To begin with, all of the Numpy functions have been subsumed into the
`scipy` namespace so that all of those functions are available
without additionally importing Numpy. In addition, the universal
functions (addition, subtraction, division) have been altered to not
raise exceptions if floating-point errors are encountered; instead,
NaN’s and Inf’s are returned in the arrays. To assist in detection of
these events, several functions (`sp.isnan`, `sp.isfinite`,
`sp.isinf`) are available.

Finally, some of the basic functions like log, sqrt, and inverse trig
functions have been modified to return complex numbers instead of
NaN’s where appropriate (*i.e.* `sp.sqrt(-1)` returns `1j`).

The purpose of the top level of scipy is to collect general-purpose
routines that the other sub-packages can use and to provide a simple
replacement for Numpy. Anytime you might think to import Numpy, you
can import scipy instead and remove yourself from direct dependence on
Numpy. These routines are divided into several files for
organizational purposes, but they are all available under the numpy
namespace (and the scipy namespace). There are routines for type
handling and type checking, shape and matrix manipulation, polynomial
processing, and other useful functions. Rather than giving a detailed
description of each of these functions (which is available in the
Numpy Reference Guide or by using the `help`, `info` and
`source` commands), this tutorial will discuss some of the more
useful commands which require a little introduction to use to their
full potential.

Note the difference between `sp.iscomplex`/`sp.isreal` and
`sp.iscomplexobj`/`sp.isrealobj`. The former command is
array based and returns byte arrays of ones and zeros providing the
result of the element-wise test. The latter command is object based
and returns a scalar describing the result of the test on the entire
object.

Often it is required to get just the real and/or imaginary part of a
complex number. While complex numbers and arrays have attributes that
return those values, if one is not sure whether or not the object will
be complex-valued, it is better to use the functional forms
`sp.real` and `sp.imag` . These functions succeed for anything
that can be turned into a Numpy array. Consider also the function
`sp.real_if_close` which transforms a complex-valued number with
tiny imaginary part into a real number.

Occasionally the need to check whether or not a number is a scalar
(Python (long)int, Python float, Python complex, or rank-0 array)
occurs in coding. This functionality is provided in the convenient
function `sp.isscalar` which returns a 1 or a 0.

Finally, ensuring that objects are a certain Numpy type occurs often
enough that it has been given a convenient interface in SciPy through
the use of the `sp.cast` dictionary. The dictionary is keyed by the
type it is desired to cast to and the dictionary stores functions to
perform the casting. Thus, `sp.cast['f'](d)` returns an array
of `sp.float32` from *d*. This function is also useful as an easy
way to get a scalar of a certain type:

```
>>> sp.cast['f'](sp.pi)
array(3.1415927410125732, dtype=float32)
```

There are some class instances that make special use of the slicing
functionality to provide efficient means for array construction. This
part will discuss the operation of `sp.mgrid` , `sp.ogrid` ,
`sp.r_` , and `sp.c_` for quickly constructing arrays.

One familiar with Matlab may complain that it is difficult to construct arrays from the interactive session with Python. Suppose, for example that one wants to construct an array that begins with 3 followed by 5 zeros and then contains 10 numbers spanning the range -1 to 1 (inclusive on both ends). Before SciPy, you would need to enter something like the following

```
>>> concatenate(([3],[0]*5,arange(-1,1.002,2/9.0)))
```

With the `r_` command one can enter this as

```
>>> r_[3,[0]*5,-1:1:10j]
```

which can ease typing and make for more readable code. Notice how objects are concatenated, and the slicing syntax is (ab)used to construct ranges. The other term that deserves a little explanation is the use of the complex number 10j as the step size in the slicing syntax. This non-standard use allows the number to be interpreted as the number of points to produce in the range rather than as a step size (note we would have used the long integer notation, 10L, but this notation may go away in Python as the integers become unified). This non-standard usage may be unsightly to some, but it gives the user the ability to quickly construct complicated vectors in a very readable fashion. When the number of points is specified in this way, the end- point is inclusive.

The “r” stands for row concatenation because if the objects between
commas are 2 dimensional arrays, they are stacked by rows (and thus
must have commensurate columns). There is an equivalent command
`c_` that stacks 2d arrays by columns but works identically to
`r_` for 1d arrays.

Another very useful class instance which makes use of extended slicing
notation is the function `mgrid`. In the simplest case, this
function can be used to construct 1d ranges as a convenient substitute
for arange. It also allows the use of complex-numbers in the step-size
to indicate the number of points to place between the (inclusive)
end-points. The real purpose of this function however is to produce N,
N-d arrays which provide coordinate arrays for an N-dimensional
volume. The easiest way to understand this is with an example of its
usage:

```
>>> mgrid[0:5,0:5]
array([[[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 3],
[4, 4, 4, 4, 4]],
[[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4]]])
>>> mgrid[0:5:4j,0:5:4j]
array([[[ 0. , 0. , 0. , 0. ],
[ 1.6667, 1.6667, 1.6667, 1.6667],
[ 3.3333, 3.3333, 3.3333, 3.3333],
[ 5. , 5. , 5. , 5. ]],
[[ 0. , 1.6667, 3.3333, 5. ],
[ 0. , 1.6667, 3.3333, 5. ],
[ 0. , 1.6667, 3.3333, 5. ],
[ 0. , 1.6667, 3.3333, 5. ]]])
```

Having meshed arrays like this is sometimes very useful. However, it
is not always needed just to evaluate some N-dimensional function over
a grid due to the array-broadcasting rules of Numpy and SciPy. If this
is the only purpose for generating a meshgrid, you should instead use
the function `ogrid` which generates an “open “grid using NewAxis
judiciously to create N, N-d arrays where only one dimension in each
array has length greater than 1. This will save memory and create the
same result if the only purpose for the meshgrid is to generate sample
points for evaluation of an N-d function.

