# HG changeset patch # User Minh Van Nguyen # Date 1249845363 25200 # Node ID d63f79700dd8888926ff2b21818e5ea60e9e19e6 # Parent 87b600f2b8d586ea15239b60720ba6a89e11e44c trac 6718: spell-check all modules under sage/matrix diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/action.pyx --- a/sage/matrix/action.pyx +++ b/sage/matrix/action.pyx @@ -87,7 +87,7 @@ cpdef Element _call_(self, g, s): """ EXAMPLES: - Respects compatable subdivisions: + Respects compatible subdivisions: sage: M = matrix(5, 5, prime_range(100)) sage: M.subdivide(2,3); M [ 2 3 5| 7 11] @@ -117,7 +117,7 @@ [1048] [3056] - If the subdivisions aren't compatable, ignore them. + If the subdivisions aren't compatible, ignore them. sage: N.subdivide(1,1); N [ 0| 1] [--+--] diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/constructor.py --- a/sage/matrix/constructor.py +++ b/sage/matrix/constructor.py @@ -46,7 +46,7 @@ To construct a multiple of the identity (`cI`), you can specify square dimensions and pass in `c`. Calling matrix() with a Sage object may return something that makes sense. Calling - matrix() with a numpy array will convert the array to a matrix. + matrix() with a NumPy array will convert the array to a matrix. The ring, number of rows, and number of columns of the matrix can be specified by setting the ring, nrows, or ncols parameters or by @@ -588,9 +588,9 @@ try: return matrix( [list(row) for row in list(num_array)]) except TypeError: - raise TypeError("cannot convert numpy matrix to SAGE matrix") + raise TypeError("cannot convert NumPy matrix to Sage matrix") else: - raise TypeError("cannot convert numpy matrix to SAGE matrix") + raise TypeError("cannot convert NumPy matrix to Sage matrix") return m elif nrows is not None and ncols is not None: @@ -954,7 +954,7 @@ def block_matrix(sub_matrices, nrows=None, ncols=None, subdivide=True): """ - Returns a larger matrix made by concatinating the sub_matrices + Returns a larger matrix made by concatenating the sub_matrices (rows first, then columns). For example, the matrix :: @@ -1010,7 +1010,7 @@ [ 3 9| -3 -9|-5/12 3/8| 300 900] [ 6 10| -6 -10| 1/4 -1/8| 600 1000] - It handle baserings nicely too:: + It handle base rings nicely too:: sage: R. = ZZ['x'] sage: block_matrix([1/2, A, 0, x-1]) @@ -1044,7 +1044,7 @@ elif ncols is None: ncols = int(n/nrows) if nrows * ncols != n: - raise ValueError, "Given number of rows (%s), columns (%s) incompatable with number of submatrices (%s)" % (nrows, ncols, n) + raise ValueError, "Given number of rows (%s), columns (%s) incompatible with number of submatrices (%s)" % (nrows, ncols, n) # empty matrix if n == 0: @@ -1085,7 +1085,7 @@ if R is not ZZ: base = sage.categories.pushout.pushout(base, R) - # finally concatinate + # finally concatenate for i in range(nrows): for j in range(ncols): # coerce diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/docs.py --- a/sage/matrix/docs.py +++ b/sage/matrix/docs.py @@ -77,7 +77,7 @@ entries. Once a matrix `A` is made immutable using ``A.set_immutable()`` the entries of `A` cannot be changed, and `A` can never be made mutable again. -However, properies of `A` such as its rank, characteristic +However, properties of `A` such as its rank, characteristic polynomial, etc., are all cached so computations involving `A` may be more efficient. Once `A` is made immutable it cannot be changed back. However, one can obtain a @@ -212,6 +212,6 @@ - Kernels of matrices Implement only a left_kernel() or right_kernel() method, whichever requires - the least overhead (usually meaning little or no transpose'ing). Let the + the least overhead (usually meaning little or no transposing). Let the methods in the matrix2 class handle left, right, generic kernel distinctions. """ diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/matrix0.pyx --- a/sage/matrix/matrix0.pyx +++ b/sage/matrix/matrix0.pyx @@ -398,12 +398,12 @@ Matrices are always mutable by default, i.e., you can change their entries using ``A[i,j] = x``. However, mutable matrices - aren't hasheable, so can't be used as keys in dictionaries, etc. + aren't hashable, so can't be used as keys in dictionaries, etc. Also, often when implementing a class, you might compute a matrix associated to it, e.g., the matrix of a Hecke operator. If you return this matrix to the user you're really returning a reference and the user could then change an entry; this could be confusing. - Thus you shoulds set such a matrix immutable. + Thus you should set such a matrix immutable. EXAMPLES:: @@ -415,7 +415,7 @@ [10 1] [ 2 3] - Mutable matrices are not hasheable, so can't be used as keys for + Mutable matrices are not hashable, so can't be used as keys for dictionaries:: sage: hash(A) @@ -427,7 +427,7 @@ ... TypeError: mutable matrices are unhashable - If we make A immutable it suddenly is hasheable. + If we make A immutable it suddenly is hashable. :: @@ -519,7 +519,7 @@ ## sage: a._get_very_unsafe(0,1) ## 1 -## If you do \code{a.