def make_plots(self, ticker1, ticker2):
self.plot = circle(ticker1 + "_returns", ticker2 + "_returns",
title="%s vs %s" %(ticker1, ticker2),
source=self.source,
plot_width=400, plot_height=400,
tools="pan,wheel_zoom,select"
)
session().plotcontext.children=[self]
session().plotcontext._dirty = True
def make_plots(self, ticker1, ticker2):
self.plot = circle(ticker1 + "_returns",
ticker2 + "_returns",
title="%s vs %s" % (ticker1, ticker2),
source=self.source,
plot_width=400,
plot_height=400,
tools="pan,wheel_zoom,select")
session().plotcontext.children = [self]
session().plotcontext._dirty = True
def wrapper(*args, **kwargs):
docname = prefix + str(uuid.uuid4())
output_server(docname, url=url)
app = func(*args, **kwargs)
session().add(app)
session().plotcontext.children=[app]
session().plotcontext._dirty = True
logger.debug("stored: %s", str(session().store_all()))
app.docname = docname
return app
def main():
app = make_plot()
docname = session().docname
return render_template('page.html', docname=docname,
bokeh_location=bokeh_location,
splitjs=splitjs
)
def main():
app = make_plot()
docname = session().docname
return render_template('page.html',
docname=docname,
bokeh_location=bokeh_location,
splitjs=splitjs)
def exampleapp():
app = make_app()
docname = session().docname
docid = session().docid
extra_generated_classes = app.extra_generated_classes
if len(extra_generated_classes) == 0:
extra_generated_classes.append((app.__view_model__, app.__view_model__, app.jsmodel))
extra_generated_classes.append(
(app.modelform.__view_model__, app.modelform.__view_model__, app.modelform.jsmodel)
)
return render_template(
"applet.html",
extra_generated_classes=extra_generated_classes,
title=app.__class__.__view_model__,
docname=docname,
docid=docid,
splitjs=bokeh_app.splitjs,
)
def exampleapp():
app = make_app()
docname = session().docname
docid = session().docid
extra_generated_classes = app.extra_generated_classes
if len(extra_generated_classes) == 0:
extra_generated_classes.append(
(app.__view_model__, app.__view_model__, app.jsmodel))
extra_generated_classes.append(
(app.modelform.__view_model__,
app.modelform.__view_model__, app.modelform.jsmodel))
return render_template(
'applet.html',
extra_generated_classes=extra_generated_classes,
title=app.__class__.__view_model__,
docname=docname,
docid=docid,
splitjs=bokeh_app.splitjs)
def make_plot():
sess = session()
data = pd.DataFrame({'a' : np.random.randn(100), 'b' : np.random.randn(100)})
source = ColumnDataSource(data=data)
scatter_plot = circle(source=source, x='a', y='b', plot_width=500,
plot_height=500)
app = App(data_source=source,
scatter_plot=scatter_plot,
stats=str(data.describe())
)
return app
def make_plot():
sess = session()
data = pd.DataFrame({'a': np.random.randn(100), 'b': np.random.randn(100)})
source = ColumnDataSource(data=data)
scatter_plot = circle(source=source,
x='a',
y='b',
plot_width=500,
plot_height=500)
app = App(data_source=source,
scatter_plot=scatter_plot,
stats=str(data.describe()))
return app
pres_y = np.array([20])
bk.output_server('Pressure')
bk.line(
time_x,
pres_y,
color='#0000FF',
tools=
'pan,wheel_zoom,box_zoom,reset,resize,crosshair,select,previewsave,embed',
width=1200,
height=300)
bk.xaxis()[0].axis_label = 'Time'
bk.yaxis()[0].axis_label = 'Pressure'
renderer = [r for r in bk.curplot().renderers if isinstance(r, Glyph)][0]
ds = renderer.data_source
while True:
ds.data["x"] = time_x
ds.data["y"] = pres_y
ds._dirty = True
bk.session().store_obj(ds)
time.sleep(1)
t = t + 1
time_x = np.append(time_x, time_x[t - 1] + 1)
pres_y = np.append(
pres_y, pres_y[t - 1] + np.random.random() * pres_y[t - 1] * 0.01)
from bokeh import pyplot
from pylab import *
from bokeh import plotting
x = linspace(-2*pi,2*pi,100)
y = sin(x)
plot(x,y,"r-")
title("Matplotlib Figure in Bokeh")
# dashed lines work
#plot(x,y,"r-x", linestyle="-.")
