SimpleModel¶
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class
lsst.cp.pipe.SimpleModel¶ Bases:
lsst.cp.pipe.OverscanModelSimple analytic overscan model.
Methods Summary
difference(params, signal, data, error, …)Calculate the flattened difference array between model and data. loglikelihood(params, signal, data, error, …)Calculate log likelihood of the model. model_results(params, signal, num_transfers)Generate a realization of the overscan model, using the specified fit parameters and input signal. negative_loglikelihood(params, signal, data, …)Calculate negative log likelihood of the model. rms_error(params, signal, data, error, …)Calculate RMS error between model and data. Methods Documentation
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difference(params, signal, data, error, *args, **kwargs)¶ Calculate the flattened difference array between model and data.
Parameters: - params :
lmfit.Parameters Object containing the model parameters.
- signal :
np.ndarray, (nMeasurements) Array of image means.
- data :
np.ndarray, (nMeasurements, nCols) Array of overscan column means from each measurement.
- error :
float Fixed error value.
- *args
Additional position arguments.
- **kwargs
Additional keyword arguments.
Returns: - difference :
np.ndarray, (nMeasurements*nCols) The rms error between the model and input data.
- params :
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loglikelihood(params, signal, data, error, *args, **kwargs)¶ Calculate log likelihood of the model.
Parameters: - params :
lmfit.Parameters Object containing the model parameters.
- signal :
np.ndarray, (nMeasurements) Array of image means.
- data :
np.ndarray, (nMeasurements, nCols) Array of overscan column means from each measurement.
- error :
float Fixed error value.
- *args
Additional position arguments.
- **kwargs
Additional keyword arguments.
Returns: - logL :
float The log-likelihood of the observed data given the model parameters.
- params :
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static
model_results(params, signal, num_transfers, start=1, stop=10)¶ Generate a realization of the overscan model, using the specified fit parameters and input signal.
Parameters: - params :
lmfit.Parameters Object containing the model parameters.
- signal :
np.ndarray, (nMeasurements) Array of image means.
- num_transfers :
int Number of serial transfers that the charge undergoes.
- start :
int, optional First overscan column to fit. This number includes the last imaging column, and needs to be adjusted by one when using the overscan bounding box.
- stop :
int, optional Last overscan column to fit. This number includes the last imaging column, and needs to be adjusted by one when using the overscan bounding box.
Returns: - res :
np.ndarray, (nMeasurements, nCols) Model results.
- params :
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negative_loglikelihood(params, signal, data, error, *args, **kwargs)¶ Calculate negative log likelihood of the model.
Parameters: - params :
lmfit.Parameters Object containing the model parameters.
- signal :
np.ndarray, (nMeasurements) Array of image means.
- data :
np.ndarray, (nMeasurements, nCols) Array of overscan column means from each measurement.
- error :
float Fixed error value.
- *args
Additional position arguments.
- **kwargs
Additional keyword arguments.
Returns: - negativelogL :
float The negative log-likelihood of the observed data given the model parameters.
- params :
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rms_error(params, signal, data, error, *args, **kwargs)¶ Calculate RMS error between model and data.
Parameters: - params :
lmfit.Parameters Object containing the model parameters.
- signal :
np.ndarray, (nMeasurements) Array of image means.
- data :
np.ndarray, (nMeasurements, nCols) Array of overscan column means from each measurement.
- error :
float Fixed error value.
- *args
Additional position arguments.
- **kwargs
Additional keyword arguments.
Returns: - rms :
float The rms error between the model and input data.
- params :
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