packages/vaex-core/vaex/dataframe.py
def cov(self, x, y=None, binby=[], limits=None, shape=default_shape, selection=False, delay=False, progress=None):
        """Calculate the covariance matrix for x and y or more expressions, possibly on a grid defined by binby.

        Either x and y are expressions, e.g:

        >>> df.cov("x", "y")

        Or only the x argument is given with a list of expressions, e,g.:

        >>> df.cov(["x, "y, "z"])

        Example:

        >>> df.cov("x", "y")
        array([[ 53.54521742,  -3.8123135 ],
        [ -3.8123135 ,  60.62257881]])
        >>> df.cov(["x", "y", "z"])
        array([[ 53.54521742,  -3.8123135 ,  -0.98260511],
        [ -3.8123135 ,  60.62257881,   1.21381057],
        [ -0.98260511,   1.21381057,  25.55517638]])

        >>> df.cov("x", "y", binby="E", shape=2)
        array([[[  9.74852878e+00,  -3.02004780e-02],
        [ -3.02004780e-02,   9.99288215e+00]],
        [[  8.43996546e+01,  -6.51984181e+00],
        [ -6.51984181e+00,   9.68938284e+01]]])


        :param x: {expression}
        :param y: {expression_single}
        :param binby: {binby}
        :param limits: {limits}
        :param shape: {shape}
        :param selection: {selection}
        :param delay: {delay}
        :return: {return_stat_scalar}, the last dimensions are of shape (2,2)
        """
        selection = _ensure_strings_from_expressions(selection)
        if y is None:
            if not _issequence(x):
                raise ValueError("if y argument is not given, x is expected to be sequence, not %r", x)
            expressions = x
        else:
            expressions = [x, y]
        N = len(expressions)
        binby = _ensure_list(binby)
        shape = _expand_shape(shape, len(binby))
        progressbar = vaex.utils.progressbars(progress)
        limits = self.limits(binby, limits, selection=selection, delay=True)

        @delayed
        def calculate(expressions, limits):
            # print('limits', limits)
            task = tasks.TaskStatistic(self, binby, shape, limits, weights=expressions, op=tasks.OP_COV, selection=selection)
            self.executor.schedule(task)
            progressbar.add_task(task, "covariance values for %r" % expressions)
            return task

        @delayed
        def finish(values):
            N = len(expressions)
            counts = values[..., :N]
            sums = values[..., N:2 * N]
            with np.errstate(divide='ignore', invalid='ignore'):
                means = sums / counts
            # matrix of means * means.T
            meansxy = means[..., None] * means[..., None, :]

            counts = values[..., 2 * N:2 * N + N**2]
            sums = values[..., 2 * N + N**2:]
            shape = counts.shape[:-1] + (N, N)
            counts = counts.reshape(shape)
            sums = sums.reshape(shape)
            with np.errstate(divide='ignore', invalid='ignore'):
                moments2 = sums / counts
            cov_matrix = moments2 - meansxy
            return cov_matrix
        progressbar = vaex.utils.progressbars(progress)
        values = calculate(expressions, limits)
        cov_matrix = finish(values)
        return self._delay(delay, cov_matrix)
def cov(self, x, y=None, binby=[], limits=None, shape=default_shape, selection=False, delay=False, progress=None):
        selection = _ensure_strings_from_expressions(selection)
        if y is None:
            if not _issequence(x):
                raise ValueError("if y argument is not given, x is expected to be sequence, not %r", x)
            expressions = x
        else:
            expressions = [x, y]
        N = len(expressions)
        binby = _ensure_list(binby)
        shape = _expand_shape(shape, len(binby))
        progressbar = vaex.utils.progressbars(progress)
        limits = self.limits(binby, limits, selection=selection, delay=True)

        @delayed
        def calculate(expressions, limits):
            # print('limits', limits)
            task = tasks.TaskStatistic(self, binby, shape, limits, weights=expressions, op=tasks.OP_COV, selection=selection)
            self.executor.schedule(task)
            progressbar.add_task(task, "covariance values for %r" % expressions)
            return task

