Exemplo n.º 1
0
    def reconstruct(c1, L, K, n=8):
        from zernike import RZern
        cart = RZern(n)

        ddx = np.linspace(-1.0, 1.0, K)
        ddy = np.linspace(-1.0, 1.0, L)
        xv, yv = np.meshgrid(ddx, ddy)
        cart.make_cart_grid(xv, yv)

        return cart.eval_grid(c1[0:cart.nk], matrix=True)
Exemplo n.º 2
0
    def test_normalisations_real(self):
        log = logging.getLogger('TestZern.test_normalisations_real')
        n_alpha = 6
        L, K = 400, 357

        # polar grid
        pol = RZern(n_alpha)
        fitAlpha = FitZern(pol, L, K)
        t1 = time()
        pol.make_pol_grid(fitAlpha.rho_j, fitAlpha.theta_i)
        t2 = time()
        log.debug('make pol grid {:.6f}'.format(t2 - t1))

        # cartesian grid
        cart = RZern(n_alpha)
        dd = np.linspace(-1.0, 1.0, max(L, K))
        xx, yy = np.meshgrid(dd, dd)
        t1 = time()
        cart.make_cart_grid(xx, yy)
        t2 = time()
        log.debug('make cart grid {:.6f}'.format(t2 - t1))

        smap = np.isfinite(cart.eval_grid(np.zeros(cart.nk)))
        scale = (1.0/np.sum(smap))
        log.debug('')
        log.debug('{} modes, {} x {} grid'.format(n_alpha, L, K))
        for i in range(pol.nk):
            a = np.zeros(pol.nk)
            a[i] = 1.0
            Phi_a = cart.eval_grid(a)
            for j in range(pol.nk):
                b = np.zeros(pol.nk)
                b[j] = 1.0
                Phi_b = cart.eval_grid(b)
                ip = scale*np.sum(Phi_a[smap]*Phi_b[smap])
                if i == j:
                    eip = 1.0
                else:
                    eip = 0.0
                iperr = abs(ip - eip)
                log.debug('<{:02},{:02}> = {:+e} {:+e}'.format(
                    i + 1, j + 1, ip, iperr))
                self.assertTrue(iperr < self.max_ip_err)
Exemplo n.º 3
0
 def phase_Zernike(self, Plot=True, Save=False):
     SLMRes = np.min([self.SLMResX, self.SLMResY])
     apertureD = int(round(self.aperture_radius / self.pixelpitch * 2))
     Zmatrix_size = np.min([SLMRes, apertureD])
     r0 = self.pixelpitch * Zmatrix_size / 2
     r0mm = r0 * 1e3
     cart = RZern(6)
     ddx = np.linspace(-1.0, 1.0, Zmatrix_size)
     ddy = np.linspace(-1.0, 1.0, Zmatrix_size)
     xv, yv = np.meshgrid(ddx, ddy)
     cart.make_cart_grid(xv, yv)
     c = np.zeros(cart.nk)
     c[[self.ind_Zernike]] = 1
     Phi = cart.eval_grid(c, matrix=True)
     # change all nan values in Phi matrix to be zero
     where_are_NaNs = np.isnan(Phi)
     Phi[where_are_NaNs] = 0
     Phi_norm = Phi
     n = cart.ntab[self.ind_Zernike]
     m = cart.mtab[self.ind_Zernike]
     zernike_str = f"Radial: {n}, Angular: {m}"
     print(zernike_str)
     # Launch the Zernike phase pattern to SLM screen
     SLM_screen_aberr = np.zeros((int(self.SLMResY), int(self.SLMResX)))
     col_Phi_norm = np.size(Phi_norm, axis=1)
     row_Phi_norm = np.size(Phi_norm, axis=0)
     startRow_screen = self.SLMResY / 2 - round(row_Phi_norm / 2)
     endRow_screen = self.SLMResY / 2 + round(row_Phi_norm / 2)
     startCol_screen = self.SLMResX / 2 - round(col_Phi_norm / 2)
     endCol_screen = self.SLMResX / 2 + round(col_Phi_norm / 2)
     SLM_screen_aberr[int(startRow_screen):int(endRow_screen), :][:, int(startCol_screen):int(endCol_screen)] = \
         Phi_norm*self.percent
     if Plot:
         im = plt.imshow(Phi_norm,
                         origin='lower',
                         extent=(-r0mm, r0mm, -r0mm, r0mm))
         plt.title(zernike_str)
         plt.colorbar(im)
         plt.show()
     if Save:
         np.savetxt(f"SLM_Zernike_{n}{m}_{percent}.csv",
                    SLM_screen_aberr,
                    delimiter=",")
     return SLM_screen_aberr, m, n
Exemplo n.º 4
0
        inds = np.where(cart.ntab == n)[0]
        ms = cart.mtab[inds]
        inds = inds[ms.argsort()]

