.. _bokeh-tutorial: ************************** Bokeh Integration Concepts ************************** **Last Updated:** November 2019 This tutorial introduces ``Bokeh Server`` integration concepts for Tethys developers. Two ``bokeh`` handlers will be created to demonstrate how to link Bokeh plots or widgets to Python functions in the brackground using both a plain Bokeh approach as well as a ``Param`` approach. The topics covered include: * Bokeh Server * Handler functions using Bokeh Widgets * Handler functions using Param and Panel Create a and install a new Tethys app named bokeh_tutorial. :: t tethys scaffold bokeh_tutorial cd tethysapp-bokeh_tutorial tethys install -d 1. Bokeh Server =============== ``Bokeh`` is an interactive visualization library for Python. ``Bokeh Server`` is a component of the ``Bokeh`` architecture. It provides a way to sync model objects in Python on the backend to JavaScript model objects on the client. This is done by levering the ``Websocket`` protocol. With the addition of ``Django Channels`` to Tethys, this ability to sync backend python objects and frontend plots has also been integrated without the need of other components such as a ``Tornado`` server (see `Tethys Bokeh Integration documentation <../../tethys_sdk/url_maps.html#bokeh-integration>`_). This integration facilitates the linking of objects and ``Bokeh`` widgets as well as the creation of the necessary ``websocket`` and ``http`` ``consumers``. The logic for creating a Bokeh widget along with other related functionality is provided in a ``handler function``. This handler will be associated to a specific ``controller function`` where the resulting Bokeh widget will be displayed in a later step. 2. Handler Functions Using Bokeh Widgets ======================================== Let's use Bokeh's sea temperature sample data to create a time series plot and link it to a slider that will provide the value to perform a rolling-window analysis on the time series. This example is based on a similar example in Bokeh's main documentation. 1. Create a ``handler function`` by adding the following imports and logic to ``controller.py``. .. code-block:: Python from bokeh.plotting import figure from bokeh.models import ColumnDataSource from bokeh.sampledata.sea_surface_temperature import sea_surface_temperature ... def home_handler(document): df = sea_surface_temperature.copy() source = ColumnDataSource(data=df) plot = figure(x_axis_type="datetime", y_range=(0, 25), y_axis_label="Temperature (Celsius)", height=500, width=800, title="Sea Surface Temperature at 43.18, -70.43") plot.line("time", "temperature", source=source) document.add_root(plot) This simple handler contains the logic for a time series plot of the sea surface temperature sample data provided by ``Bokeh``. 2. Clear the default home function in ``controller.py`` and add the following code to it. .. code-block:: Python from bokeh.embed import server_document @login_required() def home(request): script = server_document(request.build_absolute_uri()) context = {'script': script} return render(request, 'bokeh_tutorial/home.html', context) The home controller can now load the time series plot from (a) using the Bokeh ``server_document`` function. However, we still need to link the ``handler`` and the ``controller`` in the ``app.py``, and add the script context variable to the template as with any other variable. 3. Modify ``app.py`` by adding a dot-formatted path to the handler function created in (1) to the ``handler`` parameter and providing a ``handler_type`` with a value equal to 'bokeh' as shown in the code below. .. code-block:: Python from tethys_sdk.base import TethysAppBase, url_map_maker class BokehTutorial(TethysAppBase): """ Tethys app class for Bokeh Tutorial. """ name = 'Bokeh Tutorial' index = 'bokeh_tutorial:home' icon = 'bokeh_tutorial/images/icon.gif' package = 'bokeh_tutorial' root_url = 'bokeh-tutorial' color = '#2980b9' description = '' tags = '' enable_feedback = False feedback_emails = [] def url_maps(self): """ Add controllers """ UrlMap = url_map_maker(self.root_url) url_maps = ( UrlMap( name='home', url='bokeh-tutorial', controller='bokeh_tutorial.controllers.home', handler='bokeh_tutorial.controllers.home_handler', handler_type='bokeh' ), ) return url_maps 4. Clear the default ``home.html`` template and add the following code to it. .. code-block:: html+django {% extends "bokeh_tutorial/base.html" %} {% load tethys_gizmos %} {% block app_content %}

