def trip_fare_init(self, width=310, height=350, webgl=True):
def ticker():
labels = {
0: '0~5',
1: '5~10',
2: '10~25',
3: '25~50',
4: '50~100',
5: '>100'
}
return labels[tick]
self.trip_fare = figure(webgl=webgl,
toolbar_location=None,
width=width,
height=height,
title='Fare (US dolloars)')
self.trip_fare_source = ColumnDataSource(
data=dict(x=range(6), fare=self.data.get_fare()))
vbar = self.trip_fare.vbar(width=1,
bottom=0,
x='x',
top='fare',
source=self.trip_fare_source,
fill_alpha=0.7,
line_color="white",
color='#ffcc5c')
self.trip_fare.y_range.start = 0
self.trip_fare.xaxis.major_tick_line_color = None
self.trip_fare.xaxis.minor_tick_line_color = None
self.trip_fare.xaxis.formatter = FuncTickFormatter.from_py_func(ticker)
self.trip_fare.select(dict(type=GlyphRenderer))
self.trip_fare.add_tools(
HoverTool(renderers=[vbar], tooltips=[("Trips", "@fare")]))
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 _axis_properties(self,
axis,
key,
plot,
dimension,
ax_mapping={
'x': 0,
'y': 1
}):
"""
Returns a dictionary of axis properties depending
on the specified axis.
"""
axis_props = {}
if ((axis == 'x' and self.xaxis in ['bottom-bare', 'top-bare']) or
(axis == 'y' and self.yaxis in ['left-bare', 'right-bare'])):
axis_props['axis_label'] = ''
axis_props['major_label_text_font_size'] = '0pt'
axis_props['major_tick_line_color'] = None
axis_props['minor_tick_line_color'] = None
else:
rotation = self.xrotation if axis == 'x' else self.yrotation
if rotation:
axis_props['major_label_orientation'] = np.radians(rotation)
ticker = self.xticks if axis == 'x' else self.yticks
if isinstance(ticker, Ticker):
axis_props['ticker'] = ticker
elif isinstance(ticker, int):
axis_props['ticker'] = BasicTicker(desired_num_ticks=ticker)
elif isinstance(ticker, (tuple, list)):
if all(isinstance(t, tuple) for t in ticker):
pass
else:
axis_props['ticker'] = FixedTicker(ticks=ticker)
if FuncTickFormatter is not None and ax_mapping and dimension:
formatter = None
if dimension.value_format:
formatter = dimension.value_format
elif dimension.type in dimension.type_formatters:
formatter = dimension.type_formatters[dimension.type]
if formatter:
msg = ('%s dimension formatter could not be '
'converted to tick formatter. ' % dimension.name)
try:
formatter = FuncTickFormatter.from_py_func(formatter)
except RuntimeError:
self.warning(msg + 'Ensure Flexx is installed '
'("conda install -c bokeh flexx" or '
'"pip install flexx")')
except Exception as e:
error = 'Pyscript raised an error: {0}'.format(e)
error = error.replace('%', '%%')
self.warning(msg + error)
else:
axis_props['formatter'] = formatter
return axis_props
def test_functickformatter_from_py_func_no_args():
def convert_to_minutes():
return tick * 60 # noqa
formatter = FuncTickFormatter.from_py_func(convert_to_minutes)
js_code = flexx.pyscript.py2js(convert_to_minutes, 'formatter')
function_wrapper = formatter.code.replace(js_code, '')
assert function_wrapper == "return formatter();\n"
def test_functickformatter_from_py_func():
def convert_to_minutes(seconds):
