punchbowl.level1.initial_uncertainty#
Functions#
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Convert an input array from DN to photon count. |
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Generate noise based on an input data array, with specified noise parameters. |
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With an input data array compute a corresponding uncertainty array. |
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Prefect task to compute initial uncertainty. |
Module Contents#
- punchbowl.level1.initial_uncertainty.dn_to_photons(data_array: numpy.ndarray, gain_left: float = 4.9, gain_right: float = 4.9) numpy.ndarray#
Convert an input array from DN to photon count.
- punchbowl.level1.initial_uncertainty.compute_noise(data: numpy.ndarray, bias_level: float = 100, dark_level: float = 55.81, gain_left: float = 4.9, gain_right: float = 4.9, read_noise_level: float = 17, bitrate_signal: int = 16) tuple[numpy.ndarray, numpy.ndarray]#
Generate noise based on an input data array, with specified noise parameters.
- Parameters:
data – input data array (n x n)
bias_level – ccd bias level
dark_level – ccd dark level
gain_left – ccd gain (left side of CCD)
gain_right – ccd gain (right side of CCD)
read_noise_level – ccd read noise level
bitrate_signal – desired ccd data bit level
- Returns:
data (np.ndarray) – clipped data with the bias level added
noise (np.ndarray) – computed noise array corresponding to input data and ccd/noise parameters
- punchbowl.level1.initial_uncertainty.compute_uncertainty(data_array: numpy.ndarray, bias_level: float = 100, dark_level: float = 55.81, gain_left: float = 4.9, gain_right: float = 4.9, read_noise_level: float = 17, bitrate_signal: int = 16) numpy.ndarray#
With an input data array compute a corresponding uncertainty array.
- punchbowl.level1.initial_uncertainty.update_initial_uncertainty_task(data_object: ndcube.NDCube, bias_level: float = 100, dark_level: float = 55.81, gain_left: float = 4.9, gain_right: float = 4.9, read_noise_level: float = 17, bitrate_signal: int = 16) ndcube.NDCube#
Prefect task to compute initial uncertainty.