diff --git a/scripts/chapter_1_introduction/tutorial_0_visualization.py b/scripts/chapter_1_introduction/tutorial_0_visualization.py index 01d95f8..da36a34 100644 --- a/scripts/chapter_1_introduction/tutorial_0_visualization.py +++ b/scripts/chapter_1_introduction/tutorial_0_visualization.py @@ -7,12 +7,12 @@ __Contents__ -**Directories:** **PyAutoLens assumes** the working directory is `autolens_workspace` on your hard-disk. -**Dataset:** Load and plot the strong lens dataset. -**Subplots:** In addition to plotting individual figures, **PyAutoLens** can plot `subplots` which show multiple. -**Plot Customization:** Does the figure display correctly on your computer screen? -**Overlays:** Overlays such as critical curves and image positions are added using the `lines=` and `positions=`. -**Wrap Up:** Summary of the script and next steps. +- **Directories:** **PyAutoLens assumes** the working directory is `autolens_workspace` on your hard-disk. +- **Dataset:** Load and plot the strong lens dataset. +- **Subplots:** In addition to plotting individual figures, **PyAutoLens** can plot `subplots` which show multiple. +- **Plot Customization:** Does the figure display correctly on your computer screen? +- **Overlays:** Overlays such as critical curves and image positions are added using the `lines=` and `positions=`. +- **Wrap Up:** Summary of the script and next steps. """ diff --git a/scripts/chapter_1_introduction/tutorial_1_grids_and_galaxies.py b/scripts/chapter_1_introduction/tutorial_1_grids_and_galaxies.py index e41db40..425c5a2 100644 --- a/scripts/chapter_1_introduction/tutorial_1_grids_and_galaxies.py +++ b/scripts/chapter_1_introduction/tutorial_1_grids_and_galaxies.py @@ -51,10 +51,10 @@ __Contents__ -**Grids:** A `Grid2D` is a set of two-dimensional $(y,x)$ coordinates that represent points in space where we. -**Geometry:** The above grid is centered on the origin (0.0", 0.0"). -**Light Profiles:** Galaxies are collections of stars, gas, dust, and other astronomical objects that emit light. -**One Dimension Projection:** We often want to calculative 1D quantities of a light profile, for example to plot how its light. +- **Grids:** A `Grid2D` is a set of two-dimensional $(y,x)$ coordinates that represent points in space where we. +- **Geometry:** The above grid is centered on the origin (0.0", 0.0"). +- **Light Profiles:** Galaxies are collections of stars, gas, dust, and other astronomical objects that emit light. +- **One Dimension Projection:** We often want to calculative 1D quantities of a light profile, for example to plot how its light. """ diff --git a/scripts/chapter_1_introduction/tutorial_2_ray_tracing.py b/scripts/chapter_1_introduction/tutorial_2_ray_tracing.py index 31388d2..55edfc2 100644 --- a/scripts/chapter_1_introduction/tutorial_2_ray_tracing.py +++ b/scripts/chapter_1_introduction/tutorial_2_ray_tracing.py @@ -58,8 +58,8 @@ __Contents__ -**Grid:** In the previous tutorial, we created 2D grids of (y,x) coordinates and showed how shifting and. -**Mass Profiles:** To perform lensing calculations, we use mass profiles available in the `mass_profile` module. +- **Grid:** In the previous tutorial, we created 2D grids of (y,x) coordinates and showed how shifting and. +- **Mass Profiles:** To perform lensing calculations, we use mass profiles available in the `mass_profile` module. """ diff --git a/scripts/chapter_1_introduction/tutorial_3_more_ray_tracing.py b/scripts/chapter_1_introduction/tutorial_3_more_ray_tracing.py index 811b15f..6ebe436 100644 --- a/scripts/chapter_1_introduction/tutorial_3_more_ray_tracing.py +++ b/scripts/chapter_1_introduction/tutorial_3_more_ray_tracing.py @@ -25,14 +25,14 @@ __Contents__ -**Initial Setup:** To begin, lets setup the grid we'll ray-trace using. -**Concise Code:** Lets set up the tracer used in the previous tutorial. -**Critical Curves:** To end, we can finally explain what the black lines that have appeared on many of the plots. -**Caustics:** In the previous tutorial, we plotted the critical curves of the mass profile on the image-plane. -**Units:** Lets plot the lensing quantities again. -**More Complexity:** We now make a lens with some attributes we didn`t in the last tutorial. -**Multi Galaxy Ray Tracing:** Now lets pass our 4 galaxies to a `Tracer`, which means the following will occur. -**Wrap Up:** Summary of the script and next steps. +- **Initial Setup:** To begin, lets setup the grid we'll ray-trace using. +- **Concise Code:** Lets set up the tracer used in the previous tutorial. +- **Critical Curves:** To end, we can finally explain what the black lines that have appeared on many of the plots. +- **Caustics:** In the previous tutorial, we plotted the critical curves of the mass profile on the image-plane. +- **Units:** Lets plot the lensing quantities again. +- **More Complexity:** We now make a lens with some attributes we didn`t in the last tutorial. +- **Multi Galaxy Ray Tracing:** Now lets pass our 4 galaxies to a `Tracer`, which means the following will occur. +- **Wrap Up:** Summary of the script and next steps. """ diff --git a/scripts/chapter_1_introduction/tutorial_4_point_sources.py b/scripts/chapter_1_introduction/tutorial_4_point_sources.py index 9500740..a8823a1 100644 --- a/scripts/chapter_1_introduction/tutorial_4_point_sources.py +++ b/scripts/chapter_1_introduction/tutorial_4_point_sources.py @@ -11,7 +11,7 @@ __Contents__ -**Wrap Up:** Summary of the script and next steps. +- **Wrap Up:** Summary of the script and next steps. """ diff --git a/scripts/chapter_1_introduction/tutorial_5_lensing_formalism.py b/scripts/chapter_1_introduction/tutorial_5_lensing_formalism.py index 4ab94b9..45edd88 100644 --- a/scripts/chapter_1_introduction/tutorial_5_lensing_formalism.py +++ b/scripts/chapter_1_introduction/tutorial_5_lensing_formalism.py @@ -12,7 +12,7 @@ __Contents__ -**Wrap Up:** Summary of the script and next steps. +- **Wrap Up:** Summary of the script and next steps. """ diff --git a/scripts/chapter_1_introduction/tutorial_6_data.py b/scripts/chapter_1_introduction/tutorial_6_data.py index c1e7883..81224b7 100644 --- a/scripts/chapter_1_introduction/tutorial_6_data.py +++ b/scripts/chapter_1_introduction/tutorial_6_data.py @@ -28,13 +28,13 @@ __Contents__ -**Initial Setup:** To create our simulated strong lens image, we first need a 2D grid. -**Optics Blurring:** All images captured using CCDs (like those on the Hubble Space Telescope or Euclid) experience some. -**Poisson Noise:** In addition to the blurring caused by