diff --git a/.buildinfo b/.buildinfo new file mode 100644 index 0000000..89e3e55 --- /dev/null +++ b/.buildinfo @@ -0,0 +1,4 @@ +# Sphinx build info version 1 +# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. +config: 62af7e129c6c1ecc6228d4db28e9bc0a +tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/.doctrees/Tutorial_advanced.doctree b/.doctrees/Tutorial_advanced.doctree new file mode 100644 index 0000000..e47579c Binary files /dev/null and b/.doctrees/Tutorial_advanced.doctree differ diff --git a/.doctrees/Tutorial_with_GUI.doctree b/.doctrees/Tutorial_with_GUI.doctree new file mode 100644 index 0000000..a352495 Binary files /dev/null and b/.doctrees/Tutorial_with_GUI.doctree differ diff --git a/.doctrees/environment.pickle b/.doctrees/environment.pickle new file mode 100644 index 0000000..8e1d76f Binary files /dev/null and b/.doctrees/environment.pickle differ diff --git a/.doctrees/getting_started.doctree b/.doctrees/getting_started.doctree new file mode 100644 index 0000000..f058c1d Binary files /dev/null and b/.doctrees/getting_started.doctree differ diff --git a/.doctrees/index.doctree b/.doctrees/index.doctree new file mode 100644 index 0000000..6fcea56 Binary files /dev/null and b/.doctrees/index.doctree differ diff --git a/.doctrees/modules.doctree b/.doctrees/modules.doctree new file mode 100644 index 0000000..86d58ef Binary files /dev/null and b/.doctrees/modules.doctree differ diff --git a/.doctrees/simca.doctree b/.doctrees/simca.doctree new file mode 100644 index 0000000..8ce54cd Binary files /dev/null and b/.doctrees/simca.doctree differ diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 0000000..e69de29 diff --git a/Tutorial_advanced.html b/Tutorial_advanced.html new file mode 100644 index 0000000..be313a4 --- /dev/null +++ b/Tutorial_advanced.html @@ -0,0 +1,300 @@ + + + + + + + Tutorial - Advanced (only script) — simca 1.0 documentation + + + + + + + + + + + + + + + + + +
+ + +
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+ +
+
+
+
+ +
+

Tutorial - Advanced (only script)

+
+

Single acquisition

+

This tutorial walks you through the process of running a simple acquisition using the CassiSystem class from the simca package.

+
+

Setup

+

First, make sure to import the necessary modules:

+
from simca import CassiSystem, load_yaml_config
+
+
+

Next, load the configuration files:

+
config_dataset = load_yaml_config("simca/configs/dataset.yml")
+config_system = load_yaml_config("simca/configs/cassi_system.yml")
+config_patterns = load_yaml_config("simca/configs/pattern.yml")
+config_acquisition = load_yaml_config("simca/configs/acquisition.yml")
+
+
+

Then, set the name of the dataset of interest:

+
dataset_name = "indian_pines"
+
+
+
+
+

Initialize the CassiSystem

+

Initialize the CassiSystem:

+
cassi_system = CassiSystem(system_config=config_system)
+
+
+
+
+

Load the Hyperspectral dataset

+

Load the hyperspectral dataset:

+
cassi_system.load_dataset(dataset_name, config_dataset["datasets directory"])
+
+
+
+
+

Generate the Coded Aperture Pattern

+

Generate the coded aperture pattern:

+
cassi_system.generate_2D_pattern(config_patterns)
+
+
+
+
+

Propagate the Coded Aperture Grid

+

Propagate the coded aperture grid to the detector plane:

+
cassi_system.propagate_coded_aperture_grid()
+
+
+
+
+

Generate the Filtering Cube

+

Generate the filtering cube:

+
cassi_system.generate_filtering_cube()
+
+
+
+
+

(Optional) Generate the PSF

+

Generate the PSF of the optical system:

+
cassi_system.optical_model.generate_psf(type="Gaussian",radius=100)
+
+
+
+
+

Simulate the Acquisition

+

Simulate the acquisition (with PSF in this case):

+
cassi_system.image_acquisition(use_psf=True, chunck_size=50)
+
+
+
+
+

Save the Acquisition

+

Finally, save the acquisition:

+
cassi_system.save_acquisition(config_patterns, config_acquisition)
+
+
+

And that’s it! You’ve successfully run an acquisition using the CassiSystem class from the simca package.

+
+
+
+

Multiple acquisitions

+

This tutorial walks you through the process of running multiple acquisitions using the CassiSystem class from the simca package.

+
+

Setup

+

First, make sure to import the necessary modules and configurations:

+
import matplotlib.pyplot as plt
+from simca import CassiSystem
+from simca.functions_general_purpose import *
+import os
+
+config_dataset = load_yaml_config("simca/configs/dataset.yml")
+config_system = load_yaml_config("simca/configs/cassi_system.yml")
+config_patterns = load_yaml_config("simca/configs/pattern.yml")
+config_acquisition = load_yaml_config("simca/configs/acquisition.yml")
+
+dataset_name = "indian_pines"
+results_directory = "./data/results/lego_test_1"
+nb_of_acq = 10
+
+
+
+
+

Initialize the CassiSystem

+

Initialize the CassiSystem:

+
cassi_system = CassiSystem(system_config=config_system)
+
+
+
+
+

Load the Hyperspectral dataset

+

Load the hyperspectral dataset:

