def ChooseXtal(self, widget):
self.xtalID = str(widget.get_active_text())
for n,item in enumerate(self.Todo):
if str(item[0]) == self.xtalID:
self.index = n
self.db_dict_mainTable={}
self.db_dict_panddaTable={}
if str(self.Todo[self.index][0]) != None:
self.compoundID=str(self.Todo[self.index][1])
self.refinement_folder=str(self.Todo[self.index][4])
self.refinement_outcome=str(self.Todo[self.index][5])
self.ligand_confidence=str(self.Todo[self.index][6])
# updating dataset outcome radiobuttons
current_stage=0
for i,entry in enumerate(self.experiment_stage):
# if entry[1]==self.refinement_outcome: # use number as identifier, not string since this one might change
if entry[1].split()[0] == self.refinement_outcome.split()[0]:
current_stage=i
break
for i,button in enumerate(self.experiment_stage_button_list):
if i==current_stage:
button.set_active(True)
break
# updating ligand confidence radiobuttons
current_stage=0
for i,entry in enumerate(self.ligand_confidence_category):
print '--->',entry,self.ligand_confidence
# if entry==self.ligand_confidence: # use number as identifier, not string since this one might change
try:
if entry.split()[0] == self.ligand_confidence.split()[0]:
current_stage=i
break
except IndexError:
pass
for i,button in enumerate(self.ligand_confidence_button_list):
if i==current_stage:
button.set_active(True)
break
if int(self.selected_site[0]) > 0:
pandda_info=self.db.get_pandda_info_for_coot(self.xtalID,self.selected_site[0])
print 'PANDDA INDO', pandda_info
try:
self.event_map=pandda_info[0][0]
coot.set_rotation_centre(float(pandda_info[0][1]),float(pandda_info[0][2]),float(pandda_info[0][3]))
self.spider_plot=pandda_info[0][4]
except IndexError:
self.event_map=''
self.spider_plot=''
else:
self.event_map=''
self.spider_plot=''
self.RefreshData()
def cfc_process_site(site_number, imol_ligand_specs, first_ligand_spec):
imol_first = imol_ligand_specs[0][0] # others are lsq fitted to this
env_residue_specs = coot.residues_near_residue_py(imol_first, first_ligand_spec, 6)
protein_res_specs = [r for r in env_residue_specs if get_residue_name(imol_first, r) != "HOH"]
# only lsq the first (0th) one - that one has the most ligands in the site
#
if site_number == 0:
print "DEBUG:: protein_res_specs (for lsqing):"
for spec in protein_res_specs:
print " ", spec, get_residue_name(imol_first, spec)
for res_spec in protein_res_specs:
chain_id = rsu.residue_spec_to_chain_id(res_spec)
res_no = rsu.residue_spec_to_res_no(res_spec)
coot.add_lsq_match(res_no, res_no, chain_id, res_no, res_no, chain_id, 1)
for imol in imol_ligand_specs[1:]: # lsq fit others to the first in the list
coot.apply_lsq_matches_py(imol_first, imol[0])
ligand_centre = coot.residue_centre_py(
imol_first,
rsu.residue_spec_to_chain_id(first_ligand_spec),
rsu.residue_spec_to_res_no(first_ligand_spec),
"",
)
coot.set_go_to_atom_molecule(imol_first)
coot.set_rotation_centre(*ligand_centre)
combo_list = []
try:
# we have a large radius for the water selection
radius = 10 # water must be within radius of it's own ligand
radius_2 = 5 # water must be with radius_2 of any ligand atom (not just its own)
combo_list = coot.chemical_feature_clusters_py(env_residue_specs, imol_ligand_specs, radius, radius_2)
except TypeError as e:
print e
# the rest is unlikely to work if we get here
if True:
water_position_list = combo_list[0]
chemical_feature_list = combo_list[1]
# residues_sidechains_list = combo_list[1]
# ----------- handle waters -----------
w_positions_list = []
for item in [wat[2] for wat in water_position_list]:
w_positions_list.append(item)
for item in [wat[2] for wat in water_position_list]:
delta = 0.1
p1 = [item[0], item[1], item[2] + delta]
p2 = [item[0], item[1], item[2] - delta]
p3 = [item[0], item[1] + delta, item[2]]
p4 = [item[0], item[1] - delta, item[2]]
p5 = [item[0] + delta, item[1], item[2]]
p6 = [item[0] - delta, item[1], item[2]]
w_positions_list.append(p1)
w_positions_list.append(p2)
w_positions_list.append(p3)
