def convert_path_prob_to_concentration(data_dir,
atom_followed="C",
path_prob=None):
"""
convert total pathway probability to concentration
for example, C3H8, suppose [C3H8] = 1.0 and we are following "C"
atom, then the corresponding total pathway probability should be
1.0 * 3, since each C3H8 has 3 "C" atoms, in other word, concentration
should be pathway probability divide by 3.0
"""
if path_prob is None:
return None
if path_prob is []:
return None
_, spe_n_i_d = psri.parse_spe_info(data_dir)
spe_composition = psri.read_spe_composition(
os.path.join(data_dir, "input", "spe_composition.json"))
spe_idx_coefficient = dict()
for _, val in enumerate(spe_composition):
if atom_followed in spe_composition[val]:
spe_idx_coefficient[spe_n_i_d[val]] = float(
spe_composition[val][atom_followed])
else:
spe_idx_coefficient[spe_n_i_d[val]] = 0.0
#print(spe_composition, spe_idx_coefficient)
if np.shape(path_prob)[0] > 0:
if np.shape(path_prob[0]) is ():
print("1D array", "shape:\t", len(path_prob))
for idx, _ in enumerate(path_prob):
if float(spe_idx_coefficient[str(idx)]) != 0:
path_prob[idx] /= float(spe_idx_coefficient[str(idx)])
return path_prob
def convert_concentration_to_path_prob(data_dir,
atom_followed="C",
spe_conc=None,
renormalization=True):
"""
convert concentration to corresponding total pathway probability
for example, C3H8, suppose [C3H8] = 1.0 and we are following "C"
atom, then the corresponding total pathway probability should be
1.0 * 3, since each C3H8 has 3 "C" atoms
Warning: spe_conc should be read from dlsode calculation, it is
guaranteed outside that dimensions of spe_conc match the mechanism
"""
if spe_conc is None:
return None
if spe_conc is []:
return None
_, spe_n_i_d = psri.parse_spe_info(data_dir)
spe_composition = psri.read_spe_composition(
os.path.join(data_dir, "input", "spe_composition.json"))
spe_idx_coefficient = dict()
for _, val in enumerate(spe_composition):
if atom_followed in spe_composition[val]:
spe_idx_coefficient[spe_n_i_d[val]] = float(
spe_composition[val][atom_followed])
else:
spe_idx_coefficient[spe_n_i_d[val]] = 0.0
#print(spe_composition, spe_idx_coefficient)
if np.shape(spe_conc)[0] > 0:
if np.shape(spe_conc[0]) is ():
print("1D array", "shape:\t", len(spe_conc))
for idx, _ in enumerate(spe_conc):
spe_conc[idx] *= float(spe_idx_coefficient[str(idx)])
if renormalization is True:
spe_conc /= np.sum(spe_conc)
else:
print("2D array", "shape:\t", np.shape(spe_conc))
for idx in range(np.shape(spe_conc)[1]):
spe_conc[:, idx] *= float(spe_idx_coefficient[str(idx)])
if renormalization is True:
for idx, _ in enumerate(spe_conc):
spe_conc[idx, :] /= np.sum(spe_conc[idx, :])
return spe_conc
def get_species_with_top_n_concentration(data_dir,
exclude,
top_n=10,
traj_max_t=100.0,
tau=10.0,
end_t=1.0,
tag="M",
atoms=None):
"""
get species concentration at a tau, where tau is the ratio of the time_wanted/end_time
"""
if atoms is None:
atoms = ["C"]
if exclude is None:
exclude = []
time = np.loadtxt(os.path.join(data_dir, "output",
"time_dlsode_" + str(tag) + ".csv"),
delimiter=",")
conc_all = np.loadtxt(os.path.join(
data_dir, "output", "concentration_dlsode_" + str(tag) + ".csv"),
delimiter=",")
n_spe = np.shape(conc_all)[1]
data = [float] * n_spe
for i in range(n_spe):
data[i] = interpolation.interp1d(time, conc_all[:, i], tau * end_t)
c_idx_map = defaultdict(set)
for idx, val in enumerate(data):
c_idx_map[val].add(str(idx))
c_idx_map = OrderedDict(sorted(c_idx_map.items(), reverse=True))
spe_idx_name_dict, _ = psri.parse_spe_info(data_dir)
spe_composition = psri.read_spe_composition(
os.path.join(data_dir, "input", "spe_composition.json"))
spe_idx_list = []
counter = 0
for _, val in enumerate(c_idx_map):
if counter < top_n:
spe_idx = next(iter(c_idx_map[val]))
indicator = False
for _, atom in enumerate(atoms):
if atom in spe_composition[spe_idx_name_dict[spe_idx]]:
indicator = True
break
if spe_idx_name_dict[spe_idx] not in exclude and indicator:
print(val, spe_idx, spe_idx_name_dict[spe_idx])
spe_idx_list.append(int(spe_idx))
counter += 1
# species doesn't contain atom we are interested in
exclude_spe_name_list = []
for idx, s_n_t in enumerate(spe_composition):
indicator = False
for _, atom in enumerate(atoms):
if atom in spe_composition[s_n_t]:
indicator = True
if indicator is False:
exclude_spe_name_list.append(s_n_t)
spe_name_list = [str(spe_idx_name_dict[str(x)]) for x in spe_idx_list]
return spe_idx_list, spe_name_list, exclude_spe_name_list
def get_species_with_top_n_concentration(data_dir,
exclude,
top_n=10,
traj_max_t=100.0,
tau=10.0,
end_t=1.0,
tag="M",
atoms=None):
"""
get species concentration at a tau, where tau is the ratio of the time_wanted/end_time
"""
if atoms is None:
atoms = ["C"]
if exclude is None:
exclude = []
conc = np.loadtxt(os.path.join(
data_dir, "output", "concentration_dlsode_" + str(tag) + ".csv"),
delimiter=",")
# the time point where reference time tau is
tau_time_point = float(tau) / traj_max_t * len(conc)
time_idx = int(end_t * tau_time_point)
if time_idx >= len(conc):
time_idx = (len(conc) - 1)
data = conc[time_idx, :]
c_idx_map = defaultdict(set)
for idx, val in enumerate(data):
c_idx_map[val].add(str(idx))
c_idx_map = OrderedDict(sorted(c_idx_map.items(), reverse=True))
spe_idx_name_dict, _ = psri.parse_spe_info(data_dir)
spe_composition = psri.read_spe_composition(
os.path.join(data_dir, "input", "spe_composition.json"))
spe_idx_list = []
counter = 0
for _, val in enumerate(c_idx_map):
if counter < top_n:
spe_idx = next(iter(c_idx_map[val]))
indicator = False
for _, atom in enumerate(atoms):
if atom in spe_composition[spe_idx_name_dict[spe_idx]]:
indicator = True
break
if spe_idx_name_dict[spe_idx] not in exclude and indicator:
print(val, spe_idx, spe_idx_name_dict[spe_idx])
spe_idx_list.append(int(spe_idx))
counter += 1
# species doesn't contain atom we are interested in
exclude_spe_name_list = []
for idx, s_n_t in enumerate(spe_composition):
indicator = False
for _, atom in enumerate(atoms):
if atom in spe_composition[s_n_t]:
indicator = True
if indicator is False:
exclude_spe_name_list.append(s_n_t)
spe_name_list = [str(spe_idx_name_dict[str(x)]) for x in spe_idx_list]
return spe_idx_list, spe_name_list, exclude_spe_name_list