Lecture 7. Scientific Computation

Transcription

1 Lecture 7 Scientific Computation

2 for-loops: Beyond Sequences Work on iterable objects Object with an ordered collection of data This includes sequences But also much more Examples: Text Files (built-in) Web pages (urllib2) 2110: learn to design custom iterable objects def blanklines(fname): """Return: # blank lines in file fname Precondition: fname is a string""" # open makes a file object file = open('myfile.txt') # Accumulator count = 0 for line in file: # line is a string if len(line) == 0: # line is blank count = count+1 f.close() # close file when done return count 9/29/14 Scientific Computation 2

3 Issues with File I/O In Python, '\n' represents the end of a line Standard way files are handled in Unix Usually the accepted file format of all programs But other OS s may handle it differently Classic Mac OS: '\r' Windows OS: '\n\r' Cannot use a loop without '\n' Will only read a single line Example: grades.csv 9/29/14 Scientific Computation 3

4 Issues with File I/O In Python, '\n' represents the end of a line Standard way files are handled in Unix Usually the accepted file format of all programs But other OS s may handle it differently Classic Mac OS: '\r' Windows OS: '\n\r' Cannot use a loop without '\n' Will only read a single line Example: grades.csv Used by MS Excel on Mac OS X (still) 9/29/14 Scientific Computation 4

5 CSV Files (From Excel or Otherwise) Table Format NETID A1 A2 A3 EXAM aaa cms jrm jrm saf web wmw Text File Format NETID,A1,A2,A3,EXAM aaa1,97,100,100,99 cms1,19,30,42,53 jrm3,82,88,75,78 jrm4,53,30,21,42 saf7,89,92,80,83 web5,26,22,18,29 wmw6,92,89,94,90 Line Ending? 9/29/14 Scientific Computation 5

6 CSV Files (From Excel or Otherwise) Processing CSV f = open('grades.csv','r') # Read all at once s = f.read() # Split lines on Mac line endings rows = s.split('\r') # Turn it into 2x2 table for k in range(len(rows)):" rows[k] = rows[k].split(',') Text File Format NETID,A1,A2,A3,EXAM aaa1,97,100,100,99 cms1,19,30,42,53 jrm3,82,88,75,78 jrm4,53,30,21,42 saf7,89,92,80,83 web5,26,22,18,29 wmw6,92,89,94,90 Line Ending? Example: grades.csv 9/29/14 Scientific Computation 6

7 Dictionaries (Type dict) Description List of key-value pairs Keys are unique Values need not be Example: net-ids net-ids are unique (a key) names need not be (values) js1 is John Smith (class 13) js2 is John Smith (class 16) Many other applications Python Syntax Create with format: {k1:v1, k2:v2, } Keys must be non-mutable ints, floats, bools, strings Not lists or custom objects Values can be anything Example: d = {'js1':'john Smith'," 'js2':'john Smith'," 'wmw2':'walker White'} 9/29/14 Scientific Computation 7

8 Using Dictionaries (Type dict) Access elts. like a list d['js1'] evaluates to 'John' But cannot slice ranges! Dictionaries are mutable Can reassign values d['js1'] = 'Jane' Can add new keys d['aa1'] = 'Allen' Can delete keys del d['wmw2'] " d = {'js1':'john','js2':'john'," 'wmw2':'walker'} id8 'js1' 'js2' 'wmw2' d 'John' 'John' 'Walker' id8 dict Key-Value order in folder is not important 9/29/14 Scientific Computation 8

9 Using Dictionaries (Type dict) Access elts. like a list d['js1'] evaluates to 'John' But cannot slice ranges! Dictionaries are mutable Can reassign values d['js1'] = 'Jane' Can add new keys d['aa1'] = 'Allen' Can delete keys del d['wmw2'] " d = {'js1':'john','js2':'john'," 'wmw2':'walker'} id8 'js1' 'js2' 'wmw2' d 'John' 'Jane' 'John' 'Walker' id8 dict Key-Value order in folder is not important 9/29/14 Scientific Computation 9

10 Using Dictionaries (Type dict) Access elts. like a list d['js1'] evaluates to 'John' But cannot slice ranges! Dictionaries are mutable Can reassign values d['js1'] = 'Jane' Can add new keys d['aa1'] = 'Allen' Can delete keys del d['wmw2'] " d = {'js1':'john','js2':'john'," 'wmw2':'walker'} id8 'js1' 'js2' 'wmw2' 'aa1' d 'Jane' 'John' 'Walker' 'Allen' id8 dict 9/29/14 Scientific Computation 10