        left = (1 - inds.size * width1 - (inds.size - 1) * width_span) / 2
        bott = (1 - nrows * height1 - (nrows - 1) * height_span) / 2

        bt = (bott + (nrows - i - 1) * (height1 + height_span))

        for j in range(inds.size):
            lf = left + j * (width1 + width_span) + leftoff
            ax = fig.add_axes([lf, bt, width1, height1])

            c *= 0.0
            c[inds[j]] = 1.0
            Phi = cart.eval_grid(c, matrix=True)
            ax.imshow(Phi, origin='lower', extent=[0, 1, 0, 1])
            ax.axis('off')

            ze = inds[j] + 1
            zn = cart.ntab[inds[j]]
            zm = cart.mtab[inds[j]]

            ax.text(1,
                    1,
                    '$\\#' + str(ze) + '$',
                    transform=ax.transAxes,
                    fontsize=fs1)

        plt.text(0,
                 bt + height1 / 2,
Exemplo n.º 5
0
def main_Calib(filename, output, mode, alg, basis, order, figure, verbose, offset, qt, pre, split):
    '''
    # main program
    # input: radius: %+.3f, 'str' (in makefile, str is default)
    #        path: file storage path, 'str'
    #        fout: file output name as .h5, 'str' (.h5 not included')
    #        cut_max: cut off of Legendre
    # output: the gathered result EventID, ChannelID, x, y, z
    '''
    if pre != 'r':
        print('begin reading file', flush=True)
        EventID, ChannelID, Q, PETime, photonTime, PulseTime, dETime, x, y, z = pub.ReadFile(filename)
        VertexTruth = (np.vstack((x, y, z))/1e3).T
        if(offset):
            off = pub.LoadBase(offset)
        else:
            off = np.zeros_like(PMTPos[:,0])
        print('total event: %d' % np.size(np.unique(EventID)), flush=True)
        print('begin processing legendre coeff', flush=True)
        # this part for the same vertex

        tmp = time.time()
        EventNo = np.size(np.unique(EventID))
        PMTNo = np.size(PMTPos[:,0])
        if mode == 'PE':
            PMTPosRep = np.tile(PMTPos, (EventNo,1))
            vertex = np.repeat(VertexTruth, PMTNo, axis=0)
        elif mode == 'time':
            counts = np.bincount(EventID)
            counts = counts[counts!=0]
            PMTPosRep = PMTPos[ChannelID]
            vertex = np.repeat(VertexTruth, counts, axis=0)
        elif mode == 'combined':
            PMTPosRep = np.tile(PMTPos, (EventNo,1))
            vertex = np.repeat(VertexTruth, PMTNo, axis=0)

        if basis == 'Legendre':
            X, cos_theta = pub.LegendreCoeff(PMTPosRep, vertex, order, Legendre=True)
        elif basis == 'Zernike':
            from zernike import RZern
            cos_theta = pub.LegendreCoeff(PMTPosRep, vertex, order, Legendre=False)
            cart = RZern(order)
            nk = cart.nk
            m = cart.mtab
            n = cart.ntab
            rho = np.linalg.norm(vertex, axis=1)/0.65
            theta = np.arccos(cos_theta)
            X = np.zeros((rho.shape[0], nk))

            for i in np.arange(nk):
                if not i % 5:
                    print(f'process {i}-th event')
                X[:,i] = cart.Zk(i, rho, theta)
            X = X[:,m>=0]
            print(f'rank: {np.linalg.matrix_rank(X)}')    
        print(f'use {time.time() - tmp} s')

