JYP's recommended steps for learning python

If you don't know which python distribution to use and how to start the python interpreter, you should first read the Working with Python page

As can be expected, there is a lot of online python documentation available, and it's easy to get lost. You can always use google to find an answer to your problem, and you will probably end up looking at lots of answers on Stack Overflow or a similar site. But it's always better to know where you can find some good documentation… and to spend some time to read the documentation

This page tries to list some python for the scientist related resources, in a suggested reading order. Do not print anything (or at least not everything), but it's a good idea to download all the pdf files in the same place, so that you can easily open and search the documents

JYP's introduction to python

Part 1

You can start using python by reading the Bien démarrer avec python tutorial that was used during a 2013 IPSL python class:

this tutorial is in French (my apologies for the lack of translation, but it should be easy to understand)

If you have too much trouble understanding this French Tutorial, you can read the first 6 chapters of the Tutorial in the official Python documentation and chapters 1.2.1 to 1.2.5 in the Scipy Lecture Notes. Once you have read these, you can try to read the French tutorial again

it's an introduction to python (and programming) for the climate scientist: after reading this tutorial, you should be able to do most of the things you usually do in a shell script

python types, tests, loops, reading a text file

the tutorial is very detailed about string handling, because strings offer an easy way to practice working with indices (indexing and slicing), before indexing numpy arrays. And our usual pre/post-processing scripts often need to do a lot of string handling in order to generate the file/variable/experiment names

after reading this tutorial, you should practice with the following:

Basic python training test (ipython notebook version)

Basic python training test (pdf version)

Basic python training test (pdf version, with answers)

Part 2

Once you have done your first steps, you should read Plus loin avec Python (start at page 39, the previous pages are an old version of what was covered in Part 1 above)

this tutorial is in French (sorry again)

after reading this tutorial, you will be able to do more than you can do in a shell script, in an easier way

advanced string formatting

creating functions and using modules

working with file paths and handling files without calling external Linux programs
(e.g. using os.remove(file_name) instead of rm $file_name)

using command-line options for scripts, or using configuration files

calling external programs

The official python documentation

You do not need to read all the python documentation at this step, but it is really well made and you should at least have a look at it. The Tutorial is very good, and you should have a look at the table of content of the Python Standard Library. There is a lot in the default library that can make your life easier

Python 2.7

html - pdf (in a zip file)

Python 3

html - pdf (in a zip file)

Numpy and Scipy

Summary: Python provides ordered objects (e.g. lists, strings, basic arrays, …) and some math operators, but you can't do real heavy computation with these. Numpy makes it possible to work with multi-dimensional data arrays, and using array syntax and masks (instead of explicit nested loops and tests) and the apropriate numpy functions will allow you to get performance similar to what you would get with a compiled program! Scipy adds more scientific functions

Where: html and pdf documentation

Getting started

always remember that indices start at 0 and that the last element of an array is at index -1!
First learn about indexing and slicing by manipulating strings, as shown in Part 1 above (try 'This document by JY is awesome!'[::-1] and 'This document by JY is awesome!'[slice(None, None, -1)])

if you are a Matlab user (but the references are interesting for others as well), you can read the following:

Numpy for Matlab users

NumPy for MATLAB users (nice, but does not seem to be maintained any more)

