class TestZern(unittest.TestCase):
def setUp(self):
self.pupil = RZern(4)
self.max_enorm = 1e-9
self.max_ip_err = 5e-2
def test_ntab(self):
self.assertTrue(self.pupil.ntab.size == 15)
expect = np.array([0, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4])
self.assertTrue(norm(self.pupil.ntab - expect) < self.max_enorm)
def test_mtab(self):
self.assertTrue(self.pupil.mtab.size == 15)
expect = np.array([0, 1, -1, 0, -2, 2, -1, 1, -3, 3, 0, 2, -2, 4, -4])
self.assertTrue(norm(self.pupil.mtab - expect) < self.max_enorm)
def test_rhotab(self):
expect = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 13.416407864999,
12.649110640674, 12.649110640674, 3.1622776601684,
3.1622776601684, 0, 0, 0, 0, 0, 0, 8.4852813742386,
8.4852813742386, 2.8284271247462, 2.8284271247462,
0, 0, 0, 0, 0, 0, 0, 0, 3.4641016151378,
2.4494897427832, 2.4494897427832, 0, 0, 0, 0,
-13.416407864999, -9.4868329805051, -9.4868329805051,
0, 0, 0, 2, 2, 0, 0, 0, -5.6568542494924,
-5.6568542494924, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
-1.7320508075689, 0, 0, 0, 0, 0, 0, 2.2360679774998,
0, 0, 0, 0
]
for c, v in enumerate(expect):
i, j = c % 15, c // 15
err = abs(self.pupil.coefnorm[i]*self.pupil.rhotab[i, j] - v)
self.assertTrue(err < self.max_enorm)
def test_Rnm(self):
inrho = [
0.19343375193909, 0.75442458148825, 0.34626071666477,
0.41862541430271, 0.15571983035489, 0.81900058392684,
0.62492352784628, 0.73856042161065, 0.80511242070695,
0.067222657563179, 0.95079031650557, 0.49757701102216,
0.75514590470052, 0.74240507067694, 0.83112957571011,
0.1565018467502, 0.45730872055141, 0.61810049609438,
0.93218335504705, 0.83508823555935, 0.89542351634235,
0.58251852466803, 0.58274680636179, 0.85492593896995,
0.034865700047931, 0.8854200954848, 0.40773081135598,
0.036382030447832, 0.74614794273772, 0.15482877075111
]
out3 = [
-1.6024358463019, 0.2395649672106, -1.3167172040235,
-1.1249765690963, -1.6480509660176, 0.59153676922049,
-0.37921722803669, 0.157517884017, 0.5134006785331,
-1.716396928352, 1.3995047634699, -0.87439854650643,
0.24333698669463, 0.177241760149, 0.66086873705921,
-1.6472051624093, -1.0075988516153, -0.40859694027142,
1.2781350494377, 0.68371791511127, 1.0454079255648,
-0.55658471812649, -0.55566323680867, 0.79985538570113,
-1.7278397866178, 0.98369658989472, -1.1561632626913,
-1.7274655420545, 0.19654187580572, -1.6490095429101
]
out4 = [
0.091651618055082, 1.3941428842409, 0.29368520752156,
0.42926631070763, 0.0593968575795, 1.6430245322286,
0.95659779790408, 1.3361268353382, 1.5877739726481,
0.011068964146113, 2.214344179944, 0.60645172969723,
1.3968101047969, 1.3500737219024, 1.6920496368403,
0.059994931046482, 0.51226489069861, 0.93582320415359,
2.1285228321212, 1.7082064455855, 1.9639599046646,
0.83118004289954, 0.83183162858811, 1.7903280385894,
0.0029776414702216, 1.9203234007362, 0.4072139881838,
0.0032422723387354, 1.3637209645609, 0.058719041358534
]
out10 = [
1.7528544047959, -1.0538684824995, 0.82035094947331,
0.29691883664534, 1.9186268081509, -0.72681628778909,
-0.95725432162119, -1.0902934593531, -0.82334230297547,
2.1757147314369, 1.0717625678433, -0.2632155912034,