telescope optics, we also need to consider Poisson noise when. -**Background Sky:** The final effect we will consider when simulating imaging data is the background sky. -**Simulator:** The `SimulatorImaging` object lets us create simulated imaging data while including the effects of. -**Output:** We will now save these simulated data to `.fits` files, the standard format used by astronomers for. -**Wrap Up:** Summary of the script and next steps. +- **Initial Setup:** To create our simulated strong lens image, we first need a 2D grid. +- **Optics Blurring:** All images captured using CCDs (like those on the Hubble Space Telescope or Euclid) experience some. +- **Poisson Noise:** In addition to the blurring caused by telescope optics, we also need to consider Poisson noise when. +- **Background Sky:** The final effect we will consider when simulating imaging data is the background sky. +- **Simulator:** The `SimulatorImaging` object lets us create simulated imaging data while including the effects of. +- **Output:** We will now save these simulated data to `.fits` files, the standard format used by astronomers for. +- **Wrap Up:** Summary of the script and next steps. """ diff --git a/scripts/chapter_1_introduction/tutorial_7_fitting.py b/scripts/chapter_1_introduction/tutorial_7_fitting.py index add71cd..eb01303 100644 --- a/scripts/chapter_1_introduction/tutorial_7_fitting.py +++ b/scripts/chapter_1_introduction/tutorial_7_fitting.py @@ -33,12 +33,12 @@ __Contents__ -**Dataset & Mask:** Standard set up of the dataset and mask that is fitted. -**Masked Grid:** In tutorials 1 and 2, we emphasized that the `Grid2D` object is crucial for evaluating a lens's. -**Fitting:** Fit the lens model to the dataset and inspect the results. -**Incorrect Fit:** In the previous section, we successfully created and fitted a lens model to the image data. -**Model Fitting:** In the previous sections, we used the true model to fit the data, which resulted in a high log. -**Wrap Up:** Summary of the script and next steps. +- **Dataset & Mask:** Standard set up of the dataset and mask that is fitted. +- **Masked Grid:** In tutorials 1 and 2, we emphasized that the `Grid2D` object is crucial for evaluating a lens's. +- **Fitting:** Fit the lens model to the dataset and inspect the results. +- **Incorrect Fit:** In the previous section, we successfully created and fitted a lens model to the image data. +- **Model Fitting:** In the previous sections, we used the true model to fit the data, which resulted in a high log. +- **Wrap Up:** Summary of the script and next steps. """ diff --git a/scripts/chapter_1_introduction/tutorial_8_summary.py b/scripts/chapter_1_introduction/tutorial_8_summary.py index a0754d1..78b5d20 100644 --- a/scripts/chapter_1_introduction/tutorial_8_summary.py +++ b/scripts/chapter_1_introduction/tutorial_8_summary.py @@ -18,12 +18,12 @@ __Contents__ -**Start:** Below, we do all the steps we have learned this chapter, making profiles, galaxies, a tracer, etc. -**Object Composition:** Lets now consider how all of the objects we've covered throughout this chapter (`LightProfile`'s. -**Visualization:** Furthermore, using the `MatPLot2D` and `lines=`/`positions=` overlays objects we can visualize any. -**Code Design:** To end, I want to quickly talk about the **PyAutoLens** code-design and structure, which was really. -**Source Code:** If you do enjoy code, variables, functions, and parameters, you may want to dig deeper into the. -**Wrap Up:** Summary of the script and next steps. +- **Start:** Below, we do all the steps we have learned this chapter, making profiles, galaxies, a tracer, etc. +- **Object Composition:** Lets now consider how all of the objects we've covered throughout this chapter (`LightProfile`'s. +- **Visualization:** Furthermore, using the `MatPLot2D` and `lines=`/`positions=` overlays objects we can visualize any. +- **Code Design:** To end, I want to quickly talk about the **PyAutoLens** code-design and structure, which was really. +- **Source Code:** If you do enjoy code, variables, functions, and parameters, you may want to dig deeper into the. +- **Wrap Up:** Summary of the script and next steps. """ diff --git a/scripts/chapter_2_lens_modeling/tutorial_1_non_linear_search.py b/scripts/chapter_2_lens_modeling/tutorial_1_non_linear_search.py index 23f2f32..ee13f6c 100644 --- a/scripts/chapter_2_lens_modeling/tutorial_1_non_linear_search.py +++ b/scripts/chapter_2_lens_modeling/tutorial_1_non_linear_search.py @@ -44,19 +44,19 @@ __Contents__ -**Overview:** In this tutorial, we will use a non-linear search to fit a lens model to simulated imaging of. -**Parameter Space:** In mathematics, a function is defined by its parameters, which map inputs to outputs. -**Search Types:** There are different types of non-linear searches, each of which explores parameter space in a. -**Deeper Background:** **The descriptions of how searches work in this example are simplfied and phoenomenological and do. -**PyAutoFit:** Modeling uses the probabilistic programming language. -**Initial Setup:** Let's first load the `Imaging` dataset, which we will use to fit a model with a non-linear search. -**Mask:** Define the 2D mask applied to the dataset for the model-fit. -**Model:** Compose the lens model fitted to the data. -**Priors:** When we examine the `.info` of our model, we notice that each parameter (like `centre`. -**Analysis:** Create the Analysis object that defines how the model is fitted to the data. -**Searches:** To perform a non-linear search, we create an instance of a `NonLinearSearch` object. -**Nested Sampling:** **Nested Sampling** is an advanced method for model-fitting that excels in handling complex models. -**Wrap Up:** Summary of the script and next steps. +- **Overview:** In this tutorial, we will use a non-linear search to fit a lens model to simulated imaging of. +- **Parameter Space:** In mathematics, a function is defined by its parameters, which map inputs to outputs. +- **Search Types:** There are different types of non-linear searches, each of which explores parameter space in a. +- **Deeper Background:** **The descriptions of how searches work in this example are simplfied and phoenomenological and do. +- **PyAutoFit:** Modeling uses the probabilistic programming language. +- **Initial Setup:** Let's first load the `Imaging` dataset, which we will use to fit a model with a non-linear search. +- **Mask:** Define the 2D mask applied to the dataset for the model-fit. +- **Model:** Compose