def plot3():
p = figure(title='something', x_axis_label='date', x_axis_type='datetime')
df = pd.read_csv('chart.csv')
df3 = pd.read_csv('df3d.csv')
output_file("stacked_bar.html")
p = Bar(df3,
label='Country',
values='Population wo access water',
stack='category',
color=color(columns='category',
palette=['Orange', 'Red'],
sort=False),
legend='top_right')
script, div = components(p)
df4 = df[df['status'] == 'water supplier']
output_file("bar.html")
q = Bar(df4,
label='Country',
values='Possible water provider',
legend=False,
color="Green")
script2, div2 = components(q)
return render_template('plot3.html',
script=script,
div=div,
script2=script2,
div2=div2)
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 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 hitrun_chart(value, value_label, filename):
'''
Takes 3 string arguments:
column name of attribute being pivoted with HIT_RUN_FLAG as "value",
an axis label name as "value_label",
and a filename for the final JS output as "filename"
Outputs Bokeh plot in JS format for embedding
'''
df = pd.pivot_table(crashes[crashes.HIT_RUN_FLAG == 'Yes'],
values='HIT_RUN_FLAG',
index=value,
aggfunc='count')
df = df.to_frame()
hitrun_speed.reset_index(inplace=True)
hover = HoverTool(tooltips=[
("Total hit and runs", "@height"),
])
tools = [hover]
a = Bar(df,
label=CatAttr(columns=[value], sort=False),
values='HIT_RUN_FLAG',
plot_width=720,
plot_height=720,
color=color(columns='HIT_RUN_FLAG', palette=palette),
tools=tools,
legend='top_right',
sizing_mode="scale_both")
a.xaxis.axis_label = value_label
a.yaxis.axis_label = 'Total number of hit and runs'
#save file to JS
script, div = components(a)
foo = open(filename, 'w')
foo.write(script)
foo.close
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 stack_bokeh(infile):
with open(infile, "r") as file:
data = pd.read_csv(file)
print(data)
p = Bar(data,
values=blend('locus dropout',
'allelic dropout',
'biallelic coverage',
name='samp',
labels_name="samps"),
label=cat(columns='Sample', sort=False),
stack=cat(columns='samps', sort=False),
color=color(columns="samps",
palette=["green", "red", "black"],
sort=False),
legend='top_right',
title="sam")
output_file("stacked_bar.html")
curdoc().add_root(p)
show(p)
def _create_plot(self):
drought_years = self._drought_analysis.label_years()
merged = self._annual_data.to_frame(name="Annual").merge(
drought_years.to_frame(name="InDrought"),
how='left',
left_index=True,
right_index=True)
# Choose a color palette that sets drought years to red and non drought
# years to green.
drought_color = color(columns="InDrought", palette=["green", "red"])
return Bar(
data=merged,
label="index",
values="Annual",
color=drought_color,
agg='sum',
#FIXME: The label doesn't display in the legend correctly
#legend="top_right",
xlabel="Year",
**self._plotargs)
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 plot_read_dist(rinfo):
df = pd.read_table(rinfo)
data = df[['length', 'mapper', 'count']].groupby(['length',
'mapper']).count()
data = data.apply(lambda s: s / data.sum() * 100, axis=1).reset_index()
p = Bar(data,
plot_width=500,
plot_height=400,
label='length',
values='count',
agg='sum',
stack=cat(columns='mapper', sort=False),
legend='top_right',
color=color(columns='mapper',
palette=["#f98283", "#a4a4a4"],
sort=False),
xlabel='read length (nt)',
