Patch update for NLPAR

.github/workflows/publish.yml

@@ -29,7 +29,7 @@ jobs:
       # IMPORTANT: this permission is mandatory for trusted publishing:
       id-token: write
     steps:
-    - uses: actions/checkout@v6
+    - uses: actions/checkout@v7
 
     - name: Set up Python
       uses: actions/setup-python@v6

.github/workflows/tests.yml

@@ -20,7 +20,7 @@ jobs:
     name: check manifest
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v7
 
       - uses: actions/setup-python@v6
         with:
@@ -49,7 +49,7 @@ jobs:
             DEPENDENCIES: matplotlib==3.8 numba==0.56.4 pyopencl==2023.1.2
             LABEL: -oldest
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v7
 
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v6

CHANGELOG.rst

@@ -5,6 +5,39 @@ Changelog
 All notable changes to PyEBSDIndex will be documented in this file. The format is based
 on `Keep a Changelog <https://keepachangelog.com/en/1.1.0>`_.
 
+
+0.3.10.1 (2026-07-03)
+==================
+BUG/ERROR FIX RELEASE
+---------------------
+There was an error when attempting to multi-process GPU calculations in NLPAR/NLSTEM
+that caused new patterns to not be written to the NLPAR/NLSTEM files when using HDF5 as output.
+With the GPU enabled calculations, non-parallel diskIO ends up being the rate limiting step, and
+thus the multiprocess NLPAR is no longer the default method for discrete GPUs, and a single process
+GPU is now the default.  Overall safer, even if it does take longer to process files.
+
+The NLPAR calculations are still done in parallel, either with GPU or multithreaded CPU calculations.
+
+This should require no changes from the examples behaviors outlined in the tutorials.
+
+A thanks to Dan Pennachio for alerting me to this issue and useful example data.
+
+Added
+-----
+
+Changed
+-------
+
+Fixed
+-----
+- Properly send in the vendor keyword to gnomonic correction setup
+  (currently no correction is applied).
+- Removed multi-process NLPAR as default for discrete GPUs as it mostly was fasteer due-to
+  slightly risky multi-process file writes, which are not compatible with standard HDF5.
+- Fixed region-to-region minimum scaling for ``stem_scale=True`` in NLPAR, now using the file
+  global minimum.
+
+
 0.3.10 (2026-05-11)
 ==================
  Added

pyebsdindex/__version.py

@@ -1 +1 @@
-__version__ = "0.3.10"
+__version__ = "0.3.10.1"

pyebsdindex/_ebsd_index_parallel.py

@@ -48,6 +48,7 @@
     from pyebsdindex import band_detect as band_detect
 
 os.environ["GRPC_VERBOSITY"] = "ERROR"
+os.environ["RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO"] = "0"
 
 RAYIPADDRESS = '127.0.0.1'
 #RAYIPADDRESS = '0.0.0.0'
@@ -493,7 +494,7 @@ def index_pats_distributed(
                                                )
             )
         gtaskindex.append(gjob)
-
+        time.sleep(1)
         #gpu_launched += 1
 
     gpuwrker_cycles = -1000
@@ -524,7 +525,8 @@ def index_pats_distributed(
 
             gpuwrker_cycles +=1
 
-            donewrker, busy = ray.wait(gputask,num_returns=len(gputask),  timeout=0.1)
+            #donewrker, busy = ray.wait(gputask,num_returns=len(gputask),  timeout=0.1)
+            donewrker, busy = ray.wait(gputask, num_returns=min(1,len(gputask)), timeout=0.1)
 
             #print(len(donewrker), nret)
             #print()

pyebsdindex/_ebsd_index_single.py

@@ -423,6 +423,7 @@ def __init__(
                 ("nmatch", np.int32),
                 ("matchattempts", np.int32, 4),
                 ("totvotes", np.int32),
+                ("grainid", np.int64),
             ]
         )
 
@@ -560,7 +561,7 @@ def index_pats(
         except:
             pass
 
-        self.gnomonic.calccorrection(PCpat)
+        #self.gnomonic.calccorrection(PCpat)
         banddata, bandnorm = self._detectbands(pats, PCpat, xyloc=xyloc, clparams=clparams, verbose=verbose,
                                                chunksize=chunksize, gpu_id=gpuid)
         tic = timer()

pyebsdindex/ebsd_pattern.py

@@ -218,6 +218,8 @@ def __init__(self,path, filetype=None):
     self.xStep = None  # assumming square grid data, with constant step size
     self.yStep = None
     self.xyCenter = np.array([0.0, 0.0])
+    self.datamax = None
+    self.datamin = None
     # This is the location of the center of the scan relative to center of SEM field-of-view
     self.hexflag = False
     self.filetype = filetype
@@ -423,6 +425,12 @@ def set_scan_rc(self, rc=(0,0)): # helper function for pattern files that don't
     self.nRows = np.uint64(rc[0])
     self.nPatterns = np.uint64(self.nCols * self.nRows)
 
