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%% Cell type:markdown id: tags:
%% Cell type:markdown id:d9a95412 tags:
# WIN Pytreat - Python overview
This notebook intends to give you a whirlwind overview of the Python
programming language.
This notebook, and many of the others, assume that you have FSL 6.0.4 or newer
installed. To run this notebook interactively on your own computer, follow the
instructions for attendees here: https://git.fmrib.ox.ac.uk/fsl/win-pytreat
and open the `getting_started/00_overview.ipynb` notebook.
# Contents
* [Introduction](#introduction)
* [Python in a nutshell](#python-in-a-nutshell)
* [Different ways of writing and running Python](#different-ways-of-writing-and-running-python)
* [Variables and basic types](#variables-and-basic-types)
* [Integer and floating point scalars](#integer-and-floating-point-scalars)
* [Strings](#strings)
* [Lists and tuples](#lists-and-tuples)
* [Dictionaries](#dictionaries)
* [A note on mutablility](#a-note-on-mutablility)
* [Flow control](#flow-control)
* [List comprehensions](#list-comprehensions)
* [Reading and writing text files](#reading-and-writing-text-files)
* [Example: processing lesion counts](#example-processing-lesion-counts)
* [Functions](#functions)
* [Working with `numpy`](#working-with-numpy)
* [The Python list versus the `numpy` array](#the-python-list-versus-the-numpy-array)
* [Creating arrays](#creating-arrays)
* [Example: reading arrays from text files](#example-reading-arrays-from-text-files)
<a class="anchor" id="introduction"></a>
# Introduction
This notebook is an attempt to give a whirlwind overview of the Python
programming language.
We have prepared much of the PyTreat material as interactive [Jupyter
Notebooks](https://jupyter.org/) - you can run all of the code on your own
machine - click on a code block, and press **SHIFT+ENTER**. You can also "run"
the text sections, so you can just move down the document by pressing
**SHIFT+ENTER**.
It is also possible to *change* the contents of each code block (these pages
are completely interactive) so do experiment with the code you see and try
some variations!
You can get help on any Python object, function, or method by putting a `?`
before or after the thing you want help on:
%% Cell type:code id: tags:
%% Cell type:code id:bfe0abb7 tags:
```
a = 'hello!'
?a.upper
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:6b7b0a1d tags:
And you can explore the available methods on a Python object by using the
**TAB** key:
%% Cell type:code id: tags:
%% Cell type:code id:4aaf1645 tags:
```
# Put the cursor after the dot, and press the TAB key...
a.
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:5e17377c tags:
<a class="anchor" id="python-in-a-nutshell"></a>
## Python in a nutshell
**Pros**
* _Flexible_ Feel free to use functions, classes, objects, modules and
packages. Or don't - it's up to you!
* _Fast_ If you do things right (in other words, if you use `numpy`)
* _Dynamically typed_ No need to declare your variables, or specify their
types.
* _Intuitive syntax_ How do I run some code for each of the elements in my
list?
%% Cell type:code id: tags:
%% Cell type:code id:7a90987e tags:
```
mylist = [1, 2, 3, 4, 5]
for element in mylist:
print(element)
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:6c5482ab tags:
**Cons**
* _Dynamically typed_ Easier to make mistakes, harder to catch them
* _No compiler_ See above
* _Slow_ if you don't do things the right way
* _Python 2 is not the same as Python 3_ But there's an easy solution: Forget
that Python 2 exists.
* _Hard to manage different versions of python_ But we have a solution for
you: `fslpython`.
Python is a widely used language, so you can get lots of help through google
and [stackoverflow](https://stackoverflow.com). But make sure that the
information you find is for **Python 3**, and **not** for **Python 2**!
Python 2 is obsolete, but is still used by many organisations, so you will
inevitably come across many Python 2 resources.
The differences between Python 2 and 3 are small, but important. The most
visible difference is in the `print` function: in Python 3, we write
`print('hello!')`, but in Python 2, we would write `print 'hello!'`.
FSL 5.0.10 and newer comes with its own version of Python 3, bundled with
nearly all of the scientific libraries that you are likely to need.
So if you use `fslpython` for all of your development, you can forget about
Python 2, and be sure that your code will work in FSL!
