Python pylab.hstack函数代码示例

	def sigma_vectors(self,x,P):

		"""
		generates sigma vectors

		Arguments
		----------
		x : matrix
			state at time instant t
		P:  matrix
			state covariance matrix at time instant t

		Returns
		----------
		Chi : matrix
			matrix of sigma points
		"""
		State_covariance_cholesky=sp.linalg.cholesky(P).T
		State_covariance_cholesky_product=self.gamma_sigma_points*State_covariance_cholesky		
		chi_plus=[]
		chi_minus=[]
		for i in range(self.L):
			chi_plus.append(x+State_covariance_cholesky_product[:,i].reshape(self.L,1)) 
			chi_minus.append(x-State_covariance_cholesky_product[:,i].reshape(self.L,1)) 

		Chi=pb.hstack((x,pb.hstack((pb.hstack(chi_plus),pb.hstack(chi_minus))))) 
		return pb.matrix(Chi)

 def dy_Stance(self, t, y, pars, return_force = False):
     """
     This is the ode function that is passed to the solver. Internally, it calles:
         legfunc1 - force of leg 1 (overwrite for new models)
         legfunc2 - force of leg 2 (overwrite for new models)
     
     :args:
         t (float): simulation time
         y (6x float): CoM state
         pars (dict): parameters, will be passed to legfunc1 and legfunc2.
             must also include 'foot1' (3x float), 'foot2' (3x float), 'm' (float)
             and 'g' (3x float) indicating the feet positions, mass and direction of
             gravity, respectively.
         return_force (bool, default: False): return [F_leg1, F_leg2] (6x
             float) instead of dy/dt.
     """
     
     f1 = max(self.legfunc1(t, y, pars), 0) # only push
     l1 = norm(array(y[:3]) - array(pars['foot1']))
     f1_vec = (array(y[:3]) - array(pars['foot1'])) / l1 * f1
     f2 = max(self.legfunc2(t, y, pars), 0) # only push
     l2 = norm(array(y[:3]) - array(pars['foot2']))
     f2_vec = (array(y[:3]) - array(pars['foot2'])) / l2 * f2
     if return_force:
         return hstack([f1_vec, f2_vec])
     return hstack([y[3:], (f1_vec + f2_vec) / pars['m'] + pars['g']])

	def sigma_vectors(self,x,P):

		"""
		generator for the sigma vectors

		Arguments
		----------
		x : ndarray
			state at time instant t
		P:  ndarray
			state covariance matrix at time instant t

		Returns
		----------
		Xi : ndarray
			matrix of sigma points, each column is a sigma vector: [x0 x0+ x0-];nx by 2nx+1
		"""
		Pc=sp.linalg.cholesky(P,lower=1)
		Weighted_Pc=self.gamma_sigma_points*Pc		
		Xi_plus=[]
		Xi_minus=[]
		for i in range(self.nx):
			Xi_plus.append(x+Weighted_Pc[:,i].reshape(self.nx,1)) #list of ndarray with length nx
			Xi_minus.append(x-Weighted_Pc[:,i].reshape(self.nx,1)) #list of ndarray with length nx

		Xi=pb.hstack((x,pb.hstack((pb.hstack(Xi_plus),pb.hstack(Xi_minus))))) 
		return Xi

def mk_image(galaxy):
    base = './../../images_v5/GS_2.5as_matched/gs_all_'

    i_img = pyf.getdata(base+str(galaxy)+'_I.fits')
    j_img = pyf.getdata(base+str(galaxy)+'_J.fits')
    h_img = pyf.getdata(base+str(galaxy)+'_H.fits')

    #include 90% of pixels
    x = pyl.hstack(i_img)
    i_lim = scoreatpercentile(x,99)
    x = pyl.hstack(j_img)
    j_lim = scoreatpercentile(x,99)
    x = pyl.hstack(h_img)
    h_lim = scoreatpercentile(x,99)

    print galaxy, i_lim, j_lim, h_lim

    img = pyl.zeros((h_img.shape[0], h_img.shape[1], 3), dtype=float)
    img[:,:,0] = img_scale.asinh(h_img, scale_min=-0.1*h_lim, scale_max=h_lim,
            non_linear=0.5)
    img[:,:,1] = img_scale.asinh(j_img, scale_min=-0.1*j_lim, scale_max=j_lim,
            non_linear=0.5)
    img[:,:,2] = img_scale.asinh(i_img, scale_min=-0.1*i_lim, scale_max=i_lim,
            non_linear=0.5)