        # which info should be used
        if mode == 'PE':
            y = Q
        elif mode == 'time':
            y = PulseTime 
        elif mode == 'combined':
            # PulseTime = PulseTime - np.min(PulseTime)
            # PulseTime = (PulseTime - np.max(PulseTime)/2)/np.max(PulseTime)*2
            # print(np.min(PulseTime), np.max(PulseTime))
            PulseTime = (PulseTime - np.max(PulseTime)/2)/np.max(PulseTime)*2
            bins = np.arange(-1, 0.05, 0.1)
            N = 10
            # Legendre coeff
            x = pub.legval(bins, np.eye(N).reshape(N, N, 1))
            # 1st basis
            Y = np.tile(x, len(np.unique(EventID))*len(np.unique(ChannelID))).T
            # 2nd basis
            X = np.repeat(X, bins.shape[0], axis=0)
            # output
            y = np.zeros((len(np.unique(EventID)), len(np.unique(ChannelID)), len(bins)))
            '''
            basis = np.zeros((X.shape[0], X.shape[1]*Y.shape[1]))
            for i_index, i in enumerate(np.arange(X.shape[1])):
                for j_index, j in enumerate(np.arange(Y.shape[1])):
                    total_index = i_index*Y.shape[1] + j_index
                    if not total_index % 10:
                        print(total_index)
                    basis[:, total_index] = X[:,i_index]*Y[:,j_index]
            X = basis
            '''
            split_index = np.unique(EventID).shape[0]
            for k_index, k in enumerate(np.unique(EventID)): # event begin with 1
                if k_index > split_index * split:
                    break
                if not k % 100:
                    print(k)
                index = EventID == k
                CID = ChannelID[index]
                Pulse_t = PulseTime[index]
                for i in np.unique(CID): # PMT begin with 0
                    y[k_index, i, 1:], _ = np.histogram(Pulse_t[CID==i], bins=bins)
            y = np.reshape(y,(-1))
        if verbose:
            print(f'the basis shape is {X.shape}, and the dependent variable shape is {y.shape}')
    if pre =='w':
        if split != 1:
            split_index = np.int(split*y.shape[0])
            X = X[:split_index]
            Y = Y[:split_index]
            y = y[:split_index]
        import pandas as pd
        import pyarrow as pa
        import pyarrow.parquet as pq
        y = np.atleast_2d(y).T
        #data = np.hstack((X, y, np.ones_like(y)))
        df_X = pd.DataFrame(X)
        X_names = []
        for i in df_X.columns:
            X_names.append('X' + str(i))
        df_X.columns = X_names    
        
        df_Y = pd.DataFrame(Y)
        Y_names = []
        for i in df_Y.columns:
            Y_names.append('Y' + str(i))
        df_Y.columns = Y_names
        
        df_y = pd.DataFrame(y)
        df_y.columns = ['output']
        df = pd.concat([df_X, df_Y, df_y], axis=1)
        table = pa.Table.from_pandas(df)
        
        pq.write_table(table, 'test1.parquet')
        return

    if not pre:
        # Regression methods:
        if alg == 'sms':
            import statsmodels.api as sm
            if mode == 'PE':
                model = sm.GLM(y, X, family=sm.families.Poisson(), fit_intercept=False)
                result = model.fit()
                if verbose:
                    print(result.summary())
                AIC = result.aic
                coef_ = result.params
                std = result.bse
                
            elif mode == 'time':
                import pandas as pd
                data = pd.DataFrame(data = np.hstack((X, np.atleast_2d(y).T)))                
                strs = 'y ~ '
                start = data.keys().start
                stop = data.keys().stop
                step = data.keys().step

