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Dumebiz/ciscodevnet
13,125,420,084,938
47e63455dd76a3d9cc1bbb9849f947c34bed96fd
c6137f69c56956458326f221153ac204ff63ac01
/add_device.py
c1f846e5bb1e10f214b405e8992e69257a10b051
[]
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https://github.com/Dumebiz/ciscodevnet
0dbd0ed776f1acdeaab2e65cdc4e5a886b790984
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2021-01-11T18:27:19
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#!/usr/bin/env python """ Author: Dumebi Umezinne Purpose: Demonstrate Python "requests" to get an access token from Cisco DNA Center using the REST API. """ import requests from auth_token import get_token import time import json from pprint import pprint as pprint new_device_dict = { "ipAddress": ["172.20.20.20"], "snmpVersion": "v3", "snmpROCommunity": "readonly", "snmpRWCommunity": "readwrite", "snmpRetry": "1", "snmpAuthPassphrase": "kjdiDI89", "snmpAuthProtocol": "", "snmpMode": "AUTHPRIV", "snmpPrivPassphrase": "hjdahDue88299", "snmpTimeout": "120", "snmpUserName": "admin", "cliTransport": "ssh", "userName": "ambrana", "password": "diablo419!", "enablePassword": "diavolo678!" } def add_device(): token = get_token() api_path = "https://sandboxdnac.cisco.com/dna" headers ={"Content-type": "application/json", "X-Auth-Token" : token} # POST request to add a new device with device details from # dictionary created earlier add_resp = requests.post( f'{api_path}/intent/api/v1/network-device', json=new_device_dict, headers=headers ) print(add_resp.status_code) #print(add_resp) #add_data = add_resp.json() #print(add_data) #print(add_resp.json()["response"]) #print(add_resp.headers) print("***********************") if add_resp.ok: # Wait a few seconds as this is an aysnc process print(f"Request accepted: status code {add_resp.status_code}") time.sleep(10) # Query DNA center to GET the status of task (task url gotten from response to add) task_path = add_resp.json()["response"]["url"] print(f'{api_path}/intent{task_path}') task_resp = requests.get ( f'{api_path}/intent{task_path}', headers = headers ) #Check if task GET is successful if task_resp.ok: task_data = task_resp.json()["response"] #Check if device add async task completed successfully if not task_data["isError"]: print("Successfully added new device") else: print(f"Async task error see: {task_data['progress']}") print(f"Aysnc task failure: {task_data['failureReason']}") else: print(f'Async GET failed: status code {task_resp.status_code}') else: #The initial new device POST failed with details below print(f"Device addition failed with code {add_resp.status_code}") print(f"Failure body: {add_resp.text}") def main(): add_device() if __name__ == "__main__": main()
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yunkb/fibrosis
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/source/solvers/ns_steady/nsestimator.py
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''' Steady Navier-Stokes estimator module Author: David Nolte (dnolte@dim.uchile.cl) Date: 20-09-2016 ''' # TODO: Throw away bctype option?? from dolfin import * from functions import inout import numpy as np import matplotlib.pyplot as plt from scipy.optimize import minimize, brute from nsproblem import NSProblem from nssolver import NSSolver import warnings class NSEstimator: ''' Estimate the coefficients of Navier-Slip boundary conditions using the NSSolver class. The Navier-Slip BCs need to be specified for every boundary segment (defined as gmsh physical group) in the estimator input file with 'preset: 'navierslip''. Only the Nitsche method is currently supported. Note: in the iteration process, the previous solution is used as initial solution for each solve. Currently implemented optimization methods: - scipy.optimize.minimize ''' def __init__(self, opt_ref, opt_est): ''' Initialize Navier-Slip Estimator class. Args: opt_ref path to yaml file with settings for reference case opt_est path to yaml file with settings for estimation ''' self.optfile_ref = opt_ref self.optfile_est = opt_est self.pb_est = None self.pb_ref = None self.options = inout.read_parameters(opt_est) self.uref = None self.pref = None self.pref_meas = None self.uref_meas = None self.u_meas = None # TODO: in the end self.u_meas is the LAST iterate, not the optimal # one!! self.u_opt = None self.p_opt = None self._x0 = None self._xfun = None self._bounds = None self.xlegend = None self.x_opt = None self.f_opt = None self.fval = [] self.x = [] self.beta = [] self.result = None self.p_stei = None self.BO = None self._end = False self.init_problems() pass def init_problems(self): ''' Create reference problem and estimator problem. Automatically called by self.__init__(). Decoupled by the actual pb.init() calls at the beginning of self.estimate(), so parameters can be changed more easily between calls. ''' self.pb_ref = NSProblem(self.optfile_ref) self.pb_est = NSProblem(self.optfile_est) return self def _init_measurement(self): ''' Initialize measurement function space and functions.''' opt_meas = self.options['estimation']['measurement'] mesh, _, bnds = inout.read_mesh(opt_meas['mesh']) self.bnds_meas = bnds # needed for pressure_drop if opt_meas['elements'] == 'P1': deg = 1 elif opt_meas['elements'] == 'P2': deg = 2 else: raise ValueError('Element type unknown. Available options: P1, P2') V = FunctionSpace(mesh, VectorElement('Lagrange', mesh.ufl_cell(), deg)) Q = FunctionSpace(mesh, FiniteElement('Lagrange', mesh.ufl_cell(), 1)) self.pref_meas = Function(Q) self.uref_meas = Function(V) self.u_meas = Function(V) return self def _interp_measurement(self, u, ref=False): ''' Interpolate velocity field to measurement function space. Args: u velocity field to interpolate ref reference solution flag ''' if ref: if not self.uref_meas: raise Exception('uref_meas is None. Call _init_measurement ' 'first!') uinp = self.uref_meas else: if not self.u_meas: raise Exception('u_meas is None. Call _init_measurement ' 'first!') uinp = self.u_meas LI = LagrangeInterpolator() LI.interpolate(uinp, u) return self def add_gaussian_noise(self, u, scal_umax): ''' Add Gaussian noise to a velocity field u, with amplitude scal_umax*umax. Args: u (dolfin function) velocity field scal_umax scaling factor for noise amplitude ''' assert self.u_meas, 'self.u_meas doesn\'t exist yet!' if 'random_seed' in self.pb_est.options['estimation']: np.random.seed(self.pb_est.options['estimation']['random_seed']) dim = u.vector().size() umax = abs(u.vector().array()).max() noise = np.random.normal(0., scal_umax*umax, dim) noise_fun = Function(u.function_space()) noise_fun.vector()[:] = noise u.vector().axpy(1.0, noise_fun.vector()) pass def _update_parameters_inflow(self, x): ''' Update coefficient of inflow profile. Modifies Expressions stored in NSProblem.bcs. Procedure: 1) Find inlet BC in list of strong Dirichlet BCs. 2) Update value at position. According to settings, change U only or U and R. # TODO: PROBLEM IS, R = R + dR WILL BE OVERWRITTEN ! # SOLVED?: SPLIT options dict and bc_lst which is modified by BCs in class NSProblem ATTENTION: This is quite fragile and depends on the fact that the boundary conditions are processed in order ... Args: x parameter: max inflow velocity x[0], dR x[1] (if set) ''' bc_lst = self.pb_est.bc_lst param = self.options['estimation']['parameters']['inflow'] assert param['use'] val = None count_dbc = 0 for i_bc, bc in enumerate(bc_lst): # count_dbc dirichlet BCs before inlet if 'preset' in bc and bc['preset'] == 'inlet': val = bc['value'] bid = bc['id'] break elif 'method' in bc and bc['method'] == 'essential': count_dbc += 1 else: raise KeyError('Inlet BC not found in input file.') assert bc_lst[i_bc]['id'] == bid assert bc_lst[i_bc]['preset'] == 'inlet' val.U = x[0] if param['use'] == 2: # use_slip does not matter. dR will always be in second position in # parameters vector. Maybe necessary TODO this in the future. warnings.warn('Take care. inlet>value>R needs to be INNER radius.') R_inlet = self.options['boundary_conditions'][i_bc]['value']['R'] val.R = x[1] + R_inlet V = self.pb_est.W.sub(0) if self.pb_est.is_enriched(V): val = project(val, V.collapse(), solver_type='lu') self.pb_est.bcs[count_dbc].set_value(val) return self def _update_parameters_navierslip(self, x): ''' Update coefficients of Navier-Slip BCs. Modifies Expressions stored in of NSProblem.bcs_navierslip. Selects the gamma prefactor or dR according to the 'parameters' setting in the options file. Args: x parameter vector ''' param = self.options['estimation']['parameters']['navierslip'] assert param['use'] if 'boundary_id' in self.options['estimation']: boundary_selection = self.options['estimation']['boundary_id'] if type(boundary_selection) is int: boundary_selection = [boundary_selection] else: boundary_selection = [0] if boundary_selection[0] or len(self.pb_est.bcs_navierslip) > 1: raise NotImplementedError('Only one parameter per boundary ' 'supported currently.') if not self.pb_est.bcs_navierslip: raise Exception('No Navier-slip boundary found.') # find position of Navier-slip coefficient in parameter vector index = self.xlegend.index('navierslip') val = x[index] # for val, bc in zip(, self.pb_est.bcs_navierslip): # if bc[0] in boundary_selection or boundary_selection == [0]: for bc in self.pb_est.bcs_navierslip: if param['use'] == 1: bc[1].a = val elif param['use'] == 2: bc[1].dR = val return self def _update_parameters_nitsche(self, beta): ''' Update coefficient of the Nitsche boundary conditions. NOTE: Careful, this updates the betas of ALL Nitsche BCs!!! Args: beta new value for beta ''' self.pb_est.options['nitsche']['beta1'] = beta raise Exception('Nitsche Optimization not supported in the current ' 'version') return self def _update_parameters_transpiration(self, x): ''' Update coefficients of transpiration BC. Modifies Expression stored in NSProblem.bcs_transpiration. Args: x parameters vector ''' param = self.options['estimation']['parameters']['transpiration'] assert param['use'] if 'boundary_id' in self.options['estimation']: boundary_selection = self.options['estimation']['boundary_id'] if type(boundary_selection) is int: boundary_selection = [boundary_selection] else: boundary_selection = [0] if boundary_selection[0] or len(self.pb_est.bcs_transpiration) > 1: raise NotImplementedError('Only one parameter per boundary ' 'supported currently.') if not self.pb_est.bcs_transpiration: raise Exception('No transpiration boundary found.') # find correct index in parameters array index = self.xlegend.index('transpiration') val = x[index] for bc in self.pb_est.bcs_transpiration: assert self.pb_est.is_Constant(bc[1]) bc[1].assign(val) return self def _update_parameters(self, x): ''' Update coefficients of BCs. Depending on the problem: no-slip: bctype == 0. -> inflow (U) navier-slip: bctype == 1. -> any combination of inflow(U, dR), dR or gamma, beta(Nitsche/Transpiration) Args: x parameter ''' param = self.options['estimation']['parameters'] if param['inflow']['use']: self._update_parameters_inflow(x) if param['navierslip']['use']: # navier-slip self._update_parameters_navierslip(x) if param['transpiration']['use']: # transpiration self._update_parameters_transpiration(x) self.pb_est.variational_form() return self def _apply_xfun(self, x): ''' Apply 'xfun' to parameters x. xfun == 0: linear, y = x xfun == 1: exponential, y = 2**x xfun == 2: tanh, y = a + b*0.5*(np.tanh(x) + 1) Args: x (ndarray) parameter Returns: y (ndarray) result of xfun(x) ''' # check for bruteforce 1 parameter corner case if type(x) in (np.ndarray, np.float64) and not x.shape: x = np.array([x]) yi = [] for i, (xi, fi, bi) in enumerate(zip(x, self._xfun, self._bounds)): if fi == 0: # linear yi.append(xi) if fi == 1: # exponential yi.append(2**xi) if fi == 2: # tanh yi.append(self.tanh_xfun(xi, bi)) return np.array(yi) def _apply_inv_xfun(self, x): ''' Apply inverse xfun to initial parameters. See _apply_xfun(). Args: x parameters Returns y inv_xfun(x) ''' yi = [] for i, (xi, fi, bi) in enumerate(zip(x, self._xfun, self._bounds)): if fi == 0: # linear yi.append(xi) if fi == 1: # exponential yi.append(np.log2(xi)) if fi == 2: # tanh yi.append(self.inv_tanh_xfun(xi, bi)) return np.array(yi) def _tikhonov_regularization(self, val): ''' Tikhonov regularization. Returns: val contribution to fval ''' raise NotImplementedError() # if opt_est['xfun'] == 1: # val0 = 2**self.x0 # elif opt_est['xfun'] == 2: # val0 = abs(self.x0) # elif opt_est['xfun'] == 3: # val0 = self.x0**2 # else: # val0 = self.x0 # tikh = opt_est['tikhonov']*np.linalg.norm(val0 - val)**2 # if opt_est['error'] == 'rel': # tikh /= np.linalg.norm(val0)**2 return val def _compute_error(self): ''' Compute the L2 error of the calculated velocity field w.r.t to the measurement. Returns: fval error ''' u, _ = self.pb_est.w.split(deepcopy=True) self._interp_measurement(u) # -> stored to self.u_meas fval = norm(self.u_meas.vector() - self.uref_meas.vector(), 'l2') if self.options['estimation']['error'] == 'rel': fval /= norm(self.uref_meas.vector(), 'l2') return fval def _solve(self, x): ''' Solve function called by optimization method. # TODO: clean up the mess (Tikhonov??) Args: x estimation parameter Returns: fval value to be minimized: error wrt measurement ''' opt_est = self.options['estimation'] val = self._apply_xfun(x) self._update_parameters(val) solver = NSSolver(self.pb_est) solver.solve() assert id(self.pb_est.w) == id(solver.w) and self.pb_est.w == solver.w fval = self._compute_error() if opt_est['tikhonov']: fval += self._tikhonov_regularization(val) if not self._end: print('Parameters:\t {0}'.format(str(val))) print('Fval:\t\t {0}'.format(fval)) self.fval.append(fval) self.x.append(x) return fval def _setup_bruteforce(self): ''' Setup bruteforce arguments. If as_slices is set, make slices according to Npts (int: uniform, or list) and bounds. Otherwise assure that Npts is an integer. Returns: Npts (int) Number of points (no slices) bounds tuple of slices or list of limits for each parameter ''' opt_est = self.options['estimation'] bounds = self._bounds Npts = opt_est['bruteforce']['numpts'] if opt_est['bruteforce']['as_slice']: if not type(Npts) is list: Npts = [Npts]*len(bounds) slices = [] for (n, bnd) in zip(Npts, bounds): step = (bnd[1] - bnd[0])/(n - 1) slices.append(slice(bnd[0], bnd[1] + 1.e-10, step)) bounds = tuple(slices) else: assert type(Npts) is int, ( 'If [a, b] ranges are given, numpts must be int. Use ' 'slices for nonuniform grids.') return Npts, bounds def _parse_parameters(self): ''' Cast parameters into the required form. Process inflow (U, (dR)), navierslip (gamma or dR), transpiration coef. The inflow dR can be taken from the navierslip estimate, if dR is chosen to be optimized in the navierslip section, via the use_slip switch. For all optimization parameters, the initial value x0, the parameter function xfun, and the limits (if any), are added to the instance variables: self._x0, self._xfun, self._bounds The initial values x0 are expected to be the 'true' physical values, BEFORE re-parametrization. The inverse of 'xfun' (self._apply_inv_xfun) is applied on x0 in the end in order to get the correct values. The order is: [u_inflow, dR_inflow, navierslip, transpiration] ''' param = self.options['estimation']['parameters'] # create start vector, x0 self.xlegend = [] self._x0 = [] self._xfun = [] self._bounds = [] if param['inflow']['use']: self._xfun.append(param['inflow']['velocity']['xfun']) self._x0.append(param['inflow']['velocity']['x0']) self._bounds.append(param['inflow']['velocity']['bounds']) self.xlegend.append('Uin') if (param['inflow']['use'] == 2 and param['inflow']['dR']['use_slip'] == 0): self._x0.append(param['inflow']['dR']['x0']) self._xfun.append(param['inflow']['dR']['xfun']) self._bounds.append(param['inflow']['dR']['bounds']) self.xlegend.append('dR_in') elif (param['inflow']['dR']['use_slip'] == 1 and not param['navierslip']['use'] == 2): raise Exception('Inflow dR to be taken from Navier-Slip dR' ' but dR estimation via Navier-Slip set!') estim_boundaries = ['navierslip', 'transpiration'] for bnd in estim_boundaries: if param[bnd]['use']: self._x0.append(param[bnd]['x0']) self._xfun.append(param[bnd]['xfun']) self._bounds.append(param[bnd]['bounds']) self.xlegend.append(bnd) self._x0 = self._apply_inv_xfun(self._x0) return self def gpyopt_optimization(self): ''' OPtimization using GPyOpt. Returns results x_opt, f_opt dict ''' import GPyOpt bounds = self.options['estimation']['gpyopt']['bounds'] if not bounds: gpbounds = None else: gpbounds = [ {'name': 'x{0}'.format(i), 'type': 'continuous', 'domain': tuple(gpbnd)} for (i, gpbnd) in enumerate(bounds)] if ('x0' in self.options['estimation']['gpyopt'] and type(self.options['estimation']['gpyopt']['x0']) is list): Xinit = np.array(self.options['estimation']['gpyopt']['x0']) else: Xinit = None acq_type = self.options['estimation']['gpyopt']['acq_type'] model_type = self.options['estimation']['gpyopt']['model_type'] myBO = GPyOpt.methods.BayesianOptimization( f=self._solve, domain=gpbounds, acquisition_type=acq_type, model_type=model_type, X=Xinit ) max_iter = self.options['estimation']['gpyopt']['max_iter'] max_time = self.options['estimation']['gpyopt']['max_time'] eps = 1e-6 myBO.run_optimization(max_iter, max_time, eps) plt.ion() myBO.plot_acquisition() self.BO = myBO result = {'x': myBO.x_opt, 'f': myBO.fx_opt} self.x = np.array(self.x).squeeze() return result def measurement(self): ''' Makes measurement: first, compute reference solution, then interpolate to measurement mesh and add noise. ''' self.reference_solution() self._init_measurement() self._interp_measurement(self.uref, ref=True) noise_intensity = self.options['estimation']['noise'] if noise_intensity: self.add_gaussian_noise(self.uref_meas, noise_intensity) return self def reference_solution(self): ''' Compute reference solution and produce measurement (u_meas). ''' self.pb_ref.init() sol = NSSolver(self.pb_ref) sol.solve() self.uref, self.pref = sol.w.split(deepcopy=True) return self def estimate(self): '''Estimate parameters of Navier-Slip BC. Setup problem from yaml file and call optimization method with set of initial values. Note: NSSolver initialization takes 1.4us, so no reason to setup beforehand. TODO NOTE: included now beta optimization via switch in yaml file. Args: x0 (optional) initial values ''' opt_est = self.options['estimation'] self._parse_parameters() self.measurement() self.pb_est.init() method = opt_est['method'] if method == 'Powell': result = minimize(self._solve, self._x0, method='Powell') self.x_opt = result['x'] self.f_opt = result['fun'] elif method == 'Nelder-Mead': result = minimize(self._solve, self._x0, method='Nelder-Mead') # options={'disp': True, # 'xtol': 1e-2, 'ftol': 1e-2}) self.x_opt = result['x'] self.f_opt = result['fun'] elif method == 'BFGS': result = minimize(self._solve, self._x0, method='BFGS', tol=opt_est['bfgs']['tol']) # options={ # 'disp': True, 'gtol': 1e-5, 'eps': 1e-3 # }) self.x_opt = result['x'] self.f_opt = result['fun'] elif method == 'bruteforce': Npts, bfbounds = self._setup_bruteforce() result = brute(self._solve, bfbounds, Ns=Npts, disp=True, finish=None, full_output=True) # finish (default) = scipy.optimize.fmin to polish result self.x_opt = result[0] self.f_opt = result[1] elif method == 'gpyopt': raise Exception('GPyOpt dropped. Adapt...') result = self.gpyopt_optimization() self.x_opt = result['x'] self.f_opt = result['f'] # optimization done. self._end = True self.fval = np.array(self.fval) self.x = np.array(self.x) print(result) self.result = result return self def solve_opt(self, x=None, init=False): ''' Solve with the optimal parameters. Args: x (optional, numpy.ndarray) parameters; if not given, use x_opt init (optional, bool) reinitialize solution w ''' if x is None: x = self.x_opt # else: # x = self._apply_inv_xfun(x) if init: self.pb_est.w.vector().zero() print('zeroed') self._solve(x) self.u_opt, self.p_opt = self.pb_est.w.split(deepcopy=True) self.u_meas_opt = self.u_meas return self def get_radius_at_vert_boundary(self, bnds, bid): ''' Get the radius of a vertical boundary patch. Args: bnds boundary domain object bid boundary id Returns: radius ''' It_facet = SubsetIterator(bnds, bid) ycoord = [] for c in It_facet: for v in vertices(c): ycoord.append(v.point().y()) ycoord = np.array(ycoord) if np.allclose(ycoord.min(), 0) or np.allclose(ycoord.max(), -ycoord.min()): # symmetric or full (-R, R) radius = ycoord.max() else: warnings.warn('Pressure_drop: careful, geometry not symmetric! ' 'ymin = {0}, ymax = {1}'.format(ycoord.min(), ycoord.max())) radius = 0.5*(ycoord1.max() - ycoord1.min()) return radius def pressure_drop(self, p, sin=1, sout=2): ''' Calculate pressure drop for optimized NSE solution or reference pressure on the respective meshes, between two boundaries sin, sout. The pressure is integrated over the boundaries and devided by the respective measure (integral mean), then substracted. The function detects automatically if the given pressure field p is defined on a) the reference mesh, b) the estimation mesh, c) the measurement mesh, and the boundary FacetFunction is chosen appriopriately. Args: p pressure field sin index of inlet boundary sout index of outlet boundary ''' # detect reference or estimator problem mesh = p.function_space().mesh() if mesh.id() == self.pref.function_space().mesh().id(): # reference case bnds = self.pb_ref.bnds elif (mesh.id() == self.pb_est.w.split(deepcopy=True)[1].function_space(). mesh().id()): bnds = self.pb_est.bnds elif mesh.id() == self.pref_meas.function_space().mesh().id(): bnds = self.bnds_meas else: raise Exception('p not identified.') ds = Measure('ds', domain=mesh, subdomain_data=bnds) measure_sin = Constant(self.get_radius_at_vert_boundary(bnds, sin)) measure_sout = Constant(self.get_radius_at_vert_boundary(bnds, sout)) # print('measure_sin: {0}'.format(measure_sin.values()[0])) # print('measure_sout: {0}'.format(measure_sin.values()[0])) dP = assemble(p/measure_sin*ds(sin) - p/measure_sout*ds(sout)) return dP def direct_estimator(self, method='STEint', return_pressure=False): ''' Compute "standalone" pressure estimate; caller function to encompass Navier-slip optimization via self.estimation(). Args: method pressure estimation method: STE, STEint, PPE Returns: dP estimated pressure drop ''' if not self.uref_meas: self.measurement() if not self.pb_est.w: self.pb_est.init() fun = getattr(self, method) dP, p_est = fun() if return_pressure: ret = (dP, p_est) else: ret = dP return ret def PPE(self, sin=1, sout=2): ''' Compute PPE pressure approximation and pressure drop. Args (optional): sin inlet boundary id sout outlet boundary id Returns: dP ''' assert self.uref_meas, 'Reference measurement does not exist.' rho = self.pb_ref.options['rho'] mesh = self.uref_meas.function_space().mesh() E1 = FiniteElement('Lagrange', mesh.ufl_cell(), 1) P1 = FunctionSpace(mesh, E1) p = TrialFunction(P1) q = TestFunction(P1) bc = DirichletBC(P1, Constant(0.), self.bnds_meas, sout) u0 = self.uref_meas a = inner(grad(p), grad(q))*dx L = - rho*inner(grad(u0)*u0, grad(q))*dx A, b = assemble_system(a, L, bc) p_est = Function(P1) solve(A, p_est.vector(), b, 'mumps') self.p_est = p_est dP = self.pressure_drop(p_est) return dP, p_est def STE(self, sin=1, sout=2): ''' Compute STE pressure approximation and pressure drop. Args (optional): sin inlet boundary id sout outlet boundary id Returns: dP ''' assert self.uref_meas, 'Reference measurement does not exist.' rho = self.pb_ref.options['rho'] ndim = self.pb_ref.ndim mesh = self.uref_meas.function_space().mesh() P1 = FiniteElement('Lagrange', mesh.ufl_cell(), 1) B = FiniteElement('Bubble', mesh.ufl_cell(), 1 + ndim) W = FunctionSpace(mesh, MixedElement(ndim*[P1 + B])*P1) (w, p) = TrialFunctions(W) (v, q) = TestFunctions(W) zero = Constant((0,)*ndim) noslip = project(zero, W.sub(0).collapse()) bc = DirichletBC(W.sub(0), noslip, 'on_boundary') u0 = self.uref_meas a = inner(grad(w), grad(v))*dx - p*div(v)*dx + div(w)*q*dx L = - rho*inner(grad(u0)*u0, v)*dx # A = assemble(a) # b = assemble(L) # bc.apply(A, b) A, b = assemble_system(a, L, bc) w1 = Function(W) solve(A, w1.vector(), b, 'mumps') _, p_est = w1.split(deepcopy=True) self.p_est = p_est dP = self.pressure_drop(p_est) return dP, p_est def STEint(self, sin=1, sout=2): ''' Compute STEint pressure approximation and pressure drop. Args (optional): sin inlet boundary id sout outlet boundary id Returns: dP ''' assert self.uref_meas, 'Reference measurement does not exist.' mu = self.pb_ref.options['mu'] rho = self.pb_ref.options['rho'] ndim = self.pb_ref.ndim mesh = self.uref_meas.function_space().mesh() P1 = FiniteElement('Lagrange', mesh.ufl_cell(), 1) B = FiniteElement('Bubble', mesh.ufl_cell(), 1 + ndim) W = FunctionSpace(mesh, MixedElement(ndim*[P1 + B])*P1) (w, p) = TrialFunctions(W) (v, q) = TestFunctions(W) zero = Constant((0,)*ndim) noslip = project(zero, W.sub(0).collapse()) bc = DirichletBC(W.sub(0), noslip, 'on_boundary') u0 = self.uref_meas a = inner(grad(w), grad(v))*dx - p*div(v)*dx + div(w)*q*dx L = - mu*inner(grad(u0), grad(v))*dx + rho*inner(grad(v)*u0, u0)*dx # A = assemble(a) # b = assemble(L) # bc.apply(A, b) A, b = assemble_system(a, L, bc) w1 = Function(W) solve(A, w1.vector(), b, 'mumps') _, p_est = w1.split(deepcopy=True) self.p_est = p_est dP = self.pressure_drop(p_est) return dP, p_est def gamma(self, x, R_i=0.95, R_o=1.0): ''' Utility function for calculating Navier-slip Gamma from the optimization parameters. First the poiseuille base gamma is computed, then the gamma based on the estimation parameters. Since the Navier-slip BC is defined via x*gamma_pois, where gamma_pois is the Poiseuille gamma obtained from the physical parameters and only the proportionality factor x is set, both gammas are returned for comparability. Args: xi parameter(s) R_i Poiseuille gamma inner radius R_o Poiseuille gamma outer radius Returns: gamma_pois Poiseuille gamma gamma_opt optimized gamma ''' use = self.options['estimation']['parameters']['navierslip']['use'] mu = self.options['mu'] gamma_pois = 2.*mu*R_i/(R_i**2 - R_o**2) if use == 1: # xi*gamma optimization gamma_opt = x*2.*mu*R_i/(R_i**2 - R_o**2) elif use == 2: # dR optimization R_o = R_i + x gamma_opt = 2.*mu*R_i/(R_i**2 - R_o**2) return gamma_pois, gamma_opt def tanh_xfun(self, x, bounds): ''' Compute tanh(x) function. Args: x evaluation location bounds tuple with (lower, upper) limits Return: beta ''' return bounds[0] + bounds[1]*0.5*(np.tanh(x) + 1) def inv_tanh_xfun(self, x, bounds): ''' Compute inverse of tanh(x) function. Args: x evaluation location bounds tuple with (lower, upper) limits Return: beta ''' return np.arctanh(2./bounds[1]*(x - bounds[0]) - 1.)
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hack4impact/vision-zero-philly
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4fdb163f12ea089f35be7650100da428119dabad
6485b0cc51a8b449cd12ffb042968e7456c98974
/app/reports/forms.py
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2020-05-26T12:25:58.159744
2017-08-23T01:53:48
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import datetime as datetime from flask_wtf import Form from flask_wtf.file import FileField, FileAllowed from wtforms.fields import ( SelectField, StringField, SubmitField, IntegerField, TextAreaField, HiddenField, DateField, RadioField, FieldList, BooleanField ) from wtforms_components import TimeField from wtforms.ext.sqlalchemy.fields import QuerySelectField from wtforms.validators import ( InputRequired, Length, Optional, NumberRange, URL, Regexp ) from app.custom_validators import StrippedLength, ValidLocation, RequiredIf, RequireDescription from .. import db class IncidentReportForm(Form): address = StringField('Address', validators=[ InputRequired('Address is required.'), ValidLocation() ]) latitude = HiddenField('Latitude') longitude = HiddenField('Longitude') car = BooleanField('Car', validators=[ Optional() ]) bus = BooleanField('Bus', validators=[ Optional() ]) truck = BooleanField('Truck', validators=[ Optional() ]) bicycle = BooleanField('Bicycle', validators=[ Optional() ]) pedestrian = BooleanField('Pedestrian', validators=[ Optional() ]) injuries = RadioField('Did an injury occur?', choices=[ ('Yes', 'Yes'), ('No', 'No') ], validators=[InputRequired()]) injuries_description = TextAreaField('Injuries Description', validators=[ RequireDescription('injuries'), Length(max=5000) ]) witness = RadioField('Did you observe or experience the incident?', choices=[ ('Observed', 'Observed'), ('Experienced', 'Experienced') ], validators=[InputRequired()]) category = SelectField('Category', choices=[("Failure to stop", "Failure to stop"), ("Running a red light", "Running a red light"), ("Swerving vehicle", "Swerving vehicle"), ("Tailgating", "Tailgating"), ("Cycling on sidewalk", "Cycling on sidewalk"), ("Car door", "Car door"), ("Crossing against signal", "Crossing against signal"), ("Other", "Other")], validators=[ InputRequired() ]) description = TextAreaField('Description', validators=[ Optional(), Length(max=5000) ]) road_conditions = TextAreaField('Weather/Road Conditions', validators=[ Optional(), Length(max=5000) ]) today = datetime.datetime.today() date = DateField('Date of Event (year-month-day)', default=today.strftime('%m-%d-%Y'), validators=[InputRequired()]) time = TimeField('Time of Event (hours:minutes am/pm)', default=today.strftime('%I:%M %p'), validators=[InputRequired()]) picture_file = FileField( 'Upload a Photo', validators=[ Optional(), FileAllowed(['jpg', 'jpe', 'jpeg', 'png', 'gif', 'svg', 'bmp'], 'Only images are allowed.') ] ) picture_url = StringField('Picture URL', validators=[ Optional(), URL(message='Picture URL must be a valid URL. ' 'Please upload the image to an image hosting website ' 'and paste the link here.') ]) deaths = IntegerField('Number of Deaths', validators=[Optional()]) contact_name = StringField('Contact Name', validators=[ Optional(), Length(max=1000) ]) contact_phone = StringField('Contact Phone', validators=[ Optional(), Length(max=1000) ]) contact_email = StringField('Contact E-mail', validators=[ Optional(), Length(max=100) ]) submit = SubmitField('Create Report') class EditIncidentReportForm(IncidentReportForm): submit = SubmitField('Update Report')
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TushaarGVS/SC_Lab
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ee03fb09a9855ab32348c1a4a85769badebca541
/Lab3/K_Means_Clustering.py
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2017-11-06T04:56:56
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from csv import reader from math import sqrt from random import randrange def load_csv(filename): dataset = list() check = 0 with open(filename, 'r') as file: csv_reader = reader(file) for row in csv_reader: if not row: continue if check != 0: dataset.append(row) check += 1 change_dataset(dataset) return dataset def change_dataset(dataset): for element in dataset: for i in range(len(element) - 1): element[i] = float(element[i].strip()) element[-1] = 1.0 if element[-1] == 'Yes' else 0.0 def pick_centers(dataset, k): dataset_copy = list(dataset) clusters = [] for i in range(k): index = randrange(len(dataset_copy)) clusters.append(dataset_copy.pop(index)) return clusters def euclidian_distance(datapoint_1, datapoint_2): value = 0 for i in range(len(datapoint_1) - 1): value += (datapoint_1[i] - datapoint_2[i])**2 return sqrt(value) def dataset_segregation(dataset, assigned_clusters): segregated_dataset = {} for i in range(len(dataset)): if (assigned_clusters[i] not in segregated_dataset): segregated_dataset[assigned_clusters[i]] = [] segregated_dataset[assigned_clusters[i]].append(dataset[i]) return segregated_dataset def compute_new_clusters(dataset, assigned_clusters): clusters = [] segregated_dataset = dataset_segregation(dataset, assigned_clusters) for cluster_num, data_points in segregated_dataset.iteritems(): clusters.append([float(sum(i)) / len(i) for i in zip(*data_points)]) return clusters def assign_clusters(clusters, dataset, k): assigned_clusters = [] for data_point in dataset: distances = [] for i in range(k): distances.append(euclidian_distance(data_point, clusters[i])) cluster = distances.index(min(distances)) assigned_clusters.append(cluster) return assigned_clusters def k_means_clustering(dataset, k, num_iterations): clusters = pick_centers(dataset, k) prev_clusters = [] for i in range(num_iterations): if (prev_clusters == clusters): break assigned_clusters = assign_clusters(clusters, dataset, k) new_clusters = compute_new_clusters(dataset, assigned_clusters) prev_clusters = clusters clusters = new_clusters final_segregation = dataset_segregation(dataset, assigned_clusters) print("Total iterations used: %s" %(i + 1)) return final_segregation def accuracy(final_segregation): correct = 0 for cluster_num, data_points in final_segregation.iteritems(): count_0 = count_1 = 0 for data_point in data_points: if(data_point[-1] == 0): count_0 = count_0 + 1 else: count_1 = count_1 + 1 if(count_0 > count_1): class_assigned = 0 else: class_assigned = 1 print("Cluster: %s; Class Assigned: %s; Number of elements: %s" %(cluster_num, class_assigned, len(data_points))) for data_point in data_points: if(data_point[-1] == class_assigned): correct = correct + 1 return correct filename = raw_input("Enter file name: ") k = 2 num_iterations = int(raw_input("Enter the maximum number of iterations: ")) dataset = load_csv(filename) final_segregation = k_means_clustering(dataset, k, num_iterations) print("Clusters: %s" % final_segregation) correct = accuracy(final_segregation) print("Accuracy: %s" %(correct/float(len(dataset)) * 100))
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Xav83/Python
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1db70b1fa7fc99a4c824998036cc6abe2016412a
/roboc/tst/test_Robot.py
66344a9dcdd11ca7242c6f04f047cb48914971ff
[]
no_license
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import unittest from src import robot class TestRobot(unittest.TestCase): pass
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cuplv/verivita
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aaeff97796f9d7780e0b7136de45d7ac93d399a2
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/cbverifier/traces/__init__.py
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[]
no_license
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refs/heads/master
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2018-09-10T16:10:20
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# package cbverifier.traces
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romin991/rocketSales
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d6f8d1bc316935cae91137fc8c9f6d394fb4ddc2
3c3dc45d0fec06f7e4bc4c3477f3eaa66ad68bb7
/devices/serializers.py
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refs/heads/master
2021-01-22T22:03:34.967308
2017-05-29T15:52:31
2017-05-29T15:52:31
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from rest_framework import serializers from devices.models import * class DeviceSerializer(serializers.ModelSerializer): class Meta: model = Device
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PrincessGods/deco3801
9,509,057,605,346
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/application/main/routes.py
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[]
no_license
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refs/heads/master
2020-03-27T08:26:25.074661
2018-10-26T01:09:52
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from flask import render_template, request, Blueprint, flash, redirect, url_for from application.models import User, Sample_Information, Sample_Location, Search_Results from application import db, bcrypt from application.main.forms import HomeSearchForm from flask_login import current_user main = Blueprint('main', __name__) @main.route("/") @main.route("/home", methods=['GET', 'POST']) def home(): form = HomeSearchForm() user_icon = getUserIcon() if form.validate_on_submit(): c_name = form.search.data return redirect(url_for('main.search', name=c_name)) return render_template('index.html', title = "QAEHS", form = form, icon = user_icon) @main.route("/help") def help(): user_icon = getUserIcon() return render_template('tutorial.html', title = "Help", icon = user_icon) @main.route("/search/<name>", methods=['GET', 'POST']) def search(name): form = HomeSearchForm() user_icon = getUserIcon() samples = Sample_Information.query.filter_by(sample_type = name).all() return render_template('chemical_search.html', title = "Search Result", form = form, icon = user_icon, samples = samples) @main.route("/searchDetails/<id>", methods=['GET', 'POST']) def searchDetails(id): form = HomeSearchForm() user_icon = getUserIcon() sample = Sample_Information.query.filter_by(id = id).first() location = Sample_Location.query.filter_by(sample_id = id).first() return render_template('search_result_details.html', title = "Search Result Details", form = form, icon = user_icon, sample = sample, location = location) def getUserIcon(): if current_user.is_authenticated: user_icon = url_for('static', filename='imgs/' + current_user.user_icon) return user_icon
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routes.py
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nwrocketman64/sales-tax-calculator
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d8a0a1f9f09d44b4c39787d3f2986de5be54b248
66ff40f03f3f4a8f1291ac0f683a514e1b272e71
/app.py
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refs/heads/main
2023-05-04T18:26:08.596351
2021-05-20T19:57:14
2021-05-20T19:57:14
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# Import the needed librarys from flask and sessions. from flask import Flask, render_template, request, session, redirect, url_for from flask_session import Session # Create the web app in flask. app = Flask(__name__) # Configure the sessions in the web app. app.config["SESSION_PERMANENT"] = False app.config["SESSION_TYPE"] = "filesystem" app.secret_key = "\xd5$\xa2\xd5\xd8\x06\xab\xa4\xb5\x86\xec\xf1Tn[s" Session(app) # Define the main route as both a POST and GET route. @app.route('/', methods = ['POST', 'GET']) def index(): # If the method is POST, the user will be entering the calculations. if request.method == 'POST': # Clear all the values from the session if there are any. session.pop('message', None) session.pop('total', None) session.pop('subtotal', None) session.pop('taxAmount', None) # Try to receive the input from the user and validate the input. try: price = float(request.form['price']) amount = float(request.form['amount']) tax = float(request.form['tax']) # If it fails to validate, add the error message to the session and redirect back to the GET page. except: session['message'] = 'You must fill out all input fields.' return redirect(url_for('index')) # If everything worked so far, calculate the subtotal, tax, and total. subtotal = (price * amount) tax_amount = (price * amount) * (tax / 100.0) total = (price * amount) * (1 + (tax / 100.0)) # Save all the results to the session. session['subtotal'] = "${:,.2f}". format(subtotal) session['taxAmount'] = "${:,.2f}". format(tax_amount) session['total'] = "${:,.2f}". format(total) # Then redirect the user back to the GET form of the page. return redirect(url_for('index')) else: # Just render the page if it is a GET request. return render_template('index.html') # The 404 handler. @app.errorhandler(404) def not_found(e): return render_template('404.html'), 404 # Start the web application. if __name__ == '__main__': app.run()
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akangupt/a-fiery-vengeance
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/source code/hero_exit.py
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[]
no_license
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refs/heads/master
2021-01-10T14:23:02.893165
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import sys import os import math import cPickle as pk import pygame import random try: import _path except: pass import tiledtmxloader # checks if hero collides to a portal # if it does then returns the index of the portal # otherwise returns -1 def exit_map(hero,portal): plen=len(portal) i=0 for i in range(0,plen): if pygame.Rect.colliderect(hero.rect, portal[i]): return i return -1 # if hero collides to portal then it returns the next map corresponding to that portal def next_map(map_name,portal_num): sv=pk.load(open("./save.p","rb")) if map_name=='./maps/village1.tmx': if portal_num==0: return ['./maps/tunnel.tmx',0] elif portal_num==1: return ['./maps/tunnel2_4.tmx',0] elif portal_num==2: return ['./maps/tunnel3.tmx',0] elif portal_num==3: return ['./maps/tunnel2_4.tmx',1] elif map_name=='./maps/tunnel2_4.tmx': if (portal_num==0 or portal_num==1): return ['./maps/village1.tmx',1] elif map_name=='./maps/tunnel3.tmx': if portal_num==0: return ['./maps/ship.tmx',0] elif map_name=='./maps/mountainclimbing.tmx': if portal_num==0: return['./maps/mountain_top.tmx',0] elif map_name=='./maps/village2_out1.tmx': if portal_num==0: return ['./maps/village2_inside.tmx',0] elif map_name=='./maps/village2_inside.tmx': if portal_num==0: return ['./maps/village2_out1.tmx',1] elif (portal_num==1 and sv['spook']==1): # sv['spook'] == 1 denotes that hero has talked to spooky guy # 'village2_inside' contains two paths to go out of this map # portal_num == 1 denotes the first path return ['./maps/mountainclimbing.tmx',0] elif (portal_num==2 and sv['spook']==1): # portal_num == 1 denotes the first path return ['./maps/mountainclimbing.tmx',0] elif portal_num==3: # portal_num == 3 denotes the hotel map return ['./maps/hotel.tmx',0] elif map_name=='./maps/tunnel.tmx': if portal_num==0: return ['./maps/village1.tmx',1] elif portal_num==1: return ['./maps/tunnel2.tmx',0] elif map_name=='./maps/ship.tmx': if (portal_num==0 and sv['pirate']==1): # sv['pirate'] == 1 denotes that hero has talked to pirate guy if(random.randint(1,2)==1): return['./maps/maze.tmx',0] else: return['./maps/maze2.tmx',0] elif map_name=='./maps/maze.tmx': if portal_num==0: return['./maps/safe1.tmx',0] elif map_name=='./maps/maze2.tmx': if portal_num==0: return['./maps/safe1.tmx',0]
UTF-8
Python
false
false
2,917
py
59
hero_exit.py
33
0.541995
0.516627
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feyfree/Summary
4,990,752,043,863
6277df950f8afe26d414faf03cb786b3ac889649
b36b7b71693113e248c8391a871cb10df9acb1a0
/DataStructure/sort/select_sort.py
45aeca2dae7c4439e861fde5889ff4e1c60dd44a
[]
no_license
https://github.com/feyfree/Summary
81e24838de96f9e073d0a5d60ba93913dabeb05b
67f72f882a6c00f472cb4f5219b87a2cf0243017
refs/heads/master
2020-04-07T11:46:41.478087
2019-02-14T06:49:14
2019-02-14T06:49:14
158,340,544
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null
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null
null
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null
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def select_sort(nums): n = len(nums) for i in range(n): k = i for j in range(i, n): if nums[j] < nums[k]: k = j if i != k: nums[i], nums[k] = nums[k], nums[i] return nums nums = [1,3,6,7,2,4] print(select_sort(nums))
UTF-8
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py
96
select_sort.py
69
0.544304
0.518987
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13
17.307692
38
QuailAutomation/yahoo_fantasy_bot
9,053,791,090,267
f61098052211ca8cc78a524b76d9c99abe610355
04796f68651ae33a498454437804ea94e0a3f8ba
/yahoo_fantasy_bot/nhl.py
32a76db85b182382d5dd423b8d3e3a52d595b222
[ "MIT" ]
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https://github.com/QuailAutomation/yahoo_fantasy_bot
56b4d18575efd0d800c89f39d95d158a3759a778
9d3b4205f971babe20faa242ab515b2818083fc2
refs/heads/master
2020-09-13T13:05:28.067751
2019-11-17T21:15:21
2019-11-17T21:15:21
null
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null