the lens model fitted to the data. +- **Priors:** When we examine the `.info` of our model, we notice that each parameter (like `centre`. +- **Analysis:** Create the Analysis object that defines how the model is fitted to the data. +- **Searches:** To perform a non-linear search, we create an instance of a `NonLinearSearch` object. +- **Nested Sampling:** **Nested Sampling** is an advanced method for model-fitting that excels in handling complex models. +- **Wrap Up:** Summary of the script and next steps. __Parameter Space__ diff --git a/scripts/chapter_2_lens_modeling/tutorial_2_practicalities.py b/scripts/chapter_2_lens_modeling/tutorial_2_practicalities.py index 40c32fc..19bda0e 100755 --- a/scripts/chapter_2_lens_modeling/tutorial_2_practicalities.py +++ b/scripts/chapter_2_lens_modeling/tutorial_2_practicalities.py @@ -17,23 +17,23 @@ __Contents__ -**PyAutoFit:** Modeling uses the probabilistic programming language. -**Initial Setup:** Lets first load the `Imaging` dataset we'll fit a model with using a non-linear search. -**Mask:** Define the 2D mask applied to the dataset for the model-fit. -**Model:** Compose the lens model fitted to the data. -**Search:** Configure the non-linear search used to fit the model. -**Search Settings:** Nautilus samples parameter space by placing "live points" representing different galaxy models. -**Iterations Per Update:** Every N iterations, the non-linear search outputs the current results to the folder. -**Analysis:** Create the Analysis object that defines how the model is fitted to the data. -**VRAM Use:** When running AutoLens with JAX on a GPU, the analysis must fit within the GPU’s available VRAM. -**Run Times:** Profiling the expected run time of the model-fit. -**Result Info:** A concise readable summary of the results is given by printing its `info` attribute. -**Output Folder:** Now checkout the `autolens_workspace/output` folder. -**Unique Identifier:** In the output folder, you will note that results are in a folder which is a collection of random. -**Output Folder Contents:** Now this is running you should checkout the `autolens_workspace/output` folder. -**Result:** Overview of the results of the model-fit. -**Other Practicalities:** The following are examples of other practicalities which I will document fully in this example. -**Wrap Up:** Summary of the script and next steps. +- **PyAutoFit:** Modeling uses the probabilistic programming language. +- **Initial Setup:** Lets first load the `Imaging` dataset we'll fit a model with using a non-linear search. +- **Mask:** Define the 2D mask applied to the dataset for the model-fit. +- **Model:** Compose the lens model fitted to the data. +- **Search:** Configure the non-linear search used to fit the model. +- **Search Settings:** Nautilus samples parameter space by placing "live points" representing different galaxy models. +- **Iterations Per Update:** Every N iterations, the non-linear search outputs the current results to the folder. +- **Analysis:** Create the Analysis object that defines how the model is fitted to the data. +- **VRAM Use:** When running AutoLens with JAX on a GPU, the analysis must fit within the GPU’s available VRAM. +- **Run Times:** Profiling the expected run time of the model-fit. +- **Result Info:** A concise readable summary of the results is given by printing its `info` attribute. +- **Output Folder:** Now checkout the `autolens_workspace/output` folder. +- **Unique Identifier:** In the output folder, you will note that results are in a folder which is a collection of random. +- **Output Folder Contents:** Now this is running you should checkout the `autolens_workspace/output` folder. +- **Result:** Overview of the results of the model-fit. +- **Other Practicalities:** The following are examples of other practicalities which I will document fully in this example. +- **Wrap Up:** Summary of the script and next steps. __Search Settings__ diff --git a/scripts/chapter_2_lens_modeling/tutorial_3_realism_and_complexity.py b/scripts/chapter_2_lens_modeling/tutorial_3_realism_and_complexity.py index 73e082d..c17c878 100644 --- a/scripts/chapter_2_lens_modeling/tutorial_3_realism_and_complexity.py +++ b/scripts/chapter_2_lens_modeling/tutorial_3_realism_and_complexity.py @@ -24,13 +24,13 @@ __Contents__ -**Initial Setup:** we'll use new strong lensing data, where. -**Mask:** Define the 2D mask applied to the dataset for the model-fit. -**Model:** Compose the lens model fitted to the data. -**Run Time:** Profiling the expected run time of the model-fit. -**Result:** Overview of the results of the model-fit. -**Global and Local Maxima:** Up to now, all our non-linear searches have successfully found lens models that provide visibly. -**Wrap Up:** Summary of the script and next steps. +- **Initial Setup:** we'll use new strong lensing data, where. +- **Mask:** Define the 2D mask applied to the dataset for the model-fit. +- **Model:** Compose the lens model fitted to the data. +- **Run Time:** Profiling the expected run time of the model-fit. +- **Result:** Overview of the results of the model-fit. +- **Global and Local Maxima:** Up to now, all our non-linear searches have successfully found lens models that provide visibly. +- **Wrap Up:** Summary of the script and next steps. """ diff --git a/scripts/chapter_2_lens_modeling/tutorial_4_dealing_with_failure.py b/scripts/chapter_2_lens_modeling/tutorial_4_dealing_with_failure.py index fa44192..13a9aff 100644 --- a/scripts/chapter_2_lens_modeling/tutorial_4_dealing_with_failure.py +++ b/scripts/chapter_2_lens_modeling/tutorial_4_dealing_with_failure.py @@ -20,12 +20,12 @@ __Contents__ -**Initial Setup:** we'll use the same strong lensing data as the previous tutorial, where. -**Mask:** Define the 2D mask applied to the dataset for the model-fit. -**Prior Tuning:** First, we will try to assist our non-linear search by tuning our priors. -**Run Time:** Profiling the expected run time of the model-fit. -**Result:** Overview of the results of the model-fit. -**Discussion:** By tuning our priors to the specific lens model we are fitting, we increase the chances of finding. +- **Initial Setup:** we'll use the same strong lensing data as the previous tutorial, where. +- **Mask:** Define the 2D mask applied to the dataset for the model-fit. +- **Prior Tuning:** First, we will try to assist our non-linear search by tuning our priors. +- **Run Time:** Profiling the expected run time of the model-fit. +- **Result:** Overview of the results of the