                cname = []
                cname.append('X0')
                for i in np.arange(start+1, stop, step):
                    if i == start + 1:
                        strs += 'X%d ' % i
                    elif i == stop - step:
                        pass
                    else:
                        strs += ' + X%d ' % i                      

                    if i == stop - step:
                        cname.append('y')
                    else:
                        cname.append('X%d' % i)
                data.columns = cname

                mod = sm.formula.quantreg(strs, data[cname])

                result = mod.fit(q=qt,)
                coef_ = result.params
                AIC = np.zeros_like(coef_)
                std = np.zeros_like(coef_)           
                print('Waring! No AIC and std value')
            elif mode == 'combined':
                # data = pd.DataFrame(data = np.hstack((basis, np.atleast_2d(y).T)))  
                with h5py.File(output,'w') as out:        
                    out.create_dataset('X', data = X)
                    out.create_dataset('Y', data = y)
                print('begin...')
                model = sm.GLM(y, X, family=sm.families.Poisson())
                result = model.fit()
                if verbose:
                    print(result.summary())
                coef_ = result.params
                std = result.bse
                AIC = result.aic
            if verbose:
                print(result.summary())

        elif (alg == 'custom'):
            from scipy.optimize import minimize
            x0 = np.zeros_like(X[0]) # initial value (be careful of Zernike order)
            
            if mode == 'PE':
                x0[0] = 0.8 + np.log(2) # intercept is much more important
                result = minimize(pub.CalibPE, x0=x0, method='SLSQP', args = (y, PMTPos, X))
            elif mode == 'time':
                x0[0] = np.mean(y)
                qt = 0.1
                ts = 2.6
                result = minimize(pub.CalibTime, x0=x0, method='SLSQP', args = (np.hstack((EventID, EventID)), y, X, qt, ts))
            elif mode == 'combined':
                x0 = np.zeros_like(X[0])
                x0[0] = 0.8 + np.log(2) # intercept is much more important
                result = minimize(pub.CalibPE, x0=x0, method='SLSQP', args = (y, PMTPos, X))

            coef_ = np.array(result.x, dtype=float)
            if verbose:
                print(result.message)
            AIC = np.zeros_like(coef_)
            std = np.zeros_like(coef_)

            H = pub.MyHessian(result.x, pub.CalibPE, *(y, PMTPos, X))
            # H = pub.MyHessian(result.x, *(Q, PMTPos, X, pub.CalibTime))
            # std = 1/np.sqrt(-np.diag(np.linalg.pinv(H1)))
            print(coef_)
            # print(std)
            print('Waring! No AIC and std value, std is testing')

        elif alg == 'sk':
            from sklearn.linear_model import TweedieRegressor
            alpha = 0.001
            reg = TweedieRegressor(power=1, alpha=alpha, link='log', max_iter=1000, tol=1e-6, fit_intercept=False)
            reg.fit(X, y)

            # just for point data
            # pred = reg.predict(X[0:30,0:cut+1])

            print('coeff:\n', reg.coef_,'\n')

            coef_ = reg.coef_ 

            AIC = np.zeros_like(coef_)
            std = np.zeros_like(coef_)
            print('Waring! No AIC and std value')

        elif alg == 'h2o':
            import h2o
            from h2o.estimators.gbm import H2OGradientBoostingEstimator
            from h2o.estimators.glm import H2OGeneralizedLinearEstimator           
            if mode != 'combined':
                y = np.atleast_2d(y).T
                data = np.hstack((X, y, np.ones_like(y)))

                h2o.init()
                hf = h2o.H2OFrame(data)
                predictors = hf.columns[0:-2]
                response_col = hf.columns[-2]

                if mode == 'PE':
                    #offset_col = hf.columns[-1]
                    glm_model = H2OGeneralizedLinearEstimator(family= "poisson",
                        #offset_column = offset_col, 
                        lambda_ = 0,
                        compute_p_values = True)

                    glm_model.train(predictors, response_col, training_frame=hf)

                    coef_table = glm_model._model_json['output']['coefficients_table']
                    coef_ = glm_model.coef()

