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main_vlbi.py
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main_vlbi.py
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import os
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
from keras.callbacks import ModelCheckpoint
from keras.initializers import RandomUniform, Constant
import keras.models
import keras.layers
import keras.initializers
import keras.regularizers
import keras.callbacks
from keras import backend as K
from keras import losses
from keras.datasets import fashion_mnist
from keras.datasets import mnist
# from loupe import models_vlbi, layers_vlbi # loupe package
import ehtim as eh # eht imaging package
from ehtim.observing.obs_helpers import *
from scipy.ndimage import gaussian_filter
import skimage.transform
# import helpers as hp
import csv
import sys
import datetime
import warnings
from models_posci import IsingVisNet, IsingCpAmpNet, IsingMutipleVisNet, IsingMutipleCpAmpNet, IsingVisFeatureNet, IsingCpAmpFeatureNet
from losses_posci import site_sparsity, energy, Lambda_similarity, Lambda_angle_diff
from data_augmentation import elastic_transform
import gc
# mute the verbose warnings
tf.logging.set_verbosity(tf.logging.ERROR)
warnings.filterwarnings("ignore")
# initialize GPU
K.clear_session()
gpu_id = 0
gpu = '/gpu:' + str(gpu_id)
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
set_session(tf.Session(config=config))
plt.ion()
# import numpy as np
def observe_prob(params,limit):
return(1./(1.+np.exp(-params[0]*(limit-params[1]))))
cutoff = 3.0
with open('cdf_params.txt','r') as fi:
lines = fi.readlines()
cdf_params = {}
obs_prob = {}
for line in lines:
cdf_params[line.split('\t')[0]] = np.array([float(line.split('\t')[1]),float(line.split('\t')[2].rstrip())])
obs_prob[line.split('\t')[0]] = observe_prob(cdf_params[line.split('\t')[0]],cutoff)
print(obs_prob)
nsamp = 10000 #100000
def Prepare_EHT_Data(fov_param, flux_label, blur_param, sefd_param, eht_array='eht2019', target='m87', data_augmentation=False, npix=32):
"""
Prepare the EHT training data for learning a probabilistic sensing for computational imaging!
fov_param: field of view
flux_label: 0 represents varying flux, 1 represents constant flux
blur_param: fraction of nominal resolution
"""
add_th_noise = False # False if you *don't* want to add thermal error. If there are no sefds in obs_orig it will use the sigma for each data point
phasecal = True # True if you don't want to add atmospheric phase error. if False then it adds random phases to simulate atmosphere
ampcal = True # True if you don't want to add atmospheric amplitude error. if False then add random gain errors
stabilize_scan_phase = False # if true then add a single phase error for each scan to act similar to adhoc phasing
stabilize_scan_amp = False # if true then add a single gain error at each scan
jones = False # apply jones matrix for including noise in the measurements (including leakage)
inv_jones = False # no not invert the jones matrix
frcal = True # True if you do not include effects of field rotation
dcal = True # True if you do not include the effects of leakage
dterm_offset = 0.05 # a random offset of the D terms is given at each site with this standard deviation away from 1
dtermp = 0
tint_sec = 10
tadv_sec = 600
tstart_hr = 0
tstop_hr = 24
bw_hz = 4e9
###############################################################################
# define EHT array
###############################################################################
if eht_array == 'eht2019':
# please change this to the folder of ehtim
# array = '/home/groot/BoumanLab/eht-imaging/arrays/EHT2019.txt'
array = '/home/groot/BoumanLab/eht-imaging/arrays/EHT2019_no_carma.txt'
elif eht_array == 'eht2025':
# please change this to the folder of ehtim
array = '/home/groot/BoumanLab/eht-imaging/arrays/EHT2025.txt'
eht = eh.array.load_txt(array)
###############################################################################
# define observation field of view
###############################################################################
fov = fov_param * eh.RADPERUAS
###############################################################################
# define scientific target
###############################################################################
if target == 'm87':
ra = 12.513728717168174
dec = 12.39112323919932
elif target == 'sgrA':
ra = 19.414182210498385
dec = -29.24170032236311
###############################################################################
# generate the discrete Fourier transform matrix for complex visibilities
###############################################################################
rf = 230e9
# npix = 32
mjd = 57853 # day of observation
simim = eh.image.make_empty(npix, fov, ra, dec, rf=rf, source='random', mjd=mjd)
simim.imvec = np.zeros((npix, npix, 1)).reshape((-1, 1))#xdata[0, :, :, :].reshape((-1, 1))
obs = simim.observe(eht, tint_sec, tadv_sec, tstart_hr, tstop_hr, bw_hz, add_th_noise=add_th_noise, ampcal=ampcal, phasecal=phasecal,
stabilize_scan_phase=stabilize_scan_phase, stabilize_scan_amp=stabilize_scan_amp,
jones=jones,inv_jones=inv_jones,dcal=dcal, frcal=frcal, dterm_offset=dterm_offset)
obs_data = obs.unpack(['u', 'v', 'vis', 'sigma'])
uv = np.hstack((obs_data['u'].reshape(-1,1), obs_data['v'].reshape(-1,1)))