+
cassi_system.load_dataset(dataset_name, config_dataset["datasets directory"])
+
+
+
+
+

Generate Multiple Patterns for Acquisition

+

Generate multiple coded aperture patterns:

+
cassi_system.generate_multiple_patterns(config_patterns, nb_of_acq)
+
+
+
+
+

Propagate the Coded Aperture Grid

+

Propagate the coded aperture grid to the detector plane:

+
cassi_system.propagate_coded_aperture_grid()
+
+
+
+
+

Generate Multiple Filtering Cubes

+

Generate the multiple filtering cubes:

+
cassi_system.generate_multiple_filtering_cubes(nb_of_acq)
+
+
+
+
+

Simulate Multiple Acquisitions

+

Simulate multiple acquisitions:

+
cassi_system.multiple_image_acquisitions(use_psf=False, nb_of_filtering_cubes=nb_of_acq, chunck_size=50)
+
+
+
+
+

Save the Acquisition

+

Set up the results directory and save the acquisition:

+
cassi_system.result_directory = results_directory
+os.makedirs(results_directory, exist_ok=True)
+
+save_config_file("config_system", cassi_system.system_config, cassi_system.result_directory)
+save_config_file("config_pattern", config_patterns, cassi_system.result_directory)
+save_config_file("config_acquisition", config_acquisition, cassi_system.result_directory)
+save_data_in_hdf5("interpolated_scene", cassi_system.interpolated_scene, cassi_system.result_directory)
+save_data_in_hdf5("panchro", cassi_system.panchro, cassi_system.result_directory)
+save_data_in_hdf5("wavelengths", cassi_system.optical_model.system_wavelengths, cassi_system.result_directory)
+save_data_in_hdf5("list_of_compressed_measurements", cassi_system.list_of_measurements, cassi_system.result_directory)
+save_data_in_hdf5("list_of_filtering_cubes", cassi_system.list_of_filtering_cubes, cassi_system.result_directory)
+save_data_in_hdf5("list_of_patterns", cassi_system.list_of_patterns, cassi_system.result_directory)
+
+
+

Congratulations! You’ve successfully performed and saved multiple acquisitions using the CassiSystem class from the simca package.

+
+
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+ + + + \ No newline at end of file diff --git a/Tutorial_with_GUI.html b/Tutorial_with_GUI.html new file mode 100644 index 0000000..9166c0b --- /dev/null +++ b/Tutorial_with_GUI.html @@ -0,0 +1,365 @@ + + + + + + + Tutorial - Basics (with GUI) — simca 1.0 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
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+ +
+
+
+
+ +
+

Tutorial - Basics (with GUI)

+
+

Discover Main Features

+

The are 4 main features included in the application. These modules are not completely independent, using them sequentially is recommended for first usages.

+
    +

  • Dataset Analysis (only with GUI): for analyzing multi- or hyper-spectral datasets. It includes vizualization of data slices, spectrum analysis, and dataset labeling.

    • +

  • Optical Design: for evaluating and comparing the performances of various optical systems.

    • +

  • Coded Aperture: for generating various patterns and corresponding filtering cubes.

    • +

  • Acquisition: for simulating the acquisition process of coded images

    • +
+Docusaurus logo
+
+

Feature A : Dataset Analysis

+

The Dataset analysis tab is used to load & display datasets characteristics.

+layout dataset tab
+

1. Settings

+

Located on the left side of the application window.

+

Includes:

+
    +
  • +
    datasets directorypath to the datasets directory. All datasets be stored here.

    ATTENTION : click on the reload datasets button if you change the datasets directory path

    +
    +
    +
    • +

  • dataset name : a ComboBox displaying the datasets available in the selected directory

    • +
  • +
    loaded dataset dimensionsThese values are displayed once the dataset is loaded
      +

    • dimension along X : dimension of the dataset in the X direction (main spectral dispersion direction)

      • +

    • dimension along Y : dimension of the dataset in the Y direction (perpendicular to spectral dispersion direction)

      • +

    • number of spectral bands : number of spectral bands in the loaded dataset

      • +

    • minimum wavelength : minimum wavelength, usually corresponds to the spectral band n°0

      • +

    • maximum wavelength : maximum wavelength, usually corresponds to the last spectral band

      • +
    +
    +
    +
    • +
+
+
+

2. Load dataset button

+

By clicking on this button, the dataset selected in the dataset name ComboBox is loaded by the application.

+
+
+

3. Display windows

+

Located on the right side of the application window.

+

Once a dataset is loaded, one can inspect the spatial and spectral content of the dataset.

+
+

Hyperspectral cube

+

By moving the slider, you choose the spectral plane to be displayed.

+dataset layout 2
+
+

Compare Spectra

+dataset layout 2
+
+

Labelisation map

+dataset layout 2
+
+

Labelisation Histogram

+dataset layout 2
+
+
+
+

Feature B : Optical Design

+

The Optical Design tab is used for quick evaluation of the optical system characteristics (spectral dispersion & distortions).

+layout dataset tab
+

1. System Settings

+Docusaurus logo

Located on the left side of the application window.

