nn.Module:\\n pass\",\n \"def __init__(self, log=True, normalize=False):\\r\\n self.model = LinearRegression(normalize=normalize)\\r\\n self.log = log\",\n \"def __init__(self, linear_constraints, n_skip):\\n self.lincon = linear_constraints\\n self.n_skip = n_skip\",\n \"def __init__(self, n_lm, n_ang):\\n super(MVCNet, self).__init__()\\n self.convM1_sag = conv_bn_prelu_dropout(1, 64, 4, 2, 1, 64, 64, 0.25)\\n self.convM1_cor = conv_bn_prelu_dropout(1, 64, 4, 2, 1, 64, 64, 0.25)\\n self.xModule1 = xModule([64, 128, 64], 64, 4, 2, 1, 128, 128, 0.25)\\n self.xModule2 = xModule([128, 64, 32], 128, 4, 2, 1, 256, 256, 0.25)\\n self.xModule3 = xModule([256, 32, 16], 256, 4, 2, 1, 512, 512, 0.25)\\n self.SLE_sag = SLE([512, 16, 8], 512, n_lm)\\n self.SLE_cor = SLE([512, 16, 8], 512, n_lm)\\n self.CAE_sag = CAE(512, n_lm, n_ang)\\n self.CAE_cor = CAE(512, n_lm, n_ang)\",\n \"def __init__(self):\\n super().__init__(functional.domain, functional.domain,\\n linear=False)\",\n \"def __init__(self):\\n super().__init__(functional.domain, functional.domain,\\n linear=False)\",\n \"def __init__(self, D_in):\\n super(ExplainEven, self).__init__()\\n self.linear1 = torch.nn.Linear(D_in, 10)\\n self.linear2 = torch.nn.Linear(10, 1)\",\n \"def __init__(self, num_lemmas, num_pos, num_dep, num_directions=5, n_epochs=10, num_relations=2,\\n alpha=0.01, lemma_embeddings=None, dropout=0.0, use_xy_embeddings=False, num_hidden_layers=0):\\n self.n_epochs = n_epochs\\n self.num_lemmas = num_lemmas\\n self.num_pos = num_pos\\n self.num_dep = num_dep\\n self.num_directions = num_directions\\n self.num_relations = num_relations\\n self.alpha = alpha\\n self.dropout = dropout\\n self.use_xy_embeddings = use_xy_embeddings\\n self.num_hidden_layers = num_hidden_layers\\n self.update = True\\n\\n self.lemma_vectors = None\\n if lemma_embeddings is not None:\\n self.lemma_vectors = lemma_embeddings\\n self.lemma_embeddings_dim = lemma_embeddings.shape[1]\\n else:\\n self.lemma_embeddings_dim = LEMMA_DIM\\n\\n # Create the network\\n print 'Creating the network...'\\n self.builder, self.model, self.model_parameters = create_computation_graph(self.num_lemmas, self.num_pos,\\n self.num_dep, self.num_directions,\\n self.num_relations,\\n self.lemma_vectors,\\n use_xy_embeddings,\\n self.num_hidden_layers,\\n self.lemma_embeddings_dim)\\n print 'Done!'\",\n \"def __init__(self, op, expression1, expression2):\\n LinearExpression.__init__(self)\\n\\n self.op = op\\n self.expression1 = expression1\\n self.expression2 = expression2\",\n \"def linear(*args) -> core.Linear:\\n X, Y, kws = util.parseargs(*args)\\n if kws:\\n raise ValueError(\\\"linear does not take any keyword\\\")\\n return core.Linear(X, Y)\",\n \"def __init__(self):\\n super().__init__(functional.domain, functional.domain,\\n linear=False)\",\n \"def __init__(self):\\n super().__init__(functional.domain, functional.domain,\\n linear=False)\",\n \"def __init__(self):\\n super().__init__(functional.domain, functional.domain,\\n linear=False)\",\n \"def __init__(self):\\n super().__init__(functional.domain, functional.domain,\\n linear=False)\",\n \"def __init__(self):\\n super().__init__(functional.domain, functional.domain,\\n linear=False)\",\n \"def new_LP(self, LP_type: str) -> None:\\n self._LP = cplex.Cplex()\\n \\n # set objective\\n if LP_type.lower() == 'feasibility':\\n self.LP.variables.add(ub = np.ones(self.cols), \\n lb = np.zeros(self.cols))\\n elif LP_type.lower() in ['optimization','optimisation']:\\n self.LP.variables.add(obj = np.hstack((np.zeros(self.cols),[1.0])), \\n ub = np.ones(self.cols+1), \\n lb = np.zeros(self.cols+1))\\n else: \\n raise qiskit.QiskitError(\\n \\\"Invalid LP_type. Should be either 'feasibility' or \\\"\\n \\\"'optimization'. Objective hasn't been set.\\\") \\n self.LP.objective.set_sense(self.LP.objective.sense.maximize)\\n \\n # summation constraint\\n self.LP.linear_constraints.add(\\n lin_expr = [cplex.SparsePair(ind = range(self.cols), \\n val = np.ones(self.cols))], \\n rhs = [1.0], senses = 'E', range_values = [0.0], names = ['sum'])\\n \\n # restrict output\\n self.LP.set_log_stream(None)\\n self.LP.set_error_stream(None)\\n self.LP.set_warning_stream(None)\\n self.LP.set_results_stream(None)\\n \\n # solving method\\n alg = self.LP.parameters.lpmethod.values\\n self.LP.parameters.lpmethod.set(alg.dual)\\n self.LP.parameters.threads.set(3)\\n self.LP.parameters.parallel.set(1)\",\n \"def define_linear_constraints(self):\\n lower_bound = []\\n for idx, equ_val in enumerate(self.equ_vec):\\n if equ_val == 0:\\n lower_bound.append(self.rhs_vec[idx])\\n else:\\n lower_bound.append(-np.inf)\\n upper_bound = self.rhs_vec\\n\\n return LinearConstraint(self.matrix, lower_bound, upper_bound)\",\n \"def __init__(self, *args, **kwargs):\\n super(LinlLis, self).__init__(\\n ('linl', Bits(maxlen=4)),\\n ('lis', Bits(maxlen=4)),\\n *args, **kwargs\\n )\",\n \"def linear_from_internal(internal_values, constr):\\n return constr[\\\"from_internal\\\"] @ internal_values\",\n \"def __init__(self):\\n\\t\\tself.theta = 0.8\\t\\t\\t# Theta value, the constant of the line which x+y is.(1.2 is best)\\n\\t\\tself.numberOfInput = 0\\t\\t# The number of Input\\n\\t\\tself.weight = []\\t\\t\\t# The list of weight.\",\n \"def __init__(self, lam=1.0):\\n self.lam = lam\\n\\n # these are set in fit\\n self.b = None # float\\n self.w = None # (nvars, ) array\",\n \"def __init__(self, in_features, out_features):\\n super(Module, self).__init__()\\n self.in_features = in_features\\n self.out_features = out_features\\n self.w = FloatTensor(out_features, in_features).normal_()\\n self.b = FloatTensor(1, out_features).normal_()\\n self.dl_dw = FloatTensor(self.w.size())\\n self.dl_db = FloatTensor(self.b.size())\",\n \"def __init__(self, tokens):\\n self.mdl = self.train(tokens)\",\n \"def __init__(self, tokens):\\n self.mdl = self.train(tokens)\",\n \"def Linear(v):\\n bias = Symbol('h_{}'.format(v), REAL)\\n\\n min_, max_ = linear_energy_ranges[v]\\n\\n theta.assertions.add(LE(bias, limitReal(max_)))\\n theta.assertions.add(GE(bias, limitReal(min_)))\\n\\n return bias\",\n \"def __init__(self, input_dim, output_dim, hidden_dim=64, activation = F.relu, output_activation = F.tanh):\\n super().__init__(input_dim, output_dim, hidden_dim)\\n self.linear1 = nn.Linear(input_dim, hidden_dim)\\n self.linear2 = nn.Linear(hidden_dim, int(hidden_dim/2))\\n self.linear3 = nn.Linear(int(hidden_dim/2), int(hidden_dim/4))\\n self.linear4 = nn.Linear(int(hidden_dim/4), output_dim)\\n self.activation = activation\\n self.output_activation = output_activation\",\n \"def set_up_linear_system(self):\\r\\n \\r\\n import numpy as np\\r\\n import copy\\r\\n \\r\\n # Find all elements which require the solver\\r\\n # First, find all elements which are either Line Sinks, Doublets, or Inhomogeneities\\r\\n part_of_solver = [(isinstance(e, ElementHeadBoundary) or isinstance(e, ElementNoFlowBoundary) or isinstance(e, ElementInhomogeneity)) for e in self.elementlist]\\r\\n # Only keep the elements which must be part of the linear system...\\r\\n part_of_solver = [idx for idx,val in enumerate(part_of_solver) if val]\\r\\n # ...and prepare a second set of indices for its complement\\r\\n not_part_of_solver = [i for i in np.arange(len(self.elementlist)) if i not in part_of_solver]\\r\\n \\r\\n # These elements invariably consist of segments - find out how many there are in total\\r\\n num_segments = np.sum([self.elementlist[idx].segments for idx in part_of_solver])\\r\\n \\r\\n # =====================================================================\\r\\n # Now create the matrix\\r\\n # =====================================================================\\r\\n \\r\\n # Pre-allocate arrays for the linear solver\\r\\n matrix = np.zeros((num_segments,num_segments))\\r\\n \\r\\n # The counter will keep track at what row we are\\r\\n row = 0\\r\\n \\r\\n # Go through all elements\\r\\n for i in part_of_solver:\\r\\n \\r\\n # Find the corresponding element\\r\\n e = self.elementlist[i]\\r\\n \\r\\n # We need a second counter for the columns\\r\\n col = 0\\r\\n \\r\\n # e is the element we are currently looking at - the row -, now we \\r\\n # must go through all other elements which are part of the solver\\r\\n # and check what they contribute to the control points of this element\\r\\n for i2 in part_of_solver:\\r\\n \\r\\n # Find the corresponding element\\r\\n e2 = self.elementlist[i2]\\r\\n \\r\\n # If the row element is a HeadLineSink, we must extract potentials\\r\\n if isinstance(e, ElementHeadBoundary):\\r\\n \\r\\n # Evaluate the contributions of this element to the control points\\r\\n if e != e2:\\r\\n block = e2.evaluate(\\r\\n z = e.zc,\\r\\n detailed = True,\\r\\n override_parameters = True).T\\r\\n else:\\r\\n block = e2.evaluate(\\r\\n z = e.zc,\\r\\n detailed = True,\\r\\n override_parameters = True,\\r\\n evaluate_self = True).T\\r\\n \\r\\n \\r\\n elif isinstance(e, ElementNoFlowBoundary):\\r\\n \\r\\n # Evaluate the contributions of this element to the control points\\r\\n block = e2.evaluate_gradient(\\r\\n z = e.zc,\\r\\n detailed = True,\\r\\n derivatives = 'phi',\\r\\n override_parameters = True).T\\r\\n \\r\\n # Project the partial derivatives onto the normal vector\\r\\n # The projection is a->b = <a,b>/||b||^2*b\\r\\n # Let's try it with the inner product instead\\r\\n # The normal vector is already normalized\\r\\n \\r\\n # We should have as many normal vectors as we have control points\\r\\n # Go through them all, and project each gradient onto the normal vector\\r\\n for idx,nv in enumerate(e.segment_nvec):\\r\\n \\r\\n # Calculate the inner product between the returned partial\\r\\n # derivatives and the segment's normal vector\\r\\n block[idx,:] = np.inner(\\r\\n np.column_stack(( \\r\\n np.real(block[idx,:]),\\r\\n np.imag(block[idx,:]) )),\\r\\n np.asarray([np.real(nv),np.imag(nv)]).T )[:,0]\\r\\n \\r\\n elif isinstance(e, ElementInhomogeneity):\\r\\n \\r\\n # If this inhomogeneity evaluates itself\\r\\n if i == i2:\\r\\n \\r\\n # Retrieve own matrix contribution\\r\\n block = copy.copy(e2.block)\\r\\n \\r\\n # This contribution is incomplete, subtract A_star from\\r\\n # its diagonal\\r\\n \\r\\n # Prepare a vector of outside conductivities; all are\\r\\n # the background conductivity by default\\r\\n for e3 in self.elementlist:\\r\\n if isinstance(e3, ElementMoebiusBase) or isinstance(e3, ElementUniformBase):\\r\\n A_star = np.ones(e2.zc.shape)*e3.k/(e2.k - e3.k)\\r\\n \\r\\n # Get add matrix\\r\\n addmat = np.identity(block.shape[0])\\r\\n np.fill_diagonal(addmat,A_star)\\r\\n \\r\\n # Subtract it from the retrieved block\\r\\n block -= addmat\\r\\n \\r\\n else:\\r\\n \\r\\n # Evaluate the contributions of this element to the control points\\r\\n block = e2.evaluate(\\r\\n z = e.zc,\\r\\n detailed = True,\\r\\n override_parameters = True).T\\r\\n \\r\\n # Write this block into the matrix\\r\\n matrix[row:row+e.segments,col:col+e2.segments] = copy.copy(np.real(block))\\r\\n \\r\\n # Update the column counter\\r\\n col += e2.segments\\r\\n \\r\\n # Update the row counter\\r\\n row += e.segments\\r\\n \\r\\n # =====================================================================\\r\\n # Now create the solution_vector\\r\\n # =====================================================================\\r\\n \\r\\n # Pre-allocate spac efor the solution vector\\r\\n solution_vector = np.zeros(num_segments)\\r\\n \\r\\n # The counter will keep track at what row we are\\r\\n counter = 0\\r\\n \\r\\n # Go through all elements\\r\\n for i in part_of_solver:\\r\\n \\r\\n # Find the corresponding element\\r\\n e = self.elementlist[i]\\r\\n \\r\\n # If the element is a HeadLineSink, we must assign the difference\\r\\n # between the head target and the background contributions\\r\\n if isinstance(e, ElementHeadBoundary):\\r\\n \\r\\n # Step 1: Assign the head targets\\r\\n solution_vector[counter:counter+e.segments] = \\\\\\r\\n copy.copy(e.phi_target)\\r\\n # solution_vector[counter:counter+e.segments] = \\\\\\r\\n # copy.copy(e.head_target)\\r\\n \\r\\n # # Step 2: Background potential --------------------------------\\r\\n # solution_vector[counter:counter+e.segments] -= \\\\\\r\\n # np.real(self.evaluate(e.zc))\\r\\n \\r\\n # Step 3: All elements ----------------------------------------\\r\\n for idx in not_part_of_solver:\\r\\n solution_vector[counter:counter+e.segments] -= \\\\\\r\\n np.real(self.elementlist[idx].evaluate(e.zc))\\r\\n \\r\\n # If the element is a no-flow boundary, we must assign the difference\\r\\n # between the head target and the background contributions\\r\\n if isinstance(e, ElementNoFlowBoundary):\\r\\n \\r\\n # # Step 1: Background gradient ---------------------------------\\r\\n # temp = self.evaluate_gradient(e.zc,derivatives='phi')\\r\\n \\r\\n # Step 2: Gradients from all elements -------------------------\\r\\n temp = np.zeros(e.zc.shape,dtype=np.complex)\\r\\n for idx in not_part_of_solver:\\r\\n temp += \\\\\\r\\n self.elementlist[idx].evaluate_gradient(e.zc,derivatives='phi')\\r\\n \\r\\n # Step 3: Project gradients onto normal vector ----------------\\r\\n for ix,nv in enumerate(e.segment_nvec):\\r\\n solution_vector[counter+ix] = \\\\\\r\\n -np.inner(\\r\\n np.asarray([np.real(nv),np.imag(nv)])[:,0],\\r\\n np.asarray([np.real(temp[ix]),np.imag(temp[ix])]) )\\r\\n \\r\\n # If the element is an Inhomogeneity, we must simply assign the potentials\\r\\n # induced by other elements\\r\\n if isinstance(e, ElementInhomogeneity):\\r\\n \\r\\n # # Step 1: Background potential --------------------------------\\r\\n # solution_vector[counter:counter+e.segments] -= \\\\\\r\\n # np.real(self.evaluate(e.zc))\\r\\n \\r\\n # Step 2: All elements ----------------------------------------\\r\\n for idx in not_part_of_solver:\\r\\n solution_vector[counter:counter+e.segments] -= \\\\\\r\\n np.real(self.elementlist[idx].evaluate(e.zc))\\r\\n \\r\\n # Update the counter\\r\\n counter += e.segments\\r\\n \\r\\n self.matrix = matrix\\r\\n self.solvec = solution_vector\\r\\n \\r\\n return matrix, solution_vector\",\n \"def build_linear_model(inputs, columns, config):\\r\\n features = inputs['features']\\r\\n\\r\\n cols_to_vars = {}\\r\\n units = int(config['linear_model'].get('units', 1))\\r\\n combiner = config['linear_model'].get('combiner', 'sum')\\r\\n linear_logits = tf.feature_column.linear_model(\\r\\n features=features,\\r\\n feature_columns=columns,\\r\\n units=units,\\r\\n sparse_combiner=combiner,\\r\\n cols_to_vars=cols_to_vars)\\r\\n\\r\\n return linear_logits\",\n \"def new(self):\\n self.define_layers()\\n self.model = nn.Sequential(*self.layers)\\n self.model.cuda()\\n self.model = orthogonal_init(self.model)\\n\\n # Re-count N\\n self.count_params()\",\n \"def __init__(self, value):\\n\\n LinearExpression.__init__(self)\\n\\n self.value = value\",\n \"def add_linear_transform(self):\\n \\n with tf.variable_scope(\\\"linear_transform\\\"):\\n \\n # feature scales/weights\\n self.w = tf.get_variable(\\\"weights\\\", shape=[self.dim_input], \\n initializer= tf.contrib.layers.xavier_initializer())\\n #self.B = tf.get_variable(\\\"biases\\\", shape=[self.dim_input], \\n # initializer= tf.contrib.layers.xavier_initializer())\\n \\n # diagonalize and matmul\\n self.W = tf.diag(self.w)\\n #self.W = tf.get_variable(\\\"weights\\\", shape=[self.dim_input, self.dim_input], \\n # initializer= tf.contrib.layers.xavier_initializer())\\n \\n #self.X_transformed = tf.add(tf.matmul(self.X_input, self.W), self.B) \\n self.X_transformed = tf.matmul(self.X_input, self.W)\",\n \"def __init__(self, space, constant=0):\\n self.__constant = constant\\n super().__init__(space, linear=False)\",\n \"def __init__(self, rng, input, n_in, n_hidden, n_out):\\r\\n\\r\\n # Since we are dealing with a one hidden layer MLP, this will\\r\\n # translate into a TanhLayer connected to the LogisticRegression\\r\\n # layer; this can be replaced by a SigmoidalLayer, or a layer\\r\\n # implementing any other nonlinearity\\r\\n self.hiddenLayer = HiddenLayer(rng = rng, input = input,\\r\\n n_in = n_in, n_out = n_hidden,\\r\\n activation = T.tanh, name_prefix='hid_')\\r\\n\\r\\n # The logistic regression layer gets as input the hidden units\\r\\n # of the hidden layer\\r\\n self.logRegressionLayer = LogisticRegression(\\r\\n input = self.hiddenLayer.output,\\r\\n n_in = n_hidden,\\r\\n n_out = n_out, name_prefix='log_')\\r\\n\\r\\n # negative log likelihood of the MLP is given by the negative\\r\\n # log likelihood of the output of the model, computed in the\\r\\n # logistic regression layer\\r\\n self.negative_log_likelihood = self.logRegressionLayer.negative_log_likelihood\\r\\n\\r\\n # the parameters of the model are the parameters of the two layer it is\\r\\n # made out of\\r\\n self.params = self.hiddenLayer.params + self.logRegressionLayer.params\",\n \"def __init__(self,hidden_size):\\n super(mulatt, self).__init__()\\n self.linear1=torch.nn.Linear(hidden_size,hidden_size,bias=False)\",\n \"def __init__(self, rng, input, n_in, n_hidden, n_out):\\n\\n # Since we are dealing with a one hidden layer MLP, this will translate\\n # into a HiddenLayer with a tanh activation function connected to the\\n # LogisticRegression layer; the activation function can be replaced by\\n # sigmoid or any other nonlinear function\\n self.hiddenLayer = HiddenLayer(\\n rng=rng,\\n input=input,\\n n_in=n_in,\\n n_out=n_hidden,\\n activation=T.tanh\\n )\\n\\n # The logistic regression layer gets as input the hidden units\\n # of the hidden layer\\n self.logRegressionLayer = LogisticRegression(\\n input=self.hiddenLayer.output,\\n n_in=n_hidden,\\n n_out=n_out\\n )\\n # end-snippet-2 start-snippet-3\\n # L1 norm ; one regularization option is to enforce L1 norm to\\n # be small\\n self.L1 = (\\n abs(self.hiddenLayer.W).sum()\\n + abs(self.logRegressionLayer.W).sum()\\n )\\n\\n # square of L2 norm ; one regularization option is to enforce\\n # square of L2 norm to be small\\n self.L2_sqr = (\\n (self.hiddenLayer.W ** 2).sum()\\n + (self.logRegressionLayer.W ** 2).sum()\\n )\\n\\n # negative log likelihood of the MLP is given by the negative\\n # log likelihood of the output of the model, computed in the\\n # logistic regression layer\\n self.negative_log_likelihood = (\\n self.logRegressionLayer.negative_log_likelihood\\n )\\n # same holds for the function computing the number of errors\\n self.errors = self.logRegressionLayer.errors\\n\\n # the parameters of the model are the parameters of the two layer it is\\n # made out of\\n self.params = self.hiddenLayer.params + self.logRegressionLayer.params\\n # end-snippet-3\\n\\n # keep track of model input\\n self.input = input\",\n \"def __init__(self, num_nodes):\\n self.graph = sps.lil_matrix((num_nodes, num_nodes))\\n self.size = num_nodes\",\n \"def Linear(in_features, out_features, bias=True, device=None, dtype=None):\\n layer = nn.Linear(in_features, out_features, bias, device, dtype)\\n # @see: https://msdn.microsoft.com/en-us/magazine/mt833293.aspx for example\\n torch.nn.init.xavier_uniform_(layer.weight)\\n if bias:\\n torch.nn.init.zeros_(layer.bias)\\n return layer\",\n \"def __init__(self, N_sym, n_nodes, activations, N_element, bias = True, scaling = None):\\n super(MultiLayerNet, self).__init__()\\n N_layers = len(n_nodes)\\n if N_layers == 0:\\n self.net = torch.nn.Linear(N_sym, N_element, bias = bias)\\n else:\\n layers = []\\n for n in range(N_layers):\\n if n == 0:\\n layers += [torch.nn.Linear(N_sym, n_nodes[n], bias = bias)]\\n layers += [activations[n]]\\n else:\\n layers += [torch.nn.Linear(n_nodes[n-1], n_nodes[n], bias = bias)]\\n layers += [activations[n]]\\n layers += [torch.nn.Linear(n_nodes[-1], N_element, bias = bias)]\\n self.net = torch.nn.Sequential(*layers)\\n \\n self.scaling = scaling\",\n \"def build(self):\\n\\n self.W = self.init([self.n_atom_input_feat, self.n_output])\\n self.b = model_ops.zeros(shape=[\\n self.n_output,\\n ])\\n\\n self.trainable_weights = self.W + self.b\",\n \"def __init__(self, m, g, l):\\n\\n AffineDynamics.__init__(self)\\n SystemDynamics.__init__(self,n=2,m=1)\\n self.m, self.g, self.l = m, g, l\",\n \"def linear(input_dim,variances=None,ARD=False):\\r\\n part = parts.linear.Linear(input_dim,variances,ARD)\\r\\n return kern(input_dim, [part])\",\n \"def get_linear_layer(rows, columns, init_method):\\n layer = torch.nn.Linear(rows, columns)\\n init_method(layer.weight)\\n with torch.no_grad():\\n layer.bias.zero_()\\n return layer\",\n \"def __init__(self, cell_index, stimulus_type, loss='poisson_loss', optimizer='sgd',\\n weight_init='glorot_normal', l2_reg=0., mean_adapt=False):\\n\\n self.stim_shape = (40, 50, 50)\\n\\n # build the model (flatten the input, followed by a dense layer and\\n # softplus activation)\\n with notify('Building LN model'):\\n self.model = Sequential()\\n self.model.add(Flatten(input_shape=self.stim_shape))\\n self.model.add(Dense(1, activation='softplus', init=weight_init,\\n W_regularizer=l2(l2_reg)))\\n\\n # save architecture string (for markdown file)\\n self.architecture = '\\\\n'.join(['l2 regularization: {}'.format(l2_reg),\\n 'stimulus shape: {}'.format(self.stim_shape),\\n 'weight initialization: {}'.format(weight_init)])\\n\\n # compile\\n super().__init__(cell_index, stimulus_type, loss, optimizer, mean_adapt)\",\n \"def __init__(self,hidden_size):\\n super(selfatt, self).__init__()\\n self.linear1=torch.nn.Linear(2*hidden_size,hidden_size)\\n self.linear2=torch.nn.Linear(hidden_size,1)\",\n \"def create_modules(self):\\n self.bitcell = self.replica_bitcell = self.mod_replica_bitcell()\\n self.add_mod(self.bitcell)\\n\\n # This is the replica bitline load column that is the height of our array\\n self.rbl = bitcell_array(name=\\\"bitline_load\\\", cols=1, rows=self.bitcell_loads)\\n self.add_mod(self.rbl)\\n\\n # FIXME: The FO and depth of this should be tuned\\n self.delay_chain = self.mod_delay_chain([self.delay_fanout]*self.delay_stages)\\n self.add_mod(self.delay_chain)\\n\\n self.inv = pinv()\\n self.add_mod(self.inv)\\n\\n self.access_tx = ptx(tx_type=\\\"pmos\\\")\\n self.add_mod(self.access_tx)\",\n \"def from_linear(self, E=(1+0j, 0+0j)):\\n self.linear = array(E).reshape((2,1))\\n self.circular = self.to_circular()\\n self.Stokes_from_linear()\\n return self\",\n \"def __init__(self):\\n self.label = \\\"Logistic regression\\\"\\n self.description = \\\"This tool is a useful complement to Weights-of-Evidence Calculate Response tool as Logistic Regression does not make the assumption of conditional independence of the evidence with regards to the training sites. Using the evidence and assocaited weights tables, this tool creates the outputs the response and standard deviation rasters. The calculations are based on the Gen_Class attribute in the weights table and the type of evidence. Please note that the Logistic Regression tool accepts a maximum of 6,000 unique conditions or it fails. Also note that there is an upper limit of 100,000 unit cells per class in each evidence raster layer. If a class in an evidence raster goes above this, the script contains a function to increase the unit cell size to ensure an upper limit of 100,000. These issues are unable to be fixed due to a hard coded limitation in the Logistic Regression executable sdmlr.exe.\\\"\\n self.canRunInBackground = False\\n self.category = \\\"Weights of Evidence\\\"\",\n \"def linear_regression(self):\\n # Create dummies of the Types\\n\\n\\n self.truth = pd.read_csv(\\\"AllTruth/AllTruth.csv\\\")\\n self.false = pd.read_csv(\\\"AllLie/AllLie.csv\\\")\\n\\n self.truth[\\\"Type\\\"] = \\\"T\\\"\\n self.false[\\\"Type\\\"] = \\\"F\\\"\\n\\n full = self.truth.append([self.false])\\n\\n\\n\\n encoder = LabelEncoder()\\n encoder.fit(full[\\\"Type\\\"])\\n encoded_Y = encoder.transform(full[\\\"Type\\\"])\\n # convert integers to dummy variables (i.e. one hot encoded)\\n dummy_y = np_utils.to_categorical(encoded_Y)\\n\\n y_data = full[\\\"Type\\\"]\\n for label in ['Type', 'Unnamed: 0.1', 'Unnamed: 0', 'Unnamed: 0.1.1', 'Unnamed: 0.1.1.1']:\\n if label in full.columns.values:\\n full = full.drop([label], axis=1)\\n #full = full.drop(columns=['136', '137', '138', '139', '140', '141', '142'])\\n\\n print(\\\"Nan = \\\", full.isnull().any())\\n\\n \\\"\\\"\\\"removeList = full.columns.values\\n print(removeList)\\n full = createCouples(full)\\n full = full.drop(columns = removeList)\\\"\\\"\\\"\\n\\n self.labels = full.columns.values\\n # Create train & test data\\n #this.X_train, this.X_test, this.y_train, this.y_test = #train_test_split(np.array(full), np.array(y_data))\\n X_train, y_train = full, dummy_y\\n\\n print(X_train, \\\"\\\\n\\\", y_train)\\n\\n # Learn the data and check for success\\n #self.learner = NetLearner(\\\"test2\\\", 137)\\n self.learner = LinearLearner()\\n self.learner.learn(np.array(X_train), np.array(y_train))\\n\\n print(full, \\\"\\\\n\\\\nLearning Done.\\\")\\n\\n X_test_truth = pd.read_csv(\\\"TruthTest/TruthTest.csv\\\")\\n X_test_lie = pd.read_csv(\\\"LieTest/LieTest.csv\\\")\\n\\n X_test_truth[\\\"Type\\\"] = \\\"T\\\"\\n X_test_lie[\\\"Type\\\"] = \\\"F\\\"\\n\\n full_test = X_test_truth.append([X_test_lie])\\n\\n\\n encoder = LabelEncoder()\\n encoder.fit(full_test[\\\"Type\\\"])\\n encoded_Y = encoder.transform(full_test[\\\"Type\\\"])\\n # convert integers to dummy variables (i.e. one hot encoded)\\n dummy_y_test = np_utils.to_categorical(encoded_Y)\\n\\n y_test = dummy_y_test#full_test[\\\"Type\\\"]\\n for label in ['Type', 'Unnamed: 0.1', 'Unnamed: 0', 'Unnamed: 0.1.1', 'Unnamed: 0.1.1.1']:\\n if label in full_test.columns.values:\\n full_test = full_test.drop([label], axis=1)\\n\\n # self.learner.print_accuracy(full_test, dummy_y_test)\\n\\n y_test_predict = pd.DataFrame(self.learner.predict(full_test)).idxmax(axis=1)\\n y_test = pd.DataFrame(y_test).idxmax(axis=1)\\n #print(y_test, y_test_predict)\\n result = np.array(y_test) - np.array(y_test_predict)\\n\\n total = len(full_test)\\n curr = total - np.count_nonzero(result)\\n\\n # Print the success rate\\n print(\\\"Total success rate: %.2f%%\\\" % (curr/total * 100))\\n\\n importance0 = pd.DataFrame(np.abs(self.learner.lm.coef_[0]),\\n index = full_test.columns,\\n columns=['importance']).sort_values('importance', ascending=False)\\n\\n importance1 = pd.DataFrame(np.abs(self.learner.lm.coef_[1]),\\n index = full_test.columns,\\n columns=['importance']).sort_values('importance', ascending=False)\\n\\n\\n importance0 = np.array(importance0) / 2\\n importance1 = np.array(importance1) / 2\\n print(importance0, '\\\\n', importance1)\\n with open(\\\"importance.txt\\\", \\\"w\\\") as file:\\n file.write(\\\"///////Importance 0///////\\\\n\\\")\\n file.write(str(importance0))\\n file.write(\\\"\\\\n\\\\n///////Importance 1///////\\\\n\\\")\\n file.write(str(importance1))\\n \\\"\\\"\\\"\\n\\n # Merge PL+NL and PT+NT\\n def group(n):\\n if n in [0, 1]:\\n return 0\\n return 1\\n\\n # Get the predicted output for X_test and compare to\\n # the expected output\\n y_test_predict = pd.DataFrame(self.learner.predict(X_test)).idxmax(axis=1).apply(group)\\n y_test = pd.DataFrame(y_test).idxmax(axis=1).apply(group)\\n\\n # Count number of failures\\n result = np.array(y_test) - np.array(y_test_predict)\\n\\n total = len(X_test)\\n curr = total - np.count_nonzero(result)\\n\\n # Print the success rate\\n print(\\\"Total success rate: %.2f%%\\\" % (curr/total * 100))\\n \\\"\\\"\\\"\",\n \"def __init__(self,lpr='__label__',lr=0.1,lru=100,dim=100,ws=5,epoch=5,minc=1,neg=5,ngram=1,\\\\\\nloss='softmax',nbucket=0,minn=0,maxn=0,th=12,t=0.0001,verbosec=0,encoding='utf-8'):\\n\\t\\t\\tself.lr=lr\\n\\t\\t\\tself.lr_update_rate=lru\\n\\t\\t\\tself.dim=dim\\n\\t\\t\\tself.ws=ws\\n\\t\\t\\tself.epoch=epoch\\n\\t\\t\\tself.min_count=minc\\n\\t\\t\\tself.neg=neg\\n\\t\\t\\tself.word_ngrams=ngram\\n\\t\\t\\tself.loss=loss\\n\\t\\t\\tself.bucket=bucket\\n\\t\\t\\tself.minn=minn\\n\\t\\t\\tself.maxn=maxn\\n\\t\\t\\tself.n_thread=th\\n\\t\\t\\tself.samplet=t\\n\\t\\t\\tself.silent=verbosec\\n\\t\\t\\tself.enc=encodings\\n\\t\\t\\tself.model=None\\n\\t\\t\\tself.result=None\",\n \"def __init__(self, config) -> None:\\n super(Global_MP, self).__init__()\\n self.dim = config.dim\\n\\n self.h_mlp = MLP([self.dim, self.dim])\\n\\n self.res1 = Res(self.dim)\\n self.res2 = Res(self.dim)\\n self.res3 = Res(self.dim)\\n self.mlp = MLP([self.dim, self.dim])\\n\\n self.x_edge_mlp = MLP([self.dim * 3, self.dim])\\n self.linear = nn.Linear(self.dim, self.dim, bias=False)\",\n \"def __init__(self, num_parameters=1, init=0.25):\\n super(PReLU, self).__init__()\\n self.num_parameters = num_parameters\\n self.weight = Parameter(Tensor(num_parameters).fill_(init))\",\n \"def define_module(self):\\n # pylint: disable=invalid-name\\n # Small names like fc abd bn are fine in this context.\\n\\n resnet = resnet152(pretrained=True)\\n modules = list(resnet.children())[:-1] # delete the last fc layer.\\n for module in modules:\\n module.requires_grad = False\\n self.resnet = nn.Sequential(*modules)\\n self.fc = nn.Linear(resnet.fc.in_features, self.d_embed)\\n self.bn = nn.BatchNorm1d(self.d_embed, momentum=0.01)\",\n \"def __init__(self,layers,activations):\\n model = utils.buildMLP(layers, activations)\\n super().__init__(torch.nn.Sequential(model), nnType='dnn')\",\n \"def __init__(self, inputs, outputs):\\n super(ELM, self).__init__(inputs, outputs)\",\n \"def new_ll():\\n from linked_list import Linked_List\\n this_ll = Linked_List(PARAMS_SAMPLE_LIST)\\n return this_ll\",\n \"def __init__(self, config) -> None:\\n\\n super(Local_MP, self).__init__()\\n self.dim = config.dim\\n\\n self.h_mlp = MLP([self.dim, self.dim])\\n\\n self.mlp_kj = MLP([3 * self.dim, self.dim])\\n self.mlp_ji_1 = MLP([3 * self.dim, self.dim])\\n self.mlp_ji_2 = MLP([self.dim, self.dim])\\n self.mlp_jj = MLP([self.dim, self.dim])\\n\\n self.mlp_sbf1 = MLP([self.dim, self.dim, self.dim])\\n self.mlp_sbf2 = MLP([self.dim, self.dim, self.dim])\\n self.lin_rbf1 = nn.Linear(self.dim, self.dim, bias=False)\\n self.lin_rbf2 = nn.Linear(self.dim, self.dim, bias=False)\\n\\n self.res1 = Res(self.dim)\\n self.res2 = Res(self.dim)\\n self.res3 = Res(self.dim)\\n\\n self.lin_rbf_out = nn.Linear(self.dim, self.dim, bias=False)\\n\\n self.h_mlp = MLP([self.dim, self.dim])\\n\\n self.y_mlp = MLP([self.dim, self.dim, self.dim, self.dim])\\n self.y_W = nn.Linear(self.dim, 1)\",\n \"def __init__(self, input_dim: int, output_dim: int):\\n\\n super().__init__()\\n\\n self.input_dim = input_dim\\n self.output_dim = output_dim\\n\\n self.batchNorm1 = nn.BatchNorm1d(input_dim)\\n self.dense1 = nn.Linear(input_dim+output_dim, 64)\\n self.relu1 = nn.ReLU()\\n self.dense2 = nn.Linear(64, 32)\\n self.relu2 = nn.ReLU()\\n self.dense3 = nn.Linear(32, output_dim)\",\n \"def __init__(self, rng, input, n_in, n_hidden, n_out):\\r\\n\\r\\n # Since we are dealing with a one hidden layer MLP, this will translate\\r\\n # into a HiddenLayer with a tanh activation function connected to the\\r\\n # LogisticRegression layer; the activation function can be replaced by\\r\\n # sigmoid or any other nonlinear function\\r\\n self.hiddenLayer = HiddenLayer(rng=rng, input=input,\\r\\n n_in=n_in, n_out=n_hidden,\\r\\n activation=T.tanh)\\r\\n\\r\\n # The logistic regression layer gets as input the hidden units\\r\\n # of the hidden layer\\r\\n self.logRegressionLayer = LogisticRegression(\\r\\n input=self.hiddenLayer.output,\\r\\n n_in=n_hidden,\\r\\n n_out=n_out)\\r\\n\\r\\n # L1 norm ; one regularization option is to enforce L1 norm to\\r\\n # be small\\r\\n self.L1 = abs(self.hiddenLayer.W).sum() \\\\\\r\\n + abs(self.logRegressionLayer.W).sum()\\r\\n\\r\\n # square of L2 norm ; one regularization option is to enforce\\r\\n # square of L2 norm to be small\\r\\n self.L2_sqr = (self.hiddenLayer.W ** 2).sum() \\\\\\r\\n + (self.logRegressionLayer.W ** 2).sum()\\r\\n\\r\\n # negative log likelihood of the MLP is given by the negative\\r\\n # log likelihood of the output of the model, computed in the\\r\\n # logistic regression layer\\r\\n self.negative_log_likelihood = self.logRegressionLayer.negative_log_likelihood\\r\\n # same holds for the function computing the number of errors\\r\\n self.errors = self.logRegressionLayer.errors\\r\\n\\r\\n # the parameters of the model are the parameters of the two layer it is\\r\\n # made out of\\r\\n self.params = self.hiddenLayer.params + self.logRegressionLayer.params\",\n \"def build(self, lang, linearInMLP=False):\\n inputLayers, concLayers = [], []\\n inputToken = Input((3 + configuration['embedding']['useB1'] + configuration['embedding']['useB-1'],))\\n inputLayers.append(inputToken)\\n tokenEmb = Embedding(len(self.vocabulary.tokenIndices), configuration['mlp']['tokenEmb'],\\n trainable=configuration['mlp']['trainable'],\\n weights=self.getWeightMatrix(self.vocabulary.tokenIndices, lang))(inputToken)\\n tokenFlatten = Flatten()(tokenEmb)\\n concLayers.append(tokenFlatten)\\n inputPos = Input((3 + configuration['embedding']['useB1'] + configuration['embedding']['useB-1'],))\\n inputLayers.append(inputPos)\\n posEmb = Embedding(len(self.vocabulary.posIndices), configuration['mlp']['posEmb'],\\n trainable=configuration['mlp']['trainable'])(inputPos)\\n posFlatten = Flatten()(posEmb)\\n concLayers.append(posFlatten)\\n if linearInMLP:\\n linearPredInput = Input(shape=(8,))\\n inputLayers.append(linearPredInput)\\n concLayers.append(linearPredInput)\\n\\n conc = keras.layers.concatenate(concLayers) if len(concLayers) > 1 else concLayers[0]\\n dense1Layer = Dense(configuration['mlp']['dense1UnitNumber'],\\n activation=configuration['nn']['dense1Activation'])(conc)\\n lastLayer = Dropout(configuration['mlp']['dense1Dropout'])(dense1Layer)\\n if configuration['mlp2']['dense2']:\\n dense2Layer = Dense(configuration['mlp2']['dense2UnitNumber'],\\n activation=configuration['mlp2']['dense2Activation'])(lastLayer)\\n lastLayer = Dropout(configuration['mlp2']['dense2Dropout'])(dense2Layer)\\n softmaxLayer = Dense(8 if enableCategorization else 4, activation='softmax')(lastLayer)\\n return inputLayers, softmaxLayer\",\n \"def __init__(self, module, low_mem=False, num_outputs=1):\\n\\n super(TimeDistributed, self).__init__()\\n self._module = module\\n if num_outputs > 1:\\n self.forward = self._forward_low_mem_multiple_outputs if low_mem else self._forward_multiple_outputs\\n else:\\n self.forward = self._forward_low_mem if low_mem else self._forward\\n self._num_outputs = num_outputs\",\n \"def __init__(self, module, intrinsic_dimension, device=0):\\n super(DenseWrap, self).__init__()\\n\\n # Hide this from inspection by get_parameters()\\n self.m = [module]\\n\\n self.name_base_localname = []\\n\\n # Stores the initial value: \\\\theta_{0}^{D}\\n self.initial_value = dict()\\n\\n # Stores the randomly generated projection matrix P\\n self.random_matrix = dict()\\n\\n self.device = device\\n\\n # Parameter vector that is updated, initialised with zeros as per text: \\\\theta^{d}\\n V = nn.Parameter(torch.zeros((intrinsic_dimension, 1)).to(device))\\n self.register_parameter(\\\"V\\\", V)\\n v_size = (intrinsic_dimension,)\\n\\n # Iterates over layers in the Neural Network\\n for name, param in module.named_parameters():\\n # If the parameter requires gradient update\\n if param.requires_grad:\\n\\n # Saves the initial values of the initialised parameters from param.data and sets them to no grad.\\n # (initial values are the 'origin' of the search)\\n self.initial_value[name] = v0 = (\\n param.clone().detach().requires_grad_(False).to(device)\\n )\\n\\n # If v0.size() is [4, 3], then below operation makes it [4, 3, v_size]\\n matrix_size = v0.size() + v_size\\n\\n # Generates random projection matrices P, sets them to no grad\\n self.random_matrix[name] = (\\n torch.randn(matrix_size, requires_grad=False).to(device)\\n / intrinsic_dimension ** 0.5\\n )\\n\\n # NOTE!: lines below are not clear!\\n base, localname = module, name\\n while \\\".\\\" in localname:\\n prefix, localname = localname.split(\\\".\\\", 1)\\n base = base.__getattr__(prefix)\\n self.name_base_localname.append((name, base, localname))\\n\\n for name, base, localname in self.name_base_localname:\\n delattr(base, localname)\",\n \"def __init__(self, num_units=512, num_layers=4):\\n\\n self.num_units = num_units\\n self.num_layers = num_layers\\n \\n self.layers = []\\n for idx in range(self.num_layers):\\n lstm_layer = tf.contrib.cudnn_rnn.CudnnLSTM(num_units=self.num_units,\\n direction='unidirectional',\\n num_layers=1)\\n self.layers.append(lstm_layer)\",\n \"def __init__(self, nlp):\\n self._nlp = nlp\\n x = nlp.init_primals()\\n y = nlp.init_duals()\\n if np.any(np.isnan(y)):\\n # did not get initial values for y, use this default\\n y.fill(1.0)\\n\\n self._cached_x = x.copy()\\n self._cached_y = y.copy()\\n self._cached_obj_factor = 1.0\\n\\n nlp.set_primals(self._cached_x)\\n nlp.set_duals(self._cached_y)\\n\\n # get jacobian and hessian structures\\n self._jac_g = nlp.evaluate_jacobian()\\n self._hess_lag = nlp.evaluate_hessian_lag()\\n self._hess_lower_mask = self._hess_lag.row >= self._hess_lag.col\",\n \"def __init__(self,NL,kS=None):\\n assert isinstance(NL,int), \\\"Wrong data type for number of layers.\\\\n\\\\n\\\"\\n assert NL>0, \\\"At least one layer is needed.\\\\n\\\"\\n self.NL=NL\\n self.layers=[] # List self.layers will store object of type Layer\\n for i in range(NL):\\n self.layers+=[Layer(),] # Initialize with blank layers\\n \\n self.couplings=[] # List self.couplings will store coupling strengths between layers\\n for i in range(NL-1):\\n self.couplings+=[0.,] # Initialize with decoupled layers\\n \\n if kS:\\n assert isinstance(kS,kSpace), \\\"Wrong data type of momentum space.\\\\n\\\\n\\\"\\n self.kS=kS\\n else:\\n self.kS=kSpace() # System requires some momentum space discretization. If none provided taken default.\",\n \"def create_linear_layer(lin_fn, num_inputs):\\n inputs = torch.randn(num_inputs, dtype=torch.float64).requires_grad_(True)\\n bias = torch.ones((), dtype=torch.float64).requires_grad_(True)\\n out = lin_fn(inputs, bias)\\n layer = nn.Linear(num_inputs, len(out))\\n for i, x in enumerate(out):\\n inputs.grad = None\\n bias.grad = None\\n x.backward(retain_graph=True)\\n if inputs.grad is not None:\\n layer.weight.detach()[i] = inputs.grad.float()\\n else:\\n layer.weight.detach()[i].zero_()\\n if bias.grad is not None:\\n layer.bias.detach()[i] = bias.grad.float()\\n else:\\n layer.bias.detach()[i] = 0\\n return layer\",\n \"def __init__(self, layers, in_dim, lstm_dim, word_vocab, model, pre_trained=None):\\n self._model = model\\n if pre_trained is None:\\n self._E = model.add_lookup_parameters((len(word_vocab), in_dim))\\n else:\\n self._E = model.lookup_parameters_from_numpy(pre_trained)\\n self._fwd_RNN_first = dy.VanillaLSTMBuilder(layers, in_dim, lstm_dim, model)\\n self._bwd_RNN_first = dy.VanillaLSTMBuilder(layers, in_dim, lstm_dim, model)\\n self._classifier = LinearClassifier(2 * lstm_dim, 3, model)\\n self._w2i = word_vocab\",\n \"def __solve_full_linear_problem(self):\\n samples = []\\n\\n for news in self.news_pool:\\n samples += [news.sampled_quality] * self.layout_slots\\n\\n self.full_C = np.array(samples) * self.full_lambdas\\n\\n linear_problem = opt.linprog(A_ub=self.full_A, b_ub=self.full_B, c=self.full_C)\\n slots_assegnation_probabilities = []\\n slot_counter = 0\\n tmp_slot_probabilities = []\\n while slot_counter < self.layout_slots:\\n i = slot_counter\\n while i < len(linear_problem.x):\\n tmp_slot_probabilities.append(np.abs(linear_problem.x[i]))\\n i += self.layout_slots\\n slots_assegnation_probabilities.append(tmp_slot_probabilities.copy())\\n tmp_slot_probabilities.clear()\\n slot_counter += 1\\n\\n result = self.__de_randomize_LP(self.news_pool, slots_assegnation_probabilities, self.lp_rand_tech)\\n\\n return result\",\n \"def __init__(self, D_in, H, D_out):\\n super(SimpleNet, self).__init__()\\n self.linear1 = torch.nn.Linear(D_in, H)\\n self.linear2 = torch.nn.Linear(H, D_out)\",\n \"def __init__(self,L,b):\\n \\n #take the inputs and assign them to the mapping object\\n self.L = L\\n self.b = b\\n \\n #also give it the number of maps and dimension as attributes\\n n, d = np.shape(b)\\n self.n = n\\n self.d = d\\n\\n \\n #it is also of use to have the affine matrix so assign that now\\n self.A = np.zeros([n,d,d])\\n for j in range(n):\\n #use the cholesky definition of the matrix L\\n self.A[j,:,:] = L[j,:,:] @ L[j,:,:].T\",\n \"def __init__(self, D_in, D_out, H, learning_rate):\\n self.model = th.nn.Sequential(\\n th.nn.Linear(D_in, H),\\n th.nn.Tanh(),\\n th.nn.Linear(H, D_out),\\n ).double()\\n\\n for x in self.model.modules():\\n if isinstance(x, th.nn.Linear):\\n x.weight.data = th.normal(means=th.zeros(x.weight.size())).type(dtype)\\n x.bias.data = th.zeros(x.bias.size()).type(dtype)\\n\\n self.H = H\\n self.lr = learning_rate\\n self.loss_fn = th.nn.MSELoss(size_average=False)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6729752","0.64801776","0.6456392","0.63238543","0.63191384","0.621048","0.6181088","0.6168781","0.61668515","0.6132894","0.6119526","0.6119526","0.6091859","0.6075223","0.603353","0.6022447","0.60161537","0.5987431","0.5971014","0.5959086","0.595031","0.5949405","0.5948383","0.5931414","0.5926678","0.59241784","0.59175724","0.59165597","0.5912054","0.5889294","0.5863986","0.58535874","0.5845031","0.5828106","0.58262956","0.5820146","0.5820146","0.5805587","0.5796664","0.579648","0.57955635","0.57819253","0.57819253","0.57819253","0.57819253","0.57819253","0.5765124","0.5762329","0.57595795","0.5755963","0.57558405","0.5754738","0.5754165","0.5747658","0.5747658","0.57176656","0.57173026","0.5714932","0.5684966","0.5674559","0.5673827","0.56690323","0.5656559","0.5648184","0.5647463","0.56465966","0.56397516","0.5631375","0.56273854","0.5624045","0.5613716","0.56053644","0.5604636","0.5598658","0.55957985","0.5588581","0.55851907","0.5580818","0.55793804","0.5574222","0.55551505","0.55510837","0.5541929","0.5538041","0.5537079","0.5527786","0.55155176","0.5511346","0.5501844","0.54977924","0.54937905","0.5493723","0.5489718","0.5488363","0.54811734","0.54794174","0.54760706","0.547538","0.54702497","0.54687876","0.5465973"],"string":"[\n \"0.6729752\",\n \"0.64801776\",\n \"0.6456392\",\n \"0.63238543\",\n \"0.63191384\",\n \"0.621048\",\n \"0.6181088\",\n \"0.6168781\",\n \"0.61668515\",\n \"0.6132894\",\n \"0.6119526\",\n \"0.6119526\",\n \"0.6091859\",\n \"0.6075223\",\n \"0.603353\",\n \"0.6022447\",\n \"0.60161537\",\n \"0.5987431\",\n \"0.5971014\",\n \"0.5959086\",\n \"0.595031\",\n \"0.5949405\",\n \"0.5948383\",\n \"0.5931414\",\n \"0.5926678\",\n \"0.59241784\",\n \"0.59175724\",\n \"0.59165597\",\n \"0.5912054\",\n \"0.5889294\",\n \"0.5863986\",\n \"0.58535874\",\n \"0.5845031\",\n \"0.5828106\",\n \"0.58262956\",\n \"0.5820146\",\n \"0.5820146\",\n \"0.5805587\",\n \"0.5796664\",\n \"0.579648\",\n \"0.57955635\",\n \"0.57819253\",\n \"0.57819253\",\n \"0.57819253\",\n \"0.57819253\",\n \"0.57819253\",\n \"0.5765124\",\n \"0.5762329\",\n \"0.57595795\",\n \"0.5755963\",\n \"0.57558405\",\n \"0.5754738\",\n \"0.5754165\",\n \"0.5747658\",\n \"0.5747658\",\n \"0.57176656\",\n \"0.57173026\",\n \"0.5714932\",\n \"0.5684966\",\n \"0.5674559\",\n \"0.5673827\",\n \"0.56690323\",\n \"0.5656559\",\n \"0.5648184\",\n \"0.5647463\",\n \"0.56465966\",\n \"0.56397516\",\n \"0.5631375\",\n \"0.56273854\",\n \"0.5624045\",\n \"0.5613716\",\n \"0.56053644\",\n \"0.5604636\",\n \"0.5598658\",\n \"0.55957985\",\n \"0.5588581\",\n \"0.55851907\",\n \"0.5580818\",\n \"0.55793804\",\n \"0.5574222\",\n \"0.55551505\",\n \"0.55510837\",\n \"0.5541929\",\n \"0.5538041\",\n \"0.5537079\",\n \"0.5527786\",\n \"0.55155176\",\n \"0.5511346\",\n \"0.5501844\",\n \"0.54977924\",\n \"0.54937905\",\n \"0.5493723\",\n \"0.5489718\",\n \"0.5488363\",\n \"0.54811734\",\n \"0.54794174\",\n \"0.54760706\",\n \"0.547538\",\n \"0.54702497\",\n \"0.54687876\",\n \"0.5465973\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":84188,"cells":{"query":{"kind":"string","value":"Initialize layers of the head."},"document":{"kind":"string","value":"def _init_layers(self):\n self.relu = nn.ReLU(inplace=True)\n self.cls_convs = nn.ModuleList()\n self.reg_convs = nn.ModuleList()\n for i in range(self.stacked_convs):\n chn = self.in_channels if i == 0 else self.feat_channels\n self.cls_convs.append(\n ConvModule(\n chn,\n self.feat_channels,\n 3,\n stride=1,\n padding=1,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg))\n self.reg_convs.append(\n ConvModule(\n chn,\n self.feat_channels,\n 3,\n stride=1,\n padding=1,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg))\n self.retina_cls = nn.Conv2d(\n self.feat_channels,\n self.num_anchors * self.cls_out_channels,\n 3,\n padding=1)\n self.retina_reg = nn.Conv2d(\n self.feat_channels, self.num_anchors * 4, 3, padding=1)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _init_layers(self):\n self._init_predictor()\n if self.use_edge_fusion:\n self._init_edge_module()","def _init_layers(self) -> None:\n self.self_attn = MultiheadAttention(**self.self_attn_cfg)\n self.cross_attn = MultiheadAttention(**self.cross_attn_cfg)\n self.embed_dims = self.self_attn.embed_dims\n self.ffn = FFN(**self.ffn_cfg)\n norms_list = [\n build_norm_layer(self.norm_cfg, self.embed_dims)[1]\n for _ in range(3)\n ]\n self.norms = ModuleList(norms_list)","def _init_layers(self) -> None:\n self.self_attn = MultiheadAttention(**self.self_attn_cfg)\n self.embed_dims = self.self_attn.embed_dims\n self.ffn = FFN(**self.ffn_cfg)\n norms_list = [\n build_norm_layer(self.norm_cfg, self.embed_dims)[1]\n for _ in range(2)\n ]\n self.norms = ModuleList(norms_list)","def __init__(self, layers):\n\n\t\tself.layers = layers","def _init_layers(self) -> None:\n weight_nums, bias_nums = [], []\n for i in range(self.num_layers):\n if i == 0:\n weight_nums.append((self.in_channels + 2) * self.feat_channels)\n bias_nums.append(self.feat_channels)\n elif i == self.num_layers - 1:\n weight_nums.append(self.feat_channels * 1)\n bias_nums.append(1)\n else:\n weight_nums.append(self.feat_channels * self.feat_channels)\n bias_nums.append(self.feat_channels)\n\n self.weight_nums = weight_nums\n self.bias_nums = bias_nums\n self.num_params = sum(weight_nums) + sum(bias_nums)","def init_layers(self):\n\n # get caching layers activated\n caching_layers = G3WCachingLayer.objects.all()\n for caching_layer in caching_layers:\n self.add_layer(str(caching_layer), caching_layer)","def init_layer(layer):\n \n if layer.weight.ndimension() == 4:\n (n_out, n_in, height, width) = layer.weight.size()\n n = n_in * height * width\n \n elif layer.weight.ndimension() == 2:\n (n_out, n) = layer.weight.size()\n\n std = math.sqrt(2. / n)\n scale = std * math.sqrt(3.)\n layer.weight.data.uniform_(-scale, scale)\n\n if layer.bias is not None:\n layer.bias.data.fill_(0.)","def set_layers(self, sizes, init='random'):\n\t\tself.init_weights(sizes, init, None, None)","def setup_layer_structure(self):\n self.page_rank_convolution_1 = self.layer(self.feature_number, self.args.layers[0], self.args.iterations, self.args.alpha)\n self.page_rank_convolution_2 = self.layer(self.args.layers[0], self.args.layers[1], self.args.iterations, self.args.alpha)\n self.page_rank_convolution_3 = self.layer(self.args.layers[1], self.class_number, self.args.iterations, self.args.alpha)","def define_layers(self):\n\n if self.D0Flag:\n self.d = self.h\n\n self.layers = [nn.Sequential(nn.Linear(self.d, self.h),\n nn.ReLU(), )] # nn.BatchNorm1d(self.h, affine=False))]\n for l in range(1, self.L):\n self.layers.append(nn.Sequential(nn.Linear(self.h, self.h),\n nn.ReLU(), )) # nn.BatchNorm1d(self.h, affine=False)))\n\n self.layers.append(nn.Linear(self.h, 1))","def setup_layers(self):\n if self.args.model == \"exact\":\n self.layer = PPNPLayer\n else:\n self.layer = APPNPLayer\n self.setup_layer_structure()","def _init_layers(self) -> None:\n self.convs_all_levels = nn.ModuleList()\n for i in range(self.start_level, self.end_level + 1):\n convs_per_level = nn.Sequential()\n convs_per_level.add_module(\n f'conv{i}',\n ConvModule(\n self.in_channels,\n self.feat_channels,\n 3,\n padding=1,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg,\n inplace=False,\n bias=False))\n self.convs_all_levels.append(convs_per_level)\n\n conv_branch = []\n for _ in range(self.num_stacked_convs):\n conv_branch.append(\n ConvModule(\n self.feat_channels,\n self.feat_channels,\n 3,\n padding=1,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg,\n bias=False))\n self.conv_branch = nn.Sequential(*conv_branch)\n\n self.conv_pred = nn.Conv2d(\n self.feat_channels, self.out_channels, 1, stride=1)","def __init__(self, weights=[], alphas=[]):\n self._layers = [Layer(w, a) for w, a in zip(weights, alphas)]","def define_layers(self):\n if self.d != 0:\n # If we have a fixed input size we use it do define the first layer\n self.layers = [nn.Sequential(nn.Linear(self.d, self.h),\n nn.ReLU(), )] # nn.BatchNorm1d(self.h, affine=False))]\n else:\n self.layers = [nn.Sequential(nn.Linear(self.h, self.h),\n nn.ReLU(), )]\n\n l = 0\n for l in self.layers_sizes():\n self.layers.append(nn.Sequential(nn.Linear(self.h - l, self.h - l - self.delta_h),\n nn.ReLU(), )) # nn.BatchNorm1d( self.h - l - self.delta_h, affine=False)))\n self.layers.append(nn.Sequential(nn.Linear(self.h - l - self.delta_h, 1), nn.ReLU()))","def init_layers_hpm(self):\n\n self.lin_layers_hpm.append(nn.Linear(8, self.noFeatures_hpm))\n for _ in range(self.noLayers_hpm):\n self.lin_layers_hpm.append(nn.Linear(self.noFeatures_hpm, self.noFeatures_hpm))\n self.lin_layers_hpm.append(nn.Linear(self.noFeatures_hpm, 2))\n\n \n for m in self.lin_layers_hpm:\n if isinstance(m, nn.Linear):\n nn.init.xavier_normal_(m.weight)\n nn.init.constant_(m.bias, 0)","def _init_layers(self) -> None:\n super()._init_layers()\n self.controller = nn.Conv2d(\n self.feat_channels, self.num_params, 3, padding=1)","def SetUpLayerManager(self):\n pass","def __init__(self, **kwargs):\n super(ForwardLayersBase, self).__init__()\n pass","def initLayers(self):\n self.addRow(self.currentLayerData)\n self.selected = self.jobRow.child(0)\n self.setSelectedFromItem(self.selected)","def __init__(self, layers) -> None:\n super(FeaturizerPhi, self).__init__()\n self.layers = layers","def initialize_layers(self, layers_config: dict, inputs=None):\n layers_config = layers_config.copy()\n input_lyrs = []\n initiated_layers = OrderedDict()\n wrp_layer = None # indicator for wrapper layers\n first_layer = True\n\n for lyr, lyr_args in layers_config.items():\n\n lyr_config, lyr_inputs, named_outs, call_args = self.deconstruct_lyr_args(lyr, lyr_args)\n\n lyr_name, args, lyr_config, activation = self.check_lyr_config(lyr, lyr_config)\n\n if K.BACKEND == 'pytorch':\n\n if first_layer:\n first_layer = False\n\n if callable(lyr_config):\n lyr_initiated = lyr_config\n else:\n lyr_initiated = TORCH_LAYERS[lyr_name](**lyr_config)\n setattr(self, lyr, lyr_initiated)\n initiated_layers[lyr] = {\"layer\": lyr_initiated, \"named_outs\": named_outs, 'call_args': call_args,\n 'inputs': lyr_inputs}\n\n else:\n # may be user has defined layers without input layer, in this case add Input layer as first layer\n if first_layer:\n if inputs is not None: # This method was called by providing it inputs.\n assert isinstance(inputs, tf.Tensor)\n # since inputs have been defined, all the layers that will be added will be next to first layer\n first_layer = False\n layer_outputs = inputs\n initiated_layers[layer_outputs.name] = {'layer': layer_outputs, 'tf_name': lyr_name}\n\n elif lyr_name != \"Input\":\n if 'input_shape' in lyr_config: # input_shape is given in the first layer so make input layer\n initialized_layer = LAYERS[\"Input\"](shape=lyr_config['input_shape'])\n else:\n # for simple dense layer based models, lookback will not be used\n def_shape = (self.num_ins,) if self.lookback == 1 else (self.lookback, self.num_ins)\n initialized_layer = LAYERS[\"Input\"](shape=def_shape)\n\n # first layer is built so next iterations will not be for first layer\n first_layer = False\n # put the first layer in memory to be used for model compilation\n # add th layer which the user had specified as first layer\n initiated_layers[initialized_layer.name] = {'layer': initialized_layer,\n 'tf_name': lyr_name}\n input_lyrs.append(initialized_layer)\n\n # The inputs to the layer have not been specified, so either it is an Input layer\n if lyr_inputs is None:\n # or it uses the previous outputs as inputs\n if lyr_name == \"Input\":\n # it is an Input layer, hence should not be called\n initialized_layer = LAYERS[lyr_name](*args, **lyr_config)\n initiated_layers[lyr_config['name']] = {'layer': initialized_layer,\n 'tf_name': lyr_name}\n input_lyrs.append(initialized_layer)\n else:\n # it is executable and uses previous outputs as inputs\n if lyr_name in ACTIVATION_LAYERS:\n layer_outputs = ACTIVATION_LAYERS[lyr_name](name=lyr_config['name'])\n initiated_layers[lyr_config['name']] = {'layer': layer_outputs,\n 'named_outs': named_outs,\n 'call_args': call_args,\n 'inputs': lyr_inputs,\n 'tf_name': lyr_name}\n elif lyr_name in ['TimeDistributed', 'Bidirectional']:\n wrp_layer = LAYERS[lyr_name]\n # because wrapper layer name is property\n initiated_layers[lyr_config['name']] = {'layer': wrp_layer,\n 'tf_name': lyr_name}\n continue\n elif \"LAMBDA\" in lyr_name.upper():\n # lyr_config is serialized lambda layer, which needs to be deserialized\n initialized_layer = tf.keras.layers.deserialize(lyr_config)\n # layers_config['lambda']['config'] still contails lambda, so we need to replace the python\n # object (lambda) with the serialized version (lyr_config) so that it can be saved as json file.\n layers_config[lyr]['config'] = lyr_config\n initiated_layers[lyr_config['name']] = {'layer': initialized_layer,\n 'named_outs': named_outs,\n 'call_args': call_args,\n 'inputs': lyr_inputs,\n 'tf_name': lyr_name}\n else:\n if wrp_layer is not None:\n initialized_layer = wrp_layer(LAYERS[lyr_name](*args, **lyr_config))\n initiated_layers[lyr_config['name']] = {'layer': initialized_layer,\n 'named_outs': named_outs,\n 'call_args': call_args,\n 'inputs': lyr_inputs,\n 'tf_name': lyr_name}\n wrp_layer = None\n else:\n if lyr_name == \"TemporalFusionTransformer\":\n lyr_config['return_attention_components'] = True\n initialized_layer = LAYERS[lyr_name](*args, **lyr_config)\n initiated_layers[lyr_config['name']] = {'layer': initialized_layer,\n 'named_outs': named_outs,\n 'call_args': call_args,\n 'inputs': lyr_inputs,\n 'tf_name': lyr_name}\n\n else: # The inputs to this layer have been specified so they must exist in lyr_cache.\n # it is an executable\n if lyr_name in ACTIVATION_LAYERS:\n\n layer_outputs = ACTIVATION_LAYERS[lyr_name](name=lyr_config['name'])\n initiated_layers[lyr_config['name']] = {'layer': layer_outputs,\n 'named_outs': named_outs,\n 'call_args': call_args,\n 'inputs': lyr_inputs,\n 'tf_name': lyr_name}\n elif lyr_name in ['TimeDistributed', 'Bidirectional']:\n wrp_layer = LAYERS[lyr_name]\n # because wrapper layer name is property\n initiated_layers[lyr_config['name']] = {'layer': wrp_layer,\n 'tf_name': lyr_name}\n continue\n elif \"LAMBDA\" in lyr_name.upper():\n initialized_layer = tf.keras.layers.deserialize(lyr_config)\n layers_config[lyr]['config'] = lyr_config\n initiated_layers[lyr_config['name']] = {'layer': initialized_layer,\n 'named_outs': named_outs,\n 'call_args': call_args,\n 'inputs': lyr_inputs,\n 'tf_name': lyr_name}\n else:\n if wrp_layer is not None:\n initialized_layer = wrp_layer(LAYERS[lyr_name](*args, **lyr_config))\n initiated_layers[lyr_config['name']] = {'layer': initialized_layer,\n 'named_outs': named_outs,\n 'call_args': call_args,\n 'inputs': lyr_inputs,\n 'tf_name': lyr_name}\n wrp_layer = None\n else:\n layer_initialized = LAYERS[lyr_name](*args, **lyr_config)\n initiated_layers[lyr_config['name']] = {'layer': layer_initialized,\n 'named_outs': named_outs,\n 'call_args': call_args,\n 'inputs': lyr_inputs,\n 'tf_name': lyr_name}\n\n if activation is not None: # put the string back to dictionary to be saved in config file\n lyr_config['activation'] = activation\n\n first_layer = False\n\n self.jsonize_lyr_config(lyr_config)\n\n # inputs = [] todo, indentify input layers\n # for k,v in lyr_cache.items():\n # since the model is not build yet and we have access to only output tensors of each list, this is probably\n # # the only way to know that how many `Input` layers were encountered during the run of this method. Each\n # tensor (except TimeDistributed) has .op.inputs attribute, which is empty if a tensor represents output of Input layer.\n # if int(''.join(tf.__version__.split('.')[0:2]).ljust(3, '0')) < 240:\n # if k.upper() != \"TIMEDISTRIBUTED\" and hasattr(v, 'op'):\n # if hasattr(v.op, 'inputs'):\n # _ins = v.op.inputs\n # if len(_ins) == 0:\n # inputs.append(v)\n # else: # not sure if this is the proper way of checking if a layer receives an input or not!\n # if hasattr(v, '_keras_mask'):\n # inputs.append(v)\n\n setattr(self, 'initiated_layers', initiated_layers)\n setattr(self, 'input_lyrs', input_lyrs)\n\n\n # todo,\n # # for case when {Input -> Dense, Input_1}, this method wrongly makes Input_1 as output so in such case use\n # # {Input_1, Input -> Dense }, thus it makes Dense as output and first 2 as inputs, so throwing warning\n # if int(''.join(tf.__version__.split('.')[0:2]).ljust(3, '0')) < 240:\n # if len(layer_outputs.op.inputs) < 1:\n # print(\"Warning: the output is of Input tensor class type\")\n # else:\n # if 'op' not in dir(layer_outputs): # layer_outputs does not have `op`, which means it has no incoming node\n # print(\"Warning: the output is of Input tensor class type\")\n\n # outs = None\n #if BACKEND == 'tensorflow':\n # outs = self.call(input_lyrs)\n # setattr(self, 'output_lyrs', outs)\n # if BACKEND == 'tensorflow':\n # ## Reinitial\n # super(Model, self).__init__(\n # inputs=input_lyrs,\n # outputs=outs)\n #MODEL.__init__(self, inputs=inputs, outputs=outs)\n\n return input_lyrs # , outs","def __init__(self, hparams):\n super(ThreeLayerClassifier, self).__init__()\n self.hparams = hparams\n self.layer_1 = torch.nn.Linear(self.hparams[\"input_size\"], 128)\n self.layer_2 = torch.nn.Linear(128, 256)\n self.layer_3 = torch.nn.Linear(256, self.hparams[\"targets\"])","def initialize_layers(self, years):\n min_year = min(years)\n max_year = max(years)\n ordered_years = list(range(min_year, max_year + 1))\n self.layers = [Layer(y) for y in ordered_years]","def init_layers(self):\n self.user_emb = nn.Embedding(self.n_users, self.emb_dim)\n self.item_emb = nn.Embedding(self.n_items, self.emb_dim)\n init_range = 0.1 * (self.emb_dim) ** (-1 / 2)\n self.item_emb.weight.data.uniform_(-init_range, init_range)\n self.fc_u_1_mu = nn.Linear(self.user_fea_dim, self.late_dim)\n self.fc_u_2_mu = nn.Linear(self.late_dim, self.emb_dim * 2)\n self.fc_i_1_mu = nn.Linear(self.item_fea_dim, self.late_dim)\n self.fc_i_2_mu = nn.Linear(self.late_dim, self.emb_dim * 2)","def initialize_parameters(self):\n for i in range(1, self.L):\n self.W[i - 1] = np.random.randn(self.layer_dims[i], self.layer_dims[i - 1]) * 0.01\n self.b[i - 1] = np.zeros((self.layer_dims[i], 1))","def init_weight(self):\n init_layer(self.conv1)\n init_layer(self.conv2)\n init_bn(self.norm1)\n init_bn(self.norm2)","def initialize_weights(self):\n tf.nest.map_structure(\n weights_initializer.WeightsInitializer.initialize_layer_or_model,\n self._layer_nest)","def __init__(self, layer_list_info):\n self.layer_list_info = layer_list_info","def _init_layers(self) -> None:\n self.relu = nn.ReLU(inplace=True)\n self.cls_convs = nn.ModuleList()\n self.reg_convs = nn.ModuleList()\n for i in range(self.stacked_convs):\n chn = self.in_channels if i == 0 else self.feat_channels\n self.cls_convs.append(\n ConvModule(\n chn,\n self.feat_channels,\n 3,\n stride=1,\n padding=1,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg))\n self.reg_convs.append(\n ConvModule(\n chn,\n self.feat_channels,\n 3,\n stride=1,\n padding=1,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg))\n pred_pad_size = self.pred_kernel_size // 2\n self.atss_cls = nn.Conv2d(\n self.feat_channels,\n self.num_anchors * self.cls_out_channels,\n self.pred_kernel_size,\n padding=pred_pad_size)\n self.atss_reg = nn.Conv2d(\n self.feat_channels,\n self.num_base_priors * 4,\n self.pred_kernel_size,\n padding=pred_pad_size)\n self.atss_centerness = nn.Conv2d(\n self.feat_channels,\n self.num_base_priors * 1,\n self.pred_kernel_size,\n padding=pred_pad_size)\n self.scales = nn.ModuleList(\n [Scale(1.0) for _ in self.prior_generator.strides])","def __init__(self, n_input, n_output, hidden_layer_size, reg):\n self.reg = reg\n self.fulllayer1 = FullyConnectedLayer(n_input, hidden_layer_size)\n self.reglayer1 = ReLULayer()\n self.fulllayer2 = FullyConnectedLayer(hidden_layer_size, n_output)","def __init__(self):\n self.layer_scope = None\n self.out = None","def __init__(self):\n self.layers = []\n self.best_loss = None","def connect_layers(self):\n if not self.check():\n msg = \"Failed to check neural network.\"\n print(msg)\n logging.error(msg)\n return\n\n # 1. set input layer\n pre_layer = self.input_layer\n for layer in self.hidden_layers:\n layer.set_input_layer(pre_layer)\n pre_layer = layer\n self.output_layer.set_input_layer(pre_layer)\n\n # 2. set output layer\n next_layer = self.output_layer\n for layer in reversed(self.hidden_layers):\n layer.set_next_layer(next_layer)\n next_layer = layer\n self.input_layer.set_next_layer(next_layer)\n\n # 3. call layer init\n self.input_layer.init()\n for layer in self.hidden_layers:\n layer.init()\n self.output_layer.init()\n\n return","def _init_layers(self):\n self.relu = nn.ReLU(inplace=True)\n self.rpn_convs = nn.ModuleList()\n for i in range(self.stacked_convs):\n chn = self.in_channels if i == 0 else self.feat_channels\n self.rpn_convs.append(\n ConvModule(\n chn,\n self.feat_channels,\n 3,\n stride=1,\n padding=1,\n conv_cfg=self.conv_cfg,\n norm_cfg=self.norm_cfg))\n self.rpn_cls = nn.Conv2d(\n self.feat_channels, self.num_anchors * 1, 3, padding=1)\n self.rpn_reg = nn.Conv2d(\n self.feat_channels, self.num_anchors * 4 * (self.reg_max + 1), 3, padding=1)\n self.rpn_iou = nn.Conv2d(\n self.feat_channels, self.num_anchors * 1, 3, padding=1)\n self.scales = nn.ModuleList(\n [Scale(1.0) for _ in self.anchor_generator.strides])\n\n ##############V2################\n conf_vector = [nn.Conv2d(self.num_anchors * 4 * self.total_dim, self.num_anchors * self.reg_channels, 1)]\n conf_vector += [self.relu]\n conf_vector += [nn.Conv2d(self.num_anchors * self.reg_channels, self.num_anchors, 1), nn.Sigmoid()]\n\n self.reg_conf = nn.Sequential(*conf_vector)\n ##############V2################","def __init__(self, layers, r_min, r_max, learn_rate):\n\n if not isinstance(layers, list) or len(layers) < 3:\n raise ValueError('invalid layer parammeter')\n self.layers = layers\n n_layer = len(layers)\n self.n_layer = n_layer\n self.r_min = r_min\n self.r_max = r_max\n self.learn_rate = learn_rate\n\n # initialize ws\n self.ws = []\n for layer_idx in range(n_layer - 1):\n layer_size = (layers[layer_idx] + 1, layers[layer_idx + 1])\n w = self.init_w(layer_size)\n self.ws.append(w)","def layers(self, layers):\n\n self._layers = layers","def initialize(self):\n params = {}\n for i in range(1, len(self.layer_dimensions)):\n params['b_' + str(i)] = np.ones((self.layer_dimensions[i], 1))\n if self.he_initialization:\n params['W_' + str(i)] = np.random.randn(self.layer_dimensions[i],\n self.layer_dimensions[i - 1]) * np.sqrt(\n 2 / self.layer_dimensions[i - 1])\n else:\n params['W_' + str(i)] = np.random.rand(self.layer_dimensions[i], self.layer_dimensions[i - 1]) - 0.5\n return params","def __init__(self):\n\n for layer in self._layer_class_map:\n setattr(self, layer, self._layer_class_map[layer]())","def UpdateLayers(self):\n pass","def __init__(self):\n super(GatherLastLayer, self).__init__()","def build_layers(self):\n raise NotImplementedError","def init_weights(self, pretrained=None):\n\n super(EncoderDecoder_gan, self).init_weights(pretrained)\n self.backbone.init_weights(pretrained=pretrained)\n self.backbone_gan.init_weights(pretrained=pretrained)\n self.decode_head.init_weights()\n # init GAN\n # self.discriminator.init_weight()\n # self.G_head.init_weights()\n if self.with_auxiliary_head:\n if isinstance(self.auxiliary_head, nn.ModuleList):\n for aux_head in self.auxiliary_head:\n aux_head.init_weights()\n else:\n self.auxiliary_head.init_weights()","def initialisation(self):\n self.create_variables()\n self.create_placeholders()\n self.build_model()\n self.reset_lr(None, True)\n self.build_loss()\n self.initialised = True","def initialize_weights(self):\n for layer in self._cnn_layers:\n weights_initializer.WeightsInitializer.initialize_layer_or_model(layer)","def setUp(self):\n\t\tprint(\"\\n-------------------------------------\\nIn Test_PyTorchLayers:\", self._testMethodName)\n\t\tww.weightwatcher.torch = torch\n\t\tself.model = models.resnet18()\n\t\tfor layer in self.model.modules():\n\t\t\tself.last_layer = layer","def __init__(self):\r\n # A dummy layer does nothing\r\n self.weights = np.zeros(shape=(input.shape[1], 10))\r\n bias = np.zeros(shape=(10,))\r\n pass","def __init__(self, **kwargs):\n base.Layer.__init__(self, **kwargs)\n self._group = self.spec['group']\n self._conv_args = dict(self.spec)\n self._conv_args['name'] = self.spec['name'] + '_sub'\n del self._conv_args['group']\n self._bottom_sub = [base.Blob() for _ in range(self._group)]\n self._top_sub = [base.Blob() for _ in range(self._group)]\n self._conv_layers = None\n self._blocksize = 0\n self._num_kernels = self.spec['num_kernels']\n # create the convolution layers\n self._conv_layers = [\n convolution.ConvolutionLayer(**self._conv_args)\n for i in range(self._group)]\n self._param = sum((layer.param() for layer in self._conv_layers), [])\n return","def _init_layers(self):\n cls_branch = []\n for _ in range(self.num_reg_fcs):\n cls_branch.append(Linear(self.embed_dims, self.embed_dims))\n cls_branch.append(nn.LayerNorm(self.embed_dims))\n cls_branch.append(nn.ReLU(inplace=True))\n cls_branch.append(Linear(self.embed_dims, self.cls_out_channels))\n fc_cls = nn.Sequential(*cls_branch)\n\n reg_branch = []\n for _ in range(self.num_reg_fcs):\n reg_branch.append(Linear(self.embed_dims, self.embed_dims))\n reg_branch.append(nn.ReLU())\n reg_branch.append(Linear(self.embed_dims, self.code_size))\n reg_branch = nn.Sequential(*reg_branch)\n\n past_traj_reg_branch = []\n for _ in range(self.num_reg_fcs):\n past_traj_reg_branch.append(\n Linear(self.embed_dims, self.embed_dims))\n past_traj_reg_branch.append(nn.ReLU())\n past_traj_reg_branch.append(\n Linear(self.embed_dims, (self.past_steps + self.fut_steps)*2))\n past_traj_reg_branch = nn.Sequential(*past_traj_reg_branch)\n\n def _get_clones(module, N):\n return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\n\n # last reg_branch is used to generate proposal from\n # encode feature map when as_two_stage is True.\n num_pred = (self.transformer.decoder.num_layers + 1) if \\\n self.as_two_stage else self.transformer.decoder.num_layers\n\n if self.with_box_refine:\n self.cls_branches = _get_clones(fc_cls, num_pred)\n self.reg_branches = _get_clones(reg_branch, num_pred)\n self.past_traj_reg_branches = _get_clones(\n past_traj_reg_branch, num_pred)\n else:\n self.cls_branches = nn.ModuleList(\n [fc_cls for _ in range(num_pred)])\n self.reg_branches = nn.ModuleList(\n [reg_branch for _ in range(num_pred)])\n self.past_traj_reg_branches = nn.ModuleList(\n [past_traj_reg_branch for _ in range(num_pred)])\n if not self.as_two_stage:\n self.bev_embedding = nn.Embedding(\n self.bev_h * self.bev_w, self.embed_dims)","def initialize(self):\n\n\t\tparameters = {}\n\t\tL = len(self.layer_dims) # number of layers in the network\n\n\t\tfor l in range(1, L):\n\t\t\tparameters['W' + str(l)] = np.random.randn(self.layer_dims[l], self.layer_dims[l-1]) * 0.01\n\t\t\tparameters['b' + str(l)] = np.zeros((self.layer_dims[l], 1))\n\n\t\t\tassert(parameters['W' + str(l)].shape == (self.layer_dims[l], self.layer_dims[l-1]))\n\t\t\tassert(parameters['b' + str(l)].shape == (self.layer_dims[l], 1))\n\n\t\treturn parameters","def init_mask_head(self, mask_roi_extractor, mask_head):\n self.mask_head = nn.ModuleList()\n if not isinstance(mask_head, list):\n mask_head = [mask_head for _ in range(self.num_stages)]\n assert len(mask_head) == self.num_stages\n for head in mask_head:\n self.mask_head.append(build_head(head))\n if self.recursive:\n for i in range(self.num_stages):\n self.mask_head[i] = self.mask_head[0]","def init_dense(self, layer):\n pass","def __init__(self,NL,kS=None):\n assert isinstance(NL,int), \"Wrong data type for number of layers.\\n\\n\"\n assert NL>0, \"At least one layer is needed.\\n\"\n self.NL=NL\n self.layers=[] # List self.layers will store object of type Layer\n for i in range(NL):\n self.layers+=[Layer(),] # Initialize with blank layers\n \n self.couplings=[] # List self.couplings will store coupling strengths between layers\n for i in range(NL-1):\n self.couplings+=[0.,] # Initialize with decoupled layers\n \n if kS:\n assert isinstance(kS,kSpace), \"Wrong data type of momentum space.\\n\\n\"\n self.kS=kS\n else:\n self.kS=kSpace() # System requires some momentum space discretization. If none provided taken default.","def __init__(self, layer_nest):\n self._layer_nest = layer_nest\n super().__init__()","def _init_layers(self):\n self.relu = nn.ReLU(inplace=True)\n self.cls_convs = nn.ModuleList()\n self.reg_convs = nn.ModuleList()\n for i in range(self.stacked_convs):\n chn = self.in_channels if i == 0 else self.feat_channels\n self.cls_convs.append(\n ConvModule(\n chn,\n self.feat_channels,\n 3,\n stride=1,\n padding=1,\n conv_cfg=dict(type='DCN', deform_groups=1)\n if i == 0 and self.use_dcn else self.conv_cfg,\n norm_cfg=self.norm_cfg))\n self.reg_convs.append(\n ConvModule(\n chn,\n self.feat_channels,\n 3,\n stride=1,\n padding=1,\n conv_cfg=dict(type='DCN', deform_groups=1)\n if i == 0 and self.use_dcn else self.conv_cfg,\n norm_cfg=self.norm_cfg))\n self.atss_cls = nn.Conv2d(\n self.feat_channels,\n self.num_base_priors * self.cls_out_channels,\n 3,\n padding=1)\n self.atss_reg = nn.Conv2d(\n self.feat_channels, self.num_base_priors * 4, 3, padding=1)\n self.atss_iou = nn.Conv2d(\n self.feat_channels, self.num_base_priors * 1, 3, padding=1)\n self.scales = nn.ModuleList(\n [Scale(1.0) for _ in self.prior_generator.strides])\n\n # we use the global list in loss\n self.cls_num_pos_samples_per_level = [\n 0. for _ in range(len(self.prior_generator.strides))\n ]\n self.reg_num_pos_samples_per_level = [\n 0. for _ in range(len(self.prior_generator.strides))\n ]","def __init__(self, layerNeurons, initialWeights = None, layerTypes=None, **kwargs):\r\n \r\n # Ensure that there is at-least one input and one output layer in the network\r\n assert len(layerNeurons)>1, \"At least one input layer and one output layer is needed\"\r\n \r\n # Get the total number of weights needed in the network\r\n totalWeightCount = NeuralNetwork.getSizeOfWeightVector(layerNeurons)\r\n \r\n # Initialise the weights with the initializer or random values\r\n if initialWeights is None:\r\n self.weights = np.random.uniform(-1/np.sqrt(layerNeurons[0]), 1/np.sqrt(layerNeurons[0]), totalWeightCount)\r\n else:\r\n assert len(initialWeights) == totalWeightCount, (\"Length of initial weight matrix incorrect. You need \"+str(totalWeightCount)+\" weights\")\r\n self.weights = np.array(initialWeights, dtype = np.float64) \r\n \r\n # create an empty array of layers\r\n self.layers = []\r\n layerBlockStart = 0\r\n \r\n if layerTypes is None or len(layerTypes)<(len(layerNeurons)-1):\r\n layerTypes=[NetworkLayer]*(len(layerNeurons)-1)\r\n \r\n for layerInputDimention, layerOutputDimention, layerType in zip(layerNeurons, layerNeurons[1:], layerTypes):\r\n # initialise each layer with its input and output dimentions and bi-directional pointers to the relivant weights\r\n layerBlockEnd = layerBlockStart+(layerInputDimention*layerOutputDimention)\r\n layerBiasEnd = layerBlockEnd+layerOutputDimention\r\n newLayer = layerType(layerInputDimention, layerOutputDimention, \r\n self.weights[..., layerBlockStart:layerBlockEnd], \r\n self.weights[..., layerBlockEnd:layerBiasEnd], **kwargs)\r\n self.layers.append(newLayer)\r\n \r\n layerBlockStart = layerBiasEnd\r\n \r\n # Tell the output later to use a different function to calculate the delta \r\n newLayer.calcDelta = newLayer.calcDeltaOutputLayer","def __init__(self):\n super(LinearAggregationLayer, self).__init__()","def initialize_network(self):\n # intermediate layer size\n ils = int((self.specbinnum + self.numfilters) / 2)\n\n network = lasagne.layers.InputLayer((None, 1, self.specbinnum, self.numtimebins), self.input_var)\n\n network = NormalisationLayer(network, self.specbinnum)\n self.normlayer = network\n\n network, _ = custom_convlayer_2(network, in_num_chans=self.specbinnum, out_num_chans=ils)\n network = batch_norm(network)\n network, _ = custom_convlayer_2(network, in_num_chans=ils, out_num_chans=self.numfilters)\n network = batch_norm(network)\n\n network = lasagne.layers.NonlinearityLayer(network, nonlinearity=elu)\n self.latents = network\n network = ZeroOutBackgroundLatentsLayer(self.latents,\n mp_down_factor=self.mp_down_factor,\n numfilters=self.numfilters,\n numtimebins=self.numtimebins,\n background_latents_factor=self.background_latents_factor,\n use_maxpool=self.use_maxpool)\n network, _ = custom_convlayer_2(network, in_num_chans=self.numfilters, out_num_chans=ils)\n network = batch_norm(network)\n network, _ = custom_convlayer_2(network, in_num_chans=ils, out_num_chans=self.specbinnum)\n network = batch_norm(network)\n\n # output_size\n num_time_samples = int(audioframe_len/2 * (self.numtimebins + 1))\n # network = batch_norm(DenseLayer(network, num_time_samples)) # MemoryError\n network, _ = custom_convlayer_2(network, in_num_chans=self.specbinnum, out_num_chans=num_time_samples)\n network, _ = batch_norm(network)\n network, _ = custom_convlayer_2(network, in_num_chans=num_time_samples, out_num_chans=1)\n network, _ = batch_norm(network)\n\n self.network = network","def new_initial_layer(self, nodes, inputs, alpha=0.1):\n weights = [[1 for _ in range(inputs)] for i in range(nodes)]\n alphas = [alpha for _ in range(nodes)]\n self._layers.insert(0, Layer(weights, alphas))","def __init__(self, num_latents, activation=None, layer_param=None, **kwargs):\n\t\tshape_image = [64,64,3]\n\t\tsuper().__init__(\n\t\t\tlayer_param=layer_param, \n\t\t\tnum_latents=num_latents, \n\t\t\tshape_image=shape_image, \n\t\t\tactivation=activation, \n\t\t\tis_create_sequential=False, # don't create sequential as we shouldn't build just yet - we need to redefine the layers.\n\t\t\t**kwargs)","def __init__(self, layers=[2, 2, 1], activation_function=\"bentidentity\"):\n self.layers = layers\n self.activation_function = th.activation_functions[activation_function]\n self.activation_derivative = th.activation_derivatives[\n activation_function]\n self.weights = self._generate_weights()","def init_weights(self, pretrained=None):\n self.online_backbone.init_weights() # backbone\n for online_neck in self.online_necks:\n online_neck.init_weights(init_linear='kaiming') # projection\n\n for param_ol, param_tgt in zip(self.online_backbone.parameters(),\n self.target_backbone.parameters()):\n param_tgt.data.copy_(param_ol.data)\n for param_ol, param_tgt in zip(self.online_necks.parameters(),\n self.target_necks.parameters()):\n param_tgt.data.copy_(param_ol.data)\n # init the predictor in the head\n for head in self.heads:\n head.init_weights()","def _nn_initial_values(structure):\n\n # Use Xavier uniform initializer\n initializer=tf.glorot_uniform_initializer()\n\n output=[]\n last_width=None\n\n # Add biases & weights per layer\n for l in structure:\n output.append(tf.zeros(shape=[l])) # layer l biases\n if last_width is not None: # Exclude weights from layer 0\n output.append(initializer(shape=[last_width, l])) # layer l weights\n last_width=l\n\n return output","def __init__(self, layer_delay=_default_layer_delay):\n self._attached_phys = []\n self._layer_delay = layer_delay","def __init__(self, feature_length, layersNumber):\n super(SubGraph, self).__init__()\n self.layers_number = layersNumber\n self.layers = nn.ModuleList([SubGraphLayer(feature_length * (2 ** i)) for i in range(self.layers_number)])","def __init__(self, parent, layer):\n pass","def __init__(self, layer_list_info):\n super(DynaNet, self).__init__()\n self.layer_list_info = layer_list_info\n self.task_modules = nn.ModuleDict()\n self.classification_layers = nn.ModuleDict()\n self.module_generator = ModuleFactory(layer_list_info)\n self.task_module_name_path = {}\n self.nr_levels = len(layer_list_info)\n self.task_idx = None","def init_bbox_head(self, mask_roi_extractor, mask_head):\n pass","def init():\n global tube, ball, faceTextureName, woodTextureName\n tube = gluNewQuadric()\n gluQuadricDrawStyle(tube, GLU_FILL)\n ball = gluNewQuadric()\n gluQuadricDrawStyle(ball, GLU_FILL)\n\n # Set up lighting and depth-test\n glEnable(GL_LIGHTING)\n glEnable(GL_NORMALIZE) # Inefficient...\n glEnable(GL_DEPTH_TEST) # For z-buffering!\n\n generateCheckerBoardTexture()\n faceTextureName = loadImageTexture(\"brick.jpg\")\n woodTextureName = loadImageTexture(\"wood.jpg\")","def __init__(self, z_dim, initailize_weights=True):\n super().__init__()\n self.z_dim = z_dim\n\n self.img_conv1 = conv2d(3, 16, kernel_size=7, stride=2)\n self.img_conv2 = conv2d(16, 32, kernel_size=5, stride=2)\n self.img_conv3 = conv2d(32, 64, kernel_size=5, stride=2)\n self.img_conv4 = conv2d(64, 64, stride=2)\n self.img_conv5 = conv2d(64, 128, stride=2)\n self.img_conv6 = conv2d(128, self.z_dim, stride=2)\n self.img_encoder = nn.Linear(4 * self.z_dim, 2 * self.z_dim)\n self.flatten = Flatten()\n\n if initailize_weights:\n init_weights(self.modules())","def init_mask_head(self, mask_roi_extractor, mask_head):\n self.mask_head = nn.ModuleList()\n if not isinstance(mask_head, list):\n mask_head = [mask_head for _ in range(self.num_stages)]\n assert len(mask_head) == self.num_stages\n for head in mask_head:\n self.mask_head.append(build_head(head))\n if mask_roi_extractor is not None:\n self.share_roi_extractor = False\n self.mask_roi_extractor = nn.ModuleList()\n if not isinstance(mask_roi_extractor, list):\n mask_roi_extractor = [\n mask_roi_extractor for _ in range(self.num_stages)\n ]\n assert len(mask_roi_extractor) == self.num_stages\n for roi_extractor in mask_roi_extractor:\n self.mask_roi_extractor.append(\n build_roi_extractor(roi_extractor))\n else:\n self.share_roi_extractor = True\n self.mask_roi_extractor = self.bbox_roi_extractor","def init_parameters(self):\n # Create the weights and biases\n for i in range(1, len(self.layer_dimensions)):\n # Initialization from He et al.\n mu = 0\n var = 2 / self.layer_dimensions[i]\n sigma = np.sqrt(var)\n weight_shape = (self.layer_dimensions[i - 1], self.layer_dimensions[i])\n weight = np.random.normal(loc=mu, scale=sigma, size=weight_shape)\n bias = np.zeros((self.layer_dimensions[i], ))\n\n # Saving in the parameters dict\n layer_weight = \"w_\" + str(i)\n self._parameters[layer_weight] = weight\n layer_b = \"b_\" + str(i)\n self._parameters[layer_b] = bias","def __init__(self, z_dim, initailize_weights=True):\n super().__init__()\n self.z_dim = z_dim\n\n self.depth_conv1 = conv2d(1, 32, kernel_size=3, stride=2)\n self.depth_conv2 = conv2d(32, 64, kernel_size=3, stride=2)\n self.depth_conv3 = conv2d(64, 64, kernel_size=4, stride=2)\n self.depth_conv4 = conv2d(64, 64, stride=2)\n self.depth_conv5 = conv2d(64, 128, stride=2)\n self.depth_conv6 = conv2d(128, self.z_dim, stride=2)\n\n self.depth_encoder = nn.Linear(4 * self.z_dim, 2 * self.z_dim)\n self.flatten = Flatten()\n\n if initailize_weights:\n init_weights(self.modules())","def __init__(self, channels, num_classes):\n super(AuxiliaryHead, self).__init__()\n self.features = nn.Sequential(\n nn.ReLU(inplace=True),\n # image size = 2 x 2\n nn.AvgPool2d(5, stride=3, padding=0, count_include_pad=False),\n nn.Conv2d(channels, 128, 1, bias=False),\n nn.BatchNorm2d(128),\n nn.ReLU(inplace=True),\n nn.Conv2d(128, 768, 2, bias=False),\n nn.BatchNorm2d(768),\n nn.ReLU(inplace=True),\n )\n self.classifier = nn.Linear(768, num_classes)","def initialize(self):\n self.conv1.reset_parameters()\n self.conv2.reset_parameters()","def __init__(self, layer, cNextLayer, batch):\n\n self._layer = layer\n self._oldWDeltas = [[0.0] * cNextLayer for _ in layer]\n self._batch = batch\n if batch:\n self._wDeltas = [[0.0] * cNextLayer for _ in layer]","def init_params(self):\n self.conv = Conv(self.conv_layers[0][-1], self.out_channels, padding=self.padding,stride=self.stride)\n self.W = torch.randn(self.num_labels, self.cout_numel, requires_grad=True)\n self.T = torch.randn(self.num_labels, self.num_labels, requires_grad=True)","def init_weights(self, pretrained=None):\n if pretrained is not None:\n self.pretrained = pretrained\n self.backbone.init_weights(self.pretrained)\n if self.with_necks:\n for neck in self.necks:\n if hasattr(neck, 'init_weights'):\n neck.init_weights()\n for head in self.heads:\n if hasattr(head, 'init_weights'):\n head.init_weights()","def __init__(self, config):\n self.layers = [] # Store all layers in this list.\n self.x = None # Save the input to forward in this\n self.y = None # Save the output vector of model in this\n self.targets = None # Save the targets in forward in this variable\n\n # Add layers specified by layer_specs.\n for i in range(len(config['layer_specs']) - 1):\n self.layers.append(Layer(config['layer_specs'][i], config['layer_specs'][i+1]))\n if i < len(config['layer_specs']) - 2:\n self.layers.append(Activation(config['activation']))","def init():\n global neural_network\n global labels\n\n # load objects required by run() for inferencing\n model_dir = Model.get_model_path(\"mnist-fashion\")\n # neural model\n neural_network = keras.models.load_model(f\"{model_dir}/neural-network.h5\")\n # labels\n with open(f\"{model_dir}/labels.jsonpickle\", \"r\") as labels_file:\n labels = jsonpickle.decode(labels_file.read())","def init_weights(self, pretrained):\n if self.with_shared_head:\n self.shared_head.init_weights(pretrained=pretrained)\n for i in range(1):\n if self.with_bbox:\n self.bbox_roi_extractor[i].init_weights()\n self.bbox_head[i].init_weights()\n if self.with_rbbox:\n self.rbbox_roi_extractor[i].init_weights()\n self.rbbox_head[i].init_weights()\n if self.with_mask:\n if not self.share_roi_extractor:\n self.mask_roi_extractor[i].init_weights()\n self.mask_head[i].init_weights()","def __init__(self, layers, learning_rate, loss_function, l2_lambda=0, noise_type=None):\n self.layers, self.alpha = layers, learning_rate\n self.loss = loss_function\n self.l2_lambda = l2_lambda\n self.noise_type = noise_type","def setUp(self):\n # The short NSC used in this example\n self.net_nsc = [\n (1, 4, 0, 0, 0), # Layer 1: Identity(input)\n (2, 1, 3, 1, 0), # Layer 2: Convolution(Layer1)\n (3, 1, 3, 2, 0), # Layer 3: Convolution(Layer2)\n (4, 5, 0, 1, 3), # Layer 4: Convolution(Layer1)\n (5, 7, 0, 0, 0), # Layer 5: Convolution(Layer4)\n ]","def setUp(self):\n\t\tprint(\"\\n-------------------------------------\\nIn Test_KerasLayers:\", self._testMethodName)\n\t\tself.model = VGG16() \n\t\tself.last_layer = self.model.submodules[-1]\n\t\tww.weightwatcher.keras = keras","def layers(self): # -> LayerView:\n ...","def setUp(self):\n\t\t#import inspect\n\n\t\tprint(\"\\n-------------------------------------\\nIn Test_PyStateDictLayers:\", self._testMethodName)\n\t\tww.weightwatcher.torch = torch\n\t\tself.model = models.resnet18().state_dict()\n\t\t#for cls in inspect.getmro(type(self.model)):\n\t\t#\tprint(str(cls))\n \n\t\tfor key in self.model.keys():\n\t\t\tif key.endswith('.weight'):\n\t\t\t\tlayer_name = key[:-len('.weight')]\n\t\t\t\tself.fc_layer_name = layer_name","def setup_walls(self):\n self.wall_list = self.get_current_map().get_layer_by_name(\"walls\").sprite_list","def init_layer(layer):\n nn.init.xavier_uniform_(layer.weight)\n\n if hasattr(layer, \"bias\"):\n if layer.bias is not None:\n layer.bias.data.fill_(0.0)","def __init__(self, layerNeurons, numberOfLayers, initialWeights = None, lowerBound = None, upperBound = None):\r\n \r\n # Ensure that there is at-least one input and one output layer in the network\r\n assert len(layerNeurons) > 1, \"At least one input layer and one output layer is needed\"\r\n \r\n # Get the total number of weights needed in the network\r\n totalWeightCount = NeuralNetwork.getSizeOfWeightVector(layerNeurons)*numberOfLayers\r\n \r\n # Initialise the weights with the initialiser or random values\r\n if initialWeights is None:\r\n if lowerBound is None:\r\n lowerBound=-1/np.sqrt(layerNeurons[0])\r\n if upperBound is None:\r\n upperBound=1/np.sqrt(layerNeurons[0])\r\n self.weights = np.random.uniform(lowerBound, upperBound, totalWeightCount)\r\n else:\r\n assert initialWeights.size == totalWeightCount, (\"Length of initial weight matrix incorrect. You need \"+str(totalWeightCount)+\" weights\")\r\n self.weights = initialWeights.view()\r\n \r\n self.weights.shape = (numberOfLayers, -1)\r\n # create an empty array of layers\r\n self.layers = []\r\n layerBlockStart = 0\r\n \r\n for layerInputDimention, layerOutputDimention in zip(layerNeurons, layerNeurons[1:]):\r\n # initialise each layer with its input and output dimentions and bi-directional pointers to the relivant weights\r\n layerBlockEnd = layerBlockStart+(layerInputDimention*layerOutputDimention)\r\n layerBiasEnd = layerBlockEnd+layerOutputDimention\r\n newLayer = batchNetworkLayer(layerInputDimention, layerOutputDimention, numberOfLayers, \r\n self.weights[..., :, layerBlockStart:layerBlockEnd], \r\n self.weights[..., :, layerBlockEnd:layerBiasEnd])\r\n self.layers.append(newLayer)\r\n \r\n layerBlockStart = layerBiasEnd","def __init__(self, energy_model, layers, prior='normal'):\n self.energy_model = energy_model\n super().__init__(energy_model.dim, layers, prior=prior)","def layers(self, layers):\n self._layers = layers\n prev = None\n for layer in layers:\n if not layer.inputs and prev is not None:\n layer.inputs = [prev]\n prev = layer","def build_head(self, n_features, device=None):\n # By default this is a linear layer\n self.head = self.create_compatible_head(n_features, device)","def __init__(self, heads, loss_combiner):\n # TODO(zakaria): Keep _Head a pure interface.\n super(_MultiHead, self).__init__(head_name=None)\n self._logits_dimension = 0\n for head in heads:\n if not head.head_name:\n raise ValueError(\"Head must have a name.\")\n self._logits_dimension += head.logits_dimension\n\n self._heads = heads\n self._loss_combiner = loss_combiner","def construct(self):\n self.input_size = self.numplanes * self.boardsize**2\n \n if self.hidden:\n layers = [\n torch.nn.Linear(self.input_size, self.hidden), \n torch.nn.ReLU(),\n torch.nn.Linear(self.hidden, self.boardsize**2)\n ]\n else:\n layers = [torch.nn.Linear(self.input_size, self.boardsize**2)]\n\n self.layers = torch.nn.ModuleList(layers)\n self.optimizer = torch.optim.Adam(self.parameters(), lr=1e-5)\n logging.info(\"Model initialized: %s\", self)","def __init__(self,\n image_shape,\n z_dim,\n num_blocks,\n action_space,\n hiddens=[],\n dropout=False,\n subsampling=True):\n super().__init__()\n self.layers = nn.ModuleList()\n self.layers.append(\n ImageInputNetwork(image_shape, z_dim, num_blocks, dropout,\n subsampling))\n self.layers.append(ActorNet(action_space, z_dim, hiddens))","def __init__(self, stage, layer=None, proxyPrimPrefix=\"\", renderPrimPrefix=\"\"):\n\n self._stage = stage\n\n # Layer\n if layer is None:\n self._layer = Sdf.Layer.CreateAnonymous()\n elif isinstance(layer, Sdf.Layer):\n self._layer = layer\n elif isinstance(layer, str):\n self._layer = Sdf.Layer.Open(layer)\n\n self._layer.SetMuted(True)\n self._stage.GetSessionLayer().subLayerPaths.append(self._layer.identifier)\n self._proxyPrimPrefix = proxyPrimPrefix\n self._renderPrimPrefix = renderPrimPrefix","def init_weights(self):\n # We don't use the `init_weights()` function in BaseModule, since it\n # doesn't support the initialization method from `reset_parameters()`\n # in Pytorch.\n if self.with_backbone:\n self.backbone.init_weights()\n\n if self.with_neck:\n for m in self.neck.modules():\n if isinstance(m, _ConvNd) or isinstance(m, _BatchNorm):\n m.reset_parameters()\n\n if self.with_head:\n for m in self.head.modules():\n if isinstance(m, _ConvNd) or isinstance(m, _BatchNorm):\n m.reset_parameters()","def createEmptyLayer(self):\n # , wt.greeting: False , wt.ects: False, wt.preReqs: False, wt.courseCodeMentioned: False\n layer = {wt.questionWord: \"\", wt.pronoun: \"\", wt.verb: \"\", wt.websiteName: \"\", wt.timeWord: \"\", wt.about: \"\",\n wt.weather: \"\", wt.when: \"\", wt.keywords: [], wt.courseID: \"\", wt.structureUnitCode: \"\",\n wt.sentence: [], wt.hangman: \"\", wt.what: \"\"}\n return layer","def init_weights(self, pretrained=None):\n super(SingleTwoStageDetector176PRW, self).init_weights(pretrained)\n self.backbone.init_weights(pretrained=pretrained)\n if self.with_neck:\n if isinstance(self.neck, nn.Sequential):\n for m in self.neck:\n m.init_weights()\n else:\n self.neck.init_weights()\n if self.with_rpn:\n self.rpn_head.init_weights()\n if self.with_roi_head:\n self.roi_head.init_weights(pretrained)\n self.bbox_head.init_weights()","def init():\n ########################\n # OPTIONS\n ########################\n # Debugging tools\n global TIMER # displays time of every major step\n TIMER = True\n global MONITOR # displays monitoring infos\n MONITOR = False\n \n global directories\n directories = {'1Face': 'data/1Face/',\n '2Faces': 'data/2Faces/',\n '3Faces': 'data/3Faces/',\n 'test': 'data/test/'}\n \n # Opt. swicthes\n global maxfinder # to find the max dim. amongst the pictures\n maxfinder = False\n global ML_mode\n ML_mode = {'CNN_Train': False,\n 'CNN_Pred' : True,\n 'Sampler': True}\n \n # Global variables\n global num_pics\n num_pics = {'1Face': 0,\n '2Faces': 0,\n '3Faces': 0}\n global labels\n labels = {'1Face': 0,\n '2Faces': 1,\n '3Faces': 2}\n global num_data\n num_data = 0\n global splitsize # Fraction of data to build the training set\n splitsize = 0.7 \n global maxheight # Resize the pictures to a power of 2 for CNN (2^8 here)\n maxheight = 128\n global maxwidth\n maxwidth = 128\n global TreshEdge # Number of consecutive black pixels to define an edge\n TreshEdge = 2\n global TreshFace # Number of white pixels to define a face (or large edge)\n TreshFace = maxheight/16","def init_layer(layer):\n nn.init.xavier_uniform_(layer.weight)\n \n if hasattr(layer, 'bias'):\n if layer.bias is not None:\n layer.bias.data.fill_(0.)"],"string":"[\n \"def _init_layers(self):\\n self._init_predictor()\\n if self.use_edge_fusion:\\n self._init_edge_module()\",\n \"def _init_layers(self) -> None:\\n self.self_attn = MultiheadAttention(**self.self_attn_cfg)\\n self.cross_attn = MultiheadAttention(**self.cross_attn_cfg)\\n self.embed_dims = self.self_attn.embed_dims\\n self.ffn = FFN(**self.ffn_cfg)\\n norms_list = [\\n build_norm_layer(self.norm_cfg, self.embed_dims)[1]\\n for _ in range(3)\\n ]\\n self.norms = ModuleList(norms_list)\",\n \"def _init_layers(self) -> None:\\n self.self_attn = MultiheadAttention(**self.self_attn_cfg)\\n self.embed_dims = self.self_attn.embed_dims\\n self.ffn = FFN(**self.ffn_cfg)\\n norms_list = [\\n build_norm_layer(self.norm_cfg, self.embed_dims)[1]\\n for _ in range(2)\\n ]\\n self.norms = ModuleList(norms_list)\",\n \"def __init__(self, layers):\\n\\n\\t\\tself.layers = layers\",\n \"def _init_layers(self) -> None:\\n weight_nums, bias_nums = [], []\\n for i in range(self.num_layers):\\n if i == 0:\\n weight_nums.append((self.in_channels + 2) * self.feat_channels)\\n bias_nums.append(self.feat_channels)\\n elif i == self.num_layers - 1:\\n weight_nums.append(self.feat_channels * 1)\\n bias_nums.append(1)\\n else:\\n weight_nums.append(self.feat_channels * self.feat_channels)\\n bias_nums.append(self.feat_channels)\\n\\n self.weight_nums = weight_nums\\n self.bias_nums = bias_nums\\n self.num_params = sum(weight_nums) + sum(bias_nums)\",\n \"def init_layers(self):\\n\\n # get caching layers activated\\n caching_layers = G3WCachingLayer.objects.all()\\n for caching_layer in caching_layers:\\n self.add_layer(str(caching_layer), caching_layer)\",\n \"def init_layer(layer):\\n \\n if layer.weight.ndimension() == 4:\\n (n_out, n_in, height, width) = layer.weight.size()\\n n = n_in * height * width\\n \\n elif layer.weight.ndimension() == 2:\\n (n_out, n) = layer.weight.size()\\n\\n std = math.sqrt(2. / n)\\n scale = std * math.sqrt(3.)\\n layer.weight.data.uniform_(-scale, scale)\\n\\n if layer.bias is not None:\\n layer.bias.data.fill_(0.)\",\n \"def set_layers(self, sizes, init='random'):\\n\\t\\tself.init_weights(sizes, init, None, None)\",\n \"def setup_layer_structure(self):\\n self.page_rank_convolution_1 = self.layer(self.feature_number, self.args.layers[0], self.args.iterations, self.args.alpha)\\n self.page_rank_convolution_2 = self.layer(self.args.layers[0], self.args.layers[1], self.args.iterations, self.args.alpha)\\n self.page_rank_convolution_3 = self.layer(self.args.layers[1], self.class_number, self.args.iterations, self.args.alpha)\",\n \"def define_layers(self):\\n\\n if self.D0Flag:\\n self.d = self.h\\n\\n self.layers = [nn.Sequential(nn.Linear(self.d, self.h),\\n nn.ReLU(), )] # nn.BatchNorm1d(self.h, affine=False))]\\n for l in range(1, self.L):\\n self.layers.append(nn.Sequential(nn.Linear(self.h, self.h),\\n nn.ReLU(), )) # nn.BatchNorm1d(self.h, affine=False)))\\n\\n self.layers.append(nn.Linear(self.h, 1))\",\n \"def setup_layers(self):\\n if self.args.model == \\\"exact\\\":\\n self.layer = PPNPLayer\\n else:\\n self.layer = APPNPLayer\\n self.setup_layer_structure()\",\n \"def _init_layers(self) -> None:\\n self.convs_all_levels = nn.ModuleList()\\n for i in range(self.start_level, self.end_level + 1):\\n convs_per_level = nn.Sequential()\\n convs_per_level.add_module(\\n f'conv{i}',\\n ConvModule(\\n self.in_channels,\\n self.feat_channels,\\n 3,\\n padding=1,\\n conv_cfg=self.conv_cfg,\\n norm_cfg=self.norm_cfg,\\n inplace=False,\\n bias=False))\\n self.convs_all_levels.append(convs_per_level)\\n\\n conv_branch = []\\n for _ in range(self.num_stacked_convs):\\n conv_branch.append(\\n ConvModule(\\n self.feat_channels,\\n self.feat_channels,\\n 3,\\n padding=1,\\n conv_cfg=self.conv_cfg,\\n norm_cfg=self.norm_cfg,\\n bias=False))\\n self.conv_branch = nn.Sequential(*conv_branch)\\n\\n self.conv_pred = nn.Conv2d(\\n self.feat_channels, self.out_channels, 1, stride=1)\",\n \"def __init__(self, weights=[], alphas=[]):\\n self._layers = [Layer(w, a) for w, a in zip(weights, alphas)]\",\n \"def define_layers(self):\\n if self.d != 0:\\n # If we have a fixed input size we use it do define the first layer\\n self.layers = [nn.Sequential(nn.Linear(self.d, self.h),\\n nn.ReLU(), )] # nn.BatchNorm1d(self.h, affine=False))]\\n else:\\n self.layers = [nn.Sequential(nn.Linear(self.h, self.h),\\n nn.ReLU(), )]\\n\\n l = 0\\n for l in self.layers_sizes():\\n self.layers.append(nn.Sequential(nn.Linear(self.h - l, self.h - l - self.delta_h),\\n nn.ReLU(), )) # nn.BatchNorm1d( self.h - l - self.delta_h, affine=False)))\\n self.layers.append(nn.Sequential(nn.Linear(self.h - l - self.delta_h, 1), nn.ReLU()))\",\n \"def init_layers_hpm(self):\\n\\n self.lin_layers_hpm.append(nn.Linear(8, self.noFeatures_hpm))\\n for _ in range(self.noLayers_hpm):\\n self.lin_layers_hpm.append(nn.Linear(self.noFeatures_hpm, self.noFeatures_hpm))\\n self.lin_layers_hpm.append(nn.Linear(self.noFeatures_hpm, 2))\\n\\n \\n for m in self.lin_layers_hpm:\\n if isinstance(m, nn.Linear):\\n nn.init.xavier_normal_(m.weight)\\n nn.init.constant_(m.bias, 0)\",\n \"def _init_layers(self) -> None:\\n super()._init_layers()\\n self.controller = nn.Conv2d(\\n self.feat_channels, self.num_params, 3, padding=1)\",\n \"def SetUpLayerManager(self):\\n pass\",\n \"def __init__(self, **kwargs):\\n super(ForwardLayersBase, self).__init__()\\n pass\",\n \"def initLayers(self):\\n self.addRow(self.currentLayerData)\\n self.selected = self.jobRow.child(0)\\n self.setSelectedFromItem(self.selected)\",\n \"def __init__(self, layers) -> None:\\n super(FeaturizerPhi, self).__init__()\\n self.layers = layers\",\n \"def initialize_layers(self, layers_config: dict, inputs=None):\\n layers_config = layers_config.copy()\\n input_lyrs = []\\n initiated_layers = OrderedDict()\\n wrp_layer = None # indicator for wrapper layers\\n first_layer = True\\n\\n for lyr, lyr_args in layers_config.items():\\n\\n lyr_config, lyr_inputs, named_outs, call_args = self.deconstruct_lyr_args(lyr, lyr_args)\\n\\n lyr_name, args, lyr_config, activation = self.check_lyr_config(lyr, lyr_config)\\n\\n if K.BACKEND == 'pytorch':\\n\\n if first_layer:\\n first_layer = False\\n\\n if callable(lyr_config):\\n lyr_initiated = lyr_config\\n else:\\n lyr_initiated = TORCH_LAYERS[lyr_name](**lyr_config)\\n setattr(self, lyr, lyr_initiated)\\n initiated_layers[lyr] = {\\\"layer\\\": lyr_initiated, \\\"named_outs\\\": named_outs, 'call_args': call_args,\\n 'inputs': lyr_inputs}\\n\\n else:\\n # may be user has defined layers without input layer, in this case add Input layer as first layer\\n if first_layer:\\n if inputs is not None: # This method was called by providing it inputs.\\n assert isinstance(inputs, tf.Tensor)\\n # since inputs have been defined, all the layers that will be added will be next to first layer\\n first_layer = False\\n layer_outputs = inputs\\n initiated_layers[layer_outputs.name] = {'layer': layer_outputs, 'tf_name': lyr_name}\\n\\n elif lyr_name != \\\"Input\\\":\\n if 'input_shape' in lyr_config: # input_shape is given in the first layer so make input layer\\n initialized_layer = LAYERS[\\\"Input\\\"](shape=lyr_config['input_shape'])\\n else:\\n # for simple dense layer based models, lookback will not be used\\n def_shape = (self.num_ins,) if self.lookback == 1 else (self.lookback, self.num_ins)\\n initialized_layer = LAYERS[\\\"Input\\\"](shape=def_shape)\\n\\n # first layer is built so next iterations will not be for first layer\\n first_layer = False\\n # put the first layer in memory to be used for model compilation\\n # add th layer which the user had specified as first layer\\n initiated_layers[initialized_layer.name] = {'layer': initialized_layer,\\n 'tf_name': lyr_name}\\n input_lyrs.append(initialized_layer)\\n\\n # The inputs to the layer have not been specified, so either it is an Input layer\\n if lyr_inputs is None:\\n # or it uses the previous outputs as inputs\\n if lyr_name == \\\"Input\\\":\\n # it is an Input layer, hence should not be called\\n initialized_layer = LAYERS[lyr_name](*args, **lyr_config)\\n initiated_layers[lyr_config['name']] = {'layer': initialized_layer,\\n 'tf_name': lyr_name}\\n input_lyrs.append(initialized_layer)\\n else:\\n # it is executable and uses previous outputs as inputs\\n if lyr_name in ACTIVATION_LAYERS:\\n layer_outputs = ACTIVATION_LAYERS[lyr_name](name=lyr_config['name'])\\n initiated_layers[lyr_config['name']] = {'layer': layer_outputs,\\n 'named_outs': named_outs,\\n 'call_args': call_args,\\n 'inputs': lyr_inputs,\\n 'tf_name': lyr_name}\\n elif lyr_name in ['TimeDistributed', 'Bidirectional']:\\n wrp_layer = LAYERS[lyr_name]\\n # because wrapper layer name is property\\n initiated_layers[lyr_config['name']] = {'layer': wrp_layer,\\n 'tf_name': lyr_name}\\n continue\\n elif \\\"LAMBDA\\\" in lyr_name.upper():\\n # lyr_config is serialized lambda layer, which needs to be deserialized\\n initialized_layer = tf.keras.layers.deserialize(lyr_config)\\n # layers_config['lambda']['config'] still contails lambda, so we need to replace the python\\n # object (lambda) with the serialized version (lyr_config) so that it can be saved as json file.\\n layers_config[lyr]['config'] = lyr_config\\n initiated_layers[lyr_config['name']] = {'layer': initialized_layer,\\n 'named_outs': named_outs,\\n 'call_args': call_args,\\n 'inputs': lyr_inputs,\\n 'tf_name': lyr_name}\\n else:\\n if wrp_layer is not None:\\n initialized_layer = wrp_layer(LAYERS[lyr_name](*args, **lyr_config))\\n initiated_layers[lyr_config['name']] = {'layer': initialized_layer,\\n 'named_outs': named_outs,\\n 'call_args': call_args,\\n 'inputs': lyr_inputs,\\n 'tf_name': lyr_name}\\n wrp_layer = None\\n else:\\n if lyr_name == \\\"TemporalFusionTransformer\\\":\\n lyr_config['return_attention_components'] = True\\n initialized_layer = LAYERS[lyr_name](*args, **lyr_config)\\n initiated_layers[lyr_config['name']] = {'layer': initialized_layer,\\n 'named_outs': named_outs,\\n 'call_args': call_args,\\n 'inputs': lyr_inputs,\\n 'tf_name': lyr_name}\\n\\n else: # The inputs to this layer have been specified so they must exist in lyr_cache.\\n # it is an executable\\n if lyr_name in ACTIVATION_LAYERS:\\n\\n layer_outputs = ACTIVATION_LAYERS[lyr_name](name=lyr_config['name'])\\n initiated_layers[lyr_config['name']] = {'layer': layer_outputs,\\n 'named_outs': named_outs,\\n 'call_args': call_args,\\n 'inputs': lyr_inputs,\\n 'tf_name': lyr_name}\\n elif lyr_name in ['TimeDistributed', 'Bidirectional']:\\n wrp_layer = LAYERS[lyr_name]\\n # because wrapper layer name is property\\n initiated_layers[lyr_config['name']] = {'layer': wrp_layer,\\n 'tf_name': lyr_name}\\n continue\\n elif \\\"LAMBDA\\\" in lyr_name.upper():\\n initialized_layer = tf.keras.layers.deserialize(lyr_config)\\n layers_config[lyr]['config'] = lyr_config\\n initiated_layers[lyr_config['name']] = {'layer': initialized_layer,\\n 'named_outs': named_outs,\\n 'call_args': call_args,\\n 'inputs': lyr_inputs,\\n 'tf_name': lyr_name}\\n else:\\n if wrp_layer is not None:\\n initialized_layer = wrp_layer(LAYERS[lyr_name](*args, **lyr_config))\\n initiated_layers[lyr_config['name']] = {'layer': initialized_layer,\\n 'named_outs': named_outs,\\n 'call_args': call_args,\\n 'inputs': lyr_inputs,\\n 'tf_name': lyr_name}\\n wrp_layer = None\\n else:\\n layer_initialized = LAYERS[lyr_name](*args, **lyr_config)\\n initiated_layers[lyr_config['name']] = {'layer': layer_initialized,\\n 'named_outs': named_outs,\\n 'call_args': call_args,\\n 'inputs': lyr_inputs,\\n 'tf_name': lyr_name}\\n\\n if activation is not None: # put the string back to dictionary to be saved in config file\\n lyr_config['activation'] = activation\\n\\n first_layer = False\\n\\n self.jsonize_lyr_config(lyr_config)\\n\\n # inputs = [] todo, indentify input layers\\n # for k,v in lyr_cache.items():\\n # since the model is not build yet and we have access to only output tensors of each list, this is probably\\n # # the only way to know that how many `Input` layers were encountered during the run of this method. Each\\n # tensor (except TimeDistributed) has .op.inputs attribute, which is empty if a tensor represents output of Input layer.\\n # if int(''.join(tf.__version__.split('.')[0:2]).ljust(3, '0')) < 240:\\n # if k.upper() != \\\"TIMEDISTRIBUTED\\\" and hasattr(v, 'op'):\\n # if hasattr(v.op, 'inputs'):\\n # _ins = v.op.inputs\\n # if len(_ins) == 0:\\n # inputs.append(v)\\n # else: # not sure if this is the proper way of checking if a layer receives an input or not!\\n # if hasattr(v, '_keras_mask'):\\n # inputs.append(v)\\n\\n setattr(self, 'initiated_layers', initiated_layers)\\n setattr(self, 'input_lyrs', input_lyrs)\\n\\n\\n # todo,\\n # # for case when {Input -> Dense, Input_1}, this method wrongly makes Input_1 as output so in such case use\\n # # {Input_1, Input -> Dense }, thus it makes Dense as output and first 2 as inputs, so throwing warning\\n # if int(''.join(tf.__version__.split('.')[0:2]).ljust(3, '0')) < 240:\\n # if len(layer_outputs.op.inputs) < 1:\\n # print(\\\"Warning: the output is of Input tensor class type\\\")\\n # else:\\n # if 'op' not in dir(layer_outputs): # layer_outputs does not have `op`, which means it has no incoming node\\n # print(\\\"Warning: the output is of Input tensor class type\\\")\\n\\n # outs = None\\n #if BACKEND == 'tensorflow':\\n # outs = self.call(input_lyrs)\\n # setattr(self, 'output_lyrs', outs)\\n # if BACKEND == 'tensorflow':\\n # ## Reinitial\\n # super(Model, self).__init__(\\n # inputs=input_lyrs,\\n # outputs=outs)\\n #MODEL.__init__(self, inputs=inputs, outputs=outs)\\n\\n return input_lyrs # , outs\",\n \"def __init__(self, hparams):\\n super(ThreeLayerClassifier, self).__init__()\\n self.hparams = hparams\\n self.layer_1 = torch.nn.Linear(self.hparams[\\\"input_size\\\"], 128)\\n self.layer_2 = torch.nn.Linear(128, 256)\\n self.layer_3 = torch.nn.Linear(256, self.hparams[\\\"targets\\\"])\",\n \"def initialize_layers(self, years):\\n min_year = min(years)\\n max_year = max(years)\\n ordered_years = list(range(min_year, max_year + 1))\\n self.layers = [Layer(y) for y in ordered_years]\",\n \"def init_layers(self):\\n self.user_emb = nn.Embedding(self.n_users, self.emb_dim)\\n self.item_emb = nn.Embedding(self.n_items, self.emb_dim)\\n init_range = 0.1 * (self.emb_dim) ** (-1 / 2)\\n self.item_emb.weight.data.uniform_(-init_range, init_range)\\n self.fc_u_1_mu = nn.Linear(self.user_fea_dim, self.late_dim)\\n self.fc_u_2_mu = nn.Linear(self.late_dim, self.emb_dim * 2)\\n self.fc_i_1_mu = nn.Linear(self.item_fea_dim, self.late_dim)\\n self.fc_i_2_mu = nn.Linear(self.late_dim, self.emb_dim * 2)\",\n \"def initialize_parameters(self):\\n for i in range(1, self.L):\\n self.W[i - 1] = np.random.randn(self.layer_dims[i], self.layer_dims[i - 1]) * 0.01\\n self.b[i - 1] = np.zeros((self.layer_dims[i], 1))\",\n \"def init_weight(self):\\n init_layer(self.conv1)\\n init_layer(self.conv2)\\n init_bn(self.norm1)\\n init_bn(self.norm2)\",\n \"def initialize_weights(self):\\n tf.nest.map_structure(\\n weights_initializer.WeightsInitializer.initialize_layer_or_model,\\n self._layer_nest)\",\n \"def __init__(self, layer_list_info):\\n self.layer_list_info = layer_list_info\",\n \"def _init_layers(self) -> None:\\n self.relu = nn.ReLU(inplace=True)\\n self.cls_convs = nn.ModuleList()\\n self.reg_convs = nn.ModuleList()\\n for i in range(self.stacked_convs):\\n chn = self.in_channels if i == 0 else self.feat_channels\\n self.cls_convs.append(\\n ConvModule(\\n chn,\\n self.feat_channels,\\n 3,\\n stride=1,\\n padding=1,\\n conv_cfg=self.conv_cfg,\\n norm_cfg=self.norm_cfg))\\n self.reg_convs.append(\\n ConvModule(\\n chn,\\n self.feat_channels,\\n 3,\\n stride=1,\\n padding=1,\\n conv_cfg=self.conv_cfg,\\n norm_cfg=self.norm_cfg))\\n pred_pad_size = self.pred_kernel_size // 2\\n self.atss_cls = nn.Conv2d(\\n self.feat_channels,\\n self.num_anchors * self.cls_out_channels,\\n self.pred_kernel_size,\\n padding=pred_pad_size)\\n self.atss_reg = nn.Conv2d(\\n self.feat_channels,\\n self.num_base_priors * 4,\\n self.pred_kernel_size,\\n padding=pred_pad_size)\\n self.atss_centerness = nn.Conv2d(\\n self.feat_channels,\\n self.num_base_priors * 1,\\n self.pred_kernel_size,\\n padding=pred_pad_size)\\n self.scales = nn.ModuleList(\\n [Scale(1.0) for _ in self.prior_generator.strides])\",\n \"def __init__(self, n_input, n_output, hidden_layer_size, reg):\\n self.reg = reg\\n self.fulllayer1 = FullyConnectedLayer(n_input, hidden_layer_size)\\n self.reglayer1 = ReLULayer()\\n self.fulllayer2 = FullyConnectedLayer(hidden_layer_size, n_output)\",\n \"def __init__(self):\\n self.layer_scope = None\\n self.out = None\",\n \"def __init__(self):\\n self.layers = []\\n self.best_loss = None\",\n \"def connect_layers(self):\\n if not self.check():\\n msg = \\\"Failed to check neural network.\\\"\\n print(msg)\\n logging.error(msg)\\n return\\n\\n # 1. set input layer\\n pre_layer = self.input_layer\\n for layer in self.hidden_layers:\\n layer.set_input_layer(pre_layer)\\n pre_layer = layer\\n self.output_layer.set_input_layer(pre_layer)\\n\\n # 2. set output layer\\n next_layer = self.output_layer\\n for layer in reversed(self.hidden_layers):\\n layer.set_next_layer(next_layer)\\n next_layer = layer\\n self.input_layer.set_next_layer(next_layer)\\n\\n # 3. call layer init\\n self.input_layer.init()\\n for layer in self.hidden_layers:\\n layer.init()\\n self.output_layer.init()\\n\\n return\",\n \"def _init_layers(self):\\n self.relu = nn.ReLU(inplace=True)\\n self.rpn_convs = nn.ModuleList()\\n for i in range(self.stacked_convs):\\n chn = self.in_channels if i == 0 else self.feat_channels\\n self.rpn_convs.append(\\n ConvModule(\\n chn,\\n self.feat_channels,\\n 3,\\n stride=1,\\n padding=1,\\n conv_cfg=self.conv_cfg,\\n norm_cfg=self.norm_cfg))\\n self.rpn_cls = nn.Conv2d(\\n self.feat_channels, self.num_anchors * 1, 3, padding=1)\\n self.rpn_reg = nn.Conv2d(\\n self.feat_channels, self.num_anchors * 4 * (self.reg_max + 1), 3, padding=1)\\n self.rpn_iou = nn.Conv2d(\\n self.feat_channels, self.num_anchors * 1, 3, padding=1)\\n self.scales = nn.ModuleList(\\n [Scale(1.0) for _ in self.anchor_generator.strides])\\n\\n ##############V2################\\n conf_vector = [nn.Conv2d(self.num_anchors * 4 * self.total_dim, self.num_anchors * self.reg_channels, 1)]\\n conf_vector += [self.relu]\\n conf_vector += [nn.Conv2d(self.num_anchors * self.reg_channels, self.num_anchors, 1), nn.Sigmoid()]\\n\\n self.reg_conf = nn.Sequential(*conf_vector)\\n ##############V2################\",\n \"def __init__(self, layers, r_min, r_max, learn_rate):\\n\\n if not isinstance(layers, list) or len(layers) < 3:\\n raise ValueError('invalid layer parammeter')\\n self.layers = layers\\n n_layer = len(layers)\\n self.n_layer = n_layer\\n self.r_min = r_min\\n self.r_max = r_max\\n self.learn_rate = learn_rate\\n\\n # initialize ws\\n self.ws = []\\n for layer_idx in range(n_layer - 1):\\n layer_size = (layers[layer_idx] + 1, layers[layer_idx + 1])\\n w = self.init_w(layer_size)\\n self.ws.append(w)\",\n \"def layers(self, layers):\\n\\n self._layers = layers\",\n \"def initialize(self):\\n params = {}\\n for i in range(1, len(self.layer_dimensions)):\\n params['b_' + str(i)] = np.ones((self.layer_dimensions[i], 1))\\n if self.he_initialization:\\n params['W_' + str(i)] = np.random.randn(self.layer_dimensions[i],\\n self.layer_dimensions[i - 1]) * np.sqrt(\\n 2 / self.layer_dimensions[i - 1])\\n else:\\n params['W_' + str(i)] = np.random.rand(self.layer_dimensions[i], self.layer_dimensions[i - 1]) - 0.5\\n return params\",\n \"def __init__(self):\\n\\n for layer in self._layer_class_map:\\n setattr(self, layer, self._layer_class_map[layer]())\",\n \"def UpdateLayers(self):\\n pass\",\n \"def __init__(self):\\n super(GatherLastLayer, self).__init__()\",\n \"def build_layers(self):\\n raise NotImplementedError\",\n \"def init_weights(self, pretrained=None):\\n\\n super(EncoderDecoder_gan, self).init_weights(pretrained)\\n self.backbone.init_weights(pretrained=pretrained)\\n self.backbone_gan.init_weights(pretrained=pretrained)\\n self.decode_head.init_weights()\\n # init GAN\\n # self.discriminator.init_weight()\\n # self.G_head.init_weights()\\n if self.with_auxiliary_head:\\n if isinstance(self.auxiliary_head, nn.ModuleList):\\n for aux_head in self.auxiliary_head:\\n aux_head.init_weights()\\n else:\\n self.auxiliary_head.init_weights()\",\n \"def initialisation(self):\\n self.create_variables()\\n self.create_placeholders()\\n self.build_model()\\n self.reset_lr(None, True)\\n self.build_loss()\\n self.initialised = True\",\n \"def initialize_weights(self):\\n for layer in self._cnn_layers:\\n weights_initializer.WeightsInitializer.initialize_layer_or_model(layer)\",\n \"def setUp(self):\\n\\t\\tprint(\\\"\\\\n-------------------------------------\\\\nIn Test_PyTorchLayers:\\\", self._testMethodName)\\n\\t\\tww.weightwatcher.torch = torch\\n\\t\\tself.model = models.resnet18()\\n\\t\\tfor layer in self.model.modules():\\n\\t\\t\\tself.last_layer = layer\",\n \"def __init__(self):\\r\\n # A dummy layer does nothing\\r\\n self.weights = np.zeros(shape=(input.shape[1], 10))\\r\\n bias = np.zeros(shape=(10,))\\r\\n pass\",\n \"def __init__(self, **kwargs):\\n base.Layer.__init__(self, **kwargs)\\n self._group = self.spec['group']\\n self._conv_args = dict(self.spec)\\n self._conv_args['name'] = self.spec['name'] + '_sub'\\n del self._conv_args['group']\\n self._bottom_sub = [base.Blob() for _ in range(self._group)]\\n self._top_sub = [base.Blob() for _ in range(self._group)]\\n self._conv_layers = None\\n self._blocksize = 0\\n self._num_kernels = self.spec['num_kernels']\\n # create the convolution layers\\n self._conv_layers = [\\n convolution.ConvolutionLayer(**self._conv_args)\\n for i in range(self._group)]\\n self._param = sum((layer.param() for layer in self._conv_layers), [])\\n return\",\n \"def _init_layers(self):\\n cls_branch = []\\n for _ in range(self.num_reg_fcs):\\n cls_branch.append(Linear(self.embed_dims, self.embed_dims))\\n cls_branch.append(nn.LayerNorm(self.embed_dims))\\n cls_branch.append(nn.ReLU(inplace=True))\\n cls_branch.append(Linear(self.embed_dims, self.cls_out_channels))\\n fc_cls = nn.Sequential(*cls_branch)\\n\\n reg_branch = []\\n for _ in range(self.num_reg_fcs):\\n reg_branch.append(Linear(self.embed_dims, self.embed_dims))\\n reg_branch.append(nn.ReLU())\\n reg_branch.append(Linear(self.embed_dims, self.code_size))\\n reg_branch = nn.Sequential(*reg_branch)\\n\\n past_traj_reg_branch = []\\n for _ in range(self.num_reg_fcs):\\n past_traj_reg_branch.append(\\n Linear(self.embed_dims, self.embed_dims))\\n past_traj_reg_branch.append(nn.ReLU())\\n past_traj_reg_branch.append(\\n Linear(self.embed_dims, (self.past_steps + self.fut_steps)*2))\\n past_traj_reg_branch = nn.Sequential(*past_traj_reg_branch)\\n\\n def _get_clones(module, N):\\n return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\\n\\n # last reg_branch is used to generate proposal from\\n # encode feature map when as_two_stage is True.\\n num_pred = (self.transformer.decoder.num_layers + 1) if \\\\\\n self.as_two_stage else self.transformer.decoder.num_layers\\n\\n if self.with_box_refine:\\n self.cls_branches = _get_clones(fc_cls, num_pred)\\n self.reg_branches = _get_clones(reg_branch, num_pred)\\n self.past_traj_reg_branches = _get_clones(\\n past_traj_reg_branch, num_pred)\\n else:\\n self.cls_branches = nn.ModuleList(\\n [fc_cls for _ in range(num_pred)])\\n self.reg_branches = nn.ModuleList(\\n [reg_branch for _ in range(num_pred)])\\n self.past_traj_reg_branches = nn.ModuleList(\\n [past_traj_reg_branch for _ in range(num_pred)])\\n if not self.as_two_stage:\\n self.bev_embedding = nn.Embedding(\\n self.bev_h * self.bev_w, self.embed_dims)\",\n \"def initialize(self):\\n\\n\\t\\tparameters = {}\\n\\t\\tL = len(self.layer_dims) # number of layers in the network\\n\\n\\t\\tfor l in range(1, L):\\n\\t\\t\\tparameters['W' + str(l)] = np.random.randn(self.layer_dims[l], self.layer_dims[l-1]) * 0.01\\n\\t\\t\\tparameters['b' + str(l)] = np.zeros((self.layer_dims[l], 1))\\n\\n\\t\\t\\tassert(parameters['W' + str(l)].shape == (self.layer_dims[l], self.layer_dims[l-1]))\\n\\t\\t\\tassert(parameters['b' + str(l)].shape == (self.layer_dims[l], 1))\\n\\n\\t\\treturn parameters\",\n \"def init_mask_head(self, mask_roi_extractor, mask_head):\\n self.mask_head = nn.ModuleList()\\n if not isinstance(mask_head, list):\\n mask_head = [mask_head for _ in range(self.num_stages)]\\n assert len(mask_head) == self.num_stages\\n for head in mask_head:\\n self.mask_head.append(build_head(head))\\n if self.recursive:\\n for i in range(self.num_stages):\\n self.mask_head[i] = self.mask_head[0]\",\n \"def init_dense(self, layer):\\n pass\",\n \"def __init__(self,NL,kS=None):\\n assert isinstance(NL,int), \\\"Wrong data type for number of layers.\\\\n\\\\n\\\"\\n assert NL>0, \\\"At least one layer is needed.\\\\n\\\"\\n self.NL=NL\\n self.layers=[] # List self.layers will store object of type Layer\\n for i in range(NL):\\n self.layers+=[Layer(),] # Initialize with blank layers\\n \\n self.couplings=[] # List self.couplings will store coupling strengths between layers\\n for i in range(NL-1):\\n self.couplings+=[0.,] # Initialize with decoupled layers\\n \\n if kS:\\n assert isinstance(kS,kSpace), \\\"Wrong data type of momentum space.\\\\n\\\\n\\\"\\n self.kS=kS\\n else:\\n self.kS=kSpace() # System requires some momentum space discretization. If none provided taken default.\",\n \"def __init__(self, layer_nest):\\n self._layer_nest = layer_nest\\n super().__init__()\",\n \"def _init_layers(self):\\n self.relu = nn.ReLU(inplace=True)\\n self.cls_convs = nn.ModuleList()\\n self.reg_convs = nn.ModuleList()\\n for i in range(self.stacked_convs):\\n chn = self.in_channels if i == 0 else self.feat_channels\\n self.cls_convs.append(\\n ConvModule(\\n chn,\\n self.feat_channels,\\n 3,\\n stride=1,\\n padding=1,\\n conv_cfg=dict(type='DCN', deform_groups=1)\\n if i == 0 and self.use_dcn else self.conv_cfg,\\n norm_cfg=self.norm_cfg))\\n self.reg_convs.append(\\n ConvModule(\\n chn,\\n self.feat_channels,\\n 3,\\n stride=1,\\n padding=1,\\n conv_cfg=dict(type='DCN', deform_groups=1)\\n if i == 0 and self.use_dcn else self.conv_cfg,\\n norm_cfg=self.norm_cfg))\\n self.atss_cls = nn.Conv2d(\\n self.feat_channels,\\n self.num_base_priors * self.cls_out_channels,\\n 3,\\n padding=1)\\n self.atss_reg = nn.Conv2d(\\n self.feat_channels, self.num_base_priors * 4, 3, padding=1)\\n self.atss_iou = nn.Conv2d(\\n self.feat_channels, self.num_base_priors * 1, 3, padding=1)\\n self.scales = nn.ModuleList(\\n [Scale(1.0) for _ in self.prior_generator.strides])\\n\\n # we use the global list in loss\\n self.cls_num_pos_samples_per_level = [\\n 0. for _ in range(len(self.prior_generator.strides))\\n ]\\n self.reg_num_pos_samples_per_level = [\\n 0. for _ in range(len(self.prior_generator.strides))\\n ]\",\n \"def __init__(self, layerNeurons, initialWeights = None, layerTypes=None, **kwargs):\\r\\n \\r\\n # Ensure that there is at-least one input and one output layer in the network\\r\\n assert len(layerNeurons)>1, \\\"At least one input layer and one output layer is needed\\\"\\r\\n \\r\\n # Get the total number of weights needed in the network\\r\\n totalWeightCount = NeuralNetwork.getSizeOfWeightVector(layerNeurons)\\r\\n \\r\\n # Initialise the weights with the initializer or random values\\r\\n if initialWeights is None:\\r\\n self.weights = np.random.uniform(-1/np.sqrt(layerNeurons[0]), 1/np.sqrt(layerNeurons[0]), totalWeightCount)\\r\\n else:\\r\\n assert len(initialWeights) == totalWeightCount, (\\\"Length of initial weight matrix incorrect. You need \\\"+str(totalWeightCount)+\\\" weights\\\")\\r\\n self.weights = np.array(initialWeights, dtype = np.float64) \\r\\n \\r\\n # create an empty array of layers\\r\\n self.layers = []\\r\\n layerBlockStart = 0\\r\\n \\r\\n if layerTypes is None or len(layerTypes)<(len(layerNeurons)-1):\\r\\n layerTypes=[NetworkLayer]*(len(layerNeurons)-1)\\r\\n \\r\\n for layerInputDimention, layerOutputDimention, layerType in zip(layerNeurons, layerNeurons[1:], layerTypes):\\r\\n # initialise each layer with its input and output dimentions and bi-directional pointers to the relivant weights\\r\\n layerBlockEnd = layerBlockStart+(layerInputDimention*layerOutputDimention)\\r\\n layerBiasEnd = layerBlockEnd+layerOutputDimention\\r\\n newLayer = layerType(layerInputDimention, layerOutputDimention, \\r\\n self.weights[..., layerBlockStart:layerBlockEnd], \\r\\n self.weights[..., layerBlockEnd:layerBiasEnd], **kwargs)\\r\\n self.layers.append(newLayer)\\r\\n \\r\\n layerBlockStart = layerBiasEnd\\r\\n \\r\\n # Tell the output later to use a different function to calculate the delta \\r\\n newLayer.calcDelta = newLayer.calcDeltaOutputLayer\",\n \"def __init__(self):\\n super(LinearAggregationLayer, self).__init__()\",\n \"def initialize_network(self):\\n # intermediate layer size\\n ils = int((self.specbinnum + self.numfilters) / 2)\\n\\n network = lasagne.layers.InputLayer((None, 1, self.specbinnum, self.numtimebins), self.input_var)\\n\\n network = NormalisationLayer(network, self.specbinnum)\\n self.normlayer = network\\n\\n network, _ = custom_convlayer_2(network, in_num_chans=self.specbinnum, out_num_chans=ils)\\n network = batch_norm(network)\\n network, _ = custom_convlayer_2(network, in_num_chans=ils, out_num_chans=self.numfilters)\\n network = batch_norm(network)\\n\\n network = lasagne.layers.NonlinearityLayer(network, nonlinearity=elu)\\n self.latents = network\\n network = ZeroOutBackgroundLatentsLayer(self.latents,\\n mp_down_factor=self.mp_down_factor,\\n numfilters=self.numfilters,\\n numtimebins=self.numtimebins,\\n background_latents_factor=self.background_latents_factor,\\n use_maxpool=self.use_maxpool)\\n network, _ = custom_convlayer_2(network, in_num_chans=self.numfilters, out_num_chans=ils)\\n network = batch_norm(network)\\n network, _ = custom_convlayer_2(network, in_num_chans=ils, out_num_chans=self.specbinnum)\\n network = batch_norm(network)\\n\\n # output_size\\n num_time_samples = int(audioframe_len/2 * (self.numtimebins + 1))\\n # network = batch_norm(DenseLayer(network, num_time_samples)) # MemoryError\\n network, _ = custom_convlayer_2(network, in_num_chans=self.specbinnum, out_num_chans=num_time_samples)\\n network, _ = batch_norm(network)\\n network, _ = custom_convlayer_2(network, in_num_chans=num_time_samples, out_num_chans=1)\\n network, _ = batch_norm(network)\\n\\n self.network = network\",\n \"def new_initial_layer(self, nodes, inputs, alpha=0.1):\\n weights = [[1 for _ in range(inputs)] for i in range(nodes)]\\n alphas = [alpha for _ in range(nodes)]\\n self._layers.insert(0, Layer(weights, alphas))\",\n \"def __init__(self, num_latents, activation=None, layer_param=None, **kwargs):\\n\\t\\tshape_image = [64,64,3]\\n\\t\\tsuper().__init__(\\n\\t\\t\\tlayer_param=layer_param, \\n\\t\\t\\tnum_latents=num_latents, \\n\\t\\t\\tshape_image=shape_image, \\n\\t\\t\\tactivation=activation, \\n\\t\\t\\tis_create_sequential=False, # don't create sequential as we shouldn't build just yet - we need to redefine the layers.\\n\\t\\t\\t**kwargs)\",\n \"def __init__(self, layers=[2, 2, 1], activation_function=\\\"bentidentity\\\"):\\n self.layers = layers\\n self.activation_function = th.activation_functions[activation_function]\\n self.activation_derivative = th.activation_derivatives[\\n activation_function]\\n self.weights = self._generate_weights()\",\n \"def init_weights(self, pretrained=None):\\n self.online_backbone.init_weights() # backbone\\n for online_neck in self.online_necks:\\n online_neck.init_weights(init_linear='kaiming') # projection\\n\\n for param_ol, param_tgt in zip(self.online_backbone.parameters(),\\n self.target_backbone.parameters()):\\n param_tgt.data.copy_(param_ol.data)\\n for param_ol, param_tgt in zip(self.online_necks.parameters(),\\n self.target_necks.parameters()):\\n param_tgt.data.copy_(param_ol.data)\\n # init the predictor in the head\\n for head in self.heads:\\n head.init_weights()\",\n \"def _nn_initial_values(structure):\\n\\n # Use Xavier uniform initializer\\n initializer=tf.glorot_uniform_initializer()\\n\\n output=[]\\n last_width=None\\n\\n # Add biases & weights per layer\\n for l in structure:\\n output.append(tf.zeros(shape=[l])) # layer l biases\\n if last_width is not None: # Exclude weights from layer 0\\n output.append(initializer(shape=[last_width, l])) # layer l weights\\n last_width=l\\n\\n return output\",\n \"def __init__(self, layer_delay=_default_layer_delay):\\n self._attached_phys = []\\n self._layer_delay = layer_delay\",\n \"def __init__(self, feature_length, layersNumber):\\n super(SubGraph, self).__init__()\\n self.layers_number = layersNumber\\n self.layers = nn.ModuleList([SubGraphLayer(feature_length * (2 ** i)) for i in range(self.layers_number)])\",\n \"def __init__(self, parent, layer):\\n pass\",\n \"def __init__(self, layer_list_info):\\n super(DynaNet, self).__init__()\\n self.layer_list_info = layer_list_info\\n self.task_modules = nn.ModuleDict()\\n self.classification_layers = nn.ModuleDict()\\n self.module_generator = ModuleFactory(layer_list_info)\\n self.task_module_name_path = {}\\n self.nr_levels = len(layer_list_info)\\n self.task_idx = None\",\n \"def init_bbox_head(self, mask_roi_extractor, mask_head):\\n pass\",\n \"def init():\\n global tube, ball, faceTextureName, woodTextureName\\n tube = gluNewQuadric()\\n gluQuadricDrawStyle(tube, GLU_FILL)\\n ball = gluNewQuadric()\\n gluQuadricDrawStyle(ball, GLU_FILL)\\n\\n # Set up lighting and depth-test\\n glEnable(GL_LIGHTING)\\n glEnable(GL_NORMALIZE) # Inefficient...\\n glEnable(GL_DEPTH_TEST) # For z-buffering!\\n\\n generateCheckerBoardTexture()\\n faceTextureName = loadImageTexture(\\\"brick.jpg\\\")\\n woodTextureName = loadImageTexture(\\\"wood.jpg\\\")\",\n \"def __init__(self, z_dim, initailize_weights=True):\\n super().__init__()\\n self.z_dim = z_dim\\n\\n self.img_conv1 = conv2d(3, 16, kernel_size=7, stride=2)\\n self.img_conv2 = conv2d(16, 32, kernel_size=5, stride=2)\\n self.img_conv3 = conv2d(32, 64, kernel_size=5, stride=2)\\n self.img_conv4 = conv2d(64, 64, stride=2)\\n self.img_conv5 = conv2d(64, 128, stride=2)\\n self.img_conv6 = conv2d(128, self.z_dim, stride=2)\\n self.img_encoder = nn.Linear(4 * self.z_dim, 2 * self.z_dim)\\n self.flatten = Flatten()\\n\\n if initailize_weights:\\n init_weights(self.modules())\",\n \"def init_mask_head(self, mask_roi_extractor, mask_head):\\n self.mask_head = nn.ModuleList()\\n if not isinstance(mask_head, list):\\n mask_head = [mask_head for _ in range(self.num_stages)]\\n assert len(mask_head) == self.num_stages\\n for head in mask_head:\\n self.mask_head.append(build_head(head))\\n if mask_roi_extractor is not None:\\n self.share_roi_extractor = False\\n self.mask_roi_extractor = nn.ModuleList()\\n if not isinstance(mask_roi_extractor, list):\\n mask_roi_extractor = [\\n mask_roi_extractor for _ in range(self.num_stages)\\n ]\\n assert len(mask_roi_extractor) == self.num_stages\\n for roi_extractor in mask_roi_extractor:\\n self.mask_roi_extractor.append(\\n build_roi_extractor(roi_extractor))\\n else:\\n self.share_roi_extractor = True\\n self.mask_roi_extractor = self.bbox_roi_extractor\",\n \"def init_parameters(self):\\n # Create the weights and biases\\n for i in range(1, len(self.layer_dimensions)):\\n # Initialization from He et al.\\n mu = 0\\n var = 2 / self.layer_dimensions[i]\\n sigma = np.sqrt(var)\\n weight_shape = (self.layer_dimensions[i - 1], self.layer_dimensions[i])\\n weight = np.random.normal(loc=mu, scale=sigma, size=weight_shape)\\n bias = np.zeros((self.layer_dimensions[i], ))\\n\\n # Saving in the parameters dict\\n layer_weight = \\\"w_\\\" + str(i)\\n self._parameters[layer_weight] = weight\\n layer_b = \\\"b_\\\" + str(i)\\n self._parameters[layer_b] = bias\",\n \"def __init__(self, z_dim, initailize_weights=True):\\n super().__init__()\\n self.z_dim = z_dim\\n\\n self.depth_conv1 = conv2d(1, 32, kernel_size=3, stride=2)\\n self.depth_conv2 = conv2d(32, 64, kernel_size=3, stride=2)\\n self.depth_conv3 = conv2d(64, 64, kernel_size=4, stride=2)\\n self.depth_conv4 = conv2d(64, 64, stride=2)\\n self.depth_conv5 = conv2d(64, 128, stride=2)\\n self.depth_conv6 = conv2d(128, self.z_dim, stride=2)\\n\\n self.depth_encoder = nn.Linear(4 * self.z_dim, 2 * self.z_dim)\\n self.flatten = Flatten()\\n\\n if initailize_weights:\\n init_weights(self.modules())\",\n \"def __init__(self, channels, num_classes):\\n super(AuxiliaryHead, self).__init__()\\n self.features = nn.Sequential(\\n nn.ReLU(inplace=True),\\n # image size = 2 x 2\\n nn.AvgPool2d(5, stride=3, padding=0, count_include_pad=False),\\n nn.Conv2d(channels, 128, 1, bias=False),\\n nn.BatchNorm2d(128),\\n nn.ReLU(inplace=True),\\n nn.Conv2d(128, 768, 2, bias=False),\\n nn.BatchNorm2d(768),\\n nn.ReLU(inplace=True),\\n )\\n self.classifier = nn.Linear(768, num_classes)\",\n \"def initialize(self):\\n self.conv1.reset_parameters()\\n self.conv2.reset_parameters()\",\n \"def __init__(self, layer, cNextLayer, batch):\\n\\n self._layer = layer\\n self._oldWDeltas = [[0.0] * cNextLayer for _ in layer]\\n self._batch = batch\\n if batch:\\n self._wDeltas = [[0.0] * cNextLayer for _ in layer]\",\n \"def init_params(self):\\n self.conv = Conv(self.conv_layers[0][-1], self.out_channels, padding=self.padding,stride=self.stride)\\n self.W = torch.randn(self.num_labels, self.cout_numel, requires_grad=True)\\n self.T = torch.randn(self.num_labels, self.num_labels, requires_grad=True)\",\n \"def init_weights(self, pretrained=None):\\n if pretrained is not None:\\n self.pretrained = pretrained\\n self.backbone.init_weights(self.pretrained)\\n if self.with_necks:\\n for neck in self.necks:\\n if hasattr(neck, 'init_weights'):\\n neck.init_weights()\\n for head in self.heads:\\n if hasattr(head, 'init_weights'):\\n head.init_weights()\",\n \"def __init__(self, config):\\n self.layers = [] # Store all layers in this list.\\n self.x = None # Save the input to forward in this\\n self.y = None # Save the output vector of model in this\\n self.targets = None # Save the targets in forward in this variable\\n\\n # Add layers specified by layer_specs.\\n for i in range(len(config['layer_specs']) - 1):\\n self.layers.append(Layer(config['layer_specs'][i], config['layer_specs'][i+1]))\\n if i < len(config['layer_specs']) - 2:\\n self.layers.append(Activation(config['activation']))\",\n \"def init():\\n global neural_network\\n global labels\\n\\n # load objects required by run() for inferencing\\n model_dir = Model.get_model_path(\\\"mnist-fashion\\\")\\n # neural model\\n neural_network = keras.models.load_model(f\\\"{model_dir}/neural-network.h5\\\")\\n # labels\\n with open(f\\\"{model_dir}/labels.jsonpickle\\\", \\\"r\\\") as labels_file:\\n labels = jsonpickle.decode(labels_file.read())\",\n \"def init_weights(self, pretrained):\\n if self.with_shared_head:\\n self.shared_head.init_weights(pretrained=pretrained)\\n for i in range(1):\\n if self.with_bbox:\\n self.bbox_roi_extractor[i].init_weights()\\n self.bbox_head[i].init_weights()\\n if self.with_rbbox:\\n self.rbbox_roi_extractor[i].init_weights()\\n self.rbbox_head[i].init_weights()\\n if self.with_mask:\\n if not self.share_roi_extractor:\\n self.mask_roi_extractor[i].init_weights()\\n self.mask_head[i].init_weights()\",\n \"def __init__(self, layers, learning_rate, loss_function, l2_lambda=0, noise_type=None):\\n self.layers, self.alpha = layers, learning_rate\\n self.loss = loss_function\\n self.l2_lambda = l2_lambda\\n self.noise_type = noise_type\",\n \"def setUp(self):\\n # The short NSC used in this example\\n self.net_nsc = [\\n (1, 4, 0, 0, 0), # Layer 1: Identity(input)\\n (2, 1, 3, 1, 0), # Layer 2: Convolution(Layer1)\\n (3, 1, 3, 2, 0), # Layer 3: Convolution(Layer2)\\n (4, 5, 0, 1, 3), # Layer 4: Convolution(Layer1)\\n (5, 7, 0, 0, 0), # Layer 5: Convolution(Layer4)\\n ]\",\n \"def setUp(self):\\n\\t\\tprint(\\\"\\\\n-------------------------------------\\\\nIn Test_KerasLayers:\\\", self._testMethodName)\\n\\t\\tself.model = VGG16() \\n\\t\\tself.last_layer = self.model.submodules[-1]\\n\\t\\tww.weightwatcher.keras = keras\",\n \"def layers(self): # -> LayerView:\\n ...\",\n \"def setUp(self):\\n\\t\\t#import inspect\\n\\n\\t\\tprint(\\\"\\\\n-------------------------------------\\\\nIn Test_PyStateDictLayers:\\\", self._testMethodName)\\n\\t\\tww.weightwatcher.torch = torch\\n\\t\\tself.model = models.resnet18().state_dict()\\n\\t\\t#for cls in inspect.getmro(type(self.model)):\\n\\t\\t#\\tprint(str(cls))\\n \\n\\t\\tfor key in self.model.keys():\\n\\t\\t\\tif key.endswith('.weight'):\\n\\t\\t\\t\\tlayer_name = key[:-len('.weight')]\\n\\t\\t\\t\\tself.fc_layer_name = layer_name\",\n \"def setup_walls(self):\\n self.wall_list = self.get_current_map().get_layer_by_name(\\\"walls\\\").sprite_list\",\n \"def init_layer(layer):\\n nn.init.xavier_uniform_(layer.weight)\\n\\n if hasattr(layer, \\\"bias\\\"):\\n if layer.bias is not None:\\n layer.bias.data.fill_(0.0)\",\n \"def __init__(self, layerNeurons, numberOfLayers, initialWeights = None, lowerBound = None, upperBound = None):\\r\\n \\r\\n # Ensure that there is at-least one input and one output layer in the network\\r\\n assert len(layerNeurons) > 1, \\\"At least one input layer and one output layer is needed\\\"\\r\\n \\r\\n # Get the total number of weights needed in the network\\r\\n totalWeightCount = NeuralNetwork.getSizeOfWeightVector(layerNeurons)*numberOfLayers\\r\\n \\r\\n # Initialise the weights with the initialiser or random values\\r\\n if initialWeights is None:\\r\\n if lowerBound is None:\\r\\n lowerBound=-1/np.sqrt(layerNeurons[0])\\r\\n if upperBound is None:\\r\\n upperBound=1/np.sqrt(layerNeurons[0])\\r\\n self.weights = np.random.uniform(lowerBound, upperBound, totalWeightCount)\\r\\n else:\\r\\n assert initialWeights.size == totalWeightCount, (\\\"Length of initial weight matrix incorrect. You need \\\"+str(totalWeightCount)+\\\" weights\\\")\\r\\n self.weights = initialWeights.view()\\r\\n \\r\\n self.weights.shape = (numberOfLayers, -1)\\r\\n # create an empty array of layers\\r\\n self.layers = []\\r\\n layerBlockStart = 0\\r\\n \\r\\n for layerInputDimention, layerOutputDimention in zip(layerNeurons, layerNeurons[1:]):\\r\\n # initialise each layer with its input and output dimentions and bi-directional pointers to the relivant weights\\r\\n layerBlockEnd = layerBlockStart+(layerInputDimention*layerOutputDimention)\\r\\n layerBiasEnd = layerBlockEnd+layerOutputDimention\\r\\n newLayer = batchNetworkLayer(layerInputDimention, layerOutputDimention, numberOfLayers, \\r\\n self.weights[..., :, layerBlockStart:layerBlockEnd], \\r\\n self.weights[..., :, layerBlockEnd:layerBiasEnd])\\r\\n self.layers.append(newLayer)\\r\\n \\r\\n layerBlockStart = layerBiasEnd\",\n \"def __init__(self, energy_model, layers, prior='normal'):\\n self.energy_model = energy_model\\n super().__init__(energy_model.dim, layers, prior=prior)\",\n \"def layers(self, layers):\\n self._layers = layers\\n prev = None\\n for layer in layers:\\n if not layer.inputs and prev is not None:\\n layer.inputs = [prev]\\n prev = layer\",\n \"def build_head(self, n_features, device=None):\\n # By default this is a linear layer\\n self.head = self.create_compatible_head(n_features, device)\",\n \"def __init__(self, heads, loss_combiner):\\n # TODO(zakaria): Keep _Head a pure interface.\\n super(_MultiHead, self).__init__(head_name=None)\\n self._logits_dimension = 0\\n for head in heads:\\n if not head.head_name:\\n raise ValueError(\\\"Head must have a name.\\\")\\n self._logits_dimension += head.logits_dimension\\n\\n self._heads = heads\\n self._loss_combiner = loss_combiner\",\n \"def construct(self):\\n self.input_size = self.numplanes * self.boardsize**2\\n \\n if self.hidden:\\n layers = [\\n torch.nn.Linear(self.input_size, self.hidden), \\n torch.nn.ReLU(),\\n torch.nn.Linear(self.hidden, self.boardsize**2)\\n ]\\n else:\\n layers = [torch.nn.Linear(self.input_size, self.boardsize**2)]\\n\\n self.layers = torch.nn.ModuleList(layers)\\n self.optimizer = torch.optim.Adam(self.parameters(), lr=1e-5)\\n logging.info(\\\"Model initialized: %s\\\", self)\",\n \"def __init__(self,\\n image_shape,\\n z_dim,\\n num_blocks,\\n action_space,\\n hiddens=[],\\n dropout=False,\\n subsampling=True):\\n super().__init__()\\n self.layers = nn.ModuleList()\\n self.layers.append(\\n ImageInputNetwork(image_shape, z_dim, num_blocks, dropout,\\n subsampling))\\n self.layers.append(ActorNet(action_space, z_dim, hiddens))\",\n \"def __init__(self, stage, layer=None, proxyPrimPrefix=\\\"\\\", renderPrimPrefix=\\\"\\\"):\\n\\n self._stage = stage\\n\\n # Layer\\n if layer is None:\\n self._layer = Sdf.Layer.CreateAnonymous()\\n elif isinstance(layer, Sdf.Layer):\\n self._layer = layer\\n elif isinstance(layer, str):\\n self._layer = Sdf.Layer.Open(layer)\\n\\n self._layer.SetMuted(True)\\n self._stage.GetSessionLayer().subLayerPaths.append(self._layer.identifier)\\n self._proxyPrimPrefix = proxyPrimPrefix\\n self._renderPrimPrefix = renderPrimPrefix\",\n \"def init_weights(self):\\n # We don't use the `init_weights()` function in BaseModule, since it\\n # doesn't support the initialization method from `reset_parameters()`\\n # in Pytorch.\\n if self.with_backbone:\\n self.backbone.init_weights()\\n\\n if self.with_neck:\\n for m in self.neck.modules():\\n if isinstance(m, _ConvNd) or isinstance(m, _BatchNorm):\\n m.reset_parameters()\\n\\n if self.with_head:\\n for m in self.head.modules():\\n if isinstance(m, _ConvNd) or isinstance(m, _BatchNorm):\\n m.reset_parameters()\",\n \"def createEmptyLayer(self):\\n # , wt.greeting: False , wt.ects: False, wt.preReqs: False, wt.courseCodeMentioned: False\\n layer = {wt.questionWord: \\\"\\\", wt.pronoun: \\\"\\\", wt.verb: \\\"\\\", wt.websiteName: \\\"\\\", wt.timeWord: \\\"\\\", wt.about: \\\"\\\",\\n wt.weather: \\\"\\\", wt.when: \\\"\\\", wt.keywords: [], wt.courseID: \\\"\\\", wt.structureUnitCode: \\\"\\\",\\n wt.sentence: [], wt.hangman: \\\"\\\", wt.what: \\\"\\\"}\\n return layer\",\n \"def init_weights(self, pretrained=None):\\n super(SingleTwoStageDetector176PRW, self).init_weights(pretrained)\\n self.backbone.init_weights(pretrained=pretrained)\\n if self.with_neck:\\n if isinstance(self.neck, nn.Sequential):\\n for m in self.neck:\\n m.init_weights()\\n else:\\n self.neck.init_weights()\\n if self.with_rpn:\\n self.rpn_head.init_weights()\\n if self.with_roi_head:\\n self.roi_head.init_weights(pretrained)\\n self.bbox_head.init_weights()\",\n \"def init():\\n ########################\\n # OPTIONS\\n ########################\\n # Debugging tools\\n global TIMER # displays time of every major step\\n TIMER = True\\n global MONITOR # displays monitoring infos\\n MONITOR = False\\n \\n global directories\\n directories = {'1Face': 'data/1Face/',\\n '2Faces': 'data/2Faces/',\\n '3Faces': 'data/3Faces/',\\n 'test': 'data/test/'}\\n \\n # Opt. swicthes\\n global maxfinder # to find the max dim. amongst the pictures\\n maxfinder = False\\n global ML_mode\\n ML_mode = {'CNN_Train': False,\\n 'CNN_Pred' : True,\\n 'Sampler': True}\\n \\n # Global variables\\n global num_pics\\n num_pics = {'1Face': 0,\\n '2Faces': 0,\\n '3Faces': 0}\\n global labels\\n labels = {'1Face': 0,\\n '2Faces': 1,\\n '3Faces': 2}\\n global num_data\\n num_data = 0\\n global splitsize # Fraction of data to build the training set\\n splitsize = 0.7 \\n global maxheight # Resize the pictures to a power of 2 for CNN (2^8 here)\\n maxheight = 128\\n global maxwidth\\n maxwidth = 128\\n global TreshEdge # Number of consecutive black pixels to define an edge\\n TreshEdge = 2\\n global TreshFace # Number of white pixels to define a face (or large edge)\\n TreshFace = maxheight/16\",\n \"def init_layer(layer):\\n nn.init.xavier_uniform_(layer.weight)\\n \\n if hasattr(layer, 'bias'):\\n if layer.bias is not None:\\n layer.bias.data.fill_(0.)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7565452","0.73801965","0.7378758","0.70826614","0.70083666","0.68058515","0.6793121","0.6747672","0.6699219","0.6565106","0.6552534","0.6548868","0.6536724","0.6533556","0.65013635","0.64803135","0.6478405","0.6438143","0.6429889","0.6428964","0.6401287","0.6378955","0.63557523","0.63098294","0.62651575","0.62627214","0.62521","0.6234774","0.62296057","0.61282","0.608666","0.6075663","0.6065903","0.60646474","0.6062385","0.60588783","0.6053228","0.6050158","0.6049648","0.6047469","0.60096884","0.6008756","0.60029","0.6002875","0.5999899","0.5999306","0.599868","0.5997637","0.5997229","0.5972601","0.59629244","0.59565145","0.5944875","0.5935242","0.5932093","0.5930642","0.5928851","0.5925865","0.5922919","0.59212404","0.5911614","0.5894809","0.58945","0.58813417","0.58599037","0.5855468","0.5822791","0.5820226","0.58127147","0.5800078","0.5797833","0.5788313","0.57807493","0.57727146","0.57687676","0.57440096","0.57432413","0.57428473","0.57271266","0.57227576","0.57218444","0.5720404","0.57175434","0.57108533","0.5705898","0.56962997","0.5693475","0.56911105","0.5688712","0.56843495","0.5683593","0.5672732","0.5670984","0.5660436","0.565882","0.5650751","0.5649894","0.5642876","0.56427425","0.56348693"],"string":"[\n \"0.7565452\",\n \"0.73801965\",\n \"0.7378758\",\n \"0.70826614\",\n \"0.70083666\",\n \"0.68058515\",\n \"0.6793121\",\n \"0.6747672\",\n \"0.6699219\",\n \"0.6565106\",\n \"0.6552534\",\n \"0.6548868\",\n \"0.6536724\",\n \"0.6533556\",\n \"0.65013635\",\n \"0.64803135\",\n \"0.6478405\",\n \"0.6438143\",\n \"0.6429889\",\n \"0.6428964\",\n \"0.6401287\",\n \"0.6378955\",\n \"0.63557523\",\n \"0.63098294\",\n \"0.62651575\",\n \"0.62627214\",\n \"0.62521\",\n \"0.6234774\",\n \"0.62296057\",\n \"0.61282\",\n \"0.608666\",\n \"0.6075663\",\n \"0.6065903\",\n \"0.60646474\",\n \"0.6062385\",\n \"0.60588783\",\n \"0.6053228\",\n \"0.6050158\",\n \"0.6049648\",\n \"0.6047469\",\n \"0.60096884\",\n \"0.6008756\",\n \"0.60029\",\n \"0.6002875\",\n \"0.5999899\",\n \"0.5999306\",\n \"0.599868\",\n \"0.5997637\",\n \"0.5997229\",\n \"0.5972601\",\n \"0.59629244\",\n \"0.59565145\",\n \"0.5944875\",\n \"0.5935242\",\n \"0.5932093\",\n \"0.5930642\",\n \"0.5928851\",\n \"0.5925865\",\n \"0.5922919\",\n \"0.59212404\",\n \"0.5911614\",\n \"0.5894809\",\n \"0.58945\",\n \"0.58813417\",\n \"0.58599037\",\n \"0.5855468\",\n \"0.5822791\",\n \"0.5820226\",\n \"0.58127147\",\n \"0.5800078\",\n \"0.5797833\",\n \"0.5788313\",\n \"0.57807493\",\n \"0.57727146\",\n \"0.57687676\",\n \"0.57440096\",\n \"0.57432413\",\n \"0.57428473\",\n \"0.57271266\",\n \"0.57227576\",\n \"0.57218444\",\n \"0.5720404\",\n \"0.57175434\",\n \"0.57108533\",\n \"0.5705898\",\n \"0.56962997\",\n \"0.5693475\",\n \"0.56911105\",\n \"0.5688712\",\n \"0.56843495\",\n \"0.5683593\",\n \"0.5672732\",\n \"0.5670984\",\n \"0.5660436\",\n \"0.565882\",\n \"0.5650751\",\n \"0.5649894\",\n \"0.5642876\",\n \"0.56427425\",\n \"0.56348693\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.67046845"},"document_rank":{"kind":"string","value":"8"}}},{"rowIdx":84189,"cells":{"query":{"kind":"string","value":"Initialize weights of the head."},"document":{"kind":"string","value":"def init_weights(self):\n for m in self.cls_convs:\n normal_init(m.conv, std=0.01)\n for m in self.reg_convs:\n normal_init(m.conv, std=0.01)\n bias_cls = bias_init_with_prob(0.01)\n normal_init(self.retina_cls, std=0.01, bias=bias_cls)\n normal_init(self.retina_reg, std=0.01)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _initialize_weights(self):\n pass","def init_weights(self):\n # Initialize weights\n self.apply(self._init_weights)","def setWeightInitializer(self,weights):\n self.init_w = weights","def init_weights(self):\n # Initialize weights\n self.apply(self._init_weights)\n # Tie weights if needed\n self.tie_weights()","def init_weights_(self):\n raise NotImplementedError","def initialize_weights(self):\n weights_initializer.WeightsInitializer.initialize_layer_or_model(\n self._batch)","def init_weight(self):\n init_bn(self.norm0)","def initialize_weights(self):\n tf.nest.map_structure(\n weights_initializer.WeightsInitializer.initialize_layer_or_model,\n self._layer_nest)","def InitWeights(self):\n self.w = -1 + 2 * np.random.rand(self.num_of_inputs,)\n self.w0 = -1 + 2 * np.random.rand()","def init_weights(self):\n self._q_neuron.h(self._weights) \n self._q_neuron.x(self._weights)","def init_weights(self) -> None:\n nn.init.kaiming_normal_(self._U)\n nn.init.kaiming_normal_(self._W)\n nn.init.kaiming_normal_(self._V)\n\n nn.init.normal_(self._b)","def init_weights(model):\n ...","def init_weights(self):\n \n self.w = np.random.randn(self.D) / np.sqrt(self.D)","def init_weights(self):\n self.embedding.weight.data.uniform_(-0.1, 0.1)\n self.word.bias.data.fill_(0)\n self.word.weight.data.uniform_(-0.1, 0.1)","def init_weights(self, init_w=3e-3):\n self.l3.weight.data.uniform_(-init_w, init_w)\n self.l3.bias.data.uniform_(-init_w, init_w)","def init_weights(self, init_w=3e-3):\n self.l3.weight.data.uniform_(-init_w, init_w)\n self.l3.bias.data.uniform_(-init_w, init_w)","def init_weights(self):\r\n self.embedding.weight.data.uniform_(-0.1, 0.1)\r\n self.fc.bias.data.fill_(0)\r\n self.fc.weight.data.uniform_(-0.1, 0.1)","def init_weights(self):\n self.embedding.weight.data.uniform_(-0.1, 0.1)\n self.fc.bias.data.fill_(0)\n self.fc.weight.data.uniform_(-0.1, 0.1)","def init_weights(self):\n self.embedding.weight.data.uniform_(-0.1, 0.1)\n self.fc.bias.data.fill_(0)\n self.fc.weight.data.uniform_(-0.1, 0.1)","def init_weights(self):\n self.embedding.weight.data.uniform_(-0.1, 0.1)\n self.fc.bias.data.fill_(0)\n self.fc.weight.data.uniform_(-0.1, 0.1)","def init_weights(self):\n with torch.no_grad():\n self._init_weights()","def initWeights(self):\n self.weights = []\n self.bias = []\n for i, dim in enumerate(self.dimensions[1:]):\n self.weights.append(np.random.uniform(-1,1,(self.dimensions[i],dim)))\n self.bias.append(np.random.uniform(-1,1,dim))","def init_weights(self, load_weights=None):\n if load_weights:\n # TODO\n pass\n else:\n # x: lower layer nodes n\n # y: current layer nodes n\n x = self.weights_shape[1]\n y = self.weights_shape[0]\n self.weights = np.random.randn(y, x) / np.sqrt(x) # pylint: disable=no-member\n self.biases = np.random.randn(y, 1) # pylint: disable=no-member","def default_weight_initializer(self):\n self.biases = [np.random.randn(y, 1) for y in self.sizes[1:]]\n self.weights = [np.random.randn(y, x)/np.sqrt(x)\n for x, y in zip(self.sizes[:-1], self.sizes[1:])]","def init_weights(self) -> None:\n self.fc.bias.data.fill_(0)\n self.fc.weight.data.uniform_(-0.1, 0.1)","def initialize_weights(self, weights_initializer, bias_initializer):\n wshapes = [\n [self.input_size, self.hidden_size[0]],\n [self.hidden_size[0], self.hidden_size[1]],\n [self.hidden_size[1], self.output_size]\n ]\n\n bshapes = [\n [1, self.hidden_size[0]],\n [1, self.hidden_size[1]],\n [1, self.output_size]\n ]\n\n self.weights = [init_weights(s, weights_initializer) for s in wshapes]\n self.biases = [init_weights(s, bias_initializer) for s in bshapes]\n\n self.trainable_variables = self.weights + self.biases","def initialize_weights(self):\n for layer in self._cnn_layers:\n weights_initializer.WeightsInitializer.initialize_layer_or_model(layer)","def init_weights(self):\n self.transformer.init_weights()\n if self.loss_cls.use_sigmoid:\n bias_init = bias_init_with_prob(0.01)\n for m in self.cls_branches:\n nn.init.constant_(m[-1].bias, bias_init)","def initialize_weights(self, seed=None):\r\n if seed!=None:\r\n np.random.seed(seed)\r\n self.weights = np.random.randn(self.number_of_nodes,self.input_dimensions)","def init_weight(self):\n init_layer(self.conv1)\n init_layer(self.conv2)\n init_bn(self.norm1)\n init_bn(self.norm2)","def init_weights(self):\r\n constant_init(self.sampling_offsets, 0.)\r\n thetas = torch.arange(\r\n self.num_heads,\r\n dtype=torch.float32) * (2.0 * math.pi / self.num_heads)\r\n grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)\r\n grid_init = (grid_init /\r\n grid_init.abs().max(-1, keepdim=True)[0]).view(\r\n self.num_heads, 1, 1,\r\n 2).repeat(1, self.num_levels, self.num_points, 1)\r\n for i in range(self.num_points):\r\n grid_init[:, :, i, :] *= i + 1\r\n\r\n self.sampling_offsets.bias.data = grid_init.view(-1)\r\n constant_init(self.attention_weights, val=0., bias=0.)\r\n xavier_init(self.value_proj, distribution='uniform', bias=0.)\r\n xavier_init(self.output_proj, distribution='uniform', bias=0.)\r\n self._is_init = True","def init_weights(self, pretrained=None):\n if pretrained is not None:\n self.pretrained = pretrained\n self.backbone.init_weights(self.pretrained)\n if self.with_necks:\n for neck in self.necks:\n if hasattr(neck, 'init_weights'):\n neck.init_weights()\n for head in self.heads:\n if hasattr(head, 'init_weights'):\n head.init_weights()","def __init__(self, weights):\n self._weights = weights","def initialize_weights(self, seed=None):\n if seed != None:\n np.random.seed(seed)\n self.weights = np.random.randn(self.number_of_nodes, self.input_dimensions)","def weights_initializer(self):\n self.weights = [np.random.normal(0, 1 / np.sqrt(x), (x, y)) for x, y in list(zip(self.structure[1:], self.structure[:-1]))]","def _initialize_weights(self):\n for _, cell in self.cells_and_names():\n if isinstance(cell, nn.Conv2d):\n cell.weight.set_data(orthogonal(cell.weight.shape, 0.6))\n if cell.bias is not None:\n cell.bias.set_data(\n init.initializer(init.Constant(0.01), cell.bias.shape,\n cell.bias.dtype))","def init_weights(self, init_w=3e-3):\n self.mean_linear.weight.data.uniform_(-init_w, init_w)\n self.mean_linear.bias.data.uniform_(-init_w, init_w)\n self.log_std_linear.weight.data.uniform_(-init_w, init_w)\n self.log_std_linear.bias.data.uniform_(-init_w, init_w)","def initializeWeightsToZero(self):\n\t\t## YOUR CODE BELOW\n\t\t\n\t\tutil.raiseNotDefined()\n\t\treturn","def initialize_weights(self):\n # compute lmda.\n self.lmda = float(self.c)/float(self.N)\n # bias term. This should not get a regularization penalty.\n self.bias = {}\n # weight vector\n self.w = {}\n # lastW[j] = k, indicates that feature j was last updated at time k.\n self.lastW = {}\n for lbl in self.labels:\n self.bias[lbl] = 0\n self.w[lbl] = {}\n self.lastW[lbl] = {}\n pass","def init_weights(self, pretrained=None):\n self.online_backbone.init_weights() # backbone\n for online_neck in self.online_necks:\n online_neck.init_weights(init_linear='kaiming') # projection\n\n for param_ol, param_tgt in zip(self.online_backbone.parameters(),\n self.target_backbone.parameters()):\n param_tgt.data.copy_(param_ol.data)\n for param_ol, param_tgt in zip(self.online_necks.parameters(),\n self.target_necks.parameters()):\n param_tgt.data.copy_(param_ol.data)\n # init the predictor in the head\n for head in self.heads:\n head.init_weights()","def init_weights(self, num_features):\n for each_label in self.valid_labels:\n self.weights[each_label] = np.zeros(num_features)","def init_sg_weights(self):\n n = self.weights_shape[0] # size of current layer\n # pylint: disable=no-member\n A = np.random.randn(n, n) / np.sqrt(n)\n B = np.random.randn(10, n) / np.sqrt(n)\n C = np.random.randn(1, n) / np.sqrt(n)\n # pylint: enable=no-member\n self.sg_weights = [A, B, C]","def reset_weights(self):\n self.head.reset_weights()","def init_weight(w):\n shape = w.shape\n if len(shape) == 4:\n i, o, u, v = shape\n k = np.sqrt(6 / (i * u * v + o * u * v))\n w.data.uniform_(-k, k)\n elif len(shape) == 2:\n k = np.sqrt(6 / sum(shape))\n w.data.uniform_(-k, k)\n elif len(shape) == 1:\n w.data.zero_()","def _weight_initializer(self):\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n nn.init.kaiming_normal_(m.weight)\n elif isinstance(m, nn.BatchNorm2d):\n nn.init.constant_(m.weight, 1.0)\n nn.init.constant_(m.bias, 0.0)","def init_parameters(self):\n stdv = 1. / math.sqrt(self.weight.data.size(1))\n self.weight.data.uniform_(-stdv, stdv)\n if self.bias is not None:\n self.bias.data.uniform_(-stdv, stdv)","def _weight_initializer(self):\n for m in self.modules():\n if isinstance(m, nn.ConvTranspose2d):\n nn.init.kaiming_normal_(m.weight)\n elif isinstance(m, nn.BatchNorm2d):\n nn.init.constant_(m.weight, 1.0)\n nn.init.constant_(m.bias, 0.0)","def init_weights(self, pretrained=None):\n if pretrained is not None:\n self.pretrained = pretrained\n self.backbone.init_weights(self.pretrained)\n self.mesh_head.init_weights()\n if self.with_gan:\n self.discriminator.init_weights()","def weights_init(m):\n if type(m) == torch.nn.Linear:\n m.weight.data.normal_(0.0, 0.02)\n m.bias.data.fill_(0)","def init_weights(self):\n r = np.sqrt(6.) / np.sqrt(self.fc.in_features +\n self.fc.out_features)\n self.fc.weight.data.uniform_(-r, r)\n self.fc.bias.data.fill_(0)","def init_weights(self):\n r = np.sqrt(6.) / np.sqrt(self.fc.in_features +\n self.fc.out_features)\n self.fc.weight.data.uniform_(-r, r)\n self.fc.bias.data.fill_(0)","def init_weights(self, dims):\n self.W = np.random.normal(size=dims) * 0.0001","def __init__(self, weights:np.ndarray):\n self.w = weights.copy()","def _set_initial_weights(self):\n weights = {'h0': None, 'y': None}\n weights['h0'] = np.array([[-0.6, 0.4, 0.5], [-0.2, 0.8, 0.8]])\n weights['y'] = np.array([[-0.3, -0.4, 0.9]])\n \n return weights","def init_weights(self, pretrained):\n if self.with_shared_head:\n self.shared_head.init_weights(pretrained=pretrained)\n for i in range(1):\n if self.with_bbox:\n self.bbox_roi_extractor[i].init_weights()\n self.bbox_head[i].init_weights()\n if self.with_rbbox:\n self.rbbox_roi_extractor[i].init_weights()\n self.rbbox_head[i].init_weights()\n if self.with_mask:\n if not self.share_roi_extractor:\n self.mask_roi_extractor[i].init_weights()\n self.mask_head[i].init_weights()","def _init_weights(self):\n nn.init.xavier_normal_(self.out.weight)","def _init_weights(module: nn.Module, name: str, head_bias: float = 0., flax=False):\n if isinstance(module, nn.Linear):\n if name.startswith('head'):\n nn.init.zeros_(module.weight)\n nn.init.constant_(module.bias, head_bias)\n else:\n if flax:\n # Flax defaults\n lecun_normal_(module.weight)\n if module.bias is not None:\n nn.init.zeros_(module.bias)\n else:\n # like MLP init in vit (my original init)\n nn.init.xavier_uniform_(module.weight)\n if module.bias is not None:\n if 'mlp' in name:\n nn.init.normal_(module.bias, std=1e-6)\n else:\n nn.init.zeros_(module.bias)\n elif isinstance(module, nn.Conv2d):\n lecun_normal_(module.weight)\n if module.bias is not None:\n nn.init.zeros_(module.bias)\n elif isinstance(module, (nn.LayerNorm, nn.BatchNorm2d, nn.GroupNorm)):\n nn.init.ones_(module.weight)\n nn.init.zeros_(module.bias)\n elif hasattr(module, 'init_weights'):\n # NOTE if a parent module contains init_weights method, it can override the init of the\n # child modules as this will be called in depth-first order.\n module.init_weights()","def set_weights(self, weights):\r\n self.weights = weights","def init_weights(self):\n\n params = torch.load(self.resnet_weight)\n\n self.fc1.weight.data = params['state_dict']['module.fc.weight'].clone()\n self.fc1.bias.data = params['state_dict']['module.fc.bias'].clone()\n\n\n r = np.sqrt(1.) / np.sqrt(self.fc3.in_features +\n self.fc3.out_features)\n self.fc3.weight.data.uniform_(-r, r)\n self.fc3.bias.data.fill_(0)\n r = np.sqrt(1.) / np.sqrt(self.fc2.in_features +\n self.fc2.out_features)\n self.fc2.weight.data.uniform_(-r, r)\n self.fc2.bias.data.fill_(0)\n r = np.sqrt(1.) / np.sqrt(self.fc4.in_features +\n self.fc4.out_features)\n self.fc4.weight.data.uniform_(-r, r)\n self.fc4.bias.data.fill_(0)","def init_weights(self, pretrained=None):\n super(SingleTwoStageDetector176PRW, self).init_weights(pretrained)\n self.backbone.init_weights(pretrained=pretrained)\n if self.with_neck:\n if isinstance(self.neck, nn.Sequential):\n for m in self.neck:\n m.init_weights()\n else:\n self.neck.init_weights()\n if self.with_rpn:\n self.rpn_head.init_weights()\n if self.with_roi_head:\n self.roi_head.init_weights(pretrained)\n self.bbox_head.init_weights()","def init_weights(self) -> None:\n super().init_weights()\n kaiming_init(self.convs_all_levels, a=1, distribution='uniform')\n kaiming_init(self.conv_branch, a=1, distribution='uniform')\n kaiming_init(self.conv_pred, a=1, distribution='uniform')","def _init_weights(self):\n for layer in self.modules():\n if isinstance(layer, (nn.Conv1d, nn.Linear)):\n nn.init.xavier_uniform_(layer.weight)\n if layer.bias is not None:\n nn.init.constant_(layer.bias, 0)\n elif isinstance(layer, nn.BatchNorm1d):\n nn.init.constant_(layer.weight, 1)\n nn.init.constant_(layer.bias, 0)","def _init_weights(self):\n for layer in self.modules():\n if isinstance(layer, (nn.Conv1d, nn.Linear)):\n nn.init.xavier_uniform_(layer.weight)\n if layer.bias is not None:\n nn.init.constant_(layer.bias, 0)\n elif isinstance(layer, nn.BatchNorm1d):\n nn.init.constant_(layer.weight, 1)\n nn.init.constant_(layer.bias, 0)","def _init_weights(self):\n for layer in self.modules():\n if isinstance(layer, (nn.Conv1d, nn.Linear)):\n nn.init.xavier_uniform_(layer.weight)\n if layer.bias is not None:\n nn.init.constant_(layer.bias, 0)\n elif isinstance(layer, nn.BatchNorm1d):\n nn.init.constant_(layer.weight, 1)\n nn.init.constant_(layer.bias, 0)","def _initialize_weights(self, inputs):\n if self.data_init:\n self._data_dep_init(inputs)\n else:\n self._init_norm()\n self._initialized = True","def _initialize_weights(self, m):\n self.w_ = np.zeros(1 + m)\n self.w_initialized = True","def _initialize_weights(self, m):\n self.w_ = np.zeros(1 + m)\n self.w_initialized = True","def _initialize_weights(self, m):\n self.w_ = np.zeros(1 + m)\n self.w_initialized = True","def initialize_weights(self, shape):\n # Our function is not convex, so initialization with zero is not helpful\n return he_weights_initialization(shape)","def init_weights(self, pretrained=None):\n\n super(EncoderDecoder_gan, self).init_weights(pretrained)\n self.backbone.init_weights(pretrained=pretrained)\n self.backbone_gan.init_weights(pretrained=pretrained)\n self.decode_head.init_weights()\n # init GAN\n # self.discriminator.init_weight()\n # self.G_head.init_weights()\n if self.with_auxiliary_head:\n if isinstance(self.auxiliary_head, nn.ModuleList):\n for aux_head in self.auxiliary_head:\n aux_head.init_weights()\n else:\n self.auxiliary_head.init_weights()","def init_weights(self):\n # We don't use the `init_weights()` function in BaseModule, since it\n # doesn't support the initialization method from `reset_parameters()`\n # in Pytorch.\n if self.with_backbone:\n self.backbone.init_weights()\n\n if self.with_neck:\n for m in self.neck.modules():\n if isinstance(m, _ConvNd) or isinstance(m, _BatchNorm):\n m.reset_parameters()\n\n if self.with_head:\n for m in self.head.modules():\n if isinstance(m, _ConvNd) or isinstance(m, _BatchNorm):\n m.reset_parameters()","def init_weights(self):\n for i in range(5):\n default_init_weights(getattr(self, f'conv{i+1}'), 0.1)","def init_weights(self):\n\n r = np.sqrt(1.) / np.sqrt(self.fc3.in_features +\n self.fc3.out_features)\n self.fc3.weight.data.uniform_(-r, r)\n self.fc3.bias.data.fill_(0)\n r = np.sqrt(1.) / np.sqrt(self.fc2.in_features +\n self.fc2.out_features)\n self.fc2.weight.data.uniform_(-r, r)\n self.fc2.bias.data.fill_(0)\n r = np.sqrt(1.) / np.sqrt(self.fc1.in_features +\n self.fc1.out_features)\n self.fc1.weight.data.uniform_(-r, r)\n self.fc1.bias.data.fill_(0)","def test_node_weight_init(self):\n n = Node(value=2.0)\n self.assertEqual(n.weights, None)","def _initialize_weights(self):\r\n for m in self.modules():\r\n if isinstance(m, nn.Conv2d):\r\n m.weight.data.normal_(0, 0.05)\r\n if m.bias is not None:\r\n m.bias.data.zero_()","def _set_weights(self):\n self.Wc = torch.mm(self.TP.T, self.W).mm(self.TP)","def init_weights(m):\n\tif type(m) == nn.Linear:\n\t\ttorch.nn.init.xavier_normal(m.weight)\n\t\tm.bias.data.fill_(0.01)","def weights_init(self, m):\n if isinstance(m, nn.Conv1d):\n torch.nn.init.xavier_uniform(m.weight.data)\n m.bias.data.fill_(0.0)\n if isinstance(m, nn.LSTM):\n torch.nn.init.xavier_uniform(m.weight_ih_l0)\n torch.nn.init.orthogonal(m.weight_hh_l0)\n for names in m._all_weights:\n for name in filter(lambda n: \"bias\" in n, names):\n bias = getattr(m, name)\n n = bias.size(0)\n start, end = n // 4, n // 2\n bias.data[start:end].fill_(1.0)\n m.bias_ih_l0.data.fill_(0.0)","def _set_weights(self, weights):\r\n self.weights = weights.reshape(self.output_size, self.input_size+1)","def weights_init(mod):\n classname = mod.__class__.__name__\n if classname.find('Conv') != -1:\n mod.weight.data.normal_(0.0, 0.02)\n elif classname.find('BatchNorm') != -1:\n mod.weight.data.normal_(1.0, 0.02)\n mod.bias.data.fill_(0)","def init_embedding(self):\n self.embedding.weight.data.uniform_(-1./self.num_embeddings, 1./self.num_embeddings)","def __init__(self):\r\n # A dummy layer does nothing\r\n self.weights = np.zeros(shape=(input.shape[1], 10))\r\n bias = np.zeros(shape=(10,))\r\n pass","def init_weights(self, sizes, init, X, Y):\n\t\tinit = init.lower()\n\n\t\tif init == 'none':\n\t\t\tpass\n\t\telif init == 'zeros':\n\t\t\tself.wts = arr([np.zeros((sizes[i + 1],sizes[i] + 1)) for i in range(len(sizes) - 1)], dtype=object)\n\t\telif init == 'random':\n\t\t\tself.wts = arr([.0025 * np.random.randn(sizes[i+1],sizes[i]+1) for i in range(len(sizes) - 1)], dtype=object)\n\t\telse:\n\t\t\traise ValueError('NNetClassify.init_weights: ' + str(init) + ' is not a valid option for init')","def init_bn(bn):\n bn.bias.data.fill_(0.0)\n bn.weight.data.fill_(1.0)","def setWeights(self, w):\n raise NotImplementedError","def initialize_parameters(self):\n for i in range(1, self.L):\n self.W[i - 1] = np.random.randn(self.layer_dims[i], self.layer_dims[i - 1]) * 0.01\n self.b[i - 1] = np.zeros((self.layer_dims[i], 1))","def __init__(self, dimensions=2):\n assert dimensions > 0\n for d in range(0,dimensions+1):\n self.weight.append(0)","def __init__(self, num_parameters=1, init=0.25):\n super(PReLU, self).__init__()\n self.num_parameters = num_parameters\n self.weight = Parameter(Tensor(num_parameters).fill_(init))","def init_weights(self):\n r = np.sqrt(6.) / np.sqrt(self.fc1.in_features +\n self.fc1.out_features)\n self.fc1.weight.data.uniform_(-r, r)\n self.fc1.bias.data.fill_(0)\n r = np.sqrt(6.) / np.sqrt(self.fc2.in_features +\n self.fc2.out_features)\n self.fc2.weight.data.uniform_(-r, r)\n self.fc2.bias.data.fill_(0)","def init_bn(bn):\n bn.bias.data.fill_(0.)\n bn.weight.data.fill_(1.)","def init_bn(bn):\n bn.bias.data.fill_(0.)\n bn.weight.data.fill_(1.)","def init_bn(bn):\n bn.bias.data.fill_(0.)\n bn.weight.data.fill_(1.)","def reset_weights(self):\n self.policy_backbone.reset_weights()\n self.value_backbone.reset_weights()\n self.action_head.reset_weights()\n self.critic_head.reset_weights()","def __initialize_weights__(num_neurons: int) -> np.ndarray:\n\n weights = np.zeros((num_neurons, num_neurons))\n\n return weights","def init_weights(self, input_size=None, min_w=WEIGHT_MIN,\n max_w=WEIGHT_MAX):\n if input_size is None:\n input_size = self.INPUT_SIZE\n\n # Add a bias weight to each neuron\n weights_per_neuron = input_size + 1\n\n self.weights = np.random.rand(self.size, weights_per_neuron) \\\n * (max_w - min_w) + min_w","def init_weights(self):\n for m in self.cls_convs:\n normal_init(m.conv, std=0.01)\n for m in self.reg_convs:\n normal_init(m.conv, std=0.01)\n normal_init(self.atss_reg, std=0.01)\n normal_init(self.atss_iou, std=0.01)\n bias_cls = bias_init_with_prob(0.01)\n normal_init(self.atss_cls, std=0.01, bias=bias_cls)","def init_weights(self, leveledinit: bool, kernel_size: int, bias: bool) -> None:\n if leveledinit:\n nn.init.normal_(self.conv1d.weight, std=1e-3)\n nn.init.normal_(self.conv1d.bias, std=1e-6)\n with torch.no_grad():\n self.conv1d.weight[:, 0, :] += 1.0 / kernel_size\n else:\n nn.init.xavier_uniform_(self.conv1d.weight)\n\n if self.embed in (\"pre\", \"post\"):\n nn.init.xavier_uniform_(self.embedding.weight)","def set_weights(self, weights):\n self._weights = weights\n self.normalize_weights() ########################added\n #self.get_weights()","def init_weights(m):\n if isinstance(m, nn.Linear):\n nn.init.normal_(m.weight, mean=0.1, std=0.01)\n nn.init.constant_(m.bias, 0.0)","def init_weights(self):\n if self.init_cfg:\n super().init_weights()\n else:\n # Use smaller std for better stability and performance. We\n # use 0.1. See more details in \"ESRGAN: Enhanced Super-Resolution\n # Generative Adversarial Networks\"\n for m in [\n self.conv_first, self.conv_body, self.conv_up1,\n self.conv_up2, self.conv_hr, self.conv_last\n ]:\n default_init_weights(m, 0.1)"],"string":"[\n \"def _initialize_weights(self):\\n pass\",\n \"def init_weights(self):\\n # Initialize weights\\n self.apply(self._init_weights)\",\n \"def setWeightInitializer(self,weights):\\n self.init_w = weights\",\n \"def init_weights(self):\\n # Initialize weights\\n self.apply(self._init_weights)\\n # Tie weights if needed\\n self.tie_weights()\",\n \"def init_weights_(self):\\n raise NotImplementedError\",\n \"def initialize_weights(self):\\n weights_initializer.WeightsInitializer.initialize_layer_or_model(\\n self._batch)\",\n \"def init_weight(self):\\n init_bn(self.norm0)\",\n \"def initialize_weights(self):\\n tf.nest.map_structure(\\n weights_initializer.WeightsInitializer.initialize_layer_or_model,\\n self._layer_nest)\",\n \"def InitWeights(self):\\n self.w = -1 + 2 * np.random.rand(self.num_of_inputs,)\\n self.w0 = -1 + 2 * np.random.rand()\",\n \"def init_weights(self):\\n self._q_neuron.h(self._weights) \\n self._q_neuron.x(self._weights)\",\n \"def init_weights(self) -> None:\\n nn.init.kaiming_normal_(self._U)\\n nn.init.kaiming_normal_(self._W)\\n nn.init.kaiming_normal_(self._V)\\n\\n nn.init.normal_(self._b)\",\n \"def init_weights(model):\\n ...\",\n \"def init_weights(self):\\n \\n self.w = np.random.randn(self.D) / np.sqrt(self.D)\",\n \"def init_weights(self):\\n self.embedding.weight.data.uniform_(-0.1, 0.1)\\n self.word.bias.data.fill_(0)\\n self.word.weight.data.uniform_(-0.1, 0.1)\",\n \"def init_weights(self, init_w=3e-3):\\n self.l3.weight.data.uniform_(-init_w, init_w)\\n self.l3.bias.data.uniform_(-init_w, init_w)\",\n \"def init_weights(self, init_w=3e-3):\\n self.l3.weight.data.uniform_(-init_w, init_w)\\n self.l3.bias.data.uniform_(-init_w, init_w)\",\n \"def init_weights(self):\\r\\n self.embedding.weight.data.uniform_(-0.1, 0.1)\\r\\n self.fc.bias.data.fill_(0)\\r\\n self.fc.weight.data.uniform_(-0.1, 0.1)\",\n \"def init_weights(self):\\n self.embedding.weight.data.uniform_(-0.1, 0.1)\\n self.fc.bias.data.fill_(0)\\n self.fc.weight.data.uniform_(-0.1, 0.1)\",\n \"def init_weights(self):\\n self.embedding.weight.data.uniform_(-0.1, 0.1)\\n self.fc.bias.data.fill_(0)\\n self.fc.weight.data.uniform_(-0.1, 0.1)\",\n \"def init_weights(self):\\n self.embedding.weight.data.uniform_(-0.1, 0.1)\\n self.fc.bias.data.fill_(0)\\n self.fc.weight.data.uniform_(-0.1, 0.1)\",\n \"def init_weights(self):\\n with torch.no_grad():\\n self._init_weights()\",\n \"def initWeights(self):\\n self.weights = []\\n self.bias = []\\n for i, dim in enumerate(self.dimensions[1:]):\\n self.weights.append(np.random.uniform(-1,1,(self.dimensions[i],dim)))\\n self.bias.append(np.random.uniform(-1,1,dim))\",\n \"def init_weights(self, load_weights=None):\\n if load_weights:\\n # TODO\\n pass\\n else:\\n # x: lower layer nodes n\\n # y: current layer nodes n\\n x = self.weights_shape[1]\\n y = self.weights_shape[0]\\n self.weights = np.random.randn(y, x) / np.sqrt(x) # pylint: disable=no-member\\n self.biases = np.random.randn(y, 1) # pylint: disable=no-member\",\n \"def default_weight_initializer(self):\\n self.biases = [np.random.randn(y, 1) for y in self.sizes[1:]]\\n self.weights = [np.random.randn(y, x)/np.sqrt(x)\\n for x, y in zip(self.sizes[:-1], self.sizes[1:])]\",\n \"def init_weights(self) -> None:\\n self.fc.bias.data.fill_(0)\\n self.fc.weight.data.uniform_(-0.1, 0.1)\",\n \"def initialize_weights(self, weights_initializer, bias_initializer):\\n wshapes = [\\n [self.input_size, self.hidden_size[0]],\\n [self.hidden_size[0], self.hidden_size[1]],\\n [self.hidden_size[1], self.output_size]\\n ]\\n\\n bshapes = [\\n [1, self.hidden_size[0]],\\n [1, self.hidden_size[1]],\\n [1, self.output_size]\\n ]\\n\\n self.weights = [init_weights(s, weights_initializer) for s in wshapes]\\n self.biases = [init_weights(s, bias_initializer) for s in bshapes]\\n\\n self.trainable_variables = self.weights + self.biases\",\n \"def initialize_weights(self):\\n for layer in self._cnn_layers:\\n weights_initializer.WeightsInitializer.initialize_layer_or_model(layer)\",\n \"def init_weights(self):\\n self.transformer.init_weights()\\n if self.loss_cls.use_sigmoid:\\n bias_init = bias_init_with_prob(0.01)\\n for m in self.cls_branches:\\n nn.init.constant_(m[-1].bias, bias_init)\",\n \"def initialize_weights(self, seed=None):\\r\\n if seed!=None:\\r\\n np.random.seed(seed)\\r\\n self.weights = np.random.randn(self.number_of_nodes,self.input_dimensions)\",\n \"def init_weight(self):\\n init_layer(self.conv1)\\n init_layer(self.conv2)\\n init_bn(self.norm1)\\n init_bn(self.norm2)\",\n \"def init_weights(self):\\r\\n constant_init(self.sampling_offsets, 0.)\\r\\n thetas = torch.arange(\\r\\n self.num_heads,\\r\\n dtype=torch.float32) * (2.0 * math.pi / self.num_heads)\\r\\n grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)\\r\\n grid_init = (grid_init /\\r\\n grid_init.abs().max(-1, keepdim=True)[0]).view(\\r\\n self.num_heads, 1, 1,\\r\\n 2).repeat(1, self.num_levels, self.num_points, 1)\\r\\n for i in range(self.num_points):\\r\\n grid_init[:, :, i, :] *= i + 1\\r\\n\\r\\n self.sampling_offsets.bias.data = grid_init.view(-1)\\r\\n constant_init(self.attention_weights, val=0., bias=0.)\\r\\n xavier_init(self.value_proj, distribution='uniform', bias=0.)\\r\\n xavier_init(self.output_proj, distribution='uniform', bias=0.)\\r\\n self._is_init = True\",\n \"def init_weights(self, pretrained=None):\\n if pretrained is not None:\\n self.pretrained = pretrained\\n self.backbone.init_weights(self.pretrained)\\n if self.with_necks:\\n for neck in self.necks:\\n if hasattr(neck, 'init_weights'):\\n neck.init_weights()\\n for head in self.heads:\\n if hasattr(head, 'init_weights'):\\n head.init_weights()\",\n \"def __init__(self, weights):\\n self._weights = weights\",\n \"def initialize_weights(self, seed=None):\\n if seed != None:\\n np.random.seed(seed)\\n self.weights = np.random.randn(self.number_of_nodes, self.input_dimensions)\",\n \"def weights_initializer(self):\\n self.weights = [np.random.normal(0, 1 / np.sqrt(x), (x, y)) for x, y in list(zip(self.structure[1:], self.structure[:-1]))]\",\n \"def _initialize_weights(self):\\n for _, cell in self.cells_and_names():\\n if isinstance(cell, nn.Conv2d):\\n cell.weight.set_data(orthogonal(cell.weight.shape, 0.6))\\n if cell.bias is not None:\\n cell.bias.set_data(\\n init.initializer(init.Constant(0.01), cell.bias.shape,\\n cell.bias.dtype))\",\n \"def init_weights(self, init_w=3e-3):\\n self.mean_linear.weight.data.uniform_(-init_w, init_w)\\n self.mean_linear.bias.data.uniform_(-init_w, init_w)\\n self.log_std_linear.weight.data.uniform_(-init_w, init_w)\\n self.log_std_linear.bias.data.uniform_(-init_w, init_w)\",\n \"def initializeWeightsToZero(self):\\n\\t\\t## YOUR CODE BELOW\\n\\t\\t\\n\\t\\tutil.raiseNotDefined()\\n\\t\\treturn\",\n \"def initialize_weights(self):\\n # compute lmda.\\n self.lmda = float(self.c)/float(self.N)\\n # bias term. This should not get a regularization penalty.\\n self.bias = {}\\n # weight vector\\n self.w = {}\\n # lastW[j] = k, indicates that feature j was last updated at time k.\\n self.lastW = {}\\n for lbl in self.labels:\\n self.bias[lbl] = 0\\n self.w[lbl] = {}\\n self.lastW[lbl] = {}\\n pass\",\n \"def init_weights(self, pretrained=None):\\n self.online_backbone.init_weights() # backbone\\n for online_neck in self.online_necks:\\n online_neck.init_weights(init_linear='kaiming') # projection\\n\\n for param_ol, param_tgt in zip(self.online_backbone.parameters(),\\n self.target_backbone.parameters()):\\n param_tgt.data.copy_(param_ol.data)\\n for param_ol, param_tgt in zip(self.online_necks.parameters(),\\n self.target_necks.parameters()):\\n param_tgt.data.copy_(param_ol.data)\\n # init the predictor in the head\\n for head in self.heads:\\n head.init_weights()\",\n \"def init_weights(self, num_features):\\n for each_label in self.valid_labels:\\n self.weights[each_label] = np.zeros(num_features)\",\n \"def init_sg_weights(self):\\n n = self.weights_shape[0] # size of current layer\\n # pylint: disable=no-member\\n A = np.random.randn(n, n) / np.sqrt(n)\\n B = np.random.randn(10, n) / np.sqrt(n)\\n C = np.random.randn(1, n) / np.sqrt(n)\\n # pylint: enable=no-member\\n self.sg_weights = [A, B, C]\",\n \"def reset_weights(self):\\n self.head.reset_weights()\",\n \"def init_weight(w):\\n shape = w.shape\\n if len(shape) == 4:\\n i, o, u, v = shape\\n k = np.sqrt(6 / (i * u * v + o * u * v))\\n w.data.uniform_(-k, k)\\n elif len(shape) == 2:\\n k = np.sqrt(6 / sum(shape))\\n w.data.uniform_(-k, k)\\n elif len(shape) == 1:\\n w.data.zero_()\",\n \"def _weight_initializer(self):\\n for m in self.modules():\\n if isinstance(m, nn.Conv2d):\\n nn.init.kaiming_normal_(m.weight)\\n elif isinstance(m, nn.BatchNorm2d):\\n nn.init.constant_(m.weight, 1.0)\\n nn.init.constant_(m.bias, 0.0)\",\n \"def init_parameters(self):\\n stdv = 1. / math.sqrt(self.weight.data.size(1))\\n self.weight.data.uniform_(-stdv, stdv)\\n if self.bias is not None:\\n self.bias.data.uniform_(-stdv, stdv)\",\n \"def _weight_initializer(self):\\n for m in self.modules():\\n if isinstance(m, nn.ConvTranspose2d):\\n nn.init.kaiming_normal_(m.weight)\\n elif isinstance(m, nn.BatchNorm2d):\\n nn.init.constant_(m.weight, 1.0)\\n nn.init.constant_(m.bias, 0.0)\",\n \"def init_weights(self, pretrained=None):\\n if pretrained is not None:\\n self.pretrained = pretrained\\n self.backbone.init_weights(self.pretrained)\\n self.mesh_head.init_weights()\\n if self.with_gan:\\n self.discriminator.init_weights()\",\n \"def weights_init(m):\\n if type(m) == torch.nn.Linear:\\n m.weight.data.normal_(0.0, 0.02)\\n m.bias.data.fill_(0)\",\n \"def init_weights(self):\\n r = np.sqrt(6.) / np.sqrt(self.fc.in_features +\\n self.fc.out_features)\\n self.fc.weight.data.uniform_(-r, r)\\n self.fc.bias.data.fill_(0)\",\n \"def init_weights(self):\\n r = np.sqrt(6.) / np.sqrt(self.fc.in_features +\\n self.fc.out_features)\\n self.fc.weight.data.uniform_(-r, r)\\n self.fc.bias.data.fill_(0)\",\n \"def init_weights(self, dims):\\n self.W = np.random.normal(size=dims) * 0.0001\",\n \"def __init__(self, weights:np.ndarray):\\n self.w = weights.copy()\",\n \"def _set_initial_weights(self):\\n weights = {'h0': None, 'y': None}\\n weights['h0'] = np.array([[-0.6, 0.4, 0.5], [-0.2, 0.8, 0.8]])\\n weights['y'] = np.array([[-0.3, -0.4, 0.9]])\\n \\n return weights\",\n \"def init_weights(self, pretrained):\\n if self.with_shared_head:\\n self.shared_head.init_weights(pretrained=pretrained)\\n for i in range(1):\\n if self.with_bbox:\\n self.bbox_roi_extractor[i].init_weights()\\n self.bbox_head[i].init_weights()\\n if self.with_rbbox:\\n self.rbbox_roi_extractor[i].init_weights()\\n self.rbbox_head[i].init_weights()\\n if self.with_mask:\\n if not self.share_roi_extractor:\\n self.mask_roi_extractor[i].init_weights()\\n self.mask_head[i].init_weights()\",\n \"def _init_weights(self):\\n nn.init.xavier_normal_(self.out.weight)\",\n \"def _init_weights(module: nn.Module, name: str, head_bias: float = 0., flax=False):\\n if isinstance(module, nn.Linear):\\n if name.startswith('head'):\\n nn.init.zeros_(module.weight)\\n nn.init.constant_(module.bias, head_bias)\\n else:\\n if flax:\\n # Flax defaults\\n lecun_normal_(module.weight)\\n if module.bias is not None:\\n nn.init.zeros_(module.bias)\\n else:\\n # like MLP init in vit (my original init)\\n nn.init.xavier_uniform_(module.weight)\\n if module.bias is not None:\\n if 'mlp' in name:\\n nn.init.normal_(module.bias, std=1e-6)\\n else:\\n nn.init.zeros_(module.bias)\\n elif isinstance(module, nn.Conv2d):\\n lecun_normal_(module.weight)\\n if module.bias is not None:\\n nn.init.zeros_(module.bias)\\n elif isinstance(module, (nn.LayerNorm, nn.BatchNorm2d, nn.GroupNorm)):\\n nn.init.ones_(module.weight)\\n nn.init.zeros_(module.bias)\\n elif hasattr(module, 'init_weights'):\\n # NOTE if a parent module contains init_weights method, it can override the init of the\\n # child modules as this will be called in depth-first order.\\n module.init_weights()\",\n \"def set_weights(self, weights):\\r\\n self.weights = weights\",\n \"def init_weights(self):\\n\\n params = torch.load(self.resnet_weight)\\n\\n self.fc1.weight.data = params['state_dict']['module.fc.weight'].clone()\\n self.fc1.bias.data = params['state_dict']['module.fc.bias'].clone()\\n\\n\\n r = np.sqrt(1.) / np.sqrt(self.fc3.in_features +\\n self.fc3.out_features)\\n self.fc3.weight.data.uniform_(-r, r)\\n self.fc3.bias.data.fill_(0)\\n r = np.sqrt(1.) / np.sqrt(self.fc2.in_features +\\n self.fc2.out_features)\\n self.fc2.weight.data.uniform_(-r, r)\\n self.fc2.bias.data.fill_(0)\\n r = np.sqrt(1.) / np.sqrt(self.fc4.in_features +\\n self.fc4.out_features)\\n self.fc4.weight.data.uniform_(-r, r)\\n self.fc4.bias.data.fill_(0)\",\n \"def init_weights(self, pretrained=None):\\n super(SingleTwoStageDetector176PRW, self).init_weights(pretrained)\\n self.backbone.init_weights(pretrained=pretrained)\\n if self.with_neck:\\n if isinstance(self.neck, nn.Sequential):\\n for m in self.neck:\\n m.init_weights()\\n else:\\n self.neck.init_weights()\\n if self.with_rpn:\\n self.rpn_head.init_weights()\\n if self.with_roi_head:\\n self.roi_head.init_weights(pretrained)\\n self.bbox_head.init_weights()\",\n \"def init_weights(self) -> None:\\n super().init_weights()\\n kaiming_init(self.convs_all_levels, a=1, distribution='uniform')\\n kaiming_init(self.conv_branch, a=1, distribution='uniform')\\n kaiming_init(self.conv_pred, a=1, distribution='uniform')\",\n \"def _init_weights(self):\\n for layer in self.modules():\\n if isinstance(layer, (nn.Conv1d, nn.Linear)):\\n nn.init.xavier_uniform_(layer.weight)\\n if layer.bias is not None:\\n nn.init.constant_(layer.bias, 0)\\n elif isinstance(layer, nn.BatchNorm1d):\\n nn.init.constant_(layer.weight, 1)\\n nn.init.constant_(layer.bias, 0)\",\n \"def _init_weights(self):\\n for layer in self.modules():\\n if isinstance(layer, (nn.Conv1d, nn.Linear)):\\n nn.init.xavier_uniform_(layer.weight)\\n if layer.bias is not None:\\n nn.init.constant_(layer.bias, 0)\\n elif isinstance(layer, nn.BatchNorm1d):\\n nn.init.constant_(layer.weight, 1)\\n nn.init.constant_(layer.bias, 0)\",\n \"def _init_weights(self):\\n for layer in self.modules():\\n if isinstance(layer, (nn.Conv1d, nn.Linear)):\\n nn.init.xavier_uniform_(layer.weight)\\n if layer.bias is not None:\\n nn.init.constant_(layer.bias, 0)\\n elif isinstance(layer, nn.BatchNorm1d):\\n nn.init.constant_(layer.weight, 1)\\n nn.init.constant_(layer.bias, 0)\",\n \"def _initialize_weights(self, inputs):\\n if self.data_init:\\n self._data_dep_init(inputs)\\n else:\\n self._init_norm()\\n self._initialized = True\",\n \"def _initialize_weights(self, m):\\n self.w_ = np.zeros(1 + m)\\n self.w_initialized = True\",\n \"def _initialize_weights(self, m):\\n self.w_ = np.zeros(1 + m)\\n self.w_initialized = True\",\n \"def _initialize_weights(self, m):\\n self.w_ = np.zeros(1 + m)\\n self.w_initialized = True\",\n \"def initialize_weights(self, shape):\\n # Our function is not convex, so initialization with zero is not helpful\\n return he_weights_initialization(shape)\",\n \"def init_weights(self, pretrained=None):\\n\\n super(EncoderDecoder_gan, self).init_weights(pretrained)\\n self.backbone.init_weights(pretrained=pretrained)\\n self.backbone_gan.init_weights(pretrained=pretrained)\\n self.decode_head.init_weights()\\n # init GAN\\n # self.discriminator.init_weight()\\n # self.G_head.init_weights()\\n if self.with_auxiliary_head:\\n if isinstance(self.auxiliary_head, nn.ModuleList):\\n for aux_head in self.auxiliary_head:\\n aux_head.init_weights()\\n else:\\n self.auxiliary_head.init_weights()\",\n \"def init_weights(self):\\n # We don't use the `init_weights()` function in BaseModule, since it\\n # doesn't support the initialization method from `reset_parameters()`\\n # in Pytorch.