def area_charts():
defaults.width = 400
defaults.height = 400
# create some example data
data = dict(
python=[2, 3, 7, 5, 26, 221, 44, 233, 254, 265, 266, 267, 120, 111],
pypy=[
12, 33, 47, 15, 126, 121, 144, 233, 254, 225, 226, 267, 110, 130
],
jython=[
22, 43, 10, 25, 26, 101, 114, 203, 194, 215, 201, 227, 139, 160
],
)
area1 = Area(data,
title="Area Chart",
legend="top_left",
xlabel='time',
ylabel='memory')
area2 = Area(data,
title="Stacked Area Chart",
legend="top_left",
stack=True,
xlabel='time',
ylabel='memory')
output_file("area.html", title="area.py example")
show(row(area1, area2))
def plot(concdf, outputfile="test.html"):
newtime = concdf
newtime["Times"] = map(lambda x: (float(x) / 60) - 8, concdf["Times"])
data = pd.melt(newtime, id_vars="Times")
# oldtimes = data["Times"]
# resettimes = []
# for time in oldtimes:
# if time >= 24:
# resettimes.append(time-24)
# else:
# resettimes.append(time)
# data["Times"] = resettimes
area = Area(data,
x="Times",
y="value",
legend='top_right',
title="T-Rx Optimized Treatment Schedule",
xlabel="Hours (post 8am)",
ylabel="% Remaining in Patient",
color="variable")
output_file(outputfile, title="T-RxPlot")
save(area)
print os.getcwd()
# show(area)
return
def get_concentration_area_chart(df):
"""Creates area chart visualizing portfolio concentration by position"""
df = (
df[["Symbol", "Concentration"]]
.sort_values("Concentration")
.set_index("Symbol")
.cumsum()
.reset_index()
.drop("Symbol", axis=1)
)
chart = Area(
df['Concentration'].astype(float),
title='Cumulative Portfolio Concentration',
ylabel='% of Total Value',
xlabel='Number of Positions',
plot_width=1200,
plot_height=400,
legend=False,
color='#4285f4'
)
chart.x_range.start, chart.x_range.end = 0, df.shape[0]
return file_html(chart, INLINE)
def create_analysis(id):
"""
View a pre-created analysis
"""
# Get file path from database by id
file = File.query.get_or_404(id).file
# Get filename for template
file_name = os.path.basename(file)
# Get file path and file extension
file_path, file_extension = os.path.splitext(file_name)
# Get file from server to process into data frame
if file_extension == '.csv':
# Load CSV file type
df = pd.DataFrame(pd.read_csv(file, encoding="ISO-8859-1", parse_dates=['Order Date']))
else:
# Load files with excel based file extensions
df = pd.DataFrame(pd.read_excel(file, encoding="ISO-8859-1", parse_dates=['Order Date']))
# Add columns for sales where profit is plus or zero to negative
df_sales = df
df_sales['Profitable'] = df['Sales'].where(df['Profit'] > 0, 0)
df_sales['Unprofitable'] = df['Sales'].where(df['Profit'] <= 0, 0)
# Filter only columns needed
df_sales = df_sales[['Order Date', 'Customer Segment', 'Profit', 'Sales', 'Profitable', 'Unprofitable']]
# Group by Segment
df_sales2 = df_sales.groupby(['Customer Segment', 'Order Date']).sum()
df_sales2 = df_sales2.reset_index(drop=False)
# Group by Month
df_mo = df_sales2.set_index('Order Date').groupby('Customer Segment').resample('M').sum()
df_mo = df_mo.reset_index(drop=False)
# Change column header to remove space for using as index
df_segs = df_mo.rename(columns={'Customer Segment': 'Segment'})
# Setting unique axis to pick up single Customer Segment
cons_df = df_segs[df_segs['Segment'] == df_segs.Segment.unique()[0]]
# Filter only columns for chart
cons_df_area = cons_df[['Order Date', 'Profitable', 'Unprofitable']]
# Reset index for chart axis labels
cons_df_area = cons_df_area.set_index(['Order Date']).resample('M').sum()
# When adding stack=True, Y labels skew. Fixed with NumeralTickFormatter
title1 = df_segs.Segment.unique()[0]
cons_area = Area(cons_df_area, title=title1, legend="top_left",
xlabel='', ylabel='Profit', plot_width=700, plot_height=400,
stack=True,
)
cons_area.yaxis[0].formatter = NumeralTickFormatter(format="0,00")
