adjust for colab

spet2094 · Mar 14, 2021 · e52ce14 · e52ce14
1 parent 2c13223
commit e52ce14
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diff --git a/sa_demo_virus_on_network.ipynb b/sa_demo_virus_on_network.ipynb
@@ -77,7 +77,7 @@
     "ema_logging.log_to_stderr(ema_logging.INFO)\n",
     "\n",
     "# Import Mesa virus on network model\n",
-    "from virus_on_network import model"
+    "from SSF2021.virus_on_network import model"
    ]
   },
   {
@@ -127,7 +127,7 @@
     "                           gain_resistance_chance=1,\n",
     "                           steps=10):\n",
     "    \n",
-    "    from virus_on_network import model\n",
+    "    from SSF2021.virus_on_network import model\n",
     "    \n",
     "    # Initialising the model\n",
     "    virus_on_network = model.VirusOnNetwork(num_nodes=num_nodes, \n",
@@ -509,7 +509,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from ema_workbench.util.utilities import save_results, load_results"
+    "from ema_workbench.util.utilities import save_results, load_results\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Creaet a directory to store the results\n",
+    "directory = 'results/virus_on_network'\n",
+    "if not os.path.exists(directory):\n",
+    "    os.makedirs(directory)"
    ]
   },
   {
@@ -560,7 +573,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Before we proceed further, we have to do a bit of preprocessing. Let's take a closer look at the outcomes. We have 4 outcomes, 100 scenarios, 10 replications and we run the model for 30 steps. This is what you see down below:"
+    "Before we proceed further, we have to do a bit of preprocessing. Let's take a closer look at the outcomes. We have 4 outcomes, 1400 scenarios, 10 replications and we run the model for 30 steps. This is what you see down below:"
    ]
   },
   {

diff --git a/sa_practice_virus_on_network.ipynb b/sa_practice_virus_on_network.ipynb
@@ -20,7 +20,7 @@
    "source": [
     "This is the main notebook of the workshop on *sensitivity analysis* (SA) at the Social Simulation Festival 2021. Here we will demonstrate how to do Variance-based SA also know as [Sobol SA](https://en.wikipedia.org/wiki/Variance-based_sensitivity_analysis) on a relatively simple model [virus on network](https://ccl.northwestern.edu/netlogo/models/VirusonaNetwork). The idea is that you reuse (read copy-paste) this code your own model. Therefore, we tried to keep simple and avoid extensive side steps from.\n",
     "\n",
-    "This notebook is tuned to be run on [Google Colab](https://colab.research.google.com/) and has a couple of extra lines of code. If you want to use it on your local machine please use `sa_demo_local_machine.ipynb`.\n",
+    "This notebook is tuned to be run on [Google Colab](https://colab.research.google.com/) and has a couple of extra lines of code.\n",
     "\n",
     "The core packages used in this notebook are [Mesa](https://mesa.readthedocs.io/en/stable/) to define an ABM model in Python, [EMA Workbench](https://emaworkbench.readthedocs.io/en/latest/) to design and run experiments, [SALib](https://salib.readthedocs.io/en/latest/) to conduct SA (within EMA Workbench). Also, we used one pretty plotting function of [pyNetLogo](https://pynetlogo.readthedocs.io/en/latest/).\n",
     "\n",
@@ -94,7 +94,7 @@
     "ema_logging.log_to_stderr(ema_logging.INFO)\n",
     "\n",
     "# Import Mesa virus on network model\n",
-    "from src import model"
+    "from SSF2021.src import model"
    ]
   },
   {
@@ -144,7 +144,7 @@
     "                           gain_resistance_chance=1,\n",
     "                           steps=10):\n",
     "    \n",
-    "    from src import model\n",
+    "    from SSF2021.virus_on_network import model\n",
     "    \n",
     "    # Initialising the model\n",
     "    virus_on_network = model.VirusOnNetwork(num_nodes=num_nodes, \n",
@@ -500,13 +500,33 @@
     "### Epic save"
    ]
   },
+  {
+   "source": [
+    "<center><img src=\"img/save.jpg\" width=200 height=260/><center>"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from ema_workbench.util.utilities import save_results, load_results"
+    "from ema_workbench.util.utilities import save_results, load_results\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Creaet a directory to store the results\n",
+    "directory = 'results/virus_on_network'\n",
+    "if not os.path.exists(directory):\n",
+    "    os.makedirs(directory)"
    ]
   },
   {
@@ -565,7 +585,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Before we proceed further, we have to do a bit of preprocessing. Let's take a closer look at the outcomes. We have 4 outcomes, 100 scenarios, 10 replications and we run the model for 30 steps. This is what you see down below:"
+    "Before we proceed further, we have to do a bit of preprocessing. Let's take a closer look at the outcomes. We have 4 outcomes, 800 scenarios, 10 replications and we run the model for 30 steps. This is what you see down below:"
    ]
   },
   {
@@ -700,7 +720,7 @@
    "source": [
     "from SALib.analyze import sobol\n",
     "from ema_workbench.em_framework.salib_samplers import get_SALib_problem\n",
-    "from src.plot import plot_sobol_indices\n",
+    "from SSF2021.src.plot import plot_sobol_indices\n",
     "sns.set_style('white')"
    ]
   },
@@ -823,9 +843,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.3-final"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}