def main():
"""Parse args and run simulated annealing"""
opts = settings.parse_args()
print(opts)
# set up seeds
if opts.seed != None:
np.random.seed(opts.seed)
random.seed(opts.seed)
tf.random.set_seed(opts.seed)
generator, discriminator, iterator, parameters = process_opts(opts)
# grid search
if opts.grid:
print("Grid search not supported right now")
sys.exit()
#posterior, loss_lst = grid_search(discriminator, samples, simulator, \
# iterator, parameters, opts.seed)
# simulated annealing
else:
posterior, loss_lst = simulated_annealing(generator, discriminator,\
iterator, parameters, opts.seed, toy=opts.toy)
print(posterior)
print(loss_lst)
def main():
arg_parser = gen_arg_parser()
args = arg_parser.parse_args()
conf_path = args.conf_path
action = args.action
project_name = args.project_name
conf_path = find_conf_path(conf_path)
#print(conf_path)
conf = None
if conf_path is not None:
conf = parse_conf(conf_path)
if conf is None and action in ['setup', 'snapshot', None]:
return "didn't find conf. can't run command..exiting"
if action == 'init':
#dispatch_init(conf, conf_path, project_name)
dispatch_init(conf, default_conf_path, project_name)
elif action == 'setup':
dispatch_snapshot_setup(conf, conf_path, project_name, is_setup=True)
elif action in 'snapshot':
dispatch_snapshot_setup(conf, conf_path, project_name)
elif action == 'status':
dispatch_status(conf, conf_path, project_name)
return "done"
to_track_lst = None
if not file_exists(conf_path):
err_str = "can't find %s. " % (conf_path, )
print("doesn't look like .vivie folder:", err_str)
sys.exit(1)
else:
to_track_str = read_file(conf_path)
to_track_lst = to_track_str.split('\n')[:-1]
if not file_exists(data_dir):
sh.mkdir(data_dir)
cmd = parse_args()
if cmd == 'help':
print_help()
elif cmd == 'setup':
run_setup(to_track_lst)
elif cmd == 'snapshot':
take_snapshot(to_track_lst)
elif cmd == 'status':
pass
from torch.autograd import Variable
from lib import network
import lib.datasets as datasets
import lib.utils.general_utils as utils
import models as models
from models.HDN_v2.utils import interpret_relationships
import warnings
from settings import parse_args, testImageLoader
from PIL import Image
from sort.sort import Sort, iou
import interpret
import os.path as osp
import vis
from keyframe_extracion import keyframe_checker
args = parse_args()
# Set the random seed
random.seed(args.seed)
torch.manual_seed(args.seed + 1)
torch.cuda.manual_seed(args.seed + 2)
colorlist = [(random.randint(0,
230), random.randint(0,
230), random.randint(0, 230))
for i in range(10000)]
# Set options
options = {
'data': {
'dataset_option': args.dataset_option,
'batch_size': args.batch_size,
},
def main():
input_file = sys.argv[1]
output_file = sys.argv[2]
print("input file", input_file)
print("output file", output_file)
# need to remove these from the list of args or else argparser will not
# handle unspecified positionals.
sys.argv.remove(input_file)
sys.argv.remove(output_file)
opts = settings.parse_args()
generator, iterator, parameters = process_opts(opts)
values = ss_helpers.parse_output(input_file)
print("VALUES", values)
print("made it through params")
# use the parameters we inferred!
fsc=False
generator.update_params(values)
if opts.model == 'fsc':
print("\nALERT you are running FSC sim!\n")
print("FSC PARAMS!", FSC_PARAMS)
generator.update_params(FSC_PARAMS) # make sure to check the order!
fsc=True
# real
real_matrices = iterator.real_batch(NUM_TRIAL, neg1=False)
real_matrices_region = iterator.real_batch(NUM_TRIAL, neg1=False,
region_len=True)
print("got through real data")
# sim
sim_matrices = generator.simulate_batch(NUM_TRIAL, neg1=False)
generator.num_snps = None # this activates region_len mode
sim_matrices_region = generator.simulate_batch(NUM_TRIAL, neg1=False)
# one pop models
if opts.model in ['exp', 'const']:
real_sfs, real_dist, real_ld, real_stats = ss_helpers.stats_all(real_matrices, real_matrices_region, L)
sim_sfs, sim_dist, sim_ld, sim_stats = ss_helpers.stats_all(sim_matrices, sim_matrices_region, L)
plot_all_stats(real_stats, real_dist, real_sfs, real_ld, sim_stats, sim_dist, sim_sfs, sim_ld, output_file)
# two pop models
elif opts.model in ['im', 'ooa2', 'post_ooa', 'msmc', 'fsc']:
half = real_matrices.shape[1]//2
