def main(argv=None):
if(argv==None):
argv = sys.argv
options = options_desc.parse_args(argv)[0]
assert(not(options.refine_all and options.extend_all))
pool = Pool()
needy_nodes = pool.where("isa_partition and is_sampled").multilock()
# 1. Trying to detect fake convergence
for n in pool.where("state == 'converged'"):
means = kmeans(n.trajectory, k=2)
d = (means[0] - means[1]).norm2()
if(d > 2.0 and (options.refine_all or userinput("%s has converged but appears to have a bimodal distribution.\nDo you want to refine?"%n.name, "bool"))): #TODO decide upon threshold (per coordinate?)
refine(n, options)
# 2. Dealing with not-converged nodes
for n in pool.where("state == 'not-converged'"):
if(not(options.refine_all or options.extend_all)):
choice = userchoice("%s has not converged. What do you want to do?"%n.name, ['_refine', '_extend', '_ignore'])
if(options.refine_all or choice=="r"):
refine(n, options)
elif(options.extend_all or choice=="e"):
extend(n)
elif(choice=="i"):
continue
for n in needy_nodes:
n.save()
n.unlock()
zgf_setup_nodes.main()
zgf_grompp.main()
zgf_cleanup.main()
def main():
options = options_desc.parse_args(sys.argv)[0]
zgf_cleanup.main()
pool = Pool()
not_reweightable = "isa_partition and state not in ('converged'"
if (options.ignore_convergence):
not_reweightable += ",'not-converged'"
if (options.ignore_failed):
not_reweightable += ",'mdrun-failed'"
not_reweightable += ")"
if pool.where(not_reweightable):
print "Pool can not be reweighted due to the following nodes:"
for bad_guy in pool.where(not_reweightable):
print "Node %s with state %s." % (bad_guy.name, bad_guy.state)
sys.exit("Aborting.")
active_nodes = pool.where("isa_partition and state != 'mdrun-failed'")
assert (len(active_nodes) == len(active_nodes.multilock())
) # make sure we lock ALL nodes
if (options.check_restraint):
for n in active_nodes:
check_restraint_energy(n)
if (options.method == "direct"):
reweight_direct(active_nodes, options)
elif (options.method == "entropy"):
reweight_entropy(active_nodes, options)
elif (options.method == "presampling"):
reweight_presampling(active_nodes, options)
else:
raise (Exception("Method unkown: " + options.method))
weight_sum = np.sum([n.tmp['weight'] for n in active_nodes])
print "Thermodynamic weights calculated by method '%s':" % options.method
for n in active_nodes:
n.obs.weight_direct = n.tmp['weight'] / weight_sum
if (options.method == "direct"):
print(
" %s with mean_V: %f [kJ/mol], %d refpoints and weight: %f" %
(n.name, n.obs.mean_V, n.tmp['n_refpoints'],
n.obs.weight_direct))
else:
print(" %s with A: %f [kJ/mol] and weight: %f" %
(n.name, n.obs.A, n.obs.weight_direct))
print "The above weighting uses bonded energies='%s' and nonbonded energies='%s'." % (
options.e_bonded, options.e_nonbonded)
for n in active_nodes:
n.save()
active_nodes.unlock()
def main():
options = options_desc.parse_args(sys.argv)[0]
zgf_cleanup.main()
pool = Pool()
#not_reweightable = "state not in ('refined','converged')"
not_reweightable = "isa_partition and state!='converged'"
if options.ignore_convergence:
not_reweightable = "isa_partition and state not in ('converged','not-converged')"
if pool.where(not_reweightable):
print "Pool can not be reweighted due to the following nodes:"
for bad_guy in pool.where(not_reweightable):
print "Node %s with state %s."%(bad_guy.name, bad_guy.state)
sys.exit("Aborting.")
active_nodes = pool.where("isa_partition")
assert(len(active_nodes) == len(active_nodes.multilock())) # make sure we lock ALL nodes
for n in active_nodes:
check_restraint_energy(n)
# find out about number of energygrps
mdp_file = gromacs.read_mdp_file(pool.mdp_fn)
energygrps = [str(egrp) for egrp in re.findall('[\S]+', mdp_file["energygrps"])]
moi_energies = True
if len(energygrps) < 2:
moi_energies = False # Gromacs energies are named differently when there are less than two energygrps :(
if(options.method == "direct"):
reweight_direct(active_nodes, moi_energies, options.sol_energy, options.save_refpoints)
elif(options.method == "entropy"):
reweight_entropy(active_nodes, moi_energies, options.sol_energy, options.save_refpoints)
elif(options.method == "presampling"):
reweight_presampling(active_nodes, options.presamp_temp, moi_energies, options.sol_energy)
else:
raise(Exception("Method unkown: "+options.method))
weight_sum = np.sum([n.tmp['weight'] for n in active_nodes])
print "Thermodynamic weights calculated by method '%s' (sol-energy=%s):"%(options.method, options.sol_energy)
for n in active_nodes:
n.obs.weight_direct = n.tmp['weight'] / weight_sum
if(options.method == "direct"):
print(" %s with mean_V: %f [kJ/mol], %d refpoints and weight: %f" % (n.name, n.obs.mean_V, n.tmp['n_refpoints'], n.obs.weight_direct))
else:
print(" %s with A: %f [kJ/mol] and weight: %f" % (n.name, n.obs.A, n.obs.weight_direct))
for n in active_nodes:
n.save()
active_nodes.unlock()
def main():
options = options_desc.parse_args(sys.argv)[0]
#TODO put somehow into Options, e.g. min_value=1 or required=True
if(not options.doomed_nodes):
sys.exit("Option --doomed_nodes is required.")
