Custom UI Tutorial

Objective: learn the basics of the Apache Airavata Django Portal and how to make both simple and complex customizations to the user interface.

Prerequisites

Tutorial attendees should have:

• a text editor or IDE for writing Python and web code. Any text editor or IDE will do but if you're looking for recommendations, I recommend Visual Studio Code.
• latest Python (current version as of this writing is 3.9.5)
• Git client
• Docker Desktop
• If you don‘t have Docker installed or can’t install it, you'll also need:
  • Node LTS,
  • and Yarn package manager.

Installing Python

Python 3.6-3.9 are supported, but I highly recommend you download and use Python 3.9

Download and install Python 3.9.

• (macOS/Windows): Download from https://www.python.org/downloads/
• (Linux): use your distribution's package manager to install Python 3.9

Verify you have installed Python 3.9:

=== “macOS/Linux”

    $ python3.9 --version
    Python 3.9.5

=== “Windows”

Open PowerShell then run:

    PS C:\Users\username> py --version
    Python 3.9.5

Installing Git

If you don't already have Git installed, see https://git-scm.com/downloads and follow the installation instructions for your platform.

Verify that you have installed Git:

=== “macOS/Linux”

Open a terminal

    $ git --version
    git version 2.26.0

The command should print "git version X.X". The version should be at least 2.0.

=== “Windows”

Open PowerShell then run:

    PS C:\Users\username> git --version
    git version 2.32.0.windows.2

The command should print "git version X.X". The version should be at least 2.0.

Installing Docker Desktop

Follow the instructions at Docker Desktop to install Docker Desktop.

Verify that you have installed Docker Desktop:

=== “macOS/Linux”

Open a terminal

    $ docker --version
    Docker version 20.10.7, build f0df350
    $ docker run hello-world
    Unable to find image 'hello-world:latest' locally
    latest: Pulling from library/hello-world
    109db8fad215: Pull complete
    Digest: sha256:df5f5184104426b65967e016ff2ac0bfcd44ad7899ca3bbcf8e44e4461491a9e
    Status: Downloaded newer image for hello-world:latest

    Hello from Docker!
    This message shows that your installation appears to be working correctly.
    ...


The command should print "Docker version X.X". As long as it is a recent
version, you should be fine.

=== “Windows”

Open PowerShell then run:

    PS C:\Users\username> docker --version
    Docker version 20.10.7, build f0df350
    PS C:\Users\username> docker run hello-world
    Unable to find image 'hello-world:latest' locally
    latest: Pulling from library/hello-world
    b8dfde127a29: Pull complete
    Digest: sha256:df5f5184104426b65967e016ff2ac0bfcd44ad7899ca3bbcf8e44e4461491a9e
    Status: Downloaded newer image for hello-world:latest

    Hello from Docker!
    This message shows that your installation appears to be working correctly.
    ...

The command should print "Docker version X.X". As long as it is a recent
version, you should be fine.

Installing Node.js LTS

Note: you only need to install Node.js LTS if you don't have Docker installed.

Follow the instructions on the Node.js downloads page to install Node.js LTS for your platform.

Verify that you have installed Node.js LTS:

=== “macOS/Linux”

Open a terminal

    $ node --version
    v14.17.1

The command should print "vX.X". The version should be at least v14.0.

=== “Windows”

Open PowerShell then run:

    PS C:\Users\username> node --version
    v14.17.3

The command should print "vX.X". The version should be at least v14.0.

Installing Yarn

Note: you only need to install Yarn if you don't have Docker installed. Yarn requires Node.js.

To install Yarn run

npm install -g yarn

Verify that you have installed Yarn:

=== “macOS/Linux”

Open a terminal

    $ yarn --version
    1.22.10

The command should print "X.X". The version should be 1.X.

=== “Windows”

Open PowerShell then run:

    PS C:\Users\username> yarn --version
    1.22.10

The command should print "X.X". The version should be 1.X.

Outline

• Introduction
• Presentation: Overview of Airavata and Django Portal
  • History of the Airavata UI and how did we get here
• Hands on: run a basic computational experiment in the Django portal
• Tutorial exercise: customize the input user interface for an application
• Tutorial exercise: Create a custom output viewer for an output file
• Tutorial exercise: Create a custom Django app
  • use the AiravataAPI JavaScript library for utilizing the backend Airavata API
  • develop a simple custom user interface for setting up and visualizing computational experiments

Hands on: run a Gaussian computational experiment in the Django portal

Log into testdrive.airavata.org

First, you‘ll need a user account. For the in person tutorial we’ll have a set of pre-created usernames and passwords to use. If you are unable to attend the in person tutorial or would otherwise like to create your own account, go to the Create Account page and select Sign in with existing institution credentials. This will take you to the CILogon institution selection page. If you don't find your institution listed here, go back to the Create Account page and fill out the form to create an account with a username, password, etc.

Once you have an account, log into the Airavata Testdrive portal.

After you‘ve logged in, an administrator can grant you access to run the Gaussian application. During the tutorial we’ll grant you access right away and let you know. If you're at the in person tutorial and using a pre-created username and password, you should already have all of the necessary authorizations.

When you log in for the first time you will see a list of applications that are available in this science gateway. Applications that you are not able to run are greyed out but the other ones you can run. Once you are granted access, refresh the page and you should now see that you the Gaussian16 application is not greyed out.

Submit a test job

From the dashboard, click on the Gaussian16 application. The page title is Create a New Experiment.

Here you can change the Experiment Name, add a description or select a different project if you have multiple projects.

We'll focus on the Application Inputs for this hands-on. The Gaussian application requires one input, an Input-File. The following is a preconfigured Gaussian input file. Download this to your local computer and then click the Browse button to upload the file:

• npentane12diol.inp

You can click on the file to take a quick look at the file in a popup window.

Now we'll select what account to charge and where to run this job. The Allocation field should already have Default selected. Under Compute Resource make sure you select Expanse.

Then click Save and Launch.

You should then be taken to the Experiment Summary page which will update as the job progresses. When the job finishes you'll be able to download the .log file which is the primary output file of the gaussian application.