return seconds * 60
formatter = FuncTickFormatter.from_py_func(convert_to_minutes)
js_code = pyscript.py2js(convert_to_minutes, 'formatter')
function_wrapper = formatter.code.replace(js_code, '')
assert function_wrapper == "function (seconds) {return formatter(seconds)};"
def test_functickformatter_from_py_func_no_args():
def convert_to_minutes():
return tick * 60 # noqa
formatter = FuncTickFormatter.from_py_func(convert_to_minutes)
js_code = flexx.pyscript.py2js(convert_to_minutes, 'formatter')
function_wrapper = formatter.code.replace(js_code, '')
assert function_wrapper == "formatter();\n"
def evolutionCharts(df):
df['week']= df['snapshot'].apply(getWeekString)
df = df[df['end'].notnull()]
df=df.sort(['snapshot'], ascending=[1])
df['snapshotId']= range(1, len(df) + 1)
source = ColumnDataSource(df)
plots={'size':evolSize(source,df)}
last=None
for q in qa:
pt = figure( plot_width=600, plot_height=200
, min_border=10, min_border_left=50
, toolbar_location="above",responsive=True)
pt.background_fill_color = "#fafafa"
pt.legend.location = "top_left"
pt.toolbar.logo = None
pt.toolbar_location = None
hit_renderers = []
legends=[]
for m,v in q['metrics'].items():
l=pt.line(x='snapshotId',y=m.lower(), line_width=2,source=source, color=v['color'])
c=pt.circle(x='snapshotId',y=m.lower(), line_width=2,source=source, color=v['color'])
# invisible circle used for hovering
hit_target =Circle(x='snapshotId',y=m.lower(), size=10,line_color=None, fill_color=None)
hit_renderer = pt.add_glyph(source, hit_target)
legends.append((v['label']+" ["+m.lower()+"]",[l,c]))
pt.add_tools(HoverTool(renderers=[hit_renderer], tooltips={'Metric':v['label'], "Week": "@week", 'Value':"@"+m.lower()}))
pt.xaxis[0].ticker.desired_num_ticks = df.shape[0]/2
pt.xaxis.formatter=FuncTickFormatter.from_py_func(getWeekStringTick)
pt.axis.minor_tick_line_color = None
legend = Legend(location=(0, -30))
legend.items=legends
pt.add_layout(legend, 'right')
pt.xaxis[0].axis_label = 'Snapshot'
pt.yaxis[0].axis_label = 'Average quality'
plots[q['dimension']]=pt
last=pt
return plots
def test_functickformatter_from_py_func():
def convert_to_minutes(seconds):
return seconds * 60
formatter = FuncTickFormatter.from_py_func(convert_to_minutes)
js_code = pyscript.py2js(convert_to_minutes, 'formatter')
function_wrapper = formatter.code.replace(js_code, '')
assert function_wrapper == "function (seconds) {return formatter(seconds)};"
assert formatter.lang == "javascript"
def test_functickformatter_from_py_func_with_args():
slider = Slider()
def convert_to_minutes(x=slider):
return tick * 60 # noqa
formatter = FuncTickFormatter.from_py_func(convert_to_minutes)
js_code = flexx.pyscript.py2js(convert_to_minutes, 'formatter')
function_wrapper = formatter.code.replace(js_code, '')
assert function_wrapper == "formatter(x);\n"
assert formatter.args['x'] is slider
def _axis_properties(self, axis, key, plot, dimension,
ax_mapping={'x': 0, 'y': 1}):
"""
Returns a dictionary of axis properties depending
on the specified axis.
