Created starter files for the project.

venv/Lib/site-packages/numpy/doc/broadcasting.py

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+"""
+========================
+Broadcasting over arrays
+========================
+
+.. note::
+    See `this article
+    <https://numpy.org/devdocs/user/theory.broadcasting.html>`_
+    for illustrations of broadcasting concepts.
+
+
+The term broadcasting describes how numpy treats arrays with different
+shapes during arithmetic operations. Subject to certain constraints,
+the smaller array is "broadcast" across the larger array so that they
+have compatible shapes. Broadcasting provides a means of vectorizing
+array operations so that looping occurs in C instead of Python. It does
+this without making needless copies of data and usually leads to
+efficient algorithm implementations. There are, however, cases where
+broadcasting is a bad idea because it leads to inefficient use of memory
+that slows computation.
+
+NumPy operations are usually done on pairs of arrays on an
+element-by-element basis.  In the simplest case, the two arrays must
+have exactly the same shape, as in the following example:
+
+  >>> a = np.array([1.0, 2.0, 3.0])
+  >>> b = np.array([2.0, 2.0, 2.0])
+  >>> a * b
+  array([ 2.,  4.,  6.])
+
+NumPy's broadcasting rule relaxes this constraint when the arrays'
+shapes meet certain constraints. The simplest broadcasting example occurs
+when an array and a scalar value are combined in an operation:
+
+>>> a = np.array([1.0, 2.0, 3.0])
+>>> b = 2.0
+>>> a * b
+array([ 2.,  4.,  6.])
+
+The result is equivalent to the previous example where ``b`` was an array.
+We can think of the scalar ``b`` being *stretched* during the arithmetic
+operation into an array with the same shape as ``a``. The new elements in
+``b`` are simply copies of the original scalar. The stretching analogy is
+only conceptual.  NumPy is smart enough to use the original scalar value
+without actually making copies so that broadcasting operations are as
+memory and computationally efficient as possible.
+
+The code in the second example is more efficient than that in the first
+because broadcasting moves less memory around during the multiplication
+(``b`` is a scalar rather than an array).
+
+General Broadcasting Rules
+==========================
+When operating on two arrays, NumPy compares their shapes element-wise.
+It starts with the trailing dimensions and works its way forward.  Two
+dimensions are compatible when
+
+1) they are equal, or
+2) one of them is 1
+
+If these conditions are not met, a
+``ValueError: operands could not be broadcast together`` exception is 
+thrown, indicating that the arrays have incompatible shapes. The size of 
+the resulting array is the size that is not 1 along each axis of the inputs.
+
+Arrays do not need to have the same *number* of dimensions.  For example,
+if you have a ``256x256x3`` array of RGB values, and you want to scale
+each color in the image by a different value, you can multiply the image
+by a one-dimensional array with 3 values. Lining up the sizes of the
+trailing axes of these arrays according to the broadcast rules, shows that
+they are compatible::
+
+  Image  (3d array): 256 x 256 x 3
+  Scale  (1d array):             3
+  Result (3d array): 256 x 256 x 3
+
+When either of the dimensions compared is one, the other is
+used.  In other words, dimensions with size 1 are stretched or "copied"
+to match the other.
+
+In the following example, both the ``A`` and ``B`` arrays have axes with
+length one that are expanded to a larger size during the broadcast
+operation::
+
+  A      (4d array):  8 x 1 x 6 x 1
+  B      (3d array):      7 x 1 x 5
+  Result (4d array):  8 x 7 x 6 x 5
+
+Here are some more examples::
+
+  A      (2d array):  5 x 4
+  B      (1d array):      1
+  Result (2d array):  5 x 4
+
+  A      (2d array):  5 x 4
+  B      (1d array):      4
+  Result (2d array):  5 x 4
+
+  A      (3d array):  15 x 3 x 5
+  B      (3d array):  15 x 1 x 5
+  Result (3d array):  15 x 3 x 5
+
+  A      (3d array):  15 x 3 x 5
+  B      (2d array):       3 x 5
+  Result (3d array):  15 x 3 x 5
+
+  A      (3d array):  15 x 3 x 5
+  B      (2d array):       3 x 1
+  Result (3d array):  15 x 3 x 5
+
+Here are examples of shapes that do not broadcast::
+
+  A      (1d array):  3
+  B      (1d array):  4 # trailing dimensions do not match
+
+  A      (2d array):      2 x 1
+  B      (3d array):  8 x 4 x 3 # second from last dimensions mismatched
+
+An example of broadcasting in practice::
+
+ >>> x = np.arange(4)
+ >>> xx = x.reshape(4,1)
+ >>> y = np.ones(5)
+ >>> z = np.ones((3,4))
+
+ >>> x.shape
+ (4,)
+
+ >>> y.shape
+ (5,)
+
+ >>> x + y
+ ValueError: operands could not be broadcast together with shapes (4,) (5,)
+
+ >>> xx.shape
+ (4, 1)
+
+ >>> y.shape
+ (5,)
+
+ >>> (xx + y).shape
+ (4, 5)
+
+ >>> xx + y
+ array([[ 1.,  1.,  1.,  1.,  1.],
+        [ 2.,  2.,  2.,  2.,  2.],
+        [ 3.,  3.,  3.,  3.,  3.],
+        [ 4.,  4.,  4.,  4.,  4.]])
+
+ >>> x.shape
+ (4,)
+
+ >>> z.shape
+ (3, 4)
+
+ >>> (x + z).shape
+ (3, 4)
+
+ >>> x + z
+ array([[ 1.,  2.,  3.,  4.],
+        [ 1.,  2.,  3.,  4.],
+        [ 1.,  2.,  3.,  4.]])
+
+Broadcasting provides a convenient way of taking the outer product (or
+any other outer operation) of two arrays. The following example shows an
+outer addition operation of two 1-d arrays::
+
+  >>> a = np.array([0.0, 10.0, 20.0, 30.0])
+  >>> b = np.array([1.0, 2.0, 3.0])
+  >>> a[:, np.newaxis] + b
+  array([[  1.,   2.,   3.],
+         [ 11.,  12.,  13.],
+         [ 21.,  22.,  23.],
+         [ 31.,  32.,  33.]])
+
+Here the ``newaxis`` index operator inserts a new axis into ``a``,
+making it a two-dimensional ``4x1`` array.  Combining the ``4x1`` array
+with ``b``, which has shape ``(3,)``, yields a ``4x3`` array.
+
+"""