                elif mode == 'time':
                    gbm = H2OGradientBoostingEstimator(distribution="quantile", seed = 1234,
                                                      stopping_metric = "mse", stopping_tolerance = 1e-4)
                    gbm.train(x = predictors, y = response_col, training_frame = hf)
                    breakpoint()
                    print(gbm)
                    exit()
            elif mode == 'combined':
                y = np.atleast_2d(y).T
                data = np.hstack((X, Y, y, np.ones_like(y)))

                h2o.init() 
                hf = h2o.H2OFrame(data)
                predictors = hf.columns[0:-2]
                response_col = hf.columns[-2]           

            if verbose:
                print(coef_)
                if basis == 'Zernike':
                    print(f'Regession coef shape is f{np.array(coef_).shape}, Zernike shape is {nk}')
            coef_ = coef_table['coefficients']
            std = coef_table['std_error']
            AIC = glm_model.aic()

            h2o.cluster().shutdown()

    elif pre == 'r':
        import h2o
        from h2o.estimators.gbm import H2OGradientBoostingEstimator
        from h2o.estimators.glm import H2OGeneralizedLinearEstimator           
        h2o.init()
        hf = h2o.import_file("electron-1.parquet")
        pairs = []
        for i in hf.columns:
            for j in hf.columns:
                if (i.startswith('Z') and j.startswith('L')):
                    if ((i!='X0') and (j != 'Y0')):
                        pairs.append((i,j))
        predictors = hf.columns[2:]
        response_col = hf.columns[0]
        
        print(predictors)
        print(response_col)
        print(pairs)
        if mode == 'PE':
            #offset_col = hf.columns[-1]
            glm_model = H2OGeneralizedLinearEstimator(family= "poisson",
                #offset_column = offset_col, 
                lambda_ = 0,
                compute_p_values = True)

            glm_model.train(predictors, response_col, training_frame=hf)
        
        elif mode == 'combined':
            #offset_col = hf.columns[-1]
            glm_model = H2OGeneralizedLinearEstimator(family= "poisson",
                #offset_column = offset_col, 
                interaction_pairs=pairs,
                lambda_ = 0,
                #remove_collinear_columns = True, 
                compute_p_values = True)

            glm_model.train(predictors, response_col, training_frame=hf)
        breakpoint()
        coef_table = glm_model._model_json['output']['coefficients_table']
        coef_ = coef_table['coefficients']
        std = coef_table['std_error']
        AIC = glm_model.aic()
        print(f'Regession coef is f{np.array(coef_)}')             
        if (figure=='ON'):
            import matplotlib.pyplot as plt
            L, K = 500, 500
            ddx = np.linspace(-1.0, 1.0, K)
            ddy = np.linspace(-1.0, 1.0, L)
            xv, yv = np.meshgrid(ddx, ddy)
            cart.make_cart_grid(xv, yv)
            # normal scale
            # im = plt.imshow(np.exp(cart.eval_grid(np.array(coef_), matrix=True)), origin='lower', extent=(-1, 1, -1, 1))
            # log scale
            im = plt.imshow(cart.eval_grid(np.array(coef_), matrix=True), origin='lower', extent=(-1, 1, -1, 1))
            plt.colorbar()
            plt.savefig('test.png')
    else:
        print('error regression algorithm')
            
    with h5py.File(output,'w') as out:        
        out.create_dataset('coeff' + str(order), data = coef_)
        out.create_dataset('std' + str(order), data = std)
        out.create_dataset('AIC' + str(order), data = AIC)
Exemplo n.º 6
0
class ZernikePanel(QWidget):

    def_pars = {'zernike_labels': {}, 'shown_modes': 21}

    def __init__(self,
                 wavelength,
                 n_radial,
                 z0=None,
                 callback=None,
                 pars={},
                 parent=None):
        super().__init__(parent=parent)
        self.log = logging.getLogger(self.__class__.__name__)

        self.pars = {**deepcopy(self.def_pars), **deepcopy(pars)}
        self.units = 'rad'
        self.status = None
        self.mul = 1.0
        self.figphi = None
        self.ax = None
        self.im = None
        self.cb = None
        self.shape = (128, 128)
        self.P = 1