F = ftmatrix(simim.psize, simim.xdim, simim.ydim, uv, pulse=simim.pulse)
t1 = obs.data['t1']
t2 = obs.data['t2']
n_sites = np.unique(np.concatenate([t1, t2])).shape[0] + 1
###############################################################################
# generate the discrete Fourier transform matrices for closure phases
###############################################################################
# obs.add_cphase(count='min')
obs.add_cphase(count='max')
tc1 = obs.cphase['t1']
tc2 = obs.cphase['t2']
tc3 = obs.cphase['t3']
cphase_map = np.zeros((len(obs.cphase['time']), 3))
zero_symbol = 10000
for k1 in range(cphase_map.shape[0]):
for k2 in list(np.where(obs.data['time']==obs.cphase['time'][k1])[0]):
if obs.data['t1'][k2] == obs.cphase['t1'][k1] and obs.data['t2'][k2] == obs.cphase['t2'][k1]:
cphase_map[k1, 0] = k2
if k2 == 0:
cphase_map[k1, 0] = zero_symbol
elif obs.data['t2'][k2] == obs.cphase['t1'][k1] and obs.data['t1'][k2] == obs.cphase['t2'][k1]:
cphase_map[k1, 0] = -k2
if k2 == 0:
cphase_map[k1, 0] = -zero_symbol
elif obs.data['t1'][k2] == obs.cphase['t2'][k1] and obs.data['t2'][k2] == obs.cphase['t3'][k1]:
cphase_map[k1, 1] = k2
if k2 == 0:
cphase_map[k1, 1] = zero_symbol
elif obs.data['t2'][k2] == obs.cphase['t2'][k1] and obs.data['t1'][k2] == obs.cphase['t3'][k1]:
cphase_map[k1, 1] = -k2
if k2 == 0:
cphase_map[k1, 1] = -zero_symbol
elif obs.data['t1'][k2] == obs.cphase['t3'][k1] and obs.data['t2'][k2] == obs.cphase['t1'][k1]:
cphase_map[k1, 2] = k2
if k2 == 0:
cphase_map[k1, 2] = zero_symbol
elif obs.data['t2'][k2] == obs.cphase['t3'][k1] and obs.data['t1'][k2] == obs.cphase['t1'][k1]:
cphase_map[k1, 2] = -k2
if k2 == 0:
cphase_map[k1, 2] = -zero_symbol
F_cphase = np.zeros((cphase_map.shape[0], npix*npix, 3), dtype=np.complex64)
cphase_proj = np.zeros((cphase_map.shape[0], F.shape[0]), dtype=np.float32)
for k in range(cphase_map.shape[0]):
for j in range(cphase_map.shape[1]):
if cphase_map[k][j] > 0:
if int(cphase_map[k][j]) == zero_symbol:
cphase_map[k][j] = 0
F_cphase[k, :, j] = F[int(cphase_map[k][j]), :]
cphase_proj[k, int(cphase_map[k][j])] = 1
else:
if np.abs(int(cphase_map[k][j])) == zero_symbol:
cphase_map[k][j] = 0
F_cphase[k, :, j] = np.conj(F[int(-cphase_map[k][j]), :])
cphase_proj[k, int(-cphase_map[k][j])] = -1
###############################################################################
# load the data
###############################################################################
# nsamp = 10000
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
(x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = mnist.load_data()
# xdata_train = 1.0*x_train[0:int(nsamp*0.7)]
xdata_train = 1.0*x_train[[k%60000 for k in range(int(nsamp*0.7))]]
if data_augmentation:
rot_random = np.random.rand(int(nsamp*0.7)) * 360
for k in range(int(nsamp*0.7)):
im = np.expand_dims(xdata_train[k], -1)
im = np.concatenate([im, np.zeros(im.shape)], -1)
# im_deform = elastic_transform(im, 15, 2, 0.5)
im_deform = elastic_transform(im, 20, 2, 0.5)
xdata_train[k] = im_deform[:, :, 0]
xdata_train[k] = skimage.transform.rotate(xdata_train[k], rot_random[k])
xdata_train = np.pad(xdata_train, ((0,0), (2,2), (2,2)), 'constant') # get to 32x32
xdata_train = xdata_train[..., np.newaxis]/255
# xdata_test = 1.0*x_train_mnist[0:int(nsamp*0.3)]
xdata_test = 1.0*x_train_mnist[[k%60000 for k in range(int(nsamp*0.3))]]
xdata_test = np.pad(xdata_test, ((0,0), (2,2), (2,2)), 'constant') # get to 32x32
xdata_test = xdata_test[..., np.newaxis]/255
for k in range(int(0.3*nsamp)):
xdata_test[k] = 2.2 * gaussian_filter(xdata_test[k], 2)
# res = obs.res()
# for k in range(int(0.3*nsamp)):
# simim.imvec = xdata_test[k, :, :, :].reshape((-1, 1))
# im_out = simim.blur_circ(0.3*res)
# xdata_test[k, :, :, 0] = im_out.imvec.reshape((32, 32))
xdata = np.concatenate([xdata_train, xdata_test], 0)
###############################################################################
# define the flux label
###############################################################################
if flux_label != 0:
for k in range(nsamp):
xdata[k] = 224.46*xdata[k] / np.sum(xdata[k])
###############################################################################
# define blurry effect: the fraction of the nominal resolution
###############################################################################
xdata_blur = np.zeros(xdata.shape)
res = obs.res()
for k in range(xdata.shape[0]):
simim.imvec = xdata[k, :, :, :].reshape((-1, 1))
im_out = simim.blur_circ(blur_param*res)
xdata_blur[k, :, :, 0] = im_out.imvec.reshape((32, 32))
###############################################################################
# thermal noises: 0 represents no thermal noises, 1 represents site-varying thermal noises, 2 represents site-equivalent thermal noises
###############################################################################
if sefd_param == 1:
sigma = 224.46 * np.concatenate([np.expand_dims(obs.data['sigma'], -1), np.expand_dims(obs.data['sigma'], -1)], -1)
elif sefd_param == 2:
sigma = 224.46 * np.concatenate([np.expand_dims(obs.data['sigma'], -1), np.expand_dims(obs.data['sigma'], -1)], -1)
sigma = np.mean(sigma.reshape((-1, ))) * np.ones(sigma.shape)
else:
sigma = None
return xdata, xdata_blur, t1, t2, F, tc1, tc2, tc3, F_cphase, cphase_proj, sigma
