Science Camera

The ScienceCamera component represents the imaging path used to evaluate science output from the AO system. Unlike the wavefront sensor, this component is typically used to observe performance metrics such as PSF structure, long-exposure integration, tip-tilt behavior, and Strehl-related quantities.

The class manages the shared-memory outputs associated with science imaging, including short- and long-exposure PSF products.

Soft-RTC Example

The following example shows the typical soft-RTC pattern for science-camera setup.

from pyRTC.ScienceCamera import ScienceCamera
from pyRTC.utils import read_yaml_file

conf = read_yaml_file("path/to/config.yaml")
sci = ScienceCamera(conf["psf"])
sci.start()

# The short-exposure and long-exposure products are written to shared memory.
sci.expose()
sci.integrate()

Hard-RTC Example

If the science camera is tied to a specific vendor SDK or operational process boundary, it can also be launched in hard-RTC mode.

from pyRTC.Pipeline import hardwareLauncher

config = 'path/to/config.yaml'
port = 3003

sci = hardwareLauncher('path/to/pyRTC/hardware/myScienceCamera.py', config, port)
sci.launch()
sci.run("integrate")

Operational Notes

The science camera is usually responsible for image-quality observables rather than control observables. Common configuration and workflow concerns include:

short- vs long-exposure output products
dark-frame handling
model PSF loading
ROI, gain, binning, and exposure settings
downstream analysis such as Strehl and centroid-derived metrics

This class is often subclassed for site-specific cameras under pyRTC.hardware.

Parameters

class pyRTC.ScienceCamera.ScienceCamera(conf)[source]

Bases: pyRTCComponent

Base class for cameras that produce science images and image-quality metrics.

ScienceCamera centralizes the parts of imaging that are shared across real and synthetic science-camera backends: SHM publication, dark/model PSF handling, long-exposure accumulation, and simple Strehl/tip-tilt telemetry. Subclasses are expected to implement the device-facing acquisition logic and then call the parent methods so the standard pyRTC products stay updated.

Config

namestr: Name of the camera.
widthint: Width of the image. Required.
heightint: Height of the image. Required.
darkCountint: Number of dark frames to average. Required.
integrationint: Integration length. Required.
darkFilestr, optional: File to save the dark frames. Default is “”.
modelFilestr, optional: File to save the model PSF. Default is “”.

Attributes

namestr: Name of the camera.
imageShapetuple: Shape of the image.
imageRawDTypetype: Data type of the raw image.
imageDTypetype: Data type of the image.
psfLongDtypetype: Data type of the long exposure PSF.
psfShortImageSHM: Shared memory object for the short exposure PSF.
psfLongImageSHM: Shared memory object for the long exposure PSF.
strehlShmImageSHM: Shared memory object for the Strehl ratio.
tipTiltShmImageSHM: Shared memory object for the tip-tilt.
datanumpy.ndarray: Data array for the image.
darknumpy.ndarray: Dark frame.
darkCountint: Number of dark frames to average.
darkFilestr: File to save the dark frames.
modelnumpy.ndarray: Model PSF.
modelFilestr: File to save the model PSF.
strehl_ratiofloat: Strehl ratio.
peak_distfloat: Peak distance.
integrationLengthint: Integration length.
roiWidthint: Width of the region of interest.
roiHeightint: Height of the region of interest.
roiLeftint: Left coordinate of the region of interest.
roiTopint: Top coordinate of the region of interest.
exposureint: Exposure time.
binningint: Binning factor.
gainint: Gain setting.
bitDepthint: Bit depth setting.

computeStrehl(median_filter_size=1, gaussian_sigma=0)[source]

Compute the rough Strehl ratio and tip tilt offset. These values are reference to the modelPSF. If your model PSF is taken empirically, then the Strehl ratio is not absolute, and should only be used as a relative measurement for focal plane feedback.

Parameters

median_filter_sizeint, optional: Size of the median filter to apply. Default is 1.
gaussian_sigmafloat, optional: Sigma for the Gaussian filter. Default is 0.

Returns

float: Strehl ratio.

expose()[source]: Perform a single exposure.

integrate()[source]: Perform multiple exposures and integrate the results. Number of frames set by integrationLength.

loadDark(filename='')[source]

Load the dark frame from a file.

Parameters

filenamestr, optional: File to load the dark frame from. If not specified, uses the configured darkFile.

loadModelPSF(filename='')[source]

Load the model PSF from a file.

Parameters

filenamestr, optional: File to load the model PSF from. If not specified, uses the configured modelFile.

plot()[source]: Plot the current short exposure PSF.

read(block=True)[source]

Read the current short exposure PSF.

Returns

numpy.ndarray: Current short exposure PSF.

readLong()[source]

Read the current long exposure PSF.

Returns

numpy.ndarray: Current long exposure PSF.

saveDark(filename='')[source]

Save the dark frame to a file.

Parameters

filenamestr, optional: File to save the dark frame to. If not specified, uses the configured darkFile.

saveModelPSF(filename='')[source]

Save the model PSF to a file.

Parameters

filenamestr, optional: File to save the model PSF to. If not specified, uses the configured modelFile.

setBinning(binning)[source]

Set the binning factor.

Parameters

binningint: Binning factor to set.

setBitDepth(bitDepth)[source]

Set the bit depth.

Parameters

bitDepthint: Bit depth to set.

setDark(dark)[source]

Set the dark frame.

Parameters

darknumpy.ndarray: Dark frame to set.

setExposure(exposure)[source]

Set the exposure time.

Parameters

exposureint: Exposure time to set.

setGain(gain)[source]

Set the gain.

Parameters

gainint: Gain to set.

setGamma(gamma)[source]

Set the gamma.

Parameters

gammafloat: Gamma to set.

setIntegrationLength(integrationLength)[source]

Set the integration length.

Parameters

integrationLengthint: Integration length to set.

setModelPSF(model)[source]

Set the model PSF.

Parameters

modelnumpy.ndarray: Model PSF to set.

setRoi(roi)[source]

Set the region of interest (ROI).

Parameters

roituple: Tuple containing (width, height, left, top) of the ROI.

start(): Start the registered real-time functions.

stop(): Stops the registered real-time functions.

takeDark()[source]: Take dark frames and average them to create a dark frame. Number of exposures to average set by darkCount parameter.

takeModelPSF()[source]: Capture the current long exposure PSF as the model PSF.