read the really nice numpy Quickstart tutorial

have a quick look at the full documentation to know where things are

Numpy User Guide

Numpy Reference Guide

Scipy Reference Guide

Beware of the array view side effects

When you take a slice of an array, you get a View : an array that has a new shape but that still shares its data with the first array.That is not a problem when you only read the values, but if you change the values of the View, you change the values of the first array (and vice-versa)! If that is not what want, do not forget to make a copy of the data before working on it! Views are a good thing most of the time, so only make a copy of your data when needed, because otherwise copying a big array will just be a waste of CPU and computer memory. Anyway, it is always better to understand what you are doing… Check the example below and the copies and views part of the quickstart tutorial. >>> import numpy as np >>> a = np.arange(30).reshape((3,10)) >>> 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]]) >>> b = a[1, :] >>> b array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19]) >>> b[3:7] = 0 >>> b array([10, 11, 12, 0, 0, 0, 0, 17, 18, 19]) >>> a array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [10, 11, 12, 0, 0, 0, 0, 17, 18, 19], [20, 21, 22, 23, 24, 25, 26, 27, 28, 29]]) >>> a[:, 2:4] = -1 >>> a array([[ 0, 1, -1, -1, 4, 5, 6, 7, 8, 9], [10, 11, -1, -1, 0, 0, 0, 17, 18, 19], [20, 21, -1, -1, 24, 25, 26, 27, 28, 29]]) >>> b array([10, 11, -1, -1, 0, 0, 0, 17, 18, 19]) >>> c = a[1, :].copy() >>> c array([10, 11, -1, -1, 0, 0, 0, 17, 18, 19]) >>> c[:] = 9 >>> c array([9, 9, 9, 9, 9, 9, 9, 9, 9, 9]) >>> b array([10, 11, -1, -1, 0, 0, 0, 17, 18, 19]) >>> a array([[ 0, 1, -1, -1, 4, 5, 6, 7, 8, 9], [10, 11, -1, -1, 0, 0, 0, 17, 18, 19], [20, 21, -1, -1, 24, 25, 26, 27, 28, 29]])

Extra numpy information

More information about array indexing:

Examples:

indirect_indexing_2.py.txt: Take a vertical slice in a 3D zyx array, along a varying y 'path'

Indexing (index arrays, boolean index arrays, np.newaxis, Ellipsis, variable numbers of indices, …)

Fancy indexing and the ix_() function

Indexing (in the numpy reference manual)

Indexing routines

More information about arrays:

Array creation routines

Array manipulation routines

Masked arrays

Masked array operations

Dealing with special numerical values (Nan, inf)

If you know that your data has missing values, it is cleaner and safer to handle them with masked arrays!

Handling numerical exceptions

Floating point error handling

cdms2 and netCDF4

There is a good chance that your input array data will come from a file in the NetCDF format.

Depending on which python distribution you are using, you can use the cdms2 or or netCDF4 modules to read the data.

cdms2

Summary: cdms2 can read/write netCDF files (and read grads dat+ctl files) and provides a higher level interface than netCDF4. cdms2 is available in the UV-CDAT distribution, and can theoretically be installed independently of UV-CDAT (e.g. it will be installed when you install CMOR in conda). When you can use cdms2, you also have access to cdtime, that is very useful for handling time axis data.

How to get started:

read JYP's cdms tutorial, starting at page 54

the tutorial is in French (soooorry!)

you have to replace cdms with cdms2, and MV with MV2 (sooorry about that, the tutorial was written when CDAT was based on Numeric instead of numpy to handle array data)

read the official cdms documentation (link may change)

netCDF4

Summary: netCDF4 can read/write netCDF files and is available in most python distributions

Where: http://unidata.github.io/netcdf4-python/

CDAT-related resources

Some links, in case they can't be found easily on the UV-CDAT web site…

Tutorials in ipython notebooks

VCS: Visualization Control System

Colormaps in vcs examples

EzTemplate Documentation

Matplotlib

The full content of this matplotlib section has been moved to Working with matplotlib (JYP version) after becoming too big to manage here Note: Plotting maps with matplotlib+cartopy (examples provided by JYP)

Summary: there are lots of python libraries that you can use for plotting, but Matplotlib has become a de facto standard

Where: Matplotlib web site

Help on stack overflow: matplotlib help

Graphics related resources

Ten Simple Rules for Better Figures

Top 50 matplotlib Visualizations

Seaborn is a library for making attractive and informative statistical graphics in Python, built on top of matplotlib

See also: Python Seaborn Tutorial For Beginners

Communicating/displaying/plotting your data (possibly for people not of your field):