\_get\_very\_unsafe(0,10)} you'll very likely crash SAGE +## If you do \code{a.\_get\_very\_unsafe(0,10)} you'll very likely crash Sage ## completely. ## """ ## return self.get_unsafe(i, j) @@ -1679,7 +1679,7 @@ from sage.server.support import EMBEDDED_MODE # jsmath doesn't know the command \hline, so have to do things - # differently (and not as atractively) in embedded mode: + # differently (and not as attractively) in embedded mode: # construct an array with a subarray for each block. if len(row_divs) + len(col_divs) > 0 and EMBEDDED_MODE: for r in range(len(row_divs)+1): @@ -3795,7 +3795,7 @@ of coefficients. A dense matrix and a sparse matrix are equal if their coefficients are the same. - EXAMPLES: EXAMPLE cmparing sparse and dense matrices:: + EXAMPLES: EXAMPLE comparing sparse and dense matrices:: sage: matrix(QQ,2,range(4)) == matrix(QQ,2,range(4),sparse=True) True diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/matrix1.pyx --- a/sage/matrix/matrix1.pyx +++ b/sage/matrix/matrix1.pyx @@ -31,7 +31,7 @@ def _pari_init_(self): """ - Return a string defining a gp representation of self. + Return a string defining a GP representation of self. EXAMPLES:: @@ -1059,7 +1059,7 @@ - Jaap Spies (2006-02-18) - - Didier Deshommes: some pyrex speedups implemented + - Didier Deshommes: some Pyrex speedups implemented """ if not PY_TYPE_CHECK(rows, list): raise TypeError, "rows must be a list of integers" diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/matrix2.pyx --- a/sage/matrix/matrix2.pyx +++ b/sage/matrix/matrix2.pyx @@ -939,7 +939,7 @@ Return the determinant of self. ALGORITHM: For small matrices (n4), this is computed using the - naive formula For integral domains, the charpoly is computed (using + naive formula For integral domains, the characteristic polynomial is computed (using hessenberg form) Otherwise this is computed using the very stupid expansion by minors stupid *naive generic algorithm*. For matrices over more most rings more sophisticated algorithms can be used. @@ -3531,7 +3531,7 @@ if not self.base_ring().is_exact(): from warnings import warn - warn("Using generic algorithm for an inexact ring, which may result in garbarge from numerical precision issues.") + warn("Using generic algorithm for an inexact ring, which may result in garbage from numerical precision issues.") V = [] from sage.rings.qqbar import QQbar @@ -5129,7 +5129,7 @@ EXAMPLES: - Here is an example over the real double field; internally, this uses scipy:: + Here is an example over the real double field; internally, this uses SciPy:: sage: r = matrix(RDF, 5, 5, [ 0,0,0,0,1, 1,1,1,1,1, 16,8,4,2,1, 81,27,9,3,1, 256,64,16,4,1 ]) sage: m = r * r.transpose(); m @@ -5294,7 +5294,7 @@ 10000 In this example the Hadamard bound has to be computed - (automatically) using mpfr instead of doubles, since doubles + (automatically) using MPFR instead of doubles, since doubles overflow:: sage: a = matrix(ZZ, 2, [2^10000,3^10000,2^50,3^19292]) @@ -6226,7 +6226,7 @@ def _dim_cmp(x,y): """ Used internally by matrix functions. Given 2-tuples (x,y), returns - their comparision based on the first component. + their comparison based on the first component. EXAMPLES:: @@ -6285,7 +6285,7 @@ """ Returns all possible sublists of length t from range(n) - Based on algoritm T from Knuth's taocp part 4: 7.2.1.3 p.5 This + Based on algorithm T from Knuth's taocp part 4: 7.2.1.3 p.5 This function replaces the one based on algorithm L because it is faster. diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/matrix_complex_double_dense.pyx --- a/sage/matrix/matrix_complex_double_dense.pyx +++ b/sage/matrix/matrix_complex_double_dense.pyx @@ -24,7 +24,7 @@ AUTHORS: -- Jason Grout (2008-09): switch