def __init__(self, id, neighs, data, variance="false"):
"""
@type id: integer
@param id: Id of the polygon/area
@type neighs: list
@param neighs: Neighborhood ids
@type data: list.
@param data: Data releated to the area.
@type variance: boolean
@keyword variance: Boolean indicating if the data have variance matrix
"""
self.id = id
self.neighs = neighs
if variance == "false":
self.data = data
else:
n = (npsqrt(9 + 8 * (len(data) - 1)) - 3) / 2
self.var = npmatrix(npidentity(n))
index = n + 1
for i in range(int(n)):
for j in range(i + 1):
self.var[i, j] = data[int(index)]
self.var[j, i] = data[int(index)]
index += 1
self.data = data[0: int(n + 1)]
def possible(n, x, y, grid):
"""determina se é possível um numero n ser
colocado na posição x y de um grid de sudoku"""
grid = npmatrix(grid)
if n in grid[y, :]:
return False
if n in grid[:, x]:
return False
if n in grid[y // 3 * 3:y // 3 * 3 + 3, x // 3 * 3:x // 3 * 3 + 3]:
return False
else:
return True
def calculateCentroid(areaList):
"""
This function return the centroid of an area list
"""
pg = 0.0
pk = []
centroid = AreaCl(0, [], [])
for area in areaList:
pg += area.data[0]
pk = pk + [area.data[0]]
pkPg = npmatrix(pk).T / pg
data = [0.0] * len(area.data)
var = npmatrix(areaList[0].var) * 0.0
j = 0
for area in areaList:
var += area.var * pow(pkPg[j, 0], 2)
for i in range(len(area.data)):
data[i] += area.data[i] * pkPg[j, 0]
j += 1
centroid.data = data
centroid.var = var
return centroid
def calculateCentroid(areaList):
"""
This function return the centroid of an area list
"""
pg = 0.0
pk = []
centroid = AreaCl(0, [], [])
for area in areaList:
pg += area.data[0]
pk = pk + [area.data[0]]
pkPg = npmatrix(pk).T / pg
data = [0.0] * len(area.data)
var = npmatrix(areaList[0].var) * 0.0
j = 0
for area in areaList:
var += area.var * pow(pkPg[j, 0], 2)
for i in range(len(area.data)):
data[i] += area.data[i] * pkPg[j, 0]
j += 1
centroid.data = data
centroid.var = var
return centroid
def solve(_sudoku):
"""resolve um sudoku em forma de np.matrix,
depende da funçao possible"""
for y in range(9):
for x in range(9):
if _sudoku[y][x] == 0:
for n in range(1, 10):
if possible(n, x, y, _sudoku):
_sudoku[y][x] = n
solve(_sudoku)
_sudoku[y][x] = 0
#print(npmatrix(_sudoku))
return
#print('impossible sudoku')
print('\n')
print('\n')
print('\n')
print(npmatrix(_sudoku))
input('\nmore? ')
def getmatrix(block):
"""Get 1-by-n matrix corresponding to block"""
matrix = npmatrix([[letters.index(c)] for c in block])
return matrix
blocksize = len(key)
clean_text = cleanup(plaintext, blocksize)
blocks_plain = [getmatrix(clean_text[i:i+blocksize]) for i in xrange(0, len(clean_text), blocksize)]
blocks_cipher = [getblock(key*block) for block in blocks_plain]
ciphertext = ''.join(blocks_cipher)
return ciphertext
def decrypt(ciphertext, key):
blocksize = len(key)
blocks_cipher = [getmatrix(ciphertext[i:i+blocksize]) for i in xrange(0, len(ciphertext), blocksize)]
inv_key = matrix_modinv(key, 26)
blocks_plain = [getblock(inv_key*block) for block in blocks_cipher]
plaintext = (''.join(blocks_plain)).lower()
return plaintext
