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The bokeh.embed.standalonehtmlpageformodels method has been deprecated in place of bokeh.embed.filehtml. For details see pull request 5978. The validate keyword argument to bokeh.io.save has been deprecated. Future usage of bokeh.io.save will always validate the document before outputting a file. GNU Emacs For Mac OS X Pure builds of Emacs for Mac OS X. Download Emacs Version 27.1-1 Universal Binary (62.351 MB) Released 2020-08-11 Usually there's a nifty page here with a big download button. But you are using a browser which doesn't support SVG and so you get the boring looking page.

28 Oct 2011 #Nuke#Python

This is a quick guide to setting Nuke 6.3 up with a custom menu and make it work more seamlessly across operating systems.

Since I jump between my Mac laptop and a Windows based workstation, I used to mess around with broken file read nodes in The Foundry’s Nuke and other stuff related to file paths being different on Windows and Mac OS X. Well… no more!

In this article, I will explain how to set up Nuke with a few handy extras that will make it fly even smoother across Windows and Mac computers, and with custom menus. Let me know if there are any specific Windows/Mac switching woes that you are experiencing, which are not covered here.

Downloads:

The NUKE_PATH environment variable

First off, create the environment variable NUKE_PATH and point it to a centralized server location, in my case Y:/include/nuke on Windows or /Volumes/Assets/include/nuke on Mac OS X. Nuke will then look for this environment variable and intialize any customizations defined with init.py and menu.py, which we will create inside this directory.

An alternative to setting the environment variable is to run Nuke from a Python script or a bash script for the Mac, which will launch Nuke with the environment variable loaded for the particular shell it is running in. Examples of this below…

Contents of launch_nuke.py (works on Windows with Python installed):

Contents of launch_nuke.sh (works on Mac OS X):

The files and folder structure

Inside the /include/nuke folder, we will create the following files and folder structure:

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Just create init.py and menu.py as empty text files.

The init.py will serve as a script that will initialize Nuke with custom functions.The menu.py will solely handle the interactive bits of our customization, such as the custom menus in the GUI.Each folder will soon be acknowledged by Nuke to contain loadable things (scripts, gizmos etc), but I like to keep it organized and nice, thus separating icons from plugins from scripts.

Make Read nodes load up without errors on both Windows and OS X

By specifying the file server paths for both Windows and Mac OS X in init.py, we will be able to filter these paths out and replace them with the appropriate ones, for the current operating system, without forcing the user to manually changing the file paths:

Add to init.py:

You can read more about the filenameFilter in The Foundry’s Python docs. If you wish to specify file paths for Linux systems, simply check out nuke.env['LINUX'], like in their sample code on that page. For some reason, their sample code snipplet does not work with Nuke 6.3 as of writing this.

Generate OS-specific variable values

In order to make a whole lot of custom stuff work, we need to detect the current operating system. This time around I have taken a bit different route when detecting the OS type, by using the Python “sys” library. It’s up to you which flavour you like best.

Please note that the variables created here will be required by the examples below as well. And again, I have not bothered to add Linux support. Just google it. :)

Favorites, cross-platform

Add to init.py:

Add more formats

Add to init.py:

Define custom location for gizmos and plugins, cross-platform

In order to easily load gizmos and plugins from within our own custom menus, I will just add a bit of extra code to init.py:

Load OS-specific plugins, cross-platform

In this example, I am loading Peregrine Lab’s Bokeh, and since it is built for Windows and Mac OS X respectively, I need to specify which OS build I will try and load up since I will not be able to load the Windows plugin on Mac OS X or vice versa. On Mac OS X, you can use Pacifist to extract the files from the .dmg file to be put on the server location.

Assuming you have already installed the floating server licenses onto 10.0.1.100 and the RLM server is running on port 5053, add to init.py:

Please note that on Windows, you may have to copy the .dll files from the Bokeh build into the root of the build’s folder.

Load OS-specific OFX plugins, cross-platform

In this example, I’m loading Frischluft’s Lenscare. Unlike “regular” plugins, OFX plugins are identified by an environment variable. If it’s not already set, we will set it. If it had been already set, any existing content to the environment variable is preserved (any local installations of OFX plugins will be maintained).

Creating a custom toolbar menu and file menu

I am using menu.py to set this up, and I will create a file menu as well as a toolbar menu. The toolbar menu tools will show up when you hit “tab” in the node graph and start typing its name, and so, I would like these to be tools that generate a node. The file menu will only include the tools that launch a menu or perform an action that does not generate a new node in the node graph.

Enter the following into menu.py:

Just fill those menus up with your heart’s content, by editing the code above to load up any downloaded gizmo or plugin (placed in their respective centralized folders within your NUKE_PATH).

Other tweaks

The following tweaks are not cross-platform related. Just some things that might come in handy…

If write dir does not exist, make Nuke create it automatically

This is quite an annoying feature, I think, so I force Nuke to create the destination path for write nodes (if they do not already exist).