pyplot.show_bokeh(gcf(), filename="mpltest.html")
plotting.session().dumpjson(file="mpltest.json")
# Generate data. In this case, we'll make a bunch of center-points and generate
# verticies by subtracting random offsets from those center-points
numpoly, numverts = 100, 4
centers = 100 * (np.random.random((numpoly, 2)) - 0.5)
offsets = 10 * (np.random.random((numverts, numpoly, 2)) - 0.5)
verts = centers + offsets
verts = np.swapaxes(verts, 0, 1)
# In your case, "verts" might be something like:
# verts = zip(zip(lon1, lat1), zip(lon2, lat2), ...)
# If "data" in your case is a numpy array, there are cleaner ways to reorder
# things to suit.
colors = ['red', 'green', 'blue', 'cyan', 'yellow', 'magenta', 'black']
ax = plt.axes()
# Make the collection and add it to the plot.
col = PolyCollection(verts, color=colors)
ax.add_collection(col)
plt.xlim([-60, 60])
plt.ylim([-60, 60])
plt.title("MPL-PolyCollection support in Bokeh")
pyplot.show_bokeh(plt.gcf(), filename="polycollection.html")
plotting.session().dumpjson(file="polycollection.json")
def make_app():
app = cls()
app.create(session())
return app
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
from bokeh import pyplot
from bokeh import plotting
# Generate the pandas dataframe
data = np.random.multivariate_normal([0, 0], [[1, 2], [2, 20]], size=100)
data = pd.DataFrame(data, columns=["X", "Y"])
mpl.rc("figure", figsize=(6, 6))
# Just plot seaborn kde
sns.kdeplot(data, cmap="BuGn_d")
plt.title("Seaborn kdeplot in bokeh.")
pyplot.show_bokeh(plt.gcf(), filename="seaborn_kde.html")
plotting.session().dumpjson(file="seaborn_kde.json")
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
from bokeh import pyplot
from bokeh import plotting
# In order to efficiently plot many lines in a single set of axes,
# add the lines all at once. Here is a simple example showing how it is done.
N = 50
x = np.arange(N)
# Here are many sets of y to plot vs x
ys = [x + i for i in x]
colors = [
'#ff0000', '#008000', '#0000ff', '#00bfbf', '#bfbf00', '#bf00bf', '#000000'
]
line_segments = LineCollection([list(zip(x, y)) for y in ys],
color=colors,
linewidth=(0.5, 1, 1.5, 2),
linestyle='dashed')
ax = plt.axes()
ax.add_collection(line_segments)
ax.set_title('Line Collection with dashed colors')
pyplot.show_bokeh(plt.gcf(), filename="lc_dashed.html")
plotting.session().dumpjson(file="lc_dashed.json")
# Generate data. In this case, we'll make a bunch of center-points and generate
# verticies by subtracting random offsets from those center-points
numpoly, numverts = 100, 4
centers = 100 * (np.random.random((numpoly,2)) - 0.5)
offsets = 10 * (np.random.random((numverts,numpoly,2)) - 0.5)
verts = centers + offsets
verts = np.swapaxes(verts, 0, 1)
# In your case, "verts" might be something like:
# verts = zip(zip(lon1, lat1), zip(lon2, lat2), ...)
# If "data" in your case is a numpy array, there are cleaner ways to reorder
# things to suit.
colors = ['red','green','blue','cyan','yellow','magenta','black']
ax = plt.axes()
# Make the collection and add it to the plot.
col = PolyCollection(verts, color=colors)
ax.add_collection(col)
plt.xlim([-60, 60])
plt.ylim([-60, 60])
plt.title("MPL-PolyCollection support in Bokeh")
pyplot.show_bokeh(plt.gcf(), filename="mpl_polycollection.html")
plotting.session().dumpjson(file="mpl_polycollection.json")
import matplotlib.pyplot as plt
from scipy import optimize
from bokeh import pyplot
from bokeh import plotting
# Set the palette colors.
sns.set(palette="Set2")
# Build the sin wave
def sine_wave(n_x, obs_err_sd=1.5, tp_err_sd=.3):
x = np.linspace(0, (n_x - 1) / 2, n_x)
y = np.sin(x) + np.random.normal(0, obs_err_sd) + np.random.normal(0, tp_err_sd, n_x)
return y
sines = np.array([sine_wave(31) for _ in range(20)])
# Generate the Seaborn plot with "ci" bars.
ax = sns.tsplot(sines, err_style="ci_bars", interpolate=False)
xmin, xmax = ax.get_xlim()
x = np.linspace(xmin, xmax, sines.shape[1])
out, _ = optimize.leastsq(lambda p: sines.mean(0) - (np.sin(x / p[1]) + p[0]), (0, 2))
a, b = out
xx = np.linspace(xmin, xmax, 100)
plt.plot(xx, np.sin(xx / b) + a, c="#444444");
plt.title("Seaborn tsplot with CI in bokeh.")
