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Posted to commits@airavata.apache.org by ma...@apache.org on 2019/09/18 17:31:57 UTC
[airavata-django-portal] branch master updated: Updated tutorial
instructions for gateways19 deployment
This is an automated email from the ASF dual-hosted git repository.
machristie pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/airavata-django-portal.git
The following commit(s) were added to refs/heads/master by this push:
new 56c0859 Updated tutorial instructions for gateways19 deployment
56c0859 is described below
commit 56c085935872ad144fe86b31ac7cd917bb345270
Author: Marcus Christie <ma...@apache.org>
AuthorDate: Wed Sep 18 13:31:47 2019 -0400
Updated tutorial instructions for gateways19 deployment
---
docs/tutorial/data/npentane12diol.inp | 29 +++
docs/tutorial/data/oxygen.inp | 11 --
docs/tutorial/gateways2019_tutorial.md | 327 ++++++++++++++++-----------------
3 files changed, 186 insertions(+), 181 deletions(-)
diff --git a/docs/tutorial/data/npentane12diol.inp b/docs/tutorial/data/npentane12diol.inp
new file mode 100644
index 0000000..ed97da3
--- /dev/null
+++ b/docs/tutorial/data/npentane12diol.inp
@@ -0,0 +1,29 @@
+%nproc=24
+%mem=3000MB
+%Chk=npentane12diol
+#P RB3LYP/cc-pVDZ GFInput GFPrint Iop(6/7=3) Opt Freq
+
+ npentane12diol #P RB3LYP/cc-pVDZ GFInput GFPrint Iop(6/7=3) Opt Freq
+
+0 1
+C -2.2355789473684213 0.26257894736842113 -0.4898947368421052
+C -1.0085789473684212 -0.4044210526315789 0.14410526315789476
+C 0.28942105263157875 0.3595789473684211 -0.16089473684210526
+C 1.5214210526315788 -0.2264210526315789 0.5341052631578947
+H -2.325578947368421 1.3245789473684213 -0.14489473684210524
+H -2.1215789473684215 0.28157894736842115 -1.6008947368421054
+H -1.1595789473684213 -0.3864210526315789 1.2511052631578947
+H 0.17242105263157875 1.419578947368421 0.17810526315789477
+H 1.3674210526315789 -0.2114210526315789 1.6411052631578946
+C 2.799421052631579 0.541578947368421 0.21310526315789477
+H 2.710421052631579 1.6065789473684209 0.5381052631578948
+H 3.0054210526315788 0.5135789473684211 -0.8838947368421053
+O -3.425578947368421 -0.45042105263157894 -0.17989473684210522
+H -3.683578947368421 -0.2094210526315789 0.7711052631578947
+H 3.662421052631579 0.07757894736842108 0.7481052631578948
+H 1.6674210526315787 -1.2874210526315788 0.21810526315789475
+O -0.9175789473684213 -1.7714210526315788 -0.2518947368421052
+H 0.46242105263157873 0.3665789473684211 -1.2658947368421054
+H -0.7805789473684213 -1.806421052631579 -1.2588947368421053
+
+
diff --git a/docs/tutorial/data/oxygen.inp b/docs/tutorial/data/oxygen.inp
deleted file mode 100644
index 4d252a5..0000000
--- a/docs/tutorial/data/oxygen.inp
+++ /dev/null
@@ -1,11 +0,0 @@
-#P TEST STO-3G COMPLEX pop=full scf=tight
-
-Gaussian Test Job 01
-SINGLET DELTA STO-3G//STO-3G DIOXYGEN
-
- 0 1
- O
- O 1 R
-
-R 1.220
-
diff --git a/docs/tutorial/gateways2019_tutorial.md b/docs/tutorial/gateways2019_tutorial.md
index dc20ca2..b716381 100644
--- a/docs/tutorial/gateways2019_tutorial.md
+++ b/docs/tutorial/gateways2019_tutorial.md
@@ -28,37 +28,41 @@ We'll install Python and Node.js as part of the tutorial.
