def MonthGraph(request):
if request.method =='GET' :
if request.user.is_authenticated():
now = datetime.now()
now = formats.date_format(now,"SHORT_DATETIME_FORMAT")
#data = DeviceUsage.objects.values('date').annotate(sumusage=Sum('usage'))# Group by date
y = [0,0,0,0,0,0,0,0,0,0
,0,0,0,0,0,0,0,0,0,0
,0,0,0,0,0,0,0,0,0,0,0,0]
print "Don't select data"
head=""
plot = Bar(y,title=head, xlabel='date', ylabel='Unit', width=800, height=400)
plot.toolbar_location = None
plot.outline_line_color = None
script, div = components(plot, CDN)
return render(request, "monthgraph.html", {"the_script": script, "the_div": div,'devices': DeviceProfile.objects.filter(owner=request.user),"date":str(now)})
else:
return redirect('/authen/')
elif request.method == 'POST':
now = datetime.now()
now = formats.date_format(now,"SHORT_DATETIME_FORMAT")
#data = DeviceUsage.objects.values('date').annotate(sumusage=Sum('usage'))# Group by date
y = [0,0,0,0,0,0,0,0,0,0
,0,0,0,0,0,0,0,0,0,0
,0,0,0,0,0,0,0,0,0,0,0,0]
head=""
if request.POST.get('deviceid') is not None:
id = request.POST.get('deviceid')
if request.POST.get('month') is not None:
month = request.POST.get('month')
do = DeviceProfile.objects.get(device_id=id)
data = DeviceUsage.objects.filter(device_id=do,date__month=month).values('date').annotate(sumusage=Sum('usage'),sumtime=Sum('time'))
#print data # data[0].get('date').strftime("%d") get only date from year-month-date
for d in data:
hr = (float(d.get('sumtime'))/3600)
kw = float(d.get('sumusage'))
y[int(d.get('date').strftime("%d"))] = kw * hr
head = "usage of device name: "+ do.device_name +" at "+request.POST.get('month')+"th Month"
else:
print "Don't select data"
head=""
plot = Bar(y,title=head, xlabel='date', ylabel='Unit', width=800, height=400)
plot.toolbar_location = None
plot.outline_line_color = None
script, div = components(plot, CDN)
return render(request, "monthgraph.html", {"the_script": script, "the_div": div,'devices': DeviceProfile.objects.filter(owner=request.user),"date":str(now)})
def generate_chart(year):
data = get_data(year)
barchart = Bar(data,
values=blend(*SCORED_EVENTS, labels_name='event'),
label=cat(columns='Team', sort=False),
stack=cat(columns='event', sort=False),
color=color(columns='event', palette=Spectral9, sort=False),
xgrid=False, ygrid=False, legend='top_right',
plot_width=1000, plot_height=625,
tools="pan,wheel_zoom,box_zoom,reset,resize")
barchart.title = "Formula SAE Michigan " + str(year) + " Total Scores by Place"
barchart._xaxis.axis_label = "Teams"
barchart._xaxis.axis_line_color = None
barchart._xaxis.major_tick_line_color = None
barchart._xaxis.minor_tick_line_color = None
barchart._xaxis.major_label_text_font_size = '0.6em'
barchart._yaxis.axis_label = "Total Score"
barchart._yaxis.axis_line_color = None
barchart._yaxis.major_tick_line_color = None
barchart._yaxis.minor_tick_line_color = None
barchart.outline_line_color = None
barchart.toolbar_location = 'right'
barchart.logo = None
return barchart
def systemEvolutionBarPlot(df, yLabel, values):
with Timer(key='systemEvolutionBarPlot', verbose=True):
p = Bar(df, label='snapshot', values=values, agg='sum', stack='software',
legend='bottom_left', bar_width=0.5, xlabel="Snapshots", ylabel=yLabel, responsive=True, height=200,tools='hover')
glyph_renderers = p.select(GlyphRenderer)
bar_source = [glyph_renderers[i].data_source for i in range(len(glyph_renderers))]
hover = p.select(HoverTool)
hover.tooltips = [
('software',' @software'),
('value', '@height'),
]
p.xaxis.formatter=FuncTickFormatter.from_py_func(getWeekStringTick)
p.axis.minor_tick_line_color = None
p.background_fill_color = "#fafafa"
p.legend.location = "top_left"
p.toolbar.logo = None
p.toolbar_location = None
legend=p.legend[0].legends
p.legend[0].legends=[]
l = Legend( location=(0, -30))
l.items=legend
p.add_layout(l, 'right')
return p
def plotbar(events, userid):
"""
Arch: I tried to install bokeh 0.10.0 ver and that was
giving a lot of errors in the Bar(method args)- StopIteration
and also while trying to load the latest bokeh.min.js from CDN
So I am going back to the bokeh 0.8.1 ver- at least it works
"""
xr = [x.title for x in events]
y1values = [y1.prefearfactor for y1 in events]
y2values = [y2.postfearfactor for y2 in events]
yr = OrderedDict(PredictedFear=y1values, ActualFear=y2values)
"""
height=400
width=600
bar = figure(plot_width=width, plot_height=height, title="Imagined vs real")
# use the `rect` renderer to display stacked bars of the medal results. Note
# that we set y_range explicitly on the first renderer
bar.rect(xr, y1values, height=0.5, width=0.4, color="red", alpha=0.6)