-1.0518319661711, -1.082911351763, -0.62973545308711,
1.915510691432, 0.017056106891689, -0.93137658244662,
0.7084189870086, -0.59538643046011, 0.10384045987001,
-0.7716820983231, -0.77282798892389, -0.40275090649042,
2.2197785892442, -0.036158358505776, 0.37645712628727,
2.2183328268091, -1.0748457792076, 1.9221603389389
]
out13 = [
0.0044271987866637, 1.0243852641201, 0.0454582689999,
0.09711858840943, 0.0018594122493614, 1.4227770316249,
0.48228916269284, 0.9409014176698, 1.3286974659506,
6.4574746661877e-05, 2.5842766270514, 0.19383902995264,
1.0283086425533, 0.96064675024684, 1.5089503417078,
0.0018970460208737, 0.13830501642735, 0.4615687191036,
2.3878408401262, 1.5379048343826, 2.0328904888789,
0.36411532970706, 0.3646864342091, 1.6893279835042,
4.6729760834851e-06, 1.9435579666012, 0.08739651710603,
5.540484342947e-06, 0.98016633844227, 0.0018172164647309
]
outi = [3, 4, 10, 13]
outl = [out3, out4, out10, out13]
for c, i in enumerate(outi):
for j in range(len(inrho)):
a = self.pupil.coefnorm[i]*self.pupil.Rnm(i, inrho[j])
b = outl[c][j]
err = abs(a - b)
self.assertTrue(err < self.max_enorm)
def test_Zj(self):
rho = [
0, 0.11111111111111, 0.22222222222222,
0.33333333333333, 0.44444444444444,
0.55555555555556, 0.66666666666667,
0.77777777777778, 0.88888888888889, 1
]
theta = [
0, 0.69813170079773, 1.3962634015955,
2.0943951023932, 2.7925268031909,
3.4906585039887, 4.1887902047864,
4.8869219055841, 5.5850536063819,
6.2831853071796
]
c = [
0.54031374569774, 0.99075098653197, 0.98937811670199,
-0.68884118291816, -0.85683760660055, 0.048390017659746,
-0.66485340286758, 1.4527419609898, 1.3798457521444,
0.095136418969694, -0.42712706398395, 0.51080780984162,
-0.65627362438046, -0.12501677594865, -0.53046670030726
]
z = [
0.77833652275983, 0.085651919598506, -0.53679726827283,
-0.98349975448282, -1.1477173085559, -0.92148475591224,
-0.19560997786786, 1.1403260883655, 3.1979694496802,
6.090193057073, 0.77833652275983, 0.74923429103286,
0.92966992705808, 1.3229751043261, 1.8849792604988,
2.524009597409, 3.1008910810612, 3.4289464416307,
3.2739961734642, 2.3543585350795, 0.77833652275983,
1.3880071769557, 2.1381716926469, 2.8957139599519,
3.4767967033161, 3.6468614815119, 3.1206286876391,
1.5620975491243, -1.4154538722785, -6.2494686804882,
0.77833652275983, 1.6882064144334, 2.5555787288029,
3.2751193339259, 3.7304210304525, 3.7940035516249,
3.3273135632778, 2.1807246638382, 0.19353738432559,
-2.8060208116485, 0.77833652275983, 1.5982868830036,
2.2744826664559, 2.7560978140881, 3.0147604484415,
3.0445528736273, 2.8620115753266, 2.5061272207906,
2.0383446588402, 1.5425629198664, 0.77833652275983,
1.2214350933728, 1.5980879771486, 1.7569084557018,
1.5178403949563, 0.67215824514518, -1.0175329591891,
-3.8172975991948, -8.0218694717109, -13.954651789267,
0.77833652275983, 0.68350110425162, 0.77861386147368,
0.97581235178894, 1.1341939908368, 1.0598160525329,
0.50569566906943, -0.82819016908496, -3.2949046131853,
-7.3005509562103, 0.77833652275983, 0.12208636455387,
-0.30877246905582, -0.48094227889849, -0.39896357657152,
-0.10521508444048, 0.32008626436092, 0.75888532593079,
