maps.py 18.1 KB
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"""
------------------------------------------------------
Utility functions for handling mirror surface
maps. Some functions based on earlier version
in Matlab (http://www.gwoptics.org/simtools/)
Work in progress, currently these functions are
untested!

http://www.gwoptics.org/pykat/
------------------------------------------------------
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

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from pykat.optics.romhom import makeReducedBasis, makeEmpiricalInterpolant, makeWeights, makeWeightsNew
from scipy.interpolate import interp2d, interp1d
from pykat.maths.zernike import *        

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import numpy as np
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import math
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import pickle
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class surfacemap(object):
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    def __init__(self, name, maptype, size, center, step_size, scaling, data=None):
        
        self.name = name
        self.type = maptype
        self.center = center
        self.step_size = step_size
        self.scaling = scaling
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        self.__interp = None
        
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        if data is None:
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            self.data = np.zeros(size)
        else:
            self.data = data

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        self._rom_weights = None
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    def write_map(self, filename):
        with open(filename,'w') as mapfile:
            
            mapfile.write("% Surface map\n")
            mapfile.write("% Name: {0}\n".format(self.name))
            mapfile.write("% Type: {0}\n".format(self.type))
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            mapfile.write("% Size: {0} {1}\n".format(self.data.shape[0], self.data.shape[1]))
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            mapfile.write("% Optical center (x,y): {0} {1}\n".format(self.center[0], self.center[1]))
            mapfile.write("% Step size (x,y): {0} {1}\n".format(self.step_size[0], self.step_size[1]))
            mapfile.write("% Scaling: {0}\n".format(float(self.scaling)))
            mapfile.write("\n\n")
            
            for i in range(0, self.data.shape[0]):
                for j in range(0, self.data.shape[1]):
                    mapfile.write("%.15g " % self.data[i,j])
                mapfile.write("\n")
    
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    @property
    def data(self):
        return self.__data
    
    @data.setter
    def data(self, value):
        self.__data = value
        self.__interp = None
    
    @property
    def center(self):
        return self.__center
    
    @center.setter
    def center(self, value):
        self.__center = value
        self.__interp = None
    
    @property
    def step_size(self):
        return self.__step_size
    
    @step_size.setter
    def step_size(self, value):
        self.__step_size = value
        self.__interp = None

    @property
    def scaling(self):
        return self.__scaling
    
    @scaling.setter
    def scaling(self, value):
        self.__scaling = value
        self.__interp = None

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    @property
    def x(self):
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        return self.step_size[0] * (np.array(range(1, self.data.shape[0]+1)) - self.center[0])
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    @property
    def y(self):
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        return self.step_size[1] * (np.array(range(1, self.data.shape[1]+1))- self.center[1])
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    @property
    def size(self):
        return self.data.shape
            
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    @property
    def offset(self):
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        return np.array(self.step_size)*(np.array(self.center) - 1/2. - np.array(self.size)/2.0)
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    @property
    def ROMWeights(self):
        return self._rom_weights
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    def z_xy(self, x=None, y=None, wavelength=1064e-9, direction="11", nr1=1.0, nr2=1.0):
        """
        For this given map the field perturbation is computed. This data
        is used in computing the coupling coefficient. It returns a grid
        of complex values representing the change in amplitude or phase
        of the field.
        
            x, y      : Points to interpolate at, 'None' for no interpolation.
            wavelength: Wavelength of light in vacuum [m]
            direction : 11 (reflection front)
                        12 (transmission front to back)
                        21 (transmission back to front)
                        22 (reflection back)
            nr1       : refractive index on front side
            nr2       : refractive index on back side
        """
        
        assert(nr1 >= 1)
        assert(nr2 >= 1)
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        if x is None and y is None:
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            data = self.scaling * self.data
        else:
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            if self.__interp is None:
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                self.__interp = interp2d(self.x, self.y, self.data * self.scaling)
                
            data = self.__interp(x, y)
            
