def _plot(model = None, **kwargs):
session = _get_or_make_session()
# Build Gridplot:
num = _SUBPLOT_DICT["num"]
rows = _SUBPLOT_DICT["rows"]
cols = _SUBPLOT_DICT["cols"]
global _GRIDPLOT
if num == 1:
children = [[None for _ in xrange(_SUBPLOT_DICT["cols"])] for _ in xrange(_SUBPLOT_DICT["rows"])]
else:
children = _GRIDPLOT.children
newplot = model(**kwargs)
global _CURRENT_PLOT
_CURRENT_PLOT = newplot
global _ALL_PLOTS
_ALL_PLOTS[newplot._id] = newplot
row_coordinate = int(np.ceil(float(num) / cols) - 1)
col_coordinate = num % cols -1 if num % cols != 0 else cols -1
children[row_coordinate][col_coordinate] = newplot
_GRIDPLOT = GridPlot()
_GRIDPLOT.children = children
_CURRENT_SESSION.document.clear()
_GRIDPLOT._detach_document()
print _GRIDPLOT
_CURRENT_SESSION.document.add_root(_GRIDPLOT)
# global _CURRENT_PLOT
# _CURRENT_PLOT = newplot
return newplot
def make_2d_facet_plot(self, facet=None):
# print ("make_2d_facet_plot")
"""Creates the grid of plots when there are both x and y facets.
Returns:
GridPlot: grid of x and y facet combinations
"""
# ToDo: gracefully handle large combinations of facets
all_facets_x = self.make_facets('x')
all_facets_y = self.make_facets('y')
grid_plots = []
# y faceting down column
for facets_y in all_facets_y:
# x faceting across row
row = []
for facets_x in all_facets_x:
# build the facets and title
facets = facets_x + facets_y
title = self.facet_title(facets)
# must filter by any extra facets provided for facet tab
if facet:
filter_facets = facets + facet
else:
filter_facets = facets
# if len(self.filter_widgets) == 0:
# filtered_df = self.query('/bench/col/dict/bare')
# else:
filtered_df = self.df
df = self.facet_data(filter_facets, filtered_df)
plot = self.make_single_plot(df=df,
title=title,
plot_height=200,
plot_width=200,
tools="pan,wheel_zoom,reset",
facet=facets)
row.append(plot)
# append the row to the list of rows
grid_plots.append(row)
self.hide_internal_axes(grid_plots)
# return the grid of plots as the plot
return GridPlot(children=grid_plots,
plot_width=200 * len(all_facets_x))
def make_1d_facet_plot(self, facet=None):
# print ("make_1d_facet_plot")
"""Creates the faceted plots when a facet is added to the x axis.
Returns:
GridPlot: a grid of plots, where each plot has subset of data
"""
if self.facet_x:
all_facets = self.make_facets('x')
else:
all_facets = self.make_facets('y')
plots = []
# loop over facets and create single plots for data subset
for facets in all_facets:
title = self.facet_title(facets)
if facet:
facets += facet
# if len(self.filter_widgets) == 0:
# filtered_df = self.query('/bench/col/dict/bare')
# else:
filtered_df = self.df
df = self.facet_data(facets, filtered_df)
plot = self.make_single_plot(df=df,
title=title,
plot_height=200,
plot_width=200,
tools="pan,wheel_zoom,reset",
facet=facets)
# append single plot to list of plots
plots.append(plot)
# create squarish grid based on number of plots
chunk_size = int(np.ceil(np.sqrt(len(plots))))
# create list of lists of plots, where each list of plots is a row
grid_plots = []
for i in range(0, len(plots), chunk_size):
chunk = plots[i:i + chunk_size]
grid_plots.append(chunk)
self.hide_internal_axes(grid_plots)
# return the grid as the plot
return GridPlot(children=grid_plots, plot_width=200 * chunk_size)
p2.line(x='time', y='macd9', color='blue', source=source)
p2.segment(x0='time',
y0=0,
x1='time',
y1='macdh',
line_width=6,
color='black',
alpha=0.5,
source=source)
mean = Slider(title="mean", value=0, start=-0.01, end=0.01, step=0.001)
stddev = Slider(title="stddev", value=0.04, start=0.01, end=0.1, step=0.01)
mavg = Select(value=MA12, options=[MA12, MA26, EMA12, EMA26])
curdoc().add_root(
VBox(HBox(mean, stddev, mavg), GridPlot(children=[[p], [p2]])))
def _create_prices(t):
last_average = 100 if t == 0 else source.data['average'][-1]
