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‎.gitignore‎

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.ipynb_checkpoints
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‎Code/3358OS_01_Code/code1/vectorsum.py‎

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#!/usr/bin/env/python
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import sys
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from datetime import datetime
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import numpy as np
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"""
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This program demonstrates vector addition the Python way.
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Run from the command line as follows
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python vectorsum.py n
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where n is an integer that specifies the size of the vectors.
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The first vector to be added contains the squares of 0 up to n.
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The second vector contains the cubes of 0 up to n.
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The program prints the last 2 elements of the sum and the elapsed time.
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"""
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def numpysum(n):
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a = np.arange(n) ** 2
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b = np.arange(n) ** 3
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c = a + b
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return c
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def pythonsum(n):
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a = range(n)
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b = range(n)
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c = []
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for i in range(len(a)):
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a[i] = i ** 2
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b[i] = i ** 3
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c.append(a[i] + b[i])
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return c
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size = int(sys.argv[1])
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start = datetime.now()
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c = pythonsum(size)
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delta = datetime.now() - start
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print "The last 2 elements of the sum", c[-2:]
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print "PythonSum elapsed time in microseconds", delta.microseconds
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start = datetime.now()
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c = numpysum(size)
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delta = datetime.now() - start
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print "The last 2 elements of the sum", c[-2:]
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print "NumPySum elapsed time in microseconds", delta.microseconds

‎Code/3358OS_02_Code/3358OS_02_Code/code2/arrayattributes.py‎

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import numpy as np
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# Demonstrates the dtype and shape attributes
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# of ndarray.
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#
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# Run from the commandline with
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#
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# python arrayattributes.py
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a = np.arange(5)
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print "In: a = arange(5)"
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print "In: a.dtype"
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print a.dtype
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#Out: dtype('int64')
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print
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print "In: a.shape"
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print a.shape
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#Out: (5,)
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print
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print "In: a"
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print a
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#Out[4]: array([0, 1, 2, 3, 4])
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print
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m = np.array([np.arange(2), np.arange(2)])
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print "In: m"
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print m
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#Out:
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#array([[0, 1],
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# [0, 1]])
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print
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print "In: m.shape"
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print m.shape
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#Out: (2, 2)
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print "In: m.dtype"
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print m.dtype
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#Out: dtype('int64')
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print

‎Code/3358OS_02_Code/3358OS_02_Code/code2/arrayattributes2.py‎

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import numpy as np
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# Demonstrates ndarray attributes.
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#
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# Run from the commandline with
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#
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# python arrayattributes2.py
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b = np.arange(24).reshape(2, 12)
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print "In: b"
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print b
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#Out:
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#array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
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# [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]])
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print "In: b.ndim"
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print b.ndim
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#Out: 2
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print "In: b.size"
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print b.size
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#Out: 24
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print "In: b.itemsize"
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print b.itemsize
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#Out: 8
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print "In: b.nbytes"
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print b.nbytes
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#Out: 192
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print "In: b.size * b.itemsize"
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print b.size * b.itemsize
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#Out: 192
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print "In: b.resize(6,4)"
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print b.resize(6,4)
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print "In: b"
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print b
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#Out:
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#array([[ 0, 1, 2, 3],
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# [ 4, 5, 6, 7],
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# [ 8, 9, 10, 11],
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# [12, 13, 14, 15],
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# [16, 17, 18, 19],
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# [20, 21, 22, 23]])
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print "In: b.T"
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print b.T
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#Out:
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#array([[ 0, 4, 8, 12, 16, 20],
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# [ 1, 5, 9, 13, 17, 21],
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# [ 2, 6, 10, 14, 18, 22],
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# [ 3, 7, 11, 15, 19, 23]])
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print "In: b.ndim"
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print b.ndim
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#Out: 1
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print "In: b.T"
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print b.T
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#Out: array([0, 1, 2, 3, 4])
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print "In: b = array([1.j + 1, 2.j + 3])"
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b = np.array([1.j + 1, 2.j + 3])
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print "In: b"
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print b
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#Out: array([ 1.+1.j, 3.+2.j])
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print "In: b.real"
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print b.real
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#Out: array([ 1., 3.])
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print "In: b.imag"
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print b.imag
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#Out: array([ 1., 2.])
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print "In: b.dtype"
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print b.dtype
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#Out: dtype('complex128')
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print "In: b.dtype.str"
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print b.dtype.str
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#Out: '<c16'
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print "In: b = arange(4).reshape(2,2)"
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b = np.arange(4).reshape(2,2)
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print "In: b"
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print b
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#Out:
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#array([[0, 1],
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# [2, 3]])
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print "In: f = b.flat"
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f = b.flat
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print "In: f"
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print f
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#Out: <numpy.flatiter object at 0x103013e00>
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print "In: for it in f: print it"
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for it in f:
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print it
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#0
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#1
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#2
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#3
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print "In: b.flat[2]"
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print b.flat[2]
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#Out: 2
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print "In: b.flat[[1,3]]"
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print b.flat[[1,3]]
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#Out: array([1, 3])
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print "In: b"
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print b
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#Out:
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#array([[7, 7],
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# [7, 7]])
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print "In: b.flat[[1,3]] = 1"
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b.flat[[1,3]] = 1
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print "In: b"
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print b
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#Out:
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#array([[7, 1],
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# [7, 1]])
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‎Code/3358OS_02_Code/3358OS_02_Code/code2/arrayconversion.py‎