<a class="anchor" id="different-ways-of-writing-and-running-python"></a>
## Different ways of writing and running Python
Many of the Pytreat practicals are presented as *Jupyter notebooks*, which is
a way of running python code in a web browser.
Jupyter notebooks are good for presentations and practicals, and some people
find them very useful for exploratory data analysis. But they're not the only
way of running Python code.
**Run Python from a file**
This works just like it does in MATLAB:
1. Put your code in a `.py` file (e.g. `mycode.py`), using your favourite
**plain text** editor.
2. Run `fslpython mycode.py` in a terminal.
**Run python in an interpreter**
Python is an [*interpreted
language*](https://en.wikipedia.org/wiki/Interpreted_language), like MATLAB.
So you can either write your code into a file, and then run that file, or you
can type code directly into a Python interpreter.
Python has a standard interpreter built-in - run `fslpython` in a terminal,
and see what happens (use CTRL+D to exit).
**But** there is another interpreter called [IPython](https://ipython.org/)
which is vastly superior to the standard Python interpreter. Use IPython
instead! It is already installed in `fslpython`, so if you want to do some
interactive work, you can use `fslipython` in a terminal.
**Python development environments**
There are a number of Python editors, or *IDEs* (Integrated Development
Environments), which combine both a text editor and a python interpreter. Here
are a couple of our favourites:
* [PyCharm](https://www.jetbrains.com/pycharm/) has very good code editing
tools, and good file management/version control integration, so is a very
good choice for writing code.
* [Spyder](https://www.spyder-ide.org/) is a MATLAB-like environment for
Python, which makes it useful for interactive work (e.g. data exploratiion
and analysis).
* [JupyterLab](https://jupyter.org/) is the successor to Jupyter Notebook -
it provides a full Python development environment running entirely in your
web browser.
> Get in touch with a tutor if you would like help in installing a Python IDE.
<a class="anchor" id="variables-and-basic-types"></a>
# Variables and basic types
There are many different types of values in Python. Python *variables* do not
have a type though - a variable can refer to values of any type, and a
variable can be updated to refer to different values (of different
types). This is just like how things work in MATLAB.
<a class="anchor" id="integer-and-floating-point-scalars"></a>
## Integer and floating point scalars
%% Cell type:code id: tags:
%% Cell type:code id:92f979f2 tags:
```
a = 7
b = 1 / 3
c = a + b
print('a: ', a)
print('b: ', b)
print('c: ', c)
print('b: {:0.4f}'.format(b))
print('a + b:', a + b)
print('a: ', a)
print('b: ', b)
print('c: ', c)
print('b: {:0.4f}'.format(b))
print('a + b: ', a + b)
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:1bfc6ffb tags:
<a class="anchor" id="strings)"></a>
## Strings
%% Cell type:code id: tags:
%% Cell type:code id:276cb884 tags:
```
a = 'Hello'
b = "Kitty"
c = '''
c = """
Magic
multi-line
strings!
'''
"""
print(a, b)
print(a + b)
print('{}, {}!'.format(a, b))
print(f'{a}, {b}!')
print(c)
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:f233a287 tags:
String objects have a number of useful methods:
%% Cell type:code id: tags:
%% Cell type:code id:479ad215 tags:
```
s = 'This is a Test String'
print(s.upper())
print(s.lower())
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:89132c40 tags:
Another useful method is:
%% Cell type:code id: tags:
%% Cell type:code id:74773e8f tags:
```
s = 'This is a Test String'
s2 = s.replace('Test', 'Better')
print(s2)
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:3d070148 tags:
Two common and convenient string methods are `strip()` and `split()`. The
first will remove any whitespace at the beginning and end of a string:
%% Cell type:code id: tags:
%% Cell type:code id:442e0a9b tags:
```
s2 = ' A very spacy string '
print('*' + s2 + '*')
print('*' + s2.strip() + '*')
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:9ff59d32 tags:
With `split()` we can tokenize a string (to turn it into a list of strings)
like this:
%% Cell type:code id: tags:
%% Cell type:code id:7c7d9f09 tags:
```
print(s.split())
print(s2.split())
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:d5485806 tags:
We can also use the `join` method to re-construct a new string. Imagine that
we need to reformat some data from being comma-separated to being
space-separated:
%% Cell type:code id: tags:
%% Cell type:code id:e546a282 tags:
```
data = ' 1,2,3,4,5,6,7 '
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:a3f3c49e tags:
`strip`, `split` and `join` makes this job trivial:
%% Cell type:code id: tags:
%% Cell type:code id:6a6e0823 tags:
```
print('Original: {}'.format(data))
print('Strip, split, and join: {}'.format(' '.join(data.strip().split(','))))
print(f'Original: {data}')
print('Strip, split, and join:', ' '.join(data.strip().split(',')))
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:c5009202 tags:
<a class="anchor" id="lists-and-tuples"></a>
## Lists and tuples
Both tuples and lists are built-in Python types and are like cell-arrays in
MATLAB. For numerical vectors and arrays it is much better to use *numpy*
arrays, which are covered later.