def Train_IsingVisNet(eht_array, target, fov_param, flux_label, blur_param, sefd_param, lr, nb_epochs_train, sample_weight, ising_weight, batch_size = 32, n_ising_layers = 5, models_dir='../joint_opt/models/anti-aliasing/12302019/', savefile_name='nn_params', weather=False):
###############################################################################
# prepare the training data
###############################################################################
xdata, xdata_blur, t1, t2, F, tc1, tc2, tc3, F_cphase, cphase_proj, sigma = Prepare_EHT_Data(fov_param, flux_label, blur_param, sefd_param, eht_array, target)
n_sites = np.unique(np.concatenate([t1, t2])).shape[0] + 1
###############################################################################
# define the model
###############################################################################
# nsamp = 10000
# model = IsingVisNet(t1, t2, F, n_ising_layers=n_ising_layers, slope_const=3, sigma=sigma)
if weather:
model = IsingVisNet(t1, t2, F, n_ising_layers=n_ising_layers, slope_const=3, obs_prob=obs_prob)
# model = IsingCpAmpNet(t1, t2, tc1, tc2, tc3, F, F_cphase, cphase_proj, n_ising_layers=n_ising_layers, slope_const=3, sigma=sigma, obs_prob=obs_prob)
else:
model = IsingVisNet(t1, t2, F, n_ising_layers=n_ising_layers, slope_const=3)
# model = IsingCpAmpNet(t1, t2, tc1, tc2, tc3, F, F_cphase, cphase_proj, n_ising_layers=n_ising_layers, slope_const=3, sigma=sigma)
adam_opt = keras.optimizers.Adam(lr=lr, beta_1=0.9, beta_2=0.999, amsgrad=False)
recon_weight = 1.0
model.compile(optimizer=adam_opt, loss={'recon': 'mean_absolute_error', 'sampling': site_sparsity, 'ising': energy},
loss_weights={'recon': recon_weight, 'sampling': sample_weight, 'ising': ising_weight})
# modelname = os.path.join(models_dir, savefile_name+'best.h5')
checkpoint = ModelCheckpoint(models_dir+savefile_name+'best.hdf5', monitor='val_loss', verbose=1, save_best_only=True, save_weights_only=True, mode='auto', period=1)
class Save_weights(keras.callbacks.Callback):
def on_epoch_begin(self, epoch, logs=None):
model.save_weights(os.path.join(models_dir, savefile_name+'weights{:02d}.hdf5'.format(epoch)))
history = model.fit({'input': xdata}, {'recon': xdata_blur, 'sampling': np.zeros((nsamp, n_sites)), 'ising': np.zeros((nsamp, ))},
validation_split=0.3,
initial_epoch=1,
epochs=1 + nb_epochs_train,
batch_size=batch_size,
verbose=1,
callbacks=[checkpoint])#,
modelname = os.path.join(models_dir, savefile_name+'.h5')
model.save_weights(modelname)
w = csv.writer(open(os.path.join(models_dir, 'history_'+savefile_name+'.csv'), 'w'))
for key, val in history.history.items():
w.writerow([key, val])
del history
return model
def Train_IsingCpAmpNet(eht_array, target, fov_param, flux_label, blur_param, sefd_param, lr, nb_epochs_train, sample_weight, ising_weight, batch_size = 32, n_ising_layers = 5, models_dir='../joint_opt/models/anti-aliasing/12302019/', savefile_name='nn_params', weather=False):
###############################################################################
# prepare the training data
###############################################################################
xdata, xdata_blur, t1, t2, F, tc1, tc2, tc3, F_cphase, cphase_proj, sigma = Prepare_EHT_Data(fov_param, flux_label, blur_param, sefd_param, eht_array, target, data_augmentation=True)
n_sites = np.unique(np.concatenate([t1, t2])).shape[0] + 1
###############################################################################
# define the model
###############################################################################
# nsamp = 10000
vis = np.matmul(xdata.reshape((-1, 32*32)), np.transpose(F)).astype(np.complex64)
vis1 = np.matmul(xdata.reshape((-1, 32*32)), np.transpose(F_cphase[:, :, 0])).astype(np.complex64)
vis2 = np.matmul(xdata.reshape((-1, 32*32)), np.transpose(F_cphase[:, :, 1])).astype(np.complex64)
vis3 = np.matmul(xdata.reshape((-1, 32*32)), np.transpose(F_cphase[:, :, 2])).astype(np.complex64)
vis_concat = np.concatenate([vis.real, vis.imag], -1)
cphase = np.angle(vis1 * vis2 * vis3) * 180 / np.pi
vis_amp = np.abs(vis)
vis_angle = np.angle(vis) * 180 / np.pi
if weather:
# model = IsingVisNet(t1, t2, F, n_ising_layers=n_ising_layers, slope_const=3)
model = IsingCpAmpNet(t1, t2, tc1, tc2, tc3, F, F_cphase, cphase_proj, n_ising_layers=n_ising_layers, slope_const=3, sigma=sigma, obs_prob=obs_prob)
else:
model = IsingCpAmpNet(t1, t2, tc1, tc2, tc3, F, F_cphase, cphase_proj, n_ising_layers=n_ising_layers, slope_const=3, sigma=sigma)
adam_opt = keras.optimizers.Adam(lr=lr, beta_1=0.9, beta_2=0.999, amsgrad=False)
recon_weight = 1.0
vis_weight = 0.0 #2e-2
cphase_weight = 1e-1
# model.compile(optimizer=adam_opt, loss={'recon': 'mean_absolute_error', 'sampling': site_sparsity, 'ising': energy},
# loss_weights={'recon': recon_weight, 'sampling': sample_weight, 'ising': ising_weight})
model.compile(optimizer=adam_opt, loss={'recon': Lambda_similarity, 'sampling': site_sparsity, 'ising': energy, 'vis_angle_pred': Lambda_angle_diff, 'cphase_pred': Lambda_angle_diff},
loss_weights={'recon': recon_weight, 'sampling': sample_weight, 'ising': ising_weight, 'vis_angle_pred': vis_weight, 'cphase_pred': cphase_weight})
# modelname = os.path.join(models_dir, savefile_name+'best.h5')
checkpoint = ModelCheckpoint(models_dir+savefile_name+'best.hdf5', monitor='val_loss', verbose=1, save_best_only=True, save_weights_only=True, mode='auto', period=1)