html = file_html(cons_area, CDN, "html")
# this is a placeholder template
return render_template('auth/analyses/render.html', data=html, title="Area Chart")
def area_chart(df, interval, rolling):
# re-sampling
df = df_stitched.resample(str(interval) + 'S').sum().fillna(0) / (
1000 / 50) * (60 / interval) # Each sample is 50ms
# Gives number of seconds spoken per min
# rename columns if names were given
if member_names:
for member_key in member_names:
df.rename(columns=member_names, inplace=True)
if rolling:
df = df.rolling(min_periods=1, window=5,
center=True).mean() # To smooth graph
start = unix_time_ms(df.index[0])
start_datetime = datetime.datetime.utcfromtimestamp(start / 1000)
end = unix_time_ms(df.index[len(df.index) - 1])
end_datetime = datetime.datetime.utcfromtimestamp(end / 1000)
df.reset_index(level='datetime',
inplace=True) # To input x values into area chart
if rolling:
graph_title = 'Contribution per Minute per Member for Meeting ' + meeting_name + ' (with rolling mean) \
from ' + start_datetime.strftime(
'%I:%M %p') + ' to ' + end_datetime.strftime('%I:%M %p')
else:
graph_title = 'Contribution per Minute per Member for Meeting ' + meeting_name + ' (without rolling mean) \
from ' + start_datetime.strftime(
'%I:%M %p') + ' to ' + end_datetime.strftime('%I:%M %p')
area = Area(
df,
x='datetime', # Column name
title=graph_title,
legend='top_left',
stack=True,
xlabel='Time of Day',
ylabel='Number of Seconds',
xscale='datetime',
width=1700,
height=400,
tools='xpan, xwheel_zoom, box_zoom, reset, resize',
)
# Format tick labels on x-axis
area.below[0].formatter = DatetimeTickFormatter()
area.below[0].formatter.formats = dict(years=['%Y'],
months=['%b %Y'],
days=['%d %b %Y'],
hours=['%I:%M %P'],
hourmin=['%I:%M %P'],
minutes=['%I:%M %P'],
minsec=['%I:%M:%S %P'],
seconds=['%I:%M:%S %P'])
return area
def showBacktestingResult(self):
"""显示回测结果"""
d = self.calculateBacktestingResult()
# 输出
self.output('-' * 30)
self.output('First Trade：\t%s' % d['timeList'][0])
self.output('Last Trade：\t%s' % d['timeList'][-1])
self.output('Total Trades：\t%s' % formatNumber(d['totalResult']))
self.output('Total Return：\t%s' % formatNumber(d['capital']))
self.output('Maximum Drawdown: \t%s' %
formatNumber(min(d['drawdownList'])))
self.output('Ave Trade：\t%s' %
formatNumber(d['capital'] / d['totalResult']))
self.output('Ave Slippage：\t%s' %
formatNumber(d['totalSlippage'] / d['totalResult']))
self.output('Ave Commission：\t%s' %
formatNumber(d['totalCommission'] / d['totalResult']))
self.output('Win Ratio\t\t%s%%' % formatNumber(d['winningRate']))
self.output('Ave Win\t%s' % formatNumber(d['averageWinning']))
self.output('Ave Loss\t%s' % formatNumber(d['averageLosing']))
self.output('Profit Factor：\t%s' % formatNumber(d['profitLossRatio']))
# Use Bokeh to plot
from bokeh.charts import Area, Line, Histogram
from bokeh.layouts import column
from bokeh.io import show
plotdata = {
"TradeN": range(len(d['capitalList'])),
"Equity Curve": d['capitalList'],
"Maximum Drawdown": d['drawdownList'],
"Profit/Loss": d['pnlList']
}
f1 = Line(plotdata,
x="TradeN",
y="Equity Curve",
color="blue",
width=1000,
height=300)
f2 = Area(plotdata,
x="TradeN",
y="Maximum Drawdown",
color="tomato",
width=1000,
height=300)
f3 = Histogram(plotdata,
values="Profit/Loss",
bins=30,
color="green",
width=1000,
height=300)
show(column(f1, f2, f3))
def doPlot_Area(data, nrDataSource):
p = Area(data,
title="Area Chart: " + nrDataSource['name'],
legend="top_left",
xlabel=data.columns[0],
ylabel=data.columns[1])
c = components(p, resources=None, wrap_script=False, wrap_plot_info=True)
return c
def area_plot_table(table, **kwargs):
"""
Plot a tabular DataFrame with an index and multiple columns representing
categories using a Bokeh area chart.