+  def write_datamaxmin(self): # placeholder for non-HDF5 files
+    pass
+
+  def read_datamaxmin(self): # placeholder for non-HDF5 files
+    pass
+
 
 class UPFile(EBSDPatternFile):
 
@@ -1547,6 +1555,37 @@ def pat_writer(self, pat2write, patStart, nPatToWrite, typewrite):
     patterndset[patStart:patStart+nPatToWrite, :, :] = pat2write[0:nPatToWrite,:,:]
     f.close()
 
+  def write_datamaxmin(self):
+    try:
+      f = h5py.File(Path(self.filepath).expanduser(), 'r+')
+    except:
+      print("File Not Found:", str(Path(self.filepath)))
+      return -1
+
+    patterndset = f[self.h5patdatpth]
+
+    if self.datamax is not None:
+      patterndset.attrs["DataMax"] = np.float32(self.datamax)
+    if self.datamin is not None:
+      patterndset.attrs["DataMin"] = np.float32(self.datamin)
+    f.close()
+
+  def read_datamaxmin(self):
+    try:
+      f = h5py.File(Path(self.filepath).expanduser(), 'r+')
+
+    except:
+      print("File Not Found:", str(Path(self.filepath)))
+      return -1
+
+    patterndset = f[self.h5patdatpth]
+
+    if 'DataMax' in patterndset.attrs:
+      self.datamax = np.float32(patterndset.attrs["DataMax"])
+    if 'DataMin' in patterndset.attrs:
+      self.datamin = np.float32(patterndset.attrs["DataMin"])
+    f.close()
+
   def read_header(self, path=None):
     if path is not None:
       self.filepath = path
@@ -1931,10 +1970,9 @@ class DM5(HDF5PatFile):
   def __init__(self, path=None):
     HDF5PatFile.__init__(self, path)
     self.vendor = 'GATAN'
-    # OXFORDOINA only attributes
-    self.filedatatype = None  # np.uint8
-    self.patternh5id = 'Data'  # Could also be 'Raw Patterns'
 
+    self.filedatatype = None  # np.uint8
+    self.patternh5id = 'Data'
     if self.filepath is not None:
       self.get_data_paths()
 
@@ -2012,7 +2050,8 @@ def read_header(self, path=None):
       #self.yStep = 1.0 #np.float32(headerpath['Y Step'][()][0])
       self.xStep = ((f[self.h5patdatpth].parent)["Calibrations/Dimension/[2]"]).attrs['Scale'] #np.float32(headerpath['X Step'][()][0])
       self.yStep = ((f[self.h5patdatpth].parent)["Calibrations/Dimension/[3]"]).attrs['Scale']
-
+    f.close()
+    self.read_datamaxmin()
     return 0  # note this function uses multiple returns
 
   def read_data(self, path=None, convertToFloat=False, patStartCount=[0, -1], returnArrayOnly=False):
@@ -2215,4 +2254,5 @@ def write_data(self, newpatterns=None, patStartCount = [0,-1], writeHead=False,
 
         patterndset[int(rowstart):int(rowstart + nrowwrite),
                             int(colstart):int(colstart + ncolwrite), :, :] = pat2write
-    f.close()
+    f.close()
+

pyebsdindex/ebsdfile.py

@@ -279,7 +279,15 @@ def writeoh5(filename, indexer, data,
       f.create_dataset(datasetname + '/EBSD/Data/X Position', data=x)
       f.create_dataset(datasetname + '/EBSD/Data/Y Position', data=y)
       f.create_dataset(datasetname + '/EBSD/Data/Valid', data=np.zeros(npoints, dtype=np.int8))
-      f.create_dataset(datasetname + '/EBSD/Data/SEM Signal', data=np.zeros(npoints, dtype=np.int32))
+      semsig = (data[-1]['pq']).copy()
+      semsig -= semsig.min()
+      semsig *= float(int(2**8 - 1))/semsig.max()
+
+      semsig = (np.round(semsig)-int(2**7-1)).astype(np.int8)
+
+      f.create_dataset(datasetname + '/EBSD/Data/SEM Signal', data=semsig)
+      f.create_dataset(datasetname + '/EBSD/Data/Grain ID', data=data[-1]['grainid'])
+
 
       if version == '8.6':
         versiontxt = 'OIM Analysis 8.6.103 x64 [29 Sep 2022]'