w_positions_list.append(p4)
w_positions_list.append(p5)
w_positions_list.append(p6)
w_positions_np = np.array(w_positions_list)
# move these to the origin
# w_positions_np = w_positions_np_at_ligand
# for pos in w_positions_np:
# pos -= np.array(ligand_centre)
# dpgmm = mixture.DPGMM(n_components=25, covariance_type='spherical', alpha=1.101,
# n_iter=40000, params='wmc', init_params='wmc', tol=1e-4,
# verbose=0)
#
# the number of clusters is highly related to the dist_cutoff (the
# distance of an accepted water atom to any any atom in any of the
# ligands = currently 4.2)
#
gmm, cluster_assignments = cluster_and_display_waters(site_number, w_positions_np)
means = gmm.means_
cvs = gmm._get_covars()
weights = gmm.weights_
print "water means:"
for mean in means:
print " ", mean
# each water has been assigned a cluster, that is the cluster_assignments
#
# need to convert the array cluster_assignments to a list of items:
# [imol water_residue_spec cluster_number]
#
water_cluster_info_for_input = []
for i, water_pos in enumerate(water_position_list):
# print water_pos, cluster_assignments[i]
item = [water_pos[0], water_pos[1], cluster_assignments[i]]
water_cluster_info_for_input.append(item)
# cluster_info is a list of
# list of water cluster info
# list of [mean, weight, length] where length is the eigenvalue v[0],
# (same as v[1], v[2] - all the same for spherical model)
# list of cluster predictions for then input positions
#
ci = zip([[l[0], l[1], l[2]] for l in means], weights, [cv[0][0] for cv in cvs])
water_cluster_info = [ci, water_cluster_info_for_input]
# give those results back to c++ so that we can use them for display
#
coot.set_display_generic_objects_as_solid(1)
# ----------- handle chemical features -----------
# make a dictionary from the list of chemical features
chemical_features_dict = {}
for item in chemical_feature_list:
for type in ["Donor", "Acceptor", "Aromatic", "Hydrophobe", "LumpedHydrophobe"]:
if item[0] == type:
try:
chemical_features_dict[type].append(item[1:])
except KeyError:
chemical_features_dict[type] = [item[1:]]
chemical_feature_clusters_info = []
for key in chemical_features_dict:
# list of [type, features-annotated-by-cluster-number, cluster_means]
clusters = cluster_and_display_chemical_features(site_number, key, chemical_features_dict[key])
chemical_feature_clusters_info.append(clusters)
# print 'water_cluster_info'
# for wc in water_cluster_info:
# print wc
cluster_info = [water_cluster_info, chemical_feature_clusters_info]
coot.chemical_feature_clusters_accept_info_py(site_number, protein_res_specs, imol_ligand_specs, cluster_info)
def cfc_process_site(site_number, imol_ligand_specs, imol_first,
first_ligand_spec):
print("debug:: in cfc_process_site with imol_ligand_specs",
imol_ligand_specs)
print("debug:: in cfc_process_site with non-first imol_ligand_specs",
imol_ligand_specs[1:])
# print("calling residues_near_residue_py", imol_first, first_ligand_spec)
env_residue_specs = coot.residues_near_residue_py(imol_first,
first_ligand_spec, 6)
# print("env_residue_specs", env_residue_specs)
protein_res_specs = [
r for r in env_residue_specs
if get_residue_name(imol_first, r) != "HOH"
]
# only lsq the first (0th) one - that one has the most ligands in the site
#
if site_number == 0:
# print("protein_res_specs (for lsqing):")
# for spec in protein_res_specs:
# print(" ", spec, get_residue_name(imol_first, spec))
for res_spec in protein_res_specs:
chain_id = rsu.residue_spec_to_chain_id(res_spec)
res_no = rsu.residue_spec_to_res_no(res_spec)
coot.add_lsq_match(res_no, res_no, chain_id, res_no, res_no,
chain_id, 1)
for imol_and_spec in imol_ligand_specs[
1:]: # lsq fit others to the first in the list
print('============================ lsq-match ', imol_first,
imol_and_spec, imol_and_spec[0])
imol, spec = imol_and_spec
# coot.apply_lsq_matches_py(imol_first, imol_and_spec[0])
coot.apply_lsq_matches_py(imol_first, imol)
make_ball_and_stick_by_spec(imol, spec)
