def aggregate(transaction_history):
net = dict()
buy = dict()
sell = dict()
interest = dict()
dividend = dict()
historical = dict()
for t in transaction_history:
quarter = "%s-Q%i" % (t.date.year, (t.date.month-1)//3+1)
net[quarter] = float(handleNull(net.get(quarter))) + float(t.cashflow)
if t.kind == Transaction.BUY:
buy[quarter] = float(handleNull(buy.get(quarter))) + float(t.cashflow)
elif t.kind == Transaction.SELL:
sell[quarter] = float(handleNull(sell.get(quarter))) + float(t.cashflow)
elif t.kind == Transaction.INTEREST:
interest[quarter] = float(handleNull(interest.get(quarter))) + float(t.cashflow)
elif t.kind == Transaction.DIVIDEND:
dividend[quarter] = float(handleNull(dividend.get(quarter))) + float(t.cashflow)
elif t.kind == Transaction.HISTORICAL or t.kind == Transaction.CURRENT:
historical[quarter] = float(handleNull(historical.get(quarter))) + float(t.cashflow)
net = addMissingQuarters(net)
buy = addMissingQuarters(buy)
sell = addMissingQuarters(sell)
interest = addMissingQuarters(interest)
dividend = addMissingQuarters(dividend)
historical = addMissingQuarters(historical)
d = {'net': pd.Series(net),
'buy': pd.Series(buy),
'sell': pd.Series(sell),
'interest':pd.Series(interest),
'dividend':pd.Series(dividend),
'historical':pd.Series(historical)}
df = pd.DataFrame(d)
df['label']=df.index
p1 = Bar(df,
values = blend('buy','sell','interest','dividend','historical',name='cashflow', labels_name='cf'),
label=cat(columns='label',sort=False),
stack=cat(columns='cf',sort=False))
p2 = Bar(df,
values = blend('net'),
label='label')
output_file("test.html")
show(vplot(p1, p2))
def scatter():
s = Scatter(
flowers,
title="Fisher's Iris data set", tools='tap,box_select,save',
x=blend('petal_length', name='Length'),
y=blend('petal_width', name='Width'),
color='species', palette=Spectral4, legend=True,
)
# Lets move the legend off-canvas!
legend = s.legend[0]
legend.border_line_color = None
legend.orientation = 'horizontal'
legend.location = (0, 0)
s.above.append(legend)
return s
def scatter():
s = Scatter(
flowers,
title="Fisher's Iris data set", tools='tap,box_select,save',
x=blend('petal_length', name='Length'),
y=blend('petal_width', name='Width'),
color='species', palette=Spectral4, legend=True,
)
# Lets move the legend off-canvas!
legend = s.legend[0]
legend.border_line_color = None
legend.orientation = 'horizontal'
legend.location = (0, 0)
s.above.append(legend)
return s
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 plotAverage(self):
bar = Bar(\
self.__table,
values = blend('average', name = 'average', labels_name = 'average'),\
label = cat(columns = 'country', sort = False),\
stack = cat(columns = 'average'),\
agg = 'mean',\
title = 'Average of CO2 emissions in European Union',\
legend = False,\
tooltips = [('Average', '@average{1.11}' + ' g/km')])
return bar
def plotAverage(self):
bar = Bar(\
self.__table,
values = blend('average', name = 'average', labels_name = 'average'),\
label = cat(columns = 'fuel'),\
stack = cat(columns = 'average'),\
agg = 'mean',\
title = 'Average of CO2 emissions (by fuel type) in ' + self.__title,\
legend = False,\
tooltips = [('Average', '@average{1.11}' + ' g/km')])
return bar
def plot(self):
bar = Bar(\
self.__fuel_table,
values = blend('percentage', name = 'percentage', labels_name = 'percentage'),\
label = cat(columns = 'fuel'),\
stack = cat(columns = 'percentage'),\
agg = 'mean',\
title = self.__title,\
legend = False,\
tooltips = [('Percentage', '@percentage{0.00%}')])
bar._yaxis.formatter = NumeralTickFormatter(format = '0%')
return bar
def plotTotal(self):
bar = Bar(\
self.__table,
values = blend('total', name = 'total', labels_name = 'total'),\
label = cat(columns = 'fuel'),\
stack = cat(columns = 'total'),\
agg = 'mean',\
title = 'Total of CO2 emissions (by fuel type) in ' + self.__title,\
legend = False,\
tooltips = [('Total', '@total{1,1}' + ' g/km')])
bar._yaxis.formatter = NumeralTickFormatter(format = '0,0' + ' g/km')
return bar