bar.rect(xr, y2values, height=0.5, width=0.4, color="blue", alpha=0.6)
"""
bar = Bar(yr,
xr,
xlabel="Events",
ylabel="Fear factor",
title="Imagined vs real fear",
width=800,
height=600,
legend=True,
palette=['#c1ff55', '#b7feea'],
stacked=True,
tools=None,
xgrid=None,
ygrid=None)
bar.toolbar_location = None
#output_file("stacked_bar.html", autosave=True)
#show(bar) - commented out so it doesnt try to open display in linux
#generating unique filenames by- concatenating userid to a rolledindex-
#we dont expect a user to generate more than
"""
global rollingindex
userfilepath = filepath+str(userid)+str(rollingindex.next())+'.js'
"""
userfilepath = filepath + str(userid) + '.js'
js, tag = autoload_static(
bar, CDN, '/' + userfilepath + '?id=' + str(randint(1, 1000000)))
with open(userfilepath, 'w') as f:
f.write(js)
return js, tag
def generate_chart(event):
data = get_data(TIMED_EVENTS[event])
# Bokeh doesn't let me control the order of the grouping! This is
# frustrating since it will be different on every server launch
barchart = Bar(data,
values='percentage_dnf',
label='year',
color="FireBrick",
xgrid=False,
ygrid=False,
plot_width=800,
plot_height=500,
tools="pan,wheel_zoom,box_zoom,reset,resize")
barchart.title = "Formula SAE Michigan DNFs - " + event
barchart._xaxis.axis_label = "Year"
barchart._xaxis.axis_line_color = None
barchart._xaxis.major_tick_line_color = None
barchart._xaxis.minor_tick_line_color = None
barchart._yaxis.axis_label = "Percentage DNF"
barchart._yaxis.axis_line_color = None
barchart._yaxis.major_tick_line_color = None
barchart._yaxis.minor_tick_line_color = None
barchart._yaxis.formatter = NumeralTickFormatter(format="0%")
barchart.y_range = Range1d(0, 1)
barchart.outline_line_color = None
barchart.toolbar_location = 'right'
barchart.logo = None
for renderer in barchart.select(GlyphRenderer):
if renderer.data_source.data['height'] != [0]:
year = renderer.data_source.data['year']
num_dnf = data['dnfs'].loc[data['year'] == year]
num_entries = data['entries'].loc[data['year'] == year]
percent_dnf = data['percentage_dnf'].loc[data['year'] == year]
hover = HoverTool(renderers=[renderer],
tooltips=[
("# DNFs", '%d' % num_dnf.values[0]),
("# Entries", '%d' % num_entries.values[0]),
("% DNF",
'%.2f%%' % (100 * percent_dnf.values[0]))
])
barchart.add_tools(hover)
return barchart
def generate_chart(team):
data = generate_data(team)
selectable_years = list(map(str, data.index.unique()))
# Generate the chart UNFORTUNATELY using the high level plots. Streaming
# a bunch of quads resulted in lots of graphics corruption when switching
# teams. I would rather have the plots work all the time but really slow
# then have the plot show the wrong information.
barchart = Bar(data,
values=blend(*SCORED_EVENTS, labels_name='event'),
label=cat(columns='Year', sort=False),
stack=cat(columns='event', sort=False),
color=color(columns='event', palette=Spectral9, sort=False),
xgrid=False, ygrid=False,
plot_width=1000, plot_height=625,
tools="pan,wheel_zoom,box_zoom,reset,resize")
barchart.title = "Formula SAE Michigan - " + team
barchart._xaxis.axis_label = "Teams"
barchart._xaxis.axis_line_color = None
barchart._xaxis.major_tick_line_color = None
barchart._xaxis.minor_tick_line_color = None
barchart._yaxis.axis_label = "Total Score"
barchart._yaxis.axis_line_color = None
barchart._yaxis.major_tick_line_color = None
barchart._yaxis.minor_tick_line_color = None
barchart.outline_line_color = None
barchart.toolbar_location = 'right'
barchart.logo = None
# Hacky tooltips
for renderer in barchart.select(GlyphRenderer):
if renderer.data_source.data['height'] != [0]:
year = renderer.data_source.data['Year']
place = data['Place'].loc[data['Year'] == year]
score = data['Total Score'].loc[data['Year'] == year]
hover = HoverTool(renderers=[renderer],
tooltips=[("Year", '@Year'),
("Selection", '@event'),
("Event Score", '@height'),
("Total Score", '%.2f' % score.values[0]),
("Overall Place", '%d' % place.values[0])])
barchart.add_tools(hover)
return barchart
def output_chart(issues_df, output_mode='static'):
import datetime
import bokeh
from bokeh.models import HoverTool
# Add timestamp to title
issues_chart = Bar(issues_df,
label='value_delivered',
values='status',
agg='count',
stack='status',
title=ISSUES_TITLE + " (Updated " +
datetime.datetime.now().strftime('%m/%d/%Y') + ")",
xlabel="Value Delivered",
ylabel="Number of Use Cases",
legend='top_right',
tools='hover',
color=brewer["GnBu"][3])