\\n if self.with_backbone:\\n self.backbone.init_weights()\\n\\n if self.with_neck:\\n for m in self.neck.modules():\\n if isinstance(m, _ConvNd) or isinstance(m, _BatchNorm):\\n m.reset_parameters()\\n\\n if self.with_head:\\n for m in self.head.modules():\\n if isinstance(m, _ConvNd) or isinstance(m, _BatchNorm):\\n m.reset_parameters()\",\n \"def init_weights(self):\\n for i in range(5):\\n default_init_weights(getattr(self, f'conv{i+1}'), 0.1)\",\n \"def init_weights(self):\\n\\n r = np.sqrt(1.) / np.sqrt(self.fc3.in_features +\\n self.fc3.out_features)\\n self.fc3.weight.data.uniform_(-r, r)\\n self.fc3.bias.data.fill_(0)\\n r = np.sqrt(1.) / np.sqrt(self.fc2.in_features +\\n self.fc2.out_features)\\n self.fc2.weight.data.uniform_(-r, r)\\n self.fc2.bias.data.fill_(0)\\n r = np.sqrt(1.) / np.sqrt(self.fc1.in_features +\\n self.fc1.out_features)\\n self.fc1.weight.data.uniform_(-r, r)\\n self.fc1.bias.data.fill_(0)\",\n \"def test_node_weight_init(self):\\n n = Node(value=2.0)\\n self.assertEqual(n.weights, None)\",\n \"def _initialize_weights(self):\\r\\n for m in self.modules():\\r\\n if isinstance(m, nn.Conv2d):\\r\\n m.weight.data.normal_(0, 0.05)\\r\\n if m.bias is not None:\\r\\n m.bias.data.zero_()\",\n \"def _set_weights(self):\\n self.Wc = torch.mm(self.TP.T, self.W).mm(self.TP)\",\n \"def init_weights(m):\\n\\tif type(m) == nn.Linear:\\n\\t\\ttorch.nn.init.xavier_normal(m.weight)\\n\\t\\tm.bias.data.fill_(0.01)\",\n \"def weights_init(self, m):\\n if isinstance(m, nn.Conv1d):\\n torch.nn.init.xavier_uniform(m.weight.data)\\n m.bias.data.fill_(0.0)\\n if isinstance(m, nn.LSTM):\\n torch.nn.init.xavier_uniform(m.weight_ih_l0)\\n torch.nn.init.orthogonal(m.weight_hh_l0)\\n for names in m._all_weights:\\n for name in filter(lambda n: \\\"bias\\\" in n, names):\\n bias = getattr(m, name)\\n n = bias.size(0)\\n start, end = n // 4, n // 2\\n bias.data[start:end].fill_(1.0)\\n m.bias_ih_l0.data.fill_(0.0)\",\n \"def _set_weights(self, weights):\\r\\n self.weights = weights.reshape(self.output_size, self.input_size+1)\",\n \"def weights_init(mod):\\n classname = mod.__class__.__name__\\n if classname.find('Conv') != -1:\\n mod.weight.data.normal_(0.0, 0.02)\\n elif classname.find('BatchNorm') != -1:\\n mod.weight.data.normal_(1.0, 0.02)\\n mod.bias.data.fill_(0)\",\n \"def init_embedding(self):\\n self.embedding.weight.data.uniform_(-1./self.num_embeddings, 1./self.num_embeddings)\",\n \"def __init__(self):\\r\\n # A dummy layer does nothing\\r\\n self.weights = np.zeros(shape=(input.shape[1], 10))\\r\\n bias = np.zeros(shape=(10,))\\r\\n pass\",\n \"def init_weights(self, sizes, init, X, Y):\\n\\t\\tinit = init.lower()\\n\\n\\t\\tif init == 'none':\\n\\t\\t\\tpass\\n\\t\\telif init == 'zeros':\\n\\t\\t\\tself.wts = arr([np.zeros((sizes[i + 1],sizes[i] + 1)) for i in range(len(sizes) - 1)], dtype=object)\\n\\t\\telif init == 'random':\\n\\t\\t\\tself.wts = arr([.0025 * np.random.randn(sizes[i+1],sizes[i]+1) for i in range(len(sizes) - 1)], dtype=object)\\n\\t\\telse:\\n\\t\\t\\traise ValueError('NNetClassify.init_weights: ' + str(init) + ' is not a valid option for init')\",\n \"def init_bn(bn):\\n bn.bias.data.fill_(0.0)\\n bn.weight.data.fill_(1.0)\",\n \"def setWeights(self, w):\\n raise NotImplementedError\",\n \"def initialize_parameters(self):\\n for i in range(1, self.L):\\n self.W[i - 1] = np.random.randn(self.layer_dims[i], self.layer_dims[i - 1]) * 0.01\\n self.b[i - 1] = np.zeros((self.layer_dims[i], 1))\",\n \"def __init__(self, dimensions=2):\\n assert dimensions > 0\\n for d in range(0,dimensions+1):\\n self.weight.append(0)\",\n \"def __init__(self, num_parameters=1, init=0.25):\\n super(PReLU, self).__init__()\\n self.num_parameters = num_parameters\\n self.weight = Parameter(Tensor(num_parameters).fill_(init))\",\n \"def init_weights(self):\\n r = np.sqrt(6.) / np.sqrt(self.fc1.in_features +\\n self.fc1.out_features)\\n self.fc1.weight.data.uniform_(-r, r)\\n self.fc1.bias.data.fill_(0)\\n r = np.sqrt(6.) / np.sqrt(self.fc2.in_features +\\n self.fc2.out_features)\\n self.fc2.weight.data.uniform_(-r, r)\\n self.fc2.bias.data.fill_(0)\",\n \"def init_bn(bn):\\n bn.bias.data.fill_(0.)\\n bn.weight.data.fill_(1.)\",\n \"def init_bn(bn):\\n bn.bias.data.fill_(0.)\\n bn.weight.data.fill_(1.)\",\n \"def init_bn(bn):\\n bn.bias.data.fill_(0.)\\n bn.weight.data.fill_(1.)\",\n \"def reset_weights(self):\\n self.policy_backbone.reset_weights()\\n self.value_backbone.reset_weights()\\n self.action_head.reset_weights()\\n self.critic_head.reset_weights()\",\n \"def __initialize_weights__(num_neurons: int) -> np.ndarray:\\n\\n weights = np.zeros((num_neurons, num_neurons))\\n\\n return weights\",\n \"def init_weights(self, input_size=None, min_w=WEIGHT_MIN,\\n max_w=WEIGHT_MAX):\\n if input_size is None:\\n input_size = self.INPUT_SIZE\\n\\n # Add a bias weight to each neuron\\n weights_per_neuron = input_size + 1\\n\\n self.weights = np.random.rand(self.size, weights_per_neuron) \\\\\\n * (max_w - min_w) + min_w\",\n \"def init_weights(self):\\n for m in self.cls_convs:\\n normal_init(m.conv, std=0.01)\\n for m in self.reg_convs:\\n normal_init(m.conv, std=0.01)\\n normal_init(self.atss_reg, std=0.01)\\n normal_init(self.atss_iou, std=0.01)\\n bias_cls = bias_init_with_prob(0.01)\\n normal_init(self.atss_cls, std=0.01, bias=bias_cls)\",\n \"def init_weights(self, leveledinit: bool, kernel_size: int, bias: bool) -> None:\\n if leveledinit:\\n nn.init.normal_(self.conv1d.weight, std=1e-3)\\n nn.init.normal_(self.conv1d.bias, std=1e-6)\\n with torch.no_grad():\\n self.conv1d.weight[:, 0, :] += 1.0 / kernel_size\\n else:\\n nn.init.xavier_uniform_(self.conv1d.weight)\\n\\n if self.embed in (\\\"pre\\\", \\\"post\\\"):\\n nn.init.xavier_uniform_(self.embedding.weight)\",\n \"def set_weights(self, weights):\\n self._weights = weights\\n self.normalize_weights() ########################added\\n #self.get_weights()\",\n \"def init_weights(m):\\n if isinstance(m, nn.Linear):\\n nn.init.normal_(m.weight, mean=0.1, std=0.01)\\n nn.init.constant_(m.bias, 0.0)\",\n \"def init_weights(self):\\n if self.init_cfg:\\n super().init_weights()\\n else:\\n # Use smaller std for better stability and performance. We\\n # use 0.1. See more details in \\\"ESRGAN: Enhanced Super-Resolution\\n # Generative Adversarial Networks\\\"\\n for m in [\\n self.conv_first, self.conv_body, self.conv_up1,\\n self.conv_up2, self.conv_hr, self.conv_last\\n ]:\\n default_init_weights(m, 0.1)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8231931","0.8066145","0.8041016","0.8037346","0.79638714","0.78825206","0.7794443","0.7718116","0.76905215","0.76740956","0.7660412","0.76257116","0.76194113","0.75636816","0.7554319","0.7554319","0.75442475","0.75423366","0.75423366","0.75423366","0.75293475","0.74815124","0.7450458","0.7446465","0.7420917","0.7401905","0.73730576","0.7350111","0.73493385","0.7331179","0.7329561","0.7292371","0.72760737","0.72708786","0.7267815","0.726247","0.7230158","0.7228281","0.72115237","0.71850204","0.71652776","0.71649516","0.7158523","0.7146802","0.71372926","0.7135884","0.7132714","0.7110758","0.7091838","0.7078908","0.7078908","0.7067926","0.7045233","0.7028495","0.702795","0.70110863","0.70000243","0.6981812","0.6970271","0.69604796","0.695625","0.69179493","0.69179493","0.69179493","0.6893862","0.6889514","0.6889514","0.6889514","0.6869791","0.6869462","0.6867456","0.68550766","0.684829","0.679995","0.67862374","0.6769511","0.6755151","0.67410684","0.6722867","0.67045915","0.66987455","0.66966736","0.6693988","0.6690312","0.6678647","0.6675876","0.66714656","0.6669032","0.66630095","0.6660276","0.6660276","0.6660276","0.66592854","0.6655593","0.66531","0.6649832","0.66352826","0.66333497","0.6627004","0.6626462"],"string":"[\n \"0.8231931\",\n \"0.8066145\",\n \"0.8041016\",\n \"0.8037346\",\n \"0.79638714\",\n \"0.78825206\",\n \"0.7794443\",\n \"0.7718116\",\n \"0.76905215\",\n \"0.76740956\",\n \"0.7660412\",\n \"0.76257116\",\n \"0.76194113\",\n \"0.75636816\",\n \"0.7554319\",\n \"0.7554319\",\n \"0.75442475\",\n \"0.75423366\",\n \"0.75423366\",\n \"0.75423366\",\n \"0.75293475\",\n \"0.74815124\",\n \"0.7450458\",\n \"0.7446465\",\n \"0.7420917\",\n \"0.7401905\",\n \"0.73730576\",\n \"0.7350111\",\n \"0.73493385\",\n \"0.7331179\",\n \"0.7329561\",\n \"0.7292371\",\n \"0.72760737\",\n \"0.72708786\",\n \"0.7267815\",\n \"0.726247\",\n \"0.7230158\",\n \"0.7228281\",\n \"0.72115237\",\n \"0.71850204\",\n \"0.71652776\",\n \"0.71649516\",\n \"0.7158523\",\n \"0.7146802\",\n \"0.71372926\",\n \"0.7135884\",\n \"0.7132714\",\n \"0.7110758\",\n \"0.7091838\",\n \"0.7078908\",\n \"0.7078908\",\n \"0.7067926\",\n \"0.7045233\",\n \"0.7028495\",\n \"0.702795\",\n \"0.70110863\",\n \"0.70000243\",\n \"0.6981812\",\n \"0.6970271\",\n \"0.69604796\",\n \"0.695625\",\n \"0.69179493\",\n \"0.69179493\",\n \"0.69179493\",\n \"0.6893862\",\n \"0.6889514\",\n \"0.6889514\",\n \"0.6889514\",\n \"0.6869791\",\n \"0.6869462\",\n \"0.6867456\",\n \"0.68550766\",\n \"0.684829\",\n \"0.679995\",\n \"0.67862374\",\n \"0.6769511\",\n \"0.6755151\",\n \"0.67410684\",\n \"0.6722867\",\n \"0.67045915\",\n \"0.66987455\",\n \"0.66966736\",\n \"0.6693988\",\n \"0.6690312\",\n \"0.6678647\",\n \"0.6675876\",\n \"0.66714656\",\n \"0.6669032\",\n \"0.66630095\",\n \"0.6660276\",\n \"0.6660276\",\n \"0.6660276\",\n \"0.66592854\",\n \"0.6655593\",\n \"0.66531\",\n \"0.6649832\",\n \"0.66352826\",\n \"0.66333497\",\n \"0.6627004\",\n \"0.6626462\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.66495466"},"document_rank":{"kind":"string","value":"96"}}},{"rowIdx":84190,"cells":{"query":{"kind":"string","value":"Forward feature of a single scale level."},"document":{"kind":"string","value":"def forward_single(self, x):\n cls_feat = x\n reg_feat = x\n for cls_conv in self.cls_convs:\n cls_feat = cls_conv(cls_feat)\n for reg_conv in self.reg_convs:\n reg_feat = reg_conv(reg_feat)\n cls_score = self.retina_cls(cls_feat)\n bbox_pred = self.retina_reg(reg_feat)\n return cls_score, bbox_pred"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def feature_forward(self, x):\n raise NotImplementedError","def forward(self, x):\n x = self.features(x)\n return x","def forward_single(self, x, scale):\n cls_feat = x\n reg_feat = x\n for cls_conv in self.cls_convs:\n cls_feat = cls_conv(cls_feat)\n for reg_conv in self.reg_convs:\n reg_feat = reg_conv(reg_feat)\n cls_score = self.atss_cls(cls_feat)\n # we just follow atss, not apply exp in bbox_pred\n bbox_pred = scale(self.atss_reg(reg_feat)).float()\n iou_pred = self.atss_iou(reg_feat)\n return cls_score, bbox_pred, iou_pred","def forward(self, x):\n raise NotImplementedError","def forward(self, x):\n raise NotImplementedError","def forward(self, x, scale_param):\n reg_arr = torch.exp(scale_param * self.reg_conv(self.reg_branch(x)))\n label_branch_arr = self.label_branch(x)\n label_arr = self.label_conv(label_branch_arr)\n center_arr = self.center_conv(label_branch_arr)\n return (reg_arr, label_arr, center_arr)","def forward_features(self, x):\n x_size = (x.shape[2], x.shape[3])\n x = self.patch_embed(x)\n if self.ape:\n x = x + self.absolute_pos_embed\n x = self.pos_drop(x)\n\n for layer in self.layers:\n x = layer(x, x_size)\n\n x = self.norm(x) # B L C\n x = self.patch_unembed(x, x_size)\n\n return x","def forward(self, x): # pylint: disable=invalid-name\n x = self.layer4(self.layer3(self.layer2(self.layer1(x))))\n return x.mean((-2, -1))","def forward(self, x: Tuple[Tensor]) -> Tensor:\n inputs = x[self.start_level:self.end_level + 1]\n assert len(inputs) == (self.end_level - self.start_level + 1)\n feature_add_all_level = self.convs_all_levels[0](inputs[0])\n target_h, target_w = feature_add_all_level.size()[2:]\n for i in range(1, len(inputs)):\n input_p = inputs[i]\n x_p = self.convs_all_levels[i](input_p)\n h, w = x_p.size()[2:]\n factor_h = target_h // h\n factor_w = target_w // w\n assert factor_h == factor_w\n feature_per_level = aligned_bilinear(x_p, factor_h)\n feature_add_all_level = feature_add_all_level + \\\n feature_per_level\n\n feature_add_all_level = self.conv_branch(feature_add_all_level)\n feature_pred = self.conv_pred(feature_add_all_level)\n return feature_pred","def forward(self, x):\n #print('output of fetures.children() : %s'%str([i for i in self.features.children()]))\n #print(\"shape of input is %s\" % str(x.size()))\n for layer_no, layer in enumerate(self.features.children()):\n\n if layer_no is 23:\n y = layer(x)\n if layer_no is 33:\n z = layer(x)\n x = layer(x)\n\n #print('debug')\n #print('layer info: %s'%str(layer))\n #print(\"shape of x is %s\" % str(x.size()))\n\n x = self.conv1D_downstream1(x)\n x = self.conv1D_downstream2(x)\n x = self.upsample_1(x)\n\n z = self.conv1D_pool4(z)\n y = self.conv1D_pool3(y)\n #print('debug')\n #print(\"shape of x is %s\"%str(x.size()))\n #print(\"shape of z is %s\" % str(z.size()))\n\n if x.size() is not z.size():\n x = nn.functional.interpolate(x,size = (z.size()[2],z.size()[3]), mode = 'nearest')\n x = x+ z\n x = self.upsample_2(x)\n x = x+y\n x = self.upsample_3(x)\n\n return x","def forward(self, x):\n # Compute the mean norm of activations per channel.\n nu2 = x.pow(2).mean(dim=[2, 3], keepdim=True)\n\n # Perform FRN.\n x = x * torch.rsqrt(nu2 + self.eps.abs())\n\n # Scale and Bias\n if self.is_scale:\n x = self.weight * x\n if self.is_bias:\n x = x + self.bias\n return x","def forward(self, x):\n return self.layers(x)","def forward(self, x):\n levels = self.levels\n expanded_levels = levels[None, :]\n\n samples = self.est.forward(x)\n # this product is of shape (N,K) for N variables and K levels\n # it is then contracted over the levels axis\n # TODO: this can be done with tensordot / sum_of_2d_modes?\n tmp = (samples * expanded_levels)\n self.tmpshape = tmp.shape\n return tmp.sum(axis=-1)","def forward(self, x):\n pass","def scale(x, feature_range=(-1,1)):\r\n x = x * 2 - 1\r\n return x","def forward(self, x, normalize=False):\n if normalize:\n x = (x - self.state_means) / torch.sqrt(self.state_vars)\n\n x = self.features(x)\n return x","def forward(self, x):\n for i, layer in enumerate(self.layers):\n x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)\n return x.squeeze(0)","def base_forward(self, x):\r\n pass","def forward(self, x: torch.Tensor) -> torch.Tensor:\n x = self.cnn.extractor.forward(x)\n return self.cnn.regressor(x)","def forward(self, x):\n x = self.sparseModel(x)\n x = self.linear(x)\n return x","def forward(self, x):\n x = self.lin(x)\n\n return x","def forward(self, x: torch.Tensor, dim: int = 0, p: int = 1):\n raise NotImplementedError","def forward_single(self, x: Tensor, scale: Scale) -> Sequence[Tensor]:\n cls_feat = x\n reg_feat = x\n for cls_conv in self.cls_convs:\n cls_feat = cls_conv(cls_feat)\n for reg_conv in self.reg_convs:\n reg_feat = reg_conv(reg_feat)\n cls_score = self.atss_cls(cls_feat)\n # we just follow atss, not apply exp in bbox_pred\n bbox_pred = scale(self.atss_reg(reg_feat)).float()\n centerness = self.atss_centerness(reg_feat)\n return cls_score, bbox_pred, centerness","def addforwardscale(self):\n m_graph = nx.DiGraph()\n # Construct the graph with connections\n for u in range(self.nodummy_nodes):\n for v in range(self.nodummy_nodes):\n if (self.nodummyconnection[u, v] != 0):\n m_graph.add_edge(self.nodummyvariablelist[v],\n self.nodummyvariablelist[u],\n weight=self.nodummygain[u, v])\n # Add connections where out degree == 1\n counter = 1\n for node in m_graph.nodes():\n if m_graph.out_degree(node) == 1:\n nameofscale = 'DV' + str(counter)\n m_graph.add_edge(node, nameofscale, weight=1.0)\n counter = counter + 1\n\n self.scaledforwardconnection = transpose(\n nx.to_numpy_matrix(m_graph, weight=None))\n self.scaledforwardgain = transpose(\n nx.to_numpy_matrix(m_graph, weight='weight'))\n self.scaledforwardvariablelist = m_graph.nodes()","def forward(self, x: tuple, positive_infos: InstanceList) -> tuple:\n mask_feats = self.mask_feature_head(x)\n return multi_apply(self.forward_single, mask_feats, positive_infos)","def forward(self, x: Tensor, **kwargs) -> Tensor:\n if self.scale:\n return self.lut(x) * math.sqrt(self.embedding_dim)\n return self.lut(x)","def forward(self, x, **kwargs):\n pass","def forward(self, x):\n return self.l1(x)","def forward(self, x):\n # The scaled feature maps for each interim layer\n scaled_features = OrderedDict()\n for (_, module), (layer_name, features) in zip(self.adaptor.items(), x.items()):\n patches_per_axis = int(sqrt(features.shape[1]))\n # Reshape to 2d and reorder dimensions to traditional convolution dims. (B, C, H, W)\n features_2d = features.reshape(features.shape[0], patches_per_axis, patches_per_axis, features.shape[2]) \\\n .permute(0, 3, 1, 2)\n\n scaled_features[layer_name] = module(features_2d)\n\n return scaled_features","def forward(self, x: Tuple[Tensor]) -> Tuple[List[Tensor]]:\n return multi_apply(self.forward_single, x, self.scales)","def forward(self, x):\n return x","def forward(self, x):\n flows_forward, flows_backward = self.get_flow(x)\n b, n, _, h, w = x.size()\n\n # backward branch\n out_l = []\n feat_prop = x.new_zeros(b, self.num_feat, h, w)\n for i in range(n - 1, -1, -1):\n x_i = x[:, i, :, :, :]\n if i < n - 1:\n flow = flows_backward[:, i, :, :, :]\n feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1))\n feat_prop = torch.cat([x_i, feat_prop], dim=1)\n feat_prop = self.backward_trunk(feat_prop)\n out_l.insert(0, feat_prop)\n\n # forward branch\n feat_prop = torch.zeros_like(feat_prop)\n for i in range(0, n):\n x_i = x[:, i, :, :, :]\n if i > 0:\n flow = flows_forward[:, i - 1, :, :, :]\n feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1))\n\n feat_prop = torch.cat([x_i, feat_prop], dim=1)\n feat_prop = self.forward_trunk(feat_prop)\n\n # upsample\n out = torch.cat([out_l[i], feat_prop], dim=1)\n out = self.lrelu(self.fusion(out))\n out = self.lrelu(self.pixel_shuffle(self.upconv1(out)))\n out = self.lrelu(self.pixel_shuffle(self.upconv2(out)))\n out = self.lrelu(self.conv_hr(out))\n out = self.conv_last(out)\n base = F.interpolate(x_i, scale_factor=4, mode='bilinear', align_corners=False)\n out += base\n out_l[i] = out\n\n return torch.stack(out_l, dim=1)","def forward(self, x):\n return self.activation_function(self.backbone_model(x))","def forward(self, x):\n\t\t#######################################################################\n\t\t# ** START OF YOUR CODE **\n\t\t#######################################################################\n\n\t\toutput = self._layers[0].forward(x)\n\t\tfor i in range(1, len(self._layers)):\n\t\t\toutput = self._layers[i].forward(output)\n\t\treturn output\n\n\t\t#######################################################################\n\t\t# ** END OF YOUR CODE **\n\t\t#######################################################################","def forward(self, x):\n\n ########################\n # PUT YOUR CODE HERE #\n #######################\n for l in range(len(self.layers)):\n if l == 0:\n z = self.layers[l].forward(x)\n else:\n z = self.layers[l].forward(a)\n a = self.activations[l].forward(z)\n\n # output from softmax layer\n out = a\n\n ########################\n # END OF YOUR CODE #\n #######################\n\n return out","def forward(self, x):\n in_num=self.layers[1].in_features\n out_num=self.layers[-1].out_features\n x_new=x.detach().clone()\n for i in range(in_num):\n x_new[:,i]=(x[:,i] - self.beta[i]) * self.alpha[i] \n \n y=self.layers(x_new)\n for i in range(out_num):\n y[:,i]=(y[:,i] / self.alpha[in_num+i]) + self.beta[in_num+i]\n return y","def forward(self, x):\n out = self.features(x)\n out = self.avgpool(out)\n out = torch.flatten(out, 1)\n out = self.classifier(out)\n return out","def forward(self, x):\n out = self.features(x)\n out = self.avgpool(out)\n out = torch.flatten(out, 1)\n out = self.classifier(out)\n return out","def forward(self, x: torch.Tensor) -> torch.Tensor:\n out = self.embedding(x) * self.scale\n return out","def forward(self, x):\n x = self.features(x)\n x = x.view(x.size(0), 256 * 6 * 6)\n x = self.classifier(x)\n return x","def _forward(self, x, X, upto=None):\n if upto is not None: # cannot use 'if upto' here since it is 0-indexed\n # and layer0 is the first layer\n assert 0<=upto<=self._layer_counter\n counter = upto + 1\n else: counter = self._layer_counter\n\n y_previous, Y_previous = x, X\n # TODO: because we always need to compute F_i(X) at each layer i, this\n # is a huge overhead\n # feedforward\n for i in range(counter):\n layer = getattr(self, 'layer'+str(i))\n y, Y = layer(y_previous, Y_previous), layer(Y_previous, Y_previous)\n y_previous, Y_previous = y, Y\n\n return y","def forward(self, feat_T: torch.Tensor) -> torch.Tensor:\n return self.loss_weight * self.forward_train(feat_T)","def _forward(self, X, **kwargs):\n raise NotImplementedError()","def forward(self, x):\n out = self.features(x)\n out = out.view(out.size(0), -1)\n out = self.classifier(out)\n return out","def forward(self, X: Tensor) -> Tensor:\n if self.training:\n if self.transform_on_train:\n return self.transform(X)\n elif self.transform_on_eval:\n if fantasize.off() or self.transform_on_fantasize:\n return self.transform(X)\n return X","def forward(self, x: Tensor) -> Tensor: # type: ignore\n x = self.backbone(x)\n x = x.view(x.size(0), -1)\n if self.head_layers is not None:\n out = self.imagehead(x)\n return out\n else:\n return x","def forward(self, x, dt, do_normalization=True):\n raise NotImplementedError","def forward_tensor(self, x):\n pass","def forward(self, x):\n x = x.permute(0, 3, 1, 2) # [batch_size*num_slots, features, height, width]\n x = self.dec(x)\n return x","def forward(self, x):\n x = x.permute(0, 3, 1, 2) # [batch_size*num_slots, features, height, width]\n x = self.dec(x)\n return x","def layer_forward_prop(self):\n raise NotImplementedError(\"Must be created in child classes\")","def forward(self, x: torch.Tensor, x_mask: torch.Tensor, proto: torch.Tensor = None) -> torch.Tensor:\n feature = self.features(x, x_mask)\n if len(self.old_cols) > 0:\n with torch.no_grad():\n fc1_kb = [old(feature) for old in self.old_fc1s]\n feature = F.relu(self.fc1(feature))\n y = self.adaptor1(torch.cat(fc1_kb, 1))\n out = self.classifier(feature, proto) + y\n else:\n feature = F.relu(self.fc1(feature))\n out = self.classifier(feature, proto)\n return out","def forward(self, x, dt, do_normalization=True):\n return x","def forward(self, x):\n residues = []\n # Downward Pass\n x = self.layers[0](x.unsqueeze(1))\n for layer in self.layers[1:self.half]:\n x = layer(x)\n residues.insert(0, x)\n\n # Upward Pass\n for idx, layer in enumerate(self.layers[self.half:(len(self.layers)-1)]):\n x = layer(x, residues[idx])\n x = self.layers[-1](x)\n\n return(x)","def forward(self, x):\n out = self.pre_processing(x)\n out = self.a3(out)\n out = self.b3(out)\n out = self.maxpool(out)\n out = self.a4(out)\n out = self.b4(out)\n out = self.c4(out)\n out = self.d4(out)\n out = self.e4(out)\n out = self.maxpool(out)\n out = self.a5(out)\n out = self.b5(out)\n out = self.avgpool(out)\n out = out.view(out.size(0), -1) # reshape the output tensor\n out = self.linear(out)\n\n return out","def forward(self, X):\r\n N = X.size()[0]\r\n assert X.size() == (N, 3, 448, 448)\r\n X = self.features(X)\r\n assert X.size() == (N, 512, 28, 28)\r\n X = X.view(N, 512, 28**2)\r\n X = torch.bmm(X, torch.transpose(X, 1, 2)) / (28**2) # Bilinear\r\n assert X.size() == (N, 512, 512)\r\n X = X.view(N, 512**2)\r\n X = torch.sign(X)*torch.sqrt(torch.abs(X)+1e-12)\r\n # X = torch.sqrt(X + 1e-5)\r\n X = torch.nn.functional.normalize(X)\r\n X = self.fc(X)\r\n assert X.size() == (N, 11)\r\n return X","def forward(self, x: torch.Tensor) -> torch.Tensor:\n return self.model(x)","def forward(self, x):\n dims = list(range(1, len(x.shape)))\n mean = x.mean(dim=dims, keepdim=True)\n var = torch.pow(x - mean, 2).mean(dim=dims, keepdim=True)\n return self.apply_gain_and_bias((x - mean) / (var + EPS).sqrt())","def forward(self, feats):\n return multi_apply(self.forward_single, feats, self.scales)","def forward(self, x):\r\n x = x.reshape(x.shape[0], x.shape[1], 1 , 1)\r\n x = self.input(x)\r\n x = self.bn(x)\r\n x = F.relu(x)\r\n for i in range(len(self.DV)-1, -1, -1):\r\n x = self.DV[i](x)\r\n if i != 0:\r\n x = self.BN[i](x)\r\n x = F.relu(x)\r\n for col, t in enumerate(self.col_type):\r\n i = int(col/self.shape)\r\n j = col % self.shape\r\n if t == \"binary\":\r\n x[:,:,i,j] = torch.sigmoid(x[:,:,i,j])\r\n elif t == \"normalize\":\r\n x[:,:,i,j] = torch.tanh(x[:,:,i,j])\r\n else:\r\n x[:,:,i,j] = torch.relu(x[:,:,i,j])\r\n return x","def forward(self, x):\n x = self.conv(x)\n return x","def forward(self, x):\n x = F.relu(self.fc1(x))\n x = F.relu(self.fc2(x))\n x = self.fc3(x)\n return x","def forward(self, x):\n out = self.regress(x)\n \n return out","def forward(self, x):\n out = self.fc1(x)\n out = out.view(-1, 196, 4, 4)\n out = self.tconv(out)\n\n return out","def forward(self, input: torch.Tensor) -> torch.Tensor:\n\n x = self.layers(input)\n\n return x","def forward(self, x):\n x = self.feature_extractor(x)\n batch_size, hidden = x.size()\n\n x = self.layer_1(x)\n x = torch.relu(x)\n x = self.layer_2(x)\n x = torch.relu(x)\n x = self.layer_3(x)\n\n x = torch.log_softmax(x, dim=1)\n return x","def forward(self, target, ref, gain=1):\n x = torch.cat((target, ref), 1)\n x = self.body(x) * gain\n return x","def forward(self, x, feature_matching=False):\n x = x.view(-1, x.size(1), 1, 1)\n x = self.model(x)\n return self.output(x)","def forward(self, x_in):\r\n return torch.sigmoid(self.fc1(x_in)).squeeze()","def transform(self, X: Tensor) -> Tensor:\n for tf in self.values():\n X = tf.forward(X)\n return X","def scale(x, feature_range=(-1, 1)):\n \n # scale from 0-1 to feature_range\n min, max = feature_range\n #x = x * (max - min) + min\n #x = torch.add(torch.mul(x, (max-min)), min)\n x = x.mul(max-min).add_(min)\n return x","def forward(self, x):\n\n\t\tif \"_rescaleParameter\" in self.__dict__:\n\t\t\tmethod = self._rescaleParameter[\"masses\"][\"method\"]\n\t\telse:\n\t\t\tmethod = None\n\n\t\tif not self.training and method != None:\n\n\t\t\tx = x.detach().numpy()\n\n\t\t\tif method == \"minmaxScaler\":\n\n\t\t\t\tscaler = self._rescaleParameter[\"masses\"][\"scaler\"]\n\t\t\t\tx = scaler.transform(x)\n\n\t\t\telif method == \"standardScore\":\n\n\t\t\t\tmean = self._rescaleParameter[\"masses\"][\"mean\"]\n\t\t\t\tstd = self._rescaleParameter[\"masses\"][\"std\"]\n\t\t\t\tx = (x - mean) / std\n\n\t\t\tx = torch.tensor(x, dtype=torch.float64)\n\n\t\tx = self.seq(x)\n\n\t\tif \"_rescaleParameter\" in self.__dict__:\n\t\t\tmethod = self._rescaleParameter[\"targets\"][\"method\"]\n\t\telse:\n\t\t\tmethod = None\n\n\t\tif not self.trainingMode: # and method != None:\n\n\t\t\tx = x.detach().numpy()\n\n\t\t\tif method == \"boxcox\":\n\n\t\t\t\tlmbda = self._rescaleParameter[\"targets\"][\"lambda\"]\n\t\t\t\tx = [inv_boxcox(t, lmbda)[0] for t in x]\n\n\t\t\telif method == \"log\":\n\n\t\t\t\tx = [(10**t)[0] for t in x]\n\n\t\t\telif method == \"standardScore\":\n\n\t\t\t\tmean = self._rescaleParameter[\"targets\"][\"mean\"]\n\t\t\t\tstd = self._rescaleParameter[\"targets\"][\"std\"]\n\t\t\t\tx = x * std + mean\n\n\t\t\telse:\n\n\t\t\t\tx = [t[0] for t in x]\n\n\t\treturn x","def training_forward(self, x):\n if self.project_parameters.loss_function == 'BCELoss':\n return self.activation_function(self.backbone_model(x))\n elif self.project_parameters.loss_function == 'CrossEntropyLoss':\n return self.backbone_model(x)","def forward(self, x: Tensor) -> Any: # type: ignore[override]\n return self.model(x)","def forward(self,x):\n x = x.transpose(1,2).contiguous()\n x = F.leaky_relu(self.fc1(x), 0.2)\n x = F.leaky_relu(self.bn2(self.fc2(x)), 0.2)\n x = F.leaky_relu(self.bn3(self.fc3(x)), 0.2)\n x = torch.sigmoid(self.fc4(x))\n return x.transpose(1,2)","def forward(self, inp):\n out = self.features(inp)\n out = out.view(out.size(0), -1) # linearized the output of the module 'features'\n out = self.classifier(out)\n return out","def forward(self, X):\n N = X.size()[0]\n assert X.size() == (N, 3, 448, 448)\n X = self.features(X)\n assert X.size() == (N, 512, 28, 28)\n X = X.view(N, 512, 28**2)\n X = torch.bmm(X, torch.transpose(X, 1, 2)) / (28**2) # Bilinear\n assert X.size() == (N, 512, 512)\n X = X.view(N, 512**2)\n X = torch.sqrt(X + 1e-5)\n X = torch.nn.functional.normalize(X)\n X = self.fc(X)\n assert X.size() == (N, 36)\n return X","def forward(self, x):\n return trl(x, self.weight, bias=self.bias)","def forward(self, system):\n x_ligand = system[0] # retrieve the featurized component, here the ligand\n x_ligand = x_ligand.float()\n x_ligand = self._activation(self.fully_connected_1(x_ligand))\n return self.fully_connected_out(x_ligand)","def model_forward_pass(self, data):\n for key, value in data.items():\n data[key] = value.to(self.device)\n \n if self.fp16:\n with torch.cuda.amp.autocast():\n output, loss = self.model(**data)\n loss = loss / self.accumulate_grad_steps\n else:\n output, loss = self.model(**data)\n loss = loss / self.accumulate_grad_steps\n\n return output, loss","def forward(ctx, input):\n ctx.save_for_backward(input)\n e = torch.exp(input)\n n = e * e + 2 * e\n mask = input <= -0.6\n input[mask] = (input * (n / (n + 2)))[mask]\n input[~mask] = ((input - 2 * (input / (n + 2))))[~mask]\n\n return input","def forward(self, x: torch.Tensor) -> torch.Tensor:\n model_output = None\n #######################################################################\n # Student code begins\n #######################################################################\n\n (N,C,H,W) = x.shape\n\n conv_features = self.conv_layers(x)\n \n flat_features = conv_features.reshape(-1, 500)\n model_output = self.fc_layers(flat_features)\n\n\n #######################################################################\n # Student code ends\n #######################################################################\n return model_output","def forward(self, x):\n x = self.efficient_net(x)\n return x","def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:\n score = multi_scale_ssim(x=x, y=y, kernel_size=self.kernel_size, kernel_sigma=self.kernel_sigma,\n data_range=self.data_range, reduction=self.reduction, scale_weights=self.scale_weights,\n k1=self.k1, k2=self.k2)\n return torch.ones_like(score) - score","def forward(self, x):\n\n out = self.model(x)\n\n return out","def forward(self, x):\n X = np.concatenate((np.ones((x.shape[0],1)), x), axis = 1)\n W = np.concatenate((self.b, self.w), axis = 0)\n self.a = np.dot(X,W)\n return self.a\n raise NotImplementedError(\"Layer forward pass not implemented.\")","def forward_pass_on_convolutions(x, target_layer):\n net.features[-1].register_forward_hook(save_target_output)","def __feedforward(self, X):\n A = X\n for layer in self.layers:\n layer._Dense__forward(A)\n A = layer.A\n return A","def forward(self, x: torch.Tensor):\n x = self.linear1(x)\n x = torch.relu(x)\n x = self.linear2(x)\n x = self.dropout(x)\n return x","def forward(self, x, alpha=1e-8):\r\n batch_size, _, height, width = x.shape\r\n\r\n # [B x C x H x W] Subtract mean over batch.\r\n y = x - x.mean(dim=0, keepdim=True)\r\n\r\n # [1 x C x H x W] Calc standard deviation over batch\r\n y = torch.sqrt(y.pow(2.).mean(dim=0, keepdim=False) + alpha)\r\n\r\n # [1] Take average over feature_maps and pixels.\r\n y = y.mean().view(1, 1, 1, 1)\r\n\r\n # [B x 1 x H x W] Replicate over group and pixels.\r\n y = y.repeat(batch_size, 1, height, width)\r\n\r\n # [B x C x H x W] Append as new feature_map.\r\n y = torch.cat([x, y], 1)\r\n\r\n # return the computed values:\r\n return y","def _forward(self, x):\n\n batch_size = x.size()[0]\n x = self._data_augment(x)\n\n path_prob = self.inner_nodes(x)\n path_prob = torch.unsqueeze(path_prob, dim=2)\n path_prob = torch.cat((path_prob, 1 - path_prob), dim=2)\n\n mu = x.data.new(batch_size, 1, 1).fill_(1.0)\n penalty_ = torch.tensor(0.0).to(self.device)\n\n # Iterate through internal odes in each layer to compute the final path\n # probabilities and the regularization term.\n begin_idx = 0\n end_idx = 1\n\n for layer_idx in range(0, self.depth):\n path_prob_ = path_prob[:, begin_idx:end_idx, :]\n\n # Extract internal nodes in the current layer to compute the\n # regularization term\n penalty_ = penalty_ + self._cal_penalty(layer_idx, mu, path_prob_)\n mu = mu.view(batch_size, -1, 1).repeat(1, 1, 2)\n\n mu = mu * path_prob_ # update path probabilities\n\n begin_idx = end_idx\n end_idx = begin_idx + 2 ** (layer_idx + 1)\n\n mu = mu.view(batch_size, self.leaf_node_num_)\n\n return mu, penalty_","def forward(self, x):\n # Pass the input through all the layers apllying ReLU activation, but the last\n for layer in self.fc_layers[:-1]:\n x = F.relu(layer(x))\n # Pass the result through the output layer apllying hyperbolic tangent function\n x = torch.tanh(self.fc_layers[-1](x))\n # Return the better action for the input state\n return x","def forward(self, x):#input_):\n\n\t\tif \"_rescaleParameter\" in self.__dict__:\n\t\t\tmethod = self._rescaleParameter[\"masses\"][\"method\"]\n\t\telse:\n\t\t\tmethod = None\n\n\t\tif not self.trainingMode and method != None:\n\n\t\t\tx = x.detach().numpy()\n\n\t\t\tif method == \"minmaxScaler\":\n\n\t\t\t\tx = [x] # <--- SKETCHY\n\t\t\t\tscaler = self._rescaleParameter[\"masses\"][\"scaler\"]\n\t\t\t\tx = scaler.transform(x)\n\n\t\t\telif method == \"standardScore\":\n\n\t\t\t\tmean = self._rescaleParameter[\"masses\"][\"mean\"]\n\t\t\t\tstd = self._rescaleParameter[\"masses\"][\"std\"]\n\t\t\t\tx = (x - mean) / std\n\n\t\t\tx = torch.tensor(x, dtype=torch.float64)\n\n\t\tx = self.seq(x)\n\t\t\n\t\tif not self.trainingMode and self._delimiter != 0.:\n\t\t\tfor n in range(len(x)):\n\t\t\t\tif self._delimiter < x[n]: x[n] = 1.\n\t\t\t\telse: x[n] = 0.\n\t\t\n\t\treturn x","def forward(self, x):\n self.save_net()\n self.perturb_tensors()\n out = self.net.forward(x)\n return out","def forward(self, input):\n raise NotImplementedError","def forward(self, input):\n raise NotImplementedError","def forward(self, input_x):\n adv, val = self.adv_val(input_x)\n return val + (adv - adv.mean(dim=1, keepdim=True))","def forward(self, input_x):\n adv, val = self.adv_val(input_x)\n return val + (adv - adv.mean(dim=1, keepdim=True))","def forward_once(self, x):\n x = self.features(x)\n x = x.view(x.size(0), 512) # reshpe it into (batch_size, feature_dimention)\n return x","def forward(self, x: torch.Tensor) -> torch.Tensor:\n return torch.nn.functional.linear(\n x,\n self.weight_mu + self.weight_sigma * self.weight_epsilon,\n self.bias_mu + self.bias_sigma * self.bias_epsilon,\n )","def forward(self, feature_map, z):\n B, c_feat, H, W = feature_map.shape\n z = z.view(B, self.latent_dim, 1, 1).expand(B, self.latent_dim, H, W)\n concatinated = torch.cat([feature_map, z], dim=1)\n output = self.model(concatinated)\n return output"],"string":"[\n \"def feature_forward(self, x):\\n raise NotImplementedError\",\n \"def forward(self, x):\\n x = self.features(x)\\n return x\",\n \"def forward_single(self, x, scale):\\n cls_feat = x\\n reg_feat = x\\n for cls_conv in self.cls_convs:\\n cls_feat = cls_conv(cls_feat)\\n for reg_conv in self.reg_convs:\\n reg_feat = reg_conv(reg_feat)\\n cls_score = self.atss_cls(cls_feat)\\n # we just follow atss, not apply exp in bbox_pred\\n bbox_pred = scale(self.atss_reg(reg_feat)).float()\\n iou_pred = self.atss_iou(reg_feat)\\n return cls_score, bbox_pred, iou_pred\",\n \"def forward(self, x):\\n raise NotImplementedError\",\n \"def forward(self, x):\\n raise NotImplementedError\",\n \"def forward(self, x, scale_param):\\n reg_arr = torch.exp(scale_param * self.reg_conv(self.reg_branch(x)))\\n label_branch_arr = self.label_branch(x)\\n label_arr = self.label_conv(label_branch_arr)\\n center_arr = self.center_conv(label_branch_arr)\\n return (reg_arr, label_arr, center_arr)\",\n \"def forward_features(self, x):\\n x_size = (x.shape[2], x.shape[3])\\n x = self.patch_embed(x)\\n if self.ape:\\n x = x + self.absolute_pos_embed\\n x = self.pos_drop(x)\\n\\n for layer in self.layers:\\n x = layer(x, x_size)\\n\\n x = self.norm(x) # B L C\\n x = self.patch_unembed(x, x_size)\\n\\n return x\",\n \"def forward(self, x): # pylint: disable=invalid-name\\n x = self.layer4(self.layer3(self.layer2(self.layer1(x))))\\n return x.mean((-2, -1))\",\n \"def forward(self, x: Tuple[Tensor]) -> Tensor:\\n inputs = x[self.start_level:self.end_level + 1]\\n assert len(inputs) == (self.end_level - self.start_level + 1)\\n feature_add_all_level = self.convs_all_levels[0](inputs[0])\\n target_h, target_w = feature_add_all_level.size()[2:]\\n for i in range(1, len(inputs)):\\n input_p = inputs[i]\\n x_p = self.convs_all_levels[i](input_p)\\n h, w = x_p.size()[2:]\\n factor_h = target_h // h\\n factor_w = target_w // w\\n assert factor_h == factor_w\\n feature_per_level = aligned_bilinear(x_p, factor_h)\\n feature_add_all_level = feature_add_all_level + \\\\\\n feature_per_level\\n\\n feature_add_all_level = self.conv_branch(feature_add_all_level)\\n feature_pred = self.conv_pred(feature_add_all_level)\\n return feature_pred\",\n \"def forward(self, x):\\n #print('output of fetures.children() : %s'%str([i for i in self.features.children()]))\\n #print(\\\"shape of input is %s\\\" % str(x.size()))\\n for layer_no, layer in enumerate(self.features.children()):\\n\\n if layer_no is 23:\\n y = layer(x)\\n if layer_no is 33:\\n z = layer(x)\\n x = layer(x)\\n\\n #print('debug')\\n #print('layer info: %s'%str(layer))\\n #print(\\\"shape of x is %s\\\" % str(x.size()))\\n\\n x = self.conv1D_downstream1(x)\\n x = self.conv1D_downstream2(x)\\n x = self.upsample_1(x)\\n\\n z = self.conv1D_pool4(z)\\n y = self.conv1D_pool3(y)\\n #print('debug')\\n #print(\\\"shape of x is %s\\\"%str(x.size()))\\n #print(\\\"shape of z is %s\\\" % str(z.size()))\\n\\n if x.size() is not z.size():\\n x = nn.functional.interpolate(x,size = (z.size()[2],z.size()[3]), mode = 'nearest')\\n x = x+ z\\n x = self.upsample_2(x)\\n x = x+y\\n x = self.upsample_3(x)\\n\\n return x\",\n \"def forward(self, x):\\n # Compute the mean norm of activations per channel.\\n nu2 = x.pow(2).mean(dim=[2, 3], keepdim=True)\\n\\n # Perform FRN.\\n x = x * torch.rsqrt(nu2 + self.eps.abs())\\n\\n # Scale and Bias\\n if self.is_scale:\\n x = self.weight * x\\n if self.is_bias:\\n x = x + self.bias\\n return x\",\n \"def forward(self, x):\\n return self.layers(x)\",\n \"def forward(self, x):\\n levels = self.levels\\n expanded_levels = levels[None, :]\\n\\n samples = self.est.forward(x)\\n # this product is of shape (N,K) for N variables and K levels\\n # it is then contracted over the levels axis\\n # TODO: this can be done with tensordot / sum_of_2d_modes?\\n tmp = (samples * expanded_levels)\\n self.tmpshape = tmp.shape\\n return tmp.sum(axis=-1)\",\n \"def forward(self, x):\\n pass\",\n \"def scale(x, feature_range=(-1,1)):\\r\\n x = x * 2 - 1\\r\\n return x\",\n \"def forward(self, x, normalize=False):\\n if normalize:\\n x = (x - self.state_means) / torch.sqrt(self.state_vars)\\n\\n x = self.features(x)\\n return x\",\n \"def forward(self, x):\\n for i, layer in enumerate(self.layers):\\n x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)\\n return x.squeeze(0)\",\n \"def base_forward(self, x):\\r\\n pass\",\n \"def forward(self, x: torch.Tensor) -> torch.Tensor:\\n x = self.cnn.extractor.forward(x)\\n return self.cnn.regressor(x)\",\n \"def forward(self, x):\\n x = self.sparseModel(x)\\n x = self.linear(x)\\n return x\",\n \"def forward(self, x):\\n x = self.lin(x)\\n\\n return x\",\n \"def forward(self, x: torch.Tensor, dim: int = 0, p: int = 1):\\n raise NotImplementedError\",\n \"def forward_single(self, x: Tensor, scale: Scale) -> Sequence[Tensor]:\\n cls_feat = x\\n reg_feat = x\\n for cls_conv in self.cls_convs:\\n cls_feat = cls_conv(cls_feat)\\n for reg_conv in self.reg_convs:\\n reg_feat = reg_conv(reg_feat)\\n cls_score = self.atss_cls(cls_feat)\\n # we just follow atss, not apply exp in bbox_pred\\n bbox_pred = scale(self.atss_reg(reg_feat)).float()\\n centerness = self.atss_centerness(reg_feat)\\n return cls_score, bbox_pred, centerness\",\n \"def addforwardscale(self):\\n m_graph = nx.DiGraph()\\n # Construct the graph with connections\\n for u in range(self.nodummy_nodes):\\n for v in range(self.nodummy_nodes):\\n if (self.nodummyconnection[u, v] != 0):\\n m_graph.add_edge(self.nodummyvariablelist[v],\\n self.nodummyvariablelist[u],\\n weight=self.nodummygain[u, v])\\n # Add connections where out degree == 1\\n counter = 1\\n for node in m_graph.nodes():\\n if m_graph.out_degree(node) == 1:\\n nameofscale = 'DV' + str(counter)\\n m_graph.add_edge(node, nameofscale, weight=1.0)\\n counter = counter + 1\\n\\n self.scaledforwardconnection = transpose(\\n nx.to_numpy_matrix(m_graph, weight=None))\\n self.scaledforwardgain = transpose(\\n nx.to_numpy_matrix(m_graph, weight='weight'))\\n self.scaledforwardvariablelist = m_graph.nodes()\",\n \"def forward(self, x: tuple, positive_infos: InstanceList) -> tuple:\\n mask_feats = self.mask_feature_head(x)\\n return multi_apply(self.forward_single, mask_feats, positive_infos)\",\n \"def forward(self, x: Tensor, **kwargs) -> Tensor:\\n if self.scale:\\n return self.lut(x) * math.sqrt(self.embedding_dim)\\n return self.lut(x)\",\n \"def forward(self, x, **kwargs):\\n pass\",\n \"def forward(self, x):\\n return self.l1(x)\",\n \"def forward(self, x):\\n # The scaled feature maps for each interim layer\\n scaled_features = OrderedDict()\\n for (_, module), (layer_name, features) in zip(self.adaptor.items(), x.items()):\\n patches_per_axis = int(sqrt(features.shape[1]))\\n # Reshape to 2d and reorder dimensions to traditional convolution dims. (B, C, H, W)\\n features_2d = features.reshape(features.shape[0], patches_per_axis, patches_per_axis, features.shape[2]) \\\\\\n .permute(0, 3, 1, 2)\\n\\n scaled_features[layer_name] = module(features_2d)\\n\\n return scaled_features\",\n \"def forward(self, x: Tuple[Tensor]) -> Tuple[List[Tensor]]:\\n return multi_apply(self.forward_single, x, self.scales)\",\n \"def forward(self, x):\\n return x\",\n \"def forward(self, x):\\n flows_forward, flows_backward = self.get_flow(x)\\n b, n, _, h, w = x.size()\\n\\n # backward branch\\n out_l = []\\n feat_prop = x.new_zeros(b, self.num_feat, h, w)\\n for i in range(n - 1, -1, -1):\\n x_i = x[:, i, :, :, :]\\n if i < n - 1:\\n flow = flows_backward[:, i, :, :, :]\\n feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1))\\n feat_prop = torch.cat([x_i, feat_prop], dim=1)\\n feat_prop = self.backward_trunk(feat_prop)\\n out_l.insert(0, feat_prop)\\n\\n # forward branch\\n feat_prop = torch.zeros_like(feat_prop)\\n for i in range(0, n):\\n x_i = x[:, i, :, :, :]\\n if i > 0:\\n flow = flows_forward[:, i - 1, :, :, :]\\n feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1))\\n\\n feat_prop = torch.cat([x_i, feat_prop], dim=1)\\n feat_prop = self.forward_trunk(feat_prop)\\n\\n # upsample\\n out = torch.cat([out_l[i], feat_prop], dim=1)\\n out = self.lrelu(self.fusion(out))\\n out = self.lrelu(self.pixel_shuffle(self.upconv1(out)))\\n out = self.lrelu(self.pixel_shuffle(self.upconv2(out)))\\n out = self.lrelu(self.conv_hr(out))\\n out = self.conv_last(out)\\n base = F.interpolate(x_i, scale_factor=4, mode='bilinear', align_corners=False)\\n out += base\\n out_l[i] = out\\n\\n return torch.stack(out_l, dim=1)\",\n \"def forward(self, x):\\n return self.activation_function(self.backbone_model(x))\",\n \"def forward(self, x):\\n\\t\\t#######################################################################\\n\\t\\t# ** START OF YOUR CODE **\\n\\t\\t#######################################################################\\n\\n\\t\\toutput = self._layers[0].forward(x)\\n\\t\\tfor i in range(1, len(self._layers)):\\n\\t\\t\\toutput = self._layers[i].forward(output)\\n\\t\\treturn output\\n\\n\\t\\t#######################################################################\\n\\t\\t# ** END OF YOUR CODE **\\n\\t\\t#######################################################################\",\n \"def forward(self, x):\\n\\n ########################\\n # PUT YOUR CODE HERE #\\n #######################\\n for l in range(len(self.layers)):\\n if l == 0:\\n z = self.layers[l].forward(x)\\n else:\\n z = self.layers[l].forward(a)\\n a = self.activations[l].forward(z)\\n\\n # output from softmax layer\\n out = a\\n\\n ########################\\n # END OF YOUR CODE #\\n #######################\\n\\n return out\",\n \"def forward(self, x):\\n in_num=self.layers[1].in_features\\n out_num=self.layers[-1].out_features\\n x_new=x.detach().clone()\\n for i in range(in_num):\\n x_new[:,i]=(x[:,i] - self.beta[i]) * self.alpha[i] \\n \\n y=self.layers(x_new)\\n for i in range(out_num):\\n y[:,i]=(y[:,i] / self.alpha[in_num+i]) + self.beta[in_num+i]\\n return y\",\n \"def forward(self, x):\\n out = self.features(x)\\n out = self.avgpool(out)\\n out = torch.flatten(out, 1)\\n out = self.classifier(out)\\n return out\",\n \"def forward(self, x):\\n out = self.features(x)\\n out = self.avgpool(out)\\n out = torch.flatten(out, 1)\\n out = self.classifier(out)\\n return out\",\n \"def forward(self, x: torch.Tensor) -> torch.Tensor:\\n out = self.embedding(x) * self.scale\\n return out\",\n \"def forward(self, x):\\n x = self.features(x)\\n x = x.view(x.size(0), 256 * 6 * 6)\\n x = self.classifier(x)\\n return x\",\n \"def _forward(self, x, X, upto=None):\\n if upto is not None: # cannot use 'if upto' here since it is 0-indexed\\n # and layer0 is the first layer\\n assert 0<=upto<=self._layer_counter\\n counter = upto + 1\\n else: counter = self._layer_counter\\n\\n y_previous, Y_previous = x, X\\n # TODO: because we always need to compute F_i(X) at each layer i, this\\n # is a huge overhead\\n # feedforward\\n for i in range(counter):\\n layer = getattr(self, 'layer'+str(i))\\n y, Y = layer(y_previous, Y_previous), layer(Y_previous, Y_previous)\\n y_previous, Y_previous = y, Y\\n\\n return y\",\n \"def forward(self, feat_T: torch.Tensor) -> torch.Tensor:\\n return self.loss_weight * self.forward_train(feat_T)\",\n \"def _forward(self, X, **kwargs):\\n raise NotImplementedError()\",\n \"def forward(self, x):\\n out = self.features(x)\\n out = out.view(out.size(0), -1)\\n out = self.classifier(out)\\n return out\",\n \"def forward(self, X: Tensor) -> Tensor:\\n if self.training:\\n if self.transform_on_train:\\n return self.transform(X)\\n elif self.transform_on_eval:\\n if fantasize.off() or self.transform_on_fantasize:\\n return self.transform(X)\\n return X\",\n \"def forward(self, x: Tensor) -> Tensor: # type: ignore\\n x = self.backbone(x)\\n x = x.view(x.size(0), -1)\\n if self.head_layers is not None:\\n out = self.imagehead(x)\\n return out\\n else:\\n return x\",\n \"def forward(self, x, dt, do_normalization=True):\\n raise NotImplementedError\",\n \"def forward_tensor(self, x):\\n pass\",\n \"def forward(self, x):\\n x = x.permute(0, 3, 1, 2) # [batch_size*num_slots, features, height, width]\\n x = self.dec(x)\\n return x\",\n \"def forward(self, x):\\n x = x.permute(0, 3, 1, 2) # [batch_size*num_slots, features, height, width]\\n x = self.dec(x)\\n return x\",\n \"def layer_forward_prop(self):\\n raise NotImplementedError(\\\"Must be created in child classes\\\")\",\n \"def forward(self, x: torch.Tensor, x_mask: torch.Tensor, proto: torch.Tensor = None) -> torch.Tensor:\\n feature = self.features(x, x_mask)\\n if len(self.old_cols) > 0:\\n with torch.no_grad():\\n fc1_kb = [old(feature) for old in self.old_fc1s]\\n feature = F.relu(self.fc1(feature))\\n y = self.adaptor1(torch.cat(fc1_kb, 1))\\n out = self.classifier(feature, proto) + y\\n else:\\n feature = F.relu(self.fc1(feature))\\n out = self.classifier(feature, proto)\\n return out\",\n \"def forward(self, x, dt, do_normalization=True):\\n return x\",\n \"def forward(self, x):\\n residues = []\\n # Downward Pass\\n x = self.layers[0](x.unsqueeze(1))\\n for layer in self.layers[1:self.half]:\\n x = layer(x)\\n residues.insert(0, x)\\n\\n # Upward Pass\\n for idx, layer in enumerate(self.layers[self.half:(len(self.layers)-1)]):\\n x = layer(x, residues[idx])\\n x = self.layers[-1](x)\\n\\n return(x)\",\n \"def forward(self, x):\\n out = self.pre_processing(x)\\n out = self.a3(out)\\n out = self.b3(out)\\n out = self.maxpool(out)\\n out = self.a4(out)\\n out = self.b4(out)\\n out = self.c4(out)\\n out = self.d4(out)\\n out = self.e4(out)\\n out = self.maxpool(out)\\n out = self.a5(out)\\n out = self.b5(out)\\n out = self.avgpool(out)\\n out = out.view(out.size(0), -1) # reshape the output tensor\\n out = self.linear(out)\\n\\n return out\",\n \"def forward(self, X):\\r\\n N = X.size()[0]\\r\\n assert X.size() == (N, 3, 448, 448)\\r\\n X = self.features(X)\\r\\n assert X.size() == (N, 512, 28, 28)\\r\\n X = X.view(N, 512, 28**2)\\r\\n X = torch.bmm(X, torch.transpose(X, 1, 2)) / (28**2) # Bilinear\\r\\n assert X.size() == (N, 512, 512)\\r\\n X = X.view(N, 512**2)\\r\\n X = torch.sign(X)*torch.sqrt(torch.abs(X)+1e-12)\\r\\n # X = torch.sqrt(X + 1e-5)\\r\\n X = torch.nn.functional.normalize(X)\\r\\n X = self.fc(X)\\r\\n assert X.size() == (N, 11)\\r\\n return X\",\n \"def forward(self, x: torch.Tensor) -> torch.Tensor:\\n return self.model(x)\",\n \"def forward(self, x):\\n dims = list(range(1, len(x.shape)))\\n mean = x.mean(dim=dims, keepdim=True)\\n var = torch.pow(x - mean, 2).mean(dim=dims, keepdim=True)\\n return self.apply_gain_and_bias((x - mean) / (var + EPS).sqrt())\",\n \"def forward(self, feats):\\n return multi_apply(self.forward_single, feats, self.scales)\",\n \"def forward(self, x):\\r\\n x = x.reshape(x.shape[0], x.shape[1], 1 , 1)\\r\\n x = self.input(x)\\r\\n x = self.bn(x)\\r\\n x = F.relu(x)\\r\\n for i in range(len(self.DV)-1, -1, -1):\\r\\n x = self.DV[i](x)\\r\\n if i != 0:\\r\\n x = self.BN[i](x)\\r\\n x = F.relu(x)\\r\\n for col, t in enumerate(self.col_type):\\r\\n i = int(col/self.shape)\\r\\n j = col % self.shape\\r\\n if t == \\\"binary\\\":\\r\\n x[:,:,i,j] = torch.sigmoid(x[:,:,i,j])\\r\\n elif t == \\\"normalize\\\":\\r\\n x[:,:,i,j] = torch.tanh(x[:,:,i,j])\\r\\n else:\\r\\n x[:,:,i,j] = torch.relu(x[:,:,i,j])\\r\\n return x\",\n \"def forward(self, x):\\n x = self.conv(x)\\n return x\",\n \"def forward(self, x):\\n x = F.relu(self.fc1(x))\\n x = F.relu(self.fc2(x))\\n x = self.fc3(x)\\n return x\",\n \"def forward(self, x):\\n out = self.regress(x)\\n \\n return out\",\n \"def forward(self, x):\\n out = self.fc1(x)\\n out = out.view(-1, 196, 4, 4)\\n out = self.tconv(out)\\n\\n return out\",\n \"def forward(self, input: torch.Tensor) -> torch.Tensor:\\n\\n x = self.layers(input)\\n\\n return x\",\n \"def forward(self, x):\\n x = self.feature_extractor(x)\\n batch_size, hidden = x.size()\\n\\n x = self.layer_1(x)\\n x = torch.relu(x)\\n x = self.layer_2(x)\\n x = torch.relu(x)\\n x = self.layer_3(x)\\n\\n x = torch.log_softmax(x, dim=1)\\n return x\",\n \"def forward(self, target, ref, gain=1):\\n x = torch.cat((target, ref), 1)\\n x = self.body(x) * gain\\n return x\",\n \"def forward(self, x, feature_matching=False):\\n x = x.view(-1, x.size(1), 1, 1)\\n x = self.model(x)\\n return self.output(x)\",\n \"def forward(self, x_in):\\r\\n return torch.sigmoid(self.fc1(x_in)).squeeze()\",\n \"def transform(self, X: Tensor) -> Tensor:\\n for tf in self.values():\\n X = tf.forward(X)\\n return X\",\n \"def scale(x, feature_range=(-1, 1)):\\n \\n # scale from 0-1 to feature_range\\n min, max = feature_range\\n #x = x * (max - min) + min\\n #x = torch.add(torch.mul(x, (max-min)), min)\\n x = x.mul(max-min).add_(min)\\n return x\",\n \"def forward(self, x):\\n\\n\\t\\tif \\\"_rescaleParameter\\\" in self.__dict__:\\n\\t\\t\\tmethod = self._rescaleParameter[\\\"masses\\\"][\\\"method\\\"]\\n\\t\\telse:\\n\\t\\t\\tmethod = None\\n\\n\\t\\tif not self.training and method != None:\\n\\n\\t\\t\\tx = x.detach().numpy()\\n\\n\\t\\t\\tif method == \\\"minmaxScaler\\\":\\n\\n\\t\\t\\t\\tscaler = self._rescaleParameter[\\\"masses\\\"][\\\"scaler\\\"]\\n\\t\\t\\t\\tx = scaler.transform(x)\\n\\n\\t\\t\\telif method == \\\"standardScore\\\":\\n\\n\\t\\t\\t\\tmean = self._rescaleParameter[\\\"masses\\\"][\\\"mean\\\"]\\n\\t\\t\\t\\tstd = self._rescaleParameter[\\\"masses\\\"][\\\"std\\\"]\\n\\t\\t\\t\\tx = (x - mean) / std\\n\\n\\t\\t\\tx = torch.tensor(x, dtype=torch.float64)\\n\\n\\t\\tx = self.seq(x)\\n\\n\\t\\tif \\\"_rescaleParameter\\\" in self.__dict__:\\n\\t\\t\\tmethod = self._rescaleParameter[\\\"targets\\\"][\\\"method\\\"]\\n\\t\\telse:\\n\\t\\t\\tmethod = None\\n\\n\\t\\tif not self.trainingMode: # and method != None:\\n\\n\\t\\t\\tx = x.detach().numpy()\\n\\n\\t\\t\\tif method == \\\"boxcox\\\":\\n\\n\\t\\t\\t\\tlmbda = self._rescaleParameter[\\\"targets\\\"][\\\"lambda\\\"]\\n\\t\\t\\t\\tx = [inv_boxcox(t, lmbda)[0] for t in x]\\n\\n\\t\\t\\telif method == \\\"log\\\":\\n\\n\\t\\t\\t\\tx = [(10**t)[0] for t in x]\\n\\n\\t\\t\\telif method == \\\"standardScore\\\":\\n\\n\\t\\t\\t\\tmean = self._rescaleParameter[\\\"targets\\\"][\\\"mean\\\"]\\n\\t\\t\\t\\tstd = self._rescaleParameter[\\\"targets\\\"][\\\"std\\\"]\\n\\t\\t\\t\\tx = x * std + mean\\n\\n\\t\\t\\telse:\\n\\n\\t\\t\\t\\tx = [t[0] for t in x]\\n\\n\\t\\treturn x\",\n \"def training_forward(self, x):\\n if self.project_parameters.loss_function == 'BCELoss':\\n return self.activation_function(self.backbone_model(x))\\n elif self.project_parameters.loss_function == 'CrossEntropyLoss':\\n return self.backbone_model(x)\",\n \"def forward(self, x: Tensor) -> Any: # type: ignore[override]\\n return self.model(x)\",\n \"def forward(self,x):\\n x = x.transpose(1,2).contiguous()\\n x = F.leaky_relu(self.fc1(x), 0.2)\\n x = F.leaky_relu(self.bn2(self.fc2(x)), 0.2)\\n x = F.leaky_relu(self.bn3(self.fc3(x)), 0.2)\\n x = torch.sigmoid(self.fc4(x))\\n return x.transpose(1,2)\",\n \"def forward(self, inp):\\n out = self.features(inp)\\n out = out.view(out.size(0), -1) # linearized the output of the module 'features'\\n out = self.classifier(out)\\n return out\",\n \"def forward(self, X):\\n N = X.size()[0]\\n assert X.size() == (N, 3, 448, 448)\\n X = self.features(X)\\n assert X.size() == (N, 512, 28, 28)\\n X = X.view(N, 512, 28**2)\\n X = torch.bmm(X, torch.transpose(X, 1, 2)) / (28**2) # Bilinear\\n assert X.size() == (N, 512, 512)\\n X = X.view(N, 512**2)\\n X = torch.sqrt(X + 1e-5)\\n X = torch.nn.functional.normalize(X)\\n X = self.fc(X)\\n assert X.size() == (N, 36)\\n return X\",\n \"def forward(self, x):\\n return trl(x, self.weight, bias=self.bias)\",\n \"def forward(self, system):\\n x_ligand = system[0] # retrieve the featurized component, here the ligand\\n x_ligand = x_ligand.float()\\n x_ligand = self._activation(self.fully_connected_1(x_ligand))\\n return self.fully_connected_out(x_ligand)\",\n \"def model_forward_pass(self, data):\\n for key, value in data.items():\\n data[key] = value.to(self.device)\\n \\n if self.fp16:\\n with torch.cuda.amp.autocast():\\n output, loss = self.model(**data)\\n loss = loss / self.accumulate_grad_steps\\n else:\\n output, loss = self.model(**data)\\n loss = loss / self.accumulate_grad_steps\\n\\n return output, loss\",\n \"def forward(ctx, input):\\n ctx.save_for_backward(input)\\n e = torch.exp(input)\\n n = e * e + 2 * e\\n mask = input <= -0.6\\n input[mask] = (input * (n / (n + 2)))[mask]\\n input[~mask] = ((input - 2 * (input / (n + 2))))[~mask]\\n\\n return input\",\n \"def forward(self, x: torch.Tensor) -> torch.Tensor:\\n model_output = None\\n #######################################################################\\n # Student code begins\\n #######################################################################\\n\\n (N,C,H,W) = x.shape\\n\\n conv_features = self.conv_layers(x)\\n \\n flat_features = conv_features.reshape(-1, 500)\\n model_output = self.fc_layers(flat_features)\\n\\n\\n #######################################################################\\n # Student code ends\\n #######################################################################\\n return model_output\",\n \"def forward(self, x):\\n x = self.efficient_net(x)\\n return x\",\n \"def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:\\n score = multi_scale_ssim(x=x, y=y, kernel_size=self.kernel_size, kernel_sigma=self.kernel_sigma,\\n data_range=self.data_range, reduction=self.reduction, scale_weights=self.scale_weights,\\n k1=self.k1, k2=self.k2)\\n return torch.ones_like(score) - score\",\n \"def forward(self, x):\\n\\n out = self.model(x)\\n\\n return out\",\n \"def forward(self, x):\\n X = np.concatenate((np.ones((x.shape[0],1)), x), axis = 1)\\n W = np.concatenate((self.b, self.w), axis = 0)\\n self.a = np.dot(X,W)\\n return self.a\\n raise NotImplementedError(\\\"Layer forward pass not implemented.\\\")\",\n \"def forward_pass_on_convolutions(x, target_layer):\\n net.features[-1].register_forward_hook(save_target_output)\",\n \"def __feedforward(self, X):\\n A = X\\n for layer in self.layers:\\n layer._Dense__forward(A)\\n A = layer.A\\n return A\",\n \"def forward(self, x: torch.Tensor):\\n x = self.linear1(x)\\n x = torch.relu(x)\\n x = self.linear2(x)\\n x = self.dropout(x)\\n return x\",\n \"def forward(self, x, alpha=1e-8):\\r\\n batch_size, _, height, width = x.shape\\r\\n\\r\\n # [B x C x H x W] Subtract mean over batch.\\r\\n y = x - x.mean(dim=0, keepdim=True)\\r\\n\\r\\n # [1 x C x H x W] Calc standard deviation over batch\\r\\n y = torch.sqrt(y.pow(2.).mean(dim=0, keepdim=False) + alpha)\\r\\n\\r\\n # [1] Take average over feature_maps and pixels.\\r\\n y = y.mean().view(1, 1, 1, 1)\\r\\n\\r\\n # [B x 1 x H x W] Replicate over group and pixels.\\r\\n y = y.repeat(batch_size, 1, height, width)\\r\\n\\r\\n # [B x C x H x W] Append as new feature_map.\\r\\n y = torch.cat([x, y], 1)\\r\\n\\r\\n # return the computed values:\\r\\n return y\",\n \"def _forward(self, x):\\n\\n batch_size = x.size()[0]\\n x = self._data_augment(x)\\n\\n path_prob = self.inner_nodes(x)\\n path_prob = torch.unsqueeze(path_prob, dim=2)\\n path_prob = torch.cat((path_prob, 1 - path_prob), dim=2)\\n\\n mu = x.data.new(batch_size, 1, 1).fill_(1.0)\\n penalty_ = torch.tensor(0.0).to(self.device)\\n\\n # Iterate through internal odes in each layer to compute the final path\\n # probabilities and the regularization term.\\n begin_idx = 0\\n end_idx = 1\\n\\n for layer_idx in range(0, self.depth):\\n path_prob_ = path_prob[:, begin_idx:end_idx, :]\\n\\n # Extract internal nodes in the current layer to compute the\\n # regularization term\\n penalty_ = penalty_ + self._cal_penalty(layer_idx, mu, path_prob_)\\n mu = mu.view(batch_size, -1, 1).repeat(1, 1, 2)\\n\\n mu = mu * path_prob_ # update path probabilities\\n\\n begin_idx = end_idx\\n end_idx = begin_idx + 2 ** (layer_idx + 1)\\n\\n mu = mu.view(batch_size, self.leaf_node_num_)\\n\\n return mu, penalty_\",\n \"def forward(self, x):\\n # Pass the input through all the layers apllying ReLU activation, but the last\\n for layer in self.fc_layers[:-1]:\\n x = F.relu(layer(x))\\n # Pass the result through the output layer apllying hyperbolic tangent function\\n x = torch.tanh(self.fc_layers[-1](x))\\n # Return the better action for the input state\\n return x\",\n \"def forward(self, x):#input_):\\n\\n\\t\\tif \\\"_rescaleParameter\\\" in self.__dict__:\\n\\t\\t\\tmethod = self._rescaleParameter[\\\"masses\\\"][\\\"method\\\"]\\n\\t\\telse:\\n\\t\\t\\tmethod = None\\n\\n\\t\\tif not self.trainingMode and method != None:\\n\\n\\t\\t\\tx = x.detach().numpy()\\n\\n\\t\\t\\tif method == \\\"minmaxScaler\\\":\\n\\n\\t\\t\\t\\tx = [x] # <--- SKETCHY\\n\\t\\t\\t\\tscaler = self._rescaleParameter[\\\"masses\\\"][\\\"scaler\\\"]\\n\\t\\t\\t\\tx = scaler.transform(x)\\n\\n\\t\\t\\telif method == \\\"standardScore\\\":\\n\\n\\t\\t\\t\\tmean = self._rescaleParameter[\\\"masses\\\"][\\\"mean\\\"]\\n\\t\\t\\t\\tstd = self._rescaleParameter[\\\"masses\\\"][\\\"std\\\"]\\n\\t\\t\\t\\tx = (x - mean) / std\\n\\n\\t\\t\\tx = torch.tensor(x, dtype=torch.float64)\\n\\n\\t\\tx = self.seq(x)\\n\\t\\t\\n\\t\\tif not self.trainingMode and self._delimiter != 0.:\\n\\t\\t\\tfor n in range(len(x)):\\n\\t\\t\\t\\tif self._delimiter < x[n]: x[n] = 1.\\n\\t\\t\\t\\telse: x[n] = 0.\\n\\t\\t\\n\\t\\treturn x\",\n \"def forward(self, x):\\n self.save_net()\\n self.perturb_tensors()\\n out = self.net.forward(x)\\n return out\",\n \"def forward(self, input):\\n raise NotImplementedError\",\n \"def forward(self, input):\\n raise NotImplementedError\",\n \"def forward(self, input_x):\\n adv, val = self.adv_val(input_x)\\n return val + (adv - adv.mean(dim=1, keepdim=True))\",\n \"def forward(self, input_x):\\n adv, val = self.adv_val(input_x)\\n return val + (adv - adv.mean(dim=1, keepdim=True))\",\n \"def forward_once(self, x):\\n x = self.features(x)\\n x = x.view(x.size(0), 512) # reshpe it into (batch_size, feature_dimention)\\n return x\",\n \"def forward(self, x: torch.Tensor) -> torch.Tensor:\\n return torch.nn.functional.linear(\\n x,\\n self.weight_mu + self.weight_sigma * self.weight_epsilon,\\n self.bias_mu + self.bias_sigma * self.bias_epsilon,\\n )\",\n \"def forward(self, feature_map, z):\\n B, c_feat, H, W = feature_map.shape\\n z = z.view(B, self.latent_dim, 1, 1).expand(B, self.latent_dim, H, W)\\n concatinated = torch.cat([feature_map, z], dim=1)\\n output = self.model(concatinated)\\n return output\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.74690366","0.68007946","0.65777415","0.6515914","0.6515914","0.6404764","0.6320513","0.6306763","0.6299315","0.6285584","0.6279949","0.6200305","0.6179189","0.6158268","0.6132828","0.6124821","0.61077636","0.61061907","0.6104554","0.6101124","0.6098391","0.60974395","0.6088855","0.60685205","0.60625106","0.6054674","0.6030006","0.60247475","0.6018435","0.6010663","0.6005247","0.59856904","0.5982928","0.59771335","0.59663826","0.59599316","0.59437704","0.59437704","0.59417206","0.594159","0.59390265","0.5934223","0.59303796","0.5929529","0.5925676","0.59253883","0.5923631","0.592142","0.5908756","0.5908756","0.58762425","0.5865634","0.58580506","0.5852337","0.58497256","0.5839759","0.5828017","0.5814001","0.58117664","0.58109456","0.57936543","0.5790761","0.5788847","0.578523","0.5775939","0.5772099","0.57426393","0.57421404","0.57350975","0.5734979","0.57322186","0.5723641","0.5722357","0.5720455","0.57118917","0.5706432","0.57057065","0.5703893","0.5699927","0.56913584","0.56870526","0.5686476","0.5685123","0.5684999","0.5684726","0.56831676","0.5677821","0.56777817","0.5676662","0.56748825","0.5674117","0.5668848","0.5663115","0.5661122","0.5658218","0.5658218","0.56552505","0.56552505","0.5650071","0.5640212","0.5633645"],"string":"[\n \"0.74690366\",\n \"0.68007946\",\n \"0.65777415\",\n \"0.6515914\",\n \"0.6515914\",\n \"0.6404764\",\n \"0.6320513\",\n \"0.6306763\",\n \"0.6299315\",\n \"0.6285584\",\n \"0.6279949\",\n \"0.6200305\",\n \"0.6179189\",\n \"0.6158268\",\n \"0.6132828\",\n \"0.6124821\",\n \"0.61077636\",\n \"0.61061907\",\n \"0.6104554\",\n \"0.6101124\",\n \"0.6098391\",\n \"0.60974395\",\n \"0.6088855\",\n \"0.60685205\",\n \"0.60625106\",\n \"0.6054674\",\n \"0.6030006\",\n \"0.60247475\",\n \"0.6018435\",\n \"0.6010663\",\n \"0.6005247\",\n \"0.59856904\",\n \"0.5982928\",\n \"0.59771335\",\n \"0.59663826\",\n \"0.59599316\",\n \"0.59437704\",\n \"0.59437704\",\n \"0.59417206\",\n \"0.594159\",\n \"0.59390265\",\n \"0.5934223\",\n \"0.59303796\",\n \"0.5929529\",\n \"0.5925676\",\n \"0.59253883\",\n \"0.5923631\",\n \"0.592142\",\n \"0.5908756\",\n \"0.5908756\",\n \"0.58762425\",\n \"0.5865634\",\n \"0.58580506\",\n \"0.5852337\",\n \"0.58497256\",\n \"0.5839759\",\n \"0.5828017\",\n \"0.5814001\",\n \"0.58117664\",\n \"0.58109456\",\n \"0.57936543\",\n \"0.5790761\",\n \"0.5788847\",\n \"0.578523\",\n \"0.5775939\",\n \"0.5772099\",\n \"0.57426393\",\n \"0.57421404\",\n \"0.57350975\",\n \"0.5734979\",\n \"0.57322186\",\n \"0.5723641\",\n \"0.5722357\",\n \"0.5720455\",\n \"0.57118917\",\n \"0.5706432\",\n \"0.57057065\",\n \"0.5703893\",\n \"0.5699927\",\n \"0.56913584\",\n \"0.56870526\",\n \"0.5686476\",\n \"0.5685123\",\n \"0.5684999\",\n \"0.5684726\",\n \"0.56831676\",\n \"0.5677821\",\n \"0.56777817\",\n \"0.5676662\",\n \"0.56748825\",\n \"0.5674117\",\n \"0.5668848\",\n \"0.5663115\",\n \"0.5661122\",\n \"0.5658218\",\n \"0.5658218\",\n \"0.56552505\",\n \"0.56552505\",\n \"0.5650071\",\n \"0.5640212\",\n \"0.5633645\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":84191,"cells":{"query":{"kind":"string","value":"Compute losses of the head."},"document":{"kind":"string","value":"def loss(self,\n cls_scores,\n bbox_preds,\n gt_bboxes,\n gt_labels,\n img_metas,\n gt_bboxes_ignore=None):\n featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]\n assert len(featmap_sizes) == self.anchor_generator.num_levels\n\n device = cls_scores[0].device\n\n anchor_list, valid_flag_list = self.get_anchors(\n featmap_sizes, img_metas, device=device)\n label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1\n cls_reg_targets = self.get_targets(\n anchor_list,\n valid_flag_list,\n gt_bboxes,\n img_metas,\n gt_bboxes_ignore_list=gt_bboxes_ignore,\n gt_labels_list=gt_labels,\n label_channels=label_channels)\n if cls_reg_targets is None:\n return None\n (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,\n num_total_pos, num_total_neg) = cls_reg_targets\n\n all_labels=[]\n all_label_weights=[]\n all_cls_scores=[]\n all_bbox_targets=[]\n all_bbox_weights=[]\n all_bbox_preds=[]\n for labels, label_weights, cls_score, bbox_targets, bbox_weights, bbox_pred in zip(labels_list, label_weights_list,cls_scores, bbox_targets_list, bbox_weights_list, bbox_preds):\n all_labels.append(labels.reshape(-1))\n all_label_weights.append(label_weights.reshape(-1))\n all_cls_scores.append(cls_score.permute(0, 2, 3, 1).reshape(-1, self.cls_out_channels))\n \n all_bbox_targets.append(bbox_targets.reshape(-1, 4))\n all_bbox_weights.append(bbox_weights.reshape(-1, 4))\n all_bbox_preds.append(bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4))\n\n\n cls_labels = torch.cat(all_labels)\n pos_idx = cls_labels < 80\n if pos_idx.sum() > 0:\n # classification loss\n flat_labels = vectorize_labels(cls_labels, self.num_classes, torch.cat(all_label_weights))\n flat_preds=torch.cat(all_cls_scores).reshape(-1)\n\n losses_cls = self.AP_Loss.apply(flat_preds, flat_labels)\n\n\n # regression loss\n if self.reg_decoded_bbox:\n pos_pred = self.delta2bbox(torch.cat(all_bbox_preds)[pos_idx])\n pos_target = self.delta2bbox(torch.cat(all_bbox_targets)[pos_idx])\n pos_weights = torch.cat(all_bbox_weights)[pos_idx]\n else:\n pos_pred = torch.cat(all_bbox_preds)[pos_idx]\n pos_target = torch.cat(all_bbox_targets)[pos_idx]\n pos_weights = torch.cat(all_bbox_weights)[pos_idx]\n\n losses_bbox = self.loss_bbox(\n pos_pred,\n pos_target,\n pos_weights)\n else:\n losses_cls=0*torch.cat(all_cls_scores)\n losses_bbox=0*torch.cat(all_bbox_preds)\n \n return dict(loss_cls=losses_cls, loss_bbox=losses_bbox)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def losses(self):\n pass","def compute_loss(self):","def losses(self):\n # compute all kinds of losses \n\n # 1. Logits losses for classification \n\n # 2. regression loss for bbox \n\n return classification_loss, bbox_reg_loss","def compute_loss(self, obs, returns):","def compute_loss(self):\n self.prototypes = self.compute_prototypes()\n self.test_logits = self.compute_logits()\n loss = tf.nn.sparse_softmax_cross_entropy_with_logits(\n labels=self.episode.test_labels, logits=self.test_logits)\n cross_entropy_loss = tf.reduce_mean(loss)\n regularization = tf.reduce_sum(\n tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))\n loss = cross_entropy_loss + self.weight_decay * regularization\n return loss","def build_losses(self):\n self.batch_losses = tf.squared_difference(self.predicted_rv, self.label)\n self.total_loss = tf.reduce_mean(self.batch_losses)","def build_losses(self):\n self.batch_losses = tf.squared_difference(self.predicted_rv, self.label)\n self.total_loss = tf.reduce_mean(self.batch_losses)","def loss_function(self, train_head, train_tail, train_relation, train_head_corrupted, train_tail_corrupted):\n\n # train_head = tf.nn.l2_normalize(train_head, 1)\n # train_tail = tf.nn.l2_normalize(train_tail, 1)\n # train_head_corrupted = tf.nn.l2_normalize(train_head_corrupted, 1)\n # train_tail_corrupted = tf.nn.l2_normalize(train_tail_corrupted, 1)\n\n # loss = tf.reduce_mean(\n # tf.maximum(self.dict_paras['margin']\n # + self.distance(tf.add(train_head, train_relation), train_tail)\n # - self.distance(tf.add(train_head_corrupted, train_relation), train_tail_corrupted), 0.))\n\n loss = tf.reduce_mean(self.distance(tf.add(train_head, train_relation), train_tail))\n\n return loss","def losses(self):\n losses = []\n for layer in self.layers:\n losses += layer.losses\n if context.in_eager_mode():\n return losses\n\n relevant_inputs = self.inputs or []\n for i in range(1, len(self._inbound_nodes)):\n inputs = self.get_input_at(i)\n if isinstance(inputs, list):\n relevant_inputs += inputs\n else:\n relevant_inputs.append(inputs)\n reachable = tf_layers_util.get_reachable_from_inputs(relevant_inputs,\n losses)\n relevant_conditional_losses = [x for x in losses if x in reachable]\n unconditional_losses = [\n x for x in losses if x._unconditional_loss] # pylint: disable=protected-access\n return list(set(\n relevant_conditional_losses + unconditional_losses + self._losses))","def genLoss(self, *data):\r\n _, (x_unlab, _) = data\r\n z = self.getInputNoise(self.hypers['ul_BS'])\r\n fake_logits = self.D(self.G(z))\r\n g_losses = -1*logOneMinusSoftmax(fake_logits)[:,self.D.numClasses-1]\r\n return torch.mean(g_losses)","def get_losses(self):\n if self.loss is not None:\n return [self.loss]\n else:\n return []","def lossFun(inputs, targets, hprev):\n xs, hs, ys, ps = {}, {}, {}, {}\n hs[-1] = np.copy(hprev)\n loss = 0\n\n # forward pass\n for t in xrange(len(inputs)):\n xs[t] = np.zeros((vocab_size,1)) # encode in 1-of-k representation\n xs[t][inputs[t]] = 1\n hs[t] = np.tanh(np.dot(Wxh, xs[t]) + np.dot(Whh, hs[t-1]) + bh) # hidden state\n ys[t] = np.dot(Why, hs[t]) + by # unnormalized log probabilities for next chars\n ps[t] = np.exp(ys[t]-np.max(ys[t])) / np.sum(np.exp(ys[t]-np.max(ys[t]))) # probabilities for next chars\n loss += -np.log(ps[t][targets[t],0]) # softmax (cross-entropy loss)\n\n assert_array_equal(van.window_step,t)\n assert_array_equal(van.state[t-1],hs[t-1].T[0])\n assert_array_equal(van.statenet[t].forward([xs[t].T[0],hs[t-1].T[0]]),hs[t].T[0])\n assert_array_equal(van.statenet[t].net.elements[0].elements[0].elements[1].W.get(),Wxh)\n assert_array_equal(van.statenet[t].net.elements[0].elements[1].elements[1].W.get(),Whh)\n assert_array_equal(van.statenet[t].net.elements[0].elements[2].W.get(),bh.T[0])\n\n assert_array_equal(vantr.statenet[t].net.elements[0].elements[0].elements[1].W.get(),Wxh)\n assert_array_equal(vantr.statenet[t].net.elements[0].elements[1].elements[1].W.get(),Whh)\n assert_array_equal(vantr.statenet[t].net.elements[0].elements[2].W.get(),bh.T[0])\n assert_array_equal(vantr.outputnet[t].net.elements[0].elements[1].W.get(),Why)\n assert_array_equal(vantr.outputnet[t].net.elements[1].W.get(),by.T[0])\n\n #\n # #Neg\n # assert_array_almost_equal(van.outputnet[t].net.elements[0].elements[0].elements[1].W,Why)\n # assert_array_almost_equal(van.outputnet[t].net.elements[0].elements[1].W,by.T[0])\n # assert_array_almost_equal(van.outputnet[t].forward(hs[t].T[0]),ps[t].T[0])\n # assert_array_almost_equal(van.outputnet[t].forward(van.statenet[t].forward([xs[t].T[0],hs[t-1].T[0]])),ps[t].T[0])\n # assert_array_almost_equal(van.forward(xs[t].T[0]),ps[t].T[0])\n #\n # Cross\n assert_array_equal(van.outputnet[t].net.elements[0].elements[1].W.get(),Why)\n assert_array_equal(van.outputnet[t].net.elements[1].W.get(),by.T[0])\n assert_array_equal(van.outputnet[t].forward(hs[t].T[0]),ys[t].T[0])\n assert_array_equal(van.outputnet[t].forward(van.statenet[t].forward([xs[t].T[0],hs[t-1].T[0]])),ys[t].T[0])\n assert_array_equal(van.outputnet[t].forward(van.statenet[t].forward([xs[t].T[0],van.state[t-1]])),ys[t].T[0])\n assert_array_equal(van.forward(xs[t].T[0]),ys[t].T[0])\n assert_array_equal(soft.forward(ys[t].T[0]),ps[t].T[0])\n\n # backward pass: compute gradients going backwards\n dWxh, dWhh, dWhy = np.zeros_like(Wxh), np.zeros_like(Whh), np.zeros_like(Why)\n dbh, dby = np.zeros_like(bh), np.zeros_like(by)\n dhnext = np.zeros_like(hs[0])\n\n for t in reversed(xrange(len(inputs))):\n dy = np.copy(ps[t])\n dy[targets[t]] -= 1 # backprop into y. see http://cs231n.github.io/neural-networks-case-study/#grad if confused here\n dWhy += np.dot(dy, hs[t].T)\n dby += dy\n dh = np.dot(Why.T, dy) + dhnext # backprop into h\n dhraw = (1 - hs[t] * hs[t]) * dh # backprop through tanh nonlinearity\n dbh += dhraw\n dWxh += np.dot(dhraw, xs[t].T)\n dWhh += np.dot(dhraw, hs[t-1].T)\n\n #\n # #Neg\n # van.backward(negLog.dJdy_gradient(ps[t].T[0],to_one_hot_vect(targets[t],vocab_size)),opt)\n # assert_array_almost_equal(van.outputnet[t].net.elements[0].elements[1].x,hs[t].T[0])\n # assert_array_almost_equal(van.outputnet[t].net.elements[0].elements[0].elements[1].dW,dWhy)\n # assert_array_almost_equal(van.outputnet[t].net.elements[0].elements[1].dW,dby.T[0])\n # assert_array_almost_equal(van.outputnet[t].net.elements[0].elements[0].elements[1].W,Why)\n # assert_array_almost_equal(van.outputnet[t].net.elements[0].elements[1].W,by.T[0])\n #\n #Cross\n assert_array_equal(van.outputnet[t].net.elements[0].elements[1].x,hs[t].T[0])\n assert_array_equal(van.outputnet[t].net.forward(hs[t].T[0]),ys[t].T[0])\n assert_array_equal(soft.forward(van.outputnet[t].net.forward(hs[t].T[0])),ps[t].T[0])\n assert_array_equal(soft.forward(van.outputnet[t].net.forward(hs[t].T[0]))-to_one_hot_vect(targets[t],vocab_size),dy.T[0])\n\n err = cross.dJdy_gradient(ys[t].T[0],to_one_hot_vect(targets[t],vocab_size))\n\n assert_array_equal(soft.forward(van.outputnet[t].net.forward(hs[t].T[0]))-to_one_hot_vect(targets[t],vocab_size),dy.T[0])\n assert_array_equal(ps[t].T[0]-to_one_hot_vect(targets[t],vocab_size),dy.T[0])\n assert_array_equal(err,dy.T[0])\n\n van.backward(err,opt)\n\n assert_array_equal(van.outputnet[t].net.elements[0].elements[1].W.get_dW(),dWhy)\n assert_array_equal(van.outputnet[t].net.elements[0].elements[1].W.get(),Why)\n assert_array_equal(van.outputnet[t].net.elements[1].W.get_dW(),dby.T[0])\n assert_array_almost_equal(van.outputnet[t].net.elements[1].W.get(),by.T[0])\n #\n\n assert_array_equal(van.statenet[t].net.elements[0].elements[0].elements[1].W.get_dW(),dWxh)\n assert_array_equal(van.statenet[t].net.elements[0].elements[1].elements[1].W.get_dW(),dWhh)\n assert_array_equal(van.statenet[t].net.elements[0].elements[2].W.get_dW(),dbh.T[0])\n assert_array_equal(van.statenet[t].net.elements[0].elements[0].elements[1].W.get(),Wxh)\n assert_array_equal(van.statenet[t].net.elements[0].elements[1].elements[1].W.get(),Whh)\n assert_array_equal(van.statenet[t].net.elements[0].elements[2].W.get(),bh.T[0])\n assert_array_equal(van.dJdh[t],dhnext.T[0])\n\n dhnext = np.dot(Whh.T, dhraw)\n\n opt.update_model()\n trainer.learn_window(vantr,zip(to_hot_vect(inputs,vocab_size),to_hot_vect(targets,vocab_size)),crosstr,opttr)\n\n for dparam in [dWxh, dWhh, dWhy, dbh, dby]:\n np.clip(dparam, -5, 5, out=dparam) # clip to mitigate exploding gradients\n\n return loss, dWxh, dWhh, dWhy, dbh, dby, hs[len(inputs)-1]","def compute_loss(self):\n self.test_logits = self.compute_logits()\n loss = tf.nn.sparse_softmax_cross_entropy_with_logits(\n labels=self.data.test_labels, logits=self.test_logits)\n cross_entropy_loss = tf.reduce_mean(loss)\n regularization = tf.reduce_sum(\n tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))\n loss = cross_entropy_loss + self.weight_decay * regularization\n return loss","def losses(self):\n for name in self._nodes:\n if isinstance(self._nodes[name], Loss):\n yield name","def loss(self) -> KernelLoss:\n return self._loss","def calc_loss(self, x: np.ndarray, y: np.ndarray) -> float:\n return self.descent.calc_loss(x, y)","def loss(self, y: torch.Tensor, state: AlgorithmState) -> torch.Tensor:\n\n raise NotImplementedError()","def lossFun(self, inputs, targets, hprev):\n xs, hs, ys, ps = {}, {}, {}, {}\n hs[-1] = np.copy(hprev)\n loss = 0\n # forward pass\n for t in range(len(inputs)):\n xs[t] = np.zeros((self._txt_reader.vocab_size,1)) # One-hot, encode in 1-of-k representation\n xs[t][inputs[t]] = 1\n hs[t] = np.tanh(np.dot(self._Wxh, xs[t]) + np.dot(self._Whh, hs[t-1]) + self._bh) # compute chidden state\n ys[t] = np.dot(self._Why, hs[t]) + self._by # logits \n ys[t] -= ys[t].max() # for numerical stability\n ps[t] = np.exp(ys[t]) / np.sum(np.exp(ys[t])) # probabilities for next chars\n\n loss += -np.log(ps[t][targets[t],0]) # softmax (cross-entropy loss)\n\n # backward pass: compute gradients going backwards\n dWxh, dWhh, dWhy = np.zeros_like(self._Wxh), np.zeros_like(self._Whh), np.zeros_like(self._Why)\n dbh, dby = np.zeros_like(self._bh), np.zeros_like(self._by)\n\n dhnext = np.zeros_like(hs[0])\n for t in reversed(range(len(inputs))):\n dy = np.copy(ps[t])\n dy[targets[t]] -= 1 # backprop into y. see http://cs231n.github.io/neural-networks-case-study/#grad if confused here\n dWhy += np.dot(dy, hs[t].T)\n dby += dy\n dh = np.dot(self._Why.T, dy) + dhnext # backprop into h\n dhraw = (1 - hs[t] * hs[t]) * dh # backprop through tanh nonlinearity\n dbh += dhraw\n dWxh += np.dot(dhraw, xs[t].T)\n dWhh += np.dot(dhraw, hs[t-1].T)\n dhnext = np.dot(self._Whh.T, dhraw)\n\n for dparam in [dWxh, dWhh, dWhy, dbh, dby]:\n np.clip(dparam, -5, 5, out=dparam) # clip to mitigate exploding gradients\n return loss, dWxh, dWhh, dWhy, dbh, dby, hs[len(inputs)-1]","def get_loss(self):\r\n\r\n if F.loss_type==\"cosine\":\r\n self.losscos = r2d*tf.acos(1-tf.losses.cosine_distance(tf.nn.l2_normalize(self.labels,1), tf.nn.l2_normalize(self.out, 1), dim=1))\r\n self.loss = tf.losses.cosine_distance(tf.nn.l2_normalize(self.labels,1), tf.nn.l2_normalize(self.out, 1), dim=1)\r\n elif F.loss_type==\"mse2d\":\r\n xl, yl, zl = tf.split(self.labels, 3, axis=1)\r\n xo, yo, zo = tf.split(self.out, 3, axis=1)\r\n thetal, thetao = tf.asin(-yl), tf.asin(-yo)\r\n phil, phio = tf.atan2(-zl, -xl), tf.atan2(-zo, -xo)\r\n self.lb = tf.concat([thetal, phil], axis=1)\r\n self.ob = tf.concat([thetao, phio], axis=1)\r\n self.loss = tf.scalar_mul(tf.constant(r2d), tf.losses.mean_squared_error(self.lb, self.ob, 2))\r\n elif F.loss_type==\"mse3d\":\r\n self.loss = tf.losses.mean_squared_error(tf.nn.l2_normalize(self.labels, 0), tf.nn.l2_normalize(self.out, 0))","def compute_loss(self, x, gt):\n loss = sum([torch.mean((out - gt)**2) for out in self.forward(x)])\n return loss","def test_loss_hook(self, losses):\n self.runinfo[\"dev_losses\"].append(losses)","def get_loss(self):\n return self.loss / self.cnt","def compute_losses(self):\n cycle_consistency_loss_a = \\\n self._lambda_a * losses.cycle_consistency_loss(\n real_images=self.input_a, generated_images=self.cycle_images_a,\n )\n cycle_consistency_loss_b = \\\n self._lambda_b * losses.cycle_consistency_loss(\n real_images=self.input_b, generated_images=self.cycle_images_b,\n )\n\n lsgan_loss_a = losses.lsgan_loss_generator(self.prob_fake_a_is_real)\n lsgan_loss_b = losses.lsgan_loss_generator(self.prob_fake_b_is_real)\n\n g_loss_A = \\\n cycle_consistency_loss_a + cycle_consistency_loss_b + lsgan_loss_b\n g_loss_B = \\\n cycle_consistency_loss_b + cycle_consistency_loss_a + lsgan_loss_a\n\n d_loss_A = losses.lsgan_loss_discriminator(\n prob_real_is_real=self.prob_real_a_is_real,\n prob_fake_is_real=self.prob_fake_pool_a_is_real,\n )\n d_loss_B = losses.lsgan_loss_discriminator(\n prob_real_is_real=self.prob_real_b_is_real,\n prob_fake_is_real=self.prob_fake_pool_b_is_real,\n )\n\n optimizer = tf.train.AdamOptimizer(self.learning_rate, beta1=0.5)\n\n self.model_vars = tf.trainable_variables()\n\n d_A_vars = [var for var in self.model_vars if 'd_A' in var.name]\n g_A_vars = [var for var in self.model_vars if 'g_A' in var.name]\n d_B_vars = [var for var in self.model_vars if 'd_B' in var.name]\n g_B_vars = [var for var in self.model_vars if 'g_B' in var.name]\n\n self.d_A_trainer = optimizer.minimize(d_loss_A, var_list=d_A_vars)\n self.d_B_trainer = optimizer.minimize(d_loss_B, var_list=d_B_vars)\n self.g_A_trainer = optimizer.minimize(g_loss_A, var_list=g_A_vars)\n self.g_B_trainer = optimizer.minimize(g_loss_B, var_list=g_B_vars)\n\n for var in self.model_vars:\n print(var.name)\n\n # Summary variables for tensorboard\n self.g_A_loss_summ = tf.summary.scalar(\"g_A_loss\", g_loss_A)\n self.g_B_loss_summ = tf.summary.scalar(\"g_B_loss\", g_loss_B)\n self.d_A_loss_summ = tf.summary.scalar(\"d_A_loss\", d_loss_A)\n self.d_B_loss_summ = tf.summary.scalar(\"d_B_loss\", d_loss_B)","def lasso_loss(w: FloatTensor, x: FloatTensor, y: FloatTensor, lmb: float) -> float:\n return ols_loss(w, x, y, 0.0) + lmb * w.abs().sum()","def loss(self, obs: np.ndarray, acs: np.ndarray, next_obs: np.ndarray, data_statistics: dict) -> float:\n delta_pred_normalized_mus, delta_pred_normalized_stds = self.forward_pass(obs, acs, data_statistics)\n delta_pred_normalized_dist = tfd.MultivariateNormalDiag(loc=delta_pred_normalized_mus, scale_diag=delta_pred_normalized_stds)\n\n targets = normalize(next_obs - obs,\n data_statistics['delta_mean'],\n data_statistics['delta_std'])\n return -delta_pred_normalized_dist.log_prob(targets)","def get_loss(self, xs, y):\n \"*** YOUR CODE HERE ***\"\n predictedY = self.run(xs)\n return nn.SoftmaxLoss(predictedY, y)\n # return nn.SquareLoss(predictedY, y)","def _compute_loss(self):\n state, action, reward, next_state, done = self.replay_buffer.sample(self.batch_size)\n\n state = torch.FloatTensor(state)\n next_state = torch.FloatTensor(next_state)\n action = torch.LongTensor(action)\n reward = torch.FloatTensor(reward)\n done = torch.FloatTensor(done)\n\n q_values = self.dqn(state)\n q_value = q_values.gather(1, action.unsqueeze(1)).squeeze(1)\n\n next_q_values = self.target_dqn(next_state)\n next_q_value = next_q_values.max(1)[0]\n target = reward + self.discount_factor * next_q_value * (1 - done)\n\n # loss = F.smooth_l1_loss(q_value, target.detach())\n loss = F.mse_loss(q_value, target.detach())\n\n return loss","def get_loss(self, xs, y):\n \"*** YOUR CODE HERE question 4 ***\"\n return nn.SoftmaxLoss(self.run(xs), y)","def lossFun(review, target, hprev):\n xs, hs, ys, ps = {}, {}, {}, {}\n hs[-1] = np.copy(hprev)\n loss = 0\n\n # forward pass\n for t in range(len(review)):\n xs[t] = np.zeros((vector_len,1)) # encode in 1-of-k representation\n for j in range(32):\n xs[t][j] = review[t][j]\n hs[t] = np.tanh(np.dot(Wxh, xs[t]) + np.dot(Whh, hs[t-1]) + bh) # hidden state\n\n #Many 2 one\n last = len(review) - 1\n ys = np.dot(Why, hs[last]) + by # unnormalized log probabilities for next chars\n ps = np.exp(ys) / np.sum(np.exp(ys)) # probabilities for next chars\n loss = -np.log(ps[target,0]) # softmax (cross-entropy loss)\n\n # backward pass: compute gradients going backwards\n dWxh, dWhh, dWhy = np.zeros_like(Wxh), np.zeros_like(Whh), np.zeros_like(Why)\n dbh, dby = np.zeros_like(bh), np.zeros_like(by)\n dhnext = np.zeros_like(hs[0])\n\n dy = np.subtract(ps,target) # backprop into y. see http://cs231n.github.io/neural-networks-case-study/#grad if confused here\n dWhy += np.dot(dy, hs[last].T)\n dby += dy\n dh = np.dot(Why.T, dy) + dhnext # backprop into h\n for t in reversed(range(len(review))):\n dhraw = (1 - (hs[t] * hs[t].T)) * dh # backprop through tanh nonlinearity\n dbh += dhraw\n dWxh += np.dot(dhraw, xs[t].T)\n dWhh += np.dot(dhraw, hs[t-1].T)\n dhnext = np.dot(Whh.T, dhraw)\n for dparam in [dWxh, dWhh, dWhy, dbh, dby]:\n np.clip(dparam, -5, 5, out=dparam) # clip to mitigate exploding gradients\n return loss, dWxh, dWhh, dWhy, dbh, dby, hs[last]","def loss_fn(self, lbl, y):\n\n binlbl = self._to_device(lbl[:,0]>.5)\n # center = self._to_device(lbl[:,3]) \n offset = 5. * self._to_device(lbl[:,1:]) \n\n loss = self.criterion(y[:,:2], offset) \n loss2 = self.criterion2(y[:,2], binlbl)\n\n # loss3 = self.criterion(y[:,3], center)\n\n loss = loss + loss2\n return loss","def compute_loss(self):\n def calc_loss(inputs, outputs):\n reconstruction_loss = tf.metrics.binary_crossentropy(\n tf_flat(inputs), tf_flat(outputs))\n reconstruction_loss *= OUT_SIZE * OUT_SIZE\n kl_loss = -0.5 * tf.reduce_sum(1.0 + self.log_sigma - tf.square(\n self.mu) - tf.exp(self.log_sigma), 1)\n return tf.reduce_mean(reconstruction_loss + kl_loss)\n return calc_loss","def loss_fn(model):\n with flax.deprecated.nn.stateful() as state:\n with flax.deprecated.nn.stochastic(dropout_rng):\n logits = model(example, train=True)\n loss, weight_sum = compute_weighted_cross_entropy(logits, targets)\n mean_loss = loss / weight_sum\n return mean_loss, (logits, state)","def compute_loss(self, X, y):\r\n # INSERT YOUR CODE HERE\r\n #raise Exception('Function not yet implemented!')\r\n\r\n # Computing the loss using the below formula\r\n # Loss = -(1/m)*sum( (y_i)*log(σ(wTx_i)) + (1-y_i)*log(1 - σ(wTx_i)))\r\n # m = number of examples and i for ith example\r\n\r\n loss = 0\r\n X = np.append(X, np.array([[1]]*X.shape[0]), axis=1)\r\n # for idx,example in enumerate(X):\r\n # loss = loss + y[idx] * np.log(self.sigmoid(np.dot(example, self.w))) + (1 - y[idx]) * np.log(1 - self.sigmoid(np.dot(example, self.w)))\r\n # loss = -loss/ X.shape[0]\r\n\r\n loss = -np.mean(y * np.log(self.sigmoid(np.dot(X, self.w))) + (1 - y) * np.log(1 - self.sigmoid(np.dot(X, self.w))))\r\n return loss","def compute_loss(self, x, label):\n # Forward propagation\n y_hat = self.forward_pass(x)\n return -np.log(y_hat[label])","def compute_loss(self, inputs):\r\n outputs = self.net.compute_outputs(inputs)\r\n loss_grad = self.net.compute_loss_grad(outputs - inputs)\r\n loss = np.sum((inputs - outputs) ** 2, axis=0).mean() / 2.0\r\n return loss, loss_grad","def loss(self, x, y):\n (N,D) = x.shape\n k1 = np.matmul(x,np.transpose(self.w)) + self.b\n y1 = y.reshape((N,1))\n c2 = 0\n c1 = (np.log(1+np.exp(-1*y1*k1)))\n for i in range(N):\n c2 += c1[i][0]\n l = c2 / N + (0.5 * self.l2_reg * np.dot(self.w,np.transpose(self.w)))\n l1 = l[0][0]\n return l1\n\n\n #raise NotImplementedError","def l1_loss(obs, actual):\n # (tf.Tensor, tf.Tensor, float) -> tf.Tensor\n return tf.reduce_sum(tf.abs(obs - actual) , 1)","def build_loss(self):\n if self.mode != \"encode\":\n total_loss = tf.losses.get_total_loss()\n tf.summary.scalar(\"losses/total\", total_loss)\n\n self.total_loss = total_loss","def __loss(self, h, y):\n return (-y*np.log(h)-(1-y)*np.log(1-h)).mean()","def loss(self, predicts, labels, objects_num):\n\n def cond1(num, object_num, loss, predict, label, nilboy):\n return num < object_num\n\n class_loss = tf.constant(0, tf.float32)\n object_loss = tf.constant(0, tf.float32)\n noobject_loss = tf.constant(0, tf.float32)\n coord_loss = tf.constant(0, tf.float32)\n loss = [0, 0, 0, 0]\n for i in range(self.batch_size):\n predict = predicts[i, :, :, :]\n label = labels[i, :, :]\n object_num = objects_num[i]\n nilboy = tf.ones([self.cell_size, self.cell_size, 1])\n tuple_results = tf.while_loop(cond1, self.body1,\n [\n tf.constant(0), object_num,\n [class_loss, object_loss, noobject_loss, coord_loss],\n predict, label, nilboy\n ])\n for j in range(4):\n loss[j] = loss[j] + tuple_results[2][j]\n nilboy = tuple_results[5]\n\n tf.add_to_collection('losses', (loss[0] + loss[1] + loss[2] + loss[3]) / self.batch_size)\n\n tf.summary.scalar('class_loss', loss[0] / self.batch_size)\n tf.summary.scalar('object_loss', loss[1] / self.batch_size)\n tf.summary.scalar('noobject_loss', loss[2] / self.batch_size)\n tf.summary.scalar('coord_loss', loss[3] / self.batch_size)\n tf.summary.scalar('weight_loss', tf.add_n(tf.get_collection('losses')) - (\n loss[0] + loss[1] + loss[2] + loss[3]) / self.batch_size)\n\n return tf.add_n(tf.get_collection('losses'), name='total_loss'), nilboy","def get_loss(self):\n raise NotImplementedError","def setup_loss(self):\n with vs.variable_scope(\"loss\"):\n self.loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=self.label_placeholder, logits=self.label_predictions))","def calc_loss(self, outputs, labels):\n information_loss = self.bottleneck.buffer_capacity.mean() # Taking the mean is equivalent of scaling with 1/K\n cross_entropy = F.cross_entropy(outputs, target=labels)\n total = cross_entropy + self.beta * information_loss\n self.ce_loss.append(cross_entropy.cpu().detach().numpy())\n self.info_loss.append(information_loss.cpu().detach().numpy())\n self.total_loss.append(total.cpu().detach().numpy())\n return total","def get_loss(self, xs, y):\n \"*** YOUR CODE HERE ***\"\n y_pred = self.run(xs)\n return nn.SoftmaxLoss(y_pred,y)","def pseudo_loss(self, params, batches):\n loss = 0\n for batch in batches:\n states = batch[\"states\"]\n actions = batch[\"actions\"]\n returns = batch[\"returns\"]\n\n preds = self.predict_jax(params, states)\n\n baseline = jnp.mean(returns, axis=0)\n preds_select = jnp.take_along_axis(preds, jnp.expand_dims(actions, axis=2), axis=2).squeeze()\n loss += (-jnp.mean(jnp.sum(preds_select * (returns - baseline))))\n\n return loss + self.l2_regularizer(params, 0.001) # try to divide by len(batches)?","def loss_op(self):\n return self.loss","def _init_loss(self) -> None:\n labels_one_hot = tf.one_hot(self.labels, depth=Data.num_classes)\n cross_entropy_loss = tf.losses.softmax_cross_entropy(labels_one_hot, self.logits)\n # cross_entropy_loss is a scalar\n tf.add_to_collection(tf.GraphKeys.LOSSES, cross_entropy_loss)\n self.loss = tf.add_n(tf.get_collection(tf.GraphKeys.LOSSES))\n self.logger_factory.add_scalar('loss', self.loss, log_frequency=10)\n self.logger_factory.add_scalar('cross_entropy_loss', cross_entropy_loss, log_frequency=25)","def _generator_loss(self, y_hat):\n\n l = -tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels = tf.zeros(tf.shape(y_hat)),logits = y_hat ))\n print('generatorloss shape',tf.shape(l))\n return l","def lossFunc(inputs, targets, hprev):\n xs, hs, ys, ps = {}, {}, {}, {} # input, hidden, output, out_prob states for each time t\n hs[-1] = np.copy(hprev)\n loss = 0\n \n # forward pass\n for t in xrange(len(inputs)):\n xs[t] = np.zeros((vocab_size,1)) \n xs[t][inputs[t]] = 1. # convert input to one-hot\n hs[t] = np.tanh(np.dot(Wxh, xs[t]) + np.dot(Whh, hs[t-1]) + bh)\n ys[t] = np.dot(Why, hs[t]) + by\n ps[t] = np.exp(ys[t]) / np.sum(np.exp(ys[t]))\n loss += -np.log(ps[t][targets[t],0])\n \n # backward pass\n dWxh, dWhh, dWhy = np.zeros_like(Wxh), np.zeros_like(Whh), np.zeros_like(Why)\n dbh, dby = np.zeros_like(bh), np.zeros_like(by)\n dhnext = np.zeros_like(hs[0])\n for t in reversed(xrange(len(inputs))):\n # backprop into y\n dy = np.copy(ps[t])\n dy[targets[t]] -= 1\n # backprop into Why, hs, and by\n dWhy += np.dot(dy, hs[t].T)\n dby += dy\n dh = np.dot(Why.T, dy) + dhnext\n # backprop through tanh activition\n dhraw = (1 - hs[t] * hs[t]) * dh\n # backprop into Wxh, Whh, hs, and bh\n dbh += dhraw\n dWxh += np.dot(dhraw, xs[t].T)\n dWhh += np.dot(dhraw, hs[t-1].T)\n dhnext = np.dot(Whh.T, dhraw)\n # clip gradient preventing exploding\n for dparam in [dWxh, dWhh, dWhy, dbh, dby]:\n np.clip(dparam, -5, 5, out=dparam)\n\n return loss, dWxh, dWhh, dWhy, dbh, dby, hs[len(inputs)-1]","def calculate_loss(self, X, y):\n probs = self.predict(X)\n\n num_examples = X.shape[0]\n\n sub = np.subtract(probs, y)\n abs_sum = np.abs(sub)\n sm = np.sum(abs_sum)\n loss = 1 - sm / num_examples\n print(\"Current loss: [ \" + str(\"{:6.5f}\").format(loss) + \" ]\")\n return loss","def losses(self, win2: ArrayLike, lose2: ArrayLike) -> ArrayLike:\n return -1 * torch.log(self.probs(win2, lose2))","def build_loss(self):\n import tensorflow as tf\n\n y_1d = [tf.reduce_sum(tf.multiply(self.variables[\"y\"][i], self.variables[\"y_action\"][i]), axis=1) for i in range(len(self.variables[\"y\"]))]\n loss = np.sum([tf.nn.l2_loss(y_1d[i] - self.variables[\"y_true\"]) for i in range(len(y_1d))])\n\n l1_reg = 0\n l2_reg = 0\n\n keys = sorted(self.variables.keys())\n keys = [key for key in keys if critere_keys(key) and \"W\" in key]\n for key in keys:\n l1_reg += tf.reduce_sum(tf.abs(self.variables[key]))\n l2_reg += tf.nn.l2_loss(self.variables[key])\n\n self.loss = loss + self.alpha_reg * l1_reg + self.beta_reg * l2_reg\n\n self.train_step = tf.train.RMSPropOptimizer(self.decay_learning_rate,\n decay=0.99, momentum=0., centered=True).minimize(self.loss, global_step=self.global_step)","def _get_loss(self):\n raise NotImplementedError","def loss(self):\n return self._loss","def _loss(self):\n\n cross_entropy = tf.reduce_mean(-tf.log(self.probability + epsilon) * self.y)\n self.loss = cross_entropy\n\n self.accuracy = tf.reduce_mean(\n tf.cast(tf.equal(tf.argmax(self.y, 1), self.prediction), tf.float32))","def computeLoss(self):\n return sum(np.arccosh(-minkowskiArrayDot(self.examples, self.centroid)) ** 2)[0] / np.shape(self.examples)[0]","def compute_loss(self, sample):\n observations_batch, actions_batch, return_batch, masks_batch, \\\n old_action_log_probs_batch, adv_targ = sample\n\n assert old_action_log_probs_batch.shape == (self.mini_batch_size, 1)\n assert adv_targ.shape == (self.mini_batch_size, 1)\n assert return_batch.shape == (self.mini_batch_size, 1)\n\n values, action_log_probs, dist_entropy = self.evaluate_actions(\n observations_batch, actions_batch)\n\n assert values.shape == (self.mini_batch_size, 1)\n assert action_log_probs.shape == (self.mini_batch_size, 1)\n assert values.requires_grad\n assert action_log_probs.requires_grad\n assert dist_entropy.requires_grad\n\n # [TODO] Implement policy loss\n ratio = torch.exp(action_log_probs - old_action_log_probs_batch)\n surr1 = ratio * adv_targ\n surr2 = torch.clamp(ratio, 1.0 - self.clip_param, 1.0 + self.clip_param) * adv_targ\n policy_loss = -torch.min(surr1, surr2).mean()\n\n # [TODO] Implement value loss\n value_loss = F.mse_loss(return_batch, values)\n\n # This is the total loss\n loss = policy_loss + self.config.value_loss_weight * value_loss - self.config.entropy_loss_weight * dist_entropy\n\n return loss, policy_loss, value_loss, dist_entropy","def compute_loss(y, tx, w):\n # ***************************************************\n # INSERT YOUR CODE HERE\n # TODO: compute loss by MSE / MAE\n # ***************************************************\n \n # vector e\n e = compute_e(y, tx, w)\n N = compute_N(e)\n L_MSE = np.dot(np.matrix.transpose(e), e)\n L_MSE = L_MSE / (2 * N)\n \n return L_MSE","def update_losses(losses, l_loss, c_loss):\n losses[0] += l_loss\n losses[1] += c_loss\n losses[2] += l_loss\n losses[3] += c_loss","def loss(self):\n return la.norm(self.resids) / self.normX","def build_loss(self):\n\n opt = tf.train.AdamOptimizer(self.learning_rate)\n mse = tf.losses.mean_squared_error(self.label[-1], self.outputs[-1])\n loss = tf.losses.get_total_loss()\n\n return mse, loss","def loss(self, data):\n loss, smoothed, lr = data\n\n curves = []\n\n curve_keys = ['color', 'linestyle', 'linewidth', 'alpha']\n\n if loss is not None:\n loss_name = self.config.get('label', f\"loss #{self.index + 1}\")\n loss_label = f'{loss_name} ⟶ {loss[-1]:2.3f}'\n final_window = self.config.get('final_window', None)\n if final_window is not None:\n final_window = min(final_window, len(loss))\n final = np.mean(loss[-final_window:])\n loss_label += f\"\\nmean over last {final_window} iterations={final:2.3f}\"\n\n loss_config = self.config.filter(keys=curve_keys, prefix='curve_')\n loss_curve = self.ax.plot(loss, label=loss_label, **loss_config)\n curves.extend(loss_curve)\n\n if smoothed is not None:\n smoothed_color = scale_lightness(loss_config['color'], scale=.5)\n smooth_window = self.config.get('window')\n smoothed_label = self.config.get('smoothed_label', loss_name)\n smoothed_label = smoothed_label + '\\n' if smoothed_label else ''\n smoothed_label += f'smoothed with window {smooth_window}'\n smoothed_curve = self.ax.plot(smoothed, label=smoothed_label, color=smoothed_color, linestyle='--')\n curves.extend(smoothed_curve)\n\n if lr is not None:\n lr_ax = self.ax if loss is None else self.twin_ax\n lr_label = f'learning rate №{self.index + 1} ⟶ {lr[-1]:.0e}'\n lr_config = self.config.filter(keys=curve_keys, prefix='lr_')\n lr_curve = lr_ax.plot(lr, label=lr_label, **lr_config)\n lr_ax.set_ylabel('Learning rate', fontsize=12)\n curves.extend(lr_curve)\n\n return curves","def calculate_loss(self, pred, gold, smoothing=False):\n gold = gold.contiguous().view(-1)\n if smoothing:\n epsilon = 0.1\n n_class = pred.size(1)\n one_hot = torch.zeros_like(pred).scatter(1, gold.view(-1, 1), 1)\n one_hot = one_hot * (1 - epsilon) + \\\n (1 - one_hot) * epsilon / (n_class - 1)\n\n log_prb = F.log_softmax(pred, dim=1)\n # create non-padding mask with torch.ne()\n non_pad_mask = gold.ne(self.constants.PAD)\n loss = -(one_hot * log_prb).sum(dim=1)\n # losses are averaged later\n loss = loss.masked_select(non_pad_mask).sum()\n else:\n loss = F.cross_entropy(\n pred, gold, ignore_index=self.constants.PAD, reduction='sum')\n return loss","def loss(self, X, y=None):\n X = X.astype(self.dtype)\n mode = 'test' if y is None else 'train'\n\n # We are gonna store everythin in a dictionnary hidden\n hidden = {}\n hidden['h0'] = X.reshape(X.shape[0], np.prod(X.shape[1:]))\n\n for i in range(self.L):\n idx = i + 1\n # Naming of the variable\n w = self.params['W' + str(idx)]\n b = self.params['b' + str(idx)]\n h = hidden['h' + str(idx - 1)]\n\n # Computing of the forward pass.\n # Special case of the last layer (output)\n if idx == self.L:\n h, cache_h = affine_forward(h, w, b)\n hidden['h' + str(idx)] = h\n hidden['cache_h' + str(idx)] = cache_h\n\n # For all other layers\n else:\n h, cache_h = affine_relu_forward(h, w, b)\n hidden['h' + str(idx)] = h\n hidden['cache_h' + str(idx)] = cache_h\n\n scores = hidden['h' + str(self.L)]\n\n # If test mode return early\n if mode == 'test':\n return scores\n\n # Computing of the loss\n data_loss, dscores = softmax_loss(scores, y)\n reg_loss = 0\n for w in [self.params[f] for f in self.params.keys() if f[0] == 'W']:\n reg_loss += 0.5 * self.reg * np.sum(w * w)\n\n loss = data_loss + reg_loss\n\n # Backward pass\n\n hidden['dh' + str(self.L)] = dscores\n for i in range(self.L)[::-1]:\n idx = i + 1\n dh = hidden['dh' + str(idx)]\n h_cache = hidden['cache_h' + str(idx)]\n if idx == self.L:\n dh, dw, db = affine_backward(dh, h_cache)\n hidden['dh' + str(idx - 1)] = dh\n hidden['dW' + str(idx)] = dw\n hidden['db' + str(idx)] = db\n else:\n dh, dw, db = affine_relu_backward(dh, h_cache)\n hidden['dh' + str(idx - 1)] = dh\n hidden['dW' + str(idx)] = dw\n hidden['db' + str(idx)] = db\n\n # w gradients where we add the regulariation term\n list_dw = {key[1:]: val + self.reg * self.params[key[1:]]\n for key, val in hidden.iteritems() if key[:2] == 'dW'}\n # Paramerters b\n list_db = {key[1:]: val for key, val in hidden.iteritems() if key[:2] == 'db'}\n # Parameters gamma\n list_dgamma = {key[1:]: val for key, val in hidden.iteritems() if key[:6] == 'dgamma'}\n # Paramters beta\n list_dbeta = {key[1:]: val for key, val in hidden.iteritems() if key[:5] == 'dbeta'}\n grads = {}\n grads.update(list_dw)\n grads.update(list_db)\n grads.update(list_dgamma)\n grads.update(list_dbeta)\n return loss, grads","def compute_loss(self, x, y):\n\n self.batch_size = x.shape[0]\n self.x = x\n self.y = y\n self.soft = self.softmax(x) + 10**(-11)\n out = np.zeros(self.batch_size)\n for i in range(self.batch_size):\n out[i] = -(y[i] @ np.log(self.soft[i]))\n\n return out","def get_loss(self, Loss, results, inputs, device):\n return","def reset_loss_sums():\n\tglobal decoder_nat_loss, decoder_syn_loss, KLD_syn_loss, KLD_nat_loss, regressor_nat, regressor_syn\n\tdecoder_nat_loss = decoder_syn_loss = KLD_syn_loss = KLD_nat_loss = regressor_nat = regressor_syn = 0","def compute_loss(\n self,\n x: torch.Tensor,\n y: torch.Tensor,\n y_hat: torch.Tensor,\n extras: List[torch.Tensor],\n train_context: TrainContext,\n ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:","def _calc_loss(self, fvs, labels, w, b):\n\n loss = 0.5 * self.lda * (np.linalg.norm(w) ** 2)\n tmp = sum(map(lambda x, y: (x - y) ** 2, fvs.dot(w) + b, labels))\n loss += tmp / fvs.shape[0]\n\n return loss","def loss_total(self):\r\n def loss(y_true, y_pred):\r\n l2 = 1/2*K.sum(K.square(y_true-y_pred))\r\n\r\n return l2\r\n return loss","def setup_loss(self, h_s, h_e):\n with vs.variable_scope(\"loss\"):\n # masked_h_s = tf.boolean_mask(h_s, self.context_mask_placeholder)\n # masked_h_e = tf.boolean_mask(h_e, self.context_mask_placeholder)\n # loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(h_s, self.start_span_placeholder) +\n # tf.nn.softmax_cross_entropy_with_logits(h_e, self.end_span_placeholder))\n masked_h_s = tf.add(h_s, (1 - tf.cast(self.context_mask_placeholder, 'float')) * (-1e30))\n masked_h_e = tf.add(h_e, (1 - tf.cast(self.context_mask_placeholder, 'float')) * (-1e30))\n loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(masked_h_s, self.start_span_placeholder) +\n tf.nn.softmax_cross_entropy_with_logits(masked_h_e, self.end_span_placeholder))\n total_loss = loss\n return total_loss, masked_h_s, masked_h_e","def loss(self, y: np.ndarray, y_hat: np.ndarray) -> float:\n losses = -(y * np.log(y_hat) + (1 - y) * np.log(1 - y_hat))\n return losses.mean() + self.reg / self.num_parameters * (\n (self.v[:, -1] ** 2).sum() + (self.w ** 2).sum()\n )","def compute_loss_lasso(y, tx, w, lambda_):\n e = y - tx.dot(w)\n\n return e.dot(e)/(2 * len(e)) + lambda_ * sum(abs(w))","def add_loss_op(self, preds):\n ### YOUR CODE HERE (~2-4 lines)\n trans = tf.get_variable('trans',\n shape=[Config.n_classes, Config.n_classes],\n initializer=tf.contrib.layers.xavier_initializer())\n log_likelihood, _ = crf_log_likelihood(preds,\n self.labels_placeholder,\n self.length_placeholder,\n trans)\n #log_likelihood = tf.boolean_mask(log_likelihood, self.mask_placeholder)\n loss = tf.reduce_mean(-1.0 * log_likelihood)\n \n ### END YOUR CODE\n return trans, loss","def l1_loss(params):\n \"\"\" It is a vec for each branch\"\"\"\n loss_branches_vec = []\n # TODO This is hardcoded but all our cases rigth now uses four branches\n for i in range(len(params['branches']) -1):\n loss_branches_vec.append(torch.abs((params['branches'][i] - params['targets'])\n * params['controls_mask'][i])\n * params['branch_weights'][i])\n \"\"\" The last branch is a speed branch\"\"\"\n # TODO: Activate or deactivate speed branch loss\n loss_branches_vec.append(torch.abs(params['branches'][-1] - params['inputs'])\n * params['branch_weights'][-1])\n return loss_branches_vec, {}","def loss_(self, batch):\n raise NotImplementedError","def _initLoss(self):\n\n return torch.nn.MSELoss()","def get_loss(self, xs, y):\n return nn.SoftmaxLoss(self.run(xs), y)","def _build_loss(self):\n self._build_loss_D()\n self._build_loss_G()\n tf.add_to_collection('losses', self.loss_D)\n tf.add_to_collection('losses', self.loss_G)","def svm_loss(x, y):\n\n x = x.reshape((-1,1))\n y = y.reshape((-1,1))\n N,_ = x.shape\n \n y_p = np.where(y == 1,1,-1)\n \n losses = np.maximum(0,1-(x*y_p))\n loss = np.sum(losses)/N\n dx = np.where(losses > 0, 1, 0)*(-y_p)/N\n dx = dx.reshape((-1,))\n\n return loss, dx","def compute_loss(self, **kwargs):\n raise NotImplementedError","def backward(self):\n self.loss_similarity = [LNCC(warped_img, self.batch_fixed, self.corr_kernel) for warped_img in self.warped_img_list]\n self.loss_similarity_mean = torch.mean(torch.stack(self.loss_similarity))\n self.loss_smooth = [GradNorm(disp_map) for disp_map in self.disp_list]\n self.loss_smooth_mean = torch.mean(torch.stack(self.loss_smooth))\n if len(self.strain_compensated_list) > 1:\n self.loss_consistency_strain = [LNCC(self.strain_compensated_list[t-1][:,:,143:-143,:], self.strain_compensated_list[t][:,:,143:-143,:], self.corr_kernel) for t in range(1, len(self.strain_compensated_list))]\n self.loss_consistency_strain_mean = torch.mean(torch.stack(self.loss_consistency_strain))\n self.loss_total = 1 - self.loss_similarity_mean + self.loss_smooth_mean * self.alpha + (1 - self.loss_consistency_strain_mean) * self.beta\n else:\n self.loss_total = 1 - self.loss_similarity_mean + self.loss_smooth_mean * self.alpha","def loss(self, predictions, labels, labels_2, inputs, raw_inp):\n next_word = labels\n curr_label = tf.cast(labels_2, tf.float32)\n\n \n prediction_word = predictions[0]\n prediction_label = predictions[1]\n\n #initialising variables\n cross_entropy_next = tf.constant(0)\n cross_entropy_label = tf.constant(0)\n cross_entropy_label_similarity = tf.constant(0)\n cross_entropy_emb = tf.constant(0)\n \n self.prec_label, self.prec_label_op = tf.constant(1), tf.constant(1)\n self.recall_label, self.recall_label_op = tf.constant(1), tf.constant(1)\n self.label_sigmoid = tf.constant(0)\n\n \n if self.config.solver._next_node_loss:\n #<EOS> and <UNK> get encoded as 1 and 0 respectively\n #Count loss only for actual nodes\n \n raw_inp1 = tf.greater(tf.slice(raw_inp, [0,0],[-1, 1]), -1) #Make first column all True\n raw_inp2 = tf.greater(tf.slice(raw_inp, [0,1],[-1, -1]), 1) #Make only non (<EOS>,<UNK>) True\n raw_inp = tf.concat(1, [raw_inp1, raw_inp2]) #concatenate back to original shape\n raw_inp = tf.transpose(raw_inp) #Transpose raw_inp from batch*step to step*batch\n mask = [tf.reshape(tf.cast(raw_inp, tf.float32), [-1])] #Convert from bool to float and flatten array\n\n\n #<EOS> and <UNK> get encoded as 1 and 0 respectively\n #Transpose raw_inp from batch*step to shape*batch\n #Count loss only for actual nodes\n #Convert from bool to float and flatten array\n #mask = [tf.reshape(tf.cast(tf.greater(tf.transpose(raw_inp), 0), tf.float32), [-1])]\n\n #Vector to weigh different word losses\n #all_ones = [tf.ones([self.config.batch_size * self.config.num_steps])]\n\n #cross entropy loss for next word prediction\n cross_entropy_next = sequence_loss([prediction_word],[tf.reshape(next_word, [-1])], mask, self.config.data_sets._len_vocab)\n tf.add_to_collection('total_loss', cross_entropy_next)\n\n if self.config.solver._curr_label_loss:\n #Get the slice of tensor representing label '0' for all batch.seq\n #'0' label is assigned for <EOS> and the nodes whose labels are not known\n #Valid errors are only those which don't have '0' label\n valid = tf.cast(tf.less(tf.slice(curr_label, [0,0,0], [self.config.num_steps, self.config.batch_size, 1]), tf.constant(0.5)), tf.float32)\n #replicate along 3rd axis\n valid = tf.tile(valid, tf.pack([1,1,tf.shape(curr_label)[2]]))\n \n #Sigmoid activation\n self.label_sigmoid = tf.sigmoid(prediction_label)\n #binary cross entropy for labels\n cross_loss = tf.add(tf.log(1e-10 + self.label_sigmoid)*curr_label,\n tf.log(1e-10 + (1-self.label_sigmoid))*(1-curr_label))\n #only consider the loss for valid label predictions\n #[TODO] mean of all or mean of only valid ???\n cross_entropy_label = -1*tf.reduce_mean(tf.reduce_sum(cross_loss*valid,2))\n tf.add_to_collection('total_loss', cross_entropy_label)\n\n\n if self.config.solver._label_similarity_loss: \n #Label similarity loss \n label_sigmoid = tf.sigmoid(pred_label_reshaped)\n part1 = tf.slice(label_sigmoid, [0,0,0], [self.config.num_steps-1, self.config.batch_size, self.config.data_sets._len_labels])\n part2 = tf.slice(label_sigmoid, [1,0,0], [self.config.num_steps-1, self.config.batch_size, self.config.data_sets._len_labels])\n\n #Exponential weightage -> [r**(n-1), r**(n-2), ... , r**2. r**1]\n label_diffusion = tf.constant([self.config.data_sets._diffusion_rate**i for i in range(self.config.num_steps-1,0,-1)])\n cross_loss_sim = tf.add(tf.log(1e-10 + part1)*part2, tf.log(1e-10 + (1-part1))*(1-part2))\n #prediction is 3 dimensional (seq x batch x label_len), reduce along axis of label_len\n #Sum over each label error -> take mean over the batch -> sum for the sequence\n cross_entropy_label_similarity = tf.reduce_sum(tf.reduce_mean(-tf.reduce_sum(cross_loss_sim, 2),1) * label_diffusion)\n tf.add_to_collection('total_loss', cross_entropy_label_similarity)\n\n \n if self.config.solver._embedding_loss:\n #embedding similarity loss\n #Matching First input's embeddings with embeddings of other inputs\n #[TODO] reverse feed of input AND reverse diffusion rate\n \n emb_part1 = tf.slice(inputs, [self.config.num_steps-2,0,0], [1, self.config.batch_size, self.config.mRNN._embed_size])\n emb_part2 = tf.slice(inputs, [0,0,0], [self.config.num_steps-1, self.config.batch_size, self.config.mRNN._embed_size])\n\n #Exponential weightage -> [r**(n-1), r**(n-2), ... , r**2. r**1]\n label_diffusion = tf.constant([self.config.data_sets._diffusion_rate**i for i in range(self.config.num_steps-1,0,-1)])\n #Broadcastive Subtraction\n mse_emb = tf.reduce_mean(tf.square(emb_part2 - emb_part1),2)\n cross_entropy_emb = tf.reduce_sum(tf.reduce_mean(mse_emb,1) * label_diffusion) * self.config.data_sets._emb_factor\n tf.add_to_collection('total_loss', cross_entropy_emb)\n\n if self.config.solver._L2loss:\n vars = tf.trainable_variables() \n lossL2 = tf.add_n([tf.nn.l2_loss(v) for v in vars])*0.00001\n tf.add_to_collection('total_loss', lossL2)\n\n loss = tf.add_n(tf.get_collection('total_loss'))\n grads, = tf.gradients(loss, [self.embedding]) \n\n tf.summary.scalar('next_node_loss', cross_entropy_next)\n tf.summary.scalar('curr_label_loss', cross_entropy_label)\n tf.summary.scalar('label_similarity_loss', cross_entropy_label_similarity )\n tf.summary.scalar('emb_loss', cross_entropy_emb)\n tf.summary.scalar('total_loss', tf.reduce_sum(loss))\n \n return [loss, cross_entropy_next, cross_entropy_label, cross_entropy_label_similarity, cross_entropy_emb, grads]","def loss_fn(model):\n with flax.nn.stateful(state) as new_state:\n with flax.nn.stochastic(prng_key):\n logits = model(batch['image'])\n loss = cross_entropy_loss(logits, batch['label'])\n # TODO(britefury): check if applying L2 regularization to weights but\n # *not* biases improves results\n weight_penalty_params = jax.tree_leaves(model.params)\n weight_l2 = sum([jnp.sum(x ** 2)\n for x in weight_penalty_params\n if x.ndim > 1])\n weight_penalty = l2_reg * 0.5 * weight_l2\n loss = loss + weight_penalty\n return loss, (new_state, logits)","def calculate_loss(self, logits, one_hot_true_labels):\n loss = 0.0\n loss += self.calculate_loss_by_granularity(logits, one_hot_true_labels, end=self.coarse_slice)\n loss += self.calculate_loss_by_granularity(logits, one_hot_true_labels, ini=self.coarse_slice,\n end=self.fine_slice)\n loss += self.calculate_loss_by_granularity(logits, one_hot_true_labels, ini=self.fine_slice)\n return loss","def loss(\n self,\n model_in: torch.Tensor,\n target: Optional[torch.Tensor] = None,\n idx=None,\n next_obs=None,\n eval=False,\n ) -> torch.Tensor:\n loss = self._vaml_loss(model_in, target, idx=idx, next_obs=next_obs, eval=False)\n if self.add_mse:\n loss += self._mse_loss(model_in, target).mean(-1, keepdim=True)\n return loss.mean()","def loss_fn(outputs, labels, wts):\n\n # reshape labels to give a flat vector of length batch_size*seq_len\n loss_noreduce = nn.BCEWithLogitsLoss(reduce=False)\n loss = torch.mean(loss_noreduce(outputs, labels)*wts)\n\t\n # compute cross entropy loss for all tokens\n return loss","def _compute_loss(self, parameters, inputs, ground_truth):\n predictions = self.network_forward(parameters, inputs)\n loss = np.mean((ground_truth - predictions) ** 2)\n return loss","def _compute_loss(self, parameters, inputs, ground_truth):\n predictions = self.network_forward(parameters, inputs)\n loss = np.mean((ground_truth - predictions) ** 2)\n return loss","def compute_loss(self, targets, logits, seq_length):\n\n\t\twith tf.name_scope('evaluate_loss'):\n\t\t\tloss, norm = self.loss_computer(targets, logits, seq_length)\n\t\t\t\n\t\treturn loss, norm","def compute_loss(self, *args, **kwargs):\n raise NotImplementedError","def rmse_loss(y, label):\r\n losses = np.sqrt(np.mean((y - label) ** 2, axis=1))\r\n return losses","def loss(self, X, y):\n pass","def loss_fn(self, targets, outputs, model):","def getLoss(self, x_test, t_test):\n x_t = Variable(x_test, requires_grad=False)\n #Feed inputes into neural network\n t_pred = self.model(x_t)\n #Now lets compute out loss\n loss = self.loss_fn(t_pred, t_test)\n return loss","def loss(self, logits, labels):\r\n return tf.reduce_mean(tf.keras.losses.binary_crossentropy(labels,logits))","def L1(yhat, y):\n\n loss = np.sum(np.abs(y - yhat))\n \n return loss","def halo_loss(predicted, labels, weights, obj, k_neg=7.):\n\n global_loss = torch.FloatTensor([0]).to(device)\n x_lsm = F.log_softmax(predicted, dim=1)\n x_sm = F.softmax(predicted, dim=1)\n\n v_weights = weights\n v_labels = labels\n\n for i in range(predicted.size(0)): # Foreach sample\n\n # predicted[0] is preserved for background\n bg = v_weights[i, 0] * v_labels[i, 0]\n global_loss += -torch.sum(bg * x_lsm[i, 0]) / torch.sum(bg)\n\n if 0 == len(obj[i][1:]):\n continue\n\n indexes = torch.LongTensor(obj[i][1:]).to(device) # One is for labels\n\n valid_labels = v_labels[i].index_select(0, indexes)\n valid_weights = v_weights[i].index_select(0, indexes)\n\n target_pos = valid_labels * valid_weights\n target_neg = (1. - valid_labels) * valid_weights\n\n positive = -torch.mm(flatten(x_lsm[i, 1:]),\n flatten(target_pos).transpose(1, 0)) / torch.sum(target_pos)\n\n negative = -torch.mm(flatten(torch.log(1. - x_sm[i, 1:] + 1e-12)),\n flatten(target_neg).transpose(1, 0)) / torch.sum(target_neg)\n\n values, indices = torch.min(positive + k_neg * negative, 0)\n global_loss += torch.sum(positive[indices, torch.arange(0, positive.size(1)).long().to(device)])\n\n # Normalize w.r.t. image size\n return global_loss * (1000. / (predicted.size(2) * predicted.size(3)))","def kl_loss_batch(self):\n return sum([e for m in self.modules for e in m._kl_losses])","def lfads_training_loss(params, lfads_hps, key, x_bxt, kl_scale, keep_rate):\n losses = lfads_losses(params, lfads_hps, key, x_bxt, kl_scale, keep_rate)\n return losses['total']","def loss_fn(self, recons, inputs, mu, log_var, **kwargs):\n# kld_weight = kwargs['M_N'] # Account for the minibatch samples from the dataset\n recons_loss = F.mse_loss(recons, inputs)\n# recons_loss = F.binary_cross_entropy(recons, inputs)\n KLD = torch.mean(-0.5 * torch.sum(1 + log_var - mu**2 - log_var.exp(), dim=1), dim=0)\n loss = recons_loss - KLD\n return loss, recons_loss, KLD"],"string":"[\n \"def losses(self):\\n pass\",\n \"def compute_loss(self):\",\n \"def losses(self):\\n # compute all kinds of losses \\n\\n # 1. Logits losses for classification \\n\\n # 2. regression loss for bbox \\n\\n return classification_loss, bbox_reg_loss\",\n \"def compute_loss(self, obs, returns):\",\n \"def compute_loss(self):\\n self.prototypes = self.compute_prototypes()\\n self.test_logits = self.compute_logits()\\n loss = tf.nn.sparse_softmax_cross_entropy_with_logits(\\n labels=self.episode.test_labels, logits=self.test_logits)\\n cross_entropy_loss = tf.reduce_mean(loss)\\n regularization = tf.reduce_sum(\\n tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))\\n loss = cross_entropy_loss + self.weight_decay * regularization\\n return loss\",\n \"def build_losses(self):\\n self.batch_losses = tf.squared_difference(self.predicted_rv, self.label)\\n self.total_loss = tf.reduce_mean(self.batch_losses)\",\n \"def build_losses(self):\\n self.batch_losses = tf.squared_difference(self.predicted_rv, self.label)\\n self.total_loss = tf.reduce_mean(self.batch_losses)\",\n \"def loss_function(self, train_head, train_tail, train_relation, train_head_corrupted, train_tail_corrupted):\\n\\n # train_head = tf.nn.l2_normalize(train_head, 1)\\n # train_tail = tf.nn.l2_normalize(train_tail, 1)\\n # train_head_corrupted = tf.nn.l2_normalize(train_head_corrupted, 1)\\n # train_tail_corrupted = tf.nn.l2_normalize(train_tail_corrupted, 1)\\n\\n # loss = tf.reduce_mean(\\n # tf.maximum(self.dict_paras['margin']\\n # + self.distance(tf.add(train_head, train_relation), train_tail)\\n # - self.distance(tf.add(train_head_corrupted, train_relation), train_tail_corrupted), 0.))\\n\\n loss = tf.reduce_mean(self.distance(tf.add(train_head, train_relation), train_tail))\\n\\n return loss\",\n \"def losses(self):\\n losses = []\\n for layer in self.layers:\\n losses += layer.losses\\n if context.in_eager_mode():\\n return losses\\n\\n relevant_inputs = self.inputs or []\\n for i in range(1, len(self._inbound_nodes)):\\n inputs = self.get_input_at(i)\\n if isinstance(inputs, list):\\n relevant_inputs += inputs\\n else:\\n relevant_inputs.append(inputs)\\n reachable = tf_layers_util.get_reachable_from_inputs(relevant_inputs,\\n losses)\\n relevant_conditional_losses = [x for x in losses if x in reachable]\\n unconditional_losses = [\\n x for x in losses if x._unconditional_loss] # pylint: disable=protected-access\\n return list(set(\\n relevant_conditional_losses + unconditional_losses + self._losses))\",\n \"def genLoss(self, *data):\\r\\n _, (x_unlab, _) = data\\r\\n z = self.getInputNoise(self.hypers['ul_BS'])\\r\\n fake_logits = self.D(self.G(z))\\r\\n g_losses = -1*logOneMinusSoftmax(fake_logits)[:,self.D.numClasses-1]\\r\\n return torch.mean(g_losses)\",\n \"def get_losses(self):\\n if self.loss is not None:\\n return [self.loss]\\n else:\\n return []\",\n \"def lossFun(inputs, targets, hprev):\\n xs, hs, ys, ps = {}, {}, {}, {}\\n hs[-1] = np.copy(hprev)\\n loss = 0\\n\\n # forward pass\\n for t in xrange(len(inputs)):\\n xs[t] = np.zeros((vocab_size,1)) # encode in 1-of-k representation\\n xs[t][inputs[t]] = 1\\n hs[t] = np.tanh(np.dot(Wxh, xs[t]) + np.dot(Whh, hs[t-1]) + bh) # hidden state\\n ys[t] = np.dot(Why, hs[t]) + by # unnormalized log probabilities for next chars\\n ps[t] = np.exp(ys[t]-np.max(ys[t])) / np.sum(np.exp(ys[t]-np.max(ys[t]))) # probabilities for next chars\\n loss += -np.log(ps[t][targets[t],0]) # softmax (cross-entropy loss)\\n\\n assert_array_equal(van.window_step,t)\\n assert_array_equal(van.state[t-1],hs[t-1].T[0])\\n assert_array_equal(van.statenet[t].forward([xs[t].T[0],hs[t-1].T[0]]),hs[t].T[0])\\n assert_array_equal(van.statenet[t].net.elements[0].elements[0].elements[1].W.get(),Wxh)\\n assert_array_equal(van.statenet[t].net.elements[0].elements[1].elements[1].W.get(),Whh)\\n assert_array_equal(van.statenet[t].net.elements[0].elements[2].W.get(),bh.T[0])\\n\\n assert_array_equal(vantr.statenet[t].net.elements[0].elements[0].elements[1].W.get(),Wxh)\\n assert_array_equal(vantr.statenet[t].net.elements[0].elements[1].elements[1].W.get(),Whh)\\n assert_array_equal(vantr.statenet[t].net.elements[0].elements[2].W.get(),bh.T[0])\\n assert_array_equal(vantr.outputnet[t].net.elements[0].elements[1].W.get(),Why)\\n assert_array_equal(vantr.outputnet[t].net.elements[1].W.get(),by.T[0])\\n\\n #\\n # #Neg\\n # assert_array_almost_equal(van.outputnet[t].net.elements[0].elements[0].elements[1].W,Why)\\n # assert_array_almost_equal(van.outputnet[t].net.elements[0].elements[1].W,by.T[0])\\n # assert_array_almost_equal(van.outputnet[t].forward(hs[t].T[0]),ps[t].T[0])\\n # assert_array_almost_equal(van.outputnet[t].forward(van.statenet[t].forward([xs[t].T[0],hs[t-1].T[0]])),ps[t].T[0])\\n # assert_array_almost_equal(van.forward(xs[t].T[0]),ps[t].T[0])\\n #\\n # Cross\\n assert_array_equal(van.outputnet[t].net.elements[0].elements[1].W.get(),Why)\\n assert_array_equal(van.outputnet[t].net.elements[1].W.get(),by.T[0])\\n assert_array_equal(van.outputnet[t].forward(hs[t].T[0]),ys[t].T[0])\\n assert_array_equal(van.outputnet[t].forward(van.statenet[t].forward([xs[t].T[0],hs[t-1].T[0]])),ys[t].T[0])\\n assert_array_equal(van.outputnet[t].forward(van.statenet[t].forward([xs[t].T[0],van.state[t-1]])),ys[t].T[0])\\n assert_array_equal(van.forward(xs[t].T[0]),ys[t].T[0])\\n assert_array_equal(soft.forward(ys[t].T[0]),ps[t].T[0])\\n\\n # backward pass: compute gradients going backwards\\n dWxh, dWhh, dWhy = np.zeros_like(Wxh), np.zeros_like(Whh), np.zeros_like(Why)\\n dbh, dby = np.zeros_like(bh), np.zeros_like(by)\\n dhnext = np.zeros_like(hs[0])\\n\\n for t in reversed(xrange(len(inputs))):\\n dy = np.copy(ps[t])\\n dy[targets[t]] -= 1 # backprop into y. see http://cs231n.github.io/neural-networks-case-study/#grad if confused here\\n dWhy += np.dot(dy, hs[t].T)\\n dby += dy\\n dh = np.dot(Why.T, dy) + dhnext # backprop into h\\n dhraw = (1 - hs[t] * hs[t]) * dh # backprop through tanh nonlinearity\\n dbh += dhraw\\n dWxh += np.dot(dhraw, xs[t].T)\\n dWhh += np.dot(dhraw, hs[t-1].T)\\n\\n #\\n # #Neg\\n # van.backward(negLog.dJdy_gradient(ps[t].T[0],to_one_hot_vect(targets[t],vocab_size)),opt)\\n # assert_array_almost_equal(van.outputnet[t].net.elements[0].elements[1].x,hs[t].T[0])\\n # assert_array_almost_equal(van.outputnet[t].net.elements[0].elements[0].elements[1].dW,dWhy)\\n # assert_array_almost_equal(van.outputnet[t].net.elements[0].elements[1].dW,dby.T[0])\\n # assert_array_almost_equal(van.outputnet[t].net.elements[0].elements[0].elements[1].W,Why)\\n # assert_array_almost_equal(van.outputnet[t].net.elements[0].elements[1].W,by.T[0])\\n #\\n #Cross\\n assert_array_equal(van.outputnet[t].net.elements[0].elements[1].x,hs[t].T[0])\\n assert_array_equal(van.outputnet[t].net.forward(hs[t].T[0]),ys[t].T[0])\\n assert_array_equal(soft.forward(van.outputnet[t].net.forward(hs[t].T[0])),ps[t].T[0])\\n assert_array_equal(soft.forward(van.outputnet[t].net.forward(hs[t].T[0]))-to_one_hot_vect(targets[t],vocab_size),dy.T[0])\\n\\n err = cross.dJdy_gradient(ys[t].T[0],to_one_hot_vect(targets[t],vocab_size))\\n\\n assert_array_equal(soft.forward(van.outputnet[t].net.forward(hs[t].T[0]))-to_one_hot_vect(targets[t],vocab_size),dy.T[0])\\n assert_array_equal(ps[t].T[0]-to_one_hot_vect(targets[t],vocab_size),dy.T[0])\\n assert_array_equal(err,dy.T[0])\\n\\n van.backward(err,opt)\\n\\n assert_array_equal(van.outputnet[t].net.elements[0].elements[1].W.get_dW(),dWhy)\\n assert_array_equal(van.outputnet[t].net.elements[0].elements[1].W.get(),Why)\\n assert_array_equal(van.outputnet[t].net.elements[1].W.get_dW(),dby.T[0])\\n assert_array_almost_equal(van.outputnet[t].net.elements[1].W.get(),by.T[0])\\n #\\n\\n assert_array_equal(van.statenet[t].net.elements[0].elements[0].elements[1].W.get_dW(),dWxh)\\n assert_array_equal(van.statenet[t].net.elements[0].elements[1].elements[1].W.get_dW(),dWhh)\\n assert_array_equal(van.statenet[t].net.elements[0].elements[2].W.get_dW(),dbh.T[0])\\n assert_array_equal(van.statenet[t].net.elements[0].elements[0].elements[1].W.get(),Wxh)\\n assert_array_equal(van.statenet[t].net.elements[0].elements[1].elements[1].W.get(),Whh)\\n assert_array_equal(van.statenet[t].net.elements[0].elements[2].W.get(),bh.T[0])\\n assert_array_equal(van.dJdh[t],dhnext.T[0])\\n\\n dhnext = np.dot(Whh.T, dhraw)\\n\\n opt.update_model()\\n trainer.learn_window(vantr,zip(to_hot_vect(inputs,vocab_size),to_hot_vect(targets,vocab_size)),crosstr,opttr)\\n\\n for dparam in [dWxh, dWhh, dWhy, dbh, dby]:\\n np.clip(dparam, -5, 5, out=dparam) # clip to mitigate exploding gradients\\n\\n return loss, dWxh, dWhh, dWhy, dbh, dby, hs[len(inputs)-1]\",\n \"def compute_loss(self):\\n self.test_logits = self.compute_logits()\\n loss = tf.nn.sparse_softmax_cross_entropy_with_logits(\\n labels=self.data.test_labels, logits=self.test_logits)\\n cross_entropy_loss = tf.reduce_mean(loss)\\n regularization = tf.reduce_sum(\\n tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))\\n loss = cross_entropy_loss + self.weight_decay * regularization\\n return loss\",\n \"def losses(self):\\n for name in self._nodes:\\n if isinstance(self._nodes[name], Loss):\\n yield name\",\n \"def loss(self) -> KernelLoss:\\n return self._loss\",\n \"def calc_loss(self, x: np.ndarray, y: np.ndarray) -> float:\\n return self.descent.calc_loss(x, y)\",\n \"def loss(self, y: torch.Tensor, state: AlgorithmState) -> torch.Tensor:\\n\\n raise NotImplementedError()\",\n \"def lossFun(self, inputs, targets, hprev):\\n xs, hs, ys, ps = {}, {}, {}, {}\\n hs[-1] = np.copy(hprev)\\n loss = 0\\n # forward pass\\n for t in range(len(inputs)):\\n xs[t] = np.zeros((self._txt_reader.vocab_size,1)) # One-hot, encode in 1-of-k representation\\n xs[t][inputs[t]] = 1\\n hs[t] = np.tanh(np.dot(self._Wxh, xs[t]) + np.dot(self._Whh, hs[t-1]) + self._bh) # compute chidden state\\n ys[t] = np.dot(self._Why, hs[t]) + self._by # logits \\n ys[t] -= ys[t].max() # for numerical stability\\n ps[t] = np.exp(ys[t]) / np.sum(np.exp(ys[t])) # probabilities for next chars\\n\\n loss += -np.log(ps[t][targets[t],0]) # softmax (cross-entropy loss)\\n\\n # backward pass: compute gradients going backwards\\n dWxh, dWhh, dWhy = np.zeros_like(self._Wxh), np.zeros_like(self._Whh), np.zeros_like(self._Why)\\n dbh, dby = np.zeros_like(self._bh), np.zeros_like(self._by)\\n\\n dhnext = np.zeros_like(hs[0])\\n for t in reversed(range(len(inputs))):\\n dy = np.copy(ps[t])\\n dy[targets[t]] -= 1 # backprop into y. see http://cs231n.github.io/neural-networks-case-study/#grad if confused here\\n dWhy += np.dot(dy, hs[t].T)\\n dby += dy\\n dh = np.dot(self._Why.T, dy) + dhnext # backprop into h\\n dhraw = (1 - hs[t] * hs[t]) * dh # backprop through tanh nonlinearity\\n dbh += dhraw\\n dWxh += np.dot(dhraw, xs[t].T)\\n dWhh += np.dot(dhraw, hs[t-1].T)\\n dhnext = np.dot(self._Whh.T, dhraw)\\n\\n for dparam in [dWxh, dWhh, dWhy, dbh, dby]:\\n np.clip(dparam, -5, 5, out=dparam) # clip to mitigate exploding gradients\\n return loss, dWxh, dWhh, dWhy, dbh, dby, hs[len(inputs)-1]\",\n \"def get_loss(self):\\r\\n\\r\\n if F.loss_type==\\\"cosine\\\":\\r\\n self.losscos = r2d*tf.acos(1-tf.losses.cosine_distance(tf.nn.l2_normalize(self.labels,1), tf.nn.l2_normalize(self.out, 1), dim=1))\\r\\n self.loss = tf.losses.cosine_distance(tf.nn.l2_normalize(self.labels,1), tf.nn.l2_normalize(self.out, 1), dim=1)\\r\\n elif F.loss_type==\\\"mse2d\\\":\\r\\n xl, yl, zl = tf.split(self.labels, 3, axis=1)\\r\\n xo, yo, zo = tf.split(self.out, 3, axis=1)\\r\\n thetal, thetao = tf.asin(-yl), tf.asin(-yo)\\r\\n phil, phio = tf.atan2(-zl, -xl), tf.atan2(-zo, -xo)\\r\\n self.lb = tf.concat([thetal, phil], axis=1)\\r\\n self.ob = tf.concat([thetao, phio], axis=1)\\r\\n self.loss = tf.scalar_mul(tf.constant(r2d), tf.losses.mean_squared_error(self.lb, self.ob, 2))\\r\\n elif F.loss_type==\\\"mse3d\\\":\\r\\n self.loss = tf.losses.mean_squared_error(tf.nn.l2_normalize(self.labels, 0), tf.nn.l2_normalize(self.out, 0))\",\n \"def compute_loss(self, x, gt):\\n loss = sum([torch.mean((out - gt)**2) for out in self.forward(x)])\\n return loss\",\n \"def test_loss_hook(self, losses):\\n self.runinfo[\\\"dev_losses\\\"].append(losses)\",\n \"def get_loss(self):\\n return self.loss / self.cnt\",\n \"def compute_losses(self):\\n cycle_consistency_loss_a = \\\\\\n self._lambda_a * losses.cycle_consistency_loss(\\n real_images=self.input_a, generated_images=self.cycle_images_a,\\n )\\n cycle_consistency_loss_b = \\\\\\n self._lambda_b * losses.cycle_consistency_loss(\\n real_images=self.input_b, generated_images=self.cycle_images_b,\\n )\\n\\n lsgan_loss_a = losses.lsgan_loss_generator(self.prob_fake_a_is_real)\\n lsgan_loss_b = losses.lsgan_loss_generator(self.prob_fake_b_is_real)\\n\\n g_loss_A = \\\\\\n cycle_consistency_loss_a + cycle_consistency_loss_b + lsgan_loss_b\\n g_loss_B = \\\\\\n cycle_consistency_loss_b + cycle_consistency_loss_a + lsgan_loss_a\\n\\n d_loss_A = losses.lsgan_loss_discriminator(\\n prob_real_is_real=self.prob_real_a_is_real,\\n prob_fake_is_real=self.prob_fake_pool_a_is_real,\\n )\\n d_loss_B = losses.lsgan_loss_discriminator(\\n prob_real_is_real=self.prob_real_b_is_real,\\n prob_fake_is_real=self.prob_fake_pool_b_is_real,\\n )\\n\\n optimizer = tf.train.AdamOptimizer(self.learning_rate, beta1=0.5)\\n\\n self.model_vars = tf.trainable_variables()\\n\\n d_A_vars = [var for var in self.model_vars if 'd_A' in var.name]\\n g_A_vars = [var for var in self.model_vars if 'g_A' in var.name]\\n d_B_vars = [var for var in self.model_vars if 'd_B' in var.name]\\n g_B_vars = [var for var in self.model_vars if 'g_B' in var.name]\\n\\n self.d_A_trainer = optimizer.minimize(d_loss_A, var_list=d_A_vars)\\n self.d_B_trainer = optimizer.minimize(d_loss_B, var_list=d_B_vars)\\n self.g_A_trainer = optimizer.minimize(g_loss_A, var_list=g_A_vars)\\n self.g_B_trainer = optimizer.minimize(g_loss_B, var_list=g_B_vars)\\n\\n for var in self.model_vars:\\n print(var.name)\\n\\n # Summary variables for tensorboard\\n self.g_A_loss_summ = tf.summary.scalar(\\\"g_A_loss\\\", g_loss_A)\\n self.g_B_loss_summ = tf.summary.scalar(\\\"g_B_loss\\\", g_loss_B)\\n self.d_A_loss_summ = tf.summary.scalar(\\\"d_A_loss\\\", d_loss_A)\\n self.d_B_loss_summ = tf.summary.scalar(\\\"d_B_loss\\\", d_loss_B)\",\n \"def lasso_loss(w: FloatTensor, x: FloatTensor, y: FloatTensor, lmb: float) -> float:\\n return ols_loss(w, x, y, 0.0) + lmb * w.abs().sum()\",\n \"def loss(self, obs: np.ndarray, acs: np.ndarray, next_obs: np.ndarray, data_statistics: dict) -> float:\\n delta_pred_normalized_mus, delta_pred_normalized_stds = self.forward_pass(obs, acs, data_statistics)\\n delta_pred_normalized_dist = tfd.MultivariateNormalDiag(loc=delta_pred_normalized_mus, scale_diag=delta_pred_normalized_stds)\\n\\n targets = normalize(next_obs - obs,\\n data_statistics['delta_mean'],\\n data_statistics['delta_std'])\\n return -delta_pred_normalized_dist.log_prob(targets)\",\n \"def get_loss(self, xs, y):\\n \\\"*** YOUR CODE HERE ***\\\"\\n predictedY = self.run(xs)\\n return nn.SoftmaxLoss(predictedY, y)\\n # return nn.SquareLoss(predictedY, y)\",\n \"def _compute_loss(self):\\n state, action, reward, next_state, done = self.replay_buffer.sample(self.batch_size)\\n\\n state = torch.FloatTensor(state)\\n next_state = torch.FloatTensor(next_state)\\n action = torch.LongTensor(action)\\n reward = torch.FloatTensor(reward)\\n done = torch.FloatTensor(done)\\n\\n q_values = self.dqn(state)\\n q_value = q_values.gather(1, action.unsqueeze(1)).squeeze(1)\\n\\n next_q_values = self.target_dqn(next_state)\\n next_q_value = next_q_values.max(1)[0]\\n target = reward + self.discount_factor * next_q_value * (1 - done)\\n\\n # loss = F.smooth_l1_loss(q_value, target.detach())\\n loss = F.mse_loss(q_value, target.detach())\\n\\n return loss\",\n \"def get_loss(self, xs, y):\\n \\\"*** YOUR CODE HERE question 4 ***\\\"\\n return nn.SoftmaxLoss(self.run(xs), y)\",\n \"def lossFun(review, target, hprev):\\n xs, hs, ys, ps = {}, {}, {}, {}\\n hs[-1] = np.copy(hprev)\\n loss = 0\\n\\n # forward pass\\n for t in range(len(review)):\\n xs[t] = np.zeros((vector_len,1)) # encode in 1-of-k representation\\n for j in range(32):\\n xs[t][j] = review[t][j]\\n hs[t] = np.tanh(np.dot(Wxh, xs[t]) + np.dot(Whh, hs[t-1]) + bh) # hidden state\\n\\n #Many 2 one\\n last = len(review) - 1\\n ys = np.dot(Why, hs[last]) + by # unnormalized log probabilities for next chars\\n ps = np.exp(ys) / np.sum(np.exp(ys)) # probabilities for next chars\\n loss = -np.log(ps[target,0]) # softmax (cross-entropy loss)\\n\\n # backward pass: compute gradients going backwards\\n dWxh, dWhh, dWhy = np.zeros_like(Wxh), np.zeros_like(Whh), np.zeros_like(Why)\\n dbh, dby = np.zeros_like(bh), np.zeros_like(by)\\n dhnext = np.zeros_like(hs[0])\\n\\n dy = np.subtract(ps,target) # backprop into y. see http://cs231n.github.io/neural-networks-case-study/#grad if confused here\\n dWhy += np.dot(dy, hs[last].T)\\n dby += dy\\n dh = np.dot(Why.T, dy) + dhnext # backprop into h\\n for t in reversed(range(len(review))):\\n dhraw = (1 - (hs[t] * hs[t].T)) * dh # backprop through tanh nonlinearity\\n dbh += dhraw\\n dWxh += np.dot(dhraw, xs[t].T)\\n dWhh += np.dot(dhraw, hs[t-1].T)\\n dhnext = np.dot(Whh.T, dhraw)\\n for dparam in [dWxh, dWhh, dWhy, dbh, dby]:\\n np.clip(dparam, -5, 5, out=dparam) # clip to mitigate exploding gradients\\n return loss, dWxh, dWhh, dWhy, dbh, dby, hs[last]\",\n \"def loss_fn(self, lbl, y):\\n\\n binlbl = self._to_device(lbl[:,0]>.5)\\n # center = self._to_device(lbl[:,3]) \\n offset = 5. * self._to_device(lbl[:,1:]) \\n\\n loss = self.criterion(y[:,:2], offset) \\n loss2 = self.criterion2(y[:,2], binlbl)\\n\\n # loss3 = self.criterion(y[:,3], center)\\n\\n loss = loss + loss2\\n return loss\",\n \"def compute_loss(self):\\n def calc_loss(inputs, outputs):\\n reconstruction_loss = tf.metrics.binary_crossentropy(\\n tf_flat(inputs), tf_flat(outputs))\\n reconstruction_loss *= OUT_SIZE * OUT_SIZE\\n kl_loss = -0.5 * tf.reduce_sum(1.0 + self.log_sigma - tf.square(\\n self.mu) - tf.exp(self.log_sigma), 1)\\n return tf.reduce_mean(reconstruction_loss + kl_loss)\\n return calc_loss\",\n \"def loss_fn(model):\\n with flax.deprecated.nn.stateful() as state:\\n with flax.deprecated.nn.stochastic(dropout_rng):\\n logits = model(example, train=True)\\n loss, weight_sum = compute_weighted_cross_entropy(logits, targets)\\n mean_loss = loss / weight_sum\\n return mean_loss, (logits, state)\",\n \"def compute_loss(self, X, y):\\r\\n # INSERT YOUR CODE HERE\\r\\n #raise Exception('Function not yet implemented!')\\r\\n\\r\\n # Computing the loss using the below formula\\r\\n # Loss = -(1/m)*sum( (y_i)*log(σ(wTx_i)) + (1-y_i)*log(1 - σ(wTx_i)))\\r\\n # m = number of examples and i for ith example\\r\\n\\r\\n loss = 0\\r\\n X = np.append(X, np.array([[1]]*X.shape[0]), axis=1)\\r\\n # for idx,example in enumerate(X):\\r\\n # loss = loss + y[idx] * np.log(self.sigmoid(np.dot(example, self.w))) + (1 - y[idx]) * np.log(1 - self.sigmoid(np.dot(example, self.w)))\\r\\n # loss = -loss/ X.shape[0]\\r\\n\\r\\n loss = -np.mean(y * np.log(self.sigmoid(np.dot(X, self.w))) + (1 - y) * np.log(1 - self.sigmoid(np.dot(X, self.w))))\\r\\n return loss\",\n \"def compute_loss(self, x, label):\\n # Forward propagation\\n y_hat = self.forward_pass(x)\\n return -np.log(y_hat[label])\",\n \"def compute_loss(self, inputs):\\r\\n outputs = self.net.compute_outputs(inputs)\\r\\n loss_grad = self.net.compute_loss_grad(outputs - inputs)\\r\\n loss = np.sum((inputs - outputs) ** 2, axis=0).mean() / 2.0\\r\\n return loss, loss_grad\",\n \"def loss(self, x, y):\\n (N,D) = x.shape\\n k1 = np.matmul(x,np.transpose(self.w)) + self.b\\n y1 = y.reshape((N,1))\\n c2 = 0\\n c1 = (np.log(1+np.exp(-1*y1*k1)))\\n for i in range(N):\\n c2 += c1[i][0]\\n l = c2 / N + (0.5 * self.l2_reg * np.dot(self.w,np.transpose(self.w)))\\n l1 = l[0][0]\\n return l1\\n\\n\\n #raise NotImplementedError\",\n \"def l1_loss(obs, actual):\\n # (tf.Tensor, tf.Tensor, float) -> tf.Tensor\\n return tf.reduce_sum(tf.abs(obs - actual) , 1)\",\n \"def build_loss(self):\\n if self.mode != \\\"encode\\\":\\n total_loss = tf.losses.get_total_loss()\\n tf.summary.scalar(\\\"losses/total\\\", total_loss)\\n\\n self.total_loss = total_loss\",\n \"def __loss(self, h, y):\\n return (-y*np.log(h)-(1-y)*np.log(1-h)).mean()\",\n \"def loss(self, predicts, labels, objects_num):\\n\\n def cond1(num, object_num, loss, predict, label, nilboy):\\n return num < object_num\\n\\n class_loss = tf.constant(0, tf.float32)\\n object_loss = tf.constant(0, tf.float32)\\n noobject_loss = tf.constant(0, tf.float32)\\n coord_loss = tf.constant(0, tf.float32)\\n loss = [0, 0, 0, 0]\\n for i in range(self.batch_size):\\n predict = predicts[i, :, :, :]\\n label = labels[i, :, :]\\n object_num = objects_num[i]\\n nilboy = tf.ones([self.cell_size, self.cell_size, 1])\\n tuple_results = tf.while_loop(cond1, self.body1,\\n [\\n tf.constant(0), object_num,\\n [class_loss, object_loss, noobject_loss, coord_loss],\\n predict, label, nilboy\\n ])\\n for j in range(4):\\n loss[j] = loss[j] + tuple_results[2][j]\\n nilboy = tuple_results[5]\\n\\n tf.add_to_collection('losses', (loss[0] + loss[1] + loss[2] + loss[3]) / self.batch_size)\\n\\n tf.summary.scalar('class_loss', loss[0] / self.batch_size)\\n tf.summary.scalar('object_loss', loss[1] / self.batch_size)\\n tf.summary.scalar('noobject_loss', loss[2] / self.batch_size)\\n tf.summary.scalar('coord_loss', loss[3] / self.batch_size)\\n tf.summary.scalar('weight_loss', tf.add_n(tf.get_collection('losses')) - (\\n loss[0] + loss[1] + loss[2] + loss[3]) / self.batch_size)\\n\\n return tf.add_n(tf.get_collection('losses'), name='total_loss'), nilboy\",\n \"def get_loss(self):\\n raise NotImplementedError\",\n \"def setup_loss(self):\\n with vs.variable_scope(\\\"loss\\\"):\\n self.loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=self.label_placeholder, logits=self.label_predictions))\",\n \"def calc_loss(self, outputs, labels):\\n information_loss = self.bottleneck.buffer_capacity.mean() # Taking the mean is equivalent of scaling with 1/K\\n cross_entropy = F.cross_entropy(outputs, target=labels)\\n total = cross_entropy + self.beta * information_loss\\n self.ce_loss.append(cross_entropy.cpu().detach().numpy())\\n self.info_loss.append(information_loss.cpu().detach().numpy())\\n self.total_loss.append(total.cpu().detach().numpy())\\n return total\",\n \"def get_loss(self, xs, y):\\n \\\"*** YOUR CODE HERE ***\\\"\\n y_pred = self.run(xs)\\n return nn.SoftmaxLoss(y_pred,y)\",\n \"def pseudo_loss(self, params, batches):\\n loss = 0\\n for batch in batches:\\n states = batch[\\\"states\\\"]\\n actions = batch[\\\"actions\\\"]\\n returns = batch[\\\"returns\\\"]\\n\\n preds = self.predict_jax(params, states)\\n\\n baseline = jnp.mean(returns, axis=0)\\n preds_select = jnp.take_along_axis(preds, jnp.expand_dims(actions, axis=2), axis=2).squeeze()\\n loss += (-jnp.mean(jnp.sum(preds_select * (returns - baseline))))\\n\\n return loss + self.l2_regularizer(params, 0.001) # try to divide by len(batches)?\",\n \"def loss_op(self):\\n return self.loss\",\n \"def _init_loss(self) -> None:\\n labels_one_hot = tf.one_hot(self.labels, depth=Data.num_classes)\\n cross_entropy_loss = tf.losses.softmax_cross_entropy(labels_one_hot, self.logits)\\n # cross_entropy_loss is a scalar\\n tf.add_to_collection(tf.GraphKeys.LOSSES, cross_entropy_loss)\\n self.loss = tf.add_n(tf.get_collection(tf.GraphKeys.LOSSES))\\n self.logger_factory.add_scalar('loss', self.loss, log_frequency=10)\\n self.logger_factory.add_scalar('cross_entropy_loss', cross_entropy_loss, log_frequency=25)\",\n \"def _generator_loss(self, y_hat):\\n\\n l = -tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels = tf.zeros(tf.shape(y_hat)),logits = y_hat ))\\n print('generatorloss shape',tf.shape(l))\\n return l\",\n \"def lossFunc(inputs, targets, hprev):\\n xs, hs, ys, ps = {}, {}, {}, {} # input, hidden, output, out_prob states for each time t\\n hs[-1] = np.copy(hprev)\\n loss = 0\\n \\n # forward pass\\n for t in xrange(len(inputs)):\\n xs[t] = np.zeros((vocab_size,1)) \\n xs[t][inputs[t]] = 1. # convert input to one-hot\\n hs[t] = np.tanh(np.dot(Wxh, xs[t]) + np.dot(Whh, hs[t-1]) + bh)\\n ys[t] = np.dot(Why, hs[t]) + by\\n ps[t] = np.exp(ys[t]) / np.sum(np.exp(ys[t]))\\n loss += -np.log(ps[t][targets[t],0])\\n \\n # backward pass\\n dWxh, dWhh, dWhy = np.zeros_like(Wxh), np.zeros_like(Whh), np.zeros_like(Why)\\n dbh, dby = np.zeros_like(bh), np.zeros_like(by)\\n dhnext = np.zeros_like(hs[0])\\n for t in reversed(xrange(len(inputs))):\\n # backprop into y\\n dy = np.copy(ps[t])\\n dy[targets[t]] -= 1\\n # backprop into Why, hs, and by\\n dWhy += np.dot(dy, hs[t].T)\\n dby += dy\\n dh = np.dot(Why.T, dy) + dhnext\\n # backprop through tanh activition\\n dhraw = (1 - hs[t] * hs[t]) * dh\\n # backprop into Wxh, Whh, hs, and bh\\n dbh += dhraw\\n dWxh += np.dot(dhraw, xs[t].T)\\n dWhh += np.dot(dhraw, hs[t-1].T)\\n dhnext = np.dot(Whh.T, dhraw)\\n # clip gradient preventing exploding\\n for dparam in [dWxh, dWhh, dWhy, dbh, dby]:\\n np.clip(dparam, -5, 5, out=dparam)\\n\\n return loss, dWxh, dWhh, dWhy, dbh, dby, hs[len(inputs)-1]\",\n \"def calculate_loss(self, X, y):\\n probs = self.predict(X)\\n\\n num_examples = X.shape[0]\\n\\n sub = np.subtract(probs, y)\\n abs_sum = np.abs(sub)\\n sm = np.sum(abs_sum)\\n loss = 1 - sm / num_examples\\n print(\\\"Current loss: [ \\\" + str(\\\"{:6.5f}\\\").format(loss) + \\\" ]\\\")\\n return loss\",\n \"def losses(self, win2: ArrayLike, lose2: ArrayLike) -> ArrayLike:\\n return -1 * torch.log(self.probs(win2, lose2))\",\n \"def build_loss(self):\\n import tensorflow as tf\\n\\n y_1d = [tf.reduce_sum(tf.multiply(self.variables[\\\"y\\\"][i], self.variables[\\\"y_action\\\"][i]), axis=1) for i in range(len(self.variables[\\\"y\\\"]))]\\n loss = np.sum([tf.nn.l2_loss(y_1d[i] - self.variables[\\\"y_true\\\"]) for i in range(len(y_1d))])\\n\\n l1_reg = 0\\n l2_reg = 0\\n\\n keys = sorted(self.variables.keys())\\n keys = [key for key in keys if critere_keys(key) and \\\"W\\\" in key]\\n for key in keys:\\n l1_reg += tf.reduce_sum(tf.abs(self.variables[key]))\\n l2_reg += tf.nn.l2_loss(self.variables[key])\\n\\n self.loss = loss + self.alpha_reg * l1_reg + self.beta_reg * l2_reg\\n\\n self.train_step = tf.train.RMSPropOptimizer(self.decay_learning_rate,\\n decay=0.99, momentum=0., centered=True).minimize(self.loss, global_step=self.global_step)\",\n \"def _get_loss(self):\\n raise NotImplementedError\",\n \"def loss(self):\\n return self._loss\",\n \"def _loss(self):\\n\\n cross_entropy = tf.reduce_mean(-tf.log(self.probability + epsilon) * self.y)\\n self.loss = cross_entropy\\n\\n self.accuracy = tf.reduce_mean(\\n tf.cast(tf.equal(tf.argmax(self.y, 1), self.prediction), tf.float32))\",\n \"def computeLoss(self):\\n return sum(np.arccosh(-minkowskiArrayDot(self.examples, self.centroid)) ** 2)[0] / np.shape(self.examples)[0]\",\n \"def compute_loss(self, sample):\\n observations_batch, actions_batch, return_batch, masks_batch, \\\\\\n old_action_log_probs_batch, adv_targ = sample\\n\\n assert old_action_log_probs_batch.shape == (self.mini_batch_size, 1)\\n assert adv_targ.shape == (self.mini_batch_size, 1)\\n assert return_batch.shape == (self.mini_batch_size, 1)\\n\\n values, action_log_probs, dist_entropy = self.evaluate_actions(\\n observations_batch, actions_batch)\\n\\n assert values.shape == (self.mini_batch_size, 1)\\n assert action_log_probs.shape == (self.mini_batch_size, 1)\\n assert values.requires_grad\\n assert action_log_probs.requires_grad\\n assert dist_entropy.requires_grad\\n\\n # [TODO] Implement policy loss\\n ratio = torch.exp(action_log_probs - old_action_log_probs_batch)\\n surr1 = ratio * adv_targ\\n surr2 = torch.clamp(ratio, 1.0 - self.clip_param, 1.0 + self.clip_param) * adv_targ\\n policy_loss = -torch.min(surr1, surr2).mean()\\n\\n # [TODO] Implement value loss\\n value_loss = F.mse_loss(return_batch, values)\\n\\n # This is the total loss\\n loss = policy_loss + self.config.value_loss_weight * value_loss - self.config.entropy_loss_weight * dist_entropy\\n\\n return loss, policy_loss, value_loss, dist_entropy\",\n \"def compute_loss(y, tx, w):\\n # ***************************************************\\n # INSERT YOUR CODE HERE\\n # TODO: compute loss by MSE / MAE\\n # ***************************************************\\n \\n # vector e\\n e = compute_e(y, tx, w)\\n N = compute_N(e)\\n L_MSE = np.dot(np.matrix.transpose(e), e)\\n L_MSE = L_MSE / (2 * N)\\n \\n return L_MSE\",\n \"def update_losses(losses, l_loss, c_loss):\\n losses[0] += l_loss\\n losses[1] += c_loss\\n losses[2] += l_loss\\n losses[3] += c_loss\",\n \"def loss(self):\\n return la.norm(self.resids) / self.normX\",\n \"def build_loss(self):\\n\\n opt = tf.train.AdamOptimizer(self.learning_rate)\\n mse = tf.losses.mean_squared_error(self.label[-1], self.outputs[-1])\\n loss = tf.losses.get_total_loss()\\n\\n return mse, loss\",\n \"def loss(self, data):\\n loss, smoothed, lr = data\\n\\n curves = []\\n\\n curve_keys = ['color', 'linestyle', 'linewidth', 'alpha']\\n\\n if loss is not None:\\n loss_name = self.config.get('label', f\\\"loss #{self.index + 1}\\\")\\n loss_label = f'{loss_name} ⟶ {loss[-1]:2.3f}'\\n final_window = self.config.get('final_window', None)\\n if final_window is not None:\\n final_window = min(final_window, len(loss))\\n final = np.mean(loss[-final_window:])\\n loss_label += f\\\"\\\\nmean over last {final_window} iterations={final:2.3f}\\\"\\n\\n loss_config = self.config.filter(keys=curve_keys, prefix='curve_')\\n loss_curve = self.ax.plot(loss, label=loss_label, **loss_config)\\n curves.extend(loss_curve)\\n\\n if smoothed is not None:\\n smoothed_color = scale_lightness(loss_config['color'], scale=.5)\\n smooth_window = self.config.get('window')\\n smoothed_label = self.config.get('smoothed_label', loss_name)\\n smoothed_label = smoothed_label + '\\\\n' if smoothed_label else ''\\n smoothed_label += f'smoothed with window {smooth_window}'\\n smoothed_curve = self.ax.plot(smoothed, label=smoothed_label, color=smoothed_color, linestyle='--')\\n curves.extend(smoothed_curve)\\n\\n if lr is not None:\\n lr_ax = self.ax if loss is None else self.twin_ax\\n lr_label = f'learning rate №{self.index + 1} ⟶ {lr[-1]:.0e}'\\n lr_config = self.config.filter(keys=curve_keys, prefix='lr_')\\n lr_curve = lr_ax.plot(lr, label=lr_label, **lr_config)\\n lr_ax.set_ylabel('Learning rate', fontsize=12)\\n curves.extend(lr_curve)\\n\\n return curves\",\n \"def calculate_loss(self, pred, gold, smoothing=False):\\n gold = gold.contiguous().view(-1)\\n if smoothing:\\n epsilon = 0.1\\n n_class = pred.size(1)\\n one_hot = torch.zeros_like(pred).scatter(1, gold.view(-1, 1), 1)\\n one_hot = one_hot * (1 - epsilon) + \\\\\\n (1 - one_hot) * epsilon / (n_class - 1)\\n\\n log_prb = F.log_softmax(pred, dim=1)\\n # create non-padding mask with torch.ne()\\n non_pad_mask = gold.ne(self.constants.PAD)\\n loss = -(one_hot * log_prb).sum(dim=1)\\n # losses are averaged later\\n loss = loss.masked_select(non_pad_mask).sum()\\n else:\\n loss = F.cross_entropy(\\n pred, gold, ignore_index=self.constants.PAD, reduction='sum')\\n return loss\",\n \"def loss(self, X, y=None):\\n X = X.astype(self.dtype)\\n mode = 'test' if y is None else 'train'\\n\\n # We are gonna store everythin in a dictionnary hidden\\n hidden = {}\\n hidden['h0'] = X.reshape(X.shape[0], np.prod(X.shape[1:]))\\n\\n for i in range(self.L):\\n idx = i + 1\\n # Naming of the variable\\n w = self.params['W' + str(idx)]\\n b = self.params['b' + str(idx)]\\n h = hidden['h' + str(idx - 1)]\\n\\n # Computing of the forward pass.\\n # Special case of the last layer (output)\\n if idx == self.L:\\n h, cache_h = affine_forward(h, w, b)\\n hidden['h' + str(idx)] = h\\n hidden['cache_h' + str(idx)] = cache_h\\n\\n # For all other layers\\n else:\\n h, cache_h = affine_relu_forward(h, w, b)\\n hidden['h' + str(idx)] = h\\n hidden['cache_h' + str(idx)] = cache_h\\n\\n scores = hidden['h' + str(self.L)]\\n\\n # If test mode return early\\n if mode == 'test':\\n return scores\\n\\n # Computing of the loss\\n data_loss, dscores = softmax_loss(scores, y)\\n reg_loss = 0\\n for w in [self.params[f] for f in self.params.keys() if f[0] == 'W']:\\n reg_loss += 0.5 * self.reg * np.sum(w * w)\\n\\n loss = data_loss + reg_loss\\n\\n # Backward pass\\n\\n hidden['dh' + str(self.L)] = dscores\\n for i in range(self.L)[::-1]:\\n idx = i + 1\\n dh = hidden['dh' + str(idx)]\\n h_cache = hidden['cache_h' + str(idx)]\\n if idx == self.L:\\n dh, dw, db = affine_backward(dh, h_cache)\\n hidden['dh' + str(idx - 1)] = dh\\n hidden['dW' + str(idx)] = dw\\n hidden['db' + str(idx)] = db\\n else:\\n dh, dw, db = affine_relu_backward(dh, h_cache)\\n hidden['dh' + str(idx - 1)] = dh\\n hidden['dW' + str(idx)] = dw\\n hidden['db' + str(idx)] = db\\n\\n # w gradients where we add the regulariation term\\n list_dw = {key[1:]: val + self.reg * self.params[key[1:]]\\n for key, val in hidden.iteritems() if key[:2] == 'dW'}\\n # Paramerters b\\n list_db = {key[1:]: val for key, val in hidden.iteritems() if key[:2] == 'db'}\\n # Parameters gamma\\n list_dgamma = {key[1:]: val for key, val in hidden.iteritems() if key[:6] == 'dgamma'}\\n # Paramters beta\\n list_dbeta = {key[1:]: val for key, val in hidden.iteritems() if key[:5] == 'dbeta'}\\n grads = {}\\n grads.update(list_dw)\\n grads.update(list_db)\\n grads.update(list_dgamma)\\n grads.update(list_dbeta)\\n return loss, grads\",\n \"def compute_loss(self, x, y):\\n\\n self.batch_size = x.shape[0]\\n self.x = x\\n self.y = y\\n self.soft = self.softmax(x) + 10**(-11)\\n out = np.zeros(self.batch_size)\\n for i in range(self.batch_size):\\n out[i] = -(y[i] @ np.log(self.soft[i]))\\n\\n return out\",\n \"def get_loss(self, Loss, results, inputs, device):\\n return\",\n \"def reset_loss_sums():\\n\\tglobal decoder_nat_loss, decoder_syn_loss, KLD_syn_loss, KLD_nat_loss, regressor_nat, regressor_syn\\n\\tdecoder_nat_loss = decoder_syn_loss = KLD_syn_loss = KLD_nat_loss = regressor_nat = regressor_syn = 0\",\n \"def compute_loss(\\n self,\\n x: torch.Tensor,\\n y: torch.Tensor,\\n y_hat: torch.Tensor,\\n extras: List[torch.Tensor],\\n train_context: TrainContext,\\n ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\",\n \"def _calc_loss(self, fvs, labels, w, b):\\n\\n loss = 0.5 * self.lda * (np.linalg.norm(w) ** 2)\\n tmp = sum(map(lambda x, y: (x - y) ** 2, fvs.dot(w) + b, labels))\\n loss += tmp / fvs.shape[0]\\n\\n return loss\",\n \"def loss_total(self):\\r\\n def loss(y_true, y_pred):\\r\\n l2 = 1/2*K.sum(K.square(y_true-y_pred))\\r\\n\\r\\n return l2\\r\\n return loss\",\n \"def setup_loss(self, h_s, h_e):\\n with vs.variable_scope(\\\"loss\\\"):\\n # masked_h_s = tf.boolean_mask(h_s, self.context_mask_placeholder)\\n # masked_h_e = tf.boolean_mask(h_e, self.context_mask_placeholder)\\n # loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(h_s, self.start_span_placeholder) +\\n # tf.nn.softmax_cross_entropy_with_logits(h_e, self.end_span_placeholder))\\n masked_h_s = tf.add(h_s, (1 - tf.cast(self.context_mask_placeholder, 'float')) * (-1e30))\\n masked_h_e = tf.add(h_e, (1 - tf.cast(self.context_mask_placeholder, 'float')) * (-1e30))\\n loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(masked_h_s, self.start_span_placeholder) +\\n tf.nn.softmax_cross_entropy_with_logits(masked_h_e, self.end_span_placeholder))\\n total_loss = loss\\n return total_loss, masked_h_s, masked_h_e\",\n \"def loss(self, y: np.ndarray, y_hat: np.ndarray) -> float:\\n losses = -(y * np.log(y_hat) + (1 - y) * np.log(1 - y_hat))\\n return losses.mean() + self.reg / self.num_parameters * (\\n (self.v[:, -1] ** 2).sum() + (self.w ** 2).sum()\\n )\",\n \"def compute_loss_lasso(y, tx, w, lambda_):\\n e = y - tx.dot(w)\\n\\n return e.dot(e)/(2 * len(e)) + lambda_ * sum(abs(w))\",\n \"def add_loss_op(self, preds):\\n ### YOUR CODE HERE (~2-4 lines)\\n trans = tf.get_variable('trans',\\n shape=[Config.n_classes, Config.n_classes],\\n initializer=tf.contrib.layers.xavier_initializer())\\n log_likelihood, _ = crf_log_likelihood(preds,\\n self.labels_placeholder,\\n self.length_placeholder,\\n trans)\\n #log_likelihood = tf.boolean_mask(log_likelihood, self.mask_placeholder)\\n loss = tf.reduce_mean(-1.0 * log_likelihood)\\n \\n ### END YOUR CODE\\n return trans, loss\",\n \"def l1_loss(params):\\n \\\"\\\"\\\" It is a vec for each branch\\\"\\\"\\\"\\n loss_branches_vec = []\\n # TODO This is hardcoded but all our cases rigth now uses four branches\\n for i in range(len(params['branches']) -1):\\n loss_branches_vec.append(torch.abs((params['branches'][i] - params['targets'])\\n * params['controls_mask'][i])\\n * params['branch_weights'][i])\\n \\\"\\\"\\\" The last branch is a speed branch\\\"\\\"\\\"\\n # TODO: Activate or deactivate speed branch loss\\n loss_branches_vec.append(torch.abs(params['branches'][-1] - params['inputs'])\\n * params['branch_weights'][-1])\\n return loss_branches_vec, {}\",\n \"def loss_(self, batch):\\n raise NotImplementedError\",\n \"def _initLoss(self):\\n\\n return torch.nn.MSELoss()\",\n \"def get_loss(self, xs, y):\\n return nn.SoftmaxLoss(self.run(xs), y)\",\n \"def _build_loss(self):\\n self._build_loss_D()\\n self._build_loss_G()\\n tf.add_to_collection('losses', self.loss_D)\\n tf.add_to_collection('losses', self.loss_G)\",\n \"def svm_loss(x, y):\\n\\n x = x.reshape((-1,1))\\n y = y.reshape((-1,1))\\n N,_ = x.shape\\n \\n y_p = np.where(y == 1,1,-1)\\n \\n losses = np.maximum(0,1-(x*y_p))\\n loss = np.sum(losses)/N\\n dx = np.where(losses > 0, 1, 0)*(-y_p)/N\\n dx = dx.reshape((-1,))\\n\\n return loss, dx\",\n \"def compute_loss(self, **kwargs):\\n raise NotImplementedError\",\n \"def backward(self):\\n self.loss_similarity = [LNCC(warped_img, self.batch_fixed, self.corr_kernel) for warped_img in self.warped_img_list]\\n self.loss_similarity_mean = torch.mean(torch.stack(self.loss_similarity))\\n self.loss_smooth = [GradNorm(disp_map) for disp_map in self.disp_list]\\n self.loss_smooth_mean = torch.mean(torch.stack(self.loss_smooth))\\n if len(self.strain_compensated_list) > 1:\\n self.loss_consistency_strain = [LNCC(self.strain_compensated_list[t-1][:,:,143:-143,:], self.strain_compensated_list[t][:,:,143:-143,:], self.corr_kernel) for t in range(1, len(self.strain_compensated_list))]\\n self.loss_consistency_strain_mean = torch.mean(torch.stack(self.loss_consistency_strain))\\n self.loss_total = 1 - self.loss_similarity_mean + self.loss_smooth_mean * self.alpha + (1 - self.loss_consistency_strain_mean) * self.beta\\n else:\\n self.loss_total = 1 - self.loss_similarity_mean + self.loss_smooth_mean * self.alpha\",\n \"def loss(self, predictions, labels, labels_2, inputs, raw_inp):\\n next_word = labels\\n curr_label = tf.cast(labels_2, tf.float32)\\n\\n \\n prediction_word = predictions[0]\\n prediction_label = predictions[1]\\n\\n #initialising variables\\n cross_entropy_next = tf.constant(0)\\n cross_entropy_label = tf.constant(0)\\n cross_entropy_label_similarity = tf.constant(0)\\n cross_entropy_emb = tf.constant(0)\\n \\n self.prec_label, self.prec_label_op = tf.constant(1), tf.constant(1)\\n self.recall_label, self.recall_label_op = tf.constant(1), tf.constant(1)\\n self.label_sigmoid = tf.constant(0)\\n\\n \\n if self.config.solver._next_node_loss:\\n #<EOS> and <UNK> get encoded as 1 and 0 respectively\\n #Count loss only for actual nodes\\n \\n raw_inp1 = tf.greater(tf.slice(raw_inp, [0,0],[-1, 1]), -1) #Make first column all True\\n raw_inp2 = tf.greater(tf.slice(raw_inp, [0,1],[-1, -1]), 1) #Make only non (<EOS>,<UNK>) True\\n raw_inp = tf.concat(1, [raw_inp1, raw_inp2]) #concatenate back to original shape\\n raw_inp = tf.transpose(raw_inp) #Transpose raw_inp from batch*step to step*batch\\n mask = [tf.reshape(tf.cast(raw_inp, tf.float32), [-1])] #Convert from bool to float and flatten array\\n\\n\\n #<EOS> and <UNK> get encoded as 1 and 0 respectively\\n #Transpose raw_inp from batch*step to shape*batch\\n #Count loss only for actual nodes\\n #Convert from bool to float and flatten array\\n #mask = [tf.reshape(tf.cast(tf.greater(tf.transpose(raw_inp), 0), tf.float32), [-1])]\\n\\n #Vector to weigh different word losses\\n #all_ones = [tf.ones([self.config.batch_size * self.config.num_steps])]\\n\\n #cross entropy loss for next word prediction\\n cross_entropy_next = sequence_loss([prediction_word],[tf.reshape(next_word, [-1])], mask, self.config.data_sets._len_vocab)\\n tf.add_to_collection('total_loss', cross_entropy_next)\\n\\n if self.config.solver._curr_label_loss:\\n #Get the slice of tensor representing label '0' for all batch.seq\\n #'0' label is assigned for <EOS> and the nodes whose labels are not known\\n #Valid errors are only those which don't have '0' label\\n valid = tf.cast(tf.less(tf.slice(curr_label, [0,0,0], [self.config.num_steps, self.config.batch_size, 1]), tf.constant(0.5)), tf.float32)\\n #replicate along 3rd axis\\n valid = tf.tile(valid, tf.pack([1,1,tf.shape(curr_label)[2]]))\\n \\n #Sigmoid activation\\n self.label_sigmoid = tf.sigmoid(prediction_label)\\n #binary cross entropy for labels\\n cross_loss = tf.add(tf.log(1e-10 + self.label_sigmoid)*curr_label,\\n tf.log(1e-10 + (1-self.label_sigmoid))*(1-curr_label))\\n #only consider the loss for valid label predictions\\n #[TODO] mean of all or mean of only valid ???\\n cross_entropy_label = -1*tf.reduce_mean(tf.reduce_sum(cross_loss*valid,2))\\n tf.add_to_collection('total_loss', cross_entropy_label)\\n\\n\\n if self.config.solver._label_similarity_loss: \\n #Label similarity loss \\n label_sigmoid = tf.sigmoid(pred_label_reshaped)\\n part1 = tf.slice(label_sigmoid, [0,0,0], [self.config.num_steps-1, self.config.batch_size, self.config.data_sets._len_labels])\\n part2 = tf.slice(label_sigmoid, [1,0,0], [self.config.num_steps-1, self.config.batch_size, self.config.data_sets._len_labels])\\n\\n #Exponential weightage -> [r**(n-1), r**(n-2), ... , r**2. r**1]\\n label_diffusion = tf.constant([self.config.data_sets._diffusion_rate**i for i in range(self.config.num_steps-1,0,-1)])\\n cross_loss_sim = tf.add(tf.log(1e-10 + part1)*part2, tf.log(1e-10 + (1-part1))*(1-part2))\\n #prediction is 3 dimensional (seq x batch x label_len), reduce along axis of label_len\\n #Sum over each label error -> take mean over the batch -> sum for the sequence\\n cross_entropy_label_similarity = tf.reduce_sum(tf.reduce_mean(-tf.reduce_sum(cross_loss_sim, 2),1) * label_diffusion)\\n tf.add_to_collection('total_loss', cross_entropy_label_similarity)\\n\\n \\n if self.config.solver._embedding_loss:\\n #embedding similarity loss\\n #Matching First input's embeddings with embeddings of other inputs\\n #[TODO] reverse feed of input AND reverse diffusion rate\\n \\n emb_part1 = tf.slice(inputs, [self.config.num_steps-2,0,0], [1, self.config.batch_size, self.config.mRNN._embed_size])\\n emb_part2 = tf.slice(inputs, [0,0,0], [self.config.num_steps-1, self.config.batch_size, self.config.mRNN._embed_size])\\n\\n #Exponential weightage -> [r**(n-1), r**(n-2), ... , r**2. r**1]\\n label_diffusion = tf.constant([self.config.data_sets._diffusion_rate**i for i in range(self.config.num_steps-1,0,-1)])\\n #Broadcastive Subtraction\\n mse_emb = tf.reduce_mean(tf.square(emb_part2 - emb_part1),2)\\n cross_entropy_emb = tf.reduce_sum(tf.reduce_mean(mse_emb,1) * label_diffusion) * self.config.data_sets._emb_factor\\n tf.add_to_collection('total_loss', cross_entropy_emb)\\n\\n if self.config.solver._L2loss:\\n vars = tf.trainable_variables() \\n lossL2 = tf.add_n([tf.nn.l2_loss(v) for v in vars])*0.00001\\n tf.add_to_collection('total_loss', lossL2)\\n\\n loss = tf.add_n(tf.get_collection('total_loss'))\\n grads, = tf.gradients(loss, [self.embedding]) \\n\\n tf.summary.scalar('next_node_loss', cross_entropy_next)\\n tf.summary.scalar('curr_label_loss', cross_entropy_label)\\n tf.summary.scalar('label_similarity_loss', cross_entropy_label_similarity )\\n tf.summary.scalar('emb_loss', cross_entropy_emb)\\n tf.summary.scalar('total_loss', tf.reduce_sum(loss))\\n \\n return [loss, cross_entropy_next, cross_entropy_label, cross_entropy_label_similarity, cross_entropy_emb, grads]\",\n \"def loss_fn(model):\\n with flax.nn.stateful(state) as new_state:\\n with flax.nn.stochastic(prng_key):\\n logits = model(batch['image'])\\n loss = cross_entropy_loss(logits, batch['label'])\\n # TODO(britefury): check if applying L2 regularization to weights but\\n # *not* biases improves results\\n weight_penalty_params = jax.tree_leaves(model.params)\\n weight_l2 = sum([jnp.sum(x ** 2)\\n for x in weight_penalty_params\\n if x.ndim > 1])\\n weight_penalty = l2_reg * 0.5 * weight_l2\\n loss = loss + weight_penalty\\n return loss, (new_state, logits)\",\n \"def calculate_loss(self, logits, one_hot_true_labels):\\n loss = 0.0\\n loss += self.calculate_loss_by_granularity(logits, one_hot_true_labels, end=self.coarse_slice)\\n loss += self.calculate_loss_by_granularity(logits, one_hot_true_labels, ini=self.coarse_slice,\\n end=self.fine_slice)\\n loss += self.calculate_loss_by_granularity(logits, one_hot_true_labels, ini=self.fine_slice)\\n return loss\",\n \"def loss(\\n self,\\n model_in: torch.Tensor,\\n target: Optional[torch.Tensor] = None,\\n idx=None,\\n next_obs=None,\\n eval=False,\\n ) -> torch.Tensor:\\n loss = self._vaml_loss(model_in, target, idx=idx, next_obs=next_obs, eval=False)\\n if self.add_mse:\\n loss += self._mse_loss(model_in, target).mean(-1, keepdim=True)\\n return loss.mean()\",\n \"def loss_fn(outputs, labels, wts):\\n\\n # reshape labels to give a flat vector of length batch_size*seq_len\\n loss_noreduce = nn.BCEWithLogitsLoss(reduce=False)\\n loss = torch.mean(loss_noreduce(outputs, labels)*wts)\\n\\t\\n # compute cross entropy loss for all tokens\\n return loss\",\n \"def _compute_loss(self, parameters, inputs, ground_truth):\\n predictions = self.network_forward(parameters, inputs)\\n loss = np.mean((ground_truth - predictions) ** 2)\\n return loss\",\n \"def _compute_loss(self, parameters, inputs, ground_truth):\\n predictions = self.network_forward(parameters, inputs)\\n loss = np.mean((ground_truth - predictions) ** 2)\\n return loss\",\n \"def compute_loss(self, targets, logits, seq_length):\\n\\n\\t\\twith tf.name_scope('evaluate_loss'):\\n\\t\\t\\tloss, norm = self.loss_computer(targets, logits, seq_length)\\n\\t\\t\\t\\n\\t\\treturn loss, norm\",\n \"def compute_loss(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"def rmse_loss(y, label):\\r\\n losses = np.sqrt(np.mean((y - label) ** 2, axis=1))\\r\\n return losses\",\n \"def loss(self, X, y):\\n pass\",\n \"def loss_fn(self, targets, outputs, model):\",\n \"def getLoss(self, x_test, t_test):\\n x_t = Variable(x_test, requires_grad=False)\\n #Feed inputes into neural network\\n t_pred = self.model(x_t)\\n #Now lets compute out loss\\n loss = self.loss_fn(t_pred, t_test)\\n return loss\",\n \"def loss(self, logits, labels):\\r\\n return tf.reduce_mean(tf.keras.losses.binary_crossentropy(labels,logits))\",\n \"def L1(yhat, y):\\n\\n loss = np.sum(np.abs(y - yhat))\\n \\n return loss\",\n \"def halo_loss(predicted, labels, weights, obj, k_neg=7.):\\n\\n global_loss = torch.FloatTensor([0]).to(device)\\n x_lsm = F.log_softmax(predicted, dim=1)\\n x_sm = F.softmax(predicted, dim=1)\\n\\n v_weights = weights\\n v_labels = labels\\n\\n for i in range(predicted.size(0)): # Foreach sample\\n\\n # predicted[0] is preserved for background\\n bg = v_weights[i, 0] * v_labels[i, 0]\\n global_loss += -torch.sum(bg * x_lsm[i, 0]) / torch.sum(bg)\\n\\n if 0 == len(obj[i][1:]):\\n continue\\n\\n indexes = torch.LongTensor(obj[i][1:]).to(device) # One is for labels\\n\\n valid_labels = v_labels[i].index_select(0, indexes)\\n valid_weights = v_weights[i].index_select(0, indexes)\\n\\n target_pos = valid_labels * valid_weights\\n target_neg = (1. - valid_labels) * valid_weights\\n\\n positive = -torch.mm(flatten(x_lsm[i, 1:]),\\n flatten(target_pos).transpose(1, 0)) / torch.sum(target_pos)\\n\\n negative = -torch.mm(flatten(torch.log(1. - x_sm[i, 1:] + 1e-12)),\\n flatten(target_neg).transpose(1, 0)) / torch.sum(target_neg)\\n\\n values, indices = torch.min(positive + k_neg * negative, 0)\\n global_loss += torch.sum(positive[indices, torch.arange(0, positive.size(1)).long().to(device)])\\n\\n # Normalize w.r.t. image size\\n return global_loss * (1000. / (predicted.size(2) * predicted.size(3)))\",\n \"def kl_loss_batch(self):\\n return sum([e for m in self.modules for e in m._kl_losses])\",\n \"def lfads_training_loss(params, lfads_hps, key, x_bxt, kl_scale, keep_rate):\\n losses = lfads_losses(params, lfads_hps, key, x_bxt, kl_scale, keep_rate)\\n return losses['total']\",\n \"def loss_fn(self, recons, inputs, mu, log_var, **kwargs):\\n# kld_weight = kwargs['M_N'] # Account for the minibatch samples from the dataset\\n recons_loss = F.mse_loss(recons, inputs)\\n# recons_loss = F.binary_cross_entropy(recons, inputs)\\n KLD = torch.mean(-0.5 * torch.sum(1 + log_var - mu**2 - log_var.exp(), dim=1), dim=0)\\n loss = recons_loss - KLD\\n return loss, recons_loss, KLD\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7471189","0.6744396","0.6732741","0.66957474","0.6637872","0.65996355","0.65996355","0.6463765","0.6462264","0.6452444","0.6387869","0.63160586","0.6304027","0.6283066","0.6242887","0.6218127","0.61900795","0.617547","0.6174948","0.6159635","0.6144969","0.6140564","0.6126589","0.6125259","0.6096993","0.6086241","0.6081793","0.6081192","0.60706955","0.6060143","0.60575753","0.6053961","0.60465044","0.6013867","0.60102165","0.60013366","0.5991718","0.5982514","0.59802085","0.5958351","0.5955787","0.5952771","0.5950238","0.59474355","0.59453535","0.59392077","0.59339625","0.59318554","0.5927908","0.5921563","0.59176236","0.59110206","0.59089965","0.59075373","0.58940226","0.5890562","0.5889848","0.5885155","0.58816624","0.5879803","0.58713484","0.586875","0.586518","0.58632094","0.58588","0.5857977","0.5851799","0.58505857","0.5844993","0.5843693","0.58427554","0.58402056","0.5832747","0.5830337","0.58208126","0.5817909","0.5814619","0.5804701","0.5794599","0.5789212","0.5787851","0.578276","0.57808137","0.57782656","0.5775059","0.5773623","0.5767161","0.57598984","0.57598984","0.5755583","0.5750911","0.57505697","0.57434034","0.5741253","0.57391983","0.573639","0.57356954","0.5731151","0.57234466","0.5721283","0.57203996"],"string":"[\n \"0.7471189\",\n \"0.6744396\",\n \"0.6732741\",\n \"0.66957474\",\n \"0.6637872\",\n \"0.65996355\",\n \"0.65996355\",\n \"0.6463765\",\n \"0.6462264\",\n \"0.6452444\",\n \"0.6387869\",\n \"0.63160586\",\n \"0.6304027\",\n \"0.6283066\",\n \"0.6242887\",\n \"0.6218127\",\n \"0.61900795\",\n \"0.617547\",\n \"0.6174948\",\n \"0.6159635\",\n \"0.6144969\",\n \"0.6140564\",\n \"0.6126589\",\n \"0.6125259\",\n \"0.6096993\",\n \"0.6086241\",\n \"0.6081793\",\n \"0.6081192\",\n \"0.60706955\",\n \"0.6060143\",\n \"0.60575753\",\n \"0.6053961\",\n \"0.60465044\",\n \"0.6013867\",\n \"0.60102165\",\n \"0.60013366\",\n \"0.5991718\",\n \"0.5982514\",\n \"0.59802085\",\n \"0.5958351\",\n \"0.5955787\",\n \"0.5952771\",\n \"0.5950238\",\n \"0.59474355\",\n \"0.59453535\",\n \"0.59392077\",\n \"0.59339625\",\n \"0.59318554\",\n \"0.5927908\",\n \"0.5921563\",\n \"0.59176236\",\n \"0.59110206\",\n \"0.59089965\",\n \"0.59075373\",\n \"0.58940226\",\n \"0.5890562\",\n \"0.5889848\",\n \"0.5885155\",\n \"0.58816624\",\n \"0.5879803\",\n \"0.58713484\",\n \"0.586875\",\n \"0.586518\",\n \"0.58632094\",\n \"0.58588\",\n \"0.5857977\",\n \"0.5851799\",\n \"0.58505857\",\n \"0.5844993\",\n \"0.5843693\",\n \"0.58427554\",\n \"0.58402056\",\n \"0.5832747\",\n \"0.5830337\",\n \"0.58208126\",\n \"0.5817909\",\n \"0.5814619\",\n \"0.5804701\",\n \"0.5794599\",\n \"0.5789212\",\n \"0.5787851\",\n \"0.578276\",\n \"0.57808137\",\n \"0.57782656\",\n \"0.5775059\",\n \"0.5773623\",\n \"0.5767161\",\n \"0.57598984\",\n \"0.57598984\",\n \"0.5755583\",\n \"0.5750911\",\n \"0.57505697\",\n \"0.57434034\",\n \"0.5741253\",\n \"0.57391983\",\n \"0.573639\",\n \"0.57356954\",\n \"0.5731151\",\n \"0.57234466\",\n \"0.5721283\",\n \"0.57203996\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":84192,"cells":{"query":{"kind":"string","value":"Reads the discounts csv file & returns a dataframe"},"document":{"kind":"string","value":"def read_discounts(self, file_name):\n discounts = pd.read_csv(f'{file_name}.csv')\n\n return discounts"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def load_extract(cryptocurrency):\n df = pd.read_csv(f'input_12mo/{cryptocurrency}.csv')\n df = df['Close'].copy()\n df = df[-183:].copy()\n return df","def loan_data():\n return pd.read_csv(data_path / \"credit_data.csv\")","def read_csv():","def read_data(filepath):\n df = pd.read_csv(filepath)\n return df","def read_data(filepath):\n df = pd.read_csv(filepath)\n return df","def read_data(filepath):\n df = pd.read_csv(filepath)\n return df","def get_data(filename):\r\n return pd.read_csv(filename)","def read_data_from_csv(filename):\n df = pd.read_csv(filename)\n return df","def data_from_csv(self, filepath):\n self.dataframe = pd.load_csv(filepath, separator='')","def import_data():\n import pandas as pd\n \n df = pd.read_csv('Company_Bankruptcy_Prediction.csv')\n return df","def read_csv_ur10(self, csv_file):\r\n df = pd.read_csv(csv_file, sep=';', decimal=',', header=0)\r\n return df","def get_data_from_disc(symbol, usecols=['Date', 'Close', 'Open', 'High', 'Low', 'Adj Close', 'Volume']):\n print('\\n\\n\\n============================================================================')\n print('#Loading raw data usecols=',usecols)\n print('===============================================================================')\n df1 = pd.read_csv(symbol_to_path(symbol)\n , index_col='Date'\n , parse_dates=True\n , usecols=usecols\n , na_values=['nan'])\n\n print('raw columns=', df1.columns)\n print('\\nraw df1=\\n', df1)\n return df1","def _read_data(filename):\n logger.info('Reading file {}'.format(filename))\n return pd.read_csv(filename)","def reader(self):\n df = pd.read_csv(self.path)\n return df","def load_predict(cryptocurrency):\n df = pd.read_csv(f'output_12mo/{cryptocurrency}.csv')\n #df = df.drop(0, axis=0).copy()\n #df = df['Close'].copy()\n #df = df[:-25].copy()\n return df","def ReadMetrics( fileName ):\n DataDF=pd.read_csv(fileName,header=0,delimiter=',',parse_dates=[0])\n DataDF = DataDF.set_index('Date')\n #print(DataDF.head())\n return( DataDF )","def ReadData( fileName ):\n \n # define column names\n colNames = ['agency_cd', 'site_no', 'Date', 'Discharge', 'Quality']\n\n # open and read the file\n DataDF = pd.read_csv(fileName, header=1, names=colNames, \n delimiter=r\"\\s+\",parse_dates=[2], comment='#',\n na_values=['Eqp'])\n DataDF = DataDF.set_index('Date')\n \n # quantify the number of missing values\n MissingValues = DataDF[\"Discharge\"].isna().sum()\n \n ## Remove invalid streamflow data\n DataDF.Discharge[(DataDF['Discharge']<0)]=np.nan\n \n return( DataDF, MissingValues )","def _get_df_from_csv(self, filename):\n df = pd.read_csv(filename)\n df.set_index('Date', drop=True, inplace=True)\n df.index = pd.to_datetime(df.index)\n return df","def importData(filename):\r\n data = pd.read_csv(filename)\r\n return data","def extract_df(self, file_path):\n df = pd.read_csv(file_path, sep=\";\")\n df.rename(columns={\"Get\": \"Currency\"}, inplace=True)\n df = df[df[\"Pay\"] == \"Chaos Orb\"]\n df = df[[\"League\", \"Date\", \"Currency\", \"Value\"]]\n df[\"Date\"] = pd.to_datetime(df[\"Date\"])\n df[\"Date\"] = df[\"Date\"] - df.loc[0][\"Date\"]\n return df","def _parse_csv(csv_file: str) -> pd.DataFrame:\n return pd.read_csv(csv_file, header=0)","def read_from_file_no_check(file_name: str) -> pd.DataFrame:\n return pd.read_csv(file_name)","def read_csv():\n csv_file = \"dow.csv\"\n\n # read the data from the csv file, parsing the Dates to make the x-axis, setting index_col to zero to remove it\n data_frame = pd.read_csv(csv_file, parse_dates=True, index_col=0)\n return data_frame","def get_dataset(filepath):\n return pandas.read_csv(filepath, header='infer')","def read_data_from_csv(filename: str) -> pd.DataFrame:\n try:\n data = pd.read_csv(filename)\n return data\n except(FileNotFoundError):\n print('Error: Could not read the data from csv.')\n return None","def _get_liwc_df(self) -> pd.DataFrame:\n data = pd.read_csv(self.path)\n data.index = pd.to_numeric(data['Filename'].str.rstrip('.txt'))\n return data","def get_df_from_csv(input_file_name):\n df = pd.read_csv(input_file_name)\n df = df.drop(['Unnamed: 0'], axis=1)\n return df","def prepare_data():\n df = pd.read_csv('Wholesale customers data.csv')\n df_numeric = df[['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']]\n return df, df_numeric","def get_data(path):\n df = pd.read_csv(path)\n\n return df","def get_price_df(url):\n df = pd.read_csv(url).dropna()\n df.index = pd.to_datetime(df['Date'])\n df = df.drop(columns=['Date'])\n return df","def import_data(csv_file):\n # skips bad lines\n data = pd.read_csv(csv_file, error_bad_lines=False)\n return data","def run(self) -> pd.DataFrame:\n with open(self.file_path, 'r') as in_file:\n headers = in_file.readline()\n headers = headers.replace(\"\\n\", \"\")\n\n if ',' in headers:\n headers = headers.split(',')\n else:\n headers = headers.split()\n\n if headers == self.NORMAL_HEADERS:\n return self.normal_csv()\n else:\n return self.read_data_columns_to_rows()","def load_data(fn):\n return pandas.read_csv(fn, dtype={'Name': str, 'Reason': str, 'Amount': float, 'Day': int})","def _read_data(self, fp):\n names = [\n \"Year\",\n \"Month\",\n \"Day\",\n \"Hour\",\n \"Minute\",\n \"Data Source and Uncertainty Flags\",\n \"Dry Bulb Temperature\",\n \"Dew Point Temperature\",\n \"Relative Humidity\",\n \"Atmospheric Station Pressure\",\n \"Extraterrestrial Horizontal Radiation\",\n \"Extraterrestrial Direct Normal Radiation\",\n \"Horizontal Infrared Radiation Intensity\",\n \"Global Horizontal Radiation\",\n \"Direct Normal Radiation\",\n \"Diffuse Horizontal Radiation\",\n \"Global Horizontal Illuminance\",\n \"Direct Normal Illuminance\",\n \"Diffuse Horizontal Illuminance\",\n \"Zenith Luminance\",\n \"Wind Direction\",\n \"Wind Speed\",\n \"Total Sky Cover\",\n \"Opaque Sky Cover (used if Horizontal IR Intensity missing)\",\n \"Visibility\",\n \"Ceiling Height\",\n \"Present Weather Observation\",\n \"Present Weather Codes\",\n \"Precipitable Water\",\n \"Aerosol Optical Depth\",\n \"Snow Depth\",\n \"Days Since Last Snowfall\",\n \"Albedo\",\n \"Liquid Precipitation Depth\",\n \"Liquid Precipitation Quantity\",\n ]\n\n first_row = self._first_row_with_climate_data(fp)\n df = pd.read_csv(fp, skiprows=first_row, header=None, names=names)\n return df","def read_csv_to_dataframe(file_name):\n df = pd.read_csv(file_name)\n df = df.drop(['Unnamed: 0'], axis=1)\n return df","def create_dataframe():\r\n\r\n df = pd.read_csv('data/data.csv', header=0)\r\n return df","def initialize_from_file(filename):\r\n df = pd.read_csv(filename)\r\n return df","def fetch_csv(filename):\n variable = pd.read_csv(filename+'.csv', index_col=0)\n return variable","def read_csv_data(url):\n\n csv_data = pd.read_csv(url)\n\n return csv_data","def import_data():\n data = pd.read_csv('partA/bikes_October18.csv', ',')\n return data","def _read_csvs(self):\n self.data = pd.read_csv(self.path+self.name, index_col=0)","def readindata(chosen_columns, chosen_url):\n return pd.read_csv(chosen_url, usecols=chosen_columns)","def read_ticker(\n ticker:str) -> pd.DataFrame:\n df = pd.read_csv(f'../data/consolidate/{ticker}.csv',\n index_col='Unnamed: 0',parse_dates=True).fillna(0)\n\n df = df.loc[:, (df != 0).any(axis=0)] # removing 0 columns\n df = df.pct_change().replace([np.inf, -np.inf, np.nan], 0)\n\n return df","def read_csv(path):\n return pd.read_csv(path)","def read_csv_file(self):\n pass","def GetRateData(directory):\n\n rt_data = pd.read_csv(directory)\n return rt_data","def _read_csv(filename):\n csv_file = open(filename, 'r')\n data = pandas.read_csv(csv_file, comment='!', quotechar=\"'\")\n csv_file.close()\n return data","def read_rf_csv():\n if os.path.exists(\"rf.csv\"):\n #print (\"--decision trees CSV imported\\n\")\n results = pd.read_csv(\"rf.csv\", index_col=0)\n else:\n print(\"log not found\")\n\n return results","def read_csv(self) -> None:\n\n self._df = pd.read_csv(self._dataset_file)","def get_sp500_stocks_file(file_path=None):\n\n df = pd.read_csv(file_path)\n\n return df","def load_csv():\n df = pd.read_csv(datafolder+filename, decimal=decimal).astype(\n {'min': 'float', 'max': 'float'})\n return df","def read_csv(path):\n column_headers = ['Country', 'Region', 'Pop. Density (per sq. mi.)', 'Infant mortality (per 1000 births)',\n 'GDP ($ per capita) dollars']\n df = pd.read_csv(path)\n return df[column_headers]","def read_data(path):\n data = pd.read_csv(path)\n return data","def read_data(path):\n data = pd.read_csv(path)\n return data","def process_csv(filepath):\n suburb = get_suburb(filepath)\n read_file = pd.read_csv(filepath,\n infer_datetime_format=True,\n parse_dates=[\"SALE DATE\"],\n dayfirst=True)\n read_file[\"SUBURB\"] = suburb\n separate_date(read_file)\n return read_file","def read_data(self) -> pd.DataFrame:\n data = pd.read_csv(self.data_path)\n assert isinstance(data, pd.DataFrame)\n return data","def read_data(self) -> pd.DataFrame:\n data = pd.read_csv(self.data_path)\n assert isinstance(data, pd.DataFrame)\n return data","def csv_to_df(self, path=None):\n # reads the csv file and puts it to the dataframe\n df = pd.read_csv(path)\n return df","def get_raw_data_from_csv():\n data_df = pd.read_csv(static_constants.RAW_DATA_PATH)\n return data_df","def import_data(catalog='xmatch_TGAS_Simbad.csv', params=None, nrows=None, delimiter=','):\n print \"Loading %s and creating DataFrame..\" % catalog\n df_imported = pd.read_csv(catalog, delimiter=delimiter, header=0, usecols=params, nrows=nrows)\n print \"..Done\\n----------\"\n return df_imported","def read_dataset(filename):\n if not os.path.exists(filename):\n url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data'\n df = pd.read_csv(url, header = None, na_values = ['?'])\n colnames = ['symboling', 'normalized-losses', 'make', 'fuel-type',\n 'aspiration', 'num-of-doors', 'body-style', 'drive-wheels',\n 'engine-location', 'wheel-base', 'length', 'width', 'height',\n 'curb-weight', 'engine-type', 'num-of-cylinders', 'engine-size',\n 'fuel-system', 'bore', 'stroke', 'compression-ratio',\n 'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg', 'price']\n df.columns = colnames\n df.to_csv(filename, index = False)\n\n df = pd.read_csv(filename)\n return df","def normal_csv(self):\n if len(self.delimiter) > 0:\n df = pd.read_csv(self.file_path, delimiter=self.delimiter)\n else:\n df = pd.read_csv(self.file_path)\n\n return df","def load_data(filename):\n data = pd.read_csv(filename)\n\n labels = data['Revenue'].map(lambda x: 1 if x is True else 0).tolist()\n\n data['Month'] = data['Month'].map(lambda x: convert_month_to_number(x))\n data.drop(data[data['Month'] == -1].index)\n\n data['VisitorType'] = data['VisitorType'].map(lambda x: 1 if x == 'Returning_Visitor' else 0)\n data['Weekend'] = data['Weekend'].map(lambda x: 1 if x is True else 0)\n\n for i in data.keys():\n if i == 'Administrative' or i == 'Informational' or i == 'ProductRelated' or i == 'OperatingSystems' or \\\n i == 'Browser' or i == 'Region' or i == 'TrafficType' or i == 'VisitorType' or i == 'Weekend':\n if not pd.api.types.is_integer_dtype(data[i].dtype):\n data = data[i].apply(lambda x: not isinstance(x, int))\n if i == 'Administrative_Duration' or i == 'Informational_Duration' or i == 'ProductRelated_Duration' or \\\n i == 'BounceRates' or i == 'ExitRates' or i == 'PageValues' or i == 'SpecialDay':\n if not pd.api.types.is_float_dtype(data[i].dtype):\n data = data[data[i].apply(lambda x: isinstance(x, float))]\n data.drop(\"Revenue\", axis=1, inplace=True)\n evidence = data.values.tolist()\n return evidence, labels","def load_daily_data():\n return pd.read_csv(os.path.join('data', 'raw', 'full_grouped.csv'))","def read(self):\n \n self.df = pd.read_csv(self.path, encoding = \"ISO-8859-1\")","def read_data(fname, cols):\n df = (pd.read_csv(fname, header=None, sep=r\"\\s+\", comment=\"#\",\n names=cols, dtype=np.float64)\n .iloc[1:]) # First line is the total number of trees\n # Could reset_index, but we don't shuffle the DataFrame\n return df","def read_product(filename=None):\n if not filename:\n filename = settings.PRODUCT_FILENAME\n return pd.read_csv(filename, sep='|')","def create_dataframe_from_csv(path_to_csv_file):\r\n df = pd.read_csv(path_to_csv_file)\r\n return df","def read_file(file):\n return pd.read_csv(file, header=0, index_col=0,\n parse_dates=['Trading_date'],\n infer_datetime_format=True,\n keep_date_col=True)","def readSolarData(filename):\n\treturn pd.read_csv(filename)","def diamonds(path):\n import pandas as pd\n path = os.path.expanduser(path)\n filename = 'diamonds.csv'\n if not os.path.exists(os.path.join(path, filename)):\n url = 'http://dustintran.com/data/r/Stat2Data/Diamonds.csv'\n maybe_download_and_extract(path, url,\n save_file_name='diamonds.csv',\n resume=False)\n\n data = pd.read_csv(os.path.join(path, filename), index_col=0,\n parse_dates=True)\n x_train = data.values\n metadata = {'columns': data.columns}\n return x_train, metadata","def read_data():\n data = pd.read_csv('input_data/Preply_tutor_views_datasaet.csv')\n return data","def readFile(self,path):\n _file = pd.read_csv(path, sep='\\s+', engine='python', header=None)\n self._dataTable = pd.DataFrame(_file.iloc[:, 3:15])\n self._dataTable.columns = ['MGEMLEEF Avg age', 'MOSHOOFD Customer main type', 'MGODRK Roman catholic',\n 'MGODPR Protestant', 'MGODOV Other religion', 'MGODGE No religion', 'MRELGE Married',\n 'MRELSA Living together', 'MRELOV Other relation', 'MFALLEEN Singles',\n 'MFGEKIND Household without children', 'MFWEKIND Household with children']","def get_data(fpath):\n\n visits = ['SC', 'BL', 'V01', 'V02', 'V03', 'V04', 'V05', 'V06', 'V07',\n 'V08', 'V09', 'V10', 'V11', 'V12', 'V13', 'V14', 'V15']\n dtype = dict(PATNO=str,\n CLINICAL_EVENT=cdtype(visits, ordered=True),\n TESTNAME=str,\n TESTVALUE=str)\n\n fname = op.join(fpath, 'Current_Biospecimen_Analysis_Results.csv')\n data = pd.read_csv(fname, dtype=dtype)\n\n data['TESTVALUE'] = pd.to_numeric(data.TESTVALUE, errors='coerce')\n data = data.rename(columns=RENAME_COLS).assign(**ASSIGN_COLS)[RETAIN_COLS]\n data = data.dropna(axis=0, subset=['SCORE'])\n\n return data","def read_dataset():\n\n df = pd.read_csv('fake_job_postings.csv', index_col='job_id')\n return df","def read_csv(csv_path):\n \n df = pd.read_csv(csv_path)\n\n return df","def read_data_csv(path):\n df = pd.read_csv(path)\n df.drop([\"Unnamed: 0\"], axis=1, inplace=True)\n return df","def read_confounds(filename, confounds):\n df_confounds = pandas.read_csv(filename, sep='\\t', usecols=confounds)\n return df_confounds","def _read_csv(self) -> pd.DataFrame:\n\n return pd.concat(\n [\n pd.read_csv(f, usecols=[1, 2, 3, 4, 5])\n for f in self.temp_path.iterdir()\n if f.name.endswith(\".csv\")\n ]\n )","def _csv2df(data_file):\n df = pd.read_csv(data_file, encoding=\"ISO-8859-1\", low_memory=False)\n return df","def read_data(path, filename, drop_col=\"index\", dt=\"float32\"):\n\tdata = pd.read_csv(path + filename, sep=\",\", dtype=dt)\n\tdata = data.drop(drop_col, axis=1)\n\treturn data.as_matrix()","def _ReadDefinitions(filename, delimiter):\n return pd.read_csv(filename, sep=delimiter)","def load_to_dataframe(self) -> DataFrame:\n return read_csv(self._csv_path, converters={\n # Check if embedding size is the empty string,\n # as it would be for Count models\n \"Embedding size\": lambda v: int(float(v)) if len(v) > 0 else nan\n })","def read_data(self):\n fpath = './data/surveys.csv'\n self.data = pd.read_csv(fpath, header=0, low_memory=False)\n #print(self.data.head(n=5))\n print(self.data.shape)","def load_data(csv_path):\n df = pd.read_csv(csv_path)\n return df","def readData(filename):\n #defining gobal variable (dataframe) to access it outside this function\n global dataframe\n #storing full CSV file into a dataframe(data structure)\n dataframe = pd.read_csv(filename)\n #type casting temperature column of dataframe to numeric data and ignoring '***' values\n dataframe['Temperature'] = pd.to_numeric(dataframe['Temperature'], errors='coerce')\n return dataframe","def read_data():\n \n countries= pd.read_csv('../countries.csv')\n try:\n income = pd.read_excel(\"indicator gapminder gdp_per_capita_ppp.xlsx\",index_col=u\"gdp pc test\")\n except:\n print \"excel read module not found \"\n income = income.T\n print \"Q1: The head of the income dataframe\"\n print income.head()\n return countries,income","def read_csv_file_data(file_path):\n if os.path.exists(file_path):\n df = pandas.read_csv(file_path)\n else:\n raise ValueError('ERROR: file_path doesnt exist in read_csv_file_data()')\n return df","def load_data(reviews_path):\n df1 = pd.read_csv(reviews_path)\n #substituting 0 for negative reviews labeled '__label__1' and 1 for positive reviews labeled '__label__2'\n df1 = df1.replace('__label__1', 0)\n df1= df1.replace('__label__2', 1)\n \n return df1","def load(self) -> pd.DataFrame:\n if os.path.exists(self.file_name):\n df = pd.read_csv(self.file_name, index_col=0)\n df = self._clean(df)\n else:\n _LOG.debug(\"No file '%s'\", self.file_name)\n df = pd.DataFrame()\n return df","def get_decopath_df() -> pd.DataFrame:\n return pd.read_csv(DECOPATH_PATH, sep='\\t')","def get_recipe_dataset():\n df = pd.read_csv(FILENAME)\n print(\"# of rows - no filtering: %s\" % len(df))\n\n df = df.head(500)\n print(\"# of rows - reduce size of DF for testing: %s\" % len(df))\n\n return df","def _loadCSVFile(self):\n self._df = pd.read_csv(\n self._pathfile, sep=CSV_SEPARATOR, index_col=CSV_INDEX_COL)","def create_df(filename):\n data = pd.read_csv(filename)\n data = data.dropna(axis='index')\n data['inc_angle'] = np.radians(data['inc_angle'])\n data = data.astype('float64')\n data = data[data['inc_angle'] <= np.deg2rad(80)]\n return data","def parse(file_name):\n \n return pd.read_csv(file_name, na_values = '---')","def load_data(file_path):\n data = pandas.read_csv(file_path)\n\n return data","def _data_reader(file):\n # Create a dictionary so that filename matches a site name.