In addition to the keyword parameters accepted by bokeh.charts.Area, this
function accepts the following additional keyword arguments:
number_of_categories: integer
The number of categories ranked by total value to use.
xaxis_formatter: TickFormatter
The formatter to use for x axis values.
yaxis_formatter: TickFormatter
The formatter to use for y axis values.
x_range: Range1d
A range to use for the X-axis.
y_range: Range1d
A range to use for the Y-axis.
"""
if kwargs.has_key('number_of_categories'):
acreage_table = analysis.select_top_n_columns(
table, kwargs['number_of_categories'])
else:
acreage_table = table
acre_plot = Area(
acreage_table.reset_index(),
x='year',
y=map(str, acreage_table.columns),
stack=True,
**_remove_custom_keys(kwargs)
)
if kwargs.has_key('x_range'):
acre_plot.xrange = kwargs['x_range']
else:
acre_plot.x_range = Range1d(acreage_table.index.min(), acreage_table.index.max())
if kwargs.has_key('y_range'):
acre_plot.y_range = kwargs['y_range']
else:
acre_plot.y_range = Range1d(0, acreage_table.max().sum())
if kwargs.has_key('xaxis_formatter'):
acre_plot._xaxis.formatter = kwargs['xaxis_formatter']
if kwargs.has_key('yaxis_formatter'):
acre_plot._yaxis.formatter = kwargs['yaxis_formatter']
return acre_plot
def showArea(times):
data = dict(BootTime=totalboot(times))
area = Area(data,
title="Area Chart",
legend="top_left",
xlabel='Start Up #',
ylabel='Time (s)')
show(area)
def create_area_chart(data, palette):
return Area(data,
title=palette,
stack=True,
palette=standard_palettes.get(palette),
legend=None,
xlabel='',
ylabel='',
xgrid=False,
ygrid=False,
tools='')
def create_area_chart(data, palette):
_chart_styling = dict(height=300,
width=300,
xgrid=False,
ygrid=False,
tools="")
return Area(data,
title=palette,
stack=True,
palette=standard_palettes.get(palette),
**_chart_styling)
def plot_stacked(concdf, outputfile="test.html"):
# divide by 60 for time
# get x val to be time
data = concdf.set_index(['Times'])
area = Area(data,
legend='top_right',
stack=True,
title="Plasma Concentration over Time",
xlabel="Time",
ylabel="Concentration")
output_file(outputfile, title="Concentraion Plot")
# show(area)
return
def test_responsive_legacy_chart_starts_at_correct_size(
output_file_url, selenium):
values = dict(
apples=[2, 3, 7, 5, 26, 221, 44, 233, 254, 25, 2, 67, 10, 11],
oranges=[
22, 43, 10, 25, 26, 101, 114, 203, 194, 215, 201, 227, 139, 160
],
)
area = Area(values, title="Area Chart", responsive=True)
save(area)
selenium.set_window_size(width=1000, height=600)
selenium.get(output_file_url)
canvas = selenium.find_element_by_tag_name('canvas')
wait_for_canvas_resize(canvas, selenium)
# Canvas width should be just under 1000
assert canvas.size['width'] > 900
assert canvas.size['width'] < 1000
def area_plot_table(table, **kwargs):
"""
Plot a tabular DataFrame with an index and multiple columns representing
categories using a Bokeh area chart.