model-fit. +- **Discussion:** By tuning our priors to the specific lens model we are fitting, we increase the chances of finding. """ diff --git a/scripts/chapter_2_lens_modeling/tutorial_5_linear_profiles.py b/scripts/chapter_2_lens_modeling/tutorial_5_linear_profiles.py index 3c64de4..77efd3c 100644 --- a/scripts/chapter_2_lens_modeling/tutorial_5_linear_profiles.py +++ b/scripts/chapter_2_lens_modeling/tutorial_5_linear_profiles.py @@ -25,21 +25,21 @@ __Contents__ -**Initial Setup:** we'll use the same strong lensing data as the previous tutorial, where. -**Mask:** Define the 2D mask applied to the dataset for the model-fit. -**Linear Light Profiles:** We use a variant of a light profile discussed called a "linear light profile", which is accessed. -**Run Time:** Profiling the expected run time of the model-fit. -**Result:** Overview of the results of the model-fit. -**Intensities:** The intensities of linear light profiles are not a part of the model parameterization and therefore. -**Visualization:** Linear light profiles and objects containing them (e.g. -**Basis:** We can use many linear light profiles to build a `Basis`. -**Model Fit:** Perform the model-fit using the search and analysis. -**Source MGE:** We now compose a second `Basis` of 15 Gaussians to represent the source galaxy. -**Multi Gaussian Expansion Benefits:** Fitting a galaxy's light with a superposition of Gaussians is called a Multi-Gaussian Expansion. -**Disadvantage of Basis Functions:** For many science cases, the MGE can also be a less intuitive model to interpret than a Sersic. -**Positive Only Solver:** Ensuring positive-only solutions for linear light profile intensities. -**Other Basis Functions:** In addition to the Gaussians used in this example, there is another basis function implemented in. -**Wrap Up:** Summary of the script and next steps. +- **Initial Setup:** we'll use the same strong lensing data as the previous tutorial, where. +- **Mask:** Define the 2D mask applied to the dataset for the model-fit. +- **Linear Light Profiles:** We use a variant of a light profile discussed called a "linear light profile", which is accessed. +- **Run Time:** Profiling the expected run time of the model-fit. +- **Result:** Overview of the results of the model-fit. +- **Intensities:** The intensities of linear light profiles are not a part of the model parameterization and therefore. +- **Visualization:** Linear light profiles and objects containing them (e.g. +- **Basis:** We can use many linear light profiles to build a `Basis`. +- **Model Fit:** Perform the model-fit using the search and analysis. +- **Source MGE:** We now compose a second `Basis` of 15 Gaussians to represent the source galaxy. +- **Multi Gaussian Expansion Benefits:** Fitting a galaxy's light with a superposition of Gaussians is called a Multi-Gaussian Expansion. +- **Disadvantage of Basis Functions:** For many science cases, the MGE can also be a less intuitive model to interpret than a Sersic. +- **Positive Only Solver:** Ensuring positive-only solutions for linear light profile intensities. +- **Other Basis Functions:** In addition to the Gaussians used in this example, there is another basis function implemented in. +- **Wrap Up:** Summary of the script and next steps. """ diff --git a/scripts/chapter_2_lens_modeling/tutorial_6_masking_and_positions.py b/scripts/chapter_2_lens_modeling/tutorial_6_masking_and_positions.py index e3d70b1..20bb06c 100644 --- a/scripts/chapter_2_lens_modeling/tutorial_6_masking_and_positions.py +++ b/scripts/chapter_2_lens_modeling/tutorial_6_masking_and_positions.py @@ -8,13 +8,13 @@ __Contents__ -**Initial Setup:** we'll use the same strong lensing data as tutorials 1 & 2, where. -**Mask:** Define the 2D mask applied to the dataset for the model-fit. -**Run Time:** Profiling the expected run time of the model-fit. -**Search:** Configure the non-linear search used to fit the model. -**Discussion:** So, we can choose the mask we use in a model-fit. -**Positions Thresholding:** We can manually specify a set of image-plane (y,x) coordinates corresponding to the multiple images. -**Wrap Up:** Summary of the script and next steps. +- **Initial Setup:** we'll use the same strong lensing data as tutorials 1 & 2, where. +- **Mask:** Define the 2D mask applied to the dataset for the model-fit. +- **Run Time:** Profiling the expected run time of the model-fit. +- **Search:** Configure the non-linear search used to fit the model. +- **Discussion:** So, we can choose the mask we use in a model-fit. +- **Positions Thresholding:** We can manually specify a set of image-plane (y,x) coordinates corresponding to the multiple images. +- **Wrap Up:** Summary of the script and next steps. """ diff --git a/scripts/chapter_2_lens_modeling/tutorial_7_results.py b/scripts/chapter_2_lens_modeling/tutorial_7_results.py index 3697609..29de55f 100644 --- a/scripts/chapter_2_lens_modeling/tutorial_7_results.py +++ b/scripts/chapter_2_lens_modeling/tutorial_7_results.py @@ -7,12 +7,12 @@ __Contents__ -**Initial Setup:** Lets use the model-fit performed in tutorial 1 to get a `Result` object. -**Tracer & Fit:** In the previous tutorials, we saw that this result contains the maximum log likelihood fit, which. -**Samples:** The result contains a lot more information about the model-fit. -**Workspace:** We are not going into any more detail on the result variable in this tutorial, or in the. -**Database:** Once a search has completed running, we have a set of results on our hard disk which we can. -**Wrap Up:** Summary of the script and next steps. +- **Initial Setup:** Lets use the model-fit performed in tutorial 1 to get a `Result` object. +- **Tracer & Fit:** In the previous tutorials, we saw that this result contains the maximum log likelihood fit, which. +- **Samples:** The result contains a lot more information about the model-fit. +- **Workspace:** We are not going into any more detail on the result variable in this tutorial, or in the. +- **Database:** Once a search has completed running, we have a set of results on our hard disk which we can. +- **Wrap Up:** Summary of the script and next steps. """ diff --git a/scripts/chapter_2_lens_modeling/tutorial_8_need_for_speed.py b/scripts/chapter_2_lens_modeling/tutorial_8_need_for_speed.py index ffdde9e..61c4b1b 100755 --- a/scripts/chapter_2_lens_modeling/tutorial_8_need_for_speed.py +++ b/scripts/chapter_2_lens_modeling/tutorial_8_need_for_speed.py @@ -38,9 +38,9 @@ __Contents__ -**Algorithmic Optimization:** Every