"""
axis_props = {}
if ((axis == 'x' and self.xaxis in ['bottom-bare', 'top-bare']) or
(axis == 'y' and self.yaxis in ['left-bare', 'right-bare'])):
axis_props['axis_label'] = ''
axis_props['major_label_text_font_size'] = '0pt'
axis_props['major_tick_line_color'] = None
axis_props['minor_tick_line_color'] = None
else:
rotation = self.xrotation if axis == 'x' else self.yrotation
if rotation:
axis_props['major_label_orientation'] = np.radians(rotation)
ticker = self.xticks if axis == 'x' else self.yticks
if isinstance(ticker, Ticker):
axis_props['ticker'] = ticker
elif isinstance(ticker, int):
axis_props['ticker'] = BasicTicker(desired_num_ticks=ticker)
elif isinstance(ticker, (tuple, list)):
if all(isinstance(t, tuple) for t in ticker):
pass
else:
axis_props['ticker'] = FixedTicker(ticks=ticker)
if FuncTickFormatter is not None and ax_mapping and dimension:
formatter = None
if dimension.value_format:
formatter = dimension.value_format
elif dimension.type in dimension.type_formatters:
formatter = dimension.type_formatters[dimension.type]
if formatter:
msg = ('%s dimension formatter could not be '
'converted to tick formatter. ' % dimension.name)
try:
formatter = FuncTickFormatter.from_py_func(formatter)
except RuntimeError:
self.warning(msg+'Ensure Flexx is installed '
'("conda install -c bokeh flexx" or '
'"pip install flexx")')
except Exception as e:
error = 'Pyscript raised an error: {0}'.format(e)
error = error.replace('%', '%%')
self.warning(msg+error)
else:
axis_props['formatter'] = formatter
return axis_props
def test_functickformatter_from_py_func_with_args():
slider = Slider()
def convert_to_minutes(x=slider):
return tick * 60 # noqa
formatter = FuncTickFormatter.from_py_func(convert_to_minutes)
js_code = flexx.pyscript.py2js(convert_to_minutes, 'formatter')
function_wrapper = formatter.code.replace(js_code, '')
assert function_wrapper == "return formatter(x);\n"
assert formatter.args['x'] is slider
def evolutionCharts(df):
df['week'] = df['snapshot'].apply(getWeekString)
df = df[df['end'].notnull()]
df=df.sort_values(['snapshot'], ascending=[1])
df['snapshotId']= range(1, len(df) + 1)
print(df)
source = ColumnDataSource(df)
plots={'size':evolSize(source,df)}
for q in qa:
pt = figure(plot_width=600, plot_height=200
, min_border=10, min_border_left=50
, toolbar_location="above")
pt.background_fill_color = "#fafafa"
pt.legend.location = "top_left"
pt.toolbar.logo = None
pt.toolbar_location = None
legends=[]
marker = [pt.circle, pt.x, pt.cross, pt.square, pt.diamond, pt.triangle, pt.inverted_triangle, pt.asterisk, pt.square_cross]
colors = brewer['BrBG'][max(3, min(len(q['metrics']), 11))]
i = 0
for m,v in q['metrics'].items():
if m.lower() in df:
l=pt.line(x='snapshotId',y=m.lower(), line_width=2,source=source, color=colors[i%len(colors)])
c=marker[i%len(marker)](x='snapshotId',y=m.lower(), line_width=4,source=source, color=colors[i%len(colors)])
# invisible circle used for hovering
hit_target =Circle(x='snapshotId',y=m.lower(), size=10,line_color=None, fill_color=None)
hit_renderer = pt.add_glyph(source, hit_target)
legends.append((v['label']+" ["+m.lower()+"]",[l,c]))
pt.add_tools(HoverTool(renderers=[hit_renderer], tooltips={'Metric':v['label'], "Week": "@week", 'Value':"@"+m.lower()}))
pt.xaxis[0].ticker.desired_num_ticks = df.shape[0]
i += 1
pt.xaxis.formatter=FuncTickFormatter.from_py_func(getWeekStringTick)
pt.axis.minor_tick_line_color = None
legend = Legend(location=(0, -30))
legend.items=legends
pt.add_layout(legend, 'right')
pt.xaxis[0].axis_label = 'Snapshot'