pool = Pool()
old_pool_size = len(pool)
old_alpha = pool.alpha
doomed_nodes = NodeList()
#TODO: maybe this code should go into ZIBMolPy.ui
for name in options.doomed_nodes.split(","):
found = [n for n in pool if n.name == name]
if(len(found) != 1):
sys.exit("Coult not find node '%s'"%(name))
doomed_nodes.append(found[0])
for n in doomed_nodes:
if(n == pool.root):
sys.exit("Node %s is the root. Removal not allowed."%(n.name))
#if(len(n.children) > 0):
# sys.exit("Node %s has children. Removal not allowed."%(n.name)) #TODO why should we forbid this?
if not(userinput("The selected node(s) will be removed permanently. Continue?", "bool")):
sys.exit("Quit by user.")
assert(len(doomed_nodes) == len(doomed_nodes.multilock()))
for n in doomed_nodes:
print("Removing directory: "+n.dir)
shutil.rmtree(n.dir)
pool.reload_nodes()
#TODO: this code-block also exists in zgf_create_node
if(len(pool.where("isa_partition")) < 2):
pool.alpha = None
elif(options.methodalphas == "theta"):
pool.alpha = zgf_create_nodes.calc_alpha_theta(pool)
elif(options.methodalphas == "user"):
pool.alpha = userinput("Please enter a value for alpha", "float")
else:
raise(Exception("Method unkown: "+options.methodalphas))
pool.history.append({'removed_nodes': [(n.name, n.state) for n in doomed_nodes], 'size':old_pool_size, 'alpha':old_alpha, 'timestamp':datetime.now()})
pool.save()
#TODO: deal with analysis dir and dependencies
zgf_cleanup.main()
def main():
options = options_desc.parse_args(sys.argv)[0]
#TODO put somehow into Options, e.g. min_value=1 or required=True
if(not options.doomed_nodes):
sys.exit("Option --doomed_nodes is required.")
pool = Pool()
old_pool_size = len(pool)
old_alpha = pool.alpha
doomed_nodes = NodeList()
#TODO: maybe this code should go into ZIBMolPy.ui
for name in options.doomed_nodes.split(","):
found = [n for n in pool if n.name == name]
if(len(found) != 1):
sys.exit("Coult not find node '%s'"%(name))
doomed_nodes.append(found[0])
for n in doomed_nodes:
if(n == pool.root):
sys.exit("Node %s is the root. Removal not allowed."%(n.name))
#if(len(n.children) > 0):
# sys.exit("Node %s has children. Removal not allowed."%(n.name)) #TODO why should we forbid this?
if not(userinput("The selected node(s) will be removed permanently. Continue?", "bool")):
sys.exit("Quit by user.")
assert(len(doomed_nodes) == len(doomed_nodes.multilock()))
for n in doomed_nodes:
print("Removing directory: "+n.dir)
shutil.rmtree(n.dir)
pool.reload_nodes()
#TODO: this code-block also exists in zgf_create_node
if(len(pool.where("isa_partition")) < 2):
pool.alpha = None
elif(options.methodalphas == "theta"):
pool.alpha = zgf_create_nodes.calc_alpha_theta(pool)
elif(options.methodalphas == "user"):
pool.alpha = userinput("Please enter a value for alpha", "float")
else:
raise(Exception("Method unkown: "+options.methodalphas))
pool.history.append({'removed_nodes': [(n.name, n.state) for n in doomed_nodes], 'size':old_pool_size, 'alpha':old_alpha, 'timestamp':datetime.now()})
pool.save()
#TODO: deal with analysis dir and dependencies
zgf_cleanup.main()
def main():
options = options_desc.parse_args(sys.argv)[0]
zgf_cleanup.main()
pool = Pool()
not_reweightable = "isa_partition and state not in ('converged'"
if(options.ignore_convergence):
not_reweightable += ",'not-converged'"
if(options.ignore_failed):
not_reweightable += ",'mdrun-failed'"
not_reweightable += ")"
if pool.where(not_reweightable):
print "Pool can not be reweighted due to the following nodes:"
for bad_guy in pool.where(not_reweightable):
print "Node %s with state %s."%(bad_guy.name, bad_guy.state)
sys.exit("Aborting.")