+

Includes:

+
    +
  • +
    infos:
      +

    • system name: name of the studied system

      • +
    +
    +
    +
    • +

  • system architecture: All parameters that define the optical system and thus the spatial/spectral filtering

    +
    +
      +

    • system type

      • +

    • propagation type : model used for evaluating the spatial/spectral filtering

      • +

    • focal lens F [in micrometers]

      • +
    • +
      dispersive element:
        +

      • type: Prism or Grating

        • +

      • A (only when prism is selected): apex angle of the prism [in degrees]

        • +

      • m (only when grating is selected): considered order of diffraction [no units]

        • +

      • G (only when grating is selected): grating lines density [lines/mm]

        • +

      • delta alpha c [in degrees]

        • +

      • delta beta c [in degrees]

        • +

      • wavelength center [in nm]

        • +
      +
      +
      +
      • +
    +
    +
    • +

  • detector: parameters that define the detector grid

    • +

  • SLM: parameters that define the mask grid

    • +

  • spectral range: the spectral boundaries of the system and the number of spectral bands to consider

    • +
+
+
+

2. Run Simulation button

+

For each considered wavelength, the mask grid points coordinates is propagated onto the detector.

+
+
+

3. Display

+

Located on the right side of the application window. It can be used to analyse the mask grid object and its images in the detector plane.

+
+

Coded aperture grid

+Docusaurus logo
+
+

Propagated coded aperture grid

+

Spectral images of the input coded aperture grid for the minimum, maximum, and center wavelength.

+

ATTENTION: center wavelength (605 nm on the given example) is different from the system architecture center wavelength

+Docusaurus logo
+
+

Distortion maps

+

Get qualitative and quantitative distortion data:

+Docusaurus logo
+
+
+
+

Feature C : Pattern generation

+

The Coded Aperture tab is used for designing patterns and generating associated filtering cube.

+Coded Aperture design tab
+

1. Patterns Settings

+

Located on the left side of the application window.

+

The patterns characteristics depend on the chosen pattern type.

+

Available patterns:

+
    +
  • +
    slit:only one column of the coded aperture is open (perpendicular to the spectral dispersion), thus generating a spectral gradient type filter.
      +

    • slit position: relative to the center column between -100 and 100 coded aperture elements

      • +

    • slit width: between 1 and 30 coded aperture elements.

      • +
    +
    +
    +
    • +

  • random: random noise pattern with a normal law

    • +

  • blue noise: random noise pattern with boosted high frequencies

    • +

  • custon h5 pattern: custom pattern that should be a h5 file with a container named “pattern”. Once loaded, the pattern is cropped to fit SLM dimensions

    • +
+
+
+

2. Generate pattern

+

By clicking on this button, a 2D array representing a coded aperture pattern is generated through pattern generation functions contained in the functions_patterns_generation.py file.

+
+
+

3. Generate Filtering Cube button

+

By clicking on this button, a CassiSystem instance is creating the filtering cube corresponding to the detector dimensions along X and Y and the number of spectral bands.

+

Each slice of the filtering contains the projection of the coded aperture pattern on the detector grid.

+

ATTENTION : The spectral sampling of the filtering cube is not the same as the dataset’s sampling. It is defined in the spectral range section of the Optical Design tab. The wavelengths are equally spaced between “minimum wavelength” and “maximum wavelength”.

+
+
+

4. Display Pattern and Filtering Cube

+

Located on the right side of the application window.

+
+

Pattern

+

Shows the generated (or loaded) pattern:

+Pattern
+
+

Filtering Cube, slice by slice

+

Shows the corresponding filtering cube. By moving the slider, one can inspect the filtering cube slice by slice:

+filtering cube
+
+
+
+

Feature D : Acquisition

+

The Acquisition tab is used to generate compressed measurements given: a dataset and a filtering cube.

+

Note that the dataset is:

+
    +

  • cropped in the spatial dimensions to fit the filtering cube sampling (detector dimensions).

    • +

  • interpolated in the spectral dimension according to the filtering cube sampling.

    • +
+layout scene tab
+

1. Settings

+

For now, the GUI only includes one mode: single acquisition.

+

A Point-spread-function (PSF) can be added for more realism. For now, each slice of the filtered scene is convolved by the same kernel. A wavelength-dependent PSF will be added in the future.

+
+
+

2. Run Acquisition button

+

By clicking on this button:

+
    +

  • First, the dataset cube is cropped in the spatial dimensions and interpolated in the spectral dimension.

    • +

  • Second, a point by point multiplication is performed between the filtering cube and the reinterpolated scene.

    • +
+
+
+

3. Display measurements

+
+

compressed measurements

+

The image as measured by the detector.

+layout scene tab
+
+

Spectral images

+

Each slice of the filtered scene.

+layout scene tab
+
+

Panchromatic image

+

No spatial/spectral filtering, the interpolated scene is simply summed along its spectral dimension.