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        if direction == "11" or direction == "22":
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            if "phase" in self.type:
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                k = math.pi * nr1 * 2 / wavelength
                
                if direction == "11":
                    return np.exp(-2j * k * data)
                else:
                    return np.exp(2j * k * data[:, ::-1])
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            elif "absorption" in self.type:
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                if direction == "11":
                    return np.sqrt(1.0 - data)
                else:
                    return np.sqrt(1.0 - data[:, ::-1])
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            else:
                raise BasePyKatException("Map type needs handling")
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        elif direction == "12" or direction == "21":
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            if "phase" in self.type:
                k = math.pi * 2 / wavelength
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                if direction == "12":
                    return np.exp((nr1-nr2)*k * data)
                else:
                    return np.exp((nr1-nr2)*k * data[:, ::-1])
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            elif "absorption" in self.type:
                if direction == "12":
                    return np.sqrt(1.0 - data)
                else:
                    return np.sqrt(1.0 - data[:, ::-1])
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            else:
                raise BasePyKatException("Map type needs handling")
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        else:
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            raise ValueError("Direction not valid")
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    def generateROMWeights(self, EIxFilename, EIyFilename=None, verbose=False, interpolate=False, newtonCotesOrder=8):
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        if interpolate == True:
            # Use EI nodes to interpolate if we
            with open(EIxFilename, 'rb') as f:
                EIx = pickle.load(f)

            if EIyFilename is None:
                EIy = EIx
            else:
                with open(EIyFilename, 'rb') as f:
                    EIy = pickle.load(f)

            x = EIx.x
            x.sort()
            nx = np.unique(np.hstack((x, -x[::-1])))
        
            y = EIy.x
            y.sort()
            ny = np.unique(np.hstack((y, -y[::-1])))
            
            self.interpolate(nx, ny)
        
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        self._rom_weights = makeWeightsNew(self, EIxFilename, EIyFilename, verbose=verbose, newtonCotesOrderMapWeight=newtonCotesOrder)
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        return self.ROMWeights

    def interpolate(self, nx, ny, **kwargs):
        """
        Interpolates the map for some new x and y values.
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        Uses scipy.interpolate.interp2d and any keywords arguments are
        passed on to it, thus settings like interpolation type and
        fill values can be set.
        
        The range of nx and ny must contain the value zero so that the
        center point of the map can be set.
        """

        D = interp2d(self.x, self.y, self.data, **kwargs)
        
        data = D(nx-self.offset[0], ny-self.offset[0])
        
        Dx = interp1d(nx, np.arange(1,len(nx)+1))
        Dy = interp1d(ny, np.arange(1,len(ny)+1))
        
        self.center = (Dx(0), Dy(0))
        self.step_size = (nx[1]-nx[0], ny[1]-ny[0])
        self.data = data

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    def plot(self, show=True, clabel=None, xlim=None, ylim=None):
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        import pylab
        
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        if xlim is not None:
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            _x = np.logical_and(self.x<=max(xlim)/100.0, self.x>=min(xlim)/100.0)
            xmin = np.min(np.where(_x == True))
            xmax = np.max(np.where(_x == True))
        else:
            xmin = 0
            xmax = len(self.x)-1
            xlim = [self.x.min()*100, self.x.max()*100]
    
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        if ylim is not None:
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            _y = np.logical_and(self.y<=max(ylim)/100.0, self.y>=min(ylim)/100.0)
            ymin = np.min(np.where(_y == True))
            ymax = np.max(np.where(_y == True))
        else:
            ymin = 0
            ymax = len(self.y)-1
            ylim = [self.y.min()*100, self.y.max()*100]
        
        zmin = self.data[xmin:xmax,ymin:ymax].min()
        zmax = self.data[xmin:xmax,ymin:ymax].max()

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        # 100 factor for scaling to cm
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        xrange = 100*self.x
        yrange = 100*self.y
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        fig = pylab.figure()
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        axes = pylab.pcolormesh(xrange, yrange, self.data, vmin=zmin, vmax=zmax)
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        pylab.xlabel('x [cm]')
        pylab.ylabel('y [cm]')
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        if xlim is not None: pylab.xlim(xlim)
        if ylim is not None: pylab.ylim(ylim)
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        pylab.title('Surface map {0}, type {1}'.format(self.name, self.type))
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        cbar = fig.colorbar(axes)
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        cbar.set_clim(zmin, zmax)
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        if clabel is not None:
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            cbar.set_label(clabel)
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        if show:
            pylab.show()
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        return fig
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class mergedmap:
    """
    A merged map combines multiple surfaces map to form one. Such a map can be used
    for computations of coupling coefficients but it cannot be written to a file to 
    be used with Finesse. For this you must output each map separately.
    
