Single Facet Data Process

Module for processing and analyzing SOFAST data for a single facet mirror.

This script performs the following steps: 1. Load saved single facet SOFAST collection data from an HDF5 file. 2. Save projected sinusoidal fringe images to PNG format. 3. Save captured sinusoidal fringe images and mask images to PNG format. 4. Process data with SOFAST and save processed data to HDF5. 5. Generate a suite of plots and save image files.

Examples

To run the script, simply execute it as a standalone program:

>>> python example_process_single_facet.py

This will perform the processing steps and save the results to the data/output/single_facet directory with the following subfolders: 1_images_fringes_projected - The patterns sent to the display during the SOFAST measurement of the optic. 2_images_captured - The captured images of the displayed patterns as seen by the SOFAST camera 3_processed_data - The processed data from SOFAST. 4_processed_output_figures - The output figure suite from a SOFAST characterization.

Notes

  • The script assumes that the input data files are located in the specified directories.

  • Chat GPT 40 assisted with the generation of some docstrings in this file.

example.sofast_fringe.example_process_single_facet.example_process_single_facet()

Performs processing of previously collected SOFAST data of single facet mirror.

  1. Load saved single facet SOFAST collection data from HDF5 file

  2. Save projected sinusoidal fringe images to PNG format

  3. Save captured sinusoidal fringe images and mask images to PNG format

  4. Processes data with SOFAST and save processed data to HDF5

  5. Generate plot suite and save images files

Facet Ensemble Data Process

Module for processing and analyzing SOFAST data for an ensemble of mirrors.

This script performs the following steps:

  1. Load saved facet ensemble SOFAST collection data from an HDF5 file.

  2. Save projected sinusoidal fringe images to PNG format.

  3. Save captured sinusoidal fringe images and mask images to PNG format.

  4. Process data with SOFAST and save processed data to HDF5.

  5. Generate a suite of plots and save image files.

Examples

To run the script, simply execute it as a standalone program:

>>> python example_process_facet_ensemble.py

This will perform the processing steps and save the results to the data/output/single_facet directory with the following subfolders:

1_images_fringes_projected - The patterns sent to the display during the SOFAST measurement of the optic. 2_images_captured - The captured images of the displayed patterns as seen by the SOFAST camera 3_processed_data - The processed data from SOFAST. 4_processed_output_figures - The output figure suite from a SOFAST characterization.

Notes

  • The script assumes that the input data files are located in the specified directories.

  • Chat GPT 4o assisted with the generation of some docstrings in this file.

example.sofast_fringe.example_process_facet_ensemble.example_process_facet_ensemble()

Performs processing of previously collected SOFAST data of facet ensemble.

  1. Load saved facet ensemble Sofast collection data

  2. Save projected sinusoidal fringe images to PNG format

  3. Save captured sinusoidal fringe images and mask images to PNG format

  4. Processes data with SOFAST and save processed data to HDF5

  5. Generate plot suite and save images files

Undefined Facet Data Process

Module for processing and analyzing SOFAST data for a single facet mirror of unknown shape.

This script performs the following steps: 1. Load saved single facet SOFAST collection data from an HDF5 file. 2. Save projected sinusoidal fringe images to PNG format. 3. Save captured sinusoidal fringe images and mask images to PNG format. 4. Process data with SOFAST and save processed data to HDF5. 5. Generate a suite of plots and save image files.

Examples

To run the script, simply execute it as a standalone program:

>>> python example_process_undefined_shape.py

This will perform the processing steps and save the results to the data/output/single_facet directory with the following subfolders: 1_images_fringes_projected - The patterns sent to the display during the SOFAST measurement of the optic. 2_images_captured - The captured images of the displayed patterns as seen by the SOFAST camera 3_processed_data - The processed data from SOFAST. 4_processed_output_figures - The output figure suite from a SOFAST characterization.

Notes

  • The script assumes that the input data files are located in the specified directories.

  • Chat GPT 40 assisted with the generation of some docstrings in this file.

example.sofast_fringe.example_process_undefined_shape.example_process_undefined_shape_facet()

Performs processing of previously collected SOFAST data of single facet mirror with an unknown shape.

  1. Load saved single facet SOFAST collection data from HDF5 file

  2. Save projected sinusoidal fringe images to PNG format

  3. Save captured sinusoidal fringe images and mask images to PNG format

  4. Processes data with SOFAST and save processed data to HDF5

  5. Generate plot suite and save images files

Generating Standard Mirror Output Plots

example.sofast_fringe.example_standard_mirror_plot_output.example_single_facet() → None

Loads and visualizes CSP facet from saved Sofast HDF file containing measured data of an NSTTF Facet.

  1. Load Sofast measurement data

  2. Define viewing/illumination geometry

  3. Create standard output plots:

    • Perform ray trace of facet

    • Plot orthorectified slope maps

    • Plot orthorectified slope error map

    • Plot facet in 3d

    • Plot sun images on receiver

    • Plot ensquared energy curve