Tuples are defined using round brackets and lists are defined using square
brackets. For example:
%% Cell type:code id: tags:
%% Cell type:code id:8114c7c6 tags:
```
t = (3, 7.6, 'str')
l = [1, 'mj', -5.4]
print(t)
print(l)
t2 = (t, l)
l2 = [t, l]
print('t2 is: ', t2)
print('l3 is: ', l2)
print(len(t2))
print(len(l2))
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:fefe91af tags:
The key difference between lists and tuples is that tuples are *immutable*
(once created, they cannot be changed), whereas lists are *mutable*:
%% Cell type:code id: tags:
%% Cell type:code id:3277edf9 tags:
```
a = [10, 20, 30]
a[2] = 999
print(a)
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:c52a32cb tags:
Square brackets are used to index tuples, lists, strings, dictionaries, etc.
For example:
%% Cell type:code id: tags:
%% Cell type:code id:710c4f99 tags:
```
d = [10, 20, 30]
print(d[1])
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:e57f4f8a tags:
> **MATLAB pitfall:** Python uses zero-based indexing, unlike MATLAB, where
> indices start from 1.
%% Cell type:code id: tags:
%% Cell type:code id:3f0cf402 tags:
```
a = [10, 20, 30, 40, 50, 60]
print(a[0])
print(a[2])
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:44726855 tags:
A range of values for the indices can be specified to extract values from a
list or tuple using the `:` character. For example:
%% Cell type:code id: tags:
%% Cell type:code id:59c8d4a9 tags:
```
print(a[0:3])
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:0beb8aed tags:
> **MATLAB pitfall:** Note that Python's slicing syntax is different from
> MATLAB in that the second number is *exclusive*, i.e. `a[0:3]` gives us the
> elements of `a` at positions `0`, `1` and `2` , but *not* at position `3`.
When slicing a list or tuple, you can leave the start and end values out -
when you do this, Python will assume that you want to start slicing from the
beginning or the end of the list. For example:
%% Cell type:code id: tags:
%% Cell type:code id:44c5a48c tags:
```
print(a[:3])
print(a[1:])
print(a[:])
print(a[:-1])
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:56b622fa tags:
You can also change the step size, which is specified by the third value (not
the second one, as in MATLAB). For example:
%% Cell type:code id: tags:
%% Cell type:code id:2c9b61e4 tags:
```
print(a[0:4:2])
print(a[::2])
print(a[::-1])
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:3949cc29 tags:
Some methods are available on `list` objects for adding and removing items:
%% Cell type:code id: tags:
%% Cell type:code id:f96a3e31 tags:
```
print(d)
d.append(40)
print(d)
d.extend([50, 60])
print(d)
d = d + [70, 80]
print(d)
d.remove(20)
print(d)
d.pop(0)
print(d)
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:4cf71af8 tags:
What will `d.append([50,60])` do, and how is it different from
`d.extend([50,60])`?
