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)
def coeff(img, n=8):
from zernike import RZern
cart = RZern(n)
L, K = img.shape
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)
c1 = cart.fit_cart_grid(img)[0]
return c1, L, K
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
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)
def calibrate(self,
U,
images,
fringe,
wavelength,
cam_pixel_size,
cam_serial='',
dname='',
dm_serial='',
dmplot_txs=(0, 0, 0),
dm_transform=SquareRoot.name,
hash1='',
n_radial=25,
alpha=.75,
lambda1=5e-3,
status_cb=False):
if status_cb:
status_cb('Computing Zernike polynomials ...')
t1 = time()
nu, ns = U.shape
xx, yy, shape = fringe.get_unit_aperture()
assert (xx.shape == shape)
assert (yy.shape == shape)
cart = RZern(n_radial)
cart.make_cart_grid(xx, yy)
LOG.info(
f'calibrate(): Computing Zernike polynomials {time() - t1:.1f}')
if status_cb:
status_cb('Computing masks ...')
t1 = time()
zfm = cart.matrix(np.isfinite(cart.ZZ[:, 0]))
self.cart = cart
self.zfm = zfm
zfA1, zfA2, mask = self._make_zfAs()
LOG.info(f'calibrate(): Computing masks {time() - t1:.1f}')
# TODO remove me
mask1 = np.sqrt(xx**2 + yy**2) >= 1.
assert (np.allclose(mask, mask1))
assert (np.allclose(fringe.mask, mask1))
if status_cb:
status_cb('Computing phases 00.00% ...')
t1 = time()
def make_progress():
prevts = [time()]
def f(pc):
t = time()
dt = t - prevts[0]
prevts[0] = t
if dt > 1.5 or pc > 99:
status_cb(f'Computing phases {pc:05.2f}% ...')
return f
with Pool() as p:
if status_cb:
chunksize = ns // (4 * cpu_count())
if chunksize < 4:
chunksize = 4
phases = []
progress_fun = make_progress()
for i, phi in enumerate(
p.imap(PhaseExtract(fringe),
[images[i, ...] for i in range(ns)], chunksize),
1):
phases.append(phi)
progress_fun(100 * i / ns)
else:
phases = p.map(PhaseExtract(fringe),
[images[i, ...] for i in range(ns)])
phases = np.array(phases)
inds0 = fix_principal_val(U, phases)
inds1 = np.setdiff1d(np.arange(ns), inds0)
assert (np.allclose(np.arange(ns), np.sort(np.hstack((inds0, inds1)))))
phi0 = phases[inds0, :].mean(axis=0)
z0 = lstsq(np.dot(zfA1.T, zfA1), np.dot(zfA1.T, phi0), rcond=None)[0]
phases -= phi0.reshape(1, -1)
LOG.info(f'calibrate(): Computing phases {time() - t1:.1f}')
if status_cb:
status_cb('Computing least-squares matrices ...')
t1 = time()
nphi = phases.shape[1]
uiuiT = np.zeros((nu, nu))
phiiuiT = np.zeros((nphi, nu))
for i in inds1:
uiuiT += np.dot(U[:, [i]], U[:, [i]].T)
phiiuiT += np.dot(phases[[i], :].T, U[:, [i]].T)
LOG.info(
f'calibrate(): Computing least-squares matrices {time() - t1:.1f}')
if status_cb:
status_cb('Solving least-squares ...')
t1 = time()
A = np.dot(zfA1.T, zfA1)
C = np.dot(zfA1.T, phiiuiT)
B = uiuiT
U1 = cholesky(A, lower=False, overwrite_a=True)
Y = solve_triangular(U1, C, trans='T', lower=False)
D = solve_triangular(U1, Y, trans='N', lower=False)
U2 = cholesky(B, lower=False, overwrite_a=True)
YT = solve_triangular(U2, D.T, trans='T', lower=False)
XT = solve_triangular(U2, YT, trans='N', lower=False)
H = XT.T
def vaf(y, ye):
return 100 * (1 - np.var(y - ye, axis=1) / np.var(y, axis=1))
mvaf = vaf(phases.T, zfA1 @ H @ U)
LOG.info(f'calibrate(): Solving least-squares {time() - t1:.1f}')
if status_cb:
status_cb('Applying regularisation ...')
t1 = time()
if alpha > 0.:
# weighted least squares
rr = np.sqrt(xx**2 + yy**2)
win = .5 * (1 + np.cos(np.pi * ((2 * rr /
(alpha) - 2 / alpha + 1))))
win[rr < 1 - alpha / 2] = 1
win[rr >= 1] = 0
stds = np.zeros(nu)
for i in range(nu):
ind = np.where(U[i, :] == U.max())[0][0]
stds[i] = np.std(phases[ind] * win[zfm])
stds -= stds.min()
stds /= stds.max()
assert (stds.min() == 0.)
assert (stds.max() == 1.)
C = np.dot(pinv(lambda1 * np.diag(1 - stds) + np.dot(H.T, H)), H.T)
else:
C = np.linalg.pinv(H)
uflat = -np.dot(C, z0)
self.fringe = fringe
self.shape = shape
self.H = H
self.mvaf = mvaf
self.phi0 = phi0
self.z0 = z0
self.uflat = uflat
self.C = C
self.alpha = alpha
self.lambda1 = lambda1
self.wavelength = wavelength
self.dm_serial = dm_serial
self.dm_transform = dm_transform
self.cam_pixel_size = cam_pixel_size
self.cam_serial = cam_serial
self.dmplot_txs = dmplot_txs
self.dname = dname
self.hash1 = hash1
LOG.info(f'calibrate(): Applying regularisation {time() - t1:.1f}')
import matplotlib.pyplot as plt
import numpy as np
from zernike import RZern
if __name__ == '__main__':
plt.close('all')
fs = 7
fs1 = 6
cart = RZern(6)
L, K = 300, 300
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)
c = np.zeros(cart.nk)
ns = np.unique(cart.ntab)
fig = plt.figure(1)
nradial = 6
span = 0.05
leftoff = .03
while nradial >= 0:
nk = (nradial + 1) * (nradial + 2) // 2
nrows = nradial + 1
ncols = np.where(cart.ntab == nradial)[0].size
height1 = (1 - (nrows + 1) * span) / nrows
width1 = (1 - (ncols + 1) * span) / ncols
min1 = min(width1, height1)
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)
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)