We'll come back to this experiment later in the tutorial.

Tutorial exercise: customize the input user interface for an application

For this exercise we‘ll define an application based on the Computational Systems Biology Group’s eFindSite drug-binding site detection software. We'll use this application to demonstrate how to customize the user interface used for application inputs.

Basic application configuration

1. In the portal, click on the dropdown menu at the top right (currently Workspace is likely selected) and select Settings.

2. You should see the Application Catalog. Click on the New Application button.
3. For Application Name provide eFindSite-<your username>. Appending your username will allow you to distinguish your version of eFindSite from other users.
4. Click Save.
5. Click on the Interface tab.
6. This application has 4 command line inputs. We'll add them now. To add the first one, click on Add application input and provide the following information:
   • Name: Target ID
   • Type: STRING (which is the default)
   • Application Argument: -i
   • User Friendly Description: 3-10 alphanumerical characters.
   • Required: True
   • Required on Command Line: True

7. Add the next three application inputs in the same way, using the values in the table below:

Name	Type	Application Argument	Required	Required on Command Line
Target Structure	URI	-s	True	True
Screening libraries	STRING	-l	False	True
Visualization scripts	STRING	-v	False	True

(In Airavata, files are represented as URIs. When an application input has type URI it means that a file is needed for that input. From a UI point of view, this essentially means that the user will be able to upload a file for inputs of type URI.)

Normally we would also define the output files for this application, but for this exercise we are only interested in exploring the options available in customizing the application inputs and we won‘t actually run this application. We need to register a deployment to be able to invoke this application. An application deployment includes the details of how and where an application is installed on a compute resource. Since we won’t actually run this application, we'll just create a dummy deployment so that we can invoke it from the Workspace Dashboard.

8. Click Save at the bottom of the screen.
9. Click on the Deployments tab.
10. Click on the New Deployment button. Select the example-vc.jetstream-cloud.org compute resource in the drop down list and click OK.
11. For the Application Executable Path, provide the value /usr/bin/true. This is the only required field.
12. Click Save at the bottom of the screen.
13. Use the top level menu to go back to the Workspace. You should see your eFindSite application listed there.
14. Click on your eFindSite application.

If you see a form with the inputs that we registered for the application (Target ID, etc.) then you have successfully registered the application interface.

Improving the application input user interface

There are a few things to point out now:

• the Target ID input takes a string value, but only certain characters (alphanumeric) are allowed and the string value has a minimum and maximum allowed length.
• the Screening libraries and Visualization scripts only accept specific values. For example, one of the allowed values for Screening libraries is screen_drugbank

We can make this user interface more user friendly by providing more guidance in the application inputs' user interface. What we'll do:

• for Target ID (input #1) we'll provide validation feedback that verifies that the given value has an allowed length and only allowed characters.
• nothing to configure for Target Structure (input #2)
• for Screening libraries and Visualization scripts (inputs #3 and #4) we'll provide a list of labeled checkboxes for the user to select.

1. Go back to Settings and in the Application Catalog click on your eFindSite application.
2. Click on the Interface tab.
3. For Target ID (input #1), in the Advanced Input Field Modification Metadata box, add the following JSON configuration:

{
    "editor": {
        "validations": [
            {
                "type": "min-length",
                "value": 3
            },
            {
                "type": "max-length",
                "value": 10
            },
            {
                "message": "Target ID may only contain alphanumeric characters and underscores.",
                "type": "regex",
                "value": "^[a-zA-Z0-9_]+$"
            }
        ],
        "ui-component-id": "string-input-editor"
    }
}

It should look something like this:

This JSON configuration customizes the input editor in two ways:

• it adds 3 validations: min-length, max-length and a regex
• it sets the UI component of the input editor to be the string-input-editor (which is also the default)

4. Skipping past Target Structure (input #2) and on to Screening Libraries (input #3), set the Advanced Input Field Modification Metadata to:

{
    "editor": {
        "ui-component-id": "checkbox-input-editor",
        "config": {
            "options": [
                {
                    "text": "BindingDB",
                    "value": "screen_bindingdb"
                },
                {
                    "text": "ChEMBL (non-redundant, TC<0.8)",
                    "value": "screen_chembl_nr"
                },
                {
                    "text": "DrugBank",
                    "value": "screen_drugbank"
                },
                {
                    "text": "KEGG Compound",
                    "value": "screen_keggcomp"
                },
                {
                    "text": "KEGG Drug",
                    "value": "screen_keggdrug"
                },
                {
                    "text": "NCI-Open",
                    "value": "screen_nciopen"
                },
                {
                    "text": "RCSB PDB",
                    "value": "screen_rcsbpdb"
                },
                {
                    "text": "ZINC12 (non-redundant, TC<0.7)",
                    "value": "screen_zinc12_nr"
                }
            ]
        }
    }
}

This JSON configuration specifies a different UI component to use as the input editor, the checkbox-input-editor. It also provides a list of text/value pairs for the checkboxes; the values are what will be provided to the application as command line arguments.

5. Similarly for the Visualization scripts (input #4), provide the following JSON configuration:

{
    "editor": {
        "ui-component-id": "checkbox-input-editor",
        "config": {
            "options": [
                {
                    "text": "VMD",
                    "value": "visual_vmd"
                },
                {
                    "text": "PyMOL",
                    "value": "visual_pymol"
                },
                {
                    "text": "ChimeraX",
                    "value": "visual_chimerax"
                }
            ]
        }
    }
}

6. Click Save at the bottom of the page.
7. Now, go back to the Workspace and on the Dashboard click on your eFindSite application. The application inputs form should now reflect your changes.
8. Try typing an invalid character (for example, #) in Target ID. Also try typing in more than 10 alphanumeric characters. When an invalid value is provided the validation feedback informs the user of the problem so that the user can correct it.

Additional application input customizations

Other UI components are available:

• textarea
• radio buttons
• dropdown

We're working to provide a way for custom input editors to be added by the community, especially domain specific input editors. For example, a ball and stick molecule editor or a map view for selecting a bounding box of a region of interest.

