def species_production_reaction(data_dir, spe='OH', top_n=50, norm=False):
"""
species production reaction in a path multiply by pathway probability
"""
print(data_dir)
f_n_n = os.path.join(data_dir, "output", "pathway_name_candidate.csv")
f_n_p = os.path.join(data_dir, "output", "pathway_prob.csv")
pathway_name = np.genfromtxt(f_n_n, dtype=str, delimiter='\n')
pathway_prob = np.genfromtxt(f_n_p, dtype=float, delimiter='\n')
_, spe_name_ind_dict = psri.parse_spe_info(data_dir)
reaction_map = dict()
for _, (p_n, p_p) in enumerate(zip(pathway_name, pathway_prob)):
map_tmp = parse_pattern.parse_species_production_reaction(
p_n, 'S' + spe_name_ind_dict[spe])
for key, value in map_tmp.items():
if key not in reaction_map:
reaction_map[key] = value * p_p
else:
reaction_map[key] += value * p_p
d_f = pd.DataFrame(list(
sorted(reaction_map.items(), key=lambda x: x[1], reverse=True)),
columns=['reaction', 'frequency'])
if norm is True:
total = sum(d_f['frequency'])
d_f['frequency'] /= total
f_n_out1 = os.path.join(data_dir, "output",
spe + "_production_reaction_index.csv")
d_f[0:top_n].to_csv(f_n_out1,
header=False,
index=False,
sep=',',
columns=['reaction', 'frequency'])
# load reaction info
_, new_ind_reaction_dict = psri.parse_reaction_and_its_index(data_dir)
# convert species reaction index to real species and reactions
d_f['reaction'] = d_f['reaction'].apply(
lambda x: psri.reaction_name_to_real_reaction(new_ind_reaction_dict, x
).strip())
# print(d_f['reaction'])
f_n_out2 = os.path.join(data_dir, "output",
spe + "_production_reaction_name.csv")
d_f[0:top_n].to_csv(f_n_out2,
header=False,
index=False,
sep=',',
columns=['reaction', 'frequency'])
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 convert_path_prob_to_concentration(data_dir,
atom_followed="C",
path_prob=None,
default_coef=None):
"""
if default_coef is not None, use it as default coefficient
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
if default_coef is not None:
for val in spe_idx_coefficient:
if spe_idx_coefficient[val] != 0:
spe_idx_coefficient[val] = default_coef
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 parse_spe_production_along_path(data_dir,
top_n=10,
spe_idx=10,
init_spe=62,
atom_followed="C",
end_t=1.0,
species_path=False,
axis=0,
path_branching_factor=False,
s_consumption=False,
s_production=True):
"""
parse species peoduction along path, note species might not explictly shown on path
but are side products of reaction on pathway
if path_idx is None, use top_n path
if path_idx is not None, instead it is a list, use only selected path, the output file name
thereafter ends with "selected_path"
"""
id_tmp = ""
if spe_idx is None or spe_idx is []:
return
elif isinstance(spe_idx, int):
id_tmp = str(spe_idx)
spe_idx = [spe_idx]
else:
for x_t in spe_idx:
if id_tmp == "":
id_tmp = str(x_t)
else:
id_tmp += "_" + str(x_t)
suffix = naming.get_suffix(data_dir,
init_spe=init_spe,
atom_followed=atom_followed,
end_t=end_t)
prefix = ""
if species_path is True:
prefix = "species_"
f_n_path_name = os.path.join(
data_dir, "output",
prefix + "pathway_name_candidate" + suffix + ".csv")
pathname_data = np.genfromtxt(f_n_path_name, dtype=str, max_rows=top_n + 1)
# in case of two dimensional pathway name
if len(np.shape(pathname_data)) == 2:
pathname_data = pathname_data[:, axis]
net_reactant = psri.parse_reaction_net_reactant(data_dir)