In addition to the keyword parameters accepted by bokeh.charts.Area, this
function accepts the following additional keyword arguments:
number_of_categories: integer
The number of categories ranked by total value to use.
xaxis_formatter: TickFormatter
The formatter to use for x axis values.
yaxis_formatter: TickFormatter
The formatter to use for y axis values.
x_range: Range1d
A range to use for the X-axis.
y_range: Range1d
A range to use for the Y-axis.
"""
if kwargs.has_key('number_of_categories'):
acreage_table = analysis.select_top_n_columns(
table, kwargs['number_of_categories'])
else:
acreage_table = table
acre_plot = Area(acreage_table.reset_index(),
x='year',
y=map(str, acreage_table.columns),
stack=True,
**_remove_custom_keys(kwargs))
if kwargs.has_key('x_range'):
acre_plot.xrange = kwargs['x_range']
else:
acre_plot.x_range = Range1d(acreage_table.index.min(),
acreage_table.index.max())
if kwargs.has_key('y_range'):
acre_plot.y_range = kwargs['y_range']
else:
acre_plot.y_range = Range1d(0, acreage_table.max().sum())
if kwargs.has_key('xaxis_formatter'):
acre_plot._xaxis.formatter = kwargs['xaxis_formatter']
if kwargs.has_key('yaxis_formatter'):
acre_plot._yaxis.formatter = kwargs['yaxis_formatter']
return acre_plot
logging.basicConfig(level=logging.DEBUG, format="%(asctime)s - %(module)s - %(levelname)s ##\t %(message)s")
log = logging.getLogger()
# create some example data
data = dict(
python=[2, 3, 7, 5, 26, 221, 44, 233, 254, 265, 266, 267, 120, 111],
pypy=[12, 33, 47, 15, 126, 121, 144, 233, 254, 225, 226, 267, 110, 130],
jython=[22, 43, 10, 25, 26, 101, 114, 203, 194, 215, 201, 227, 139, 160],
)
area = Area(data, title="Area Chart", legend="top_left",
xlabel='time', ylabel='memory')
@app.route("/api_analytics/nrendpoint", methods=["GET"])
def getNodeRedEndpoint():
e = {"url":appConfig.nodered_url}
return Response(json.dumps(e), mimetype="application/json")
def doPlot1(data, nrDataSource):
p = Bar(data, data.columns[0], values=data.columns[1], title="Bar graph: " + nrDataSource['name'], xlabel=data.columns[0], ylabel=data.columns[1], plot_width=900, responsive=True)
c = components(p, resources=None, wrap_script=False, wrap_plot_info=True)
return c
timelineChart.line("date","polarity", color = "green", line_width=1, source = timelineSource)
timelineChart.line("date","magnitude", color = "gray", line_width=1, source = timelineSource)
timelineChart.y_range = Range1d(0, 100)
#area chart
areadata = dict(
surprise=surpriseArray,
like=likeArray,
excite=exciteArray,
disappoint=disappointArray,
angry=angryArray,
hate=hateArray
)
area = Area(areadata, title="情感走势图", legend="top_left",
stack=True, xlabel='时间', ylabel='百分比', plot_width=730, plot_height=300, responsive=True, toolbar_location="below",
toolbar_sticky=False,tools = ["wheel_zoom","box_zoom","reset","save"])
area.toolbar.logo = None
area.x_range = Range1d(0, 152)
area.y_range = Range1d(0, 1)
#scatter chart
scatterhover = HoverTool(
tooltips="""
<div>
<div>
<span style="font-size: 15px;">极性 @polarity</span>
</div>
<div>
<span style="font-size: 15px;">浓度 @magnitude</span>
from collections import OrderedDict
from bokeh.charts import Area, show, vplot, output_file, defaults
defaults.width = 400
defaults.height = 400
# create some example data
xyvalues = OrderedDict(
python=[2, 3, 7, 5, 26, 221, 44, 233, 254, 265, 266, 267, 120, 111],
pypy=[12, 33, 47, 15, 126, 121, 144, 233, 254, 225, 226, 267, 110, 130],
jython=[22, 43, 10, 25, 26, 101, 114, 203, 194, 215, 201, 227, 139, 160],
)
output_file(filename="area.html")
area1 = Area(xyvalues,
title="Area Chart",
xlabel='time',
ylabel='memory',
legend="top_left")
area2 = Area(xyvalues,
title="Stacked Area Chart",