\n site_dict = {'D05536000': 'NB Niles', 'D05536101': 'NS Channel-Wilmette',\n 'D05536105': 'NB Albany', 'D05536118': 'NB Grand Avenue',\n 'D05536121': 'CH River-Lock', 'D05536123': 'CH River-Columbus',\n 'D05536137': 'CSSC-Western Avenue', 'D05536140': 'CSSC-Stickney',\n 'D05536275': 'Thorn Creek', 'D05536290': 'Little Calument',\n 'D05536340': 'Midlothian Creek', 'D05536343': 'Natalie Creek',\n 'D05536357': 'Grand Calumet', 'D05536500': 'Tinley Creek',\n 'D05536700': 'Calumet-Sag Channel', 'D05536890': 'CSSC-Lemont',\n 'D05536995': 'CSSC-Romeoville'}\n df_raw = pd.read_csv(file)\n df_raw['dateTime'] = pd.to_datetime(df_raw['dateTime'])\n # Creating a dataframe with the data we only need.\n df = df_raw[['dateTime', 'X_00065_00000']]\n df = df.set_index(df_raw['dateTime'])\n\n # Retrieve site information to be used in saved excel filenames.\n site_code = file[-9:]\n site_name = [v for v in site_dict.items() if site_code in v][0]\n site = site_code + '_' + site_name[1].replace(' ', '-')\n\n # Convert index into a datetime index for easier indexing.\n df.index = pd.to_datetime(df.index)\n return df_raw, df, site, site_code","def _get_dataframe(self, filePathList):\n filePath = filePathList[0]\n df = pd.read_csv(filePath, sep=\"\\t\", comment=\"#\")\n return df","def read_covid():\n return pd.read_csv(_COVID_FILE, parse_dates=[\"date\"])","def load_data():\n df = pd.read_csv(\"https://raw.githubusercontent.com/Andrea-Giuliani/Python-Project/master/data/final_dataset.csv\",sep=',') \n return df"],"string":"[\n \"def load_extract(cryptocurrency):\\n df = pd.read_csv(f'input_12mo/{cryptocurrency}.csv')\\n df = df['Close'].copy()\\n df = df[-183:].copy()\\n return df\",\n \"def loan_data():\\n return pd.read_csv(data_path / \\\"credit_data.csv\\\")\",\n \"def read_csv():\",\n \"def read_data(filepath):\\n df = pd.read_csv(filepath)\\n return df\",\n \"def read_data(filepath):\\n df = pd.read_csv(filepath)\\n return df\",\n \"def read_data(filepath):\\n df = pd.read_csv(filepath)\\n return df\",\n \"def get_data(filename):\\r\\n return pd.read_csv(filename)\",\n \"def read_data_from_csv(filename):\\n df = pd.read_csv(filename)\\n return df\",\n \"def data_from_csv(self, filepath):\\n self.dataframe = pd.load_csv(filepath, separator='')\",\n \"def import_data():\\n import pandas as pd\\n \\n df = pd.read_csv('Company_Bankruptcy_Prediction.csv')\\n return df\",\n \"def read_csv_ur10(self, csv_file):\\r\\n df = pd.read_csv(csv_file, sep=';', decimal=',', header=0)\\r\\n return df\",\n \"def get_data_from_disc(symbol, usecols=['Date', 'Close', 'Open', 'High', 'Low', 'Adj Close', 'Volume']):\\n print('\\\\n\\\\n\\\\n============================================================================')\\n print('#Loading raw data usecols=',usecols)\\n print('===============================================================================')\\n df1 = pd.read_csv(symbol_to_path(symbol)\\n , index_col='Date'\\n , parse_dates=True\\n , usecols=usecols\\n , na_values=['nan'])\\n\\n print('raw columns=', df1.columns)\\n print('\\\\nraw df1=\\\\n', df1)\\n return df1\",\n \"def _read_data(filename):\\n logger.info('Reading file {}'.format(filename))\\n return pd.read_csv(filename)\",\n \"def reader(self):\\n df = pd.read_csv(self.path)\\n return df\",\n \"def load_predict(cryptocurrency):\\n df = pd.read_csv(f'output_12mo/{cryptocurrency}.csv')\\n #df = df.drop(0, axis=0).copy()\\n #df = df['Close'].copy()\\n #df = df[:-25].copy()\\n return df\",\n \"def ReadMetrics( fileName ):\\n DataDF=pd.read_csv(fileName,header=0,delimiter=',',parse_dates=[0])\\n DataDF = DataDF.set_index('Date')\\n #print(DataDF.head())\\n return( DataDF )\",\n \"def ReadData( fileName ):\\n \\n # define column names\\n colNames = ['agency_cd', 'site_no', 'Date', 'Discharge', 'Quality']\\n\\n # open and read the file\\n DataDF = pd.read_csv(fileName, header=1, names=colNames, \\n delimiter=r\\\"\\\\s+\\\",parse_dates=[2], comment='#',\\n na_values=['Eqp'])\\n DataDF = DataDF.set_index('Date')\\n \\n # quantify the number of missing values\\n MissingValues = DataDF[\\\"Discharge\\\"].isna().sum()\\n \\n ## Remove invalid streamflow data\\n DataDF.Discharge[(DataDF['Discharge']<0)]=np.nan\\n \\n return( DataDF, MissingValues )\",\n \"def _get_df_from_csv(self, filename):\\n df = pd.read_csv(filename)\\n df.set_index('Date', drop=True, inplace=True)\\n df.index = pd.to_datetime(df.index)\\n return df\",\n \"def importData(filename):\\r\\n data = pd.read_csv(filename)\\r\\n return data\",\n \"def extract_df(self, file_path):\\n df = pd.read_csv(file_path, sep=\\\";\\\")\\n df.rename(columns={\\\"Get\\\": \\\"Currency\\\"}, inplace=True)\\n df = df[df[\\\"Pay\\\"] == \\\"Chaos Orb\\\"]\\n df = df[[\\\"League\\\", \\\"Date\\\", \\\"Currency\\\", \\\"Value\\\"]]\\n df[\\\"Date\\\"] = pd.to_datetime(df[\\\"Date\\\"])\\n df[\\\"Date\\\"] = df[\\\"Date\\\"] - df.loc[0][\\\"Date\\\"]\\n return df\",\n \"def _parse_csv(csv_file: str) -> pd.DataFrame:\\n return pd.read_csv(csv_file, header=0)\",\n \"def read_from_file_no_check(file_name: str) -> pd.DataFrame:\\n return pd.read_csv(file_name)\",\n \"def read_csv():\\n csv_file = \\\"dow.csv\\\"\\n\\n # read the data from the csv file, parsing the Dates to make the x-axis, setting index_col to zero to remove it\\n data_frame = pd.read_csv(csv_file, parse_dates=True, index_col=0)\\n return data_frame\",\n \"def get_dataset(filepath):\\n return pandas.read_csv(filepath, header='infer')\",\n \"def read_data_from_csv(filename: str) -> pd.DataFrame:\\n try:\\n data = pd.read_csv(filename)\\n return data\\n except(FileNotFoundError):\\n print('Error: Could not read the data from csv.')\\n return None\",\n \"def _get_liwc_df(self) -> pd.DataFrame:\\n data = pd.read_csv(self.path)\\n data.index = pd.to_numeric(data['Filename'].str.rstrip('.txt'))\\n return data\",\n \"def get_df_from_csv(input_file_name):\\n df = pd.read_csv(input_file_name)\\n df = df.drop(['Unnamed: 0'], axis=1)\\n return df\",\n \"def prepare_data():\\n df = pd.read_csv('Wholesale customers data.csv')\\n df_numeric = df[['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']]\\n return df, df_numeric\",\n \"def get_data(path):\\n df = pd.read_csv(path)\\n\\n return df\",\n \"def get_price_df(url):\\n df = pd.read_csv(url).dropna()\\n df.index = pd.to_datetime(df['Date'])\\n df = df.drop(columns=['Date'])\\n return df\",\n \"def import_data(csv_file):\\n # skips bad lines\\n data = pd.read_csv(csv_file, error_bad_lines=False)\\n return data\",\n \"def run(self) -> pd.DataFrame:\\n with open(self.file_path, 'r') as in_file:\\n headers = in_file.readline()\\n headers = headers.replace(\\\"\\\\n\\\", \\\"\\\")\\n\\n if ',' in headers:\\n headers = headers.split(',')\\n else:\\n headers = headers.split()\\n\\n if headers == self.NORMAL_HEADERS:\\n return self.normal_csv()\\n else:\\n return self.read_data_columns_to_rows()\",\n \"def load_data(fn):\\n return pandas.read_csv(fn, dtype={'Name': str, 'Reason': str, 'Amount': float, 'Day': int})\",\n \"def _read_data(self, fp):\\n names = [\\n \\\"Year\\\",\\n \\\"Month\\\",\\n \\\"Day\\\",\\n \\\"Hour\\\",\\n \\\"Minute\\\",\\n \\\"Data Source and Uncertainty Flags\\\",\\n \\\"Dry Bulb Temperature\\\",\\n \\\"Dew Point Temperature\\\",\\n \\\"Relative Humidity\\\",\\n \\\"Atmospheric Station Pressure\\\",\\n \\\"Extraterrestrial Horizontal Radiation\\\",\\n \\\"Extraterrestrial Direct Normal Radiation\\\",\\n \\\"Horizontal Infrared Radiation Intensity\\\",\\n \\\"Global Horizontal Radiation\\\",\\n \\\"Direct Normal Radiation\\\",\\n \\\"Diffuse Horizontal Radiation\\\",\\n \\\"Global Horizontal Illuminance\\\",\\n \\\"Direct Normal Illuminance\\\",\\n \\\"Diffuse Horizontal Illuminance\\\",\\n \\\"Zenith Luminance\\\",\\n \\\"Wind Direction\\\",\\n \\\"Wind Speed\\\",\\n \\\"Total Sky Cover\\\",\\n \\\"Opaque Sky Cover (used if Horizontal IR Intensity missing)\\\",\\n \\\"Visibility\\\",\\n \\\"Ceiling Height\\\",\\n \\\"Present Weather Observation\\\",\\n \\\"Present Weather Codes\\\",\\n \\\"Precipitable Water\\\",\\n \\\"Aerosol Optical Depth\\\",\\n \\\"Snow Depth\\\",\\n \\\"Days Since Last Snowfall\\\",\\n \\\"Albedo\\\",\\n \\\"Liquid Precipitation Depth\\\",\\n \\\"Liquid Precipitation Quantity\\\",\\n ]\\n\\n first_row = self._first_row_with_climate_data(fp)\\n df = pd.read_csv(fp, skiprows=first_row, header=None, names=names)\\n return df\",\n \"def read_csv_to_dataframe(file_name):\\n df = pd.read_csv(file_name)\\n df = df.drop(['Unnamed: 0'], axis=1)\\n return df\",\n \"def create_dataframe():\\r\\n\\r\\n df = pd.read_csv('data/data.csv', header=0)\\r\\n return df\",\n \"def initialize_from_file(filename):\\r\\n df = pd.read_csv(filename)\\r\\n return df\",\n \"def fetch_csv(filename):\\n variable = pd.read_csv(filename+'.csv', index_col=0)\\n return variable\",\n \"def read_csv_data(url):\\n\\n csv_data = pd.read_csv(url)\\n\\n return csv_data\",\n \"def import_data():\\n data = pd.read_csv('partA/bikes_October18.csv', ',')\\n return data\",\n \"def _read_csvs(self):\\n self.data = pd.read_csv(self.path+self.name, index_col=0)\",\n \"def readindata(chosen_columns, chosen_url):\\n return pd.read_csv(chosen_url, usecols=chosen_columns)\",\n \"def read_ticker(\\n ticker:str) -> pd.DataFrame:\\n df = pd.read_csv(f'../data/consolidate/{ticker}.csv',\\n index_col='Unnamed: 0',parse_dates=True).fillna(0)\\n\\n df = df.loc[:, (df != 0).any(axis=0)] # removing 0 columns\\n df = df.pct_change().replace([np.inf, -np.inf, np.nan], 0)\\n\\n return df\",\n \"def read_csv(path):\\n return pd.read_csv(path)\",\n \"def read_csv_file(self):\\n pass\",\n \"def GetRateData(directory):\\n\\n rt_data = pd.read_csv(directory)\\n return rt_data\",\n \"def _read_csv(filename):\\n csv_file = open(filename, 'r')\\n data = pandas.read_csv(csv_file, comment='!', quotechar=\\\"'\\\")\\n csv_file.close()\\n return data\",\n \"def read_rf_csv():\\n if os.path.exists(\\\"rf.csv\\\"):\\n #print (\\\"--decision trees CSV imported\\\\n\\\")\\n results = pd.read_csv(\\\"rf.csv\\\", index_col=0)\\n else:\\n print(\\\"log not found\\\")\\n\\n return results\",\n \"def read_csv(self) -> None:\\n\\n self._df = pd.read_csv(self._dataset_file)\",\n \"def get_sp500_stocks_file(file_path=None):\\n\\n df = pd.read_csv(file_path)\\n\\n return df\",\n \"def load_csv():\\n df = pd.read_csv(datafolder+filename, decimal=decimal).astype(\\n {'min': 'float', 'max': 'float'})\\n return df\",\n \"def read_csv(path):\\n column_headers = ['Country', 'Region', 'Pop. Density (per sq. mi.)', 'Infant mortality (per 1000 births)',\\n 'GDP ($ per capita) dollars']\\n df = pd.read_csv(path)\\n return df[column_headers]\",\n \"def read_data(path):\\n data = pd.read_csv(path)\\n return data\",\n \"def read_data(path):\\n data = pd.read_csv(path)\\n return data\",\n \"def process_csv(filepath):\\n suburb = get_suburb(filepath)\\n read_file = pd.read_csv(filepath,\\n infer_datetime_format=True,\\n parse_dates=[\\\"SALE DATE\\\"],\\n dayfirst=True)\\n read_file[\\\"SUBURB\\\"] = suburb\\n separate_date(read_file)\\n return read_file\",\n \"def read_data(self) -> pd.DataFrame:\\n data = pd.read_csv(self.data_path)\\n assert isinstance(data, pd.DataFrame)\\n return data\",\n \"def read_data(self) -> pd.DataFrame:\\n data = pd.read_csv(self.data_path)\\n assert isinstance(data, pd.DataFrame)\\n return data\",\n \"def csv_to_df(self, path=None):\\n # reads the csv file and puts it to the dataframe\\n df = pd.read_csv(path)\\n return df\",\n \"def get_raw_data_from_csv():\\n data_df = pd.read_csv(static_constants.RAW_DATA_PATH)\\n return data_df\",\n \"def import_data(catalog='xmatch_TGAS_Simbad.csv', params=None, nrows=None, delimiter=','):\\n print \\\"Loading %s and creating DataFrame..\\\" % catalog\\n df_imported = pd.read_csv(catalog, delimiter=delimiter, header=0, usecols=params, nrows=nrows)\\n print \\\"..Done\\\\n----------\\\"\\n return df_imported\",\n \"def read_dataset(filename):\\n if not os.path.exists(filename):\\n url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data'\\n df = pd.read_csv(url, header = None, na_values = ['?'])\\n colnames = ['symboling', 'normalized-losses', 'make', 'fuel-type',\\n 'aspiration', 'num-of-doors', 'body-style', 'drive-wheels',\\n 'engine-location', 'wheel-base', 'length', 'width', 'height',\\n 'curb-weight', 'engine-type', 'num-of-cylinders', 'engine-size',\\n 'fuel-system', 'bore', 'stroke', 'compression-ratio',\\n 'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg', 'price']\\n df.columns = colnames\\n df.to_csv(filename, index = False)\\n\\n df = pd.read_csv(filename)\\n return df\",\n \"def normal_csv(self):\\n if len(self.delimiter) > 0:\\n df = pd.read_csv(self.file_path, delimiter=self.delimiter)\\n else:\\n df = pd.read_csv(self.file_path)\\n\\n return df\",\n \"def load_data(filename):\\n data = pd.read_csv(filename)\\n\\n labels = data['Revenue'].map(lambda x: 1 if x is True else 0).tolist()\\n\\n data['Month'] = data['Month'].map(lambda x: convert_month_to_number(x))\\n data.drop(data[data['Month'] == -1].index)\\n\\n data['VisitorType'] = data['VisitorType'].map(lambda x: 1 if x == 'Returning_Visitor' else 0)\\n data['Weekend'] = data['Weekend'].map(lambda x: 1 if x is True else 0)\\n\\n for i in data.keys():\\n if i == 'Administrative' or i == 'Informational' or i == 'ProductRelated' or i == 'OperatingSystems' or \\\\\\n i == 'Browser' or i == 'Region' or i == 'TrafficType' or i == 'VisitorType' or i == 'Weekend':\\n if not pd.api.types.is_integer_dtype(data[i].dtype):\\n data = data[i].apply(lambda x: not isinstance(x, int))\\n if i == 'Administrative_Duration' or i == 'Informational_Duration' or i == 'ProductRelated_Duration' or \\\\\\n i == 'BounceRates' or i == 'ExitRates' or i == 'PageValues' or i == 'SpecialDay':\\n if not pd.api.types.is_float_dtype(data[i].dtype):\\n data = data[data[i].apply(lambda x: isinstance(x, float))]\\n data.drop(\\\"Revenue\\\", axis=1, inplace=True)\\n evidence = data.values.tolist()\\n return evidence, labels\",\n \"def load_daily_data():\\n return pd.read_csv(os.path.join('data', 'raw', 'full_grouped.csv'))\",\n \"def read(self):\\n \\n self.df = pd.read_csv(self.path, encoding = \\\"ISO-8859-1\\\")\",\n \"def read_data(fname, cols):\\n df = (pd.read_csv(fname, header=None, sep=r\\\"\\\\s+\\\", comment=\\\"#\\\",\\n names=cols, dtype=np.float64)\\n .iloc[1:]) # First line is the total number of trees\\n # Could reset_index, but we don't shuffle the DataFrame\\n return df\",\n \"def read_product(filename=None):\\n if not filename:\\n filename = settings.PRODUCT_FILENAME\\n return pd.read_csv(filename, sep='|')\",\n \"def create_dataframe_from_csv(path_to_csv_file):\\r\\n df = pd.read_csv(path_to_csv_file)\\r\\n return df\",\n \"def read_file(file):\\n return pd.read_csv(file, header=0, index_col=0,\\n parse_dates=['Trading_date'],\\n infer_datetime_format=True,\\n keep_date_col=True)\",\n \"def readSolarData(filename):\\n\\treturn pd.read_csv(filename)\",\n \"def diamonds(path):\\n import pandas as pd\\n path = os.path.expanduser(path)\\n filename = 'diamonds.csv'\\n if not os.path.exists(os.path.join(path, filename)):\\n url = 'http://dustintran.com/data/r/Stat2Data/Diamonds.csv'\\n maybe_download_and_extract(path, url,\\n save_file_name='diamonds.csv',\\n resume=False)\\n\\n data = pd.read_csv(os.path.join(path, filename), index_col=0,\\n parse_dates=True)\\n x_train = data.values\\n metadata = {'columns': data.columns}\\n return x_train, metadata\",\n \"def read_data():\\n data = pd.read_csv('input_data/Preply_tutor_views_datasaet.csv')\\n return data\",\n \"def readFile(self,path):\\n _file = pd.read_csv(path, sep='\\\\s+', engine='python', header=None)\\n self._dataTable = pd.DataFrame(_file.iloc[:, 3:15])\\n self._dataTable.columns = ['MGEMLEEF Avg age', 'MOSHOOFD Customer main type', 'MGODRK Roman catholic',\\n 'MGODPR Protestant', 'MGODOV Other religion', 'MGODGE No religion', 'MRELGE Married',\\n 'MRELSA Living together', 'MRELOV Other relation', 'MFALLEEN Singles',\\n 'MFGEKIND Household without children', 'MFWEKIND Household with children']\",\n \"def get_data(fpath):\\n\\n visits = ['SC', 'BL', 'V01', 'V02', 'V03', 'V04', 'V05', 'V06', 'V07',\\n 'V08', 'V09', 'V10', 'V11', 'V12', 'V13', 'V14', 'V15']\\n dtype = dict(PATNO=str,\\n CLINICAL_EVENT=cdtype(visits, ordered=True),\\n TESTNAME=str,\\n TESTVALUE=str)\\n\\n fname = op.join(fpath, 'Current_Biospecimen_Analysis_Results.csv')\\n data = pd.read_csv(fname, dtype=dtype)\\n\\n data['TESTVALUE'] = pd.to_numeric(data.TESTVALUE, errors='coerce')\\n data = data.rename(columns=RENAME_COLS).assign(**ASSIGN_COLS)[RETAIN_COLS]\\n data = data.dropna(axis=0, subset=['SCORE'])\\n\\n return data\",\n \"def read_dataset():\\n\\n df = pd.read_csv('fake_job_postings.csv', index_col='job_id')\\n return df\",\n \"def read_csv(csv_path):\\n \\n df = pd.read_csv(csv_path)\\n\\n return df\",\n \"def read_data_csv(path):\\n df = pd.read_csv(path)\\n df.drop([\\\"Unnamed: 0\\\"], axis=1, inplace=True)\\n return df\",\n \"def read_confounds(filename, confounds):\\n df_confounds = pandas.read_csv(filename, sep='\\\\t', usecols=confounds)\\n return df_confounds\",\n \"def _read_csv(self) -> pd.DataFrame:\\n\\n return pd.concat(\\n [\\n pd.read_csv(f, usecols=[1, 2, 3, 4, 5])\\n for f in self.temp_path.iterdir()\\n if f.name.endswith(\\\".csv\\\")\\n ]\\n )\",\n \"def _csv2df(data_file):\\n df = pd.read_csv(data_file, encoding=\\\"ISO-8859-1\\\", low_memory=False)\\n return df\",\n \"def read_data(path, filename, drop_col=\\\"index\\\", dt=\\\"float32\\\"):\\n\\tdata = pd.read_csv(path + filename, sep=\\\",\\\", dtype=dt)\\n\\tdata = data.drop(drop_col, axis=1)\\n\\treturn data.as_matrix()\",\n \"def _ReadDefinitions(filename, delimiter):\\n return pd.read_csv(filename, sep=delimiter)\",\n \"def load_to_dataframe(self) -> DataFrame:\\n return read_csv(self._csv_path, converters={\\n # Check if embedding size is the empty string,\\n # as it would be for Count models\\n \\\"Embedding size\\\": lambda v: int(float(v)) if len(v) > 0 else nan\\n })\",\n \"def read_data(self):\\n fpath = './data/surveys.csv'\\n self.data = pd.read_csv(fpath, header=0, low_memory=False)\\n #print(self.data.head(n=5))\\n print(self.data.shape)\",\n \"def load_data(csv_path):\\n df = pd.read_csv(csv_path)\\n return df\",\n \"def readData(filename):\\n #defining gobal variable (dataframe) to access it outside this function\\n global dataframe\\n #storing full CSV file into a dataframe(data structure)\\n dataframe = pd.read_csv(filename)\\n #type casting temperature column of dataframe to numeric data and ignoring '***' values\\n dataframe['Temperature'] = pd.to_numeric(dataframe['Temperature'], errors='coerce')\\n return dataframe\",\n \"def read_data():\\n \\n countries= pd.read_csv('../countries.csv')\\n try:\\n income = pd.read_excel(\\\"indicator gapminder gdp_per_capita_ppp.xlsx\\\",index_col=u\\\"gdp pc test\\\")\\n except:\\n print \\\"excel read module not found \\\"\\n income = income.T\\n print \\\"Q1: The head of the income dataframe\\\"\\n print income.head()\\n return countries,income\",\n \"def read_csv_file_data(file_path):\\n if os.path.exists(file_path):\\n df = pandas.read_csv(file_path)\\n else:\\n raise ValueError('ERROR: file_path doesnt exist in read_csv_file_data()')\\n return df\",\n \"def load_data(reviews_path):\\n df1 = pd.read_csv(reviews_path)\\n #substituting 0 for negative reviews labeled '__label__1' and 1 for positive reviews labeled '__label__2'\\n df1 = df1.replace('__label__1', 0)\\n df1= df1.replace('__label__2', 1)\\n \\n return df1\",\n \"def load(self) -> pd.DataFrame:\\n if os.path.exists(self.file_name):\\n df = pd.read_csv(self.file_name, index_col=0)\\n df = self._clean(df)\\n else:\\n _LOG.debug(\\\"No file '%s'\\\", self.file_name)\\n df = pd.DataFrame()\\n return df\",\n \"def get_decopath_df() -> pd.DataFrame:\\n return pd.read_csv(DECOPATH_PATH, sep='\\\\t')\",\n \"def get_recipe_dataset():\\n df = pd.read_csv(FILENAME)\\n print(\\\"# of rows - no filtering: %s\\\" % len(df))\\n\\n df = df.head(500)\\n print(\\\"# of rows - reduce size of DF for testing: %s\\\" % len(df))\\n\\n return df\",\n \"def _loadCSVFile(self):\\n self._df = pd.read_csv(\\n self._pathfile, sep=CSV_SEPARATOR, index_col=CSV_INDEX_COL)\",\n \"def create_df(filename):\\n data = pd.read_csv(filename)\\n data = data.dropna(axis='index')\\n data['inc_angle'] = np.radians(data['inc_angle'])\\n data = data.astype('float64')\\n data = data[data['inc_angle'] <= np.deg2rad(80)]\\n return data\",\n \"def parse(file_name):\\n \\n return pd.read_csv(file_name, na_values = '---')\",\n \"def load_data(file_path):\\n data = pandas.read_csv(file_path)\\n\\n return data\",\n \"def _data_reader(file):\\n # Create a dictionary so that filename matches a site name.\\n site_dict = {'D05536000': 'NB Niles', 'D05536101': 'NS Channel-Wilmette',\\n 'D05536105': 'NB Albany', 'D05536118': 'NB Grand Avenue',\\n 'D05536121': 'CH River-Lock', 'D05536123': 'CH River-Columbus',\\n 'D05536137': 'CSSC-Western Avenue', 'D05536140': 'CSSC-Stickney',\\n 'D05536275': 'Thorn Creek', 'D05536290': 'Little Calument',\\n 'D05536340': 'Midlothian Creek', 'D05536343': 'Natalie Creek',\\n 'D05536357': 'Grand Calumet', 'D05536500': 'Tinley Creek',\\n 'D05536700': 'Calumet-Sag Channel', 'D05536890': 'CSSC-Lemont',\\n 'D05536995': 'CSSC-Romeoville'}\\n df_raw = pd.read_csv(file)\\n df_raw['dateTime'] = pd.to_datetime(df_raw['dateTime'])\\n # Creating a dataframe with the data we only need.\\n df = df_raw[['dateTime', 'X_00065_00000']]\\n df = df.set_index(df_raw['dateTime'])\\n\\n # Retrieve site information to be used in saved excel filenames.\\n site_code = file[-9:]\\n site_name = [v for v in site_dict.items() if site_code in v][0]\\n site = site_code + '_' + site_name[1].replace(' ', '-')\\n\\n # Convert index into a datetime index for easier indexing.\\n df.index = pd.to_datetime(df.index)\\n return df_raw, df, site, site_code\",\n \"def _get_dataframe(self, filePathList):\\n filePath = filePathList[0]\\n df = pd.read_csv(filePath, sep=\\\"\\\\t\\\", comment=\\\"#\\\")\\n return df\",\n \"def read_covid():\\n return pd.read_csv(_COVID_FILE, parse_dates=[\\\"date\\\"])\",\n \"def load_data():\\n df = pd.read_csv(\\\"https://raw.githubusercontent.com/Andrea-Giuliani/Python-Project/master/data/final_dataset.csv\\\",sep=',') \\n return df\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67762727","0.66502154","0.657781","0.65579677","0.65579677","0.65579677","0.6523957","0.6514698","0.64671755","0.6462768","0.6447732","0.63911945","0.63872385","0.6359474","0.6340352","0.6323935","0.6310876","0.62966776","0.6278205","0.6277503","0.6268801","0.62184405","0.62075233","0.61809576","0.6168928","0.6160777","0.61494035","0.61352897","0.6119682","0.6108777","0.6088929","0.60772026","0.6066652","0.6060962","0.605868","0.60586464","0.6050886","0.60480785","0.6040749","0.6032217","0.60265833","0.6024235","0.6013658","0.6010649","0.6009872","0.600774","0.60049784","0.59956384","0.59757143","0.59729284","0.5971864","0.5968474","0.59382087","0.59382087","0.5925088","0.5908872","0.5908872","0.5908008","0.59002924","0.5898458","0.5898034","0.58973324","0.589338","0.5892399","0.58912295","0.58904946","0.58897555","0.587711","0.5869674","0.5868592","0.58658636","0.5855429","0.58458227","0.5835578","0.5833001","0.58285785","0.5825782","0.5820849","0.5819668","0.5816386","0.5811755","0.58098483","0.58051795","0.58022887","0.5801158","0.579923","0.57870936","0.578341","0.57807946","0.57805824","0.57669353","0.57652634","0.5764185","0.5762713","0.57586056","0.5758392","0.5756842","0.5753926","0.5752839","0.57501656"],"string":"[\n \"0.67762727\",\n \"0.66502154\",\n \"0.657781\",\n \"0.65579677\",\n \"0.65579677\",\n \"0.65579677\",\n \"0.6523957\",\n \"0.6514698\",\n \"0.64671755\",\n \"0.6462768\",\n \"0.6447732\",\n \"0.63911945\",\n \"0.63872385\",\n \"0.6359474\",\n \"0.6340352\",\n \"0.6323935\",\n \"0.6310876\",\n \"0.62966776\",\n \"0.6278205\",\n \"0.6277503\",\n \"0.6268801\",\n \"0.62184405\",\n \"0.62075233\",\n \"0.61809576\",\n \"0.6168928\",\n \"0.6160777\",\n \"0.61494035\",\n \"0.61352897\",\n \"0.6119682\",\n \"0.6108777\",\n \"0.6088929\",\n \"0.60772026\",\n \"0.6066652\",\n \"0.6060962\",\n \"0.605868\",\n \"0.60586464\",\n \"0.6050886\",\n \"0.60480785\",\n \"0.6040749\",\n \"0.6032217\",\n \"0.60265833\",\n \"0.6024235\",\n \"0.6013658\",\n \"0.6010649\",\n \"0.6009872\",\n \"0.600774\",\n \"0.60049784\",\n \"0.59956384\",\n \"0.59757143\",\n \"0.59729284\",\n \"0.5971864\",\n \"0.5968474\",\n \"0.59382087\",\n \"0.59382087\",\n \"0.5925088\",\n \"0.5908872\",\n \"0.5908872\",\n \"0.5908008\",\n \"0.59002924\",\n \"0.5898458\",\n \"0.5898034\",\n \"0.58973324\",\n \"0.589338\",\n \"0.5892399\",\n \"0.58912295\",\n \"0.58904946\",\n \"0.58897555\",\n \"0.587711\",\n \"0.5869674\",\n \"0.5868592\",\n \"0.58658636\",\n \"0.5855429\",\n \"0.58458227\",\n \"0.5835578\",\n \"0.5833001\",\n \"0.58285785\",\n \"0.5825782\",\n \"0.5820849\",\n \"0.5819668\",\n \"0.5816386\",\n \"0.5811755\",\n \"0.58098483\",\n \"0.58051795\",\n \"0.58022887\",\n \"0.5801158\",\n \"0.579923\",\n \"0.57870936\",\n \"0.578341\",\n \"0.57807946\",\n \"0.57805824\",\n \"0.57669353\",\n \"0.57652634\",\n \"0.5764185\",\n \"0.5762713\",\n \"0.57586056\",\n \"0.5758392\",\n \"0.5756842\",\n \"0.5753926\",\n \"0.5752839\",\n \"0.57501656\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7847184"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":84193,"cells":{"query":{"kind":"string","value":"Consumes discount dataframe & returns multiple metadata from it"},"document":{"kind":"string","value":"def get_product_vs_offer_and_volume(self, df):\n\n product_vs_min_volume = {}\n product_offer = {}\n base_product_vs_offer_product = {}\n\n for i in df.itertuples():\n product_vs_min_volume[i[4].lower()] = i[5]\n product_offer[i[4].lower()] = i[1]\n base_product_vs_offer_product[i[4].lower()] = i[7].lower()\n\n return product_vs_min_volume, product_offer, base_product_vs_offer_product"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def metadata(self, df):\n raise NotImplementedError(\"missing metadata() method\")","def get_metadata(self):\n df_data = pd.read_csv(self.path_bin)\n # df_data = df_data[df_data['subject'] == 0] # limit data to certain subject.\n\n df = pd.DataFrame()\n df['mean'] = np.mean(df_data)\n\n def cond_mean(df, cond_value, target):\n result = list()\n for f in df:\n tmp_df = df[df[f] == cond_value]\n result.append(np.mean(tmp_df[target]))\n return result\n\n df['mean|f=0'] = cond_mean(df_data, cond_value=0, target=self.target)\n df['mean|f=1'] = cond_mean(df_data, cond_value=1, target=self.target)\n df['std'] = np.std(df_data)\n\n df['H'] = [H(df_data[x]) for x in df_data]\n h_x = _H([df.loc[self.target]['mean'], 1-df.loc[self.target]['mean']])\n df['IG'] = df.apply(IG_from_series, axis='columns', h_x=h_x, identifier='mean')\n df['IG ratio'] = df.apply(lambda x: x['IG']/x['H'], axis='columns') # correct?\n df = df.sort_values(by=['IG'], ascending=False)\n df.to_csv(self.path_meta, index=True)","def instruments_with_meta_data(self):\n if len(self._instruments_with_meta_data) > 0:\n return self._instruments_with_meta_data\n else:\n self._borsdata_api = BorsdataAPI(constants.API_KEY)\n # fetching data from api\n countries = self._borsdata_api.get_countries()\n branches = self._borsdata_api.get_branches()\n sectors = self._borsdata_api.get_sectors()\n markets = self._borsdata_api.get_markets()\n instruments = self._borsdata_api.get_instruments()\n # instrument type dict for conversion (https://github.com/Borsdata-Sweden/API/wiki/Instruments)\n instrument_type_dict = {0: 'Aktie', 1: 'Pref', 2: 'Index', 3: 'Stocks2', 4: 'SectorIndex', 5: 'BranschIndex'}\n # creating an empty dataframe\n instrument_df = pd.DataFrame()\n # loop through the whole dataframe (table) i.e. row-wise-iteration.\n for index, instrument in instruments.iterrows():\n name = instrument['name']\n ins_id = instrument['insId']\n ticker = instrument['ticker']\n isin = instrument['isin']\n # locating meta-data in various ways\n # dictionary-lookup\n instrument_type = instrument_type_dict[instrument['instrument']]\n # .loc locates the rows where the criteria (inside the brackets, []) is fulfilled\n # located rows (should be only one) get the column 'name' and return its value-array\n # take the first value in that array ([0], should be only one value)\n market = markets.loc[markets['id'] == instrument['marketId']]['name'].values[0]\n country = countries.loc[countries['id'] == instrument['countryId']]['name'].values[0]\n sector = 'N/A'\n branch = 'N/A'\n # index-typed instruments does not have a sector or branch\n if market.lower() != 'index':\n sector = sectors.loc[sectors['id'] == instrument['sectorId']]['name'].values[0]\n branch = branches.loc[branches['id'] == instrument['branchId']]['name'].values[0]\n # appending current data to dataframe, i.e. adding a row to the table.\n instrument_df = instrument_df.append({'name': name, 'ins_id': ins_id, 'ticker': ticker, 'isin': isin, 'instrument_type': instrument_type,\n 'market': market, 'country': country, 'sector': sector, 'branch': branch}, ignore_index=True)\n # create directory if it do not exist\n if not os.path.exists(constants.EXPORT_PATH):\n os.makedirs(constants.EXPORT_PATH)\n # to csv\n instrument_df.to_csv(constants.EXPORT_PATH + 'instrument_with_meta_data.csv')\n # creating excel-document\n excel_writer = pd.ExcelWriter(constants.EXPORT_PATH + 'instrument_with_meta_data.xlsx')\n # adding one sheet\n instrument_df.to_excel(excel_writer, 'instruments_with_meta_data')\n # saving the document\n excel_writer.save()\n self._instruments_with_meta_data = instrument_df\n return instrument_df","def get_metadata(self):\n # Load file\n retouch_file = pd.read_csv(self.file)\n \n # Loop over rows\n for index, row in retouch_file.iterrows():\n try:\n info = IPTCInfo(f\"{settings.MEDIA_ROOT}/images/{row['image_name']}.jpg\")\n row['headline'] = info['headline']\n print(\"Headline\", info['headline'])\n row['keywords'] = info['keywords']\n x = \"\".join(info['keywords'])\n print(\"Keywords\", x)\n row['creator'] = info['creator']\n print(\"Creator\", info['creator'])\n row['date created'] = info['date_created']\n print(\"Date Created\", info['date_created'])\n row['sub-location'] = info['sub-location']\n print(\"Sub-location\", info['sub-location'])\n row['city'] = info['city']\n print(\"City\", info['city'])\n row['province/state'] = info['province/state']\n print(\"Province/ State\", info['province/state'])\n row['country'] = info['country/primary location name']\n print(\"Country\", info['country/primary location name'])\n row['category'] = info['category']\n print(\"Category\", info['category'])\n row['description'] = info['description']\n print(\"Description\", info['description'])\n except:\n pass\n # retouch_file.to_csv(\"output.csv\")\n return \"IPTC Metadata returned.\"","def getCouponFeatures(coupon_dict, master_list):\n purchase_small_area_name_dict, capsule_text_dict, genre_name_dict, coupon_large_area_name_dict, coupon_small_area_name_dict, coupon_ken_name_dict, price_rate_list, catalog_price_list, discount_price_list, dispperiod_list, valid_period_list, usable_date_mon_list, usable_date_tue_list, usable_date_wed_list, usable_date_thu_list, usable_date_fri_list, usable_date_sat_list, usable_date_sun_list, usable_date_hol_list, usable_date_before_hol_list = master_list\n\n feat_header = [\"CouponCapsuleText\", \"CouponGenreName\", \"CouponPriceRate\", \"CouponCatalogPrice\", \"CouponDiscountPrice\", \"CouponDispPeriod\", \"CouponValidPeriod\", \"CouponUsableMon\", \"CouponUsableTue\", \"CouponUsableWed\", \"CouponUsableThu\", \"CouponUsableFri\", \"CouponUsableSat\", \"CouponUsableSun\", \"CouponUsableHol\", \"CouponUsableBeforeHol\", \"CouponLargeAreaName\", \"CouponKenName\", \"CouponSmallAreaName\", \"CapsuleTextCount\", \"CapsuleTextCountNorm\", \"GenreNameCount\", \"GenreNameCountNorm\", \"LargeAreaNameCount\", \"LargeAreaNameCountNorm\", \"SmallAreaNameCount\", \"SmallAreaNameCountNorm\", \"KenNameCount\",\"KenNameCountNorm\", \"PriceRateMin\", \"PriceRateMax\", \"PriceRateMean\", \"PriceRateBetMeanMax\", \"PriceRateBetMinMean\", \"PriceRateGrtMax\", \"PriceRateLessMin\", \"CatalogPriceMin\", \"CatalogPriceMax\", \"CatalogPriceMean\", \"CatalogPriceBetMeanMax\", \"CatalogPriceBetMinMean\", \"CatalogPriceGrtMax\", \"CatalogPriceLessMin\", \"DiscountPriceMin\", \"DiscountPriceMax\", \"DiscountPriceMean\", \"DiscountPriceBetMeanMax\", \"DiscountPriceBetMinMean\", \"DiscountPriceGrtMax\", \"DiscountPriceLessMin\", \"DispPeriodMin\", \"DispPeriodMax\", \"DispPeriodMean\", \"DispPeriodBetMeanMax\", \"DispPeriodBetMinMean\", \"DispPeriodGrtMax\", \"DispPeriodLessMin\", \"MondayCount\", \"MondayCountNorm\", \"TuesdayCount\", \"TuesdayCountNorm\", \"WednesdayCount\", \"WednesdayCountNorm\", \"ThursdayCount\", \"ThursdayCountNorm\", \"FridayCount\", \"FridayCountNorm\", \"SaturdayCount\", \"SaturdayCountNorm\", \"SundayCount\", \"SundayCountNorm\", \"HolidayCount\", \"HolidayCountNorm\", \"BeforeHolidayCount\", \"BeforeHolidayCountNorm\"]\n\n if coupon_dict['USABLE_DATE_MON'] in ('','NA'):\n coupon_dict[\"USABLE_DATE_MON\"] = 3.0\n coupon_dict[\"USABLE_DATE_TUE\"] = 3.0\n coupon_dict[\"USABLE_DATE_WED\"] = 3.0\n coupon_dict[\"USABLE_DATE_THU\"] = 3.0\n coupon_dict[\"USABLE_DATE_FRI\"] = 3.0\n coupon_dict[\"USABLE_DATE_SAT\"] = 3.0\n coupon_dict[\"USABLE_DATE_SUN\"] = 3.0\n coupon_dict[\"USABLE_DATE_HOLIDAY\"] = 3.0\n coupon_dict[\"USABLE_DATE_BEFORE_HOLIDAY\"] = 3.0\n\n feat_list = [coupon_dict[\"CAPSULE_TEXT\"], coupon_dict[\"GENRE_NAME\"], coupon_dict[\"PRICE_RATE\"], coupon_dict[\"CATALOG_PRICE\"], coupon_dict[\"DISCOUNT_PRICE\"], coupon_dict[\"DISPPERIOD\"], coupon_dict[\"VALIDPERIOD\"], coupon_dict[\"USABLE_DATE_MON\"], coupon_dict[\"USABLE_DATE_TUE\"], coupon_dict[\"USABLE_DATE_WED\"], coupon_dict[\"USABLE_DATE_THU\"], coupon_dict[\"USABLE_DATE_FRI\"], coupon_dict[\"USABLE_DATE_SAT\"], coupon_dict[\"USABLE_DATE_SUN\"], coupon_dict[\"USABLE_DATE_HOLIDAY\"], coupon_dict[\"USABLE_DATE_BEFORE_HOLIDAY\"], coupon_dict[\"large_area_name\"], coupon_dict[\"ken_name\"], coupon_dict[\"small_area_name\"]]\n\n capsule_text_count, capsule_text_count_norm = getCountFromDict(coupon_dict[\"CAPSULE_TEXT\"], capsule_text_dict)\n feat_list.extend([capsule_text_count, capsule_text_count_norm])\n\n genre_name_count, genre_name_count_norm = getCountFromDict(coupon_dict[\"GENRE_NAME\"], genre_name_dict)\n feat_list.extend([genre_name_count, genre_name_count_norm])\n\n large_area_name_count, large_area_name_count_norm = getCountFromDict(coupon_dict[\"large_area_name\"], coupon_large_area_name_dict)\n feat_list.extend([large_area_name_count, large_area_name_count_norm])\n\n small_area_name_count, small_area_name_count_norm = getCountFromDict(coupon_dict[\"small_area_name\"], coupon_small_area_name_dict)\n feat_list.extend([small_area_name_count, small_area_name_count_norm])\n\n ken_name_count, ken_name_count_norm = getCountFromDict(coupon_dict[\"ken_name\"], coupon_ken_name_dict)\n feat_list.extend([ken_name_count, ken_name_count_norm])\n\n price_rate_out_list = getMinMaxMeanFromList(coupon_dict[\"PRICE_RATE\"], price_rate_list)\n feat_list.extend( price_rate_out_list )\n\n catalog_price_out_list = getMinMaxMeanFromList(coupon_dict[\"CATALOG_PRICE\"], catalog_price_list)\n feat_list.extend( catalog_price_out_list )\n\n discount_price_out_list = getMinMaxMeanFromList(coupon_dict[\"DISCOUNT_PRICE\"], discount_price_list)\n feat_list.extend( discount_price_out_list )\n\n dispperiod_out_list = getMinMaxMeanFromList(coupon_dict[\"DISPPERIOD\"], dispperiod_list)\n feat_list.extend( dispperiod_out_list )\n\n monday_count, monday_count_norm = getCountFromDict(coupon_dict[\"USABLE_DATE_MON\"], usable_date_mon_list)\n feat_list.extend([monday_count, monday_count_norm])\n\n tuesday_count, tuesday_count_norm = getCountFromDict(coupon_dict[\"USABLE_DATE_TUE\"], usable_date_tue_list)\n feat_list.extend([tuesday_count, tuesday_count_norm])\n\n wednesday_count, wednesday_count_norm = getCountFromDict(coupon_dict[\"USABLE_DATE_WED\"], usable_date_wed_list)\n feat_list.extend([wednesday_count, wednesday_count_norm])\n\n thursday_count, thursday_count_norm = getCountFromDict(coupon_dict[\"USABLE_DATE_THU\"], usable_date_thu_list)\n feat_list.extend([thursday_count, thursday_count_norm])\n\n friday_count, friday_count_norm = getCountFromDict(coupon_dict[\"USABLE_DATE_FRI\"], usable_date_fri_list)\n feat_list.extend([friday_count, friday_count_norm])\n\n saturday_count, saturday_count_norm = getCountFromDict(coupon_dict[\"USABLE_DATE_SAT\"], usable_date_sat_list)\n feat_list.extend([saturday_count, saturday_count_norm])\n\n sunday_count, sunday_count_norm = getCountFromDict(coupon_dict[\"USABLE_DATE_SUN\"], usable_date_sun_list)\n feat_list.extend([sunday_count, sunday_count_norm])\n\n holiday_count, holiday_count_norm = getCountFromDict(coupon_dict[\"USABLE_DATE_HOLIDAY\"], usable_date_hol_list)\n feat_list.extend([holiday_count, holiday_count_norm])\n\n before_holiday_count, before_holiday_count_norm = getCountFromDict(coupon_dict[\"USABLE_DATE_BEFORE_HOLIDAY\"], usable_date_before_hol_list)\n feat_list.extend([before_holiday_count, before_holiday_count_norm])\n\n\tvar_list, var_header = getDummyVars(capsule_text_dict, unique_capsule_text, prefix=\"CapsuleText_\")\n\tfeat_header.extend(var_header)\n\tfeat_list.extend(var_list)\n\n\tvar_list, var_header = getDummyVars(genre_name_dict, unique_genre_name, prefix=\"GenreName_\")\n\tfeat_header.extend(var_header)\n feat_list.extend(var_list)\n\n\tvar_list, var_header = getDummyVars(coupon_large_area_name_dict, unique_large_area_name, prefix=\"LargeAreaName_\")\n feat_header.extend(var_header)\n feat_list.extend(var_list)\n\n\tvar_list, var_header = getDummyVars(coupon_small_area_name_dict, unique_small_area_name, prefix=\"SmallAreaName_\")\n feat_header.extend(var_header)\n feat_list.extend(var_list)\n\n\tvar_list, var_header = getDummyVars(coupon_ken_name_dict, unique_ken_name, prefix=\"KenName_\")\n feat_header.extend(var_header)\n feat_list.extend(var_list)\n\n\tvar_list, var_header = getDummyVarsDays(usable_date_mon_list, prefix=\"UsableMonday_\")\n\tfeat_header.extend(var_header)\n feat_list.extend(var_list)\n\n\tvar_list, var_header = getDummyVarsDays(usable_date_tue_list, prefix=\"UsableTuesday_\")\n feat_header.extend(var_header)\n feat_list.extend(var_list)\n\n\tvar_list, var_header = getDummyVarsDays(usable_date_wed_list, prefix=\"UsableWednesday_\")\n feat_header.extend(var_header)\n feat_list.extend(var_list)\n\n\tvar_list, var_header = getDummyVarsDays(usable_date_thu_list, prefix=\"UsableThursday_\")\n feat_header.extend(var_header)\n feat_list.extend(var_list)\n\n\tvar_list, var_header = getDummyVarsDays(usable_date_fri_list, prefix=\"UsableFriday_\")\n feat_header.extend(var_header)\n feat_list.extend(var_list)\n\n\tvar_list, var_header = getDummyVarsDays(usable_date_sat_list, prefix=\"UsableSaturday_\")\n feat_header.extend(var_header)\n feat_list.extend(var_list)\n\n\tvar_list, var_header = getDummyVarsDays(usable_date_sun_list, prefix=\"UsableSunday_\")\n feat_header.extend(var_header)\n feat_list.extend(var_list)\n\n\tvar_list, var_header = getDummyVarsDays(usable_date_hol_list, prefix=\"UsableHoliday_\")\n feat_header.extend(var_header)\n feat_list.extend(var_list)\n\n\tvar_list, var_header = getDummyVarsDays(usable_date_before_hol_list, prefix=\"UsableBeforeHoliday_\")\n feat_header.extend(var_header)\n feat_list.extend(var_list)\n\t\n return feat_list, feat_header","def get_ytm_discount_data(self):\n ytm=Bootstrapping.get_ytm_dict(self)\n data = pd.DataFrame()\n for i in ytm.keys():\n data.loc[i / 2, 'Yield to maturity'] = ytm[i]\n data.loc[i / 2, 'discount_rate'] = Bootstrapping.P_Tn(self,ytm[i], i)\n return data","def get_metaData():\n #get all movies from db\n movies_df = movie_helper.get_movies_df()\n \n #get movie meta data\n with tqdm(total=len(movies_df)) as pbar:\n for index, row in movies_df.iterrows(): \n \n #if an imdbid exists use it to look up the API\n if (row['imdbId']):\n \n #get base meta data from imdb\n movie = ia.get_movie(str(row['imdbId']))\n year = movie['year']\n \n #created delimited list of genre strings\n if (movie.get('genres')): \n genres = ','.join(movie.get('genres')) \n \n rating = movie.get('rating')\n votes = movie.get('votes')\n \n #create delimited list of movie certificates\n certificates = None\n if (movie.get('certificates')): \n certificates = ','.join(movie.get('certificates'))\n \n #update database with collected meta data\n update_params = {\n \"year\" : year,\n \"genres\" : genres,\n \"rating\" : rating,\n \"votes\" : votes,\n \"certificates\" : certificates\n }\n select_params = { \"movieId\" : row[\"movieId\"] }\n database_helper.update_data(\"movies\", update_params = update_params, select_params = select_params)\n \n pbar.update(1)","def _extract_metadata(self) -> None:\n self.log(\"Extracting metadata.\")\n image_paths: list[Path] = []\n for ext in (\"jpg\", \"jpeg\", \"png\"):\n image_paths.extend(self._base_dir.glob(f\"**/*.{ext}\"))\n image_paths_str = [str(image.relative_to(self._base_dir)) for image in image_paths]\n filepaths = pd.Series(image_paths_str)\n metadata = cast(\n pd.DataFrame,\n filepaths.str.split(\"/\", expand=True).rename( # type: ignore[attr-defined]\n columns={0: \"superclass\", 1: \"concept\", 2: \"context\", 3: \"filename\"}\n ),\n )\n metadata[\"filepath\"] = filepaths\n metadata.sort_index(axis=1, inplace=True)\n metadata.sort_values(by=[\"filepath\"], axis=0, inplace=True)\n metadata = self._label_encode_metadata(metadata)\n metadata.to_csv(self._metadata_path)","def gather_technical_indicators(pricing_data, lookback_window):\r\n master_df = pd.DataFrame()\r\n master_df['Price'] = pricing_data\r\n master_df['SMA'] = find_SMA(pricing_data, lookback_window)\r\n master_df['Bandwidth'] = find_bolling_bandwidth(pricing_data, lookback_window)\r\n master_df['PM'] = find_price_momentum(pricing_data, lookback_window)\r\n return master_df.dropna()","def metadata(filename):\n import numpy as np\n import pandas as pd\n\n infos = \"\"\"IGRAID 1- 11 Character\nWMOID 13- 17 Integer\nNAME 19- 48 Character\nNAMFLAG 50- 50 Character\nLATITUDE 52- 60 Real\nLATFLAG 62- 62 Character\nLONGITUDE 64- 72 Real\nLONFLAG 74- 74 Character\nELEVATION 76- 81 Real\nELVFLAG 83- 83 Character\nYEAR 85- 88 Integer\nMONTH 90- 91 Integer\nDAY 93- 94 Integer\nHOUR 96- 97 Integer\nDATEIND 99- 99 Integer\nEVENT 101-119 Character\nALTIND 121-122 Character\nBEFINFO 124-163 Character\nBEFFLAG 164-164 Character\nLINK 166-167 Character\nAFTINFO 169-208 Character\nAFTFLAG 209-209 Character\nREFERENCE 211-235 Character\nCOMMENT 236-315 Character\nUPDCOM 316-346 Character\nUPDDATE 348-354 Character\n\"\"\"\n\n colspecs = []\n header = []\n types = {}\n for iline in infos.splitlines():\n if iline == '':\n continue\n ih = iline[0:11].strip().lower()\n header.append(ih)\n ii = int(iline[13:16]) - 1\n ij = int(iline[17:20])\n colspecs.append((ii, ij))\n it = iline[22:].strip()\n if it == 'Character':\n it = 'str'\n\n elif it == 'Real':\n it = 'float'\n\n else:\n it = 'int'\n\n types[ih] = it\n\n data = pd.read_fwf(filename, colspecs=colspecs, header=None, dtype=types, names=header)\n data = data.replace('nan', '')\n data['date'] = pd.to_datetime((data.year * 1000000 +\n np.where(data.month.values == 99, 6, data.month.values) * 10000 +\n np.where(data.day.values == 99, 15, data.day.values) * 100 +\n np.where(data.hour.values == 99, 0, data.hour.values)).apply(str), format='%Y%m%d%H')\n return data","def lcia_methods__metadata(self):\r\n with UnicodeReader(os.path.join(dirpath, \"categoryUUIDs.csv\"), \r\n encoding='latin-1', \r\n delimiter=dt) as csv_file:\r\n next(csv_file) \r\n csv_data = [{'name': (line[0], line[2], line[4]),\r\n 'description': line[7]\r\n } for line in csv_file]\r\n \r\n filename = \"LCIA_implementation_2019.xlsx\" # this was donwloaded and updated on Oct 2019 from ecoinvent website. \r\n wb = xlrd.open_workbook(os.path.join(dirpath, filename))\r\n #characterizaton factors\r\n sheet= wb.sheet_by_name(\"CFs\")\r\n cf_data = [{\r\n 'method': (sheet.cell(row, 0).value,\r\n sheet.cell(row, 1).value,\r\n sheet.cell(row, 2).value),\r\n 'name': sheet.cell(row, 3).value,\r\n 'categories': (sheet.cell(row, 4).value, sheet.cell(row, 5).value),\r\n 'amount': sheet.cell(row, 7).value\r\n }\r\n for row in range(1, sheet.nrows)\r\n if sheet.cell(row, 0).value not in \r\n {'selected LCI results, additional', 'selected LCI results'} and isinstance(sheet.cell(row, 7).value, Number)]\r\n #units\r\n sheet= wb.sheet_by_name(\"units\")\r\n units = {(sheet.cell(row, 0).value, sheet.cell(row, 1).value, \r\n sheet.cell(row, 2).value): sheet.cell(row, 4).value for row in range(1, sheet.nrows)}\r\n return csv_data, cf_data, units, filename","def prepare_data():\n df = pd.read_csv('Wholesale customers data.csv')\n df_numeric = df[['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']]\n return df, df_numeric","def _get_dsmeta(self, bids):\n # STEP 1: Extract metadata from `dataset_description.json`\n metadata = self._get_bids_dsdescription(bids)\n # STEP 2: Extract README text\n metadata[\"description\"] = self._get_bids_readme()\n # STEP 3: Extract information about entities and add to metadata\n metadata[\"entities\"] = self._get_bids_entities(bids)\n # STEP 4: Extract variable collection information on multiple levels\n metadata[\"variables\"] = self._get_bids_variables(bids)\n # STEP 5: Add context to metadata output\n metadata[\"@context\"] = BIDSCONTEXT\n return metadata","def get_metafeatures(df):\n metafeatures = []\n rows = df.shape[0]\n for col in df.columns:\n d = {'column': col,\n 'n_unique': df[col].nunique(),\n 'missing': df[col].isnull().sum() * 1.0 / rows,\n 'type': df[col].dtype}\n metafeatures.append(d)\n return pd.DataFrame(metafeatures)","def get_data_from_disc(symbol, usecols=['Date', 'Close', 'Open', 'High', 'Low', 'Adj Close', 'Volume']):\n print('\\n\\n\\n============================================================================')\n print('#Loading raw data usecols=',usecols)\n print('===============================================================================')\n df1 = pd.read_csv(symbol_to_path(symbol)\n , index_col='Date'\n , parse_dates=True\n , usecols=usecols\n , na_values=['nan'])\n\n print('raw columns=', df1.columns)\n print('\\nraw df1=\\n', df1)\n return df1","def get_details(i, df):\n print(\"Name:\", df.name.loc[i])\n print(\"Gender:\", df.gender.loc[i])\n print(\"Age in months:\", df.age.loc[i])\n print(\"Breed:\", df.breed1_desc.loc[i].title().replace(\"_\",\" \"),df.breed2_desc.loc[i].title().replace(\"_\",\" \"))\n print(\"Color/s:\", df.color1_desc.loc[i].title(), df.color2_desc.loc[i].title(), df.color3_desc.loc[i].title())\n print(\"Fur Length:\", df.fur_length.loc[i])\n print(\"Vaccinated:\", df.vaccinated.loc[i])\n print(\"Dewormed:\", df.dewormed.loc[i])\n print(\"Spayed or Neutered:\", df.sterilized.loc[i])\n print(\"Health:\", df.health.loc[i])\n print(\"No. of Pets in this Listing:\", df.quantity.loc[i])\n if df.fee.loc[i] == 0:\n print(\"Adoption Fee: FREE\")\n else:\n print(\"Adoption Fee: MYR\", round(df.fee.loc[i],2))\n print(\"Location :\", df.state_desc.loc[i].title().replace(\"_\",\" \"))\n print(\"Description :\", df.description.loc[i])\n print_images(i, df)","def sample_metadata(sample):\n sel = [\n Mental_health.year,\n Mental_health.physical_importance,\n Mental_health.mental_importance,\n Mental_health.industry_support,\n ]\n \n results = db.session.query(*sel).all() \n# Create a dictionary entry for each row of metadata information\n sample_metadata = {}\n for result in results:\n Mental_health[\"year\"] = result[0]\n Mental_health[\"physical_importance\"] = result[1]\n Mental_health[\"mental_importance\"] = result[2]\n Mental_health[\"industry_support\"] = result[3]\n \n\n print(sample_metadata)\n return jsonify(sample_metadata)","def getIndicators(fileName: str):\n df = pd.read_json(f'data/{fileName}')\n df.columns = ['TIMESTAMP', 'OPEN', 'HIGH', 'LOW', 'CLOSE', 'VOLUME']\n df['PROFIT_TOTAL'] = df['CLOSE'] - df['OPEN']\n df['PROFIT_PERCENTAGE'] = percentage(df['PROFIT_TOTAL'], df['OPEN'])\n df['DATE'] = pd.to_datetime(df['TIMESTAMP'], unit='ms')\n df['RSI'] = getRSI(df, 20)\n df['SMA_20'] = getSMA(df, 20)\n df['SMA_50'] = getSMA(df, 50)\n df['SMA_100'] = getSMA(df, 100)\n df['SMA_200'] = getSMA(df, 200)\n df['EMA_20'] = getEMA(df, 20)\n df['EMA_50'] = getEMA(df, 50)\n df['EMA_100'] = getEMA(df, 100)\n df['EMA_200'] = getEMA(df, 200)\n df['BB_HIGH_1'] = getBollingerUpperBand(df, 1, 20)\n df['BB_LOW_1'] = getBollingerLowerBand(df, 1, 20)\n df['BB_WIDTH_1'] = getBollingerBandWidth(df, 1, 20)\n df['BB_HIGH_2'] = getBollingerUpperBand(df, 2, 20)\n df['BB_LOW_2'] = getBollingerLowerBand(df, 2, 20)\n df['BB_WIDTH_2'] = getBollingerBandWidth(df, 2, 20)\n df['MACD_DEFAULT'] = getMACD(df, 26, 12, 9)\n df['MACD_200_50_20'] = getMACD(df, 200, 50, 20)\n\n return [df, len(df)]","def sample_metadata(iso_code):\n sel = [\n Freedom_short.iso_code,\n Freedom_short.year,\n Freedom_short.country,\n Freedom_short.region,\n Freedom_short.hf_score,\n Freedom_short.hf_rank,\n Freedom_short.hf_quartile,\n ]\n\n results = db.session.query(*sel).filter(Freedom_short.iso_code == iso_code,Freedom_short.year == 2017).all()\n\n # Create a dictionary entry for each row of metadata information\n freedom_sdata_list = []\n for result in results:\n freedom_sdata = {}\n freedom_sdata[\"iso_code\"] = result[0]\n freedom_sdata[\"year\"] = result[1]\n freedom_sdata[\"country\"] = result[2]\n freedom_sdata[\"region\"] = result[3]\n freedom_sdata[\"hf_score\"] = str(result[4])\n freedom_sdata[\"hf_rank\"] = str(result[5])\n freedom_sdata[\"hf_quartile\"] = result[6]\n freedom_sdata_list.append(freedom_sdata) \n\n print(freedom_sdata_list)\n return jsonify(freedom_sdata_list[0])","def _compute_overview(df: EDAFrame, cfg: Config) -> Dict[str, Any]:\n data: Dict[str, Any] = {}\n # overview\n if cfg.overview.enable:\n data[\"ov\"] = calc_stats(df, cfg)\n data[\"insights\"] = []\n for col in df.columns:\n col_dtype = df.get_eda_dtype(col)\n\n # when srs is full NA, the column type will be float\n # and need to be transformed into str.\n if df.get_missing_cnt(col) == df.shape[0]:\n srs = df.get_col_as_str(col, na_as_str=False).dropna().astype(str)\n data[\"insights\"].append((col, Nominal(), _nom_calcs(srs, cfg)))\n elif isinstance(col_dtype, Continuous):\n data[\"insights\"].append(\n (col, Continuous(), _cont_calcs(df.frame[col].dropna(), cfg))\n )\n elif isinstance(col_dtype, (Nominal, GeoGraphy, GeoPoint, SmallCardNum)):\n srs = df.get_col_as_str(col, na_as_str=False).dropna()\n data[\"insights\"].append((col, Nominal(), _nom_calcs(srs, cfg)))\n # elif isinstance(col_dtype, DateTime):\n # data[\"insights\"].append(\n # (col, DateTime(), dask.delayed(_calc_line_dt)(df.frame[[col]], cfg.line.unit))\n # )\n else:\n raise RuntimeError(f\"unprocessed data type: col:{col}, dtype: {type(col_dtype)}\")\n return data","def read_strong_meta(strong_meta):\n \n df = pd.read_csv(strong_meta, sep='\\t')\n df = pd.DataFrame(df)\n \n events_list = []\n \n for row in df.iterrows():\n event = {'filename': row[1]['filename'], \n 'onset': row[1]['onset'], \n 'offset': row[1]['offset'], \n 'event_label': row[1]['event_label']}\n \n events_list.append(event)\n \n return events_list","def data_specific_processing(self, dataframe):\n return dataframe","def get_ctffind_4_1_0_meta(file_name: str) -> pd.DataFrame:\n extract_dict: typing.Dict[str, str]\n ctffind_meta_data: pd.DataFrame\n lines: typing.List[str]\n match: typing.Optional[typing.Match[str]]\n non_string_values: typing.Set[str]\n\n extract_dict = get_ctffind_4_1_0_extract_dict()\n ctffind_meta_data = pd.DataFrame(index=[0], columns=extract_dict.keys())\n with open(file_name, 'r') as read:\n lines = read.readlines()\n\n non_string_values = set([\n 'MicrographNameNoDW',\n 'version'\n ])\n for line in lines:\n for key, value in extract_dict.items():\n match = re.match(value, line)\n if match is not None:\n try:\n ctffind_meta_data[key] = float(match.group(1))\n except ValueError:\n assert key in non_string_values, f'{key}: {match.group(1)}'\n ctffind_meta_data[key] = match.group(1)\n else:\n pass\n return ctffind_meta_data","def get_events_metadata(events, beh_df):\n assert len(events[events[:, 2] == cfg.EVENTS_ID[\"answer\"]]) == len(beh_df)\n assert len(events[events[:, 2] == cfg.EVENTS_ID[\"fixcross\"]]) == len(\n beh_df\n )\n i_question = 0\n beh_data = {\"confidence\": [], \"is_correct\": [], \"question_num\": []}\n for i, ev in enumerate(events):\n if i_question == 0 or ev[2] == cfg.EVENTS_ID[\"question/second\"]:\n confidence, is_correct, question_num = get_question_data(\n i_question, beh_df\n )\n if ev[2] == cfg.EVENTS_ID[\"confidence\"]:\n i_question += 1\n beh_data[\"confidence\"].append(confidence)\n beh_data[\"is_correct\"].append(is_correct)\n beh_data[\"question_num\"].append(question_num)\n\n metadata = pd.DataFrame(beh_data)\n return metadata","def load_extract(cryptocurrency):\n df = pd.read_csv(f'input_12mo/{cryptocurrency}.csv')\n df = df['Close'].copy()\n df = df[-183:].copy()\n return df","def get_data_from_disc(symbol, skipFirstLines, size_output = 2):\n\n df1 = pd.read_csv( symbol_to_path(symbol)\n , index_col = 'Date'\n , parse_dates= True\n , usecols = ['Date', 'Close', 'Open', 'High', 'Low', 'Adj Close', 'Volume']\n , na_values = ['nan'])\n\n # Clean NaN values\n df = utils.dropna(df1)\n\n # Add ta features filling NaN values\n #df1 = add_all_ta_features(df, \"Open\", \"High\", \"Low\", \"Close\", fillna=True)#, \"Volume_BTC\",\n\n # Add bollinger band high indicator filling NaN values\n df1['bb_hi10' ] = bollinger_hband_indicator(df1[\"Close\"], n=10 , ndev=2, fillna=True)\n df1['bb_lo10' ] = bollinger_lband_indicator(df1[\"Close\"], n=10 , ndev=2, fillna=True)\n df1['bb_hi20' ] = bollinger_hband_indicator(df1[\"Close\"], n=20 , ndev=2, fillna=True)\n df1['bb_lo20' ] = bollinger_lband_indicator(df1[\"Close\"], n=20 , ndev=2, fillna=True)\n df1['bb_hi50' ] = bollinger_hband_indicator(df1[\"Close\"], n=50 , ndev=2, fillna=True)\n df1['bb_lo50' ] = bollinger_lband_indicator(df1[\"Close\"], n=50 , ndev=2, fillna=True)\n df1['bb_hi200'] = bollinger_hband_indicator(df1[\"Close\"], n=200, ndev=2, fillna=True)\n df1['bb_lo200'] = bollinger_lband_indicator(df1[\"Close\"], n=200, ndev=2, fillna=True)\n\n df1['rsi5' ] = rsi (df1[\"Close\"], n=5 , fillna=True)\n df1['rsi10' ] = rsi (df1[\"Close\"], n=10, fillna=True)\n df1['rsi20' ] = rsi (df1[\"Close\"], n=20, fillna=True)\n df1['rsi50' ] = rsi (df1[\"Close\"], n=50, fillna=True)\n\n\n df1['stoc10' ] = stoch (df1[\"High\"],df1[\"Low\"],df1[\"Close\"], n=10 , fillna=True)\n df1['stoc20' ] = stoch (df1[\"High\"],df1[\"Low\"],df1[\"Close\"], n=20 , fillna=True)\n df1['stoc50' ] = stoch (df1[\"High\"],df1[\"Low\"],df1[\"Close\"], n=50 , fillna=True)\n df1['stoc200'] = stoch (df1[\"High\"],df1[\"Low\"],df1[\"Close\"], n=200, fillna=True)\n\n df1['mom5' ] = wr (df1[\"High\"],df1[\"Low\"],df1[\"Close\"], lbp=5 , fillna=True)\n df1['mom10' ] = wr (df1[\"High\"],df1[\"Low\"],df1[\"Close\"], lbp=10 , fillna=True)\n df1['mom20' ] = wr (df1[\"High\"],df1[\"Low\"],df1[\"Close\"], lbp=20 , fillna=True)\n df1['mom50' ] = wr (df1[\"High\"],df1[\"Low\"],df1[\"Close\"], lbp=50 , fillna=True)\n\n df1['sma10' ] = df1['Close'].rolling(window=10 ).mean()\n df1['sma20' ] = df1['Close'].rolling(window=20 ).mean()\n df1['sma50' ] = df1['Close'].rolling(window=50 ).mean()\n df1['sma200'] = df1['Close'].rolling(window=200).mean()\n df1['sma400'] = df1['Close'].rolling(window=400).mean()\n #df1['mom']=pandas.stats.\n df1 = df1[-(df1.shape[0]-skipFirstLines):] # skip 1st x rows, x years due to NAN in sma, range\n\n df1['nvo' ] = df1['Volume'] / df1['sma10'] / 100 # normalized volume\n\n\n df1['range'] = df1['Close'] - df1['Open']\n df1['percentage'] = df1['range'] / df1['Open'] * 100\n\n #df/df.iloc[0,:]\n df1['range_sma'] = (df1 ['Close'] - df1 ['sma10']) / df1['Close']\n df1['range_sma1'] = (df1 ['sma10'] - df1 ['sma20']) / df1['sma10']#small sma above big sma indicates that price is going up\n df1['range_sma2'] = (df1 ['sma20'] - df1 ['sma50']) / df1['sma20']#small sma above big sma indicates that price is going up\n df1['range_sma3'] = (df1 ['sma50'] - df1['sma200']) / df1['sma50']#small sma above big sma indicates that price is going up\n df1['range_sma4'] = (df1['sma200'] - df1['sma400']) / df1['sma200']#small sma above big sma indicates that price is going up\n\n df1['rel_bol_hi10'] = (df1 ['High'] - df1 ['bb_hi10']) / df1['High']\n df1['rel_bol_lo10'] = (df1 ['Low'] - df1 ['bb_lo10']) / df1['Low']\n df1['rel_bol_hi20'] = (df1 ['High'] - df1 ['bb_hi20']) / df1['High']\n df1['rel_bol_lo20'] = (df1 ['Low'] - df1 ['bb_lo20']) / df1['Low']\n df1['rel_bol_hi50'] = (df1 ['High'] - df1 ['bb_hi50']) / df1['High']\n df1['rel_bol_lo50'] = (df1 ['Low'] - df1 ['bb_lo50']) / df1['Low']\n df1['rel_bol_hi200'] = (df1 ['High'] - df1 ['bb_hi200']) / df1['High']\n df1['rel_bol_lo200'] = (df1 ['Low'] - df1 ['bb_lo200']) / df1['Low']\n\n #df1['isUp'] = 0\n print(df1)\n # print ('\\ndf1=\\n',df1.tail())\n # print ('\\nsma_10=\\n',df1['sma10'] )\n # print ('\\nsma_200=\\n',df1['sma200'] )\n # print ('\\nrsi10=\\n',df1['rsi10'] )\n # print ('\\nrsi5=\\n',df1['rsi5'] )\n # print ('\\nstoc10=\\n',df1['stoc10'] )\n # print ('\\nstoc200=\\n',df1['stoc200'] )\n # print ('\\nrangesma=\\n',df1['rangesma'])\n # print ('\\nrangesma4=\\n',df1['rangesma4'])\n # print ('\\nrel_bol_hi10=\\n',df1['rel_bol_hi10'])\n # print ('\\nrel_bol_hi200=\\n',df1['rel_bol_hi200'])\n\n # df1['sma4002' ] = sma\n # df1['ema' ] = ema\n # df1['macd' ] = macd\n # df1['stoc' ] = stoc\n # df1['rsi' ] = rsi\n#tech_ind = pd.concat([sma, ema, macd, stoc, rsi, adx, cci, aroon, bands, ad, obv, wma, mom, willr], axis=1)\n\n ## labeling\n ## smart labeling\n if size_output == 2:\n df1.loc[df1.range > 0.0, 'isUp'] = 1\n df1.loc[df1.range <= 0.0, 'isUp'] = 0\n if size_output == 3:\n df1['isUp'] = 0\n df1.loc[ df1.percentage >= +0.1 , 'isUp'] = 1\n df1.loc[ df1.percentage <= -0.1 , 'isUp'] = -1\n # df1.loc[(-0.1 < df1.percentage < +0.1), 'isUp'] = 0\n #df1['isUp'] = np.random.randint(2, size=df1.shape[0])# if u the model accuracy with random labaling expect to get 0.5\n\n\n #direction = (close > close.shift()).astype(int)\n #target = direction.shift(-1).fillna(0).astype(int)\n #target.name = 'target'\n #sma10 = sma10.rename(columns={symbol: symbol+'sma10'})\n #sma20 = sma20.rename(columns={symbol: symbol+'sma20'})\n #df1 = df1.rename(columns={'Close': symbol+'Close'})\n '''\n# loss: 0.1222 - acc: 0.9000 - val_loss: 0.1211 - val_acc: 0.9364 epoch50 sma+range+close+open (range tell model the answer)\n# loss: 0.6932 - acc: 0.4860 - val_loss: 0.6932 - val_acc: 0.4969 random data >>> random results\n# loss: 0.6922 - acc: 0.5205 - val_loss: 0.6911 - val_acc: 0.5364 epoch50 sma\n# loss: 0.6923 - acc: 0.5198 - val_loss: 0.6914 - val_acc: 0.5360\n# loss: 0.6922 - acc: 0.5217 - val_loss: 0.6911 - val_acc: 0.5353\n# loss: 0.6431 - acc: 0.5846 - val_loss: 0.7373 - val_acc: 0.5364\n# loss: 0.5373 - acc: 0.7114 - val_loss: 0.6112 - val_acc: 0.6773 epoch50\n# loss: 0.5198 - acc: 0.7225 - val_loss: 0.5632 - val_acc: 0.6797 epoch100 sma+stoc+rsi\n# loss: 0.5487 - acc: 0.7079 - val_loss: 0.6115 - val_acc: 0.6740 SPY 1970 epoch100 sma+stoc+rsi+bol 1970\n# loss: 0.4112 - acc: 0.8140 - val_loss: 0.5576 - val_acc: 0.7324 SPY 1970 epoch500 nvo+mom+sma+stoc+rsi+bol 1970\n\n# loss: 0.6047 - acc: 0.6574 - val_loss: 0.6257 - val_acc: 0.6580 SPY 2000\n# loss: nan - acc: 0.4711 - val_loss: nan - val_acc: 0.4563 DJI 2000\n# loss: nan - acc: 0.4906 - val_loss: nan - val_acc: 0.4626 QQQ 2000\n\n nvo Open High Low Close range_sma isUp\nDate \n1964-05-01 748.525452 79.459999 80.470001 79.459999 80.169998 0.001821 1.0\n1964-05-04 669.824179 80.169998 81.010002 79.870003 80.470001 0.005580 1.0\n2019-07-11 10607.754714 2999.620117 3002.330078 2988.800049 2999.909912 0.008677 1.0\n2019-07-12 9973.829690 3003.360107 3013.919922 3001.870117 3013.770020 0.010287 1.0\n'''\n pd.set_option('display.max_columns', 500)\n pd.set_option('display.width' , 1000)\n print('columns=', df1.columns)\n print ('\\ndf1=\\n',df1.loc[:, ['nvo', 'Open' ,'High' , 'Low' , 'Close', 'range_sma', 'isUp']])\n print ('\\ndf1=\\n',df1.loc[:, ['sma10','sma20','sma50','sma200', 'range_sma1']])\n print ('\\ndf1=\\n',df1.loc[:, ['rsi10','rsi20','rsi50','rsi5']])\n print ('\\ndf1=\\n',df1.loc[:, ['stoc10','stoc20','stoc50','stoc200']])\n print ('\\ndf1=\\n',df1.loc[:, ['bb_hi10','bb_hi20','bb_hi50','bb_hi200']])#, 'sma4002']])\n print ('\\ndf1=\\n',df1.loc[:, ['bb_lo10','bb_lo20','bb_lo50','bb_lo200']])#, 'sma4002']])\n print ('\\ndf1=\\n',df1.loc[:, ['rel_bol_hi10','rel_bol_hi20','rel_bol_hi50','rel_bol_hi200']])#, 'sma4002']])\n print ('\\ndf1[ 0]=\\n',df1.iloc[0])#, 'sma4002']])\n print ('\\ndf1[ 1]=\\n',df1.iloc[1])#, 'sma4002']])\n print ('\\ndf1[9308]=\\n',df1.iloc[9308])#, 'sma4002']])\n print ('\\ndf1[-2]=\\n',df1.iloc[-2])#, 'sma4002']])\n print ('\\ndf1[-1]=\\n',df1.iloc[-1])#, 'sma4002']])\n #df = pd.DataFrame(record, columns = ['Name', 'Age', 'Stream', 'Percentage'])\n # rslt_df = df[df1['isUp'] == 1]\n # print ('\\ndf1 describe direction = +1\\n',rslt_df.describe())\n # rslt_df = df[df1['isUp'] == -1]\n # print ('\\ndf1 describe direction = -1\\n',rslt_df.describe())\n # rslt_df = df[df1['isUp'] == 0]\n # print ('\\ndf1 describe direction = 0\\n',rslt_df.describe())\n # # print ('\\ndf1=\\n',df1.loc[:, ['ema','macd','stoc', 'rsi']])\n print('\\ndf11 describe=\\n',df1.loc[:, ['percentage', 'nvo', 'range', 'mom5', 'mom10', 'mom20', 'mom50', 'rsi5', 'rsi10', 'rsi20', 'rsi50', 'stoc10', 'stoc20', 'stoc50', 'stoc200']].describe())\n return df1","def read_data(transferencias):\n # read in triples of user/artist/playcount from the input dataset\n transfer_count = transferencias.groupby([\"REFERENCIA_1\", \"REFERENCIA_ORIGEN\"]).IMPORTE.count().reset_index()\n\n # for index, row in transfer_count.iterrows():\n # cliente = Empresa.objects.get(fiscal_id=str(row['REFERENCIA_ORIGEN']))\n # proveedor = Empresa.objects.get(fiscal_id=str(row['REFERENCIA_1']))\n # proveedor.clients.add(cliente)\n # cliente.providers.add(proveedor)\n\n print(\"clientes y proveedores cargados\")\n\n data = pandas.DataFrame()\n data['user'] = transfer_count['REFERENCIA_ORIGEN'].astype(\"category\")\n data['artist'] = transfer_count['REFERENCIA_1'].astype(\"category\")\n data['transfers'] = transfer_count['IMPORTE'].astype(\"category\")\n\n transfers = coo_matrix((data['transfers'].astype(float),\n (data['artist'].cat.codes.copy(),\n data['user'].cat.codes.copy())))\n\n return data, transfers","def metadata(self, rel_index) -> Metadata:\n\n row = self.image_df.iloc[rel_index]\n\n (\n site,\n well,\n replicate,\n plate,\n compound,\n concentration,\n moa,\n image_idx,\n ) = row[\n [\n \"site\",\n \"well\",\n \"replicate\",\n \"plate\",\n \"compound\",\n \"concentration\",\n \"moa\",\n \"image_idx\",\n ]\n ]\n\n metadata = Metadata(\n Plate(site, str(well), replicate, plate),\n Compound(compound, concentration, moa),\n image_idx,\n )\n\n return metadata","def fetch_tracks_info_df(df):\n\n gen_df = df.copy()\n gen_df = gen_df[['artist_name', 'title', 'release', 'track_id', 'song_id']]\n\n for column_name in gen_df.columns:\n gen_df[column_name] = gen_df[column_name].map(lambda x: ast.literal_eval(x).decode('utf-8'))\n\n gen_df.rename(columns={'release': 'album_name'}, inplace=True)\n gen_df['year'] = df['year']\n\n return gen_df","def fetchAndCleanDataframe(self):\n\n df = pd.read_csv('/Users/apple4u/Desktop/goksel tez/results_with_scenarios.csv')\n df.insider_label.fillna(0, inplace=True) # replaces null fields with 0\n df = df.drop(columns=['employee_name', 'scenario', 'role'])\n df = df.rename(columns={'insider_label':'label'})\n #df['label'] = df['insider_label'].astype('int64')\n #df.drop(columns='insider_label', inplace=True)\n df.set_index('user_id', inplace=True)\n X = df.iloc[:, :5].values #fetch all records first 5 columns\n y = df.label.values\n print(df.head())\n return X, y","def extract_data():\n logging.info(f'Reading data from {impftermine.agg_export_file_name()}...')\n df_wl = pd.read_csv(impftermine.agg_export_file_name())\n vacc_report_file = os.path.join(credentials.vmdl_path, 'vaccination_report_bs_age_group_long.csv')\n logging.info(f'Reading data from {vacc_report_file}...')\n df_impf = pd.read_csv(vacc_report_file)\n return df_wl, df_impf","def load_predict(cryptocurrency):\n df = pd.read_csv(f'output_12mo/{cryptocurrency}.csv')\n #df = df.drop(0, axis=0).copy()\n #df = df['Close'].copy()\n #df = df[:-25].copy()\n return df","def create_metadata(data_dir):\n pool = multiprocessing.Pool()\n raw_feature_paths = [os.path.join(data_dir, \"features.jsonl\")]\n records = list(pool.imap(read_metadata_record, raw_feature_iterator(raw_feature_paths)))\n records = [dict(record, **{\"subset\": \"train\"}) for record in records]\n\n metadf = pd.DataFrame(records)[[\"sha256\", \"appeared\", \"subset\", \"label\"]]\n metadf.to_csv(os.path.join(data_dir, \"metadata.csv\"))\n print(\"\\n[Done] create_metadata\\n\")\n \n return metadf","def get_ticker_details(self):\n\n ticker_data = sequential_data_scrape(\n scrape.download_ticker_details,\n [\n f\"https://finviz.com/quote.ashx?&t={row.get('Ticker')}\"\n for row in self.data\n ],\n self._user_agent,\n )\n\n for entry in ticker_data:\n for key, value in entry.items():\n for ticker_generic in self.data:\n if ticker_generic.get(\"Ticker\") == key:\n if \"Sales\" not in self.headers:\n self.headers.extend(list(value[0].keys()))\n\n ticker_generic.update(value[0])\n self.analysis.extend(value[1])\n\n return self.data","def getUserHistFeatures(transaction_list, coupon_dict, model_start_date, purchase_date):\n feat_header = [\"NoOfPurchases\", \"DaysSinceLastPurchase\", \"NoOfPurchasesLastweek\", \"NoOfPurchasesLast15Days\", \"NoOfPurchasesLast30Days\", \"NoOfPurchasesLast60Days\", \"NoOfPurchasesLast90Days\", \"NoOfPurchasesLast180Days\", \"DaysSincePrevPurchase\", \"NoOfPurchasesPrevweek\", \"NoOfPurchasesPrev15Days\", \"NoOfPurchasesPrev30Days\", \"NoOfPurchasesPrev60Days\", \"NoOfPurchasesPrev90Days\", \"NoOfPurchasesPrev180Days\"]\n\n # getting number of purchases #\n feat_list = [len(transaction_list)]\n\n # initializing variables #\n purchase_small_area_name_dict = {}\n puchase_date_list = []\n capsule_text_dict = {}\n genre_name_dict = {}\n price_rate_list = []\n catalog_price_list = []\n discount_price_list = []\n dispperiod_list = []\n valid_period_list = []\n usable_date_mon_list = {}\n usable_date_tue_list = {}\n usable_date_wed_list = {}\n usable_date_thu_list = {}\n usable_date_fri_list = {}\n usable_date_sat_list = {}\n usable_date_sun_list = {}\n usable_date_hol_list = {}\n usable_date_before_hol_list = {}\n coupon_large_area_name_dict = {}\n coupon_small_area_name_dict = {}\n coupon_ken_name_dict = {}\n days_since_last_purchase = 9999\n last_week_purchase = 0\n last_fifteendays_purchase = 0\n last_thirtydays_purchase = 0\n last_sixtydays_purchase = 0\n last_nintydays_purchase = 0\n\tlast_oneeightydays_purchase = 0\n\tdays_since_prev_purchase = 9999\n\tprev_week_purchase = 0\n prev_fifteendays_purchase = 0\n prev_thirtydays_purchase = 0\n prev_sixtydays_purchase = 0\n prev_nintydays_purchase = 0\n prev_oneeightydays_purchase = 0\n for transaction in transaction_list:\n diff_days = (model_start_date - datetime.datetime.strptime(transaction['I_DATE'], \"%Y-%m-%d %H:%M:%S\").date()).days\n if diff_days < days_since_last_purchase:\n days_since_last_purchase = diff_days\n if diff_days <= 7:\n last_week_purchase += 1\n if diff_days <= 15:\n last_fifteendays_purchase += 1\n if diff_days <= 30:\n last_thirtydays_purchase += 1\n if diff_days <= 60:\n last_sixtydays_purchase += 1\n if diff_days <= 90:\n last_nintydays_purchase += 1\n\t\tif diff_days <= 180:\n last_oneeightydays_purchase += 1\n\t\t\n\t\tdiff_days = (purchase_date - datetime.datetime.strptime(transaction['I_DATE'], \"%Y-%m-%d %H:%M:%S\").date()).days\n if diff_days < days_since_last_purchase:\n days_since_prev_purchase = diff_days\n if diff_days <= 7:\n prev_week_purchase += 1\n if diff_days <= 15:\n prev_fifteendays_purchase += 1\n if diff_days <= 30:\n prev_thirtydays_purchase += 1\n if diff_days <= 60:\n prev_sixtydays_purchase += 1\n if diff_days <= 90:\n prev_nintydays_purchase += 1\n if diff_days <= 180:\n prev_oneeightydays_purchase += 1\n\n coupon_id_dict = coupon_dict[ transaction['COUPON_ID_hash'] ]\n purchase_small_area_name_dict[transaction['SMALL_AREA_NAME']] = purchase_small_area_name_dict.get( transaction['SMALL_AREA_NAME'],0) + 1\n capsule_text_dict[ coupon_id_dict['CAPSULE_TEXT'] ] = capsule_text_dict.get( coupon_id_dict['CAPSULE_TEXT'], 0) + 1\n genre_name_dict[ coupon_id_dict['GENRE_NAME'] ] = genre_name_dict.get( coupon_id_dict['GENRE_NAME'],0 ) + 1\n coupon_large_area_name_dict[ coupon_id_dict['large_area_name'] ] = coupon_large_area_name_dict.get( coupon_id_dict['large_area_name'],0 ) + 1\n coupon_small_area_name_dict[ coupon_id_dict['small_area_name'] ] = coupon_small_area_name_dict.get( coupon_id_dict['small_area_name'],0 ) + 1\n coupon_ken_name_dict[ coupon_id_dict['ken_name'] ] = coupon_ken_name_dict.get( coupon_id_dict['ken_name'],0 ) + 1\n price_rate_list.append( float(coupon_id_dict['PRICE_RATE']) )\n catalog_price_list.append( float(coupon_id_dict['CATALOG_PRICE']) )\n discount_price_list.append( float(coupon_id_dict['DISCOUNT_PRICE']) )\n dispperiod_list.append( float(coupon_id_dict['DISPPERIOD']) )\n if coupon_id_dict['VALIDPERIOD'] not in ('','NA'):\n valid_period_list.append( float(coupon_id_dict['VALIDPERIOD']) )\n if coupon_id_dict['USABLE_DATE_MON'] not in ('','NA'):\n usable_date_mon_list[ float(coupon_id_dict['USABLE_DATE_MON']) ] = usable_date_mon_list.get( float(coupon_id_dict['USABLE_DATE_MON']),0 ) + 1\n usable_date_tue_list[ float(coupon_id_dict['USABLE_DATE_TUE']) ] = usable_date_tue_list.get( float(coupon_id_dict['USABLE_DATE_TUE']),0 ) + 1\n usable_date_wed_list[ float(coupon_id_dict['USABLE_DATE_WED']) ] = usable_date_wed_list.get( float(coupon_id_dict['USABLE_DATE_WED']),0 ) + 1\n usable_date_thu_list[ float(coupon_id_dict['USABLE_DATE_THU']) ] = usable_date_thu_list.get( float(coupon_id_dict['USABLE_DATE_THU']),0 ) + 1\n usable_date_fri_list[ float(coupon_id_dict['USABLE_DATE_FRI']) ] = usable_date_fri_list.get( float(coupon_id_dict['USABLE_DATE_FRI']),0 ) + 1\n usable_date_sat_list[ float(coupon_id_dict['USABLE_DATE_SAT']) ] = usable_date_sat_list.get( float(coupon_id_dict['USABLE_DATE_SAT']),0 ) + 1\n usable_date_sun_list[ float(coupon_id_dict['USABLE_DATE_SUN']) ] = usable_date_sun_list.get( float(coupon_id_dict['USABLE_DATE_SUN']),0 ) + 1\n usable_date_hol_list[ float(coupon_id_dict['USABLE_DATE_HOLIDAY']) ] = usable_date_hol_list.get( float(coupon_id_dict['USABLE_DATE_HOLIDAY']),0 ) + 1\n usable_date_before_hol_list[ float(coupon_id_dict['USABLE_DATE_BEFORE_HOLIDAY']) ] = usable_date_before_hol_list.get( float(coupon_id_dict['USABLE_DATE_BEFORE_HOLIDAY']),0 )+1\n else:\n usable_date_mon_list[3.0] = usable_date_mon_list.get( 3.0,0 ) + 1\n usable_date_tue_list[3.0] = usable_date_tue_list.get( 3.0,0 ) + 1\n usable_date_wed_list[3.0] = usable_date_wed_list.get( 3.0,0 ) + 1\n usable_date_thu_list[3.0] = usable_date_thu_list.get( 3.0,0 ) + 1\n usable_date_fri_list[3.0] = usable_date_fri_list.get( 3.0,0 ) + 1\n usable_date_sat_list[3.0] = usable_date_sat_list.get( 3.0,0 ) + 1\n usable_date_sun_list[3.0] = usable_date_sun_list.get( 3.0,0 ) + 1\n usable_date_hol_list[3.0] = usable_date_hol_list.get( 3.0,0 ) + 1\n usable_date_before_hol_list[3.0] = usable_date_before_hol_list.get( 3.0,0 ) + 1\n\n feat_list.extend([days_since_last_purchase, last_week_purchase, last_fifteendays_purchase, last_thirtydays_purchase, last_sixtydays_purchase, last_nintydays_purchase, last_oneeightydays_purchase, days_since_prev_purchase, prev_week_purchase, prev_fifteendays_purchase, prev_thirtydays_purchase, prev_sixtydays_purchase, prev_nintydays_purchase, prev_oneeightydays_purchase])\n return feat_list, feat_header, [purchase_small_area_name_dict, capsule_text_dict, genre_name_dict, coupon_large_area_name_dict, coupon_small_area_name_dict, coupon_ken_name_dict, price_rate_list, catalog_price_list, discount_price_list, dispperiod_list, valid_period_list, usable_date_mon_list, usable_date_tue_list, usable_date_wed_list, usable_date_thu_list, usable_date_fri_list, usable_date_sat_list, usable_date_sun_list, usable_date_hol_list, usable_date_before_hol_list]","def extract_summary(self):\n metadata = {}\n\n ## document Id\n documentId = self.tree.find(\"./id\")\n documentId = documentId.attrib['root'] if documentId is not None and \"root\" in documentId.attrib else \"\"\n metadata[\"documentId\"] = documentId\n\n ## setId\n setid = self.tree.find(\"./setId\")\n setid = setid.attrib['root'] if setid is not None and \"root\" in setid.attrib else \"\"\n metadata[\"setId\"] = setid\n\n ## version number\n splversion = self.tree.find(\"./versionNumber\")\n versionNumber = \"\"\n if splversion is not None:\n if \"value\" in splversion.attrib:\n versionNumber = splversion.attrib[\"value\"]\n metadata[\"versionNumber\"] = versionNumber\n\n ## product type \n code = self.tree.find(\"./code\")\n check_if_attrib_exists = lambda x, key: x[key] if key in x else ''\n product_type = check_if_attrib_exists(code.attrib, \"displayName\")\n metadata[\"productType\"] = product_type\n\n ## title\n title_text = self.tree_et.xpath(\"./title//text()\")\n title = (\" \".join([self.strip_newline_tab(t) for t in title_text]) if len(title_text) > 0 else \"\")\n metadata[\"title\"] = title\n\n ## manufacturer\n manufacturer = self.tree.find(\"./author//representedOrganization/name\")\n if manufacturer != None and manufacturer.text != None:\n manufacturer = self.strip_newline_tab(manufacturer.text)\n else:\n manufacturer = \"\"\n metadata[\"manufacturer\"] = manufacturer\n\n ## effectivetime\n effectiveTime = self.tree_et.xpath(\"./effectiveTime/@value\")\n effectiveTime = self.__normalize_date(effectiveTime)\n\n metadata[\"effectiveTime\"] = effectiveTime\n metadata[\"publishedDate\"] = effectiveTime\n\n ## From manufacturedProduct section\n brand_name = self.tree_et.xpath(\".//manufacturedProduct//name\")\n brand_name = self.strip_newline_tab(brand_name[0].text) if len(brand_name) > 0 else \"\"\n metadata[\"drugName\"] = brand_name\n\n route = self.tree_et.xpath(\".//manufacturedProduct//formCode/@code\")\n route = self.strip_newline_tab(route[0]) if len(route) > 0 else \"\"\n metadata[\"routeOfAdministration\"] = route\n\n product_ndc = self.tree_et.xpath(\".//manufacturedProduct//code/@code\")\n product_ndc = self.strip_newline_tab(product_ndc[0]) if len(product_ndc) > 0 else \"\"\n metadata[\"ndcCode\"] = product_ndc\n\n generic_name = self.tree_et.xpath(\".//manufacturedProduct//asEntityWithGeneric//genericMedicine/name\")\n generic_name = self.strip_newline_tab(generic_name[0].text) if len(generic_name) > 0 else \"\"\n metadata[\"genericName\"] = generic_name\n\n ## dosage form\n dosage_form = self.tree_et.xpath(\".//manufacturedProduct//formCode/@displayName\")\n dosage_form = dosage_form[0] if len(dosage_form) > 0 else \"\"\n metadata[\"dosageForm\"] = dosage_form\n\n # active ingredients\n substance_name = sorted([self.strip_newline_tab(a.text) for a in\n self.tree_et.xpath(\".//.//manufacturedProduct//activeMoiety/activeMoiety/name\")])\n substance_name = \", \".join(set(substance_name))\n metadata[\"substanceName\"] = substance_name\n\n ## inactive ingredients\n inactive_ingredients = sorted([self.strip_newline_tab(inactive.text) for inactive in self.tree_et.xpath(\n \".//manufacturedProduct//inactiveIngredient/inactiveIngredientSubstance/name\")])\n\n if len(inactive_ingredients) == 0:\n inactive_ingredients = \"\"\n else:\n inactive_ingredients = \",\".join(set(inactive_ingredients))\n\n metadata[\"inactiveIngredients\"] = inactive_ingredients\n\n ## other ingredients\n ingredients = sorted([self.strip_newline_tab(ingredient.text) for ingredient in\n self.tree_et.xpath(\".//manufacturedProduct//ingredient/ingredientSubstance/name\")])\n\n if len(ingredients) == 0:\n ingredients = \"\"\n else:\n ingredients = \", \".join(set(ingredients))\n metadata[\"ingredients\"] = ingredients\n\n # marketing_category\n marketing_category = self.tree_et.xpath(\".//manufacturedProduct/subjectOf/approval/code/@displayName\")\n marketing_category = self.strip_newline_tab(marketing_category[0]) if len(marketing_category) > 0 else \"\"\n metadata[\"marketingCategory\"] = marketing_category\n\n # consumed in\n consumed_in = self.tree_et.xpath(\n \".//manufacturedProduct//consumedIn/substanceAdministration/routeCode/@displayName\")\n consumed_in = consumed_in[0] if len(consumed_in) > 0 else \"\"\n metadata[\"consumedIn\"] = consumed_in\n\n # revision date\n marketing_date = self.tree_et.xpath(\".//manufacturedProduct//marketingAct/effectiveTime/low/@value\")\n marketing_date = self.__normalize_date(marketing_date)\n metadata[\"marketingDate\"] = marketing_date\n\n return metadata","def screwdriver_data(df):\n print('Only screwdriver data taken')\n\n screwdriver_df = df[['output_double_register_24',\n 'output_double_register_25',\n 'output_double_register_26',\n 'output_double_register_27',\n 'output_bit_register_70',\n 'output_bit_register_71',\n 'output_bit_register_72',\n 'label']]\n\n return screwdriver_df","def _get_metadata(conn):\n metadata_sql = \"SELECT * FROM metadata\"\n metadata = conn.execute(metadata_sql).fetchall()\n return {\n row[0]: {\n \"name\": row[0],\n \"field_name\": row[1],\n \"field_expr\": row[1],\n \"type\": row[2],\n \"minimum\": row[3],\n \"maximum\": row[4],\n \"average\": row[5],\n \"variance\": row[6],\n \"total\": row[7],\n \"stddev\": row[8],\n \"other\": json.loads(row[9]) if row[9] else None,\n }\n for row in metadata\n }","def build_dataset_by_df(file_path):\n data = pd.read_csv(file_path, sep=',')\n print(data.head())\n\n user_ids = data['CustomerID'].unique()\n prod_ids = data['StockCode'].unique()\n user_num = len(user_ids)\n product_num = len(prod_ids)\n product_lists_by_user = dict(data.groupby('CustomerID')['StockCode'].apply(list)).values()\n product_to_desc = dict(data.groupby('StockCode')['Description'].apply(set))\n dictionary = {}\n for id in prod_ids:\n dictionary[id] = len(dictionary)\n reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\n return product_lists_by_user, product_to_desc, dictionary, reversed_dictionary, user_num, product_num","def make_emotion_data(emotion: str, dict_to_use: dd.DataFrame, ck=True):\n emotion_data = []\n target_data = []\n\n for row in tqdm(dict_to_use.iterrows(), total = len(dict_to_use.index)):\n index, data = row\n annotated_emote = data[\"annotated\"]\n\n if annotated_emote == \"N/A\":\n continue\n all_columns = data.keys()\n columns_to_use = [x for x in all_columns if 'annotated' not in x and 'predicted' not in x and 'patient' not in x]\n # data = data.loc[:, columns_to_use].compute()\n data = data[columns_to_use]\n # data = data.loc[:, df.columns != \"annotated\" and df.columns != \"Happy_predicted\"].compute()\n emotion_data.append(data.tolist())\n\n if annotated_emote == emotion:\n target_data.append(1)\n else:\n target_data.append(0)\n\n return emotion_data, target_data\n\n\n # if dict_to_use is None:\n # dict_to_use = json.load(open('au_emotes.txt'))\n # emotion_data = [\n # item for sublist in (b for b in ((a for a in x.values() if a)\n # for x in dict_to_use.values()\n\n # if x) if b) for item in sublist\n # ]\n\n # if ck:\n # ck_dict = json.load(open('ck_dict.txt'))\n\n # for patient_list in ck_dict.values():\n # to_add = AUScorer.TrainList\n # au_dict = {\n # str(int(float(x))): y\n\n # for x, y in patient_list[0].items()\n # }\n\n # for add in to_add:\n # if add not in au_dict:\n # au_dict[add] = 0\n # emotion_data.append([au_dict, patient_list[1]])\n\n # au_data = []\n # target_data = []\n # aus_list = AUScorer.TrainList\n\n # for frame in emotion_data:\n # aus = frame[0]\n\n # if frame[1] == emotion:\n # au_data.append([float(aus[str(x)]) for x in aus_list])\n # target_data.append(1)\n # index = 0\n # happy_len = len(target_data)\n\n # for frame in emotion_data:\n # aus = frame[0]\n\n # if frame[1] and frame[1] != emotion:\n # au_data.append([float(aus[str(x)]) for x in aus_list])\n # target_data.append(0)\n # index += 1\n\n # if index == happy_len:\n # break\n\n # return au_data, target_data","def get_discounts(table):\n\n discounts_money_spent = {}\n discount_levels = [100, 1000, 5000]\n discount_percents = [\"2%\", \"5%\", \"10%\"]\n\n customer_id_index = 2\n amount_sold_index = 4\n id_index = 0\n product_id_index = 3\n name_index = 1\n price_index = 3\n\n store_table = store.get_table()\n store.check_table(store_table)\n\n crm_table = crm.get_table('model/crm/customers.csv')\n\n for record in table:\n product_id = record[product_id_index]\n amount_sold = record[amount_sold_index]\n customer_id = record[customer_id_index]\n\n for customer in crm_table:\n customer_name = customer[name_index]\n client_id = customer[id_index]\n\n if customer_id == client_id:\n\n for game in store_table:\n game_id = game[id_index]\n price = game[price_index]\n if product_id == game_id:\n money_spent = int(price) * int(amount_sold)\n \n if customer_name in discounts_money_spent:\n discounts_money_spent[customer_name] += int(money_spent)\n else:\n discounts_money_spent[customer_name] = int(money_spent)\n \n discounts = {}\n\n for customer_name in discounts_money_spent:\n money_spent = discounts_money_spent[customer_name]\n if money_spent >= discount_levels[0] and money_spent < discount_levels[1]:\n discount = discount_percents[0]\n elif money_spent >= discount_levels[1] and money_spent < discount_levels[2]:\n discount = discount_percents[1]\n elif money_spent >= discount_levels[2]:\n discount = discount_percents[2]\n else:\n discount = \"0%\"\n\n discounts[customer_name] = discount\n\n return discounts","def pre_get_data(df):\n\n df_len = len(df.iloc[0, :]) - 1\n\n select_cols = []\n\n for i in range(df_len): #Get Columns that contain number values\n\n if type(df.iloc[0, i + 1]) is np.float64:\n if math.isnan(df.iloc[0, i + 1]) == False:\n select_cols.append(i + 1)\n elif type(df.iloc[0, i + 1]) is np.float:\n if math.isnan(df.iloc[0, i + 1]) == False:\n select_cols.append(i + 1)\n\n\n res_df = df.iloc[:, select_cols]\n\n list_pop = list(res_df)\n list_res = ['B_F1_Bool_Result', 'Event_Date', 'B_WClass']\n list_pop.pop()\n\n for item in list_pop:\n if \"F1\" in item:\n aa = item\n bb = aa.replace(\"F1\", \"F2\")\n if bb in list_pop:\n cc = aa.replace(\"F1\", \"F12\")\n df[cc] = df[aa] - df[bb]\n list_res.append(cc)\n\n elif \"F2\" not in item:\n list_res.append(item)\n\n\n\n bw = df['B_WClass']\n i = -1\n j = df.columns.get_loc('B_WClass')\n\n for item in bw:\n i = i + 1\n if item != item:\n df.iloc[i, j] = np.nan\n else:\n df.iloc[i, j] = get_weight[item]\n\n df['B_WClass'] = df['B_WClass'].astype(float)\n res_df = df[list_res]\n\n return res_df","def data():\n df = gen_sliced_df()\n df = df[[\"x\", \"z_categ\", \"y\", \"residual\"]]\n new_df = df.iloc[[1, 100, 150, 200, 250, 300, 305, 400, 405, 500, 550, 609]].copy()\n return {\"df\": df, \"new_df\": new_df}","def extract(self) -> pd.core.frame.DataFrame:\n pass","def get_metadata(sf):\r\n data = {}\r\n all_describe = sf.describe()\r\n\r\n s_objs = all_describe['sobjects']\r\n # scan the objects and save to a list...\r\n for obj in s_objs:\r\n row = {}\r\n row['name'] = obj['name']\r\n row['label'] = obj['label']\r\n row['custom'] = obj['custom']\r\n row['activateable'] = obj['activateable']\r\n row['keyPrefix'] = obj['keyPrefix']\r\n row['labelPlural'] = obj['labelPlural']\r\n\r\n row['raw'] = obj\r\n\r\n logger.info('\\t{}\\t-> {}'.format(obj['label'], obj['name']))\r\n data[row['name']] = row\r\n\r\n return data","def get_market_inf():\n r = requests.get(vs.DSE_URL+vs.DSE_MARKET_INF_URL)\n soup = BeautifulSoup(r.text, 'html.parser')\n quotes = [] # a list to store quotes\n\n table = soup.find('table', attrs={'class': 'table table-bordered background-white text-center', '_id': 'data-table'})\n\n for row in table.find_all('tr')[1:]:\n cols = row.find_all('td')\n quotes.append({'Date': cols[0].text.strip().replace(\",\", \"\"),\n 'Total Trade': cols[1].text.strip().replace(\",\", \"\"),\n 'Total Volume': cols[2].text.strip().replace(\",\", \"\"),\n 'Total Value (mn)': cols[3].text.strip().replace(\",\", \"\"),\n 'Total Market Cap. (mn)': cols[4].text.strip().replace(\",\", \"\"),\n 'DSEX Index': cols[5].text.strip().replace(\",\", \"\"),\n 'DSES Index': cols[6].text.strip().replace(\",\", \"\"),\n 'DS30 Index': cols[7].text.strip().replace(\",\", \"\"),\n 'DGEN Index': cols[8].text.strip().replace(\",\", \"\")\n })\n df = pd.DataFrame(quotes)\n return df","def sample_meta(self):\n return self._data.iloc[:, self.n_genes :]","def get_scandata(scandict):\n headers = scandict[\"Header\"].split(\",\")\n scannames = [i for i in scandict.keys() if not i == \"Header\"]\n no_scans = len(scannames)\n df = pd.DataFrame(columns=headers, index=scannames)\n for s in scannames:\n scandata = re.findall(r'\".+?\"|[\\w-]+', scandict[s])\n df.loc[s, headers[: len(scandata)]] = scandata\n\n for c in [\n \"Description\",\n \"Vertex List\",\n \"Preablation Settings\",\n \"Ablation Settings\",\n ]:\n df[c] = df[c].str.replace('\"', \"\")\n\n df[\"Vertex List\"] = (\n df[\"Vertex List\"]\n .str.split(\";\")\n .apply(lambda x: np.array([i.split(\",\") for i in x]))\n )\n df[\"Preablation Settings\"] = df[\"Preablation Settings\"].apply(split_config)\n df[\"Ablation Settings\"] = df[\"Ablation Settings\"].apply(split_config)\n df[\"Data\"] = df[\"Data\"].apply(split_config)\n return df","def information(self):\n \n \n x = list(zip(self.df.count(), self.df.dtypes, (self.df.isnull().sum() / self.df.shape[0])))\n y = dict(zip(self.df.columns, x))\n return pd.DataFrame(y, index=['Number of Values', 'Data Type', 'Percent Missing']).transpose()","def make_claim_df(claim_list, columns = ['Sl','Name of Bank','Name of Branch','A/C Number (15 digit)','A/C Title','Amount of Remittance in BDT','Date of A/C Credit','Remittance Received through BEFTN/RTGS','Name of Remittance Collecting/BEFTN Processing Bank','Date of Claim']):\n sl=[]\n nrbc_bank = []\n branch = []\n ac_no = []\n ac_title = []\n amount=[]\n date_account_credit=[]\n channel = []\n other_bank=[]\n claim_date=[]\n i=1\n for claim in claim_list:\n sl.append(i)\n i=i+1\n nrbc_bank.append(\"NRBC Bank Ltd.\")\n branch.append(claim.branch.name.upper())\n ac_no.append(claim.account_no)\n ac_title.append(claim.account_title)\n amount.append(claim.remittance_amount)\n date_account_credit.append(claim.date_account_credit)\n channel.append(claim.get_channel_display())\n other_bank.append(claim.collecting_bank.name)\n claim_date.append(claim.date_claim.date())\n dc = {\n 'SL':sl,\n 'Name of Bank':nrbc_bank,\n 'Name of Branch': branch,\n 'A/C Number': ac_no,\n 'A/C Title': ac_title,\n 'Amount of Remittance in BDT': amount,\n 'Date of A/C Credit': date_account_credit,\n 'Remittance Received Through BEFTN/RTGS': channel,\n 'Name of Remittance Processing Bank': other_bank,\n 'Date of Claim': claim_date\n }\n df = pd.DataFrame(dc)\n return df.sort_values(by=['Name of Remittance Processing Bank',])","def get_price_info(price_filename, commodity):\n prices = []\n prices = pd.read_csv(price_filename, sep=' ', index_col=0)\n\n five_day_avg = pd.Series(pd.rolling_mean(prices[commodity], 5), name='five_day_avg')\n ten_day_avg = pd.Series(pd.rolling_mean(prices[commodity], 10), name='ten_day_avg')\n thirty_day_avg = pd.Series(pd.rolling_mean(prices[commodity], 30), name='thirty_day_avg')\n \n price_info = pd.DataFrame(pd.concat([prices, five_day_avg, ten_day_avg, thirty_day_avg], axis=1))\n \n price_diffs = prices[commodity].diff()\n # price_changes_series = pd.Series(np.array(price_diffs > 0), dtype=int, index=price_info.index.values)\n price_changes_series = pd.Series([0.333 if (np.isnan(x) or np.isnan(y)) else 100000.0 * x / y for (x, y) in zip(price_diffs, prices[commodity])], dtype=int, index=price_info.index.values)\n price_changes_series = prices[commodity]\n return price_info, price_changes_series","def apifields(sample):\n metadata\n sel = [\n metadata.ID,\n metadata.Count_of_Masked_Loan_Number,\n # metadata.Note_Sale_NPL,\n # metadata.Short_Payoff,\n # metadata.REO_Sale_DIL,\n # metadata.Foreclosure_Auction,\n # metadata.Full_Payoff,\n # metadata.Note_Sale_PL\n ]\n\n results = db.session.query(*sel).filter(metadata.ID == sample).all()\n\n # Create a dictionary entry for each row of metadata information\n apifields = {}\n for result in results:\n apifields[\"ID\"] = result[0]\n apifields[\"Count_of_Masked_Loan_Number\"] = result[1]\n # apifields[\"Note_Sale_NPL\"] = result[2]\n # apifields[\"Short_Payoff\"] = result[3]\n # apifields[\"REO_Sale_DIL\"] = result[4]\n # apifields[\"Foreclosure_Auction\"] = result[5]\n # apifields[\"Full_Payoff\"] = result[6]\n # apifields[\"Note_Sale_PL\"] = result[7]\n\n print(apifields)\n return jsonify(apifields)","def get_info(self, frame, headings):\n return self.df.loc[frame, headings].values","def _read_dataset(self):\n import pandas as pd\n\n freesolv_path = get_data_file_path(FREESOLV_PATH)\n\n freesolv_db = pd.read_csv(freesolv_path, delimiter=';',\n skipinitialspace=True,\n skiprows=[0, 1, 2], header=0,\n names=['compound id', 'SMILES',\n 'iupac name',\n 'experimental value',\n 'experimental uncertainty',\n 'calculated value (GAFF)',\n 'calculated uncertainty',\n 'experimental reference',\n 'calculated reference',\n 'notes'])\n\n compound_ids = freesolv_db['compound id'].to_list()\n smiles_tags = freesolv_db['SMILES'].to_list()\n experimental_v = freesolv_db['experimental value'].to_list()\n return compound_ids, smiles_tags, experimental_v","def _unpack_sentiment_data(self):\n get_neg = lambda x: x.get('probability').get('neg')\n get_pos = lambda x: x.get('probability').get('pos')\n get_neutral = lambda x: x.get('probability').get('neutral')\n get_label = lambda x: x.get('label')\n self.dataframe['negative_sentiment'] = self.dataframe['sentiment'].map(get_neg)\n self.dataframe['positive_sentiment'] = self.dataframe['sentiment'].map(get_pos)\n self.dataframe['neutral_sentiment'] = self.dataframe['sentiment'].map(get_neutral)\n self.dataframe['sentiment_label'] = self.dataframe['sentiment'].map(get_label)","def ca_to_coils_second_df(agent_df):","def read(self):\n self._load_metadata()\n return self._df.compute()","def fetch_bank_df(preprocess=False):\n (train_X, train_y), (test_X, test_y) = lale.datasets.openml.fetch(\n \"bank-marketing\", \"classification\", astype=\"pandas\", preprocess=preprocess\n )\n orig_X = pd.concat([train_X, test_X]).sort_index()\n orig_y = pd.concat([train_y, test_y]).sort_index().astype(np.float64)\n column_map = {\n \"v1\": \"age\",\n \"v2\": \"job\",\n \"v3\": \"marital\",\n \"v4\": \"education\",\n \"v5\": \"default\",\n \"v6\": \"balance\",\n \"v7\": \"housing\",\n \"v8\": \"loan\",\n \"v9\": \"contact\",\n \"v10\": \"day\",\n \"v11\": \"month\",\n \"v12\": \"duration\",\n \"v13\": \"campaign\",\n \"v14\": \"pdays\",\n \"v15\": \"previous\",\n \"v16\": \"poutcome\",\n }\n if preprocess:\n\n def map_col(col):\n if col.find(\"_\") == -1:\n return column_map[col]\n prefix, suffix = col.split(\"_\")\n return column_map[prefix] + \"_\" + suffix\n\n orig_X.columns = [map_col(col) for col in orig_X.columns]\n age = pd.Series(orig_X[\"age\"] >= 25, dtype=np.float64)\n encoded_X = orig_X.assign(age=age)\n encoded_y = pd.Series(orig_y == 0, dtype=np.float64, name=orig_y.name)\n fairness_info = {\n \"favorable_labels\": [1],\n \"protected_attributes\": [\n {\"feature\": \"age\", \"reference_group\": [1]},\n ],\n }\n return encoded_X, encoded_y, fairness_info\n else:\n orig_X.columns = [column_map[col] for col in orig_X.columns]\n fairness_info = {\n \"favorable_labels\": [1],\n \"protected_attributes\": [\n {\"feature\": \"age\", \"reference_group\": [[25, 1000]]},\n ],\n }\n return orig_X, orig_y, fairness_info","def get_dataframe() -> pandas.DataFrame:\n database_connection = processing.establish_connection(database_path)\n dataframe = database_connection.to_dataframe(['CustomerId', 'InvoiceDate', 'Total'], table_name)\n database_connection.close()\n dataframe = processing.get_invoice_date_fixed(dataframe)\n analyze_dataframe = dataframe.copy()\n total_sum_dataframe = processing.get_column_sum(analyze_dataframe)\n\n customer_count_dataframe = processing.drop_duplicates(analyze_dataframe)\n customer_count_dataframe = processing.get_column_count(customer_count_dataframe)\n return customer_count_dataframe.merge(total_sum_dataframe, how='inner', on='InvoiceDate')","def process_book_extra_info(\n book_extra_info_rows: List[Tuple[int, str, str]]\n) -> pd.DataFrame:\n book_info_labels = ['work_id', 'isbn13', 'description']\n book_extra_info_df = pd.DataFrame.from_records(\n book_extra_info_rows, columns=book_info_labels)\n\n html_tag_regexp = '<[^<]+?>'\n multiple_whitespaces_regexp = r\"\\s\\s+\"\n book_extra_info_df.description = book_extra_info_df.description.astype(\n str).str.replace(html_tag_regexp, ' ')\n book_extra_info_df.description = book_extra_info_df.description.astype(\n str).str.replace(multiple_whitespaces_regexp, ' ')\n\n return book_extra_info_df","def data_process_deprecated(self, CustomerID, InvoiceDate, InvoiceNo, Description, Quantity\\\n , UnitPrice ):\n dict_invoice = {'InvoiceDate':InvoiceDate, 'Description':Description\\\n , 'Quantity':Quantity, 'UnitPrice':UnitPrice}\n dict_invoice['CustomerID'] = CustomerID\n dict_invoice['InvoiceNo'] = InvoiceNo\n df_invoice_line \\\n = pd.DataFrame(dict_invoice, columns=dict_invoice.keys(), index=[0])\n \n self.data_transform(df_invoice_line)\n\n #self.feature_rfm_encode()\n\n self.feature_scale()\n\n self.list_feature_drop()\n\n self.feature_description_nlp()\n return","def clean_qpcr_data(df) -> pd.DataFrame:\n ASSAYDATE_COL = \"Assay Date\"\n INSTRUMENT_COL = \"Instrument\"\n POOL_COL = \"Pool\"\n\n # Names and types of all our columns. We also add the columns INSTRUMENT_COL and POOL_COL\n original_columns = [ASSAYDATE_COL, DATE_COL, \"Sample ID\", \"GENE\", \"Ct [1]\", \"Ct [2]\", \"Ct [3]\", \"Ct Avg\", \"Ct Stdev\", \"Copies [1]\", \"Copies [2]\", \"Copies [3]\", \"Copies AVG\", \"Copies Stdev\", \"PMMoV Ct [1]\", \"PMMoV Ct [2]\", \"PMMoV Ct [3]\", \"PMMoV Avg\", \"PMMoV Stdev\", \"Empty tube weight (g)\", \"Full tube weight (g)\", \"Pellet weight (g)\", \"Extracted Mass (in 100 uL) (g)\", \"Copies per Extracted Mass (copies/g) [1]\", \"Copies per Extracted Mass (copies/g) [2]\", \"Copies per Extracted Mass (copies/g) [3]\", \"Copies per Extracted Mass (copies/g) Avg\", \"Copies per Extracted Mass Stdev\", \"2^Ct\", \"2^Ct normalized to a value\", \"(2^Ct normalized to a value per Extracted Mass)\", \"PMMoV Copies [1]\", \"PMMoV Copies [2]\", \"PMMoV Copies [3]\", \"PMMoV Copies Avg\", \"PMMoV Copies Stdev\", \"Copies per Copies of PMMoV Avg\", \"Copies per Copies of PMMoV * 10^3 Avg\", \"Copies per Copies normalized to a value\", \"Copies per Copies of PMMoV Stdev\", \"Copies per Copies of PMMoV * 10^3 Stdev\", \"Copies per Copies normalized to a value Stdev\", \"Date [2]\", \"Gene\", \"APPROVED: Copies per Copies of PMMoV [1]\", \"APPROVED: Copies per Copies of PMMoV [2]\", \"APPROVED: Copies per Copies of PMMoV [3]\", \"APPROVED: Copies per Copies of PMMoV Avg\", \"APPROVED: Copies per Copies of PMMoV Stdev\", \"APPROVED: Copies per Extracted Mass (copies/g) [1]\", \"APPROVED: Copies per Extracted Mass (copies/g) [2]\", \"APPROVED: Copies per Extracted Mass (copies/g) [3]\", \"APPROVED: Copies per Extracted Mass (copies/g) Avg\", \"APPROVED: Copies/L [1]\", \"APPROVED: Copies/L [2]\", \"APPROVED: Copies/L [3]\", \"APPROVED: Copies/L Avg\", \"MESP UPLOAD: PMMoV Copies per Extracted Mass (copies/g)\", \"MESP UPLOAD: PMMoV Copies/L\", \"INHIBITION CTRL: Date\", \"INHIBITION CTRL: Sample Name\", \"INHIBITION CTRL: Pepper 1/10 [1]\", \"INHIBITION CTRL: Pepper 1/10 [2]\", \"INHIBITION CTRL: Pepper 1/10 [3]\", \"INHIBITION CTRL: Pepper 1/10 Avg\", \"INHIBITION CTRL: Pepper 1/40 [1]\", \"INHIBITION CTRL: Pepper 1/40 [2]\", \"INHIBITION CTRL: Pepper 1/40 [3]\", \"INHIBITION CTRL: Pepper 1/40 Avg\", \"INHIBITION CTRL: Pepper No Dilution [1]\", \"INHIBITION CTRL: Pepper No Dilution [2]\", \"INHIBITION CTRL: Pepper No Dilution [3]\", \"INHIBITION CTRL: Pepper No Dilution Avg\", \"Pepper 1/10 ΔCt\", \"Pepper 1/40 ΔCt\"]\n original_types = [\"date\", \"date\", \"text\", \"text\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"date\", \"text\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"date\", \"text\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\"]\n new_columns = original_columns.copy()\n new_columns[2:2] = [INSTRUMENT_COL, POOL_COL]\n new_types = original_types.copy()\n new_types[2:2] = [\"text\", \"text\"]\n\n # Remove unwanted columns at end of dataframe, and add new columns in the correct order\n df = df[df.columns[:len(original_columns)]].copy()\n df.columns = original_columns\n df[list(set(new_columns) - set(original_columns))] = None\n df = df[new_columns]\n\n # Go through all rows and clean them, assign appropriate values for current instrument, pool, assay date, etc\n cur_instrument, cur_pool = None, None\n cur_assaydate = None\n has_inhibition_controls = False\n has_copies_per_liter = False\n for i,row in df.iterrows():\n cur_instrument, cur_pool = get_inst_and_pool(row, cur_instrument, cur_pool)\n if row[ASSAYDATE_COL]:\n row_date = parse_date(row[ASSAYDATE_COL])\n cur_assaydate = row_date or cur_assaydate\n if row_date is not None:\n has_inhibition_controls = False\n has_copies_per_liter = False\n\n if np.any([\"Pepper 1/10\" in c for c in row if isinstance(c, str)]):\n has_inhibition_controls = True\n if np.any([\"Copies / VS\" in c or \"Copies / L\" in c for c in row if isinstance(c, str)]):\n has_copies_per_liter = True\n df.loc[i, ASSAYDATE_COL] = cur_assaydate\n df.loc[i, INSTRUMENT_COL] = cur_instrument\n df.loc[i, POOL_COL] = cur_pool\n if not has_inhibition_controls:\n df.loc[i, [c for c in df.columns if c.startswith(\"INHIBITION CTRL: \")]] = \"\"\n if not has_copies_per_liter:\n index = [idx for idx, c in enumerate(df.columns) if c.startswith(\"APPROVED: Copies/L\")][0]\n df.loc[i, df.columns[index:]] = \"\"\n\n # Remove invalid rows (where no valid \"Date\" is specified)\n df[DATE_COL] = pd.to_datetime(df[DATE_COL], errors=\"coerce\")\n df = df.loc[~df[DATE_COL].isna()]\n\n # 6 rows have a year of 1900 (from \"Septembe 27th\" assay date), change these to 2020.\n # 12 rows have a year of 2014 (from \"Septembe 17th\" assay date), change these to 2020.\n f = (df[DATE_COL].dt.year == 1900) | (df[DATE_COL].dt.year == 2014)\n df.loc[f, DATE_COL] = pd.to_datetime(df.loc[f, DATE_COL].dt.strftime(\"2020-%m-%d\"), format=\"%Y-%m-%d\")\n\n # Rows from assay date \"Tuesday, December 22, 2020\" and \"Wednesday, December 23, 2020\" have incorrect\n # year in \"Date\" column (2021 instead of 2020)\n df[ASSAYDATE_COL] = pd.to_datetime(df[ASSAYDATE_COL], errors=\"coerce\")\n f = (df[ASSAYDATE_COL] == pd.to_datetime(\"December 22, 2020\")) | (df[ASSAYDATE_COL] == pd.to_datetime(\"December 23, 2020\"))\n df.loc[f, DATE_COL] = pd.to_datetime(df.loc[f, DATE_COL].dt.strftime(\"2020-%m-%d\"), format=\"%Y-%m-%d\")\n\n # Add row at start that specifies the column types (date, text, number)\n df.loc[-1] = new_types\n df.index = df.index + 1\n df = df.sort_index()\n df.index = list(range(len(df.index)))\n\n return df","def fetch_creditg_df(preprocess=False):\n (train_X, train_y), (test_X, test_y) = lale.datasets.openml.fetch(\n \"credit-g\", \"classification\", astype=\"pandas\", preprocess=preprocess\n )\n orig_X = pd.concat([train_X, test_X]).sort_index()\n orig_y = pd.concat([train_y, test_y]).sort_index()\n if preprocess:\n sex = pd.Series(\n (orig_X[\"personal_status_male div/sep\"] == 1)\n | (orig_X[\"personal_status_male mar/wid\"] == 1)\n | (orig_X[\"personal_status_male single\"] == 1),\n dtype=np.float64,\n )\n age = pd.Series(orig_X[\"age\"] > 25, dtype=np.float64)\n dropped_X = orig_X.drop(\n labels=[\n \"personal_status_female div/dep/mar\",\n \"personal_status_male div/sep\",\n \"personal_status_male mar/wid\",\n \"personal_status_male single\",\n ],\n axis=1,\n )\n encoded_X = dropped_X.assign(sex=sex, age=age)\n fairness_info = {\n \"favorable_labels\": [1],\n \"protected_attributes\": [\n {\"feature\": \"sex\", \"reference_group\": [1]},\n {\"feature\": \"age\", \"reference_group\": [1]},\n ],\n }\n return encoded_X, orig_y, fairness_info\n else:\n fairness_info = {\n \"favorable_labels\": [\"good\"],\n \"protected_attributes\": [\n {\n \"feature\": \"personal_status\",\n \"reference_group\": [\n \"male div/sep\",\n \"male mar/wid\",\n \"male single\",\n ],\n },\n {\"feature\": \"age\", \"reference_group\": [[26, 1000]]},\n ],\n }\n return orig_X, orig_y, fairness_info","def fetch_ricci_df(preprocess=False):\n (train_X, train_y), (test_X, test_y) = lale.datasets.openml.fetch(\n \"ricci\", \"classification\", astype=\"pandas\", preprocess=preprocess\n )\n orig_X = pd.concat([train_X, test_X]).sort_index()\n orig_y = pd.concat([train_y, test_y]).sort_index()\n if preprocess:\n race = pd.Series(orig_X[\"race_W\"] == 1, dtype=np.float64)\n dropped_X = orig_X.drop(labels=[\"race_B\", \"race_H\", \"race_W\"], axis=1)\n encoded_X = dropped_X.assign(race=race)\n fairness_info = {\n \"favorable_labels\": [1],\n \"protected_attributes\": [{\"feature\": \"race\", \"reference_group\": [1]}],\n }\n return encoded_X, orig_y, fairness_info\n else:\n fairness_info = {\n \"favorable_labels\": [\"Promotion\"],\n \"protected_attributes\": [{\"feature\": \"race\", \"reference_group\": [\"W\"]}],\n }\n return orig_X, orig_y, fairness_info","def do_statistics(df):\n length = len(df.index)\n df['estimated_sigma'] = [None] * length\n df['spot_price_using_estimation'] = [None] * length\n df['gap'] = [None] * length","def _extract_metadata(self, header, cleaner):\n metadata = []\n for k, v in header.items():\n key = str(cleaner(k)) # clean key and ensure it is a string\n val = str(cleaner(v)) # clean value and ensure it is a string\n if (key and val):\n metadata.append(Metadatum(key, val))\n return metadata","def _get_metadata(dataset, path_to_cdf):\n metadata = []\n cdf = cdflib.CDF(path_to_cdf)\n if dataset=='SOHO_ERNE-HED_L2-1MIN' or dataset=='SOHO_ERNE-LED_L2-1MIN':\n if dataset=='SOHO_ERNE-HED_L2-1MIN':\n m = 'H'\n if dataset=='SOHO_ERNE-LED_L2-1MIN':\n m = 'L'\n metadata = {'He_E_label': cdf.varget('He_E_label')[0],\n 'He_energy': cdf.varget('He_energy'),\n 'He_energy_delta': cdf.varget('He_energy_delta'),\n f'A{m}_LABL': cdf.varattsget(f'A{m}')['LABLAXIS'],\n f'A{m}_UNITS': cdf.varattsget(f'A{m}')['UNITS'],\n f'A{m}_FILLVAL': cdf.varattsget(f'A{m}')['FILLVAL'],\n 'P_E_label': cdf.varget('P_E_label')[0],\n 'P_energy': cdf.varget('P_energy'),\n 'P_energy_delta': cdf.varget('P_energy_delta'),\n f'P{m}_LABL': cdf.varattsget(f'P{m}')['LABLAXIS'],\n f'P{m}_UNITS': cdf.varattsget(f'P{m}')['UNITS'],\n f'P{m}_FILLVAL': cdf.varattsget(f'P{m}')['FILLVAL'],\n }\n\n channels_dict_df_He = pd.DataFrame(cdf.varget('He_E_label')[0], columns=['ch_strings'])\n channels_dict_df_He['lower_E'] = cdf.varget(\"He_energy\")-cdf.varget(\"He_energy_delta\")\n channels_dict_df_He['upper_E'] = cdf.varget(\"He_energy\")+cdf.varget(\"He_energy_delta\")\n channels_dict_df_He['DE'] = cdf.varget(\"He_energy_delta\")\n # channels_dict_df_He['mean_E'] = np.sqrt(channels_dict_df_He['upper_E'] * channels_dict_df_He['lower_E'])\n channels_dict_df_He['mean_E'] = cdf.varget(\"He_energy\")\n\n channels_dict_df_p = pd.DataFrame(cdf.varget('P_E_label')[0], columns=['ch_strings'])\n channels_dict_df_p['lower_E'] = cdf.varget(\"P_energy\")-cdf.varget(\"P_energy_delta\")\n channels_dict_df_p['upper_E'] = cdf.varget(\"P_energy\")+cdf.varget(\"P_energy_delta\")\n channels_dict_df_p['DE'] = cdf.varget(\"P_energy_delta\")\n # channels_dict_df_p['mean_E'] = np.sqrt(channels_dict_df_p['upper_E'] * channels_dict_df_p['lower_E'])\n channels_dict_df_p['mean_E'] = cdf.varget(\"P_energy\")\n\n metadata.update({'channels_dict_df_He': channels_dict_df_He})\n metadata.update({'channels_dict_df_p': channels_dict_df_p})\n return metadata","def process_input_df(df_raw, market, price_col_id):\n # drop all non required columns from data frame\n df = drop_columns(df_raw, [price_col_id])\n\n # rename last price column to uniform convention\n df = df.rename(columns={price_col_id: 'close'})\n\n # calculate required indicators\n df[market + '_rt1'] = np.log(df['close']).diff().shift(1) * 100\n df[market + '_asy5_r'] = df[market + '_rt1'].rolling(window=5).sum() / 5\n df[market + '_asy4_r'] = df[market + '_rt1'].rolling(window=4).sum() / 4\n df[market + '_asy3_r'] = df[market + '_rt1'].rolling(window=3).sum() / 3\n df[market + '_asy2_r'] = df[market + '_rt1'].rolling(window=2).sum() / 2\n\n return df","def get_summary(self, df):\n results_df = pd.DataFrame({'Energy kWh': self.get_all_periods(df).sum()})\n results_df['Prices $/kWh'] = self.deliveryPrice + self.get_rates()\n results_df['Value $'] = results_df['Energy kWh'] * results_df['Prices $/kWh']\n return(results_df)","def add_all_features(df):\n df.reset_index(drop=True, inplace=True)\n df = target_indicators(df)\n df = momentum_indicators(df)\n df = trend_indicators(df)\n df = volatility_indicators(df)\n df = volume_indicators(df)\n df = special_indicators(df)\n return df","def extract(self):\n \n print('Extracting Metrics data... ',end=''),\n self.df = pd.read_excel(self.file_path, index_col=0)\n print('Done')","def get_dataset(reader: DataReader):\n\n xs = []\n ys = []\n\n for annotation_sentences in reader.annotations:\n for annotation in annotation_sentences:\n xs.append([annotation.fee_raw] + annotation.sentence)\n ys.append(annotation.frame)\n\n return xs, ys","def load_data(self, identifier):\n propertyType = self.soup.find('h2', class_='listing-details-h1').text\n propertyType = propertyType.replace(' for sale', '')\n beds = re.findall(r'\\d{1} bed', propertyType)\n if beds == []:\n # get first word\n beds = '1 bed'\n else:\n beds = beds[0]\n # get general info of property\n propertyType = propertyType.replace(str(beds) + ' ', '')\n address = self.soup.find('h2', {'itemprop':'streetAddress'}).text \n postCode = address.rsplit(None, 1)[-1]\n address = address.replace(postCode, '')\n latLong = self.soup.find('meta', {'itemprop':'latitude'}).attrs\n latitude = latLong['content']\n latLong = self.soup.find('meta', {'itemprop':'longitude'}).attrs\n longitude = latLong['content']\n # original price and changes to price are in sidebar\n for sidebar in self.soup.find_all('div', {\"class\":sCLASS_SECTION}):\n count = 0\n # price info in sidebar called 'Listing history' \n if sidebar.find(text=sSB_NAME):\n try:\n originalPriceAndDate = sidebar.find(text=sFIRST_LISTED).next_element.next_element\n originalPrice = originalPriceAndDate[:originalPriceAndDate.find(' on')]\n originalPrice = self.find_number(originalPrice)\n originalDate = originalPriceAndDate[originalPriceAndDate.find(' on') + 3:]\n originalDate = self.get_date(originalDate)\n # store original listing - in same order as headers!\n result = [identifier + \"_\" + str(count), originalDate, np.NaN, originalPrice, np.NaN, \n beds, propertyType, postCode, address, latitude, longitude, self.url]\n series = pd.Series(result, name=identifier + \"_\" + str(count), index=headers)\n# self.df = self.df.append(pd.Series(result, index=headers), ignore_index=True) \n# self.df = self.df.append(series) \n# self.df = pd.concat([self.df, series])\n self.df = self.df.append(series, ignore_index=False)\n except (AttributeError, UnboundLocalError):\n print('Error viewing this property')\n pass\n try:\n # store any changes to original listing\n for changes in sidebar.find_all('ul', {'class':sCHANGES}):\n for change in changes.find_all('li'):\n count += 1\n # get date and new price\n date = change.find('span').text\n date = date.replace('Reduced on:', '')\n date = date.replace('\\n', '')\n date = self.get_date(date)\n newPrice = self.find_number(change.next_element)\n # store result in order of headers\n result = [identifier + \"_\" + str(count), originalDate, date, originalPrice, newPrice, \n beds, propertyType, postCode, address, latitude, longitude, self.url]\n series = pd.Series(result, name=identifier + \"_\" + str(count), index=headers) \n# self.df = self.df.append(pd.Series(result, index=headers), ignore_index=True)\n self.df = self.df.append(series, ignore_index=False)\n# self.df = pd.concat([self.df, series])\n except (AttributeError, UnboundLocalError):\n print('No changes')\n pass","def create_df_recommendations(api_results):\r\n track_name = []\r\n track_id = []\r\n artist = []\r\n album = []\r\n duration = []\r\n popularity = []\r\n for items in api_results['tracks']:\r\n try:\r\n track_name.append(items['name'])\r\n track_id.append(items['id'])\r\n artist.append(items[\"artists\"][0][\"name\"])\r\n duration.append(items[\"duration_ms\"])\r\n album.append(items[\"album\"][\"name\"])\r\n popularity.append(items[\"popularity\"])\r\n except TypeError:\r\n pass\r\n df = pd.DataFrame({ \"track_name\": track_name, \r\n \"album\": album, \r\n \"track_id\": track_id,\r\n \"artist\": artist, \r\n \"duration\": duration, \r\n \"popularity\": popularity})\r\n\r\n return df","def respuesta(response):\n for report in response.get('reports', []):\n\n columnHeader = report.get('columnHeader', {})\n dimensionHeaders = columnHeader.get('dimensions', [])\n metricHeaders = columnHeader.get(\n 'metricHeader', {}).get('metricHeaderEntries', [])\n\n return_data = []\n\n for row in report.get('data', {}).get('rows', []):\n\n dimensions = row.get('dimensions', [])\n dateRangeValues = row.get('metrics', [])\n pipeline_insert = {}\n for header, dimension in zip(dimensionHeaders, dimensions):\n pipeline_insert[header] = dimension\n\n for i, values in enumerate(dateRangeValues):\n\n for metricHeader, value in zip(metricHeaders, values.get('values')):\n pipeline_insert[metricHeader.get('name')] = value\n return_data.append(pipeline_insert)\n\n return return_data","def process_to_dataframe(chkpts, tags=['final_energy', 'job_name']):\n\n pass","def get_recipe_dataset():\n df = pd.read_csv(FILENAME)\n print(\"# of rows - no filtering: %s\" % len(df))\n\n df = df.head(500)\n print(\"# of rows - reduce size of DF for testing: %s\" % len(df))\n\n return df","def get_data(self)->pd.DataFrame:\n pass","def general_analysis(df):\n pass","def data_transform(self, df) :\n\n #-------------------------------------------------------------------------\n # Copy of given dataframe to be transformed\n #-------------------------------------------------------------------------\n self.df_invoice_line = df\n \n #-------------------------------------------------------------------------\n # Features issued from InvoiceDate are created\n #-------------------------------------------------------------------------\n if self.is_transform_timeFeature is True:\n self.strprint(\"\\n*** Time features transformation ***\")\n self.data_transform_timeFeature()\n\n #-------------------------------------------------------------------------\n # RFM is computed and encoded\n #-------------------------------------------------------------------------\n if self.is_transform_rfm is True:\n self.strprint(\"\\n*** RFM transformation ***\")\n self.data_transform_rfm()\n\n #-------------------------------------------------------------------------\n # NLP features issued from Description are created\n #-------------------------------------------------------------------------\n if self.is_transform_nlp is True:\n self.strprint(\"\\n*** NLP transformation ***\")\n self.data_transform_nlp()\n \n return self.df_invoice_line","def _extract(self):\r\n self._data = []\r\n for fname in self.files:\r\n meta = dict(filename=fname)\r\n\r\n # Perform the actual metadata extraction\r\n fname = os.path.splitext(self.filter_filename(fname))[0]\r\n values = fname.split(self.sep)\r\n\r\n # Handle the case where number of fields is less than the length\r\n # of the extracted values, ie cases where we only want to extract\r\n # a subset of available fields.\r\n if self.index:\r\n values = [val for i, val in enumerate(values) if i in self.index]\r\n\r\n meta.update(dict(zip(self.fields, values)))\r\n if self.split_by in self.fields:\r\n meta[self.split_by] = self._get_split_field_values(meta['filename'])\r\n self._data.append(meta)","def _dataframe_preprocess(self):\n # 1. add baisc feature like date, time in day, ....\n if self.data_type != 'porto':\n self.df['TIMESTAMP'] = self.df.apply(lambda df: df['TIMESTAMPS'][0], axis=1)\n self.df['TIME'] = pd.to_datetime(self.df['TIMESTAMP'], unit='s', utc=True)\n \n self.df.TIME = self.df.TIME.dt.tz_convert(self.timezone)\n # 2. group df for specific driver analysis\n self.grouped_df = self.df.groupby('LABEL')\n if self.count_od_info:\n if 'SD' not in self.df.columns:\n self._add_OD_info()\n self.grouped_od = self.df.groupby('SD')","def data(self):\n dfdata = pd.concat([self.weights, self.returns, self.category], axis=1)\n dfdata.columns = ['weights', 'returns', self.category_name]\n if self.period is not None:\n dfdata['date'] = self.period\n return dfdata","def parse_df(data):\n\n # First column should be the ids\n ids = list(data.iloc[:, 0])\n\n # Second column should hold the labels\n labels = list(data.iloc[:, 1])\n\n # From third columns, we should have the features\n features = list(data.iloc[:, 2:].values)\n\n return ids, labels, features","def get_Metadata(metafile):\n\n mslist_file = open(metafile, 'r')\n LINES = mslist_file.readlines()\n mslist_file.close()\n\n nBlocks = 6 # these are the number of correlator cards (PILOT survey value)\n \n obs_date = 'Observed from'\n code = 'Code'\n duration = 'Total elapsed time'\n antenna = 'antennas'\n frame = 'Frame'\n \n for i in range(len(LINES)):\n line = LINES[i]\n if line.find(antenna) >=0:\n TOKS = line.split()\n n_ant = TOKS[5][-2:]\n if line.find(obs_date) >=0:\n TOKS = line.split()\n start_obs_date = TOKS[6]\n end_obs_date = TOKS[8]\n if line.find(duration) >=0:\n TOKS = line.split()\n tobs = float(TOKS[10]) # in second\n if line.find(code) >= 0:\n next_line = LINES[i+1]\n TOKS = next_line.split()\n field = TOKS[5]\n ra = TOKS[6][:-5]\n dec = TOKS[7][:-4]\n if line.find(frame) >= 0:\n next_line = LINES[i+1]\n TOKS = next_line.split()\n total_obs_bw = float(TOKS[10])*nBlocks/1000.0 # kHz to MHz \n \n return n_ant, start_obs_date, end_obs_date, tobs, field, ra, dec, total_obs_bw","def load_data(database_filepath):\n engine = create_engine('sqlite:///'+ database_filepath)\n df = pd.read_sql(\"SELECT * FROM DisasterResponse\", engine)\n #exclude colums that are not needed in model\n col=[i for i in df.columns if i not in ['id','original', 'genre']]\n X = df[\"message\"]\n Y = df.iloc[:,4:]\n #global category_names\n category_names = Y.columns\n return X, Y, category_names","def extract_details(df):\n df_RSinfo = df[['pentamer', 'Step details', 'RouteScore details',\n 'Isolated', 'RouteScore', 'log(RouteScore)']]\n\n last3_rxns = ['Buchwald_deprotection', 'Buchwald', 'SNAr']\n for rxn in last3_rxns:\n df_RSinfo[rxn] = [next(step for step in row[-3:] if step['reaction'] == rxn) for row in df['Step details']]\n\n for key in df_RSinfo['RouteScore details'][0].keys():\n df_RSinfo[key] = [row[key] for row in df['RouteScore details']]\n\n return df_RSinfo","def parse_metadata_file(self, file):\n \n file_keys = list(file.keys())\n \n if 'labelAnnotations' in file_keys:\n #file_annots = file['labelAnnotations'][:int(len(file['labelAnnotations']) * 0.5)]\n file_annots = file['labelAnnotations'][:]\n file_top_score = np.asarray([x['score'] for x in file_annots]).mean()\n file_top_desc = [x['description'] for x in file_annots]\n else:\n file_top_score = np.nan\n file_top_desc = ['']\n \n file_colors = file['imagePropertiesAnnotation']['dominantColors']['colors']\n file_crops = file['cropHintsAnnotation']['cropHints']\n\n file_color_score = np.asarray([x['score'] for x in file_colors]).mean()\n file_color_pixelfrac = np.asarray([x['pixelFraction'] for x in file_colors]).mean()\n\n file_crop_conf = np.asarray([x['confidence'] for x in file_crops]).mean()\n \n if 'importanceFraction' in file_crops[0].keys():\n file_crop_importance = np.asarray([x['importanceFraction'] for x in file_crops]).mean()\n else:\n file_crop_importance = np.nan\n\n df_metadata = {\n 'annots_score': file_top_score,\n 'color_score': file_color_score,\n 'color_pixelfrac': file_color_pixelfrac,\n 'crop_conf': file_crop_conf,\n 'crop_importance': file_crop_importance,\n 'annots_top_desc': self.sentence_sep.join(file_top_desc)\n }\n \n df_metadata = pd.DataFrame.from_dict(df_metadata, orient='index').T\n df_metadata = df_metadata.add_prefix('metadata_')\n \n return df_metadata","def desc_df(df):\n print(df.shape)\n print(df.columns)","def _process_data(self, jd, expiry):\n now = dt.datetime.now()\n\n columns = ['Last', 'Bid', 'Ask', 'Chg', 'PctChg', 'Vol',\n 'Open_Int', 'Root', 'Underlying_Price', 'Quote_Time']\n indexes = ['Strike', 'Expiry', 'Type', 'Symbol']\n rows_list, index = self._process_rows(jd, now, expiry)\n df = DataFrame(rows_list, columns=columns,\n index=MultiIndex.from_tuples(index, names=indexes))\n\n # Make dtype consistent, requires float64 as there can be NaNs\n df['Vol'] = df['Vol'].astype('float64')\n df['Open_Int'] = df['Open_Int'].astype('float64')\n\n return df.sort_index()","def read_meta(self):\n meta = cPickle.load(open('../sugar_analysis_data/META-CABALLO2.pkl'))\n self.meta_sn_name_list = []\n self.meta_zcmb = []\n self.meta_x0 =[]\n self.meta_x0_err = []\n self.meta_x1 =[]\n self.meta_x1_err = []\n self.meta_c = []\n self.meta_c_err = []\n self.meta_mb = []\n self.meta_mb_err = []\n self.meta_cov_x0_x1 = [] \n self.meta_cov_x0_c = []\n self.meta_cov_x1_c = []\n self.meta_cov_mb_x1 = []\n self.meta_cov_mb_c = [] \n self.meta_zhl = []\n self.meta_zhl_err = []\n self.meta_idr = []\n for meta_sn_name in meta.keys(): \n \n if meta[meta_sn_name]['idr.subset'] != 'bad' and meta[meta_sn_name]['idr.subset'] != 'auxiliary':\n \n self.meta_sn_name_list.append(meta_sn_name)\n self.meta_zhl_err.append(meta[meta_sn_name]['host.zhelio.err'])\n self.meta_zhl.append(meta[meta_sn_name]['host.zhelio'])\n self.meta_zcmb.append(meta[meta_sn_name]['host.zcmb'])\n self.meta_x0.append(meta[meta_sn_name]['salt2.X0'])\n self.meta_x0_err.append(meta[meta_sn_name]['salt2.X0.err'])\n self.meta_x1.append(meta[meta_sn_name]['salt2.X1'])\n self.meta_x1_err.append(meta[meta_sn_name]['salt2.X1.err'])\n self.meta_c.append(meta[meta_sn_name]['salt2.Color'])\n self.meta_c_err.append(meta[meta_sn_name]['salt2.Color.err'])\n self.meta_mb.append(meta[meta_sn_name]['salt2.RestFrameMag_0_B'])\n self.meta_mb_err.append(meta[meta_sn_name]['salt2.RestFrameMag_0_B.err'])\n self.meta_cov_x0_x1.append(meta[meta_sn_name]['salt2.CovX0X1'])\n self.meta_cov_x0_c.append(meta[meta_sn_name]['salt2.CovColorX0'])\n self.meta_cov_x1_c.append(meta[meta_sn_name]['salt2.CovColorX1'])\n self.meta_cov_mb_x1.append(meta[meta_sn_name]['salt2.CovRestFrameMag_0_BX1'])\n self.meta_cov_mb_c.append(meta[meta_sn_name]['salt2.CovColorRestFrameMag_0_B'])\n self.meta_idr.append(meta[meta_sn_name]['idr.subset'])\n \n self.meta_idr = np.array(self.meta_idr)\n self.meta_zcmb = np.array(self.meta_zcmb)\n self.meta_zhl = np.array(self.meta_zhl)\n self.meta_zhl_err = np.array(self.meta_zhl_err)\n self.meta_x0 = np.array(self.meta_x0)\n self.meta_x0_err = np.array(self.meta_x0_err)\n self.meta_x1 = np.array(self.meta_x1)\n self.meta_x1_err = np.array(self.meta_x1_err) \n self.meta_c = np.array(self.meta_c)\n self.meta_c_err = np.array(self.meta_c_err)\n self.meta_mb = np.array(self.meta_mb)\n self.meta_mb_err = np.array(self.meta_mb_err)\n self.meta_cov_x0_x1 = np.array(self.meta_cov_x0_x1)\n self.meta_cov_x0_c = np.array(self.meta_cov_x0_c)\n self.meta_cov_x1_c = np.array(self.meta_cov_x1_c)\n self.meta_cov_mb_x1 = np.array(self.meta_cov_mb_x1)\n self.meta_cov_mb_c = np.array(self.meta_cov_mb_c)","def get_metadata_options(ensemble):\n\n\tif \";\" in ensemble: # Prevent SQL injection since table names aren't parameterizable\n\t\treturn None\n\n\tquery = \"SELECT * FROM %(ensemble)s LIMIT 1\" % {'ensemble': ensemble,}\n\n\ttry:\n\t\tdf = pd.read_sql(query, db.get_engine(current_app, 'methylation_data'))\n\texcept exc.ProgrammingError as e:\n\t\tnow = datetime.datetime.now()\n\t\tprint(\"[{}] ERROR in app(get_metadata_options): {}\".format(str(now), e))\n\t\tsys.stdout.flush()\n\t\treturn None\n\n\tdf_tsne = df.filter(regex='^tsne_x_', axis='columns')\n\tdf_cluster = df.filter(regex='^cluster_', axis='columns')\n\n\tlist_tsne_types = [x.split('tsne_x_')[1] for x in df_tsne.columns.values]\n\tlist_mc_types_tsne = sorted(list(set([x.split('_')[0] for x in list_tsne_types])), key=lambda mC_type: methylation_types_order.index(mC_type))\n\tlist_dims_tsne_first = sorted(list(set([int(x.split('_')[1].replace('ndim','')) for x in list_tsne_types if list_mc_types_tsne[0] == x.split('_')[0]])))\n\tlist_perp_tsne_first = sorted(list(set([int(x.split('_')[2].replace('perp', '')) for x in list_tsne_types if (list_mc_types_tsne[0]+'_ndim'+str(list_dims_tsne_first[0])) == (x.split('_')[0] +'_'+ x.split('_')[1])])))\n\n\tlist_clustering_types = [x.split('cluster_')[1] for x in df_cluster.columns.values]\n\n\t#generate query for getting number clusters for each clustering type\n\tnum_clusters_query = \"SELECT \"\n\tfor i, clustering in enumerate(list_clustering_types):\n\t\tnum_clusters_query += \"MAX(cluster_{0}) as {0}, \".format(clustering)\n\tnum_clusters_query = num_clusters_query[:-2] #Gets rid of last \", \" which causes a MySQL syntax error\n\tnum_clusters_query += \" FROM {}\".format(ensemble)\n\n\tresult = db.get_engine(current_app, 'methylation_data').execute(num_clusters_query).fetchone()\n\n\tdict_clustering_types_and_numclusters = OrderedDict()\n\tfor clustering_type, num_clusters in zip(list_clustering_types, result):\n\t\tdict_clustering_types_and_numclusters[clustering_type] = num_clusters\n\n\tlist_algorithms_clustering = list(set([x.split('_')[1] for x in list_clustering_types]))\n\n\t#list_mc_types_clustering = sorted(list(set([x.split('_')[0] for x in list_clustering_types])), key=lambda mC_type: methylation_types_order.index(mC_type))\n\t#list_algorithms_clustering = sorted(list(set([x.split('_')[1] for x in list_clustering_types if list_mc_types_clustering[0] == x.split('_')[0]])))\n\t#list_npc_clustering = sorted(list(set([int(x.split('_')[2].replace('npc', '')) for x in list_clustering_types if (list_mc_types_clustering[0]+'_'+list_algorithms_clustering[0]) in x])))\n\t#list_k_clustering = sorted(list(set([int(x.split('_')[3].replace('k', '')) for x in list_clustering_types if (list_mc_types_clustering[0]+'_'+list_algorithms_clustering[0]+'_npc'+str(list_npc_clustering[0])) in x])))\n\n\tall_metadata = dict()\n\tfor modality in ['methylation','snATAC','RNA']:\n\t\tall_metadata[modality] = ['cluster','annotation']\n\t\tif ensemble_exists(ensemble, modality=modality):\n\t\t\tall_metadata[modality] += ['dataset','sex','brain_region','broad_brain_region']\n\t\t\tquery = \"SELECT count(*) AS count FROM ensembles \\\n\t\t\tRIGHT JOIN datasets ON ensembles.datasets LIKE %(x)s \\\n\t\t\tWHERE datasets.target_region!='NULL' \\\n\t\t\tAND ensembles.ensemble_id={0}\".format(ensemble.strip('Ens'))\n\t\t\tdf_metadata = pd.read_sql(query, con=db.get_engine(current_app, modality+'_data'), params={'x': \"CONCAT('%',datasets.dataset,'%')\"})\n\t\t\tif (not df_metadata['count'].isnull().all()) and max(df_metadata['count'])>0:\n\t\t\t\tall_metadata[modality] += ['target_region']\n\n\t\t\tquery = \"SELECT * FROM cells LIMIT 1;\"\n\t\t\tdf_metadata = pd.read_sql(query, con=db.get_engine(current_app, modality+'_data'))\n\t\t\tdf_metadata = df_metadata.drop(list(df_metadata.filter(regex='cell_.*', axis='columns')), axis=1)\n\t\t\tall_metadata[modality] += list([i for i in df_metadata.columns.values])\n\n\treturn {'all_tsne_settings': list_tsne_types,\n\t\t\t'tsne_methylation': list_mc_types_tsne,\n\t\t\t'all_clustering_settings': list_clustering_types,\n\t\t\t'all_clustering_settings2': dict_clustering_types_and_numclusters,\n\t\t\t'clustering_algorithms': list_algorithms_clustering,\n\t\t\t'tsne_dimensions': list_dims_tsne_first,\n\t\t\t'tsne_perplexity': list_perp_tsne_first,\n\t\t\t'methylation_metadata_fields': all_metadata['methylation'],\n\t\t\t'snATAC_metadata_fields': all_metadata['snATAC'],\n\t\t\t'RNA_metadata_fields': all_metadata['RNA'],}","def get_out_d(out_df):\n out_l = []\n if out_df.empty :\n return {'sorry, no':'predictions'}\n for row in out_df.itertuples():\n row_d = {\n 'date,price':[row.cdate,row.cp]\n ,'pct_lead': row.pct_lead\n ,'prediction,effectiveness,accuracy':[row.prediction,row.effectiveness,row.accuracy]\n }\n out_l.append(row_d)\n lo_acc = sum((1+np.sign(out_df.pct_lead))/2) / out_df.accuracy.size\n out_d = {'Long-Only-Accuracy': lo_acc }\n out_d['Long-Only-Effectivness'] = sum(out_df.pct_lead)\n out_d['Model-Effectivness'] = sum(out_df.effectiveness)\n out_d['Model-Accuracy'] = sum(out_df.accuracy) / out_df.accuracy.size\n out_d['Prediction-Count'] = out_df.prediction.size\n out_d['Prediction-Details'] = out_l\n return out_d","def create_catalog_dataframe(save_dataframe):\n if not save_dataframe:\n return pd.read_pickle(f'{path_dictionary[\"catalog_dataframe_grouped_path\"]}')\n\n catalog_items = DbHelper.get_all_catalog_items()\n\n # Create dataframe to put all information together\n columns = ['user_id', 'item_id', 'session_id', 'window_size_x', 'window_size_y', 'page_size_x', 'page_size_y', 'catalog_item_list', 'user_log_list']\n catalog_items_df = pd.DataFrame(catalog_items, columns=columns)\n\n # Clean 'Catalog Items' that user see during a session\n catalog_items_df['catalog_item_list'] = catalog_items_df.apply(clean_objects_listed, axis=1)\n\n # Clean Log Files\n catalog_items_df['user_log_list'] = catalog_items_df.apply(clean_logs, axis=1)\n\n # Get Catalog Items that user hover or has a click action, her/his mouse\n catalog_items_df = get_interacted_catalog_items(catalog_items_df)\n\n # Label the catalog items as 0\n catalog_items_df['catalog_item_list'] = catalog_items_df.apply(label_page_type, axis=1)\n\n catalog_items_df_grouped = catalog_items_df.groupby(['user_id', 'session_id'], as_index=False).agg(lambda x: list(x))\n catalog_items_df_grouped.drop(['item_id', 'window_size_x', 'window_size_y', 'page_size_x', 'page_size_y'], axis=1, inplace=True)\n\n if save_dataframe:\n catalog_items_df.to_pickle(f'{path_dictionary[\"path_raw_catalog_dataframe\"]}')\n catalog_items_df_grouped.to_pickle(f'{path_dictionary[\"path_catalog_dataframe\"]}')\n catalog_items_df_grouped.to_csv(f'{path_dictionary[\"path_catalog_csv\"]}', index=False, sep='|')\n return catalog_items_df_grouped","def get_management(symbol: str) -> pd.DataFrame:\n url_market_business_insider = (\n f\"https://markets.businessinsider.com/stocks/{symbol.lower()}-stock\"\n )\n text_soup_market_business_insider = BeautifulSoup(\n request(\n url_market_business_insider, headers={\"User-Agent\": get_user_agent()}\n ).text,\n \"lxml\",\n )\n\n found_h2s = {}\n\n for next_h2 in text_soup_market_business_insider.findAll(\n \"h2\", {\"class\": \"header-underline\"}\n ):\n next_table = next_h2.find_next_sibling(\"table\", {\"class\": \"table\"})\n\n if next_table:\n found_h2s[next_h2.text] = next_table\n\n # Business Insider changed management display convention from 'Management' to\n # 'Ticker Management'. These next few lines simply find 'Ticker Management'\n # header key and copy it to a 'Management' key as to not alter the rest of\n # the function\n ticker_management_to_be_deleted = \"\"\n management_data_available = False\n for key in found_h2s:\n if \"Management\" in key:\n ticker_management_to_be_deleted = key\n management_data_available = True\n if management_data_available:\n found_h2s[\"Management\"] = found_h2s[ticker_management_to_be_deleted]\n del found_h2s[ticker_management_to_be_deleted]\n\n if found_h2s.get(\"Management\") is None:\n console.print(\n f\"[red]No management information in Business Insider for {symbol}[/red]\"\n )\n return pd.DataFrame()\n\n l_titles = [\n s_title.text.strip()\n for s_title in found_h2s[\"Management\"].findAll(\n \"td\", {\"class\": \"table__td text-right\"}\n )\n if any(c.isalpha() for c in s_title.text.strip())\n and (\"USD\" not in s_title.text.strip())\n ]\n\n l_names = [\n s_name.text.strip()\n for s_name in found_h2s[\"Management\"].findAll(\n \"td\", {\"class\": \"table__td table--allow-wrap\"}\n )\n ]\n\n df_management = pd.DataFrame(\n {\"Name\": l_names[-len(l_titles) :], \"Title\": l_titles}, # noqa: E203\n columns=[\"Name\", \"Title\"],\n )\n\n df_management[\"Info\"] = \"-\"\n df_management = df_management.set_index(\"Name\")\n\n for s_name in df_management.index:\n df_management.loc[s_name][\n \"Info\"\n ] = f\"http://www.google.com/search?q={s_name} {symbol.upper()}\".replace(\n \" \", \"%20\"\n )\n\n return df_management","def get_object_metadata(sf, name):\r\n data = {}\r\n \r\n result = get_object(sf, name)\r\n\r\n # now split out the result\r\n data['raw'] = result\r\n describe = result['describe']\r\n relationships = []\r\n for entry in describe['childRelationships']:\r\n row = {}\r\n row['table'] = entry['childSObject']\r\n row['field'] = entry['field']\r\n row['name'] = entry['relationshipName']\r\n\r\n relationships.append(row)\r\n data['relationships'] = relationships\r\n\r\n fields = []\r\n for entry in describe['fields']:\r\n row = {}\r\n row['name'] = entry['name']\r\n row['label'] = entry['label']\r\n row['type'] = entry['type']\r\n row['length'] = entry['length']\r\n row['byteLength'] = entry['byteLength']\r\n row['custom'] = entry['custom']\r\n row['scale'] = entry['scale']\r\n row['precision'] = entry['precision']\r\n row['calculated'] = entry['calculated']\r\n row['calculatedFormula'] = entry['calculatedFormula']\r\n row['soapType'] = entry['soapType']\r\n row['picklist'] = entry['picklistValues']\r\n if len(row['picklist']) > 0:\r\n p_rows = []\r\n for p in row['picklist']:\r\n p_row = {}\r\n p_row['active'] = p['active']\r\n p_row['defaultValue'] = p['defaultValue']\r\n p_row['label'] = p['label']\r\n p_row['validFor'] = p['validFor']\r\n p_row['value'] = p['value']\r\n\r\n p_rows.append(row)\r\n\r\n fields.append(row)\r\n \r\n data['fields'] = fields\r\n\r\n # get the record types\r\n rt_rows = []\r\n for rt in describe['recordTypeInfos']:\r\n rt_row = {}\r\n rt_row['available'] = rt['available']\r\n rt_row['default'] = rt['defaultRecordTypeMapping']\r\n rt_row['master'] = rt['master']\r\n rt_row['name'] = rt['name']\r\n rt_row['recordTypeId'] = rt['recordTypeId']\r\n\r\n rt_rows.append(rt_row)\r\n\r\n data['record_type'] = rt_rows\r\n\r\n return data","def get_data(list_data_tuples):\n \n \n benchmark_symbol=list_data_tuples[0][0]; # First element is the benchmark symbol\n \n #print benchmark_symbol\n \n df=pd.DataFrame(index=list_data_tuples[0][1]['data'].index) # First dataframe index is nothing but date\n \n for tpl in list_data_tuples:\n #print tpl[0]\n df_temp = pd.DataFrame(tpl[1]['data']['Adj. Close'],index=tpl[1]['data'].index)\n df_temp = df_temp.rename(columns={'Adj. Close': tpl[0]}) # tpl[0] is the symbol\n #print df_temp,tpl[0]\n df = df.join(df_temp)\n if tpl[0] == benchmark_symbol: # drop dates SPY did not trade\n df = df.dropna(subset=[benchmark_symbol])\n\n df=df.dropna(axis=0) # This drops any NaN values especially if the stock price has no information\n \n return df","def get_metadata(self):\n items = self.get_playlist_items()\n uris = [item[\"track\"][\"uri\"] for item in items]\n features = self.API.audio_features(uris)\n\n list_data = []\n\n for idx, item in enumerate(items):\n data = {\"name\": item[\"track\"][\"name\"], \"uri\": item[\"track\"][\"uri\"]}\n data.update(features[idx])\n list_data.append(data)\n\n self.metadata = pd.DataFrame(data=list_data,\n index=range(len(list_data)))\n\n return self.metadata","def get_data(self, category, ontologies, include_negative_enrichment=True, fdr=0.05):\n\n df = self._get_data(category, ontologies)\n\n # some extra information for convenience\n df[\"pct_diff_expr\"] = df[\"number_in_list\"] * 100 / df[\"number_in_reference\"]\n\n # could happen that all fold enrichment are set to 'NaN'\n df = df[df['fold_enrichment'] != 'NaN']\n df[\"log2_fold_enrichment\"] = pylab.log2(df[\"fold_enrichment\"])\n df[\"abs_log2_fold_enrichment\"] = abs(pylab.log2(df[\"fold_enrichment\"]))\n df[\"expected\"] = [int(x) for x in df.expected]\n\n # Some user may want to include GO terms with fold enrichment\n # significanyly below 1 or not.\n if include_negative_enrichment is False:\n df = df.query(\"fold_enrichment>=1\").copy()\n logger.info(\"Found {} GO terms after keeping only positive enrichment\".format(len(df)))\n\n # filter out FDR>0.05\n df = df.query(\"fdr<=@fdr\").copy()\n logger.info(\"Found {} GO terms after keeping only FDR<{}\".format(len(df), fdr))\n\n return df","def df_customers_fileRead(self):\n \n #-------------------------------------------------------------------------\n # RFM features are restored\n #-------------------------------------------------------------------------\n df_customers_rfm \\\n = p5_util.object_load(self.df_customers_rfm_fileName)\n self.strprint(\"RFM features : \"+str(df_customers_rfm.shape))\n \n #-------------------------------------------------------------------------\n # Time features are restored\n #-------------------------------------------------------------------------\n df_customers_timeFeature \\\n = p5_util.object_load(self._df_customers_timeFeature_fileName)\n self.strprint(\"Time features : \"+str(df_customers_timeFeature.shape))\n \n #-------------------------------------------------------------------------\n # NLP features are restored\n #-------------------------------------------------------------------------\n df_customers_nlp = p5_util.object_load(self._df_customers_nlp_fileName)\n self.strprint(\"NLP features : \"+str(df_customers_nlp.shape))\n\n if False:\n df_customers_rfm = self._df_customers_rfm.copy()\n df_customers_timeFeature = self._df_customers_timeFeature.copy()\n df_customers_nlp = self._df_customers_pca_nlp.copy()\n\n #-------------------------------------------------------------------------\n # Dataframe are aggregated; note that indexes are customerID.\n #-------------------------------------------------------------------------\n df_customers = pd.DataFrame()\n\n df_customers = pd.concat([df_customers,df_customers_rfm], axis=1)\n\n df_customers = pd.concat([df_customers,df_customers_timeFeature]\\\n , join='inner', axis=1)\n\n df_customers = pd.concat([df_customers,df_customers_nlp]\\\n , join='inner', axis=1)\n\n self.strprint(\"All features : \"+str(df_customers.shape))\n\n #----------------------------------------------------------------------\n # Dataframe is dumped into a file\n #----------------------------------------------------------------------\n p5_util.object_dump(df_customers, self._df_customers_fileName)\n if False:\n #----------------------------------------------------------------------\n # Dataframe is copied as an attribute\n #----------------------------------------------------------------------\n self._df_customers = df_customers.copy()\n \n return","def extract_imits(spark_session: SparkSession) -> DataFrame:\n print(spark_session)"],"string":"[\n \"def metadata(self, df):\\n raise NotImplementedError(\\\"missing metadata() method\\\")\",\n \"def get_metadata(self):\\n df_data = pd.read_csv(self.path_bin)\\n # df_data = df_data[df_data['subject'] == 0] # limit data to certain subject.\\n\\n df = pd.DataFrame()\\n df['mean'] = np.mean(df_data)\\n\\n def cond_mean(df, cond_value, target):\\n result = list()\\n for f in df:\\n tmp_df = df[df[f] == cond_value]\\n result.append(np.mean(tmp_df[target]))\\n return result\\n\\n df['mean|f=0'] = cond_mean(df_data, cond_value=0, target=self.target)\\n df['mean|f=1'] = cond_mean(df_data, cond_value=1, target=self.target)\\n df['std'] = np.std(df_data)\\n\\n df['H'] = [H(df_data[x]) for x in df_data]\\n h_x = _H([df.loc[self.target]['mean'], 1-df.loc[self.target]['mean']])\\n df['IG'] = df.apply(IG_from_series, axis='columns', h_x=h_x, identifier='mean')\\n df['IG ratio'] = df.apply(lambda x: x['IG']/x['H'], axis='columns') # correct?\\n df = df.sort_values(by=['IG'], ascending=False)\\n df.to_csv(self.path_meta, index=True)\",\n \"def instruments_with_meta_data(self):\\n if len(self._instruments_with_meta_data) > 0:\\n return self._instruments_with_meta_data\\n else:\\n self._borsdata_api = BorsdataAPI(constants.API_KEY)\\n # fetching data from api\\n countries = self._borsdata_api.get_countries()\\n branches = self._borsdata_api.get_branches()\\n sectors = self._borsdata_api.get_sectors()\\n markets = self._borsdata_api.get_markets()\\n instruments = self._borsdata_api.get_instruments()\\n # instrument type dict for conversion (https://github.com/Borsdata-Sweden/API/wiki/Instruments)\\n instrument_type_dict = {0: 'Aktie', 1: 'Pref', 2: 'Index', 3: 'Stocks2', 4: 'SectorIndex', 5: 'BranschIndex'}\\n # creating an empty dataframe\\n instrument_df = pd.DataFrame()\\n # loop through the whole dataframe (table) i.e. row-wise-iteration.\\n for index, instrument in instruments.iterrows():\\n name = instrument['name']\\n ins_id = instrument['insId']\\n ticker = instrument['ticker']\\n isin = instrument['isin']\\n # locating meta-data in various ways\\n # dictionary-lookup\\n instrument_type = instrument_type_dict[instrument['instrument']]\\n # .loc locates the rows where the criteria (inside the brackets, []) is fulfilled\\n # located rows (should be only one) get the column 'name' and return its value-array\\n # take the first value in that array ([0], should be only one value)\\n market = markets.loc[markets['id'] == instrument['marketId']]['name'].values[0]\\n country = countries.loc[countries['id'] == instrument['countryId']]['name'].values[0]\\n sector = 'N/A'\\n branch = 'N/A'\\n # index-typed instruments does not have a sector or branch\\n if market.lower() != 'index':\\n sector = sectors.loc[sectors['id'] == instrument['sectorId']]['name'].values[0]\\n branch = branches.loc[branches['id'] == instrument['branchId']]['name'].values[0]\\n # appending current data to dataframe, i.e. adding a row to the table.\\n instrument_df = instrument_df.append({'name': name, 'ins_id': ins_id, 'ticker': ticker, 'isin': isin, 'instrument_type': instrument_type,\\n 'market': market, 'country': country, 'sector': sector, 'branch': branch}, ignore_index=True)\\n # create directory if it do not exist\\n if not os.path.exists(constants.EXPORT_PATH):\\n os.makedirs(constants.EXPORT_PATH)\\n # to csv\\n instrument_df.to_csv(constants.EXPORT_PATH + 'instrument_with_meta_data.csv')\\n # creating excel-document\\n excel_writer = pd.ExcelWriter(constants.EXPORT_PATH + 'instrument_with_meta_data.xlsx')\\n # adding one sheet\\n instrument_df.to_excel(excel_writer, 'instruments_with_meta_data')\\n # saving the document\\n excel_writer.save()\\n self._instruments_with_meta_data = instrument_df\\n return instrument_df\",\n \"def get_metadata(self):\\n # Load file\\n retouch_file = pd.read_csv(self.file)\\n \\n # Loop over rows\\n for index, row in retouch_file.iterrows():\\n try:\\n info = IPTCInfo(f\\\"{settings.MEDIA_ROOT}/images/{row['image_name']}.jpg\\\")\\n row['headline'] = info['headline']\\n print(\\\"Headline\\\", info['headline'])\\n row['keywords'] = info['keywords']\\n x = \\\"\\\".join(info['keywords'])\\n print(\\\"Keywords\\\", x)\\n row['creator'] = info['creator']\\n print(\\\"Creator\\\", info['creator'])\\n row['date created'] = info['date_created']\\n print(\\\"Date Created\\\", info['date_created'])\\n row['sub-location'] = info['sub-location']\\n print(\\\"Sub-location\\\", info['sub-location'])\\n row['city'] = info['city']\\n print(\\\"City\\\", info['city'])\\n row['province/state'] = info['province/state']\\n print(\\\"Province/ State\\\", info['province/state'])\\n row['country'] = info['country/primary location name']\\n print(\\\"Country\\\", info['country/primary location name'])\\n row['category'] = info['category']\\n print(\\\"Category\\\", info['category'])\\n row['description'] = info['description']\\n print(\\\"Description\\\", info['description'])\\n except:\\n pass\\n # retouch_file.to_csv(\\\"output.csv\\\")\\n return \\\"IPTC Metadata returned.\\\"\",\n \"def getCouponFeatures(coupon_dict, master_list):\\n purchase_small_area_name_dict, capsule_text_dict, genre_name_dict, coupon_large_area_name_dict, coupon_small_area_name_dict, coupon_ken_name_dict, price_rate_list, catalog_price_list, discount_price_list, dispperiod_list, valid_period_list, usable_date_mon_list, usable_date_tue_list, usable_date_wed_list, usable_date_thu_list, usable_date_fri_list, usable_date_sat_list, usable_date_sun_list, usable_date_hol_list, usable_date_before_hol_list = master_list\\n\\n feat_header = [\\\"CouponCapsuleText\\\", \\\"CouponGenreName\\\", \\\"CouponPriceRate\\\", \\\"CouponCatalogPrice\\\", \\\"CouponDiscountPrice\\\", \\\"CouponDispPeriod\\\", \\\"CouponValidPeriod\\\", \\\"CouponUsableMon\\\", \\\"CouponUsableTue\\\", \\\"CouponUsableWed\\\", \\\"CouponUsableThu\\\", \\\"CouponUsableFri\\\", \\\"CouponUsableSat\\\", \\\"CouponUsableSun\\\", \\\"CouponUsableHol\\\", \\\"CouponUsableBeforeHol\\\", \\\"CouponLargeAreaName\\\", \\\"CouponKenName\\\", \\\"CouponSmallAreaName\\\", \\\"CapsuleTextCount\\\", \\\"CapsuleTextCountNorm\\\", \\\"GenreNameCount\\\", \\\"GenreNameCountNorm\\\", \\\"LargeAreaNameCount\\\", \\\"LargeAreaNameCountNorm\\\", \\\"SmallAreaNameCount\\\", \\\"SmallAreaNameCountNorm\\\", \\\"KenNameCount\\\",\\\"KenNameCountNorm\\\", \\\"PriceRateMin\\\", \\\"PriceRateMax\\\", \\\"PriceRateMean\\\", \\\"PriceRateBetMeanMax\\\", \\\"PriceRateBetMinMean\\\", \\\"PriceRateGrtMax\\\", \\\"PriceRateLessMin\\\", \\\"CatalogPriceMin\\\", \\\"CatalogPriceMax\\\", \\\"CatalogPriceMean\\\", \\\"CatalogPriceBetMeanMax\\\", \\\"CatalogPriceBetMinMean\\\", \\\"CatalogPriceGrtMax\\\", \\\"CatalogPriceLessMin\\\", \\\"DiscountPriceMin\\\", \\\"DiscountPriceMax\\\", \\\"DiscountPriceMean\\\", \\\"DiscountPriceBetMeanMax\\\", \\\"DiscountPriceBetMinMean\\\", \\\"DiscountPriceGrtMax\\\", \\\"DiscountPriceLessMin\\\", \\\"DispPeriodMin\\\", \\\"DispPeriodMax\\\", \\\"DispPeriodMean\\\", \\\"DispPeriodBetMeanMax\\\", \\\"DispPeriodBetMinMean\\\", \\\"DispPeriodGrtMax\\\", \\\"DispPeriodLessMin\\\", \\\"MondayCount\\\", \\\"MondayCountNorm\\\", \\\"TuesdayCount\\\", \\\"TuesdayCountNorm\\\", \\\"WednesdayCount\\\", \\\"WednesdayCountNorm\\\", \\\"ThursdayCount\\\", \\\"ThursdayCountNorm\\\", \\\"FridayCount\\\", \\\"FridayCountNorm\\\", \\\"SaturdayCount\\\", \\\"SaturdayCountNorm\\\", \\\"SundayCount\\\", \\\"SundayCountNorm\\\", \\\"HolidayCount\\\", \\\"HolidayCountNorm\\\", \\\"BeforeHolidayCount\\\", \\\"BeforeHolidayCountNorm\\\"]\\n\\n if coupon_dict['USABLE_DATE_MON'] in ('','NA'):\\n coupon_dict[\\\"USABLE_DATE_MON\\\"] = 3.0\\n coupon_dict[\\\"USABLE_DATE_TUE\\\"] = 3.0\\n coupon_dict[\\\"USABLE_DATE_WED\\\"] = 3.0\\n coupon_dict[\\\"USABLE_DATE_THU\\\"] = 3.0\\n coupon_dict[\\\"USABLE_DATE_FRI\\\"] = 3.0\\n coupon_dict[\\\"USABLE_DATE_SAT\\\"] = 3.0\\n coupon_dict[\\\"USABLE_DATE_SUN\\\"] = 3.0\\n coupon_dict[\\\"USABLE_DATE_HOLIDAY\\\"] = 3.0\\n coupon_dict[\\\"USABLE_DATE_BEFORE_HOLIDAY\\\"] = 3.0\\n\\n feat_list = [coupon_dict[\\\"CAPSULE_TEXT\\\"], coupon_dict[\\\"GENRE_NAME\\\"], coupon_dict[\\\"PRICE_RATE\\\"], coupon_dict[\\\"CATALOG_PRICE\\\"], coupon_dict[\\\"DISCOUNT_PRICE\\\"], coupon_dict[\\\"DISPPERIOD\\\"], coupon_dict[\\\"VALIDPERIOD\\\"], coupon_dict[\\\"USABLE_DATE_MON\\\"], coupon_dict[\\\"USABLE_DATE_TUE\\\"], coupon_dict[\\\"USABLE_DATE_WED\\\"], coupon_dict[\\\"USABLE_DATE_THU\\\"], coupon_dict[\\\"USABLE_DATE_FRI\\\"], coupon_dict[\\\"USABLE_DATE_SAT\\\"], coupon_dict[\\\"USABLE_DATE_SUN\\\"], coupon_dict[\\\"USABLE_DATE_HOLIDAY\\\"], coupon_dict[\\\"USABLE_DATE_BEFORE_HOLIDAY\\\"], coupon_dict[\\\"large_area_name\\\"], coupon_dict[\\\"ken_name\\\"], coupon_dict[\\\"small_area_name\\\"]]\\n\\n capsule_text_count, capsule_text_count_norm = getCountFromDict(coupon_dict[\\\"CAPSULE_TEXT\\\"], capsule_text_dict)\\n feat_list.extend([capsule_text_count, capsule_text_count_norm])\\n\\n genre_name_count, genre_name_count_norm = getCountFromDict(coupon_dict[\\\"GENRE_NAME\\\"], genre_name_dict)\\n feat_list.extend([genre_name_count, genre_name_count_norm])\\n\\n large_area_name_count, large_area_name_count_norm = getCountFromDict(coupon_dict[\\\"large_area_name\\\"], coupon_large_area_name_dict)\\n feat_list.extend([large_area_name_count, large_area_name_count_norm])\\n\\n small_area_name_count, small_area_name_count_norm = getCountFromDict(coupon_dict[\\\"small_area_name\\\"], coupon_small_area_name_dict)\\n feat_list.extend([small_area_name_count, small_area_name_count_norm])\\n\\n ken_name_count, ken_name_count_norm = getCountFromDict(coupon_dict[\\\"ken_name\\\"], coupon_ken_name_dict)\\n feat_list.extend([ken_name_count, ken_name_count_norm])\\n\\n price_rate_out_list = getMinMaxMeanFromList(coupon_dict[\\\"PRICE_RATE\\\"], price_rate_list)\\n feat_list.extend( price_rate_out_list )\\n\\n catalog_price_out_list = getMinMaxMeanFromList(coupon_dict[\\\"CATALOG_PRICE\\\"], catalog_price_list)\\n feat_list.extend( catalog_price_out_list )\\n\\n discount_price_out_list = getMinMaxMeanFromList(coupon_dict[\\\"DISCOUNT_PRICE\\\"], discount_price_list)\\n feat_list.extend( discount_price_out_list )\\n\\n dispperiod_out_list = getMinMaxMeanFromList(coupon_dict[\\\"DISPPERIOD\\\"], dispperiod_list)\\n feat_list.extend( dispperiod_out_list )\\n\\n monday_count, monday_count_norm = getCountFromDict(coupon_dict[\\\"USABLE_DATE_MON\\\"], usable_date_mon_list)\\n feat_list.extend([monday_count, monday_count_norm])\\n\\n tuesday_count, tuesday_count_norm = getCountFromDict(coupon_dict[\\\"USABLE_DATE_TUE\\\"], usable_date_tue_list)\\n feat_list.extend([tuesday_count, tuesday_count_norm])\\n\\n wednesday_count, wednesday_count_norm = getCountFromDict(coupon_dict[\\\"USABLE_DATE_WED\\\"], usable_date_wed_list)\\n feat_list.extend([wednesday_count, wednesday_count_norm])\\n\\n thursday_count, thursday_count_norm = getCountFromDict(coupon_dict[\\\"USABLE_DATE_THU\\\"], usable_date_thu_list)\\n feat_list.extend([thursday_count, thursday_count_norm])\\n\\n friday_count, friday_count_norm = getCountFromDict(coupon_dict[\\\"USABLE_DATE_FRI\\\"], usable_date_fri_list)\\n feat_list.extend([friday_count, friday_count_norm])\\n\\n saturday_count, saturday_count_norm = getCountFromDict(coupon_dict[\\\"USABLE_DATE_SAT\\\"], usable_date_sat_list)\\n feat_list.extend([saturday_count, saturday_count_norm])\\n\\n sunday_count, sunday_count_norm = getCountFromDict(coupon_dict[\\\"USABLE_DATE_SUN\\\"], usable_date_sun_list)\\n feat_list.extend([sunday_count, sunday_count_norm])\\n\\n holiday_count, holiday_count_norm = getCountFromDict(coupon_dict[\\\"USABLE_DATE_HOLIDAY\\\"], usable_date_hol_list)\\n feat_list.extend([holiday_count, holiday_count_norm])\\n\\n before_holiday_count, before_holiday_count_norm = getCountFromDict(coupon_dict[\\\"USABLE_DATE_BEFORE_HOLIDAY\\\"], usable_date_before_hol_list)\\n feat_list.extend([before_holiday_count, before_holiday_count_norm])\\n\\n\\tvar_list, var_header = getDummyVars(capsule_text_dict, unique_capsule_text, prefix=\\\"CapsuleText_\\\")\\n\\tfeat_header.extend(var_header)\\n\\tfeat_list.extend(var_list)\\n\\n\\tvar_list, var_header = getDummyVars(genre_name_dict, unique_genre_name, prefix=\\\"GenreName_\\\")\\n\\tfeat_header.extend(var_header)\\n feat_list.extend(var_list)\\n\\n\\tvar_list, var_header = getDummyVars(coupon_large_area_name_dict, unique_large_area_name, prefix=\\\"LargeAreaName_\\\")\\n feat_header.extend(var_header)\\n feat_list.extend(var_list)\\n\\n\\tvar_list, var_header = getDummyVars(coupon_small_area_name_dict, unique_small_area_name, prefix=\\\"SmallAreaName_\\\")\\n feat_header.extend(var_header)\\n feat_list.extend(var_list)\\n\\n\\tvar_list, var_header = getDummyVars(coupon_ken_name_dict, unique_ken_name, prefix=\\\"KenName_\\\")\\n feat_header.extend(var_header)\\n feat_list.extend(var_list)\\n\\n\\tvar_list, var_header = getDummyVarsDays(usable_date_mon_list, prefix=\\\"UsableMonday_\\\")\\n\\tfeat_header.extend(var_header)\\n feat_list.extend(var_list)\\n\\n\\tvar_list, var_header = getDummyVarsDays(usable_date_tue_list, prefix=\\\"UsableTuesday_\\\")\\n feat_header.extend(var_header)\\n feat_list.extend(var_list)\\n\\n\\tvar_list, var_header = getDummyVarsDays(usable_date_wed_list, prefix=\\\"UsableWednesday_\\\")\\n feat_header.extend(var_header)\\n feat_list.extend(var_list)\\n\\n\\tvar_list, var_header = getDummyVarsDays(usable_date_thu_list, prefix=\\\"UsableThursday_\\\")\\n feat_header.extend(var_header)\\n feat_list.extend(var_list)\\n\\n\\tvar_list, var_header = getDummyVarsDays(usable_date_fri_list, prefix=\\\"UsableFriday_\\\")\\n feat_header.extend(var_header)\\n feat_list.extend(var_list)\\n\\n\\tvar_list, var_header = getDummyVarsDays(usable_date_sat_list, prefix=\\\"UsableSaturday_\\\")\\n feat_header.extend(var_header)\\n feat_list.extend(var_list)\\n\\n\\tvar_list, var_header = getDummyVarsDays(usable_date_sun_list, prefix=\\\"UsableSunday_\\\")\\n feat_header.extend(var_header)\\n feat_list.extend(var_list)\\n\\n\\tvar_list, var_header = getDummyVarsDays(usable_date_hol_list, prefix=\\\"UsableHoliday_\\\")\\n feat_header.extend(var_header)\\n feat_list.extend(var_list)\\n\\n\\tvar_list, var_header = getDummyVarsDays(usable_date_before_hol_list, prefix=\\\"UsableBeforeHoliday_\\\")\\n feat_header.extend(var_header)\\n feat_list.extend(var_list)\\n\\t\\n return feat_list, feat_header\",\n \"def get_ytm_discount_data(self):\\n ytm=Bootstrapping.get_ytm_dict(self)\\n data = pd.DataFrame()\\n for i in ytm.keys():\\n data.loc[i / 2, 'Yield to maturity'] = ytm[i]\\n data.loc[i / 2, 'discount_rate'] = Bootstrapping.P_Tn(self,ytm[i], i)\\n return data\",\n \"def get_metaData():\\n #get all movies from db\\n movies_df = movie_helper.get_movies_df()\\n \\n #get movie meta data\\n with tqdm(total=len(movies_df)) as pbar:\\n for index, row in movies_df.iterrows(): \\n \\n #if an imdbid exists use it to look up the API\\n if (row['imdbId']):\\n \\n #get base meta data from imdb\\n movie = ia.get_movie(str(row['imdbId']))\\n year = movie['year']\\n \\n #created delimited list of genre strings\\n if (movie.get('genres')): \\n genres = ','.join(movie.get('genres')) \\n \\n rating = movie.get('rating')\\n votes = movie.get('votes')\\n \\n #create delimited list of movie certificates\\n certificates = None\\n if (movie.get('certificates')): \\n certificates = ','.join(movie.get('certificates'))\\n \\n #update database with collected meta data\\n update_params = {\\n \\\"year\\\" : year,\\n \\\"genres\\\" : genres,\\n \\\"rating\\\" : rating,\\n \\\"votes\\\" : votes,\\n \\\"certificates\\\" : certificates\\n }\\n select_params = { \\\"movieId\\\" : row[\\\"movieId\\\"] }\\n database_helper.update_data(\\\"movies\\\", update_params = update_params, select_params = select_params)\\n \\n pbar.update(1)\",\n \"def _extract_metadata(self) -> None:\\n self.log(\\\"Extracting metadata.\\\")\\n image_paths: list[Path] = []\\n for ext in (\\\"jpg\\\", \\\"jpeg\\\", \\\"png\\\"):\\n image_paths.extend(self._base_dir.glob(f\\\"**/*.{ext}\\\"))\\n image_paths_str = [str(image.relative_to(self._base_dir)) for image in image_paths]\\n filepaths = pd.Series(image_paths_str)\\n metadata = cast(\\n pd.DataFrame,\\n filepaths.str.split(\\\"/\\\", expand=True).rename( # type: ignore[attr-defined]\\n columns={0: \\\"superclass\\\", 1: \\\"concept\\\", 2: \\\"context\\\", 3: \\\"filename\\\"}\\n ),\\n )\\n metadata[\\\"filepath\\\"] = filepaths\\n metadata.sort_index(axis=1, inplace=True)\\n metadata.sort_values(by=[\\\"filepath\\\"], axis=0, inplace=True)\\n metadata = self._label_encode_metadata(metadata)\\n metadata.to_csv(self._metadata_path)\",\n \"def gather_technical_indicators(pricing_data, lookback_window):\\r\\n master_df = pd.DataFrame()\\r\\n master_df['Price'] = pricing_data\\r\\n master_df['SMA'] = find_SMA(pricing_data, lookback_window)\\r\\n master_df['Bandwidth'] = find_bolling_bandwidth(pricing_data, lookback_window)\\r\\n master_df['PM'] = find_price_momentum(pricing_data, lookback_window)\\r\\n return master_df.dropna()\",\n \"def metadata(filename):\\n import numpy as np\\n import pandas as pd\\n\\n infos = \\\"\\\"\\\"IGRAID 1- 11 Character\\nWMOID 13- 17 Integer\\nNAME 19- 48 Character\\nNAMFLAG 50- 50 Character\\nLATITUDE 52- 60 Real\\nLATFLAG 62- 62 Character\\nLONGITUDE 64- 72 Real\\nLONFLAG 74- 74 Character\\nELEVATION 76- 81 Real\\nELVFLAG 83- 83 Character\\nYEAR 85- 88 Integer\\nMONTH 90- 91 Integer\\nDAY 93- 94 Integer\\nHOUR 96- 97 Integer\\nDATEIND 99- 99 Integer\\nEVENT 101-119 Character\\nALTIND 121-122 Character\\nBEFINFO 124-163 Character\\nBEFFLAG 164-164 Character\\nLINK 166-167 Character\\nAFTINFO 169-208 Character\\nAFTFLAG 209-209 Character\\nREFERENCE 211-235 Character\\nCOMMENT 236-315 Character\\nUPDCOM 316-346 Character\\nUPDDATE 348-354 Character\\n\\\"\\\"\\\"\\n\\n colspecs = []\\n header = []\\n types = {}\\n for iline in infos.splitlines():\\n if iline == '':\\n continue\\n ih = iline[0:11].strip().lower()\\n header.append(ih)\\n ii = int(iline[13:16]) - 1\\n ij = int(iline[17:20])\\n colspecs.append((ii, ij))\\n it = iline[22:].strip()\\n if it == 'Character':\\n it = 'str'\\n\\n elif it == 'Real':\\n it = 'float'\\n\\n else:\\n it = 'int'\\n\\n types[ih] = it\\n\\n data = pd.read_fwf(filename, colspecs=colspecs, header=None, dtype=types, names=header)\\n data = data.replace('nan', '')\\n data['date'] = pd.to_datetime((data.year * 1000000 +\\n np.where(data.month.values == 99, 6, data.month.values) * 10000 +\\n np.where(data.day.values == 99, 15, data.day.values) * 100 +\\n np.where(data.hour.values == 99, 0, data.hour.values)).apply(str), format='%Y%m%d%H')\\n return data\",\n \"def lcia_methods__metadata(self):\\r\\n with UnicodeReader(os.path.join(dirpath, \\\"categoryUUIDs.csv\\\"), \\r\\n encoding='latin-1', \\r\\n delimiter=dt) as csv_file:\\r\\n next(csv_file) \\r\\n csv_data = [{'name': (line[0], line[2], line[4]),\\r\\n 'description': line[7]\\r\\n } for line in csv_file]\\r\\n \\r\\n filename = \\\"LCIA_implementation_2019.xlsx\\\" # this was donwloaded and updated on Oct 2019 from ecoinvent website. \\r\\n wb = xlrd.open_workbook(os.path.join(dirpath, filename))\\r\\n #characterizaton factors\\r\\n sheet= wb.sheet_by_name(\\\"CFs\\\")\\r\\n cf_data = [{\\r\\n 'method': (sheet.cell(row, 0).value,\\r\\n sheet.cell(row, 1).value,\\r\\n sheet.cell(row, 2).value),\\r\\n 'name': sheet.cell(row, 3).value,\\r\\n 'categories': (sheet.cell(row, 4).value, sheet.cell(row, 5).value),\\r\\n 'amount': sheet.cell(row, 7).value\\r\\n }\\r\\n for row in range(1, sheet.nrows)\\r\\n if sheet.cell(row, 0).value not in \\r\\n {'selected LCI results, additional', 'selected LCI results'} and isinstance(sheet.cell(row, 7).value, Number)]\\r\\n #units\\r\\n sheet= wb.sheet_by_name(\\\"units\\\")\\r\\n units = {(sheet.cell(row, 0).value, sheet.cell(row, 1).value, \\r\\n sheet.cell(row, 2).value): sheet.cell(row, 4).value for row in range(1, sheet.nrows)}\\r\\n return csv_data, cf_data, units, filename\",\n \"def prepare_data():\\n df = pd.read_csv('Wholesale customers data.csv')\\n df_numeric = df[['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']]\\n return df, df_numeric\",\n \"def _get_dsmeta(self, bids):\\n # STEP 1: Extract metadata from `dataset_description.json`\\n metadata = self._get_bids_dsdescription(bids)\\n # STEP 2: Extract README text\\n metadata[\\\"description\\\"] = self._get_bids_readme()\\n # STEP 3: Extract information about entities and add to metadata\\n metadata[\\\"entities\\\"] = self._get_bids_entities(bids)\\n # STEP 4: Extract variable collection information on multiple levels\\n metadata[\\\"variables\\\"] = self._get_bids_variables(bids)\\n # STEP 5: Add context to metadata output\\n metadata[\\\"@context\\\"] = BIDSCONTEXT\\n return metadata\",\n \"def get_metafeatures(df):\\n metafeatures = []\\n rows = df.shape[0]\\n for col in df.columns:\\n d = {'column': col,\\n 'n_unique': df[col].nunique(),\\n 'missing': df[col].isnull().sum() * 1.0 / rows,\\n 'type': df[col].dtype}\\n metafeatures.append(d)\\n return pd.DataFrame(metafeatures)\",\n \"def get_data_from_disc(symbol, usecols=['Date', 'Close', 'Open', 'High', 'Low', 'Adj Close', 'Volume']):\\n print('\\\\n\\\\n\\\\n============================================================================')\\n print('#Loading raw data usecols=',usecols)\\n print('===============================================================================')\\n df1 = pd.read_csv(symbol_to_path(symbol)\\n , index_col='Date'\\n , parse_dates=True\\n , usecols=usecols\\n , na_values=['nan'])\\n\\n print('raw columns=', df1.columns)\\n print('\\\\nraw df1=\\\\n', df1)\\n return df1\",\n \"def get_details(i, df):\\n print(\\\"Name:\\\", df.name.loc[i])\\n print(\\\"Gender:\\\", df.gender.loc[i])\\n print(\\\"Age in months:\\\", df.age.loc[i])\\n print(\\\"Breed:\\\", df.breed1_desc.loc[i].title().replace(\\\"_\\\",\\\" \\\"),df.breed2_desc.loc[i].title().replace(\\\"_\\\",\\\" \\\"))\\n print(\\\"Color/s:\\\", df.color1_desc.loc[i].title(), df.color2_desc.loc[i].title(), df.color3_desc.loc[i].title())\\n print(\\\"Fur Length:\\\", df.fur_length.loc[i])\\n print(\\\"Vaccinated:\\\", df.vaccinated.loc[i])\\n print(\\\"Dewormed:\\\", df.dewormed.loc[i])\\n print(\\\"Spayed or Neutered:\\\", df.sterilized.loc[i])\\n print(\\\"Health:\\\", df.health.loc[i])\\n print(\\\"No. of Pets in this Listing:\\\", df.quantity.loc[i])\\n if df.fee.loc[i] == 0:\\n print(\\\"Adoption Fee: FREE\\\")\\n else:\\n print(\\\"Adoption Fee: MYR\\\", round(df.fee.loc[i],2))\\n print(\\\"Location :\\\", df.state_desc.loc[i].title().replace(\\\"_\\\",\\\" \\\"))\\n print(\\\"Description :\\\", df.description.loc[i])\\n print_images(i, df)\",\n \"def sample_metadata(sample):\\n sel = [\\n Mental_health.year,\\n Mental_health.physical_importance,\\n Mental_health.mental_importance,\\n Mental_health.industry_support,\\n ]\\n \\n results = db.session.query(*sel).all() \\n# Create a dictionary entry for each row of metadata information\\n sample_metadata = {}\\n for result in results:\\n Mental_health[\\\"year\\\"] = result[0]\\n Mental_health[\\\"physical_importance\\\"] = result[1]\\n Mental_health[\\\"mental_importance\\\"] = result[2]\\n Mental_health[\\\"industry_support\\\"] = result[3]\\n \\n\\n print(sample_metadata)\\n return jsonify(sample_metadata)\",\n \"def getIndicators(fileName: str):\\n df = pd.read_json(f'data/{fileName}')\\n df.columns = ['TIMESTAMP', 'OPEN', 'HIGH', 'LOW', 'CLOSE', 'VOLUME']\\n df['PROFIT_TOTAL'] = df['CLOSE'] - df['OPEN']\\n df['PROFIT_PERCENTAGE'] = percentage(df['PROFIT_TOTAL'], df['OPEN'])\\n df['DATE'] = pd.to_datetime(df['TIMESTAMP'], unit='ms')\\n df['RSI'] = getRSI(df, 20)\\n df['SMA_20'] = getSMA(df, 20)\\n df['SMA_50'] = getSMA(df, 50)\\n df['SMA_100'] = getSMA(df, 100)\\n df['SMA_200'] = getSMA(df, 200)\\n df['EMA_20'] = getEMA(df, 20)\\n df['EMA_50'] = getEMA(df, 50)\\n df['EMA_100'] = getEMA(df, 100)\\n df['EMA_200'] = getEMA(df, 200)\\n df['BB_HIGH_1'] = getBollingerUpperBand(df, 1, 20)\\n df['BB_LOW_1'] = getBollingerLowerBand(df, 1, 20)\\n df['BB_WIDTH_1'] = getBollingerBandWidth(df, 1, 20)\\n df['BB_HIGH_2'] = getBollingerUpperBand(df, 2, 20)\\n df['BB_LOW_2'] = getBollingerLowerBand(df, 2, 20)\\n df['BB_WIDTH_2'] = getBollingerBandWidth(df, 2, 20)\\n df['MACD_DEFAULT'] = getMACD(df, 26, 12, 9)\\n df['MACD_200_50_20'] = getMACD(df, 200, 50, 20)\\n\\n return [df, len(df)]\",\n \"def sample_metadata(iso_code):\\n sel = [\\n Freedom_short.iso_code,\\n Freedom_short.year,\\n Freedom_short.country,\\n Freedom_short.region,\\n Freedom_short.hf_score,\\n Freedom_short.hf_rank,\\n Freedom_short.hf_quartile,\\n ]\\n\\n results = db.session.query(*sel).filter(Freedom_short.iso_code == iso_code,Freedom_short.year == 2017).all()\\n\\n # Create a dictionary entry for each row of metadata information\\n freedom_sdata_list = []\\n for result in results:\\n freedom_sdata = {}\\n freedom_sdata[\\\"iso_code\\\"] = result[0]\\n freedom_sdata[\\\"year\\\"] = result[1]\\n freedom_sdata[\\\"country\\\"] = result[2]\\n freedom_sdata[\\\"region\\\"] = result[3]\\n freedom_sdata[\\\"hf_score\\\"] = str(result[4])\\n freedom_sdata[\\\"hf_rank\\\"] = str(result[5])\\n freedom_sdata[\\\"hf_quartile\\\"] = result[6]\\n freedom_sdata_list.append(freedom_sdata) \\n\\n print(freedom_sdata_list)\\n return jsonify(freedom_sdata_list[0])\",\n \"def _compute_overview(df: EDAFrame, cfg: Config) -> Dict[str, Any]:\\n data: Dict[str, Any] = {}\\n # overview\\n if cfg.overview.enable:\\n data[\\\"ov\\\"] = calc_stats(df, cfg)\\n data[\\\"insights\\\"] = []\\n for col in df.columns:\\n col_dtype = df.get_eda_dtype(col)\\n\\n # when srs is full NA, the column type will be float\\n # and need to be transformed into str.\\n if df.get_missing_cnt(col) == df.shape[0]:\\n srs = df.get_col_as_str(col, na_as_str=False).dropna().astype(str)\\n data[\\\"insights\\\"].append((col, Nominal(), _nom_calcs(srs, cfg)))\\n elif isinstance(col_dtype, Continuous):\\n data[\\\"insights\\\"].append(\\n (col, Continuous(), _cont_calcs(df.frame[col].dropna(), cfg))\\n )\\n elif isinstance(col_dtype, (Nominal, GeoGraphy, GeoPoint, SmallCardNum)):\\n srs = df.get_col_as_str(col, na_as_str=False).dropna()\\n data[\\\"insights\\\"].append((col, Nominal(), _nom_calcs(srs, cfg)))\\n # elif isinstance(col_dtype, DateTime):\\n # data[\\\"insights\\\"].append(\\n # (col, DateTime(), dask.delayed(_calc_line_dt)(df.frame[[col]], cfg.line.unit))\\n # )\\n else:\\n raise RuntimeError(f\\\"unprocessed data type: col:{col}, dtype: {type(col_dtype)}\\\")\\n return data\",\n \"def read_strong_meta(strong_meta):\\n \\n df = pd.read_csv(strong_meta, sep='\\\\t')\\n df = pd.DataFrame(df)\\n \\n events_list = []\\n \\n for row in df.iterrows():\\n event = {'filename': row[1]['filename'], \\n 'onset': row[1]['onset'], \\n 'offset': row[1]['offset'], \\n 'event_label': row[1]['event_label']}\\n \\n events_list.append(event)\\n \\n return events_list\",\n \"def data_specific_processing(self, dataframe):\\n return dataframe\",\n \"def get_ctffind_4_1_0_meta(file_name: str) -> pd.DataFrame:\\n extract_dict: typing.Dict[str, str]\\n ctffind_meta_data: pd.DataFrame\\n lines: typing.List[str]\\n match: typing.Optional[typing.Match[str]]\\n non_string_values: typing.Set[str]\\n\\n extract_dict = get_ctffind_4_1_0_extract_dict()\\n ctffind_meta_data = pd.DataFrame(index=[0], columns=extract_dict.keys())\\n with open(file_name, 'r') as read:\\n lines = read.readlines()\\n\\n non_string_values = set([\\n 'MicrographNameNoDW',\\n 'version'\\n ])\\n for line in lines:\\n for key, value in extract_dict.items():\\n match = re.match(value, line)\\n if match is not None:\\n try:\\n ctffind_meta_data[key] = float(match.group(1))\\n except ValueError:\\n assert key in non_string_values, f'{key}: {match.group(1)}'\\n ctffind_meta_data[key] = match.group(1)\\n else:\\n pass\\n return ctffind_meta_data\",\n \"def get_events_metadata(events, beh_df):\\n assert len(events[events[:, 2] == cfg.EVENTS_ID[\\\"answer\\\"]]) == len(beh_df)\\n assert len(events[events[:, 2] == cfg.EVENTS_ID[\\\"fixcross\\\"]]) == len(\\n beh_df\\n )\\n i_question = 0\\n beh_data = {\\\"confidence\\\": [], \\\"is_correct\\\": [], \\\"question_num\\\": []}\\n for i, ev in enumerate(events):\\n if i_question == 0 or ev[2] == cfg.EVENTS_ID[\\\"question/second\\\"]:\\n confidence, is_correct, question_num = get_question_data(\\n i_question, beh_df\\n )\\n if ev[2] == cfg.EVENTS_ID[\\\"confidence\\\"]:\\n i_question += 1\\n beh_data[\\\"confidence\\\"].append(confidence)\\n beh_data[\\\"is_correct\\\"].append(is_correct)\\n beh_data[\\\"question_num\\\"].append(question_num)\\n\\n metadata = pd.DataFrame(beh_data)\\n return metadata\",\n \"def load_extract(cryptocurrency):\\n df = pd.read_csv(f'input_12mo/{cryptocurrency}.csv')\\n df = df['Close'].copy()\\n df = df[-183:].copy()\\n return df\",\n \"def get_data_from_disc(symbol, skipFirstLines, size_output = 2):\\n\\n df1 = pd.read_csv( symbol_to_path(symbol)\\n , index_col = 'Date'\\n , parse_dates= True\\n , usecols = ['Date', 'Close', 'Open', 'High', 'Low', 'Adj Close', 'Volume']\\n , na_values = ['nan'])\\n\\n # Clean NaN values\\n df = utils.dropna(df1)\\n\\n # Add ta features filling NaN values\\n #df1 = add_all_ta_features(df, \\\"Open\\\", \\\"High\\\", \\\"Low\\\", \\\"Close\\\", fillna=True)#, \\\"Volume_BTC\\\",\\n\\n # Add bollinger band high indicator filling NaN values\\n df1['bb_hi10' ] = bollinger_hband_indicator(df1[\\\"Close\\\"], n=10 , ndev=2, fillna=True)\\n df1['bb_lo10' ] = bollinger_lband_indicator(df1[\\\"Close\\\"], n=10 , ndev=2, fillna=True)\\n df1['bb_hi20' ] = bollinger_hband_indicator(df1[\\\"Close\\\"], n=20 , ndev=2, fillna=True)\\n df1['bb_lo20' ] = bollinger_lband_indicator(df1[\\\"Close\\\"], n=20 , ndev=2, fillna=True)\\n df1['bb_hi50' ] = bollinger_hband_indicator(df1[\\\"Close\\\"], n=50 , ndev=2, fillna=True)\\n df1['bb_lo50' ] = bollinger_lband_indicator(df1[\\\"Close\\\"], n=50 , ndev=2, fillna=True)\\n df1['bb_hi200'] = bollinger_hband_indicator(df1[\\\"Close\\\"], n=200, ndev=2, fillna=True)\\n df1['bb_lo200'] = bollinger_lband_indicator(df1[\\\"Close\\\"], n=200, ndev=2, fillna=True)\\n\\n df1['rsi5' ] = rsi (df1[\\\"Close\\\"], n=5 , fillna=True)\\n df1['rsi10' ] = rsi (df1[\\\"Close\\\"], n=10, fillna=True)\\n df1['rsi20' ] = rsi (df1[\\\"Close\\\"], n=20, fillna=True)\\n df1['rsi50' ] = rsi (df1[\\\"Close\\\"], n=50, fillna=True)\\n\\n\\n df1['stoc10' ] = stoch (df1[\\\"High\\\"],df1[\\\"Low\\\"],df1[\\\"Close\\\"], n=10 , fillna=True)\\n df1['stoc20' ] = stoch (df1[\\\"High\\\"],df1[\\\"Low\\\"],df1[\\\"Close\\\"], n=20 , fillna=True)\\n df1['stoc50' ] = stoch (df1[\\\"High\\\"],df1[\\\"Low\\\"],df1[\\\"Close\\\"], n=50 , fillna=True)\\n df1['stoc200'] = stoch (df1[\\\"High\\\"],df1[\\\"Low\\\"],df1[\\\"Close\\\"], n=200, fillna=True)\\n\\n df1['mom5' ] = wr (df1[\\\"High\\\"],df1[\\\"Low\\\"],df1[\\\"Close\\\"], lbp=5 , fillna=True)\\n df1['mom10' ] = wr (df1[\\\"High\\\"],df1[\\\"Low\\\"],df1[\\\"Close\\\"], lbp=10 , fillna=True)\\n df1['mom20' ] = wr (df1[\\\"High\\\"],df1[\\\"Low\\\"],df1[\\\"Close\\\"], lbp=20 , fillna=True)\\n df1['mom50' ] = wr (df1[\\\"High\\\"],df1[\\\"Low\\\"],df1[\\\"Close\\\"], lbp=50 , fillna=True)\\n\\n df1['sma10' ] = df1['Close'].rolling(window=10 ).mean()\\n df1['sma20' ] = df1['Close'].rolling(window=20 ).mean()\\n df1['sma50' ] = df1['Close'].rolling(window=50 ).mean()\\n df1['sma200'] = df1['Close'].rolling(window=200).mean()\\n df1['sma400'] = df1['Close'].rolling(window=400).mean()\\n #df1['mom']=pandas.stats.\\n df1 = df1[-(df1.shape[0]-skipFirstLines):] # skip 1st x rows, x years due to NAN in sma, range\\n\\n df1['nvo' ] = df1['Volume'] / df1['sma10'] / 100 # normalized volume\\n\\n\\n df1['range'] = df1['Close'] - df1['Open']\\n df1['percentage'] = df1['range'] / df1['Open'] * 100\\n\\n #df/df.iloc[0,:]\\n df1['range_sma'] = (df1 ['Close'] - df1 ['sma10']) / df1['Close']\\n df1['range_sma1'] = (df1 ['sma10'] - df1 ['sma20']) / df1['sma10']#small sma above big sma indicates that price is going up\\n df1['range_sma2'] = (df1 ['sma20'] - df1 ['sma50']) / df1['sma20']#small sma above big sma indicates that price is going up\\n df1['range_sma3'] = (df1 ['sma50'] - df1['sma200']) / df1['sma50']#small sma above big sma indicates that price is going up\\n df1['range_sma4'] = (df1['sma200'] - df1['sma400']) / df1['sma200']#small sma above big sma indicates that price is going up\\n\\n df1['rel_bol_hi10'] = (df1 ['High'] - df1 ['bb_hi10']) / df1['High']\\n df1['rel_bol_lo10'] = (df1 ['Low'] - df1 ['bb_lo10']) / df1['Low']\\n df1['rel_bol_hi20'] = (df1 ['High'] - df1 ['bb_hi20']) / df1['High']\\n df1['rel_bol_lo20'] = (df1 ['Low'] - df1 ['bb_lo20']) / df1['Low']\\n df1['rel_bol_hi50'] = (df1 ['High'] - df1 ['bb_hi50']) / df1['High']\\n df1['rel_bol_lo50'] = (df1 ['Low'] - df1 ['bb_lo50']) / df1['Low']\\n df1['rel_bol_hi200'] = (df1 ['High'] - df1 ['bb_hi200']) / df1['High']\\n df1['rel_bol_lo200'] = (df1 ['Low'] - df1 ['bb_lo200']) / df1['Low']\\n\\n #df1['isUp'] = 0\\n print(df1)\\n # print ('\\\\ndf1=\\\\n',df1.tail())\\n # print ('\\\\nsma_10=\\\\n',df1['sma10'] )\\n # print ('\\\\nsma_200=\\\\n',df1['sma200'] )\\n # print ('\\\\nrsi10=\\\\n',df1['rsi10'] )\\n # print ('\\\\nrsi5=\\\\n',df1['rsi5'] )\\n # print ('\\\\nstoc10=\\\\n',df1['stoc10'] )\\n # print ('\\\\nstoc200=\\\\n',df1['stoc200'] )\\n # print ('\\\\nrangesma=\\\\n',df1['rangesma'])\\n # print ('\\\\nrangesma4=\\\\n',df1['rangesma4'])\\n # print ('\\\\nrel_bol_hi10=\\\\n',df1['rel_bol_hi10'])\\n # print ('\\\\nrel_bol_hi200=\\\\n',df1['rel_bol_hi200'])\\n\\n # df1['sma4002' ] = sma\\n # df1['ema' ] = ema\\n # df1['macd' ] = macd\\n # df1['stoc' ] = stoc\\n # df1['rsi' ] = rsi\\n#tech_ind = pd.concat([sma, ema, macd, stoc, rsi, adx, cci, aroon, bands, ad, obv, wma, mom, willr], axis=1)\\n\\n ## labeling\\n ## smart labeling\\n if size_output == 2:\\n df1.loc[df1.range > 0.0, 'isUp'] = 1\\n df1.loc[df1.range <= 0.0, 'isUp'] = 0\\n if size_output == 3:\\n df1['isUp'] = 0\\n df1.loc[ df1.percentage >= +0.1 , 'isUp'] = 1\\n df1.loc[ df1.percentage <= -0.1 , 'isUp'] = -1\\n # df1.loc[(-0.1 < df1.percentage < +0.1), 'isUp'] = 0\\n #df1['isUp'] = np.random.randint(2, size=df1.shape[0])# if u the model accuracy with random labaling expect to get 0.5\\n\\n\\n #direction = (close > close.shift()).astype(int)\\n #target = direction.shift(-1).fillna(0).astype(int)\\n #target.name = 'target'\\n #sma10 = sma10.rename(columns={symbol: symbol+'sma10'})\\n #sma20 = sma20.rename(columns={symbol: symbol+'sma20'})\\n #df1 = df1.rename(columns={'Close': symbol+'Close'})\\n '''\\n# loss: 0.1222 - acc: 0.9000 - val_loss: 0.1211 - val_acc: 0.9364 epoch50 sma+range+close+open (range tell model the answer)\\n# loss: 0.6932 - acc: 0.4860 - val_loss: 0.6932 - val_acc: 0.4969 random data >>> random results\\n# loss: 0.6922 - acc: 0.5205 - val_loss: 0.6911 - val_acc: 0.5364 epoch50 sma\\n# loss: 0.6923 - acc: 0.5198 - val_loss: 0.6914 - val_acc: 0.5360\\n# loss: 0.6922 - acc: 0.5217 - val_loss: 0.6911 - val_acc: 0.5353\\n# loss: 0.6431 - acc: 0.5846 - val_loss: 0.7373 - val_acc: 0.5364\\n# loss: 0.5373 - acc: 0.7114 - val_loss: 0.6112 - val_acc: 0.6773 epoch50\\n# loss: 0.5198 - acc: 0.7225 - val_loss: 0.5632 - val_acc: 0.6797 epoch100 sma+stoc+rsi\\n# loss: 0.5487 - acc: 0.7079 - val_loss: 0.6115 - val_acc: 0.6740 SPY 1970 epoch100 sma+stoc+rsi+bol 1970\\n# loss: 0.4112 - acc: 0.8140 - val_loss: 0.5576 - val_acc: 0.7324 SPY 1970 epoch500 nvo+mom+sma+stoc+rsi+bol 1970\\n\\n# loss: 0.6047 - acc: 0.6574 - val_loss: 0.6257 - val_acc: 0.6580 SPY 2000\\n# loss: nan - acc: 0.4711 - val_loss: nan - val_acc: 0.4563 DJI 2000\\n# loss: nan - acc: 0.4906 - val_loss: nan - val_acc: 0.4626 QQQ 2000\\n\\n nvo Open High Low Close range_sma isUp\\nDate \\n1964-05-01 748.525452 79.459999 80.470001 79.459999 80.169998 0.001821 1.0\\n1964-05-04 669.824179 80.169998 81.010002 79.870003 80.470001 0.005580 1.0\\n2019-07-11 10607.754714 2999.620117 3002.330078 2988.800049 2999.909912 0.008677 1.0\\n2019-07-12 9973.829690 3003.360107 3013.919922 3001.870117 3013.770020 0.010287 1.0\\n'''\\n pd.set_option('display.max_columns', 500)\\n pd.set_option('display.width' , 1000)\\n print('columns=', df1.columns)\\n print ('\\\\ndf1=\\\\n',df1.loc[:, ['nvo', 'Open' ,'High' , 'Low' , 'Close', 'range_sma', 'isUp']])\\n print ('\\\\ndf1=\\\\n',df1.loc[:, ['sma10','sma20','sma50','sma200', 'range_sma1']])\\n print ('\\\\ndf1=\\\\n',df1.loc[:, ['rsi10','rsi20','rsi50','rsi5']])\\n print ('\\\\ndf1=\\\\n',df1.loc[:, ['stoc10','stoc20','stoc50','stoc200']])\\n print ('\\\\ndf1=\\\\n',df1.loc[:, ['bb_hi10','bb_hi20','bb_hi50','bb_hi200']])#, 'sma4002']])\\n print ('\\\\ndf1=\\\\n',df1.loc[:, ['bb_lo10','bb_lo20','bb_lo50','bb_lo200']])#, 'sma4002']])\\n print ('\\\\ndf1=\\\\n',df1.loc[:, ['rel_bol_hi10','rel_bol_hi20','rel_bol_hi50','rel_bol_hi200']])#, 'sma4002']])\\n print ('\\\\ndf1[ 0]=\\\\n',df1.iloc[0])#, 'sma4002']])\\n print ('\\\\ndf1[ 1]=\\\\n',df1.iloc[1])#, 'sma4002']])\\n print ('\\\\ndf1[9308]=\\\\n',df1.iloc[9308])#, 'sma4002']])\\n print ('\\\\ndf1[-2]=\\\\n',df1.iloc[-2])#, 'sma4002']])\\n print ('\\\\ndf1[-1]=\\\\n',df1.iloc[-1])#, 'sma4002']])\\n #df = pd.DataFrame(record, columns = ['Name', 'Age', 'Stream', 'Percentage'])\\n # rslt_df = df[df1['isUp'] == 1]\\n # print ('\\\\ndf1 describe direction = +1\\\\n',rslt_df.describe())\\n # rslt_df = df[df1['isUp'] == -1]\\n # print ('\\\\ndf1 describe direction = -1\\\\n',rslt_df.describe())\\n # rslt_df = df[df1['isUp'] == 0]\\n # print ('\\\\ndf1 describe direction = 0\\\\n',rslt_df.describe())\\n # # print ('\\\\ndf1=\\\\n',df1.loc[:, ['ema','macd','stoc', 'rsi']])\\n print('\\\\ndf11 describe=\\\\n',df1.loc[:, ['percentage', 'nvo', 'range', 'mom5', 'mom10', 'mom20', 'mom50', 'rsi5', 'rsi10', 'rsi20', 'rsi50', 'stoc10', 'stoc20', 'stoc50', 'stoc200']].describe())\\n return df1\",\n \"def read_data(transferencias):\\n # read in triples of user/artist/playcount from the input dataset\\n transfer_count = transferencias.groupby([\\\"REFERENCIA_1\\\", \\\"REFERENCIA_ORIGEN\\\"]).IMPORTE.count().reset_index()\\n\\n # for index, row in transfer_count.iterrows():\\n # cliente = Empresa.objects.get(fiscal_id=str(row['REFERENCIA_ORIGEN']))\\n # proveedor = Empresa.objects.get(fiscal_id=str(row['REFERENCIA_1']))\\n # proveedor.clients.add(cliente)\\n # cliente.providers.add(proveedor)\\n\\n print(\\\"clientes y proveedores cargados\\\")\\n\\n data = pandas.DataFrame()\\n data['user'] = transfer_count['REFERENCIA_ORIGEN'].astype(\\\"category\\\")\\n data['artist'] = transfer_count['REFERENCIA_1'].astype(\\\"category\\\")\\n data['transfers'] = transfer_count['IMPORTE'].astype(\\\"category\\\")\\n\\n transfers = coo_matrix((data['transfers'].astype(float),\\n (data['artist'].cat.codes.copy(),\\n data['user'].cat.codes.copy())))\\n\\n return data, transfers\",\n \"def metadata(self, rel_index) -> Metadata:\\n\\n row = self.image_df.iloc[rel_index]\\n\\n (\\n site,\\n well,\\n replicate,\\n plate,\\n compound,\\n concentration,\\n moa,\\n image_idx,\\n ) = row[\\n [\\n \\\"site\\\",\\n \\\"well\\\",\\n \\\"replicate\\\",\\n \\\"plate\\\",\\n \\\"compound\\\",\\n \\\"concentration\\\",\\n \\\"moa\\\",\\n \\\"image_idx\\\",\\n ]\\n ]\\n\\n metadata = Metadata(\\n Plate(site, str(well), replicate, plate),\\n Compound(compound, concentration, moa),\\n image_idx,\\n )\\n\\n return metadata\",\n \"def fetch_tracks_info_df(df):\\n\\n gen_df = df.copy()\\n gen_df = gen_df[['artist_name', 'title', 'release', 'track_id', 'song_id']]\\n\\n for column_name in gen_df.columns:\\n gen_df[column_name] = gen_df[column_name].map(lambda x: ast.literal_eval(x).decode('utf-8'))\\n\\n gen_df.rename(columns={'release': 'album_name'}, inplace=True)\\n gen_df['year'] = df['year']\\n\\n return gen_df\",\n \"def fetchAndCleanDataframe(self):\\n\\n df = pd.read_csv('/Users/apple4u/Desktop/goksel tez/results_with_scenarios.csv')\\n df.insider_label.fillna(0, inplace=True) # replaces null fields with 0\\n df = df.drop(columns=['employee_name', 'scenario', 'role'])\\n df = df.rename(columns={'insider_label':'label'})\\n #df['label'] = df['insider_label'].astype('int64')\\n #df.drop(columns='insider_label', inplace=True)\\n df.set_index('user_id', inplace=True)\\n X = df.iloc[:, :5].values #fetch all records first 5 columns\\n y = df.label.values\\n print(df.head())\\n return X, y\",\n \"def extract_data():\\n logging.info(f'Reading data from {impftermine.agg_export_file_name()}...')