        self.rzern = RZern(n_radial)
        dd = np.linspace(-1, 1, self.shape[0])
        xv, yv = np.meshgrid(dd, dd)
        self.rzern.make_cart_grid(xv, yv)
        self.rad_to_nm = wavelength / (2 * np.pi)
        self.callback = callback
        self.zernike_rows = []

        if z0 is None:
            self.z = np.zeros(self.rzern.nk)
        else:
            self.z = z0.copy()
        assert (self.rzern.nk == self.z.size)

        group_phase = QGroupBox('phase')
        lay_phase = QGridLayout()
        group_phase.setLayout(lay_phase)
        self.figphi = FigureCanvas(Figure(figsize=(2, 2)))
        self.ax = self.figphi.figure.add_subplot(1, 1, 1)
        phi = self.rzern.matrix(self.rzern.eval_grid(np.dot(self.P, self.z)))
        self.im = self.ax.imshow(phi, origin='lower')
        self.cb = self.figphi.figure.colorbar(self.im)
        self.cb.locator = ticker.MaxNLocator(nbins=5)
        self.cb.update_ticks()
        self.ax.axis('off')
        self.status = QLabel('')
        lay_phase.addWidget(self.figphi, 0, 0)
        lay_phase.addWidget(self.status, 1, 0)

        def nmodes():
            return min(self.pars['shown_modes'], self.rzern.nk)

        bot = QGroupBox('Zernike')
        lay_zern = QGridLayout()
        bot.setLayout(lay_zern)
        labzm = QLabel('shown modes')
        lezm = QLineEdit(str(nmodes()))
        lezm.setMaximumWidth(50)
        lezmval = MyQIntValidator(1, self.rzern.nk)
        lezmval.setFixup(nmodes())
        lezm.setValidator(lezmval)

        brad = QCheckBox('rad')
        brad.setChecked(True)
        breset = QPushButton('reset')
        lay_zern.addWidget(labzm, 0, 0)
        lay_zern.addWidget(lezm, 0, 1)
        lay_zern.addWidget(brad, 0, 2)
        lay_zern.addWidget(breset, 0, 3)

        scroll = QScrollArea()
        lay_zern.addWidget(scroll, 1, 0, 1, 5)
        scroll.setWidget(QWidget())
        scrollLayout = QGridLayout(scroll.widget())
        scroll.setWidgetResizable(True)

        def make_hand_slider(ind):
            def f(r):
                self.z[ind] = r
                self.update_phi_plot()

            return f

        def make_hand_lab(le, i):
            def f():
                self.pars['zernike_labels'][str(i)] = le.text()

            return f

        def default_zernike_name(i, n, m):
            if i == 1:
                return 'piston'
            elif i == 2:
                return 'tip'
            elif i == 3:
                return 'tilt'
            elif i == 4:
                return 'defocus'
            elif m == 0:
                return 'spherical'
            elif abs(m) == 1:
                return 'coma'
            elif abs(m) == 2:
                return 'astigmatism'
            elif abs(m) == 3:
                return 'trefoil'
            elif abs(m) == 4:
                return 'quadrafoil'
            elif abs(m) == 5:
                return 'pentafoil'
            else:
                return ''

        def make_update_zernike_rows():
            def f(mynk=None):
                if mynk is None:
                    mynk = len(self.zernike_rows)
                ntab = self.rzern.ntab
                mtab = self.rzern.mtab
                if len(self.zernike_rows) < mynk:
                    for i in range(len(self.zernike_rows), mynk):
                        lab = QLabel(
                            f'Z<sub>{i + 1}</sub> ' +
                            f'Z<sub>{ntab[i]}</sub><sup>{mtab[i]}</sup>')
                        slider = RelSlider(self.z[i], make_hand_slider(i))