Also you can define dependencies between application inputs and show or hide inputs based on the values of other inputs.

Tutorial exercise: Create a custom output viewer for an output file

By default, the Django portal provides a very simple view for output files that allows users to download the file to their local machine. However, it is possible to provide additional custom views for output files. Examples include:

• image (visualization)
• link (perhaps to another web application that can visualize the file)
• chart
• parameterized notebook

To be able to create a custom output viewer we‘ll need to write some Python code. First we’ll generate the custom Django app code.

Generate a custom Django app

We have a local develop environment created. Now we can start adding custom code. First, we'll create a custom Django app, which is the standard way to package a Django extension.

Create and activate a Python virtual environment

=== “macOS/Linux”

    $ cd $HOME
    $ python3.9 -m venv tutorial-env
    $ source tutorial-env/bin/activate
    (tutorial-env) $

=== “Windows”

    PS C:\Users\username> cd $HOME
    PS C:\Users\username> py -m venv tutorial-env
    PS C:\Users\username> .\tutorial-env\Scripts\Activate.ps1
    (tutorial-env) PS C:\Users\username>

Run the django app cookiecutter

1. Install the latest version of cookiecutter. Cookiecutter is a tool for generating project source code from a template.

pip install -U cookiecutter

2. Use cookiecutter to run the Airavata Django app template.

cookiecutter https://github.com/machristie/cookiecutter-airavata-django-app.git

You'll need to answer some questions. You can name it whatever you want, but to follow along with the tutorial, for project_name give Custom UI Tutorial App. For the rest of the questions, you can simply accept the defaults:

project_name [My Custom Django App]: Custom UI Tutorial App
project_slug [custom_ui_tutorial_app]:
project_short_description [Custom Django app with everything needed to be installed in the airavata-django-portal]:
app_config_class_name [CustomUiTutorialAppConfig]:
version [0.1.0]:

Setup local Django portal development environment

For running the local Django portal development environment, there are a few options:

• Docker: Run the portal as a Docker container. If you have Docker installed, this is the recommended option for the tutorial.
• Python: Install the portal dependencies (Python and Nodejs) and then run it directly on your computer. This is recommended when you don‘t or can’t have Docker installed. It is also what we recommend when you are developing a real custom django app extension.

Regardless of which approach you use, you'll need to get a config file for setting up a local development environment that has the same settings as Testdrive. Go to https://testdrive.airavata.org/admin/developers/ and download the settings_local.py file for local development. Move or copy it to the $HOME/custom_ui_tutorial_app/ directory.

=== “Docker (macOS/Linux/Windows)”

Note for **Windows** users, the following commands assume PowerShell.

1. Make sure you have
[Docker installed](https://www.docker.com/products/docker-desktop).
2. Run the following to create a Docker container called **custom-ui-tutorial**.

        cd $HOME/custom_ui_tutorial_app
        docker run -d --name custom-ui-tutorial -p 8000:8000 -v "${PWD}:/extensions" -v "${PWD}/settings_local.py:/code/django_airavata/settings_local.py" machristie/airavata-django-portal
3. Wait until the Docker container starts up. Go to <http://localhost:8000>
and when it loads and you see **Welcome to your new Wagtail site!**, then
you're ready to proceed to the next step.
4. Run the following to load the default set of CMS pages:

        docker exec custom-ui-tutorial python manage.py load_cms_data new_default_theme

Go to [http://localhost:8000](http://localhost:8000), click on **Login in**,
enter your username and password. On the dashboard you should see the your
experiments listed on the right hand side.

=== “Python (Windows)”

Verify that you have the following installed

-   Python 3.9
-   Node LTS
-   Yarn
-   Git

The following instructions assume that you start in your home directory, but you
could technically checkout and build the code anywhere.

1. Make sure that you have activated your `tutorial-env` virtual
environment. You should see `(tutorial-env)` at the beginning of the CMD
prompt. See the [virtual environment instructions if
needed](#create-and-activate-a-python-virtual-environment).

2. Clone the custom_ui_tutorial_app and airavata-django-portal repositories.

        (tutorial-env) PS C:\Users\username>cd $HOME
        (tutorial-env) PS C:\Users\username>git clone https://github.com/apache/airavata-django-portal.git

3. Install the airavata-django-portal dependencies.

        (tutorial-env) PS C:\Users\username>cd airavata-django-portal
        (tutorial-env) PS C:\Users\username\airavata-django-portal>pip install -U pip
        (tutorial-env) PS C:\Users\username\airavata-django-portal>pip install -r requirements.txt

4. Copy in the settings_local.py file.

        (tutorial-env) PS C:\Users\username\airavata-django-portal>copy ..\custom_ui_tutorial_app\settings_local.py django_airavata\

5. Run Django database migrations

        (tutorial-env) PS C:\Users\username\airavata-django-portal>python manage.py migrate

6. Load the default Wagtail CMS pages.

        (tutorial-env) PS C:\Users\username\airavata-django-portal>python manage.py load_cms_data new_default_theme

7. Build the JavaScript frontend code.

        (tutorial-env) PS C:\Users\username\airavata-django-portal>.\build_js.bat

   This last step can take a few minutes to complete.

=== “Python (macOS/Linux)”

Verify that you have the following installed

-   Python 3.9
-   Node LTS
-   Yarn
-   Git

The following instructions assume that you start in your home directory, but you
could technically checkout and build the code anywhere.

1. Make sure that you have activated your `tutorial-env` virtual
environment. You should see `(tutorial-env)` at the beginning of the shell
prompt. See the [virtual environment instructions if
needed](#create-and-activate-a-python-virtual-environment).

2. Clone the custom_ui_tutorial_app and airavata-django-portal repositories.

        (tutorial-env) $ cd $HOME
        (tutorial-env) $ git clone https://github.com/apache/airavata-django-portal.git

3. Install the airavata-django-portal dependencies.

        (tutorial-env) $ cd airavata-django-portal
        (tutorial-env) $ pip install -U pip
        (tutorial-env) $ pip install -r requirements.txt

4. Copy in the settings_local.py file.

        (tutorial-env) $ cp ../custom_ui_tutorial_app/settings_local.py django_airavata/

5. Run Django database migrations

        (tutorial-env) $ python manage.py migrate

6. Load the default Wagtail CMS pages.

        (tutorial-env) $ python manage.py load_cms_data new_default_theme

7. Build the JavaScript frontend code.

        (tutorial-env) $ ./build_js.sh

   This last step can take a few minutes to complete.