pt.yaxis[0].axis_label = 'Average quality'
plots[q['dimension']]=pt
return plots
def fetchProcessChart(data,cnts):
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
bp.xaxis[0].formatter = NumeralTickFormatter(format="0.0%")
bp.yaxis[0].formatter=FuncTickFormatter.from_py_func(hm)
bp.xaxis[0].axis_label = '% of portals'
bp.yaxis[0].axis_label = 'Time elapsed'
mx=None
c=0
for sn in sorted(data.keys()):
d=data[sn]
d_sorted = np.sort(np.array(d))
y=[e for e in d_sorted] #datetime.datetime.fromtimestamp(e)
x = 1. * arange(len(d)) / (len(d) - 1)
mx=max(x) if max(x)>mx else mx
if sn == max(data.keys()):
ci=bp.circle(x,y, size=5, alpha=0.5, color='red', legend="current week: "+getWeekString(sn))
li=bp.line(x,y, line_width=2,line_color='red', legend="current week: "+getWeekString(sn))
else:
ci=bp.circle(x,y, size=5, alpha=0.5, color='gray')
li=bp.line(x,y, line_width=2,line_color='gray')
#hit_target =Circle(x,y, size=10,line_color=None, fill_color=None)
#c.select(dict(type=HoverTool)).tooltips = {"Week": "@week",m:"@"+m.lower()}
#hit_renderers.append(hit_renderer)
bp.add_tools(HoverTool(renderers=[li], tooltips={"Week": getWeekString(sn)}))
c+=1
#bp.text(,y[-1], line_width=2,line_color=OrRd9[c],legend=str(sn))
no_olympics_glyph = Text(x=x[-1], y=y[-1], x_offset=100, text=["%s of %s portals"%(len(d), cnts[sn])],
text_align="right", text_baseline="top",
text_font_size="9pt", text_font_style="italic", text_color="black")
bp.add_glyph(no_olympics_glyph)
bp.x_range=Range1d(0, mx*1.2)
bp.background_fill_color = "#fafafa"
bp.legend.location = "top_left"
return bp
def evolSize(source,df):
p = figure( plot_width=600, plot_height=200
, min_border=10, min_border_left=50
, toolbar_location="above",responsive=True)
p.background_fill_color = "#fafafa"
p.legend.location = "top_left"
p.toolbar.logo = None
p.toolbar_location = None
legends=[]
l=p.line(x='snapshotId',y='datasetcount', line_width=2,source=source)
c=p.circle(x='snapshotId',y='datasetcount', line_width=2,source=source)
hit_target =Circle(x='snapshotId',y='datasetcount', size=10,line_color=None, fill_color=None)
hit_renderer = p.add_glyph(source, hit_target)
legends.append(("Datasets",[l,c]))
p.add_tools(HoverTool(renderers=[hit_renderer], tooltips={'Metric':"Size", "Week": "@week", 'Value':"@datasetcount"}))
#######
l=p.line(x='snapshotId',y='resourcecount', line_width=2,source=source)
c=p.circle(x='snapshotId',y='resourcecount', line_width=2,source=source)
hit_target =Circle(x='snapshotId',y='resourcecount', size=10,line_color=None, fill_color=None)
hit_renderer = p.add_glyph(source, hit_target)
legends.append(("Resources",[l,c]))
p.add_tools(HoverTool(renderers=[hit_renderer], tooltips={'Metric':"Size", "Week": "@week", 'Value':"@resourcecount"}))
p.xaxis[0].ticker.desired_num_ticks = df.shape[0]/2
p.xaxis.formatter=FuncTickFormatter.from_py_func(getWeekStringTick)
p.axis.minor_tick_line_color = None
legend = Legend( location=(0, -30))
legend.items = legends
p.add_layout(legend, 'right')
p.xaxis[0].axis_label = 'Snapshot'
p.yaxis[0].axis_label = 'Count'
return p
def test_functickformatter_bad_pyfunc_formats():
def has_positional_arg(x):
return None
with pytest.raises(ValueError):
FuncTickFormatter.from_py_func(has_positional_arg)
def has_positional_arg_with_kwargs(y, x=5):
return None
with pytest.raises(ValueError):
FuncTickFormatter.from_py_func(has_positional_arg_with_kwargs)
def has_non_Model_keyword_argument(x=10):
return None
with pytest.raises(ValueError):
FuncTickFormatter.from_py_func(has_non_Model_keyword_argument)
def test_functickformatter_bad_pyfunc_formats():