### Create a portal user account
-First, you'll need a user account. Go to the
-[Create Account](https://pearc19.scigap.org/auth/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.
+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](https://gateways19.scigap.org/auth/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.
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.
+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 _Gaussian_ application is not
+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 **Gaussian** application. The page title is
+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 is you have another project if you have multiple projects.
+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 are preconfigured
-Gaussian input files. Download one of these to your laptop and then click the
-**Browse** button to upload the file:
+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:
-- [oxygen.inp](./data/oxygen.inp)
+- [npentane12diol.inp](./data/npentane12diol.inp)
You can click on **View File** to take a quick look at the file.
@@ -70,7 +74,9 @@ 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 _Gaussian_.
+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
@@ -136,7 +142,7 @@ Dashboard.
13. 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 register the application
+(_Target ID_, etc.) then you have successfully registered the application
interface.
### Improving the application input user interface
@@ -323,57 +329,42 @@ use as a development environment.
1. Make sure you have Python 3.6+ installed. See
<https://www.python.org/downloads/> for downloadable packages or use your
system's package manager.
-2. You'll also need npm 6.4.1+ to build the JavaScript frontend code. Please
- install
- [the most recent LTS version of Node.js](https://nodejs.org/en/download/) or
- use your system's package manager.
-3. Clone the airavata-django-portal project and create a virtual environment.
-
-```bash
-git clone https://github.com/apache/airavata-django-portal.git
-cd airavata-django-portal
-python3 -m venv venv
-source venv/bin/activate
-pip install -r requirements.txt
-```
-
-4. Now we'll clone another repository that has some supporting files for this
- tutorial. Change into the parent directory and clone
- <https://github.com/machristie/gateways19-tutorial>
+2. Now we'll clone a repository that has some supporting files for this
+ tutorial. Change to a directory on your system where you will keep the
+ tutorial files and clone <https://github.com/machristie/gateways19-tutorial>
```bash
-cd ..
git clone https://github.com/machristie/gateways19-tutorial.git
```
-5. Copy the `settings_local.py` file from this repo into the Django portal repo:
+3. For the sake of convenience, a zip file of the airavata-django-portal code
+ was created for this tutorial. Unzip the airavata-django-portal code into the
+ parent directory.
```bash
-cp gateways19-tutorial/settings_local.py airavata-django-portal/django_airavata/
+unzip gateways19-tutorial/airavata-django-portal.zip
```
-6. Back in the Django portal repo we'll run the Django migrate command.
+4. Create a virtual environment.
```bash
-cd airavata-django-portal
-python manage.py migrate
+python3 -m venv venv
```
-7. Load a starting set of CMS pages for the portal
+5. Activate the virtual environment.
-```bash
-python manage.py load_default_gateway
+```
+source venv/bin/activate
```
-8. Build the JavaScript sources for the portal. This one will take some time to
- complete.
+6. Install the airavata-django-portal dependencies in the virtual environment.
-```bash
-./build_js.sh
+```
+cd airavata-django-portal
+pip install -r requirements.txt
```
-Once the build finishes we can start the Django server and log in and see our
-experiments.
+7. We can now start the Django server and log in and see our experiments.
```bash
export OAUTHLIB_INSECURE_TRANSPORT=1
@@ -384,13 +375,16 @@ 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.
+After confirming you can log in, go ahead and shutdown the local Django server
+for now by typing `Control-C` in the console.
+
### Setup the custom output viewer package
1. Change back into the _gateways19-tutorial_ directory and install it into the
Django portal's virtual environment. Make sure you still have the Django
portal's virtual environment activated; your terminal prompt should start
with `(venv)`. If the Django portal virtual environment isn't activated, see
- step 3 in the previous section. We'll also use pip to install the output
+ step 5 in the previous section. We'll also use pip to install the output
viewer's dependencies.
