Make optimal number of components of all criteria accessible

mapca/__init__.py

@@ -3,9 +3,9 @@
 GIFT.
 """
 
-from .due import due, Doi
 from ._version import get_versions
-from .mapca import ma_pca
+from .due import Doi, due
+from .mapca import MovingAveragePCA, ma_pca
 
 __version__ = get_versions()["version"]
 
@@ -16,6 +16,7 @@
 
 __all__ = [
     "ma_pca",
+    "MovingAveragePCA",
     "__version__",
 ]
 

mapca/mapca.py

@@ -254,19 +254,35 @@ def _fit(self, img, mask):
 
         itc = np.row_stack([self.aic_, self.kic_, self.mdl_])
 
+        dlap = np.diff(itc, axis=1)
+
+        # AIC
+        a_aic = np.where(dlap[0, :] > 0)[0] + 1
+        if a_aic.size == 0:
+            self.n_aic_ = itc[0, :].shape[0]
+        else:
+            self.n_aic_ = a_aic[0]
+
+        # KIC
+        a_kic = np.where(dlap[1, :] > 0)[0] + 1
+        if a_kic.size == 0:
+            self.n_kic_ = itc[1, :].shape[0]
+        else:
+            self.n_kic_ = a_kic[0]
+
+        # MDL
+        a_mdl = np.where(dlap[2, :] > 0)[0] + 1
+        if a_mdl.size == 0:
+            self.n_mdl_ = itc[2, :].shape[0]
+        else:
+            self.n_mdl_ = a_mdl[0]
+
         if self.criterion == "aic":
-            criteria_idx = 0
+            n_components = self.n_aic_
         elif self.criterion == "kic":
-            criteria_idx = 1
+            n_components = self.n_kic_
         elif self.criterion == "mdl":
-            criteria_idx = 2
-
-        dlap = np.diff(itc[criteria_idx, :])
-        a = np.where(dlap > 0)[0] + 1  # Plus 1 to
-        if a.size == 0:
-            n_components = itc[criteria_idx, :].shape[0]
-        else:
-            n_components = a[0]
+            n_components = self.n_mdl_
 
         LGR.info("Estimated number of components is %d" % n_components)