%% Cell type:code id: tags:
%% Cell type:code id:ac1fce51 tags:
```
d.append([50, 60])
print(d)
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:15a01466 tags:
<a class="anchor" id="dictionaries"></a>
## Dictionaries
Dictionaries (or *dicts*) can be used to store key-value pairs. Almost
anything can used as a key, and anything can be stored as a value; it is
common to use strings as keys:
%% Cell type:code id: tags:
%% Cell type:code id:051312df tags:
```
e = {'a' : 10, 'b': 20}
print(len(e))
print(e.keys())
print(e.values())
print(e['a'])
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:e39b639f tags:
Like lists (and unlike tuples), dicts are mutable, and have a number of
methods for manipulating them:
%% Cell type:code id: tags:
%% Cell type:code id:db998eb1 tags:
```
e['c'] = 30
e.pop('a')
e.update({'a' : 100, 'd' : 400})
print(e)
e.clear()
print(e)
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:11002f02 tags:
<a class="anchor" id="a-note-on-mutability"></a>
## A note on mutablility
Python variables can refer to values which are either mutable, or
immutable. Examples of immutable values are strings, tuples, and integer and
floating point scalars. Examples of mutable values are lists, dicts, and most
user-defined types.
When you pass an immutable value around (e.g. into a function, or to another
variable), it works the same as if you were to copy the value and pass in the
copy - the original value is not changed:
%% Cell type:code id: tags:
%% Cell type:code id:14b8bfc8 tags:
```
a = 'abcde'
b = a
b = b.upper()
print('a:', a)
print('b:', b)
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:ad4a61a6 tags:
In contrast, when you pass a mutable value around, you are passing a
*reference* to that value - there is only ever one value in existence, but
multiple variables refer to it. You can manipulate the value through any of
the variables that refer to it:
%% Cell type:code id: tags:
%% Cell type:code id:2852dd29 tags:
```
a = [1, 2, 3, 4, 5]
b = a
a[3] = 999
b.append(6)
print('a', a)
print('b', b)
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:3405fef2 tags:
<a class="anchor" id="flow-control"></a>
# Flow control
Python also has a boolean type which can be either `True` or `False`. Most
Python types can be implicitly converted into booleans when used in a
conditional expression.
Relevant boolean and comparison operators include: `not`, `and`, `or`, `==`
and `!=`
For example:
%% Cell type:code id: tags:
%% Cell type:code id:fa3a015c tags:
```
a = True
b = False
print('Not a is:', not a)
print('a or b is:', a or b)
print('a and b is:', a and b)
print('Not 1 is:', not 1)
print('Not 0 is:', not 0)
print('Not {} is:', not {})
print('{}==0 is:', {}==0)
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:66809170 tags:
There is also the `in` test for strings, lists, etc:
%% Cell type:code id: tags:
%% Cell type:code id:69c83d4a tags:
```
print('the' in 'a number of words')
print('of' in 'a number of words')
print(3 in [1, 2, 3, 4])
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:5c2a73d3 tags:
We can use boolean values in `if`-`else` conditional expressions:
%% Cell type:code id: tags:
%% Cell type:code id:ae10c994 tags:
```
a = [1, 2, 3, 4]
val = 3
if val in a:
print('Found {}!'.format(val))
print(f'Found {val}!')
else:
print('{} not found :('.format(val))
print(f'{val} not found :(')
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:bbaad787 tags:
Note that the indentation in the `if`-`else` statement is **crucial**.
**All** python control blocks are delineated purely by indentation. We
recommend using **four spaces** and no tabs, as this is a standard practice
and will help a lot when collaborating with others.
You can use the `for` statement to loop over elements in a list:
%% Cell type:code id: tags:
%% Cell type:code id:feb11366 tags:
```
d = [10, 20, 30]
for x in d:
print(x)
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:2d0b9802 tags:
You can also loop over the key-value pairs in a dict:
%% Cell type:code id: tags:
%% Cell type:code id:016a0b9a tags:
```
a = {'a' : 10, 'b' : 20, 'c' : 30}
print('a.items()')
for key, val in a.items():
print(key, val)
print('a.keys()')
for key in a.keys():
print(key, a[key])
print('a.values()')
for val in a.values():
print(val)
print('a.items()')
for key, val in a.items():
print(key, val)
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:5ecfcb80 tags:
> In older versions of Python 3, there was no guarantee of ordering when using dictionaries.
> However, a of Python 3.7, dictionaries will remember the order in which items are inserted,
> and the `keys()`, `values()`, and `items()` methods will return elements in that order.
>
> If you want a dictionary with ordering, *and* you want your code to work with
> Python versions older than 3.7, you can use the
> [`OrderedDict`](https://docs.python.org/3/library/collections.html#collections.OrderedDict)
> class.