issues_chart.plot_width = DESTINATION_FRAME_WIDTH - (HTML_BODY_MARGIN * 2)
issues_chart.plot_height = DESTINATION_FRAME_HEIGHT - (HTML_BODY_MARGIN *
2)
issues_chart.logo = None
issues_chart.toolbar_location = None
hover = issues_chart.select(dict(type=HoverTool))
hover.tooltips = [("Value Delivered", "$x")]
#--- Configure output ---
reset_output()
if output_mode == 'static':
# Static file. CDN is most space efficient
output_file(ISSUES_FILE,
title=ISSUES_TITLE,
autosave=False,
mode='cdn',
root_dir=None) # Generate file
save(issues_chart, filename=ISSUES_FILE)
elif output_mode == 'notebook':
output_notebook() # Show inline
show(issues_chart)
else:
# Server (using internal server IP, rather than localhost or external)
session = bokeh.session.Session(root_url=BOKEH_SERVER_IP,
load_from_config=False)
output_server("ddod_chart", session=session)
show(issues_chart)
def generate_chart(event):
data = get_data(TIMED_EVENTS[event])
# Bokeh doesn't let me control the order of the grouping! This is
# frustrating since it will be different on every server launch
barchart = Bar(data,
values='percentage_dnf',
label='year',
color="FireBrick",
xgrid=False, ygrid=False,
plot_width=800, plot_height=500,
tools="pan,wheel_zoom,box_zoom,reset,resize")
barchart.title = "Formula SAE Michigan DNFs - " + event
barchart._xaxis.axis_label = "Year"
barchart._xaxis.axis_line_color = None
barchart._xaxis.major_tick_line_color = None
barchart._xaxis.minor_tick_line_color = None
barchart._yaxis.axis_label = "Percentage DNF"
barchart._yaxis.axis_line_color = None
barchart._yaxis.major_tick_line_color = None
barchart._yaxis.minor_tick_line_color = None
barchart._yaxis.formatter = NumeralTickFormatter(format="0%")
barchart.y_range = Range1d(0, 1)
barchart.outline_line_color = None
barchart.toolbar_location = 'right'
barchart.logo = None
for renderer in barchart.select(GlyphRenderer):
if renderer.data_source.data['height'] != [0]:
year = renderer.data_source.data['year']
num_dnf = data['dnfs'].loc[data['year'] == year]
num_entries = data['entries'].loc[data['year'] == year]
percent_dnf = data['percentage_dnf'].loc[data['year'] == year]
hover = HoverTool(renderers=[renderer],
tooltips=[("# DNFs", '%d' % num_dnf.values[0]),
("# Entries", '%d' % num_entries.values[0]),
("% DNF", '%.2f%%' % (100 * percent_dnf.values[0]))])
barchart.add_tools(hover)
return barchart
def output_chart(issues_df,output_mode='static'):
import datetime
import bokeh
from bokeh.models import HoverTool
# Add timestamp to title
issues_chart = Bar(issues_df, label='value_delivered',
values='status', agg='count', stack='status',
title=ISSUES_TITLE+" (Updated "+datetime.datetime.now().strftime('%m/%d/%Y')+")",
xlabel="Value Delivered",ylabel="Number of Use Cases",
legend='top_right',
tools='hover',
color=brewer["GnBu"][3]
)
issues_chart.plot_width = DESTINATION_FRAME_WIDTH - (HTML_BODY_MARGIN * 2)
issues_chart.plot_height = DESTINATION_FRAME_HEIGHT - (HTML_BODY_MARGIN * 2)
issues_chart.logo = None
issues_chart.toolbar_location = None
hover = issues_chart.select(dict(type=HoverTool))
hover.tooltips = [ ("Value Delivered", "$x")]
#--- Configure output ---
reset_output()
if output_mode == 'static':
# Static file. CDN is most space efficient
output_file(ISSUES_FILE, title=ISSUES_TITLE,
autosave=False, mode='cdn',
root_dir=None
) # Generate file
save(issues_chart,filename=ISSUES_FILE)
elif output_mode == 'notebook':
output_notebook() # Show inline
show(issues_chart)
else:
# Server (using internal server IP, rather than localhost or external)
session = bokeh.session.Session(root_url = BOKEH_SERVER_IP, load_from_config=False)
output_server("ddod_chart", session=session)
show(issues_chart)
def generate_chart(year):
data = get_data(year)
plot_data = {
'country': data.index.tolist(),
'count': [float(i) for i in data.values.tolist()]
}
barchart = Bar(plot_data,
label='country',
values='count',
color="red",
xgrid=False,
ygrid=False,
plot_width=800,
plot_height=500,
tools="pan,wheel_zoom,box_zoom,reset,resize")
barchart.title = "Formula SAE Michigan " + year + " Countries"
barchart.x_range = FactorRange(factors=data.index.tolist())
barchart._xaxis.axis_label = "Country"
barchart._xaxis.axis_line_color = None
barchart._xaxis.major_tick_line_color = None
barchart._xaxis.minor_tick_line_color = None
barchart._yaxis.axis_label = "Number of Teams"
barchart._yaxis.axis_line_color = None
barchart._yaxis.major_tick_line_color = None
barchart._yaxis.minor_tick_line_color = None
barchart.outline_line_color = None
barchart.toolbar_location = 'right'