\\n df_wl = pd.read_csv(impftermine.agg_export_file_name())\\n vacc_report_file = os.path.join(credentials.vmdl_path, 'vaccination_report_bs_age_group_long.csv')\\n logging.info(f'Reading data from {vacc_report_file}...')\\n df_impf = pd.read_csv(vacc_report_file)\\n return df_wl, df_impf\",\n \"def load_predict(cryptocurrency):\\n df = pd.read_csv(f'output_12mo/{cryptocurrency}.csv')\\n #df = df.drop(0, axis=0).copy()\\n #df = df['Close'].copy()\\n #df = df[:-25].copy()\\n return df\",\n \"def create_metadata(data_dir):\\n pool = multiprocessing.Pool()\\n raw_feature_paths = [os.path.join(data_dir, \\\"features.jsonl\\\")]\\n records = list(pool.imap(read_metadata_record, raw_feature_iterator(raw_feature_paths)))\\n records = [dict(record, **{\\\"subset\\\": \\\"train\\\"}) for record in records]\\n\\n metadf = pd.DataFrame(records)[[\\\"sha256\\\", \\\"appeared\\\", \\\"subset\\\", \\\"label\\\"]]\\n metadf.to_csv(os.path.join(data_dir, \\\"metadata.csv\\\"))\\n print(\\\"\\\\n[Done] create_metadata\\\\n\\\")\\n \\n return metadf\",\n \"def get_ticker_details(self):\\n\\n ticker_data = sequential_data_scrape(\\n scrape.download_ticker_details,\\n [\\n f\\\"https://finviz.com/quote.ashx?&t={row.get('Ticker')}\\\"\\n for row in self.data\\n ],\\n self._user_agent,\\n )\\n\\n for entry in ticker_data:\\n for key, value in entry.items():\\n for ticker_generic in self.data:\\n if ticker_generic.get(\\\"Ticker\\\") == key:\\n if \\\"Sales\\\" not in self.headers:\\n self.headers.extend(list(value[0].keys()))\\n\\n ticker_generic.update(value[0])\\n self.analysis.extend(value[1])\\n\\n return self.data\",\n \"def getUserHistFeatures(transaction_list, coupon_dict, model_start_date, purchase_date):\\n feat_header = [\\\"NoOfPurchases\\\", \\\"DaysSinceLastPurchase\\\", \\\"NoOfPurchasesLastweek\\\", \\\"NoOfPurchasesLast15Days\\\", \\\"NoOfPurchasesLast30Days\\\", \\\"NoOfPurchasesLast60Days\\\", \\\"NoOfPurchasesLast90Days\\\", \\\"NoOfPurchasesLast180Days\\\", \\\"DaysSincePrevPurchase\\\", \\\"NoOfPurchasesPrevweek\\\", \\\"NoOfPurchasesPrev15Days\\\", \\\"NoOfPurchasesPrev30Days\\\", \\\"NoOfPurchasesPrev60Days\\\", \\\"NoOfPurchasesPrev90Days\\\", \\\"NoOfPurchasesPrev180Days\\\"]\\n\\n # getting number of purchases #\\n feat_list = [len(transaction_list)]\\n\\n # initializing variables #\\n purchase_small_area_name_dict = {}\\n puchase_date_list = []\\n capsule_text_dict = {}\\n genre_name_dict = {}\\n price_rate_list = []\\n catalog_price_list = []\\n discount_price_list = []\\n dispperiod_list = []\\n valid_period_list = []\\n usable_date_mon_list = {}\\n usable_date_tue_list = {}\\n usable_date_wed_list = {}\\n usable_date_thu_list = {}\\n usable_date_fri_list = {}\\n usable_date_sat_list = {}\\n usable_date_sun_list = {}\\n usable_date_hol_list = {}\\n usable_date_before_hol_list = {}\\n coupon_large_area_name_dict = {}\\n coupon_small_area_name_dict = {}\\n coupon_ken_name_dict = {}\\n days_since_last_purchase = 9999\\n last_week_purchase = 0\\n last_fifteendays_purchase = 0\\n last_thirtydays_purchase = 0\\n last_sixtydays_purchase = 0\\n last_nintydays_purchase = 0\\n\\tlast_oneeightydays_purchase = 0\\n\\tdays_since_prev_purchase = 9999\\n\\tprev_week_purchase = 0\\n prev_fifteendays_purchase = 0\\n prev_thirtydays_purchase = 0\\n prev_sixtydays_purchase = 0\\n prev_nintydays_purchase = 0\\n prev_oneeightydays_purchase = 0\\n for transaction in transaction_list:\\n diff_days = (model_start_date - datetime.datetime.strptime(transaction['I_DATE'], \\\"%Y-%m-%d %H:%M:%S\\\").date()).days\\n if diff_days < days_since_last_purchase:\\n days_since_last_purchase = diff_days\\n if diff_days <= 7:\\n last_week_purchase += 1\\n if diff_days <= 15:\\n last_fifteendays_purchase += 1\\n if diff_days <= 30:\\n last_thirtydays_purchase += 1\\n if diff_days <= 60:\\n last_sixtydays_purchase += 1\\n if diff_days <= 90:\\n last_nintydays_purchase += 1\\n\\t\\tif diff_days <= 180:\\n last_oneeightydays_purchase += 1\\n\\t\\t\\n\\t\\tdiff_days = (purchase_date - datetime.datetime.strptime(transaction['I_DATE'], \\\"%Y-%m-%d %H:%M:%S\\\").date()).days\\n if diff_days < days_since_last_purchase:\\n days_since_prev_purchase = diff_days\\n if diff_days <= 7:\\n prev_week_purchase += 1\\n if diff_days <= 15:\\n prev_fifteendays_purchase += 1\\n if diff_days <= 30:\\n prev_thirtydays_purchase += 1\\n if diff_days <= 60:\\n prev_sixtydays_purchase += 1\\n if diff_days <= 90:\\n prev_nintydays_purchase += 1\\n if diff_days <= 180:\\n prev_oneeightydays_purchase += 1\\n\\n coupon_id_dict = coupon_dict[ transaction['COUPON_ID_hash'] ]\\n purchase_small_area_name_dict[transaction['SMALL_AREA_NAME']] = purchase_small_area_name_dict.get( transaction['SMALL_AREA_NAME'],0) + 1\\n capsule_text_dict[ coupon_id_dict['CAPSULE_TEXT'] ] = capsule_text_dict.get( coupon_id_dict['CAPSULE_TEXT'], 0) + 1\\n genre_name_dict[ coupon_id_dict['GENRE_NAME'] ] = genre_name_dict.get( coupon_id_dict['GENRE_NAME'],0 ) + 1\\n coupon_large_area_name_dict[ coupon_id_dict['large_area_name'] ] = coupon_large_area_name_dict.get( coupon_id_dict['large_area_name'],0 ) + 1\\n coupon_small_area_name_dict[ coupon_id_dict['small_area_name'] ] = coupon_small_area_name_dict.get( coupon_id_dict['small_area_name'],0 ) + 1\\n coupon_ken_name_dict[ coupon_id_dict['ken_name'] ] = coupon_ken_name_dict.get( coupon_id_dict['ken_name'],0 ) + 1\\n price_rate_list.append( float(coupon_id_dict['PRICE_RATE']) )\\n catalog_price_list.append( float(coupon_id_dict['CATALOG_PRICE']) )\\n discount_price_list.append( float(coupon_id_dict['DISCOUNT_PRICE']) )\\n dispperiod_list.append( float(coupon_id_dict['DISPPERIOD']) )\\n if coupon_id_dict['VALIDPERIOD'] not in ('','NA'):\\n valid_period_list.append( float(coupon_id_dict['VALIDPERIOD']) )\\n if coupon_id_dict['USABLE_DATE_MON'] not in ('','NA'):\\n usable_date_mon_list[ float(coupon_id_dict['USABLE_DATE_MON']) ] = usable_date_mon_list.get( float(coupon_id_dict['USABLE_DATE_MON']),0 ) + 1\\n usable_date_tue_list[ float(coupon_id_dict['USABLE_DATE_TUE']) ] = usable_date_tue_list.get( float(coupon_id_dict['USABLE_DATE_TUE']),0 ) + 1\\n usable_date_wed_list[ float(coupon_id_dict['USABLE_DATE_WED']) ] = usable_date_wed_list.get( float(coupon_id_dict['USABLE_DATE_WED']),0 ) + 1\\n usable_date_thu_list[ float(coupon_id_dict['USABLE_DATE_THU']) ] = usable_date_thu_list.get( float(coupon_id_dict['USABLE_DATE_THU']),0 ) + 1\\n usable_date_fri_list[ float(coupon_id_dict['USABLE_DATE_FRI']) ] = usable_date_fri_list.get( float(coupon_id_dict['USABLE_DATE_FRI']),0 ) + 1\\n usable_date_sat_list[ float(coupon_id_dict['USABLE_DATE_SAT']) ] = usable_date_sat_list.get( float(coupon_id_dict['USABLE_DATE_SAT']),0 ) + 1\\n usable_date_sun_list[ float(coupon_id_dict['USABLE_DATE_SUN']) ] = usable_date_sun_list.get( float(coupon_id_dict['USABLE_DATE_SUN']),0 ) + 1\\n usable_date_hol_list[ float(coupon_id_dict['USABLE_DATE_HOLIDAY']) ] = usable_date_hol_list.get( float(coupon_id_dict['USABLE_DATE_HOLIDAY']),0 ) + 1\\n usable_date_before_hol_list[ float(coupon_id_dict['USABLE_DATE_BEFORE_HOLIDAY']) ] = usable_date_before_hol_list.get( float(coupon_id_dict['USABLE_DATE_BEFORE_HOLIDAY']),0 )+1\\n else:\\n usable_date_mon_list[3.0] = usable_date_mon_list.get( 3.0,0 ) + 1\\n usable_date_tue_list[3.0] = usable_date_tue_list.get( 3.0,0 ) + 1\\n usable_date_wed_list[3.0] = usable_date_wed_list.get( 3.0,0 ) + 1\\n usable_date_thu_list[3.0] = usable_date_thu_list.get( 3.0,0 ) + 1\\n usable_date_fri_list[3.0] = usable_date_fri_list.get( 3.0,0 ) + 1\\n usable_date_sat_list[3.0] = usable_date_sat_list.get( 3.0,0 ) + 1\\n usable_date_sun_list[3.0] = usable_date_sun_list.get( 3.0,0 ) + 1\\n usable_date_hol_list[3.0] = usable_date_hol_list.get( 3.0,0 ) + 1\\n usable_date_before_hol_list[3.0] = usable_date_before_hol_list.get( 3.0,0 ) + 1\\n\\n feat_list.extend([days_since_last_purchase, last_week_purchase, last_fifteendays_purchase, last_thirtydays_purchase, last_sixtydays_purchase, last_nintydays_purchase, last_oneeightydays_purchase, days_since_prev_purchase, prev_week_purchase, prev_fifteendays_purchase, prev_thirtydays_purchase, prev_sixtydays_purchase, prev_nintydays_purchase, prev_oneeightydays_purchase])\\n return feat_list, feat_header, [purchase_small_area_name_dict, capsule_text_dict, genre_name_dict, coupon_large_area_name_dict, coupon_small_area_name_dict, coupon_ken_name_dict, price_rate_list, catalog_price_list, discount_price_list, dispperiod_list, valid_period_list, usable_date_mon_list, usable_date_tue_list, usable_date_wed_list, usable_date_thu_list, usable_date_fri_list, usable_date_sat_list, usable_date_sun_list, usable_date_hol_list, usable_date_before_hol_list]\",\n \"def extract_summary(self):\\n metadata = {}\\n\\n ## document Id\\n documentId = self.tree.find(\\\"./id\\\")\\n documentId = documentId.attrib['root'] if documentId is not None and \\\"root\\\" in documentId.attrib else \\\"\\\"\\n metadata[\\\"documentId\\\"] = documentId\\n\\n ## setId\\n setid = self.tree.find(\\\"./setId\\\")\\n setid = setid.attrib['root'] if setid is not None and \\\"root\\\" in setid.attrib else \\\"\\\"\\n metadata[\\\"setId\\\"] = setid\\n\\n ## version number\\n splversion = self.tree.find(\\\"./versionNumber\\\")\\n versionNumber = \\\"\\\"\\n if splversion is not None:\\n if \\\"value\\\" in splversion.attrib:\\n versionNumber = splversion.attrib[\\\"value\\\"]\\n metadata[\\\"versionNumber\\\"] = versionNumber\\n\\n ## product type \\n code = self.tree.find(\\\"./code\\\")\\n check_if_attrib_exists = lambda x, key: x[key] if key in x else ''\\n product_type = check_if_attrib_exists(code.attrib, \\\"displayName\\\")\\n metadata[\\\"productType\\\"] = product_type\\n\\n ## title\\n title_text = self.tree_et.xpath(\\\"./title//text()\\\")\\n title = (\\\" \\\".join([self.strip_newline_tab(t) for t in title_text]) if len(title_text) > 0 else \\\"\\\")\\n metadata[\\\"title\\\"] = title\\n\\n ## manufacturer\\n manufacturer = self.tree.find(\\\"./author//representedOrganization/name\\\")\\n if manufacturer != None and manufacturer.text != None:\\n manufacturer = self.strip_newline_tab(manufacturer.text)\\n else:\\n manufacturer = \\\"\\\"\\n metadata[\\\"manufacturer\\\"] = manufacturer\\n\\n ## effectivetime\\n effectiveTime = self.tree_et.xpath(\\\"./effectiveTime/@value\\\")\\n effectiveTime = self.__normalize_date(effectiveTime)\\n\\n metadata[\\\"effectiveTime\\\"] = effectiveTime\\n metadata[\\\"publishedDate\\\"] = effectiveTime\\n\\n ## From manufacturedProduct section\\n brand_name = self.tree_et.xpath(\\\".//manufacturedProduct//name\\\")\\n brand_name = self.strip_newline_tab(brand_name[0].text) if len(brand_name) > 0 else \\\"\\\"\\n metadata[\\\"drugName\\\"] = brand_name\\n\\n route = self.tree_et.xpath(\\\".//manufacturedProduct//formCode/@code\\\")\\n route = self.strip_newline_tab(route[0]) if len(route) > 0 else \\\"\\\"\\n metadata[\\\"routeOfAdministration\\\"] = route\\n\\n product_ndc = self.tree_et.xpath(\\\".//manufacturedProduct//code/@code\\\")\\n product_ndc = self.strip_newline_tab(product_ndc[0]) if len(product_ndc) > 0 else \\\"\\\"\\n metadata[\\\"ndcCode\\\"] = product_ndc\\n\\n generic_name = self.tree_et.xpath(\\\".//manufacturedProduct//asEntityWithGeneric//genericMedicine/name\\\")\\n generic_name = self.strip_newline_tab(generic_name[0].text) if len(generic_name) > 0 else \\\"\\\"\\n metadata[\\\"genericName\\\"] = generic_name\\n\\n ## dosage form\\n dosage_form = self.tree_et.xpath(\\\".//manufacturedProduct//formCode/@displayName\\\")\\n dosage_form = dosage_form[0] if len(dosage_form) > 0 else \\\"\\\"\\n metadata[\\\"dosageForm\\\"] = dosage_form\\n\\n # active ingredients\\n substance_name = sorted([self.strip_newline_tab(a.text) for a in\\n self.tree_et.xpath(\\\".//.//manufacturedProduct//activeMoiety/activeMoiety/name\\\")])\\n substance_name = \\\", \\\".join(set(substance_name))\\n metadata[\\\"substanceName\\\"] = substance_name\\n\\n ## inactive ingredients\\n inactive_ingredients = sorted([self.strip_newline_tab(inactive.text) for inactive in self.tree_et.xpath(\\n \\\".//manufacturedProduct//inactiveIngredient/inactiveIngredientSubstance/name\\\")])\\n\\n if len(inactive_ingredients) == 0:\\n inactive_ingredients = \\\"\\\"\\n else:\\n inactive_ingredients = \\\",\\\".join(set(inactive_ingredients))\\n\\n metadata[\\\"inactiveIngredients\\\"] = inactive_ingredients\\n\\n ## other ingredients\\n ingredients = sorted([self.strip_newline_tab(ingredient.text) for ingredient in\\n self.tree_et.xpath(\\\".//manufacturedProduct//ingredient/ingredientSubstance/name\\\")])\\n\\n if len(ingredients) == 0:\\n ingredients = \\\"\\\"\\n else:\\n ingredients = \\\", \\\".join(set(ingredients))\\n metadata[\\\"ingredients\\\"] = ingredients\\n\\n # marketing_category\\n marketing_category = self.tree_et.xpath(\\\".//manufacturedProduct/subjectOf/approval/code/@displayName\\\")\\n marketing_category = self.strip_newline_tab(marketing_category[0]) if len(marketing_category) > 0 else \\\"\\\"\\n metadata[\\\"marketingCategory\\\"] = marketing_category\\n\\n # consumed in\\n consumed_in = self.tree_et.xpath(\\n \\\".//manufacturedProduct//consumedIn/substanceAdministration/routeCode/@displayName\\\")\\n consumed_in = consumed_in[0] if len(consumed_in) > 0 else \\\"\\\"\\n metadata[\\\"consumedIn\\\"] = consumed_in\\n\\n # revision date\\n marketing_date = self.tree_et.xpath(\\\".//manufacturedProduct//marketingAct/effectiveTime/low/@value\\\")\\n marketing_date = self.__normalize_date(marketing_date)\\n metadata[\\\"marketingDate\\\"] = marketing_date\\n\\n return metadata\",\n \"def screwdriver_data(df):\\n print('Only screwdriver data taken')\\n\\n screwdriver_df = df[['output_double_register_24',\\n 'output_double_register_25',\\n 'output_double_register_26',\\n 'output_double_register_27',\\n 'output_bit_register_70',\\n 'output_bit_register_71',\\n 'output_bit_register_72',\\n 'label']]\\n\\n return screwdriver_df\",\n \"def _get_metadata(conn):\\n metadata_sql = \\\"SELECT * FROM metadata\\\"\\n metadata = conn.execute(metadata_sql).fetchall()\\n return {\\n row[0]: {\\n \\\"name\\\": row[0],\\n \\\"field_name\\\": row[1],\\n \\\"field_expr\\\": row[1],\\n \\\"type\\\": row[2],\\n \\\"minimum\\\": row[3],\\n \\\"maximum\\\": row[4],\\n \\\"average\\\": row[5],\\n \\\"variance\\\": row[6],\\n \\\"total\\\": row[7],\\n \\\"stddev\\\": row[8],\\n \\\"other\\\": json.loads(row[9]) if row[9] else None,\\n }\\n for row in metadata\\n }\",\n \"def build_dataset_by_df(file_path):\\n data = pd.read_csv(file_path, sep=',')\\n print(data.head())\\n\\n user_ids = data['CustomerID'].unique()\\n prod_ids = data['StockCode'].unique()\\n user_num = len(user_ids)\\n product_num = len(prod_ids)\\n product_lists_by_user = dict(data.groupby('CustomerID')['StockCode'].apply(list)).values()\\n product_to_desc = dict(data.groupby('StockCode')['Description'].apply(set))\\n dictionary = {}\\n for id in prod_ids:\\n dictionary[id] = len(dictionary)\\n reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\\n return product_lists_by_user, product_to_desc, dictionary, reversed_dictionary, user_num, product_num\",\n \"def make_emotion_data(emotion: str, dict_to_use: dd.DataFrame, ck=True):\\n emotion_data = []\\n target_data = []\\n\\n for row in tqdm(dict_to_use.iterrows(), total = len(dict_to_use.index)):\\n index, data = row\\n annotated_emote = data[\\\"annotated\\\"]\\n\\n if annotated_emote == \\\"N/A\\\":\\n continue\\n all_columns = data.keys()\\n columns_to_use = [x for x in all_columns if 'annotated' not in x and 'predicted' not in x and 'patient' not in x]\\n # data = data.loc[:, columns_to_use].compute()\\n data = data[columns_to_use]\\n # data = data.loc[:, df.columns != \\\"annotated\\\" and df.columns != \\\"Happy_predicted\\\"].compute()\\n emotion_data.append(data.tolist())\\n\\n if annotated_emote == emotion:\\n target_data.append(1)\\n else:\\n target_data.append(0)\\n\\n return emotion_data, target_data\\n\\n\\n # if dict_to_use is None:\\n # dict_to_use = json.load(open('au_emotes.txt'))\\n # emotion_data = [\\n # item for sublist in (b for b in ((a for a in x.values() if a)\\n # for x in dict_to_use.values()\\n\\n # if x) if b) for item in sublist\\n # ]\\n\\n # if ck:\\n # ck_dict = json.load(open('ck_dict.txt'))\\n\\n # for patient_list in ck_dict.values():\\n # to_add = AUScorer.TrainList\\n # au_dict = {\\n # str(int(float(x))): y\\n\\n # for x, y in patient_list[0].items()\\n # }\\n\\n # for add in to_add:\\n # if add not in au_dict:\\n # au_dict[add] = 0\\n # emotion_data.append([au_dict, patient_list[1]])\\n\\n # au_data = []\\n # target_data = []\\n # aus_list = AUScorer.TrainList\\n\\n # for frame in emotion_data:\\n # aus = frame[0]\\n\\n # if frame[1] == emotion:\\n # au_data.append([float(aus[str(x)]) for x in aus_list])\\n # target_data.append(1)\\n # index = 0\\n # happy_len = len(target_data)\\n\\n # for frame in emotion_data:\\n # aus = frame[0]\\n\\n # if frame[1] and frame[1] != emotion:\\n # au_data.append([float(aus[str(x)]) for x in aus_list])\\n # target_data.append(0)\\n # index += 1\\n\\n # if index == happy_len:\\n # break\\n\\n # return au_data, target_data\",\n \"def get_discounts(table):\\n\\n discounts_money_spent = {}\\n discount_levels = [100, 1000, 5000]\\n discount_percents = [\\\"2%\\\", \\\"5%\\\", \\\"10%\\\"]\\n\\n customer_id_index = 2\\n amount_sold_index = 4\\n id_index = 0\\n product_id_index = 3\\n name_index = 1\\n price_index = 3\\n\\n store_table = store.get_table()\\n store.check_table(store_table)\\n\\n crm_table = crm.get_table('model/crm/customers.csv')\\n\\n for record in table:\\n product_id = record[product_id_index]\\n amount_sold = record[amount_sold_index]\\n customer_id = record[customer_id_index]\\n\\n for customer in crm_table:\\n customer_name = customer[name_index]\\n client_id = customer[id_index]\\n\\n if customer_id == client_id:\\n\\n for game in store_table:\\n game_id = game[id_index]\\n price = game[price_index]\\n if product_id == game_id:\\n money_spent = int(price) * int(amount_sold)\\n \\n if customer_name in discounts_money_spent:\\n discounts_money_spent[customer_name] += int(money_spent)\\n else:\\n discounts_money_spent[customer_name] = int(money_spent)\\n \\n discounts = {}\\n\\n for customer_name in discounts_money_spent:\\n money_spent = discounts_money_spent[customer_name]\\n if money_spent >= discount_levels[0] and money_spent < discount_levels[1]:\\n discount = discount_percents[0]\\n elif money_spent >= discount_levels[1] and money_spent < discount_levels[2]:\\n discount = discount_percents[1]\\n elif money_spent >= discount_levels[2]:\\n discount = discount_percents[2]\\n else:\\n discount = \\\"0%\\\"\\n\\n discounts[customer_name] = discount\\n\\n return discounts\",\n \"def pre_get_data(df):\\n\\n df_len = len(df.iloc[0, :]) - 1\\n\\n select_cols = []\\n\\n for i in range(df_len): #Get Columns that contain number values\\n\\n if type(df.iloc[0, i + 1]) is np.float64:\\n if math.isnan(df.iloc[0, i + 1]) == False:\\n select_cols.append(i + 1)\\n elif type(df.iloc[0, i + 1]) is np.float:\\n if math.isnan(df.iloc[0, i + 1]) == False:\\n select_cols.append(i + 1)\\n\\n\\n res_df = df.iloc[:, select_cols]\\n\\n list_pop = list(res_df)\\n list_res = ['B_F1_Bool_Result', 'Event_Date', 'B_WClass']\\n list_pop.pop()\\n\\n for item in list_pop:\\n if \\\"F1\\\" in item:\\n aa = item\\n bb = aa.replace(\\\"F1\\\", \\\"F2\\\")\\n if bb in list_pop:\\n cc = aa.replace(\\\"F1\\\", \\\"F12\\\")\\n df[cc] = df[aa] - df[bb]\\n list_res.append(cc)\\n\\n elif \\\"F2\\\" not in item:\\n list_res.append(item)\\n\\n\\n\\n bw = df['B_WClass']\\n i = -1\\n j = df.columns.get_loc('B_WClass')\\n\\n for item in bw:\\n i = i + 1\\n if item != item:\\n df.iloc[i, j] = np.nan\\n else:\\n df.iloc[i, j] = get_weight[item]\\n\\n df['B_WClass'] = df['B_WClass'].astype(float)\\n res_df = df[list_res]\\n\\n return res_df\",\n \"def data():\\n df = gen_sliced_df()\\n df = df[[\\\"x\\\", \\\"z_categ\\\", \\\"y\\\", \\\"residual\\\"]]\\n new_df = df.iloc[[1, 100, 150, 200, 250, 300, 305, 400, 405, 500, 550, 609]].copy()\\n return {\\\"df\\\": df, \\\"new_df\\\": new_df}\",\n \"def extract(self) -> pd.core.frame.DataFrame:\\n pass\",\n \"def get_metadata(sf):\\r\\n data = {}\\r\\n all_describe = sf.describe()\\r\\n\\r\\n s_objs = all_describe['sobjects']\\r\\n # scan the objects and save to a list...\\r\\n for obj in s_objs:\\r\\n row = {}\\r\\n row['name'] = obj['name']\\r\\n row['label'] = obj['label']\\r\\n row['custom'] = obj['custom']\\r\\n row['activateable'] = obj['activateable']\\r\\n row['keyPrefix'] = obj['keyPrefix']\\r\\n row['labelPlural'] = obj['labelPlural']\\r\\n\\r\\n row['raw'] = obj\\r\\n\\r\\n logger.info('\\\\t{}\\\\t-> {}'.format(obj['label'], obj['name']))\\r\\n data[row['name']] = row\\r\\n\\r\\n return data\",\n \"def get_market_inf():\\n r = requests.get(vs.DSE_URL+vs.DSE_MARKET_INF_URL)\\n soup = BeautifulSoup(r.text, 'html.parser')\\n quotes = [] # a list to store quotes\\n\\n table = soup.find('table', attrs={'class': 'table table-bordered background-white text-center', '_id': 'data-table'})\\n\\n for row in table.find_all('tr')[1:]:\\n cols = row.find_all('td')\\n quotes.append({'Date': cols[0].text.strip().replace(\\\",\\\", \\\"\\\"),\\n 'Total Trade': cols[1].text.strip().replace(\\\",\\\", \\\"\\\"),\\n 'Total Volume': cols[2].text.strip().replace(\\\",\\\", \\\"\\\"),\\n 'Total Value (mn)': cols[3].text.strip().replace(\\\",\\\", \\\"\\\"),\\n 'Total Market Cap. (mn)': cols[4].text.strip().replace(\\\",\\\", \\\"\\\"),\\n 'DSEX Index': cols[5].text.strip().replace(\\\",\\\", \\\"\\\"),\\n 'DSES Index': cols[6].text.strip().replace(\\\",\\\", \\\"\\\"),\\n 'DS30 Index': cols[7].text.strip().replace(\\\",\\\", \\\"\\\"),\\n 'DGEN Index': cols[8].text.strip().replace(\\\",\\\", \\\"\\\")\\n })\\n df = pd.DataFrame(quotes)\\n return df\",\n \"def sample_meta(self):\\n return self._data.iloc[:, self.n_genes :]\",\n \"def get_scandata(scandict):\\n headers = scandict[\\\"Header\\\"].split(\\\",\\\")\\n scannames = [i for i in scandict.keys() if not i == \\\"Header\\\"]\\n no_scans = len(scannames)\\n df = pd.DataFrame(columns=headers, index=scannames)\\n for s in scannames:\\n scandata = re.findall(r'\\\".+?\\\"|[\\\\w-]+', scandict[s])\\n df.loc[s, headers[: len(scandata)]] = scandata\\n\\n for c in [\\n \\\"Description\\\",\\n \\\"Vertex List\\\",\\n \\\"Preablation Settings\\\",\\n \\\"Ablation Settings\\\",\\n ]:\\n df[c] = df[c].str.replace('\\\"', \\\"\\\")\\n\\n df[\\\"Vertex List\\\"] = (\\n df[\\\"Vertex List\\\"]\\n .str.split(\\\";\\\")\\n .apply(lambda x: np.array([i.split(\\\",\\\") for i in x]))\\n )\\n df[\\\"Preablation Settings\\\"] = df[\\\"Preablation Settings\\\"].apply(split_config)\\n df[\\\"Ablation Settings\\\"] = df[\\\"Ablation Settings\\\"].apply(split_config)\\n df[\\\"Data\\\"] = df[\\\"Data\\\"].apply(split_config)\\n return df\",\n \"def information(self):\\n \\n \\n x = list(zip(self.df.count(), self.df.dtypes, (self.df.isnull().sum() / self.df.shape[0])))\\n y = dict(zip(self.df.columns, x))\\n return pd.DataFrame(y, index=['Number of Values', 'Data Type', 'Percent Missing']).transpose()\",\n \"def make_claim_df(claim_list, columns = ['Sl','Name of Bank','Name of Branch','A/C Number (15 digit)','A/C Title','Amount of Remittance in BDT','Date of A/C Credit','Remittance Received through BEFTN/RTGS','Name of Remittance Collecting/BEFTN Processing Bank','Date of Claim']):\\n sl=[]\\n nrbc_bank = []\\n branch = []\\n ac_no = []\\n ac_title = []\\n amount=[]\\n date_account_credit=[]\\n channel = []\\n other_bank=[]\\n claim_date=[]\\n i=1\\n for claim in claim_list:\\n sl.append(i)\\n i=i+1\\n nrbc_bank.append(\\\"NRBC Bank Ltd.\\\")\\n branch.append(claim.branch.name.upper())\\n ac_no.append(claim.account_no)\\n ac_title.append(claim.account_title)\\n amount.append(claim.remittance_amount)\\n date_account_credit.append(claim.date_account_credit)\\n channel.append(claim.get_channel_display())\\n other_bank.append(claim.collecting_bank.name)\\n claim_date.append(claim.date_claim.date())\\n dc = {\\n 'SL':sl,\\n 'Name of Bank':nrbc_bank,\\n 'Name of Branch': branch,\\n 'A/C Number': ac_no,\\n 'A/C Title': ac_title,\\n 'Amount of Remittance in BDT': amount,\\n 'Date of A/C Credit': date_account_credit,\\n 'Remittance Received Through BEFTN/RTGS': channel,\\n 'Name of Remittance Processing Bank': other_bank,\\n 'Date of Claim': claim_date\\n }\\n df = pd.DataFrame(dc)\\n return df.sort_values(by=['Name of Remittance Processing Bank',])\",\n \"def get_price_info(price_filename, commodity):\\n prices = []\\n prices = pd.read_csv(price_filename, sep=' ', index_col=0)\\n\\n five_day_avg = pd.Series(pd.rolling_mean(prices[commodity], 5), name='five_day_avg')\\n ten_day_avg = pd.Series(pd.rolling_mean(prices[commodity], 10), name='ten_day_avg')\\n thirty_day_avg = pd.Series(pd.rolling_mean(prices[commodity], 30), name='thirty_day_avg')\\n \\n price_info = pd.DataFrame(pd.concat([prices, five_day_avg, ten_day_avg, thirty_day_avg], axis=1))\\n \\n price_diffs = prices[commodity].diff()\\n # price_changes_series = pd.Series(np.array(price_diffs > 0), dtype=int, index=price_info.index.values)\\n price_changes_series = pd.Series([0.333 if (np.isnan(x) or np.isnan(y)) else 100000.0 * x / y for (x, y) in zip(price_diffs, prices[commodity])], dtype=int, index=price_info.index.values)\\n price_changes_series = prices[commodity]\\n return price_info, price_changes_series\",\n \"def apifields(sample):\\n metadata\\n sel = [\\n metadata.ID,\\n metadata.Count_of_Masked_Loan_Number,\\n # metadata.Note_Sale_NPL,\\n # metadata.Short_Payoff,\\n # metadata.REO_Sale_DIL,\\n # metadata.Foreclosure_Auction,\\n # metadata.Full_Payoff,\\n # metadata.Note_Sale_PL\\n ]\\n\\n results = db.session.query(*sel).filter(metadata.ID == sample).all()\\n\\n # Create a dictionary entry for each row of metadata information\\n apifields = {}\\n for result in results:\\n apifields[\\\"ID\\\"] = result[0]\\n apifields[\\\"Count_of_Masked_Loan_Number\\\"] = result[1]\\n # apifields[\\\"Note_Sale_NPL\\\"] = result[2]\\n # apifields[\\\"Short_Payoff\\\"] = result[3]\\n # apifields[\\\"REO_Sale_DIL\\\"] = result[4]\\n # apifields[\\\"Foreclosure_Auction\\\"] = result[5]\\n # apifields[\\\"Full_Payoff\\\"] = result[6]\\n # apifields[\\\"Note_Sale_PL\\\"] = result[7]\\n\\n print(apifields)\\n return jsonify(apifields)\",\n \"def get_info(self, frame, headings):\\n return self.df.loc[frame, headings].values\",\n \"def _read_dataset(self):\\n import pandas as pd\\n\\n freesolv_path = get_data_file_path(FREESOLV_PATH)\\n\\n freesolv_db = pd.read_csv(freesolv_path, delimiter=';',\\n skipinitialspace=True,\\n skiprows=[0, 1, 2], header=0,\\n names=['compound id', 'SMILES',\\n 'iupac name',\\n 'experimental value',\\n 'experimental uncertainty',\\n 'calculated value (GAFF)',\\n 'calculated uncertainty',\\n 'experimental reference',\\n 'calculated reference',\\n 'notes'])\\n\\n compound_ids = freesolv_db['compound id'].to_list()\\n smiles_tags = freesolv_db['SMILES'].to_list()\\n experimental_v = freesolv_db['experimental value'].to_list()\\n return compound_ids, smiles_tags, experimental_v\",\n \"def _unpack_sentiment_data(self):\\n get_neg = lambda x: x.get('probability').get('neg')\\n get_pos = lambda x: x.get('probability').get('pos')\\n get_neutral = lambda x: x.get('probability').get('neutral')\\n get_label = lambda x: x.get('label')\\n self.dataframe['negative_sentiment'] = self.dataframe['sentiment'].map(get_neg)\\n self.dataframe['positive_sentiment'] = self.dataframe['sentiment'].map(get_pos)\\n self.dataframe['neutral_sentiment'] = self.dataframe['sentiment'].map(get_neutral)\\n self.dataframe['sentiment_label'] = self.dataframe['sentiment'].map(get_label)\",\n \"def ca_to_coils_second_df(agent_df):\",\n \"def read(self):\\n self._load_metadata()\\n return self._df.compute()\",\n \"def fetch_bank_df(preprocess=False):\\n (train_X, train_y), (test_X, test_y) = lale.datasets.openml.fetch(\\n \\\"bank-marketing\\\", \\\"classification\\\", astype=\\\"pandas\\\", preprocess=preprocess\\n )\\n orig_X = pd.concat([train_X, test_X]).sort_index()\\n orig_y = pd.concat([train_y, test_y]).sort_index().astype(np.float64)\\n column_map = {\\n \\\"v1\\\": \\\"age\\\",\\n \\\"v2\\\": \\\"job\\\",\\n \\\"v3\\\": \\\"marital\\\",\\n \\\"v4\\\": \\\"education\\\",\\n \\\"v5\\\": \\\"default\\\",\\n \\\"v6\\\": \\\"balance\\\",\\n \\\"v7\\\": \\\"housing\\\",\\n \\\"v8\\\": \\\"loan\\\",\\n \\\"v9\\\": \\\"contact\\\",\\n \\\"v10\\\": \\\"day\\\",\\n \\\"v11\\\": \\\"month\\\",\\n \\\"v12\\\": \\\"duration\\\",\\n \\\"v13\\\": \\\"campaign\\\",\\n \\\"v14\\\": \\\"pdays\\\",\\n \\\"v15\\\": \\\"previous\\\",\\n \\\"v16\\\": \\\"poutcome\\\",\\n }\\n if preprocess:\\n\\n def map_col(col):\\n if col.find(\\\"_\\\") == -1:\\n return column_map[col]\\n prefix, suffix = col.split(\\\"_\\\")\\n return column_map[prefix] + \\\"_\\\" + suffix\\n\\n orig_X.columns = [map_col(col) for col in orig_X.columns]\\n age = pd.Series(orig_X[\\\"age\\\"] >= 25, dtype=np.float64)\\n encoded_X = orig_X.assign(age=age)\\n encoded_y = pd.Series(orig_y == 0, dtype=np.float64, name=orig_y.name)\\n fairness_info = {\\n \\\"favorable_labels\\\": [1],\\n \\\"protected_attributes\\\": [\\n {\\\"feature\\\": \\\"age\\\", \\\"reference_group\\\": [1]},\\n ],\\n }\\n return encoded_X, encoded_y, fairness_info\\n else:\\n orig_X.columns = [column_map[col] for col in orig_X.columns]\\n fairness_info = {\\n \\\"favorable_labels\\\": [1],\\n \\\"protected_attributes\\\": [\\n {\\\"feature\\\": \\\"age\\\", \\\"reference_group\\\": [[25, 1000]]},\\n ],\\n }\\n return orig_X, orig_y, fairness_info\",\n \"def get_dataframe() -> pandas.DataFrame:\\n database_connection = processing.establish_connection(database_path)\\n dataframe = database_connection.to_dataframe(['CustomerId', 'InvoiceDate', 'Total'], table_name)\\n database_connection.close()\\n dataframe = processing.get_invoice_date_fixed(dataframe)\\n analyze_dataframe = dataframe.copy()\\n total_sum_dataframe = processing.get_column_sum(analyze_dataframe)\\n\\n customer_count_dataframe = processing.drop_duplicates(analyze_dataframe)\\n customer_count_dataframe = processing.get_column_count(customer_count_dataframe)\\n return customer_count_dataframe.merge(total_sum_dataframe, how='inner', on='InvoiceDate')\",\n \"def process_book_extra_info(\\n book_extra_info_rows: List[Tuple[int, str, str]]\\n) -> pd.DataFrame:\\n book_info_labels = ['work_id', 'isbn13', 'description']\\n book_extra_info_df = pd.DataFrame.from_records(\\n book_extra_info_rows, columns=book_info_labels)\\n\\n html_tag_regexp = '<[^<]+?>'\\n multiple_whitespaces_regexp = r\\\"\\\\s\\\\s+\\\"\\n book_extra_info_df.description = book_extra_info_df.description.astype(\\n str).str.replace(html_tag_regexp, ' ')\\n book_extra_info_df.description = book_extra_info_df.description.astype(\\n str).str.replace(multiple_whitespaces_regexp, ' ')\\n\\n return book_extra_info_df\",\n \"def data_process_deprecated(self, CustomerID, InvoiceDate, InvoiceNo, Description, Quantity\\\\\\n , UnitPrice ):\\n dict_invoice = {'InvoiceDate':InvoiceDate, 'Description':Description\\\\\\n , 'Quantity':Quantity, 'UnitPrice':UnitPrice}\\n dict_invoice['CustomerID'] = CustomerID\\n dict_invoice['InvoiceNo'] = InvoiceNo\\n df_invoice_line \\\\\\n = pd.DataFrame(dict_invoice, columns=dict_invoice.keys(), index=[0])\\n \\n self.data_transform(df_invoice_line)\\n\\n #self.feature_rfm_encode()\\n\\n self.feature_scale()\\n\\n self.list_feature_drop()\\n\\n self.feature_description_nlp()\\n return\",\n \"def clean_qpcr_data(df) -> pd.DataFrame:\\n ASSAYDATE_COL = \\\"Assay Date\\\"\\n INSTRUMENT_COL = \\\"Instrument\\\"\\n POOL_COL = \\\"Pool\\\"\\n\\n # Names and types of all our columns. We also add the columns INSTRUMENT_COL and POOL_COL\\n original_columns = [ASSAYDATE_COL, DATE_COL, \\\"Sample ID\\\", \\\"GENE\\\", \\\"Ct [1]\\\", \\\"Ct [2]\\\", \\\"Ct [3]\\\", \\\"Ct Avg\\\", \\\"Ct Stdev\\\", \\\"Copies [1]\\\", \\\"Copies [2]\\\", \\\"Copies [3]\\\", \\\"Copies AVG\\\", \\\"Copies Stdev\\\", \\\"PMMoV Ct [1]\\\", \\\"PMMoV Ct [2]\\\", \\\"PMMoV Ct [3]\\\", \\\"PMMoV Avg\\\", \\\"PMMoV Stdev\\\", \\\"Empty tube weight (g)\\\", \\\"Full tube weight (g)\\\", \\\"Pellet weight (g)\\\", \\\"Extracted Mass (in 100 uL) (g)\\\", \\\"Copies per Extracted Mass (copies/g) [1]\\\", \\\"Copies per Extracted Mass (copies/g) [2]\\\", \\\"Copies per Extracted Mass (copies/g) [3]\\\", \\\"Copies per Extracted Mass (copies/g) Avg\\\", \\\"Copies per Extracted Mass Stdev\\\", \\\"2^Ct\\\", \\\"2^Ct normalized to a value\\\", \\\"(2^Ct normalized to a value per Extracted Mass)\\\", \\\"PMMoV Copies [1]\\\", \\\"PMMoV Copies [2]\\\", \\\"PMMoV Copies [3]\\\", \\\"PMMoV Copies Avg\\\", \\\"PMMoV Copies Stdev\\\", \\\"Copies per Copies of PMMoV Avg\\\", \\\"Copies per Copies of PMMoV * 10^3 Avg\\\", \\\"Copies per Copies normalized to a value\\\", \\\"Copies per Copies of PMMoV Stdev\\\", \\\"Copies per Copies of PMMoV * 10^3 Stdev\\\", \\\"Copies per Copies normalized to a value Stdev\\\", \\\"Date [2]\\\", \\\"Gene\\\", \\\"APPROVED: Copies per Copies of PMMoV [1]\\\", \\\"APPROVED: Copies per Copies of PMMoV [2]\\\", \\\"APPROVED: Copies per Copies of PMMoV [3]\\\", \\\"APPROVED: Copies per Copies of PMMoV Avg\\\", \\\"APPROVED: Copies per Copies of PMMoV Stdev\\\", \\\"APPROVED: Copies per Extracted Mass (copies/g) [1]\\\", \\\"APPROVED: Copies per Extracted Mass (copies/g) [2]\\\", \\\"APPROVED: Copies per Extracted Mass (copies/g) [3]\\\", \\\"APPROVED: Copies per Extracted Mass (copies/g) Avg\\\", \\\"APPROVED: Copies/L [1]\\\", \\\"APPROVED: Copies/L [2]\\\", \\\"APPROVED: Copies/L [3]\\\", \\\"APPROVED: Copies/L Avg\\\", \\\"MESP UPLOAD: PMMoV Copies per Extracted Mass (copies/g)\\\", \\\"MESP UPLOAD: PMMoV Copies/L\\\", \\\"INHIBITION CTRL: Date\\\", \\\"INHIBITION CTRL: Sample Name\\\", \\\"INHIBITION CTRL: Pepper 1/10 [1]\\\", \\\"INHIBITION CTRL: Pepper 1/10 [2]\\\", \\\"INHIBITION CTRL: Pepper 1/10 [3]\\\", \\\"INHIBITION CTRL: Pepper 1/10 Avg\\\", \\\"INHIBITION CTRL: Pepper 1/40 [1]\\\", \\\"INHIBITION CTRL: Pepper 1/40 [2]\\\", \\\"INHIBITION CTRL: Pepper 1/40 [3]\\\", \\\"INHIBITION CTRL: Pepper 1/40 Avg\\\", \\\"INHIBITION CTRL: Pepper No Dilution [1]\\\", \\\"INHIBITION CTRL: Pepper No Dilution [2]\\\", \\\"INHIBITION CTRL: Pepper No Dilution [3]\\\", \\\"INHIBITION CTRL: Pepper No Dilution Avg\\\", \\\"Pepper 1/10 ΔCt\\\", \\\"Pepper 1/40 ΔCt\\\"]\\n original_types = [\\\"date\\\", \\\"date\\\", \\\"text\\\", \\\"text\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"date\\\", \\\"text\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"date\\\", \\\"text\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\"]\\n new_columns = original_columns.copy()\\n new_columns[2:2] = [INSTRUMENT_COL, POOL_COL]\\n new_types = original_types.copy()\\n new_types[2:2] = [\\\"text\\\", \\\"text\\\"]\\n\\n # Remove unwanted columns at end of dataframe, and add new columns in the correct order\\n df = df[df.columns[:len(original_columns)]].copy()\\n df.columns = original_columns\\n df[list(set(new_columns) - set(original_columns))] = None\\n df = df[new_columns]\\n\\n # Go through all rows and clean them, assign appropriate values for current instrument, pool, assay date, etc\\n cur_instrument, cur_pool = None, None\\n cur_assaydate = None\\n has_inhibition_controls = False\\n has_copies_per_liter = False\\n for i,row in df.iterrows():\\n cur_instrument, cur_pool = get_inst_and_pool(row, cur_instrument, cur_pool)\\n if row[ASSAYDATE_COL]:\\n row_date = parse_date(row[ASSAYDATE_COL])\\n cur_assaydate = row_date or cur_assaydate\\n if row_date is not None:\\n has_inhibition_controls = False\\n has_copies_per_liter = False\\n\\n if np.any([\\\"Pepper 1/10\\\" in c for c in row if isinstance(c, str)]):\\n has_inhibition_controls = True\\n if np.any([\\\"Copies / VS\\\" in c or \\\"Copies / L\\\" in c for c in row if isinstance(c, str)]):\\n has_copies_per_liter = True\\n df.loc[i, ASSAYDATE_COL] = cur_assaydate\\n df.loc[i, INSTRUMENT_COL] = cur_instrument\\n df.loc[i, POOL_COL] = cur_pool\\n if not has_inhibition_controls:\\n df.loc[i, [c for c in df.columns if c.startswith(\\\"INHIBITION CTRL: \\\")]] = \\\"\\\"\\n if not has_copies_per_liter:\\n index = [idx for idx, c in enumerate(df.columns) if c.startswith(\\\"APPROVED: Copies/L\\\")][0]\\n df.loc[i, df.columns[index:]] = \\\"\\\"\\n\\n # Remove invalid rows (where no valid \\\"Date\\\" is specified)\\n df[DATE_COL] = pd.to_datetime(df[DATE_COL], errors=\\\"coerce\\\")\\n df = df.loc[~df[DATE_COL].isna()]\\n\\n # 6 rows have a year of 1900 (from \\\"Septembe 27th\\\" assay date), change these to 2020.\\n # 12 rows have a year of 2014 (from \\\"Septembe 17th\\\" assay date), change these to 2020.\\n f = (df[DATE_COL].dt.year == 1900) | (df[DATE_COL].dt.year == 2014)\\n df.loc[f, DATE_COL] = pd.to_datetime(df.loc[f, DATE_COL].dt.strftime(\\\"2020-%m-%d\\\"), format=\\\"%Y-%m-%d\\\")\\n\\n # Rows from assay date \\\"Tuesday, December 22, 2020\\\" and \\\"Wednesday, December 23, 2020\\\" have incorrect\\n # year in \\\"Date\\\" column (2021 instead of 2020)\\n df[ASSAYDATE_COL] = pd.to_datetime(df[ASSAYDATE_COL], errors=\\\"coerce\\\")\\n f = (df[ASSAYDATE_COL] == pd.to_datetime(\\\"December 22, 2020\\\")) | (df[ASSAYDATE_COL] == pd.to_datetime(\\\"December 23, 2020\\\"))\\n df.loc[f, DATE_COL] = pd.to_datetime(df.loc[f, DATE_COL].dt.strftime(\\\"2020-%m-%d\\\"), format=\\\"%Y-%m-%d\\\")\\n\\n # Add row at start that specifies the column types (date, text, number)\\n df.loc[-1] = new_types\\n df.index = df.index + 1\\n df = df.sort_index()\\n df.index = list(range(len(df.index)))\\n\\n return df\",\n \"def fetch_creditg_df(preprocess=False):\\n (train_X, train_y), (test_X, test_y) = lale.datasets.openml.fetch(\\n \\\"credit-g\\\", \\\"classification\\\", astype=\\\"pandas\\\", preprocess=preprocess\\n )\\n orig_X = pd.concat([train_X, test_X]).sort_index()\\n orig_y = pd.concat([train_y, test_y]).sort_index()\\n if preprocess:\\n sex = pd.Series(\\n (orig_X[\\\"personal_status_male div/sep\\\"] == 1)\\n | (orig_X[\\\"personal_status_male mar/wid\\\"] == 1)\\n | (orig_X[\\\"personal_status_male single\\\"] == 1),\\n dtype=np.float64,\\n )\\n age = pd.Series(orig_X[\\\"age\\\"] > 25, dtype=np.float64)\\n dropped_X = orig_X.drop(\\n labels=[\\n \\\"personal_status_female div/dep/mar\\\",\\n \\\"personal_status_male div/sep\\\",\\n \\\"personal_status_male mar/wid\\\",\\n \\\"personal_status_male single\\\",\\n ],\\n axis=1,\\n )\\n encoded_X = dropped_X.assign(sex=sex, age=age)\\n fairness_info = {\\n \\\"favorable_labels\\\": [1],\\n \\\"protected_attributes\\\": [\\n {\\\"feature\\\": \\\"sex\\\", \\\"reference_group\\\": [1]},\\n {\\\"feature\\\": \\\"age\\\", \\\"reference_group\\\": [1]},\\n ],\\n }\\n return encoded_X, orig_y, fairness_info\\n else:\\n fairness_info = {\\n \\\"favorable_labels\\\": [\\\"good\\\"],\\n \\\"protected_attributes\\\": [\\n {\\n \\\"feature\\\": \\\"personal_status\\\",\\n \\\"reference_group\\\": [\\n \\\"male div/sep\\\",\\n \\\"male mar/wid\\\",\\n \\\"male single\\\",\\n ],\\n },\\n {\\\"feature\\\": \\\"age\\\", \\\"reference_group\\\": [[26, 1000]]},\\n ],\\n }\\n return orig_X, orig_y, fairness_info\",\n \"def fetch_ricci_df(preprocess=False):\\n (train_X, train_y), (test_X, test_y) = lale.datasets.openml.fetch(\\n \\\"ricci\\\", \\\"classification\\\", astype=\\\"pandas\\\", preprocess=preprocess\\n )\\n orig_X = pd.concat([train_X, test_X]).sort_index()\\n orig_y = pd.concat([train_y, test_y]).sort_index()\\n if preprocess:\\n race = pd.Series(orig_X[\\\"race_W\\\"] == 1, dtype=np.float64)\\n dropped_X = orig_X.drop(labels=[\\\"race_B\\\", \\\"race_H\\\", \\\"race_W\\\"], axis=1)\\n encoded_X = dropped_X.assign(race=race)\\n fairness_info = {\\n \\\"favorable_labels\\\": [1],\\n \\\"protected_attributes\\\": [{\\\"feature\\\": \\\"race\\\", \\\"reference_group\\\": [1]}],\\n }\\n return encoded_X, orig_y, fairness_info\\n else:\\n fairness_info = {\\n \\\"favorable_labels\\\": [\\\"Promotion\\\"],\\n \\\"protected_attributes\\\": [{\\\"feature\\\": \\\"race\\\", \\\"reference_group\\\": [\\\"W\\\"]}],\\n }\\n return orig_X, orig_y, fairness_info\",\n \"def do_statistics(df):\\n length = len(df.index)\\n df['estimated_sigma'] = [None] * length\\n df['spot_price_using_estimation'] = [None] * length\\n df['gap'] = [None] * length\",\n \"def _extract_metadata(self, header, cleaner):\\n metadata = []\\n for k, v in header.items():\\n key = str(cleaner(k)) # clean key and ensure it is a string\\n val = str(cleaner(v)) # clean value and ensure it is a string\\n if (key and val):\\n metadata.append(Metadatum(key, val))\\n return metadata\",\n \"def _get_metadata(dataset, path_to_cdf):\\n metadata = []\\n cdf = cdflib.CDF(path_to_cdf)\\n if dataset=='SOHO_ERNE-HED_L2-1MIN' or dataset=='SOHO_ERNE-LED_L2-1MIN':\\n if dataset=='SOHO_ERNE-HED_L2-1MIN':\\n m = 'H'\\n if dataset=='SOHO_ERNE-LED_L2-1MIN':\\n m = 'L'\\n metadata = {'He_E_label': cdf.varget('He_E_label')[0],\\n 'He_energy': cdf.varget('He_energy'),\\n 'He_energy_delta': cdf.varget('He_energy_delta'),\\n f'A{m}_LABL': cdf.varattsget(f'A{m}')['LABLAXIS'],\\n f'A{m}_UNITS': cdf.varattsget(f'A{m}')['UNITS'],\\n f'A{m}_FILLVAL': cdf.varattsget(f'A{m}')['FILLVAL'],\\n 'P_E_label': cdf.varget('P_E_label')[0],\\n 'P_energy': cdf.varget('P_energy'),\\n 'P_energy_delta': cdf.varget('P_energy_delta'),\\n f'P{m}_LABL': cdf.varattsget(f'P{m}')['LABLAXIS'],\\n f'P{m}_UNITS': cdf.varattsget(f'P{m}')['UNITS'],\\n f'P{m}_FILLVAL': cdf.varattsget(f'P{m}')['FILLVAL'],\\n }\\n\\n channels_dict_df_He = pd.DataFrame(cdf.varget('He_E_label')[0], columns=['ch_strings'])\\n channels_dict_df_He['lower_E'] = cdf.varget(\\\"He_energy\\\")-cdf.varget(\\\"He_energy_delta\\\")\\n channels_dict_df_He['upper_E'] = cdf.varget(\\\"He_energy\\\")+cdf.varget(\\\"He_energy_delta\\\")\\n channels_dict_df_He['DE'] = cdf.varget(\\\"He_energy_delta\\\")\\n # channels_dict_df_He['mean_E'] = np.sqrt(channels_dict_df_He['upper_E'] * channels_dict_df_He['lower_E'])\\n channels_dict_df_He['mean_E'] = cdf.varget(\\\"He_energy\\\")\\n\\n channels_dict_df_p = pd.DataFrame(cdf.varget('P_E_label')[0], columns=['ch_strings'])\\n channels_dict_df_p['lower_E'] = cdf.varget(\\\"P_energy\\\")-cdf.varget(\\\"P_energy_delta\\\")\\n channels_dict_df_p['upper_E'] = cdf.varget(\\\"P_energy\\\")+cdf.varget(\\\"P_energy_delta\\\")\\n channels_dict_df_p['DE'] = cdf.varget(\\\"P_energy_delta\\\")\\n # channels_dict_df_p['mean_E'] = np.sqrt(channels_dict_df_p['upper_E'] * channels_dict_df_p['lower_E'])\\n channels_dict_df_p['mean_E'] = cdf.varget(\\\"P_energy\\\")\\n\\n metadata.update({'channels_dict_df_He': channels_dict_df_He})\\n metadata.update({'channels_dict_df_p': channels_dict_df_p})\\n return metadata\",\n \"def process_input_df(df_raw, market, price_col_id):\\n # drop all non required columns from data frame\\n df = drop_columns(df_raw, [price_col_id])\\n\\n # rename last price column to uniform convention\\n df = df.rename(columns={price_col_id: 'close'})\\n\\n # calculate required indicators\\n df[market + '_rt1'] = np.log(df['close']).diff().shift(1) * 100\\n df[market + '_asy5_r'] = df[market + '_rt1'].rolling(window=5).sum() / 5\\n df[market + '_asy4_r'] = df[market + '_rt1'].rolling(window=4).sum() / 4\\n df[market + '_asy3_r'] = df[market + '_rt1'].rolling(window=3).sum() / 3\\n df[market + '_asy2_r'] = df[market + '_rt1'].rolling(window=2).sum() / 2\\n\\n return df\",\n \"def get_summary(self, df):\\n results_df = pd.DataFrame({'Energy kWh': self.get_all_periods(df).sum()})\\n results_df['Prices $/kWh'] = self.deliveryPrice + self.get_rates()\\n results_df['Value $'] = results_df['Energy kWh'] * results_df['Prices $/kWh']\\n return(results_df)\",\n \"def add_all_features(df):\\n df.reset_index(drop=True, inplace=True)\\n df = target_indicators(df)\\n df = momentum_indicators(df)\\n df = trend_indicators(df)\\n df = volatility_indicators(df)\\n df = volume_indicators(df)\\n df = special_indicators(df)\\n return df\",\n \"def extract(self):\\n \\n print('Extracting Metrics data... ',end=''),\\n self.df = pd.read_excel(self.file_path, index_col=0)\\n print('Done')\",\n \"def get_dataset(reader: DataReader):\\n\\n xs = []\\n ys = []\\n\\n for annotation_sentences in reader.annotations:\\n for annotation in annotation_sentences:\\n xs.append([annotation.fee_raw] + annotation.sentence)\\n ys.append(annotation.frame)\\n\\n return xs, ys\",\n \"def load_data(self, identifier):\\n propertyType = self.soup.find('h2', class_='listing-details-h1').text\\n propertyType = propertyType.replace(' for sale', '')\\n beds = re.findall(r'\\\\d{1} bed', propertyType)\\n if beds == []:\\n # get first word\\n beds = '1 bed'\\n else:\\n beds = beds[0]\\n # get general info of property\\n propertyType = propertyType.replace(str(beds) + ' ', '')\\n address = self.soup.find('h2', {'itemprop':'streetAddress'}).text \\n postCode = address.rsplit(None, 1)[-1]\\n address = address.replace(postCode, '')\\n latLong = self.soup.find('meta', {'itemprop':'latitude'}).attrs\\n latitude = latLong['content']\\n latLong = self.soup.find('meta', {'itemprop':'longitude'}).attrs\\n longitude = latLong['content']\\n # original price and changes to price are in sidebar\\n for sidebar in self.soup.find_all('div', {\\\"class\\\":sCLASS_SECTION}):\\n count = 0\\n # price info in sidebar called 'Listing history' \\n if sidebar.find(text=sSB_NAME):\\n try:\\n originalPriceAndDate = sidebar.find(text=sFIRST_LISTED).next_element.next_element\\n originalPrice = originalPriceAndDate[:originalPriceAndDate.find(' on')]\\n originalPrice = self.find_number(originalPrice)\\n originalDate = originalPriceAndDate[originalPriceAndDate.find(' on') + 3:]\\n originalDate = self.get_date(originalDate)\\n # store original listing - in same order as headers!\\n result = [identifier + \\\"_\\\" + str(count), originalDate, np.NaN, originalPrice, np.NaN, \\n beds, propertyType, postCode, address, latitude, longitude, self.url]\\n series = pd.Series(result, name=identifier + \\\"_\\\" + str(count), index=headers)\\n# self.df = self.df.append(pd.Series(result, index=headers), ignore_index=True) \\n# self.df = self.df.append(series) \\n# self.df = pd.concat([self.df, series])\\n self.df = self.df.append(series, ignore_index=False)\\n except (AttributeError, UnboundLocalError):\\n print('Error viewing this property')\\n pass\\n try:\\n # store any changes to original listing\\n for changes in sidebar.find_all('ul', {'class':sCHANGES}):\\n for change in changes.find_all('li'):\\n count += 1\\n # get date and new price\\n date = change.find('span').text\\n date = date.replace('Reduced on:', '')\\n date = date.replace('\\\\n', '')\\n date = self.get_date(date)\\n newPrice = self.find_number(change.next_element)\\n # store result in order of headers\\n result = [identifier + \\\"_\\\" + str(count), originalDate, date, originalPrice, newPrice, \\n beds, propertyType, postCode, address, latitude, longitude, self.url]\\n series = pd.Series(result, name=identifier + \\\"_\\\" + str(count), index=headers) \\n# self.df = self.df.append(pd.Series(result, index=headers), ignore_index=True)\\n self.df = self.df.append(series, ignore_index=False)\\n# self.df = pd.concat([self.df, series])\\n except (AttributeError, UnboundLocalError):\\n print('No changes')\\n pass\",\n \"def create_df_recommendations(api_results):\\r\\n track_name = []\\r\\n track_id = []\\r\\n artist = []\\r\\n album = []\\r\\n duration = []\\r\\n popularity = []\\r\\n for items in api_results['tracks']:\\r\\n try:\\r\\n track_name.append(items['name'])\\r\\n track_id.append(items['id'])\\r\\n artist.append(items[\\\"artists\\\"][0][\\\"name\\\"])\\r\\n duration.append(items[\\\"duration_ms\\\"])\\r\\n album.append(items[\\\"album\\\"][\\\"name\\\"])\\r\\n popularity.append(items[\\\"popularity\\\"])\\r\\n except TypeError:\\r\\n pass\\r\\n df = pd.DataFrame({ \\\"track_name\\\": track_name, \\r\\n \\\"album\\\": album, \\r\\n \\\"track_id\\\": track_id,\\r\\n \\\"artist\\\": artist, \\r\\n \\\"duration\\\": duration, \\r\\n \\\"popularity\\\": popularity})\\r\\n\\r\\n return df\",\n \"def respuesta(response):\\n for report in response.get('reports', []):\\n\\n columnHeader = report.get('columnHeader', {})\\n dimensionHeaders = columnHeader.get('dimensions', [])\\n metricHeaders = columnHeader.get(\\n 'metricHeader', {}).get('metricHeaderEntries', [])\\n\\n return_data = []\\n\\n for row in report.get('data', {}).get('rows', []):\\n\\n dimensions = row.get('dimensions', [])\\n dateRangeValues = row.get('metrics', [])\\n pipeline_insert = {}\\n for header, dimension in zip(dimensionHeaders, dimensions):\\n pipeline_insert[header] = dimension\\n\\n for i, values in enumerate(dateRangeValues):\\n\\n for metricHeader, value in zip(metricHeaders, values.get('values')):\\n pipeline_insert[metricHeader.get('name')] = value\\n return_data.append(pipeline_insert)\\n\\n return return_data\",\n \"def process_to_dataframe(chkpts, tags=['final_energy', 'job_name']):\\n\\n pass\",\n \"def get_recipe_dataset():\\n df = pd.read_csv(FILENAME)\\n print(\\\"# of rows - no filtering: %s\\\" % len(df))\\n\\n df = df.head(500)\\n print(\\\"# of rows - reduce size of DF for testing: %s\\\" % len(df))\\n\\n return df\",\n \"def get_data(self)->pd.DataFrame:\\n pass\",\n \"def general_analysis(df):\\n pass\",\n \"def data_transform(self, df) :\\n\\n #-------------------------------------------------------------------------\\n # Copy of given dataframe to be transformed\\n #-------------------------------------------------------------------------\\n self.df_invoice_line = df\\n \\n #-------------------------------------------------------------------------\\n # Features issued from InvoiceDate are created\\n #-------------------------------------------------------------------------\\n if self.is_transform_timeFeature is True:\\n self.strprint(\\\"\\\\n*** Time features transformation ***\\\")\\n self.data_transform_timeFeature()\\n\\n #-------------------------------------------------------------------------\\n # RFM is computed and encoded\\n #-------------------------------------------------------------------------\\n if self.is_transform_rfm is True:\\n self.strprint(\\\"\\\\n*** RFM transformation ***\\\")\\n self.data_transform_rfm()\\n\\n #-------------------------------------------------------------------------\\n # NLP features issued from Description are created\\n #-------------------------------------------------------------------------\\n if self.is_transform_nlp is True:\\n self.strprint(\\\"\\\\n*** NLP transformation ***\\\")\\n self.data_transform_nlp()\\n \\n return self.df_invoice_line\",\n \"def _extract(self):\\r\\n self._data = []\\r\\n for fname in self.files:\\r\\n meta = dict(filename=fname)\\r\\n\\r\\n # Perform the actual metadata extraction\\r\\n fname = os.path.splitext(self.filter_filename(fname))[0]\\r\\n values = fname.split(self.sep)\\r\\n\\r\\n # Handle the case where number of fields is less than the length\\r\\n # of the extracted values, ie cases where we only want to extract\\r\\n # a subset of available fields.\\r\\n if self.index:\\r\\n values = [val for i, val in enumerate(values) if i in self.index]\\r\\n\\r\\n meta.update(dict(zip(self.fields, values)))\\r\\n if self.split_by in self.fields:\\r\\n meta[self.split_by] = self._get_split_field_values(meta['filename'])\\r\\n self._data.append(meta)\",\n \"def _dataframe_preprocess(self):\\n # 1. add baisc feature like date, time in day, ....\\n if self.data_type != 'porto':\\n self.df['TIMESTAMP'] = self.df.apply(lambda df: df['TIMESTAMPS'][0], axis=1)\\n self.df['TIME'] = pd.to_datetime(self.df['TIMESTAMP'], unit='s', utc=True)\\n \\n self.df.TIME = self.df.TIME.dt.tz_convert(self.timezone)\\n # 2. group df for specific driver analysis\\n self.grouped_df = self.df.groupby('LABEL')\\n if self.count_od_info:\\n if 'SD' not in self.df.columns:\\n self._add_OD_info()\\n self.grouped_od = self.df.groupby('SD')\",\n \"def data(self):\\n dfdata = pd.concat([self.weights, self.returns, self.category], axis=1)\\n dfdata.columns = ['weights', 'returns', self.category_name]\\n if self.period is not None:\\n dfdata['date'] = self.period\\n return dfdata\",\n \"def parse_df(data):\\n\\n # First column should be the ids\\n ids = list(data.iloc[:, 0])\\n\\n # Second column should hold the labels\\n labels = list(data.iloc[:, 1])\\n\\n # From third columns, we should have the features\\n features = list(data.iloc[:, 2:].values)\\n\\n return ids, labels, features\",\n \"def get_Metadata(metafile):\\n\\n mslist_file = open(metafile, 'r')\\n LINES = mslist_file.readlines()\\n mslist_file.close()\\n\\n nBlocks = 6 # these are the number of correlator cards (PILOT survey value)\\n \\n obs_date = 'Observed from'\\n code = 'Code'\\n duration = 'Total elapsed time'\\n antenna = 'antennas'\\n frame = 'Frame'\\n \\n for i in range(len(LINES)):\\n line = LINES[i]\\n if line.find(antenna) >=0:\\n TOKS = line.split()\\n n_ant = TOKS[5][-2:]\\n if line.find(obs_date) >=0:\\n TOKS = line.split()\\n start_obs_date = TOKS[6]\\n end_obs_date = TOKS[8]\\n if line.find(duration) >=0:\\n TOKS = line.split()\\n tobs = float(TOKS[10]) # in second\\n if line.find(code) >= 0:\\n next_line = LINES[i+1]\\n TOKS = next_line.split()\\n field = TOKS[5]\\n ra = TOKS[6][:-5]\\n dec = TOKS[7][:-4]\\n if line.find(frame) >= 0:\\n next_line = LINES[i+1]\\n TOKS = next_line.split()\\n total_obs_bw = float(TOKS[10])*nBlocks/1000.0 # kHz to MHz \\n \\n return n_ant, start_obs_date, end_obs_date, tobs, field, ra, dec, total_obs_bw\",\n \"def load_data(database_filepath):\\n engine = create_engine('sqlite:///'+ database_filepath)\\n df = pd.read_sql(\\\"SELECT * FROM DisasterResponse\\\", engine)\\n #exclude colums that are not needed in model\\n col=[i for i in df.columns if i not in ['id','original', 'genre']]\\n X = df[\\\"message\\\"]\\n Y = df.iloc[:,4:]\\n #global category_names\\n category_names = Y.columns\\n return X, Y, category_names\",\n \"def extract_details(df):\\n df_RSinfo = df[['pentamer', 'Step details', 'RouteScore details',\\n 'Isolated', 'RouteScore', 'log(RouteScore)']]\\n\\n last3_rxns = ['Buchwald_deprotection', 'Buchwald', 'SNAr']\\n for rxn in last3_rxns:\\n df_RSinfo[rxn] = [next(step for step in row[-3:] if step['reaction'] == rxn) for row in df['Step details']]\\n\\n for key in df_RSinfo['RouteScore details'][0].keys():\\n df_RSinfo[key] = [row[key] for row in df['RouteScore details']]\\n\\n return df_RSinfo\",\n \"def parse_metadata_file(self, file):\\n \\n file_keys = list(file.keys())\\n \\n if 'labelAnnotations' in file_keys:\\n #file_annots = file['labelAnnotations'][:int(len(file['labelAnnotations']) * 0.5)]\\n file_annots = file['labelAnnotations'][:]\\n file_top_score = np.asarray([x['score'] for x in file_annots]).mean()\\n file_top_desc = [x['description'] for x in file_annots]\\n else:\\n file_top_score = np.nan\\n file_top_desc = ['']\\n \\n file_colors = file['imagePropertiesAnnotation']['dominantColors']['colors']\\n file_crops = file['cropHintsAnnotation']['cropHints']\\n\\n file_color_score = np.asarray([x['score'] for x in file_colors]).mean()\\n file_color_pixelfrac = np.asarray([x['pixelFraction'] for x in file_colors]).mean()\\n\\n file_crop_conf = np.asarray([x['confidence'] for x in file_crops]).mean()\\n \\n if 'importanceFraction' in file_crops[0].keys():\\n file_crop_importance = np.asarray([x['importanceFraction'] for x in file_crops]).mean()\\n else:\\n file_crop_importance = np.nan\\n\\n df_metadata = {\\n 'annots_score': file_top_score,\\n 'color_score': file_color_score,\\n 'color_pixelfrac': file_color_pixelfrac,\\n 'crop_conf': file_crop_conf,\\n 'crop_importance': file_crop_importance,\\n 'annots_top_desc': self.sentence_sep.join(file_top_desc)\\n }\\n \\n df_metadata = pd.DataFrame.from_dict(df_metadata, orient='index').T\\n df_metadata = df_metadata.add_prefix('metadata_')\\n \\n return df_metadata\",\n \"def desc_df(df):\\n print(df.shape)\\n print(df.columns)\",\n \"def _process_data(self, jd, expiry):\\n now = dt.datetime.now()\\n\\n columns = ['Last', 'Bid', 'Ask', 'Chg', 'PctChg', 'Vol',\\n 'Open_Int', 'Root', 'Underlying_Price', 'Quote_Time']\\n indexes = ['Strike', 'Expiry', 'Type', 'Symbol']\\n rows_list, index = self._process_rows(jd, now, expiry)\\n df = DataFrame(rows_list, columns=columns,\\n index=MultiIndex.from_tuples(index, names=indexes))\\n\\n # Make dtype consistent, requires float64 as there can be NaNs\\n df['Vol'] = df['Vol'].astype('float64')\\n df['Open_Int'] = df['Open_Int'].astype('float64')\\n\\n return df.sort_index()\",\n \"def read_meta(self):\\n meta = cPickle.load(open('../sugar_analysis_data/META-CABALLO2.pkl'))\\n self.meta_sn_name_list = []\\n self.meta_zcmb = []\\n self.meta_x0 =[]\\n self.meta_x0_err = []\\n self.meta_x1 =[]\\n self.meta_x1_err = []\\n self.meta_c = []\\n self.meta_c_err = []\\n self.meta_mb = []\\n self.meta_mb_err = []\\n self.meta_cov_x0_x1 = [] \\n self.meta_cov_x0_c = []\\n self.meta_cov_x1_c = []\\n self.meta_cov_mb_x1 = []\\n self.meta_cov_mb_c = [] \\n self.meta_zhl = []\\n self.meta_zhl_err = []\\n self.meta_idr = []\\n for meta_sn_name in meta.keys(): \\n \\n if meta[meta_sn_name]['idr.subset'] != 'bad' and meta[meta_sn_name]['idr.subset'] != 'auxiliary':\\n \\n self.meta_sn_name_list.append(meta_sn_name)\\n self.meta_zhl_err.append(meta[meta_sn_name]['host.zhelio.err'])\\n self.meta_zhl.append(meta[meta_sn_name]['host.zhelio'])\\n self.meta_zcmb.append(meta[meta_sn_name]['host.zcmb'])\\n self.meta_x0.append(meta[meta_sn_name]['salt2.X0'])\\n self.meta_x0_err.append(meta[meta_sn_name]['salt2.X0.err'])\\n self.meta_x1.append(meta[meta_sn_name]['salt2.X1'])\\n self.meta_x1_err.append(meta[meta_sn_name]['salt2.X1.err'])\\n self.meta_c.append(meta[meta_sn_name]['salt2.Color'])\\n self.meta_c_err.append(meta[meta_sn_name]['salt2.Color.err'])\\n self.meta_mb.append(meta[meta_sn_name]['salt2.RestFrameMag_0_B'])\\n self.meta_mb_err.append(meta[meta_sn_name]['salt2.RestFrameMag_0_B.err'])\\n self.meta_cov_x0_x1.append(meta[meta_sn_name]['salt2.CovX0X1'])\\n self.meta_cov_x0_c.append(meta[meta_sn_name]['salt2.CovColorX0'])\\n self.meta_cov_x1_c.append(meta[meta_sn_name]['salt2.CovColorX1'])\\n self.meta_cov_mb_x1.append(meta[meta_sn_name]['salt2.CovRestFrameMag_0_BX1'])\\n self.meta_cov_mb_c.append(meta[meta_sn_name]['salt2.CovColorRestFrameMag_0_B'])\\n self.meta_idr.append(meta[meta_sn_name]['idr.subset'])\\n \\n self.meta_idr = np.array(self.meta_idr)\\n self.meta_zcmb = np.array(self.meta_zcmb)\\n self.meta_zhl = np.array(self.meta_zhl)\\n self.meta_zhl_err = np.array(self.meta_zhl_err)\\n self.meta_x0 = np.array(self.meta_x0)\\n self.meta_x0_err = np.array(self.meta_x0_err)\\n self.meta_x1 = np.array(self.meta_x1)\\n self.meta_x1_err = np.array(self.meta_x1_err) \\n self.meta_c = np.array(self.meta_c)\\n self.meta_c_err = np.array(self.meta_c_err)\\n self.meta_mb = np.array(self.meta_mb)\\n self.meta_mb_err = np.array(self.meta_mb_err)\\n self.meta_cov_x0_x1 = np.array(self.meta_cov_x0_x1)\\n self.meta_cov_x0_c = np.array(self.meta_cov_x0_c)\\n self.meta_cov_x1_c = np.array(self.meta_cov_x1_c)\\n self.meta_cov_mb_x1 = np.array(self.meta_cov_mb_x1)\\n self.meta_cov_mb_c = np.array(self.meta_cov_mb_c)\",\n \"def get_metadata_options(ensemble):\\n\\n\\tif \\\";\\\" in ensemble: # Prevent SQL injection since table names aren't parameterizable\\n\\t\\treturn None\\n\\n\\tquery = \\\"SELECT * FROM %(ensemble)s LIMIT 1\\\" % {'ensemble': ensemble,}\\n\\n\\ttry:\\n\\t\\tdf = pd.read_sql(query, db.get_engine(current_app, 'methylation_data'))\\n\\texcept exc.ProgrammingError as e:\\n\\t\\tnow = datetime.datetime.now()\\n\\t\\tprint(\\\"[{}] ERROR in app(get_metadata_options): {}\\\".format(str(now), e))\\n\\t\\tsys.stdout.flush()\\n\\t\\treturn None\\n\\n\\tdf_tsne = df.filter(regex='^tsne_x_', axis='columns')\\n\\tdf_cluster = df.filter(regex='^cluster_', axis='columns')\\n\\n\\tlist_tsne_types = [x.split('tsne_x_')[1] for x in df_tsne.columns.values]\\n\\tlist_mc_types_tsne = sorted(list(set([x.split('_')[0] for x in list_tsne_types])), key=lambda mC_type: methylation_types_order.index(mC_type))\\n\\tlist_dims_tsne_first = sorted(list(set([int(x.split('_')[1].replace('ndim','')) for x in list_tsne_types if list_mc_types_tsne[0] == x.split('_')[0]])))\\n\\tlist_perp_tsne_first = sorted(list(set([int(x.split('_')[2].replace('perp', '')) for x in list_tsne_types if (list_mc_types_tsne[0]+'_ndim'+str(list_dims_tsne_first[0])) == (x.split('_')[0] +'_'+ x.split('_')[1])])))\\n\\n\\tlist_clustering_types = [x.split('cluster_')[1] for x in df_cluster.columns.values]\\n\\n\\t#generate query for getting number clusters for each clustering type\\n\\tnum_clusters_query = \\\"SELECT \\\"\\n\\tfor i, clustering in enumerate(list_clustering_types):\\n\\t\\tnum_clusters_query += \\\"MAX(cluster_{0}) as {0}, \\\".format(clustering)\\n\\tnum_clusters_query = num_clusters_query[:-2] #Gets rid of last \\\", \\\" which causes a MySQL syntax error\\n\\tnum_clusters_query += \\\" FROM {}\\\".format(ensemble)\\n\\n\\tresult = db.get_engine(current_app, 'methylation_data').execute(num_clusters_query).fetchone()\\n\\n\\tdict_clustering_types_and_numclusters = OrderedDict()\\n\\tfor clustering_type, num_clusters in zip(list_clustering_types, result):\\n\\t\\tdict_clustering_types_and_numclusters[clustering_type] = num_clusters\\n\\n\\tlist_algorithms_clustering = list(set([x.split('_')[1] for x in list_clustering_types]))\\n\\n\\t#list_mc_types_clustering = sorted(list(set([x.split('_')[0] for x in list_clustering_types])), key=lambda mC_type: methylation_types_order.index(mC_type))\\n\\t#list_algorithms_clustering = sorted(list(set([x.split('_')[1] for x in list_clustering_types if list_mc_types_clustering[0] == x.split('_')[0]])))\\n\\t#list_npc_clustering = sorted(list(set([int(x.split('_')[2].replace('npc', '')) for x in list_clustering_types if (list_mc_types_clustering[0]+'_'+list_algorithms_clustering[0]) in x])))\\n\\t#list_k_clustering = sorted(list(set([int(x.split('_')[3].replace('k', '')) for x in list_clustering_types if (list_mc_types_clustering[0]+'_'+list_algorithms_clustering[0]+'_npc'+str(list_npc_clustering[0])) in x])))\\n\\n\\tall_metadata = dict()\\n\\tfor modality in ['methylation','snATAC','RNA']:\\n\\t\\tall_metadata[modality] = ['cluster','annotation']\\n\\t\\tif ensemble_exists(ensemble, modality=modality):\\n\\t\\t\\tall_metadata[modality] += ['dataset','sex','brain_region','broad_brain_region']\\n\\t\\t\\tquery = \\\"SELECT count(*) AS count FROM ensembles \\\\\\n\\t\\t\\tRIGHT JOIN datasets ON ensembles.datasets LIKE %(x)s \\\\\\n\\t\\t\\tWHERE datasets.target_region!='NULL' \\\\\\n\\t\\t\\tAND ensembles.ensemble_id={0}\\\".format(ensemble.strip('Ens'))\\n\\t\\t\\tdf_metadata = pd.read_sql(query, con=db.get_engine(current_app, modality+'_data'), params={'x': \\\"CONCAT('%',datasets.dataset,'%')\\\"})\\n\\t\\t\\tif (not df_metadata['count'].isnull().all()) and max(df_metadata['count'])>0:\\n\\t\\t\\t\\tall_metadata[modality] += ['target_region']\\n\\n\\t\\t\\tquery = \\\"SELECT * FROM cells LIMIT 1;\\\"\\n\\t\\t\\tdf_metadata = pd.read_sql(query, con=db.get_engine(current_app, modality+'_data'))\\n\\t\\t\\tdf_metadata = df_metadata.drop(list(df_metadata.filter(regex='cell_.*', axis='columns')), axis=1)\\n\\t\\t\\tall_metadata[modality] += list([i for i in df_metadata.columns.values])\\n\\n\\treturn {'all_tsne_settings': list_tsne_types,\\n\\t\\t\\t'tsne_methylation': list_mc_types_tsne,\\n\\t\\t\\t'all_clustering_settings': list_clustering_types,\\n\\t\\t\\t'all_clustering_settings2': dict_clustering_types_and_numclusters,\\n\\t\\t\\t'clustering_algorithms': list_algorithms_clustering,\\n\\t\\t\\t'tsne_dimensions': list_dims_tsne_first,\\n\\t\\t\\t'tsne_perplexity': list_perp_tsne_first,\\n\\t\\t\\t'methylation_metadata_fields': all_metadata['methylation'],\\n\\t\\t\\t'snATAC_metadata_fields': all_metadata['snATAC'],\\n\\t\\t\\t'RNA_metadata_fields': all_metadata['RNA'],}\",\n \"def get_out_d(out_df):\\n out_l = []\\n if out_df.empty :\\n return {'sorry, no':'predictions'}\\n for row in out_df.itertuples():\\n row_d = {\\n 'date,price':[row.cdate,row.cp]\\n ,'pct_lead': row.pct_lead\\n ,'prediction,effectiveness,accuracy':[row.prediction,row.effectiveness,row.accuracy]\\n }\\n out_l.append(row_d)\\n lo_acc = sum((1+np.sign(out_df.pct_lead))/2) / out_df.accuracy.size\\n out_d = {'Long-Only-Accuracy': lo_acc }\\n out_d['Long-Only-Effectivness'] = sum(out_df.pct_lead)\\n out_d['Model-Effectivness'] = sum(out_df.effectiveness)\\n out_d['Model-Accuracy'] = sum(out_df.accuracy) / out_df.accuracy.size\\n out_d['Prediction-Count'] = out_df.prediction.size\\n out_d['Prediction-Details'] = out_l\\n return out_d\",\n \"def create_catalog_dataframe(save_dataframe):\\n if not save_dataframe:\\n return pd.read_pickle(f'{path_dictionary[\\\"catalog_dataframe_grouped_path\\\"]}')\\n\\n catalog_items = DbHelper.get_all_catalog_items()\\n\\n # Create dataframe to put all information together\\n columns = ['user_id', 'item_id', 'session_id', 'window_size_x', 'window_size_y', 'page_size_x', 'page_size_y', 'catalog_item_list', 'user_log_list']\\n catalog_items_df = pd.DataFrame(catalog_items, columns=columns)\\n\\n # Clean 'Catalog Items' that user see during a session\\n catalog_items_df['catalog_item_list'] = catalog_items_df.apply(clean_objects_listed, axis=1)\\n\\n # Clean Log Files\\n catalog_items_df['user_log_list'] = catalog_items_df.apply(clean_logs, axis=1)\\n\\n # Get Catalog Items that user hover or has a click action, her/his mouse\\n catalog_items_df = get_interacted_catalog_items(catalog_items_df)\\n\\n # Label the catalog items as 0\\n catalog_items_df['catalog_item_list'] = catalog_items_df.apply(label_page_type, axis=1)\\n\\n catalog_items_df_grouped = catalog_items_df.groupby(['user_id', 'session_id'], as_index=False).agg(lambda x: list(x))\\n catalog_items_df_grouped.drop(['item_id', 'window_size_x', 'window_size_y', 'page_size_x', 'page_size_y'], axis=1, inplace=True)\\n\\n if save_dataframe:\\n catalog_items_df.to_pickle(f'{path_dictionary[\\\"path_raw_catalog_dataframe\\\"]}')\\n catalog_items_df_grouped.to_pickle(f'{path_dictionary[\\\"path_catalog_dataframe\\\"]}')\\n catalog_items_df_grouped.to_csv(f'{path_dictionary[\\\"path_catalog_csv\\\"]}', index=False, sep='|')\\n return catalog_items_df_grouped\",\n \"def get_management(symbol: str) -> pd.DataFrame:\\n url_market_business_insider = (\\n f\\\"https://markets.businessinsider.com/stocks/{symbol.lower()}-stock\\\"\\n )\\n text_soup_market_business_insider = BeautifulSoup(\\n request(\\n url_market_business_insider, headers={\\\"User-Agent\\\": get_user_agent()}\\n ).text,\\n \\\"lxml\\\",\\n )\\n\\n found_h2s = {}\\n\\n for next_h2 in text_soup_market_business_insider.findAll(\\n \\\"h2\\\", {\\\"class\\\": \\\"header-underline\\\"}\\n ):\\n next_table = next_h2.find_next_sibling(\\\"table\\\", {\\\"class\\\": \\\"table\\\"})\\n\\n if next_table:\\n found_h2s[next_h2.text] = next_table\\n\\n # Business Insider changed management display convention from 'Management' to\\n # 'Ticker Management'. These next few lines simply find 'Ticker Management'\\n # header key and copy it to a 'Management' key as to not alter the rest of\\n # the function\\n ticker_management_to_be_deleted = \\\"\\\"\\n management_data_available = False\\n for key in found_h2s:\\n if \\\"Management\\\" in key:\\n ticker_management_to_be_deleted = key\\n management_data_available = True\\n if management_data_available:\\n found_h2s[\\\"Management\\\"] = found_h2s[ticker_management_to_be_deleted]\\n del found_h2s[ticker_management_to_be_deleted]\\n\\n if found_h2s.get(\\\"Management\\\") is None:\\n console.print(\\n f\\\"[red]No management information in Business Insider for {symbol}[/red]\\\"\\n )\\n return pd.DataFrame()\\n\\n l_titles = [\\n s_title.text.strip()\\n for s_title in found_h2s[\\\"Management\\\"].findAll(\\n \\\"td\\\", {\\\"class\\\": \\\"table__td text-right\\\"}\\n )\\n if any(c.isalpha() for c in s_title.text.strip())\\n and (\\\"USD\\\" not in s_title.text.strip())\\n ]\\n\\n l_names = [\\n s_name.text.strip()\\n for s_name in found_h2s[\\\"Management\\\"].findAll(\\n \\\"td\\\", {\\\"class\\\": \\\"table__td table--allow-wrap\\\"}\\n )\\n ]\\n\\n df_management = pd.DataFrame(\\n {\\\"Name\\\": l_names[-len(l_titles) :], \\\"Title\\\": l_titles}, # noqa: E203\\n columns=[\\\"Name\\\", \\\"Title\\\"],\\n )\\n\\n df_management[\\\"Info\\\"] = \\\"-\\\"\\n df_management = df_management.set_index(\\\"Name\\\")\\n\\n for s_name in df_management.index:\\n df_management.loc[s_name][\\n \\\"Info\\\"\\n ] = f\\\"http://www.google.com/search?q={s_name} {symbol.upper()}\\\".replace(\\n \\\" \\\", \\\"%20\\\"\\n )\\n\\n return df_management\",\n \"def get_object_metadata(sf, name):\\r\\n data = {}\\r\\n \\r\\n result = get_object(sf, name)\\r\\n\\r\\n # now split out the result\\r\\n data['raw'] = result\\r\\n describe = result['describe']\\r\\n relationships = []\\r\\n for entry in describe['childRelationships']:\\r\\n row = {}\\r\\n row['table'] = entry['childSObject']\\r\\n row['field'] = entry['field']\\r\\n row['name'] = entry['relationshipName']\\r\\n\\r\\n relationships.append(row)\\r\\n data['relationships'] = relationships\\r\\n\\r\\n fields = []\\r\\n for entry in describe['fields']:\\r\\n row = {}\\r\\n row['name'] = entry['name']\\r\\n row['label'] = entry['label']\\r\\n row['type'] = entry['type']\\r\\n row['length'] = entry['length']\\r\\n row['byteLength'] = entry['byteLength']\\r\\n row['custom'] = entry['custom']\\r\\n row['scale'] = entry['scale']\\r\\n row['precision'] = entry['precision']\\r\\n row['calculated'] = entry['calculated']\\r\\n row['calculatedFormula'] = entry['calculatedFormula']\\r\\n row['soapType'] = entry['soapType']\\r\\n row['picklist'] = entry['picklistValues']\\r\\n if len(row['picklist']) > 0:\\r\\n p_rows = []\\r\\n for p in row['picklist']:\\r\\n p_row = {}\\r\\n p_row['active'] = p['active']\\r\\n p_row['defaultValue'] = p['defaultValue']\\r\\n p_row['label'] = p['label']\\r\\n p_row['validFor'] = p['validFor']\\r\\n p_row['value'] = p['value']\\r\\n\\r\\n p_rows.append(row)\\r\\n\\r\\n fields.append(row)\\r\\n \\r\\n data['fields'] = fields\\r\\n\\r\\n # get the record types\\r\\n rt_rows = []\\r\\n for rt in describe['recordTypeInfos']:\\r\\n rt_row = {}\\r\\n rt_row['available'] = rt['available']\\r\\n rt_row['default'] = rt['defaultRecordTypeMapping']\\r\\n rt_row['master'] = rt['master']\\r\\n rt_row['name'] = rt['name']\\r\\n rt_row['recordTypeId'] = rt['recordTypeId']\\r\\n\\r\\n rt_rows.append(rt_row)\\r\\n\\r\\n data['record_type'] = rt_rows\\r\\n\\r\\n return data\",\n \"def get_data(list_data_tuples):\\n \\n \\n benchmark_symbol=list_data_tuples[0][0]; # First element is the benchmark symbol\\n \\n #print benchmark_symbol\\n \\n df=pd.DataFrame(index=list_data_tuples[0][1]['data'].index) # First dataframe index is nothing but date\\n \\n for tpl in list_data_tuples:\\n #print tpl[0]\\n df_temp = pd.DataFrame(tpl[1]['data']['Adj. Close'],index=tpl[1]['data'].index)\\n df_temp = df_temp.rename(columns={'Adj. Close': tpl[0]}) # tpl[0] is the symbol\\n #print df_temp,tpl[0]\\n df = df.join(df_temp)\\n if tpl[0] == benchmark_symbol: # drop dates SPY did not trade\\n df = df.dropna(subset=[benchmark_symbol])\\n\\n df=df.dropna(axis=0) # This drops any NaN values especially if the stock price has no information\\n \\n return df\",\n \"def get_metadata(self):\\n items = self.get_playlist_items()\\n uris = [item[\\\"track\\\"][\\\"uri\\\"] for item in items]\\n features = self.API.audio_features(uris)\\n\\n list_data = []\\n\\n for idx, item in enumerate(items):\\n data = {\\\"name\\\": item[\\\"track\\\"][\\\"name\\\"], \\\"uri\\\": item[\\\"track\\\"][\\\"uri\\\"]}\\n data.update(features[idx])\\n list_data.append(data)\\n\\n self.metadata = pd.DataFrame(data=list_data,\\n index=range(len(list_data)))\\n\\n return self.metadata\",\n \"def get_data(self, category, ontologies, include_negative_enrichment=True, fdr=0.05):\\n\\n df = self._get_data(category, ontologies)\\n\\n # some extra information for convenience\\n df[\\\"pct_diff_expr\\\"] = df[\\\"number_in_list\\\"] * 100 / df[\\\"number_in_reference\\\"]\\n\\n # could happen that all fold enrichment are set to 'NaN'\\n df = df[df['fold_enrichment'] != 'NaN']\\n df[\\\"log2_fold_enrichment\\\"] = pylab.log2(df[\\\"fold_enrichment\\\"])\\n df[\\\"abs_log2_fold_enrichment\\\"] = abs(pylab.log2(df[\\\"fold_enrichment\\\"]))\\n df[\\\"expected\\\"] = [int(x) for x in df.expected]\\n\\n # Some user may want to include GO terms with fold enrichment\\n # significanyly below 1 or not.\\n if include_negative_enrichment is False:\\n df = df.query(\\\"fold_enrichment>=1\\\").copy()\\n logger.info(\\\"Found {} GO terms after keeping only positive enrichment\\\".format(len(df)))\\n\\n # filter out FDR>0.05\\n df = df.query(\\\"fdr<=@fdr\\\").copy()\\n logger.info(\\\"Found {} GO terms after keeping only FDR<{}\\\".format(len(df), fdr))\\n\\n return df\",\n \"def df_customers_fileRead(self):\\n \\n #-------------------------------------------------------------------------\\n # RFM features are restored\\n #-------------------------------------------------------------------------\\n df_customers_rfm \\\\\\n = p5_util.object_load(self.df_customers_rfm_fileName)\\n self.strprint(\\\"RFM features : \\\"+str(df_customers_rfm.shape))\\n \\n #-------------------------------------------------------------------------\\n # Time features are restored\\n #-------------------------------------------------------------------------\\n df_customers_timeFeature \\\\\\n = p5_util.object_load(self._df_customers_timeFeature_fileName)\\n self.strprint(\\\"Time features : \\\"+str(df_customers_timeFeature.shape))\\n \\n #-------------------------------------------------------------------------\\n # NLP features are restored\\n #-------------------------------------------------------------------------\\n df_customers_nlp = p5_util.object_load(self._df_customers_nlp_fileName)\\n self.strprint(\\\"NLP features : \\\"+str(df_customers_nlp.shape))\\n\\n if False:\\n df_customers_rfm = self._df_customers_rfm.copy()\\n df_customers_timeFeature = self._df_customers_timeFeature.copy()\\n df_customers_nlp = self._df_customers_pca_nlp.copy()\\n\\n #-------------------------------------------------------------------------\\n # Dataframe are aggregated; note that indexes are customerID.\\n #-------------------------------------------------------------------------\\n df_customers = pd.DataFrame()\\n\\n df_customers = pd.concat([df_customers,df_customers_rfm], axis=1)\\n\\n df_customers = pd.concat([df_customers,df_customers_timeFeature]\\\\\\n , join='inner', axis=1)\\n\\n df_customers = pd.concat([df_customers,df_customers_nlp]\\\\\\n , join='inner', axis=1)\\n\\n self.strprint(\\\"All features : \\\"+str(df_customers.shape))\\n\\n #----------------------------------------------------------------------\\n # Dataframe is dumped into a file\\n #----------------------------------------------------------------------\\n p5_util.object_dump(df_customers, self._df_customers_fileName)\\n if False:\\n #----------------------------------------------------------------------\\n # Dataframe is copied as an attribute\\n #----------------------------------------------------------------------\\n self._df_customers = df_customers.copy()\\n \\n return\",\n \"def extract_imits(spark_session: SparkSession) -> DataFrame:\\n print(spark_session)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6035103","0.5869","0.5865952","0.58466834","0.5742667","0.57354236","0.5657524","0.5648695","0.56444234","0.5626451","0.5606854","0.5591722","0.55566615","0.55493385","0.5523179","0.5518336","0.5508248","0.5500367","0.54583555","0.54141194","0.54134256","0.54126364","0.5409198","0.5405545","0.5393958","0.5387398","0.5312582","0.53058755","0.53054357","0.5295633","0.52952886","0.52718794","0.5271009","0.5262873","0.5250299","0.5238617","0.52226835","0.52145714","0.52051276","0.5200363","0.51975906","0.51936936","0.5188086","0.5183932","0.51799047","0.5168468","0.51541364","0.5152651","0.51521164","0.5151881","0.5150782","0.5148182","0.51392204","0.5123063","0.51189333","0.5111233","0.5104366","0.5101723","0.5089721","0.5085755","0.50851136","0.5082722","0.5081033","0.5080918","0.5074834","0.50708497","0.50693405","0.50578415","0.5057302","0.5054017","0.50466406","0.50403476","0.5039168","0.50387335","0.50325465","0.5023418","0.50181806","0.5012783","0.50124055","0.50089645","0.50079924","0.50040483","0.500239","0.50023377","0.5000329","0.4998902","0.4995512","0.49951503","0.49885586","0.49870872","0.49831077","0.4982846","0.49774423","0.4975472","0.49728444","0.49720812","0.49715292","0.49675375","0.49665886","0.49568692","0.4956533"],"string":"[\n \"0.6035103\",\n \"0.5869\",\n \"0.5865952\",\n \"0.58466834\",\n \"0.5742667\",\n \"0.57354236\",\n \"0.5657524\",\n \"0.5648695\",\n \"0.56444234\",\n \"0.5626451\",\n \"0.5606854\",\n \"0.5591722\",\n \"0.55566615\",\n \"0.55493385\",\n \"0.5523179\",\n \"0.5518336\",\n \"0.5508248\",\n \"0.5500367\",\n \"0.54583555\",\n \"0.54141194\",\n \"0.54134256\",\n \"0.54126364\",\n \"0.5409198\",\n \"0.5405545\",\n \"0.5393958\",\n \"0.5387398\",\n \"0.5312582\",\n \"0.53058755\",\n \"0.53054357\",\n \"0.5295633\",\n \"0.52952886\",\n \"0.52718794\",\n \"0.5271009\",\n \"0.5262873\",\n \"0.5250299\",\n \"0.5238617\",\n \"0.52226835\",\n \"0.52145714\",\n \"0.52051276\",\n \"0.5200363\",\n \"0.51975906\",\n \"0.51936936\",\n \"0.5188086\",\n \"0.5183932\",\n \"0.51799047\",\n \"0.5168468\",\n \"0.51541364\",\n \"0.5152651\",\n \"0.51521164\",\n \"0.5151881\",\n \"0.5150782\",\n \"0.5148182\",\n \"0.51392204\",\n \"0.5123063\",\n \"0.51189333\",\n \"0.5111233\",\n \"0.5104366\",\n \"0.5101723\",\n \"0.5089721\",\n \"0.5085755\",\n \"0.50851136\",\n \"0.5082722\",\n \"0.5081033\",\n \"0.5080918\",\n \"0.5074834\",\n \"0.50708497\",\n \"0.50693405\",\n \"0.50578415\",\n \"0.5057302\",\n \"0.5054017\",\n \"0.50466406\",\n \"0.50403476\",\n \"0.5039168\",\n \"0.50387335\",\n \"0.50325465\",\n \"0.5023418\",\n \"0.50181806\",\n \"0.5012783\",\n \"0.50124055\",\n \"0.50089645\",\n \"0.50079924\",\n \"0.50040483\",\n \"0.500239\",\n \"0.50023377\",\n \"0.5000329\",\n \"0.4998902\",\n \"0.4995512\",\n \"0.49951503\",\n \"0.49885586\",\n \"0.49870872\",\n \"0.49831077\",\n \"0.4982846\",\n \"0.49774423\",\n \"0.4975472\",\n \"0.49728444\",\n \"0.49720812\",\n \"0.49715292\",\n \"0.49675375\",\n \"0.49665886\",\n \"0.49568692\",\n \"0.4956533\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":84194,"cells":{"query":{"kind":"string","value":"Takes in the product volume dictionary & returns applicable discounts, any residuals (product codes) which doesnt have any offers"},"document":{"kind":"string","value":"def get_applicable_discounts(self, actual_volume):\n\n applicable_discounts = []\n residual = {}\n\n min_vol_criteria, product_vs_offer, base_product_vs_offer_product = self.get_product_vs_offer_and_volume(self.discounts)\n\n for basket_item, volume in actual_volume.items():\n offer_on_product = base_product_vs_offer_product.get(basket_item)\n if min_vol_criteria.get(basket_item):\n if volume >= min_vol_criteria.get(basket_item) and actual_volume.get(offer_on_product):\n applicable_discounts.append(product_vs_offer.get(basket_item))\n else:\n residual[basket_item.upper()] = volume\n else:\n for prod, offering in base_product_vs_offer_product.items():\n if (offering == basket_item) and (prod not in actual_volume):\n residual[basket_item.upper()] = volume\n # else:\n\n\n if \"APPL\" in applicable_discounts and \"APOM\" in applicable_discounts:\n applicable_discounts.remove(\"APOM\")\n residual['OM1'] = actual_volume.get('om1')\n\n return applicable_discounts, residual"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_basket_with_discounts(self, actual_volume, applicable_discounts, residual):\n\n # product_vs_min_volume, product_offer = self.get_product_vs_offer_and_volume(self.discounts)\n pr = Product('items')\n product_prices = pr.product_meta\n\n basket = []\n\n for discount in applicable_discounts:\n offer_info = Offer('discount', discount)\n\n if offer_info.offer_type.lower() == \"discount\":\n temp_basket = self.get_basket_items_discount(offer_info, actual_volume, product_prices)\n for item in temp_basket:\n basket.append(item)\n elif offer_info.offer_type.lower() == \"pricedrop\":\n temp_basket = self.get_basket_items_pricedrop(offer_info, actual_volume, product_prices)\n for item in temp_basket:\n basket.append(item)\n\n if residual:\n for product, volume in residual.items():\n for i in range(0, volume):\n basket.append((product, product_prices.get(product.lower()).get('price')))\n\n return basket","def get_basket_items_discount(self, offer_info, actual_volume, product_prices):\n prod_code = offer_info.base_prod_code\n base_prod_vol = actual_volume.get(prod_code.lower())\n\n discount_basket = []\n\n if base_prod_vol >= offer_info.min_vol:\n offer_on_prod = offer_info.offer_on\n if actual_volume.get(offer_on_prod.lower()):\n print(f\"Base product volume is greater than minimum required volume & product on offer is also available \"\n f\"in cart..\")\n if offer_info.is_limited:\n print(f\"Limited offer..\")\n if prod_code == offer_on_prod:\n # total_allowed_items_on_offer = Limit Volume of base product * (Offer Product Max Volume/Minimum volume of base product)\n total_allowed_items_on_offer = offer_info.limit_vol * (offer_info.offer_prod_volume/offer_info.min_vol)\n max_limit = 1\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n discount_basket.append((prod_code, base_prod_actual_price))\n while max_limit <= total_allowed_items_on_offer:\n discounted_price = (base_prod_actual_price *(offer_info.discount_perc/100))*-1\n discount_basket.append((offer_info.offer_code, discounted_price))\n max_limit += 1\n else:\n total_allowed_items_on_offer = offer_info.limit_vol * (offer_info.offer_prod_volume / offer_info.min_vol)\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n discount_basket.append((prod_code, base_prod_actual_price))\n max_limit = 1\n while max_limit <= total_allowed_items_on_offer:\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\n discounted_price = (offer_onprod_actual_price *(offer_info.discount_perc/100))*-1\n for j in range(0, actual_volume.get(offer_on_prod.lower())):\n discount_basket.append((offer_on_prod, offer_onprod_actual_price))\n discount_basket.append((offer_info.offer_code, discounted_price))\n max_limit += 1\n else:\n print(f\"Unlimited offer..\")\n if prod_code == offer_on_prod:\n if base_prod_vol > offer_info.min_vol:\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n discount_basket.append((prod_code, base_prod_actual_price))\n if i%2 != 0:\n discounted_price = (base_prod_actual_price *(offer_info.discount_perc/100))*-1\n discount_basket.append((offer_info.offer_code, discounted_price))\n else:\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n discount_basket.append((prod_code, base_prod_actual_price))\n else:\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n discount_basket.append((prod_code, base_prod_actual_price))\n\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\n discounted_price = (offer_onprod_actual_price * (offer_info.discount_perc / 100))*-1\n\n for j in range(0, actual_volume.get(offer_on_prod.lower())):\n discount_basket.append((offer_on_prod, offer_onprod_actual_price))\n discount_basket.append((offer_info.offer_code, discounted_price))\n\n\n return discount_basket","def get_basket_items_pricedrop(self, offer_info, actual_volume, product_prices):\n prod_code = offer_info.base_prod_code\n base_prod_vol = actual_volume.get(prod_code.lower())\n\n pricedrop_basket = []\n\n if base_prod_vol >= offer_info.min_vol:\n offer_on_prod = offer_info.offer_on\n if actual_volume.get(offer_on_prod.lower()):\n print(\n f\"Base product volume is greater than minimum required volume & product on offer is also available \"\n f\"in cart..\")\n if offer_info.is_limited:\n print(f\"Limited offer..\")\n if prod_code == offer_on_prod:\n # total_allowed_items_on_offer = Limit Volume of base product * (Offer Product Max Volume/Minimum volume of base product)\n total_allowed_items_on_offer = offer_info.limit_vol * (\n offer_info.offer_prod_volume / offer_info.min_vol)\n max_limit = 1\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n pricedrop_basket.append((prod_code, base_prod_actual_price))\n while max_limit <= total_allowed_items_on_offer:\n new_price = (base_prod_actual_price - (offer_info.new_price)) * -1\n pricedrop_basket.append((offer_info.offer_code, new_price))\n max_limit += 1\n else:\n total_allowed_items_on_offer = offer_info.limit_vol * (\n offer_info.offer_prod_volume / offer_info.min_vol)\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n pricedrop_basket.append((prod_code, base_prod_actual_price))\n max_limit = 1\n while max_limit <= total_allowed_items_on_offer:\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\n new_price = (base_prod_actual_price - (offer_info.new_price)) * -1\n for j in range(0, actual_volume.get(offer_on_prod).lower()):\n pricedrop_basket.append((offer_on_prod, offer_onprod_actual_price))\n pricedrop_basket.append((offer_info.offer_code, new_price))\n max_limit += 1\n else:\n print(f\"Unlimited offer..\")\n if prod_code == offer_on_prod:\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n pricedrop_basket.append((prod_code, base_prod_actual_price))\n new_price = (base_prod_actual_price - (offer_info.new_price))*-1\n pricedrop_basket.append((offer_info.offer_code, new_price))\n else:\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n pricedrop_basket.append((prod_code, base_prod_actual_price))\n\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\n new_price = (offer_onprod_actual_price - (offer_info.new_price)) * -1\n\n for j in range(0, actual_volume.get(offer_on_prod).lower()):\n pricedrop_basket.append((offer_on_prod, offer_onprod_actual_price))\n pricedrop_basket.append((offer_info.offer_code, new_price))\n\n return pricedrop_basket","def get_product_vs_offer_and_volume(self, df):\n\n product_vs_min_volume = {}\n product_offer = {}\n base_product_vs_offer_product = {}\n\n for i in df.itertuples():\n product_vs_min_volume[i[4].lower()] = i[5]\n product_offer[i[4].lower()] = i[1]\n base_product_vs_offer_product[i[4].lower()] = i[7].lower()\n\n return product_vs_min_volume, product_offer, base_product_vs_offer_product","def __init__(self, name, product, production_volume):\n self.name = name\n self.product = product\n self.production_volume = production_volume\n self.suppliers = []\n self.costumers = []\n self.production_drop = 0\n self.market_shrinkage = 0\n self.sale_drop = 0\n self.out_edge_capacity_drop = {}\n self.in_edge_capacity_drop = {}","def show_available_products(): # {{{\n products_available = {}\n try:\n with MONGO:\n product_collection = MONGO.connection.assignment_07[\"product\"].find(\n )\n\n for product in product_collection:\n if int(product[\"quantity_available\"]) > 0:\n products_available[product[\"product_id\"]] = {\n \"description\": product[\"description\"],\n \"product_type\": product[\"product_type\"],\n \"quantity_available\": product[\"quantity_available\"],\n }\n except TypeError as excep:\n LOGGER.warning(\"Error looking up available products\")\n LOGGER.warning(excep)\n else:\n if not products_available:\n LOGGER.info('No products found')\n else:\n LOGGER.info(\"Available products retrieved successfully.\")\n return products_available # }}}","def get_product_kpis(self):\n kpi_1 = self.products[['products', 'client']].reset_index().groupby([\"client\", \"products\"]).agg(\n {\"index\": \"count\"}).reset_index().groupby(\"products\").agg(\n {\"index\": \"mean\"}).reset_index().rename(columns={\"index\": \"average_product_sold_per_user_kpi\"})\n kpi_2 = self.products[['products', 'payment_amount']].groupby(\"products\").agg(\n {\"payment_amount\": \"sum\"}).reset_index().rename(columns={\"payment_amount\": \"total_product_revenue_kpi\"})\n try:\n kpi_3 = self.products[['products', 'discount_amount']].query(\"discount_amount == discount_amount\")\n kpi_3['discount_amount'] = kpi_3['discount_amount'].apply(lambda x: float(x))\n kpi_3 = kpi_3.groupby(\"products\").agg({\"discount_amount\": \"sum\"}).reset_index().rename(\n columns={\"discount_amount\": \"total_product_discount_kpi\"})\n except:\n kpi_3 = self.products[['products']]\n kpi_3['total_product_discount_kpi'] = 0\n\n kpi_4 = self.products[['products', 'client']].groupby(\"products\").agg(\n {\"client\": lambda x: len(np.unique(x))}).reset_index().rename(columns={\"client\": \"total_product_cust_kpi\"})\n\n self.product_kpis = pd.DataFrame(self.products.groupby('products').count().reset_index()[['products']]).merge(\n kpi_1, on='products', how='left').merge(\n kpi_2, on='products', how='left').merge(\n kpi_3, on='products', how='left').merge(kpi_4, on='products', how='left').fillna(0)\n\n return self.product_kpis","def available_products(self):\n # TODO - take into account bands, and what bands available in input products, etc\n return {k: self.__products__[k].description for k in self.__products__.keys()}","def inventory_report(products):\n unique_names = []\n total_price = 0\n total_weight = 0\n total_flammability = 0\n num_products = len(products)\n for i in range(num_products):\n if products[i].name not in unique_names:\n unique_names.append(products[i].name) \n total_price += products[i].price\n total_weight += products[i].weight\n total_flammability += products[i].flammability\n mean_price = total_price / num_products\n mean_weight = total_weight / num_products\n mean_flammability = total_flammability / num_products\n print('ACME CORPORATION OFFICIAL INVENTORY REPORT')\n print(f'Unique product names: {len(unique_names)}')\n print(f'Average price: {mean_price}')\n print(f'Average weight {mean_weight}')\n print(f'Average flammabilitiy {mean_flammability}')\n return unique_names, mean_price, mean_weight, mean_flammability","def retrieve_product_infos(self):\n\n # PRODUCT NAME\n try:\n product_name = self.product['product_name'].capitalize()\n except KeyError:\n product_name = None\n\n # PRODUCT CODE\n try:\n product_code = self.product['code'].capitalize()\n except KeyError:\n product_code = None\n\n # URL\n try:\n product_url = self.product['url'].lower()\n except KeyError:\n product_url = None\n\n # IMAGE URL\n try:\n image_url = self.product['image_url'].lower()\n except KeyError:\n image_url = None\n\n # QUANTITY\n try:\n quantity = self.product['quantity'].capitalize()\n except KeyError:\n quantity = None\n\n # INGREDIENTS\n try:\n ingredients = self.product['ingredients_text_fr'].capitalize()\n except KeyError:\n ingredients = None\n\n # BRAND\n brands = []\n try:\n for brand in self.product['brands'].split(','):\n brand = brand.strip().capitalize()\n if (\n brand != ''\n and brand not in brands\n ):\n brands.append(brand)\n except KeyError:\n pass\n\n # STORES\n stores = []\n try:\n for store in self.product['stores'].split(','):\n store = store.strip().capitalize()\n if (\n store != ''\n and store not in stores\n ):\n stores.append(store)\n except KeyError:\n pass\n\n # COUNTRY\n try:\n countries = self.product['countries'].capitalize()\n except KeyError:\n countries = None\n if 'France' in countries:\n countries = 'France'\n else:\n countries = None\n\n # COMPARE TO CATEGORY\n try:\n compare_to = self.product['compared_to_category'].capitalize().split(':')[1]\n except KeyError:\n compare_to = None\n try:\n Categories.objects.get(\n name=compare_to\n )\n except Categories.DoesNotExist:\n compare_to = None\n except:\n importable = False\n\n # CATEGORIES HIERARCHY\n try:\n categories_hierarchy = [\n category.split(':')[1] for category in self.product['categories_hierarchy']\n ]\n except KeyError:\n categories_hierarchy = None\n\n # NUTRISCORE GRADE\n nutriscore_labels = [\n 'nutrition_grade_fr',\n 'nutriscore_grade'\n ]\n nutriscore = 'F'\n i = 0\n while (\n i < len(nutriscore_labels)\n and nutriscore == 'F'\n ):\n try:\n nutriscore = self.product[nutriscore_labels[i]].upper()\n except KeyError:\n i += 1\n\n product_infos = {\n 'product_name': product_name,\n 'product_code': product_code,\n 'product_url': product_url,\n 'image_url': image_url,\n 'quantity': quantity,\n 'ingredients': ingredients,\n 'brands': brands,\n 'stores': stores,\n 'countries': countries,\n 'compare_to': compare_to,\n 'categories_hierarchy': categories_hierarchy,\n 'nutriscore': nutriscore\n }\n\n nutriments = self.product['nutriments']\n for nutriment in self.list_nutriments:\n try:\n product_infos[nutriment] = float(nutriments[nutriment])\n except KeyError:\n product_infos[nutriment] = 0\n\n return product_infos","def show_available_products(*args):\n logger.info(f\"Preparing dict of available prodcuts...\")\n available_products = {}\n\n with MONGO:\n mdb = eval(Settings.connect_string)\n products = mdb[\"product\"]\n for doc in products.find():\n del doc[\"_id\"]\n if int(doc[\"quantity_available\"]) > 0:\n product_id = doc[\"product_id\"]\n del doc[\"product_id\"]\n available_products[product_id] = doc\n\n return available_products","def show_available_products():\n\n mongo = MongoDBConnection()\n result = {}\n\n with mongo:\n db = mongo.connection.HPNorton\n productcollection = db[\"products\"]\n for document in productcollection.find({\"quantity_available\": {\"$gt\": \"0\"}}):\n key = document['product_id']\n\n result[key] = {\n 'description': document['description'],\n 'product_type': document['product_type'],\n 'quantity_available': document['quantity_available']\n }\n\n return result","def show_available_products():\n products = DATABASE['product'].find({'quantity_available': {'$ne':'0'}})\n products_dict = {prod['product_id']:\n {'description': prod['description'],\n 'product_type': prod['product_type'],\n 'quantity_available': int(prod['quantity_available'])}\n for prod in products}\n return products_dict","def solve_prices(self):\n return None","def inventory_report(prod_list):\n prod_list = list(set(prod_list))\n x = 0\n price = 0\n weight = 0\n flammability = 0\n stealability = 0\n for item in prod_list:\n x += 1\n price += item.price\n weight += item.weight\n flammability += item.flammability\n if stealability != 'Not so stealable...':\n stealability += 1\n\n avg_price = price / x\n avg_weight = weight / x\n avg_flammability = flammability / x\n print(f'There are {x} unique products in this list. The average price is {avg_price}, '\n f'average weight is {avg_weight},'\n f'and the average flammability is {avg_flammability}.')\n if stealability >= len(prod_list) / 2:\n print('Many of these items are highly stealable!')\n return avg_price, avg_weight, avg_flammability","def compute_volume(self, windkessel_pressures):\n # Extract relevant model parameters.\n cart = self.parameters['arterial_compliance']\n vart_rest = self.parameters['arterial_resting_volume']\n return {'art': windkessel_pressures['art']*cart + vart_rest}","def show_vendor_product():\n vendor = input(\"Enter the Vendor: \")\n product = input(\"Enter the product: \")\n filter_string = input(\"Enter Optional Search string (i.e. HTTP): \")\n logger.debug(\"Searching: {} from {} -- Filter = {}\".format(product, vendor, filter_string))\n search_url = \"http://cve.circl.lu/api/search/{}/{}\".format(vendor, product)\n req = call_api(search_url)\n if not req:\n logger.debug(\"something no workie with the vendor product call\")\n else:\n print(\"Searching: {} from {} -- Filter = {}\".format(product, vendor, filter_string))\n for item in req:\n if filter_string != '' or not filter_string:\n if filter_string in item['summary']:\n print(\"\\nSummary: \" + item['summary'])\n print(\"CVE: \" + item['id'])\n print(\"CVSS: \" + str(item['cvss']))\n else:\n print(\"\\nSummary: \" + item['summary'])\n print(\"CVE: \" + item['id'])\n print(\"CVSS: \" + str(item['cvss']))\n menu()","def _get_book_summary(self):\n self._get_book_prices()\n for price in self.ask_prices:\n volume = 0\n for k in self.ask_snapshot.keys():\n if self.ask_snapshot[k].price == price:\n volume += self.ask_snapshot[k].volume\n self.ask_volumes.append(volume)\n for price in self.bid_prices:\n volume = 0\n for k in self.bid_snapshot.keys():\n if self.bid_snapshot[k].price == price:\n volume += self.bid_snapshot[k].volume\n self.bid_volumes.append(volume)","def prod_parser(self, response):\n products = []\n pk = Product.objects.last()\n if pk:\n i = pk.id\n else:\n i = 0\n for product in response:\n # crawling product for name, desc, API_url, image_url, nutriscore, nutient_100g\n if 'ingredients_text_fr' not in product:\n desc = \"\"\n else:\n desc = product['ingredients_text_fr']\n nutrigrade = \"\".join(product[\"nutrition_grades_tags\"])\n if nutrigrade in (\"a\", \"b\", \"c\", \"d\", \"e\") \\\n and 'fat_100g' in product['nutriments'] \\\n and 'image_url' in product \\\n and 'product_name_fr' in product:\n i += 1\n product[i] = {\n \"id\": i,\n \"name\": product['product_name_fr'],\n \"desc\": desc,\n \"categories\": product[\"categories\"].split(\", \"),\n \"API_link\": product['url'],\n \"photo\": product['image_url'],\n \"nutriscore\": nutrigrade,\n \"nutrient_100g\":\n \"saturated_fat_100g:{}:{}, \".format(\n product['nutriments']['saturated-fat_100g'],\n product['nutrient_levels']['saturated-fat']) +\n \"fat_100g:{}:{}, \".format(\n product['nutriments']['fat_100g'], product['nutrient_levels']['fat']) +\n \"salt_100g:{}:{}, \".format(\n product['nutriments']['salt_100g'], product['nutrient_levels']['salt']) +\n \"sugars_100g:{}:{} \".format(\n product['nutriments']['sugars_100g'], product['nutrient_levels']['sugars'])\n }\n products.append(product[i])\n else:\n pass\n return products","def test_instrument_dataproduct_filtering():\n dp = 'spectrum'\n data = mm.instrument_inventory('nirspec', dataproduct=dp, caom=True,\n return_data=True)\n\n dps = [row['dataproduct_type'] for row in data['data']]\n\n assert all([i == dp for i in dps])","def get_product_pack(self, ):\n\n # Parse response dict for product data\n\n result = {}\n\n # Iterate over incoming raw data and pick values corresponding to \"Product\" db table\n for item in self.raw_data['searchResult']['item']:\n for field in item.keys():\n if format_ebay_col_name(field) in product_table_columns:\n if type(item[field]) == str:\n if '\"' in item[field]: item[field] = item[field].replace('\"', '\\\\\"')\n result[format_ebay_col_name(field)] = item[field]\n\n # Handle several nested values\n while Switch(format_ebay_col_name(field)):\n if case('PRODUCT_ID'):\n result['PRODUCT_ID'] = item[field]['value']\n if case('PRIMARY_CATEGORY'):\n result['CATEGORY_ID'] = item[field]['categoryId']\n result['CATEGORY_NAME'] = item[field]['categoryName']\n if case('CONDITION'):\n result['CONDITION_DISPLAY_NAME'] = item[field]['conditionDisplayName']\n result['CONDITION_ID'] = item[field]['conditionId']\n break\n\n # Fill missing values with \"NULL\"s\n for table_filed in product_table_columns:\n if table_filed not in result.keys(): result[table_filed] = 'NULL'\n\n return result","def variations():","def inventory_report(products):\n name_list = set()\n price_list = []\n wt_list = []\n flamablity_list = []\n\n for p in products:\n name_list.add(p.name)\n price_list.append(p.price)\n wt_list.append(p.weight)\n flamablity_list.append(p.flammability)\n# Calculating average for report\n unique_names = len(name_list)\n avg_price = sum(price_list)/len(price_list)\n avg_weight = sum(wt_list)/len(wt_list)\n avg_flammability = sum(flamablity_list)/len(flamablity_list)\n# Printing\n print(\"$ python acme_report.py \")\n print(\"ACME CORPORATION OFFICIAL INVENTORY REPORT\")\n print(\"Unique product names:\", unique_names)\n print(\"Average price:\", round(avg_price, 2))\n print(\"Average weight:\", avg_weight)\n print(\"Average flammability:\", avg_flammability)","def process_product_data(cls, pricing_info):\n if 'prices' not in pricing_info or 'vat_bands' not in pricing_info:\n raise PricingException('Json data does not contain required '\n 'product and vat band information')\n\n product_prices = pricing_info['prices']\n vat_bands = pricing_info['vat_bands']\n\n processed_vat_bands = {}\n for vat_name, vat_rate in vat_bands.items():\n if vat_name in processed_vat_bands:\n raise PricingException('Vat names need to be unique. '\n f'Vat names duplicated {vat_name}')\n\n try:\n rate = float(vat_rate)\n except ValueError:\n raise PricingException('Vat rates need to be a decimal. '\n f'Vat rate: {vat_rate}')\n\n processed_vat_bands[vat_name] = {\n 'name': vat_name,\n 'rate': rate\n }\n\n processed_products = {}\n for product_price in product_prices:\n product_id = product_price['product_id']\n if product_id in processed_products:\n raise PricingException('Product ids need to be unique. '\n f'Product id duplicated {product_id}')\n\n associated_vat_band = product_price['vat_band']\n if associated_vat_band not in processed_vat_bands:\n raise PricingException('Vat band does not exist. '\n f'Vat band: {associated_vat_band} on '\n f'product id: {product_id}')\n\n try:\n price = int(product_price['price'])\n except ValueError:\n raise PricingException('Product prices need to be numbers. '\n f'Price: {price} on '\n f'product id: {product_id}')\n\n processed_products[product_id] = {\n 'product_id': product_id,\n 'price': price,\n 'vat_band': associated_vat_band\n }\n\n return processed_vat_bands, processed_products","def buy_one_get_one(products):\n if 'p1' in products and products['p1'] >= 2:\n return -20\n else:\n return 0","def add_non_furniture_nor_appliance(itemcode, itemdescription,\n itemprice, itemrentalprice):\n\n newitem = Inventory(itemcode, itemdescription,\n itemprice, itemrentalprice)\n FULLINVENTORY[itemcode] = newitem.returnasdictionary()","def coproduct_sum(sys):\r\n \r\n streams=coproduct_finder(sys)\r\n BD_mass=0\r\n Glycerol_mass=0\r\n products = {}\r\n for i in streams:\r\n if i == 'Waste':\r\n pass\r\n else:\r\n mass = find(i).mass\r\n BD_index=find(i).get_index('Biodiesel')\r\n Glycerol_index=find(i).get_index('Glycerol')\r\n BD = mass[BD_index]\r\n Glycerol = mass[Glycerol_index]\r\n BD_mass = BD_mass+ BD\r\n Glycerol_mass = Glycerol_mass + Glycerol\r\n products = {'Biodiesel (kg/hr)':BD_mass, 'Glycerol (kg/hr)': Glycerol_mass}\r\n return products","def inventory_report(products, prices, weights, flammabilities):\n num_product = len(products)\n avg_price = mean(prices)\n avg_weight = mean(weights)\n avg_flam = mean(flammabilities)\n\n print(\"ACME CORPORATION OFFICIAL INVENTORY REPORT\")\n print(\"Unique product names: {}\".format(num_product))\n print(\"Average price: {}\".format(avg_price))\n print(\"Average weight: {}\".format(avg_weight))\n print(\"Average flammability: {}\".format(avg_flam))","def get_most_and_least_expensive_high_review_product(df):\n try:\n df3 = merge_metadata(df)\n product_filter = df3['overall'] >= 4.0\n high_reviewed_products = df3[product_filter]\n # print high_reviewed_products[:10]\n # The data contained NaN so we use the nanmax/min funtions to get max/min\n most_exp = round(np.nanmax(high_reviewed_products['price'])[0], 2)\n least_exp = round(np.nanmin(high_reviewed_products['price'])[0], 2)\n\n most_exp_prod = df3.loc[df3['price'] == most_exp, 'asin'].iloc[0]\n least_exp_prod = df3.loc[df3['price'] == least_exp, 'asin'].iloc[0]\n write_text_tofile(\"Most Expensive Product: \" + str(most_exp_prod) + \", Price: \" + str(most_exp))\n write_text_tofile(\"Least Expensive Product: \" + str(least_exp_prod) + \", Price: \" + str(least_exp))\n return {most_exp_prod: most_exp, least_exp_prod: least_exp}\n except Exception as e:\n print \"Error getting most and least expensive high review product\"\n print str(e)\n pass","def get_products_dict(products):\n # lang = get_language()[:2]\n lang = ''\n products_dict = {}\n try:\n if products and products[0].get('source') == 'greedy':\n for product in products:\n key = product['name']\n products_dict[key] = products_dict.get(key, {})\n products_dict[key].setdefault('products', []).append(key)\n products_dict[key]['price'] = products_dict[key].get('price', 0) + product['net_price']\n else:\n product_objs = list(Product.objects.using('slave').in_bulk([p['product_id'] for p in products]).values())\n bundled_products = []\n for product in product_objs:\n for bundled_product in product.bundled.all():\n bundled_product.price = 0\n bundled_products.append(bundled_product)\n product_objs.extend(bundled_products)\n for product in product_objs:\n key = getattr(product.parent, 'name_%s' % lang)\n products_dict[key] = products_dict.get(key, {\n 'expire_in': product.expire_in,\n 'never_expire': product.never_expire\n })\n products_dict[key].setdefault('products', []).append(mark_safe(product.name))\n products_dict[key]['price'] = products_dict[key].get('price', 0) + product.price\n # Convert it to a format which is easy to handle in email templates\n products_dict = [{\n 'title': key,\n 'body': value,\n } for key, value in products_dict.items()]\n except (ValueError, KeyError, AttributeError):\n products_dict = list({'title': p['name'], 'body': {'expire_in': None, 'never_expire': None}} for p in products)\n\n return products_dict","def show_available_products():\n LOGGER.debug('Listing all available products.')\n available_products = {}\n with MongoDBConnection() as mongo:\n database = mongo.connection.hp_norton\n for product in database.products.find(\n {'quantity_available': {'$gt': 0}}):\n available_products[product['product_id']] = {\n 'description': product['description'],\n 'product_type': product['product_type'],\n 'quantity_available': product['quantity_available']}\n return available_products","def show_available_products():\n available_product = {}\n\n if not collection_exist(DATABASE, PRODUCT_COLLECTION):\n return available_product\n\n with MongoDBConnection() as mongo:\n database = mongo.connection[DATABASE]\n\n available_product__count = 0\n for product in database[PRODUCT_COLLECTION].find({\"quantity_available\": {\"$ne\": '0'}}):\n available_product[product['product_id']] = \\\n {'description': product['description'],\n 'product_type': product['product_type'],\n 'quantity_available': product['quantity_available']}\n available_product__count += 1\n\n return available_product__count","def getChannelVolumes(query, volDimensions, scale, intensityrange, mod):\n \n # query = {'preIF' : preIF, 'preIF_z' : preIF_z, 'postIF' : postIF, 'postIF_z' : postIF_z};\n\n #presynaptic volumes\n presynapticvolumes = []\n preIF = query['preIF']\n\n for n in range(0, len(preIF)):\n\n print preIF[n]\n\n volume = getVolumeFromRender(\n preIF[n], volDimensions, scale, intensityrange, mod)\n presynapticvolumes.append(volume)\n\n #postsynaptic volumes\n postsynapticvolumes = []\n postIF = query['postIF']\n\n for n in range(0, len(postIF)):\n print postIF[n]\n volume = getVolumeFromRender(\n postIF[n], volDimensions, scale, intensityrange, mod)\n postsynapticvolumes.append(volume)\n\n synapticVolumes = {'presynaptic': presynapticvolumes,\n 'postsynaptic': postsynapticvolumes};\n return synapticVolumes","def _get_normal_sold_prices(self, jdict):\n price_dict = jdict['props']['homeDetails']['price']\n try:\n sales_price_text = price_dict['formattedPrice'].replace(',','')\\\n .replace('$', '')\n sales_price = self._extract_num(sales_price_text)\n sales_date = price_dict['formattedSoldDate']\n sales_date_formatted = datetime.datetime\\\n .strptime(sales_date, '%b %d, %Y')\\\n .strftime('%Y-%m-%d')\n try:\n asking_price_text = price_dict['listingPrice']['formattedPrice'].replace(',','')\\\n .replace('$', '')\n if 'k' in asking_price_text.lower():\n asking_price = self._extract_num(asking_price_text)*1e3\n elif 'm' in asking_price_text.lower():\n asking_price = self._extract_num(asking_price_text)*1e6\n else:\n asking_price = self._extract_num(asking_price_text)\n return sales_date_formatted, sales_price, asking_price\n except:\n return sales_date_formatted, sales_price, np.nan\n except:\n return None, None, None","def compute_volume(self, windkessel_pressures = None):\n # Extract the relevant volumes from the inputs.\n if windkessel_pressures is None:\n part = self.pressure['art']\n pven = self.pressure['ven']\n else:\n part = windkessel_pressures['art']\n pven = windkessel_pressures['ven']\n\n # Extract relevant model parameters.\n cven = self.parameters['venous_compliance']\n cart = self.parameters['arterial_compliance']\n vven_rest = self.parameters['venous_resting_volume']\n vart_rest = self.parameters['arterial_resting_volume']\n\n # Compute the venous and arterial volumes.\n v = {'art': part*cart + vart_rest, 'ven': pven*cven + vven_rest}\n return v","def execQ4():\n frame = pan.DataFrame(data, columns=['Product', 'Price', 'Period'])\n tuna = frame[dFrame.Series_title_1 == \"Tuna - canned (supermarket only), 185g\"]\n expensive = tuna.sort_values(by=\"Price\", ascending=False).head(1)\n return expensive","def get_prices_dict(name,products,sales):\r\n return {x:(1-sales[name])*products[x] for x in products}","def test_product_buy_missing_goods(self):\n result_buy = self.info_list.product_buy(\"хлеб серый хлебозавод\", 3)\n self.assertFalse(result_buy)","def get_volume_product_on_date(product_barcode, date, store_id, transactions):\n transactions_day = transactions[(transactions['STO_EAN'] == store_id) &\n (transactions['BARCODE'] == product_barcode) &\n (transactions['TRX_DATETIME'] >= pd.to_datetime(date).date())\n &(transactions['TRX_DATETIME'] < (pd.to_datetime(date) + pd.DateOffset(1)))]\n\n # If the transaction does not exist return None\n if transactions_day.empty:\n return None\n\n return {\"price\": np.sum(transactions_day['SAL_AMT_WTAX'].values),\n \"weight\": np.sum(transactions_day['SAL_UNIT_QTY_WEIGHT'].values)}","def getCurrencies():","def query_part_info(parts, distributors, currency):\n solved = set()\n # Loop through the parts looking for those sourced by local distributors\n # that won't be found online. Place any user-added info for these parts\n # (such as pricing) into the part dictionary.\n for p in parts:\n # Find the manufacturer's part number if it exists.\n pn = p.fields.get('manf#') # Returns None if no manf# field.\n\n # Now look for catalog number, price list and webpage link for this part.\n for dist in distributors:\n cat_num = p.fields.get(dist + ':cat#')\n pricing = p.fields.get(dist + ':pricing')\n link = p.fields.get(dist + ':link')\n avail = p.fields.get(dist + ':avail')\n if cat_num is None and pricing is None and link is None:\n continue\n\n cat_num = cat_num or pn or make_unique_catalog_number(p, dist)\n p.fields[dist + ':cat#'] = cat_num # Store generated cat#.\n # Get the DistData for this distributor\n dd = p.dd.get(dist, DistData())\n dd.part_num = cat_num\n\n if link:\n url_parts = list(urlsplit(link))\n if url_parts[0] == '':\n url_parts[0] = u'http'\n link = urlunsplit(url_parts)\n else:\n # This happens when no part URL is found.\n debug_obsessive('No part URL found for local \\'{}\\' distributor!'.format(dist))\n dd.url = link\n\n price_tiers = {}\n try:\n local_currency = re.findall('[a-zA-Z]{3}', pricing)[0].upper()\n except Exception:\n local_currency = currency\n old_pricing = pricing\n pricing = re.sub('[^0-9.;:]', '', pricing) # Keep only digits, decimals, delimiters.\n for qty_price in pricing.split(';'):\n splitted = qty_price.split(SEPRTR)\n if len(splitted) == 2:\n qty, price = splitted\n if local_currency:\n dd.currency = local_currency\n try:\n price_tiers[int(qty)] = float(price)\n except ValueError:\n warning(W_BADPRICE, 'Malformed pricing number: `{}` at {}'.format(old_pricing, p.refs))\n else:\n warning(W_BADPRICE, 'Malformed pricing entry: `{}` at {}'.format(qty_price, p.refs))\n # dd.moq = min(price_tiers.keys())\n if not price_tiers:\n # This happens when no pricing info is found.\n debug_obsessive('No pricing information found for local \\'{}\\' distributor!'.format(dist))\n dd.price_tiers = price_tiers\n\n # Availability\n if avail is not None:\n dd.qty_avail = avail\n\n # Update the DistData for this distributor\n p.dd[dist] = dd\n # We have data for this distributor. Avoid marking normal distributors.\n if dist in dist_local_template.api_distributors:\n solved.add(dist)\n return solved","def rateLaws(rxnList,formModus):\n \n eqList = dict()\n texeqList = dict()\n \n for rxn in rxnList.keys():\n subst_num = str()\n prod_num = str()\n subst_km = str()\n prod_km =str() # only needed if irreversible backwards\n \n # create first the numerator, as it is invariant over all rate laws\n for rct in rxnList[rxn]['rct'].keys():\n tID = 't_' + rct\n km = 'K_r_' + rxn + '_' + tID + rxnList[rxn]['rct'][rct]['suffix']\n #numerator\n t_num = tID+'^'+str(abs(rxnList[rxn]['rct'][rct]['stoi']))\n # process substrates\n if rxnList[rxn]['rct'][rct]['stoi'] < 0:\n if (subst_num == str()):\n subst_num = t_num\n subst_km = km + '^' + str(abs(rxnList[rxn]['rct'][rct]['stoi']))\n else:\n subst_num = subst_num + '*' + t_num\n subst_km = subst_km + '*' + km + '^' + str(abs(rxnList[rxn]['rct'][rct]['stoi']))\n else:\n if prod_num == str():\n prod_num = t_num\n prod_km = km + '^' + str(abs(rxnList[rxn]['rct'][rct]['stoi']))\n else:\n prod_num = prod_num + '*' + t_num\n prod_km = prod_km + '*' + km + '^' + str(abs(rxnList[rxn]['rct'][rct]['stoi']))\n \n # Create denominator in the different reaction form cases\n if formModus == 'rm':\n subSites = dict()\n prodSites = dict()\n denSites = dict()\n for rct in rxnList[rxn]['rct'].keys():\n tDen = '(t_' + rct + '/' +'K_r_' + rxn + '_t_' + rct + rxnList[rxn]['rct'][rct]['suffix'] + ')'\n for i in range(abs(rxnList[rxn]['rct'][rct]['stoi'])):\n if rxnList[rxn]['rct'][rct]['stoi'] < 0:\n subSites[str(rxnList[rxn]['rct'][rct]['actsit']+i*0.1)]=tDen\n else:\n prodSites[str(rxnList[rxn]['rct'][rct]['actsit']+i*0.1)]=tDen\n # sort all sites in a decreasing order\n for site in sorted(set(prodSites.keys()+subSites.keys()),reverse=1):\n if site in denSites.keys():\n 1\n elif (site in prodSites.keys()) and (site in subSites.keys()):\n denSites[site] = '(1+' + subSites[site] + '+' + prodSites[site]\n elif (site in prodSites.keys()) and float(site).is_integer():\n denSites[site] = '(1+' + prodSites[site]\n elif (site in subSites.keys()) and float(site).is_integer():\n denSites[site] = '(1+' + subSites[site]\n elif site in subSites.keys():\n intSite = str(float(site).__floordiv__(1))\n if intSite in prodSites.keys():\n denSites[intSite] = '(' + prodSites[intSite] + '+' + '(1+' + subSites[site] + ')*(1+' + subSites[intSite] +')'\n else:\n denSites[site] = '(1+' + subSites[site]\n elif site in prodSites.keys():\n intSite = str(float(site).__floordiv__(1))\n if intSite in subSites.keys():\n denSites[intSite] = '(' + subSites[intSite]+ '+' + '(1+' + prodSites[site] + ')*(1+'+ prodSites[intSite] + ')'\n else:\n denSites[site] = '(1+' + prodSites[site]\n \n # Add the inhibitors to the denominators\n for rct in rxnList[rxn]['inh'].keys():\n tDen = '(t_' + rct + '/' +'K_r_' + rxn + '_t_' + rct + rxnList[rxn]['inh'][rct]['suffix'] + ')'\n if rxnList[rxn]['inh'][rct]['hill'] == 0:\n tDen = tDen + '^' + 'KH_r_' + rxn + '_t_' + rct + rxnList[rxn]['inh'][rct]['suffix']\n elif rxnList[rxn]['inh'][rct]['hill'] != 1:\n tDen = tDen + '^' + str(rxnList[rxn]['inh'][rct]['hill'])\n \n site = str(rxnList[rxn]['inh'][rct]['actsit'])\n if not(site in denSites.keys()):\n site = str(float(site).__floordiv__(1))\n if rxnList[rxn]['inh'][rct]['subtype'] == 'uncompetitive': # uncompetitive inh\n denSites['i'+rct] = '(1+' + tDen\n elif rxnList[rxn]['inh'][rct]['subtype'] == 'competitive':# comp inhibition\n denSites[site] = denSites[site] + '+' + tDen\n elif rxnList[rxn]['inh'][rct]['subtype'] == 'noncompetitive':# noncomp inhibition\n denSites[site] = denSites[site] + '+' + tDen\n denSites['i'+rct] = '(1+' + tDen\n else: raise NameError('invalid inhibitor type (valid: uncompetitive,competitive,noncompetitive) in ' + rxn)\n \n # stitch the denominator together\n denTerm = ''\n for site in denSites.keys():\n denTerm = denTerm + denSites[site] + ')*'\n denTerm = denTerm[:-1]\n \n # convinience kinetics denominator\n elif formModus == 'cc':\n subSites = dict()\n prodSites = dict()\n for rct in rxnList[rxn]['rct'].keys():\n tDen = '(t_' + rct + '/' +'K_r_' + rxn + '_t_' + rct + rxnList[rxn]['rct'][rct]['suffix'] + ')'\n for i in range(abs(rxnList[rxn]['rct'][rct]['stoi'])):\n if i == 0:\n t_den = '(1'\n t_den = t_den + '+' + tDen + '^' + str(i+1)\n if rxnList[rxn]['rct'][rct]['stoi'] < 0:\n subSites[rct] = t_den\n else:\n prodSites[rct] = t_den\n \n denTerm = ''\n for site in subSites.keys():\n denTerm = denTerm + subSites[site] + ')*'\n \n denTerm = denTerm[:-1] + '+'\n for site in prodSites.keys():\n denTerm = denTerm + prodSites[site] + ')*'\n denTerm = denTerm[:-1]\n # Add the inhibitors\n for rct in rxnList[rxn]['inh']:\n tDen = '(t_' + rct + '/' +'K_r_' + rxn + '_t_' + rct + rxnList[rxn]['inh'][rct]['suffix'] + ')'\n if rxnList[rxn]['inh'][rct]['hill'] == 0:\n tDen = tDen + '^' + 'KH_r_' + rxn + '_t_' + rct + rxnList[rxn]['inh'][rct]['suffix']\n elif rxnList[rxn]['inh'][rct]['hill'] != 1:\n tDen = tDen + '^' + str(rxnList[rxn]['inh'][rct]['hill']) \n denTerm = '(' + denTerm + ')*(1+' + tDen + ')'\n \n \n # Stitch the equations together:\n # reversible:\n if rxnList[rxn]['rev'] == '0':\n eq = '1/('+subst_km +')*(' + subst_num + '-' + prod_num + '/Keqr_' + rxn + ')/(' + denTerm + ')'\n texeq = '\\\\frac{\\\\frac{1}{' + subst_km + '}*\\\\left('+ subst_num + '-' + '\\\\frac{' + prod_num + '}{Keqr_' + rxn + '}\\\\right)}{' + denTerm + '}'\n \n elif rxnList[rxn]['rev'] == '1':#irreversible\n eq = '(('+ subst_num +')/('+ subst_km +'))/(' + denTerm + ')'\n texeq = '\\\\frac{\\\\frac{'+ subst_num +'}{'+ subst_km +'}}{' + denTerm + '}'\n else: #irreversibly backwards\n eq = '-(('+ prod_num +')/('+ prod_km +'))/(' + denTerm + ')'\n texeq = '-\\\\frac{\\\\frac{'+ prod_num+'}{'+ prod_km +'}}{' + denTerm + '}'\n # Add the activator term:\n for rct in rxnList[rxn]['act']:\n \n if rxnList[rxn]['act'][rct]['subtype'] == 'mm':\n # 1 / ((K/L)^n + 1)\n tDen = '(' + 'K_r_' + rxn + '_t_' + rct + rxnList[rxn]['act'][rct]['suffix'] + '/' + 't_' + rct + ')'\n \n if rxnList[rxn]['act'][rct]['hill'] == 0:\n tDen = tDen + '^' + 'KH_r_' + rxn + '_t_' + rct + rxnList[rxn]['act'][rct]['suffix']\n elif rxnList[rxn]['act'][rct]['hill'] != 1:\n tDen = tDen + '^' + str(rxnList[rxn]['act'][rct]['hill'])\n \n eq = '(1/(' + tDen + '+1))*' + eq\n texeq = '(1/(' + tDen + '+1))*' + texeq\n \n elif rxnList[rxn]['act'][rct]['subtype'] == 'cc':\n # 1 + (L/K^n)\n tDen = '(t_' + rct + '/' +'K_r_' + rxn + '_t_' + rct + rxnList[rxn]['act'][rct]['suffix'] + ')'\n \n if rxnList[rxn]['act'][rct]['hill'] == 0:\n tDen = tDen + '^' + 'KH_r_' + rxn + '_t_' + rct + rxnList[rxn]['act'][rct]['suffix']\n elif rxnList[rxn]['act'][rct]['hill'] != 1:\n tDen = tDen + '^' + str(rxnList[rxn]['act'][rct]['hill'])\n eq = '(1+' + tDen + ')*' + eq\n texeq = '(1+' + tDen + ')*' + texeq\n \n else:\n raise NameError('invalid activator style (valid: mm,cc) in ' + rxn)\n \n eq = re.sub('\\^1(?![0-9\\.])','',eq)\n eq = re.sub('\\\\)\\\\(',')*(',eq)\n texeq = re.sub('\\^1(?![0-9\\.])','',texeq)\n texeq = re.sub('\\\\)\\\\(',')*(',texeq)\n \n # do some latex editing\n texeq = re.sub('\\((?=t\\_.{4}\\/K.{14}\\))',r'\\\\frac{',texeq)\n texeq = re.sub('\\/(?=K.{14}\\))','}{',texeq)\n texeq = re.sub('(?<=}{K.{14})\\)','}',texeq)\n texeq = re.sub('(?<!left)\\(',r'\\\\left(',texeq)\n texeq = re.sub('(?<!right)\\)',r'\\\\right)',texeq)\n texeq = texeq.replace('_','\\_')\n texeq = re.sub(r'\\\\',r'\\\\\\\\',texeq)\n eqList[rxn] = eq\n texeqList[rxn] = re.sub('\\\\\\\\+', '\\\\\\\\', texeq)\n \n return(dict(eqList = eqList,texeqList = texeqList))","def _initialize_products(self, products: List) -> Dict[str, int]:\n\n product_request = urllib.request.Request(url=URL_PRODUCTS, headers={'User-Agent': URL_USER_AGENT})\n product_response = urllib.request.urlopen(product_request)\n all_products = json.load(product_response)\n\n product_details = {}\n\n for product in products:\n for cbpro_product in all_products:\n if cbpro_product[\"id\"] == product:\n quote_increment = float(cbpro_product[\"quote_increment\"])\n num_decimal_places = int(math.log10(1 / quote_increment))\n product_details[product] = num_decimal_places\n logging.debug(\n \"Retrieved quote increment for {}: {} = {} decimal places\".\n format(product, quote_increment, num_decimal_places))\n\n return product_details","def apply(self, catalog):\n out = []\n for product in catalog:\n valid = True\n for key, filtr in self.filters.items():\n valid = valid and filtr(product[key])\n if valid:\n out.append(product)\n out = sorted(out, key=lambda x: x['onDemandUsdPrice'])\n return self.limit_size(out)","def inventory_report(products):\r\n names = set()\r\n total_price = 0\r\n total_weight = 0\r\n total_flammability = 0\r\n for product in products:\r\n names.add(product.name)\r\n total_price += product.price\r\n total_weight += product.weight\r\n total_flammability += product.flammability\r\n\r\n print(\"ACME CORPORATION OFFICIAL INVENTORY REPORT\")\r\n print(\"Unique product names: {}\".format(len(names)))\r\n print(\"Average price: {}\".format(total_price / len(products)))\r\n print(\"Average weight: {}\".format(total_weight / len(products)))\r\n print(\"Average flammability:{}\".format(\r\n total_flammability / len(products)))\r\n\r\n print(\"Following is useful starting code for acme_report.py:\")","def inventory_report(self):\n mean_price = sum(Product.price for Product in sample) / len(sample)\n mean_weight = sum(Product.weight for Product in sample) / len(sample)\n mean_flam = sum(Product.flammability for Product in sample) / len(sample)\n return 'Unique Product Names: ', sample.unique, '/n Average Price: ', mean_price, \n '/n Average Weight: ', mean_weight, '/n Average Flammability: ', mean_flam","def generate_dollar_volume_weights(close, volume):\n \n product = close*volume \n \n \n \n weights=product.apply(lambda r : r/sum(r),axis=1) \n \n assert close.index.equals(volume.index)\n assert close.columns.equals(volume.columns)\n \n #TODO: Implement function\n\n return weights","def get_products_from_page(url):\n\n def get_data_from_book(book):\n \"\"\"Return data from one book.\"\"\"\n src_img = book.find(\"img\").get(\"src\")\n src_img = src_img.replace(\"../\", \"\")\n image = \"http://books.toscrape.com/\" + src_img\n\n in_stock = False\n in_stock_or_not = book.find(\"p\", {\"class\", \"instock\"}).text\n if \"In stock\" in in_stock_or_not:\n in_stock = True\n\n name = book.find(\"h3\").find(\"a\").text\n\n price = book.find(\"p\", {\"class\", \"price_color\"}).text\n price = price.replace(\"Â\", \"\")\n\n rating = book.find(\"p\", {\"class\", \"star-rating\"}).get(\"class\")[1]\n rating = w2n.word_to_num(rating)\n\n return {\n \"image\": image,\n \"in_stock\": in_stock,\n \"name\": name,\n \"price\": price,\n \"rating\": rating,\n }\n\n r = requests.get(url)\n soup = BeautifulSoup(r.text, \"html.parser\")\n books = soup.find_all(\"article\", {\"class\", \"product_pod\"})\n\n result = list(map(get_data_from_book, books))\n return result","def apply_peddler_effect(self, player: Player) -> None:\n \"\"\"\n Buy 1 cube (any resource but gold) from the stock with 1 denier.\n \"\"\"\n # Remark: Hard-coded! We don't use the tags <cost><n_deniers>-1 and <gain><CHOICES>... in <game_elements><buildings><player_buildings><player_building><secondary_effect>.\n money_resource_cost, qty_cost = Money.money, -1 # type: MoneyResource, int\n if player.current_money_resources[money_resource_cost] + \\\n qty_cost < 0: # Has the player enough money or resource?\n print(indent(4) + player.txt_name_money_resources_workers_PPs_deck(True, True, False, False, False) +\n ' and can\\'t apply the effect because he/she doesn\\'t have enough money or resource as ' +\n str(qty_cost) + ' ' + money_resource_cost.name + '(s) required.')\n else:\n resource_gain_choices, qty_gain = [resource for resource in Resource.resources.values()\n if not resource.is_wild()], \\\n +1 # type: List[Resource], int\n resource_gain = player.choose_buy_resource(money_resource_cost, qty_cost, resource_gain_choices,\n qty_gain) # type: Resource\n if resource_gain is None:\n print(indent(4) + player.txt_name_money_resources_workers_PPs_deck(True, True, False, False, False) +\n ' and had chosen to don\\'t apply the effect.')\n else:\n print(indent(4) + player.name() + ' wants to consume ' + str(qty_cost) + ' ' +\n money_resource_cost.name + '(s) to obtain ' + str(qty_gain) + ' ' + resource_gain.name + '(s).')\n player.current_money_resources[money_resource_cost] += qty_cost\n player.current_money_resources[resource_gain] += qty_gain\n print(indent(4) + player.txt_name_money_resources_workers_PPs_deck(True, True, False, False, False) +\n ' once the effect applied.')","def get_products_statistics(loop, products):\n description_placeholder = config[\"placeholders\"][\"description\"]\n img_url_placeholder = config[\"placeholders\"][\"img_url\"]\n\n # collect offers for all products asynchronously\n futures = [get_offers_async(product[\"productId\"]) for product in products]\n all_offers = loop.run_until_complete(asyncio.gather(*futures))\n\n for product, offers in zip(products, all_offers):\n product[\"normalized_title\"] = clean_string(product[\"title\"])\n product[\"min_price\"] = sys.maxsize\n product[\"max_price\"] = 0\n product[\"description\"] = description_placeholder\n product[\"img_url\"] = img_url_placeholder\n\n for offer in offers:\n product[\"min_price\"] = min(product[\"min_price\"], offer[\"price\"])\n product[\"max_price\"] = max(product[\"max_price\"], offer[\"price\"])\n\n if product[\"description\"] == description_placeholder and offer.get(\"description\"):\n product[\"description\"] = offer[\"description\"]\n\n if product[\"img_url\"] == img_url_placeholder and offer.get(\"img_url\"):\n product[\"img_url\"] = offer[\"img_url\"]\n\n return products","def pro_code_dict(code=False, inverse=False, return_all=False):\n\n pro_code_dict = {\"0500\": \"Date\",\n \"0501\": \"height [> 0: top, < 0: bottom of elem.] (cm)\",\n \"0502\": \"element density (kg m-3)\",\n \"0503\": \"element temperature (degC)\",\n \"0504\": \"element ID (1)\",\n \"0506\": \"liquid water content by volume (%)\",\n \"0508\": \"dendricity (1)\",\n \"0509\": \"sphericity (1)\",\n \"0510\": \"coordination number (1)\",\n \"0511\": \"bond size (mm)\",\n \"0512\": \"grain size (mm)\",\n \"0513\": \"grain type (Swiss Code F1F2F3)\",\n \"0514\": \"grain type, grain size (mm), and density (kg m-3) of SH at surface\",\n \"0515\": \"ice volume fraction (%)\",\n \"0516\": \"air volume fraction (%)\",\n \"0517\": \"stress in (kPa)\",\n \"0518\": \"viscosity (GPa s)\",\n \"0519\": \"soil volume fraction (%)\",\n \"0520\": \"temperature gradient (K m-1)\",\n \"0521\": \"thermal conductivity (W K-1 m-1)\",\n \"0522\": \"absorbed shortwave radiation (W m-2)\",\n \"0523\": \"viscous deformation rate (1.e-6 s-1)\",\n \"0531\": \"deformation rate stability index Sdef\",\n \"0532\": \"natural stability index Sn38\",\n \"0533\": \"stability index Sk38\",\n \"0534\": \"hand hardness either (N) or index steps (1)\",\n \"0535\": \"optical equivalent grain size (mm)\",\n \"0540\": \"bulk salinity (g/kg)\",\n \"0541\": \"brine salinity (g/kg)\",\n \"0601\": \"snow shear strength (kPa)\",\n \"0602\": \"grain size difference (mm)\",\n \"0603\": \"hardness difference (1)\",\n \"0604\": \"ssi\",\n \"0605\": \"inverse texture index ITI (Mg m-4)\",\n \"0606\": \"critical cut length (m)\", }\n\n if inverse:\n inverse = {value: key for key, value in pro_code_dict.items()}\n return(inverse[code])\n if code:\n return (pro_code_dict[code])\n if return_all:\n return (pro_code_dict)","def get_product_purity(product):\n return product.imass[products].sum() / product.F_mass","def get_complete_volume_info(vol_name, vol_info_dict=None):\n return_dict = {}\n try:\n if not vol_info_dict:\n vol_info_dict, err = get_basic_volume_info(vol_name)\n if err:\n raise Exception(err)\n\n return_dict = vol_info_dict\n\n vol_status_dict = {}\n if vol_info_dict['status'] == 1:\n vol_status_dict, err = get_volume_status(\n vol_info_dict['name'], vol_info_dict)\n if err:\n raise Exception(err)\n\n if vol_info_dict['status'] == 1:\n if vol_status_dict:\n # Add the status and usage info\n return_dict.update(vol_status_dict)\n vol_process_dict, err = get_volume_process_status(\n vol_name, vol_info_dict, vol_status_dict)\n if err:\n raise Exception(err)\n\n return_dict['processes_ok'] = vol_process_dict['processes_ok']\n\n for br in return_dict['brick_status']:\n if br in vol_process_dict['brick_status']:\n return_dict['brick_status'][br].update(\n vol_process_dict['brick_status'][br])\n\n quotas, err = get_volume_quota(vol_name, vol_info_dict)\n if err:\n raise Exception(err)\n return_dict['quotas'] = quotas\n\n except Exception, e:\n return None, 'Error getting complete volume information : %s' % str(e)\n else:\n return return_dict, None","def outputFxVolCalibrated(result,data):\n output = {\"fxVolatilitiesValues\":[]}\n indexVol = 0\n for fxvol in data[\"marketDataDefinitions\"][\"fxVolatilities\"]:\n output[\"fxVolatilitiesValues\"].append({\"id\":fxvol[\"id\"],\"date\" : data[\"asOfDate\"],\"values\":{}})\n smileAxis = fxvol[\"smileAxis\"]\n dictionnary = {\"DELTA_CALL\" : [0,\"deltaCalls\"], \"DELTA_PUT\" : [1,\"deltaPuts\"], \"STRIKE\":[3,\"strikes\"],\"DELTA\":[2,\"deltas\"],\"LOG_MONEYNESS\":[6,\"logMoneyness\"]}\n volValues = result[indexVol]\n jump =0\n for expiry in fxvol[\"expiries\"]:\n output[\"fxVolatilitiesValues\"][indexVol][\"values\"].update({expiry[\"expiryDate\"]: {\n \"deltaConvention\":expiry[\"deltaConvention\"],\"delivery\":expiry[\"deliveryDate\"],\"atmIndex\" : volValues[0+jump][len(result[indexVol][0])-1],\n \"volatilityValues\": result[indexVol][4][0:5].tolist()}})\n for axis in smileAxis:\n output[\"fxVolatilitiesValues\"][indexVol][\"values\"][expiry[\"expiryDate\"]].update({dictionnary[axis][1]: volValues[dictionnary[axis][0]][0:5].tolist()})\n jump = jump + 7\n indexVol = indexVol + 1\n return output","def show_products():\n\n print \"These are the products in sale\"\n for key, value in ADD_PRODUCTS.iteritems():\n print \"%s: Q%.2f\" % (key, value)","def show_proposal(self, req):\n z = 1\n y = z\n x = 1\n self.dico_product = {}\n for prod in req:\n if z <= 5:\n List_store = orm_imp.find_store(prod.id)\n print(\"Choix numéro\", z, \":\", prod.name, \"| score : \",\n prod.nutriscore, \"| Magasins : \", List_store,\n \"| Lien :\",\n prod.url, \"| \\n ==> description :\",\n prod.ingredient, \"\\n==================================\")\n self.dico_product.update({z: prod.id})\n x += 1\n z += 1\n return self.dico_product, y, x - 1","def handle_get_prep_term(self, context: 'IconScoreContext') -> dict:\n if self.term is None:\n raise ServiceNotReadyException(\"Term is not ready\")\n\n preps_data = []\n\n # Collect Main and Sub P-Reps\n for prep_snapshot in self.term.preps:\n prep = self.preps.get_by_address(prep_snapshot.address)\n preps_data.append(prep.to_dict(PRepDictType.FULL))\n\n # Collect P-Reps which got penalized for consecutive 660 block validation failure\n def _func(node: 'PRep') -> bool:\n return node.penalty == PenaltyReason.BLOCK_VALIDATION and node.status == PRepStatus.ACTIVE\n\n # Sort preps in descending order by delegated\n preps_on_block_validation_penalty = \\\n sorted(filter(_func, self.preps), key=lambda x: x.order())\n\n for prep in preps_on_block_validation_penalty:\n preps_data.append(prep.to_dict(PRepDictType.FULL))\n\n return {\n \"blockHeight\": context.block.height,\n \"sequence\": self.term.sequence,\n \"startBlockHeight\": self.term.start_block_height,\n \"endBlockHeight\": self.term.end_block_height,\n \"totalSupply\": self.term.total_supply,\n \"totalDelegated\": self.term.total_delegated,\n \"irep\": self.term.irep,\n \"preps\": preps_data\n }","def get_prices(country, year, square=2, name='p_', store=yearly):\n\n year1 = 'y' + str(year) + '_'\n year0 = 'y' + str(year - 1) + '_'\n\n df1 = yearly[year1 + 'price_' + country]\n df0 = yearly[year0 + 'price_' + country]\n\n df1.name = 'p' + str(year)\n df0.name = 'p' + str(year - 1)\n\n gr1 = df1.groupby(axis=0, level='PRODUCT_NC')\n gr0 = df0.groupby(axis=0, level='PRODUCT_NC')\n\n l1 = []\n drops1 = []\n for product in gr1.groups.keys():\n try:\n l1.append((product, ref_dict[product]))\n except KeyError:\n drops1.append(product)\n\n l0 = []\n drops0 = []\n for product in gr0.groups.keys():\n try:\n l0.append((product, ref_dict[product]))\n except KeyError:\n drops0.append(product)\n\n return pd.DataFrame((np.log(df1) - np.log(df0) - (\n np.log(df1.ix[l1].reset_index(level='PARTNER')['p' + str(year)].reindex(df1.index, level='PRODUCT_NC')) - (\n np.log(df0.ix[l0].reset_index(level='PARTNER')['p' + str(year - 1)].reindex(df0.index, level='PRODUCT_NC'))))), columns=[name + str(year)]) ** square","def get_silverpeak_costs(self, nodePair, site, accessories):\n bundleNode = nodePair.get_bundleNode()\n core = self.core\n bundle = {\n \"hardware\" : 0,\n \"software\" : 0,\n \"support\" : 0,\n \"license\" : 0,\n \"accessories\": 0,\n \"BOM\":[]\n }\n \n bundle[\"hardware\"] = float(core.get_attribute(bundleNode, \"cost\")) * (1 - float(core.get_attribute(bundleNode, \"discount\")))\n bundle[\"BOM\"].append(create_BOM_row(siteID=str(site[\"Site ID 1\"]) + str(site[\"Site ID 2\"]),code=core.get_attribute(bundleNode, \"vendorCode\"),description=core.get_attribute(bundleNode, \"description\"), quantity=site[\"Device quantity\"], discount=core.get_attribute(bundleNode, \"discount\"),unit_list=core.get_attribute(bundleNode, \"cost\")))\n bundle[\"weight\"] = core.get_attribute(bundleNode, \"weight\")\n\n parts = core.load_children(bundleNode)\n for part in parts:\n if core.get_attribute(part, \"type\") == \"Support\" and \"1M\" in core.get_attribute(part,\"name\"):\n core.set_registry(part, \"color\", \"#00FF00\")\n bundle[\"support\"] = core.get_attribute(part,\"cost\") * (1 - core.get_attribute(part,\"discount\")) * site[\"Contract term\"]\n bundle[\"BOM\"].append(create_BOM_row(siteID=str(site[\"Site ID 1\"]) + str(site[\"Site ID 2\"]),code=core.get_attribute(part, \"vendorCode\"), description=core.get_attribute(part, \"description\"), quantity=site[\"Device quantity\"], discount=core.get_attribute(part, \"discount\"), unit_list=core.get_attribute(part, \"cost\"), contract_term=site['Contract term']))\n for accessory in accessories:\n for part in parts:\n if core.get_attribute(part, \"type\") == \"Accessory\" and accessory['code'] == core.get_attribute(part, \"vendorCode\"):\n core.set_registry(part, \"color\", \"#00FF00\")\n bundle[\"accessories\"] += core.get_attribute(part,\"cost\") * (1 - float(core.get_attribute(bundleNode, \"discount\"))) * accessory['quantity']\n bundle[\"BOM\"].append(create_BOM_row(siteID=str(site[\"Site ID 1\"]) + str(site[\"Site ID 2\"]),code=core.get_attribute(part, \"vendorCode\"), description=core.get_attribute(part, \"description\"), quantity=accessory['quantity'], discount=core.get_attribute(bundleNode, \"discount\"), unit_list=core.get_attribute(part, \"cost\")))\n return bundle","def calculateVolumes(data):\n print \"Calculating volumes...\"\n results = {}\n for dataLine in data:\n name = dataLine['name']\n r1 = dataLine['r1']\n r2 = dataLine['r2']\n r3 = dataLine['r3']\n r4 = dataLine['r4']\n t1 = dataLine['t1']\n t2 = dataLine['t2']\n t3 = dataLine['t3']\n volCup = (math.pi/3.0) * t1 * ((r1**2) + (r4**2) - (r1*r4))\n volPeanut = math.pi * (t1 - t2 - t3) * ((r2**2) + (r3**2) - (r2*r3)) / 3.0\n volChoc = volCup - volPeanut\n ratio = volChoc/volPeanut\n print \"Ratio for \" + name + \" is \" + str(ratio)\n results[name] = [r1, volChoc, volPeanut, volCup, ratio]\n return results","def test_gather_location_no_product(self):\n gathered_items = self.Quant._gather(self.apple, self.test_stock_location_02)\n # Check the number of apple quants returned is correct\n self.assertFalse(len(gathered_items))","def extract_data(product):\n if not isinstance(product, dict) and product:\n return\n image = product.get('mediumImageUrls', None)\n price = product.get('itemPrice', None)\n data = {\n 'service': 'rakuten',\n 'currency': None,\n 'price': price and int(price) or price,\n 'image': image[0] if image else 0,\n 'id': product.get('itemCode', None),\n # 'ProductId': product['itemCode', None],\n 'DetailPageURL': product.get('itemUrl', None),\n 'Label': product.get('itemCaption', None),\n 'EditorialReview': [\n {'name': 'Description',\n 'value': product.get('itemCaption', None)}],\n 'ProductGroup': product.get('genreId', None), # get it name to display\n 'Title': product.get('itemName', None),\n 'Manufacturer': product.get('shopName', None),\n 'CustomerReviews': product.get('itemUrl', None), # INFO: no such thing\n 'images': [\n {'SmallImage': small,\n 'LargeImage': small.rsplit('?', 1)[0]}\n for small in product.get('smallImageUrls', [])],\n 'ItemAttributes': [],\n }\n return data","def recheck_conts_package(conts_dict, si_result):\n print('######## BEGIN CHECKING PACKAGES ? ########')\n packages = si_result.get('packages', True)\n description = si_result.get('description_of_goods', False)\n pUnit = None\n print(packages == description)\n\n if packages == description:\n conts_package = []\n for unit in current_app.config['CONTAINER_PACKAGE_UNITS']:\n re_unit = re.compile(\n r'(\\d+\\W*(' +\n unit + r'(s|S)?\\s?(\\Ws\\W|\\WS\\W)?' +\n r')\\W*)|(\\W*(' +\n unit + r'(s|S)?\\s?(\\Ws\\W|\\WS\\W)?' +\n r')\\W*\\d+)')\n units_result = re.findall(re_unit, packages)\n\n if (\n len(units_result) == len(conts_dict)\n ) or (\n len(units_result) == len(conts_dict) + 1\n ):\n for result in units_result:\n print(result)\n if len(result[0]) > len(unit):\n print('result[0]', result[0])\n conts_package.append(\n re.sub(r'[^\\d]+', '', result[0]))\n elif len(result[4]) > len(unit):\n print('result[4]', result[4])\n conts_package.append(\n re.sub(r'[^\\d]+', '', result[4]))\n\n if unit.isupper():\n pUnit = unit + 'S'\n else:\n pUnit = unit + 's'\n\n break\n\n if len(conts_package) >= len(conts_dict):\n for i, cont in enumerate(conts_dict):\n cont['packages'] = str(conts_package[i]) + ' ' + pUnit\n\n return conts_dict","def __Dict2PrintList(self, dProducts: dict) -> list:\n lAvailableProducts = []\n for Product in dProducts.values():\n if Product.iQuantity > 0:\n sAvailableProductWithPrice = \"{} {} - {}: {} {}\".format(Product.iUid, Product.sName, \"Price\", Product.iPrice, Consts.CURRENCY_TYPE)\n lAvailableProducts.append(sAvailableProductWithPrice)\n return lAvailableProducts","def two_bad_cars():\n return [\n {\n \"mpg\": maintenance_factor(21.6, False),\n \"annual_miles\": 4000,\n \"maintenance\": False, # this entry in the dictionary can probably be deleted\n \"lbs_CO2\": (11000 / 21.6) * 19.6,\n },\n {\n \"mpg\": maintenance_factor(21.6, False),\n \"annual_miles\": 4000,\n \"maintenance\": False, # this entry in the dictionary can probably be deleted\n \"lbs_CO2\": (11000 / 21.6) * 19.6,\n },\n ]","def _get_discount(self):\n\n # For every 2 PENS, one free discount\n number_of_pens = len([x for x in self._products if x.code == 'PEN'])\n discount = 5.0 * int(number_of_pens / 2)\n\n # If there are more than 3 T-Shirts in the basket, 5 EUR of discount in every of them (25%)\n number_of_tshirts = len([x for x in self._products if x.code == 'TSHIRT'])\n if number_of_tshirts >= 3:\n discount += 5.0 * number_of_tshirts\n\n return discount","def vertical_production(type_id: int, quantity: int, me: int = 0, prod_type: str = 'manufacturing' , prices: bool = False) -> pd.DataFrame:\n actID = {'manufacturing': 1, 'reaction': 11}\n mats = pd.DataFrame(columns=['type_id', 'type_name', 'quantity'])\n\n if type_id in quantities.loc[(quantities['activityID'] == actID[prod_type]) & (quantities['productTypeID'] == type_id)].values: # item can be manufactured\n\n if prod_type == 'reaction':\n bpid = productToFormula(type_id)\n\n elif prod_type == 'manufacturing':\n bpid = productToBP(type_id)\n \n else: \n raise ValueError(\"thats not a valid manufacturing type. options are 'reaction', 'manufacturing'\")\n\n qPerRun = quantPerRun(bpid)\n runs = quantity // qPerRun\n\n if runs > 0:\n\n for _, row in materials.loc[(materials['activityID'] == actID[prod_type]) & (materials['typeID'] == bpid)].iterrows():\n\n if prod_type == 'reaction':\n quant = row['quantity'] * runs\n\n elif prod_type == 'manufacturing':\n quant = me_formula(row['quantity'],me) * runs\n\n mats = mats.append(vertical_production(row['materialTypeID'], quant, prod_type = prod_type) ,ignore_index=True)\n \n \n # buys the product instead of manufacturing more of it than needed\n if int(runs * qPerRun) < int(quantity):\n mats = mats.append({'type_id': type_id, 'type_name': emt.typeIDToName(type_id), 'quantity': quantity - (runs * qPerRun)},ignore_index=True)\n\n else: # item cannot be manufactured\n mats = mats.append({'type_id': type_id, 'type_name': emt.typeIDToName(type_id), 'quantity': quantity}, ignore_index=True)\n\n mats = mats.groupby(['type_id', 'type_name']).sum().reset_index()\n mats = mats.astype({\"type_id\": int, \"quantity\": int})\n\n if prices:\n mats = emt.add_price(mats)\n\n return mats","def products(self):\n return [k for k, v in iteritems(self._metabolites) if v >= 0]","def prepare_data_with_warehouse(self,from_date,to_date,warehouses,all_products):\n data_dict = {}\n stock_quant_obj=self.env['stock.quant']\n for warehouse in warehouses:\n all_locations = self.get_all_locations(warehouse)\n if not all_locations:\n continue\n \n #here we are finding the opening stock for these we are using base query\n #of inventory at date v10\n result = self.get_product_qty(all_locations,from_date)\n qty_dict = dict((x,y) for x, y in result)\n \n for product in all_products:\n last_sales = ''\n qty_purchase_in_duration = 0\n qty_sales_in_duration = 0\n last_purchase_date = ''\n scrap_location_qty = 0\n adjusted_qty_in_duration = 0\n warehouse_out_qty = 0\n warehouse_in_qty = 0\n# here from result of inventory at date we are seaching for specific product.\n opening_product_qty = qty_dict.get(product.id)\n\n #finding last sales qty\n last_sales = self.find_last_sales_qty(from_date,to_date,warehouse,all_locations,product)\n #finding last purchase date of product\n last_purchase_date = self.find_last_purchase_date(from_date,to_date,all_locations,product)\n #fiding date purchase qty in duration for specific product\n qty_purchase_in_duration = self.find_purchase_qty_in_duration(from_date,to_date,all_locations,product)\n #fiding scrap qty of precific product\n scrap_location_qty = self.find_scap_location_qty(from_date,to_date,product,all_locations)\n #finding sales qty in duration\n qty_sales_in_duration = self.find_sale_qty_in_duration(from_date,to_date,warehouse,all_locations,product)\n #fidning adjusted qty in duration\n adjusted_qty_in_duration = self.find_adjusted_qty_in_duration(from_date, to_date, product, all_locations)\n \n dest_location_lst = self.get_other_wahouse_locations(warehouse)\n \n if any(all_locations) and any(dest_location_lst):\n #fidning warehouse in qty \n warehouse_in_qty = self.find_warehouse_transer_in_qty(product, all_locations, dest_location_lst,from_date,to_date)\n #fidning warehouse out qty for specific product.\n warehouse_out_qty = self.find_warehouse_transer_out_qty(product, all_locations, dest_location_lst,from_date,to_date)\n \n if warehouse_out_qty:\n warehouse_out_qty = warehouse_out_qty and warehouse_out_qty[0][0] or ''\n if warehouse_in_qty:\n warehouse_in_qty = warehouse_in_qty and warehouse_in_qty[0][0] or ''\n \n if adjusted_qty_in_duration:\n adjusted_qty_in_duration = adjusted_qty_in_duration and adjusted_qty_in_duration[0][0] or '' \n if scrap_location_qty:\n scrap_location_qty = scrap_location_qty and scrap_location_qty[0][0] or ''\n \n # if qty_sales_in_duration:\n # qty_sales_in_duration = qty_sales_in_duration and qty_sales_in_duration[0][0] or ''\n # if qty_purchase_in_duration:\n # qty_purchase_in_duration = qty_purchase_in_duration[0][0] or ''\n if last_sales:\n last_sales = datetime.strptime(last_sales and last_sales[0][0], '%Y-%m-%d %H:%M:%S').strftime('%d-%m-%Y') or ''\n \n if last_purchase_date:\n last_purchase_date = datetime.strptime(last_purchase_date and last_purchase_date[0][0], '%Y-%m-%d %H:%M:%S').strftime('%d-%m-%Y') or ''\n \n if data_dict.has_key(warehouse.id):\n data_lst=data_dict.get(warehouse.id)\n data_lst.append({'product':product,'sku':product.default_code or '','name':product.name,\n 'Cost':product.standard_price or '','sales_price':product.lst_price or '',\n 'opening_qty':opening_product_qty or 0,'last_sales':last_sales or '',\n 'last_purchase_date':last_purchase_date or '','qty_purchase_in_duration':qty_purchase_in_duration or 0,\n 'qty_sales_in_duration': qty_sales_in_duration or 0,'scrap_location_qty':scrap_location_qty or 0,\n 'adjusted_qty_in_duration':adjusted_qty_in_duration or 0\n ,'warehouse_in_qty':warehouse_in_qty or 0,\n 'warehouse_out_qty':warehouse_out_qty or 0 \n })\n data_dict.update({warehouse.id:data_lst})\n continue\n data_dict.update({warehouse.id:[{'product':product,'sku':product.default_code or '','name':product.name,\n 'Cost':product.standard_price or '','sales_price':product.lst_price or '',\n 'opening_qty':opening_product_qty or 0,\n 'last_sales':last_sales or '','last_purchase_date':last_purchase_date or '',\n 'qty_purchase_in_duration':qty_purchase_in_duration or 0,\n 'qty_sales_in_duration': qty_sales_in_duration or 0,\n 'scrap_location_qty':scrap_location_qty or 0,\n 'adjusted_qty_in_duration':adjusted_qty_in_duration or 0,\n 'warehouse_in_qty':warehouse_in_qty or 0,\n 'warehouse_out_qty':warehouse_out_qty or 0\n }]})\n return data_dict","def free_item_discount(self, items):\n new_items = []\n occurrences = collections.Counter(item.sku for item in items)\n if occurrences.get(MacbookPro.sku, 0) > 0:\n for item in items:\n if item.sku == VGAadapter.sku and item.price != 0:\n item.price = 0\n else:\n new_items.append(CheckoutItem(sku=VGAadapter.sku, price=0))\n items.extend(new_items)\n return items","def _all_products(q_1: Q, q_2: Q) -> Dict:\n\n all_dict = _commuting_products(q_1, q_2)\n all_dict.update(_anti_commuting_products(q_1, q_2))\n\n return all_dict","def parse_product_installment(advertise: Dict[str, Any]) -> Optional[Dict[str, Union[float, int]]]:\n if \"product_installment\" in advertise.keys():\n\n tmp_dict = {\"parcell\": None, \"price\": None, \"no_interest\": None}\n tmp: List[str] = re.split(\"[ \\t]{2,}\", advertise[\"product_installment\"])\n\n for term in tmp:\n if \"x\" in term:\n tmp_vec = term.split()\n if tmp_vec[0].isnumeric():\n tmp_dict[\"parcell\"] = int(tmp_vec[0])\n continue\n if \"R$\" in term:\n tmp_vec = term.split()\n if \"R$\" in tmp_vec:\n tmp_vec.remove(\"R$\")\n if all([term.isnumeric() for term in tmp_vec]):\n tmp_dict[\"price\"] = float(\".\".join(tmp_vec))\n continue\n if \"sem juros\" in term:\n tmp_dict[\"no_interest\"] = 1\n continue\n return tmp_dict","def compute_volume(self, windkessel_pressures = None):\n # Extract the relevant volumes from the inputs.\n if windkessel_pressures is None:\n part = self.pressure['art']\n else:\n part = windkessel_pressures['art']\n\n # Extract relevant model parameters.\n cart = self.parameters['arterial_compliance']\n\n # Compute the arterial volume.\n v = {'art': part*cart}\n return v","def main():\n\n parser = argparse.ArgumentParser(description='MODIS product table')\n parser.add_argument('--product', help='MODIS product ID', metavar='')\n parser.add_argument('--resolution', help='Filter for pixel size', type=str, metavar='')\n parser.add_argument('--vampc', help='Filter for VAM product code', metavar='')\n args = parser.parse_args()\n\n # Load product table\n this_dir = Path(__file__).parent\n with open(this_dir.parent.joinpath('data', 'MODIS_V6_PT.pkl').as_posix(), 'rb') as table_raw:\n product_table = pickle.load(table_raw)\n\n tbl = pd.DataFrame(product_table).T\n print('\\n')\n\n # Subset table by product\n if args.product:\n try:\n tbl = tbl.loc[args.product.upper()]\n except:\n raise SystemExit('Product ID not recognized!')\n # Subset table by resolution\n if args.resolution and not args.product:\n try:\n tbl = tbl[tbl['pixel_size'] == args.resolution]\n except:\n raise ValueError('Resolution not valid! (possible: 250,500,1000,5600)')\n # Subset table by parameter\n if args.vampc and not args.product:\n helper_dict = dict(zip(['VIM', 'VEM', 'LTD', 'LTN'],\n ['Vegetation Indices',\n 'Vegetation Indices',\n 'Temperature, Emissivity',\n 'Temperature, Emissivity']))\n try:\n tbl = tbl[tbl['product'] == helper_dict[args.vampc.upper()]]\n except:\n raise ValueError('VAM product code not valid! (possible: VIM, VEM, LTD, LTN)')\n\n # print results\n with pd.option_context('display.max_rows', None, 'display.max_columns', 4):\n print(tbl)\n print('\\n')","def product_summary(self):\n # products = self.products.filter(type='S') # TODO: explain the usage of this commented line or remove it\n from .utils import process_products\n\n subscription_products = SubscriptionProduct.objects.filter(subscription=self)\n dict_all_products = {}\n for sp in subscription_products:\n dict_all_products[str(sp.product.id)] = str(sp.copies)\n return process_products(dict_all_products)","def calculate_prices(self, good=None):\n\n stock = self.calculate_init_stock(good)\n buy = self.buying_price()\n\n if stock == 0:\n sell = 0\n buy = buy + (buy * 0.5)\n\n elif stock < 500:\n # mild bug: stock, without selling price\n sell = self.selling_price()\n elif stock >= 500:\n # higher production, lower prices\n sell = self.selling_price() / 2\n buy = buy - (buy * 0.5)\n\n return [buy, sell, stock]","def premium(self):\n\n return blackscholes_norm(self.data['moneyness'],\n self.data['maturity'],\n self.impvol(), self.data['call'])","def prep_visualize_cost(self, dat):\n # Assume if null then there are no in-app purchases since all others have a value\n dat['In-app Purchases'].fillna('0', inplace = True)\n\n # An app is only actually free if you cant buy anything once in it\n dat['true_free'] = (dat.Price == 0) & (dat['In-app Purchases'] == '0')\n\n # Look at only if there is a base cost first and see if rating difference exists\n\n print('Investigate differences in ratings by free vs. paid App')\n plt.hist(dat[dat.true_free == True]['Average User Rating'].dropna().values, label='Free', color = 'green')\n plt.hist(dat[(dat.true_free == False) & (dat.Price != 0)]['Average User Rating'].dropna().values, label='Paid')\n\n plt.legend(loc='upper right')\n plt.show()\n\n print('Many Apps included pain content within them. These should not truly be considered \"free\"')\n\n plt.hist(dat[dat.true_free == False]['Average User Rating'].dropna().values, label='Paid')\n plt.hist(dat[dat.true_free == True]['Average User Rating'].dropna().values, label='Free', color = 'green')\n\n plt.legend(loc='upper right')\n plt.show()\n\n print('Which are truly best?')\n\n non_true_free_rating = dat[dat.true_free == False]['Average User Rating'].dropna().mean()\n in_app_purchases = dat[(dat.true_free == False) & (dat.Price == 0)]['Average User Rating'].dropna().mean()\n true_free_rating = dat[dat.true_free == True]['Average User Rating'].dropna().mean()\n zero_cost_rating = dat[dat.Price == 0]['Average User Rating'].dropna().mean()\n paid_rating = dat[dat.Price != 0]['Average User Rating'].dropna().mean()\n\n non_true_free_rating_cnt = dat[dat.true_free == False]['Average User Rating'].dropna().shape[0]\n in_app_purchases_cnt = dat[(dat.true_free == False) & (dat.Price == 0)]['Average User Rating'].dropna().shape[0]\n true_free_rating_cnt = dat[dat.true_free == True]['Average User Rating'].dropna().shape[0]\n zero_cost_rating_cnt = dat[dat.Price == 0]['Average User Rating'].dropna().shape[0]\n paid_rating_cnt = dat[dat.Price != 0]['Average User Rating'].dropna().shape[0]\n\n plt.bar(['Zero Cost','Paid App'],\n [zero_cost_rating,paid_rating])\n plt.ylim((3.8,4.18))\n plt.xticks(rotation = 'vertical')\n plt.show()\n\n plt.bar(['Truly Free','Any Cost'],\n [true_free_rating,non_true_free_rating])\n plt.ylim((3.8,4.18))\n plt.xticks(rotation = 'vertical')\n plt.show()\n\n plt.bar(['Zero Cost','Truly Free','Paid App','Any Cost','In app-purchases'],\n [zero_cost_rating,true_free_rating,paid_rating,non_true_free_rating, in_app_purchases])\n plt.ylim((3.8,4.18))\n plt.xticks(rotation = 'vertical')\n plt.show()\n\n N = 5\n\n ind = np.arange(N) # the x locations for the groups\n width = 0.35 # the width of the bars\n\n fig, ax = plt.subplots()\n rects1 = ax.bar(ind, [zero_cost_rating,true_free_rating,paid_rating,non_true_free_rating, in_app_purchases], \n width, color='r')\n\n\n # add some text for labels, title and axes ticks\n ax.set_ylabel('Scores')\n ax.set_title('Scores by group and gender')\n ax.set_xticks(ind + width / 2)\n ax.set_xticklabels(['Zero Cost','Truly Free','Paid App','Any Cost','In app-purchases'])\n\n plt.ylim((3.85,4.18))\n\n counts = [zero_cost_rating_cnt,true_free_rating_cnt,paid_rating_cnt,non_true_free_rating_cnt, in_app_purchases_cnt]\n\n for x, rect in zip(counts, rects1):\n height = rect.get_height()\n ax.text(rect.get_x() + rect.get_width()/2., height,\n 'n = ' + str(x),\n ha='center', va='bottom')\n\n plt.show()","def product_db() -> List[Text]:\n\n return [\n \"credit\",\n \"forex\",\n \"debit\",\n \"atm\"\n ]","def calc_Cinv_boiler(Q_design_W, technology_type, boiler_cost_data):\n Capex_a_Boiler_USD = 0.0\n Opex_a_fix_Boiler_USD = 0.0\n Capex_Boiler_USD = 0.0\n\n if Q_design_W > 0.0:\n boiler_cost_data = boiler_cost_data[boiler_cost_data['code'] == technology_type]\n # if the Q_design is below the lowest capacity available for the technology, then it is replaced by the least\n # capacity for the corresponding technology from the database\n if Q_design_W < boiler_cost_data.iloc[0]['cap_min']:\n Q_design_W = boiler_cost_data.iloc[0]['cap_min']\n max_boiler_size = boiler_cost_data.iloc[0]['cap_max']\n\n if Q_design_W <= max_boiler_size:\n\n boiler_cost_data = boiler_cost_data[\n (boiler_cost_data['cap_min'] <= Q_design_W) & (boiler_cost_data['cap_max'] > Q_design_W)]\n\n Inv_a = boiler_cost_data.iloc[0]['a']\n Inv_b = boiler_cost_data.iloc[0]['b']\n Inv_c = boiler_cost_data.iloc[0]['c']\n Inv_d = boiler_cost_data.iloc[0]['d']\n Inv_e = boiler_cost_data.iloc[0]['e']\n Inv_IR = boiler_cost_data.iloc[0]['IR_%']\n Inv_LT = boiler_cost_data.iloc[0]['LT_yr']\n Inv_OM = boiler_cost_data.iloc[0]['O&M_%'] / 100.0\n\n InvC = Inv_a + Inv_b * (Q_design_W) ** Inv_c + (Inv_d + Inv_e * Q_design_W) * log(Q_design_W)\n\n Capex_a_Boiler_USD = calc_capex_annualized(InvC, Inv_IR, Inv_LT)\n Opex_a_fix_Boiler_USD = InvC * Inv_OM\n Capex_Boiler_USD = InvC\n\n else:\n number_of_boilers = int(ceil(Q_design_W / max_boiler_size))\n Q_nom_W = Q_design_W / number_of_boilers\n\n boiler_cost_data = boiler_cost_data[\n (boiler_cost_data['cap_min'] <= Q_nom_W) & (boiler_cost_data['cap_max'] > Q_nom_W)]\n\n Inv_a = boiler_cost_data.iloc[0]['a']\n Inv_b = boiler_cost_data.iloc[0]['b']\n Inv_c = boiler_cost_data.iloc[0]['c']\n Inv_d = boiler_cost_data.iloc[0]['d']\n Inv_e = boiler_cost_data.iloc[0]['e']\n Inv_IR = boiler_cost_data.iloc[0]['IR_%']\n Inv_LT = boiler_cost_data.iloc[0]['LT_yr']\n Inv_OM = boiler_cost_data.iloc[0]['O&M_%'] / 100.0\n\n InvC = (Inv_a + Inv_b * (Q_nom_W) ** Inv_c + (Inv_d + Inv_e * Q_nom_W) * log(Q_nom_W)) * number_of_boilers\n\n Capex_a_Boiler_USD = calc_capex_annualized(InvC, Inv_IR, Inv_LT)\n Opex_a_fix_Boiler_USD = InvC * Inv_OM\n Capex_Boiler_USD = InvC\n\n return Capex_a_Boiler_USD, Opex_a_fix_Boiler_USD, Capex_Boiler_USD","def get_product_details(product_url: str) -> dict:\n def get_available_sizes(postID, sizeStr, product_mainID):\n \"\"\"\n List size haye mojood va tamoom shode ro ba API mide\n POST: https://store.lining.com/ajax/goods_details.htm\n \"\"\"\n api_url = 'https://store.lining.com/ajax/goods_details.html'\n data = {\n 'postID': postID,\n 'sizeStr': sizeStr,\n 'product_mainID': product_mainID\n }\n r = get_json(api_url, data=data)\n onsale_sizes = r['data']['onSale']\n logging.debug('Onsale Sizes: ' + repr(onsale_sizes))\n return onsale_sizes\n\n def get_pid_from_url(url):\n \"\"\" ID mahsool ro az URL darmiare \"\"\"\n return re.findall(r'store.lining.com/shop/goods-(\\w+).html\\w*', url)[0]\n\n def translate_keyword(keyword):\n \"\"\" tarjome key marboot be desctioption \"\"\"\n define = {\n '运动类型': 'Sport Type',\n '性别': 'Sex',\n '颜色': 'Color',\n '鞋透气指数': 'Shoes breathability index',\n '鞋软硬指数': 'Shoe soft and hard index',\n }\n if keyword in define:\n return define[keyword]\n else:\n return keyword\n ###########################################################\n\n details = dict()\n soup = get_soup(product_url)\n\n # product ID\n pid = get_pid_from_url(product_url)\n logging.debug('PID: ' + pid)\n details['pid'] = pid\n\n # product name\n name = soup.find('h1', {'id': 'product_name'}).text.strip()\n logging.debug('Name: ' + name)\n details['name'] = name\n\n # part number\n sku = soup.find('span', {'id': 'partNumber'}).find('span', {'class': 'v'}).text.strip()\n part_number = sku[0:sku.find('-')]\n logging.debug('Part Number: ' + part_number)\n details['sku'] = sku\n details['part_number'] = part_number\n\n # price\n price = soup.find('span', {'id': 'listPrice'}).find('span', {'class': 'v'}).text.strip().replace('￥', '')\n price_offer = soup.find('span', {'id': 'offerPrice'}).find('span', {'class': 'v'}).text.strip().replace('￥', '')\n logging.debug('Price: %s [offer]-> %s' % (price, price_offer))\n details['price'] = price\n details['price_offer'] = price_offer\n\n # all sizes\n all_sizes = list()\n for tag in soup.find('div', {'id': 'sizelist'}).find_all('div', 'size-layer'):\n tag = tag.find('input')\n # all_size -> [(id, size, status), ...]\n all_sizes.append(\n (\n tag.get('id').replace('size_list_', ''),\n tag.get('value'),\n None,\n )\n )\n available_sizes = get_available_sizes(\n postID=pid,\n product_mainID=part_number,\n # first element of all_sizes list\n # all_size -> [(id, size, status), ...]\n sizeStr=','.join([s[0] for s in all_sizes]),\n )\n # update all sizes status\n for i in range(len(all_sizes)):\n if all_sizes[i][1] in available_sizes:\n all_sizes[i] = (\n all_sizes[i][0],\n all_sizes[i][1],\n 'onsale',\n )\n else:\n all_sizes[i] = (\n all_sizes[i][0],\n all_sizes[i][1],\n 'stockout',\n )\n logging.debug('All Sizes: %s' % repr(all_sizes))\n details['all_sizes'] = all_sizes\n\n # description images\n description_images = list()\n desc = soup.find('div', {'id': 'PD_desc_picture'})\n for img in desc.find_all('img'):\n img = img.get('orginalsrc')\n logging.debug('description_images[]: ' + img)\n description_images.append(img)\n details['description_images'] = description_images\n\n # description key/value\n description = dict()\n for li in soup.find('ul', {'id': 'p_spec'}).find_all('li'):\n key = li.find('span', {'class': 't'}).text.replace(':', '').strip()\n key = translate_keyword(key)\n value = li.find('span', {'class': 'v'}).text.strip()\n description[key] = value\n logging.debug('%s -> %s' % (key, value))\n details['description'] = description\n\n # slider images\n slider_images = list()\n for li in soup.find('div', {'class': 'box'}).find_all('li'):\n img = li.find('img').get('big')\n logging.debug('slider_images[]: ' + img)\n slider_images.append(img)\n details['slider_images'] = slider_images\n\n # related products\n related_products_id = list()\n for li in soup.find('div', {'id': 'f_litimg'}).find_all('li'):\n url = li.find('a').get('href')\n url = 'store.lining.com' + url\n pid = get_pid_from_url(url)\n logging.debug('related_products_id[]: %s -> %s' % (pid, url))\n related_products_id.append(pid)\n details['related_products_id'] = related_products_id\n\n return details","def eod_risk_by_underlyer_report(positions, underlyer_positions, domain, headers, pricing_environment):\n # find books and underlyers\n books = set([p['bookName'] for p in positions])\n books.update(underlyer_positions.keys())\n book_underlyer = {b: set() for b in books}\n for p in positions:\n book_underlyer[p['bookName']].add(p['underlyerInstrumentId'])\n for b, u in underlyer_positions.items():\n book_underlyer[b].update([l['underlyerInstrumentId'] for l in u.values()])\n # get quotes\n underlyers = set()\n for b in book_underlyer.values():\n for u in b:\n underlyers.add(u)\n underlyers = list(underlyers)\n quotes = get_underlyer_quotes(underlyers, datetime.now(), domain, headers)\n yst_quotes = get_underlyer_quotes(underlyers, datetime.now() - timedelta(days=1), domain, headers)\n # empty report\n risks = {b: {} for b in books}\n for b in book_underlyer:\n for u in book_underlyer[b]:\n spot = quotes[u]['close']\n if spot is None or spot == 0:\n continue\n spot_change_ratio = (spot - np.float64(yst_quotes.get(u).get('close'))) / spot\n risks[b][u] = {\n 'bookName': b,\n 'underlyerInstrumentId': u,\n 'underlyerPrice': spot,\n 'underlyerNetPosition': 0,\n 'delta': 0,\n 'deltaCash': 0,\n 'netDelta': 0,\n 'deltaDecay': 0,\n 'deltaWithDecay': 0,\n 'gamma': 0,\n 'gammaCash': 0,\n 'vega': 0,\n 'theta': 0,\n 'rho': 0\n }\n if not utils.is_nan(spot_change_ratio):\n risks[b][u]['underlyerPriceChangePercent'] = spot_change_ratio\n # underlyer positions\n for b in underlyer_positions.values():\n for u in b.values():\n if risks[u['bookId']].get(u['instrumentId'], None) is None:\n continue\n r = risks[u['bookId']][u['instrumentId']]\n spot = u['underlyerPrice']\n multiplier = u['underlyerInstrumentMultiplier']\n r['underlyerMultiplier'] = multiplier\n if u['instrumentId'] == u['underlyerInstrumentId']:\n r['underlyerNetPosition'] += u['netPosition']\n else:\n r['delta'] += u['delta'] / multiplier\n r['gamma'] += u['gamma'] * spot / 100 / multiplier\n r['gammaCash'] += u['gamma'] * spot * spot / 100 / multiplier\n r['vega'] += u['vega'] / 100\n r['theta'] += u['theta'] / 365\n r['rho'] += u['rhoR'] / 100\n # option risks\n for p in positions:\n if has_pricing_error(p):\n continue\n r = risks[p['bookName']][p['underlyerInstrumentId']]\n r['delta'] += p['delta']\n r['deltaDecay'] += p['deltaDecay']\n r['deltaWithDecay'] += p['deltaWithDecay']\n r['gamma'] += p['gamma']\n r['gammaCash'] += p['gammaCash']\n r['vega'] += p['vega']\n r['theta'] += p['theta']\n r['rho'] += p['rho']\n if r.get('underlyerMultiplier', None) is None:\n r['underlyerMultiplier'] = p['underlyerMultiplier']\n\n for b in risks:\n for u in risks[b]:\n r = risks[b][u]\n if r.get('underlyerMultiplier', None) is None:\n continue\n r['netDelta'] = r['delta'] + r['underlyerNetPosition']\n r['deltaCash'] = r['netDelta'] * r['underlyerPrice'] * r['underlyerMultiplier']\n r.pop('underlyerMultiplier')\n # flatten\n report = []\n for b in risks:\n report.extend(list(risks[b].values()))\n for re in report:\n re['pricingEnvironment'] = pricing_environment\n return report","def _product_available(self, cr, uid, ids, field_names=None, arg=False, context=None):\n if not field_names:\n field_names = []\n if context is None:\n context = {}\n res = {}\n \n for product_loc in self.browse(cr, uid, ids):\n c = context.copy()\n c.update({ 'states': ('done',), 'what': ('in', 'out'), 'location': product_loc.location_id.id})\n stock = self.pool.get('product.product').get_product_available(cr, uid, [product_loc.product_id.id], context=c)\n res[product_loc.id] = stock.get(product_loc.product_id.id, 0.0)\n return res","def calculate_possible_loss(sub_data: pandas.core.frame.DataFrame) -> str:\n sub_data1 = sub_data.copy()\n sub_data1 = sub_data1[sub_data1['StockQty']!=0] # remove non-stock day\n \n loss_INV = sub_data1['Margin'].mean() \n loss_NS = sub_data1['NetSales'].mean() \n loss_QTY = sub_data1['QtySold'].mean()\n \n return loss_INV, loss_NS, loss_QTY","def sexp(p,n,res_field=RR):\n assert n>=2, \"Carleman matrix must at least be of size 2 to retrieve the coefficients. But given was \" + repr(n)\n CM = p.carleman_matrix(n)\n ev = [ r[0] for r in CM.charpoly().roots(QQbar) ]\n assert len(ev) == n, \"Carleman matrix must have exactly \" + repr(n) + \"eigenvalues, but has \" + repr(len(ev))\n\n #We want to compute:\n #sum over k: evk^t*(CM-ev1*I)*(CM-ev2*I)*. omit k * (CM-evn*I)/(evk-ev1)*.. omit k *(evk-evn)\n Char = [0]*n\n for k in range(n):\n #here is possibility for improvement of precision\n #to separate the fractional from the root parts\n #expanding the product\n Char[k] = CM - ev[k]*identity_matrix(n)\n\n #we want to have the first row of the product of the matrices\n #thatswhy we mulitply in front with:\n prod = vector(QQbar,[0,1]+[0]*(n-2))\n prodwo = [0]*n\n for k in range(n):\n prodwo[k]=prod #these are the first terms until k-1\n\n #no need to continue\n if k == n-1:\n break\n\n #and we add the terms starting with k+1\n for i in range(k+1,n):\n prodwo[k] = prodwo[k] * Char[i]\n\n prod = prod * Char[k]\n\n sprodwo = [0]*n\n for k in range(n):\n if k==0:\n sprodwo[k] = ev[k] - ev[1]\n start = 2\n else:\n sprodwo[k] = ev[k] - ev[0]\n start = 1\n\n for i in range(start,n):\n if i != k:\n sprodwo[k] = sprodwo[k] * (ev[k] - ev[i])\n\n for k in range(n):\n print ev[k]\n print prodwo[k][0]/sprodwo[k]\n print res_field\n #return lambda t: sum(res_field(ev[k])**t*res_field(prodwo[k][0]/sprodwo[k]) for k in range(n))\n return [ev,[prodwo[k][0]/sprodwo[k] for k in range(n)]]","def sell_products():\n\n print \"\\nInsert the products you want to buy: \"\n print \"Press done to finish\\n\"\n while True:\n sell = insert_product_to_sell()\n if ADD_PRODUCTS.has_key(sell) == True: #Verifies if the product belongs to the dictionary\n print \" Q%.2f \" % (ADD_PRODUCTS.get(sell)) #This prints its price respective\n SAVE_EXISTENT.append(sell) #This adds the product to a list\n SAVE_PRICE.append(ADD_PRODUCTS[sell]) #This saves the prices in a list\n elif sell == \"done\":\n verify_done()\n main_menu()\n elif sell == \"gold\":\n CARDS.append(\"gold\") #This adds the gold card\n elif sell == \"silver\":\n CARDS.append(\"silver\") #This adds the silver card\n else:\n print \"\\nThis product is not available\"","def product(self):\n return None","def product(self):\n return None","def one_good_car():\n return [\n {\n \"mpg\": maintenance_factor(50, False),\n \"annual_miles\": 5000,\n \"maintenance\": True, # this entry in the dictionary can probably be deleted\n \"lbs_CO2\": (11000 / 50) * 19.6,\n }\n ]","def _r_inv_data(self):\n return self.sites.precisions","def check_primal_and_dual_solutions(self):\r\n\r\n # Solve primal and dual models\r\n self.primal.solve_model(self.m_p)\r\n self.dual.solve_model(self.m_d)\r\n\r\n # Elements to check\r\n elements = {\r\n 'power - existing thermal':\r\n {\r\n 'primal': {'id': 'p', 'is_dual': False},\r\n 'dual': {'id': 'POWER_OUTPUT_EXISTING_THERMAL', 'is_dual': True}\r\n },\r\n 'power - existing wind':\r\n {\r\n 'primal': {'id': 'p', 'is_dual': False},\r\n 'dual': {'id': 'POWER_OUTPUT_EXISTING_WIND', 'is_dual': True}\r\n },\r\n 'power - existing solar':\r\n {\r\n 'primal': {'id': 'p', 'is_dual': False},\r\n 'dual': {'id': 'POWER_OUTPUT_EXISTING_SOLAR', 'is_dual': True}\r\n },\r\n 'power - existing hydro':\r\n {\r\n 'primal': {'id': 'p', 'is_dual': False},\r\n 'dual': {'id': 'POWER_OUTPUT_HYDRO', 'is_dual': True}\r\n },\r\n # 'charging power - existing storage':\r\n # {\r\n # 'primal': {'id': 'p_in', 'is_dual': False},\r\n # 'dual': {'id': 'CHARGING_POWER_EXISTING_STORAGE', 'is_dual': True}\r\n # },\r\n # 'discharging power - existing storage':\r\n # {\r\n # 'primal': {'id': 'p_out', 'is_dual': False},\r\n # 'dual': {'id': 'DISCHARGING_POWER_EXISTING_STORAGE', 'is_dual': True}\r\n # },\r\n 'power - candidate thermal':\r\n {\r\n 'primal': {'id': 'p', 'is_dual': False},\r\n 'dual': {'id': 'POWER_OUTPUT_CANDIDATE_THERMAL', 'is_dual': True}\r\n },\r\n 'power - candidate wind':\r\n {\r\n 'primal': {'id': 'p', 'is_dual': False},\r\n 'dual': {'id': 'POWER_OUTPUT_CANDIDATE_WIND', 'is_dual': True}\r\n },\r\n 'power - candidate solar':\r\n {\r\n 'primal': {'id': 'p', 'is_dual': False},\r\n 'dual': {'id': 'POWER_OUTPUT_CANDIDATE_SOLAR', 'is_dual': True}\r\n },\r\n 'charging power - candidate storage':\r\n {\r\n 'primal': {'id': 'p_in', 'is_dual': False},\r\n 'dual': {'id': 'CHARGING_POWER_CANDIDATE_STORAGE', 'is_dual': True}\r\n },\r\n 'discharging power - candidate storage':\r\n {\r\n 'primal': {'id': 'p_out', 'is_dual': False},\r\n 'dual': {'id': 'DISCHARGING_POWER_CANDIDATE_STORAGE', 'is_dual': True}\r\n },\r\n 'locational marginal prices':\r\n {\r\n 'primal': {'id': 'POWER_BALANCE', 'is_dual': True},\r\n 'dual': {'id': 'lamb', 'is_dual': False}\r\n },\r\n 'load shedding':\r\n {\r\n 'primal': {'id': 'p_V', 'is_dual': False},\r\n 'dual': {'id': 'LOAD_SHEDDING_POWER', 'is_dual': True}\r\n },\r\n }\r\n\r\n print(f\"Checking primal and dual solutions\")\r\n for el_id, e in elements.items():\r\n # Absolute, non-zero and max absolute difference\r\n absolute_diff, non_zero_diff, max_abs_diff = self.compare_primal_and_dual_components(e)\r\n\r\n print('---------------------------------------------------------------------------')\r\n print(f\"Primal component: {e['primal']['id']}, Dual component: {e['dual']['id']}')\")\r\n print(f'Total keys: {len(absolute_diff)}')\r\n print(f\"Max absolute difference: {max_abs_diff}\")\r\n print(f\"Non-zero diff: {non_zero_diff}\")","def get_variable_OM_costs(fs, production_rate, resources, rates, prices={}):\n\n # check the units on production_rate\n if isinstance(production_rate, Expression) and production_rate.is_indexed:\n production_units = pyunits.get_units(\n production_rate[production_rate.index_set().first()]\n )\n else:\n production_units = pyunits.get_units(production_rate)\n\n try:\n pyunits.convert(production_units, pyunits.MW)\n mode = \"power\"\n cost_units = pyunits.USD_2018 / pyunits.MWh\n unit_tag = \"$/MWh\"\n except InconsistentUnitsError:\n try:\n pyunits.convert(production_units, pyunits.kg / pyunits.s)\n mode = \"hydrogen\"\n cost_units = pyunits.USD_2018 / pyunits.kg\n unit_tag = \"$/kg\"\n except InconsistentUnitsError:\n raise Exception(\n \"units not compatable, make sure production rate\"\n \"has dimensions of power or mass flowrate\"\n )\n\n # assert arguments are correct types\n if type(resources) is not list:\n raise TypeError(\"resources argument must be a list\")\n if type(rates) is not list:\n raise TypeError(\"rates argument must be a list\")\n if type(prices) is not dict:\n raise TypeError(\"prices argument must be a dictionary\")\n\n # assert lists are the same length\n if len(resources) != len(rates):\n raise Exception(\"resources and rates must be lists of the same length\")\n\n # check if flowsheet level costing block exists\n if not hasattr(fs, \"costing\"):\n fs.get_costing(year=\"2018\")\n\n # dictionary of default prices\n default_prices = {\n \"natural gas\": 4.42 * pyunits.USD_2018 / pyunits.MBtu, # $/MMbtu\n \"coal\": 51.96 * pyunits.USD_2018 / pyunits.ton,\n \"water\": 1.90e-3 * pyunits.USD_2018 / pyunits.gallon,\n \"water treatment chemicals\": 550 * pyunits.USD_2018 / pyunits.ton,\n \"ammonia\": 300 * pyunits.USD_2018 / pyunits.ton,\n \"SCR catalyst\": 150 * pyunits.USD_2018 / pyunits.ft**3,\n \"triethylene glycol\": 6.80 * pyunits.USD_2018 / pyunits.gallon,\n \"SCR catalyst waste\": 2.50 * pyunits.USD_2018 / pyunits.ft**3,\n \"triethylene glycol waste\": 0.35 * pyunits.USD_2018 / pyunits.gallon,\n \"amine purification unit waste\": 38 * pyunits.USD_2018 / pyunits.ton,\n \"thermal reclaimer unit waste\": 38 * pyunits.USD_2018 / pyunits.ton,\n }\n\n # add entrys from prices to defualt_prices\n for key in prices.keys():\n default_prices[key] = prices[key]\n\n # raise error if the user included a resource not in default_prices\n if not set(resources).issubset(default_prices.keys()):\n raise Exception(\n \"A resource was included that does not contain a \"\n \"price. Prices exist for the following resources: \"\n \"{}\".format(list(default_prices.keys()))\n )\n # create list of prices\n prices = [default_prices[r] for r in resources]\n\n # zip rates and prices into a dict accessible by resource\n resource_rates = dict(zip(resources, rates))\n resource_prices = dict(zip(resources, prices))\n\n # if costing power make vars in fs.costing, if costing hydrogen make vars\n # in fs.H2_costing\n if mode == \"power\":\n costing = fs.costing\n elif mode == \"hydrogen\":\n fs.H2_costing = Block()\n costing = fs.H2_costing\n\n # make vars\n costing.variable_operating_costs = Var(\n fs.time,\n resources,\n initialize=1,\n doc=\"variable operating costs\",\n units=cost_units,\n )\n\n costing.other_variable_costs = Var(\n fs.time,\n initialize=0,\n doc=\"a variable to include non-standard O&M costs\",\n units=cost_units,\n )\n # assume the user is not using this\n costing.other_variable_costs.fix(0)\n\n costing.total_variable_OM_cost = Var(\n fs.time,\n initialize=1,\n doc=\"total variable operating and maintenance costs\",\n units=cost_units,\n )\n\n # make constraints\n @costing.Constraint(fs.time, resources)\n def variable_cost_rule(c, t, r):\n return c.variable_operating_costs[t, r] == (\n pyunits.convert(\n resource_prices[r] * resource_rates[r][t] / production_rate[t],\n cost_units,\n )\n )\n\n if mode == \"power\" and hasattr(fs.costing, \"maintenance_material_cost\"):\n\n @fs.costing.Constraint(fs.time)\n def total_variable_cost_rule(c, t):\n return (\n c.total_variable_OM_cost[t]\n == sum(c.variable_operating_costs[t, r] for r in resources)\n + c.maintenance_material_cost\n + c.other_variable_costs[t]\n )\n\n else:\n\n @costing.Constraint(fs.time)\n def total_variable_cost_rule(c, t):\n return (\n c.total_variable_OM_cost[t]\n == sum(c.variable_operating_costs[t, r] for r in resources)\n + c.other_variable_costs[t]\n )\n\n if mode == \"power\":\n _log.warning(\n \"The variable fs.costing.maintenance_material_cost \"\n \"could not be found, total_variable_cost_rule was \"\n \"constructed without in. get_fixed_OM_costs should be\"\n \" called before get_variable_OM_costs\"\n )","def get_disparity(code, k, w=1):\n est_price, shares, value, net_worth, roe, excess_earning = estimate_price(code, k, w)\n cur_price = reader.get_current_price(code)\n\n try:\n disparity = (cur_price / est_price) * 100\n except:\n disparity = None\n return disparity, cur_price, est_price, shares, value, net_worth, roe, excess_earning","def update(self, arm, reward, alpha=0.05, l=0.05):\n\n # Get context\n context = self.context.iloc[self.t, :]\n\n\n # Add price\n price_dict = {}\n productid_dict = {}\n \n for var in context.keys():\n price_dict[var + '_price'] = context[var] * self.df_arm_dummies.ix[arm, 'price']\n\n for i in range(10, 26):\n productid_dict[var + '_productid_' + str(i)] = context[var] * \\\n self.df_arm_dummies.ix[arm, 'productid_' + str(i)]\n\n print(\"Price dict is\")\n print(price_dict)\n print(productid_dict)\n \n\n#Age_price = context.Age * self.df_arm_dummies.ix[arm, 'price']\n#Agent_Linux_price = self.df_arm_dummies.ix[arm, 'price'] * context.Agent_Linux\n#Agent_OSX_price = self.df_arm_dummies.ix[arm, 'price'] * context.Agent_OSX\n#Agent_Windows_price = self.df_arm_dummies.ix[arm, 'price'] * context.Agent_Windows\n#Agent_mobile_price = self.df_arm_dummies.ix[arm, 'price'] * context.Agent_mobile\n#\n#\n#Language_EN_price = self.df_arm_dummies.ix[arm, 'price'] * context.Language_EN\n#Language_GE_price = self.df_arm_dummies.ix[arm, 'price'] * context.Language_GE\n#Language_NL_price = self.df_arm_dummies.ix[arm, 'price'] * context.Language_NL\n#Referer_Bing_price = self.df_arm_dummies.ix[arm, 'price'] * context.Referer_Bing\n#Referer_Google_price = self.df_arm_dummies.ix[arm, 'price'] * context.Referer_Google\n#\n\n combined = np.append(context, self.df_arm_dummies.iloc[arm, :])#.reshape(-1, 1)\n\n prices = prict_dict.items()\n\n # Combine with arm\n combined = np.append(combined,\n [Age_price,\n Agent_Linux_price,\n Agent_OSX_price,\n Agent_Windows_price,\n Agent_mobile_price,\n Language_EN_price,\n Language_GE_price,\n Language_NL_price,\n Referer_Bing_price,\n Referer_Google_price\n ]).reshape(-1, 1)\n \n if reward > 0:\n reward = 1\n else:\n reward = -1\n\n # Bayes\n self.B = self.B + np.dot(context, context)\n \n self.f = self.f + combined * reward\n\n self.mu_hat = np.dot(np.linalg.inv(self.B), self.f)\n\n self.mu = min(5, self.mu + 0.1 * (-0.5 + int(bool(reward))))\n\n # Update time step\n self.t += 1","def abc_scan(self, code):\n products = self.env['product.product'].search(['|', ('ean13', '=', code), ('default_code', '=', code)])\n if products:\n return {\n 'type': 'product.product',\n 'product': self.abc_make_records(products)}\n picking = self.env['stock.picking'].search_read([('name', '=', code)], ['id'])\n if picking:\n return {\n 'type': 'stock.picking',\n 'picking': picking[0]\n }\n return {'type': 'no hit'}","def get_sealed_products_data(\n self, set_code: str\n ) -> List[MtgjsonSealedProductObject]:\n LOGGER.info(f\"Getting booster data for {set_code}\")\n products_list = []\n for sealed_product_name, sealed_product in self.sealed_products.get(\n set_code.lower(), {}\n ).items():\n product_obj = MtgjsonSealedProductObject()\n product_obj.name = sealed_product_name\n product_obj.release_date = sealed_product.get(\"release_date\")\n\n try:\n product_obj.category = getattr(\n MtgjsonSealedProductCategory,\n sealed_product.get(\"category\", \"UNKNOWN\").upper(),\n )\n except AttributeError:\n product_obj.category = None\n try:\n product_obj.subtype = getattr(\n MtgjsonSealedProductSubtype,\n sealed_product.get(\"subtype\", \"UNKNOWN\").upper(),\n )\n except AttributeError:\n product_obj.subtype = None\n\n product_obj.raw_purchase_urls = sealed_product.get(\"purchase_url\", {})\n products_list.append(product_obj)\n\n for location, identifier in sealed_product.get(\"identifiers\", {}).items():\n setattr(product_obj.identifiers, location, identifier)\n return products_list","def _get_conditional_negative_energy(self):\n return [layer._get_conditional_negative_energy()\n for layer in self.layers]","def init_stock():\n return {\"five\":0, \"one\": 0, \"quarter\": 25, \"dime\": 25, \"nickel\":25}","def __init__(self, product_code, description, market_price,\n rental_price, brand, voltage):\n Inventory.__init__(self, product_code, description, market_price,\n rental_price)\n\n self.brand = brand\n self.voltage = voltage","def getStockData():\n pass"],"string":"[\n \"def get_basket_with_discounts(self, actual_volume, applicable_discounts, residual):\\n\\n # product_vs_min_volume, product_offer = self.get_product_vs_offer_and_volume(self.discounts)\\n pr = Product('items')\\n product_prices = pr.product_meta\\n\\n basket = []\\n\\n for discount in applicable_discounts:\\n offer_info = Offer('discount', discount)\\n\\n if offer_info.offer_type.lower() == \\\"discount\\\":\\n temp_basket = self.get_basket_items_discount(offer_info, actual_volume, product_prices)\\n for item in temp_basket:\\n basket.append(item)\\n elif offer_info.offer_type.lower() == \\\"pricedrop\\\":\\n temp_basket = self.get_basket_items_pricedrop(offer_info, actual_volume, product_prices)\\n for item in temp_basket:\\n basket.append(item)\\n\\n if residual:\\n for product, volume in residual.items():\\n for i in range(0, volume):\\n basket.append((product, product_prices.get(product.lower()).get('price')))\\n\\n return basket\",\n \"def get_basket_items_discount(self, offer_info, actual_volume, product_prices):\\n prod_code = offer_info.base_prod_code\\n base_prod_vol = actual_volume.get(prod_code.lower())\\n\\n discount_basket = []\\n\\n if base_prod_vol >= offer_info.min_vol:\\n offer_on_prod = offer_info.offer_on\\n if actual_volume.get(offer_on_prod.lower()):\\n print(f\\\"Base product volume is greater than minimum required volume & product on offer is also available \\\"\\n f\\\"in cart..\\\")\\n if offer_info.is_limited:\\n print(f\\\"Limited offer..