In this category of functions are routines for squeezing out length- one dimensions from N-dimensional arrays, ensuring that an array is at least 1-, 2-, or 3-dimensional, and stacking (concatenating) arrays by rows, columns, and “pages “(in the third dimension). Routines for splitting arrays (roughly the opposite of stacking arrays) are also available.

There are two (interchangeable) ways to deal with 1-d polynomials in
SciPy. The first is to use the `poly1d` class from Numpy. This
class accepts coefficients or polynomial roots to initialize a
polynomial. The polynomial object can then be manipulated in algebraic
expressions, integrated, differentiated, and evaluated. It even prints
like a polynomial:

```
>>> p = poly1d([3,4,5])
>>> print p
2
3 x + 4 x + 5
>>> print p*p
4 3 2
9 x + 24 x + 46 x + 40 x + 25
>>> print p.integ(k=6)
3 2
x + 2 x + 5 x + 6
>>> print p.deriv()
6 x + 4
>>> p([4,5])
array([ 69, 100])
```

The other way to handle polynomials is as an array of coefficients with the first element of the array giving the coefficient of the highest power. There are explicit functions to add, subtract, multiply, divide, integrate, differentiate, and evaluate polynomials represented as sequences of coefficients.

One of the features that NumPy provides is a class `vectorize` to
convert an ordinary Python function which accepts scalars and returns
scalars into a “vectorized-function” with the same broadcasting rules
as other Numpy functions (*i.e.* the Universal functions, or
ufuncs). For example, suppose you have a Python function named
`addsubtract` defined as:

```
>>> def addsubtract(a,b):
... if a > b:
... return a - b
... else:
... return a + b
```

which defines a function of two scalar variables and returns a scalar result. The class vectorize can be used to “vectorize “this function so that

```
>>> vec_addsubtract = vectorize(addsubtract)
```

returns a function which takes array arguments and returns an array result:

```
>>> vec_addsubtract([0,3,6,9],[1,3,5,7])
array([1, 6, 1, 2])
```

This particular function could have been written in vector form
without the use of `vectorize` . But, what if the function you have written is the result of some
optimization or integration routine. Such functions can likely only be
vectorized using `vectorize.`

There are several other functions in the scipy_base package including most of the other functions that are also in the Numpy package. The reason for duplicating these functions is to allow SciPy to potentially alter their original interface and make it easier for users to know how to get access to functions

```
>>> from scipy import *
```

Functions which should be mentioned are `mod(x,y)` which can
replace `x % y` when it is desired that the result take the sign of
*y* instead of *x* . Also included is `fix` which always rounds
to the nearest integer towards zero. For doing phase processing, the
functions `angle`, and `unwrap` are also useful. Also, the
`linspace` and `logspace` functions return equally spaced samples
in a linear or log scale. Finally, it’s useful to be aware of the indexing
capabilities of Numpy. Mention should be made of the new
function `select` which extends the functionality of `where` to
include multiple conditions and multiple choices. The calling
convention is `select(condlist,choicelist,default=0).` `select` is
a vectorized form of the multiple if-statement. It allows rapid
construction of a function which returns an array of results based on
a list of conditions. Each element of the return array is taken from
the array in a `choicelist` corresponding to the first condition in
`condlist` that is true. For example

```
>>> x = r_[-2:3]
>>> x
array([-2, -1, 0, 1, 2])
>>> select([x > 3, x >= 0],[0,x+2])
array([0, 0, 2, 3, 4])
```

Some functions depend on sub-packages of SciPy but should be available
from the top-level of SciPy due to their common use. These are
functions that might have been placed in scipy_base except for their
dependence on other sub-packages of SciPy. For example the
`factorial` and `comb` functions compute and
using either exact integer arithmetic (thanks to
Python’s Long integer object), or by using floating-point precision
and the gamma function. The functions `rand` and `randn`
are used so often that they warranted a place at the top level. There
are convenience functions for the interactive use: `disp`
(similar to print), and `who` (returns a list of defined
variables and memory consumption–upper bounded). Another function
returns a common image used in image processing: `lena`.

Finally, two functions are provided that are useful for approximating
derivatives of functions using discrete-differences. The function
`central_diff_weights` returns weighting coefficients for an
equally-spaced -point approximation to the derivative of
order *o*. These weights must be multiplied by the function
corresponding to these points and the results added to obtain the
derivative approximation. This function is intended for use when only
samples of the function are avaiable. When the function is an object
that can be handed to a routine and evaluated, the function
`derivative` can be used to automatically evaluate the object at
the correct points to obtain an N-point approximation to the *o*-th
derivative at a given point.