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RROMPy/main/utilities.py
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utilities.py
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#!/usr/bin/python
import os
import warnings
import numpy as np
from RROMPyTypes import Any, ListAny, DictAny, Np1DLst, List, Np1D
def findDictStrKey(key:Any, keyList:ListAny):
for akey in keyList:
if isinstance(key, str) and key.lower() == akey.lower():
return akey
return None
def purgeList(lst:ListAny, allowedEntries : ListAny = [],
silent : bool = False, complement : bool = False,
listname : str = ""):
if listname != "":
listname = " in " + listname
lstcp = []
for x in lst:
ax = findDictStrKey(x, allowedEntries)
if (ax is None) != complement:
if not silent:
warnings.warn("Ignoring entry {0}{1}.".format(x, listname))
else:
lstcp = lstcp + [ax]
return lstcp
def purgeDict(dct:DictAny, allowedKeys : ListAny = [], silent : bool = False,
complement : bool = False, dictname : str = ""):
if dictname != "":
dictname = " in " + dictname
dctcp = {}
for key in dct.keys():
akey = findDictStrKey(key, allowedKeys)
if (akey is None) != complement:
if not silent:
warnings.warn("Ignoring key {0}{2} with value {1}."\
.format(key, dct[key], dictname))
else:
if akey is None:
akey = key
dctcp[akey] = dct[key]
return dctcp
def getNewFilename(prefix : str = "", extension : str = "dat") -> str:
n = len(extension) + 1
filename = "{}{}.{}".format(prefix, np.random.randint(0, 10), extension)
while os.path.exists(filename):
filename = filename[: - n] + "{}.{}".format(np.random.randint(10),
extension)
return filename
prime_v = [] #memoization vector
def squareResonances(a:int, b:int, zero_terms : bool = True):
spectrum = []
a = max(int(np.floor(a)), 0)
b = max(int(np.ceil(b)), 0)
global prime_v
if len(prime_v) == 0:
prime_v = [2, 3]
if a > prime_v[-1]:
for i in range(prime_v[-1], a, 2):
get_next_prime_factor(i)
for i in range(a, b + 1):
spectrum = spectrum + [i] * count_square_sums(i, zero_terms)
return np.array(spectrum)
def get_next_prime_factor(n:int):
global prime_v
for x in prime_v:
if n % x == 0:
return x
if prime_v[-1] < n:
prime_v = prime_v + [n]
return n
def prime_factorize(n:int):
factors = []
number = n
while number > 1:
factor = get_next_prime_factor(number)
factors.append(factor)
number = int(number / factor)
if n < -1:
factors[0] = -factors[0]
return list(factors)
def count_square_sums(n:int, zero_terms : bool = True):
if n < 2: return (n + 1) * zero_terms
factors = prime_factorize(n)
funique, fcounts = np.unique(factors, return_counts = True)
count = 1
for fac, con in zip(funique, fcounts): #using number theory magic
if fac % 4 == 1:
count = count * (con + 1)
elif fac % 4 == 3 and con % 2 == 1:
return 0
return count + (2 * zero_terms - 1) * (int(n ** .5) ** 2 == n)
def listify(u:Np1DLst, d:int) -> List[Np1D]:
"""
Convert numpy array into list-like format.
Args:
u: numpy complex array with function dofs or list of such.
d: secondary dimension.
Returns:
List-like numpy array of numpy arrays.
"""
if isinstance(u, (list,)) or len(u) == d:
if len(u) != d:
raise Exception(("Input list lenght must be equal to "
"augmentation dimension d."))
N = len(u[0])
for x in u[1 :]:
if len(x) != N:
raise Exception(("Elements of input list must all have "
"the same lenght."))
return np.array(u)
else:
N = int(len(u) / d)
if not np.isclose(len(u), d * N):
raise Exception(("Input vector lenght must be multiple of "
"augmentation dimension d."))
v = [None] * d
for i in range(d):
v[i] = u[i * N : (i + 1) * N]
return np.array(v)
def vectorify(u:Np1DLst, d:int) -> Np1D:
"""
Convert list-like object into numpy array.
Args:
u: numpy complex array with function dofs or list of such.
d: secondary dimension.
Returns:
Numpy arrays.
"""
if isinstance(u, (list,)) or len(u) == d:
if len(u) != d:
raise Exception(("Input list lenght must be equal to "
"augmentation dimension d."))
N = len(u[0])
for x in u[1 :]:
if len(x) != N:
raise Exception(("Elements of input list must all have "
"the same lenght."))
return np.concatenate(u)
else:
N = int(len(u) / d)
if not np.isclose(len(u), d * N):
raise Exception(("Input vector lenght must be multiple of"
"augmentation dimension d."))
return u
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