operation **PyAutoLens** performs to fit strong lens data with a model takes time, for. -**Data Quantity:** The final factor driving run-speed is the quantity of data that is fitted. -**Wrap Up:** Summary of the script and next steps. +- **Algorithmic Optimization:** Every operation **PyAutoLens** performs to fit strong lens data with a model takes time, for. +- **Data Quantity:** The final factor driving run-speed is the quantity of data that is fitted. +- **Wrap Up:** Summary of the script and next steps. __Algorithmic Optimization__ diff --git a/scripts/chapter_3_search_chaining/tutorial_1_search_chaining.py b/scripts/chapter_3_search_chaining/tutorial_1_search_chaining.py index 58b98db..7b2732c 100644 --- a/scripts/chapter_3_search_chaining/tutorial_1_search_chaining.py +++ b/scripts/chapter_3_search_chaining/tutorial_1_search_chaining.py @@ -40,13 +40,13 @@ __Contents__ -**Initial Setup:** we'll use the same strong lensing data as the previous tutorial, where. -**Model:** Compose the lens model fitted to the data. -**Result:** Overview of the results of the model-fit. -**Prior Passing:** Now all we need to do is look at the results of search 1 and pass the results as priors for search. -**Run Time:** Profiling the expected run time of the model-fit. -**Model Fit:** Perform the model-fit using the search and analysis. -**Wrap Up:** Summary of the script and next steps. +- **Initial Setup:** we'll use the same strong lensing data as the previous tutorial, where. +- **Model:** Compose the lens model fitted to the data. +- **Result:** Overview of the results of the model-fit. +- **Prior Passing:** Now all we need to do is look at the results of search 1 and pass the results as priors for search. +- **Run Time:** Profiling the expected run time of the model-fit. +- **Model Fit:** Perform the model-fit using the search and analysis. +- **Wrap Up:** Summary of the script and next steps. """ diff --git a/scripts/chapter_3_search_chaining/tutorial_2_prior_passing.py b/scripts/chapter_3_search_chaining/tutorial_2_prior_passing.py index 11188d8..5b40def 100644 --- a/scripts/chapter_3_search_chaining/tutorial_2_prior_passing.py +++ b/scripts/chapter_3_search_chaining/tutorial_2_prior_passing.py @@ -12,14 +12,14 @@ __Contents__ -**Initial Setup:** we'll use the same strong lensing data as the previous tutorial, where. -**Model:** Compose the lens model fitted to the data. -**Search:** Configure the non-linear search used to fit the model. -**Prior Passing:** We are now going to use the prior passing API to pass these results, in a way which does not. -**Result:** Overview of the results of the model-fit. -**Wrap Up:** Summary of the script and next steps. -**Detailed Explanation Of Prior Passing:** To end, I provide a detailed overview of how prior passing works and illustrate tools that can be. -**EXAMPLE:** Lets go through an example using a real parameter. +- **Initial Setup:** we'll use the same strong lensing data as the previous tutorial, where. +- **Model:** Compose the lens model fitted to the data. +- **Search:** Configure the non-linear search used to fit the model. +- **Prior Passing:** We are now going to use the prior passing API to pass these results, in a way which does not. +- **Result:** Overview of the results of the model-fit. +- **Wrap Up:** Summary of the script and next steps. +- **Detailed Explanation Of Prior Passing:** To end, I provide a detailed overview of how prior passing works and illustrate tools that can be. +- **EXAMPLE:** Lets go through an example using a real parameter. """ diff --git a/scripts/chapter_3_search_chaining/tutorial_3_lens_and_source.py b/scripts/chapter_3_search_chaining/tutorial_3_lens_and_source.py index 310979a..e373807 100644 --- a/scripts/chapter_3_search_chaining/tutorial_3_lens_and_source.py +++ b/scripts/chapter_3_search_chaining/tutorial_3_lens_and_source.py @@ -24,11 +24,11 @@ __Contents__ -**Dated Tutorial:** This example tutorial was written ~4 years ago, when **PyAutoLens** was in its infancy and had a. -**Initial Setup:** we'll use strong lensing data, where. -**Paths:** All three searches will use the same `path_prefix`, so we write it here to avoid repetition. -**Notes:** We use linear light profiles througout this script, given that the model is quite complex and this. -**Wrap Up:** Summary of the script and next steps. +- **Dated Tutorial:** This example tutorial was written ~4 years ago, when **PyAutoLens** was in its infancy and had a. +- **Initial Setup:** we'll use strong lensing data, where. +- **Paths:** All three searches will use the same `path_prefix`, so we write it here to avoid repetition. +- **Notes:** We use linear light profiles througout this script, given that the model is quite complex and this. +- **Wrap Up:** Summary of the script and next steps. __Dated Tutorial__ diff --git a/scripts/chapter_3_search_chaining/tutorial_4_x2_lens_galaxies.py b/scripts/chapter_3_search_chaining/tutorial_4_x2_lens_galaxies.py index d098913..d2c73bc 100644 --- a/scripts/chapter_3_search_chaining/tutorial_4_x2_lens_galaxies.py +++ b/scripts/chapter_3_search_chaining/tutorial_4_x2_lens_galaxies.py @@ -17,11 +17,11 @@ __Contents__ -**Initial Setup:** we'll use new strong lensing data, where. -**Mask:** Define the 2D mask applied to the dataset for the model-fit. -**Paths:** All four searches will use the same `path_prefix`, so we write it here to avoid repetition. -**Search Chaining Approach:** Looking at the image, there are two blobs of light corresponding to the two lens galaxies. -**Wrap Up:** Summary of the script and next steps. +- **Initial Setup:** we'll use new strong lensing data, where. +- **Mask:** Define the 2D mask applied to the dataset for the model-fit. +- **Paths:** All four searches will use the same `path_prefix`, so we write it here to avoid repetition. +- **Search Chaining Approach:** Looking at the image, there are two blobs of light corresponding to the two lens galaxies. +- **Wrap Up:** Summary of the script and next steps. """ diff --git a/scripts/chapter_3_search_chaining/tutorial_5_complex_source.py b/scripts/chapter_3_search_chaining/tutorial_5_complex_source.py index 8a1e2e2..bf01fc3 100644 --- a/scripts/chapter_3_search_chaining/tutorial_5_complex_source.py +++ b/scripts/chapter_3_search_chaining/tutorial_5_complex_source.py @@ -14,11 +14,11 @@ __Contents__ -**Initial Setup:** we'll use new strong lensing data, where. -**Paths:** All four searches will use the same `path_prefix`, so we write it here to avoid repetition. -**Search