    return img

def simCSLIP_xp(x0, x0R, x0L, p0R, p0L, AR, AL, SLIP_param0, n=50):
    """
    simulates the controlled 2step-SLIP, using [x,p]-referenced control
    input:
        x0 - initial (augmented) state, e.g. [x0L, p0R].T
        x0R - reference right apex (y, vx, vz)
        x0L - reference left apex     -"-
        p0R - reference right parameters
        p0L - reference left parameters
        AR - parameter control right leg
        AL - parameter control left leg
        SLIP_param0: dict, containing {'m': ..., 'g': ... }
        n - number of strides to simulate at most
    """
    res = []
    refStateL = hstack([x0L, squeeze(sp_d2a(p0R))])[:,newaxis]
    refStateR = hstack([x0R, squeeze(sp_d2a(p0L))])[:,newaxis]
    currState = array(x0)
    slip_params = copy.deepcopy(SLIP_param0)
    if currState.ndim == 1:
        currState = currState[:,newaxis]
    elif currState.shape[0] == 1:
        currState = currState.T
    for step in range(n):
        #print 'AL: ', AL.shape, 'p0L: ', sp_d2a(p0L).shape
        pL = sp_d2a(p0L) + dot(AL, currState - refStateL)
        #print 'pL changed:', not allclose(pL,sp_d2a(p0L))
        slip_params.update(sp_a2d(pL))
        try:
            resL = sl.SLIP_step3D(currState[:3,0], slip_params)
        except ValueError:
            print 'simulation aborted (l1)\n'
            break
        if resL['sim_fail']:
            print 'simulation aborted (l2)\n'
            break
        res.append(resL)
        currState = hstack([resL['y'][-1],
                            resL['vx'][-1],
                            resL['vz'][-1],
                            squeeze(pL)])[:,newaxis]
        pR = sp_d2a(p0R) + dot(AR, currState - refStateR)
        #print 'pR changed:', not allclose(pR,sp_d2a(p0R))
        slip_params.update(sp_a2d(pR))
        try:
            resR = sl.SLIP_step3D(currState[:3,0], slip_params)
        except ValueError:
            print 'simulation aborted (r1)\n'
            break
        if resR['sim_fail']:
            print 'simulation aborted (r2)\n'
            break
        res.append(resR)
        currState = hstack([resR['y'][-1],
                            resR['vx'][-1],
                            resR['vz'][-1],
                            squeeze(pR)])[:,newaxis]
    return res

def int_f(a, fs=1.):
    """
    A fourier-based integrator.

    ===========
    Parameters:
    ===========
    a : *array* (1D)
        The array which should be integrated
    fs : *float*
        sampling time of the data

    ========
    Returns:
    ========
    y : *array* (1D)
        The integrated array

    """

    if False:
    # version with "mirrored" code
        xp = hstack([a, a[::-1]])
        int_fluc = int_f0(xp, float(fs))[:len(a)]
        baseline = mean(a) * arange(len(a)) / float(fs)
        return int_fluc + baseline - int_fluc[0]
    
    # old version
    baseline = mean(a) * arange(len(a)) / float(fs)
    int_fluc = int_f0(a, float(fs))
    return int_fluc + baseline - int_fluc[0]

    # old code - remove eventually (comment on 02/2014)
    # periodify
    if False:
        baseline = linspace(a[0], a[-1], len(a))
        a0 = a - baseline
        m = a0[-1] - a0[-2]
        b2 = linspace(0, -.5 * m, len(a))
        baseline -= b2
        a0 += b2
        a2 = hstack([a0, -1. * a0[1:][::-1]]) # "smooth" periodic signal  

        dbase = baseline[1] - baseline[0]
        t_vec = arange(len(a)) / float(fs)
        baseint = baseline[0] * t_vec + .5 * dbase * t_vec ** 2
        
        # define frequencies
        T = len(a2) / float(fs)
        freqs = 1. / T * arange(len(a2))
        freqs[len(freqs) // 2 + 1 :] -= float(fs)

        spec = fft.fft(a2)
        spec_i = zeros_like(spec, dtype=complex)
        spec_i[1:] = spec[1:] / (2j * pi* freqs[1:])
        res_int = fft.ifft(spec_i).real[:len(a0)] + baseint
        return res_int - res_int[0]

  def extrude_mesh(self,l,z_offset):
    # accepts the number of layers and the length of extrusion

    # Extrude vertices
    all_coords = []
    for i in linspace(0,z_offset,l):
      all_coords.append(hstack((mesh.coordinates(),i*ones((self.n_v2,1)))))
    self.global_vertices = vstack(all_coords)

    # Extrude cells (tris to tetrahedra)
    for i in range(l-1):
      for c in self.mesh.cells():
        # Make a prism out of 2 stacked triangles
        vertices = hstack((c+i*self.n_v2,c+(i+1)*self.n_v2))