barchart.logo = None
hover = HoverTool(tooltips=[("Country", '@x'), ("# Teams", '@height')])
barchart.add_tools(hover)
return barchart
def generate_chart():
data = get_data()
# Bokeh doesn't let me control the order of the grouping! This is
# frustrating since it will be different on every server launch
barchart = Bar(data,
label='year',
values='count',
group=cat(columns='size', ascending=True, sort=True),
color=color(columns='size', palette=Spectral4),
legend='top_left',
xgrid=False,
ygrid=False,
plot_width=800,
plot_height=500,
tools="pan,wheel_zoom,box_zoom,reset,resize")
barchart.title = "Formula SAE Michigan Engine Cylinders"
barchart._xaxis.axis_label = "Year"
barchart._xaxis.axis_line_color = None
barchart._xaxis.major_tick_line_color = None
barchart._xaxis.minor_tick_line_color = None
barchart._yaxis.axis_label = "Frequency"
barchart._yaxis.axis_line_color = None
barchart._yaxis.major_tick_line_color = None
barchart._yaxis.minor_tick_line_color = None
barchart.outline_line_color = None
barchart.toolbar_location = 'right'
barchart.logo = None
hover = HoverTool(tooltips=[("Engine", '@size'), ("# Teams", '@height')])
barchart.add_tools(hover)
return barchart
def generate_chart(year):
data = get_data(year)
plot_data = {'country': data.index.tolist(),
'count': [float(i) for i in data.values.tolist()]}
barchart = Bar(plot_data,
label='country',
values='count',
color="red",
xgrid=False, ygrid=False,
plot_width=800, plot_height=500,
tools="pan,wheel_zoom,box_zoom,reset,resize")
barchart.title = "Formula SAE Michigan " + year + " Countries"
barchart.x_range = FactorRange(factors=data.index.tolist())
barchart._xaxis.axis_label = "Country"
barchart._xaxis.axis_line_color = None
barchart._xaxis.major_tick_line_color = None
barchart._xaxis.minor_tick_line_color = None
barchart._yaxis.axis_label = "Number of Teams"
barchart._yaxis.axis_line_color = None
barchart._yaxis.major_tick_line_color = None
barchart._yaxis.minor_tick_line_color = None
barchart.outline_line_color = None
barchart.toolbar_location = 'right'
barchart.logo = None
hover = HoverTool(tooltips=[("Country", '@x'),
("# Teams", '@height')])
barchart.add_tools(hover)
return barchart
def generate_chart():
data = get_data()
# Bokeh doesn't let me control the order of the grouping! This is
# frustrating since it will be different on every server launch
barchart = Bar(data,
label='year',
values='count',
group=cat(columns='size', ascending=True, sort=True),
color=color(columns='size', palette=Spectral4),
legend='top_left',
xgrid=False, ygrid=False,
plot_width=800, plot_height=500,
tools="pan,wheel_zoom,box_zoom,reset,resize")
barchart.title = "Formula SAE Michigan Engine Cylinders"
barchart._xaxis.axis_label = "Year"
barchart._xaxis.axis_line_color = None
barchart._xaxis.major_tick_line_color = None
barchart._xaxis.minor_tick_line_color = None
barchart._yaxis.axis_label = "Frequency"
barchart._yaxis.axis_line_color = None
barchart._yaxis.major_tick_line_color = None
barchart._yaxis.minor_tick_line_color = None
barchart.outline_line_color = None
barchart.toolbar_location = 'right'
barchart.logo = None
hover = HoverTool(tooltips=[("Engine", '@size'),
("# Teams", '@height')])
barchart.add_tools(hover)
return barchart
def generate_chart(year):
data = get_data(year)
barchart = Bar(data,
values=blend(*SCORED_EVENTS, labels_name='event'),
label=cat(columns='Team', sort=False),
stack=cat(columns='event', sort=False),
color=color(columns='event', palette=Spectral9, sort=False),
xgrid=False,
ygrid=False,
legend='top_right',
plot_width=1000,
plot_height=625,
tools="pan,wheel_zoom,box_zoom,reset,resize")
barchart.title = "Formula SAE Michigan " + str(
year) + " Total Scores by Place"
barchart._xaxis.axis_label = "Teams"
barchart._xaxis.axis_line_color = None
barchart._xaxis.major_tick_line_color = None
barchart._xaxis.minor_tick_line_color = None
barchart._xaxis.major_label_text_font_size = '0.6em'
barchart._yaxis.axis_label = "Total Score"
barchart._yaxis.axis_line_color = None
barchart._yaxis.major_tick_line_color = None
barchart._yaxis.minor_tick_line_color = None
barchart.outline_line_color = None
barchart.toolbar_location = 'right'
barchart.logo = None
return barchart
def MonthGraph(request):
if request.method == 'GET':
if request.user.is_authenticated():
now = datetime.now()
now = formats.date_format(now, "SHORT_DATETIME_FORMAT")
#data = DeviceUsage.objects.values('date').annotate(sumusage=Sum('usage'))# Group by date
y = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
]
print "Don't select data"
