v2.7.2

Author: fbordignon

src/ltrace/ltrace/algorithms/microporosity/modelling.py

@@ -86,6 +86,10 @@ class DataVar:
     color: tuple = (0, 0, 0)
 
 
+class ModelDataTypeError(Exception):
+    pass
+
+
 class SampleModel:
     def __init__(self, pcrRange=(0.0, 1.0)):
         self.variables: typing.Dict[str, DataVar] = {}
@@ -95,16 +99,19 @@ def __init__(self, pcrRange=(0.0, 1.0)):
         self.limits = None
 
     def addData(self, image: np.ndarray, mask: np.ndarray, label: int = 0, color: tuple = (0, 0, 0)):
+        if not np.issubdtype(image.dtype, np.integer):
+            raise ValueError("Input Image must be of integer type")
+
         self.image = image
         self.addSubGroup("Data", mask, label, color, markers=[0.0001, 0.99])
         self.limits = [int(np.round(v)) for v in self.variables["Data"].threshold]
 
     def addSubGroup(self, name: str, mask: np.ndarray, label: int, color: tuple, markers: typing.List[float] = None):
         if self.image is None:
-            raise ValueError("Image is missing")
+            raise ModelDataTypeError("Image is missing")
 
         if np.any(mask.shape != self.image.shape):
-            raise ValueError("Mask and image must have the same shape")
+            raise ModelDataTypeError("Mask and image must have the same shape")
 
         counts = fast1DBinCountUInt64(self.image[mask])
         counts[0] = 0  # exclude background 0

src/ltrace/ltrace/pore_networks/functions_extract.py

@@ -346,14 +346,17 @@ def general_pn_extract(
         # Convert from adimensional voxel size to node scale
         # TODO: Deal with anisotropic data PL-1370
         scale = inputNode.GetSpacing()[::-1]
-        pn_properties = _porespy_extract(
-            input_multiphase,
-            input_watershed,
-            scale,
-            porosity_map=porosity_map,
-            watershed_blur=watershed_blur,
-            force_cpu=force_cpu,
-        )
+        try:
+            pn_properties = _porespy_extract(
+                input_multiphase,
+                input_watershed,
+                scale,
+                porosity_map=porosity_map,
+                watershed_blur=watershed_blur,
+                force_cpu=force_cpu,
+            )
+        except TypeError:
+            raise RuntimeError("Empty network extracted from porespy.")
         if pn_properties is False:
             return False
     elif method == "PNExtract":

src/ltrace/ltrace/pore_networks/pnflow_parameter_defs.py

@@ -204,13 +204,21 @@
         "default_value": False,
     },
     "max_subprocesses": {
-        "display_name": "Max subprocesses",
+        "display_name": "Max subprocesses (Local)",
         "layout": "options",
         "dtype": "integerspinbox",
         "minimum_value": 1,
         "maximum_value": 256,
         "default_value": 8,
     },
+    "n_jobs": {
+        "display_name": "Number of jobs (Remote)",
+        "layout": "options",
+        "dtype": "integerspinbox",
+        "minimum_value": 1,
+        "maximum_value": 8,
+        "default_value": 2,
+    },
     "timeout_enabled": {
         "display_name": "Enable timeout",
         "layout": "options",

src/ltrace/ltrace/pore_networks/subres_models.py

@@ -91,8 +91,8 @@ def get_capillary_radius_function(cls, params, pore_network, volume):
             pore_network["throat.phases"][:, 1],
         )
         resolved_radii = radii[resolved_throats]
-        smallest_raddi = resolved_radii.min()
-        highest_pc = estimate_pressure(smallest_raddi)
+        smallest_radii = resolved_radii.min()
+        highest_pc = estimate_pressure(smallest_radii)
         highest_pc_index = Pc >= highest_pc
         if highest_pc_index.sum() == 0:
             return lambda _: highest_pc
@@ -154,8 +154,8 @@ def get_capillary_radius_function(cls, params, pore_network, volume):
             pore_network["throat.phases"][:, 1],
         )
         resolved_radii = radii[resolved_throats]
-        smallest_raddi = resolved_radii.min()
-        highest_pc = estimate_pressure(smallest_raddi)
+        smallest_radii = resolved_radii.min()
+        highest_pc = estimate_pressure(smallest_radii)
         highest_pc_index = Pc >= highest_pc
         if highest_pc_index.sum() == 0:
             return lambda _: highest_pc
@@ -197,7 +197,7 @@ def _get_cumulative_dist_points(cls, points):
 
 
 class PressureCurveLogic:
-    required_params = ("throat radii", "capillary pressure", "dsn", "smallest_raddi_multiplier")
+    required_params = ("throat radii", "capillary pressure", "dsn", "smallest_radii_multiplier")
 
     def __init__(self):
         pass
@@ -216,15 +216,15 @@ def get_capillary_radius_function(cls, params, pore_network, volume):
         if resolved_radii.size == 0:
             return lambda _: None
 
-        smallest_resolved_raddi = resolved_radii.min()
+        smallest_resolved_radii = resolved_radii.min()
 
         if params["throat radii"] is not None:
             subresolution_radii_bool_index = np.logical_and(
                 params["throat radii"] > 1e-8,
-                params["throat radii"] <= params["smallest_raddi_multiplier"] * smallest_resolved_raddi,
+                params["throat radii"] <= params["smallest_radii_multiplier"] * smallest_resolved_radii,
             )
             if subresolution_radii_bool_index.sum() == 0:
-                return lambda _: smallest_resolved_raddi / 2
+                return lambda _: smallest_resolved_radii / 2
             elif subresolution_radii_bool_index.sum() == 1:
                 return lambda _: params["throat radii"][subresolution_radii_bool_index][0]