                        if str(i) in self.pars['zernike_labels'].keys():
                            zname = self.pars['zernike_labels'][str(i)]
                        else:
                            zname = default_zernike_name(
                                i + 1, ntab[i], mtab[i])
                            self.pars['zernike_labels'][str(i)] = zname
                        lbn = QLineEdit(zname)
                        lbn.setMaximumWidth(120)
                        hand_lab = make_hand_lab(lbn, i)
                        lbn.editingFinished.connect(hand_lab)

                        scrollLayout.addWidget(lab, i, 0)
                        scrollLayout.addWidget(lbn, i, 1)
                        slider.add_to_layout(scrollLayout, i, 2)

                        self.zernike_rows.append((lab, slider, lbn, hand_lab))

                    assert (len(self.zernike_rows) == mynk)

                elif len(self.zernike_rows) > mynk:
                    for i in range(len(self.zernike_rows) - 1, mynk - 1, -1):
                        lab, slider, lbn, hand_lab = self.zernike_rows.pop()

                        scrollLayout.removeWidget(lab)
                        slider.remove_from_layout(scrollLayout)
                        scrollLayout.removeWidget(lbn)

                        lbn.editingFinished.disconnect(hand_lab)
                        lab.setParent(None)
                        lbn.setParent(None)

                    assert (len(self.zernike_rows) == mynk)

            return f

        self.update_zernike_rows = make_update_zernike_rows()

        def reset_fun():
            self.z *= 0.
            self.update_gui_controls()
            self.update_phi_plot()

        def change_nmodes():
            try:
                ival = int(lezm.text())
                assert (ival > 0)
                assert (ival <= self.rzern.nk)
            except Exception:
                lezm.setText(str(len(self.zernike_rows)))
                return

            if ival != len(self.zernike_rows):
                self.update_zernike_rows(ival)
                self.update_phi_plot()
                lezm.setText(str(len(self.zernike_rows)))

        def f2():
            def f(b):
                if b:
                    self.units = 'rad'
                    self.mul = 1.0
                else:
                    self.units = 'nm'
                    self.mul = self.rad_to_nm
                self.update_phi_plot()

            return f

        self.update_zernike_rows(nmodes())

        brad.stateChanged.connect(f2())
        breset.clicked.connect(reset_fun)
        lezm.editingFinished.connect(change_nmodes)

        splitv = QSplitter(Qt.Vertical)
        top = QSplitter(Qt.Horizontal)
        top.addWidget(group_phase)
        splitv.addWidget(top)
        splitv.addWidget(bot)
        self.top = top
        self.bot = bot
        l1 = QGridLayout()
        l1.addWidget(splitv)
        self.setLayout(l1)
        self.lezm = lezm

    def save_parameters(self, merge={}):
        d = {**merge, **self.pars}
        d['shown_modes'] = len(self.zernike_rows)
        return d

    def load_parameters(self, d):
        self.pars = {**deepcopy(self.def_pars), **deepcopy(d)}
        nmodes = min(self.pars['shown_modes'], self.rzern.nk)
        self.pars['shown_modes'] = nmodes
        self.lezm.blockSignals(True)
        self.lezm.setText(str(nmodes))
        self.lezm.blockSignals(False)
        self.update_zernike_rows(0)
        self.update_zernike_rows(nmodes)

    def update_gui_controls(self):
        for i, t in enumerate(self.zernike_rows):
            slider = t[1]
            slider.block()
            slider.set_value(self.z[i])
            slider.unblock()

    def update_phi_plot(self, run_callback=True):
        phi = self.mul * self.rzern.matrix(
            self.rzern.eval_grid(np.dot(self.P, self.z)))
        inner = phi[np.isfinite(phi)]
        min1 = inner.min()
        max1 = inner.max()
        rms = self.mul * norm(self.z)
        self.status.setText(
            '{} [{: 03.2f} {: 03.2f}] {: 03.2f} PV {: 03.2f} RMS'.format(
                self.units, min1, max1, max1 - min1, rms))
        self.im.set_data(phi)
        self.im.set_clim(inner.min(), inner.max())
        self.figphi.figure.canvas.draw()

        if self.callback and run_callback:
            self.callback(self.z)