Archived discussions · Captured October 28, 2026

facets (or aligned plots)

Jonathan_ShoreDec 07, 2014 #1

I just started using Bokeh’s python API. I have multiple time series that I want to plot on the same x-axis, but plot in separate vertically stacked areas (figures) due to distinct y-axes and to avoid visual clutter.

In ggplot I could accomplish this with vertical facets. Is there a way I can do something like this in Bokeh? I am aware of the gridplot() function to present a series of graphs in a grid, but:

want a vertical stacking (and not horizontal + vertical)
want to keep the x axis aligned
want to allow for panning synchronously across the graphs

A typical layout I use with ggplot is a large facet (cartesian plotting area) for prices, and one or more smaller areas for signals. For example, 1 larger pane + 2 vertically smaller panes aligned on the same axis:

-----------------|

}-----------------|

-----------------|

}-----------------|

0 1 2 3 4 5 6 7

Facets are quite popular in the R / ggplot world, so suspect there are others who would like to do this.

···

–

Jonathan Shore

Systematic Trading LLC

⌁ Link to post

Jonathan_ShoreDec 07, 2014 #2

I upgraded to Bokeh 0.7 so I could use VBox (which I noticed in the git repo). However, I have not figured the following out, if anyone can help:

how does one avoid displaying the tool bar (I only want the toolbar on the top plot)
can I latch on to a pan event (or drag) in one pane and apply to the other panes?
Thanks for any and all help.

···

On Saturday, December 6, 2014 7:24:51 PM UTC-5, Jonathan Shore wrote:

I just started using Bokeh’s python API. I have multiple time series that I want to plot on the same x-axis, but plot in separate vertically stacked areas (figures) due to distinct y-axes and to avoid visual clutter.

In ggplot I could accomplish this with vertical facets. Is there a way I can do something like this in Bokeh? I am aware of the gridplot() function to present a series of graphs in a grid, but:

want a vertical stacking (and not horizontal + vertical)

want to keep the x axis aligned

want to allow for panning synchronously across the graphs

A typical layout I use with ggplot is a large facet (cartesian plotting area) for prices, and one or more smaller areas for signals. For example, 1 larger pane + 2 vertically smaller panes aligned on the same axis:

-----------------|

}-----------------|

-----------------|

}-----------------|

0 1 2 3 4 5 6 7

Facets are quite popular in the R / ggplot world, so suspect there are others who would like to do this.

–

Jonathan Shore

Systematic Trading LLC

⌁ Link to post

Jonathan_ShoreDec 08, 2014 #3

As an update to this, i examined the source code and also found some related posts. The keys to doing this were:

VBox (in 0.7 for vertically aligned widgets)
toolbar_location=None to remove toolbar for sub-plots
assignment of x_range from main plot to sub-plots, to link panning behavior
Here is an example that puts this together, for future reference:

from bokeh.plotting import *

import numpy as np

from datetime import *

from bokeh.models import DatetimeTickFormatter

def concat(a, b):

return np.append(np.array(a), np.array(b))

def SMA(x, N):

return np.convolve(x, np.ones((N,))/N, mode=‘valid’)

def mr (n: int, sd=0.5, kappa=0.01, x0 = 100):

x = x0

vec =

for dw in np.random.normal(scale=sd, size=n):

dX = -kappa*(x - x0) + dw

x += dX

vec.append(x)

return vec

def daterange (Tstart: datetime, dt: int, n: int):

Ts = Tstart.timestamp()

return [datetime.utcfromtimestamp(Ts + i * dt) for i in range(0,n)]

yprices = mr (800, kappa=0.001)

xprices = daterange(datetime(2014,4,1,9,0,0), dt=30, n=800)

yma = SMA(yprices, 12)

xma = xprices[(len(yprices)-len(yma)):]

dfs = concat (np.zeros((2+len(yprices)-len(yma))), np.diff (yma,2))

yvol = np.array([abs(dfs[i]) * 10000 for i in range(0,len(yprices),4)])

xvol = daterange(datetime(2014,4,1,9,0,0), dt=120, n=200)

output_file(“/tmp/test.html”, “test”)

config_top = dict(

x_axis_type=“datetime”, x_range=[xprices[0], xprices[-1]],

plot_width=800, plot_height=400, background_fill = “#f0f0f0”, title_text_font_size=“14pt”,

min_border_bottom=0, min_border_top=0,

tools=“pan,wheel_zoom,box_zoom,select”)

config_lower = dict(

x_axis_type=“datetime”, x_range=[xprices[0], xprices[-1]],

plot_width=800, plot_height=200, background_fill = “#f0f0f0”, title_text_font_size=“14pt”,

min_border_bottom=0, min_border_top=0, toolbar_location=None)

xformatter = DatetimeTickFormatter(formats=dict(hours=[“%H:%m”]))

p1 = figure(title=‘prices’, **config_top)

p1.line(xprices, yprices, color=‘black’)

p1.line(xma, yma, color=‘red’)

p1.xaxis.formatter = xformatter

p2 = figure(title=None, **config_lower)

p2.rect(xvol, yvol/2, width=120, height=yvol, fill=‘blue’)

p2.x_range = p1.x_range

vbox = VBox()

vbox.children.append(p1)

vbox.children.append(p2)

show(vbox)

