import numpy as np
[docs]def times(s, n):
"""
Concats string ``s`` ``n`` times.
Examples
--------
>>> times("hi",5)
'hihihihihi'
"""
return s.join(["" for i in range(0, n+1)])
[docs]def get(key, ddict, default):
"""
Returns dict[key] if this exists else default.
Examples
--------
>>> d = {'a' : 1 , 'b' : 2 , 'c' : 3}
>>> get('a',d,5)
1
>>> get('x',d,5)
5
"""
if has(key, ddict):
return ddict[key]
else:
return default
[docs]def has(key, ddict):
"""
Checks if the key is in the dict and not None.
Examples
--------
>>> d = {'a' : 1 , 'b' : 2 , 'c' : 3}
>>> has('a',d)
True
>>> has('x',d)
False
"""
return key in ddict and not ddict[key] is None
[docs]def true(key, ddict):
"""
Checks if the key is in the dict and not None and True.
Examples
--------
>>> d = {'a' : True , 'b' : True , 'c' : False}
>>> true('a', d)
True
>>> true('c', d)
False
>>> true('x', d)
False
"""
return has(key, ddict) and ddict[key]
[docs]def find_nearest_index(array, value):
"""
Returns the index of the element in ``array`` closest to ``value``
Examples
--------
>>> find_nearest_index([1,7,6,2] , 1.9)
3
"""
array = np.asarray(array)
idx = (np.abs(array - value)).argmin()
return idx
[docs]def find_nearest(array, value):
"""
Return the element in ``array`` closest to ``value``
Examples
--------
>>> find_nearest([1,7,6,2] , 1.9)
2
"""
return array[find_nearest_index(array, value)]