1.0552887455991, 1.0155649579277, 0.77833652275983,
-0.16765298701795, -1.0634255875761, -1.8055846686938,
-2.2742293038136, -2.3329542500415, -1.8288499481469,
-0.59250252256298, 1.5620062186141, 4.8370987836243,
0.77833652275983, 0.085651919598506, -0.53679726827283,
-0.98349975448283, -1.1477173085559, -0.92148475591224,
-0.19560997786786, 1.1403260883655, 3.1979694496802,
6.090193057073
]
count = 0
for th in theta:
for rh in rho:
zv = 0.0
for j, ci in enumerate(c):
zv += ci*self.pupil.Zk(j, rh, th)
err = abs(zv - z[count])
self.assertTrue(err <= self.max_enorm)
count += 1
def test_eval_a(self):
rho = [
0, 0.11111111111111, 0.22222222222222,
0.33333333333333, 0.44444444444444,
0.55555555555556, 0.66666666666667,
0.77777777777778, 0.88888888888889, 1
]
theta = [
0, 0.69813170079773, 1.3962634015955,
2.0943951023932, 2.7925268031909,
3.4906585039887, 4.1887902047864,
4.8869219055841, 5.5850536063819,
6.2831853071796
]
c = [
0.54031374569774, 0.99075098653197, 0.98937811670199,
-0.68884118291816, -0.85683760660055, 0.048390017659746,
-0.66485340286758, 1.4527419609898, 1.3798457521444,
0.095136418969694, -0.42712706398395, 0.51080780984162,
-0.65627362438046, -0.12501677594865, -0.53046670030726
]
phi = [
0.77833652275983, 0.085651919598506, -0.53679726827283,
-0.98349975448282, -1.1477173085559, -0.92148475591224,
-0.19560997786786, 1.1403260883655, 3.1979694496802,
6.090193057073, 0.77833652275983, 0.74923429103286,
0.92966992705808, 1.3229751043261, 1.8849792604988,
2.524009597409, 3.1008910810612, 3.4289464416307,
3.2739961734642, 2.3543585350795, 0.77833652275983,
1.3880071769557, 2.1381716926469, 2.8957139599519,
3.4767967033161, 3.6468614815119, 3.1206286876391,
1.5620975491243, -1.4154538722785, -6.2494686804882,
0.77833652275983, 1.6882064144334, 2.5555787288029,
3.2751193339259, 3.7304210304525, 3.7940035516249,
3.3273135632778, 2.1807246638382, 0.19353738432559,
-2.8060208116485, 0.77833652275983, 1.5982868830036,
2.2744826664559, 2.7560978140881, 3.0147604484415,
3.0445528736273, 2.8620115753266, 2.5061272207906,
2.0383446588402, 1.5425629198664, 0.77833652275983,
1.2214350933728, 1.5980879771486, 1.7569084557018,
1.5178403949563, 0.67215824514518, -1.0175329591891,
-3.8172975991948, -8.0218694717109, -13.954651789267,
0.77833652275983, 0.68350110425162, 0.77861386147368,
0.97581235178894, 1.1341939908368, 1.0598160525329,
0.50569566906943, -0.82819016908496, -3.2949046131853,
-7.3005509562103, 0.77833652275983, 0.12208636455387,
-0.30877246905582, -0.48094227889849, -0.39896357657152,
-0.10521508444048, 0.32008626436092, 0.75888532593079,
1.0552887455991, 1.0155649579277, 0.77833652275983,
-0.16765298701795, -1.0634255875761, -1.8055846686938,
-2.2742293038136, -2.3329542500415, -1.8288499481469,
-0.59250252256298, 1.5620062186141, 4.8370987836243,
0.77833652275983, 0.085651919598506, -0.53679726827283,
-0.98349975448283, -1.1477173085559, -0.92148475591224,
-0.19560997786786, 1.1403260883655, 3.1979694496802,
6.090193057073
]
count = 0
for th in theta:
for rh in rho:
phi2 = self.pupil.eval_a(c, rh, th)
err = abs(phi2 - phi[count])
self.assertTrue(err <= self.max_enorm)