···

On Sunday, December 7, 2014 10:32:00 AM UTC-5, Jonathan Shore wrote:

I upgraded to Bokeh 0.7 so I could use VBox (which I noticed in the git repo). However, I have not figured the following out, if anyone can help:

how does one avoid displaying the tool bar (I only want the toolbar on the top plot)

can I latch on to a pan event (or drag) in one pane and apply to the other panes?
Thanks for any and all help.

On Saturday, December 6, 2014 7:24:51 PM UTC-5, Jonathan Shore wrote:

I just started using Bokeh’s python API. I have multiple time series that I want to plot on the same x-axis, but plot in separate vertically stacked areas (figures) due to distinct y-axes and to avoid visual clutter.

In ggplot I could accomplish this with vertical facets. Is there a way I can do something like this in Bokeh? I am aware of the gridplot() function to present a series of graphs in a grid, but:

want a vertical stacking (and not horizontal + vertical)

want to keep the x axis aligned

want to allow for panning synchronously across the graphs

A typical layout I use with ggplot is a large facet (cartesian plotting area) for prices, and one or more smaller areas for signals. For example, 1 larger pane + 2 vertically smaller panes aligned on the same axis:

-----------------|

}-----------------|

-----------------|

}-----------------|

0 1 2 3 4 5 6 7

Facets are quite popular in the R / ggplot world, so suspect there are others who would like to do this.

–

Jonathan Shore

Systematic Trading LLC

⌁ Link to post

BryanDec 08, 2014 #4

Hi Jonathan,

I just wanted to drop you a note that this post is not being overlooked, I'm in the middle of some traveling but I hope to make a proper response tonight or tomorrow.

Thanks,

Bryan

···

On Dec 8, 2014, at 12:33 PM, Jonathan Shore <jonathan.shore@gmail.com> wrote:

As an update to this, i examined the source code and also found some related posts. The keys to doing this were:

  • VBox (in 0.7 for vertically aligned widgets)
  • toolbar_location=None to remove toolbar for sub-plots
  • assignment of x_range from main plot to sub-plots, to link panning behavior
Here is an example that puts this together, for future reference:

from bokeh.plotting import *
import numpy as np
from datetime import *
from bokeh.models import DatetimeTickFormatter

def concat(a, b):
   return np.append(np.array(a), np.array(b))

def SMA(x, N):
    return np.convolve(x, np.ones((N,))/N, mode='valid')

def mr (n: int, sd=0.5, kappa=0.01, x0 = 100):
    x = x0
    vec =
    for dw in np.random.normal(scale=sd, size=n):
        dX = -kappa*(x - x0) + dw
        x += dX
        vec.append(x)
    return vec

def daterange (Tstart: datetime, dt: int, n: int):
    Ts = Tstart.timestamp()
    return [datetime.utcfromtimestamp(Ts + i * dt) for i in range(0,n)]

yprices = mr (800, kappa=0.001)
xprices = daterange(datetime(2014,4,1,9,0,0), dt=30, n=800)
yma = SMA(yprices, 12)
xma = xprices[(len(yprices)-len(yma)):]

dfs = concat (np.zeros((2+len(yprices)-len(yma))), np.diff (yma,2))

yvol = np.array([abs(dfs[i]) * 10000 for i in range(0,len(yprices),4)])
xvol = daterange(datetime(2014,4,1,9,0,0), dt=120, n=200)

output_file("/tmp/test.html", "test")

config_top = dict(
    x_axis_type="datetime", x_range=[xprices[0], xprices[-1]],
    plot_width=800, plot_height=400, background_fill = "#f0f0f0", title_text_font_size="14pt",
    min_border_bottom=0, min_border_top=0,
    tools="pan,wheel_zoom,box_zoom,select")
config_lower = dict(
    x_axis_type="datetime", x_range=[xprices[0], xprices[-1]],
    plot_width=800, plot_height=200, background_fill = "#f0f0f0", title_text_font_size="14pt",
    min_border_bottom=0, min_border_top=0, toolbar_location=None)

xformatter = DatetimeTickFormatter(formats=dict(hours=["%H:%m"]))
p1 = figure(title='prices', **config_top)
p1.line(xprices, yprices, color='black')
p1.line(xma, yma, color='red')
p1.xaxis.formatter = xformatter

p2 = figure(title=None, **config_lower)
p2.rect(xvol, yvol/2, width=120, height=yvol, fill='blue')
p2.x_range = p1.x_range

vbox = VBox()
vbox.children.append(p1)
vbox.children.append(p2)

show(vbox)