\\\")\\n if prod_code == offer_on_prod:\\n # total_allowed_items_on_offer = Limit Volume of base product * (Offer Product Max Volume/Minimum volume of base product)\\n total_allowed_items_on_offer = offer_info.limit_vol * (offer_info.offer_prod_volume/offer_info.min_vol)\\n max_limit = 1\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n discount_basket.append((prod_code, base_prod_actual_price))\\n while max_limit <= total_allowed_items_on_offer:\\n discounted_price = (base_prod_actual_price *(offer_info.discount_perc/100))*-1\\n discount_basket.append((offer_info.offer_code, discounted_price))\\n max_limit += 1\\n else:\\n total_allowed_items_on_offer = offer_info.limit_vol * (offer_info.offer_prod_volume / offer_info.min_vol)\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n discount_basket.append((prod_code, base_prod_actual_price))\\n max_limit = 1\\n while max_limit <= total_allowed_items_on_offer:\\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\\n discounted_price = (offer_onprod_actual_price *(offer_info.discount_perc/100))*-1\\n for j in range(0, actual_volume.get(offer_on_prod.lower())):\\n discount_basket.append((offer_on_prod, offer_onprod_actual_price))\\n discount_basket.append((offer_info.offer_code, discounted_price))\\n max_limit += 1\\n else:\\n print(f\\\"Unlimited offer..\\\")\\n if prod_code == offer_on_prod:\\n if base_prod_vol > offer_info.min_vol:\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n discount_basket.append((prod_code, base_prod_actual_price))\\n if i%2 != 0:\\n discounted_price = (base_prod_actual_price *(offer_info.discount_perc/100))*-1\\n discount_basket.append((offer_info.offer_code, discounted_price))\\n else:\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n discount_basket.append((prod_code, base_prod_actual_price))\\n else:\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n discount_basket.append((prod_code, base_prod_actual_price))\\n\\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\\n discounted_price = (offer_onprod_actual_price * (offer_info.discount_perc / 100))*-1\\n\\n for j in range(0, actual_volume.get(offer_on_prod.lower())):\\n discount_basket.append((offer_on_prod, offer_onprod_actual_price))\\n discount_basket.append((offer_info.offer_code, discounted_price))\\n\\n\\n return discount_basket\",\n \"def get_basket_items_pricedrop(self, offer_info, actual_volume, product_prices):\\n prod_code = offer_info.base_prod_code\\n base_prod_vol = actual_volume.get(prod_code.lower())\\n\\n pricedrop_basket = []\\n\\n if base_prod_vol >= offer_info.min_vol:\\n offer_on_prod = offer_info.offer_on\\n if actual_volume.get(offer_on_prod.lower()):\\n print(\\n f\\\"Base product volume is greater than minimum required volume & product on offer is also available \\\"\\n f\\\"in cart..\\\")\\n if offer_info.is_limited:\\n print(f\\\"Limited offer..\\\")\\n if prod_code == offer_on_prod:\\n # total_allowed_items_on_offer = Limit Volume of base product * (Offer Product Max Volume/Minimum volume of base product)\\n total_allowed_items_on_offer = offer_info.limit_vol * (\\n offer_info.offer_prod_volume / offer_info.min_vol)\\n max_limit = 1\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n pricedrop_basket.append((prod_code, base_prod_actual_price))\\n while max_limit <= total_allowed_items_on_offer:\\n new_price = (base_prod_actual_price - (offer_info.new_price)) * -1\\n pricedrop_basket.append((offer_info.offer_code, new_price))\\n max_limit += 1\\n else:\\n total_allowed_items_on_offer = offer_info.limit_vol * (\\n offer_info.offer_prod_volume / offer_info.min_vol)\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n pricedrop_basket.append((prod_code, base_prod_actual_price))\\n max_limit = 1\\n while max_limit <= total_allowed_items_on_offer:\\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\\n new_price = (base_prod_actual_price - (offer_info.new_price)) * -1\\n for j in range(0, actual_volume.get(offer_on_prod).lower()):\\n pricedrop_basket.append((offer_on_prod, offer_onprod_actual_price))\\n pricedrop_basket.append((offer_info.offer_code, new_price))\\n max_limit += 1\\n else:\\n print(f\\\"Unlimited offer..\\\")\\n if prod_code == offer_on_prod:\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n pricedrop_basket.append((prod_code, base_prod_actual_price))\\n new_price = (base_prod_actual_price - (offer_info.new_price))*-1\\n pricedrop_basket.append((offer_info.offer_code, new_price))\\n else:\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n pricedrop_basket.append((prod_code, base_prod_actual_price))\\n\\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\\n new_price = (offer_onprod_actual_price - (offer_info.new_price)) * -1\\n\\n for j in range(0, actual_volume.get(offer_on_prod).lower()):\\n pricedrop_basket.append((offer_on_prod, offer_onprod_actual_price))\\n pricedrop_basket.append((offer_info.offer_code, new_price))\\n\\n return pricedrop_basket\",\n \"def get_product_vs_offer_and_volume(self, df):\\n\\n product_vs_min_volume = {}\\n product_offer = {}\\n base_product_vs_offer_product = {}\\n\\n for i in df.itertuples():\\n product_vs_min_volume[i[4].lower()] = i[5]\\n product_offer[i[4].lower()] = i[1]\\n base_product_vs_offer_product[i[4].lower()] = i[7].lower()\\n\\n return product_vs_min_volume, product_offer, base_product_vs_offer_product\",\n \"def __init__(self, name, product, production_volume):\\n self.name = name\\n self.product = product\\n self.production_volume = production_volume\\n self.suppliers = []\\n self.costumers = []\\n self.production_drop = 0\\n self.market_shrinkage = 0\\n self.sale_drop = 0\\n self.out_edge_capacity_drop = {}\\n self.in_edge_capacity_drop = {}\",\n \"def show_available_products(): # {{{\\n products_available = {}\\n try:\\n with MONGO:\\n product_collection = MONGO.connection.assignment_07[\\\"product\\\"].find(\\n )\\n\\n for product in product_collection:\\n if int(product[\\\"quantity_available\\\"]) > 0:\\n products_available[product[\\\"product_id\\\"]] = {\\n \\\"description\\\": product[\\\"description\\\"],\\n \\\"product_type\\\": product[\\\"product_type\\\"],\\n \\\"quantity_available\\\": product[\\\"quantity_available\\\"],\\n }\\n except TypeError as excep:\\n LOGGER.warning(\\\"Error looking up available products\\\")\\n LOGGER.warning(excep)\\n else:\\n if not products_available:\\n LOGGER.info('No products found')\\n else:\\n LOGGER.info(\\\"Available products retrieved successfully.\\\")\\n return products_available # }}}\",\n \"def get_product_kpis(self):\\n kpi_1 = self.products[['products', 'client']].reset_index().groupby([\\\"client\\\", \\\"products\\\"]).agg(\\n {\\\"index\\\": \\\"count\\\"}).reset_index().groupby(\\\"products\\\").agg(\\n {\\\"index\\\": \\\"mean\\\"}).reset_index().rename(columns={\\\"index\\\": \\\"average_product_sold_per_user_kpi\\\"})\\n kpi_2 = self.products[['products', 'payment_amount']].groupby(\\\"products\\\").agg(\\n {\\\"payment_amount\\\": \\\"sum\\\"}).reset_index().rename(columns={\\\"payment_amount\\\": \\\"total_product_revenue_kpi\\\"})\\n try:\\n kpi_3 = self.products[['products', 'discount_amount']].query(\\\"discount_amount == discount_amount\\\")\\n kpi_3['discount_amount'] = kpi_3['discount_amount'].apply(lambda x: float(x))\\n kpi_3 = kpi_3.groupby(\\\"products\\\").agg({\\\"discount_amount\\\": \\\"sum\\\"}).reset_index().rename(\\n columns={\\\"discount_amount\\\": \\\"total_product_discount_kpi\\\"})\\n except:\\n kpi_3 = self.products[['products']]\\n kpi_3['total_product_discount_kpi'] = 0\\n\\n kpi_4 = self.products[['products', 'client']].groupby(\\\"products\\\").agg(\\n {\\\"client\\\": lambda x: len(np.unique(x))}).reset_index().rename(columns={\\\"client\\\": \\\"total_product_cust_kpi\\\"})\\n\\n self.product_kpis = pd.DataFrame(self.products.groupby('products').count().reset_index()[['products']]).merge(\\n kpi_1, on='products', how='left').merge(\\n kpi_2, on='products', how='left').merge(\\n kpi_3, on='products', how='left').merge(kpi_4, on='products', how='left').fillna(0)\\n\\n return self.product_kpis\",\n \"def available_products(self):\\n # TODO - take into account bands, and what bands available in input products, etc\\n return {k: self.__products__[k].description for k in self.__products__.keys()}\",\n \"def inventory_report(products):\\n unique_names = []\\n total_price = 0\\n total_weight = 0\\n total_flammability = 0\\n num_products = len(products)\\n for i in range(num_products):\\n if products[i].name not in unique_names:\\n unique_names.append(products[i].name) \\n total_price += products[i].price\\n total_weight += products[i].weight\\n total_flammability += products[i].flammability\\n mean_price = total_price / num_products\\n mean_weight = total_weight / num_products\\n mean_flammability = total_flammability / num_products\\n print('ACME CORPORATION OFFICIAL INVENTORY REPORT')\\n print(f'Unique product names: {len(unique_names)}')\\n print(f'Average price: {mean_price}')\\n print(f'Average weight {mean_weight}')\\n print(f'Average flammabilitiy {mean_flammability}')\\n return unique_names, mean_price, mean_weight, mean_flammability\",\n \"def retrieve_product_infos(self):\\n\\n # PRODUCT NAME\\n try:\\n product_name = self.product['product_name'].capitalize()\\n except KeyError:\\n product_name = None\\n\\n # PRODUCT CODE\\n try:\\n product_code = self.product['code'].capitalize()\\n except KeyError:\\n product_code = None\\n\\n # URL\\n try:\\n product_url = self.product['url'].lower()\\n except KeyError:\\n product_url = None\\n\\n # IMAGE URL\\n try:\\n image_url = self.product['image_url'].lower()\\n except KeyError:\\n image_url = None\\n\\n # QUANTITY\\n try:\\n quantity = self.product['quantity'].capitalize()\\n except KeyError:\\n quantity = None\\n\\n # INGREDIENTS\\n try:\\n ingredients = self.product['ingredients_text_fr'].capitalize()\\n except KeyError:\\n ingredients = None\\n\\n # BRAND\\n brands = []\\n try:\\n for brand in self.product['brands'].split(','):\\n brand = brand.strip().capitalize()\\n if (\\n brand != ''\\n and brand not in brands\\n ):\\n brands.append(brand)\\n except KeyError:\\n pass\\n\\n # STORES\\n stores = []\\n try:\\n for store in self.product['stores'].split(','):\\n store = store.strip().capitalize()\\n if (\\n store != ''\\n and store not in stores\\n ):\\n stores.append(store)\\n except KeyError:\\n pass\\n\\n # COUNTRY\\n try:\\n countries = self.product['countries'].capitalize()\\n except KeyError:\\n countries = None\\n if 'France' in countries:\\n countries = 'France'\\n else:\\n countries = None\\n\\n # COMPARE TO CATEGORY\\n try:\\n compare_to = self.product['compared_to_category'].capitalize().split(':')[1]\\n except KeyError:\\n compare_to = None\\n try:\\n Categories.objects.get(\\n name=compare_to\\n )\\n except Categories.DoesNotExist:\\n compare_to = None\\n except:\\n importable = False\\n\\n # CATEGORIES HIERARCHY\\n try:\\n categories_hierarchy = [\\n category.split(':')[1] for category in self.product['categories_hierarchy']\\n ]\\n except KeyError:\\n categories_hierarchy = None\\n\\n # NUTRISCORE GRADE\\n nutriscore_labels = [\\n 'nutrition_grade_fr',\\n 'nutriscore_grade'\\n ]\\n nutriscore = 'F'\\n i = 0\\n while (\\n i < len(nutriscore_labels)\\n and nutriscore == 'F'\\n ):\\n try:\\n nutriscore = self.product[nutriscore_labels[i]].upper()\\n except KeyError:\\n i += 1\\n\\n product_infos = {\\n 'product_name': product_name,\\n 'product_code': product_code,\\n 'product_url': product_url,\\n 'image_url': image_url,\\n 'quantity': quantity,\\n 'ingredients': ingredients,\\n 'brands': brands,\\n 'stores': stores,\\n 'countries': countries,\\n 'compare_to': compare_to,\\n 'categories_hierarchy': categories_hierarchy,\\n 'nutriscore': nutriscore\\n }\\n\\n nutriments = self.product['nutriments']\\n for nutriment in self.list_nutriments:\\n try:\\n product_infos[nutriment] = float(nutriments[nutriment])\\n except KeyError:\\n product_infos[nutriment] = 0\\n\\n return product_infos\",\n \"def show_available_products(*args):\\n logger.info(f\\\"Preparing dict of available prodcuts...\\\")\\n available_products = {}\\n\\n with MONGO:\\n mdb = eval(Settings.connect_string)\\n products = mdb[\\\"product\\\"]\\n for doc in products.find():\\n del doc[\\\"_id\\\"]\\n if int(doc[\\\"quantity_available\\\"]) > 0:\\n product_id = doc[\\\"product_id\\\"]\\n del doc[\\\"product_id\\\"]\\n available_products[product_id] = doc\\n\\n return available_products\",\n \"def show_available_products():\\n\\n mongo = MongoDBConnection()\\n result = {}\\n\\n with mongo:\\n db = mongo.connection.HPNorton\\n productcollection = db[\\\"products\\\"]\\n for document in productcollection.find({\\\"quantity_available\\\": {\\\"$gt\\\": \\\"0\\\"}}):\\n key = document['product_id']\\n\\n result[key] = {\\n 'description': document['description'],\\n 'product_type': document['product_type'],\\n 'quantity_available': document['quantity_available']\\n }\\n\\n return result\",\n \"def show_available_products():\\n products = DATABASE['product'].find({'quantity_available': {'$ne':'0'}})\\n products_dict = {prod['product_id']:\\n {'description': prod['description'],\\n 'product_type': prod['product_type'],\\n 'quantity_available': int(prod['quantity_available'])}\\n for prod in products}\\n return products_dict\",\n \"def solve_prices(self):\\n return None\",\n \"def inventory_report(prod_list):\\n prod_list = list(set(prod_list))\\n x = 0\\n price = 0\\n weight = 0\\n flammability = 0\\n stealability = 0\\n for item in prod_list:\\n x += 1\\n price += item.price\\n weight += item.weight\\n flammability += item.flammability\\n if stealability != 'Not so stealable...':\\n stealability += 1\\n\\n avg_price = price / x\\n avg_weight = weight / x\\n avg_flammability = flammability / x\\n print(f'There are {x} unique products in this list. The average price is {avg_price}, '\\n f'average weight is {avg_weight},'\\n f'and the average flammability is {avg_flammability}.')\\n if stealability >= len(prod_list) / 2:\\n print('Many of these items are highly stealable!')\\n return avg_price, avg_weight, avg_flammability\",\n \"def compute_volume(self, windkessel_pressures):\\n # Extract relevant model parameters.\\n cart = self.parameters['arterial_compliance']\\n vart_rest = self.parameters['arterial_resting_volume']\\n return {'art': windkessel_pressures['art']*cart + vart_rest}\",\n \"def show_vendor_product():\\n vendor = input(\\\"Enter the Vendor: \\\")\\n product = input(\\\"Enter the product: \\\")\\n filter_string = input(\\\"Enter Optional Search string (i.e. HTTP): \\\")\\n logger.debug(\\\"Searching: {} from {} -- Filter = {}\\\".format(product, vendor, filter_string))\\n search_url = \\\"http://cve.circl.lu/api/search/{}/{}\\\".format(vendor, product)\\n req = call_api(search_url)\\n if not req:\\n logger.debug(\\\"something no workie with the vendor product call\\\")\\n else:\\n print(\\\"Searching: {} from {} -- Filter = {}\\\".format(product, vendor, filter_string))\\n for item in req:\\n if filter_string != '' or not filter_string:\\n if filter_string in item['summary']:\\n print(\\\"\\\\nSummary: \\\" + item['summary'])\\n print(\\\"CVE: \\\" + item['id'])\\n print(\\\"CVSS: \\\" + str(item['cvss']))\\n else:\\n print(\\\"\\\\nSummary: \\\" + item['summary'])\\n print(\\\"CVE: \\\" + item['id'])\\n print(\\\"CVSS: \\\" + str(item['cvss']))\\n menu()\",\n \"def _get_book_summary(self):\\n self._get_book_prices()\\n for price in self.ask_prices:\\n volume = 0\\n for k in self.ask_snapshot.keys():\\n if self.ask_snapshot[k].price == price:\\n volume += self.ask_snapshot[k].volume\\n self.ask_volumes.append(volume)\\n for price in self.bid_prices:\\n volume = 0\\n for k in self.bid_snapshot.keys():\\n if self.bid_snapshot[k].price == price:\\n volume += self.bid_snapshot[k].volume\\n self.bid_volumes.append(volume)\",\n \"def prod_parser(self, response):\\n products = []\\n pk = Product.objects.last()\\n if pk:\\n i = pk.id\\n else:\\n i = 0\\n for product in response:\\n # crawling product for name, desc, API_url, image_url, nutriscore, nutient_100g\\n if 'ingredients_text_fr' not in product:\\n desc = \\\"\\\"\\n else:\\n desc = product['ingredients_text_fr']\\n nutrigrade = \\\"\\\".join(product[\\\"nutrition_grades_tags\\\"])\\n if nutrigrade in (\\\"a\\\", \\\"b\\\", \\\"c\\\", \\\"d\\\", \\\"e\\\") \\\\\\n and 'fat_100g' in product['nutriments'] \\\\\\n and 'image_url' in product \\\\\\n and 'product_name_fr' in product:\\n i += 1\\n product[i] = {\\n \\\"id\\\": i,\\n \\\"name\\\": product['product_name_fr'],\\n \\\"desc\\\": desc,\\n \\\"categories\\\": product[\\\"categories\\\"].split(\\\", \\\"),\\n \\\"API_link\\\": product['url'],\\n \\\"photo\\\": product['image_url'],\\n \\\"nutriscore\\\": nutrigrade,\\n \\\"nutrient_100g\\\":\\n \\\"saturated_fat_100g:{}:{}, \\\".format(\\n product['nutriments']['saturated-fat_100g'],\\n product['nutrient_levels']['saturated-fat']) +\\n \\\"fat_100g:{}:{}, \\\".format(\\n product['nutriments']['fat_100g'], product['nutrient_levels']['fat']) +\\n \\\"salt_100g:{}:{}, \\\".format(\\n product['nutriments']['salt_100g'], product['nutrient_levels']['salt']) +\\n \\\"sugars_100g:{}:{} \\\".format(\\n product['nutriments']['sugars_100g'], product['nutrient_levels']['sugars'])\\n }\\n products.append(product[i])\\n else:\\n pass\\n return products\",\n \"def test_instrument_dataproduct_filtering():\\n dp = 'spectrum'\\n data = mm.instrument_inventory('nirspec', dataproduct=dp, caom=True,\\n return_data=True)\\n\\n dps = [row['dataproduct_type'] for row in data['data']]\\n\\n assert all([i == dp for i in dps])\",\n \"def get_product_pack(self, ):\\n\\n # Parse response dict for product data\\n\\n result = {}\\n\\n # Iterate over incoming raw data and pick values corresponding to \\\"Product\\\" db table\\n for item in self.raw_data['searchResult']['item']:\\n for field in item.keys():\\n if format_ebay_col_name(field) in product_table_columns:\\n if type(item[field]) == str:\\n if '\\\"' in item[field]: item[field] = item[field].replace('\\\"', '\\\\\\\\\\\"')\\n result[format_ebay_col_name(field)] = item[field]\\n\\n # Handle several nested values\\n while Switch(format_ebay_col_name(field)):\\n if case('PRODUCT_ID'):\\n result['PRODUCT_ID'] = item[field]['value']\\n if case('PRIMARY_CATEGORY'):\\n result['CATEGORY_ID'] = item[field]['categoryId']\\n result['CATEGORY_NAME'] = item[field]['categoryName']\\n if case('CONDITION'):\\n result['CONDITION_DISPLAY_NAME'] = item[field]['conditionDisplayName']\\n result['CONDITION_ID'] = item[field]['conditionId']\\n break\\n\\n # Fill missing values with \\\"NULL\\\"s\\n for table_filed in product_table_columns:\\n if table_filed not in result.keys(): result[table_filed] = 'NULL'\\n\\n return result\",\n \"def variations():\",\n \"def inventory_report(products):\\n name_list = set()\\n price_list = []\\n wt_list = []\\n flamablity_list = []\\n\\n for p in products:\\n name_list.add(p.name)\\n price_list.append(p.price)\\n wt_list.append(p.weight)\\n flamablity_list.append(p.flammability)\\n# Calculating average for report\\n unique_names = len(name_list)\\n avg_price = sum(price_list)/len(price_list)\\n avg_weight = sum(wt_list)/len(wt_list)\\n avg_flammability = sum(flamablity_list)/len(flamablity_list)\\n# Printing\\n print(\\\"$ python acme_report.py \\\")\\n print(\\\"ACME CORPORATION OFFICIAL INVENTORY REPORT\\\")\\n print(\\\"Unique product names:\\\", unique_names)\\n print(\\\"Average price:\\\", round(avg_price, 2))\\n print(\\\"Average weight:\\\", avg_weight)\\n print(\\\"Average flammability:\\\", avg_flammability)\",\n \"def process_product_data(cls, pricing_info):\\n if 'prices' not in pricing_info or 'vat_bands' not in pricing_info:\\n raise PricingException('Json data does not contain required '\\n 'product and vat band information')\\n\\n product_prices = pricing_info['prices']\\n vat_bands = pricing_info['vat_bands']\\n\\n processed_vat_bands = {}\\n for vat_name, vat_rate in vat_bands.items():\\n if vat_name in processed_vat_bands:\\n raise PricingException('Vat names need to be unique. '\\n f'Vat names duplicated {vat_name}')\\n\\n try:\\n rate = float(vat_rate)\\n except ValueError:\\n raise PricingException('Vat rates need to be a decimal. '\\n f'Vat rate: {vat_rate}')\\n\\n processed_vat_bands[vat_name] = {\\n 'name': vat_name,\\n 'rate': rate\\n }\\n\\n processed_products = {}\\n for product_price in product_prices:\\n product_id = product_price['product_id']\\n if product_id in processed_products:\\n raise PricingException('Product ids need to be unique. '\\n f'Product id duplicated {product_id}')\\n\\n associated_vat_band = product_price['vat_band']\\n if associated_vat_band not in processed_vat_bands:\\n raise PricingException('Vat band does not exist. '\\n f'Vat band: {associated_vat_band} on '\\n f'product id: {product_id}')\\n\\n try:\\n price = int(product_price['price'])\\n except ValueError:\\n raise PricingException('Product prices need to be numbers. '\\n f'Price: {price} on '\\n f'product id: {product_id}')\\n\\n processed_products[product_id] = {\\n 'product_id': product_id,\\n 'price': price,\\n 'vat_band': associated_vat_band\\n }\\n\\n return processed_vat_bands, processed_products\",\n \"def buy_one_get_one(products):\\n if 'p1' in products and products['p1'] >= 2:\\n return -20\\n else:\\n return 0\",\n \"def add_non_furniture_nor_appliance(itemcode, itemdescription,\\n itemprice, itemrentalprice):\\n\\n newitem = Inventory(itemcode, itemdescription,\\n itemprice, itemrentalprice)\\n FULLINVENTORY[itemcode] = newitem.returnasdictionary()\",\n \"def coproduct_sum(sys):\\r\\n \\r\\n streams=coproduct_finder(sys)\\r\\n BD_mass=0\\r\\n Glycerol_mass=0\\r\\n products = {}\\r\\n for i in streams:\\r\\n if i == 'Waste':\\r\\n pass\\r\\n else:\\r\\n mass = find(i).mass\\r\\n BD_index=find(i).get_index('Biodiesel')\\r\\n Glycerol_index=find(i).get_index('Glycerol')\\r\\n BD = mass[BD_index]\\r\\n Glycerol = mass[Glycerol_index]\\r\\n BD_mass = BD_mass+ BD\\r\\n Glycerol_mass = Glycerol_mass + Glycerol\\r\\n products = {'Biodiesel (kg/hr)':BD_mass, 'Glycerol (kg/hr)': Glycerol_mass}\\r\\n return products\",\n \"def inventory_report(products, prices, weights, flammabilities):\\n num_product = len(products)\\n avg_price = mean(prices)\\n avg_weight = mean(weights)\\n avg_flam = mean(flammabilities)\\n\\n print(\\\"ACME CORPORATION OFFICIAL INVENTORY REPORT\\\")\\n print(\\\"Unique product names: {}\\\".format(num_product))\\n print(\\\"Average price: {}\\\".format(avg_price))\\n print(\\\"Average weight: {}\\\".format(avg_weight))\\n print(\\\"Average flammability: {}\\\".format(avg_flam))\",\n \"def get_most_and_least_expensive_high_review_product(df):\\n try:\\n df3 = merge_metadata(df)\\n product_filter = df3['overall'] >= 4.0\\n high_reviewed_products = df3[product_filter]\\n # print high_reviewed_products[:10]\\n # The data contained NaN so we use the nanmax/min funtions to get max/min\\n most_exp = round(np.nanmax(high_reviewed_products['price'])[0], 2)\\n least_exp = round(np.nanmin(high_reviewed_products['price'])[0], 2)\\n\\n most_exp_prod = df3.loc[df3['price'] == most_exp, 'asin'].iloc[0]\\n least_exp_prod = df3.loc[df3['price'] == least_exp, 'asin'].iloc[0]\\n write_text_tofile(\\\"Most Expensive Product: \\\" + str(most_exp_prod) + \\\", Price: \\\" + str(most_exp))\\n write_text_tofile(\\\"Least Expensive Product: \\\" + str(least_exp_prod) + \\\", Price: \\\" + str(least_exp))\\n return {most_exp_prod: most_exp, least_exp_prod: least_exp}\\n except Exception as e:\\n print \\\"Error getting most and least expensive high review product\\\"\\n print str(e)\\n pass\",\n \"def get_products_dict(products):\\n # lang = get_language()[:2]\\n lang = ''\\n products_dict = {}\\n try:\\n if products and products[0].get('source') == 'greedy':\\n for product in products:\\n key = product['name']\\n products_dict[key] = products_dict.get(key, {})\\n products_dict[key].setdefault('products', []).append(key)\\n products_dict[key]['price'] = products_dict[key].get('price', 0) + product['net_price']\\n else:\\n product_objs = list(Product.objects.using('slave').in_bulk([p['product_id'] for p in products]).values())\\n bundled_products = []\\n for product in product_objs:\\n for bundled_product in product.bundled.all():\\n bundled_product.price = 0\\n bundled_products.append(bundled_product)\\n product_objs.extend(bundled_products)\\n for product in product_objs:\\n key = getattr(product.parent, 'name_%s' % lang)\\n products_dict[key] = products_dict.get(key, {\\n 'expire_in': product.expire_in,\\n 'never_expire': product.never_expire\\n })\\n products_dict[key].setdefault('products', []).append(mark_safe(product.name))\\n products_dict[key]['price'] = products_dict[key].get('price', 0) + product.price\\n # Convert it to a format which is easy to handle in email templates\\n products_dict = [{\\n 'title': key,\\n 'body': value,\\n } for key, value in products_dict.items()]\\n except (ValueError, KeyError, AttributeError):\\n products_dict = list({'title': p['name'], 'body': {'expire_in': None, 'never_expire': None}} for p in products)\\n\\n return products_dict\",\n \"def show_available_products():\\n LOGGER.debug('Listing all available products.')\\n available_products = {}\\n with MongoDBConnection() as mongo:\\n database = mongo.connection.hp_norton\\n for product in database.products.find(\\n {'quantity_available': {'$gt': 0}}):\\n available_products[product['product_id']] = {\\n 'description': product['description'],\\n 'product_type': product['product_type'],\\n 'quantity_available': product['quantity_available']}\\n return available_products\",\n \"def show_available_products():\\n available_product = {}\\n\\n if not collection_exist(DATABASE, PRODUCT_COLLECTION):\\n return available_product\\n\\n with MongoDBConnection() as mongo:\\n database = mongo.connection[DATABASE]\\n\\n available_product__count = 0\\n for product in database[PRODUCT_COLLECTION].find({\\\"quantity_available\\\": {\\\"$ne\\\": '0'}}):\\n available_product[product['product_id']] = \\\\\\n {'description': product['description'],\\n 'product_type': product['product_type'],\\n 'quantity_available': product['quantity_available']}\\n available_product__count += 1\\n\\n return available_product__count\",\n \"def getChannelVolumes(query, volDimensions, scale, intensityrange, mod):\\n \\n # query = {'preIF' : preIF, 'preIF_z' : preIF_z, 'postIF' : postIF, 'postIF_z' : postIF_z};\\n\\n #presynaptic volumes\\n presynapticvolumes = []\\n preIF = query['preIF']\\n\\n for n in range(0, len(preIF)):\\n\\n print preIF[n]\\n\\n volume = getVolumeFromRender(\\n preIF[n], volDimensions, scale, intensityrange, mod)\\n presynapticvolumes.append(volume)\\n\\n #postsynaptic volumes\\n postsynapticvolumes = []\\n postIF = query['postIF']\\n\\n for n in range(0, len(postIF)):\\n print postIF[n]\\n volume = getVolumeFromRender(\\n postIF[n], volDimensions, scale, intensityrange, mod)\\n postsynapticvolumes.append(volume)\\n\\n synapticVolumes = {'presynaptic': presynapticvolumes,\\n 'postsynaptic': postsynapticvolumes};\\n return synapticVolumes\",\n \"def _get_normal_sold_prices(self, jdict):\\n price_dict = jdict['props']['homeDetails']['price']\\n try:\\n sales_price_text = price_dict['formattedPrice'].replace(',','')\\\\\\n .replace('$', '')\\n sales_price = self._extract_num(sales_price_text)\\n sales_date = price_dict['formattedSoldDate']\\n sales_date_formatted = datetime.datetime\\\\\\n .strptime(sales_date, '%b %d, %Y')\\\\\\n .strftime('%Y-%m-%d')\\n try:\\n asking_price_text = price_dict['listingPrice']['formattedPrice'].replace(',','')\\\\\\n .replace('$', '')\\n if 'k' in asking_price_text.lower():\\n asking_price = self._extract_num(asking_price_text)*1e3\\n elif 'm' in asking_price_text.lower():\\n asking_price = self._extract_num(asking_price_text)*1e6\\n else:\\n asking_price = self._extract_num(asking_price_text)\\n return sales_date_formatted, sales_price, asking_price\\n except:\\n return sales_date_formatted, sales_price, np.nan\\n except:\\n return None, None, None\",\n \"def compute_volume(self, windkessel_pressures = None):\\n # Extract the relevant volumes from the inputs.\\n if windkessel_pressures is None:\\n part = self.pressure['art']\\n pven = self.pressure['ven']\\n else:\\n part = windkessel_pressures['art']\\n pven = windkessel_pressures['ven']\\n\\n # Extract relevant model parameters.\\n cven = self.parameters['venous_compliance']\\n cart = self.parameters['arterial_compliance']\\n vven_rest = self.parameters['venous_resting_volume']\\n vart_rest = self.parameters['arterial_resting_volume']\\n\\n # Compute the venous and arterial volumes.\\n v = {'art': part*cart + vart_rest, 'ven': pven*cven + vven_rest}\\n return v\",\n \"def execQ4():\\n frame = pan.DataFrame(data, columns=['Product', 'Price', 'Period'])\\n tuna = frame[dFrame.Series_title_1 == \\\"Tuna - canned (supermarket only), 185g\\\"]\\n expensive = tuna.sort_values(by=\\\"Price\\\", ascending=False).head(1)\\n return expensive\",\n \"def get_prices_dict(name,products,sales):\\r\\n return {x:(1-sales[name])*products[x] for x in products}\",\n \"def test_product_buy_missing_goods(self):\\n result_buy = self.info_list.product_buy(\\\"хлеб серый хлебозавод\\\", 3)\\n self.assertFalse(result_buy)\",\n \"def get_volume_product_on_date(product_barcode, date, store_id, transactions):\\n transactions_day = transactions[(transactions['STO_EAN'] == store_id) &\\n (transactions['BARCODE'] == product_barcode) &\\n (transactions['TRX_DATETIME'] >= pd.to_datetime(date).date())\\n &(transactions['TRX_DATETIME'] < (pd.to_datetime(date) + pd.DateOffset(1)))]\\n\\n # If the transaction does not exist return None\\n if transactions_day.empty:\\n return None\\n\\n return {\\\"price\\\": np.sum(transactions_day['SAL_AMT_WTAX'].values),\\n \\\"weight\\\": np.sum(transactions_day['SAL_UNIT_QTY_WEIGHT'].values)}\",\n \"def getCurrencies():\",\n \"def query_part_info(parts, distributors, currency):\\n solved = set()\\n # Loop through the parts looking for those sourced by local distributors\\n # that won't be found online. Place any user-added info for these parts\\n # (such as pricing) into the part dictionary.\\n for p in parts:\\n # Find the manufacturer's part number if it exists.\\n pn = p.fields.get('manf#') # Returns None if no manf# field.\\n\\n # Now look for catalog number, price list and webpage link for this part.\\n for dist in distributors:\\n cat_num = p.fields.get(dist + ':cat#')\\n pricing = p.fields.get(dist + ':pricing')\\n link = p.fields.get(dist + ':link')\\n avail = p.fields.get(dist + ':avail')\\n if cat_num is None and pricing is None and link is None:\\n continue\\n\\n cat_num = cat_num or pn or make_unique_catalog_number(p, dist)\\n p.fields[dist + ':cat#'] = cat_num # Store generated cat#.\\n # Get the DistData for this distributor\\n dd = p.dd.get(dist, DistData())\\n dd.part_num = cat_num\\n\\n if link:\\n url_parts = list(urlsplit(link))\\n if url_parts[0] == '':\\n url_parts[0] = u'http'\\n link = urlunsplit(url_parts)\\n else:\\n # This happens when no part URL is found.\\n debug_obsessive('No part URL found for local \\\\'{}\\\\' distributor!'.format(dist))\\n dd.url = link\\n\\n price_tiers = {}\\n try:\\n local_currency = re.findall('[a-zA-Z]{3}', pricing)[0].upper()\\n except Exception:\\n local_currency = currency\\n old_pricing = pricing\\n pricing = re.sub('[^0-9.;:]', '', pricing) # Keep only digits, decimals, delimiters.\\n for qty_price in pricing.split(';'):\\n splitted = qty_price.split(SEPRTR)\\n if len(splitted) == 2:\\n qty, price = splitted\\n if local_currency:\\n dd.currency = local_currency\\n try:\\n price_tiers[int(qty)] = float(price)\\n except ValueError:\\n warning(W_BADPRICE, 'Malformed pricing number: `{}` at {}'.format(old_pricing, p.refs))\\n else:\\n warning(W_BADPRICE, 'Malformed pricing entry: `{}` at {}'.format(qty_price, p.refs))\\n # dd.moq = min(price_tiers.keys())\\n if not price_tiers:\\n # This happens when no pricing info is found.\\n debug_obsessive('No pricing information found for local \\\\'{}\\\\' distributor!'.format(dist))\\n dd.price_tiers = price_tiers\\n\\n # Availability\\n if avail is not None:\\n dd.qty_avail = avail\\n\\n # Update the DistData for this distributor\\n p.dd[dist] = dd\\n # We have data for this distributor. Avoid marking normal distributors.\\n if dist in dist_local_template.api_distributors:\\n solved.add(dist)\\n return solved\",\n \"def rateLaws(rxnList,formModus):\\n \\n eqList = dict()\\n texeqList = dict()\\n \\n for rxn in rxnList.keys():\\n subst_num = str()\\n prod_num = str()\\n subst_km = str()\\n prod_km =str() # only needed if irreversible backwards\\n \\n # create first the numerator, as it is invariant over all rate laws\\n for rct in rxnList[rxn]['rct'].keys():\\n tID = 't_' + rct\\n km = 'K_r_' + rxn + '_' + tID + rxnList[rxn]['rct'][rct]['suffix']\\n #numerator\\n t_num = tID+'^'+str(abs(rxnList[rxn]['rct'][rct]['stoi']))\\n # process substrates\\n if rxnList[rxn]['rct'][rct]['stoi'] < 0:\\n if (subst_num == str()):\\n subst_num = t_num\\n subst_km = km + '^' + str(abs(rxnList[rxn]['rct'][rct]['stoi']))\\n else:\\n subst_num = subst_num + '*' + t_num\\n subst_km = subst_km + '*' + km + '^' + str(abs(rxnList[rxn]['rct'][rct]['stoi']))\\n else:\\n if prod_num == str():\\n prod_num = t_num\\n prod_km = km + '^' + str(abs(rxnList[rxn]['rct'][rct]['stoi']))\\n else:\\n prod_num = prod_num + '*' + t_num\\n prod_km = prod_km + '*' + km + '^' + str(abs(rxnList[rxn]['rct'][rct]['stoi']))\\n \\n # Create denominator in the different reaction form cases\\n if formModus == 'rm':\\n subSites = dict()\\n prodSites = dict()\\n denSites = dict()\\n for rct in rxnList[rxn]['rct'].keys():\\n tDen = '(t_' + rct + '/' +'K_r_' + rxn + '_t_' + rct + rxnList[rxn]['rct'][rct]['suffix'] + ')'\\n for i in range(abs(rxnList[rxn]['rct'][rct]['stoi'])):\\n if rxnList[rxn]['rct'][rct]['stoi'] < 0:\\n subSites[str(rxnList[rxn]['rct'][rct]['actsit']+i*0.1)]=tDen\\n else:\\n prodSites[str(rxnList[rxn]['rct'][rct]['actsit']+i*0.1)]=tDen\\n # sort all sites in a decreasing order\\n for site in sorted(set(prodSites.keys()+subSites.keys()),reverse=1):\\n if site in denSites.keys():\\n 1\\n elif (site in prodSites.keys()) and (site in subSites.keys()):\\n denSites[site] = '(1+' + subSites[site] + '+' + prodSites[site]\\n elif (site in prodSites.keys()) and float(site).is_integer():\\n denSites[site] = '(1+' + prodSites[site]\\n elif (site in subSites.keys()) and float(site).is_integer():\\n denSites[site] = '(1+' + subSites[site]\\n elif site in subSites.keys():\\n intSite = str(float(site).__floordiv__(1))\\n if intSite in prodSites.keys():\\n denSites[intSite] = '(' + prodSites[intSite] + '+' + '(1+' + subSites[site] + ')*(1+' + subSites[intSite] +')'\\n else:\\n denSites[site] = '(1+' + subSites[site]\\n elif site in prodSites.keys():\\n intSite = str(float(site).__floordiv__(1))\\n if intSite in subSites.keys():\\n denSites[intSite] = '(' + subSites[intSite]+ '+' + '(1+' + prodSites[site] + ')*(1+'+ prodSites[intSite] + ')'\\n else:\\n denSites[site] = '(1+' + prodSites[site]\\n \\n # Add the inhibitors to the denominators\\n for rct in rxnList[rxn]['inh'].keys():\\n tDen = '(t_' + rct + '/' +'K_r_' + rxn + '_t_' + rct + rxnList[rxn]['inh'][rct]['suffix'] + ')'\\n if rxnList[rxn]['inh'][rct]['hill'] == 0:\\n tDen = tDen + '^' + 'KH_r_' + rxn + '_t_' + rct + rxnList[rxn]['inh'][rct]['suffix']\\n elif rxnList[rxn]['inh'][rct]['hill'] != 1:\\n tDen = tDen + '^' + str(rxnList[rxn]['inh'][rct]['hill'])\\n \\n site = str(rxnList[rxn]['inh'][rct]['actsit'])\\n if not(site in denSites.keys()):\\n site = str(float(site).__floordiv__(1))\\n if rxnList[rxn]['inh'][rct]['subtype'] == 'uncompetitive': # uncompetitive inh\\n denSites['i'+rct] = '(1+' + tDen\\n elif rxnList[rxn]['inh'][rct]['subtype'] == 'competitive':# comp inhibition\\n denSites[site] = denSites[site] + '+' + tDen\\n elif rxnList[rxn]['inh'][rct]['subtype'] == 'noncompetitive':# noncomp inhibition\\n denSites[site] = denSites[site] + '+' + tDen\\n denSites['i'+rct] = '(1+' + tDen\\n else: raise NameError('invalid inhibitor type (valid: uncompetitive,competitive,noncompetitive) in ' + rxn)\\n \\n # stitch the denominator together\\n denTerm = ''\\n for site in denSites.keys():\\n denTerm = denTerm + denSites[site] + ')*'\\n denTerm = denTerm[:-1]\\n \\n # convinience kinetics denominator\\n elif formModus == 'cc':\\n subSites = dict()\\n prodSites = dict()\\n for rct in rxnList[rxn]['rct'].keys():\\n tDen = '(t_' + rct + '/' +'K_r_' + rxn + '_t_' + rct + rxnList[rxn]['rct'][rct]['suffix'] + ')'\\n for i in range(abs(rxnList[rxn]['rct'][rct]['stoi'])):\\n if i == 0:\\n t_den = '(1'\\n t_den = t_den + '+' + tDen + '^' + str(i+1)\\n if rxnList[rxn]['rct'][rct]['stoi'] < 0:\\n subSites[rct] = t_den\\n else:\\n prodSites[rct] = t_den\\n \\n denTerm = ''\\n for site in subSites.keys():\\n denTerm = denTerm + subSites[site] + ')*'\\n \\n denTerm = denTerm[:-1] + '+'\\n for site in prodSites.keys():\\n denTerm = denTerm + prodSites[site] + ')*'\\n denTerm = denTerm[:-1]\\n # Add the inhibitors\\n for rct in rxnList[rxn]['inh']:\\n tDen = '(t_' + rct + '/' +'K_r_' + rxn + '_t_' + rct + rxnList[rxn]['inh'][rct]['suffix'] + ')'\\n if rxnList[rxn]['inh'][rct]['hill'] == 0:\\n tDen = tDen + '^' + 'KH_r_' + rxn + '_t_' + rct + rxnList[rxn]['inh'][rct]['suffix']\\n elif rxnList[rxn]['inh'][rct]['hill'] != 1:\\n tDen = tDen + '^' + str(rxnList[rxn]['inh'][rct]['hill']) \\n denTerm = '(' + denTerm + ')*(1+' + tDen + ')'\\n \\n \\n # Stitch the equations together:\\n # reversible:\\n if rxnList[rxn]['rev'] == '0':\\n eq = '1/('+subst_km +')*(' + subst_num + '-' + prod_num + '/Keqr_' + rxn + ')/(' + denTerm + ')'\\n texeq = '\\\\\\\\frac{\\\\\\\\frac{1}{' + subst_km + '}*\\\\\\\\left('+ subst_num + '-' + '\\\\\\\\frac{' + prod_num + '}{Keqr_' + rxn + '}\\\\\\\\right)}{' + denTerm + '}'\\n \\n elif rxnList[rxn]['rev'] == '1':#irreversible\\n eq = '(('+ subst_num +')/('+ subst_km +'))/(' + denTerm + ')'\\n texeq = '\\\\\\\\frac{\\\\\\\\frac{'+ subst_num +'}{'+ subst_km +'}}{' + denTerm + '}'\\n else: #irreversibly backwards\\n eq = '-(('+ prod_num +')/('+ prod_km +'))/(' + denTerm + ')'\\n texeq = '-\\\\\\\\frac{\\\\\\\\frac{'+ prod_num+'}{'+ prod_km +'}}{' + denTerm + '}'\\n # Add the activator term:\\n for rct in rxnList[rxn]['act']:\\n \\n if rxnList[rxn]['act'][rct]['subtype'] == 'mm':\\n # 1 / ((K/L)^n + 1)\\n tDen = '(' + 'K_r_' + rxn + '_t_' + rct + rxnList[rxn]['act'][rct]['suffix'] + '/' + 't_' + rct + ')'\\n \\n if rxnList[rxn]['act'][rct]['hill'] == 0:\\n tDen = tDen + '^' + 'KH_r_' + rxn + '_t_' + rct + rxnList[rxn]['act'][rct]['suffix']\\n elif rxnList[rxn]['act'][rct]['hill'] != 1:\\n tDen = tDen + '^' + str(rxnList[rxn]['act'][rct]['hill'])\\n \\n eq = '(1/(' + tDen + '+1))*' + eq\\n texeq = '(1/(' + tDen + '+1))*' + texeq\\n \\n elif rxnList[rxn]['act'][rct]['subtype'] == 'cc':\\n # 1 + (L/K^n)\\n tDen = '(t_' + rct + '/' +'K_r_' + rxn + '_t_' + rct + rxnList[rxn]['act'][rct]['suffix'] + ')'\\n \\n if rxnList[rxn]['act'][rct]['hill'] == 0:\\n tDen = tDen + '^' + 'KH_r_' + rxn + '_t_' + rct + rxnList[rxn]['act'][rct]['suffix']\\n elif rxnList[rxn]['act'][rct]['hill'] != 1:\\n tDen = tDen + '^' + str(rxnList[rxn]['act'][rct]['hill'])\\n eq = '(1+' + tDen + ')*' + eq\\n texeq = '(1+' + tDen + ')*' + texeq\\n \\n else:\\n raise NameError('invalid activator style (valid: mm,cc) in ' + rxn)\\n \\n eq = re.sub('\\\\^1(?![0-9\\\\.])','',eq)\\n eq = re.sub('\\\\\\\\)\\\\\\\\(',')*(',eq)\\n texeq = re.sub('\\\\^1(?![0-9\\\\.])','',texeq)\\n texeq = re.sub('\\\\\\\\)\\\\\\\\(',')*(',texeq)\\n \\n # do some latex editing\\n texeq = re.sub('\\\\((?=t\\\\_.{4}\\\\/K.{14}\\\\))',r'\\\\\\\\frac{',texeq)\\n texeq = re.sub('\\\\/(?=K.{14}\\\\))','}{',texeq)\\n texeq = re.sub('(?<=}{K.{14})\\\\)','}',texeq)\\n texeq = re.sub('(?<!left)\\\\(',r'\\\\\\\\left(',texeq)\\n texeq = re.sub('(?<!right)\\\\)',r'\\\\\\\\right)',texeq)\\n texeq = texeq.replace('_','\\\\_')\\n texeq = re.sub(r'\\\\\\\\',r'\\\\\\\\\\\\\\\\',texeq)\\n eqList[rxn] = eq\\n texeqList[rxn] = re.sub('\\\\\\\\\\\\\\\\+', '\\\\\\\\\\\\\\\\', texeq)\\n \\n return(dict(eqList = eqList,texeqList = texeqList))\",\n \"def _initialize_products(self, products: List) -> Dict[str, int]:\\n\\n product_request = urllib.request.Request(url=URL_PRODUCTS, headers={'User-Agent': URL_USER_AGENT})\\n product_response = urllib.request.urlopen(product_request)\\n all_products = json.load(product_response)\\n\\n product_details = {}\\n\\n for product in products:\\n for cbpro_product in all_products:\\n if cbpro_product[\\\"id\\\"] == product:\\n quote_increment = float(cbpro_product[\\\"quote_increment\\\"])\\n num_decimal_places = int(math.log10(1 / quote_increment))\\n product_details[product] = num_decimal_places\\n logging.debug(\\n \\\"Retrieved quote increment for {}: {} = {} decimal places\\\".\\n format(product, quote_increment, num_decimal_places))\\n\\n return product_details\",\n \"def apply(self, catalog):\\n out = []\\n for product in catalog:\\n valid = True\\n for key, filtr in self.filters.items():\\n valid = valid and filtr(product[key])\\n if valid:\\n out.append(product)\\n out = sorted(out, key=lambda x: x['onDemandUsdPrice'])\\n return self.limit_size(out)\",\n \"def inventory_report(products):\\r\\n names = set()\\r\\n total_price = 0\\r\\n total_weight = 0\\r\\n total_flammability = 0\\r\\n for product in products:\\r\\n names.add(product.name)\\r\\n total_price += product.price\\r\\n total_weight += product.weight\\r\\n total_flammability += product.flammability\\r\\n\\r\\n print(\\\"ACME CORPORATION OFFICIAL INVENTORY REPORT\\\")\\r\\n print(\\\"Unique product names: {}\\\".format(len(names)))\\r\\n print(\\\"Average price: {}\\\".format(total_price / len(products)))\\r\\n print(\\\"Average weight: {}\\\".format(total_weight / len(products)))\\r\\n print(\\\"Average flammability:{}\\\".format(\\r\\n total_flammability / len(products)))\\r\\n\\r\\n print(\\\"Following is useful starting code for acme_report.py:\\\")\",\n \"def inventory_report(self):\\n mean_price = sum(Product.price for Product in sample) / len(sample)\\n mean_weight = sum(Product.weight for Product in sample) / len(sample)\\n mean_flam = sum(Product.flammability for Product in sample) / len(sample)\\n return 'Unique Product Names: ', sample.unique, '/n Average Price: ', mean_price, \\n '/n Average Weight: ', mean_weight, '/n Average Flammability: ', mean_flam\",\n \"def generate_dollar_volume_weights(close, volume):\\n \\n product = close*volume \\n \\n \\n \\n weights=product.apply(lambda r : r/sum(r),axis=1) \\n \\n assert close.index.equals(volume.index)\\n assert close.columns.equals(volume.columns)\\n \\n #TODO: Implement function\\n\\n return weights\",\n \"def get_products_from_page(url):\\n\\n def get_data_from_book(book):\\n \\\"\\\"\\\"Return data from one book.\\\"\\\"\\\"\\n src_img = book.find(\\\"img\\\").get(\\\"src\\\")\\n src_img = src_img.replace(\\\"../\\\", \\\"\\\")\\n image = \\\"http://books.toscrape.com/\\\" + src_img\\n\\n in_stock = False\\n in_stock_or_not = book.find(\\\"p\\\", {\\\"class\\\", \\\"instock\\\"}).text\\n if \\\"In stock\\\" in in_stock_or_not:\\n in_stock = True\\n\\n name = book.find(\\\"h3\\\").find(\\\"a\\\").text\\n\\n price = book.find(\\\"p\\\", {\\\"class\\\", \\\"price_color\\\"}).text\\n price = price.replace(\\\"Â\\\", \\\"\\\")\\n\\n rating = book.find(\\\"p\\\", {\\\"class\\\", \\\"star-rating\\\"}).get(\\\"class\\\")[1]\\n rating = w2n.word_to_num(rating)\\n\\n return {\\n \\\"image\\\": image,\\n \\\"in_stock\\\": in_stock,\\n \\\"name\\\": name,\\n \\\"price\\\": price,\\n \\\"rating\\\": rating,\\n }\\n\\n r = requests.get(url)\\n soup = BeautifulSoup(r.text, \\\"html.parser\\\")\\n books = soup.find_all(\\\"article\\\", {\\\"class\\\", \\\"product_pod\\\"})\\n\\n result = list(map(get_data_from_book, books))\\n return result\",\n \"def apply_peddler_effect(self, player: Player) -> None:\\n \\\"\\\"\\\"\\n Buy 1 cube (any resource but gold) from the stock with 1 denier.\\n \\\"\\\"\\\"\\n # Remark: Hard-coded! We don't use the tags <cost><n_deniers>-1 and <gain><CHOICES>... in <game_elements><buildings><player_buildings><player_building><secondary_effect>.\\n money_resource_cost, qty_cost = Money.money, -1 # type: MoneyResource, int\\n if player.current_money_resources[money_resource_cost] + \\\\\\n qty_cost < 0: # Has the player enough money or resource?\\n print(indent(4) + player.txt_name_money_resources_workers_PPs_deck(True, True, False, False, False) +\\n ' and can\\\\'t apply the effect because he/she doesn\\\\'t have enough money or resource as ' +\\n str(qty_cost) + ' ' + money_resource_cost.name + '(s) required.')\\n else:\\n resource_gain_choices, qty_gain = [resource for resource in Resource.resources.values()\\n if not resource.is_wild()], \\\\\\n +1 # type: List[Resource], int\\n resource_gain = player.choose_buy_resource(money_resource_cost, qty_cost, resource_gain_choices,\\n qty_gain) # type: Resource\\n if resource_gain is None:\\n print(indent(4) + player.txt_name_money_resources_workers_PPs_deck(True, True, False, False, False) +\\n ' and had chosen to don\\\\'t apply the effect.')\\n else:\\n print(indent(4) + player.name() + ' wants to consume ' + str(qty_cost) + ' ' +\\n money_resource_cost.name + '(s) to obtain ' + str(qty_gain) + ' ' + resource_gain.name + '(s).')\\n player.current_money_resources[money_resource_cost] += qty_cost\\n player.current_money_resources[resource_gain] += qty_gain\\n print(indent(4) + player.txt_name_money_resources_workers_PPs_deck(True, True, False, False, False) +\\n ' once the effect applied.')\",\n \"def get_products_statistics(loop, products):\\n description_placeholder = config[\\\"placeholders\\\"][\\\"description\\\"]\\n img_url_placeholder = config[\\\"placeholders\\\"][\\\"img_url\\\"]\\n\\n # collect offers for all products asynchronously\\n futures = [get_offers_async(product[\\\"productId\\\"]) for product in products]\\n all_offers = loop.run_until_complete(asyncio.gather(*futures))\\n\\n for product, offers in zip(products, all_offers):\\n product[\\\"normalized_title\\\"] = clean_string(product[\\\"title\\\"])\\n product[\\\"min_price\\\"] = sys.maxsize\\n product[\\\"max_price\\\"] = 0\\n product[\\\"description\\\"] = description_placeholder\\n product[\\\"img_url\\\"] = img_url_placeholder\\n\\n for offer in offers:\\n product[\\\"min_price\\\"] = min(product[\\\"min_price\\\"], offer[\\\"price\\\"])\\n product[\\\"max_price\\\"] = max(product[\\\"max_price\\\"], offer[\\\"price\\\"])\\n\\n if product[\\\"description\\\"] == description_placeholder and offer.get(\\\"description\\\"):\\n product[\\\"description\\\"] = offer[\\\"description\\\"]\\n\\n if product[\\\"img_url\\\"] == img_url_placeholder and offer.get(\\\"img_url\\\"):\\n product[\\\"img_url\\\"] = offer[\\\"img_url\\\"]\\n\\n return products\",\n \"def pro_code_dict(code=False, inverse=False, return_all=False):\\n\\n pro_code_dict = {\\\"0500\\\": \\\"Date\\\",\\n \\\"0501\\\": \\\"height [> 0: top, < 0: bottom of elem.] (cm)\\\",\\n \\\"0502\\\": \\\"element density (kg m-3)\\\",\\n \\\"0503\\\": \\\"element temperature (degC)\\\",\\n \\\"0504\\\": \\\"element ID (1)\\\",\\n \\\"0506\\\": \\\"liquid water content by volume (%)\\\",\\n \\\"0508\\\": \\\"dendricity (1)\\\",\\n \\\"0509\\\": \\\"sphericity (1)\\\",\\n \\\"0510\\\": \\\"coordination number (1)\\\",\\n \\\"0511\\\": \\\"bond size (mm)\\\",\\n \\\"0512\\\": \\\"grain size (mm)\\\",\\n \\\"0513\\\": \\\"grain type (Swiss Code F1F2F3)\\\",\\n \\\"0514\\\": \\\"grain type, grain size (mm), and density (kg m-3) of SH at surface\\\",\\n \\\"0515\\\": \\\"ice volume fraction (%)\\\",\\n \\\"0516\\\": \\\"air volume fraction (%)\\\",\\n \\\"0517\\\": \\\"stress in (kPa)\\\",\\n \\\"0518\\\": \\\"viscosity (GPa s)\\\",\\n \\\"0519\\\": \\\"soil volume fraction (%)\\\",\\n \\\"0520\\\": \\\"temperature gradient (K m-1)\\\",\\n \\\"0521\\\": \\\"thermal conductivity (W K-1 m-1)\\\",\\n \\\"0522\\\": \\\"absorbed shortwave radiation (W m-2)\\\",\\n \\\"0523\\\": \\\"viscous deformation rate (1.e-6 s-1)\\\",\\n \\\"0531\\\": \\\"deformation rate stability index Sdef\\\",\\n \\\"0532\\\": \\\"natural stability index Sn38\\\",\\n \\\"0533\\\": \\\"stability index Sk38\\\",\\n \\\"0534\\\": \\\"hand hardness either (N) or index steps (1)\\\",\\n \\\"0535\\\": \\\"optical equivalent grain size (mm)\\\",\\n \\\"0540\\\": \\\"bulk salinity (g/kg)\\\",\\n \\\"0541\\\": \\\"brine salinity (g/kg)\\\",\\n \\\"0601\\\": \\\"snow shear strength (kPa)\\\",\\n \\\"0602\\\": \\\"grain size difference (mm)\\\",\\n \\\"0603\\\": \\\"hardness difference (1)\\\",\\n \\\"0604\\\": \\\"ssi\\\",\\n \\\"0605\\\": \\\"inverse texture index ITI (Mg m-4)\\\",\\n \\\"0606\\\": \\\"critical cut length (m)\\\", }\\n\\n if inverse:\\n inverse = {value: key for key, value in pro_code_dict.items()}\\n return(inverse[code])\\n if code:\\n return (pro_code_dict[code])\\n if return_all:\\n return (pro_code_dict)\",\n \"def get_product_purity(product):\\n return product.imass[products].sum() / product.F_mass\",\n \"def get_complete_volume_info(vol_name, vol_info_dict=None):\\n return_dict = {}\\n try:\\n if not vol_info_dict:\\n vol_info_dict, err = get_basic_volume_info(vol_name)\\n if err:\\n raise Exception(err)\\n\\n return_dict = vol_info_dict\\n\\n vol_status_dict = {}\\n if vol_info_dict['status'] == 1:\\n vol_status_dict, err = get_volume_status(\\n vol_info_dict['name'], vol_info_dict)\\n if err:\\n raise Exception(err)\\n\\n if vol_info_dict['status'] == 1:\\n if vol_status_dict:\\n # Add the status and usage info\\n return_dict.update(vol_status_dict)\\n vol_process_dict, err = get_volume_process_status(\\n vol_name, vol_info_dict, vol_status_dict)\\n if err:\\n raise Exception(err)\\n\\n return_dict['processes_ok'] = vol_process_dict['processes_ok']\\n\\n for br in return_dict['brick_status']:\\n if br in vol_process_dict['brick_status']:\\n return_dict['brick_status'][br].update(\\n vol_process_dict['brick_status'][br])\\n\\n quotas, err = get_volume_quota(vol_name, vol_info_dict)\\n if err:\\n raise Exception(err)\\n return_dict['quotas'] = quotas\\n\\n except Exception, e:\\n return None, 'Error getting complete volume information : %s' % str(e)\\n else:\\n return return_dict, None\",\n \"def outputFxVolCalibrated(result,data):\\n output = {\\\"fxVolatilitiesValues\\\":[]}\\n indexVol = 0\\n for fxvol in data[\\\"marketDataDefinitions\\\"][\\\"fxVolatilities\\\"]:\\n output[\\\"fxVolatilitiesValues\\\"].append({\\\"id\\\":fxvol[\\\"id\\\"],\\\"date\\\" : data[\\\"asOfDate\\\"],\\\"values\\\":{}})\\n smileAxis = fxvol[\\\"smileAxis\\\"]\\n dictionnary = {\\\"DELTA_CALL\\\" : [0,\\\"deltaCalls\\\"], \\\"DELTA_PUT\\\" : [1,\\\"deltaPuts\\\"], \\\"STRIKE\\\":[3,\\\"strikes\\\"],\\\"DELTA\\\":[2,\\\"deltas\\\"],\\\"LOG_MONEYNESS\\\":[6,\\\"logMoneyness\\\"]}\\n volValues = result[indexVol]\\n jump =0\\n for expiry in fxvol[\\\"expiries\\\"]:\\n output[\\\"fxVolatilitiesValues\\\"][indexVol][\\\"values\\\"].update({expiry[\\\"expiryDate\\\"]: {\\n \\\"deltaConvention\\\":expiry[\\\"deltaConvention\\\"],\\\"delivery\\\":expiry[\\\"deliveryDate\\\"],\\\"atmIndex\\\" : volValues[0+jump][len(result[indexVol][0])-1],\\n \\\"volatilityValues\\\": result[indexVol][4][0:5].tolist()}})\\n for axis in smileAxis:\\n output[\\\"fxVolatilitiesValues\\\"][indexVol][\\\"values\\\"][expiry[\\\"expiryDate\\\"]].update({dictionnary[axis][1]: volValues[dictionnary[axis][0]][0:5].tolist()})\\n jump = jump + 7\\n indexVol = indexVol + 1\\n return output\",\n \"def show_products():\\n\\n print \\\"These are the products in sale\\\"\\n for key, value in ADD_PRODUCTS.iteritems():\\n print \\\"%s: Q%.2f\\\" % (key, value)\",\n \"def show_proposal(self, req):\\n z = 1\\n y = z\\n x = 1\\n self.dico_product = {}\\n for prod in req:\\n if z <= 5:\\n List_store = orm_imp.find_store(prod.id)\\n print(\\\"Choix numéro\\\", z, \\\":\\\", prod.name, \\\"| score : \\\",\\n prod.nutriscore, \\\"| Magasins : \\\", List_store,\\n \\\"| Lien :\\\",\\n prod.url, \\\"| \\\\n ==> description :\\\",\\n prod.ingredient, \\\"\\\\n==================================\\\")\\n self.dico_product.update({z: prod.id})\\n x += 1\\n z += 1\\n return self.dico_product, y, x - 1\",\n \"def handle_get_prep_term(self, context: 'IconScoreContext') -> dict:\\n if self.term is None:\\n raise ServiceNotReadyException(\\\"Term is not ready\\\")\\n\\n preps_data = []\\n\\n # Collect Main and Sub P-Reps\\n for prep_snapshot in self.term.preps:\\n prep = self.preps.get_by_address(prep_snapshot.address)\\n preps_data.append(prep.to_dict(PRepDictType.FULL))\\n\\n # Collect P-Reps which got penalized for consecutive 660 block validation failure\\n def _func(node: 'PRep') -> bool:\\n return node.penalty == PenaltyReason.BLOCK_VALIDATION and node.status == PRepStatus.ACTIVE\\n\\n # Sort preps in descending order by delegated\\n preps_on_block_validation_penalty = \\\\\\n sorted(filter(_func, self.preps), key=lambda x: x.order())\\n\\n for prep in preps_on_block_validation_penalty:\\n preps_data.append(prep.to_dict(PRepDictType.FULL))\\n\\n return {\\n \\\"blockHeight\\\": context.block.height,\\n \\\"sequence\\\": self.term.sequence,\\n \\\"startBlockHeight\\\": self.term.start_block_height,\\n \\\"endBlockHeight\\\": self.term.end_block_height,\\n \\\"totalSupply\\\": self.term.total_supply,\\n \\\"totalDelegated\\\": self.term.total_delegated,\\n \\\"irep\\\": self.term.irep,\\n \\\"preps\\\": preps_data\\n }\",\n \"def get_prices(country, year, square=2, name='p_', store=yearly):\\n\\n year1 = 'y' + str(year) + '_'\\n year0 = 'y' + str(year - 1) + '_'\\n\\n df1 = yearly[year1 + 'price_' + country]\\n df0 = yearly[year0 + 'price_' + country]\\n\\n df1.name = 'p' + str(year)\\n df0.name = 'p' + str(year - 1)\\n\\n gr1 = df1.groupby(axis=0, level='PRODUCT_NC')\\n gr0 = df0.groupby(axis=0, level='PRODUCT_NC')\\n\\n l1 = []\\n drops1 = []\\n for product in gr1.groups.keys():\\n try:\\n l1.append((product, ref_dict[product]))\\n except KeyError:\\n drops1.append(product)\\n\\n l0 = []\\n drops0 = []\\n for product in gr0.groups.keys():\\n try:\\n l0.append((product, ref_dict[product]))\\n except KeyError:\\n drops0.append(product)\\n\\n return pd.DataFrame((np.log(df1) - np.log(df0) - (\\n np.log(df1.ix[l1].reset_index(level='PARTNER')['p' + str(year)].reindex(df1.index, level='PRODUCT_NC')) - (\\n np.log(df0.ix[l0].reset_index(level='PARTNER')['p' + str(year - 1)].reindex(df0.index, level='PRODUCT_NC'))))), columns=[name + str(year)]) ** square\",\n \"def get_silverpeak_costs(self, nodePair, site, accessories):\\n bundleNode = nodePair.get_bundleNode()\\n core = self.core\\n bundle = {\\n \\\"hardware\\\" : 0,\\n \\\"software\\\" : 0,\\n \\\"support\\\" : 0,\\n \\\"license\\\" : 0,\\n \\\"accessories\\\": 0,\\n \\\"BOM\\\":[]\\n }\\n \\n bundle[\\\"hardware\\\"] = float(core.get_attribute(bundleNode, \\\"cost\\\")) * (1 - float(core.get_attribute(bundleNode, \\\"discount\\\")))\\n bundle[\\\"BOM\\\"].append(create_BOM_row(siteID=str(site[\\\"Site ID 1\\\"]) + str(site[\\\"Site ID 2\\\"]),code=core.get_attribute(bundleNode, \\\"vendorCode\\\"),description=core.get_attribute(bundleNode, \\\"description\\\"), quantity=site[\\\"Device quantity\\\"], discount=core.get_attribute(bundleNode, \\\"discount\\\"),unit_list=core.get_attribute(bundleNode, \\\"cost\\\")))\\n bundle[\\\"weight\\\"] = core.get_attribute(bundleNode, \\\"weight\\\")\\n\\n parts = core.load_children(bundleNode)\\n for part in parts:\\n if core.get_attribute(part, \\\"type\\\") == \\\"Support\\\" and \\\"1M\\\" in core.get_attribute(part,\\\"name\\\"):\\n core.set_registry(part, \\\"color\\\", \\\"#00FF00\\\")\\n bundle[\\\"support\\\"] = core.get_attribute(part,\\\"cost\\\") * (1 - core.get_attribute(part,\\\"discount\\\")) * site[\\\"Contract term\\\"]\\n bundle[\\\"BOM\\\"].append(create_BOM_row(siteID=str(site[\\\"Site ID 1\\\"]) + str(site[\\\"Site ID 2\\\"]),code=core.get_attribute(part, \\\"vendorCode\\\"), description=core.get_attribute(part, \\\"description\\\"), quantity=site[\\\"Device quantity\\\"], discount=core.get_attribute(part, \\\"discount\\\"), unit_list=core.get_attribute(part, \\\"cost\\\"), contract_term=site['Contract term']))\\n for accessory in accessories:\\n for part in parts:\\n if core.get_attribute(part, \\\"type\\\") == \\\"Accessory\\\" and accessory['code'] == core.get_attribute(part, \\\"vendorCode\\\"):\\n core.set_registry(part, \\\"color\\\", \\\"#00FF00\\\")\\n bundle[\\\"accessories\\\"] += core.get_attribute(part,\\\"cost\\\") * (1 - float(core.get_attribute(bundleNode, \\\"discount\\\"))) * accessory['quantity']\\n bundle[\\\"BOM\\\"].append(create_BOM_row(siteID=str(site[\\\"Site ID 1\\\"]) + str(site[\\\"Site ID 2\\\"]),code=core.get_attribute(part, \\\"vendorCode\\\"), description=core.get_attribute(part, \\\"description\\\"), quantity=accessory['quantity'], discount=core.get_attribute(bundleNode, \\\"discount\\\"), unit_list=core.get_attribute(part, \\\"cost\\\")))\\n return bundle\",\n \"def calculateVolumes(data):\\n print \\\"Calculating volumes...\\\"\\n results = {}\\n for dataLine in data:\\n name = dataLine['name']\\n r1 = dataLine['r1']\\n r2 = dataLine['r2']\\n r3 = dataLine['r3']\\n r4 = dataLine['r4']\\n t1 = dataLine['t1']\\n t2 = dataLine['t2']\\n t3 = dataLine['t3']\\n volCup = (math.pi/3.0) * t1 * ((r1**2) + (r4**2) - (r1*r4))\\n volPeanut = math.pi * (t1 - t2 - t3) * ((r2**2) + (r3**2) - (r2*r3)) / 3.0\\n volChoc = volCup - volPeanut\\n ratio = volChoc/volPeanut\\n print \\\"Ratio for \\\" + name + \\\" is \\\" + str(ratio)\\n results[name] = [r1, volChoc, volPeanut, volCup, ratio]\\n return results\",\n \"def test_gather_location_no_product(self):\\n gathered_items = self.Quant._gather(self.apple, self.test_stock_location_02)\\n # Check the number of apple quants returned is correct\\n self.assertFalse(len(gathered_items))\",\n \"def extract_data(product):\\n if not isinstance(product, dict) and product:\\n return\\n image = product.get('mediumImageUrls', None)\\n price = product.get('itemPrice', None)\\n data = {\\n 'service': 'rakuten',\\n 'currency': None,\\n 'price': price and int(price) or price,\\n 'image': image[0] if image else 0,\\n 'id': product.get('itemCode', None),\\n # 'ProductId': product['itemCode', None],\\n 'DetailPageURL': product.get('itemUrl', None),\\n 'Label': product.get('itemCaption', None),\\n 'EditorialReview': [\\n {'name': 'Description',\\n 'value': product.get('itemCaption', None)}],\\n 'ProductGroup': product.get('genreId', None), # get it name to display\\n 'Title': product.get('itemName', None),\\n 'Manufacturer': product.get('shopName', None),\\n 'CustomerReviews': product.get('itemUrl', None), # INFO: no such thing\\n 'images': [\\n {'SmallImage': small,\\n 'LargeImage': small.rsplit('?', 1)[0]}\\n for small in product.get('smallImageUrls', [])],\\n 'ItemAttributes': [],\\n }\\n return data\",\n \"def recheck_conts_package(conts_dict, si_result):\\n print('######## BEGIN CHECKING PACKAGES ? ########')\\n packages = si_result.get('packages', True)\\n description = si_result.get('description_of_goods', False)\\n pUnit = None\\n print(packages == description)\\n\\n if packages == description:\\n conts_package = []\\n for unit in current_app.config['CONTAINER_PACKAGE_UNITS']:\\n re_unit = re.compile(\\n r'(\\\\d+\\\\W*(' +\\n unit + r'(s|S)?\\\\s?(\\\\Ws\\\\W|\\\\WS\\\\W)?' +\\n r')\\\\W*)|(\\\\W*(' +\\n unit + r'(s|S)?\\\\s?(\\\\Ws\\\\W|\\\\WS\\\\W)?' +\\n r')\\\\W*\\\\d+)')\\n units_result = re.findall(re_unit, packages)\\n\\n if (\\n len(units_result) == len(conts_dict)\\n ) or (\\n len(units_result) == len(conts_dict) + 1\\n ):\\n for result in units_result:\\n print(result)\\n if len(result[0]) > len(unit):\\n print('result[0]', result[0])\\n conts_package.append(\\n re.sub(r'[^\\\\d]+', '', result[0]))\\n elif len(result[4]) > len(unit):\\n print('result[4]', result[4])\\n conts_package.append(\\n re.sub(r'[^\\\\d]+', '', result[4]))\\n\\n if unit.isupper():\\n pUnit = unit + 'S'\\n else:\\n pUnit = unit + 's'\\n\\n break\\n\\n if len(conts_package) >= len(conts_dict):\\n for i, cont in enumerate(conts_dict):\\n cont['packages'] = str(conts_package[i]) + ' ' + pUnit\\n\\n return conts_dict\",\n \"def __Dict2PrintList(self, dProducts: dict) -> list:\\n lAvailableProducts = []\\n for Product in dProducts.values():\\n if Product.iQuantity > 0:\\n sAvailableProductWithPrice = \\\"{} {} - {}: {} {}\\\".format(Product.iUid, Product.sName, \\\"Price\\\", Product.iPrice, Consts.CURRENCY_TYPE)\\n lAvailableProducts.append(sAvailableProductWithPrice)\\n return lAvailableProducts\",\n \"def two_bad_cars():\\n return [\\n {\\n \\\"mpg\\\": maintenance_factor(21.6, False),\\n \\\"annual_miles\\\": 4000,\\n \\\"maintenance\\\": False, # this entry in the dictionary can probably be deleted\\n \\\"lbs_CO2\\\": (11000 / 21.6) * 19.6,\\n },\\n {\\n \\\"mpg\\\": maintenance_factor(21.6, False),\\n \\\"annual_miles\\\": 4000,\\n \\\"maintenance\\\": False, # this entry in the dictionary can probably be deleted\\n \\\"lbs_CO2\\\": (11000 / 21.6) * 19.6,\\n },\\n ]\",\n \"def _get_discount(self):\\n\\n # For every 2 PENS, one free discount\\n number_of_pens = len([x for x in self._products if x.code == 'PEN'])\\n discount = 5.0 * int(number_of_pens / 2)\\n\\n # If there are more than 3 T-Shirts in the basket, 5 EUR of discount in every of them (25%)\\n number_of_tshirts = len([x for x in self._products if x.code == 'TSHIRT'])\\n if number_of_tshirts >= 3:\\n discount += 5.0 * number_of_tshirts\\n\\n return discount\",\n \"def vertical_production(type_id: int, quantity: int, me: int = 0, prod_type: str = 'manufacturing' , prices: bool = False) -> pd.DataFrame:\\n actID = {'manufacturing': 1, 'reaction': 11}\\n mats = pd.DataFrame(columns=['type_id', 'type_name', 'quantity'])\\n\\n if type_id in quantities.loc[(quantities['activityID'] == actID[prod_type]) & (quantities['productTypeID'] == type_id)].values: # item can be manufactured\\n\\n if prod_type == 'reaction':\\n bpid = productToFormula(type_id)\\n\\n elif prod_type == 'manufacturing':\\n bpid = productToBP(type_id)\\n \\n else: \\n raise ValueError(\\\"thats not a valid manufacturing type. options are 'reaction', 'manufacturing'\\\")\\n\\n qPerRun = quantPerRun(bpid)\\n runs = quantity // qPerRun\\n\\n if runs > 0:\\n\\n for _, row in materials.loc[(materials['activityID'] == actID[prod_type]) & (materials['typeID'] == bpid)].iterrows():\\n\\n if prod_type == 'reaction':\\n quant = row['quantity'] * runs\\n\\n elif prod_type == 'manufacturing':\\n quant = me_formula(row['quantity'],me) * runs\\n\\n mats = mats.append(vertical_production(row['materialTypeID'], quant, prod_type = prod_type) ,ignore_index=True)\\n \\n \\n # buys the product instead of manufacturing more of it than needed\\n if int(runs * qPerRun) < int(quantity):\\n mats = mats.append({'type_id': type_id, 'type_name': emt.typeIDToName(type_id), 'quantity': quantity - (runs * qPerRun)},ignore_index=True)\\n\\n else: # item cannot be manufactured\\n mats = mats.append({'type_id': type_id, 'type_name': emt.typeIDToName(type_id), 'quantity': quantity}, ignore_index=True)\\n\\n mats = mats.groupby(['type_id', 'type_name']).sum().reset_index()\\n mats = mats.astype({\\\"type_id\\\": int, \\\"quantity\\\": int})\\n\\n if prices:\\n mats = emt.add_price(mats)\\n\\n return mats\",\n \"def products(self):\\n return [k for k, v in iteritems(self._metabolites) if v >= 0]\",\n \"def prepare_data_with_warehouse(self,from_date,to_date,warehouses,all_products):\\n data_dict = {}\\n stock_quant_obj=self.env['stock.quant']\\n for warehouse in warehouses:\\n all_locations = self.get_all_locations(warehouse)\\n if not all_locations:\\n continue\\n \\n #here we are finding the opening stock for these we are using base query\\n #of inventory at date v10\\n result = self.get_product_qty(all_locations,from_date)\\n qty_dict = dict((x,y) for x, y in result)\\n \\n for product in all_products:\\n last_sales = ''\\n qty_purchase_in_duration = 0\\n qty_sales_in_duration = 0\\n last_purchase_date = ''\\n scrap_location_qty = 0\\n adjusted_qty_in_duration = 0\\n warehouse_out_qty = 0\\n warehouse_in_qty = 0\\n# here from result of inventory at date we are seaching for specific product.\\n opening_product_qty = qty_dict.get(product.id)\\n\\n #finding last sales qty\\n last_sales = self.find_last_sales_qty(from_date,to_date,warehouse,all_locations,product)\\n #finding last purchase date of product\\n last_purchase_date = self.find_last_purchase_date(from_date,to_date,all_locations,product)\\n #fiding date purchase qty in duration for specific product\\n qty_purchase_in_duration = self.find_purchase_qty_in_duration(from_date,to_date,all_locations,product)\\n #fiding scrap qty of precific product\\n scrap_location_qty = self.find_scap_location_qty(from_date,to_date,product,all_locations)\\n #finding sales qty in duration\\n qty_sales_in_duration = self.find_sale_qty_in_duration(from_date,to_date,warehouse,all_locations,product)\\n #fidning adjusted qty in duration\\n adjusted_qty_in_duration = self.find_adjusted_qty_in_duration(from_date, to_date, product, all_locations)\\n \\n dest_location_lst = self.get_other_wahouse_locations(warehouse)\\n \\n if any(all_locations) and any(dest_location_lst):\\n #fidning warehouse in qty \\n warehouse_in_qty = self.find_warehouse_transer_in_qty(product, all_locations, dest_location_lst,from_date,to_date)\\n #fidning warehouse out qty for specific product.\\n warehouse_out_qty = self.find_warehouse_transer_out_qty(product, all_locations, dest_location_lst,from_date,to_date)\\n \\n if warehouse_out_qty:\\n warehouse_out_qty = warehouse_out_qty and warehouse_out_qty[0][0] or ''\\n if warehouse_in_qty:\\n warehouse_in_qty = warehouse_in_qty and warehouse_in_qty[0][0] or ''\\n \\n if adjusted_qty_in_duration:\\n adjusted_qty_in_duration = adjusted_qty_in_duration and adjusted_qty_in_duration[0][0] or '' \\n if scrap_location_qty:\\n scrap_location_qty = scrap_location_qty and scrap_location_qty[0][0] or ''\\n \\n # if qty_sales_in_duration:\\n # qty_sales_in_duration = qty_sales_in_duration and qty_sales_in_duration[0][0] or ''\\n # if qty_purchase_in_duration:\\n # qty_purchase_in_duration = qty_purchase_in_duration[0][0] or ''\\n if last_sales:\\n last_sales = datetime.strptime(last_sales and last_sales[0][0], '%Y-%m-%d %H:%M:%S').strftime('%d-%m-%Y') or ''\\n \\n if last_purchase_date:\\n last_purchase_date = datetime.strptime(last_purchase_date and last_purchase_date[0][0], '%Y-%m-%d %H:%M:%S').strftime('%d-%m-%Y') or ''\\n \\n if data_dict.has_key(warehouse.id):\\n data_lst=data_dict.get(warehouse.id)\\n data_lst.append({'product':product,'sku':product.default_code or '','name':product.name,\\n 'Cost':product.standard_price or '','sales_price':product.lst_price or '',\\n 'opening_qty':opening_product_qty or 0,'last_sales':last_sales or '',\\n 'last_purchase_date':last_purchase_date or '','qty_purchase_in_duration':qty_purchase_in_duration or 0,\\n 'qty_sales_in_duration': qty_sales_in_duration or 0,'scrap_location_qty':scrap_location_qty or 0,\\n 'adjusted_qty_in_duration':adjusted_qty_in_duration or 0\\n ,'warehouse_in_qty':warehouse_in_qty or 0,\\n 'warehouse_out_qty':warehouse_out_qty or 0 \\n })\\n data_dict.update({warehouse.id:data_lst})\\n continue\\n data_dict.update({warehouse.id:[{'product':product,'sku':product.default_code or '','name':product.name,\\n 'Cost':product.standard_price or '','sales_price':product.lst_price or '',\\n 'opening_qty':opening_product_qty or 0,\\n 'last_sales':last_sales or '','last_purchase_date':last_purchase_date or '',\\n 'qty_purchase_in_duration':qty_purchase_in_duration or 0,\\n 'qty_sales_in_duration': qty_sales_in_duration or 0,\\n 'scrap_location_qty':scrap_location_qty or 0,\\n 'adjusted_qty_in_duration':adjusted_qty_in_duration or 0,\\n 'warehouse_in_qty':warehouse_in_qty or 0,\\n 'warehouse_out_qty':warehouse_out_qty or 0\\n }]})\\n return data_dict\",\n \"def free_item_discount(self, items):\\n new_items = []\\n occurrences = collections.Counter(item.sku for item in items)\\n if occurrences.get(MacbookPro.sku, 0) > 0:\\n for item in items:\\n if item.sku == VGAadapter.sku and item.price != 0:\\n item.price = 0\\n else:\\n new_items.append(CheckoutItem(sku=VGAadapter.sku, price=0))\\n items.extend(new_items)\\n return items\",\n \"def _all_products(q_1: Q, q_2: Q) -> Dict:\\n\\n all_dict = _commuting_products(q_1, q_2)\\n all_dict.update(_anti_commuting_products(q_1, q_2))\\n\\n return all_dict\",\n \"def parse_product_installment(advertise: Dict[str, Any]) -> Optional[Dict[str, Union[float, int]]]:\\n if \\\"product_installment\\\" in advertise.keys():\\n\\n tmp_dict = {\\\"parcell\\\": None, \\\"price\\\": None, \\\"no_interest\\\": None}\\n tmp: List[str] = re.split(\\\"[ \\\\t]{2,}\\\", advertise[\\\"product_installment\\\"])\\n\\n for term in tmp:\\n if \\\"x\\\" in term:\\n tmp_vec = term.split()\\n if tmp_vec[0].isnumeric():\\n tmp_dict[\\\"parcell\\\"] = int(tmp_vec[0])\\n continue\\n if \\\"R$\\\" in term:\\n tmp_vec = term.split()\\n if \\\"R$\\\" in tmp_vec:\\n tmp_vec.remove(\\\"R$\\\")\\n if all([term.isnumeric() for term in tmp_vec]):\\n tmp_dict[\\\"price\\\"] = float(\\\".\\\".join(tmp_vec))\\n continue\\n if \\\"sem juros\\\" in term:\\n tmp_dict[\\\"no_interest\\\"] = 1\\n continue\\n return tmp_dict\",\n \"def compute_volume(self, windkessel_pressures = None):\\n # Extract the relevant volumes from the inputs.\\n if windkessel_pressures is None:\\n part = self.pressure['art']\\n else:\\n part = windkessel_pressures['art']\\n\\n # Extract relevant model parameters.\\n cart = self.parameters['arterial_compliance']\\n\\n # Compute the arterial volume.\\n v = {'art': part*cart}\\n return v\",\n \"def main():\\n\\n parser = argparse.ArgumentParser(description='MODIS product table')\\n parser.add_argument('--product', help='MODIS product ID', metavar='')\\n parser.add_argument('--resolution', help='Filter for pixel size', type=str, metavar='')\\n parser.add_argument('--vampc', help='Filter for VAM product code', metavar='')\\n args = parser.parse_args()\\n\\n # Load product table\\n this_dir = Path(__file__).parent\\n with open(this_dir.parent.joinpath('data', 'MODIS_V6_PT.pkl').as_posix(), 'rb') as table_raw:\\n product_table = pickle.load(table_raw)\\n\\n tbl = pd.DataFrame(product_table).T\\n print('\\\\n')\\n\\n # Subset table by product\\n if args.product:\\n try:\\n tbl = tbl.loc[args.product.upper()]\\n except:\\n raise SystemExit('Product ID not recognized!')\\n # Subset table by resolution\\n if args.resolution and not args.product:\\n try:\\n tbl = tbl[tbl['pixel_size'] == args.resolution]\\n except:\\n raise ValueError('Resolution not valid! (possible: 250,500,1000,5600)')\\n # Subset table by parameter\\n if args.vampc and not args.product:\\n helper_dict = dict(zip(['VIM', 'VEM', 'LTD', 'LTN'],\\n ['Vegetation Indices',\\n 'Vegetation Indices',\\n 'Temperature, Emissivity',\\n 'Temperature, Emissivity']))\\n try:\\n tbl = tbl[tbl['product'] == helper_dict[args.vampc.upper()]]\\n except:\\n raise ValueError('VAM product code not valid! (possible: VIM, VEM, LTD, LTN)')\\n\\n # print results\\n with pd.option_context('display.max_rows', None, 'display.max_columns', 4):\\n print(tbl)\\n print('\\\\n')\",\n \"def product_summary(self):\\n # products = self.products.filter(type='S') # TODO: explain the usage of this commented line or remove it\\n from .utils import process_products\\n\\n subscription_products = SubscriptionProduct.objects.filter(subscription=self)\\n dict_all_products = {}\\n for sp in subscription_products:\\n dict_all_products[str(sp.product.id)] = str(sp.copies)\\n return process_products(dict_all_products)\",\n \"def calculate_prices(self, good=None):\\n\\n stock = self.calculate_init_stock(good)\\n buy = self.buying_price()\\n\\n if stock == 0:\\n sell = 0\\n buy = buy + (buy * 0.5)\\n\\n elif stock < 500:\\n # mild bug: stock, without selling price\\n sell = self.selling_price()\\n elif stock >= 500:\\n # higher production, lower prices\\n sell = self.selling_price() / 2\\n buy = buy - (buy * 0.5)\\n\\n return [buy, sell, stock]\",\n \"def premium(self):\\n\\n return blackscholes_norm(self.data['moneyness'],\\n self.data['maturity'],\\n self.impvol(), self.data['call'])\",\n \"def prep_visualize_cost(self, dat):\\n # Assume if null then there are no in-app purchases since all others have a value\\n dat['In-app Purchases'].fillna('0', inplace = True)\\n\\n # An app is only actually free if you cant buy anything once in it\\n dat['true_free'] = (dat.Price == 0) & (dat['In-app Purchases'] == '0')\\n\\n # Look at only if there is a base cost first and see if rating difference exists\\n\\n print('Investigate differences in ratings by free vs. paid App')\\n plt.hist(dat[dat.true_free == True]['Average User Rating'].dropna().values, label='Free', color = 'green')\\n plt.hist(dat[(dat.true_free == False) & (dat.Price != 0)]['Average User Rating'].dropna().values, label='Paid')\\n\\n plt.legend(loc='upper right')\\n plt.show()\\n\\n print('Many Apps included pain content within them. These should not truly be considered \\\"free\\\"')\\n\\n plt.hist(dat[dat.true_free == False]['Average User Rating'].dropna().values, label='Paid')\\n plt.hist(dat[dat.true_free == True]['Average User Rating'].dropna().values, label='Free', color = 'green')\\n\\n plt.legend(loc='upper right')\\n plt.show()\\n\\n print('Which are truly best?')\\n\\n non_true_free_rating = dat[dat.true_free == False]['Average User Rating'].dropna().mean()\\n in_app_purchases = dat[(dat.true_free == False) & (dat.Price == 0)]['Average User Rating'].dropna().mean()\\n true_free_rating = dat[dat.true_free == True]['Average User Rating'].dropna().mean()\\n zero_cost_rating = dat[dat.Price == 0]['Average User Rating'].dropna().mean()\\n paid_rating = dat[dat.Price != 0]['Average User Rating'].dropna().mean()\\n\\n non_true_free_rating_cnt = dat[dat.true_free == False]['Average User Rating'].dropna().shape[0]\\n in_app_purchases_cnt = dat[(dat.true_free == False) & (dat.Price == 0)]['Average User Rating'].dropna().shape[0]\\n true_free_rating_cnt = dat[dat.true_free == True]['Average User Rating'].dropna().shape[0]\\n zero_cost_rating_cnt = dat[dat.Price == 0]['Average User Rating'].dropna().shape[0]\\n paid_rating_cnt = dat[dat.Price != 0]['Average User Rating'].dropna().shape[0]\\n\\n plt.bar(['Zero Cost','Paid App'],\\n [zero_cost_rating,paid_rating])\\n plt.ylim((3.8,4.18))\\n plt.xticks(rotation = 'vertical')\\n plt.show()\\n\\n plt.bar(['Truly Free','Any Cost'],\\n [true_free_rating,non_true_free_rating])\\n plt.ylim((3.8,4.18))\\n plt.xticks(rotation = 'vertical')\\n plt.show()\\n\\n plt.bar(['Zero Cost','Truly Free','Paid App','Any Cost','In app-purchases'],\\n [zero_cost_rating,true_free_rating,paid_rating,non_true_free_rating, in_app_purchases])\\n plt.ylim((3.8,4.18))\\n plt.xticks(rotation = 'vertical')\\n plt.show()\\n\\n N = 5\\n\\n ind = np.arange(N) # the x locations for the groups\\n width = 0.35 # the width of the bars\\n\\n fig, ax = plt.subplots()\\n rects1 = ax.bar(ind, [zero_cost_rating,true_free_rating,paid_rating,non_true_free_rating, in_app_purchases], \\n width, color='r')\\n\\n\\n # add some text for labels, title and axes ticks\\n ax.set_ylabel('Scores')\\n ax.set_title('Scores by group and gender')\\n ax.set_xticks(ind + width / 2)\\n ax.set_xticklabels(['Zero Cost','Truly Free','Paid App','Any Cost','In app-purchases'])\\n\\n plt.ylim((3.85,4.18))\\n\\n counts = [zero_cost_rating_cnt,true_free_rating_cnt,paid_rating_cnt,non_true_free_rating_cnt, in_app_purchases_cnt]\\n\\n for x, rect in zip(counts, rects1):\\n height = rect.get_height()\\n ax.text(rect.get_x() + rect.get_width()/2., height,\\n 'n = ' + str(x),\\n ha='center', va='bottom')\\n\\n plt.show()\",\n \"def product_db() -> List[Text]:\\n\\n return [\\n \\\"credit\\\",\\n \\\"forex\\\",\\n \\\"debit\\\",\\n \\\"atm\\\"\\n ]\",\n \"def calc_Cinv_boiler(Q_design_W, technology_type, boiler_cost_data):\\n Capex_a_Boiler_USD = 0.0\\n Opex_a_fix_Boiler_USD = 0.0\\n Capex_Boiler_USD = 0.0\\n\\n if Q_design_W > 0.0:\\n boiler_cost_data = boiler_cost_data[boiler_cost_data['code'] == technology_type]\\n # if the Q_design is below the lowest capacity available for the technology, then it is replaced by the least\\n # capacity for the corresponding technology from the database\\n if Q_design_W < boiler_cost_data.iloc[0]['cap_min']:\\n Q_design_W = boiler_cost_data.iloc[0]['cap_min']\\n max_boiler_size = boiler_cost_data.iloc[0]['cap_max']\\n\\n if Q_design_W <= max_boiler_size:\\n\\n boiler_cost_data = boiler_cost_data[\\n (boiler_cost_data['cap_min'] <= Q_design_W) & (boiler_cost_data['cap_max'] > Q_design_W)]\\n\\n Inv_a = boiler_cost_data.iloc[0]['a']\\n Inv_b = boiler_cost_data.iloc[0]['b']\\n Inv_c = boiler_cost_data.iloc[0]['c']\\n Inv_d = boiler_cost_data.iloc[0]['d']\\n Inv_e = boiler_cost_data.iloc[0]['e']\\n Inv_IR = boiler_cost_data.iloc[0]['IR_%']\\n Inv_LT = boiler_cost_data.iloc[0]['LT_yr']\\n Inv_OM = boiler_cost_data.iloc[0]['O&M_%'] / 100.0\\n\\n InvC = Inv_a + Inv_b * (Q_design_W) ** Inv_c + (Inv_d + Inv_e * Q_design_W) * log(Q_design_W)\\n\\n Capex_a_Boiler_USD = calc_capex_annualized(InvC, Inv_IR, Inv_LT)\\n Opex_a_fix_Boiler_USD = InvC * Inv_OM\\n Capex_Boiler_USD = InvC\\n\\n else:\\n number_of_boilers = int(ceil(Q_design_W / max_boiler_size))\\n Q_nom_W = Q_design_W / number_of_boilers\\n\\n boiler_cost_data = boiler_cost_data[\\n (boiler_cost_data['cap_min'] <= Q_nom_W) & (boiler_cost_data['cap_max'] > Q_nom_W)]\\n\\n Inv_a = boiler_cost_data.iloc[0]['a']\\n Inv_b = boiler_cost_data.iloc[0]['b']\\n Inv_c = boiler_cost_data.iloc[0]['c']\\n Inv_d = boiler_cost_data.iloc[0]['d']\\n Inv_e = boiler_cost_data.iloc[0]['e']\\n Inv_IR = boiler_cost_data.iloc[0]['IR_%']\\n Inv_LT = boiler_cost_data.iloc[0]['LT_yr']\\n Inv_OM = boiler_cost_data.iloc[0]['O&M_%'] / 100.0\\n\\n InvC = (Inv_a + Inv_b * (Q_nom_W) ** Inv_c + (Inv_d + Inv_e * Q_nom_W) * log(Q_nom_W)) * number_of_boilers\\n\\n Capex_a_Boiler_USD = calc_capex_annualized(InvC, Inv_IR, Inv_LT)\\n Opex_a_fix_Boiler_USD = InvC * Inv_OM\\n Capex_Boiler_USD = InvC\\n\\n return Capex_a_Boiler_USD, Opex_a_fix_Boiler_USD, Capex_Boiler_USD\",\n \"def get_product_details(product_url: str) -> dict:\\n def get_available_sizes(postID, sizeStr, product_mainID):\\n \\\"\\\"\\\"\\n List size haye mojood va tamoom shode ro ba API mide\\n POST: https://store.lining.com/ajax/goods_details.htm\\n \\\"\\\"\\\"\\n api_url = 'https://store.lining.com/ajax/goods_details.html'\\n data = {\\n 'postID': postID,\\n 'sizeStr': sizeStr,\\n 'product_mainID': product_mainID\\n }\\n r = get_json(api_url, data=data)\\n onsale_sizes = r['data']['onSale']\\n logging.debug('Onsale Sizes: ' + repr(onsale_sizes))\\n return onsale_sizes\\n\\n def get_pid_from_url(url):\\n \\\"\\\"\\\" ID mahsool ro az URL darmiare \\\"\\\"\\\"\\n return re.findall(r'store.lining.com/shop/goods-(\\\\w+).html\\\\w*', url)[0]\\n\\n def translate_keyword(keyword):\\n \\\"\\\"\\\" tarjome key marboot be desctioption \\\"\\\"\\\"\\n define = {\\n '运动类型': 'Sport Type',\\n '性别': 'Sex',\\n '颜色': 'Color',\\n '鞋透气指数': 'Shoes breathability index',\\n '鞋软硬指数': 'Shoe soft and hard index',\\n }\\n if keyword in define:\\n return define[keyword]\\n else:\\n return keyword\\n ###########################################################\\n\\n details = dict()\\n soup = get_soup(product_url)\\n\\n # product ID\\n pid = get_pid_from_url(product_url)\\n logging.debug('PID: ' + pid)\\n details['pid'] = pid\\n\\n # product name\\n name = soup.find('h1', {'id': 'product_name'}).text.strip()\\n logging.debug('Name: ' + name)\\n details['name'] = name\\n\\n # part number\\n sku = soup.find('span', {'id': 'partNumber'}).find('span', {'class': 'v'}).text.strip()\\n part_number = sku[0:sku.find('-')]\\n logging.debug('Part Number: ' + part_number)\\n details['sku'] = sku\\n details['part_number'] = part_number\\n\\n # price\\n price = soup.find('span', {'id': 'listPrice'}).find('span', {'class': 'v'}).text.strip().replace('￥', '')\\n price_offer = soup.find('span', {'id': 'offerPrice'}).find('span', {'class': 'v'}).text.strip().replace('￥', '')\\n logging.debug('Price: %s [offer]-> %s' % (price, price_offer))\\n details['price'] = price\\n details['price_offer'] = price_offer\\n\\n # all sizes\\n all_sizes = list()\\n for tag in soup.find('div', {'id': 'sizelist'}).find_all('div', 'size-layer'):\\n tag = tag.find('input')\\n # all_size -> [(id, size, status), ...]\\n all_sizes.append(\\n (\\n tag.get('id').replace('size_list_', ''),\\n tag.get('value'),\\n None,\\n )\\n )\\n available_sizes = get_available_sizes(\\n postID=pid,\\n product_mainID=part_number,\\n # first element of all_sizes list\\n # all_size -> [(id, size, status), ...]\\n sizeStr=','.join([s[0] for s in all_sizes]),\\n )\\n # update all sizes status\\n for i in range(len(all_sizes)):\\n if all_sizes[i][1] in available_sizes:\\n all_sizes[i] = (\\n all_sizes[i][0],\\n all_sizes[i][1],\\n 'onsale',\\n )\\n else:\\n all_sizes[i] = (\\n all_sizes[i][0],\\n all_sizes[i][1],\\n 'stockout',\\n )\\n logging.debug('All Sizes: %s' % repr(all_sizes))\\n details['all_sizes'] = all_sizes\\n\\n # description images\\n description_images = list()\\n desc = soup.find('div', {'id': 'PD_desc_picture'})\\n for img in desc.find_all('img'):\\n img = img.get('orginalsrc')\\n logging.debug('description_images[]: ' + img)\\n description_images.append(img)\\n details['description_images'] = description_images\\n\\n # description key/value\\n description = dict()\\n for li in soup.find('ul', {'id': 'p_spec'}).find_all('li'):\\n key = li.find('span', {'class': 't'}).text.replace(':', '').strip()\\n key = translate_keyword(key)\\n value = li.find('span', {'class': 'v'}).text.strip()\\n description[key] = value\\n logging.debug('%s -> %s' % (key, value))\\n details['description'] = description\\n\\n # slider images\\n slider_images = list()\\n for li in soup.find('div', {'class': 'box'}).find_all('li'):\\n img = li.find('img').get('big')\\n logging.debug('slider_images[]: ' + img)\\n slider_images.append(img)\\n details['slider_images'] = slider_images\\n\\n # related products\\n related_products_id = list()\\n for li in soup.find('div', {'id': 'f_litimg'}).find_all('li'):\\n url = li.find('a').get('href')\\n url = 'store.lining.com' + url\\n pid = get_pid_from_url(url)\\n logging.debug('related_products_id[]: %s -> %s' % (pid, url))\\n related_products_id.append(pid)\\n details['related_products_id'] = related_products_id\\n\\n return details\",\n \"def eod_risk_by_underlyer_report(positions, underlyer_positions, domain, headers, pricing_environment):\\n # find books and underlyers\\n books = set([p['bookName'] for p in positions])\\n books.update(underlyer_positions.keys())\\n book_underlyer = {b: set() for b in books}\\n for p in positions:\\n book_underlyer[p['bookName']].add(p['underlyerInstrumentId'])\\n for b, u in underlyer_positions.items():\\n book_underlyer[b].update([l['underlyerInstrumentId'] for l in u.values()])\\n # get quotes\\n underlyers = set()\\n for b in book_underlyer.values():\\n for u in b:\\n underlyers.add(u)\\n underlyers = list(underlyers)\\n quotes = get_underlyer_quotes(underlyers, datetime.now(), domain, headers)\\n yst_quotes = get_underlyer_quotes(underlyers, datetime.now() - timedelta(days=1), domain, headers)\\n # empty report\\n risks = {b: {} for b in books}\\n for b in book_underlyer:\\n for u in book_underlyer[b]:\\n spot = quotes[u]['close']\\n if spot is None or spot == 0:\\n continue\\n spot_change_ratio = (spot - np.float64(yst_quotes.get(u).get('close'))) / spot\\n risks[b][u] = {\\n 'bookName': b,\\n 'underlyerInstrumentId': u,\\n 'underlyerPrice': spot,\\n 'underlyerNetPosition': 0,\\n 'delta': 0,\\n 'deltaCash': 0,\\n 'netDelta': 0,\\n 'deltaDecay': 0,\\n 'deltaWithDecay': 0,\\n 'gamma': 0,\\n 'gammaCash': 0,\\n 'vega': 0,\\n 'theta': 0,\\n 'rho': 0\\n }\\n if not utils.is_nan(spot_change_ratio):\\n risks[b][u]['underlyerPriceChangePercent'] = spot_change_ratio\\n # underlyer positions\\n for b in underlyer_positions.values():\\n for u in b.values():\\n if risks[u['bookId']].get(u['instrumentId'], None) is None:\\n continue\\n r = risks[u['bookId']][u['instrumentId']]\\n spot = u['underlyerPrice']\\n multiplier = u['underlyerInstrumentMultiplier']\\n r['underlyerMultiplier'] = multiplier\\n if u['instrumentId'] == u['underlyerInstrumentId']:\\n r['underlyerNetPosition'] += u['netPosition']\\n else:\\n r['delta'] += u['delta'] / multiplier\\n r['gamma'] += u['gamma'] * spot / 100 / multiplier\\n r['gammaCash'] += u['gamma'] * spot * spot / 100 / multiplier\\n r['vega'] += u['vega'] / 100\\n r['theta'] += u['theta'] / 365\\n r['rho'] += u['rhoR'] / 100\\n # option risks\\n for p in positions:\\n if has_pricing_error(p):\\n continue\\n r = risks[p['bookName']][p['underlyerInstrumentId']]\\n r['delta'] += p['delta']\\n r['deltaDecay'] += p['deltaDecay']\\n r['deltaWithDecay'] += p['deltaWithDecay']\\n r['gamma'] += p['gamma']\\n r['gammaCash'] += p['gammaCash']\\n r['vega'] += p['vega']\\n r['theta'] += p['theta']\\n r['rho'] += p['rho']\\n if r.get('underlyerMultiplier', None) is None:\\n r['underlyerMultiplier'] = p['underlyerMultiplier']\\n\\n for b in risks:\\n for u in risks[b]:\\n r = risks[b][u]\\n if r.get('underlyerMultiplier', None) is None:\\n continue\\n r['netDelta'] = r['delta'] + r['underlyerNetPosition']\\n r['deltaCash'] = r['netDelta'] * r['underlyerPrice'] * r['underlyerMultiplier']\\n r.pop('underlyerMultiplier')\\n # flatten\\n report = []\\n for b in risks:\\n report.extend(list(risks[b].values()))\\n for re in report:\\n re['pricingEnvironment'] = pricing_environment\\n return report\",\n \"def _product_available(self, cr, uid, ids, field_names=None, arg=False, context=None):\\n if not field_names:\\n field_names = []\\n if context is None:\\n context = {}\\n res = {}\\n \\n for product_loc in self.browse(cr, uid, ids):\\n c = context.copy()\\n c.update({ 'states': ('done',), 'what': ('in', 'out'), 'location': product_loc.location_id.id})\\n stock = self.pool.get('product.product').get_product_available(cr, uid, [product_loc.product_id.id], context=c)\\n res[product_loc.id] = stock.get(product_loc.product_id.id, 0.0)\\n return res\",\n \"def calculate_possible_loss(sub_data: pandas.core.frame.DataFrame) -> str:\\n sub_data1 = sub_data.copy()\\n sub_data1 = sub_data1[sub_data1['StockQty']!=0] # remove non-stock day\\n \\n loss_INV = sub_data1['Margin'].mean() \\n loss_NS = sub_data1['NetSales'].mean() \\n loss_QTY = sub_data1['QtySold'].mean()\\n \\n return loss_INV, loss_NS, loss_QTY\",\n \"def sexp(p,n,res_field=RR):\\n assert n>=2, \\\"Carleman matrix must at least be of size 2 to retrieve the coefficients. But given was \\\" + repr(n)\\n CM = p.carleman_matrix(n)\\n ev = [ r[0] for r in CM.charpoly().roots(QQbar) ]\\n assert len(ev) == n, \\\"Carleman matrix must have exactly \\\" + repr(n) + \\\"eigenvalues, but has \\\" + repr(len(ev))\\n\\n #We want to compute:\\n #sum over k: evk^t*(CM-ev1*I)*(CM-ev2*I)*. omit k * (CM-evn*I)/(evk-ev1)*.. omit k *(evk-evn)\\n Char = [0]*n\\n for k in range(n):\\n #here is possibility for improvement of precision\\n #to separate the fractional from the root parts\\n #expanding the product\\n Char[k] = CM - ev[k]*identity_matrix(n)\\n\\n #we want to have the first row of the product of the matrices\\n #thatswhy we mulitply in front with:\\n prod = vector(QQbar,[0,1]+[0]*(n-2))\\n prodwo = [0]*n\\n for k in range(n):\\n prodwo[k]=prod #these are the first terms until k-1\\n\\n #no need to continue\\n if k == n-1:\\n break\\n\\n #and we add the terms starting with k+1\\n for i in range(k+1,n):\\n prodwo[k] = prodwo[k] * Char[i]\\n\\n prod = prod * Char[k]\\n\\n sprodwo = [0]*n\\n for k in range(n):\\n if k==0:\\n sprodwo[k] = ev[k] - ev[1]\\n start = 2\\n else:\\n sprodwo[k] = ev[k] - ev[0]\\n start = 1\\n\\n for i in range(start,n):\\n if i != k:\\n sprodwo[k] = sprodwo[k] * (ev[k] - ev[i])\\n\\n for k in range(n):\\n print ev[k]\\n print prodwo[k][0]/sprodwo[k]\\n print res_field\\n #return lambda t: sum(res_field(ev[k])**t*res_field(prodwo[k][0]/sprodwo[k]) for k in range(n))\\n return [ev,[prodwo[k][0]/sprodwo[k] for k in range(n)]]\",\n \"def sell_products():\\n\\n print \\\"\\\\nInsert the products you want to buy: \\\"\\n print \\\"Press done to finish\\\\n\\\"\\n while True:\\n sell = insert_product_to_sell()\\n if ADD_PRODUCTS.has_key(sell) == True: #Verifies if the product belongs to the dictionary\\n print \\\" Q%.2f \\\" % (ADD_PRODUCTS.get(sell)) #This prints its price respective\\n SAVE_EXISTENT.append(sell) #This adds the product to a list\\n SAVE_PRICE.append(ADD_PRODUCTS[sell]) #This saves the prices in a list\\n elif sell == \\\"done\\\":\\n verify_done()\\n main_menu()\\n elif sell == \\\"gold\\\":\\n CARDS.append(\\\"gold\\\") #This adds the gold card\\n elif sell == \\\"silver\\\":\\n CARDS.append(\\\"silver\\\") #This adds the silver card\\n else:\\n print \\\"\\\\nThis product is not available\\\"\",\n \"def product(self):\\n return None\",\n \"def product(self):\\n return None\",\n \"def one_good_car():\\n return [\\n {\\n \\\"mpg\\\": maintenance_factor(50, False),\\n \\\"annual_miles\\\": 5000,\\n \\\"maintenance\\\": True, # this entry in the dictionary can probably be deleted\\n \\\"lbs_CO2\\\": (11000 / 50) * 19.6,\\n }\\n ]\",\n \"def _r_inv_data(self):\\n return self.sites.precisions\",\n \"def check_primal_and_dual_solutions(self):\\r\\n\\r\\n # Solve primal and dual models\\r\\n self.primal.solve_model(self.m_p)\\r\\n self.dual.solve_model(self.m_d)\\r\\n\\r\\n # Elements to check\\r\\n elements = {\\r\\n 'power - existing thermal':\\r\\n {\\r\\n 'primal': {'id': 'p', 'is_dual': False},\\r\\n 'dual': {'id': 'POWER_OUTPUT_EXISTING_THERMAL', 'is_dual': True}\\r\\n },\\r\\n 'power - existing wind':\\r\\n {\\r\\n 'primal': {'id': 'p', 'is_dual': False},\\r\\n 'dual': {'id': 'POWER_OUTPUT_EXISTING_WIND', 'is_dual': True}\\r\\n },\\r\\n 'power - existing solar':\\r\\n {\\r\\n 'primal': {'id': 'p', 'is_dual': False},\\r\\n 'dual': {'id': 'POWER_OUTPUT_EXISTING_SOLAR', 'is_dual': True}\\r\\n },\\r\\n 'power - existing hydro':\\r\\n {\\r\\n 'primal': {'id': 'p', 'is_dual': False},\\r\\n 'dual': {'id': 'POWER_OUTPUT_HYDRO', 'is_dual': True}\\r\\n },\\r\\n # 'charging power - existing storage':\\r\\n # {\\r\\n # 'primal': {'id': 'p_in', 'is_dual': False},\\r\\n # 'dual': {'id': 'CHARGING_POWER_EXISTING_STORAGE', 'is_dual': True}\\r\\n # },\\r\\n # 'discharging power - existing storage':\\r\\n # {\\r\\n # 'primal': {'id': 'p_out', 'is_dual': False},\\r\\n # 'dual': {'id': 'DISCHARGING_POWER_EXISTING_STORAGE', 'is_dual': True}\\r\\n # },\\r\\n 'power - candidate thermal':\\r\\n {\\r\\n 'primal': {'id': 'p', 'is_dual': False},\\r\\n 'dual': {'id': 'POWER_OUTPUT_CANDIDATE_THERMAL', 'is_dual': True}\\r\\n },\\r\\n 'power - candidate wind':\\r\\n {\\r\\n 'primal': {'id': 'p', 'is_dual': False},\\r\\n 'dual': {'id': 'POWER_OUTPUT_CANDIDATE_WIND', 'is_dual': True}\\r\\n },\\r\\n 'power - candidate solar':\\r\\n {\\r\\n 'primal': {'id': 'p', 'is_dual': False},\\r\\n 'dual': {'id': 'POWER_OUTPUT_CANDIDATE_SOLAR', 'is_dual': True}\\r\\n },\\r\\n 'charging power - candidate storage':\\r\\n {\\r\\n 'primal': {'id': 'p_in', 'is_dual': False},\\r\\n 'dual': {'id': 'CHARGING_POWER_CANDIDATE_STORAGE', 'is_dual': True}\\r\\n },\\r\\n 'discharging power - candidate storage':\\r\\n {\\r\\n 'primal': {'id': 'p_out', 'is_dual': False},\\r\\n 'dual': {'id': 'DISCHARGING_POWER_CANDIDATE_STORAGE', 'is_dual': True}\\r\\n },\\r\\n 'locational marginal prices':\\r\\n {\\r\\n 'primal': {'id': 'POWER_BALANCE', 'is_dual': True},\\r\\n 'dual': {'id': 'lamb', 'is_dual': False}\\r\\n },\\r\\n 'load shedding':\\r\\n {\\r\\n 'primal': {'id': 'p_V', 'is_dual': False},\\r\\n 'dual': {'id': 'LOAD_SHEDDING_POWER', 'is_dual': True}\\r\\n },\\r\\n }\\r\\n\\r\\n print(f\\\"Checking primal and dual solutions\\\")\\r\\n for el_id, e in elements.items():\\r\\n # Absolute, non-zero and max absolute difference\\r\\n absolute_diff, non_zero_diff, max_abs_diff = self.compare_primal_and_dual_components(e)\\r\\n\\r\\n print('---------------------------------------------------------------------------')\\r\\n print(f\\\"Primal component: {e['primal']['id']}, Dual component: {e['dual']['id']}')\\\")\\r\\n print(f'Total keys: {len(absolute_diff)}')\\r\\n print(f\\\"Max absolute difference: {max_abs_diff}\\\")\\r\\n print(f\\\"Non-zero diff: {non_zero_diff}\\\")\",\n \"def get_variable_OM_costs(fs, production_rate, resources, rates, prices={}):\\n\\n # check the units on production_rate\\n if isinstance(production_rate, Expression) and production_rate.is_indexed:\\n production_units = pyunits.get_units(\\n production_rate[production_rate.index_set().first()]\\n )\\n else:\\n production_units = pyunits.get_units(production_rate)\\n\\n try:\\n pyunits.convert(production_units, pyunits.MW)\\n mode = \\\"power\\\"\\n cost_units = pyunits.USD_2018 / pyunits.MWh\\n unit_tag = \\\"$/MWh\\\"\\n except InconsistentUnitsError:\\n try:\\n pyunits.convert(production_units, pyunits.kg / pyunits.s)\\n mode = \\\"hydrogen\\\"\\n cost_units = pyunits.USD_2018 / pyunits.kg\\n unit_tag = \\\"$/kg\\\"\\n except InconsistentUnitsError:\\n raise Exception(\\n \\\"units not compatable, make sure production rate\\\"\\n \\\"has dimensions of power or mass flowrate\\\"\\n )\\n\\n # assert arguments are correct types\\n if type(resources) is not list:\\n raise TypeError(\\\"resources argument must be a list\\\")\\n if type(rates) is not list:\\n raise TypeError(\\\"rates argument must be a list\\\")\\n if type(prices) is not dict:\\n raise TypeError(\\\"prices argument must be a dictionary\\\")\\n\\n # assert lists are the same length\\n if len(resources) != len(rates):\\n raise Exception(\\\"resources and rates must be lists of the same length\\\")\\n\\n # check if flowsheet level costing block exists\\n if not hasattr(fs, \\\"costing\\\"):\\n fs.get_costing(year=\\\"2018\\\")\\n\\n # dictionary of default prices\\n default_prices = {\\n \\\"natural gas\\\": 4.42 * pyunits.USD_2018 / pyunits.MBtu, # $/MMbtu\\n \\\"coal\\\": 51.96 * pyunits.USD_2018 / pyunits.ton,\\n \\\"water\\\": 1.90e-3 * pyunits.USD_2018 / pyunits.gallon,\\n \\\"water treatment chemicals\\\": 550 * pyunits.USD_2018 / pyunits.ton,\\n \\\"ammonia\\\": 300 * pyunits.USD_2018 / pyunits.ton,\\n \\\"SCR catalyst\\\": 150 * pyunits.USD_2018 / pyunits.ft**3,\\n \\\"triethylene glycol\\\": 6.80 * pyunits.USD_2018 / pyunits.gallon,\\n \\\"SCR catalyst waste\\\": 2.50 * pyunits.USD_2018 / pyunits.ft**3,\\n \\\"triethylene glycol waste\\\": 0.35 * pyunits.USD_2018 / pyunits.gallon,\\n \\\"amine purification unit waste\\\": 38 * pyunits.USD_2018 / pyunits.ton,\\n \\\"thermal reclaimer unit waste\\\": 38 * pyunits.USD_2018 / pyunits.ton,\\n }\\n\\n # add entrys from prices to defualt_prices\\n for key in prices.keys():\\n default_prices[key] = prices[key]\\n\\n # raise error if the user included a resource not in default_prices\\n if not set(resources).issubset(default_prices.keys()):\\n raise Exception(\\n \\\"A resource was included that does not contain a \\\"\\n \\\"price. Prices exist for the following resources: \\\"\\n \\\"{}\\\".format(list(default_prices.keys()))\\n )\\n # create list of prices\\n prices = [default_prices[r] for r in resources]\\n\\n # zip rates and prices into a dict accessible by resource\\n resource_rates = dict(zip(resources, rates))\\n resource_prices = dict(zip(resources, prices))\\n\\n # if costing power make vars in fs.costing, if costing hydrogen make vars\\n # in fs.H2_costing\\n if mode == \\\"power\\\":\\n costing = fs.costing\\n elif mode == \\\"hydrogen\\\":\\n fs.H2_costing = Block()\\n costing = fs.H2_costing\\n\\n # make vars\\n costing.variable_operating_costs = Var(\\n fs.time,\\n resources,\\n initialize=1,\\n doc=\\\"variable operating costs\\\",\\n units=cost_units,\\n )\\n\\n costing.other_variable_costs = Var(\\n fs.time,\\n initialize=0,\\n doc=\\\"a variable to include non-standard O&M costs\\\",\\n units=cost_units,\\n )\\n # assume the user is not using this\\n costing.other_variable_costs.fix(0)\\n\\n costing.total_variable_OM_cost = Var(\\n fs.time,\\n initialize=1,\\n doc=\\\"total variable operating and maintenance costs\\\",\\n units=cost_units,\\n )\\n\\n # make constraints\\n @costing.Constraint(fs.time, resources)\\n def variable_cost_rule(c, t, r):\\n return c.variable_operating_costs[t, r] == (\\n pyunits.convert(\\n resource_prices[r] * resource_rates[r][t] / production_rate[t],\\n cost_units,\\n )\\n )\\n\\n if mode == \\\"power\\\" and hasattr(fs.costing, \\\"maintenance_material_cost\\\"):\\n\\n @fs.costing.Constraint(fs.time)\\n def total_variable_cost_rule(c, t):\\n return (\\n c.total_variable_OM_cost[t]\\n == sum(c.variable_operating_costs[t, r] for r in resources)\\n + c.maintenance_material_cost\\n + c.other_variable_costs[t]\\n )\\n\\n else:\\n\\n @costing.Constraint(fs.time)\\n def total_variable_cost_rule(c, t):\\n return (\\n c.total_variable_OM_cost[t]\\n == sum(c.variable_operating_costs[t, r] for r in resources)\\n + c.other_variable_costs[t]\\n )\\n\\n if mode == \\\"power\\\":\\n _log.warning(\\n \\\"The variable fs.costing.maintenance_material_cost \\\"\\n \\\"could not be found, total_variable_cost_rule was \\\"\\n \\\"constructed without in. get_fixed_OM_costs should be\\\"\\n \\\" called before get_variable_OM_costs\\\"\\n )\",\n \"def get_disparity(code, k, w=1):\\n est_price, shares, value, net_worth, roe, excess_earning = estimate_price(code, k, w)\\n cur_price = reader.get_current_price(code)\\n\\n try:\\n disparity = (cur_price / est_price) * 100\\n except:\\n disparity = None\\n return disparity, cur_price, est_price, shares, value, net_worth, roe, excess_earning\",\n \"def update(self, arm, reward, alpha=0.05, l=0.05):\\n\\n # Get context\\n context = self.context.iloc[self.t, :]\\n\\n\\n # Add price\\n price_dict = {}\\n productid_dict = {}\\n \\n for var in context.keys():\\n price_dict[var + '_price'] = context[var] * self.df_arm_dummies.ix[arm, 'price']\\n\\n for i in range(10, 26):\\n productid_dict[var + '_productid_' + str(i)] = context[var] * \\\\\\n self.df_arm_dummies.ix[arm, 'productid_' + str(i)]\\n\\n print(\\\"Price dict is\\\")\\n print(price_dict)\\n print(productid_dict)\\n \\n\\n#Age_price = context.Age * self.df_arm_dummies.ix[arm, 'price']\\n#Agent_Linux_price = self.df_arm_dummies.ix[arm, 'price'] * context.Agent_Linux\\n#Agent_OSX_price = self.df_arm_dummies.ix[arm, 'price'] * context.Agent_OSX\\n#Agent_Windows_price = self.df_arm_dummies.ix[arm, 'price'] * context.Agent_Windows\\n#Agent_mobile_price = self.df_arm_dummies.ix[arm, 'price'] * context.Agent_mobile\\n#\\n#\\n#Language_EN_price = self.df_arm_dummies.ix[arm, 'price'] * context.Language_EN\\n#Language_GE_price = self.df_arm_dummies.ix[arm, 'price'] * context.Language_GE\\n#Language_NL_price = self.df_arm_dummies.ix[arm, 'price'] * context.Language_NL\\n#Referer_Bing_price = self.df_arm_dummies.ix[arm, 'price'] * context.Referer_Bing\\n#Referer_Google_price = self.df_arm_dummies.ix[arm, 'price'] * context.Referer_Google\\n#\\n\\n combined = np.append(context, self.df_arm_dummies.iloc[arm, :])#.reshape(-1, 1)\\n\\n prices = prict_dict.items()\\n\\n # Combine with arm\\n combined = np.append(combined,\\n [Age_price,\\n Agent_Linux_price,\\n Agent_OSX_price,\\n Agent_Windows_price,\\n Agent_mobile_price,\\n Language_EN_price,\\n Language_GE_price,\\n Language_NL_price,\\n Referer_Bing_price,\\n Referer_Google_price\\n ]).reshape(-1, 1)\\n \\n if reward > 0:\\n reward = 1\\n else:\\n reward = -1\\n\\n # Bayes\\n self.B = self.B + np.dot(context, context)\\n \\n self.f = self.f + combined * reward\\n\\n self.mu_hat = np.dot(np.linalg.inv(self.B), self.f)\\n\\n self.mu = min(5, self.mu + 0.1 * (-0.5 + int(bool(reward))))\\n\\n # Update time step\\n self.t += 1\",\n \"def abc_scan(self, code):\\n products = self.env['product.product'].search(['|', ('ean13', '=', code), ('default_code', '=', code)])\\n if products:\\n return {\\n 'type': 'product.product',\\n 'product': self.abc_make_records(products)}\\n picking = self.env['stock.picking'].search_read([('name', '=', code)], ['id'])\\n if picking:\\n return {\\n 'type': 'stock.picking',\\n 'picking': picking[0]\\n }\\n return {'type': 'no hit'}\",\n \"def get_sealed_products_data(\\n self, set_code: str\\n ) -> List[MtgjsonSealedProductObject]:\\n LOGGER.info(f\\\"Getting booster data for {set_code}\\\")\\n products_list = []\\n for sealed_product_name, sealed_product in self.sealed_products.get(\\n set_code.lower(), {}\\n ).items():\\n product_obj = MtgjsonSealedProductObject()\\n product_obj.name = sealed_product_name\\n product_obj.release_date = sealed_product.get(\\\"release_date\\\")\\n\\n try:\\n product_obj.category = getattr(\\n MtgjsonSealedProductCategory,\\n sealed_product.get(\\\"category\\\", \\\"UNKNOWN\\\").upper(),\\n )\\n except AttributeError:\\n product_obj.category = None\\n try:\\n product_obj.subtype = getattr(\\n MtgjsonSealedProductSubtype,\\n sealed_product.get(\\\"subtype\\\", \\\"UNKNOWN\\\").upper(),\\n )\\n except AttributeError:\\n product_obj.subtype = None\\n\\n product_obj.raw_purchase_urls = sealed_product.get(\\\"purchase_url\\\", {})\\n products_list.append(product_obj)\\n\\n for location, identifier in sealed_product.get(\\\"identifiers\\\", {}).items():\\n setattr(product_obj.identifiers, location, identifier)\\n return products_list\",\n \"def _get_conditional_negative_energy(self):\\n return [layer._get_conditional_negative_energy()\\n for layer in self.layers]\",\n \"def init_stock():\\n return {\\\"five\\\":0, \\\"one\\\": 0, \\\"quarter\\\": 25, \\\"dime\\\": 25, \\\"nickel\\\":25}\",\n \"def __init__(self, product_code, description, market_price,\\n rental_price, brand, voltage):\\n Inventory.__init__(self, product_code, description, market_price,\\n rental_price)\\n\\n self.brand = brand\\n self.voltage = voltage\",\n \"def getStockData():\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list 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Dicsount. Buy 1 get 1 or any other discounts"},"document":{"kind":"string","value":"def get_basket_items_discount(self, offer_info, actual_volume, product_prices):\n prod_code = offer_info.base_prod_code\n base_prod_vol = actual_volume.get(prod_code.lower())\n\n discount_basket = []\n\n if base_prod_vol >= offer_info.min_vol:\n offer_on_prod = offer_info.offer_on\n if actual_volume.get(offer_on_prod.lower()):\n print(f\"Base product volume is greater than minimum required volume & product on offer is also available \"\n f\"in cart..\")\n if offer_info.is_limited:\n print(f\"Limited offer..\")\n if prod_code == offer_on_prod:\n # total_allowed_items_on_offer = Limit Volume of base product * (Offer Product Max Volume/Minimum volume of base product)\n total_allowed_items_on_offer = offer_info.limit_vol * (offer_info.offer_prod_volume/offer_info.min_vol)\n max_limit = 1\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n discount_basket.append((prod_code, base_prod_actual_price))\n while max_limit <= total_allowed_items_on_offer:\n discounted_price = (base_prod_actual_price *(offer_info.discount_perc/100))*-1\n discount_basket.append((offer_info.offer_code, discounted_price))\n max_limit += 1\n else:\n total_allowed_items_on_offer = offer_info.limit_vol * (offer_info.offer_prod_volume / offer_info.min_vol)\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n discount_basket.append((prod_code, base_prod_actual_price))\n max_limit = 1\n while max_limit <= total_allowed_items_on_offer:\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\n discounted_price = (offer_onprod_actual_price *(offer_info.discount_perc/100))*-1\n for j in range(0, actual_volume.get(offer_on_prod.lower())):\n discount_basket.append((offer_on_prod, offer_onprod_actual_price))\n discount_basket.append((offer_info.offer_code, discounted_price))\n max_limit += 1\n else:\n print(f\"Unlimited offer..\")\n if prod_code == offer_on_prod:\n if base_prod_vol > offer_info.min_vol:\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n discount_basket.append((prod_code, base_prod_actual_price))\n if i%2 != 0:\n discounted_price = (base_prod_actual_price *(offer_info.discount_perc/100))*-1\n discount_basket.append((offer_info.offer_code, discounted_price))\n else:\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n discount_basket.append((prod_code, base_prod_actual_price))\n else:\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n discount_basket.append((prod_code, base_prod_actual_price))\n\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\n discounted_price = (offer_onprod_actual_price * (offer_info.discount_perc / 100))*-1\n\n for j in range(0, actual_volume.get(offer_on_prod.lower())):\n discount_basket.append((offer_on_prod, offer_onprod_actual_price))\n discount_basket.append((offer_info.offer_code, discounted_price))\n\n\n return discount_basket"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _compute_offers_to_send(self) -> Dict[Tuple[float, float], float]:\n partial_asgt = self._neighbors_values.copy()\n offers = dict()\n\n for limited_asgt in generate_assignment_as_dict([self.variable, self._partner]):\n partial_asgt.update(limited_asgt)\n cost = self._compute_cost(**partial_asgt)\n if self.logger.isEnabledFor(logging.DEBUG):\n self.logger.debug(\n f\"looking for offer : {partial_asgt} - cost {cost}\"\n f\" current {self.current_cost} {self._mode}\"\n )\n\n if (self.current_cost > cost and self._mode == \"min\") or (\n self.current_cost < cost and self._mode == \"max\"\n ):\n offers[(limited_asgt[self.name], limited_asgt[self._partner.name])] = (\n self.current_cost - cost\n )\n return offers","def apply_discounts(self):\n # for each valid discount...\n for discount in list(DiscountTypes):\n # only apply the discount if it is set in the cart\n if(discount in self.cart.discounts):\n getattr(self, discount.value)()","def calculate_adoption_fee(cursor, dog_id):\n query = f\"SELECT Dog.dogID, IF (Dog.surrender_by_animal_control = 1, 0.15 * sum( Expense.amount ), 1.15 * sum( Expense.amount )) \" \\\n f\"FROM Dog LEFT JOIN Expense ON Dog.dogID = Expense.dogID LEFT JOIN Adoption ON Adoption.dog = Dog.dogID \" \\\n f\"WHERE Dog.dogID = {dog_id};\"\n cursor.execute(query)\n query_result = cursor.fetchall()\n return query_result","def decider(wallet):\n found = False\n if len(wallet.get_cards()) == 0:\n return found\n elif len(wallet.get_cards()) == 1:\n card = wallet.get_cards()[0]\n found = list()\n found.append(card.get_issuer())\n found.append(card.get_card_name())\n found.append(-1)\n return found\n \"\"\"\n First, we need to check for any valid SUB. If so, if there's one,\n then that will be selected, otherwise, narrow the options to just \n those with active sign_up_bonus and do the usual process.\n \"\"\"\n sub_cards = list()\n subs = False\n for card in wallet.get_cards():\n sub = card.get_sign_up_bonus()\n if sub.check_active():\n sub_cards.append(card)\n subs = True\n if len(sub_cards) == 1:\n found = list()\n card = sub_cards[0]\n found.append(card.get_issuer())\n found.append(card.get_card_name())\n found.append(0)\n return found\n elif len(sub_cards) > 1:\n subs = True\n category = decider_menu()\n # PayPal is currently a quarterly category on several cards\n paypal = \"\"\n while paypal != \"N\" and paypal != \"Y\":\n paypal = input(\n \"Will you be purchasing through PayPal? (Y/N): \")\n if paypal == \"Y\":\n category = category + \"(PayPal)\"\n break\n elif paypal == \"N\":\n break\n else:\n print(\"Invalid input\")\n main_categories = wallet.get_generic_category_names()\n if category in main_categories:\n best_card = wallet.find_best_for_category(category)\n found = list()\n found.append(best_card.get_issuer())\n found.append(best_card.get_card_name())\n found.append(category)\n value = best_card.check_categories(category)\n if best_card.get_sign_up_bonus().check_active():\n value += best_card.get_sign_up_bonus().get_return_on_spend() * 100\n found.append(value)\n return found\n best = list()\n best.append(0)\n best.append(0)\n \"\"\"\n Here, depending on whether of not there are active sign-up bonuses, the\n function will go through each card in the wallet to find the best value.\n A future goal is implementing the Wallet class, in which I will have a \n dictionary attribute which will contain the best card mapped to its\n category i.e. {\"dining\":AMEX Gold}, and whenever new cards are added, it\n will check then so as to prevent algorithmic backups which occur now.\n \"\"\"\n card_list = wallet.get_cards()\n if subs:\n card_list = sub_cards\n for card in card_list:\n sub = card.get_sign_up_bonus()\n value = card.check_categories(category)\n if \"(\" in category:\n if \"PayPal\" in category:\n category = category[:len(category) - 8]\n if (card.check_categories(\"quarterly\") !=\n card.check_categories(\"else\")):\n value = card.check_categories(\"quarterly\")\n value += card.check_categories(category)\n if \"IHG\" in category:\n if value != 25 * .6:\n value = card.check_categories(\"travel\")\n if subs:\n value += sub.get_return_on_spend() * 100\n if value > best[0]:\n best[0] = value\n best[1] = card\n if subs:\n print(\"Note: This recommendation is made because\"\n \" of a sign-up bonus, not only multipliers!\")\n found = list()\n card = best[1]\n found.append(card.get_issuer())\n found.append(card.get_card_name())\n found.append(category)\n found.append(best[0])\n return found","def cost(books):\n\n num_types = len(set(books))\n assert num_types < 6, \"Discounts indeterminate\"\n return DISCOUNT_TABLE.get(num_types, 0) * 8 * len(books) * 100","def filter_viable_offers(want, have, offers) -> List:\n return [x for x in offers if is_offer_viable(want, have, x) is True]","def duty_free(price: int, discount: int, holiday_cost: int) -> int:\n if holiday_cost == 500:\n return holiday_cost\n\n discount /= 100\n price = holiday_cost / (price * discount)\n price = int(price)\n return price","def discount(self,discountFactor,type='geometric'):\n for e in self.estimators:\n e.discount(discountFactor,type)\n return","def get_applicable_discounts(self, actual_volume):\n\n applicable_discounts = []\n residual = {}\n\n min_vol_criteria, product_vs_offer, base_product_vs_offer_product = self.get_product_vs_offer_and_volume(self.discounts)\n\n for basket_item, volume in actual_volume.items():\n offer_on_product = base_product_vs_offer_product.get(basket_item)\n if min_vol_criteria.get(basket_item):\n if volume >= min_vol_criteria.get(basket_item) and actual_volume.get(offer_on_product):\n applicable_discounts.append(product_vs_offer.get(basket_item))\n else:\n residual[basket_item.upper()] = volume\n else:\n for prod, offering in base_product_vs_offer_product.items():\n if (offering == basket_item) and (prod not in actual_volume):\n residual[basket_item.upper()] = volume\n # else:\n\n\n if \"APPL\" in applicable_discounts and \"APOM\" in applicable_discounts:\n applicable_discounts.remove(\"APOM\")\n residual['OM1'] = actual_volume.get('om1')\n\n return applicable_discounts, residual","def apply_discounts(order_obj):\n all_dedits = order_obj.debits\n other_debit = filter(lambda x: x[\"coll_name\"] != discounts.Discount.coll_name(), all_dedits)\n all_discounts = discounts.get_all()\n valid_discounts = []\n for item_dic in order_obj.items:\n for d in all_discounts:\n item_obj = items.get(coerce_bson_id(item_dic[\"obj_id\"]))\n if item_obj is None: continue\n if discounts.valid_on_item(d, item_obj):\n valid_discounts += [{\n \"obj_id\": d._id,\n \"coll_name\": discounts.Discount.coll_name(),\n \"amount\": discounts.discounted_value(item_obj.price, d),\n }]\n break\n order_obj.debits = other_debit + valid_discounts\n return valid_discounts","def is_offer_viable(want, have, offer) -> bool:\n # lower tier value means higher value\n have_tier = constants.currencies[have][\"tier\"]\n want_tier = constants.currencies[want][\"tier\"]\n conversion_rate = offer[\"conversion_rate\"]\n\n # if `want` is worth more than `have`, the conversion rate cannot be bigger than 1\n # This should be enough to successfully filter out price-fixing offers.\n if have_tier > want_tier and conversion_rate > 1:\n return False\n\n # if `have` is worth more than `have`, the conversion rate cannot be smaller than 1\n # We shouldn't need this case, since conversion rates below 1 will typically\n # result in unprofitable conversions and are therefore ignored anyway.\n # if have_tier < want_tier and conversion_rate < 1:\n # return False\n\n return True","def check_for_offer(self, bid, commodity, limit, actual, quantity, price):\n if bid:\n if len(self.trades[\"buys\"][commodity]) == 0:\n return 0\n else: # tally up how much trying to buy.\n total = 0.0\n total_price = 0.0\n for offer in self.trades[\"buys\"][commodity]:\n total += offer.quantity\n total_price += offer.price\n\n avg_price = total_price / len(self.trades[\"buys\"][commodity])\n\n # if total < limit:\n # #PLACE MORE BIDS.\n return total\n\n else:\n if len(self.trades[\"asks\"][commodity]) == 0:\n return 0\n else: # tally up how much trying to buy.\n total = 0.0\n total_price = 0.0\n for offer in self.trades[\"asks\"][commodity]:\n total += offer.quantity\n total_price += offer.price\n\n avg_price = total_price / len(self.trades[\"asks\"][commodity])\n #\n # if total < limit:\n # #PLACE MORE asks.\n # return total\n # if total < limit:\n # #PLACE MORE asks.\n return total # - limit","def budget_for_necessities():\n print(\"========== Displaying hotel options ==========\")\n for i in range(len(hotel_list)):\n print(\" -- Enter\", i+1, \"to choose -- \")\n hotel_list[i].print_hotel()\n print(\" \")\n while True:\n try:\n hotel_num = int(input(\"Please choose your hotel option (Enter a number between 1 to 7): \"))\n hotel_num -= 1\n if hotel_num in range(len(hotel_list)): break\n except ValueError:\n print(\"Please enter an positive integer. Try again... \")\n while True:\n try:\n hotel_stay = int(input(\"Please enter the duration (in days) of your stay: \"))\n if hotel_stay > 0: break\n except ValueError:\n print(\"Please enter an positive integer. Try again... \")\n user_hotel = hotel_list[hotel_num]\n user_hotel_price = user_hotel.get_price()\n user_hotel_name = user_hotel.get_name()\n # display car option and ask for user input\n print(\"\\n======== Displaying rental car options =========\")\n for i in range(len(car_list)):\n print(\" -- Enter\", i+1, \"to choose -- \")\n car_list[i].print_car()\n print(\" \")\n while True:\n try:\n car_num = int(input(\"Please choose your car rental option (Enter a number between 1 to 6): \"))\n car_num -= 1\n if car_num in range(len(hotel_list)): break\n except ValueError:\n print(\"Please enter an positive integer. Try again... \")\n while True:\n try:\n car_rental_day = int(input(\"Please enter the duration (in days) of your car rental: \"))\n if car_rental_day > 0: break\n except ValueError:\n print(\"Please enter an positive integer. Try again... \")\n # calculate user's total cost for car rental and hotel\n user_car = car_list[car_num]\n user_car_price = user_car.get_price()\n user_car_name = user_car.get_name()\n total_hotel_cost = hotel_stay * user_hotel_price\n total_car_rental_cost = car_rental_day * user_car_price\n print(\"\\n=== Displaying your hotel and car rental information ===\")\n print(\"Hotel: \", user_hotel.get_name())\n print(\"Hotel total cost: $\", total_hotel_cost)\n print(\"Car Rental: \", user_car.get_name())\n print(\"Car rental total cost: $\", total_car_rental_cost)\n print(\" \")\n # calculate remaining budget based on hotel and car's cost and/or ask for higher budget\n user_budget.calculate_new_budget(total_hotel_cost + total_car_rental_cost)\n print(\" \")\n return total_hotel_cost, total_car_rental_cost, user_hotel_name, user_car_name","def calc_earning(self, data=None):\n result = Result()\n if data is None:\n data = self.security\n self.calcDecision()\n first_purchase_method = self.check_first_purchase_method()\n for i in np.arange(len(data['Close'])):\n if data['FinalDecision'].iloc[i] is None:\n pass\n elif data['FinalDecision'].iloc[i] == TransactionType.BUY:\n if data['FinalDecision'].iloc[i-1] == TransactionType.BUY:\n pass\n else:\n if (self.buys_made + self.sells_made) == 0:\n if first_purchase_method == FirstTransactionType.INIT_CAPITAL:\n self.shares_own = int((self.init_capital/data['Close'].iloc[i]))\n self.buys_made += 1\n elif first_purchase_method == FirstTransactionType.STOCK_QUANTITY:\n self.shares_own = self.stock_quantity\n self.buys_made += 1\n else:\n self.shares_own = int(self.final_capital / data['Close'].iloc[i])\n self.final_capital = self.final_capital % data['Close'].iloc[i]\n #print(self.shares_own)\n\n elif data['FinalDecision'].iloc[i] == TransactionType.SELL:\n if data['FinalDecision'].iloc[i-1] == TransactionType.SELL:\n pass\n else:\n if (self.buys_made + self.sells_made) == 0:\n pass\n else:\n self.final_capital += self.shares_own * data['Close'].iloc[i]\n self.shares_own = 0\n self.sells_made +=1\n #Checar si es el momento mas alto o bajo de ganancias\n if self.shares_own == 0:\n if (self.highest_point is None\n or self.highest_point < self.final_capital):\n self.highest_point = self.final_capital\n if (self.lowest_point is None\n or self.lowest_point > self.final_capital\n or self.lowest_point == 0):\n self.lowest_point = self.final_capital\n else:\n if (self.highest_point is None\n or self.highest_point < (self.shares_own * data['Close'].iloc[i])):\n self.highest_point = self.final_capital\n if (self.lowest_point is None\n or self.lowest_point > (self.shares_own * data['Close'].iloc[i])\n or self.lowest_point == 0):\n self.lowest_point = self.final_capital\n self.calcRealFinalCapital()\n self.calcDiferencePercentage()","def get_basket_with_discounts(self, actual_volume, applicable_discounts, residual):\n\n # product_vs_min_volume, product_offer = self.get_product_vs_offer_and_volume(self.discounts)\n pr = Product('items')\n product_prices = pr.product_meta\n\n basket = []\n\n for discount in applicable_discounts:\n offer_info = Offer('discount', discount)\n\n if offer_info.offer_type.lower() == \"discount\":\n temp_basket = self.get_basket_items_discount(offer_info, actual_volume, product_prices)\n for item in temp_basket:\n basket.append(item)\n elif offer_info.offer_type.lower() == \"pricedrop\":\n temp_basket = self.get_basket_items_pricedrop(offer_info, actual_volume, product_prices)\n for item in temp_basket:\n basket.append(item)\n\n if residual:\n for product, volume in residual.items():\n for i in range(0, volume):\n basket.append((product, product_prices.get(product.lower()).get('price')))\n\n return basket","async def _create_offer_for_ids(\n self, offer: Dict[int, int]\n ) -> Tuple[bool, Optional[TradeRecord], Optional[str]]:\n spend_bundle = None\n try:\n for id in offer.keys():\n amount = offer[id]\n wallet_id = uint32(int(id))\n wallet = self.wallet_state_manager.wallets[wallet_id]\n if isinstance(wallet, CCWallet):\n balance = await wallet.get_confirmed_balance()\n if balance < abs(amount) and amount < 0:\n raise Exception(f\"insufficient funds in wallet {wallet_id}\")\n if amount > 0:\n if spend_bundle is None:\n to_exclude: List[Coin] = []\n else:\n to_exclude = spend_bundle.removals()\n zero_spend_bundle: Optional[\n SpendBundle\n ] = await wallet.generate_zero_val_coin(False, to_exclude)\n\n if zero_spend_bundle is None:\n raise Exception(\n \"Failed to generate offer. Zero value coin not created.\"\n )\n if spend_bundle is None:\n spend_bundle = zero_spend_bundle\n else:\n spend_bundle = SpendBundle.aggregate(\n [spend_bundle, zero_spend_bundle]\n )\n\n additions = zero_spend_bundle.additions()\n removals = zero_spend_bundle.removals()\n zero_val_coin: Optional[Coin] = None\n for add in additions:\n if add not in removals and add.amount == 0:\n zero_val_coin = add\n new_spend_bundle = await wallet.create_spend_bundle_relative_amount(\n amount, zero_val_coin\n )\n else:\n new_spend_bundle = await wallet.create_spend_bundle_relative_amount(\n amount\n )\n elif isinstance(wallet, Wallet):\n if spend_bundle is None:\n to_exclude = []\n else:\n to_exclude = spend_bundle.removals()\n new_spend_bundle = await wallet.create_spend_bundle_relative_chia(\n amount, to_exclude\n )\n else:\n return False, None, \"unsupported wallet type\"\n if new_spend_bundle is None or new_spend_bundle.removals() == []:\n raise Exception(f\"Wallet {id} was unable to create offer.\")\n if spend_bundle is None:\n spend_bundle = new_spend_bundle\n else:\n spend_bundle = SpendBundle.aggregate(\n [spend_bundle, new_spend_bundle]\n )\n\n if spend_bundle is None:\n return False, None, None\n\n now = uint64(int(time.time()))\n trade_offer: TradeRecord = TradeRecord(\n confirmed_at_index=uint32(0),\n accepted_at_time=None,\n created_at_time=now,\n my_offer=True,\n sent=uint32(0),\n spend_bundle=spend_bundle,\n tx_spend_bundle=None,\n additions=spend_bundle.additions(),\n removals=spend_bundle.removals(),\n trade_id=std_hash(spend_bundle.name() + bytes(now)),\n status=uint32(TradeStatus.PENDING_ACCEPT.value),\n sent_to=[],\n )\n return True, trade_offer, None\n except Exception as e:\n tb = traceback.format_exc()\n self.log.error(f\"Error with creating trade offer: {type(e)}{tb}\")\n return False, None, str(e)","def is_satisfied(self, offer, basket):\n covered_ids = []\n for line in basket.all_lines():\n if not line.is_available_for_offer_discount(offer):\n continue\n product = line.product\n if self.can_apply_condition(line) and product.id not in covered_ids:\n covered_ids.append(product.id)\n if len(covered_ids) >= self.value:\n return True\n return False","def apply_20percent_shipping_offer(self):\n product_range = Range.objects.create(\n name=\"All products range\", includes_all_products=True\n )\n benefit = Benefit.objects.create(\n range=product_range, type=Benefit.SHIPPING_PERCENTAGE, value=20\n )\n offer = factories.create_offer(product_range=product_range, benefit=benefit)\n Applicator().apply_offers(self.basket, [offer])\n return offer","def test_market_1_2(self):\n\n def check_1_2(buyers: List[float], sellers: List[float], expected_num_of_deals: int,\n expected_prices: List[float]):\n market = Market([\n AgentCategory(\"buyer\", buyers),\n AgentCategory(\"seller\", sellers),\n ])\n ps_recipe = [1, 2]\n self._check_market(market, ps_recipe, expected_num_of_deals, expected_prices)\n\n check_1_2(buyers=[9], sellers=[-4, -3],\n expected_num_of_deals=0, expected_prices=[9, -4.5])\n check_1_2(buyers=[9, 8, 7, 6], sellers=[-6, -5, -4, -3, -2, -1],\n expected_num_of_deals=1, expected_prices=[8, -4])\n check_1_2(buyers=[9, 8], sellers=[-4, -3, -2, -1],\n expected_num_of_deals=1, expected_prices=[8, -4])\n check_1_2(buyers=[9, 8], sellers=[-6, -3, -2, -1],\n expected_num_of_deals=1, expected_prices=[8, -4])\n check_1_2(buyers=[9, 8], sellers=[-4, -3, -2, -1],\n expected_num_of_deals=1, expected_prices=[8, -4])\n\n # PRICE CROSSES ZERO AT FIRST PHASE\n check_1_2(buyers=list(range(20)), sellers=[-3, -2, -1],\n expected_num_of_deals=1, expected_prices=[18, -9])","def base_offers_list():\n offers_list = Offers()\n offers_list.add_new_offer('Offer1', 'Baked Beans', 3, 1)\n offers_list.add_new_offer('Offer2', 'Sardines', 1, 0.25)\n return offers_list","def _find_best_offer(\n self, all_offers: List[Tuple[str, Dict]]\n ) -> Tuple[List, float]:\n bests, best_gain = [], 0\n\n for partner, offers in all_offers:\n partial_asgt = self._neighbors_values.copy()\n current_partner = self._neighbor_var(partner)\n\n # Filter out the constraints linking those two variables to avoid\n # counting their cost twice.\n shared = find_dependent_relations(current_partner, self._constraints)\n concerned = [rel for rel in self._constraints if rel not in shared]\n\n for (val_p, my_offer_val), partner_local_gain in offers.items():\n partial_asgt.update({partner: val_p, self.variable.name: my_offer_val})\n\n # Then we evaluate the agent constraint's for the offer\n # and add the partner's local gain.\n cost = assignment_cost(partial_asgt, concerned)\n global_gain = self.current_cost - cost + partner_local_gain\n\n if (global_gain > best_gain and self._mode == \"min\") or (\n global_gain < best_gain and self._mode == \"max\"\n ):\n bests = [(val_p, my_offer_val, partner)]\n best_gain = global_gain\n elif global_gain == best_gain:\n bests.append((val_p, my_offer_val, partner))\n\n return bests, best_gain","def decide_trade_plan(self):\n self.trade_amount = 5\n most_demanded_goods = self.home.market.goods_demand_ratio(day_range=1)\n most_demanded_goods = sorted(most_demanded_goods.items(), key=lambda i: i[1], reverse=True)\n\n # if we already had a trade good, refresh ideal inventory\n if self.trade_good:\n self.update_ideal_inventory()\n\n if DEBUG: print(\"Finding a Good to trade:\")\n\n for good, demand in most_demanded_goods:\n if demand > 0:\n # find nearby provinces where this has inventory and the price is lower\n price_at_home = self.home.market.mean_price(good)\n if DEBUG: print(\"Good: {}, Demand: {}, Price: ${}\".format(good.title, demand, price_at_home))\n neighboring_markets = [p.market for p in self.location.owned_neighbors]\n neighboring_markets = [m for m in neighboring_markets if m.supply_for(good) > self.trade_amount]\n neighboring_markets.sort(key=lambda m: m.supply_for(good), reverse=True)\n\n if len(neighboring_markets) > 0:\n # we found places where this good is cheaper and in inventory\n target = neighboring_markets[0].location\n price_at_target = target.market.mean_price(good)\n\n # only trade with prices where we can make money\n if price_at_home > price_at_target:\n offset = 0\n if good is Good.bread:\n offset = 1\n self.inventory.set_ideal(good, self.trade_amount + offset)\n self.trade_location = target\n if DEBUG:\n print(\"\\tTarget: {}, Supply: {}, Price: ${}, Price at home: ${}\".format(\n self.trade_location.name,\n self.trade_location.market.supply_for(good),\n self.trade_location.market.mean_price(good),\n price_at_home)\n )\n self.trade_good = good\n return\n else:\n if DEBUG: print(\"\\tPrice is higher at target (home: ${} target: ${})\".format(price_at_home, price_at_target))\n else:\n if DEBUG: print(\"\\tNo markets selling {} found\".format(good))","def options_to_withdraw(self, amount):\n counter = PaperMoneyCounter() # aux class\n options = [] # options to withdraw\n remaining_cash = 0 # aux var\n\n if (amount % 20 == 0 or amount % 50 == 0) and (amount <= 1000): # is it allowed to withdraw?\n # prioritizing 100-dollar bills\n qtt_100s = counter.how_many_100s(amount)\n remaining_cash = counter.remaining_cash_without_100s(amount)\n\n qtt_50s = counter.how_many_50s(remaining_cash)\n remaining_cash = counter.remaining_cash_without_50s(remaining_cash)\n\n qtt_20s = counter.how_many_20s(remaining_cash)\n remaining_cash = counter.remaining_cash_without_20s(remaining_cash)\n\n if counter.cash(qtt_100s, qtt_50s, qtt_20s) == amount:\n options.append([int(qtt_100s), int(qtt_50s), int(qtt_20s)])\n\n # prioritizing 50-dollar bills\n qtt_100s = 0\n\n qtt_50s = counter.how_many_50s(amount)\n remaining_cash = counter.remaining_cash_without_50s(amount)\n\n qtt_20s = counter.how_many_20s(remaining_cash)\n remaining_cash = counter.remaining_cash_without_20s(remaining_cash)\n\n if counter.cash(qtt_100s, qtt_50s, qtt_20s) == amount:\n if not(options[0] == [qtt_100s, qtt_50s, qtt_20s]):\n options.append([int(qtt_100s), int(qtt_50s), int(qtt_20s)])\n\n # prioritizing 20-dollar bills\n qtt_100s = 0\n\n qtt_50s = 0\n\n qtt_20s = counter.how_many_20s(amount)\n\n if counter.cash(qtt_100s, qtt_50s, qtt_20s) == amount:\n if not(options[0] == [qtt_100s, qtt_50s, qtt_20s]):\n if not(options[1] == [qtt_100s, qtt_50s, qtt_20s]):\n options.append([int(qtt_100s), int(qtt_50s), int(qtt_20s)])\n\n return options\n\n return None # if it wasn't allowed to withdraw","def option_price(volatilities,\n strikes,\n expiries,\n spots=None,\n forwards=None,\n discount_factors=None,\n is_call_options=None,\n dtype=None,\n name=None):\n if (spots is None) == (forwards is None):\n raise ValueError('Either spots or forwards must be supplied but not both.')\n\n with tf.compat.v1.name_scope(\n name,\n default_name='option_price',\n values=[\n forwards, strikes, volatilities, expiries, discount_factors,\n is_call_options\n ]):\n strikes = tf.convert_to_tensor(strikes, dtype=dtype, name='strikes')\n volatilities = tf.convert_to_tensor(\n volatilities, dtype=dtype, name='volatilities')\n expiries = tf.convert_to_tensor(expiries, dtype=dtype, name='expiries')\n\n if discount_factors is None:\n discount_factors = tf.convert_to_tensor(\n 1.0, dtype=dtype, name='discount_factors')\n else:\n discount_factors = tf.convert_to_tensor(\n discount_factors, dtype=dtype, name='discount_factors')\n\n if forwards is not None:\n forwards = tf.convert_to_tensor(forwards, dtype=dtype, name='forwards')\n else:\n spots = tf.convert_to_tensor(spots, dtype=dtype, name='spots')\n forwards = spots / discount_factors\n\n sqrt_var = volatilities * tf.math.sqrt(expiries)\n d1 = (tf.math.log(forwards / strikes) + sqrt_var * sqrt_var / 2) / sqrt_var\n d2 = d1 - sqrt_var\n undiscounted_calls = forwards * _ncdf(d1) - strikes * _ncdf(d2)\n if is_call_options is None:\n return discount_factors * undiscounted_calls\n undiscounted_forward = forwards - strikes\n undiscounted_puts = undiscounted_calls - undiscounted_forward\n predicate = tf.broadcast_to(is_call_options, tf.shape(undiscounted_calls))\n return discount_factors * tf.where(predicate, undiscounted_calls,\n undiscounted_puts)","def buyEssentials(self, amount=5): \n ls = self.location.getEssentialList()\n print(f\"{bcolors.WHITE}\\nGreat job so far! Now, it's time to arm yourself with some essentials you would need to survive in the {self.location.worldType}.{bcolors.ENDC}\")\n print(f\"\\n{bcolors.CYAN}Following are the essential items for {self.location.worldName}. Please choose a minimum of 3 items to proceed.{bcolors.ENDC}\")\n outputList = [str(i+1) + \". \" + ls[i] + \"\\n\" for i in range(len(ls))]\n print(f\"\\n{bcolors.CYAN}{''.join(outputList)}{bcolors.ENDC}\")\n sizeEssentialList = len(ls)\n essentialsList = []\n\n \n choiceInput = False\n while choiceInput is False:\n choices = input(f\"{bcolors.CYAN}Input your selection as numbers 1, 2, 3, 4, or 5 separated by comma: {bcolors.ENDC}\")\n choiceInput = True\n choices = choices.split(',')\n for choice in choices:\n if choice not in ('1', '2', '3', '4', '5', 'quit', 'QUIT', 'Quit'):\n print(f\"\\n{bcolors.PINK}Please enter a valid Input{bcolors.ENDC}\\n\")\n choiceInput = False\n break\n \n for choice in choices:\n if choice.capitalize() == \"Quit\":\n # User input \"Quit\" at this stage. So, just quit the game.\n return choices\n \n\n try:\n # Convert input to integer for index in essentialList item\n choices = [int(i) for i in choices]\n except ValueError:\n # If input is not a number, Quit gracefully!\n print(\"Input is not a number. Quit\")\n return essentialsList\n\n if max(choices) > sizeEssentialList:\n print(f\"Invalid input! Input is not in essentialList\")\n return essentialsList\n\n for j in choices:\n if self.spendCoin(amount):\n essentialsList.append(ls[j-1])\n else:\n print(f\"You don't have enough money to buy {j}. You only have {self.coins} coins left.\")\n break\n self.assets = essentialsList\n print(f\"\\n{bcolors.WHITE}Thank you for buying the essentials. Now you are officially ready to enter into the {self.location.worldType}.\\nHere is your current asset bag with essential items and the available coins.{bcolors.ENDC}\")\n print(f\"\\n{bcolors.YELLOW}Asset Bag Contents: {self.assets}\\nCoins: {self.coins}{bcolors.ENDC}\")\n\n return self.assets","def test_market_1_1(self):\n\n def check_1_1(buyers: List[float], sellers: List[float], expected_num_of_deals: int,\n expected_prices: List[float]):\n market = Market([\n AgentCategory(\"buyer\", buyers),\n AgentCategory(\"seller\", sellers),\n ])\n ps_recipe = [1, 1]\n self._check_market(market, ps_recipe, expected_num_of_deals, expected_prices)\n\n check_1_1(buyers=[9], sellers=[-4],\n expected_num_of_deals=0, expected_prices=[None, None])\n check_1_1(buyers=[9, 8], sellers=[-4],\n expected_num_of_deals=0, expected_prices=[None, None])\n check_1_1(buyers=[9], sellers=[-4, -3],\n expected_num_of_deals=1, expected_prices=[4, -4])\n check_1_1(buyers=[9, 8], sellers=[-4, -3],\n expected_num_of_deals=1, expected_prices=[8, -8])\n\n # ALL POSITIVE VALUES:\n check_1_1(buyers=[4, 3], sellers=[9, 8],\n expected_num_of_deals=1, expected_prices=[3, -3])\n\n # ALL NEGATIVE VALUES:\n check_1_1(buyers=[-4, -3], sellers=[-9, -8],\n expected_num_of_deals=0, expected_prices=[None, None])\n\n # LARGER EXAMPLE\n check_1_1(buyers=[19, 17, 15, 13, 11, 9], sellers=[-12, -10, -8, -6, -4, -2],\n expected_num_of_deals=4, expected_prices=[11, -11])","def dp_all_td(foods, done_count, cal_left, pro_left, fat_left, carb_left):\n # using a tolerance of 50 cal/5 carb/fat/pro\n if cal_left < 50 and pro_left < 5 and fat_left < 5 and carb_left < 5:\n return {}\n\n # done going through all the foods\n if len(foods) <= done_count:\n return {0: 9999999}\n\n # need everything in ints for data struct\n cal_left = int(cal_left)\n pro_left = int(pro_left)\n fat_left = int(fat_left)\n carb_left = int(carb_left)\n\n # try to use solution already calculated\n try:\n foods_used_a = \\\n dp[(str(done_count + 1) + '-'\n + str(cal_left) + '-'\n + str(pro_left) + '-'\n + str(fat_left) + '-'\n + str(carb_left))]\n except KeyError:\n # calculate scenario where you don't use current food\n foods_used_a = dp_all_td(\n foods, done_count + 1, cal_left, pro_left, fat_left, carb_left)\n\n # if current food violates reqs, then don't bother calc-ing using it\n if ((cal_left - foods[done_count]['calories']) < -50\n or (pro_left - foods[done_count]['protein']) < -5\n or (fat_left - foods[done_count]['fat']) < -5\n or (carb_left - foods[done_count]['carbs']) < -5):\n return foods_used_a\n\n # try to use solution already calculated\n try:\n foods_used_b = \\\n dp[(str(done_count) + '-'\n + str(cal_left - foods[done_count]['calories']) + '-'\n + str(pro_left - foods[done_count]['protein']) + '-'\n + str(fat_left - foods[done_count]['fat']) + '-'\n + str(carb_left - foods[done_count]['carbs']))]\n except KeyError:\n # calculate for scenario where you use the current food\n foods_used_b = dp_all_td(\n foods,\n done_count,\n cal_left - foods[done_count]['calories'],\n pro_left - foods[done_count]['protein'],\n fat_left - foods[done_count]['fat'],\n carb_left - foods[done_count]['carbs'])\n try:\n foods_used_b[done_count] += 1\n except KeyError:\n foods_used_b[done_count] = 1\n\n # store cheapest, then return it\n if cost(foods, foods_used_a) > cost(foods, foods_used_b):\n dp[(str(done_count) + '-'\n + str(cal_left) + '-'\n + str(pro_left) + '-'\n + str(fat_left) + '-'\n + str(carb_left))] = foods_used_b\n return foods_used_b\n dp[(str(done_count) + '-'\n + str(cal_left) + '-'\n + str(pro_left) + '-'\n + str(fat_left) + '-'\n + str(carb_left))] = foods_used_a\n return foods_used_a","def evaluate_power(soldier_list: List[Soldier]):\n inf_count = 0\n inf_avg_weapon = 0.0\n inf_avg_armor = 0.0\n arc_count = 0\n arc_avg_weapon = 0.0\n arc_avg_armor = 0.0\n cvl_count = 0\n cvl_avg_weapon = 0.0\n cvl_avg_armor = 0.0\n \n for soldier in soldier_list:\n ################################# YOUR CODE HERE #################################\n if soldier.typecode == \"ARC\":\n arc_count += 1\n arc_avg_armor += soldier.armor\n arc_avg_weapon += soldier.weapon\n elif soldier.typecode == \"INF\":\n inf_count += 1\n inf_avg_armor += soldier.armor\n inf_avg_weapon += soldier.weapon\n elif soldier.typecode == \"CVL\":\n cvl_count += 1\n cvl_avg_armor += soldier.armor\n cvl_avg_weapon += soldier.weapon\n if arc_count != 0:\n arc_avg_armor /= arc_count\n arc_avg_weapon /= arc_count\n\n if cvl_count != 0:\n cvl_avg_armor /= cvl_count\n cvl_avg_weapon /= cvl_count\n\n if inf_count != 0:\n inf_avg_armor /= inf_count\n inf_avg_weapon /= inf_count\n ##################################################################################\n return (inf_count, inf_avg_weapon, inf_avg_armor), (arc_count, arc_avg_weapon, arc_avg_armor), (cvl_count, cvl_avg_weapon, cvl_avg_armor)","def prep_visualize_cost(self, dat):\n # Assume if null then there are no in-app purchases since all others have a value\n dat['In-app Purchases'].fillna('0', inplace = True)\n\n # An app is only actually free if you cant buy anything once in it\n dat['true_free'] = (dat.Price == 0) & (dat['In-app Purchases'] == '0')\n\n # Look at only if there is a base cost first and see if rating difference exists\n\n print('Investigate differences in ratings by free vs. paid App')\n plt.hist(dat[dat.true_free == True]['Average User Rating'].dropna().values, label='Free', color = 'green')\n plt.hist(dat[(dat.true_free == False) & (dat.Price != 0)]['Average User Rating'].dropna().values, label='Paid')\n\n plt.legend(loc='upper right')\n plt.show()\n\n print('Many Apps included pain content within them. These should not truly be considered \"free\"')\n\n plt.hist(dat[dat.true_free == False]['Average User Rating'].dropna().values, label='Paid')\n plt.hist(dat[dat.true_free == True]['Average User Rating'].dropna().values, label='Free', color = 'green')\n\n plt.legend(loc='upper right')\n plt.show()\n\n print('Which are truly best?')\n\n non_true_free_rating = dat[dat.true_free == False]['Average User Rating'].dropna().mean()\n in_app_purchases = dat[(dat.true_free == False) & (dat.Price == 0)]['Average User Rating'].dropna().mean()\n true_free_rating = dat[dat.true_free == True]['Average User Rating'].dropna().mean()\n zero_cost_rating = dat[dat.Price == 0]['Average User Rating'].dropna().mean()\n paid_rating = dat[dat.Price != 0]['Average User Rating'].dropna().mean()\n\n non_true_free_rating_cnt = dat[dat.true_free == False]['Average User Rating'].dropna().shape[0]\n in_app_purchases_cnt = dat[(dat.true_free == False) & (dat.Price == 0)]['Average User Rating'].dropna().shape[0]\n true_free_rating_cnt = dat[dat.true_free == True]['Average User Rating'].dropna().shape[0]\n zero_cost_rating_cnt = dat[dat.Price == 0]['Average User Rating'].dropna().shape[0]\n paid_rating_cnt = dat[dat.Price != 0]['Average User Rating'].dropna().shape[0]\n\n plt.bar(['Zero Cost','Paid App'],\n [zero_cost_rating,paid_rating])\n plt.ylim((3.8,4.18))\n plt.xticks(rotation = 'vertical')\n plt.show()\n\n plt.bar(['Truly Free','Any Cost'],\n [true_free_rating,non_true_free_rating])\n plt.ylim((3.8,4.18))\n plt.xticks(rotation = 'vertical')\n plt.show()\n\n plt.bar(['Zero Cost','Truly Free','Paid App','Any Cost','In app-purchases'],\n [zero_cost_rating,true_free_rating,paid_rating,non_true_free_rating, in_app_purchases])\n plt.ylim((3.8,4.18))\n plt.xticks(rotation = 'vertical')\n plt.show()\n\n N = 5\n\n ind = np.arange(N) # the x locations for the groups\n width = 0.35 # the width of the bars\n\n fig, ax = plt.subplots()\n rects1 = ax.bar(ind, [zero_cost_rating,true_free_rating,paid_rating,non_true_free_rating, in_app_purchases], \n width, color='r')\n\n\n # add some text for labels, title and axes ticks\n ax.set_ylabel('Scores')\n ax.set_title('Scores by group and gender')\n ax.set_xticks(ind + width / 2)\n ax.set_xticklabels(['Zero Cost','Truly Free','Paid App','Any Cost','In app-purchases'])\n\n plt.ylim((3.85,4.18))\n\n counts = [zero_cost_rating_cnt,true_free_rating_cnt,paid_rating_cnt,non_true_free_rating_cnt, in_app_purchases_cnt]\n\n for x, rect in zip(counts, rects1):\n height = rect.get_height()\n ax.text(rect.get_x() + rect.get_width()/2., height,\n 'n = ' + str(x),\n ha='center', va='bottom')\n\n plt.show()","def get_discounts(table):\n\n discounts_money_spent = {}\n discount_levels = [100, 1000, 5000]\n discount_percents = [\"2%\", \"5%\", \"10%\"]\n\n customer_id_index = 2\n amount_sold_index = 4\n id_index = 0\n product_id_index = 3\n name_index = 1\n price_index = 3\n\n store_table = store.get_table()\n store.check_table(store_table)\n\n crm_table = crm.get_table('model/crm/customers.csv')\n\n for record in table:\n product_id = record[product_id_index]\n amount_sold = record[amount_sold_index]\n customer_id = record[customer_id_index]\n\n for customer in crm_table:\n customer_name = customer[name_index]\n client_id = customer[id_index]\n\n if customer_id == client_id:\n\n for game in store_table:\n game_id = game[id_index]\n price = game[price_index]\n if product_id == game_id:\n money_spent = int(price) * int(amount_sold)\n \n if customer_name in discounts_money_spent:\n discounts_money_spent[customer_name] += int(money_spent)\n else:\n discounts_money_spent[customer_name] = int(money_spent)\n \n discounts = {}\n\n for customer_name in discounts_money_spent:\n money_spent = discounts_money_spent[customer_name]\n if money_spent >= discount_levels[0] and money_spent < discount_levels[1]:\n discount = discount_percents[0]\n elif money_spent >= discount_levels[1] and money_spent < discount_levels[2]:\n discount = discount_percents[1]\n elif money_spent >= discount_levels[2]:\n discount = discount_percents[2]\n else:\n discount = \"0%\"\n\n discounts[customer_name] = discount\n\n return discounts","def dp_all(foods, cal_goal, pro_goal, carb_goal, fat_goal):\n costs = init_four_d_array((cal_goal, pro_goal, carb_goal, fat_goal),\n 999999999)\n foods_used = init_four_d_array((cal_goal, pro_goal, carb_goal, fat_goal),\n {})\n\n for i in range(cal_goal):\n for j in range(pro_goal):\n for k in range(carb_goal):\n for l in range(fat_goal):\n for n in range(len(foods)):\n food = foods[n]\n if (int(food['calories']) > i\n or int(food['protein']) > j\n or int(food['carbs']) > k\n or int(food['fat']) > l):\n continue\n if (costs[i - int(food['calories'])]\n [j - int(food['protein'])]\n [k - int(food['carbs'])]\n [l - int(food['fat'])]\n == 999999999):\n prev_cost = 0\n prev_foods_used = {}\n else:\n prev_cost = (macros[i - int(food['calories'])]\n [j - int(food['protein'])]\n [j - int(food['carbs'])]\n [j - int(food['fat'])])\n prev_foods_used = \\\n (foods_used[i - int(food['calories'])]\n [j - int(food['protein'])]\n [k - int(food['carbs'])]\n [l - int(food['fat'])]).copy()\n new_cal = calories(\n foods, prev_foods_used) + food['calories']\n new_pro = protein(\n foods, prev_foods_used) + food['protein']\n new_car = carbs(\n foods, prev_foods_used) + food['protein']\n new_fat = fat(\n foods, prev_foods_used) + food['protein']\n if (costs[i][j] > prev_cost + food['serving_cost']\n and new_cal > i - 20 and new_cal < i + 10\n and new_pro < j + 5 and new_pro < j + 5\n and new_car < j + 5 and new_car < j + 5\n and new_fat < j + 5 and new_fat < j + 5):\n costs[i][j][k][l] = prev_cost + \\\n food['serving_cost']\n try:\n prev_foods_used[n] += 1\n except KeyError:\n prev_foods_used[n] = 1\n foods_used[i][j][k][l] = prev_foods_used\n return foods_used[cal_goal - 1][pro_goal - 1][carb_goal - 1][fat_goal - 1]","def _get_discount(self):\n\n # For every 2 PENS, one free discount\n number_of_pens = len([x for x in self._products if x.code == 'PEN'])\n discount = 5.0 * int(number_of_pens / 2)\n\n # If there are more than 3 T-Shirts in the basket, 5 EUR of discount in every of them (25%)\n number_of_tshirts = len([x for x in self._products if x.code == 'TSHIRT'])\n if number_of_tshirts >= 3:\n discount += 5.0 * number_of_tshirts\n\n return discount","def search_for_contracts(self, market=None, buy_sell=None, type_=None, contracts=\"All\"):\n if not contracts:\n contracts = []\n if not type_:\n pass\n elif type_.lower() in ['yes', 'long'] and buy_sell == 'buy':\n type_ = {'long': 'BestBuyYesCost'}\n elif type_.lower() in ['no', 'short'] and buy_sell == 'buy':\n type_ = {'short': 'BestBuyNoCost'}\n elif type_.lower() in ['yes', 'long'] and buy_sell == 'sell':\n type_ = {'long': 'BestSellYesCost'}\n elif type_.lower() in ['no', 'short'] and buy_sell == 'sell':\n type_ = {'short': 'BestSellNoCost'}\n \n if not market:\n market_links = [(\"us_election\", 'https://www.predictit.org/api/marketdata/category/6'), (\"us_politics\", 'https://www.predictit.org/api/marketdata/category/13'), (\"world_politics\", 'https://www.predictit.org/api/marketdata/category/4')]\n elif 'us' and 'election' in market.replace('.', '').lower():\n market_links = [(\"us_elections\", 'https://www.predictit.org/api/marketdata/category/6')]\n elif 'us' and 'politic' in market.replace('.', '').lower():\n market_links = [(\"us_politics\",'https://www.predictit.org/api/marketdata/category/13')]\n elif 'world' in market.lower():\n market_links = [(\"world_politics\", 'https://www.predictit.org/api/marketdata/category/4')]\n \n \n\n market_data=[]\n for category, market_link in market_links:\n markets = list(self.browser.get(market_link).json()['Markets'])\n for market in markets:\n market = market\n market[\"Category\"] = category\n market[\"References\"]=[]\n wikidict={\"Trump\": \"http://dbpedia.org/resource/Donald_Trump\", \"Clinton\": \"http://dbpedia.org/resource/Hillary_Clinton\", \"Ossoff\": \"https://en.wikipedia.org/wiki/Jon_Ossoff\", \"Virginia\": \"https://en.wikipedia.org/wiki/Virginia\", \"Georgia\": \"https://en.wikipedia.org/wiki/Georgia_(U.S._state)\",\"Election\":\"https://en.wikipedia.org/wiki/Elections_in_the_United_States\"}\n \n for thing in [\"Trump\", \"Clinton\", \"Ossoff\", \"Virginia\", \"Georgia\",\"Election\"]:\n if thing.lower() in [element.lower() for element in market[\"Name\"].split()]:\n market[\"References\"].append(wikidict[thing])\n market_data.append(json.dumps(market))\n \n return market_data","def calculate_training_cost(soldier_list: List[Soldier]):\n total_cost = 0.0\n \n for soldier in soldier_list:\n ################################# YOUR CODE HERE #################################\n if soldier.typecode == \"INF\":\n cost = 2.5 * soldier.weapon + 1.0 * soldier.armor\n elif soldier.typecode == \"ARC\":\n cost = 1.5 * soldier.weapon + 3.0 * soldier.armor\n elif soldier.typecode == \"CVL\":\n cost = 4.0 * soldier.weapon + 6.0 * soldier.armor\n if soldier.vitality < 0.35:\n cost *= 0.5\n total_cost += cost\n ##################################################################################\n return total_cost","def get_prices(self, grab, subject):\n prices = []\n try:\n extras = grab.doc.rex_text(\n '<h3 class\\=\"h6 copy-sp-m\">.*?%s.*?</h3>(.+?)</ul>' % subject,\n flags=re.S\n )\n except DataNotFound:\n logging.debug(\n \"Price %s is not found on %s\"\n % (subject, grab.doc.url)\n )\n return None\n\n sel = XpathSelector(fromstring(extras))\n prices = []\n for li in sel.select('//li[@class=\"list__item u-cf\"]'):\n obligatory = OrderedDict()\n obligatory['name'] = li.select('node()').text()\n money = li.select('node()/strong').text()\n obligatory['value'] = money[1:].replace(',', '')\n\n # Find perweek or perday\n if li.select(\n 'span[@class=\"boatview__extras-amount\"' +\n ' and contains(text(),\"per week\")]'\n ).exists():\n obligatory['perweek'] = True\n elif li.select(\n 'span[@class=\"boatview__extras-amount\"' +\n ' and contains(text(),\"per day\")]'\n ).exists():\n obligatory['perday'] = True\n obligatory['currency'] = money[0]\n prices.append(obligatory)\n\n if len(prices) < 1:\n logging.debug(\n \"Price %s contains less than one element on: %s\"\n % (subject, grab.doc.url)\n )\n return None\n\n return prices","def apply(self, catalog):\n out = []\n for product in catalog:\n valid = True\n for key, filtr in self.filters.items():\n valid = valid and filtr(product[key])\n if valid:\n out.append(product)\n out = sorted(out, key=lambda x: x['onDemandUsdPrice'])\n return self.limit_size(out)","def priceGetAll(soup):\n try:\n price = priceGetMost(soup)\n except:\n price = priceGetSome(soup)\n\n return price","def test_reasonable_auction(self):\n # I have no preferences\n bids = [Cost(ITEM1, ACTOR1, 1000),\n Cost(ITEM2, ACTOR1, 1000),\n Cost(ITEM3, ACTOR1, 1000),\n Cost(ITEM4, ACTOR1, 1000),\n Cost(ITEM5, ACTOR1, 1000),\n\n # I have linear preferences\n Cost(ITEM1, ACTOR2, 700),\n Cost(ITEM2, ACTOR2, 800),\n Cost(ITEM3, ACTOR2, 1000),\n Cost(ITEM4, ACTOR2, 1200),\n Cost(ITEM5, ACTOR2, 1300),\n\n # I have non-linear preferences\n Cost(ITEM1, ACTOR3, 400),\n Cost(ITEM2, ACTOR3, 800),\n Cost(ITEM3, ACTOR3, 1000),\n Cost(ITEM4, ACTOR3, 1200),\n Cost(ITEM5, ACTOR3, 1600),\n\n # I have arbitrary preference\n Cost(ITEM1, ACTOR4, 2435),\n Cost(ITEM2, ACTOR4, 305),\n Cost(ITEM3, ACTOR4, 310),\n Cost(ITEM4, ACTOR4, 1725),\n Cost(ITEM5, ACTOR4, 225),\n\n # I have strong preferences\n Cost(ITEM1, ACTOR5, 0),\n Cost(ITEM2, ACTOR5, 0),\n Cost(ITEM3, ACTOR5, 0),\n Cost(ITEM4, ACTOR5, 0),\n Cost(ITEM5, ACTOR5, 5000)]\n result = self.splitter.split(ITEMS[:5], ACTORS[:5], bids)\n expected = [(ITEM1, ACTOR4, None),\n (ITEM2, ACTOR1, None),\n (ITEM3, ACTOR3, None),\n (ITEM4, ACTOR2, None),\n (ITEM5, ACTOR5, None)]\n item_assignments_present(self, result, expected)","async def detect_available_dps(self):\n # type_0d devices need a sort of bruteforce querying in order to detect the\n # list of available dps experience shows that the dps available are usually\n # in the ranges [1-25] and [100-110] need to split the bruteforcing in\n # different steps due to request payload limitation (max. length = 255)\n self.dps_cache = {}\n ranges = [(2, 11), (11, 21), (21, 31), (100, 111)]\n\n for dps_range in ranges:\n # dps 1 must always be sent, otherwise it might fail in case no dps is found\n # in the requested range\n self.dps_to_request = {\"1\": None}\n self.add_dps_to_request(range(*dps_range))\n try:\n data = await self.status()\n except Exception as ex:\n self.exception(\"Failed to get status: %s\", ex)\n raise\n if \"dps\" in data:\n self.dps_cache.update(data[\"dps\"])\n\n if self.dev_type == \"type_0a\":\n return self.dps_cache\n self.debug(\"Detected dps: %s\", self.dps_cache)\n return self.dps_cache","def price(self, irc, msg, args, optlist, typeName):\n\n try:\n typeID = self._get_typeID(typeName)\n itemType = self._get_type(typeID)\n except:\n irc.error('Unknown type')\n return\n\n if len(optlist) == 1:\n location = optlist[0][1]\n else:\n location = 'Jita'\n\n try:\n locationID = self._get_locationID(location)\n location = self._get_location(locationID)\n except:\n irc.error('Unknown location')\n return\n\n market = self._sql(\"\"\"\n SELECT * FROM evecentral_market\n WHERE \"locationID\"=%s\"\"\", [locationID])\n if not market:\n irc.reply('No data for that market location')\n return\n\n marketitem = self._sql(\"\"\"\n SELECT * FROM evecentral_marketitem\n WHERE \"locationID\"=%s AND \"typeID\"=%s\"\"\", [locationID, typeID])\n if marketitem:\n irc.reply('{0} in {1}: buy max: {2} (volume: {3:,d}). sell min: {4} (volume: {5:,d}).'.format(\n ircutils.bold(itemType['typeName']),\n self._colorize_system(location),\n ircutils.mircColor(\n '{:,.2f}'.format(marketitem['buy_max']),\n fg='green'),\n int(marketitem['buy_volume']),\n ircutils.mircColor(\n '{:,.2f}'.format(marketitem['sell_min']),\n fg='green'),\n int(marketitem['sell_volume']),\n ), prefixNick=False)\n else:\n irc.reply(\"Prices for {0} in {1} isn't updated yet.\".format(\n itemType['typeName'],\n location['itemName']\n ))","def value_offers(self):\n return OrderBookUtils.book_value(self.offer)","def price(usr, item, searches = 2, method = \"AVERAGE\", deduct = 0):\n if not method in ShopWizard.methods: raise invalidMethod()\n\n if isinstance(item, Item):\n item = item.name\n\n prices = []\n dets = {}\n for x in range(0, searches):\n print('performing search: {}'.format(x))\n results = ShopWizard.search(usr, item)\n\n # Set to -1 if not found\n if not results:\n prices.append(-1)\n continue\n\n prices.append(int(results[item].price))\n dets[str(results[item].price)] = (results[item].owner, results[item].id)\n\n time.sleep(ShopWizard.waitTime)\n\n # Determines if item was UB\n print(prices)\n if sum(prices) == len(prices) * -1:\n return False\n\n prices = list(filter(lambda x: x != -1, prices))\n\n if method == ShopWizard.RETLOW:\n price = sorted(prices)[0]\n return (price, dets[str(price)][0], dets[str(price)][1])\n\n return ShopWizard.__determinePrice(prices, method, deduct)","def get_the_sum_of_prices(item_ids):\n\n # your code","def getActiveCurrencies():","def daffodils(flNeeded,amtPaid, dzCost):\n\n\n import math\n\n dz = flNeeded / 12\n dozens = math.ceil (dz) #Rounds up to the nearest dozen\n\n totCost = dzCost * dozens\n toPay = totCost - amtPaid\n\n print (\"You will need to contribute\", toPay)","def sales_orders_options(m,\n price=None,\n prefer_exercise=True,\n cheap_first=True,\n restricted=True,\n vested=True,\n nso_first=False):\n\n # default to the ipo price\n if price is None: price = m.ipo_price_usd\n assert(0 <= price)\n\n # Set up the list of sales orders\n option_lst = []\n nso_lst = grant_lst_for_vehicle(m, 'nso', cheap_first=cheap_first)\n iso_lst = grant_lst_for_vehicle(m, 'iso', cheap_first=cheap_first)\n \n if nso_first:\n option_lst = nso_lst + iso_lst\n else:\n option_lst = iso_lst + nso_lst\n\n retval = []\n\n # How many shares can be legally sold?\n remaining_restricted = m.shares_sellable_restricted_n\n\n for g in option_lst:\n rate = getattr(m, 'shares_withheld_%s_rate'%g.vehicle)\n if vested:\n n = g.available(m.query_date, rate)\n else:\n n = g.n_shares - g.liquidated\n if restricted:\n n = min(remaining_restricted, n)\n remaining_restricted -= n\n retval.append({\n 'id': g.name,\n 'qty': n,\n 'price': price,\n 'prefer_exercise': prefer_exercise\n })\n\n return retval","def spread(self):\n if self.bid and self.offer:\n return self.offer[0].price - self.bid[-1].price\n\n return 0","def execute_deposits(self):\n deposits = [v for v in self.action_register if v['action'] == 'deposit']\n for deposit in deposits:\n self.model.schedule.agents_by_type['Customer'][deposit['unique_id']].euro_wallet += deposit['value']","def get_basket_items_pricedrop(self, offer_info, actual_volume, product_prices):\n prod_code = offer_info.base_prod_code\n base_prod_vol = actual_volume.get(prod_code.lower())\n\n pricedrop_basket = []\n\n if base_prod_vol >= offer_info.min_vol:\n offer_on_prod = offer_info.offer_on\n if actual_volume.get(offer_on_prod.lower()):\n print(\n f\"Base product volume is greater than minimum required volume & product on offer is also available \"\n f\"in cart..\")\n if offer_info.is_limited:\n print(f\"Limited offer..\")\n if prod_code == offer_on_prod:\n # total_allowed_items_on_offer = Limit Volume of base product * (Offer Product Max Volume/Minimum volume of base product)\n total_allowed_items_on_offer = offer_info.limit_vol * (\n offer_info.offer_prod_volume / offer_info.min_vol)\n max_limit = 1\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n pricedrop_basket.append((prod_code, base_prod_actual_price))\n while max_limit <= total_allowed_items_on_offer:\n new_price = (base_prod_actual_price - (offer_info.new_price)) * -1\n pricedrop_basket.append((offer_info.offer_code, new_price))\n max_limit += 1\n else:\n total_allowed_items_on_offer = offer_info.limit_vol * (\n offer_info.offer_prod_volume / offer_info.min_vol)\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n pricedrop_basket.append((prod_code, base_prod_actual_price))\n max_limit = 1\n while max_limit <= total_allowed_items_on_offer:\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\n new_price = (base_prod_actual_price - (offer_info.new_price)) * -1\n for j in range(0, actual_volume.get(offer_on_prod).lower()):\n pricedrop_basket.append((offer_on_prod, offer_onprod_actual_price))\n pricedrop_basket.append((offer_info.offer_code, new_price))\n max_limit += 1\n else:\n print(f\"Unlimited offer..\")\n if prod_code == offer_on_prod:\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n pricedrop_basket.append((prod_code, base_prod_actual_price))\n new_price = (base_prod_actual_price - (offer_info.new_price))*-1\n pricedrop_basket.append((offer_info.offer_code, new_price))\n else:\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n pricedrop_basket.append((prod_code, base_prod_actual_price))\n\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\n new_price = (offer_onprod_actual_price - (offer_info.new_price)) * -1\n\n for j in range(0, actual_volume.get(offer_on_prod).lower()):\n pricedrop_basket.append((offer_on_prod, offer_onprod_actual_price))\n pricedrop_basket.append((offer_info.offer_code, new_price))\n\n return pricedrop_basket","def refuse_and_delete_pending_offers(item, offer_id):\n for o in item.offers_received.filter(~Q(pk=offer_id), answered=False):\n refuse_offer(o)\n\n for o in item.offers_done.filter(~Q(pk=offer_id), answered=False):\n delete_offer(o)","def calc_Cinv_boiler(Q_design_W, technology_type, boiler_cost_data):\n Capex_a_Boiler_USD = 0.0\n Opex_a_fix_Boiler_USD = 0.0\n Capex_Boiler_USD = 0.0\n\n if Q_design_W > 0.0:\n boiler_cost_data = boiler_cost_data[boiler_cost_data['code'] == technology_type]\n # if the Q_design is below the lowest capacity available for the technology, then it is replaced by the least\n # capacity for the corresponding technology from the database\n if Q_design_W < boiler_cost_data.iloc[0]['cap_min']:\n Q_design_W = boiler_cost_data.iloc[0]['cap_min']\n max_boiler_size = boiler_cost_data.iloc[0]['cap_max']\n\n if Q_design_W <= max_boiler_size:\n\n boiler_cost_data = boiler_cost_data[\n (boiler_cost_data['cap_min'] <= Q_design_W) & (boiler_cost_data['cap_max'] > Q_design_W)]\n\n Inv_a = boiler_cost_data.iloc[0]['a']\n Inv_b = boiler_cost_data.iloc[0]['b']\n Inv_c = boiler_cost_data.iloc[0]['c']\n Inv_d = boiler_cost_data.iloc[0]['d']\n Inv_e = boiler_cost_data.iloc[0]['e']\n Inv_IR = boiler_cost_data.iloc[0]['IR_%']\n Inv_LT = boiler_cost_data.iloc[0]['LT_yr']\n Inv_OM = boiler_cost_data.iloc[0]['O&M_%'] / 100.0\n\n InvC = Inv_a + Inv_b * (Q_design_W) ** Inv_c + (Inv_d + Inv_e * Q_design_W) * log(Q_design_W)\n\n Capex_a_Boiler_USD = calc_capex_annualized(InvC, Inv_IR, Inv_LT)\n Opex_a_fix_Boiler_USD = InvC * Inv_OM\n Capex_Boiler_USD = InvC\n\n else:\n number_of_boilers = int(ceil(Q_design_W / max_boiler_size))\n Q_nom_W = Q_design_W / number_of_boilers\n\n boiler_cost_data = boiler_cost_data[\n (boiler_cost_data['cap_min'] <= Q_nom_W) & (boiler_cost_data['cap_max'] > Q_nom_W)]\n\n Inv_a = boiler_cost_data.iloc[0]['a']\n Inv_b = boiler_cost_data.iloc[0]['b']\n Inv_c = boiler_cost_data.iloc[0]['c']\n Inv_d = boiler_cost_data.iloc[0]['d']\n Inv_e = boiler_cost_data.iloc[0]['e']\n Inv_IR = boiler_cost_data.iloc[0]['IR_%']\n Inv_LT = boiler_cost_data.iloc[0]['LT_yr']\n Inv_OM = boiler_cost_data.iloc[0]['O&M_%'] / 100.0\n\n InvC = (Inv_a + Inv_b * (Q_nom_W) ** Inv_c + (Inv_d + Inv_e * Q_nom_W) * log(Q_nom_W)) * number_of_boilers\n\n Capex_a_Boiler_USD = calc_capex_annualized(InvC, Inv_IR, Inv_LT)\n Opex_a_fix_Boiler_USD = InvC * Inv_OM\n Capex_Boiler_USD = InvC\n\n return Capex_a_Boiler_USD, Opex_a_fix_Boiler_USD, Capex_Boiler_USD","def get_addents(data: List[int], answer: int, amount: int) -> List[int]:\n for addents in itertools.combinations(data, amount):\n if sum(addents) == answer:\n return addents","def ItemDed(_posagi, e17500, e18400, e18500, e18800, e18900,\n e20500, e20400, e19200, e20550, e20600, e20950, e19500, e19570,\n e19400, e19550, e19800, e20100, e20200, e20900, e21000, e21010,\n MARS, _sep, c00100, ID_ps, ID_Medical_frt, ID_Casualty_frt,\n ID_Miscellaneous_frt, ID_Charity_crt_Cash, ID_Charity_crt_Asset,\n ID_prt, ID_crt, ID_StateLocalTax_HC, ID_Charity_frt, puf):\n # Medical\n c17750 = ID_Medical_frt * _posagi\n c17000 = max(0, e17500 - c17750)\n\n # State and Local Income Tax, or Sales Tax\n _statax = max(e18400, 0)\n\n # Other Taxes (including state and local)\n c18300 = _statax + e18500 + e18800 + e18900\n\n # Casulty\n if e20500 > 0:\n c37703 = e20500 + ID_Casualty_frt * _posagi\n c20500 = c37703 - ID_Casualty_frt * _posagi\n else:\n c37703 = 0.\n c20500 = 0.\n\n # Miscellaneous\n c20750 = ID_Miscellaneous_frt * _posagi\n if puf:\n c20400 = e20400\n else:\n c20400 = e20550 + e20600 + e20950\n c20800 = max(0, c20400 - c20750)\n\n # Interest paid deduction\n if puf:\n c19200 = e19200\n else:\n c19200 = e19500 + e19570 + e19400 + e19550\n\n # Charity (assumes carryover is non-cash)\n base_charity = e19800 + e20100 + e20200\n if base_charity <= 0.2 * _posagi:\n c19700 = base_charity\n else:\n lim50 = min(ID_Charity_crt_Cash * _posagi, e19800)\n lim30 = min(ID_Charity_crt_Asset * _posagi, e20100 + e20200)\n c19700 = min(0.5 * _posagi, lim30 + lim50)\n\n charity_floor = ID_Charity_frt * _posagi # frt is zero in present law\n c19700 = max(0, c19700 - charity_floor)\n\n # Gross Itemized Deductions\n\n c21060 = (e20900 + c17000 + (1 - ID_StateLocalTax_HC) * c18300 + c19200 +\n c19700 + c20500 + c20800 + e21000 + e21010)\n\n # Limitations on deductions excluding medical, charity etc\n _phase2_i = ID_ps[MARS - 1]\n\n _nonlimited = c17000 + c20500 + e19570 + e21010 + e20900\n _phase2_i = ID_ps[MARS - 1]\n _limitratio = _phase2_i / _sep\n\n if c21060 > _nonlimited and c00100 > _limitratio:\n dedmin = ID_crt * (c21060 - _nonlimited)\n dedpho = ID_prt * max(0, _posagi - _limitratio)\n c21040 = min(dedmin, dedpho)\n else:\n c21040 = 0.0\n\n # Itemized deductions amount after limitation if any\n c04470 = c21060 - c21040\n\n # the variables that are casted as floats below can be either floats or\n # ints depending n which if/else branches they follow in the above code.\n # they need to always be the same type\n\n c20400 = float(c20400)\n c20400 = float(c20400)\n c19200 = float(c19200)\n c37703 = float(c37703)\n c20500 = float(c20500)\n\n return (c17750, c17000, _statax, c18300, c37703, c20500,\n c20750, c20400, c19200, c20800, c19700, c21060, _phase2_i,\n _nonlimited, _limitratio, c04470, c21040)","def accumulate_prices(name,products,sales,types,add):\r\n return reduce(add,[get_prices_by_type(name,products,sales,types)[i] for i in get_prices_by_type(name,products,sales,types)])","def GetSpeculated(self):\n return self.money + sum([self.share[i] * self.price[i][0] * (1 + self.taxe) for i in self.price])","def compute_costs(timesheet, biller, date1=None, date2=None): \n # Slice\n f = slice_by_dates(timesheet, date1, date2)\n\n # Resample and add start/end dates\n if biller.freq is not None:\n freq = biller.freq\n f = timesheet.set_index('date')[['duration']].resample(freq).sum()\n f = f.reset_index()\n f['period'] = f['date'].map(lambda x: pd.Period(x, freq))\n f['start_date'] = f['period'].map(lambda x: x.start_time)\n f['end_date'] = f['period'].map(lambda x: x.end_time)\n else:\n start_date, end_date = f['date'].min(), f['date'].max()\n f['start_date'] = start_date\n f['end_date'] = end_date\n\n # Get bins for aggregating\n if biller.base_fee:\n bins = [0] + biller.bins\n else:\n bins = biller.bins\n\n def my_agg(group):\n d = OrderedDict()\n d['start_date'] = group['start_date'].iat[0]\n d['end_date'] = group['end_date'].iat[0]\n t = group['duration'].iat[0]\n d['duration'] = pd.Series(decompose(t, bins))\n c1 = d['duration'].cumsum().map(biller)\n c2 = c1.shift(1).fillna(0)\n cost = c1 - c2\n d['rate'] = cost/d['duration']\n d['cost'] = cost\n return pd.DataFrame(d)\n \n f = f.groupby('date').apply(my_agg\n ).reset_index().drop(['level_1', 'date'], axis=1)\n\n # Drop NaN rate items\n f = f.dropna(subset=['rate'])\n\n return f","def solve_prices(self):\n return None","def cost(foods, foods_used):\n cost = 0.00\n for i, count in foods_used.items():\n cost += (foods[i]['serving_cost'] * count)\n return cost","def getTransferListSummary(self):\n p_ids_and_prices = {}\n players = self.getAllPlayerInfoTransferlist()\n\n # Get IDs of all players\n log_event(self.queue, \"Gathering player prices... \")\n for p in players:\n p_bidstatus = p[1]\n p_id = p[8]\n # removed Filter for unlisted / expired players\n if p_id not in p_ids_and_prices:\n p_sellprice = self.getPlayerSellPrice(p_id)\n # If sell price returns 0, need to fetch from Futbin\n if p_sellprice == 0:\n p_sellprice = self.getFutbinPrice_opentab(p_id)\n self.sleep_approx(5) # Delay iteration to not anger futbin\n # Add player ID and price to dict\n p_ids_and_prices[p_id] = p_sellprice\n\n for p_id in p_ids_and_prices:\n p_price = p_ids_and_prices[p_id]\n p_name = self.getPlayerCardName(p_id)\n log_event(self.queue, str(p_name) + \" - #\" +\n str(p_id) + \" Price \" + str(p_price))\n\n num_p_sold = 0\n num_p_expired = 0\n num_p_unlisted = 0\n num_p_listed = 0\n\n sold_p_value = 0\n expired_p_value = 0\n unlisted_p_value = 0\n listed_p_value = 0\n\n for p in players:\n p_bidstatus = p[1]\n p_id = p[8]\n p_soldprice = p[5] # is 0 if unlisted\n p_sellprice = int(p_ids_and_prices[p_id])\n\n if \"won\" in p_bidstatus:\n num_p_sold += 1\n sold_p_value += p_soldprice\n if \"expired\" in p_bidstatus:\n num_p_expired += 1\n expired_p_value += p_sellprice\n if (p_bidstatus == \"listFUTItem\"):\n num_p_unlisted += 1\n unlisted_p_value += p_sellprice\n if (p_bidstatus == \"listFUTItem has-auction-data\"):\n num_p_listed += 1\n listed_p_value += p_sellprice\n\n log_event(self.queue, \"Players sold: \" + str(num_p_sold))\n log_event(self.queue, \"Players expired: \" + str(num_p_expired))\n log_event(self.queue, \"Players listed: \" + str(num_p_listed))\n log_event(self.queue, \"Players unlisted: \" + str(num_p_unlisted))\n log_event(self.queue, \" - - - \")\n log_event(self.queue, \"Sold players value: \" + str(sold_p_value))\n log_event(self.queue, \"Expired players value: \" +\n str(expired_p_value))\n log_event(self.queue, \"Unlisted players value: \" +\n str(unlisted_p_value))\n log_event(self.queue, \"Listed players value: \" + str(listed_p_value))\n\n # TODO subtract bought price\n self.user_players_won += int(num_p_unlisted)\n self.p_ids_and_prices = p_ids_and_prices\n intel = [p_ids_and_prices, num_p_sold, num_p_expired, num_p_unlisted,\n num_p_listed, sold_p_value, expired_p_value, unlisted_p_value, listed_p_value]\n return intel","def get_prices(self):\n pass","def test_get_offers(self):\n pass","def deposits_for_period(self):\n cash_transaction = CashTransaction(self.user)\n limits = self.user.limits\n deposits = 0\n if limits.exists():\n deposit_limit = self.user.limits.get(type=Limit.DEPOSIT)\n deposits = \\\n cash_transaction.get_all_deposits(date_range=deposit_limit.time_period_boundaries)[\n 'amount__sum']\n return deposits","def getCurrencies():","def is_satisfied(self, offer, basket):\n value_of_matches = D(\"0.00\")\n for line in basket.all_lines():\n if (\n self.can_apply_condition(line)\n and line.quantity_without_offer_discount(offer) > 0\n ):\n price = unit_price(offer, line)\n value_of_matches += price * int(\n line.quantity_without_offer_discount(offer)\n )\n if value_of_matches >= self.value:\n return True\n return False","def nsquaredDist(points, weights = None, potent = None, dType = \"EUCLIDEAN\"):\n\n n,k = NUM.shape(points)\n maxMinSumDist = 3.402823466E+38\n\n if weights == None: \n weights = NUM.ones((n,), float) \n\n if potent == None:\n potent = NUM.zeros((n,), float)\n\n weightedPotential = weights * potent\n\n res = {}\n\n #### Calculate Sum of Weighted Distances For Each Feature ####\n weights.shape = n,1\n if dType == \"EUCLIDEAN\":\n for idx, point in enumerate(points):\n weightedDist = eucDistArray(point, points, weights) \n res[idx] = weightedDist + (weights[idx] * potent[idx])\n ARCPY.SetProgressorPosition()\n else:\n for idx, point in enumerate(points):\n weightedDist = manDistArray(point, points, weights) \n res[idx] = weightedDist + weightedPotential[idx]\n ARCPY.SetProgressorPosition()\n\n #### Minimum Sum of Weighted Distances (Central Feature) ####\n minSumDist = min(res.itervalues())\n final = [ key for key,val in res.iteritems() if val == minSumDist ]\n \n return final, minSumDist","def buy(self, buyDate, sellDate, buyLimit, unitLimit, repeat):\n\t\tstartDate, startPrice = self.getOpeningPrice()\n\t\tmaxAmount = int(buyLimit)/int(startPrice)\n\t\t\t\t\n\t\t# If you've selected a one-off stock purchase\n\t\tif repeat == 'never' and maxAmount > 0:\n\t\t\tself.purchased = (maxAmount * startPrice)\n\t\t\tself.units = maxAmount\n\t\t\tself.purchasedList.append(maxAmount * startPrice)\n\t\t\tself.unitsList.append(maxAmount)\n\t\t\tself.dateList = [startDate]\t\n\t\t\t\n\t\t# Otherwise there's some iteration required\n\t\telse:\n\t\t\tcurrentDate = startDate\n\t\t\tendDate, endPrice = self.getClosingPrice()\n\t\t\t\n\t\t\t# Iterate on the date value\n\t\t\twhile currentDate <= endDate:\n\t\t\t\tdate, price = self.getPrice(currentDate)\n\n\t\t\t\tif date is not None and price is not None:\n\t\t\t\t\tmaxAmount = int(buyLimit)/int(price)\n\t\t\t\t\t\n\t\t\t\t\t# Check there's no unit limit\t\t\t\t\n\t\t\t\t\tif maxAmount > unitLimit and unitLimit > 0:\n\t\t\t\t\t\tmaxAmount = unitLimit\n\t\t\t\t\t\n\t\t\t\t\t# If you can afford it, but the stock, otherwise skip it\n\t\t\t\t\tif maxAmount > 0:\n\t\t\t\t\t\tself.purchased += (maxAmount * price)\n\t\t\t\t\t\tself.units += maxAmount\n\t\t\t\t\t\tself.purchasedList.append(maxAmount * price)\n\t\t\t\t\t\tself.unitsList.append(maxAmount)\n\t\t\t\t\t\tself.dateList.append(date)\t\n\t\t\t\t\n\t\t\t\t# If there's no later stock data remaining break the loop\n\t\t\t\tcurrentDate = self.getNextDate(currentDate, startDate, repeat)\n\t\t\t\tif currentDate is None:\n\t\t\t\t\tbreak","def get_good_price_by_deal(self):\n if self.deal == SALE:\n return self.new_price\n elif self.deal == AUCTION:\n return self.max_price\n else:\n return self.price","def get_prices_by_type(name,products,sales,types):\r\n return {x:products[x]*(lambda y:1-sales[name][y])(tuple(types)[0] if x==tuple(types.values())[0][1] or x==tuple(types.values())[0][0] else tuple(types)[1]) for x in products}","def check_restrictions(self):\n from .signals import determine_availability\n\n responses = determine_availability.send(\n self.item.event, item=self.item,\n variations=[self.to_variation_dict()], context=None,\n cache=self.item.event.get_cache()\n )\n price = self.default_price if self.default_price is not None else self.item.default_price\n for receiver, response in responses:\n if 'available' in response[0] and not response[0]['available']:\n return False\n elif 'price' in response[0] and response[0]['price'] is not None and response[0]['price'] < price:\n price = response[0]['price']\n return price","def is_satisfied(self, offer, basket):\n num_matches = 0\n for line in basket.all_lines():\n if self.can_apply_condition(line):\n num_matches += line.quantity_without_offer_discount(offer)\n if num_matches >= self.value:\n return True\n return False","def binary_price(volatilities,\n strikes,\n expiries,\n spots=None,\n forwards=None,\n discount_factors=None,\n is_call_options=None,\n dtype=None,\n name=None):\n if (spots is None) == (forwards is None):\n raise ValueError('Either spots or forwards must be supplied but not both.')\n\n with tf.compat.v1.name_scope(\n name,\n default_name='option_price',\n values=[\n forwards, strikes, volatilities, expiries, discount_factors,\n is_call_options\n ]):\n strikes = tf.convert_to_tensor(strikes, dtype=dtype, name='strikes')\n volatilities = tf.convert_to_tensor(\n volatilities, dtype=dtype, name='volatilities')\n expiries = tf.convert_to_tensor(expiries, dtype=dtype, name='expiries')\n\n if discount_factors is None:\n discount_factors = tf.convert_to_tensor(\n 1.0, dtype=dtype, name='discount_factors')\n else:\n discount_factors = tf.convert_to_tensor(\n discount_factors, dtype=dtype, name='discount_factors')\n\n if forwards is not None:\n forwards = tf.convert_to_tensor(forwards, dtype=dtype, name='forwards')\n else:\n spots = tf.convert_to_tensor(spots, dtype=dtype, name='spots')\n forwards = spots / discount_factors\n\n sqrt_var = volatilities * tf.math.sqrt(expiries)\n d1 = (tf.math.log(forwards / strikes) + sqrt_var * sqrt_var / 2) / sqrt_var\n d2 = d1 - sqrt_var\n undiscounted_calls = _ncdf(d2)\n if is_call_options is None:\n return discount_factors * undiscounted_calls\n undiscounted_puts = 1 - undiscounted_calls\n predicate = tf.broadcast_to(is_call_options, tf.shape(undiscounted_calls))\n return discount_factors * tf.where(predicate, undiscounted_calls,\n undiscounted_puts)","async def sellall(self, ctx:commands.Context, fish_type:str='', fish_quality:str=''):\r\n\r\n if not fish_type in ['specific', 'name', 'school', 'rarity', '']:\r\n await ctx.send(f'{fish_type} must be one of the following:\\n```name\\nschool\\nrarity```')\r\n return\r\n\r\n all_fish = await self.config.member(ctx.message.author).bucket()\r\n new_fish = []\r\n sell_fish = []\r\n msg = ''\r\n total = 0\r\n for i in all_fish:\r\n i.update({'specific': f'{i[\"name\"]} {i[\"size\"]}'})\r\n if fish_type == '' or i[fish_type] == fish_quality:\r\n sell_fish.append(i)\r\n\r\n mod = await self.CheckMerchants(ctx.guild, i)\r\n if await self.CompletedSchool(ctx.message.author, i['school']):\r\n mod += await self.GetModifier(ctx.guild, 'school_complete_mod')\r\n\r\n i[\"value\"] = int(i[\"value\"] * mod * await self.GetModifier(ctx.guild, i['rarity']))\r\n total += i[\"value\"]\r\n msg += f'{i[\"name\"]}{\" \" * (self.longestFishName - len(i[\"name\"]))}{i[\"value\"]} {await bank.get_currency_name(ctx.guild)} ({mod}x)\\n'\r\n else:\r\n new_fish.append(i)\r\n\r\n msg = await ctx.send(f'Are you sure you want to sell:\\n```{msg}```for {total} {await bank.get_currency_name(ctx.guild)}')\r\n\r\n start_adding_reactions(msg, ReactionPredicate.YES_OR_NO_EMOJIS)\r\n pred = ReactionPredicate.yes_or_no(msg, ctx.author)\r\n try:\r\n await ctx.bot.wait_for(\"reaction_add\", check=pred, timeout=20)\r\n except asyncio.TimeoutError:\r\n await msg.clear_reactions()\r\n await msg.edit(content='Sale timed out')\r\n return\r\n\r\n if pred.result is True:\r\n await self.config.member(ctx.message.author).bucket.set(new_fish)\r\n await msg.edit(content=f'{len(sell_fish)} fish sold for {total} {await bank.get_currency_name(ctx.guild)}')\r\n await bank.deposit_credits(ctx.message.author, total)\r\n else:\r\n await msg.edit(content='Sale cancelled')\r\n\r\n await msg.clear_reactions()","def getTransferListSummaryWithoutPrices(self):\n players = self.getAllPlayerInfoTransferlist()\n\n num_p_sold = 0\n num_p_expired = 0\n num_p_unlisted = 0\n num_p_listed = 0\n sold_p_value = 0\n\n for p in players:\n p_bidstatus = p[1]\n p_id = p[8]\n p_soldprice = p[5] # is 0 if unlisted\n\n if \"won\" in p_bidstatus:\n num_p_sold += 1\n sold_p_value += p_soldprice\n if \"expired\" in p_bidstatus:\n num_p_expired += 1\n if (p_bidstatus == \"listFUTItem\"):\n num_p_unlisted += 1\n if (p_bidstatus == \"listFUTItem has-auction-data\"):\n num_p_listed += 1\n\n # TODO subtract bought price\n return num_p_sold, num_p_expired, num_p_unlisted, num_p_listed","def prices(parsed, appearance_value, removal_value, gate_single_value, gate_double_value):\n\t\tprices = []\n\n\t\tfor item in parsed:\n\t\t\tif item.entityType == \"fence\":\n\t\t\t\tprices.append(QuoteCalculation._fencePrice(item, appearance_value, removal_value))\n\n\t\t\telif item.entityType == \"gate\":\n\t\t\t\tprices.append(QuoteCalculation._gatePrice(item, gate_single_value, gate_double_value, removal_value))\n\n\t\t\t# Not required?\n\t\t\t#elif item.entityType == \"post\":\n\t\t\t\t#prices.append(QuoteCalculation._postPrice(item))\n\n\t\treturn prices","def get_duty_cate_score(chosen_duty_list: list) -> pmag.MagicDict:\n res = pmag.MagicDict()\n for w, cate in chosen_duty_list:\n freq = MODEL[cate]['duty'][w]['freq']\n prob = MODEL[cate]['duty'][w]['prob']\n score = prob # freq * prob / DUTY_NF[cate]\n if cate in res:\n res[cate] += score\n else:\n res[cate] = score\n return res","def computeMealFrecuency(self):\n self.getOrdersData()\n self.getOrderValues()\n meals = set(self.meals)\n for meal in meals:\n self.labels.append(meal)\n self.quantity.append(self.meals.count(meal))","def calculate_cost(self):\n booking_days, booking_hours = self.calculate_daily_hourly_billable_counts()\n day_cost = booking_days * Decimal(self.vehicle.type.daily_rate)\n hour_cost = booking_hours * Decimal(self.vehicle.type.hourly_rate)\n if hour_cost > self.vehicle.type.daily_rate:\n hour_cost = self.vehicle.type.daily_rate\n return float(day_cost + hour_cost)","def consume_items(self, offer, basket, affected_lines):\n # Determine value of items already consumed as part of discount\n value_consumed = D(\"0.00\")\n for line, __, qty in affected_lines:\n price = unit_price(offer, line)\n value_consumed += price * qty\n\n to_consume = max(0, self.value - value_consumed)\n if to_consume == 0:\n return\n\n for price, line in self.get_applicable_lines(\n offer, basket, most_expensive_first=True\n ):\n quantity_to_consume = min(\n line.quantity_without_offer_discount(offer),\n (to_consume / price).quantize(D(1), ROUND_UP),\n )\n line.consume(quantity_to_consume, offer=offer)\n to_consume -= price * quantity_to_consume\n if to_consume <= 0:\n break","def implied_discount_factor(p1: Instrument, c1: Instrument, p2: Instrument, c2: Instrument) -> float:\n return (c1.price - p1.price - c2.price + p2.price)/ (c2.strike - c1.strike)","async def loaddndshop(self, ctx):\n await self.bot.di.add_shop_items(ctx.guild,\n {item: value[\"meta\"][\"Cost\"] for item, value in self.bot.dnditems.items() if\n value[\"meta\"].get(\"Cost\", \"\").isdigit()})\n await ctx.send(await _(ctx, \"Successfully added all D&D items to shop!\"))","def recalc_for_combo_birds(self, early_birds):\n for early_bird in early_birds:\n if early_bird.price_type == 1:\n self.recalc_for_bird(early_bird)\n return self.rates","def get_discount(self, price):\r\n pass","def apply_peddler_effect(self, player: Player) -> None:\n \"\"\"\n Buy 1 cube (any resource but gold) from the stock with 1 denier.\n \"\"\"\n # Remark: Hard-coded! We don't use the tags <cost><n_deniers>-1 and <gain><CHOICES>... in <game_elements><buildings><player_buildings><player_building><secondary_effect>.\n money_resource_cost, qty_cost = Money.money, -1 # type: MoneyResource, int\n if player.current_money_resources[money_resource_cost] + \\\n qty_cost < 0: # Has the player enough money or resource?\n print(indent(4) + player.txt_name_money_resources_workers_PPs_deck(True, True, False, False, False) +\n ' and can\\'t apply the effect because he/she doesn\\'t have enough money or resource as ' +\n str(qty_cost) + ' ' + money_resource_cost.name + '(s) required.')\n else:\n resource_gain_choices, qty_gain = [resource for resource in Resource.resources.values()\n if not resource.is_wild()], \\\n +1 # type: List[Resource], int\n resource_gain = player.choose_buy_resource(money_resource_cost, qty_cost, resource_gain_choices,\n qty_gain) # type: Resource\n if resource_gain is None:\n print(indent(4) + player.txt_name_money_resources_workers_PPs_deck(True, True, False, False, False) +\n ' and had chosen to don\\'t apply the effect.')\n else:\n print(indent(4) + player.name() + ' wants to consume ' + str(qty_cost) + ' ' +\n money_resource_cost.name + '(s) to obtain ' + str(qty_gain) + ' ' + resource_gain.name + '(s).')\n player.current_money_resources[money_resource_cost] += qty_cost\n player.current_money_resources[resource_gain] += qty_gain\n print(indent(4) + player.txt_name_money_resources_workers_PPs_deck(True, True, False, False, False) +\n ' once the effect applied.')","def calculateCosts(self):\n self.costs = 0\n for house in self.houses:\n if not house.distance == 1000:\n self.costs += house.distance * 9\n for battery in self.batteries:\n self.costs += battery.costs\n return self.costs","def best_buy(self):\n return Library.functions.best_buy(self._book)","def get_all_cost_per_click(self,bid):\n cost1 = self.get_cost_per_click(bid, 1)\n cost2 = self.get_cost_per_click(bid, 2)\n cost3 = self.get_cost_per_click(bid, 3)\n return cost1, cost2, cost3","def GetAllDifferentAmountOfCost():\n\n logs.logger.debug(\n \"Start to get back all different amount of \"\n \"Cost objects from database.\")\n try:\n ListOfAllDifferentAmountOfCost = []\n searchedCostsItems = GetAllAmountOfCost()\n for item in searchedCostsItems:\n if item not in ListOfAllDifferentAmountOfCost:\n ListOfAllDifferentAmountOfCost.append(item)\n logs.logger.info(\n \"Get back all different amount of Cost objects from database.