    """
    
    def __init__(self, name, size, center, step_size, scaling):
        
        self.name = name
        self.center = center
        self.step_size = step_size
        self.scaling = scaling
        self.__interp = None
        self._rom_weights = None
        self.__maps = []
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        self.weighting = None
        
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    def addMap(self, m):
        self.__maps.append(m)
    
    @property
    def center(self):
        return self.__center
    
    @center.setter
    def center(self, value):
        self.__center = value
        self.__interp = None
    
    @property
    def step_size(self):
        return self.__step_size
    
    @step_size.setter
    def step_size(self, value):
        self.__step_size = value
        self.__interp = None

    @property
    def scaling(self):
        return self.__scaling
    
    @scaling.setter
    def scaling(self, value):
        self.__scaling = value
        self.__interp = None
    
    @property
    def x(self):
        return self.step_size[0] * (np.array(range(1, self.size[0]+1)) - self.center[0])
        
    @property
    def y(self):
        return self.step_size[1] * (np.array(range(1, self.size[1]+1))- self.center[1])

    @property
    def size(self):
        return self.__maps[0].data.shape
            
    @property
    def offset(self):
        return np.array(self.step_size)*(np.array(self.center) - 1/2. - np.array(self.size)/2.0)
    
    @property
    def ROMWeights(self):
        return self._rom_weights
    
    def z_xy(self, wavelength=1064e-9, direction="reflection", nr1=1.0, nr2=1.0):
        
        z_xy = np.ones(self.size, dtype=np.complex128)
        
        for m in self.__maps:
            z_xy *= m.z_xy(wavelength=wavelength, direction=direction, nr1=nr1, nr2=nr2)
            
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        if self.weighting is None:
            return z_xy
        else:
            return z_xy * self.weighting
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    def generateROMWeights(self, EIxFilename, EIyFilename=None, verbose=False, interpolate=False, newtonCotesOrder=8):
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        if interpolate == True:
            # Use EI nodes to interpolate if we
            with open(EIxFilename, 'rb') as f:
                EIx = pickle.load(f)

            if EIyFilename is None:
                EIy = EIx
            else:
                with open(EIyFilename, 'rb') as f:
                    EIy = pickle.load(f)

            x = EIx.x
            x.sort()
            nx = np.unique(np.hstack((x, -x[::-1])))
        
            y = EIy.x
            y.sort()
            ny = np.unique(np.hstack((y, -y[::-1])))
            
            self.interpolate(nx, ny)
        
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        self._rom_weights = makeWeightsNew(self, EIxFilename, EIyFilename, verbose=verbose, newtonCotesOrderMapWeight=newtonCotesOrder)
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        return self.ROMWeights

    def interpolate(self, nx, ny, **kwargs):
        """
        Interpolates all the maps that are used to fc
        
        Uses scipy.interpolate.interp2d and any keywords arguments are
        passed on to it, thus settings like interpolation type and
        fill values can be set.
        
        The range of nx and ny must contain the value zero so that the
        center point of the map can be set.
        """

        for m in self.__maps:
            m.interpolate(nx, ny)

    def plot(self, mode="absorption", show=True, clabel=None, xlim=None, ylim=None, wavelength=1064e-9):
        
        import pylab
        
        if xlim is not None:
            _x = np.logical_and(self.x<=max(xlim)/100.0, self.x>=min(xlim)/100.0)
            xmin = np.min(np.where(_x == True))
            xmax = np.max(np.where(_x == True))
        else:
            xmin = 0
            xmax = len(self.x)-1
            xlim = [self.x.min()*100, self.x.max()*100]
    
        if ylim is not None:
            _y = np.logical_and(self.y<=max(ylim)/100.0, self.y>=min(ylim)/100.0)
            ymin = np.min(np.where(_y == True))
            ymax = np.max(np.where(_y == True))
        else:
            ymin = 0
            ymax = len(self.y)-1
            ylim = [self.y.min()*100, self.y.max()*100]

        if mode == "absorption":
            # plots how much of field is absorbed
            data = 1-np.abs(self.z_xy())
        elif mode == "meter":
            # plot the phase in terms of meters of displacement
            k = 2*np.pi/wavelength
            data = np.angle(self.z_xy()) / (2*k)
            
        zmin = data[xmin:xmax,ymin:ymax].min()
        zmax = data[xmin:xmax,ymin:ymax].max()

        # 100 factor for scaling to cm
        xrange = 100*self.x
        yrange = 100*self.y

        fig = pylab.figure()
        axes = pylab.pcolormesh(xrange, yrange, data, vmin=zmin, vmax=zmax)
        pylab.xlabel('x [cm]')
        pylab.ylabel('y [cm]')

        if xlim is not None: pylab.xlim(xlim)
        if ylim is not None: pylab.ylim(ylim)

        pylab.title('Merged map {0}, mode {1}'.format(self.name, mode))

        cbar = fig.colorbar(axes)
        cbar.set_clim(zmin, zmax)
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        if clabel is not None:
            cbar.set_label(clabel)
    
        if show:
            pylab.show()
        
        return fig

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class aperturemap(surfacemap):
    
    def __init__(self, name, size, step_size, R):
        surfacemap.__init__(self, name, "absorption both", size, (np.array(size)+1)/2.0, step_size, 1)
        self.R = R
        