On Sunday, December 7, 2014 10:32:00 AM UTC-5, Jonathan Shore wrote:
I upgraded to Bokeh 0.7 so I could use VBox (which I noticed in the git repo). However, I have not figured the following out, if anyone can help:
  • how does one avoid displaying the tool bar (I only want the toolbar on the top plot)
  • can I latch on to a pan event (or drag) in one pane and apply to the other panes?
Thanks for any and all help.

On Saturday, December 6, 2014 7:24:51 PM UTC-5, Jonathan Shore wrote:
I just started using Bokeh's python API. I have multiple time series that I want to plot on the same x-axis, but plot in separate vertically stacked areas (figures) due to distinct y-axes and to avoid visual clutter.

In ggplot I could accomplish this with vertical facets. Is there a way I can do something like this in Bokeh? I am aware of the gridplot() function to present a series of graphs in a grid, but:

- want a vertical stacking (and not horizontal + vertical)
- want to keep the x axis aligned
- want to allow for panning synchronously across the graphs

A typical layout I use with ggplot is a large facet (cartesian plotting area) for prices, and one or more smaller areas for signals. For example, 1 larger pane + 2 vertically smaller panes aligned on the same axis:

>-----------------|
> >
> >
>-----------------|

>-----------------|
}-----------------|

>-----------------|
}-----------------|
0 1 2 3 4 5 6 7

Facets are quite popular in the R / ggplot world, so suspect there are others who would like to do this.
--
Jonathan Shore
Systematic Trading LLC

--
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For more options, visit https://groups.google.com/a/continuum.io/d/optout\.

⌁ Link to post

Juan_Nunez-IglesiasDec 09, 2014 #5

Hi guys,

Thanks for all your great work, and thanks Jonathan for your complete example.

Can I PLEASE implore everyone here to stop using “from x import *”? It’s bad form and makes code less readable. (IPython already learned this the hard way with all their later-unusable “%pylab inline” notebooks, now deprecated in favor of “%matplotlib inline”.) Namespaces are a good thing. There is a reason the SciPy community has embraced them.

IF there is some acknowledgement from the Bokeh devs, I’m happy to help migrate the existing docs to new syntax.

Thoughts?

Juan.

···

On Tue, Dec 9, 2014 at 6:27 AM, Bryan Van de Ven bryan@bokeh.org wrote:

Hi Jonathan,

I just wanted to drop you a note that this post is not being overlooked, I’m in the middle of some traveling but I hope to make a proper response tonight or tomorrow.

Thanks,

Bryan

On Dec 8, 2014, at 12:33 PM, Jonathan Shore jonathan.shore@gmail.com wrote:

As an update to this, i examined the source code and also found some related posts. The keys to doing this were:

• VBox (in 0.7 for vertically aligned widgets)

• toolbar_location=None to remove toolbar for sub-plots

• assignment of x_range from main plot to sub-plots, to link panning behavior

Here is an example that puts this together, for future reference:

from bokeh.plotting import *

import numpy as np

from datetime import *

from bokeh.models import DatetimeTickFormatter

def concat(a, b):

return np.append(np.array(a), np.array(b))

def SMA(x, N):

return np.convolve(x, np.ones((N,))/N, mode=‘valid’)

def mr (n: int, sd=0.5, kappa=0.01, x0 = 100):

x = x0

vec =

for dw in np.random.normal(scale=sd, size=n):

dX = -kappa*(x - x0) + dw

x += dX

vec.append(x)

return vec

def daterange (Tstart: datetime, dt: int, n: int):

Ts = Tstart.timestamp()

return [datetime.utcfromtimestamp(Ts + i * dt) for i in range(0,n)]

yprices = mr (800, kappa=0.001)

xprices = daterange(datetime(2014,4,1,9,0,0), dt=30, n=800)

yma = SMA(yprices, 12)

xma = xprices[(len(yprices)-len(yma)):]

dfs = concat (np.zeros((2+len(yprices)-len(yma))), np.diff (yma,2))

yvol = np.array([abs(dfs[i]) * 10000 for i in range(0,len(yprices),4)])

xvol = daterange(datetime(2014,4,1,9,0,0), dt=120, n=200)

output_file(“/tmp/test.html”, “test”)

