olive"
j.legend.location = "top_left"
j.legend.click_policy = "mute"
#desc.text = " <br > <b> Region:</b> <i> </i> <br /> "
pre.text = " <b>Start date:</b> <i>{}</i> <br /> <b>End date:</b> <i>{}</i> <br /> <b>Time period:</b> <i>{}</i> days <br /> <b> Total Number of Nodes:</b> <i>{}</i> <br /> <b>Policy</b> = <i>{}</i><br /> ".format(
sdate, edate, days, nodes, policy)
#fuction to update data on selection
callback = CustomJS(args=dict(source=source,
original_source=original_source,
location_select_obj=location,
region_select_obj=region,
div=desc,
text_input=text_input),
code="""
var data = source.get('data');
var original_data = original_source.get('data');
var loc = location_select_obj.get('value');
var reg = region_select_obj.get('value');
var line = " <br /> <b> Region:</b>"+ reg + "<br /> <b>Location:</b> " + loc;
var text_input =text_input.get('value');
div.text=line;
for (var key in original_data) {
data[key] = [];
for (var i = 0; i < original_data['location_reference_id'].length; ++i) {
if ((original_data['location_reference_id'][i] === loc) && (original_data['region'][i] === reg) ) {
data[key].push(original_data[key][i]);
} } }
source.trigger('change');
""")
#controls = [location, region]
#for control in controls:
#control.js_on_change("value", callback)
#source.js_on_change("value", callback)
desc.js_on_event('event', callback)
location.js_on_change('value', callback)
region.js_on_change('value', callback)
text_input.js_on_change('value', callback)
#inputs = widgetbox(*controls, sizing_mode="fixed")
#inputs = widgetbox(*controls,width=220,height=500)
inputs = widgetbox(location, region, desc, pre, width=220, height=500)
# controls number of tabs
tab1 = Panel(child=p, title='Backroom OH')
tab2 = Panel(child=s, title='Stockouts')
tab3 = Panel(child=f, title='BOH vs Ideal')
tab4 = Panel(child=g, title='Pog Fit')
tab5 = Panel(child=m, title='Inbound/Outbound')
tab6 = Panel(child=h, title='BOH vs POG')
tab7 = Panel(child=t, title='Fill Rate')
tab8 = Panel(child=j, title='Inventory')
tab9 = Panel(child=w, title='Prop OH in Backroom')
#data table columns to summarize data
columns = [
TableColumn(field="location_reference_id", title="Location"),
TableColumn(field="Backroom_OH",
title="Backroom_OH",
formatter=NumberFormatter(format="0,0")),
TableColumn(field="Outbound",
title="Outbound",
formatter=NumberFormatter(format="0,0")),
TableColumn(field="Inbound",
title="Inbound",
formatter=NumberFormatter(format="0,0")),
TableColumn(field="OTL",
title="OTL",
formatter=NumberFormatter(format="0,0")),
TableColumn(field="DFE_Q98",
title="DFE_Q98",
formatter=NumberFormatter(format="0,0")),
TableColumn(field="BOH",
title="BOH",
formatter=NumberFormatter(format="0,0")),
TableColumn(field="EOH",
title="EOH",
formatter=NumberFormatter(format="0,0")),
TableColumn(field="BOH_OOS",
title="BOH_OOS",
formatter=NumberFormatter(format="0,0")),
TableColumn(field="EOH_OOS",
title="EOH_OOS",
formatter=NumberFormatter(format="0,0"))
]
data_table = DataTable(source=source1, columns=columns, width=1250)
tab10 = Panel(child=data_table, title='Summary Table')
view = Tabs(
tabs=[tab1, tab2, tab5, tab8, tab6, tab3, tab7, tab4, tab9, tab10])
layout_text = column(inputs)
layout1 = row(layout_text, view)
#laying out plot
layout2 = layout(children=[[layout_text, view]],
sizing_mode='scale_height')
#update plots
return layout2
'border-color': ' #05BAC5',\
'background-color': 'white',\
'padding': '.5em'})
if check_file['check'][0] == 0:
source = ColumnDataSource(data={
'File_1': [decode(data64_1)],
'File_2': [decode(data64_2)]
})
else:
source = ColumnDataSource(
data={
'File_1': [pd.read_csv(main_path + 'file1_data.csv')],
'File_2': [pd.read_csv(main_path + 'file2_data.csv')]
})
running_info.text = '<b>Status Bar</b>: Datasource is Updated ->->-> <b>Blend</b>, <b>Reset</b>, or <b>Select</b> New Data.'
