from copy import deepcopy
from random import sample as sample_rand
import numpy as np
import numpy.random as rand
from upxo.pxtal.mcgs2_temporal_slice import mcgs2_grain_structure as GS2d
import random
from numba import njit
@njit
def mcloop_alg200(cbp, sbp, S, AIA0, AIA1, NLM):
"""Mcloop alg200."""
for s0 in range(S.shape[0]): # along axis 0
s00, s01, s02 = s0+0, s0+1, s0+2
for s1 in range(S.shape[1]): # along axis 1
s10, s11, s12 = s1+0, s1+1, s1+2
ssub_00 = S[AIA0[s00, s10], AIA1[s00, s10]]
ssub_01 = S[AIA0[s01, s10], AIA1[s01, s10]]
ssub_02 = S[AIA0[s02, s10], AIA1[s02, s10]]
ssub_10 = S[AIA0[s00, s11], AIA1[s00, s11]]
ssub_11 = S[AIA0[s01, s11], AIA1[s01, s11]]
ssub_12 = S[AIA0[s02, s11], AIA1[s02, s11]]
ssub_20 = S[AIA0[s00, s12], AIA1[s00, s12]]
ssub_21 = S[AIA0[s01, s12], AIA1[s01, s12]]
ssub_22 = S[AIA0[s02, s12], AIA1[s02, s12]]
Neigh = np.array([ssub_00, ssub_01, ssub_02,
ssub_10, ssub_11, ssub_12,
ssub_20, ssub_21, ssub_22])
if Neigh.min() != Neigh.max():
DelH1 = NLM[0, 0]*int(ssub_11 == ssub_00) + \
NLM[0, 1]*int(ssub_11 == ssub_01) + \
NLM[0, 2]*int(ssub_11 == ssub_02) + \
NLM[1, 0]*int(ssub_11 == ssub_10) + \
NLM[1, 2]*int(ssub_11 == ssub_12) + \
NLM[2, 0]*int(ssub_11 == ssub_20) + \
NLM[2, 1]*int(ssub_11 == ssub_21) + \
NLM[2, 2]*int(ssub_11 == ssub_22)
# ---------------------------------------------
Neigh = Neigh[Neigh != ssub_11]
# ---------------------------------------------
random_index = np.random.randint(0, len(Neigh))
ssub_11_b = Neigh[random_index]
# ---------------------------------------------
DelH2 = NLM[0, 0]*int(ssub_11_b == ssub_00) + \
NLM[0, 1]*int(ssub_11_b == ssub_01) + \
NLM[0, 2]*int(ssub_11_b == ssub_02) + \
NLM[1, 0]*int(ssub_11_b == ssub_10) + \
NLM[1, 2]*int(ssub_11_b == ssub_12) + \
NLM[2, 0]*int(ssub_11_b == ssub_20) + \
NLM[2, 1]*int(ssub_11_b == ssub_21) + \
NLM[2, 2]*int(ssub_11_b == ssub_22)
if DelH2 >= DelH1:
S[s0, s1] = ssub_11_b
elif cbp:
if sbp[int(ssub_11_b-1)] < np.random.random():
S[s0, s1] = ssub_11_b
return S
[docs]
def run(uisim, uiint, uidata, uigrid,
xgr, ygr, zgr, px_size,
_a, _b, _c,
user_LIWM, LIWM,
S, AIA0, AIA1, display_messages):
"""Run."""
# ---------------------------------------------
print("Using ALG-200: SA's SL NL-1 TP1 C2 unweighted Q-Pott's model:")
print('|' + 15*'-'+' MC SIM RUN IN PROGRESS on: ALG200' + 15*'-' + '|')
gs = {}
# Build the Non-Locality Matrix
NLM_00, NLM_01, NLM_02 = _a # Unpack 3 colms of 1st row
NLM_10, NLM_11, NLM_12 = _b # Unpack 3 colms of 2nd row
NLM_20, NLM_21, NLM_22 = _c # Unpack 3 colms of 3rd row
NLM = np.array([[NLM_00, NLM_01, NLM_02],
[NLM_10, NLM_11, NLM_12],
[NLM_20, NLM_21, NLM_22]
])
if user_LIWM:
NLM = LIWM
# ---------------------------------------------
# Begin modified Markov-Chain annealing iterations
fully_annealed = False
fully_annealed_at_m = None
for m in range(uisim.mcsteps):
if S.min() == S.max():
print(30*'.')
