Warning
This documentation is work-in-progress and unorganized.
Various utilities that don’t have another home.
Note that the Python Imaging Library (PIL) is not a dependency of SciPy and therefore the pilutil module is not available on systems that don’t have PIL installed.
common | Functions which are common and require SciPy Base and Level 1 SciPy |
doccer | Utilities to allow inserting docstring fragments for common |
pilutil | |
setup | |
setupscons |
bytescale | |
central_diff_weights(Np[, ndiv]) | Return weights for an Np-point central derivative of order ndiv |
comb(N, k[, exact]) | The number of combinations of N things taken k at a time. |
derivative(func, x0[, dx, n, args, order]) | Find the n-th derivative of a function at point x0. |
factorial(n[, exact]) | The factorial function, n! = special.gamma(n+1). |
factorial2(n[, exact]) | Double factorial. |
factorialk(n, k[, exact]) | n(!!...!) = multifactorial of order k |
fromimage | |
imfilter | |
imread | |
imresize | |
imrotate | |
imsave | |
imshow | |
info([object, maxwidth, output, toplevel]) | Get help information for a function, class, or module. |
lena() | Get classic image processing example image, Lena, at 8-bit grayscale |
pade(an, m) | Given Taylor series coefficients in an, return a Pade approximation to |
radon | |
toimage |
Print the Numpy arrays in the given dictionary.
If there is no dictionary passed in or vardict is None then returns Numpy arrays in the globals() dictionary (all Numpy arrays in the namespace).
Parameters : | vardict : dict, optional
|
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Returns : | out : None
|
Notes
Prints out the name, shape, bytes and type of all of the ndarrays present in vardict.
Examples
>>> a = np.arange(10)
>>> b = np.ones(20)
>>> np.who()
Name Shape Bytes Type
===========================================================
a 10 40 int32
b 20 160 float64
Upper bound on total bytes = 200
>>> d = {'x': np.arange(2.0), 'y': np.arange(3.0), 'txt': 'Some str',
... 'idx':5}
>>> np.who(d)
Name Shape Bytes Type
===========================================================
y 3 24 float64
x 2 16 float64
Upper bound on total bytes = 40
Print or write to a file the source code for a Numpy object.
The source code is only returned for objects written in Python. Many functions and classes are defined in C and will therefore not return useful information.
Parameters : | object : numpy object
output : file object, optional
|
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See also
lookfor, info
Examples
>>> np.source(np.interp)
In file: /usr/lib/python2.6/dist-packages/numpy/lib/function_base.py
def interp(x, xp, fp, left=None, right=None):
""".... (full docstring printed)"""
if isinstance(x, (float, int, number)):
return compiled_interp([x], xp, fp, left, right).item()
else:
return compiled_interp(x, xp, fp, left, right)
The source code is only returned for objects written in Python.
>>> np.source(np.array)
Not available for this object.
Get help information for a function, class, or module.
Parameters : | object : object or str, optional
maxwidth : int, optional
output : file like object, optional
toplevel : str, optional
|
---|
See also
source, lookfor
Notes
When used interactively with an object, np.info(obj) is equivalent to help(obj) on the Python prompt or obj? on the IPython prompt.
Examples
>>> np.info(np.polyval)
polyval(p, x)
Evaluate the polynomial p at x.
...
When using a string for object it is possible to get multiple results.
>>> np.info('fft')
*** Found in numpy ***
Core FFT routines
...
*** Found in numpy.fft ***
fft(a, n=None, axis=-1)
...
*** Repeat reference found in numpy.fft.fftpack ***
*** Total of 3 references found. ***
The factorial function, n! = special.gamma(n+1).
If exact is 0, then floating point precision is used, otherwise exact long integer is computed.
Parameters : | n : int or array_like of ints
exact : bool, optional
|
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Returns : | nf : float or int
|
Examples
>>> arr = np.array([3,4,5])
>>> sc.factorial(arr, exact=False)
array([ 6., 24., 120.])
>>> sc.factorial(5, exact=True)
120L
Double factorial.
This is the factorial with every second value skipped, i.e., 7!! = 7 * 5 * 3 * 1. It can be approximated numerically as:
n!! = special.gamma(n/2+1)*2**((m+1)/2)/sqrt(pi) n odd
= 2**(n/2) * (n/2)! n even
Parameters : | n : int or array_like
exact : bool, optional
|
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Returns : | nff : float or int
|
Examples
>>> factorial2(7, exact=False)
array(105.00000000000001)
>>> factorial2(7, exact=True)
105L
n(!!...!) = multifactorial of order k k times
Parameters : | n : int, array_like
exact : bool, optional
|
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Returns : | val : int
|
Raises : | NotImplementedError :
|
Examples
>>> sc.factorialk(5, 1, exact=True)
120L
>>> sc.factorialk(5, 3, exact=True)
10L
The number of combinations of N things taken k at a time. This is often expressed as “N choose k”.
Parameters : | N : int, array
k : int, array
exact : int, optional
|
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Returns : | val : int, array
|
Notes
Examples
>>> k = np.array([3, 4])
>>> n = np.array([10, 10])
>>> sc.comb(n, k, exact=False)
array([ 120., 210.])
>>> sc.comb(10, 3, exact=True)
120L
Return weights for an Np-point central derivative of order ndiv assuming equally-spaced function points.
If weights are in the vector w, then derivative is w[0] * f(x-ho*dx) + ... + w[-1] * f(x+h0*dx)
Notes
Can be inaccurate for large number of points.
Find the n-th derivative of a function at point x0.
Given a function, use a central difference formula with spacing dx to compute the n-th derivative at x0.
Parameters : | func : function
x0 : float
dx : int, optional
n : int, optional
args : tuple, optional
order : int, optional
|
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Notes
Decreasing the step size too small can result in round-off error.
Examples
>>> def x2(x):
... return x*x
...
>>> derivative(x2, 2)
4.0
Given Taylor series coefficients in an, return a Pade approximation to the function as the ratio of two polynomials p / q where the order of q is m.
Get classic image processing example image, Lena, at 8-bit grayscale bit-depth, 512 x 512 size.
Parameters : | None : |
---|---|
Returns : | lena : ndarray
|
Examples
>>> import scipy.misc
>>> lena = scipy.misc.lena()
>>> lena.shape
(512, 512)
>>> lena.max()
245
>>> lena.dtype
dtype('int32')
>>> import matplotlib.pyplot as plt
>>> plt.gray()
>>> plt.imshow(lena)
>>> plt.show()