Create the custom output viewer

Now we'll also generate and implement a Gaussian Eigenvalues View provider.

1. We'll run another cookiecutter template to generate the output view provider code. First, change into the custom_ui_tutorial_app generated in the previous step:

=== “macOS/Linux”

    (tutorial-env) $ cd $HOME/custom_ui_tutorial_app

=== “Windows”

    (tutorial-env) PS C:\Users\username>cd $HOME\custom_ui_tutorial_app

2. Run the following cookiecutter template:

cookiecutter https://github.com/machristie/cookiecutter-airavata-django-output-view.git -f

You'll need to answer some questions again. For project_name give Gaussian Eigenvalues View. For custom_django_app_module_name, you need to provide the name of the Python module that was generated by cookiecutter-airavata-django-app, which for this tutorial is custom_ui_tutorial_app. For all of the other questions you can accept the default.

project_name [My Custom Output View]: Gaussian Eigenvalues View
project_slug [gaussian_eigenvalues_view]:
project_short_description [Gaussian Eigenvalues View generates data for an output view in the Airavata Django Portal]:
output_view_provider_class_name [GaussianEigenvaluesViewProvider]:
custom_django_app_module_name []: custom_ui_tutorial_app
output_views_directory_name [output_views]:
Select output_view_display_type:
1 - image
2 - link
3 - html
Choose from 1, 2, 3 [1]:
Select number_of_output_files:
1 - single (URI)
2 - multiple (URI_COLLECTION)
Choose from 1, 2 [1]:

3. This creates a custom output view provider, called GaussianEigenvaluesViewProvider, in custom_ui_tutorial_app/output_views/. Open $HOME/custom_ui_tutorial_app/custom_ui_tutorial_app/output_views/gaussian_eigenvalues_view.py in your editor and we‘ll look at the generated code. The cookiecutter template has generated a GaussianEigenvaluesViewProvider class with a method called generate_data. The generate_data method has some commented out code samples and links to further information. There is also guidance at the end for how to prepare the values expected in the returned dictionary. Let’s start filling in the implementation.

4. As a final result, the output_views/gaussian_eigenvalues_view.py file should have the following contents. I'll explain each part of this in the following steps, but you can go ahead and copy and paste the following into gaussian_eigenvalues_view.py:

import io
import os

import numpy as np
from cclib.parser import ccopen
from django.conf import settings
from matplotlib.figure import Figure

from airavata_django_portal_sdk import user_storage


class GaussianEigenvaluesViewProvider:
    display_type = 'image'
    name = "Gaussian Eigenvalues"

    def generate_data(self, request, experiment_output, experiment, output_file=None, **kwargs):

        # Parse output_file
        output_text = io.TextIOWrapper(output_file)
        gaussian = ccopen(output_text)
        data = gaussian.parse()
        data.listify()
        homo_eigenvalues = None
        lumo_eigenvalues = None
        if hasattr(data, 'homos') and hasattr(data, 'moenergies'):
            homos = data.homos[0] + 1
            moenergies = data.moenergies[0]
            if homos > 9 and len(moenergies) >= homos:
                homo_eigenvalues = [data.moenergies[0][homos - 1 - i] for i in range(1, 10)]
            if homos + 9 <= len(moenergies):
                lumo_eigenvalues = [data.moenergies[0][homos + i] for i in range(1, 10)]

        # Create plot
        fig = Figure()
        if homo_eigenvalues and lumo_eigenvalues:
            fig.suptitle("Eigenvalues")
            ax = fig.subplots(2, 1)
            ax[0].plot(range(1, 10), homo_eigenvalues, label='Homo')
            ax[0].set_ylabel('eV')
            ax[0].legend()
            ax[1].plot(range(1, 10), lumo_eigenvalues, label='Lumo')
            ax[1].set_ylabel('eV')
            ax[1].legend()
        else:
            ax = fig.subplots()
            ax.text(0.5, 0.5, "No applicable data", horizontalalignment='center',
                verticalalignment='center', transform=ax.transAxes)

        # Export plot as image buffer
        buffer = io.BytesIO()
        fig.savefig(buffer, format='png')
        image_bytes = buffer.getvalue()
        buffer.close()

        # return dictionary with image data
        return {
            'image': image_bytes,
            'mime-type': 'image/png'
        }

5. Let's take a look at the implementation. First we added some imports at the top:

import io
import os

import numpy as np
from cclib.parser import ccopen
from django.conf import settings
from matplotlib.figure import Figure

from airavata_django_portal_sdk import user_storage

6. Next we implemented the generate_data function. This function should return a dictionary with values that are expected for this display_type. For a display type of image, the required return values are image which should be a bytes array or file-like object with the image bytes and mime-type which should be the image's mime type. There implementation plots the eigenvalues of molecular orbital energies calculated by Gaussian and has three parts:

   1. Use the cclib library to parse the Gaussian log file. cclib is a Python computational chemistry library which is used to read the molecular orbital energies.
   2. Generate a plot using matplotlib.
   3. Save the plot as a PNG image into an in-memory array of bytes.