stack=True,
xlabel='time',
ylabel='memory',
legend="top_left")
show(vplot(area1, area2))
from collections import OrderedDict
from bokeh.charts import Area
# create some example data
xyvalues = OrderedDict(
python=[2, 3, 7, 5, 26, 221, 44, 233, 254, 265, 266, 267, 120, 111],
pypy=[12, 33, 47, 15, 126, 121, 144, 233, 254, 225, 226, 267, 110, 130],
jython=[22, 43, 10, 25, 26, 101, 114, 203, 194, 215, 201, 227, 139, 160],
)
# create an area chart
area = Area(xyvalues,
title="Area Chart",
xlabel='time',
ylabel='memory',
filename="area.html",
stacked=True,
legend="top_left").legend("top_left")
area.show()
from collections import OrderedDict
from bokeh.charts import Area
# create some example data
xyvalues = OrderedDict(
python=[2, 3, 7, 5, 26, 221, 44, 233, 254, 265, 266, 267, 120, 111],
pypy=[12, 33, 47, 15, 126, 121, 144, 233, 254, 225, 226, 267, 110, 130],
jython=[22, 43, 10, 25, 26, 101, 114, 203, 194, 215, 201, 227, 139, 160],
)
# create an area chart
area = Area(
xyvalues, title="Area Chart", xlabel='time',
ylabel='memory', filename="area.html",
facet=False, stacked=True,
)
area.legend("top_left").show()
def _plot_public_data_statistics(self, all_data, version_attr_name,
title_name, label_cb):
"""
generic method to plot flight hours one data type
:param all_data: list with all types as string
:param version_attr_name: attribute name of _VersionData
:param title_name: name of the data for the title (and hover tool)
:param label_cb: callback to create the label
:return: bokeh plot
"""
# change data structure
data_hours = {} # key=data id, value=list of hours for each version
for d in all_data:
data_hours[d] = []
versions = [] # sorted list of all versions
for ver in sorted(self._version_data,
key=functools.cmp_to_key(_Log.compare_version)):
versions.append(ver)
# all data points of the requested type for this version
version_type_data = getattr(self._version_data[ver],
version_attr_name)
for d in all_data:
if not d in version_type_data:
version_type_data[d] = 0.
data_hours[d].append(version_type_data[d])
# cumulative over each version
for key in all_data:
data_hours[key] = np.array(data_hours[key])
data_hours[key + "_cum"] = np.cumsum(data_hours[key])
# create a 2D numpy array. We could directly pass the dict to the bokeh
# plot, but then we don't have control over the sorting order
X = np.zeros((len(all_data), len(versions)))
i = 0
all_data_sorted = []
for key in sorted(
all_data,
key=lambda data_key: data_hours[data_key + "_cum"][-1]):
X[i, :] = data_hours[key + "_cum"]
all_data_sorted.append(key)
i += 1
all_data = all_data_sorted
colors = viridis(len(all_data))
# alternative: use patches: http://bokeh.pydata.org/en/latest/docs/gallery/brewer.html
area = Area(X,
title="Flight Hours per " + title_name,
tools=TOOLS,
active_scroll=ACTIVE_SCROLL_TOOLS,
stack=True,
xlabel='version (including development states)',
ylabel='',
color=colors)
# now set the labels
for i in range(len(all_data)):
area.legend[0].items[i].label = props.value(
label_cb(all_data[i], False))
area.legend[0].items.reverse()
# stack the data: we'll need it for the hover tool
last = np.zeros(len(versions))
for i in range(len(all_data)):
last = last + X[i, :]
data_hours[all_data[i] + '_stacked'] = last
data_hours['x'] = np.arange(len(versions))
# hover tool
source = ColumnDataSource(data=data_hours)
for d in all_data:
renderer = area.circle(x='x',
y=d + '_stacked',
source=source,
size=10,
alpha=0,
name=d)
g1_hover = HoverTool(renderers=[renderer],
tooltips=[
(title_name, label_cb(d, True)),
('Flight hours (only this version)',
'@' + d + '{0,0.0}'),
('Flight hours (up to this version)',
'@' + d + '_cum{0,0.0}')
])
area.add_tools(g1_hover)