config_top = dict(

x_axis_type=“datetime”, x_range=[xprices[0], xprices[-1]],

plot_width=800, plot_height=400, background_fill = “#f0f0f0”, title_text_font_size=“14pt”,

min_border_bottom=0, min_border_top=0,

tools=“pan,wheel_zoom,box_zoom,select”)

config_lower = dict(

x_axis_type=“datetime”, x_range=[xprices[0], xprices[-1]],

plot_width=800, plot_height=200, background_fill = “#f0f0f0”, title_text_font_size=“14pt”,

min_border_bottom=0, min_border_top=0, toolbar_location=None)

xformatter = DatetimeTickFormatter(formats=dict(hours=[“%H:%m”]))

p1 = figure(title=‘prices’, **config_top)

p1.line(xprices, yprices, color=‘black’)

p1.line(xma, yma, color=‘red’)

p1.xaxis.formatter = xformatter

p2 = figure(title=None, **config_lower)

p2.rect(xvol, yvol/2, width=120, height=yvol, fill=‘blue’)

p2.x_range = p1.x_range

vbox = VBox()

vbox.children.append(p1)

vbox.children.append(p2)

show(vbox)

On Sunday, December 7, 2014 10:32:00 AM UTC-5, Jonathan Shore wrote:

I upgraded to Bokeh 0.7 so I could use VBox (which I noticed in the git repo). However, I have not figured the following out, if anyone can help:

• how does one avoid displaying the tool bar (I only want the toolbar on the top plot)

• can I latch on to a pan event (or drag) in one pane and apply to the other panes?

Thanks for any and all help.

On Saturday, December 6, 2014 7:24:51 PM UTC-5, Jonathan Shore wrote:

I just started using Bokeh’s python API. I have multiple time series that I want to plot on the same x-axis, but plot in separate vertically stacked areas (figures) due to distinct y-axes and to avoid visual clutter.

In ggplot I could accomplish this with vertical facets. Is there a way I can do something like this in Bokeh? I am aware of the gridplot() function to present a series of graphs in a grid, but:

want a vertical stacking (and not horizontal + vertical)

want to keep the x axis aligned

want to allow for panning synchronously across the graphs

A typical layout I use with ggplot is a large facet (cartesian plotting area) for prices, and one or more smaller areas for signals. For example, 1 larger pane + 2 vertically smaller panes aligned on the same axis:

-----------------|

-----------------|

-----------------|

}-----------------|

-----------------|

}-----------------|

0 1 2 3 4 5 6 7

Facets are quite popular in the R / ggplot world, so suspect there are others who would like to do this.

–

Jonathan Shore

Systematic Trading LLC

–
You received this message because you are subscribed to the Google Groups “Bokeh Discussion - Public” group.

To unsubscribe from this group and stop receiving emails from it, send an email to bokeh+unsubscribe@continuum.io.

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To view this discussion on the web visit https://groups.google.com/a/continuum.io/d/msgid/bokeh/aef610ac-5af6-4b34-8dea-1f4415f07bb3%40continuum.io.

For more options, visit https://groups.google.com/a/continuum.io/d/optout.

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For more options, visit https://groups.google.com/a/continuum.io/d/optout.

⌁ Link to post

BryanDec 09, 2014 #6

It's funny you should mention this, I am actually in the process of restoring the tutorial (it never got updated to new plotting.py api) and am removing all the * imports as this email came in. In the past, with the old style plotting API I would have resisted this, but now that the glyph functions are methods on plots, users can get by with just "import figure, output_file, show" ninety percent of the time, and that does not seem too burdensome. Maybe another good reason the old API is gone.

Anyway, help updating the docs is always welcome.

Thanks!

Bryan

···

On Dec 8, 2014, at 7:18 PM, Juan Nunez-Iglesias <jni.soma@gmail.com> wrote:

Hi guys,

Thanks for all your great work, and thanks Jonathan for your complete example.

Can I PLEASE implore everyone here to stop using "from x import *"? It's bad form and makes code less readable. (IPython already learned this the hard way with all their later-unusable "%pylab inline" notebooks, now deprecated in favor of "%matplotlib inline".) Namespaces are a good thing. There is a reason the SciPy community has embraced them.

IF there is some acknowledgement from the Bokeh devs, I'm happy to help migrate the existing docs to new syntax.

Thoughts?

Juan.

On Tue, Dec 9, 2014 at 6:27 AM, Bryan Van de Ven <bryan@bokeh.org> wrote:

Hi Jonathan,

I just wanted to drop you a note that this post is not being overlooked, I'm in the middle of some traveling but I hope to make a proper response tonight or tomorrow.