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#!/usr/bin/python import pandas as pd import numpy as np from nhl_scraper import nhl import logging import datetime logger = logging.getLogger() class Builder: """Class that constructs prediction datasets for hockey players. The datasets it generates are fully populated with projected stats taken from csv files. :param lg: Yahoo! league :type lg: yahoo_fantasy_api.league.League :param skaters_csv: csv file containing skater predictions :type skaters_csv: str :param goalies_csv: csv file containing goalie predictions :type goalies_csv: str """ def __init__(self, lg, skaters_csv, goalies_csv): skaters = pd.read_csv(skaters_csv, index_col='name') goalies = pd.read_csv(goalies_csv, index_col='name') self.ppool = pd.concat([skaters, goalies], sort=True) self.nhl_scraper = nhl.Scraper() wk_start_date = lg.edit_date() assert(wk_start_date.weekday() == 0) wk_end_date = wk_start_date + datetime.timedelta(days=6) self.team_game_count = self.nhl_scraper.games_count(wk_start_date, wk_end_date) self.nhl_players = self.nhl_scraper.players() def predict(self, roster_cont): """Build a dataset of hockey predictions for the week The pool of players is passed into this function through roster_const. It will generate a DataFrame for these players with their predictions. The returning DataFrame has rows for each player, and columns for each prediction stat. :param roster_cont: Roster of players to generate predictions for :type roster_cont: roster.Container object :return: Dataset of predictions :rtype: DataFrame """ # Produce a DataFrame using preds as the base. We'll filter out # all of the players not in roster_cont by doing a join of the two # data frames. This also has the affect of attaching eligible # positions and Yahoo! player ID from the input player pool. my_roster = pd.DataFrame(roster_cont.get_roster()) df = my_roster.join(self.ppool, on='name') # Then we'll figure out the number of games each player is playing # this week. To do this, we'll verify the team each player players # for then using the game count added as a column. team_ids = [] wk_g = [] for plyr_series in df.iterrows(): plyr = plyr_series[1] (team_id, g) = self._find_players_schedule(plyr['name']) team_ids.append(team_id) wk_g.append(g) df['team_id'] = team_ids df['WK_G'] = wk_g return df def _find_players_schedule(self, plyr_name): """Find a players schedule for the upcoming week :param plyr_name: Name of the player :type plyr_name: str :return: Pair of team_id (from NHL) and the number of games :rtype: (int, int) """ df = self.nhl_players[self.nhl_players['name'] == plyr_name] if len(df.index) == 1: team_id = df['teamId'].iloc(0)[0] return (team_id, self.team_game_count[team_id]) else: return(np.nan, 0) def init_prediction_builder(lg, cfg): return Builder(lg, "espn.skaters.proj.csv", "espn.goalies.proj.csv") class PlayerPrinter: def __init__(self, cfg): pass def printRoster(self, lineup, bench, injury_reserve): """Print out the roster to standard out :param cfg: Instance of the config :type cfg: configparser :param lineup: Roster to print out :type lineup: List :param bench: Players on the bench :type bench: List :param injury_reserve: Players on the injury reserve :type injury_reserve: List """ first_goalie = True print("{:4}: {:20} " "{:4} {}/{}/{}/{}/{}". format('B', '', 'WK_G', 'G', 'A', 'PPP', 'SOG', 'PIM')) for pos in ['C', 'LW', 'RW', 'D', 'G']: for plyr in lineup: if plyr['selected_position'] == pos: if pos in ["G"]: if first_goalie: print("") print("{:4}: {:20} " "{:4} {}/{}". format('G', '', 'WK_G', 'W', 'SV%')) first_goalie = False print("{:4}: {:20} " "{:4} {:.1f}/{:.3f}". format(plyr['selected_position'], plyr['name'], plyr['WK_G'], plyr['W'], plyr['SV%'])) else: print("{:4}: {:20} " "{:4} {:.1f}/{:.1f}/{:.1f}/{:.1f}/{:.1f}". format(plyr['selected_position'], plyr['name'], plyr['WK_G'], plyr['G'], plyr['A'], plyr['PPP'], plyr['SOG'], plyr['PIM'])) print("") print("Bench") for plyr in bench: print(plyr['name']) print("") print("Injury Reserve") for plyr in injury_reserve: print(plyr['name']) def printListPlayerHeading(self, pos): if pos in ['G']: print("{:20} {} {}/{}".format('name', 'WK_G', 'W', 'SV%')) else: print("{:20} {} {}/{}/{}/{}/{}".format('name', 'WK_G', 'G', 'A', 'PPP', 'SOG', 'PIM')) def printPlayer(self, pos, plyr): if pos in ['G']: if self._does_player_have_valid_stats(plyr, ['W', 'SV%']): print("{:20} {:.1f}/{:.3f}". format(plyr[1]['name'], plyr[1]['W'], plyr[1]['SV%'])) else: if self._does_player_have_valid_stats(plyr, ['G', 'A', 'PPP', 'SOG', 'PIM']): print("{:20} {} {:.1f}/{:.1f}/{:.1f}/{:.1f}/{:.1f}". format(plyr[1]['name'], plyr[1]['WK_G'], plyr[1]['G'], plyr[1]['A'], plyr[1]['PPP'], plyr[1]['SOG'], plyr[1]['PIM'])) def _does_player_have_valid_stats(self, plyr, stats): for stat in stats: if np.isnan(plyr[1][stat]): return False return True class Scorer: """Class that scores rosters that it is given""" def __init__(self, cfg): self.cfg = cfg self.use_weekly_sched = cfg['Scorer'].getboolean('useWeeklySchedule') def summarize(self, df): """Summarize the dataframe into individual stat categories :param df: Roster predictions to summarize :type df: DataFrame :return: Summarized predictions :rtype: Series """ temp_stat_cols = ['GA', 'SV'] stat_cols = ['G', 'A', 'SOG', 'PPP', 'PIM', 'W'] + temp_stat_cols res = dict.fromkeys(stat_cols, 0) for plyr in df.iterrows(): p = plyr[1] for stat in stat_cols: if not np.isnan(p[stat]): if self.use_weekly_sched: res[stat] += p[stat] / 82 * p['WK_G'] else: res[stat] += p[stat] # Handle ratio stats if res['SV'] > 0: res['SV%'] = res['SV'] / (res['SV'] + res['GA']) else: res['SV%'] = None # Drop the temporary values used to calculate the ratio stats for stat in temp_stat_cols: del res[stat] return res def is_counting_stat(self, stat): return stat not in ['SV%'] def is_highest_better(self, stat): return True
UTF-8
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false
false
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py
15
nhl.py
11
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0.488785
0
215
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wax911/anime-meta
17,557,826,308,111
e0c5ae475cf2c3b8e407c24e86bd3c05be443033
c6b719f46b52535c991fbf4ef8270fd4e290e29d
/di/__init__.py
d2575e17b16f7faa105818e949c9ec3eb333f7b5
[ "Apache-2.0" ]
permissive
https://github.com/wax911/anime-meta
2d143845b999ada949de497ab2adcdab1a2ed988
5a707a75b277a9c0dc5b9d9447cea44a73d2f83c
refs/heads/main
2023-06-17T02:12:34.599542
2021-07-15T09:37:20
2021-07-15T09:37:20
355,594,879
2
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null
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null
null
null
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from .dependencies import LocalSourceProvider, \ RemoteSourceProvider, \ RepositoryProvider, \ UseCaseProvider, \ UtilityClientScopeProvider, \ MapperScopeProvider, \ SourceUtilityProvider
UTF-8
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py
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__init__.py
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rishabmamgai/PCA
19,628,000,550,606
222fb27c789d1d469c2ef7f3ad6feeead7f70635
02f26085369cecbb55c2f61e4143bc99f52c8cf9
/main.py
bbda05b2a9480106b02c2c6a6e21072a40b8584e
[]
no_license
https://github.com/rishabmamgai/PCA
24a36faa0d97f7b088c0b612ef1f73c0cd326cde
43ce54abd0ffac07b22b8805a62de65f47040984
refs/heads/main
2023-07-11T12:27:45.231336
2021-08-13T15:10:35
2021-08-13T15:10:35
395,694,484
2
0
null
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from matplotlib.pyplot import plot from model import pca, find_k import numpy as np import functions # Loading data and plotting faces X = functions.load_data(r'D:\ML\PCA\faces.mat') functions.plot_faces(X) # Normalizing data X_normalized = X / 255 # Running PCA U, S, V = pca(X_normalized) # Plotting first 36 eigen vectors functions.plot_faces(np.transpose(U[:, :36]) * 255) # Finding number of pricipal components k, variance_retained = find_k(S) print(f"\nnumber of principal components = {k}") # Reduction U_reduce = U[:, :k] z = np.dot(X_normalized, U_reduce) # Recovering features X_recovered = np.dot(z, np.transpose(U_reduce)) X_recovered *= 255 functions.plot_faces(X_recovered)
UTF-8
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py
3
main.py
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0.685135
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kamau96/Binary-Tree
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61fd592afee1c7606f7c1afabc82150914bffe38
/main.py
e7cfe22f5fed827bb270de067ac7e8cc1ec8de7e
[]
no_license
https://github.com/kamau96/Binary-Tree
6eadffd5804e205b90d5ed8c4621e9d30e7cdb2f
890b3f6b62b6c350cfabc1d557e9fe6fb9287994
refs/heads/master
2022-12-28T04:44:19.243167
2020-10-15T02:05:35
2020-10-15T02:05:35
304,183,442
0
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null
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class Node: def __init__(self,data): self.left=None self.right=None self.data=data def inorder(temp): if (not temp): return inorder(temp.left) print(temp.key,end = " ") inorder(temp.right) def insert(temp, key): if not temp: root= Node(key) return queue=[] queue.append(temp) while (len(queue)): temp=queue[0] queue.pop(0) if (not temp.left): temp.left=Node(key) break else: queue.append(temp.left) if (not temp.right): temp.right=Node(key) break else: queue.append(temp.right) def deleteDeepest(root,d_node): queue=[] queue.append(root) while len(queue): temp=queue.pop(0) if temp is d_node: temp=None return if temp.right: if temp.right is d_node: temp.right=None else: queue.append(temp.right) if temp.left: if temp.left is d_node: temp.left=None else: queue.append(temp.left) def deletion(root,key): if root==None: return None if root.left==None and root.right==None: if root.data==key: return None else: return root key_node=None queue=[] queue.append(root) while len(queue): temp=queue.pop(0) if temp.data==key: key_node=temp if temp.left: queue.append(temp.left) if temp.right: queue.append(temp.right) if key_node: x=temp.data deleteDeepest(root,temp) key_node.data=x return root
UTF-8
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false
false
1,470
py
2
main.py
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0.597279
0.594558
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74
18.878378
42
juiyangchang/LeetCoding
7,636,451,892,843
034810de3a5718b8cdde54dc14bade2dfd505d42
a357fa6608a03f86a9511fac7f7678a94120b366
/python/166_Fraction_to_Recurring_Decimal.py
eb1dfadf2e4d1c953fbb1533ca09a721d122c081
[]
no_license
https://github.com/juiyangchang/LeetCoding
e33e52b256c54da9a7bf007272c891fe11f8da24
d9590bf791ece34e391bca0055c8536ee2c8061e
refs/heads/master
2021-09-09T11:43:24.371160
2018-03-15T19:10:52
2018-03-15T19:10:52
110,507,013
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null
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class Solution: def fractionToDecimal(self, numerator, denominator): """ :type numerator: int :type denominator: int :rtype: str """ if numerator == 0: return "0" if (numerator < 0) != (denominator < 0): sgn = True else: sgn = False numerator, denominator = abs(numerator), abs(denominator) lookup = {} quotient, remainder = numerator // denominator, numerator % denominator if remainder == 0: return '-' + str(quotient) if sgn else str(quotient) int_quotient = quotient fraction = [] while remainder not in lookup and remainder != 0: lookup[remainder] = len(fraction) remainder *= 10 quotient, remainder = remainder // denominator, remainder % denominator fraction.append(quotient) if remainder == 0: return ''.join(map(str, ['-' if sgn else ''] + [int_quotient] + ['.'] + fraction)) else: return ''.join(map(str, ['-' if sgn else ''] + [int_quotient] + ['.'] + fraction[:lookup[remainder]] + ['('] + fraction[lookup[remainder]:] + [')']))
UTF-8
Python
false
false
1,306
py
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166_Fraction_to_Recurring_Decimal.py
63
0.491577
0.484686
0
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34.324324
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desingdeveloperdayday/PickedUpMole_ForEarthForUs
635,655,196,055
0922cc651fa73eb9c0a4241206d9dc65f1d7816a
8de379a6efeb54ee6df94109feb258e9839f4ab2
/backend/ForEarthForUs_backend/api/models/category_models.py
158e6bd31c87f5168b27834cf22032b50e547fc8
[]
no_license
https://github.com/desingdeveloperdayday/PickedUpMole_ForEarthForUs
b8c557037846b4863f985f6b21aaa4cd5495f9b4
85e5ab4ab3f5e7011aa6ede983cc66ece6e489bd
refs/heads/master
2022-12-12T15:19:42.553227
2019-09-30T08:28:47
2019-09-30T08:28:47
181,449,109
6
3
null
false
2022-12-08T05:04:07
2019-04-15T08:54:55
2022-04-06T15:21:15
2022-12-08T05:04:06
45,501
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Kotlin
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false
from django.db import models from django.db.models.signals import post_delete from api.utils.media_clean import file_cleanup class Category(models.Model): categoryId = models.IntegerField(primary_key=True, unique=True) image = models.FileField(null=False, upload_to='images/category/') completeMessage = models.CharField(max_length=100) class Meta: db_table = 'categories' verbose_name = 'category' verbose_name_plural = 'categories' post_delete.connect(file_cleanup, sender=Category, dispatch_uid="category.file_cleanup")
UTF-8
Python
false
false
566
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category_models.py
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0.736749
0.731449
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15
36.733333
88
kozakusek/ipp-2020-testy
12,850,542,185,002
b3cb8d168c990f1c3209c1b19652dc126fde4dc3
ca75f7099b93d8083d5b2e9c6db2e8821e63f83b
/z2/part2/interactive/jm/random_fuzzy_arrows_1/753097229.py
a747d802b452446032d933bc6ad307ea5b7daecc
[ "MIT" ]
permissive
https://github.com/kozakusek/ipp-2020-testy
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refs/heads/master
2022-10-04T18:55:37.875713
2020-06-09T21:15:37
2020-06-09T21:15:37
262,290,632
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0
MIT
true
2020-06-09T21:15:38
2020-05-08T10:10:47
2020-05-12T20:07:47
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C
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from part1 import ( gamma_board, gamma_busy_fields, gamma_delete, gamma_free_fields, gamma_golden_move, gamma_golden_possible, gamma_move, gamma_new, ) """ scenario: test_random_actions uuid: 753097229 """ """ random actions, total chaos """ board = gamma_new(4, 6, 6, 3) assert board is not None assert gamma_move(board, 1, 2, 3) == 1 assert gamma_move(board, 2, 0, 1) == 1 assert gamma_busy_fields(board, 2) == 1 assert gamma_free_fields(board, 2) == 22 assert gamma_golden_move(board, 2, 3, 2) == 0 assert gamma_move(board, 3, 3, 2) == 1 assert gamma_move(board, 4, 1, 1) == 1 assert gamma_move(board, 4, 0, 5) == 1 assert gamma_free_fields(board, 4) == 19 assert gamma_move(board, 5, 4, 3) == 0 assert gamma_move(board, 5, 1, 0) == 1 assert gamma_busy_fields(board, 5) == 1 assert gamma_move(board, 6, 5, 1) == 0 assert gamma_move(board, 6, 2, 2) == 1 assert gamma_move(board, 1, 3, 3) == 1 assert gamma_move(board, 2, 3, 1) == 1 assert gamma_move(board, 2, 0, 1) == 0 assert gamma_move(board, 3, 5, 2) == 0 assert gamma_move(board, 4, 1, 5) == 1 assert gamma_move(board, 4, 0, 2) == 1 assert gamma_move(board, 6, 2, 2) == 0 assert gamma_free_fields(board, 6) == 13 assert gamma_golden_possible(board, 6) == 1 assert gamma_move(board, 1, 4, 1) == 0 assert gamma_move(board, 1, 2, 1) == 1 assert gamma_move(board, 2, 0, 4) == 1 assert gamma_move(board, 3, 4, 1) == 0 assert gamma_move(board, 3, 2, 2) == 0 assert gamma_move(board, 4, 2, 1) == 0 assert gamma_move(board, 5, 3, 0) == 1 board717240267 = gamma_board(board) assert board717240267 is not None assert board717240267 == ("44..\n" "2...\n" "..11\n" "4.63\n" "2412\n" ".5.5\n") del board717240267 board717240267 = None assert gamma_move(board, 6, 2, 1) == 0 assert gamma_move(board, 6, 1, 5) == 0 assert gamma_golden_possible(board, 6) == 1 assert gamma_move(board, 1, 1, 4) == 1 assert gamma_free_fields(board, 1) == 4 assert gamma_move(board, 2, 3, 3) == 0 assert gamma_move(board, 2, 1, 0) == 0 assert gamma_move(board, 3, 4, 2) == 0 assert gamma_move(board, 3, 0, 2) == 0 assert gamma_golden_possible(board, 3) == 1 assert gamma_move(board, 4, 0, 4) == 0 assert gamma_move(board, 4, 3, 4) == 0 assert gamma_move(board, 5, 4, 3) == 0 assert gamma_move(board, 5, 0, 3) == 1 assert gamma_golden_possible(board, 5) == 1 assert gamma_move(board, 6, 4, 2) == 0 assert gamma_move(board, 1, 3, 0) == 0 assert gamma_move(board, 2, 0, 2) == 0 assert gamma_move(board, 2, 2, 1) == 0 assert gamma_busy_fields(board, 3) == 1 assert gamma_golden_possible(board, 3) == 1 assert gamma_move(board, 4, 0, 2) == 0 assert gamma_move(board, 5, 4, 2) == 0 assert gamma_golden_possible(board, 5) == 1 assert gamma_move(board, 6, 4, 3) == 0 assert gamma_move(board, 2, 2, 1) == 0 assert gamma_move(board, 2, 1, 2) == 0 assert gamma_move(board, 3, 4, 2) == 0 assert gamma_move(board, 4, 1, 4) == 0 assert gamma_busy_fields(board, 4) == 4 assert gamma_move(board, 5, 3, 1) == 0 assert gamma_move(board, 5, 3, 0) == 0 assert gamma_busy_fields(board, 5) == 3 assert gamma_move(board, 6, 1, 5) == 0 assert gamma_move(board, 1, 3, 5) == 0 assert gamma_free_fields(board, 1) == 4 assert gamma_move(board, 2, 3, 5) == 0 assert gamma_golden_possible(board, 2) == 1 assert gamma_golden_possible(board, 3) == 1 assert gamma_move(board, 4, 5, 2) == 0 assert gamma_move(board, 5, 5, 2) == 0 assert gamma_move(board, 5, 3, 1) == 0 assert gamma_move(board, 6, 4, 3) == 0 assert gamma_move(board, 6, 2, 1) == 0 assert gamma_move(board, 1, 1, 5) == 0 assert gamma_move(board, 2, 0, 2) == 0 assert gamma_move(board, 2, 2, 3) == 0 assert gamma_golden_move(board, 2, 0, 1) == 0 assert gamma_move(board, 3, 3, 1) == 0 assert gamma_move(board, 4, 5, 2) == 0 assert gamma_move(board, 4, 0, 1) == 0 assert gamma_busy_fields(board, 4) == 4 assert gamma_move(board, 5, 2, 1) == 0 assert gamma_move(board, 6, 0, 2) == 0 assert gamma_busy_fields(board, 6) == 1 assert gamma_free_fields(board, 6) == 8 gamma_delete(board)
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/Python/066.Plus One/Solution.py
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[]
no_license
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3aaa3ea69776b060ece6dc7380b2dee8b1b2a07f
bdd2808db9d629e84523e203f55b4493c3fc286f
refs/heads/master
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#coding=utf-8 class Solution: def plusOne(self, digits): """ :type digits: List[int] :rtype: List[int] """ for idx in range(len(digits) - 1, -1, -1): if digits[idx] != 9: digits[idx] += 1 break else: digits[idx] = 0 if 0 == digits[0]: digits.insert(0, 1) return digits if __name__ == "__main__": digits = [1, 2, 3] print(Solution().plusOne(digits)) digits = [4, 3, 2, 1] print(Solution().plusOne(digits))
UTF-8
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jacksonyoudi/smt
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/controller/lib/csv_handle.py
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[]
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refs/heads/master
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# -*- coding: utf-8 -*- import csv import datetime import time def table(data): import sqlite3 con = sqlite3.connect('/Users/changyouliang/project/others/smt/first.db') cur = con.cursor() sql = "insert into acv_tab (wo_no,time,c3 ,c4 ,R70 ,c6 ,C158 ,c8 ,R69 ,c10 ,R3 ,c12 ,IC8 ,c14 ,IC3 ,c16 ,D11 ,c18 ,L5 ,c20 ,C150 ,c22 ,C100 ,C24) values ('{}','{}','{}','{}','{}','{}','{}','{}','{}','{}','{}','{}','{}','{}','{}','{}','{}','{}','{}','{}','{}','{}','{}','{}')".format( *data) try: cur.execute(sql) except Exception as e: print(e) finally: con.commit() cur.close() con.close() def parse_csv(csv_file, encoding=None): """ 读取数据返回列表的数据 :param csv_file: :return: """ data = [] with open(csv_file, encoding=encoding) as f: f_csv = csv.reader(f) # 去除头部数据 header = next(f_csv) for row in f_csv: data.append(row) return header, data if __name__ == '__main__': f = "../../1HZ9011113-05_6433997B.csv" header, data = parse_csv(f) result = [] print(header) length = len(data) stops = 0 pre_time = None cur_time = None start_time = data[0][1] end_time = data[-1][1] row = None for i in range(0, length): row = data[i] time_array = time.strptime(row[1], "%Y/%m/%d %H:%M:%S") other_style_time = int(time.mktime(time_array)) cur_time = other_style_time if pre_time: if (cur_time - pre_time) <= 60 * 5 and (cur_time - pre_time) > 0: stops += 1 pre_time = other_style_time item = { "type": "detail", "品番": row[0][10:19], "工单号": row[0][22:30], "面番": row[0][-1], "开始时间": row[1], "结束时间": end_time, "批量": length, "导入成功时间": datetime.datetime.now() } item = { "type": "agg", "品番": row[0][10:19], "工单号": row[0][22:30], "面番": row[0][-1], "开始时间": start_time, "结束时间": end_time, "批量": length, "短暂停回数": stops, "导入成功时间": datetime.datetime.now() } result.insert(0, item) # print(result[0]) # print(stops) # one = data[0] # table(one)
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/lib-python/DrumbeatNode.py
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#!/usr/bin/env python3.4 import os import sys import time import hmac import hashlib sys.path.append('../lib') import ACNode class DrumbeatNode(ACNode.ACNode): # import SharedSecret # class DrumbeatNode(SharedSecret.SharedSecret): default_interval = 60 default_node = "drumbeat" def parseArguments(self): self.parser.add('--interval','-i',default=self.default_interval,action='store',type=int, help='DrumbeatNode interval, in seconds (default: '+str(self.default_interval)+' seconds)'), super().parseArguments() last_time = 0 def loop(self): if time.time() - self.last_time > self.cnf.interval: self.last_time = time.time() self.send(self.cnf.node, "beat") if self.cnf.secrets: for node in self.cnf.secrets.keys(): self.send(node, "beat") super().loop() # Allow this class to auto instanciate if # we run it on its own. # if __name__ == "__main__": drumbeat = DrumbeatNode() if not drumbeat: sys.exit(1) exitcode = drumbeat.run() sys.exit(exitcode)
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takuseno/d3rlpy
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/tests/logging/test_logger.py
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from typing import Any, Dict import pytest from d3rlpy.logging import D3RLPyLogger from d3rlpy.logging.logger import SaveProtocol class StubLoggerAdapter: def __init__(self, experiment_name: str): self.experiment_name = experiment_name self.is_write_params_called = False self.is_before_write_metric_called = False self.is_write_metric_called = False self.is_after_write_metric_called = False self.is_save_model_called = False self.is_close_called = False def write_params(self, params: Dict[str, Any]) -> None: self.is_write_params_called = True def before_write_metric(self, epoch: int, step: int) -> None: self.is_before_write_metric_called = True def write_metric( self, epoch: int, step: int, name: str, value: float ) -> None: assert self.is_before_write_metric_called self.is_write_metric_called = True def after_write_metric(self, epoch: int, step: int) -> None: assert self.is_before_write_metric_called assert self.is_write_metric_called self.is_after_write_metric_called = True def save_model(self, epoch: int, algo: SaveProtocol) -> None: self.is_save_model_called = True def close(self) -> None: self.is_close_called = True class StubLoggerAdapterFactory: def create(self, experiment_name: str) -> StubLoggerAdapter: return StubLoggerAdapter(experiment_name) class StubAlgo: def save(self, fname: str) -> None: pass @pytest.mark.parametrize("with_timestamp", [False, True]) def test_d3rlpy_logger(with_timestamp: bool) -> None: logger = D3RLPyLogger(StubLoggerAdapterFactory(), "test", with_timestamp) # check experiment_name adapter = logger.adapter assert isinstance(adapter, StubLoggerAdapter) if with_timestamp: assert adapter.experiment_name != "test" else: assert adapter.experiment_name == "test" assert not adapter.is_write_params_called logger.add_params({"test": 1}) assert adapter.is_write_params_called logger.add_metric("test", 1) with logger.measure_time("test"): pass assert not adapter.is_before_write_metric_called assert not adapter.is_write_metric_called assert not adapter.is_after_write_metric_called metrics = logger.commit(1, 1) assert "test" in metrics assert "time_test" in metrics assert adapter.is_before_write_metric_called assert adapter.is_write_metric_called assert adapter.is_after_write_metric_called assert not adapter.is_save_model_called logger.save_model(1, StubAlgo()) assert adapter.is_save_model_called assert not adapter.is_close_called logger.close() assert adapter.is_close_called
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pyvista/pyvista
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/examples_trame/advanced/contour.py
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from trame.app import get_server from trame.ui.vuetify import SinglePageLayout from trame.widgets import vuetify from vtkmodules.vtkFiltersCore import vtkContourFilter import pyvista as pv from pyvista import examples from pyvista.trame.ui import plotter_ui # ----------------------------------------------------------------------------- # Trame initialization # ----------------------------------------------------------------------------- pv.OFF_SCREEN = True server = get_server() state, ctrl = server.state, server.controller state.trame__title = "Contour" ctrl.on_server_ready.add(ctrl.view_update) # ----------------------------------------------------------------------------- # Pipeline # ----------------------------------------------------------------------------- volume = examples.download_head_2() contour = vtkContourFilter() contour.SetInputDataObject(volume) # contour.SetComputeNormals(True) # contour.SetComputeScalars(False) # Extract data range => Update store/state data_range = tuple(volume.get_data_range()) contour_value = 0.5 * (data_range[0] + data_range[1]) state.contour_value = contour_value state.data_range = (float(data_range[0]), float(data_range[1])) # Configure contour with valid values contour.SetNumberOfContours(1) contour.SetValue(0, contour_value) # ----------------------------------------------------------------------------- # Plotting # ----------------------------------------------------------------------------- pl = pv.Plotter() actor = pl.add_mesh(contour, cmap="viridis", clim=data_range) # ----------------------------------------------------------------------------- # Callbacks # ----------------------------------------------------------------------------- @state.change("contour_value") def update_contour(contour_value, **kwargs): contour.SetValue(0, contour_value) ctrl.view_update_image() # ----------------------------------------------------------------------------- # GUI # ----------------------------------------------------------------------------- with SinglePageLayout(server) as layout: layout.title.set_text("Contour") with layout.toolbar: vuetify.VSpacer() vuetify.VSlider( v_model="contour_value", min=("data_range[0]",), max=("data_range[1]",), hide_details=True, dense=True, style="max-width: 300px", start="trigger('demoAnimateStart')", end="trigger('demoAnimateStop')", change=ctrl.view_update, ) vuetify.VProgressLinear( indeterminate=True, absolute=True, bottom=True, active=("trame__busy",), ) with layout.content: with vuetify.VContainer( fluid=True, classes="pa-0 fill-height", ): # Use PyVista UI template for Plotters view = plotter_ui(pl, namespace='demo') ctrl.view_update = view.update ctrl.view_update_image = view.update_image server.start()
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sharathghosh/Python-Intro
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/tutorial_python_basics.py
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[]
no_license
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d3c746d41997ff0c8b28d45706e63bfaa12aab9b
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# -*- coding: utf-8 -*- """ Created on Wed Nov 16 14:26:46 2016 @author: sharath """ # Variables # a,b,c are integer variables a = 5 # assignment operator b = 6 c = -8 # d,e,f are floating point variables d = 5.6 e = 9.0000e-4 f = -1.4 # Find out the type of a variable print type(a) print type(e) # Boolean types - True and False are defined keywords in Python True False # Operators print (a + b) print (c - d) print (a * c) print (a / b) print (a % 2) print (c ** a) print (b // a) x = 1 a = a + 1 # shorthand x += 1 # Conditions, make sure the indentation is correct if b >= 5: print("if clause is true") elif b >= 3: print("elif clause is true") else: print("else clause is true") # conditional operators # == equal to # > greater than # < less than # >= greater than or equal to # <= less than or equal to # != not equal to # Functions def function_dummy_1(): # This function does not accept any arguments # Function body that does something meaningful return 0 # return some results def function_add(x, y): # This function accepts 2 arguments return x + y # return sum of the two arguments def function_mul(a, b): # This function accepts 2 arguments, note that the a and b in the function # definition is not the same as the variables a and b return a * b # return product of the two arguments # Scope of variables within functions def function_dummy_2(): # This function does not accept any arguments print y # y is not declared before and is not visible within the function return y # return some results def function_dummy_3(): # This function does not accept any arguments y = 10 z = 15 return True # return some results print y, z # y and z are declared within the function, and are not visible outside # Loops, note the indent # whatever is indented is considered to be a part of the repeatable section # of the loop for a in range(1, 4): print (a) # range() generates a sequence of integers, often helpful for iterating over a # series of items print range(5, 10) print range(5, 10, 2) print range(5, 10, -2) print range(10, 5, -2) # Another way of looping in Python, note the increment step # If you forget the increment step, this code will run forever printing values of u u = 1 while u < 10: print (u) u+=1 # Strings foo = "This is my string" print foo bar = "This is your string" print bar baz = "This is our string" print baz bat = 'We can use single quotes; this is also a string' print bat woot = 'We can "mix" quotes' print woot toot = " We can 'mix' quotes like this too " print toot zoot = 'People\'s Republic' print zoot # counts the number of occurrences of 'x' print woot.count('x') # returns the position of character 'x' print foo.find('x') # returns the stringVar in lowercase (this is temporary) print foo.lower() # returns the stringVar in uppercase (this is temporary) print foo.upper() # replaces all occurrences of 'my' with 'your' in the string print foo.replace('my', 'your') # remove preceeding and trailing spaces print toot.strip() # Slicing strings print foo[0:4] print foo[4:7] print foo[4:] print foo[:-1] print foo[4:-1] print foo[-5:] # Lists int_list = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15] print int_list # iterating over list items for item in int_list: print item, item**2 for idx in range(len(int_list)): print int_list[idx], int_list[idx]**2 # appends element to end of the list int_list.append(16) print int_list # counts the number of occurrences of 4s in the list int_list.count(4) # returns the index of 10 in the list int_list.index(10) # inserts 4 at location 2 int_list.insert(2, 4) print int_list # returns last element then removes it from the list int_list.pop() print int_list # finds and removes first 4 from list int_list.remove(4) print int_list # reverses the elements in the list int_list.reverse() #sorts the list alphabetically in ascending order, or numerical in ascending order int_list.sort() # Lists could also be sliced # Strings could be interpreted as lists for letter in foo: print letter # replace multiple instances in lists - to do t = (1, 2, 3) print (t) t.append(4) # will fail because tuples are immutable print t[0], t[1], t[2] t[0] = -1 # will fail because tupes are immutable # Dictionaries # Key value data structure, unordered - what you type in need not be the order # in which the data gets stored my_dictionary = {'name': 'Foo Bar', 'Age': -220, 'Profession': 'Jobless', 'Qualifications': 'Worthless'} print(my_dictionary['Profession']) # Iterating and accessing dictionary keys and values for k in my_dictionary: print 'Key:', k, ', - Value:', my_dictionary[k] # The dictionary data type provides in built methods to access keys and values print my_dictionary.keys() print my_dictionary.values() # Another way of accessing keys and values from a dictionary for key in my_dictionary.keys(): print key, ' - ', my_dictionary[key] # Adding a new key value pair my_dictionary['IQ'] = -911 print my_dictionary # Removing a key value pair my_dictionary.pop('IQ') # pop-ing a non-existent key will result in an error my_dictionary.pop('IQ') # already pop-ed, cannot pop 'IQ' again # Update a value for a key my_dictionary['name'] = 'Foo Baz' # References and copying objects, mutable types # We had an int_list # Let's create a new_list by assignment new_list = int_list print new_list print int_list # Let's change the first item of new_list new_list[0] = -10 print new_list # nothing unexpected print int_list # Why did the first item change here? # For mutable composite data types, python creates references # If another copy is reuqired, it must be explicitly created import copy new_list = copy.deepcopy(int_list) new_list[0] = 1 print new_list print int_list # This behaviour is not applicable for basic data types like integers, floats etc. my_input = input('Type in something: ') print my_input, len(my_input) my_raw_input = raw_input('Type in some more: ') print my_raw_input, len(my_raw_input) file_handle = open('temp.txt', 'w') file_handle.writelines(['this is a string\n', 'this is another string\n']) file_handle.close() file_handle = open('temp.txt', 'r') x = file_handle.readlines() file_handle.close() # there are the readline(), writeline() functions # there are the read() and write() functions too #enumerate list_of_strings = ['aa', 'bb', 'cc', 'dd'] for idx, string in enumerate(list_of_strings): if idx%2 == 1: print idx, string #generators mygenerator = (x*x for x in range(3)) for i in mygenerator: print i #importing modules, code organization etc. # create folder, two files with classes, __init__ method and another method # __init__.py file # __name__ variable, significance
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Nyumat/Pathfinding-Algorithm-Tool
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2023-03-06T08:01:03
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import pygame import math from queue import PriorityQueue # RGB Colors RED = (255, 0, 0) GREEN = (0, 255, 0) BLUE = (0, 0, 255) YELLOW = (255, 255, 0) WHITE = (255, 255, 255) BLACK = (0, 0, 0) PURPLE = (128, 0, 128) ORANGE = (255, 165 ,0) GREY = (128, 128, 128) TURQUOISE = (64, 224, 208) # Node class, which contains the object initizalizers and methods that will be used throughout the project. class Node: def __init__(self, row, col, window_size, total_rows): self.row = row self.col = col self.x = row * window_size self.y = col * window_size self.color = BLACK self.neighbors = [] self.window_size = window_size self.total_rows = total_rows def get_pos(self): return self.row, self.col def is_closed(self): return self.color == TURQUOISE def is_open(self): return self.color == RED def is_barrier(self): return self.color == WHITE def is_start(self): return self.color == ORANGE def is_end(self): return self.color == RED def reset(self): self.color = BLACK def make_start(self): self.color = ORANGE def make_closed(self): self.color = TURQUOISE def make_open(self): self.color = RED def make_barrier(self): self.color = WHITE def make_end(self): self.color = RED def make_path(self): self.color = PURPLE def draw(self, client): pygame.draw.rect(client, self.color, (self.x, self.y, self.window_size, self.window_size)) # Manhattan Heuristic Implementation # By using this Heuristic, we can only move along the grid in four directions and not "Diagonally" (up,down,left,right) def update_neighbors(self, grid): self.neighbors = [] if self.row < self.total_rows - 1 and not grid[self.row + 1][self.col].is_barrier(): # DOWN self.neighbors.append(grid[self.row + 1][self.col]) if self.row > 0 and not grid[self.row - 1][self.col].is_barrier(): # UP self.neighbors.append(grid[self.row - 1][self.col]) if self.col < self.total_rows - 1 and not grid[self.row][self.col + 1].is_barrier(): # RIGHT self.neighbors.append(grid[self.row][self.col + 1]) if self.col > 0 and not grid[self.row][self.col - 1].is_barrier(): # LEFT self.neighbors.append(grid[self.row][self.col - 1]) def __lt__(self, other): return False window_size = 600 client = pygame.display.set_mode((window_size, window_size)) pygame.display.set_caption("[Thomas's Pathfinding Visualization Tool V1] Made by @Nyumat") def h(p1, p2): x1, y1 = p1 x2, y2 = p2 return abs(x1 - x2) + abs(y1 - y2) # Creates path from node to node. def reconstruct_path(came_from, current, draw): while current in came_from: current = came_from[current] current.make_path() draw() # A* Pathfinding Search def algorithm(draw, grid, start, end): count = 0 open_set = PriorityQueue() open_set.put((0, count, start)) came_from = {} # G score is the cost "so far" to reach node n, which is why it starts at 0. g_score = {node: float("inf") for row in grid for node in row} g_score[start] = 0 # F score will represent the total estimated cost of the path through the neighbors f_score = {node: float("inf") for row in grid for node in row} f_score[start] = h(start.get_pos(), end.get_pos()) open_set_hash = {start} while not open_set.empty(): for event in pygame.event.get(): if event.type == pygame.QUIT: pygame.quit() current = open_set.get()[2] open_set_hash.remove(current) if current == end: # Draw the optimal path once the search reaches the second placed node. reconstruct_path(came_from, end, draw) end.make_end() start.make_start() return True # Algo to simultaneously evaluate the neighbor and search for the ending node in the open set for neighbor in current.neighbors: temp_g_score = g_score[current] + 1 # SEE README.md to understand this algorithm. # To put it simply, it traces through each neighbor to find the end node. if temp_g_score < g_score[neighbor]: came_from[neighbor] = current g_score[neighbor] = temp_g_score f_score[neighbor] = temp_g_score + h(neighbor.get_pos(), end.get_pos()) # If the node isnt found within the open set, the search spreads. if neighbor not in open_set_hash: count += 1 open_set.put((f_score[neighbor], count, neighbor)) open_set_hash.add(neighbor) neighbor.make_open() draw() if current != start: current.make_closed() return False # Function controls how our interface, or "grid" will be created. def make_grid(rows, window_size): grid = [] gap = window_size // rows for i in range(rows): grid.append([]) for j in range(rows): node = Node(i, j, gap, rows) grid[i].append(node) return grid # Function draws the border and lines for the tool def draw_grid(client, rows, window_size): gap = window_size // rows for i in range(rows): pygame.draw.line(client, GREY, (0, i * gap), (window_size, i * gap)) for j in range(rows): pygame.draw.line(client, GREY, (j * gap, 0), (j * gap, window_size)) # Function that will draw the plane for the tool to be used in def draw(client, grid, rows, window_size): client.fill(BLACK) for row in grid: for node in row: node.draw(client) draw_grid(client, rows, window_size) pygame.display.update() # Determines the part of the grid we're clicking so we can interact with it. def get_clicked_pos(pos, rows, window_size): gap = window_size // rows y, x = pos row = y // gap col = x // gap return row, col # Main function to hold a lot of the logic and controls. def main(client, window_size): ROWS = 40 grid = make_grid(ROWS, window_size) start = None end = None run = True while run: draw(client, grid, ROWS, window_size) for event in pygame.event.get(): if event.type == pygame.QUIT: run = False # Left click if pygame.mouse.get_pressed()[0]: pos = pygame.mouse.get_pos() row, col = get_clicked_pos(pos, ROWS, window_size) node = grid[row][col] if not start and node != end: start = node start.make_start() elif not end and node != start: end = node end.make_end() elif node != end and node != start: node.make_barrier() # Right Click elif pygame.mouse.get_pressed()[2]: pos = pygame.mouse.get_pos() row, col = get_clicked_pos(pos, ROWS, window_size) node = grid[row][col] node.reset() if node == start: start = None elif node == end: end = None if event.type == pygame.KEYDOWN: # Draw Path to the other node on run (space bar) if event.key == pygame.K_SPACE and start and end: for row in grid: for node in row: node.update_neighbors(grid) # Call algorithm object for pathfinding. algorithm(lambda: draw(client, grid, ROWS, window_size), grid, start, end) if event.key == pygame.K_r: start = None end = None grid = make_grid(ROWS, window_size) pygame.quit() if __name__ == "__main__": main(client,window_size)
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Coburn37/NASS-Search
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cfc7a44dbeaa54b823eb1c652403c9ccffa28a96
cbba218ea18839f221595c8466838c87b2b1d9cd
/nassAPI/nassGlobal.py
c739c54727aec76208b00663d80e5cb09a80ae5f
[]
no_license
https://github.com/Coburn37/NASS-Search
f1d86ca4dcd4366d481e5cdf6b4712f4c283bfe9
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""" NASS Search Tool Global Preferences and Data This module holds the global preferences and data structures. All prefs & data should be read from nassGlobal.prefs and nassGlobal.data. """ import os.path import json #DummyReadOnlyDict - A dict that once read will call init() but only once across all instances #Only implements __getitem__ so should be read only class DummyReadOnlyDict(): triggered = False def __init__(self, target): self.target = target def noCheck__getitem__(self, key): return self.target[key] def __getitem__(self, key): #Multiple dicts might be accessed at different times #Only trigger if none before if not DummyReadOnlyDict.triggered: init() DummyReadOnlyDict.triggered = True #Replace this function with the noCheck version if a trigger has occured previously if DummyReadOnlyDict.triggered: self.__getitem__ = self.noCheck__getitem__ return self.target[key] #PassThroughDict - Dictionary that will __getitem__ the values of a different dict if they exist #Finalization will join the two dicts, overwriting everything in self with keys from target #This allows for values in one dict to be used if they exist in calculating new values (specifically in init()) #but to also allow the programmer to give a different value in place of these calculated values which will #be applied in the finalization. class PassThroughDict(dict): def __init__(self, target, *args, **kwargs): super().__init__(*args, **kwargs) self.target = target def __getitem__(self, key): if key in self.target: return self.target.__getitem__(key) return dict.__getitem__(self, key) #Turn this dict back into a normal dict, overwriting all values with the ones in target dict def finalizeDict(self): self.update(self.target) self.__getitem__ = dict.__getitem__ #Default configuration #USER PREFERENCES userPrefs = {} _prefs = PassThroughDict(userPrefs) #The final values for preferences that userPrefs will be joined over top of once an init() occurs prefs = DummyReadOnlyDict(_prefs) #Where all API values are read from (if one is read, we trigger an init()) #GLOBAL DATA _data = {} data = DummyReadOnlyDict(_data) def updateUserPrefs(moreUserPrefs): """ Overwrites global API prefs (only before init() is called) Takes a dict with key-value pairs representing the prefs to be overridden and the value to override with """ #They can only update the preferences (or should only update them) when we haven't init'd if DummyReadOnlyDict.triggered: raise RuntimeError("NASS has already been inited. User preferences shouldn't be changed now") #Join the user prefs overwriting the default prefs userPrefs.update(moreUserPrefs) def init(): """ Inits the global state of prefs and data Called automatically when either nassGlobal.prefs and nassGlobal.data is accessed. Calculates all default preferences, substituting those specified by the programmer in updateUserPrefs when necessary (stored in userPrefs dict) """ #DEFAULT USER PREFERENCES #Folders and files configuration _prefs["rootPath"] = os.path.realpath(".") _prefs["dbPath"] = os.path.join(_prefs["rootPath"], "nassDB") _prefs["configPath"] = _prefs["rootPath"] _prefs["preprocessJSONFile"] = os.path.join(_prefs["configPath"], "preprocessDBInfo.json") _prefs["staticJSONFile"] = os.path.join(_prefs["configPath"], "staticDBInfo.json") #Directories that we're looking for in the nassDB folder _prefs["dataDirNames"] = [ "ASCII", "Unformatted Data", "Expanded SAS", os.path.normpath("Expanded SAS/UNFORMATTED")] #Keys kept for stub cases _prefs["stubKeys"] = ["CASENO", "PSU", "VEHNO", "OCCNO"] #Default compare functions #TODO: Shouldn't go in API, should be in web application def stringIn(found, find): return str(find) in str(found) def equal(found, find): return str(find) == str(found) def startsWith(found, find): return str(found).startswith(str(find)) _prefs["supportedCompareFuncs"] = { "String Inside" : stringIn, "Equal" : equal, "Starts With" : startsWith } _prefs.finalizeDict() #GLOBAL DATA #Json info on dbs fstaticDBInfo = open(_prefs["staticJSONFile"], "r") _data["staticDBInfo"] = json.loads(fstaticDBInfo.read()) if not os.path.isfile(_prefs["preprocessJSONFile"]): raise RuntimeError("No preprocessDBInfo found! Run the preprocessor first!") else: fpreprocessDBInfo = open(_prefs["preprocessJSONFile"],"r") _data["preprocessDBInfo"] = json.loads(fpreprocessDBInfo.read()) #COMMON FUNCTIONALITY def userYN(msg): while True: userIn = input(msg) if userIn.lower() == "y": return True elif userIn.lower() == "n": return False print("Invalid response, please choose y or n") class NASSJSONEncoder(json.JSONEncoder): def default(self, o, *args, **kwargs): if getattr(o, "toJSONHelper", None) and callable(o.toJSONHelper): return o.toJSONHelper() else: return super().default(o, *args, **kwargs)
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vramana30/newSnia
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/api_emulator/redfish/templates/Subscription.py
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# Copyright Notice: # Copyright 2017 Distributed Management Task Force, Inc. All rights reserved. # License: BSD 3-Clause License. For full text see link: https://github.com/DMTF/Redfish-Interface-Emulator/LICENSE.md # Example Resoruce Template import copy import strgen _TEMPLATE = \ { "@Redfish.Copyright":"Copyright 2014-2016 Distributed Management Task Force, Inc. (DMTF). All rights reserved.", "@odata.context": "{rb}$metadata#EventDestination.EventDestination", "@odata.id": "{rb}EventService/Subscriptions/{id}", "@odata.type": "#EventDestination.v1_0_0.EventDestination", "Id": "{id}", "Name": "EventSubscription {id}", "Destination": "http://www.dnsname.com/Destination{id}", "EventTypes": [ "Alert" ], "Context": "ABCDEFGHJLKJ", "Protocol": "Redfish" } def get_Subscription_instance(wildcards): """ Instantiate and format the template Arguments: wildcard - A dictionary of wildcards strings and their repalcement values """ c = copy.deepcopy(_TEMPLATE) c['@odata.context'] = c['@odata.context'].format(**wildcards) c['@odata.id'] = c['@odata.id'].format(**wildcards) c['Id'] = c['Id'].format(**wildcards) c['Destination'] = c['Destination'].format(**wildcards) c['Name'] = c['Name'].format(**wildcards) return c
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cocofile/mango-explorer
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/mango/logmessages.py
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refs/heads/main