def has_positional_arg(x):
return None
with pytest.raises(ValueError):
FuncTickFormatter.from_py_func(has_positional_arg)
def has_positional_arg_with_kwargs(y, x=5):
return None
with pytest.raises(ValueError):
FuncTickFormatter.from_py_func(has_positional_arg_with_kwargs)
def has_non_Model_keyword_argument(x=10):
return None
with pytest.raises(ValueError):
FuncTickFormatter.from_py_func(has_non_Model_keyword_argument)
def __init__(self, xVals, yVals, colours=None, width=None):
Max = 0
Min = 0
N = len(xVals)
# create list of colours
if (colours == None):
colours = list(xVals)
for i in range(0, N):
colours[i] = "red"
else:
if (not isinstance(colours, list)):
colours = [colours]
for i in range(1, N):
colours.append(colours[0])
# create list of widths
if (width == None):
width = []
for i in range(0, N):
width.append(1)
# initialise values for loop
self.fig = figure(tools="")
self.barSources = []
x = 0
places = []
label_places = []
index = {}
for i in range(0, N):
# add ColumnDataSource describing each bar
self.barSources.append(
ColumnDataSource(
data=dict(x=[x, x, x + width[i], x + width[i]],
y=[0, yVals[i], yVals[i], 0])))
# update Max and Min for y_range
if (yVals[i] + 1 > Max):
Max = yVals[i] + 1
elif (yVals[i] < 0 and yVals[i] - 1 < Min):
Min = yVals[i] - 1
# create bar
self.fig.patch(x='x',
y='y',
fill_color=colours[i],
source=self.barSources[i],
line_color=None)
br = xVals[i].find('\n')
places.append(x + width[i] / 2.0)
if (br == -1):
# remember bar position
label_places.append(x + width[i] / 2.0)
# remember label that should be written at that postion
index[str(int(100 * (x + width[i] / 2.0)))] = [xVals[i]]
else:
label = []
while (br != -1):
label.append(xVals[i][0:br])
xVals[i] = xVals[i][br + 1:]
br = xVals[i].find('\n')
label.append(xVals[i])
N = len(label)
for j in range(0, N):
index[str(int(100 * (x + width[i] * (j + 1) /
(N + 1.0))))] = [label[j]]
label_places.append((floor(100 * (x + width[i] * (j + 1) /
(N + 1.0))) / 100.0))
# increase x
x += width[i] + 1
# set figure properties
self.fig.x_range = Range1d(-1, x)
self.fig.y_range = Range1d(Min, Max)
self.fig.grid.visible = False
self.fig.xaxis.major_label_text_font_size = "15pt"
self.fig.xaxis.major_tick_line_color = None
self.fig.xaxis.major_label_orientation = 0
self.fig.yaxis.axis_label = "speed (m/second)"
self.fig.yaxis.axis_label_text_font_size = "14pt"
self.fig.min_border_bottom = 5
self.fig.title.text = 'Boat'
self.fig.title_location = 'below'
self.fig.title.text_font_size = "15pt"
self.fig.title.align = "center"
# only give x ticks at bars
self.fig.xaxis[0].ticker = FixedTicker(ticks=label_places)
# save vals in ColumnDataSource so ticker_func can use it as default val
index_obj = ColumnDataSource(data=index)
# define function which assigns values to tick labels
def ticker_func(labels=index_obj):
return labels.data[str(tick * 100)]
# call ticker_func
self.fig.xaxis[0].formatter = FuncTickFormatter.from_py_func(
ticker_func)
def make_violin_plot(x,
y,
hue,
data,
plot_type="factor",
palette="GnBu",
width=750,
height=500):
data = data[[x, y, hue]]
sns_palette = sns.color_palette(palette, 2)
sns.set_style("whitegrid")
if plot_type == "factor":
violin_plot_sns = sns.factorplot(x=x,
y=y,
hue=hue,
data=data,
kind="violin",
palette=sns_palette)
else:
violin_plot_sns = sns.violinplot(x=x,
y=y,
hue=hue,
data=data,
palette=sns_palette,
split=True,
scale="area",
inner="box",
orient="v",
bw=.15,
responsive=True)
violin_plot = mpl.to_bokeh()
violin_plot.toolbar_location = "above"
violin_plot.toolbar_sticky = False