Thanks,

Bryan

> On Dec 8, 2014, at 12:33 PM, Jonathan Shore <jonathan.shore@gmail.com> wrote:
>
> As an update to this, i examined the source code and also found some related posts. The keys to doing this were:
>
> • VBox (in 0.7 for vertically aligned widgets)
> • toolbar_location=None to remove toolbar for sub-plots
> • assignment of x_range from main plot to sub-plots, to link panning behavior
> Here is an example that puts this together, for future reference:
>
> from bokeh.plotting import *
> import numpy as np
> from datetime import *
> from bokeh.models import DatetimeTickFormatter
>
> def concat(a, b):
> return np.append(np.array(a), np.array(b))
>
> def SMA(x, N):
> return np.convolve(x, np.ones((N,))/N, mode='valid')
>
> def mr (n: int, sd=0.5, kappa=0.01, x0 = 100):
> x = x0
> vec =
> for dw in np.random.normal(scale=sd, size=n):
> dX = -kappa*(x - x0) + dw
> x += dX
> vec.append(x)
> return vec
>
> def daterange (Tstart: datetime, dt: int, n: int):
> Ts = Tstart.timestamp()
> return [datetime.utcfromtimestamp(Ts + i * dt) for i in range(0,n)]
>
>
> yprices = mr (800, kappa=0.001)
> xprices = daterange(datetime(2014,4,1,9,0,0), dt=30, n=800)
> yma = SMA(yprices, 12)
> xma = xprices[(len(yprices)-len(yma)):]
>
> dfs = concat (np.zeros((2+len(yprices)-len(yma))), np.diff (yma,2))
>
> yvol = np.array([abs(dfs[i]) * 10000 for i in range(0,len(yprices),4)])
> xvol = daterange(datetime(2014,4,1,9,0,0), dt=120, n=200)
>
> output_file("/tmp/test.html", "test")
>
> config_top = dict(
> x_axis_type="datetime", x_range=[xprices[0], xprices[-1]],
> plot_width=800, plot_height=400, background_fill = "#f0f0f0", title_text_font_size="14pt",
> min_border_bottom=0, min_border_top=0,
> tools="pan,wheel_zoom,box_zoom,select")
> config_lower = dict(
> x_axis_type="datetime", x_range=[xprices[0], xprices[-1]],
> plot_width=800, plot_height=200, background_fill = "#f0f0f0", title_text_font_size="14pt",
> min_border_bottom=0, min_border_top=0, toolbar_location=None)
>
> xformatter = DatetimeTickFormatter(formats=dict(hours=["%H:%m"]))
> p1 = figure(title='prices', **config_top)
> p1.line(xprices, yprices, color='black')
> p1.line(xma, yma, color='red')
> p1.xaxis.formatter = xformatter
>
> p2 = figure(title=None, **config_lower)
> p2.rect(xvol, yvol/2, width=120, height=yvol, fill='blue')
> p2.x_range = p1.x_range
>
> vbox = VBox()
> vbox.children.append(p1)
> vbox.children.append(p2)
>
> show(vbox)
>
>
>
> On Sunday, December 7, 2014 10:32:00 AM UTC-5, Jonathan Shore wrote:
> I upgraded to Bokeh 0.7 so I could use VBox (which I noticed in the git repo). However, I have not figured the following out, if anyone can help:
> • how does one avoid displaying the tool bar (I only want the toolbar on the top plot)
> • can I latch on to a pan event (or drag) in one pane and apply to the other panes?
> Thanks for any and all help.
>
> On Saturday, December 6, 2014 7:24:51 PM UTC-5, Jonathan Shore wrote:
> I just started using Bokeh's python API. I have multiple time series that I want to plot on the same x-axis, but plot in separate vertically stacked areas (figures) due to distinct y-axes and to avoid visual clutter.
>
> In ggplot I could accomplish this with vertical facets. Is there a way I can do something like this in Bokeh? I am aware of the gridplot() function to present a series of graphs in a grid, but:
>
> - want a vertical stacking (and not horizontal + vertical)
> - want to keep the x axis aligned
> - want to allow for panning synchronously across the graphs
>
> A typical layout I use with ggplot is a large facet (cartesian plotting area) for prices, and one or more smaller areas for signals. For example, 1 larger pane + 2 vertically smaller panes aligned on the same axis:
>
> >-----------------|
> > >
> > >
> >-----------------|
>
> >-----------------|
> }-----------------|
>
> >-----------------|
> }-----------------|
> 0 1 2 3 4 5 6 7
>
> Facets are quite popular in the R / ggplot world, so suspect there are others who would like to do this.
> --
> Jonathan Shore
> Systematic Trading LLC
>
>
>
> --
> You received this message because you are subscribed to the Google Groups "Bokeh Discussion - Public" group.
> To unsubscribe from this group and stop receiving emails from it, send an email to bokeh+unsubscribe@continuum.io.
> To post to this group, send email to bokeh@continuum.io.
> To view this discussion on the web visit https://groups.google.com/a/continuum.io/d/msgid/bokeh/aef610ac-5af6-4b34-8dea-1f4415f07bb3%40continuum.io\.
> For more options, visit https://groups.google.com/a/continuum.io/d/optout\.

