diff options
Diffstat (limited to 'numpy/matrixlib/defmatrix.py')
| -rw-r--r-- | numpy/matrixlib/defmatrix.py | 50 |
1 files changed, 25 insertions, 25 deletions
diff --git a/numpy/matrixlib/defmatrix.py b/numpy/matrixlib/defmatrix.py index 1ca835af2..0a73725c2 100644 --- a/numpy/matrixlib/defmatrix.py +++ b/numpy/matrixlib/defmatrix.py @@ -39,7 +39,7 @@ else: del k def _eval(astr): - str_ = astr.translate(_table,_todelete) + str_ = astr.translate(_table, _todelete) if not str_: raise TypeError("Invalid data string supplied: " + astr) else: @@ -95,7 +95,7 @@ def asmatrix(data, dtype=None): """ return matrix(data, dtype=dtype, copy=False) -def matrix_power(M,n): +def matrix_power(M, n): """ Raise a square matrix to the (integer) power `n`. @@ -169,7 +169,7 @@ def matrix_power(M,n): M = asanyarray(M) if len(M.shape) != 2 or M.shape[0] != M.shape[1]: raise ValueError("input must be a square array") - if not issubdtype(type(n),int): + if not issubdtype(type(n), int): raise TypeError("exponent must be an integer") from numpy.linalg import inv @@ -185,21 +185,21 @@ def matrix_power(M,n): result = M if n <= 3: for _ in range(n-1): - result=N.dot(result,M) + result=N.dot(result, M) return result # binary decomposition to reduce the number of Matrix # multiplications for n > 3. beta = binary_repr(n) - Z,q,t = M,0,len(beta) + Z, q, t = M, 0, len(beta) while beta[t-q-1] == '0': - Z = N.dot(Z,Z) + Z = N.dot(Z, Z) q += 1 result = Z - for k in range(q+1,t): - Z = N.dot(Z,Z) + for k in range(q+1, t): + Z = N.dot(Z, Z) if beta[t-k-1] == '1': - result = N.dot(result,Z) + result = N.dot(result, Z) return result @@ -271,9 +271,9 @@ class matrix(N.ndarray): if (ndim > 2): raise ValueError("matrix must be 2-dimensional") elif ndim == 0: - shape = (1,1) + shape = (1, 1) elif ndim == 1: - shape = (1,shape[0]) + shape = (1, shape[0]) order = False if (ndim == 2) and arr.flags.fortran: @@ -304,9 +304,9 @@ class matrix(N.ndarray): else: newshape = self.shape if ndim == 0: - self.shape = (1,1) + self.shape = (1, 1) elif ndim == 1: - self.shape = (1,newshape[0]) + self.shape = (1, newshape[0]) return def __getitem__(self, index): @@ -330,13 +330,13 @@ class matrix(N.ndarray): except: n = 0 if n > 1 and isscalar(index[1]): - out.shape = (sh,1) + out.shape = (sh, 1) else: - out.shape = (1,sh) + out.shape = (1, sh) return out def __mul__(self, other): - if isinstance(other,(N.ndarray, list, tuple)) : + if isinstance(other, (N.ndarray, list, tuple)) : # This promotes 1-D vectors to row vectors return N.dot(self, asmatrix(other)) if isscalar(other) or not hasattr(other, '__rmul__') : @@ -378,7 +378,7 @@ class matrix(N.ndarray): orientation. """ if axis is None: - return self[0,0] + return self[0, 0] elif axis==0: return self elif axis==1: @@ -391,7 +391,7 @@ class matrix(N.ndarray): to a scalar like _align, but are using keepdims=True """ if axis is None: - return self[0,0] + return self[0, 0] else: return self @@ -862,7 +862,7 @@ class matrix(N.ndarray): [ 0., 1.]]) """ - M,N = self.shape + M, N = self.shape if M == N: from numpy.dual import inv as func else: @@ -997,7 +997,7 @@ class matrix(N.ndarray): H = property(getH, None, doc="hermitian (conjugate) transpose") I = property(getI, None, doc="inverse") -def _from_string(str,gdict,ldict): +def _from_string(str, gdict, ldict): rows = str.split(';') rowtup = [] for row in rows: @@ -1018,8 +1018,8 @@ def _from_string(str,gdict,ldict): raise KeyError("%s not found" % (col,)) coltup.append(thismat) - rowtup.append(concatenate(coltup,axis=-1)) - return concatenate(rowtup,axis=0) + rowtup.append(concatenate(coltup, axis=-1)) + return concatenate(rowtup, axis=0) def bmat(obj, ldict=None, gdict=None): @@ -1084,10 +1084,10 @@ def bmat(obj, ldict=None, gdict=None): arr_rows = [] for row in obj: if isinstance(row, N.ndarray): # not 2-d - return matrix(concatenate(obj,axis=-1)) + return matrix(concatenate(obj, axis=-1)) else: - arr_rows.append(concatenate(row,axis=-1)) - return matrix(concatenate(arr_rows,axis=0)) + arr_rows.append(concatenate(row, axis=-1)) + return matrix(concatenate(arr_rows, axis=0)) if isinstance(obj, N.ndarray): return matrix(obj) |