pyplot.show_bokeh(plt.gcf(), filename="mpl_seaborn_sinerror.html")
plotting.session().dumpjson(file="mpl_seaborn_sinerror.json")
plt.plot(x, y1, 'ks', xfit, fit(xfit), 'r-', lw=2)
plt.axis([2, 20, 2, 14])
plt.setp(plt.gca(), xticklabels=[], yticks=(4, 8, 12), xticks=(0, 10, 20))
plt.ylabel('I', fontsize=20)
plt.subplot(222)
plt.plot(x, y2, 'ks', xfit, fit(xfit), 'r-', lw=2)
plt.axis([2, 20, 2, 14])
plt.setp(plt.gca(), xticklabels=[], yticks=(4, 8, 12), yticklabels=[], xticks=(0, 10, 20))
plt.ylabel('II', fontsize=20)
plt.subplot(223)
plt.plot(x, y3, 'ks', xfit, fit(xfit), 'r-', lw=2)
plt.axis([2, 20, 2, 14])
plt.ylabel('III', fontsize=20)
plt.setp(plt.gca(), yticks=(4, 8, 12), xticks=(0, 10, 20))
plt.subplot(224)
xfit = np.array([np.amin(x4), np.amax(x4)])
plt.plot(x4, y4, 'ks', xfit, fit(xfit), 'r-', lw=2)
plt.axis([2, 20, 2, 14])
plt.setp(plt.gca(), yticklabels=[], yticks=(4, 8, 12), xticks=(0, 10, 20))
plt.ylabel('IV', fontsize=20)
# We create the figure in matplotlib and then we "pass it" to Bokeh
pyplot.show_bokeh(plt.gcf(), filename="subplots.html")
plotting.session().dumpjson(file="subplots.json")
# colors = ['#ff0000', '#008000', '#0000ff', '#00bfbf', '#bfbf00', '#bf00bf', '#000000']
# colors = [(1.0, 0.0, 0.0, 1.0), (0.0, 0.5, 0.0, 1.0), (0.0, 0.0, 1.0, 1.0), (0.0, 0.75, 0.75, 1.0),
# (0.75, 0.75, 0, 1.0), (0.75, 0, 0.75, 1.0), (0.0, 0.0, 0.0, 1.0)]
colors = ['r', 'g', 'b', 'c', 'y', 'm', 'k']
widths = [5, 10, 20, 40, 20, 10, 5]
segments = make_segments(x, y)
lc = LineCollection(segments, colors=colors, linewidth=widths, alpha=alpha)
ax = plt.gca()
ax.add_collection(lc)
return lc
# Colored sine wave
x = np.linspace(0, 4 * np.pi, 100)
y = np.sin(x)
colorline(x, y)
plt.title("MPL support for ListCollection in Bokeh")
plt.xlim(x.min(), x.max())
plt.ylim(-1.0, 1.0)
pyplot.show_bokeh(plt.gcf(), filename="listcollection.html")
plotting.session().dumpjson(file="listcollection.json")
nverts = 60
ncurves = 20
offs = (0.1, 0.0)
rs = np.random.RandomState([12345678])
yy = np.linspace(0, 2*np.pi, nverts)
ym = np.amax(yy)
xx = (0.2 + (ym-yy)/ym)**2 * np.cos(yy-0.4) * 0.5
segs = []
for i in range(ncurves):
xxx = xx + 0.02*rs.randn(nverts)
curve = list(zip(xxx, yy*100))
segs.append(curve)
colors = [(1.0, 0.0, 0.0, 1.0), (0.0, 0.5, 0.0, 1.0), (0.0, 0.0, 1.0, 1.0), (0.0, 0.75, 0.75, 1.0),
(0.75, 0.75, 0, 1.0), (0.75, 0, 0.75, 1.0), (0.0, 0.0, 0.0, 1.0)]
col = LineCollection(segs, linewidth=5, offsets=offs)
ax = plt.axes()
ax.add_collection(col, autolim=True)
col.set_color(colors)
ax.set_title('Successive data offsets')
fig = plt.gcf()
pyplot.show_bokeh(plt.gcf(), filename="mpl_lc_offsets.html")
plotting.session().dumpjson(file="mpl_lc_offsets.json")
