def main(): #Always start by updating with newest summary.json utilities.update() #Check fletching info OnyxBolts() DiamondBolts() RubyBolts()
def main(): #Always start by updating with newest summary.json utilities.update() #Check fletching info Dharok() Verac() Guthan() Torag() Karil() Ahrim()
def test_update():
data, source_probs = dummy_ini('test_init.log')
print "Initial priorities : ",
print source_probs
print "Updating based on data : ",
print data
source_probs = utilities.update(source_probs, data, 0, 'test_update.log')
print "Final priorities (with smoothing factor 0) : ",
print source_probs
async def appDateCheck():
"""
Checks app updates
"""
if not utilities.isonline(): # if we're not online return false
return False
data = utilities.checkUpdate( 'https://github.com/ENDERZOMBI102/BEE-manipulator', config.version )
if data.url is None:
if getattr(root, 'instance', False):
wx.MessageBox(
parent=root.instance,
message='No updates found!',
caption='BEE Manipulator'
)
return
data = wx.GenericMessageDialog(
parent=getattr(root, 'instance', None),
message=f'An update for the app is available, do you want to update now?\n\n{data.description}',
caption=f'Update Available - new version: {data.version}',
style=wx.YES_NO | wx.ICON_WARNING | wx.STAY_ON_TOP | wx.NO_DEFAULT
)
if data.ShowModal() == wx.ID_NO:
return # user don't want to update
utilities.update()
def iterate(k, j, t, wall_time, hexagon_mf_operators, ts, Ma, uab_term, u_term,
v_term, mu_term, t_term, var_terms, dig_h, Pr, Psi_s, Ns,
Nsquare_s, EVals, EVecs, err):
t_begin = time()
mu = Ma.flat[j]
# initial d_hex_min is the minimum of eigenvalue
d_hex_min, v_hex_min, vec_hex, d_hex = 1.0e5, None, None, None
phi_s = None
# t_init_begin = time()
for lp in range(0, len(Pr)):
psi_s = np.repeat(Pr.flat[lp], 12)
# import the 6 single-site mean-field Hamiltonians for a Honeycomb lattice
# with two species of Pseudospins
h_hexa = calc_h_hexa(t, mu, psi_s, uab_term, u_term, v_term, mu_term,
t_term, var_terms, dig_h, ts)
# solve the Hamilton with Eigenvectors and Eigenvalues
# python returns array of Eigenvalues and normalized Eigenvectors
try:
d_hex, vec_hex = sparse.linalg.eigsh(h_hexa, which='SA', k=1)
d_hex0, v_hex0 = d_hex[0], vec_hex[:, 0]
except sparse.linalg.ArpackNoConvergence:
continue
# find phi1up(down)---the trial solution corresponding to the lowest eigenvalues of Hsite
if d_hex0 < d_hex_min:
d_hex_min, v_hex_min = d_hex0, v_hex0
phi_s = psi_s
# Values of Order parameters corresponding to the trial solution of ground state above
# # value difference for designated order parameters with the trial solutions
is_self_consistent, Phi_s, v_hex_min = update(h_hexa, hexagon_mf_operators,
phi_s, err)
for lp in range(0, wall_time):
if is_self_consistent or Phi_s is None:
break
else:
psi_s = Phi_s
h_hexa = calc_h_hexa(t, mu, psi_s, uab_term, u_term, v_term,
mu_term, t_term, var_terms, dig_h, ts)
is_self_consistent, Phi_s, v_hex_min = update(
h_hexa, hexagon_mf_operators, psi_s, err)
if not is_self_consistent:
print(f" {k}, {j} iteration fail to converge", flush=True)
# Phi_s[2] = np.nan
if Phi_s is not None:
evals, evecs = sparse.linalg.eigsh(h_hexa, which='SA', k=10)
args = np.argsort(evals)
EVals[j, k] = evals[args]
EVecs[j, k] = evecs[:, args].T