Bokeh Integration Example

{{ script|safe }} {% endblock %} As you can see, the script context variable has been added to the app_content block. If you start tethys and go to the home page of this app you should see something like this: .. figure:: ../images/tutorial/bokeh_integration/bokeh_integration_1.png :width: 650px This is a simple Bokeh plot. We will now add the rest of the logic to make it an interactive plot. We will add a ``Slider`` widget. Then, we will create a callback function to modify the time-series plot based on the slider. Finally, we will add both our plot and slider to the document tree using a ``Column`` layout. 5. Modify the ``handler function`` from ``controller.py`` to look like this. .. code-block:: python from bokeh.models import ColumnDataSource, Slider from bokeh.layouts import column ... def home_handler(document): df = sea_surface_temperature.copy() source = ColumnDataSource(data=df) plot = figure(x_axis_type="datetime", y_range=(0, 25), y_axis_label="Temperature (Celsius)", height=500, width=800, title="Sea Surface Temperature at 43.18, -70.43") plot.line("time", "temperature", source=source) slider = Slider(start=0, end=30, value=0, step=1, title="Smoothing by N Days") def callback(attr, old, new): if new == 0: data = df else: data = df.rolling(f'{new}D').mean() source.data = dict(ColumnDataSource(data=data).data) slider.on_change("value", callback) document.add_root(column(slider, plot)) If you start tethys and go to the home page of this app you should see something like this: .. figure:: ../images/tutorial/bokeh_integration/bokeh_integration_2.png :width: 650px The ``Slider`` and ``Plot`` will appear in the order they were added to the ``Column`` layout. If the value of the ``Slider`` changes, the data in the ``Plot`` will reflect this change based on this expression: `data = df.rolling(f'{new}D').mean()`. Where `df` is the sample data and `new` is the new ``Slider`` value. 3. Handler Functions Using Param and Panel ========================================== ``Param`` is a Python library for providing parameters with dynamically generated values. One of the main advantages of ``Param`` is that parameters are provided using declarative programming. ``Panel``, on the other hand, is a visualization library for creating custom dashboards that rely on the use of widgets to render plots, images, and tables. These libraries can be used in combination with ``Bokeh Server`` to attain the same result of creating interactive tools within an app that are connected to Python objects. Given the depth of these libraries, the resulting code structure, and the level of difficulty for creating complex visualizations may be simplified. In this example we will build on top of the ``bokeh_tutorial`` app to demonstrate how to use ``Param`` and ``Panel`` in combination with ``bokeh Server``. This same example can be found in `Panel's documentation `_. 1. Install the ``param`` library by running the following with your Tethys environment activated: .. code-block:: bash conda install -c conda-forge panel param 2. Add the new dependencies to your :file:`install.yml` as follows so that the app will work when installed in a new environment: .. code-block:: yaml # This file should be committed to your app code. version: 1.0 # This should match the app - package name in your setup.py name: bokeh_tutorial requirements: # Putting in a skip true param will skip the entire section. Ignoring the option will assume it be set to False skip: false conda: channels: - conda-forge packages: - panel - param pip: post: 3. Add the following objects to a new file called ``param_model.py``. .. code-block:: python import param import panel as pn import numpy as np from bokeh.plotting import figure ... class Shape(param.Parameterized): radius = param.Number(default=1, bounds=(0, 1)) def __init__(self, **params): super(Shape, self).