There are some handy built-in functions that you can use with `for` loops:
%% Cell type:code id: tags:
%% Cell type:code id:bf4d0dbb tags:
```
d = [10, 20, 30]
print('Using the range function')
for i in range(len(d)):
print('element at position {}: {}'.format(i, d[i]))
print(f'element at position {i}: {d[i]}')
print('Using the enumerate function')
for i, elem in enumerate(d):
print('element at position {}: {}'.format(i, elem))
print(f'element at position {i}: {elem}')
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:2537a8f3 tags:
<a class="anchor" id=" list-comprehensions"></a>
## List comprehensions
Python has a really neat way to create lists (and dicts), called
*comprehensions*. Let's say we have some strings, and we want to count the
number of characters in each of them:
%% Cell type:code id: tags:
%% Cell type:code id:7f2bd1ce tags:
```
strings = ['hello', 'howdy', 'hi', 'hey']
nchars = [len(s) for s in strings]
for s, c in zip(strings, nchars):
print('{}: {}'.format(s, c))
print(f'{s}: {c}')
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:c7bd81f1 tags:
> The `zip` function "zips" two or more sequences, so you can loop over them
> together.
Or we could store the character counts in a dict:
%% Cell type:code id: tags:
%% Cell type:code id:e3542daa tags:
```
nchars = { s : len(s) for s in strings }
for s, c in nchars.items():
print('{}: {}'.format(s, c))
print(f'{s}: {c}')
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:b09e20ec tags:
<a class="anchor" id="reading-and-writing-text-files"></a>
# Reading and writing text files
The syntax to open a file in python is
> ```
> with open(<filename>, <mode>) as <file_object>:
> <block of code>
> ```
* `<filename>` is a string with the name of the file
* `<mode>` is one of `'r'` (for read-only access), `'w'` (for writing a file,
this wipes out any existing content), `'a'` (for appending to an existing
file).
* `<file_object>` is a variable name which will be used within the
`<block of code>` to access the opened file.
For example the following will read all the text in `00_overview/file.txt` and
print it:
%% Cell type:code id: tags:
%% Cell type:code id:386b53ba tags:
```
with open('00_overview/file.txt', 'r') as f:
print(f.read())
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:888c2a4c tags:
A very similar syntax is used to write files:
%% Cell type:code id: tags:
%% Cell type:code id:5bdb2fcc tags:
```
with open('new_file.txt', 'w') as f:
f.write('This is my first line\n')
f.writelines(['Second line\n', 'and the third\n'])
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:21cca2ce tags:
<a class="anchor" id="example-processing-lesion-counts"></a>
## Example: processing lesion counts
Imagine that we have written an amazing algorithm in Python which
automatically counts the number of lesions in an individual's structural MRI
image.
%% Cell type:code id: tags:
%% Cell type:code id:3be76086 tags:
```
subject_ids = ['01', '07', '21', '32']
lesion_counts = [ 4, 9, 13, 2]
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:0c6e4b1f tags:
We may wish to process this data in another application (e.g. Excel or SPSS).
Let's save the results out to a CSV (comma-separated value) file:
%% Cell type:code id: tags:
%% Cell type:code id:4ee09755 tags:
```
with open('lesion_counts.csv', 'w') as f:
f.write('Subject ID, Lesion count\n')
for subj_id, count in zip(subject_ids, lesion_counts):
f.write('{}, {}\n'.format(subj_id, count))
f.write(f'{subj_id}, {count}\n')
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:01538f4b tags:
We can now load the `lesion_counts.csv` file into our analysis software of
choice. Or we could load it back into another Python session, and store
the data in a dict:
%% Cell type:code id: tags:
%% Cell type:code id:9df720ba tags:
```
lesion_counts = {}
with open('lesion_counts.csv', 'r') as f:
# skip the header
f.readline()
for line in f.readlines():
subj_id, count = line.split(',')
lesion_counts[subj_id] = int(count)
print('Loaded lesion counts:')
for subj, count in lesion_counts.items():
print('{}: {}'.format(subj, count))
print(f'{subj}: {count}')
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:25ecd7db tags:
<a class="anchor" id="functions"></a>
## Functions
You will find functions pretty familiar in python to start with, although they
have a few options which are really handy and different from C++ or matlab (to
be covered in a later practical). To start with we'll look at a simple
function but note a few key points:
* you *must* indent everything inside the function (it is a code block and
indentation is the only way of determining this - just like for the guts of a
loop)
* you can return *whatever you want* from a python function, but only a single
object - it is usual to package up multiple things in a tuple or list, which
is easily unpacked by the calling invocation: e.g., `a, b, c = myfunc(x)`
* parameters are passed by *reference* (more on this below)
%% Cell type:code id: tags:
%% Cell type:code id:70ea8549 tags:
```
def myfunc(x, y, z=0):
r2 = x*x + y*y + z*z
r = r2**0.5
return r, r2
rad = myfunc(10, 20)
print(rad)
rad, dummy = myfunc(10, 20, 30)
print(rad)
rad, _ = myfunc(10,20,30)
print(rad)
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:60d3d252 tags:
> Note that the `_` is used as shorthand here for a dummy variable
> that you want to throw away.