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# # ⚠ Warning # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT # LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN # NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, # WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # # [🥭 Mango Markets](https://mango.markets/) support is available at: # [Docs](https://docs.mango.markets/) # [Discord](https://discord.gg/67jySBhxrg) # [Twitter](https://twitter.com/mangomarkets) # [Github](https://github.com/blockworks-foundation) # [Email](mailto:hello@blockworks.foundation) import typing from .idl import IdlParser, lazy_load_cached_idl_parser def expand_log_messages(original_messages: typing.Sequence[str]) -> typing.Sequence[str]: idl_parser: IdlParser = lazy_load_cached_idl_parser("mango_logs.json") expanded_messages: typing.List[str] = [] parse_next_line: bool = False for message in original_messages: if parse_next_line: encoded: str = message[len("Program log: "):] name, parsed = idl_parser.decode_and_parse(encoded) expanded_messages += ["Mango " + name + " " + str(parsed)] parse_next_line = False elif message == "Program log: mango-log": parse_next_line = True else: expanded_messages += [message] parse_next_line = False return expanded_messages
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SplashTheBatya/netologia_homeworks
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/databases_2/m2m-relations/articles/admin.py
47c4cca99bcc61cab668f2886eb13bf4474f3dd9
[]
no_license
https://github.com/SplashTheBatya/netologia_homeworks
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from django.contrib import admin from django.core.exceptions import ValidationError from django.forms import BaseInlineFormSet from .models import Article, Thematics, ArticleThematics class ArticleThematicsInlineFormset(BaseInlineFormSet): def clean(self): counter = 0 for form in self.forms: if form.cleaned_data.get('main_thematic', False): counter += 1 if counter > 1: raise ValidationError('Выберите только 1 основную тематику') elif counter < 1: raise ValidationError('Выберите хотя-бы 1 основную тематику') return super().clean() class ArticleThematicsInline(admin.TabularInline): model = ArticleThematics formset = ArticleThematicsInlineFormset @admin.register(Article) class ArticleAdmin(admin.ModelAdmin): inlines = [ArticleThematicsInline] @admin.register(Thematics) class ThematicsAdmin(admin.ModelAdmin): pass
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grillzwitu/alx-higher_level_programming
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/0x01-python-if_else_loops_functions/1-last_digit.py
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[]
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https://github.com/grillzwitu/alx-higher_level_programming
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#!/usr/bin/python3 import random number = random.randint(-10000, 10000) if number < 0: lastDigit = (int(repr(-number)[-1])) * -1 else: lastDigit = int(repr(number)[-1]) if lastDigit > 5: print("Last digit of {} is {} and is greater than 5" .format(number, lastDigit)) elif lastDigit == 0: print("Last digit of {} is {} and is 0" .format(number, lastDigit)) elif lastDigit < 6 and not 0: print("Last digit of {} is {} and is less than 6 and not 0" .format(number, lastDigit))
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py
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1-last_digit.py
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DesignInformaticsLab/adversarial_challenge
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/imagenet_sample_code/centerloss.py
72e37ae7101fcc38ec600488dc65423c534031ec
[]
no_license
https://github.com/DesignInformaticsLab/adversarial_challenge
a880be7d0f8a1bb8552d3ce7464cef6e9ab8d458
139baceaae81f1d61c523231390212792947a5c0
refs/heads/master
2020-03-28T00:51:47.532432
2018-10-10T10:56:05
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LAMBDA = 1e-1 CENTER_LOSS_ALPHA = 0.5 NUM_CLASSES = 200 batch_size = 128 import os import numpy as np import tensorflow as tf import tflearn slim = tf.contrib.slim os.environ['CUDA_VISIBLE_DEVICES'] = '0' with tf.name_scope('input'): input_images = tf.placeholder(tf.float32, shape=(batch_size,64,64,3), name='input_images') labels = tf.placeholder(tf.int64, shape=(batch_size), name='labels') global_step = tf.Variable(0, trainable=False, name='global_step') def get_center_loss(features, labels, alpha, num_classes): """获取center loss及center的更新op Arguments: features: Tensor,表征样本特征,一般使用某个fc层的输出,shape应该为[batch_size, feature_length]. labels: Tensor,表征样本label,非one-hot编码,shape应为[batch_size]. alpha: 0-1之间的数字,控制样本类别中心的学习率,细节参考原文. num_classes: 整数,表明总共有多少个类别,网络分类输出有多少个神经元这里就取多少. Return: loss: Tensor,可与softmax loss相加作为总的loss进行优化. centers: Tensor,存储样本中心值的Tensor,仅查看样本中心存储的具体数值时有用. centers_update_op: op,用于更新样本中心的op,在训练时需要同时运行该op,否则样本中心不会更新 """ # 获取特征的维数,例如256维 len_features = features.get_shape()[1] # 建立一个Variable,shape为[num_classes, len_features],用于存储整个网络的样本中心, # 设置trainable=False是因为样本中心不是由梯度进行更新的 centers = tf.get_variable('centers', [num_classes, len_features], dtype=tf.float32, initializer=tf.constant_initializer(0), trainable=False) # 将label展开为一维的,输入如果已经是一维的,则该动作其实无必要 labels = tf.reshape(labels, [-1]) # 根据样本label,获取mini-batch中每一个样本对应的中心值 centers_batch = tf.gather(centers, labels) # 计算loss loss = tf.nn.l2_loss(features - centers_batch) # 当前mini-batch的特征值与它们对应的中心值之间的差 diff = centers_batch - features # 获取mini-batch中同一类别样本出现的次数,了解原理请参考原文公式(4) unique_label, unique_idx, unique_count = tf.unique_with_counts(labels) appear_times = tf.gather(unique_count, unique_idx) appear_times = tf.reshape(appear_times, [-1, 1]) diff = diff / tf.cast((1 + appear_times), tf.float32) diff = alpha * diff centers_update_op = tf.scatter_sub(centers, labels, diff) return loss, centers, centers_update_op def inference(input_images, num_class=200, reuse=False): with slim.arg_scope([slim.conv2d], kernel_size=3, padding='SAME'): with slim.arg_scope([slim.max_pool2d], kernel_size=2): x = slim.conv2d(input_images, num_outputs=64, scope='conv1_1') x = slim.conv2d(x, num_outputs=64, scope='conv1_2') x = slim.max_pool2d(x, scope='pool1') x = slim.conv2d(x, num_outputs=128, scope='conv2_1') x = slim.conv2d(x, num_outputs=128, scope='conv2_2') x = slim.max_pool2d(x, scope='pool2') x = slim.conv2d(x, num_outputs=256, scope='conv3_1') x = slim.conv2d(x, num_outputs=256, scope='conv3_2') x = slim.max_pool2d(x, scope='pool3') x = slim.conv2d(x, num_outputs=512, scope='conv4_1') x = slim.conv2d(x, num_outputs=512, scope='conv4_2') x = slim.max_pool2d(x, scope='pool3') x = slim.flatten(x, scope='flatten') feature3 = x = slim.fully_connected(x, num_outputs=512, activation_fn=None, scope='fc0') feature2 = x = slim.fully_connected(x, num_outputs=32, activation_fn=None, scope='fc1') feature1 = x =slim.fully_connected(x, num_outputs=2, activation_fn=None, scope='fc2') x = tflearn.prelu(feature3) x = slim.fully_connected(x, num_outputs=num_class, activation_fn=None, scope='fc3') feature_list = [feature1, feature2] return x, feature_list def build_network(input_images, labels, ratio=0.5, reuse=False): logits, feature_list = inference(input_images, num_class=NUM_CLASSES) with tf.name_scope('loss'): with tf.variable_scope('center_loss1'): center_loss1, centers1, centers_update_op1 = get_center_loss(feature_list[0], labels, CENTER_LOSS_ALPHA, NUM_CLASSES) with tf.variable_scope('center_loss2'): center_loss2, centers2, centers_update_op2 = get_center_loss(feature_list[1], labels, CENTER_LOSS_ALPHA, NUM_CLASSES) with tf.name_scope('softmax_loss'): softmax_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits)) with tf.name_scope('total_loss'): total_loss = softmax_loss + ratio * center_loss1#(center_loss1*0.8 + center_loss2*0.2) * 4 with tf.name_scope('acc'): accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.arg_max(logits, 1), labels), tf.float32)) with tf.name_scope('loss/'): tf.summary.scalar('CenterLoss1', center_loss1) tf.summary.scalar('CenterLoss2', center_loss2) tf.summary.scalar('SoftmaxLoss', softmax_loss) tf.summary.scalar('TotalLoss', total_loss) centers_update_op_list = [centers_update_op1, centers_update_op2] return logits, feature_list, total_loss, accuracy, centers_update_op_list with tf.variable_scope("build_network", reuse=False): logits, feature_list, total_loss, accuracy, centers_update_op_list = build_network(input_images, labels, ratio=LAMBDA) features = feature_list[0] centers_update_op1 = centers_update_op_list[0] centers_update_op2 = centers_update_op_list[1] train_images = np.load('/home/doi6/Documents/Guangyu/tiny-imagenet-200/train_data.npy',encoding=('latin1')).item()['image'] / 255. train_labels = np.load('/home/doi6/Documents/Guangyu/tiny-imagenet-200/train_data.npy',encoding=('latin1')).item()['label'] if 0: train_image_5 = train_images[train_labels==5] train_image_7 = train_images[train_labels==7] train_images = np.concatenate([train_image_5, train_image_7],0) train_labels = np.asarray( [0]*len(train_image_5) + [1]*len(train_image_7) ) from random import shuffle idx = list(range(len(train_images))) shuffle(idx) train_images = train_images[idx] train_labels = train_labels[idx] test_images = train_images[:200] test_labels = train_labels[:200] val_images = np.load('/home/doi6/Documents/Guangyu/tiny-imagenet-200/val_data.npy',encoding=('latin1')).item()['image'] / 255. val_labels = np.load('/home/doi6/Documents/Guangyu/tiny-imagenet-200/val_data.npy',encoding=('latin1')).item()['label'] if 0: val_image_5 = val_images[val_labels==5] val_image_7 = val_images[val_labels==7] val_images = np.concatenate([val_image_5, val_image_7],0) val_labels = np.asarray( [0]*len(val_image_5) + [1]*len(val_image_7) ) idx_v = list(range(len(val_images))) shuffle(idx_v) val_images = val_images[idx_v] val_labels = val_labels[idx_v] val_images = val_images[:200] val_labels = val_labels[:200] optimizer = tf.train.AdamOptimizer(0.0001) with tf.control_dependencies([centers_update_op1, centers_update_op2]): train_op = optimizer.minimize(total_loss, global_step=global_step) summary_op = tf.summary.merge_all() saver = tf.train.Saver() sess = tf.Session() sess.run(tf.global_variables_initializer()) step = sess.run(global_step) for ep_i in range(50): train_acc = [] train_loss = [] for jj in range(train_images.shape[0]//batch_size): _, summary_str, train_acc_i, train_loss_i = sess.run( [train_op, summary_op, accuracy, total_loss], feed_dict={ input_images: train_images[jj*batch_size:(1+jj)*batch_size], labels: train_labels[jj*batch_size:(1+jj)*batch_size] }) train_acc += [train_acc_i] train_loss += [train_loss_i] testing_acc = sess.run(accuracy,feed_dict={input_images: val_images[0:128],labels: val_labels[0:128]}) print(("epoch: {}, train_acc:{:.4f}, train_loss:{:.4f},testing_acc:{:.4f}". format(ep_i, np.mean(train_acc), np.mean(train_loss),testing_acc))) saver.save(sess,'./model_save/center_loss.ckpt')
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import torch import wandb from Trainer import Trainer MAX_SUMMARY_IMAGES = 4 DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu') assert torch.cuda.is_available() # LR = 2e-4 EPOCHS = 100 # BATCH_SIZE = 64 NUM_WORKERS = 4 # LAMBDA_L1 = 100 sweep_config = { 'method': 'bayes', # grid, random 'metric': { 'name': 'loss_g', 'goal': 'minimize' }, 'parameters': { 'lambda_l1': { 'values': [80, 90, 100, 110, 120, 130] }, 'batch_size': { 'values': [64] }, 'learning_rate': { 'values': [1e-5, 1e-4, 2e-4, 3e-4] } } } if __name__ == '__main__': def train_wrapper(): wandb.init() config = wandb.config print(f'Config: {config}') trainer = Trainer( lr=config.learning_rate, device=DEVICE, batch_size=config.batch_size, epochs=EPOCHS, lambda_l1=config.learning_rate, dataloader_num_workers=NUM_WORKERS, max_summary_images=MAX_SUMMARY_IMAGES ) trainer.train() sweep_id = wandb.sweep(sweep_config, project="poke-gan") wandb.agent(sweep_id, train_wrapper)
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# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # usage example #python ckpt_quantization.py --init_checkpoint=squad_model/QAT_noresidualQuant/model.ckpt-5474 --quantized_checkpoint=squad_model/QAT_noresidualQuant_quantized/model.ckpt import tensorflow as tf import numpy as np from tensorflow.contrib.framework.python.framework import checkpoint_utils from tensorflow.python.ops import io_ops from tensorflow.python.training.saver import BaseSaverBuilder import os import re build_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../lib') transformer_op_module = tf.load_op_library( os.path.join(build_path, 'libtf_weight_quantize.so')) ACTIVATION_AMAX_NUM = 80 INT8O_GEMM_NUM = 8 TRT_FUSED_MHA_AMAX_NUM = 3 def checkpoint_quantization(in_checkpoint_file, out_checkpoint_file, per_channel_quantization): var_list = checkpoint_utils.list_variables(tf.flags.FLAGS.init_checkpoint) def init_graph(): restore_vars = [] layer_num = 0 regex = re.compile('layer_\d+') amaxTotalNum = 0 for name, shape in var_list: var = checkpoint_utils.load_variable(tf.flags.FLAGS.init_checkpoint, name) if "intermediate/dense/kernel" in name and amaxTotalNum == 0: amaxTotalNum = ACTIVATION_AMAX_NUM + 9*shape[0] + INT8O_GEMM_NUM + TRT_FUSED_MHA_AMAX_NUM print(amaxTotalNum, shape[0]) recon_dtype = var.dtype restore_vars.append(tf.get_variable(name, shape=shape, dtype=var.dtype)) tmp = regex.findall(name) if len(tmp) < 1: continue num_tmp = int(tmp[0].replace("layer_", "")) if layer_num < num_tmp: layer_num = num_tmp layer_num = layer_num + 1 #add new var for amax for i in range(layer_num): tf.get_variable("bert/encoder/layer_{}/amaxList".format(i), shape=[amaxTotalNum], dtype=tf.float32) return layer_num, amaxTotalNum, restore_vars layer_num, amaxTotalNum, restore_vars = init_graph() restorer = tf.train.Saver(restore_vars) saver = tf.train.Saver() config = tf.ConfigProto() config.gpu_options.allow_growth = True with tf.Session(config=config) as sess: restorer.restore(sess, in_checkpoint_file) kernel_name_list = ["attention/self/query", "attention/self/key", "attention/self/value", "attention/output/dense", "intermediate/dense", "output/dense"] #input_scale, 0 amax_name_list = ["attention/self/query/input_quantizer", #Q_aftergemm_scale, 1 "attention/self/query/aftergemm_quantizer", #Qbias_scale, 2 "attention/self/matmul_q_input_quantizer", #K_aftergemm_scale, 3 "attention/self/key/aftergemm_quantizer", #Kbias_scale, 4 "attention/self/matmul_k_input_quantizer", #V_aftergemm_scale, 5 "attention/self/value/aftergemm_quantizer", #Vbias_scale, 6 "attention/self/matmul_v_input_quantizer", #bmm1_scale, 7 "attention/self/softmax_input_quantizer", #Softmax_scale, 8 "attention/self/matmul_a_input_quantizer", #bmm2_scale, 9 "attention/output/dense/input_quantizer", #Proj_aftergemm_scale, 10 "attention/output/dense/aftergemm_quantizer", #ProjBiasNorm_scale, 11 "intermediate/dense/input_quantizer", #FC1_aftergemm_scale, 12 "intermediate/dense/aftergemm_quantizer", #F1Bias_scale, 13 "output/dense/input_quantizer", #FC2_aftergemm_scale, 14 "output/dense/aftergemm_quantizer", #F2Bias_scale, 15 "special_F2Bias_scale", ] int8O_gemm_weight_amax_list = [0 for i in range(INT8O_GEMM_NUM)] #Q_aftergemm int8O_gemm_weight_list = ["attention/self/query", #K_aftergemm "attention/self/key", #V_aftergemm "attention/self/value", #bmm1_aftergemm "attention/self/matmul_k_input_quantizer", #bmm2_aftergemm "attention/self/matmul_v_input_quantizer", #Proj_aftergemm "attention/output/dense", #FC1_aftergemm "intermediate/dense", #FC2_aftergemm "output/dense"] int8O_gemm_input_amax_list = [0 for i in range(INT8O_GEMM_NUM)] #Q_aftergemm int8O_gemm_input_list = ["attention/self/query/input_quantizer", #K_aftergemm "attention/self/key/input_quantizer", #V_aftergemm "attention/self/value/input_quantizer", #bmm1_aftergemm "attention/self/matmul_q_input_quantizer", #bmm2_aftergemm "attention/self/matmul_a_input_quantizer", #Proj_aftergemm "attention/output/dense/input_quantizer", #FC1_aftergemm "intermediate/dense/input_quantizer", #FC2_aftergemm "output/dense/input_quantizer"] int8O_gemm_output_amax_list = [0 for i in range(INT8O_GEMM_NUM)] #Q_aftergemm int8O_gemm_output_list = ["attention/self/query/aftergemm_quantizer", #K_aftergemm "attention/self/key/aftergemm_quantizer", #V_aftergemm "attention/self/value/aftergemm_quantizer", #bmm1_aftergemm "attention/self/softmax_input_quantizer", #bmm2_aftergemm "attention/output/dense/input_quantizer", #Proj_aftergemm "attention/output/dense/aftergemm_quantizer", #FC1_aftergemm "intermediate/dense/aftergemm_quantizer", #FC2_aftergemm "output/dense/aftergemm_quantizer"] factor = 1000000.0 for i in range(layer_num): amaxList = np.zeros([amaxTotalNum]) amax_id = 0 for amax_name in amax_name_list: if amax_name == "special_F2Bias_scale": if i != layer_num - 1: name = "bert/encoder/layer_{}/{}/quant_max:0".format(i+1, amax_name_list[0]) quant_max = checkpoint_utils.load_variable(tf.flags.FLAGS.init_checkpoint, name) name = "bert/encoder/layer_{}/{}/quant_min:0".format(i+1, amax_name_list[0]) quant_min = checkpoint_utils.load_variable(tf.flags.FLAGS.init_checkpoint, name) if abs(quant_max) > abs(quant_min): amax = abs(quant_max)#int(abs(quant_max)*factor)/factor else: amax = abs(quant_min)#int(abs(quant_min)*factor)/factor else: #not used, placeholder amax = 1.0 amaxList[amax_id] = amax amax_id += 1 amaxList[amax_id] = amax/127.0 amax_id += 1 amaxList[amax_id] = amax/127.0/127.0 amax_id += 1 amaxList[amax_id] = 127.0/amax amax_id += 1 continue name = "bert/encoder/layer_{}/{}/quant_max:0".format(i, amax_name) quant_max = checkpoint_utils.load_variable(tf.flags.FLAGS.init_checkpoint, name) name = "bert/encoder/layer_{}/{}/quant_min:0".format(i, amax_name) quant_min = checkpoint_utils.load_variable(tf.flags.FLAGS.init_checkpoint, name) if abs(quant_max) > abs(quant_min): amax = abs(quant_max)#int(abs(quant_max)*factor)/factor else: amax = abs(quant_min)#int(abs(quant_min)*factor)/factor if amax_name in int8O_gemm_input_list: int8O_gemm_input_amax_list[int8O_gemm_input_list.index(amax_name)] = amax if amax_name == "attention/self/query/input_quantizer": int8O_gemm_input_amax_list[int8O_gemm_input_list.index("attention/self/key/input_quantizer")] = amax int8O_gemm_input_amax_list[int8O_gemm_input_list.index("attention/self/value/input_quantizer")] = amax if amax_name in int8O_gemm_output_list: int8O_gemm_output_amax_list[int8O_gemm_output_list.index(amax_name)] = amax if amax_name in int8O_gemm_weight_list: int8O_gemm_weight_amax_list[int8O_gemm_weight_list.index(amax_name)] = amax amaxList[amax_id] = amax amax_id += 1 amaxList[amax_id] = amax/127.0 amax_id += 1 amaxList[amax_id] = amax/127.0/127.0 amax_id += 1 amaxList[amax_id] = 127.0/amax amax_id += 1 print("done process layer_{} activation amax".format(i)) #kernel amax starts from ACTIVATION_AMAX_NUM amax_id = ACTIVATION_AMAX_NUM for kernel_id, kernel_name in enumerate(kernel_name_list): kernel = tf.get_default_graph().get_tensor_by_name("bert/encoder/layer_{}/{}/kernel:0".format(i, kernel_name)) name = "bert/encoder/layer_{}/{}/kernel_quantizer/quant_max:0".format(i, kernel_name) quant_max2 = tf.convert_to_tensor(checkpoint_utils.load_variable(tf.flags.FLAGS.init_checkpoint, name)) name = "bert/encoder/layer_{}/{}/kernel_quantizer/quant_min:0".format(i, kernel_name) quant_min2 = tf.convert_to_tensor(checkpoint_utils.load_variable(tf.flags.FLAGS.init_checkpoint, name)) kernel_processed, quant_max_processed = transformer_op_module.weight_quantize(kernel, quant_max2, quant_min2, per_channel_quantization = per_channel_quantization) kernel_processed_, quant_max_processed_ = sess.run([kernel_processed, quant_max_processed]) sess.run(tf.assign(kernel, kernel_processed_)) if kernel_name in int8O_gemm_weight_list: int8O_gemm_weight_amax_list[int8O_gemm_weight_list.index(kernel_name)] = quant_max_processed_[0] for e in quant_max_processed_: amaxList[amax_id] = e amax_id += 1 #for int8O gemm deQuant for j in range(INT8O_GEMM_NUM): amaxList[amax_id] = (int8O_gemm_input_amax_list[j]*int8O_gemm_weight_amax_list[j])/(127.0*int8O_gemm_output_amax_list[j]) amax_id += 1 #for trt fused MHA amax #### QKV_addBias_amax amaxList[amax_id] = np.maximum(np.maximum(amaxList[8],amaxList[16]), amaxList[24]) amax_id += 1 #### softmax amax amaxList[amax_id] = amaxList[32] amax_id += 1 #### bmm2 amax amaxList[amax_id] = amaxList[36] amax_id += 1 amaxL = tf.get_default_graph().get_tensor_by_name("bert/encoder/layer_{}/amaxList:0".format(i)) sess.run(tf.assign(amaxL, amaxList)) print("done process layer_{} kernel weight".format(i)) saver.save(sess, out_checkpoint_file) if __name__ == '__main__': tf.flags.DEFINE_string("quantized_checkpoint", None, "quantized checkpoint file") tf.flags.DEFINE_string("init_checkpoint", None, "initial checkpoint file") tf.flags.DEFINE_integer("int8_mode", 1, "int8 mode in FasterTransformer, default as 1") if tf.flags.FLAGS.int8_mode == 1: per_channel_quantization = True elif tf.flags.FLAGS.int8_mode == 2 or tf.flags.FLAGS.int8_mode == 3: per_channel_quantization = False else: raise ValueError("wrong int8_mode argument") quantized_checkpoint_folder = "/".join(tf.flags.FLAGS.quantized_checkpoint.split("/")[:-1]) if not os.path.exists(quantized_checkpoint_folder): os.system("mkdir -p " + quantized_checkpoint_folder) checkpoint_quantization(tf.flags.FLAGS.init_checkpoint, tf.flags.FLAGS.quantized_checkpoint, per_channel_quantization)
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""" 订单退订模块 """ from flask import request from config import Status from flask_restful import Resource from ky_omm.common.query_order import QueryOrder class QueryOrders(Resource): def post(self): """ 查询订单号 业务逻辑:判断是否符合视频id和订单id长度 条件成立:执行QueryOrder.query_order() 获取查询结果,然后进行判断,如果count =1 就是订单id号,大于1就是视频id号 :return: 状态码和信息 """ if request.json: orders_id = request.json.get("orders_id", None).strip() if orders_id is None: return {'status': Status.FAILED, 'data': "video_type or order_id none"} query_result = QueryOrder(orders_id=orders_id).query_order() if query_result.get('count', None) is None: return {'status': Status.FAILED, 'data': "订单号不存在"} return {'status': Status.SUCCEED, 'data': query_result.get('data')} else: return {'status': Status.FAILED, 'msg': 'no data'}
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ncfeo/mystie
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/blog/migrations/0003_blogcomment.py
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no_license
https://github.com/ncfeo/mystie
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# -*- coding: utf-8 -*- # Generated by Django 1.9.8 on 2016-08-31 14:42 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('blog', '0002_auto_20160830_0732'), ] operations = [ migrations.CreateModel( name='BlogComment', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('user_name', models.CharField(max_length=100, verbose_name=b'\xe8\xaf\x84\xe8\xae\xba\xe8\x80\x85\xe5\x90\x8d\xe5\xad\x97')), ('user_email', models.EmailField(max_length=255, verbose_name=b'\xe8\xaf\x84\xe8\xae\xba\xe8\x80\x85\xe9\x82\xae\xe7\xae\xb1')), ('body', models.TextField(verbose_name=b'\xe8\xaf\x84\xe8\xae\xba\xe5\x86\x85\xe5\xae\xb9')), ('created_time', models.DateTimeField(auto_now_add=True, verbose_name=b'\xe8\xaf\x84\xe8\xae\xba\xe5\x8f\x91\xe8\xa1\xa8\xe6\x97\xb6\xe9\x97\xb4')), ('article', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='blog.Article', verbose_name=b'\xe8\xaf\x84\xe8\xae\xba\xe6\x89\x80\xe5\xb1\x9e\xe6\x96\x87\xe7\xab\xa0')), ], ), ]
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dzintars2/mongoDB_sample
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/MD3.py
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[]
no_license
https://github.com/dzintars2/mongoDB_sample
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#MongoDB import os, sys, json, pymongo, random from json import dumps from flask import Flask, g, Response, request, render_template from pathlib import Path from dateutil import parser import sampleData from faker import Faker #nejaušu datu ģerenators app = Flask(__name__) app.debug = True #DB MongoDB Atlas client = pymongo.MongoClient("localhost", 27017) db = client.test mydb = client["lietvediba"] tableDokVeidi = mydb["dokumentu_veidi"] tableDarbinieki = mydb["darbinieki"] tableUznemumi = mydb["uznemumi"] tableDokumenti = mydb["dokumenti"] fake = Faker('lv_LV') #neīstu datu ģenerators @app.teardown_appcontext def close_db(error): if hasattr(g, 'neo4j_db'): g.neo4j_db.close() @app.route("/") def get_index(): text = '<img src="https://webassets.mongodb.com/_com_assets/cms/mongodb-logo-rgb-j6w271g1xn.jpg" width="100%">' return render_template('index.html', saturs=text) #klasifikatoru ierakstu ģenerēšana @app.route("/generateData") def get_generateData(): text = '<br>' tableDarbinieki.insert_many(sampleData.datiPersonas) tableDokVeidi.insert_many(sampleData.datiDokumentuVeidi) tableUznemumi.insert_many(sampleData.datiUznemumi) get_generateDocuments() text = '<br><table class="table table-hover table-sm"><thead><tr><th>Izveidota kolekcija</th><th>Ierakstu skaits</th></tr></thead><tbody>' #izdrukā kolekcijas un tajās esošo ierakstu skaitu for collection in mydb.list_collection_names(): text += '<tr><td>' + collection + '</td><td>'+str(mydb[collection].count()) + '</td></tr>' text += '</tbody></table>' return render_template('index.html', saturs='<b>Klasifikatoru datu ģenerēšana</b>'+text) #dokumentu datu ģenerēšana @app.route("/generateDocuments") def get_generateDocuments(): text = '<br>' mongoData = [] for i in range(500): sys.stdout.write("\rUzģenerēti %d ieraksti no 500" % i) sys.stdout.flush() isParent = 1 while (isParent==1): #mūs interesē tikai dokumentu veidi, kuriem ir "parent_id" jeb kuri nav dokumentu veidu grupas dokumentaTips = tableDokVeidi.aggregate([{ "$sample": {"size": 1} }]).next() if ('parent_id' in dokumentaTips): isParent = 0 nejaussDarbinieks = tableDarbinieki.aggregate([{ "$sample": {"size": 1} }]).next() datums = fake.date_between(start_date='-1y', end_date='now') datums = parser.parse(str(datums)) if (dokumentaTips["_id"]=="rikojums_darb"): rikojumaNr = "2018-" + dokumentaTips["case_id"] + "/"+str(random.randint(1, 999)) apraksts = fake.text() data = {"dokumentaTips": dokumentaTips["_id"], "persona": nejaussDarbinieks["_id"], "numurs": rikojumaNr, "temats": "Rīkojums par darbu", "datums":datums, "apraksts": apraksts} elif (dokumentaTips["_id"]=="rikojums_visp"): rikojumaNr = "2018-" + dokumentaTips["case_id"] + "/"+str(random.randint(1, 999)) data = {"dokumentaTips": dokumentaTips["_id"], "persona": nejaussDarbinieks["_id"], "numurs": rikojumaNr, "temats": "Rīkojums", "datums": datums} elif (dokumentaTips["_id"]=="ligums_darba"): epastaAdrese = fake.email() amats = fake.job() bankasKonts = fake.iban() data = {"dokumentaTips": dokumentaTips["_id"], "ligumsledzejs": nejaussDarbinieks["_id"], "epasts": epastaAdrese, "temats": "Darba līgums", "datums": datums, "amats": amats, "bankas_konts":bankasKonts} elif (dokumentaTips["_id"]=="ligums_kredits" or dokumentaTips["_id"]=="ligums_saimn"): ligumsledzejs = tableUznemumi.aggregate([{ "$sample": {"size": 1} }]).next() if (dokumentaTips["_id"]=="ligums_kredits"): summa = (random.randint(1, 1000))*1000 temats = "Aizdevuma līgums" else: summa = (random.randint(1, 9999999))/100 temats = "Saimnieciskais līgums" apraksts = fake.text() apmaksasTermins = fake.date_between(start_date='+1y', end_date='+20y') apmaksasTermins = parser.parse(str(apmaksasTermins)) ligumsledzejaPersona = fake.name() bankasKonts = fake.iban() pastaAdrese = fake.address() epastaAdrese = fake.email() amats = fake.job() data = {"dokumentaTips": dokumentaTips["_id"], "ligumsledzejs": ligumsledzejs["_id"], "pasta_adrese":pastaAdrese, "epasts": epastaAdrese, "darbinieks": nejaussDarbinieks["_id"], "summa": summa, "temats": temats, "datums": datums, "termins": apmaksasTermins, "apraksts": apraksts, "ligumsledzejaPersona": ligumsledzejaPersona, "amats": amats, "bankas_konts":bankasKonts} mongoData.append(data) tableDokumenti.insert_many(mongoData) return render_template('index.html', saturs='<b>Dokumentu datu ģenerēšana</b>'+text) @app.route("/deleteData") def get_deleteData(): tableDarbinieki.drop() tableDokVeidi.drop() tableUznemumi.drop() tableDokumenti.drop() return render_template('index.html', saturs='<b>Datu dzēšana</b><br>Kolekcijas izdzēstas') #informācija par ierakstu skaitu kolekcijās @app.route("/statistics") def get_statistics(): text = '<br><table class="table table-hover table-sm"><thead><tr><th>Kolekcija</th><th>Ierakstu skaits</th></tr></thead><tbody>' for collection in mydb.list_collection_names(): text += '<tr><td>' + collection + '</td><td>'+str(mydb[collection].count()) + '</td></tr>' text += '</tbody></table><a class="nav-link" href="/generateDocuments">Papildus dokumentu ģenerēšana</a>' return render_template('index.html', saturs='<b>Statistika</b>'+text) @app.route("/report1") def get_report1(): apraksts = """<b>1.atskaite</b><br> Kopsavilkums pa dokumentiem<br> (grupēts pa dokumementiem - summa (ja tāda ir noteikta), skaits (dati sakārtoti pēc dokumentu skaita)""" result = tableDokumenti.aggregate([ {"$lookup": { "from": "dokumentu_veidi", "localField": "dokumentaTips", "foreignField": "_id", "as": "dokumenta_veidi_dati" } }, { "$group": { "_id": "$dokumentaTips", "skaits": { "$sum": 1 }, "summa": {"$sum": "$summa"}, "dokumentaTipaNosaukums": {"$min":"$dokumenta_veidi_dati.name"}} }, { "$sort":{ "skaits": -1} }]) table = '<br><table class="table table-hover table-sm"><thead><tr><th>Dokumenta tips</th><th>Dokumentu skaits</th><th>Dokumentos norādīta kopsumma (ja norādīts)</th></tr></thead><tbody>' for ieraksts in result: table += '<tr><td>'+ieraksts["dokumentaTipaNosaukums"][0]+'</td><td>'+str(ieraksts["skaits"])+'</td><td>'+str('{:5.2f}'.format(ieraksts["summa"]))+'</td></tr>' table += '</tbody></table>' return render_template('index.html', saturs=apraksts+table) @app.route("/report2") def get_report2(): apraksts = """<b>2.atskaite</b><br> Aizdevuma līgumu kopsavilkums<br> Atmaksas termiņa gads, atmaksājamā summa. Iekļauti aizdevumi ar atmaksas termiņu līdz 31.12.2029""" table = '<br><table class="table table-hover table-sm"><thead><tr><th>Termiņs (gads)</th><th>Atmaksājamā summa</th></tr></thead><tbody>' datums = parser.parse(str("2030-01-01")) result = tableDokumenti.aggregate([ {"$match": {"dokumentaTips":"ligums_kredits", "termins": { "$lte" : datums}}}, { "$group": { "_id": { "gads": { "$year": "$termins" } }, "summa": {"$sum": "$summa"}}}, {"$sort":{"_id": 1}} ]) for ieraksts in result: table += '<tr><td>'+str(ieraksts["_id"]["gads"])+'.gads</td><td>'+str(ieraksts["summa"])+'</td></tr>' table += '</tbody></table>' return render_template('index.html', saturs=apraksts+table) @app.route("/report3") def get_report3(): apraksts = """<b>3.atskaite</b><br> Kopsavilkums pa rīkojumiem (darbinieks, rīkojumu skaits, TOP10 darbinieki pēc rīkojumu skaita)""" table = '<br><table class="table table-hover table-sm"><thead><tr><th>#</th><th>Darbinieks</th><th>Rīkojumu skaits</th></tr></thead><tbody>' result = tableDokumenti.aggregate([ {"$lookup": { "from": "darbinieki", "localField": "persona", "foreignField": "_id", "as": "darbinieks" } }, {"$lookup": { "from": "dokumentu_veidi", "localField": "dokumentaTips", "foreignField": "_id", "as": "dokumenta_veidi_dati" } }, {"$addFields": { "dok_veida_grupa": "$dokumenta_veidi_dati.parent_id", "darbinieks_vards": "$darbinieks.name" }}, {"$match": {"dok_veida_grupa": "rikojums"}}, {"$group":{ "_id": "$persona", "vards_uzvards": {"$min":"$darbinieks_vards"}, "dokumentu_skaits": {"$sum": 1} } }, {"$sort":{"dokumentu_skaits": -1}}, {"$limit": 10} ]) i = 1 for ieraksts in result: table += '<tr><td>'+str(i)+'</td><td>' + ieraksts["vards_uzvards"][0] + '</td><td>' + str(ieraksts["dokumentu_skaits"]) + '</td></tr>' i += 1 table += '</tbody></table>' return render_template('index.html', saturs=apraksts+table) @app.route("/report4") def get_report4(): apraksts = """<b>4.atskaite</b><br> Līgumi pa līgumslēdzējiem<br> Darījuma partnera nosaukums, līguma veids, līgumu slēgšanas periods (līguma datums min-max), līgumu skaits, TOP10 partneri pēc līgumu skaita """ table = '<br><table class="table table-hover table-sm"><thead><tr><th>#</th><th>Partneris</th><th>Līguma veids</th><th>Līgumu periods</th><th>Līgumu skaits</th><th>Līgumu kopsumma</th></tr></thead><tbody>' result = tableDokumenti.aggregate([ {"$lookup": { "from": "uznemumi", "localField": "ligumsledzejs", "foreignField": "_id", "as": "uznemums" } }, {"$lookup": { "from": "dokumentu_veidi", "localField": "dokumentaTips", "foreignField": "_id", "as": "dokumenta_veidi_dati" } }, {"$addFields": { "dok_veida_grupa": "$dokumenta_veidi_dati.parent_id", "dok_veida_nosaukums": "$dokumenta_veidi_dati.name", "partneris_nos": "$uznemums.name_in_quotes", "partnera_tips": "$uznemums.type" }}, {"$match": {"dokumentaTips": {"$in": ["ligums_kredits","ligums_saimn"]}} }, {"$group":{ "_id": {"partneraId":"$ligumsledzejs", "dok_veids": "$dokumentaTips"}, "dok_veida_nosaukums": {"$min": "$dok_veida_nosaukums"}, "partneris": {"$min":"$partneris_nos"}, "partn_tips": {"$min":"$partnera_tips"}, "ligumsumma": {"$sum":"$summa"}, "datums_no": {"$min":"$datums"}, "datums_lidz": {"$max": "$datums"}, "dokumentu_skaits": {"$sum": 1} } }, {"$sort":{"dokumentu_skaits": -1}}, {"$limit": 10} ]) i = 1 for ieraksts in result: table += ('<tr><td>'+str(i)+'</td><td>'+ieraksts["partneris"][0]+' '+ieraksts["partn_tips"][0]+'</td><td>'+ ieraksts["dok_veida_nosaukums"][0] + '</td><td>'+ str(ieraksts["datums_no"])[0:10] + ' - ' + str(ieraksts["datums_lidz"])[0:10] + '</td><td>'+str(ieraksts["dokumentu_skaits"]) + '</td><td>'+str('{:5.2f}'.format(ieraksts["ligumsumma"])) + '</td></tr>') i += 1 table += '</tbody></table>' return render_template('index.html', saturs=apraksts+table) @app.route("/report5") def get_report5(): apraksts = """<b>5.atskaite</b><br> Lielākie līgumi pa darbiniekiem, kuri tos ir slēguši. Sakārtots pēc lielākās līgumsummas. TOP10 darbinieki. """ table = '<br><table class="table table-hover table-sm"><thead><tr><th>#</th><th>Darbinieks</th><th>Līgumu skaits</th><th>Līgumsummu kopsumma</th><th>Maksimālā līgumsumma</th></tr></thead><tbody>' result = tableDokumenti.aggregate([ {"$lookup": { "from": "darbinieki", "localField": "darbinieks", "foreignField": "_id", "as": "darbinieka_dati" } }, {"$lookup": { "from": "dokumentu_veidi", "localField": "dokumentaTips", "foreignField": "_id", "as": "dokumenta_veidi_dati" } }, {"$addFields": { "dok_veida_grupa": "$dokumenta_veidi_dati.parent_id", "darbinieka_vards": "$darbinieka_dati.name" }}, {"$match": {"dokumentaTips": {"$in": ["ligums_kredits","ligums_saimn"]}} }, {"$group":{ "_id": {"partneraId":"$darbinieks"}, "darbinieks": {"$min":"$darbinieka_vards"}, "ligumu_kopsumma": {"$sum":"$summa"}, "max_ligums": {"$max":"$summa"}, "dokumentu_skaits": {"$sum": 1} } }, {"$sort":{"max_ligums": -1}}, {"$limit": 10} ]) i = 1 for ieraksts in result: table += ('<tr><td>'+str(i)+'</td><td>'+ieraksts["darbinieks"][0] + '</td><td>' + str(ieraksts["dokumentu_skaits"]) + '</td><td>' + str('{:5.2f}'.format(ieraksts["ligumu_kopsumma"])) + '</td><td>' + str('{:5.2f}'.format(ieraksts["max_ligums"])) + '</td></tr>') i += 1 table += '</tbody></table>' return render_template('index.html', saturs=apraksts+table) if __name__ == '__main__': app.run(port=8080)
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Bojanovski/ChessANN
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/Code/dependencies/chess/polyglot.py
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# -*- coding: utf-8 -*- # # This file is part of the python-chess library. # Copyright (C) 2012-2016 Niklas Fiekas <niklas.fiekas@backscattering.de> # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. import chess import struct import os import mmap import random try: import backport_collections as collections except ImportError: import collections ENTRY_STRUCT = struct.Struct(">QHHI") class Entry(collections.namedtuple("Entry", ["key", "raw_move", "weight", "learn"])): """An entry from a polyglot opening book.""" def move(self, chess960=False): """Gets the move (as a :class:`~chess.Move` object).""" # Extract source and target square. to_square = self.raw_move & 0x3f from_square = (self.raw_move >> 6) & 0x3f # Extract the promotion type. promotion_part = (self.raw_move >> 12) & 0x7 promotion = promotion_part + 1 if promotion_part else None # Convert castling moves. if not chess960 and not promotion: if from_square == chess.E1: if to_square == chess.H1: return chess.Move(chess.E1, chess.G1) elif to_square == chess.A1: return chess.Move(chess.E1, chess.C1) elif from_square == chess.E8: if to_square == chess.H8: return chess.Move(chess.E8, chess.G8) elif to_square == chess.A8: return chess.Move(chess.E8, chess.C8) return chess.Move(from_square, to_square, promotion) class MemoryMappedReader(object): """Maps a polyglot opening book to memory.""" def __init__(self, filename): self.fd = os.open(filename, os.O_RDONLY | os.O_BINARY if hasattr(os, "O_BINARY") else os.O_RDONLY) try: self.mmap = mmap.mmap(self.fd, 0, access=mmap.ACCESS_READ) except (ValueError, mmap.error): # Can not memory map empty opening books. self.mmap = None def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): return self.close() def close(self): """Closes the reader.""" if self.mmap is not None: self.mmap.close() try: os.close(self.fd) except OSError: pass def __len__(self): if self.mmap is None: return 0 else: return self.mmap.size() // ENTRY_STRUCT.size def __getitem__(self, key): if self.mmap is None: raise IndexError() if key < 0: key = len(self) + key try: key, raw_move, weight, learn = ENTRY_STRUCT.unpack_from(self.mmap, key * ENTRY_STRUCT.size) except struct.error: raise IndexError() return Entry(key, raw_move, weight, learn) def __iter__(self): i = 0 size = len(self) while i < size: yield self[i] i += 1 def bisect_key_left(self, key): lo = 0 hi = len(self) while lo < hi: mid = (lo + hi) // 2 mid_key, _, _, _ = ENTRY_STRUCT.unpack_from(self.mmap, mid * ENTRY_STRUCT.size) if mid_key < key: lo = mid + 1 else: hi = mid return lo def __contains__(self, entry): return any(current == entry for current in self.find_all(entry.key, entry.weight)) def find_all(self, board, minimum_weight=1, exclude_moves=()): """Seeks a specific position and yields corresponding entries.""" try: zobrist_hash = board.zobrist_hash() except AttributeError: zobrist_hash = int(board) board = None i = self.bisect_key_left(zobrist_hash) size = len(self) while i < size: entry = self[i] i += 1 if entry.key != zobrist_hash: break if entry.weight < minimum_weight: continue if board: move = entry.move(chess960=board.chess960) elif exclude_moves: move = entry.move() if exclude_moves and move in exclude_moves: continue if board and not board.is_legal(move): continue yield entry def find(self, board, minimum_weight=1, exclude_moves=()): """ Finds the main entry for the given position or zobrist hash. The main entry is the first entry with the highest weight. By default entries with weight ``0`` are excluded. This is a common way to delete entries from an opening book without compacting it. Pass *minimum_weight* ``0`` to select all entries. Raises :exc:`IndexError` if no entries are found. """ try: return max(self.find_all(board, minimum_weight, exclude_moves), key=lambda entry: entry.weight) except ValueError: raise IndexError() def choice(self, board, minimum_weight=1, exclude_moves=(), random=random): """ Uniformly selects a random entry for the given position. Raises :exc:`IndexError` if no entries are found. """ chosen_entry = None for i, entry in enumerate(self.find_all(board, minimum_weight, exclude_moves)): if chosen_entry is None or random.randint(0, i) == i: chosen_entry = entry if chosen_entry is None: raise IndexError() return chosen_entry def weighted_choice(self, board, exclude_moves=(), random=random): """ Selects a random entry for the given position, distributed by the weights of the entries. Raises :exc:`IndexError` if no entries are found. """ total_weights = sum(entry.weight for entry in self.find_all(board, exclude_moves=exclude_moves)) if not total_weights: raise IndexError() choice = random.randint(0, total_weights - 1) current_sum = 0 for entry in self.find_all(board, exclude_moves=exclude_moves): current_sum += entry.weight if current_sum > choice: return entry assert False def open_reader(path): """ Creates a reader for the file at the given path. >>> with open_reader("data/polyglot/performance.bin") as reader: ... for entry in reader.find_all(board): ... print(entry.move(), entry.weight, entry.learn) e2e4 1 0 d2d4 1 0 c2c4 1 0 """ return MemoryMappedReader(path)
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from django.db import models from django.contrib.auth.models import AbstractBaseUser, PermissionsMixin from .managers import CustomUserManager class Profile(AbstractBaseUser, PermissionsMixin): email = models.CharField(max_length=128, unique=True) date_joined = models.DateTimeField(auto_now_add=True) first_name = models.CharField(max_length=40, default='') second_name = models.CharField(max_length=40, default='') is_staff = models.BooleanField(default=False) is_active = models.BooleanField(default=True) difficult_password = models.PositiveSmallIntegerField(default=0) objects = CustomUserManager() USERNAME_FIELD = 'email' class Meta: verbose_name = 'Profile' verbose_name_plural = 'Profiles'
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# 문제4. # 다음과 같은 출력이 되도록 구구단을 작성하세요. (이중 for~in) for dan in range(1, 10): for gob in range(1, 10): print(dan, 'x', gob, '=', dan * gob, end='\t') print()
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#!/usr/bin/env python3 from bisect import* (n,), a, b, c = [sorted(map(int, o.split())) for o in open(0)] print(sum(bisect_left(a, i) * (n - bisect(c, i)) for i in b))
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from math import isclose from _pytest.monkeypatch import MonkeyPatch def test_input(monkeypatch): monkeypatch.setattr('builtins.input', lambda _: "M") test_input(MonkeyPatch()) testTC = 15. #coolant temperature celsius testPP = 980. #density of plastic part kg/m^3 testCP = 1300. #specific heat capacity of plastic part J/KG*K testLP = 0.001 #half the plastic part thickness m testW = 0.010 #cooling line pitch distance m testD = 0.005 #cooling line diameter m testLM = 0.004 #distance from cooling line to mold wall testTMelt = 180. #Part melted temperature testTEject = 64.9 #Part ejection temperature testTCycle = 10. #Cycle time seconds testTMO = 13. #Initial mold temperature testCVV = 0.227 #coolant velocity liters/sec testDV = 1.002 * 10**-3 #coolant dynamic viscosity tesWDV = 0.0009775 #coolant dynamic viscosity when near wall testKC = 0.5918 #thermal conductivity of coolant testPC = 998.2 #coolant density testCC = 4187 #specific heat capacity of coolant testL = 1.15 #coolant line length testrho_m = 7930. #First comparison Mold density kg/m^3: 316 steel testCp_m = 510. #First comparison Mold specific heat 316 steel testeps = 0.00015 #First comparison average height of pipe surface irregularities (m) 316 steel testKM = 16.5 #First comparison thermal conductivity of mold: 316 steel testPR = 7.089175397093613 testCD = 0.057 from ..paccman import FVfunc from ..paccman import KVfunc from ..paccman import REfunc from ..paccman import PRfunc from ..paccman import DFfunc from ..paccman import htc from ..paccman import GNU from ..paccman import ATMfunc from ..paccman import TConstantfunc from ..paccman import pdropfunc from ..paccman import helicalDFfunc_lam_bigv from ..paccman import helicalDFfunc_turb from ..paccman import helicalNU_lam from ..paccman import helicalNU_turb class TestClass: def testFV(self): testFV = FVfunc(testCVV, testD) assert testFV == 11.561015066195278 return testFV def testKV(self): testKV = KVfunc(testDV,testPC) assert testKV == 1.0038068523342016e-06 return testKV def testRE(self): testRE = REfunc(11.561015066195278,testD,1.0038068523342016e-06) assert testRE == 57585.854486407814 return testRE def testPR(self): testPR = PRfunc(testDV,testCC,testKC) assert testPR == 7.089175397093613 return testPR def testDF(self): testDF = DFfunc(testeps,testD,57585.854486407814) assert testDF == 0.010906214575733224 return testDF def testhtc(self): testh = htc(testKC,testD,GNU(0.010906214575733224,57585.854486407814,7.089175397093613)) assert testh == 28621.292587276246 return testh def testATM(self): testATM = ATMfunc(testPP,testCP,testLP,testKM,testW,28621.292587276246,testD,testLM,testTMelt,testTEject,testTCycle,testTC) assert testATM == 19.207173469816603 return testATM def testTConstant(self): testTConstant = TConstantfunc(testrho_m,testCp_m,testLM,testKM,testW,28621.292587276246,testD) assert testTConstant == 4.641400260500878 return testTConstant def testpdrop(self): testpdrop = pdropfunc(0.010906214575733224,testL,testD,testPC,11.561015066195278) assert testpdrop == 167332.91558708664 return testpdrop def testhelicalDFfunc_lam(self): testhelicalDF_lam = helicalDFfunc_lam_bigv(2492,12/112,1) testhelicalDF_lam2 = helicalDFfunc_lam_bigv(7912,12/112,1) assert isclose(testhelicalDF_lam, 0.07922, abs_tol=2.1e-3) assert isclose(testhelicalDF_lam2, 0.04899, abs_tol=1e-3) #data from Hydraulic Performance... (2001) by Xu, et al. pulled with WebPlotDigitizer def testhelicalDFfunc_turb(self): testhelicalDF_turb = helicalDFfunc_turb(0,10394,12/112,1) testhelicalDF_turb2 = helicalDFfunc_turb(0,21310,12/112,1) assert isclose(testhelicalDF_turb, 0.05457, abs_tol=2.5e-3) assert isclose(testhelicalDF_turb2, 0.05246, abs_tol=5.5e-3) #data from Hydraulic Performance... (2001) by Xu, et al. pulled with WebPlotDigitizer def testhelicalNU_lam(self): testhelicalNU_lam = helicalNU_lam(765.17,testPR) testhelicalNU_lam2 = helicalNU_lam(15.227,testPR) assert isclose(testhelicalNU_lam, 23.751*testPR**0.175, abs_tol=3) assert isclose(testhelicalNU_lam2, 3.9104*testPR**0.175, abs_tol=1.4e-1) #data from The Effects of... (1997) by Xin, et al. pulled with WebPlotDigitizer and tested using generic Prandtl def testhelicalNU_turb(self): testhelicalNU_turb = helicalNU_turb(18152.641,testPR,testD,testCD) testhelicalNU_turb2 = helicalNU_turb(112074.9,testPR,testD,testCD) assert isclose(testhelicalNU_turb, 51.304*(testPR**0.4*(1+3.455*(testD/testCD))), abs_tol=1e-1) assert isclose(testhelicalNU_turb2, 279.538*(testPR**0.4*(1+3.455*(testD/testCD))), abs_tol=17) #data from The Effects of... (1997) by Xin, et al. pulled with WebPlotDigitizer and tested using generic Prandtl, Diameter, Coil Diameter
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shsingh/secureCodeBox
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/scanners/zap-advanced/scanner/tests/test_zap_context.py