   Here's the generate_data function:

    def generate_data(self, request, experiment_output, experiment, output_file=None, **kwargs):
        # Parse output_file
        output_text = io.TextIOWrapper(output_file)
        gaussian = ccopen(output_text)
        data = gaussian.parse()
        data.listify()
        homo_eigenvalues = None
        lumo_eigenvalues = None
        if hasattr(data, 'homos') and hasattr(data, 'moenergies'):
            homos = data.homos[0] + 1
            moenergies = data.moenergies[0]
            if homos > 9 and len(moenergies) >= homos:
                homo_eigenvalues = [data.moenergies[0][homos - 1 - i] for i in range(1, 10)]
            if homos + 9 <= len(moenergies):
                lumo_eigenvalues = [data.moenergies[0][homos + i] for i in range(1, 10)]

        # Create plot
        fig = Figure()
        if homo_eigenvalues and lumo_eigenvalues:
            fig.suptitle("Eigenvalues")
            ax = fig.subplots(2, 1)
            ax[0].plot(range(1, 10), homo_eigenvalues, label='Homo')
            ax[0].set_ylabel('eV')
            ax[0].legend()
            ax[1].plot(range(1, 10), lumo_eigenvalues, label='Lumo')
            ax[1].set_ylabel('eV')
            ax[1].legend()
        else:
            ax = fig.subplots()
            ax.text(0.5, 0.5, "No applicable data", horizontalalignment='center',
                verticalalignment='center', transform=ax.transAxes)

        # Export plot as image buffer
        buffer = io.BytesIO()
        fig.savefig(buffer, format='png')
        image_bytes = buffer.getvalue()
        buffer.close()

        # return dictionary with image data
        return {
            'image': image_bytes,
            'mime-type': 'image/png'
        }

7. Now we need to register our output view provider with the package metadata so that the Django Portal will be able to discover it. The cookiecutter template already created this when it generated the gaussian_eigenvalues_view.py code. We can take a look and make sure it added an airavata.output_view_providers entry to the [options.entry_points] section in the $HOME/custom_ui_tutorial_app/setup.cfg file:

[options.entry_points]
airavata.djangoapp =
    custom_ui_tutorial_app = custom_ui_tutorial_app.apps:CustomUiTutorialAppConfig
airavata.output_view_providers =
    gaussian_eigenvalues_view = custom_ui_tutorial_app.output_views.gaussian_eigenvalues_view:GaussianEigenvaluesViewProvider

gaussian_eigenvalues_view is the output view provider id. custom_ui_tutorial_app.output_views.gaussian_eigenvalues_view is the module in which the GaussianEigenvaluesViewProvider output view provider class is found.

9. While we‘re looking at setup.cfg, let’s add our output view providers Python dependencies. Under install_requires add cclib, numpy and matplotlib, so that it looks like:

install_requires =
    django >= 2.2
    airavata-django-portal-sdk
    cclib
    numpy
    matplotlib

10. Now we need to install the custom_ui_tutorial_app package into the Django portal's virtual environment.

=== “Docker (macOS/Linux/Windows)”

    docker exec -w /extensions custom-ui-tutorial pip install -e .
    docker exec custom-ui-tutorial touch /code/django_airavata/wsgi.py

These commands:

1. install our custom django app package and its dependencies into the
container's Python environment, and
2. touches the wsgi.py to trigger a reload of the Django portal dev server.

=== “Python (Windows)”

    (tutorial-env) PS C:\Users\username\airavata-django-portal> cd $HOME\custom_ui_tutorial_app
    (tutorial-env) PS C:\Users\username\custom_ui_tutorial_app> pip install -e .
    (tutorial-env) PS C:\Users\username\custom_ui_tutorial_app> cd ..\airavata-django-portal
    (tutorial-env) PS C:\Users\username\airavata-django-portal> python manage.py runserver

=== “Python (macOS/Linux)”

    (tutorial-env) $ cd $HOME/custom_ui_tutorial_app
    (tutorial-env) $ pip install -e .
    (tutorial-env) $ cd ../airavata-django-portal
    (tutorial-env) $ python manage.py runserver

Use the GaussianEigenvaluesViewProvider with the Gaussian log output file

Back in the Django Portal, we'll make sure the application interface for Gaussian is configured to add the GaussianEigenvaluesViewProvider as an additional output view of the file.

1. Log into your local Django Portal instance at http://localhost:8000.
2. In the menu at the top, select Settings.
3. Click on the Gaussian16 application.
4. Click on the Interface tab.
5. Scroll down to the Output Field: Gaussian-Application-Output.
6. Verify that the following is in the Metadata section:

{
    "output-view-providers": ["gaussian_eigenvalues_view"]
}

It should look something like this:

7. Go back to the Workspace using the menu at the top.
8. Select your Gaussian16 experiment from the right sidebar.
9. For the .log output file there should be a dropdown menu allowing you to select an alternate view. Select Gaussian Eigenvalues. Now you should see the image generated by the custom output view provider.

(Optional) Interactive parameter

In additional to producing static visualizations, output view providers can declare interactive parameters that can be manipulated by the user. We can add a simple boolean interactive parameter to toggle the display of the matplotlib grid as an example.

1. Open $HOME/custom_ui_tutorial_app/custom_ui_tutorial_app/output_views/gaussian_eigenvalues_view.py. Change the generate_data function so that it has an additional show_grid parameter with a default value of False:

    def generate_data(self, request, experiment_output, experiment, output_file=None, show_grid=False, **kwargs):

2. Add the following .grid() lines to the matplotlib code:

...
            fig.suptitle("Eigenvalues")
            ax = fig.subplots(2, 1)
            ax[0].plot(range(1, 10), homo_eigenvalues, label='Homo')
            ax[0].set_ylabel('eV')
            ax[0].legend()
            ax[0].grid(show_grid)
            ax[1].plot(range(1, 10), lumo_eigenvalues, label='Lumo')
            ax[1].set_ylabel('eV')
            ax[1].legend()
            ax[1].grid(show_grid)
...

3. Change the resulting dictionary to have the special interactive property and declare the show_grid parameter:

...
        # return dictionary with image data
        return {
            'image': image_bytes,
            'mime-type': 'image/png'
            'interactive': [
                {'name': 'show_grid', 'value': show_grid}
            ]
        }

This will provider the user with a checkbox for manipulating the show_grid parameter. Every time the user changes it, the GaussianEigenvaluesViewProvider will be again invoked. It should look something like the following:

There are several more interactive parameter types and additional options. You can learn more about them in the custom output view provider documentation.

Tutorial exercise: Create a custom Django app

In this tutorial exercise we'll create a fully custom user interface that lives within the Django Portal.

