University:
Boston University
Course:
GRS CS 910 | Directed Study: Systems
Academic year:

2020
Views:

220

Pages:

17
Author:

Mya Lowe

Numerical and Scientific Computing in Python RCS Team and Expertise  Our Team  Scientific Programmers  Systems Administrators  Graphics/Visualization Specialists  Account/Project Managers  Special Initiatives (Grants)  Maintains and administers the Shared Computing Cluster  Located in Holyoke, MA  ~19,000 CPUs running Linux  Consulting Focus:       Bioinformatics Data Analysis / Statistics Molecular modeling Geographic Information Systems Scientific / Engineering Simulation Visualization Running Python for the Tutorial  If you have an SCC account, log on and use Python there.  Run: module load python/3.6.2 spyder & source /net/scc2/scratch/Numpy_examples.sh  Note that the spyder program takes a while to load! SCC On-Demand  Go to: scc-ondemand.bu.edu  Go to Interactive Apps, choose Spyder.  Once available, open a terminal window and run: source /net/scc2/scratch/Numpy_examples.sh Run Spyder  Click on the Start Menu in the bottom left corner and type: spyder  After a second or two it will be found. Click to run it.  Be patient…it takes a while to start. SCC Python Tutorials        Introduction to Python, Part one Introduction to Python, Part two Numerical and Scientific Computing in Python Python for Data Analysis Data Visualization in Python Introduction to Python Scikit-learn Python Optimization Outline  Python lists  The numpy library  Speeding up numpy: numba and numexpr  Libraries: scipy and opencv  Alternatives to Python Python’s strengths  Python is a general purpose language.  In contrast with R or Matlab which started out as specialized languages  Python lends itself to implementing complex or specialized algorithms for solving computational problems.  It is a highly productive language to work with that’s been applied to hundreds of subject areas. Extending its Capabilities  However…for number crunching some aspects of the language are not optimal:  Runtime type checks  No compiler to analyze a whole program for optimizations  General purpose built-in data structures are not optimal for numeric calculations  “regular” Python code is not competitive with compiled languages (C, C++, Fortran) for numeric computing.  The solution: specialized libraries that extend Python with data structures and algorithms for numeric computing.  Keep the good stuff, speed up the parts that are slow! Outline  The numpy library  Libraries: scipy and opencv  When numpy / scipy isn’t fast enough NumPy  NumPy provides optimized data structures and basic routines for manipulating multidimensional numerical data.  Mostly implemented in compiled C code.  NumPy underlies many other numeric and algorithm libraries available for Python, such as:  SciPy, matplotlib, pandas, OpenCV’s Python API, and more Ndarray – the basic NumPy data type  NumPy ndarray’s are:  Typed  Fixed size  Fixed dimensionality  An ndarray can be constructed from:     Conversion from a Python list, set, tuple, or similar data structure NumPy initialization routines Copies or computations with other ndarray’s NumPy-based functions as a return value ndarray vs list  List:     General purpose Untyped 1 dimension Resizable  Add/remove elements anywhere  Accessed with [ ] notation and integer indices  Ndarray:  Intended to store and process (mostly) numeric data  Typed  N-dimensions  Chosen at creation time  Fixed size  Chosen at creation time  Accessed with [ ] notation and integer indices List Review x = ['a','b',3.14] Operation Syntax Indexing – starting from 0 x[0]  ‘a’ Notes x[1]  ‘b’ Indexing backwards from -1 x[-1]  3.14 x[-3]  ‘a’ Slicing x[start:end:incr] Slicing produces a COPY of the original list! x[0:2]  [‘a’,’b’] x[-1:-3:-1]  [3.14,’b’] x[:]  [‘a’,’b’,3.14] Sorting x.sort()  in-place sort sorted(x)  returns a new sorted list Size of a list len(x) Depending on list contents a sorting function might be req’d List Implementation x = ['a','b',3.14]  A Python list mimics a linked list data structure  It’s implemented as a resizable array of pointers to Python objects for performance reasons. Pointer to a Python object x 'a' Pointer to a Python object 'b' Pointer to a Python object 3.14 Allocated anywhere in memory  x[1]  get the pointer (memory address) at index 1  resolve pointer to retrieve the Python object in memory  get the value from the object  return ‘b’ import numpy as np # Initialize a NumPy array # from a Python list y = np.array([1,2,3]) NumPy ndarray  The basic data type is a class called ndarray.  The object has:  a data that describes the array (data type, number of dimensions, number of elements, memory format, etc.)  A contiguous array in memory containing the data. y Data description (integer, 3 elements, 1-D) 1 2 Values are physically adjacent in memory 3  y[1]  check the ndarray data type  retrieve the value at offset 1 in the data array  return 2 dtype  Every ndarray has a dtype, the type of data that it holds.  This is used to interpret the block of data stored in the ndarray.  Can be assigned at creation time:  Conversion from one type to another is done with the astype() method: a = np.array([1,2,3]) a.dtype  dtype('int64') c = np.array([-1,4,124], dtype='int8') c.dtype --> dtype('int8') b = a.astype('float') b.dtype  dtype('float64')

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