9 files changed, 63 insertions, 87 deletions
diff --git a/doc/source/reference/c-api/iterator.rst b/doc/source/reference/c-api/iterator.rst
index b4adaef9b..07187e7f1 100644
--- a/doc/source/reference/c-api/iterator.rst
+++ b/doc/source/reference/c-api/iterator.rst
@@ -203,6 +203,66 @@ is used to control the memory layout of the allocated result, typically
     }
 
 
+Multi Index Tracking Example
+----------------------------
+
+This example shows you how to work with the :c:data:`NPY_ITER_MULTI_INDEX` flag. For simplicity, we assume the argument is a two-dimensional array.
+
+.. code-block:: c
+
+   int PrintMultiIndex(PyArrayObject *arr) {
+       NpyIter *iter;
+       NpyIter_IterNextFunc *iternext;
+       npy_intp multi_index[2];
+
+       iter = NpyIter_New(
+           arr, NPY_ITER_READONLY | NPY_ITER_MULTI_INDEX | NPY_ITER_REFS_OK,
+           NPY_KEEPORDER, NPY_NO_CASTING, NULL);
+       if (iter == NULL) {
+           return -1;
+       }
+       if (NpyIter_GetNDim(iter) != 2) {
+           NpyIter_Deallocate(iter);
+           PyErr_SetString(PyExc_ValueError, "Array must be 2-D");
+           return -1;
+       }
+       if (NpyIter_GetIterSize(iter) != 0) {
+           iternext = NpyIter_GetIterNext(iter, NULL);
+           if (iternext == NULL) {
+               NpyIter_Deallocate(iter);
+               return -1;
+           }
+           NpyIter_GetMultiIndexFunc *get_multi_index =
+               NpyIter_GetGetMultiIndex(iter, NULL);
+           if (get_multi_index == NULL) {
+               NpyIter_Deallocate(iter);
+               return -1;
+           }
+
+           do {
+               get_multi_index(iter, multi_index);
+               printf("multi_index is [%" NPY_INTP_FMT ", %" NPY_INTP_FMT "]\n",
+                      multi_index[0], multi_index[1]);
+           } while (iternext(iter));
+       }
+       if (!NpyIter_Deallocate(iter)) {
+           return -1;
+       }
+       return 0;
+   }
+
+When called with a 2x3 array, the above example prints:
+
+.. code-block:: sh
+
+   multi_index is [0, 0]
+   multi_index is [0, 1]
+   multi_index is [0, 2]
+   multi_index is [1, 0]
+   multi_index is [1, 1]
+   multi_index is [1, 2]
+
+
 Iterator Data Types
 ---------------------
 
diff --git a/doc/source/user/absolute_beginners.rst b/doc/source/user/absolute_beginners.rst
index a4a82afb6..dae2c4f06 100644
--- a/doc/source/user/absolute_beginners.rst
+++ b/doc/source/user/absolute_beginners.rst
@@ -1504,7 +1504,7 @@ You can use ``np.load()`` to reconstruct your array. ::
 
   >>> b = np.load('filename.npy')
 
-If you want to check your array, you can run:::
+If you want to check your array, you can run::
 
   >>> print(b)
   [1 2 3 4 5 6]
diff --git a/doc/source/user/c-info.python-as-glue.rst b/doc/source/user/c-info.python-as-glue.rst
index 4db789856..363099fe3 100644
--- a/doc/source/user/c-info.python-as-glue.rst
+++ b/doc/source/user/c-info.python-as-glue.rst
@@ -53,7 +53,7 @@ needs is to call out from Python code to a fast, machine-code routine
 relatively easy to do is a big reason why Python is such an excellent
 high-level language for scientific and engineering programming.
 