def plot(self):
bar = Bar(\
self.__table,
values = blend('registrations', name = 'registrations', labels_name = 'registrations'),\
label = cat(columns = 'country', sort = False),\
stack = cat(columns = 'registrations'),\
agg = 'mean',\
title = 'Total of new registrations in European Union',\
legend = False,\
tooltips = [('Registrations', '@registrations{1,1}')])
bar._yaxis.formatter = NumeralTickFormatter(format='0,0')
return bar
def create_bar(data):
op = blend(*countries, labels_name='countries', name='energy')
return Bar(data,
label='year',
values=op,
color='countries',
group='countries',
width=1400,
height=600,
ylabel='Energy use per capita',
palette=['purple', 'green', 'blue', 'pink'],
legend=True)
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 createBokehChart(self):
data = self.getWorkingPandasDataFrame()
keyFields = self.getKeyFields()
valueFields = self.getValueFields()
clusterby = self.options.get("clusterby")
stacked = self.options.get("charttype", "grouped") == "stacked"
subplots = self.isSubplot()
# self.debug("keyF={0}, valueF={1}, cluster={2}, stacked={3}".format(keyFields[0], valueFields[0], clusterby, stacked))
charts = []
params = []
if subplots and len(valueFields) > 1:
for val in valueFields:
series = clusterby if clusterby is not None else False
values = val
params.append((values, series))
elif clusterby is not None and len(valueFields) <= 1:
params.append((valueFields[0], clusterby))
else:
series = '_'.join(valueFields)
values = blend(*valueFields,
name=series.replace('_', ','),
labels_name=series)
if clusterby is not None:
self.addMessage(
"Warning: 'Cluster By' ignored when you have multiple Value Fields but subplots option is not selected"
)
params.append((values, series))
for p in params:
if stacked:
b = Bar(data,
label=keyFields[0],
values=p[0],
stack=p[1],
legend=self.showLegend())
else:
b = Bar(data,
label=keyFields[0],
values=p[0],
group=p[1],
legend=self.showLegend())
charts.append(b)
return charts
def plot_stacked_bar_chart(df):
bar = Bar(df,
values=blend('Murder',
'Rape',
'Robbery',
'Aggravated Assault',
'Burglary',
'Larceny Theft',
'Motor Vehicle Theft',
name='Crime',
labels_name='crime'),
label=cat(columns='State', sort=False),
stack=cat(columns='crime', sort=False),
legend='top_right',
title="Crime per state",
width=1000,
height=500,
tooltips=[('crime', '@crime'), ('state', '@State')])
#output_file("stacked_bar.html", title="stacked_bar.py example")
show(bar)
def plot_opt(name):
with open('{}.txt'.format(name), 'r') as f:
data = f.read()
N = int(re.findall('\[I(\d*) \d*\]\n.* .* .*', data)[0])
B = int(re.findall('\[I\d* (\d*)\]\n.* .* .*', data)[0])
TP = ['with', 'without']
T1 = [float(x) for x in re.findall('\[I\d* \d*\]\n(.*) .* .*', data)]
T2 = [float(x) for x in re.findall('\[I\d* \d*\]\n.* (.*) .*', data)]
T3 = [float(x) for x in re.findall('\[I\d* \d*\]\n.* .* (.*)', data)]
T1 += [
float(x)
for x in re.findall('\[I\d* \d*\]\n.* .* .*\n(.*) .* .*', data)
]
T2 += [
float(x)
for x in re.findall('\[I\d* \d*\]\n.* .* .*\n.* (.*) .*', data)
]
T3 += [
float(x)
for x in re.findall('\[I\d* \d*\]\n.* .* .*\n.* .* (.*)', data)
]
df = pd.DataFrame({
'type': TP,
'computation': T1,
'communication': T2,
'memcpy': T3
})
bar = Bar(df,
label=['type'],
values=blend('computation',
'communication',
'memcpy',
name='times',
labels_name='time'),
xlabel='comparison',
ylabel='total time(s)',
stack=cat(columns=['time'], sort=False),
title='{}'.format(name))
output_file('{}.html'.format(name), title='N={}, B={}'.format(N, B))
save(bar)
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 plot_weak(name):
with open('{}.tot'.format(name), 'r') as f:
data = f.read()
Ns = [int(x) for x in re.findall('\[I(\d*) \d*\]\n.* .* .*', data)]
Bs = [int(x) for x in re.findall('\[I\d* (\d*)\]\n.* .* .*', data)]
T1s = [float(x) for x in re.findall('\[I\d* \d*\]\n(.*) .* .*', data)]
T2s = [float(x) for x in re.findall('\[I\d* \d*\]\n.* (.*) .*', data)]
T3s = [float(x) for x in re.findall('\[I\d* \d*\]\n.* .* (.*)', data)]