What we're going to build is a very simple user interface that will:

• allow a user to pick a greeting in one of several languages
• submit a simple echo job to a batch scheduler to echo that greeting
• display the echoed greeting by displaying the STDOUT file produced by the job

This is an intentionally simple example to demonstrate the general principle of using custom REST APIs and UI to setup, execute and post-process/visualize the output of a computational experiment.

We've already registered the Echo application with the portal, meaning we registered its interface and on which compute resource it is deployed.

A Django application or app is a Python package that may include Django views, url mappings, models, etc. It‘s a way of creating a kind of plug-in that integrates with a Django server. We’ll create this custom user interface by developing a Django app that uses the Django framework as well as the Airavata Django Portal REST APIs and JS library.

Setting up the Django app

To start, we'll just create a simple “Hello World” page for the Django app and get it properly registered with the local Django Portal instance.

1. In the custom_ui_tutorial_app directory, open $HOME/custom_ui_tutorial_app/custom_ui_tutorial_app/templates/custom_ui_tutorial_app/home.html. Copy this file to hello.html in the same directory.

2. Change the title of the page, in the <h1> tag, to Hello World and save the file.

{% extends 'base.html' %}

{% block content %}
<div class="main-content-wrapper">
    <main class="main-content">
        <div class="container-fluid">
            <h1>Hello World</h1>
        </div>
    </main>
</div>
{% endblock content %}

3. Open the file $HOME/custom_ui_tutorial_app/custom_ui_tutorial_app/views.py and add the following hello_world view function at the end of the file:

@login_required
def hello_world(request):
    return render(request, "custom_ui_tutorial_app/hello.html")

This view will simply display the template created in the previous step.

4. Open the file $HOME/custom_ui_tutorial_app/custom_ui_tutorial_app/urls.py and add a URL mapping for of hello/ to the hello_world view function:

from django.urls import path

from . import views

app_name = 'custom_ui_tutorial_app'
urlpatterns = [
    path('home/', views.home, name='home'),
    path('hello/', views.hello_world, name='hello_world'),
]

This maps the /hello/ URL to the hello_world view.

5. Open the file $HOME/custom_ui_tutorial_app/custom_ui_tutorial_app/apps.py and update the fa_icon_class attribute and the url_home attribute to the CustomUiTutorialAppConfig class:

from django.apps import AppConfig


class CustomUiTutorialAppConfig(AppConfig):
    name = 'custom_ui_tutorial_app'
    label = name
    verbose_name = "Custom UI Tutorial App"
    fa_icon_class = "fa-comment"
    url_home = "custom_ui_tutorial_app:hello_world"

This the main metadata for this custom Django app. Besides the normal metadata that the Django framework expects, this also defines a display name (verbose_name) and an icon (fa_icon_class) to use for this custom app. The url_home attribute specifies the initial view that should be rendered when navigating to this app.

---

Now you should be able to log into the portal locally and see Custom UI Tutorial App in the drop down menu in the header (click on Workspace then you should see it in that menu).

Adding a list of “Hello” greetings

Now we'll create a REST endpoint in our custom Django app that will return greetings in several languages.

1. In the $HOME/custom_ui_tutorial_app/custom_ui_tutorial_app/views.py file, we add the following import:

from django.http import JsonResponse

2. Also we add the following view:

@login_required
def languages(request):
    return JsonResponse({'languages': [{
        'lang': 'French',
        'greeting': 'bonjour',
    }, {
        'lang': 'German',
        'greeting': 'guten tag'
    }, {
        'lang': 'Hindi',
        'greeting': 'namaste'
    }, {
        'lang': 'Japanese',
        'greeting': 'konnichiwa'
    }, {
        'lang': 'Swahili',
        'greeting': 'jambo'
    }, {
        'lang': 'Turkish',
        'greeting': 'merhaba'
    }]})

3. In $HOME/custom_ui_tutorial_app/custom_ui_tutorial_app/urls.py we add a url mapping for the languages view:

urlpatterns = [
    path('home/', views.home, name='home'),
    path('hello/', views.hello_world, name="home"),
    path('languages/', views.languages, name="languages"),
]

4. In $HOME/custom_ui_tutorial_app/custom_ui_tutorial_app/templates/custom_ui_tutorial_app/hello.html, add the lines between the STARTING HERE and ENDING HERE comments. This adds a <select> element to the template which will be used to display the greeting options:

...
<div class="main-content-wrapper">
    <main class="main-content">
        <div class="container-fluid">
            <h1>Hello World</h1>

            <!-- STARTING HERE -->
            <div class="card">
                <div class="card-header">
                    Run "echo" for different languages
                </div>
                <div class="card-body">
                    <select id="greeting-select"></select>
                    <button id="run-button" class="btn btn-primary">Run</button>
                </div>
            </div>
            <!-- ENDING HERE -->
        </div>
    </main>
</div>
...

5. The hello.html template already has the {% load static %} directive and a scripts block at the end. This will load the AiravataAPI JavaScript library which has utilities for interacting with the Django portal‘s REST API (which can also be used for custom developed REST endpoints) and model classes for Airavata’s data models. The utils.FetchUtils is used to load the languages REST endpoint.

   Add to hello.html the code between the STARTING HERE and ENDING HERE comments.

{% block scripts %}
<script src="{% static 'django_airavata_api/dist/airavata-api.js' %}"></script>
<script>
    const { models, services, session, utils } = AiravataAPI;

    // STARTING HERE
    utils.FetchUtils.get("/custom_ui_tutorial_app/languages/").then((data) => {
        data.languages.forEach((language) => {
            $("#greeting-select").append(
                `<option value="${language.greeting}">
                    ${language.lang} - "${language.greeting}"
                 </option>`
            );
        });
    });
    // ENDING HERE
</script>
{% endblock scripts %}

Now when you view the custom app at http://localhost:8000/custom_ui_tutorial_app/hello/ you should see a dropdown of greetings in several languages, like so:

Displaying a list of recent experiments

Now we‘ll use the AiravataAPI library to load the user’s recent experiments.