net_product = psri.parse_reaction_net_product(data_dir)
s_p_r_c = psri.parse_species_pair_reaction(data_dir)
if path_branching_factor is True:
atom_scheme = asch.get_atom_scheme(data_dir)
s_idx_name, _ = psri.parse_spe_info(data_dir)
s_p_c = []
for _, p_n in enumerate(pathname_data):
spe_consumption_count = 0
spe_production_count = 0
for s_i in spe_idx:
if s_consumption is True:
spe_consumption_count += parse_pattern.parse_species_along_path_using_reaction(
p_n, net_reactant, s_i, s_p_r_c)
if s_production is True:
spe_production_count += parse_pattern.parse_species_along_path_using_reaction(
p_n, net_product, s_i, s_p_r_c)
path_branching_number = 1
if path_branching_factor is True:
path_branching_number = parse_pattern.calculate_path_branching_number(
pathname=p_n,
net_reactant=net_reactant,
net_product=net_product,
s_idx_name=s_idx_name,
atom_scheme=atom_scheme,
atom_followed=atom_followed)
s_p_c.append((spe_production_count - spe_consumption_count) *
path_branching_number)
if id_tmp != "":
suffix += "_" + id_tmp
f_n_spe_production_count = os.path.join(
data_dir, "output",
prefix + "pathway_species_production_count" + suffix + ".csv")
np.savetxt(f_n_spe_production_count, s_p_c, fmt='%d')
def plot_network(data_dir,
fname="",
pathname="",
pathprob=1.0,
path_idx=None,
end_t=1.0,
suffix="",
atom_followed="C",
species_path=False):
"""
plot network manually
"""
print(fname)
n_coordinate = get_names_coordinates(data_dir, fname)
prefix = ""
if species_path is True:
prefix = "species_"
# figure name
if suffix is "":
fig_name = prefix + "network_path_" + str(path_idx) + ".jpg"
else:
fig_name = prefix + "network_path_" + \
str(path_idx) + str(suffix) + ".jpg"
# specify label for lines
labels = []
x = []
y = []
name_idx_dict = dict()
for i_tmp, val in enumerate(n_coordinate):
labels.append(val)
name_idx_dict[val] = i_tmp
x.append(float(n_coordinate[val][0]))
y.append(float(n_coordinate[val][1]))
# read in species index name
spe_idx_name_dict, spe_name_idx_dict = psri.parse_spe_info(data_dir)
spe_alias_latex = read_spe_alias(
os.path.join(data_dir, "input", "spe_alias_latex.json"))
_, new_ind_reaction_dict = psri.parse_reaction_and_its_index(data_dir)
# modify labels
spe_union_find_group = global_settings.get_union_find_group(
DATA_DIR, atom_followed)
for idx, val in enumerate(labels):
spe_i = spe_name_idx_dict[val]
if spe_i in spe_union_find_group:
labels[idx] = ",".join([
str(spe_idx_name_dict[str(x)])
for x in spe_union_find_group[spe_i]
])
print(labels)
for idx, val in enumerate(labels):
labels[idx] = change_spe_name(val, spe_alias_latex, None)
print(labels)
fig, a_x = plt.subplots(1, 1, sharex=True, sharey=False)
# background
a_x.scatter(x, y, color='b', marker="o", alpha=0.3)
for i, _ in enumerate(x):
t_h = a_x.annotate(labels[i], (x[i], y[i]))
t_h.set_alpha(0.15)
# get rid of R-1000003S90, don't need it here
pathname = re.sub(r"R-\d+S\d+", r'', pathname)
# parse pathway
matched_spe = re.findall(r"S(\d+)", pathname)
matched_reaction = re.findall(r"R(\d+)", pathname)
print(matched_spe, matched_reaction)
node_list = [
name_idx_dict[change_spe_name(str(x), spe_idx_name_dict,
spe_union_find_group)]
for x in matched_spe
]
print(node_list)
for idx, curr_idx in enumerate(node_list):
if idx >= 1:
pre_idx = node_list[idx - 1]
a_h = a_x.annotate('',
xy=(x[curr_idx], y[curr_idx]),
