def asAMatrix(spl):
if isinstance(spl, obj.MSSample) or not spl.spectra:
return
matrix_=[]
for spectrum in spl.spectra:
matrix.append(spectrum.x_data)
return matrix(matrix_)
def calcDCT():
matrix = []
for y in range(0, 8):
row = []
for x in range(0, 8):
if(y == 0):
row.append(1/sqrt(8))
else:
row.append(sqrt(2/8.0) * cos(((2*x+1)*y * pi)/16.0))
matrix.append(row)
return matrix
def qcolumn_vector(raw_numbers, quantities):
'''
A quick constructor for a column vector.
>>> qcolumn_vector((1,2,3),(meter,second,mole))
1
m 1.0
s 2.0
mol 3.0
'''
matrix = []
for element in raw_numbers:
matrix.append([element])
return QuantMatrix(numpy.array(matrix), [list(quantities),[dimensionless()]])
def qcolumn_vector(raw_numbers, quantities):
'''
A quick constructor for a column vector.
>>> qcolumn_vector((1,2,3),(meter,second,mole))
1
m 1.0
s 2.0
mol 3.0
'''
matrix = []
for element in raw_numbers:
matrix.append([element])
return QuantMatrix(numpy.array(matrix),
[list(quantities), [dimensionless()]])
def find_coeffs_similarity(pa, pb):
matrix = []
for p1, p2 in zip(pa, pb):
matrix.append([p1[0], p1[1], 1, 0])
matrix.append([p1[1], -p1[0], 0, 1])
A = numpy.matrix(matrix, dtype=numpy.float)
B = numpy.array(pb).reshape(2 * len(pa))
res = numpy.dot(numpy.linalg.inv(A.T * A) * A.T, B)
res = res[0][0]
T = numpy.matrix([[res[0, 0], res[0, 1], res[0, 2]],
[-res[0, 1], res[0, 0], res[0, 3]]])
return (T[0, 0], T[0, 1], T[0, 2], T[1, 0], T[1, 1], T[1, 2])
def _get_matrix(name, kmatrix):
"""Converts a numpy matrix into a matrix digestable by e.g. matlab.
@param name Name of the matrix
@param kmatrix The matrix
@return String which contains a matrix digestable by e.g. matlab
"""
line = list()
list_numbers = (int, long, float, double, ubyte, ushort, short, uint16,
int32, int64, uint32, uint64)
matrix = []
is_string = True
try:
# assume, all elements have same data type
if isinstance(kmatrix[0, 0], list_numbers):
is_string = False
for x in range(kmatrix.shape[0]):
for y in range(kmatrix.shape[1]):
if is_string:
line.append("'%s'" % kmatrix[x, y])
else:
line.append('%.9g' % kmatrix[x, y])
matrix.append(', '.join(line))
line = list()
except IndexError:
if isinstance(kmatrix[0], list_numbers):
is_string = False
for x in range(kmatrix.shape[0]):
if is_string:
line.append("'%s'" % kmatrix[x])
else:
line.append('%.9g' % kmatrix[x])
matrix.append(', '.join(line))
line = list()
matrix = ';'.join(matrix)
if is_string:
matrix = ''.join([name, ' = {', matrix, '}'])
else:
matrix = ''.join([name, ' = [', matrix, ']'])
return matrix.replace('\n', '')
def _get_matrix (name, kmatrix):
"""Converts a numpy matrix into a matrix digestable by e.g. matlab.
@param name Name of the matrix
@param kmatrix The matrix
@return String which contains a matrix digestable by e.g. matlab
"""
line=list()
list_numbers=(int, long, float, double, ubyte, ushort, short, uint16, int32, int64, uint32, uint64)
matrix=[]
is_string=True
try:
# assume, all elements have same data type
if isinstance(kmatrix[0, 0], list_numbers):
is_string=False
for x in range(kmatrix.shape[0]):
for y in range(kmatrix.shape[1]):
if is_string:
line.append("'%s'" % kmatrix[x, y])
else:
line.append('%.9g' % kmatrix[x, y])
matrix.append(', '.join(line))
line=list()
except IndexError:
if isinstance(kmatrix[0], list_numbers):
is_string=False
for x in range(kmatrix.shape[0]):
if is_string:
line.append("'%s'" % kmatrix[x])
else:
line.append('%.9g' % kmatrix[x])
matrix.append(', '.join(line))
line=list()
matrix=';'.join(matrix)
if is_string:
matrix=''.join([name, ' = {', matrix, '}'])
else:
matrix=''.join([name, ' = [', matrix, ']'])
return matrix.replace('\n', '')
def random_matrix(shape=[2,100], min=-1, max=1, type=REAL, include_threshold_multiplier=True):
"""generates a 2-D list representing a matrix
Retrieves rows pertaining to the given keys from the Table instance
represented by big_table. Silly things may happen if
other_silly_variable is not None.
Args:
shape: The shape of the desired matrix
min: The minimum value of any given value in the matrix
(defaults to real numbers between 0-1 by default).
This is exclusive i.e to get a min integer of 1 use min=0.
max: The max value of any given value in the matrix.