# pass
print("Here with first_ligand_spec:", first_ligand_spec)
ligand_centre = coot.residue_centre_py(
imol_first, rsu.residue_spec_to_chain_id(first_ligand_spec),
rsu.residue_spec_to_res_no(first_ligand_spec), '')
coot.set_go_to_atom_molecule(imol_first)
coot.set_rotation_centre(*ligand_centre)
combo_list = []
try:
# we have a large radius for the water selection
radius = 10 # water must be within radius of it's own ligand
radius_2 = 5 # water must be with radius_2 of any ligand atom (not just its own)
combo_list = coot.chemical_feature_clusters_py(env_residue_specs,
imol_ligand_specs,
radius, radius_2)
except TypeError as e:
print(e)
# the rest is unlikely to work if we get here
if True:
water_position_list = combo_list[0]
chemical_feature_list = combo_list[1]
# residues_sidechains_list = combo_list[1]
# ----------- handle waters -----------
w_positions_list = []
for item in [wat[2] for wat in water_position_list]:
w_positions_list.append(item)
for item in [wat[2] for wat in water_position_list]:
delta = 0.1
p1 = [item[0], item[1], item[2] + delta]
p2 = [item[0], item[1], item[2] - delta]
p3 = [item[0], item[1] + delta, item[2]]
p4 = [item[0], item[1] - delta, item[2]]
p5 = [item[0] + delta, item[1], item[2]]
p6 = [item[0] - delta, item[1], item[2]]
w_positions_list.append(p1)
w_positions_list.append(p2)
w_positions_list.append(p3)
w_positions_list.append(p4)
w_positions_list.append(p5)
w_positions_list.append(p6)
w_positions_np = np.array(w_positions_list)
# move these to the origin
# w_positions_np = w_positions_np_at_ligand
# for pos in w_positions_np:
# pos -= np.array(ligand_centre)
# dpgmm = mixture.DPGMM(n_components=25, covariance_type='spherical', alpha=1.101,
# n_iter=40000, params='wmc', init_params='wmc', tol=1e-4,
# verbose=0)
#
# the number of clusters is highly related to the dist_cutoff (the
# distance of an accepted water atom to any any atom in any of the
# ligands = currently 4.2)
#
gmm, cluster_assignments = cluster_and_display_waters(
site_number, w_positions_np)
means = gmm.means_
cvs = gmm._get_covars()
weights = gmm.weights_
print("water means:")
for mean in means:
print(" ", mean)
# each water has been assigned a cluster, that is the cluster_assignments
#
# need to convert the array cluster_assignments to a list of items:
# [imol water_residue_spec cluster_number]
#
water_cluster_info_for_input = []
for i, water_pos in enumerate(water_position_list):
# print water_pos, cluster_assignments[i]
item = [water_pos[0], water_pos[1], cluster_assignments[i]]
water_cluster_info_for_input.append(item)
# cluster_info is a list of
# list of water cluster info
# list of [mean, weight, length] where length is the eigenvalue v[0],
# (same as v[1], v[2] - all the same for spherical model)
# list of cluster predictions for then input positions
#
ci = list(
zip([[l[0], l[1], l[2]] for l in means], weights,
[cv[0][0] for cv in cvs]))
water_cluster_info = [ci, water_cluster_info_for_input]
# give those results back to c++ so that we can use them for display
#
coot.set_display_generic_objects_as_solid(1)
# ----------- handle chemical features -----------
# make a dictionary from the list of chemical features
chemical_features_dict = {}
for item in chemical_feature_list:
for type in [
'Donor', 'Acceptor', 'Aromatic', 'Hydrophobe',
'LumpedHydrophobe'
]:
if item[0] == type:
try:
chemical_features_dict[type].append(item[1:])
except KeyError:
chemical_features_dict[type] = [item[1:]]
chemical_feature_clusters_info = []
for key in chemical_features_dict:
# list of [type, features-annotated-by-cluster-number, cluster_means]
clusters = cluster_and_display_chemical_features(
site_number, key, chemical_features_dict[key])
chemical_feature_clusters_info.append(clusters)
# print 'water_cluster_info'
# for wc in water_cluster_info:
# print wc
cluster_info = [water_cluster_info, chemical_feature_clusters_info]
coot.chemical_feature_clusters_accept_info_py(site_number,
protein_res_specs,
imol_ligand_specs,
cluster_info)