active_nodes = pool.where("isa_partition and state != 'mdrun-failed'")
assert(len(active_nodes) == len(active_nodes.multilock())) # make sure we lock ALL nodes
if(options.check_restraint):
for n in active_nodes:
check_restraint_energy(n)
if(options.method == "direct"):
reweight_direct(active_nodes, options)
elif(options.method == "entropy"):
reweight_entropy(active_nodes, options)
elif(options.method == "presampling"):
reweight_presampling(active_nodes, options)
else:
raise(Exception("Method unkown: "+options.method))
weight_sum = np.sum([n.tmp['weight'] for n in active_nodes])
print "Thermodynamic weights calculated by method '%s':"%options.method
for n in active_nodes:
n.obs.weight_direct = n.tmp['weight'] / weight_sum
if(options.method == "direct"):
print(" %s with mean_V: %f [kJ/mol], %d refpoints and weight: %f" % (n.name, n.obs.mean_V, n.tmp['n_refpoints'], n.obs.weight_direct))
else:
print(" %s with A: %f [kJ/mol] and weight: %f" % (n.name, n.obs.A, n.obs.weight_direct))
print "The above weighting uses bonded energies='%s' and nonbonded energies='%s'."%(options.e_bonded, options.e_nonbonded)
for n in active_nodes:
n.save()
active_nodes.unlock()
def main():
options = options_desc.parse_args(sys.argv)[0]
zgf_cleanup.main()
pool = Pool()
needy_nodes = pool.where("state == '%s'"%options.current_state).multilock()
for n in needy_nodes:
print "Recovering node %s with state %s to state %s ..."%(n.name, n.state, options.recover_state)
n.state = options.recover_state
n.save()
n.unlock()
def main():
options = options_desc.parse_args(sys.argv)[0]
zgf_cleanup.main()
pool = Pool()
needy_nodes = pool.where("state == '%s'" %
options.current_state).multilock()
for n in needy_nodes:
print "Recovering node %s with state %s to state %s ..." % (
n.name, n.state, options.recover_state)
n.state = options.recover_state
n.save()
n.unlock()
def main(argv=None):
if (argv == None):
argv = sys.argv
options = options_desc.parse_args(argv)[0]
assert (not (options.refine_all and options.extend_all))
pool = Pool()
needy_nodes = pool.where("isa_partition and is_sampled").multilock()
# 1. Trying to detect fake convergence
for n in pool.where("state == 'converged'"):
means = kmeans(n.trajectory, k=2)
d = (means[0] - means[1]).norm2()
if (d > 2.0 and (options.refine_all or userinput(
"%s has converged but appears to have a bimodal distribution.\nDo you want to refine?"
% n.name,
"bool"))): #TODO decide upon threshold (per coordinate?)
refine(n, options)
# 2. Dealing with not-converged nodes
for n in pool.where("state == 'not-converged'"):
if (not (options.refine_all or options.extend_all)):
choice = userchoice(
"%s has not converged. What do you want to do?" % n.name,
['_refine', '_extend', '_ignore'])
if (options.refine_all or choice == "r"):
refine(n, options)
elif (options.extend_all or choice == "e"):
extend(n)
elif (choice == "i"):
continue
for n in needy_nodes:
n.save()
n.unlock()
zgf_setup_nodes.main()
zgf_grompp.main()
zgf_cleanup.main()
def main():
options = options_desc.parse_args(sys.argv)[0]
zgf_cleanup.main()
print("Options:\n%s\n"%pformat(eval(str(options))))
pool = Pool()
parent = pool.root
active_nodes = pool.where("isa_partition")
assert(len(active_nodes) == len(active_nodes.multilock())) # make sure we lock ALL nodes
if active_nodes.where("'weight_direct' not in obs"):
sys.exit("Q-Matrix calculation not possible: Not all of the nodes have been reweighted.")
node_weights = np.array([node.obs.weight_direct for node in active_nodes])
print "### Generate bins: equidist ###"
result = q_equidist(parent, options.numnodes)
chosen_idx=result['chosen_idx']
frames_chosen=result['frames_chosen']
theta=result['theta']
chosen_idx.sort() # makes preview-trajectory easier to understand
dimension=len(chosen_idx)
print "chosen_idx"
print chosen_idx
print "### Generate bin weights ###"
bin_weights=np.zeros(dimension)
for (i,n) in enumerate(active_nodes):
w_denom = np.sum(n.frameweights)
for t in range(len(n.trajectory)):
diffs = (frames_chosen - n.trajectory.getframe(t)).norm()
j = np.argmin(diffs)
bin_weights[j] = bin_weights[j] + node_weights[i] * n.frameweights[t] / w_denom
print "bin_weights"
print bin_weights
print "### Generate q_all (entries only for neighboring bins) ###"
q_all = np.empty((dimension, dimension), dtype=np.float)
for i in range(dimension):
sum_row = 0.0
diffs = (frames_chosen - frames_chosen.getframe(i)).norm()
print "diffs"
print diffs
for j in range(dimension):
if (diffs[j] < 2.0 * theta) and (bin_weights[i] > 0.0):
q_all[i,j] = np.sqrt(bin_weights[j]) / np.sqrt(bin_weights[i])
sum_row = sum_row + q_all[i , j]
else:
q_all[i,j] = 0
q_all[i, i] = q_all[i, i]- sum_row
print "Q_All"
print q_all
if options.export_matlab:
savemat(pool.analysis_dir+"q_all.mat", {"q_all":q_all})
active_nodes.unlock()
zgf_cleanup.main()
def main():
options = options_desc.parse_args(sys.argv)[0]
zgf_cleanup.main()
pool = Pool()
npz_file = np.load(pool.chi_mat_fn)
chi_matrix = npz_file['matrix']
node_names = npz_file['node_names']
n_clusters = npz_file['n_clusters']
active_nodes = [Node(nn) for nn in node_names]
# create and open dest_files, intialize counters for statistics
dest_filenames = [
pool.analysis_dir + "cluster%d.trr" % (c + 1)
for c in range(n_clusters)
]
dest_files = [open(fn, "wb") for fn in dest_filenames]
dest_frame_counters = np.zeros(n_clusters)