xytext=(x[pre_idx], y[pre_idx]),
arrowprops={
'arrowstyle': '->',
'lw': 4,
'color': 'red'
},
va='center')
a_h.set_alpha(0.9)
# re-draw points and labels on canvas
for _, val in enumerate(node_list):
a_x.scatter(x[val], y[val], color='r', marker="o", alpha=0.9)
t_h = a_x.annotate(labels[val], (x[val], y[val]))
t_h.set_alpha(0.9)
# draw reaction along path
if species_path is False:
# check for duplicate transition
idx_label_dict = {}
for idx, curr_idx in enumerate(node_list):
if idx >= 1:
pre_idx = node_list[idx - 1]
rxn_idx = matched_reaction[idx - 1]
if tuple([pre_idx, curr_idx, rxn_idx]) in idx_label_dict:
idx_label_dict[tuple([pre_idx, curr_idx,
rxn_idx])] += "," + str(idx)
else:
idx_label_dict[tuple([pre_idx, curr_idx,
rxn_idx])] = str(idx)
for idx, curr_idx in enumerate(node_list):
if idx >= 1:
pre_idx = node_list[idx - 1]
rxn_idx = matched_reaction[idx - 1]
rxn_name = idx_label_dict[tuple(
[pre_idx, curr_idx, rxn_idx])] + ": " + str(
new_ind_reaction_dict[matched_reaction[idx - 1]])
if x[pre_idx] <= x[curr_idx]:
x_tmp = x[pre_idx]
else:
x_tmp = x[curr_idx]
y_tmp = y[pre_idx] * 0.7 + y[curr_idx] * 0.3
t_h = a_x.annotate(rxn_name, (x_tmp, y_tmp),
color='g',
size=8.0)
t_h.set_alpha(0.5)
else:
# build idx->label
idx_label_dict = {}
for idx, curr_idx in enumerate(node_list):
if idx >= 1:
pre_idx = node_list[idx - 1]
if tuple([pre_idx, curr_idx]) in idx_label_dict:
idx_label_dict[tuple([pre_idx,
curr_idx])] += "," + str(idx)
else:
idx_label_dict[tuple([pre_idx, curr_idx])] = str(idx)
for idx, curr_idx in enumerate(node_list):
if idx >= 1:
pre_idx = node_list[idx - 1]
rxn_name = idx_label_dict[tuple([pre_idx, curr_idx])]
t_h = a_x.annotate(rxn_name,
(x[pre_idx] * 0.7 + x[curr_idx] * 0.3,
y[pre_idx] * 0.7 + y[curr_idx] * 0.3),
color='g',
size=8.0)
t_h.set_alpha(0.5)
a_x.set_xlim([
np.min(x) - 0.01 * (np.max(x) - np.min(x)),
np.max(x) + 0.25 * (np.max(x) - np.min(x))
])
# a_x.grid('on')
a_x.axis('off')
a_x.set_frame_on(False)
a_x.set_xticks([]) # this is needed for bbox_inches
a_x.set_yticks([])
if (path_idx == 1):
a_x.set_title("P$_{" + str(path_idx) + "}$" + " = " +
"{:.6e}".format(float(pathprob)))
else:
a_x.set_title("P$_{" + str(path_idx) + "}$" + " = " +
"{:.2e}".format(float(pathprob)))
# fig.tight_layout()
# plt.subplots_adjust(left=0.01, right=0.9, top=0.9, bottom=0.01)
fig.savefig(os.path.join(data_dir, "output", fig_name),
bbox_inches='tight',
dpi=500)
# bbox_inches='tight', pad_inches=0, dpi=500)
plt.close()
return
def init_directed_network_from_X_and_R_at_a_time(data_dir,
tag="M",
tau=10.0,
end_t=0.5,
end_t2=None,
x_y_dict=None):
"""
init directed network
without parallel edges
return networkx.DiGraph
at least time-snapshot network at a time, end_t,
a second time can be added, but don't change network structure,
instead add a second attribute "weight2" to edges
"""
s_idx_2_name, _ = psri.parse_spe_info(data_dir)
spe_alias = read_spe_alias(
os.path.join(data_dir, "input", "spe_alias.json"))
time_v = np.loadtxt(os.path.join(data_dir, "output", "time_dlsode_M.csv"),
dtype=float,
delimiter=',')
conc_mat = np.loadtxt(os.path.join(
data_dir, "output", "concentration_dlsode_" + str(tag) + ".csv"),
delimiter=",")
rxn_rates_mat = np.loadtxt(os.path.join(
data_dir, "output", "reaction_rate_dlsode_" + str(tag) + ".csv"),
delimiter=",")
