drop support for ols ramp fitting

changes/1398.ramp_fitting.rst

@@ -0,0 +1 @@
+Drop support for ``ols`` ramp fitting.

docs/roman/ramp_fitting/arguments.rst

@@ -4,27 +4,8 @@ Arguments
 =========
 The ramp fitting step has the following optional argument that can be set by the user:
 
-* ``--algorithm``: Algorithm to use. Possible values are `ols` and `ols_cas22`.
-  `ols` is the same algorithm used by JWST and can only be used with even ramps.
-  `ols_cas22` needs to be used for uneven ramps. `ols_cas22` is the default.
-
-The following optional arguments are valid only if using the `ols` algorithm.
-
-* ``--save_opt``: A True/False value that specifies whether to write
-  the optional output product. Default if False.
-
-* ``--opt_name``: A string that can be used to override the default name
-  for the optional output product.
-
-* ``--maximum_cores``: The fraction of available cores that will be
-  used for multi-processing in this step. The default value is 'none' which does not use
-  multi-processing. The other options are 'quarter', 'half', and 'all'. Note that these
-  fractions refer to the total available cores and on most CPUs these include physical
-  and virtual cores. The clock time for the step is reduced
-  almost linearly by the number of physical cores used on all machines. For example, on an Intel CPU with
-  six real cores and 6 virtual cores setting maximum_cores to 'half' results in a
-  decrease of a factor of six in the clock time for the step to run. Depending on the system
-  the clock time can also decrease even more with maximum_cores is set to 'all'.
+* ``--algorithm``: Algorithm to use. At the moment the only supported option
+  is  `ols_cas22` (the default).
 
 * ``--use_ramp_jump_detection``: A True/False value that specifies whether to use
   the unevenly-spaced jump detection integrated into the ramp fitting algorithm.

docs/roman/ramp_fitting/description.rst

@@ -15,11 +15,7 @@ more detail below.
 
 The count rate for each pixel is determined by a linear fit to the
 cosmic-ray-free and saturation-free ramp intervals for each pixel; hereafter
-this interval will be referred to as a "segment." There are two algorithms used:
-Optimal Least-Square ('ols') and Optimal Least-Square for Uneven ramps
-('ols_cas22'). The 'ols' algorithm is the one
-`used by JWST <https://jwst-pipeline.readthedocs.io/en/stable/jwst/ramp_fitting/description.html>`_
-and is further :ref:`described here <rampfit-algorithm-ols>`.
+this interval will be referred to as a "segment."
 
 The 'ols_cas22' algorithm is based on `Casertano et al, STScI Technical Document,
 2022

docs/roman/ramp_fitting/index.rst

@@ -11,10 +11,4 @@ Ramp Fitting
    arguments.rst
    reference_files.rst
 
-.. toctree::
-   :maxdepth: 1
-
-   description_ols.rst
-
-
 .. automodapi:: romancal.ramp_fitting

docs/roman/ramp_fitting/reference_files.rst

@@ -1,12 +1,13 @@
 Reference Files
 ===============
-The ``ramp_fit`` step uses two reference file types: :ref:`GAIN <gain_reffile>`
-and :ref:`READNOISE <readnoise_reffile>`.
+The ``ramp_fit`` step uses three reference file types:
+
+  - :ref:`GAIN <gain_reffile>`
+  - :ref:`READNOISE <readnoise_reffile>`
+  - :ref:`DARK <dark_reffile>`
+
 During ramp fitting, the GAIN values are used to temporarily convert the pixel
 values from units of DN to electrons, and convert the results of ramp fitting
 back to DN.  The READNOISE values are used as part of the noise estimate for
-each pixel. Both are necessary for proper computation of noise estimates.
-
-:ref:`GAIN <gain_reffile>`
-
-:ref:`READNOISE <readnoise_reffile>`
+each pixel. The DARK values are needed to estimate the Poisson noise. See
+:ref:`rampfit-algorithm-ols22` for more details.