print(f'Single crystal achieved at iteration {m}.')
fully_annealed, fully_annealed_at_m = True, m
# Store the last temporal slice as a UPXO grain structure by
# default
gs[m] = GS2d(m=m,
dim=uigrid.dim,
uidata=uidata,
px_size=px_size,
S_total=uisim.S,
xgr=xgr,
ygr=ygr,
uigrid=uigrid,
)
gs[m].s = deepcopy(S)
if display_messages:
print(f"GS temporal slice {m} stored\n\n"
"!! MONTE-CARLO ALG.202 run ended !!\n")
break
else:
cbp = uisim.consider_boltzmann_probability
sbp = uisim.s_boltz_prob
S = mcloop_alg200(cbp, sbp, S, AIA0, AIA1, NLM)
# ------------------------------------------------
cond_1 = m % uiint.mcint_save_at_mcstep_interval == 0.0
save_msg = False
if m==0 or cond_1 or fully_annealed:
gs[m] = GS2d(m=m,
dim=uigrid.dim,
uidata=uidata,
px_size=px_size,
S_total=uisim.S,
xgr=xgr,
ygr=ygr,
uigrid=uigrid,
)
gs[m].s = deepcopy(S)
save_msg = True
if display_messages:
print(f"GS temporal slice {m} stored")
if m % uiint.mcint_promt_display == 0:
if display_messages:
if not save_msg:
print(f"Monte-Carlo temporal step = {m}")
print('|' + 15*'-'+' MC SIM RUN COMPLETED on: ALG200' + 15*'-' + '|')
fully_annealed = {'fully_annealed': fully_annealed,
'm': fully_annealed_at_m}
return gs, fully_annealed
"""
def run(uisim, uiint, uidata, uigrid,
xgr, ygr, zgr, px_size,
_a, _b, _c, S, AIA0, AIA1, display_messages):
# ---------------------------------------------
print("Using ALG-200: SA's SL NL-1 TP1 C2 unweighted Q-Pott's model:")
print('|' + 15*'-'+' MC SIM RUN IN PROGRESS on: ALG200' + 15*'-' + '|')
gs = {}
# Build the Non-Locality Matrix
NLM_00, NLM_01, NLM_02 = _a # Unpack 3 colms of 1st row
NLM_10, NLM_11, NLM_12 = _b # Unpack 3 colms of 2nd row
NLM_20, NLM_21, NLM_22 = _c # Unpack 3 colms of 3rd row
NLM = [
[_a[0], _a[1], _a[2]],
[_b[0], _b[1], _b[2]],
[_c[0], _c[1], _c[2]]
]
# ---------------------------------------------
# Begin modified Markov-Chain annealing iterations
fully_annealed = False
fully_annealed_at_m = None
for m in range(uisim.mcsteps):
if S.min() == S.max():
print(30*'.')
print(f'Single crystal achieved at iteration {m}.')
fully_annealed, fully_annealed_at_m = True, m
# Store the last temporal slice as a UPXO grain structure by
# default
gs[m] = GS2d(m=m,
dim=uigrid.dim,
uidata=uidata,
px_size=px_size,
S_total=uisim.S,
xgr=xgr,
ygr=ygr,
uigrid=uigrid,
)
gs[m].s = deepcopy(S)
if display_messages:
print(f"GS temporal slice {m} stored\n\n"
"!! MONTE-CARLO ALG.202 run ended !!\n"
"...............................")