head = ""
plot = Bar(y,
title=head,
xlabel='date',
ylabel='Unit',
width=800,
height=400)
plot.toolbar_location = None
plot.outline_line_color = None
script, div = components(plot, CDN)
return render(
request, "monthgraph.html", {
"the_script": script,
"the_div": div,
'devices':
DeviceProfile.objects.filter(owner=request.user),
"date": str(now)
})
else:
return redirect('/authen/')
elif request.method == 'POST':
now = datetime.now()
now = formats.date_format(now, "SHORT_DATETIME_FORMAT")
#data = DeviceUsage.objects.values('date').annotate(sumusage=Sum('usage'))# Group by date
y = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0
]
head = ""
if request.POST.get('deviceid') is not None:
id = request.POST.get('deviceid')
if request.POST.get('month') is not None:
month = request.POST.get('month')
do = DeviceProfile.objects.get(device_id=id)
data = DeviceUsage.objects.filter(
device_id=do, date__month=month).values('date').annotate(
sumusage=Sum('usage'), sumtime=Sum('time'))
#print data # data[0].get('date').strftime("%d") get only date from year-month-date
for d in data:
hr = (float(d.get('sumtime')) / 3600)
kw = float(d.get('sumusage'))
y[int(d.get('date').strftime("%d"))] = kw * hr
head = "usage of device name: " + do.device_name + " at " + request.POST.get(
'month') + "th Month"
else:
print "Don't select data"
head = ""
plot = Bar(y,
title=head,
xlabel='date',
ylabel='Unit',
width=800,
height=400)
plot.toolbar_location = None
plot.outline_line_color = None
script, div = components(plot, CDN)
return render(
request, "monthgraph.html", {
"the_script": script,
"the_div": div,
'devices': DeviceProfile.objects.filter(owner=request.user),
"date": str(now)
})
def portalDynamicity(df):
def getWeekString(yearweek):
if yearweek is None or len(str(yearweek)) == 0:
return ''
year = "'" + str(yearweek)[:2]
week = int(str(yearweek)[2:])
# d = d - timedelta(d.weekday())
# dd=(week)*7
# dlt = timedelta(days = dd)
# first= d + dlt
# dlt = timedelta(days = (week)*7)
# last= d + dlt + timedelta(days=6)
return 'W' + str(week) + '-' + str(year)
bp = figure(plot_width=600, plot_height=300, y_axis_type="datetime", responsive=True,
tools='') # ,toolbar_location=None
bp.toolbar.logo = None
bp.toolbar_location = None
label_dict={}
for i, s in enumerate(df['snapshot']):
label_dict[i] = getWeekString1(s)
bp.yaxis[0].formatter = NumeralTickFormatter(format="0.0%")
bp.xaxis[0].axis_label = 'Snapshots'
bp.yaxis[0].axis_label = '% of portals'
li = bp.line(df.index.values.tolist(), df['dyratio'], line_width=2, line_color='red', legend="dyratio")
c = bp.circle(df.index.values.tolist(), df['dyratio'], line_width=2, line_color='red', legend="dyratio")
li1 = bp.line(df.index.values.tolist(), df['adddelratio'], line_width=2, line_color='blue', legend="adddelratio")
c = bp.circle(df.index.values.tolist(), df['adddelratio'], line_width=2, line_color='blue', legend="adddelratio")
legend = bp.legend[0].legends
bp.legend[0].legends = []
l = Legend(location=(0, -30))
l.items = legend
bp.add_layout(l, 'right')
labels=["staticRatio","updatedRatio","addRatio","delRatio"]
#for l in labels:
# df[l]= df[l]*100
print brewer.keys()
colors = brewer["Pastel2"][len(labels)]
bar = Bar(df,
values=blend("staticRatio","updatedRatio","addRatio","delRatio", name='medals', labels_name='medal'),
label=cat(columns='snapshot', sort=False),
stack=cat(columns='medal', sort=False),
color=color(columns='medal', palette=colors,
sort=False),
legend='top_right',
bar_width=0.5, responsive=True,
tooltips=[('ratio', '@medal'), ('snapshot', '@snapshot'),('Value of Total',' @height{0.00%}')])
legend = bar.legend[0].legends
bar.legend[0].legends = []
l = Legend(location=(0, -30))
l.items = legend
bar.add_layout(l, 'right')
bar.xaxis[0].axis_label = 'Snapshots'
bar.yaxis[0].axis_label = '% of datasets'
bar.width=600
bar.height=300
bar.xaxis[0].formatter = FuncTickFormatter.from_py_func(getWeekStringTick)
bar.toolbar.logo = None
bar.toolbar_location = None
bar.yaxis[0].formatter = NumeralTickFormatter(format="0.0%")
return {'bar':bar,'lines':bp}
# Make the chart
TOOLS = ''
plt2 = Bar(portions_df,
label="Name",
values="Portion",
stack = "SaleStatus",
legend="bottom_right",
color=['Green','Red'],
xlabel="Listing feature",
ylabel="Portion sold or unsold",
title="Impact of listing features on likelihood of sale",
tools=TOOLS)
plt2.logo = None
plt2.toolbar_location = None
plt2.y_range = Range1d(start=0,end=1)
# Save the plot
output_file("./templates/plot3_new.html")
# show(plt2)
#######################################################