Chaining Approach:** The source is clearly complex, with more than 4 peaks of light. -**Run Times:** Profiling the expected run time of the model-fit. -**Wrap Up:** Summary of the script and next steps. +- **Initial Setup:** we'll use new strong lensing data, where. +- **Paths:** All four searches will use the same `path_prefix`, so we write it here to avoid repetition. +- **Search Chaining Approach:** The source is clearly complex, with more than 4 peaks of light. +- **Run Times:** Profiling the expected run time of the model-fit. +- **Wrap Up:** Summary of the script and next steps. """ diff --git a/scripts/chapter_4_pixelizations/tutorial_10_brightness_adaption.py b/scripts/chapter_4_pixelizations/tutorial_10_brightness_adaption.py index f9d79c4..d6a4c8c 100644 --- a/scripts/chapter_4_pixelizations/tutorial_10_brightness_adaption.py +++ b/scripts/chapter_4_pixelizations/tutorial_10_brightness_adaption.py @@ -18,12 +18,12 @@ __Contents__ -**Initial Setup:** we'll use the same strong lensing data as the previous tutorial, where. -**Adapt Image:** We can use this fit to set up our adapt image. -**Adaption:** Now lets take a look at brightness based adaption in action. -**Hilbert:** So how does the `adapt_image` adapt the pixelization to the source's brightness? -**Weight Map:** We now have a sense of how our `Hilbert` image-mesh is computed, so lets look at how we create the. -**Wrap Up:** Summary of the script and next steps. +- **Initial Setup:** we'll use the same strong lensing data as the previous tutorial, where. +- **Adapt Image:** We can use this fit to set up our adapt image. +- **Adaption:** Now lets take a look at brightness based adaption in action. +- **Hilbert:** So how does the `adapt_image` adapt the pixelization to the source's brightness? +- **Weight Map:** We now have a sense of how our `Hilbert` image-mesh is computed, so lets look at how we create the. +- **Wrap Up:** Summary of the script and next steps. """ diff --git a/scripts/chapter_4_pixelizations/tutorial_11_adaptive_regularization.py b/scripts/chapter_4_pixelizations/tutorial_11_adaptive_regularization.py index 6dd0b1a..79d9133 100644 --- a/scripts/chapter_4_pixelizations/tutorial_11_adaptive_regularization.py +++ b/scripts/chapter_4_pixelizations/tutorial_11_adaptive_regularization.py @@ -12,10 +12,10 @@ __Contents__ -**Initial Setup:** we'll use the same strong lensing data as the previous tutorial, where. -**Convenience Function:** We are going to fit the image using a magnification based grid. -**Adaptive Regularization:** Lets now look at adaptive regularization in action, by setting up a adapt-image and using the. -**Wrap Up:** Summary of the script and next steps. +- **Initial Setup:** we'll use the same strong lensing data as the previous tutorial, where. +- **Convenience Function:** We are going to fit the image using a magnification based grid. +- **Adaptive Regularization:** Lets now look at adaptive regularization in action, by setting up a adapt-image and using the. +- **Wrap Up:** Summary of the script and next steps. """ diff --git a/scripts/chapter_4_pixelizations/tutorial_1_pixelizations.py b/scripts/chapter_4_pixelizations/tutorial_1_pixelizations.py index 634b1da..381b5f3 100644 --- a/scripts/chapter_4_pixelizations/tutorial_1_pixelizations.py +++ b/scripts/chapter_4_pixelizations/tutorial_1_pixelizations.py @@ -9,9 +9,9 @@ __Contents__ -**Initial Setup:** Lets setup a lensed source-plane grid, using a lens galaxy and tracer. -**Mesh:** Next, lets set up a `Mesh` using the `mesh` module. -**Wrap Up:** Summary of the script and next steps. +- **Initial Setup:** Lets setup a lensed source-plane grid, using a lens galaxy and tracer. +- **Mesh:** Next, lets set up a `Mesh` using the `mesh` module. +- **Wrap Up:** Summary of the script and next steps. """ diff --git a/scripts/chapter_4_pixelizations/tutorial_2_mappers.py b/scripts/chapter_4_pixelizations/tutorial_2_mappers.py index c6f76a3..52d4bbc 100644 --- a/scripts/chapter_4_pixelizations/tutorial_2_mappers.py +++ b/scripts/chapter_4_pixelizations/tutorial_2_mappers.py @@ -11,10 +11,10 @@ __Contents__ -**Initial Setup:** we'll use new strong lensing data, where. -**Mappers:** We now setup a `Pixelization` and use it to create a `Mapper` via the tracer`s source-plane grid. -**Mask:** Define the 2D mask applied to the dataset for the model-fit. -**Wrap Up:** Summary of the script and next steps. +- **Initial Setup:** we'll use new strong lensing data, where. +- **Mappers:** We now setup a `Pixelization` and use it to create a `Mapper` via the tracer`s source-plane grid. +- **Mask:** Define the 2D mask applied to the dataset for the model-fit. +- **Wrap Up:** Summary of the script and next steps. """ diff --git a/scripts/chapter_4_pixelizations/tutorial_3_inversions.py b/scripts/chapter_4_pixelizations/tutorial_3_inversions.py index eae7d6a..c8c0440 100644 --- a/scripts/chapter_4_pixelizations/tutorial_3_inversions.py +++ b/scripts/chapter_4_pixelizations/tutorial_3_inversions.py @@ -12,11 +12,11 @@ __Contents__ -**Initial Setup:** we'll use the same strong lensing data as the previous tutorial, where. -**Pixelization:** Finally, we can now use the `Mapper` to reconstruct the source via an `Inversion`. -**Positive Only Solver:** Ensuring positive-only solutions for linear light profile intensities. -**Wrap Up:** Summary of the script and next steps. -**Detailed Explanation:** If you are interested in a more detailed description of how inversions work, then checkout the file. +- **Initial Setup:** we'll use the same strong lensing data as the previous tutorial, where. +- **Pixelization:** Finally, we can now use the `Mapper` to reconstruct the source via an `Inversion`. +- **Positive Only Solver:** Ensuring positive-only solutions for linear light profile intensities. +- **Wrap Up:** Summary of the script and next steps. +- **Detailed Explanation:** If you are interested in a more detailed description of how inversions work, then checkout the file. """ diff --git a/scripts/chapter_4_pixelizations/tutorial_4_bayesian_regularization.py b/scripts/chapter_4_pixelizations/tutorial_4_bayesian_regularization.py index cc9acb7..f651892 100644 --- a/scripts/chapter_4_pixelizations/tutorial_4_bayesian_regularization.py +++ b/scripts/chapter_4_pixelizations/tutorial_4_bayesian_regularization.py @@ -14,12 +14,12 @@ __Contents__ -**Initial Setup:** we'll use the same strong lensing data as the previous tutorial, where. -**Convenience Function:** we're going to perform a lot of