# For each active node...
for (i, n) in enumerate(active_nodes):
# ... find the clusters to which it belongs (might be more than one)...
belonging_clusters = np.argwhere(
chi_matrix[i] > options.node_threshold)
# ... and find all typical frames of this node.
#TODO not an optimal solution... discuss
# per default, we take every frame with above average weight
frame_threshold = options.frame_threshold * 2 * np.mean(n.frameweights)
typical_frame_nums = np.argwhere(n.frameweights > frame_threshold)
# Go through the node's trajectory ...
trr_in = TrrFile(n.trr_fn)
curr_frame = trr_in.first_frame
for i in typical_frame_nums:
# ...stop at each typical frame...
while (i != curr_frame.number):
curr_frame = curr_frame.next()
assert (curr_frame.number == i)
#... and copy it into the dest_file of each belonging cluster.
for c in belonging_clusters:
dest_files[c].write(curr_frame.raw_data)
dest_frame_counters[c] += 1
trr_in.close() # close source file
# close dest_files
for f in dest_files:
f.close()
del (dest_files)
# desolvate cluster-trajectories 'in-place'
if (not options.write_sol):
for dest_fn in dest_filenames:
tmp_fn = mktemp(suffix='.trr', dir=pool.analysis_dir)
os.rename(dest_fn, tmp_fn) # works as both files are in same dir
cmd = ["trjconv", "-f", tmp_fn, "-o", dest_fn, "-n", pool.ndx_fn]
p = Popen(cmd, stdin=PIPE)
p.communicate(input="MOI\n")
assert (p.wait() == 0)
os.remove(tmp_fn)
# register dependencies
for fn in dest_filenames:
register_file_dependency(fn, pool.chi_mat_fn)
# check number of written frames
sys.stdout.write("Checking lenghts of written trajectories... ")
for i in range(n_clusters):
f = TrrFile(dest_filenames[i])
assert (f.count_frames() == dest_frame_counters[i])
f.close()
print("done.")
#output statistics
print "\n### Extraction summary ###\nnode threshold: %1.1f, frame threshold: %1.1f" % (
options.node_threshold, options.frame_threshold)
print "Cluster trajectories were written to %s:" % pool.analysis_dir
for (c, f) in enumerate(dest_frame_counters):
print "cluster%d.trr [%d frames] from node(s):" % (c + 1, f)
print list(np.argwhere(chi_matrix[:, c] > options.node_threshold).flat)
def main(argv=None):
if(argv==None):
argv = sys.argv
options = options_desc.parse_args(argv)[0]
print("Options:\n%s\n"%pformat(eval(str(options))))
if(options.random_seed):
# using numpy-random because python-random differs beetween 32 and 64 bit
np.random.seed(hash(options.random_seed))
pool = Pool()
old_pool_size = len(pool)
print "pool", pool
if(options.parent_node == "root"):
parent = pool.root
else:
found = [n for n in pool if n.name == options.parent_node]
assert(len(found) == 1)
parent = found[0]
print "### Generate nodes: %s ###" % options.methodnodes
if(options.methodnodes == "kmeans"):
chosen_idx = mknodes_kmeans(parent, options.numnodes)
elif(options.methodnodes == "equidist"):
chosen_idx = mknodes_equidist(parent, options.numnodes)
elif(options.methodnodes == "maxdist"):
chosen_idx = mknodes_maxdist(parent, options.numnodes)
elif(options.methodnodes == "all"):
chosen_idx = mknodes_all(parent)
else:
raise(Exception("Method unknown: "+options.methodnodes))
chosen_idx.sort() # makes preview-trajectory easier to understand
if(options.write_preview):
write_node_preview(pool, parent, chosen_idx)
for i in chosen_idx:
n = Node()
n.parent_frame_num = i
n.parent = parent
n.state = "creating-a-partition" # will be set to "created" at end of script
n.extensions_counter = 0
n.extensions_max = options.ext_max
n.extensions_length = options.ext_length
n.sampling_length = options.sampling_length
n.internals = parent.trajectory.getframe(i)
pool.append(n)
print "\n### Obtain alpha: %s ###" % options.methodalphas
old_alpha = pool.alpha
if(options.methodalphas == "theta"):
pool.alpha = calc_alpha_theta(pool)
elif(options.methodalphas == "user"):
pool.alpha = userinput("Please enter a value for alpha", "float")
else:
raise(Exception("Method unknown: "+options.methodalphas))
pool.history.append({'refined_node': (parent.name, parent.state), 'size':old_pool_size, 'alpha':old_alpha, 'timestamp':datetime.now()})
pool.save() # alpha might have changed
print "\n### Obtain phi fit: %s ###" % options.methodphifit
if(options.methodphifit == "harmonic"):
do_phifit_harmonic(pool)
elif(options.methodphifit == "switch"):
do_phifit_switch(pool)
elif(options.methodphifit == "leastsq"):
do_phifit_leastsq(pool)
else:
raise(Exception("Method unkown: "+options.methodphifit))
for n in pool.where("state == 'creating-a-partition'"):
n.state = "created"
n.save()
print "saving " +str(n)
zgf_cleanup.main()
def main():
options = options_desc.parse_args(sys.argv)[0]
zgf_cleanup.main()
pool = Pool()
active_nodes = pool.where("isa_partition")
if(options.ignore_failed):
active_nodes = pool.where("isa_partition and not state=='mdrun-failed'")
assert(len(active_nodes) == len(active_nodes.multilock())) # make sure we lock ALL nodes
if active_nodes.where("'weight_direct' not in obs"):
active_nodes.unlock()
sys.exit("Matrix calculation not possible: Not all of the nodes have been reweighted.")
