def test_paths():
net_flux = np.array([[0.0, 0.5, 0.5, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.3, 0.0, 0.2],
[0.0, 0.0, 0.0, 0.0, 0.5, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.3],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0]])
sources = np.array([0])
sinks = np.array([4, 5])
ref_paths = [[0, 2, 4],
[0, 1, 3, 5],
[0, 1, 5]]
ref_fluxes = np.array([0.5, 0.3, 0.2])
res_bottle = tpt.paths(sources, sinks, net_flux, remove_path='bottleneck')
res_subtract = tpt.paths(sources, sinks, net_flux, remove_path='subtract')
for paths, fluxes in [res_bottle, res_subtract]:
npt.assert_array_almost_equal(fluxes, ref_fluxes)
assert len(paths) == len(ref_paths)
for i in range(len(paths)):
npt.assert_array_equal(paths[i], ref_paths[i])
def test_paths():
net_flux = np.array([[0.0, 0.5, 0.5, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.3, 0.0, 0.2],
[0.0, 0.0, 0.0, 0.0, 0.5, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.3],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0]])
sources = np.array([0])
sinks = np.array([4, 5])
ref_paths = [[0, 2, 4], [0, 1, 3, 5], [0, 1, 5]]
ref_fluxes = np.array([0.5, 0.3, 0.2])
res_bottle = tpt.paths(sources, sinks, net_flux, remove_path='bottleneck')
res_subtract = tpt.paths(sources, sinks, net_flux, remove_path='subtract')
for paths, fluxes in [res_bottle, res_subtract]:
npt.assert_array_almost_equal(fluxes, ref_fluxes)
assert len(paths) == len(ref_paths)
for i in xrange(len(paths)):
npt.assert_array_equal(paths[i], ref_paths[i])
def generate_tpt_traj_index_series(msm_object, sources, sinks, clusters_map,
num_paths, remove_path, save_file):
net_flux = tpt.net_fluxes(sources, sinks, msm_object)
tpt_paths = tpt.paths(sources,
sinks,
net_flux,
remove_path=remove_path,
num_paths=num_paths,
flux_cutoff=0.5)
inv_map = {v: k for k, v in msm_object.mapping_.items()}
print(tpt_paths)
traj_index_pairs_list = []
for path in tpt_paths[0]:
print("path = %s" % (str(path)))
traj_index_pairs = []
for state in path:
cluster = inv_map[state]
traj_index_pair = random.choice(list(clusters_map[cluster]))
traj_index_pairs.append(traj_index_pair)
traj_index_pairs_list.append(traj_index_pairs)
verbosedump(traj_index_pairs_list, save_file)
inv_tpt_paths = []
for tpt_path in tpt_paths[0]:
inv_tpt_paths.append([inv_map[i] for i in tpt_path])
return tpt_paths[0], inv_tpt_paths, traj_index_pairs_list
def make_json_paths(msm, request):
sources = map(int,
request.get_argument('sources').replace(" ", "").split(","))
sinks = map(int, request.get_argument('sinks').replace(" ", "").split(","))
n = int(request.get_argument('num_paths'))
net_flux = tpt.net_fluxes(sources, sinks, msm)
paths = tpt.paths(sources, sinks, net_flux, num_paths=n)
G = nx.DiGraph()
for j, i in enumerate(paths[0][::-1]):
G.add_node(i[0], type="source")
for k in range(1, len(i)):
G.add_node(i[k], type="none")
G.add_edge(i[k-1], i[k], weight=paths[1][::-1][j])
G.add_node(i[-1], type="sink")
return json_graph.node_link_data(G)
def plot_tpaths(
msm, sources, sinks, for_committors=None, num_paths=1, pos=None, node_color="c", edge_color="k", ax=None, **kwargs
):
net_flux = tpt.net_fluxes(sources, sinks, msm, for_committors=for_committors)
paths, _ = tpt.paths(sources, sinks, net_flux, num_paths=num_paths)
graph = nx.DiGraph()
for path in paths:
for u, v in zip(path[:-1], path[1:]):
graph.add_edge(u, v, weight=net_flux[u, v])
if not ax:
ax = pp.gca()
nx.draw_networkx(graph, pos=pos, node_color=node_color, edge_color=edge_color, ax=ax, **kwargs)
return ax
def do_tpt(ev_id):
plt.figure(figsize=(15, 10))
# TPT "FROM":
sources = [7]
# TPT "To":
sinks = [10]
net_flux = tpt.net_fluxes(sources, sinks, msm, for_committors=None)
np.savetxt('net_flux.txt', net_flux)
pfold = tpt.committors(sources, sinks, msm)
np.savetxt('pfold.txt', pfold)
paths = tpt.paths(sources,
sinks,
net_flux,
remove_path='subtract',
flux_cutoff=0.9999999999)
mfpts = tpt.mfpts(
msm, sinks=None, lag_time=1.0
) # Default is (1) which is in units of the lag time of the MSM.