class Save_weights(keras.callbacks.Callback):
def on_epoch_begin(self, epoch, logs=None):
model.save_weights(os.path.join(models_dir, savefile_name+'weights{:02d}.hdf5'.format(epoch)))
history = model.fit({'input': xdata}, {'recon': xdata_blur, 'sampling': np.zeros((nsamp, n_sites)), 'ising': np.zeros((nsamp, )), 'vis_angle_pred': vis_angle, 'cphase_pred': cphase},
validation_split=0.3,
initial_epoch=1,
epochs=1 + nb_epochs_train,
batch_size=batch_size,
verbose=1,
callbacks=[checkpoint])
modelname = os.path.join(models_dir, savefile_name+'.h5')
model.save_weights(modelname)
w = csv.writer(open(os.path.join(models_dir, 'history_'+savefile_name+'.csv'), 'w'))
for key, val in history.history.items():
w.writerow([key, val])
K.clear_session()
del history, checkpoint, adam_opt
del xdata, xdata_blur, t1, t2, F, tc1, tc2, tc3, F_cphase, cphase_proj, sigma
return model
def Train_IsingMutipleVisNet(eht_array, target_list, fov_param, flux_label, blur_param, sefd_param, lr, nb_epochs_train, sample_weight, ising_weight, batch_size = 32, n_ising_layers = 5, models_dir='../joint_opt/models/anti-aliasing/12302019/', savefile_name='nn_params'):
###############################################################################
# prepare the training data
###############################################################################
t1_list = []
t2_list = []
F_list = []
tc1_list = []
tc2_list = []
tc3_list = []
F_cphase_list = []
cphase_proj_list = []
for k in range(len(target_list)):
target = target_list[k]
if k == 0:
xdata, xdata_blur, t1, t2, F, tc1, tc2, tc3, F_cphase, cphase_proj, sigma = Prepare_EHT_Data(fov_param, flux_label, blur_param, sefd_param, eht_array, target)
else:
_, _, t1, t2, F, tc1, tc2, tc3, F_cphase, cphase_proj, _ = Prepare_EHT_Data(fov_param, flux_label, blur_param, sefd_param, eht_array, target)
t1_list.append(t1)
t2_list.append(t2)
F_list.append(F)
tc1_list.append(tc1)
tc2_list.append(tc2)
tc3_list.append(tc3)
F_cphase_list.append(F_cphase)
cphase_proj_list.append(cphase_proj)
n_sites = np.unique(np.concatenate([t1, t2])).shape[0] + 1
###############################################################################
# define the model
###############################################################################
# nsamp = 10000
# model = IsingVisNet(t1, t2, F, n_ising_layers=n_ising_layers, slope_const=3, sigma=sigma)
model = IsingMutipleVisNet(t1_list, t2_list, F_list, n_ising_layers=5, slope_const=1e2, sigma=sigma)
adam_opt = keras.optimizers.Adam(lr=lr, beta_1=0.9, beta_2=0.999, amsgrad=False)
recon_weight = 1.0
loss_dict = {'sampling': site_sparsity, 'ising': energy}
for k in range(len(t1_list)):
loss_dict['recon'+str(k)] = 'mean_absolute_error'
loss_weights_dict = {'sampling': sample_weight, 'ising': ising_weight}
for k in range(len(t1_list)):
loss_weights_dict['recon'+str(k)] = recon_weight / len(t1_list)
# model.compile(optimizer=adam_opt, loss={'recon': 'mean_absolute_error', 'sampling': site_sparsity, 'ising': energy},
# loss_weights={'recon': recon_weight, 'sampling': sample_weight, 'ising': ising_weight})
model.compile(optimizer=adam_opt, loss=loss_dict, loss_weights=loss_weights_dict)
# modelname = os.path.join(models_dir, savefile_name+'best.h5')
checkpoint = ModelCheckpoint(models_dir+savefile_name+'best.hdf5', monitor='val_loss', verbose=1, save_best_only=True, save_weights_only=True, mode='auto', period=1)
class Save_weights(keras.callbacks.Callback):
def on_epoch_begin(self, epoch, logs=None):
model.save_weights(os.path.join(models_dir, savefile_name+'weights{:02d}.hdf5'.format(epoch)))
fitdata_dict = {'sampling': np.zeros((nsamp, n_sites)), 'ising': np.zeros((nsamp, ))}
for k in range(len(t1_list)):
fitdata_dict['recon'+str(k)] = xdata_blur
history = model.fit({'input': xdata}, fitdata_dict,
validation_split=0.3,
initial_epoch=1,
epochs=1 + nb_epochs_train,
batch_size=batch_size,
verbose=1,
callbacks=[checkpoint])#,
modelname = os.path.join(models_dir, savefile_name+'.h5')
model.save_weights(modelname)
w = csv.writer(open(os.path.join(models_dir, 'history_'+savefile_name+'.csv'), 'w'))
for key, val in history.history.items():
w.writerow([key, val])
del history, checkpoint
del xdata, xdata_blur, t1, t2, F, tc1, tc2, tc3, F_cphase, cphase_proj, sigma
return model
def Train_IsingMutipleCpAmpNet(eht_array, target_list, fov_param, flux_label, blur_param, sefd_param, lr, nb_epochs_train, sample_weight, ising_weight, batch_size = 32, n_ising_layers = 5, models_dir='../joint_opt/models/anti-aliasing/12302019/', savefile_name='nn_params'):
###############################################################################
# prepare the training data
###############################################################################
t1_list = []
t2_list = []
F_list = []
tc1_list = []
tc2_list = []
tc3_list = []
F_cphase_list = []
cphase_proj_list = []
for k in range(len(target_list)):
target = target_list[k]
if k == 0:
xdata, xdata_blur, t1, t2, F, tc1, tc2, tc3, F_cphase, cphase_proj, sigma = Prepare_EHT_Data(fov_param, flux_label, blur_param, sefd_param, eht_array, target, data_augmentation=True)
else:
_, _, t1, t2, F, tc1, tc2, tc3, F_cphase, cphase_proj, _ = Prepare_EHT_Data(fov_param, flux_label, blur_param, sefd_param, eht_array, target, data_augmentation=True)
