support Single Precision OpenFOAM output

fluiddyn · Apr 16, 2024 · d29cd2d · d29cd2d
1 parent 25b70fd
commit d29cd2d
Show file tree

Hide file tree

Showing 4 changed files with 46 additions and 20 deletions.
diff --git a/README.rst b/README.rst
@@ -26,8 +26,8 @@ What is this repository for?
 ----------------------------
 
 * Openfoam Tools
-* Version : 0.2.5
-* Supported OpenFoam Versions : 2.4.0, 4.1 to 9, v1712plus to v2212plus
+* Version : 0.2.6
+* Supported OpenFoam Versions : 2.4.0, 4.1 to 9, v1712plus to v2312plus
 * Supported Python Versions : >= 3.8
 
 Documentation and Examples

diff --git a/doc/index.rst b/doc/index.rst
@@ -13,8 +13,8 @@ What is this repository for?
 ----------------------------
 
 * Openfoam Tools
-* Version : 0.2.5
-* Supported OpenFoam Versions : 2.4.0, 4.1 to 9, v1712plus to v2212plus
+* Version : 0.2.6
+* Supported OpenFoam Versions : 2.4.0, 4.1 to 9, v1712plus to v2312plus
 * Supported Python Versions : >= 3.8
 
 Deployment instructions

diff --git a/fluidfoam/_version.py b/fluidfoam/_version.py
@@ -11,4 +11,4 @@
 'a' or 'alpha' means alpha version (internal testing),
 'b' or 'beta' means beta version (external testing).
 """
-__version__ = "0.2.5"
+__version__ = "0.2.6"
diff --git a/fluidfoam/readof.py b/fluidfoam/readof.py
@@ -112,9 +112,11 @@ def __init__(
 
         try:
             self.is_ascii = self.header[b"format"] == b"ascii"
+            self.is_SP    = b"scalar=32" in self.header[b"arch"]
             self.noheader = False
         except KeyError:
             self.is_ascii = True
+            self.is_SP    = False
             self.noheader = True
 
         for line in self.lines_stripped:
@@ -289,12 +291,20 @@ def _parse_data(self, boundary, datatype, precision=15):
                 nb_numbers = 6 * nb_pts
             elif self.type_data == "tensor":
                 nb_numbers = 9 * nb_pts
-            self.values = np.array(
-                struct.unpack(
-                    "{}d".format(nb_numbers),
-                    data[: nb_numbers * struct.calcsize("d")],
+            if self.is_SP:
+                self.values = np.array(
+                    struct.unpack(
+                        "{}f".format(nb_numbers),
+                        data[: nb_numbers * struct.calcsize("f")],
+                    )
+                )
+            else:
+                self.values = np.array(
+                    struct.unpack(
+                        "{}d".format(nb_numbers),
+                        data[: nb_numbers * struct.calcsize("d")],
+                    )
                 )
-            )
         else:
             if self.type_data == "scalar":
                 self.values = np.array(
@@ -386,12 +396,20 @@ def _nearest_data(self, boundary, precision):
                 nb_numbers = 6 * nb_pts
             elif self.type_data == "tensor":
                 nb_numbers = 9 * nb_pts
-            values = np.array(
-                struct.unpack(
-                    "{}d".format(nb_numbers),
-                    data[: nb_numbers * struct.calcsize("d")],
+            if self.is_SP:
+                values = np.array(
+                    struct.unpack(
+                        "{}f".format(nb_numbers),
+                        data[: nb_numbers * struct.calcsize("f")],
+                    )
+                )
+            else:
+                values = np.array(
+                    struct.unpack(
+                        "{}d".format(nb_numbers),
+                        data[: nb_numbers * struct.calcsize("d")],
+                    )
                 )
-            )
         else:
             if self.type_data == "scalar":
                 values = np.array(
@@ -498,12 +516,20 @@ def _parse_points(self, precision):
         if not self.is_ascii:
             nb_numbers = 3 * self.nb_pts
             data = b"\n(".join(data.split(b"\n(")[1:])
-            self.values = np.array(
-                struct.unpack(
-                    "{}d".format(nb_numbers),
-                    data[: nb_numbers * struct.calcsize("d")],
+            if self.is_SP:
+                self.values = np.array(
+                    struct.unpack(
+                        "{}f".format(nb_numbers),
+                        data[: nb_numbers * struct.calcsize("f")],
+                    )
+                )
+            else:
+                self.values = np.array(
+                    struct.unpack(
+                        "{}d".format(nb_numbers),
+                        data[: nb_numbers * struct.calcsize("d")],
+                    )
                 )
-            )
         else:
             lines = data.split(b"\n(")
             lines = [line.split(b")")[0] for line in lines]