Add to init.py:

Make Quicktime Write nodes default to sRGB

In Mac OS X prior to 10.6, the default system gamma value was 1.8. Today all new Macs are shipped running Mac OS X in gamma 2.2. Therefore, leaving Nuke’s default value of gamma 1.8 is mostly a legacy thing. In order to change this default into sRGB, enter the following into init.py:

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Bokeh is an interactive visualization library for modern web browsers. Itprovides elegant, concise construction of versatile graphics, and affordshigh-performance interactivity over large or streaming datasets. Bokeh can helpanyone who would like to quickly and easily make interactive plots, dashboards,and data applications.

To offer both simplicity and the powerful and flexible features needed foradvanced customizations, Bokeh exposes two interface levels to users:

bokeh.models

A low-level interface that provides the most flexibility toapplication developers.

bokeh.plotting

A higher-level interface centered around composing visual glyphs.

This Quickstart focuses on the bokeh.plotting interface.

Installation¶

There are multiple ways to install Bokeh, and we recommend the easiest one,which is to use the Anaconda Python distribution and enter this command ata Bash or Windows command prompt:

This installs all the dependencies that Bokeh needs. Anaconda minimizesinstallation effort on all platforms and configurations, including Windows,and also installs the examples into the examples/ subdirectory of yourAnaconda or Miniconda installation directory.

If you are confident that you have installed all needed dependencies, such asNumPy, you may instead use pip at the command line:

Note

The pip method does not install the examples. Clone the Git repositoryand look in the examples/ directory of the checkout to see examples.

Getting Started¶

Bokeh is a large library that exposes many capabilities, so this section isonly a quick tour of some common Bokeh use cases and workflows. For moredetailed information please consult the full User Guide.

Let’s begin with some examples.

Plotting data in basic Python lists as a line plot, including zoom,pan, save, and other tools, is simple and straightforward:

When you execute this script, you will see that a new output file'lines.html' is created and that a browser automatically opens a new tabto display it (for presentation purposes we have included the plot outputdirectly inline in this document).

The basic steps to creating plots with the bokeh.plotting interface are:

Prepare some data

In this case, plain Python lists, but NumPy arrays or Pandas series alsowork.

Tell Bokeh where to generate output

In this case, using output_file(), with the filename 'lines.html'.Another option is output_notebook() for use in Jupyter notebooks.

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Call figure()

This creates a plot with typical default options and easy customizationof title, tools, and axes labels.

Add renderers

In this case, we use line() for our data, specifying visualcustomizations like colors, legends, and widths.

Ask Bokeh to show() or save() the results

These functions save the plot to an HTML file and optionally display it ina browser.

Steps three and four can be repeated to create more than one plot, as shown insome of the examples below.

The bokeh.plotting interface is also quite handy if we need to customizethe output a bit more by adding more data series, glyphs, logarithmic axis,and so on. It’s easy to combine multiple glyphs together on one plot, as shownbelow:

Jupyter Notebooks¶

At this point we should mention Jupyter notebooks.

Jupyter notebooks are a common tool for exploratory data analysis, widelyused across the PyData community. Bokeh integrates seamlessly with Jupyternotebooks. To view the above examples in a notebook, you would onlychange output_file() to output_notebook() instead.

You can interact directly with live tutorial notebooks hosted online byMyBinder. Static versions of these may be viewed at theBokeh NBViewer Gallery.

The Bokeh GitHub repository also has a number of example notebooks in theexamples/howto directory. After cloning the repository, navigate there and run:

You can open and interact with any of the notebooks listed on the index pagethat automatically opens up. In particular, you might check out these examples,which show how Bokeh can be used together with Jupyter interactive widgets:

examples/howto/notebook_comms/Jupyter Interactors.ipynb

A basic example that combines Bokeh’s interactive capabilities withJupyter’s dropdowns and sliders.

examples/howto/notebook_comms/Numba Image Example.ipynb

A more advanced example that uses Numba to efficiently perform imageprocessing interactively based on Jupyter widget controls.

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Note

Bokeh plots will not display inline in GitHub notebook previews. Bokehplots use JavaScript code to render, but GitHub scrubs all JavaScript frompreviewed content.

Sample Data¶

Some of the examples included in the Bokeh source make use of sample data filesthat are distributed separately. To download this data, execute the followingcommands at a Bash or Windows command prompt:

For more information see the bokeh.sampledata reference.

Concepts¶

Let’s consider the plots above, and use them to help define some core concepts.

Plot¶

Plots are a central concept in Bokeh. They are containers that hold all thevarious objects (renderers, guides, data, and tools) that comprise the finalvisualization that is presented to users. The bokeh.plotting interfaceprovides a figure() function to help with assembling all the necessary objects.

