Top-level notebook to analyze aggregated instance data. Incomplete since can't run due to Perlmutter down for maintenance.

Author: markcoletti

examples-proposed/024-aggregated-compute-ensemble/driver.py

@@ -9,21 +9,21 @@
 from ipsframework import Component
 from ipsframework.ipsutil import params_from_csv
 
+# The notebook that will be copied for each ensemble instance
+SOURCE_NOTEBOOK_NAME='global_notebook.ipynb'
+
 
 class EnsembleDriver(Component):
     """Kicks off a simple ensemble"""
 
     def init(self, timestamp=0.0):
-        pass
-        # TODO temporarily commenting this out until the actual
-        # example is ready to consider adding a notebook to the portal.
-
-        # NOTEBOOK_TEMPLATE = 'notebook.ipynb'
-        # self.services.stage_input_files([NOTEBOOK_TEMPLATE])
-        # try:
-        #     self.services.initialize_jupyter_notebook(NOTEBOOK_TEMPLATE)
-        # except Exception:
-        #     print('did not add notebook to portal')
+        self.services.stage_input_files([SOURCE_NOTEBOOK_NAME])
+
+        self.services.initialize_jupyter_notebook(
+                dest_notebook_name='jupyterhub_global_notebook.ipynb',
+                source_notebook_path=SOURCE_NOTEBOOK_NAME,
+        )
+
 
     def step(self, timestamp=0.0):
         # This CSV file contains the parameters used for the

examples-proposed/024-aggregated-compute-ensemble/input_dir/global_notebook.ipynb

@@ -0,0 +1,84 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# This is the top-level notebook for analysing all the data from\n",
+    "# the ensemble instances."
+   ]
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-21T15:24:56.068204Z",
+     "start_time": "2026-01-21T15:24:56.059637Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import json\n",
+    "import csv\n",
+    "from pathlib import Path\n",
+    "\n",
+    "%matplotlib notebook\n",
+    "import matplotlib.pyplot as plt"
+   ],
+   "id": "857879d9069625cf",
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-21T15:24:48.049301Z",
+     "start_time": "2026-01-21T15:24:48.038184Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "a = ips_analysis_api.get_data()\n",
+    "b = ips_analysis_api.get_child_data()\n",
+    "c = ips_analysis_api.get_child_data_not_ensembles()\n",
+    "d = ips_analysis_api.get_child_data_by_ensemble_names()"
+   ],
+   "id": "3d8fa1127a2ab4ef",
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "c2e3d3e1518509f8"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}