def _calBiasingForce(self, coord_x, coord_y, d):
if self.p["abfCheckFlag"] == "yes":
bsForce = copy.deepcopy(self.colvars_FreeE)
if self.p["ndims"] == 1:
bsForce = np.diff(bsForce)
bsForce = np.append(bsForce,
bsForce[-1]) # padding to the right legnth
bsForce = (bsForce / self.binw)
return bsForce[getIndices(coord_x, self.bins)]
if self.p["ndims"] == 2:
if d == 0:
bsForce = np.diff(bsForce,
axis=0) # axis=0 for x; axis=1 for y
bsForce = np.append(bsForce, [bsForce[-1, :]],
axis=0) # padding to the right length
bsForce = (bsForce / self.binw)
elif d == 1:
bsForce = np.diff(bsForce, axis=1)
bsForce = np.append(bsForce,
bsForce[:, -1][:, np.newaxis],
axis=1) # padding to the right length
bsForce = (bsForce / self.binw)
return bsForce[getIndices(coord_x, self.bins)][getIndices(
coord_y, self.bins)]
else:
return 0
def _calBiasingForce(self, coord_x, coord_y, d):
if (self.p["abfCheckFlag"] == "yes" and self.p["nnCheckFlag"]
== "no") or (self.p["abfCheckFlag"] == "yes"
and self.p["nnCheckFlag"] == "yes"
and self.criteriaFEBool == 0):
if self.p["ndims"] == 1:
if self.colvars_count[getIndices(coord_x, self.bins)] == 0:
return 0
else:
return -(
(self.colvars_force[getIndices(coord_x, self.bins)] /
self.colvars_count[getIndices(coord_x, self.bins)] +
self._entropicCorrection()) * self._inverseGradient())
if self.p["ndims"] == 2:
if self.colvars_count[d][getIndices(coord_x,
self.bins)][getIndices(
coord_y,
self.bins)] == 0:
return 0
else:
return -((self.colvars_force[d][getIndices(coord_x, self.bins)][getIndices(coord_y, self.bins)] / self.colvars_count[d][getIndices(coord_x, self.bins)][getIndices(coord_y, self.bins)] +\
self._entropicCorrection()) * self._inverseGradient())
if (self.p["abfCheckFlag"] == "yes" and self.p["nnCheckFlag"]
== "yes") and self.criteriaFEBool > 0:
if self.p["ndims"] == 1:
return -self.colvars_force_NN[getIndices(coord_x, self.bins)]
if self.p["ndims"] == 2:
return -self.colvars_force_NN[d][getIndices(
coord_x, self.bins)][getIndices(coord_y, self.bins)]
def _forceHistDistrRecord(self, coord_x, coord_y, d):
if self.p["ndims"] == 1:
self.colvars_count[getIndices(coord_x, self.bins)] += 1
if self.p["ndims"] == 2:
self.colvars_count[d][getIndices(coord_x, self.bins)][getIndices(
coord_y, self.bins)] += 1
def _forceDistrRecord(self, coord_x, updated_Fsys, coord_y, d):
if self.p["ndims"] == 1:
self.colvars_force[getIndices(coord_x, self.bins)] += updated_Fsys
if self.p["ndims"] == 2:
self.colvars_force[d][getIndices(coord_x, self.bins)][getIndices(
coord_y, self.bins)] += updated_Fsys
def _accumulateColvarsHist(self):
for n in range(self.p["nparticle"]):
if self.p["ndims"] == 1:
self.colvars_hist[getIndices(self.current_coord[n][0],
self.bins)] += 1
elif self.p["ndims"] == 2:
self.colvars_hist[getIndices(self.current_coord[n][0],
self.bins)][getIndices(
self.current_coord[n][1],
self.bins)] += 1
def _calBiasingForce(self, coord_x, coord_y, d):
if (self.p["abfCheckFlag"] == "yes" and self.p["nnCheckFlag"]
== "no") or (self.p["abfCheckFlag"] == "yes"
and self.p["nnCheckFlag"] == "yes" and
self.p["init_frame"] <= self.p["earlyStopCheck"]):
bsForce = copy.deepcopy(self.colvars_FreeE)
elif (self.p["abfCheckFlag"] == "yes" and self.p["nnCheckFlag"]
