Metadata-Version: 1.1
Name: mymat
Version: 1.1.0
Summary: Subclass of numpy.matrix behaving as matrices in matlab.
Home-page: https://git.oschina.net/williamzjc/mymat
Author: William Song
Author-email: songcwzjut@163.com
License: MIT
Description: =============
        Introduction
        =============
        This is my module about matrix operation. It imitates matlab grammar.
        If you love matlab as well as python, then this is your choice.
        It will be a good experience to operating matrices matlab-like.
        
        Sugar: one can use single index to refer to the elements in a matrix. (see following examples)
        
        ============
        Orgnization
        ============
        
        mymat
        +—— mymat
        +—- matshow
        +—- matdemo
        +—- test_mat
        +—— linalg
        +—- denoise
        
        
        ============
        Feature
        ============
         
        Current Version: 
            1. fix some bugs. 
            2. Demonstrate Gauss elimination with tkinter.
            3. Define LinearEquation class
        
            import mymat.matdemo
            >>> mymat.matdemo.main()
        
        Main Feature(>0.1.x):
            1. MyMat, PyMat is now the subclass of MatBase
            2. improve some essential methods, fix some bugs
            3. the index can be in reserved order, such as A[3:1:-1,1]
            4. see numerical experiment in mat_demo (improved)
            5. add more methods (introduced below) and A[is,js]=[] now is legal
            6. fix some bugs, make the codes more robust
            finally, another improvement is that when create a matrix, we use following codes to set dtype (may temporarily)
        
            if data.dtype != np.complex128 and data.dtype != object:
                kwargs.setdefault('dtype', np.float64)
        
        Main Feature(0.0.x):
            1. introduce operator | and & to concatenate matrices
            2. in setitem, the index is allowed to be out of range as matlab with the help of update method (see below)
            3. correct the codes of delete, improve the codes of many method
            4. add poly/expm (Tylor approximation) method to calculate p(A) and e^A
            5. add totex method, transforming a matrix to its tex-form
            6. the default dtype of MyMat is float64(complex128 when it is complex), but the integer matrix is int32. so, don't forget to convert the dtype if neccessary. But this is temporary.
        
        Grammar
        =========
        
        basic grammar
        ---------------
        
        import::
        
            >>> import mymat
            >>> A = mymat.MyMat([]) # use import mymat.pymat to import PyMat
        
        operators (Python left, Matlab right)::
        
            A*B := A*B  (B*A := B*A)
            A/B := A/B == A*B.I (B/A := B/A == B*A.I)
            A ** B := A .* B  (B ** A := B .* A)
            A//B := A./B  (B//A := B./A)
            A<<B := A.^B  (B<<A := B.^A)
            A^B := A^B
            A|B := [A,B]   A&B :=[A;B]
        
        
        We use matlab-type index, instead of python-type index, for example::
        
            >>> A=TestMat(5)
            [1, 2, 3, 4, 5;
             6, 7, 8, 9, 10;
             11, 12, 13, 14, 15;
             16, 17, 18, 19, 20;
             21, 22, 23, 24, 25]: M(5 X 5)
            >>> A[[3,4,7,10]]    # with single index as in matlab
            [11, 16, 7, 22]: M(1 X 4)
            >>> A[[2,3],1:4]
            [6, 7, 8, 9;
            11, 12, 13, 14]: M(2 X 4)
            >>> A[[1,3],[2,4]]   # use A.get(([1,3],[2,4])) to get matrix([2, 14])
            [2, 4;
            12, 14]: M(2 X 2)
        
            >>> A[3:1:-1,:]     # reversing order
            [11, 12, 13, 14, 15;
            6, 7, 8, 9, 10;
            1, 2, 3, 4, 5]: M(3 X 5)
        
        Use delete method to delete some rows or columns, as in matlab::
        
            >>> A=H(7)
            >>> B=A.delete([1,3],slice(3))   #  <=> B=A.copy(); B[[1,3],[1,2,3]]=[]
            >>> B.shape
            (5, 4)
        
        Linear equation::
        
            >>> le = LinearEquation(A, b)
            >>> print(le.totex())    # print tex of a linear equation
        
        
        Demonstration and Visualization
        ---------------------------------
        
        demonstration and numerical experiment::
        
            >>> import mymat
            >>> import mymat.matdemo        # see Gauss elimination
            >>> A=mymat.MyMat('1,1,1,6;0,4,-1,5;2,-2,1,1')  # or    A=mymat.MyMat('1&1&1&6\\0&4&-1&5\\2&-2&1&1') just copying the latex codes
            >>> mymat.matdemo.guassDemo(A)  # show the process of getting the echelon form of A
            >>> mymat.matdemo.denoiseDemo([n:noised signal(row vector)]) # see a denoising experiment
        
        draw a matrix::
        
            >>> import mymat.matshow    # draw a matrix on axes(require matplotlib)
            >>> ms = mymat.matshow.MatrixShow(A); ms.show()
        
        
        Methods and Functions
        ---------------------
        
        other methods::
        
            __call__: A(ind) == A[ind]
            delete(ind1=row, ind2=col): delete row-rows and col-columns
            proj(ind1=row, ind2=col): =0 out of A[row, col], for example A.proj(ind1=COLON, ind2=[2,3])=[2,3] where A=[1,2,3,4]
            repmat((ind1, ind2)|ind): repeat matrix as in Matlab (like tile)
            just: cut matrix to a certain size, and supplement zeros if the size is too large.
            cat: as concatenate
            equal: (A == B == C).all()
            apply: A.apply(lambda x:x+1) == A+1
            plus: A.plus(n) == A + nI
            robinson: A.robinson(j, x) == A[j<-x], namely A[:,j]=x used in Cramer rule
            echelon: get the echelon form (include the corresponding column indexes)
            tril, triu, diag are similar to matlab
            row(col)_transform1/2/3: elementary row (column) transforms (Gauss tranforms)
            comat: get the co-matrix (similar with delete)
            cofactor: A.cofactor(i,j)=Aij get the cofactor based on comat
            rho: the spectral radius
            totex: to tex form of matrix
            tolineq: to tex form of linear equations wrt augment matrix
            argmin, argmax
        
        
        class methods::
        
            MyMat.zeros, MyMat.ones, MyMat.random, MyMat.randint, MyMat.eye
        
        
        functions and variables::
        
            ind2ind: the most essential function
            times: translate single index to double index
            compind: get complementary index (called in proj)
            COLON: slice(None), COLON2=(COLON,COLON)
        
        
        matrices::
        
            FM: Fourier matrix
            FIM: Fourier inverse matrix
            FUM: Fourier unitary matrix
            Ho: Horsehold matrix
            Ref: reflection matrix
            H: Hilbert matrix
            Elm1,Elm2,Elm3: elementary matrices (3 types)
Keywords: numpy matrix matlab
Platform: UNKNOWN
Classifier: License :: Public Domain
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.4
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Natural Language :: English
