PyAutoLabs · Jammy2211 · Jul 24, 2026 · Jul 24, 2026
diff --git a/notebooks/chapter_1_introduction/tutorial_0_visualization.ipynb b/notebooks/chapter_1_introduction/tutorial_0_visualization.ipynb
@@ -294,13 +294,6 @@
     "Great! Hopefully, visualization in **PyAutoGalaxy** is displaying nicely for us to get on with the **HowToGalaxy**\n",
     "lecture series."
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/chapter_1_introduction/tutorial_1_grids_and_galaxies.ipynb b/notebooks/chapter_1_introduction/tutorial_1_grids_and_galaxies.ipynb
@@ -1089,13 +1089,6 @@
     "Once you are more experienced, you should read up on over-sampling in more detail via \n",
     "the `autogalaxy_workspace/*/guides/over_sampling.ipynb` notebook."
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/chapter_1_introduction/tutorial_2_data.ipynb b/notebooks/chapter_1_introduction/tutorial_2_data.ipynb
@@ -634,13 +634,6 @@
     "\n",
     "- **Output**: We saved the simulated data to `.fits` files, the standard format used by astronomers for storing images."
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/chapter_1_introduction/tutorial_3_fitting.ipynb b/notebooks/chapter_1_introduction/tutorial_3_fitting.ipynb
@@ -1102,13 +1102,6 @@
     "- **Model Fitting**: We performed a basic model fit on a simple dataset, adjusting the model parameters to improve the\n",
     "  fit quality."
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/chapter_1_introduction/tutorial_4_methods.ipynb b/notebooks/chapter_1_introduction/tutorial_4_methods.ipynb
@@ -79,13 +79,6 @@
    "source": [
     "__Wrap Up__"
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/chapter_1_introduction/tutorial_5_summary.ipynb b/notebooks/chapter_1_introduction/tutorial_5_summary.ipynb
@@ -283,13 +283,6 @@
     "\n",
     "Even the smallest bit of exposure via a GitHub star can help our project grow!"
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/chapter_2_modeling/tutorial_1_non_linear_search.ipynb b/notebooks/chapter_2_modeling/tutorial_1_non_linear_search.ipynb
@@ -995,13 +995,6 @@
     "scientific research and data analysis. In the next tutorials, these concepts will be further expanded upon to \n",
     "deepen your understanding and provide more advanced techniques for model fitting and analysis."
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/chapter_2_modeling/tutorial_2_practicalities.ipynb b/notebooks/chapter_2_modeling/tutorial_2_practicalities.ipynb
@@ -535,16 +535,8 @@
     "parameter `n`). These mappings ate specified in the `config/notation.yaml` file and can be customized by users.\n",
     "\n",
     "The superscripts of labels correspond to the name each component was given in the model (e.g. for the `Isothermal`\n",
-    "mass its name `mass` defined when making the `Model` above is used)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [
+    "mass its name `mass` defined when making the `Model` above is used).\n",
     "\n",
-    "# %%\n",
-    "'''\n",
     "__Other Practicalities__\n",
     "\n",
     "The following are examples of other practicalities which I will document fully in this example script in the future,\n",
@@ -569,18 +561,8 @@
     "- How to navigate the output folder and examine model-fit results to assess quality.\n",
     "\n",
     "- How to estimate the run-time of a model-fit before initiating it, and change settings to make it faster or run the\n",
-    "  analysis in parallel.\n",
-    "'''"
-   ],
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
+    "  analysis in parallel."
+   ]
   }
  ],
  "metadata": {