fits using an `Inversion` this tutorial. -**Pixelization:** Okay, so lets look at our fit from the previous tutorial in more detail. -**Regularization:** The source reconstruction looks excellent! -**Bayesian Evidence:** For inversions, we therefore need a different goodness-of-fit measure to choose the appropriate. -**Detailed Description:** Below, I provide a more detailed discussion of the Bayesian evidence. +- **Initial Setup:** we'll use the same strong lensing data as the previous tutorial, where. +- **Convenience Function:** we're going to perform a lot of fits using an `Inversion` this tutorial. +- **Pixelization:** Okay, so lets look at our fit from the previous tutorial in more detail. +- **Regularization:** The source reconstruction looks excellent! +- **Bayesian Evidence:** For inversions, we therefore need a different goodness-of-fit measure to choose the appropriate. +- **Detailed Description:** Below, I provide a more detailed discussion of the Bayesian evidence. """ diff --git a/scripts/chapter_4_pixelizations/tutorial_5_borders.py b/scripts/chapter_4_pixelizations/tutorial_5_borders.py index b581dea..1b69f0b 100644 --- a/scripts/chapter_4_pixelizations/tutorial_5_borders.py +++ b/scripts/chapter_4_pixelizations/tutorial_5_borders.py @@ -11,9 +11,9 @@ __Contents__ -**Initial Setup:** we'll use the same strong lensing data as the previous tutorial, where. -**Borders:** So, what is a border? -**Wrap Up:** Summary of the script and next steps. +- **Initial Setup:** we'll use the same strong lensing data as the previous tutorial, where. +- **Borders:** So, what is a border? +- **Wrap Up:** Summary of the script and next steps. """ diff --git a/scripts/chapter_4_pixelizations/tutorial_6_lens_modeling.py b/scripts/chapter_4_pixelizations/tutorial_6_lens_modeling.py index 5b43add..ed13973 100644 --- a/scripts/chapter_4_pixelizations/tutorial_6_lens_modeling.py +++ b/scripts/chapter_4_pixelizations/tutorial_6_lens_modeling.py @@ -16,11 +16,11 @@ __Contents__ -**Initial Setup:** We'll use the same strong lensing data as the previous tutorial, where. -**Unphysical Solutions:** The code below illustrates a systematic set of solutions called demagnified solutions, which. -**Brief Description:** To see the short-comings of an inversion, we begin by performing a fit where the lens galaxy has an. -**Light Profiles:** We can also model strong lenses using light profiles and an inversion at the same time. -**Wrap Up:** Summary of the script and next steps. +- **Initial Setup:** We'll use the same strong lensing data as the previous tutorial, where. +- **Unphysical Solutions:** The code below illustrates a systematic set of solutions called demagnified solutions, which. +- **Brief Description:** To see the short-comings of an inversion, we begin by performing a fit where the lens galaxy has an. +- **Light Profiles:** We can also model strong lenses using light profiles and an inversion at the same time. +- **Wrap Up:** Summary of the script and next steps. """ diff --git a/scripts/chapter_4_pixelizations/tutorial_7_adaptive_pixelization.py b/scripts/chapter_4_pixelizations/tutorial_7_adaptive_pixelization.py index ea0198c..8d5ecdf 100644 --- a/scripts/chapter_4_pixelizations/tutorial_7_adaptive_pixelization.py +++ b/scripts/chapter_4_pixelizations/tutorial_7_adaptive_pixelization.py @@ -11,11 +11,11 @@ __Contents__ -**Initial Setup:** We'll use the same strong lensing data as the previous tutorial, where. -**Advantages and Disadvatanges:** Lets think about the rectangular pixelization. -**Image Mesh:** The Delaunay mesh is an irregular grid of pixels (or triangles) in the source-plane. -**Regularization:** On the rectangular grid, we regularized each source pixel with its 4 neighbors. -**Wrap Up:** Summary of the script and next steps. +- **Initial Setup:** We'll use the same strong lensing data as the previous tutorial, where. +- **Advantages and Disadvatanges:** Lets think about the rectangular pixelization. +- **Image Mesh:** The Delaunay mesh is an irregular grid of pixels (or triangles) in the source-plane. +- **Regularization:** On the rectangular grid, we regularized each source pixel with its 4 neighbors. +- **Wrap Up:** Summary of the script and next steps. """ diff --git a/scripts/chapter_4_pixelizations/tutorial_9_fit_problems.py b/scripts/chapter_4_pixelizations/tutorial_9_fit_problems.py index 79d5fc3..f6ea57f 100644 --- a/scripts/chapter_4_pixelizations/tutorial_9_fit_problems.py +++ b/scripts/chapter_4_pixelizations/tutorial_9_fit_problems.py @@ -46,13 +46,13 @@ __Contents__ -**Initial Setup:** we'll use 3 sources whose `effective_radius` and `sersic_index` are changed such that each is more. -**Mask:** Define the 2D mask applied to the dataset for the model-fit. -**Simulator:** Now, lets simulate all 3 of our source's as to create `Imaging` data. -**Fitting:** Fit the lens model to the dataset and inspect the results. -**Fit Problems:** Lets fit our first source which was simulated using the flattest light profile. -**Discussion:** Okay, so what did we learn? -**Wrap Up:** Summary of the script and next steps. +- **Initial Setup:** we'll use 3 sources whose `effective_radius` and `sersic_index` are changed such that each is more. +- **Mask:** Define the 2D mask applied to the dataset for the model-fit. +- **Simulator:** Now, lets simulate all 3 of our source's as to create `Imaging` data. +- **Fitting:** Fit the lens model to the dataset and inspect the results. +- **Fit Problems:** Lets fit our first source which was simulated using the flattest light profile. +- **Discussion:** Okay, so what did we learn? +- **Wrap Up:** Summary of the script and next steps. """ diff --git a/scripts/chapter_optional/tutorial_searches.py b/scripts/chapter_optional/tutorial_searches.py index 7e5c4a0..20cddcb 100644 --- a/scripts/chapter_optional/tutorial_searches.py +++ b/scripts/chapter_optional/tutorial_searches.py @@ -13,9 +13,9 @@ __Contents__ -**Nested Sampling:** Lets first perform the model-fit using Nautilus, but look at different parameters that control how. -**Optimizers:** There are a class of non-linear searches called optimizers, which seek to optimize the log likelihood. -**MCMC:** For users familiar with Markov Chain Monte Carlo (MCMC) non-linear samplers, PyAutoFit supports the. +- **Nested Sampling:** Lets first perform the model-fit using Nautilus, but look at different parameters that control how. +- **Optimizers:** There are a class of non-linear searches called optimizers, which seek to optimize