t1_list.append(t1)
t2_list.append(t2)
F_list.append(F)
tc1_list.append(tc1)
tc2_list.append(tc2)
tc3_list.append(tc3)
F_cphase_list.append(F_cphase)
cphase_proj_list.append(cphase_proj)
n_sites = np.unique(np.concatenate([t1, t2])).shape[0] + 1
###############################################################################
# define the model
###############################################################################
# nsamp = 10000
model = IsingMutipleCpAmpNet(t1_list, t2_list, tc1_list, tc2_list, tc3_list, F_list, F_cphase_list, cphase_proj_list, n_ising_layers=n_ising_layers, slope_const=3, sigma=sigma)
adam_opt = keras.optimizers.Adam(lr=lr, beta_1=0.9, beta_2=0.999, amsgrad=False)
recon_weight = 1.0
loss_dict = {'sampling': site_sparsity, 'ising': energy}
for k in range(len(t1_list)):
loss_dict['recon'+str(k)] = Lambda_similarity
loss_weights_dict = {'sampling': sample_weight, 'ising': ising_weight}
for k in range(len(t1_list)):
loss_weights_dict['recon'+str(k)] = recon_weight / len(t1_list)
model.compile(optimizer=adam_opt, loss=loss_dict, loss_weights=loss_weights_dict)
# model.compile(optimizer=adam_opt, loss={'recon': Lambda_similarity, 'sampling': site_sparsity, 'ising': energy},
# loss_weights={'recon': recon_weight, 'sampling': sample_weight, 'ising': ising_weight})
# modelname = os.path.join(models_dir, savefile_name+'best.h5')
checkpoint = ModelCheckpoint(models_dir+savefile_name+'best.hdf5', monitor='val_loss', verbose=1, save_best_only=True, save_weights_only=True, mode='auto', period=1)
class Save_weights(keras.callbacks.Callback):
def on_epoch_begin(self, epoch, logs=None):
model.save_weights(os.path.join(models_dir, savefile_name+'weights{:02d}.hdf5'.format(epoch)))
fitdata_dict = {'sampling': np.zeros((nsamp, n_sites)), 'ising': np.zeros((nsamp, ))}
for k in range(len(t1_list)):
fitdata_dict['recon'+str(k)] = xdata_blur
history = model.fit({'input': xdata}, fitdata_dict,
validation_split=0.3,
initial_epoch=1,
epochs=1 + nb_epochs_train,
batch_size=batch_size,
verbose=1,
callbacks=[checkpoint])
modelname = os.path.join(models_dir, savefile_name+'.h5')
model.save_weights(modelname)
w = csv.writer(open(os.path.join(models_dir, 'history_'+savefile_name+'.csv'), 'w'))
for key, val in history.history.items():
w.writerow([key, val])
del history
return model
def Prepare_BlackHole_Feature_Data(sefd_param = 0, target = 'm87'):
###############################################################################
# prepare the training data with features
###############################################################################
bh_sim_data = np.load('/home/groot/BoumanLab/joint_opt/data/bh_sim_data.npy', allow_pickle=True)
image = bh_sim_data.item()['image']
spin_label = bh_sim_data.item()['spin']
class_label = bh_sim_data.item()['type']
nsamp = image.shape[0]
combined_class_label = [(class_label[k], spin_label[k]) for k in range(nsamp)]
combined_class_unique = np.unique(combined_class_label, axis=0)
n_combined_class = len(combined_class_unique)
combined_class_array = np.zeros((nsamp, n_combined_class))
for k in range(nsamp):
for i in range(n_combined_class):
if class_label[k] == combined_class_unique[i][0] and spin_label[k] == combined_class_unique[i][1]:
combined_class_array[k, i] = 1.0
else:
combined_class_array[k, i] = 0.0
spin_unique = np.unique(spin_label)
spin_array = np.zeros((nsamp, len(spin_unique)))
for k in range(nsamp):
for i in range(len(spin_unique)):
if spin_label[k] == spin_unique[i]:
spin_array[k, i] = 1.0
else:
spin_array[k, i] = 0.0
class_unique = np.unique(class_label)
class_array = np.zeros((nsamp, len(class_unique)))
for k in range(nsamp):
for i in range(len(class_unique)):
if class_label[k] == class_unique[i]:
class_array[k, i] = 1.0
else:
class_array[k, i] = 0.0
shuffle_order = np.random.RandomState(seed=42).permutation(nsamp)
image_shuffle = image[shuffle_order]
# spin_shuffle = spin_array[shuffle_order]
# class_shuffle = class_array[shuffle_order]
combined_class_shuffle = combined_class_array[shuffle_order]
class_shuffle = class_array[shuffle_order]
spin_shuffle = spin_array[shuffle_order]
###############################################################################
# generate eht observation data
###############################################################################
add_th_noise = False # False if you *don't* want to add thermal error. If there are no sefds in obs_orig it will use the sigma for each data point
phasecal = True # True if you don't want to add atmospheric phase error. if False then it adds random phases to simulate atmosphere
ampcal = True # True if you don't want to add atmospheric amplitude error. if False then add random gain errors
stabilize_scan_phase = False # if true then add a single phase error for each scan to act similar to adhoc phasing
stabilize_scan_amp = False # if true then add a single gain error at each scan
jones = False # apply jones matrix for including noise in the measurements (including leakage)
inv_jones = False # no not invert the jones matrix
frcal = True # True if you do not include effects of field rotation
dcal = True # True if you do not include the effects of leakage
dterm_offset = 0.05 # a random offset of the D terms is given at each site with this standard deviation away from 1