1. In $HOME/custom_ui_tutorial_app/custom_ui_tutorial_app/templates/custom_ui_tutorial_app/hello.html, add the following lines between the STARTING HERE and ENDING HERE comments. This adds table to display recent experiments to the bottom of hello.html:

...
            <div class="card">
                <div class="card-header">
                    Run "echo" for different languages
                </div>
                <div class="card-body">
                    <select id="greeting-select"></select>
                    <button id="run-button" class="btn btn-primary">Run</button>
                </div>
            </div>

            <!-- STARTING HERE -->
            <div class="card">
                <div class="card-header">
                    Experiments
                </div>
                <div class="card-body">
                    <button id="refresh-button" class="btn btn-secondary">Refresh</button>
                    <table class="table">
                        <thead>
                            <tr>
                                <th scope="col">Name</th>
                                <th scope="col">Application</th>
                                <th scope="col">Creation Time</th>
                                <th scope="col">Status</th>
                                <th scope="col">Output</th>
                            </tr>
                        </thead>
                        <tbody id="experiment-list">
                        </tbody>
                    </table>
                </div>
            </div>
            <!-- ENDING HERE -->

        </div>
    </main>
</div>
{% endblock content %}

2. Now we‘ll use the ExperimentSearchService to load the user’s most recent 5 Echo experiments and display them in the table. We add the following to the end of the scripts block in hello.html:

// ...
    // STARTING HERE
    const appInterfaceId = "Echo_23d67491-1bef-47bd-a0f5-faf069e09773";

    function loadExperiments() {

        return services.ExperimentSearchService
            .list({limit: 5,
                [models.ExperimentSearchFields.USER_NAME.name]: session.Session.username,
                [models.ExperimentSearchFields.APPLICATION_ID.name]: appInterfaceId,
            })
            .then(data => {
                $('#experiment-list').empty();
                data.results.forEach((exp, index) => {
                    $('#experiment-list').append(
                    `<tr>
                        <td>${exp.name}</td>
                        <td>${exp.executionId}</td>
                        <td>${exp.creationTime}</td>
                        <td>${exp.experimentStatus.name}</td>
                        <td id="output_${index}"></td>
                    </tr>`);
                });
        });
    }

    loadExperiments();
    $("#refresh-button").click(loadExperiments);
    // ENDING HERE

</script>

{% endblock scripts %}

The user interface should now look something like:

Submitting an Echo job

Now we‘ll use AiravataAPI to submit an Echo job. Let’s take a look at what we'll need to do make this work.

1. We'll need to add a click handler to the Run button that gets the selected greeting value:

$("#run-button").click((e) => {
    const greeting = $("#greeting-select").val();
});

2. There are a couple key pieces of information that are needed to submit a computational experiment. We can use the REST API to find these. The application we want to use is called Echo and it has id Echo_37eb38ac-74c8-4aa4-a037-c656ab5bc6b8. We can browse the API for this application using: https://testdrive.airavata.org/api/applications/Echo_37eb38ac-74c8-4aa4-a037-c656ab5bc6b8/. First, we need the Application Interface for the application, which defines the inputs and outputs of the application. We can get its id by following the link to applicationInterface: https://testdrive.airavata.org/api/applications/Echo_37eb38ac-74c8-4aa4-a037-c656ab5bc6b8/application_interface/. We'll create an Experiment instance from the Application Interface definition:

const loadAppInterface = services.ApplicationInterfaceService.retrieve({
    lookup: appInterfaceId,
});

3. Second, we need to know where and how the application is deployed. We could let the user then pick where they want to run this application. For this exercise we're going to hard code the resource and the application deployment that will be used for executing the application, but we still need the application deployment information so we can get default values for the application that can be used when submitting the job to that scheduler. The application deployment id we get from https://testdrive.airavata.org/api/applications/Echo_37eb38ac-74c8-4aa4-a037-c656ab5bc6b8/application_deployments/.

const appDeploymentId =
    "example-vc.jetstream-cloud.org_Echo_37eb38ac-74c8-4aa4-a037-c656ab5bc6b8";
const loadQueues = services.ApplicationDeploymentService.getQueues({
    lookup: appDeploymentId,
});

4. We also need to know a few other pieces of information, like the id of the compute resource which https://testdrive.airavata.org/api/applications/Echo_37eb38ac-74c8-4aa4-a037-c656ab5bc6b8/application_deployments/ also provides with the value computeHostId. The queue name we can get from following the link from the deployment to the queues: https://testdrive.airavata.org/api/application-deployments/example-vc.jetstream-cloud.org_Echo_37eb38ac-74c8-4aa4-a037-c656ab5bc6b8/queues/. Here we see that the queueName is cloud. We also need the account to use to submit the job and that is specified via a “Group Resource Profile”. https://testdrive.airavata.org/api/group-resource-profiles/ lists profiles you have access to and the compute resources each profile can use for job submission. We‘ll use the tutorial reservation one. Finally, experiments are organized by projects so we’ll also load the user‘s most recently used project which is stored in the user’s WorkspacePreferences:

const resourceHostId =
    "example-vc.jetstream-cloud.org_794fd026-101a-46af-8868-5d7a86f813a1";
const queueName = "cloud";
const groupResourceProfileId = "fc245311-a7d1-41af-b8ae-a4142989c9a1";
const loadWorkspacePrefs = services.WorkspacePreferencesService.get();

5. Once we have all of this information we can then create an Experiment object then save and launch it. Here's the complete click handler. We add the following to the end of the scripts block in hello.html:

// ...

