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Python

Python is widely used general-purpose high-level programming language. Its design philosophy emphasizes code readability. It is very popular in science.

Jupyter

The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text.

  • Evolved from IPython notebook
  • In addition to Python it supports many other programming languages (Julija, R, Haskell, etc..)
  • http://jupyter.org/

Getting started

Anaconda/Conda (need to install)

Web hosted (only need a web browser)

The notebook

Cell types - markdown and code

This is Markdown cell





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print('This is cell with code')



    


















    






This is cell with code

















In [ ]:

Variables, lists and dictionaries





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var1 = 1
my_string = "This is a string"



    











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var1



    


















    
Out[3]:








1

















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print(my_string)



    


















    






This is a string

















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my_list = [1, 2, 3, 'x', 'y']
my_list



    


















    
Out[5]:








[1, 2, 3, 'x', 'y']

















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my_list[0]



    


















    
Out[6]:








1

















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my_list[1:3]



    


















    
Out[7]:








[2, 3]

















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salaries = {'Mike':2000, 'Ann':3000}



    











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salaries['Mike']



    


















    
Out[9]:








2000

















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salaries['Jake'] = 2500



    











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salaries



    


















    
Out[11]:








{'Ann': 3000, 'Jake': 2500, 'Mike': 2000}

Strings





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long_string = 'This is a string \n Second line of the string'



    











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print(long_string)



    


















    






This is a string 
 Second line of the string

















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long_string.split(" ")



    


















    
Out[14]:








['This', 'is', 'a', 'string', '\n', 'Second', 'line', 'of', 'the', 'string']

















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long_string.split("\n")



    


















    
Out[15]:








['This is a string ', ' Second line of the string']

















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long_string.count('s') # case sensitive!



    


















    
Out[16]:








4

















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long_string.upper()



    


















    
Out[17]:








'THIS IS A STRING \n SECOND LINE OF THE STRING'

Conditionals





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if long_string.startswith('X'):
    print('Yes')
elif long_string.startswith('T'):
    print('It has T')
else:
    print('No')



    


















    






It has T

Loops





In [19]:



    


for line in long_string.split('\n'):
    print line



    


















    






This is a string 
 Second line of the string

















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c = 0
while c < 10:
    c += 2
    print c



    


















    






2
4
6
8
10

List comprehensions





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some_numbers = [1,2,3,4]



    











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[x**2 for x in some_numbers]



    


















    
Out[22]:








[1, 4, 9, 16]

File operations





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with open('../README.md', 'r') as f:
    content = f.read()



    











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print(content)



    


















    






# HUB-ipython
##HUB (Heidelberg Unseminars in Bioinformatics) about IPython and chemoinformatics
This repository contains material for [HUB 21](http://www.hub-hub.de/wordpress/?tribe_events=hub-21-interactive-notebooks-try-out-programming-jupyteripython-chemoinformatics)
  
### Location and date
BioMed X, Im Neuenheimer feld 583, Heidelbeg, 21 January 2016

### How to run code from this repo
I recommend [Anaconda](https://www.continuum.io/downloads) Python distribution with Python 2.7. In Anaconda terminal type `jupyter notebook` and navigate to folder with notebooks.  
You can install [RDKit](http://www.rdkit.org/) (chemoinformatics library) with `conda install -c https://conda.anaconda.org/rdkit rdkit`
  
If you can't install Python on your computer you can use [tmpnb.org](tmpnb.org) to experiment with Jupyter (rdkit is not available in tmpnb.org).
  