\")\n return ListOfAllDifferentAmountOfCost\n except Exception as e:\n logs.logger.error(e, exc_info=True)","def get_shop_list_by_dishes(cook_dict, dishes, person_count):\n temp_dict = dict()\n for dish_name in dishes:\n for dish_type, ingredient_list in cook_dict.items():\n if dish_type == dish_name:\n for ingr_dict in ingredient_list:\n ingr_name = ingr_dict['ingredient_name']\n if temp_dict.setdefault(ingr_name) is None:\n temp_dict[ingr_name] = {\n 'quantity': ingr_dict['quantity'] * person_count, 'measure': ingr_dict['measure']\n }\n else:\n temp_dict[ingr_name]['quantity'] += ingr_dict['quantity'] * person_count\n return temp_dict","def dishlist_prices(n: list) -> list:\r\n return [dish.price for dish in n]","def get_all_desserts():\n return get_data_from_category_name(\"Desserts\")","def discount_card(subtotal):\n\n if \"gold\" in CARDS:\n return gold_card(subtotal) #This calculates the 5%\n\n elif \"silver\" in CARDS:\n return silver_card(subtotal) #This calculates the 2%\n\n elif \"gold\" in CARDS and \"silver\" in CARDS:\n return gold_card(subtotal)\n\n else:\n return 0 #Whitout discount","def get_price(self, name: str, set_name: str, foil: bool = False):\n self.search_card(name, set_name)\n time.sleep(1.5)\n self.select_singles()\n all_product = self.browser.find_elements_by_class_name('product')\n product_1 = all_product[0]\n card_finishes = product_1.find_elements_by_class_name('finish-option')\n for finish in card_finishes:\n foil_attribute = finish.text\n if foil and (foil_attribute == 'Foil'):\n finish.click()\n self.foil = True\n elif (not foil) and (foil_attribute == 'Non-Foil'):\n finish.click()\n self.foil = False\n\n card_name = product_1.find_element_by_xpath('//article/div[1]/h4/a').text\n card_set = product_1.find_element_by_xpath('//article/div[1]/h4/p').text\n all_price = product_1.find_elements_by_class_name('price-section')\n list_prices = []\n for price in all_price:\n current_price = price.find_elements_by_class_name('price')[0].text\n if len(current_price) > 0:\n if current_price[0] == '$':\n list_prices.append(float(current_price[1:]))\n else:\n list_prices.append(float(current_price))\n self.price_ftf = max(list_prices)\n self.name = card_name\n self.set = card_set","def free_item_discount(self, items):\n new_items = []\n occurrences = collections.Counter(item.sku for item in items)\n if occurrences.get(MacbookPro.sku, 0) > 0:\n for item in items:\n if item.sku == VGAadapter.sku and item.price != 0:\n item.price = 0\n else:\n new_items.append(CheckoutItem(sku=VGAadapter.sku, price=0))\n items.extend(new_items)\n return items","def calculate_prices(self, good=None):\n\n stock = self.calculate_init_stock(good)\n buy = self.buying_price()\n\n if stock == 0:\n sell = 0\n buy = buy + (buy * 0.5)\n\n elif stock < 500:\n # mild bug: stock, without selling price\n sell = self.selling_price()\n elif stock >= 500:\n # higher production, lower prices\n sell = self.selling_price() / 2\n buy = buy - (buy * 0.5)\n\n return [buy, sell, stock]","def bulk_item(order: Order) -> Decimal:\n discount = Decimal(0)\n for item in order.cart:\n if item.quantity >= 20:\n discount += item.total() * Decimal('0.1')\n return discount","def bulk_item(order):\n discount = 0\n for item in order.cart:\n if item.quantity >= 20:\n discount += item.total() * .1\n return discount","def _compute_taxed_lst_price2(self):\n company_id = self._context.get(\n 'company_id', self.env.user.company_id.id)\n for product in self:\n product.taxed_lst_price2 = product.taxes_id.filtered(\n lambda x: x.company_id.id == company_id).compute_all(\n product.list_price2,\n self.env.user.company_id.currency_id,\n product=product)['total_included']","def recalc_for_combo_birds(self, early_birds):\n\n for early_bird in early_birds:\n if early_bird.price_type == 1:\n self.recalc_for_bird(early_bird)\n return self.rates","def fin_advice(input_data, num_advice=3):\n # Initialize the advice list\n advice = []\n # Compute the Z-score for a certain company\n inc_rate = compute_increase_rate(input_data)\n zscore = {}\n # Map each Z-Score to each company\n for comp_name in input_data.keys():\n zscore[comp_name] = compute_zscore(inc_rate, comp_name)\n # Reverse the dictionary z-score. (This help to find the max z-score)\n rev_zscore = rev_dict(zscore)\n for i in range(num_advice):\n if len(rev_zscore) == 0:\n break\n # Find and delete the company with the greatest absolute z-score\n use_zscore = max(rev_zscore.keys())\n use_comp = rev_zscore[use_zscore]\n del rev_zscore[use_zscore]\n # Determine the advice given for that certain company\n if zscore[use_comp] > 1:\n advice.append(\"Sell \" + use_comp + \" Stock\")\n elif zscore[use_comp] < -1:\n advice.append(\"Buy \" + use_comp + \" Stock\")\n else:\n advice.append(\"Keep \" + use_comp + \" Stock\")\n return advice","def lot_leverage(self): \n return 20"],"string":"[\n \"def _compute_offers_to_send(self) -> Dict[Tuple[float, float], float]:\\n partial_asgt = self._neighbors_values.copy()\\n offers = dict()\\n\\n for limited_asgt in generate_assignment_as_dict([self.variable, self._partner]):\\n partial_asgt.update(limited_asgt)\\n cost = self._compute_cost(**partial_asgt)\\n if self.logger.isEnabledFor(logging.DEBUG):\\n self.logger.debug(\\n f\\\"looking for offer : {partial_asgt} - cost {cost}\\\"\\n f\\\" current {self.current_cost} {self._mode}\\\"\\n )\\n\\n if (self.current_cost > cost and self._mode == \\\"min\\\") or (\\n self.current_cost < cost and self._mode == \\\"max\\\"\\n ):\\n offers[(limited_asgt[self.name], limited_asgt[self._partner.name])] = (\\n self.current_cost - cost\\n )\\n return offers\",\n \"def apply_discounts(self):\\n # for each valid discount...\\n for discount in list(DiscountTypes):\\n # only apply the discount if it is set in the cart\\n if(discount in self.cart.discounts):\\n getattr(self, discount.value)()\",\n \"def calculate_adoption_fee(cursor, dog_id):\\n query = f\\\"SELECT Dog.dogID, IF (Dog.surrender_by_animal_control = 1, 0.15 * sum( Expense.amount ), 1.15 * sum( Expense.amount )) \\\" \\\\\\n f\\\"FROM Dog LEFT JOIN Expense ON Dog.dogID = Expense.dogID LEFT JOIN Adoption ON Adoption.dog = Dog.dogID \\\" \\\\\\n f\\\"WHERE Dog.dogID = {dog_id};\\\"\\n cursor.execute(query)\\n query_result = cursor.fetchall()\\n return query_result\",\n \"def decider(wallet):\\n found = False\\n if len(wallet.get_cards()) == 0:\\n return found\\n elif len(wallet.get_cards()) == 1:\\n card = wallet.get_cards()[0]\\n found = list()\\n found.append(card.get_issuer())\\n found.append(card.get_card_name())\\n found.append(-1)\\n return found\\n \\\"\\\"\\\"\\n First, we need to check for any valid SUB. If so, if there's one,\\n then that will be selected, otherwise, narrow the options to just \\n those with active sign_up_bonus and do the usual process.\\n \\\"\\\"\\\"\\n sub_cards = list()\\n subs = False\\n for card in wallet.get_cards():\\n sub = card.get_sign_up_bonus()\\n if sub.check_active():\\n sub_cards.append(card)\\n subs = True\\n if len(sub_cards) == 1:\\n found = list()\\n card = sub_cards[0]\\n found.append(card.get_issuer())\\n found.append(card.get_card_name())\\n found.append(0)\\n return found\\n elif len(sub_cards) > 1:\\n subs = True\\n category = decider_menu()\\n # PayPal is currently a quarterly category on several cards\\n paypal = \\\"\\\"\\n while paypal != \\\"N\\\" and paypal != \\\"Y\\\":\\n paypal = input(\\n \\\"Will you be purchasing through PayPal? (Y/N): \\\")\\n if paypal == \\\"Y\\\":\\n category = category + \\\"(PayPal)\\\"\\n break\\n elif paypal == \\\"N\\\":\\n break\\n else:\\n print(\\\"Invalid input\\\")\\n main_categories = wallet.get_generic_category_names()\\n if category in main_categories:\\n best_card = wallet.find_best_for_category(category)\\n found = list()\\n found.append(best_card.get_issuer())\\n found.append(best_card.get_card_name())\\n found.append(category)\\n value = best_card.check_categories(category)\\n if best_card.get_sign_up_bonus().check_active():\\n value += best_card.get_sign_up_bonus().get_return_on_spend() * 100\\n found.append(value)\\n return found\\n best = list()\\n best.append(0)\\n best.append(0)\\n \\\"\\\"\\\"\\n Here, depending on whether of not there are active sign-up bonuses, the\\n function will go through each card in the wallet to find the best value.\\n A future goal is implementing the Wallet class, in which I will have a \\n dictionary attribute which will contain the best card mapped to its\\n category i.e. {\\\"dining\\\":AMEX Gold}, and whenever new cards are added, it\\n will check then so as to prevent algorithmic backups which occur now.\\n \\\"\\\"\\\"\\n card_list = wallet.get_cards()\\n if subs:\\n card_list = sub_cards\\n for card in card_list:\\n sub = card.get_sign_up_bonus()\\n value = card.check_categories(category)\\n if \\\"(\\\" in category:\\n if \\\"PayPal\\\" in category:\\n category = category[:len(category) - 8]\\n if (card.check_categories(\\\"quarterly\\\") !=\\n card.check_categories(\\\"else\\\")):\\n value = card.check_categories(\\\"quarterly\\\")\\n value += card.check_categories(category)\\n if \\\"IHG\\\" in category:\\n if value != 25 * .6:\\n value = card.check_categories(\\\"travel\\\")\\n if subs:\\n value += sub.get_return_on_spend() * 100\\n if value > best[0]:\\n best[0] = value\\n best[1] = card\\n if subs:\\n print(\\\"Note: This recommendation is made because\\\"\\n \\\" of a sign-up bonus, not only multipliers!\\\")\\n found = list()\\n card = best[1]\\n found.append(card.get_issuer())\\n found.append(card.get_card_name())\\n found.append(category)\\n found.append(best[0])\\n return found\",\n \"def cost(books):\\n\\n num_types = len(set(books))\\n assert num_types < 6, \\\"Discounts indeterminate\\\"\\n return DISCOUNT_TABLE.get(num_types, 0) * 8 * len(books) * 100\",\n \"def filter_viable_offers(want, have, offers) -> List:\\n return [x for x in offers if is_offer_viable(want, have, x) is True]\",\n \"def duty_free(price: int, discount: int, holiday_cost: int) -> int:\\n if holiday_cost == 500:\\n return holiday_cost\\n\\n discount /= 100\\n price = holiday_cost / (price * discount)\\n price = int(price)\\n return price\",\n \"def discount(self,discountFactor,type='geometric'):\\n for e in self.estimators:\\n e.discount(discountFactor,type)\\n return\",\n \"def get_applicable_discounts(self, actual_volume):\\n\\n applicable_discounts = []\\n residual = {}\\n\\n min_vol_criteria, product_vs_offer, base_product_vs_offer_product = self.get_product_vs_offer_and_volume(self.discounts)\\n\\n for basket_item, volume in actual_volume.items():\\n offer_on_product = base_product_vs_offer_product.get(basket_item)\\n if min_vol_criteria.get(basket_item):\\n if volume >= min_vol_criteria.get(basket_item) and actual_volume.get(offer_on_product):\\n applicable_discounts.append(product_vs_offer.get(basket_item))\\n else:\\n residual[basket_item.upper()] = volume\\n else:\\n for prod, offering in base_product_vs_offer_product.items():\\n if (offering == basket_item) and (prod not in actual_volume):\\n residual[basket_item.upper()] = volume\\n # else:\\n\\n\\n if \\\"APPL\\\" in applicable_discounts and \\\"APOM\\\" in applicable_discounts:\\n applicable_discounts.remove(\\\"APOM\\\")\\n residual['OM1'] = actual_volume.get('om1')\\n\\n return applicable_discounts, residual\",\n \"def apply_discounts(order_obj):\\n all_dedits = order_obj.debits\\n other_debit = filter(lambda x: x[\\\"coll_name\\\"] != discounts.Discount.coll_name(), all_dedits)\\n all_discounts = discounts.get_all()\\n valid_discounts = []\\n for item_dic in order_obj.items:\\n for d in all_discounts:\\n item_obj = items.get(coerce_bson_id(item_dic[\\\"obj_id\\\"]))\\n if item_obj is None: continue\\n if discounts.valid_on_item(d, item_obj):\\n valid_discounts += [{\\n \\\"obj_id\\\": d._id,\\n \\\"coll_name\\\": discounts.Discount.coll_name(),\\n \\\"amount\\\": discounts.discounted_value(item_obj.price, d),\\n }]\\n break\\n order_obj.debits = other_debit + valid_discounts\\n return valid_discounts\",\n \"def is_offer_viable(want, have, offer) -> bool:\\n # lower tier value means higher value\\n have_tier = constants.currencies[have][\\\"tier\\\"]\\n want_tier = constants.currencies[want][\\\"tier\\\"]\\n conversion_rate = offer[\\\"conversion_rate\\\"]\\n\\n # if `want` is worth more than `have`, the conversion rate cannot be bigger than 1\\n # This should be enough to successfully filter out price-fixing offers.\\n if have_tier > want_tier and conversion_rate > 1:\\n return False\\n\\n # if `have` is worth more than `have`, the conversion rate cannot be smaller than 1\\n # We shouldn't need this case, since conversion rates below 1 will typically\\n # result in unprofitable conversions and are therefore ignored anyway.\\n # if have_tier < want_tier and conversion_rate < 1:\\n # return False\\n\\n return True\",\n \"def check_for_offer(self, bid, commodity, limit, actual, quantity, price):\\n if bid:\\n if len(self.trades[\\\"buys\\\"][commodity]) == 0:\\n return 0\\n else: # tally up how much trying to buy.\\n total = 0.0\\n total_price = 0.0\\n for offer in self.trades[\\\"buys\\\"][commodity]:\\n total += offer.quantity\\n total_price += offer.price\\n\\n avg_price = total_price / len(self.trades[\\\"buys\\\"][commodity])\\n\\n # if total < limit:\\n # #PLACE MORE BIDS.\\n return total\\n\\n else:\\n if len(self.trades[\\\"asks\\\"][commodity]) == 0:\\n return 0\\n else: # tally up how much trying to buy.\\n total = 0.0\\n total_price = 0.0\\n for offer in self.trades[\\\"asks\\\"][commodity]:\\n total += offer.quantity\\n total_price += offer.price\\n\\n avg_price = total_price / len(self.trades[\\\"asks\\\"][commodity])\\n #\\n # if total < limit:\\n # #PLACE MORE asks.\\n # return total\\n # if total < limit:\\n # #PLACE MORE asks.\\n return total # - limit\",\n \"def budget_for_necessities():\\n print(\\\"========== Displaying hotel options ==========\\\")\\n for i in range(len(hotel_list)):\\n print(\\\" -- Enter\\\", i+1, \\\"to choose -- \\\")\\n hotel_list[i].print_hotel()\\n print(\\\" \\\")\\n while True:\\n try:\\n hotel_num = int(input(\\\"Please choose your hotel option (Enter a number between 1 to 7): \\\"))\\n hotel_num -= 1\\n if hotel_num in range(len(hotel_list)): break\\n except ValueError:\\n print(\\\"Please enter an positive integer. Try again... \\\")\\n while True:\\n try:\\n hotel_stay = int(input(\\\"Please enter the duration (in days) of your stay: \\\"))\\n if hotel_stay > 0: break\\n except ValueError:\\n print(\\\"Please enter an positive integer. Try again... \\\")\\n user_hotel = hotel_list[hotel_num]\\n user_hotel_price = user_hotel.get_price()\\n user_hotel_name = user_hotel.get_name()\\n # display car option and ask for user input\\n print(\\\"\\\\n======== Displaying rental car options =========\\\")\\n for i in range(len(car_list)):\\n print(\\\" -- Enter\\\", i+1, \\\"to choose -- \\\")\\n car_list[i].print_car()\\n print(\\\" \\\")\\n while True:\\n try:\\n car_num = int(input(\\\"Please choose your car rental option (Enter a number between 1 to 6): \\\"))\\n car_num -= 1\\n if car_num in range(len(hotel_list)): break\\n except ValueError:\\n print(\\\"Please enter an positive integer. Try again... \\\")\\n while True:\\n try:\\n car_rental_day = int(input(\\\"Please enter the duration (in days) of your car rental: \\\"))\\n if car_rental_day > 0: break\\n except ValueError:\\n print(\\\"Please enter an positive integer. Try again... \\\")\\n # calculate user's total cost for car rental and hotel\\n user_car = car_list[car_num]\\n user_car_price = user_car.get_price()\\n user_car_name = user_car.get_name()\\n total_hotel_cost = hotel_stay * user_hotel_price\\n total_car_rental_cost = car_rental_day * user_car_price\\n print(\\\"\\\\n=== Displaying your hotel and car rental information ===\\\")\\n print(\\\"Hotel: \\\", user_hotel.get_name())\\n print(\\\"Hotel total cost: $\\\", total_hotel_cost)\\n print(\\\"Car Rental: \\\", user_car.get_name())\\n print(\\\"Car rental total cost: $\\\", total_car_rental_cost)\\n print(\\\" \\\")\\n # calculate remaining budget based on hotel and car's cost and/or ask for higher budget\\n user_budget.calculate_new_budget(total_hotel_cost + total_car_rental_cost)\\n print(\\\" \\\")\\n return total_hotel_cost, total_car_rental_cost, user_hotel_name, user_car_name\",\n \"def calc_earning(self, data=None):\\n result = Result()\\n if data is None:\\n data = self.security\\n self.calcDecision()\\n first_purchase_method = self.check_first_purchase_method()\\n for i in np.arange(len(data['Close'])):\\n if data['FinalDecision'].iloc[i] is None:\\n pass\\n elif data['FinalDecision'].iloc[i] == TransactionType.BUY:\\n if data['FinalDecision'].iloc[i-1] == TransactionType.BUY:\\n pass\\n else:\\n if (self.buys_made + self.sells_made) == 0:\\n if first_purchase_method == FirstTransactionType.INIT_CAPITAL:\\n self.shares_own = int((self.init_capital/data['Close'].iloc[i]))\\n self.buys_made += 1\\n elif first_purchase_method == FirstTransactionType.STOCK_QUANTITY:\\n self.shares_own = self.stock_quantity\\n self.buys_made += 1\\n else:\\n self.shares_own = int(self.final_capital / data['Close'].iloc[i])\\n self.final_capital = self.final_capital % data['Close'].iloc[i]\\n #print(self.shares_own)\\n\\n elif data['FinalDecision'].iloc[i] == TransactionType.SELL:\\n if data['FinalDecision'].iloc[i-1] == TransactionType.SELL:\\n pass\\n else:\\n if (self.buys_made + self.sells_made) == 0:\\n pass\\n else:\\n self.final_capital += self.shares_own * data['Close'].iloc[i]\\n self.shares_own = 0\\n self.sells_made +=1\\n #Checar si es el momento mas alto o bajo de ganancias\\n if self.shares_own == 0:\\n if (self.highest_point is None\\n or self.highest_point < self.final_capital):\\n self.highest_point = self.final_capital\\n if (self.lowest_point is None\\n or self.lowest_point > self.final_capital\\n or self.lowest_point == 0):\\n self.lowest_point = self.final_capital\\n else:\\n if (self.highest_point is None\\n or self.highest_point < (self.shares_own * data['Close'].iloc[i])):\\n self.highest_point = self.final_capital\\n if (self.lowest_point is None\\n or self.lowest_point > (self.shares_own * data['Close'].iloc[i])\\n or self.lowest_point == 0):\\n self.lowest_point = self.final_capital\\n self.calcRealFinalCapital()\\n self.calcDiferencePercentage()\",\n \"def get_basket_with_discounts(self, actual_volume, applicable_discounts, residual):\\n\\n # product_vs_min_volume, product_offer = self.get_product_vs_offer_and_volume(self.discounts)\\n pr = Product('items')\\n product_prices = pr.product_meta\\n\\n basket = []\\n\\n for discount in applicable_discounts:\\n offer_info = Offer('discount', discount)\\n\\n if offer_info.offer_type.lower() == \\\"discount\\\":\\n temp_basket = self.get_basket_items_discount(offer_info, actual_volume, product_prices)\\n for item in temp_basket:\\n basket.append(item)\\n elif offer_info.offer_type.lower() == \\\"pricedrop\\\":\\n temp_basket = self.get_basket_items_pricedrop(offer_info, actual_volume, product_prices)\\n for item in temp_basket:\\n basket.append(item)\\n\\n if residual:\\n for product, volume in residual.items():\\n for i in range(0, volume):\\n basket.append((product, product_prices.get(product.lower()).get('price')))\\n\\n return basket\",\n \"async def _create_offer_for_ids(\\n self, offer: Dict[int, int]\\n ) -> Tuple[bool, Optional[TradeRecord], Optional[str]]:\\n spend_bundle = None\\n try:\\n for id in offer.keys():\\n amount = offer[id]\\n wallet_id = uint32(int(id))\\n wallet = self.wallet_state_manager.wallets[wallet_id]\\n if isinstance(wallet, CCWallet):\\n balance = await wallet.get_confirmed_balance()\\n if balance < abs(amount) and amount < 0:\\n raise Exception(f\\\"insufficient funds in wallet {wallet_id}\\\")\\n if amount > 0:\\n if spend_bundle is None:\\n to_exclude: List[Coin] = []\\n else:\\n to_exclude = spend_bundle.removals()\\n zero_spend_bundle: Optional[\\n SpendBundle\\n ] = await wallet.generate_zero_val_coin(False, to_exclude)\\n\\n if zero_spend_bundle is None:\\n raise Exception(\\n \\\"Failed to generate offer. Zero value coin not created.\\\"\\n )\\n if spend_bundle is None:\\n spend_bundle = zero_spend_bundle\\n else:\\n spend_bundle = SpendBundle.aggregate(\\n [spend_bundle, zero_spend_bundle]\\n )\\n\\n additions = zero_spend_bundle.additions()\\n removals = zero_spend_bundle.removals()\\n zero_val_coin: Optional[Coin] = None\\n for add in additions:\\n if add not in removals and add.amount == 0:\\n zero_val_coin = add\\n new_spend_bundle = await wallet.create_spend_bundle_relative_amount(\\n amount, zero_val_coin\\n )\\n else:\\n new_spend_bundle = await wallet.create_spend_bundle_relative_amount(\\n amount\\n )\\n elif isinstance(wallet, Wallet):\\n if spend_bundle is None:\\n to_exclude = []\\n else:\\n to_exclude = spend_bundle.removals()\\n new_spend_bundle = await wallet.create_spend_bundle_relative_chia(\\n amount, to_exclude\\n )\\n else:\\n return False, None, \\\"unsupported wallet type\\\"\\n if new_spend_bundle is None or new_spend_bundle.removals() == []:\\n raise Exception(f\\\"Wallet {id} was unable to create offer.\\\")\\n if spend_bundle is None:\\n spend_bundle = new_spend_bundle\\n else:\\n spend_bundle = SpendBundle.aggregate(\\n [spend_bundle, new_spend_bundle]\\n )\\n\\n if spend_bundle is None:\\n return False, None, None\\n\\n now = uint64(int(time.time()))\\n trade_offer: TradeRecord = TradeRecord(\\n confirmed_at_index=uint32(0),\\n accepted_at_time=None,\\n created_at_time=now,\\n my_offer=True,\\n sent=uint32(0),\\n spend_bundle=spend_bundle,\\n tx_spend_bundle=None,\\n additions=spend_bundle.additions(),\\n removals=spend_bundle.removals(),\\n trade_id=std_hash(spend_bundle.name() + bytes(now)),\\n status=uint32(TradeStatus.PENDING_ACCEPT.value),\\n sent_to=[],\\n )\\n return True, trade_offer, None\\n except Exception as e:\\n tb = traceback.format_exc()\\n self.log.error(f\\\"Error with creating trade offer: {type(e)}{tb}\\\")\\n return False, None, str(e)\",\n \"def is_satisfied(self, offer, basket):\\n covered_ids = []\\n for line in basket.all_lines():\\n if not line.is_available_for_offer_discount(offer):\\n continue\\n product = line.product\\n if self.can_apply_condition(line) and product.id not in covered_ids:\\n covered_ids.append(product.id)\\n if len(covered_ids) >= self.value:\\n return True\\n return False\",\n \"def apply_20percent_shipping_offer(self):\\n product_range = Range.objects.create(\\n name=\\\"All products range\\\", includes_all_products=True\\n )\\n benefit = Benefit.objects.create(\\n range=product_range, type=Benefit.SHIPPING_PERCENTAGE, value=20\\n )\\n offer = factories.create_offer(product_range=product_range, benefit=benefit)\\n Applicator().apply_offers(self.basket, [offer])\\n return offer\",\n \"def test_market_1_2(self):\\n\\n def check_1_2(buyers: List[float], sellers: List[float], expected_num_of_deals: int,\\n expected_prices: List[float]):\\n market = Market([\\n AgentCategory(\\\"buyer\\\", buyers),\\n AgentCategory(\\\"seller\\\", sellers),\\n ])\\n ps_recipe = [1, 2]\\n self._check_market(market, ps_recipe, expected_num_of_deals, expected_prices)\\n\\n check_1_2(buyers=[9], sellers=[-4, -3],\\n expected_num_of_deals=0, expected_prices=[9, -4.5])\\n check_1_2(buyers=[9, 8, 7, 6], sellers=[-6, -5, -4, -3, -2, -1],\\n expected_num_of_deals=1, expected_prices=[8, -4])\\n check_1_2(buyers=[9, 8], sellers=[-4, -3, -2, -1],\\n expected_num_of_deals=1, expected_prices=[8, -4])\\n check_1_2(buyers=[9, 8], sellers=[-6, -3, -2, -1],\\n expected_num_of_deals=1, expected_prices=[8, -4])\\n check_1_2(buyers=[9, 8], sellers=[-4, -3, -2, -1],\\n expected_num_of_deals=1, expected_prices=[8, -4])\\n\\n # PRICE CROSSES ZERO AT FIRST PHASE\\n check_1_2(buyers=list(range(20)), sellers=[-3, -2, -1],\\n expected_num_of_deals=1, expected_prices=[18, -9])\",\n \"def base_offers_list():\\n offers_list = Offers()\\n offers_list.add_new_offer('Offer1', 'Baked Beans', 3, 1)\\n offers_list.add_new_offer('Offer2', 'Sardines', 1, 0.25)\\n return offers_list\",\n \"def _find_best_offer(\\n self, all_offers: List[Tuple[str, Dict]]\\n ) -> Tuple[List, float]:\\n bests, best_gain = [], 0\\n\\n for partner, offers in all_offers:\\n partial_asgt = self._neighbors_values.copy()\\n current_partner = self._neighbor_var(partner)\\n\\n # Filter out the constraints linking those two variables to avoid\\n # counting their cost twice.\\n shared = find_dependent_relations(current_partner, self._constraints)\\n concerned = [rel for rel in self._constraints if rel not in shared]\\n\\n for (val_p, my_offer_val), partner_local_gain in offers.items():\\n partial_asgt.update({partner: val_p, self.variable.name: my_offer_val})\\n\\n # Then we evaluate the agent constraint's for the offer\\n # and add the partner's local gain.\\n cost = assignment_cost(partial_asgt, concerned)\\n global_gain = self.current_cost - cost + partner_local_gain\\n\\n if (global_gain > best_gain and self._mode == \\\"min\\\") or (\\n global_gain < best_gain and self._mode == \\\"max\\\"\\n ):\\n bests = [(val_p, my_offer_val, partner)]\\n best_gain = global_gain\\n elif global_gain == best_gain:\\n bests.append((val_p, my_offer_val, partner))\\n\\n return bests, best_gain\",\n \"def decide_trade_plan(self):\\n self.trade_amount = 5\\n most_demanded_goods = self.home.market.goods_demand_ratio(day_range=1)\\n most_demanded_goods = sorted(most_demanded_goods.items(), key=lambda i: i[1], reverse=True)\\n\\n # if we already had a trade good, refresh ideal inventory\\n if self.trade_good:\\n self.update_ideal_inventory()\\n\\n if DEBUG: print(\\\"Finding a Good to trade:\\\")\\n\\n for good, demand in most_demanded_goods:\\n if demand > 0:\\n # find nearby provinces where this has inventory and the price is lower\\n price_at_home = self.home.market.mean_price(good)\\n if DEBUG: print(\\\"Good: {}, Demand: {}, Price: ${}\\\".format(good.title, demand, price_at_home))\\n neighboring_markets = [p.market for p in self.location.owned_neighbors]\\n neighboring_markets = [m for m in neighboring_markets if m.supply_for(good) > self.trade_amount]\\n neighboring_markets.sort(key=lambda m: m.supply_for(good), reverse=True)\\n\\n if len(neighboring_markets) > 0:\\n # we found places where this good is cheaper and in inventory\\n target = neighboring_markets[0].location\\n price_at_target = target.market.mean_price(good)\\n\\n # only trade with prices where we can make money\\n if price_at_home > price_at_target:\\n offset = 0\\n if good is Good.bread:\\n offset = 1\\n self.inventory.set_ideal(good, self.trade_amount + offset)\\n self.trade_location = target\\n if DEBUG:\\n print(\\\"\\\\tTarget: {}, Supply: {}, Price: ${}, Price at home: ${}\\\".format(\\n self.trade_location.name,\\n self.trade_location.market.supply_for(good),\\n self.trade_location.market.mean_price(good),\\n price_at_home)\\n )\\n self.trade_good = good\\n return\\n else:\\n if DEBUG: print(\\\"\\\\tPrice is higher at target (home: ${} target: ${})\\\".format(price_at_home, price_at_target))\\n else:\\n if DEBUG: print(\\\"\\\\tNo markets selling {} found\\\".format(good))\",\n \"def options_to_withdraw(self, amount):\\n counter = PaperMoneyCounter() # aux class\\n options = [] # options to withdraw\\n remaining_cash = 0 # aux var\\n\\n if (amount % 20 == 0 or amount % 50 == 0) and (amount <= 1000): # is it allowed to withdraw?\\n # prioritizing 100-dollar bills\\n qtt_100s = counter.how_many_100s(amount)\\n remaining_cash = counter.remaining_cash_without_100s(amount)\\n\\n qtt_50s = counter.how_many_50s(remaining_cash)\\n remaining_cash = counter.remaining_cash_without_50s(remaining_cash)\\n\\n qtt_20s = counter.how_many_20s(remaining_cash)\\n remaining_cash = counter.remaining_cash_without_20s(remaining_cash)\\n\\n if counter.cash(qtt_100s, qtt_50s, qtt_20s) == amount:\\n options.append([int(qtt_100s), int(qtt_50s), int(qtt_20s)])\\n\\n # prioritizing 50-dollar bills\\n qtt_100s = 0\\n\\n qtt_50s = counter.how_many_50s(amount)\\n remaining_cash = counter.remaining_cash_without_50s(amount)\\n\\n qtt_20s = counter.how_many_20s(remaining_cash)\\n remaining_cash = counter.remaining_cash_without_20s(remaining_cash)\\n\\n if counter.cash(qtt_100s, qtt_50s, qtt_20s) == amount:\\n if not(options[0] == [qtt_100s, qtt_50s, qtt_20s]):\\n options.append([int(qtt_100s), int(qtt_50s), int(qtt_20s)])\\n\\n # prioritizing 20-dollar bills\\n qtt_100s = 0\\n\\n qtt_50s = 0\\n\\n qtt_20s = counter.how_many_20s(amount)\\n\\n if counter.cash(qtt_100s, qtt_50s, qtt_20s) == amount:\\n if not(options[0] == [qtt_100s, qtt_50s, qtt_20s]):\\n if not(options[1] == [qtt_100s, qtt_50s, qtt_20s]):\\n options.append([int(qtt_100s), int(qtt_50s), int(qtt_20s)])\\n\\n return options\\n\\n return None # if it wasn't allowed to withdraw\",\n \"def option_price(volatilities,\\n strikes,\\n expiries,\\n spots=None,\\n forwards=None,\\n discount_factors=None,\\n is_call_options=None,\\n dtype=None,\\n name=None):\\n if (spots is None) == (forwards is None):\\n raise ValueError('Either spots or forwards must be supplied but not both.')\\n\\n with tf.compat.v1.name_scope(\\n name,\\n default_name='option_price',\\n values=[\\n forwards, strikes, volatilities, expiries, discount_factors,\\n is_call_options\\n ]):\\n strikes = tf.convert_to_tensor(strikes, dtype=dtype, name='strikes')\\n volatilities = tf.convert_to_tensor(\\n volatilities, dtype=dtype, name='volatilities')\\n expiries = tf.convert_to_tensor(expiries, dtype=dtype, name='expiries')\\n\\n if discount_factors is None:\\n discount_factors = tf.convert_to_tensor(\\n 1.0, dtype=dtype, name='discount_factors')\\n else:\\n discount_factors = tf.convert_to_tensor(\\n discount_factors, dtype=dtype, name='discount_factors')\\n\\n if forwards is not None:\\n forwards = tf.convert_to_tensor(forwards, dtype=dtype, name='forwards')\\n else:\\n spots = tf.convert_to_tensor(spots, dtype=dtype, name='spots')\\n forwards = spots / discount_factors\\n\\n sqrt_var = volatilities * tf.math.sqrt(expiries)\\n d1 = (tf.math.log(forwards / strikes) + sqrt_var * sqrt_var / 2) / sqrt_var\\n d2 = d1 - sqrt_var\\n undiscounted_calls = forwards * _ncdf(d1) - strikes * _ncdf(d2)\\n if is_call_options is None:\\n return discount_factors * undiscounted_calls\\n undiscounted_forward = forwards - strikes\\n undiscounted_puts = undiscounted_calls - undiscounted_forward\\n predicate = tf.broadcast_to(is_call_options, tf.shape(undiscounted_calls))\\n return discount_factors * tf.where(predicate, undiscounted_calls,\\n undiscounted_puts)\",\n \"def buyEssentials(self, amount=5): \\n ls = self.location.getEssentialList()\\n print(f\\\"{bcolors.WHITE}\\\\nGreat job so far! Now, it's time to arm yourself with some essentials you would need to survive in the {self.location.worldType}.{bcolors.ENDC}\\\")\\n print(f\\\"\\\\n{bcolors.CYAN}Following are the essential items for {self.location.worldName}. Please choose a minimum of 3 items to proceed.{bcolors.ENDC}\\\")\\n outputList = [str(i+1) + \\\". \\\" + ls[i] + \\\"\\\\n\\\" for i in range(len(ls))]\\n print(f\\\"\\\\n{bcolors.CYAN}{''.join(outputList)}{bcolors.ENDC}\\\")\\n sizeEssentialList = len(ls)\\n essentialsList = []\\n\\n \\n choiceInput = False\\n while choiceInput is False:\\n choices = input(f\\\"{bcolors.CYAN}Input your selection as numbers 1, 2, 3, 4, or 5 separated by comma: {bcolors.ENDC}\\\")\\n choiceInput = True\\n choices = choices.split(',')\\n for choice in choices:\\n if choice not in ('1', '2', '3', '4', '5', 'quit', 'QUIT', 'Quit'):\\n print(f\\\"\\\\n{bcolors.PINK}Please enter a valid Input{bcolors.ENDC}\\\\n\\\")\\n choiceInput = False\\n break\\n \\n for choice in choices:\\n if choice.capitalize() == \\\"Quit\\\":\\n # User input \\\"Quit\\\" at this stage. So, just quit the game.\\n return choices\\n \\n\\n try:\\n # Convert input to integer for index in essentialList item\\n choices = [int(i) for i in choices]\\n except ValueError:\\n # If input is not a number, Quit gracefully!\\n print(\\\"Input is not a number. Quit\\\")\\n return essentialsList\\n\\n if max(choices) > sizeEssentialList:\\n print(f\\\"Invalid input! Input is not in essentialList\\\")\\n return essentialsList\\n\\n for j in choices:\\n if self.spendCoin(amount):\\n essentialsList.append(ls[j-1])\\n else:\\n print(f\\\"You don't have enough money to buy {j}. You only have {self.coins} coins left.\\\")\\n break\\n self.assets = essentialsList\\n print(f\\\"\\\\n{bcolors.WHITE}Thank you for buying the essentials. Now you are officially ready to enter into the {self.location.worldType}.\\\\nHere is your current asset bag with essential items and the available coins.{bcolors.ENDC}\\\")\\n print(f\\\"\\\\n{bcolors.YELLOW}Asset Bag Contents: {self.assets}\\\\nCoins: {self.coins}{bcolors.ENDC}\\\")\\n\\n return self.assets\",\n \"def test_market_1_1(self):\\n\\n def check_1_1(buyers: List[float], sellers: List[float], expected_num_of_deals: int,\\n expected_prices: List[float]):\\n market = Market([\\n AgentCategory(\\\"buyer\\\", buyers),\\n AgentCategory(\\\"seller\\\", sellers),\\n ])\\n ps_recipe = [1, 1]\\n self._check_market(market, ps_recipe, expected_num_of_deals, expected_prices)\\n\\n check_1_1(buyers=[9], sellers=[-4],\\n expected_num_of_deals=0, expected_prices=[None, None])\\n check_1_1(buyers=[9, 8], sellers=[-4],\\n expected_num_of_deals=0, expected_prices=[None, None])\\n check_1_1(buyers=[9], sellers=[-4, -3],\\n expected_num_of_deals=1, expected_prices=[4, -4])\\n check_1_1(buyers=[9, 8], sellers=[-4, -3],\\n expected_num_of_deals=1, expected_prices=[8, -8])\\n\\n # ALL POSITIVE VALUES:\\n check_1_1(buyers=[4, 3], sellers=[9, 8],\\n expected_num_of_deals=1, expected_prices=[3, -3])\\n\\n # ALL NEGATIVE VALUES:\\n check_1_1(buyers=[-4, -3], sellers=[-9, -8],\\n expected_num_of_deals=0, expected_prices=[None, None])\\n\\n # LARGER EXAMPLE\\n check_1_1(buyers=[19, 17, 15, 13, 11, 9], sellers=[-12, -10, -8, -6, -4, -2],\\n expected_num_of_deals=4, expected_prices=[11, -11])\",\n \"def dp_all_td(foods, done_count, cal_left, pro_left, fat_left, carb_left):\\n # using a tolerance of 50 cal/5 carb/fat/pro\\n if cal_left < 50 and pro_left < 5 and fat_left < 5 and carb_left < 5:\\n return {}\\n\\n # done going through all the foods\\n if len(foods) <= done_count:\\n return {0: 9999999}\\n\\n # need everything in ints for data struct\\n cal_left = int(cal_left)\\n pro_left = int(pro_left)\\n fat_left = int(fat_left)\\n carb_left = int(carb_left)\\n\\n # try to use solution already calculated\\n try:\\n foods_used_a = \\\\\\n dp[(str(done_count + 1) + '-'\\n + str(cal_left) + '-'\\n + str(pro_left) + '-'\\n + str(fat_left) + '-'\\n + str(carb_left))]\\n except KeyError:\\n # calculate scenario where you don't use current food\\n foods_used_a = dp_all_td(\\n foods, done_count + 1, cal_left, pro_left, fat_left, carb_left)\\n\\n # if current food violates reqs, then don't bother calc-ing using it\\n if ((cal_left - foods[done_count]['calories']) < -50\\n or (pro_left - foods[done_count]['protein']) < -5\\n or (fat_left - foods[done_count]['fat']) < -5\\n or (carb_left - foods[done_count]['carbs']) < -5):\\n return foods_used_a\\n\\n # try to use solution already calculated\\n try:\\n foods_used_b = \\\\\\n dp[(str(done_count) + '-'\\n + str(cal_left - foods[done_count]['calories']) + '-'\\n + str(pro_left - foods[done_count]['protein']) + '-'\\n + str(fat_left - foods[done_count]['fat']) + '-'\\n + str(carb_left - foods[done_count]['carbs']))]\\n except KeyError:\\n # calculate for scenario where you use the current food\\n foods_used_b = dp_all_td(\\n foods,\\n done_count,\\n cal_left - foods[done_count]['calories'],\\n pro_left - foods[done_count]['protein'],\\n fat_left - foods[done_count]['fat'],\\n carb_left - foods[done_count]['carbs'])\\n try:\\n foods_used_b[done_count] += 1\\n except KeyError:\\n foods_used_b[done_count] = 1\\n\\n # store cheapest, then return it\\n if cost(foods, foods_used_a) > cost(foods, foods_used_b):\\n dp[(str(done_count) + '-'\\n + str(cal_left) + '-'\\n + str(pro_left) + '-'\\n + str(fat_left) + '-'\\n + str(carb_left))] = foods_used_b\\n return foods_used_b\\n dp[(str(done_count) + '-'\\n + str(cal_left) + '-'\\n + str(pro_left) + '-'\\n + str(fat_left) + '-'\\n + str(carb_left))] = foods_used_a\\n return foods_used_a\",\n \"def evaluate_power(soldier_list: List[Soldier]):\\n inf_count = 0\\n inf_avg_weapon = 0.0\\n inf_avg_armor = 0.0\\n arc_count = 0\\n arc_avg_weapon = 0.0\\n arc_avg_armor = 0.0\\n cvl_count = 0\\n cvl_avg_weapon = 0.0\\n cvl_avg_armor = 0.0\\n \\n for soldier in soldier_list:\\n ################################# YOUR CODE HERE #################################\\n if soldier.typecode == \\\"ARC\\\":\\n arc_count += 1\\n arc_avg_armor += soldier.armor\\n arc_avg_weapon += soldier.weapon\\n elif soldier.typecode == \\\"INF\\\":\\n inf_count += 1\\n inf_avg_armor += soldier.armor\\n inf_avg_weapon += soldier.weapon\\n elif soldier.typecode == \\\"CVL\\\":\\n cvl_count += 1\\n cvl_avg_armor += soldier.armor\\n cvl_avg_weapon += soldier.weapon\\n if arc_count != 0:\\n arc_avg_armor /= arc_count\\n arc_avg_weapon /= arc_count\\n\\n if cvl_count != 0:\\n cvl_avg_armor /= cvl_count\\n cvl_avg_weapon /= cvl_count\\n\\n if inf_count != 0:\\n inf_avg_armor /= inf_count\\n inf_avg_weapon /= inf_count\\n ##################################################################################\\n return (inf_count, inf_avg_weapon, inf_avg_armor), (arc_count, arc_avg_weapon, arc_avg_armor), (cvl_count, cvl_avg_weapon, cvl_avg_armor)\",\n \"def prep_visualize_cost(self, dat):\\n # Assume if null then there are no in-app purchases since all others have a value\\n dat['In-app Purchases'].fillna('0', inplace = True)\\n\\n # An app is only actually free if you cant buy anything once in it\\n dat['true_free'] = (dat.Price == 0) & (dat['In-app Purchases'] == '0')\\n\\n # Look at only if there is a base cost first and see if rating difference exists\\n\\n print('Investigate differences in ratings by free vs. paid App')\\n plt.hist(dat[dat.true_free == True]['Average User Rating'].dropna().values, label='Free', color = 'green')\\n plt.hist(dat[(dat.true_free == False) & (dat.Price != 0)]['Average User Rating'].dropna().values, label='Paid')\\n\\n plt.legend(loc='upper right')\\n plt.show()\\n\\n print('Many Apps included pain content within them. These should not truly be considered \\\"free\\\"')\\n\\n plt.hist(dat[dat.true_free == False]['Average User Rating'].dropna().values, label='Paid')\\n plt.hist(dat[dat.true_free == True]['Average User Rating'].dropna().values, label='Free', color = 'green')\\n\\n plt.legend(loc='upper right')\\n plt.show()\\n\\n print('Which are truly best?')\\n\\n non_true_free_rating = dat[dat.true_free == False]['Average User Rating'].dropna().mean()\\n in_app_purchases = dat[(dat.true_free == False) & (dat.Price == 0)]['Average User Rating'].dropna().mean()\\n true_free_rating = dat[dat.true_free == True]['Average User Rating'].dropna().mean()\\n zero_cost_rating = dat[dat.Price == 0]['Average User Rating'].dropna().mean()\\n paid_rating = dat[dat.Price != 0]['Average User Rating'].dropna().mean()\\n\\n non_true_free_rating_cnt = dat[dat.true_free == False]['Average User Rating'].dropna().shape[0]\\n in_app_purchases_cnt = dat[(dat.true_free == False) & (dat.Price == 0)]['Average User Rating'].dropna().shape[0]\\n true_free_rating_cnt = dat[dat.true_free == True]['Average User Rating'].dropna().shape[0]\\n zero_cost_rating_cnt = dat[dat.Price == 0]['Average User Rating'].dropna().shape[0]\\n paid_rating_cnt = dat[dat.Price != 0]['Average User Rating'].dropna().shape[0]\\n\\n plt.bar(['Zero Cost','Paid App'],\\n [zero_cost_rating,paid_rating])\\n plt.ylim((3.8,4.18))\\n plt.xticks(rotation = 'vertical')\\n plt.show()\\n\\n plt.bar(['Truly Free','Any Cost'],\\n [true_free_rating,non_true_free_rating])\\n plt.ylim((3.8,4.18))\\n plt.xticks(rotation = 'vertical')\\n plt.show()\\n\\n plt.bar(['Zero Cost','Truly Free','Paid App','Any Cost','In app-purchases'],\\n [zero_cost_rating,true_free_rating,paid_rating,non_true_free_rating, in_app_purchases])\\n plt.ylim((3.8,4.18))\\n plt.xticks(rotation = 'vertical')\\n plt.show()\\n\\n N = 5\\n\\n ind = np.arange(N) # the x locations for the groups\\n width = 0.35 # the width of the bars\\n\\n fig, ax = plt.subplots()\\n rects1 = ax.bar(ind, [zero_cost_rating,true_free_rating,paid_rating,non_true_free_rating, in_app_purchases], \\n width, color='r')\\n\\n\\n # add some text for labels, title and axes ticks\\n ax.set_ylabel('Scores')\\n ax.set_title('Scores by group and gender')\\n ax.set_xticks(ind + width / 2)\\n ax.set_xticklabels(['Zero Cost','Truly Free','Paid App','Any Cost','In app-purchases'])\\n\\n plt.ylim((3.85,4.18))\\n\\n counts = [zero_cost_rating_cnt,true_free_rating_cnt,paid_rating_cnt,non_true_free_rating_cnt, in_app_purchases_cnt]\\n\\n for x, rect in zip(counts, rects1):\\n height = rect.get_height()\\n ax.text(rect.get_x() + rect.get_width()/2., height,\\n 'n = ' + str(x),\\n ha='center', va='bottom')\\n\\n plt.show()\",\n \"def get_discounts(table):\\n\\n discounts_money_spent = {}\\n discount_levels = [100, 1000, 5000]\\n discount_percents = [\\\"2%\\\", \\\"5%\\\", \\\"10%\\\"]\\n\\n customer_id_index = 2\\n amount_sold_index = 4\\n id_index = 0\\n product_id_index = 3\\n name_index = 1\\n price_index = 3\\n\\n store_table = store.get_table()\\n store.check_table(store_table)\\n\\n crm_table = crm.get_table('model/crm/customers.csv')\\n\\n for record in table:\\n product_id = record[product_id_index]\\n amount_sold = record[amount_sold_index]\\n customer_id = record[customer_id_index]\\n\\n for customer in crm_table:\\n customer_name = customer[name_index]\\n client_id = customer[id_index]\\n\\n if customer_id == client_id:\\n\\n for game in store_table:\\n game_id = game[id_index]\\n price = game[price_index]\\n if product_id == game_id:\\n money_spent = int(price) * int(amount_sold)\\n \\n if customer_name in discounts_money_spent:\\n discounts_money_spent[customer_name] += int(money_spent)\\n else:\\n discounts_money_spent[customer_name] = int(money_spent)\\n \\n discounts = {}\\n\\n for customer_name in discounts_money_spent:\\n money_spent = discounts_money_spent[customer_name]\\n if money_spent >= discount_levels[0] and money_spent < discount_levels[1]:\\n discount = discount_percents[0]\\n elif money_spent >= discount_levels[1] and money_spent < discount_levels[2]:\\n discount = discount_percents[1]\\n elif money_spent >= discount_levels[2]:\\n discount = discount_percents[2]\\n else:\\n discount = \\\"0%\\\"\\n\\n discounts[customer_name] = discount\\n\\n return discounts\",\n \"def dp_all(foods, cal_goal, pro_goal, carb_goal, fat_goal):\\n costs = init_four_d_array((cal_goal, pro_goal, carb_goal, fat_goal),\\n 999999999)\\n foods_used = init_four_d_array((cal_goal, pro_goal, carb_goal, fat_goal),\\n {})\\n\\n for i in range(cal_goal):\\n for j in range(pro_goal):\\n for k in range(carb_goal):\\n for l in range(fat_goal):\\n for n in range(len(foods)):\\n food = foods[n]\\n if (int(food['calories']) > i\\n or int(food['protein']) > j\\n or int(food['carbs']) > k\\n or int(food['fat']) > l):\\n continue\\n if (costs[i - int(food['calories'])]\\n [j - int(food['protein'])]\\n [k - int(food['carbs'])]\\n [l - int(food['fat'])]\\n == 999999999):\\n prev_cost = 0\\n prev_foods_used = {}\\n else:\\n prev_cost = (macros[i - int(food['calories'])]\\n [j - int(food['protein'])]\\n [j - int(food['carbs'])]\\n [j - int(food['fat'])])\\n prev_foods_used = \\\\\\n (foods_used[i - int(food['calories'])]\\n [j - int(food['protein'])]\\n [k - int(food['carbs'])]\\n [l - int(food['fat'])]).copy()\\n new_cal = calories(\\n foods, prev_foods_used) + food['calories']\\n new_pro = protein(\\n foods, prev_foods_used) + food['protein']\\n new_car = carbs(\\n foods, prev_foods_used) + food['protein']\\n new_fat = fat(\\n foods, prev_foods_used) + food['protein']\\n if (costs[i][j] > prev_cost + food['serving_cost']\\n and new_cal > i - 20 and new_cal < i + 10\\n and new_pro < j + 5 and new_pro < j + 5\\n and new_car < j + 5 and new_car < j + 5\\n and new_fat < j + 5 and new_fat < j + 5):\\n costs[i][j][k][l] = prev_cost + \\\\\\n food['serving_cost']\\n try:\\n prev_foods_used[n] += 1\\n except KeyError:\\n prev_foods_used[n] = 1\\n foods_used[i][j][k][l] = prev_foods_used\\n return foods_used[cal_goal - 1][pro_goal - 1][carb_goal - 1][fat_goal - 1]\",\n \"def _get_discount(self):\\n\\n # For every 2 PENS, one free discount\\n number_of_pens = len([x for x in self._products if x.code == 'PEN'])\\n discount = 5.0 * int(number_of_pens / 2)\\n\\n # If there are more than 3 T-Shirts in the basket, 5 EUR of discount in every of them (25%)\\n number_of_tshirts = len([x for x in self._products if x.code == 'TSHIRT'])\\n if number_of_tshirts >= 3:\\n discount += 5.0 * number_of_tshirts\\n\\n return discount\",\n \"def search_for_contracts(self, market=None, buy_sell=None, type_=None, contracts=\\\"All\\\"):\\n if not contracts:\\n contracts = []\\n if not type_:\\n pass\\n elif type_.lower() in ['yes', 'long'] and buy_sell == 'buy':\\n type_ = {'long': 'BestBuyYesCost'}\\n elif type_.lower() in ['no', 'short'] and buy_sell == 'buy':\\n type_ = {'short': 'BestBuyNoCost'}\\n elif type_.lower() in ['yes', 'long'] and buy_sell == 'sell':\\n type_ = {'long': 'BestSellYesCost'}\\n elif type_.lower() in ['no', 'short'] and buy_sell == 'sell':\\n type_ = {'short': 'BestSellNoCost'}\\n \\n if not market:\\n market_links = [(\\\"us_election\\\", 'https://www.predictit.org/api/marketdata/category/6'), (\\\"us_politics\\\", 'https://www.predictit.org/api/marketdata/category/13'), (\\\"world_politics\\\", 'https://www.predictit.org/api/marketdata/category/4')]\\n elif 'us' and 'election' in market.replace('.', '').lower():\\n market_links = [(\\\"us_elections\\\", 'https://www.predictit.org/api/marketdata/category/6')]\\n elif 'us' and 'politic' in market.replace('.', '').lower():\\n market_links = [(\\\"us_politics\\\",'https://www.predictit.org/api/marketdata/category/13')]\\n elif 'world' in market.lower():\\n market_links = [(\\\"world_politics\\\", 'https://www.predictit.org/api/marketdata/category/4')]\\n \\n \\n\\n market_data=[]\\n for category, market_link in market_links:\\n markets = list(self.browser.get(market_link).json()['Markets'])\\n for market in markets:\\n market = market\\n market[\\\"Category\\\"] = category\\n market[\\\"References\\\"]=[]\\n wikidict={\\\"Trump\\\": \\\"http://dbpedia.org/resource/Donald_Trump\\\", \\\"Clinton\\\": \\\"http://dbpedia.org/resource/Hillary_Clinton\\\", \\\"Ossoff\\\": \\\"https://en.wikipedia.org/wiki/Jon_Ossoff\\\", \\\"Virginia\\\": \\\"https://en.wikipedia.org/wiki/Virginia\\\", \\\"Georgia\\\": \\\"https://en.wikipedia.org/wiki/Georgia_(U.S._state)\\\",\\\"Election\\\":\\\"https://en.wikipedia.org/wiki/Elections_in_the_United_States\\\"}\\n \\n for thing in [\\\"Trump\\\", \\\"Clinton\\\", \\\"Ossoff\\\", \\\"Virginia\\\", \\\"Georgia\\\",\\\"Election\\\"]:\\n if thing.lower() in [element.lower() for element in market[\\\"Name\\\"].split()]:\\n market[\\\"References\\\"].append(wikidict[thing])\\n market_data.append(json.dumps(market))\\n \\n return market_data\",\n \"def calculate_training_cost(soldier_list: List[Soldier]):\\n total_cost = 0.0\\n \\n for soldier in soldier_list:\\n ################################# YOUR CODE HERE #################################\\n if soldier.typecode == \\\"INF\\\":\\n cost = 2.5 * soldier.weapon + 1.0 * soldier.armor\\n elif soldier.typecode == \\\"ARC\\\":\\n cost = 1.5 * soldier.weapon + 3.0 * soldier.armor\\n elif soldier.typecode == \\\"CVL\\\":\\n cost = 4.0 * soldier.weapon + 6.0 * soldier.armor\\n if soldier.vitality < 0.35:\\n cost *= 0.5\\n total_cost += cost\\n ##################################################################################\\n return total_cost\",\n \"def get_prices(self, grab, subject):\\n prices = []\\n try:\\n extras = grab.doc.rex_text(\\n '<h3 class\\\\=\\\"h6 copy-sp-m\\\">.*?%s.*?</h3>(.+?)</ul>' % subject,\\n flags=re.S\\n )\\n except DataNotFound:\\n logging.debug(\\n \\\"Price %s is not found on %s\\\"\\n % (subject, grab.doc.url)\\n )\\n return None\\n\\n sel = XpathSelector(fromstring(extras))\\n prices = []\\n for li in sel.select('//li[@class=\\\"list__item u-cf\\\"]'):\\n obligatory = OrderedDict()\\n obligatory['name'] = li.select('node()').text()\\n money = li.select('node()/strong').text()\\n obligatory['value'] = money[1:].replace(',', '')\\n\\n # Find perweek or perday\\n if li.select(\\n 'span[@class=\\\"boatview__extras-amount\\\"' +\\n ' and contains(text(),\\\"per week\\\")]'\\n ).exists():\\n obligatory['perweek'] = True\\n elif li.select(\\n 'span[@class=\\\"boatview__extras-amount\\\"' +\\n ' and contains(text(),\\\"per day\\\")]'\\n ).exists():\\n obligatory['perday'] = True\\n obligatory['currency'] = money[0]\\n prices.append(obligatory)\\n\\n if len(prices) < 1:\\n logging.debug(\\n \\\"Price %s contains less than one element on: %s\\\"\\n % (subject, grab.doc.url)\\n )\\n return None\\n\\n return prices\",\n \"def apply(self, catalog):\\n out = []\\n for product in catalog:\\n valid = True\\n for key, filtr in self.filters.items():\\n valid = valid and filtr(product[key])\\n if valid:\\n out.append(product)\\n out = sorted(out, key=lambda x: x['onDemandUsdPrice'])\\n return self.limit_size(out)\",\n \"def priceGetAll(soup):\\n try:\\n price = priceGetMost(soup)\\n except:\\n price = priceGetSome(soup)\\n\\n return price\",\n \"def test_reasonable_auction(self):\\n # I have no preferences\\n bids = [Cost(ITEM1, ACTOR1, 1000),\\n Cost(ITEM2, ACTOR1, 1000),\\n Cost(ITEM3, ACTOR1, 1000),\\n Cost(ITEM4, ACTOR1, 1000),\\n Cost(ITEM5, ACTOR1, 1000),\\n\\n # I have linear preferences\\n Cost(ITEM1, ACTOR2, 700),\\n Cost(ITEM2, ACTOR2, 800),\\n Cost(ITEM3, ACTOR2, 1000),\\n Cost(ITEM4, ACTOR2, 1200),\\n Cost(ITEM5, ACTOR2, 1300),\\n\\n # I have non-linear preferences\\n Cost(ITEM1, ACTOR3, 400),\\n Cost(ITEM2, ACTOR3, 800),\\n Cost(ITEM3, ACTOR3, 1000),\\n Cost(ITEM4, ACTOR3, 1200),\\n Cost(ITEM5, ACTOR3, 1600),\\n\\n # I have arbitrary preference\\n Cost(ITEM1, ACTOR4, 2435),\\n Cost(ITEM2, ACTOR4, 305),\\n Cost(ITEM3, ACTOR4, 310),\\n Cost(ITEM4, ACTOR4, 1725),\\n Cost(ITEM5, ACTOR4, 225),\\n\\n # I have strong preferences\\n Cost(ITEM1, ACTOR5, 0),\\n Cost(ITEM2, ACTOR5, 0),\\n Cost(ITEM3, ACTOR5, 0),\\n Cost(ITEM4, ACTOR5, 0),\\n Cost(ITEM5, ACTOR5, 5000)]\\n result = self.splitter.split(ITEMS[:5], ACTORS[:5], bids)\\n expected = [(ITEM1, ACTOR4, None),\\n (ITEM2, ACTOR1, None),\\n (ITEM3, ACTOR3, None),\\n (ITEM4, ACTOR2, None),\\n (ITEM5, ACTOR5, None)]\\n item_assignments_present(self, result, expected)\",\n \"async def detect_available_dps(self):\\n # type_0d devices need a sort of bruteforce querying in order to detect the\\n # list of available dps experience shows that the dps available are usually\\n # in the ranges [1-25] and [100-110] need to split the bruteforcing in\\n # different steps due to request payload limitation (max. length = 255)\\n self.dps_cache = {}\\n ranges = [(2, 11), (11, 21), (21, 31), (100, 111)]\\n\\n for dps_range in ranges:\\n # dps 1 must always be sent, otherwise it might fail in case no dps is found\\n # in the requested range\\n self.dps_to_request = {\\\"1\\\": None}\\n self.add_dps_to_request(range(*dps_range))\\n try:\\n data = await self.status()\\n except Exception as ex:\\n self.exception(\\\"Failed to get status: %s\\\", ex)\\n raise\\n if \\\"dps\\\" in data:\\n self.dps_cache.update(data[\\\"dps\\\"])\\n\\n if self.dev_type == \\\"type_0a\\\":\\n return self.dps_cache\\n self.debug(\\\"Detected dps: %s\\\", self.dps_cache)\\n return self.dps_cache\",\n \"def price(self, irc, msg, args, optlist, typeName):\\n\\n try:\\n typeID = self._get_typeID(typeName)\\n itemType = self._get_type(typeID)\\n except:\\n irc.error('Unknown type')\\n return\\n\\n if len(optlist) == 1:\\n location = optlist[0][1]\\n else:\\n location = 'Jita'\\n\\n try:\\n locationID = self._get_locationID(location)\\n location = self._get_location(locationID)\\n except:\\n irc.error('Unknown location')\\n return\\n\\n market = self._sql(\\\"\\\"\\\"\\n SELECT * FROM evecentral_market\\n WHERE \\\"locationID\\\"=%s\\\"\\\"\\\", [locationID])\\n if not market:\\n irc.reply('No data for that market location')\\n return\\n\\n marketitem = self._sql(\\\"\\\"\\\"\\n SELECT * FROM evecentral_marketitem\\n WHERE \\\"locationID\\\"=%s AND \\\"typeID\\\"=%s\\\"\\\"\\\", [locationID, typeID])\\n if marketitem:\\n irc.reply('{0} in {1}: buy max: {2} (volume: {3:,d}). sell min: {4} (volume: {5:,d}).'.format(\\n ircutils.bold(itemType['typeName']),\\n self._colorize_system(location),\\n ircutils.mircColor(\\n '{:,.2f}'.format(marketitem['buy_max']),\\n fg='green'),\\n int(marketitem['buy_volume']),\\n ircutils.mircColor(\\n '{:,.2f}'.format(marketitem['sell_min']),\\n fg='green'),\\n int(marketitem['sell_volume']),\\n ), prefixNick=False)\\n else:\\n irc.reply(\\\"Prices for {0} in {1} isn't updated yet.\\\".format(\\n itemType['typeName'],\\n location['itemName']\\n ))\",\n \"def value_offers(self):\\n return OrderBookUtils.book_value(self.offer)\",\n \"def price(usr, item, searches = 2, method = \\\"AVERAGE\\\", deduct = 0):\\n if not method in ShopWizard.methods: raise invalidMethod()\\n\\n if isinstance(item, Item):\\n item = item.name\\n\\n prices = []\\n dets = {}\\n for x in range(0, searches):\\n print('performing search: {}'.format(x))\\n results = ShopWizard.search(usr, item)\\n\\n # Set to -1 if not found\\n if not results:\\n prices.append(-1)\\n continue\\n\\n prices.append(int(results[item].price))\\n dets[str(results[item].price)] = (results[item].owner, results[item].id)\\n\\n time.sleep(ShopWizard.waitTime)\\n\\n # Determines if item was UB\\n print(prices)\\n if sum(prices) == len(prices) * -1:\\n return False\\n\\n prices = list(filter(lambda x: x != -1, prices))\\n\\n if method == ShopWizard.RETLOW:\\n price = sorted(prices)[0]\\n return (price, dets[str(price)][0], dets[str(price)][1])\\n\\n return ShopWizard.__determinePrice(prices, method, deduct)\",\n \"def get_the_sum_of_prices(item_ids):\\n\\n # your code\",\n \"def getActiveCurrencies():\",\n \"def daffodils(flNeeded,amtPaid, dzCost):\\n\\n\\n import math\\n\\n dz = flNeeded / 12\\n dozens = math.ceil (dz) #Rounds up to the nearest dozen\\n\\n totCost = dzCost * dozens\\n toPay = totCost - amtPaid\\n\\n print (\\\"You will need to contribute\\\", toPay)\",\n \"def sales_orders_options(m,\\n price=None,\\n prefer_exercise=True,\\n cheap_first=True,\\n restricted=True,\\n vested=True,\\n nso_first=False):\\n\\n # default to the ipo price\\n if price is None: price = m.ipo_price_usd\\n assert(0 <= price)\\n\\n # Set up the list of sales orders\\n option_lst = []\\n nso_lst = grant_lst_for_vehicle(m, 'nso', cheap_first=cheap_first)\\n iso_lst = grant_lst_for_vehicle(m, 'iso', cheap_first=cheap_first)\\n \\n if nso_first:\\n option_lst = nso_lst + iso_lst\\n else:\\n option_lst = iso_lst + nso_lst\\n\\n retval = []\\n\\n # How many shares can be legally sold?\\n remaining_restricted = m.shares_sellable_restricted_n\\n\\n for g in option_lst:\\n rate = getattr(m, 'shares_withheld_%s_rate'%g.vehicle)\\n if vested:\\n n = g.available(m.query_date, rate)\\n else:\\n n = g.n_shares - g.liquidated\\n if restricted:\\n n = min(remaining_restricted, n)\\n remaining_restricted -= n\\n retval.append({\\n 'id': g.name,\\n 'qty': n,\\n 'price': price,\\n 'prefer_exercise': prefer_exercise\\n })\\n\\n return retval\",\n \"def spread(self):\\n if self.bid and self.offer:\\n return self.offer[0].price - self.bid[-1].price\\n\\n return 0\",\n \"def execute_deposits(self):\\n deposits = [v for v in self.action_register if v['action'] == 'deposit']\\n for deposit in deposits:\\n self.model.schedule.agents_by_type['Customer'][deposit['unique_id']].euro_wallet += deposit['value']\",\n \"def get_basket_items_pricedrop(self, offer_info, actual_volume, product_prices):\\n prod_code = offer_info.base_prod_code\\n base_prod_vol = actual_volume.get(prod_code.lower())\\n\\n pricedrop_basket = []\\n\\n if base_prod_vol >= offer_info.min_vol:\\n offer_on_prod = offer_info.offer_on\\n if actual_volume.get(offer_on_prod.lower()):\\n print(\\n f\\\"Base product volume is greater than minimum required volume & product on offer is also available \\\"\\n f\\\"in cart..\\\")\\n if offer_info.is_limited:\\n print(f\\\"Limited offer..\\\")\\n if prod_code == offer_on_prod:\\n # total_allowed_items_on_offer = Limit Volume of base product * (Offer Product Max Volume/Minimum volume of base product)\\n total_allowed_items_on_offer = offer_info.limit_vol * (\\n offer_info.offer_prod_volume / offer_info.min_vol)\\n max_limit = 1\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n pricedrop_basket.append((prod_code, base_prod_actual_price))\\n while max_limit <= total_allowed_items_on_offer:\\n new_price = (base_prod_actual_price - (offer_info.new_price)) * -1\\n pricedrop_basket.append((offer_info.offer_code, new_price))\\n max_limit += 1\\n else:\\n total_allowed_items_on_offer = offer_info.limit_vol * (\\n offer_info.offer_prod_volume / offer_info.min_vol)\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n pricedrop_basket.append((prod_code, base_prod_actual_price))\\n max_limit = 1\\n while max_limit <= total_allowed_items_on_offer:\\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\\n new_price = (base_prod_actual_price - (offer_info.new_price)) * -1\\n for j in range(0, actual_volume.get(offer_on_prod).lower()):\\n pricedrop_basket.append((offer_on_prod, offer_onprod_actual_price))\\n pricedrop_basket.append((offer_info.offer_code, new_price))\\n max_limit += 1\\n else:\\n print(f\\\"Unlimited offer..\\\")\\n if prod_code == offer_on_prod:\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n pricedrop_basket.append((prod_code, base_prod_actual_price))\\n new_price = (base_prod_actual_price - (offer_info.new_price))*-1\\n pricedrop_basket.append((offer_info.offer_code, new_price))\\n else:\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n pricedrop_basket.append((prod_code, base_prod_actual_price))\\n\\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\\n new_price = (offer_onprod_actual_price - (offer_info.new_price)) * -1\\n\\n for j in range(0, actual_volume.get(offer_on_prod).lower()):\\n pricedrop_basket.append((offer_on_prod, offer_onprod_actual_price))\\n pricedrop_basket.append((offer_info.offer_code, new_price))\\n\\n return pricedrop_basket\",\n \"def refuse_and_delete_pending_offers(item, offer_id):\\n for o in item.offers_received.filter(~Q(pk=offer_id), answered=False):\\n refuse_offer(o)\\n\\n for o in item.offers_done.filter(~Q(pk=offer_id), answered=False):\\n delete_offer(o)\",\n \"def calc_Cinv_boiler(Q_design_W, technology_type, boiler_cost_data):\\n Capex_a_Boiler_USD = 0.0\\n Opex_a_fix_Boiler_USD = 0.0\\n Capex_Boiler_USD = 0.0\\n\\n if Q_design_W > 0.0:\\n boiler_cost_data = boiler_cost_data[boiler_cost_data['code'] == technology_type]\\n # if the Q_design is below the lowest capacity available for the technology, then it is replaced by the least\\n # capacity for the corresponding technology from the database\\n if Q_design_W < boiler_cost_data.iloc[0]['cap_min']:\\n Q_design_W = boiler_cost_data.iloc[0]['cap_min']\\n max_boiler_size = boiler_cost_data.iloc[0]['cap_max']\\n\\n if Q_design_W <= max_boiler_size:\\n\\n boiler_cost_data = boiler_cost_data[\\n (boiler_cost_data['cap_min'] <= Q_design_W) & (boiler_cost_data['cap_max'] > Q_design_W)]\\n\\n Inv_a = boiler_cost_data.iloc[0]['a']\\n Inv_b = boiler_cost_data.iloc[0]['b']\\n Inv_c = boiler_cost_data.iloc[0]['c']\\n Inv_d = boiler_cost_data.iloc[0]['d']\\n Inv_e = boiler_cost_data.iloc[0]['e']\\n Inv_IR = boiler_cost_data.iloc[0]['IR_%']\\n Inv_LT = boiler_cost_data.iloc[0]['LT_yr']\\n Inv_OM = boiler_cost_data.iloc[0]['O&M_%'] / 100.0\\n\\n InvC = Inv_a + Inv_b * (Q_design_W) ** Inv_c + (Inv_d + Inv_e * Q_design_W) * log(Q_design_W)\\n\\n Capex_a_Boiler_USD = calc_capex_annualized(InvC, Inv_IR, Inv_LT)\\n Opex_a_fix_Boiler_USD = InvC * Inv_OM\\n Capex_Boiler_USD = InvC\\n\\n else:\\n number_of_boilers = int(ceil(Q_design_W / max_boiler_size))\\n Q_nom_W = Q_design_W / number_of_boilers\\n\\n boiler_cost_data = boiler_cost_data[\\n (boiler_cost_data['cap_min'] <= Q_nom_W) & (boiler_cost_data['cap_max'] > Q_nom_W)]\\n\\n Inv_a = boiler_cost_data.iloc[0]['a']\\n Inv_b = boiler_cost_data.iloc[0]['b']\\n Inv_c = boiler_cost_data.iloc[0]['c']\\n Inv_d = boiler_cost_data.iloc[0]['d']\\n Inv_e = boiler_cost_data.iloc[0]['e']\\n Inv_IR = boiler_cost_data.iloc[0]['IR_%']\\n Inv_LT = boiler_cost_data.iloc[0]['LT_yr']\\n Inv_OM = boiler_cost_data.iloc[0]['O&M_%'] / 100.0\\n\\n InvC = (Inv_a + Inv_b * (Q_nom_W) ** Inv_c + (Inv_d + Inv_e * Q_nom_W) * log(Q_nom_W)) * number_of_boilers\\n\\n Capex_a_Boiler_USD = calc_capex_annualized(InvC, Inv_IR, Inv_LT)\\n Opex_a_fix_Boiler_USD = InvC * Inv_OM\\n Capex_Boiler_USD = InvC\\n\\n return Capex_a_Boiler_USD, Opex_a_fix_Boiler_USD, Capex_Boiler_USD\",\n \"def get_addents(data: List[int], answer: int, amount: int) -> List[int]:\\n for addents in itertools.combinations(data, amount):\\n if sum(addents) == answer:\\n return addents\",\n \"def ItemDed(_posagi, e17500, e18400, e18500, e18800, e18900,\\n e20500, e20400, e19200, e20550, e20600, e20950, e19500, e19570,\\n e19400, e19550, e19800, e20100, e20200, e20900, e21000, e21010,\\n MARS, _sep, c00100, ID_ps, ID_Medical_frt, ID_Casualty_frt,\\n ID_Miscellaneous_frt, ID_Charity_crt_Cash, ID_Charity_crt_Asset,\\n ID_prt, ID_crt, ID_StateLocalTax_HC, ID_Charity_frt, puf):\\n # Medical\\n c17750 = ID_Medical_frt * _posagi\\n c17000 = max(0, e17500 - c17750)\\n\\n # State and Local Income Tax, or Sales Tax\\n _statax = max(e18400, 0)\\n\\n # Other Taxes (including state and local)\\n c18300 = _statax + e18500 + e18800 + e18900\\n\\n # Casulty\\n if e20500 > 0:\\n c37703 = e20500 + ID_Casualty_frt * _posagi\\n c20500 = c37703 - ID_Casualty_frt * _posagi\\n else:\\n c37703 = 0.\\n c20500 = 0.\\n\\n # Miscellaneous\\n c20750 = ID_Miscellaneous_frt * _posagi\\n if puf:\\n c20400 = e20400\\n else:\\n c20400 = e20550 + e20600 + e20950\\n c20800 = max(0, c20400 - c20750)\\n\\n # Interest paid deduction\\n if puf:\\n c19200 = e19200\\n else:\\n c19200 = e19500 + e19570 + e19400 + e19550\\n\\n # Charity (assumes carryover is non-cash)\\n base_charity = e19800 + e20100 + e20200\\n if base_charity <= 0.2 * _posagi:\\n c19700 = base_charity\\n else:\\n lim50 = min(ID_Charity_crt_Cash * _posagi, e19800)\\n lim30 = min(ID_Charity_crt_Asset * _posagi, e20100 + e20200)\\n c19700 = min(0.5 * _posagi, lim30 + lim50)\\n\\n charity_floor = ID_Charity_frt * _posagi # frt is zero in present law\\n c19700 = max(0, c19700 - charity_floor)\\n\\n # Gross Itemized Deductions\\n\\n c21060 = (e20900 + c17000 + (1 - ID_StateLocalTax_HC) * c18300 + c19200 +\\n c19700 + c20500 + c20800 + e21000 + e21010)\\n\\n # Limitations on deductions excluding medical, charity etc\\n _phase2_i = ID_ps[MARS - 1]\\n\\n _nonlimited = c17000 + c20500 + e19570 + e21010 + e20900\\n _phase2_i = ID_ps[MARS - 1]\\n _limitratio = _phase2_i / _sep\\n\\n if c21060 > _nonlimited and c00100 > _limitratio:\\n dedmin = ID_crt * (c21060 - _nonlimited)\\n dedpho = ID_prt * max(0, _posagi - _limitratio)\\n c21040 = min(dedmin, dedpho)\\n else:\\n c21040 = 0.0\\n\\n # Itemized deductions amount after limitation if any\\n c04470 = c21060 - c21040\\n\\n # the variables that are casted as floats below can be either floats or\\n # ints depending n which if/else branches they follow in the above code.\\n # they need to always be the same type\\n\\n c20400 = float(c20400)\\n c20400 = float(c20400)\\n c19200 = float(c19200)\\n c37703 = float(c37703)\\n c20500 = float(c20500)\\n\\n return (c17750, c17000, _statax, c18300, c37703, c20500,\\n c20750, c20400, c19200, c20800, c19700, c21060, _phase2_i,\\n _nonlimited, _limitratio, c04470, c21040)\",\n \"def accumulate_prices(name,products,sales,types,add):\\r\\n return reduce(add,[get_prices_by_type(name,products,sales,types)[i] for i in get_prices_by_type(name,products,sales,types)])\",\n \"def GetSpeculated(self):\\n return self.money + sum([self.share[i] * self.price[i][0] * (1 + self.taxe) for i in self.price])\",\n \"def compute_costs(timesheet, biller, date1=None, date2=None): \\n # Slice\\n f = slice_by_dates(timesheet, date1, date2)\\n\\n # Resample and add start/end dates\\n if biller.freq is not None:\\n freq = biller.freq\\n f = timesheet.set_index('date')[['duration']].resample(freq).sum()\\n f = f.reset_index()\\n f['period'] = f['date'].map(lambda x: pd.Period(x, freq))\\n f['start_date'] = f['period'].map(lambda x: x.start_time)\\n f['end_date'] = f['period'].map(lambda x: x.end_time)\\n else:\\n start_date, end_date = f['date'].min(), f['date'].max()\\n f['start_date'] = start_date\\n f['end_date'] = end_date\\n\\n # Get bins for aggregating\\n if biller.base_fee:\\n bins = [0] + biller.bins\\n else:\\n bins = biller.bins\\n\\n def my_agg(group):\\n d = OrderedDict()\\n d['start_date'] = group['start_date'].iat[0]\\n d['end_date'] = group['end_date'].iat[0]\\n t = group['duration'].iat[0]\\n d['duration'] = pd.Series(decompose(t, bins))\\n c1 = d['duration'].cumsum().map(biller)\\n c2 = c1.shift(1).fillna(0)\\n cost = c1 - c2\\n d['rate'] = cost/d['duration']\\n d['cost'] = cost\\n return pd.DataFrame(d)\\n \\n f = f.groupby('date').apply(my_agg\\n ).reset_index().drop(['level_1', 'date'], axis=1)\\n\\n # Drop NaN rate items\\n f = f.dropna(subset=['rate'])\\n\\n return f\",\n \"def solve_prices(self):\\n return None\",\n \"def cost(foods, foods_used):\\n cost = 0.00\\n for i, count in foods_used.items():\\n cost += (foods[i]['serving_cost'] * count)\\n return cost\",\n \"def getTransferListSummary(self):\\n p_ids_and_prices = {}\\n players = self.getAllPlayerInfoTransferlist()\\n\\n # Get IDs of all players\\n log_event(self.queue, \\\"Gathering player prices... \\\")\\n for p in players:\\n p_bidstatus = p[1]\\n p_id = p[8]\\n # removed Filter for unlisted / expired players\\n if p_id not in p_ids_and_prices:\\n p_sellprice = self.getPlayerSellPrice(p_id)\\n # If sell price returns 0, need to fetch from Futbin\\n if p_sellprice == 0:\\n p_sellprice = self.getFutbinPrice_opentab(p_id)\\n self.sleep_approx(5) # Delay iteration to not anger futbin\\n # Add player ID and price to dict\\n p_ids_and_prices[p_id] = p_sellprice\\n\\n for p_id in p_ids_and_prices:\\n p_price = p_ids_and_prices[p_id]\\n p_name = self.getPlayerCardName(p_id)\\n log_event(self.queue, str(p_name) + \\\" - #\\\" +\\n str(p_id) + \\\" Price \\\" + str(p_price))\\n\\n num_p_sold = 0\\n num_p_expired = 0\\n num_p_unlisted = 0\\n num_p_listed = 0\\n\\n sold_p_value = 0\\n expired_p_value = 0\\n unlisted_p_value = 0\\n listed_p_value = 0\\n\\n for p in players:\\n p_bidstatus = p[1]\\n p_id = p[8]\\n p_soldprice = p[5] # is 0 if unlisted\\n p_sellprice = int(p_ids_and_prices[p_id])\\n\\n if \\\"won\\\" in p_bidstatus:\\n num_p_sold += 1\\n sold_p_value += p_soldprice\\n if \\\"expired\\\" in p_bidstatus:\\n num_p_expired += 1\\n expired_p_value += p_sellprice\\n if (p_bidstatus == \\\"listFUTItem\\\"):\\n num_p_unlisted += 1\\n unlisted_p_value += p_sellprice\\n if (p_bidstatus == \\\"listFUTItem has-auction-data\\\"):\\n num_p_listed += 1\\n listed_p_value += p_sellprice\\n\\n log_event(self.queue, \\\"Players sold: \\\" + str(num_p_sold))\\n log_event(self.queue, \\\"Players expired: \\\" + str(num_p_expired))\\n log_event(self.queue, \\\"Players listed: \\\" + str(num_p_listed))\\n log_event(self.queue, \\\"Players unlisted: \\\" + str(num_p_unlisted))\\n log_event(self.queue, \\\" - - - \\\")\\n log_event(self.queue, \\\"Sold players value: \\\" + str(sold_p_value))\\n log_event(self.queue, \\\"Expired players value: \\\" +\\n str(expired_p_value))\\n log_event(self.queue, \\\"Unlisted players value: \\\" +\\n str(unlisted_p_value))\\n log_event(self.queue, \\\"Listed players value: \\\" + str(listed_p_value))\\n\\n # TODO subtract bought price\\n self.user_players_won += int(num_p_unlisted)\\n self.p_ids_and_prices = p_ids_and_prices\\n intel = [p_ids_and_prices, num_p_sold, num_p_expired, num_p_unlisted,\\n num_p_listed, sold_p_value, expired_p_value, unlisted_p_value, listed_p_value]\\n return intel\",\n \"def get_prices(self):\\n pass\",\n \"def test_get_offers(self):\\n pass\",\n \"def deposits_for_period(self):\\n cash_transaction = CashTransaction(self.user)\\n limits = self.user.limits\\n deposits = 0\\n if limits.exists():\\n deposit_limit = self.user.limits.get(type=Limit.DEPOSIT)\\n deposits = \\\\\\n cash_transaction.get_all_deposits(date_range=deposit_limit.time_period_boundaries)[\\n 'amount__sum']\\n return deposits\",\n \"def getCurrencies():\",\n \"def is_satisfied(self, offer, basket):\\n value_of_matches = D(\\\"0.00\\\")\\n for line in basket.all_lines():\\n if (\\n self.can_apply_condition(line)\\n and line.quantity_without_offer_discount(offer) > 0\\n ):\\n price = unit_price(offer, line)\\n value_of_matches += price * int(\\n line.quantity_without_offer_discount(offer)\\n )\\n if value_of_matches >= self.value:\\n return True\\n return False\",\n \"def nsquaredDist(points, weights = None, potent = None, dType = \\\"EUCLIDEAN\\\"):\\n\\n n,k = NUM.shape(points)\\n maxMinSumDist = 3.402823466E+38\\n\\n if weights == None: \\n weights = NUM.ones((n,), float) \\n\\n if potent == None:\\n potent = NUM.zeros((n,), float)\\n\\n weightedPotential = weights * potent\\n\\n res = {}\\n\\n #### Calculate Sum of Weighted Distances For Each Feature ####\\n weights.shape = n,1\\n if dType == \\\"EUCLIDEAN\\\":\\n for idx, point in enumerate(points):\\n weightedDist = eucDistArray(point, points, weights) \\n res[idx] = weightedDist + (weights[idx] * potent[idx])\\n ARCPY.SetProgressorPosition()\\n else:\\n for idx, point in enumerate(points):\\n weightedDist = manDistArray(point, points, weights) \\n res[idx] = weightedDist + weightedPotential[idx]\\n ARCPY.SetProgressorPosition()\\n\\n #### Minimum Sum of Weighted Distances (Central Feature) ####\\n minSumDist = min(res.itervalues())\\n final = [ key for key,val in res.iteritems() if val == minSumDist ]\\n \\n return final, minSumDist\",\n \"def buy(self, buyDate, sellDate, buyLimit, unitLimit, repeat):\\n\\t\\tstartDate, startPrice = self.getOpeningPrice()\\n\\t\\tmaxAmount = int(buyLimit)/int(startPrice)\\n\\t\\t\\t\\t\\n\\t\\t# If you've selected a one-off stock purchase\\n\\t\\tif repeat == 'never' and maxAmount > 0:\\n\\t\\t\\tself.purchased = (maxAmount * startPrice)\\n\\t\\t\\tself.units = maxAmount\\n\\t\\t\\tself.purchasedList.append(maxAmount * startPrice)\\n\\t\\t\\tself.unitsList.append(maxAmount)\\n\\t\\t\\tself.dateList = [startDate]\\t\\n\\t\\t\\t\\n\\t\\t# Otherwise there's some iteration required\\n\\t\\telse:\\n\\t\\t\\tcurrentDate = startDate\\n\\t\\t\\tendDate, endPrice = self.getClosingPrice()\\n\\t\\t\\t\\n\\t\\t\\t# Iterate on the date value\\n\\t\\t\\twhile currentDate <= endDate:\\n\\t\\t\\t\\tdate, price = self.getPrice(currentDate)\\n\\n\\t\\t\\t\\tif date is not None and price is not None:\\n\\t\\t\\t\\t\\tmaxAmount = int(buyLimit)/int(price)\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t# Check there's no unit limit\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tif maxAmount > unitLimit and unitLimit > 0:\\n\\t\\t\\t\\t\\t\\tmaxAmount = unitLimit\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t# If you can afford it, but the stock, otherwise skip it\\n\\t\\t\\t\\t\\tif maxAmount > 0:\\n\\t\\t\\t\\t\\t\\tself.purchased += (maxAmount * price)\\n\\t\\t\\t\\t\\t\\tself.units += maxAmount\\n\\t\\t\\t\\t\\t\\tself.purchasedList.append(maxAmount * price)\\n\\t\\t\\t\\t\\t\\tself.unitsList.append(maxAmount)\\n\\t\\t\\t\\t\\t\\tself.dateList.append(date)\\t\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# If there's no later stock data remaining break the loop\\n\\t\\t\\t\\tcurrentDate = self.getNextDate(currentDate, startDate, repeat)\\n\\t\\t\\t\\tif currentDate is None:\\n\\t\\t\\t\\t\\tbreak\",\n \"def get_good_price_by_deal(self):\\n if self.deal == SALE:\\n return self.new_price\\n elif self.deal == AUCTION:\\n return self.max_price\\n else:\\n return self.price\",\n \"def get_prices_by_type(name,products,sales,types):\\r\\n return {x:products[x]*(lambda y:1-sales[name][y])(tuple(types)[0] if x==tuple(types.values())[0][1] or x==tuple(types.values())[0][0] else tuple(types)[1]) for x in products}\",\n \"def check_restrictions(self):\\n from .signals import determine_availability\\n\\n responses = determine_availability.send(\\n self.item.event, item=self.item,\\n variations=[self.to_variation_dict()], context=None,\\n cache=self.item.event.get_cache()\\n )\\n price = self.default_price if self.default_price is not None else self.item.default_price\\n for receiver, response in responses:\\n if 'available' in response[0] and not response[0]['available']:\\n return False\\n elif 'price' in response[0] and response[0]['price'] is not None and response[0]['price'] < price:\\n price = response[0]['price']\\n return price\",\n \"def is_satisfied(self, offer, basket):\\n num_matches = 0\\n for line in basket.all_lines():\\n if self.can_apply_condition(line):\\n num_matches += line.quantity_without_offer_discount(offer)\\n if num_matches >= self.value:\\n return True\\n return False\",\n \"def binary_price(volatilities,\\n strikes,\\n expiries,\\n spots=None,\\n forwards=None,\\n discount_factors=None,\\n is_call_options=None,\\n dtype=None,\\n name=None):\\n if (spots is None) == (forwards is None):\\n raise ValueError('Either spots or forwards must be supplied but not both.')\\n\\n with tf.compat.v1.name_scope(\\n name,\\n default_name='option_price',\\n values=[\\n forwards, strikes, volatilities, expiries, discount_factors,\\n is_call_options\\n ]):\\n strikes = tf.convert_to_tensor(strikes, dtype=dtype, name='strikes')\\n volatilities = tf.convert_to_tensor(\\n volatilities, dtype=dtype, name='volatilities')\\n expiries = tf.convert_to_tensor(expiries, dtype=dtype, name='expiries')\\n\\n if discount_factors is None:\\n discount_factors = tf.convert_to_tensor(\\n 1.0, dtype=dtype, name='discount_factors')\\n else:\\n discount_factors = tf.convert_to_tensor(\\n discount_factors, dtype=dtype, name='discount_factors')\\n\\n if forwards is not None:\\n forwards = tf.convert_to_tensor(forwards, dtype=dtype, name='forwards')\\n else:\\n spots = tf.convert_to_tensor(spots, dtype=dtype, name='spots')\\n forwards = spots / discount_factors\\n\\n sqrt_var = volatilities * tf.math.sqrt(expiries)\\n d1 = (tf.math.log(forwards / strikes) + sqrt_var * sqrt_var / 2) / sqrt_var\\n d2 = d1 - sqrt_var\\n undiscounted_calls = _ncdf(d2)\\n if is_call_options is None:\\n return discount_factors * undiscounted_calls\\n undiscounted_puts = 1 - undiscounted_calls\\n predicate = tf.broadcast_to(is_call_options, tf.shape(undiscounted_calls))\\n return discount_factors * tf.where(predicate, undiscounted_calls,\\n undiscounted_puts)\",\n \"async def sellall(self, ctx:commands.Context, fish_type:str='', fish_quality:str=''):\\r\\n\\r\\n if not fish_type in ['specific', 'name', 'school', 'rarity', '']:\\r\\n await ctx.send(f'{fish_type} must be one of the following:\\\\n```name\\\\nschool\\\\nrarity```')\\r\\n return\\r\\n\\r\\n all_fish = await self.config.member(ctx.message.author).bucket()\\r\\n new_fish = []\\r\\n sell_fish = []\\r\\n msg = ''\\r\\n total = 0\\r\\n for i in all_fish:\\r\\n i.update({'specific': f'{i[\\\"name\\\"]} {i[\\\"size\\\"]}'})\\r\\n if fish_type == '' or i[fish_type] == fish_quality:\\r\\n sell_fish.append(i)\\r\\n\\r\\n mod = await self.CheckMerchants(ctx.guild, i)\\r\\n if await self.CompletedSchool(ctx.message.author, i['school']):\\r\\n mod += await self.GetModifier(ctx.guild, 'school_complete_mod')\\r\\n\\r\\n i[\\\"value\\\"] = int(i[\\\"value\\\"] * mod * await self.GetModifier(ctx.guild, i['rarity']))\\r\\n total += i[\\\"value\\\"]\\r\\n msg += f'{i[\\\"name\\\"]}{\\\" \\\" * (self.longestFishName - len(i[\\\"name\\\"]))}{i[\\\"value\\\"]} {await bank.get_currency_name(ctx.guild)} ({mod}x)\\\\n'\\r\\n else:\\r\\n new_fish.append(i)\\r\\n\\r\\n msg = await ctx.send(f'Are you sure you want to sell:\\\\n```{msg}```for {total} {await bank.get_currency_name(ctx.guild)}')\\r\\n\\r\\n start_adding_reactions(msg, ReactionPredicate.YES_OR_NO_EMOJIS)\\r\\n pred = ReactionPredicate.yes_or_no(msg, ctx.author)\\r\\n try:\\r\\n await ctx.bot.wait_for(\\\"reaction_add\\\", check=pred, timeout=20)\\r\\n except asyncio.TimeoutError:\\r\\n await msg.clear_reactions()\\r\\n await msg.edit(content='Sale timed out')\\r\\n return\\r\\n\\r\\n if pred.result is True:\\r\\n await self.config.member(ctx.message.author).bucket.set(new_fish)\\r\\n await msg.edit(content=f'{len(sell_fish)} fish sold for {total} {await bank.get_currency_name(ctx.guild)}')\\r\\n await bank.deposit_credits(ctx.message.author, total)\\r\\n else:\\r\\n await msg.edit(content='Sale cancelled')\\r\\n\\r\\n await msg.clear_reactions()\",\n \"def getTransferListSummaryWithoutPrices(self):\\n players = self.getAllPlayerInfoTransferlist()\\n\\n num_p_sold = 0\\n num_p_expired = 0\\n num_p_unlisted = 0\\n num_p_listed = 0\\n sold_p_value = 0\\n\\n for p in players:\\n p_bidstatus = p[1]\\n p_id = p[8]\\n p_soldprice = p[5] # is 0 if unlisted\\n\\n if \\\"won\\\" in p_bidstatus:\\n num_p_sold += 1\\n sold_p_value += p_soldprice\\n if \\\"expired\\\" in p_bidstatus:\\n num_p_expired += 1\\n if (p_bidstatus == \\\"listFUTItem\\\"):\\n num_p_unlisted += 1\\n if (p_bidstatus == \\\"listFUTItem has-auction-data\\\"):\\n num_p_listed += 1\\n\\n # TODO subtract bought price\\n return num_p_sold, num_p_expired, num_p_unlisted, num_p_listed\",\n \"def prices(parsed, appearance_value, removal_value, gate_single_value, gate_double_value):\\n\\t\\tprices = []\\n\\n\\t\\tfor item in parsed:\\n\\t\\t\\tif item.entityType == \\\"fence\\\":\\n\\t\\t\\t\\tprices.append(QuoteCalculation._fencePrice(item, appearance_value, removal_value))\\n\\n\\t\\t\\telif item.entityType == \\\"gate\\\":\\n\\t\\t\\t\\tprices.append(QuoteCalculation._gatePrice(item, gate_single_value, gate_double_value, removal_value))\\n\\n\\t\\t\\t# Not required?\\n\\t\\t\\t#elif item.entityType == \\\"post\\\":\\n\\t\\t\\t\\t#prices.append(QuoteCalculation._postPrice(item))\\n\\n\\t\\treturn prices\",\n \"def get_duty_cate_score(chosen_duty_list: list) -> pmag.MagicDict:\\n res = pmag.MagicDict()\\n for w, cate in chosen_duty_list:\\n freq = MODEL[cate]['duty'][w]['freq']\\n prob = MODEL[cate]['duty'][w]['prob']\\n score = prob # freq * prob / DUTY_NF[cate]\\n if cate in res:\\n res[cate] += score\\n else:\\n res[cate] = score\\n return res\",\n \"def computeMealFrecuency(self):\\n self.getOrdersData()\\n self.getOrderValues()\\n meals = set(self.meals)\\n for meal in meals:\\n self.labels.append(meal)\\n self.quantity.append(self.meals.count(meal))\",\n \"def calculate_cost(self):\\n booking_days, booking_hours = self.calculate_daily_hourly_billable_counts()\\n day_cost = booking_days * Decimal(self.vehicle.type.daily_rate)\\n hour_cost = booking_hours * Decimal(self.vehicle.type.hourly_rate)\\n if hour_cost > self.vehicle.type.daily_rate:\\n hour_cost = self.vehicle.type.daily_rate\\n return float(day_cost + hour_cost)\",\n \"def consume_items(self, offer, basket, affected_lines):\\n # Determine value of items already consumed as part of discount\\n value_consumed = D(\\\"0.00\\\")\\n for line, __, qty in affected_lines:\\n price = unit_price(offer, line)\\n value_consumed += price * qty\\n\\n to_consume = max(0, self.value - value_consumed)\\n if to_consume == 0:\\n return\\n\\n for price, line in self.get_applicable_lines(\\n offer, basket, most_expensive_first=True\\n ):\\n quantity_to_consume = min(\\n line.quantity_without_offer_discount(offer),\\n (to_consume / price).quantize(D(1), ROUND_UP),\\n )\\n line.consume(quantity_to_consume, offer=offer)\\n to_consume -= price * quantity_to_consume\\n if to_consume <= 0:\\n break\",\n \"def implied_discount_factor(p1: Instrument, c1: Instrument, p2: Instrument, c2: Instrument) -> float:\\n return (c1.price - p1.price - c2.price + p2.price)/ (c2.strike - c1.strike)\",\n \"async def loaddndshop(self, ctx):\\n await self.bot.di.add_shop_items(ctx.guild,\\n {item: value[\\\"meta\\\"][\\\"Cost\\\"] for item, value in self.bot.dnditems.items() if\\n value[\\\"meta\\\"].get(\\\"Cost\\\", \\\"\\\").isdigit()})\\n await ctx.send(await _(ctx, \\\"Successfully added all D&D items to shop!\\\"))\",\n \"def recalc_for_combo_birds(self, early_birds):\\n for early_bird in early_birds:\\n if early_bird.price_type == 1:\\n self.recalc_for_bird(early_bird)\\n return self.rates\",\n \"def get_discount(self, price):\\r\\n pass\",\n \"def apply_peddler_effect(self, player: Player) -> None:\\n \\\"\\\"\\\"\\n Buy 1 cube (any resource but gold) from the stock with 1 denier.\\n \\\"\\\"\\\"\\n # Remark: Hard-coded! We don't use the tags <cost><n_deniers>-1 and <gain><CHOICES>... in <game_elements><buildings><player_buildings><player_building><secondary_effect>.\\n money_resource_cost, qty_cost = Money.money, -1 # type: MoneyResource, int\\n if player.current_money_resources[money_resource_cost] + \\\\\\n qty_cost < 0: # Has the player enough money or resource?\\n print(indent(4) + player.txt_name_money_resources_workers_PPs_deck(True, True, False, False, False) +\\n ' and can\\\\'t apply the effect because he/she doesn\\\\'t have enough money or resource as ' +\\n str(qty_cost) + ' ' + money_resource_cost.name + '(s) required.')\\n else:\\n resource_gain_choices, qty_gain = [resource for resource in Resource.resources.values()\\n if not resource.is_wild()], \\\\\\n +1 # type: List[Resource], int\\n resource_gain = player.choose_buy_resource(money_resource_cost, qty_cost, resource_gain_choices,\\n qty_gain) # type: Resource\\n if resource_gain is None:\\n print(indent(4) + player.txt_name_money_resources_workers_PPs_deck(True, True, False, False, False) +\\n ' and had chosen to don\\\\'t apply the effect.')\\n else:\\n print(indent(4) + player.name() + ' wants to consume ' + str(qty_cost) + ' ' +\\n money_resource_cost.name + '(s) to obtain ' + str(qty_gain) + ' ' + resource_gain.name + '(s).')\\n player.current_money_resources[money_resource_cost] += qty_cost\\n player.current_money_resources[resource_gain] += qty_gain\\n print(indent(4) + player.txt_name_money_resources_workers_PPs_deck(True, True, False, False, False) +\\n ' once the effect applied.')\",\n \"def calculateCosts(self):\\n self.costs = 0\\n for house in self.houses:\\n if not house.distance == 1000:\\n self.costs += house.distance * 9\\n for battery in self.batteries:\\n self.costs += battery.costs\\n return self.costs\",\n \"def best_buy(self):\\n return Library.functions.best_buy(self._book)\",\n \"def get_all_cost_per_click(self,bid):\\n cost1 = self.get_cost_per_click(bid, 1)\\n cost2 = self.get_cost_per_click(bid, 2)\\n cost3 = self.get_cost_per_click(bid, 3)\\n return cost1, cost2, cost3\",\n \"def GetAllDifferentAmountOfCost():\\n\\n logs.logger.debug(\\n \\\"Start to get back all different amount of \\\"\\n \\\"Cost objects from database.\\\")\\n try:\\n ListOfAllDifferentAmountOfCost = []\\n searchedCostsItems = GetAllAmountOfCost()\\n for item in searchedCostsItems:\\n if item not in ListOfAllDifferentAmountOfCost:\\n ListOfAllDifferentAmountOfCost.append(item)\\n logs.logger.info(\\n \\\"Get back all different amount of Cost objects from database.\\\")\\n return ListOfAllDifferentAmountOfCost\\n except Exception as e:\\n logs.logger.error(e, exc_info=True)\",\n \"def get_shop_list_by_dishes(cook_dict, dishes, person_count):\\n temp_dict = dict()\\n for dish_name in dishes:\\n for dish_type, ingredient_list in cook_dict.items():\\n if dish_type == dish_name:\\n for ingr_dict in ingredient_list:\\n ingr_name = ingr_dict['ingredient_name']\\n if temp_dict.setdefault(ingr_name) is None:\\n temp_dict[ingr_name] = {\\n 'quantity': ingr_dict['quantity'] * person_count, 'measure': ingr_dict['measure']\\n }\\n else:\\n temp_dict[ingr_name]['quantity'] += ingr_dict['quantity'] * person_count\\n return temp_dict\",\n \"def dishlist_prices(n: list) -> list:\\r\\n return [dish.price for dish in n]\",\n \"def get_all_desserts():\\n return get_data_from_category_name(\\\"Desserts\\\")\",\n \"def discount_card(subtotal):\\n\\n if \\\"gold\\\" in CARDS:\\n return gold_card(subtotal) #This calculates the 5%\\n\\n elif \\\"silver\\\" in CARDS:\\n return silver_card(subtotal) #This calculates the 2%\\n\\n elif \\\"gold\\\" in CARDS and \\\"silver\\\" in CARDS:\\n return gold_card(subtotal)\\n\\n else:\\n return 0 #Whitout discount\",\n \"def get_price(self, name: str, set_name: str, foil: bool = False):\\n self.search_card(name, set_name)\\n time.sleep(1.5)\\n self.select_singles()\\n all_product = self.browser.find_elements_by_class_name('product')\\n product_1 = all_product[0]\\n card_finishes = product_1.find_elements_by_class_name('finish-option')\\n for finish in card_finishes:\\n foil_attribute = finish.text\\n if foil and (foil_attribute == 'Foil'):\\n finish.click()\\n self.foil = True\\n elif (not foil) and (foil_attribute == 'Non-Foil'):\\n finish.click()\\n self.foil = False\\n\\n card_name = product_1.find_element_by_xpath('//article/div[1]/h4/a').text\\n card_set = product_1.find_element_by_xpath('//article/div[1]/h4/p').text\\n all_price = product_1.find_elements_by_class_name('price-section')\\n list_prices = []\\n for price in all_price:\\n current_price = price.find_elements_by_class_name('price')[0].text\\n if len(current_price) > 0:\\n if current_price[0] == '$':\\n list_prices.append(float(current_price[1:]))\\n else:\\n list_prices.append(float(current_price))\\n self.price_ftf = max(list_prices)\\n self.name = card_name\\n self.set = card_set\",\n \"def free_item_discount(self, items):\\n new_items = []\\n occurrences = collections.Counter(item.sku for item in items)\\n if occurrences.get(MacbookPro.sku, 0) > 0:\\n for item in items:\\n if item.sku == VGAadapter.sku and item.price != 0:\\n item.price = 0\\n else:\\n new_items.append(CheckoutItem(sku=VGAadapter.sku, price=0))\\n items.extend(new_items)\\n return items\",\n \"def calculate_prices(self, good=None):\\n\\n stock = self.calculate_init_stock(good)\\n buy = self.buying_price()\\n\\n if stock == 0:\\n sell = 0\\n buy = buy + (buy * 0.5)\\n\\n elif stock < 500:\\n # mild bug: stock, without selling price\\n sell = self.selling_price()\\n elif stock >= 500:\\n # higher production, lower prices\\n sell = self.selling_price() / 2\\n buy = buy - (buy * 0.5)\\n\\n return [buy, sell, stock]\",\n \"def bulk_item(order: Order) -> Decimal:\\n discount = Decimal(0)\\n for item in order.cart:\\n if item.quantity >= 20:\\n discount += item.total() * Decimal('0.1')\\n return discount\",\n \"def bulk_item(order):\\n discount = 0\\n for item in order.cart:\\n if item.quantity >= 20:\\n discount += item.total() * .1\\n return discount\",\n \"def _compute_taxed_lst_price2(self):\\n company_id = self._context.get(\\n 'company_id', self.env.user.company_id.id)\\n for product in self:\\n product.taxed_lst_price2 = product.taxes_id.filtered(\\n lambda x: x.company_id.id == company_id).compute_all(\\n product.list_price2,\\n self.env.user.company_id.currency_id,\\n product=product)['total_included']\",\n \"def recalc_for_combo_birds(self, early_birds):\\n\\n for early_bird in early_birds:\\n if early_bird.price_type == 1:\\n self.recalc_for_bird(early_bird)\\n return self.rates\",\n \"def fin_advice(input_data, num_advice=3):\\n # Initialize the advice list\\n advice = []\\n # Compute the Z-score for a certain company\\n inc_rate = compute_increase_rate(input_data)\\n zscore = {}\\n # Map each Z-Score to each company\\n for comp_name in input_data.keys():\\n zscore[comp_name] = compute_zscore(inc_rate, comp_name)\\n # Reverse the dictionary z-score. (This help to find the max z-score)\\n rev_zscore = rev_dict(zscore)\\n for i in range(num_advice):\\n if len(rev_zscore) == 0:\\n break\\n # Find and delete the company with the greatest absolute z-score\\n use_zscore = max(rev_zscore.keys())\\n use_comp = rev_zscore[use_zscore]\\n del rev_zscore[use_zscore]\\n # Determine the advice given for that certain company\\n if zscore[use_comp] > 1:\\n advice.append(\\\"Sell \\\" + use_comp + \\\" Stock\\\")\\n elif zscore[use_comp] < -1:\\n advice.append(\\\"Buy \\\" + use_comp + \\\" Stock\\\")\\n else:\\n advice.append(\\\"Keep \\\" + use_comp + \\\" Stock\\\")\\n return advice\",\n \"def lot_leverage(self): \\n return 20\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.55473804","0.5530387","0.55122185","0.5472553","0.5421471","0.5414957","0.53732795","0.53643686","0.53457385","0.5306167","0.52392435","0.523554","0.52336174","0.52059656","0.5203071","0.51802623","0.51542354","0.5132325","0.5119246","0.50583845","0.5051806","0.50509536","0.5037654","0.5015832","0.4955219","0.49412382","0.4941033","0.49224937","0.49199638","0.49114248","0.49056065","0.49007237","0.48921868","0.48918733","0.48859522","0.48791823","0.4859512","0.48441193","0.4834774","0.48333693","0.4831466","0.48303455","0.4829535","0.48223448","0.48219764","0.48206735","0.4816856","0.47942048","0.47916836","0.47806808","0.4774819","0.47725764","0.47719848","0.4769608","0.4768722","0.47652847","0.4756459","0.4751339","0.47497404","0.47359088","0.4732478","0.47312558","0.4730096","0.4728302","0.47270387","0.47160718","0.47149193","0.47148928","0.47029915","0.4702478","0.46971577","0.46871808","0.46869528","0.46842015","0.46647242","0.4650581","0.46481878","0.46395546","0.46380663","0.46370587","0.46339583","0.46339568","0.4626355","0.4625517","0.4616729","0.46077597","0.4605139","0.4603911","0.45957464","0.45943373","0.458906","0.4588353","0.45732066","0.45701542","0.4568646","0.4567742","0.45632467","0.4559581","0.4559155","0.45591375"],"string":"[\n \"0.55473804\",\n \"0.5530387\",\n \"0.55122185\",\n \"0.5472553\",\n \"0.5421471\",\n \"0.5414957\",\n \"0.53732795\",\n \"0.53643686\",\n \"0.53457385\",\n \"0.5306167\",\n \"0.52392435\",\n \"0.523554\",\n \"0.52336174\",\n \"0.52059656\",\n \"0.5203071\",\n \"0.51802623\",\n \"0.51542354\",\n \"0.5132325\",\n \"0.5119246\",\n \"0.50583845\",\n \"0.5051806\",\n \"0.50509536\",\n \"0.5037654\",\n \"0.5015832\",\n \"0.4955219\",\n \"0.49412382\",\n \"0.4941033\",\n \"0.49224937\",\n \"0.49199638\",\n \"0.49114248\",\n \"0.49056065\",\n \"0.49007237\",\n \"0.48921868\",\n \"0.48918733\",\n \"0.48859522\",\n \"0.48791823\",\n \"0.4859512\",\n \"0.48441193\",\n \"0.4834774\",\n \"0.48333693\",\n \"0.4831466\",\n \"0.48303455\",\n \"0.4829535\",\n \"0.48223448\",\n \"0.48219764\",\n \"0.48206735\",\n \"0.4816856\",\n \"0.47942048\",\n \"0.47916836\",\n \"0.47806808\",\n \"0.4774819\",\n \"0.47725764\",\n \"0.47719848\",\n \"0.4769608\",\n \"0.4768722\",\n \"0.47652847\",\n \"0.4756459\",\n \"0.4751339\",\n \"0.47497404\",\n \"0.47359088\",\n \"0.4732478\",\n \"0.47312558\",\n \"0.4730096\",\n \"0.4728302\",\n \"0.47270387\",\n \"0.47160718\",\n \"0.47149193\",\n \"0.47148928\",\n \"0.47029915\",\n \"0.4702478\",\n \"0.46971577\",\n \"0.46871808\",\n \"0.46869528\",\n \"0.46842015\",\n \"0.46647242\",\n \"0.4650581\",\n \"0.46481878\",\n \"0.46395546\",\n \"0.46380663\",\n \"0.46370587\",\n \"0.46339583\",\n \"0.46339568\",\n \"0.4626355\",\n \"0.4625517\",\n \"0.4616729\",\n \"0.46077597\",\n \"0.4605139\",\n \"0.4603911\",\n \"0.45957464\",\n \"0.45943373\",\n \"0.458906\",\n \"0.4588353\",\n \"0.45732066\",\n \"0.45701542\",\n \"0.4568646\",\n \"0.4567742\",\n \"0.45632467\",\n \"0.4559581\",\n \"0.4559155\",\n \"0.45591375\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5732468"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":84196,"cells":{"query":{"kind":"string","value":"Computes all applicable offers if the offer type is PriceDrop."},"document":{"kind":"string","value":"def get_basket_items_pricedrop(self, offer_info, actual_volume, product_prices):\n prod_code = offer_info.base_prod_code\n base_prod_vol = actual_volume.get(prod_code.lower())\n\n pricedrop_basket = []\n\n if base_prod_vol >= offer_info.min_vol:\n offer_on_prod = offer_info.offer_on\n if actual_volume.get(offer_on_prod.lower()):\n print(\n f\"Base product volume is greater than minimum required volume & product on offer is also available \"\n f\"in cart..\")\n if offer_info.is_limited:\n print(f\"Limited offer..\")\n if prod_code == offer_on_prod:\n # total_allowed_items_on_offer = Limit Volume of base product * (Offer Product Max Volume/Minimum volume of base product)\n total_allowed_items_on_offer = offer_info.limit_vol * (\n offer_info.offer_prod_volume / offer_info.min_vol)\n max_limit = 1\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n pricedrop_basket.append((prod_code, base_prod_actual_price))\n while max_limit <= total_allowed_items_on_offer:\n new_price = (base_prod_actual_price - (offer_info.new_price)) * -1\n pricedrop_basket.append((offer_info.offer_code, new_price))\n max_limit += 1\n else:\n total_allowed_items_on_offer = offer_info.limit_vol * (\n offer_info.offer_prod_volume / offer_info.min_vol)\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n pricedrop_basket.append((prod_code, base_prod_actual_price))\n max_limit = 1\n while max_limit <= total_allowed_items_on_offer:\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\n new_price = (base_prod_actual_price - (offer_info.new_price)) * -1\n for j in range(0, actual_volume.get(offer_on_prod).lower()):\n pricedrop_basket.append((offer_on_prod, offer_onprod_actual_price))\n pricedrop_basket.append((offer_info.offer_code, new_price))\n max_limit += 1\n else:\n print(f\"Unlimited offer..\")\n if prod_code == offer_on_prod:\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n pricedrop_basket.append((prod_code, base_prod_actual_price))\n new_price = (base_prod_actual_price - (offer_info.new_price))*-1\n pricedrop_basket.append((offer_info.offer_code, new_price))\n else:\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n pricedrop_basket.append((prod_code, base_prod_actual_price))\n\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\n new_price = (offer_onprod_actual_price - (offer_info.new_price)) * -1\n\n for j in range(0, actual_volume.get(offer_on_prod).lower()):\n pricedrop_basket.append((offer_on_prod, offer_onprod_actual_price))\n pricedrop_basket.append((offer_info.offer_code, new_price))\n\n return pricedrop_basket"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def filter_viable_offers(want, have, offers) -> List:\n return [x for x in offers if is_offer_viable(want, have, x) is True]","def _compute_offers_to_send(self) -> Dict[Tuple[float, float], float]:\n partial_asgt = self._neighbors_values.copy()\n offers = dict()\n\n for limited_asgt in generate_assignment_as_dict([self.variable, self._partner]):\n partial_asgt.update(limited_asgt)\n cost = self._compute_cost(**partial_asgt)\n if self.logger.isEnabledFor(logging.DEBUG):\n self.logger.debug(\n f\"looking for offer : {partial_asgt} - cost {cost}\"\n f\" current {self.current_cost} {self._mode}\"\n )\n\n if (self.current_cost > cost and self._mode == \"min\") or (\n self.current_cost < cost and self._mode == \"max\"\n ):\n offers[(limited_asgt[self.name], limited_asgt[self._partner.name])] = (\n self.current_cost - cost\n )\n return offers","def get_prices(self):\n price = self.get_price()\n if price:\n return [price]\n return []","def get_prices(self):\n pass","def spread(self):\n if self.bid and self.offer:\n return self.offer[0].price - self.bid[-1].price\n\n return 0","def priceGetAll(soup):\n try:\n price = priceGetMost(soup)\n except:\n price = priceGetSome(soup)\n\n return price","def base_offers_list():\n offers_list = Offers()\n offers_list.add_new_offer('Offer1', 'Baked Beans', 3, 1)\n offers_list.add_new_offer('Offer2', 'Sardines', 1, 0.25)\n return offers_list","def solve_prices(self):\n return None","def calculate_prices(self, good=None):\n\n stock = self.calculate_init_stock(good)\n buy = self.buying_price()\n\n if stock == 0:\n sell = 0\n buy = buy + (buy * 0.5)\n\n elif stock < 500:\n # mild bug: stock, without selling price\n sell = self.selling_price()\n elif stock >= 500:\n # higher production, lower prices\n sell = self.selling_price() / 2\n buy = buy - (buy * 0.5)\n\n return [buy, sell, stock]","def get_basket_items_discount(self, offer_info, actual_volume, product_prices):\n prod_code = offer_info.base_prod_code\n base_prod_vol = actual_volume.get(prod_code.lower())\n\n discount_basket = []\n\n if base_prod_vol >= offer_info.min_vol:\n offer_on_prod = offer_info.offer_on\n if actual_volume.get(offer_on_prod.lower()):\n print(f\"Base product volume is greater than minimum required volume & product on offer is also available \"\n f\"in cart..\")\n if offer_info.is_limited:\n print(f\"Limited offer..\")\n if prod_code == offer_on_prod:\n # total_allowed_items_on_offer = Limit Volume of base product * (Offer Product Max Volume/Minimum volume of base product)\n total_allowed_items_on_offer = offer_info.limit_vol * (offer_info.offer_prod_volume/offer_info.min_vol)\n max_limit = 1\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n discount_basket.append((prod_code, base_prod_actual_price))\n while max_limit <= total_allowed_items_on_offer:\n discounted_price = (base_prod_actual_price *(offer_info.discount_perc/100))*-1\n discount_basket.append((offer_info.offer_code, discounted_price))\n max_limit += 1\n else:\n total_allowed_items_on_offer = offer_info.limit_vol * (offer_info.offer_prod_volume / offer_info.min_vol)\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n discount_basket.append((prod_code, base_prod_actual_price))\n max_limit = 1\n while max_limit <= total_allowed_items_on_offer:\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\n discounted_price = (offer_onprod_actual_price *(offer_info.discount_perc/100))*-1\n for j in range(0, actual_volume.get(offer_on_prod.lower())):\n discount_basket.append((offer_on_prod, offer_onprod_actual_price))\n discount_basket.append((offer_info.offer_code, discounted_price))\n max_limit += 1\n else:\n print(f\"Unlimited offer..\")\n if prod_code == offer_on_prod:\n if base_prod_vol > offer_info.min_vol:\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n discount_basket.append((prod_code, base_prod_actual_price))\n if i%2 != 0:\n discounted_price = (base_prod_actual_price *(offer_info.discount_perc/100))*-1\n discount_basket.append((offer_info.offer_code, discounted_price))\n else:\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n discount_basket.append((prod_code, base_prod_actual_price))\n else:\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n discount_basket.append((prod_code, base_prod_actual_price))\n\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\n discounted_price = (offer_onprod_actual_price * (offer_info.discount_perc / 100))*-1\n\n for j in range(0, actual_volume.get(offer_on_prod.lower())):\n discount_basket.append((offer_on_prod, offer_onprod_actual_price))\n discount_basket.append((offer_info.offer_code, discounted_price))\n\n\n return discount_basket","def is_offer_viable(want, have, offer) -> bool:\n # lower tier value means higher value\n have_tier = constants.currencies[have][\"tier\"]\n want_tier = constants.currencies[want][\"tier\"]\n conversion_rate = offer[\"conversion_rate\"]\n\n # if `want` is worth more than `have`, the conversion rate cannot be bigger than 1\n # This should be enough to successfully filter out price-fixing offers.\n if have_tier > want_tier and conversion_rate > 1:\n return False\n\n # if `have` is worth more than `have`, the conversion rate cannot be smaller than 1\n # We shouldn't need this case, since conversion rates below 1 will typically\n # result in unprofitable conversions and are therefore ignored anyway.\n # if have_tier < want_tier and conversion_rate < 1:\n # return False\n\n return True","def get_prices(self, grab, subject):\n prices = []\n try:\n extras = grab.doc.rex_text(\n '<h3 class\\=\"h6 copy-sp-m\">.*?%s.*?</h3>(.+?)</ul>' % subject,\n flags=re.S\n )\n except DataNotFound:\n logging.debug(\n \"Price %s is not found on %s\"\n % (subject, grab.doc.url)\n )\n return None\n\n sel = XpathSelector(fromstring(extras))\n prices = []\n for li in sel.select('//li[@class=\"list__item u-cf\"]'):\n obligatory = OrderedDict()\n obligatory['name'] = li.select('node()').text()\n money = li.select('node()/strong').text()\n obligatory['value'] = money[1:].replace(',', '')\n\n # Find perweek or perday\n if li.select(\n 'span[@class=\"boatview__extras-amount\"' +\n ' and contains(text(),\"per week\")]'\n ).exists():\n obligatory['perweek'] = True\n elif li.select(\n 'span[@class=\"boatview__extras-amount\"' +\n ' and contains(text(),\"per day\")]'\n ).exists():\n obligatory['perday'] = True\n obligatory['currency'] = money[0]\n prices.append(obligatory)\n\n if len(prices) < 1:\n logging.debug(\n \"Price %s contains less than one element on: %s\"\n % (subject, grab.doc.url)\n )\n return None\n\n return prices","def _find_best_offer(\n self, all_offers: List[Tuple[str, Dict]]\n ) -> Tuple[List, float]:\n bests, best_gain = [], 0\n\n for partner, offers in all_offers:\n partial_asgt = self._neighbors_values.copy()\n current_partner = self._neighbor_var(partner)\n\n # Filter out the constraints linking those two variables to avoid\n # counting their cost twice.\n shared = find_dependent_relations(current_partner, self._constraints)\n concerned = [rel for rel in self._constraints if rel not in shared]\n\n for (val_p, my_offer_val), partner_local_gain in offers.items():\n partial_asgt.update({partner: val_p, self.variable.name: my_offer_val})\n\n # Then we evaluate the agent constraint's for the offer\n # and add the partner's local gain.\n cost = assignment_cost(partial_asgt, concerned)\n global_gain = self.current_cost - cost + partner_local_gain\n\n if (global_gain > best_gain and self._mode == \"min\") or (\n global_gain < best_gain and self._mode == \"max\"\n ):\n bests = [(val_p, my_offer_val, partner)]\n best_gain = global_gain\n elif global_gain == best_gain:\n bests.append((val_p, my_offer_val, partner))\n\n return bests, best_gain","def apply_20percent_shipping_offer(self):\n product_range = Range.objects.create(\n name=\"All products range\", includes_all_products=True\n )\n benefit = Benefit.objects.create(\n range=product_range, type=Benefit.SHIPPING_PERCENTAGE, value=20\n )\n offer = factories.create_offer(product_range=product_range, benefit=benefit)\n Applicator().apply_offers(self.basket, [offer])\n return offer","def compute_market_prices(prices):\n denom = prices.bid_volume + prices.ask_volume\n numer = (prices.bid_price * prices.ask_volume +\n prices.ask_price * prices.bid_volume)\n mask = denom == 0\n denom[mask] = 2\n numer[mask] = prices.bid_price[mask] + prices.ask_price[mask]\n prices = prices.copy()\n prices['market_price'] = numer / denom\n return prices","def check_restrictions(self):\n from .signals import determine_availability\n\n responses = determine_availability.send(\n self.item.event, item=self.item,\n variations=[self.to_variation_dict()], context=None,\n cache=self.item.event.get_cache()\n )\n price = self.default_price if self.default_price is not None else self.item.default_price\n for receiver, response in responses:\n if 'available' in response[0] and not response[0]['available']:\n return False\n elif 'price' in response[0] and response[0]['price'] is not None and response[0]['price'] < price:\n price = response[0]['price']\n return price","def option_price(volatilities,\n strikes,\n expiries,\n spots=None,\n forwards=None,\n discount_factors=None,\n is_call_options=None,\n dtype=None,\n name=None):\n if (spots is None) == (forwards is None):\n raise ValueError('Either spots or forwards must be supplied but not both.')\n\n with tf.compat.v1.name_scope(\n name,\n default_name='option_price',\n values=[\n forwards, strikes, volatilities, expiries, discount_factors,\n is_call_options\n ]):\n strikes = tf.convert_to_tensor(strikes, dtype=dtype, name='strikes')\n volatilities = tf.convert_to_tensor(\n volatilities, dtype=dtype, name='volatilities')\n expiries = tf.convert_to_tensor(expiries, dtype=dtype, name='expiries')\n\n if discount_factors is None:\n discount_factors = tf.convert_to_tensor(\n 1.0, dtype=dtype, name='discount_factors')\n else:\n discount_factors = tf.convert_to_tensor(\n discount_factors, dtype=dtype, name='discount_factors')\n\n if forwards is not None:\n forwards = tf.convert_to_tensor(forwards, dtype=dtype, name='forwards')\n else:\n spots = tf.convert_to_tensor(spots, dtype=dtype, name='spots')\n forwards = spots / discount_factors\n\n sqrt_var = volatilities * tf.math.sqrt(expiries)\n d1 = (tf.math.log(forwards / strikes) + sqrt_var * sqrt_var / 2) / sqrt_var\n d2 = d1 - sqrt_var\n undiscounted_calls = forwards * _ncdf(d1) - strikes * _ncdf(d2)\n if is_call_options is None:\n return discount_factors * undiscounted_calls\n undiscounted_forward = forwards - strikes\n undiscounted_puts = undiscounted_calls - undiscounted_forward\n predicate = tf.broadcast_to(is_call_options, tf.shape(undiscounted_calls))\n return discount_factors * tf.where(predicate, undiscounted_calls,\n undiscounted_puts)","def price(self, irc, msg, args, optlist, typeName):\n\n try:\n typeID = self._get_typeID(typeName)\n itemType = self._get_type(typeID)\n except:\n irc.error('Unknown type')\n return\n\n if len(optlist) == 1:\n location = optlist[0][1]\n else:\n location = 'Jita'\n\n try:\n locationID = self._get_locationID(location)\n location = self._get_location(locationID)\n except:\n irc.error('Unknown location')\n return\n\n market = self._sql(\"\"\"\n SELECT * FROM evecentral_market\n WHERE \"locationID\"=%s\"\"\", [locationID])\n if not market:\n irc.reply('No data for that market location')\n return\n\n marketitem = self._sql(\"\"\"\n SELECT * FROM evecentral_marketitem\n WHERE \"locationID\"=%s AND \"typeID\"=%s\"\"\", [locationID, typeID])\n if marketitem:\n irc.reply('{0} in {1}: buy max: {2} (volume: {3:,d}). sell min: {4} (volume: {5:,d}).'.format(\n ircutils.bold(itemType['typeName']),\n self._colorize_system(location),\n ircutils.mircColor(\n '{:,.2f}'.format(marketitem['buy_max']),\n fg='green'),\n int(marketitem['buy_volume']),\n ircutils.mircColor(\n '{:,.2f}'.format(marketitem['sell_min']),\n fg='green'),\n int(marketitem['sell_volume']),\n ), prefixNick=False)\n else:\n irc.reply(\"Prices for {0} in {1} isn't updated yet.\".format(\n itemType['typeName'],\n location['itemName']\n ))","def apply(self, catalog):\n out = []\n for product in catalog:\n valid = True\n for key, filtr in self.filters.items():\n valid = valid and filtr(product[key])\n if valid:\n out.append(product)\n out = sorted(out, key=lambda x: x['onDemandUsdPrice'])\n return self.limit_size(out)","def _get_applicable_offers(self, current_protocol, path):\n\n edges = []\n\n for from_protocol_name, offers in self._adaptation_offers.items():\n from_protocol = offers[0].from_protocol\n mro_distance = self.mro_distance_to_protocol(\n current_protocol, from_protocol\n )\n\n if mro_distance is not None:\n\n for offer in offers:\n # Avoid cycles by checking that we did not consider this\n # offer in this path.\n if offer not in path:\n edges.append((mro_distance, offer))\n\n return edges","def refuse_and_delete_pending_offers(item, offer_id):\n for o in item.offers_received.filter(~Q(pk=offer_id), answered=False):\n refuse_offer(o)\n\n for o in item.offers_done.filter(~Q(pk=offer_id), answered=False):\n delete_offer(o)","def parse_offers_html(html):\n def parse_single_offer(html_offer):\n try:\n info_row = html_offer.table.tbody.tr\n return Offer(info_row.td.a['href'], info_row.td.a.img['alt'], info_row.find(class_='price').strong.string)\n except:\n pass\n\n raw_offers = Soup(html, 'html.parser').find('table', {'id': 'offers_table'}).find_all('td', class_=re.compile('offer$'))\n return reducer((partial(map, parse_single_offer), partial(filter, None.__ne__), list), raw_offers)","def main():\n tte, url_pt2 = get_tte(expiration)\n url2 = url + '&date=' + url_pt2\n dfs = pd.read_html(url2)\n\n call_df = dfs[0]\n put_df = dfs[1]\n \n \n \"\"\"Should have created an Option class here. Like:\n class Option:\n def __init__(self,type, spot, strike, time, rate, div, vol, vol_guess,price):\n self.type = type\n self.spot = spot\n .....\n def theo(self):\n return Theo(type, spot, strike,time, rate, div, vol)\n def delta(self):\n return Delta(type, spot, strike,time, rate, div, vol)\n ...\n \n \"\"\"\n \n \n #instead, a bunch of lists: \n call_theos = []\n put_theos = []\n call_vols= []\n put_vols= []\n\n call_deltas = []\n call_gammas = []\n call_thetas = []\n call_vegas = []\n put_deltas = []\n put_gammas = []\n put_thetas = []\n put_vegas = []\n\n call_imp_vols =[]\n put_imp_vols = []\n\n spot = get_spot(spot_url)\n print('Spot:', spot)\n vol = get_vol(vol_url,20)\n print('20day Vol:' ,vol)\n rate = interest_rate(tte)\n div = .0\n\n #Don't really care about options going for pennies\n call_df = call_df.ix[call_df['Volume'] >=100].ix[call_df['Last Price'] >= .10]\n put_df = put_df.ix[put_df['Volume'] >= 100].ix[put_df['Last Price'] >= .10]\n\n\n for index, row in call_df.iterrows():\n call_vol = row['Implied Volatility'][:-1]\n call_vols.append(float(call_vol))\n\n\n for index, row in put_df.iterrows():\n put_vol = row['Implied Volatility'][:-1]\n put_vols.append(float(put_vol))\n\n\n # extremely crude method for increasing put vol to help account for skew\n call_sd = statistics.stdev(call_vols)\n call_mean = sum(call_vols)/len(call_vols)\n call_vols = [vol for vol in call_vols if abs(vol-call_mean) <= 3 * call_sd]\n call_vol_factor = sum(call_vols)/len(call_vols) / 100.0\n\n put_sd = statistics.stdev(put_vols)\n put_mean = sum(put_vols)/len(put_vols)\n put_vols = [vol for vol in put_vols if abs(vol-put_mean) <= 1 * put_sd]\n put_vol_factor = sum(put_vols)/len(put_vols) / 100.0\n\n call_vol = vol\n put_vol = vol * (put_vol_factor/call_vol_factor)\n\n for index, row in call_df.iterrows():\n strike = float(row['Strike'])\n time = tte\n call_theos.append(Theo('C', spot, strike, time, rate, div, call_vol))\n call_deltas.append(Delta('C', spot, strike, time, rate, div, call_vol))\n call_gammas.append(Gamma(spot, strike, time, rate, div, call_vol))\n call_thetas.append(Theta('C', spot, strike, time, rate, div, call_vol))\n call_vegas.append(Vega(spot, strike, time, rate, div, call_vol))\n\n\n midpoint = (float(row['Bid']) + float(row['Ask']))/2.0\n call_imp_vols.append(ImpliedVol('C', spot,strike,time,rate,div,midpoint, call_vol))\n\n for index, row in put_df.iterrows():\n strike = float(row['Strike'])\n time = tte\n put_theos.append(Theo('P', spot, strike, time, rate, div, put_vol))\n put_deltas.append(Delta('P', spot, strike, time, rate, div, put_vol))\n put_gammas.append(Gamma(spot, strike, time, rate, div, put_vol))\n put_thetas.append(Theta('P', spot, strike, time, rate, div, put_vol))\n put_vegas.append(Vega(spot, strike, time, rate, div, put_vol))\n\n midpoint = (float(row['Bid']) + float(row['Ask'])) / 2.0\n put_imp_vols.append(ImpliedVol('P', spot, strike, time, rate, div, midpoint, put_vol))\n\n\n call_df['Theo'] = call_theos\n call_df['Delta'] = call_deltas\n call_df['Gamma'] = call_gammas\n call_df['Theta'] = call_thetas\n call_df['Vega'] = call_vegas\n\n put_df['Theo'] = put_theos\n put_df['Delta'] = put_deltas\n put_df['Gamma'] = put_gammas\n put_df['Theta'] = put_thetas\n put_df['Vega'] = put_vegas\n\n call_imp_vols = [str(i*100)[0:5] + '%' for i in call_imp_vols]\n put_imp_vols = [str(i*100)[0:5] + '%' for i in put_imp_vols]\n\n call_df['Calc Imp Vol'] =call_imp_vols\n put_df['Calc Imp Vol'] = put_imp_vols\n\n\n #Message alert for large discrepancies between theo and midpoint of bid/ask\n default = sec+ ' Alert ' + expiration + '\\n'\n alertstring = default\n for index, row in call_df.iterrows():\n\n midpoint = (float(row['Ask']) + float(row['Bid']))/2.0\n if abs(row['Theo'] - midpoint ) >= 1.0:\n\n edge = str(abs(row['Theo'] - midpoint))\n if row['Theo'] > midpoint:\n updown = 'under'\n else:\n updown = 'over'\n\n\n alertstring += str(int(row['Strike'])) + 'C ' + edge[0:4] + ' ' + updown + '\\n'\n\n for index, row in put_df.iterrows():\n\n midpoint = (float(row['Ask']) + float(row['Bid'])) / 2.0\n if abs(row['Theo'] - midpoint) >= 1.0:\n\n edge = str(abs(row['Theo'] - midpoint))\n if row['Theo'] > midpoint:\n updown = 'under'\n else:\n updown = 'over'\n\n alertstring += str(int(row['Strike'])) + 'P ' + edge[0:4] + ' ' + updown + '\\n'\n if alertstring != default:\n send_message(alertstring)\n\n plotVolSeries(sec)\n plotImpVol(call_df,put_df,spot, sec)\n writer = pd.ExcelWriter(sec+'_options_' + expiration +'.xlsx')\n call_df.to_excel(writer, 'Calls')\n put_df.to_excel(writer, 'Puts')\n writer.save()","def target_prices(self) -> List[float]:\n return self._target_prices","def prices(parsed, appearance_value, removal_value, gate_single_value, gate_double_value):\n\t\tprices = []\n\n\t\tfor item in parsed:\n\t\t\tif item.entityType == \"fence\":\n\t\t\t\tprices.append(QuoteCalculation._fencePrice(item, appearance_value, removal_value))\n\n\t\t\telif item.entityType == \"gate\":\n\t\t\t\tprices.append(QuoteCalculation._gatePrice(item, gate_single_value, gate_double_value, removal_value))\n\n\t\t\t# Not required?\n\t\t\t#elif item.entityType == \"post\":\n\t\t\t\t#prices.append(QuoteCalculation._postPrice(item))\n\n\t\treturn prices","def get_best_offer(self,way):\n if way==\"BUY\":\n return self.book[Trade.WAY_BUY][0].get_price()\n elif way==\"SELL\":\n return self.book[Trade.WAY_SELL][len(self.book[Trade.WAY_SELL])-1].get_price()","def calc_energy_and_price(self) -> (float, float):\n\n cost_sum = 0\n energy_sum = 0\n for pump_id in self.pumps:\n pump_energy, pump_cost = self.pumps[pump_id].calculate_energy_and_cost()\n cost_sum += pump_cost\n energy_sum += pump_energy\n\n pump_id.append_index = 0\n\n assert energy_sum >= 0, \"The pumping energy cant be negative!\"\n assert cost_sum >= 0, \"The pumping cost cant be negative!\"\n return energy_sum, cost_sum","def option_price_curve(self, payoff='Call'):\n return dict(zip(self.strike_grid, list(map(lambda K: self.option_price(K, payoff=payoff), self.strike_grid))))","def compute(self):\r\n \r\n if self.option_type == \"European call\":\r\n option_price = BS_call(self.S0, self.K, self.T_scaled, self.vola, self.div_yield, r=self.rf)\r\n \r\n elif self.option_type == \"European put\":\r\n option_price = BS_put(self.S0, self.K, self.T_scaled, self.vola, self.div_yield, r=self.rf)\r\n \r\n elif self.option_type == \"Asian call\":\r\n option_price = monte_carlo_call(self.S0, self.K, self.T_scaled, self.vola, self.div_yield, rf=self.rf ) # function in other file\r\n else:\r\n # Asian put\r\n option_price = monte_carlo_put(self.S0, self.K, self.T_scaled, self.vola, self.div_yield, rf=self.rf)\r\n \r\n\r\n self.display_option_price.setText(str(round(option_price,2)))\r\n self.display_option_price.adjustSize()","def _get_book_prices(self):\n for k in self.orders.keys():\n if self.orders[k].type == 'ask':\n self.ask_prices.append(self.orders[k].price)\n self.ask_snapshot[k] = self.orders[k]\n elif self.orders[k].type == 'bid':\n self.bid_prices.append(self.orders[k].price)\n self.bid_snapshot[k] = self.orders[k]\n # Sorting and removing dubbing\n self.ask_prices = list(dict.fromkeys(sorted(self.ask_prices)))\n self.bid_prices = list(dict.fromkeys(sorted(self.bid_prices, reverse=True)))","def sell_all(self):\n\n from_symbol = self.symbol\n to_symbol = self.currency\n price = self.data[0].close\n amount = self.portfolio['buy_sell_amount'][self.currency]\n date = self.date\n\n if self.slippage:\n slip_factor = (self.data[-1].high - self.data[-1].close)*self.slippage\n price -= np.abs(slip_factor)\n\n self.trade_manager.sell_all(from_symbol, to_symbol, price, amount, date)","def compare_price(self):\n if self.__product_price < self.__alert_price:\n #print(\"price drop...\")\n self.__alert_client = True\n self.__price_difference = self.__product_price - self.__alert_price\n else:\n #print(\"Price not reduced...\")\n self.__alert_client = False\n self.__price_difference = self.__product_price - self.__alert_price","def value_offers(self):\n return OrderBookUtils.book_value(self.offer)","def recalc_for_combo_birds(self, early_birds):\n for early_bird in early_birds:\n if early_bird.price_type == 1:\n self.recalc_for_bird(early_bird)\n return self.rates","def sell(self, amount):\n trades = []\n sell_amount = 0\n precision = pow(10, self.pair.get_base_token().get_decimals() - self.pair.get_quote_token().get_decimals())\n for i in range(len(self.book[Trade.WAY_BUY])):\n offer = self.book[Trade.WAY_BUY][i]\n amount_quote = offer.get_quote_amount()\n amount_base = offer.get_base_amount()\n price = offer.get_price()\n\n if amount_quote >= amount:\n tmp = amount * price * precision\n tmp = int(tmp)\n trade = Trade(self.pair, Trade.WAY_SELL, price, tmp, amount, time.time(), fee_currency=self.pair.get_exchange().get_fee_token())\n sell_amount = sell_amount + trade.get_amount_base()\n trades.append(trade)\n return trades, int(sell_amount)\n\n '''\n Is the offered amount less than needed, you can only buy the offered amount and continue\n '''\n trade = Trade(self.pair, Trade.WAY_SELL, price, amount_base, amount_quote, time.time(), fee_currency=self.pair.get_exchange().get_fee_token())\n amount = amount - amount_quote\n sell_amount = sell_amount + trade.get_amount_base()\n trades = trades + [trade]\n\n '''\n Not enough volume or amount to high\n '''\n raise KeyError(\"Not enough offers in orderbook. Low volume or amount to high.\")","def get_prices(variation_object,Map):\n\n sale_price = variation_object[Map['itemtype']['fields']['sale_price']]\n price = sale_price\n\n if variation_object[Map['itemtype']['fields']['price']]:\n price = variation_object[Map['itemtype']['fields']['price']]\n\n return price,sale_price","def aggregate_offer_qty(self, trade_price):\n qty = 0\n for i in range(len(self.offer)):\n # print(\"trade_price = {} offer[{}] = {}\".format(trade_price, i, self.offer[i].price))\n if self.offer[i].price <= trade_price:\n qty += self.offer[i].qty\n # print(\"Running qty = {}\".format(qty))\n return qty","def extract_price_excluding_tax(soup):\r\n price_excluding_tax = extract_attributs_book(soup)['Price (excl. tax)']\r\n return price_excluding_tax","def getTransferListSummary(self):\n p_ids_and_prices = {}\n players = self.getAllPlayerInfoTransferlist()\n\n # Get IDs of all players\n log_event(self.queue, \"Gathering player prices... \")\n for p in players:\n p_bidstatus = p[1]\n p_id = p[8]\n # removed Filter for unlisted / expired players\n if p_id not in p_ids_and_prices:\n p_sellprice = self.getPlayerSellPrice(p_id)\n # If sell price returns 0, need to fetch from Futbin\n if p_sellprice == 0:\n p_sellprice = self.getFutbinPrice_opentab(p_id)\n self.sleep_approx(5) # Delay iteration to not anger futbin\n # Add player ID and price to dict\n p_ids_and_prices[p_id] = p_sellprice\n\n for p_id in p_ids_and_prices:\n p_price = p_ids_and_prices[p_id]\n p_name = self.getPlayerCardName(p_id)\n log_event(self.queue, str(p_name) + \" - #\" +\n str(p_id) + \" Price \" + str(p_price))\n\n num_p_sold = 0\n num_p_expired = 0\n num_p_unlisted = 0\n num_p_listed = 0\n\n sold_p_value = 0\n expired_p_value = 0\n unlisted_p_value = 0\n listed_p_value = 0\n\n for p in players:\n p_bidstatus = p[1]\n p_id = p[8]\n p_soldprice = p[5] # is 0 if unlisted\n p_sellprice = int(p_ids_and_prices[p_id])\n\n if \"won\" in p_bidstatus:\n num_p_sold += 1\n sold_p_value += p_soldprice\n if \"expired\" in p_bidstatus:\n num_p_expired += 1\n expired_p_value += p_sellprice\n if (p_bidstatus == \"listFUTItem\"):\n num_p_unlisted += 1\n unlisted_p_value += p_sellprice\n if (p_bidstatus == \"listFUTItem has-auction-data\"):\n num_p_listed += 1\n listed_p_value += p_sellprice\n\n log_event(self.queue, \"Players sold: \" + str(num_p_sold))\n log_event(self.queue, \"Players expired: \" + str(num_p_expired))\n log_event(self.queue, \"Players listed: \" + str(num_p_listed))\n log_event(self.queue, \"Players unlisted: \" + str(num_p_unlisted))\n log_event(self.queue, \" - - - \")\n log_event(self.queue, \"Sold players value: \" + str(sold_p_value))\n log_event(self.queue, \"Expired players value: \" +\n str(expired_p_value))\n log_event(self.queue, \"Unlisted players value: \" +\n str(unlisted_p_value))\n log_event(self.queue, \"Listed players value: \" + str(listed_p_value))\n\n # TODO subtract bought price\n self.user_players_won += int(num_p_unlisted)\n self.p_ids_and_prices = p_ids_and_prices\n intel = [p_ids_and_prices, num_p_sold, num_p_expired, num_p_unlisted,\n num_p_listed, sold_p_value, expired_p_value, unlisted_p_value, listed_p_value]\n return intel","def decide_trade_plan(self):\n self.trade_amount = 5\n most_demanded_goods = self.home.market.goods_demand_ratio(day_range=1)\n most_demanded_goods = sorted(most_demanded_goods.items(), key=lambda i: i[1], reverse=True)\n\n # if we already had a trade good, refresh ideal inventory\n if self.trade_good:\n self.update_ideal_inventory()\n\n if DEBUG: print(\"Finding a Good to trade:\")\n\n for good, demand in most_demanded_goods:\n if demand > 0:\n # find nearby provinces where this has inventory and the price is lower\n price_at_home = self.home.market.mean_price(good)\n if DEBUG: print(\"Good: {}, Demand: {}, Price: ${}\".format(good.title, demand, price_at_home))\n neighboring_markets = [p.market for p in self.location.owned_neighbors]\n neighboring_markets = [m for m in neighboring_markets if m.supply_for(good) > self.trade_amount]\n neighboring_markets.sort(key=lambda m: m.supply_for(good), reverse=True)\n\n if len(neighboring_markets) > 0:\n # we found places where this good is cheaper and in inventory\n target = neighboring_markets[0].location\n price_at_target = target.market.mean_price(good)\n\n # only trade with prices where we can make money\n if price_at_home > price_at_target:\n offset = 0\n if good is Good.bread:\n offset = 1\n self.inventory.set_ideal(good, self.trade_amount + offset)\n self.trade_location = target\n if DEBUG:\n print(\"\\tTarget: {}, Supply: {}, Price: ${}, Price at home: ${}\".format(\n self.trade_location.name,\n self.trade_location.market.supply_for(good),\n self.trade_location.market.mean_price(good),\n price_at_home)\n )\n self.trade_good = good\n return\n else:\n if DEBUG: print(\"\\tPrice is higher at target (home: ${} target: ${})\".format(price_at_home, price_at_target))\n else:\n if DEBUG: print(\"\\tNo markets selling {} found\".format(good))","def trade_offer(self, price):\n if self.is_sold:\n raise AlreadySoldError(\"Electricity product already sold\")\n\n if self.remaining_slots <= 0:\n raise LeadtimePassedError(\"Lead time passed\")\n\n succesful_trade = random.random() < self.selling_chance(price)\n self.remaining_slots -= 1\n\n if succesful_trade:\n profit = price - self.product_price\n self.is_sold = True\n elif self.remaining_slots == 0:\n profit = -self.product_price\n else:\n profit = 0\n\n return (profit, self.is_sold)","def apply_prices(data, conditions=None, price='open', stop_loss=0, order='B'):\n if order.upper() == 'B':\n multiplier = 1 - (stop_loss * 0.01)\n elif order.upper() == 'S':\n multiplier = 1 + (stop_loss * 0.01)\n else:\n raise ValueError('Order should be either B or S')\n\n # All conditions are forced to lower case AND ed\n if conditions:\n big_condition = '&'.join(['(' + c.lower() + ')' for c in conditions])\n # TO DO: Deal with Memory Error in case of lots of conditions\n # f shorthand for filtered\n f = data.query(big_condition).copy()\n else:\n f = data.copy()\n f['price'] = f.eval(price).apply(tick)\n f['stop_loss'] = (f['price'] * multiplier).apply(tick)\n\n # Price map to determine whether buy or sell is the entry\n p_map = {\n 'B': ('buy', 'sell'),\n 'S': ('sell', 'buy')\n }\n\n col_price, col_sl = p_map.get(order)\n\n f.loc[:, col_price] = [isPrice(p,h,l) for p,h,l in zip(\n f.price, f.high, f.low)] * f['price']\n f.loc[:, col_sl] = [isPrice(p,h,l) for p,h,l in zip(\n f.stop_loss, f.high, f.low)] * f['stop_loss']\n f.loc[f.buy == 0, 'buy'] = f.loc[f.buy == 0, 'close']\n f.loc[f.sell == 0, 'sell'] = f.loc[f.sell == 0, 'close']\n return f","def accumulate_prices(name,products,sales,types,add):\r\n return reduce(add,[get_prices_by_type(name,products,sales,types)[i] for i in get_prices_by_type(name,products,sales,types)])","def recalc_for_combo_birds(self, early_birds):\n\n for early_bird in early_birds:\n if early_bird.price_type == 1:\n self.recalc_for_bird(early_bird)\n return self.rates","def validate(cls, prices):\n super(GiftCardPrice, cls).validate(prices)\n\n for price in prices:\n price.check_price()","def getTransferListSummaryWithoutPrices(self):\n players = self.getAllPlayerInfoTransferlist()\n\n num_p_sold = 0\n num_p_expired = 0\n num_p_unlisted = 0\n num_p_listed = 0\n sold_p_value = 0\n\n for p in players:\n p_bidstatus = p[1]\n p_id = p[8]\n p_soldprice = p[5] # is 0 if unlisted\n\n if \"won\" in p_bidstatus:\n num_p_sold += 1\n sold_p_value += p_soldprice\n if \"expired\" in p_bidstatus:\n num_p_expired += 1\n if (p_bidstatus == \"listFUTItem\"):\n num_p_unlisted += 1\n if (p_bidstatus == \"listFUTItem has-auction-data\"):\n num_p_listed += 1\n\n # TODO subtract bought price\n return num_p_sold, num_p_expired, num_p_unlisted, num_p_listed","def price_data(sp_available=False, sp_traded=False, ex_best_offers=False, ex_all_offers=False, ex_traded=False):\n args = locals()\n return [\n k.upper() for k, v in args.items() if v is True\n ]","def check_for_offer(self, bid, commodity, limit, actual, quantity, price):\n if bid:\n if len(self.trades[\"buys\"][commodity]) == 0:\n return 0\n else: # tally up how much trying to buy.\n total = 0.0\n total_price = 0.0\n for offer in self.trades[\"buys\"][commodity]:\n total += offer.quantity\n total_price += offer.price\n\n avg_price = total_price / len(self.trades[\"buys\"][commodity])\n\n # if total < limit:\n # #PLACE MORE BIDS.\n return total\n\n else:\n if len(self.trades[\"asks\"][commodity]) == 0:\n return 0\n else: # tally up how much trying to buy.\n total = 0.0\n total_price = 0.0\n for offer in self.trades[\"asks\"][commodity]:\n total += offer.quantity\n total_price += offer.price\n\n avg_price = total_price / len(self.trades[\"asks\"][commodity])\n #\n # if total < limit:\n # #PLACE MORE asks.\n # return total\n # if total < limit:\n # #PLACE MORE asks.\n return total # - limit","def extract_listing_price_from_result(soup, prices):\r\n for description in soup.find_all(name='div', class_='descr'):\r\n price = description.find(name='div', class_='price')\r\n if price == None:\r\n prices.append('No Price')\r\n else:\r\n prices.append(price.get_text())\r\n # print(prices)\r\n return prices","def offers(self, offers):\n\n self._offers = offers","def get_all_available_variations(self, use_cache: bool=False):\n if use_cache and hasattr(self, '_get_all_available_variations_cache'):\n return self._get_all_available_variations_cache\n\n from .signals import determine_availability\n\n variations = self._get_all_generated_variations()\n responses = determine_availability.send(\n self.event, item=self,\n variations=variations, context=None,\n cache=self.event.get_cache()\n )\n\n for i, var in enumerate(variations):\n var['available'] = var['variation'].active if 'variation' in var else True\n if 'variation' in var:\n if var['variation'].default_price:\n var['price'] = var['variation'].default_price\n else:\n var['price'] = self.default_price\n else:\n var['price'] = self.default_price\n\n # It is possible, that *multiple* restriction plugins change the default price.\n # In this case, the cheapest one wins. As soon as there is a restriction\n # that changes the price, the default price has no effect.\n\n newprice = None\n for receiver, response in responses:\n if 'available' in response[i] and not response[i]['available']:\n var['available'] = False\n break\n if 'price' in response[i] and response[i]['price'] is not None \\\n and (newprice is None or response[i]['price'] < newprice):\n newprice = response[i]['price']\n var['price'] = newprice or var['price']\n\n variations = [var for var in variations if var['available']]\n\n self._get_all_available_variations_cache = variations\n return variations","def stop_loss_prices(self) -> List[float]:\n return self._stop_loss_prices","def liquid_with_loans(self):\n from Game.models import LoanOffer\n loan_offers = LoanOffer.objects.filter(lender=self)\n return self.liquid - sum(l.offered for l in loan_offers)","def test_get_offers(self):\n pass","def export_tier_prices_to_magento(self):\n instance = self.website.instance\n\n for mag_product_template in self.website.magento_product_templates:\n product_template = mag_product_template.template\n product = product_template.products[0]\n\n # Get the price tiers from the product if the product has a price\n # tier table else get the default price tiers from current store\n price_tiers = product_template.price_tiers or self.price_tiers\n\n price_data = []\n for tier in price_tiers:\n if hasattr(tier, 'product'):\n # The price tier comes from a product, then it has a\n # function field for price, we use it directly\n price = tier.price\n else:\n # The price tier comes from the default tiers on store,\n # we dont have a product on tier, so we use the current\n # product in loop for computing the price for this tier\n price = self.price_list.compute(\n None, product, product.list_price, tier.quantity,\n self.website.default_uom\n )\n\n price_data.append({\n 'qty': tier.quantity,\n 'price': float(price),\n })\n\n # Update stock information to magento\n with magento.ProductTierPrice(\n instance.url, instance.api_user, instance.api_key\n ) as tier_price_api:\n tier_price_api.update(\n mag_product_template.magento_id, price_data\n )\n\n return len(self.website.magento_product_templates)","def calculate_prices(self, merged_data):\n calculated_prices = []\n for record in merged_data:\n prices_dict = dict()\n supplier_price_id = record.get('supplier_detail').get('identifier') # get the supplier price id\n session_id = record.get('supplier_transaction').get('session_id') # get the transaction session\n supplier_trans_fee_price = self.compute_fee_price(\n record) # Get the fee price for each transaction if needed\n supplier_trans_time_price = self.compute_time_price(\n record) # Get the time price for each transaction if needed\n supplier_trans_kwh_price = self.compute_kwh_price(record)\n total_price = supplier_trans_fee_price + supplier_trans_time_price + supplier_trans_kwh_price\n prices_dict.update({'fee_price': supplier_trans_fee_price,\n 'time_price': supplier_trans_time_price,\n 'kwh_price': supplier_trans_kwh_price,\n 'total_price': total_price,\n 'session_id': session_id,\n 'supplier_price_id': supplier_price_id})\n calculated_prices.append(prices_dict)\n\n return calculated_prices","def check_restrictions(self):\n if self.properties.count() > 0: # NOQA\n raise ValueError('Do not call this directly on items which have properties '\n 'but call this on their ItemVariation objects')\n from .signals import determine_availability\n\n vd = VariationDict()\n responses = determine_availability.send(\n self.event, item=self,\n variations=[vd], context=None,\n cache=self.event.get_cache()\n )\n price = self.default_price\n for receiver, response in responses:\n if 'available' in response[0] and not response[0]['available']:\n return False\n elif 'price' in response[0] and response[0]['price'] is not None and response[0]['price'] < price:\n price = response[0]['price']\n return price","def get_good_price_by_deal(self):\n if self.deal == SALE:\n return self.new_price\n elif self.deal == AUCTION:\n return self.max_price\n else:\n return self.price","def sellOptionsAtExpiration(self):\n\n open_positions = self.open_positions.find(\n {\"Trader\": self.user[\"Name\"], \"Asset_Type\": \"OPTION\"})\n\n dt = getDatetime()\n\n for position in open_positions:\n\n day_before = (position[\"Exp_Date\"] -\n timedelta(days=1)).strftime(\"%Y-%m-%d\")\n\n if day_before == dt.strftime(\"%Y-%m-%d\"):\n\n trade_data = {\n \"Symbol\": position[\"Symbol\"],\n \"Pre_Symbol\": position[\"Pre_Symbol\"],\n \"Side\": \"SELL_TO_CLOSE\",\n \"Option_Type\": position[\"Option_Type\"],\n \"Strategy\": position[\"Strategy\"],\n \"Asset_Type\": position[\"Asset_Type\"],\n \"Exp_Date\": position[\"Exp_Date\"]\n }\n\n self.placeOrder(trade_data, position)","def update_price_model(self, good, order_type, is_successful, clearing_price=0):\n\n SIGNIFICANT = 0.25 # 25% more or less is \"significant\"\n SIG_IMBALANCE = 0.33\n LOW_INVENTORY = 0.1 # 10% of ideal inventory = \"LOW\"\n HIGH_INVENTORY = 2.0 # 200% of ideal inventory = \"HIGH\"\n MIN_PRICE = 0.01 # lowest allowed price of a Good\n\n if is_successful:\n # add this trade to the observed trading range\n self.observed_trading_range[good].append(clearing_price)\n\n public_mean_price = self.market.mean_price(good)\n belief = self.price_belief[good]\n mean = belief.mean()\n wobble = 0.05 # the degree which the Pop should bid outside the belief\n\n # how different the public mean price is from the price belief\n delta_to_mean = mean - public_mean_price\n\n if is_successful:\n if order_type is OrderType.buy_order and delta_to_mean > SIGNIFICANT:\n # this Pop overpaid, shift belief towards mean\n belief.low -= delta_to_mean / 2\n belief.high -= delta_to_mean / 2\n elif order_type is OrderType.sell_order and delta_to_mean < -SIGNIFICANT:\n # this Pop underpaid!, shift belief towards mean\n belief.low -= delta_to_mean / 2\n belief.high -= delta_to_mean / 2\n\n # increase the belief's certainty\n belief.low += wobble * mean\n belief.high -= wobble * mean\n\n else:\n # shift towards mean\n belief.low -= delta_to_mean / 2\n belief.high -= delta_to_mean / 2\n\n # check for inventory special cases\n stocks = self.inventory.get_amount(good)\n ideal = self.inventory.get_ideal(good)\n\n # if we're buying and inventory is too low\n # meaning we're desperate to buy\n if order_type is OrderType.buy_order and stocks < LOW_INVENTORY * ideal:\n wobble *= 2\n\n # if we're selling and inventory is too high\n # meaning we're desperate to sell\n elif order_type is OrderType.sell_order and stocks > HIGH_INVENTORY * ideal:\n wobble *= 2\n # all other cases\n else:\n sells = self.market.history.sell_orders.average(good, 1)\n buys = self.market.history.buy_orders.average(good, 1)\n\n # TODO: figure out why this is sometimes 0\n if sells + buys > 0:\n\n supply_vs_demand = (sells - buys) / (sells + buys)\n\n if supply_vs_demand > SIG_IMBALANCE or supply_vs_demand < -SIG_IMBALANCE:\n # too much supply? lower bid lower to sell faster\n # too much demand? raise price to buy faster\n\n new_mean = public_mean_price * (1 - supply_vs_demand)\n delta_to_mean = mean - new_mean\n\n # shift the price belief to the new price mean\n belief.low -= delta_to_mean / 2\n belief.high -= delta_to_mean / 2\n\n\n # decrease belief's certainty since we've just changed it (we could be wrong)\n belief.low -= wobble * mean\n belief.high += wobble * mean\n\n # make sure the price belief doesn't decrease below the minimum\n if belief.low < MIN_PRICE:\n belief.low = MIN_PRICE\n elif belief.high < MIN_PRICE:\n belief.high = MIN_PRICE","async def _create_offer_for_ids(\n self, offer: Dict[int, int]\n ) -> Tuple[bool, Optional[TradeRecord], Optional[str]]:\n spend_bundle = None\n try:\n for id in offer.keys():\n amount = offer[id]\n wallet_id = uint32(int(id))\n wallet = self.wallet_state_manager.wallets[wallet_id]\n if isinstance(wallet, CCWallet):\n balance = await wallet.get_confirmed_balance()\n if balance < abs(amount) and amount < 0:\n raise Exception(f\"insufficient funds in wallet {wallet_id}\")\n if amount > 0:\n if spend_bundle is None:\n to_exclude: List[Coin] = []\n else:\n to_exclude = spend_bundle.removals()\n zero_spend_bundle: Optional[\n SpendBundle\n ] = await wallet.generate_zero_val_coin(False, to_exclude)\n\n if zero_spend_bundle is None:\n raise Exception(\n \"Failed to generate offer. Zero value coin not created.\"\n )\n if spend_bundle is None:\n spend_bundle = zero_spend_bundle\n else:\n spend_bundle = SpendBundle.aggregate(\n [spend_bundle, zero_spend_bundle]\n )\n\n additions = zero_spend_bundle.additions()\n removals = zero_spend_bundle.removals()\n zero_val_coin: Optional[Coin] = None\n for add in additions:\n if add not in removals and add.amount == 0:\n zero_val_coin = add\n new_spend_bundle = await wallet.create_spend_bundle_relative_amount(\n amount, zero_val_coin\n )\n else:\n new_spend_bundle = await wallet.create_spend_bundle_relative_amount(\n amount\n )\n elif isinstance(wallet, Wallet):\n if spend_bundle is None:\n to_exclude = []\n else:\n to_exclude = spend_bundle.removals()\n new_spend_bundle = await wallet.create_spend_bundle_relative_chia(\n amount, to_exclude\n )\n else:\n return False, None, \"unsupported wallet type\"\n if new_spend_bundle is None or new_spend_bundle.removals() == []:\n raise Exception(f\"Wallet {id} was unable to create offer.\")\n if spend_bundle is None:\n spend_bundle = new_spend_bundle\n else:\n spend_bundle = SpendBundle.aggregate(\n [spend_bundle, new_spend_bundle]\n )\n\n if spend_bundle is None:\n return False, None, None\n\n now = uint64(int(time.time()))\n trade_offer: TradeRecord = TradeRecord(\n confirmed_at_index=uint32(0),\n accepted_at_time=None,\n created_at_time=now,\n my_offer=True,\n sent=uint32(0),\n spend_bundle=spend_bundle,\n tx_spend_bundle=None,\n additions=spend_bundle.additions(),\n removals=spend_bundle.removals(),\n trade_id=std_hash(spend_bundle.name() + bytes(now)),\n status=uint32(TradeStatus.PENDING_ACCEPT.value),\n sent_to=[],\n )\n return True, trade_offer, None\n except Exception as e:\n tb = traceback.format_exc()\n self.log.error(f\"Error with creating trade offer: {type(e)}{tb}\")\n return False, None, str(e)","def add(self, offer):\n other_offer = self.get(offer.get_price(), offer.get_way())\n if other_offer:\n other_offer.add_quote_amount(offer.get_quote_amount())\n other_offer.add_base_amount(offer.get_base_amount())\n return\n self.book[offer.get_way()].append(offer)\n self.book[offer.get_way()] = sorted(self.book[offer.get_way()], key=lambda entry: entry.get_price(),\n reverse=(offer.get_way() == Trade.WAY_BUY))","def test_option_prices(self, dtype):\n spots = np.array([1.0, 2.0, 3.0, 4.0, 5.0], dtype=dtype)\n strikes = np.array([3.0, 3.0, 3.0, 3.0, 3.0], dtype=dtype)\n volatilities = np.array([0.1, 0.22, 0.32, 0.01, 0.4], dtype=dtype)\n is_call_options = np.array([True, True, False, False, False])\n is_american = np.array([False, True, True, False, True])\n discount_rates = np.array(0.035, dtype=dtype)\n dividend_rates = np.array([0.02, 0.0, 0.07, 0.01, 0.0], dtype=dtype)\n expiries = np.array(1.0, dtype=dtype)\n\n prices = self.evaluate(\n tff.black_scholes.option_price_binomial(\n volatilities=volatilities,\n strikes=strikes,\n expiries=expiries,\n spots=spots,\n discount_rates=discount_rates,\n dividend_rates=dividend_rates,\n is_call_options=is_call_options,\n is_american=is_american,\n dtype=dtype))\n expected_prices = np.array(\n [0., 0.0098847, 0.41299509, 0., 0.06046989])\n self.assertArrayNear(expected_prices, prices, 1e-5)","def get(self, price, way):\n for offer in self.book[way]:\n if offer.get_price() == price:\n return offer\n return None","def apply_time_leverage_fees(self):\n if len(self.active_long_positions + self.active_short_positions) == 0:\n return\n\n if self.leverage > 1:\n for pos in (self.active_long_positions + self.active_short_positions):\n pos.apply_time_leverage_fees()\n\n self.update_capital_stats()","def get_price(self, name: str, set_name: str, foil: bool = False):\n self.search_card(name, set_name)\n time.sleep(1.5)\n self.select_singles()\n all_product = self.browser.find_elements_by_class_name('product')\n product_1 = all_product[0]\n card_finishes = product_1.find_elements_by_class_name('finish-option')\n for finish in card_finishes:\n foil_attribute = finish.text\n if foil and (foil_attribute == 'Foil'):\n finish.click()\n self.foil = True\n elif (not foil) and (foil_attribute == 'Non-Foil'):\n finish.click()\n self.foil = False\n\n card_name = product_1.find_element_by_xpath('//article/div[1]/h4/a').text\n card_set = product_1.find_element_by_xpath('//article/div[1]/h4/p').text\n all_price = product_1.find_elements_by_class_name('price-section')\n list_prices = []\n for price in all_price:\n current_price = price.find_elements_by_class_name('price')[0].text\n if len(current_price) > 0:\n if current_price[0] == '$':\n list_prices.append(float(current_price[1:]))\n else:\n list_prices.append(float(current_price))\n self.price_ftf = max(list_prices)\n self.name = card_name\n self.set = card_set","def show_hide_offer_price_options(n_clicks, is_open):\n if n_clicks:\n return not is_open\n return is_open","def get_safe_price(side: str):\n return (\n const.MIN_SUM if side == const.BUY\n else const.MAX_SUM\n )","def get_price_data(self):\n additional_json = self.get_additional_json()\n price_data = {'price': None, 'code': None, 'flag': None}\n\n try:\n model = additional_json.get('ActionPanel', {}).get('isModel', {})\n price_data['code'] = model.get('currencyCode')\n\n if model.get('bid'):\n price_data['flag'] = 'bid'\n price_data['price'] = self.find_price(model.get('bidPrice'))\n else:\n price_data['price'] = self.find_price(model.get('binPriceOnly'))\n\n except Exception:\n error = traceback.format_exc()\n capture_exception(error)\n\n return price_data","def sell_strategy_generator(params: Dict[str, Any]) -> Callable:\n def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:\n \"\"\"\n Sell strategy Hyperopt will build and use.\n \"\"\"\n conditions = []\n\n conditions.append(\n ((dataframe['bull'] > 0) & (dataframe['rsi'] > params['bull-sell-rsi-value'])) |\n (~(dataframe['bull'] > 0) & (dataframe['rsi'] > params['bear-sell-rsi-value']))\n )\n\n conditions.append(dataframe['volume'] > 0)\n\n dataframe.loc[\n reduce(lambda x, y: x & y, conditions),\n 'sell'] = 1\n\n return dataframe\n\n return populate_sell_trend","def step(self):\n highest_offer = None\n\n if self.manager is None:\n highest_rep = 0\n\n else:\n highest_rep = self.manager.reputation\n\n for offer in self.offers:\n if offer.manager.reputation > highest_rep:\n highest_offer = offer\n\n if highest_offer is not None:\n highest_offer.accept()\n\n self.offers = []","def action_compute_price(self):\n self._prepare_printing()\n\n providers = set(self.mapped('provider_id.id'))\n for provider_id in providers: # process by provider id\n records = self.filtered(lambda r: r.provider_id.id == provider_id)\n # call provider implementation\n provider_name = records[0].provider_id.provider\n if hasattr(records, '_%s_action_compute_price' % provider_name):\n getattr(records, '_%s_action_compute_price' % provider_name)()","def makes_offer(self) -> object:\n return self._makes_offer","def get_basket_with_discounts(self, actual_volume, applicable_discounts, residual):\n\n # product_vs_min_volume, product_offer = self.get_product_vs_offer_and_volume(self.discounts)\n pr = Product('items')\n product_prices = pr.product_meta\n\n basket = []\n\n for discount in applicable_discounts:\n offer_info = Offer('discount', discount)\n\n if offer_info.offer_type.lower() == \"discount\":\n temp_basket = self.get_basket_items_discount(offer_info, actual_volume, product_prices)\n for item in temp_basket:\n basket.append(item)\n elif offer_info.offer_type.lower() == \"pricedrop\":\n temp_basket = self.get_basket_items_pricedrop(offer_info, actual_volume, product_prices)\n for item in temp_basket:\n basket.append(item)\n\n if residual:\n for product, volume in residual.items():\n for i in range(0, volume):\n basket.append((product, product_prices.get(product.lower()).get('price')))\n\n return basket","def ex_best_offers_overrides(best_prices_depth=None, rollup_model=None, rollup_limit=None,\n rollup_liability_threshold=None, rollup_liability_factor=None):\n\n args = locals()\n return {\n to_camel_case(k): v for k, v in args.items() if v is not None\n }","def _compute_taxed_lst_price2(self):\n company_id = self._context.get(\n 'company_id', self.env.user.company_id.id)\n for product in self:\n product.taxed_lst_price2 = product.taxes_id.filtered(\n lambda x: x.company_id.id == company_id).compute_all(\n product.list_price2,\n self.env.user.company_id.currency_id,\n product=product)['total_included']","def _getOrdersToExecute(self, markPrice):\n ordersToExecute = []\n takeProfitOrder = None\n stopLossOrder = None\n for order in self.openOrders.values():\n # if market order, execute at mark price\n if order.market:\n order.price = markPrice\n ordersToExecute.append(order)\n continue\n\n # if limit order, define buy and sell conditions\n if order.type=='SL':\n if (order.size < 0 and markPrice <= order.price) or (order.size > 0 and markPrice >= order.price):\n stopLossOrder = order\n else:\n triggerBuy = order.size > 0 and markPrice <= order.price\n triggerSell = order.size < 0 and markPrice >= order.price\n if triggerBuy or triggerSell:\n if order.type=='TP':\n takeProfitOrder = order\n else:\n ordersToExecute.append(order)\n\n # make sure that the take profit is executed first to simplify the simulation\n if takeProfitOrder is not None:\n return [takeProfitOrder] + ordersToExecute\n\n # make sure that the stop loss is executed last to simplify the simulation\n elif stopLossOrder is not None:\n return ordersToExecute + [stopLossOrder]\n\n else:\n return ordersToExecute","def _dynamic_price(self):\n adjust = PriceAdjustmentCalc(self)\n signals.satchmo_price_query.send(self, adjustment=adjust,\n slug=self.product.slug, discountable=self.product.is_discountable)\n return adjust.final_price()","def test_market_1_2(self):\n\n def check_1_2(buyers: List[float], sellers: List[float], expected_num_of_deals: int,\n expected_prices: List[float]):\n market = Market([\n AgentCategory(\"buyer\", buyers),\n AgentCategory(\"seller\", sellers),\n ])\n ps_recipe = [1, 2]\n self._check_market(market, ps_recipe, expected_num_of_deals, expected_prices)\n\n check_1_2(buyers=[9], sellers=[-4, -3],\n expected_num_of_deals=0, expected_prices=[9, -4.5])\n check_1_2(buyers=[9, 8, 7, 6], sellers=[-6, -5, -4, -3, -2, -1],\n expected_num_of_deals=1, expected_prices=[8, -4])\n check_1_2(buyers=[9, 8], sellers=[-4, -3, -2, -1],\n expected_num_of_deals=1, expected_prices=[8, -4])\n check_1_2(buyers=[9, 8], sellers=[-6, -3, -2, -1],\n expected_num_of_deals=1, expected_prices=[8, -4])\n check_1_2(buyers=[9, 8], sellers=[-4, -3, -2, -1],\n expected_num_of_deals=1, expected_prices=[8, -4])\n\n # PRICE CROSSES ZERO AT FIRST PHASE\n check_1_2(buyers=list(range(20)), sellers=[-3, -2, -1],\n expected_num_of_deals=1, expected_prices=[18, -9])","def limit_on_close_order(liability, price):\n return locals()","def binary_price(volatilities,\n strikes,\n expiries,\n spots=None,\n forwards=None,\n discount_factors=None,\n is_call_options=None,\n dtype=None,\n name=None):\n if (spots is None) == (forwards is None):\n raise ValueError('Either spots or forwards must be supplied but not both.')\n\n with tf.compat.v1.name_scope(\n name,\n default_name='option_price',\n values=[\n forwards, strikes, volatilities, expiries, discount_factors,\n is_call_options\n ]):\n strikes = tf.convert_to_tensor(strikes, dtype=dtype, name='strikes')\n volatilities = tf.convert_to_tensor(\n volatilities, dtype=dtype, name='volatilities')\n expiries = tf.convert_to_tensor(expiries, dtype=dtype, name='expiries')\n\n if discount_factors is None:\n discount_factors = tf.convert_to_tensor(\n 1.0, dtype=dtype, name='discount_factors')\n else:\n discount_factors = tf.convert_to_tensor(\n discount_factors, dtype=dtype, name='discount_factors')\n\n if forwards is not None:\n forwards = tf.convert_to_tensor(forwards, dtype=dtype, name='forwards')\n else:\n spots = tf.convert_to_tensor(spots, dtype=dtype, name='spots')\n forwards = spots / discount_factors\n\n sqrt_var = volatilities * tf.math.sqrt(expiries)\n d1 = (tf.math.log(forwards / strikes) + sqrt_var * sqrt_var / 2) / sqrt_var\n d2 = d1 - sqrt_var\n undiscounted_calls = _ncdf(d2)\n if is_call_options is None:\n return discount_factors * undiscounted_calls\n undiscounted_puts = 1 - undiscounted_calls\n predicate = tf.broadcast_to(is_call_options, tf.shape(undiscounted_calls))\n return discount_factors * tf.where(predicate, undiscounted_calls,\n undiscounted_puts)","def get_applicable_discounts(self, actual_volume):\n\n applicable_discounts = []\n residual = {}\n\n min_vol_criteria, product_vs_offer, base_product_vs_offer_product = self.get_product_vs_offer_and_volume(self.discounts)\n\n for basket_item, volume in actual_volume.items():\n offer_on_product = base_product_vs_offer_product.get(basket_item)\n if min_vol_criteria.get(basket_item):\n if volume >= min_vol_criteria.get(basket_item) and actual_volume.get(offer_on_product):\n applicable_discounts.append(product_vs_offer.get(basket_item))\n else:\n residual[basket_item.upper()] = volume\n else:\n for prod, offering in base_product_vs_offer_product.items():\n if (offering == basket_item) and (prod not in actual_volume):\n residual[basket_item.upper()] = volume\n # else:\n\n\n if \"APPL\" in applicable_discounts and \"APOM\" in applicable_discounts:\n applicable_discounts.remove(\"APOM\")\n residual['OM1'] = actual_volume.get('om1')\n\n return applicable_discounts, residual","def test_visualize_price_breakdown(self):\n pass","def _collect_quantizers_descriptions(self) -> List[QuantizerDescription]:\n # `True` for weight quantizer, `False` otherwise.\n quantizers = chain(\n map(lambda x: (True, x), self._weight_quantizers.values()),\n map(lambda x: (False, x), self._non_weight_quantizers.values()),\n )\n\n quantizers_descriptions = []\n for is_weight_quantizer, q in quantizers:\n is_symmetric = isinstance(q, SymmetricQuantizer)\n\n quantizers_descriptions.append(\n QuantizerDescription(\n q.num_bits, q.per_channel, q.signed, is_symmetric, is_weight_quantizer, q.is_enabled_quantization()\n )\n )\n\n return quantizers_descriptions","def price(usr, item, searches = 2, method = \"AVERAGE\", deduct = 0):\n if not method in ShopWizard.methods: raise invalidMethod()\n\n if isinstance(item, Item):\n item = item.name\n\n prices = []\n dets = {}\n for x in range(0, searches):\n print('performing search: {}'.format(x))\n results = ShopWizard.search(usr, item)\n\n # Set to -1 if not found\n if not results:\n prices.append(-1)\n continue\n\n prices.append(int(results[item].price))\n dets[str(results[item].price)] = (results[item].owner, results[item].id)\n\n time.sleep(ShopWizard.waitTime)\n\n # Determines if item was UB\n print(prices)\n if sum(prices) == len(prices) * -1:\n return False\n\n prices = list(filter(lambda x: x != -1, prices))\n\n if method == ShopWizard.RETLOW:\n price = sorted(prices)[0]\n return (price, dets[str(price)][0], dets[str(price)][1])\n\n return ShopWizard.__determinePrice(prices, method, deduct)","def get_transaction_prices(self):\n cleaned_data = self.cleaned_data()\n supplier_cleaned_data = cleaned_data.get('cleaned_supplier_data')\n transaction_cleaned_data = cleaned_data.get('cleaned_transaction_data')\n merged_data = self.merge_supplier_transaction(supplier_cleaned_data, transaction_cleaned_data)\n calculated_data = self.calculate_prices(merged_data)\n self.export_calculated_prices(calculated_data)\n return calculated_data","def marketDataParser(self,data,quick=True):\n\t\trt={'bids':[],'asks':[]}\n\t\tfor [price,sz,numb] in data['bids']:\n\t\t\tif quick is False:\n\t\t\t\trt['bids'].append([Decimal(price),Decimal(sz),Decimal(numb)])\n\t\t\telse:\n\t\t\t\trt['bids'].append([float(price),float(sz),float(numb)])\t\t\t\t\n\t\tfor [price,sz,numb] in data['asks']:\n\t\t\tif quick is False:\n\t\t\t\trt['asks'].append([Decimal(price),Decimal(sz),Decimal(numb)])\n\t\t\telse:\n\t\t\t\trt['asks'].append([float(price),float(sz),float(numb)])\t\t\t\t\n\t\tBuy=[]\t#[price,size,numb-order,sum-size,sum-vl]\n\t\tif quick is False:\n\t\t\tlastVL=Decimal(0)\t\t\n\t\t\tsumsz=Decimal(0)\n\t\telse:\n\t\t\tlastVL=0\t\t\n\t\t\tsumsz=0\n\t\tfor [pr,sz,nb] in rt['bids']:\n\t\t\tlastVL=pr*sz+lastVL\n\t\t\tsumsz+=sz\t\t\t\t\n\t\t\tBuy.append((pr,sz,nb,sumsz,lastVL)) \n\t\t\t\t\t\t\t\t\n\t\tSell=[]\t#(price,size,numb-order,sum-size,sum-vl)\n\t\tif quick is False:\n\t\t\tlastVL=Decimal(0)\t\t\n\t\t\tsumsz=Decimal(0)\n\t\telse:\n\t\t\tlastVL=0\t\t\n\t\t\tsumsz=0\n\t\tfor [pr,sz,nb] in rt['asks']:\n\t\t\tlastVL=pr*sz+lastVL\n\t\t\tsumsz+=sz\t\t\t\t\n\t\t\tSell.append((pr,sz,nb,sumsz, lastVL)) \n\t\treturn {'buy':Buy,'sell':Sell}","def get_price(self):\n if self.price is None:\n price = self.data['pizza'][self.item_type][self.size]\n for topping in self.toppings:\n price += self.data['topping'][topping]\n return price\n return self.price","def get_prices(paths, side):\n prices = {}\n\n for path in paths:\n data = get_data(path)\n\n for pair in data.keys():\n\n if pair not in prices:\n prices[pair] = []\n\n if side == 'bids': # Best bid\n\n bids = data[pair]['bids'].keys()\n float_bids = [float(b) for b in bids]\n sorted_bids = sorted(float_bids)\n best_bid = sorted_bids[-1]\n prices[pair].append(best_bid)\n\n elif side == 'asks': # Best ask\n\n asks = data[pair]['asks'].keys()\n float_asks = [float(a) for a in asks]\n sorted_asks = sorted(float_asks)\n best_ask = sorted_asks[0]\n prices[pair].append(best_ask)\n\n else:\n raise Exception('Side must be either \"asks\" or \"bids\".')\n\n return prices","def filter_potential_deal_table(apply_n_clicks, potential_deal_table_data,\n selected_year, selected_make, selected_model, min_odometer,\n max_odometer, min_price, max_price, min_offer_price,\n max_offer_price):\n potential_deal_db_data = DBApi.get_instance().potential_records\n filtered_year_ids = []\n filtered_make_ids = []\n filtered_model_ids = []\n filtered_min_odometer_ids = []\n filtered_max_odometer_ids = []\n filtered_min_price_ids = []\n filtered_max_price_ids = []\n filtered_min_offer_price_ids = []\n filtered_max_offer_price_ids = []\n\n if selected_year or selected_make or min_odometer or max_odometer or min_price or \\\n max_price or min_offer_price or max_offer_price:\n # Odometer filter\n if min_odometer and max_odometer:\n if int(max_odometer) < int(min_odometer):\n return [\n potential_deal_table_data,\n \"Max odometer value should be greater than min odometer value\",\n True,\n 5000\n ]\n if min_price and max_price:\n if int(max_price) < int(min_price):\n return [\n potential_deal_table_data,\n \"Max price value should be greater than min price value\",\n True,\n 5000\n ]\n\n if min_offer_price and max_offer_price:\n if int(max_offer_price) < int(min_offer_price):\n return [\n potential_deal_table_data,\n \"Max offer price MMR value should be greater \"\n \"than min offer price MMR value\",\n True,\n 5000\n ]\n\n for data in potential_deal_db_data:\n # Year filter\n if selected_year:\n for year in selected_year:\n if year in data[\"make_model_year\"]:\n filtered_year_ids.append(data[\"PotentialDealID\"])\n break\n else:\n filtered_year_ids.append(data[\"PotentialDealID\"])\n\n # Make filter\n if selected_make:\n for make in selected_make:\n if make in data[\"make_model_year\"].lower():\n filtered_make_ids.append(data[\"PotentialDealID\"])\n else:\n filtered_make_ids.append(data[\"PotentialDealID\"])\n\n # Model filter\n if selected_model:\n for model in selected_model:\n if model in data[\"make_model_year\"].lower():\n filtered_model_ids.append(data[\"PotentialDealID\"])\n else:\n filtered_model_ids.append(data[\"PotentialDealID\"])\n\n # Min odometer filter\n if min_odometer:\n if int(data[\"odometer\"]) >= int(min_odometer):\n filtered_min_odometer_ids.append(data[\"PotentialDealID\"])\n else:\n filtered_min_odometer_ids.append(data[\"PotentialDealID\"])\n\n # Max odometer filter\n if max_odometer:\n if int(data[\"odometer\"]) <= int(max_odometer):\n filtered_max_odometer_ids.append(data[\"PotentialDealID\"])\n else:\n filtered_max_odometer_ids.append(data[\"PotentialDealID\"])\n\n # Min price filter\n if min_price:\n if int(data[\"price\"]) >= int(min_price):\n filtered_min_price_ids.append(data[\"PotentialDealID\"])\n else:\n filtered_min_price_ids.append(data[\"PotentialDealID\"])\n\n # Max price filter\n if max_price:\n if int(data[\"price\"]) <= int(max_price):\n filtered_max_price_ids.append(data[\"PotentialDealID\"])\n else:\n filtered_max_price_ids.append(data[\"PotentialDealID\"])\n\n # Min offer price filter\n if min_offer_price:\n if int(data[\"OfferPricePctMMR\"]) >= int(min_offer_price):\n filtered_min_offer_price_ids.append(data[\"PotentialDealID\"])\n else:\n filtered_min_offer_price_ids.append(data[\"PotentialDealID\"])\n\n # Max offer price filter\n if max_offer_price:\n if int(data[\"OfferPricePctMMR\"]) <= int(max_offer_price):\n filtered_max_offer_price_ids.append(data[\"PotentialDealID\"])\n else:\n filtered_max_offer_price_ids.append(data[\"PotentialDealID\"])\n\n filtered_ids = set(filtered_year_ids).intersection(\n set(filtered_make_ids),\n set(filtered_model_ids),\n set(filtered_min_odometer_ids),\n set(filtered_max_odometer_ids),\n set(filtered_min_price_ids),\n set(filtered_max_price_ids),\n set(filtered_min_offer_price_ids),\n set(filtered_max_offer_price_ids)\n )\n filtered_data = [\n data\n for data in potential_deal_db_data\n if data[\"PotentialDealID\"] in filtered_ids\n ]\n return [filtered_data, \"\", False, 2000]\n return [potential_deal_db_data, \"\", False, 2000]","def depth_offers(self):\n return len(self.offer)","def set_final_settle_prices(pr_trades, exer_date, mode, TestMode):\n\n if not pr_trades:\n msg = ('No Exercise/Assign trades made on date {0}'.format(exer_date))\n Logme()(msg, 'WARNING')\n return\n\n for t in pr_trades:\n ins = t.insaddr\n settle_price = getSettlePriceFromMarket(ins, exer_date, \"SETTLEMENT\")\n if not settle_price:\n msg = ('Will skip trade {0} since there is no price for this '\n 'instrument {1}.'.format(t.trdnbr, ins.insid))\n Logme()(msg)\n continue\n\n strike_price = convert_price_to_und_or_strike_quotation(ins,\n ins.strike_price, 1)\n\n if ins.settlement == 'Cash':\n if ins.call_option:\n p_der = FBDPCommon.create_quotetype_price(ins,\n settle_price - strike_price)\n elif ins.instype == 'Future/Forward':\n p_der = settle_price\n else:\n p_der = FBDPCommon.create_quotetype_price(ins,\n strike_price - settle_price)\n\n else: # Physical settlement\n p_phys = 0.0 # price to be set in the physical trade\n t_phys = get_physical_trade(t)\n if not t_phys:\n Logme()('Physical settlement trade does not exist for trade '\n '{0}.'.format(t.trdnbr))\n continue\n if mode == 'Market':\n p_phys = settle_price\n if ins.instype == 'Option':\n p_phys = settle_price\n if ins.call_option:\n p_der = FBDPCommon.create_quotetype_price(ins,\n settle_price - strike_price)\n else:\n p_der = FBDPCommon.create_quotetype_price(ins,\n strike_price - settle_price)\n else: # Future\n p_der = settle_price\n\n else: # Physical is done to the strike price (Strike mode)\n p_der = 0.0\n if ins.instype == 'Option':\n p_phys = ins.strike_price\n else: # Future\n p_phys = settle_price\n\n if (abs(ins.phys_contr_size) > 0.000001 and\n abs(ins.phys_contr_size - ins.contr_size) > 0.000001):\n update_exercise_payment(t, settle_price, mode, TestMode)\n\n phys_clone = t_phys.clone()\n phys_clone.price = p_phys\n if (ins.instype in ['Option', 'Warrant'] and\n ins.und_instype == 'Curr'):\n phys_clone.fx_update_non_dealt_amount(p_phys)\n else:\n phys_clone.premium = trade_premium_from_quote(\n phys_clone.trdnbr, p_phys, phys_clone.acquire_day)\n if not TestMode:\n phys_clone.commit()\n\n der_clone = t.clone()\n der_clone.price = p_der\n der_clone.premium = trade_premium_from_quote(der_clone.trdnbr, p_der,\n t.acquire_day)\n\n if not TestMode:\n der_clone.commit()\n ael.poll","def _compute_taxed_lst_price2(self):\n company_id = self._context.get(\n 'company_id', self.env.user.company_id.id)\n for product in self:\n product.taxed_lst_price = product.taxes_id.filtered(\n lambda x: x.company_id.id == company_id).compute_all(\n product.lst_price,\n self.env.user.company_id.currency_id,\n product=product)['total_included']","def processMarketOrders(self):\n try:\n nextRound = self.currentRound+1\n resultsList = []\n master = {}\n self.genMarketStat()\n myMarketStat = self.marketStats[str(self.currentRound)]\n \n # sorted lists of market orders\n master['buyAL'] = anwp.func.funcs.sortDictByChildObjValue(self.marketOrders, 'max', True, {'value':'AL', 'min':0})\n master['buyEC'] = anwp.func.funcs.sortDictByChildObjValue(self.marketOrders, 'max', True, {'value':'EC', 'min':0})\n master['buyIA'] = anwp.func.funcs.sortDictByChildObjValue(self.marketOrders, 'max', True, {'value':'IA', 'min':0})\n master['sellAL'] = anwp.func.funcs.sortDictByChildObjValue(self.marketOrders, 'min', False, {'value':'AL', 'max':0})\n master['sellEC'] = anwp.func.funcs.sortDictByChildObjValue(self.marketOrders, 'min', False, {'value':'EC', 'max':0})\n master['sellIA'] = anwp.func.funcs.sortDictByChildObjValue(self.marketOrders, 'min', False, {'value':'IA', 'max':0})\n \n for res in ['AL', 'EC', 'IA']:\n for sellOrder in master['sell%s' % res]:\n # min sell order gets first chance to sell its product\n if sellOrder.amountUsed == sellOrder.amount:\n pass # seller has sold all he wants with this order\n else:\n i = 0\n for buyOrder in master['buy%s' % res]:\n # determine price, allow for bidding on price\n try:\n nextBuyOrder = master['buy%s' % res][i+1]\n if nextBuyOrder.max < buyOrder.max and (nextBuyOrder.max+1) >= sellOrder.min:\n price = nextBuyOrder.max + 1\n else:\n price = buyOrder.max\n except IndexError:\n price = buyOrder.max\n # max buy order gets first chance to buy sellers product\n resultsList.append(self.processMarketTransaction(buyOrder, sellOrder, price))\n i += 1\n \n # set the average market prices for this round\n if getattr(myMarketStat, 'volSold%s' % res) > 0:\n setattr(myMarketStat, 'avgSold%s' % res, (getattr(myMarketStat, 'sumSold%s' % res) / \n getattr(myMarketStat, 'volSold%s' % res)))\n \n # clean up market orders for next round\n for orderID in self.marketOrders.keys():\n myMarketOrder = self.marketOrders[orderID]\n myMarketOrder.cleanUp()\n if myMarketOrder.amount == 0:\n resultsList.append('cancel market Order=%s' % orderID)\n self.cancelMarketOrder(orderID)\n \n return str(resultsList)\n except:\n return 'galaxy->processMarketOrders error'","def vol_from_price( self, price ):\n def target_func( price, vol ):\n return self.price_from_vol( vol ) - price \n \n return brentq( partial( target_func, price ), 1e-8, 10 )","def prices_available(self, prices_available):\n\n self._prices_available = prices_available","def sell_cost(self):\n return self._manager.get_sell_price(self.name)","def drop_fairings(vessel):\n fairings = filter(lambda f: getattr(f, 'tag', None) != \"noauto\",\n find_all_fairings(vessel))\n for f in fairings:\n jettison_fairing(f)","def get_product_vs_offer_and_volume(self, df):\n\n product_vs_min_volume = {}\n product_offer = {}\n base_product_vs_offer_product = {}\n\n for i in df.itertuples():\n product_vs_min_volume[i[4].lower()] = i[5]\n product_offer[i[4].lower()] = i[1]\n base_product_vs_offer_product[i[4].lower()] = i[7].lower()\n\n return product_vs_min_volume, product_offer, base_product_vs_offer_product","def get_item_price(self, soup: BeautifulSoup) -> None:\n try:\n price = soup.find(\"span\", class_=\"_olc9rf0\").get_text()\n price = re.findall(\"\\d+(?:\\.\\d+)?\", price)[0]\n except (AttributeError, IndexError):\n price = None\n self.__collected_dic[\"price\"].append(price)"],"string":"[\n \"def filter_viable_offers(want, have, offers) -> List:\\n return [x for x in offers if is_offer_viable(want, have, x) is True]\",\n \"def _compute_offers_to_send(self) -> Dict[Tuple[float, float], float]:\\n partial_asgt = self._neighbors_values.copy()\\n offers = dict()\\n\\n for limited_asgt in generate_assignment_as_dict([self.variable, self._partner]):\\n partial_asgt.update(limited_asgt)\\n cost = self._compute_cost(**partial_asgt)\\n if self.logger.isEnabledFor(logging.DEBUG):\\n self.logger.debug(\\n f\\\"looking for offer : {partial_asgt} - cost {cost}\\\"\\n f\\\" current {self.current_cost} {self._mode}\\\"\\n )\\n\\n if (self.current_cost > cost and self._mode == \\\"min\\\") or (\\n self.current_cost < cost and self._mode == \\\"max\\\"\\n ):\\n offers[(limited_asgt[self.name], limited_asgt[self._partner.name])] = (\\n self.current_cost - cost\\n )\\n return offers\",\n \"def get_prices(self):\\n price = self.get_price()\\n if price:\\n return [price]\\n return []\",\n \"def get_prices(self):\\n pass\",\n \"def spread(self):\\n if self.bid and self.offer:\\n return self.offer[0].price - self.bid[-1].price\\n\\n return 0\",\n \"def priceGetAll(soup):\\n try:\\n price = priceGetMost(soup)\\n except:\\n price = priceGetSome(soup)\\n\\n return price\",\n \"def base_offers_list():\\n offers_list = Offers()\\n offers_list.add_new_offer('Offer1', 'Baked Beans', 3, 1)\\n offers_list.add_new_offer('Offer2', 'Sardines', 1, 0.25)\\n return offers_list\",\n \"def solve_prices(self):\\n return None\",\n \"def calculate_prices(self, good=None):\\n\\n stock = self.calculate_init_stock(good)\\n buy = self.buying_price()\\n\\n if stock == 0:\\n sell = 0\\n buy = buy + (buy * 0.5)\\n\\n elif stock < 500:\\n # mild bug: stock, without selling price\\n sell = self.selling_price()\\n elif stock >= 500:\\n # higher production, lower prices\\n sell = self.selling_price() / 2\\n buy = buy - (buy * 0.5)\\n\\n return [buy, sell, stock]\",\n \"def get_basket_items_discount(self, offer_info, actual_volume, product_prices):\\n prod_code = offer_info.base_prod_code\\n base_prod_vol = actual_volume.get(prod_code.lower())\\n\\n discount_basket = []\\n\\n if base_prod_vol >= offer_info.min_vol:\\n offer_on_prod = offer_info.offer_on\\n if actual_volume.get(offer_on_prod.lower()):\\n print(f\\\"Base product volume is greater than minimum required volume & product on offer is also available \\\"\\n f\\\"in cart..\\\")\\n if offer_info.is_limited:\\n print(f\\\"Limited offer..\\\")\\n if prod_code == offer_on_prod:\\n # total_allowed_items_on_offer = Limit Volume of base product * (Offer Product Max Volume/Minimum volume of base product)\\n total_allowed_items_on_offer = offer_info.limit_vol * (offer_info.offer_prod_volume/offer_info.min_vol)\\n max_limit = 1\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n discount_basket.append((prod_code, base_prod_actual_price))\\n while max_limit <= total_allowed_items_on_offer:\\n discounted_price = (base_prod_actual_price *(offer_info.discount_perc/100))*-1\\n discount_basket.append((offer_info.offer_code, discounted_price))\\n max_limit += 1\\n else:\\n total_allowed_items_on_offer = offer_info.limit_vol * (offer_info.offer_prod_volume / offer_info.min_vol)\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n discount_basket.append((prod_code, base_prod_actual_price))\\n max_limit = 1\\n while max_limit <= total_allowed_items_on_offer:\\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\\n discounted_price = (offer_onprod_actual_price *(offer_info.discount_perc/100))*-1\\n for j in range(0, actual_volume.get(offer_on_prod.lower())):\\n discount_basket.append((offer_on_prod, offer_onprod_actual_price))\\n discount_basket.append((offer_info.offer_code, discounted_price))\\n max_limit += 1\\n else:\\n print(f\\\"Unlimited offer..\\\")\\n if prod_code == offer_on_prod:\\n if base_prod_vol > offer_info.min_vol:\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n discount_basket.append((prod_code, base_prod_actual_price))\\n if i%2 != 0:\\n discounted_price = (base_prod_actual_price *(offer_info.discount_perc/100))*-1\\n discount_basket.append((offer_info.offer_code, discounted_price))\\n else:\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n discount_basket.append((prod_code, base_prod_actual_price))\\n else:\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n discount_basket.append((prod_code, base_prod_actual_price))\\n\\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\\n discounted_price = (offer_onprod_actual_price * (offer_info.discount_perc / 100))*-1\\n\\n for j in range(0, actual_volume.get(offer_on_prod.lower())):\\n discount_basket.append((offer_on_prod, offer_onprod_actual_price))\\n discount_basket.append((offer_info.offer_code, discounted_price))\\n\\n\\n return discount_basket\",\n \"def is_offer_viable(want, have, offer) -> bool:\\n # lower tier value means higher value\\n have_tier = constants.currencies[have][\\\"tier\\\"]\\n want_tier = constants.currencies[want][\\\"tier\\\"]\\n conversion_rate = offer[\\\"conversion_rate\\\"]\\n\\n # if `want` is worth more than `have`, the conversion rate cannot be bigger than 1\\n # This should be enough to successfully filter out price-fixing offers.\\n if have_tier > want_tier and conversion_rate > 1:\\n return False\\n\\n # if `have` is worth more than `have`, the conversion rate cannot be smaller than 1\\n # We shouldn't need this case, since conversion rates below 1 will typically\\n # result in unprofitable conversions and are therefore ignored anyway.\\n # if have_tier < want_tier and conversion_rate < 1:\\n # return False\\n\\n return True\",\n \"def get_prices(self, grab, subject):\\n prices = []\\n try:\\n extras = grab.doc.rex_text(\\n '<h3 class\\\\=\\\"h6 copy-sp-m\\\">.*?%s.*?</h3>(.+?)</ul>' % subject,\\n flags=re.S\\n )\\n except DataNotFound:\\n logging.debug(\\n \\\"Price %s is not found on %s\\\"\\n % (subject, grab.doc.url)\\n )\\n return None\\n\\n sel = XpathSelector(fromstring(extras))\\n prices = []\\n for li in sel.select('//li[@class=\\\"list__item u-cf\\\"]'):\\n obligatory = OrderedDict()\\n obligatory['name'] = li.select('node()').text()\\n money = li.select('node()/strong').text()\\n obligatory['value'] = money[1:].replace(',', '')\\n\\n # Find perweek or perday\\n if li.select(\\n 'span[@class=\\\"boatview__extras-amount\\\"' +\\n ' and contains(text(),\\\"per week\\\")]'\\n ).exists():\\n obligatory['perweek'] = True\\n elif li.select(\\n 'span[@class=\\\"boatview__extras-amount\\\"' +\\n ' and contains(text(),\\\"per day\\\")]'\\n ).exists():\\n obligatory['perday'] = True\\n obligatory['currency'] = money[0]\\n prices.append(obligatory)\\n\\n if len(prices) < 1:\\n logging.debug(\\n \\\"Price %s contains less than one element on: %s\\\"\\n % (subject, grab.doc.url)\\n )\\n return None\\n\\n return prices\",\n \"def _find_best_offer(\\n self, all_offers: List[Tuple[str, Dict]]\\n ) -> Tuple[List, float]:\\n bests, best_gain = [], 0\\n\\n for partner, offers in all_offers:\\n partial_asgt = self._neighbors_values.copy()\\n current_partner = self._neighbor_var(partner)\\n\\n # Filter out the constraints linking those two variables to avoid\\n # counting their cost twice.\\n shared = find_dependent_relations(current_partner, self._constraints)\\n concerned = [rel for rel in self._constraints if rel not in shared]\\n\\n for (val_p, my_offer_val), partner_local_gain in offers.items():\\n partial_asgt.update({partner: val_p, self.variable.name: my_offer_val})\\n\\n # Then we evaluate the agent constraint's for the offer\\n # and add the partner's local gain.\\n cost = assignment_cost(partial_asgt, concerned)\\n global_gain = self.current_cost - cost + partner_local_gain\\n\\n if (global_gain > best_gain and self._mode == \\\"min\\\") or (\\n global_gain < best_gain and self._mode == \\\"max\\\"\\n ):\\n bests = [(val_p, my_offer_val, partner)]\\n best_gain = global_gain\\n elif global_gain == best_gain:\\n bests.append((val_p, my_offer_val, partner))\\n\\n return bests, best_gain\",\n \"def apply_20percent_shipping_offer(self):\\n product_range = Range.objects.create(\\n name=\\\"All products range\\\", includes_all_products=True\\n )\\n benefit = Benefit.objects.create(\\n range=product_range, type=Benefit.SHIPPING_PERCENTAGE, value=20\\n )\\n offer = factories.create_offer(product_range=product_range, benefit=benefit)\\n Applicator().apply_offers(self.basket, [offer])\\n return offer\",\n \"def compute_market_prices(prices):\\n denom = prices.bid_volume + prices.ask_volume\\n numer = (prices.bid_price * prices.ask_volume +\\n prices.ask_price * prices.bid_volume)\\n mask = denom == 0\\n denom[mask] = 2\\n numer[mask] = prices.bid_price[mask] + prices.ask_price[mask]\\n prices = prices.copy()\\n prices['market_price'] = numer / denom\\n return prices\",\n \"def check_restrictions(self):\\n from .signals import determine_availability\\n\\n responses = determine_availability.send(\\n self.item.event, item=self.item,\\n variations=[self.to_variation_dict()], context=None,\\n cache=self.item.event.get_cache()\\n )\\n price = self.default_price if self.default_price is not None else self.item.default_price\\n for receiver, response in responses:\\n if 'available' in response[0] and not response[0]['available']:\\n return False\\n elif 'price' in response[0] and response[0]['price'] is not None and response[0]['price'] < price:\\n price = response[0]['price']\\n return price\",\n \"def option_price(volatilities,\\n strikes,\\n expiries,\\n spots=None,\\n forwards=None,\\n discount_factors=None,\\n is_call_options=None,\\n dtype=None,\\n name=None):\\n if (spots is None) == (forwards is None):\\n raise ValueError('Either spots or forwards must be supplied but not both.')\\n\\n with tf.compat.v1.name_scope(\\n name,\\n default_name='option_price',\\n values=[\\n forwards, strikes, volatilities, expiries, discount_factors,\\n is_call_options\\n ]):\\n strikes = tf.convert_to_tensor(strikes, dtype=dtype, name='strikes')\\n volatilities = tf.convert_to_tensor(\\n volatilities, dtype=dtype, name='volatilities')\\n expiries = tf.convert_to_tensor(expiries, dtype=dtype, name='expiries')\\n\\n if discount_factors is None:\\n discount_factors = tf.convert_to_tensor(\\n 1.0, dtype=dtype, name='discount_factors')\\n else:\\n discount_factors = tf.convert_to_tensor(\\n discount_factors, dtype=dtype, name='discount_factors')\\n\\n if forwards is not None:\\n forwards = tf.convert_to_tensor(forwards, dtype=dtype, name='forwards')\\n else:\\n spots = tf.convert_to_tensor(spots, dtype=dtype, name='spots')\\n forwards = spots / discount_factors\\n\\n sqrt_var = volatilities * tf.math.sqrt(expiries)\\n d1 = (tf.math.log(forwards / strikes) + sqrt_var * sqrt_var / 2) / sqrt_var\\n d2 = d1 - sqrt_var\\n undiscounted_calls = forwards * _ncdf(d1) - strikes * _ncdf(d2)\\n if is_call_options is None:\\n return discount_factors * undiscounted_calls\\n undiscounted_forward = forwards - strikes\\n undiscounted_puts = undiscounted_calls - undiscounted_forward\\n predicate = tf.broadcast_to(is_call_options, tf.shape(undiscounted_calls))\\n return discount_factors * tf.where(predicate, undiscounted_calls,\\n undiscounted_puts)\",\n \"def price(self, irc, msg, args, optlist, typeName):\\n\\n try:\\n typeID = self._get_typeID(typeName)\\n itemType = self._get_type(typeID)\\n except:\\n irc.error('Unknown type')\\n return\\n\\n if len(optlist) == 1:\\n location = optlist[0][1]\\n else:\\n location = 'Jita'\\n\\n try:\\n locationID = self._get_locationID(location)\\n location = self._get_location(locationID)\\n except:\\n irc.error('Unknown location')\\n return\\n\\n market = self._sql(\\\"\\\"\\\"\\n SELECT * FROM evecentral_market\\n WHERE \\\"locationID\\\"=%s\\\"\\\"\\\", [locationID])\\n if not market:\\n irc.reply('No data for that market location')\\n return\\n\\n marketitem = self._sql(\\\"\\\"\\\"\\n SELECT * FROM evecentral_marketitem\\n WHERE \\\"locationID\\\"=%s AND \\\"typeID\\\"=%s\\\"\\\"\\\", [locationID, typeID])\\n if marketitem:\\n irc.reply('{0} in {1}: buy max: {2} (volume: {3:,d}). sell min: {4} (volume: {5:,d}).'.format(\\n ircutils.bold(itemType['typeName']),\\n self._colorize_system(location),\\n ircutils.mircColor(\\n '{:,.2f}'.format(marketitem['buy_max']),\\n fg='green'),\\n int(marketitem['buy_volume']),\\n ircutils.mircColor(\\n '{:,.2f}'.format(marketitem['sell_min']),\\n fg='green'),\\n int(marketitem['sell_volume']),\\n ), prefixNick=False)\\n else:\\n irc.reply(\\\"Prices for {0} in {1} isn't updated yet.\\\".format(\\n itemType['typeName'],\\n location['itemName']\\n ))\",\n \"def apply(self, catalog):\\n out = []\\n for product in catalog:\\n valid = True\\n for key, filtr in self.filters.items():\\n valid = valid and filtr(product[key])\\n if valid:\\n out.append(product)\\n out = sorted(out, key=lambda x: x['onDemandUsdPrice'])\\n return self.limit_size(out)\",\n \"def _get_applicable_offers(self, current_protocol, path):\\n\\n edges = []\\n\\n for from_protocol_name, offers in self._adaptation_offers.items():\\n from_protocol = offers[0].from_protocol\\n mro_distance = self.mro_distance_to_protocol(\\n current_protocol, from_protocol\\n )\\n\\n if mro_distance is not None:\\n\\n for offer in offers:\\n # Avoid cycles by checking that we did not consider this\\n # offer in this path.\\n if offer not in path:\\n edges.append((mro_distance, offer))\\n\\n return edges\",\n \"def refuse_and_delete_pending_offers(item, offer_id):\\n for o in item.offers_received.filter(~Q(pk=offer_id), answered=False):\\n refuse_offer(o)\\n\\n for o in item.offers_done.filter(~Q(pk=offer_id), answered=False):\\n delete_offer(o)\",\n \"def parse_offers_html(html):\\n def parse_single_offer(html_offer):\\n try:\\n info_row = html_offer.table.tbody.tr\\n return Offer(info_row.td.a['href'], info_row.td.a.img['alt'], info_row.find(class_='price').strong.string)\\n except:\\n pass\\n\\n raw_offers = Soup(html, 'html.parser').find('table', {'id': 'offers_table'}).find_all('td', class_=re.compile('offer$'))\\n return reducer((partial(map, parse_single_offer), partial(filter, None.__ne__), list), raw_offers)\",\n \"def main():\\n tte, url_pt2 = get_tte(expiration)\\n url2 = url + '&date=' + url_pt2\\n dfs = pd.read_html(url2)\\n\\n call_df = dfs[0]\\n put_df = dfs[1]\\n \\n \\n \\\"\\\"\\\"Should have created an Option class here. Like:\\n class Option:\\n def __init__(self,type, spot, strike, time, rate, div, vol, vol_guess,price):\\n self.type = type\\n self.spot = spot\\n .....\\n def theo(self):\\n return Theo(type, spot, strike,time, rate, div, vol)\\n def delta(self):\\n return Delta(type, spot, strike,time, rate, div, vol)\\n ...