count += 1
def test_rhoitab(self):
z = self.pupil
for k in range(z.nk):
for i in range(z.n + 1):
a = (z.rhoitab[k, i])*(z.n + 2 - i)
b = z.rhotab[k, i]
self.assertTrue(abs(a - b) < self.max_enorm)
def test_eval_grid(self):
log = logging.getLogger('TestZern.test_eval_grid')
z = self.pupil
rho_j = np.array([
0, 0.11111111111111, 0.22222222222222,
0.33333333333333, 0.44444444444444,
0.55555555555556, 0.66666666666667,
0.77777777777778, 0.88888888888889, 1])
theta_i = np.array([
0, 0.69813170079773, 1.3962634015955,
2.0943951023932, 2.7925268031909,
3.4906585039887, 4.1887902047864,
4.8869219055841, 5.5850536063819,
6.2831853071796])
a = normal(size=z.nk)
t1 = time()
Phi1 = [z.eval_a(a, rh, th) for rh in rho_j for th in theta_i]
t2 = time()
log.debug('list eval {:.6f}'.format(t2 - t1))
z.make_pol_grid(rho_j, theta_i)
t1 = time()
Phi2 = z.eval_grid(np.array(a))
t2 = time()
log.debug('numpy eval {:.6f}'.format(t2 - t1))
Phi1 = np.array(Phi1).ravel(order='F')
Phi2 = np.array(Phi2).ravel(order='F')
log.debug('norm(Phi1 - Phi2) {}'.format(norm(Phi1 - Phi2)))
self.assertTrue(norm(Phi1 - Phi2) < self.max_enorm)
def test_save_load(self):
rho_j = np.array([
0, 0.11111111111111, 0.22222222222222,
0.33333333333333, 0.44444444444444,
0.55555555555556, 0.66666666666667,
0.77777777777778, 0.88888888888889, 1])
theta_i = np.array([
0, 0.69813170079773, 1.3962634015955,
2.0943951023932, 2.7925268031909,
3.4906585039887, 4.1887902047864,
4.8869219055841, 5.5850536063819,
6.2831853071796])
def do_test(cls, complex_a):
rz1 = cls(4)
if complex_a:
a = normal(size=rz1.nk) + 1j*normal(size=rz1.nk)
else:
a = normal(size=rz1.nk)
rz1.make_pol_grid(rho_j, theta_i)
PhiA = rz1.eval_grid(a)
# create tmp path
tmpfile = NamedTemporaryFile()
tmppath = tmpfile.name
tmpfile.close()
rz1.save(tmppath)
rz2 = cls.load(tmppath)
PhiB = rz2.eval_grid(a)
self.assertTrue(norm(PhiA - PhiB) < self.max_enorm)
self.assertTrue(type(rz1) == type(rz2))
self.assertTrue(norm(rz1.coefnorm - rz2.coefnorm) == 0)
self.assertTrue(norm(rz1.ntab - rz2.ntab) == 0)
self.assertTrue(norm(rz1.mtab - rz2.mtab) == 0)
self.assertTrue(rz1.n == rz2.n)
self.assertTrue(rz1.nk == rz2.nk)
self.assertTrue(rz1.normalise == rz2.normalise)
self.assertTrue(norm(rz1.rhoitab - rz2.rhoitab) == 0)
self.assertTrue(norm(rz1.rhotab - rz2.rhotab) == 0)
self.assertTrue(rz1.numpy_dtype == rz2.numpy_dtype)
self.assertTrue(norm(rz1.ZZ - rz2.ZZ) == 0)
os.unlink(tmppath)
del rz1, rz2, a, PhiA, PhiB
do_test(RZern, False)
do_test(CZern, True)
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 test_normalisations_complex(self):
log = logging.getLogger('TestZern.test_normalisations_complex')
n_beta = 6
L, K = 400, 393
# polar grid
pol = CZern(n_beta)
fitBeta = FitZern(pol, L, K)
t1 = time()
pol.make_pol_grid(fitBeta.rho_j, fitBeta.theta_i)
t2 = time()
log.debug('make pol grid {:.6f}'.format(t2 - t1))
# cartesian grid
cart = CZern(n_beta)
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_beta, 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].conj()))
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 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)