+layout scene tab
+
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+ + + + \ No newline at end of file diff --git a/_images/SIMCA_logo-2-cropped.png b/_images/SIMCA_logo-2-cropped.png new file mode 100644 index 0000000..e13a59d Binary files /dev/null and b/_images/SIMCA_logo-2-cropped.png differ diff --git a/_images/acquisition_tab.svg b/_images/acquisition_tab.svg new file mode 100644 index 0000000..be35deb --- /dev/null +++ b/_images/acquisition_tab.svg @@ -0,0 +1,130 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/_images/distortion_maps.svg b/_images/distortion_maps.svg new file mode 100644 index 0000000..72dfdbb --- /dev/null +++ b/_images/distortion_maps.svg @@ -0,0 +1,9486 @@ + + + + + + + + 2023-05-17T18:31:36.489245 + image/svg+xml + + + Matplotlib v3.7.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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b/_sources/Tutorial_advanced.rst.txt new file mode 100644 index 0000000..cb35a51 --- /dev/null +++ b/_sources/Tutorial_advanced.rst.txt @@ -0,0 +1,228 @@ +Tutorial - Advanced (only script) +================================= + + + + +Single acquisition +------------------ + + +This tutorial walks you through the process of running a simple acquisition using the `CassiSystem` class from the :code:`simca` package. + +Setup +..... + +First, make sure to import the necessary modules: + +.. code-block:: python + + from simca import CassiSystem, load_yaml_config + +Next, load the configuration files: + +.. code-block:: python + + config_dataset = load_yaml_config("simca/configs/dataset.yml") + config_system = load_yaml_config("simca/configs/cassi_system.yml") + config_patterns = load_yaml_config("simca/configs/pattern.yml") + config_acquisition = load_yaml_config("simca/configs/acquisition.yml") + +Then, set the name of the dataset of interest: + +.. code-block:: python + + dataset_name = "indian_pines" + +Initialize the CassiSystem +.............................. + +Initialize the `CassiSystem`: + +.. code-block:: python + + cassi_system = CassiSystem(system_config=config_system) + +Load the Hyperspectral dataset +.............................. + +Load the hyperspectral dataset: + +.. code-block:: python + + cassi_system.load_dataset(dataset_name, config_dataset["datasets directory"]) + +Generate the Coded Aperture Pattern +........................................ + +Generate the coded aperture pattern: + +.. code-block:: python + + cassi_system.generate_2D_pattern(config_patterns) + +Propagate the Coded Aperture Grid +................................... + +Propagate the coded aperture grid to the detector plane: + +.. code-block:: python + + cassi_system.propagate_coded_aperture_grid() + +Generate the Filtering Cube +.............................. + +Generate the filtering cube: + +.. code-block:: python + + cassi_system.generate_filtering_cube() + +(Optional) Generate the PSF +.............................. + +Generate the PSF of the optical system: + +.. code-block:: python + + cassi_system.optical_model.generate_psf(type="Gaussian",radius=100) + +Simulate the Acquisition +......................... + +Simulate the acquisition (with PSF in this case): + +.. code-block:: python + + cassi_system.image_acquisition(use_psf=True, chunck_size=50) + +Save the Acquisition +......................... + +Finally, save the acquisition: + +.. code-block:: python + + cassi_system.save_acquisition(config_patterns, config_acquisition) + +And that's it! You've successfully run an acquisition using the `CassiSystem` class from the :code:`simca` package. + + +Multiple acquisitions +---------------------- + +This tutorial walks you through the process of running multiple acquisitions using the `CassiSystem` class from the :code:`simca` package. + +Setup +......................... + +First, make sure to import the necessary modules and configurations: + + +.. code-block:: python + + import matplotlib.pyplot as plt + from simca import CassiSystem + from simca.functions_general_purpose import * + import os + + config_dataset = load_yaml_config("simca/configs/dataset.yml") + config_system = load_yaml_config("simca/configs/cassi_system.yml") + config_patterns = load_yaml_config("simca/configs/pattern.yml") + config_acquisition = load_yaml_config("simca/configs/acquisition.yml") + + dataset_name = "indian_pines" + results_directory = "./data/results/lego_test_1" + nb_of_acq = 10 + + +Initialize the CassiSystem +.................................................. + +Initialize the `CassiSystem`: + + +.. code-block:: python + + cassi_system = CassiSystem(system_config=config_system) + + +Load the Hyperspectral dataset +.................................................. + +Load the hyperspectral dataset: + + +.. code-block:: python + + cassi_system.load_dataset(dataset_name, config_dataset["datasets directory"]) + + +Generate Multiple Patterns for Acquisition +........................................................................... + +Generate multiple coded aperture patterns: + + +.. code-block:: python + + cassi_system.generate_multiple_patterns(config_patterns, nb_of_acq) + + +Propagate the Coded Aperture Grid +.................................................. + +Propagate the coded aperture grid to the detector plane: + + +.. code-block:: python + + cassi_system.propagate_coded_aperture_grid() + + +Generate Multiple Filtering Cubes +.................................................. + +Generate the multiple filtering cubes: + + +.. code-block:: python + + cassi_system.generate_multiple_filtering_cubes(nb_of_acq) + + +Simulate Multiple Acquisitions +.................................................. + +Simulate multiple acquisitions: + + +.. code-block:: python + + cassi_system.multiple_image_acquisitions(use_psf=False, nb_of_filtering_cubes=nb_of_acq, chunck_size=50) + + +Save the Acquisition +......................... + +Set up the results directory and save the acquisition: + + +.. code-block:: python + + cassi_system.result_directory = results_directory + os.makedirs(results_directory, exist_ok=True) + + save_config_file("config_system", cassi_system.system_config, cassi_system.result_directory) + save_config_file("config_pattern", config_patterns, cassi_system.result_directory) + save_config_file("config_acquisition", config_acquisition, cassi_system.result_directory) + save_data_in_hdf5("interpolated_scene", cassi_system.interpolated_scene, cassi_system.result_directory) + save_data_in_hdf5("panchro", cassi_system.panchro, cassi_system.result_directory) + save_data_in_hdf5("wavelengths", cassi_system.optical_model.system_wavelengths, cassi_system.result_directory) + save_data_in_hdf5("list_of_compressed_measurements", cassi_system.list_of_measurements, cassi_system.result_directory) + save_data_in_hdf5("list_of_filtering_cubes", cassi_system.list_of_filtering_cubes, cassi_system.result_directory) + save_data_in_hdf5("list_of_patterns", cassi_system.list_of_patterns, cassi_system.result_directory) + +Congratulations! You've successfully performed and saved multiple acquisitions using the `CassiSystem` class from the :code:`simca` package. + diff --git a/_sources/Tutorial_with_GUI.rst.txt b/_sources/Tutorial_with_GUI.rst.txt new file mode 100644 index 0000000..cddec06 --- /dev/null +++ b/_sources/Tutorial_with_GUI.rst.txt @@ -0,0 +1,279 @@ +Tutorial - Basics (with GUI) +============================ + + + +Discover Main Features +----------------------- + +The are 4 main features included in the application. These modules are not completely independent, using them sequentially is recommended for first usages. + +- **Dataset Analysis** (only with GUI): for analyzing multi- or hyper-spectral datasets. It includes vizualization of data slices, spectrum analysis, and dataset labeling. + +- **Optical Design**: for evaluating and comparing the performances of various optical systems. + +- **Coded Aperture**: for generating various patterns and corresponding filtering cubes. + +- **Acquisition**: for simulating the acquisition process of coded images + +.. image:: /resources/layout_general.svg + :alt: Docusaurus logo + +Feature A : Dataset Analysis +---------------------------- + +The Dataset analysis tab is used to **load & display datasets characteristics**. + +.. image:: /resources/layout_dataset_tab.svg + :alt: layout dataset tab + +1. Settings +............. + +Located on the left side of the application window. + +Includes: + +- `datasets directory` : path to the datasets directory. All datasets be stored here. + **ATTENTION** : click on the `reload datasets` button if you change the datasets directory path + +- **dataset name : a ComboBox displaying the datasets available in the selected directory** + +- `loaded dataset dimensions` : These values are displayed once the dataset is loaded + - `dimension along X` : dimension of the dataset in the X direction (main spectral dispersion direction) + - `dimension along Y` : dimension of the dataset in the Y direction (perpendicular to spectral dispersion direction) + - `number of spectral bands` : number of spectral bands in the loaded dataset + - `minimum wavelength` : minimum wavelength, usually corresponds to the spectral band n°0 + - `maximum wavelength` : maximum wavelength, usually corresponds to the last spectral band + +2. Load dataset button +....................... + +By clicking on this button, the dataset selected in the `dataset name` ComboBox is loaded by the application. + +3. Display windows +.................... + +Located on the right side of the application window. + +**Once a dataset is loaded**, one can inspect the spatial and spectral content of the dataset. + +Hyperspectral cube +^^^^^^^^^^^^^^^^^^ + +By moving the slider, you choose the spectral plane to be displayed. + +.. image:: /resources/layout_dataset_2.svg + :alt: dataset layout 2 + +Compare Spectra +^^^^^^^^^^^^^^^ + +.. image:: /resources/layout_dataset_3.svg + :alt: dataset layout 2 + +Labelisation map +^^^^^^^^^^^^^^^^ + +.. image:: /resources/layout_dataset_4.svg + :alt: dataset layout 2 + +Labelisation Histogram +^^^^^^^^^^^^^^^^^^^^^^ + +.. image:: /resources/layout_dataset_5.svg + :alt: dataset layout 2 + + +Feature B : Optical Design +-------------------------- + +The Optical Design tab is used for quick evaluation of the optical system characteristics (spectral dispersion & distortions). + +.. image:: /resources/layout_optical_design_tab.svg + :alt: layout dataset tab + +1. System Settings +................... + +.. image:: /resources/mask_to_detector.svg + :alt: Docusaurus logo + +Located on the left side of the application window. + +Includes: + +- **infos**: + - *system name*: name of the studied system +- **system architecture**: All parameters that define the optical system and thus the spatial/spectral filtering + + - *system type* + - *propagation type* : model used for evaluating the spatial/spectral filtering + - *focal lens F* [in micrometers] + - *dispersive element*: + - *type*: Prism or Grating + - *A (only when prism is selected)*: apex angle of the prism [in degrees] + - *m (only when grating is selected)*: considered order of diffraction [no units] + - *G (only when grating is selected)*: grating lines density [lines/mm] + - *delta alpha c* [in degrees] + - *delta beta c* [in degrees] + - *wavelength center* [in nm] +- **detector**: parameters that define the detector grid +- **SLM**: parameters that define the mask grid +- **spectral range**: the spectral boundaries of the system and the number of spectral bands to consider + +2. Run Simulation button +......................... + +For each considered wavelength, the mask grid points coordinates is propagated onto the detector. + +3. Display +........... + +Located on the right side of the application window. It can be used to analyse the mask grid object and its images in the detector plane. + +Coded aperture grid +^^^^^^^^^^^^^^^^^^^^ + +.. image:: /resources/input_grid.svg + :alt: Docusaurus logo + +Propagated coded aperture grid +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Spectral images of the input coded aperture grid for the minimum, maximum, and center wavelength. + +**ATTENTION**: center wavelength (605 nm on the given example) is different from the system architecture center wavelength + +.. image:: /resources/propagated_grids.svg + :alt: Docusaurus logo + +Distortion maps +^^^^^^^^^^^^^^^ + +Get qualitative and quantitative distortion data: + +.. image:: /resources/distortion_maps.svg + :alt: Docusaurus logo + + +Feature C : Pattern generation +------------------------------- + +The Coded Aperture tab is used for designing patterns and generating associated filtering cube. + +.. image:: /resources/layout_coded_aperture.svg + :alt: Coded Aperture design tab + + + +1. Patterns Settings +..................... + +Located on the left side of the application window. + +The patterns characteristics depend on the chosen pattern type. + +Available patterns: + +- **slit**:*only one column of the coded aperture is open (perpendicular to the spectral dispersion), thus generating a spectral gradient type filter.