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import numpy as np
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# Demonstrates the NumPy record data type.
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#
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# Run from the commandline with
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#
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# python arrayconversion.py
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b = np.array([ 1.+1.j, 3.+2.j])
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print "In: b"
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print b
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#Out: array([ 1.+1.j, 3.+2.j])
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print "In: b.tolist()"
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print b.tolist()
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#Out: [(1+1j), (3+2j)]
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print "In: b.tostring()"
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print b.tostring()
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#Out: '\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\x08@\x00\x00\x00\x00\x00\x00\x00@'
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print "In: fromstring('\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\x08@\x00\x00\x00\x00\x00\x00\x00@', dtype=complex)"
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print np.fromstring('\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\x08@\x00\x00\x00\x00\x00\x00\x00@', dtype=complex)
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#Out: array([ 1.+1.j, 3.+2.j]
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print "In: fromstring('20:42:52',sep=':', dtype=int)"
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print np.fromstring('20:42:52',sep=':', dtype=int)
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#Out: array([20, 42, 52])
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print "In: b"
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print b
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#Out: array([ 1.+1.j, 3.+2.j])
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print "In: b.astype(int)"
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print b.astype(int)
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#/usr/local/bin/ipython:1: ComplexWarning: Casting complex values to real discards the imaginary part
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# #!/usr/bin/python
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#Out: array([1, 3])
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print "In: b.astype('complex')"
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print b.astype('complex')
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#Out: array([ 1.+1.j, 3.+2.j])
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‎Code/3358OS_02_Code/3358OS_02_Code/code2/boolean_indexing.py‎

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import scipy.misc
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import matplotlib.pyplot as plt
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import numpy as np
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# Load the Lena array
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lena = scipy.misc.lena()
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def get_indices(size):
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arr = np.arange(size)
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return arr % 4 == 0
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# Plot Lena
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lena1 = lena.copy()
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xindices = get_indices(lena.shape[0])
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yindices = get_indices(lena.shape[1])
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lena1[xindices, yindices] = 0
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plt.subplot(211)
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plt.imshow(lena1)
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lena2 = lena.copy()
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# Between quarter and 3 quarters of the max value
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lena2[(lena > lena.max()/4) & (lena < 3 * lena.max()/4)] = 0
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plt.subplot(212)
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plt.imshow(lena2)
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plt.show()

‎Code/3358OS_02_Code/3358OS_02_Code/code2/broadcasting.py‎

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import scipy.io.wavfile
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import matplotlib.pyplot as plt
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import urllib2
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import numpy as np
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response = urllib2.urlopen('http://www.thesoundarchive.com/austinpowers/smashingbaby.wav')
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print response.info()
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WAV_FILE = 'smashingbaby.wav'
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filehandle = open(WAV_FILE, 'w')
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filehandle.write(response.read())
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filehandle.close()
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sample_rate, data = scipy.io.wavfile.read(WAV_FILE)
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print "Data type", data.dtype, "Shape", data.shape
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plt.subplot(2, 1, 1)
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plt.title("Original")
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plt.plot(data)
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newdata = data * 0.2
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newdata = newdata.astype(np.uint8)
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print "Data type", newdata.dtype, "Shape", newdata.shape
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scipy.io.wavfile.write("quiet.wav",
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sample_rate, newdata)
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plt.subplot(2, 1, 2)
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plt.title("Quiet")
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plt.plot(newdata)
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plt.show()
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‎Code/3358OS_02_Code/3358OS_02_Code/code2/charcodes.py‎

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import numpy as np
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# Demonstrates the NumPy dtype character codes.
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#
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# Run from the commandline with
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#
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# python charcodes.py
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print "In: arange(7, dtype='f')"
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print np.arange(7, dtype='f')
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#Out: array([ 0., 1., 2., 3., 4., 5., 6.], dtype=float32)
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print "In: arange(7, dtype='D')"
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print np.arange(7, dtype='D')
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#Out: array([ 0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j, 4.+0.j, 5.+0.j, 6.+0.j])
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‎Code/3358OS_02_Code/3358OS_02_Code/code2/copy_view.py‎

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import scipy.misc
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import matplotlib.pyplot as plt
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lena = scipy.misc.lena()
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print lena.shape
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acopy = lena.copy()
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aview = lena.view()
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# Plot the Lena array
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plt.subplot(221)
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plt.imshow(lena)
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#Plot the copy
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plt.subplot(222)
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plt.imshow(acopy)
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#Plot the view
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plt.subplot(223)
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plt.imshow(aview)
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# Plot the view after changes
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aview.flat = 0
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plt.subplot(224)
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plt.imshow(aview)
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plt.show()
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