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)