print "\n### Getting S matrix ..."
s_matrix = cache_matrix(pool.s_mat_fn, active_nodes, overwrite=options.overwrite_mat, fast=options.fast_mat)
register_file_dependency(pool.s_mat_fn, pool.filename)
node_weights = np.array([node.obs.weight_direct for node in active_nodes])
print "\n### Symmetrizing S matrix ..."
(corr_s_matrix, corr_node_weights) = symmetrize(s_matrix, node_weights, correct_weights=True, error=float(options.error))
# store intermediate results
register_file_dependency(pool.s_corr_mat_fn, pool.s_mat_fn)
np.savez(pool.s_corr_mat_fn, matrix=corr_s_matrix, node_names=[n.name for n in active_nodes])
if options.export_matlab:
savemat(pool.analysis_dir+"node_weights.mat", {"node_weights":node_weights, "node_weights_corrected":corr_node_weights})
savemat(pool.analysis_dir+"s_mats.mat", {"s_matrix":s_matrix, "s_matrix_corrected":corr_s_matrix})
for (n, cw) in zip(active_nodes, corr_node_weights):
n.obs.weight_corrected = cw
print "\n### Node weights after symmetrization of S matrix:"
for n in active_nodes:
print "%s: initial weight: %f, corrected weight: %f, weight change: %f" % (n.name, n.obs.weight_direct, n.obs.weight_corrected, abs(n.obs.weight_direct - n.obs.weight_corrected))
n.save()
active_nodes.unlock()
# calculate and sort eigenvalues in descending order
(eigvalues, eigvectors) = np.linalg.eig(corr_s_matrix)
argsorted_eigvalues = np.argsort(-eigvalues)
eigvalues = eigvalues[argsorted_eigvalues]
eigvectors = eigvectors[:, argsorted_eigvalues]
gaps = np.abs(eigvalues[1:]-eigvalues[:-1])
gaps = np.append(gaps, 0.0)
wgaps = gaps*eigvalues
print "\n### Sorted eigenvalues of symmetrized S matrix:"
for (idx, ev, gap, wgap) in zip(range(1, len(eigvalues)+1), eigvalues, gaps, wgaps):
print "EV%04d: %f, gap to next: %f, EV-weighted gap to next: %f" % (idx, ev, gap, wgap)
n_clusters = np.argmax(wgaps)+1
print "\n### Maximum gap %f after top %d eigenvalues." % (np.max(gaps), n_clusters)
print "### Maximum EV-weighted gap %f after top %d eigenvalues." % (np.max(wgaps), np.argmax(wgaps)+1)
sys.stdout.flush()
if not options.auto_cluster:
n_clusters = userinput("Please enter the number of clusters for PCCA+", "int", "x>0")
print "### Using %d clusters for PCCA+ ..."%n_clusters
if options.export_matlab:
savemat(pool.analysis_dir+"evs.mat", {"evs":eigvectors})
# orthogonalize and normalize eigenvectors
eigvectors = orthogonalize(eigvalues, eigvectors, corr_node_weights)
# perform PCCA+
# First two return-values "c_f" and "indicator" are not needed
(chi_matrix, rot_matrix) = cluster_by_isa(eigvectors, n_clusters)[2:]
if(options.optimize_chi):
print "\n### Optimizing chi matrix ..."