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For more options, visit https://groups.google.com/a/continuum.io/d/optout\.

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Juan_Nunez-IglesiasDec 09, 2014 #7

Awesome! =D

I don’t want us stepping on each other’s toes, so if you send me a list of files to update, I’ll put in a PR later today. =)

Juan.

···

On Tue, Dec 9, 2014 at 11:32 AM, Bryan Van de Ven bryan@bokeh.org wrote:

It’s funny you should mention this, I am actually in the process of restoring the tutorial (it never got updated to new plotting.py api) and am removing all the * imports as this email came in. In the past, with the old style plotting API I would have resisted this, but now that the glyph functions are methods on plots, users can get by with just “import figure, output_file, show” ninety percent of the time, and that does not seem too burdensome. Maybe another good reason the old API is gone.

Anyway, help updating the docs is always welcome.

Thanks!

Bryan

On Dec 8, 2014, at 7:18 PM, Juan Nunez-Iglesias jni.soma@gmail.com wrote:

Hi guys,

Thanks for all your great work, and thanks Jonathan for your complete example.

Can I PLEASE implore everyone here to stop using “from x import *”? It’s bad form and makes code less readable. (IPython already learned this the hard way with all their later-unusable “%pylab inline” notebooks, now deprecated in favor of “%matplotlib inline”.) Namespaces are a good thing. There is a reason the SciPy community has embraced them.

IF there is some acknowledgement from the Bokeh devs, I’m happy to help migrate the existing docs to new syntax.

Thoughts?

Juan.

On Tue, Dec 9, 2014 at 6:27 AM, Bryan Van de Ven bryanv@continuum.io wrote:

Hi Jonathan,

I just wanted to drop you a note that this post is not being overlooked, I’m in the middle of some traveling but I hope to make a proper response tonight or tomorrow.

Thanks,

Bryan

On Dec 8, 2014, at 12:33 PM, Jonathan Shore jonathan.shore@gmail.com wrote:

As an update to this, i examined the source code and also found some related posts. The keys to doing this were:

• VBox (in 0.7 for vertically aligned widgets)
• toolbar_location=None to remove toolbar for sub-plots
• assignment of x_range from main plot to sub-plots, to link panning behavior
Here is an example that puts this together, for future reference:

from bokeh.plotting import *
import numpy as np
from datetime import *
from bokeh.models import DatetimeTickFormatter

def concat(a, b):
return np.append(np.array(a), np.array(b))

def SMA(x, N):
return np.convolve(x, np.ones((N,))/N, mode=‘valid’)

def mr (n: int, sd=0.5, kappa=0.01, x0 = 100):
x = x0
vec =
for dw in np.random.normal(scale=sd, size=n):
dX = -kappa*(x - x0) + dw
x += dX
vec.append(x)
return vec

def daterange (Tstart: datetime, dt: int, n: int):
Ts = Tstart.timestamp()
return [datetime.utcfromtimestamp(Ts + i * dt) for i in range(0,n)]

yprices = mr (800, kappa=0.001)
xprices = daterange(datetime(2014,4,1,9,0,0), dt=30, n=800)
yma = SMA(yprices, 12)
xma = xprices[(len(yprices)-len(yma)):]

dfs = concat (np.zeros((2+len(yprices)-len(yma))), np.diff (yma,2))

yvol = np.array([abs(dfs[i]) * 10000 for i in range(0,len(yprices),4)])
xvol = daterange(datetime(2014,4,1,9,0,0), dt=120, n=200)

output_file(“/tmp/test.html”, “test”)

config_top = dict(
x_axis_type=“datetime”, x_range=[xprices[0], xprices[-1]],
plot_width=800, plot_height=400, background_fill = “#f0f0f0”, title_text_font_size=“14pt”,
min_border_bottom=0, min_border_top=0,
tools=“pan,wheel_zoom,box_zoom,select”)
config_lower = dict(
x_axis_type=“datetime”, x_range=[xprices[0], xprices[-1]],
plot_width=800, plot_height=200, background_fill = “#f0f0f0”, title_text_font_size=“14pt”,
min_border_bottom=0, min_border_top=0, toolbar_location=None)

xformatter = DatetimeTickFormatter(formats=dict(hours=[“%H:%m”]))
p1 = figure(title=‘prices’, **config_top)
p1.line(xprices, yprices, color=‘black’)
p1.line(xma, yma, color=‘red’)
p1.xaxis.formatter = xformatter

p2 = figure(title=None, **config_lower)
p2.rect(xvol, yvol/2, width=120, height=yvol, fill=‘blue’)
p2.x_range = p1.x_range

vbox = VBox()
vbox.children.append(p1)
vbox.children.append(p2)

show(vbox)