// STARTING HERE
$("#run-button").click((e) => {
    const greeting = $("#greeting-select").val();
    const loadAppInterface = services.ApplicationInterfaceService.retrieve({
        lookup: appInterfaceId,
    });
    const appDeploymentId =
        "example-vc.jetstream-cloud.org_Echo_37eb38ac-74c8-4aa4-a037-c656ab5bc6b8";
    const loadQueues = services.ApplicationDeploymentService.getQueues({
        lookup: appDeploymentId,
    });
    const resourceHostId =
        "example-vc.jetstream-cloud.org_794fd026-101a-46af-8868-5d7a86f813a1";
    const queueName = "cloud";
    const groupResourceProfileId = "fc245311-a7d1-41af-b8ae-a4142989c9a1";
    const loadWorkspacePrefs = services.WorkspacePreferencesService.get();
    Promise.all([loadAppInterface, loadWorkspacePrefs, loadQueues])
        .then(([appInterface, workspacePrefs, queues]) => {
            const experiment = appInterface.createExperiment();
            experiment.experimentName = "Echo " + greeting;
            experiment.projectId = workspacePrefs.most_recent_project_id;
            const cloudQueue = queues.find((q) => q.queueName === queueName);
            experiment.userConfigurationData.groupResourceProfileId =
                groupResourceProfileId;
            experiment.userConfigurationData.computationalResourceScheduling.resourceHostId =
                resourceHostId;
            experiment.userConfigurationData.computationalResourceScheduling.totalCPUCount =
                cloudQueue.defaultCPUCount;
            experiment.userConfigurationData.computationalResourceScheduling.nodeCount =
                cloudQueue.defaultNodeCount;
            experiment.userConfigurationData.computationalResourceScheduling.wallTimeLimit =
                cloudQueue.defaultWalltime;
            experiment.userConfigurationData.computationalResourceScheduling.queueName =
                queueName;
            // Copy the selected greeting to the value of the first input
            experiment.experimentInputs[0].value = greeting;

            return services.ExperimentService.create({ data: experiment });
        })
        .then((exp) => {
            return services.ExperimentService.launch({
                lookup: exp.experimentId,
            });
        });
});
// ENDING HERE

</script>

{% endblock scripts %}

Now that we can launch the experiment we can go ahead and give it a try.

You can also try this out in the production deployment at https://testdrive.airavata.org/custom_ui_tutorial_app/hello/.

Displaying the experiment output

Instead of simply reporting the status of the job we would also like to do something with the output. The STDOUT of the Echo job has a format like the following:

bonjour

We'll read the STDOUT file and display that in our experiment listing table.

1. What we need to do is get the identifier for the experiment's STDOUT file. In Airavata, this identifier is called the Data Product ID. Once we have that we can get the DataProduct object which has the files metadata, including a downloadURL. For each exp we can use the FullExperimentService to get these details like so:

if (exp.experimentStatus === models.ExperimentState.COMPLETED) {
    services.FullExperimentService.retrieve({ lookup: exp.experimentId }).then(
        (fullDetails) => {
            const stdoutDataProductId =
                fullDetails.experiment.experimentOutputs.find(
                    (o) => o.name === "Echo-STDOUT"
                ).value;
            const stdoutDataProduct = fullDetails.outputDataProducts.find(
                (dp) => dp.productUri === stdoutDataProductId
            );
            if (stdoutDataProduct && stdoutDataProduct.downloadURL) {
                return fetch(stdoutDataProduct.downloadURL, {
                    credentials: "same-origin",
                }).then((result) => result.text());
            }
        }
    );
}

2. Then we'll simply display the value in the table.

if (exp.experimentStatus === models.ExperimentState.COMPLETED) {
    services.FullExperimentService.retrieve({ lookup: exp.experimentId })
        .then((fullDetails) => {
            const stdoutDataProductId =
                fullDetails.experiment.experimentOutputs.find(
                    (o) => o.name === "Echo-STDOUT"
                ).value;
            const stdoutDataProduct = fullDetails.outputDataProducts.find(
                (dp) => dp.productUri === stdoutDataProductId
            );
            if (stdoutDataProduct && stdoutDataProduct.downloadURL) {
                return fetch(stdoutDataProduct.downloadURL, {
                    credentials: "same-origin",
                }).then((result) => result.text());
            }
        })
        .then((text) => {
            $(`#output_${index}`).text(text);
        });
}

3. To enable this, add the lines between the STARTING HERE and ENDING HERE comments to the loadExperiments function. Here's the update to the loadExperiments function:

function loadExperiments() {
    return services.ExperimentSearchService.list({
        limit: 5,
        [models.ExperimentSearchFields.USER_NAME.name]:
            session.Session.username,
        [models.ExperimentSearchFields.APPLICATION_ID.name]: appInterfaceId,
    }).then((data) => {
        $("#experiment-list").empty();
        data.results.forEach((exp, index) => {
            $("#experiment-list").append(
                `<tr>
                            <td>${exp.name}</td>
                            <td>${exp.executionId}</td>
                            <td>${exp.creationTime}</td>
                            <td>${exp.experimentStatus.name}</td>
                            <td id="output_${index}"></td>
                        </tr>`
            );

            // STARTING HERE
            // If experiment has finished, load full details, then parse the stdout file
            if (exp.experimentStatus === models.ExperimentState.COMPLETED) {
                services.FullExperimentService.retrieve({
                    lookup: exp.experimentId,
                })
                    .then((fullDetails) => {
                        const stdoutDataProductId =
                            fullDetails.experiment.experimentOutputs.find(
                                (o) => o.name === "Echo-STDOUT"
                            ).value;
                        const stdoutDataProduct =
                            fullDetails.outputDataProducts.find(
                                (dp) => dp.productUri === stdoutDataProductId
                            );
                        if (
                            stdoutDataProduct &&
                            stdoutDataProduct.downloadURL
                        ) {
                            return fetch(stdoutDataProduct.downloadURL, {
                                credentials: "same-origin",
                            }).then((result) => result.text());
                        }
                    })
                    .then((text) => {
                        $(`#output_${index}`).text(text);
                    });
            }
            // ENDING HERE
        });
    });
}

Resources

You can browser the final version of the code at https://github.com/machristie/custom_ui_tutorial_app.

Airavata API

The Django portal provides a REST API bridge to the backend Airavata API. So it's helpful to look at what is available in the backend Airavata API. See the Apache Airavata API docs

To see what is in the AiravataAPI JavaScript library, take a look at it's index.js file in the airavata-django-portal repo.

Airavata Gateway Hosting

SciGaP provides free Airavata Gateways hosting services. Log in or create an account at scigap.org to request gateway hosting.