From 833a4ad8d355033d665404fd0e481ae5a62c2db7 Mon Sep 17 00:00:00 2001
From: Gabriel Studer <gabriel.studer@unibas.ch>
Date: Wed, 13 Apr 2022 10:46:19 +0200
Subject: [PATCH] variable renaming
---
modules/mol/alg/pymod/lddt.py | 42 +++++++++++++++++------------------
1 file changed, 21 insertions(+), 21 deletions(-)
diff --git a/modules/mol/alg/pymod/lddt.py b/modules/mol/alg/pymod/lddt.py
index dcd27c5c3..70b9ca9b1 100644
--- a/modules/mol/alg/pymod/lddt.py
+++ b/modules/mol/alg/pymod/lddt.py
@@ -311,11 +311,11 @@ class lDDTScorer:
generic float property of that name
:type local_lddt_prop: :class:`str`
:param local_contact_prop: If set, number of expected contacts as well
- as number of fulfilled contacts will be
+ as number of conserved contacts will be
assigned as generic int property.
Excected contacts will be set as
- <local_contact_prop>_exp, fulfilled contacts
- as <local_contact_prop>_fulfilled. Values
+ <local_contact_prop>_exp, conserved contacts
+ as <local_contact_prop>_conserved. Values
are summed over all thresholds.
:param local_contact_prop: :class:`str`
:param chain_mapping: Mapping of model chains (key) onto target chains
@@ -360,7 +360,7 @@ class lDDTScorer:
Third: numpy matrix of shape
(len(scored_residues), len(thresholds))
specifying how many for each threshold are
- fulfilled.
+ conserved.
:returns: global and per-residue lDDT scores as a tuple -
first element is global lDDT score and second element
a list of per-residue scores with length len(*model*.residues)
@@ -478,7 +478,7 @@ class lDDTScorer:
per_res_exp = np.asarray([self._GetNExp(res_ref_atom_indices[idx],
ref_indices) for idx in range(len(res_indices))], dtype=np.int32)
- per_res_fulfilled = self._EvalResidues(pos, thresholds,
+ per_res_conserved = self._EvalResidues(pos, thresholds,
res_atom_indices,
ref_indices, ref_distances)
@@ -488,14 +488,14 @@ class lDDTScorer:
per_res_lDDT = [None] * len(model.residues)
for idx in range(len(res_indices)):
n_exp = n_thresh * per_res_exp[idx]
- score = np.sum(per_res_fulfilled[idx,:]) / n_exp
+ score = np.sum(per_res_conserved[idx,:]) / n_exp
per_res_lDDT[res_indices[idx]] = score
# do full model score
if penalize_extra_chains:
n_distances += self._GetExtraModelChainPenalty(model, chain_mapping)
- lDDT = np.sum(per_res_fulfilled) / (n_thresh * n_distances)
+ lDDT = np.sum(per_res_conserved) / (n_thresh * n_distances)
# set properties if necessary
if local_lddt_prop:
@@ -506,14 +506,14 @@ class lDDTScorer:
if local_contact_prop:
residues = model.residues
exp_prop = local_contact_prop + "_exp"
- fulfilled_prop = local_contact_prop + "_fulfilled"
+ conserved_prop = local_contact_prop + "_conserved"
for idx in res_indices:
residues[idx].SetIntProp(exp_prop, n_thresh * per_res_exp[idx])
- residues[idx].SetIntProp(fulfilled_prop,
- np.sum(per_res_fulfilled[idx,:]))
+ residues[idx].SetIntProp(conserved_prop,
+ np.sum(per_res_conserved[idx,:]))
if return_dist_test:
- return lDDT, per_res_lDDT, res_indices, per_res_exp, per_res_fulfilled
+ return lDDT, per_res_lDDT, res_indices, per_res_exp, per_res_conserved
else:
return lDDT, per_res_lDDT
@@ -858,12 +858,12 @@ class lDDTScorer:
returns numpy matrix of shape (n_atoms, len(threshold))
"""
- fulfilled = np.zeros((len(atom_indices), len(thresholds)),
+ conserved = np.zeros((len(atom_indices), len(thresholds)),
dtype=np.int32)
for a_idx, a in enumerate(atom_indices):
- fulfilled[a_idx, :] = self._EvalAtom(pos, a, thresholds,
+ conserved[a_idx, :] = self._EvalAtom(pos, a, thresholds,
ref_indices, ref_distances)
- return fulfilled
+ return conserved
def _EvalResidues(self, pos, thresholds, res_atom_indices, ref_indices,
ref_distances):
@@ -872,12 +872,12 @@ class lDDTScorer:
residues are defined in *res_atom_indices* as lists of atom indices
returns numpy matrix of shape (n_residues, len(thresholds)).
"""
- fulfilled = np.zeros((len(res_atom_indices), len(thresholds)),
+ conserved = np.zeros((len(res_atom_indices), len(thresholds)),
dtype=np.int32)
for rai_idx, rai in enumerate(res_atom_indices):
- fulfilled[rai_idx,:] = np.sum(self._EvalAtoms(pos, rai, thresholds,
+ conserved[rai_idx,:] = np.sum(self._EvalAtoms(pos, rai, thresholds,
ref_indices, ref_distances), axis=0)
- return fulfilled
+ return conserved
def _ProcessSequenceSeparation(self):
if self.sequence_separation != 0:
@@ -914,7 +914,7 @@ class lDDTScorer:
continue # nothing to do
# score as is
- sym_one_fulfilled = self._EvalAtoms(
+ sym_one_conserved = self._EvalAtoms(
pos,
atom_indices,
thresholds,
@@ -926,7 +926,7 @@ class lDDTScorer:
for pair in sym:
pos[[pair[0], pair[1]]] = pos[[pair[1], pair[0]]]
- sym_two_fulfilled = self._EvalAtoms(
+ sym_two_conserved = self._EvalAtoms(
pos,
atom_indices,
thresholds,
@@ -934,8 +934,8 @@ class lDDTScorer:
sym_ref_distances,
)
- sym_one_score = np.sum(sym_one_fulfilled) / (len(thresholds) * tot)
- sym_two_score = np.sum(sym_two_fulfilled) / (len(thresholds) * tot)
+ sym_one_score = np.sum(sym_one_conserved) / (len(thresholds) * tot)
+ sym_two_score = np.sum(sym_two_conserved) / (len(thresholds) * tot)
if sym_one_score >= sym_two_score:
# switch back, initial positions were better or equal
--
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