ylabel='proportion (%)',
ygrid=False,
tooltips=[('length', '@length'), ('mapper', '@mapper'),
('percent', '@height')])
p.toolbar.logo = None
p.outline_line_color = None
return p
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
from bokeh.charts import Bar, output_file, show
from bokeh.charts.operations import blend
from bokeh.charts.attributes import cat, color
from bokeh.charts.utils import df_from_json
from bokeh.sampledata.olympics2014 import data
# utilize utility to make it easy to get json/dict data converted to a dataframe
df = df_from_json(data)
# filter by countries with at least one medal and sort by total medals
df = df[df['total'] > 0]
df = df.sort("total", ascending=False)
bar = Bar(df,
values=blend('bronze',
'silver',
'gold',
name='medals',
labels_name='medal'),
label=cat(columns='abbr', sort=False),
stack=cat(columns='medal', sort=False),
color=color(columns='medal',
palette=['SaddleBrown', 'Silver', 'Goldenrod'],
sort=False),
legend='top_right',
title="Medals per Country, Sorted by Total Medals",
tooltips=[('medal', '@medal'), ('country', '@abbr')])
output_file("stacked_bar.html")
show(bar)
def output_components(prefix, order, maxaln):
rinfo = "{0}.rinfo".format(prefix)
comp = "{0}.comp".format(prefix)
def plot_read_dist(rinfo):
df = pd.read_table(rinfo)
data = df[['length', 'mapper', 'count']].groupby(['length',
'mapper']).count()
data = data.apply(lambda s: s / data.sum() * 100, axis=1).reset_index()
p = Bar(data,
plot_width=500,
plot_height=400,
label='length',
values='count',
agg='sum',
stack=cat(columns='mapper', sort=False),
legend='top_right',
color=color(columns='mapper',
palette=["#f98283", "#a4a4a4"],
sort=False),
xlabel='read length (nt)',
ylabel='proportion (%)',
ygrid=False,
tooltips=[('length', '@length'), ('mapper', '@mapper'),
('percent', '@height')])
p.toolbar.logo = None
p.outline_line_color = None
return p
rdist = plot_read_dist(rinfo)
df = pd.read_table(comp, index_col=0)
total = df.sum()
total = total * 100 / total.sum()
df = df.apply(lambda s: s * 100 / s.sum(), axis=1)
df = df.reset_index()
#ftypes = df.columns[1:].tolist()
ftypes = order
colors = sns.color_palette("hls", len(ftypes)).as_hex()
bar = Bar(df,
values=blend(*ftypes, name="ftypes", labels_name="ftype"),
x_range=rdist.x_range,
y_range=(0, 100),
label=cat(columns='rlen', sort=False),
stack=cat(columns='ftype', sort=False),
xlabel='read length (nt)',
ylabel='proportion (%)',
legend="top_right",
ygrid=False,
width=500,
height=400,
color=color(columns='ftype', palette=colors, sort=False),
fill_alpha=1,
tooltips=[("length", "@rlen"), ("feature", "@ftype"),
("percent", "@height")])
bar.toolbar.logo = None
bar.outline_line_color = None
start_angles = {}
end_angles = {}
start = 0
for ftype in ftypes:
end = 2 * pi * total[ftype] / 100
start_angles[ftype] = start
end_angles[ftype] = start + end
start += end
colors = dict(zip(ftypes, colors))
df = pd.DataFrame(total).reset_index()
df.columns = ["ftype", "percent"]
df["start"] = df.apply(lambda s: start_angles[s["ftype"]], axis=1)
df["end"] = df.apply(lambda s: end_angles[s["ftype"]], axis=1)
df["color"] = df.apply(lambda s: colors[s["ftype"]], axis=1)
df["x"] = df.apply(lambda s: 1.2 * cos(
(start_angles[s["ftype"]] + end_angles[s["ftype"]]) / 2),
axis=1)
df["y"] = df.apply(lambda s: 1.2 * sin(