# This is important! Save curdoc() to make sure all threads
# see the same document.
doc = curdoc()
@gen.coroutine
def update(data):
source.stream(data)
def blocking_task():
while True:
# do some blocking computation
time.sleep(10)
def selection_tab(rtdap_data):
"""
return selection tab contents
Keyword arguments:
rtdap_data - dataframe containing rtdap vds detail data
"""
#-----------------------------------------------------------------------------------------------------------------
#submit_selection -- Data Selection Update Function
def submit_selection():
"""
python callback to update table and visual content based on
user selections in the data review panel
"""
avgs_speed = rtdap_avg(rtdap_data, corridor_select.value, 'avgSpeed')
avgs_occ = rtdap_avg(rtdap_data, corridor_select.value, 'avgOccupancy')
avgs_volume = rtdap_avg(rtdap_data, corridor_select.value, 'avgVolume')
filtered_data = filter_selection(rtdap_data, corridor_select.value,
str(date_picker_start.value),
str(date_picker_end.value),
day_of_week.value, time_of_day.value)
speed = summarize_metrics(filtered_data, corridor_select.value,
'corridor', avgs_speed, 'avgSpeed', 'Speed',
'missing_speed')
occ = summarize_metrics(filtered_data, corridor_select.value,
'corridor', avgs_occ, 'avgOccupancy',
'Occupancy', 'missing_occ')
volume = summarize_metrics(filtered_data, corridor_select.value,
'corridor', avgs_volume, 'avgVolume',
'Volume', 'missing_vol')
summary_df = speed.append(occ)
summary_df = summary_df.append(volume)
summary_title.text = "<h1>" + corridor_select.value + " Summary</h1>"
summary_df_tbl = summary_df.copy()
summary_df_tbl['Mean Diff'] = summary_df_tbl['Mean Diff'] * 100
summary_df_tbl = summary_df_tbl.reset_index()
summary_table.text = str(
summary_df_tbl.fillna(0).to_html(
index=False,
formatters=[
str, '{:20,}'.format, '{:20,.1f}'.format,
'{:20,.1f}%'.format, '{:20,}'.format
],
classes=["w3-table", "w3-hoverable", "w3-small"]))
if len(summary_df) > 0:
new_df = summary_df.fillna(0)
bar_viz_new = hbar_chart(new_df, 'Mean Diff')[0]
bar_viz_src.data.update(bar_viz_new.data)
#-----------------------------------------------------------------------------------------------------------------
#Data Review Panel
panel_title = Div(
text="Data Review",
css_classes=["panel-heading", "text-center", "w3-text-white"])
panel_text = Div(
text=
"""Lorem Ipsum is simply dummy text of the printing and typesetting industry.
Lorem Ipsum has been the industry's standard dummy text ever since the 1500s,
when an unknown printer took a galley of type and scrambled it to make a type
specimen book.""",
css_classes=["panel-content", "w3-text-white"])
#Panel Buttons
corridor_select = Select(
options=rtdap_data['corridor'].drop_duplicates().values.tolist(),
title='Corridor:',
height=60,
value='Dan Ryan Express Lane',
css_classes=["panel-content"])
date_picker_start = DatePicker(min_date=date(2015, 1, 1),
max_date=date(2018, 12, 31),
css_classes=["panel-content"],
title="Start Date:",
height=60,
value=date(2015, 12, 31))
date_picker_end = DatePicker(min_date=date(2015, 1, 1),
max_date=date(2018, 12, 31),
css_classes=["panel-content"],
title="End Date:",
height=60,
value=date(2017, 12, 31))
time_of_day = RangeSlider(start=1,
end=8,
step=1,
value=(1, 2),
title="Time of Day:",
bar_color="black",
css_classes=["panel-content"])
tod_description = Div(
text="""Time of Day Categories:<br>
<ol>
<li>8pm-6am</li>
<li>6pm-7am</li>
<li>7am-8am</li>
<li>9am-10am</li>
<li>10am-2pm</li>
<li>2pm-4pm</li>
<li>4pm-6pm</li>
<li>6pm-8pm</li>
</ol>""",
css_classes=["panel-content", "caption", "w3-text-white"])
day_of_week = Select(options=['All'] +
rtdap_data['dow'].drop_duplicates().values.tolist(),
title="Day of Week:",
css_classes=["panel-content"],
height=60,
value="All")
select_data = Button(label="Select Subset",
css_classes=["panel-content"],
height=60)
select_data.on_click(submit_selection)
#-----------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------
#Create initial content
avgs_speed = rtdap_avg(rtdap_data, corridor_select.value, 'avgSpeed')
