def __init__(self, accountID, top_config):
self.accountID = accountID
self.top_config = top_config
self.db = db_handler.DBHandler()
self.corr = correlation.Correlation(accountID)
account = self.db.get_websim_account(accountID)
self.ws = websim.WebSim(login = account[0], password = account[1])
self.ws.authorise()
def setUp(self):
cosmo_multi = cosmology.MultiEpoch(0.0, 5.0, cosmo_dict=c_dict)
lens_dist = kernel.dNdzMagLim(z_min=0.0, z_max=2.0,
a=2, z0=0.3, b=2)
source_dist = kernel.dNdzGaussian(z_min=0.0, z_max=2.0,
z0=1.0, sigma_z=0.2)
lens_window = kernel.WindowFunctionGalaxy(
lens_dist, cosmo_multi_epoch=cosmo_multi)
source_window = kernel.WindowFunctionConvergence(
source_dist, cosmo_multi_epoch=cosmo_multi)
kern = kernel.Kernel(0.001*0.001*deg_to_rad, 1.0*100.0*deg_to_rad,
window_function_a=lens_window,
window_function_b=source_window,
cosmo_multi_epoch=cosmo_multi)
zheng = hod.HODZheng(hod_dict)
cosmo_single = cosmology.SingleEpoch(0.0, cosmo_dict=c_dict)
h = halo.Halo(input_hod=zheng, cosmo_single_epoch=cosmo_single)
self.corr = correlation.Correlation(0.001, 1.0,
input_kernel=kern,
input_halo=h,
power_spec='power_mm')
self.theta_array = numpy.logspace(-3, 0, 4)*deg_to_rad
import correlation
import torch
B, C, H, W = 1, 1, 32, 32
a = torch.randint(1, 4, (B, C, H, W), dtype=torch.float32).cuda()
b = torch.randint_like(a, 1, 4).cuda()
print(a.dtype)
print(a.shape, b.shape)
print(a.device, b.device)
corr = correlation.Correlation(pad_size=4,
kernel_size=1,
max_displacement=4,
stride1=1,
stride2=1,
corr_multiply=1).cuda()
c = corr(a, b)
print(c.shape)
lens_dist_low = kernel.dNdzInterpolation(
p_z[:25, 0], p_z[:25, 1]) #dNdzGaussian(0.0, 5.0, 3.1, 0.05)#0.1)#0.5
lens_window_low = kernel.WindowFunctionGalaxy(lens_dist_low, cosmo_multi)
con_kernel_low = kernel.Kernel(ktheta_min=0.001 * 0.001 * deg_to_rad,
ktheta_max=100.0 * 1.0 * deg_to_rad,
window_function_a=lens_window_low,
window_function_b=lens_window_low,
cosmo_multi_epoch=cosmo_multi)
V = comoving_volume(3., 0.0356402381150449, 0.5) #0.01745,0.5)
gal_dens, dens_err = galaxy_density(
'/users/bhernandez/thesis/work/galdens_u.txt', V)
corr_low = correlation.Correlation(theta_min_deg=0.01,
theta_max_deg=2.0,
input_kernel=con_kernel_low,
input_halo=halo_model_low) #,
#power_spec='power_gg')
corr_low.compute_correlation()
data = np.loadtxt(
'/users/bhernandez/thesis/work/Wtheta_23.0t24.5_individual_weights_udropouts_density'
) #'/vol/fohlen11/fohlen11_1/bhernandez/data/corr/udropouts/final/Wtheta_23t24_individual_weights')#'/vol/fohlen11/fohlen11_1/bhernandez/data/corr/udropouts/final/Wtheta_udropouts_m23t24_with_proper_weight')#'/vol/fohlen11/fohlen11_1/bhernandez/data/corr/magnitude_bins/udropouts_0.2/Wtheta_combined_24.2t24.4_udropouts') #'/users/bhernandez/thesis/work/Wtheta_23t24_udropouts')#'/vol/fohlen11/fohlen11_1/bhernandez/data/corr/udropouts/final/Wtheta_pointings_weights_small_scales_m23t24_RR.txt')#Wtheta_combined_small_scales_noregions_m23t24')
#data=np.log10(data)
density = data[-1, 1]
data = data[5:-7, :]
Wtheta = data[:, 1]
theta = data[:, 0]
covariance_tmp = np.loadtxt(
'/users/bhernandez/thesis/work/Wcovar_23.0t24.5_individual_weights_udropouts_density'
) #'/vol/fohlen11/fohlen11_1/bhernandez/data/corr/udropouts/final/Wcovar_23t24_individual_weights')#/users/bhernandez/thesis/work/Wcovar_23t24.5_combined')#'/vol/fohlen11/fohlen11_1/bhernandez/data/corr/udropouts/final/Wcovar_udropouts_m23t24_with_proper_weight')#'/vol/fohlen11/fohlen11_1/bhernandez/data/corr/magnitude_bins/udropouts_0.2/Wcovar_combined_24.2t24.4_udropouts') #'/users/bhernandez/thesis/work/covariance_23t24_udropouts')#'/vol/fohlen11/fohlen11_1/bhernandez/data/corr/udropouts/final/Wcovar_pointings_weights_small_scales_m23t24_RR.txt')#Wtheta_combined_small_scales_noregions_m23t24')