"""
if 'cuda' not in name:
Ns += Ns
Bs += Bs
T1s += [float(x) for x in re.findall('\[I\d* \d*\]\n.* .* .*\n(.*) .* .*', data)]
T2s += [float(x) for x in re.findall('\[I\d* \d*\]\n.* .* .*\n.* (.*) .*', data)]
T3s += [float(x) for x in re.findall('\[I\d* \d*\]\n.* .* .*\n.* .* (.*)', data)]
"""
df = pd.DataFrame({
'N': Ns,
'B': Bs,
'computation': T1s,
'communication': T2s,
'memcpy': T3s
})
bar = Bar(df,
label=['N', 'B'],
values=blend('computation',
'communication',
'memcpy',
name='times',
labels_name='time'),
xlabel='#node',
ylabel='total time(s)',
stack=cat(columns=['time'], sort=False),
title='Weak Scalability')
output_file('weak_{}.html'.format(name))
save(bar)
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)
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)
def create_bar(data):
op = blend(*countries, labels_name='countries', name='energy')
return Bar(data, label='year', values=op, color='countries', group='countries',
width=1400, height=600, responsive = False, ylabel='Energy use per capita',
palette=['purple', 'green', 'blue', 'pink'],
legend=True)
""" This example uses the Iris data to demonstrate the specification of
combined variables using chart operations.
This specific instance uses a blend, which stacks columns, and renames
the combined column. This can be used where the column itself is a type
of categorical variable. Here, length and width are derived from the
petal and sepal measurements.
"""
from bokeh.charts import Scatter, output_file, show
from bokeh.charts.operations import blend
from bokeh.sampledata.iris import flowers as data
scatter = Scatter(data,
x=blend('petal_length', 'sepal_length', name='length'),
y=blend('petal_width', 'sepal_width', name='width'),
color='species',
title='x=petal_length+sepal_length, y=petal_width+sepal_width, color=species',
legend='top_right')
output_file("iris_blend.html")
show(scatter)
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 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}
scatter4 = Scatter(
df, x='mpg', y='hp', color='cyl', marker='origin', title="x='mpg', y='hp', color='cyl', marker='origin'",
xlabel="Miles Per Gallon", ylabel="Horsepower", legend='top_right')
# Example with nested json/dict like data, which has been pre-aggregated and pivoted
df2 = df_from_json(data)
df2 = df2.sort('total', ascending=False)
df2 = df2.head(10)
df2 = pd.melt(df2, id_vars=['abbr', 'name'])
scatter5 = Scatter(
df2, x='value', y='name', color='variable', title="x='value', y='name', color='variable'",
xlabel="Medals", ylabel="Top 10 Countries", legend='bottom_right')
scatter6 = Scatter(flowers, x=blend('petal_length', 'sepal_length', name='length'),
y=blend('petal_width', 'sepal_width', name='width'), color='species',
title='x=petal_length+sepal_length, y=petal_width+sepal_width, color=species',
legend='top_right')
output_file("scatter_multi.html")
show(vplot(
hplot(scatter0, scatter1),
hplot(scatter2, scatter3),
hplot(scatter4, scatter5),
hplot(scatter6)
))
def createBokehChart(self):
keyFields = self.getKeyFields()
valueFields = self.getValueFields()
clusterby = self.options.get("clusterby")
stacked = self.options.get("charttype", "grouped") == "stacked"
subplots = self.isSubplot()
workingPDF = self.getWorkingPandasDataFrame().copy()
def convertPDFDate(df, col):
#Bokeh doesn't support datetime as index in Bar chart. Convert to String
if len(keyFields) == 1:
dtype = df[col].dtype.type if col in df else None
if numpy.issubdtype(dtype, numpy.datetime64):
dateFormat = self.options.get("dateFormat", None)
try:
df[col] = df[col].apply(lambda x: str(x).replace(':','-') if dateFormat is None else x.strftime(dateFormat))
except:
self.exception("Error converting dateFormat {}".format(dateFormat))
df[col] = df[col].apply(lambda x: str(x).replace(':','-'))
for index, row in workingPDF.iterrows():
for k in keyFields:
if isinstance(row[k], str if sys.version >= '3' else basestring):
row[k] = row[k].replace(':', '.')
