+- **2D self-patterning** is the most basic use-case implemented within the **2d_discrete** pipeline. The ability of MCPM to generate a diversity of patterns with network characteristics is achieved by *disabling the data marker deposition*, leaving only the MCPM agents to generate the marker responsible for maintaining structure.
-- **2D procedural pipeline** provide an easy environment to experiment with the behavior of *PolyPhy* in the presence of discrete data with different spatial frequencies. Editing (adding new data points) is also supported. This pipeline is implemented in the **./experiments/Jupyter/PolyPhy_2D_discrete_data** notebook.+- **2D procedural pipeline** provide an easy environment to experiment with the behavior of *PolyPhy* in the presence of discrete data with different spatial frequencies. Editing (adding new data points) is also supported. This is invoked by specifying **2d_discrete** pipeline without providing any input data file, thus prompting *PolyPhy* to generate the data procedurally.
-- **2D discrete pipeline** implements the canonical way of working with custom data defined by a CSV file. The example below demonstrates fitting to a 2D projection of the SDSS galaxy dataset. This pipeline is implemented in the **./experiments/Jupyter/PolyPhy_2D_discrete_data** notebook.+- **2D discrete pipeline** implements the canonical way of working with custom data defined by a CSV file. The example below demonstrates fitting to a 2D projection of the SDSS galaxy dataset. It is invoked by specifying **2d_discrete** pipeline and a custom input data file.
-- **2D continuous pipeline** demonstrates the workflow with a continuous user-provided dataset. Instead of a discrete set of points as in the previous use-cases, the data is defined by a scalar field, which in 2D amounts to a grayscale image. The example below approximates the US road network using only a sparse population density map as the input. This pipeline is implemented in the **./experiments/Jupyter/PolyPhy_2D_continuous_data** notebook.+- **2D continuous pipeline** demonstrates the workflow with a continuous user-provided dataset. Instead of a discrete set of points as in the previous use-cases, the data is defined by a scalar field, which in 2D amounts to a grayscale image. The example below approximates the US road network using only a sparse population density map as the input. This pipeline is implemented in the **./experiments/Jupyter/PolyPhy_2D_continuous_data** notebook, to be ported to the main build.
-- **3D discrete pipeline** represents an equivalent functionality to the original *Polyphorm* implementation. The dataset consists of SDSS galaxies defined as a weighted collection of 3D points. THe visualization is based on volumetric ray marching simultaneously fetching the deposit and the trace fields. This pipeline is implemented in the **./experiments/Jupyter/PolyPhy_3D_discrete_data** notebook.+- **3D discrete pipeline** represents an equivalent functionality to the original *Polyphorm* implementation. The dataset consists of SDSS galaxies defined as a weighted collection of 3D points. The visualization is based on volumetric ray marching simultaneously fetching the deposit and the trace fields. This pipeline is invoked through the **3d_discrete** parameter.
## How to Use PolyPhy Below is a recording of the [PolyPhy Workshop](https://elek.pub/workshop_cross2022.html) given as part of the [OSPO Symposium 2022](https://ospo.ucsc.edu/event/20220927/).