__init__(**params) self.figure = figure(x_range=(-1, 1), y_range=(-1, 1), width=500, height=500) self.renderer = self.figure.line(*self._get_coords()) def _get_coords(self): return [], [] def view(self): return self.figure class Circle(Shape): n = param.Integer(default=100, precedence=-1) def __init__(self, **params): super(Circle, self).__init__(**params) def _get_coords(self): angles = np.linspace(0, 2 * np.pi, self.n + 1) return (self.radius * np.sin(angles), self.radius * np.cos(angles)) @param.depends('radius', watch=True) def update(self): xs, ys = self._get_coords() self.renderer.data_source.data.update({'x': xs, 'y': ys}) class NGon(Circle): n = param.Integer(default=3, bounds=(3, 10), precedence=1) @param.depends('radius', 'n', watch=True) def update(self): xs, ys = self._get_coords() self.renderer.data_source.data.update({'x': xs, 'y': ys}) shapes = [NGon(name='NGon'), Circle(name='Circle')] class ShapeViewer(param.Parameterized): shape = param.ObjectSelector(default=shapes[0], objects=shapes) @param.depends('shape') def view(self): return self.shape.view() @param.depends('shape', 'shape.radius') def title(self): return '## %s (radius=%.1f)' % (type(self.shape).__name__, self.shape.radius) def panel(self): return pn.Column(self.title, self.view) The added classes depend on ``Bokeh``. The `Circle` and `NGon` classes depend on the `Shape` class, while the `ShapeViewer` allows the user to pick one of the two available shapes. 4. Add a ``handler function`` that uses the classes created in the previous step by adding the following code to ``controller.py``. .. code-block:: python import panel as pn from .param_model import ShapeViewer ... def shapes_handler(document): viewer = ShapeViewer() panel = pn.Row(viewer.param, viewer.panel()) panel.server_doc(document) 5. Add a ``controller function`` to pass the ``Panel`` object to a template and to link it with the ``handler`` created in the previous step. .. code-block:: python def shapes_with_panel(request): script = server_document(request.build_absolute_uri()) context = {'script': script} return render(request, "bokeh_tutorial/shapes.html", context) 6. Create a new ``UrlMap`` in ``app.py`` to link the new ``handler-controller pair`` to an endpoint. .. code-block:: python def url_maps(self): """ Add controllers """ UrlMap = url_map_maker(self.root_url) url_maps = ( UrlMap( name='home', url='bokeh-tutorial', controller='bokeh_tutorial.controllers.home', handler='bokeh_tutorial.controllers.home_handler', handler_type='bokeh' ), UrlMap( name='shapes', url='bokeh-tutorial/shapes', controller='bokeh_tutorial.controllers.shapes_with_panel', handler='bokeh_tutorial.controllers.shapes_handler', handler_type='bokeh' ), ) return url_maps 7. Add a new template to match the path rendered in the new ``controller`` from (c) (`bokeh_tutorial/shapes.html`). .. code-block:: html+django {% extends "bokeh_tutorial/base.html" %} {% load tethys_gizmos %} {% block header_buttons %}
{% endblock %} {% block app_content %}

Bokeh Integration Example using Param and Panel

{{ script|safe }} {% endblock %} 8. To add the new endpoint to the app navigation bar, go to the ``base.html`` template and replace the ``app_navigation`` block content with the code below. .. code-block:: html+django {% block app_navigation_items %} {% url 'bokeh_tutorial:home' as home_url %} {% url 'bokeh_tutorial:shapes' as shapes_url %}
  • Examples
  • Sea Surface
  • Shapes
  • {% endblock %} If you start tethys and go to the shapes endpoint of this app you should see something like this: .. figure:: ../images/tutorial/bokeh_integration/bokeh_integration_3.png :width: 650px 4. Solution =========== This concludes the ``Bokeh Integration`` tutorial. You can view the solution on GitHub at ``_ or clone it as follows: .. parsed-literal:: git clone https://github.com/tethysplatform/tethysapp-bokeh_tutorial.git cd tethysapp-bokeh_tutorial git checkout -b solution solution-|version|