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#!/usr/bin/env python # SPDX-FileCopyrightText: the secureCodeBox authors # # SPDX-License-Identifier: Apache-2.0 # -*- coding: utf-8 -*- import pytest from unittest.mock import MagicMock, Mock, patch from unittest import TestCase from zapv2 import ZAPv2 from zapclient.configuration import ZapConfiguration from zapclient.context.zap_context import ZapConfigureContext class ZapScannerTests(TestCase): @pytest.mark.unit def test_context_empty(self): pass # # build our dependencies # mock_zap = mock.create_autospec(ZAPv2.context.context_list) # mock_config = mock.create_autospec(ZapConfiguration) # testobject = ZapConfigureContext(mock_zap, mock_config) # testobject.configure_contexts()
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JacekPierzchlewski/fsCS-repro
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/RxCS-15Jan2015/rxcs/ana/SNR.py
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""" This module contains SNR evaluation function of the reconstructed signals. |br| *Author*: Jacek Pierzchlewski, Aalborg University, Denmark. <jap@es.aau.dk> *Version*: 0.1 | 20-MAY-2014 : * Initial version. |br| 0.2 | 21-MAY-2014 : * Success Ratio computation is added. |br| 0.3 | 21-MAY-2014 : * Docstrings added. |br| 0.4 | 21-MAY-2014 : * Configuration with a dictionary |br| 0.5 | 21-MAY-2014 : * Progress and results printing |br| 1.0 | 21-MAY-2014 : * Version 1.0 released. |br| *License*: BSD 2-Clause """ from __future__ import division import numpy as np import rxcs def main(dSigOrig, dSigRecon, dAna, dAnaConf): """ This the main function of the generator and the only one which should be accessed by a user. |br| The function computes a noise of reconstrucion for every signal; this noise is equal to the difference between the reconstructed and the original signals. Afterwards the function computes the signal-to-noise ratio of the reconstruction for every signal. The ratio is computed as: SNR = 10log10(iPs/iPn), where: iPs - power of the original signal iPn - power of noise The function computes also the average signal-to-noise ratio of the reconstruction. |br| Additionally. the function computes the success ratio, which is equal to the ratio of reconstructed signal with reconstruction SNR higher than success threshold. |br| Args: dSigOrig (dict): dict. with the original signals dSigRecon (dict): dict. with the reconstructed signals dAna (dict): dict. with results of system analysis dAnaConf (dict): dict. with configuration for system analysis Returns: dAna (dict): dict. with results of system analysis """ # ------------------------------------------------------------------- # Get the signals (mSig_orig, mSig_recon, nSigs_orig, iSiz_orig) = \ _getSignals(dSigOrig, dSigRecon) # Get the configuration # bMute - 'mute the console output' flag # iSNRSuccess - success threshold (bMute, iSNRSuccess) = _getConf(dAnaConf) # ------------------------------------------------------------------- # Print out the header of the SNR analysis if bMute == 0: rxcs.console.progress('System analysis', 'SNR of the reconstructed signal') tStart = rxcs.console.module_progress('SNR analysis starts!!!') # ------------------------------------------------------------------- # Compute the SNR # Compute the noise mNoise = np.abs(mSig_orig - mSig_recon) (_, iSizNoise) = mNoise.shape # Size of the noise # Compute the power of noise vNoiseP = (np.sum(mNoise * mNoise, axis=1) / iSizNoise) # Compute the power of orignal signals vSigP = (np.sum(mSig_orig * mSig_orig, axis=1) / iSiz_orig) # Compute the SNR for every reconstructed signal and the average SNR vSNR = 10 * np.log10(vSigP / vNoiseP) iSNR = vSNR.mean() # ------------------------------------------------------------------- # Compute the success ratio iSR = (vSNR >= iSNRSuccess).mean() # ------------------------------------------------------------------- # Add the vector with computed SNR to the dictionary with system # analysis results dAna['vSNR'] = vSNR # Add the average SNR to the dictionary with system analysis results dAna['iSNR'] = iSNR # Add the success ratio to the dictionary with system analysis results dAna['iSR'] = iSR # ------------------------------------------------------------------- # SNR analysis is done if bMute == 0: rxcs.console.module_progress_done(tStart) # ------------------------------------------------------------------- # Print results _printResults(iSNR, iSR, iSNRSuccess, bMute) # ------------------------------------------------------------------- return dAna # ================================================================= # Get the signals # ================================================================= def _getSignals(dSigOrig, dSigRecon): """ This function gets the reconstructed and the original signals from the data dicionaries. The function checks if: - the signals are present in the dictionaries - the signals have the same length - there is the same number of signals Args: dSigOrig (dict): dict. with the original signals dSigRecon (dict): dict. with the reconstructed signals Returns: mSig_orig (matrix): the original non noisy signal mSig_recon (matrix): the reconstructed signal nSigs (float): the number of signals iSigSiz (float): the length of signals """ # ------------------------------------------------------------------- # Get the original non noisy signals, the number of orignal signals # and their length strErr = 'The original signals (mSigNN) are missing in the "dSigOrig"' if not 'mSigNN' in dSigOrig: raise NameError(strErr) mSig_orig = dSigOrig['mSigNN'] (nSigs_orig, iSiz_orig) = mSig_orig.shape # ------------------------------------------------------------------- # Get the reconstructed signals, the number of reconstructed signals, # and their length strErr = 'The reconstructed signals (mSig) are missing in the "dSigRecon"' if not 'mSig' in dSigRecon: raise NameError(strErr) mSig_recon = dSigRecon['mSig'] (nSigs_recon, iSiz_recon) = mSig_orig.shape # ------------------------------------------------------------------- # Check if the original and the reconstructed signals have the same # length strErr = 'The original and reconstructed signals must have the same length' if iSiz_orig != iSiz_recon: raise ValueError(strErr) # ------------------------------------------------------------------- # Check if there is the same number of original and reconstructed # signals strErr = 'There are more original signals than reconstructed signals!' if nSigs_orig > nSigs_recon: raise ValueError(strErr) strErr = 'There are more reconstructed signals than original signals!' if nSigs_recon > nSigs_orig: raise ValueError(strErr) # ------------------------------------------------------------------- nSigs = nSigs_orig iSigSiz = iSiz_orig return (mSig_orig, mSig_recon, nSigs, iSigSiz) # ================================================================= # Get the configuration # ================================================================= def _getConf(dAnaConf): """ This function gets the configuration of the module from the system analysis configuration dictionary. The function checks if the correct configuration fields are given in the configuration dictionary. If not, the default values are assigned to the configuration values. Args: dAnaConf (dict): dict. with the system analysis configuration Returns: bMute (float): 'mute the console output' flag iSNRSuccess (float): success threshold """ # ------------------------------------------------------------------- # Get the mute flag if not 'bMute' in dAnaConf: bMute = 0 else: bMute = dAnaConf['bMute'] # ------------------------------------------------------------------- # Get the success threshold if not 'iSNRSuccess' in dAnaConf: iSNRSuccess = 20 else: iSNRSuccess = dAnaConf['iSNRSuccess'] return (bMute, iSNRSuccess) # ================================================================= # Print results of the analysis # ================================================================= def _printResults(iSNR, iSR, iSNRSuccess, bMute): """ This function print the results of the SNR analysis to the console, if the 'mute' flag is not set. Args: iSNR (float): the measured average SNR of the reconstrucion iSR (float): success ratio iSNRSuccess (float): success threshold bMute (float): 'mute the console output' flag Returns: nothing """ if bMute == 0: rxcs.console.bullet_param('The average SNR of the reconstruction', iSNR, '-', 'dB') rxcs.console.bullet_param('The Success Ratio', iSR, ' ', '') rxcs.console.param('(success threshold)', iSNRSuccess, '-', 'dB') return
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#!/usr/bin/env python3 """Correlation parser Query correlation file with a package name and get recommended tests to run. Written for Python3 but should support Python2 as well. """ from collections import defaultdict import argparse import json import sys import operator MAX_NBR_OF_TESTS = 1203 MODE_CHOICES = ['WIDE', 'wide', 'NARROW', 'narrow'] def read_data(filename): """Read json data from filename""" data = {} try: with open(filename, 'r') as fileh: data = json.loads(fileh.read()) except (OSError, IOError): pass return data def get_tests(module, data): """Get tests correlated to module in dict data""" tests = {} try: tests = data[module] except KeyError: pass return tests def parse_args(): """Setup argparser""" parser = argparse.ArgumentParser() parser.add_argument('modules', nargs='+', help='module(s) to get ' 'recommendations on. Space separated list of modules. ' 'Ignored if wide mode is specified.') parser.add_argument('-f', '--correlation-data', required=True, help='json file to analyze') parser.add_argument('-c', '--cutoff', help='cutoff limit for correlation ' 'weights', default=0, type=int) parser.add_argument('--mode', default='NARROW', choices=MODE_CHOICES, help='regression test strategy.') parser.add_argument('-v', '--verbose', action="store_true", help='prints additional information') parser.add_argument( '--sort', default='weight', help="Option to sort output in different ways", choices=['weight', 'weight-reverse', 'alphabet', 'alphabet-reverse'], dest='order', ) return parser.parse_args() def sort_tests(tests: dict, order: str) -> list: """Takes a dict containing str/int key/value pairs like: {'testsname': correlation_weight} Returns a list ordered as specified by parameter `order`. """ if order == 'weight': return sorted(tests.items(), key=operator.itemgetter(1, 0)) elif order == 'weight-reverse': return sorted(tests.items(), key=operator.itemgetter(1, 0))[::-1] elif order == 'alphabet': return sorted(tests.items()) elif order == 'alphabet-reverse': return sorted(tests.items())[::-1] raise ValueError("Order '%s' is not supported" % order) def narrow(filename, args): """Perform narrow test selection.""" # TODO: Refactor this modules = args.modules if args.verbose: print("\nParsing using narrow selection on file '{}' for " "recommendations on {}\n".format(filename, modules)) data = read_data(filename) if not data: print("ERROR: File {} not found".format(filename)) sys.exit(1) tests = {} empty_tests = [] for module in modules: current_module_tests = get_tests(module, data) if not current_module_tests: empty_tests.append(module) tests = {k: tests.get(k, 0) + current_module_tests.get(k, 0) for k in set(tests) | set(current_module_tests)} if not tests: print("WARNING: No tests correlated to specified module(s):" "{}".format(modules)) sys.exit(1) ordered_tests = sort_tests(tests, args.order) if args.verbose: print("Recommended tests:") for test in ordered_tests: if test[1] >= args.cutoff: print("{: <5} {}".format(test[1], test[0])) if args.cutoff: print("(cutoff at weight {})".format(args.cutoff)) print("\nTotal recommended tests: {}".format(len(ordered_tests))) time_saved = MAX_NBR_OF_TESTS - len(ordered_tests) print("\nTime savings running only recommended tests: {} units " "".format(time_saved)) if empty_tests: print("[INFO]: no tests found for {}".format(', '.join(empty_tests))) else: print_list([item[0] for item in ordered_tests]) def wide(filename, args): """Perform wide test selection.""" # TODO: Refactor this if args.verbose: print("\nParsing using wide selection on file '{}'" "\n".format(filename)) with open(filename, 'r') as fileh: string_data = fileh.read() data = json.loads(string_data) tests = sum_tests(data) sorted_tests = sort_tests(tests, args.order) if args.verbose: for item in sorted_tests: print("{: >6} | {}".format(item[1], item[0])) print("weight | name") time_savings_percentage = ((1 - len(sorted_tests) / MAX_NBR_OF_TESTS) * 100) print("Nbr of correlated tests: {}".format(len(sorted_tests))) print("Time savings: {:.1f}%".format(time_savings_percentage)) else: print_list([item[0] for item in sorted_tests]) def print_list(test_list, sep='\n'): """Print a list of test items, newline separated by default.""" print(sep.join(test_list)) def sum_tests(data): """Go through each package in data, get the tests and their correlations, and add test and correlation to a list. If test already exists, increment weight by the weight of the test found.""" tests = defaultdict(int) for package in data: for test in data[package]: tests[test] += data[package][test] return tests def main(): """Main method""" args = parse_args() filename = args.correlation_data if args.mode.lower() == 'narrow': narrow(filename, args) if args.mode.lower() == 'wide': wide(filename, args) if __name__ == "__main__": main()
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pankajkhatiwada/Sen_Machine_Deep_Learning
4,011,499,465,660
49c8590eaff0a64d772eef12300f35bb0523944f
84d3368a4536d9a1b3cd7548b298ebc28f6b1e81
/Regression.py
80a6a9b4700268017d60da355f90bb33f59a1811
[]
no_license
https://github.com/pankajkhatiwada/Sen_Machine_Deep_Learning
306ed6158707af9eea12bb18227e190cea7f1e2e
86f9d284d9538c6a4e845cdf991ecdea8dc0c748
refs/heads/master
2022-12-11T08:38:16.490245
2020-09-10T13:18:46
2020-09-10T13:18:46
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import pandas as pd import math import quandl df = quandl.get("WIKI/GOOGL") df = df[["Adj. Open", "Adj. High", "Adj. Low", "Adj. Close", "Adj. Volume"]] df["HCL_PCT"] = (df["Adj. High"] - df["Adj. Close"]) / df["Adj. Close"] * 100 df["PCT_change"] = (df["Adj. Close"] - df["Adj. Open"]) / df["Adj. Open"] * 100 df = df[["Adj. Close", "HCL_PCT", "PCT_change", "Adj. Volume"]] forecast_col = "Adj. Close" df.fillna(-99999, inplace=True) forecast_out = int(math.ceil(0.1*len(df))) df["label"] = df[forecast_col].shift(-forecast_col) print(df.head())
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py
1
Regression.py
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0.608696
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vandemaelefelix/sudoku_solver
1,065,151,892,797
f820cc020cdf338f9d09aff0bfe344db8389daae
682e2b36cda6df4e046b7006a9c5b731e2692d41
/python code/extract_sudoku.py
508618193118f387b04c7e8593f3843f06984d1a
[]
no_license
https://github.com/vandemaelefelix/sudoku_solver
410637dd681fe6506807fbf2a027cb9b48984222
862ed6881de6ad3993a102d639d7e4423f9734cc
refs/heads/master
2022-07-09T22:40:54.265131
2020-05-20T11:15:02
2020-05-20T11:15:02
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import cv2 import numpy as np import matplotlib.pyplot as plt import tkinter as tk from tkinter import filedialog import glob, os import time import operator def preprocess_image(image): # Make image grayscale to remove colors processed_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Blur image so lines stand out more processed_img = cv2.GaussianBlur(processed_img.copy(), (9, 9), 3) # Use tresholding to differentiate background and foreground processed_img = cv2.adaptiveThreshold(processed_img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2) # Invert black and white processed_img = cv2.bitwise_not(processed_img, processed_img) return processed_img def find_corners(image): contours, _ = cv2.findContours(image.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) contour = sorted(contours, key=cv2.contourArea, reverse=True)[0] bottom_right, _ = max(enumerate([pt[0][0] + pt[0][1] for pt in contour]), key=operator.itemgetter(1)) top_left, _ = min(enumerate([pt[0][0] + pt[0][1] for pt in contour]), key=operator.itemgetter(1)) bottom_left, _ = min(enumerate([pt[0][0] - pt[0][1] for pt in contour]), key=operator.itemgetter(1)) top_right, _ = max(enumerate([pt[0][0] - pt[0][1] for pt in contour]), key=operator.itemgetter(1)) return [contour[top_left][0], contour[top_right][0], contour[bottom_right][0], contour[bottom_left][0]] def four_point_transform(pts, image): width = image.shape[0] height = image.shape[1] # Corner coördinates in original image pts1 = np.float32(pts) # Destination coördinates pts2 = np.float32([[0, 0], [width, 0], [width, height], [0, height]]) # Apply Perspective Transform Algorithm matrix = cv2.getPerspectiveTransform(pts1, pts2) result = cv2.warpPerspective(image, matrix, (width, height)) return result root = tk.Tk() root.withdraw() file_paths = filedialog.askopenfilenames() for file in file_paths: # Read image of sudoku and resize image = cv2.imread(file) image = cv2.resize(image, (720, 720)) # print('Starting...') start = time.time() # Some preprocessing to eliminate everything but the sudoku from the picture preprocessed_image = preprocess_image(image) index = 0 contours, _ = cv2.findContours(preprocessed_image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) contour = sorted(contours, key=cv2.contourArea, reverse=True)[index] approx = cv2.approxPolyDP(contour, 0.01*cv2.arcLength(contour, True), True) print(approx) while len(approx) > 4: index += 1 contours, _ = cv2.findContours(preprocessed_image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) contour = sorted(contours, key=cv2.contourArea, reverse=True)[index] approx = cv2.approxPolyDP(contour, 0.01*cv2.arcLength(contour, True), True) cv2.drawContours(image, [contour], 0, (0, 255, 0), 5) corners = find_corners(preprocessed_image) result = four_point_transform(corners, image) cv2.imshow('sudoku', image) key = cv2.waitKey(0) if key == 27: break
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jtokaz/checkio-mission-univocalic-davasaan
5,299,989,646,147
e5992a4eaaa7cbed403c2cc3b03205548366f161
9b0eac9a0f264a1e4968cfd40f7daa203948dba3
/verification/tests.py
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[]
no_license
https://github.com/jtokaz/checkio-mission-univocalic-davasaan
bba35d00bd1b2b0d6f0f999d8bc7f984712481cb
997de1894e11f757cf1f68fbbb48f1d395e66878
refs/heads/master
2021-01-23T14:05:25.984848
2015-09-02T12:13:22
2015-09-02T12:13:22
41,778,812
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import random TESTS = {"Basic": [ {"input": 0, "answer": 0}, {"input": 9, "answer": 0}, {"input": 41, "answer": 4}, {"input": 65, "answer": 6}, {"input": 79, "answer": 7}, ]} for x in random.sample(range(0,1000),10): TESTS["Basic"].append({"input": x, "answer": x//10}) R = range(0,2000000001) for x in random.sample(range(0,2000000000),30): TESTS["Basic"].append({"input": x, "answer": x//10})
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rohit-devops-test/python_training
14,156,212,229,762
207033c22b1d59cce57db3ba689b36d00350040d
d01954425c95f59e96b0abae335e2c06ad400ae6
/day_01/labs/lab_04.py
ca2d2b9aff4c575d5a2e0dca01ffcb524c9e4915
[]
no_license
https://github.com/rohit-devops-test/python_training
2ec20f1a03ea8df75d4706b024670822232d4786
d65ebed4f2c7ef5724614a35bc111bdf5d473d26
refs/heads/master
2023-02-18T22:06:13.367733
2021-01-21T07:56:20
2021-01-21T07:56:20
291,370,991
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# Program to filter the 2 digit numbers whose sum of individual # digits is some value say 10 # [100 random numbers] # [filtered values] # 64 => 6 + 4 = 10 # 33 => X # 73 => & + 3 = 10 import random # Input RN = [] for i in range(100): RN.append(random.randint(10, 99)) print(RN) print('_'*60) # Process FN = [] for n in RN: if((n//10 + n%10) == 10): # Manjeeth FN.append(n) # Output print(FN)
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lab_04.py
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JoaoMFachinetto/servicedrivenapplication
19,207,093,785,393
99e733cbe67f78ed43329a1b9c13e802c71a66b7
8ca8fc63e9db0916a4fec4a54da9888ef9f2fc2e
/flaskr/modules/__init__.py
8025758ebe25a1be351535b970a2de8f7b499097
[]
no_license
https://github.com/JoaoMFachinetto/servicedrivenapplication
3d3465de64a3d118a31611d50dac61810d4ed21e
0b370166c4198bdb8a6d5b689b0da233cda5d6c4
refs/heads/master
2021-05-28T06:23:27.505496
2015-01-14T13:10:09
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null
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null
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import sys sys.path.append('/modules')
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py
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supernnova/SuperNNova
9,526,237,485,474
bd22ee37d99aeb642b0ab6399e9cddc87e628beb
13b7614d34150fcaa2f4cd7d379bf4769a2e4423
/supernnova/utils/experiment_settings.py
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permissive
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refs/heads/master
2023-06-21T21:56:07.052935
2023-06-13T09:22:53
2023-06-13T09:22:53
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MIT
false
2023-06-13T09:22:55
2019-04-04T10:08:22
2023-05-18T19:17:04
2023-06-13T09:22:54
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import os import json import h5py import itertools import numpy as np from pathlib import Path from collections import OrderedDict class ExperimentSettings: """Mother class to control experiment parameters This class is responsible for the following - Defining paths and model names - Choosing the device on which to run computations - Specifying all hyperparameters such as model configuration, datasets, features etc Args: cli_args (argparse.Namespace) command line arguments """ def __init__(self, cli_args): # Transfer attributes if isinstance(cli_args, dict): self.__dict__.update(cli_args) self.cli_args = cli_args else: self.__dict__.update(cli_args.__dict__) self.cli_args = cli_args.__dict__ self.device = "cpu" if self.use_cuda: self.device = "cuda" if self.model == "variational": self.weight_decay = self.weight_decay else: self.weight_decay = 0.0 # Load simulation and training settings and prepare directories if self.no_dump: pass else: self.setup_dir() # Set the database file names self.set_database_file_names() self.randomforest_features = self.get_randomforest_features() # Set the feature lists if "all_features" not in cli_args: self.set_feature_lists() self.overwrite = not self.no_overwrite # filter combination list_filters_combination = [] for i in range(1, len(self.list_filters) + 1): tmp = [ "".join(t) for t in list(itertools.combinations(self.list_filters, i)) ] list_filters_combination = list_filters_combination + tmp self.list_filters_combination = list_filters_combination self.set_randomforest_model_name() self.set_pytorch_model_name() # Get the feature normalization dict self.load_normalization() def get_randomforest_features(self): """Specify list of features to be used for RandomForest training""" features = [ "x1", "x1ERR", "c", "cERR", "mB", "mBERR", "x0", "x0ERR", # 'COV_x1_c', 'COV_x1_x0','COV_c_x0', 'NDOF', "FITCHI2", "m0obs_r", "m0obs_i", "m0obs_g", "m0obs_z", "em0obs_i", "em0obs_r", "em0obs_g", "em0obs_z", ] if self.redshift == "zpho": features += ["HOSTGAL_PHOTOZ", "HOSTGAL_PHOTOZ_ERR"] elif self.redshift == "zspe": features += ["HOSTGAL_SPECZ", "HOSTGAL_SPECZ_ERR"] return features def setup_dir(self): """Configure directories where data is read from or dumped to during the course of an experiment """ for path in [ # f"{self.raw_dir}", # f"{self.fits_dir}", f"{self.dump_dir}/explore", f"{self.dump_dir}/stats", f"{self.dump_dir}/figures", f"{self.dump_dir}/lightcurves", f"{self.dump_dir}/latex", f"{self.dump_dir}/processed", f"{self.dump_dir}/preprocessed", f"{self.dump_dir}/models", ]: setattr(self, Path(path).name + "_dir", path) Path(path).mkdir(exist_ok=True, parents=True) def set_pytorch_model_name(self): """Define the model name for all NN based classifiers""" name = f"{self.model}_S_{self.seed}_CLF_{self.nb_classes}" name += f"_R_{self.redshift}" name += f"_{self.source_data}_DF_{self.data_fraction}_N_{self.norm}" name += f"_{self.layer_type}_{self.hidden_dim}x{self.num_layers}" name += f"_{self.dropout}" name += f"_{self.batch_size}" name += f"_{self.bidirectional}" name += f"_{self.rnn_output_option}" if "bayesian" in self.model: name += ( f"_Bayes_{self.pi}_{self.log_sigma1}_{self.log_sigma2}" f"_{self.rho_scale_lower}_{self.rho_scale_upper}" f"_{self.log_sigma1_output}_{self.log_sigma2_output}" f"_{self.rho_scale_lower_output}_{self.rho_scale_upper_output}" ) if self.cyclic: name += "_C" if self.weight_decay > 0: name += f"_WD_{self.weight_decay}" self.pytorch_model_name = name self.rnn_dir = f"{self.models_dir}/{self.pytorch_model_name}" # deserializing numpy arrays to save as json d_tmp = {} for k, v in self.__dict__.items(): if isinstance(v, np.ndarray): v = v.tolist() d_tmp[k] = v if self.train_rnn: os.makedirs(self.rnn_dir, exist_ok=True) # Dump the command line arguments (for model restoration) with open(Path(self.rnn_dir) / "cli_args.json", "w") as f: json.dump(d_tmp, f, indent=4, sort_keys=True) def set_randomforest_model_name(self): """Define the model name for all RandomForest based classifiers""" name = f"randomforest_S_{self.seed}_CLF_{self.nb_classes}" name += f"_R_{self.redshift}" name += f"_{self.source_data}_DF_{self.data_fraction}_N_{self.norm}" self.randomforest_model_name = name self.rf_dir = f"{self.models_dir}/{self.randomforest_model_name}" if self.train_rf: os.makedirs(self.rf_dir, exist_ok=True) # Dump the command line arguments (for model restoration) with open(Path(self.rf_dir) / "cli_args.json", "w") as f: json.dump(self.cli_args, f, indent=4, sort_keys=True) return name def check_data_exists(self): """Utility to check the database has been built""" database_file = f"{self.processed_dir}/database.h5" assert os.path.isfile(database_file) def set_feature_lists(self): """Utility to define the features used to train NN=based models""" self.training_features_to_normalize = [ f"FLUXCAL_{f}" for f in self.list_filters ] self.training_features_to_normalize += [ f"FLUXCALERR_{f}" for f in self.list_filters ] self.training_features_to_normalize += ["delta_time"] if not self.data: # If the database has been created, add the list of all features with h5py.File(self.hdf5_file_name, "r") as hf: self.all_features = hf["features"][:].astype(str) self.non_redshift_features = [ f for f in self.all_features if "HOSTGAL" not in f ] # Optionally add redshift self.redshift_features = [] if self.redshift == "zpho": self.redshift_features = [ f for f in self.all_features if "HOSTGAL_PHOTOZ" in f ] elif self.redshift == "zspe": self.redshift_features = [ f for f in self.all_features if "HOSTGAL_SPECZ" in f ] self.training_features = ( self.non_redshift_features + self.redshift_features ) if self.additional_train_var: self.training_features += [ k for k in self.additional_train_var if k not in self.training_features ] def set_database_file_names(self): """Create a unique database name based on the dataset required by the settings """ out_file = f"{self.processed_dir}/database" self.pickle_file_name = out_file + ".pickle" self.hdf5_file_name = out_file + ".h5" def load_normalization(self): """Create an array holding the data-normalization parameters used to normalize certain features in the NN-based classification pipeline """ if not self.data: self.idx_features = [ i for (i, f) in enumerate(self.all_features) if f in self.training_features ] self.idx_specz = [ i for (i, f) in enumerate(self.training_features) if "HOSTGAL_SPECZ" in f ] self.idx_flux = [ i for (i, f) in enumerate(self.training_features) if "FLUXCAL_" in f ] self.idx_fluxerr = [ i for (i, f) in enumerate(self.training_features) if "FLUXCALERR_" in f ] self.idx_delta_time = [ i for (i, f) in enumerate(self.training_features) if "delta_time" in f ] self.idx_features_to_normalize = [ i for (i, f) in enumerate(self.all_features) if f in self.training_features_to_normalize ] self.d_feat_to_idx = {f: i for i, f in enumerate(self.all_features)} list_norm = [] with h5py.File(self.hdf5_file_name, "r") as hf: for f in self.training_features_to_normalize: if self.norm == "perfilter": minv = np.array(hf[f"normalizations/{f}/min"]) meanv = np.array(hf[f"normalizations/{f}/mean"]) stdv = np.array(hf[f"normalizations/{f}/std"]) list_norm.append([minv, meanv, stdv]) else: if "FLUX" in f: prefix = f.split("_")[0] minv = np.array(hf[f"normalizations_global/{prefix}/min"]) meanv = np.array(hf[f"normalizations_global/{prefix}/mean"]) stdv = np.array(hf[f"normalizations_global/{prefix}/std"]) else: minv = np.array(hf[f"normalizations/{f}/min"]) meanv = np.array(hf[f"normalizations/{f}/mean"]) stdv = np.array(hf[f"normalizations/{f}/std"]) list_norm.append([minv, meanv, stdv]) self.arr_norm = np.array(list_norm)
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i-DAT-Qualia/Card-Backend
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from django.core.management.base import BaseCommand, CommandError from cards.models import * import datetime, math, time from decimal import Decimal def updater(reader_id,start_date,end_date,reader_location_id): scans = Scan.objects.filter(readerLocation__reader__id = reader_id) scans = scans.filter(added__range=[start_date, end_date]) new_reader_location = ReaderLocation.objects.get(id=reader_location_id) for scan in scans: print scan scan.readerLocation = new_reader_location scan.save() print scan class Command(BaseCommand): help = 'Updates scans to the correct location' def handle(self, *args, **options): print "Updating scans" #Use this if the scheduler fails updater('1', '2014-03-21', '2014-03-24','2')
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Sparsh239/Python-Decision-Analysis
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/Decision_Tree_Analysis.py
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https://github.com/Sparsh239/Python-Decision-Analysis
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2020-03-20T23:45:20
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# -*- coding: utf-8 -*- """ Created on Tue Mar 3 17:59:17 2020 @author: skans """ # -*- coding: utf-8 -*- """ Created on Sat Feb 22 11:39:33 2020 @author: skans """ import json from operator import itemgetter class Node: def __init__(self,node_name,node_type,parent_name,cost,benefits, probability): self.node_type = node_type self.node_name = node_name self.parent_name = parent_name self.data = {'Cost': cost, 'Benefits': benefits, 'Probability': probability} self.branches = [] def printJSON(self): print( json.dumps(self, default=lambda o: o.__dict__, sort_keys=False, indent=4)) def toJSON(self): json_string = json.dumps(self, default=lambda o: o.__dict__, sort_keys=False, indent=4) return json.loads(json_string) def solve(self): if self.node_type == "Payoff": payoff = self.data['Benefits']-self.data['Cost'] self.data['Payoff'] = payoff return payoff elif self.node_type == "Chance": sum = 0 for child in self.branches: prob = child.data['Probability'] value = child.solve() final_value = prob*(value - self.data['Cost']) sum = sum + final_value self.data['EV'] = sum return sum else: maximization_list = [] for child in self.branches: value = child.solve() node_recognition ={'name':child.node_name,'value':value} maximization_list.append(node_recognition) print(maximization_list) sorted_list = sorted(maximization_list, key=itemgetter('value'), reverse = True) return sorted_list[0] def insert(self,parent_name, child_node): if self.node_name == None: try: raise KeyboardInterrupt finally: print("There is no node") elif self.node_name != parent_name: for child in self.branches: if child.node_name == parent_name: child.insert(child.node_name, child_node) else: self.branches.append(child_node) @staticmethod def read_json_formation_node(input_json_data): nodes_dict = {} for nodes in input_json_data: if nodes['parent_node'] == "": ## Basically trying to access the root node andI think we can have a better condition node_type = nodes['node_type'] node_name = nodes['node_name'] cost = nodes['data']['Cost'] benefits = nodes['data']['Benefits'] probability = nodes['data']['Probability'] parent_node = nodes['parent_node'] new_node = Node(node_name,node_type,parent_node, cost,benefits,probability) nodes_dict[node_name] = new_node else: node_type = nodes['node_type'] node_name = nodes['node_name'] cost = nodes['data']['Cost'] benefits = nodes['data']['Benefits'] probability = nodes['data']['Probability'] parent_node = nodes['parent_node'] new_node = Node(node_name,node_type,parent_node,cost,benefits,probability) if node_type == "Chance": node_data = new_node.data node_data['probability_checker'] = [] nodes_dict[node_name] = new_node else: nodes_dict[node_name] = new_node for decision_tree_node in nodes_dict.keys(): decision_tree_node_class = nodes_dict[decision_tree_node] if decision_tree_node_class.parent_name == "": root_node = decision_tree_node_class# It is a root node and that needs to be returned continue else: nodes_parent_name = decision_tree_node_class.parent_name parent_node_class = nodes_dict.get(nodes_parent_name) #The list includes parent_node, its parent_name if parent_node_class.node_type == "Chance": #Here we wil access the probability checker list data_info = decision_tree_node.data # Accessing the data indicator of the node probability_childnode = data_info['Probability'] # Data info is a dictionary and we will take the probability value probability_checker_list = parent_node_class.data['probability_checker'] # Accessig the probability checker list of the parent node if sum(probability_checker_list) > 1: raise NameError("""The probability sum is greater than 1, thus cant add a new node anmore """) else: probability_checker_list.append(probability_childnode) parent_node_class.branches.append(decision_tree_node_class) elif parent_node_class.node_type == "Payoff": raise NameError("We cant add a child to the parent node") else: parent_node_class.branches.append(childnode_with_node_parentnode[0]) return root_node # Question 1 # final_decision = Node("Computer System", "Final Decision", 0 , 0 , 0) # advanced_computer_system = Node("Advanced Computer System", "Chance",20,0, 0) # current_computer_system = Node("Current Computer System", "Payoff", 20, 30, 0 ) # high_prob_adc = Node("High Probability", "Payoff", 0, 60 ,0.70) # low_prob_adc = Node("Low Probability", "Payoff", 0,30,0.30) # advanced_computer_system.insert("Advanced Computer System", high_prob_adc) # advanced_computer_system.insert("Advanced Computer System", low_prob_adc) # final_decision.insert("Computer System",advanced_computer_system) # final_decision.insert("Computer System", current_computer_system) # print("The decision should be:",final_decision.solve()) # final_decision.printJSON() input_json_data = [ { "node_type":"Root|Choice", "node_name":"Computer System", "data":{ "Cost":0, "Benefits":0, "Probability":0 }, "parent_node":""}, { "node_type":"Chance", "node_name":"Advanced Computer System", "data":{ "Cost":20, "Benefits":0, "Probability":0 }, "parent_node":"Computer System"}, { "node_type":"Payoff", "node_name":"High Probability", "data":{ "Cost":0, "Benefits":60, "Probability":0.7 }, "parent_node":"Advanced Computer System"}, { "node_type":"Payoff", "node_name":"Low Probability", "data":{ "Cost":0, "Benefits":30, "Probability":0.3 }, "parent_node":"Advanced Computer System"}, { "node_type": "Payoff", "node_name": "Current Computer System", "data": { "Cost": 20, "Benefits": 30, "Probability": 0, "Payoff": 10 }, "parent_node":"Computer System"} ] decision_tree = Node.read_json_formation_node(input_json_data) decision_tree.printJSON() print(decision_tree.solve())
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lilium513/competition_programing
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refs/heads/master
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2019-07-31T18:22:31
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import itertools import math LIM = 50 def do(): ans = 0 A,B,C,D,E,F= list(map(int,input().split(" "))) waters = [] for i in range(31): for j in range(31): water = A*i* 100+B*j* 100 if water <= F and water != 0: waters.append(water) solts = [] for i in range(3000): for j in range(3000): solt = C * i + D * j if solt > E * F /100: break solts.append(solt) max_nodo = -1 ans_water = 0 ans_solt = 0 for solt in solts: for water in waters: if solt/(water/100) <= E: if (100 * solt)/(solt+water) >= max_nodo and water + solt <= F: max_nodo =(100 * solt)/(solt+water) ans_water = water + solt ans_solt = solt print(ans_water,ans_solt) if __name__ == "__main__": do()
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alexweav/Deep-Learning
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https://github.com/alexweav/Deep-Learning
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refs/heads/master
2016-09-14T12:43:58.432584
2016-05-22T23:19:54
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# -*- coding: utf-8 -*- """ Created on Fri May 06 17:56:07 2016 @author: Alexander Weaver """ import numpy as np import LearnyMcLearnface as lml def main(): d = lml.layers.DropoutLayer(10, 1) opts = { 'input_dim' : 700, 'init_scheme' : 'xavier' } nn = lml.NeuralNetwork(opts) nn.add_layer('Affine', {'neurons':500}) nn.add_layer('PReLU', {}) nn.add_layer('Dropout', {'dropout_param':0.9}) nn.add_layer('Affine', {'neurons':10}) nn.add_layer('SoftmaxLoss', {}) test_data = np.random.randn(100, 700) test_y = np.random.randint(1, 10, 100) d.forward_train(test_data) data = { 'X_train' : test_data, 'y_train' : test_y, 'X_val' : test_data, 'y_val' : test_y } opts = { 'update_options' : {'update_rule' : 'sgd', 'learning_rate' : 1}, 'reg_param' : 0, 'num_epochs' : 6 } trainer = lml.Trainer(nn, data, opts) accuracy = trainer.accuracy(test_data, test_y) print('Initial model accuracy:', accuracy) trainer.train() accuracy = trainer.accuracy(test_data, test_y) print('Final model accuracy:', accuracy) main()
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stuartarchibald/awkward-1.0
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/tests/test_0331-pandas-indexedarray.py
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refs/heads/master
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# BSD 3-Clause License; see https://github.com/scikit-hep/awkward-1.0/blob/master/LICENSE from __future__ import absolute_import import sys import pytest import numpy import awkward1 pandas = pytest.importorskip("pandas") def test(): simple = awkward1.Array([0.0, 1.1, 2.2, 3.3, 4.4, 5.5]) assert awkward1.to_pandas(simple)["values"].values.tolist() == [0.0, 1.1, 2.2, 3.3, 4.4, 5.5] index = awkward1.layout.Index64(numpy.array([3, 3, 1, 5], dtype=numpy.int64)) indexed = awkward1.Array(awkward1.layout.IndexedArray64(index, simple.layout)) assert indexed.tolist() == [3.3, 3.3, 1.1, 5.5] assert awkward1.to_pandas(indexed)["values"].values.tolist() == [3.3, 3.3, 1.1, 5.5] tuples = awkward1.Array(awkward1.layout.RecordArray([simple.layout, simple.layout])) assert awkward1.to_pandas(tuples)["1"].values.tolist() == [0.0, 1.1, 2.2, 3.3, 4.4, 5.5] offsets = awkward1.layout.Index64(numpy.array([0, 1, 1, 3, 4], dtype=numpy.int64)) nested = awkward1.Array(awkward1.layout.ListOffsetArray64(offsets, indexed.layout)) assert awkward1.to_pandas(nested)["values"].values.tolist() == [3.3, 3.3, 1.1, 5.5] offsets2 = awkward1.layout.Index64(numpy.array([0, 3, 3, 4, 6], dtype=numpy.int64)) nested2 = awkward1.Array(awkward1.layout.ListOffsetArray64(offsets2, tuples.layout)) assert awkward1.to_pandas(nested2)["1"].values.tolist() == [0.0, 1.1, 2.2, 3.3, 4.4, 5.5] recrec = awkward1.Array([{"x": {"y": 1}}, {"x": {"y": 2}}, {"x": {"y": 3}}]) assert awkward1.to_pandas(recrec)["x", "y"].values.tolist() == [1, 2, 3] recrec2 = awkward1.Array([{"x": {"a": 1, "b": 2}, "y": {"c": 3, "d": 4}}, {"x": {"a": 10, "b": 20}, "y": {"c": 30, "d": 40}}]) assert awkward1.to_pandas(recrec2)["y", "c"].values.tolist() == [3, 30] recrec3 = awkward1.Array([{"x": 1, "y": {"c": 3, "d": 4}}, {"x": 10, "y": {"c": 30, "d": 40}}]) assert awkward1.to_pandas(recrec3)["y", "c"].values.tolist() == [3, 30] tuptup = awkward1.Array([(1.0, (1.1, 1.2)), (2.0, (2.1, 2.2)), (3.0, (3.1, 3.2))]) assert awkward1.to_pandas(tuptup)["1", "0"].values.tolist() == [1.1, 2.1, 3.1] recrec4 = awkward1.Array([[{"x": 1, "y": {"c": 3, "d": 4}}], [{"x": 10, "y": {"c": 30, "d": 40}}]]) assert awkward1.to_pandas(recrec4)["y", "c"].values.tolist() == [3, 30] def test_broken(): ex = awkward1.Array([[1, 2, 3], [], [4, 5]]) p4 = awkward1.zip({"x": ex}) p4c = awkward1.cartesian({"a": p4, "b": p4}) df = awkward1.to_pandas(p4c) assert df["a", "x"].values.tolist() == [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 5, 5] assert df["b", "x"].values.tolist() == [1, 2, 3, 1, 2, 3, 1, 2, 3, 4, 5, 4, 5] def test_union_to_record(): recordarray1 = awkward1.Array([{"x": 1, "y": 1.1}, {"x": 3, "y": 3.3}]).layout recordarray2 = awkward1.Array([{"y": 2.2, "z": 999}]).layout tags = awkward1.layout.Index8(numpy.array([0, 1, 0], dtype=numpy.int8)) index = awkward1.layout.Index64(numpy.array([0, 0, 1], dtype=numpy.int64)) unionarray = awkward1.layout.UnionArray8_64(tags, index, [recordarray1, recordarray2]) assert awkward1.to_list(unionarray) == [{"x": 1, "y": 1.1}, {"y": 2.2, "z": 999}, {"x": 3, "y": 3.3}] converted = awkward1._util.union_to_record(unionarray, "values") assert isinstance(converted, awkward1.layout.RecordArray) assert awkward1.to_list(converted) == [{"x": 1, "y": 1.1, "z": None}, {"x": None, "y": 2.2, "z": 999}, {"x": 3, "y": 3.3, "z": None}] otherarray = awkward1.Array(["one", "two"]).layout tags2 = awkward1.layout.Index8(numpy.array([0, 2, 1, 2, 0], dtype=numpy.int8)) index2 = awkward1.layout.Index64(numpy.array([0, 0, 0, 1, 1], dtype=numpy.int64)) unionarray2 = awkward1.layout.UnionArray8_64(tags2, index2, [recordarray1, recordarray2, otherarray]) assert awkward1.to_list(unionarray2) == [{"x": 1, "y": 1.1}, "one", {"y": 2.2, "z": 999}, "two", {"x": 3, "y": 3.3}] converted2 = awkward1._util.union_to_record(unionarray2, "values") assert isinstance(converted2, awkward1.layout.RecordArray) assert awkward1.to_list(converted2) == [{"x": 1, "y": 1.1, "z": None, "values": None}, {"x": None, "y": None, "z": None, "values": "one"}, {"x": None, "y": 2.2, "z": 999, "values": None}, {"x": None, "y": None, "z": None, "values": "two"}, {"x": 3, "y": 3.3, "z": None, "values": None}] df_unionarray = awkward1.to_pandas(unionarray) numpy.testing.assert_array_equal(df_unionarray["x"].values, numpy.array([1, numpy.nan, 3])) numpy.testing.assert_array_equal(df_unionarray["y"].values, numpy.array([1.1, 2.2, 3.3])) numpy.testing.assert_array_equal(df_unionarray["z"].values, numpy.array([numpy.nan, 999, numpy.nan])) df_unionarray2 = awkward1.to_pandas(unionarray2) numpy.testing.assert_array_equal(df_unionarray2["x"].values, [1, numpy.nan, numpy.nan, numpy.nan, 3]) numpy.testing.assert_array_equal(df_unionarray2["y"].values, [1.1, numpy.nan, 2.2, numpy.nan, 3.3]) numpy.testing.assert_array_equal(df_unionarray2["z"].values, [numpy.nan, numpy.nan, 999, numpy.nan, numpy.nan]) numpy.testing.assert_array_equal(df_unionarray2["values"].values, ["nan", "one", "nan", "two", "nan"])
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elvincalex/Assignment1
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/main.py
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[]
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refs/heads/master
2023-07-28T17:56:30.436422
2021-09-11T10:20:10
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import random import sys history = {0:''} choices_dict = {"r": "Rock", "p": "Paper", "s": "Scissors"} beats = {'r': 'p', 's': 'r', 'p': 's'} choices = ["r", "p", "s"] def play_again(): while True: print("\n\nPlay another game?\nEnter y for Yes or n for No") print("\n--------------------------------") play_again_user_response = input("...") if (play_again_user_response == "y") or (play_again_user_response == "Y"): num_games_func() break if (play_again_user_response == "n") or (play_again_user_response == "N"): print("Are you sure you want to quit?\nEnter y to confirm") play_again_user_response_confirm = input("...") if (play_again_user_response_confirm == "y") or (play_again_user_response_confirm == "Y"): sys.exit() continue print("Error: Invalid input\nPlease enter y or n") def result(initial_rounds_num, final_scores): print("\n--------------------------------") if final_scores[0] > final_scores[1]: print("Player Wins with score:"+ str(final_scores[0]) + "/" + str(initial_rounds_num)+"\nComputer lost with a score of " + str(final_scores[1]) +"/"+str(initial_rounds_num) ) elif (final_scores[0] < final_scores[1]): print("Computer Wins with a score of " + str(final_scores[1]) +"/"+str(initial_rounds_num)+"\nPlayer lost with score:"+ str(final_scores[0]) + "/" + str(initial_rounds_num)) else: print("Player and Computer have drawn at a score of " + str(final_scores[0]) + "/" + str( initial_rounds_num)) while True: print("Enter the round which you need more information, to exit enter 99") round_check = input("..") if (round_check.isdecimal()): round_check = int(round_check) if(round_check==99): play_again() elif (round_check>0)or(round_check<=initial_rounds_num): uc = history[round_check] uc =list(uc) ucc=uc[0][1] cc=uc[0][0] outc=uc[0][2] print("Player choice:"+ucc+"\nComputer Choice:"+cc+"\n"+outc+" the round\n") def game_run(rounds, scores): initial_rounds_num = rounds decrease_rounds = True i = 0 while True: comp_choice = random.choice(choices) user_input = input("...") if (user_input not in choices): print("Enter a valid input") decrease_rounds = False else: decrease_rounds = True if (comp_choice == user_input): i = i + 1 history[i] = {(choices_dict[comp_choice],choices_dict[user_input],'Tied')} elif comp_choice == beats[user_input]: i = i + 1 scores[1] += 1 history[i] = {(choices_dict[comp_choice], choices_dict[user_input],'Computer wins')} else: i = i + 1 scores[0] += 1 history[i] = {(choices_dict[comp_choice], choices_dict[user_input],"Player wins")} if (rounds == 1 and decrease_rounds): final_scores = scores result(initial_rounds_num, final_scores) if decrease_rounds: rounds -= 1 def start(rounds, scores): print( "\n-------------------\nBest of " + str(rounds) + ":\n-------------------\nEnter:\n\nr for Rock\np for Paper\ns for Scissors") game_run(rounds, scores) def num_games_func(): print("This is a new game :\n") scores = [0, 0] while True: num_games_input = input("...") if (num_games_input.isdecimal()): num_games_input = int(num_games_input) if (num_games_input == 10): rounds_remaining = num_games_input start(num_games_input, scores) elif (num_games_input<10) or (num_games_input>10): rounds_remaining = num_games_input start(num_games_input, scores) else: print("Error: Invalid input\nEnter a valid input") else: print("Error: Invalid input\nEnter a valid input") print("\nEnter the number of rounds for Rock, Paper, Scissors\n") num_games_func()