        # Determine prism orientation
        smallest_vertex_index = argmin(vertices)

        # Map to I-ordering of Dompierre et al.
        mapping = self.indirection_table[smallest_vertex_index]

        # Determine which subdivision scheme to use.
        if min(vertices[mapping][[1,5]]) < min(vertices[mapping][[2,4]]):
          local_tets = vstack((vertices[mapping][[0,1,2,5]],\
                               vertices[mapping][[0,1,5,4]],\
                               vertices[mapping][[0,4,5,3]]))
        else:
          local_tets = vstack((vertices[mapping][[0,1,2,4]],\
                               vertices[mapping][[0,4,2,5]],\
                               vertices[mapping][[0,4,5,3]]))
        # Concatenate local tet to cell array
        self.global_tets = vstack((self.global_tets,local_tets))

    # Eliminate phantom initialization tet
    self.global_tets = self.global_tets[1:,:]

    # Query number of vertices and tets in new mesh
    self.n_verts = self.global_vertices.shape[0]
    self.n_tets = self.global_tets.shape[0]

    # Initialize new dolfin mesh of dimension 3
    self.new_mesh = Mesh()
    m = MeshEditor()
    m.open(self.new_mesh,3,3)
    m.init_vertices(self.n_verts,self.n_verts)
    m.init_cells(self.n_tets,self.n_tets)

    # Copy vertex data into new mesh
    for i,v in enumerate(self.global_vertices):
      m.add_vertex(i,Point(*v))

    # Copy cell data into new mesh
    for j,c in enumerate(self.global_tets):
      m.add_cell(j,*c)

    m.close()

def getPeriodicOrbit(statesL, T_L, ymin_L,
                     statesR, T_R, ymin_R,
                     baseParams ,
                     startParams=[14000, 1.16, 1, 0.] ):
    """
    returns a tuple of SLIP parameters, that result in the two-step periodic
    solution defined by <statesL> -> <statesR> -> >statesL>,
    with step time left (right) = <T_L> (<T_R>)
    minimal vertical position left (right) = <ymin_L> (<ymin_R>)
    statesL/R: a list of (left/right) apex states y, vx, vz
    baseParams: dict of base SLIP parameters: g, m (gravity acceleration, mass)
    
    returns: [SL, paramsL, dEL], [SR, paramsR, dER] 
             two tuples of initial apex states and corresponding SLIP
             parameters that yield the two-step periodic solution
             (dE: energy fluctuation)
        
    """    
    SL = mean(vstack(statesL), axis=0) if len(statesL) > 1 else statesL
    SR = mean(vstack(statesR), axis=0) if len(statesR) > 1 else statesR
    tr = mean(hstack(T_R))
    tl = mean(hstack(T_L))
    yminl = mean(hstack(ymin_L))
    yminr = mean(hstack(ymin_R))
    m = baseParams['m']
    g = baseParams['g']
    # energy input right (left) step
    dER = (SL[0]-SR[0])*m*abs(g) + .5*m*(SL[1]**2 + SL[2]**2 
                                       - SR[1]**2 - SR[2]**2)
    dEL = -dER

    # initialize parameters
    PR = copy.deepcopy( baseParams )
    PL = copy.deepcopy( baseParams )
    PL['IC'] = SL    
    PL['dE'] = dEL
    PR['IC'] = SR
    PR['dE'] = dER
    
    # define step params: (y_apex2, T, y_min, vz_apex2)
    spL = (SR[0], tl, yminl, SR[2])
    spR = (SL[0], tr, yminr, SL[2])
    
    # compute necessary model parameters
    paramsL = fl.calcSlipParams3D(spL, PL, startParams)
    paramsR = fl.calcSlipParams3D(spR, PR, startParams)
    
    
    return ([SL, paramsL, dEL],[SR, paramsR, dER])

    def SVMAF(self,freq,n,l):
        #Apply the SVMAF filter to the material parameters
        runningMean=lambda x,N: py.hstack((x[:N-1],py.convolve(x,py.ones((N,))/N,mode='same')[N-1:-N+1],x[(-N+1):]))
        #calculate the moving average of 3 points
        n_smoothed=runningMean(n,3)
        #evaluate H_smoothed from n_smoothed
        H_smoothed=self.H_theory(freq,[n_smoothed.real,n_smoothed.imag],l)
        
        H_r=H_smoothed.real
        H_i=H_smoothed.imag
        f=1
        #the uncertainty margins
        lb_r=self.H.getFReal()-self.H.getFRealUnc()*f
        lb_i=self.H.getFImag()-self.H.getFImagUnc()*f
        ub_r=self.H.getFReal()+self.H.getFRealUnc()*f
        ub_i=self.H.getFImag()+self.H.getFImagUnc()*f
        