>
> The return statement implicitly creates a tuple to return and is equivalent
> to `return (r, r2)`
One nice feature of python functions is that you can name the arguments when
you call them, rather than only doing it by position. For example:
%% Cell type:code id: tags:
%% Cell type:code id:4b3b5f32 tags:
```
def myfunc(x, y, z=0, flag=''):
if flag=='L1':
r = abs(x) + abs(y) + abs(z)
else:
r = (x*x + y*y + z*z)**0.5
return r
rA = myfunc(10, 20)
rB = myfunc(10, 20, flag='L1')
rC = myfunc(10, 20, flag='L1', z=30)
print(rA, rB, rC)
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:a5983a86 tags:
You will often see python functions called with these named arguments. In
fact, for functions with more than 2 or 3 variables this naming of arguments
is recommended, because it clarifies what each of the arguments does for
anyone reading the code.
Arguments passed into a python function are *passed by reference* - this is
where the difference between *mutable* and *immutable* types becomes
important - if you pass a mutable object into a function, the function
might change it!
%% Cell type:code id: tags:
%% Cell type:code id:e0e53f1c tags:
```
def changelist(l):
l[0] = 'mwahahaha!'
mylist = [1,2,3,4,5]
print('before:', mylist)
changelist(mylist)
print('after:', mylist)
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:89b37ef2 tags:
<a class="anchor" id="working-with-numpy"></a>
# Working with `numpy`
This section introduces you to [`numpy`](http://www.numpy.org/), Python's
numerical computing library. Numpy adds a new data type to the Python
language - the `array` (more specifically, the `ndarray`). A Numpy `array`
is a N-dimensional array of homogeneously-typed numerical data.
Pretty much every scientific computing library in Python is built on top of
Numpy - whenever you want to access some data, you will be accessing it in the
form of a Numpy array. So it is worth getting to know the basics.
<a class="anchor" id="the-python-list-versus-the-numpy-array"></a>
## The Python list versus the `numpy` array
You have already been introduced to the Python `list`, which you can easily
use to store a handful of numbers (or anything else):
%% Cell type:code id: tags:
%% Cell type:code id:ea408674 tags:
```
data = [10, 8, 12, 14, 7, 6, 11]
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:0fe6304b tags:
You could also emulate a 2D or ND matrix by using lists of lists, for example:
%% Cell type:code id: tags:
%% Cell type:code id:0f3e62a6 tags:
```
xyz_coords = [[-11.4, 1.0, 22.6],
[ 22.7, -32.8, 19.1],
[ 62.8, -18.2, -34.5]]
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:cd708cc7 tags:
For simple tasks, you could stick with processing your data using python
lists, and the built-in
[`math`](https://docs.python.org/3/library/math.html) library. And this
might be tempting, because it does look quite a lot like what you might type
into Matlab.
But **BEWARE!** A Python list is a terrible data structure for scientific
computing!
This is a major source of confusion for people who are learning Python, and
are trying to write efficient code. It is _crucial_ to be able to distinguish
between a Python list and a Numpy array.
**Python list == Matlab cell array:** A list in Python is akin to a cell
array in Matlab - they can store anything, but are extremely inefficient, and
unwieldy when you have more than a couple of dimensions.
**Numpy array == Matlab matrix:** These are in contrast to the Numpy array
and Matlab matrix, which are both thin wrappers around a contiguous chunk of
memory, and which provide blazing-fast performance (because behind the scenes
in both Numpy and Matlab, it's C, C++ and FORTRAN all the way down).