violin_plot.grid.grid_line_color = "SlateGray"
violin_plot.grid.grid_line_alpha = .5
violin_plot.grid.minor_grid_line_color = "SlateGray"
violin_plot.grid.minor_grid_line_alpha = .2
violin_plot.plot_height = height
violin_plot.plot_width = width
if "amount" in y:
violin_plot.title.text = "Fare Violin Plot"
violin_plot.yaxis[0].formatter = NumeralTickFormatter(format="$ 0.00")
if "distance" in y:
violin_plot.title.text = "Distance Violin Plot"
violin_plot.yaxis[0].formatter = PrintfTickFormatter(
format="%5.2f miles")
def day_ticker():
days = [
"Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday",
"Sunday"
]
if abs(int(round(tick)) - tick) < .05:
return days[int(round(tick))]
else:
return ""
def month_ticker():
months = [
"January", "February", "March", "April", "May", "June", "July"
]
if abs(int(round(tick)) - tick) < .05:
return months[int(round(tick))]
else:
return ""
if "day" in x:
violin_plot.xaxis[0].formatter = FuncTickFormatter.from_py_func(
day_ticker)
if "month" in x:
violin_plot.xaxis[0].formatter = FuncTickFormatter.from_py_func(
month_ticker)
return violin_plot
def make_stacked_bar_chart(buckets,
stack,
data,
palette,
width=500,
height=500):
cp = data
if buckets == "day":
cp[buckets] = cp.apply(lambda row: row['pickup_datetime'].weekday(),
axis=1)
elif buckets == "month":
cp[buckets] = cp.apply(lambda row: row['pickup_datetime'].month,
axis=1)
TOOLS = "pan,reset,hover,save"
stacked_bar = Bar(cp,
label=buckets,
values=buckets,
agg='count',
stack=cat(stack, sort=True),
tools=TOOLS,
title="{0} Stacked Bar for {1}".format(
("Daily" if buckets == "day" else "Monthly"),
stack.replace("_", " ").title()),
palette=palette,
plot_width=width,
plot_height=height,
toolbar_location="above",
toolbar_sticky=False,
legend_sort_field='color',
legend_sort_direction='ascending',
responsive=True)
hover = stacked_bar.select_one(HoverTool)
hover.point_policy = "follow_mouse"
hover.tooltips = [("Frequency", "@height"),
("{0}".format(stack.replace("_", " ").title()),
"@{0}".format(stack))]
def day_ticker():
days = [
"Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday",
"Sunday"
]
if abs(int(round(tick)) - tick) < .05:
return days[int(round(tick))]
else:
return ""
def month_ticker():
months = [
"January", "February", "March", "April", "May", "June", "July"
]
if abs(int(round(tick)) - tick) < .05:
return months[int(round(tick))]
else:
return ""
if buckets == "day":
stacked_bar.xaxis.formatter = FuncTickFormatter.from_py_func(
day_ticker)
else:
stacked_bar.xaxis.formatter = FuncTickFormatter.from_py_func(
month_ticker)
stacked_bar.grid.grid_line_color = "SlateGray"
stacked_bar.grid.grid_line_alpha = .5
stacked_bar.grid.minor_grid_line_color = "SlateGray"
stacked_bar.grid.minor_grid_line_alpha = .2
return stacked_bar
df['Gun_Seconds'] = map(lambda i: time_to_seconds(i),df['Gun Tim'])
df['Pace'] = map(lambda i: time_to_seconds(i),df['Pace'])
#df['Net Tim']= map(lambda i: if (str(i).split(':')),df['Div/Tot'])
together = pd.concat(dfs)
together['Rank'] = together['Net_Seconds'].rank()
gun_color_women ="#ff3399"
gun_color_men ='#0000ff'
net_color_women ="#9900cc"
net_color_men = "#006666"
####PLOT ONE#### DATA DISTRIBUTIONS - GUN TIME
plot_one = figure(plot_width=900,plot_height=500)
plot_one.xaxis.formatter = FuncTickFormatter.from_py_func(seconds_formatted)
#hover_one =HoverTool(
# tooltips=[
# ("index", "$index"),
# ("(x,y)", "($x, $y)"),
# ("desc", "@desc"),
# ]
# )
plot_one.add_tools(HoverTool())
#WOMEN
density = stats.kde.gaussian_kde(females['Gun_Seconds'].dropna())