diff --git a/notebooks/chapter_2_modeling/tutorial_3_realism_and_complexity.ipynb b/notebooks/chapter_2_modeling/tutorial_3_realism_and_complexity.ipynb
@@ -427,13 +427,6 @@
     " disk, bulge, bar, star-forming knot) and there may even be more than 1 galaxy! Do you think there is any hope of \n",
     " us navigating a parameter space if the galaxies contributes 30+ parameters?"
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/chapter_2_modeling/tutorial_4_dealing_with_failure.ipynb b/notebooks/chapter_2_modeling/tutorial_4_dealing_with_failure.ipynb
@@ -727,13 +727,6 @@
     "To wrap up chapter 2, we have a few more tutorials, where we will discuss masking in more detail, the `Result` object\n",
     "and how to make **PyAutoGalaxy** run faster."
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/chapter_2_modeling/tutorial_6_masking.ipynb b/notebooks/chapter_2_modeling/tutorial_6_masking.ipynb
@@ -261,13 +261,6 @@
     " 1) Customizing the mask for the analysis of one galaxy gets the analysis running fast and can provide accurate \n",
     " non-linear sampling. However, for a large sample of galaxies, this high level of customization may take a lot of time. "
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/chapter_2_modeling/tutorial_7_results.ipynb b/notebooks/chapter_2_modeling/tutorial_7_results.ipynb
@@ -259,13 +259,6 @@
     "This is because it makes it simple to perform all analyse in a Jupyter notebook, which is the most flexible and \n",
     "versatile way to check results and make figures."
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/chapter_2_modeling/tutorial_8_need_for_speed.ipynb b/notebooks/chapter_2_modeling/tutorial_8_need_for_speed.ipynb
@@ -124,13 +124,6 @@
     "\n",
     "_setup_colab.setup(\"howtogalaxy\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/chapter_3_search_chaining/tutorial_1_search_chaining.ipynb b/notebooks/chapter_3_search_chaining/tutorial_1_search_chaining.ipynb
@@ -550,13 +550,6 @@
     "Fortunately, we can pass priors in **PyAutoGalaxy** without specifying the specific values. The API for this technique,\n",
     "called prior passing, is the topic of the next tutorial."
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/chapter_3_search_chaining/tutorial_2_prior_passing.ipynb b/notebooks/chapter_3_search_chaining/tutorial_2_prior_passing.ipynb
@@ -526,13 +526,6 @@
     "\n",
     "And with that, we're done. Chaining priors is a bit of an art form, but one that works really well. "
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/chapter_3_search_chaining/tutorial_3_x2_galaxies.ipynb b/notebooks/chapter_3_search_chaining/tutorial_3_x2_galaxies.ipynb
@@ -491,13 +491,6 @@
     "\n",
     "We have successfully fitted multiple galaxies, but fitting each one-by-one."
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/chapter_4_pixelizations/tutorial_1_pixelizations.ipynb b/notebooks/chapter_4_pixelizations/tutorial_1_pixelizations.ipynb
@@ -314,13 +314,6 @@
     " 1) The rectangular pixelization`s edges are aligned with the most exterior coordinates of the source-grid. This is \n",
     " intentional, why do you think this is?"
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/chapter_4_pixelizations/tutorial_2_mappers.ipynb b/notebooks/chapter_4_pixelizations/tutorial_2_mappers.ipynb
@@ -334,13 +334,6 @@
     " on the pixel-grid. So, how does knowing how each pixel maps to the image actually help us? If you`ve not got \n",
     " any bright ideas, then worry not, that exactly what we're going to cover in the next tutorial."
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/chapter_4_pixelizations/tutorial_3_inversions.ipynb b/notebooks/chapter_4_pixelizations/tutorial_3_inversions.ipynb
@@ -398,13 +398,6 @@
     "`autogalaxy_workspace/*/imaging/log_likelihood_function/inversion.ipynb` which gives a visual step-by-step\n",
     "guide of the process alongside equations and references to literature on the subject."
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/chapter_4_pixelizations/tutorial_4_bayesian_regularization.ipynb b/notebooks/chapter_4_pixelizations/tutorial_4_bayesian_regularization.ipynb
@@ -441,13 +441,6 @@
     "this framework throughout, **PyAutoGalaxy** objectively determines the final model following the principles of Bayesian\n",
     "analysis and Occam\u2019s Razor."
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/chapter_4_pixelizations/tutorial_5_model_fit.ipynb b/notebooks/chapter_4_pixelizations/tutorial_5_model_fit.ipynb
@@ -363,13 +363,6 @@
     "\n",
     "And with that, we now have a pipeline to model galaxies using an inversion! "
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/chapter_optional/tutorial_searches.ipynb b/notebooks/chapter_optional/tutorial_searches.ipynb
@@ -471,13 +471,6 @@
     "For the vast majority of models in **PyAutoGalaxy**, the default nested sampling search `Nautilus` is recommended, as\n",
     "it is robust, efficient and requires little manual tuning."
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/simulators/sersic.ipynb b/notebooks/simulators/sersic.ipynb
@@ -324,13 +324,6 @@
    "source": [
     "The dataset can be viewed in the folder `autogalaxy_workspace/imaging/simple__sersic`."
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/simulators/sersic_x2.ipynb b/notebooks/simulators/sersic_x2.ipynb
@@ -332,13 +332,6 @@
    "source": [
     "The dataset can be viewed in the folder `autogalaxy_workspace/imaging/sersic_x2`."
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/notebooks/simulators/simple.ipynb b/notebooks/simulators/simple.ipynb
@@ -420,13 +420,6 @@
     "See `scripts/guides/api/data_structures.py` for the broader \"JIT-it-\n",
     "yourself\" pattern."
    ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {},
-   "source": [],
-   "outputs": [],
-   "execution_count": null
   }
  ],
  "metadata": {

diff --git a/scripts/chapter_2_modeling/tutorial_2_practicalities.py b/scripts/chapter_2_modeling/tutorial_2_practicalities.py
@@ -362,9 +362,7 @@
 
 The superscripts of labels correspond to the name each component was given in the model (e.g. for the `Isothermal`
 mass its name `mass` defined when making the `Model` above is used).
-"""
 
-"""
 __Other Practicalities__
 
 The following are examples of other practicalities which I will document fully in this example script in the future,