        @delayed
        def finish(values):
            N = len(expressions)
            counts = values[..., :N]
            sums = values[..., N:2 * N]
            with np.errstate(divide='ignore', invalid='ignore'):
                means = sums / counts
            # matrix of means * means.T
            meansxy = means[..., None] * means[..., None, :]

            counts = values[..., 2 * N:2 * N + N**2]
            sums = values[..., 2 * N + N**2:]
            shape = counts.shape[:-1] + (N, N)
            counts = counts.reshape(shape)
            sums = sums.reshape(shape)
            with np.errstate(divide='ignore', invalid='ignore'):
                moments2 = sums / counts
            cov_matrix = moments2 - meansxy
            return cov_matrix
        progressbar = vaex.utils.progressbars(progress)
        values = calculate(expressions, limits)
        cov_matrix = finish(values)
        return self._delay(delay, cov_matrix)
def cov ( self , x , y = None , binby = [ ] , limits = None , shape = default_shape , selection = False , delay = False , progress = None ) : selection = _ensure_strings_from_expressions ( selection ) if y is None : if not _issequence ( x ) : raise ValueError ( "if y argument is not given, x is expected to be sequence, not %r" , x ) expressions = x else : expressions = [ x , y ] N = len ( expressions ) binby = _ensure_list ( binby ) shape = _expand_shape ( shape , len ( binby ) ) progressbar = vaex . utils . progressbars ( progress ) limits = self . limits ( binby , limits , selection = selection , delay = True ) @ delayed def calculate ( expressions , limits ) : # print('limits', limits) task = tasks . TaskStatistic ( self , binby , shape , limits , weights = expressions , op = tasks . OP_COV , selection = selection ) self . executor . schedule ( task ) progressbar . add_task ( task , "covariance values for %r" % expressions ) return task @ delayed def finish ( values ) : N = len ( expressions ) counts = values [ ... , : N ] sums = values [ ... , N : 2 * N ] with np . errstate ( divide = 'ignore' , invalid = 'ignore' ) : means = sums / counts # matrix of means * means.T meansxy = means [ ... , None ] * means [ ... , None , : ] counts = values [ ... , 2 * N : 2 * N + N ** 2 ] sums = values [ ... , 2 * N + N ** 2 : ] shape = counts . shape [ : - 1 ] + ( N , N ) counts = counts . reshape ( shape ) sums = sums . reshape ( shape ) with np . errstate ( divide = 'ignore' , invalid = 'ignore' ) : moments2 = sums / counts cov_matrix = moments2 - meansxy return cov_matrix progressbar = vaex . utils . progressbars ( progress ) values = calculate ( expressions , limits ) cov_matrix = finish ( values ) return self . _delay ( delay , cov_matrix )
Calculate the covariance matrix for x and y or more expressions, possibly on a grid defined by binby.

        Either x and y are expressions, e.g:

        >>> df.cov("x", "y")

        Or only the x argument is given with a list of expressions, e,g.:

        >>> df.cov(["x, "y, "z"])

        Example:

        >>> df.cov("x", "y")
        array([[ 53.54521742,  -3.8123135 ],
        [ -3.8123135 ,  60.62257881]])
        >>> df.cov(["x", "y", "z"])
        array([[ 53.54521742,  -3.8123135 ,  -0.98260511],
        [ -3.8123135 ,  60.62257881,   1.21381057],
        [ -0.98260511,   1.21381057,  25.55517638]])

        >>> df.cov("x", "y", binby="E", shape=2)
        array([[[  9.74852878e+00,  -3.02004780e-02],
        [ -3.02004780e-02,   9.99288215e+00]],
        [[  8.43996546e+01,  -6.51984181e+00],
        [ -6.51984181e+00,   9.68938284e+01]]])


        :param x: {expression}
        :param y: {expression_single}
        :param binby: {binby}
        :param limits: {limits}
        :param shape: {shape}
        :param selection: {selection}
        :param delay: {delay}
        :return: {return_stat_scalar}, the last dimensions are of shape (2,2)
Calculate the covariance matrix for x and y or more expressions possibly on a grid defined by binby .