Gstore

abipy/eph/gstore.py

@@ -157,8 +157,9 @@ class Gqk:
     vkmat_cart_ibz: np.ndarray | None
 
     @classmethod
-    def from_gstore(cls, gstore, spin: int):
+    def from_gstore(cls, gstore: GstoreFile, spin: int):
         """
+        Build an istance from a GstoreFile and the spin index.
         """
         ncr = gstore.r
         path = f"gqk_spin{spin+1}"
@@ -213,25 +214,27 @@ def to_string(self, verbose=0) -> str:
 
         return "\n".join(lines)
 
-    def to_dataframe(self, what="g2"):
+    def get_dataframe(self, what="g2"):
         """
         """
         if what == "g2":
             g2 = self.g2 if self.g2 is not None else np.abs(self.gvals) ** 2
             shape, ndim = g2.shape, g2.ndim
-            # Flatten the array , get the indices and combine indices and values into a DataFrame
+            # Flatten the array, get the indices and combine indices and values into a DataFrame
             indices = np.indices(shape).reshape(ndim, -1).T
             df = pd.DataFrame(indices, columns=["iq", "ik", "imode", "m_kq", "n_k"])
             df["g2"] = g2.flatten()
 
         elif what == "v2":
-            if self.vcart_ibz is None:
-                raise ValueError("vcart_ibz is not available!")
+            if self.vk_cart_ibz is None:
+                raise ValueError("vk_cart_ibz is not available!")
 
             # Compute the squared norm of each vector
-            v2 = np.sum(self.vcart_ibz ** 2, axis=2)
+            v2 = np.sum(self.vk_cart_ibz ** 2, axis=2)
             shape, ndim = v2.shape, v2.ndim
-            df = pd.DataFrame(indices, columns=["ik", "imode", "ib"])
+            # Flatten the array, get the indices and combine indices and values into a DataFrame
+            indices = np.indices(shape).reshape(ndim, -1).T
+            df = pd.DataFrame(indices, columns=["ik", "ib"])
             df["v2"] = v2.flatten()
 
         else:
@@ -241,7 +244,7 @@ def to_dataframe(self, what="g2"):
 
     def get_gdf_at_qpt_kpt(self, qpoint, kpoint) -> pd.DataFrame:
         """
-        Build and return a Dataframe with the |g(k+q,k)|^2 for the given (qpoint, kpoint) pair.
+        Build and return a dataframe with the |g(k+q,k)|^2 for the given (qpoint, kpoint) pair.
         """
         spin = self.spin
 
@@ -350,7 +353,7 @@ def __init__(self, filepath: PathLike):
         self.kglob2bz -= 1
 
     def find_iq_glob_qpoint(self, qpoint, spin: int):
-        """Find the internal index of qpoint"""
+        """Find the internal index of qpoint needed to access the gvals array."""
         qpoint = np.array(qpoint)
         for iq_g, iq_bz in enumerate(self.qglob2bz[spin]):
             if np.allclose(qpoint, self.qbz[iq_bz]):
@@ -359,7 +362,7 @@ def find_iq_glob_qpoint(self, qpoint, spin: int):
         raise ValueError(f"Cannot find {qpoint=} in GSTORE.nc")
 
     def find_ik_glob_kpoint(self, kpoint, spin: int):
-        """Find the internal indices of kpoint"""
+        """Find the internal indices of kpoint needed in access the gvals array."""
         kpoint = np.array(kpoint)
         for ik_g, ik_bz in enumerate(self.kglob2bz[spin]):
             if np.allclose(kpoint, self.kbz[ik_bz]):

abipy/ml/aseml.py

@@ -1547,6 +1547,7 @@ class MyM3GNetCalculator(_MyCalculator, M3GNetCalculator):
                 else:
                     #model_name = "M3GNet-MP-2021.2.8-PES" if self.model_name is None else self.model_name
                     model_name = "M3GNet-MP-2021.2.8-DIRECT-PES" if self.model_name is None else self.model_name
+                    print("Using model_name:", model_name)
                     self._model = matgl.load_model(model_name)
 
             class MyM3GNetCalculator(_MyCalculator, M3GNetCalculator):