[ENH] utils: Minimize imports for utils

i18n/si/msgs.jaml

@@ -1,6 +1,11 @@
 __init__.py:
     def `networks`:
         networks: false
+utils.py:
+    def `items_from_distmatrix`:
+        label: oznaka
+    def `weights_from_distances`:
+        All distances must be positive: Vse razdalje morajo biti pozitivne
 network/base.py:
     class `Network`:
         def `__init__`:
@@ -758,11 +763,6 @@ widgets/ownxsinglemode.py:
     def `main`:
         /Users/janez/Downloads/100_petrozavodsk_171_events_no_sqrt.net: false
     __main__: false
-widgets/utils.py:
-    def `items_from_distmatrix`:
-        label: oznaka
-    def `weights_from_distances`:
-        All distances must be positive: Vse razdalje morajo biti pozitivne
 widgets/tests/utils.py:
     class `NetworkTest`:
         def `_read_network`:

orangecontrib/network/widgets/tests/test_utils.py → orangecontrib/network/tests/test_utils.py

@@ -4,7 +4,7 @@
 
 from Orange.misc import DistMatrix
 from Orange.data import Table, Domain, ContinuousVariable
-from orangecontrib.network.widgets.utils import items_from_distmatrix, weights_from_distances
+from orangecontrib.network.utils import items_from_distmatrix, weights_from_distances
 
 
 class TestItemsFromMatrix(unittest.TestCase):

orangecontrib/network/utils.py

@@ -0,0 +1,41 @@
+import numpy as np
+from Orange.data import Table, StringVariable, Domain
+
+
+def items_from_distmatrix(matrix):
+    if matrix.row_items is not None:
+        row_items = matrix.row_items
+        if isinstance(row_items, Table):
+            if matrix.axis == 1:
+                items = row_items
+            else:
+                items = [[v.name] for v in row_items.domain.attributes]
+        else:
+            items = [[str(x)] for x in matrix.row_items]
+    else:
+        items = [[str(i)] for i in range(1, 1 + matrix.shape[0])]
+    if not isinstance(items, Table):
+        items = Table.from_list(
+            Domain([], metas=[StringVariable('label')]),
+            items)
+    return items
+
+
+def weights_from_distances(weights):
+    weights = weights.astype(np.float64)
+
+    if weights.size == 0:
+        return weights
+
+    mi, ma = np.nanmin(weights), np.nanmax(weights)
+    assert mi >= 0, "All distances must be positive"
+
+    if ma - mi < 1e-6:
+        return np.ones(weights.shape)
+
+    weights /= ma
+    weights *= np.log(10)
+    np.exp(weights, out=weights)
+    np.reciprocal(weights, out=weights)
+
+    return weights

orangecontrib/network/widgets/OWNxFromDistances.py

@@ -19,8 +19,7 @@
 from orangecontrib.network.network import Network
 from orangecontrib.network.network.base import UndirectedEdges, DirectedEdges
 # This enables summarize in widget preview, pylint: disable=unused-import
-import orangecontrib.network.widgets
-from orangecontrib.network.widgets.utils import items_from_distmatrix, weights_from_distances
+from orangecontrib.network.utils import items_from_distmatrix, weights_from_distances
 
 
 class QIntValidatorWithFixup(QIntValidator):

orangecontrib/network/widgets/OWNxNeighbor.py

@@ -1,5 +1,4 @@
 import numpy as np
-from numpy.lib.stride_tricks import as_strided
 import scipy.sparse as sp
 
 from AnyQt.QtCore import Qt
@@ -15,8 +14,7 @@
 from orangecontrib.network.network import Network
 from orangecontrib.network.network.base import UndirectedEdges, DirectedEdges
 # This enables summarize in widget preview, pylint: disable=unused-import
-import orangecontrib.network.widgets
-from orangecontrib.network.widgets.utils import items_from_distmatrix, weights_from_distances
+from orangecontrib.network.utils import items_from_distmatrix, weights_from_distances
 
 
 class OWNxNeighbor(widget.OWWidget):

orangecontrib/network/widgets/utils.py

@@ -1,41 +1 @@
-import numpy as np
-from Orange.data import Table, StringVariable, Domain
-
-
-def items_from_distmatrix(matrix):
-    if matrix.row_items is not None:
-        row_items = matrix.row_items
-        if isinstance(row_items, Table):
-            if matrix.axis == 1:
-                items = row_items
-            else:
-                items = [[v.name] for v in row_items.domain.attributes]
-        else:
-            items = [[str(x)] for x in matrix.row_items]
-    else:
-        items = [[str(i)] for i in range(1, 1 + matrix.shape[0])]
-    if not isinstance(items, Table):
-        items = Table.from_list(
-            Domain([], metas=[StringVariable('label')]),
-            items)
-    return items
-
-
-def weights_from_distances(weights):
-    weights = weights.astype(np.float64)
-
-    if weights.size == 0:
-        return weights
-
-    mi, ma = np.nanmin(weights), np.nanmax(weights)
-    assert mi >= 0, "All distances must be positive"
-
-    if ma - mi < 1e-6:
-        return np.ones(weights.shape)
-
-    weights /= ma
-    weights *= np.log(10)
-    np.exp(weights, out=weights)
-    np.reciprocal(weights, out=weights)
-
-    return weights
+from orangecontrib.network.utils import items_from_distmatrix, weights_from_distances