outliers = 5
mean_weight = np.mean(corr_node_weights)
threshold = mean_weight/100*outliers
print "Light-weight node threshold (%d%% of mean corrected node weight): %.4f."%(outliers, threshold)
# accumulate nodes for optimization
edges = np.where(np.max(chi_matrix, axis=1) > 0.9999)[0] # edges of simplex
heavies = np.where( corr_node_weights > threshold)[0] # heavy-weight nodes
filtered_eigvectors = eigvectors[ np.union1d(edges, heavies) ]
# perform the actual optimization
rot_matrix = opt_soft(filtered_eigvectors, rot_matrix, n_clusters)
chi_matrix = np.dot(eigvectors[:,:n_clusters], rot_matrix)
# deal with light-weight nodes: shift and scale
for i in np.where(corr_node_weights <= threshold)[0]:
if(i in edges):
print "Column %d belongs to (potentially dangerous) light-weight node, but its node is a simplex edge."%(i+1)
continue
print "Column %d is shifted and scaled."%(i+1)
col_min = np.min( chi_matrix[i,:] )
chi_matrix[i,:] -= col_min
chi_matrix[i,:] /= 1-(n_clusters*col_min)
qc_matrix = np.dot( np.dot( np.linalg.inv(rot_matrix), np.diag(eigvalues[range(n_clusters)]) ), rot_matrix ) - np.eye(n_clusters)
cluster_weights = rot_matrix[0]
print "\n### Matrix numerics check"
print "-- Q_c matrix row sums --"
print np.sum(qc_matrix, axis=1)
print "-- cluster weights: first column of rot_matrix --"
print cluster_weights
print "-- cluster weights: numpy.dot(node_weights, chi_matrix) --"
print np.dot(corr_node_weights, chi_matrix)
print "-- chi matrix column max values --"
print np.max(chi_matrix, axis=0)
print "-- chi matrix row sums --"
print np.sum(chi_matrix, axis=1)
# store final results
np.savez(pool.chi_mat_fn, matrix=chi_matrix, n_clusters=n_clusters, node_names=[n.name for n in active_nodes])
np.savez(pool.qc_mat_fn, matrix=qc_matrix, n_clusters=n_clusters, node_names=[n.name for n in active_nodes], weights=cluster_weights)
if options.export_matlab:
savemat(pool.analysis_dir+"chi_mat.mat", {"chi_matrix":chi_matrix})
savemat(pool.analysis_dir+"qc_mat.mat", {"qc_matrix":qc_matrix, "weights":cluster_weights})
register_file_dependency(pool.chi_mat_fn, pool.s_corr_mat_fn)
register_file_dependency(pool.qc_mat_fn, pool.s_corr_mat_fn)
for fn in (pool.s_mat_fn, pool.s_corr_mat_fn):
register_file_dependency(pool.chi_mat_fn, fn)
register_file_dependency(pool.qc_mat_fn, fn)
# touch analysis directory (triggering update in zgf_browser)
atime = mtime = time.time()
os.utime(pool.analysis_dir, (atime, mtime))
# show summary
if(options.summary):
print "\n### Preparing cluster summary ..."
chi_threshold = 1E-3
from pprint import pformat
for i in range(n_clusters):
involved_nodes = [active_nodes[ni] for ni in np.argwhere(chi_matrix[:,i] > chi_threshold)]
max_chi_node = active_nodes[ np.argmax(chi_matrix[:,i]) ]
c_max = []
for c in pool.converter:
coord_range = pool.coord_range(c)
scale = c.plot_scale
edges = scale(np.linspace(np.min(coord_range), np.max(coord_range), num=50))
hist_cluster = np.zeros(edges.size-1)
for (n, chi) in zip([n for n in active_nodes], chi_matrix[:,i]):
samples = scale( n.trajectory.getcoord(c) )
hist_node = np.histogram(samples, bins=edges, weights=n.frameweights, normed=True)[0]
hist_cluster += n.obs.weight_corrected * hist_node * chi
c_max.append( scale(np.linspace(np.min(coord_range), np.max(coord_range), num=50))[np.argmax(hist_cluster)] )
msg = "### Cluster %d (weight=%.4f, #involved nodes=%d, representative='%s'):"%(i+1, cluster_weights[i], len(involved_nodes), max_chi_node.name)
print "\n"+msg
print "-- internal coordinates --"
print "%s"%pformat(["%.2f"%cm for cm in c_max])
print "-- involved nodes --"
print "%s"%pformat([n.name for n in involved_nodes])
print "-"*len(msg)
def main():
options = options_desc.parse_args(sys.argv)[0]
zgf_cleanup.main()
print("Options:\n%s\n"%pformat(eval(str(options))))
pool = Pool()
parent = pool.root
active_nodes = pool.where("isa_partition")
assert(len(active_nodes) == len(active_nodes.multilock())) # make sure we lock ALL nodes
if active_nodes.where("'weight_direct' not in obs"):
sys.exit("Q-Matrix calculation not possible: Not all of the nodes have been reweighted.")
