Difference between revisions of "CompSciHW6"

From Predictive Chemistry
Jump to: navigation, search
(Created page with "Homework 6 - Due Friday, March 18 <pre> 1) Create a function that parses the following files: a) mat33 = """ 1.0 2.0 3.0 -2.0 0.0 1.0 1.0 1.1 1.2 """ b) csv = """ # time te...")
 
 
(2 intermediate revisions by the same user not shown)
Line 1: Line 1:
Homework 6 - Due Friday, March 18
+
Homework 6 - Due Friday, March 25
 
<pre>
 
<pre>
 
1) Create a function that parses the following files:
 
1) Create a function that parses the following files:
Line 6: Line 6:
 
-2.0 0.0 1.0
 
-2.0 0.0 1.0
 
1.0 1.1 1.2
 
1.0 1.1 1.2
"""
 
  +
""" # output a matrix
  +
# example function structure
  +
def parse_mat(s):
  +
mat = [] # build a list of lists
  +
...
  +
return array(mat) # make into an array
   
 
b) csv = """
 
b) csv = """
Line 14: Line 19:
 
3.0, 299.8, 44.2
 
3.0, 299.8, 44.2
 
4.5, 301.0, 43.9
 
4.5, 301.0, 43.9
"""
 
  +
""" # output a list of strings and a matrix
   
 
c) name_val = """
 
c) name_val = """
Line 21: Line 26:
 
trials = 10
 
trials = 10
 
tolerance = 1e-8
 
tolerance = 1e-8
"""
 
  +
""" # output a dictionary
   
 
d) group_data = """
 
d) group_data = """
Line 32: Line 37:
 
[Group 3]
 
[Group 3]
 
7 8 9 10 11
 
7 8 9 10 11
"""
 
  +
""" # output a dictionary of [int]
   
2) Write code to carry out matrix multiplication manually
 
  +
2) Complete this function by adding a for loop to fill each
and test it against the result of numpy.dot.
 
  +
element of a vector with a function (g(i)):
  +
from numpy import *
  +
def vec_from_func(n, g): # int -> (int -> float) -> array
  +
x = zeros(n, float)
  +
for ...
  +
return x
   
3) Write code to multiply every column of matrix, A,
 
  +
# example call
with every other column. Use this to find the set of angles
 
  +
def square_x(x): # int -> float
between the column vectors.
 
  +
return 1.0*x*x
  +
  +
g_arr = vec_from_func(10, square_x)
  +
  +
3) Complete this function by adding a nested for-loop to fill
  +
each element of a matrix with a function, u(i,j):
  +
from numpy import *
  +
def mat_from_func(n,m, g): # int -> int -> (int -> int -> float) -> array
  +
M = zeros((n,m), float)
  +
for ...
  +
for ...
  +
return M
 
</pre>
 
</pre>
  +
  +
  +
Hint:
  +
<source lang="python">
  +
# parse key-value pairs
  +
def get_kv(x): # str -> (str, str)
  +
n = x.find('=')
  +
if n < 0:
  +
raise ValueError
  +
return x[:n].strip(), x[n+1:].strip()
  +
  +
# basic parser structure:
  +
def parse_cif(name):
  +
f = open(name, 'r')
  +
out = {}
  +
state = 0
  +
vals = []
  +
for line in f.readlines():
  +
k = line.strip()
  +
if not k:
  +
continue
  +
# test whether line contains useful info.
  +
return out
  +
</source>

Latest revision as of 12:50, 21 March 2016

Homework 6 - Due Friday, March 25

1) Create a function that parses the following files:
 a) mat33 = """
1.0 2.0 3.0
-2.0 0.0 1.0
1.0 1.1 1.2
""" # output a matrix
# example function structure
def parse_mat(s):
  mat = [] # build a list of lists
  ...
  return array(mat) # make into an array

 b) csv = """
# time temperature sea_level
0.0, 300.0, 44.5
1.5, 299.5, 44.0
3.0, 299.8, 44.2
4.5, 301.0, 43.9
""" # output a list of strings and a matrix

 c) name_val = """
seed = 1553
name = "test name"
trials = 10
tolerance = 1e-8
""" # output a dictionary

 d) group_data = """
[Group 1]
1 2 3 4 5 6

[Group 2]
4 5 11 14 1

[Group 3]
7 8 9 10 11
""" # output a dictionary of [int]

2) Complete this function by adding a for loop to fill each 
  element of a vector with a function (g(i)):
  from numpy import *
  def vec_from_func(n, g): # int -> (int -> float) -> array
    x = zeros(n, float)
    for ...
    return x

  # example call
  def square_x(x): # int -> float
    return 1.0*x*x

  g_arr = vec_from_func(10, square_x)

3) Complete this function by adding a nested for-loop to fill
   each element of a matrix with a function, u(i,j):
   from numpy import *
   def mat_from_func(n,m, g): # int -> int -> (int -> int -> float) -> array
   M = zeros((n,m), float)
   for ...
     for ...
   return M


Hint: <source lang="python">

  1. parse key-value pairs

def get_kv(x): # str -> (str, str)

 n = x.find('=')
 if n < 0:
   raise ValueError
 return x[:n].strip(), x[n+1:].strip()
  1. basic parser structure:

def parse_cif(name):

   f = open(name, 'r')
   out = {}
   state = 0
   vals = []
   for line in f.readlines():
       k = line.strip()
       if not k:
           continue
       # test whether line contains useful info.
   return out

</source>