dtermp = 0
tint_sec = 10
tadv_sec = 600
tstart_hr = 0
tstop_hr = 24
bw_hz = 4e9
array = '/home/groot/BoumanLab/eht-imaging/arrays/EHT2019.txt'
eht = eh.array.load_txt(array)
fov = 160 * eh.RADPERUAS
###############################################################################
# define scientific target
###############################################################################
if target == 'm87':
ra = 12.513728717168174
dec = 12.39112323919932
elif target == 'sgrA':
ra = 19.414182210498385
dec = -29.24170032236311
# ra = 12.513728717168174
# dec = 12.39112323919932
rf = 230e9
npix = 160
mjd = 57853 # day of observation
simim = eh.image.make_empty(npix, fov, ra, dec, rf=rf, source='random', mjd=mjd)
simim.imvec = np.zeros((npix, npix, 1)).reshape((-1, 1))#xdata[0, :, :, :].reshape((-1, 1))
obs = simim.observe(eht, tint_sec, tadv_sec, tstart_hr, tstop_hr, bw_hz, add_th_noise=add_th_noise, ampcal=ampcal, phasecal=phasecal,
stabilize_scan_phase=stabilize_scan_phase, stabilize_scan_amp=stabilize_scan_amp,
jones=jones,inv_jones=inv_jones,dcal=dcal, frcal=frcal, dterm_offset=dterm_offset)
t1 = obs.data['t1']
t2 = obs.data['t2']
n_sites = np.unique(np.concatenate([t1, t2])).shape[0] + 1
obs_data = obs.unpack(['u', 'v', 'vis', 'sigma'])
uv = np.hstack((obs_data['u'].reshape(-1,1), obs_data['v'].reshape(-1,1)))
F = ftmatrix(simim.psize, simim.xdim, simim.ydim, uv, pulse=simim.pulse)
# complex visibilities
vis_data = np.matmul(image_shuffle, F.T)
# obs.add_cphase(count='min')
obs.add_cphase(count='max')
tc1 = obs.cphase['t1']
tc2 = obs.cphase['t2']
tc3 = obs.cphase['t3']
cphase_map = np.zeros((len(obs.cphase['time']), 3))
zero_symbol = 10000
for k1 in range(cphase_map.shape[0]):
for k2 in list(np.where(obs.data['time']==obs.cphase['time'][k1])[0]):
if obs.data['t1'][k2] == obs.cphase['t1'][k1] and obs.data['t2'][k2] == obs.cphase['t2'][k1]:
cphase_map[k1, 0] = k2
if k2 == 0:
cphase_map[k1, 0] = zero_symbol
elif obs.data['t2'][k2] == obs.cphase['t1'][k1] and obs.data['t1'][k2] == obs.cphase['t2'][k1]:
cphase_map[k1, 0] = -k2
if k2 == 0:
cphase_map[k1, 0] = -zero_symbol
elif obs.data['t1'][k2] == obs.cphase['t2'][k1] and obs.data['t2'][k2] == obs.cphase['t3'][k1]:
cphase_map[k1, 1] = k2
if k2 == 0:
cphase_map[k1, 1] = zero_symbol
elif obs.data['t2'][k2] == obs.cphase['t2'][k1] and obs.data['t1'][k2] == obs.cphase['t3'][k1]:
cphase_map[k1, 1] = -k2
if k2 == 0:
cphase_map[k1, 1] = -zero_symbol
elif obs.data['t1'][k2] == obs.cphase['t3'][k1] and obs.data['t2'][k2] == obs.cphase['t1'][k1]:
cphase_map[k1, 2] = k2
if k2 == 0:
cphase_map[k1, 2] = zero_symbol
elif obs.data['t2'][k2] == obs.cphase['t3'][k1] and obs.data['t1'][k2] == obs.cphase['t1'][k1]:
cphase_map[k1, 2] = -k2
if k2 == 0:
cphase_map[k1, 2] = -zero_symbol
cphase_proj = np.zeros((cphase_map.shape[0], F.shape[0]), dtype=np.float32)
for k in range(cphase_map.shape[0]):
for j in range(cphase_map.shape[1]):
if cphase_map[k][j] > 0:
if int(cphase_map[k][j]) == zero_symbol:
cphase_map[k][j] = 0
cphase_proj[k, int(cphase_map[k][j])] = 1
else:
if np.abs(int(cphase_map[k][j])) == zero_symbol:
cphase_map[k][j] = 0
cphase_proj[k, int(-cphase_map[k][j])] = -1
###############################################################################
# thermal noises: 0 represents no thermal noises, 1 represents site-varying thermal noises, 2 represents site-equivalent thermal noises
###############################################################################
if sefd_param == 1:
sigma = 224.46 * np.concatenate([np.expand_dims(obs.data['sigma'], -1), np.expand_dims(obs.data['sigma'], -1)], -1)
elif sefd_param == 2:
sigma = 224.46 * np.concatenate([np.expand_dims(obs.data['sigma'], -1), np.expand_dims(obs.data['sigma'], -1)], -1)
sigma = np.mean(sigma.reshape((-1, ))) * np.ones(sigma.shape)
else:
sigma = None
# return vis_data, spin_shuffle, class_shuffle, t1, t2, sigma
return vis_data, combined_class_shuffle, class_shuffle, spin_shuffle, t1, t2, tc1, tc2, tc3, cphase_proj, sigma, combined_class_unique, class_unique, spin_unique
def Train_IsingFeatureNet(eht_array, target, sefd_param, lr, nb_epochs_train, sample_weight, ising_weight, batch_size = 32, n_ising_layers = 5, models_dir='../joint_opt/models/anti-aliasing/12302019/', savefile_name='nn_params', network='vis', feature_name='both'):
###############################################################################
# prepare the training data
###############################################################################
fov_param = 160
flux_label = 1
blur_param = 1
# vis_shuffle, spin_shuffle, class_shuffle, t1, t2, sigma = Prepare_BlackHole_Feature_Data(sefd_param)
vis_shuffle, combined_class_shuffle, class_shuffle, spin_shuffle, t1, t2, tc1, tc2, tc3, cphase_proj, sigma, combined_class_unique, class_unique, spin_unique = Prepare_BlackHole_Feature_Data(sefd_param, target)
n_sites = np.unique(np.concatenate([t1, t2])).shape[0] + 1
nsamp = vis_shuffle.shape[0]
###############################################################################
# define the model
###############################################################################
if network == 'vis':
model = IsingVisFeatureNet(t1, t2, n_ising_layers=n_ising_layers, slope_const=3, n_layers=3, n_hid=200, sigma=sigma, feature_name=feature_name)