to numpy backend +- Jason Grout (2008-09): switch to NumPy backend - Josh Kantor @@ -93,7 +93,7 @@ # * set_unsafe # * get_unsafe # * __richcmp__ -- always the same - # * __hash__ -- alway simple + # * __hash__ -- always simple ######################################################################## def __new__(self, parent, entries, copy, coerce): global numpy diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/matrix_cyclo_dense.pyx --- a/sage/matrix/matrix_cyclo_dense.pyx +++ b/sage/matrix/matrix_cyclo_dense.pyx @@ -1081,7 +1081,7 @@ OUTPUT: matrix over GF(p) whose columns correspond to the entries - of all the charpolys of the reduction of self modulo all + of all the characteristic polynomials of the reduction of self modulo all the primes over p. EXAMPLES: @@ -1095,12 +1095,12 @@ [4 0 0] [0 0 0] """ - tm = verbose("Computing characteristic polynomial of cyclomotic matrix modulo %s."%p) + tm = verbose("Computing characteristic polynomial of cyclotomic matrix modulo %s."%p) # Reduce self modulo all primes over p R, denom = self._reductions(p) # Compute the characteristic polynomial of each reduced matrix F = [A.charpoly('x') for A in R] - # Put the charpolys together as the rows of a mod-p matrix + # Put the characteristic polynomials together as the rows of a mod-p matrix k = R[0].base_ring() S = matrix(k, len(F), self.nrows()+1, [f.list() for f in F]) # multiply by inverse of reduction matrix to lift @@ -1467,7 +1467,7 @@ found += 1 else: # this means that the rank profile mod this - # prime is worse than those that came befroe, + # prime is worse than those that came before, # so we just loop p = previous_prime(p) continue diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/matrix_dense.pyx --- a/sage/matrix/matrix_dense.pyx +++ b/sage/matrix/matrix_dense.pyx @@ -204,7 +204,7 @@ def antitranspose(self): """ - Returns the anittranspose of self, without changing self. + Returns the antitranspose of self, without changing self. EXAMPLES:: diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/matrix_double_dense.pyx --- a/sage/matrix/matrix_double_dense.pyx +++ b/sage/matrix/matrix_double_dense.pyx @@ -1,5 +1,5 @@ """ -Dense matrices using a numpy backend. This serves as a base class for +Dense matrices using a NumPy backend. This serves as a base class for dense matrices over Real Double Field and Complex Double Field. EXAMPLES: @@ -23,7 +23,7 @@ True AUTHORS: - -- Jason Grout, Sep 2008: switch to numpy backend, factored out the Matrix_double_dense class + -- Jason Grout, Sep 2008: switch to NumPy backend, factored out the Matrix_double_dense class -- Josh Kantor -- William Stein: many bug fixes and touch ups. """ @@ -86,7 +86,7 @@ # * set_unsafe # * get_unsafe # * __richcmp__ -- always the same - # * __hash__ -- alway simple + # * __hash__ -- always simple ######################################################################## def __new__(self, parent, entries, copy, coerce): """ @@ -104,7 +104,7 @@ EXAMPLE: In this example, we throw away the current matrix and make a - new unitialized matrix representing the data for the class. + new uninitialized matrix representing the data for the class. sage: a=matrix(RDF, 3, range(9)) sage: a.__create_matrix__() """ @@ -260,7 +260,7 @@ # We call the self._python_dtype function on the value since # numpy does not know how to deal with complex numbers other - # than the builtin complex number type. + # than the built-in complex number type. cdef int status status = cnumpy.PyArray_SETITEM(self._matrix_numpy, cnumpy.PyArray_GETPTR2(self._matrix_numpy, i, j), @@ -883,7 +883,7 @@ Compute the log of the absolute value of the determinant using LU decomposition. - NOTE: This is useful if the usual determinant overlows. + NOTE: This is useful if the usual determinant overflows. EXAMPLES: sage: m = matrix(RDF,2,2,range(4)); m diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/matrix_generic_dense.pyx --- a/sage/matrix/matrix_generic_dense.pyx +++ b/sage/matrix/matrix_generic_dense.pyx @@ -73,7 +73,7 @@ entries = list(entries) is_list = 1 except TypeError: - raise TypeError, "entries must be coercible to a list or the basering" + raise TypeError, "entries must be coercible to a list or the base ring" else: is_list = 1 diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/matrix_integer_2x2.pyx --- a/sage/matrix/matrix_integer_2x2.pyx +++ b/sage/matrix/matrix_integer_2x2.pyx @@ -159,7 +159,7 @@ entries = list(entries) is_list = 1 except TypeError: - raise TypeError, "entries must be coercible to a list or the basering" + raise TypeError, "entries must be coercible to a list or the base ring" else: is_list = 1 diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/matrix_integer_dense.pyx --- a/sage/matrix/matrix_integer_dense.pyx +++ b/sage/matrix/matrix_integer_dense.pyx @@ -609,7 +609,7 @@ # TODO: This is about 6-10 slower than MAGMA doing what seems to be the same thing. # Moreover, NTL can also do this quickly. Why? I think both have specialized # small integer classes. (dmharvey: yes, NTL does not allocate any memory when - # intialising a ZZ to zero.) + # initializing a ZZ to zero.) _sig_on cdef Py_ssize_t i for i from 0 <= i < self._nrows * self._ncols: @@ -1139,7 +1139,7 @@ mpz_clear(h) mpz_clear(x) - return 0 # no error occured. + return 0 # no error occurred. def _multiply_multi_modular(left, Matrix_integer_dense right): """ @@ -1223,7 +1223,7 @@ cdef mod_int **row_list row_list = sage_malloc(sizeof(mod_int*) * n) if row_list == NULL: - raise MemoryError, "out of memory allocating multi-modular coefficent list" + raise MemoryError, "out of memory allocating multi-modular coefficient list" _sig_on for i from 0 <= i < nr: @@ -1280,13 +1280,13 @@ raise NotImplementedError, "modulus to big" cdef mod_int** matrix = sage_malloc(sizeof(mod_int*) * self._nrows) if matrix == NULL: - raise MemoryError, "out of memory allocating multi-modular coefficent list" + raise MemoryError, "out of memory allocating multi-modular coefficient list" cdef Py_ssize_t i, j for i from 0 <= i < self._nrows: matrix[i] = sage_malloc(sizeof(mod_int) * self._ncols) if matrix[i] == NULL: - raise MemoryError, "out of memory allocating multi-modular coefficent list" + raise MemoryError, "out of memory allocating multi-modular coefficient list" for j from 0 <= j < self._ncols: matrix[i][j] = mpz_fdiv_ui(self._matrix[i][j], n) @@ -1762,7 +1762,7 @@ ALGORITHM: 1. Replace input by a matrix of full rank got from a subset of the rows. 2. Divide out any common factors from rows. 3. - Check max_dets random dets of submatrices to see if their gcd + Check max_dets random dets of submatrices to see if their GCD (with p) is 1 - if so matrix is saturated and we're done. 4. Finally, use that if A is a matrix of full rank, then `hnf(transpose(A))^{-1}*A` is a saturation of A. @@ -2310,7 +2310,7 @@ if False, impacts how determinants are computed. - By convention if self has 0 columnss, the right kernel is of dimension 0, + By convention if self has 0 columns, the right kernel is of dimension 0, whereas the right kernel is the whole domain if self has 0 rows. EXAMPLES:: @@ -3124,8 +3124,8 @@ The rank is cached. - ALGORITHM: First check if the matrix has maxim posible rank by - working modulo one random prime. If not call Linbox's rank + ALGORITHM: First check if the matrix has maxim possible rank by + working modulo one random prime. If not call LinBox's rank function. EXAMPLES:: @@ -3157,7 +3157,7 @@ if r == self._nrows or r == self._ncols: self.cache('rank', r) return r - # Detecting full rank didn't work -- use Linbox's general algorithm. + # Detecting full rank didn't work -- use LinBox's general algorithm. r = self._rank_linbox() self.cache('rank', r) return r @@ -3252,7 +3252,7 @@ sage: A.determinant(algorithm='linbox') Traceback (most recent call last): ... - RuntimeError: you must pass the proof=False option to the determinant command to use Linbox's det algorithm + RuntimeError: you must pass the proof=False option to the determinant command to use LinBox's det algorithm sage: A.determinant(algorithm='linbox',proof=False) -21 sage: A._clear_cache() @@ -3293,7 +3293,7 @@ return matrix_integer_dense_hnf.det_padic(self, proof=proof, stabilize=stabilize) elif algorithm == 'linbox': if proof: - raise RuntimeError, "you must pass the proof=False option to the determinant command to use Linbox's det algorithm" + raise RuntimeError, "you must pass the proof=False option to the determinant command to use LinBox's det algorithm" d = self._det_linbox() elif algorithm == 'pari': d = self._det_pari() @@ -3358,7 +3358,7 @@ cdef long dim cdef mpz_t *mp_N - time = verbose('computing nullspace of %s x %s matrix using