break
else:
cbp = uisim.consider_boltzmann_probability
sbp = uisim.s_boltz_prob
# --------------------------------
#def numba_optimized_loop(S, AIA0, AIA1, NLM, xgr, ygr):
# --------------------------------
for s0 in range(S.shape[0]): # along axis 0
s00, s01, s02 = s0+0, s0+1, s0+2
for s1 in range(S.shape[1]): # along axis 1
s10, s11, s12 = s1+0, s1+1, s1+2
ssub_00 = S[AIA0[s00, s10], AIA1[s00, s10]]
ssub_01 = S[AIA0[s01, s10], AIA1[s01, s10]]
ssub_02 = S[AIA0[s02, s10], AIA1[s02, s10]]
ssub_10 = S[AIA0[s00, s11], AIA1[s00, s11]]
ssub_11 = S[AIA0[s01, s11], AIA1[s01, s11]]
ssub_12 = S[AIA0[s02, s11], AIA1[s02, s11]]
ssub_20 = S[AIA0[s00, s12], AIA1[s00, s12]]
ssub_21 = S[AIA0[s01, s12], AIA1[s01, s12]]
ssub_22 = S[AIA0[s02, s12], AIA1[s02, s12]]
Neigh = [ssub_00, ssub_01, ssub_02,
ssub_10, ssub_11, ssub_12,
ssub_20, ssub_21, ssub_22]
if min(Neigh) != max(Neigh):
DelH1 = NLM[0][0]*int(ssub_11 == ssub_00) + \
NLM[0][1]*int(ssub_11 == ssub_01) + \
NLM[0][2]*int(ssub_11 == ssub_02) + \
NLM[1][0]*int(ssub_11 == ssub_10) + \
NLM[1][2]*int(ssub_11 == ssub_12) + \
NLM[2][0]*int(ssub_11 == ssub_20) + \
NLM[2][1]*int(ssub_11 == ssub_21) + \
NLM[2][2]*int(ssub_11 == ssub_22)
# ---------------------------------------------
# If the sampling is to be selected without
# weightage to dominant neighbour state, then:
'Neigh = set([x for x in Neigh if x != ssub_11])'
'Neigh = Neigh[Neigh != ssub_11]'
Neigh = [x for x in Neigh if x != ssub_11]
# ---------------------------------------------
'ssub_11_b = sample_rand(Neigh, 1)[0]'
random_index = random.randint(0, len(Neigh) - 1)
ssub_11_b = Neigh[random_index]
DelH2 = NLM[0][0]*int(ssub_11_b == ssub_00) + \
NLM[0][1]*int(ssub_11_b == ssub_01) + \
NLM[0][2]*int(ssub_11_b == ssub_02) + \
NLM[1][0]*int(ssub_11_b == ssub_10) + \
NLM[1][2]*int(ssub_11_b == ssub_12) + \
NLM[2][0]*int(ssub_11_b == ssub_20) + \
NLM[2][1]*int(ssub_11_b == ssub_21) + \
NLM[2][2]*int(ssub_11_b == ssub_22)
if DelH2 >= DelH1:
S[s0, s1] = ssub_11_b
elif cbp:
if sbp[int(ssub_11_b-1)] < random.random():
S[s0, s1] = ssub_11_b
cond_1 = m % uiint.mcint_save_at_mcstep_interval == 0.0
save_msg = False
if m==0 or cond_1 or fully_annealed:
gs[m] = GS2d(m=m,
dim=uigrid.dim,
uidata=uidata,
px_size=px_size,
S_total=uisim.S,
xgr=xgr,
ygr=ygr,
uigrid=uigrid,
)
gs[m].s = deepcopy(S)
save_msg = True
if display_messages:
print(f"GS temporal slice {m} stored\n", 30*".")
if m % uiint.mcint_promt_display == 0:
if display_messages:
if not save_msg:
print(f"Monte-Carlo temporal step = {m}")
print('|' + 15*'-'+' MC SIM RUN COMPLETED on: ALG200' + 15*'-' + '|')
fully_annealed = {'fully_annealed': fully_annealed,
'm': fully_annealed_at_m}
return gs, fully_annealed
"""
"""
def run(uisim, uiint, uidata, uigrid,
xgr, ygr, zgr, px_size,
_a, _b, _c, S, AIA0, AIA1, display_messages):
# ---------------------------------------------
print("Using ALG-200: SA's SL NL-1 TP1 C2 unweighted Q-Pott's model:")
print('|' + 15*'-'+' MC SIM RUN IN PROGRESS on: ALG200' + 15*'-' + '|')
gs = {}
# Build the Non-Locality Matrix
NLM_00, NLM_01, NLM_02 = _a # Unpack 3 colms of 1st row
NLM_10, NLM_11, NLM_12 = _b # Unpack 3 colms of 2nd row
NLM_20, NLM_21, NLM_22 = _c # Unpack 3 colms of 3rd row
# ---------------------------------------------
# Begin modified Markov-Chain annealing iterations
fully_annealed = False
fully_annealed_at_m = None
for m in range(uisim.mcsteps):
if S.min() == S.max():
print(30*'.')