Returns:
A list representing a random generated matrix
Raises:
"""
matrix = []
for i in range(shape[1]):
matrix.append(DataGenerator.random_vector(shape[0], min, max, type, include_threshold_multiplier))
return matrix
def build_matrix(self, constraints, point_to_matrix):
n = len(self._all_points)
targets = []
matrix = []
for (coeffs, target) in constraints:
row = [0] * 2 * n
for (pt, coeffx, coeffy) in coeffs:
row[2 * point_to_matrix[pt]] = coeffx
row[2 * point_to_matrix[pt] + 1] = coeffy
matrix.append(row)
targets.append(target)
ortho_matrix = self.build_ortho(matrix)
self._target_map_x = {}
self._target_map_y = {}
for pt in self._point_lru:
coords = self._point_coords[pt]
row1 = [0] * 2 * n
row2 = [0] * 2 * n
row1[2 * point_to_matrix[pt]] = 1
row2[2 * point_to_matrix[pt] + 1] = 1
if self.can_add(ortho_matrix, 2 * point_to_matrix[pt]):
self._target_map_x[pt] = len(targets)
targets.append(coords[0])
matrix.append(row1)
self.add_ortho_row(ortho_matrix, row1)
if self.can_add(ortho_matrix, 2 * point_to_matrix[pt] + 1):
self._target_map_y[pt] = len(targets)
targets.append(coords[1])
matrix.append(row2)
self.add_ortho_row(ortho_matrix, row2)
if len(matrix) == 2 * n:
break
# TODO: optimize this
return (array(matrix), array(targets))
def build_matrix(self, constraints, point_to_matrix):
n = len(self._all_points)
targets = []
matrix = []
for (coeffs, target) in constraints:
row = [0] * 2 * n
for (pt, coeffx, coeffy) in coeffs:
row[2 * point_to_matrix[pt]] = coeffx
row[2 * point_to_matrix[pt] + 1] = coeffy
matrix.append(row)
targets.append(target)
ortho_matrix = self.build_ortho(matrix)
self._target_map_x = {}
self._target_map_y = {}
for pt in self._point_lru:
coords = self._point_coords[pt]
row1 = [0] * 2 * n
row2 = [0] * 2 * n
row1[2 * point_to_matrix[pt]] = 1
row2[2 * point_to_matrix[pt] + 1] = 1
if self.can_add(ortho_matrix, 2 * point_to_matrix[pt]):
self._target_map_x[pt] = len(targets)
targets.append(coords[0])
matrix.append(row1)
self.add_ortho_row(ortho_matrix, row1)
if self.can_add(ortho_matrix, 2 * point_to_matrix[pt] + 1):
self._target_map_y[pt] = len(targets)
targets.append(coords[1])
matrix.append(row2)
self.add_ortho_row(ortho_matrix, row2)
if len(matrix) == 2 * n:
break
# TODO: optimize this
return (array(matrix), array(targets))
def get_responses_matrix(self):
matrix = []
for candidate in self.candidates:
matrix.append(self.get_positions_from_candidate(candidate))
return matrix
def get_responses_matrix(self):
matrix = []
for candidate in self.candidates:
matrix.append(self.get_positions_from_candidate(candidate))
return matrix
def cubicSplineInterpolate(x_axis, y_axis, z_axis):
'''
prepare right-side vector
'''
dx = []
dy = []
dz = []
matrix = []
n = 2
while n < len(x_axis):
dx.append(3 * (x_axis[n] - 2 * x_axis[n - 1] + x_axis[n - 2]))
dy.append(3 * (y_axis[n] - 2 * y_axis[n - 1] + y_axis[n - 2]))
dz.append(3 * (z_axis[n] - 2 * z_axis[n - 1] + z_axis[n - 2]))
n = n + 1
'''
produce square matrix looks like :
[[2.0, 0.5, 0.0, 0.0], [0.5, 2.0, 0.5, 0.0], [0.0, 0.5, 2.0, 0.5], [0.0, 0.0, 2.0, 0.5]]
the classes of the matrix depends on the length of x_axis(number of nodes)
'''
matrix.append([float(2), float(0.5)])
for m in range(len(x_axis) - 4):
matrix[0].append(float(0))
n = 2
while n < len(x_axis) - 2:
matrix.append([])
for m in range(n - 2):
matrix[n - 1].append(float(0))
matrix[n - 1].append(float(0.5))
matrix[n - 1].append(float(2))
matrix[n - 1].append(float(0.5))
for m in range(len(x_axis) - n - 3):
matrix[n - 1].append(float(0))
n = n + 1
matrix.append([])
for m in range(n - 2):
matrix[n - 1].append(float(0))
matrix[n - 1].append(float(0.5))
matrix[n - 1].append(float(2))
'''
LU Factorization may not be optimal method to solve this regular matrix.
If you guys have better idea to solve the Equation, please contact me.
'''
P, L, U = doLUFactorization(matrix)
u = solveEquations(P, L, U, dx)
v = solveEquations(P, L, U, dy)
w = solveEquations(P, L, U, dz)
'''
define gradient of start/end point
'''
m = 0
U = [0]
V = [0]
W = [0]
while m < len(u):
U.append(u[m])
V.append(v[m])
W.append(w[m])
m = m + 1
U.append(0)
V.append(0)
W.append(0)
plotCubicSpline(U, V, W, x_axis, y_axis, z_axis)
elif kak == 2:
jaj = randint(M, N)
jazz = jazz + jaj
ark.append(jaj)
else:
print("game over, snake\n")
exit()
print(f"вот-с ваш массив - {ark}\n")
jazz = []
kazz = []
for i in range(len(ark)):
if i % 2 == 0 and ark[i] > 0:
jazz.append(ark[i])
kazz.append(i)
jazz.sort()
matrix.append(kazz)
matrix.append(jazz)
print(matrix)
for j in range(len(matrix[0])):
k = matrix[0][j]
ark[k] = matrix[1][j]
print(ark)
print("tenth task\n")
ark = []
skok = int(input("сколько будет массивов ?\n"))
kak = int(input("хотите сами ввести числа ? -1 или автоматом ? -2 \n"))
if kak == 2:
M = int(input("в таком случае введите от какого числа\n"))
N = int(input("И до какого\n"))
jazz = 0