    @property
    def R(self):
        return self.__R
    
    @R.setter
    def R(self, value):
        self.__R = value
    
        xx, yy = np.meshgrid(self.x, self.y)
        
        radius = np.sqrt(xx**2 + yy**2)
        
        self.data = np.zeros(self.size)
        self.data[radius > self.R] = 1.0
        
        
class curvedmap(surfacemap):
    
    def __init__(self, name, size, step_size, Rc):
        surfacemap.__init__(self, name, "phase reflection", size, (np.array(size)+1)/2.0, step_size, 1e-6)
        self.Rc = Rc
        
    @property
    def Rc(self):
        return self.__Rc
    
    @Rc.setter
    def Rc(self, value):
        self.__Rc = value
    
        xx, yy = np.meshgrid(self.x, self.y)
        
        Rsq = xx**2 + yy**2
        self.data = (self.Rc - math.copysign(1.0, self.Rc) * np.sqrt(self.Rc**2 - Rsq))/ self.scaling
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class tiltmap(surfacemap):
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    """
    To create a tiltmap, plot it and write it to a file to use with Finesse:
        
        tilts = (1e-6, 1e-8) # tilt in (x, y) radians\
        dx = 1e-4
        L = 0.2
        N = L/dx
        
        tmap = tiltmap("tilt", (N, N), (dx,dx), tilts)
        tmap.plot()
        tmap.write_map("mytilt.map")
    """
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    def __init__(self, name, size, step_size, tilt):
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        surfacemap.__init__(self, name, "phase reflection", size, (np.array(size)+1)/2.0, step_size, 1e-9)
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        self.tilt = tilt
        
    @property
    def tilt(self):
        return self.__tilt
    
    @tilt.setter
    def tilt(self, value):
        self.__tilt = value
        
        xx, yy = np.meshgrid(self.x, self.y)
        
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        self.data = (yy * self.tilt[1] + xx * self.tilt[0])/self.scaling
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class zernikemap(surfacemap):
	def __init__(self, name, size, step_size, radius, scaling=1e-9):
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		surfacemap.__init__(self, name, "phase reflection", size, (np.array(size)+1)/2.0, step_size, scaling)
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		self.__zernikes = {}
		self.radius = radius
		
	@property
	def radius(self): return self.__radius

	@radius.setter
	def radius(self, value, update=True):
		self.__radius = float(value)
		if update: self.update_data()

	def setZernike(self, m, n, amplitude, update=True):
		self.__zernikes["%i%i" % (m, n)] = (m,n,amplitude)
		if update: self.update_data()

	def update_data(self):
		X,Y = np.meshgrid(self.x, self.y)
		R = np.sqrt(X**2 + Y**2)
		PHI = np.arctan2(Y, X)

		data = np.zeros(np.shape(R))

		for i in self.__zernikes.items():
			data += i[1][2] * zernike(i[1][0], i[1][1], R/self.radius, PHI)

		self.data = data
	
			
	
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def read_map(filename):
    with open(filename, 'r') as f:
        
        f.readline()
        name = f.readline().split(':')[1].strip()
        maptype = f.readline().split(':')[1].strip()
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        size = tuple(map(lambda x: float(x), f.readline().split(':')[1].strip().split()))
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        center = tuple(map(lambda x: float(x), f.readline().split(':')[1].strip().split()))
        step = tuple(map(lambda x: float(x), f.readline().split(':')[1].strip().split()))
        scaling = float(f.readline().split(':')[1].strip())
        
        
        
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    data = np.loadtxt(filename, dtype=np.float64,ndmin=2,comments='%')    
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    return surfacemap(name,maptype,size,center,step,scaling,data)
    
    
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# TODO: Recreate functions from Simtools:, List taken from: ligo_maps/FT_convert_ligo_map_for_finesse.m
# map=FT_recenter_mirror_map(map);
# [map2,A2,Rc_out]=FT_remove_zernike_curvatures_from_map(map,Rc_in);
# [map2,Rc_out]=FT_remove_curvature_from_mirror_map(map,Rc_in,w, display_style);
# [map2,offset]=FT_remove_offset_from_mirror_map(map2,1e-2);
# [map3,x_tilt,y_tilt,offset2]=FT_remove_piston_from_mirror_map(map2,w, display_style);
# map3=FT_invert_mirror_map(map3, invert);