== "yes") and self.p["init_frame"] > self.p["earlyStopCheck"]:
bsForce = copy.deepcopy(self.colvars_FreeE_NN)
elif self.p["abfCheckFlag"] == "no" and self.p["nnCheckFlag"] == "no":
return 0
if self.p["ndims"] == 1:
bsForce = np.diff(bsForce)
bsForce = np.append(bsForce,
bsForce[-1]) # padding to the right legnth
bsForce = (bsForce / self.binw)
return bsForce[getIndices(coord_x, self.bins)]
if self.p["ndims"] == 2:
if d == 0:
bsForce = np.diff(bsForce,
axis=0) # axis=0 for x; axis=1 for y
bsForce = np.append(bsForce, [bsForce[-1, :]],
axis=0) # padding to the right length
bsForce = (bsForce / self.binw)
elif d == 1:
bsForce = np.diff(bsForce, axis=1)
bsForce = np.append(bsForce,
bsForce[:, -1][:, np.newaxis],
axis=1) # padding to the right length
bsForce = (bsForce / self.binw)
return bsForce[getIndices(coord_x, self.bins)][getIndices(
coord_y, self.bins)]
def getCurrentForce(self, coord_x, d, vel, coord_y):
if self.p["abfCheckFlag"] == "no" and self.p["nnCheckFlag"] == "no":
Fabf = 0
elif self.p["abfCheckFlag"] == "yes" and self.p["nnCheckFlag"] == "no":
Fabf = self._calBiasingForce(coord_x, coord_y, d)
elif self.p["abfCheckFlag"] == "yes" and self.p["nnCheckFlag"] == "yes":
if self.p["init_frame"] <= self.p["trainingFreq"]:
Fabf = self._calBiasingForce(coord_x, coord_y, d)
else: # ANN takes over here
if self.p["ndims"] == 1:
Fabf = self.gradient[getIndices(coord_x, self.bins)]
elif self.p["ndims"] == 2:
Fabf = self.gradient[d][getIndices(coord_x,
self.bins)][getIndices(
coord_y, self.bins)]
return Fabf
def _calBiasingForce(self, coord_x, coord_y, d):
if self.p["ndims"] == 1:
if self.colvars_count[getIndices(coord_x, self.bins)] == 0:
return 0
else:
return -(
(self.colvars_force[getIndices(coord_x, self.bins)] /
self.colvars_count[getIndices(coord_x, self.bins)] +
self._entropicCorrection()) * self._inverseGradient())
if self.p["ndims"] == 2:
if self.colvars_count[d][getIndices(coord_x,
self.bins)][getIndices(
coord_y, self.bins)] == 0:
return 0
else:
return -((self.colvars_force[d][getIndices(coord_x, self.bins)][getIndices(coord_y, self.bins)] / self.colvars_count[d][getIndices(coord_x, self.bins)][getIndices(coord_y, self.bins)] +\
self._entropicCorrection()) * self._inverseGradient())
def _biasingPotential(self, coord_x, coord_y=None):
if self.p["abfCheckFlag"] == "yes" and self.p["nnCheckFlag"] == "yes":
if self.p["ndims"] == 1:
if self.p["init_frame"] <= self.p[
"earlyStopCheck"]: # initial sweep
return 0
else:
return self.biasingPotentialFromNN[getIndices(
coord_x, self.bins)]
if self.p["ndims"] == 2:
if self.p["init_frame"] <= self.p[
"earlyStopCheck"]: # initial sweep
return 0
else:
return self.biasingPotentialFromNN[getIndices(
coord_x, self.bins)][getIndices(coord_y, self.bins)]
elif self.p["abfCheckFlag"] == "yes" and self.p["nnCheckFlag"] == "no":
if self.p["ndims"] == 1:
if self.p["init_frame"] <= self.p["earlyStopCheck"]:
return 0
else:
return self.biasingPotentialConv[getIndices(
coord_x, self.bins)]
if self.p["ndims"] == 2:
if self.p["init_frame"] <= self.p["earlyStopCheck"]:
return 0
else:
return self.biasingPotentialConv[getIndices(
coord_x, self.bins)][getIndices(coord_y, self.bins)]
else:
return 0
