ENH: Modify intersect1d to return common indices (#10684)

* added function commonpts1d

* Update arraysetops.py

* Update arraysetops.py

* modified intersect1d to return common indices

Proposed this idea in previous pull request (https://github.com/numpy/numpy/pull/10683) and made suggested changes for implementing this idea to have relevant common indices that correspond to the values in the intersection of the two arrays.

* update intersect1d with suggested changes

* implemented return_indices options for intersect1d

I've tested out the above code and for the ~10 different test sets I've tried it's worked thus far. It's probably not the cleanest implementation but it works and is vectorized.

* cleaned up structure for intersect1d

* fixed copy-paste error, added second test, changed a conditional

* Testing return_indices in intersect1d

* formatting

* created separate test function  for intersect1d indices, added spaces after commas

* fixed up example and some style

* fixed style

* style change

* removed one example

* removed extra space

* added version number for return_indices

* added 'return_indices' keyword for np.intersect1d

* fixed formatting

* updated return_indices entry

* fixed some typos and style

* added bit about first instance of a value being used

* STY: Fix comment formats

* DOC: missing space

* DOC: correct parameter names in docstring

* made suggested changes

* fixed a mistake from previous update

also added documentation for comm1, comm2 to match doc from np.unique

* added in tests for 2d inputs

* STY: Add missing spaces around commas

* TST: Correct array to actually be unique

* STY: Spaces at beginning of comments

1 files changed, 57 insertions, 8 deletions

diff --git a/numpy/lib/arraysetops.py b/numpy/lib/arraysetops.py
index e8eda297f..4d3f35183 100644
--- a/numpy/lib/arraysetops.py
+++ b/numpy/lib/arraysetops.py
@@ -298,7 +298,7 @@ def _unique1d(ar, return_index=False, return_inverse=False,
     return ret
 
 
-def intersect1d(ar1, ar2, assume_unique=False):
+def intersect1d(ar1, ar2, assume_unique=False, return_indices=False):
     """
     Find the intersection of two arrays.
 
@@ -307,15 +307,28 @@ def intersect1d(ar1, ar2, assume_unique=False):
     Parameters
     ----------
     ar1, ar2 : array_like
-        Input arrays.
+        Input arrays. Will be flattened if not already 1D.
     assume_unique : bool
         If True, the input arrays are both assumed to be unique, which
         can speed up the calculation.  Default is False.
-
+    return_indices : bool
+        If True, the indices which correspond to the intersection of the 
+        two arrays are returned. The first instance of a value is used 
+        if there are multiple. Default is False. 
+    
+        .. versionadded:: 1.15.0    
+        
     Returns
     -------
     intersect1d : ndarray
         Sorted 1D array of common and unique elements.
+    comm1 : ndarray
+        The indices of the first occurrences of the common values in `ar1`.
+        Only provided if `return_indices` is True.
+    comm2 : ndarray
+        The indices of the first occurrences of the common values in `ar2`. 
+        Only provided if `return_indices` is True.
+
 
     See Also
     --------
@@ -332,14 +345,49 @@ def intersect1d(ar1, ar2, assume_unique=False):
     >>> from functools import reduce
     >>> reduce(np.intersect1d, ([1, 3, 4, 3], [3, 1, 2, 1], [6, 3, 4, 2]))
     array([3])
+    
+    To return the indices of the values common to the input arrays
+    along with the intersected values:
+    >>> x = np.array([1, 1, 2, 3, 4])
+    >>> y = np.array([2, 1, 4, 6])
+    >>> xy, x_ind, y_ind = np.intersect1d(x, y, return_indices=True)
+    >>> x_ind, y_ind
+    (array([0, 2, 4]), array([1, 0, 2]))
+    >>> xy, x[x_ind], y[y_ind]
+    (array([1, 2, 4]), array([1, 2, 4]), array([1, 2, 4]))
+    
     """
     if not assume_unique:
-        # Might be faster than unique( intersect1d( ar1, ar2 ) )?
-        ar1 = unique(ar1)
-        ar2 = unique(ar2)
+        if return_indices:
+            ar1, ind1 = unique(ar1, return_index=True)
+            ar2, ind2 = unique(ar2, return_index=True)
+        else:
+            ar1 = unique(ar1)
+            ar2 = unique(ar2)
+    else:
+        ar1 = ar1.ravel()
+        ar2 = ar2.ravel()
+        
     aux = np.concatenate((ar1, ar2))
-    aux.sort()
-    return aux[:-1][aux[1:] == aux[:-1]]
+    if return_indices:
+        aux_sort_indices = np.argsort(aux, kind='mergesort')
+        aux = aux[aux_sort_indices]
+    else:
+        aux.sort()
+
+    mask = aux[1:] == aux[:-1]
+    int1d = aux[:-1][mask]
+
+    if return_indices:
+        ar1_indices = aux_sort_indices[:-1][mask]
+        ar2_indices = aux_sort_indices[1:][mask] - ar1.size
+        if not assume_unique:
+            ar1_indices = ind1[ar1_indices]
+            ar2_indices = ind2[ar2_indices]
+
+        return int1d, ar1_indices, ar2_indices
+    else:
+        return int1d
 
 def setxor1d(ar1, ar2, assume_unique=False):
     """
@@ -660,3 +708,4 @@ def setdiff1d(ar1, ar2, assume_unique=False):
         ar1 = unique(ar1)
         ar2 = unique(ar2)
     return ar1[in1d(ar1, ar2, assume_unique=True, invert=True)]
+