Specify location on categorical axis for BoxAnnotation

[Till]

Hi,

I would like to place a BoxAnnotation on a figure with a categorical axis,

but can’t figure out how to specify the exact location on the axis.

Any help is very much appreciated.

The BoxAnnotation in the picture was placed with help of a numerical axis location.

p_normal = figure(y_range=scales, x_range=[25,110], plot_width=w, plot_height=h, toolbar_location=None, title=‘Normal’)

for scoring_variable in scored_variables[1:]:

normal_and_patho_curves = make_ridge(scoring_variable, source_data, x)

p_normal.patch(‘x’, ‘y_normal’, alpha=0.3, line_color=‘black’, source=normal_and_patho_curves, color=‘lightsteelblue’)

ValueError: expected an element of either Auto or NumberSpec(String, Dict(Enum('expr', 'field', 'value', 'transform'), Either(String, Instance(Transform), Instance(Expression), Float)), Float), got ['Lacunes']

p_normal.text(27, y=dodge(‘text’, 0.8, range=p_normal.y_range), text=‘text’, source=ColumnDataSource(data=dict(text=scored_variables[1:])), **text_props)

p_normal.add_layout(BoxAnnotation(top=[‘Lacunes’))

``

Thanks!!

[Mateusz_Paprocki2]

Hi,

Hi,

I would like to place a BoxAnnotation on a figure with a categorical axis,

but can’t figure out how to specify the exact location on the axis.

Any help is very much appreciated.

The BoxAnnotation in the picture was placed with help of a numerical axis location.

p_normal = figure(y_range=scales, x_range=[25,110], plot_width=w, plot_height=h, toolbar_location=None, title=‘Normal’)

for scoring_variable in scored_variables[1:]:

normal_and_patho_curves = make_ridge(scoring_variable, source_data, x)

p_normal.patch(‘x’, ‘y_normal’, alpha=0.3, line_color=‘black’, source=normal_and_patho_curves, color=‘lightsteelblue’)

p_normal.text(27, y=dodge(‘text’, 0.8, range=p_normal.y_range), text=‘text’, source=ColumnDataSource(data=dict(text=scored_variables[1:])), **text_props)

p_normal.add_layout(BoxAnnotation(top=[‘Lacunes’))

``

BoxAnnotation expects a single coordinate (most annotation aren’t vectorized). It should work when you drop the list.

Mateusz

···

On Thu, Nov 22, 2018 at 1:33 PM landge [email protected] wrote:

ValueError: expected an element of either Auto or NumberSpec(String, Dict(Enum('expr', 'field', 'value', 'transform'), Either(String, Instance(Transform), Instance(Expression), Float)), Float), got ['Lacunes']

``

Thanks!!

–

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 [email protected].

To post to this group, send email to [email protected].

To view this discussion on the web visit https://groups.google.com/a/continuum.io/d/msgid/bokeh/e554fb73-bd4e-43d8-b4bb-4f6a53c83325%40continuum.io.

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

[Till]

Thanks!

I still get this error:

Javascript Error: Number property 'top' given invalid value: "Lacunes"

When I pass a simple string and not a list.

Do I miss anything?

···

On Thursday, November 22, 2018 at 3:16:31 PM UTC+1, mateusz.paprocki wrote:

Hi,
On Thu, Nov 22, 2018 at 1:33 PM landge [email protected] wrote:

Hi,

I would like to place a BoxAnnotation on a figure with a categorical axis,

but can’t figure out how to specify the exact location on the axis.

Any help is very much appreciated.

The BoxAnnotation in the picture was placed with help of a numerical axis location.

p_normal = figure(y_range=scales, x_range=[25,110], plot_width=w, plot_height=h, toolbar_location=None, title=‘Normal’)

for scoring_variable in scored_variables[1:]:

normal_and_patho_curves = make_ridge(scoring_variable, source_data, x)

p_normal.patch(‘x’, ‘y_normal’, alpha=0.3, line_color=‘black’, source=normal_and_patho_curves, color=‘lightsteelblue’)

p_normal.text(27, y=dodge(‘text’, 0.8, range=p_normal.y_range), text=‘text’, source=ColumnDataSource(data=dict(text=scored_variables[1:])), **text_props)

p_normal.add_layout(BoxAnnotation(top=[‘Lacunes’))

``

BoxAnnotation expects a single coordinate (most annotation aren’t vectorized). It should work when you drop the list.

Mateusz

ValueError: expected an element of either Auto or NumberSpec(String, Dict(Enum('expr', 'field', 'value', 'transform'), Either(String, Instance(Transform), Instance(Expression), Float)), Float), got ['Lacunes']

``

Thanks!!

–

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 [email protected].

To post to this group, send email to [email protected].

To view this discussion on the web visit https://groups.google.com/a/continuum.io/d/msgid/bokeh/e554fb73-bd4e-43d8-b4bb-4f6a53c83325%40continuum.io.

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

[Bryan]

I believe BoxAnnotation will require work to be able to understand categorical coordinates. A GitHub issue would be reasonable, but please make sure to include a *complete* minimal example script/test case that demonstrates things failing.

Otherwise the default "synthetic" width of each category is "1" so you could use that knowledge to refine the placement using numerical coordinates.

Thanks,

Bryan

···

On Nov 22, 2018, at 09:00, [email protected] wrote:

Thanks!
I still get this error:

Javascript Error: Number property 'top' given invalid value: "Lacunes"

When I pass a simple string and not a list.
Do I miss anything?