key = npmatrix([[6,24,1],[13,16,10],[20,17,15]]) #Key matrix
def main():
mode, inputfile, outputfile = sys.argv[1:4]
ifile = open(inputfile, 'r')
ofile = open(outputfile, 'w')
if mode=='e':
plaintext = ifile.read()
ciphertext = encrypt(plaintext, key)
ofile.write(ciphertext)
frequencygraph.freqbars_percentage('Hill Cipher frequency graph)'.format(key), plaintext, ciphertext)
elif mode=='d':
ciphertext = ifile.read()
plaintext = decrypt(ciphertext, key)
ofile.write(plaintext)
ifile.close()
def getmatrix(block):
"""Get 1-by-n matrix corresponding to block"""
matrix = npmatrix([[letters.index(c)] for c in block])
return matrix
return ciphertext
def decrypt(ciphertext, key):
blocksize = len(key)
blocks_cipher = [
getmatrix(ciphertext[i:i + blocksize])
for i in xrange(0, len(ciphertext), blocksize)
]
inv_key = matrix_modinv(key, 26)
blocks_plain = [getblock(inv_key * block) for block in blocks_cipher]
plaintext = (''.join(blocks_plain)).lower()
return plaintext
key = npmatrix([[6, 24, 1], [13, 16, 10], [20, 17, 15]]) #Key matrix
def main():
mode, inputfile, outputfile = sys.argv[1:4]
ifile = open(inputfile, 'r')
ofile = open(outputfile, 'w')
if mode == 'e':
plaintext = ifile.read()
ciphertext = encrypt(plaintext, key)
ofile.write(ciphertext)
frequencygraph.freqbars_percentage(
'Hill Cipher frequency graph)'.format(key), plaintext, ciphertext)
elif mode == 'd':
ciphertext = ifile.read()
plaintext = decrypt(ciphertext, key)
def __init__(self):
self.coeffs = [(npmatrix((-1, )), npmatrix((-1, )))]
self.rpn = [(rpncalc.RPNProgram([-1]), rpncalc.RPNProgram([-1]))]
# In case this class is utilized by multiple threads.
self.lock = Lock()
def _Del(m):
return int0(npmatrix(diag(xrange(1, int(m)), k=1)[:-1]))
def _X(m):
return int0(npmatrix(diag((1, ) * int(m), k=-1)[:, :-1]))
def _E(m):
return int0(npmatrix(diag((1, ) * int(m + 1), k=0)[:, :-1]))
def A(self, n, eps=0.0000001, p=16):
# Compute matrix $\mathbf{A}_n$.
H, W = self.DFQf.shape
# Initialize the An matrix (as an array for now).
An = zeros(self.R.shape, dtype=complex128)
# First compute a partial sum for the upper triangular part.
# Start with $m=0$
mask = self.S < self.R
Sum = zeros(self.R.shape, dtype=complex128)
for m in xrange(0, p + 1, 2):
cm_rpn, dm_rpn = self._cd[m]
Term = self._tm(v=self.V[mask],
v2=self.V[mask]**2,
dlnr=self.dr / self.R[mask],
n=n,
n2=n**2,
m=m,
cm=cm_rpn,
dm=dm_rpn)
Sum[mask] += Term
mask[mask] *= abs(Term) >= eps
if not mask.any():
break
mask = self.S < self.R
An[mask] = 2 * self.W[mask]**n * self.Factor[mask] * Sum[mask]
# Now to do the diagonal.
# Since $r=s$ here, we have $q=1$, $u=\phi$, $v=-\tan\phi$,
# and $w=1$.
mask = self.S == self.R
Sum = zeros(self.R.shape, dtype=complex128)
for m in xrange(0, p + 1):
cm_rpn, dm_rpn = self._cd[m]
Term = self._tm(v=-tan(self.Phi),
v2=tan(self.Phi)**2,
dlnr=self.dr / self.R[mask],
n=n,
n2=n**2,
m=m,
cm=cm_rpn,
dm=dm_rpn)
Sum[mask] += Term
mask[mask] *= abs(Term) >= eps
if not mask.any():
break
mask = self.S == self.R
An[mask] = self.Factor[mask] * Sum[mask] + \
array([1 - 1 / cos(self.Phi)] * W)
return npmatrix(An)