# the time point where reference time tau is
# use interpolation here
idx_array = [i for i in range(len(time_v))]
time_axis = int(
round(interpolation.interp1d(time_v, idx_array, tau * end_t)))
if time_axis >= len(time_v):
time_axis = len(time_v) - 1
conc_v = conc_mat[time_axis, :]
rxn_rates_v = rxn_rates_mat[time_axis, :]
if end_t2 is not None:
time_axis2 = int(
round(interpolation.interp1d(time_v, idx_array, tau * end_t2)))
if time_axis2 >= len(time_v):
time_axis2 = len(time_v) - 1
rxn_rates_v2 = rxn_rates_mat[time_axis2, :]
species_set = set()
species_pair_weight = {}
if end_t2 is not None:
species_pair_weight2 = {}
# species pairs-reactions-coefficient
s_p_r_c = psri.parse_species_pair_reaction(data_dir)
# print(s_p_r_c)
for s1, s2 in s_p_r_c:
species_set.add(int(s1))
# print(s1, s2)
species_set.add(int(s2))
if (int(s1), int(s2)) not in species_pair_weight:
species_pair_weight.update({(int(s1), int(s2)): 0.0})
if end_t2 is not None:
if (int(s1), int(s2)) not in species_pair_weight2:
species_pair_weight2.update({(int(s1), int(s2)): 0.0})
for idx in s_p_r_c[(s1, s2)]:
r_idx = int(s_p_r_c[(s1, s2)][idx]['r_idx'])
c1 = float(s_p_r_c[(s1, s2)][idx]['c1'])
c2 = float(s_p_r_c[(s1, s2)][idx]['c2'])
flux = rxn_rates_v[r_idx] * c2 / c1
species_pair_weight[(int(s1), int(s2))] += flux
if end_t2 is not None:
flux2 = rxn_rates_v2[r_idx] * c2 / c1
species_pair_weight2[(int(s1), int(s2))] += flux2
# print(species_set)
# print(species_pair_weight)
edge_weight_v = []
for idx, key in enumerate(species_pair_weight):
edge_weight_v.append(float(species_pair_weight[key]))
if end_t2 is not None:
edge_weight_v2 = []
for idx, key in enumerate(species_pair_weight2):
edge_weight_v2.append(float(species_pair_weight2[key]))
# rescase concentrations
# conc_v = rescale_array(conc_v, 10.0, 25.0)
conc_v = rescale_array_v2(conc_v, 1.0, 5.0, 15.0, 25.0, -12)
# edge_weight_v = rescale_array(edge_weight_v, 2.0, 25.0)
edge_weight_v = rescale_array_v2(edge_weight_v, 0.5, 1.0, 15.0, 25.0, -9)
# edge weights write to file
e_w_fn1 = os.path.join(data_dir, "output",
"edge_weight1_" + str(end_t) + ".csv")
f_hanlder1 = open(e_w_fn1, 'w')
f_hanlder1.write("Source,Target,Weight" + str(end_t) + "\n")
# np.savetxt(e_w_fn1, edge_weight_v, fmt='%.18e', newline='\n')
if end_t2 is not None:
# edge_weight_v2 = rescale_array(edge_weight_v2, 2.0, 25.0)
edge_weight_v2 = rescale_array_v2(edge_weight_v2, 1.5, 2.5, 15.0, 25.0,
-9)
e_w_fn2 = os.path.join(data_dir, "output",
"edge_weight2_" + str(end_t2) + ".csv")
# np.savetxt(e_w_fn2, edge_weight_v2, fmt='%.18e', newline='\n')
f_hanlder2 = open(e_w_fn2, 'w')
f_hanlder2.write("Source,Target,Weight" + str(end_t2) + "\n")
# final directed graph
di_graph = nx.DiGraph()
# add nodes first
for idx, val in enumerate(species_set):
weight = float(conc_v[int(val)])
node_name = change_spe_name(s_idx_2_name[str(val)], spe_alias, None)
# add a layer to control whether to show the species label name
label_name = ''
if s_idx_2_name[str(val)] in spe_alias:
label_name = node_name
if x_y_dict is None:
di_graph.add_node(node_name, label=label_name, weight=weight)
else:
di_graph.add_node(node_name,
label=label_name,
weight=weight,
x=x_y_dict[node_name][0],
y=x_y_dict[node_name][1])
# add edges
for idx, key in enumerate(species_pair_weight):
src = key[0]
dst = key[1]
src_name = change_spe_name(s_idx_2_name[str(src)], spe_alias, None)