On Sunday, December 7, 2014 10:32:00 AM UTC-5, Jonathan Shore wrote:
I upgraded to Bokeh 0.7 so I could use VBox (which I noticed in the git repo). However, I have not figured the following out, if anyone can help:
• how does one avoid displaying the tool bar (I only want the toolbar on the top plot)
• can I latch on to a pan event (or drag) in one pane and apply to the other panes?
Thanks for any and all help.

On Saturday, December 6, 2014 7:24:51 PM UTC-5, Jonathan Shore wrote:
I just started using Bokeh’s python API. I have multiple time series that I want to plot on the same x-axis, but plot in separate vertically stacked areas (figures) due to distinct y-axes and to avoid visual clutter.

In ggplot I could accomplish this with vertical facets. Is there a way I can do something like this in Bokeh? I am aware of the gridplot() function to present a series of graphs in a grid, but:

want a vertical stacking (and not horizontal + vertical)

want to keep the x axis aligned

want to allow for panning synchronously across the graphs

A typical layout I use with ggplot is a large facet (cartesian plotting area) for prices, and one or more smaller areas for signals. For example, 1 larger pane + 2 vertically smaller panes aligned on the same axis:

-----------------|

-----------------|

-----------------|
}-----------------|

-----------------|
}-----------------|
0 1 2 3 4 5 6 7

Facets are quite popular in the R / ggplot world, so suspect there are others who would like to do this.

Jonathan Shore
Systematic Trading LLC

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BryanDec 09, 2014 #8

Everything I am doing will be under sphinx/source/tutorial

···

On Dec 8, 2014, at 7:36 PM, Juan Nunez-Iglesias <jni.soma@gmail.com> wrote:

Awesome! =D

I don't want us stepping on each other's toes, so if you send me a list of files to update, I'll put in a PR later today. =)

Juan.

On Tue, Dec 9, 2014 at 11:32 AM, Bryan Van de Ven <bryan@bokeh.org> wrote:

It's funny you should mention this, I am actually in the process of restoring the tutorial (it never got updated to new plotting.py api) and am removing all the * imports as this email came in. In the past, with the old style plotting API I would have resisted this, but now that the glyph functions are methods on plots, users can get by with just "import figure, output_file, show" ninety percent of the time, and that does not seem too burdensome. Maybe another good reason the old API is gone.

Anyway, help updating the docs is always welcome.

Thanks!