-Their are two basic approaches to calling compiled code: writing an
+There are two basic approaches to calling compiled code: writing an
 extension module that is then imported to Python using the import
 command, or calling a shared-library subroutine directly from Python
 using the `ctypes <https://docs.python.org/3/library/ctypes.html>`_
@@ -97,7 +97,7 @@ control over how the C-library gets used and called which can lead to
 a lean and tight interface with minimal over-head. The disadvantage is
 that you have to write, debug, and maintain C-code, although most of
 it can be adapted using the time-honored technique of
-"cutting-pasting-and-modifying" from other extension modules. Because,
+"cutting-pasting-and-modifying" from other extension modules. Because
 the procedure of calling out to additional C-code is fairly
 regimented, code-generation procedures have been developed to make
 this process easier. One of these code-generation techniques is
diff --git a/doc/source/user/plot_approx.py b/doc/source/user/plot_approx.py
deleted file mode 100644
index a2d6981d9..000000000
--- a/doc/source/user/plot_approx.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from scipy import misc
-import matplotlib.pyplot as plt
-import numpy as np
-from numpy import linalg
-
-img = misc.face()
-img_array = img / 255
-img_gray = img_array @ [0.2126, 0.7152, 0.0722]
-
-U, s, Vt = linalg.svd(img_gray)
-
-Sigma = np.zeros((768, 1024))
-for i in range(768):
-    Sigma[i, i] = s[i]
-
-k = 10
-
-approx = U @ Sigma[:, :k] @ Vt[:k, :]
-plt.imshow(approx, cmap="gray")
diff --git a/doc/source/user/plot_face.py b/doc/source/user/plot_face.py
deleted file mode 100644
index c0891e770..000000000
--- a/doc/source/user/plot_face.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from scipy import misc
-import matplotlib.pyplot as plt
-
-img = misc.face()
-plt.imshow(img)
diff --git a/doc/source/user/plot_final.py b/doc/source/user/plot_final.py
deleted file mode 100644
index 10cb097dd..000000000
--- a/doc/source/user/plot_final.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from scipy import misc
-import matplotlib.pyplot as plt
-import numpy as np
-from numpy import linalg
-
-img = misc.face()
-img_array = img / 255
-img_array_transposed = np.transpose(img_array, (2, 0, 1))
-
-U, s, Vt = linalg.svd(img_array_transposed)
-
-Sigma = np.zeros((3, 768, 1024))
-for j in range(3):
-    np.fill_diagonal(Sigma[j, :, :], s[j, :])
-
-k = 10
-
-approx_img = U @ Sigma[..., :k] @ Vt[..., :k, :]
-plt.imshow(np.transpose(approx_img, (1, 2, 0)))
diff --git a/doc/source/user/plot_gray.py b/doc/source/user/plot_gray.py
deleted file mode 100644
index 6cb46bbe4..000000000
--- a/doc/source/user/plot_gray.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from scipy import misc
-import matplotlib.pyplot as plt
-import numpy as np
-
-img = misc.face()
-img_array = img / 255
-img_gray = img_array @ [0.2126, 0.7152, 0.0722]
-plt.imshow(img_gray, cmap="gray")
diff --git a/doc/source/user/plot_gray_svd.py b/doc/source/user/plot_gray_svd.py
deleted file mode 100644
index 95439939d..000000000
--- a/doc/source/user/plot_gray_svd.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from scipy import misc
-import matplotlib.pyplot as plt
-import numpy as np
-from numpy import linalg
-
-img = misc.face()
-img_array = img / 255
-img_gray = img_array @ [0.2126, 0.7152, 0.0722]
-
-U, s, Vt = linalg.svd(img_gray)
-
-Sigma = np.zeros((768, 1024))
-for i in range(768):
-    Sigma[i, i] = s[i]
-
-plt.plot(s)
diff --git a/doc/source/user/plot_reconstructed.py b/doc/source/user/plot_reconstructed.py
deleted file mode 100644
index 37cf3c626..000000000
--- a/doc/source/user/plot_reconstructed.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from scipy import misc
-import matplotlib.pyplot as plt
-import numpy as np
-from numpy import linalg
-
-img = misc.face()
-img_array = img / 255
-img_array_transposed = np.transpose(img_array, (2, 0, 1))
-
-U, s, Vt = linalg.svd(img_array_transposed)
-
-Sigma = np.zeros((3, 768, 1024))
-for j in range(3):
-    np.fill_diagonal(Sigma[j, :, :], s[j, :])
-
-reconstructed = U @ Sigma @ Vt
-plt.imshow(np.transpose(reconstructed, (1, 2, 0)))