So you should strongly consider turning those lists into Numpy arrays:
%% Cell type:code id: tags:
%% Cell type:code id:dc215a0b tags:
```
import numpy as np
data = np.array([10, 8, 12, 14, 7, 6, 11])
xyz_coords = np.array([[-11.4, 1.0, 22.6],
[ 22.7, -32.8, 19.1],
[ 62.8, -18.2, -34.5]])
print('data: ', data)
print('xyz_coords: ', xyz_coords)
print('data.shape: ', data.shape)
print('xyz_coords.shape:', xyz_coords.shape)
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:bfacc99a tags:
> Numpy is not a "built-in" library, so we have to import it. The statement
> `import numpy as np` tells Python to *Import the `numpy` library, and make
> it available as a variable called `np`.*
<a class="anchor" id="creating-arrays"></a>
## Creating arrays
Numpy has quite a few functions which behave similarly to their equivalents in
Matlab:
%% Cell type:code id: tags:
%% Cell type:code id:a1c2c0d1 tags:
```
print('np.zeros gives us zeros: ', np.zeros(5))
print('np.ones gives us ones: ', np.ones(5))
print('np.arange gives us a range: ', np.arange(5))
print('np.linspace gives us N linearly spaced numbers:', np.linspace(0, 1, 5))
print('np.random.random gives us random numbers [0-1]:', np.random.random(5))
print('np.random.randint gives us random integers: ', np.random.randint(1, 10, 5))
print('np.eye gives us an identity matrix:')
print(np.eye(4))
print('np.diag gives us a diagonal matrix:')
print(np.diag([1, 2, 3, 4]))
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:3db10698 tags:
The `zeros` and `ones` functions can also be used to generate N-dimensional
arrays:
%% Cell type:code id: tags:
%% Cell type:code id:a24da68d tags:
```
z = np.zeros((3, 4))
o = np.ones((2, 10))
print(z)
print(o)
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:fdbb9c24 tags:
> Note that, in a 2D Numpy array, the first axis corresponds to rows, and the
> second to columns - just like in Matlab.
> **MATLAB pitfall:** Arithmetic operations on arrays in Numpy work on an
> *elementwise* basis. In particular, if you multiply two arrays together,
> you will get the elementwise product. You **won't** get the dot product,
> like you would in MATLAB. You can, however, use the `@` operator to perform
> matrix multiplication on numpy arrays.
<a class="anchor" id="example-reading-arrays-from-text-files"></a>
## Example: reading arrays from text files
The `numpy.loadtxt` function is capable of loading numerical data from
plain-text files. By default it expects space-separated data:
%% Cell type:code id: tags:
%% Cell type:code id:7072de1e tags:
```
data = np.loadtxt('00_overview/space_separated.txt')
print('data in 00_overview/space_separated.txt:')
print(data)
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:bbe6f261 tags:
But you can also specify the delimiter to expect<sup>1</sup>:
%% Cell type:code id: tags:
%% Cell type:code id:d7eca1cd tags:
```
data = np.loadtxt('00_overview/comma_separated.txt', delimiter=',')
print('data in 00_overview/comma_separated.txt:')
print(data)
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:337383bc tags:
> <sup>1</sup> And many other things such as file headers, footers, comments,
> and newline characters - see the
> [docs](https://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html)
> for more information.
Of course you can also save data out to a text file just as easily, with
[`numpy.savetxt`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.savetxt.html):
%% Cell type:code id: tags:
%% Cell type:code id:6cb9d979 tags:
```
data = np.random.randint(1, 10, (10, 10))
np.savetxt('mydata.txt', data, delimiter=',', fmt='%i')
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:84461b92 tags:
Jupyter notebooks have a special feature - if you start a line with a `!`
character, you can run a `bash` command. Let's look at the file we just
generated:
%% Cell type:code id: tags:
%% Cell type:code id:e4e1bee1 tags:
```
!cat mydata.txt
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id:dc4cf4a2 tags:
> The `!` feature won't work in regular Python scripts.