```bash
@@ -399,8 +393,9 @@ pip install -r requirements.txt
python setup.py develop
```
-2. Implement the GaussianEigenvaluesViewProvider in output_views.py. First we'll
- add some imports
+2. Implement the GaussianEigenvaluesViewProvider in
+ `gateways19-tutorial/gateways19_tutorial/output_views.py`. First we'll add
+ some imports
```python
import io
@@ -430,48 +425,48 @@ class GaussianEigenvaluesViewProvider:
which should be the image's mime type. Here's the `generate_data` function:
```python
-def generate_data(self, request, experiment_output, experiment, output_file=None):
- # Parse output_file
- gaussian = ccopen(output_file)
- 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'
- }
+ def generate_data(self, request, experiment_output, experiment, output_file=None):
+ # Parse output_file
+ gaussian = ccopen(output_file)
+ 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'
+ }
```
This plots the eigenvalues of molecular orbital energies calculated by Gaussian.
@@ -582,24 +577,29 @@ gaussian-eigenvalues-plot = gateways19_tutorial.output_views:GaussianEigenvalues
```bash
# Activate the airavata-django-portal virtual environment if not already activated
-cd ../airavata-django-portal
-source venv/bin/activate
-cd ../gateways19-tutorial
+source ../venv/bin/activate
python setup.py develop
```
### Use the GaussianEigenvaluesViewProvider with the Gaussian log output file
-Back in the Django Portal, we'll update the application interface for Gaussian
-to add the GaussianEigenvaluesViewProvider as an additional output view of the
-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. Start the local Django server again.
-1. Log into your local Django Portal instance.
-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. Add the following in the _Metadata_ section:
+```
+cd ../airavata-django-portal/
+python manage.py runserver
+```
+
+2. Log into your local Django Portal instance.
+3. In the menu at the top, select **Settings**.
+4. Click on the **Gaussian16** application.
+5. Click on the **Interface** tab.
+6. Scroll down to the _Output Field: Gaussian-Application-Output_.
+7. Verify that the following is in the _Metadata_ section:
```json
{
@@ -611,15 +611,16 @@ It should look something like this:
-7. Click **Save**.
8. Go back to the **Workspace** using the menu at the top.
-9. Select your Gaussian16 experiment.
+9. Select your Gaussian16 experiment from the right sidebar.
10. 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.
+11. Go ahead and shutdown the Django server again.
+
## Tutorial exercise: Create a custom Django app
In this tutorial exercise we'll create a fully custom user interface that lives
@@ -641,7 +642,7 @@ output of a computational experiment.
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 `gateways19-tutorial`, create a file with the path
+1. In the `gateways19-tutorial` directory, create a file with the path
`gateways19_tutorial/templates/gateways19_tutorial/hello.html` with the
following contents:
@@ -673,6 +674,10 @@ class Gateways19TutorialAppConfig(AppConfig):
fa_icon_class = "fa-comment"
```
+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.
+
3. Create a file with the path `gateways19_tutorial/views.py` with the following
contents:
@@ -726,10 +731,9 @@ gateways19_tutorial = gateways19_tutorial.apps:Gateways19TutorialAppConfig
environment is activated and run:
```bash
-# Activate the airavata-django-portal virtual environment if not already activated
-cd ../airavata-django-portal
-source venv/bin/activate
cd ../gateways19-tutorial
+# Activate the airavata-django-portal virtual environment if not already activated
+source ../venv/bin/activate
python setup.py develop
```
@@ -810,13 +814,18 @@ urlpatterns = [
<!-- ... --->
```
-5. We'll also add a `scripts` block to the end of `hello.html`. 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. Then the
- `utils.FetchUtils` is used to load the languages REST endpoint.
+5. We'll also add the `{% load static %}` directive and then a `scripts` block
+ to the end of `hello.html`. 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. Then the `utils.FetchUtils` is used to load the
+ languages REST endpoint.
```xml
+{% extends 'base.html' %}
+
+{% load static %}
+
<!-- ... -->
{% endblock content %}
@@ -894,7 +903,7 @@ Now we'll use the `AiravataAPI` library to load the user's recent experiments.
```javascript
// ...