dst_name = change_spe_name(s_idx_2_name[str(dst)], spe_alias, None)
name = src_name + "," + dst_name
# write weight1 to file
f_hanlder1.write(src_name + "," + dst_name + "," + str(weight) + "\n")
if end_t2 is None:
weight = float(edge_weight_v[idx])
di_graph.add_edge(src_name,
dst_name,
name=name,
weight=weight,
weight2=weight)
else:
weight = float(edge_weight_v[idx])
weight2 = float(edge_weight_v2[idx])
di_graph.add_edge(src_name,
dst_name,
name=name,
weight=weight,
weight2=weight2)
# write weight2 to file
f_hanlder2.write(src_name + "," + dst_name + "," + str(weight2) +
"\n")
f_hanlder1.close()
if end_t2 is not None:
f_hanlder2.close()
return di_graph
def init_directed_network(data_dir,
path_idx=None,
init_spe=None,
atom_followed="C",
end_t=None,
species_path=False,
time_axis=0):
"""
init directed network
without parallel edges
return networkx.DiGraph
"""
spe_idx_name_dict, _ = psri.parse_spe_info(data_dir)
suffix = get_suffix(data_dir,
init_spe=init_spe,
atom_followed=atom_followed,
end_t=end_t)
prefix = ""
if species_path is True:
prefix = "species_"
f_n_path_name = os.path.join(
data_dir, "output",
prefix + "pathway_name_candidate" + suffix + ".csv")
f_n_path_prob = os.path.join(data_dir, "output",
prefix + "pathway_prob" + suffix + ".csv")
print(f_n_path_name, f_n_path_prob)
p_n = np.genfromtxt(f_n_path_name, dtype=str, delimiter=',')
p_p = np.genfromtxt(f_n_path_prob, dtype=float, delimiter=',')
# in case of two dimensional pathway name
if len(np.shape(p_n)) == 2:
p_n = p_n[:, time_axis]
if len(np.shape(p_p)) == 2:
p_p = p_p[:, time_axis]
# retrieve pathway name and pathway probability before sort
p_n = [p_n[i] for i in path_idx]
p_p = [p_p[i] for i in path_idx]
# set the data type seperately
d_f_n = pd.DataFrame(p_n, columns=['name'], dtype=str)
d_f_p = pd.DataFrame(p_p, columns=['prob'], dtype=float)
d_f = pd.concat([d_f_n, d_f_p], axis=1)
d_f.sort_values(by='prob',
ascending=False,
inplace=True,
na_position='last')
d_f.reset_index(drop=True, inplace=True)
print(d_f.head())
# temporary directed graph
d_g_tmp = nx.DiGraph()
# modify labels
spe_union_find_group = global_settings.get_union_find_group(
DATA_DIR, atom_followed)
# record all nodes
nodes = set()
for _, val in d_f.iterrows():
matched_spe = re.findall(r"S(\d+)", val['name'])
for _, spe in enumerate(matched_spe):
nodes.add(
change_spe_name(spe,
spe_idx_name_dict,
union_find=spe_union_find_group))
for _, val in enumerate(nodes):
d_g_tmp.add_node(val, weight=0.0, label=str(val))
for _, val in d_f.iterrows():
prob = float(val['prob'])
# get rid of R-1000003S90, don't need it here
print(val['name'])
path_name_tmp = re.sub(r"R-\d+S\d+", r'', val['name'])
print(path_name_tmp)
# pathway contains both reaction and species
if species_path is False:
matched_spe = re.findall(r"S(\d+)", path_name_tmp)
matched_reaction = re.findall(r"R(\d+)", path_name_tmp)
for idx, spe in enumerate(matched_spe):
d_g_tmp.node[change_spe_name(
spe, spe_idx_name_dict,
union_find=spe_union_find_group)]['weight'] += 1.0 * prob
if idx > 0:
src = change_spe_name(matched_spe[idx - 1],
spe_idx_name_dict,
union_find=spe_union_find_group)
dest = change_spe_name(spe,
spe_idx_name_dict,
union_find=spe_union_find_group)
rxn = change_rxn_name(matched_reaction[idx - 1])
if d_g_tmp.has_edge(src, dest):
d_g_tmp[src][dest]['weight'] += 1.0 * prob
d_g_tmp[src][dest]['reactions'].add(rxn)