### the limits to k_min*theta_min - k_max*theta_max where k_min and k_max are
### set in the code as 0.001 and 100.0 respectively.
con_kernel = kernel.Kernel(ktheta_min=0.001 * 0.001 * deg_to_rad,
ktheta_max=100.0 * 1.0 * deg_to_rad,
window_function_a=lens_window,
window_function_b=source_window,
cosmo_multi_epoch=cosmo_multi)
con_kernel.write('test_kernel.ascii')
### Finally we define and run our correlation function, writing the results out
### to test_corr.ascii. Correlation does the job of defining the k space
### integral for a given theta. It also takes responsibility for setting the
### halo model object redshift to that of the peak kernel redshift. It also
### convenient allows for the setting of both the kernel and halo model
### cosmologies through the set_cosmology method. Note that like the kernel
### module, cosmology takes an input as radians.
corr = correlation.Correlation(theta_min_deg=0.001,
theta_max_deg=1.0,
input_kernel=con_kernel,
input_halo=halo_model,
power_spec='power_gm')
corr.compute_correlation()
corr.write('test_corr.ascii')
### and done, to make this a proper magnification correlation though, the user
### will have to multiply the output wtheta by 2.
### If you want to make this a script that could MCMCed create all of the
### objects as shown here and then in the MCMC loop call corr.set_cosmology
### and corr.set_hod (in this case) to change the cosmology/HOD and recompute.
def __init__(self, args, use_batch_norm=True, div_flow=20):
r"""FlowNet2 C module. Check out the FlowNet2 paper for more details
https://arxiv.org/abs/1612.01925
Args:
args (obj): Network initialization arguments
use_batch_norm (bool): Use batch norm or not. Default is true.
div_flow (int): Flow devision factor. Default is 20.
"""
super(FlowNetC, self).__init__()
self.use_batch_norm = use_batch_norm
self.div_flow = div_flow
self.conv1 = conv(self.use_batch_norm, 3, 64, kernel_size=7, stride=2)
self.conv2 = conv(self.use_batch_norm,
64,
128,
kernel_size=5,
stride=2)
self.conv3 = conv(self.use_batch_norm,
128,
256,
kernel_size=5,
stride=2)
self.conv_redir = conv(self.use_batch_norm,
256,
32,
kernel_size=1,
stride=1)
self.args = args
# if args.fp16:
# self.corr = nn.Sequential(
# tofp32(),
# correlation.Correlation(pad_size=20, kernel_size=1,
# max_displacement=20, stride1=1,
# stride2=2, corr_multiply=1),
# tofp16())
# else:
self.corr = correlation.Correlation(pad_size=20,
kernel_size=1,
max_displacement=20,
stride1=1,
stride2=2,
corr_multiply=1)
self.corr_activation = nn.LeakyReLU(0.1, inplace=True)
self.conv3_1 = conv(self.use_batch_norm, 473, 256)
self.conv4 = conv(self.use_batch_norm, 256, 512, stride=2)
self.conv4_1 = conv(self.use_batch_norm, 512, 512)
self.conv5 = conv(self.use_batch_norm, 512, 512, stride=2)
self.conv5_1 = conv(self.use_batch_norm, 512, 512)
self.conv6 = conv(self.use_batch_norm, 512, 1024, stride=2)
self.conv6_1 = conv(self.use_batch_norm, 1024, 1024)
self.deconv5 = deconv(1024, 512)
self.deconv4 = deconv(1026, 256)
self.deconv3 = deconv(770, 128)
self.deconv2 = deconv(386, 64)
self.predict_flow6 = predict_flow(1024)
self.predict_flow5 = predict_flow(1026)
self.predict_flow4 = predict_flow(770)
self.predict_flow3 = predict_flow(386)
self.predict_flow2 = predict_flow(194)
self.upsampled_flow6_to_5 = nn.ConvTranspose2d(2,
2,
4,
2,
1,
bias=True)
self.upsampled_flow5_to_4 = nn.ConvTranspose2d(2,
2,
4,
2,
1,
bias=True)
self.upsampled_flow4_to_3 = nn.ConvTranspose2d(2,
2,
4,
2,
1,
bias=True)
self.upsampled_flow3_to_2 = nn.ConvTranspose2d(2,
2,
4,
2,
1,
bias=True)
for m in self.modules():
if isinstance(m, nn.Conv2d):
if m.bias is not None:
init.uniform_(m.bias)
init.xavier_uniform_(m.weight)
if isinstance(m, nn.ConvTranspose2d):
if m.bias is not None:
init.uniform_(m.bias)
init.xavier_uniform_(m.weight)
# init_deconv_bilinear(m.weight)
self.upsample1 = nn.Upsample(scale_factor=4,
mode='bilinear',
align_corners=False)
import db_handler import websim import correlation c = correlation.Correlation(1) #c.request_several(['f838811ba41a421ea2a993fac447440b', 'e86d7948449449c9b9264fd45a872914', '2abd00ab916042df9addc0bbb92645e8', '23203453305048599977d15dbd1c44dd', 'd8ac4db69c7b43288ec1ccdab4c45542', '4ebc11aa196c4a128a55dd187db317a3', '95c55b460029421bb047231f3994d478', '3a47b524f5774c5eaab0afbc21bf79f9', '4d9f4c43a95841e383afc143e646b647', 'f22261104c1d42f58d7bd04bcdd667fa']) print c.correlation(49, 51, False)