elif network == 'cpamp':
model = IsingCpAmpFeatureNet(t1, t2, tc1, tc2, tc3, cphase_proj, n_ising_layers=n_ising_layers, slope_const=3, n_layers=3, n_hid=200, sigma=sigma, feature_name=feature_name)
adam_opt = keras.optimizers.Adam(lr=lr, beta_1=0.9, beta_2=0.999, amsgrad=False)
class_weight = 1.0
# model.compile(optimizer=adam_opt, loss={'bh_class': 'categorical_crossentropy', 'spin_class': 'categorical_crossentropy', 'sampling': site_sparsity, 'ising': energy},
# loss_weights={'bh_class': class_weight, 'spin_class': class_weight, 'sampling': sample_weight, 'ising': ising_weight})
if feature_name == 'class':
model.compile(optimizer=adam_opt, loss={'bh_class': 'categorical_crossentropy', 'sampling': site_sparsity, 'ising': energy},
loss_weights={'bh_class': class_weight, 'sampling': sample_weight, 'ising': ising_weight})
elif feature_name == 'spin':
model.compile(optimizer=adam_opt, loss={'spin_class': 'categorical_crossentropy', 'sampling': site_sparsity, 'ising': energy},
loss_weights={'spin_class': class_weight, 'sampling': sample_weight, 'ising': ising_weight})
elif feature_name == 'both':
model.compile(optimizer=adam_opt, loss={'combined_class': 'categorical_crossentropy', 'sampling': site_sparsity, 'ising': energy},
loss_weights={'combined_class': class_weight, 'sampling': sample_weight, 'ising': ising_weight})
# modelname = os.path.join(models_dir, savefile_name+'best.h5')
checkpoint = ModelCheckpoint(models_dir+savefile_name+'best.hdf5', monitor='val_loss', verbose=1, save_best_only=True, save_weights_only=True, mode='auto', period=1)
class Save_weights(keras.callbacks.Callback):
def on_epoch_begin(self, epoch, logs=None):
model.save_weights(os.path.join(models_dir, savefile_name+'weights{:02d}.hdf5'.format(epoch)))
# history = model.fit({'input': vis_shuffle}, {'bh_class': class_shuffle, 'spin_class': spin_shuffle, 'sampling': np.zeros((nsamp, n_sites)), 'ising': np.zeros((nsamp, ))},
# validation_split=0.3,
# initial_epoch=1,
# epochs=1 + nb_epochs_train,
# batch_size=batch_size,
# verbose=1,
# callbacks=[checkpoint])
if feature_name == 'class':
history = model.fit({'input': vis_shuffle}, {'bh_class': class_shuffle, 'sampling': np.zeros((nsamp, n_sites)), 'ising': np.zeros((nsamp, ))},
validation_split=0.3,
initial_epoch=1,
epochs=1 + nb_epochs_train,
batch_size=batch_size,
verbose=1,
callbacks=[checkpoint])
elif feature_name == 'spin':
history = model.fit({'input': vis_shuffle}, {'spin_class': spin_shuffle, 'sampling': np.zeros((nsamp, n_sites)), 'ising': np.zeros((nsamp, ))},
validation_split=0.3,
initial_epoch=1,
epochs=1 + nb_epochs_train,
batch_size=batch_size,
verbose=1,
callbacks=[checkpoint])
elif feature_name == 'both':
history = model.fit({'input': vis_shuffle}, {'combined_class': combined_class_shuffle, 'sampling': np.zeros((nsamp, n_sites)), 'ising': np.zeros((nsamp, ))},
validation_split=0.3,
initial_epoch=1,
epochs=1 + nb_epochs_train,
batch_size=batch_size,
verbose=1,
callbacks=[checkpoint])
modelname = os.path.join(models_dir, savefile_name+'.h5')
model.save_weights(modelname)
w = csv.writer(open(os.path.join(models_dir, 'history_'+savefile_name+'.csv'), 'w'))
for key, val in history.history.items():
w.writerow([key, val])
del history
return model
if __name__ == '__main__':
###############################################################################
# record the command line inputs
###############################################################################
script = str(sys.argv[0])
eht_array = str(sys.argv[1])
target = str(sys.argv[2])
lr = float(sys.argv[3])
nb_epochs_train = int(sys.argv[4])
sample_weight = float(sys.argv[5])
ising_weight = float(sys.argv[6])
blur_param = float(sys.argv[7])
fov_param = float(sys.argv[8])
sefd_param = int(sys.argv[9])
flux_label = int(sys.argv[10])
file_index = sys.argv[11]
weather = str(sys.argv[12])
if weather == 'True':
weather = True
else:
weather = False
# eht_array = 'eht2019'
# target = 'm87'#'sgrA'#'both'#
# lr = 0.001#0.001
# nb_epochs_train = 50
# sample_weight = 0.05
# ising_weight = 0.0001
# blur_param = 0.25#0.75
# fov_param = 100.0
# sefd_param = 0
# flux_label = 1
# file_index = 'cpamp1'#'featurecpamp1'#'featurevis1'
# weather = False
savefile_name = eht_array+'_'+target+'_'+file_index+'_sample'+str(sample_weight)+'_ising'+str(ising_weight)+'_blur'+str(blur_param)+'_fov'+str(fov_param)+'_sefd'+str(sefd_param)+'_flux'+str(flux_label)+'_weather'+str(weather)
models_dir = './data/20200606/'#'../joint_opt/models/anti-aliasing/02012020/'
###############################################################################
# complex visibility
###############################################################################
if file_index[0:3] == 'vis':
if target == 'both':
target_list = ['sgrA', 'm87']
model = Train_IsingMutipleVisNet(eht_array, target_list, fov_param, flux_label, blur_param, sefd_param, lr, nb_epochs_train, sample_weight, ising_weight, batch_size = 32, n_ising_layers = 5, models_dir=models_dir, savefile_name=savefile_name)
else:
model = Train_IsingVisNet(eht_array, target, fov_param, flux_label, blur_param, sefd_param, lr, nb_epochs_train, sample_weight, ising_weight, batch_size = 64, n_ising_layers = 5, models_dir=models_dir, savefile_name=savefile_name, weather=weather)