# Look at dependence of sale outcome on seller feedback score
#######################################################
# hist = Histogram(data_sold, values='feedbackScore', color='listingType',
# title="Distribution of feedback scores for sold items", legend='top_left')
#
def summarize_plans(state, age, npi):
todaysdate = str(datetime.now())
age = str(age)
if age > '65':
age = '65'
elif age < '20':
age = '20'
# filter based on what plans are current (not expired) and age
filteredplans = pd.read_hdf(
'webapp/data/plan-rates.h5',
state,
where=[
'(Age==age) & (RateExpirationDate > todaysdate) & (RateEffectiveDate < todaysdate)'
],
columns=[
'IndividualRate', 'MetalLevel', 'Age',
'URLForSummaryofBenefitsCoverage', 'PlanMarketingName'
])
stateave = filteredplans.IndividualRate.mean()
myave = stateave
# plot it
#filteredplans.groupby('MetalLevel').IndividualRate.mean().plot(kind='bar')
#statebardf = filteredplans.groupby('MetalLevel').IndividualRate.mean()
statebardf = filteredplans.groupby('MetalLevel', as_index=False).mean()
#p = mpl.to_bokeh()
if npi == '':
p = Bar(filteredplans.groupby('MetalLevel').IndividualRate.mean(),
values='IndividualRate',
xlabel="",
ylabel="Montly Premium ($)")
else:
print 'input npi is ' + npi
# read hdf5 of provider-plan pairings
provnplanpd = pd.read_hdf('webapp/data/plan_providers.h5', state)
# make a list of planids for the input npi
planlst = provnplanpd[provnplanpd.npi == npi].plan_id.values
print planlst
# read in hdf5 of plan info
planinfo = pd.read_hdf(
'webapp/data/plan-rates.h5',
state,
where=[
'(Age==age) & (RateExpirationDate > todaysdate) & (RateEffectiveDate < todaysdate)'
])
# for each planid, get the first entry and concatenate them together.
filteredplans = planinfo[planinfo.PlanId == planlst[0]].groupby(
'PlanId', as_index=False).first()
filteredplans.head()
#pd.concat([filteredplans, filteredplans])
for ii in range(1, len(planlst)):
filteredplans = pd.concat([
filteredplans,
planinfo[planinfo.PlanId == planlst[ii]].groupby(
'PlanId', as_index=False).first()
])
provbardf = filteredplans.groupby('MetalLevel', as_index=False).mean()
provbardf['average'] = ['provider rates'] * len(
provbardf.MetalLevel.values)
statebardf['average'] = ['state average'] * len(
provbardf.MetalLevel.values)
myave = statebardf.IndividualRate.mean()
#provbardf['average'] = [1, 1]
#statebardf['average'] = [2, 2]
#plotdf = pd.merge(statebardf, provbardf, on='MetalLevel')
plotdf = pd.concat([provbardf, statebardf])
#plotdf['state average'] = statebardf['IndividualRate']
#plotdf['provider rates'] = provbardf['IndividualRate']
print plotdf
p = Bar(plotdf,
label='MetalLevel',
values='IndividualRate',
group='average',
legend='top_right',
xlabel="",
ylabel="Montly Premium ($)")
p.logo = None
p.plot_height = 400
p.toolbar_location = None
script, div = components(p)
print filteredplans.to_dict(orient='records')
return {
'num_plans':
len(filteredplans),
'script':
script,
'plot_div':
div,
'national_comp':
format_price_comp(float(age2nationalAverage(age)) - myave),
'state_comp':
format_price_comp(float(stateave) - myave),
'plans':
render_template('plans.html',
plans=filteredplans.sort_values(
by='IndividualRate').to_dict(orient='records'))
}
def index():
if request.method == 'GET':
return render_template('index.html')
else:
# request was a POST
# store the city code
# all processing is based on filtering summary tables on city code
app.vars['citycode'] = request.form['city']
citycode = app.vars['citycode']
# app.vars['features'] = request.form.getlist('features')
#######################
# Summary table loads #
#######################
# coll_inj = pd.read_csv('1_coll_inj_ann.csv', encoding='utf-8')
# top_10 = pd.read_csv('2_top_10.csv', encoding='utf-8')
# surf_vs_high = pd.read_csv('3_surf_vs_high.csv', encoding='utf-8')
# inj_causes = pd.read_csv('4_inj_causes.csv', encoding='utf-8')
# weather = pd.read_csv('5_weather.csv', encoding='utf-8')
# hour = pd.read_csv('6_hour.csv', encoding='utf-8')
# city_stats14 = pd.read_csv('7_2014_city_stats.csv', encoding='utf-8')
# ins_est13 = pd.read_csv('8_2013_ins_est.csv', encoding='utf-8')
# kfinal = pd.read_csv('9_kmeans_and_pop.csv', encoding='utf-8')
################################
# Hardcoded summary statistics #
################################
# California collision stats
capop2014 = 38357121
cacol2014 = 384646
caco1k = np.round(1000.0 * cacol2014 / capop2014, 1)
cainj2014 = 225044
cainj1k = np.round(1000.0 * cainj2014 / capop2014, 1)
cakill2014 = 3068
cak1m = np.round(1000000.0 * cakill2014 / capop2014, 1)