avgs_occ = rtdap_avg(rtdap_data, corridor_select.value, 'avgOccupancy')
avgs_volume = rtdap_avg(rtdap_data, corridor_select.value, 'avgVolume')
filtered_data = filter_selection(rtdap_data, corridor_select.value,
str(date_picker_start.value),
str(date_picker_end.value),
day_of_week.value, time_of_day.value)
speed = summarize_metrics(filtered_data, corridor_select.value, 'corridor',
avgs_speed, 'avgSpeed', 'Speed', 'missing_speed')
occ = summarize_metrics(filtered_data, corridor_select.value, 'corridor',
avgs_occ, 'avgOccupancy', 'Occupancy',
'missing_occ')
volume = summarize_metrics(filtered_data, corridor_select.value,
'corridor', avgs_volume, 'avgVolume', 'Volume',
'missing_vol')
summary_df = speed.append(occ)
summary_df = summary_df.append(volume)
summary_df_tbl = summary_df.copy()
summary_df_tbl['Mean Diff'] = summary_df_tbl['Mean Diff'] * 100
summary_df_tbl = summary_df_tbl.reset_index()
summary_title = Div(text="<h1>" + corridor_select.value + " Summary</h1>",
width=2000,
css_classes=["w3-panel", "w3-white"])
summary_table = Div(text="", width=550, height=150)
summary_table.text = str(
summary_df_tbl.fillna(0).to_html(
index=False,
formatters=[
str, '{:20,}'.format, '{:20,.1f}'.format, '{:20,.1f}%'.format,
'{:20,}'.format
],
classes=["w3-table", "w3-hoverable", "w3-small"]))
line = Div(text="<hr>", css_classes=["w3-container"], width=1000)
#-----------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------
#Create initial graphics
'''#horizontal bar chart
p = figure(plot_width=300, plot_height=100)
p.hbar(y=[1, 2, 3], height=0.5, left=0,
right=[1.2, 2.5, 3.7], color="navy")
p.yaxis.visible = False
p.xaxis.formatter = NumeralTickFormatter(format="0.0f%")'''
bar_viz = hbar_chart(summary_df.fillna(0), 'Mean Diff')
bar_viz_src = bar_viz[0]
bar_viz_chart = bar_viz[1]
volume_scatter = scatter_plot('Volumes')
time_scatter = scatter_plot('Time')
corr_df = rtdap_data.loc[rtdap_data['corridor'] == corridor_select.value]
speed_diff_vbar = (vbar_chart(corr_df, filtered_data))
occ_diff_vbar = (vbar_chart(corr_df, filtered_data))
volume_diff_vbar = (vbar_chart(corr_df, filtered_data))
base_map = make_base_map(map_width=450,
map_height=960,
xaxis=None,
yaxis=None,
xrange=(-9990000, -9619944),
yrange=(5011119, 5310000),
plot_tools="pan,wheel_zoom,reset,save")
return row(
#PANEL
column(panel_title,
panel_text,
corridor_select,
date_picker_start,
date_picker_end,
day_of_week,
time_of_day,
tod_description,
select_data,
height=1000,
css_classes=["w3-sidebar", "w3-bar-block", "w3-darkgrey"]),
column(css_classes=["w3-col"], width=275),
#CONTENT
column(summary_title,
row(
Spacer(width=20),
column(
Spacer(height=10),
row(summary_table,
Spacer(width=50),
bar_viz_chart,
css_classes=["w3-panel", "w3-white", "w3-card-4"]),
Spacer(height=10),
row(volume_scatter,
Spacer(width=10),
time_scatter,
css_classes=["w3-panel", "w3-white", "w3-card-4"],
width=650),
Spacer(height=10),
row(column(speed_diff_vbar, occ_diff_vbar,
volume_diff_vbar),
css_classes=["w3-panel", "w3-white", "w3-card-4"],
width=1050),
),
row(
Spacer(width=20),
column(
Spacer(height=10),
column(base_map,
css_classes=[
"w3-panel", "w3-white", "w3-card-4"
],
width=500)))),
css_classes=["w3-container", "w3-row-padding"]),
css_classes=["w3-container", "w3-light-grey"],
width=2000,
height=1200)
def make_sheet_div(self, text):
d = Div()
d.text = "<h3>" + text + "</h3>"
return d
def make_title_div(self, text):
d = Div()
d.text = "<h1>" + text + "</h1>"
return d
print text
div.update(text=text)
fd = figure(tools=[TOOLS])
scatter = fd.circle(
x='sumhobbit24hour',
y='area24hour',
size=sizescale,
fill_color=colors,
fill_alpha=0.9,
line_color=None,
selection_line_color="firebrick",
source=source,
nonselection_fill_color='colors',
nonselection_fill_alpha=0.5,
)
div.text = '<img src=\"' + df['url24hour'][0] + '" width="700"/>'
p = row(fd, div)
#output_file("color_scatter.html", title="color_scatter.py example")
#show(p)
curdoc().add_root(p)
print scatter.data_source.properties()
print "testing"
scatter.data_source.on_change('selected', update)
# scatter.data_source.