8. Tricks and Tips
===============

Here we give a list of short and useful tips.

8.1 "Automatic" Reshaping
---------------------

To change the dimensions of an array, you can omit one of the sizes
which will then be deduced automatically:

>>> a = np.arange(30)
>>> a.shape = 2,-1,3  # -1 means "whatever is needed"
>>> a.shape
(2, 5, 3)
>>> a
array([[[ 0,  1,  2],
[ 3,  4,  5],
[ 6,  7,  8],
[ 9, 10, 11],
[12, 13, 14]],
[[15, 16, 17],
[18, 19, 20],
[21, 22, 23],
[24, 25, 26],
[27, 28, 29]]])

[demo]

import numpy as np
a = np.arange(30)
a.shape = 2,-1,3  # -1 means "whatever is needed"
print(a.shape)
print(a)

[/demo]


8.2 Vector Stacking
---------------

How do we construct a 2D array from a list of equally-sized row vectors?
In MATLAB this is quite easy: if ``x`` and ``y`` are two vectors of the
same length you only need do ``m=[x;y]``. In NumPy this works via the
functions ``column_stack``, ``dstack``, ``hstack`` and ``vstack``,
depending on the dimension in which the stacking is to be done. For
example:

::

x = np.arange(0,10,2)                     # x=([0,2,4,6,8])
y = np.arange(5)                          # y=([0,1,2,3,4])
m = np.vstack([x,y])                      # m=([[0,2,4,6,8],
#     [0,1,2,3,4]])
xy = np.hstack([x,y])                     # xy =([0,2,4,6,8,0,1,2,3,4])

[demo]

import numpy as np
x = np.arange(0,10,2)                     # x=([0,2,4,6,8])
y = np.arange(5)                          # y=([0,1,2,3,4])
m = np.vstack([x,y])                      # m=([[0,2,4,6,8],
xy = np.hstack([x,y])                     # xy =([0,2,4,6,8,0,1,2,3,4])
print(x)
print(y)
print(m)
print(xy)

[/demo]


The logic behind those functions in more than two dimensions can be
strange.

.. seealso::

:doc:`numpy-for-matlab-users`

8.3 Histograms
----------

The NumPy ``histogram`` function applied to an array returns a pair of
vectors: the histogram of the array and the vector of bins. Beware:
``matplotlib`` also has a function to build histograms (called ``hist``,
as in Matlab) that differs from the one in NumPy. The main difference is
that ``pylab.hist`` plots the histogram automatically, while
``numpy.histogram`` only generates the data.

.. plot::

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> # Build a vector of 10000 normal deviates with variance 0.5^2 and mean 2
>>> mu, sigma = 2, 0.5
>>> v = np.random.normal(mu,sigma,10000)
>>> # Plot a normalized histogram with 50 bins
>>> plt.hist(v, bins=50, normed=1)       # matplotlib version (plot)
>>> plt.show()
>>> # Compute the histogram with numpy and then plot it
>>> (n, bins) = np.histogram(v, bins=50, normed=True)  # NumPy version (no plot)
>>> plt.plot(.5*(bins[1:]+bins[:-1]), n)
>>> plt.show()


[demo]

import numpy as np
import matplotlib.pyplot as plt
# Build a vector of 10000 normal deviates with variance 0.5^2 and mean 2
mu, sigma = 2, 0.5
v = np.random.normal(mu,sigma,10000)
# Plot a normalized histogram with 50 bins
plt.hist(v, bins=50, normed=1)       # matplotlib version (plot)
plt.show()
# Compute the histogram with numpy and then plot it
(n, bins) = np.histogram(v, bins=50, normed=True)  # NumPy version (no plot)
plt.plot(.5*(bins[1:]+bins[:-1]), n)
plt.show()

[/demo]