reshape output arrays by default when reading from the file

python/test/test_api.py

@@ -9,7 +9,7 @@ import trexio as tr
 #=========================================================#
 
 # 0: TREXIO_HDF5 ; 1: TREXIO_TEXT
-TEST_TREXIO_BACKEND = tr.TREXIO_TEXT
+TEST_TREXIO_BACKEND = 0
 OUTPUT_FILENAME_TEXT = 'test_py_swig.dir'
 OUTPUT_FILENAME_HDF5 = 'test_py_swig.h5'
 
@@ -36,6 +36,8 @@ except:
 #============ WRITE THE DATA IN THE TEST FILE ============#
 #=========================================================#
 
+
+
 # create TREXIO file and open it for writing
 #test_file = tr.open(output_filename, 'w', TEST_TREXIO_BACKEND)
 test_file = tr.File(output_filename, mode='w', back_end=TEST_TREXIO_BACKEND)
@@ -114,6 +116,8 @@ tr.write_nucleus_label(test_file,labels)
 # tr.close function. This is only an issue when the data is getting written and read in the same session (e.g. in Jupyter notebook)
 del test_file
 
+
+
 #==========================================================#
 #============ READ THE DATA FROM THE TEST FILE ============#
 #==========================================================#
@@ -156,7 +160,12 @@ for i in range(nucleus_num):
 
 # read nuclear coordinates without providing optional argument dim
 rcoords_np = tr.read_nucleus_coord(test_file2)
+
 assert rcoords_np.size==nucleus_num*3
+np.testing.assert_array_almost_equal(rcoords_np, np.array(coords).reshape(nucleus_num,3), decimal=8)
+
+# set doReshape to False to get a flat 1D array (e.g. when reading matrices like nuclear coordinates)
+#rcoords_reshaped_2 = tr.read_nucleus_coord(test_file2, doReshape=False)
 
 # read array of nuclear labels
 rlabels_2d = tr.read_nucleus_label(test_file2, dim=nucleus_num)

src/templates_front/templator_front.org

@@ -1993,7 +1993,7 @@ def write_$group_dset$(trexio_file, dset_w) -> None:
      #+end_src
 
      #+begin_src python :tangle read_dset_data_front.py
-def read_$group_dset$(trexio_file, dim = None, dtype = None): 
+def read_$group_dset$(trexio_file, dim = None, doReshape = None, dtype = None): 
     """Read the $group_dset$ array of numbers from the TREXIO file.
 
     Parameters:
@@ -2007,6 +2007,10 @@ def read_$group_dset$(trexio_file, dim = None, dtype = None):
 
     dtype (Optional): type
         NumPy data type of the output (e.g. np.int32|int16 or np.float32|float16). If specified, the output array will be converted from the default double precision.
+    
+    doReshape (Optional): bool
+        Flag to determine whether the output NumPy array has be reshaped or not. Be default, reshaping is performed
+        based on the dimensions from the ~trex.json~ file. Otherwise, ~shape~ array (list or tuple) is used if provided by the user.
 
     Returns:
  ~dset_64~ if dtype is None or ~dset_converted~ otherwise: numpy.ndarray 
@@ -2015,17 +2019,30 @@ def read_$group_dset$(trexio_file, dim = None, dtype = None):
     Raises:
         - Exception from AssertionError if TREXIO return code ~rc~ is different from TREXIO_SUCCESS and prints the error message using trexio_string_of_error.
         - Exception from some other error (e.g. RuntimeError).
-    """
-    
+"""
+
+    try:
+        import numpy as np
+    except ImportError:
+        raise Exception("NumPy cannot be imported.")
+
+    if doReshape is None:
+        doReshape = True
+        
     # if dim is not specified, read dimensions from the TREXIO file
-    if dim is None:
+    dims_list = None
+    if dim is None or doReshape:
         $group_dset_dim$ = read_$group_dset_dim$(trexio_file) 
         
         dims_list = [$group_dset_dim_list$]
         dim = 1
         for i in range($group_dset_rank$):
             dim *= dims_list[i]
-    
+
+        
+    shape = tuple(dims_list)
+    if shape is None and doReshape:
+        raise ValueError("Reshaping failure: shape is None.")
 
     try:
         rc, dset_64 = pytr.trexio_read_safe_$group_dset$_64(trexio_file.pytrexio_s, dim)
@@ -2039,10 +2056,6 @@ def read_$group_dset$(trexio_file, dim = None, dtype = None):
     isConverted = False
     dset_converted = None
     if dtype is not None:
-        try:
-            import numpy as np
-        except ImportError:
-            raise Exception("NumPy cannot be imported.")
         
         try:
             assert isinstance(dtype, type)
@@ -2059,8 +2072,21 @@ def read_$group_dset$(trexio_file, dim = None, dtype = None):
             isConverted = True
 
     # additional assert can be added here to check that read_safe functions returns numpy array of proper dimension
-    if isConverted:
+
+    if doReshape:
+        try:
+            # in-place reshaping did not work so I have to make a copy
+            if isConverted:    
+                dset_reshaped = np.reshape(dset_converted, shape, order='C')
+            else:
+                dset_reshaped = np.reshape(dset_64, shape, order='C')
+        except:
+            raise
+
+    if isConverted:    
         return dset_converted
+    elif doReshape:
+        return dset_reshaped
     else:
         return dset_64
      #+end_src