print(f'Single crystal achieved at iteration {m}.')
fully_annealed, fully_annealed_at_m = True, m
# Store the last temporal slice as a UPXO grain structure by
# default
gs[m] = GS2d(m=m,
dim=uigrid.dim,
uidata=uidata,
px_size=px_size,
S_total=uisim.S,
xgr=xgr,
ygr=ygr,
uigrid=uigrid,
)
gs[m].s = deepcopy(S)
if display_messages:
print(f"GS temporal slice {m} stored\n\n"
"!! MONTE-CARLO ALG.202 run ended !!\n"
"...............................")
break
else:
for s0 in list(range(S.shape[0])): # along axis 0
s00, s01, s02 = s0+0, s0+1, s0+2
for s1 in list(range(S.shape[1])): # along axis 1
s10, s11, s12 = s1+0, s1+1, s1+2
ssub_00 = S[AIA0[s00, s10], AIA1[s00, s10]]
ssub_01 = S[AIA0[s01, s10], AIA1[s01, s10]]
ssub_02 = S[AIA0[s02, s10], AIA1[s02, s10]]
ssub_10 = S[AIA0[s00, s11], AIA1[s00, s11]]
ssub_11 = S[AIA0[s01, s11], AIA1[s01, s11]]
ssub_12 = S[AIA0[s02, s11], AIA1[s02, s11]]
ssub_20 = S[AIA0[s00, s12], AIA1[s00, s12]]
ssub_21 = S[AIA0[s01, s12], AIA1[s01, s12]]
ssub_22 = S[AIA0[s02, s12], AIA1[s02, s12]]
Neigh = [ssub_00, ssub_01, ssub_02,
ssub_10, ssub_11, ssub_12,
ssub_20, ssub_21, ssub_22]
if min(Neigh) != max(Neigh):
DelH1 = NLM_00*int(ssub_11 == ssub_00) + \
NLM_01*int(ssub_11 == ssub_01) + \
NLM_02*int(ssub_11 == ssub_02) + \
NLM_10*int(ssub_11 == ssub_10) + \
NLM_12*int(ssub_11 == ssub_12) + \
NLM_20*int(ssub_11 == ssub_20) + \
NLM_21*int(ssub_11 == ssub_21) + \
NLM_22*int(ssub_11 == ssub_22)
# ---------------------------------------------
# If the sampling is to be selected without
# weightage to dominant neighbour state, then:
Neigh = set([x for x in Neigh if x != ssub_11])
# ---------------------------------------------
ssub_11_b = sample_rand(Neigh, 1)[0]
DelH2 = NLM_00*int(ssub_11_b == ssub_00) + \
NLM_01*int(ssub_11_b == ssub_01) + \
NLM_02*int(ssub_11_b == ssub_02) + \
NLM_10*int(ssub_11_b == ssub_10) + \
NLM_12*int(ssub_11_b == ssub_12) + \
NLM_20*int(ssub_11_b == ssub_20) + \
NLM_21*int(ssub_11_b == ssub_21) + \
NLM_22*int(ssub_11_b == ssub_22)
if DelH2 >= DelH1:
S[s0, s1] = ssub_11_b
elif uisim.consider_boltzmann_probability:
if uisim.s_boltz_prob[int(ssub_11_b-1)] < rand.random():
S[s0, s1] = ssub_11_b
cond_1 = m % uiint.mcint_save_at_mcstep_interval == 0.0
save_msg = False
if m==0 or cond_1 or fully_annealed:
gs[m] = GS2d(m=m,
dim=uigrid.dim,
uidata=uidata,
px_size=px_size,
S_total=uisim.S,
xgr=xgr,
ygr=ygr,
uigrid=uigrid,
)
gs[m].s = deepcopy(S)
save_msg = True
if display_messages:
print(f"GS temporal slice {m} stored\n", 30*".")
if m % uiint.mcint_promt_display == 0:
if display_messages:
if not save_msg:
print(f"Monte-Carlo temporal step = {m}")
print('|' + 15*'-'+' MC SIM RUN COMPLETED on: ALG200' + 15*'-' + '|')
fully_annealed = {'fully_annealed': fully_annealed,
'm': fully_annealed_at_m}
return gs, fully_annealed
"""