        #ix=all indices for which after smoothening n H is still inbetwen the bounds        
        ix=py.all([H_r>=lb_r,H_r<ub_r,H_i>=lb_i,H_i<ub_i],axis=0)
#        #dont have a goood idea at the moment, so manually:
        for i in range(len(n_smoothed)):
            if ix[i]==0:
                n_smoothed[i]=n[i]
        print("SVMAF changed the refractive index at " + str(sum(ix)) + " frequencies")
        return n_smoothed      

def createSimilarAR(data):
    """
    creates an AR-process that is similar to a given data set.
    data must be given in n x d-format
    """
    # step 1: get "average" fit matrix
    l_A = []
    for rep in arange(100):
        idx = randint(0,data.shape[0]-1,data.shape[0]-1)
        idat = data[idx,:]
        odat = data[idx+1,:]
        l_A.append(lstsq(idat,odat)[0])

    sysmat = meanMat(l_A).T
    # idea: get "dynamic noise" from input data as difference of
    # expected vs. predicted data:
    # eta_i = (sysmat*(data[:,i-1]).T - data[:,i])
    # however, in order to destroy any possible correlations in the
    # input noise (they would also occur in the output), the
    # noise per section has to be permuted.
    prediction = dot(sysmat,data[:-1,:].T)
    dynNoise = data[1:,:].T - prediction
    res = [zeros((dynNoise.shape[0],1)), ]
    for nidx in permutation(dynNoise.shape[1]):
        res.append( dot(sysmat,res[-1]) + dynNoise[:,nidx][:,newaxis] )
    
    return hstack(res).T

    def SLIP_ode(y,t,params):
        """
        defines the ODE of the SLIP, under stance condition
        state: 
            [x
             y
             z
             vx
             vy
             vz]
        params:
            {'L0' : leg rest length
             'x0' : leg touchdown position
             'k'  : spring stiffness
             'm'  : mass
             'xF' : anterior foot position
             'zF' : lateral foot position }
        """

        dy0 = y[3]
        dy1 = y[4]
        dy2 = y[5]
        L = sqrt((y[0]-params['xF'])**2 + y[1]**2 + (y[2]-params['zF'])**2)
        F = params['k']*(params['L0']-L)
        Fx = F*(y[0]-params['xF'])/L
        Fy = F*y[1]/L
        Fz = F*(y[2]-params['zF'])/L
        dy3 = Fx/m
        dy4 = Fy/m + params['g']
        dy5 = Fz/m
        return hstack([dy0,dy1,dy2,dy3,dy4,dy5])

def stackSimRes(simRes):
    """
    input: a *list* of single steps
    returns: an array that contains the complete gait (consecutive time & way)
    """
    resDat = []
    res_t = []
    for part in simRes:
        if len(resDat) == 0:
            res_t.append(part['t'])
            resDat.append(vstack( [ part['x'],
                                    part['y'],
                                    part['z'],
                                    part['vx'],
                                    part['vy'],
                                    part['vz'],
                                    ]).T)
        else:
            res_t.append(part['t'][1:] + res_t[-1][-1])
            # compensate x and z translation
            resDat.append(vstack( [ part['x'][1:] + resDat[-1][-1,0],
                                    part['y'][1:],
                                    part['z'][1:] + resDat[-1][-1,2],
                                    part['vx'][1:],
                                    part['vy'][1:],
                                    part['vz'][1:],
                                    ]).T)
    return hstack(res_t), vstack(resDat)

 def my_medfilt(data, tailLength):
     """
     returns the median-filtered data; edges are "extrapolated" (constant)
     """
     data1 = hstack([data[tailLength:0:-1], data, data[-tailLength:]])
     out = medfilt(data1, 2 * tailLength + 1)
     return out[tailLength:-tailLength]

    def _sample_posteriors(self,true_N_A,p_A,N_samples):
        true_N_B = self.N_u-true_N_A  
        N_values = pl.shape(true_N_A)[0]
        posteriors = pl.zeros((N_samples,N_values))
        for i in range(N_samples):
            for (j,(t_N_A,t_N_B)) in enumerate(zip(true_N_A,true_N_B)):
                A_given_A = pl.ones(t_N_A)*self.p_uA_given_A
                A_given_B = pl.ones(t_N_B)*self.p_uA_given_B
                A_probs = pl.hstack((A_given_A,A_given_B))
                B_given_A = pl.ones(t_N_A)*self.p_uB_given_A
                B_given_B = pl.ones(t_N_B)*self.p_uB_given_B
                B_probs = pl.hstack((B_given_A,B_given_B))

                N_A = pl.sum(A_probs>pl.rand(self.N_u))
                N_B = pl.sum(B_probs>pl.rand(self.N_u))

                posteriors[i,j] = self._p_A_given_N_A(N_A,N_B)
         
        return pl.mean(posteriors,0)

def block_hankel(data, f):
    """
    Create a block hankel matrix.
    f : number of rows
    """
    data = pl.matrix(data)
    assert len(data.shape) == 2
    n = data.shape[1] - f
    return pl.matrix(pl.hstack([
        pl.vstack([data[:, i+j] for i in range(f)])
        for j in range(n)]))