* + - *slit position*: relative to the center column between -100 and 100 coded aperture elements + - *slit width*: between 1 and 30 coded aperture elements. +- **random**: random noise pattern with a normal law +- **blue noise**: random noise pattern with boosted high frequencies +- **custon h5 pattern**: custom pattern that should be a h5 file with a container named "pattern". Once loaded, the pattern is cropped to fit SLM dimensions + +2. Generate pattern +.................... + +By clicking on this button, a 2D array representing a coded aperture pattern is generated through pattern generation functions contained in the `functions_patterns_generation.py` file. + + +3. Generate Filtering Cube button +.................................. + + +By clicking on this button, a `CassiSystem` instance is creating the filtering cube corresponding to the **detector dimensions along X and Y** and the **number of spectral bands**. + +Each slice of the filtering contains the projection of the coded aperture pattern on the detector grid. + +**ATTENTION** : The spectral sampling of the filtering cube is not the same as the dataset's sampling. It is defined in the spectral range section of the Optical Design tab. The wavelengths are equally spaced between "minimum wavelength" and "maximum wavelength". + +4. Display Pattern and Filtering Cube +...................................... + +Located on the right side of the application window. + +Pattern +^^^^^^^ + +Shows the generated (or loaded) pattern: + +.. image:: /resources/pattern_display.svg + :alt: Pattern + +Filtering Cube, slice by slice +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Shows the corresponding filtering cube. By moving the slider, one can inspect the filtering cube slice by slice: + +.. image:: /resources/filtering-cube.svg + :alt: filtering cube + +Feature D : Acquisition +------------------------ + +The Acquisition tab is used to generate compressed measurements given: a **dataset** and a **filtering cube**. + +Note that the dataset is: + +- cropped in the spatial dimensions to fit the filtering cube sampling (detector dimensions). +- interpolated in the spectral dimension according to the filtering cube sampling. + + + +.. image:: /resources/acquisition_tab.svg + :alt: layout scene tab + +1. Settings +............ + +For now, the GUI only includes one mode: single acquisition. + +A Point-spread-function (PSF) can be added for more realism. For now, each slice of the filtered scene is convolved by the same kernel. A wavelength-dependent PSF will be added in the future. + +2. Run Acquisition button +.......................... + +By clicking on this button: + +- First, the dataset cube is cropped in the spatial dimensions and interpolated in the spectral dimension. +- Second, a point by point multiplication is performed between the filtering cube and the reinterpolated scene. + +3. Display measurements +........................ + +compressed measurements +^^^^^^^^^^^^^^^^^^^^^^^^ + +The image as measured by the detector. + +.. image:: /resources/layout_acquisition_1.svg + :alt: layout scene tab + +Spectral images +^^^^^^^^^^^^^^^ + +Each slice of the filtered scene. + +.. image:: /resources/layout_acquisition_2.svg + :alt: layout scene tab + +Panchromatic image +^^^^^^^^^^^^^^^^^^ + +No spatial/spectral filtering, the interpolated scene is simply summed along its spectral dimension. + +.. image:: /resources/layout_acquisition_3.svg + :alt: layout scene tab + diff --git a/_sources/getting_started.rst.txt b/_sources/getting_started.rst.txt new file mode 100644 index 0000000..55f7cc3 --- /dev/null +++ b/_sources/getting_started.rst.txt @@ -0,0 +1,65 @@ +.. _getting_started: + +Getting started +=============== + + + +Installation +------------ + +To install :code:`simca`, follow the steps below: + +1. Clone the repository from GitLab: + +.. code-block:: bash + + git clone git@gitlab.laas.fr:arouxel/simca.git + cd simca + +2. Create a dedicated Python environment using Miniconda. If you don't have Miniconda installed, you can find the instructions `here `_. + +.. code-block:: bash + + # Create a new Python environment + conda create -n simca-env python=3.9 + + # Activate the environment + conda activate simca-env + +3. Install the necessary Python packages that simca relies on. These are listed in the `requirements.txt` file in the repository. + +.. code-block:: bash + + # Install necessary Python packages with pip + pip install -r requirements.txt + + +Usage +----- + +Download datasets +^^^^^^^^^^^^^^^^^^ + +4. Download the standard datasets from this `link `_, then unzip and paste the `datasets` folder in the root directory of SIMCA. + +Quick Start with GUI (option 1) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +5. Start the application: + +.. code-block:: bash + + # run the app + python main.py + +Quick Start with API (option 2) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +5. Run the example script : + +.. code-block:: bash + + # run the script + python simple_script.py + diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt new file mode 100644 index 0000000..d762858 --- /dev/null +++ b/_sources/index.rst.txt @@ -0,0 +1,84 @@ +.. simca documentation master file, created by + sphinx-quickstart on Fri Aug 4 17:05:16 2023. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +SIMCA : optical simulations for coded spectral imaging +======================================================= + + +.. image:: ./resources/SIMCA_logo-2-cropped.png + +SIMCA is a python-based tool designed to perform optical simulations of Coded Aperture Snapshot Spectral Imaging (CASSI) systems. +We provide a python package and a graphical user-interface developed in PyQt5. + +It is built upon ray-tracing equations and interpolation methods to estimate the image formation process and generate realistic measurements of various cassi instruments. + +Available **system architectures** are: + +- Single-Disperser CASSI (:cite:`Wagadarikar2008`) +- Double-Disperser CASSI (:cite:`Gehm2007`) + +Available **propagation models** are: + +- Higher-Order from :cite:`Arguello2013` +- Ray-tracing (first implementation in :cite:`Hemsley2020a`, another paper will be submitted soon) + +Available **optical components** and related characteristics are: + +- Lens (params: focal length) +- Prism (params: apex angle, glass type, orientation misalignments) +- Grating (params: groove density, orientation misalignments) + +More system architectures and optical components will be added in the future. + + +Main Features +============= + +SIMCA includes four main features: + +- **Scene Analysis** (only with GUI): for analyzing multi- or hyper-spectral datasets. It includes vizualization of data slices, spectrum analysis, and dataset labeling. + +- **Optical Design**: for evaluating and comparing the performances of various optical systems. + +- **Coded Aperture patterns Generation**: for generating various patterns and corresponding filtering cubes. + +- **Acquisition Coded Images**: for simulating the acquisition process + +For more detailed information about each feature and further instructions, please visit our `Tutorial - Basics (with GUI) `_ and `Tutorial - Advanced (only script) `_. + + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + getting_started + Tutorial_with_GUI + Tutorial_advanced + simca + + + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` + + +License +======= + +SIMCA is licensed under the `MIT License `_. + +Contact +======= + +For any questions or feedback, please contact us at arouxel@laas.fr + +References +========== +.. bibliography:: ./resources/biblio.bib diff --git a/_sources/modules.rst.txt b/_sources/modules.rst.txt new file mode 100644 index 0000000..4ca8528 --- /dev/null +++ b/_sources/modules.rst.txt @@ -0,0 +1,7 @@ +cassi_systems +============= + +.. toctree:: + :maxdepth: 4 + + cassi_systems diff --git a/_sources/simca.rst.txt b/_sources/simca.rst.txt new file mode 100644 index 0000000..e6929c4 --- /dev/null +++ b/_sources/simca.rst.txt @@ -0,0 +1,48 @@ +API Reference +============= + +.. toctree:: + :maxdepth: 4 + +The :code:`simca` package is based on the work :cite:`rouxelphdthesis` + + + +classes +------- + +.. automodule:: simca.CassiSystem + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: simca.OpticalModel + :members: + :undoc-members: + :show-inheritance: + +functions +---------- + + .. automodule:: simca.functions_scenes + :members: + :undoc-members: + :show-inheritance: + + +.. automodule:: simca.functions_patterns_generation + :members: + :undoc-members: + :show-inheritance: + + +.. automodule:: simca.functions_acquisition + :members: + :undoc-members: + :show-inheritance: + + +.. automodule:: simca.functions_general_purpose + :members: + :undoc-members: + :show-inheritance: diff --git a/_static/_sphinx_javascript_frameworks_compat.js b/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 0000000..8141580 --- /dev/null +++ b/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,123 @@ +/* Compatability shim for jQuery and underscores.js. + * + * Copyright Sphinx contributors + * Released under the two clause BSD licence + */ + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. 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+ +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + `Search finished, found ${resultCount} page(s) matching the search query.` + ); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent !== undefined) return docContent.textContent; + console.warn( + "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + /** + * execute search (requires search index to be loaded) + */ + query: (query) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + // array of [docname, title, anchor, descr, score, filename] + let results = []; + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + results.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id] of foundEntries) { + let score = Math.round(100 * queryLower.length / entry.length) + results.push([ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // lookup as object + objectTerms.forEach((term) => + results.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); + + // now sort the results by score (in opposite order of appearance, since the + // display function below uses pop() to retrieve items) and then + // alphabetically + results.sort((a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; + }); + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + results = results.reverse(); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord) && !terms[word]) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord) && !titleTerms[word]) + arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); + }); + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) + fileMap.get(file).push(word); + else fileMap.set(file, [word]); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords) => { + const text = Search.htmlToText(htmlText); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js new file mode 100644 index 0000000..8a96c69 --- /dev/null +++ b/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/genindex.html b/genindex.html new file mode 100644 index 0000000..a7da84b --- /dev/null +++ b/genindex.html @@ -0,0 +1,108 @@ + + + + + + Index — simca 1.0 documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Index
    • +
  • +
    • +
+
+
+
+
+ + +