\\n \\n \\\"\\\"\\\"\\n \\n \\n #instead, a bunch of lists: \\n call_theos = []\\n put_theos = []\\n call_vols= []\\n put_vols= []\\n\\n call_deltas = []\\n call_gammas = []\\n call_thetas = []\\n call_vegas = []\\n put_deltas = []\\n put_gammas = []\\n put_thetas = []\\n put_vegas = []\\n\\n call_imp_vols =[]\\n put_imp_vols = []\\n\\n spot = get_spot(spot_url)\\n print('Spot:', spot)\\n vol = get_vol(vol_url,20)\\n print('20day Vol:' ,vol)\\n rate = interest_rate(tte)\\n div = .0\\n\\n #Don't really care about options going for pennies\\n call_df = call_df.ix[call_df['Volume'] >=100].ix[call_df['Last Price'] >= .10]\\n put_df = put_df.ix[put_df['Volume'] >= 100].ix[put_df['Last Price'] >= .10]\\n\\n\\n for index, row in call_df.iterrows():\\n call_vol = row['Implied Volatility'][:-1]\\n call_vols.append(float(call_vol))\\n\\n\\n for index, row in put_df.iterrows():\\n put_vol = row['Implied Volatility'][:-1]\\n put_vols.append(float(put_vol))\\n\\n\\n # extremely crude method for increasing put vol to help account for skew\\n call_sd = statistics.stdev(call_vols)\\n call_mean = sum(call_vols)/len(call_vols)\\n call_vols = [vol for vol in call_vols if abs(vol-call_mean) <= 3 * call_sd]\\n call_vol_factor = sum(call_vols)/len(call_vols) / 100.0\\n\\n put_sd = statistics.stdev(put_vols)\\n put_mean = sum(put_vols)/len(put_vols)\\n put_vols = [vol for vol in put_vols if abs(vol-put_mean) <= 1 * put_sd]\\n put_vol_factor = sum(put_vols)/len(put_vols) / 100.0\\n\\n call_vol = vol\\n put_vol = vol * (put_vol_factor/call_vol_factor)\\n\\n for index, row in call_df.iterrows():\\n strike = float(row['Strike'])\\n time = tte\\n call_theos.append(Theo('C', spot, strike, time, rate, div, call_vol))\\n call_deltas.append(Delta('C', spot, strike, time, rate, div, call_vol))\\n call_gammas.append(Gamma(spot, strike, time, rate, div, call_vol))\\n call_thetas.append(Theta('C', spot, strike, time, rate, div, call_vol))\\n call_vegas.append(Vega(spot, strike, time, rate, div, call_vol))\\n\\n\\n midpoint = (float(row['Bid']) + float(row['Ask']))/2.0\\n call_imp_vols.append(ImpliedVol('C', spot,strike,time,rate,div,midpoint, call_vol))\\n\\n for index, row in put_df.iterrows():\\n strike = float(row['Strike'])\\n time = tte\\n put_theos.append(Theo('P', spot, strike, time, rate, div, put_vol))\\n put_deltas.append(Delta('P', spot, strike, time, rate, div, put_vol))\\n put_gammas.append(Gamma(spot, strike, time, rate, div, put_vol))\\n put_thetas.append(Theta('P', spot, strike, time, rate, div, put_vol))\\n put_vegas.append(Vega(spot, strike, time, rate, div, put_vol))\\n\\n midpoint = (float(row['Bid']) + float(row['Ask'])) / 2.0\\n put_imp_vols.append(ImpliedVol('P', spot, strike, time, rate, div, midpoint, put_vol))\\n\\n\\n call_df['Theo'] = call_theos\\n call_df['Delta'] = call_deltas\\n call_df['Gamma'] = call_gammas\\n call_df['Theta'] = call_thetas\\n call_df['Vega'] = call_vegas\\n\\n put_df['Theo'] = put_theos\\n put_df['Delta'] = put_deltas\\n put_df['Gamma'] = put_gammas\\n put_df['Theta'] = put_thetas\\n put_df['Vega'] = put_vegas\\n\\n call_imp_vols = [str(i*100)[0:5] + '%' for i in call_imp_vols]\\n put_imp_vols = [str(i*100)[0:5] + '%' for i in put_imp_vols]\\n\\n call_df['Calc Imp Vol'] =call_imp_vols\\n put_df['Calc Imp Vol'] = put_imp_vols\\n\\n\\n #Message alert for large discrepancies between theo and midpoint of bid/ask\\n default = sec+ ' Alert ' + expiration + '\\\\n'\\n alertstring = default\\n for index, row in call_df.iterrows():\\n\\n midpoint = (float(row['Ask']) + float(row['Bid']))/2.0\\n if abs(row['Theo'] - midpoint ) >= 1.0:\\n\\n edge = str(abs(row['Theo'] - midpoint))\\n if row['Theo'] > midpoint:\\n updown = 'under'\\n else:\\n updown = 'over'\\n\\n\\n alertstring += str(int(row['Strike'])) + 'C ' + edge[0:4] + ' ' + updown + '\\\\n'\\n\\n for index, row in put_df.iterrows():\\n\\n midpoint = (float(row['Ask']) + float(row['Bid'])) / 2.0\\n if abs(row['Theo'] - midpoint) >= 1.0:\\n\\n edge = str(abs(row['Theo'] - midpoint))\\n if row['Theo'] > midpoint:\\n updown = 'under'\\n else:\\n updown = 'over'\\n\\n alertstring += str(int(row['Strike'])) + 'P ' + edge[0:4] + ' ' + updown + '\\\\n'\\n if alertstring != default:\\n send_message(alertstring)\\n\\n plotVolSeries(sec)\\n plotImpVol(call_df,put_df,spot, sec)\\n writer = pd.ExcelWriter(sec+'_options_' + expiration +'.xlsx')\\n call_df.to_excel(writer, 'Calls')\\n put_df.to_excel(writer, 'Puts')\\n writer.save()\",\n \"def target_prices(self) -> List[float]:\\n return self._target_prices\",\n \"def prices(parsed, appearance_value, removal_value, gate_single_value, gate_double_value):\\n\\t\\tprices = []\\n\\n\\t\\tfor item in parsed:\\n\\t\\t\\tif item.entityType == \\\"fence\\\":\\n\\t\\t\\t\\tprices.append(QuoteCalculation._fencePrice(item, appearance_value, removal_value))\\n\\n\\t\\t\\telif item.entityType == \\\"gate\\\":\\n\\t\\t\\t\\tprices.append(QuoteCalculation._gatePrice(item, gate_single_value, gate_double_value, removal_value))\\n\\n\\t\\t\\t# Not required?\\n\\t\\t\\t#elif item.entityType == \\\"post\\\":\\n\\t\\t\\t\\t#prices.append(QuoteCalculation._postPrice(item))\\n\\n\\t\\treturn prices\",\n \"def get_best_offer(self,way):\\n if way==\\\"BUY\\\":\\n return self.book[Trade.WAY_BUY][0].get_price()\\n elif way==\\\"SELL\\\":\\n return self.book[Trade.WAY_SELL][len(self.book[Trade.WAY_SELL])-1].get_price()\",\n \"def calc_energy_and_price(self) -> (float, float):\\n\\n cost_sum = 0\\n energy_sum = 0\\n for pump_id in self.pumps:\\n pump_energy, pump_cost = self.pumps[pump_id].calculate_energy_and_cost()\\n cost_sum += pump_cost\\n energy_sum += pump_energy\\n\\n pump_id.append_index = 0\\n\\n assert energy_sum >= 0, \\\"The pumping energy cant be negative!\\\"\\n assert cost_sum >= 0, \\\"The pumping cost cant be negative!\\\"\\n return energy_sum, cost_sum\",\n \"def option_price_curve(self, payoff='Call'):\\n return dict(zip(self.strike_grid, list(map(lambda K: self.option_price(K, payoff=payoff), self.strike_grid))))\",\n \"def compute(self):\\r\\n \\r\\n if self.option_type == \\\"European call\\\":\\r\\n option_price = BS_call(self.S0, self.K, self.T_scaled, self.vola, self.div_yield, r=self.rf)\\r\\n \\r\\n elif self.option_type == \\\"European put\\\":\\r\\n option_price = BS_put(self.S0, self.K, self.T_scaled, self.vola, self.div_yield, r=self.rf)\\r\\n \\r\\n elif self.option_type == \\\"Asian call\\\":\\r\\n option_price = monte_carlo_call(self.S0, self.K, self.T_scaled, self.vola, self.div_yield, rf=self.rf ) # function in other file\\r\\n else:\\r\\n # Asian put\\r\\n option_price = monte_carlo_put(self.S0, self.K, self.T_scaled, self.vola, self.div_yield, rf=self.rf)\\r\\n \\r\\n\\r\\n self.display_option_price.setText(str(round(option_price,2)))\\r\\n self.display_option_price.adjustSize()\",\n \"def _get_book_prices(self):\\n for k in self.orders.keys():\\n if self.orders[k].type == 'ask':\\n self.ask_prices.append(self.orders[k].price)\\n self.ask_snapshot[k] = self.orders[k]\\n elif self.orders[k].type == 'bid':\\n self.bid_prices.append(self.orders[k].price)\\n self.bid_snapshot[k] = self.orders[k]\\n # Sorting and removing dubbing\\n self.ask_prices = list(dict.fromkeys(sorted(self.ask_prices)))\\n self.bid_prices = list(dict.fromkeys(sorted(self.bid_prices, reverse=True)))\",\n \"def sell_all(self):\\n\\n from_symbol = self.symbol\\n to_symbol = self.currency\\n price = self.data[0].close\\n amount = self.portfolio['buy_sell_amount'][self.currency]\\n date = self.date\\n\\n if self.slippage:\\n slip_factor = (self.data[-1].high - self.data[-1].close)*self.slippage\\n price -= np.abs(slip_factor)\\n\\n self.trade_manager.sell_all(from_symbol, to_symbol, price, amount, date)\",\n \"def compare_price(self):\\n if self.__product_price < self.__alert_price:\\n #print(\\\"price drop...\\\")\\n self.__alert_client = True\\n self.__price_difference = self.__product_price - self.__alert_price\\n else:\\n #print(\\\"Price not reduced...\\\")\\n self.__alert_client = False\\n self.__price_difference = self.__product_price - self.__alert_price\",\n \"def value_offers(self):\\n return OrderBookUtils.book_value(self.offer)\",\n \"def recalc_for_combo_birds(self, early_birds):\\n for early_bird in early_birds:\\n if early_bird.price_type == 1:\\n self.recalc_for_bird(early_bird)\\n return self.rates\",\n \"def sell(self, amount):\\n trades = []\\n sell_amount = 0\\n precision = pow(10, self.pair.get_base_token().get_decimals() - self.pair.get_quote_token().get_decimals())\\n for i in range(len(self.book[Trade.WAY_BUY])):\\n offer = self.book[Trade.WAY_BUY][i]\\n amount_quote = offer.get_quote_amount()\\n amount_base = offer.get_base_amount()\\n price = offer.get_price()\\n\\n if amount_quote >= amount:\\n tmp = amount * price * precision\\n tmp = int(tmp)\\n trade = Trade(self.pair, Trade.WAY_SELL, price, tmp, amount, time.time(), fee_currency=self.pair.get_exchange().get_fee_token())\\n sell_amount = sell_amount + trade.get_amount_base()\\n trades.append(trade)\\n return trades, int(sell_amount)\\n\\n '''\\n Is the offered amount less than needed, you can only buy the offered amount and continue\\n '''\\n trade = Trade(self.pair, Trade.WAY_SELL, price, amount_base, amount_quote, time.time(), fee_currency=self.pair.get_exchange().get_fee_token())\\n amount = amount - amount_quote\\n sell_amount = sell_amount + trade.get_amount_base()\\n trades = trades + [trade]\\n\\n '''\\n Not enough volume or amount to high\\n '''\\n raise KeyError(\\\"Not enough offers in orderbook. Low volume or amount to high.\\\")\",\n \"def get_prices(variation_object,Map):\\n\\n sale_price = variation_object[Map['itemtype']['fields']['sale_price']]\\n price = sale_price\\n\\n if variation_object[Map['itemtype']['fields']['price']]:\\n price = variation_object[Map['itemtype']['fields']['price']]\\n\\n return price,sale_price\",\n \"def aggregate_offer_qty(self, trade_price):\\n qty = 0\\n for i in range(len(self.offer)):\\n # print(\\\"trade_price = {} offer[{}] = {}\\\".format(trade_price, i, self.offer[i].price))\\n if self.offer[i].price <= trade_price:\\n qty += self.offer[i].qty\\n # print(\\\"Running qty = {}\\\".format(qty))\\n return qty\",\n \"def extract_price_excluding_tax(soup):\\r\\n price_excluding_tax = extract_attributs_book(soup)['Price (excl. tax)']\\r\\n return price_excluding_tax\",\n \"def getTransferListSummary(self):\\n p_ids_and_prices = {}\\n players = self.getAllPlayerInfoTransferlist()\\n\\n # Get IDs of all players\\n log_event(self.queue, \\\"Gathering player prices... \\\")\\n for p in players:\\n p_bidstatus = p[1]\\n p_id = p[8]\\n # removed Filter for unlisted / expired players\\n if p_id not in p_ids_and_prices:\\n p_sellprice = self.getPlayerSellPrice(p_id)\\n # If sell price returns 0, need to fetch from Futbin\\n if p_sellprice == 0:\\n p_sellprice = self.getFutbinPrice_opentab(p_id)\\n self.sleep_approx(5) # Delay iteration to not anger futbin\\n # Add player ID and price to dict\\n p_ids_and_prices[p_id] = p_sellprice\\n\\n for p_id in p_ids_and_prices:\\n p_price = p_ids_and_prices[p_id]\\n p_name = self.getPlayerCardName(p_id)\\n log_event(self.queue, str(p_name) + \\\" - #\\\" +\\n str(p_id) + \\\" Price \\\" + str(p_price))\\n\\n num_p_sold = 0\\n num_p_expired = 0\\n num_p_unlisted = 0\\n num_p_listed = 0\\n\\n sold_p_value = 0\\n expired_p_value = 0\\n unlisted_p_value = 0\\n listed_p_value = 0\\n\\n for p in players:\\n p_bidstatus = p[1]\\n p_id = p[8]\\n p_soldprice = p[5] # is 0 if unlisted\\n p_sellprice = int(p_ids_and_prices[p_id])\\n\\n if \\\"won\\\" in p_bidstatus:\\n num_p_sold += 1\\n sold_p_value += p_soldprice\\n if \\\"expired\\\" in p_bidstatus:\\n num_p_expired += 1\\n expired_p_value += p_sellprice\\n if (p_bidstatus == \\\"listFUTItem\\\"):\\n num_p_unlisted += 1\\n unlisted_p_value += p_sellprice\\n if (p_bidstatus == \\\"listFUTItem has-auction-data\\\"):\\n num_p_listed += 1\\n listed_p_value += p_sellprice\\n\\n log_event(self.queue, \\\"Players sold: \\\" + str(num_p_sold))\\n log_event(self.queue, \\\"Players expired: \\\" + str(num_p_expired))\\n log_event(self.queue, \\\"Players listed: \\\" + str(num_p_listed))\\n log_event(self.queue, \\\"Players unlisted: \\\" + str(num_p_unlisted))\\n log_event(self.queue, \\\" - - - \\\")\\n log_event(self.queue, \\\"Sold players value: \\\" + str(sold_p_value))\\n log_event(self.queue, \\\"Expired players value: \\\" +\\n str(expired_p_value))\\n log_event(self.queue, \\\"Unlisted players value: \\\" +\\n str(unlisted_p_value))\\n log_event(self.queue, \\\"Listed players value: \\\" + str(listed_p_value))\\n\\n # TODO subtract bought price\\n self.user_players_won += int(num_p_unlisted)\\n self.p_ids_and_prices = p_ids_and_prices\\n intel = [p_ids_and_prices, num_p_sold, num_p_expired, num_p_unlisted,\\n num_p_listed, sold_p_value, expired_p_value, unlisted_p_value, listed_p_value]\\n return intel\",\n \"def decide_trade_plan(self):\\n self.trade_amount = 5\\n most_demanded_goods = self.home.market.goods_demand_ratio(day_range=1)\\n most_demanded_goods = sorted(most_demanded_goods.items(), key=lambda i: i[1], reverse=True)\\n\\n # if we already had a trade good, refresh ideal inventory\\n if self.trade_good:\\n self.update_ideal_inventory()\\n\\n if DEBUG: print(\\\"Finding a Good to trade:\\\")\\n\\n for good, demand in most_demanded_goods:\\n if demand > 0:\\n # find nearby provinces where this has inventory and the price is lower\\n price_at_home = self.home.market.mean_price(good)\\n if DEBUG: print(\\\"Good: {}, Demand: {}, Price: ${}\\\".format(good.title, demand, price_at_home))\\n neighboring_markets = [p.market for p in self.location.owned_neighbors]\\n neighboring_markets = [m for m in neighboring_markets if m.supply_for(good) > self.trade_amount]\\n neighboring_markets.sort(key=lambda m: m.supply_for(good), reverse=True)\\n\\n if len(neighboring_markets) > 0:\\n # we found places where this good is cheaper and in inventory\\n target = neighboring_markets[0].location\\n price_at_target = target.market.mean_price(good)\\n\\n # only trade with prices where we can make money\\n if price_at_home > price_at_target:\\n offset = 0\\n if good is Good.bread:\\n offset = 1\\n self.inventory.set_ideal(good, self.trade_amount + offset)\\n self.trade_location = target\\n if DEBUG:\\n print(\\\"\\\\tTarget: {}, Supply: {}, Price: ${}, Price at home: ${}\\\".format(\\n self.trade_location.name,\\n self.trade_location.market.supply_for(good),\\n self.trade_location.market.mean_price(good),\\n price_at_home)\\n )\\n self.trade_good = good\\n return\\n else:\\n if DEBUG: print(\\\"\\\\tPrice is higher at target (home: ${} target: ${})\\\".format(price_at_home, price_at_target))\\n else:\\n if DEBUG: print(\\\"\\\\tNo markets selling {} found\\\".format(good))\",\n \"def trade_offer(self, price):\\n if self.is_sold:\\n raise AlreadySoldError(\\\"Electricity product already sold\\\")\\n\\n if self.remaining_slots <= 0:\\n raise LeadtimePassedError(\\\"Lead time passed\\\")\\n\\n succesful_trade = random.random() < self.selling_chance(price)\\n self.remaining_slots -= 1\\n\\n if succesful_trade:\\n profit = price - self.product_price\\n self.is_sold = True\\n elif self.remaining_slots == 0:\\n profit = -self.product_price\\n else:\\n profit = 0\\n\\n return (profit, self.is_sold)\",\n \"def apply_prices(data, conditions=None, price='open', stop_loss=0, order='B'):\\n if order.upper() == 'B':\\n multiplier = 1 - (stop_loss * 0.01)\\n elif order.upper() == 'S':\\n multiplier = 1 + (stop_loss * 0.01)\\n else:\\n raise ValueError('Order should be either B or S')\\n\\n # All conditions are forced to lower case AND ed\\n if conditions:\\n big_condition = '&'.join(['(' + c.lower() + ')' for c in conditions])\\n # TO DO: Deal with Memory Error in case of lots of conditions\\n # f shorthand for filtered\\n f = data.query(big_condition).copy()\\n else:\\n f = data.copy()\\n f['price'] = f.eval(price).apply(tick)\\n f['stop_loss'] = (f['price'] * multiplier).apply(tick)\\n\\n # Price map to determine whether buy or sell is the entry\\n p_map = {\\n 'B': ('buy', 'sell'),\\n 'S': ('sell', 'buy')\\n }\\n\\n col_price, col_sl = p_map.get(order)\\n\\n f.loc[:, col_price] = [isPrice(p,h,l) for p,h,l in zip(\\n f.price, f.high, f.low)] * f['price']\\n f.loc[:, col_sl] = [isPrice(p,h,l) for p,h,l in zip(\\n f.stop_loss, f.high, f.low)] * f['stop_loss']\\n f.loc[f.buy == 0, 'buy'] = f.loc[f.buy == 0, 'close']\\n f.loc[f.sell == 0, 'sell'] = f.loc[f.sell == 0, 'close']\\n return f\",\n \"def accumulate_prices(name,products,sales,types,add):\\r\\n return reduce(add,[get_prices_by_type(name,products,sales,types)[i] for i in get_prices_by_type(name,products,sales,types)])\",\n \"def recalc_for_combo_birds(self, early_birds):\\n\\n for early_bird in early_birds:\\n if early_bird.price_type == 1:\\n self.recalc_for_bird(early_bird)\\n return self.rates\",\n \"def validate(cls, prices):\\n super(GiftCardPrice, cls).validate(prices)\\n\\n for price in prices:\\n price.check_price()\",\n \"def getTransferListSummaryWithoutPrices(self):\\n players = self.getAllPlayerInfoTransferlist()\\n\\n num_p_sold = 0\\n num_p_expired = 0\\n num_p_unlisted = 0\\n num_p_listed = 0\\n sold_p_value = 0\\n\\n for p in players:\\n p_bidstatus = p[1]\\n p_id = p[8]\\n p_soldprice = p[5] # is 0 if unlisted\\n\\n if \\\"won\\\" in p_bidstatus:\\n num_p_sold += 1\\n sold_p_value += p_soldprice\\n if \\\"expired\\\" in p_bidstatus:\\n num_p_expired += 1\\n if (p_bidstatus == \\\"listFUTItem\\\"):\\n num_p_unlisted += 1\\n if (p_bidstatus == \\\"listFUTItem has-auction-data\\\"):\\n num_p_listed += 1\\n\\n # TODO subtract bought price\\n return num_p_sold, num_p_expired, num_p_unlisted, num_p_listed\",\n \"def price_data(sp_available=False, sp_traded=False, ex_best_offers=False, ex_all_offers=False, ex_traded=False):\\n args = locals()\\n return [\\n k.upper() for k, v in args.items() if v is True\\n ]\",\n \"def check_for_offer(self, bid, commodity, limit, actual, quantity, price):\\n if bid:\\n if len(self.trades[\\\"buys\\\"][commodity]) == 0:\\n return 0\\n else: # tally up how much trying to buy.\\n total = 0.0\\n total_price = 0.0\\n for offer in self.trades[\\\"buys\\\"][commodity]:\\n total += offer.quantity\\n total_price += offer.price\\n\\n avg_price = total_price / len(self.trades[\\\"buys\\\"][commodity])\\n\\n # if total < limit:\\n # #PLACE MORE BIDS.\\n return total\\n\\n else:\\n if len(self.trades[\\\"asks\\\"][commodity]) == 0:\\n return 0\\n else: # tally up how much trying to buy.\\n total = 0.0\\n total_price = 0.0\\n for offer in self.trades[\\\"asks\\\"][commodity]:\\n total += offer.quantity\\n total_price += offer.price\\n\\n avg_price = total_price / len(self.trades[\\\"asks\\\"][commodity])\\n #\\n # if total < limit:\\n # #PLACE MORE asks.\\n # return total\\n # if total < limit:\\n # #PLACE MORE asks.\\n return total # - limit\",\n \"def extract_listing_price_from_result(soup, prices):\\r\\n for description in soup.find_all(name='div', class_='descr'):\\r\\n price = description.find(name='div', class_='price')\\r\\n if price == None:\\r\\n prices.append('No Price')\\r\\n else:\\r\\n prices.append(price.get_text())\\r\\n # print(prices)\\r\\n return prices\",\n \"def offers(self, offers):\\n\\n self._offers = offers\",\n \"def get_all_available_variations(self, use_cache: bool=False):\\n if use_cache and hasattr(self, '_get_all_available_variations_cache'):\\n return self._get_all_available_variations_cache\\n\\n from .signals import determine_availability\\n\\n variations = self._get_all_generated_variations()\\n responses = determine_availability.send(\\n self.event, item=self,\\n variations=variations, context=None,\\n cache=self.event.get_cache()\\n )\\n\\n for i, var in enumerate(variations):\\n var['available'] = var['variation'].active if 'variation' in var else True\\n if 'variation' in var:\\n if var['variation'].default_price:\\n var['price'] = var['variation'].default_price\\n else:\\n var['price'] = self.default_price\\n else:\\n var['price'] = self.default_price\\n\\n # It is possible, that *multiple* restriction plugins change the default price.\\n # In this case, the cheapest one wins. As soon as there is a restriction\\n # that changes the price, the default price has no effect.\\n\\n newprice = None\\n for receiver, response in responses:\\n if 'available' in response[i] and not response[i]['available']:\\n var['available'] = False\\n break\\n if 'price' in response[i] and response[i]['price'] is not None \\\\\\n and (newprice is None or response[i]['price'] < newprice):\\n newprice = response[i]['price']\\n var['price'] = newprice or var['price']\\n\\n variations = [var for var in variations if var['available']]\\n\\n self._get_all_available_variations_cache = variations\\n return variations\",\n \"def stop_loss_prices(self) -> List[float]:\\n return self._stop_loss_prices\",\n \"def liquid_with_loans(self):\\n from Game.models import LoanOffer\\n loan_offers = LoanOffer.objects.filter(lender=self)\\n return self.liquid - sum(l.offered for l in loan_offers)\",\n \"def test_get_offers(self):\\n pass\",\n \"def export_tier_prices_to_magento(self):\\n instance = self.website.instance\\n\\n for mag_product_template in self.website.magento_product_templates:\\n product_template = mag_product_template.template\\n product = product_template.products[0]\\n\\n # Get the price tiers from the product if the product has a price\\n # tier table else get the default price tiers from current store\\n price_tiers = product_template.price_tiers or self.price_tiers\\n\\n price_data = []\\n for tier in price_tiers:\\n if hasattr(tier, 'product'):\\n # The price tier comes from a product, then it has a\\n # function field for price, we use it directly\\n price = tier.price\\n else:\\n # The price tier comes from the default tiers on store,\\n # we dont have a product on tier, so we use the current\\n # product in loop for computing the price for this tier\\n price = self.price_list.compute(\\n None, product, product.list_price, tier.quantity,\\n self.website.default_uom\\n )\\n\\n price_data.append({\\n 'qty': tier.quantity,\\n 'price': float(price),\\n })\\n\\n # Update stock information to magento\\n with magento.ProductTierPrice(\\n instance.url, instance.api_user, instance.api_key\\n ) as tier_price_api:\\n tier_price_api.update(\\n mag_product_template.magento_id, price_data\\n )\\n\\n return len(self.website.magento_product_templates)\",\n \"def calculate_prices(self, merged_data):\\n calculated_prices = []\\n for record in merged_data:\\n prices_dict = dict()\\n supplier_price_id = record.get('supplier_detail').get('identifier') # get the supplier price id\\n session_id = record.get('supplier_transaction').get('session_id') # get the transaction session\\n supplier_trans_fee_price = self.compute_fee_price(\\n record) # Get the fee price for each transaction if needed\\n supplier_trans_time_price = self.compute_time_price(\\n record) # Get the time price for each transaction if needed\\n supplier_trans_kwh_price = self.compute_kwh_price(record)\\n total_price = supplier_trans_fee_price + supplier_trans_time_price + supplier_trans_kwh_price\\n prices_dict.update({'fee_price': supplier_trans_fee_price,\\n 'time_price': supplier_trans_time_price,\\n 'kwh_price': supplier_trans_kwh_price,\\n 'total_price': total_price,\\n 'session_id': session_id,\\n 'supplier_price_id': supplier_price_id})\\n calculated_prices.append(prices_dict)\\n\\n return calculated_prices\",\n \"def check_restrictions(self):\\n if self.properties.count() > 0: # NOQA\\n raise ValueError('Do not call this directly on items which have properties '\\n 'but call this on their ItemVariation objects')\\n from .signals import determine_availability\\n\\n vd = VariationDict()\\n responses = determine_availability.send(\\n self.event, item=self,\\n variations=[vd], context=None,\\n cache=self.event.get_cache()\\n )\\n price = self.default_price\\n for receiver, response in responses:\\n if 'available' in response[0] and not response[0]['available']:\\n return False\\n elif 'price' in response[0] and response[0]['price'] is not None and response[0]['price'] < price:\\n price = response[0]['price']\\n return price\",\n \"def get_good_price_by_deal(self):\\n if self.deal == SALE:\\n return self.new_price\\n elif self.deal == AUCTION:\\n return self.max_price\\n else:\\n return self.price\",\n \"def sellOptionsAtExpiration(self):\\n\\n open_positions = self.open_positions.find(\\n {\\\"Trader\\\": self.user[\\\"Name\\\"], \\\"Asset_Type\\\": \\\"OPTION\\\"})\\n\\n dt = getDatetime()\\n\\n for position in open_positions:\\n\\n day_before = (position[\\\"Exp_Date\\\"] -\\n timedelta(days=1)).strftime(\\\"%Y-%m-%d\\\")\\n\\n if day_before == dt.strftime(\\\"%Y-%m-%d\\\"):\\n\\n trade_data = {\\n \\\"Symbol\\\": position[\\\"Symbol\\\"],\\n \\\"Pre_Symbol\\\": position[\\\"Pre_Symbol\\\"],\\n \\\"Side\\\": \\\"SELL_TO_CLOSE\\\",\\n \\\"Option_Type\\\": position[\\\"Option_Type\\\"],\\n \\\"Strategy\\\": position[\\\"Strategy\\\"],\\n \\\"Asset_Type\\\": position[\\\"Asset_Type\\\"],\\n \\\"Exp_Date\\\": position[\\\"Exp_Date\\\"]\\n }\\n\\n self.placeOrder(trade_data, position)\",\n \"def update_price_model(self, good, order_type, is_successful, clearing_price=0):\\n\\n SIGNIFICANT = 0.25 # 25% more or less is \\\"significant\\\"\\n SIG_IMBALANCE = 0.33\\n LOW_INVENTORY = 0.1 # 10% of ideal inventory = \\\"LOW\\\"\\n HIGH_INVENTORY = 2.0 # 200% of ideal inventory = \\\"HIGH\\\"\\n MIN_PRICE = 0.01 # lowest allowed price of a Good\\n\\n if is_successful:\\n # add this trade to the observed trading range\\n self.observed_trading_range[good].append(clearing_price)\\n\\n public_mean_price = self.market.mean_price(good)\\n belief = self.price_belief[good]\\n mean = belief.mean()\\n wobble = 0.05 # the degree which the Pop should bid outside the belief\\n\\n # how different the public mean price is from the price belief\\n delta_to_mean = mean - public_mean_price\\n\\n if is_successful:\\n if order_type is OrderType.buy_order and delta_to_mean > SIGNIFICANT:\\n # this Pop overpaid, shift belief towards mean\\n belief.low -= delta_to_mean / 2\\n belief.high -= delta_to_mean / 2\\n elif order_type is OrderType.sell_order and delta_to_mean < -SIGNIFICANT:\\n # this Pop underpaid!, shift belief towards mean\\n belief.low -= delta_to_mean / 2\\n belief.high -= delta_to_mean / 2\\n\\n # increase the belief's certainty\\n belief.low += wobble * mean\\n belief.high -= wobble * mean\\n\\n else:\\n # shift towards mean\\n belief.low -= delta_to_mean / 2\\n belief.high -= delta_to_mean / 2\\n\\n # check for inventory special cases\\n stocks = self.inventory.get_amount(good)\\n ideal = self.inventory.get_ideal(good)\\n\\n # if we're buying and inventory is too low\\n # meaning we're desperate to buy\\n if order_type is OrderType.buy_order and stocks < LOW_INVENTORY * ideal:\\n wobble *= 2\\n\\n # if we're selling and inventory is too high\\n # meaning we're desperate to sell\\n elif order_type is OrderType.sell_order and stocks > HIGH_INVENTORY * ideal:\\n wobble *= 2\\n # all other cases\\n else:\\n sells = self.market.history.sell_orders.average(good, 1)\\n buys = self.market.history.buy_orders.average(good, 1)\\n\\n # TODO: figure out why this is sometimes 0\\n if sells + buys > 0:\\n\\n supply_vs_demand = (sells - buys) / (sells + buys)\\n\\n if supply_vs_demand > SIG_IMBALANCE or supply_vs_demand < -SIG_IMBALANCE:\\n # too much supply? lower bid lower to sell faster\\n # too much demand? raise price to buy faster\\n\\n new_mean = public_mean_price * (1 - supply_vs_demand)\\n delta_to_mean = mean - new_mean\\n\\n # shift the price belief to the new price mean\\n belief.low -= delta_to_mean / 2\\n belief.high -= delta_to_mean / 2\\n\\n\\n # decrease belief's certainty since we've just changed it (we could be wrong)\\n belief.low -= wobble * mean\\n belief.high += wobble * mean\\n\\n # make sure the price belief doesn't decrease below the minimum\\n if belief.low < MIN_PRICE:\\n belief.low = MIN_PRICE\\n elif belief.high < MIN_PRICE:\\n belief.high = MIN_PRICE\",\n \"async def _create_offer_for_ids(\\n self, offer: Dict[int, int]\\n ) -> Tuple[bool, Optional[TradeRecord], Optional[str]]:\\n spend_bundle = None\\n try:\\n for id in offer.keys():\\n amount = offer[id]\\n wallet_id = uint32(int(id))\\n wallet = self.wallet_state_manager.wallets[wallet_id]\\n if isinstance(wallet, CCWallet):\\n balance = await wallet.get_confirmed_balance()\\n if balance < abs(amount) and amount < 0:\\n raise Exception(f\\\"insufficient funds in wallet {wallet_id}\\\")\\n if amount > 0:\\n if spend_bundle is None:\\n to_exclude: List[Coin] = []\\n else:\\n to_exclude = spend_bundle.removals()\\n zero_spend_bundle: Optional[\\n SpendBundle\\n ] = await wallet.generate_zero_val_coin(False, to_exclude)\\n\\n if zero_spend_bundle is None:\\n raise Exception(\\n \\\"Failed to generate offer. Zero value coin not created.\\\"\\n )\\n if spend_bundle is None:\\n spend_bundle = zero_spend_bundle\\n else:\\n spend_bundle = SpendBundle.aggregate(\\n [spend_bundle, zero_spend_bundle]\\n )\\n\\n additions = zero_spend_bundle.additions()\\n removals = zero_spend_bundle.removals()\\n zero_val_coin: Optional[Coin] = None\\n for add in additions:\\n if add not in removals and add.amount == 0:\\n zero_val_coin = add\\n new_spend_bundle = await wallet.create_spend_bundle_relative_amount(\\n amount, zero_val_coin\\n )\\n else:\\n new_spend_bundle = await wallet.create_spend_bundle_relative_amount(\\n amount\\n )\\n elif isinstance(wallet, Wallet):\\n if spend_bundle is None:\\n to_exclude = []\\n else:\\n to_exclude = spend_bundle.removals()\\n new_spend_bundle = await wallet.create_spend_bundle_relative_chia(\\n amount, to_exclude\\n )\\n else:\\n return False, None, \\\"unsupported wallet type\\\"\\n if new_spend_bundle is None or new_spend_bundle.removals() == []:\\n raise Exception(f\\\"Wallet {id} was unable to create offer.\\\")\\n if spend_bundle is None:\\n spend_bundle = new_spend_bundle\\n else:\\n spend_bundle = SpendBundle.aggregate(\\n [spend_bundle, new_spend_bundle]\\n )\\n\\n if spend_bundle is None:\\n return False, None, None\\n\\n now = uint64(int(time.time()))\\n trade_offer: TradeRecord = TradeRecord(\\n confirmed_at_index=uint32(0),\\n accepted_at_time=None,\\n created_at_time=now,\\n my_offer=True,\\n sent=uint32(0),\\n spend_bundle=spend_bundle,\\n tx_spend_bundle=None,\\n additions=spend_bundle.additions(),\\n removals=spend_bundle.removals(),\\n trade_id=std_hash(spend_bundle.name() + bytes(now)),\\n status=uint32(TradeStatus.PENDING_ACCEPT.value),\\n sent_to=[],\\n )\\n return True, trade_offer, None\\n except Exception as e:\\n tb = traceback.format_exc()\\n self.log.error(f\\\"Error with creating trade offer: {type(e)}{tb}\\\")\\n return False, None, str(e)\",\n \"def add(self, offer):\\n other_offer = self.get(offer.get_price(), offer.get_way())\\n if other_offer:\\n other_offer.add_quote_amount(offer.get_quote_amount())\\n other_offer.add_base_amount(offer.get_base_amount())\\n return\\n self.book[offer.get_way()].append(offer)\\n self.book[offer.get_way()] = sorted(self.book[offer.get_way()], key=lambda entry: entry.get_price(),\\n reverse=(offer.get_way() == Trade.WAY_BUY))\",\n \"def test_option_prices(self, dtype):\\n spots = np.array([1.0, 2.0, 3.0, 4.0, 5.0], dtype=dtype)\\n strikes = np.array([3.0, 3.0, 3.0, 3.0, 3.0], dtype=dtype)\\n volatilities = np.array([0.1, 0.22, 0.32, 0.01, 0.4], dtype=dtype)\\n is_call_options = np.array([True, True, False, False, False])\\n is_american = np.array([False, True, True, False, True])\\n discount_rates = np.array(0.035, dtype=dtype)\\n dividend_rates = np.array([0.02, 0.0, 0.07, 0.01, 0.0], dtype=dtype)\\n expiries = np.array(1.0, dtype=dtype)\\n\\n prices = self.evaluate(\\n tff.black_scholes.option_price_binomial(\\n volatilities=volatilities,\\n strikes=strikes,\\n expiries=expiries,\\n spots=spots,\\n discount_rates=discount_rates,\\n dividend_rates=dividend_rates,\\n is_call_options=is_call_options,\\n is_american=is_american,\\n dtype=dtype))\\n expected_prices = np.array(\\n [0., 0.0098847, 0.41299509, 0., 0.06046989])\\n self.assertArrayNear(expected_prices, prices, 1e-5)\",\n \"def get(self, price, way):\\n for offer in self.book[way]:\\n if offer.get_price() == price:\\n return offer\\n return None\",\n \"def apply_time_leverage_fees(self):\\n if len(self.active_long_positions + self.active_short_positions) == 0:\\n return\\n\\n if self.leverage > 1:\\n for pos in (self.active_long_positions + self.active_short_positions):\\n pos.apply_time_leverage_fees()\\n\\n self.update_capital_stats()\",\n \"def get_price(self, name: str, set_name: str, foil: bool = False):\\n self.search_card(name, set_name)\\n time.sleep(1.5)\\n self.select_singles()\\n all_product = self.browser.find_elements_by_class_name('product')\\n product_1 = all_product[0]\\n card_finishes = product_1.find_elements_by_class_name('finish-option')\\n for finish in card_finishes:\\n foil_attribute = finish.text\\n if foil and (foil_attribute == 'Foil'):\\n finish.click()\\n self.foil = True\\n elif (not foil) and (foil_attribute == 'Non-Foil'):\\n finish.click()\\n self.foil = False\\n\\n card_name = product_1.find_element_by_xpath('//article/div[1]/h4/a').text\\n card_set = product_1.find_element_by_xpath('//article/div[1]/h4/p').text\\n all_price = product_1.find_elements_by_class_name('price-section')\\n list_prices = []\\n for price in all_price:\\n current_price = price.find_elements_by_class_name('price')[0].text\\n if len(current_price) > 0:\\n if current_price[0] == '$':\\n list_prices.append(float(current_price[1:]))\\n else:\\n list_prices.append(float(current_price))\\n self.price_ftf = max(list_prices)\\n self.name = card_name\\n self.set = card_set\",\n \"def show_hide_offer_price_options(n_clicks, is_open):\\n if n_clicks:\\n return not is_open\\n return is_open\",\n \"def get_safe_price(side: str):\\n return (\\n const.MIN_SUM if side == const.BUY\\n else const.MAX_SUM\\n )\",\n \"def get_price_data(self):\\n additional_json = self.get_additional_json()\\n price_data = {'price': None, 'code': None, 'flag': None}\\n\\n try:\\n model = additional_json.get('ActionPanel', {}).get('isModel', {})\\n price_data['code'] = model.get('currencyCode')\\n\\n if model.get('bid'):\\n price_data['flag'] = 'bid'\\n price_data['price'] = self.find_price(model.get('bidPrice'))\\n else:\\n price_data['price'] = self.find_price(model.get('binPriceOnly'))\\n\\n except Exception:\\n error = traceback.format_exc()\\n capture_exception(error)\\n\\n return price_data\",\n \"def sell_strategy_generator(params: Dict[str, Any]) -> Callable:\\n def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:\\n \\\"\\\"\\\"\\n Sell strategy Hyperopt will build and use.\\n \\\"\\\"\\\"\\n conditions = []\\n\\n conditions.append(\\n ((dataframe['bull'] > 0) & (dataframe['rsi'] > params['bull-sell-rsi-value'])) |\\n (~(dataframe['bull'] > 0) & (dataframe['rsi'] > params['bear-sell-rsi-value']))\\n )\\n\\n conditions.append(dataframe['volume'] > 0)\\n\\n dataframe.loc[\\n reduce(lambda x, y: x & y, conditions),\\n 'sell'] = 1\\n\\n return dataframe\\n\\n return populate_sell_trend\",\n \"def step(self):\\n highest_offer = None\\n\\n if self.manager is None:\\n highest_rep = 0\\n\\n else:\\n highest_rep = self.manager.reputation\\n\\n for offer in self.offers:\\n if offer.manager.reputation > highest_rep:\\n highest_offer = offer\\n\\n if highest_offer is not None:\\n highest_offer.accept()\\n\\n self.offers = []\",\n \"def action_compute_price(self):\\n self._prepare_printing()\\n\\n providers = set(self.mapped('provider_id.id'))\\n for provider_id in providers: # process by provider id\\n records = self.filtered(lambda r: r.provider_id.id == provider_id)\\n # call provider implementation\\n provider_name = records[0].provider_id.provider\\n if hasattr(records, '_%s_action_compute_price' % provider_name):\\n getattr(records, '_%s_action_compute_price' % provider_name)()\",\n \"def makes_offer(self) -> object:\\n return self._makes_offer\",\n \"def get_basket_with_discounts(self, actual_volume, applicable_discounts, residual):\\n\\n # product_vs_min_volume, product_offer = self.get_product_vs_offer_and_volume(self.discounts)\\n pr = Product('items')\\n product_prices = pr.product_meta\\n\\n basket = []\\n\\n for discount in applicable_discounts:\\n offer_info = Offer('discount', discount)\\n\\n if offer_info.offer_type.lower() == \\\"discount\\\":\\n temp_basket = self.get_basket_items_discount(offer_info, actual_volume, product_prices)\\n for item in temp_basket:\\n basket.append(item)\\n elif offer_info.offer_type.lower() == \\\"pricedrop\\\":\\n temp_basket = self.get_basket_items_pricedrop(offer_info, actual_volume, product_prices)\\n for item in temp_basket:\\n basket.append(item)\\n\\n if residual:\\n for product, volume in residual.items():\\n for i in range(0, volume):\\n basket.append((product, product_prices.get(product.lower()).get('price')))\\n\\n return basket\",\n \"def ex_best_offers_overrides(best_prices_depth=None, rollup_model=None, rollup_limit=None,\\n rollup_liability_threshold=None, rollup_liability_factor=None):\\n\\n args = locals()\\n return {\\n to_camel_case(k): v for k, v in args.items() if v is not None\\n }\",\n \"def _compute_taxed_lst_price2(self):\\n company_id = self._context.get(\\n 'company_id', self.env.user.company_id.id)\\n for product in self:\\n product.taxed_lst_price2 = product.taxes_id.filtered(\\n lambda x: x.company_id.id == company_id).compute_all(\\n product.list_price2,\\n self.env.user.company_id.currency_id,\\n product=product)['total_included']\",\n \"def _getOrdersToExecute(self, markPrice):\\n ordersToExecute = []\\n takeProfitOrder = None\\n stopLossOrder = None\\n for order in self.openOrders.values():\\n # if market order, execute at mark price\\n if order.market:\\n order.price = markPrice\\n ordersToExecute.append(order)\\n continue\\n\\n # if limit order, define buy and sell conditions\\n if order.type=='SL':\\n if (order.size < 0 and markPrice <= order.price) or (order.size > 0 and markPrice >= order.price):\\n stopLossOrder = order\\n else:\\n triggerBuy = order.size > 0 and markPrice <= order.price\\n triggerSell = order.size < 0 and markPrice >= order.price\\n if triggerBuy or triggerSell:\\n if order.type=='TP':\\n takeProfitOrder = order\\n else:\\n ordersToExecute.append(order)\\n\\n # make sure that the take profit is executed first to simplify the simulation\\n if takeProfitOrder is not None:\\n return [takeProfitOrder] + ordersToExecute\\n\\n # make sure that the stop loss is executed last to simplify the simulation\\n elif stopLossOrder is not None:\\n return ordersToExecute + [stopLossOrder]\\n\\n else:\\n return ordersToExecute\",\n \"def _dynamic_price(self):\\n adjust = PriceAdjustmentCalc(self)\\n signals.satchmo_price_query.send(self, adjustment=adjust,\\n slug=self.product.slug, discountable=self.product.is_discountable)\\n return adjust.final_price()\",\n \"def test_market_1_2(self):\\n\\n def check_1_2(buyers: List[float], sellers: List[float], expected_num_of_deals: int,\\n expected_prices: List[float]):\\n market = Market([\\n AgentCategory(\\\"buyer\\\", buyers),\\n AgentCategory(\\\"seller\\\", sellers),\\n ])\\n ps_recipe = [1, 2]\\n self._check_market(market, ps_recipe, expected_num_of_deals, expected_prices)\\n\\n check_1_2(buyers=[9], sellers=[-4, -3],\\n expected_num_of_deals=0, expected_prices=[9, -4.5])\\n check_1_2(buyers=[9, 8, 7, 6], sellers=[-6, -5, -4, -3, -2, -1],\\n expected_num_of_deals=1, expected_prices=[8, -4])\\n check_1_2(buyers=[9, 8], sellers=[-4, -3, -2, -1],\\n expected_num_of_deals=1, expected_prices=[8, -4])\\n check_1_2(buyers=[9, 8], sellers=[-6, -3, -2, -1],\\n expected_num_of_deals=1, expected_prices=[8, -4])\\n check_1_2(buyers=[9, 8], sellers=[-4, -3, -2, -1],\\n expected_num_of_deals=1, expected_prices=[8, -4])\\n\\n # PRICE CROSSES ZERO AT FIRST PHASE\\n check_1_2(buyers=list(range(20)), sellers=[-3, -2, -1],\\n expected_num_of_deals=1, expected_prices=[18, -9])\",\n \"def limit_on_close_order(liability, price):\\n return locals()\",\n \"def binary_price(volatilities,\\n strikes,\\n expiries,\\n spots=None,\\n forwards=None,\\n discount_factors=None,\\n is_call_options=None,\\n dtype=None,\\n name=None):\\n if (spots is None) == (forwards is None):\\n raise ValueError('Either spots or forwards must be supplied but not both.')\\n\\n with tf.compat.v1.name_scope(\\n name,\\n default_name='option_price',\\n values=[\\n forwards, strikes, volatilities, expiries, discount_factors,\\n is_call_options\\n ]):\\n strikes = tf.convert_to_tensor(strikes, dtype=dtype, name='strikes')\\n volatilities = tf.convert_to_tensor(\\n volatilities, dtype=dtype, name='volatilities')\\n expiries = tf.convert_to_tensor(expiries, dtype=dtype, name='expiries')\\n\\n if discount_factors is None:\\n discount_factors = tf.convert_to_tensor(\\n 1.0, dtype=dtype, name='discount_factors')\\n else:\\n discount_factors = tf.convert_to_tensor(\\n discount_factors, dtype=dtype, name='discount_factors')\\n\\n if forwards is not None:\\n forwards = tf.convert_to_tensor(forwards, dtype=dtype, name='forwards')\\n else:\\n spots = tf.convert_to_tensor(spots, dtype=dtype, name='spots')\\n forwards = spots / discount_factors\\n\\n sqrt_var = volatilities * tf.math.sqrt(expiries)\\n d1 = (tf.math.log(forwards / strikes) + sqrt_var * sqrt_var / 2) / sqrt_var\\n d2 = d1 - sqrt_var\\n undiscounted_calls = _ncdf(d2)\\n if is_call_options is None:\\n return discount_factors * undiscounted_calls\\n undiscounted_puts = 1 - undiscounted_calls\\n predicate = tf.broadcast_to(is_call_options, tf.shape(undiscounted_calls))\\n return discount_factors * tf.where(predicate, undiscounted_calls,\\n undiscounted_puts)\",\n \"def get_applicable_discounts(self, actual_volume):\\n\\n applicable_discounts = []\\n residual = {}\\n\\n min_vol_criteria, product_vs_offer, base_product_vs_offer_product = self.get_product_vs_offer_and_volume(self.discounts)\\n\\n for basket_item, volume in actual_volume.items():\\n offer_on_product = base_product_vs_offer_product.get(basket_item)\\n if min_vol_criteria.get(basket_item):\\n if volume >= min_vol_criteria.get(basket_item) and actual_volume.get(offer_on_product):\\n applicable_discounts.append(product_vs_offer.get(basket_item))\\n else:\\n residual[basket_item.upper()] = volume\\n else:\\n for prod, offering in base_product_vs_offer_product.items():\\n if (offering == basket_item) and (prod not in actual_volume):\\n residual[basket_item.upper()] = volume\\n # else:\\n\\n\\n if \\\"APPL\\\" in applicable_discounts and \\\"APOM\\\" in applicable_discounts:\\n applicable_discounts.remove(\\\"APOM\\\")\\n residual['OM1'] = actual_volume.get('om1')\\n\\n return applicable_discounts, residual\",\n \"def test_visualize_price_breakdown(self):\\n pass\",\n \"def _collect_quantizers_descriptions(self) -> List[QuantizerDescription]:\\n # `True` for weight quantizer, `False` otherwise.\\n quantizers = chain(\\n map(lambda x: (True, x), self._weight_quantizers.values()),\\n map(lambda x: (False, x), self._non_weight_quantizers.values()),\\n )\\n\\n quantizers_descriptions = []\\n for is_weight_quantizer, q in quantizers:\\n is_symmetric = isinstance(q, SymmetricQuantizer)\\n\\n quantizers_descriptions.append(\\n QuantizerDescription(\\n q.num_bits, q.per_channel, q.signed, is_symmetric, is_weight_quantizer, q.is_enabled_quantization()\\n )\\n )\\n\\n return quantizers_descriptions\",\n \"def price(usr, item, searches = 2, method = \\\"AVERAGE\\\", deduct = 0):\\n if not method in ShopWizard.methods: raise invalidMethod()\\n\\n if isinstance(item, Item):\\n item = item.name\\n\\n prices = []\\n dets = {}\\n for x in range(0, searches):\\n print('performing search: {}'.format(x))\\n results = ShopWizard.search(usr, item)\\n\\n # Set to -1 if not found\\n if not results:\\n prices.append(-1)\\n continue\\n\\n prices.append(int(results[item].price))\\n dets[str(results[item].price)] = (results[item].owner, results[item].id)\\n\\n time.sleep(ShopWizard.waitTime)\\n\\n # Determines if item was UB\\n print(prices)\\n if sum(prices) == len(prices) * -1:\\n return False\\n\\n prices = list(filter(lambda x: x != -1, prices))\\n\\n if method == ShopWizard.RETLOW:\\n price = sorted(prices)[0]\\n return (price, dets[str(price)][0], dets[str(price)][1])\\n\\n return ShopWizard.__determinePrice(prices, method, deduct)\",\n \"def get_transaction_prices(self):\\n cleaned_data = self.cleaned_data()\\n supplier_cleaned_data = cleaned_data.get('cleaned_supplier_data')\\n transaction_cleaned_data = cleaned_data.get('cleaned_transaction_data')\\n merged_data = self.merge_supplier_transaction(supplier_cleaned_data, transaction_cleaned_data)\\n calculated_data = self.calculate_prices(merged_data)\\n self.export_calculated_prices(calculated_data)\\n return calculated_data\",\n \"def marketDataParser(self,data,quick=True):\\n\\t\\trt={'bids':[],'asks':[]}\\n\\t\\tfor [price,sz,numb] in data['bids']:\\n\\t\\t\\tif quick is False:\\n\\t\\t\\t\\trt['bids'].append([Decimal(price),Decimal(sz),Decimal(numb)])\\n\\t\\t\\telse:\\n\\t\\t\\t\\trt['bids'].append([float(price),float(sz),float(numb)])\\t\\t\\t\\t\\n\\t\\tfor [price,sz,numb] in data['asks']:\\n\\t\\t\\tif quick is False:\\n\\t\\t\\t\\trt['asks'].append([Decimal(price),Decimal(sz),Decimal(numb)])\\n\\t\\t\\telse:\\n\\t\\t\\t\\trt['asks'].append([float(price),float(sz),float(numb)])\\t\\t\\t\\t\\n\\t\\tBuy=[]\\t#[price,size,numb-order,sum-size,sum-vl]\\n\\t\\tif quick is False:\\n\\t\\t\\tlastVL=Decimal(0)\\t\\t\\n\\t\\t\\tsumsz=Decimal(0)\\n\\t\\telse:\\n\\t\\t\\tlastVL=0\\t\\t\\n\\t\\t\\tsumsz=0\\n\\t\\tfor [pr,sz,nb] in rt['bids']:\\n\\t\\t\\tlastVL=pr*sz+lastVL\\n\\t\\t\\tsumsz+=sz\\t\\t\\t\\t\\n\\t\\t\\tBuy.append((pr,sz,nb,sumsz,lastVL)) \\n\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\tSell=[]\\t#(price,size,numb-order,sum-size,sum-vl)\\n\\t\\tif quick is False:\\n\\t\\t\\tlastVL=Decimal(0)\\t\\t\\n\\t\\t\\tsumsz=Decimal(0)\\n\\t\\telse:\\n\\t\\t\\tlastVL=0\\t\\t\\n\\t\\t\\tsumsz=0\\n\\t\\tfor [pr,sz,nb] in rt['asks']:\\n\\t\\t\\tlastVL=pr*sz+lastVL\\n\\t\\t\\tsumsz+=sz\\t\\t\\t\\t\\n\\t\\t\\tSell.append((pr,sz,nb,sumsz, lastVL)) \\n\\t\\treturn {'buy':Buy,'sell':Sell}\",\n \"def get_price(self):\\n if self.price is None:\\n price = self.data['pizza'][self.item_type][self.size]\\n for topping in self.toppings:\\n price += self.data['topping'][topping]\\n return price\\n return self.price\",\n \"def get_prices(paths, side):\\n prices = {}\\n\\n for path in paths:\\n data = get_data(path)\\n\\n for pair in data.keys():\\n\\n if pair not in prices:\\n prices[pair] = []\\n\\n if side == 'bids': # Best bid\\n\\n bids = data[pair]['bids'].keys()\\n float_bids = [float(b) for b in bids]\\n sorted_bids = sorted(float_bids)\\n best_bid = sorted_bids[-1]\\n prices[pair].append(best_bid)\\n\\n elif side == 'asks': # Best ask\\n\\n asks = data[pair]['asks'].keys()\\n float_asks = [float(a) for a in asks]\\n sorted_asks = sorted(float_asks)\\n best_ask = sorted_asks[0]\\n prices[pair].append(best_ask)\\n\\n else:\\n raise Exception('Side must be either \\\"asks\\\" or \\\"bids\\\".')\\n\\n return prices\",\n \"def filter_potential_deal_table(apply_n_clicks, potential_deal_table_data,\\n selected_year, selected_make, selected_model, min_odometer,\\n max_odometer, min_price, max_price, min_offer_price,\\n max_offer_price):\\n potential_deal_db_data = DBApi.get_instance().potential_records\\n filtered_year_ids = []\\n filtered_make_ids = []\\n filtered_model_ids = []\\n filtered_min_odometer_ids = []\\n filtered_max_odometer_ids = []\\n filtered_min_price_ids = []\\n filtered_max_price_ids = []\\n filtered_min_offer_price_ids = []\\n filtered_max_offer_price_ids = []\\n\\n if selected_year or selected_make or min_odometer or max_odometer or min_price or \\\\\\n max_price or min_offer_price or max_offer_price:\\n # Odometer filter\\n if min_odometer and max_odometer:\\n if int(max_odometer) < int(min_odometer):\\n return [\\n potential_deal_table_data,\\n \\\"Max odometer value should be greater than min odometer value\\\",\\n True,\\n 5000\\n ]\\n if min_price and max_price:\\n if int(max_price) < int(min_price):\\n return [\\n potential_deal_table_data,\\n \\\"Max price value should be greater than min price value\\\",\\n True,\\n 5000\\n ]\\n\\n if min_offer_price and max_offer_price:\\n if int(max_offer_price) < int(min_offer_price):\\n return [\\n potential_deal_table_data,\\n \\\"Max offer price MMR value should be greater \\\"\\n \\\"than min offer price MMR value\\\",\\n True,\\n 5000\\n ]\\n\\n for data in potential_deal_db_data:\\n # Year filter\\n if selected_year:\\n for year in selected_year:\\n if year in data[\\\"make_model_year\\\"]:\\n filtered_year_ids.append(data[\\\"PotentialDealID\\\"])\\n break\\n else:\\n filtered_year_ids.append(data[\\\"PotentialDealID\\\"])\\n\\n # Make filter\\n if selected_make:\\n for make in selected_make:\\n if make in data[\\\"make_model_year\\\"].lower():\\n filtered_make_ids.append(data[\\\"PotentialDealID\\\"])\\n else:\\n filtered_make_ids.append(data[\\\"PotentialDealID\\\"])\\n\\n # Model filter\\n if selected_model:\\n for model in selected_model:\\n if model in data[\\\"make_model_year\\\"].lower():\\n filtered_model_ids.append(data[\\\"PotentialDealID\\\"])\\n else:\\n filtered_model_ids.append(data[\\\"PotentialDealID\\\"])\\n\\n # Min odometer filter\\n if min_odometer:\\n if int(data[\\\"odometer\\\"]) >= int(min_odometer):\\n filtered_min_odometer_ids.append(data[\\\"PotentialDealID\\\"])\\n else:\\n filtered_min_odometer_ids.append(data[\\\"PotentialDealID\\\"])\\n\\n # Max odometer filter\\n if max_odometer:\\n if int(data[\\\"odometer\\\"]) <= int(max_odometer):\\n filtered_max_odometer_ids.append(data[\\\"PotentialDealID\\\"])\\n else:\\n filtered_max_odometer_ids.append(data[\\\"PotentialDealID\\\"])\\n\\n # Min price filter\\n if min_price:\\n if int(data[\\\"price\\\"]) >= int(min_price):\\n filtered_min_price_ids.append(data[\\\"PotentialDealID\\\"])\\n else:\\n filtered_min_price_ids.append(data[\\\"PotentialDealID\\\"])\\n\\n # Max price filter\\n if max_price:\\n if int(data[\\\"price\\\"]) <= int(max_price):\\n filtered_max_price_ids.append(data[\\\"PotentialDealID\\\"])\\n else:\\n filtered_max_price_ids.append(data[\\\"PotentialDealID\\\"])\\n\\n # Min offer price filter\\n if min_offer_price:\\n if int(data[\\\"OfferPricePctMMR\\\"]) >= int(min_offer_price):\\n filtered_min_offer_price_ids.append(data[\\\"PotentialDealID\\\"])\\n else:\\n filtered_min_offer_price_ids.append(data[\\\"PotentialDealID\\\"])\\n\\n # Max offer price filter\\n if max_offer_price:\\n if int(data[\\\"OfferPricePctMMR\\\"]) <= int(max_offer_price):\\n filtered_max_offer_price_ids.append(data[\\\"PotentialDealID\\\"])\\n else:\\n filtered_max_offer_price_ids.append(data[\\\"PotentialDealID\\\"])\\n\\n filtered_ids = set(filtered_year_ids).intersection(\\n set(filtered_make_ids),\\n set(filtered_model_ids),\\n set(filtered_min_odometer_ids),\\n set(filtered_max_odometer_ids),\\n set(filtered_min_price_ids),\\n set(filtered_max_price_ids),\\n set(filtered_min_offer_price_ids),\\n set(filtered_max_offer_price_ids)\\n )\\n filtered_data = [\\n data\\n for data in potential_deal_db_data\\n if data[\\\"PotentialDealID\\\"] in filtered_ids\\n ]\\n return [filtered_data, \\\"\\\", False, 2000]\\n return [potential_deal_db_data, \\\"\\\", False, 2000]\",\n \"def depth_offers(self):\\n return len(self.offer)\",\n \"def set_final_settle_prices(pr_trades, exer_date, mode, TestMode):\\n\\n if not pr_trades:\\n msg = ('No Exercise/Assign trades made on date {0}'.format(exer_date))\\n Logme()(msg, 'WARNING')\\n return\\n\\n for t in pr_trades:\\n ins = t.insaddr\\n settle_price = getSettlePriceFromMarket(ins, exer_date, \\\"SETTLEMENT\\\")\\n if not settle_price:\\n msg = ('Will skip trade {0} since there is no price for this '\\n 'instrument {1}.'.format(t.trdnbr, ins.insid))\\n Logme()(msg)\\n continue\\n\\n strike_price = convert_price_to_und_or_strike_quotation(ins,\\n ins.strike_price, 1)\\n\\n if ins.settlement == 'Cash':\\n if ins.call_option:\\n p_der = FBDPCommon.create_quotetype_price(ins,\\n settle_price - strike_price)\\n elif ins.instype == 'Future/Forward':\\n p_der = settle_price\\n else:\\n p_der = FBDPCommon.create_quotetype_price(ins,\\n strike_price - settle_price)\\n\\n else: # Physical settlement\\n p_phys = 0.0 # price to be set in the physical trade\\n t_phys = get_physical_trade(t)\\n if not t_phys:\\n Logme()('Physical settlement trade does not exist for trade '\\n '{0}.'.format(t.trdnbr))\\n continue\\n if mode == 'Market':\\n p_phys = settle_price\\n if ins.instype == 'Option':\\n p_phys = settle_price\\n if ins.call_option:\\n p_der = FBDPCommon.create_quotetype_price(ins,\\n settle_price - strike_price)\\n else:\\n p_der = FBDPCommon.create_quotetype_price(ins,\\n strike_price - settle_price)\\n else: # Future\\n p_der = settle_price\\n\\n else: # Physical is done to the strike price (Strike mode)\\n p_der = 0.0\\n if ins.instype == 'Option':\\n p_phys = ins.strike_price\\n else: # Future\\n p_phys = settle_price\\n\\n if (abs(ins.phys_contr_size) > 0.000001 and\\n abs(ins.phys_contr_size - ins.contr_size) > 0.000001):\\n update_exercise_payment(t, settle_price, mode, TestMode)\\n\\n phys_clone = t_phys.clone()\\n phys_clone.price = p_phys\\n if (ins.instype in ['Option', 'Warrant'] and\\n ins.und_instype == 'Curr'):\\n phys_clone.fx_update_non_dealt_amount(p_phys)\\n else:\\n phys_clone.premium = trade_premium_from_quote(\\n phys_clone.trdnbr, p_phys, phys_clone.acquire_day)\\n if not TestMode:\\n phys_clone.commit()\\n\\n der_clone = t.clone()\\n der_clone.price = p_der\\n der_clone.premium = trade_premium_from_quote(der_clone.trdnbr, p_der,\\n t.acquire_day)\\n\\n if not TestMode:\\n der_clone.commit()\\n ael.poll\",\n \"def _compute_taxed_lst_price2(self):\\n company_id = self._context.get(\\n 'company_id', self.env.user.company_id.id)\\n for product in self:\\n product.taxed_lst_price = product.taxes_id.filtered(\\n lambda x: x.company_id.id == company_id).compute_all(\\n product.lst_price,\\n self.env.user.company_id.currency_id,\\n product=product)['total_included']\",\n \"def processMarketOrders(self):\\n try:\\n nextRound = self.currentRound+1\\n resultsList = []\\n master = {}\\n self.genMarketStat()\\n myMarketStat = self.marketStats[str(self.currentRound)]\\n \\n # sorted lists of market orders\\n master['buyAL'] = anwp.func.funcs.sortDictByChildObjValue(self.marketOrders, 'max', True, {'value':'AL', 'min':0})\\n master['buyEC'] = anwp.func.funcs.sortDictByChildObjValue(self.marketOrders, 'max', True, {'value':'EC', 'min':0})\\n master['buyIA'] = anwp.func.funcs.sortDictByChildObjValue(self.marketOrders, 'max', True, {'value':'IA', 'min':0})\\n master['sellAL'] = anwp.func.funcs.sortDictByChildObjValue(self.marketOrders, 'min', False, {'value':'AL', 'max':0})\\n master['sellEC'] = anwp.func.funcs.sortDictByChildObjValue(self.marketOrders, 'min', False, {'value':'EC', 'max':0})\\n master['sellIA'] = anwp.func.funcs.sortDictByChildObjValue(self.marketOrders, 'min', False, {'value':'IA', 'max':0})\\n \\n for res in ['AL', 'EC', 'IA']:\\n for sellOrder in master['sell%s' % res]:\\n # min sell order gets first chance to sell its product\\n if sellOrder.amountUsed == sellOrder.amount:\\n pass # seller has sold all he wants with this order\\n else:\\n i = 0\\n for buyOrder in master['buy%s' % res]:\\n # determine price, allow for bidding on price\\n try:\\n nextBuyOrder = master['buy%s' % res][i+1]\\n if nextBuyOrder.max < buyOrder.max and (nextBuyOrder.max+1) >= sellOrder.min:\\n price = nextBuyOrder.max + 1\\n else:\\n price = buyOrder.max\\n except IndexError:\\n price = buyOrder.max\\n # max buy order gets first chance to buy sellers product\\n resultsList.append(self.processMarketTransaction(buyOrder, sellOrder, price))\\n i += 1\\n \\n # set the average market prices for this round\\n if getattr(myMarketStat, 'volSold%s' % res) > 0:\\n setattr(myMarketStat, 'avgSold%s' % res, (getattr(myMarketStat, 'sumSold%s' % res) / \\n getattr(myMarketStat, 'volSold%s' % res)))\\n \\n # clean up market orders for next round\\n for orderID in self.marketOrders.keys():\\n myMarketOrder = self.marketOrders[orderID]\\n myMarketOrder.cleanUp()\\n if myMarketOrder.amount == 0:\\n resultsList.append('cancel market Order=%s' % orderID)\\n self.cancelMarketOrder(orderID)\\n \\n return str(resultsList)\\n except:\\n return 'galaxy->processMarketOrders error'\",\n \"def vol_from_price( self, price ):\\n def target_func( price, vol ):\\n return self.price_from_vol( vol ) - price \\n \\n return brentq( partial( target_func, price ), 1e-8, 10 )\",\n \"def prices_available(self, prices_available):\\n\\n self._prices_available = prices_available\",\n \"def sell_cost(self):\\n return self._manager.get_sell_price(self.name)\",\n \"def drop_fairings(vessel):\\n fairings = filter(lambda f: getattr(f, 'tag', None) != \\\"noauto\\\",\\n find_all_fairings(vessel))\\n for f in fairings:\\n jettison_fairing(f)\",\n \"def get_product_vs_offer_and_volume(self, df):\\n\\n product_vs_min_volume = {}\\n product_offer = {}\\n base_product_vs_offer_product = {}\\n\\n for i in df.itertuples():\\n product_vs_min_volume[i[4].lower()] = i[5]\\n product_offer[i[4].lower()] = i[1]\\n base_product_vs_offer_product[i[4].lower()] = i[7].lower()\\n\\n return product_vs_min_volume, product_offer, base_product_vs_offer_product\",\n \"def get_item_price(self, soup: BeautifulSoup) -> None:\\n try:\\n price = soup.find(\\\"span\\\", class_=\\\"_olc9rf0\\\").get_text()\\n price = re.findall(\\\"\\\\d+(?:\\\\.\\\\d+)?\\\", price)[0]\\n except (AttributeError, IndexError):\\n price = None\\n self.__collected_dic[\\\"price\\\"].append(price)\"\n]","isConversational":false},"negative_scores":{"kind":"list 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computation methods"},"document":{"kind":"string","value":"def get_basket_with_discounts(self, actual_volume, applicable_discounts, residual):\n\n # product_vs_min_volume, product_offer = self.get_product_vs_offer_and_volume(self.discounts)\n pr = Product('items')\n product_prices = pr.product_meta\n\n basket = []\n\n for discount in applicable_discounts:\n offer_info = Offer('discount', discount)\n\n if offer_info.offer_type.lower() == \"discount\":\n temp_basket = self.get_basket_items_discount(offer_info, actual_volume, product_prices)\n for item in temp_basket:\n basket.append(item)\n elif offer_info.offer_type.lower() == \"pricedrop\":\n temp_basket = self.get_basket_items_pricedrop(offer_info, actual_volume, product_prices)\n for item in temp_basket:\n basket.append(item)\n\n if residual:\n for product, volume in residual.items():\n for i in range(0, volume):\n basket.append((product, product_prices.get(product.lower()).get('price')))\n\n return basket"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def discount(self, cart):","def get_discount(self, price):\r\n pass","def apply_discount(self, product):\n pass","def compute(self):\r\n \r\n if self.option_type == \"European call\":\r\n option_price = BS_call(self.S0, self.K, self.T_scaled, self.vola, self.div_yield, r=self.rf)\r\n \r\n elif self.option_type == \"European put\":\r\n option_price = BS_put(self.S0, self.K, self.T_scaled, self.vola, self.div_yield, r=self.rf)\r\n \r\n elif self.option_type == \"Asian call\":\r\n option_price = monte_carlo_call(self.S0, self.K, self.T_scaled, self.vola, self.div_yield, rf=self.rf ) # function in other file\r\n else:\r\n # Asian put\r\n option_price = monte_carlo_put(self.S0, self.K, self.T_scaled, self.vola, self.div_yield, rf=self.rf)\r\n \r\n\r\n self.display_option_price.setText(str(round(option_price,2)))\r\n self.display_option_price.adjustSize()","def calculate_profit(self):","def get_basket_items_discount(self, offer_info, actual_volume, product_prices):\n prod_code = offer_info.base_prod_code\n base_prod_vol = actual_volume.get(prod_code.lower())\n\n discount_basket = []\n\n if base_prod_vol >= offer_info.min_vol:\n offer_on_prod = offer_info.offer_on\n if actual_volume.get(offer_on_prod.lower()):\n print(f\"Base product volume is greater than minimum required volume & product on offer is also available \"\n f\"in cart..\")\n if offer_info.is_limited:\n print(f\"Limited offer..\")\n if prod_code == offer_on_prod:\n # total_allowed_items_on_offer = Limit Volume of base product * (Offer Product Max Volume/Minimum volume of base product)\n total_allowed_items_on_offer = offer_info.limit_vol * (offer_info.offer_prod_volume/offer_info.min_vol)\n max_limit = 1\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n discount_basket.append((prod_code, base_prod_actual_price))\n while max_limit <= total_allowed_items_on_offer:\n discounted_price = (base_prod_actual_price *(offer_info.discount_perc/100))*-1\n discount_basket.append((offer_info.offer_code, discounted_price))\n max_limit += 1\n else:\n total_allowed_items_on_offer = offer_info.limit_vol * (offer_info.offer_prod_volume / offer_info.min_vol)\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n discount_basket.append((prod_code, base_prod_actual_price))\n max_limit = 1\n while max_limit <= total_allowed_items_on_offer:\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\n discounted_price = (offer_onprod_actual_price *(offer_info.discount_perc/100))*-1\n for j in range(0, actual_volume.get(offer_on_prod.lower())):\n discount_basket.append((offer_on_prod, offer_onprod_actual_price))\n discount_basket.append((offer_info.offer_code, discounted_price))\n max_limit += 1\n else:\n print(f\"Unlimited offer..\")\n if prod_code == offer_on_prod:\n if base_prod_vol > offer_info.min_vol:\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n discount_basket.append((prod_code, base_prod_actual_price))\n if i%2 != 0:\n discounted_price = (base_prod_actual_price *(offer_info.discount_perc/100))*-1\n discount_basket.append((offer_info.offer_code, discounted_price))\n else:\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n discount_basket.append((prod_code, base_prod_actual_price))\n else:\n for i in range(0, base_prod_vol):\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\n discount_basket.append((prod_code, base_prod_actual_price))\n\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\n discounted_price = (offer_onprod_actual_price * (offer_info.discount_perc / 100))*-1\n\n for j in range(0, actual_volume.get(offer_on_prod.lower())):\n discount_basket.append((offer_on_prod, offer_onprod_actual_price))\n discount_basket.append((offer_info.offer_code, discounted_price))\n\n\n return discount_basket","def calculate(self):","def implied_discount_factor(p1: Instrument, c1: Instrument, p2: Instrument, c2: Instrument) -> float:\n return (c1.price - p1.price - c2.price + p2.price)/ (c2.strike - c1.strike)","def calculate(self):\r\n\r\n pass","def cmd_calculation():","def __call__(self, rate:'kW'):\n self.rate = rate\n self.cost = self.price * rate","def __calculation(self, *args, **kwargs):\n pass","def discount(self,discountFactor,type='geometric'):\n for e in self.estimators:\n e.discount(discountFactor,type)\n return","def price(function):\n\t\tdef wrapper( self , *args, **kwargs) :\n\t\t\tresult = function( self , *args, **kwargs)\n\t\t\tself.compute_price()\n\t\t\treturn result\n\t\treturn wrapper","def discount(ir, period):\n\treturn ir.discount(period)","def calc_earning(self, data=None):\n result = Result()\n if data is None:\n data = self.security\n self.calcDecision()\n first_purchase_method = self.check_first_purchase_method()\n for i in np.arange(len(data['Close'])):\n if data['FinalDecision'].iloc[i] is None:\n pass\n elif data['FinalDecision'].iloc[i] == TransactionType.BUY:\n if data['FinalDecision'].iloc[i-1] == TransactionType.BUY:\n pass\n else:\n if (self.buys_made + self.sells_made) == 0:\n if first_purchase_method == FirstTransactionType.INIT_CAPITAL:\n self.shares_own = int((self.init_capital/data['Close'].iloc[i]))\n self.buys_made += 1\n elif first_purchase_method == FirstTransactionType.STOCK_QUANTITY:\n self.shares_own = self.stock_quantity\n self.buys_made += 1\n else:\n self.shares_own = int(self.final_capital / data['Close'].iloc[i])\n self.final_capital = self.final_capital % data['Close'].iloc[i]\n #print(self.shares_own)\n\n elif data['FinalDecision'].iloc[i] == TransactionType.SELL:\n if data['FinalDecision'].iloc[i-1] == TransactionType.SELL:\n pass\n else:\n if (self.buys_made + self.sells_made) == 0:\n pass\n else:\n self.final_capital += self.shares_own * data['Close'].iloc[i]\n self.shares_own = 0\n self.sells_made +=1\n #Checar si es el momento mas alto o bajo de ganancias\n if self.shares_own == 0:\n if (self.highest_point is None\n or self.highest_point < self.final_capital):\n self.highest_point = self.final_capital\n if (self.lowest_point is None\n or self.lowest_point > self.final_capital\n or self.lowest_point == 0):\n self.lowest_point = self.final_capital\n else:\n if (self.highest_point is None\n or self.highest_point < (self.shares_own * data['Close'].iloc[i])):\n self.highest_point = self.final_capital\n if (self.lowest_point is None\n or self.lowest_point > (self.shares_own * data['Close'].iloc[i])\n or self.lowest_point == 0):\n self.lowest_point = self.final_capital\n self.calcRealFinalCapital()\n self.calcDiferencePercentage()","def calculate(self):\r\n pass","def testPolicy( \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t \t \t\t \t\t \t\n self, \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t \t \t\t \t\t \t\n symbol=\"jpm\", \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t \t \t\t \t\t \t\n sd=dt.datetime(2008, 1, 1), \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t \t \t\t \t\t \t\n ed=dt.datetime(2009, 12, 31), \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t \t \t\t \t\t \t\n sv=10000, \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t \t \t\t \t\t \t\n ): \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t \t \t\t \t\t \t\n \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t \t \t\t \t\t \t\n dates = pd.date_range(sd,ed)\n df_prices = ind.get_price(symbol, dates)\n\n daily_rets = (df_prices / df_prices.shift(1)) - 1\n daily_rets = daily_rets[1:]\n\n\n sd_older = sd - dt.timedelta(days=365)\n dates_older = pd.date_range(sd_older,ed)\n df_prices_older = ind.get_price(symbol, dates_older)\n sd_key = df_prices.index[0]\n sd_index = df_prices_older.index.get_loc(sd_key)\n\n\n df_holdings = df_prices.copy()\n df_holdings['Holdings'] = np.nan\n del df_holdings[symbol]\n # print(df_holdings)\n\n cum_ret_prev = 0\n iters = 0\n\n\n num_bins = len(self.bins)\n\n _,_,ind1 = ind.get_BB(df_prices_older, self.lookback)\n ind2 = ind.get_CCI(df_prices_older, self.lookback)\n _,_,ind3 = ind.get_SMA_Cross(self.lookback, 100, df_prices_older)\n ind4 = ind.get_momentum(df_prices_older, self.lookback)\n _,_,ind5 = ind.get_MACD(df_prices_older)\n BB = ind1.iloc[sd_index:].values\n CCI = ind2.iloc[sd_index:].values\n SMA_Cross = ind3.iloc[sd_index:].values\n Momentum = ind4.iloc[sd_index:].values\n MACD = ind5.iloc[sd_index:].values\n _,self.x0bins = pd.qcut(BB[:,0], num_bins,labels=False,retbins=True)\n _,self.x1bins = pd.qcut(CCI[:,0],num_bins,labels=False,retbins=True)\n _,self.x2bins = pd.qcut(SMA_Cross[:,0],num_bins,labels=False,retbins=True)\n _,self.x3bins = pd.qcut(Momentum[:,0],num_bins,labels=False,retbins=True)\n _,self.x4bins = pd.qcut(MACD[:,0],num_bins,labels=False,retbins=True)\n x_0 = np.digitize(BB[:,0], self.x0bins[1:-1])\n x_1 = np.digitize(CCI[:,0], self.x1bins[1:-1])\n x_2 = np.digitize(SMA_Cross[:,0], self.x2bins[1:-1])\n x_3 = np.digitize(Momentum[:,0], self.x3bins[1:-1])\n x_4 = np.digitize(MACD[:,0], self.x4bins[1:-1])\n state = x_0 + x_3*10 + x_4*100\n\n\n\n\n\n self.learner.rar = 0\n\n action = self.learner.querysetstate(state[0])\n\n daily_return = daily_rets.iloc[0][symbol]\n df_holdings.iloc[0]['Holdings'] = 0\n\n\n for day_idx in range(1,daily_rets.shape[0]):\n\n # implement action\n cur_price = df_prices.iloc[day_idx-1][symbol]\n next_price = df_prices.iloc[day_idx][symbol]\n action = self.learner.querysetstate(state[day_idx])\n df_holdings.iloc[day_idx]['Holdings'],_ = self.take_action(df_holdings.iloc[day_idx-1]['Holdings'], action, cur_price, next_price)\n\n\n df_holdings.iloc[-1]['Holdings'] = 0\n df_trades = df_holdings.diff()\n df_trades['Trades'] = df_trades['Holdings']\n del df_trades['Holdings']\n df_trades.iloc[0]['Trades'] = 0\n return df_trades","def action_compute_price(self):\n self._prepare_printing()\n\n providers = set(self.mapped('provider_id.id'))\n for provider_id in providers: # process by provider id\n records = self.filtered(lambda r: r.provider_id.id == provider_id)\n # call provider implementation\n provider_name = records[0].provider_id.provider\n if hasattr(records, '_%s_action_compute_price' % provider_name):\n getattr(records, '_%s_action_compute_price' % provider_name)()","def duty_free(price: int, discount: int, holiday_cost: int) -> int:\n if holiday_cost == 500:\n return holiday_cost\n\n discount /= 100\n price = holiday_cost / (price * discount)\n price = int(price)\n return price","def calc_price(self):\n price = self.price\n action = self.action\n mortage = 5 # here set mortage multiplier \n\n if action == 'RESIDENTIAL_SALE':\n return price * 12 * mortage\n\n\n if price >= 10000:\n return price * 0.7\n elif price < 10000 & price >= 5000:\n return price * 0.55\n elif price < 5000 & price >= 2800:\n return price * 0.475\n else:\n return price * 0.4","def calculate(self):\n pass","def test_callPrice(self):\n call_price1 = calculator.BlackScholes.call_price(**self.params_1)\n call_price2 = calculator.BlackScholes.call_price(**self.params_2)\n self.assertAlmostEqual(call_price1,10.45,delta=0.01)\n self.assertAlmostEqual(call_price2,7.965,delta=0.01)","def apply_discounts(self):\n # for each valid discount...\n for discount in list(DiscountTypes):\n # only apply the discount if it is set in the cart\n if(discount in self.cart.discounts):\n getattr(self, discount.value)()","def cost(self,e1,e2):\n pass","def calculate_price(self, order: Order, price: float, quantity: float, bar: Bar, volume_used: float):\n raise NotImplementedError()","def _dynamic_price(self):\n adjust = PriceAdjustmentCalc(self)\n signals.satchmo_price_query.send(self, adjustment=adjust,\n slug=self.product.slug, discountable=self.product.is_discountable)\n return adjust.final_price()","def _cost_method(self, *args, **kwargs):\n\n cost_val = 0.5 * np.linalg.norm(self.obs_data - self.op(args[0])) ** 2\n\n if 'verbose' in kwargs and kwargs['verbose']:\n print(' - DATA FIDELITY (X):', cost_val)\n\n return cost_val","def optimal_pricing_engine(model_ID, quote_df, COP_l=0, COP_m=0, COP_h=0):\r\n '''\r\n The purpose of this function is to process a quote using the optimal\r\n pricing model (as stored in the segmentation tree) to determine the \r\n optimal price and win probability data.\r\n \r\n Created: 05 May 2016 by Glenn Melzer\r\n Updated: 01 Aug 2016 by Glenn Melzer\r\n \r\n INPUTS:\r\n model_ID = the name of the model to be used for creating the optimal price \r\n quote_df = the dataframe of quote components (with the same column \r\n format as the historical data input dataframe).\r\n COP_l = Low bottom line customized optimal price\r\n COP_m = Median bottom line customized optimal price\r\n COP_h = High bottom line customized optimal price\r\n \r\n KEY INTERNAL OBJECTS NEEDED FOR THE ENGINE: \r\n seg_df = the quote segmentation tree built previously.\r\n ref_prc_col = the column that contains the reference price (typically list price)\r\n first = the name of the first column in quote_df that contains a segment \r\n classification identifier to be used.\r\n last = the name of the last column in quote_df that contains a classification \r\n identifier to be used. All columns from the first through the last\r\n will be used.\r\n prc_col = the column index name in quote_df that contains the historical\r\n quote price (as a % of reference price) as modified by the segmentation\r\n process.\r\n x_col = the name of the independant variable that affects pricing used in\r\n the regression to calculate the m and b parameters of the linear models. \r\n quote_attribute_cols = the list of column index names of quote_df that \r\n contain the historical data quote attributes.\r\n y_intercept = True or False. Indicate if the regression should have a\r\n y intercept. The default is False.\r\n \r\n OUTPUTS:\r\n quote_df = the original quote_df updated with columns for optimal price,\r\n win probability, etc.\r\n total_deal_stats = the statistics of the total deal\r\n \r\n ACCEPTANCE CRITERIA:\r\n The quote_df contains the required output column populated with data.\r\n '''\r\n start_time = time.time() #start a timer\r\n \r\n #this loads the correct model into seg_df based on the model_ID\r\n etext = 'seg_df = ' + rules_df.loc[model_ID,'seg_model_file_name'] + '_df.copy()'\r\n exec(etext)\r\n #this looks up the needed variables given the model_ID\r\n quote_id_col = rules_df.loc[model_ID,'quote_id_col']\r\n ref_prc_col = rules_df.loc[model_ID,'ref_prc_col']\r\n cost_col = rules_df.loc[model_ID,'cost_col']\r\n quote_prc_col = rules_df.loc[model_ID,'quote_prc_col']\r\n prc_col = rules_df.loc[model_ID,'prc_col']\r\n x_col = rules_df.loc[model_ID,'x_col']\r\n first = rules_df.loc[model_ID,'first']\r\n last = rules_df.loc[model_ID,'last']\r\n psych_factor = rules_df.loc[model_ID,'psych_factor']\r\n quote_attribute_cols = eval(rules_df.loc[model_ID,'quote_attribute_cols'])\r\n y_intercept = rules_df.loc[model_ID,'y_intercept']\r\n \r\n #The following defines objects needed to manage the segmentation tree building process\r\n cols = quote_df.columns\r\n for i in np.arange(len(cols)): #this for loop assigns column names to a list (cols)\r\n if (cols[i] == first):\r\n first_coln = i\r\n if (cols[i] == last):\r\n last_coln = i\r\n if (cols[i] == prc_col):\r\n prc_coln = i \r\n print ' Segmentation starts with Column (first_coln)(first): % r' % first_coln, first\r\n print ' Segmentation ends with Column (last_coln)(last): % r' % last_coln, last\r\n column_nums = range(first_coln, last_coln + 1) #this creates a list of the numbers of the columns used for component segmentation\r\n column_names = list(quote_df.columns[column_nums])\r\n print ' Column numbers of the index columns (column_nums): ',column_nums\r\n print ' Column names of the index column (column_names): ',column_names\r\n print ' Price Column (prc_coln)(prc_col): % r' % prc_coln, prc_col\r\n print ' quote_attribute_cols: % r' % quote_attribute_cols\r\n \r\n \r\n #this section ensures that needed columns are added to quote_df to match format of input_dataframe_expanded_05.csv\r\n quote_df.loc[:,'ComLowPofL'] = ''\r\n quote_df.loc[:,'ComMedPofL'] = ''\r\n quote_df.loc[:,'ComHighPofL'] = ''\r\n quote_df.loc[:,'ComMedPrice'] = ''\r\n quote_df.loc[:,'DealSize'] = ''\r\n quote_df.loc[:,'LogDealSize'] = ''\r\n quote_df.loc[:,'ComPctContrib'] = ''\r\n quote_df.loc[:,'TreeNode'] = ''\r\n #this section adds more columns to support optimal price calculations\r\n quote_df.loc[:,'ComTMCPofL'] = ''\r\n quote_df.loc[:,'AdjComLowPofL'] = ''\r\n quote_df.loc[:,'AdjComMedPofL'] = ''\r\n quote_df.loc[:,'AdjComHighPofL'] = ''\r\n quote_df.loc[:,'AdjComLowPrice'] = ''\r\n quote_df.loc[:,'AdjComMedPrice'] = ''\r\n quote_df.loc[:,'AdjComHighPrice'] = ''\r\n #this section creates columns for the optimal price data\r\n quote_df.loc[:,'OptimalPricePofL'] = ''\r\n quote_df.loc[:,'OptimalPrice'] = ''\r\n quote_df.loc[:,'OptimalPriceWinProb'] = ''\r\n quote_df.loc[:,'OptimalPriceGP'] = ''\r\n quote_df.loc[:,'OptimalPriceExpectedGP'] = ''\r\n quote_df.loc[:,'OptimalPriceIntervalLow'] = ''\r\n quote_df.loc[:,'OptimalPriceIntervalHigh'] = ''\r\n #this section creates columns for the quoted price data\r\n quote_df.loc[:,'QuotePricePofL'] = ''\r\n quote_df.loc[:,'QuotePrice'] = ''\r\n quote_df.loc[:,'QuotePriceWinProb'] = ''\r\n quote_df.loc[:,'QuotePriceGP'] = ''\r\n quote_df.loc[:,'QuotePriceExpectedGP'] = ''\r\n #this section creates columns for statistics\r\n quote_df.loc[:,'PredictedQuotePricePofL'] = ''\r\n quote_df.loc[:,'PredictedQuotePrice'] = ''\r\n #this section is for COP (Customized Optimal Price)\r\n # - L, M, H price points\r\n quote_df.loc[:,'COPComLowPrice'] = ''\r\n quote_df.loc[:,'COPComMedPrice'] = ''\r\n quote_df.loc[:,'COPComHighPrice'] = ''\r\n # - L, M, H price points as a % of List\r\n quote_df.loc[:,'COPComLowPofL'] = ''\r\n quote_df.loc[:,'COPComMedPofL'] = ''\r\n quote_df.loc[:,'COPComHighPofL'] = ''\r\n # - optimal prices and win probabilities\r\n quote_df.loc[:,'COPOptimalPrice'] = ''\r\n quote_df.loc[:,'COPOptimalPricePofL'] = ''\r\n quote_df.loc[:,'COPOptimalPriceWinProb'] = ''\r\n quote_df.loc[:,'COPOptimalPriceGP'] = ''\r\n quote_df.loc[:,'COPOptimalPriceExpectedGP'] = ''\r\n quote_df.loc[:,'COPOptimalPriceIntervalLow'] = ''\r\n quote_df.loc[:,'COPOptimalPriceIntervalHigh'] = ''\r\n # - quoted price data within customized optimal price (COP)\r\n quote_df.loc[:,'COPQuotePriceWinProb'] = ''\r\n quote_df.loc[:,'COPQuotePriceGP'] = ''\r\n quote_df.loc[:,'COPQuotePriceExpectedGP'] = ''\r\n \r\n #this section determines all of the column data needed for optimal price calculation\r\n #this determines the segment tree node to be used for each component of the quote\r\n column_headings = list(cols[column_nums])\r\n #seg_df.set_index(column_headings, inplace = True)\r\n for i in range(len(quote_df)): #this goes through each component in the quote_df\r\n column_values = list(quote_df.loc[i,column_headings])\r\n #print column_values\r\n works = False\r\n j = -1\r\n while works == False: #this finds the most specific valid node in the component segment tree\r\n try:\r\n Low_m = float(seg_df.loc[tuple(column_values), 'Low_m'])\r\n Low_b = float(seg_df.loc[tuple(column_values), 'Low_b'])\r\n Med_m = float(seg_df.loc[tuple(column_values), 'Med_m'])\r\n Med_b = float(seg_df.loc[tuple(column_values), 'Med_b'])\r\n High_m = float(seg_df.loc[tuple(column_values), 'High_m'])\r\n High_b = float(seg_df.loc[tuple(column_values), 'High_b'])\r\n Tree_Node = [seg_df.index.get_loc(tuple(column_values))][0]\r\n works = True\r\n except KeyError:\r\n column_values[j] = ''\r\n j -= 1\r\n \r\n #this calculates the low, med, and high (%ofList) price points\r\n x = quote_df.loc[i,x_col]\r\n low = Low_m * x + Low_b\r\n med = Med_m * x + Med_b\r\n high = High_m * x + High_b \r\n low,med,high = BPF.PriceAdj(low, med, high) #this makes any needed adjustments to the low, med, and high price points to eliminate anomolies\r\n #this writes the low, med, and high to the quote_df\r\n quote_df.loc[i,'ComLowPofL'] = low\r\n quote_df.loc[i,'ComMedPofL'] = med\r\n quote_df.loc[i,'ComHighPofL'] = high\r\n quote_df.loc[i,'TreeNode'] = Tree_Node\r\n #print 'Component Row, Tree_Node, & ComMedPofL: ', i, Tree_Node, med\r\n \r\n #this calculates and sets the ComMedPrice\r\n quote_df.loc[:,'ComMedPrice'] = (quote_df[ref_prc_col] * quote_df['ComMedPofL'])#.round(decimals = 2)\r\n #this calculates and sets the DealSize\r\n quote_df.loc[:,'DealSize'] = quote_df['ComMedPrice'].sum().round(decimals = 2)\r\n #this calculates and sets the Log of the DealSize\r\n quote_df.loc[:,'LogDealSize'] = np.log10(quote_df['DealSize'].astype(float))\r\n #this calculates the component's percent price contribution to the quote (based on component median price)\r\n quote_df.loc[:,'ComPctContrib'] = quote_df['ComMedPrice'] / quote_df['DealSize']\r\n \r\n #this section calculates the adjusted L, M, H values\r\n seg_df.reset_index(inplace = True)\r\n for i in range(len(quote_df)): #this goes through each component in the quote_df\r\n #set adjusted values for low and high to match original values for low and high\r\n adjlow = quote_df.loc[i,'ComLowPofL']\r\n adjhigh = quote_df.loc[i,'ComHighPofL']\r\n #adjust median point in price sensitivity curve for quote level attributes\r\n adjmed = 0\r\n for j in range(len(quote_attribute_cols)): #this calculates the value of the multiple linear regression\r\n adjmed += quote_df.loc[i, quote_attribute_cols[j]] * seg_df.loc[quote_df.loc[i, 'TreeNode'], 'QP_' + quote_attribute_cols[j]]\r\n #print 'Factors: ',i,j,quote_df.loc[i, quote_attribute_cols[j]], seg_df.loc[quote_df.loc[i, 'TreeNode'], 'QP_' + quote_attribute_cols[j]]\r\n if y_intercept == True:\r\n adjmed += seg_df.loc[quote_df.loc[i, 'TreeNode'], 'QP_intercept']\r\n #adjust median point in price sensitivity curve for skew in data\r\n quote_df.loc[i, 'PredictedQuotePricePofL'] = adjmed\r\n quote_df.loc[i, 'PredictedQuotePrice'] = adjmed * quote_df.loc[i, 'ComListPrice']\r\n adjmed += seg_df.loc[quote_df.loc[i, 'TreeNode'], 'Adj_YtoMedPofL']\r\n #adjust all points in price sensitivity curve for bias from using win data only\r\n adj = seg_df.loc[quote_df.loc[i, 'TreeNode'], 'Adj_psych']\r\n adjlow *= adj\r\n adjmed *= adj\r\n adjhigh *= adj\r\n #adjust all points in price sensitivity curve for brand input\r\n adj = seg_df.loc[quote_df.loc[i, 'TreeNode'], 'Adj_InputByBrand']\r\n adjlow += adj\r\n adjmed += adj\r\n adjhigh += adj\r\n adjlow,adjmed,adjhigh = BPF.PriceAdj(adjlow, adjmed, adjhigh) #this makes any needed adjustments to the low, med, and high price points to eliminate anomolies\r\n #store adjusted values\r\n quote_df.loc[i, 'AdjComLowPofL'] = adjlow\r\n quote_df.loc[i, 'AdjComMedPofL'] = adjmed\r\n quote_df.loc[i, 'AdjComHighPofL'] = adjhigh\r\n ListPrice = quote_df.loc[i, 'ComListPrice']\r\n quote_df.loc[i, 'AdjComLowPrice'] = adjlow * ListPrice\r\n quote_df.loc[i, 'AdjComMedPrice'] = adjmed * ListPrice\r\n quote_df.loc[i, 'AdjComHighPrice'] = adjhigh * ListPrice\r\n \r\n #this section calculates the optimal price data\r\n for i in range(len(quote_df)): #this goes through each component in the quote_df\r\n L = quote_df.loc[i,'AdjComLowPofL']\r\n M = quote_df.loc[i,'AdjComMedPofL']\r\n H = quote_df.loc[i,'AdjComHighPofL']\r\n Cf = 1.0 * quote_df.loc[i,'ComTMC'] / quote_df.loc[i,'ComListPrice']\r\n quote_df.loc[i, 'ComTMCPofL'] = Cf\r\n quote_df.loc[i, 'OptimalPricePofL'] = BPF.OptPrice(L, M, H, Cf, 0, 0, 0)\r\n quote_df.loc[i, 'OptimalPrice'] = quote_df.loc[i, 'OptimalPricePofL'] * quote_df.loc[i, 'ComListPrice']\r\n quote_df.loc[i, 'OptimalPriceWinProb'] = BPF.ProbOfWin(quote_df.loc[i, 'OptimalPricePofL'], L, M, H)\r\n quote_df.loc[i, 'OptimalPriceGP'] = quote_df.loc[i, 'OptimalPrice'] - quote_df.loc[i,'ComTMC']\r\n quote_df.loc[i, 'OptimalPriceExpectedGP'] = quote_df.loc[i, 'OptimalPriceGP'] * quote_df.loc[i, 'OptimalPriceWinProb']\r\n quote_df.loc[i, 'OptimalPriceIntervalLow'], quote_df.loc[i, 'OptimalPriceIntervalHigh'] = BPF.OptPriceConfIntervl(quote_df.loc[i, 'OptimalPrice'], quote_df.loc[i,'AdjComLowPrice'], quote_df.loc[i,'AdjComMedPrice'], quote_df.loc[i,'AdjComHighPrice'], quote_df.loc[i, 'ComTMC'])\r\n #this section shows quoted (i.e. requested) price data\r\n quote_df.loc[i, 'QuotePricePofL'] = quote_df.loc[i, 'ComQuotePricePofL']\r\n quote_df.loc[i, 'QuotePrice'] = quote_df.loc[i, 'ComQuotePrice']\r\n quote_df.loc[i, 'QuotePriceWinProb'] = BPF.ProbOfWin(quote_df.loc[i, 'QuotePricePofL'], L, M, H)\r\n quote_df.loc[i, 'QuotePriceGP'] = quote_df.loc[i, 'QuotePrice'] - quote_df.loc[i,'ComTMC']\r\n quote_df.loc[i, 'QuotePriceExpectedGP'] = quote_df.loc[i, 'QuotePriceGP'] * quote_df.loc[i, 'QuotePriceWinProb']\r\n \r\n #this section calculates the Customized Optimal Price (COP) data\r\n if (COP_h > COP_m) and (COP_m > COP_l):\r\n Lt = quote_df['AdjComLowPrice'].sum()\r\n Mt = quote_df['AdjComMedPrice'].sum()\r\n Ht = quote_df['AdjComHighPrice'].sum()\r\n LISTt = quote_df['ComListPrice'].sum()\r\n for i in range(len(quote_df)): #this goes through each component in the quote_df\r\n Li = quote_df.loc[i, 'AdjComLowPrice'].round(decimals=2)\r\n Mi = quote_df.loc[i, 'AdjComMedPrice'].round(decimals=2)\r\n Hi = quote_df.loc[i, 'AdjComHighPrice'].round(decimals=2)\r\n LISTi = quote_df.loc[i, 'ComListPrice'].round(decimals=2)\r\n #this section uses linear interpolation for setting COP component prices from the bottom line price\r\n quote_df.loc[i, 'COPComLowPrice'] = BPF.PriceConv(Lt, Mt, Ht, LISTt, COP_l, Li, Mi, Hi, LISTi).round(decimals=2)\r\n quote_df.loc[i, 'COPComMedPrice'] = BPF.PriceConv(Lt, Mt, Ht, LISTt, COP_m, Li, Mi, Hi, LISTi).round(decimals=2)\r\n quote_df.loc[i, 'COPComHighPrice'] = BPF.PriceConv(Lt, Mt, Ht, LISTt, COP_h, Li, Mi, Hi, LISTi).round(decimals=2)\r\n #this section calculate the COP PofL prices\r\n quote_df.loc[i, 'COPComLowPofL'] = quote_df.loc[i, 'COPComLowPrice'] / LISTi\r\n quote_df.loc[i, 'COPComMedPofL'] = quote_df.loc[i, 'COPComMedPrice'] / LISTi\r\n quote_df.loc[i, 'COPComHighPofL'] = quote_df.loc[i, 'COPComHighPrice'] / LISTi\r\n #this section calculate the COP optimal prices, win probabilities, and profitability\r\n quote_df.loc[i, 'COPOptimalPrice'] = BPF.OptPrice(quote_df.loc[i, 'COPComLowPrice'], quote_df.loc[i, 'COPComMedPrice'], quote_df.loc[i, 'COPComHighPrice'], quote_df.loc[i, 'ComTMC'], 0, 0, 0).round(decimals=2)\r\n quote_df.loc[i, 'COPOptimalPricePofL'] = quote_df.loc[i, 'COPOptimalPrice'] / LISTi\r\n quote_df.loc[i, 'COPOptimalPriceWinProb'] = BPF.ProbOfWin(quote_df.loc[i, 'COPOptimalPrice'], quote_df.loc[i, 'COPComLowPrice'], quote_df.loc[i, 'COPComMedPrice'], quote_df.loc[i, 'COPComHighPrice'])\r\n quote_df.loc[i, 'COPOptimalPriceGP'] = quote_df.loc[i, 'COPOptimalPrice'] - quote_df.loc[i, 'ComTMC']\r\n quote_df.loc[i, 'COPOptimalPriceExpectedGP'] = (quote_df.loc[i, 'COPOptimalPriceGP'] * quote_df.loc[i, 'COPOptimalPriceWinProb']).round(decimals=2)\r\n quote_df.loc[i, 'COPOptimalPriceIntervalLow'], quote_df.loc[i, 'COPOptimalPriceIntervalHigh'] = BPF.OptPriceConfIntervl(quote_df.loc[i, 'COPOptimalPrice'], quote_df.loc[i,'COPComLowPrice'], quote_df.loc[i,'COPComMedPrice'], quote_df.loc[i,'COPComHighPrice'], quote_df.loc[i, 'ComTMC'])\r\n #this section calculates the quoted price statistics given the COP prices\r\n quote_df.loc[i, 'COPQuotePriceWinProb'] = BPF.ProbOfWin(quote_df.loc[i, 'QuotePrice'], quote_df.loc[i, 'COPComLowPrice'], quote_df.loc[i, 'COPComMedPrice'], quote_df.loc[i, 'COPComHighPrice'])\r\n quote_df.loc[i, 'COPQuotePriceGP'] = quote_df.loc[i, 'QuotePriceGP']\r\n quote_df.loc[i, 'COPQuotePriceExpectedGP'] = quote_df.loc[i, 'COPQuotePriceGP'] * quote_df.loc[i, 'COPQuotePriceWinProb']\r\n print; print ' quote_df update complete'\r\n \r\n \r\n #this section calculates the total deal values\r\n # this section contains general quote totals\r\n total_deal_stats = pd.Series('', index=[' General Total Quote Data'])\r\n total_deal_stats['DealListPrice'] = quote_df['ComListPrice'].sum()\r\n total_deal_stats['DealSize'] = quote_df['ComMedPrice'].sum().round(decimals=2)\r\n total_deal_stats['DealTMC'] = quote_df['ComTMC'].sum()\r\n total_deal_stats['DealPredictedQuotePrice'] = quote_df['PredictedQuotePrice'].sum().round(decimals=0)\r\n # this section contains Price Range Data (Line Item Sum)\r\n total_deal_stats[' Price Range Data (Line Item Sum)'] = ''\r\n total_deal_stats['DealAdjLowPrice'] = quote_df['AdjComLowPrice'].sum().round(decimals=0)\r\n total_deal_stats['DealAdjMedPrice'] = quote_df['AdjComMedPrice'].sum().round(decimals=0)\r\n total_deal_stats['DealAdjHighPrice'] = quote_df['AdjComHighPrice'].sum().round(decimals=0)\r\n # this section contains Quoted Price Data (Line Item Sum)\r\n total_deal_stats[' Quoted Price Data (Line Item Sum)'] = ''\r\n total_deal_stats['DealQuotePrice'] = quote_df['ComQuotePrice'].sum()\r\n total_deal_stats['DealQuotePriceWinProb'] = ''\r\n total_deal_stats['DealQuotePriceGP'] = total_deal_stats['DealQuotePrice'] - total_deal_stats['DealTMC']\r\n total_deal_stats['DealQuotePriceExpectedGP'] = quote_df['QuotePriceExpectedGP'].sum()\r\n total_deal_stats['DealQuotePriceWinProb'] = total_deal_stats['DealQuotePriceExpectedGP'] / total_deal_stats['DealQuotePriceGP']\r\n # this section contains optimal price data\r\n total_deal_stats[' Optimal Price Data (Line Item Sum)'] = ''\r\n total_deal_stats['DealOptimalPrice'] = quote_df['OptimalPrice'].sum().round(decimals=0)\r\n total_deal_stats['DealOptimalPriceWinProb'] = ''\r\n total_deal_stats['DealOptimalPriceGP'] = quote_df['OptimalPriceGP'].sum().round(decimals=2)\r\n total_deal_stats['DealOptimalPriceExpectedGP'] = quote_df['OptimalPriceExpectedGP'].sum().round(decimals=2)\r\n total_deal_stats['DealOptimalPriceWinProb'] = total_deal_stats['DealOptimalPriceExpectedGP'] / total_deal_stats['DealOptimalPriceGP']\r\n total_deal_stats['DealOptimalPriceIntervalLow'] = quote_df['OptimalPriceIntervalLow'].sum().round(decimals=0)\r\n total_deal_stats['DealOptimalPriceIntervalHigh'] = quote_df['OptimalPriceIntervalHigh'].sum().round(decimals=0)\r\n # this section contains Quoted Price Data (Bottom-Line)\r\n total_deal_stats[' Quoted Price Data (Bottom-Line)'] = ''\r\n total_deal_stats['DealBotLineQuotePrice'] = total_deal_stats['DealQuotePrice']\r\n total_deal_stats['DealBotLineQuotePriceWinProb'] = BPF.ProbOfWin(total_deal_stats['DealBotLineQuotePrice'], total_deal_stats['DealAdjLowPrice'], total_deal_stats['DealAdjMedPrice'], total_deal_stats['DealAdjHighPrice'])\r\n total_deal_stats['DealBotLineQuotePriceGP'] = total_deal_stats['DealBotLineQuotePrice'] - total_deal_stats['DealTMC']\r\n total_deal_stats['DealBotLineQuotePriceExpectedGP'] = total_deal_stats['DealBotLineQuotePriceGP'] * total_deal_stats['DealBotLineQuotePriceWinProb']\r\n # this section contains Optimal Price Data (Bottom-Line)\r\n total_deal_stats[' Optimal Price Data (Bottom-Line)'] = ''\r\n total_deal_stats['DealBotLineOptimalPrice'] = BPF.OptPrice(total_deal_stats['DealAdjLowPrice'], total_deal_stats['DealAdjMedPrice'], total_deal_stats['DealAdjHighPrice'], total_deal_stats['DealTMC'], 0, 0, 0)\r\n total_deal_stats['DealBotLineOptimalPriceWinProb'] = BPF.ProbOfWin(total_deal_stats['DealBotLineOptimalPrice'], total_deal_stats['DealAdjLowPrice'], total_deal_stats['DealAdjMedPrice'], total_deal_stats['DealAdjHighPrice'])\r\n total_deal_stats['DealBotLineOptimalPriceGP'] = total_deal_stats['DealBotLineOptimalPrice'] - total_deal_stats['DealTMC']\r\n total_deal_stats['DealBotLineOptimalPriceExpectedGP'] = total_deal_stats['DealBotLineOptimalPriceGP'] * total_deal_stats['DealBotLineOptimalPriceWinProb']\r\n total_deal_stats['DealBotLineOptimalPriceIntervalLow'], total_deal_stats['DealBotLineOptimalPriceIntervalHigh'] = BPF.OptPriceConfIntervl(total_deal_stats['DealBotLineOptimalPrice'], total_deal_stats['DealAdjLowPrice'], total_deal_stats['DealAdjMedPrice'], total_deal_stats['DealAdjHighPrice'], total_deal_stats['DealTMC'])\r\n \r\n #this section is executed only if customized optimal pricing (COP) is needed\r\n if (COP_h > COP_m) and (COP_m > COP_l):\r\n # this section contains COP Price Range Data (Line Item Sum)\r\n total_deal_stats[' COP Price Range Data (Line Item Sum)'] = ''\r\n total_deal_stats['DealCOPLowPrice'] = quote_df['COPComLowPrice'].sum().round(decimals=0)\r\n total_deal_stats['DealCOPMedPrice'] = quote_df['COPComMedPrice'].sum().round(decimals=0)\r\n total_deal_stats['DealCOPHighPrice'] = quote_df['COPComHighPrice'].sum().round(decimals=0)\r\n # this section contains COP Quote Price Data (Line Item Sum)\r\n total_deal_stats[' COP Quote Price Data (Line Item Sum)'] = ''\r\n total_deal_stats['DealCOPQuotePrice'] = quote_df['ComQuotePrice'].sum()\r\n total_deal_stats['DealCOPQuotePriceWinProb'] = ''\r\n total_deal_stats['DealCOPQuotePriceGP'] = quote_df['COPQuotePriceGP'].sum().round(decimals=0)\r\n total_deal_stats['DealCOPQuotePriceExpectedGP'] = quote_df['COPQuotePriceExpectedGP'].sum().round(decimals=0)\r\n total_deal_stats['DealCOPQuotePriceWinProb'] = total_deal_stats['DealCOPQuotePriceExpectedGP'] / total_deal_stats['DealCOPQuotePriceGP']\r\n # this section contains COP Optimal Price Data (Line Item Sum)\r\n total_deal_stats[' COP Optimal Price Data (Line Item Sum)'] = ''\r\n total_deal_stats['DealCOPOptimalPrice'] = quote_df['COPOptimalPrice'].sum().round(decimals=0)\r\n total_deal_stats['DealCOPOptimalPriceWinProb'] = ''\r\n total_deal_stats['DealCOPOptimalPriceGP'] = quote_df['COPOptimalPriceGP'].sum().round(decimals=2)\r\n total_deal_stats['DealCOPOptimalPriceExpectedGP'] = quote_df['COPOptimalPriceExpectedGP'].sum().round(decimals=2)\r\n total_deal_stats['DealCOPOptimalPriceWinProb'] = total_deal_stats['DealCOPOptimalPriceExpectedGP'] / total_deal_stats['DealCOPOptimalPriceGP']\r\n total_deal_stats['DealCOPOptimalPriceIntervalLow'] = quote_df['COPOptimalPriceIntervalLow'].sum().round(decimals=0)\r\n total_deal_stats['DealCOPOptimalPriceIntervalHigh'] = quote_df['COPOptimalPriceIntervalHigh'].sum().round(decimals=0)\r\n # this section contains quoted price data within the Customized Optimal Price (COP) estimates\r\n total_deal_stats[' COP Quote Price Data (Bottom-Line)'] = ''\r\n total_deal_stats['DealCOPBotLineQuotePrice'] = total_deal_stats['DealQuotePrice']\r\n total_deal_stats['DealCOPBotLineQuotePriceWinProb'] = BPF.ProbOfWin(total_deal_stats['DealCOPQuotePrice'], total_deal_stats['DealCOPLowPrice'], total_deal_stats['DealCOPMedPrice'], total_deal_stats['DealCOPHighPrice'])\r\n total_deal_stats['DealCOPBotLineQuotePriceGP'] = total_deal_stats['DealCOPBotLineQuotePrice'] - total_deal_stats['DealTMC']\r\n total_deal_stats['DealCOPBotLineQuotePriceExpectedGP'] = total_deal_stats['DealCOPBotLineQuotePriceGP'] *total_deal_stats['DealCOPBotLineQuotePriceWinProb']\r\n # this section contains COP Optimal Price Data (Bottom-Line)\r\n total_deal_stats[' COP Optimal Price Data (Bottom-Line)'] = ''\r\n total_deal_stats['DealCOPBotLineOptimalPrice'] = BPF.OptPrice(total_deal_stats['DealCOPLowPrice'], total_deal_stats['DealCOPMedPrice'], total_deal_stats['DealCOPHighPrice'], total_deal_stats['DealTMC'], 0, 0, 0).round(decimals=0)\r\n total_deal_stats['DealCOPBotLineOptimalPriceWinProb'] = BPF.ProbOfWin(total_deal_stats['DealCOPBotLineOptimalPrice'], total_deal_stats['DealCOPLowPrice'], total_deal_stats['DealCOPMedPrice'], total_deal_stats['DealCOPHighPrice'])\r\n total_deal_stats['DealCOPBotLineOptimalPriceGP'] = total_deal_stats['DealCOPBotLineOptimalPrice'] - total_deal_stats['DealTMC']\r\n total_deal_stats['DealCOPBotLineOptimalPriceExpectedGP'] = total_deal_stats['DealCOPBotLineOptimalPriceGP'] * total_deal_stats['DealCOPBotLineOptimalPriceWinProb']\r\n total_deal_stats['DealCOPBotLineOptimalPriceIntervalLow'], total_deal_stats['DealCOPBotLineOptimalPriceIntervalHigh'] = BPF.OptPriceConfIntervl(total_deal_stats['DealCOPBotLineOptimalPrice'], total_deal_stats['DealCOPLowPrice'], total_deal_stats['DealCOPMedPrice'], total_deal_stats['DealCOPHighPrice'], total_deal_stats['DealTMC'])\r\n \r\n print ' total_deal_stats creation complete'\r\n \r\n #This stores copies of the returned data\r\n quote_df.to_csv(data_path + 'quote_return.csv')\r\n total_deal_stats.to_csv(data_path + 'total_deal_stats_return.csv') \r\n \r\n end_time = time.time() #stop a timer\r\n print 'Elapsed time (milliseconds): ', int(1000 * (end_time - start_time))\r\n \r\n return quote_df, total_deal_stats","def cost_volume_profit():\r\n c = float(input(\"Please Enter Total Fixed Costs Value: \"))\r\n a = float(input(\"Please Enter Sale Price Per Unit: \"))\r\n b = float(input(\"Please Enter Variable Cost Per Unit: \"))\r\n ccm = float(a)-float(b)\r\n cuu = float(c)/float(ccm)\r\n ccmr = (float(ccm)/float(a))*float(100)\r\n cda = float(c)/(float(ccmr)/float(100))\r\n print \">> Your Contribution Margin is\",ccm\r\n print \">> Your Breakeven Sales in Units is\",round(cuu)\r\n print \">> Your Contribution Margin Ratio is\",ccmr,\"%\"\r\n print \">> Your Breakeven Sales in Dollars is\",cda,\"\\n\"\r\n qq = input(\" Press 1 To Compute Target Profit\\n Press 2 To Compute Margin of Safety\\n Press 3 To Perform Sensitivity Analysis\\n Or Press 0 To Exit: \")\r\n if(qq == 1):\r\n dds = float(input(\"Please Enter Your Target Profit: \"))\r\n xxx = (float(c)+float(dds))/float(ccm)\r\n xxxx = (float(c)+float(dds))/(float(ccmr)/float(100))\r\n print \">> Your Target Profit in Units To Earn\",dds,\"$ is\",round(xxx)\r\n print \">> Your Target Profit in Dollars To Earn\",dds,\"$ is\",xxxx\r\n elif(qq == 0):\r\n print \"Canceled\"\r\n elif(qq == 2):\r\n xc = float(input(\"Please Enter Expected Sales in Units: \"))\r\n zzz = float(xc)-float(cuu)\r\n zzzz = float(zzz)*float(a)\r\n print \">> Your Margin of Safety in Units is\",round(zzz)\r\n print \">> Your Margin of Safety in Dollars is\",zzzz\r\n elif(qq == 3):\r\n i = input(\"Please Enter Total Fixed Costs Value: \")\r\n o = input(\"Please Enter Sale Price Per Unit: \")\r\n p = input(\"Please Enter Variable Cost Per Unit: \")\r\n n = 0\r\n for x,y,z in zip(i,o,p):\r\n cm = float(y)-float(z)\r\n uu = float(x)/float(cm)\r\n cmr = (float(cm)/float(y))*float(100)\r\n da = float(x)/(float(cmr)/float(100))\r\n n += 1\r\n print \"Your Results in Case\",int(n),\"is :\"\r\n print \">> Your Contribution Margin is\",cm\r\n print \">> Your Breakeven Sales in Units is\",round(uu)\r\n print \">> Your Contribution Margin Ratio is\",cmr,\"%\"\r\n print \">> Your Breakeven Sales in Dollars is\",da,\"\\n\"\r\n if(cm > ccm):\r\n a = float(cm)-float(ccm)\r\n print \">> Your Contribution Margin Increased by\",a\r\n elif(ccm > cm):\r\n a = float(ccm)-float(cm)\r\n print \">> Your Contribution Margin Decreased by\",a\r\n if(uu > cuu):\r\n b = float(uu)-float(cuu)\r\n print \">> Your Breakeven Sales in Units Increased by\",round(b)\r\n elif(cuu > uu):\r\n b = float(cuu)-float(uu)\r\n print \">> Your Breakeven Sales in Units Decreased by\",round(b)\r\n if(cmr > ccmr):\r\n c = float(cmr)-float(ccmr)\r\n print \">> Your Contribution Margin Ratio Increased by\",c,\"%\"\r\n elif(ccmr > cmr):\r\n c = float(ccmr)-float(cmr)\r\n print \">> Your Contribution Margin Ratio Decreased by\",c,\"%\"\r\n if(da > cda):\r\n d = float(da)-float(cda)\r\n print \">> Your Breakeven Sales in Dollars Increased by\",d\r\n elif(cda > da):\r\n d = float(cda)-float(da)\r\n print \">> Your Breakeven Sales in Dollars Decreased by\",d,\"\\n\"","def calc_price(p_counts, K, r, T, M, option):\n if p_counts.shape[0] == 0: # not possible to exercise any option\n return(0)\n if option == 'Call':\n price = np.mean(p_counts[:,-1]-K)*np.exp(-1*r*T)*(p_counts.shape[0]/M)\n elif option == 'Put':\n price = np.mean(K-p_counts[:,-1])*np.exp(-1*r*T)*(p_counts.shape[0]/M)\n return price","def calculate_and_save(self):\n # Calculate the quotation price\n self.quotationPrice = self.compute_quotation_price()\n self.save()","def do_submit(self, price_float, volume_float):\r\n raise NotImplementedError()","def calc_price(p_counts, K, r, T, M, option):\n if p_counts.shape[0] == 0: # not possible to exercise any option\n return (0)\n if option == 'Call':\n price = np.mean(p_counts[:, -1] - K) * np.exp(-1 * r * T) * (p_counts.shape[0] / M)\n elif option == 'Put':\n price = np.mean(K - p_counts[:, -1]) * np.exp(-1 * r * T) * (p_counts.shape[0] / M)\n else:\n raise KeyError(\"Options Type Input Error. Either Call or Put\")\n return price","def test_basicSalePC(self):\n # Basic price check\n self.log.info(\"Price checking Generic Item via speedkey\")\n pos.click(\"Price Check\")\n pos.click_speed_key(\"Generic Item\")\n \n # Confirm the right item, at the right price\n self.read_price_check(\"Generic Item\", \"$0.01\")\n # Add the item\n pos.click(\"Sell Item\")\n \n # Confirm we added the item\n ret = self.confirm_line(-1, \"Generic Item\", \"$0.01\")\n if ret == True:\n self.log.info(\"Confirmed item added\")\n else:\n self.tc_fail(ret)\n \n # Setup for next test\n self.recover()","def test_price_return(self, basic_factory, garment_factory, affiliate_item_factory):\n basic = basic_factory()\n garment = garment_factory(basic=basic)\n affiliate_item_factory(garment=garment, price=Decimal(100))\n\n budget_end, luxury_start = update_basic_price_points(basic)\n\n assert budget_end == Decimal(100)\n assert luxury_start == Decimal(100)","def price(usr, item, searches = 2, method = \"AVERAGE\", deduct = 0):\n if not method in ShopWizard.methods: raise invalidMethod()\n\n if isinstance(item, Item):\n item = item.name\n\n prices = []\n dets = {}\n for x in range(0, searches):\n print('performing search: {}'.format(x))\n results = ShopWizard.search(usr, item)\n\n # Set to -1 if not found\n if not results:\n prices.append(-1)\n continue\n\n prices.append(int(results[item].price))\n dets[str(results[item].price)] = (results[item].owner, results[item].id)\n\n time.sleep(ShopWizard.waitTime)\n\n # Determines if item was UB\n print(prices)\n if sum(prices) == len(prices) * -1:\n return False\n\n prices = list(filter(lambda x: x != -1, prices))\n\n if method == ShopWizard.RETLOW:\n price = sorted(prices)[0]\n return (price, dets[str(price)][0], dets[str(price)][1])\n\n return ShopWizard.__determinePrice(prices, method, deduct)","def bond_price(fv, c,n,m,r):\n return sum([a*b for a,b in zip(discount_factors(r,n,m),bond_cashflows(fv, c, n, m))])","def calculate(self, order):\n pass","def calculations():\r\n\t\r\n\tpayload, avionics, booster = weight_input()\r\n\r\n\tdrogue_size, drogue_force = drogue_calc()\r\n\tmain_size, main_force = main_calc(avionics, booster, drogue_force) #total mass, payload detaches\r\n\r\n\tprint(\"Drogue is diameter is \" + str(drogue_size) + \" inches\")\r\n\tprint(\"Main is diameter is \" + str(main_size) + \" inches\")","def main():\n tte, url_pt2 = get_tte(expiration)\n url2 = url + '&date=' + url_pt2\n dfs = pd.read_html(url2)\n\n call_df = dfs[0]\n put_df = dfs[1]\n \n \n \"\"\"Should have created an Option class here. Like:\n class Option:\n def __init__(self,type, spot, strike, time, rate, div, vol, vol_guess,price):\n self.type = type\n self.spot = spot\n .....\n def theo(self):\n return Theo(type, spot, strike,time, rate, div, vol)\n def delta(self):\n return Delta(type, spot, strike,time, rate, div, vol)\n ...\n \n \"\"\"\n \n \n #instead, a bunch of lists: \n call_theos = []\n put_theos = []\n call_vols= []\n put_vols= []\n\n call_deltas = []\n call_gammas = []\n call_thetas = []\n call_vegas = []\n put_deltas = []\n put_gammas = []\n put_thetas = []\n put_vegas = []\n\n call_imp_vols =[]\n put_imp_vols = []\n\n spot = get_spot(spot_url)\n print('Spot:', spot)\n vol = get_vol(vol_url,20)\n print('20day Vol:' ,vol)\n rate = interest_rate(tte)\n div = .0\n\n #Don't really care about options going for pennies\n call_df = call_df.ix[call_df['Volume'] >=100].ix[call_df['Last Price'] >= .10]\n put_df = put_df.ix[put_df['Volume'] >= 100].ix[put_df['Last Price'] >= .10]\n\n\n for index, row in call_df.iterrows():\n call_vol = row['Implied Volatility'][:-1]\n call_vols.append(float(call_vol))\n\n\n for index, row in put_df.iterrows():\n put_vol = row['Implied Volatility'][:-1]\n put_vols.append(float(put_vol))\n\n\n # extremely crude method for increasing put vol to help account for skew\n call_sd = statistics.stdev(call_vols)\n call_mean = sum(call_vols)/len(call_vols)\n call_vols = [vol for vol in call_vols if abs(vol-call_mean) <= 3 * call_sd]\n call_vol_factor = sum(call_vols)/len(call_vols) / 100.0\n\n put_sd = statistics.stdev(put_vols)\n put_mean = sum(put_vols)/len(put_vols)\n put_vols = [vol for vol in put_vols if abs(vol-put_mean) <= 1 * put_sd]\n put_vol_factor = sum(put_vols)/len(put_vols) / 100.0\n\n call_vol = vol\n put_vol = vol * (put_vol_factor/call_vol_factor)\n\n for index, row in call_df.iterrows():\n strike = float(row['Strike'])\n time = tte\n call_theos.append(Theo('C', spot, strike, time, rate, div, call_vol))\n call_deltas.append(Delta('C', spot, strike, time, rate, div, call_vol))\n call_gammas.append(Gamma(spot, strike, time, rate, div, call_vol))\n call_thetas.append(Theta('C', spot, strike, time, rate, div, call_vol))\n call_vegas.append(Vega(spot, strike, time, rate, div, call_vol))\n\n\n midpoint = (float(row['Bid']) + float(row['Ask']))/2.0\n call_imp_vols.append(ImpliedVol('C', spot,strike,time,rate,div,midpoint, call_vol))\n\n for index, row in put_df.iterrows():\n strike = float(row['Strike'])\n time = tte\n put_theos.append(Theo('P', spot, strike, time, rate, div, put_vol))\n put_deltas.append(Delta('P', spot, strike, time, rate, div, put_vol))\n put_gammas.append(Gamma(spot, strike, time, rate, div, put_vol))\n put_thetas.append(Theta('P', spot, strike, time, rate, div, put_vol))\n put_vegas.append(Vega(spot, strike, time, rate, div, put_vol))\n\n midpoint = (float(row['Bid']) + float(row['Ask'])) / 2.0\n put_imp_vols.append(ImpliedVol('P', spot, strike, time, rate, div, midpoint, put_vol))\n\n\n call_df['Theo'] = call_theos\n call_df['Delta'] = call_deltas\n call_df['Gamma'] = call_gammas\n call_df['Theta'] = call_thetas\n call_df['Vega'] = call_vegas\n\n put_df['Theo'] = put_theos\n put_df['Delta'] = put_deltas\n put_df['Gamma'] = put_gammas\n put_df['Theta'] = put_thetas\n put_df['Vega'] = put_vegas\n\n call_imp_vols = [str(i*100)[0:5] + '%' for i in call_imp_vols]\n put_imp_vols = [str(i*100)[0:5] + '%' for i in put_imp_vols]\n\n call_df['Calc Imp Vol'] =call_imp_vols\n put_df['Calc Imp Vol'] = put_imp_vols\n\n\n #Message alert for large discrepancies between theo and midpoint of bid/ask\n default = sec+ ' Alert ' + expiration + '\\n'\n alertstring = default\n for index, row in call_df.iterrows():\n\n midpoint = (float(row['Ask']) + float(row['Bid']))/2.0\n if abs(row['Theo'] - midpoint ) >= 1.0:\n\n edge = str(abs(row['Theo'] - midpoint))\n if row['Theo'] > midpoint:\n updown = 'under'\n else:\n updown = 'over'\n\n\n alertstring += str(int(row['Strike'])) + 'C ' + edge[0:4] + ' ' + updown + '\\n'\n\n for index, row in put_df.iterrows():\n\n midpoint = (float(row['Ask']) + float(row['Bid'])) / 2.0\n if abs(row['Theo'] - midpoint) >= 1.0:\n\n edge = str(abs(row['Theo'] - midpoint))\n if row['Theo'] > midpoint:\n updown = 'under'\n else:\n updown = 'over'\n\n alertstring += str(int(row['Strike'])) + 'P ' + edge[0:4] + ' ' + updown + '\\n'\n if alertstring != default:\n send_message(alertstring)\n\n plotVolSeries(sec)\n plotImpVol(call_df,put_df,spot, sec)\n writer = pd.ExcelWriter(sec+'_options_' + expiration +'.xlsx')\n call_df.to_excel(writer, 'Calls')\n put_df.to_excel(writer, 'Puts')\n writer.save()","def option_price(volatilities,\n strikes,\n expiries,\n spots=None,\n forwards=None,\n discount_factors=None,\n is_call_options=None,\n dtype=None,\n name=None):\n if (spots is None) == (forwards is None):\n raise ValueError('Either spots or forwards must be supplied but not both.')\n\n with tf.compat.v1.name_scope(\n name,\n default_name='option_price',\n values=[\n forwards, strikes, volatilities, expiries, discount_factors,\n is_call_options\n ]):\n strikes = tf.convert_to_tensor(strikes, dtype=dtype, name='strikes')\n volatilities = tf.convert_to_tensor(\n volatilities, dtype=dtype, name='volatilities')\n expiries = tf.convert_to_tensor(expiries, dtype=dtype, name='expiries')\n\n if discount_factors is None:\n discount_factors = tf.convert_to_tensor(\n 1.0, dtype=dtype, name='discount_factors')\n else:\n discount_factors = tf.convert_to_tensor(\n discount_factors, dtype=dtype, name='discount_factors')\n\n if forwards is not None:\n forwards = tf.convert_to_tensor(forwards, dtype=dtype, name='forwards')\n else:\n spots = tf.convert_to_tensor(spots, dtype=dtype, name='spots')\n forwards = spots / discount_factors\n\n sqrt_var = volatilities * tf.math.sqrt(expiries)\n d1 = (tf.math.log(forwards / strikes) + sqrt_var * sqrt_var / 2) / sqrt_var\n d2 = d1 - sqrt_var\n undiscounted_calls = forwards * _ncdf(d1) - strikes * _ncdf(d2)\n if is_call_options is None:\n return discount_factors * undiscounted_calls\n undiscounted_forward = forwards - strikes\n undiscounted_puts = undiscounted_calls - undiscounted_forward\n predicate = tf.broadcast_to(is_call_options, tf.shape(undiscounted_calls))\n return discount_factors * tf.where(predicate, undiscounted_calls,\n undiscounted_puts)","def _compute_amount(self):\n raise NotImplementedError()","def _cost(self, action):\n raise NotImplementedError","def compare_price(self):\n if self.__product_price < self.__alert_price:\n #print(\"price drop...\")\n self.__alert_client = True\n self.__price_difference = self.__product_price - self.__alert_price\n else:\n #print(\"Price not reduced...\")\n self.__alert_client = False\n self.__price_difference = self.__product_price - self.__alert_price","def cvp():\r\n c = float(input(\"Please Enter Total Fixed Costs Value: \"))\r\n a = float(input(\"Please Enter Sale Price Per Unit: \"))\r\n b = float(input(\"Please Enter Variable Cost Per Unit: \"))\r\n ccm = float(a)-float(b)\r\n cuu = float(c)/float(ccm)\r\n ccmr = (float(ccm)/float(a))*float(100)\r\n cda = float(c)/(float(ccmr)/float(100))\r\n print \">> Your Contribution Margin is\",ccm\r\n print \">> Your Breakeven Sales in Units is\",round(cuu)\r\n print \">> Your Contribution Margin Ratio is\",ccmr,\"%\"\r\n print \">> Your Breakeven Sales in Dollars is\",cda,\"\\n\"\r\n qq = input(\" Press 1 To Compute Target Profit\\n Press 2 To Compute Margin of Safety\\n Press 3 To Perform Sensitivity Analysis\\n Or Press 0 To Exit: \")\r\n if(qq == 1):\r\n dds = float(input(\"Please Enter Your Target Profit: \"))\r\n xxx = (float(c)+float(dds))/float(ccm)\r\n xxxx = (float(c)+float(dds))/(float(ccmr)/float(100))\r\n print \">> Your Target Profit in Units To Earn\",dds,\"$ is\",round(xxx)\r\n print \">> Your Target Profit in Dollars To Earn\",dds,\"$ is\",xxxx\r\n elif(qq == 0):\r\n print \"Canceled\"\r\n elif(qq == 2):\r\n xc = float(input(\"Please Enter Expected Sales in Units: \"))\r\n zzz = float(xc)-float(cuu)\r\n zzzz = float(zzz)*float(a)\r\n print \">> Your Margin of Safety in Units is\",round(zzz)\r\n print \">> Your Margin of Safety in Dollars is\",zzzz\r\n elif(qq == 3):\r\n i = input(\"Please Enter Total Fixed Costs Value: \")\r\n o = input(\"Please Enter Sale Price Per Unit: \")\r\n p = input(\"Please Enter Variable Cost Per Unit: \")\r\n n = 0\r\n for x,y,z in zip(i,o,p):\r\n cm = float(y)-float(z)\r\n uu = float(x)/float(cm)\r\n cmr = (float(cm)/float(y))*float(100)\r\n da = float(x)/(float(cmr)/float(100))\r\n n += 1\r\n print \"Your Results in Case\",int(n),\"is :\"\r\n print \">> Your Contribution Margin is\",cm\r\n print \">> Your Breakeven Sales in Units is\",round(uu)\r\n print \">> Your Contribution Margin Ratio is\",cmr,\"%\"\r\n print \">> Your Breakeven Sales in Dollars is\",da,\"\\n\"\r\n if(cm > ccm):\r\n a = float(cm)-float(ccm)\r\n print \">> Your Contribution Margin Increased by\",a\r\n elif(ccm > cm):\r\n a = float(ccm)-float(cm)\r\n print \">> Your Contribution Margin Decreased by\",a\r\n if(uu > cuu):\r\n b = float(uu)-float(cuu)\r\n print \">> Your Breakeven Sales in Units Increased by\",round(b)\r\n elif(cuu > uu):\r\n b = float(cuu)-float(uu)\r\n print \">> Your Breakeven Sales in Units Decreased by\",round(b)\r\n if(cmr > ccmr):\r\n c = float(cmr)-float(ccmr)\r\n print \">> Your Contribution Margin Ratio Increased by\",c,\"%\"\r\n elif(ccmr > cmr):\r\n c = float(ccmr)-float(cmr)\r\n print \">> Your Contribution Margin Ratio Decreased by\",c,\"%\"\r\n if(da > cda):\r\n d = float(da)-float(cda)\r\n print \">> Your Breakeven Sales in Dollars Increased by\",d\r\n elif(cda > da):\r\n d = float(cda)-float(da)\r\n print \">> Your Breakeven Sales in Dollars Decreased by\",d,\"\\n\"","def compute_quotation_price(self):\n result = decimal.Decimal('0')\n if self.vehiculePrice:\n result = self.vehiculePrice * 2 / 100\n if self.covWind:\n result += get_coverage_price_by_name(\"WIND\")\n if self.covPass:\n result += get_coverage_price_by_name(\"PASS\")\n if self.covFlood:\n result += get_coverage_price_by_name(\"FLOOD\")\n return result","def _calculate(self):\n raise NotYetImplemented()","def get_the_sum_of_prices(item_ids):\n\n # your code","def __call__(self, auctioneer):\n curr_bid = auctioneer.current_bid\n bid_price = curr_bid * self._bid_increase_perc\n if bid_price <= self._budget and self.get_bid_probability() > 0.3:\n self._highest_bid = bid_price\n return bid_price\n return 0","def calculate_price(self, amount: float, action: str, price:float, contract:str):\n def get_ticked_price(amount, action, price):\n if contract not in self.tick_information:\n return price\n if (action == 'Close'):\n if amount > 0:\n return (price - self.tick_information[contract] * self.tick) * (1-self.fee)\n else:\n return (price + self.tick_information[contract] * self.tick) * (1+self.fee)\n elif action == 'Long':\n return (price + self.tick_information[contract] * self.tick) * (1+self.fee)\n elif action == 'Short':\n return (price - self.tick_information[contract] * self.tick) * (1-self.fee)\n return get_ticked_price(amount, action, price)","def calc(self):\n return None","def precalculate():\n pass","def precalculate():\n pass","def test_market_1_2(self):\n\n def check_1_2(buyers: List[float], sellers: List[float], expected_num_of_deals: int,\n expected_prices: List[float]):\n market = Market([\n AgentCategory(\"buyer\", buyers),\n AgentCategory(\"seller\", sellers),\n ])\n ps_recipe = [1, 2]\n self._check_market(market, ps_recipe, expected_num_of_deals, expected_prices)\n\n check_1_2(buyers=[9], sellers=[-4, -3],\n expected_num_of_deals=0, expected_prices=[9, -4.5])\n check_1_2(buyers=[9, 8, 7, 6], sellers=[-6, -5, -4, -3, -2, -1],\n expected_num_of_deals=1, expected_prices=[8, -4])\n check_1_2(buyers=[9, 8], sellers=[-4, -3, -2, -1],\n expected_num_of_deals=1, expected_prices=[8, -4])\n check_1_2(buyers=[9, 8], sellers=[-6, -3, -2, -1],\n expected_num_of_deals=1, expected_prices=[8, -4])\n check_1_2(buyers=[9, 8], sellers=[-4, -3, -2, -1],\n expected_num_of_deals=1, expected_prices=[8, -4])\n\n # PRICE CROSSES ZERO AT FIRST PHASE\n check_1_2(buyers=list(range(20)), sellers=[-3, -2, -1],\n expected_num_of_deals=1, expected_prices=[18, -9])","def calculate_event_price_and_parameters(self, event_id):\n try:\n price = 0\n proffesional_staff = 0\n middle_staff = 0\n new_staff = 0\n\n req_id, _, client_id, title, location, date_starts, date_ends, guests, event_type_id, event_class_id, _ = self.db_handler.get_event_request_extended_info_by_id(event_id)\n _, event_class_name, guests_per_waiter = self.db_handler.get_event_class_by_id(event_class_id)\n\n distance_in_km = self.get_geopy_distance(location)\n price = distance_in_km * float(self.get_config_value('PRICE_OF_KM'))\n num_of_staff = math.ceil(guests / guests_per_waiter)\n\n if event_class_name == 'Найвищий': #85% of pro\n new_staff = 0\n proffesional_staff = math.ceil(num_of_staff * 0.85)\n middle_staff = num_of_staff - proffesional_staff\n elif event_class_name == 'Високий': #75% of pro\n new_staff = 0\n proffesional_staff = math.ceil(num_of_staff * 0.75)\n middle_staff = num_of_staff - proffesional_staff\n elif event_class_name == 'Середній':\n new_staff = round(num_of_staff * 0.2)\n middle_staff = num_of_staff - new_staff\n elif event_class_name == 'Початковий':\n middle_staff = math.ceil(num_of_staff * 0.3)\n new_staff = round(num_of_staff - middle_staff)\n\n hours_of_shift = (date_ends - date_starts).seconds / 3600\n\n price += proffesional_staff * float(self.get_config_value('PRO_RATE')) * hours_of_shift\n price += middle_staff * float(self.get_config_value('MID_RATE')) * hours_of_shift\n price += new_staff * float(self.get_config_value('NEW_RATE')) * hours_of_shift\n\n price += price * 0.4\n\n return (price,proffesional_staff,middle_staff,new_staff)\n except Exception as err:\n method_name = sys._getframe().f_code.co_name\n\n self.logger.write_to_log('exception', 'model')\n self.logger.write_to_err_log(f'exception in method {method_name} - {err}', 'model')","def cost(self) -> float:","def add_evidence( \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t \t \t\t \t\t \t\n self, \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t \t \t\t \t\t \t\n symbol=\"IBM\", \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t \t \t\t \t\t \t\n sd=dt.datetime(2008, 1, 1), \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t \t \t\t \t\t \t\n ed=dt.datetime(2009, 1, 1), \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t \t \t\t \t\t \t\n sv=10000, \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t \t \t\t \t\t \t\n ): \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t \t \t\t \t\t \t\n \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t \t \t\t \t\t \t\n # add your code to do learning here\n\n converged = False\n x = np.zeros((3,1))\n dates = pd.date_range(sd,ed)\n df_prices = ind.get_price(symbol, dates)\n\n daily_rets = (df_prices / df_prices.shift(1)) - 1\n daily_rets = daily_rets[1:]\n\n\n sd_older = sd - dt.timedelta(days=365)\n dates_older = pd.date_range(sd_older,ed)\n df_prices_older = ind.get_price(symbol, dates_older)\n sd_key = df_prices.index[0]\n sd_index = df_prices_older.index.get_loc(sd_key)\n\n num_bins = len(self.bins)\n max_state_idx = num_bins + num_bins*10 + num_bins*100\n\n # Call Q-Learner Constructor\n self.learner = QLearner( \n num_states=(max_state_idx + 1), \n num_actions=3, \n alpha=0.01, \n gamma=0.0, \n rar=0.98, \n radr=0.9995, \n dyna=0, \n verbose=False, \n )\n\n # df_trades = df_prices.copy()\n df_holdings = df_prices.copy()\n df_holdings['Holdings'] = np.nan\n del df_holdings[symbol] # print(df_holdings)\n\n # Initlialize Vars\n cum_ret_prev = 0\n iters = 0\n conv_counter = 0\n Q_prev = np.copy(self.learner.Q)\n\n # Get Indicator Values\n _,_,ind1 = ind.get_BB(df_prices_older, self.lookback)\n ind2 = ind.get_CCI(df_prices_older, self.lookback)\n _,_,ind3 = ind.get_SMA_Cross(self.lookback, 100, df_prices_older)\n ind4 = ind.get_momentum(df_prices_older, self.lookback)\n _,_,ind5 = ind.get_MACD(df_prices_older)\n BB = ind1.iloc[sd_index:].values\n CCI = ind2.iloc[sd_index:].values\n SMA_Cross = ind3.iloc[sd_index:].values\n Momentum = ind4.iloc[sd_index:].values\n MACD = ind5.iloc[sd_index:].values\n _,self.x0bins = pd.qcut(BB[:,0], num_bins,labels=False,retbins=True)\n _,self.x1bins = pd.qcut(CCI[:,0],num_bins,labels=False,retbins=True)\n _,self.x2bins = pd.qcut(SMA_Cross[:,0],num_bins,labels=False,retbins=True)\n _,self.x3bins = pd.qcut(Momentum[:,0],num_bins,labels=False,retbins=True)\n _,self.x4bins = pd.qcut(MACD[:,0],num_bins,labels=False,retbins=True)\n x_0 = np.digitize(BB[:,0], self.x0bins[1:-1])\n x_1 = np.digitize(CCI[:,0], self.x1bins[1:-1])\n x_2 = np.digitize(SMA_Cross[:,0], self.x2bins[1:-1])\n x_3 = np.digitize(Momentum[:,0], self.x3bins[1:-1])\n x_4 = np.digitize(MACD[:,0], self.x4bins[1:-1])\n state = x_0 + x_3*10 + x_4*100\n\n \n while not converged:\n\n action = self.learner.querysetstate(state[0])\n daily_return = daily_rets.iloc[0][symbol]\n cur_price = df_prices.iloc[0][symbol]\n next_price = df_prices.iloc[1][symbol]\n df_holdings.iloc[0]['Holdings'], reward = self.take_action(0, action, cur_price, next_price)\n\n\n\n for day_idx in range(1,daily_rets.shape[0]):\n\n\n daily_return = daily_rets.iloc[day_idx][symbol]\n cur_price = df_prices.iloc[day_idx-1][symbol]\n next_price = df_prices.iloc[day_idx][symbol]\n df_holdings.iloc[day_idx]['Holdings'], reward = self.take_action(df_holdings.iloc[day_idx-1]['Holdings'], action, cur_price, next_price)\n action = self.learner.query(state[day_idx], reward)\n\n df_holdings.iloc[-1]['Holdings'] = 0\n df_trades = df_holdings.diff()\n df_trades['Trades'] = df_trades['Holdings']\n del df_trades['Holdings']\n df_trades.iloc[0]['Trades'] = 0\n\n\n portvals = msc.compute_portvals( \n df_trades,\n symbol, \n sv, \n self.commission, \n self.impact, \n )\n\n cum_ret = (portvals[-1] / portvals[0]) - 1\n Q_diff = np.abs(self.learner.Q - Q_prev)\n Q_max_diff = Q_diff.max()\n\n if iters > 20:\n\n # if abs(cum_ret - cum_ret_prev) < 0.0001:\n if Q_max_diff < 0.001:\n conv_counter += 1\n else:\n conv_counter = 0\n\n if conv_counter > 5 or iters > 20000:\n converged = True\n # if iters > 100:\n # if iters % 100 == 0:\n # print(\"Iteration #\", iters)\n print(\"----------------------------------------------\")\n print(\"-- --\")\n print(\"Iteration #\", iters)\n print(\"Error = \", abs(cum_ret - cum_ret_prev))\n print(\"Q Diff: \", Q_max_diff)\n print(\"Epsilon: \", self.learner.rar)\n\n cum_ret_prev = cum_ret\n Q_prev = np.copy(self.learner.Q)\n iters += 1\n self.learner.rar *= self.learner.radr\n # print(\"Iters = \", iters)\n print(\"Mode Trained in \", iters, \" iterations!\")\n np.savetxt('Q_Table.csv', self.learner.Q, delimiter=',')","def compute_amount_discounted(promotion, amount):\n if promotion.promo_type == '1': # % off\n amount_discounted = promotion.promo_amount * amount / Decimal(100)\n amount_discounted = Decimal(str(round(amount_discounted, 2)))\n elif promotion.promo_type == '2': # $ off\n if promotion.promo_amount < amount:\n amount_discounted = promotion.promo_amount\n else:\n amount_discounted = amount\n elif promotion.promo_type == '3': # fixed $ cost\n if promotion.promo_amount < amount:\n amount_discounted = amount - promotion.promo_amount\n else:\n # If you have a fixed cost promo of $20, but your items \n # only cost $10, you don't save.\n amount_discounted = 0\n LOG.debug('compute discount: amount_discounted = %s' % amount_discounted)\n return amount_discounted","def dblcal(self, lowstrike, highstrike, expiry1, expiry2):\n assert lowstrike < highstrike\n assert pd.Timestamp(expiry1) < pd.Timestamp(expiry2)\n _rows1 = {}\n _rows2 = {}\n _prices1 = {}\n _prices2 = {}\n _index = ['Near Call', 'Far Call', 'Call Ratio', 'Near Put', 'Far Put', \n 'Put Ratio', 'Near to Far Ratio', 'Debit', 'Underlying_Price', 'Quote_Time']\n _metrics = pd.DataFrame(index=_index, columns=['Value'])\n _errmsg = \"No key for {} strike {} {}\"\n _opttype = 'call'\n _rows1[_opttype] = _relevant_rows(self.data, (highstrike, expiry1, _opttype),\n _errmsg.format(expiry1, highstrike, _opttype))\n _prices1[_opttype] = _getprice(_rows1[_opttype])\n _rows2[_opttype] = _relevant_rows(self.data, (highstrike, expiry2, _opttype),\n _errmsg.format(expiry2, highstrike, _opttype))\n _prices2[_opttype] = _getprice(_rows2[_opttype])\n _metrics.loc['Near Call', 'Value'] = _prices1[_opttype]\n _metrics.loc['Far Call', 'Value'] = _prices2[_opttype]\n _metrics.loc['Call Ratio', 'Value'] = _prices1[_opttype] / _prices2[_opttype]\n _metrics.loc['Underlying_Price', 'Value'], _metrics.loc['Quote_Time', 'Value'] =\\\n _getkeys(_rows1[_opttype], ['Underlying_Price', 'Quote_Time'])\n _opttype = 'put'\n _rows1[_opttype] = _relevant_rows(self.data, (lowstrike, expiry1, _opttype),\n _errmsg.format(expiry1, lowstrike, _opttype))\n _prices1[_opttype] = _getprice(_rows1[_opttype])\n _rows2[_opttype] = _relevant_rows(self.data, (lowstrike, expiry2, _opttype),\n _errmsg.format(expiry2, lowstrike, _opttype))\n _prices2[_opttype] = _getprice(_rows2[_opttype])\n _metrics.loc['Near Put', 'Value'] = _prices1[_opttype]\n _metrics.loc['Far Put', 'Value'] = _prices2[_opttype]\n _metrics.loc['Put Ratio', 'Value'] = _prices1[_opttype] / _prices2[_opttype]\n _neartot = sum(_prices1.values())\n _fartot = sum(_prices2.values())\n _metrics.loc['Near to Far Ratio', 'Value'] = float(_neartot) / _fartot\n _metrics.loc['Debit', 'Value'] = _fartot - _neartot\n return _metrics","def bulk_item(order: Order) -> Decimal:\n discount = Decimal(0)\n for item in order.cart:\n if item.quantity >= 20:\n discount += item.total() * Decimal('0.1')\n return discount","def calculate(self) -> float:","def _calc_return(self, order_original, perf_df):\r\n\r\n order = order_original.copy()\r\n no_sec = len(self.perf_data)\r\n price_names = np.array(['price_' + str(i) for i in xrange(1, no_sec + 1)])\r\n ret = np.zeros((np.shape(order)[0], no_sec))\r\n\r\n transaction_cost = 0\r\n\r\n # buy_list vs sell_list contains order bought vs sold that cannot be matched yet to determine the return\r\n # For example when something has been bought, but nothing or not enough has been sold yet, the residue will be\r\n # listed in these lists.\r\n buy_shares = np.zeros((np.shape(order)[0], no_sec))\r\n buy_price = np.zeros((np.shape(order)[0], no_sec))\r\n sell_shares = np.zeros((np.shape(order)[0], no_sec))\r\n sell_price = np.zeros((np.shape(order)[0], no_sec))\r\n\r\n # bl_first vs sl_first indicates which row in buy_list vs sell_list can be used to \"match\" bought/sold shares.\r\n # It automatically points to the oldest row with still outstanding shares. Initial value is -1\r\n # bl_last vs sl_last indicates which row in buy_list vs sell_list can be used to write outstanding shares to.\r\n bl_first = np.ones(no_sec).astype(int) * -1\r\n bl_last = np.zeros(no_sec).astype(int)\r\n sl_first = np.ones(no_sec).astype(int) * -1\r\n sl_last = np.zeros(no_sec).astype(int)\r\n\r\n for ind in range(0, np.shape(order)[0]):\r\n bl_first[(bl_first == -1) & (bl_last > 0)] = 0\r\n sl_first[(sl_first == -1) & (sl_last > 0)] = 0\r\n\r\n # Three situations, per type: buy, sell, nothing\r\n # If nothing, skip to next day\r\n # Only returns made on one day are determined, later they will be accumulated.\r\n\r\n # Situation A.A: Sell order & outstanding buys larger than sell order\r\n col_to_change = (order[ind, :] < 0) & (np.sum(buy_shares, 0) > -order[ind, :])\r\n if sum(col_to_change) != 0:\r\n share_cumsum = np.cumsum(buy_shares, 0)\r\n share_compl = (share_cumsum < -order[ind, :]) & col_to_change\r\n numb_shares = sum(buy_shares * share_compl, 0)[col_to_change]\r\n ret[ind, col_to_change] += numb_shares * perf_df.loc[ind, price_names[col_to_change]] \\\r\n - sum(buy_shares * buy_price * share_compl, 0)[col_to_change]\r\n buy_shares[share_compl] = 0\r\n bl_first += sum(share_compl)\r\n order[col_to_change] += numb_shares\r\n\r\n ret[ind, col_to_change] += perf_df.loc[ind, price_names[col_to_change]] * -order[ind, col_to_change] * (1 - transaction_cost) \\\r\n - buy_price[bl_first[col_to_change], col_to_change] \\\r\n * -order[ind, col_to_change] * (1 + transaction_cost)\r\n buy_shares[bl_first[col_to_change], col_to_change] += order[ind, col_to_change]\r\n order[ind, col_to_change] = 0\r\n\r\n # Situation A.B: Sell order & outstanding buys smaller than or equal to sell order\r\n # --> just fill out all outstanding buys, and change order. This order will be added to sell list in A.C\r\n col_to_change = (order[ind, :] < 0) & (np.sum(buy_shares, 0) > 0) \\\r\n & (np.sum(buy_shares, 0) <= -order[ind, :])\r\n if sum(col_to_change) != 0:\r\n numb_shares = buy_shares[:, col_to_change]\r\n price_shares = buy_price[:, col_to_change]\r\n ret[ind, col_to_change] += np.sum(numb_shares, 0) * \\\r\n perf_df.loc[ind, price_names[col_to_change]].values * (1 - transaction_cost) \\\r\n - np.sum(numb_shares * price_shares, 0) * (1 + transaction_cost)\r\n order[ind, col_to_change] += np.sum(numb_shares, 0)\r\n buy_shares[:, col_to_change] = 0\r\n bl_first[col_to_change] = bl_last[col_to_change] - 1\r\n\r\n # Situation A.C: Sell order & no outstanding buys\r\n col_to_change = (order[ind, :] < 0) & (np.sum(buy_shares, 0) == 0)\r\n if sum(col_to_change) != 0:\r\n row_to_change = bl_last[col_to_change]\r\n sell_shares[row_to_change, col_to_change] = -order[ind, col_to_change]\r\n sell_price[row_to_change, col_to_change] = perf_df.loc[ind, price_names[col_to_change]]\r\n sl_last[col_to_change] += 1\r\n\r\n # Situation B.A: Buy order & outstanding sells larger than buy order\r\n col_to_change = (order[ind, :] > 0) & (np.sum(sell_shares, 0) > order[ind, :])\r\n if sum(col_to_change) != 0:\r\n share_cumsum = np.cumsum(sell_shares, 0)\r\n share_compl = (share_cumsum < order[ind, :]) & col_to_change\r\n numb_shares = sum(sell_shares * share_compl, 0)[col_to_change]\r\n ret[ind, col_to_change] += sum(sell_shares * sell_price * share_compl, 0)[col_to_change] * (1 - transaction_cost)\\\r\n - numb_shares * perf_df.loc[ind, price_names[col_to_change]] * (1 + transaction_cost)\r\n sell_shares[share_compl] = 0\r\n sl_first += sum(share_compl)\r\n order[col_to_change] += -numb_shares\r\n\r\n ret[ind, col_to_change] += sell_price[sl_first[col_to_change], col_to_change] * order[ind, col_to_change] * (1 - transaction_cost)\\\r\n - perf_df.loc[ind, price_names[col_to_change]] * order[ind, col_to_change] * (1 + transaction_cost)\r\n sell_shares[sl_first[col_to_change], col_to_change] += -order[ind, col_to_change]\r\n order[ind, col_to_change] = 0\r\n\r\n # Situation B.B: Buy order & outstanding sells smaller than buy order\r\n # --> just fill out all outstanding sells, and change order. This order will be added to buy list in B.C\r\n col_to_change = (order[ind, :] > 0) & \\\r\n (np.sum(sell_shares, 0) > 0) & (np.sum(sell_shares, 0) <= order[ind, :])\r\n if sum(col_to_change) != 0:\r\n numb_shares = sell_shares[:, col_to_change]\r\n price_shares = sell_price[:, col_to_change]\r\n ret[ind, col_to_change] += np.sum(numb_shares * price_shares, 0) * (1 - transaction_cost) \\\r\n - np.sum(numb_shares, 0) * perf_df.loc[ind, price_names[col_to_change]] * (1 + transaction_cost)\r\n order[ind, col_to_change] += -np.sum(numb_shares, 0)\r\n sell_shares[:, col_to_change] = 0\r\n sl_first[col_to_change] = sl_last[col_to_change] - 1\r\n\r\n # Situation B.C: Buy order & no outstanding sells\r\n col_to_change = (order[ind, :] > 0) & (np.sum(sell_shares, 0) == 0)\r\n if sum(col_to_change) != 0:\r\n row_to_change = bl_last[col_to_change]\r\n buy_shares[row_to_change, col_to_change] = order[ind, col_to_change]\r\n buy_price[row_to_change, col_to_change] = perf_df.loc[ind, price_names[col_to_change]]\r\n bl_last[col_to_change] += 1\r\n\r\n ret_abs = np.array([sum(ret[:r]) for r in range(1, len(ret) + 1)])\r\n returns_abs = np.sum(ret_abs, 1)\r\n returns_rel = [i / self.context['max_notional'] + 1 for i in returns_abs]\r\n\r\n return returns_rel, returns_abs, ret_abs","def main():\r\n # Create an instance of the MyCallCostCalc class.\r\n my_callcost = MyCallCostCalc()","def get_prices(self):\n pass","def solve_prices(self):\n return None","def compute_total(price):\n\n quantity = 20\n return price * quantity","def compute(self, *args):\n\n pass","def main(price, service, vat):\n service = (price * 10)/100\n if service < 50:\n service = 50\n elif service > 1000:\n service = 1000\n price += service\n vat = (price * 7)/100\n price += vat\n print(\"%.2f\" % (price))","def check_for_offer(self, bid, commodity, limit, actual, quantity, price):\n if bid:\n if len(self.trades[\"buys\"][commodity]) == 0:\n return 0\n else: # tally up how much trying to buy.\n total = 0.0\n total_price = 0.0\n for offer in self.trades[\"buys\"][commodity]:\n total += offer.quantity\n total_price += offer.price\n\n avg_price = total_price / len(self.trades[\"buys\"][commodity])\n\n # if total < limit:\n # #PLACE MORE BIDS.\n return total\n\n else:\n if len(self.trades[\"asks\"][commodity]) == 0:\n return 0\n else: # tally up how much trying to buy.\n total = 0.0\n total_price = 0.0\n for offer in self.trades[\"asks\"][commodity]:\n total += offer.quantity\n total_price += offer.price\n\n avg_price = total_price / len(self.trades[\"asks\"][commodity])\n #\n # if total < limit:\n # #PLACE MORE asks.\n # return total\n # if total < limit:\n # #PLACE MORE asks.\n return total # - limit","def do_work(self) -> None:\n self._get_btc_eur_15min()\n print(\n f\"1 BTC = {self.btc_eur_15min} EUR\"\n f\"\\t\\t(15min delayed market price)\"\n )\n\n self._get_eur_gbp_last_month()\n print(\n f\"1 EUR = {self.eur_gbp_last_month} GBP\"\n f\"\\t(last month average rate)\"\n )\n\n self._get_btc_gbp_15min()\n print(\n f\"1 BTC = {self.btc_gbp_15min:.6f} GBP\"\n f\"\\t(BTC 15min delayed market price; GBP latest daily average rate)\"\n )","def calc_Cinv_boiler(Q_design_W, technology_type, boiler_cost_data):\n Capex_a_Boiler_USD = 0.0\n Opex_a_fix_Boiler_USD = 0.0\n Capex_Boiler_USD = 0.0\n\n if Q_design_W > 0.0:\n boiler_cost_data = boiler_cost_data[boiler_cost_data['code'] == technology_type]\n # if the Q_design is below the lowest capacity available for the technology, then it is replaced by the least\n # capacity for the corresponding technology from the database\n if Q_design_W < boiler_cost_data.iloc[0]['cap_min']:\n Q_design_W = boiler_cost_data.iloc[0]['cap_min']\n max_boiler_size = boiler_cost_data.iloc[0]['cap_max']\n\n if Q_design_W <= max_boiler_size:\n\n boiler_cost_data = boiler_cost_data[\n (boiler_cost_data['cap_min'] <= Q_design_W) & (boiler_cost_data['cap_max'] > Q_design_W)]\n\n Inv_a = boiler_cost_data.iloc[0]['a']\n Inv_b = boiler_cost_data.iloc[0]['b']\n Inv_c = boiler_cost_data.iloc[0]['c']\n Inv_d = boiler_cost_data.iloc[0]['d']\n Inv_e = boiler_cost_data.iloc[0]['e']\n Inv_IR = boiler_cost_data.iloc[0]['IR_%']\n Inv_LT = boiler_cost_data.iloc[0]['LT_yr']\n Inv_OM = boiler_cost_data.iloc[0]['O&M_%'] / 100.0\n\n InvC = Inv_a + Inv_b * (Q_design_W) ** Inv_c + (Inv_d + Inv_e * Q_design_W) * log(Q_design_W)\n\n Capex_a_Boiler_USD = calc_capex_annualized(InvC, Inv_IR, Inv_LT)\n Opex_a_fix_Boiler_USD = InvC * Inv_OM\n Capex_Boiler_USD = InvC\n\n else:\n number_of_boilers = int(ceil(Q_design_W / max_boiler_size))\n Q_nom_W = Q_design_W / number_of_boilers\n\n boiler_cost_data = boiler_cost_data[\n (boiler_cost_data['cap_min'] <= Q_nom_W) & (boiler_cost_data['cap_max'] > Q_nom_W)]\n\n Inv_a = boiler_cost_data.iloc[0]['a']\n Inv_b = boiler_cost_data.iloc[0]['b']\n Inv_c = boiler_cost_data.iloc[0]['c']\n Inv_d = boiler_cost_data.iloc[0]['d']\n Inv_e = boiler_cost_data.iloc[0]['e']\n Inv_IR = boiler_cost_data.iloc[0]['IR_%']\n Inv_LT = boiler_cost_data.iloc[0]['LT_yr']\n Inv_OM = boiler_cost_data.iloc[0]['O&M_%'] / 100.0\n\n InvC = (Inv_a + Inv_b * (Q_nom_W) ** Inv_c + (Inv_d + Inv_e * Q_nom_W) * log(Q_nom_W)) * number_of_boilers\n\n Capex_a_Boiler_USD = calc_capex_annualized(InvC, Inv_IR, Inv_LT)\n Opex_a_fix_Boiler_USD = InvC * Inv_OM\n Capex_Boiler_USD = InvC\n\n return Capex_a_Boiler_USD, Opex_a_fix_Boiler_USD, Capex_Boiler_USD","def discount_card(subtotal):\n\n if \"gold\" in CARDS:\n return gold_card(subtotal) #This calculates the 5%\n\n elif \"silver\" in CARDS:\n return silver_card(subtotal) #This calculates the 2%\n\n elif \"gold\" in CARDS and \"silver\" in CARDS:\n return gold_card(subtotal)\n\n else:\n return 0 #Whitout discount","def test_visualize_price_breakdown(self):\n pass","def discount(self, period):\n\t\treturn 1.0/compound(period)","def bulk_item(order):\n discount = 0\n for item in order.cart:\n if item.quantity >= 20:\n discount += item.total() * .1\n return discount","def update_price_model(self, good, order_type, is_successful, clearing_price=0):\n\n SIGNIFICANT = 0.25 # 25% more or less is \"significant\"\n SIG_IMBALANCE = 0.33\n LOW_INVENTORY = 0.1 # 10% of ideal inventory = \"LOW\"\n HIGH_INVENTORY = 2.0 # 200% of ideal inventory = \"HIGH\"\n MIN_PRICE = 0.01 # lowest allowed price of a Good\n\n if is_successful:\n # add this trade to the observed trading range\n self.observed_trading_range[good].append(clearing_price)\n\n public_mean_price = self.market.mean_price(good)\n belief = self.price_belief[good]\n mean = belief.mean()\n wobble = 0.05 # the degree which the Pop should bid outside the belief\n\n # how different the public mean price is from the price belief\n delta_to_mean = mean - public_mean_price\n\n if is_successful:\n if order_type is OrderType.buy_order and delta_to_mean > SIGNIFICANT:\n # this Pop overpaid, shift belief towards mean\n belief.low -= delta_to_mean / 2\n belief.high -= delta_to_mean / 2\n elif order_type is OrderType.sell_order and delta_to_mean < -SIGNIFICANT:\n # this Pop underpaid!, shift belief towards mean\n belief.low -= delta_to_mean / 2\n belief.high -= delta_to_mean / 2\n\n # increase the belief's certainty\n belief.low += wobble * mean\n belief.high -= wobble * mean\n\n else:\n # shift towards mean\n belief.low -= delta_to_mean / 2\n belief.high -= delta_to_mean / 2\n\n # check for inventory special cases\n stocks = self.inventory.get_amount(good)\n ideal = self.inventory.get_ideal(good)\n\n # if we're buying and inventory is too low\n # meaning we're desperate to buy\n if order_type is OrderType.buy_order and stocks < LOW_INVENTORY * ideal:\n wobble *= 2\n\n # if we're selling and inventory is too high\n # meaning we're desperate to sell\n elif order_type is OrderType.sell_order and stocks > HIGH_INVENTORY * ideal:\n wobble *= 2\n # all other cases\n else:\n sells = self.market.history.sell_orders.average(good, 1)\n buys = self.market.history.buy_orders.average(good, 1)\n\n # TODO: figure out why this is sometimes 0\n if sells + buys > 0:\n\n supply_vs_demand = (sells - buys) / (sells + buys)\n\n if supply_vs_demand > SIG_IMBALANCE or supply_vs_demand < -SIG_IMBALANCE:\n # too much supply? lower bid lower to sell faster\n # too much demand? raise price to buy faster\n\n new_mean = public_mean_price * (1 - supply_vs_demand)\n delta_to_mean = mean - new_mean\n\n # shift the price belief to the new price mean\n belief.low -= delta_to_mean / 2\n belief.high -= delta_to_mean / 2\n\n\n # decrease belief's certainty since we've just changed it (we could be wrong)\n belief.low -= wobble * mean\n belief.high += wobble * mean\n\n # make sure the price belief doesn't decrease below the minimum\n if belief.low < MIN_PRICE:\n belief.low = MIN_PRICE\n elif belief.high < MIN_PRICE:\n belief.high = MIN_PRICE","def run(self):\n self.evaluate()\n self.accumulate()\n self.summarize()","def binary_price(volatilities,\n strikes,\n expiries,\n spots=None,\n forwards=None,\n discount_factors=None,\n is_call_options=None,\n dtype=None,\n name=None):\n if (spots is None) == (forwards is None):\n raise ValueError('Either spots or forwards must be supplied but not both.')\n\n with tf.compat.v1.name_scope(\n name,\n default_name='option_price',\n values=[\n forwards, strikes, volatilities, expiries, discount_factors,\n is_call_options\n ]):\n strikes = tf.convert_to_tensor(strikes, dtype=dtype, name='strikes')\n volatilities = tf.convert_to_tensor(\n volatilities, dtype=dtype, name='volatilities')\n expiries = tf.convert_to_tensor(expiries, dtype=dtype, name='expiries')\n\n if discount_factors is None:\n discount_factors = tf.convert_to_tensor(\n 1.0, dtype=dtype, name='discount_factors')\n else:\n discount_factors = tf.convert_to_tensor(\n discount_factors, dtype=dtype, name='discount_factors')\n\n if forwards is not None:\n forwards = tf.convert_to_tensor(forwards, dtype=dtype, name='forwards')\n else:\n spots = tf.convert_to_tensor(spots, dtype=dtype, name='spots')\n forwards = spots / discount_factors\n\n sqrt_var = volatilities * tf.math.sqrt(expiries)\n d1 = (tf.math.log(forwards / strikes) + sqrt_var * sqrt_var / 2) / sqrt_var\n d2 = d1 - sqrt_var\n undiscounted_calls = _ncdf(d2)\n if is_call_options is None:\n return discount_factors * undiscounted_calls\n undiscounted_puts = 1 - undiscounted_calls\n predicate = tf.broadcast_to(is_call_options, tf.shape(undiscounted_calls))\n return discount_factors * tf.where(predicate, undiscounted_calls,\n undiscounted_puts)","def bulk_item_promo(order: Order):\n discount = 0\n for item in order.cart:\n if item.quantity >= 20:\n discount += item.total() * 0.1\n return discount","def compute(self):\n\t\tmontant = self.spn_montant.value() #recuperation de la valeur de la spn\n\t\tdevise_from = self.cbb_devisesFrom.currentText() #recuperation de la valeur de la cbb\n\t\tdevise_to = self.cbb_devisesTo.currentText()\n\t\t\n\t\t# on effectue la conversion grace a currency_converter\n\t\t# on fait une gestion d'erreur pour eviter les conversions non trouvees\n\t\ttry :\n\t\t\t\"\"\"on essaie\"\"\"\n\t\t\tresultat = self.c.convert(montant, devise_from, devise_to)\n\t\t\n\t\texcept currency_converter.currency_converter.RateNotFoundError :\n\t\t\t\"\"\"si erreur\"\"\"\n\t\t\tprint(\"le taux de conversion n'a pas ete trouve\")\n\t\t\n\t\telse :\n\t\t\t\"\"\"si pas d'erreur\"\"\"\n\t\t\tself.spn_montantConverti.setValue(resultat) #affichage dans la cbb","def calculate_adoption_fee(cursor, dog_id):\n query = f\"SELECT Dog.dogID, IF (Dog.surrender_by_animal_control = 1, 0.15 * sum( Expense.amount ), 1.15 * sum( Expense.amount )) \" \\\n f\"FROM Dog LEFT JOIN Expense ON Dog.dogID = Expense.dogID LEFT JOIN Adoption ON Adoption.dog = Dog.dogID \" \\\n f\"WHERE Dog.dogID = {dog_id};\"\n cursor.execute(query)\n query_result = cursor.fetchall()\n return query_result","def set_costs(self) -> None:\n self[\"glider cost\"] = (\n self[\"glider base mass\"] * self[\"glider cost slope\"]\n + self[\"glider cost intercept\"]\n )\n self[\"lightweighting cost\"] = (\n self[\"glider base mass\"]\n * self[\"lightweighting\"]\n * self[\"glider lightweighting cost per kg\"]\n )\n self[\"electric powertrain cost\"] = (\n self[\"electric powertrain cost per kW\"] * self[\"electric power\"]\n )\n self[\"combustion powertrain cost\"] = (\n self[\"combustion power\"] * self[\"combustion powertrain cost per kW\"]\n )\n self[\"fuel cell cost\"] = self[\"fuel cell power\"] * self[\"fuel cell cost per kW\"]\n self[\"power battery cost\"] = (\n self[\"battery power\"] * self[\"power battery cost per kW\"]\n )\n self[\"energy battery cost\"] = (\n self[\"energy battery cost per kWh\"] * self[\"electric energy stored\"]\n )\n self[\"fuel tank cost\"] = self[\"fuel tank cost per kg\"] * self[\"fuel mass\"]\n # Per km\n self[\"energy cost\"] = self[\"energy cost per kWh\"] * self[\"TtW energy\"] / 3600\n\n # For battery, need to divide cost of electricity\n # at battery by efficiency of charging\n # to get costs at the \"wall socket\".\n\n _ = lambda x: np.where(x == 0, 1, x)\n self[\"energy cost\"] /= _(self[\"battery charge efficiency\"])\n\n self[\"component replacement cost\"] = (\n self[\"energy battery cost\"] * self[\"battery lifetime replacements\"]\n + self[\"fuel cell cost\"] * self[\"fuel cell lifetime replacements\"]\n )\n\n with open(DATA_DIR / \"purchase_cost_params.yaml\", \"r\") as stream:\n to_markup = yaml.safe_load(stream)[\"markup\"]\n\n self[to_markup] *= self[\"markup factor\"]\n\n # calculate costs per km:\n self[\"lifetime\"] = self[\"lifetime kilometers\"] / self[\"kilometers per year\"]\n\n with open(DATA_DIR / \"purchase_cost_params.yaml\", \"r\") as stream:\n purchase_cost_params = yaml.safe_load(stream)[\"purchase\"]\n\n self[\"purchase cost\"] = self[purchase_cost_params].sum(axis=2)\n # per km\n amortisation_factor = self[\"interest rate\"] + (\n self[\"interest rate\"]\n / (\n (np.array(1) + self[\"interest rate\"]) ** self[\"lifetime kilometers\"]\n - np.array(1)\n )\n )\n self[\"amortised purchase cost\"] = (\n self[\"purchase cost\"] * amortisation_factor / self[\"kilometers per year\"]\n )\n\n # per km\n self[\"maintenance cost\"] = (\n self[\"maintenance cost per glider cost\"]\n * self[\"glider cost\"]\n / self[\"kilometers per year\"]\n )\n\n # simple assumption that component replacement\n # occurs at half of life.\n self[\"amortised component replacement cost\"] = (\n (\n self[\"component replacement cost\"]\n * (\n (np.array(1) - self[\"interest rate\"]) ** self[\"lifetime kilometers\"]\n / 2\n )\n )\n * amortisation_factor\n / self[\"kilometers per year\"]\n )\n\n self[\"total cost per km\"] = (\n self[\"energy cost\"]\n + self[\"amortised purchase cost\"]\n + self[\"maintenance cost\"]\n + self[\"amortised component replacement cost\"]\n )","def test_basicNoSalePC(self):\n # Basic price check\n self.log.info(\"Price checking Generic Item via speedkey\")\n pos.click(\"Price Check\")\n pos.click_speed_key(\"Generic Item\")\n \n # Confirm the right item, at the right price\n self.read_price_check(\"Generic Item\", \"$0.01\")\n # Don't add the item\n pos.click(\"Ok\")\n \n # Confirm we aren't in a transaction\n if self.in_transaction():\n self.tc_fail(\"Unintentionally In Transaction\")\n else:\n self.log.info(\"Confirmed we are not in a transaction\")\n \n # Setup for next test\n self.recover()","def run (self, scalers = {'capital costs':1.0}):\n self.was_run = True\n self.reason = \"OK\"\n\n tag = self.cd['file id'].split('+')\n if len(tag) > 1 and tag[1] != 'biomass_pellet':\n self.was_run = False\n self.reason = (\"Not a biomass pellet project.\")\n return\n\n if not self.cd[\"on road system\"]:\n self.diagnostics.add_warning(self.component_name,\n \"not on road system\")\n self.max_boiler_output = 0\n self.heat_displaced_sqft = 0\n self.biomass_fuel_consumed = 0\n self.fuel_price_per_unit = 0\n self.heat_diesel_displaced = 0\n self.reason = \\\n \"Not on road or marine highway system, so it is assumed that\" +\\\n \" pellets cannot be delivered cost effectively.\"\n return\n\n if np.isnan(float(self.comp_specs['peak month % of total'])):\n self.diagnostics.add_warning(self.component_name,\n \"bad config value for 'peak month % of total'\")\n self.max_boiler_output = 0\n self.heat_displaced_sqft = 0\n self.biomass_fuel_consumed = 0\n self.fuel_price_per_unit = 0\n self.heat_diesel_displaced = 0\n self.reason = \"bad config value for 'peak month % of total'\"\n return\n\n if self.cd[\"model heating fuel\"]:\n self.calc_heat_displaced_sqft()\n self.calc_energy_output()\n efficiency = self.comp_specs[\"pellet efficiency\"]\n self.calc_max_boiler_output(efficiency)\n factor = self.comp_specs['capacity factor']\n self.calc_biomass_fuel_consumed(factor)\n self.calc_diesel_displaced()\n\n\n if self.cd[\"model financial\"]:\n self.get_diesel_prices()\n\n self.calc_capital_costs()\n self.calc_maintenance_cost()\n\n\n self.fuel_price_per_unit = self.cd['pellet price']\n\n self.calc_proposed_biomass_cost(self.fuel_price_per_unit)\n self.calc_displaced_heating_oil_price()\n\n\n self.calc_annual_electric_savings()\n self.calc_annual_heating_savings()\n\n self.calc_annual_total_savings()\n self.calc_annual_costs(self.cd['interest rate'],\n scalers['capital costs'])\n self.calc_annual_net_benefit()\n self.calc_npv(self.cd['discount rate'], self.cd[\"current year\"])\n\n fuel_cost = self.biomass_fuel_consumed * self.fuel_price_per_unit\n self.calc_levelized_costs(self.maintenance_cost + fuel_cost)","def calc_b_a(self, data):\n # Calculate our expected bid / ask\n mkt_bid = data['Bid'].values[-1]\n mkt_ask = data['Ask'].values[-1]\n last_trade = data['Trade Price'].values[-1]\n shares = self.calc_shares(data=data, last_trade=last_trade)\n\n k = (mkt_bid - mkt_ask) / (mkt_bid + mkt_ask) * -100\n\n our_bid = np.average(data['Bid'], weights=self.weights) - k\n our_ask = np.average(data['Ask'], weights=self.weights) + k\n\n self.portfoolio['Unrealized_PnL'] = self.portfoolio['Shares'] * last_trade - self.portfoolio['Avg_Cost'] * self.portfoolio['Shares']\n\n if shares == 0:\n # Skip 0 share orders\n pass\n elif abs(self.portfoolio['Cost']) >= self.max_pos * .75:\n # If position size at or above 95% of max, reduce position\n self.risk_control(bid=mkt_bid, ask=mkt_ask, last_trade=last_trade)\n\n elif our_bid >= mkt_bid:\n # Buy at bid\n self.trade(shares=shares, price=mkt_bid, last_trade=last_trade)\n elif our_ask <= mkt_ask:\n # Sell at ask\n self.trade(shares=-shares, price=mkt_ask, last_trade=last_trade)\n else:\n print('No order placed')","def calculate_total(self):\n if self.total_price == 0:\n for discount in self.discounts:\n for item in self.items:\n item.add_discount(discount)\n\n for item in self.items:\n self.total_price += item.final_price()\n\n return self.total_price","def opinion():\n pass","def stoploss(self):\n price = float(self.price)\n print(\"orderPrice1:\",self.dentry[\"orderPrice1\" + self.chartnumber])\n if (self.dentry[\"orderPrice1\" + self.chartnumber] - price) / self.dentry[\"orderPrice1\" + self.chartnumber] * 100 >= self.stopPercent:\n self.sell(stopped=True)","def tickets(number, day, premium_seating):\n #fill in your code here. \n return 0.0","def lab10_q2():\n return \"\"\"\n Make a 'total' to add up the total revenue as while loop plays.\n\titem = qvm.dispense, makes it so item is the juice. \n\tWhile item: because if there is a juice this while loop will continue. If there aren't two fruits, there wont be a juice so while loop stops.\n\ttotal += qvm.collect_money(item). (this should be += not = right?) to add the revenue from that 'item'\n\trevalue item to the next juice.\n\treturn total\n \"\"\"","def transact_shares(self, action, quantity, price, commission, bid=None, ask=None):\n if bid is None: \n bid = price\n if ask is None:\n ask = price\n\n if action is None:\n return\n\n self.total_commission += commission\n\n # Adjust total bought and sold\n if action == \"BOT\":\n self.avg_bot = (self.avg_bot * self.buys + price * quantity) / (self.buys + quantity)\n\n if self.net < 0:\n self.realised_pnl += min(quantity, abs(self.net)) * (self.avg_price - price) - commission # Adjust realised PNL\n commission = 0 # assume commission is all in realised_pnl\n # Increasing long position\n self.avg_price = (self.avg_price * self.net + price * quantity + commission) / (self.net + quantity)\n self.buys += quantity\n self.total_bot = self.buys * self.avg_bot\n\n # action == \"SLD\"\n else:\n self.avg_sld = (self.avg_sld * self.sells + price * quantity) / (self.sells + quantity)\n\n if self.net > 0:\n self.realised_pnl += min(quantity, abs(self.net)) * (price - self.avg_price) - commission # Adjust realised PNL\n commission = 0 # assume commission is all in realised_pnl\n\n self.avg_price = (self.avg_price * self.net - price * quantity - commission) / (self.net - quantity)\n self.sells += quantity\n self.total_sld = self.sells * self.avg_sld\n\n # Adjust net values, including commissions\n self.net = self.buys - self.sells\n self.net_total = self.total_sld - self.total_bot\n self.net_incl_comm = self.net_total - self.total_commission\n self.cost_basis = self.net * self.avg_price\n\n self.update_market_value(bid, ask)","def compute_price(self, date = None):\n\t\tif date is None:\n\t\t\tdate = datetime.now()\n\t\tself.price = 0\n\t\t# Getting list of product in cart\n\t\tcontent = self.cart.cart_content_set.all()\n\t\t# Dictionnary in order to compute minimum state of multi promotion\n\t\tstate = {\n\t\t\t'products':{},\n\t\t\t'promotions':{}\n\t\t}\n\t\trequirements = {}\n\n\t\tfor element in content:\n\t\t\tproduct = element.product\n\t\t\tquantity = element.quantity\n\n\t\t\t# First look for promotion\n\t\t\tsimple_promotions = product.promotion_set.filter(end__gte = date, type = 's').distinct('reference', 'end').order_by('-end', 'reference')\n\t\t\tmulti_promotions = product.promotion_set.filter(end__gte = date, type = 'm').distinct('reference', 'end').order_by('-end', 'reference')\n\t\t\tif len(simple_promotions)>0:\n\t\t\t\tpromotion = simple_promotions[0]\n\t\t\t\tself.price = self.price + quantity*promotion.after\n\t\t\t\n\t\t\telif len(multi_promotions)>0:\n\t\t\t\tfor promotion in multi_promotions:\n\t\t\t\t\tprice_before = promotion.before\n\t\t\t\t\tprice_after = promotion.after\n\t\t\t\t\tcontent = [ (p, 1) for p in promotion.content.all()]\n\t\t\t\t\tfound, requirement = self.get_promotion_requirement(content, price_before)\n\t\t\t\t\tif found and requirement is not None:\n\t\t\t\t\t\trequirements[promotion.id] = { p.id:q for p, q in requirement} # updating promotion multi requirements\n\n\t\t\t\t\t# Updating promotion multi state\n\t\t\t\t\tprod, price = self.get_simple_price([{'product':product, 'quantity':1}], date)[0]\n\t\t\t\t\t# print quantity\n\t\t\t\t\tstate['products'][product.id] = {'price': price, 'qte':quantity}\n\t\t\t\t\t# print state['products'][product.id]\n\t\t\t\t\tstate['promotions'][promotion.id] = {'price': price_after, 'qte':0}\n\t\t\telse:\n\t\t\t\thistory = product.history_set.filter(created__gte = date-timedelta(hours = 24)).order_by('-created')\n\t\t\t\tif len(history)>0:\n\t\t\t\t\tself.price = self.price + quantity*history[0].price\n\t\t\t\telse:\n\t\t\t\t\thistory = product.history_set.all().order_by('-created')\n\t\t\t\t\tif len(history)>0:\n\t\t\t\t\t\tself.price = self.price + quantity*history[0].price\n\n\t\t# Dealing with multi promotion:\n\t\tmin_state, min_price = self.get_min_state(state, requirements)\n\t\tself.price = self.price + min_price\n\n\t\tself.save()\n\n\t\treturn self.price","def use(self):\n if self.price_of_trip == 0:\n print(\"Sorry your card has been used\")\n else:\n self.price_of_trip -= self.price_of_trip\n print(\"Done\")","def budget_for_necessities():\n print(\"========== Displaying hotel options ==========\")\n for i in range(len(hotel_list)):\n print(\" -- Enter\", i+1, \"to choose -- \")\n hotel_list[i].print_hotel()\n print(\" \")\n while True:\n try:\n hotel_num = int(input(\"Please choose your hotel option (Enter a number between 1 to 7): \"))\n hotel_num -= 1\n if hotel_num in range(len(hotel_list)): break\n except ValueError:\n print(\"Please enter an positive integer. Try again... \")\n while True:\n try:\n hotel_stay = int(input(\"Please enter the duration (in days) of your stay: \"))\n if hotel_stay > 0: break\n except ValueError:\n print(\"Please enter an positive integer. Try again... \")\n user_hotel = hotel_list[hotel_num]\n user_hotel_price = user_hotel.get_price()\n user_hotel_name = user_hotel.get_name()\n # display car option and ask for user input\n print(\"\\n======== Displaying rental car options =========\")\n for i in range(len(car_list)):\n print(\" -- Enter\", i+1, \"to choose -- \")\n car_list[i].print_car()\n print(\" \")\n while True:\n try:\n car_num = int(input(\"Please choose your car rental option (Enter a number between 1 to 6): \"))\n car_num -= 1\n if car_num in range(len(hotel_list)): break\n except ValueError:\n print(\"Please enter an positive integer. Try again... \")\n while True:\n try:\n car_rental_day = int(input(\"Please enter the duration (in days) of your car rental: \"))\n if car_rental_day > 0: break\n except ValueError:\n print(\"Please enter an positive integer. Try again... \")\n # calculate user's total cost for car rental and hotel\n user_car = car_list[car_num]\n user_car_price = user_car.get_price()\n user_car_name = user_car.get_name()\n total_hotel_cost = hotel_stay * user_hotel_price\n total_car_rental_cost = car_rental_day * user_car_price\n print(\"\\n=== Displaying your hotel and car rental information ===\")\n print(\"Hotel: \", user_hotel.get_name())\n print(\"Hotel total cost: $\", total_hotel_cost)\n print(\"Car Rental: \", user_car.get_name())\n print(\"Car rental total cost: $\", total_car_rental_cost)\n print(\" \")\n # calculate remaining budget based on hotel and car's cost and/or ask for higher budget\n user_budget.calculate_new_budget(total_hotel_cost + total_car_rental_cost)\n print(\" \")\n return total_hotel_cost, total_car_rental_cost, user_hotel_name, user_car_name","def __init__(self, rank_discount_fn):\n self._rank_discount_fn = rank_discount_fn","def compute(self, **kwargs):\n raise NotImplementedError","def __init__(self, price, date=datetime.datetime.now(), dividend=0., annualDividend=0.):\n self.price = price\n self.date = date\n self.dividend = dividend\n self.annualDividend = annualDividend","def getAmount2(*args):","def trade(self, action=None):\n #print(\"Trading {}\".format(action))\n # Buy\n if action > 0.2 : self.posture = 1\n # Hold\n if action < 0.2 and action > -0.2: self.posture = 0\n # Sell\n if action < -0.2: self.posture = -1\n \n # Evaluate posture and calculare actual cost of trade\n #print(\"Posture: {}\".format(self.posture))\n if self.posture == 1:\n _amt = self.amt_buy\n _base = (_amt * self.state['price'] \\\n + (_amt * self.commission)) * -1\n \n elif self.posture == -1:\n _amt = self.amt_sell\n _base = _amt * self.state['price'] \\\n + (_amt * self.commission) \\\n + (_amt * self.gap)\n _amt = _amt * -1 \n\n # Set posture to 0 if no balance available\n if (self.posture == 1 and self.balance < abs(_base)) \\\n or (self.posture == -1 and self.bag < abs(_amt)):\n print(\"NOT enough amount!!\")\n self.stop=True\n self.posture = 0\n\n if self.posture == 0:\n _amt = 0\n _base = 0\n\n # Modify balances\n self.transaction = _base\n self.amt = _amt\n self.balance = self.balance + _base\n self.bag = self.bag + _amt\n self.value = self.calculate_value()\n #print(\"Posture : {} // Transaction: {}\".format(self.posture, self.transaction))\n\n return self","def eval_sltp(self):\n\n pos_size = float(self.get_position()['positionAmt'])\n if pos_size == 0:\n return\n # tp\n tp_order = self.get_open_order('TP') \n \n is_tp_full_size = False \n is_sl_full_size = False \n\n if tp_order is not None:\n origQty = float(tp_order['origQty'])\n is_tp_full_size = origQty == abs(pos_size) if True else False\n #pos_size = pos_size - origQty \n \n tp_percent_long = self.get_sltp_values()['profit_long']\n tp_percent_short = self.get_sltp_values()['profit_short'] \n\n avg_entry = self.get_position_avg_price()\n\n # tp execution logic \n if tp_percent_long > 0 and is_tp_full_size == False:\n if pos_size > 0: \n tp_price_long = round(avg_entry +(avg_entry*tp_percent_long), self.round_decimals) \n if tp_order is not None:\n time.sleep(2) \n self.cancel(id=tp_order['clientOrderId'])\n time.sleep(2)\n self.order(\"TP\", False, abs(pos_size), limit=tp_price_long, reduce_only=True)\n else: \n self.order(\"TP\", False, abs(pos_size), limit=tp_price_long, reduce_only=True)\n if tp_percent_short > 0 and is_tp_full_size == False:\n if pos_size < 0: \n tp_price_short = round(avg_entry -(avg_entry*tp_percent_short), self.round_decimals)\n if tp_order is not None:\n time.sleep(2) \n self.cancel(id=tp_order['clientOrderId'])\n time.sleep(2)\n self.order(\"TP\", True, abs(pos_size), limit=tp_price_short, reduce_only=True)\n else:\n self.order(\"TP\", True, abs(pos_size), limit=tp_price_short, reduce_only=True)\n #sl\n sl_order = self.get_open_order('SL')\n if sl_order is not None:\n origQty = float(sl_order['origQty'])\n orig_side = sl_order['side'] == \"BUY\" if True else False\n if orig_side == False:\n origQty = -origQty \n is_sl_full_size = origQty == -pos_size if True else False \n\n sl_percent_long = self.get_sltp_values()['stop_long']\n sl_percent_short = self.get_sltp_values()['stop_short']\n\n # sl execution logic\n if sl_percent_long > 0 and is_sl_full_size == False:\n if pos_size > 0:\n sl_price_long = round(avg_entry - (avg_entry*sl_percent_long), self.round_decimals)\n if sl_order is not None:\n time.sleep(2) \n self.cancel(id=sl_order['clientOrderId'])\n time.sleep(2)\n self.order(\"SL\", False, abs(pos_size), stop=sl_price_long, reduce_only=True)\n else: \n self.order(\"SL\", False, abs(pos_size), stop=sl_price_long, reduce_only=True)\n if sl_percent_short > 0 and is_sl_full_size == False:\n if pos_size < 0:\n sl_price_short = round(avg_entry + (avg_entry*sl_percent_short), self.round_decimals)\n if sl_order is not None: \n time.sleep(2) \n self.cancel(id=sl_order['clientOrderId'])\n time.sleep(2)\n self.order(\"SL\", True, abs(pos_size), stop=sl_price_short, reduce_only=True) \n else: \n self.order(\"SL\", True, abs(pos_size), stop=sl_price_short, reduce_only=True)"],"string":"[\n \"def discount(self, cart):\",\n \"def get_discount(self, price):\\r\\n pass\",\n \"def apply_discount(self, product):\\n pass\",\n \"def compute(self):\\r\\n \\r\\n if self.option_type == \\\"European call\\\":\\r\\n option_price = BS_call(self.S0, self.K, self.T_scaled, self.vola, self.div_yield, r=self.rf)\\r\\n \\r\\n elif self.option_type == \\\"European put\\\":\\r\\n option_price = BS_put(self.S0, self.K, self.T_scaled, self.vola, self.div_yield, r=self.rf)\\r\\n \\r\\n elif self.option_type == \\\"Asian call\\\":\\r\\n option_price = monte_carlo_call(self.S0, self.K, self.T_scaled, self.vola, self.div_yield, rf=self.rf ) # function in other file\\r\\n else:\\r\\n # Asian put\\r\\n option_price = monte_carlo_put(self.S0, self.K, self.T_scaled, self.vola, self.div_yield, rf=self.rf)\\r\\n \\r\\n\\r\\n self.display_option_price.setText(str(round(option_price,2)))\\r\\n self.display_option_price.adjustSize()\",\n \"def calculate_profit(self):\",\n \"def get_basket_items_discount(self, offer_info, actual_volume, product_prices):\\n prod_code = offer_info.base_prod_code\\n base_prod_vol = actual_volume.get(prod_code.lower())\\n\\n discount_basket = []\\n\\n if base_prod_vol >= offer_info.min_vol:\\n offer_on_prod = offer_info.offer_on\\n if actual_volume.get(offer_on_prod.lower()):\\n print(f\\\"Base product volume is greater than minimum required volume & product on offer is also available \\\"\\n f\\\"in cart..\\\")\\n if offer_info.is_limited:\\n print(f\\\"Limited offer..\\\")\\n if prod_code == offer_on_prod:\\n # total_allowed_items_on_offer = Limit Volume of base product * (Offer Product Max Volume/Minimum volume of base product)\\n total_allowed_items_on_offer = offer_info.limit_vol * (offer_info.offer_prod_volume/offer_info.min_vol)\\n max_limit = 1\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n discount_basket.append((prod_code, base_prod_actual_price))\\n while max_limit <= total_allowed_items_on_offer:\\n discounted_price = (base_prod_actual_price *(offer_info.discount_perc/100))*-1\\n discount_basket.append((offer_info.offer_code, discounted_price))\\n max_limit += 1\\n else:\\n total_allowed_items_on_offer = offer_info.limit_vol * (offer_info.offer_prod_volume / offer_info.min_vol)\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n discount_basket.append((prod_code, base_prod_actual_price))\\n max_limit = 1\\n while max_limit <= total_allowed_items_on_offer:\\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\\n discounted_price = (offer_onprod_actual_price *(offer_info.discount_perc/100))*-1\\n for j in range(0, actual_volume.get(offer_on_prod.lower())):\\n discount_basket.append((offer_on_prod, offer_onprod_actual_price))\\n discount_basket.append((offer_info.offer_code, discounted_price))\\n max_limit += 1\\n else:\\n print(f\\\"Unlimited offer..\\\")\\n if prod_code == offer_on_prod:\\n if base_prod_vol > offer_info.min_vol:\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n discount_basket.append((prod_code, base_prod_actual_price))\\n if i%2 != 0:\\n discounted_price = (base_prod_actual_price *(offer_info.discount_perc/100))*-1\\n discount_basket.append((offer_info.offer_code, discounted_price))\\n else:\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n discount_basket.append((prod_code, base_prod_actual_price))\\n else:\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n discount_basket.append((prod_code, base_prod_actual_price))\\n\\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\\n discounted_price = (offer_onprod_actual_price * (offer_info.discount_perc / 100))*-1\\n\\n for j in range(0, actual_volume.get(offer_on_prod.lower())):\\n discount_basket.append((offer_on_prod, offer_onprod_actual_price))\\n discount_basket.append((offer_info.offer_code, discounted_price))\\n\\n\\n return discount_basket\",\n \"def calculate(self):\",\n \"def implied_discount_factor(p1: Instrument, c1: Instrument, p2: Instrument, c2: Instrument) -> float:\\n return (c1.price - p1.price - c2.price + p2.price)/ (c2.strike - c1.strike)\",\n \"def calculate(self):\\r\\n\\r\\n pass\",\n \"def cmd_calculation():\",\n \"def __call__(self, rate:'kW'):\\n self.rate = rate\\n self.cost = self.price * rate\",\n \"def __calculation(self, *args, **kwargs):\\n pass\",\n \"def discount(self,discountFactor,type='geometric'):\\n for e in self.estimators:\\n e.discount(discountFactor,type)\\n return\",\n \"def price(function):\\n\\t\\tdef wrapper( self , *args, **kwargs) :\\n\\t\\t\\tresult = function( self , *args, **kwargs)\\n\\t\\t\\tself.compute_price()\\n\\t\\t\\treturn result\\n\\t\\treturn wrapper\",\n \"def discount(ir, period):\\n\\treturn ir.discount(period)\",\n \"def calc_earning(self, data=None):\\n result = Result()\\n if data is None:\\n data = self.security\\n self.calcDecision()\\n first_purchase_method = self.check_first_purchase_method()\\n for i in np.arange(len(data['Close'])):\\n if data['FinalDecision'].iloc[i] is None:\\n pass\\n elif data['FinalDecision'].iloc[i] == TransactionType.BUY:\\n if data['FinalDecision'].iloc[i-1] == TransactionType.BUY:\\n pass\\n else:\\n if (self.buys_made + self.sells_made) == 0:\\n if first_purchase_method == FirstTransactionType.INIT_CAPITAL:\\n self.shares_own = int((self.init_capital/data['Close'].iloc[i]))\\n self.buys_made += 1\\n elif first_purchase_method == FirstTransactionType.STOCK_QUANTITY:\\n self.shares_own = self.stock_quantity\\n self.buys_made += 1\\n else:\\n self.shares_own = int(self.final_capital / data['Close'].iloc[i])\\n self.final_capital = self.final_capital % data['Close'].iloc[i]\\n #print(self.shares_own)\\n\\n elif data['FinalDecision'].iloc[i] == TransactionType.SELL:\\n if data['FinalDecision'].iloc[i-1] == TransactionType.SELL:\\n pass\\n else:\\n if (self.buys_made + self.sells_made) == 0:\\n pass\\n else:\\n self.final_capital += self.shares_own * data['Close'].iloc[i]\\n self.shares_own = 0\\n self.sells_made +=1\\n #Checar si es el momento mas alto o bajo de ganancias\\n if self.shares_own == 0:\\n if (self.highest_point is None\\n or self.highest_point < self.final_capital):\\n self.highest_point = self.final_capital\\n if (self.lowest_point is None\\n or self.lowest_point > self.final_capital\\n or self.lowest_point == 0):\\n self.lowest_point = self.final_capital\\n else:\\n if (self.highest_point is None\\n or self.highest_point < (self.shares_own * data['Close'].iloc[i])):\\n self.highest_point = self.final_capital\\n if (self.lowest_point is None\\n or self.lowest_point > (self.shares_own * data['Close'].iloc[i])\\n or self.lowest_point == 0):\\n self.lowest_point = self.final_capital\\n self.calcRealFinalCapital()\\n self.calcDiferencePercentage()\",\n \"def calculate(self):\\r\\n pass\",\n \"def testPolicy( \\t\\t \\t \\t\\t \\t \\t\\t\\t \\t\\t \\t\\t\\t \\t \\t \\t\\t \\t\\t \\t\\n self, \\t\\t \\t \\t\\t \\t \\t\\t\\t \\t\\t \\t\\t\\t \\t \\t \\t\\t \\t\\t \\t\\n symbol=\\\"jpm\\\", \\t\\t \\t \\t\\t \\t \\t\\t\\t \\t\\t \\t\\t\\t \\t \\t \\t\\t \\t\\t \\t\\n sd=dt.datetime(2008, 1, 1), \\t\\t \\t \\t\\t \\t \\t\\t\\t \\t\\t \\t\\t\\t \\t \\t \\t\\t \\t\\t \\t\\n ed=dt.datetime(2009, 12, 31), \\t\\t \\t \\t\\t \\t \\t\\t\\t \\t\\t \\t\\t\\t \\t \\t \\t\\t \\t\\t \\t\\n sv=10000, \\t\\t \\t \\t\\t \\t \\t\\t\\t \\t\\t \\t\\t\\t \\t \\t \\t\\t \\t\\t \\t\\n ): \\t\\t \\t \\t\\t \\t \\t\\t\\t \\t\\t \\t\\t\\t \\t \\t \\t\\t \\t\\t \\t\\n \\t\\t \\t \\t\\t \\t \\t\\t\\t \\t\\t \\t\\t\\t \\t \\t \\t\\t \\t\\t \\t\\n dates = pd.date_range(sd,ed)\\n df_prices = ind.get_price(symbol, dates)\\n\\n daily_rets = (df_prices / df_prices.shift(1)) - 1\\n daily_rets = daily_rets[1:]\\n\\n\\n sd_older = sd - dt.timedelta(days=365)\\n dates_older = pd.date_range(sd_older,ed)\\n df_prices_older = ind.get_price(symbol, dates_older)\\n sd_key = df_prices.index[0]\\n sd_index = df_prices_older.index.get_loc(sd_key)\\n\\n\\n df_holdings = df_prices.copy()\\n df_holdings['Holdings'] = np.nan\\n del df_holdings[symbol]\\n # print(df_holdings)\\n\\n cum_ret_prev = 0\\n iters = 0\\n\\n\\n num_bins = len(self.bins)\\n\\n _,_,ind1 = ind.get_BB(df_prices_older, self.lookback)\\n ind2 = ind.get_CCI(df_prices_older, self.lookback)\\n _,_,ind3 = ind.get_SMA_Cross(self.lookback, 100, df_prices_older)\\n ind4 = ind.get_momentum(df_prices_older, self.lookback)\\n _,_,ind5 = ind.get_MACD(df_prices_older)\\n BB = ind1.iloc[sd_index:].values\\n CCI = ind2.iloc[sd_index:].values\\n SMA_Cross = ind3.iloc[sd_index:].values\\n Momentum = ind4.iloc[sd_index:].values\\n MACD = ind5.iloc[sd_index:].values\\n _,self.x0bins = pd.qcut(BB[:,0], num_bins,labels=False,retbins=True)\\n _,self.x1bins = pd.qcut(CCI[:,0],num_bins,labels=False,retbins=True)\\n _,self.x2bins = pd.qcut(SMA_Cross[:,0],num_bins,labels=False,retbins=True)\\n _,self.x3bins = pd.qcut(Momentum[:,0],num_bins,labels=False,retbins=True)\\n _,self.x4bins = pd.qcut(MACD[:,0],num_bins,labels=False,retbins=True)\\n x_0 = np.digitize(BB[:,0], self.x0bins[1:-1])\\n x_1 = np.digitize(CCI[:,0], self.x1bins[1:-1])\\n x_2 = np.digitize(SMA_Cross[:,0], self.x2bins[1:-1])\\n x_3 = np.digitize(Momentum[:,0], self.x3bins[1:-1])\\n x_4 = np.digitize(MACD[:,0], self.x4bins[1:-1])\\n state = x_0 + x_3*10 + x_4*100\\n\\n\\n\\n\\n\\n self.learner.rar = 0\\n\\n action = self.learner.querysetstate(state[0])\\n\\n daily_return = daily_rets.iloc[0][symbol]\\n df_holdings.iloc[0]['Holdings'] = 0\\n\\n\\n for day_idx in range(1,daily_rets.shape[0]):\\n\\n # implement action\\n cur_price = df_prices.iloc[day_idx-1][symbol]\\n next_price = df_prices.iloc[day_idx][symbol]\\n action = self.learner.querysetstate(state[day_idx])\\n df_holdings.iloc[day_idx]['Holdings'],_ = self.take_action(df_holdings.iloc[day_idx-1]['Holdings'], action, cur_price, next_price)\\n\\n\\n df_holdings.iloc[-1]['Holdings'] = 0\\n df_trades = df_holdings.diff()\\n df_trades['Trades'] = df_trades['Holdings']\\n del df_trades['Holdings']\\n df_trades.iloc[0]['Trades'] = 0\\n return df_trades\",\n \"def action_compute_price(self):\\n self._prepare_printing()\\n\\n providers = set(self.mapped('provider_id.id'))\\n for provider_id in providers: # process by provider id\\n records = self.filtered(lambda r: r.provider_id.id == provider_id)\\n # call provider implementation\\n provider_name = records[0].provider_id.provider\\n if hasattr(records, '_%s_action_compute_price' % provider_name):\\n getattr(records, '_%s_action_compute_price' % provider_name)()\",\n \"def duty_free(price: int, discount: int, holiday_cost: int) -> int:\\n if holiday_cost == 500:\\n return holiday_cost\\n\\n discount /= 100\\n price = holiday_cost / (price * discount)\\n price = int(price)\\n return price\",\n \"def calc_price(self):\\n price = self.price\\n action = self.action\\n mortage = 5 # here set mortage multiplier \\n\\n if action == 'RESIDENTIAL_SALE':\\n return price * 12 * mortage\\n\\n\\n if price >= 10000:\\n return price * 0.7\\n elif price < 10000 & price >= 5000:\\n return price * 0.55\\n elif price < 5000 & price >= 2800:\\n return price * 0.475\\n else:\\n return price * 0.4\",\n \"def calculate(self):\\n pass\",\n \"def test_callPrice(self):\\n call_price1 = calculator.BlackScholes.call_price(**self.params_1)\\n call_price2 = calculator.BlackScholes.call_price(**self.params_2)\\n self.assertAlmostEqual(call_price1,10.45,delta=0.01)\\n self.assertAlmostEqual(call_price2,7.965,delta=0.01)\",\n \"def apply_discounts(self):\\n # for each valid discount...\\n for discount in list(DiscountTypes):\\n # only apply the discount if it is set in the cart\\n if(discount in self.cart.discounts):\\n getattr(self, discount.value)()\",\n \"def cost(self,e1,e2):\\n pass\",\n \"def calculate_price(self, order: Order, price: float, quantity: float, bar: Bar, volume_used: float):\\n raise NotImplementedError()\",\n \"def _dynamic_price(self):\\n adjust = PriceAdjustmentCalc(self)\\n signals.satchmo_price_query.send(self, adjustment=adjust,\\n slug=self.product.slug, discountable=self.product.is_discountable)\\n return adjust.final_price()\",\n \"def _cost_method(self, *args, **kwargs):\\n\\n cost_val = 0.5 * np.linalg.norm(self.obs_data - self.op(args[0])) ** 2\\n\\n if 'verbose' in kwargs and kwargs['verbose']:\\n print(' - DATA FIDELITY (X):', cost_val)\\n\\n return cost_val\",\n \"def optimal_pricing_engine(model_ID, quote_df, COP_l=0, COP_m=0, COP_h=0):\\r\\n '''\\r\\n The purpose of this function is to process a quote using the optimal\\r\\n pricing model (as stored in the segmentation tree) to determine the \\r\\n optimal price and win probability data.\\r\\n \\r\\n Created: 05 May 2016 by Glenn Melzer\\r\\n Updated: 01 Aug 2016 by Glenn Melzer\\r\\n \\r\\n INPUTS:\\r\\n model_ID = the name of the model to be used for creating the optimal price \\r\\n quote_df = the dataframe of quote components (with the same column \\r\\n format as the historical data input dataframe).\\r\\n COP_l = Low bottom line customized optimal price\\r\\n COP_m = Median bottom line customized optimal price\\r\\n COP_h = High bottom line customized optimal price\\r\\n \\r\\n KEY INTERNAL OBJECTS NEEDED FOR THE ENGINE: \\r\\n seg_df = the quote segmentation tree built previously.\\r\\n ref_prc_col = the column that contains the reference price (typically list price)\\r\\n first = the name of the first column in quote_df that contains a segment \\r\\n classification identifier to be used.\\r\\n last = the name of the last column in quote_df that contains a classification \\r\\n identifier to be used. All columns from the first through the last\\r\\n will be used.\\r\\n prc_col = the column index name in quote_df that contains the historical\\r\\n quote price (as a % of reference price) as modified by the segmentation\\r\\n process.\\r\\n x_col = the name of the independant variable that affects pricing used in\\r\\n the regression to calculate the m and b parameters of the linear models. \\r\\n quote_attribute_cols = the list of column index names of quote_df that \\r\\n contain the historical data quote attributes.\\r\\n y_intercept = True or False. Indicate if the regression should have a\\r\\n y intercept. The default is False.\\r\\n \\r\\n OUTPUTS:\\r\\n quote_df = the original quote_df updated with columns for optimal price,\\r\\n win probability, etc.\\r\\n total_deal_stats = the statistics of the total deal\\r\\n \\r\\n ACCEPTANCE CRITERIA:\\r\\n The quote_df contains the required output column populated with data.