node_weights = np.array([node.obs.weight_direct for node in active_nodes])
print "### Generate bins: equidist ###"
result = q_equidist(parent, options.numnodes)
chosen_idx=result['chosen_idx']
frames_chosen=result['frames_chosen']
theta=result['theta']
chosen_idx.sort() # makes preview-trajectory easier to understand
dimension=len(chosen_idx)
print "chosen_idx"
print chosen_idx
print "### Generate bin weights ###"
bin_weights=np.zeros(dimension)
for (i,n) in enumerate(active_nodes):
w_denom = np.sum(n.frameweights)
for t in range(len(n.trajectory)):
diffs = (frames_chosen - n.trajectory.getframe(t)).norm()
j = np.argmin(diffs)
bin_weights[j] = bin_weights[j] + node_weights[i] * n.frameweights[t] / w_denom
print "bin_weights"
print bin_weights
print "### Generate q_all (entries only for neighboring bins) ###"
q_all = np.empty((dimension, dimension), dtype=np.float)
for i in range(dimension):
sum_row = 0.0
diffs = (frames_chosen - frames_chosen.getframe(i)).norm()
print "diffs"
print diffs
for j in range(dimension):
if (diffs[j] < 2.0 * theta) and (bin_weights[i] > 0.0):
q_all[i,j] = np.sqrt(bin_weights[j]) / np.sqrt(bin_weights[i])
sum_row = sum_row + q_all[i , j]
else:
q_all[i,j] = 0
q_all[i, i] = q_all[i, i]- sum_row
print "Q_All"
print q_all
if options.export_matlab:
savemat(pool.analysis_dir+"q_all.mat", {"q_all":q_all})
active_nodes.unlock()
zgf_cleanup.main()
def main():
options = options_desc.parse_args(sys.argv)[0]
zgf_cleanup.main()
pool = Pool()
npz_file = np.load(pool.chi_mat_fn)
chi_matrix = npz_file['matrix']
node_names = npz_file['node_names']
n_clusters = npz_file['n_clusters']
active_nodes = [Node(nn) for nn in node_names]
# create and open dest_files, intialize counters for statistics
dest_filenames = [ pool.analysis_dir+"cluster%d.trr"%(c+1) for c in range(n_clusters) ]
dest_files = [ open(fn, "wb") for fn in dest_filenames ]
dest_frame_counters = np.zeros(n_clusters)
# For each active node...
for (i, n) in enumerate(active_nodes):
# ... find the clusters to which it belongs (might be more than one)...
belonging_clusters = np.argwhere(chi_matrix[i] > options.node_threshold)
# ... and find all typical frames of this node.
#TODO not an optimal solution... discuss
# per default, we take every frame with above average weight
frame_threshold = options.frame_threshold*2*np.mean(n.frameweights)
typical_frame_nums = np.argwhere(n.frameweights > frame_threshold)
# Go through the node's trajectory ...
trr_in = TrrFile(n.trr_fn)
curr_frame = trr_in.first_frame
for i in typical_frame_nums:
# ...stop at each typical frame...
while(i != curr_frame.number):
curr_frame = curr_frame.next()
assert(curr_frame.number == i)
#... and copy it into the dest_file of each belonging cluster.
for c in belonging_clusters:
dest_files[c].write(curr_frame.raw_data)
dest_frame_counters[c] += 1
trr_in.close() # close source file
# close dest_files
for f in dest_files:
f.close()
del(dest_files)
# desolvate cluster-trajectories 'in-place'
if(not options.write_sol):
for dest_fn in dest_filenames:
tmp_fn = mktemp(suffix='.trr', dir=pool.analysis_dir)
os.rename(dest_fn, tmp_fn) # works as both files are in same dir
cmd = ["trjconv", "-f", tmp_fn, "-o", dest_fn, "-n", pool.ndx_fn]
p = Popen(cmd, stdin=PIPE)
p.communicate(input="MOI\n")
assert(p.wait() == 0)
os.remove(tmp_fn)
# register dependencies
for fn in dest_filenames:
register_file_dependency(fn, pool.chi_mat_fn)
# check number of written frames
sys.stdout.write("Checking lenghts of written trajectories... ")
for i in range(n_clusters):
f = TrrFile(dest_filenames[i])
assert(f.count_frames() == dest_frame_counters[i])
f.close()
print("done.")
#output statistics
print "\n### Extraction summary ###\nnode threshold: %1.1f, frame threshold: %1.1f"%(options.node_threshold, options.frame_threshold)
print "Cluster trajectories were written to %s:"%pool.analysis_dir
for (c, f) in enumerate(dest_frame_counters):
print "cluster%d.trr [%d frames] from node(s):"%(c+1, f)
print list(np.argwhere(chi_matrix[:,c] > options.node_threshold).flat)
def main():
options = options_desc.parse_args(sys.argv)[0]
zgf_cleanup.main()
pool = Pool()
active_nodes = pool.where("isa_partition")
assert(len(active_nodes) == len(active_nodes.multilock())) # make sure we lock ALL nodes
if active_nodes.where("'weight_direct' not in obs"):
active_nodes.unlock()
sys.exit("Matrix calculation not possible: Not all of the nodes have been reweighted.")
print "\n### Getting S matrix ..."
s_matrix = cache_matrix(pool.s_mat_fn, active_nodes, overwrite=options.overwrite_mat)
register_file_dependency(pool.s_mat_fn, pool.filename)
print "\n### Getting K matrix ..."
k_matrix = cache_matrix(pool.k_mat_fn, active_nodes, shift=options.lag_time, overwrite=options.overwrite_mat)
register_file_dependency(pool.k_mat_fn, pool.filename)
node_weights = np.array([node.obs.weight_direct for node in active_nodes])
print "\n### Symmetrizing S matrix ..."
(corr_s_matrix, corr_node_weights) = symmetrize(s_matrix, node_weights, correct_weights=True, error=float(options.error))
print "\n### Symmetrizing K matrix ..."