print "mfpts:", mfpts
np.savetxt('mfpts_from_i_to_j.txt', np.array(mfpts))
total_flux = np.sum(paths[1])
print "total_flux:", total_flux
sort = np.argsort(pfold)
total_line_width = np.sum(paths[1][0:5])
# top 5 paths, to get all paths set this number higher
for j in range(5):
print "path:", paths[0][j]
print "flux:", paths[1][j], paths[1][j] / float(np.sum(paths[1]))
x = []
for k in range(len(paths[0][j])):
x.extend(np.where(np.arange(100)[sort] == paths[0][j][k])[0])
plt.plot(x,
pfold[paths[0][j]],
linewidth=np.log(paths[1][j] / float(total_line_width)))
plt.legend(['%1.8f' % i for i in paths[1][0:5]],
fontsize=18,
loc='upper left')
for i in range(len(pfold)):
plt.plot(i, pfold[sort[i]], 'o')
plt.text(i, pfold[sort[i]], sort[i])
plt.savefig('pfold_ev%d_0.01.png' % ev_id)
from sklearn.externals import joblib
ids = range(6383, 6391)
for p_id in ids:
msm = joblib.load(
'MSMs-{pid}-macro40/MSMs-{pid}-macro40.pkl'.format(pid=p_id))
committors = tpt.committors(sources=[4], sinks=[13], msm=msm)
net_fluxes = tpt.net_fluxes(sources=[4],
sinks=[13],
msm=msm,
for_committors=committors)
top_10_paths_sub, top_10_fluxes_sub = tpt.paths(sources=[4],
sinks=[13],
net_flux=net_fluxes,
num_paths=10,
remove_path='subtract')
top_10_paths_bot, top_10_fluxes_bot = tpt.paths(sources=[4],
sinks=[13],
net_flux=net_fluxes,
num_paths=10,
remove_path='bottleneck')
output_dir = "Data-{pid}-macro40".format(pid=p_id)
print top_10_paths_sub, top_10_fluxes_sub
print top_10_paths_bot, top_10_fluxes_bot
fn_committors = os.path.join(output_dir, "committors.npy")
fn_n_fluxes = os.path.join(output_dir, "net_Fluxes.mtx")
fn_10_paths_sub = os.path.join(output_dir, "top_paths_substract.npy")
fn_10_fluxes_sub = os.path.join(output_dir, "top_fluxes_substract.npy")
def plot_tpaths(msm,
sources,
sinks,
for_committors=None,
num_paths=1,
pos=None,
node_size=None,
node_color='pomegranate',
edge_color='carbon',
alpha=.7,
with_labels=True,
ax=None,
**kwargs):
"""
Plot TPT network diagram.
Parameters
----------
msm : msmbuilder.msm
MSMBuilder MarkovStateModel
sources : int or list, optional
TPT source states
sinks : int or list, optional
TPT sink states
sinks : ndarray, optional
Pre-computed forward committors
pos : dict, optional
Node positions in dict format (e.g. {node_id : [x, y]})
node_color : str or [r, g, b], optional
networkx node colors
node_size : int or list, optional
networkx node size
node_color : str, optional
networkx edge color
alpha : float, optional (default: 0.7)
Opacity of nodes
ax : matplotlib axis, optional (default: None)
Axis to plot on, otherwise uses current axis.