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
from bokeh import pyplot
from bokeh import plotting
# In order to efficiently plot many lines in a single set of axes,
# add the lines all at once. Here is a simple example showing how it is done.
N = 50
x = np.arange(N)
# Here are many sets of y to plot vs x
ys = [x+i for i in x]
colors = ['#ff0000', '#008000', '#0000ff', '#00bfbf', '#bfbf00', '#bf00bf', '#000000']
line_segments = LineCollection([list(zip(x,y)) for y in ys], color=colors,
linewidth=(0.5,1,1.5,2), linestyle='dashed')
ax = plt.axes()
ax.add_collection(line_segments)
ax.set_title('Line Collection with dashed colors')
pyplot.show_bokeh(plt.gcf(), filename="lc_dashed.html")
plotting.session().dumpjson(file="lc_dashed.json")
# Set the palette colors.
sns.set(palette="Set2")
# Build the sin wave
def sine_wave(n_x, obs_err_sd=1.5, tp_err_sd=.3):
x = np.linspace(0, (n_x - 1) / 2, n_x)
y = np.sin(x) + np.random.normal(0, obs_err_sd) + np.random.normal(
0, tp_err_sd, n_x)
return y
sines = np.array([sine_wave(31) for _ in range(20)])
# Generate the Seaborn plot with "ci" bars.
ax = sns.tsplot(sines, err_style="ci_bars", interpolate=False)
xmin, xmax = ax.get_xlim()
x = np.linspace(xmin, xmax, sines.shape[1])
out, _ = optimize.leastsq(lambda p: sines.mean(0) - (np.sin(x / p[1]) + p[0]),
(0, 2))
a, b = out
xx = np.linspace(xmin, xmax, 100)
plt.plot(xx, np.sin(xx / b) + a, c="#444444")
plt.title("Seaborn tsplot with CI in bokeh.")
pyplot.show_bokeh(plt.gcf(), filename="seaborn_sinerror.html")
plotting.session().dumpjson(file="seaborn_sinerror.json")
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from bokeh import pyplot
from bokeh import plotting
# We generated random data
data = 1 + np.random.randn(20, 6)
# And then just call the violinplot from Seaborn
sns.violinplot(data, color="Set3")
plt.title("Seaborn violin plot in bokeh.")
pyplot.show_bokeh(plt.gcf(), filename="mpl_seaborn_violet.html")
plotting.session().dumpjson(file="mpl_seaborn_violet.json")
import numpy as np
import matplotlib.pyplot as plt
from bokeh import pyplot
from bokeh import plotting
x = np.linspace(-2 * np.pi, 2 * np.pi, 100)
y = np.sin(x)
plt.plot(x, y, "r-")
plt.title("Matplotlib Figure in Bokeh")
# dashed lines work
#plt.plot(x,y,"r-x", linestyle="-.")
pyplot.show_bokeh(plt.gcf(), filename="mpltest.html")
plotting.session().dumpjson(file="mpltest.json")
def make_app():
app = cls()
app.create(session())
return app
t = 0
time_x = np.array([0])
pres_y = np.array([20])
bk.output_server('Pressure')
bk.line(time_x, pres_y, color='#0000FF',
tools='pan,wheel_zoom,box_zoom,reset,resize,crosshair,select,previewsave,embed',
width=1200,height=300)
bk.xaxis()[0].axis_label = 'Time'
bk.yaxis()[0].axis_label = 'Pressure'
renderer = [r for r in bk.curplot().renderers if isinstance(r, Glyph)][0]
ds = renderer.data_source
while True:
ds.data["x"] = time_x
ds.data["y"] = pres_y
ds._dirty = True
bk.session().store_obj(ds)
time.sleep(1)
t = t + 1
time_x = np.append(time_x,
time_x[t-1]+1)
pres_y = np.append(pres_y,
pres_y[t-1] + np.random.random()*pres_y[t-1]*0.01)
# colors = ['red','green','blue','cyan','yellow','magenta','black']
# colors = ['#ff0000', '#008000', '#0000ff', '#00bfbf', '#bfbf00', '#bf00bf', '#000000']
# colors = [(1.0, 0.0, 0.0, 1.0), (0.0, 0.5, 0.0, 1.0), (0.0, 0.0, 1.0, 1.0), (0.0, 0.75, 0.75, 1.0),
# (0.75, 0.75, 0, 1.0), (0.75, 0, 0.75, 1.0), (0.0, 0.0, 0.0, 1.0)]
colors = ['r','g','b','c','y','m','k']
widths = [5, 10, 20, 40, 20, 10, 5]
segments = make_segments(x, y)
lc = LineCollection(segments, colors=colors, linewidth=widths, alpha=alpha)
ax = plt.gca()
ax.add_collection(lc)
return lc
# Colored sine wave
x = np.linspace(0, 4 * np.pi, 100)
y = np.sin(x)
colorline(x, y)
plt.title("MPL support for ListCollection in Bokeh")
plt.xlim(x.min(), x.max())
plt.ylim(-1.0, 1.0)
pyplot.show_bokeh(plt.gcf(), filename="listcollection.html")
plotting.session().dumpjson(file="listcollection.json")