Introduction to Designing Data Visualizations — Part 1

Tables & Other Charts — Data Visualization Part 2

Tables & Other Charts — Data Visualization Part 3

Working with colors

Choosing colormaps

cmocean: Beautiful colormaps for oceanography

Palettable: Color palettes for Python

ColorBrewer 2.0 is a tool that can help you understand, and experiment with sequential, diverging and qualitative colormaps

Basemap

Basemap is going to be slowly phased out, in favor of cartopy More information in this: cartopy github issue basemap github issue

Summary: Basemap is an extension of Matplotlib that you can use for plotting maps, using different projections

Where: Basemap web site

Help on stack overflow: basemap help

How to use basemap?

look at the examples

check the different projections

read some documentation!

the really nice basemap tutorial seems much better than the official documentation below

look at the detailed official documentation

Cartopy + Iris

Summary: Cartopy is a Python package for advanced map generation with a simple matplotlib interface and Iris is a Python package for analysing and visualising meteorological and oceanographic data sets

Where: Cartopy and Iris web sites

Examples:

Examples provided by JYP

Gallery on the Cartopy web site

Gallery on the Iris web site

Examples on the Iris web site

Help on stack overflow: cartopy help

Maps and projections resources

About projections

Map projections from USGS poster

Map projections - A working manual (USGS)

Libraries

Projections in vcs

Projections in basemap

Projections in cartopy

3D resources

Ipyvolume

Animate your 3D plots with Python’s Matplotlib

How to get vertical Z axis in 3D surface plot of Matplotlib?

Data file formats

We list here some resources about non-NetCDF data formats that can be useful

The shelve package

The built-in shelve package, can be easily used for storing data (python objects like lists, dictionaries, numpy arrays that are not too big, …) on disk and retrieving them later

Use case:

Use a script do to the heavy data pre-processing and store the (intermediate) results in a file using shelve, or update the results

Use another script for plotting the results stored with shelve. This way you don't have to wait for the pre-processing step to finish each time you want to improve your plot(s)

Warning:

read the documentation and the example carefully (it's quite small)

if you get the impression that the data is not saved correctly, re-read the parts about updating correctly the content of the shelve file

you should be able to store most python objects in a shelve file, but it is safer to make tests

do not forget to close the output file

if you are dealing with big arrays and want to avoid performance issues, you should use netCDF files for storing the intermediate results

json files

More and more applications use json files as configuration files or as a mean to use text files to exchange data (through serialization/deserialization ).

json files look basically like a list of (nested) python dictionaries that would have been dumped to a text file

json module documentation

Working With JSON Data in Python tutorial

example script: /home/users/jypeter/CDAT/Progs/Devel/beaugendre/nc2json.py

A compact (not easy to read…) json file can be pretty-printed with
cat file.json | python -m json.tool | less

LiPD files

Resources for Linked PaleoData:

LiPD

Technical note: The Linked Paleo Data framework – a common tongue for paleoclimatology @ GMD

LiPD-utilities @ github

BagIt files

BagIt, a set of hierarchical file layout conventions for storage and transfer of arbitrary digital content.

The BagIt File Packaging Format

Bagger (BagIt GUI)

bagit-python

Pandas

Summary: pandas is a library providing high-performance, easy-to-use data structures and data analysis tools

Where: Pandas web site

JYP's comment: pandas is supposed to be quite good for loading, processing and plotting time series, without writing custom code. It is very convenient for processing tables in xlsx files (or csv, etc…). You should at least have a quick look at:

Some Cheat Sheets (in the following order):

Basics: Pandas basics (associated with the Pandas Cheat Sheet for Data Science in Python pandas introduction page)

Intermediate: github Pandas doc page

Advanced: the cheat sheet on the Enthought workshops advertising page

Some tutorials:

Pandas Cheat Sheet for Data Science in Python pandas introduction page

The Statistics in Python tutorial that combines Pandas, Statsmodels and Seaborn

Scipy Lecture Notes

Summary: One document to learn numerics, science, and data with Python

Where: pdf - html

This is a really nice and useful document that is regularly updated and used for the EuroScipy tutorials.