else:
d_g_tmp.add_edge(src,
dest,
reactions=set([rxn]),
weight=1.0 * prob)
else:
matched_spe = re.findall(r"S(\d+)", path_name_tmp)
for idx, spe in enumerate(matched_spe):
d_g_tmp.node[change_spe_name(
spe, spe_idx_name_dict,
union_find=spe_union_find_group)]['weight'] += 1.0 * prob
if idx > 0:
src = change_spe_name(matched_spe[idx - 1],
spe_idx_name_dict,
union_find=spe_union_find_group)
dest = change_spe_name(spe,
spe_idx_name_dict,
union_find=spe_union_find_group)
rxn = '-1'
if d_g_tmp.has_edge(src, dest):
d_g_tmp[src][dest]['weight'] += 1.0 * prob
d_g_tmp[src][dest]['reactions'].add(rxn)
else:
d_g_tmp.add_edge(src,
dest,
reactions=set([rxn]),
weight=1.0 * prob)
# update directed graph, for example,
# 1. reactions is originally a set, combine to get a string of reactions
# 2. smooth and re-normalize node weight
# 3. re-normalize edge weight
node_weight = []
for _, val in enumerate(d_g_tmp.nodes()):
node_weight.append(d_g_tmp.node[val]['weight'])
edge_weight = []
for _, val in enumerate(d_g_tmp.edges()):
edge_weight.append(d_g_tmp[val[0]][val[1]]['weight'])
node_weight = rescale_array(node_weight, 1.0, 5.0)
edge_weight = rescale_array(edge_weight, 3.0, 15.0)
# final directed graph
di_graph = nx.DiGraph()
for idx, val in enumerate(d_g_tmp.nodes()):
di_graph.add_node(val, weight=node_weight[idx])
for idx, val in enumerate(d_g_tmp.edges()):
src = val[0]
dest = val[1]
rxn_set = d_g_tmp[src][dest]['reactions']
rxn_set = sorted(rxn_set, key=lambda x: int(x), reverse=False)
name = ",".join(x for x in rxn_set)
weight = edge_weight[idx]
di_graph.add_edge(src, dest, name=name, weight=weight)
return di_graph
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 plot_spe_drc(data_dir, spe_idx=None, tau=10.0, end_t=1.0, tag="fraction", reciprocal=False):
"""
plot species destruction rate constant, give species index list
"""
spe_idx_tmp = deepcopy(spe_idx)
if spe_idx_tmp is None:
spe_idx_tmp = [0]
colors, markers, _ = get_colors_markers_linestyles()
s_idx_n, _ = psri.parse_spe_info(data_dir)
s_idx_n["-1"] = "Temp"
spe_idx_tmp.append(-1)
time = np.loadtxt(os.path.join(
data_dir, "output", "time_dlsode_" + str(tag) + ".csv"), delimiter=",")
temp = np.loadtxt(os.path.join(data_dir, "output",
"temperature_dlsode_" + str(tag) + ".csv"), delimiter=",")
spe_drc = np.loadtxt(os.path.join(data_dir, "output",
"drc_dlsode_" + str(tag) + ".csv"), delimiter=",")
counter = 0
# the time point where reference time tau is
tau_time_point = float(tau) / time[-1] * len(time)
end_point = int(end_t * tau_time_point)
delta_n = int(end_point / 10)
if delta_n is 0:
delta_n = 1
fig, a_x_left = plt.subplots(1, 1, sharex=True, sharey=False)
for s_idx in spe_idx_tmp:
if s_idx == -1:
a_x_right = a_x_left.twinx()
a_x_right.plot(time[0:end_point], temp[0:end_point], markevery=delta_n,
color=colors[-1], label=s_idx_n[str(s_idx)])
else:
if counter < len(colors) - 1:
m_k = None
else:
m_k = markers[(counter + 1 - len(colors)) % (len(markers))]
if reciprocal is False:
a_x_left.semilogy(time[0:end_point], spe_drc[0:end_point, s_idx], marker=m_k, markevery=delta_n,
color=colors[counter % (len(colors) - 1)], label=s_idx_n[str(s_idx)])
else:
a_x_left.semilogy(time[0:end_point], 1.0 / spe_drc[0:end_point, s_idx], marker=m_k, markevery=delta_n,
color=colors[counter % (len(colors) - 1)], label=s_idx_n[str(s_idx)])