# model = Train_IsingVisNet(eht_array, target, fov_param, flux_label, blur_param, sefd_param, lr, nb_epochs_train, sample_weight, ising_weight, batch_size = 32, n_ising_layers = 10, models_dir=models_dir, savefile_name=savefile_name, weather=weather)
###############################################################################
# closure phase and amplitude
###############################################################################
if file_index[0:5] == 'cpamp':
if target == 'both':
target_list = ['sgrA', 'm87']
model = Train_IsingMutipleCpAmpNet(eht_array, target_list, fov_param, flux_label, blur_param, sefd_param, lr, nb_epochs_train, sample_weight, ising_weight, batch_size = 32, n_ising_layers = 5, models_dir=models_dir, savefile_name=savefile_name)
else:
model = Train_IsingCpAmpNet(eht_array, target, fov_param, flux_label, blur_param, sefd_param, lr, nb_epochs_train, sample_weight, ising_weight, batch_size = 64, n_ising_layers = 5, models_dir=models_dir, savefile_name=savefile_name, weather=weather)
# model = Train_IsingCpAmpNet(eht_array, target, fov_param, flux_label, blur_param, sefd_param, lr, nb_epochs_train, sample_weight, ising_weight, batch_size = 32, n_ising_layers = 5, models_dir=models_dir, savefile_name=savefile_name, weather=weather)
###############################################################################
# complex visibility feature
###############################################################################
if file_index[0:10] == 'featurevis':
model = Train_IsingFeatureNet(eht_array, target, sefd_param, lr, nb_epochs_train, sample_weight, ising_weight, batch_size = 32, n_ising_layers = 5, models_dir=models_dir, savefile_name=savefile_name, network='vis', feature_name=file_index[10:-1])
###############################################################################
# closure phase and amplitude feature
###############################################################################
if file_index[0:12] == 'featurecpamp':
model = Train_IsingFeatureNet(eht_array, target, sefd_param, lr, nb_epochs_train, sample_weight, ising_weight, batch_size = 32, n_ising_layers = 5, models_dir=models_dir, savefile_name=savefile_name, network='cpamp', feature_name=file_index[12:-1])
del model
for k in range(20):
gc.collect()
# eht_array = 'eht2019'
# target = 'm87'#'both'#
# lr = 0.001#0.001
# nb_epochs_train = 200
# sample_weight = 0.2#0.005
# ising_weight = 0.2#0.005
# blur_param = 0.75
# fov_param = 100.0
# sefd_param = 0
# flux_label = 1
# file_index = 'featurevis1'#'featurecpamp2'#
# savefile_name = eht_array+'_'+target+'_'+file_index+'_sample'+str(sample_weight)+'_ising'+str(ising_weight)+'_blur'+str(blur_param)+'_fov'+str(fov_param)+'_sefd'+str(sefd_param)+'_flux'+str(flux_label)
# model = IsingCpAmpFeatureNet(t1, t2, tc1, tc2, tc3, cphase_proj, n_ising_layers=5, slope_const=3, n_layers=3, n_hid=200, sigma=sigma)
# model = IsingVisFeatureNet(t1, t2, n_ising_layers=3, slope_const=3, n_layers=3, n_hid=200, sigma=sigma)
# models_dir = '../joint_opt/models/anti-aliasing/01032020/'#'../models/anti-aliasing/11212019/'#'../models/anti-aliasing/11202019/'#'../models/anti-aliasing/11162019/'#'../models/anti-aliasing/11142019/'
# modelname = os.path.join(models_dir, savefile_name+'best'+'.hdf5')
# model.load_weights(modelname)
# Q_vec = model.get_layer('ising').get_weights()[0]
# delta = model.get_layer('ising').get_weights()[1]
# n_sites = len(delta)
# Q = np.zeros((n_sites, n_sites))
# k = 0
# for k1 in range(n_sites):
# for k2 in range(k1):
# Q[k1, k2] = Q_vec[k]
# k += 1
# Q = Q + Q.T
# tlib1 = {'PV': 0, 'PDB': 1, 'ALMA': 2, 'APEX': 3, 'LMT': 4, 'SMT': 5, 'SPT': 6, 'OVRO': 7, 'JCMT': 8, 'SMA': 9, 'KP': 10, 'GLT': 11}
# tlib2 = {'ALMA': 0, 'APEX': 1, 'PDB': 2, 'PV': 3, 'LMT': 4, 'SMT': 5, 'KP': 6, 'OVRO': 7, 'JCMT': 8, 'SMA': 9, 'GLT': 10, 'SPT': 11}
# tlib_list = list(tlib1)
# tlib2_order = []
# for k in range(len(tlib_list)):
# tlib2_order.append(tlib2[tlib_list[k]])
# if target == 'm87':
# Q_new = np.array(Q)
# delta_new = np.array(delta)
# for j1 in range(n_sites):
# delta_new[j1] = delta[tlib2_order[j1]]
# for j2 in range(n_sites):
# Q_new[j1, j2] = Q[tlib2_order[j1], tlib2_order[j2]]
# Q = Q_new
# delta = delta_new
# plt.figure(),
# plt.plot(np.arange(12), delta, 'ro')
# plt.ylim([-0.1, 0.5])
# plt.title(r'Ising model parameter ($\theta_{jj}$)', fontsize=18)
# plt.xticks(range(len(tlib_list)), list(tlib_list), size='small', rotation=35, fontsize=12)
# plt.yticks(fontsize=14)
# # plt.savefig(os.path.join(data_dir, 'delta2_'+savefile_name+'.pdf'))
# plt.figure(), plt.imshow(Q), plt.set_cmap('seismic')
# cb = plt.colorbar()
# cb.ax.tick_params(labelsize=14)
# plt.title(r'Ising model parameter ($\theta_{jk}$)', fontsize=18)
# plt.clim(-0.6, 0.6)
# plt.xticks(range(len(tlib_list)), list(tlib_list), size='small', rotation=35, fontsize=12)
# plt.yticks(range(len(tlib_list)), list(tlib_list), size='small', fontsize=12)
# plt.ylim(-0.5, n_sites-0.5)
# combined_class_prob, mask_pred, energy_pred = model.predict(np.concatenate([vis_shuffle[-900::]]*5, 0))
# class_true = np.argmax(np.concatenate([combined_class_shuffle[-900::]]*5, 0), 1)
# class_pred = np.argmax(np.concatenate([combined_class_prob[-900::]]*5, 0), 1)
# val_mask_pred = mask_pred
# class_accuracy = np.sum(class_pred == class_true)/len(class_pred)
# print('Prediction Accuracy {}'.format(class_accuracy))