On Thursday, November 22, 2018 at 3:16:31 PM UTC+1, mateusz.paprocki wrote:
Hi,
On Thu, Nov 22, 2018 at 1:33 PM landge <[email protected]> wrote:
Hi,

I would like to place a BoxAnnotation on a figure with a categorical axis,
but can't figure out how to specify the exact location on the axis.

Any help is very much appreciated.
The BoxAnnotation in the picture was placed with help of a numerical axis location.

p_normal = figure(y_range=scales, x_range=[25,110], plot_width=w, plot_height=h, toolbar_location=None, title='Normal')

for scoring_variable in scored_variables[1:]:
    normal_and_patho_curves = make_ridge(scoring_variable, source_data, x)
    p_normal.patch('x', 'y_normal', alpha=0.3, line_color='black', source=normal_and_patho_curves, color='lightsteelblue')

p_normal.text(27, y=dodge('text', 0.8, range=p_normal.y_range), text='text', source=ColumnDataSource(data=dict(text=scored_variables[1:])), **text_props)

p_normal.add_layout(BoxAnnotation(top=['Lacunes'))

BoxAnnotation expects a single coordinate (most annotation aren't vectorized). It should work when you drop the list.

Mateusz

ValueError: expected an element of either Auto or NumberSpec(String, Dict(Enum('expr', 'field', 'value', 'transform'), Either(String, Instance(Transform), Instance(Expression), Float)), Float), got ['Lacunes']

Thanks!!

--
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+un...@continuum.io.
To post to this group, send email to bo...@continuum.io.
To view this discussion on the web visit https://groups.google.com/a/continuum.io/d/msgid/bokeh/e554fb73-bd4e-43d8-b4bb-4f6a53c83325%40continuum.io\.
For more options, visit https://groups.google.com/a/continuum.io/d/optout\.

--
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 [email protected].
To post to this group, send email to [email protected].
To view this discussion on the web visit https://groups.google.com/a/continuum.io/d/msgid/bokeh/64a4805f-bc48-4b7d-909e-3524bd552dfd%40continuum.io\.
For more options, visit https://groups.google.com/a/continuum.io/d/optout\.

[Till]

Thanks for your reply.

I’ve opened an issue on github:

https://github.com/bokeh/bokeh/issues/8482

···

On Tue, Nov 27, 2018 at 2:30 AM Bryan Van de ven [email protected] wrote:

I believe BoxAnnotation will require work to be able to understand categorical coordinates. A GitHub issue would be reasonable, but please make sure to include a complete minimal example script/test case that demonstrates things failing.

Otherwise the default “synthetic” width of each category is “1” so you could use that knowledge to refine the placement using numerical coordinates.

Thanks,

Bryan

On Nov 22, 2018, at 09:00, [email protected] wrote:

Thanks!

I still get this error:

Javascript Error: Number property ‘top’ given invalid value: “Lacunes”

When I pass a simple string and not a list.

Do I miss anything?

On Thursday, November 22, 2018 at 3:16:31 PM UTC+1, mateusz.paprocki wrote:

Hi,

On Thu, Nov 22, 2018 at 1:33 PM landge [email protected] wrote:

Hi,

I would like to place a BoxAnnotation on a figure with a categorical axis,

but can’t figure out how to specify the exact location on the axis.

Any help is very much appreciated.

The BoxAnnotation in the picture was placed with help of a numerical axis location.

p_normal = figure(y_range=scales, x_range=[25,110], plot_width=w, plot_height=h, toolbar_location=None, title=‘Normal’)

for scoring_variable in scored_variables[1:]:

normal_and_patho_curves = make_ridge(scoring_variable, source_data, x)

p_normal.patch('x', 'y_normal', alpha=0.3, line_color='black', source=normal_and_patho_curves, color='lightsteelblue')

p_normal.text(27, y=dodge(‘text’, 0.8, range=p_normal.y_range), text=‘text’, source=ColumnDataSource(data=dict(text=scored_variables[1:])), **text_props)

p_normal.add_layout(BoxAnnotation(top=[‘Lacunes’))

BoxAnnotation expects a single coordinate (most annotation aren’t vectorized). It should work when you drop the list.

Mateusz

ValueError: expected an element of either Auto or NumberSpec(String, Dict(Enum(‘expr’, ‘field’, ‘value’, ‘transform’), Either(String, Instance(Transform), Instance(Expression), Float)), Float), got [‘Lacunes’]

Thanks!!

–

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 [email protected].

To post to this group, send email to [email protected].

To view this discussion on the web visit https://groups.google.com/a/continuum.io/d/msgid/bokeh/e554fb73-bd4e-43d8-b4bb-4f6a53c83325%40continuum.io.

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

–

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 [email protected].

To post to this group, send email to [email protected].

To view this discussion on the web visit https://groups.google.com/a/continuum.io/d/msgid/bokeh/64a4805f-bc48-4b7d-909e-3524bd552dfd%40continuum.io.

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

–

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

To unsubscribe from this topic, visit https://groups.google.com/a/continuum.io/d/topic/bokeh/sbz8Xmb-qak/unsubscribe.

To unsubscribe from this group and all its topics, send an email to [email protected].

To post to this group, send email to [email protected].

To view this discussion on the web visit https://groups.google.com/a/continuum.io/d/msgid/bokeh/77CFC941-AE36-423A-9B38-CFBB2E517259%40anaconda.com.

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