# Califonia insurance stats
human_claims_2013 = 4207773612
prop_claims_2013 = 7422643259
inj_2013 = 223128
sev_inj_2013 = 10664
kill_2013 = 3104
parties_2013 = 763086
# cost per X insurance estimates
# crude!
cost_per_inj = np.round(human_claims_2013 / \
(1.0 * inj_2013 + 9.0 * sev_inj_2013 + 19.0 * kill_2013), 1)
cost_per_party = np.round(prop_claims_2013 / (1.0 * parties_2013), 1)
cacpc = np.round(1.0 * (human_claims_2013 + prop_claims_2013) / 37965755, 2)
# City collision stats
citystats = city_stats14[city_stats14['cccode'] == int(citycode)]
cityname = citystats['COUNTY/CITY'].values[0]
collper1k = np.round(citystats['collper1k'].values[0],1)
injper1k = np.round(citystats['injper1k'].values[0],1)
killper1m = np.round(citystats['killper1m'].values[0],1)
# City insurance Stats
insstats = ins_est13[ins_est13['cccode'] == int(citycode)]
citycpc = np.round(insstats['estpercapita'].values[0], 2)
tools='save'
# Other City Stats
citypop = kfinal[kfinal['cccode'] == int(citycode)]['pop2014'].values[0]
citypop = locale.format("%d", citypop, grouping=True)
cluster = kfinal[kfinal['cccode'] == int(citycode)]['cluster'].values[0]
################
# Annual Trend #
################
df = coll_inj[coll_inj['cccode'] == int(citycode)]
print 'before bar definition'
p1 = Bar(df,
label='year',
values='count',
title="Collisions & Injuries by Year",
group='type',
xlabel='Year',
ylabel='Collisions/Injuries',
height=400,
# width=600,
# bar_width=0.1,
tools=tools,
legend='bottom_left'
)
p1.toolbar_location=None
p1.responsive=True
print 'end p1 definition'
##############################
# Top 10 Worst Intersections #
##############################
columns = ['inter', 'numcoll', 'SUM(NUMBER_INJURED)', 'SUM(NUMBER_KILLED)']
temp = top_10[top_10['CNTY_CITY_LOC'] == int(citycode)][columns]
newcolumns = ['Intersection', 'Collisions', 'Injuries', 'Fatalities']
temp.columns = newcolumns
interhtml = temp.to_html(index=False).replace('border="1"', 'border="0"')
interhtml = interhtml.replace('\n', '')
###############################
# Surface Streets vs Highways #
###############################
df3 = surf_vs_high[surf_vs_high['cccode'] == int(citycode)]
df3 = df3[['type', 'count', 'dtype']]
p2 = Bar(df3,
label='dtype',
values='count',
stack='type',
title="Surface Streets vs Freeways and Highways",
xlabel='',
ylabel='Proportion',
height=400,
# width=600,
bar_width=0.9,
tools=tools,
legend='bottom_right'
)
p2.toolbar_location=None
p2.responsive=True
print 'end p2 definition'
##############################################
# Primary Causes of Collisions with Injuries #
##############################################
df4 = inj_causes[inj_causes['CNTY_CITY_LOC'] == int(citycode)]
p3 = Bar(df4,
label='pcf',
values='SUM(NUMBER_INJURED)',
title="Primary Violations Leading to Injuries",
xlabel='',
ylabel='Injuries',
height=400,
# width=600,
bar_width=0.9,
tools=tools,
color='black'
)
p3.toolbar_location=None
p3.responsive=True
print 'end p3 definition'
################################
# Weather at time of collision #
################################
df5 = weather[weather['CNTY_CITY_LOC'] == int(citycode)]
p4 = Bar(df5,
label='weather',
values='numcoll',
title="Weather Conditions for Collisions",
xlabel='',
ylabel='Collisions',
height=400,
# width=600,
bar_width=0.9,
tools=tools,
color='yellow'
)
p4.toolbar_location=None
p4.responsive=True
print 'end p4 definition'
###############
# Time of Day #
###############
df6 = hour[hour['CNTY_CITY_LOC'] == int(citycode)]
p5 = Bar(df6,
label='hour',
values='numcoll',
title="Time of Day",
xlabel='Hour',
ylabel='Collisions',
height=400,
# width=600,
bar_width=0.9,
tools=tools,
color='black'
)
p5.toolbar_location=None
p5.responsive=True
#################
# Plot encoding #
#################
plots = {'trend': p1, 'streettypes': p2, 'injurycauses': p3,
'weather': p4, 'hour': p5}
script, div = components(plots, INLINE)
# script1 = encode_utf8(script1)
#############
# Rendering #
#############
print 'before render'
html = render_template('index_post.html',
citycode=citycode, cityname=cityname,