returns = asarray(lognormal(mean.value, stddev.value, 1))
average = last_average * cumprod(returns)
high = average * exp(abs(gamma(1, 0.03, size=1)))
low = average / exp(abs(gamma(1, 0.03, size=1)))
delta = high - low
open = low + delta * uniform(0.05, 0.95, size=1)
close = low + delta * uniform(0.05, 0.95, size=1)
return open[0], high[0], low[0], close[0], average[0]
def _moving_avg(prices, days=10):
rect = Rect(x="holidays_days", y="holidays_weeks", width=0.9, height=0.9, fill_color="pink", line_color="indianred")
rect_renderer = plot.add_glyph(holidays_source, rect)
text = Text(x="days", y="weeks", text="month_days", angle=0, text_align="center", text_baseline="middle")
plot.add_glyph(source, text)
xaxis = CategoricalAxis()
xaxis.major_label_text_font_size = "8pt"
xaxis.major_label_standoff = 0
xaxis.major_tick_line_color = None
xaxis.axis_line_color = None
plot.add_layout(xaxis, 'above')
hover_tool = HoverTool(plot=plot, renderers=[rect_renderer], tooltips=[("Holiday", "@month_holidays")])
plot.tools.append(hover_tool)
return plot
months = [ [ make_calendar(2014, 3*i + j + 1) for j in range(3) ] for i in range(4) ]
grid = GridPlot(title="Calendar 2014", toolbar_location=None, children=months)
doc = Document()
doc.add(grid)
if __name__ == "__main__":
filename = "calendars.html"
with open(filename, "w") as f:
f.write(file_html(doc, INLINE, "Calendar 2014"))
print("Wrote %s" % filename)
view(filename)
plot.add_glyph(source, text)
xaxis = CategoricalAxis()
xaxis.major_label_text_font_size = "8pt"
xaxis.major_label_standoff = 0
xaxis.major_tick_line_color = None
xaxis.axis_line_color = None
plot.add_layout(xaxis, 'above')
hover_tool = HoverTool(plot=plot,
renderers=[rect_renderer],
tooltips=[("Holiday", "@month_holidays")])
plot.tools.append(hover_tool)
return plot
months = [[make_calendar(2014, 3 * i + j + 1) for j in range(3)]
for i in range(4)]
grid = GridPlot(toolbar_location=None, children=months)
doc = Document()
doc.add_root(grid)
if __name__ == "__main__":
filename = "calendars.html"
with open(filename, "w") as f:
f.write(file_html(doc, INLINE, "Calendar 2014"))
print("Wrote %s" % filename)
view(filename)
p2.line(x='time', y='macd9', color='blue', source=source)
p2.segment(x0='time',
y0=0,
x1='time',
y1='macdh',
line_width=6,
color='black',
alpha=0.5,
source=source)
mean = Slider(title="mean", value=0, start=-0.01, end=0.01, step=0.001)
stddev = Slider(title="stddev", value=0.04, start=0.01, end=0.1, step=0.01)
mavg = Select(value=MA12, options=[MA12, MA26, EMA12, EMA26])
curdoc().add_root(
VBox(HBox(mean, stddev, mavg, width=800), GridPlot(children=[[p], [p2]])))
def _create_prices(t):
last_average = 100 if t == 0 else source.data['average'][-1]
returns = asarray(lognormal(mean.value, stddev.value, 1))
average = last_average * cumprod(returns)
high = average * exp(abs(gamma(1, 0.03, size=1)))
low = average / exp(abs(gamma(1, 0.03, size=1)))
delta = high - low
open = low + delta * uniform(0.05, 0.95, size=1)
close = low + delta * uniform(0.05, 0.95, size=1)
return open[0], high[0], low[0], close[0], average[0]
def _moving_avg(prices, days=10):
'Boston': {
'airport': 'BOS',
'title': 'Boston, MA',
},
'Seattle': {
'airport': 'SEA',
'title': 'Seattle, WA',
}
}
city_select = Select(value=city, title='City', options=sorted(cities.keys()))
distribution_select = Select(value=distribution,
title='Distribution',
options=['Discrete', 'Smooth'])
df = pd.read_csv(join(dirname(__file__), 'data/2015_weather.csv'))
source = get_dataset(df, cities[city]['airport'], distribution)
tplot = make_temperature_plot(source, cities[city]['title'])
pplot = make_precipitation_plot(source)
city_select.on_change('value', update_plot)
distribution_select.on_change('value', update_plot)
controls = VBox(city_select, distribution_select)