x = np.arange(0.,max(females['Gun_Seconds'].dropna()), 1)
y = [density(y) for y in x]
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}
def create_figure(result, var, test, position, number):
color_mapper1 = LinearColorMapper(palette="Viridis256",
low=np.amin(result),
high=np.amax(result))
color_mapper2 = LinearColorMapper(palette="Greys256",
low=np.amin(var),
high=np.amax(var))
color_mapper3 = LinearColorMapper(palette="Greys256",
low=np.amin(test),
high=np.amax(test))
p = figure(title="Channel Probability",
x_range=(0, 5),
y_range=(0, 5),
toolbar_location=None)
q = figure(title="Seismic Amplitude",
x_range=(0, 5),
y_range=(0, 5),
toolbar_location=None)
r = figure(title="Model Uncertainty",
x_range=(0, 5),
y_range=(0, 5),
toolbar_location=None)
#s = figure(title="Model Uncertainty Max Vote", x_range=(0,10), y_range=(0,10), toolbar_location=None)
i = number
color_bar1 = ColorBar(color_mapper=color_mapper1,
ticker=BasicTicker(),
label_standoff=12,
border_line_color=None,
location=(0, 0),
formatter=FuncTickFormatter.from_py_func(ticker))
p.add_layout(color_bar1, 'right')
color_bar2 = ColorBar(color_mapper=color_mapper2,
ticker=BasicTicker(),
label_standoff=12,
border_line_color=None,
location=(0, 0),
formatter=FuncTickFormatter.from_py_func(ticker))
color_bar3 = ColorBar(color_mapper=color_mapper3,
ticker=BasicTicker(),
label_standoff=20,
border_line_color=None,
location=(0, 0),
formatter=FuncTickFormatter.from_py_func(ticker1))
q.add_layout(color_bar3, 'right')
r.add_layout(color_bar2, 'right')
#s.add_layout(color_bar2, 'right')
if position == 1:
# must give a vector of image data for image parameter
p.image(image=[np.transpose(result[i, :, :])],
x=0,
y=0,
dw=5,
dh=5,
color_mapper=color_mapper1)
# must give a vector of image data for image parameter
q.image(image=[np.transpose(test[i, :, :])],
x=0,
y=0,
dw=5,
dh=5,
color_mapper=color_mapper3)
r.image(image=[np.transpose(var[i, :, :])],
x=0,
y=0,
dw=5,
dh=5,
color_mapper=color_mapper2)
#s.image(image=[np.transpose(var2[i,:,:])], x=0, y=0, dw=10, dh=10, color_mapper=color_mapper2)
elif position == 2:
# must give a vector of image data for image parameter
p.image(image=[np.transpose(result[:, i, :])],
x=0,
y=0,
dw=5,
dh=5,
color_mapper=color_mapper1)
# must give a vector of image data for image parameter
q.image(image=[np.transpose(test[:, i, :])],
x=0,
y=0,
dw=5,
dh=5,
color_mapper=color_mapper3)
r.image(image=[np.transpose(var[:, i, :])],
x=0,
y=0,
dw=5,
dh=5,
color_mapper=color_mapper2)
#s.image(image=[np.transpose(var2[:,i,:])], x=0, y=0, dw=10, dh=10,color_mapper=color_mapper2)
else:
# must give a vector of image data for image parameter
p.image(image=[np.transpose(result[:, :, i])],
x=0,
y=0,
dw=5,
dh=5,
color_mapper=color_mapper1)
# must give a vector of image data for image parameter
q.image(image=[np.transpose(test[:, :, i])],
x=0,
y=0,
dw=5,
dh=5,
color_mapper=color_mapper3)
r.image(image=[np.transpose(var[:, :, i])],
x=0,
y=0,
dw=5,
dh=5,
color_mapper=color_mapper2)
#s.image(image=[np.transpose(var2[:,:,i])], x=0, y=0, dw=10, dh=10,color_mapper=color_mapper2)
t = column(row(p, r), q)
return t
def _create_time_series(dfincident, agg_by, pattern, group_by,
types, width=500, height=350):
""" Create a time series plot of the incident rate.
params
------
agg_field: the column name to aggregate by.
pattern_field: the column name that represents the pattern length to
be investigated / plotted.
incident_types: array, the incident types to be included in the plot.
return
------
tuple of (Bokeh figure, glyph) showing the incident rate over time.