collper1k=collper1k, caco1k=caco1k,
injper1k=injper1k, cainj1k=cainj1k,
killper1m=killper1m, cak1m=cak1m,
citycpc=citycpc, cacpc=cacpc,
plot_scripts=script, div=div,
interhtml=interhtml, citypop=citypop,
cluster=cluster)
print 'after render'
return encode_utf8(html)
columns=["Name", "Portion", "HasFeature", "SaleStatus", "Feature"])
# Make the chart
TOOLS = ''
plt2 = Bar(portions_df,
label="Name",
values="Portion",
stack="SaleStatus",
legend="bottom_right",
color=['Green', 'Red'],
xlabel="Listing feature",
ylabel="Portion sold or unsold",
title="Impact of listing features on likelihood of sale",
tools=TOOLS)
plt2.logo = None
plt2.toolbar_location = None
plt2.y_range = Range1d(start=0, end=1)
# Save the plot
output_file("./templates/plot3_new.html")
# show(plt2)
#######################################################
# Look at dependence of sale outcome on seller feedback score
#######################################################
# hist = Histogram(data_sold, values='feedbackScore', color='listingType',
# title="Distribution of feedback scores for sold items", legend='top_left')
#
# # Save the plot
# output_file("./templates/data_exploration.html")
def summarize_plans(state, age, npi):
todaysdate = str(datetime.now())
age = str(age)
if age > '65':
age = '65'
elif age < '20':
age = '20'
# filter based on what plans are current (not expired) and age
filteredplans = pd.read_hdf('webapp/data/plan-rates.h5', state, where=['(Age==age) & (RateExpirationDate > todaysdate) & (RateEffectiveDate < todaysdate)'],
columns = ['IndividualRate', 'MetalLevel', 'Age','URLForSummaryofBenefitsCoverage','PlanMarketingName'])
stateave = filteredplans.IndividualRate.mean()
myave = stateave
# plot it
#filteredplans.groupby('MetalLevel').IndividualRate.mean().plot(kind='bar')
#statebardf = filteredplans.groupby('MetalLevel').IndividualRate.mean()
statebardf = filteredplans.groupby('MetalLevel', as_index=False).mean()
#p = mpl.to_bokeh()
if npi == '':
p = Bar(filteredplans.groupby('MetalLevel').IndividualRate.mean(), values='IndividualRate',
xlabel="", ylabel="Montly Premium ($)")
else:
print 'input npi is ' + npi
# read hdf5 of provider-plan pairings
provnplanpd = pd.read_hdf('webapp/data/plan_providers.h5', state)
# make a list of planids for the input npi
planlst = provnplanpd[provnplanpd.npi == npi].plan_id.values
print planlst
# read in hdf5 of plan info
planinfo = pd.read_hdf('webapp/data/plan-rates.h5', state, where=['(Age==age) & (RateExpirationDate > todaysdate) & (RateEffectiveDate < todaysdate)'])
# for each planid, get the first entry and concatenate them together.
filteredplans = planinfo[planinfo.PlanId==planlst[0]].groupby('PlanId', as_index=False).first()
filteredplans.head()
#pd.concat([filteredplans, filteredplans])
for ii in range(1, len(planlst)):
filteredplans = pd.concat([filteredplans, planinfo[planinfo.PlanId==planlst[ii]].groupby('PlanId', as_index=False).first()])
provbardf = filteredplans.groupby('MetalLevel', as_index=False).mean()
provbardf['average'] = ['provider rates']*len(provbardf.MetalLevel.values)
statebardf['average'] = ['state average']*len(provbardf.MetalLevel.values)
myave = statebardf.IndividualRate.mean()
#provbardf['average'] = [1, 1]
#statebardf['average'] = [2, 2]
#plotdf = pd.merge(statebardf, provbardf, on='MetalLevel')
plotdf = pd.concat([provbardf, statebardf])
#plotdf['state average'] = statebardf['IndividualRate']
#plotdf['provider rates'] = provbardf['IndividualRate']
print plotdf
p = Bar(plotdf, label='MetalLevel', values='IndividualRate', group='average', legend='top_right',
xlabel="", ylabel="Montly Premium ($)")
p.logo = None
p.plot_height=400
p.toolbar_location = None
script,div = components(p)
print filteredplans.to_dict(orient='records')
return {'num_plans':len(filteredplans), 'script': script, 'plot_div': div,
'national_comp': format_price_comp(float(age2nationalAverage(age))-myave),
'state_comp': format_price_comp(float(stateave)-myave),
'plans': render_template('plans.html', plans=filteredplans.sort_values(by='IndividualRate').to_dict(orient='records'))}