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harimohanraj/neural-network-reference
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/networks.py
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# -*- coding: utf-8 -*- """ Created on Thu Jan 14 17:37:32 2016 @author: Hari """ import numpy as np from layers import * from optimizations import * from costs import * class Network(): def __init__(self, name, optimizer, cost): self.name = name self.layers = [] self.optimizer = optimizer self.cost = cost self.weights = None self.biases = None # self.dropout = True # self.regularization_type = "L2" def add_layer(self, layer): self.layers.append(layer) def generate_weights_and_biases(self, training_x): input_size = training_x[0].shape[0] layer_sizes = [input_size] + [layer.size for layer in self.layers] weights = [np.random.randn(y, x) / np.sqrt(x) \ for x, y in zip(layer_sizes, layer_sizes[1:])] biases = [np.random.randn(1) for y in layer_sizes[1:]] return weights, biases def backpropagation(self, x, y): # 0. Initialize empty arrays to hold gradients gradients_at_w = [np.zeros(layer.size) for layer in self.layers] gradients_at_b = [np.zeros(1) for layer in self.layers] # 1. Initialize first activation as training example activations = [] weighted_inputs = [] activation = x # 2. Feedforward for layer in self.layers: weighted_input = np.dot(layer.weights, activation) weighted_inputs.append(weighted_input) activation = layer.function(weighted_input) activations.append(activation) # 3. Compute output "error" (node delta) and gradients at output sigma_prime_of_wi = self.layers[-1].derivative(weighted_inputs[-1]) error_delta = self.cost.derivative(y, activations[-1]) * sigma_prime_of_wi gradients_at_w[-1] = np.dot(error_delta, activations[-2].T) gradients_at_b[-1] = error_delta # 4. Backpropagate error (node deltas) for i in range(2,len(self.layers)): sigma_prime_of_wi = self.layers[-i].derivative(weighted_inputs[-i]) error_delta = np.dot(self.layers[-i+1].weights.T, error_delta) * sigma_prime_of_wi gradients_at_w[-i] = np.dot(error_delta, activations[-i-1].T) gradients_at_b[-i]= error_delta # 5. Output gradient return gradients_at_w, gradients_at_b def train(self, training_x, training_y, learning_rate, \ batch_size=10, iterations=5000): # initialize weights self.weights, self.biases = self.generate_weights_and_biases(training_x) # stochastic gradient descent for i in range(0,iterations, batch_size): # shuffle data and create batch data = np.concatenate((training_x, training_y), axis=1) np.random.shuffle(data) batch = np.data[i::batch_size] grad_b = [np.zeros(b.shape) for b in self.biases] grad_w = [np.zeros(w.shape) for w in self.weights] for i in batch: x = data[i, :len(training_x)] y = data[i, len(training_y):] delta_w, delta_b = self.backpropagation(x, y) grad_w = [nw+dnw for nw, dnw in zip(grad_w, delta_w)] grad_b = [nb+dnb for nb, dnb in zip(grad_b, delta_b)] # update weights and biases self.weights = [w-learning_rate*grad for w,grad in zip(self.weights, grad_w)] self.biases = [b-learning_rate*grad for b,grad in zip(self.biases, grad_b)] # calculate training cost, etc for epoch def __str__(self): opt_method = "Optimizer: " + self.optimizer + "\n" cost_func = "Cost Function: " + self.cost.__class__.__name__ + "\n" architecture = "Input Layer => " + " => ".join([layer.name for layer in self.layers]) + "\n\n" layer_list = "\n\n".join([str(layer) for layer in self.layers]) + "\n\n" return "~" + self.name + "~" + "\n" + opt_method + cost_func + "\n" + \ architecture + layer_list # tests x = np.array([[1,0],[1,1],[0,1],[0,0]]) y = np.array([[1,0],[1,1],[0,1],[1,1]]) network = Network(name="Test Network", \ optimizer="SGD", \ cost=MeanSquaredError()) hidden_layer1 = sigmoidLayer(3, "Hidden Layer 1") output_layer = sigmoidLayer(2, "Output Layer") network.add_layer(hidden_layer1) network.add_layer(output_layer) network.train(x, y)
UTF-8
Python
false
false
4,662
py
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networks.py
4
0.556628
0.5429
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37.520661
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kp258/gitrepo
17,179,886,986
51b1896030fe9ede6b386aaf632c736ae1c459e6
9432e9c485c2055b0bd0559e3ef8ffee2e3af9d0
/package/alerts.py
15e4d0dffee265468914db1199a0271c19af4344
[]
no_license
https://github.com/kp258/gitrepo
4b9abd62bcf1f1af22e6bda0c29127b1e2a7d1d8
caa4cbf52489ff957ebbb4477aa23882cda00b62
refs/heads/master
2016-09-19T08:12:16.137000
2016-09-08T12:28:03
2016-09-08T12:28:03
67,767,036
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def get_alert_level(obj_package): """ determines the alert level basis the current location vs the destination of the given package if the package is at its destination, the level is L1, else, L2 """ if (obj_package.get('cn', '') in obj_package.cs.get('sl', '') and (obj_package.get('cn', None))): level = u'L1' else: level = u'L2' return level
UTF-8
Python
false
false
411
py
1,103
alerts.py
744
0.588808
0.579075
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13
30.615385
76
aarjavjain1/Team-Website
5,592,047,459,049
6f957e9579f63f9fab43897b00a60ab74248062c
4754e61096d32950a06be9c77f44bb15fca43d68
/WebApp/urls.py
26217e172dbb7adddaaa086501bcf3a72b28df48
[ "Apache-2.0" ]
permissive
https://github.com/aarjavjain1/Team-Website
c94e173de68a43cd8986534d8bf9b351f762dd34
e74e10a95d1ffad89eaebb8cc530f3df1280e691
refs/heads/master
2020-06-10T03:17:58.222249
2019-06-24T19:36:16
2019-06-24T19:36:16
193,565,760
0
0
Apache-2.0
true
2019-06-24T19:23:44
2019-06-24T19:23:43
2019-06-23T22:16:44
2019-06-23T22:16:42
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from django.urls import path from . import views app_name = 'webapp' urlpatterns = [ path('', views.index, name='index'), path('timeline/', views.timeline, name='timeline'), ]
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dmarcos1982/Felicidades_Miguel_Telegram
2,353,642,104,277
4e007161004928b5194676160cef5690ad02dce3
8828c6ea2cbc7ccbb3210a38ea13855f016b1f49
/game_NoToken.py
85592b40aceb5dbb5576cf87ad4f45accf4eb92a
[]
no_license
https://github.com/dmarcos1982/Felicidades_Miguel_Telegram
4d15855b39ed57e82cc87d6dc128bc3f0a41e5a7
f44321737032640287f30cb465e309954c494668
refs/heads/master
2020-03-19T16:32:19.156314
2018-06-10T08:46:37
2018-06-10T08:46:37
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#!/usr/bin/python2.7 # -*- coding: utf-8 -*- # Importamos el mapa import World.map as map # Librería de la API del bot import telebot # Tipos para la API del bot from telebot import types # Librería para hacer que el programa que controla el bot no se acabe. import time # Libreria para los temporizadores from threading import Timer # Libreria para los numeros aleatorios from random import randint # Librerias para correr el bot en threads import threading from time import sleep # Token y otros parametros del bot TOKEN = '' BOT_INTERVAL = 3 BOT_TIMEOUT = 30 # Creamos la clase del jugador class player(): def __init__(self): self.location = 'a0' self.gameOver = False self.inventory = [] self.examenStarted = False self.bjPlayedRounds = 0 self.s3PlayedRounds = 0 self.trasteroQuestionsAnswered = 0 self.testarrosaSung = 0 self.diabolicHen = 0 self.falloAlfonso8 = 0 # Instanciamos el jugador myPlayer = player() # Funcion para inicializar el juego def game_initialize(): myPlayer.location = 'a0' myPlayer.gameOver = False myPlayer.inventory = [] myPlayer.examenStarted = False myPlayer.bjPlayedRounds = 0 myPlayer.s3PlayedRounds = 0 myPlayer.trasteroQuestionsAnswered = 0 myPlayer.testarrosaSung = 0 myPlayer.diabolicHen = 0 myPlayer.falloAlfonso8 = 0 # Creamos una lista de articulos para manejar los objetos articleList = ['el', 'la', 'los', 'las', 'un', 'una'] # Creamos el objeto de nuestro bot. bot = telebot.TeleBot(TOKEN) def bot_polling(): #global bot #Keep the bot object as global variable if needed print("Starting bot polling now") while True: try: print("New @jblasbot instance started") bot = telebot.TeleBot(TOKEN) #Generate new bot instance bot.set_update_listener(listener) bot.polling(none_stop=True, interval=BOT_INTERVAL, timeout=BOT_TIMEOUT) except Exception as ex: #Error in polling print("Bot polling failed, restarting in {}sec. Error:\n{}".format(BOT_TIMEOUT, ex)) bot.stop_polling() sleep(BOT_TIMEOUT) else: #Clean exit bot.stop_polling() print("Bot polling loop finished") break #End loop # Texto de introduccion display_intro = """Has tenido la inmensa suerte de encontrar a tu media naranja y, además, esta chica que te comprende, que llena tus noches y tus sueños, ha accedido a casarse contigo.\n Organizar la boda no ha sido tan fácil como te imaginabas en un principio, pero todas las dificultades ya han sido vencidas y el gran día ha llegado.\n Esta mañana Estefanía te ha dado el 'Sí, quiero' en su resplandeciente vestido blanco, los ojos le brillaban como nunca y tú… Tú estás en una nube de la que no te quieres bajar. Estás en el salón de tu boda, ha empezado la barra libre hace un ratito y todos tus invitados bailan o, al menos, intentan mover la cabeza y los dedos de los pies al ritmo de la música mientras consumen con fruición gin-tonics, whysky-colas y vodka-naranjas.\n Tu deslumbrante esposa está dándolo todo en la pista con sus amigas mientras las inconfundibles voces de 'Siempre Así' a todo volumen hacen casi imposible el mantener una conversación. Así, no has entendido una sola palabra de las que te ha dicho un joven, más o menos de la edad de Estefanía, al tiempo que te encasquetaba un voluminoso paquete envuelto en papel de regalo. No tienes ni idea de quién era el chico, pero bueno, te pasa con muchos de los invitados, así que das por supuesto que se trata de algún primo lejano de tu mujer.\n Como la canción de 'Siempre Así' es larguísima y tú no tienes nada mejor que hacer en ese momento, empiezas a romper el envoltorio del regalo que acabas de recibir, mientras recuerdas la sospechosa mirada de la persona que te lo ha dado… No sabes decir el qué pero hay algo raro en ese chico… Tienes ante ti una caja de cartón normal y corriente. Levantas la tapa y…\n\n""" # Funcion para mostrar la ayuda def display_help(m): bot.send_message(m.chat.id, '\n- Siempre que quieras indicar algo con un verbo, usa el *verbo en infinitivo*.\n- Las opciones para moverte son los 4 puntos cardinales.\n- Hay preguntas que se pueden responder con *sí* o *no*.', parse_mode='Markdown') # Funcion para mostrar nombre de la habitacion y descripcion def introduce_room(m): bot.send_message(m.chat.id, '\n' + map.zonemap[myPlayer.location]['NAME'], parse_mode='Markdown') time.sleep(1) bot.send_message(m.chat.id, '\n' + map.zonemap[myPlayer.location]['DESCRIPTION'], parse_mode='Markdown') # Teleco # PROBABLEMENTE MODIFIQUE EL QUE SE PUEDA RESPONDER DIRECTAMENTE CON EL VALOR, SIN TENER QUE PONER RESPONDER, CONTESTAR O ESCRIBIR ANTES def room_examen(m): # Si expira el timer, vamos al Alfonso VIII def timeout(): if myPlayer.examenStarted is True: tExamen.cancel() myPlayer.examenStarted = False bot.send_message(m.chat.id, "El tiempo del examen ha expirado. Juan Blas va recogiendo los exámenes por los pupitres. Cuando llega a tu puesto, tú te aferras al folio porque no lo has rellenado y realmente quieres hacer ese examen y terminar la carrera de una puñetera vez. El breve tira y afloja es vencido por Juan Blas, que tira con decisión de la hoja de papel y te arranca el exámen de las manos. ¿Y qué vas a hacer ahora? Como no tienes respuesta a esa pregunta, decides ir a un lugar en el que te sientes seguro...") map.zonemap[myPlayer.location]['VISITED'] = True myPlayer.location = 'z0' time.sleep(2) introduce_room(m) # Inicializamos el timer del examen tExamen = Timer(300.0, timeout) # Solo iniciamos el timer cuando se inicia el juego if myPlayer.examenStarted is False: myPlayer.examenStarted = True tExamen.start() # Abrimos la imagen que contiene el problema del examen cuadripolo = open('Recursos/cuadripolo.png', 'rb') # Comandos que entendemos en esta habitacion acceptableExamenActions = ['coger', 'responder', 'contestar', 'escribir', 'decir'] # Cogemos el texto que nos ha enviado y lo dividimos en palabras mSplit = m.text.lower().split() # Si la primera palabra no esta en la lista de los comandos que entendemos, no hacemos nada if mSplit[0] not in acceptableExamenActions: bot.send_message(m.chat.id, "No entiendo eso que dices") # Si quiere coger algo elif mSplit[0] == "coger": # Si quiere coger el boli if "boli" in mSplit: # Si aun no ha cogido el boli if 'boli' not in myPlayer.inventory: # Añadimos el boli al inventario myPlayer.inventory.append("boli") # Le mostramos la pregunta del examen bot.send_message(m.chat.id, "Con mano temblorosa coges el bolígrafo y lees el enunciado de la única pregunta que hay en el folio: Una instalación de telefonía está compuesta por un cuadripolo transmisor, un generador y un receptor. Determinar la potencia máxima que puede recibir el receptor.") bot.send_photo(m.chat.id, cuadripolo) else: bot.send_message(m.chat.id, "Ya tienes el boli") # Si lo que quiere coger no existe, no hacemos nada else: bot.send_message(m.chat.id, "No veo eso que dices") # Si quiere responder el examen elif ((mSplit[0] == "responder") or (mSplit[0] == "contestar") or (mSplit[0] == "escribir")): # Si no ha cogido el boli previamente, no puede hacerlo if 'boli' not in myPlayer.inventory: bot.send_message(m.chat.id, "No se como vas a hacer eso sin el boli") else: # Si responde correctamente, paramos el temporizador y pasamos al Tenere if ((mSplit[1] == '49uw') or ((mSplit[1] == '49') and (mSplit[2] == 'uw'))): tExamen.cancel() myPlayer.examenStarted = False bot.send_message(m.chat.id, "¡Enhorabuena, Miguel! Has terminado la carrera y sales a celebrarlo con tus amigos (bueno, sabes que tienes que intentar volver a tu boda de alguna manera, pero ¿a quién no le apetece reverdecer viejos laureles de vez en cuando?).") map.zonemap[myPlayer.location]['VISITED'] = True myPlayer.location = 'b3' time.sleep(2) introduce_room(m) # Si responde incorrectamente, muere miserablemente else: bot.send_message(m.chat.id, 'Tu respuesta es tan absurda que cuando Juan Blas corrige el examen monta en cólera. En tantos años de exposición a ondas electromagnéticas, ha desarrollado superpoderes como los de Hulk y el mal humor le hace multiplicar su tamaño por 10000 y su fuerza por 1E-06. El edificio de teleco revienta con su crecimiento y toda Castilla y León desaparece con el primer paso que da. Con el segundo paso hace desestabilizar el eje de La Tierra, que interrumpe su rotación y se sale de su órbita. Aún mucho antes de que el planeta azul llegue a chocar contra el rojo, la vida en La Tierra ya se ha hecho imposible debido a los desórdenes climatológicos. Todos los seres vivos, incluidos Estefanía y tú, *desaparecen miserablemente*.\n\nIntroduce _/start_ para iniciar de nuevo el juego.', parse_mode='Markdown') game_initialize() # Si quiere hablar con Juan Blas elif mSplit[0] == "decir": # Si decide no presentar, paramos el temporizador y vamos al Alfonso VIII if ((mSplit[1] == 'no') and (mSplit[2] == 'presentar')): tExamen.cancel() myPlayer.examenStarted = False bot.send_message(m.chat.id, "Entregas el exámen con una mezcla entre alivio y tristeza por no haber sido capaz de completarlo. Con la mente hecha un lío decides vagar sin rumbo fijo...") map.zonemap[myPlayer.location]['VISITED'] = True myPlayer.location = 'z0' time.sleep(2) introduce_room(m) # Cualquier otra cosa que diga, Juan Blas le dice que solo hay una cosa que entiende else: bot.send_message(m.chat.id, "Juan Blas te mira con mala cara y te dice: _Si quieres irte y 'no presentar' no tienes mas que decirlo_", parse_mode='Markdown') # Alfonso VIII def room_alfonso8(m): # Comandos que entendemos en esta habitacion acceptableAlfonso8Actions = ['poner'] # Cogemos el texto que nos ha enviado y lo dividimos en palabras mSplit = m.text.lower().split() # Marcamos la habitacion como visitada map.zonemap[myPlayer.location]['VISITED'] = True # Si la primera palabra no esta en la lista de los comandos que entendemos, no hacemos nada if mSplit[0] not in acceptableAlfonso8Actions: if 0 <= myPlayer.falloAlfonso8 <= 2: bot.send_message(m.chat.id, "No entiendo eso que dices.") sleep(2) bot.send_message(m.chat.id, "Vamos, que ya venden turrones en los supermercados.") myPlayer.falloAlfonso8 += 1 else: bot.send_message(m.chat.id, 'El director te arrea el collejazo del milenio, el cual te deja sin sentido. Nunca lo vuelves a recuperar y *mueres miserablemente*.\n\nIntroduce _/start_ para iniciar de nuevo el juego.', parse_mode='Markdown') game_initialize() else: if ((('decoracion' in mSplit) or (u'decoración' in mSplit)) and (('navidad' in mSplit) or (u'navideña' in mSplit))): bot.send_message(m.chat.id, 'El suelo de la Alfonso VIII siempre ha tenido fama por su perpetuo lustre. La escalera a la que te has subido para poner guirnaldas en el techo resbala, se abre como una cáscara de plátano y caes al suelo. El golpe te *mata miserablemente*.\n\nIntroduce _/start_ para iniciar de nuevo el juego.', parse_mode='Markdown') game_initialize() else: bot.send_message(m.chat.id, 'El director te arrea el collejazo del milenio, el cual te deja sin sentido. Nunca lo vuelves a recuperar y *mueres miserablemente*.\n\nIntroduce _/start_ para iniciar de nuevo el juego.', parse_mode='Markdown') game_initialize() # Tenere def room_tenere(m): # Comandos que entendemos en esta habitacion acceptableTenereActions = ['si', u'sí', 'no', 'plantarme', 'plantarse', 'pedir'] # Cogemos el texto que nos ha enviado y lo dividimos en palabras mSplit = m.text.lower().split() # Bucle del blackjack def blackjack_loop(): rNumber = randint(0, 9) if 0 <= rNumber <= 4: bot.send_message(m.chat.id, 'El crupier reparte de nuevo. Tienes una buena jugada en la mesa, así que pides otra carta. Pero desde luego hoy no es tu noche y te pasas de nuevo. Pero este estúpido juego no va a poder contigo, ¿o sí? *¿Deseas jugar otra partida?*', parse_mode='Markdown') elif 5 <= rNumber <= 9: bot.send_message(m.chat.id, 'Esta vez decides ser más conservador y te quedas cerca del BlackJack. Cuando el crupier levanta su carta observas con incredulidad cómo la suma de sus cartas es 21. Encima pone una sonrisilla de suficiencia que le borrarías de la cara con un guantazo. El crupier recoge la mesa y te dice: *¿Deseas jugar otra partida? Esto al final es cuestión de estadística...*', parse_mode='Markdown') # Marcamos la habitacion como visitada map.zonemap[myPlayer.location]['VISITED'] = True # Si la primera palabra no esta en la lista de los comandos que entendemos, no hacemos nada if mSplit[0] not in acceptableTenereActions: bot.send_message(m.chat.id, "No entiendo eso que dices") # La primera vez que responde solo puede hacerlo con pedir carta, plantarme o plantarse elif myPlayer.bjPlayedRounds == 0: # Si se planta vamos al Alfonso VIII if ((mSplit[0] == 'plantarme') or (mSplit[0] == 'plantarse')): bot.send_message(m.chat.id, "No estás para jueguecitos, así que te plantas y que sea lo que dios quiera. Evidentemente pierdes, pero casi mejor, ¿no? Asqueado de cómo se está desarrollando el día en esta realidad paralela decides ir a un lugar seguro y reconfortante.") map.zonemap[myPlayer.location]['VISITED'] = True myPlayer.location = 'z0' time.sleep(2) introduce_room(m) elif ((mSplit[0] == 'si') or (mSplit[0] == u'sí')): bot.send_message(m.chat.id, '¿Sí qué?') elif mSplit[0] == 'no': bot.send_message(m.chat.id, '¿No qué?') elif mSplit[0] == 'pedir': if "carta" in mSplit: bot.send_message (m.chat.id, 'Parece que la suerte del exámen no te ha acompañado ahora, sacas una figura y te pasas. El crupier te desea mejor suerte la próxima vez y antes de volver a repartir te pregunta: *¿Deseas jugar otra partida?*', parse_mode='Markdown') myPlayer.bjPlayedRounds += 1 else: bot.send_message(m.chat.id, "No veo eso que dices") else: bot.send_message(m.chat.id, "No entiendo eso que dices") elif 1 <= myPlayer.bjPlayedRounds <= 3: # Si decide jugar, iniciamos el bucle del blackjack if ((mSplit[0] == 'si') or (mSplit[0] == u'sí')): blackjack_loop() myPlayer.bjPlayedRounds += 1 # Si decide no jugar vamos al Alfonso VIII elif mSplit[0] == 'no': bot.send_message(m.chat.id, "Parece que el croupier está riéndose de tí, o haciendo trampas (o ambas cosas), así que decides que ya es hora de irte a descansar...") map.zonemap[myPlayer.location]['VISITED'] = True myPlayer.location = 'z0' time.sleep(2) introduce_room(m) else: bot.send_message(m.chat.id, "No entiendo eso que dices") # Una vez llega al numero de partidas indicado puede elegir el premio elif myPlayer.bjPlayedRounds == 4: bot.send_message(m.chat.id, 'El crupier reparte las cartas y tienes un 4 y un 6 sobre la mesa. No te queda otra que pedir carta... así que la pides ¡y sale un as! Por fin la suerte (esa perra caprichosa) ha decidido cambiar de bando. El crupier, ya cansado y con ganas de irse a su casa te da la enhorabuena y te pregunta: *¿Qué quieres pedir de premio?*', parse_mode='Markdown') myPlayer.bjPlayedRounds += 1 elif myPlayer.bjPlayedRounds == 5: if mSplit[0] == 'pedir': if ((' '.join(mSplit[1::]) == 'dos botellas de bourbon') or (' '.join(mSplit[1::]) == '2 botellas de bourbon')): bot.send_message(m.chat.id, 'El camarero te da tus dos botellas de Bourbon. Con ellas bajo el brazo decides que es hora de cambiar de garito, así que sales a la plaza a decidir cual será tu próximo destino.') # Añadimos las dos botellas de Bourbon al inventario myPlayer.inventory.append("dos botellas de bourbon") # Marcamos la habitacion como resuelta map.zonemap[myPlayer.location]['SOLVED'] = True # Movemos al jugador a la plaza myPlayer.location = 'b0' time.sleep(2) introduce_room(m) else: bot.send_message(m.chat.id, 'De eso no tenemos, pide otra cosa') else: bot.send_message(m.chat.id, "No entiendo eso que dices") # La Ducha def room_ducha(m): # Comandos que entendemos en esta habitacion acceptableDuchaActions = ['tirar'] # Cogemos el texto que nos ha enviado y lo dividimos en palabras mSplit = m.text.lower().split() # Marcamos la habitacion como visitada map.zonemap[myPlayer.location]['VISITED'] = True # Si la primera palabra no esta en la lista de los comandos que entendemos, no hacemos nada if mSplit[0] not in acceptableDuchaActions: bot.send_message(m.chat.id, "No entiendo eso que dices") else: if 'dados' in mSplit: if 0 <= myPlayer.s3PlayedRounds <= 4: dice1Number = randint(1, 6) dice2Number = randint(1, 6) # Si la suma de los dados es multiplo de 3, bebe if ((dice1Number + dice2Number)%3) == 0: bot.send_message(m.chat.id, '¡Has sacado un ' + str(dice1Number+dice2Number) + '! Procedes a beberte ese sol y sombra que te toca.') myPlayer.s3PlayedRounds += 1 # Si no, mostramos la suma else: bot.send_message(m.chat.id, 'Sacas un ' + str(dice1Number+dice2Number)) # Cuando llega a 5 partidas, pasa al baño de la ducha if myPlayer.s3PlayedRounds == 5: bot.send_message(m.chat.id, 'Ha sido divertido pero basta ya de jueguecitos por hoy. La verdad es que ya llevas un buen rato bebiendo y el señor Roca te llama a gritos, por lo que entras en el baño.') # Movemos al jugador al baño myPlayer.location = 'b4a' time.sleep(2) introduce_room(m) # No puede tirar otra cosa que no sean los dados else: bot.send_message(m.chat.id, "No entiendo eso que dices") # El baño de la Ducha def room_bano_ducha(m): # Comandos que entendemos en esta habitacion acceptableBanoDuchaActions = ['subir', 'subirte', 'abrir', 'tirar'] # Cogemos el texto que nos ha enviado y lo dividimos en palabras mSplit = m.text.lower().split() # Si la primera palabra no esta en la lista de los comandos que entendemos, no hacemos nada if mSplit[0] not in acceptableBanoDuchaActions: bot.send_message(m.chat.id, "No entiendo eso que dices") # Si decide subirse al retrete, muere miserablemente elif ((mSplit[0] == 'subir') or (mSplit[0] == 'subirte')): if 'retrete' in mSplit: bot.send_message(m.chat.id, 'La endeble tapa se hunde y te quedas atascado. Gritas pidiendo auxilio pero nadie puede entrar a rescatarte porque la puerta del baño está atascada. Pasa el tiempo, dejas de sentir las piernas. En cuestión de horas la gangrena te corroe y *mueres miserablemente*.\n\nIntroduce _/start_ para iniciar de nuevo el juego.', parse_mode='Markdown') game_initialize() else: bot.send_message(m.chat.id, 'No veo eso que dices.') # Si decide tirar de la cadena, muere miserablemente elif mSplit[0] == 'tirar': if 'cadena' in mSplit: bot.send_message(m.chat.id, 'Como estás un poco borracho, te haces un lío con la cadena y acabas ahorcándote. *Mueres miserablemente*.\n\nIntroduce _/start_ para iniciar de nuevo el juego.', parse_mode='Markdown') game_initialize() else: bot.send_message(m.chat.id, 'No veo eso que dices.') # Si decide abrir el grifo, resuelve el puzzle y vuelve a la Plaza elif mSplit[0] == 'abrir': if 'grifo' in mSplit: bot.send_message(m.chat.id, '¡Qué astuto, Miguel! Esta solución digna de dibujos animados es la que te salva la vida. El baño se va inundando poco a poco. Te mantienes a flote y, cuando el nivel del agua es lo suficientemente elevado, consigues salir por la ventana. ¡Enhorabuena! Pero ahora estás completamente calado… ni que acabaras de salir de La Ducha (LoL). Así no puedes volver a tu boda, de ninguna manera, qué dirá tu suegra. Lo mejor es que sigas de bares a ver si algún camarero amigo te puede dejar algo con lo que secarte, aunque sea el trapo de secar los vasos, que no ha visto una lavadora desde 1999.') myPlayer.location = 'b0' time.sleep(2) introduce_room(m) else: bot.send_message(m.chat.id, 'No veo eso que dices.') # El Trastero def room_trastero(m): # Comandos que entendemos en esta habitacion acceptableTrasteroActions = ['pedir', 'decir', 'hablar', 'si', u'sí', 'no', '24', 'veinticuatro', '10', 'diez', '4', 'cuatro'] # Cogemos el texto que nos ha enviado y lo dividimos en palabras mSplit = m.text.lower().split() # Marcamos la habitacion como visitada map.zonemap[myPlayer.location]['VISITED'] = True # Si la primera palabra no esta en la lista de los comandos que entendemos, no hacemos nada if mSplit[0] not in acceptableTrasteroActions: bot.send_message(m.chat.id, "No entiendo eso que dices") # Si todavia no le ha dicho si se atreve elif myPlayer.trasteroQuestionsAnswered == 0: if ((mSplit[0] == 'si') or (mSplit[0] == u'sí')): bot.send_message(m.chat.id, '¿Sí qué?') elif mSplit[0] == 'no': bot.send_message(m.chat.id, '¿No qué?') elif ((mSplit[0] == 'pedir') and ('llaves' not in mSplit)): bot.send_message(m.chat.id, 'No veo eso que dices.') else: bot.send_message(m.chat.id, 'Sí, efectivamente tus amigos me han dejado tus llaves, pero me han pagado muy bien para que no te las dé a no ser que aciertes la respuesta a 3 preguntas… un poco estúpidas la verdad… pero qué le voy a hacer, no seré yo el que discuta el color del dinero… ¿Te atreves?') myPlayer.trasteroQuestionsAnswered += 1 # Cuando le ha planteado el juego. Primera pregunta elif myPlayer.trasteroQuestionsAnswered == 1: if ((mSplit[0] == 'si') or (mSplit[0] == u'sí')): bot.send_message(m.chat.id, '_Primera pregunta:_ *¿Cuál es el record mundial, en días, de tuppers olvidados en la nevera?*', parse_mode='Markdown') myPlayer.trasteroQuestionsAnswered += 1 # Si dice que no, vuelve a la Plaza - REVISAR elif mSplit[0] == 'no': bot.send_message(m.chat.id, 'Con toda la cogorza te vuelves a la plaza.') # Si dice cualquier otra cosa, vuelve a la Plaza - REVISAR else: bot.send_message(m.chat.id, 'A Jose se le acaba la paciencia y decide que no va a darte las llaves, así que te vuelves a la plaza.') # Segunda pregunta elif myPlayer.trasteroQuestionsAnswered == 2: if ((mSplit[0] == '24') or (mSplit[0] == 'veinticuatro')): bot.send_message(m.chat.id, '_Segunda pregunta:_ *¿Cuál es el récord mundial, en días, de ropa tendida y olvidada en el tendedero?*', parse_mode='Markdown') myPlayer.trasteroQuestionsAnswered += 1 else: bot.send_message(m.chat.id, 'Jose te obliga a beber un chupito de Jack Daniels por haber respondido mal, pero tu cuerpo no soporta más cantidad de alcohol en sangre y *mueres miserablemente* de un coma etílico.\n\nIntroduce _/start_ para iniciar de nuevo el juego.', parse_mode='Markdown') game_initialize() # Tercera pregunta elif myPlayer.trasteroQuestionsAnswered == 3: if ((mSplit[0] == '10') or (mSplit[0] == 'diez')): bot.send_message(m.chat.id, '_Tercera pregunta:_ *¿Cuál es el record mundial, en días, de ropa olvidada en el tambor de la lavadora?*', parse_mode='Markdown') myPlayer.trasteroQuestionsAnswered += 1 else: bot.send_message(m.chat.id, 'Jose te obliga a beber un chupito de Jack Daniels por haber respondido mal, pero tu cuerpo no soporta más cantidad de alcohol en sangre y *mueres miserablemente* de un coma etílico.\n\nIntroduce _/start_ para iniciar de nuevo el juego.', parse_mode='Markdown') game_initialize() elif myPlayer.trasteroQuestionsAnswered == 4: if ((mSplit[0] == '4') or (mSplit[0] == 'cuatro')): bot.send_message(m.chat.id, '_Toma las llaves chico, te lo has ganado. Yo no daba un duro por tí y has conseguido acertar las 3 preguntas._\nUn largo escalofrío recorre tu espalda mientras Jose saca del centro de sus pantalones las llaves de tu piso. El sudor de su entrepierna adherido al metal las hace relucir como nunca antes. Tragas saliva, extiendes la mano, las coges y con un cuidado extremo las metes en tu bolsillo mientras te convences de que no volverás nunca a este antro y vuelves a la plaza.', parse_mode='Markdown') map.zonemap[myPlayer.location]['SOLVED'] = True myPlayer.location = 'b0' time.sleep(2) introduce_room(m) else: bot.send_message(m.chat.id, 'Jose te obliga a beber un chupito de Jack Daniels por haber respondido mal, pero tu cuerpo no soporta más cantidad de alcohol en sangre y *mueres miserablemente* de un coma etílico.\n\nIntroduce _/start_ para iniciar de nuevo el juego.', parse_mode='Markdown') game_initialize() # Testarrosa def room_testarrosa(m): # Comandos que entendemos en esta habitacion acceptableTestarrosaActions = [u'pacharán', 'pacharan', u'patxarán', 'patxaran'] # Cogemos el texto que nos ha enviado y lo dividimos en palabras mSplit = m.text.lower().split() def horrible_singing_die(): bot.send_message(m.chat.id, 'Menos mal que no te ganas la vida como vocalista, lo haces fatal. La clientela del bar, enfurecida por tu actuación, te despelleja vivo allí mismo, *muriendo miserablemente*.\n\nIntroduce _/start_ para iniciar de nuevo el juego.', parse_mode='Markdown') game_initialize() # Marcamos la habitacion como visitada map.zonemap[myPlayer.location]['VISITED'] = True # Si la primera palabra no esta en la lista de los comandos que entendemos, no hacemos nada if myPlayer.testarrosaSung == 0: if ((mSplit[0] == u'pacharán') or (mSplit[0] == 'pacharan') or (mSplit[0] == u'patxarán') or (mSplit[0] == 'patxaran')): bot.send_message(m.chat.id, "Con tu tubo de pacharan en la mano, escuchas cómo el DJ pincha una canción. Todos los asistentes enardecen con ella y empiezan a sacudir violentamente sus cabezas. Cuando empieza la letra, todo el mundo corea:\n\n_Ohhhh!! De nuevo solos tú y yo. Un lago y una canción,\necho de menos oír tu voz_\n\nLa música se interrumpe bruscamente, todo el mundo calla. Todas las miradas se centran en un punto. ¡Tú! *¡Están esperando a que cantes!*", parse_mode='Markdown') myPlayer.testarrosaSung += 1 else: bot.send_message(m.chat.id, "De eso no tenemos, pide otra cosa.") elif myPlayer.testarrosaSung == 1: if ((m.text.lower() == u'una estrella te eclipsó') or (m.text.lower() == 'una estrella te eclipso')): bot.send_message(m.chat.id, "_Los momentos que no volverá a sentir tu piel,\nella no deja de pensar que un día te encontrará..._\n\nDe nuevo todo el mundo calla y te mira, *es tu turno de nuevo para cantar.*", parse_mode='Markdown') myPlayer.testarrosaSung += 1 else: # Muere miserablemente horrible_singing_die() elif myPlayer.testarrosaSung == 2: if ((m.text.lower() == u'acércate') or (m.text.lower() == 'acercate')): bot.send_message(m.chat.id, "_A veces siento al despertar como un susurro, tu calor,\nella no deja de pensar que un día te encontrará..._\n\n¡Ella! ¡Estefanía! Te estará buscando, se preguntará dónde estás… Basta de karaokes y de tragos, es hora de volver a tu boda. ¿Dónde se encontrará la salida de este bucle temporal? No tienes ni idea. De momento, intentas salir del bar, pero una montonera de objetos bloquea la salida: un *oso panda*, un *palé*, un *enano*, una *oveja*, una *gallina* y un *señor disfrazado de hitita*.", parse_mode='Markdown') myPlayer.testarrosaSung += 1 else: # Muere miserablemente horrible_singing_die() elif myPlayer.testarrosaSung == 3: if (('oso' in mSplit) and ('panda' in mSplit)): bot.send_message(m.chat.id, '¡Pero cómo se te ocurre meterte con un oso panda, por mucho que parezca un peluche! El primer zarpazo te secciona la vena subclavia y el segundo, la yugular. *Mueres miserablemente*.\n\nIntroduce _/start_ para iniciar de nuevo el juego.', parse_mode='Markdown') game_initialize() elif 'gallina' in mSplit: bot.send_message(m.chat.id, 'Te has topado con un animal especialmente feroz. Esta gallina está cosiéndote a picotazos y más vale que encuentres un remedio rápido o no sabes si podrás sobrevivir. *¿Qué haces?*', parse_mode='Markdown') myPlayer.testarrosaSung = 10 # Bucle de la gallina elif myPlayer.testarrosaSung == 10: if 0 <= myPlayer.diabolicHen <= 9: if mSplit[0] == 'darle': if 'bourbon' in mSplit: if 'dos botellas de bourbon' in myPlayer.inventory: bot.send_message(m.chat.id, 'Has emborrachado a la gallina y por fin te ha dejado de molestar, pero el resto de objetos aún bloquean la salida: un *oso panda*, un *palé*, un *enano*, una *oveja* y un *señor disfrazado de hitita*.', parse_mode='Markdown') myPlayer.inventory.remove ('dos botellas de bourbon') myPlayer.testarrosaSung = 4 else: bot.send_message(m.chat.id, 'No encuentro eso que dices.') sleep (1) bot.send_message(m.chat.id, 'La gallina continúa picoteándote.') myPlayer.diabolicHen += 1 else: bot.send_message(m.chat.id, 'No encuentro eso que dices.') sleep(1) bot.send_message(m.chat.id, 'La gallina continúa picoteándote.') myPlayer.diabolicHen += 1 else: bot.send_message(m.chat.id, 'No entiendo eso que dices.') sleep(1) bot.send_message(m.chat.id, 'La gallina continúa picoteándote.') myPlayer.diabolicHen += 1 else: bot.send_message(m.chat.id, '*Mueres miserablemente* picoteado por la gallina.\n\nIntroduce _/start_ para iniciar de nuevo el juego.', parse_mode='Markdown') game_initialize() ################################################################################ # La Plaza def room_plaza(m): # Comandos que entendemos en esta habitacion acceptablePlazaActions = ['norte', 'sur', 'este', 'oeste'] # Cogemos el texto que nos ha enviado y lo dividimos en palabras mSplit = m.text.lower().split() # Tenere if mSplit[0] == 'sur': bot.send_message(m.chat.id, 'Ya has estado mucho tiempo en el Teneré, casi mejor ir a otro garito, ¿no?') # La Ducha elif mSplit[0] == 'oeste': if map.zonemap['b4']['VISITED'] == True: bot.send_message(m.chat.id, '¿Estás seguro de que es una buena idea volver a entrar en La Ducha? Elige otro sitio.') else: myPlayer.location = 'b4' time.sleep(2) introduce_room(m) room_ducha(m) # Trastero elif mSplit[0] == 'norte': if ((map.zonemap['b1']['VISITED'] == True) and (map.zonemap['b1']['SOLVED'] == False)): bot.send_message(m.chat.id, 'Ha quedado muy claro que Jose no piensa darte las llaves, así que es mejor que elijas otro sitio.') elif ((map.zonemap['b1']['VISITED'] == True) and (map.zonemap['b1']['SOLVED'] == True)): bot.send_message(m.chat.id, 'Una vez has recuperado tus llaves, ¿no sería mejor ir a otro sitio?.') else: myPlayer.location = 'b1' time.sleep(2) introduce_room(m) room_trastero(m) # Testarrosa elif mSplit[0] == 'este': myPlayer.location = 'b2' time.sleep(2) introduce_room(m) room_testarrosa(m) # Bucle principal del juego, en el que se decide en que habitacion esta def play_rooms(m): if myPlayer.location == 'a0': room_examen(m) elif myPlayer.location == 'b3': room_tenere(m) elif myPlayer.location == 'b0': room_plaza(m) elif myPlayer.location == 'b4': room_ducha(m) elif myPlayer.location == 'b4a': room_bano_ducha(m) elif myPlayer.location == 'b1': room_trastero(m) elif myPlayer.location == 'b2': room_testarrosa(m) elif myPlayer.location == 'z0': room_alfonso8(m) # Definimos una función llamada 'listener', que recibe como parámetro un dato llamado 'messages' def listener(messages): # Por cada dato 'm' en el dato 'messages' for m in messages: # Filtramos mensajes que sean tipo texto if m.content_type == 'text': # Almacenaremos el ID de la conversación cid = m.chat.id # Y haremos que imprima algo parecido a esto -> [52033876]: /start print "[" + str(cid) + "]: " + m.text # Si se lanza el comando /start se inicia el juego if m.text == '/start': # Inicializamos el juego y le mandamos la introduccion y el texto de Teleco game_initialize() bot.send_message(cid, display_intro) time.sleep(2) introduce_room(m) # Mostramos la ayuda elif m.text == '/help': display_help(m) # PARA TESTING EXCLUSIVAMENTE - HAY QUE ELIMINAR ESTOS ELIF elif m.text == '/tenere': myPlayer.location = 'b3' introduce_room(m) elif m.text == '/ducha': myPlayer.location = 'b4' introduce_room(m) elif m.text == '/banoducha': myPlayer.location = 'b4a' introduce_room(m) elif m.text == '/trastero': myPlayer.location = 'b1' introduce_room(m) elif m.text == '/testarrosa': myPlayer.inventory.append('dos botellas de bourbon') myPlayer.location = 'b2' introduce_room(m) elif m.text == '/testarrosagallina': myPlayer.inventory.append('dos botellas de bourbon') myPlayer.testarrosaSung = 10 myPlayer.location = 'b2' introduce_room(m) elif m.text == '/alfonso8': myPlayer.location = 'z0' introduce_room(m) else: play_rooms(m) # Le decimos al bot que utilice como función escuchadora nuestra función 'listener' # bot.set_update_listener(listener) # Le decimos al bot que siga funcionando incluso si encuentra algún fallo # bot.infinity_polling() polling_thread = threading.Thread(target=bot_polling) polling_thread.daemon = True polling_thread.start() #Keep main program running while bot runs threaded if __name__ == "__main__": while True: try: sleep(120) except KeyboardInterrupt: print("\n@jblasbot instance ended") break
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flashlightli/math_question
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/leetcode_question/easy_question/83_Remove_Duplicates_from_Sorted_List.py
424dcd73d4edb4666bc516e70cb62885fd5a9734
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https://github.com/flashlightli/math_question
0009e707c43f36b8bbbfa98725191e8810e70126
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2022-04-17T01:43:53
2022-04-17T01:43:53
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""" 给定一个排序链表,删除所有重复的元素,使得每个元素只出现一次。 示例 1: 输入: 1->1->2 输出: 1->2 示例 2: 输入: 1->1->2->3->3 输出: 1->2->3 来源:力扣(LeetCode) 链接:https://leetcode-cn.com/problems/remove-duplicates-from-sorted-list """ # Definition for singly-linked list. class ListNode: def __init__(self, x): self.val = x self.next = None class Solution: def deleteDuplicates(self, head: ListNode) -> ListNode: # 44ms 3.7MB if head == None or head.next == None: return head temp, temp.next = ListNode(0), head while head and head.next: if head.val == head.next.val: head.next = head.next.next else: head = head.next return temp.next head = ListNode(1) second = ListNode(2) third = ListNode(2) head.next = second second.next = third test = Solution() print(test.deleteDuplicates( head ))
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/httprunner/loader.py
