def load_from_archive(names, arch):
cs = []
for name in names:
cs.append(Chest(path = "{:s}-results".format(name),
open = partial(glopen, endpoint=arch),
open_many = partial(glopen_many, endpoint=arch),
available_memory = 1e12))
scs = [CachedSlict(c) for c in cs]
ps = []
for name in names:
with glopen(
"{:s}.json".format(name), mode='r',
endpoint = arch,
) as f:
ps.append(json.load(f))
if len(names) == 1:
return cs[0], scs[0], ps[0]
return cs, scs, ps
def test_CachedSlict_3d():
d = {}
d[2, 2, 2] = 6
d[2, 2, 3] = 7
d[1, 2, 8] = 11
d[8, 2, 1] = 11
d[2, 3, 3] = 8
sd = CachedSlict(d)
sd2 = sd[:, 2, :]
for k in sd2:
assert sd2[k] == sum(k) + 2
last = (0, 0)
for k in sd2.keys():
assert k > last
last = k
items = sd2.items()
vals = sd2.values()
for k, v in items:
assert v == sd2[k]
assert v in vals
def test_CachedSlict_2d():
d = {}
d[3, 2] = 5
d[2, 2] = 4
d[1, 2] = 3
d[8, 2] = 10
d[2, 3] = 5
sd = CachedSlict(d)
sd2 = sd[:, 2]
for k in sd2:
assert sd2[k] == k + 2
last = 0
for k in sd2.keys():
assert k > last
last = k
items = sd2.items()
vals = sd2.values()
for k, v in items:
assert v == sd2[k]
assert v in vals
def test_CachedSlict_1d():
d = {}
d[3] = 3
d[2] = 2
d[1] = 1
d[8] = 8
d[2] = 2
sd = CachedSlict(d)
sd2 = sd[:]
for k in sd2:
assert sd2[k] == k
last = 0
for k in sd2.keys():
assert k > last
last = k
items = sd2.items()
vals = sd2.values()
for k, v in items:
assert v == sd2[k]
assert v in vals
from json import loads
img_format = 'eps'
title = False
parser = ArgumentParser(description="Plotter for smRTI data")
parser.add_argument("--traj", action="store_true", default=False)
parser.add_argument("--only", type=str, default=None)
parser.add_argument("params", type=str, default="fit_results.p")
args = parser.parse_args()
with open("data_table.p", 'rb') as f:
data_table_d = pickle.load(f)
data_table = CachedSlict(data_table_d)
from model import exp_mix, mix_model
from model import exp_dyn, dyn_model
from model import both_error, full_model, filter_trajectory
with open(args.params, "rb") as f:
results_in = pickle.load(f)
if args.only is not None:
todo = loads(args.only)
else:
todo = data_table[:, :, 'time'].keys()
if not args.traj:
todo = []
import pickle
from slict import CachedSlict
import numpy as np
from scipy.optimize import minimize
import cma
from os.path import exists
from scipy.interpolate import UnivariateSpline
from scipy.optimize import basinhopping, minimize
with open("data_table.p", 'rb') as f:
data_table_d = pickle.load(f)
data_table = CachedSlict(data_table_d)
from model import filter_trajectory, error
from model import dyn_error, guess_dyn, bounds_dyn, exp_dyn, scaling_dyn, bounds_dyn_t
from model import mix_error, guess_mix, bounds_mix, exp_mix
if exists("fit_results.p"):
with open("fit_results.p", "rb") as f:
results = pickle.load(f)
with open("reg_fit_results.p", "rb") as f:
reg_results = pickle.load(f)
else:
results = {}
todo = list(data_table[:, :, 'time'].keys())
todo.reverse()
args = command_line_ui()
# load params from genrun.py input dictionary
import json
#from utils.custom_json import CustomDecoder
with open("{:s}.json".format(args.name), 'r') as f:
params = json.load(f)
# insert new results into the dictionary
fname = '{:s}-results.dat'.format(args.name)
#with open(fname, 'r') as f:
# results = json.load(f, cls=CustomDecoder)
from chest import Chest
from slict import CachedSlict
results = CachedSlict(Chest(path="{:s}-results".format(args.name)))
from importlib import import_module
xx = import_module(args.post)
import time as clock
start_time = clock.time()
i = 0
#for time in results[:,"frame"].keys():
# xx.plot_frame(results[time,:], params, args)
# i = i + 1
# print("Processed t={:f} ({:f} fps)".format(time, (clock.time() - start_time) / i))
# Post-post process the contents of the results dictionary
xx.post_series(results, params, args)
from os.path import join
workdirs = [join(getcwd(), x["name"]) for x in overrides]
configs = [
configure(base, override, workdir)
for override, workdir in zip(overrides, workdirs)
]
data_table = {}
max_index = -1
height = 'H_exp'
for p, wd in zip(configs, workdirs):
path = join(wd, "{}-results".format(p['name']))
print(path)
if exists(path):
c = Chest(path=path)
sc = CachedSlict(c)
times = sc[:, height].keys()[:max_index]
data_table[p['viscosity'], p['conductivity'], 'time'] = np.array(times)
data_table[p['viscosity'], p['conductivity'],
'height'] = np.array([sc[t, height] for t in times])
data_table[p['viscosity'], p['conductivity'], 'atwood'] = np.array(
[4 * np.mean(sc[t, 't_abs_proj_z']) for t in times])
for k, v in p.items():
data_table[p['viscosity'], p['conductivity'], k] = v
import pickle
with open("data_table.p", "wb") as f:
pickle.dump(data_table, f)
print(data_table)