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

        Example:

        >>> df.covar("x**2+y**2+z**2", "-log(-E+1)")
        array(52.69461456005138)
        >>> df.covar("x**2+y**2+z**2", "-log(-E+1)")/(df.std("x**2+y**2+z**2") * df.std("-log(-E+1)"))
        0.63666373822156686
        >>> df.covar("x**2+y**2+z**2", "-log(-E+1)", binby="Lz", shape=4)
        array([ 10.17387143,  51.94954078,  51.24902796,  20.2163929 ])



        :param x: {expression}
        :param y: {expression}
        :param binby: {binby}
        :param limits: {limits}
        :param shape: {shape}
        :param selection: {selection}
        :param delay: {delay}
        :param progress: {progress}
        :return: {return_stat_scalar}
        """
        @delayed
        def cov(mean_x, mean_y, mean_xy):
            return mean_xy - mean_x * mean_y

        waslist, [xlist, ylist] = vaex.utils.listify(x, y)
        # print("limits", limits)
        limits = self.limits(binby, limits, selection=selection, delay=True)
        # print("limits", limits)

        @delayed
        def calculate(limits):
            results = []
            for x, y in zip(xlist, ylist):
                mx = self.mean(x, binby=binby, limits=limits, shape=shape, selection=selection, delay=True, progress=progressbar)
                my = self.mean(y, binby=binby, limits=limits, shape=shape, selection=selection, delay=True, progress=progressbar)
                cxy = self.mean("(%s)*(%s)" % (x, y), binby=binby, limits=limits, shape=shape, selection=selection,
                                delay=True, progress=progressbar)
                results.append(cov(mx, my, cxy))
            return results

        progressbar = vaex.utils.progressbars(progress)
        covars = calculate(limits)

        @delayed
        def finish(covars):
            value = np.array(vaex.utils.unlistify(waslist, covars))
            return value
        return self._delay(delay, finish(delayed_list(covars)))
def covar(self, x, y, binby=[], limits=None, shape=default_shape, selection=False, delay=False, progress=None):
        """Calculate the covariance cov[x,y] between and x and y, possibly on a grid defined by binby.

        Example:

        >>> df.covar("x**2+y**2+z**2", "-log(-E+1)")
        array(52.69461456005138)
        >>> df.covar("x**2+y**2+z**2", "-log(-E+1)")/(df.std("x**2+y**2+z**2") * df.std("-log(-E+1)"))
        0.63666373822156686
        >>> df.covar("x**2+y**2+z**2", "-log(-E+1)", binby="Lz", shape=4)
        array([ 10.17387143,  51.94954078,  51.24902796,  20.2163929 ])



        :param x: {expression}
        :param y: {expression}
        :param binby: {binby}
        :param limits: {limits}
        :param shape: {shape}
        :param selection: {selection}
        :param delay: {delay}
        :param progress: {progress}
        :return: {return_stat_scalar}
        """
        @delayed
        def cov(mean_x, mean_y, mean_xy):
            return mean_xy - mean_x * mean_y

        waslist, [xlist, ylist] = vaex.utils.listify(x, y)
        # print("limits", limits)
        limits = self.limits(binby, limits, selection=selection, delay=True)
        # print("limits", limits)

        @delayed
        def calculate(limits):
            results = []
            for x, y in zip(xlist, ylist):
                mx = self.mean(x, binby=binby, limits=limits, shape=shape, selection=selection, delay=True, progress=progressbar)
                my = self.mean(y, binby=binby, limits=limits, shape=shape, selection=selection, delay=True, progress=progressbar)
                cxy = self.mean("(%s)*(%s)" % (x, y), binby=binby, limits=limits, shape=shape, selection=selection,
                                delay=True, progress=progressbar)
                results.append(cov(mx, my, cxy))
            return results

        progressbar = vaex.utils.progressbars(progress)
        covars = calculate(limits)

        @delayed
        def finish(covars):
            value = np.array(vaex.utils.unlistify(waslist, covars))
            return value
        return self._delay(delay, finish(delayed_list(covars)))
def covar ( self , x , y , binby = [ ] , limits = None , shape = default_shape , selection = False , delay = False , progress = None ) : @ delayed def cov ( mean_x , mean_y , mean_xy ) : return mean_xy - mean_x * mean_y waslist , [ xlist , ylist ] = vaex . utils . listify ( x , y ) # print("limits", limits) limits = self . limits ( binby , limits , selection = selection , delay = True ) # print("limits", limits) @ delayed def calculate ( limits ) : results = [ ] for x , y in zip ( xlist , ylist ) : mx = self . mean ( x , binby = binby , limits = limits , shape = shape , selection = selection , delay = True , progress = progressbar ) my = self . mean ( y , binby = binby , limits = limits , shape = shape , selection = selection , delay = True , progress = progressbar ) cxy = self . mean ( "(%s)*(%s)" % ( x , y ) , binby = binby , limits = limits , shape = shape , selection = selection , delay = True , progress = progressbar ) results . append ( cov ( mx , my , cxy ) ) return results progressbar = vaex . utils . progressbars ( progress ) covars = calculate ( limits ) @ delayed def finish ( covars ) : value = np . array ( vaex . utils . unlistify ( waslist , covars ) ) return value return self . _delay ( delay , finish ( delayed_list ( covars ) ) )
Calculate the covariance cov[x,y] between and x and y, possibly on a grid defined by binby.

        Example:

        >>> df.covar("x**2+y**2+z**2", "-log(-E+1)")
        array(52.69461456005138)
        >>> df.covar("x**2+y**2+z**2", "-log(-E+1)")/(df.std("x**2+y**2+z**2") * df.std("-log(-E+1)"))
        0.63666373822156686
        >>> df.covar("x**2+y**2+z**2", "-log(-E+1)", binby="Lz", shape=4)
        array([ 10.17387143,  51.94954078,  51.24902796,  20.2163929 ])



        :param x: {expression}
        :param y: {expression}
        :param binby: {binby}
        :param limits: {limits}
        :param shape: {shape}
        :param selection: {selection}
        :param delay: {delay}
        :param progress: {progress}
        :return: {return_stat_scalar}
Calculate the covariance cov [ x y ] between and x and y possibly on a grid defined by binby .