# add to document
curdoc().add_root(
HBox(controls,
GridPlot(children=[[tplot], [pplot]], toolbar_location=None),
width=1200))
plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
line = Line(x='x', y='y', line_color="#666699", line_width=2)
plot.add_glyph(lines_source, line)
circle = Circle(
x=xname, y=yname, size=12,
fill_color="#cc6633", line_color="#cc6633", fill_alpha=0.5
)
plot.add_glyph(circles_source, circle)
return plot
#where will this comment show up
I = make_plot('I', 'xi', 'yi')
II = make_plot('II', 'xii', 'yii')
III = make_plot('III', 'xiii', 'yiii')
IV = make_plot('IV', 'xiv', 'yiv')
grid = GridPlot(children=[[I, II], [III, IV]])
doc = Document()
doc.add(grid)
if __name__ == "__main__":
filename = "anscombe.html"
with open(filename, "w") as f:
f.write(file_html(doc, INLINE, "Anscombe's Quartet"))
print("Wrote %s" % filename)
view(filename)
plot.add_tools(PanTool(), WheelZoomTool())
return plot
xattrs = ["petal_length", "petal_width", "sepal_width", "sepal_length"]
yattrs = list(reversed(xattrs))
plots = []
for y in yattrs:
row = []
for x in xattrs:
xax = (y == yattrs[-1])
yax = (x == xattrs[0])
plot = make_plot(x, y, xax, yax)
row.append(plot)
plots.append(row)
grid = GridPlot(children=plots)
doc = Document()
doc.add_root(grid)
if __name__ == "__main__":
filename = "iris_splom.html"
with open(filename, "w") as f:
f.write(file_html(doc, INLINE, "Iris Data SPLOM"))
print("Wrote %s" % filename)
view(filename)
if ydr is None: ydr = DataRange1d()
plot = Plot(x_range=xdr, y_range=ydr, min_border=50)
plot.add_layout(LinearAxis(), 'below')
plot.add_layout(LinearAxis(), 'left')
plot.add_glyph(source, Line(x=xname, y=yname, line_color=line_color))
plot.add_tools(PanTool(), WheelZoomTool())
return plot
plot1 = make_plot(source, "x", "y1", "blue")
plot2 = make_plot(source, "x", "y2", "red", xdr=plot1.x_range)
plot3 = make_plot(source, "x", "y3", "green")
plot4 = make_plot(source, "x", "y4", "black")
grid = GridPlot(children=[[plot1, plot2], [plot3, plot4]])
doc = Document()
doc.add_root(grid)
if __name__ == "__main__":
filename = "grid.html"
with open(filename, "w") as f:
f.write(file_html(doc, INLINE, "Grid Plot Example"))
print("Wrote %s" % filename)
view(filename)
def test_grid_plot_responsive_property_is_fixed_by_default():
gp = GridPlot()
assert gp.responsive is 'fixed'
# Categorical plot
source = ColumnDataSource(dict(
names = list(css3_colors.Name),
groups = list(css3_colors.Group),
colors = list(css3_colors.Color),
))
xdr = FactorRange(factors=list(css3_colors.Group.unique()))
ydr = FactorRange(factors=list(reversed(css3_colors.Name)))
#cat_plot = Plot(title="CSS3 Color Names", x_range=xdr, y_range=ydr, min_border=1, responsive='box')
#rect = Rect(x="groups", y="names", width=1, height=1, fill_color="colors", line_color=None)
#cat_plot.add_glyph(source, rect)
#xaxis = CategoricalAxis(major_label_orientation=pi/4)
#cat_plot.add_layout(xaxis, 'below')
#cat_plot.add_layout(CategoricalAxis(), 'left')
#row1 = Row(children=[plot1, plot2])
#row2col1 = Column(children=[plot3, plot4])
#row2 = Row(children=[row2col1, cat_plot])
#row3 = Row(children=[plot3, plot4])
doc = Document()
#doc.add_root(Column(children=[row1, row2]))
doc.add_root(GridPlot([[plot1, plot2], [plot3, plot4]], toolbar_location='above', responsive='fixed'))
if __name__ == "__main__":
filename = "grid_layout.html"
with open(filename, "w") as f:
f.write(file_html(doc, INLINE, "Grid Layout"))
print("Wrote %s" % filename)
view(filename)
def _plot_bokeh_grid(self):
if len(self.plots) > 0:
grid = GridPlot(children=partition(self.plots, self.nrows), title=self.title)
plotting.show(grid)
)
plot.add_glyph(text_source, text)
return plot
xattrs = ["petal_length", "petal_width", "sepal_width", "sepal_length"]
yattrs = list(reversed(xattrs))
plots = []
for y in yattrs:
row = []
for x in xattrs:
xax = (y == yattrs[-1])
yax = (x == xattrs[0])
text = x if (x==y) else None
plot = make_plot(x, y, xax, yax, text)
row.append(plot)
plots.append(row)
grid = GridPlot(children=plots, title="iris_splom")
doc = Document()
doc.add(grid)
if __name__ == "__main__":
filename = "iris_splom.html"
with open(filename, "w") as f:
f.write(file_html(doc, INLINE, "Iris Data SPLOM"))
print("Wrote %s" % filename)
view(filename)
def test_grid_plot_sizing_mode_property_is_fixed_by_default():
gp = GridPlot()
assert gp.sizing_mode is 'fixed'
p.title = 'Color Scatter 1'
p.title_location = 'right'
p.scatter(x,
y,
radius=radii,
fill_color=colors,
fill_alpha=0.6,
line_color=None)
return p
layout = GridPlot(children=[
[p(), p(), p(), p()],
[p(), p(), p(), p()],
[p(), p(), p(), p()],
[p(), p(), p(), p()],
],
toolbar_location=None,
sizing_mode='stretch_both')
toolbar_locations = [None, 'above', 'right', 'below', 'left']
tb = 1
#layout = GridPlot(
# children=[
# [p(), p()],
# ],
# toolbar_location=toolbar_locations[tb],
# responsive='fixed'
#)
show(layout)
plot.add_glyph(lines_source, line)
circle = Circle(x=xname,
y=yname,
size=12,
fill_color="#cc6633",
line_color="#cc6633",
fill_alpha=0.5)
plot.add_glyph(circles_source, circle)
return plot
#where will this comment show up
I = make_plot('I', 'xi', 'yi')
II = make_plot('II', 'xii', 'yii')
III = make_plot('III', 'xiii', 'yiii')
IV = make_plot('IV', 'xiv', 'yiv')
grid = GridPlot(children=[[I, II], [III, IV]], plot_width=800)
doc = Document()
doc.add(grid)
if __name__ == "__main__":
filename = "anscombe.html"
with open(filename, "w") as f:
f.write(file_html(doc, INLINE, "Anscombe's Quartet"))
print("Wrote %s" % filename)
view(filename)
corr.title = '%s returns vs. %s returns' % (t1, t2)
ts1.title, ts2.title = t1, t2
def update_stats(data, t1, t2):
stats.text = str(data[[t1, t2, t1+'_returns', t2+'_returns']].describe())
ticker1.on_change('value', ticker1_change)
ticker2.on_change('value', ticker2_change)
def selection_change(attrname, old, new):
t1, t2 = ticker1.value, ticker2.value
data = get_data(t1, t2)
selected = source.selected['1d']['indices']
if selected:
data = data.iloc[selected, :]
update_stats(data, t1, t2)
source.on_change('selected', selection_change)
# set up layout
stats_box = VBox(stats)
input_box = VBox(ticker1, ticker2)
main_row = HBox(input_box, corr, stats_box)
layout = VBox(main_row, GridPlot(children=[[ts1], [ts2]]))
# initialize
update()
curdoc().add_root(layout)
p.line(x='time', y='average', alpha=0.2, line_width=3, color='navy', source=source)
p.line(x='time', y='ma', alpha=0.8, line_width=2, color='orange', source=source)
p.segment(x0='time', y0='low', x1='time', y1='high', line_width=2, color='black', source=source)
p.segment(x0='time', y0='open', x1='time', y1='close', line_width=8, color='color', source=source)
p2 = Figure(plot_height=250, x_range=p.x_range, tools="xpan,xwheel_zoom,xbox_zoom,reset")
p2.line(x='time', y='macd', color='red', source=source)
p2.line(x='time', y='macd9', color='blue', source=source)
p2.segment(x0='time', y0=0, x1='time', y1='macdh', line_width=6, color='black', alpha=0.5, source=source)
mean = Slider(title="mean", value=0, start=-0.01, end=0.01, step=0.001)
stddev = Slider(title="stddev", value=0.04, start=0.01, end=0.1, step=0.01)
mavg = Select(value=MA12, options=[MA12, MA26, EMA12, EMA26])
curdoc().add_root(VBox(HBox(mean, stddev, mavg, width=800), GridPlot(children=[[p], [p2]])))
def create_random_corr_matrix(n):
m = 2 * np.random.random_sample((n, n)) - 5
return np.tril(m) + np.tril(m, -1).T
def create_random_corr_dataframe(n):
m = create_random_corr_matrix(n)
def convert_to_title(num):
title = ''
alist = string.uppercase
while num:
mod = (num - 1) % 26
def _make_grid(grid):
return GridPlot(children=partition(grid.plots, grid.n_rows),
name=grid.title)