with_labels : boolean, optional
Whether or not to include node labels (default: True)
**kwargs : dict, optional
Extra arguments to pass to networkx.draw_networkx
Returns
-------
ax : Axis
matplotlib figure axis
"""
if hasattr(msm, 'all_populations_'):
pop = msm.all_populations_.mean(0)
elif hasattr(msm, 'populations_'):
pop = msm.populations_
net_flux = tpt.net_fluxes(sources,
sinks,
msm,
for_committors=for_committors)
paths, _ = tpt.paths(sources, sinks, net_flux, num_paths=num_paths)
graph = nx.DiGraph()
for path in paths:
for u, v in zip(path[:-1], path[1:]):
graph.add_edge(u, v, weight=net_flux[u, v])
if not ax:
ax = pp.gca()
if pos is None:
pos = nx.spring_layout(graph)
if node_size is None:
node_size = 5000. * pop
if isinstance(node_size, (list, np.ndarray)):
node_size = [node_size[i] for i in graph.nodes()]
if isinstance(node_color, (list, np.ndarray)):
node_color = [node_color[i] for i in graph.nodes()]
nx.draw_networkx(graph,
pos=pos,
node_color=node_color,
alpha=alpha,
node_size=node_size,
edge_color=edge_color,
ax=ax,
with_labels=with_labels,
**kwargs)
return ax
ax = msme.plot_tpaths(msm, start_ind[0], [pop_ind], pos=pos)
plt.tight_layout()
plt.savefig('tpath.eps')
plt.clf()
ax = msme.plot_tpaths(msm, start_ind[0], [pop_ind], pos=pos13)
plt.tight_layout()
plt.savefig('tpath_13.eps')
plt.clf()
net_flux_matrix = net_fluxes(start_ind[0], [pop_ind], msm)
flux_matrix = fluxes(start_ind[0], [pop_ind], msm)
paths_list, fluxes1 = paths(start_ind[0], [pop_ind], net_flux_matrix)
paths_list_d, fluxes1_d = paths(start_ind[0], [pop_ind], net_flux_matrix, remove_path='bottleneck')
print('num states in path 1', len(paths_list[0]))
test_3 = np.unique(np.concatenate((paths_list[0],paths_list[1], paths_list[2]), 0))
test_5 = np.unique(np.concatenate((paths_list[0],paths_list[1], paths_list[2], paths_list[3], paths_list[4]), 0))
test_3d = np.unique(np.concatenate((paths_list_d[0],paths_list_d[1], paths_list_d[2]), 0))
test_5d = np.unique(np.concatenate((paths_list_d[0],paths_list_d[1], paths_list_d[2], paths_list_d[3], paths_list_d[4]), 0))
tot_flux = fluxes1.sum()
print('sum of fluxes is ', tot_flux)
def plot_tpaths(msm,
sources,
sinks,
for_committors=None,
num_paths=1,
pos=None,
node_color='pomegranate',
edge_color='carbon',
ax=None,
**kwargs):
"""
Plot TPT network diagram.
Parameters
----------
msm : msmbuilder.msm
MSMBuilder MarkovStateModel
sources : int or list, optional
TPT source states
sinks : int or list, optional
TPT sink states
sinks : ndarray, optional
Pre-computed forward committors
pos : dict, optional
Node positions in dict format (e.g. {node_id : [x, y]})
node_color : str or [r, g, b], optional
networkx node colors
node_size : int, optional
networkx node size
node_size : str or [r, g, b], optional
networkx edge color
ax : matplotlib axis, optional (default: None)
Axis to plot on, otherwise uses current axis.
with_labels : boolean, optional
Whether or not to include node labels (default: True)
**kwargs : dict, optional
Extra arguments to pass to networkx.draw_networkx
Returns
-------
ax : Axis
matplotlib figure axis
"""
net_flux = tpt.net_fluxes(sources,
sinks,
msm,
for_committors=for_committors)
paths, _ = tpt.paths(sources, sinks, net_flux, num_paths=num_paths)
graph = nx.DiGraph()
for path in paths:
for u, v in zip(path[:-1], path[1:]):
graph.add_edge(u, v, weight=net_flux[u, v])
if not ax:
ax = pp.gca()
if not pos:
pos = nx.spring_layout(graph)
nx.draw_networkx(graph,
pos=pos,
node_color=node_color,
edge_color=edge_color,
ax=ax,
**kwargs)
return ax