IML'%(self._nrows, self._ncols)) + time = verbose('computing null space of %s x %s matrix using IML'%(self._nrows, self._ncols)) _sig_on dim = nullspaceMP (self._nrows, self._ncols, self._entries, &mp_N) _sig_off @@ -3372,7 +3372,7 @@ mpz_clear(mp_N[i]) free(mp_N) - verbose("finished computing nullspace", time) + verbose("finished computing null space", time) M._initialized = True return M @@ -4656,7 +4656,7 @@ def _det_pari(self, int flag=0): """ Determinant of this matrix using Gauss-Bareiss. If (optional) - flag is set to 1, use classical gaussian elimination. + flag is set to 1, use classical Gaussian elimination. For efficiency purposes, this det is computed entirely on the PARI stack then the PARI stack is cleared. This function is @@ -4897,7 +4897,7 @@ cdef mod_int **row_list row_list = sage_malloc(sizeof(mod_int*) * n) if row_list == NULL: - raise MemoryError, "out of memory allocating multi-modular coefficent list" + raise MemoryError, "out of memory allocating multi-modular coefficient list" _sig_on for i from 0 <= i < nr: diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/matrix_integer_dense_hnf.py --- a/sage/matrix/matrix_integer_dense_hnf.py +++ b/sage/matrix/matrix_integer_dense_hnf.py @@ -16,7 +16,7 @@ def max_det_prime(n): """ Return the largest prime so that it is reasonably efficiency to - compute modulo that prime with n x n matrices in Linbox. + compute modulo that prime with n x n matrices in LinBox. INPUT: n -- a positive integer @@ -299,7 +299,7 @@ Solve B*x = a when the last row of $B$ contains huge entries using a clever trick that reduces the problem to solve C*x = a where $C$ is $B$ but with the last row replaced by something small, along - with one easy nullspace computation. The latter are both solved + with one easy null space computation. The latter are both solved $p$-adically. INPUT: @@ -784,7 +784,7 @@ def probable_hnf(A, include_zero_rows, proof): """ - Return the HNF of A or raise an exception if someting involving + Return the HNF of A or raise an exception if something involving the randomized nature of the algorithm goes wrong along the way. Calling this function again a few times should result it in it working, at least if proof=True. @@ -990,7 +990,7 @@ H, pivots = probable_hnf(A, include_zero_rows = include_zero_rows, proof=True) except (AssertionError, ZeroDivisionError, TypeError): raise - #verbose("Assertion occured when computing HNF; guessed pivot columns likely wrong.") + #verbose("Assertion occurred when computing HNF; guessed pivot columns likely wrong.") #continue else: if is_in_hnf_form(H, pivots): @@ -1103,7 +1103,7 @@ both dense and sparse. INPUT: - times -- number of times to randomly try matrices with eash shape + times -- number of times to randomly try matrices with each shape n -- number of rows m -- number of columns proof -- test with proof true diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/matrix_integer_sparse.pyx --- a/sage/matrix/matrix_integer_sparse.pyx +++ b/sage/matrix/matrix_integer_sparse.pyx @@ -50,7 +50,7 @@ # * set_unsafe # * get_unsafe # * __richcmp__ -- always the same - # * __hash__ -- alway simple + # * __hash__ -- always simple ######################################################################## def __new__(self, parent, entries, copy, coerce): self._initialized = False diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/matrix_mod2_dense.pyx --- a/sage/matrix/matrix_mod2_dense.pyx +++ b/sage/matrix/matrix_mod2_dense.pyx @@ -80,7 +80,7 @@ [0 0 1] TODO: - - make linbox frontend and use it + - make LinBox frontend and use it - charpoly ? - minpoly ? - make Matrix_modn_frontend and use it (?) @@ -248,7 +248,7 @@ there is a position smallest (i,j) in \code{self} where \code{self[i,j]} is zero but \code{right[i,j]} is one. This (i,j) is smaller than the (i,j) if \code{self} and - \code{right} are exchanged for the comparision. + \code{right} are exchanged for the comparison. INPUT: right -- a matrix @@ -376,7 +376,7 @@ # would cancel, so we only need the parity of how many of each # possible i0 occur. This is stored in the bits of running_xor. # Similarly, running_parity is the xor of the i1 needed. It's called - # parity because i1 is constant accross a word, and for each word + # parity because i1 is constant across a word, and for each word # the number of i1 to add is equal to