# real split
real_matrices1 = real_matrices[:,:half,:,:]
real_matrices2 = real_matrices[:,half:,:,:]
real_matrices_region1 = []
real_matrices_region2 = []
for item in real_matrices_region:
real_matrices_region1.append(item[:half,:,:])
real_matrices_region2.append(item[half:,:,:])
# sim split
sim_matrices1 = sim_matrices[:,:half,:,:]
sim_matrices2 = sim_matrices[:,half:,:,:]
sim_matrices_region1 = []
sim_matrices_region2 = []
for item in sim_matrices_region:
sim_matrices_region1.append(item[:half,:,:])
sim_matrices_region2.append(item[half:,:,:])
# stats for pop 1
real_sfs1, real_dist1, real_ld1, real_stats1 = ss_helpers.stats_all(real_matrices1, real_matrices_region1, L)
sim_sfs1, sim_dist1, sim_ld1, sim_stats1 = ss_helpers.stats_all(sim_matrices1, sim_matrices_region1, L)
# stats for pop 2
real_sfs2, real_dist2, real_ld2, real_stats2 = ss_helpers.stats_all(real_matrices2, real_matrices_region2, L)
sim_sfs2, sim_dist2, sim_ld2, sim_stats2 = ss_helpers.stats_all(sim_matrices2, sim_matrices_region2, L)
# two pop stats
real_fst = ss_helpers.fst_all(real_matrices)
sim_fst = ss_helpers.fst_all(sim_matrices)
plot_stats_twopop(real_stats1, real_dist1, real_sfs1, real_ld1, real_stats2, real_dist2, real_sfs2, real_ld2, real_fst, \
sim_stats1, sim_dist1, sim_sfs1, sim_ld1, sim_stats2, sim_dist2, sim_sfs2, sim_ld2, sim_fst, output_file, fsc=fsc)
# OOA3
elif opts.model in ['ooa3']:
third = real_matrices.shape[1]//3
# real split
real_matrices1 = real_matrices[:,:third,:,:]
real_matrices2 = real_matrices[:,third:third*2,:,:]
real_matrices3 = real_matrices[:,third*2:,:,:]
real_matrices_region1 = []
real_matrices_region2 = []
real_matrices_region3 = []
for item in real_matrices_region:
real_matrices_region1.append(item[:third,:,:])
real_matrices_region2.append(item[third:third*2,:,:])
real_matrices_region3.append(item[third*2:,:,:])
# sim split
sim_matrices1 = sim_matrices[:,:third,:,:]
sim_matrices2 = sim_matrices[:,third:third*2,:,:]
sim_matrices3 = sim_matrices[:,third*2:,:,:]
sim_matrices_region1 = []
sim_matrices_region2 = []
sim_matrices_region3 = []
for item in sim_matrices_region:
sim_matrices_region1.append(item[:third,:,:])
sim_matrices_region2.append(item[third:third*2,:,:])
sim_matrices_region3.append(item[third*2:,:,:])
# stats for pop 1
real_sfs1, real_dist1, real_ld1, real_stats1 = ss_helpers.stats_all(real_matrices1, real_matrices_region1, L)
sim_sfs1, sim_dist1, sim_ld1, sim_stats1 = ss_helpers.stats_all(sim_matrices1, sim_matrices_region1, L)
# stats for pop 2
real_sfs2, real_dist2, real_ld2, real_stats2 = ss_helpers.stats_all(real_matrices2, real_matrices_region2, L)
sim_sfs2, sim_dist2, sim_ld2, sim_stats2 = ss_helpers.stats_all(sim_matrices2, sim_matrices_region2, L)
# stats for pop 3
real_sfs3, real_dist3, real_ld3, real_stats3 = ss_helpers.stats_all(real_matrices3, real_matrices_region3, L)
sim_sfs3, sim_dist3, sim_ld3, sim_stats3 = ss_helpers.stats_all(sim_matrices3, sim_matrices_region3, L)
# two pop stats
real_matrices12 = np.concatenate((np.array(real_matrices1), np.array(real_matrices2)), axis=1)
real_matrices13 = np.concatenate((np.array(real_matrices1), np.array(real_matrices3)), axis=1)
real_matrices23 = np.concatenate((np.array(real_matrices2), np.array(real_matrices3)), axis=1)
sim_matrices12 = np.concatenate((np.array(sim_matrices1), np.array(sim_matrices2)), axis=1)
sim_matrices13 = np.concatenate((np.array(sim_matrices1), np.array(sim_matrices3)), axis=1)
sim_matrices23 = np.concatenate((np.array(sim_matrices2), np.array(sim_matrices3)), axis=1)
print("fst shape", real_matrices12.shape)
real_fst12 = ss_helpers.fst_all(real_matrices12)
sim_fst12 = ss_helpers.fst_all(sim_matrices12)
real_fst13 = ss_helpers.fst_all(real_matrices13)
sim_fst13 = ss_helpers.fst_all(sim_matrices13)
real_fst23 = ss_helpers.fst_all(real_matrices23)
sim_fst23 = ss_helpers.fst_all(sim_matrices23)
plot_stats_threepop(
real_stats1, real_dist1, real_sfs1, real_ld1,
real_stats2, real_dist2, real_sfs2, real_ld2,
real_stats3, real_dist3, real_sfs3, real_ld3,
sim_stats1, sim_dist1, sim_sfs1, sim_ld1,
sim_stats2, sim_dist2, sim_sfs2, sim_ld2,
sim_stats3, sim_dist3, sim_sfs3, sim_ld3,
real_fst12, real_fst13, real_fst23,
sim_fst12, sim_fst13, sim_fst23,
output_file)
else:
print("unsupported", opts.model)