11 Using Dictionaries (Type dict) Access elts. like a list d['js1'] evaluates to 'John' But cannot slice ranges! Dictionaries are mutable Can reassign values d['js1'] = 'Jane' Can add new keys d['aa1'] = 'Allen' Can delete keys del d['wmw2'] " d = {'js1':'john','js2':'john'," 'wmw2':'walker'} id8 'js1' 'js2' 'wmw2' 'aa1' d 'Jane' 'John' 'Walker' 'Allen' id8 dict Deleting key deletes both 9/29/14 Scientific Computation 11

12 Dictionaries and For-Loops Dictionaries!= sequences Cannot slice them Different inside for loop Loop variable gets the key Then use key to get value Has methods to convert dictionary to a sequence Seq of keys: d.keys() Seq of values: d.values() key-value pairs: d.items() for k in d:" # Loops over keys print k # key" print d[k] # value" # To loop over values only" for v in d.values():" print v # value See grades.py 9/29/14 Scientific Computation 12

13 CSV Files to Dictionary Table Format Python Dictionary NETID A1 A2 A3 EXAM aaa cms jrm jrm saf web wmw id2 dict 'aaa1' id3 'cms1' id4 'jrm3' id5 d id2 id3 dict 'A1' 97 'A2' 100 'A3' 100 'EXAM' 99 9/29/14 Scientific Computation 13

14 Functions are Objects A function definition Creates a global variable (same name as function) Creates a folder for body Puts folder id in variable Variable vs. Call >>> to_centigrade <fun to_centigrade at 0x100498de8> >>> to_centigrade (32) 0.0 def to_centigrade(x):" return 5*(x-32)/9.0 Global Space to_centigrade id6 Heap Space id6 Body function Body 9/29/14 Scientific Computation 14

15 Advanced Tricks with Functions Functions are just objects Can put in other variables Functions with functions Example: Integration Takes f, a, b Want Z b a f(x) dx Use numerical techniques (trapezoidal, Simpson s) Easy solution to ODEs # Integration function from scipy.integrate import quad # Function to integrate def normal(x):" z = (x * x)/2" return np.e ** z # Numerical integration z = quad(norm,0,10) Function a b 9/29/14 Scientific Computation 15

16 The Map Function map( function, list ) Function has to have exactly 1 parameter Otherwise, get an error Returns a new list Does the same thing as def map(f,x): result = [] # empty list for y in x: result.append(f(y)) return result map(f, x) [f(x[0]), f(x[1]),, f(x[n 1])] calls the function f once for each item map(len, ['a', 'bc', 'defg']) returns [1, 2, 4] 9/29/14 Scientific Computation 16

17 The Map Function map( function, list ) Function has to have exactly 1 parameter Otherwise, get an error Returns a new list Does the same thing as def map(f,x): result = [] # empty list for y in x: result.append(f(y)) return result map(f, x) [f(x[0]), f(x[1]),, f(x[n 1])] calls the function f once for each item map(len, ['a', 'bc', 'defg']) returns [1, 2, 4] 9/29/14 Scientific Computation 17

18 Example: Converting CSV to Numbers >>> s = ['aaa1', '97', '100', '100', '99'] >>> s = s[:1] + map(int, s[1:]) Keep netid >>> print s Convert the rest ['aaa1', 97, 100, 100, 99] Not Strings Example: csv.py 9/29/14 Scientific Computation 18

19 State Machine Modules Sometimes want more than computation Launch a new window (e.g. window.py) Display a graph or other visualization A state machine is A module with a collection of functions These functions have visual side effects Effects remain until dismissed by other functions Example: MatPlotLib 9/29/14 Scientific Computation 19

20 Bar Graph Example # Create the bar graph plt.barh(y_vals, perform, xerr=errors) # Label y with people plt.yticks(y_vals, people) # Other labels plt.xlabel('performance') plt.title('how fast do you want to go today?') # Finally, display the graph. plt.show() 9/29/14 Scientific Computation 20

21 Bar Graph Example List of y values List of error values # Create the bar graph plt.barh(y_vals, perform, xerr=errors) # Label y with people List of x values plt.yticks(y_vals, people) # Other labels plt.xlabel('performance') plt.title('how fast do you want to go today?') # Finally, display the graph. plt.show() 9/29/14 Scientific Computation 21

22 Bar Graph Example # Create the bar graph plt.barh(y_vals, perform, xerr=errors) # Label y with people plt.yticks(y_vals, people) # Other labelslabels for y values plt.xlabel('performance') plt.title('how fast do you want to go today?') # Finally, display the graph. plt.show() Other graph labels 9/29/14 Scientific Computation 22

23 Bar Graph Example # Create the bar graph plt.barh(y_vals, perform, xerr=errors) # Label y with people plt.yticks(y_vals, people) # Other labels plt.xlabel('performance') plt.title('how fast do you want to go today?') # Finally, display the graph. plt.show() Nothing happens until you show it 9/29/14 Scientific Computation 23