- const appInterfaceId = "Echo_3f480d1f-ea86-4018-94bb-015423d66a1c";
+ const appInterfaceId = "Echo_4be0e1b0-24c5-417d-8d57-a695c2890a05";
function loadExperiments() {
@@ -963,7 +972,7 @@ const loadAppInterface = services.ApplicationInterfaceService.retrieve({
```javascript
const appDeploymentId =
- "bigred2.uits.iu.edu_Echo_19dc358d-d241-43d8-918c-f5a21a3b0845";
+ "pearc19-sgrc-cluster.jetstream-cloud.org_Echo_b0e05d53-cace-420b-b280-3626e5e1c94b";
const loadQueues = services.ApplicationDeploymentService.getQueues({
lookup: appDeploymentId
});
@@ -976,9 +985,9 @@ const loadQueues = services.ApplicationDeploymentService.getQueues({
```javascript
const resourceHostId =
- "bigred2.uits.iu.edu_ac140dca-3c88-46d8-b9ed-875d96ea6908";
-const queueName = "cpu";
-const groupResourceProfileId = "6a642772-15fd-4d10-a847-8aef89b71830";
+ "pearc19-sgrc-cluster.jetstream-cloud.org_cf5b3c5d-f524-4c66-883a-03662caa8178";
+const queueName = "cloud";
+const groupResourceProfileId = "573573a8-8084-459f-9a4a-138ab835b7b3";
const loadWorkspacePrefs = services.WorkspacePreferencesService.get();
```
@@ -991,30 +1000,30 @@ $("#run-button").click(e => {
const loadAppInterface = services.ApplicationInterfaceService.retrieve({
lookup: appInterfaceId
});
- const loadWorkspacePrefs = services.WorkspacePreferencesService.get();
- const resourceHostId =
- "bigred2.uits.iu.edu_ac140dca-3c88-46d8-b9ed-875d96ea6908";
const appDeploymentId =
- "bigred2.uits.iu.edu_Echo_19dc358d-d241-43d8-918c-f5a21a3b0845";
- const queueName = "cpu";
- const groupResourceProfileId = "6a642772-15fd-4d10-a847-8aef89b71830";
+ "pearc19-sgrc-cluster.jetstream-cloud.org_Echo_b0e05d53-cace-420b-b280-3626e5e1c94b";
const loadQueues = services.ApplicationDeploymentService.getQueues({
lookup: appDeploymentId
});
+ const resourceHostId =
+ "pearc19-sgrc-cluster.jetstream-cloud.org_cf5b3c5d-f524-4c66-883a-03662caa8178";
+ const queueName = "cloud";
+ const groupResourceProfileId = "573573a8-8084-459f-9a4a-138ab835b7b3";
+ 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 cpuQueue = queues.find(q => q.queueName === queueName);
+ const cloudQueue = queues.find(q => q.queueName === queueName);
experiment.userConfigurationData.groupResourceProfileId = groupResourceProfileId;
experiment.userConfigurationData.computationalResourceScheduling.resourceHostId = resourceHostId;
experiment.userConfigurationData.computationalResourceScheduling.totalCPUCount =
- cpuQueue.defaultCPUCount;
+ cloudQueue.defaultCPUCount;
experiment.userConfigurationData.computationalResourceScheduling.nodeCount =
- cpuQueue.defaultNodeCount;
+ cloudQueue.defaultNodeCount;
experiment.userConfigurationData.computationalResourceScheduling.wallTimeLimit =
- cpuQueue.defaultWalltime;
+ cloudQueue.defaultWalltime;
experiment.userConfigurationData.computationalResourceScheduling.queueName = queueName;
// Copy the selected greeting to the value of the first input
experiment.experimentInputs[0].value = greeting;
@@ -1037,27 +1046,19 @@ so that Airavata could SCP the file back to our local instance). But this custom
Django app is also deployed in the hosted tutorial Django instance so you can
run it there to verify it works.