# save the final optimal value of both order parameters£¬also save the
# corresponding state eigenvector
for i in range(0, 12):
Psi_s[i][j, k] = Phi_s[i]
# if not is_self_consistent:
# Psi_s[2][j, k] = np.nan
Psi_s[12][j, k] = (v_hex_min.getH().dot(
hexagon_mf_operators[0].getH().dot(
hexagon_mf_operators[2].dot(v_hex_min)))).data[0]
Psi_s[13][j, k] = (v_hex_min.getH().dot(
hexagon_mf_operators[1].getH().dot(
hexagon_mf_operators[3].dot(v_hex_min)))).data[0]
Psi_s[14][j, k] = (v_hex_min.getH().dot(
hexagon_mf_operators[0].getH().dot(
hexagon_mf_operators[1].dot(v_hex_min)))).data[0]
Psi_s[15][j, k] = (v_hex_min.getH().dot(
hexagon_mf_operators[2].getH().dot(
hexagon_mf_operators[3].dot(v_hex_min)))).data[0]
Psi_s[16][j, k] = (v_hex_min.getH().dot(
hexagon_mf_operators[0].getH().dot(
hexagon_mf_operators[3].dot(v_hex_min)))).data[0]
Psi_s[17][j, k] = (v_hex_min.getH().dot(
hexagon_mf_operators[1].getH().dot(
hexagon_mf_operators[2].dot(v_hex_min)))).data[0]
Psi_s[18][j, k] = (v_hex_min.getH().dot(
(hexagon_mf_operators[0] +
hexagon_mf_operators[1]).dot(v_hex_min))).data[0]
Psi_s[19][j, k] = (v_hex_min.getH().dot(
(hexagon_mf_operators[2] +
hexagon_mf_operators[3]).dot(v_hex_min))).data[0]
for i in range(0, 12):
Ns[i][j, k] = (v_hex_min.getH().dot(
hexagon_mf_operators[i].getH().dot(
hexagon_mf_operators[i].dot(v_hex_min)))).data[0]
for i in range(0, 12):
tmp = hexagon_mf_operators[i].getH().dot(hexagon_mf_operators[i])
Nsquare_s[i][j, k] = (v_hex_min.getH().dot(
tmp.dot(tmp.dot(v_hex_min)))).data[0]
else:
for i in range(0, 20):
Psi_s[i][j, k] = np.nan
# for i in range(12, 20):
# Psi_s[i][j, k] = np.nan
for i in range(0, 4):
Ns[i][j, k] = np.nan
for i in range(4, 8):
Ns[i][j, k] = np.nan
print(
f"{k}, {j} iteration finished in {time()-t_begin:.4} seconds with Psi1up{j,k}={Psi_s[0][j, k]}",
flush=True)
return Psi_s, Ns, Nsquare_s, EVals, EVecs
def update_account_codes(self): try : data = utilities.update(ACCOUNT_CODES, UPDATE, self) utilities.log_req_res(data, self) except: utilities.get_error_message(self)
def update_tax_codes(self): try : data = utilities.update(TAX_CODES, UPDATE, self) utilities.log_req_res(data, self) except: utilities.get_error_message(self)
def update_supplier_invoices(self): try : data = utilities.update(SUPPLIER_INVOICES, UPDATE, self) utilities.log_req_res(data, self) except: utilities.get_error_message(self)
def update_purchase_orders(self): try : data = utilities.update(PURCHASE_ORDERS, UPDATE, self) utilities.log_req_res(data, self) except: utilities.get_error_message(self)
def update_customer_invoices(self): try : data = utilities.update(CUSTOMER_INVOICES, UPDATE, self) utilities.log_req_res(data, self) except: utilities.get_error_message(self)
def update_assets(self): try : data = utilities.update(ASSETS, UPDATE, self) utilities.log_req_res(data, self) except: utilities.get_error_message(self)
def update_jobs(self): try : data = utilities.update(JOBS, UPDATE, self) utilities.log_req_res(data, self) except: utilities.get_error_message(self)
def update_opportunities(self): try : data = utilities.update(OPPORTUNITIES, UPDATE, self) utilities.log_req_res(data, self) except: utilities.get_error_message(self)
def update_contacts(self): try : data = utilities.update(CONTACTS, UPDATE, self) utilities.log_req_res(data, self) except: utilities.get_error_message(self)
