Revert removal of ramp_fit code to stcal

CHANGES.rst

@@ -26,7 +26,7 @@ datamodels
 
 - Removed obsolete keyword NDITHPTS and updated attributes for NRIMDTPT [#6083]
 
-- Added units to CombinedSpecModel table output [#6082]
+- Added units to ``CombinedSpecModel`` table output [#6082]
 
 - Added keywords OSS_VER, DETMODE, CMD_TSEL, NOD_TYPE, and GS_V3_PA to
   the core schema [#6086]
@@ -39,14 +39,20 @@ general
 
 - Make CRDS context reporting pytest plugin disabled by default. [#6070]
 
-- Removed all usage of sys.path, in associations and jwst.stpipe [#6098]
+- Removed all usage of sys.path, in associations and ``jwst.stpipe`` [#6098]
 
 lib
 ---
 
 - Updated set_telescope_pointing to populate ENGQLPTG keyword with new
   allowed values [#6088]
 
+ramp_fitting
+------------
+
+- Re-enable multiprocessing in ``RampFitStep`` by moving code back from
+  stcal package. [#6119]
+
 scripts
 -------
 

jwst/ramp_fitting/ramp_fit.py

@@ -0,0 +1,113 @@
+#! /usr/bin/env python
+#
+#  ramp_fit.py - calculate weighted mean of slope, based on Massimo
+#                Robberto's "On the Optimal Strategy to fit MULTIACCUM
+#                ramps in the presence of cosmic rays."
+#                (JWST-STScI-0001490,SM-12; 07/25/08).   The derivation
+#                is a generalization for >1 cosmic rays, calculating
+#                the slope and variance of the slope for each section
+#                of the ramp (in between cosmic rays). The intervals are
+#                determined from the input data quality arrays.
+#
+# Note:
+# In this module, comments on the 'first group','second group', etc are
+#    1-based, unless noted otherwise.
+
+import logging
+
+
+from . import gls_fit           # used only if algorithm is "GLS"
+from . import ols_fit           # used only if algorithm is "OLS"
+from . import utils
+
+log = logging.getLogger(__name__)
+log.setLevel(logging.DEBUG)
+
+BUFSIZE = 1024 * 300000  # 300Mb cache size for data section
+
+
+def ramp_fit(model, buffsize, save_opt, readnoise_model, gain_model,
+             algorithm, weighting, max_cores):
+    """
+    Calculate the count rate for each pixel in all data cube sections and all
+    integrations, equal to the slope for all sections (intervals between
+    cosmic rays) of the pixel's ramp divided by the effective integration time.
+    The weighting parameter must currently be set to 'optim', to use the optimal
+    weighting (paper by Fixsen, ref. TBA) will be used in the fitting; this is
+    currently the only supported weighting scheme.
+
+    Parameters
+    ----------
+    model : data model
+        input data model, assumed to be of type RampModel
+
+    buffsize : int
+        size of data section (buffer) in bytes
+
+    save_opt : boolean
+       calculate optional fitting results
+
+    readnoise_model : instance of data Model
+        readnoise for all pixels
+
+    gain_model : instance of gain model
+        gain for all pixels
+
+    algorithm : string
+        'OLS' specifies that ordinary least squares should be used;
+        'GLS' specifies that generalized least squares should be used.
+
+    weighting : string
+        'optimal' specifies that optimal weighting should be used;
+         currently the only weighting supported.
+
+    max_cores : string
+        Number of cores to use for multiprocessing. If set to 'none' (the
+        default), then no multiprocessing will be done. The other allowable
+        values are 'quarter', 'half', and 'all'. This is the fraction of cores
+        to use for multi-proc. The total number of cores includes the SMT cores
+        (Hyper Threading for Intel).
+
+    Returns
+    -------
+    new_model : Data Model object
+        DM object containing a rate image averaged over all integrations in
+        the exposure
+
+    int_model : Data Model object or None
+        DM object containing rate images for each integration in the exposure
+
+    opt_model : RampFitOutputModel object or None
+        DM object containing optional OLS-specific ramp fitting data for the
+        exposure
+
+    gls_opt_model : GLS_RampFitModel object or None
+        Object containing optional GLS-specific ramp fitting data for the
+        exposure
+    """
+    if algorithm.upper() == "GLS":
+        new_model, int_model, gls_opt_model = gls_fit.gls_ramp_fit(
+            model, buffsize, save_opt, readnoise_model, gain_model, max_cores)
+        opt_model = None
+    else:
+        # Get readnoise array for calculation of variance of noiseless ramps, and
+        #   gain array in case optimal weighting is to be done
+        frames_per_group = model.meta.exposure.nframes
+        readnoise_2d, gain_2d = \
+            utils.get_ref_subs(model, readnoise_model, gain_model, frames_per_group)
+
+        # Compute ramp fitting using ordinary least squares.
+        new_model, int_model, opt_model = ols_fit.ols_ramp_fit_multi(
+            model, buffsize, save_opt, readnoise_2d, gain_2d, weighting, max_cores)
+        gls_opt_model = None
+
+    # Update data units in output models
+    if new_model is not None:
+        new_model.meta.bunit_data = 'DN/s'
+        new_model.meta.bunit_err = 'DN/s'
+
+    if int_model is not None:
+        int_model.meta.bunit_data = 'DN/s'
+        int_model.meta.bunit_err = 'DN/s'
+
+    return new_model, int_model, opt_model, gls_opt_model