OverscanModel¶
- class lsst.cp.pipe.OverscanModel¶
Bases:
object
Base class for handling model/data fit comparisons.
This handles all of the methods needed for the lmfit Minimizer to run.
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
- 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.
- params
- Returns:
- difference
np.ndarray
, (nMeasurements*nCols) The rms error between the model and input data.
- difference
- 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.
- params
- Returns:
- logL
float
The log-likelihood of the observed data given the model parameters.
- logL
- 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.
- params
- Returns:
- results
np.ndarray
, (nMeasurements, nCols) Model results.
- results
- 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.
- params
- Returns:
- negativelogL
float
The negative log-likelihood of the observed data given the model parameters.
- negativelogL
- 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.
- params
- Returns:
- rms
float
The rms error between the model and input data.
- rms