# TODO: space x-axis entries according to version release date?
area.xaxis.formatter = FuncTickFormatter(code="""
var versions = """ + str(versions) + """;
return versions[Math.floor(tick)]
""")
area.xaxis.ticker = FixedTicker(ticks=list(range(len(versions))))
self._setup_plot(area)
return area
def generate_graph(self):
"""
Generate the graph; return a 2-tuple of strings, script to place in the
head of the HTML document and div content for the graph itself.
:return: 2-tuple (script, div)
:rtype: tuple
"""
logger.debug('Generating graph for %s', self._graph_id)
# tools to use
tools = [
PanTool(),
BoxZoomTool(),
WheelZoomTool(),
SaveTool(),
ResetTool(),
ResizeTool()
]
# generate the stacked area graph
try:
g = Area(
self._data, x='Date', y=self._y_series_names,
title=self._title, stack=True, xlabel='Date',
ylabel='Downloads', tools=tools,
# note the width and height will be set by JavaScript
plot_height=400, plot_width=800,
toolbar_location='above', legend=False
)
except Exception as ex:
logger.error("Error generating %s graph", self._graph_id)
logger.error("Data: %s", self._data)
logger.error("y=%s", self._y_series_names)
raise ex
lines = []
legend_parts = []
# add a line at the top of each Patch (stacked area) for hovertool
for renderer in g.select(GlyphRenderer):
if not isinstance(renderer.glyph, Patches):
continue
series_name = renderer.data_source.data['series'][0]
logger.debug('Adding line for Patches %s (series: %s)', renderer,
series_name)
line = self._line_for_patches(self._data, g, renderer, series_name)
if line is not None:
lines.append(line)
legend_parts.append((series_name, [line]))
# add the Hovertool, specifying only our line glyphs
g.add_tools(
HoverTool(
tooltips=[
(self._y_name, '@SeriesName'),
('Date', '@FmtDate'),
('Downloads', '@Downloads'),
],
renderers=lines,
line_policy='nearest'
)
)
# legend outside chart area
legend = Legend(legends=legend_parts, location=(0, 0))
g.add_layout(legend, 'right')
return components(g)
from collections import OrderedDict
from bokeh.charts import Area, show, output_file
# create some example data
xyvalues = OrderedDict(
python=[2, 3, 7, 5, 26, 221, 44, 233, 254, 265, 266, 267, 120, 111],
pypy=[12, 33, 47, 15, 126, 121, 144, 233, 254, 225, 226, 267, 110, 130],
jython=[22, 43, 10, 25, 26, 101, 114, 203, 194, 215, 201, 227, 139, 160],
)
output_file(filename="area.html")
area = Area(xyvalues,
title="Area Chart",
xlabel='time',
ylabel='memory',
stacked=True,
legend="top_left").legend("top_left")
show(area)
from bokeh.charts import Area, show, output_file, defaults
from bokeh.layouts import row
defaults.width = 400
defaults.height = 400
# create some example data
data = dict(
python=[2, 3, 7, 5, 26, 221, 44, 233, 254, 265, 266, 267, 120, 111],
pypy=[12, 33, 47, 15, 126, 121, 144, 233, 254, 225, 226, 267, 110, 130],
jython=[22, 43, 10, 25, 26, 101, 114, 203, 194, 215, 201, 227, 139, 160],
)
area1 = Area(data, title="Area Chart", legend="top_left",
xlabel='time', ylabel='memory')
area2 = Area(data, title="Stacked Area Chart", legend="top_left",
stack=True, xlabel='time', ylabel='memory')
