Pipeline

Pipeline#

The core algorithm is the detection pipeline which includes various models at each stage of the pipeline. It is an instance of DetectionPipeline class. Each model’s input parameters can be configured using the YAML configuration file.

Inside the pipeline the models are grouped into different levels per detector type mirroring the operational principles of the detector, for example in case of a CCD the model groups are scene generation, photon collection, charge generation, charge collection, charge transfer, charge measurement, readout electronics and data processing in this order.

Each group is structured around a for loop, iterating over all included and selected models in a predefined sequence, which can be customized by the user. All models within a pipeline sequentially access and modify the same Detector object. Upon completion, the pipeline yields the modified Detector object as output, ready for generating output files based on the results.

Since version 2.0, Pyxel includes the ability to accommodate multi-wavelength models. These models, along with their associated groups, are visually highlighted by distinct colors in the accompanying image. Integration of multi-wavelength photons occurs either in the photon collection or in the charge generation model group, ensuring that they are consolidated across the specified wavelength range.

ccd_pipeline

Models#

When referring to “models,” we are discussing various analytical functions, numerical methods, or algorithms designed to approximate, calculate, and visualize electro-optical performance and degradation resulting from operational environments such as space or laboratory tests, including their associated effects like radiation damage.

These models are Python functions that require a Detector object as their input argument. To incorporate a model, it must be added to the YAML configuration file. Subsequently, Pyxel automatically invokes the function within a loop of its corresponding model group, passing the Detector object to it. The model function has the capability to modify this object, which is then utilized and further modified by subsequent models in the pipeline.

Model groups#

A set of models is associated with a model group according to which object of the Detector data container is used or modified by the models. These groups correspond roughly to the detector fundamental functions, e.g. generating charge, so converting photons to charge or modifying the charge bucket.

Models in Pyxel makes changes and storing the data in data buckets (Scene, Photon, Charge, Phase, Pixel, Signal or Image, datatree.DataTree class). The data buckets are not initialized before running a pipeline. The models inside the model groups must initialize the data buckets.

Models can also modify any detector attributes (like Quantum Efficiency, gains, temperature, etc.) stored in a Detector subclass (Characteristics, Environment, Material).

Detector attributes changes can happen globally (on detector level) or locally (on pixel level or only for a specific detector area).

models

Most of the model groups work for CCD, CMOS, MKID and APD detectors, which are imitating the physical working principles of imaging detectors. They are grouped according to which physics data storing objects are modified by them. Note that among the 10 groups, three are dedicated to a single detector type. They are visually highlighted in the accompanying image.

Model functions#

A model function is a function that takes in the Detector object as one of the arguments and edits the data stored in it. The Detector object serves as the mandatory input argument, and may vary in type, such as a CCD or a CMOS type Detector object, depending on the simulation requirements of the model. Any other (optional) input arguments can be defined for the model as well, which will be loaded from the YAML file automatically. Users can change model parameters or enable/disable them by modifying with the configuration file. For example, a model function that multiplies the photon array with the input argument would look like this in the code:

from pyxel.detectors import Detector


def my_model_function(detector: Detector, arg: int = 0):
    """This is my model that will multiply pixel array with the argument.

    Parameters
    ----------
    detector
    arg
    """
    detector.photon.array = detector.photon.array * arg
    return None

Adding a new model#

Users and developers can easily add any kind of new or already existing model to Pyxel, thanks to the easy-to-use model plug-in mechanism developed for this purpose.

For more details, see the adding new models page.

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