the number of set bits in that # word (but because two cancel, we only need keep track of the # parity. @@ -1633,7 +1633,7 @@ def from_png(filename): r""" Returns a dense matrix over GF(2) from a 1-bit PNG image read from - \code{filename}. No attempt is made to verify that the filname string + \code{filename}. No attempt is made to verify that the filename string actually points to a PNG image. INPUT: @@ -1679,7 +1679,7 @@ INPUT: A -- a matrix over GF(2) - filename -- a string for a file in a writeable position + filename -- a string for a file in a writable position EXAMPLE: sage: from sage.matrix.matrix_mod2_dense import from_png, to_png diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/matrix_modn_dense.pyx --- a/sage/matrix/matrix_modn_dense.pyx +++ b/sage/matrix/matrix_modn_dense.pyx @@ -1284,7 +1284,7 @@ [1 5 1] [1 5 1] - Recaling need not include the entire row. + Rescaling need not include the entire row. :: diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/matrix_mpolynomial_dense.pyx --- a/sage/matrix/matrix_mpolynomial_dense.pyx +++ b/sage/matrix/matrix_mpolynomial_dense.pyx @@ -1,5 +1,5 @@ """ -Dense matrice over multivariate polynomials over fields. +Dense matrices over multivariate polynomials over fields. This implementation inherits from Matrix_generic_dense, i.e. it is not optimized for speed only some methods were added. @@ -9,7 +9,7 @@ #***************************************************************************** # -# SAGE: System for Algebra and Geometry Experimentation +# Sage: System for Algebra and Geometry Experimentation # # Copyright (C) 2007 William Stein # diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/matrix_rational_dense.pyx --- a/sage/matrix/matrix_rational_dense.pyx +++ b/sage/matrix/matrix_rational_dense.pyx @@ -1040,7 +1040,7 @@ - ``algorithm`` - - 'default': use whatever is the defalt for A\*B when A, B + - 'default': use whatever is the default for A\*B when A, B are over ZZ. - 'multimodular': use a multimodular algorithm @@ -1726,7 +1726,7 @@ since then we know that ker(g(A)) = `ker(g(A)^n)`. If dual is True, also returns the corresponding decomposition of V - under the action of the transpose of A. The factors are guarenteed + under the action of the transpose of A. The factors are guaranteed to correspond. INPUT: diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/matrix_rational_sparse.pyx --- a/sage/matrix/matrix_rational_sparse.pyx +++ b/sage/matrix/matrix_rational_sparse.pyx @@ -54,7 +54,7 @@ # * set_unsafe # * get_unsafe # * __richcmp__ -- always the same - # * __hash__ -- alway simple + # * __hash__ -- always simple ######################################################################## def __new__(self, parent, entries, copy, coerce): # set the parent, nrows, ncols, etc. @@ -403,7 +403,7 @@ Return the denominator of this matrix. OUTPUT: - -- SAGE Integer + -- Sage Integer EXAMPLES: sage: b = matrix(QQ,2,range(6)); b[0,0]=-5007/293; b @@ -544,7 +544,7 @@ # Change self's data to point to E's. self._matrix = E._matrix - # Make sure that E's destructure doesn't delete self's data. + # Make sure that E's destructor doesn't delete self's data. E._matrix = NULL E._initialized = False return E.pivots() diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/matrix_real_double_dense.pyx --- a/sage/matrix/matrix_real_double_dense.pyx +++ b/sage/matrix/matrix_real_double_dense.pyx @@ -24,7 +24,7 @@ AUTHORS: -- Jason Grout (2008-09): switch to numpy backend, factored out the +- Jason Grout (2008-09): switch to NumPy backend, factored out the Matrix_double_dense class - Josh Kantor @@ -95,7 +95,7 @@ # * set_unsafe # * get_unsafe # * __richcmp__ -- always the same - # * __hash__ -- alway simple + # * __hash__ -- always simple ######################################################################## def __new__(self, parent, entries, copy, coerce): global numpy @@ -111,8 +111,8 @@ cdef set_unsafe_double(self, Py_ssize_t i, Py_ssize_t j, double value): """ - Set the (i,j) entry to value without any typechecking or - boundchecking. + Set the (i,j) entry to value without any type checking or + bound checking. This currently isn't faster than calling self.set_unsafe; should we speed it up or is it just a convenience function that has the @@ -122,7 +122,7 @@ cdef double get_unsafe_double(self, Py_ssize_t i, Py_ssize_t