output_file("area.html", title="area.py example")
show(row(area1, area2))
#!/usr/bin/python
#coding:utf-8
from bokeh.plotting import figure, output_file, show
from bokeh.charts import Area, show, vplot, output_file
# create some example data
data = dict(
# p_top = [[25,100],[1000,100],[10000,100]],
python=[110, 110, 100],
# bottom = [100,100,90]
)
#zhushi = '''
area = Area(
data, #x = [25,1000,10000],
title="Area Chart",
legend="top_left",
# x_axis_type="log",
# y_range=(60,120),
# stack=True,
# xlabel='频率（Hz）', ylabel='限值（dBμA）'
)
output_file('area.html')
p = area
show(p)
#'''
#print data[p_top]
output_file('output/bokeh/stocks.html', mode='cdn')
show(p)
#
pri_fin_mat2 = pri_fin_mat
pri_fin_mat2['Date'] = pri_fin_mat2.index
cdf = ColumnDataSource(pri_fin_mat2)
del pri_fin_mat2
p = figure()
p.line(x='Date', y='Gold', source=cdf)
output_file('output/bokeh/stocks.html', mode='cdn')
show(p)
# 2
p2 = Area(pri_fin_mat.Stocks.fillna(0))
output_file("output/bokeh/p2.html")
show(p2)
# 3
cdf = ColumnDataSource(pri_fin_mat)
p3 = figure()
#p3.line('date', 'Stocks', source=cdf)
p3.line(pri_fin_mat.index, pri_fin_mat.Bonds)
hover = HoverTool()
p3.add_tools(hover)
output_file('output/bokeh/s2.html', mode='cdn')
show(p3)
# 4
def simple_chart(request,counter_name):
counter_len = len(Counter.objects.values())
Date = [Counter.objects.values()[i]["pub_date"] for i in range(counter_len)]
name = counter_name
y_values = Counter.objects.values_list("counter_value",flat=True).filter(counter_name=counter_name)
points = zip(Date,y_values)
ddict = OrderedDict({'Date':Date})
#ddict[name] = y_values
TOOLS="pan,wheel_zoom,box_zoom,reset,hover,save"
p = figure(width=1200, height=400, x_axis_type="datetime",tools=TOOLS,title=name+"'s Metrics" )
p.min_border_left = 100
p.min_border_top = 50
p.border_fill = 'whitesmoke'
p.ygrid.band_fill_color = "#E6E6FA"
p.title_text_color = "olive"
p.title_text_font = "times"
p.title_text_font_style = "italic"
p.outline_line_width = 7
p.outline_line_alpha = 0.3
p.outline_line_color = "black"
source = ColumnDataSource(
data=dict(
rates = [slope(points[i][0].microsecond,points[i][1],points[i+1][0].microsecond,points[i+1][1]) for i in range(len(points)-1)]
)
)
hover = p.select(dict(type=HoverTool))
hover.point_policy = "follow_mouse"
hover.tooltips = OrderedDict([
("Counter Name",name),
("Rate of count","@rates c/us"),
])
p.line(Date,y_values,line_width=1,source=source)
p.square(Date, y_values, fill_color=None, line_color="green",size=4)
script1, div1 = components(p, CDN)
hist = Histogram(list(y_values),bins=50,title='Histogram',color="#778899")
hist.border_fill = 'whitesmoke'
hist.background_fill = "beige"
script2, div2 = components(hist, CDN)
area = Area(list(y_values),title="CDF")
area.border_fill ="whitesmoke"
area.background_fill = "#191970"
script3, div3 = components(area,CDN)
context = RequestContext(request,{"the_script1":script1, "the_div1":div1,"the_script2":script2,"the_div2":div2,"the_script3":script3,"the_div3":div3})
return render(request, "counters_app/simple_bokeh.html",context)