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import collections import csv import importlib import io import json import os import sys import yaml from httprunner import built_in, exceptions, logger, parser, utils, validator from httprunner.compat import OrderedDict ############################################################################### ## file loader ############################################################################### def _check_format(file_path, content): """ check testcase format if valid """ # TODO: replace with JSON schema validation if not content: # testcase file content is empty err_msg = u"Testcase file content is empty: {}".format(file_path) logger.log_error(err_msg) raise exceptions.FileFormatError(err_msg) elif not isinstance(content, (list, dict)): # testcase file content does not match testcase format err_msg = u"Testcase file content format invalid: {}".format(file_path) logger.log_error(err_msg) raise exceptions.FileFormatError(err_msg) def load_yaml_file(yaml_file): """ load yaml file and check file content format """ with io.open(yaml_file, 'r', encoding='utf-8') as stream: yaml_content = yaml.load(stream) _check_format(yaml_file, yaml_content) return yaml_content def load_json_file(json_file): """ load json file and check file content format """ with io.open(json_file, encoding='utf-8') as data_file: try: json_content = json.load(data_file) except exceptions.JSONDecodeError: err_msg = u"JSONDecodeError: JSON file format error: {}".format(json_file) logger.log_error(err_msg) raise exceptions.FileFormatError(err_msg) _check_format(json_file, json_content) return json_content def load_csv_file(csv_file): """ load csv file and check file content format @param csv_file: csv file path e.g. csv file content: username,password test1,111111 test2,222222 test3,333333 @return list of parameter, each parameter is in dict format e.g. [ {'username': 'test1', 'password': '111111'}, {'username': 'test2', 'password': '222222'}, {'username': 'test3', 'password': '333333'} ] """ csv_content_list = [] with io.open(csv_file, encoding='utf-8') as csvfile: reader = csv.DictReader(csvfile) for row in reader: csv_content_list.append(row) return csv_content_list def load_file(file_path): if not os.path.isfile(file_path): raise exceptions.FileNotFound("{} does not exist.".format(file_path)) file_suffix = os.path.splitext(file_path)[1].lower() if file_suffix == '.json': return load_json_file(file_path) elif file_suffix in ['.yaml', '.yml']: return load_yaml_file(file_path) elif file_suffix == ".csv": return load_csv_file(file_path) else: # '' or other suffix err_msg = u"Unsupported file format: {}".format(file_path) logger.log_warning(err_msg) return [] def load_folder_files(folder_path, recursive=True): """ load folder path, return all files endswith yml/yaml/json in list. Args: folder_path (str): specified folder path to load recursive (bool): load files recursively if True Returns: list: files endswith yml/yaml/json """ if isinstance(folder_path, (list, set)): files = [] for path in set(folder_path): files.extend(load_folder_files(path, recursive)) return files if not os.path.exists(folder_path): return [] file_list = [] for dirpath, dirnames, filenames in os.walk(folder_path): filenames_list = [] for filename in filenames: if not filename.endswith(('.yml', '.yaml', '.json')): continue filenames_list.append(filename) for filename in filenames_list: file_path = os.path.join(dirpath, filename) file_list.append(file_path) if not recursive: break return file_list def load_dot_env_file(dot_env_path): """ load .env file. Args: dot_env_path (str): .env file path Returns: dict: environment variables mapping { "UserName": "debugtalk", "Password": "123456", "PROJECT_KEY": "ABCDEFGH" } Raises: exceptions.FileFormatError: If .env file format is invalid. """ if not os.path.isfile(dot_env_path): raise exceptions.FileNotFound(".env file path is not exist.") logger.log_info("Loading environment variables from {}".format(dot_env_path)) env_variables_mapping = {} with io.open(dot_env_path, 'r', encoding='utf-8') as fp: for line in fp: # maxsplit=1 if "=" in line: variable, value = line.split("=", 1) elif ":" in line: variable, value = line.split(":", 1) else: raise exceptions.FileFormatError(".env format error") env_variables_mapping[variable.strip()] = value.strip() utils.set_os_environ(env_variables_mapping) return env_variables_mapping def locate_file(start_path, file_name): """ locate filename and return file path. searching will be recursive upward until current working directory. Args: start_path (str): start locating path, maybe file path or directory path Returns: str: located file path. None if file not found. Raises: exceptions.FileNotFound: If failed to locate file. """ if os.path.isfile(start_path): start_dir_path = os.path.dirname(start_path) elif os.path.isdir(start_path): start_dir_path = start_path else: raise exceptions.FileNotFound("invalid path: {}".format(start_path)) file_path = os.path.join(start_dir_path, file_name) if os.path.isfile(file_path): return file_path # current working directory if os.path.abspath(start_dir_path) in [os.getcwd(), os.path.abspath(os.sep)]: raise exceptions.FileNotFound("{} not found in {}".format(file_name, start_path)) # locate recursive upward return locate_file(os.path.dirname(start_dir_path), file_name) ############################################################################### ## debugtalk.py module loader ############################################################################### def load_python_module(module): """ load python module. Args: module: python module Returns: dict: variables and functions mapping for specified python module { "variables": {}, "functions": {} } """ debugtalk_module = { "variables": {}, "functions": {} } for name, item in vars(module).items(): if validator.is_function((name, item)): debugtalk_module["functions"][name] = item elif validator.is_variable((name, item)): if isinstance(item, tuple): continue debugtalk_module["variables"][name] = item else: pass return debugtalk_module def load_builtin_module(): """ load built_in module """ built_in_module = load_python_module(built_in) return built_in_module def load_debugtalk_module(): """ load project debugtalk.py module debugtalk.py should be located in project working directory. Returns: dict: debugtalk module mapping { "variables": {}, "functions": {} } """ # load debugtalk.py module imported_module = importlib.import_module("debugtalk") debugtalk_module = load_python_module(imported_module) return debugtalk_module def get_module_item(module_mapping, item_type, item_name): """ get expected function or variable from module mapping. Args: module_mapping(dict): module mapping with variables and functions. { "variables": {}, "functions": {} } item_type(str): "functions" or "variables" item_name(str): function name or variable name Returns: object: specified variable or function object. Raises: exceptions.FunctionNotFound: If specified function not found in module mapping exceptions.VariableNotFound: If specified variable not found in module mapping """ try: return module_mapping[item_type][item_name] except KeyError: err_msg = "{} not found in debugtalk.py module!\n".format(item_name) err_msg += "module mapping: {}".format(module_mapping) if item_type == "functions": raise exceptions.FunctionNotFound(err_msg) else: raise exceptions.VariableNotFound(err_msg) ############################################################################### ## testcase loader ############################################################################### def _load_test_file(file_path, project_mapping): """ load testcase file or testsuite file Args: file_path (str): absolute valid file path. file_path should be in the following format: [ { "config": { "name": "", "def": "suite_order()", "request": {} } }, { "test": { "name": "add product to cart", "api": "api_add_cart()", "validate": [] } }, { "test": { "name": "add product to cart", "suite": "create_and_check()", "validate": [] } }, { "test": { "name": "checkout cart", "request": {}, "validate": [] } } ] project_mapping (dict): project_mapping Returns: dict: testcase dict { "config": {}, "teststeps": [teststep11, teststep12] } """ testcase = { "config": {}, "teststeps": [] } for item in load_file(file_path): # TODO: add json schema validation if not isinstance(item, dict) or len(item) != 1: raise exceptions.FileFormatError("Testcase format error: {}".format(file_path)) key, test_block = item.popitem() if not isinstance(test_block, dict): raise exceptions.FileFormatError("Testcase format error: {}".format(file_path)) if key == "config": testcase["config"].update(test_block) elif key == "test": def extend_api_definition(block): ref_call = block["api"] def_block = _get_block_by_name(ref_call, "def-api", project_mapping) _extend_block(block, def_block) # reference api if "api" in test_block: extend_api_definition(test_block) testcase["teststeps"].append(test_block) # reference testcase elif "suite" in test_block: # TODO: replace suite with testcase ref_call = test_block["suite"] block = _get_block_by_name(ref_call, "def-testcase", project_mapping) # TODO: bugfix lost block config variables for teststep in block["teststeps"]: if "api" in teststep: extend_api_definition(teststep) testcase["teststeps"].append(teststep) # define directly else: testcase["teststeps"].append(test_block) else: logger.log_warning( "unexpected block key: {}. block key should only be 'config' or 'test'.".format(key) ) return testcase def _get_block_by_name(ref_call, ref_type, project_mapping): """ get test content by reference name. Args: ref_call (str): call function. e.g. api_v1_Account_Login_POST($UserName, $Password) ref_type (enum): "def-api" or "def-testcase" project_mapping (dict): project_mapping Returns: dict: api/testcase definition. Raises: exceptions.ParamsError: call args number is not equal to defined args number. """ function_meta = parser.parse_function(ref_call) func_name = function_meta["func_name"] call_args = function_meta["args"] block = _get_test_definition(func_name, ref_type, project_mapping) def_args = block.get("function_meta", {}).get("args", []) if len(call_args) != len(def_args): err_msg = "{}: call args number is not equal to defined args number!\n".format(func_name) err_msg += "defined args: {}\n".format(def_args) err_msg += "reference args: {}".format(call_args) logger.log_error(err_msg) raise exceptions.ParamsError(err_msg) args_mapping = {} for index, item in enumerate(def_args): if call_args[index] == item: continue args_mapping[item] = call_args[index] if args_mapping: block = parser.substitute_variables(block, args_mapping) return block def _get_test_definition(name, ref_type, project_mapping): """ get expected api or testcase. Args: name (str): api or testcase name ref_type (enum): "def-api" or "def-testcase" project_mapping (dict): project_mapping Returns: dict: expected api/testcase info if found. Raises: exceptions.ApiNotFound: api not found exceptions.TestcaseNotFound: testcase not found """ block = project_mapping.get(ref_type, {}).get(name) if not block: err_msg = "{} not found!".format(name) if ref_type == "def-api": raise exceptions.ApiNotFound(err_msg) else: # ref_type == "def-testcase": raise exceptions.TestcaseNotFound(err_msg) return block def _extend_block(ref_block, def_block): """ extend ref_block with def_block. Args: def_block (dict): api definition dict. ref_block (dict): reference block Returns: dict: extended reference block. Examples: >>> def_block = { "name": "get token 1", "request": {...}, "validate": [{'eq': ['status_code', 200]}] } >>> ref_block = { "name": "get token 2", "extract": [{"token": "content.token"}], "validate": [{'eq': ['status_code', 201]}, {'len_eq': ['content.token', 16]}] } >>> _extend_block(def_block, ref_block) { "name": "get token 2", "request": {...}, "extract": [{"token": "content.token"}], "validate": [{'eq': ['status_code', 201]}, {'len_eq': ['content.token', 16]}] } """ # TODO: override variables def_validators = def_block.get("validate") or def_block.get("validators", []) ref_validators = ref_block.get("validate") or ref_block.get("validators", []) def_extrators = def_block.get("extract") \ or def_block.get("extractors") \ or def_block.get("extract_binds", []) ref_extractors = ref_block.get("extract") \ or ref_block.get("extractors") \ or ref_block.get("extract_binds", []) ref_block.update(def_block) ref_block["validate"] = _merge_validator( def_validators, ref_validators ) ref_block["extract"] = _merge_extractor( def_extrators, ref_extractors ) def _convert_validators_to_mapping(validators): """ convert validators list to mapping. Args: validators (list): validators in list Returns: dict: validators mapping, use (check, comparator) as key. Examples: >>> validators = [ {"check": "v1", "expect": 201, "comparator": "eq"}, {"check": {"b": 1}, "expect": 200, "comparator": "eq"} ] >>> _convert_validators_to_mapping(validators) { ("v1", "eq"): {"check": "v1", "expect": 201, "comparator": "eq"}, ('{"b": 1}', "eq"): {"check": {"b": 1}, "expect": 200, "comparator": "eq"} } """ validators_mapping = {} for validator in validators: validator = parser.parse_validator(validator) if not isinstance(validator["check"], collections.Hashable): check = json.dumps(validator["check"]) else: check = validator["check"] key = (check, validator["comparator"]) validators_mapping[key] = validator return validators_mapping def _merge_validator(def_validators, ref_validators): """ merge def_validators with ref_validators. Args: def_validators (list): ref_validators (list): Returns: list: merged validators Examples: >>> def_validators = [{'eq': ['v1', 200]}, {"check": "s2", "expect": 16, "comparator": "len_eq"}] >>> ref_validators = [{"check": "v1", "expect": 201}, {'len_eq': ['s3', 12]}] >>> _merge_validator(def_validators, ref_validators) [ {"check": "v1", "expect": 201, "comparator": "eq"}, {"check": "s2", "expect": 16, "comparator": "len_eq"}, {"check": "s3", "expect": 12, "comparator": "len_eq"} ] """ if not def_validators: return ref_validators elif not ref_validators: return def_validators else: def_validators_mapping = _convert_validators_to_mapping(def_validators) ref_validators_mapping = _convert_validators_to_mapping(ref_validators) def_validators_mapping.update(ref_validators_mapping) return list(def_validators_mapping.values()) def _merge_extractor(def_extrators, ref_extractors): """ merge def_extrators with ref_extractors Args: def_extrators (list): [{"var1": "val1"}, {"var2": "val2"}] ref_extractors (list): [{"var1": "val111"}, {"var3": "val3"}] Returns: list: merged extractors Examples: >>> def_extrators = [{"var1": "val1"}, {"var2": "val2"}] >>> ref_extractors = [{"var1": "val111"}, {"var3": "val3"}] >>> _merge_extractor(def_extrators, ref_extractors) [ {"var1": "val111"}, {"var2": "val2"}, {"var3": "val3"} ] """ if not def_extrators: return ref_extractors elif not ref_extractors: return def_extrators else: extractor_dict = OrderedDict() for api_extrator in def_extrators: if len(api_extrator) != 1: logger.log_warning("incorrect extractor: {}".format(api_extrator)) continue var_name = list(api_extrator.keys())[0] extractor_dict[var_name] = api_extrator[var_name] for test_extrator in ref_extractors: if len(test_extrator) != 1: logger.log_warning("incorrect extractor: {}".format(test_extrator)) continue var_name = list(test_extrator.keys())[0] extractor_dict[var_name] = test_extrator[var_name] extractor_list = [] for key, value in extractor_dict.items(): extractor_list.append({key: value}) return extractor_list def load_folder_content(folder_path): """ load api/testcases/testsuites definitions from folder. Args: folder_path (str): api/testcases/testsuites files folder. Returns: dict: api definition mapping. { "tests/api/basic.yml": [ {"api": {"def": "api_login", "request": {}, "validate": []}}, {"api": {"def": "api_logout", "request": {}, "validate": []}} ] } """ items_mapping = {} for file_path in load_folder_files(folder_path): items_mapping[file_path] = load_file(file_path) return items_mapping def load_api_folder(api_folder_path): """ load api definitions from api folder. Args: api_folder_path (str): api files folder. api file should be in the following format: [ { "api": { "def": "api_login", "request": {}, "validate": [] } }, { "api": { "def": "api_logout", "request": {}, "validate": [] } } ] Returns: dict: api definition mapping. { "api_login": { "function_meta": {"func_name": "api_login", "args": [], "kwargs": {}} "request": {} }, "api_logout": { "function_meta": {"func_name": "api_logout", "args": [], "kwargs": {}} "request": {} } } """ api_definition_mapping = {} api_items_mapping = load_folder_content(api_folder_path) for api_file_path, api_items in api_items_mapping.items(): # TODO: add JSON schema validation for api_item in api_items: key, api_dict = api_item.popitem() api_def = api_dict.pop("def") function_meta = parser.parse_function(api_def) func_name = function_meta["func_name"] if func_name in api_definition_mapping: logger.log_warning("API definition duplicated: {}".format(func_name)) api_dict["function_meta"] = function_meta api_definition_mapping[func_name] = api_dict return api_definition_mapping def load_test_folder(test_folder_path): """ load testcases definitions from folder. Args: test_folder_path (str): testcases files folder. testcase file should be in the following format: [ { "config": { "def": "create_and_check", "request": {}, "validate": [] } }, { "test": { "api": "get_user", "validate": [] } } ] Returns: dict: testcases definition mapping. { "create_and_check": [ {"config": {}}, {"test": {}}, {"test": {}} ], "tests/testcases/create_and_get.yml": [ {"config": {}}, {"test": {}}, {"test": {}} ] } """ test_definition_mapping = {} test_items_mapping = load_folder_content(test_folder_path) for test_file_path, items in test_items_mapping.items(): # TODO: add JSON schema validation testcase = { "config": {}, "teststeps": [] } for item in items: key, block = item.popitem() if key == "config": testcase["config"].update(block) if "def" not in block: test_definition_mapping[test_file_path] = testcase continue testcase_def = block.pop("def") function_meta = parser.parse_function(testcase_def) func_name = function_meta["func_name"] if func_name in test_definition_mapping: logger.log_warning("API definition duplicated: {}".format(func_name)) testcase["function_meta"] = function_meta test_definition_mapping[func_name] = testcase else: # key == "test": testcase["teststeps"].append(block) return test_definition_mapping def locate_debugtalk_py(start_path): """ locate debugtalk.py file. Args: start_path (str): start locating path, maybe testcase file path or directory path """ try: debugtalk_path = locate_file(start_path, "debugtalk.py") return os.path.abspath(debugtalk_path) except exceptions.FileNotFound: return None def load_project_tests(test_path, dot_env_path=None): """ load api, testcases, .env, builtin module and debugtalk.py. api/testcases folder is relative to project_working_directory Args: test_path (str): test file/folder path, locate pwd from this path. dot_env_path (str): specified .env file path Returns: dict: project loaded api/testcases definitions, environments and debugtalk.py module. """ project_mapping = {} debugtalk_path = locate_debugtalk_py(test_path) # locate PWD with debugtalk.py path if debugtalk_path: # The folder contains debugtalk.py will be treated as PWD. project_working_directory = os.path.dirname(debugtalk_path) else: # debugtalk.py is not found, use os.getcwd() as PWD. project_working_directory = os.getcwd() # add PWD to sys.path sys.path.insert(0, project_working_directory) # load .env dot_env_path = dot_env_path or os.path.join(project_working_directory, ".env") if os.path.isfile(dot_env_path): project_mapping["env"] = load_dot_env_file(dot_env_path) else: project_mapping["env"] = {} # load debugtalk.py if debugtalk_path: project_mapping["debugtalk"] = load_debugtalk_module() else: project_mapping["debugtalk"] = { "variables": {}, "functions": {} } project_mapping["def-api"] = load_api_folder(os.path.join(project_working_directory, "api")) # TODO: replace suite with testcases project_mapping["def-testcase"] = load_test_folder(os.path.join(project_working_directory, "suite")) return project_mapping def load_tests(path, dot_env_path=None): """ load testcases from file path, extend and merge with api/testcase definitions. Args: path (str/list): testcase file/foler path. path could be in several types: - absolute/relative file path - absolute/relative folder path - list/set container with file(s) and/or folder(s) dot_env_path (str): specified .env file path Returns: list: testcases list, each testcase is corresponding to a file [ { # testcase data structure "config": { "name": "desc1", "path": "testcase1_path", "variables": [], # optional "request": {} # optional "refs": { "debugtalk": { "variables": {}, "functions": {} }, "env": {}, "def-api": {}, "def-testcase": {} } }, "teststeps": [ # teststep data structure { 'name': 'test step desc2', 'variables': [], # optional 'extract': [], # optional 'validate': [], 'request': {}, 'function_meta': {} }, teststep2 # another teststep dict ] }, testcase_dict_2 # another testcase dict ] """ if isinstance(path, (list, set)): testcases_list = [] for file_path in set(path): testcases = load_tests(file_path, dot_env_path) if not testcases: continue testcases_list.extend(testcases) return testcases_list if not os.path.exists(path): err_msg = "path not exist: {}".format(path) logger.log_error(err_msg) raise exceptions.FileNotFound(err_msg) if not os.path.isabs(path): path = os.path.join(os.getcwd(), path) if os.path.isdir(path): files_list = load_folder_files(path) testcases_list = load_tests(files_list, dot_env_path) elif os.path.isfile(path): try: project_mapping = load_project_tests(path, dot_env_path) testcase = _load_test_file(path, project_mapping) testcase["config"]["path"] = path testcase["config"]["refs"] = project_mapping testcases_list = [testcase] except exceptions.FileFormatError: testcases_list = [] return testcases_list
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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Dec 4 06:43:32 2018 @author: maximoskaliakatsos-papakostas """ import os cwd = os.getcwd() import glob import music21 as m21 import numpy as np from sklearn.decomposition import PCA import MBL_melody_features_functions as mff import CM_user_output_functions as uof import MBL_music_processing_functions as mpf import pickle import matplotlib.pyplot as plt remakedata = True test_plot = True if remakedata: mainFolder = cwd + os.sep + 'all_xmls' + os.sep styles_folders = ['han' + os.sep, 'jazz' + os.sep] session_names = ['han', 'jazz'] blending_sessions = [['han0120','fried_bananas'] , ['han0351','i_fall_in_love_too_easy'] , ['han0238','i_hear_rapsody'] , ['han0207','my_silient_love']] all_names = [] all_features = [] all_features_np = [] blend_names = [] for j in range(14): blend_names.append( 'blend_' + str(j) + '.xml' ) blending_indexes = [] # first construct the features matrix of all pieces in both styles for i in range( len( styles_folders ) ): print('Processing initial: ', styles_folders[i]) folderName = mainFolder + styles_folders[i] all_files = glob.glob(folderName + "*.xml") tmp_feats = [] # for all pieces extract features and put them in respective np.arrays for j in range( len( all_files ) ): fileName = all_files[j] all_names.append( fileName.split(os.sep)[-1] ) # print('Processing initial: ', fileName) p = m21.converter.parse( fileName ) tmp_val = mff.get_features_of_stream( p ) tmp_feats.append( tmp_val ) all_features.append( tmp_val ) # end for styles # for each blending sessions, append features for i in range( len( blending_sessions ) ): session_folder = 'bl'+str(i+1)+'_'+blending_sessions[i][1]+'_'+blending_sessions[i][0]+os.sep blending_indexes.append( range( len(all_features), len(all_features)+len(blend_names), 1 ) ) print('Processing blend: ', session_folder) for j in range( len( blend_names ) ): # print('Processing blend: ', blend_names[j]) fileName = cwd+os.sep+'full'+os.sep+session_folder+ blend_names[j] p = m21.converter.parse( fileName ) tmp_val = mff.get_features_of_stream( p ) tmp_feats.append( tmp_val ) all_features.append( tmp_val ) # do PCA to all features # PCA pca = PCA(n_components=2) all_features_np = np.vstack( all_features ) # normalise x = all_features_np x_max = np.max(x, axis=0) x_min = np.min(x, axis=0) y = (x-x_min)/(x_max-x_min); # all_pca = pca.fit_transform( np.vstack( all_features_np ) ) all_pca = pca.fit_transform( np.vstack( y ) ) tmp_pca = pca.fit(np.vstack( y )) explained = tmp_pca.explained_variance_ratio_ print('PCA explained variances: ', explained) print('PCA axes correlations:') for i in range(2): for j in range(4): print('PCA_', i, ' - f_', j, ': ', np.corrcoef( all_pca[:,i], all_features_np[:,j] )[0][1]) print('PCA_0 - f_0+f_2: ', np.corrcoef( all_pca[:,0], all_features_np[:,0]+all_features_np[:,2] )[0][1]) # keep the pca coordinates of the original (not blended) pieces all_original_pca = all_pca[ :len(all_names) , : ] for i in range( len( blending_sessions ) ): # keep indexes of the pieces to be highlighted han2show = blending_sessions[i][0] jazz2show = blending_sessions[i][1] # get indexes to highlight han_idx = all_names.index( han2show+'.xml' ) jazz_idx = all_names.index( jazz2show+'.xml' ) # get pca of blends on features matrix blends_pca = all_pca[ blending_indexes[i] , : ] # plot pca of original pieces plt.plot(all_original_pca[:488,0], all_original_pca[:488,1], '|', color='grey', alpha=0.5, label='Han') plt.plot(all_original_pca[488:,0], all_original_pca[488:,1], '_', color='grey', alpha=0.5, label='Jazz') # highlight inputs plt.plot(all_original_pca[han_idx,0], all_original_pca[han_idx,1], 'd', color='grey', markerSize=10, label='Han input') plt.plot(all_original_pca[jazz_idx,0], all_original_pca[jazz_idx,1], 's', color='grey', markerSize=10, label='Jazz input') # plot blends for j in range(14): if j==0: plt.plot(blends_pca[j,0], blends_pca[j,1], '.', color='black', label='blend') else: plt.plot(blends_pca[j,0], blends_pca[j,1], '.', color='black') plt.text(blends_pca[j,0], blends_pca[j,1], str(j), color='black', fontsize=9, bbox=dict(facecolor='white', alpha=0.2)) plt.xticks([]) plt.yticks([]) plt.legend() plt.savefig('figs/pca_'+blending_sessions[i][1]+'_'+blending_sessions[i][0]+'.png', dpi=500); plt.clf() # save with open('saved_data/all_features.pickle', 'wb') as handle: pickle.dump(all_features, handle, protocol=pickle.HIGHEST_PROTOCOL) with open('saved_data/all_pca.pickle', 'wb') as handle: pickle.dump(all_pca, handle, protocol=pickle.HIGHEST_PROTOCOL) with open('saved_data/all_names.pickle', 'wb') as handle: pickle.dump(all_names, handle, protocol=pickle.HIGHEST_PROTOCOL) else: with open('saved_data/all_features.pickle', 'rb') as handle: all_features = pickle.load(handle) with open('saved_data/all_pca.pickle', 'rb') as handle: all_pca = pickle.load(handle) with open('saved_data/all_names.pickle', 'rb') as handle: all_names = pickle.load(handle) # PCA pca = PCA(n_components=2) all_features_np = np.vstack( all_features ) all_pca = pca.fit_transform( np.vstack( all_features_np ) ) plt.plot(all_pca[:447,0], all_pca[:447,1], '|', color='grey', alpha=0.5) plt.plot(all_pca[447:,0], all_pca[447:,1], '_', color='grey', alpha=0.5) for i in range( len( han_highlight ) ): plt.plot(all_pca[han_highlight[i],0], all_pca[han_highlight[i],1], 'd', color='black') plt.plot(all_pca[jazz_highlight[i],0], all_pca[jazz_highlight[i],1], 's', color='black') plt.savefig('figs/pca_all.png', dpi=500); plt.clf() ''' # PCA pca = PCA(n_components=2) all_features_np = np.vstack( all_features ) all_pca = pca.fit_transform( np.vstack( all_features_np ) ) # sort by distance to other centroid np_styles_idx = np.array( all_styles_idx ) pca_1 = all_pca[ np_styles_idx == 0 , : ] pca_2 = all_pca[ np_styles_idx == 1 , : ] features_1 = all_features_np[ np_styles_idx == 0 , : ] features_2 = all_features_np[ np_styles_idx == 1 , : ] centr_1 = np.mean(pca_1, axis = 0) centr_2 = np.mean(pca_2, axis = 0) # distances x = np.linalg.norm(pca_1 - centr_2, axis=1) y = 1/(np.linalg.norm(pca_1 - centr_1, axis=1)+1) d_pca_1 = x/np.max(x) + 0.3*y/np.max(y) x = np.linalg.norm(pca_2 - centr_1, axis=1) y = 1/(np.linalg.norm(pca_2 - centr_2, axis=1)+1) d_pca_2 = x/np.max(x) + 0.3*y/np.max(y) # get indexes of sorted distances sidx1 = np.argsort( d_pca_1 )[::-1] sidx2 = np.argsort( d_pca_2 )[::-1] # keep names of each style idxs_1 = np.where( np_styles_idx == 0 )[0] names_1 = [all_names[i] for i in idxs_1] idxs_2 = np.where( np_styles_idx == 1 )[0] names_2 = [all_names[i] for i in idxs_2] # keep shorted names s_names_1 = [names_1[i] for i in sidx1] s_names_2 = [names_2[i] for i in sidx2] # keep sorted pcas s_pca_1 = pca_1[ sidx1, : ] s_pca_2 = pca_2[ sidx2, : ] s_features_1 = features_1[ sidx1, : ] s_features_2 = features_2[ sidx2, : ] with open('saved_data/all_names.pickle', 'wb') as handle: pickle.dump(all_names, handle, protocol=pickle.HIGHEST_PROTOCOL) with open('saved_data/all_styles.pickle', 'wb') as handle: pickle.dump(all_styles, handle, protocol=pickle.HIGHEST_PROTOCOL) with open('saved_data/all_styles_idx.pickle', 'wb') as handle: pickle.dump(all_styles_idx, handle, protocol=pickle.HIGHEST_PROTOCOL) with open('saved_data/all_features.pickle', 'wb') as handle: pickle.dump(all_features, handle, protocol=pickle.HIGHEST_PROTOCOL) with open('saved_data/all_pca.pickle', 'wb') as handle: pickle.dump(all_pca, handle, protocol=pickle.HIGHEST_PROTOCOL) with open('saved_data/style_folders.pickle', 'wb') as handle: pickle.dump(style_folders, handle, protocol=pickle.HIGHEST_PROTOCOL) # save sorted pcas and names with open('saved_data/s_pca_1.pickle', 'wb') as handle: pickle.dump(s_pca_1, handle, protocol=pickle.HIGHEST_PROTOCOL) with open('saved_data/s_pca_2.pickle', 'wb') as handle: pickle.dump(s_pca_2, handle, protocol=pickle.HIGHEST_PROTOCOL) with open('saved_data/s_features_1.pickle', 'wb') as handle: pickle.dump(s_features_1, handle, protocol=pickle.HIGHEST_PROTOCOL) with open('saved_data/s_features_2.pickle', 'wb') as handle: pickle.dump(s_features_2, handle, protocol=pickle.HIGHEST_PROTOCOL) with open('saved_data/s_names_1.pickle', 'wb') as handle: pickle.dump(s_names_1, handle, protocol=pickle.HIGHEST_PROTOCOL) with open('saved_data/s_names_2.pickle', 'wb') as handle: pickle.dump(s_names_2, handle, protocol=pickle.HIGHEST_PROTOCOL) else: with open('saved_data/all_names.pickle', 'rb') as handle: all_names = pickle.load(handle) with open('saved_data/all_styles.pickle', 'rb') as handle: all_styles = pickle.load(handle) with open('saved_data/all_styles_idx.pickle', 'rb') as handle: all_styles_idx = pickle.load(handle) with open('saved_data/all_features.pickle', 'rb') as handle: all_features = pickle.load(handle) with open('saved_data/all_pca.pickle', 'rb') as handle: all_pca = pickle.load(handle) with open('saved_data/style_folders.pickle', 'rb') as handle: style_folders = pickle.load(handle) # load sorted pcas and names with open('saved_data/s_pca_1.pickle', 'rb') as handle: s_pca_1 = pickle.load(handle) with open('saved_data/s_pca_2.pickle', 'rb') as handle: s_pca_2 = pickle.load(handle) with open('saved_data/s_features_1.pickle', 'rb') as handle: s_features_1 = pickle.load(handle) with open('saved_data/s_features_2.pickle', 'rb') as handle: s_features_2 = pickle.load(handle) with open('saved_data/s_names_1.pickle', 'rb') as handle: s_names_1 = pickle.load(handle) with open('saved_data/s_names_2.pickle', 'rb') as handle: s_names_2 = pickle.load(handle) # end if remakedata if test_plot: how_many = 100 # style 1 hm = min( [how_many, s_pca_1.shape[0]] ) plt.plot( s_pca_1[ :hm , 0 ], s_pca_1[ :hm , 1 ], 'o' , label=style_folders[0] ) # style 2 hm = min( [how_many, s_pca_2.shape[0]] ) plt.plot( s_pca_2[ :hm , 0 ], s_pca_2[ :hm , 1 ], 'x' , label=style_folders[1] ) plt.legend() plt.savefig('figs/pca.png', dpi=300); plt.clf() '''
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Python
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11,142
py
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MelVis_styles_N_blends.py
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poojirules180/Python-Learning-
4,131,758,547,239
4c89fc41abb848fe2df555c209c38af0aaf197e6
f49498ec6b53221f8cfdfcc1a33aa614d6441f4b
/Data points.py
56e5c1fd75bf79566359497576b6539e79cb4168
[]
no_license
https://github.com/poojirules180/Python-Learning-
460a97847d32af6f08854c9b15fdc4203f5972ad
c3ae8ada8a8a9c590e49fd13827e0d5568b6625f
refs/heads/master
2022-11-02T09:45:43.390577
2020-06-19T05:06:16
2020-06-19T05:06:16
270,888,898
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# Opening file readFile = open("poojith.txt", "r") #number of words data = readFile.read() #using data.split, It will help me dissect each word in the file. words = data.split() numberofWord = ('Number of words in text file :', str(len(words))) print(numberofWord) #code for number of lines lines = 0 with open("poojith.txt", 'r') as f: for line in f: lines += 1 numberofLines = ("Total number of lines is:", str(lines)) print(numberofLines) #total number of spaces in the files spaces=0 for spacesinText in data: #isspace method will bascially see if there are any spaces in the file, it will say either true and false. If it says yes, It will add 1 to spaces. if (spacesinText.isspace()) == True: spaces += 1 numberofSpaces = ("The number of blank spaces is: ",str(spaces)) print(numberofSpaces) #total number of characters in the file characters = 0 for line in data: #You can use the len method to find how many chracters there are. characters = characters + len(line) numberofChar = ("Number of characters in the file: ", str(characters)) print(numberofChar) #total number of times Python is mentioned in the file pythoninText = 0 #It is checking each word to see if it says "python" and then it will add 1 everytime it sees the word "Python". for line in words: if 'Python' in line: pythoninText = pythoninText + 1 numberofPython = ("Number of times Python was mentioned: ", str(pythoninText)) print(numberofPython)
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Data points.py
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uw-it-cte/uw-restclients
19,688,130,107,599
a2523a18061ff12518edb568489e0df70db02fb7
551dc5c9b361ee2ca2f41c762e101980983f8523
/restclients/canvas/courses.py
407fd51c939698d3ee6b143f8bcfecfd9cccaafa
[ "Apache-2.0" ]
permissive
https://github.com/uw-it-cte/uw-restclients
d20fe667bea13d0b146af90764579c4cf665ed72
2b09348bf066e5508304401f93f281805e965af5
refs/heads/master
2021-01-17T05:59:19.818654
2017-01-10T23:01:39
2017-01-10T23:01:39
26,886,439
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from restclients.canvas import Canvas from restclients.models.canvas import CanvasCourse, CanvasTerm import re class Courses(Canvas): def get_course(self, course_id, params={}): """ Return course resource for given canvas course id. https://canvas.instructure.com/doc/api/courses.html#method.courses.show """ include = params.get("include", []) if "term" not in include: include.append("term") params["include"] = include url = "/api/v1/courses/%s" % (course_id) return self._course_from_json(self._get_resource(url, params=params)) def get_course_by_sis_id(self, sis_course_id, params={}): """ Return course resource for given sis id. """ return self.get_course(self._sis_id(sis_course_id, sis_field="course"), params) def get_courses_in_account(self, account_id, params={}): """ Returns a list of courses for the passed account ID. https://canvas.instructure.com/doc/api/accounts.html#method.accounts.courses_api """ if "published" in params: params["published"] = "true" if params["published"] else "" url = "/api/v1/accounts/%s/courses" % (account_id) courses = [] for data in self._get_paged_resource(url, params=params): courses.append(self._course_from_json(data)) return courses def get_courses_in_account_by_sis_id(self, sis_account_id, params={}): """ Return a list of courses for the passed account SIS ID. """ return self.get_courses_in_account( self._sis_id(sis_account_id, sis_field="account"), params) def get_published_courses_in_account(self, account_id, params={}): """ Return a list of published courses for the passed account ID. """ params["published"] = True return self.get_courses_in_account(account_id, params) def get_published_courses_in_account_by_sis_id(self, sis_account_id, params={}): """ Return a list of published courses for the passed account SIS ID. """ return self.get_published_courses_in_account( self._sis_id(sis_account_id, sis_field="account"), params) def get_courses_for_regid(self, regid, params={}): """ Return a list of courses for the passed regid. https://canvas.instructure.com/doc/api/courses.html#method.courses.index """ params["as_user_id"] = self._sis_id(regid, sis_field="user") data = self._get_resource("/api/v1/courses", params=params) del params["as_user_id"] courses = [] for datum in data: if "sis_course_id" in datum: courses.append(self._course_from_json(datum)) else: courses.append(self.get_course(datum["id"], params)) return courses def create_course(self, account_id, course_name): """ Create a canvas course with the given subaccount id and course name. https://canvas.instructure.com/doc/api/courses.html#method.courses.create """ url = "/api/v1/accounts/%s/courses" % account_id body = {"course": {"name": course_name}} data = self._post_resource(url, body) return self._course_from_json(data) def update_sis_id(self, course_id, sis_course_id): """ Updates the SIS ID for the course identified by the passed course ID. https://canvas.instructure.com/doc/api/courses.html#method.courses.update """ url = "/api/v1/courses/%s" % course_id body = {"course": {"sis_course_id": sis_course_id}} data = self._put_resource(url, body) return self._course_from_json(data) def _course_from_json(self, data): course = CanvasCourse() course.course_id = data["id"] course.sis_course_id = data.get("sis_course_id", None) course.account_id = data["account_id"] course.code = data["course_code"] course.name = data["name"] course.workflow_state = data["workflow_state"] course.public_syllabus = data["public_syllabus"] course_url = data["calendar"]["ics"] course_url = re.sub(r"(.*?[a-z]/).*", r"\1", course_url) course.course_url = "%scourses/%s" % (course_url, data["id"]) # Optional attributes specified in the course URL if "term" in data: canvas_term = data["term"] course.term = CanvasTerm( term_id=canvas_term.get("id"), sis_term_id=canvas_term.get("sis_term_id", None), name=canvas_term.get("name")) if "syllabus_body" in data: course.syllabus_body = data["syllabus_body"] return course
UTF-8
Python
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false
4,911
py
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courses.py
80
0.583181
0.581959
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liamattard/Language_model-1.0.
7,395,933,698,088
86770e62429c797de7c720bab30d4769cb6a45c8
6d74a3665181f78defe9b8ccf44c64c22e202c9f
/language_model/probabilityCalc.py
49a0177c7be14c617d7a9516cdeaaa5bac97460a
[]
no_license
https://github.com/liamattard/Language_model-1.0.
62f067cfe9180ca44da3562539d3b35c26da2ee5
d22cdfa7c0c8d7c0fb1eeefe6a98f34cf95ee013
refs/heads/master
2022-04-22T23:55:26.943656
2020-04-16T08:11:30
2020-04-16T08:11:30
256,150,601
0
1
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import random import numpy as np from collections import Counter def calculateProbabilityFromUnigram(unigram,sentence,word): return unigram[word] def calculateProbabilityFromBigram(bigram,sentence,word): if (sentence[-1],word) in bigram: return bigram[sentence[-1],word] else: return 0 def calculateProbabilityFromTrigram(trigram,sentence,word): if (sentence[-2],sentence[-1],word) in trigram: return trigram[sentence[-2],sentence[-1],word] else: return 0 def calculateProbabilityFromLaplaceBigram(trainCount,bigramCounts,sentence,lastWord): if (sentence[-1],lastWord) in bigramCounts: return (bigramCounts[sentence[-1],lastWord] +1)/(trainCount[sentence[-1]] + len(trainCount)) else: if (sentence[-1] in trainCount) and (lastWord in trainCount): return 1/(trainCount[sentence[-1]] + len(trainCount)) else: return 0 def calculateProbabilityFromLaplaceTrigram(trainCount, bigramCounts,trigramCounts,sentence,lastWord): if (sentence[-2],sentence[-1],lastWord) in trigramCounts: return (trigramCounts[sentence[-2],sentence[-1],lastWord]+1)/(bigramCounts[sentence[-2],sentence[-1]] + len(trainCount)) else: if ((sentence[-2],sentence[-1]) in bigramCounts) and (lastWord in trainCount): return 1/(bigramCounts[sentence[-2],sentence[-1]] + len(trainCount)) else: return 0 def calculateProbabilityInterpolation(unigram,bigram,trigram,sentence,word): probabilityUnigram = 0.1* (unigram[word]) probabilityBigram = 0.3 * (bigram[sentence[-1],word]) probabilityTrigram = 0.6 * (trigram[sentence[-2],sentence[-1],word]) return probabilityUnigram + probabilityBigram + probabilityTrigram def calculateProbabilityLaplaceInterpolation(trainCount,unigramCount,bigramCounts,trigramCounts,sentence,lastWord): probabilityUnigram = 0.1 * (unigramCount[lastWord]) probabilityBigram = 0.3 * (calculateProbabilityFromLaplaceBigram(trainCount,bigramCounts,sentence,lastWord)) probabilityTrigram = 0.6 * (calculateProbabilityFromLaplaceTrigram(trainCount,bigramCounts,trigramCounts,sentence,lastWord)) return probabilityUnigram + probabilityBigram + probabilityTrigram
UTF-8
Python
false
false
2,264
py
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probabilityCalc.py
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olse/faq
12,687,333,426,614
3e965c3b4d50571084b5cedb88491cfde9c4e57c
49572f3e1beb5cbdf97262c772251743783a1274
/faq/admin.py
00ec6f0b055b826e9c7b2b8c31a58be5d6b57ca3
[]
no_license
https://github.com/olse/faq
fac1eeef09a1007e1de3dfb5d5a85cf93dfff740
c4ae87c38e5f719f3b8beb27beae22a515ec8c24
refs/heads/master
2015-07-31T17:38:09.731216
2012-07-17T16:16:39
2012-07-17T16:16:39
null
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from django.contrib import admin from faq.models import Language class LanguageAdmin(admin.ModelAdmin): list_display= ['name','code'] admin.site.register(Language,LanguageAdmin)
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Python
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admin.py
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Festus254/commentsprediction
13,520,557,091,926
e3c1ec07af8fcf75af35de176ce419c9d1c25ddb
f55ce0cf1573c95db6b7c07098d6bb6c2ecb113e
/app.py
c6b4ffc9d84c5f9919335a8f7d585cfc6bc56be5
[]
no_license
https://github.com/Festus254/commentsprediction
9abe6e8678e245744dc1eadf7f64e8d53cd13a73
fc6bf88369381503229c73a7ce6ad40698696bd5
refs/heads/main
2023-05-02T09:56:59.004484
2021-05-24T14:55:13
2021-05-24T14:55:13
345,622,505
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import streamlit as st import pandas as pd import numpy as np import nltk import re import string import pickle import joblib import timeit #from sklearn import #from prob_svm import LinearSVC_proba nltk.download('popular') stopwords = nltk.corpus.stopwords.words('english') ps = nltk.PorterStemmer() wn = nltk.WordNetLemmatizer() option = st.sidebar.selectbox( 'Which ML model would you like to use?', ('Logistic Regression', 'Linear SVC', 'Naive Bayes')) st.write('You selected:', option) st.title('Toxic Comment Analysis.') st.markdown(''' Used Natural language Processing to clean and vectorize input data and Machine learning algorithmto predict if a comment is toxic or not. The implemented models named on the sidebar had a F1 accuracy of 72.1%, 72.3% and 65.1% respectively. ''') # function to remove punctuation, tokenize, remove stopwords and stem @st.cache def clean_text(text): text = ''.join([i for i in text if not i.isdigit()]) #remove integer values text = "".join([word.lower() for word in text if word not in string.punctuation])#make lowercase and remove punctuation text = ' '.join( [word for word in text.split() if len(word)>2] )#remove words less than 2 letters tokens = re.split('\W+', text) #words = [wn.lemmatize(word, 'v') for word in tokens] text = [ps.stem(word) for word in tokens if word not in stopwords] text = [wn.lemmatize(word) for word in text] text = " ".join(text) return text @st.cache def vectorizing(text): new_question = text tfidf_vectorizer = pickle.load(open("tfidf.pickle", "rb")) vectorized_question = tfidf_vectorizer.transform([new_question]) return vectorized_question @st.cache def create_features(cleaned_text, vectorized_text): text = cleaned_text vectorized_text = vectorized_text label = ['toxic', 'severe_toxic', 'obscene', 'threat','insult', 'identity_hate'] toxic = ['fuck', 'shit', 'suck', 'stupid', 'bitch', 'idiot', 'asshol', 'gay', 'dick'] severe_toxic = ['fuck', 'bitch', 'suck', 'shit', 'asshol', 'dick', 'cunt', 'faggot', 'cock'] obscene =['fuck', 'shit', 'suck', 'bitch', 'asshol', 'dick', 'cunt', 'faggot', 'stupid'] threat =['kill', 'die', 'fuck', 'shit', 'rape', 'hope', 'bitch', 'death', 'hell'] insult = ['fuck', 'bitch', 'suck', 'shit', 'idiot', 'asshol', 'stupid', 'faggot', 'cunt'] identity_hate = ['fuck', 'gay', 'nigger', 'faggot', 'shit', 'jew', 'bitch', 'homosexu', 'suck'] contains_toxic = [] contains_severe_toxic = [] contains_obscene = [] contains_threat = [] contains_insult = [] contains_identity_hate =[] for col in range(len(label)): toxic_list = vars()[label[col]] #st.write(toxic_list) value = "contains_"+label[col] check = any(substring in text for substring in toxic_list) if check is True: vars()[value].append(1) #st.write("True") else: vars()[value].append(0) #st.write("False") inp = list([contains_toxic[0],contains_severe_toxic[0],contains_obscene[0], contains_threat[0], contains_insult[0], contains_identity_hate[0]]) df = pd.DataFrame([inp], columns=['contains_toxic_word', 'contains_severe_toxic_word', 'contains_obscene_word', 'contains_threat_word', 'contains_insult_word', 'contains_identity_hate_word']) X = pd.concat([df, pd.DataFrame(vectorized_text.toarray())], axis=1) return X def predict(features, model = 'Linear SVC'): start_time = timeit.default_timer() if model == 'Logistic Regression': svc_from_joblib = joblib.load('lintoxicmodel.pkl') y = svc_from_joblib.predict_proba(features) elapsed = timeit.default_timer() - start_time if model == 'Linear SVC': svc_from_joblib = joblib.load('svctoxicmodel.pkl') y = svc_from_joblib.decision_function(features) elapsed = timeit.default_timer() - start_time if model == 'Naive Bayes': svc_from_joblib = joblib.load('bayestoxicmodel.pkl') y = svc_from_joblib.predict_proba(features) elapsed = timeit.default_timer() - start_time return y,elapsed def main(): message = st.text_area('write a comment here:') if st.button('Predict'): #st.write(message) cleaned_text = clean_text(message) #st.write(cleaned_text) vectorized_text = vectorizing(cleaned_text) #st.write(vectorized_text) features = create_features(cleaned_text, vectorized_text) #st.write(features) prediction, elapsed = predict(features, model = option) st.write("Time elapsed to predict is {:2f} minutes". format(elapsed/60)) df = pd.DataFrame({ "contains_toxic": prediction[:, 0], "contains_severe_toxic": prediction[:, 1], "contains_obscene": prediction[:, 2], "contains_threat": prediction[:, 3], "contains_insult":prediction[:, 4], "contains_identity_hate": prediction[:, 5] }, index=['Comment']) st.write(df.T) if __name__ == '__main__': main()
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app.py
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0.682057
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120
38.708333
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poeks/twitterbelle
19,292,993,118,772
8e7ec29ee1a5c318a97412a8de534cdb02c52ac6
f3c0ec2252db6a5f9ec1418a56ab67bee5e75c55
/lib/parser.py
0b4f87ef323c12a4fd60e5671238fa3db26fd242
[ "LicenseRef-scancode-generic-cla", "Apache-2.0" ]
permissive
https://github.com/poeks/twitterbelle
b10d8cd78019dc88e5953ab881d609488fcb3f95
48d7de8bbfa089391607b3089890128446447056
refs/heads/master
2021-01-25T00:16:29.496568
2010-10-18T14:35:45
2010-10-18T14:35:45
null
0
0
null
null
null
null
null
null
null
null
null
null
null
null
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from lib.BeautifulSoup import BeautifulSoup import urllib2 from django.conf import settings import re from lib.exception import PoeksException from lib.BeautifulSoup import BeautifulSoup import urllib2 class Parser: def __init__(self): pass def open_url(self, page): try: this_url = "%s%s" % (self.url, page) return urllib2.urlopen(this_url) except: raise Exception def get_element(self, search_string, search_type='id', search_contents='string', search_method='method'): try: el = self.soup.findAll(**{search_type:search_string}) except: raise Exception retval = "" try: if search_method == 'dict': retval = el[0][search_contents] else: retval = getattr(el[0], search_contents) except Exception, e: #PoeksException(e, "Couldn't do that thing with el %s" % el) #print "get_element: Couldn't do that thing with el %s search_string %s" % (el, search_string) pass return retval def get_elements(self, search_string, search_type='id', search_contents='string', search_method='method'): els = self.soup.findAll(**{search_type:search_string}) elements = [] for el in els: retval = "" try: if search_method == 'dict': retval = el[search_contents] #print "dict: "+retval else: retval = getattr(el, search_contents) #print "method: "+retval except Exception, e: #PoeksException(e, "Couldn't do that thing with el %s" % el) #print "Oops" pass elements.append(retval) return elements
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false
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1,539
py
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parser.py
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0.65822
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107
sdsubhajitdas/Algorithm-Collection
18,717,467,491,332
c364989c97265f02feb3694f53af8a9e106e2e7b
b87387634f2ab0497210513a727addb94a06b1a0
/Data Structures/Stack/Stack.py
75aa795171e93a56408514c3fda3baa6a0e50075
[]
no_license
https://github.com/sdsubhajitdas/Algorithm-Collection
93036fa3f08bed26dc87bf727a65e99a61e05b4b
85d644cd4f3737bfdf2ff115d2d56b7d01f7bbb3
refs/heads/master
2020-05-30T06:50:21.641213
2019-07-13T18:11:11
2019-07-13T18:11:11
189,586,819
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null
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class Stack(): def __init__(self,length): self.length = length self.stack = [None]*length self.pointer = -1 def push(self, element): if self.pointer >=self.length-1: print("Stack Overflow") return self.pointer+=1 self.stack[self.pointer]=element def pop(self): if self.pointer < 0: print("Stack Underflow") return self.pointer-=1 return self.stack[self.pointer + 1] def peek(self): return self.stack[self.pointer] if self.pointer >= 0 else None def print(self): print(*self.stack if self.pointer>=0 else None) if __name__ == "__main__": stack = Stack(3) stack.push(2) stack.push(4) stack.push(6) stack.push(8) print(stack.peek()) stack.print() print(stack.pop()) print(stack.pop()) print(stack.pop()) print(stack.pop())
UTF-8
Python
false
false
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py
37
Stack.py
37
0.545358
0.531483
0
39
23.051282
70
openstack/cloudkitty-dashboard
8,160,437,880,606
e85dcc92430f21c97ecb2e54fe1659cabe9f837b
f18df31d4ba8569b420219f5d52da311a32581d6
/cloudkittydashboard/dashboards/admin/modules/forms.py
dd0388810d497c6c71a8159d3db8829c2cd83498
[ "Apache-2.0" ]
permissive
https://github.com/openstack/cloudkitty-dashboard
418b54a59a93201c79e422ee4571c9f24b6234e5
4ed8863c1b15d489a2a78e767b737402647bc4da
refs/heads/master
2023-08-23T06:09:10.473334
2023-07-12T15:40:17
2023-07-12T15:40:17
23,157,716
25
14
Apache-2.0
false
2022-01-18T10:16:11
2014-08-20T17:35:14
2021-12-28T21:37:49
2022-01-18T10:15:37
525
46
15
0
Python
false
false
# Copyright 2017 Objectif Libre # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from django.utils.translation import gettext_lazy as _ from horizon import exceptions from horizon import forms from horizon import messages from cloudkittydashboard.api import cloudkitty as api class EditPriorityForm(forms.SelfHandlingForm): priority = forms.IntegerField(label=_("Priority")) def handle(self, request, data): ck_client = api.cloudkittyclient(request) try: priority = ck_client.rating.update_module( module_id=self.initial["module_id"], priority=data["priority"]) messages.success( request, _('Successfully updated priority')) return priority except Exception: exceptions.handle(request, _("Unable to update priority."))