Pair programming presentation can be converted to html and served with:
```bash
jupyter nbconvert Pair\ programming.ipynb --to slides --post serve --ServePostProcessor.port=8910
```
#### Current plans are to include this:

* short intro about BioMed X
* an ice-breaker - maybe the 'classic' standing in a line/grid to describe our experience coding, which we can also use for teaming people up for later activities - a group of experienced coders goes to the part of the room where people haven't done any coding before, and we find experienced partners for each of them
* a demo from Samo of IPython in chemoinformatic context
* maybe someone else demos something similar using a different language (R? Perl?)
* pair-programming together, using examples from [Rosalind](http://rosalind.info/problems/locations/)

angle we'd use to publicise the event would be:

* never coded before? come and try out coding with experts, to see what it's like
* interested in seeing what iPython/jupyter can do? come along and check it out - especially if you're into chemoinformatics - note that similar stuff is available for other languages
* chance to try out pair programming

For this, we'll ideally have a bunch of people joining the event who will bring their own laptops, have appropriate software set up and running, so that we have enough laptops to pair-program with everyone - if not, maybe we try doing it in groups of 3. 

### TODO
- [ ] prepare intro notebook
- [X] install instructions
- [ ] list optional chemo/bioinfo and visualisation libs
- [X] check how to make jupyter work with other programming languages
- [ ] guest wifi vouchers 
- [ ] buy beer

Functions





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def average(numbers):
    return float(sum(numbers)/len(numbers))



    











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average([1,2,2,2.5,3,])



    


















    
Out[26]:








2.1

















In [27]:



    


map(average, [[1,2,2,2.5,3,],[3,2.3,4.2,2.5,5,]])



    


















    
Out[27]:








[2.1, 3.4]

















In [28]:



    


# %load cool_events.py
#!/usr/bin/env python
from IPython.display import HTML

class HUB:
    """
    HUB event class
    """
    def __init__(self, version):
        self.full_name = "Heidelberg Unseminars in Bioinformatics"
        self.info = HTML("<p>Heidelberg Unseminars in Bioinformatics are participant-"
            "driven meetings where people with an interest in bioinformatics " 
            "come together to discuss hot topics and exchange ideas and then go "
            "for a drink and a snack afterwards.</p>")
        self.version = version
    def __repr__(self):
        return self.full_name



    











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this_event = HUB(21)



    











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this_event



    


















    
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Heidelberg Unseminars in Bioinformatics

















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this_event.full_name



    


















    
Out[31]:








'Heidelberg Unseminars in Bioinformatics'

















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this_event.version



    


















    
Out[32]:








21

Python libraries

Library is a collection of resources. These include pre-written code, subroutines, classes, etc.





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from math import exp



    











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exp(2) #shift tab to access documentation



    


















    
Out[34]:








7.38905609893065

















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import math



    











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math.exp(10)



    


















    
Out[36]:








22026.465794806718

















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import numpy as np # Numpy - package for scientifc computing



    











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#import pandas as pd # Pandas - package for working with data frames (tables)



    











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#import Bio # BioPython - package for bioinformatics



    











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#import sklearn # scikit-learn - package for machine larning



    











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#from rdkit import Chem # RDKit - Chemoinformatics library

Plotting





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%matplotlib inline



    











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import matplotlib.pyplot as plt



    











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x_values = np.arange(0, 20, 0.1)
y_values = [math.sin(x) for x in x_values]



    











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plt.plot(x_values, y_values)



    


















    
Out[45]:








[<matplotlib.lines.Line2D at 0x7fc5da94a550>]










    
























In [46]:



    


plt.scatter(x_values, y_values)



    


















    
Out[46]:








<matplotlib.collections.PathCollection at 0x7fc5da8a2890>










    
























In [47]:



    


plt.boxplot(y_values)



    


















    
Out[47]:








{'boxes': [<matplotlib.lines.Line2D at 0x7fc5da7eb5d0>],
 'caps': [<matplotlib.lines.Line2D at 0x7fc5da77a490>,
  <matplotlib.lines.Line2D at 0x7fc5da77aad0>],
 'fliers': [<matplotlib.lines.Line2D at 0x7fc5da784790>],
 'means': [],
 'medians': [<matplotlib.lines.Line2D at 0x7fc5da784150>],
 'whiskers': [<matplotlib.lines.Line2D at 0x7fc5ffcb3bd0>,
  <matplotlib.lines.Line2D at 0x7fc5da7ebe10>]}










    
























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Content source: samoturk/HUB-ipython

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