Index

+ +
+ +
+ + +
+
+
+ +
+ +
+

© Copyright 2023, Antoine Rouxel.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/getting_started.html b/getting_started.html new file mode 100644 index 0000000..8de6ec8 --- /dev/null +++ b/getting_started.html @@ -0,0 +1,178 @@ + + + + + + + Getting started — simca 1.0 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Getting started

+
+

Installation

+

To install simca, follow the steps below:

+
    +

  1. Clone the repository from GitLab:

    2. +
+
git clone git@gitlab.laas.fr:arouxel/simca.git
+cd simca
+
+
+
    +

  1. Create a dedicated Python environment using Miniconda. If you don’t have Miniconda installed, you can find the instructions here.

    2. +
+
# Create a new Python environment
+conda create -n simca-env python=3.9
+
+# Activate the environment
+conda activate simca-env
+
+
+
    +

  1. Install the necessary Python packages that simca relies on. These are listed in the requirements.txt file in the repository.

    2. +
+
# Install necessary Python packages with pip
+pip install -r requirements.txt
+
+
+
+
+

Usage

+
+

Download datasets

+
    +

  1. Download the standard datasets from this link, then unzip and paste the datasets folder in the root directory of SIMCA.

    2. +
+
+
+

Quick Start with GUI (option 1)

+
    +

  1. Start the application:

    2. +
+
# run the app
+python main.py
+
+
+
+
+

Quick Start with API (option 2)

+
    +

  1. Run the example script :

    2. +
+
# run the script
+python simple_script.py
+
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 0000000..d708512 --- /dev/null +++ b/index.html @@ -0,0 +1,212 @@ + + + + + + + SIMCA : optical simulations for coded spectral imaging — simca 1.0 documentation + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • SIMCA : optical simulations for coded spectral imaging
    • +
  • + View page source +
    • +
+
+
+
+
+ +
+

SIMCA : optical simulations for coded spectral imaging

+_images/SIMCA_logo-2-cropped.png +

SIMCA is a python-based tool designed to perform optical simulations of Coded Aperture Snapshot Spectral Imaging (CASSI) systems. +We provide a python package and a graphical user-interface developed in PyQt5.

+

It is built upon ray-tracing equations and interpolation methods to estimate the image formation process and generate realistic measurements of various cassi instruments.

+

Available system architectures are:

+
    +

  • Single-Disperser CASSI ([WJWB08])

    • +

  • Double-Disperser CASSI ([GJB+07])

    • +
+

Available propagation models are:

+
    +

  • Higher-Order from [ARW+13]

    • +

  • Ray-tracing (first implementation in [HLCM20], another paper will be submitted soon)

    • +
+

Available optical components and related characteristics are:

+
    +

  • Lens (params: focal length)

    • +

  • Prism (params: apex angle, glass type, orientation misalignments)

    • +

  • Grating (params: groove density, orientation misalignments)

    • +
+

More system architectures and optical components will be added in the future.

+
+
+

Main Features

+

SIMCA includes four main features:

+
    +

  • Scene Analysis (only with GUI): for analyzing multi- or hyper-spectral datasets. It includes vizualization of data slices, spectrum analysis, and dataset labeling.

    • +

  • Optical Design: for evaluating and comparing the performances of various optical systems.

    • +

  • Coded Aperture patterns Generation: for generating various patterns and corresponding filtering cubes.

    • +

  • Acquisition Coded Images: for simulating the acquisition process

    • +
+

For more detailed information about each feature and further instructions, please visit our Tutorial - Basics (with GUI) and Tutorial - Advanced (only script).

+
+

Contents:

+ +
+
+
+

Indices and tables

+ +
+
+

License

+

SIMCA is licensed under the MIT License.

+
+
+

Contact

+

For any questions or feedback, please contact us at arouxel@laas.fr

+
+
+

References

+
+
+[ARW+13] +

Henry Arguello, Hoover Rueda, Yuehao Wu, Dennis W. Prather, and Gonzalo R. Arce. Higher-order computational model for coded aperture spectral imaging. Applied Optics, 52(10):D12, mar 2013. doi:10.1364/ao.52.000d12.

+
+
+[GJB+07] +

M. E. Gehm, R. John, D. J. Brady, R. M. Willett, and T. J. Schulz. Single-shot compressive spectral imaging with a dual-disperser architecture. Optics Express, 15(21):14013, oct 2007. doi:10.1364/oe.15.014013.

+
+
+[HLCM20] +

Elizabeth Hemsley, Simon Lacroix, Hervé Carfantan, and Antoine Monmayrant. Calibration of programmable spectral imager with dual disperser architecture. Optics Communications, 468:125767, aug 2020. doi:10.1016/j.optcom.2020.125767.

+
+
+[Rou22] +

Antoine Rouxel. Étude d'un imageur hyperspectral adaptatif dans un contexte d'observation de la terre. Theses, INSA de Toulouse, June 2022. URL: https://theses.hal.science/tel-03997931.

+
+
+[WJWB08] +

A. Wagadarikar, T. John, R. Willett, and D. Brady. Single disperser design for coded aperture snapshot spectral imaging. Applied Optics, 47(10):B44–B51, aug 2008. doi:10.1364/ao.47.000b44.

+
+
+
+
+ + +
+ + + + + + + + + + \ No newline at end of file diff --git a/modules.html b/modules.html new file mode 100644 index 0000000..140e859 --- /dev/null +++ b/modules.html @@ -0,0 +1,109 @@ + + + + + + + cassi_systems — simca 1.0 documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

cassi_systems

+
+
+
+ + +
+
+
+ +
+ +
+

© Copyright 2023, Antoine Rouxel.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/objects.inv b/objects.inv new file mode 100644 index 0000000..fe1d922 Binary files /dev/null and b/objects.inv differ diff --git a/search.html b/search.html new file mode 100644 index 0000000..6ae8620 --- /dev/null +++ b/search.html @@ -0,0 +1,123 @@ + + + + + + Search — simca 1.0 documentation + + + + + + + + + + + + + + + + + + +
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+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
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API Reference

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The simca package is based on the work [Rou22]

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classes

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functions

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