Bryan

> On Dec 8, 2014, at 7:18 PM, Juan Nunez-Iglesias <jni.soma@gmail.com> wrote:
>
> Hi guys,
>
> Thanks for all your great work, and thanks Jonathan for your complete example.
>
> Can I PLEASE implore everyone here to stop using "from x import *"? It's bad form and makes code less readable. (IPython already learned this the hard way with all their later-unusable "%pylab inline" notebooks, now deprecated in favor of "%matplotlib inline".) Namespaces are a good thing. There is a reason the SciPy community has embraced them.
>
> IF there is some acknowledgement from the Bokeh devs, I'm happy to help migrate the existing docs to new syntax.
>
> Thoughts?
>
> Juan.
>
>
>
>
> On Tue, Dec 9, 2014 at 6:27 AM, Bryan Van de Ven <bryan@bokeh.org> wrote:
>
> Hi Jonathan,
>
> I just wanted to drop you a note that this post is not being overlooked, I'm in the middle of some traveling but I hope to make a proper response tonight or tomorrow.
>
> Thanks,
>
> Bryan
>
>
> > On Dec 8, 2014, at 12:33 PM, Jonathan Shore <jonathan.shore@gmail.com> wrote:
> >
> > As an update to this, i examined the source code and also found some related posts. The keys to doing this were:
> >
> > • VBox (in 0.7 for vertically aligned widgets)
> > • toolbar_location=None to remove toolbar for sub-plots
> > • assignment of x_range from main plot to sub-plots, to link panning behavior
> > Here is an example that puts this together, for future reference:
> >
> > from bokeh.plotting import *
> > import numpy as np
> > from datetime import *
> > from bokeh.models import DatetimeTickFormatter
> >
> > def concat(a, b):
> > return np.append(np.array(a), np.array(b))
> >
> > def SMA(x, N):
> > return np.convolve(x, np.ones((N,))/N, mode='valid')
> >
> > def mr (n: int, sd=0.5, kappa=0.01, x0 = 100):
> > x = x0
> > vec =
> > for dw in np.random.normal(scale=sd, size=n):
> > dX = -kappa*(x - x0) + dw
> > x += dX
> > vec.append(x)
> > return vec
> >
> > def daterange (Tstart: datetime, dt: int, n: int):
> > Ts = Tstart.timestamp()
> > return [datetime.utcfromtimestamp(Ts + i * dt) for i in range(0,n)]
> >
> >
> > yprices = mr (800, kappa=0.001)
> > xprices = daterange(datetime(2014,4,1,9,0,0), dt=30, n=800)
> > yma = SMA(yprices, 12)
> > xma = xprices[(len(yprices)-len(yma)):]
> >
> > dfs = concat (np.zeros((2+len(yprices)-len(yma))), np.diff (yma,2))
> >
> > yvol = np.array([abs(dfs[i]) * 10000 for i in range(0,len(yprices),4)])
> > xvol = daterange(datetime(2014,4,1,9,0,0), dt=120, n=200)
> >
> > output_file("/tmp/test.html", "test")
> >
> > config_top = dict(
> > x_axis_type="datetime", x_range=[xprices[0], xprices[-1]],
> > plot_width=800, plot_height=400, background_fill = "#f0f0f0", title_text_font_size="14pt",
> > min_border_bottom=0, min_border_top=0,
> > tools="pan,wheel_zoom,box_zoom,select")
> > config_lower = dict(
> > x_axis_type="datetime", x_range=[xprices[0], xprices[-1]],
> > plot_width=800, plot_height=200, background_fill = "#f0f0f0", title_text_font_size="14pt",
> > min_border_bottom=0, min_border_top=0, toolbar_location=None)
> >
> > xformatter = DatetimeTickFormatter(formats=dict(hours=["%H:%m"]))
> > p1 = figure(title='prices', **config_top)
> > p1.line(xprices, yprices, color='black')
> > p1.line(xma, yma, color='red')
> > p1.xaxis.formatter = xformatter
> >
> > p2 = figure(title=None, **config_lower)
> > p2.rect(xvol, yvol/2, width=120, height=yvol, fill='blue')
> > p2.x_range = p1.x_range
> >
> > vbox = VBox()
> > vbox.children.append(p1)
> > vbox.children.append(p2)
> >
> > show(vbox)
> >
> >
> >
> > On Sunday, December 7, 2014 10:32:00 AM UTC-5, Jonathan Shore wrote:
> > I upgraded to Bokeh 0.7 so I could use VBox (which I noticed in the git repo). However, I have not figured the following out, if anyone can help:
> > • how does one avoid displaying the tool bar (I only want the toolbar on the top plot)
> > • can I latch on to a pan event (or drag) in one pane and apply to the other panes?
> > Thanks for any and all help.
> >
> > On Saturday, December 6, 2014 7:24:51 PM UTC-5, Jonathan Shore wrote:
> > I just started using Bokeh's python API. I have multiple time series that I want to plot on the same x-axis, but plot in separate vertically stacked areas (figures) due to distinct y-axes and to avoid visual clutter.
> >
> > In ggplot I could accomplish this with vertical facets. Is there a way I can do something like this in Bokeh? I am aware of the gridplot() function to present a series of graphs in a grid, but:
> >
> > - want a vertical stacking (and not horizontal + vertical)
> > - want to keep the x axis aligned
> > - want to allow for panning synchronously across the graphs
> >
> > A typical layout I use with ggplot is a large facet (cartesian plotting area) for prices, and one or more smaller areas for signals. For example, 1 larger pane + 2 vertically smaller panes aligned on the same axis:
> >
> > >-----------------|
> > > >
> > > >
> > >-----------------|
> >
> > >-----------------|
> > }-----------------|
> >
> > >-----------------|
> > }-----------------|
> > 0 1 2 3 4 5 6 7
> >
> > Facets are quite popular in the R / ggplot world, so suspect there are others who would like to do this.
> > --
> > Jonathan Shore
> > Systematic Trading LLC
> >
> >
> >
> > --
> > You received this message because you are subscribed to the Google Groups "Bokeh Discussion - Public" group.
> > To unsubscribe from this group and stop receiving emails from it, send an email to bokeh+unsubscribe@continuum.io.
> > To post to this group, send email to bokeh@continuum.io.
> > To view this discussion on the web visit https://groups.google.com/a/continuum.io/d/msgid/bokeh/aef610ac-5af6-4b34-8dea-1f4415f07bb3%40continuum.io\.
> > For more options, visit https://groups.google.com/a/continuum.io/d/optout\.
>
> --
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systemJul 21, 2021 #9

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