Here's how we can load a 2D array fom a file, and calculate the mean of each
column:
%% Cell type:code id: tags:
%% Cell type:code id:07142a65 tags:
```
data = np.loadtxt('00_overview/2d_array.txt', comments='%')
colmeans = data.mean(axis=0)
print('Column means')
print('\n'.join(['{}: {:0.2f}'.format(i, m) for i, m in enumerate(colmeans)]))
print('\n'.join([f'{i}: {m:0.2f}' for i, m in enumerate(colmeans)]))
```
......
getting_started/00_overview.md
+ 22
− 22
View file @ 89baef30
......@@ -218,11 +218,11 @@ types). This is just like how things work in MATLAB.
a = 7
b = 1 / 3
c = a + b
print('a: ', a)
print('b: ', b)
print('c: ', c)
print('b: {:0.4f}'.format(b))
print('a + b:', a + b)
print('a: ', a)
print('b: ', b)
print('c: ', c)
print('b: {:0.4f}'.format(b))
print('a + b: ', a + b)
```
......@@ -233,15 +233,15 @@ print('a + b:', a + b)
```
a = 'Hello'
b = "Kitty"
c = '''
c = """
Magic
multi-line
strings!
'''
"""
print(a, b)
print(a + b)
print('{}, {}!'.format(a, b))
print(f'{a}, {b}!')
print(c)
```
......@@ -301,8 +301,8 @@ data = ' 1,2,3,4,5,6,7 '
```
print('Original: {}'.format(data))
print('Strip, split, and join: {}'.format(' '.join(data.strip().split(','))))
print(f'Original: {data}')
print('Strip, split, and join:', ' '.join(data.strip().split(',')))
```
......@@ -551,9 +551,9 @@ We can use boolean values in `if`-`else` conditional expressions:
a = [1, 2, 3, 4]
val = 3
if val in a:
print('Found {}!'.format(val))
print(f'Found {val}!')
else:
print('{} not found :('.format(val))
print(f'{val} not found :(')
```
......@@ -578,15 +578,15 @@ You can also loop over the key-value pairs in a dict:
```
a = {'a' : 10, 'b' : 20, 'c' : 30}
print('a.items()')
for key, val in a.items():
print(key, val)
print('a.keys()')
for key in a.keys():
print(key, a[key])
print('a.values()')
for val in a.values():
print(val)
print('a.items()')
for key, val in a.items():
print(key, val)
```
......@@ -608,11 +608,11 @@ There are some handy built-in functions that you can use with `for` loops:
d = [10, 20, 30]
print('Using the range function')
for i in range(len(d)):
print('element at position {}: {}'.format(i, d[i]))
print(f'element at position {i}: {d[i]}')
print('Using the enumerate function')
for i, elem in enumerate(d):
print('element at position {}: {}'.format(i, elem))
print(f'element at position {i}: {elem}')
```
......@@ -629,7 +629,7 @@ number of characters in each of them:
strings = ['hello', 'howdy', 'hi', 'hey']
nchars = [len(s) for s in strings]
for s, c in zip(strings, nchars):
print('{}: {}'.format(s, c))
print(f'{s}: {c}')
```
......@@ -644,7 +644,7 @@ Or we could store the character counts in a dict:
nchars = { s : len(s) for s in strings }
for s, c in nchars.items():
print('{}: {}'.format(s, c))
print(f'{s}: {c}')
```
......@@ -710,7 +710,7 @@ Let's save the results out to a CSV (comma-separated value) file:
with open('lesion_counts.csv', 'w') as f:
f.write('Subject ID, Lesion count\n')
for subj_id, count in zip(subject_ids, lesion_counts):
f.write('{}, {}\n'.format(subj_id, count))
f.write(f'{subj_id}, {count}\n')
```
......@@ -731,7 +731,7 @@ with open('lesion_counts.csv', 'r') as f:
print('Loaded lesion counts:')
for subj, count in lesion_counts.items():
print('{}: {}'.format(subj, count))
print(f'{subj}: {count}')
```
......@@ -1016,5 +1016,5 @@ data = np.loadtxt('00_overview/2d_array.txt', comments='%')
colmeans = data.mean(axis=0)
print('Column means')
print('\n'.join(['{}: {:0.2f}'.format(i, m) for i, m in enumerate(colmeans)]))
print('\n'.join([f'{i}: {m:0.2f}' for i, m in enumerate(colmeans)]))
```
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