(start_angles[s["ftype"]] + end_angles[s["ftype"]]) / 2),
axis=1)
df["text"] = df.apply(lambda s: "{0:.3f}%".format(s["percent"]), axis=1)
source = ColumnDataSource(data=df)
pie = figure(width=400,
height=400,
x_range=(-1.4, 1.4),
y_range=(-1.4, 1.4))
pie.toolbar.logo = None
wr = pie.annular_wedge(x=0,
y=0,
inner_radius=0.5,
outer_radius=1,
start_angle="start",
end_angle="end",
fill_color="color",
line_color="#ffffff",
line_width=0.5,
source=source)
pie.axis.visible = False
pie.grid.grid_line_color = None
pie.outline_line_color = None
hover = HoverTool(tooltips=[("feature", "@ftype"),
("percent", "@percent")],
renderers=[wr])
pie.add_tools(hover)
text_props = {
"source": source,
"angle": 0,
"color": "black",
"text_align": "center",
"text_baseline": "middle",
"text_font_size": "8pt",
"text_font_style": "normal"
}
pie.text(x="x", y="y", text="text", **text_props)
empty = figure(width=400,
height=400,
x_range=(-1.1, 1.1),
y_range=(-1.1, 1.1))
empty.toolbar.logo = None
empty.axis.visible = False
empty.grid.grid_line_color = None
empty.outline_line_color = None
empty.toolbar_location = None
stat = get_stat(prefix, maxaln)
source = ColumnDataSource(data=stat)
columns = [
TableColumn(field="Statistics", title="Statistics", width=200),
TableColumn(field="Number of reads",
title="Number of reads",
formatter=NumberFormatter(format="0,0"),
width=150),
TableColumn(field="Proportion",
title="Proportion",
formatter=NumberFormatter(format="0.000%"),
width=100),
]
data_table = DataTable(source=source,
columns=columns,
width=450,
row_headers=False)
script, div = components(
layout([[data_table, rdist], [pie, bar]], sizing_mode="scale_width"))
return script, div
from bokeh.charts import Bar, output_file, show
from bokeh.charts.attributes import cat, color
from bokeh.charts.operations import blend
from bokeh.charts.utils import df_from_json
from bokeh.sampledata.olympics2014 import data
# utilize utility to make it easy to get json/dict data converted to a dataframe
df = df_from_json(data)
# filter by countries with at least one medal and sort by total medals
df = df[df['total'] > 0]
df = df.sort_values(by="total", ascending=False)
bar = Bar(df,
values=blend('bronze', 'silver', 'gold', name='medals', labels_name='medal'),
label=cat(columns='abbr', sort=False),
stack=cat(columns='medal', sort=False),
color=color(columns='medal', palette=['SaddleBrown', 'Silver', 'Goldenrod'],
sort=False),
legend='top_right',
title="Medals per Country, Sorted by Total Medals",
tooltips=[('medal', '@medal'), ('country', '@abbr')])
output_file("stacked_bar.html", title="stacked_bar.py example")
show(bar)
from bokeh.charts import Bar, output_file, show
from bokeh.charts.attributes import cat, color
from bokeh.charts.operations import blend
from bokeh.charts.utils import df_from_json
from bokeh.sampledata.olympics2014 import data
# utilize utility to make it easy to get json/dict data converted to a dataframe
df = df_from_json(data)
# filter by countries with at least one medal and sort by total medals
df = df[df["total"] > 0]
df = df.sort("total", ascending=False)
bar = Bar(
df,
values=blend("bronze", "silver", "gold", name="medals", labels_name="medal"),
label=cat(columns="abbr", sort=False),
stack=cat(columns="medal", sort=False),
color=color(columns="medal", palette=["SaddleBrown", "Silver", "Goldenrod"], sort=False),