counter += 1
if reciprocal is False:
leg_left = a_x_left.legend(loc=9, fancybox=True, prop={'size': 10.0})
else:
leg_left = a_x_left.legend(loc=8, fancybox=True, prop={'size': 10.0})
leg_right = a_x_right.legend(loc=4, fancybox=True, prop={'size': 10.0})
leg_left.get_frame().set_alpha(0.7)
leg_right.get_frame().set_alpha(0.7)
a_x_left.grid()
a_x_left.set_xlim([0.05, time[end_point]])
a_x_left.set_xlabel("time/s")
if reciprocal is False:
a_x_left.set_ylabel("k/s$^{-1}$")
else:
a_x_left.set_ylabel("k$^{-1}/s$")
a_x_right.set_ylabel("T/K")
s_n_str = "_".join(s_idx_n[str(x)] for x in spe_idx_tmp)
# plt.title(s_n_str)
if reciprocal is False:
fig.savefig(os.path.join(data_dir, "output",
"spe_drc_" + s_n_str + ".jpg"), dpi=500)
else:
fig.savefig(os.path.join(data_dir, "output",
"spe_drc_reciprocal_" + s_n_str + ".jpg"), dpi=500)
plt.close()
def plot_concentrations(data_dir, spe_idx=None, tau=10.0, end_t=1.0, tag="fraction", exclude_names=None,
renormalization=True, semilogy=False, hasTemp=True):
"""
plot concentrations give species index list, if exclude is not None, means we are going
to renormalize the molelar fraction
"""
if exclude_names is None:
exclude_names = []
spe_idx_tmp = deepcopy(spe_idx)
if spe_idx_tmp is None:
spe_idx_tmp = [0]
colors, markers, _ = get_colors_markers_linestyles()
s_idx_n, _ = psri.parse_spe_info(data_dir)
if hasTemp is True:
s_idx_n["-1"] = "Temp"
spe_idx_tmp.append(-1)
time = np.loadtxt(os.path.join(
data_dir, "output", "time_dlsode_" + str(tag) + ".csv"), delimiter=",")
temp = np.loadtxt(os.path.join(data_dir, "output",
"temperature_dlsode_" + str(tag) + ".csv"), delimiter=",")
conc = trajectory.get_normalized_concentration(
data_dir, tag=tag, exclude_names=exclude_names, renormalization=renormalization)
counter = 0
# the time point where reference time tau is
tau_time_point = float(tau) / time[-1] * len(time)
end_point = int(end_t * tau_time_point)
delta_n = int(end_point / 10)
if delta_n is 0:
delta_n = 1
fig, a_x_left = plt.subplots(1, 1, sharex=True, sharey=False)
for s_idx in spe_idx_tmp:
if s_idx == -1:
a_x_right = a_x_left.twinx()
a_x_right.plot(time[0:end_point], temp[0:end_point],
color=colors[-1], label=s_idx_n[str(s_idx)])
else:
if counter < len(colors) - 1:
m_k = None
else:
m_k = markers[(counter + 1 - len(colors)) % (len(markers))]
if semilogy is True:
a_x_left.semilogy(time[0:end_point], conc[0:end_point, s_idx], marker=m_k, markevery=delta_n,
color=colors[counter % (len(colors) - 1)], label=s_idx_n[str(s_idx)])
else:
a_x_left.plot(time[0:end_point], conc[0:end_point, s_idx], marker=m_k, markevery=delta_n,
color=colors[counter % (len(colors) - 1)], label=s_idx_n[str(s_idx)])
counter += 1
leg_left = a_x_left.legend(loc=8, fancybox=True, prop={'size': 10.0})
leg_left.get_frame().set_alpha(0.7)
a_x_left.grid()
a_x_left.set_xlim([0, tau * end_t])
a_x_left.xaxis.set_major_formatter(FormatStrFormatter('%.1e'))
a_x_left.set_xlabel("Time/sec")
a_x_left.set_ylabel("[X]")
if hasTemp is True:
leg_right = a_x_right.legend(loc=2, fancybox=True, prop={'size': 10.0})
leg_right.get_frame().set_alpha(0.7)
a_x_right.set_ylabel("T/K")
s_n_str = "_".join(s_idx_n[str(x)] for x in spe_idx_tmp)
# plt.title(s_n_str)
fig.savefig(os.path.join(data_dir, "output",
"trajectory_" + s_n_str + ".jpg"), dpi=500)
plt.close()
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