Update plot API to flat functions (PyAutoFit PR #1174)

notebooks/cookbooks/analysis.ipynb

@@ -119,7 +119,33 @@
       "cell_type": "code",
       "metadata": {},
       "source": [
-        "dataset_path = path.join(\"dataset\", \"example_1d\", \"gaussian_x1\")\n",
+        "dataset_path = path.join(\"dataset\", \"example_1d\", \"gaussian_x1\")"
+      ],
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "__Dataset Auto-Simulation__\n",
+        "\n",
+        "If the dataset does not already exist on your system, it will be created by running the corresponding\n",
+        "simulator script. This ensures that all example scripts can be run without manually simulating data first."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "if not path.exists(dataset_path):\n",
+        "    import subprocess\n",
+        "    import sys\n",
+        "    subprocess.run(\n",
+        "        [sys.executable, \"scripts/simulators/simulators.py\"],\n",
+        "        check=True,\n",
+        "    )\n",
+        "\n",
         "data = af.util.numpy_array_from_json(file_path=path.join(dataset_path, \"data.json\"))\n",
         "noise_map = af.util.numpy_array_from_json(\n",
         "    file_path=path.join(dataset_path, \"noise_map.json\")\n",

notebooks/cookbooks/configs.ipynb

@@ -21,7 +21,7 @@
         "__Contents__\n",
         "\n",
         " - No Config Behaviour: An example of what happens when a model component does not have a config file.\n",
-        " - Template: A template config file for specifying default model component priors.\n",
+        " - Templates: A template config file for specifying default model component priors.\n",
         " - Modules: Writing prior config files based on the Python module the model component Python class is contained in.\n",
         " - Labels: Config files which specify the labels of model component parameters for visualization."
       ]
@@ -112,7 +112,33 @@
       "source": [
         "model = af.Model(GaussianNoConfig)\n",
         "\n",
-        "dataset_path = path.join(\"dataset\", \"example_1d\", \"gaussian_x1\")\n",
+        "dataset_path = path.join(\"dataset\", \"example_1d\", \"gaussian_x1\")"
+      ],
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "__Dataset Auto-Simulation__\n",
+        "\n",
+        "If the dataset does not already exist on your system, it will be created by running the corresponding\n",
+        "simulator script. This ensures that all example scripts can be run without manually simulating data first."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "if not path.exists(dataset_path):\n",
+        "    import subprocess\n",
+        "    import sys\n",
+        "    subprocess.run(\n",
+        "        [sys.executable, \"scripts/simulators/simulators.py\"],\n",
+        "        check=True,\n",
+        "    )\n",
+        "\n",
         "data = af.util.numpy_array_from_json(file_path=path.join(dataset_path, \"data.json\"))\n",
         "noise_map = af.util.numpy_array_from_json(\n",
         "    file_path=path.join(dataset_path, \"noise_map.json\")\n",

notebooks/cookbooks/model.ipynb

@@ -51,7 +51,8 @@
         " - Instances (af.Array): Create an instance of a numpy array model via input parameters.\n",
         " - Model Customization (af.Array): Customize a numpy array model (e.g. fixing parameters or linking them to one another).\n",
         " - Json Output (af.Array): Output a numpy array model in human readable text via a .json file and loading it back again.\n",
-        " - Extensible Models (af.Array): Using numpy arrays to compose models with a flexible number of parameters."
+        " - Extensible Models (af.Array): Using numpy arrays to compose models with a flexible number of parameters.\n",
+        " - Wrap Up: A summary of model composition in PyAutoFit."
       ]
     },
     {