legend="top_right",
title="Medals per Country, Sorted by Total Medals",
tooltips=[("medal", "@medal"), ("country", "@abbr")],
)
output_file("stacked_bar.html")
show(bar)
def comparison(request, stock_id1, stock_id2):
stock1 = get_object_or_404(Stock, pk=stock_id1)
stock2 = get_object_or_404(Stock, pk=stock_id2)
#update(stock1)
#update(stock2)
# Graph
# Last known weekday
current_day = weekday().isoformat()
# Retrieve live data YYYY-MM-DD
historical_price1 = ystockquote.get_historical_prices(stock1, '2013-01-24', current_day)
correct_order1 = sorted(historical_price1)
stock_prices1 = []
dates1 = []
for values in correct_order1:
stock_prices1.append(historical_price1[values]['Adj Close'])
dates1.append(values)
# Convert to Float
for p in range(len(stock_prices1)):
stock_prices1[p] = float(stock_prices1[p])
# Convert to Datetime Format
dates_objects1 = []
for d in dates1:
dates_objects1.append(datetime.strptime(d, '%Y-%m-%d'))
# Retrieve live data YYYY-MM-DD
historical_price2 = ystockquote.get_historical_prices(stock2, '2013-01-24', current_day)
correct_order2 = sorted(historical_price2)
stock_prices2 = []
dates2 = []
for values in correct_order2:
stock_prices2.append(historical_price2[values]['Adj Close'])
dates2.append(values)
# Convert to Float
for p in range(len(stock_prices2)):
stock_prices2[p] = float(stock_prices2[p])
# Convert to Datetime Format
dates_objects2 = []
for d in dates2:
dates_objects2.append(datetime.strptime(d, '%Y-%m-%d'))
#Tools
hover = HoverTool(mode='vline')
crosshair = CrosshairTool(dimensions=['height'])
TOOLS = [BoxZoomTool(), crosshair, PreviewSaveTool(), ResetTool()]
p1 = figure(x_axis_type="datetime", responsive=True, plot_height=250,tools=TOOLS)
p1.border_fill = "whitesmoke"
# Multiple Axises
min1 = min(stock_prices1)
max1 = max(stock_prices1)
min2 = min(stock_prices2)
max2 = max(stock_prices2)
p1.y_range = Range1d(start= min1- (min1/10),end=max1+ (min1/10))
p1.extra_y_ranges = {'range2':Range1d(start= min2- (min2/10),end=max2+ (min2/10))}
p1.add_layout(LinearAxis(y_range_name="range2"), 'right')
p1.line(np.array(dates_objects1, dtype=np.datetime64), stock_prices1, color='#b41f2e', legend=stock1.ticker)
p1.line(np.array(dates_objects2, dtype=np.datetime64), stock_prices2, y_range_name='range2', color='#1F78B4', legend=stock2.ticker)
p1.grid.grid_line_alpha = 0.3
p1.xaxis.axis_label = 'Date'
p1.yaxis.axis_label = 'Price'
script, div = components(p1)
# Bar Charts
mc1 = float((stock1.market_cap[:-1]))
mc2 = float((stock2.market_cap[:-1]))
pe1 = float(stock1.pe_ratio)
pe2 = float(stock2.pe_ratio)
pb1 =float(stock1.pb_ratio)
pb2 = float(stock2.pb_ratio)
vol1 = float(stock1.volume)
vol2 = float(stock2.volume)
rev1 = float((stock1.revenue[:-1]))
rev2 = float((stock2.revenue[:-1]))
peg1 = float(stock1.peg)
peg2 = float(stock2.peg)
mc_dict= {'tickers':[stock1.ticker,stock2.ticker],
'market cap':[mc1,mc2],
'PB Ratio':[pb1,pb2],
'PE Ratio':[pe1,pe2],
'Volume': [vol1,vol2],
'Revenue': [rev1,rev2],
'PE Growth': [peg1,peg2]
}
#Hover Tools - Not working
#hover_mc = HoverTool(tooltips=[('Market Cap', '@height'), ('Ticker', '@cat'), ])
mc_bar = Bar(mc_dict,width=250, height=250,values='market cap',label='tickers',
color=color(columns='market cap',palette=['#b41f2e','#1F78B4']), title="Market Cap", ylabel='Billions')