UTF-8
Python
false
false
1,407
py
53
forms.py
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0.677328
0.671642
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37.027027
79
lrutl/nudgebc.com
6,562,710,032,845
d8bcafcdeccd5ff2b0275517c8236bc77bace9d8
3b6042325da6dbf24fdbed8e1f35fcadf2c34140
/nudgeproject/urls.py
101db6d589f8687eca6e6dd315a84daef66e01f1
[]
no_license
https://github.com/lrutl/nudgebc.com
45c731f8340b8a4d24a1255f59b0a253f3850768
710eb94e72070bb01b734e85d32d8a7fa31db907
refs/heads/master
2023-08-25T18:10:01.941857
2021-10-28T20:51:54
2021-10-28T20:51:54
null
0
0
null
null
null
null
null
null
null
null
null
null
null
null
null
from django.contrib import admin from django.urls import include, path from django.conf.urls import include, url from django.views import generic from material.frontend import urls as frontend_urls from django.conf import settings from django.conf.urls.static import static urlpatterns =[ url(r'', include(frontend_urls)), path('', include('nudge.urls')), ] urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT) urlpatterns += static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)
UTF-8
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false
false
523
py
21
urls.py
8
0.782027
0.782027
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36.357143
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pyfibot/pyfibot3
11,613,591,585,495
39a7926d82f32bc9b3318b8a5fa669c42cde9ffb
d17863f84626328917c36ad36622e4ef4ce26bb4
/pyfibot/plugins/available/posti.py
5ddff43102396e61988942112e3bd61a1c5f4368
[]
no_license
https://github.com/pyfibot/pyfibot3
48b479e7b099e6743b23c62e0d9670a8066ddcad
75b63e60f17735150fdcdb1a72c181efbef9ca08
refs/heads/master
2021-01-20T19:34:34.014768
2018-12-28T12:33:30
2018-12-28T12:33:30
63,968,611
0
0
null
false
2019-03-06T07:56:34
2016-07-22T16:42:20
2018-12-28T12:33:42
2018-12-28T12:33:40
124
0
0
6
Python
false
null
""" Get shipment tracking info from Posti """ from pyfibot.plugin import Plugin from pyfibot.url import URL from pyfibot.utils import parse_datetime, get_relative_time_string from urllib.parse import quote_plus class Posti(Plugin): def init(self): self.lang = self.config.get('language', 'en') @Plugin.command('posti') def posti(self, sender, message, raw_message): ''' Get latest tracking event for a shipment from Posti. Usage: .posti JJFI00000000000000 ''' if not message: return self.bot.respond('Tracking ID is required.', raw_message) url = 'http://www.posti.fi/henkiloasiakkaat/seuranta/api/shipments/%s' % quote_plus(message) data = URL.get_json(url) if not data or not data.get('shipments'): return self.bot.respond('Error while getting tracking data. Check the tracking ID or try again later.', raw_message) shipment = data['shipments'][0] phase = shipment['phase'] eta_timestamp = shipment.get('estimatedDeliveryTime') latest_event = shipment['events'][0] event_time = get_relative_time_string(parse_datetime(latest_event['timestamp']), lang=self.lang) description = latest_event['description'][self.lang] location = '%s %s' % (latest_event['locationCode'], latest_event['locationName']) msg = ' - '.join([event_time, description, location]) if phase != 'DELIVERED' and eta_timestamp: eta_dt = parse_datetime(eta_timestamp) eta_txt = eta_dt.strftime('%d.%m.%Y %H:%M') msg = 'ETA %s - %s' % (eta_txt, msg) self.bot.respond(msg, raw_message)
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false
false
1,662
py
42
posti.py
34
0.643201
0.633574
0
43
37.651163
128
New-arkssac/dwm
2,027,224,574,870
9e3df0d0c84e91baf236f74e9365429034b5962e
41331559f499fdcefaec97a31abbfaceff42e43a
/statusbar/bar/vol.py
2e84c8e9dcc165c0825fe3cfa09262c22c62a68e
[ "MIT" ]
permissive
https://github.com/New-arkssac/dwm
967cfdf387d3acaab9c62eb4b1b25a42b75f7cef
d2ee335a6730c46873cd13cdfe30a631a339258f
refs/heads/master
2023-03-15T15:16:52.743029
2023-02-28T12:25:03
2023-02-28T12:25:03
580,392,527
0
0
MIT
true
2022-12-20T12:58:40
2022-12-20T12:58:39
2022-12-19T17:11:37
2022-12-15T04:10:42
21,485
0
0
0
null
false
false
#!/bin/python3 import os import subprocess import re class MyVol: def __init__(self, *args) -> None: self.this = "vol" self.dwm = os.environ["DWM"] self.s2d_reset = "^d^" # self.color = "^c#1A1A1A^^b#516FAB^" self.color = "^c#babbf1^^b#1a1b26^" self.signal = f"^s{self.this}^" self.handle() match args[0]: case "update": self.update() case "notify": self.notify() case _: self.click(args[1]) def handle(self): byte, _ = subprocess.Popen( ["/bin/pactl", "info"], stdout=subprocess.PIPE ).communicate() sink_stdout = byte.decode() sink_stdout = re.search("Default Sink: .*", sink_stdout) self.sink_stdout = sink_stdout and sink_stdout.group().replace( "\n", "" ).replace("Default Sink: ", "").replace("\n", "") byte, _ = subprocess.Popen( [ "/bin/bash", "-c", f"pactl list sinks | grep {self.sink_stdout} -A 6 | sed -n '7p' | grep 'Mute: no'", ], stdout=subprocess.PIPE, ).communicate() mute_stdout = byte.decode() byte, _ = subprocess.Popen( [ "/bin/bash", "-c", f"pactl list sinks | grep {self.sink_stdout} -A 7 | sed -n '8p' | awk '{{printf int($5)}}'", ], stdout=subprocess.PIPE, ).communicate() vol = int(byte.decode()) self.num = vol self.vol, self.icon = ( not mute_stdout and ("--", "ﱝ") or vol == 0 and ("00" + "%", "婢") or vol < 10 and ("0" + str(vol) + "%", "奄") or vol <= 50 and (str(vol) + "%", "奔") or (str(vol) + "%", "墳") ) def update(self) -> None: text = f"{self.icon} {self.vol} " print(text) with open(self.dwm + "/statusbar/tmp.py", "r+") as f: lines = f.readlines() tmp = [] f.seek(0) for line in lines: _ = re.search(rf"{self.this} = .*$", line) or tmp.append(line) tmp.append( f'{self.this} = "{self.color}{self.signal}{text}{self.s2d_reset}"\n' ) f.truncate() f.writelines(tmp) def notify(self): byte, _ = subprocess.Popen( [ "/bin/bash", "-c", f"pactl list sinks | grep '{self.sink_stdout}' -A 10 | grep 'Description: ' | awk -F 'Description: ' '{{print $2}}'", ], stdout=subprocess.PIPE, ).communicate() card_name = byte.decode().split("\n")[0] subprocess.Popen( [ "/bin/bash", "-c", f"notify-send -r 9527 -h int:value:{self.num} -h string:hlcolor:#7F7FFF ' {card_name}[{self.icon} {self.vol}]'", ], ) def click(self, mode): match mode: case "L": self.notify() case "M": subprocess.Popen( ["/bin/bash", "-c", "pactl set-sink-mute @DEFAULT_SINK@ toggle"], ) case "R": subprocess.Popen( ["/bin/bash", "-c", "killall pavucontrol || pavucontrol &"], ) case "U": subprocess.Popen( ["/bin/bash", "-c", "pactl set-sink-volume @DEFAULT_SINK@ +5%"], ) self.notify() case "D": subprocess.Popen( ["/bin/bash", "-c", "pactl set-sink-volume @DEFAULT_SINK@ -5%"], ) self.notify()
UTF-8
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false
false
3,878
py
14
vol.py
10
0.417486
0.406622
0
126
29.68254
133
arivolispark/datastructuresandalgorithms
14,001,593,420,932
351f109958fb7c9c118e4b5acaa3185639d20b1f
9a5ad43ce6add59f266074c463c402f4ff717dc5
/leetcode/30_day_leetcoding_challenge/202004/20200423_bitwise_AND_of_numbers_range/bitwise_AND_of_numbers_range.py
c264df0e5e67c2a936282a3b72f630884411ba39
[]
no_license
https://github.com/arivolispark/datastructuresandalgorithms
9cb1cd66f61ab22471d7378fce51f29fcf0ef553
57534898c17d058ef1dba2b1cb8cdcd8d1d2a41c
refs/heads/master
2021-06-24T15:51:04.438627
2021-01-12T05:14:37
2021-01-12T05:14:37
84,909,655
0
1
null
false
2021-01-12T05:14:38
2017-03-14T05:38:16
2021-01-05T10:18:42
2021-01-12T05:14:38
2,529
0
1
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Python
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""" Title: Bitwise AND of Numbers Range Given a range [m, n] where 0 <= m <= n <= 2147483647, return the bitwise AND of all numbers in this range, inclusive. Example 1: Input: [5,7] Output: 4 Example 2: Input: [0,1] Output: 0 """ class Solution: def rangeBitwiseAnd(self, m: int, n: int) -> int: result = 0 while m > 0 and n > 0: msb_p1 = most_significant_bit_position(m) msb_p2 = most_significant_bit_position(n) if msb_p1 != msb_p2: break # add 2^msb_p1 to result msb_val = (1 << msb_p1) result += msb_val # subtract 2^msb_p1 from m and n. m -= msb_val n -= msb_val return result def most_significant_bit_position(n: int): msb_p = -1 while n > 0: n = n >> 1 msb_p += 1 return msb_p def get_test_case_1() -> (int, int): m, n = 0, 1 return m, n def get_test_case_2() -> (int, int): m, n = 5, 7 return m, n def get_test_case_3() -> (int, int): m, n = 1, 10 return m, n def get_test_case_4() -> (int, int): m, n = 1, 1 return m, n def get_test_case_5() -> (int, int): m, n = 2, 2 return m, n def get_test_case_6() -> (int, int): m, n = 3, 3 return m, n def get_test_case_7() -> (int, int): m, n = 0, 2147483647 return m, n def get_test_case_8() -> (int, int): m, n = 20000, 2147483647 return m, n if __name__ == "__main__": solution = Solution() #m, n = get_test_case_1() #m, n = get_test_case_2() #m, n = get_test_case_3() #m, n = get_test_case_4() #m, n = get_test_case_5() #m, n = get_test_case_6() #m, n = get_test_case_7() m, n = get_test_case_8() print("\n m: ", m) print(" n: ", n) result = solution.rangeBitwiseAnd(m, n) print("\n result: ", result)
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py
370
bitwise_AND_of_numbers_range.py
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Fritas/destroyer-airplanes
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9a75523ff021b40476f9d5d78adef40240309692
dba9dc48a6d97ac4fdce93ffc89fdfe689773d1d
/model/objeto_aeronave.py
566bfaa36eee44435cc759da737c17afcfabba99
[]
no_license
https://github.com/Fritas/destroyer-airplanes
569679f5df0007e6f54b15e634662d9a5fdc4bf5
628279541d7f89adb2e9c5eb24e05dfd884146b0
refs/heads/master
2020-03-28T00:09:44.831421
2018-10-23T19:16:00
2018-10-23T19:16:00
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0
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""" Created on jun de 2017 @author: Adriano @author: Andrei @author: Joao """ from .objeto import Objeto class ObjetoAeronave(Objeto): """ Objeto base para aeronaves do jogo """ def __init__(self, ambiente, img_aeronave, img_projetil, dano, resistencia, vida, velocidade_tiro, velocidade, pos_x=0, pos_y=0): """ O metodo inicia um objeto aeronave :param ambiente: ambiente pygame que o jogo esta rodando :param img_aeronave: caminho da imagem da aeronave :param img_projetil: caminho da imagem do projetil :param dano: valor numerico do dano :param resistencia: valor numerico da resistencia :param vida: valor numerico da vida :param velocidade_tiro: valor numerico da velocidade do tiro :param velocidade: tupla com as velocidades da nave (velocidade_x, velocidade_y) :param pos_x: posicao x inicial no mapa :param pos_y: posicao y inicial no mapa """ super().__init__(ambiente, img_aeronave, velocidade[0], velocidade[1], pos_x, pos_y) self.definir_velocidade_tiro(velocidade_tiro) self.dano = dano self.resistencia = resistencia self.vida = vida self.img_projetil = img_projetil self.grup_tiros = self.ambiente.sprite.Group() def definir_velocidade_tiro(self, velocidade): """ O metodo define velocidade como um numero inteiro :param velocidade: :return: None """ self.velocidade_tiro = int(velocidade) def update(self): """ Metodo que deve ser reimplementado em toda aeronave para definir as atualizaoes dela a cada loop do jogo :return: """ pass def tratar_evento(self, key=None, evento=None): """ Metodo que deve ser reimplementado em toda aeronave para definir os comportamentos :param key: :param evento: :return: """ pass def status_vida(self): """ verifica se a aeronave ainda esta viva :return: se esta vivo retorna True, se estiver morto retorna False """ if self.vida > 0: return True return False def atirar(self): """ Metodo que deve ser reimplementado com o tiro da aeronave :return: """ pass
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objeto_aeronave.py
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collinwat/doku
16,028,817,982,602
a97d26c54b9660b1f690f9a0a31a725b508b9ceb
910564164b1dccfec90ebcbbf427472fd0e1fd7c
/doku/sudoku/game.py
b81db36f8a7694d7a898d9131e64563cc3c9116c
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refs/heads/master
2021-01-25T05:15:21.684140
2013-04-19T21:08:58
2013-04-19T21:08:58
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import doku.utils as utils import solve import parse class Board(object): def __init__(self, board, size=None): board = board.strip() if size is None: size = utils.box_size(len(board)) if not size: msg = 'Size not specified and board string is not a perfect square' raise ValueError(msg) self.size = size self.box_size = boxes = utils.box_size(self.size) self.hr = '+%s+' % '+'.join(['-' * (boxes * 2 + 1)] * boxes) self.parser = parse.StringParser(self.size) self.known = self.parser.parse(board) self.rebuild() @property def solutions(self): if not getattr(self, '_solutions', None): self._solutions = self.solver.solve() return self._solutions def rebuild(self): self.solver = solve.DLXSolver(self.size, known=self.known) self._solutions = None self.reset() def reset(self): self.guesses = set() self.grid = [[None] * self.size for i in xrange(self.size)] for known in self.known: self.grid[known[0]][known[1]] = known[2] + 1 def guess(self, guess): row, column, number = guess if guess in self.known or \ guess in self.guesses or \ row < 0 or row >= self.size or \ column < 0 or column >= self.size or \ number < 0 or number >= self.size: return row = self.grid[row] old = row[column] row[column] = number + 1 self.guesses.add(guess) if old: old = (guess[0], guess[1], old) if old in self.guesses: self.guesses.remove(old) @property def ilines(self): boxes = self.box_size for row_index, row in enumerate(self.grid): if row_index % self.box_size == 0: yield self.hr row = ['%s' % i if i else '.' for i in row] segments = [] for i in xrange(boxes): cells = row[i * boxes:i * boxes + boxes] segments.append(' '.join(cells)) yield '| %s |' % ' | '.join(segments) if row_index == len(self.grid) - 1: yield self.hr @property def lines(self): return list(self.ilines) @property def text(self): return '\n'.join(self.ilines) @property def line(self): return ''.join(['%s' % cell if cell else '.' for row in self.grid for cell in row]) def solve(self, index=0): if len(self.solutions) < 1: return for solution in self.solutions[index]: self.guess(solution)
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game.py
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dllm-pk/ETI_Team02_assignment1
12,163,347,400,245
c53a2d3a9f2322f8d92b3c7a3beecb7f456aad2e
8f3031b2ec53e9c4f728156e9986faadfb20e6a3
/code/movement.py
d31a01bd4f1faddf53d6989ed13b6cf25d24b8a6
[]
no_license
https://github.com/dllm-pk/ETI_Team02_assignment1
b452d75a8bafcf48196c0ac2f7d4bd498097ed6c
e515dc72ee334310f7d98ca07173e656f0df8402
refs/heads/main
2023-03-04T20:37:55.470642
2021-02-19T04:35:46
2021-02-19T04:35:46
312,141,726
0
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2021-02-19T04:25:17
2020-11-12T02:06:05
2021-02-19T04:18:51
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from new_save_exit_game import * from map import * #User inputs move def get_move(): moves = [W, A, S, D] while True: s = " "+W+ " = up; " + A+ " = left; " + S+ " = down; " + D+ " = right" print(s) prompt="Enter move: " move = input(prompt).upper() if move in moves: return move print("invalid input") #Hero moves def game_move(game): view_map(game) position = 1 x = game[X_INDEX] y = game[Y_INDEX] game_map = game[MAP_INDEX] n = len(game_map [0]) print() while True: move = get_move() bad = False if move == S: if x == n-1: bad = True print("Cannot move DOWN") else: x = x+1 if move == W: if x == 0: bad = True print("Cannot move UP") else: x = x-1 if move == D: if y == n-1: bad = True print("Cannot move RIGHT") else: y = y+1 if move == A: if y == 0: bad = True print("Cannot move :LEFT") else: y = y-1 if not bad: break game[X_INDEX] = x game[Y_INDEX] = y def town_move(game): game_move(game) game[STATE_INDEX] = OUT_DOOR view_map(game) game[DAY_INDEX] = game[DAY_INDEX] + 1 town_move(game)
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movement.py
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tkincaid/tkincaid.github.com
4,466,766,020,246
3712de82e2fc9912d170ca7b5b9a67983494a736
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/tests/ModelingMachine/test_Numeric_impute.py
b69de47b9de19c3f8205fa577498c24986b4ac26
[]
no_license
https://github.com/tkincaid/tkincaid.github.com
cf349c143056b847c8281d8d363b686a679f6499
8a9ab9ea4a061573328b5fcca6706536062e3be5
refs/heads/master
2016-09-05T09:24:48.325828
2014-09-24T16:49:14
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######################################################### # # Unit Test for Numeric_impute_arbitrary Task # # Author: Sergey Yurgenson # # Copyright DataRobot, Inc. 2014 # ######################################################## import unittest import pandas as pd import numpy as np import os import sys tests_dir = os.path.dirname(os.path.abspath(__file__) ) modeling_machine_dir = os.path.join(tests_dir, '../..') sys.path.append(modeling_machine_dir) from ModelingMachine.engine.tasks.converters import Numeric_impute_arbitrary from ModelingMachine.engine.tasks.converters import Numeric_impute from ModelingMachine.engine.container import Container class TestNumericImputeArbitrary(unittest.TestCase): def setUp(self): self.c = pd.DataFrame(data=np.array([ [ 1, 2, 11, float('NaN') ], [ 2, 3, 17, 18 ], [ 3, 2, float('NaN'), 16 ], [ 4, 1, 13, 14 ], [ 20, 1, 45, 46 ] ]),columns=['a','b','c','d']) self.correct1 = np.array([ [ 1, 2, 11, -9999 ], [ 2, 3, 17, 18 ], [ 3, 2, -9999, 16 ], [ 4, 1, 13, 14 ], [ 20, 1, 45, 46 ] ]) self.correct2 = np.array([ [ 1, 2, 11, 100 ], [ 2, 3, 17, 18 ], [ 3, 2, 100, 16 ], [ 4, 1, 13, 14 ], [ 20, 1, 45, 46 ] ]) def test_transform_arbitrary_imputation(self): nip = Numeric_impute_arbitrary() X = nip.fit_transform(Container(self.c)) print 'Result is \n{}'.format(X()) self.assertTrue(np.all(X() - self.correct1 < 0.0001)) nip = Numeric_impute_arbitrary('m=100') X = nip.fit_transform(Container(self.c)) print 'Result is \n{}'.format(X()) self.assertTrue(np.all(X() - self.correct2 < 0.0001)) class TestNumericImpute(unittest.TestCase): def test_transform_imputation_with_object_type(self): self.c = pd.DataFrame(data=np.array([ [ 1, 2, 11, float('NaN') ], [ 2, 3, 17, 18 ], [ 3, 2, float('NaN'), 16 ], [ 4, 1, 13, 14 ], [ 20, 1, 45, 46 ] ]).astype('object'),columns=['a','b','c','d']) nip = Numeric_impute() X = nip.fit_transform(Container(self.c)) # passes if no error, should probably assert something def test_transform_missing_test_only(self): train = pd.DataFrame(data=np.array([ [ 1, 2, 11, 11 ], [ 2, 3, 17, 18 ], [ 3, 2, 14, 16 ], [ 4, 1, 13, 14 ], [ 20, 1, 45, 46 ]], dtype=np.float)) test = pd.DataFrame(data=np.array([ [ 1, 2, 11, np.nan ], [ 2, 3, 17, np.nan ], [ 3, 2, np.nan, np.nan ], [ 4, 1, 13, np.nan ], [ 20, 1, 45, np.nan ]], dtype=np.float)) print(test) nip = Numeric_impute() nip.fit(Container(train)) np.testing.assert_array_equal(nip.nan_count_, np.zeros(4)) out = nip.transform(Container(test)) print(out()) self.assertTrue(np.all(np.isfinite(out()))) if __name__ == '__main__': unittest.main()
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py
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test_Numeric_impute.py
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hima03/log-decorator
11,244,224,410,100
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9a935990abcadf94abbc2ce32ab693a0ebb6a87a
/log-decorators/calculator.py
4304451c6a64f43ea8e9fa95f6e68979715febcb
[]
no_license
https://github.com/hima03/log-decorator
d0aabefe74dce4ab920fcad62323b1f9899c9921
63697d914a5cd423a16dd74fb3cf774498da7197
refs/heads/master
2023-05-03T06:33:22.897743
2020-08-28T10:30:26
2020-08-28T10:30:26
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21
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2020-08-26T07:21:07
2022-09-02T14:01:28
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import log_decorator import log class Calculator(): def __init__(self, first=0, second=0, log_file_name='', log_file_dir=''): self.first = first self.second = second #log file name and directory which we want to keep self.log_file_name = log_file_name self.log_file_dir = log_file_dir # Initializing logger object to write custom logs self.logger_obj = log.get_logger(log_file_name=self.log_file_name, log_sub_dir=self.log_file_dir) @log_decorator.log_decorator() def add(self, third=0, fourth=0): # writing custom logs specific to function, outside of log decorator, if needed self.logger_obj.info("Add function custom log, outside decorator") try: return self.first + self.second + third + fourth except: raise @log_decorator.log_decorator() def divide(self): self.logger_obj.info("Divide function custom log, outside decorator") try: return self.first / self.second except: raise if __name__ == '__main__': calculator = Calculator(5, 0, 'calculator_file', 'calculator_dir') calculator.add(third=2,fourth=3) calculator.divide()
UTF-8
Python
false
false
1,226
py
3
calculator.py
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Sentinel-One/jenkinsapi
12,902,081,799,300
f8d38d1ff8dbcb205d41d09f3f6af4fc5c38176d
fefd3cc9ae20245ca53c05bec40bac00ce1172ac
/examples/how_to/search_artifacts.py
fcd745170f08da2c38a3117efbb6a67fe4be003a
[ "MIT" ]
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https://github.com/Sentinel-One/jenkinsapi
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deaaef56dc582bd32d31a54b873c8eda5ffdce9c
refs/heads/master
2021-01-18T10:01:13.555912
2015-04-19T12:21:06
2015-04-19T12:21:06
34,204,081
0
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2015-04-19T11:30:23
2015-04-19T11:30:23
2015-04-18T23:21:50
2015-04-18T23:21:50
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from __future__ import print_function from jenkinsapi.api import search_artifacts jenkinsurl = "http://localhost:8080" jobid = "foo" artifact_ids = ["test1.txt", "test2.txt"] # I need a build that contains all of these result = search_artifacts(jenkinsurl, jobid, artifact_ids) print((repr(result)))
UTF-8
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false
false
302
py
1
search_artifacts.py
1
0.741722
0.721854
0
8
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ashutosh-narkar/LeetCode
2,937,757,664,406
efcd4539b7975db77d19cb84380a811191726603
aa49120740b051eed9b7199340b371a9831c3050
/level_order_zigzag.py
6d52246e70c27e8e4b98b2d57a9c6acb5f899fe1
[]
no_license
https://github.com/ashutosh-narkar/LeetCode
cd8d75389e1ab730b34ecd860b317b331b1dfa97
b62862b90886f85c33271b881ac1365871731dcc
refs/heads/master
2021-05-07T08:37:42.536436
2017-11-22T05:18:23
2017-11-22T05:18:23
109,366,819
0
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#!/usr/bin/env python """ Given a binary tree, return the zigzag level order traversal of its nodes' values. (ie, from left to right, then right to left for the next level and alternate between). For example: Given binary tree {3,9,20,#,#,15,7}, 3 / \ 9 20 / \ 15 7 return its zigzag level order traversal as: [ [3], [20,9], [15,7] ] """ # Solution 1 : Runtime O(n) #1) pop from stack "current" and store the node’s value. #2) Whenever the current level’s order is from left->right, you push the node’s left child, # then its right child to stack "next". # Remember a Stack is a Last In First OUT (LIFO) structure, # so the next time when nodes are popped off nextLevel, it will be in the reverse order. # 3) On the other hand, when the current level’s order is from right->left, # you would push the node’s right child first, then its left child. # 4) Finally, don’t forget to swap those two stacks at the # end of each level (ie, when currentLevel is empty). def zigzagLevelOrder_1(root): if not root: return [] current, next = [], [] current.append(root) leftToRight = True result = [] temp = [] while current: node = current.pop() temp.append(node.val) if leftToRight: if node.left: next.append(node.left) if node.right: next.append(node.right) else: if node.right: next.append(node.right) if node.left: next.append(node.left) if not current: result.append(temp) temp = [] leftToRight = not leftToRight current, next = next, current return result """ Solution 1: Minor change in levelOrder.py Runtime - O(n^2) """ def zigzagLevelOrder_2(root): """ :type root: TreeNode :rtype: List[List[int]] """ if not root: return [] result = [] level = [root] direction = 1 while level: result.append([node.val for node in level][::direction]) # reverse the direction direction *= -1 LRpair = [(node.left, node.right) for node in level] level = [] for pair in LRpair: for node in pair: if node: level.append(node) return result
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py
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Dom88Finch/recipe-scraper
1,099,511,643,777
7491904374bb38a6dab77f51e8f53ecbb2faeed5
4eaa0a86ab8dcd96584afa5d37f36824e4212f5d
/migrations/versions/5e78cc772642_.py
1a540557a8f0b56cd624877296fd14d1e52515ef
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permissive
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9a41c3db75fd28ebd57d442ed9a12635605cd810
refs/heads/master
2023-04-22T14:36:00.126343
2021-04-30T06:01:27
2021-04-30T06:01:27
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"""empty message Revision ID: 5e78cc772642 Revises: ec21bd75ea92 Create Date: 2020-07-22 22:44:45.754328 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = '5e78cc772642' down_revision = 'ec21bd75ea92' branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.create_table('recipe', sa.Column('id', sa.Integer(), nullable=False), sa.Column('recipe_name', sa.Text(), nullable=True), sa.Column('recipe_link', sa.Text(), nullable=True), sa.Column('image_link', sa.Text(), nullable=True), sa.Column('instructions', sa.Text(), nullable=True), sa.Column('servings', sa.Text(), nullable=True), sa.PrimaryKeyConstraint('id') ) op.create_table('user', sa.Column('id', sa.Integer(), nullable=False), sa.Column('username', sa.String(length=64), nullable=True), sa.Column('email', sa.String(length=120), nullable=True), sa.Column('password_hash', sa.String(length=128), nullable=True), sa.PrimaryKeyConstraint('id') ) op.create_index(op.f('ix_user_email'), 'user', ['email'], unique=True) op.create_index(op.f('ix_user_username'), 'user', ['username'], unique=True) op.drop_index('ix_users_email', table_name='users') op.drop_index('ix_users_username', table_name='users') op.drop_table('users') op.drop_table('recipes') op.drop_constraint(None, 'ingredients', type_='foreignkey') op.create_foreign_key(None, 'ingredients', 'recipe', ['recipe_id'], ['id']) op.drop_constraint(None, 'saved_recipes', type_='foreignkey') op.drop_constraint(None, 'saved_recipes', type_='foreignkey') op.create_foreign_key(None, 'saved_recipes', 'user', ['user_id'], ['id']) op.create_foreign_key(None, 'saved_recipes', 'recipe', ['recipe_id'], ['id']) # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.drop_constraint(None, 'saved_recipes', type_='foreignkey') op.drop_constraint(None, 'saved_recipes', type_='foreignkey') op.create_foreign_key(None, 'saved_recipes', 'users', ['user_id'], ['id']) op.create_foreign_key(None, 'saved_recipes', 'recipes', ['recipe_id'], ['id']) op.drop_constraint(None, 'ingredients', type_='foreignkey') op.create_foreign_key(None, 'ingredients', 'recipes', ['recipe_id'], ['id']) op.create_table('recipes', sa.Column('id', sa.INTEGER(), nullable=False), sa.Column('recipe_name', sa.TEXT(), nullable=True), sa.Column('recipe_link', sa.TEXT(), nullable=True), sa.Column('image_link', sa.TEXT(), nullable=True), sa.Column('instructions', sa.TEXT(), nullable=True), sa.Column('servings', sa.TEXT(), nullable=True), sa.PrimaryKeyConstraint('id') ) op.create_table('users', sa.Column('id', sa.INTEGER(), nullable=False), sa.Column('username', sa.VARCHAR(length=64), nullable=True), sa.Column('email', sa.VARCHAR(length=120), nullable=True), sa.Column('password_hash', sa.VARCHAR(length=128), nullable=True), sa.PrimaryKeyConstraint('id') ) op.create_index('ix_users_username', 'users', ['username'], unique=1) op.create_index('ix_users_email', 'users', ['email'], unique=1) op.drop_index(op.f('ix_user_username'), table_name='user') op.drop_index(op.f('ix_user_email'), table_name='user') op.drop_table('user') op.drop_table('recipe') # ### end Alembic commands ###
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py
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Alex-zhai/learn_practise
704,374,662,115
145dd011f88daf202f084c88dd8755815936bfb2
07604219235be913dc4deccfdba454a4a2e12456
/kaggle_prac/Titanic/pred_xgboost1.py
8f45826d2324477bf1a9f527abf47a80ae97a617
[]
no_license
https://github.com/Alex-zhai/learn_practise
e5a9fc1f9d2791d450f76c4087a74713a5359251
994630abd67c1403893f9a027855abca736dfcd1
refs/heads/master
2020-12-01T17:01:46.128171
2019-12-29T05:15:00
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# -*- coding:utf-8 -*- import numpy as np import pandas as pd from sklearn.feature_extraction import DictVectorizer from xgboost import XGBClassifier from sklearn.model_selection import GridSearchCV train = pd.read_csv("train.csv") test = pd.read_csv("test.csv") # print(train.info()) # print(test.info()) # choose useful features by hand selected_feat = ['Pclass', 'Sex', 'Age', 'Embarked', 'SibSp', 'Parch', 'Fare'] x_train = train[selected_feat] x_test = test[selected_feat] y_train = train['Survived'] print(x_train['Embarked'].value_counts()) print(x_test['Embarked'].value_counts()) # 对于类别形特征,使用频率最高的特征值来填充缺失值 x_train['Embarked'].fillna('S', inplace=True) x_test['Embarked'].fillna('S', inplace=True) # 对于数值型特征,使用平均值来填充缺失值 x_train['Age'].fillna(x_train['Age'].mean(), inplace=True) x_test['Age'].fillna(x_test['Age'].mean(), inplace=True) x_test['Fare'].fillna(x_test['Fare'].mean(), inplace=True) # print(x_train.info()) # print(x_test.info()) # 特征向量化 dict_vec = DictVectorizer(sparse=False) x_train = dict_vec.fit_transform(x_train.to_dict(orient='record')) print(dict_vec.feature_names_) x_test = dict_vec.transform(x_test.to_dict(orient='record')) params = {'max_depth': list(range(2, 7)), 'n_estimators': list(range(100, 1100, 200)), 'learning_rate': [0.05, 0.1, 0.25, 0.5, 1.0]} xgbc_best = XGBClassifier() gs = GridSearchCV(xgbc_best, params, verbose=1) gs.fit(x_train, y_train) print(gs.best_score_) print(gs.best_params_) pred_values = gs.predict(x_test) xgbc_submission = pd.DataFrame({'PassengerId': test['PassengerId'], 'Survived': pred_values}) xgbc_submission.to_csv("submission_xgbc_best.csv", index=False)
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import pickle from collections import OrderedDict, defaultdict, deque from collections.abc import Sequence, Mapping, Iterable import pytest from webob.multidict import MultiDict as WebObMultiDict from werkzeug.datastructures import MultiDict as WerkzeugMultiDict from multidict import MultiDict from validx import exc NoneType = type(None) @pytest.fixture(params=[WebObMultiDict, WerkzeugMultiDict, MultiDict]) def multidict_class(request): return request.param class CustomSequence(Sequence): def __init__(self, *items): self.items = items def __getitem__(self, index): return self.items[index] def __len__(self): return len(self.items) class CustomIterable(Iterable): def __init__(self, *items): self.items = items def __iter__(self): return iter(self.items) class CustomMapping(Mapping): def __init__(self, content): self.content = content def __getitem__(self, key): return self.content[key] def __iter__(self): return iter(self.content) def __len__(self): return len(self.content) # ============================================================================= def test_list(module): v = module.List(module.Int()) assert v([1, 2, 3]) == [1, 2, 3] assert v((1, 2, 3)) == [1, 2, 3] assert v({1}) == [1] assert v(frozenset([1])) == [1] assert v(CustomSequence(1, 2, 3)) == [1, 2, 3] assert v(CustomIterable(1, 2, 3)) == [1, 2, 3] assert v.clone() == v assert pickle.loads(pickle.dumps(v)) == v # Any ``Iterable``, but not ``str`` is allowed with pytest.raises(exc.InvalidTypeError) as info: v("1, 2, 3") assert info.value.expected == Iterable assert info.value.actual == str # Any ``Iterable``, but not ``bytes`` is allowed with pytest.raises(exc.InvalidTypeError) as info: v(b"1, 2, 3") assert info.value.expected == Iterable assert info.value.actual == bytes # Any ``Iterable``, but not ``dict`` is allowed with pytest.raises(exc.InvalidTypeError) as info: v({1: 1, 2: 2, 3: 3}) assert info.value.expected == Iterable assert info.value.actual == dict # Any ``Iterable``, but not ``Mapping`` is allowed with pytest.raises(exc.InvalidTypeError) as info: v(CustomMapping({1: 1, 2: 2, 3: 3})) assert info.value.expected == Iterable assert info.value.actual == CustomMapping # Test error context from sequence with pytest.raises(exc.SchemaError) as info: v([1, "2", 3, None]) assert len(info.value) == 2 assert isinstance(info.value[0], exc.InvalidTypeError) assert info.value[0].context == deque([1]) assert info.value[0].expected == int assert info.value[0].actual == str assert isinstance(info.value[1], exc.InvalidTypeError) assert info.value[1].context == deque([3]) assert info.value[1].expected == int assert info.value[1].actual == NoneType # Test error context from iterable with pytest.raises(exc.SchemaError) as info: v(CustomIterable(1, "2", 3, None)) assert len(info.value) == 2 assert isinstance(info.value[0], exc.InvalidTypeError) assert info.value[0].context == deque([None]) assert info.value[0].expected == int assert info.value[0].actual == str assert isinstance(info.value[1], exc.InvalidTypeError) assert info.value[1].context == deque([None]) assert info.value[1].expected == int assert info.value[1].actual == NoneType @pytest.mark.parametrize("nullable", [None, False, True]) def test_list_nullable(module, nullable): v = module.List(module.Int(), nullable=nullable) assert v([1, 2, 3]) == [1, 2, 3] assert v.clone() == v assert pickle.loads(pickle.dumps(v)) == v if nullable: assert v(None) is None else: with pytest.raises(exc.InvalidTypeError) as info: v(None) assert info.value.expected == Iterable assert info.value.actual == NoneType @pytest.mark.parametrize("sort", [None, 1, -1]) def test_list_sort(module, sort): v = module.List(module.Int(), sort=sort) assert v.clone() == v assert pickle.loads(pickle.dumps(v)) == v if not sort: assert v([3, 1, 2]) == [3, 1, 2] elif sort > 0: assert v([3, 1, 2]) == [1, 2, 3] elif sort < 0: assert v([3, 1, 2]) == [3, 2, 1] v = module.List( module.Dict({"x": module.Int()}), sort=sort, sort_key=lambda item: item["x"], ) assert v.clone() == v # assert pickle.loads(pickle.dumps(v)) == v # lambda is not pickleable if not sort: assert v([{"x": 1}, {"x": 3}, {"x": 2}]) == [{"x": 1}, {"x": 3}, {"x": 2}] elif sort > 0: assert v([{"x": 1}, {"x": 3}, {"x": 2}]) == [{"x": 1}, {"x": 2}, {"x": 3}] elif sort < 0: assert v([{"x": 1}, {"x": 3}, {"x": 2}]) == [{"x": 3}, {"x": 2}, {"x": 1}] @pytest.mark.parametrize("minlen", [None, 2]) @pytest.mark.parametrize("maxlen", [None, 5]) def test_list_minlen_maxlen(module, minlen, maxlen): v = module.List(module.Int(), minlen=minlen, maxlen=maxlen) assert v([1, 2, 3]) == [1, 2, 3] assert v.clone() == v assert pickle.loads(pickle.dumps(v)) == v if minlen is None: assert v([1]) == [1] else: with pytest.raises(exc.MinLengthError) as info: v([1]) assert info.value.expected == minlen assert info.value.actual == 1 # First item doesn't pass validation, so the result length is 1. # However, it should not raise MinLengthError, but SchemaError instead. with pytest.raises(exc.SchemaError) as info: v(["1", 2]) assert len(info.value) == 1 if maxlen is None: assert v([1, 2, 3, 4, 5, 6]) == [1, 2, 3, 4, 5, 6] else: with pytest.raises(exc.MaxLengthError) as info: v([1, 2, 3, 4, 5, 6]) assert info.value.expected == maxlen assert info.value.actual == 6 def test_list_minlen_maxlen_unique(module): v = module.List(module.Int(), minlen=2, maxlen=5, unique=True) assert v([1, 2, 3]) == [1, 2, 3] assert v.clone() == v assert pickle.loads(pickle.dumps(v)) == v assert v([1, 1, 1, 2, 2, 2, 3, 3, 3]) == [1, 2, 3] with pytest.raises(exc.MinLengthError) as info: v([1, 1, 1]) assert info.value.expected == 2 assert info.value.actual == 1 with pytest.raises(exc.MaxLengthError) as info: v([1, 1, 1, 2, 3, 4, 5, 6]) assert info.value.expected == 5 assert info.value.actual == 6 @pytest.mark.parametrize("unique", [None, False, True]) def test_list_unique(module, unique): v = module.List(module.Int(), unique=unique) assert v([1, 2, 3]) == [1, 2, 3] assert v.clone() == v assert pickle.loads(pickle.dumps(v)) == v if unique: assert v([1, 2, 3, 3, 2, 1]) == [1, 2, 3] else: assert v([1, 2, 3, 3, 2, 1]) == [1, 2, 3, 3, 2, 1] def test_list_context(module): class MarkContext(module.Validator): def __call__(self, value, __context=None): __context["marked"] = True return value v = module.List(MarkContext()) context = {} v([None], context) assert context["marked"] # ============================================================================= def test_set(module): v = module.Set(module.Int()) assert v([1, 2, 3]) == {1, 2, 3} assert v((1, 2, 3)) == {1, 2, 3} assert v({1, 2, 3}) == {1, 2, 3} assert v(frozenset([1, 2, 3])) == {1, 2, 3} assert v(CustomSequence(1, 2, 3)) == {1, 2, 3} assert v(CustomIterable(1, 2, 3)) == {1, 2, 3} assert v.clone() == v assert pickle.loads(pickle.dumps(v)) == v # Any ``Iterable``, but not ``str`` is allowed with pytest.raises(exc.InvalidTypeError) as info: v("1, 2, 3") assert info.value.expected == Iterable assert info.value.actual == str # Any ``Iterable``, but not ``bytes`` is allowed with pytest.raises(exc.InvalidTypeError) as info: v(b"1, 2, 3") assert info.value.expected == Iterable assert info.value.actual == bytes # Any ``Iterable``, but not ``dict`` is allowed with pytest.raises(exc.InvalidTypeError) as info: v({1: 1, 2: 2, 3: 3}) assert info.value.expected == Iterable assert info.value.actual == dict # Any ``Iterable``, but not ``Mapping`` is allowed with pytest.raises(exc.InvalidTypeError) as info: v(CustomMapping({1: 1, 2: 2, 3: 3})) assert info.value.expected == Iterable assert info.value.actual == CustomMapping # Test error context from sequence with pytest.raises(exc.SchemaError) as info: v([1, "2", 3, None]) assert len(info.value) == 2 assert isinstance(info.value[0], exc.InvalidTypeError) assert info.value[0].context == deque([1]) assert info.value[0].expected == int assert info.value[0].actual == str assert isinstance(info.value[1], exc.InvalidTypeError) assert info.value[1].context == deque([3]) assert info.value[1].expected == int assert info.value[1].actual == NoneType # Test error context from iterable with pytest.raises(exc.SchemaError) as info: v(CustomIterable(1, "2", 3, None)) assert len(info.value) == 2 assert isinstance(info.value[0], exc.InvalidTypeError) assert info.value[0].context == deque([None]) assert info.value[0].expected == int assert info.value[0].actual == str assert isinstance(info.value[1], exc.InvalidTypeError) assert info.value[1].context == deque([None]) assert info.value[1].expected == int assert info.value[1].actual == NoneType @pytest.mark.parametrize("nullable", [None, False, True]) def test_set_nullable(module, nullable): v = module.Set(module.Int(), nullable=nullable) assert v([1, 2, 3]) == {1, 2, 3} assert v.clone() == v assert pickle.loads(pickle.dumps(v)) == v if nullable: assert v(None) is None else: with pytest.raises(exc.InvalidTypeError) as info: v(None) assert info.value.expected == Iterable assert info.value.actual == NoneType @pytest.mark.parametrize("minlen", [None, 2]) @pytest.mark.parametrize("maxlen", [None, 5]) def test_set_minlen_maxlen(module, minlen, maxlen): v = module.Set(module.Int(), minlen=minlen, maxlen=maxlen) assert v([1, 2, 3]) == {1, 2, 3} assert v.clone() == v assert pickle.loads(pickle.dumps(v)) == v if minlen is None: assert v([1]) == {1} else: with pytest.raises(exc.MinLengthError) as info: v([1]) assert info.value.expected == minlen assert info.value.actual == 1 # First item doesn't pass validation, so the result length is 1. # However, it should not raise MinLengthError, but SchemaError instead. with pytest.raises(exc.SchemaError) as info: v(["1", 2]) assert len(info.value) == 1 if maxlen is None: assert v([1, 2, 3, 4, 5, 6]) == {1, 2, 3, 4, 5, 6} else: with pytest.raises(exc.MaxLengthError) as info: v([1, 2, 3, 4, 5, 6]) assert info.value.expected == maxlen assert info.value.actual == 6 def test_set_context(module): class MarkContext(module.Validator): def __call__(self, value, __context=None): __context["marked"] = True return value v = module.Set(MarkContext()) context = {} v([None], context) assert context["marked"] # ============================================================================= def test_tuple(module): v = module.Tuple(module.Int(), module.Int()) assert v([1, 2]) == (1, 2) assert v((1, 2)) == (1, 2) assert v(CustomSequence(1, 2)) == (1, 2) assert v.clone() == v assert pickle.loads(pickle.dumps(v)) == v with pytest.raises(exc.InvalidTypeError) as info: v("1, 2") assert info.value.expected == Sequence assert info.value.actual == str with pytest.raises(exc.TupleLengthError) as info: v([1, 2, 3]) assert info.value.expected == 2 assert info.value.actual == 3 with pytest.raises(exc.SchemaError) as info: v(["1", None]) assert len(info.value) == 2 assert isinstance(info.value[0], exc.InvalidTypeError) assert info.value[0].context == deque([0]) assert