the log likelihood. +- **MCMC:** For users familiar with Markov Chain Monte Carlo (MCMC) non-linear samplers, PyAutoFit supports the. """ diff --git a/scripts/simulator/lens_sersic.py b/scripts/simulator/lens_sersic.py index 59c3361..9fa5bcf 100644 --- a/scripts/simulator/lens_sersic.py +++ b/scripts/simulator/lens_sersic.py @@ -9,13 +9,13 @@ __Contents__ -**Model:** Compose the lens model fitted to the data. -**Dataset Paths:** The `dataset_type` describes the type of data being simulated and `dataset_name` gives it a. -**Simulate:** Simulate the image using a (y,x) grid with the adaptive over sampling scheme. -**Ray Tracing:** Setup the lens galaxy's light, mass and source galaxy light for this simulated lens. -**Output:** Output the simulated dataset to the dataset path as .fits files. -**Visualize:** Output a subplot of the simulated dataset, the image and the tracer's quantities to the dataset. -**Tracer json:** Save the `Tracer` in the dataset folder as a .json file, ensuring the true light profiles, mass. +- **Model:** Compose the lens model fitted to the data. +- **Dataset Paths:** The `dataset_type` describes the type of data being simulated and `dataset_name` gives it a. +- **Simulate:** Simulate the image using a (y,x) grid with the adaptive over sampling scheme. +- **Ray Tracing:** Setup the lens galaxy's light, mass and source galaxy light for this simulated lens. +- **Output:** Output the simulated dataset to the dataset path as .fits files. +- **Visualize:** Output a subplot of the simulated dataset, the image and the tracer's quantities to the dataset. +- **Tracer json:** Save the `Tracer` in the dataset folder as a .json file, ensuring the true light profiles, mass. __Model__ diff --git a/scripts/simulator/lens_x2.py b/scripts/simulator/lens_x2.py index 9ecc6a9..fb93dbe 100644 --- a/scripts/simulator/lens_x2.py +++ b/scripts/simulator/lens_x2.py @@ -15,13 +15,13 @@ __Contents__ -**Model:** Compose the lens model fitted to the data. -**Dataset Paths:** The `dataset_type` describes the type of data being simulated (in this case, `Imaging` data) and. -**Simulate:** Simulate the image using a (y,x) grid with the adaptive over sampling scheme. -**Ray Tracing:** Setup the lens galaxy's light, mass and source galaxy light for this simulated lens. -**Output:** Output the simulated dataset to the dataset path as .fits files. -**Visualize:** Output a subplot of the simulated dataset, the image and the tracer's quantities to the dataset. -**Tracer json:** Save the `Tracer` in the dataset folder as a .json file, ensuring the true light profiles, mass. +- **Model:** Compose the lens model fitted to the data. +- **Dataset Paths:** The `dataset_type` describes the type of data being simulated (in this case, `Imaging` data) and. +- **Simulate:** Simulate the image using a (y,x) grid with the adaptive over sampling scheme. +- **Ray Tracing:** Setup the lens galaxy's light, mass and source galaxy light for this simulated lens. +- **Output:** Output the simulated dataset to the dataset path as .fits files. +- **Visualize:** Output a subplot of the simulated dataset, the image and the tracer's quantities to the dataset. +- **Tracer json:** Save the `Tracer` in the dataset folder as a .json file, ensuring the true light profiles, mass. __Model__ diff --git a/scripts/simulator/no_lens_light__mass_sis.py b/scripts/simulator/no_lens_light__mass_sis.py index be186a4..9fc014d 100644 --- a/scripts/simulator/no_lens_light__mass_sis.py +++ b/scripts/simulator/no_lens_light__mass_sis.py @@ -9,13 +9,13 @@ __Contents__ -**Model:** Compose the lens model fitted to the data. -**Dataset Paths:** The `dataset_type` describes the type of data being simulated and `dataset_name` gives it a. -**Simulate:** Simulate the image using a (y,x) grid with the adaptive over sampling scheme. -**Ray Tracing:** Setup the lens galaxy's light, mass and source galaxy light for this simulated lens. -**Output:** Output the simulated dataset to the dataset path as .fits files. -**Visualize:** Output a subplot of the simulated dataset, the image and the tracer's quantities to the dataset. -**Tracer json:** Save the `Tracer` in the dataset folder as a .json file, ensuring the true light profiles, mass. +- **Model:** Compose the lens model fitted to the data. +- **Dataset Paths:** The `dataset_type` describes the type of data being simulated and `dataset_name` gives it a. +- **Simulate:** Simulate the image using a (y,x) grid with the adaptive over sampling scheme. +- **Ray Tracing:** Setup the lens galaxy's light, mass and source galaxy light for this simulated lens. +- **Output:** Output the simulated dataset to the dataset path as .fits files. +- **Visualize:** Output a subplot of the simulated dataset, the image and the tracer's quantities to the dataset. +- **Tracer json:** Save the `Tracer` in the dataset folder as a .json file, ensuring the true light profiles, mass. __Model__ diff --git a/scripts/simulator/source_complex.py b/scripts/simulator/source_complex.py index 3a7d5cf..830299c 100644 --- a/scripts/simulator/source_complex.py +++ b/scripts/simulator/source_complex.py @@ -10,13 +10,13 @@ __Contents__ -**Model:** Compose the lens model fitted to the data. -**Dataset Paths:** The `dataset_type` describes the type of data being simulated and `dataset_name` gives it a. -**Simulate:** Simulate the image using a (y,x) grid with the adaptive over sampling scheme. -**Ray Tracing:** Setup the lens galaxy's light, mass and source galaxy light for this simulated lens. -**Output:** Output the simulated dataset to the dataset path as .fits files. -**Visualize:** Output a subplot of the simulated dataset, the image and the tracer's quantities to the dataset. -**Tracer json:** Save the `Tracer` in the dataset folder as a .json file, ensuring the true light profiles, mass. +- **Model:** Compose the lens model fitted to the data. +- **Dataset Paths:** The `dataset_type` describes the type of data being simulated and `dataset_name` gives it a. +- **Simulate:** Simulate the image using a (y,x) grid with the adaptive over sampling scheme. +- **Ray Tracing:** Setup the lens galaxy's light, mass and source galaxy light for this simulated lens. +- **Output:** Output the simulated dataset to the dataset path as .fits files. +- **Visualize:** Output a subplot of the simulated dataset, the image and the tracer's quantities to the dataset. +- **Tracer json:** Save the `Tracer` in the dataset folder as a .json file, ensuring the true light profiles, mass. __Model__