This document will teach you even more things about python, numpy and matplotlib, debugging and optimizing scripts, and about using python for statistics, image processing, machine learning, washing dishes (this is just to check if you have read this page), etc…

Example: the Statistics in Python tutorial that combines Pandas, Statsmodels and Seaborn

Quick Reference and cheat sheets

The nice and convenient Python 2.7 Quick Reference: pdf - html

A possibly more up-date-version

Python 3 Quick reference and Cheat sheet

Jupyter Notebook Keyboard Shortcuts

Misc tutorials

PyFormat: With this site we try to show you the most common use-cases covered by the old and new style string formatting API with practical examples

Some good coding tips

The official Style Guide for Python Code (aka PEP 0008)

A Pragmatic Quick Reference

Debugging your code

There is only so much you can do with staring at your code in your favorite text editor, and adding print lines in your code (or using logging instead of print). The next step is to use the python debugger!

Debugging in text mode

Start the script with: python -m pdb my_script.py

Type run (or r) to go to the first line of the script

Type continue (or c) to execute the script to the end, or till the first breakpoint or error is reached

Use where (or w) to check the call stack that led to the current stop. Use up and down to navigate through the call stack and examine the values of the functions' parameters

Type break NNN to stop at line NNN

Use type(var) and print var to check the type and values of variables. You can also change the variables' values on the fly!

Type run (or r) to restart the script

Use next and step to execute some parts of the script line by line. If a code line calls a function:

next (or n) will execute a function and stop on the next line

step (or s) will stop at the first line inside the function

Check the debugger commands for details, or type help in the debugger for using the built-in help

Using pydebug

Depending on the distribution, the editor and the programming environment you use, you may have access to a graphical version of the debugger. UV-CDAT users can use pydebug my_script.py

Using a Python IDE

IDE = Integrated Development Environment

There are lots of ways to use Python and develop scripts, from using a lightweight approach (your favorite text editor with builtin python syntax highlighting, e.g. emacs and python -i myscript.py) to a full-fledged IDE. You'll find below some IDE related links

Top 5 Python IDEs For Data Science

Python IDE: The10 Best IDEs for Python Programmers

Get the Best Python IDE

https://wiki.python.org/moin/IntegratedDevelopmentEnvironments

Spyder

Home page

Documentation

Improving the performance of your code

You can already get a very efficient script by checking the following:

make sure that your script is not using too much memory (the amount depends on the computer you are using)! Your script should be scalable (e.g. keeps on working even when your data gets bigger), so it's a good idea to load only the data you need in memory (e.g. not all the time steps), and learn how to load chunks of data

make sure that you are using array/vector syntax and masks, instead of using explicit loops and tests. The numpy documentation is big, because there are lots of optimized functions to help you! If you are stuck, ask JY or somebody else who is used to numpy.

If your script is still not fast enough, there is a lot you can do to improve it, without resorting to parallelization (that may introduce extra bugs rather that extra performance). See the sections below

Hint: before optimizing your script, you should spent some time profiling it, in order to only spend time improving the slow parts of your script

Useful packages

Numexpr: Numexpr is a fast numerical expression evaluator for NumPy. With it, expressions that operate on arrays (like “3*a+4*b”) are accelerated and use less memory than doing the same calculation in Python.

PyTables: PyTables is a package for managing hierarchical datasets and designed to efficiently and easily cope with extremely large amounts of data

Tutorials by Ian Osvald

Tutorials from EuroScipy 2011

Tutorials from PyCon 2012

Python 2.7 vs Python 3

The official Porting Python 2 Code to Python 3 page gives the required information to make the transition from python 2 to python 3. It is still safe to use Python 2.7, so there is no rush to change to Python 3.

What now?

You can do a lot more with python! But if you have read at least a part of this page, you should be able to find and use the modules you need. Make sure you do not reinvent the wheel! Use existing packages when possible, and make sure to report bugs or errors in the documentations when you find some

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