peri/comp/ilms.py
def randomize_parameters(self, ptp=0.2, fourier=False, vmin=None, vmax=None):
        """
        Create random parameters for this ILM that mimic experiments
        as closely as possible without real assumptions.
        """
        if vmin is not None and vmax is not None:
            ptp = vmax - vmin
        elif vmax is not None and vmin is None:
            vmin = vmax - ptp
        elif vmin is not None and vmax is None:
            vmax = vmin + ptp
        else:
            vmax = 1.0
            vmin = vmax - ptp

        self.set_values(self.category+'-scale', 1.0)
        self.set_values(self.category+'-off', 0.0)

        for k, v in iteritems(self.poly_params):
            norm = (self.zorder + 1.0)*2
            self.set_values(k, ptp*(np.random.rand() - 0.5) / norm)

        for i, p in enumerate(self.barnes_params):
            N = len(p)
            if fourier:
                t = ((np.random.rand(N)-0.5) + 1.j*(np.random.rand(N)-0.5))/(np.arange(N)+1)
                q = np.real(np.fft.ifftn(t)) / (i+1)
            else:
                t = ptp*np.sqrt(N)*(np.random.rand(N)-0.5)
                q = np.cumsum(t) / (i+1)

            q = ptp * q / q.ptp() / len(self.barnes_params)
            q -= q.mean()
            self.set_values(p, q)

        self._norm_stat = [ptp, vmin]

        if self.shape:
            self.initialize()

        if self._parent:
            param = self.category+'-scale'
            self.trigger_update(param, self.get_values(param))
def randomize_parameters(self, ptp=0.2, fourier=False, vmin=None, vmax=None):
        """
        Create random parameters for this ILM that mimic experiments
        as closely as possible without real assumptions.
        """
        if vmin is not None and vmax is not None:
            ptp = vmax - vmin
        elif vmax is not None and vmin is None:
            vmin = vmax - ptp
        elif vmin is not None and vmax is None:
            vmax = vmin + ptp
        else:
            vmax = 1.0
            vmin = vmax - ptp

        self.set_values(self.category+'-scale', 1.0)
        self.set_values(self.category+'-off', 0.0)

        for k, v in iteritems(self.poly_params):
            norm = (self.zorder + 1.0)*2
            self.set_values(k, ptp*(np.random.rand() - 0.5) / norm)

        for i, p in enumerate(self.barnes_params):
            N = len(p)
            if fourier:
                t = ((np.random.rand(N)-0.5) + 1.j*(np.random.rand(N)-0.5))/(np.arange(N)+1)
                q = np.real(np.fft.ifftn(t)) / (i+1)
            else:
                t = ptp*np.sqrt(N)*(np.random.rand(N)-0.5)
                q = np.cumsum(t) / (i+1)

            q = ptp * q / q.ptp() / len(self.barnes_params)
            q -= q.mean()
            self.set_values(p, q)

        self._norm_stat = [ptp, vmin]

        if self.shape:
            self.initialize()

        if self._parent:
            param = self.category+'-scale'
            self.trigger_update(param, self.get_values(param))
def randomize_parameters ( self , ptp = 0.2 , fourier = False , vmin = None , vmax = None ) : if vmin is not None and vmax is not None : ptp = vmax - vmin elif vmax is not None and vmin is None : vmin = vmax - ptp elif vmin is not None and vmax is None : vmax = vmin + ptp else : vmax = 1.0 vmin = vmax - ptp self . set_values ( self . category + '-scale' , 1.0 ) self . set_values ( self . category + '-off' , 0.0 ) for k , v in iteritems ( self . poly_params ) : norm = ( self . zorder + 1.0 ) * 2 self . set_values ( k , ptp * ( np . random . rand ( ) - 0.5 ) / norm ) for i , p in enumerate ( self . barnes_params ) : N = len ( p ) if fourier : t = ( ( np . random . rand ( N ) - 0.5 ) + 1.j * ( np . random . rand ( N ) - 0.5 ) ) / ( np . arange ( N ) + 1 ) q = np . real ( np . fft . ifftn ( t ) ) / ( i + 1 ) else : t = ptp * np . sqrt ( N ) * ( np . random . rand ( N ) - 0.5 ) q = np . cumsum ( t ) / ( i + 1 ) q = ptp * q / q . ptp ( ) / len ( self . barnes_params ) q -= q . mean ( ) self . set_values ( p , q ) self . _norm_stat = [ ptp , vmin ] if self . shape : self . initialize ( ) if self . _parent : param = self . category + '-scale' self . trigger_update ( param , self . get_values ( param ) )
Create random parameters for this ILM that mimic experiments
        as closely as possible without real assumptions.
Create random parameters for this ILM that mimic experiments as closely as possible without real assumptions .