"""
def xticker():
""" Custom function for positioning ticks """
if (int(tick)%10 == 0) | (len(tick)>2):
return tick
else:
return ""
x, y, labels = aggregate_data_for_time_series(dfincident, agg_by,
pattern, group_by,
types, None)
if group_by != "None":
colors, ngroups = get_colors(len(labels))
source = ColumnDataSource({"xs": x[0:ngroups],
"ys": y[0:ngroups],
"cs": colors,
"label": labels[0:ngroups]})
else:
source = ColumnDataSource({"xs": [x],
"ys": [y],
"cs": ["green"],
"label": ["avg incidents count"]})
# create plot
timeseries_tools = "pan,wheel_zoom,reset,xbox_select,hover,save"
p = figure(tools=timeseries_tools, width=width, height=height,
x_range=FactorRange(*x))
glyph = p.multi_line(xs="xs", ys="ys", legend="label", line_color="cs",
source=source, line_width=3)
# format legend
p.legend.label_text_font_size = "7pt"
p.legend.background_fill_alpha = 0.5
p.legend.location = 'top_left'
# format ticks
p.xaxis.formatter = FuncTickFormatter.from_py_func(xticker)
p.xaxis.major_tick_line_width = 0.1
p.xaxis.major_label_text_font_size = "5pt"
p.xaxis.group_text_font_size = "6pt"
p.xaxis.major_tick_line_color = None
p.x_range.group_padding = 0.0
p.x_range.range_padding = 0.0
p.x_range.subgroup_padding = 0.0
#p.yaxis.major_tick_line_color = "Red"
#p.yaxis.major_label_text_font_size = "6pt"
#p.y_range = Range1d(np.min(y)*0.9, np.max(y)*1.1)
return p, glyph
def plot_tweet_distributions(frame,
terms: list,
title='tweet frequencies',
**kwargs):
"""
Uses bokeh to make a jittered plot of tweet timestamps
:param frame: Dataframe with columns tweetTime (datetime), tweet (int), term (str)
:param terms: List of terms appearing in the frame
:param title: Title to disply in plot
:param kwargs: Possibly contains color_generator, ticker
If a ticker is included, it should be a function which returns labels for integer
inputs like this:
def ticker():
# Replaces the numeric y axis label with the correct term
# The dict seems to need to be hardcoded since bokeh
# messes with any args or values which seem like they should be
# in scope
dd = {
1: 'crps',
2: 'migraine',
3: 'fibromyalgia'
}
term = dd.get( tick )
return "{}".format( term )
"""
try:
# If was passed a custom color generator, initialize it
colorgen = kwargs['color_generator'](len(terms))
except:
# Otherwise use the default color generator
colorgen = color_generator(len(terms))
# initialize the notebook output
output_notebook()
# create a new plot with a title and axis labels
p = figure(title=title,
x_axis_type="datetime",
plot_width=800,
plot_height=500,
x_axis_label='timestamp',
y_axis_label='term')
for term in terms:
color = next(colorgen)
source = ColumnDataSource(frame[frame.term == term])
p.circle(x='tweetTime',
y=jitter('tweet', width=0.5, range=p.y_range),
fill_color=color,
source=source,
alpha=0.6)
p.x_range.range_padding = 0
p.ygrid.grid_line_color = None
# p.legend.orientation = "horizontal"
try:
# If we were passed a ticker function for labeling the y axis
# we use it here
ticker = kwargs['ticker']
# limit the displayed tick locations to those corresponding to the
# terms in the dataframe
tick_locations = [x for x in range(1, len(terms) + 1)]
p.yaxis.ticker = FixedTicker(ticks=tick_locations)
# Now add the labels instead of the numbers to the y axis
p.yaxis.formatter = FuncTickFormatter.from_py_func(ticker)
except:
# If we didn't get one, as will likely be true if we are only plotting
# a single term, no worries.
pass
# show the results
show(p)
