Processing Modules#

Noise#

class skcomponents.processing.noise.DarkCurrent(exposure_time: Optional[float] = None, temperature: float = 273.15, tref: float = 273.15, e_pixel_s: float = 200, c1: float = 1140000.0, c2: float = 9080, ratio_exp: float = 3.0)[source]#

Bases: skcomponents.processing.processor.SignalProcessor

Add dark current to a signal

Parameters

exposure_time (float) – exposure time of the measurement [s]
temperature (float) – ccd temperature [K]
tref (float) – reference temperature of the dark current parameters [K]
e_pixel_s (float) – electrons per

dark_signal_rate(ccd_temperature: Optional[float] = None)[source]#

process_signal(signal: numpy.ndarray, temperature: Optional[float] = None, exposure_time=None) → numpy.ndarray[source]#

Process the signal.

Parameters: signal (np.ndarray) – input signal
Return type: np.ndarray

class skcomponents.processing.noise.GaussianNoise(noise_level: float = 0.01, relative=True)[source]#

Bases: skcomponents.processing.processor.SignalProcessor

Add gaussian noise to a signal

Parameters: noise_level (float) – noise level in fraction of signal (1% = 0.01)

noise_estimate(signal: numpy.ndarray) → numpy.ndarray[source]#: Provide an estimate of the noise generated on the signal

process_signal(signal: numpy.ndarray) → numpy.ndarray[source]#

Process the signal.

Parameters: signal (np.ndarray) – input signal
Return type: np.ndarray

class skcomponents.processing.noise.PoissonCounting[source]#

Bases: skcomponents.processing.processor.SignalProcessor

Add poisson counting error to the signal

noise_estimate(signal: numpy.ndarray) → numpy.ndarray[source]#: Provide an estimate of the noise generated on the signal

process_signal(signal: numpy.ndarray) → numpy.ndarray[source]#

Process the signal.

Parameters: signal (np.ndarray) – input signal
Return type: np.ndarray

class skcomponents.processing.noise.SimpleDarkCurrent(dark_current: Union[float, numpy.ndarray], exposure_time: float = 1.0, temperature: Optional[numpy.ndarray] = None, sensor_temperature: Optional[float] = None, noisy: bool = True)[source]#

Bases: skcomponents.processing.processor.SignalProcessor

property electrons_per_second#

noise_estimate(signal: numpy.ndarray) → numpy.ndarray[source]#: Provide an estimate of the noise generated on the signal

process_signal(signal: numpy.ndarray) → numpy.ndarray[source]#

Process the signal.

Parameters: signal (np.ndarray) – input signal
Return type: np.ndarray

Analogue to Digital Conversion#

class skcomponents.processing.adc.ADC(bits: int, max_value: Optional[float] = None, min_value: Optional[float] = None, rescale: bool = True)[source]#

Bases: skcomponents.processing.processor.SignalProcessor

property adu#: Analog to Ditigit Units.

noise_estimate(signal: numpy.ndarray) → numpy.ndarray[source]#: Provide an estimate of the noise generated on the signal

process_signal(values: numpy.ndarray)[source]#

Process the signal.

Parameters: signal (np.ndarray) – input signal
Return type: np.ndarray