workingPDF.loc[index] = row
charts=[]
def goChart(label, stack_or_group, values, ylabel=None, color=None):
convertPDFDate(workingPDF, keyFields[0])
if ylabel is None:
ylabel=values
label=label if isinstance(label, (list, tuple)) else [label]
if stacked:
charts.append( Bar(workingPDF, label=CatAttr(columns=label, sort=False), stack=stack_or_group, color=color, values=values, legend=self.showLegend(), ylabel=ylabel))
else:
charts.append( Bar(workingPDF, label=CatAttr(columns=label, sort=False), group=stack_or_group, color=color, values=values, legend=self.showLegend(), ylabel=ylabel))
if clusterby is not None and (subplots or len(valueFields)<=1):
subplots = subplots if len(valueFields)==1 or subplots else False
if subplots:
for j, valueField in enumerate(valueFields):
pivot = workingPDF.pivot(
index=keyFields[0], columns=clusterby, values=valueField
)
for i,col in enumerate(pivot.columns[:10]): #max 10
data = pd.DataFrame({'values':pivot[col].values, 'names': pivot.index.values})
convertPDFDate(data, 'names')
if subplots:
charts.append(
Bar(data, label=CatAttr(columns=['names'], sort=False), color = Colors.hexRGB( 1.*i/2 ), values='values', ylabel=valueField, legend=False,
title="{0} = {1}".format(clusterby, pivot.columns[i])
)
)
else:
goChart( keyFields[0], clusterby, valueFields[0])
else:
if subplots:
for i,valueField in enumerate(valueFields):
goChart( keyFields[0], None, valueField, color=Colors.hexRGB( 1.*i/2 ))
else:
if len(valueFields) > 1:
series = '_'.join(valueFields)
values = blend(*valueFields, name=series.replace('_', ','), labels_name=series)
else:
series = False
values = valueFields[0]
goChart(keyFields, series, values, ylabel=','.join(valueFields))
if clusterby is not None:
self.addMessage("Warning: 'Cluster By' ignored when grouped option with multiple Value Fields is selected")
return charts
""" This example uses the Iris data to demonstrate the specification of
combined variables using chart operations.
This specific instance uses a blend, which stacks columns, and renames
the combined column. This can be used where the column itself is a type
of categorical variable. Here, length and width are derived from the
petal and sepal measurements.
"""
from bokeh.charts import Scatter, output_file, show
from bokeh.charts.operations import blend
from bokeh.sampledata.iris import flowers as data
scatter = Scatter(
data,
x=blend('petal_length', 'sepal_length', name='length'),
y=blend('petal_width', 'sepal_width', name='width'),
color='species',
title=
'x=petal_length+sepal_length, y=petal_width+sepal_width, color=species',
legend='top_right')
output_file("iris_blend.html", title="iris_blend.py example")
show(scatter)
scatter3 = Scatter(
df, x='mpg', y='hp', color='origin', title="x='mpg', y='hp', color='origin', with tooltips",
xlabel="Miles Per Gallon", ylabel="Horsepower",
legend='top_right', tooltips=[('origin', "@origin")])
scatter4 = Scatter(
df, x='mpg', y='hp', color='cyl', marker='origin', title="x='mpg', y='hp', color='cyl', marker='origin'",
xlabel="Miles Per Gallon", ylabel="Horsepower", legend='top_right')
# Example with nested json/dict like data, which has been pre-aggregated and pivoted
df2 = df_from_json(data)
df2 = df2.sort('total', ascending=False)
df2 = df2.head(10)
df2 = pd.melt(df2, id_vars=['abbr', 'name'])
scatter5 = Scatter(
df2, x='value', y='name', color='variable', title="x='value', y='name', color='variable'",
xlabel="Medals", ylabel="Top 10 Countries", legend='bottom_right')
scatter6 = Scatter(flowers, x=blend('petal_length', 'sepal_length', name='length'),
y=blend('petal_width', 'sepal_width', name='width'), color='species',
title='x=petal_length+sepal_length, y=petal_width+sepal_width, color=species',
legend='top_right')
output_file("scatter_multi.html", title="scatter_multi.py example")
show(gridplot(scatter0, scatter2, scatter3, scatter4,
scatter5, scatter6, ncols=2))
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
data['ACCEPTABLE'] = []
data['UNACCEPTABLE'] = []
data['Date'] = []
for m in range(len(stat)):
data['ACCEPTABLE'].append(stat[m][0])
data['UNACCEPTABLE'].append(stat[m][1] + stat[m][2])
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)
# print(days)
# Put data into dataframe broken into min, mean, and max values each for month
ts = Series(vals, index=days[0])
firstmerge = pd.merge(ts.resample('M').min().to_frame(name="min"),
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']