(corr_k_matrix, corr_node_weights) = symmetrize(k_matrix, corr_node_weights)
# store intermediate results
register_file_dependency(pool.s_corr_mat_fn, pool.s_mat_fn)
register_file_dependency(pool.k_corr_mat_fn, pool.k_mat_fn)
np.savez(pool.s_corr_mat_fn, matrix=corr_s_matrix, node_names=[n.name for n in active_nodes])
np.savez(pool.k_corr_mat_fn, matrix=corr_k_matrix, node_names=[n.name for n in active_nodes])
if options.export_matlab:
savemat(pool.analysis_dir+"node_weights.mat", {"node_weights":node_weights, "node_weights_corrected":corr_node_weights})
savemat(pool.analysis_dir+"s_mats.mat", {"s_matrix":s_matrix, "s_matrix_corrected":corr_s_matrix})
savemat(pool.analysis_dir+"k_mats.mat", {"k_matrix":k_matrix, "k_matrix_corrected":corr_k_matrix})
for (n, cw) in zip(active_nodes, corr_node_weights):
n.obs.weight_corrected = cw
print "\n### Node weights after symmetrization of S matrix:"
for n in active_nodes:
print "%s: initial weight: %f, corrected weight: %f, weight change: %f" % (n.name, n.obs.weight_direct, n.obs.weight_corrected, abs(n.obs.weight_direct - n.obs.weight_corrected))
n.save()
active_nodes.unlock()
# calculate and sort eigenvalues in descending order
(eigvalues, eigvectors) = np.linalg.eig(corr_s_matrix)
argsorted_eigvalues = np.argsort(-eigvalues)
eigvalues = eigvalues[argsorted_eigvalues]
eigvectors = eigvectors[:, argsorted_eigvalues]
gaps = np.abs(eigvalues[1:]-eigvalues[:-1])
gaps = np.append(gaps, 0.0)
wgaps = gaps*eigvalues
print "\n### Sorted eigenvalues of symmetrized S matrix:"
for (idx, ev, gap, wgap) in zip(range(1, len(eigvalues)+1), eigvalues, gaps, wgaps):
print "EV%04d: %f, gap to next: %f, EV-weighted gap to next: %f" % (idx, ev, gap, wgap)
n_clusters = np.argmax(wgaps)+1
print "\n### Maximum gap %f after top %d eigenvalues." % (np.max(gaps), n_clusters)
print "### Maximum EV-weighted gap %f after top %d eigenvalues." % (np.max(wgaps), np.argmax(wgaps)+1)
sys.stdout.flush()
if not options.auto_cluster:
n_clusters = userinput("Please enter the number of clusters for PCCA+", "int", "x>0")
print "### Using %d clusters for PCCA+ ..."%n_clusters
print "eigenvectors"
print eigvectors[:, :n_clusters]
if options.export_matlab:
savemat(pool.analysis_dir+"evs.mat", {"evs":eigvectors})
# orthogonalize and normalize eigenvectors
eigvectors = orthogonalize(eigvalues, eigvectors, corr_node_weights)
# perform PCCA+
# First two return-values "c_f" and "indicator" are not needed
(chi_matrix, rot_matrix) = cluster_by_isa(eigvectors, n_clusters)[2:]
#TODO at the moment, K-matrix is not used
#xi = [] # calculate eigenvalues of Q_c, xi
#for eigvec in np.transpose(eigvectors)[: n_clusters]:
# num = np.dot( np.dot( np.transpose(eigvec), corr_k_matrix ), eigvec )
# denom = np.dot( np.dot( np.transpose(eigvec), corr_s_matrix ), eigvec )
# xi.append(num/denom-1)
#print np.diag(xi) #TODO what does this tell us? Marcus-check
qc_matrix = np.dot( np.dot( np.linalg.inv(rot_matrix), np.diag(eigvalues[range(n_clusters)]) ), rot_matrix ) - np.eye(n_clusters)
cluster_weights = rot_matrix[0]
print "Q_c matrix:"
print qc_matrix
print "Q_c matrix row sums:"
print np.sum(qc_matrix, axis=1)
print "cluster weights (calculated twice for checking):"
print cluster_weights
print np.dot(corr_node_weights, chi_matrix)
print "chi matrix column sums:"
print np.sum(chi_matrix, axis=0)
print "chi matrix row sums:"
print np.sum(chi_matrix, axis=1)
# store final results
np.savez(pool.chi_mat_fn, matrix=chi_matrix, n_clusters=n_clusters, node_names=[n.name for n in active_nodes])
np.savez(pool.qc_mat_fn, matrix=qc_matrix, n_clusters=n_clusters, node_names=[n.name for n in active_nodes], weights=cluster_weights)
if options.export_matlab:
savemat(pool.analysis_dir+"chi_mat.mat", {"chi_matrix":chi_matrix})
savemat(pool.analysis_dir+"qc_mat.mat", {"qc_matrix":qc_matrix, "weights":cluster_weights})
register_file_dependency(pool.chi_mat_fn, pool.s_corr_mat_fn)
register_file_dependency(pool.qc_mat_fn, pool.s_corr_mat_fn)
for fn in (pool.s_mat_fn, pool.s_corr_mat_fn, pool.k_mat_fn, pool.k_corr_mat_fn):
register_file_dependency(pool.chi_mat_fn, fn)
register_file_dependency(pool.qc_mat_fn, fn)
zgf_cleanup.main()