Glyphs¶

Glyphs are the basic visual marks that Bokeh can display. At the lowest level,there are glyph objects, such as Line. If you are using the low-levelbokeh.models interface, it is your responsibility to create and coordinateall the various Bokeh objects, including glyph objects and their data sources.To make life easier, the bokeh.plotting interface exposes higher-levelglyph methods such as the line() method used in the first example.The second example also adds in calls to circle() to display circleand line glyphs together on the same plot. Besides lines and circles, Bokehmakes many additional glyphs and markers available.

The visual appearance of a glyph is tied directly to the data values that areassociated with the glyph’s various attributes. In the example above we seethat positional attributes like x and y can be set to vectors of data.But glyphs also have some combination of Line Properties, Fill Properties, andHatch Properties to control their appearance. All of these attributes can be setwith “vectorized” values as well. We will show examples of this below.

Guides and Annotations¶

Bokeh plots can also have other visual components that aid presentation orhelp the user make comparisons. These fall into two categories. Guidesare visual aids that help users judge distances, angles, etc. These includegrid lines or bands, axes (such as linear, log, or datetime) that may haveticks and tick labels as well. Annotations are visual aids that label orname parts of the plot. These include titles, legends, etc.

Ranges¶

Ranges describe the to the output_file() function.

More Examples¶

Here are a few more examples to demonstrate other common tasks and use caseswith the bokeh.plotting interface.

Bokeh For Mac Os X 10.7

Vectorized Colors and Sizes¶

This example shows how it is possible to provide sequences of data values forglyph attributes like fill_color and radius. Other things to look outfor in this example:

• supplying an explicit list of tool names to figure()

• fetching BokehJS resources from CDN using the mode argument

• setting the x_range and y_range explicitly

• turning a line off (by setting its value to None)

• using NumPy arrays for supplying data

Linked Panning and Brushing¶

Linking together various aspects of different plots can be a useful techniquefor data visualization. In Bokeh, such linkages are typically accomplished bysharing some plot component between plots. Below is an example thatdemonstrates linked panning (where changing the range of one plot causesothers to update) by sharing range objects between the plots. Some otherthings to look out for in this example:

• calling figure() multiple times to create multiple plots

• using gridplot() to arrange several plots in an array

• showing new glyphs using new glyph methods triangle() andsquare()

• hiding the toolbar by setting toolbar_location to None

• setting convenience arguments color (sets both line_color andfill_color) and alpha (sets both line_alpha andfill_alpha)

Although the toolbar is hidden, the pan tool is still present and active. Clickand drag the above plots to pan them, and see how their ranges are linkedtogether.

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Another linkage that is often useful is linked brushing (where a selectionon one plot causes a selection to update on other plots). Below is an examplethat demonstrates linked brushing by sharing a ColumnDataSource between twoplots:

Choose the box or lasso select tool, and click and drag to make aselection on one plot, which will update the selection on the otherplot.

Datetime Axes¶

Dealing with date and time series is another common task. Bokeh has asophisticated DatetimeAxis that can change the displayed ticks basedon the current scale of the plot. There are some inputs for which Bokehwill automatically default to DatetimeAxis, but you can alwaysexplicitly ask for one by passing the value 'datetime' to thex_axis_type or y_axis_type parameters to figure(). A few thingsof interest to look out for in this example:

• setting the plot_width and plot_height arguments to figure()

• customizing plots and other objects by assigning values to their attributes

• accessing guides and annotations with convenience Figure attributes:legend, grid, xgrid, ygrid, axis, xaxis, yaxis

Bokeh Applications¶

Bokeh also comes with an optional server component, the Bokeh Server. It ispossible to create many interesting and interactive visualizations withoutusing the Bokeh server, as we have seen above. However, the Bokeh serveraffords many novel and powerful capabilities, including:

• UI widgets and plot selections driving computations and plot updates.

• Intelligent server-side downsampling of large datasets.

• Streaming data automatically updating plots.

• Sophisticated glyph re-writing and transformations for “Big Data”.

• Plot and dashboard publishing for wider audiences.

Details of Bokeh server usage require more space than a Quickstart allows,but you can see (and interact with) a simple Bokeh server app below:

More examples of hosted Bokeh applications can be found in theServer App Examples section of the Gallery. Forinformation about how to use the server and write Bokeh server plotsand apps, consult the Running a Bokeh Server section of theUser Guide.

What’s Next?¶

This Quickstart barely scratches the surface of Bokeh’s capabilities.

For more information about the different plotting APIs Bokeh offers,using the Bokeh server, and how to embed Bokeh plots in your own apps anddocuments, check out the User Guide. For detailed information aboutall modules, classes, models, and objects, consult the Reference.If you are interested in learning how to build and develop Bokeh, or forinformation about how to create a new language binding, see theDevelopers Guide.

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To see ready-made examples of how you might use Bokeh with your own data,check out the Gallery. To see detailed examples and walkthroughs aswell as find exercises for learning Bokeh by doing, work through thelive tutorial notebooks.

For questions and technical assistance, come join the Bokeh Discourse.

Visit the Bokeh GitHub repository and try the examples.

Be sure to follow us on Twitter @bokeh!