Python for Science
Shane Grigsby
What is python? Why python?
• Interpreted, object oriented language
• Free and open source
• Focus is on readability
• Fast to write and debug code
• Large community
– Lots of documentation
– Lots of packages…
• General purpose language
The python
scientific stack:
Python: Fast to write, slow to run?
• Depends on how you use it– if something is slow there
is probably a faster way to do it!
Are you using a numeric library?
Are you slicing through arrays, or looping lists?
Is your code vectorized?
Numpy calls fortran code to do array operations
Many other libraries call C for operations…
…or have functions written in both C and python
e.g., scipy.spatial.kdtree vs. scipy.spatial.cKDTree
How is python different from MATLAB?
• Indexing starts at 0
• Space delimited
• Default behavior is element-by-element when dealing with
arrays
• Functions use ()’s, indexes use []’s, tuples and dictionaries use
{}’s
• You don’t have to use a ‘;’ on every command
• Object oriented
See also:
http://mathesaurus.sourceforge.net/matlab-python-xref.pdf
Today’s tutorial
• Intro to the scientific stack
• Importing modules
• Intro to the development environments:
– Spyder
– iPython
•
•
•
•
•
•
Notebooks:
• SARP python tutorial
Indexing
• Prism Data
Defining functions
• Regression loops
Plotting and graphics
Files:
Intro to data structures
• Leaf_Angles.csv
Basic looping (maybe…)
• good_12_leafangle.h5
Additional pandas
• monthly.nc # Optional
– data import from clipboard • *_spec.txt
– time series (on your own)
Terminals and Prompts
• We’ll use python and python tools from three
different ‘prompts’:
– The system prompt (i.e., cmd)
– From spyder
– From the iPython Notebook
• Note that these will all run the same version
of python, but with slightly different behaviors
Notebooks:
• SARP python tutorial
• Prism Data
• Regression loops
Files:
• Leaf_Angles.csv
• good_12_leafangle.h5
• monthly.nc # Optional
Imports
Basic python is sparse…
…but we can import!
import tables
import numpy as np
from pylab import *
From scipy import ones, array
Example time:
Using the iPython notebook
• Notes:
– ‘%’s are specific to the iPython NB’s; they won’t
work in your terminal
– We’ll use: %pylab inline
• This doesn’t work for 3D or interactive plots (yet)
• Use spyder or ipython (without the notebook) to access
interactive graphics.
Imports
• Pull from the python install directory first
– i.e., lib/python2.7/site-packages
• Pull from the current directory second
• Conflicting imports are replaced with the last
import
Defining a function
from scipy.constants import *
• Multiline comment
• Keyword arguments def Xwave(wavelength, temp, unit=1):
– can use default
values
• Definition syntax
– return is optional
• Constants defined at
the top of the script
• Top line brings in
physical constants, so
we don’t have to
define them
ourselves…
X_wave = (h*c)/(k*(wavelength*unit)*temp)
return X_wave
def Lwave(wavelength, temp, unit=1):
"""Calculates L given wavelength and Temp
To get M, multiply by pi
Note that units are:
W * m**-2 * sr**-1 * m**-1
I.e, units are given in meter of spectrum
multiply by nm to get:
W * m**-2 *sr**-1 nm**-1""”
ang = 1E-10
nm = 1E-9
um = 1E-6
Cm = 1E-2
hH = 1.0
kH = 1E3
mH = 1E6
gH = 1E9
tH = 1E12
X_funct= Xwave(wavelength, temp, unit)
L=(2*h*(c**2))/(((wavelength*unit)**5)*(exp(X_funct)-1))
return L
Defining Functions
• Functions are defined using ‘def’, function
name, ‘()’’s with parameters, and an ending ‘:’
• The function body is demarcated using white
space (as in for loops)
• Functions are called using the function name,
‘()’’s, and input parameters
– Note that the input parameters don’t have to
match the names that requested…
Looping in python
for item in list:
print item
for i in range(len(list)): #  NO…
print list[i]
# executes, but is wrong
• ‘item’ is a variable name; it is not declared in advance; it is arbitrary
(i.e., could be ‘i’, ‘point’, or some other label). There could also be
more them one variable here—see next slide…
• ‘for’ and ‘in’ are syntactically required; they bracket our variables.
• ‘list’ could be another data structure (dictionary, array, etc.), or
could be a function in advanced use.
• Note: else and elseif are not required, but can be used
• Note: the white space is required—either a tab or four spaces
• Note: we don’t need to know how many items are in the data
structure
A more advanced loop:
from liblas import file
import scipy
f = file.File('/Users/grigsbye/Downloads/Alameda_park_trees_pts.las',mode='r')
treeData = scipy.ones((len(f),3))
for i, p in enumerate(f):
treeData[i,0], treeData[i,1], treeData[i,2] = p.x,p.y,p.z
•
•
•
•
•
First line imports a special module to read .las files
Third line reads a .las file into a python object we can loop over
Fourth line creates a scipy/numpy array to hold our data
‘Enumerate’ returns an index number for each point that we are looping over
The last line assigns the x, y, and z values to our array, incrementing the index by
one with each loop
• For a more complete guide to looping, see:
http://nedbatchelder.com/text/iter.html