j): """ - Get the (i,j) entry without any typechecking or boundchecking. + Get the (i,j) entry without any type checking or bound checking. This currently isn't faster than calling self.get_unsafe; should we speed it up or is it just a convenience function that has the diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/matrix_space.py --- a/sage/matrix/matrix_space.py +++ b/sage/matrix/matrix_space.py @@ -29,7 +29,7 @@ import weakref import operator -# SAGE matrix imports +# Sage matrix imports import matrix import matrix_generic_dense import matrix_generic_sparse @@ -63,7 +63,7 @@ import sage.groups.matrix_gps.matrix_group_element -# SAGE imports +# Sage imports import sage.structure.coerce import sage.structure.parent_gens as parent_gens import sage.rings.ring as ring @@ -456,13 +456,13 @@ elif sage.modules.free_module.is_FreeModule(S): return matrix_action.MatrixVectorAction(self, S) else: - # action of basering + # action of base ring return sage.structure.coerce.RightModuleAction(S, self) else: if sage.modules.free_module.is_FreeModule(S): return matrix_action.VectorMatrixAction(self, S) else: - # action of basering + # action of base ring return sage.structure.coerce.LeftModuleAction(S, self) else: return None @@ -727,7 +727,7 @@ ... NotImplementedError: object does not support iteration """ - #Make sure that we can interate over the base ring + #Make sure that we can iterate over the base ring base_ring = self.base_ring() base_iter = iter(base_ring) @@ -1196,7 +1196,7 @@ Given a dictionary of coordinate tuples, return the list given by reading off the nrows\*ncols matrix in row order. - EXAMLES:: + EXAMPLES:: sage: from sage.matrix.matrix_space import dict_to_list sage: d = {} @@ -1325,7 +1325,7 @@ assert(m00.rank() == 0) # Check that the empty 0x3 and 3x0 matrices are not invertible and that - # computing the detemininant raise the proper exception. + # computing the determinant raise the proper exception. for ms0 in [MatrixSpace(ring, 0, 3, sparse=sparse), MatrixSpace(ring, 3, 0, sparse=sparse)]: mn0 = ms0(0) diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/matrix_sparse.pyx --- a/sage/matrix/matrix_sparse.pyx +++ b/sage/matrix/matrix_sparse.pyx @@ -697,7 +697,7 @@ - Jaap Spies (2006-02-18) - - Didier Deshommes: some pyrex speedups implemented + - Didier Deshommes: some Pyrex speedups implemented - Jason Grout: sparse matrix optimizations """ diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/matrix_symbolic_dense.pyx --- a/sage/matrix/matrix_symbolic_dense.pyx +++ b/sage/matrix/matrix_symbolic_dense.pyx @@ -401,7 +401,7 @@ def factor(self): """ - Operates pointwise on each element. + Operates point-wise on each element. EXAMPLES:: @@ -416,7 +416,7 @@ def expand(self): """ - Operates pointwise on each element. + Operates point-wise on each element. EXAMPLES:: diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/strassen.pyx --- a/sage/matrix/strassen.pyx +++ b/sage/matrix/strassen.pyx @@ -30,7 +30,7 @@ Uses strassen multiplication at high levels and then uses MatrixWindow methods at low levels. EXAMPLES: The following matrix dimensions are chosen especially to exercise the eight possible - parity combinations that ocould ccur while subdividing the matrix + parity combinations that could occur while subdividing the matrix in the strassen recursion. The base case in both cases will be a (4x5) matrix times a (5x6) matrix. @@ -263,7 +263,7 @@ - ``A`` - matrix window - ``cutoff`` - size at which algorithm reverts to - naive gaussian elemination and multiplication must be at least 1. + naive Gaussian elimination and multiplication must be at least 1. OUTPUT: The list of pivot columns @@ -586,7 +586,7 @@ ## EXAMPLES: ## The following matrix dimensions are chosen especially to exercise the -## eight possible parity combinations that ocould ccur while subdividing +## eight possible parity combinations that could occur while subdividing ## the matrix in the strassen recursion. The base case in both cases will ## be a (4x5) matrix times a (5x6) matrix. diff -r 87b600f2b8d5 -r d63f79700dd8 sage/matrix/tests.py --- a/sage/matrix/tests.py +++ b/sage/matrix/tests.py @@ -45,7 +45,7 @@ sage: A.parent() Full MatrixSpace of 2 by 2 dense matrices over Symbolic Ring -We test an example det computation where linbox gave an incorrect +We test an example