-### Parsing the experiment output
+### 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:
```
-stdout [Echo bonjour]
-Echoed_Output=bonjour
-Job Cleanup ran on Fri Jul 19 12:34:39 EDT 2019
-===============================
-submit_args : submit_args = /N/dc2/scratch/cgateway/gta-work-dirs/PROCESS_3db6a570-f150-4e01-b659-d422c5e38b32/job_349221662.pbs
-NIDS : 5
-NID Placement: 5/4
+bonjour
```
-The value echoed is displayed as a value of the `Echoed_Output` variable on the
-second line of the output. We'll parse that value out and display that in our
-experiment listing table.
+We'll read the STDOUT file and display that in our experiment listing table.
-1. What we need to do is get identifier for the experiment's STDOUT file. In
+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
@@ -1068,7 +1069,7 @@ if (exp.experimentStatus === models.ExperimentState.COMPLETED) {
services.FullExperimentService.retrieve({ lookup: exp.experimentId }).then(
fullDetails => {
const stdoutDataProductId = fullDetails.experiment.experimentOutputs.find(
- o => o.name === "Standard Out"
+ o => o.name === "Echo-Standard-Out"
).value;
const stdoutDataProduct = fullDetails.outputDataProducts.find(
dp => dp.productUri === stdoutDataProductId
@@ -1083,14 +1084,14 @@ if (exp.experimentStatus === models.ExperimentState.COMPLETED) {
}
```
-2. Then to parse it we need simply apply a regular expression to get the value:
+2. Then we'll simply display the value in the table.
```javascript
if (exp.experimentStatus === models.ExperimentState.COMPLETED) {
services.FullExperimentService.retrieve({ lookup: exp.experimentId })
.then(fullDetails => {
const stdoutDataProductId = fullDetails.experiment.experimentOutputs.find(
- o => o.name === "Standard Out"
+ o => o.name === "Echo-Standard-Out"
).value;
const stdoutDataProduct = fullDetails.outputDataProducts.find(
dp => dp.productUri === stdoutDataProductId
@@ -1102,11 +1103,7 @@ if (exp.experimentStatus === models.ExperimentState.COMPLETED) {
}
})
.then(text => {
- const regex = /Echoed_Output=(.*)$/m;
- const result = regex.exec(text);
- if (result) {
- $(`#output_${index}`).text(result[1]);
- }
+ $(`#output_${index}`).text(text);
});
}
```
@@ -1118,13 +1115,7 @@ if (exp.experimentStatus === models.ExperimentState.COMPLETED) {
```javascript
const FAKE_STDOUT = `
-stdout [Echo bonjour]
-Echoed_Output=bonjour
-Job Cleanup ran on Fri Jul 19 12:34:39 EDT 2019
-===============================
-submit_args : submit_args = /N/dc2/scratch/cgateway/gta-work-dirs/PROCESS_3db6a570-f150-4e01-b659-d422c5e38b32/job_349221662.pbs
-NIDS : 5
-NID Placement: 5/4
+bonjour
`;
function loadExperiments() {
@@ -1138,12 +1129,12 @@ function loadExperiments() {
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>`
+ <td>${exp.name}</td>
+ <td>${exp.executionId}</td>
+ <td>${exp.creationTime}</td>
+ <td>${exp.experimentStatus.name}</td>
+ <td id="output_${index}"></td>
+ </tr>`
);
// If experiment has finished, load full details, then parse the stdout file
if (exp.experimentStatus === models.ExperimentState.COMPLETED) {
@@ -1152,7 +1143,7 @@ function loadExperiments() {
})
.then(fullDetails => {
const stdoutDataProductId = fullDetails.experiment.experimentOutputs.find(
- o => o.name === "Standard Out"
+ o => o.name === "Echo-Standard-Out"
).value;
const stdoutDataProduct = fullDetails.outputDataProducts.find(
dp => dp.productUri === stdoutDataProductId
@@ -1170,11 +1161,7 @@ function loadExperiments() {
}
})
.then(text => {
- const regex = /Echoed_Output=(.*)$/m;
- const result = regex.exec(text);
- if (result) {
- $(`#output_${index}`).text(result[1]);
- }
+ $(`#output_${index}`).text(text);
});
}
});