vol_bar = Bar(mc_dict, width=250, height=250,values='Volume', label='tickers',
color=color(columns='Volume',palette=['#b41f2e','#1F78B4']), title="Volume",ylabel='Volume')
pb_bar = Bar(mc_dict, width=250, height=250,values='PB Ratio', label='tickers',
color=color(columns='PB Ratio',palette=['#b41f2e','#1F78B4']),title="PB Ratio",ylabel='Ratio')
pe_bar = Bar(mc_dict, width=250, height=250,values='PE Ratio', label='tickers',
color=color(columns='PE Ratio',palette=['#b41f2e','#1F78B4']), title="PE Ratio",ylabel='Ratio')
peg_bar = Bar(mc_dict, width=250, height=250, values='PE Growth', label='tickers',
color=color(columns='PE Growth', palette=['#b41f2e', '#1F78B4']), title="PE Growth", ylabel='Percentage(%)')
rev_bar = Bar(mc_dict, width=250, height=250, values='Revenue', label='tickers',
color=color(columns='Revenue', palette=['#b41f2e', '#1F78B4']), title="Revenue", ylabel='Billions')
mc_bar.border_fill_color = "whitesmoke"
vol_bar.border_fill_color = "whitesmoke"
pb_bar.border_fill_color = "whitesmoke"
pe_bar.border_fill_color = "whitesmoke"
peg_bar.border_fill_color = "whitesmoke"
rev_bar.border_fill_color = "whitesmoke"
grid = gridplot([[mc_bar,rev_bar, pe_bar],[vol_bar, pb_bar, peg_bar]])
mscript, mdiv = components(grid)
context = {'stock1': stock1,
'stock2': stock2,
'the_script': script,
'the_div': div,
'mc1': mscript,
'mc2': mdiv,
}
return render(request, 'stocktracker/comparison.html', context)
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}
ts.resample('M').mean().to_frame(name="mean"),
left_index=True, right_index=True)
frame = pd.merge(firstmerge, ts.resample('M').max().to_frame(name="max"),
left_index=True, right_index=True)
# You can use DataFrame index for bokeh x values but it doesn't like timestamp
frame['Month'] = frame.index.strftime('%m-%Y')
# Main object to render with stacking
bar = Bar(frame,
values=blend('min', 'mean', 'max',
name='values', labels_name='stats'),
label=cat(columns='Month', sort=False),
stack=cat(columns='values', sort=False),
color=color(columns='values',
palette=['SaddleBrown', 'Silver', 'Goldenrod'],
sort=True),
legend=None,
title="Statistical Values Grouped by Month",
tooltips=[('Value', '@values')]
)
# Legend info (if legend attribute is used it gets ugly with large dataset)
factors = ["min", "mean", "max"]
x = [0] * len(factors)
y = factors
pal = ['SaddleBrown', 'Silver', 'Goldenrod']
p = figure(width=100, toolbar_location=None, y_range=factors)
p.rect(x, y, color=pal, width=10, height=1)
p.xaxis.major_label_text_color = None
p.xaxis.major_tick_line_color = None
data['Date'].append(unique_dates[m])
data['Total'] = [
data['ACCEPTABLE'][k] + data['UNACCEPTABLE'][k]
for k in range(len(data['Date']))
]
df = pd.DataFrame(data)
bar = Bar(df,
values=blend('ACCEPTABLE',
'UNACCEPTABLE',
name='Hours',
labels_name='medal'),
label=cat(columns='Date', sort=False),
stack=cat(columns='medal', sort=False),
color=color(columns='medal',
palette=['Green', 'Red'],
sort=False),
legend='top_left',
title="Respiration data quality of participant id " +
str(ids[i]),
width=800)
output_file('Respiration_DataQuality_of_' + str(ids[i]) + ".html",
title="RIP_BAR_Plot")
source = ColumnDataSource(data)
columns = [
TableColumn(field="Date", title="Date"),
TableColumn(field="ACCEPTABLE", title="ACCEPTABLE(Hours)"),
TableColumn(field="UNACCEPTABLE", title="UNACCEPTABLE(Hours)"),