determinant computation where LinBox gave an incorrect result: sage: L=[-32672924, 402859388, -140623668, 430658721, 106946787, 621276047,-192782447, 431682021, 102255307, 94626176, -34905583, -95358049, 19932420, 123725915, 52076617, -202693998, -104950285, 75183320, 90638691, -10508577, -159993345, 544819075, -205041193, 530536794, 34425198, 812190067, -260981874, 580644585, 123763815, 100094135, -69769038, -119580389, 66415448, 141833716, 62768834, -269408072, -133259211, 100392022, 122810015, -14169559, -116742143, 255636730, -101946387, 229909806, -23983454, 370713224, -122485286, 271167855, 52843557, 36798922, -40542776, -53268126, 45195782, 57579762, 26577120, -124502451, -59510085, 46572885, 57450083, -6605856, 15000427, -30582942, 12516546, -26821359, 4119829, -44014478, 14639368, -32285711, -6171530, -4068360, 4862805, 6668897, -5647699, -6841120, -3067309, 14771590, 7115903, -5537058, -6739292, 775422, 7350404, 230921981, -76923414, 258735233, 86438125, 362368415, -110372559, 248213386, 62007340, 60606320, -15120637, -56148382, 2793135, 76364724, 31673380, -117264550, -61940375, 43368039, 52065723, -6050134, -151307549, 78679036, -46266959, 18647207, -117585287, 85433954, -37642136, 80027656, 2018840, -16574471, -34635562, -6981346, 53375886, -7793292, 519003, -33205642, -9545608, 12868856, 17687995, -1966305, -363614357, 1120703653, -426407076, 1075510629, 37584638, 1662076505, -536823334, 1193214389, 250295666, 197458670, -149948886, -243590352, 148290926, 284338202, 126789390, -552585008, -271574842, 206055077, 252487220, -29112407, -24118132, 125489794, -45531102, 127679943, 19878772, 189727909, -59873328, 133891226, 30235317, 26062022, -13427908, -28437884, 11088522, 35371542, 15314003, -62344332, -31541764, 23159538, 28169710, -3257928, 23875931, -55361113, 21777071, -50092756, 4072588, -80196200, 26290253, -58432798, -11789547, -8211985, 8277760, 11589279, -9463444, -12750488, -5909614, 26780705, 12871707, -9986858, -12330558, 1418788, -123640072, 350287850, -134402037, 331686546, 2311861, 516848375, -167690026, 372647615, 77107961, 59330726, -48837778, -74960963, 49935779, 86475403, 39012673, -172208210, -84008378, 64235307, 78969119, -9098718, 29206401, -58665698, 23367942, -51628286, 7704232, -84008009, 27841118, -62026237, -12049237, -7882950, 9778971, 11364995, -11528692, -12326307, -6027486, 28258762, 13229006, -10541794, -13232024, 1518814, -15226123, -162474710, 52643231, -186629243, -70538436, -257338018, 77581589, -174915120, -44964751, -45100633, 8768165, 39990351, 1319955, -55787679, -23007336, 82911599, 44234087, -30609520, -36691281, 4268583, -3067101, -187862053, 62406108, -208934856, -67593203, -293436726, 89390112, -201449078, -50438313, -48568925, 12749582, 44690794, -3756544, -61132699, -25770007, 94967436, 49821864, -35071658, -42392728, 4922722, -83860746, 387091065, -141493308, 390611827, 53261700, 584183024, -185296915, 413534644, 91854856, 78421794, -44059309, -86512345, 37363320, 106897933, 46669076, -192648548, -96693655, 71636948, 87410231, -10100657, -56763493, 123532651, -49071162, 111379401, -11435705, 179568616, -59393552, 131295311, 25428711, 17842335, -20018876, -25373934, 22014146, 27645710, 12901369, -60462971, -28718167, 22624087, 28023128, -3220309, 74635684, 42414796, -4598855, 80612787, 85891705, 84744646, -20275295, 47713903, 20441907, 28852448, 11300266, -16245887, -24534126, 30219206, 10703344, -24779029, -16921909, 8890606, 9568309, -1154549, -129928022, 450121954, -169305350, 439469539, 30630839, 672148465, -215901643, 479926644, 102311525, 83254236, -57276284, -99501795, 53823660, 117995313, 51970498, -222955007, -110479289, 83108429, 101494954, -11712185, -144442660, 396456359, -153843052, 373857888, -1782212, 585684397, -190815470, 422383108, 85815948, 66107666, -56349192, -86531733, 56862804, 97958280, 43498721, -195572942, -95613139, 73115455, 89344289, -10295212, 10964325, -5070815, 2648146, -839025, 8356359, -4976355, 2255047, -5129380, -209043, 1262716, 2672540, -558453, -4261247, 1210201, -101067, 2031870, 246414, -752782, -1334683, 146103, 117556641, -635133625, 229805379, -649558626, -106084208, -963569121, 304047081, -678780364, -153015989, -133365649, 68083651, 144472798, -54146381, -180316114, -77858131, 316948599, 160367139, -117807494, -143206514, 16562032] sage: M = Matrix(Integers(),20,20,L)