info.value[0].expected == int assert info.value[0].actual == str assert isinstance(info.value[1], exc.InvalidTypeError) assert info.value[1].context == deque([1]) assert info.value[1].expected == int assert info.value[1].actual == NoneType @pytest.mark.parametrize("nullable", [None, False, True]) def test_tuple_nullable(module, nullable): v = module.Tuple(module.Int(), module.Int(), nullable=nullable) assert v([1, 2]) == (1, 2) assert v((1, 2)) == (1, 2) assert v(CustomSequence(1, 2)) == (1, 2) assert v.clone() == v assert pickle.loads(pickle.dumps(v)) == v if nullable: assert v(None) is None else: with pytest.raises(exc.InvalidTypeError) as info: v(None) assert info.value.expected == Sequence assert info.value.actual == NoneType def test_tuple_context(module): class MarkContext(module.Validator): def __call__(self, value, __context=None): __context["marked"] = True return value v = module.Tuple(MarkContext()) context = {} v((None,), context) assert context["marked"] # ============================================================================= def test_dict(module): v = module.Dict({"x": module.Int(), "y": module.Int()}) assert v({"x": 1, "y": 2}) == {"x": 1, "y": 2} assert v(OrderedDict({"x": 1, "y": 2})) == {"x": 1, "y": 2} assert v(defaultdict(None, {"x": 1, "y": 2})) == {"x": 1, "y": 2} assert v(CustomMapping({"x": 1, "y": 2})) == {"x": 1, "y": 2} assert v.clone() == v assert pickle.loads(pickle.dumps(v)) == v with pytest.raises(exc.InvalidTypeError) as info: v([("x", 1), ("y", 2)]) assert info.value.expected == Mapping assert info.value.actual == list with pytest.raises(exc.SchemaError) as info: v({"x": "1", "y": None}) assert len(info.value) == 2 info.value.sort() assert isinstance(info.value[0], exc.InvalidTypeError) assert info.value[0].context == deque(["x"]) assert info.value[0].expected == int assert info.value[0].actual == str assert isinstance(info.value[1], exc.InvalidTypeError) assert info.value[1].context == deque(["y"]) assert info.value[1].expected == int assert info.value[1].actual == NoneType @pytest.mark.parametrize("nullable", [None, False, True]) def test_dict_nullable(module, nullable): v = module.Dict({"x": module.Int(), "y": module.Int()}, nullable=nullable) assert v({"x": 1, "y": 2}) == {"x": 1, "y": 2} assert v(OrderedDict({"x": 1, "y": 2})) == {"x": 1, "y": 2} assert v(defaultdict(None, {"x": 1, "y": 2})) == {"x": 1, "y": 2} assert v(CustomMapping({"x": 1, "y": 2})) == {"x": 1, "y": 2} assert v.clone() == v assert pickle.loads(pickle.dumps(v)) == v if nullable: assert v(None) is None else: with pytest.raises(exc.InvalidTypeError) as info: v(None) assert info.value.expected == Mapping assert info.value.actual == NoneType @pytest.mark.parametrize("minlen", [None, 2]) @pytest.mark.parametrize("maxlen", [None, 3]) def test_dict_minlen_maxlen(module, minlen, maxlen): v = module.Dict(extra=(module.Str(), module.Int()), minlen=minlen, maxlen=maxlen) assert v({"x": 1, "y": 2}) == {"x": 1, "y": 2} assert v(OrderedDict({"x": 1, "y": 2})) == {"x": 1, "y": 2} assert v(defaultdict(None, {"x": 1, "y": 2})) == {"x": 1, "y": 2} assert v(CustomMapping({"x": 1, "y": 2})) == {"x": 1, "y": 2} assert v.clone() == v assert pickle.loads(pickle.dumps(v)) == v if minlen is None: assert v({"x": 1}) == {"x": 1} else: with pytest.raises(exc.MinLengthError) as info: v({"x": 1}) assert info.value.expected == minlen assert info.value.actual == 1 # First key doesn't pass validation, so the result length is 1. # However, it should not raise MinLengthError, but SchemaError instead. with pytest.raises(exc.SchemaError) as info: v({"x": "1", "y": 2}) assert len(info.value) == 1 if maxlen is None: assert v({"x": 1, "y": 2, "z": 3, "a": 4}) == { "x": 1, "y": 2, "z": 3, "a": 4, } else: with pytest.raises(exc.MaxLengthError) as info: v({"x": 1, "y": 2, "z": 3, "a": 4}) assert info.value.expected == maxlen assert info.value.actual == 4 def default_x(): """Pickable version of callable default value""" return 0 @pytest.mark.parametrize("defaults", [None, {"x": 0}, {"x": default_x}]) @pytest.mark.parametrize("optional", [None, ["x"]]) def test_dict_defaults_and_optional(module, defaults, optional): v = module.Dict( {"x": module.Int(), "y": module.Int()}, defaults=defaults, optional=optional ) assert v({"x": 1, "y": 2}) == {"x": 1, "y": 2} assert v(OrderedDict({"x": 1, "y": 2})) == {"x": 1, "y": 2} assert v(defaultdict(None, {"x": 1, "y": 2})) == {"x": 1, "y": 2} assert v(CustomMapping({"x": 1, "y": 2})) == {"x": 1, "y": 2} assert v.clone() == v assert pickle.loads(pickle.dumps(v)) == v with pytest.raises(exc.SchemaError) as info: v({"x": 1}) assert len(info.value) == 1 assert isinstance(info.value[0], exc.MissingKeyError) assert info.value[0].context == deque(["y"]) if defaults: assert v({"y": 2}) == {"x": 0, "y": 2} elif optional: assert v({"y": 2}) == {"y": 2} else: with pytest.raises(exc.SchemaError) as info: v({"y": 2}) assert len(info.value) == 1 assert isinstance(info.value[0], exc.MissingKeyError) assert info.value[0].context == deque(["x"]) def test_dict_defaults_validation(module): v = module.Dict( {"x": module.Dict({"y": module.Int()}, defaults={"y": 1})}, defaults={"x": {}}, ) assert v.clone() == v assert pickle.loads(pickle.dumps(v)) == v assert v({}) == {"x": {"y": 1}} v = module.Dict( {"x": module.Dict({"y": module.Int()}, defaults={"y": 1})}, defaults={"x": []}, ) with pytest.raises(exc.SchemaError) as info: v({}) assert len(info.value) == 1 assert isinstance(info.value[0], exc.InvalidTypeError) assert info.value[0].context == deque(["x"]) assert info.value[0].expected == Mapping assert info.value[0].actual == list def test_dict_defaults_and_minlen_maxlen(module): v = module.Dict( {"x": module.Int()}, defaults={"x": 1}, extra=(module.Str(), module.Int()), minlen=2, maxlen=3, ) assert v.clone() == v assert pickle.loads(pickle.dumps(v)) == v assert v({"y": 2, "z": 3}) == {"x": 1, "y": 2, "z": 3} with pytest.raises(exc.MinLengthError) as info: v({}) assert info.value.expected == 2 assert info.value.actual == 1 with pytest.raises(exc.MaxLengthError) as info: v({"y": 2, "z": 3, "too_much": 4}) assert info.value.expected == 3 assert info.value.actual == 4 @pytest.mark.parametrize("extra", [None, True]) def test_dict_extra(module, extra): if extra: extra = (module.Str(), module.Int()) v = module.Dict({"x": module.Int(), "y": module.Int()}, extra=extra) assert v({"x": 1, "y": 2}) == {"x": 1, "y": 2} assert v(OrderedDict({"x": 1, "y": 2})) == {"x": 1, "y": 2} assert v(defaultdict(None, {"x": 1, "y": 2})) == {"x": 1, "y": 2} assert v(CustomMapping({"x": 1, "y": 2})) == {"x": 1, "y": 2} assert v.clone() == v assert pickle.loads(pickle.dumps(v)) == v if extra: assert v({"x": 1, "y": 2, "z": 3}) == {"x": 1, "y": 2, "z": 3} with pytest.raises(exc.SchemaError) as info: v({"x": 1, "y": 2, 3: None}) assert len(info.value) == 2 info.value.sort() assert isinstance(info.value[0], exc.InvalidTypeError) assert info.value[0].context == deque([3, exc.EXTRA_KEY]) assert info.value[0].expected == str assert info.value[0].actual == int assert isinstance(info.value[1], exc.InvalidTypeError) assert info.value[1].context == deque([3, exc.EXTRA_VALUE]) assert info.value[1].expected == int assert info.value[1].actual == NoneType else: with pytest.raises(exc.SchemaError) as info: v({"x": 1, "y": 2, "z": 3}) assert len(info.value) == 1 assert isinstance(info.value[0], exc.ForbiddenKeyError) assert info.value[0].context == deque(["z"]) @pytest.mark.parametrize("dispose", [None, ["z"]]) def test_dict_dispose(module, dispose): v = module.Dict({"x": module.Int(), "y": module.Int()}, dispose=dispose) assert v({"x": 1, "y": 2}) == {"x": 1, "y": 2} assert v(OrderedDict({"x": 1, "y": 2})) == {"x": 1, "y": 2} assert v(defaultdict(None, {"x": 1, "y": 2})) == {"x": 1, "y": 2} assert v(CustomMapping({"x": 1, "y": 2})) == {"x": 1, "y": 2} assert v.clone() == v assert pickle.loads(pickle.dumps(v)) == v if dispose: assert v({"x": 1, "y": 2, "z": 3}) == {"x": 1, "y": 2} else: with pytest.raises(exc.SchemaError) as info: v({"x": 1, "y": 2, "z": 3}) assert len(info.value) == 1 assert isinstance(info.value[0], exc.ForbiddenKeyError) assert info.value[0].context == deque(["z"]) def test_dict_multikeys(module, multidict_class): v1 = module.Dict({"x": module.Int(), "y": module.Int()}) v2 = module.Dict( {"x": module.Int(), "y": module.List(module.Int())}, multikeys=["y"] ) data = multidict_class([("x", 1), ("y", 2), ("y", 3)]) assert v1(data) == {"x": 1, "y": 3} or v1(data) == {"x": 1, "y": 2} assert v2(data) == {"x": 1, "y": [2, 3]} assert v1.clone() == v1 assert v2.clone() == v2 def test_dict_context(module): class MarkContext(module.Validator): def __call__(self, value, __context=None): __context["marked"] = True return value v = module.Dict({"x": MarkContext()}) context = {} v({"x": None}, context) assert context["marked"] v = module.Dict({"x": MarkContext()}, defaults={"x": None}) context = {} v({}, context) assert context["marked"] v = module.Dict(extra=(module.Str(), MarkContext())) context = {} v({"x": None}, context) assert context["marked"] v = module.Dict(extra=(MarkContext(), module.Any())) context = {} v({"x": None}, context) assert context["marked"]
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# -*- coding: utf-8 -*- """ @file @brief Ce module contient la fonction trace_ligne qui retourne l'ensemble des pixels concernés par le tracé d'une ligne en 8-connexité entre deux pixels. """ def trace_ligne_simple(x1, y1, x2, y2): """ Trace une ligne entre les points de coordonnées *(x1,y1)* et *(x2,y2)*, on suppose que *x2 > x1*, *y2 >= y1*, retourne la ligne sous la forme d'un ensemble de pixels *(x,y)*.""" if y2 - y1 <= x2 - x1: # droite en dessous de la première bissectrice vx = x2 - x1 vy = y2 - y1 b = vx / 2 y = y1 x = x1 ligne = [] while x <= x2: ligne.append((x, y)) b -= vy x += 1 if b < 0: b += vx y += 1 return ligne else: # droite au dessus de la première bissectrice vx = x2 - x1 vy = y2 - y1 b = vy / 2 y = y1 x = x1 ligne = [] while y <= y2: ligne.append((x, y)) b -= vx y += 1 if b < 0: b += vy x += 1 return ligne def draw_line(x1, y1, x2, y2): """ Trace une ligne entre les points de coordonnées *(x1,y1)* et *(x2,y2)*, aucune contrainte sur les coordonnées, retourne la ligne sous la forme d'un ensemble de pixels *(x,y)*. Utilise l'algorithme de :epkg:`Bresenham`. """ if x1 == x2: if y1 <= y2: return [(x1, i) for i in range(y1, y2 + 1)] else: return [(x1, i) for i in range(y2, y1 + 1)] if y1 == y2: if x1 <= x2: return [(i, y1) for i in range(x1, x2 + 1)] else: return [(i, y1) for i in range(x2, x1 + 1)] if x1 < x2: if y1 < y2: return trace_ligne_simple(x1, y1, x2, y2) else: ligne = trace_ligne_simple(x1, y2, x2, y1) return [(x, y1 + y2 - y) for (x, y) in ligne] if x2 < x1: if y1 < y2: ligne = trace_ligne_simple(x2, y1, x1, y2) return [(x1 + x2 - x, y) for (x, y) in ligne] else: ligne = trace_ligne_simple(x2, y2, x1, y1) return [(x1 + x2 - x, y1 + y2 - y) for (x, y) in ligne] raise RuntimeError("All cases have already been processed.") def draw_ellipse(xc, yc, a, b): """ Dessine une ellipse de centre *xc, yc*, de demi axe horizontal *a*, de demi-axe vertical b, l'ellipse a pour équation x²/a² + y²/b² = 1 si l'origine est placée en *xc, yc*, l'équation de la tangente au point *x0, y0* est : :math:`\frac{x x_0}{a^2} + \frac{y y_0}{b^2}=0`, ou :math:`x x_0 b^2 + y y_0 a^2 = 0`. Utilise l'algorithme de :epkg:`Bresenham`. """ # on évite les cas litigieux if a == 0: return [(xc, yc + y) for y in range(-b, b)] if b == 0: return [(xc + x, yc) for x in range(-a, a)] bb = b * b aa = a * a # on trace l'ellipse de centre 0,0 ellipse = [] # premier huitième vx = a * bb vy = 0 x = a y = 0 bl = vx / 2 while vx >= vy and x >= 0: ellipse.append((x, y)) y += 1 vy += aa # vy = y * aa bl -= vy if bl < 0: x -= 1 vx -= bb # vx = x * bb bl += vx # second huitième while x >= 0: ellipse.append((x, y)) x -= 1 vx -= bb # vx = x * bb bl += vx if bl > 0: y += 1 vy += aa # vy = y * aa bl -= vy # second quart, symétrique par rapport à l'axe des ordonnées ellipse2 = [(-x, y) for (x, y) in ellipse] ellipse2.reverse() ellipse.extend(ellipse2) # troisième et quatrième quarts : symétrique par rapport à l'axe des # abscisse ellipse2 = [(x, -y) for (x, y) in ellipse] ellipse2.reverse() ellipse.extend(ellipse2) return [(x + xc, y + yc) for (x, y) in ellipse] def display_line(ligne, screen, pygame): """ Affiche une ligne à l'écran. """ color = 0, 0, 0 for p in ligne: pygame.draw.line(screen, color, p, p) pygame.display.flip()
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/upload/migrations/0002_auto_20200820_0958.py
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# Generated by Django 2.0.2 on 2020-08-20 09:58 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('upload', '0001_initial'), ] operations = [ migrations.RenameField( model_name='document', old_name='description', new_name='name', ), ]
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DistributedSystemsGroup/zoe-applications
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/applications/tensorflow/tf-google.py
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2017-07-11T11:23:28
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import json import sys sys.path.append('../..') import applications.app_base def tf_batch_service(mem_limit, image): """ :type mem_limit: int :type image: str :rtype: dict """ service = { 'name': "tensorflow", 'docker_image': image, 'monitor': True, 'required_resources': {"memory": mem_limit}, 'ports': [ { 'name': "Tensorboard web interface", 'protocol': "http", 'port_number': 6006, 'path': "/", 'is_main_endpoint': False, 'expose': True }, { 'name': "Notebook web interface", 'protocol': "http", 'port_number': 8888, 'path': "/", 'is_main_endpoint': False, 'expose': True } ], 'networks': [], 'total_count': 1, 'essential_count': 1, 'startup_order': 0 } return service APP_NAME = 'tensorflow' def gen_app(mem_limit, tf_image): services = [tf_batch_service(mem_limit, tf_image)] return applications.app_base.fill_app_template(APP_NAME, False, services) DOCKER_REGISTRY = 'docker-registry:5000/' options = [ ('mem_limit', 16 * (1024**3), 'Tensorflow memory limit (bytes)'), ('tf_image', 'gcr.io/tensorflow/tensorflow', 'Tensorflow image'), ] if __name__ == "__main__": args = {} for opt in options: args[opt[0]] = opt[1] app_dict = gen_app(**args) json.dump(app_dict, sys.stdout, sort_keys=True, indent=4) sys.stdout.write('\n')
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"""Sample plugin which renders custom panels on certain pages.""" from part.views import PartDetail from plugin import InvenTreePlugin from plugin.mixins import PanelMixin, SettingsMixin from stock.views import StockLocationDetail class CustomPanelSample(PanelMixin, SettingsMixin, InvenTreePlugin): """A sample plugin which renders some custom panels.""" NAME = "CustomPanelExample" SLUG = "samplepanel" TITLE = "Custom Panel Example" DESCRIPTION = "An example plugin demonstrating how custom panels can be added to the user interface" VERSION = "0.1" SETTINGS = { 'ENABLE_HELLO_WORLD': { 'name': 'Enable Hello World', 'description': 'Enable a custom hello world panel on every page', 'default': False, 'validator': bool, }, 'ENABLE_BROKEN_PANEL': { 'name': 'Enable Broken Panel', 'description': 'Enable a panel with rendering issues', 'default': False, 'validator': bool, } } def get_panel_context(self, view, request, context): """Returns enriched context.""" ctx = super().get_panel_context(view, request, context) # If we are looking at a StockLocationDetail view, add location context object if isinstance(view, StockLocationDetail): ctx['location'] = view.get_object() return ctx def get_custom_panels(self, view, request): """You can decide at run-time which custom panels you want to display! - Display on every page - Only on a single page or set of pages - Only for a specific instance (e.g. part) - Based on the user viewing the page! """ panels = [ { # Simple panel without any actual content 'title': 'No Content', } ] if self.get_setting('ENABLE_HELLO_WORLD'): # We can use template rendering in the raw content content = """ <strong>Hello world!</strong> <hr> <div class='alert-alert-block alert-info'> <em>We can render custom content using the templating system!</em> </div> <hr> <table class='table table-striped'> <tr><td><strong>Path</strong></td><td>{{ request.path }}</tr> <tr><td><strong>User</strong></td><td>{{ user.username }}</tr> </table> """ panels.append({ # This 'hello world' panel will be displayed on any view which implements custom panels 'title': 'Hello World', 'icon': 'fas fa-boxes', 'content': content, 'description': 'A simple panel which renders hello world', 'javascript': 'console.log("Hello world, from a custom panel!");', }) if self.get_setting('ENABLE_BROKEN_PANEL'): # Enabling this panel will cause panel rendering to break, # due to the invalid tags panels.append({ 'title': 'Broken Panel', 'icon': 'fas fa-times-circle', 'content': '{% tag_not_loaded %}', 'description': 'This panel is broken', 'javascript': '{% another_bad_tag %}', }) # This panel will *only* display on the PartDetail view if isinstance(view, PartDetail): panels.append({ 'title': 'Custom Part Panel', 'icon': 'fas fa-shapes', 'content': '<em>This content only appears on the PartDetail page, you know!</em>', }) # This panel will *only* display on the StockLocation view, # and *only* if the StockLocation has *no* child locations if isinstance(view, StockLocationDetail): try: loc = view.get_object() if not loc.get_descendants(include_self=False).exists(): panels.append({ 'title': 'Childless Location', 'icon': 'fa-user', 'content_template': 'panel_demo/childless.html', # Note that the panel content is rendered using a template file! }) except Exception: # pragma: no cover pass return panels
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theromis/mlpiper
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/mlcomp/parallelm/components/spark_data_component.py
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import abc from pyspark.sql import DataFrame from parallelm.common.mlcomp_exception import MLCompException from parallelm.components.spark_session_component import SparkSessionComponent import traceback class SparkDataComponent(SparkSessionComponent): def __init__(self, ml_engine): super(SparkDataComponent, self).__init__(ml_engine) def _materialize(self, spark, parent_data_objs, user_data): df = self._dataframe(spark, user_data) df_list = df if type(df) is list else [df] self._logger.debug("Data component '{}' returns: {}".format(self.name(), df_list)) return df_list # Used by child connectable component def _post_validation(self, df): self._ml_engine.set_dataframe(df) # Used by Spark ml pipeline return df @abc.abstractmethod def _dataframe(self, spark, user_data): """ Supposed to return spark data-frame """ pass
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obligate/python3-king
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__author__ = "Peter" class C: def __init__(self): self.name = 'peter'
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import re import json import datetime import requests class Downloader: def log_info(self): print('='*36) print('= K-Load ') print('='*36) print('= Name:\t', self.name) print('= Type:\t', self.platform) print('= URL:\t', self.url) print('= Format:\t', self.format) print('='*36) def __repr__(self): return '<object \'%s\' name=\'%s\'>' % (self.__class__.__name__, self.name) class KSong(Downloader): platform = '全民K歌' def __init__(self, url): self.url = url res = requests.get(self.url) pattern = re.compile(r'(?<=window\.__DATA__ = ).+(?=;.+</script>)') json_str = re.search(pattern, res.text).group() self.info = json.loads(json_str) self.info.update(self.info['detail']) self.info['ctime'] = datetime.datetime.fromtimestamp(int(self.info['ctime'])).strftime("%Y-%m-%d %H:%M:%S") self.download_url = self.info['playurl'] self.name = '%s-%s' % (self.info['song_name'], self.info['nick']) self.format = '.m4a' def download(self): print('Downloading...') res = requests.get(self.download_url) filename = '.\\%s_k-load%s' % (self.name, self.format) with open(filename, 'wb') as f: f.write(res.content) print('Finished.') print('Saved as %s' % filename)
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/app1/urls.py
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from django.urls import path from app1 import views app_mame="app1" urlpatterns = [ path("",views.samp1, name="samp1") ]
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hitandaway100/caba
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/src/dprj/platinumegg/app/cabaret/management/commands/orig_migrate.py
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[]
no_license
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refs/heads/master
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# coding: utf-8 import os import glob from django.db import connection from django.db import connections from django.core.management.base import BaseCommand import settings import settings_sub from platinumegg.app.cabaret.models import sql class Command(BaseCommand): """マイグレーション用. django の 1.4 に既存のテーブルに対しての変更を検知して Alter Table を当ててくれるものが無いので. >> python manage.py orig_migrate このコマンドは, バージョン番号がファイルの先頭に付いている sql を実行します. バージョン番号の定義は models/sql/__init__.py に書いていますので, 次のカラム変更を行ないたい日をバージョンに設定しておくと良いです. 下記例のファイルの様に, どのモデルに対して何をするのか (add, delete, ... 等) を名前に付ける事を推奨. ex) dprj/platinumegg/app/cabaret/models/sql/20150914_add_column_gachamaster.sql """ def handle(self, *args, **options): self.stdout.write('Change table ...\n') if os.path.isdir(sql.DIR): migrates = glob.glob(sql.DIR + sql.VERSION + '*.sql') if migrates: cursor = connections[settings.DB_DEFAULT].cursor() self.stdout.write( '!!! Migrate Version: {}, MasterDB: {} !!!'.format( sql.VERSION, settings.DATABASES[cursor.db.alias]['HOST'] ) ) for migrate in migrates: self.stdout.write('\033[0m# {}'.format(os.path.basename(migrate))) with open(migrate, 'r') as sql_file: success_count = 0 errors = [] for sql_line in sql_file.readlines(): try: self.stdout.write('\033[0mexec: {}'.format(sql_line)) cursor.execute(sql_line) success_count += 1 self.stdout.write('\033[32mexec OK.') except Exception as errno: errors.append('\033[31mExecError: {} \n'.format(errno)) self.stdout.write('Success {0}, Error : {1}.'.format(success_count, len(errors))) for error in errors: self.stdout.write(error) else: self.stdout.write('Not Change SQL ...\n')
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fwq777/siut
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import cv2 import os import numpy as np from torch.utils import data as data_ from tqdm import tqdm from utils.config import opt from data.datasetG import Dataset from utils.vis_tool import Visualizer from model.DeviceAdapt_Net import Gtrainer def test_model(dataloader, model, epoch, ifsave=False, test_num=100000): LOSS = 0.0 num = 0 model.eval() dir = './result/resultG0405/'+str(epoch)+'/' if not os.path.exists(dir) and ifsave: os.makedirs(dir) for ii, (img, oriimg) in enumerate(dataloader): outputimg, loss = model(img, oriimg, vis=True) LOSS += loss num += 1 if ifsave: # for i in range(len(outputimg)): i = 0 img = img[i][0].numpy() img = img*255 outimg = outputimg[i] outimg = outimg.transpose((1, 2, 0)) outimg = outimg*255 img = img.astype(np.uint8) outimg = outimg.astype(np.uint8) cv2.imwrite(dir + 'out' + str(ii) + '_' + str(i) + '.jpg', outimg) cv2.imwrite(dir + 'input' + str(ii) + '_' + str(i) + '.jpg', img) if ii > test_num: break return {"SNR": round(LOSS/num, 5)} def train(): opt._parse() vis_tool = Visualizer(env=opt.env) print("init vis_tool") print('load data') train_dataset = Dataset(opt.rootpath, mode="train/") val_dataset = Dataset(opt.rootpath, mode="val/") trainer = Gtrainer(opt, image_size=opt.image_size) # if opt.load_G: # trainer.load_G(opt.load_G) # print('model construct completed') best_map = 0.0 for epoch in range(opt.epoch): trainer.train() train_dataloader = data_.DataLoader(train_dataset, batch_size=opt.train_batch_size, num_workers=opt.num_workers, shuffle=True) val_dataloader = data_.DataLoader(val_dataset, batch_size=opt.test_batch_size, num_workers=opt.num_workers, shuffle=False) # test_model(test_dataloader, trainer, epoch, ifsave=True, test_num=opt.test_num) for ii, (img, oriseg) in tqdm(enumerate(train_dataloader), total=len(train_dataloader)): trainer.train_onebatch(img, oriseg) if ii % 50 == 0: trainer.eval() outputimg, loss = trainer(img, oriseg, vis=True) vis_tool.plot("loss", loss) input = img[0][0].numpy() input = (input*255).astype(np.uint8) vis_tool.img("input", input) label = oriseg[0].numpy() label = (label*255).astype(np.uint8) vis_tool.img("label", label) trainer.train() ifsave=False if (epoch+1)%1 == 0: ifsave=True eval_result = test_model(val_dataloader, trainer, epoch, ifsave=ifsave, test_num=opt.test_num) print('eval_loss: ', eval_result) best_map = eval_result["SNR"] best_path = trainer.save_G(best_map=best_map) print("save to %s !" % best_path) if __name__ == '__main__': train()
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Python
false
false
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py
10
train_G.py
10
0.529749
0.514648
0
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102
iamaaditya/advent_of_code
16,913,581,246,716
707e2a32e8955879ddac30f4e417bd505785c85e
18b7e4d3833b61377ac7219778a73d32353f4a7a
/day14.py
6f94f91edd7888738dae5f72a627a4ce3c0407a9
[]
no_license
https://github.com/iamaaditya/advent_of_code
3fb7bcdd25d15456709876ef0257793221432ba8
9f77ea45d95c0921b69178b5592e235dbcf5db50
refs/heads/master
2021-01-10T10:16:16.134709
2015-12-21T06:54:04
2015-12-21T06:54:04
47,854,069
0
0
null
null
null
null
null
null
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import re from itertools import repeat input = open('./input_data/input14.txt').read().splitlines() regex = r'(\w+) can fly (\d+) km/s for (\d+) seconds, but then must rest for (\d+) seconds.' data = [(name, int(speed),int(fly),int(sleep)) for ip in input for name,speed,fly,sleep in re.findall(regex, ip)] distance = list(repeat(0, len(data))) points = list(repeat(0, len(data))) deer_fly = [ik[2] for ik in data] deer_sleep = [ik[3] for ik in data] time_ = 2503 for t in xrange(1,time_+1): for index, r in enumerate(data): if deer_fly[index] : distance[index] += r[1] deer_fly[index] -= 1 elif deer_sleep[index] : deer_sleep[index] -= 1 else: deer_fly[index] = r[2] -1 deer_sleep[index] = r[3] distance[index] += r[1] md = max(distance) for i,d in enumerate(distance): if d == max(distance): points[i] += 1 print max(distance) print max( points )
UTF-8
Python
false
false
998
py
21
day14.py
21
0.568136
0.548096
0
38
25.184211
113
maayan20-meet/meet2018y1lab5
283,467,866,611
d55babf4ce00cb728bcb0a3b4548ef16cd2a7611
a3f381360745d146ca9a98c480b7d00db0d263a3
/fruit_sorter.py
1426cf10650bca239b3db2d9e868e7a57c2e4a4c
[ "MIT" ]
permissive
https://github.com/maayan20-meet/meet2018y1lab5
f6bd44f7606fca1ca2aa7cedb8d3737af5521130
18e9d8c9947c9d568f61f32545ba4b246a36ad3f
refs/heads/master
2020-03-22T13:26:08.852159
2018-08-15T13:59:45
2018-08-15T13:59:45
140,107,870
0
0
null
true
2018-07-07T18:21:53
2018-07-07T18:21:53
2018-06-23T22:48:38
2018-06-23T22:48:37
1
0
0
0
null
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null
u_fruit = input('What fruit am I sorting') if u_fruit == apples or u_fruit == Apples: print('Bin 1 (Apples)') elif u_fruit == oranges or u_fruit == Oranges: print('Bin 2 (Oranges)') elif u_fruit == olives or u_fruit == Olives: print('Bin 3 (Olives)') else: print('dont know that fruit')
UTF-8
Python
false
false
307
py
26
fruit_sorter.py
26
0.625407
0.615635
0
13
22.615385
46
vicky12348/avil1245
7,232,724,960,317
f3e7970cd85399bcaf51b3f2768591c4f5c85ff2
bb07f2f846e96b3a354466f061798139289465fc
/MOv.py
bb9f42727b8ee290a719217c9063548298eaede0
[]
no_license
https://github.com/vicky12348/avil1245
b62feb3753dd445875d88147bd0eddcb8ea12bfe
a80d8a60e9ec5905b95256c2ba3ea496af1a0ffa
refs/heads/master
2022-12-17T22:38:19.186375
2020-08-30T06:13:04
2020-08-30T06:13:04
291,409,008
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class parent(): def fees(self): print("fees paying") class child(parent): a="movies" def fees(self): print("movies") c=child() c.fees() print("this is a variable",c.a)
UTF-8
Python
false
false
201
py
10
MOv.py
9
0.572139
0.572139
0
10
18.6
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excess30/CompetitiveProgramming
5,497,558,147,766
83de3a611f7e48c28524cb557ecd58131d07a9fb
7d34183fc38360631bd67def135e48be43f0a2f3
/possiblePaths.py
a01125e521f3fe6d7bbe3da47c4bc5a07a91ba47
[]
no_license
https://github.com/excess30/CompetitiveProgramming
4703ff49d6bb5cecd052a3c18bb70c72eb712e25
30dec16e5434fd024919572bba570b168608a62f
refs/heads/master
2020-11-24T01:33:36.549023
2019-12-13T19:09:47
2019-12-13T19:09:47
227,905,509
0
0
null
null
null
null
null
null
null
null
null
null
null
null
null
# function to return count of possible paths # to reach cell at row number m and column # number n from the topmost leftmost # cell (cell at 1, 1) def numberOfPaths(m, n): # If either given row number is first # or given column number is first if(m == 1 or n == 1): return 1 # If diagonal movements are allowed # then the last addition # is required. return numberOfPaths(m-1, n) + numberOfPaths(m, n-1) #Main Function m = 3 n = 3 print(numberOfPaths(m, n))
UTF-8
Python
false
false
485
py
7
possiblePaths.py
7
0.678351
0.659794
0
20
23.3
56
atulvidyarthi29/BlueTech
6,528,350,319,203
4f3686b58bfa376548f0982cae5387ef57feb783
021a37a1b948d017975566613219cd287d713038
/BlueTech/Users/views.py
a554f7d5dd825853ba0d8acbd0d6be2b739d7884
[]
no_license
https://github.com/atulvidyarthi29/BlueTech
23efd89ae5a68e8dfa5d27635809fc22a82d4838
a837a3c851b6f80ece4faac3e2d47b2806364f75
refs/heads/master
2022-12-10T11:00:46.118588
2020-10-27T20:02:55
2020-10-27T20:02:55
209,316,140
3
2
null
false
2022-12-08T11:42:45
2019-09-18T13:30:46
2022-10-26T12:11:56
2022-12-08T11:42:44
37,794
1
2
7
JavaScript
false
false
from django.contrib.auth import login, authenticate from django.contrib.auth.decorators import login_required from django.contrib.sites.shortcuts import get_current_site from django.core.mail import EmailMessage from django.http import HttpResponse from django.shortcuts import render, redirect, get_object_or_404 from django.template.loader import render_to_string from django.utils.encoding import force_bytes, force_text from django.utils.http import urlsafe_base64_encode, urlsafe_base64_decode from django.views.decorators.csrf import csrf_exempt from . import paytm_checksum from . import payment_config from .content import team_content, index_content from .forms import UserRegistrationForm, ProductKeyForm, ProfileEditForm, PaymentForm from .models import License, Employee from .models import User def buy_erp(request): if request.method == 'POST': payment_form = PaymentForm(request.POST) if payment_form.is_valid(): payment_form = payment_form.save(commit=False) payment_form.validated = False payment_form.save() absolute_url = request.build_absolute_uri()[0:-1] CALLBACK_URL = absolute_url[0:absolute_url.rindex('/')] + '/handle_request/' print(CALLBACK_URL) param_dict = { 'MID': payment_config.MID, 'ORDER_ID': str(payment_form.licence), 'TXN_AMOUNT': payment_config.TXN_AMOUNT, 'CUST_ID': payment_form.email, 'INDUSTRY_TYPE_ID': payment_config.INDUSTRY_TYPE_ID, 'WEBSITE': payment_config.WEBSITE, 'CHANNEL_ID': payment_config.CHANNEL_ID, 'CALLBACK_URL': CALLBACK_URL, } print(param_dict) param_dict['CHECKSUMHASH'] = paytm_checksum.generateSignature(param_dict, payment_config.MERCHANT_KEY) return render(request, 'users/payment_processing.html', {'param_dict': param_dict}) payment_form = PaymentForm() return render(request, "users/checkout_page.html", {'payment_form': payment_form}) @csrf_exempt def handle_request(request): form = request.POST response = {} for key in form.keys(): response[key] = form[key] check = response['CHECKSUMHASH'] verify = paytm_checksum.verifySignature(response, payment_config.MERCHANT_KEY, check) print(response) if verify: if response['RESPCODE'] == '01': license_object = License.objects.get(licence=response['ORDERID']) send_email(request, license_object) return HttpResponse('Thank you for the payment. Please check your email for further instructions.') else: return HttpResponse("Something went wrong.") else: return HttpResponse("Checksum Verification failed") def send_email(request, license_object): tup = str(license_object.email) + ' ' + 'CEO' + ' ' + str(license_object.licence) current_site = get_current_site(request) mail_subject = 'Join using this link!' message = render_to_string('hr/recruitment_email.html', { 'unique_code': license_object.licence, 'domain': current_site.domain, 'uid': urlsafe_base64_encode(force_bytes(tup)), 'token': str(tup), }) email = EmailMessage( mail_subject, message, to=[license_object.email] ) email.send() def home(request): if not request.user.is_anonymous: return redirect('users:dashboard') try: license_obj = License.objects.first() validated = license_obj.validated except: validated = False return render(request, 'home/homepage.html', context={'validated': validated, 'true': True, 'content': index_content}) def team(request): if not request.user.is_anonymous: return redirect('users:dashboard') try: license_obj = License.objects.first() validated = license_obj.validated except: validated = False return render(request, 'home/team.html', context={'content': team_content, 'validated': validated}) def terms_of_service(request): if not request.user.is_anonymous: return redirect('users:dashboard') try: license_obj = License.objects.first() validated = license_obj.validated except: validated = False return render(request, 'home/terms_of_service.html', context={'validated': validated, }) def privacy_policy(request): if not request.user.is_anonymous: return redirect('users:dashboard') try: license_obj = License.objects.first() validated = license_obj.validated except: validated = False return render(request, 'home/privacy_policy.html', context={'validated': validated, }) def disclaimer(request): if not request.user.is_anonymous: return redirect('users:dashboard') try: license_obj = License.objects.first() validated = license_obj.validated except: validated = False return render(request, 'home/disclaimer.html', context={'validated': validated, }) def add_user(request, uidb64, token): try: uid = force_text(urlsafe_base64_decode(uidb64).decode()) tup = uid.split() print(tup) if tup[1] == 'CEO': element = License.objects.get(licence=tup[2]) element.validated = True element.save() except(TypeError, ValueError, OverflowError): return HttpResponse('Could not verify you!') if request.method == 'POST': user_form = UserRegistrationForm(request.POST) if user_form.is_valid(): user_form2 = user_form.save(commit=False) user_form2.is_active = True user_form2.save() user = authenticate(username=user_form.cleaned_data['username'], password=user_form.cleaned_data['password1']) login(request, user) return redirect('users:post_login', tup[1]) return render(request, 'users/register.html', {'user_form': user_form, 'errors': "Unauthorized"}) else: p = User() p.email = tup[0] user_form = UserRegistrationForm(instance=p) key_form = ProductKeyForm() return render(request, 'users/register.html', {'user_form': user_form, 'key_form': key_form, 'dept': uid[1]}) @login_required def to_post_login(request): return redirect('users:post_login', '-') @login_required def post_login(request, dept): try: is_profile_complete = request.user.employee.is_complete except: is_profile_complete = False if is_profile_complete: return redirect('users:dashboard') return redirect('users:profile', dept) @login_required def profile(request, dept): if request.method == 'POST': try: profile_edit_form = ProfileEditForm(request.POST, request.FILES, instance=Employee.objects.get(user=request.user)) except: profile_edit_form = ProfileEditForm(request.POST, request.FILES) user_form = UserRegistrationForm(request.POST, instance=request.user) if profile_edit_form.is_valid(): form_object = profile_edit_form.save(commit=False) form_object.dept = dept form_object.user = request.user if dept == 'CEO': form_object.reporting_to = None if form_object.dept and profile_edit_form.cleaned_data['gender'] and \ profile_edit_form.cleaned_data['date'] and profile_edit_form.cleaned_data['date_of_joining'] and \ profile_edit_form.cleaned_data['phone_no']: form_object.is_complete = True else: form_object.is_complete = False form_object.is_verified = False form_object.save() return redirect('users:dashboard') print(profile_edit_form.errors) return render(request, 'users/profile.html', {'profile_edit_form': profile_edit_form, 'user_form': user_form, 'errors': profile_edit_form.errors, 'deptat': dept, }) else: profile_edit_form = ProfileEditForm() user_form = UserRegistrationForm(instance=request.user) return render(request, 'users/profile.html', {'profile_edit_form': profile_edit_form, 'user_form': user_form, 'dept': dept}) @login_required def dashboard(request): try: department = request.user.employee.dept if department == 'CEO': return redirect('users:ceo_dashboard') elif department == 'HR': return redirect('users:hr:hr_dashboard') elif department == 'ACCOUNTS': return redirect('finance:finance_home') elif department == 'SALES': return redirect('sales:sales_dashboard') except: return render(request, 'home/404.html') @login_required def ceo_dashboard(request): department = request.user.employee.dept return render(request, 'home/dashboard.html', context={'department': department, 'user': request.user}) @login_required def update_profile(request, pk): employee = get_object_or_404(Employee, id=pk) profile_update_form = ProfileEditForm(request.POST, request.FILES, instance=employee) if request.method == 'POST': if profile_update_form.is_valid(): profile_update_form.save() return redirect(request.META.get('HTTP_REFERER')) context = { 'profile_update_form': profile_update_form, 'department': request.user.employee.dept, 'profile': employee, 'user': request.user, } return render(request, 'users/profile_update.html', context) # def boot_start(request): # if request.method == 'GET': # get_response = [request.GET.get('payment_id'), request.GET.get('status')] # print(get_response) # if len(get_response) == 2 and get_response[1] == 'success': # un = uuid.uuid4() # License.objects.create(licence=un, validated=False) # email_form = EmailForm() # return render(request, 'users/ceo_email_info.html', context={'email_form': email_form, 'un': str(un)}) # else: # return render(request, 'home/404.html') # else: # return HttpResponse("Something went wrong!") # # # def send_ceo_method(request, un): # if request.method == 'POST': # email_form = EmailForm(request.POST) # if email_form.is_valid(): # to_email = email_form.cleaned_data['email'] # tup = str(to_email) + ' ' + 'CEO' + ' ' + str(un) # current_site = get_current_site(request) # mail_subject = 'Join using this link!' # message = render_to_string('hr/recruitment_email.html', { # 'unique_code': un, # 'domain': current_site.domain, # 'uid': urlsafe_base64_encode(force_bytes(tup)), # 'token': str(tup), # }) # email = EmailMessage( # mail_subject, message, to=[to_email] # ) # email.send() # return HttpResponse('Thank you for the payment. Please check your email for further instructions.') # redirect(request.META.get('HTTP_REFERER')) # else: # redirect(request.META.get('HTTP_REFERER')) # def register(request): # if request.method == 'POST': # user_form = UserRegistrationForm(request.POST) # key_form = ProductKeyForm(request.POST) # if key_form.is_valid() and user_form.is_valid(): # pd_key = key_form.cleaned_data['product_key'] # lic_obj = License.objects.first() # if lic_obj.licence == pd_key: # lic_obj.validated = True # lic_obj.save() # user_form2 = user_form.save(commit=False) # user_form2.is_active = False # user_form2.save() # current_site = get_current_site(request) # mail_subject = 'Please, verify your Email!' # message = render_to_string('users/activate_email.html', { # 'user': user_form2, # 'domain': current_site.domain, # 'uid': urlsafe_base64_encode(force_bytes(user_form2.pk)).decode(), # 'token': account_activation_token.make_token(user_form2), # }) # to_email = user_form.cleaned_data.get('email') # email = EmailMessage( # mail_subject, message, to=[to_email] # ) # email.send() # return HttpResponse('Please confirm your email address to complete the registration') # return render(request, 'users/register.html', # {'user_form': user_form, 'key_form': key_form, 'errors': "Unauthorized"}) # else: # user_form = UserRegistrationForm() # key_form = ProductKeyForm() # return render(request, 'users/register.html', {'user_form': user_form, 'key_form': key_form}) # # # def activate(request, uidb64, token): # try: # print(uidb64) # uid = force_text(urlsafe_base64_decode(uidb64).decode()) # print(uid) # user = User.objects.get(pk=uid) # except(TypeError, ValueError, OverflowError, User.DoesNotExist): # user = None # if user is not None and account_activation_token.check_token(user, token): # user.is_active = True # user.save() # login(request, user) # return redirect('users:post_login', 'CEO') # else: # return HttpResponse('Activation link is invalid!')
UTF-8
Python
false
false
14,218
py
93
views.py
32
0.589323
0.585174
0
352
38.392045
118