\\r\\n '''\\r\\n start_time = time.time() #start a timer\\r\\n \\r\\n #this loads the correct model into seg_df based on the model_ID\\r\\n etext = 'seg_df = ' + rules_df.loc[model_ID,'seg_model_file_name'] + '_df.copy()'\\r\\n exec(etext)\\r\\n #this looks up the needed variables given the model_ID\\r\\n quote_id_col = rules_df.loc[model_ID,'quote_id_col']\\r\\n ref_prc_col = rules_df.loc[model_ID,'ref_prc_col']\\r\\n cost_col = rules_df.loc[model_ID,'cost_col']\\r\\n quote_prc_col = rules_df.loc[model_ID,'quote_prc_col']\\r\\n prc_col = rules_df.loc[model_ID,'prc_col']\\r\\n x_col = rules_df.loc[model_ID,'x_col']\\r\\n first = rules_df.loc[model_ID,'first']\\r\\n last = rules_df.loc[model_ID,'last']\\r\\n psych_factor = rules_df.loc[model_ID,'psych_factor']\\r\\n quote_attribute_cols = eval(rules_df.loc[model_ID,'quote_attribute_cols'])\\r\\n y_intercept = rules_df.loc[model_ID,'y_intercept']\\r\\n \\r\\n #The following defines objects needed to manage the segmentation tree building process\\r\\n cols = quote_df.columns\\r\\n for i in np.arange(len(cols)): #this for loop assigns column names to a list (cols)\\r\\n if (cols[i] == first):\\r\\n first_coln = i\\r\\n if (cols[i] == last):\\r\\n last_coln = i\\r\\n if (cols[i] == prc_col):\\r\\n prc_coln = i \\r\\n print ' Segmentation starts with Column (first_coln)(first): % r' % first_coln, first\\r\\n print ' Segmentation ends with Column (last_coln)(last): % r' % last_coln, last\\r\\n column_nums = range(first_coln, last_coln + 1) #this creates a list of the numbers of the columns used for component segmentation\\r\\n column_names = list(quote_df.columns[column_nums])\\r\\n print ' Column numbers of the index columns (column_nums): ',column_nums\\r\\n print ' Column names of the index column (column_names): ',column_names\\r\\n print ' Price Column (prc_coln)(prc_col): % r' % prc_coln, prc_col\\r\\n print ' quote_attribute_cols: % r' % quote_attribute_cols\\r\\n \\r\\n \\r\\n #this section ensures that needed columns are added to quote_df to match format of input_dataframe_expanded_05.csv\\r\\n quote_df.loc[:,'ComLowPofL'] = ''\\r\\n quote_df.loc[:,'ComMedPofL'] = ''\\r\\n quote_df.loc[:,'ComHighPofL'] = ''\\r\\n quote_df.loc[:,'ComMedPrice'] = ''\\r\\n quote_df.loc[:,'DealSize'] = ''\\r\\n quote_df.loc[:,'LogDealSize'] = ''\\r\\n quote_df.loc[:,'ComPctContrib'] = ''\\r\\n quote_df.loc[:,'TreeNode'] = ''\\r\\n #this section adds more columns to support optimal price calculations\\r\\n quote_df.loc[:,'ComTMCPofL'] = ''\\r\\n quote_df.loc[:,'AdjComLowPofL'] = ''\\r\\n quote_df.loc[:,'AdjComMedPofL'] = ''\\r\\n quote_df.loc[:,'AdjComHighPofL'] = ''\\r\\n quote_df.loc[:,'AdjComLowPrice'] = ''\\r\\n quote_df.loc[:,'AdjComMedPrice'] = ''\\r\\n quote_df.loc[:,'AdjComHighPrice'] = ''\\r\\n #this section creates columns for the optimal price data\\r\\n quote_df.loc[:,'OptimalPricePofL'] = ''\\r\\n quote_df.loc[:,'OptimalPrice'] = ''\\r\\n quote_df.loc[:,'OptimalPriceWinProb'] = ''\\r\\n quote_df.loc[:,'OptimalPriceGP'] = ''\\r\\n quote_df.loc[:,'OptimalPriceExpectedGP'] = ''\\r\\n quote_df.loc[:,'OptimalPriceIntervalLow'] = ''\\r\\n quote_df.loc[:,'OptimalPriceIntervalHigh'] = ''\\r\\n #this section creates columns for the quoted price data\\r\\n quote_df.loc[:,'QuotePricePofL'] = ''\\r\\n quote_df.loc[:,'QuotePrice'] = ''\\r\\n quote_df.loc[:,'QuotePriceWinProb'] = ''\\r\\n quote_df.loc[:,'QuotePriceGP'] = ''\\r\\n quote_df.loc[:,'QuotePriceExpectedGP'] = ''\\r\\n #this section creates columns for statistics\\r\\n quote_df.loc[:,'PredictedQuotePricePofL'] = ''\\r\\n quote_df.loc[:,'PredictedQuotePrice'] = ''\\r\\n #this section is for COP (Customized Optimal Price)\\r\\n # - L, M, H price points\\r\\n quote_df.loc[:,'COPComLowPrice'] = ''\\r\\n quote_df.loc[:,'COPComMedPrice'] = ''\\r\\n quote_df.loc[:,'COPComHighPrice'] = ''\\r\\n # - L, M, H price points as a % of List\\r\\n quote_df.loc[:,'COPComLowPofL'] = ''\\r\\n quote_df.loc[:,'COPComMedPofL'] = ''\\r\\n quote_df.loc[:,'COPComHighPofL'] = ''\\r\\n # - optimal prices and win probabilities\\r\\n quote_df.loc[:,'COPOptimalPrice'] = ''\\r\\n quote_df.loc[:,'COPOptimalPricePofL'] = ''\\r\\n quote_df.loc[:,'COPOptimalPriceWinProb'] = ''\\r\\n quote_df.loc[:,'COPOptimalPriceGP'] = ''\\r\\n quote_df.loc[:,'COPOptimalPriceExpectedGP'] = ''\\r\\n quote_df.loc[:,'COPOptimalPriceIntervalLow'] = ''\\r\\n quote_df.loc[:,'COPOptimalPriceIntervalHigh'] = ''\\r\\n # - quoted price data within customized optimal price (COP)\\r\\n quote_df.loc[:,'COPQuotePriceWinProb'] = ''\\r\\n quote_df.loc[:,'COPQuotePriceGP'] = ''\\r\\n quote_df.loc[:,'COPQuotePriceExpectedGP'] = ''\\r\\n \\r\\n #this section determines all of the column data needed for optimal price calculation\\r\\n #this determines the segment tree node to be used for each component of the quote\\r\\n column_headings = list(cols[column_nums])\\r\\n #seg_df.set_index(column_headings, inplace = True)\\r\\n for i in range(len(quote_df)): #this goes through each component in the quote_df\\r\\n column_values = list(quote_df.loc[i,column_headings])\\r\\n #print column_values\\r\\n works = False\\r\\n j = -1\\r\\n while works == False: #this finds the most specific valid node in the component segment tree\\r\\n try:\\r\\n Low_m = float(seg_df.loc[tuple(column_values), 'Low_m'])\\r\\n Low_b = float(seg_df.loc[tuple(column_values), 'Low_b'])\\r\\n Med_m = float(seg_df.loc[tuple(column_values), 'Med_m'])\\r\\n Med_b = float(seg_df.loc[tuple(column_values), 'Med_b'])\\r\\n High_m = float(seg_df.loc[tuple(column_values), 'High_m'])\\r\\n High_b = float(seg_df.loc[tuple(column_values), 'High_b'])\\r\\n Tree_Node = [seg_df.index.get_loc(tuple(column_values))][0]\\r\\n works = True\\r\\n except KeyError:\\r\\n column_values[j] = ''\\r\\n j -= 1\\r\\n \\r\\n #this calculates the low, med, and high (%ofList) price points\\r\\n x = quote_df.loc[i,x_col]\\r\\n low = Low_m * x + Low_b\\r\\n med = Med_m * x + Med_b\\r\\n high = High_m * x + High_b \\r\\n low,med,high = BPF.PriceAdj(low, med, high) #this makes any needed adjustments to the low, med, and high price points to eliminate anomolies\\r\\n #this writes the low, med, and high to the quote_df\\r\\n quote_df.loc[i,'ComLowPofL'] = low\\r\\n quote_df.loc[i,'ComMedPofL'] = med\\r\\n quote_df.loc[i,'ComHighPofL'] = high\\r\\n quote_df.loc[i,'TreeNode'] = Tree_Node\\r\\n #print 'Component Row, Tree_Node, & ComMedPofL: ', i, Tree_Node, med\\r\\n \\r\\n #this calculates and sets the ComMedPrice\\r\\n quote_df.loc[:,'ComMedPrice'] = (quote_df[ref_prc_col] * quote_df['ComMedPofL'])#.round(decimals = 2)\\r\\n #this calculates and sets the DealSize\\r\\n quote_df.loc[:,'DealSize'] = quote_df['ComMedPrice'].sum().round(decimals = 2)\\r\\n #this calculates and sets the Log of the DealSize\\r\\n quote_df.loc[:,'LogDealSize'] = np.log10(quote_df['DealSize'].astype(float))\\r\\n #this calculates the component's percent price contribution to the quote (based on component median price)\\r\\n quote_df.loc[:,'ComPctContrib'] = quote_df['ComMedPrice'] / quote_df['DealSize']\\r\\n \\r\\n #this section calculates the adjusted L, M, H values\\r\\n seg_df.reset_index(inplace = True)\\r\\n for i in range(len(quote_df)): #this goes through each component in the quote_df\\r\\n #set adjusted values for low and high to match original values for low and high\\r\\n adjlow = quote_df.loc[i,'ComLowPofL']\\r\\n adjhigh = quote_df.loc[i,'ComHighPofL']\\r\\n #adjust median point in price sensitivity curve for quote level attributes\\r\\n adjmed = 0\\r\\n for j in range(len(quote_attribute_cols)): #this calculates the value of the multiple linear regression\\r\\n adjmed += quote_df.loc[i, quote_attribute_cols[j]] * seg_df.loc[quote_df.loc[i, 'TreeNode'], 'QP_' + quote_attribute_cols[j]]\\r\\n #print 'Factors: ',i,j,quote_df.loc[i, quote_attribute_cols[j]], seg_df.loc[quote_df.loc[i, 'TreeNode'], 'QP_' + quote_attribute_cols[j]]\\r\\n if y_intercept == True:\\r\\n adjmed += seg_df.loc[quote_df.loc[i, 'TreeNode'], 'QP_intercept']\\r\\n #adjust median point in price sensitivity curve for skew in data\\r\\n quote_df.loc[i, 'PredictedQuotePricePofL'] = adjmed\\r\\n quote_df.loc[i, 'PredictedQuotePrice'] = adjmed * quote_df.loc[i, 'ComListPrice']\\r\\n adjmed += seg_df.loc[quote_df.loc[i, 'TreeNode'], 'Adj_YtoMedPofL']\\r\\n #adjust all points in price sensitivity curve for bias from using win data only\\r\\n adj = seg_df.loc[quote_df.loc[i, 'TreeNode'], 'Adj_psych']\\r\\n adjlow *= adj\\r\\n adjmed *= adj\\r\\n adjhigh *= adj\\r\\n #adjust all points in price sensitivity curve for brand input\\r\\n adj = seg_df.loc[quote_df.loc[i, 'TreeNode'], 'Adj_InputByBrand']\\r\\n adjlow += adj\\r\\n adjmed += adj\\r\\n adjhigh += adj\\r\\n adjlow,adjmed,adjhigh = BPF.PriceAdj(adjlow, adjmed, adjhigh) #this makes any needed adjustments to the low, med, and high price points to eliminate anomolies\\r\\n #store adjusted values\\r\\n quote_df.loc[i, 'AdjComLowPofL'] = adjlow\\r\\n quote_df.loc[i, 'AdjComMedPofL'] = adjmed\\r\\n quote_df.loc[i, 'AdjComHighPofL'] = adjhigh\\r\\n ListPrice = quote_df.loc[i, 'ComListPrice']\\r\\n quote_df.loc[i, 'AdjComLowPrice'] = adjlow * ListPrice\\r\\n quote_df.loc[i, 'AdjComMedPrice'] = adjmed * ListPrice\\r\\n quote_df.loc[i, 'AdjComHighPrice'] = adjhigh * ListPrice\\r\\n \\r\\n #this section calculates the optimal price data\\r\\n for i in range(len(quote_df)): #this goes through each component in the quote_df\\r\\n L = quote_df.loc[i,'AdjComLowPofL']\\r\\n M = quote_df.loc[i,'AdjComMedPofL']\\r\\n H = quote_df.loc[i,'AdjComHighPofL']\\r\\n Cf = 1.0 * quote_df.loc[i,'ComTMC'] / quote_df.loc[i,'ComListPrice']\\r\\n quote_df.loc[i, 'ComTMCPofL'] = Cf\\r\\n quote_df.loc[i, 'OptimalPricePofL'] = BPF.OptPrice(L, M, H, Cf, 0, 0, 0)\\r\\n quote_df.loc[i, 'OptimalPrice'] = quote_df.loc[i, 'OptimalPricePofL'] * quote_df.loc[i, 'ComListPrice']\\r\\n quote_df.loc[i, 'OptimalPriceWinProb'] = BPF.ProbOfWin(quote_df.loc[i, 'OptimalPricePofL'], L, M, H)\\r\\n quote_df.loc[i, 'OptimalPriceGP'] = quote_df.loc[i, 'OptimalPrice'] - quote_df.loc[i,'ComTMC']\\r\\n quote_df.loc[i, 'OptimalPriceExpectedGP'] = quote_df.loc[i, 'OptimalPriceGP'] * quote_df.loc[i, 'OptimalPriceWinProb']\\r\\n quote_df.loc[i, 'OptimalPriceIntervalLow'], quote_df.loc[i, 'OptimalPriceIntervalHigh'] = BPF.OptPriceConfIntervl(quote_df.loc[i, 'OptimalPrice'], quote_df.loc[i,'AdjComLowPrice'], quote_df.loc[i,'AdjComMedPrice'], quote_df.loc[i,'AdjComHighPrice'], quote_df.loc[i, 'ComTMC'])\\r\\n #this section shows quoted (i.e. requested) price data\\r\\n quote_df.loc[i, 'QuotePricePofL'] = quote_df.loc[i, 'ComQuotePricePofL']\\r\\n quote_df.loc[i, 'QuotePrice'] = quote_df.loc[i, 'ComQuotePrice']\\r\\n quote_df.loc[i, 'QuotePriceWinProb'] = BPF.ProbOfWin(quote_df.loc[i, 'QuotePricePofL'], L, M, H)\\r\\n quote_df.loc[i, 'QuotePriceGP'] = quote_df.loc[i, 'QuotePrice'] - quote_df.loc[i,'ComTMC']\\r\\n quote_df.loc[i, 'QuotePriceExpectedGP'] = quote_df.loc[i, 'QuotePriceGP'] * quote_df.loc[i, 'QuotePriceWinProb']\\r\\n \\r\\n #this section calculates the Customized Optimal Price (COP) data\\r\\n if (COP_h > COP_m) and (COP_m > COP_l):\\r\\n Lt = quote_df['AdjComLowPrice'].sum()\\r\\n Mt = quote_df['AdjComMedPrice'].sum()\\r\\n Ht = quote_df['AdjComHighPrice'].sum()\\r\\n LISTt = quote_df['ComListPrice'].sum()\\r\\n for i in range(len(quote_df)): #this goes through each component in the quote_df\\r\\n Li = quote_df.loc[i, 'AdjComLowPrice'].round(decimals=2)\\r\\n Mi = quote_df.loc[i, 'AdjComMedPrice'].round(decimals=2)\\r\\n Hi = quote_df.loc[i, 'AdjComHighPrice'].round(decimals=2)\\r\\n LISTi = quote_df.loc[i, 'ComListPrice'].round(decimals=2)\\r\\n #this section uses linear interpolation for setting COP component prices from the bottom line price\\r\\n quote_df.loc[i, 'COPComLowPrice'] = BPF.PriceConv(Lt, Mt, Ht, LISTt, COP_l, Li, Mi, Hi, LISTi).round(decimals=2)\\r\\n quote_df.loc[i, 'COPComMedPrice'] = BPF.PriceConv(Lt, Mt, Ht, LISTt, COP_m, Li, Mi, Hi, LISTi).round(decimals=2)\\r\\n quote_df.loc[i, 'COPComHighPrice'] = BPF.PriceConv(Lt, Mt, Ht, LISTt, COP_h, Li, Mi, Hi, LISTi).round(decimals=2)\\r\\n #this section calculate the COP PofL prices\\r\\n quote_df.loc[i, 'COPComLowPofL'] = quote_df.loc[i, 'COPComLowPrice'] / LISTi\\r\\n quote_df.loc[i, 'COPComMedPofL'] = quote_df.loc[i, 'COPComMedPrice'] / LISTi\\r\\n quote_df.loc[i, 'COPComHighPofL'] = quote_df.loc[i, 'COPComHighPrice'] / LISTi\\r\\n #this section calculate the COP optimal prices, win probabilities, and profitability\\r\\n quote_df.loc[i, 'COPOptimalPrice'] = BPF.OptPrice(quote_df.loc[i, 'COPComLowPrice'], quote_df.loc[i, 'COPComMedPrice'], quote_df.loc[i, 'COPComHighPrice'], quote_df.loc[i, 'ComTMC'], 0, 0, 0).round(decimals=2)\\r\\n quote_df.loc[i, 'COPOptimalPricePofL'] = quote_df.loc[i, 'COPOptimalPrice'] / LISTi\\r\\n quote_df.loc[i, 'COPOptimalPriceWinProb'] = BPF.ProbOfWin(quote_df.loc[i, 'COPOptimalPrice'], quote_df.loc[i, 'COPComLowPrice'], quote_df.loc[i, 'COPComMedPrice'], quote_df.loc[i, 'COPComHighPrice'])\\r\\n quote_df.loc[i, 'COPOptimalPriceGP'] = quote_df.loc[i, 'COPOptimalPrice'] - quote_df.loc[i, 'ComTMC']\\r\\n quote_df.loc[i, 'COPOptimalPriceExpectedGP'] = (quote_df.loc[i, 'COPOptimalPriceGP'] * quote_df.loc[i, 'COPOptimalPriceWinProb']).round(decimals=2)\\r\\n quote_df.loc[i, 'COPOptimalPriceIntervalLow'], quote_df.loc[i, 'COPOptimalPriceIntervalHigh'] = BPF.OptPriceConfIntervl(quote_df.loc[i, 'COPOptimalPrice'], quote_df.loc[i,'COPComLowPrice'], quote_df.loc[i,'COPComMedPrice'], quote_df.loc[i,'COPComHighPrice'], quote_df.loc[i, 'ComTMC'])\\r\\n #this section calculates the quoted price statistics given the COP prices\\r\\n quote_df.loc[i, 'COPQuotePriceWinProb'] = BPF.ProbOfWin(quote_df.loc[i, 'QuotePrice'], quote_df.loc[i, 'COPComLowPrice'], quote_df.loc[i, 'COPComMedPrice'], quote_df.loc[i, 'COPComHighPrice'])\\r\\n quote_df.loc[i, 'COPQuotePriceGP'] = quote_df.loc[i, 'QuotePriceGP']\\r\\n quote_df.loc[i, 'COPQuotePriceExpectedGP'] = quote_df.loc[i, 'COPQuotePriceGP'] * quote_df.loc[i, 'COPQuotePriceWinProb']\\r\\n print; print ' quote_df update complete'\\r\\n \\r\\n \\r\\n #this section calculates the total deal values\\r\\n # this section contains general quote totals\\r\\n total_deal_stats = pd.Series('', index=[' General Total Quote Data'])\\r\\n total_deal_stats['DealListPrice'] = quote_df['ComListPrice'].sum()\\r\\n total_deal_stats['DealSize'] = quote_df['ComMedPrice'].sum().round(decimals=2)\\r\\n total_deal_stats['DealTMC'] = quote_df['ComTMC'].sum()\\r\\n total_deal_stats['DealPredictedQuotePrice'] = quote_df['PredictedQuotePrice'].sum().round(decimals=0)\\r\\n # this section contains Price Range Data (Line Item Sum)\\r\\n total_deal_stats[' Price Range Data (Line Item Sum)'] = ''\\r\\n total_deal_stats['DealAdjLowPrice'] = quote_df['AdjComLowPrice'].sum().round(decimals=0)\\r\\n total_deal_stats['DealAdjMedPrice'] = quote_df['AdjComMedPrice'].sum().round(decimals=0)\\r\\n total_deal_stats['DealAdjHighPrice'] = quote_df['AdjComHighPrice'].sum().round(decimals=0)\\r\\n # this section contains Quoted Price Data (Line Item Sum)\\r\\n total_deal_stats[' Quoted Price Data (Line Item Sum)'] = ''\\r\\n total_deal_stats['DealQuotePrice'] = quote_df['ComQuotePrice'].sum()\\r\\n total_deal_stats['DealQuotePriceWinProb'] = ''\\r\\n total_deal_stats['DealQuotePriceGP'] = total_deal_stats['DealQuotePrice'] - total_deal_stats['DealTMC']\\r\\n total_deal_stats['DealQuotePriceExpectedGP'] = quote_df['QuotePriceExpectedGP'].sum()\\r\\n total_deal_stats['DealQuotePriceWinProb'] = total_deal_stats['DealQuotePriceExpectedGP'] / total_deal_stats['DealQuotePriceGP']\\r\\n # this section contains optimal price data\\r\\n total_deal_stats[' Optimal Price Data (Line Item Sum)'] = ''\\r\\n total_deal_stats['DealOptimalPrice'] = quote_df['OptimalPrice'].sum().round(decimals=0)\\r\\n total_deal_stats['DealOptimalPriceWinProb'] = ''\\r\\n total_deal_stats['DealOptimalPriceGP'] = quote_df['OptimalPriceGP'].sum().round(decimals=2)\\r\\n total_deal_stats['DealOptimalPriceExpectedGP'] = quote_df['OptimalPriceExpectedGP'].sum().round(decimals=2)\\r\\n total_deal_stats['DealOptimalPriceWinProb'] = total_deal_stats['DealOptimalPriceExpectedGP'] / total_deal_stats['DealOptimalPriceGP']\\r\\n total_deal_stats['DealOptimalPriceIntervalLow'] = quote_df['OptimalPriceIntervalLow'].sum().round(decimals=0)\\r\\n total_deal_stats['DealOptimalPriceIntervalHigh'] = quote_df['OptimalPriceIntervalHigh'].sum().round(decimals=0)\\r\\n # this section contains Quoted Price Data (Bottom-Line)\\r\\n total_deal_stats[' Quoted Price Data (Bottom-Line)'] = ''\\r\\n total_deal_stats['DealBotLineQuotePrice'] = total_deal_stats['DealQuotePrice']\\r\\n total_deal_stats['DealBotLineQuotePriceWinProb'] = BPF.ProbOfWin(total_deal_stats['DealBotLineQuotePrice'], total_deal_stats['DealAdjLowPrice'], total_deal_stats['DealAdjMedPrice'], total_deal_stats['DealAdjHighPrice'])\\r\\n total_deal_stats['DealBotLineQuotePriceGP'] = total_deal_stats['DealBotLineQuotePrice'] - total_deal_stats['DealTMC']\\r\\n total_deal_stats['DealBotLineQuotePriceExpectedGP'] = total_deal_stats['DealBotLineQuotePriceGP'] * total_deal_stats['DealBotLineQuotePriceWinProb']\\r\\n # this section contains Optimal Price Data (Bottom-Line)\\r\\n total_deal_stats[' Optimal Price Data (Bottom-Line)'] = ''\\r\\n total_deal_stats['DealBotLineOptimalPrice'] = BPF.OptPrice(total_deal_stats['DealAdjLowPrice'], total_deal_stats['DealAdjMedPrice'], total_deal_stats['DealAdjHighPrice'], total_deal_stats['DealTMC'], 0, 0, 0)\\r\\n total_deal_stats['DealBotLineOptimalPriceWinProb'] = BPF.ProbOfWin(total_deal_stats['DealBotLineOptimalPrice'], total_deal_stats['DealAdjLowPrice'], total_deal_stats['DealAdjMedPrice'], total_deal_stats['DealAdjHighPrice'])\\r\\n total_deal_stats['DealBotLineOptimalPriceGP'] = total_deal_stats['DealBotLineOptimalPrice'] - total_deal_stats['DealTMC']\\r\\n total_deal_stats['DealBotLineOptimalPriceExpectedGP'] = total_deal_stats['DealBotLineOptimalPriceGP'] * total_deal_stats['DealBotLineOptimalPriceWinProb']\\r\\n total_deal_stats['DealBotLineOptimalPriceIntervalLow'], total_deal_stats['DealBotLineOptimalPriceIntervalHigh'] = BPF.OptPriceConfIntervl(total_deal_stats['DealBotLineOptimalPrice'], total_deal_stats['DealAdjLowPrice'], total_deal_stats['DealAdjMedPrice'], total_deal_stats['DealAdjHighPrice'], total_deal_stats['DealTMC'])\\r\\n \\r\\n #this section is executed only if customized optimal pricing (COP) is needed\\r\\n if (COP_h > COP_m) and (COP_m > COP_l):\\r\\n # this section contains COP Price Range Data (Line Item Sum)\\r\\n total_deal_stats[' COP Price Range Data (Line Item Sum)'] = ''\\r\\n total_deal_stats['DealCOPLowPrice'] = quote_df['COPComLowPrice'].sum().round(decimals=0)\\r\\n total_deal_stats['DealCOPMedPrice'] = quote_df['COPComMedPrice'].sum().round(decimals=0)\\r\\n total_deal_stats['DealCOPHighPrice'] = quote_df['COPComHighPrice'].sum().round(decimals=0)\\r\\n # this section contains COP Quote Price Data (Line Item Sum)\\r\\n total_deal_stats[' COP Quote Price Data (Line Item Sum)'] = ''\\r\\n total_deal_stats['DealCOPQuotePrice'] = quote_df['ComQuotePrice'].sum()\\r\\n total_deal_stats['DealCOPQuotePriceWinProb'] = ''\\r\\n total_deal_stats['DealCOPQuotePriceGP'] = quote_df['COPQuotePriceGP'].sum().round(decimals=0)\\r\\n total_deal_stats['DealCOPQuotePriceExpectedGP'] = quote_df['COPQuotePriceExpectedGP'].sum().round(decimals=0)\\r\\n total_deal_stats['DealCOPQuotePriceWinProb'] = total_deal_stats['DealCOPQuotePriceExpectedGP'] / total_deal_stats['DealCOPQuotePriceGP']\\r\\n # this section contains COP Optimal Price Data (Line Item Sum)\\r\\n total_deal_stats[' COP Optimal Price Data (Line Item Sum)'] = ''\\r\\n total_deal_stats['DealCOPOptimalPrice'] = quote_df['COPOptimalPrice'].sum().round(decimals=0)\\r\\n total_deal_stats['DealCOPOptimalPriceWinProb'] = ''\\r\\n total_deal_stats['DealCOPOptimalPriceGP'] = quote_df['COPOptimalPriceGP'].sum().round(decimals=2)\\r\\n total_deal_stats['DealCOPOptimalPriceExpectedGP'] = quote_df['COPOptimalPriceExpectedGP'].sum().round(decimals=2)\\r\\n total_deal_stats['DealCOPOptimalPriceWinProb'] = total_deal_stats['DealCOPOptimalPriceExpectedGP'] / total_deal_stats['DealCOPOptimalPriceGP']\\r\\n total_deal_stats['DealCOPOptimalPriceIntervalLow'] = quote_df['COPOptimalPriceIntervalLow'].sum().round(decimals=0)\\r\\n total_deal_stats['DealCOPOptimalPriceIntervalHigh'] = quote_df['COPOptimalPriceIntervalHigh'].sum().round(decimals=0)\\r\\n # this section contains quoted price data within the Customized Optimal Price (COP) estimates\\r\\n total_deal_stats[' COP Quote Price Data (Bottom-Line)'] = ''\\r\\n total_deal_stats['DealCOPBotLineQuotePrice'] = total_deal_stats['DealQuotePrice']\\r\\n total_deal_stats['DealCOPBotLineQuotePriceWinProb'] = BPF.ProbOfWin(total_deal_stats['DealCOPQuotePrice'], total_deal_stats['DealCOPLowPrice'], total_deal_stats['DealCOPMedPrice'], total_deal_stats['DealCOPHighPrice'])\\r\\n total_deal_stats['DealCOPBotLineQuotePriceGP'] = total_deal_stats['DealCOPBotLineQuotePrice'] - total_deal_stats['DealTMC']\\r\\n total_deal_stats['DealCOPBotLineQuotePriceExpectedGP'] = total_deal_stats['DealCOPBotLineQuotePriceGP'] *total_deal_stats['DealCOPBotLineQuotePriceWinProb']\\r\\n # this section contains COP Optimal Price Data (Bottom-Line)\\r\\n total_deal_stats[' COP Optimal Price Data (Bottom-Line)'] = ''\\r\\n total_deal_stats['DealCOPBotLineOptimalPrice'] = BPF.OptPrice(total_deal_stats['DealCOPLowPrice'], total_deal_stats['DealCOPMedPrice'], total_deal_stats['DealCOPHighPrice'], total_deal_stats['DealTMC'], 0, 0, 0).round(decimals=0)\\r\\n total_deal_stats['DealCOPBotLineOptimalPriceWinProb'] = BPF.ProbOfWin(total_deal_stats['DealCOPBotLineOptimalPrice'], total_deal_stats['DealCOPLowPrice'], total_deal_stats['DealCOPMedPrice'], total_deal_stats['DealCOPHighPrice'])\\r\\n total_deal_stats['DealCOPBotLineOptimalPriceGP'] = total_deal_stats['DealCOPBotLineOptimalPrice'] - total_deal_stats['DealTMC']\\r\\n total_deal_stats['DealCOPBotLineOptimalPriceExpectedGP'] = total_deal_stats['DealCOPBotLineOptimalPriceGP'] * total_deal_stats['DealCOPBotLineOptimalPriceWinProb']\\r\\n total_deal_stats['DealCOPBotLineOptimalPriceIntervalLow'], total_deal_stats['DealCOPBotLineOptimalPriceIntervalHigh'] = BPF.OptPriceConfIntervl(total_deal_stats['DealCOPBotLineOptimalPrice'], total_deal_stats['DealCOPLowPrice'], total_deal_stats['DealCOPMedPrice'], total_deal_stats['DealCOPHighPrice'], total_deal_stats['DealTMC'])\\r\\n \\r\\n print ' total_deal_stats creation complete'\\r\\n \\r\\n #This stores copies of the returned data\\r\\n quote_df.to_csv(data_path + 'quote_return.csv')\\r\\n total_deal_stats.to_csv(data_path + 'total_deal_stats_return.csv') \\r\\n \\r\\n end_time = time.time() #stop a timer\\r\\n print 'Elapsed time (milliseconds): ', int(1000 * (end_time - start_time))\\r\\n \\r\\n return quote_df, total_deal_stats\",\n \"def cost_volume_profit():\\r\\n c = float(input(\\\"Please Enter Total Fixed Costs Value: \\\"))\\r\\n a = float(input(\\\"Please Enter Sale Price Per Unit: \\\"))\\r\\n b = float(input(\\\"Please Enter Variable Cost Per Unit: \\\"))\\r\\n ccm = float(a)-float(b)\\r\\n cuu = float(c)/float(ccm)\\r\\n ccmr = (float(ccm)/float(a))*float(100)\\r\\n cda = float(c)/(float(ccmr)/float(100))\\r\\n print \\\">> Your Contribution Margin is\\\",ccm\\r\\n print \\\">> Your Breakeven Sales in Units is\\\",round(cuu)\\r\\n print \\\">> Your Contribution Margin Ratio is\\\",ccmr,\\\"%\\\"\\r\\n print \\\">> Your Breakeven Sales in Dollars is\\\",cda,\\\"\\\\n\\\"\\r\\n qq = input(\\\" Press 1 To Compute Target Profit\\\\n Press 2 To Compute Margin of Safety\\\\n Press 3 To Perform Sensitivity Analysis\\\\n Or Press 0 To Exit: \\\")\\r\\n if(qq == 1):\\r\\n dds = float(input(\\\"Please Enter Your Target Profit: \\\"))\\r\\n xxx = (float(c)+float(dds))/float(ccm)\\r\\n xxxx = (float(c)+float(dds))/(float(ccmr)/float(100))\\r\\n print \\\">> Your Target Profit in Units To Earn\\\",dds,\\\"$ is\\\",round(xxx)\\r\\n print \\\">> Your Target Profit in Dollars To Earn\\\",dds,\\\"$ is\\\",xxxx\\r\\n elif(qq == 0):\\r\\n print \\\"Canceled\\\"\\r\\n elif(qq == 2):\\r\\n xc = float(input(\\\"Please Enter Expected Sales in Units: \\\"))\\r\\n zzz = float(xc)-float(cuu)\\r\\n zzzz = float(zzz)*float(a)\\r\\n print \\\">> Your Margin of Safety in Units is\\\",round(zzz)\\r\\n print \\\">> Your Margin of Safety in Dollars is\\\",zzzz\\r\\n elif(qq == 3):\\r\\n i = input(\\\"Please Enter Total Fixed Costs Value: \\\")\\r\\n o = input(\\\"Please Enter Sale Price Per Unit: \\\")\\r\\n p = input(\\\"Please Enter Variable Cost Per Unit: \\\")\\r\\n n = 0\\r\\n for x,y,z in zip(i,o,p):\\r\\n cm = float(y)-float(z)\\r\\n uu = float(x)/float(cm)\\r\\n cmr = (float(cm)/float(y))*float(100)\\r\\n da = float(x)/(float(cmr)/float(100))\\r\\n n += 1\\r\\n print \\\"Your Results in Case\\\",int(n),\\\"is :\\\"\\r\\n print \\\">> Your Contribution Margin is\\\",cm\\r\\n print \\\">> Your Breakeven Sales in Units is\\\",round(uu)\\r\\n print \\\">> Your Contribution Margin Ratio is\\\",cmr,\\\"%\\\"\\r\\n print \\\">> Your Breakeven Sales in Dollars is\\\",da,\\\"\\\\n\\\"\\r\\n if(cm > ccm):\\r\\n a = float(cm)-float(ccm)\\r\\n print \\\">> Your Contribution Margin Increased by\\\",a\\r\\n elif(ccm > cm):\\r\\n a = float(ccm)-float(cm)\\r\\n print \\\">> Your Contribution Margin Decreased by\\\",a\\r\\n if(uu > cuu):\\r\\n b = float(uu)-float(cuu)\\r\\n print \\\">> Your Breakeven Sales in Units Increased by\\\",round(b)\\r\\n elif(cuu > uu):\\r\\n b = float(cuu)-float(uu)\\r\\n print \\\">> Your Breakeven Sales in Units Decreased by\\\",round(b)\\r\\n if(cmr > ccmr):\\r\\n c = float(cmr)-float(ccmr)\\r\\n print \\\">> Your Contribution Margin Ratio Increased by\\\",c,\\\"%\\\"\\r\\n elif(ccmr > cmr):\\r\\n c = float(ccmr)-float(cmr)\\r\\n print \\\">> Your Contribution Margin Ratio Decreased by\\\",c,\\\"%\\\"\\r\\n if(da > cda):\\r\\n d = float(da)-float(cda)\\r\\n print \\\">> Your Breakeven Sales in Dollars Increased by\\\",d\\r\\n elif(cda > da):\\r\\n d = float(cda)-float(da)\\r\\n print \\\">> Your Breakeven Sales in Dollars Decreased by\\\",d,\\\"\\\\n\\\"\",\n \"def calc_price(p_counts, K, r, T, M, option):\\n if p_counts.shape[0] == 0: # not possible to exercise any option\\n return(0)\\n if option == 'Call':\\n price = np.mean(p_counts[:,-1]-K)*np.exp(-1*r*T)*(p_counts.shape[0]/M)\\n elif option == 'Put':\\n price = np.mean(K-p_counts[:,-1])*np.exp(-1*r*T)*(p_counts.shape[0]/M)\\n return price\",\n \"def calculate_and_save(self):\\n # Calculate the quotation price\\n self.quotationPrice = self.compute_quotation_price()\\n self.save()\",\n \"def do_submit(self, price_float, volume_float):\\r\\n raise NotImplementedError()\",\n \"def calc_price(p_counts, K, r, T, M, option):\\n if p_counts.shape[0] == 0: # not possible to exercise any option\\n return (0)\\n if option == 'Call':\\n price = np.mean(p_counts[:, -1] - K) * np.exp(-1 * r * T) * (p_counts.shape[0] / M)\\n elif option == 'Put':\\n price = np.mean(K - p_counts[:, -1]) * np.exp(-1 * r * T) * (p_counts.shape[0] / M)\\n else:\\n raise KeyError(\\\"Options Type Input Error. Either Call or Put\\\")\\n return price\",\n \"def test_basicSalePC(self):\\n # Basic price check\\n self.log.info(\\\"Price checking Generic Item via speedkey\\\")\\n pos.click(\\\"Price Check\\\")\\n pos.click_speed_key(\\\"Generic Item\\\")\\n \\n # Confirm the right item, at the right price\\n self.read_price_check(\\\"Generic Item\\\", \\\"$0.01\\\")\\n # Add the item\\n pos.click(\\\"Sell Item\\\")\\n \\n # Confirm we added the item\\n ret = self.confirm_line(-1, \\\"Generic Item\\\", \\\"$0.01\\\")\\n if ret == True:\\n self.log.info(\\\"Confirmed item added\\\")\\n else:\\n self.tc_fail(ret)\\n \\n # Setup for next test\\n self.recover()\",\n \"def test_price_return(self, basic_factory, garment_factory, affiliate_item_factory):\\n basic = basic_factory()\\n garment = garment_factory(basic=basic)\\n affiliate_item_factory(garment=garment, price=Decimal(100))\\n\\n budget_end, luxury_start = update_basic_price_points(basic)\\n\\n assert budget_end == Decimal(100)\\n assert luxury_start == Decimal(100)\",\n \"def price(usr, item, searches = 2, method = \\\"AVERAGE\\\", deduct = 0):\\n if not method in ShopWizard.methods: raise invalidMethod()\\n\\n if isinstance(item, Item):\\n item = item.name\\n\\n prices = []\\n dets = {}\\n for x in range(0, searches):\\n print('performing search: {}'.format(x))\\n results = ShopWizard.search(usr, item)\\n\\n # Set to -1 if not found\\n if not results:\\n prices.append(-1)\\n continue\\n\\n prices.append(int(results[item].price))\\n dets[str(results[item].price)] = (results[item].owner, results[item].id)\\n\\n time.sleep(ShopWizard.waitTime)\\n\\n # Determines if item was UB\\n print(prices)\\n if sum(prices) == len(prices) * -1:\\n return False\\n\\n prices = list(filter(lambda x: x != -1, prices))\\n\\n if method == ShopWizard.RETLOW:\\n price = sorted(prices)[0]\\n return (price, dets[str(price)][0], dets[str(price)][1])\\n\\n return ShopWizard.__determinePrice(prices, method, deduct)\",\n \"def bond_price(fv, c,n,m,r):\\n return sum([a*b for a,b in zip(discount_factors(r,n,m),bond_cashflows(fv, c, n, m))])\",\n \"def calculate(self, order):\\n pass\",\n \"def calculations():\\r\\n\\t\\r\\n\\tpayload, avionics, booster = weight_input()\\r\\n\\r\\n\\tdrogue_size, drogue_force = drogue_calc()\\r\\n\\tmain_size, main_force = main_calc(avionics, booster, drogue_force) #total mass, payload detaches\\r\\n\\r\\n\\tprint(\\\"Drogue is diameter is \\\" + str(drogue_size) + \\\" inches\\\")\\r\\n\\tprint(\\\"Main is diameter is \\\" + str(main_size) + \\\" inches\\\")\",\n \"def main():\\n tte, url_pt2 = get_tte(expiration)\\n url2 = url + '&date=' + url_pt2\\n dfs = pd.read_html(url2)\\n\\n call_df = dfs[0]\\n put_df = dfs[1]\\n \\n \\n \\\"\\\"\\\"Should have created an Option class here. Like:\\n class Option:\\n def __init__(self,type, spot, strike, time, rate, div, vol, vol_guess,price):\\n self.type = type\\n self.spot = spot\\n .....\\n def theo(self):\\n return Theo(type, spot, strike,time, rate, div, vol)\\n def delta(self):\\n return Delta(type, spot, strike,time, rate, div, vol)\\n ...\\n \\n \\\"\\\"\\\"\\n \\n \\n #instead, a bunch of lists: \\n call_theos = []\\n put_theos = []\\n call_vols= []\\n put_vols= []\\n\\n call_deltas = []\\n call_gammas = []\\n call_thetas = []\\n call_vegas = []\\n put_deltas = []\\n put_gammas = []\\n put_thetas = []\\n put_vegas = []\\n\\n call_imp_vols =[]\\n put_imp_vols = []\\n\\n spot = get_spot(spot_url)\\n print('Spot:', spot)\\n vol = get_vol(vol_url,20)\\n print('20day Vol:' ,vol)\\n rate = interest_rate(tte)\\n div = .0\\n\\n #Don't really care about options going for pennies\\n call_df = call_df.ix[call_df['Volume'] >=100].ix[call_df['Last Price'] >= .10]\\n put_df = put_df.ix[put_df['Volume'] >= 100].ix[put_df['Last Price'] >= .10]\\n\\n\\n for index, row in call_df.iterrows():\\n call_vol = row['Implied Volatility'][:-1]\\n call_vols.append(float(call_vol))\\n\\n\\n for index, row in put_df.iterrows():\\n put_vol = row['Implied Volatility'][:-1]\\n put_vols.append(float(put_vol))\\n\\n\\n # extremely crude method for increasing put vol to help account for skew\\n call_sd = statistics.stdev(call_vols)\\n call_mean = sum(call_vols)/len(call_vols)\\n call_vols = [vol for vol in call_vols if abs(vol-call_mean) <= 3 * call_sd]\\n call_vol_factor = sum(call_vols)/len(call_vols) / 100.0\\n\\n put_sd = statistics.stdev(put_vols)\\n put_mean = sum(put_vols)/len(put_vols)\\n put_vols = [vol for vol in put_vols if abs(vol-put_mean) <= 1 * put_sd]\\n put_vol_factor = sum(put_vols)/len(put_vols) / 100.0\\n\\n call_vol = vol\\n put_vol = vol * (put_vol_factor/call_vol_factor)\\n\\n for index, row in call_df.iterrows():\\n strike = float(row['Strike'])\\n time = tte\\n call_theos.append(Theo('C', spot, strike, time, rate, div, call_vol))\\n call_deltas.append(Delta('C', spot, strike, time, rate, div, call_vol))\\n call_gammas.append(Gamma(spot, strike, time, rate, div, call_vol))\\n call_thetas.append(Theta('C', spot, strike, time, rate, div, call_vol))\\n call_vegas.append(Vega(spot, strike, time, rate, div, call_vol))\\n\\n\\n midpoint = (float(row['Bid']) + float(row['Ask']))/2.0\\n call_imp_vols.append(ImpliedVol('C', spot,strike,time,rate,div,midpoint, call_vol))\\n\\n for index, row in put_df.iterrows():\\n strike = float(row['Strike'])\\n time = tte\\n put_theos.append(Theo('P', spot, strike, time, rate, div, put_vol))\\n put_deltas.append(Delta('P', spot, strike, time, rate, div, put_vol))\\n put_gammas.append(Gamma(spot, strike, time, rate, div, put_vol))\\n put_thetas.append(Theta('P', spot, strike, time, rate, div, put_vol))\\n put_vegas.append(Vega(spot, strike, time, rate, div, put_vol))\\n\\n midpoint = (float(row['Bid']) + float(row['Ask'])) / 2.0\\n put_imp_vols.append(ImpliedVol('P', spot, strike, time, rate, div, midpoint, put_vol))\\n\\n\\n call_df['Theo'] = call_theos\\n call_df['Delta'] = call_deltas\\n call_df['Gamma'] = call_gammas\\n call_df['Theta'] = call_thetas\\n call_df['Vega'] = call_vegas\\n\\n put_df['Theo'] = put_theos\\n put_df['Delta'] = put_deltas\\n put_df['Gamma'] = put_gammas\\n put_df['Theta'] = put_thetas\\n put_df['Vega'] = put_vegas\\n\\n call_imp_vols = [str(i*100)[0:5] + '%' for i in call_imp_vols]\\n put_imp_vols = [str(i*100)[0:5] + '%' for i in put_imp_vols]\\n\\n call_df['Calc Imp Vol'] =call_imp_vols\\n put_df['Calc Imp Vol'] = put_imp_vols\\n\\n\\n #Message alert for large discrepancies between theo and midpoint of bid/ask\\n default = sec+ ' Alert ' + expiration + '\\\\n'\\n alertstring = default\\n for index, row in call_df.iterrows():\\n\\n midpoint = (float(row['Ask']) + float(row['Bid']))/2.0\\n if abs(row['Theo'] - midpoint ) >= 1.0:\\n\\n edge = str(abs(row['Theo'] - midpoint))\\n if row['Theo'] > midpoint:\\n updown = 'under'\\n else:\\n updown = 'over'\\n\\n\\n alertstring += str(int(row['Strike'])) + 'C ' + edge[0:4] + ' ' + updown + '\\\\n'\\n\\n for index, row in put_df.iterrows():\\n\\n midpoint = (float(row['Ask']) + float(row['Bid'])) / 2.0\\n if abs(row['Theo'] - midpoint) >= 1.0:\\n\\n edge = str(abs(row['Theo'] - midpoint))\\n if row['Theo'] > midpoint:\\n updown = 'under'\\n else:\\n updown = 'over'\\n\\n alertstring += str(int(row['Strike'])) + 'P ' + edge[0:4] + ' ' + updown + '\\\\n'\\n if alertstring != default:\\n send_message(alertstring)\\n\\n plotVolSeries(sec)\\n plotImpVol(call_df,put_df,spot, sec)\\n writer = pd.ExcelWriter(sec+'_options_' + expiration +'.xlsx')\\n call_df.to_excel(writer, 'Calls')\\n put_df.to_excel(writer, 'Puts')\\n writer.save()\",\n \"def option_price(volatilities,\\n strikes,\\n expiries,\\n spots=None,\\n forwards=None,\\n discount_factors=None,\\n is_call_options=None,\\n dtype=None,\\n name=None):\\n if (spots is None) == (forwards is None):\\n raise ValueError('Either spots or forwards must be supplied but not both.')\\n\\n with tf.compat.v1.name_scope(\\n name,\\n default_name='option_price',\\n values=[\\n forwards, strikes, volatilities, expiries, discount_factors,\\n is_call_options\\n ]):\\n strikes = tf.convert_to_tensor(strikes, dtype=dtype, name='strikes')\\n volatilities = tf.convert_to_tensor(\\n volatilities, dtype=dtype, name='volatilities')\\n expiries = tf.convert_to_tensor(expiries, dtype=dtype, name='expiries')\\n\\n if discount_factors is None:\\n discount_factors = tf.convert_to_tensor(\\n 1.0, dtype=dtype, name='discount_factors')\\n else:\\n discount_factors = tf.convert_to_tensor(\\n discount_factors, dtype=dtype, name='discount_factors')\\n\\n if forwards is not None:\\n forwards = tf.convert_to_tensor(forwards, dtype=dtype, name='forwards')\\n else:\\n spots = tf.convert_to_tensor(spots, dtype=dtype, name='spots')\\n forwards = spots / discount_factors\\n\\n sqrt_var = volatilities * tf.math.sqrt(expiries)\\n d1 = (tf.math.log(forwards / strikes) + sqrt_var * sqrt_var / 2) / sqrt_var\\n d2 = d1 - sqrt_var\\n undiscounted_calls = forwards * _ncdf(d1) - strikes * _ncdf(d2)\\n if is_call_options is None:\\n return discount_factors * undiscounted_calls\\n undiscounted_forward = forwards - strikes\\n undiscounted_puts = undiscounted_calls - undiscounted_forward\\n predicate = tf.broadcast_to(is_call_options, tf.shape(undiscounted_calls))\\n return discount_factors * tf.where(predicate, undiscounted_calls,\\n undiscounted_puts)\",\n \"def _compute_amount(self):\\n raise NotImplementedError()\",\n \"def _cost(self, action):\\n raise NotImplementedError\",\n \"def compare_price(self):\\n if self.__product_price < self.__alert_price:\\n #print(\\\"price drop...\\\")\\n self.__alert_client = True\\n self.__price_difference = self.__product_price - self.__alert_price\\n else:\\n #print(\\\"Price not reduced...\\\")\\n self.__alert_client = False\\n self.__price_difference = self.__product_price - self.__alert_price\",\n \"def cvp():\\r\\n c = float(input(\\\"Please Enter Total Fixed Costs Value: \\\"))\\r\\n a = float(input(\\\"Please Enter Sale Price Per Unit: \\\"))\\r\\n b = float(input(\\\"Please Enter Variable Cost Per Unit: \\\"))\\r\\n ccm = float(a)-float(b)\\r\\n cuu = float(c)/float(ccm)\\r\\n ccmr = (float(ccm)/float(a))*float(100)\\r\\n cda = float(c)/(float(ccmr)/float(100))\\r\\n print \\\">> Your Contribution Margin is\\\",ccm\\r\\n print \\\">> Your Breakeven Sales in Units is\\\",round(cuu)\\r\\n print \\\">> Your Contribution Margin Ratio is\\\",ccmr,\\\"%\\\"\\r\\n print \\\">> Your Breakeven Sales in Dollars is\\\",cda,\\\"\\\\n\\\"\\r\\n qq = input(\\\" Press 1 To Compute Target Profit\\\\n Press 2 To Compute Margin of Safety\\\\n Press 3 To Perform Sensitivity Analysis\\\\n Or Press 0 To Exit: \\\")\\r\\n if(qq == 1):\\r\\n dds = float(input(\\\"Please Enter Your Target Profit: \\\"))\\r\\n xxx = (float(c)+float(dds))/float(ccm)\\r\\n xxxx = (float(c)+float(dds))/(float(ccmr)/float(100))\\r\\n print \\\">> Your Target Profit in Units To Earn\\\",dds,\\\"$ is\\\",round(xxx)\\r\\n print \\\">> Your Target Profit in Dollars To Earn\\\",dds,\\\"$ is\\\",xxxx\\r\\n elif(qq == 0):\\r\\n print \\\"Canceled\\\"\\r\\n elif(qq == 2):\\r\\n xc = float(input(\\\"Please Enter Expected Sales in Units: \\\"))\\r\\n zzz = float(xc)-float(cuu)\\r\\n zzzz = float(zzz)*float(a)\\r\\n print \\\">> Your Margin of Safety in Units is\\\",round(zzz)\\r\\n print \\\">> Your Margin of Safety in Dollars is\\\",zzzz\\r\\n elif(qq == 3):\\r\\n i = input(\\\"Please Enter Total Fixed Costs Value: \\\")\\r\\n o = input(\\\"Please Enter Sale Price Per Unit: \\\")\\r\\n p = input(\\\"Please Enter Variable Cost Per Unit: \\\")\\r\\n n = 0\\r\\n for x,y,z in zip(i,o,p):\\r\\n cm = float(y)-float(z)\\r\\n uu = float(x)/float(cm)\\r\\n cmr = (float(cm)/float(y))*float(100)\\r\\n da = float(x)/(float(cmr)/float(100))\\r\\n n += 1\\r\\n print \\\"Your Results in Case\\\",int(n),\\\"is :\\\"\\r\\n print \\\">> Your Contribution Margin is\\\",cm\\r\\n print \\\">> Your Breakeven Sales in Units is\\\",round(uu)\\r\\n print \\\">> Your Contribution Margin Ratio is\\\",cmr,\\\"%\\\"\\r\\n print \\\">> Your Breakeven Sales in Dollars is\\\",da,\\\"\\\\n\\\"\\r\\n if(cm > ccm):\\r\\n a = float(cm)-float(ccm)\\r\\n print \\\">> Your Contribution Margin Increased by\\\",a\\r\\n elif(ccm > cm):\\r\\n a = float(ccm)-float(cm)\\r\\n print \\\">> Your Contribution Margin Decreased by\\\",a\\r\\n if(uu > cuu):\\r\\n b = float(uu)-float(cuu)\\r\\n print \\\">> Your Breakeven Sales in Units Increased by\\\",round(b)\\r\\n elif(cuu > uu):\\r\\n b = float(cuu)-float(uu)\\r\\n print \\\">> Your Breakeven Sales in Units Decreased by\\\",round(b)\\r\\n if(cmr > ccmr):\\r\\n c = float(cmr)-float(ccmr)\\r\\n print \\\">> Your Contribution Margin Ratio Increased by\\\",c,\\\"%\\\"\\r\\n elif(ccmr > cmr):\\r\\n c = float(ccmr)-float(cmr)\\r\\n print \\\">> Your Contribution Margin Ratio Decreased by\\\",c,\\\"%\\\"\\r\\n if(da > cda):\\r\\n d = float(da)-float(cda)\\r\\n print \\\">> Your Breakeven Sales in Dollars Increased by\\\",d\\r\\n elif(cda > da):\\r\\n d = float(cda)-float(da)\\r\\n print \\\">> Your Breakeven Sales in Dollars Decreased by\\\",d,\\\"\\\\n\\\"\",\n \"def compute_quotation_price(self):\\n result = decimal.Decimal('0')\\n if self.vehiculePrice:\\n result = self.vehiculePrice * 2 / 100\\n if self.covWind:\\n result += get_coverage_price_by_name(\\\"WIND\\\")\\n if self.covPass:\\n result += get_coverage_price_by_name(\\\"PASS\\\")\\n if self.covFlood:\\n result += get_coverage_price_by_name(\\\"FLOOD\\\")\\n return result\",\n \"def _calculate(self):\\n raise NotYetImplemented()\",\n \"def get_the_sum_of_prices(item_ids):\\n\\n # your code\",\n \"def __call__(self, auctioneer):\\n curr_bid = auctioneer.current_bid\\n bid_price = curr_bid * self._bid_increase_perc\\n if bid_price <= self._budget and self.get_bid_probability() > 0.3:\\n self._highest_bid = bid_price\\n return bid_price\\n return 0\",\n \"def calculate_price(self, amount: float, action: str, price:float, contract:str):\\n def get_ticked_price(amount, action, price):\\n if contract not in self.tick_information:\\n return price\\n if (action == 'Close'):\\n if amount > 0:\\n return (price - self.tick_information[contract] * self.tick) * (1-self.fee)\\n else:\\n return (price + self.tick_information[contract] * self.tick) * (1+self.fee)\\n elif action == 'Long':\\n return (price + self.tick_information[contract] * self.tick) * (1+self.fee)\\n elif action == 'Short':\\n return (price - self.tick_information[contract] * self.tick) * (1-self.fee)\\n return get_ticked_price(amount, action, price)\",\n \"def calc(self):\\n return None\",\n \"def precalculate():\\n pass\",\n \"def precalculate():\\n pass\",\n \"def test_market_1_2(self):\\n\\n def check_1_2(buyers: List[float], sellers: List[float], expected_num_of_deals: int,\\n expected_prices: List[float]):\\n market = Market([\\n AgentCategory(\\\"buyer\\\", buyers),\\n AgentCategory(\\\"seller\\\", sellers),\\n ])\\n ps_recipe = [1, 2]\\n self._check_market(market, ps_recipe, expected_num_of_deals, expected_prices)\\n\\n check_1_2(buyers=[9], sellers=[-4, -3],\\n expected_num_of_deals=0, expected_prices=[9, -4.5])\\n check_1_2(buyers=[9, 8, 7, 6], sellers=[-6, -5, -4, -3, -2, -1],\\n expected_num_of_deals=1, expected_prices=[8, -4])\\n check_1_2(buyers=[9, 8], sellers=[-4, -3, -2, -1],\\n expected_num_of_deals=1, expected_prices=[8, -4])\\n check_1_2(buyers=[9, 8], sellers=[-6, -3, -2, -1],\\n expected_num_of_deals=1, expected_prices=[8, -4])\\n check_1_2(buyers=[9, 8], sellers=[-4, -3, -2, -1],\\n expected_num_of_deals=1, expected_prices=[8, -4])\\n\\n # PRICE CROSSES ZERO AT FIRST PHASE\\n check_1_2(buyers=list(range(20)), sellers=[-3, -2, -1],\\n expected_num_of_deals=1, expected_prices=[18, -9])\",\n \"def calculate_event_price_and_parameters(self, event_id):\\n try:\\n price = 0\\n proffesional_staff = 0\\n middle_staff = 0\\n new_staff = 0\\n\\n req_id, _, client_id, title, location, date_starts, date_ends, guests, event_type_id, event_class_id, _ = self.db_handler.get_event_request_extended_info_by_id(event_id)\\n _, event_class_name, guests_per_waiter = self.db_handler.get_event_class_by_id(event_class_id)\\n\\n distance_in_km = self.get_geopy_distance(location)\\n price = distance_in_km * float(self.get_config_value('PRICE_OF_KM'))\\n num_of_staff = math.ceil(guests / guests_per_waiter)\\n\\n if event_class_name == 'Найвищий': #85% of pro\\n new_staff = 0\\n proffesional_staff = math.ceil(num_of_staff * 0.85)\\n middle_staff = num_of_staff - proffesional_staff\\n elif event_class_name == 'Високий': #75% of pro\\n new_staff = 0\\n proffesional_staff = math.ceil(num_of_staff * 0.75)\\n middle_staff = num_of_staff - proffesional_staff\\n elif event_class_name == 'Середній':\\n new_staff = round(num_of_staff * 0.2)\\n middle_staff = num_of_staff - new_staff\\n elif event_class_name == 'Початковий':\\n middle_staff = math.ceil(num_of_staff * 0.3)\\n new_staff = round(num_of_staff - middle_staff)\\n\\n hours_of_shift = (date_ends - date_starts).seconds / 3600\\n\\n price += proffesional_staff * float(self.get_config_value('PRO_RATE')) * hours_of_shift\\n price += middle_staff * float(self.get_config_value('MID_RATE')) * hours_of_shift\\n price += new_staff * float(self.get_config_value('NEW_RATE')) * hours_of_shift\\n\\n price += price * 0.4\\n\\n return (price,proffesional_staff,middle_staff,new_staff)\\n except Exception as err:\\n method_name = sys._getframe().f_code.co_name\\n\\n self.logger.write_to_log('exception', 'model')\\n self.logger.write_to_err_log(f'exception in method {method_name} - {err}', 'model')\",\n \"def cost(self) -> float:\",\n \"def add_evidence( \\t\\t \\t \\t\\t \\t \\t\\t\\t \\t\\t \\t\\t\\t \\t \\t \\t\\t \\t\\t \\t\\n self, \\t\\t \\t \\t\\t \\t \\t\\t\\t \\t\\t \\t\\t\\t \\t \\t \\t\\t \\t\\t \\t\\n symbol=\\\"IBM\\\", \\t\\t \\t \\t\\t \\t \\t\\t\\t \\t\\t \\t\\t\\t \\t \\t \\t\\t \\t\\t \\t\\n sd=dt.datetime(2008, 1, 1), \\t\\t \\t \\t\\t \\t \\t\\t\\t \\t\\t \\t\\t\\t \\t \\t \\t\\t \\t\\t \\t\\n ed=dt.datetime(2009, 1, 1), \\t\\t \\t \\t\\t \\t \\t\\t\\t \\t\\t \\t\\t\\t \\t \\t \\t\\t \\t\\t \\t\\n sv=10000, \\t\\t \\t \\t\\t \\t \\t\\t\\t \\t\\t \\t\\t\\t \\t \\t \\t\\t \\t\\t \\t\\n ): \\t\\t \\t \\t\\t \\t \\t\\t\\t \\t\\t \\t\\t\\t \\t \\t \\t\\t \\t\\t \\t\\n \\t\\t \\t \\t\\t \\t \\t\\t\\t \\t\\t \\t\\t\\t \\t \\t \\t\\t \\t\\t \\t\\n # add your code to do learning here\\n\\n converged = False\\n x = np.zeros((3,1))\\n dates = pd.date_range(sd,ed)\\n df_prices = ind.get_price(symbol, dates)\\n\\n daily_rets = (df_prices / df_prices.shift(1)) - 1\\n daily_rets = daily_rets[1:]\\n\\n\\n sd_older = sd - dt.timedelta(days=365)\\n dates_older = pd.date_range(sd_older,ed)\\n df_prices_older = ind.get_price(symbol, dates_older)\\n sd_key = df_prices.index[0]\\n sd_index = df_prices_older.index.get_loc(sd_key)\\n\\n num_bins = len(self.bins)\\n max_state_idx = num_bins + num_bins*10 + num_bins*100\\n\\n # Call Q-Learner Constructor\\n self.learner = QLearner( \\n num_states=(max_state_idx + 1), \\n num_actions=3, \\n alpha=0.01, \\n gamma=0.0, \\n rar=0.98, \\n radr=0.9995, \\n dyna=0, \\n verbose=False, \\n )\\n\\n # df_trades = df_prices.copy()\\n df_holdings = df_prices.copy()\\n df_holdings['Holdings'] = np.nan\\n del df_holdings[symbol] # print(df_holdings)\\n\\n # Initlialize Vars\\n cum_ret_prev = 0\\n iters = 0\\n conv_counter = 0\\n Q_prev = np.copy(self.learner.Q)\\n\\n # Get Indicator Values\\n _,_,ind1 = ind.get_BB(df_prices_older, self.lookback)\\n ind2 = ind.get_CCI(df_prices_older, self.lookback)\\n _,_,ind3 = ind.get_SMA_Cross(self.lookback, 100, df_prices_older)\\n ind4 = ind.get_momentum(df_prices_older, self.lookback)\\n _,_,ind5 = ind.get_MACD(df_prices_older)\\n BB = ind1.iloc[sd_index:].values\\n CCI = ind2.iloc[sd_index:].values\\n SMA_Cross = ind3.iloc[sd_index:].values\\n Momentum = ind4.iloc[sd_index:].values\\n MACD = ind5.iloc[sd_index:].values\\n _,self.x0bins = pd.qcut(BB[:,0], num_bins,labels=False,retbins=True)\\n _,self.x1bins = pd.qcut(CCI[:,0],num_bins,labels=False,retbins=True)\\n _,self.x2bins = pd.qcut(SMA_Cross[:,0],num_bins,labels=False,retbins=True)\\n _,self.x3bins = pd.qcut(Momentum[:,0],num_bins,labels=False,retbins=True)\\n _,self.x4bins = pd.qcut(MACD[:,0],num_bins,labels=False,retbins=True)\\n x_0 = np.digitize(BB[:,0], self.x0bins[1:-1])\\n x_1 = np.digitize(CCI[:,0], self.x1bins[1:-1])\\n x_2 = np.digitize(SMA_Cross[:,0], self.x2bins[1:-1])\\n x_3 = np.digitize(Momentum[:,0], self.x3bins[1:-1])\\n x_4 = np.digitize(MACD[:,0], self.x4bins[1:-1])\\n state = x_0 + x_3*10 + x_4*100\\n\\n \\n while not converged:\\n\\n action = self.learner.querysetstate(state[0])\\n daily_return = daily_rets.iloc[0][symbol]\\n cur_price = df_prices.iloc[0][symbol]\\n next_price = df_prices.iloc[1][symbol]\\n df_holdings.iloc[0]['Holdings'], reward = self.take_action(0, action, cur_price, next_price)\\n\\n\\n\\n for day_idx in range(1,daily_rets.shape[0]):\\n\\n\\n daily_return = daily_rets.iloc[day_idx][symbol]\\n cur_price = df_prices.iloc[day_idx-1][symbol]\\n next_price = df_prices.iloc[day_idx][symbol]\\n df_holdings.iloc[day_idx]['Holdings'], reward = self.take_action(df_holdings.iloc[day_idx-1]['Holdings'], action, cur_price, next_price)\\n action = self.learner.query(state[day_idx], reward)\\n\\n df_holdings.iloc[-1]['Holdings'] = 0\\n df_trades = df_holdings.diff()\\n df_trades['Trades'] = df_trades['Holdings']\\n del df_trades['Holdings']\\n df_trades.iloc[0]['Trades'] = 0\\n\\n\\n portvals = msc.compute_portvals( \\n df_trades,\\n symbol, \\n sv, \\n self.commission, \\n self.impact, \\n )\\n\\n cum_ret = (portvals[-1] / portvals[0]) - 1\\n Q_diff = np.abs(self.learner.Q - Q_prev)\\n Q_max_diff = Q_diff.max()\\n\\n if iters > 20:\\n\\n # if abs(cum_ret - cum_ret_prev) < 0.0001:\\n if Q_max_diff < 0.001:\\n conv_counter += 1\\n else:\\n conv_counter = 0\\n\\n if conv_counter > 5 or iters > 20000:\\n converged = True\\n # if iters > 100:\\n # if iters % 100 == 0:\\n # print(\\\"Iteration #\\\", iters)\\n print(\\\"----------------------------------------------\\\")\\n print(\\\"-- --\\\")\\n print(\\\"Iteration #\\\", iters)\\n print(\\\"Error = \\\", abs(cum_ret - cum_ret_prev))\\n print(\\\"Q Diff: \\\", Q_max_diff)\\n print(\\\"Epsilon: \\\", self.learner.rar)\\n\\n cum_ret_prev = cum_ret\\n Q_prev = np.copy(self.learner.Q)\\n iters += 1\\n self.learner.rar *= self.learner.radr\\n # print(\\\"Iters = \\\", iters)\\n print(\\\"Mode Trained in \\\", iters, \\\" iterations!\\\")\\n np.savetxt('Q_Table.csv', self.learner.Q, delimiter=',')\",\n \"def compute_amount_discounted(promotion, amount):\\n if promotion.promo_type == '1': # % off\\n amount_discounted = promotion.promo_amount * amount / Decimal(100)\\n amount_discounted = Decimal(str(round(amount_discounted, 2)))\\n elif promotion.promo_type == '2': # $ off\\n if promotion.promo_amount < amount:\\n amount_discounted = promotion.promo_amount\\n else:\\n amount_discounted = amount\\n elif promotion.promo_type == '3': # fixed $ cost\\n if promotion.promo_amount < amount:\\n amount_discounted = amount - promotion.promo_amount\\n else:\\n # If you have a fixed cost promo of $20, but your items \\n # only cost $10, you don't save.\\n amount_discounted = 0\\n LOG.debug('compute discount: amount_discounted = %s' % amount_discounted)\\n return amount_discounted\",\n \"def dblcal(self, lowstrike, highstrike, expiry1, expiry2):\\n assert lowstrike < highstrike\\n assert pd.Timestamp(expiry1) < pd.Timestamp(expiry2)\\n _rows1 = {}\\n _rows2 = {}\\n _prices1 = {}\\n _prices2 = {}\\n _index = ['Near Call', 'Far Call', 'Call Ratio', 'Near Put', 'Far Put', \\n 'Put Ratio', 'Near to Far Ratio', 'Debit', 'Underlying_Price', 'Quote_Time']\\n _metrics = pd.DataFrame(index=_index, columns=['Value'])\\n _errmsg = \\\"No key for {} strike {} {}\\\"\\n _opttype = 'call'\\n _rows1[_opttype] = _relevant_rows(self.data, (highstrike, expiry1, _opttype),\\n _errmsg.format(expiry1, highstrike, _opttype))\\n _prices1[_opttype] = _getprice(_rows1[_opttype])\\n _rows2[_opttype] = _relevant_rows(self.data, (highstrike, expiry2, _opttype),\\n _errmsg.format(expiry2, highstrike, _opttype))\\n _prices2[_opttype] = _getprice(_rows2[_opttype])\\n _metrics.loc['Near Call', 'Value'] = _prices1[_opttype]\\n _metrics.loc['Far Call', 'Value'] = _prices2[_opttype]\\n _metrics.loc['Call Ratio', 'Value'] = _prices1[_opttype] / _prices2[_opttype]\\n _metrics.loc['Underlying_Price', 'Value'], _metrics.loc['Quote_Time', 'Value'] =\\\\\\n _getkeys(_rows1[_opttype], ['Underlying_Price', 'Quote_Time'])\\n _opttype = 'put'\\n _rows1[_opttype] = _relevant_rows(self.data, (lowstrike, expiry1, _opttype),\\n _errmsg.format(expiry1, lowstrike, _opttype))\\n _prices1[_opttype] = _getprice(_rows1[_opttype])\\n _rows2[_opttype] = _relevant_rows(self.data, (lowstrike, expiry2, _opttype),\\n _errmsg.format(expiry2, lowstrike, _opttype))\\n _prices2[_opttype] = _getprice(_rows2[_opttype])\\n _metrics.loc['Near Put', 'Value'] = _prices1[_opttype]\\n _metrics.loc['Far Put', 'Value'] = _prices2[_opttype]\\n _metrics.loc['Put Ratio', 'Value'] = _prices1[_opttype] / _prices2[_opttype]\\n _neartot = sum(_prices1.values())\\n _fartot = sum(_prices2.values())\\n _metrics.loc['Near to Far Ratio', 'Value'] = float(_neartot) / _fartot\\n _metrics.loc['Debit', 'Value'] = _fartot - _neartot\\n return _metrics\",\n \"def bulk_item(order: Order) -> Decimal:\\n discount = Decimal(0)\\n for item in order.cart:\\n if item.quantity >= 20:\\n discount += item.total() * Decimal('0.1')\\n return discount\",\n \"def calculate(self) -> float:\",\n \"def _calc_return(self, order_original, perf_df):\\r\\n\\r\\n order = order_original.copy()\\r\\n no_sec = len(self.perf_data)\\r\\n price_names = np.array(['price_' + str(i) for i in xrange(1, no_sec + 1)])\\r\\n ret = np.zeros((np.shape(order)[0], no_sec))\\r\\n\\r\\n transaction_cost = 0\\r\\n\\r\\n # buy_list vs sell_list contains order bought vs sold that cannot be matched yet to determine the return\\r\\n # For example when something has been bought, but nothing or not enough has been sold yet, the residue will be\\r\\n # listed in these lists.\\r\\n buy_shares = np.zeros((np.shape(order)[0], no_sec))\\r\\n buy_price = np.zeros((np.shape(order)[0], no_sec))\\r\\n sell_shares = np.zeros((np.shape(order)[0], no_sec))\\r\\n sell_price = np.zeros((np.shape(order)[0], no_sec))\\r\\n\\r\\n # bl_first vs sl_first indicates which row in buy_list vs sell_list can be used to \\\"match\\\" bought/sold shares.\\r\\n # It automatically points to the oldest row with still outstanding shares. Initial value is -1\\r\\n # bl_last vs sl_last indicates which row in buy_list vs sell_list can be used to write outstanding shares to.\\r\\n bl_first = np.ones(no_sec).astype(int) * -1\\r\\n bl_last = np.zeros(no_sec).astype(int)\\r\\n sl_first = np.ones(no_sec).astype(int) * -1\\r\\n sl_last = np.zeros(no_sec).astype(int)\\r\\n\\r\\n for ind in range(0, np.shape(order)[0]):\\r\\n bl_first[(bl_first == -1) & (bl_last > 0)] = 0\\r\\n sl_first[(sl_first == -1) & (sl_last > 0)] = 0\\r\\n\\r\\n # Three situations, per type: buy, sell, nothing\\r\\n # If nothing, skip to next day\\r\\n # Only returns made on one day are determined, later they will be accumulated.\\r\\n\\r\\n # Situation A.A: Sell order & outstanding buys larger than sell order\\r\\n col_to_change = (order[ind, :] < 0) & (np.sum(buy_shares, 0) > -order[ind, :])\\r\\n if sum(col_to_change) != 0:\\r\\n share_cumsum = np.cumsum(buy_shares, 0)\\r\\n share_compl = (share_cumsum < -order[ind, :]) & col_to_change\\r\\n numb_shares = sum(buy_shares * share_compl, 0)[col_to_change]\\r\\n ret[ind, col_to_change] += numb_shares * perf_df.loc[ind, price_names[col_to_change]] \\\\\\r\\n - sum(buy_shares * buy_price * share_compl, 0)[col_to_change]\\r\\n buy_shares[share_compl] = 0\\r\\n bl_first += sum(share_compl)\\r\\n order[col_to_change] += numb_shares\\r\\n\\r\\n ret[ind, col_to_change] += perf_df.loc[ind, price_names[col_to_change]] * -order[ind, col_to_change] * (1 - transaction_cost) \\\\\\r\\n - buy_price[bl_first[col_to_change], col_to_change] \\\\\\r\\n * -order[ind, col_to_change] * (1 + transaction_cost)\\r\\n buy_shares[bl_first[col_to_change], col_to_change] += order[ind, col_to_change]\\r\\n order[ind, col_to_change] = 0\\r\\n\\r\\n # Situation A.B: Sell order & outstanding buys smaller than or equal to sell order\\r\\n # --> just fill out all outstanding buys, and change order. This order will be added to sell list in A.C\\r\\n col_to_change = (order[ind, :] < 0) & (np.sum(buy_shares, 0) > 0) \\\\\\r\\n & (np.sum(buy_shares, 0) <= -order[ind, :])\\r\\n if sum(col_to_change) != 0:\\r\\n numb_shares = buy_shares[:, col_to_change]\\r\\n price_shares = buy_price[:, col_to_change]\\r\\n ret[ind, col_to_change] += np.sum(numb_shares, 0) * \\\\\\r\\n perf_df.loc[ind, price_names[col_to_change]].values * (1 - transaction_cost) \\\\\\r\\n - np.sum(numb_shares * price_shares, 0) * (1 + transaction_cost)\\r\\n order[ind, col_to_change] += np.sum(numb_shares, 0)\\r\\n buy_shares[:, col_to_change] = 0\\r\\n bl_first[col_to_change] = bl_last[col_to_change] - 1\\r\\n\\r\\n # Situation A.C: Sell order & no outstanding buys\\r\\n col_to_change = (order[ind, :] < 0) & (np.sum(buy_shares, 0) == 0)\\r\\n if sum(col_to_change) != 0:\\r\\n row_to_change = bl_last[col_to_change]\\r\\n sell_shares[row_to_change, col_to_change] = -order[ind, col_to_change]\\r\\n sell_price[row_to_change, col_to_change] = perf_df.loc[ind, price_names[col_to_change]]\\r\\n sl_last[col_to_change] += 1\\r\\n\\r\\n # Situation B.A: Buy order & outstanding sells larger than buy order\\r\\n col_to_change = (order[ind, :] > 0) & (np.sum(sell_shares, 0) > order[ind, :])\\r\\n if sum(col_to_change) != 0:\\r\\n share_cumsum = np.cumsum(sell_shares, 0)\\r\\n share_compl = (share_cumsum < order[ind, :]) & col_to_change\\r\\n numb_shares = sum(sell_shares * share_compl, 0)[col_to_change]\\r\\n ret[ind, col_to_change] += sum(sell_shares * sell_price * share_compl, 0)[col_to_change] * (1 - transaction_cost)\\\\\\r\\n - numb_shares * perf_df.loc[ind, price_names[col_to_change]] * (1 + transaction_cost)\\r\\n sell_shares[share_compl] = 0\\r\\n sl_first += sum(share_compl)\\r\\n order[col_to_change] += -numb_shares\\r\\n\\r\\n ret[ind, col_to_change] += sell_price[sl_first[col_to_change], col_to_change] * order[ind, col_to_change] * (1 - transaction_cost)\\\\\\r\\n - perf_df.loc[ind, price_names[col_to_change]] * order[ind, col_to_change] * (1 + transaction_cost)\\r\\n sell_shares[sl_first[col_to_change], col_to_change] += -order[ind, col_to_change]\\r\\n order[ind, col_to_change] = 0\\r\\n\\r\\n # Situation B.B: Buy order & outstanding sells smaller than buy order\\r\\n # --> just fill out all outstanding sells, and change order. This order will be added to buy list in B.C\\r\\n col_to_change = (order[ind, :] > 0) & \\\\\\r\\n (np.sum(sell_shares, 0) > 0) & (np.sum(sell_shares, 0) <= order[ind, :])\\r\\n if sum(col_to_change) != 0:\\r\\n numb_shares = sell_shares[:, col_to_change]\\r\\n price_shares = sell_price[:, col_to_change]\\r\\n ret[ind, col_to_change] += np.sum(numb_shares * price_shares, 0) * (1 - transaction_cost) \\\\\\r\\n - np.sum(numb_shares, 0) * perf_df.loc[ind, price_names[col_to_change]] * (1 + transaction_cost)\\r\\n order[ind, col_to_change] += -np.sum(numb_shares, 0)\\r\\n sell_shares[:, col_to_change] = 0\\r\\n sl_first[col_to_change] = sl_last[col_to_change] - 1\\r\\n\\r\\n # Situation B.C: Buy order & no outstanding sells\\r\\n col_to_change = (order[ind, :] > 0) & (np.sum(sell_shares, 0) == 0)\\r\\n if sum(col_to_change) != 0:\\r\\n row_to_change = bl_last[col_to_change]\\r\\n buy_shares[row_to_change, col_to_change] = order[ind, col_to_change]\\r\\n buy_price[row_to_change, col_to_change] = perf_df.loc[ind, price_names[col_to_change]]\\r\\n bl_last[col_to_change] += 1\\r\\n\\r\\n ret_abs = np.array([sum(ret[:r]) for r in range(1, len(ret) + 1)])\\r\\n returns_abs = np.sum(ret_abs, 1)\\r\\n returns_rel = [i / self.context['max_notional'] + 1 for i in returns_abs]\\r\\n\\r\\n return returns_rel, returns_abs, ret_abs\",\n \"def main():\\r\\n # Create an instance of the MyCallCostCalc class.\\r\\n my_callcost = MyCallCostCalc()\",\n \"def get_prices(self):\\n pass\",\n \"def solve_prices(self):\\n return None\",\n \"def compute_total(price):\\n\\n quantity = 20\\n return price * quantity\",\n \"def compute(self, *args):\\n\\n pass\",\n \"def main(price, service, vat):\\n service = (price * 10)/100\\n if service < 50:\\n service = 50\\n elif service > 1000:\\n service = 1000\\n price += service\\n vat = (price * 7)/100\\n price += vat\\n print(\\\"%.2f\\\" % (price))\",\n \"def check_for_offer(self, bid, commodity, limit, actual, quantity, price):\\n if bid:\\n if len(self.trades[\\\"buys\\\"][commodity]) == 0:\\n return 0\\n else: # tally up how much trying to buy.\\n total = 0.0\\n total_price = 0.0\\n for offer in self.trades[\\\"buys\\\"][commodity]:\\n total += offer.quantity\\n total_price += offer.price\\n\\n avg_price = total_price / len(self.trades[\\\"buys\\\"][commodity])\\n\\n # if total < limit:\\n # #PLACE MORE BIDS.\\n return total\\n\\n else:\\n if len(self.trades[\\\"asks\\\"][commodity]) == 0:\\n return 0\\n else: # tally up how much trying to buy.\\n total = 0.0\\n total_price = 0.0\\n for offer in self.trades[\\\"asks\\\"][commodity]:\\n total += offer.quantity\\n total_price += offer.price\\n\\n avg_price = total_price / len(self.trades[\\\"asks\\\"][commodity])\\n #\\n # if total < limit:\\n # #PLACE MORE asks.\\n # return total\\n # if total < limit:\\n # #PLACE MORE asks.\\n return total # - limit\",\n \"def do_work(self) -> None:\\n self._get_btc_eur_15min()\\n print(\\n f\\\"1 BTC = {self.btc_eur_15min} EUR\\\"\\n f\\\"\\\\t\\\\t(15min delayed market price)\\\"\\n )\\n\\n self._get_eur_gbp_last_month()\\n print(\\n f\\\"1 EUR = {self.eur_gbp_last_month} GBP\\\"\\n f\\\"\\\\t(last month average rate)\\\"\\n )\\n\\n self._get_btc_gbp_15min()\\n print(\\n f\\\"1 BTC = {self.btc_gbp_15min:.6f} GBP\\\"\\n f\\\"\\\\t(BTC 15min delayed market price; GBP latest daily average rate)\\\"\\n )\",\n \"def calc_Cinv_boiler(Q_design_W, technology_type, boiler_cost_data):\\n Capex_a_Boiler_USD = 0.0\\n Opex_a_fix_Boiler_USD = 0.0\\n Capex_Boiler_USD = 0.0\\n\\n if Q_design_W > 0.0:\\n boiler_cost_data = boiler_cost_data[boiler_cost_data['code'] == technology_type]\\n # if the Q_design is below the lowest capacity available for the technology, then it is replaced by the least\\n # capacity for the corresponding technology from the database\\n if Q_design_W < boiler_cost_data.iloc[0]['cap_min']:\\n Q_design_W = boiler_cost_data.iloc[0]['cap_min']\\n max_boiler_size = boiler_cost_data.iloc[0]['cap_max']\\n\\n if Q_design_W <= max_boiler_size:\\n\\n boiler_cost_data = boiler_cost_data[\\n (boiler_cost_data['cap_min'] <= Q_design_W) & (boiler_cost_data['cap_max'] > Q_design_W)]\\n\\n Inv_a = boiler_cost_data.iloc[0]['a']\\n Inv_b = boiler_cost_data.iloc[0]['b']\\n Inv_c = boiler_cost_data.iloc[0]['c']\\n Inv_d = boiler_cost_data.iloc[0]['d']\\n Inv_e = boiler_cost_data.iloc[0]['e']\\n Inv_IR = boiler_cost_data.iloc[0]['IR_%']\\n Inv_LT = boiler_cost_data.iloc[0]['LT_yr']\\n Inv_OM = boiler_cost_data.iloc[0]['O&M_%'] / 100.0\\n\\n InvC = Inv_a + Inv_b * (Q_design_W) ** Inv_c + (Inv_d + Inv_e * Q_design_W) * log(Q_design_W)\\n\\n Capex_a_Boiler_USD = calc_capex_annualized(InvC, Inv_IR, Inv_LT)\\n Opex_a_fix_Boiler_USD = InvC * Inv_OM\\n Capex_Boiler_USD = InvC\\n\\n else:\\n number_of_boilers = int(ceil(Q_design_W / max_boiler_size))\\n Q_nom_W = Q_design_W / number_of_boilers\\n\\n boiler_cost_data = boiler_cost_data[\\n (boiler_cost_data['cap_min'] <= Q_nom_W) & (boiler_cost_data['cap_max'] > Q_nom_W)]\\n\\n Inv_a = boiler_cost_data.iloc[0]['a']\\n Inv_b = boiler_cost_data.iloc[0]['b']\\n Inv_c = boiler_cost_data.iloc[0]['c']\\n Inv_d = boiler_cost_data.iloc[0]['d']\\n Inv_e = boiler_cost_data.iloc[0]['e']\\n Inv_IR = boiler_cost_data.iloc[0]['IR_%']\\n Inv_LT = boiler_cost_data.iloc[0]['LT_yr']\\n Inv_OM = boiler_cost_data.iloc[0]['O&M_%'] / 100.0\\n\\n InvC = (Inv_a + Inv_b * (Q_nom_W) ** Inv_c + (Inv_d + Inv_e * Q_nom_W) * log(Q_nom_W)) * number_of_boilers\\n\\n Capex_a_Boiler_USD = calc_capex_annualized(InvC, Inv_IR, Inv_LT)\\n Opex_a_fix_Boiler_USD = InvC * Inv_OM\\n Capex_Boiler_USD = InvC\\n\\n return Capex_a_Boiler_USD, Opex_a_fix_Boiler_USD, Capex_Boiler_USD\",\n \"def discount_card(subtotal):\\n\\n if \\\"gold\\\" in CARDS:\\n return gold_card(subtotal) #This calculates the 5%\\n\\n elif \\\"silver\\\" in CARDS:\\n return silver_card(subtotal) #This calculates the 2%\\n\\n elif \\\"gold\\\" in CARDS and \\\"silver\\\" in CARDS:\\n return gold_card(subtotal)\\n\\n else:\\n return 0 #Whitout discount\",\n \"def test_visualize_price_breakdown(self):\\n pass\",\n \"def discount(self, period):\\n\\t\\treturn 1.0/compound(period)\",\n \"def bulk_item(order):\\n discount = 0\\n for item in order.cart:\\n if item.quantity >= 20:\\n discount += item.total() * .1\\n return discount\",\n \"def update_price_model(self, good, order_type, is_successful, clearing_price=0):\\n\\n SIGNIFICANT = 0.25 # 25% more or less is \\\"significant\\\"\\n SIG_IMBALANCE = 0.33\\n LOW_INVENTORY = 0.1 # 10% of ideal inventory = \\\"LOW\\\"\\n HIGH_INVENTORY = 2.0 # 200% of ideal inventory = \\\"HIGH\\\"\\n MIN_PRICE = 0.01 # lowest allowed price of a Good\\n\\n if is_successful:\\n # add this trade to the observed trading range\\n self.observed_trading_range[good].append(clearing_price)\\n\\n public_mean_price = self.market.mean_price(good)\\n belief = self.price_belief[good]\\n mean = belief.mean()\\n wobble = 0.05 # the degree which the Pop should bid outside the belief\\n\\n # how different the public mean price is from the price belief\\n delta_to_mean = mean - public_mean_price\\n\\n if is_successful:\\n if order_type is OrderType.buy_order and delta_to_mean > SIGNIFICANT:\\n # this Pop overpaid, shift belief towards mean\\n belief.low -= delta_to_mean / 2\\n belief.high -= delta_to_mean / 2\\n elif order_type is OrderType.sell_order and delta_to_mean < -SIGNIFICANT:\\n # this Pop underpaid!, shift belief towards mean\\n belief.low -= delta_to_mean / 2\\n belief.high -= delta_to_mean / 2\\n\\n # increase the belief's certainty\\n belief.low += wobble * mean\\n belief.high -= wobble * mean\\n\\n else:\\n # shift towards mean\\n belief.low -= delta_to_mean / 2\\n belief.high -= delta_to_mean / 2\\n\\n # check for inventory special cases\\n stocks = self.inventory.get_amount(good)\\n ideal = self.inventory.get_ideal(good)\\n\\n # if we're buying and inventory is too low\\n # meaning we're desperate to buy\\n if order_type is OrderType.buy_order and stocks < LOW_INVENTORY * ideal:\\n wobble *= 2\\n\\n # if we're selling and inventory is too high\\n # meaning we're desperate to sell\\n elif order_type is OrderType.sell_order and stocks > HIGH_INVENTORY * ideal:\\n wobble *= 2\\n # all other cases\\n else:\\n sells = self.market.history.sell_orders.average(good, 1)\\n buys = self.market.history.buy_orders.average(good, 1)\\n\\n # TODO: figure out why this is sometimes 0\\n if sells + buys > 0:\\n\\n supply_vs_demand = (sells - buys) / (sells + buys)\\n\\n if supply_vs_demand > SIG_IMBALANCE or supply_vs_demand < -SIG_IMBALANCE:\\n # too much supply? lower bid lower to sell faster\\n # too much demand? raise price to buy faster\\n\\n new_mean = public_mean_price * (1 - supply_vs_demand)\\n delta_to_mean = mean - new_mean\\n\\n # shift the price belief to the new price mean\\n belief.low -= delta_to_mean / 2\\n belief.high -= delta_to_mean / 2\\n\\n\\n # decrease belief's certainty since we've just changed it (we could be wrong)\\n belief.low -= wobble * mean\\n belief.high += wobble * mean\\n\\n # make sure the price belief doesn't decrease below the minimum\\n if belief.low < MIN_PRICE:\\n belief.low = MIN_PRICE\\n elif belief.high < MIN_PRICE:\\n belief.high = MIN_PRICE\",\n \"def run(self):\\n self.evaluate()\\n self.accumulate()\\n self.summarize()\",\n \"def binary_price(volatilities,\\n strikes,\\n expiries,\\n spots=None,\\n forwards=None,\\n discount_factors=None,\\n is_call_options=None,\\n dtype=None,\\n name=None):\\n if (spots is None) == (forwards is None):\\n raise ValueError('Either spots or forwards must be supplied but not both.')\\n\\n with tf.compat.v1.name_scope(\\n name,\\n default_name='option_price',\\n values=[\\n forwards, strikes, volatilities, expiries, discount_factors,\\n is_call_options\\n ]):\\n strikes = tf.convert_to_tensor(strikes, dtype=dtype, name='strikes')\\n volatilities = tf.convert_to_tensor(\\n volatilities, dtype=dtype, name='volatilities')\\n expiries = tf.convert_to_tensor(expiries, dtype=dtype, name='expiries')\\n\\n if discount_factors is None:\\n discount_factors = tf.convert_to_tensor(\\n 1.0, dtype=dtype, name='discount_factors')\\n else:\\n discount_factors = tf.convert_to_tensor(\\n discount_factors, dtype=dtype, name='discount_factors')\\n\\n if forwards is not None:\\n forwards = tf.convert_to_tensor(forwards, dtype=dtype, name='forwards')\\n else:\\n spots = tf.convert_to_tensor(spots, dtype=dtype, name='spots')\\n forwards = spots / discount_factors\\n\\n sqrt_var = volatilities * tf.math.sqrt(expiries)\\n d1 = (tf.math.log(forwards / strikes) + sqrt_var * sqrt_var / 2) / sqrt_var\\n d2 = d1 - sqrt_var\\n undiscounted_calls = _ncdf(d2)\\n if is_call_options is None:\\n return discount_factors * undiscounted_calls\\n undiscounted_puts = 1 - undiscounted_calls\\n predicate = tf.broadcast_to(is_call_options, tf.shape(undiscounted_calls))\\n return discount_factors * tf.where(predicate, undiscounted_calls,\\n undiscounted_puts)\",\n \"def bulk_item_promo(order: Order):\\n discount = 0\\n for item in order.cart:\\n if item.quantity >= 20:\\n discount += item.total() * 0.1\\n return discount\",\n \"def compute(self):\\n\\t\\tmontant = self.spn_montant.value() #recuperation de la valeur de la spn\\n\\t\\tdevise_from = self.cbb_devisesFrom.currentText() #recuperation de la valeur de la cbb\\n\\t\\tdevise_to = self.cbb_devisesTo.currentText()\\n\\t\\t\\n\\t\\t# on effectue la conversion grace a currency_converter\\n\\t\\t# on fait une gestion d'erreur pour eviter les conversions non trouvees\\n\\t\\ttry :\\n\\t\\t\\t\\\"\\\"\\\"on essaie\\\"\\\"\\\"\\n\\t\\t\\tresultat = self.c.convert(montant, devise_from, devise_to)\\n\\t\\t\\n\\t\\texcept currency_converter.currency_converter.RateNotFoundError :\\n\\t\\t\\t\\\"\\\"\\\"si erreur\\\"\\\"\\\"\\n\\t\\t\\tprint(\\\"le taux de conversion n'a pas ete trouve\\\")\\n\\t\\t\\n\\t\\telse :\\n\\t\\t\\t\\\"\\\"\\\"si pas d'erreur\\\"\\\"\\\"\\n\\t\\t\\tself.spn_montantConverti.setValue(resultat) #affichage dans la cbb\",\n \"def calculate_adoption_fee(cursor, dog_id):\\n query = f\\\"SELECT Dog.dogID, IF (Dog.surrender_by_animal_control = 1, 0.15 * sum( Expense.amount ), 1.15 * sum( Expense.amount )) \\\" \\\\\\n f\\\"FROM Dog LEFT JOIN Expense ON Dog.dogID = Expense.dogID LEFT JOIN Adoption ON Adoption.dog = Dog.dogID \\\" \\\\\\n f\\\"WHERE Dog.dogID = {dog_id};\\\"\\n cursor.execute(query)\\n query_result = cursor.fetchall()\\n return query_result\",\n \"def set_costs(self) -> None:\\n self[\\\"glider cost\\\"] = (\\n self[\\\"glider base mass\\\"] * self[\\\"glider cost slope\\\"]\\n + self[\\\"glider cost intercept\\\"]\\n )\\n self[\\\"lightweighting cost\\\"] = (\\n self[\\\"glider base mass\\\"]\\n * self[\\\"lightweighting\\\"]\\n * self[\\\"glider lightweighting cost per kg\\\"]\\n )\\n self[\\\"electric powertrain cost\\\"] = (\\n self[\\\"electric powertrain cost per kW\\\"] * self[\\\"electric power\\\"]\\n )\\n self[\\\"combustion powertrain cost\\\"] = (\\n self[\\\"combustion power\\\"] * self[\\\"combustion powertrain cost per kW\\\"]\\n )\\n self[\\\"fuel cell cost\\\"] = self[\\\"fuel cell power\\\"] * self[\\\"fuel cell cost per kW\\\"]\\n self[\\\"power battery cost\\\"] = (\\n self[\\\"battery power\\\"] * self[\\\"power battery cost per kW\\\"]\\n )\\n self[\\\"energy battery cost\\\"] = (\\n self[\\\"energy battery cost per kWh\\\"] * self[\\\"electric energy stored\\\"]\\n )\\n self[\\\"fuel tank cost\\\"] = self[\\\"fuel tank cost per kg\\\"] * self[\\\"fuel mass\\\"]\\n # Per km\\n self[\\\"energy cost\\\"] = self[\\\"energy cost per kWh\\\"] * self[\\\"TtW energy\\\"] / 3600\\n\\n # For battery, need to divide cost of electricity\\n # at battery by efficiency of charging\\n # to get costs at the \\\"wall socket\\\".\\n\\n _ = lambda x: np.where(x == 0, 1, x)\\n self[\\\"energy cost\\\"] /= _(self[\\\"battery charge efficiency\\\"])\\n\\n self[\\\"component replacement cost\\\"] = (\\n self[\\\"energy battery cost\\\"] * self[\\\"battery lifetime replacements\\\"]\\n + self[\\\"fuel cell cost\\\"] * self[\\\"fuel cell lifetime replacements\\\"]\\n )\\n\\n with open(DATA_DIR / \\\"purchase_cost_params.yaml\\\", \\\"r\\\") as stream:\\n to_markup = yaml.safe_load(stream)[\\\"markup\\\"]\\n\\n self[to_markup] *= self[\\\"markup factor\\\"]\\n\\n # calculate costs per km:\\n self[\\\"lifetime\\\"] = self[\\\"lifetime kilometers\\\"] / self[\\\"kilometers per year\\\"]\\n\\n with open(DATA_DIR / \\\"purchase_cost_params.yaml\\\", \\\"r\\\") as stream:\\n purchase_cost_params = yaml.safe_load(stream)[\\\"purchase\\\"]\\n\\n self[\\\"purchase cost\\\"] = self[purchase_cost_params].sum(axis=2)\\n # per km\\n amortisation_factor = self[\\\"interest rate\\\"] + (\\n self[\\\"interest rate\\\"]\\n / (\\n (np.array(1) + self[\\\"interest rate\\\"]) ** self[\\\"lifetime kilometers\\\"]\\n - np.array(1)\\n )\\n )\\n self[\\\"amortised purchase cost\\\"] = (\\n self[\\\"purchase cost\\\"] * amortisation_factor / self[\\\"kilometers per year\\\"]\\n )\\n\\n # per km\\n self[\\\"maintenance cost\\\"] = (\\n self[\\\"maintenance cost per glider cost\\\"]\\n * self[\\\"glider cost\\\"]\\n / self[\\\"kilometers per year\\\"]\\n )\\n\\n # simple assumption that component replacement\\n # occurs at half of life.\\n self[\\\"amortised component replacement cost\\\"] = (\\n (\\n self[\\\"component replacement cost\\\"]\\n * (\\n (np.array(1) - self[\\\"interest rate\\\"]) ** self[\\\"lifetime kilometers\\\"]\\n / 2\\n )\\n )\\n * amortisation_factor\\n / self[\\\"kilometers per year\\\"]\\n )\\n\\n self[\\\"total cost per km\\\"] = (\\n self[\\\"energy cost\\\"]\\n + self[\\\"amortised purchase cost\\\"]\\n + self[\\\"maintenance cost\\\"]\\n + self[\\\"amortised component replacement cost\\\"]\\n )\",\n \"def test_basicNoSalePC(self):\\n # Basic price check\\n self.log.info(\\\"Price checking Generic Item via speedkey\\\")\\n pos.click(\\\"Price Check\\\")\\n pos.click_speed_key(\\\"Generic Item\\\")\\n \\n # Confirm the right item, at the right price\\n self.read_price_check(\\\"Generic Item\\\", \\\"$0.01\\\")\\n # Don't add the item\\n pos.click(\\\"Ok\\\")\\n \\n # Confirm we aren't in a transaction\\n if self.in_transaction():\\n self.tc_fail(\\\"Unintentionally In Transaction\\\")\\n else:\\n self.log.info(\\\"Confirmed we are not in a transaction\\\")\\n \\n # Setup for next test\\n self.recover()\",\n \"def run (self, scalers = {'capital costs':1.0}):\\n self.was_run = True\\n self.reason = \\\"OK\\\"\\n\\n tag = self.cd['file id'].split('+')\\n if len(tag) > 1 and tag[1] != 'biomass_pellet':\\n self.was_run = False\\n self.reason = (\\\"Not a biomass pellet project.\\\")\\n return\\n\\n if not self.cd[\\\"on road system\\\"]:\\n self.diagnostics.add_warning(self.component_name,\\n \\\"not on road system\\\")\\n self.max_boiler_output = 0\\n self.heat_displaced_sqft = 0\\n self.biomass_fuel_consumed = 0\\n self.fuel_price_per_unit = 0\\n self.heat_diesel_displaced = 0\\n self.reason = \\\\\\n \\\"Not on road or marine highway system, so it is assumed that\\\" +\\\\\\n \\\" pellets cannot be delivered cost effectively.\\\"\\n return\\n\\n if np.isnan(float(self.comp_specs['peak month % of total'])):\\n self.diagnostics.add_warning(self.component_name,\\n \\\"bad config value for 'peak month % of total'\\\")\\n self.max_boiler_output = 0\\n self.heat_displaced_sqft = 0\\n self.biomass_fuel_consumed = 0\\n self.fuel_price_per_unit = 0\\n self.heat_diesel_displaced = 0\\n self.reason = \\\"bad config value for 'peak month % of total'\\\"\\n return\\n\\n if self.cd[\\\"model heating fuel\\\"]:\\n self.calc_heat_displaced_sqft()\\n self.calc_energy_output()\\n efficiency = self.comp_specs[\\\"pellet efficiency\\\"]\\n self.calc_max_boiler_output(efficiency)\\n factor = self.comp_specs['capacity factor']\\n self.calc_biomass_fuel_consumed(factor)\\n self.calc_diesel_displaced()\\n\\n\\n if self.cd[\\\"model financial\\\"]:\\n self.get_diesel_prices()\\n\\n self.calc_capital_costs()\\n self.calc_maintenance_cost()\\n\\n\\n self.fuel_price_per_unit = self.cd['pellet price']\\n\\n self.calc_proposed_biomass_cost(self.fuel_price_per_unit)\\n self.calc_displaced_heating_oil_price()\\n\\n\\n self.calc_annual_electric_savings()\\n self.calc_annual_heating_savings()\\n\\n self.calc_annual_total_savings()\\n self.calc_annual_costs(self.cd['interest rate'],\\n scalers['capital costs'])\\n self.calc_annual_net_benefit()\\n self.calc_npv(self.cd['discount rate'], self.cd[\\\"current year\\\"])\\n\\n fuel_cost = self.biomass_fuel_consumed * self.fuel_price_per_unit\\n self.calc_levelized_costs(self.maintenance_cost + fuel_cost)\",\n \"def calc_b_a(self, data):\\n # Calculate our expected bid / ask\\n mkt_bid = data['Bid'].values[-1]\\n mkt_ask = data['Ask'].values[-1]\\n last_trade = data['Trade Price'].values[-1]\\n shares = self.calc_shares(data=data, last_trade=last_trade)\\n\\n k = (mkt_bid - mkt_ask) / (mkt_bid + mkt_ask) * -100\\n\\n our_bid = np.average(data['Bid'], weights=self.weights) - k\\n our_ask = np.average(data['Ask'], weights=self.weights) + k\\n\\n self.portfoolio['Unrealized_PnL'] = self.portfoolio['Shares'] * last_trade - self.portfoolio['Avg_Cost'] * self.portfoolio['Shares']\\n\\n if shares == 0:\\n # Skip 0 share orders\\n pass\\n elif abs(self.portfoolio['Cost']) >= self.max_pos * .75:\\n # If position size at or above 95% of max, reduce position\\n self.risk_control(bid=mkt_bid, ask=mkt_ask, last_trade=last_trade)\\n\\n elif our_bid >= mkt_bid:\\n # Buy at bid\\n self.trade(shares=shares, price=mkt_bid, last_trade=last_trade)\\n elif our_ask <= mkt_ask:\\n # Sell at ask\\n self.trade(shares=-shares, price=mkt_ask, last_trade=last_trade)\\n else:\\n print('No order placed')\",\n \"def calculate_total(self):\\n if self.total_price == 0:\\n for discount in self.discounts:\\n for item in self.items:\\n item.add_discount(discount)\\n\\n for item in self.items:\\n self.total_price += item.final_price()\\n\\n return self.total_price\",\n \"def opinion():\\n pass\",\n \"def stoploss(self):\\n price = float(self.price)\\n print(\\\"orderPrice1:\\\",self.dentry[\\\"orderPrice1\\\" + self.chartnumber])\\n if (self.dentry[\\\"orderPrice1\\\" + self.chartnumber] - price) / self.dentry[\\\"orderPrice1\\\" + self.chartnumber] * 100 >= self.stopPercent:\\n self.sell(stopped=True)\",\n \"def tickets(number, day, premium_seating):\\n #fill in your code here. \\n return 0.0\",\n \"def lab10_q2():\\n return \\\"\\\"\\\"\\n Make a 'total' to add up the total revenue as while loop plays.\\n\\titem = qvm.dispense, makes it so item is the juice. \\n\\tWhile item: because if there is a juice this while loop will continue. If there aren't two fruits, there wont be a juice so while loop stops.\\n\\ttotal += qvm.collect_money(item). (this should be += not = right?) to add the revenue from that 'item'\\n\\trevalue item to the next juice.\\n\\treturn total\\n \\\"\\\"\\\"\",\n \"def transact_shares(self, action, quantity, price, commission, bid=None, ask=None):\\n if bid is None: \\n bid = price\\n if ask is None:\\n ask = price\\n\\n if action is None:\\n return\\n\\n self.total_commission += commission\\n\\n # Adjust total bought and sold\\n if action == \\\"BOT\\\":\\n self.avg_bot = (self.avg_bot * self.buys + price * quantity) / (self.buys + quantity)\\n\\n if self.net < 0:\\n self.realised_pnl += min(quantity, abs(self.net)) * (self.avg_price - price) - commission # Adjust realised PNL\\n commission = 0 # assume commission is all in realised_pnl\\n # Increasing long position\\n self.avg_price = (self.avg_price * self.net + price * quantity + commission) / (self.net + quantity)\\n self.buys += quantity\\n self.total_bot = self.buys * self.avg_bot\\n\\n # action == \\\"SLD\\\"\\n else:\\n self.avg_sld = (self.avg_sld * self.sells + price * quantity) / (self.sells + quantity)\\n\\n if self.net > 0:\\n self.realised_pnl += min(quantity, abs(self.net)) * (price - self.avg_price) - commission # Adjust realised PNL\\n commission = 0 # assume commission is all in realised_pnl\\n\\n self.avg_price = (self.avg_price * self.net - price * quantity - commission) / (self.net - quantity)\\n self.sells += quantity\\n self.total_sld = self.sells * self.avg_sld\\n\\n # Adjust net values, including commissions\\n self.net = self.buys - self.sells\\n self.net_total = self.total_sld - self.total_bot\\n self.net_incl_comm = self.net_total - self.total_commission\\n self.cost_basis = self.net * self.avg_price\\n\\n self.update_market_value(bid, ask)\",\n \"def compute_price(self, date = None):\\n\\t\\tif date is None:\\n\\t\\t\\tdate = datetime.now()\\n\\t\\tself.price = 0\\n\\t\\t# Getting list of product in cart\\n\\t\\tcontent = self.cart.cart_content_set.all()\\n\\t\\t# Dictionnary in order to compute minimum state of multi promotion\\n\\t\\tstate = {\\n\\t\\t\\t'products':{},\\n\\t\\t\\t'promotions':{}\\n\\t\\t}\\n\\t\\trequirements = {}\\n\\n\\t\\tfor element in content:\\n\\t\\t\\tproduct = element.product\\n\\t\\t\\tquantity = element.quantity\\n\\n\\t\\t\\t# First look for promotion\\n\\t\\t\\tsimple_promotions = product.promotion_set.filter(end__gte = date, type = 's').distinct('reference', 'end').order_by('-end', 'reference')\\n\\t\\t\\tmulti_promotions = product.promotion_set.filter(end__gte = date, type = 'm').distinct('reference', 'end').order_by('-end', 'reference')\\n\\t\\t\\tif len(simple_promotions)>0:\\n\\t\\t\\t\\tpromotion = simple_promotions[0]\\n\\t\\t\\t\\tself.price = self.price + quantity*promotion.after\\n\\t\\t\\t\\n\\t\\t\\telif len(multi_promotions)>0:\\n\\t\\t\\t\\tfor promotion in multi_promotions:\\n\\t\\t\\t\\t\\tprice_before = promotion.before\\n\\t\\t\\t\\t\\tprice_after = promotion.after\\n\\t\\t\\t\\t\\tcontent = [ (p, 1) for p in promotion.content.all()]\\n\\t\\t\\t\\t\\tfound, requirement = self.get_promotion_requirement(content, price_before)\\n\\t\\t\\t\\t\\tif found and requirement is not None:\\n\\t\\t\\t\\t\\t\\trequirements[promotion.id] = { p.id:q for p, q in requirement} # updating promotion multi requirements\\n\\n\\t\\t\\t\\t\\t# Updating promotion multi state\\n\\t\\t\\t\\t\\tprod, price = self.get_simple_price([{'product':product, 'quantity':1}], date)[0]\\n\\t\\t\\t\\t\\t# print quantity\\n\\t\\t\\t\\t\\tstate['products'][product.id] = {'price': price, 'qte':quantity}\\n\\t\\t\\t\\t\\t# print state['products'][product.id]\\n\\t\\t\\t\\t\\tstate['promotions'][promotion.id] = {'price': price_after, 'qte':0}\\n\\t\\t\\telse:\\n\\t\\t\\t\\thistory = product.history_set.filter(created__gte = date-timedelta(hours = 24)).order_by('-created')\\n\\t\\t\\t\\tif len(history)>0:\\n\\t\\t\\t\\t\\tself.price = self.price + quantity*history[0].price\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\thistory = product.history_set.all().order_by('-created')\\n\\t\\t\\t\\t\\tif len(history)>0:\\n\\t\\t\\t\\t\\t\\tself.price = self.price + quantity*history[0].price\\n\\n\\t\\t# Dealing with multi promotion:\\n\\t\\tmin_state, min_price = self.get_min_state(state, requirements)\\n\\t\\tself.price = self.price + min_price\\n\\n\\t\\tself.save()\\n\\n\\t\\treturn self.price\",\n \"def use(self):\\n if self.price_of_trip == 0:\\n print(\\\"Sorry your card has been used\\\")\\n else:\\n self.price_of_trip -= self.price_of_trip\\n print(\\\"Done\\\")\",\n \"def budget_for_necessities():\\n print(\\\"========== Displaying hotel options ==========\\\")\\n for i in range(len(hotel_list)):\\n print(\\\" -- Enter\\\", i+1, \\\"to choose -- \\\")\\n hotel_list[i].print_hotel()\\n print(\\\" \\\")\\n while True:\\n try:\\n hotel_num = int(input(\\\"Please choose your hotel option (Enter a number between 1 to 7): \\\"))\\n hotel_num -= 1\\n if hotel_num in range(len(hotel_list)): break\\n except ValueError:\\n print(\\\"Please enter an positive integer. Try again... \\\")\\n while True:\\n try:\\n hotel_stay = int(input(\\\"Please enter the duration (in days) of your stay: \\\"))\\n if hotel_stay > 0: break\\n except ValueError:\\n print(\\\"Please enter an positive integer. Try again... \\\")\\n user_hotel = hotel_list[hotel_num]\\n user_hotel_price = user_hotel.get_price()\\n user_hotel_name = user_hotel.get_name()\\n # display car option and ask for user input\\n print(\\\"\\\\n======== Displaying rental car options =========\\\")\\n for i in range(len(car_list)):\\n print(\\\" -- Enter\\\", i+1, \\\"to choose -- \\\")\\n car_list[i].print_car()\\n print(\\\" \\\")\\n while True:\\n try:\\n car_num = int(input(\\\"Please choose your car rental option (Enter a number between 1 to 6): \\\"))\\n car_num -= 1\\n if car_num in range(len(hotel_list)): break\\n except ValueError:\\n print(\\\"Please enter an positive integer. Try again... \\\")\\n while True:\\n try:\\n car_rental_day = int(input(\\\"Please enter the duration (in days) of your car rental: \\\"))\\n if car_rental_day > 0: break\\n except ValueError:\\n print(\\\"Please enter an positive integer. Try again... \\\")\\n # calculate user's total cost for car rental and hotel\\n user_car = car_list[car_num]\\n user_car_price = user_car.get_price()\\n user_car_name = user_car.get_name()\\n total_hotel_cost = hotel_stay * user_hotel_price\\n total_car_rental_cost = car_rental_day * user_car_price\\n print(\\\"\\\\n=== Displaying your hotel and car rental information ===\\\")\\n print(\\\"Hotel: \\\", user_hotel.get_name())\\n print(\\\"Hotel total cost: $\\\", total_hotel_cost)\\n print(\\\"Car Rental: \\\", user_car.get_name())\\n print(\\\"Car rental total cost: $\\\", total_car_rental_cost)\\n print(\\\" \\\")\\n # calculate remaining budget based on hotel and car's cost and/or ask for higher budget\\n user_budget.calculate_new_budget(total_hotel_cost + total_car_rental_cost)\\n print(\\\" \\\")\\n return total_hotel_cost, total_car_rental_cost, user_hotel_name, user_car_name\",\n \"def __init__(self, rank_discount_fn):\\n self._rank_discount_fn = rank_discount_fn\",\n \"def compute(self, **kwargs):\\n raise NotImplementedError\",\n \"def __init__(self, price, date=datetime.datetime.now(), dividend=0., annualDividend=0.):\\n self.price = price\\n self.date = date\\n self.dividend = dividend\\n self.annualDividend = annualDividend\",\n \"def getAmount2(*args):\",\n \"def trade(self, action=None):\\n #print(\\\"Trading {}\\\".format(action))\\n # Buy\\n if action > 0.2 : self.posture = 1\\n # Hold\\n if action < 0.2 and action > -0.2: self.posture = 0\\n # Sell\\n if action < -0.2: self.posture = -1\\n \\n # Evaluate posture and calculare actual cost of trade\\n #print(\\\"Posture: {}\\\".format(self.posture))\\n if self.posture == 1:\\n _amt = self.amt_buy\\n _base = (_amt * self.state['price'] \\\\\\n + (_amt * self.commission)) * -1\\n \\n elif self.posture == -1:\\n _amt = self.amt_sell\\n _base = _amt * self.state['price'] \\\\\\n + (_amt * self.commission) \\\\\\n + (_amt * self.gap)\\n _amt = _amt * -1 \\n\\n # Set posture to 0 if no balance available\\n if (self.posture == 1 and self.balance < abs(_base)) \\\\\\n or (self.posture == -1 and self.bag < abs(_amt)):\\n print(\\\"NOT enough amount!!\\\")\\n self.stop=True\\n self.posture = 0\\n\\n if self.posture == 0:\\n _amt = 0\\n _base = 0\\n\\n # Modify balances\\n self.transaction = _base\\n self.amt = _amt\\n self.balance = self.balance + _base\\n self.bag = self.bag + _amt\\n self.value = self.calculate_value()\\n #print(\\\"Posture : {} // Transaction: {}\\\".format(self.posture, self.transaction))\\n\\n return self\",\n \"def eval_sltp(self):\\n\\n pos_size = float(self.get_position()['positionAmt'])\\n if pos_size == 0:\\n return\\n # tp\\n tp_order = self.get_open_order('TP') \\n \\n is_tp_full_size = False \\n is_sl_full_size = False \\n\\n if tp_order is not None:\\n origQty = float(tp_order['origQty'])\\n is_tp_full_size = origQty == abs(pos_size) if True else False\\n #pos_size = pos_size - origQty \\n \\n tp_percent_long = self.get_sltp_values()['profit_long']\\n tp_percent_short = self.get_sltp_values()['profit_short'] \\n\\n avg_entry = self.get_position_avg_price()\\n\\n # tp execution logic \\n if tp_percent_long > 0 and is_tp_full_size == False:\\n if pos_size > 0: \\n tp_price_long = round(avg_entry +(avg_entry*tp_percent_long), self.round_decimals) \\n if tp_order is not None:\\n time.sleep(2) \\n self.cancel(id=tp_order['clientOrderId'])\\n time.sleep(2)\\n self.order(\\\"TP\\\", False, abs(pos_size), limit=tp_price_long, reduce_only=True)\\n else: \\n self.order(\\\"TP\\\", False, abs(pos_size), limit=tp_price_long, reduce_only=True)\\n if tp_percent_short > 0 and is_tp_full_size == False:\\n if pos_size < 0: \\n tp_price_short = round(avg_entry -(avg_entry*tp_percent_short), self.round_decimals)\\n if tp_order is not None:\\n time.sleep(2) \\n self.cancel(id=tp_order['clientOrderId'])\\n time.sleep(2)\\n self.order(\\\"TP\\\", True, abs(pos_size), limit=tp_price_short, reduce_only=True)\\n else:\\n self.order(\\\"TP\\\", True, abs(pos_size), limit=tp_price_short, reduce_only=True)\\n #sl\\n sl_order = self.get_open_order('SL')\\n if sl_order is not None:\\n origQty = float(sl_order['origQty'])\\n orig_side = sl_order['side'] == \\\"BUY\\\" if True else False\\n if orig_side == False:\\n origQty = -origQty \\n is_sl_full_size = origQty == -pos_size if True else False \\n\\n sl_percent_long = self.get_sltp_values()['stop_long']\\n sl_percent_short = self.get_sltp_values()['stop_short']\\n\\n # sl execution logic\\n if sl_percent_long > 0 and is_sl_full_size == False:\\n if pos_size > 0:\\n sl_price_long = round(avg_entry - (avg_entry*sl_percent_long), self.round_decimals)\\n if sl_order is not None:\\n time.sleep(2) \\n self.cancel(id=sl_order['clientOrderId'])\\n time.sleep(2)\\n self.order(\\\"SL\\\", False, abs(pos_size), stop=sl_price_long, reduce_only=True)\\n else: \\n self.order(\\\"SL\\\", False, abs(pos_size), stop=sl_price_long, reduce_only=True)\\n if sl_percent_short > 0 and is_sl_full_size == False:\\n if pos_size < 0:\\n sl_price_short = round(avg_entry + (avg_entry*sl_percent_short), self.round_decimals)\\n if sl_order is not None: \\n time.sleep(2) \\n self.cancel(id=sl_order['clientOrderId'])\\n time.sleep(2)\\n self.order(\\\"SL\\\", True, abs(pos_size), stop=sl_price_short, reduce_only=True) \\n else: \\n self.order(\\\"SL\\\", True, abs(pos_size), stop=sl_price_short, reduce_only=True)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69941723","0.6785887","0.66767174","0.6378575","0.6193816","0.6177383","0.61754054","0.60723627","0.5998024","0.59970456","0.5993197","0.59507436","0.59506613","0.59471047","0.59038407","0.58806396","0.5844756","0.58214176","0.58164537","0.57721055","0.5763402","0.5715087","0.5704697","0.56677085","0.5641842","0.56325424","0.56237334","0.5606406","0.5601377","0.5595319","0.55691355","0.5557798","0.55505186","0.55390334","0.553191","0.5508789","0.5506599","0.5505627","0.54936403","0.5483043","0.54745233","0.5473832","0.54702514","0.54650176","0.5462069","0.54536283","0.5440936","0.54290736","0.5426047","0.54200464","0.5411951","0.5409296","0.5407163","0.5407163","0.5402556","0.53992355","0.5399216","0.53963023","0.5393825","0.53888047","0.5386183","0.53835523","0.5372682","0.5361","0.535171","0.5347671","0.5340559","0.5339047","0.5334416","0.53255427","0.5320362","0.5319221","0.53150046","0.5310642","0.52995616","0.5296152","0.52960247","0.5293781","0.52869236","0.5286832","0.5274163","0.5272548","0.5267323","0.5265925","0.5263708","0.5263032","0.52601147","0.525334","0.5245129","0.52445525","0.52363","0.52348596","0.52338105","0.5232304","0.5230389","0.52247316","0.5223909","0.52227926","0.52202857","0.5218857","0.5213575"],"string":"[\n \"0.69941723\",\n \"0.6785887\",\n \"0.66767174\",\n \"0.6378575\",\n \"0.6193816\",\n \"0.6177383\",\n \"0.61754054\",\n \"0.60723627\",\n \"0.5998024\",\n \"0.59970456\",\n \"0.5993197\",\n \"0.59507436\",\n \"0.59506613\",\n \"0.59471047\",\n \"0.59038407\",\n \"0.58806396\",\n \"0.5844756\",\n \"0.58214176\",\n \"0.58164537\",\n \"0.57721055\",\n \"0.5763402\",\n \"0.5715087\",\n \"0.5704697\",\n \"0.56677085\",\n \"0.5641842\",\n \"0.56325424\",\n \"0.56237334\",\n \"0.5606406\",\n \"0.5601377\",\n \"0.5595319\",\n \"0.55691355\",\n \"0.5557798\",\n \"0.55505186\",\n \"0.55390334\",\n \"0.553191\",\n \"0.5508789\",\n \"0.5506599\",\n \"0.5505627\",\n \"0.54936403\",\n \"0.5483043\",\n \"0.54745233\",\n \"0.5473832\",\n \"0.54702514\",\n \"0.54650176\",\n \"0.5462069\",\n \"0.54536283\",\n \"0.5440936\",\n \"0.54290736\",\n \"0.5426047\",\n \"0.54200464\",\n \"0.5411951\",\n \"0.5409296\",\n \"0.5407163\",\n \"0.5407163\",\n \"0.5402556\",\n \"0.53992355\",\n \"0.5399216\",\n \"0.53963023\",\n \"0.5393825\",\n \"0.53888047\",\n \"0.5386183\",\n \"0.53835523\",\n \"0.5372682\",\n \"0.5361\",\n \"0.535171\",\n \"0.5347671\",\n \"0.5340559\",\n \"0.5339047\",\n \"0.5334416\",\n \"0.53255427\",\n \"0.5320362\",\n \"0.5319221\",\n \"0.53150046\",\n \"0.5310642\",\n \"0.52995616\",\n \"0.5296152\",\n \"0.52960247\",\n \"0.5293781\",\n \"0.52869236\",\n \"0.5286832\",\n \"0.5274163\",\n \"0.5272548\",\n \"0.5267323\",\n \"0.5265925\",\n \"0.5263708\",\n \"0.5263032\",\n \"0.52601147\",\n \"0.525334\",\n \"0.5245129\",\n \"0.52445525\",\n \"0.52363\",\n \"0.52348596\",\n \"0.52338105\",\n \"0.5232304\",\n \"0.5230389\",\n \"0.52247316\",\n \"0.5223909\",\n \"0.52227926\",\n \"0.52202857\",\n \"0.5218857\",\n \"0.5213575\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":84198,"cells":{"query":{"kind":"string","value":"Generates background and labels for the Cafeteria Menu"},"document":{"kind":"string","value":"def menu_handler(menu,fileout_path):\r\n \r\n image = Image.open(\"icons/menu_template.jpg\")\r\n draw = ImageDraw.Draw (image)\r\n \r\n if menu is None: \r\n \r\n TITLE_X_OFFSET=0.05*image.size[0]\r\n TITLE_Y_OFFSET=0.25*image.size[1]\r\n \r\n huge_font=ImageFont.truetype(\"fonts\\Ariblk.ttf\",int(image.size[0]*HUGE_FONT/1000)) \r\n\r\n draw.text ( (TITLE_X_OFFSET,TITLE_Y_OFFSET), u'Ementa não disponível', fill=BLACK,font=huge_font)\r\n \r\n else:\r\n\r\n TITLE_X_OFFSET=0.05*image.size[0]\r\n MEAL_X_OFFSET=0.07*image.size[0]\r\n MENU_X_OFFSET=0.10*image.size[0]\r\n DISH_X_OFFSET=0.15*image.size[0]\r\n\r\n TITLE_Y_OFFSET=0.12*image.size[1]\r\n MEAL_Y_OFFSET=0.10*image.size[1]\r\n MENU_Y_OFFSET=0.08*image.size[1]\r\n DISH_Y_OFFSET=0.06*image.size[1]\r\n \r\n \r\n large_font=ImageFont.truetype(\"fonts\\Ariblk.ttf\",int(image.size[0]*LARGE_FONT/1000))\r\n medium_font=ImageFont.truetype(\"fonts\\Ariblk.ttf\",int(image.size[0]*MEDIUM_FONT/1000))\r\n small_font=ImageFont.truetype(\"fonts\\Ariblk.ttf\",int(image.size[0]*SMALL_FONT/1000))\r\n\r\n large_font_bold=ImageFont.truetype(\"fonts\\Ariblk.ttf\",int(image.size[0]*LARGE_FONT/1000))\r\n medium_font_bold=ImageFont.truetype(\"fonts\\Ariblk.ttf\",int(image.size[0]*MEDIUM_FONT/1000))\r\n small_font_bold=ImageFont.truetype(\"fonts\\Ariblk.ttf\",int(image.size[0]*SMALL_FONT/1000))\r\n\r\n y_offset=0\r\n \r\n y_offset+=TITLE_Y_OFFSET\r\n draw.text ( (TITLE_X_OFFSET,y_offset), u'Ementa', fill=BLACK,font=large_font)\r\n \r\n if menu.has_key(u'Almoço'):\r\n y_offset+=MEAL_Y_OFFSET\r\n draw.text ( (MEAL_X_OFFSET,y_offset), u'Almoço', fill=BLACK,font=medium_font_bold)\r\n \r\n\r\n if menu[u'Almoço'].has_key(u'Menú Tradicional'): \r\n y_offset+=MENU_Y_OFFSET \r\n draw.text ( (MENU_X_OFFSET,y_offset), u'- Menú Tradicional', fill= BLACK,font=small_font) \r\n \r\n for elem in menu[u'Almoço'][u'Menú Tradicional']:\r\n y_offset+=DISH_Y_OFFSET\r\n draw.text ( (DISH_X_OFFSET,y_offset), u'\\u2022 ' + elem, fill= BLACK,font=small_font) \r\n \r\n \r\n if menu[u'Almoço'].has_key(u'Menú Macrobiótica'): \r\n y_offset+=MENU_Y_OFFSET \r\n draw.text ( (MENU_X_OFFSET,y_offset), u'- Menú Macrobiótica', fill= BLACK,font=small_font) \r\n \r\n for elem in menu[u'Almoço'][u'Menú Macrobiótica']:\r\n y_offset+=DISH_Y_OFFSET\r\n draw.text ( (DISH_X_OFFSET,y_offset), u'\\u2022 ' + elem, fill= BLACK,font=small_font) \r\n \r\n if menu.has_key(u'Jantar'): \r\n y_offset+=MEAL_Y_OFFSET\r\n draw.text ( (MEAL_X_OFFSET,y_offset), 'Jantar', fill= BLACK,font=medium_font_bold) \r\n \r\n if menu[u'Jantar'].has_key(u'Menú Tradicional'): \r\n y_offset+=MENU_Y_OFFSET \r\n draw.text ( (MENU_X_OFFSET,y_offset), u'- Menú Tradicional', fill= BLACK,font=small_font) \r\n \r\n for elem in menu[u'Jantar'][u'Menú Tradicional']:\r\n y_offset+=DISH_Y_OFFSET\r\n draw.text ( (DISH_X_OFFSET,y_offset), u'\\u2022 ' + elem, fill= BLACK,font=small_font) \r\n \r\n \r\n image.save(join(fileout_path,'menu_file.jpg'))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create_menu():","def create_menus( self ):","def draw_main_menu():\n draw_cover()\n draw_menu_buttons()\n draw_border()","def main_menu(self):\n\n # Set the window background\n self.palette = QPalette()\n self.pixmap = QPixmap('./pictures/menu_cat.png').scaled(860, 640)\n self.palette.setBrush(QPalette.Background, QBrush(self.pixmap))\n self.setPalette(self.palette)\n\n for item in self.mainmenu_items:\n item.show()\n for item in self.mapmenu_items:\n item.hide()","def _add_color_menu(self):\n print 'adding color menu'\n self.menuBar.addcascademenu('Color', 'Color Atoms'); \n c_lambda = lambda: self.color_wireframe('cpk');\n self.menuBar.addmenuitem('Color Atoms','command','Color wireframes cpk', command=c_lambda, label='cpk')\n c_lambda = lambda: self.color_wireframe('type');\n self.menuBar.addmenuitem('Color Atoms','command','Color wireframes by type', command=c_lambda, label='type')\n c_lambda = lambda: self.color_wireframe('chain');\n self.menuBar.addmenuitem('Color Atoms','command','color wireframes by chain', command=c_lambda, label='chain')\n c_lambda = lambda: self.color_wireframe('hydrogen_type');\n self.menuBar.addmenuitem('Color Atoms','command','color wireframes by H type', command=c_lambda, label='H Type')\n \n self.menuBar.addcascademenu('Color', 'Color Trace')\n self.menuBar.addmenuitem('Color Trace','command','Color tubes by secondary', command=self.color_trace_by_secondary,label='secondary')\n self.menuBar.addmenuitem('Color Trace','command','Color tubes by type', command=self.color_tubes_type,label='type')\n self.menuBar.addmenuitem('Color Trace','command','Color tubes by chain', command=self.color_tubes_chain,label='chain')\n\n self.menuBar.addcascademenu('Color', 'Color Volumes')\n self.menuBar.addmenuitem('Color Volumes','command','Color volumes cpk', command=self.color_volumes_cpk,label='cpk')\n self.menuBar.addmenuitem('Color Volumes','command','Color volumes by type', command=self.color_volumes_type,label='type')\n self.menuBar.addmenuitem('Color Volumes','command','Color volumes by chain', command=self.color_volumes_chain,label='chain')\n\n # create menu items for .features keys for atoms and residues\n if self.system != 'None' and self.system != None:\n key_store = {}\n key_store['atom'] = self.system.ProteinList[0].atoms[0].features.keys()\n key_store['residue'] = self.system.ProteinList[0].residues[0].features.keys()\n for run_type in ['atom', 'residue']:\n broken = 0\n for key in key_store[run_type]:\n for pol in self.system.ProteinList:\n if key == 'domain':\n self.print_domain_info(pol)\n normalized = 1\n # if the feature includes non-digits, pass. if it is all digits, see if \n # it is normalized\n if run_type == 'atom':\n item_list = pol.atoms\n elif run_type == 'residue':\n item_list = pol.residues\n same_val_count = 0\n try:\n item_list[0].features[key]\n except KeyError:\n continue\n else:\n first_val = item_list[0].features[key]\n for item in item_list:\n try:\n feature = item.features[key]\n except KeyError:\n print 'key error on %s, breaking'%(key)\n broken = 1\n break\n try:\n int(feature)\n except ValueError:\n print '%s not digit, breaking'%(feature)\n broken = 1\n break\n else:\n if feature != -1 and (feature < 0.0 or feature > 1.0):\n normalized = 0\n if feature == first_val:\n same_val_count += 1\n if same_val_count == len(item_list):\n print '%s all the same value; breaking'%(key)\n broken = 1\n break\n if key == 'domain':\n if item.features[key] == 0.0:\n item.features[key] = -1\n else:\n # if not normalized, make a new key called key+'_normalized', and swap the old\n # key with the new key to color by it\n old_key = copy.copy(key)\n if not normalized and (key+'_normalized' not in item.features.keys()):\n min_f = 1000000\n max_f = -1000000\n for item2 in item_list:\n feature = item2.features[key]\n if feature != -1:\n if feature < min_f:\n min_f = feature\n if feature > max_f:\n max_f = feature\n key = key + '_normalized'\n for item2 in item_list:\n if item2.features[old_key] != -1.0:\n d = (item2.features[old_key]-min_f) / (max_f-min_f+0.0)\n item2.features[key] = d\n else:\n item2.features[key] = -1.0\n if run_type == 'residue':\n c_lambda1 = lambda p=pol, k=key: self.color_trace_by_residue_feature(p, k)\n self.menuBar.addmenuitem('Color Trace','command','Color trace by res '+key, command=c_lambda1, label='%s %s'%(pol.chain_name, key))\n c_lambda2 = lambda p=pol, k=key: self.color_volume_by_residue_feature(p, k)\n self.menuBar.addmenuitem('Color Volumes','command','Color volumes by res '+key, command=c_lambda2, label='%s %s'%(pol.chain_name, key))\n c_lambda3 = lambda p=pol, k=key: self.color_atoms_by_residue_feature(p, k)\n self.menuBar.addmenuitem('Color Atoms','command','Color atoms by res '+key, command=c_lambda3, label='%s %s'%(pol.chain_name, key))\n elif run_type == 'atom':\n c_lambda1 = lambda p=pol, k=key: self.color_trace_by_atom_feature(p, k)\n self.menuBar.addmenuitem('Color Trace','command','Color trace by atom '+key, command=c_lambda1, label='%s %s'%(pol.chain_name, key))\n c_lambda2 = lambda p=pol, k=key: self.color_volume_by_atom_feature(p, k)\n self.menuBar.addmenuitem('Color Volumes','command','Color volumes by atom '+key, command=c_lambda2, label='%s %s'%(pol.chain_name, key))\n c_lambda3 = lambda p=pol, k=key: self.color_atoms_by_atom_feature(p, k)\n self.menuBar.addmenuitem('Color Atoms','command','Color atoms by atom '+key, command=c_lambda3, label='%s %s'%(pol.chain_name, key))\n key = old_key\n #broken = 1\n #break\n if broken:\n break","def make_top_menus(self):\n menubar = tk.Menu(self)\n\n # create a pulldown menu for languages, and add it to the menu bar\n language_menu = tk.Menu(menubar, tearoff=0)\n language_menu.add_command(label=self.translate(\"English\"), command=lambda: self.replace_language('english'))\n language_menu.add_command(label=self.translate(\"Spanish\"), command=lambda: self.replace_language('spanish'))\n language_menu.add_command(label=self.translate(\"Portuguese\"), command=lambda: self.replace_language('portuguese'))\n menubar.add_cascade(label=self.translate(\"Languages\"), menu=language_menu)\n \n # create a pulldown menu for switching context areas, and add it to the menu bar\n context_menu = tk.Menu(menubar, tearoff=0)\n context_menu.add_command(label=self.translate(\"Chile\"), command=lambda: self.switch_context('Chile'))\n context_menu.add_command(label=self.translate(\"Indonesia\"), command=lambda: self.switch_context('Indonesia'))\n context_menu.add_command(label=self.translate(\"Luanda\"), command=lambda: self.switch_context('Luanda'))\n context_menu.add_command(label=self.translate(\"Querétaro\"), command=lambda: self.switch_context('Querétaro'))\n context_menu.add_command(label=self.translate(\"Rio de Janeiro\"), command=lambda: self.switch_context('Rio de Janeiro'))\n context_menu.add_command(label=self.translate(\"Santiago\"), command=lambda: self.switch_context('Santiago'))\n menubar.add_cascade(label=self.translate(\"Locations\"), menu=context_menu)\n \n # create a pulldown menu for arrangment, and add it to the menu bar\n language_menu = tk.Menu(menubar, tearoff=0)\n language_menu.add_command(label=self.translate(\"Graphs-Graphs\"), command=lambda: self.switch_arrangment(['Graph', 'Graph']))\n language_menu.add_command(label=self.translate(\"Graphs-Map\"), command=lambda: self.switch_arrangment(['Graph', 'Map']))\n language_menu.add_command(label=self.translate(\"Map-Graphs\"), command=lambda: self.switch_arrangment(['Map', 'Graph']))\n language_menu.add_command(label=self.translate(\"Map-Map\"), command=lambda: self.switch_arrangment(['Map', 'Map']))\n menubar.add_cascade(label=self.translate(\"Arrange\"), menu=language_menu)\n \n # create an exit command that closes the UI\n menubar.add_command(label=self.translate(\"Exit\"), command=self.destroy)\n \n # display the menu\n menubar.config(font=self.small_font)\n self.config(menu=menubar)\n \n return menubar","def _create_menu(self):\n menu = ImageMenu(0.04, 0.16, 0.23, self.y_for_x(0.23) * 0.7)\n menu.items_per_col = 2\n menu.visible_rows = 2\n menu.visible_cols = 4\n\n clips = self.video_library.get_video_clips()\n clips_list = [[clip.thumbnail_url, clip] for clip in clips]\n menu.async_add_clips(clips_list)\n\n # Create list indicator\n self.list_indicator = ListIndicator(0.7, 0.8, 0.2, 0.045,\n ListIndicator.HORIZONTAL)\n self.list_indicator.set_maximum(len(clips))\n self.list_indicator.show()\n self.add(self.list_indicator)\n\n # Create information labels\n self.clip_title = Label(0.042, \"title\", 0.15, 0.77, \"\",\n font_weight=\"bold\")\n self.clip_title.set_ellipsize(pango.ELLIPSIZE_END)\n self.clip_title.set_line_wrap(False)\n self.clip_title.width = 0.5\n self.add(self.clip_title)\n\n self.clip_info = Label(0.034, \"subtitle\", 0.15, 0.85, \"\")\n self.clip_info.set_ellipsize(pango.ELLIPSIZE_END)\n self.clip_info.set_line_wrap(False)\n self.clip_info.width = 0.5\n self.add(self.clip_info)\n\n return menu","def make_background_graphics(self):\n # Title.\n centered = self.graphics.center_just(1, \"CHOOSE DIFFICULTY\")\n title = TextBox(*centered)\n self.uielements.append(title)\n\n # Bars.\n bar_a = TextBox(Point(0, 2), \"_\"*self.graphics.LENGTH)\n self.uielements.append(bar_a)\n\n bar_b = TextBox(Point(0, 8), \"_\"*self.graphics.LENGTH)\n self.uielements.append(bar_b)\n\n # Controls.\n text = [\n \"_\"*self.graphics.LENGTH,\n \" wasd: Move | m: Select | q: Quit\"\n ]\n controls = LongTextBox(Point(0, self.graphics.HEIGHT-3), text)\n self.uielements.append(controls)","def mainmenu_background():\n gameDisplay.fill((40, 0, 40))","def mainmenu_background():\n gameDisplay.fill((40, 0, 40))","def setup_menu(self, surface, background_colour=BLACK):\n # flood fill the surface with the background colour\n surface.fill(background_colour)\n\n # set up the fixed items on the menu\n # Add buttons and labels\n menu_config = [\n (\"Speed\", 6, 6, BLUE, WHITE), #, 62, 100, WHITE),\n (\"Maze\", 122, 6, BLUE, WHITE), #, 62, 100, WHITE),\n (\"Rainbow\", 6, 70, BLUE, WHITE), #, 62, 100, WHITE),\n (\"Golf\", 122, 70, BLUE, WHITE), #, 62, 100, WHITE),\n (\"Pi Noon\", 6, 134, BLUE, WHITE), #, 62, 100, WHITE),\n (\"Obstacle\", 122, 134, BLUE, WHITE), #, 62, 100, WHITE),\n (\"Shooting\", 6, 198, BLUE, WHITE), #, 62, 100, WHITE),\n (\"RC\", 122, 198, BLUE, WHITE), #, 62, 100, WHITE),\n (\"Exit\", 6, 262, BLUE, WHITE), #, 40, 210, WHITE),\n (\"Calibrate\", 122, 262, BLUE, WHITE),\n ]\n\n # perform list comprehension on menu_config, wherein we call\n # make_button with the index, and individual item arguments\n # note *item performs unpacking of the tuple and provides them\n # as individual arguments to make_button\n return [\n self.make_button(index, *item)\n for index, item\n in enumerate(menu_config)\n ]","def mainmenu_background():\n surface.fill((40, 0, 40))","def draw_labels(self):\n x = PygameUI.Label('Inactive Events')\n x.frame = pygame.Rect(4, 4, 150, 30)\n self.scene.add_child(x)\n \n x = PygameUI.Label('Active Events')\n x.frame = pygame.Rect(Menu.scene.frame.w-150, 4, 150, 30)\n self.scene.add_child(x)","def menu_principal(self):\r\n print('=== Bem Vindo ao Sistema de Cadastro de Clientes ===')\r\n print('\\nEscolha uma das opções abaixo.')\r\n print('1. Visualizar contatos')\r\n print('2. Busca contatos')\r\n print('3. Adicionar contato')\r\n print('4. Remover contato')\r\n print('5. Alterar contato')\r\n print('6. Carregar lista Parte I') \r\n print('7. Visualizar grupo')\r\n print('8. Gestão de grupos')\r\n print('9. Sair')","def draw_main(self, window, font, xpos, ypos):\n\n if not self.is_selected_main(xpos, ypos):\n # Text\n name_label = font.render(self.selected_option, 1, self.menu_colour1)\n window.blit(\n name_label,\n (\n self.x + (self.width // 2 - name_label.get_width() // 2),\n self.y + (self.height // 2 - name_label.get_height() // 2),\n ),\n )\n\n else:\n # Define label\n # Generated first so it's width can be used in the background placement calculations\n name_label = font.render(self.selected_option, 1, self.menu_colour2)\n\n # Box\n # x defined in that way so the background is centered\n # width is multiplied by 1.1 to give each option a bit more background on it's sides\n pygame.draw.rect(\n window,\n self.menu_colour1,\n (\n self.x + (self.width // 2 - name_label.get_width() * 1.1 // 2),\n self.y,\n name_label.get_width() * 1.1,\n self.height,\n ),\n 0,\n )\n # Place label\n window.blit(\n name_label,\n (\n self.x + (self.width // 2 - name_label.get_width() // 2),\n self.y + (self.height // 2 - name_label.get_height() // 2),\n ),\n )","def Crearmenu():\n layoutM = [\n [sg.T(\"ScrabbleAR\", size=(16, 1), justification=\"center\",\n font=(\"Times New Roman\", 25))],\n [sg.T(\" Bienvenido a ScrabbleAR!, el juego donde \")],\n [sg.T(\" hay que armar palabras para ganar \")],\n [sg.B(\"Iniciar nuevo juego\", size=(17, 1), key=\"inicio\"),\n sg.B(\"Configuracion\", size=(17, 1), key=\"config\")],\n [sg.B(\"Puntuaciones\", size=(17, 1), key=\"puntos\"),\n sg.B(\"Salir\", size=(17, 1), key=\"exit\")]\n ]\n\n if(os.path.isfile(\"Guardado.json\")):\n layoutM += [[sg.B(\"Continuar partida\", size=(36, 1), key=\"continue\")]]\n\n window = sg.Window(\"ScrabbleAR - Menu\", layoutM)\n\n return window","def menu_items():\r\n menu_name = \"Coloring\"\r\n algorithm_list = [[\"Brute Force\", board_brute_force_coloring],\r\n [\"separator\", \"separator\"],\r\n [\"Greedy Coloring\", board_greedy_coloring],\r\n [\"Tabu Coloring Search\", board_tabu_coloring],\r\n [\"Tabu Pre-Coloring Search\", board_tabu_precoloring]]\r\n \r\n return [menu_name, algorithm_list]","def draw(self, context):\n layout = self.layout\n\n pie = layout.menu_pie()\n pie.operator(\"object.view_menu\", text=\"Node Editor\", icon='NODETREE').vp = \"NODE_EDITOR\"\n pie.operator(\"object.view_menu\", text=\"UV Image Editor\", icon='IMAGE_COL').vp = \"IMAGE_EDITOR\"\n pie.operator(\"object.view_menu\", text=\"Video Sequece Editor\", icon='SEQUENCE').vp = \"SEQUENCE_EDITOR\"\n pie.operator(\"object.view_menu\", text=\"Movie Clip Editor\", icon='CLIP').vp = \"CLIP_EDITOR\"","def create(self):\n # Positioning background and pointer indicator for main menu\n self.surface.blit(self.main_menu_background, (0, 0))\n self.surface.blit(self.main_menu_greets, self.main_menu_greets_position)\n self.show_mouse_position_with_px()\n self.main_menu_buttons()","def _createDisplayMenu(ned, menu):\n pass","def menu():\n ferme_fenetre()\n Menu()","def create_frame_icons(self):\n self.text = \"{}\".format(self.name)\n self.y = self.startY - 10 if self.startY - 10 > 10 else self.startY + 10\n self.colorIndex = LABELS.index(self.name)","def Render(self, background):\r\n ui_back = rpg_image.Load('data/ui/board_500.png')\r\n rpg_gfx.Draw(ui_back, background, (70,55))\r\n \r\n ui_back_slot = rpg_image.Load('data/ui/board_500_slot_61.png')\r\n ui_back_slot_small = rpg_image.Load('data/ui/board_500_slot_35.png')\r\n \r\n # Print option background borders\r\n if len(self.options) >= 1:\r\n rpg_gfx.Draw(ui_back_slot, background, (70+30,55+75))\r\n if len(self.options) >= 2:\r\n rpg_gfx.Draw(ui_back_slot, background, (70+30,55+145))\r\n if len(self.options) >= 3:\r\n rpg_gfx.Draw(ui_back_slot, background, (70+30,55+215))\r\n if len(self.options) >= 4:\r\n rpg_gfx.Draw(ui_back_slot, background, (70+30,55+285))\r\n \r\n # Draw the Prompt\r\n rpg_image.DrawText(self.dialogue.prompt, 30, (255, 255, 255), (100,80),\r\n background, outline=1)\r\n \r\n # Print all the option texts\r\n for count in range(0, len(self.options)):\r\n #TODO(g): Make the show_always data option check and force this option\r\n # to be the last option, if we have it set to be on\r\n \r\n # Only drawing 4 now\r\n if count >= 4:\r\n break\r\n \r\n option = self.options[count]\r\n \r\n x = 70+30 + 15\r\n y = 55+75 + (70 * count) + 15\r\n \r\n # If this option is selected, highlight it\r\n if self.selected_option == count:\r\n color = (255, 0, 0)\r\n else:\r\n color = (255, 255, 255)\r\n \r\n rpg_image.DrawText(option['text'], 20, color, (x,y), background,\r\n outline=1)","def buildKeyframeMenu(*args, **kwargs)->None:\n pass","def create_menu():\n MenuData = [\n (\"&Draw Variables\",drawable.ask),\n (\"&Show Variables\",printall),\n (\"&Print Variables\",printval),\n (\"&Edit Variable\",edit),\n (\"&Rename Variable\",rename),\n (\"&Forget Variables\",forget),\n (\"---\",None),\n (\"&Create Plane\",\n [(\"Coordinates\", \n [(\"Point and normal\", createPlaneCoordsPointNormal),\n (\"Three points\", createPlaneCoords3Points),\n ]), \n (\"Visually\", \n [(\"Three points\", createPlaneVisual3Points),\n ]),\n ]),\n (\"&Select Plane\",planes.ask),\n (\"&Draw Selection\",planes.draw),\n (\"&Forget Selection\",planes.forget),\n (\"---\",None),\n (\"&Pick Actors\",pick_actors),\n (\"&Pick Elements\",pick_elements),\n (\"&Pick Points\",pick_points),\n (\"&Pick Edges\",pick_edges),\n (\"---\",None),\n ('&Selection',\n [('&Create Report',report_selection),\n ('&Set Property',setprop_selection),\n ('&Grow',grow_selection),\n ('&Partition',partition_selection),\n ('&Get Partition',get_partition),\n ('&Export',export_selection),\n ]),\n (\"---\",None),\n ('&Query',\n [('&Actors',query_actors),\n ('&Elements',query_elements),\n ('&Points',query_points),\n ('&Edges',query_edges),\n ('&Distances',query_distances),\n ]),\n (\"---\",None),\n (\"&Close\",close_menu),\n ]\n return widgets.Menu('Tools',items=MenuData,parent=GD.gui.menu,before='help')","def drawMenu(self):\r\n menuText, menuSize = self.menuView.draw() \r\n self.drawCenteredText(menuText, menuSize, .5, 11.0/16)","def map_menu(self):\n\n # Set the window background\n self.palette = QPalette()\n gradient = QLinearGradient(0, 0, 0, 800)\n gradient.setColorAt(0.0, QColor(227, 0, 77))\n gradient.setColorAt(1.0, QColor(255, 255, 255))\n self.palette.setBrush(QPalette.Window, QBrush(gradient))\n self.setPalette(self.palette)\n\n for item in self.mapmenu_items:\n item.show()\n for item in self.mainmenu_items:\n item.hide()","def menu():\n ########################## HELLO #############################\n print(\"\"\" \n \n mm db 7MM db \n MM MM \n`7MMpdMAo. `7M' `MF' ,pW\"Wq. mmMMmm `7MM ,M\"\"bMM ,pP\"Ybd ,6\"Yb. 7MM \n MM `Wb VA ,V 6W' `Wb MM MM ,AP MM 8I `\" 8) MM MM \n MM M8 VA ,V 8M M8 MM MM 8MI MM `YMMMa. ,pm9MM MM \n MM ,AP VVV YA. ,A9 MM MM `Mb MM L. I8 8M MM MM \n MMbmmd' ,V `Ybmd9' `Mbmo .JMML. `Wbmd\"MML.M9mmmP' `Moo9^Yo..JMML.\n MM ,V \n.JMML. OOb\" \n\nVersion: 1.1 Autor: Paloma Sánchez y Juan Pablo Egido OS: Linux/Debian\n\"\"\")\n\n ######################### MENU ############################\n print(\"\\n [!] Bienvenid@ a Pyotidsai.\")\n print('\\n [!] Introduce la opción deseada '\n '\\n [!] (1) Crear reglas'\n '\\n [!] (2) Detectar malware(SNORT)'\n '\\n [!] (3) Machine Learning Classifier'\n '\\n [!] (0) Salir')","def makeTitle(self):\n l1=Label(self.app, text=\"Asset Allocation Combinations\")\n l1.grid(row=0, column=0)","def __init__(self, MENUWIDTH, MENUHEIGHT):\n \n self._menu_items = []\n self.menu_Text = []\n #Background\n self.menuRect = pygame.Rect((0, 0), (MENUWIDTH, MENUHEIGHT))","def main():\n os.makedirs(\"./img/event_generated/\", exist_ok=True)\n for category in categories():\n render_icon(category + \".png\")","def draw_menu(self, context):\n if context.engine == 'RPR':\n layout = self.layout\n layout.popover('RPR_VIEW3D_PT_panel')","def _draw_main_menu(client, commands):\n client.send(\"\\n\")\n client.send_wrapped(\"~=\" * 20)\n client.send(\" \" * 16 +\"Welcome to \" + PRODUCT_NAME + \"\\n\")\n client.send_wrapped(\"~=\" * 20)\n client.send(\"\\n\")\n client.send(\" | \".join(commands))\n client.send(\"\\n\")","def printMenu():\n print(\"\\nBienvenido\")\n print(\"1- Cargar Datos\")\n print(\"2- Cargar Catalogo de peliculas\")\n print(\"3- Buscar productoras\")\n print(\"0- Salir\")","def createConnectionMarkingMenu(ned):\n pass","def menuBar(self):\n\n\t\tmenu = Menu(self.master)\n\t\tself.master.config(menu=menu)\n\n\t\t#File Menu\n\t\tfyle = Menu(menu)\n\t\tfyle.add_command(label='New',command=self.newPad)\n\t\tfyle.add_command(label='Open File',command=self.open_file)\n\t\tfyle.add_command(label='Save', command=self.saveFile)\n\t\tfyle.add_command(label='Save As',command=self.saveFileAs)\n\t\tfyle.add_command(label='Exit', command=outer.destroy)\n\t\tmenu.add_cascade(label='File',menu=fyle)\n\n\t\t#Edit Menu\n\t\tedit = Menu(menu)\n\t\tedit.add_command(label='Cut')\n\t\tedit.add_command(label='Copy')\n\t\tedit.add_command(label='Paste')\n\t\tedit.add_command(label='Undo')\n\t\tedit.add_command(label='Redo')\n\t\tmenu.add_cascade(label='Edit',menu=edit)\n\n\t\t#View Menu\n\t\tview = Menu(menu)\n\t\tview.add_command(label='Line Numbers')\n\t\tmenu.add_cascade(label='View', menu=view)\n\n\n\t\t#Help Menu\n\t\thelp = Menu(menu)\n\t\thelp.add_command(label='About')\n\t\tmenu.add_cascade(label='Help',menu=help)","def __init__(self, gui, text, callback=None, shortcut=None):\r\n menu_text = pi3d.String(font=gui.font, string=text, is_3d=False,\r\n camera=gui.camera, justify='L')\r\n menu_text.set_shader(gui.shader)\r\n super(MenuItem, self).__init__(gui, [menu_text], 0, 0,\r\n callback=callback, shortcut=shortcut)\r\n self.child_menu = None\r\n self.own_menu = None","def __init__(self, screen, bg_color=(0, 0, 0), font=None, font_size=35, font_color=(255, 255, 255)):\n self.screen = screen\n self.scr_width = self.screen.get_rect().width\n self.scr_height = self.screen.get_rect().height\n self.bg_color = bg_color\n self.clock = pygame.time.Clock()\n self.font = pygame.font.SysFont(font, font_size)\n self.font_color = font_color\n self.items = []\n self.funcs = OrderedDict()\n\n self.text_color = (255, 255, 255)\n\n self.funcs[\"Back\"] = self.menu\n self.funcs[\"Quit\"] = sys.exit\n items = self.funcs.keys()\n for index, item in enumerate(items):\n menu_item = MenuItem(item)\n t_h = len(items) * menu_item.height\n pos_x = (self.scr_width / 2) - (menu_item.width / 2)\n pos_y = (self.scr_height / 2) - (t_h / 2) + ((index * 2) + index * menu_item.height) + 120\n menu_item.set_position(pos_x, pos_y)\n self.items.append(menu_item)\n\n self.mouse_is_visible = True\n self.cur_item = None\n self.text_wall = TextWall()\n self.text_wall.parse_text(instruction_text)","def STARTMENU_LOOP():\n pass","def ibl_options_panel(self, context):\r\n \r\n AM = context.window_manager.asset_m\r\n node_group = bpy.context.scene.world.node_tree.nodes\r\n layout = self.layout\r\n \r\n box = layout.box()\r\n row = box.row()\r\n row.alignment = 'CENTER'\r\n row.label(\"IMAGE\")\r\n row = box.row(align = True)\r\n row.label(\"Rotation:\")\r\n col = row.column()\r\n col.prop(node_group[\"Mapping\"], \"rotation\", text = \"\")\r\n row = box.row(align = True)\r\n row.label(\"Projection:\")\r\n row.prop(AM, \"projection\", text = \"\")\r\n row = box.row(align = True)\r\n row.label(\"Blur:\")\r\n row.prop(node_group[\"ImageBlur\"].inputs[1], \"default_value\", text = \"\")\r\n row = box.row(align = True)\r\n row.label(\"Visible:\")\r\n row.prop(bpy.context.scene.world.cycles_visibility, \"camera\")\r\n row = box.row(align = True)\r\n row.label(\"Transparent:\")\r\n row.prop(bpy.context.scene.cycles, \"film_transparent\")\r\n \r\n \r\n \r\n box = layout.box()\r\n row = box.row(align = True)\r\n row.label(\"Gamma:\")\r\n row.prop(node_group[\"AM_IBL_Tool\"].inputs[0], \"default_value\", text = \"\")\r\n \r\n box = layout.box()\r\n row = box.row()\r\n row.alignment = 'CENTER'\r\n row.label(\"LIGHT\")\r\n row = box.row(align = True)\r\n row.label(\"Strength:\")\r\n row.prop(node_group[\"AM_IBL_Tool\"].inputs[2], \"default_value\", text = \"\")\r\n row = box.row(align = True)\r\n row.label(\"Saturation:\")\r\n row.prop(node_group[\"AM_IBL_Tool\"].inputs[3], \"default_value\", text = \"\")\r\n row = box.row(align = True)\r\n row.label(\"Hue:\")\r\n row.prop(node_group[\"AM_IBL_Tool\"].inputs[4], \"default_value\", text = \"\")\r\n row = box.row(align = True)\r\n row.label(\"Mix Hue:\")\r\n row.prop(node_group[\"AM_IBL_Tool\"].inputs[5], \"default_value\", text = \"\")\r\n \r\n box = layout.box()\r\n row = box.row()\r\n row.alignment = 'CENTER'\r\n row.label(\"GLOSSY\")\r\n row = box.row(align = True)\r\n row.label(\"Strength:\")\r\n row.prop(node_group[\"AM_IBL_Tool\"].inputs[7], \"default_value\", text = \"\")\r\n row = box.row(align = True)\r\n row.label(\"Saturation:\")\r\n row.prop(node_group[\"AM_IBL_Tool\"].inputs[8], \"default_value\", text = \"\")\r\n row = box.row(align = True)\r\n row.label(\"Hue:\")\r\n row.prop(node_group[\"AM_IBL_Tool\"].inputs[9], \"default_value\", text = \"\")\r\n row = box.row(align = True)\r\n row.label(\"Mix Hue:\")\r\n row.prop(node_group[\"AM_IBL_Tool\"].inputs[10], \"default_value\", text = \"\")\r\n \r\n layout.operator(\"wm.save_ibl_settings\", text = \"Save settings\", icon = 'FILE_TICK')","def makeMenu(self):\n\t\tself.fileMenu = self.menuBar().addMenu(self.tr(\"&Arquivo\"))\n\t\tself.fileMenu.addAction(self.newAct)\n\t\tself.fileMenu.addAction(self.openAct)\n\t\tself.fileMenu.addAction(self.saveAct)\n\t\tself.fileMenu.addAction(self.exportAct)\n\t\tself.fileMenu.addSeparator() \n\t\tself.fileMenu.addAction(self.exitAct)\n\n\t\tself.editMenu = self.menuBar().addMenu(self.tr(\"&Editar\"))\n\t\t\n\t\tself.helpMenu = self.menuBar().addMenu(self.tr(\"&Ajuda\"))\n\t\tself.helpMenu.addAction(self.aboutAct)","def __init__(self, parent_menu, label):\n self._parent_menu = parent_menu\n self._options = {}\n self._label = label\n self._menu = Menu(parent_menu)\n self._key_underline = 0","def controls_setup(self):\n self.dashboard = element.Link(self, class_name='menu-dashboard', alias='Dashboard Menu')\n # catalogue and sub-menus\n self.catalogue = element.LinkButton(self, class_name='menu-catalogue', alias='Catalogue Menu')\n self.products = element.Link(self, class_name='submenu-products', alias='Catalogue->Products Menu')\n self.product_types = element.Link(self, class_name='submenu-product-types',\n alias='Catalogue->Product Types Menu')\n self.categories = element.Link(self, class_name='submenu-categories', alias='Catalogue->Categories Menu')\n self.ranges = element.Link(self, class_name='submenu-ranges', alias='Catalogue->Ranges Menu')\n self.stock_alerts = element.Link(self, class_name='submenu-low-stock-alerts',\n alias='Catalogue->Low Stock Alerts Menu')\n # fulfilment and sub-menus\n self.fulfilment = element.LinkButton(self, class_name='menu-fulfilment', alias='Fulfilment Menu')\n self.orders = element.Link(self, class_name='submenu-orders', alias='Fulfilment->Orders Menu')\n self.statistics = element.Link(self, class_name='submenu-statistics', alias='Fulfilment->Statistics Menu')\n self.partners = element.Link(self, class_name='submenu-partners', alias='Fulfilment->Partners Menu')\n # customers and sub-menus\n self.customers = element.LinkButton(self, class_name='menu-customers', alias='Customers Menu')\n self.customers_submenu = element.Link(self, class_name='submenu-customers', alias='Customers->Customers Menu')\n self.deleted_accounts = element.Link(self, class_name='submenu-deleted_accounts',\n alias='Customers->Deleted Accounts Menu')\n self.stock_alert_requests = element.Link(self, class_name='submenu-stock-alert-requests-customers',\n alias='Customers->Stock Alert Requests Menu')\n # offers and sub-menus\n self.offers = element.LinkButton(self, class_name='menu-offers', alias='Offers Menu')\n self.offers_submenu = element.Link(self, class_name='submenu-offers', alias='Offers->Offers Menu')\n self.vouchers = element.Link(self, class_name='submenu-vouchers', alias='Offers->Vouchers Menu')\n # content and sub-menus\n self.content_menu = element.LinkButton(self, class_name='menu-content', alias='Content Menu')\n self.pages = element.Link(self, class_name='menu-pages', alias='Content->Pages Menu')\n self.announcements = element.Link(self, class_name='menu-announcements', alias='Content->Announcements Menu')\n self.reviews = element.Link(self, class_name='menu-reviews', alias='Content->Reviews Menu')\n\n self.reports = element.Link(self, class_name='menu-reports', alias='Reports Menu')\n self.store_info = element.LinkButton(self, css_selector='.dropdown.store > a', alias='Store Info Menu')\n self.partner_store = []","def side_menu(self, screen):\n font = pg.font.Font('freesansbold.ttf', 30) \n text = font.render('Menu', True, (120,130,135))\n textRect = text.get_rect()\n textRect.center = (1100, 180)\n screen.blit(text, textRect)","def draw_title(self):\n title = text_helper.create_text(\"Indefinite Loop\", menu_fonts, 50, white)\n self.main_menu_surface.blit(title, (center_horizontally(title, self.screen_dimensions), 50))","def controls_setup(self):\n\n self.new_items = element.TopMenuLink(self, alias='New Items Menu',\n css_selector='#browse > li:nth-child(1) > a')\n if self.fixed:\n self.jewelry = element.TopMenuLink(self, alias='Jewelry Menu',\n css_selector='#browse > li:nth-child(2) > a')\n self.artwork = element.TopMenuLink(self, alias='Artwork Menu',\n css_selector='#browse > li:nth-child(3) > a')\n self.homeware = element.TopMenuLink(self, alias='Homeware Menu',\n css_selector='#browse > li:nth-child(4) > a')\n self.clothing = element.TopMenuLink(self, alias='Clothing Menu',\n css_selector='#browse > li:nth-child(5) > a')\n self.vintage = element.TopMenuLink(self, alias='Vintage Menu',\n css_selector='#browse > li:nth-child(6) > a')\n self.food = element.TopMenuLink(self, alias='Food Menu',\n css_selector='#browse > li:nth-child(7) > a')\n self.rings = element.SubMenuLink(self, alias='Jewelry->Rings Submenu',\n css_selector='#browse > li:nth-child(2) > ul > li:nth-child(1) > a')\n self.necklaces = element.SubMenuLink(self, alias='Jewelry->Necklaces Submenu',\n css_selector='#browse > li:nth-child(2) > ul > li:nth-child(2) > a')\n self.bracelets = element.SubMenuLink(self, alias='Jewelry->Bracelets Submenu',\n css_selector='#browse > li:nth-child(2) > ul > li:nth-child(3) > a')\n self.earrings = element.SubMenuLink(self, alias='Jewelry->Earrings Submenu',\n css_selector='#browse > li:nth-child(2) > ul > li:nth-child(4) > a')\n self.broaches = element.SubMenuLink(self, alias='Jewelry->Broaches and Pins Submenu',\n css_selector='#browse > li:nth-child(2) > ul > li:nth-child(5) > a')\n self.wall_art = element.SubMenuLink(self, alias='Artwork->Wall Art Submenu',\n css_selector='#browse > li:nth-child(3) > ul > li:nth-child(1) > a')\n self.ceramics = element.SubMenuLink(self, alias='Jewelry->Ceramics Submenu',\n css_selector='#browse > li:nth-child(3) > ul > li:nth-child(2) > a')\n self.sculpture = element.SubMenuLink(self, alias='Jewelry->Sculpture Submenu',\n css_selector='#browse > li:nth-child(3) > ul > li:nth-child(3) > a')\n self.living = element.SubMenuLink(self, alias='Homeware->Living Submenu',\n css_selector='#browse > li:nth-child(4) > ul > li:nth-child(1) > a')\n self.furniture = element.SubMenuLink(self, alias='Homeware->Furniture Submenu',\n css_selector='#browse > li:nth-child(4) > ul > li:nth-child(2) > a')\n self.kitchen = element.SubMenuLink(self, alias='Homeware->Kitchen Submenu',\n css_selector='#browse > li:nth-child(4) > ul > li:nth-child(3) > a')\n self.lighting = element.SubMenuLink(self, alias='Homeware->Lighting Submenu',\n css_selector='#browse > li:nth-child(4) > ul > li:nth-child(4) > a')\n self.outdoor = element.SubMenuLink(self, alias='Homeware->Outdoor Submenu',\n css_selector='#browse > li:nth-child(4) > ul > li:nth-child(5) > a')\n self.organize = element.SubMenuLink(self, alias='Homeware->Organize Submenu',\n css_selector='#browse > li:nth-child(4) > ul > li:nth-child(6) > a')\n self.party = element.SubMenuLink(self, alias='Homeware->Party Submenu',\n css_selector='#browse > li:nth-child(4) > ul > li:nth-child(7) > a')\n self.sub_food = element.SubMenuLink(self, alias='Homeware->Food Submenu',\n css_selector='#browse > li:nth-child(4) > ul > li:nth-child(8) > a')\n self.pets = element.SubMenuLink(self, alias='Homeware->For Pets Submenu',\n css_selector='#browse > li:nth-child(4) > ul > li:nth-child(9) > a')\n self.women = element.SubMenuLink(self, alias='Clothing->Women Submenu',\n css_selector='#browse > li:nth-child(5) > ul > li:nth-child(1) > a')\n self.men = element.SubMenuLink(self, alias='Clothing->Men Submenu',\n css_selector='#browse > li:nth-child(5) > ul > li:nth-child(2) > a')\n self.kids = element.SubMenuLink(self, alias='Clothing->Kids Submenu',\n css_selector='#browse > li:nth-child(5) > ul > li:nth-child(3) > a')\n self.pet_lovers = element.SubMenuLink(self, alias='Clothing->Pet Lovers Submenu',\n css_selector='#browse > li:nth-child(5) > ul > li:nth-child(4) > a')\n self.antiques = element.SubMenuLink(self, alias='Vintage->Antiques Submenu',\n css_selector='#browse > li:nth-child(6) > ul > li:nth-child(1) > a')\n self.accessories = element.SubMenuLink(self, alias='Vintage->Accessories Submenu',\n css_selector='#browse > li:nth-child(6) > ul > li:nth-child(2) > a')\n self.sub_clothing = element.SubMenuLink(self, alias='Vintage->Clothing Submenu',\n css_selector='#browse > li:nth-child(6) > ul > li:nth-child(3) > a')\n self.sub_homeware = element.SubMenuLink(self, alias='Vintage->Homeware Submenu',\n css_selector='#browse > li:nth-child(6) > ul > li:nth-child(4) > a')\n self.toys = element.SubMenuLink(self, alias='Vintage->Toys Submenu',\n css_selector='#browse > li:nth-child(6) > ul > li:nth-child(5) > a')\n self.books = element.SubMenuLink(self, alias='Vintage->Books Submenu',\n css_selector='#browse > li:nth-child(6) > ul > li:nth-child(6) > a')\n else:\n self.menus = []","def get_menus():\n\n pass","def main_menu():\n \n print(\"\"\"\n ( \n )\\ ) ( ) \n (()/( ( ( ( )\\ ) ( ( ( /( ( ( \n /(_)))\\))( ( )( (()/( ( ))\\ ))\\ )\\()) ))\\ )( \n (_)) ((_)()\\ )\\ (()\\ ((_)) )\\ /((_)/((_)((_)\\ /((_)(()\\ \n / __|_(()((_) ((_) ((_) _| | ((_)(_)) (_)) | |(_)(_)) ((_) \n \\__ \\\\ V V // _ \\| '_|/ _` | (_-</ -_)/ -_) | / / / -_) | '_| \n |___/ \\_/\\_/ \\___/|_| \\__,_| /__/\\___|\\___| |_\\_\\ \\___| |_| )\"\"\")\n for i in range(3):\n print()\n print(\"\"\" Main menu:\n\n (1) Play\n (2) Highscores\n (3) Exit\n \n (0) About authors\"\"\")","def printMenu():\n print(\"\\nBienvenido\")\n print(\"1- Cargar Datos\")\n print(\"2- Contar los elementos de la Lista\")\n print(\"3- Contar elementos filtrados por palabra clave\")\n print(\"4- Consultar elementos a partir de dos listas\")\n print(\"5- Consultar buenas peliculas\")\n print(\"0- Salir\")","def __init__(self, win):\n Menu.__init__(self, None, FONT_ADV, win)","def draw_menu(self, menu):\n for but in menu.get_buttons():\n self.win.blit(but.image, but.rect)","def draw_menu_buttons():\n global menu_leds\n \n def draw_menu_led(x, y, color):\n \"\"\"Draws menu lights to launchpad and stores them in menu_leds\n \n :param x: x-coord of led\n :param y: y-coord of led\n :param color: color of led\n :return: None\n \"\"\"\n lp.led_ctrl_xy(x, y, *color)\n if y != 0:\n menu_leds[7 + y] = color\n else:\n menu_leds[x] = color\n \n # Clear all circular buttons\n for i in range(8):\n draw_menu_led(i, 0, (0, 0))\n draw_menu_led(8, i + 1, (0, 0))\n \n # Color all menu buttons\n draw_menu_led(*BUT_LEFT, (3, 3)) # Select buttons yellow\n draw_menu_led(*BUT_RIGHT, (3, 3))\n draw_menu_led(*BUT_START, (0, 3)) # Start button green\n draw_menu_led(*BUT_QUIT, (3, 0)) # Quit button red","def addBgMarkupcallback(self):\n if self.currentSeedNameComboBox.currentIndex != self.currentSeedNameComboBox.count - 1:\n self.lastLabelIndex = self.currentSeedNameComboBox.currentIndex\n self.currentSeedNameComboBox.setCurrentIndex( self.currentSeedNameComboBox.count - 1 )\n interactionNode = slicer.app.applicationLogic().GetInteractionNode()\n interactionNode.SetCurrentInteractionMode(interactionNode.Place)","def MakeCustomMenu(content): #py:MakeCustomMenu\n RUR._MakeCustomMenu_(content)","def hlp(self):\r\n \r\n self.menubar.entryconfig(\"Help\", state = 'disabled')\r\n self.menubar.entryconfig(\"File\", state = 'disabled')\r\n self.hlpframe = LabelFrame(self.master, text = \"Help & About\", font = ('impact', 10), width = 580, height = 662)\r\n self.hlpframe.place(x = 5, y = 5)\r\n self.abt = LabelFrame(self.hlpframe, width = 566, height = 111)\r\n self.abt.place(x = 5)\r\n self.abtproj = Label(self.abt, text = \"A Program for citating web pages using APA 6th edition, commonly used in social sciences:\")\r\n self.abtproj.place(x=35, y = 20)\r\n self.projtitle = Label(self.abt, text = \"APA 6th Edition Web Citation Generator\", font = ('impact', 25))\r\n self.projtitle.place(x = 16, y = 40)\r\n self.how2use = LabelFrame(self.hlpframe, text = \"How to use?\", font = ('impact', 8), width = 566, height = 400)\r\n self.how2use.place(x = 5, y = 110)\r\n self.inst1 = Label(self.how2use, text = \"◾ Enter the required details first and click \\\"Create\\\":\", justify = LEFT)\r\n self.inst1.place(x = 1, y = 1)\r\n self.inst1auth = Label(self.how2use, text = \"▸ You can choose if the article's author is one or more author or a group of authors.\", justify = LEFT)\r\n self.inst1auth.place(x = 25, y = 20)\r\n self.inst1auth2 = Label(self.how2use, text = \"▸ By adding another author, enter the name of the first author to activate the \\\"+\\\" button to add \\n another one.\", justify = LEFT)\r\n self.inst1auth2.place(x=25, y = 40)\r\n self.inst1dates = Label(self.how2use, text = \"▸ Click the dropdown menus of Months, Days, and Years to choose for the article's date of \\n publication and the date of when did you retrieved it.\",justify = LEFT)\r\n self.inst1dates.place(x=25, y= 75)\r\n self.inst1datestip = Label(self.how2use, text = \"▸ If the article does not have a date of publication, just leave it blank or you can set it to none, \\n same goes for retrieval date if you don't want to include it.\",justify = LEFT)\r\n self.inst1datestip.place(x = 25, y = 113)\r\n self.inst2edit = Label(self.how2use, text = \"◾ In Editable result you can make changes on your created citation:\", justify = LEFT)\r\n self.inst2edit.place(x = 1, y = 160)\r\n self.inst2conf = Label(self.how2use, text = \"▸ Click \\\"Confirm\\\" to save changes and move on to Final result or \\\"Discard\\\" to go back to creating \\n a new citation.\",justify = LEFT)\r\n self.inst2conf.place(x=25, y = 180)\r\n self.inst3final = Label(self.how2use, text = \"◾ The Final result adds indents and handles the citations alphabetically:\",justify = LEFT)\r\n self.inst3final.place(x = 1, y = 225)\r\n self.inst3add = Label(self.how2use, text = \"▸ To add another citation to the reference list click \\\"Add another citation!\\\".\",justify = LEFT)\r\n self.inst3add.place(x = 25, y = 245)\r\n self.inst3dis = Label(self.how2use, text = \"▸ If you clicked \\\"Clear all\\\" it'll prompt you to clear the reference list, once confirmed it cannot \\n be undone.\", justify = LEFT)\r\n self.inst3dis.place(x = 25, y = 265)\r\n self.inst4sav = Label(self.how2use, text = \"◾ Saving the reference list by \\\".txt\\\" or \\\".doc\\\" type:\",justify = LEFT)\r\n self.inst4sav.place(x = 1, y = 310)\r\n self.inst4how = Label(self.how2use, text = \"▸ You can save the reference list by going to \\\"File\\\", \\\"Save as...\\\".\", justify=LEFT)\r\n self.inst4how.place(x = 25, y = 330)\r\n self.inst4note = Label(self.how2use, text = \"▸ Once the file has been saved, you can't make changes on it using the program.\", justify = LEFT)\r\n self.inst4note.place(x = 25, y = 350)\r\n self.lbl = LabelFrame(self.hlpframe, width = 566, height = 55)\r\n self.lbl.place(x = 5, y = 515)\r\n self.built = Label(self.lbl, text = \"Built using Python\", font = 'impact')\r\n self.built.place(x = 225)\r\n self.jp = Label(self.lbl, text = \"John Paul G. Zoleta Bachelor of Science in Computer Science College of Mary Immaculate\")\r\n self.jp.place(x =25, y = 25)\r\n\r\n self.ok = Button(self.hlpframe, text = \"Okay ⌐■_■\",activebackground = 'gray', command = self.hlpframeclear, width = 79, height = 3)\r\n self.ok.place(x = 6, y = 575)","def create_menu(self, root):\n menubar = Menu(root)\n root['menu'] = menubar\n\n menu_file = Menu(menubar)\n menu_run = Menu(menubar)\n menu_folders = Menu(menubar)\n menu_links = Menu(menubar)\n menu_help = Menu(menubar)\n menu_beta = Menu(menubar)\n menubar.add_cascade(menu=menu_file, label='File')\n menubar.add_cascade(menu=menu_run, label='Run')\n menubar.add_cascade(menu=menu_folders, label='Folders')\n menubar.add_cascade(menu=menu_links, label='Links')\n menubar.add_cascade(menu=menu_help, label='Help')\n menubar.add_cascade(menu=menu_beta, label='Experimental')\n\n menu_file.add_command(\n label='Re-load param set', command=self.load_params,\n accelerator='Ctrl+L')\n menu_file.add_command(\n label='Re-save param set', command=self.save_params,\n accelerator='Ctrl+S')\n menu_file.add_command(\n label='Output log', command=lambda: LogWindow(self.root))\n if sys.platform != 'darwin':\n menu_file.add_command(\n label='Exit', command=self.exit_program, accelerator='Alt+F4')\n root.bind_all('<Control-l>', lambda e: self.load_params())\n root.bind_all('<Control-s>', lambda e: self.save_params())\n\n menu_run.add_command(\n label='Dwarf Fortress', command=self.lnp.run_df,\n accelerator='Ctrl+R')\n menu_run.add_command(\n label='Init Editor', command=self.run_init, accelerator='Ctrl+I')\n root.bind_all('<Control-r>', lambda e: self.lnp.run_df())\n root.bind_all('<Control-i>', lambda e: self.run_init())\n\n menu_folders.add_command(\n label='Savegame Folder', command=self.lnp.open_savegames)\n menu_folders.add_command(\n label='Utilities Folder', command=self.lnp.open_utils)\n menu_folders.add_command(\n label='Graphics Folder', command=self.lnp.open_graphics)\n menu_folders.add_separator()\n menu_folders.add_command(\n label='Main Folder', command=self.lnp.open_main_folder)\n menu_folders.add_command(\n label='LNP Folder', command=self.lnp.open_lnp_folder)\n menu_folders.add_command(\n label='Dwarf Fortress Folder', command=self.lnp.open_df_folder)\n menu_folders.add_command(\n label='Init Folder', command=self.lnp.open_init_folder)\n\n menu_links.add_command(\n label=\"DF Homepage\", command=self.lnp.open_df_web)\n menu_links.add_command(label=\"DF Wiki\", command=self.lnp.open_wiki)\n menu_links.add_command(label=\"DF Forums\", command=self.lnp.open_forums)\n\n menu_help.add_command(\n label=\"Help\", command=self.show_help, accelerator='F1')\n menu_help.add_command(\n label=\"About\", command=self.show_about, accelerator='Alt+F1')\n root.bind_all('<F1>', lambda e: self.show_help())\n root.bind_all('<Alt-F1>', lambda e: self.show_about())\n root.createcommand('tkAboutDialog', self.show_about)\n\n menu_beta.add_command(\n label='Toggle graphics pack patching', command=self.toggle_patching)","def create_menu(self):\n about = gtk.ImageMenuItem(gtk.STOCK_ABOUT)\n about.connect_object('activate', self.about, 'about')\n about.show()\n\n# prefs = gtk.ImageMenuItem(gtk.STOCK_PREFERENCES)\n# prefs.connect_object('activate', self.prefs, 'prefs')\n# prefs.show()\n\n quit = gtk.ImageMenuItem(gtk.STOCK_QUIT)\n quit.connect_object('activate', self.exit, 'quit')\n quit.show()\n\n menu = gtk.Menu()\n menu.append(about)\n# menu.append(prefs)\n menu.append(quit)\n return menu","def __admin_menu(self):\n log.debug(\"Displaying __admin_menu\")\n self.menu = TelegramMenu(\"config/comunda_admin_menu.bpmn\", self, \"MenuStart\")\n self.menu.admin_menu(\"MenuStart\", \"menu_admin_main_txt\")\n return","def menu(self):\n self.font = pygame.font.SysFont(\"comicsansms\", 72)\n i = 0\n self.window.fill((60,50,20))\n self.menu_buttons = [0,0,0]\n for i in range(3):\n self.menu_buttons[i] = pygame.draw.rect(self.window, (255, 255, 255), (270, 200+i*120, 300, 100)) \n text = self.font.render(\"H x H\", True, (0, 128, 0))\n self.window.blit(text, (330, 200))\n text = self.font.render(\"H x C\", True, (0, 128, 0))\n self.window.blit(text, (330, 320))\n text = self.font.render(\"C x C\", True, (0, 128, 0))\n self.window.blit(text, (330, 440))\n pygame.display.flip()\n while True: \n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n self.quit = True\n return\n elif event.type == pygame.KEYDOWN:\n if evenvt.key == pygame.K_r:\n self.play()\n elif event.key == pygame.K_t:\n self.tip()\n elif event.key == pygame.K_m:\n self.__init__()\n elif event.type == pygame.MOUSEBUTTONDOWN and event.button == 1:\n pos = pygame.mouse.get_pos()\n for i in range(len(self.menu_buttons)):\n if self.menu_buttons[i].collidepoint(pos):\n y_pos=pos[1]\n difficulty=0\n if y_pos<=300 and y_pos>=200:\n return [1,difficulty]\n elif y_pos<=420 and y_pos>=320:\n difficulty=self.difficulty_menu(\"Computer difficulty:\")\n return [2,difficulty]\n elif y_pos<=540 and y_pos>=420:\n difficulty_bot1=self.difficulty_menu(\"Computer 1 difficulty:\")\n difficulty_bot2=self.difficulty_menu(\"Computer 2 difficulty:\")\n return [3,difficulty_bot1, difficulty_bot2]","def __init__(self, win):\n Menu.__init__(self, ['START', 'QUIT'], FONT_LA_CASA, win)","def build_labels():\n l_title = GLabel('Which one is Karel?')\n l_title.font = 'Courier-25'\n l_title.color = 'black'\n window.add(l_title, x=260, y=60)\n l_num = GLabel('19')\n l_num.font = 'Courier-50'\n l_num.color = 'whitesmoke'\n window.add(l_num, x=37, y=242)\n l_skip = GLabel('skip')\n l_skip.font = 'Courier-20'\n l_skip.color = 'whitesmoke'\n window.add(l_skip, x=726, y=152)\n l_ans1 = GLabel('Answers')\n l_ans1.font = 'Courier-20-italic'\n l_ans1.color = 'black'\n window.add(l_ans1, x=698, y=270)\n l_ans2 = GLabel('0')\n l_ans2.font = 'Courier-50-italic'\n l_ans2.color = 'black'\n window.add(l_ans2, x=722, y=252)\n l_game_pin = GLabel('Game PIN: SC101')\n l_game_pin.font = 'Courier-20'\n l_game_pin.color = 'black'\n window.add(l_game_pin, x=20, y=540)\n l_1 = GPolygon()\n l_1.add_vertex((210, 360))\n l_1.add_vertex((197, 380))\n l_1.add_vertex((221, 380))\n l_1.filled = True\n l_1.color = 'whitesmoke'\n l_1.fill_color= 'whitesmoke'\n window.add(l_1)\n l_2_1 = GPolygon()\n l_2_1.add_vertex((210+380, 359))\n l_2_1.add_vertex((198+380, 370))\n l_2_1.add_vertex((221+380, 370))\n l_2_1.filled = True\n l_2_1.fill_color = 'whitesmoke'\n l_2_1.color = 'whitesmoke'\n window.add(l_2_1)\n l_2_2 = GPolygon()\n l_2_2.add_vertex((210+380, 381))\n l_2_2.add_vertex((198+380, 370))\n l_2_2.add_vertex((221+380, 370))\n l_2_2.filled = True\n l_2_2.fill_color = 'whitesmoke'\n l_2_2.color = 'whitesmoke'\n window.add(l_2_2)\n l_3 = GOval(23, 23, x=198, y=450)\n l_3.filled = True\n l_3.fill_color = 'whitesmoke'\n l_3.color = 'whitesmoke'\n window.add(l_3)\n l_4 = GRect(20, 20, x=583, y=450)\n l_4.filled = True\n l_4.fill_color = 'whitesmoke'\n l_4.color = 'whitesmoke'\n window.add(l_4)","def render(self):\n self.delete()\n self.__create_background(self._imfname)\n # XXX must be last after successor implementation, but works without this line\n #self.c.event_generate(\"<Configure>\")\n #self.c.update_idletasks()","def create_animation_menu(master: Widget) -> None:\r\n\r\n def create_gen_labels(master: Widget) -> None:\r\n \"\"\"Create generation labels with the parent MASTER.\"\"\"\r\n\r\n gen_label = Label(master, text='Gen:', font=self.FONT_NORMAL, bg=self.MAIN_BG)\r\n gen_label.pack(side=LEFT)\r\n self.gen_number = Label(master, text=0, font=self.FONT_NORMAL, bg=self.MAIN_BG)\r\n self.gen_number.pack(side=LEFT)\r\n\r\n def create_rule_labels(master: Widget) -> None:\r\n \"\"\"Create rule labels with the parent MASTER.\"\"\"\r\n\r\n rule_label = Label(master, text='Rule:', font=self.FONT_NORMAL, bg=self.MAIN_BG)\r\n rule_label.pack(side=LEFT, padx=(50,0))\r\n self.rule_name = Label(master, text=self.INITIAL_RULE, font=self.FONT_NORMAL,\r\n bg=self.MAIN_BG)\r\n self.rule_name.pack(side=LEFT)\r\n\r\n def create_anim_buttons(master: Widget) -> None:\r\n \"\"\"Create animation buttons with the parent MASTER.\"\"\"\r\n\r\n self.reset_button = Button(master, text='Reset', font=self.FONT_NORMAL,\r\n command=self.on_reset)\r\n self.reset_button.pack(side=RIGHT)\r\n self.step_button = Button(master, text='Step', font=self.FONT_NORMAL,\r\n command=self.on_step)\r\n self.step_button.pack(side=RIGHT, padx=self.WIDGET_PAD)\r\n self.play_button = Button(master, text='Play', font=self.FONT_NORMAL,\r\n command=self.on_play)\r\n self.play_button.pack(side=RIGHT)\r\n\r\n animation_menu = Frame(master, bg=self.MAIN_BG, pady=self.WIDGET_PAD)\r\n animation_menu.pack(side=TOP, fill=X)\r\n\r\n create_gen_labels(animation_menu)\r\n create_rule_labels(animation_menu)\r\n create_anim_buttons(animation_menu)\r\n TkState.disable([self.play_button, self.step_button, self.reset_button])","def printMenu():\n # tWelc = PrettyTable(['Welcome to the CLI-of the repository classifier'])\n print('Welcome to the CLI of the repository classifier')\n print(strStopper1)\n t = PrettyTable(['Action', ' Shortcut '])\n t.add_row(['Show Menu', '- m -'])\n t.add_row([' Predict repositories form txt-file ', '- i -'])\n t.add_row(['Input URL', '- u -'])\n t.add_row(['Show Info', '- f -'])\n t.add_row(['Train Model', '- t -'])\n t.add_row(['set GitHub-Token', '- g -'])\n t.add_row(['Help', '- h -'])\n t.add_row(['Quit', '- q -'])\n print(t)\n print('')","def display_menu(self):\n menu_view = self.get_view.menu_view\n menu_view.blit_background()\n menu_view.draw_menu_view()\n menu_view.update_display()","def menu(self):\n from mainmenu import Menu\n gm = Menu(self.screen)\n gm.run()","def draw(self, context):\n layout = self.layout\n pie = layout.menu_pie()\n\n pie.operator(\"wm.call_menu_pie\", text=\"Images\", icon='IMAGE_COL').name = \"PieAreaViewsImage\"\n pie.operator(\"wm.call_menu_pie\", text=\"Anim\", icon='IPO').name = \"PieAreaViewsAnim\"\n pie.operator(\"screen.screen_full_area\", text=\"Full Screen\", icon='FULLSCREEN_ENTER')\n pie.operator(\"object.view_menu\", text=\"3D View\", icon='VIEW3D').vp = \"VIEW_3D\"\n\n # DIAGONALS\n pie.operator(\"wm.call_menu_pie\", text=\"Utils\", icon='BUTS').name = \"PieAreaViewsUtils\"\n pie.separator()\n pie.operator(\"area.joinarea\", text=\"Join\", icon='X')\n pie.operator(\"wm.call_menu_pie\", text=\"Split\", icon='SPLITSCREEN').name = \"pie.split_viewport\"","def create_main(self: object) -> None:\n\n welcome_msg = \"Welcome to dnazip! \\n A graphical \" + \\\n \"representation of Burros-Wheeler and Huffman \" + \\\n \"Coding algorithms\"\n rand = \"You don't have a DNA sequence? we'll generate a small \" + \\\n \"random one for you\"\n Label(self, text=welcome_msg, bg='#ebebeb', font=(None, 15)).grid(row=0, columnspan=3, padx=5, pady=6)\n Label(self, text=\"Compression\", bg='#ebebeb', borderwidth=2, relief=\"groove\", font=(None, 13, 'bold')).grid(row=1, columnspan=3, padx=5, pady=6)\n Label(self, text=\"Decompression\", bg='#ebebeb', borderwidth=2, relief=\"groove\", font=(None, 13, 'bold')).grid(row=5, columnspan=3, padx=5, pady=6)\n separator_one = ttk.Separator(self, orient='horizontal')\n separator_one.grid(row=4, columnspan=3, ipadx=500, padx=5, pady=6) \n Label(self, text=rand, bg='#ebebeb', font=(None, 12, 'bold')).grid(row=8, columnspan=3, padx=5, pady=6)\n self.random = StringVar()\n self.entry = Entry(self, textvariable=self.random, width=50)\n self.entry.grid(row=9, columnspan=3, padx=5, pady=10)\n separator_two = ttk.Separator(self, orient='horizontal')\n separator_two.grid(row=7, columnspan=3, ipadx=500, padx=5, pady=6)","def setup(self):\n self.ui_manager.purge_ui_elements()\n\n button = buttons.MenuButton(\n 'Menu',\n center_x=self.window.width // 2,\n center_y=self.window.height // 9,\n width=200,\n user=self.user.text\n )\n button.set_style_attrs(\n bg_color_hover=(159, 255, 233),\n bg_color_press=(51, 201, 166),\n )\n self.ui_manager.add_ui_element(button)","def build_pcb(self, bg=None):","def MainMenu():\n\n # You have to open an object container to produce the icons you want to appear on this page.\n oc = ObjectContainer()\n main_list = [('New Videos', ''), ('Best Videos', '/best'), ('Pornstars', '/pornstars')]\n for pt, h in main_list:\n oc.add(DirectoryObject(\n key=Callback(ShowHTML, pTitle=pt, href=h),\n title=pt, thumb=Callback(GetThumb, url=BASE_URL)))\n oc.add(DirectoryObject(\n key=Callback(ShowHTML, pTitle=\"Pornstars\", href='/channels'),\n title=\"Channels\", thumb=Callback(GetThumb, url=BASE_URL)))\n\n oc.add(InputDirectoryObject(key=Callback(Search), title='Search Videos', prompt='Search Videos'))\n\n return oc","def _createColorProfileMenuItems(node):\n pass","def _create_menu(self):\n menubar = Menu(self.root)\n # Game dropdown menu\n gamemenu = Menu(menubar, tearoff=0)\n gamemenu.add_command(\n label='New Game', command=self.new_game)\n gamemenu.add_separator()\n gamemenu.add_command(label='Exit', command=self.root.quit)\n menubar.add_cascade(label='Game', menu=gamemenu)\n\n # Options dropdown menu\n optionmenu = Menu(menubar, tearoff=0)\n optionmenu.add_command(label='Scattershot', command=self.game.spray)\n optionmenu.add_command(label='All Ships Attack', command=None)\n menubar.add_cascade(label='Options', menu=optionmenu)\n\n # Help dropdown menu\n helpmenu = Menu(menubar, tearoff=0)\n helpmenu.add_command(label='Help Index', command=None)\n helpmenu.add_command(label='About...', command=None)\n menubar.add_cascade(label='Help', menu=helpmenu)\n\n self.root.config(menu=menubar)","def build_UI(self):\n\n #Common local coordinates to change the UI positions\n common_x = 0\n common_y = 5\n\n #Create the Main Title\n self.titleFont = font.Font(family = FONTS[\"lucida grande\"], size = 30)\n self.title = cGUIf.get_TextLabel(self,\n \"File Converter App \",\n self.titleFont,\n 135 + common_x,\n 40 + common_y)\n\n #Add the \"Main Icon\"\n self.mainIcon = cGUIf.get_ImgLabel(self,\n self.__pics[\"main_icon\"],\n 280 + common_x,\n 125 + common_y)\n\n\n #Create a subtitle that says \"options\"\n self.subtitleFont = font.Font(family = FONTS[\"courier new\"], size = 22)\n self.subtitle = cGUIf.get_TextLabel(self,\n \"Options\",\n self.subtitleFont,\n 240 + common_x,\n 195 + common_y)\n\n #Create a label that says \"Image Conversion\"\n self.conversionFont = font.Font(family = FONTS[\"times new roman\"], size = 15)\n self.imageConversionLabel = cGUIf.get_TextLabel(self,\n \" Image\\n Conversion\",\n self.conversionFont,\n 60 + common_x,\n 285 + common_y)\n\n #Create a button for Image Conversion\n self.imageButton = cGUIf.get_Button(self,\n \"\",\n lambda : self.switch_frames(\"image_frame\"),\n 190 + common_x,\n 270 + common_y)\n self.imageButton.configure(image = self.__pics[\"image_icon\"])\n\n #Create a label that says \"Audio Conversion\"\n self.audioConversionLabel = cGUIf.get_TextLabel(self,\n \" Audio\\n Conversion\",\n self.conversionFont,\n 440 + common_x,\n 285 + common_y)\n\n #Create a button for Audio Conversion\n self.audioButton = cGUIf.get_Button(self,\n \"\",\n lambda : self.switch_frames(\"audio_frame\"),\n 340 + common_x,\n 270 + common_y)\n self.audioButton.configure(image = self.__pics[\"audio_icon\"])\n\n #Create a label that says \"Doc Conversion\"\n self.docConversionLabel = cGUIf.get_TextLabel(self,\n \" Doc\\n Conversion\",\n self.conversionFont,\n 60 + common_x,\n 410 + common_y)\n\n\n #Create a button for Doc Conversion\n self.docButton = cGUIf.get_Button(self,\n \"\",\n lambda : self.switch_frames(\"doc_frame\"),\n 190 + common_x,\n 400 + common_y)\n self.docButton.configure(image = self.__pics[\"doc_icon\"])\n\n\n #Create a label that says \"Video Conversion\"\n self.videoConversionLabel = cGUIf.get_TextLabel(self,\n \" Video\\n Conversion\",\n self.conversionFont,\n 440 + common_x,\n 410 + common_y)\n\n #Create a button for Video Conversion\n self.videoButton = cGUIf.get_Button(self,\n \"\",\n lambda : self.switch_frames(\"video_frame\"),\n 340 + common_x,\n 400 + common_y)\n self.videoButton.configure(image = self.__pics[\"video_icon\"])","def menu():\n #On créer une nouvelle fenetre\n win = createNewWin(curses)\n\n #On charge le logo et on l'affiche\n logoLines = loadLogo()\n i = 1\n for line in logoLines:\n win.addstr(i, 16, line, curses.color_pair(2))\n i+=1\n win.border(0)\n\n chooseMenu = 0 \n\n #On ajoute les entrées\n win.addstr(1, 4, 'SnakePY', curses.color_pair(1))\n win.addstr(2, 4, 'Choose option', curses.color_pair(1))\n win.addstr(3, 4, '1. Launch game', curses.color_pair(1))\n win.addstr(4, 4, '2. Show HighScore', curses.color_pair(1))\n win.addstr(5, 4, '3. Credits', curses.color_pair(1))\n win.addstr(6, 4, '4. Exit game', curses.color_pair(1))\n win.addstr(7, 4, '')\n\n #On effectue une boucle\n while chooseMenu != ord('4'):\n #On récupere la touche appuyée par l'utilisateur\n chooseMenu = win.getch()\n #Si il s'agit du 1 ...\n if chooseMenu == ord('1'):\n #...on lance le jeu\n destroyWin()\n launchGame()\n #Si il s'agit du 2 ...\n elif chooseMenu == ord('2'):\n #...on affiche le highscore\n destroyWin()\n showHG()\n #Si il s'agit du 3 ...\n elif chooseMenu == ord('3'):\n #... on affiche les crédits\n destroyWin()\n showCredits()\n #Si on sort de la boucle (4), alors on\n #détruit les fenetres\n destroyWin()\n\n #On ferme toutes les fenêtres de curses\n closeCurse()","def setup(self):\n self.ui_manager.purge_ui_elements()\n\n button = buttons.MenuButton(\n 'Menu',\n center_x=self.window.width // 2,\n center_y=self.window.height // 6,\n width=200,\n user=self.user.text\n )\n button.set_style_attrs(\n bg_color=(255, 153, 204),\n bg_color_hover=(255, 102, 178),\n bg_color_press=(204, 0, 102),\n )\n self.ui_manager.add_ui_element(button)","def menu(self):\n ## This is a DICTIONARY, it's a list with custom index values. Python is cool.\n # Please feel free to change the menu and add options.\n print(\"\\n *** MENU ***\") \n menu = {\"c\": (\"Calibrate\", self.calibrate),\n \"d\": (\"Dance\", self.dance),\n \"h\": (\"Hold position\", self.hold_position),\n \"n\": (\"Navigate\", self.nav),\n \"o\": (\"Obstacle count\", self.obstacle_count),\n \"q\": (\"Quit\", self.quit),\n \"v\": (\"Veer\", self.slither)\n }\n # loop and print the menu...\n for key in sorted(menu.keys()):\n print(key + \":\" + menu[key][0])\n # store the user's answer\n ans = str.lower(input(\"Your selection: \"))\n # activate the item selected\n menu.get(ans, [None, self.quit])[1]()","def display_menu(self):\n print(\"\"\"\nLogistic System Menu\n1. Add Vehicles\n2. Add Item To The Cart\n3. Complete The Order\n4. Track The Order\n5. Quit \"\"\")","def _helpmenu_about():\n self.helpindex = Toplevel(self.master)\n self.helpindex.title(\"About\")\n self.helpindex.geometry(\"500x300\")\n self.helpindex.label()","def CreateLayout(self):\n # Defines the title of the Dialog\n self.SetTitle(\"A Custom Dialog with a Top Menu\")\n\n # Flushes all the already existing menu to create our one. The content will be on the left.\n self.MenuFlushAll()\n\n # Creates a Sub menu begin to insert new menu entry\n self.MenuSubBegin(\"Left Menu\")\n\n # Adds a string with a given ID, so it will trigger a call to Command once clicked\n self.MenuAddString(self.ID_LEFT_MENU_FIRST_ITEM, \"Close\")\n\n # Finalizes the Sub Menu\n self.MenuSubEnd()\n\n # Finalizes the menu\n self.MenuFinished()\n\n # Creates a Group in the Menu. The content will be on the right\n if self.GroupBeginInMenuLine():\n # Creates a BitmapButtonCustomGui with the find icon\n settings = c4d.BaseContainer()\n settings[c4d.BITMAPBUTTON_BUTTON] = True\n settings[c4d.BITMAPBUTTON_BORDER] = False\n settings[c4d.BITMAPBUTTON_TOGGLE] = True\n settings[c4d.BITMAPBUTTON_ICONID1] = c4d.RESOURCEIMAGE_SCENEBROWSER_FIND2\n settings[c4d.BITMAPBUTTON_ICONID2] = c4d.RESOURCEIMAGE_SCENEBROWSER_FIND1\n\n self.displayContentButtonDlg = self.AddCustomGui(self.ID_RIGHT_MENU_SHOW_CONTENT,\n c4d.CUSTOMGUI_BITMAPBUTTON, \"\",\n c4d.BFH_CENTER | c4d.BFV_CENTER, 0, 0, settings)\n\n self.GroupEnd()\n\n # Creates a group that will contain the content that will be hidden when the BitmapButton is pressed. It's\n # important to have a parent group to the group that needs to be hidden since you need to redraw this parent\n # group after the visibility definition.\n if self.GroupBegin(self.ID_MAIN_GROUP, c4d.BFH_LEFT | c4d.BFV_CENTER):\n\n # The group that will be hidden\n if self.GroupBegin(self.ID_HIDDEN_GROUP, c4d.BFH_LEFT | c4d.BFV_CENTER):\n # Adds the content you want to toggle\n self.AddStaticText(0, c4d.BFH_LEFT | c4d.BFV_CENTER, name=\"test\")\n\n self.GroupEnd()\n self.GroupEnd()\n\n # Adds two buttons, Ok and Cancel\n self.AddDlgGroup(c4d.DLG_OK | c4d.DLG_CANCEL)\n\n return True","def get_all_menu():","def create_menu_set(name):\n\t\t\tmenu = getattr(self, name + \"_menu\")\n\t\t\ttv = getattr(self, \"tv_\" + name)\n\t\t\tcid_index = getattr(self, \"cid_\" + name)\n\n\t\t\t# bind menu helper\n\t\t\tdef bind_menu(label):\n\t\t\t\tdef bind_menu_inner(func):\n\t\t\t\t\tmenu.add_command(label=label, command=func)\n\t\t\t\t\treturn func\n\t\t\t\treturn bind_menu_inner\n\n\t\t\t# add commands...\n\t\t\t@bind_menu(\"刪除\")\n\t\t\tdef tvdelete():\n\t\t\t\tif messagebox.askyesno(\"Comic Crawler\", \"確定刪除？\"):\n\t\t\t\t\tselected = tv.selection()\n\t\t\t\t\tself.remove(name, *[cid_index[cid] for cid in selected])\n\n\t\t\t@bind_menu(\"移至頂部\")\n\t\t\tdef tvlift():\n\t\t\t\tselected = tv.selection()\n\t\t\t\tself.downloader.mission_manager.lift(name, *[cid_index[cid] for cid in selected])\n\n\t\t\t@bind_menu(\"移至底部\")\n\t\t\tdef tvdrop():\n\t\t\t\tselected = tv.selection()\n\t\t\t\tself.downloader.mission_manager.drop(name, *[cid_index[cid] for cid in selected])\n\n\t\t\t@bind_menu(\"改名\")\n\t\t\tdef tvchangetitle():\n\t\t\t\tselected = tv.selection()\n\t\t\t\tmission = cid_index[selected[0]]\n\t\t\t\tselect_title(self.root, mission)\n\n\t\t\t@bind_menu(\"重新選擇集數\")\n\t\t\tdef tvReselectEP():\n\t\t\t\ts = tv.selection()\n\t\t\t\tmissions = [ cid_index[i] for i in s ]\n\t\t\t\tfor mission in missions:\n\t\t\t\t\treselect_episodes(self.root, mission)\n\n\t\t\t@bind_menu(\"開啟資料夾\")\n\t\t\tdef tvOpen():\n\t\t\t\ts = tv.selection()\n\t\t\t\tmissions = [ cid_index[i] for i in s ]\n\t\t\t\tsavepath = setting[\"savepath\"]\n\t\t\t\tfor mission in missions:\n\t\t\t\t\tfolder = os.path.join(savepath, safefilepath(mission.title))\n\t\t\t\t\tos.startfile(os.path.expanduser(folder))\n\n\t\t\t@bind_menu(\"開啟網頁\")\n\t\t\tdef tvOpenBrowser():\n\t\t\t\ts = tv.selection()\n\t\t\t\tmissions = [ cid_index[i] for i in s ]\n\t\t\t\tfor mission in missions:\n\t\t\t\t\twebbrowser.open(mission.url)\n\n\t\t\tif name == \"view\":\n\t\t\t\t@bind_menu(\"加入圖書館\")\n\t\t\t\tdef tvAddToLib():\n\t\t\t\t\ts = tv.selection()\n\t\t\t\t\tmissions = [ cid_index[i] for i in s ]\n\t\t\t\t\ttitles = [ m.title for m in missions ]\n\t\t\t\t\tself.downloader.mission_manager.add(\"library\", *missions)\n\t\t\t\t\tsafeprint(\"已加入圖書館︰{}\".format(\", \".join(titles)))\n\n\t\t\t# menu call\n\t\t\tdef tvmenucall(event):\n\t\t\t\tmenu.post(event.x_root, event.y_root)\n\t\t\ttv.bind(\"<Button-3>\", tvmenucall)","def build_schematic(self, bg=None):","def __init__(self: object) -> None:\n super().__init__()\n self.title(\"dnazip\")\n self.configure(bg='#ebebeb')\n self.create_main()\n self.create_menu()\n self.create_buttons()\n self.file = None","def _render(self):\n self._renderer.render_menu()\n pg.display.update()","def render(self,session,context):\n #TODO: at some point this should actually get implemented, but to do so\n # would need to convert self.menu_items from list to a dict, where the\n # keys are the items that should be selected in the menu. \n # to do so would need to update MenuItem as well.\n menu = [] if self.title_str == '' else [\" %s\" % self.title_str]\n for idx , item in enumerate(self.menu_items):\n if item.hide_index: menu_text = str(item)\n else:\n menu_text = \"%s. %s\"%(item.custom_index, item)\n menu.append( menu_text )\n return \"\\n\".join(menu)","def menuItem(*args):\n\toptionsWindow()","def create_options(self, n):\n f = Frame(n)\n f.pack(side=TOP, fill=BOTH, expand=Y)\n options = Labelframe(f, text='Options')\n options.pack(side=TOP, fill=BOTH, expand=Y)\n Grid.columnconfigure(options, 0, weight=1)\n Grid.columnconfigure(options, 1, weight=1)\n\n population = Button(\n options, text='Population Cap', command=self.set_pop_cap)\n create_tooltip(population, 'Maximum population in your fort')\n population.grid(column=0, row=0, sticky=\"nsew\")\n self.controls['popcap'] = population\n invaders = Button(\n options, text='Invaders',\n command=lambda: self.cycle_option('invaders'))\n create_tooltip(\n invaders, 'Toggles whether invaders (goblins, etc.) show up')\n invaders.grid(column=1, row=0, sticky=\"nsew\")\n self.controls['invaders'] = invaders\n childcap = Button(\n options, text='Child Cap', command=self.set_child_cap)\n create_tooltip(childcap, 'Maximum children in your fort')\n childcap.grid(column=0, row=1, sticky=\"nsew\")\n self.controls['childcap'] = childcap\n caveins = Button(\n options, text='Cave-ins',\n command=lambda: self.cycle_option('caveins'))\n create_tooltip(\n caveins,\n 'Toggles whether unsupported bits of terrain will collapse')\n caveins.grid(column=1, row=1, sticky=\"nsew\")\n self.controls['caveins'] = caveins\n temperature = Button(\n options, text='Temperature',\n command=lambda: self.cycle_option('temperature'))\n create_tooltip(\n temperature,\n 'Toggles whether things will burn, melt, freeze, etc.')\n temperature.grid(column=0, row=2, sticky=\"nsew\")\n self.controls['temperature'] = temperature\n liquid_depth = Button(\n options, text='Liquid Depth',\n command=lambda: self.cycle_option('liquidDepth'))\n create_tooltip(\n liquid_depth,\n 'Displays the depth of liquids with numbers 1-7')\n liquid_depth.grid(column=1, row=2, sticky=\"nsew\")\n self.controls['liquidDepth'] = liquid_depth\n weather = Button(\n options, text='Weather',\n command=lambda: self.cycle_option('weather'))\n create_tooltip(weather, 'Rain, snow, etc.')\n weather.grid(column=0, row=3, sticky=\"nsew\")\n self.controls['weather'] = weather\n varied_ground = Button(\n options, text='Varied Ground',\n command=lambda: self.cycle_option('variedGround'))\n create_tooltip(\n varied_ground,\n 'If ground tiles use a variety of punctuation, or only periods')\n varied_ground.grid(column=1, row=3, sticky=\"nsew\")\n self.controls['variedGround'] = varied_ground\n starting_labors = Button(\n options, text='Starting Labors',\n command=lambda: self.cycle_option('laborLists'))\n create_tooltip(\n starting_labors, 'Which labors are enabled by default:'\n 'by skill level of dwarves, by their unit type, or none')\n starting_labors.grid(column=0, row=4, columnspan=2, sticky=\"nsew\")\n self.controls['laborLists'] = starting_labors\n\n modifications = Labelframe(f, text='Modifications', width=192)\n modifications.pack(side=TOP, expand=Y, anchor=\"w\")\n Grid.columnconfigure(modifications, 0, weight=1)\n Grid.columnconfigure(modifications, 1, weight=1)\n\n aquifers = Button(\n modifications, text='Aquifers',\n command=lambda: self.cycle_option('aquifers'))\n create_tooltip(\n aquifers, 'Whether newly created worlds will have Aquifers in them '\n '(Infinite sources of underground water, but may flood your fort')\n aquifers.grid(column=0, row=0, sticky=\"nsew\")\n self.controls['aquifers'] = aquifers\n\n keybindings = Labelframe(f, text='Key Bindings')\n keybindings.pack(side=BOTTOM, fill=BOTH, expand=Y, anchor=\"s\")\n Grid.columnconfigure(keybindings, 0, weight=2)\n Grid.columnconfigure(keybindings, 1, weight=1)\n Grid.columnconfigure(keybindings, 2, weight=1)\n\n keybinding_files = Listbox(\n keybindings, height=4, listvariable=self.keybinds,\n activestyle='dotbox')\n keybinding_files.grid(column=0, row=0, rowspan=2, sticky=\"nsew\")\n\n load_keyb = Button(\n keybindings, text='Load',\n command=lambda: self.load_keybinds(keybinding_files))\n load_keyb.grid(column=1, row=0)\n create_tooltip(load_keyb, 'Load selected keybindings')\n refresh_keyb = Button(\n keybindings, text='Refresh', command=self.read_keybinds)\n create_tooltip(refresh_keyb, 'Refresh keybinding list')\n refresh_keyb.grid(column=2, row=0)\n save_keyb = Button(keybindings, text='Save', command=self.save_keybinds)\n create_tooltip(save_keyb, 'Save your current keybindings')\n save_keyb.grid(column=1, row=1)\n delete_keyb = Button(\n keybindings, text='Delete',\n command=lambda: self.delete_keybinds(keybinding_files))\n create_tooltip(delete_keyb, 'Delete selected keybinding')\n delete_keyb.grid(column=2, row=1)\n return f","def createMenubar(self):\r\n # Create menubar\r\n self.menubar = tk.Menu(tearoff=False)\r\n self.root.config(menu=self.menubar)\r\n filemenu = tk.Menu(self.menubar,tearoff=False)\r\n filemenu.add_command(label=\"Edit Video/fNIRS Sources\",command=self.launchImportWindow)\r\n filemenu.add_command(label=\"Synchronise Video/fNIRS\",command=self.launchSyncToolWindow)\r\n filemenu.add_command(label=\"Help\",command=self.launchHelpWindow)\r\n filemenu.add_command(label=\"Quit\",command=self.quit)\r\n self.menubar.add_cascade(label=\"Project\",menu=filemenu)","def setupNewGame(self):\r\n self.level = 1\r\n self.num_cows = 2\r\n self.num_farmers = 1\r\n self.levelHeading = Text(self.gameDisplay, 120, 425, 175, self.light_orange, \"Farm 1\")\r\n self.shield_indicator.image = self.greenShield\r\n updatedHeading = self.levelHeading\r\n self.startUX[0] = updatedHeading","def _context_menu_make(self, pos):\n format = self._control.cursorForPosition(pos).charFormat()\n name = format.stringProperty(QtGui.QTextFormat.ImageName)\n if name:\n menu = QtGui.QMenu()\n\n menu.addAction('Copy Image', lambda: self._copy_image(name))\n menu.addAction('Save Image As...', lambda: self._save_image(name))\n menu.addSeparator()\n\n svg = self._name_to_svg_map.get(name, None)\n if svg is not None:\n menu.addSeparator()\n menu.addAction('Copy SVG', lambda: svg_to_clipboard(svg))\n menu.addAction('Save SVG As...',\n lambda: save_svg(svg, self._control))\n else:\n menu = super(RichJupyterWidget, self)._context_menu_make(pos)\n return menu","def _print_menu(self):\n # Create header line.\n header = \"%s Menu:\" % (self.__name)\n header = header.title()\n print(header)\n\n # Show the iterations counter.\n iterations = self._status.get_value(\"iterations\")\n print(\"(Iteration %d)\" % (iterations))\n\n self._print_custom()\n\n # Display the options alphabetically.\n option_names = list(self.__options.keys())\n option_names.sort()\n for option in option_names:\n desc, command = self.__options[option]\n print(\"\\t%s: %s\" % (option, desc))","def add_menus_and_status(self):\r\n# Adding the statusbar and menubar\r\n self.statusBar().showMessage(\"\")\r\n menubar = self.menuBar()\r\n# Adding the first menu to the menubar\r\n menu = menubar.addMenu(\"Menu\")\r\n# Adding the first \"choise\" in the menu\r\n contact_action = QAction(\"Contact\", self)\r\n contact_action.setStatusTip(\"Contact PyCrypt\")\r\n contact_action.triggered.connect(self.email)\r\n menu.addAction(contact_action)\r\n# Adding a separator line to the menu\r\n menu.addSeparator()\r\n# Adding the second \"choise\" to the menu\r\n exit_action = QAction(\"Exit\", self) # Create an exit action\r\n exit_action.setStatusTip(\"Click to exit the application\")\r\n exit_action.triggered.connect(self.close) # Close application when clicked\r\n exit_action.setShortcut(\"Ctrl+Q\") # Keyboard shortcut to exit app\r\n menu.addAction(exit_action)","def add_menu():\n\n def _(*args, **kwargs):\n args = (cast_str(i) if isinstance(i, six.text_type) else i for i in args)\n kwargs = tuple(\n {\n k: cast_str(v) if isinstance(v, six.text_type) else v\n for k, v in kwargs.items()\n }.items()\n )\n return (args, kwargs)\n\n def _auto_comp():\n try:\n comp.Comp().create_nodes()\n except comp.FootageError:\n nuke.message(cast_str(\"请先导入素材\"))\n\n all_menu = [\n {\n _(\"工具\"): [\n {\n _(\"按素材名组装\"): [\n _(\"对当前工程执行\", _auto_comp, icon=\"autocomp.png\"),\n _(\n \"批量执行\",\n lambda: comp.panels.BatchCompPanel().showModalDialog(),\n icon=\"autocomp.png\",\n ),\n _(\n \"设置\",\n lambda: comp.panels.CompConfigPanel().showModalDialog(),\n icon=\"autocomp.png\",\n ),\n ],\n },\n {\n _(\"转换为序列工程\"): [\n _(\"对当前工程执行\", edit.script_use_seq.execute),\n _(\n \"批量执行\",\n lambda: edit.script_use_seq.panels.BatchPanel().showModalDialog(),\n ),\n _(\n \"设置\",\n lambda: edit.script_use_seq.panels.ConfigPanel().showModalDialog(),\n ),\n ]\n },\n ]\n }\n ]\n\n # Add all menu.\n def _add_menu(menu, parent=nuke.menu(cast_str(\"Nuke\"))):\n # type: (..., nuke.Menu) -> None\n assert isinstance(menu, dict)\n\n for k, v in menu.items():\n m = parent.addMenu(*k[0], **dict(k[1]))\n for i in v:\n if i is None:\n _ = m.addSeparator()\n elif isinstance(i, dict):\n _add_menu(i, m)\n elif isinstance(i, tuple):\n _ = m.addCommand(*i[0], **dict(i[1]))\n\n for menu in all_menu:\n _add_menu(menu)","def init_mapmenu(self):\n\n self.level1 = QPushButton('', self)\n self.level1.setFixedSize(192, 192)\n self.level1.setStyleSheet('''\n color: black;\n background-image: url(./pictures/level1.png);\n border: none;\n ''')\n self.level1.move(80, 60)\n self.level1.clicked.connect(self.start_level1)\n self.mapmenu_items.append(self.level1)\n\n self.level2 = QPushButton('', self)\n self.level2.setFixedSize(192, 192)\n self.level2.setStyleSheet('''\n color: black;\n background-image: url(./pictures/level2.png);\n border: none;\n ''')\n self.level2.move(330, 70)\n self.level2.clicked.connect(self.start_level2)\n self.mapmenu_items.append(self.level2)\n\n self.level3 = QPushButton('', self)\n self.level3.setFixedSize(192, 192)\n self.level3.setStyleSheet('''\n color: black;\n background-image: url(./pictures/level3.png);\n border: none;\n ''')\n self.level3.move(580, 70)\n self.level3.clicked.connect(self.start_level3)\n self.mapmenu_items.append(self.level3)\n\n self.level_n = QPushButton('', self)\n self.level_n.setFixedSize(192, 192)\n self.level_n.setStyleSheet('''\n color: black;\n background-image: url(./pictures/level_n.png);\n border: none;\n ''')\n self.level_n.move(80, 330)\n self.level_n.clicked.connect(self.start_level_n)\n self.mapmenu_items.append(self.level_n)\n\n self.cancel = QPushButton('Cancel', self)\n self.cancel.setFixedSize(256, 128)\n self.cancel.setStyleSheet('''\n background-image: url(./pictures/cancel.png);\n border: none;\n ''')\n self.cancel.move(578, 470)\n self.cancel.clicked.connect(self.main_menu)\n self.mapmenu_items.append(self.cancel)","def BuildMenuTemplate(menuJSONFile, isCheeseburgerBao, isCoconutBao, isEggSausageBao, isIMPOSSIBLEBao, isBundles, isPotDumpCombined):\n with open(menuJSONFile) as file:\n data = json.load(file)\n \n template_menu = Menu(None, None)\n for category in data['Categories']:\n #have fun figuring out this part n00b\n if(category.get(\"bundleDependency\", isBundles)==isBundles):\n if(category.get(\"potDumpDependency\", isPotDumpCombined)==isPotDumpCombined):\n template_menu.categories.append(category[\"Name\"])\n \n #setting standard modifier sets\n #no use encoding the same 4 bao flavors over and over again in the json with hella dependencies\n #modifier sets are the only parts of the menu you have to edit in the code\n #IF YOU NEED TO CHANGE MODIFIERS DO IT HERE\n\n #base modifiers\n bao_modifiers = [\"BBQ Berkshire Pork\", \"Teriyaki Chicken\", \"Spicy Mongolian Beef\", \"Whole Wheat Vegetable\"]\n pot_dump_modifiers = [\"Ginger Chicken\", \"Green Vegetable\"]\n bowl_modifiers = [\"Teriyaki Chicken Bowl\", \"Spicy Kung Pao Chicken Bowl\", \"Orange Chicken Bowl\"]\n\n #contingent modifiers (atm only bao)\n if(isCheeseburgerBao):\n bao_modifiers.append(\"Cheeseburger\")\n if(isCoconutBao):\n bao_modifiers.append(\"Coconut Custard\")\n if(isEggSausageBao):\n bao_modifiers.append(\"Egg & Sausage\")\n if(isIMPOSSIBLEBao):\n bao_modifiers.append(\"IMPOSSIBLE\")\n\n modifier_sets = {\"Bao\":bao_modifiers, \"Potsticker/Dumpling\":pot_dump_modifiers, \"Bowl\":bowl_modifiers}\n \n\n\n for i in data['Items']:\n if(i.get(\"bundleDependency\", isBundles)==isBundles):\n mgs = []\n for m in i.get(\"ModifierGroups\", []):\n mgs.append(ModifierGroup(m[\"Name\"], modifier_sets[m[\"ModifierSet\"]]))\n item = Item(i[\"Name\"], i[\"Description\"], mgs)\n template_menu.items.append(item)\n \n return template_menu","def mainmenu(self):\n\n global con\n global lt\n global prg\n global afi\n global plv\n \n con = self.sql_conexion()\n lt = lv.Lotes(con)\n prg = prgva.Agenda(con)\n afi = af.Afiliado(con)\n plv = pl.Plan(con)\n \n afi.tabla_afiliados()\n lt._Lotes__tabla_vacunas()\n plv._Plan__tabla_plan()\n prg._Agenda__tabla_prog()\n\n \"\"\"\n este es el menu principal apartir de este se despliegan los submenus de cada modulo\n \"\"\"\n while True:\n print('\\nS I S T E M A D E G E S T I Ó N D E V A C U N A C I Ó N ')\n print(\"Selecciona una opción:\")\n print(\"\\t1 - Administrar Afiliados\")\n print(\"\\t2 - Administrar Vacunas\")\n print(\"\\t3 - Plan Vacunacion\")\n print(\"\\t4 - Programa Vacunacion\")\n print(\"\\t5 - salir\")\n\n # Mostramos el menu\n # solicituamos una opción al usuario\n opcionmenu = input(\"Seleccione una opcion: \")\n\n if opcionmenu == \"1\":\n self.menuafi()\n elif opcionmenu == \"2\":\n self.menulote()\n elif opcionmenu == \"3\":\n self.menuvac()\n elif opcionmenu == \"4\":\n self.provac()\n elif opcionmenu == \"5\":\n con.close()\n break\n else:\n input(\"\\nNo has pulsado ninguna opción correcta...\\npulsa una tecla para continuar\")","def make_control_frame(self):\n \n #Generate and place the frame for housing the controls\n control_frame = tk.Frame(self, padx = 0,\n bg=self.default_background)\n control_frame.grid(column=0, row=4, columnspan = 1)\n \n #Generate the time indicator\n time_label = tk.Label(control_frame, text=self.translate('Day')+': ',\n bg=self.default_background)\n time_label.grid(column=0, row=0)\n self.timev = tk.StringVar()\n self.timev.set(str(self.timeSeries[-1]))\n self.time_value_label = tk.Label(control_frame, textvariable=self.timev,\n bg=self.default_background)\n self.time_value_label.grid(column=1, row=0, sticky='W')\n \n #set width of the control dropdowns\n boxwidth = 30\n \n #Generate each of the policy control dropdowns\n index = 0 \n self.policy_option_vars = dict()\n self.option_menus = []\n fontsizelist = []\n for policy in self.PolicyDicts.keys():\n option1_label = self.DynamicLabel(400, control_frame, text=self.translate(policy)+': ',\n bg=self.default_background)\n option1_label.grid(column=0, row=index+1)\n self.option1_label_list.append(option1_label)\n fontsizelist.append(option1_label.font.actual(\"size\"))\n option1_list = []\n for entry in list(self.PolicyDicts[policy].keys()):\n option1_list.append(self.translate(entry))\n \n self.policy_option_vars[policy] = tk.StringVar()\n defaultpolicy = self.translate(self.PolicyDictsInv[policy][self.SD_Map.retrieve_ob(policy).value()])\n \n self.policy_option_vars[policy].set(defaultpolicy)\n self.option_menus.append(tk.OptionMenu(control_frame, self.policy_option_vars[policy], \n *option1_list, \n command=lambda value, policy=policy: self.update_Policy(policy)\n ))\n self.option_menus[-1].config(width=boxwidth, anchor='w',\n bg=self.button_color,\n highlightbackground=self.highlight_color)\n self.option_menus[-1]['menu'].config(bg=self.button_color)\n self.option_menus[-1].grid(column=1, row=index+1, columnspan=2)\n \n index+=1\n \n #Resize the fontsize of all the labels to match that of the smallest\n newsize = min(fontsizelist)\n for policylabel in self.option1_label_list:\n policylabel.font.configure(size=newsize)\n\n \n button_font_sizes = []\n \n #Generate the Next Week simulation button\n run_button = self.DynamicButton(300,control_frame, text=self.translate('Next Week'), \n command = lambda: self.increment_time(),\n bg=self.button_color,\n highlightbackground=self.highlight_color)\n run_button.grid(column=0, row=8, columnspan=1, sticky='E')\n button_font_sizes.append(run_button.font.actual(\"size\"))\n \n #Generate the Run Autonomously button\n automatic_button = self.DynamicButton(300,control_frame, text=self.translate('Run Autonomously'), \n command = lambda: self.automatic_window(),\n bg=self.button_color,\n highlightbackground=self.highlight_color)\n automatic_button.grid(column=1, row=8, columnspan=1)\n button_font_sizes.append(automatic_button.font.actual(\"size\"))\n \n #Generate the Clear Simulation Button\n clear_button = self.DynamicButton(300,control_frame, text = self.translate('Clear Simulation'),\n command = lambda: self.clear_simulation(),\n bg=self.button_color,\n highlightbackground=self.highlight_color,)\n clear_button.grid(column=2, row=8, columnspan=2)\n button_font_sizes.append(clear_button.font.actual(\"size\"))\n \n #Resize all the button texts to match that of the smallest\n new_button_font_size = min(button_font_sizes)\n run_button.font.configure(size=new_button_font_size)\n automatic_button.font.configure(size=new_button_font_size)\n clear_button.font.configure(size=new_button_font_size)\n \n return control_frame","def on_show(self): \n arcade.set_background_color(arcade.color.BLACK)","def main():\n\tmain_menu_title = txt_sns + \"\\nBIENVENIDO AL SANSANITO POKEMON. QUE DESEA HACER?\\n\"\n\t# Opciones de menu principal\n\tmain_menu_items = [\"Crear un registro (create)\", \"Ingresar un pokemon\", \"Buscar en tabla (read)\", \"Opciones especiales de busqueda\",\\\n\t\t\t\t\t\t\"Cambiar datos de pokemon ingresado (update)\", \"Borrar registro (delete)\",\\\n\t\t\t\t\t\t\"Ver la tabla Poyo\", \"Ver la tabla Sansanito Pokemon\", \"Capacidad actual\", \"Salir\"]\n\tmain_menu_cursor = \"> \"\n\tmain_menu_cursor_style = (\"fg_red\", \"bold\")\n\tmain_menu_style = (\"bg_purple\", \"fg_yellow\")\n\tmain_menu_exit = False\n\n\tmain_menu = TerminalMenu(menu_entries=main_menu_items,\n\t\t\t\t\t\t\t title=main_menu_title,\n\t\t\t\t\t\t\t menu_cursor=main_menu_cursor,\n\t\t\t\t\t\t\t menu_cursor_style=main_menu_cursor_style,\n\t\t\t\t\t\t\t menu_highlight_style=main_menu_style,\n\t\t\t\t\t\t\t cycle_cursor=True,\n\t\t\t\t\t\t\t clear_screen=True)\n\n\twhile not main_menu_exit:\n\t\tmain_sel = main_menu.show()\n\t\tsubmenu_flag = True\n\t\t# Create de CRUD y Ingresar pokemon\n\t\tif main_sel == 0 or main_sel == 1:\n\t\t\tsubmenu_flag = False\n\t\t\tprint(\"TABLA ORIGINAL:\")\n\t\t\tprint_sansanito()\n\t\t\tcreate()\n\t\t\tprint(\"\\nTABLA FINAL:\")\n\t\t\tprint_sansanito()\n\t\t\tprint(\"Ingrese X para volver al MENU PRINCIPAL.\")\n\t\t\tcondicion = input()\n\t\t\twhile(condicion != \"X\" and condicion != \"x\"):\n\t\t\t\tcondicion = input()\n\n\t\telif main_sel == 2:\n\t\t\tmenu1_title = \"BUSQUEDA DE REGISTRO EN SANSANITO POKEMON\\n\"\n\t\t\tmenu1_items = [\"Buscar por ID\", \"Salir\"]\n\t\t\tmenu1 = TerminalMenu(menu_entries=menu1_items,\n\t\t\t\t\t\t\t title=menu1_title,\n\t\t\t\t\t\t\t menu_cursor=main_menu_cursor,\n\t\t\t\t\t\t\t menu_cursor_style=main_menu_cursor_style,\n\t\t\t\t\t\t\t menu_highlight_style=main_menu_style,\n\t\t\t\t\t\t\t cycle_cursor=True,\n\t\t\t\t\t\t\t clear_screen=True)\n\t\t\twhile submenu_flag:\n\t\t\t\tmenu1_sel = menu1.show()\n\t\t\t\tif menu1_sel == 0:\n\t\t\t\t\tread()\n\t\t\t\t\tprint(\"Ingrese X para volver al MENU PRINCIPAL.\")\n\t\t\t\t\tcondicion = input()\n\t\t\t\t\twhile(condicion != \"X\" and condicion != \"x\"):\n\t\t\t\t\t\tcondicion = input()\n\t\t\t\telif menu1_sel == 1:\n\t\t\t\t\tsubmenu_flag = False \n\n\t\telif main_sel == 3:\n\t\t\tmenu2_title = \"BUSQUEDA ESPECIAL EN SANSANITO POKEMON. ELIGA UNA OPCION.\\n\"\n\t\t\tmenu2_items = [\"10 pokemon con mayor prioridad\", \"10 pokemon con menor prioridad\",\\\n\t\t\t\"Pokemon con estado especifico\", \"Pokemon legendarios ingresados\",\\\n\t\t\t\"Pokemon que lleva mas tiempo ingresado\", \"Pokemon mas repetido\",\\\n\t\t\t\"Pokemon ingresados, ordenados por su prioridad\", \"Salir\"]\n\t\t\tmenu2 = TerminalMenu(menu_entries=menu2_items,\n\t\t\t\t\t\t\t\ttitle=menu2_title,\n\t\t\t\t\t\t\t\tmenu_cursor=main_menu_cursor,\n\t\t\t\t\t\t\t\tmenu_cursor_style=main_menu_cursor_style,\n\t\t\t\t\t\t\t\tmenu_highlight_style=main_menu_style,\n\t\t\t\t\t\t\t\tcycle_cursor=True,\n\t\t\t\t\t\t\t\tclear_screen=True)\n\t\t\twhile submenu_flag:\n\t\t\t\tmenu2_sel = menu2.show()\n\t\t\t\t# 10 con mayor prioridad\n\t\t\t\tif menu2_sel == 0:\n\t\t\t\t\tmaxprio_sansanito()\n\t\t\t\t\tprint(\"Ingrese X para volver al MENU PRINCIPAL.\")\n\t\t\t\t\tcondicion = input()\n\t\t\t\t\twhile(condicion != \"X\" and condicion != \"x\"):\n\t\t\t\t\t\tcondicion = input()\n\t\t\t\t# 10 con menor prioidad\n\t\t\t\telif menu2_sel == 1:\n\t\t\t\t\tminprio_sansanito()\n\t\t\t\t\tprint(\"Ingrese X para volver al MENU PRINCIPAL.\")\n\t\t\t\t\tcondicion = input()\n\t\t\t\t\twhile(condicion != \"X\" and condicion != \"x\"):\n\t\t\t\t\t\tcondicion = input()\n\t\t\t\t# Filtrado por estado\n\t\t\t\telif menu2_sel == 2:\n\t\t\t\t\tprint(\"NOTA: Estados disponibles son: Envenenado, Paralizado, Quemado, Dormido, Congelado\")\n\t\t\t\t\tprint(\"Para ver pokemons sin estado, ingrese X.\\n\")\n\t\t\t\t\testado = input(\"Ingrese un estado para filtrar los datos: \")\n\t\t\t\t\tif estado.upper() == \"X\":\n\t\t\t\t\t\testado = None\n\t\t\t\t\testado_sansanito(estado)\n\t\t\t\t\tprint(\"Ingrese X para volver al MENU PRINCIPAL.\")\n\t\t\t\t\tcondicion = input()\n\t\t\t\t\twhile(condicion != \"X\" and condicion != \"x\"):\n\t\t\t\t\t\tcondicion = input()\n\t\t\t\t# Los legendarios ingresados\n\t\t\t\telif menu2_sel == 3:\n\t\t\t\t\tlegendarios_sansanito()\n\t\t\t\t\tprint(\"Ingrese X para volver al MENU PRINCIPAL.\")\n\t\t\t\t\tcondicion = input()\n\t\t\t\t\twhile(condicion != \"X\" and condicion != \"x\"):\n\t\t\t\t\t\tcondicion = input()\n\t\t\t\t# El mas antiguo\n\t\t\t\telif menu2_sel == 4:\n\t\t\t\t\tantiguedad_sansanito()\n\t\t\t\t\tprint(\"Ingrese X para volver al MENU PRINCIPAL.\")\n\t\t\t\t\tcondicion = input()\n\t\t\t\t\twhile(condicion != \"X\" and condicion != \"x\"):\n\t\t\t\t\t\tcondicion = input()\n\t\t\t\t# Pokemon mas repetido\n\t\t\t\telif menu2_sel == 5:\n\t\t\t\t\trepetido_sansanito()\n\t\t\t\t\tprint(\"Ingrese X para volver al MENU PRINCIPAL.\")\n\t\t\t\t\tcondicion = input()\n\t\t\t\t\twhile(condicion != \"X\" and condicion != \"x\"):\n\t\t\t\t\t\tcondicion = input()\n\t\t\t\t# Ordenados por prioidad\n\t\t\t\telif menu2_sel == 6:\n\t\t\t\t\torden = input(\"Ingrese 1 si desea el orden DESCENDIENTE, 0 en caso contrario: \")\n\t\t\t\t\tif orden != \"1\" and orden != \"0\":\n\t\t\t\t\t\tprint(\"Input invalido! Intente de nuevo.\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tif orden == \"1\":\n\t\t\t\t\t\t\tordenado_sansanito(\"DESC\")\n\t\t\t\t\t\t\tprint(\"Ingrese X para volver al MENU PRINCIPAL.\")\n\t\t\t\t\t\t\tcondicion = input()\n\t\t\t\t\t\t\twhile(condicion != \"X\" and condicion != \"x\"):\n\t\t\t\t\t\t\t\tcondicion = input()\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tordenado_sansanito(\"ASC\")\n\t\t\t\t\t\t\tprint(\"Ingrese X para volver al MENU PRINCIPAL.\")\n\t\t\t\t\t\t\tcondicion = input()\n\t\t\t\t\t\t\twhile(condicion != \"X\" and condicion != \"x\"):\n\t\t\t\t\t\t\t\tcondicion = input()\n\t\t\t\telif menu2_sel == 7:\n\t\t\t\t\tsubmenu_flag = False\n\t\telif main_sel == 4:\n\t\t\tupdate()\n\t\t# Delete de CRUD\n\t\telif main_sel == 5:\n\t\t\ta_borrar = int(input(\"Ingrese el ID de registro a borrar: \"))\n\t\t\tdelete(a_borrar)\n\t\t\ttime.sleep(1)\n\t\t# Muestra la tabla de poyo\n\t\telif main_sel == 6:\n\t\t\tprint_poyo()\n\t\t\tprint(\"Ingrese X para volver al MENU PRINCIPAL.\")\n\t\t\tcondicion = input()\n\t\t\twhile(condicion.upper() != \"X\"):\n\t\t\t\tcondicion = input()\n\t\t# Muestra la tabla de sansanito\n\t\telif main_sel == 7:\n\t\t\tprint_sansanito()\n\t\t\tprint(\"Ingrese X para volver al MENU PRINCIPAL.\")\n\t\t\tcondicion = input()\n\t\t\twhile(condicion.upper() != \"X\"):\n\t\t\t\tcondicion = input()\n\t\t# Funcion extra que muestra la ocupacion actual de sansanito\n\t\telif main_sel == 8:\n\t\t\tprint(\"La capacidad actual es: {}/50\".format(calcular_ocupacion()))\n\t\t\tprint(\"Ingrese X para volver al MENU PRINCIPAL.\")\n\t\t\tcondicion = input()\n\t\t\twhile(condicion != \"X\" and condicion != \"x\"):\n\t\t\t\tcondicion = input()\n\t\t# Salir\n\t\telif main_sel == 9:\n\t\t\tmain_menu_exit = True"],"string":"[\n \"def create_menu():\",\n \"def create_menus( self ):\",\n \"def draw_main_menu():\\n draw_cover()\\n draw_menu_buttons()\\n draw_border()\",\n \"def main_menu(self):\\n\\n # Set the window background\\n self.palette = QPalette()\\n self.pixmap = QPixmap('./pictures/menu_cat.png').scaled(860, 640)\\n self.palette.setBrush(QPalette.Background, QBrush(self.pixmap))\\n self.setPalette(self.palette)\\n\\n for item in self.mainmenu_items:\\n item.show()\\n for item in self.mapmenu_items:\\n item.hide()\",\n \"def _add_color_menu(self):\\n print 'adding color menu'\\n self.menuBar.addcascademenu('Color', 'Color Atoms'); \\n c_lambda = lambda: self.color_wireframe('cpk');\\n self.menuBar.addmenuitem('Color Atoms','command','Color wireframes cpk', command=c_lambda, label='cpk')\\n c_lambda = lambda: self.color_wireframe('type');\\n self.menuBar.addmenuitem('Color Atoms','command','Color wireframes by type', command=c_lambda, label='type')\\n c_lambda = lambda: self.color_wireframe('chain');\\n self.menuBar.addmenuitem('Color Atoms','command','color wireframes by chain', command=c_lambda, label='chain')\\n c_lambda = lambda: self.color_wireframe('hydrogen_type');\\n self.menuBar.addmenuitem('Color Atoms','command','color wireframes by H type', command=c_lambda, label='H Type')\\n \\n self.menuBar.addcascademenu('Color', 'Color Trace')\\n self.menuBar.addmenuitem('Color Trace','command','Color tubes by secondary', command=self.color_trace_by_secondary,label='secondary')\\n self.menuBar.addmenuitem('Color Trace','command','Color tubes by type', command=self.color_tubes_type,label='type')\\n self.menuBar.addmenuitem('Color Trace','command','Color tubes by chain', command=self.color_tubes_chain,label='chain')\\n\\n self.menuBar.addcascademenu('Color', 'Color Volumes')\\n self.menuBar.addmenuitem('Color Volumes','command','Color volumes cpk', command=self.color_volumes_cpk,label='cpk')\\n self.menuBar.addmenuitem('Color Volumes','command','Color volumes by type', command=self.color_volumes_type,label='type')\\n self.menuBar.addmenuitem('Color Volumes','command','Color volumes by chain', command=self.color_volumes_chain,label='chain')\\n\\n # create menu items for .features keys for atoms and residues\\n if self.system != 'None' and self.system != None:\\n key_store = {}\\n key_store['atom'] = self.system.ProteinList[0].atoms[0].features.keys()\\n key_store['residue'] = self.system.ProteinList[0].residues[0].features.keys()\\n for run_type in ['atom', 'residue']:\\n broken = 0\\n for key in key_store[run_type]:\\n for pol in self.system.ProteinList:\\n if key == 'domain':\\n self.print_domain_info(pol)\\n normalized = 1\\n # if the feature includes non-digits, pass. if it is all digits, see if \\n # it is normalized\\n if run_type == 'atom':\\n item_list = pol.atoms\\n elif run_type == 'residue':\\n item_list = pol.residues\\n same_val_count = 0\\n try:\\n item_list[0].features[key]\\n except KeyError:\\n continue\\n else:\\n first_val = item_list[0].features[key]\\n for item in item_list:\\n try:\\n feature = item.features[key]\\n except KeyError:\\n print 'key error on %s, breaking'%(key)\\n broken = 1\\n break\\n try:\\n int(feature)\\n except ValueError:\\n print '%s not digit, breaking'%(feature)\\n broken = 1\\n break\\n else:\\n if feature != -1 and (feature < 0.0 or feature > 1.0):\\n normalized = 0\\n if feature == first_val:\\n same_val_count += 1\\n if same_val_count == len(item_list):\\n print '%s all the same value; breaking'%(key)\\n broken = 1\\n break\\n if key == 'domain':\\n if item.features[key] == 0.0:\\n item.features[key] = -1\\n else:\\n # if not normalized, make a new key called key+'_normalized', and swap the old\\n # key with the new key to color by it\\n old_key = copy.copy(key)\\n if not normalized and (key+'_normalized' not in item.features.keys()):\\n min_f = 1000000\\n max_f = -1000000\\n for item2 in item_list:\\n feature = item2.features[key]\\n if feature != -1:\\n if feature < min_f:\\n min_f = feature\\n if feature > max_f:\\n max_f = feature\\n key = key + '_normalized'\\n for item2 in item_list:\\n if item2.features[old_key] != -1.0:\\n d = (item2.features[old_key]-min_f) / (max_f-min_f+0.0)\\n item2.features[key] = d\\n else:\\n item2.features[key] = -1.0\\n if run_type == 'residue':\\n c_lambda1 = lambda p=pol, k=key: self.color_trace_by_residue_feature(p, k)\\n self.menuBar.addmenuitem('Color Trace','command','Color trace by res '+key, command=c_lambda1, label='%s %s'%(pol.chain_name, key))\\n c_lambda2 = lambda p=pol, k=key: self.color_volume_by_residue_feature(p, k)\\n self.menuBar.addmenuitem('Color Volumes','command','Color volumes by res '+key, command=c_lambda2, label='%s %s'%(pol.chain_name, key))\\n c_lambda3 = lambda p=pol, k=key: self.color_atoms_by_residue_feature(p, k)\\n self.menuBar.addmenuitem('Color Atoms','command','Color atoms by res '+key, command=c_lambda3, label='%s %s'%(pol.chain_name, key))\\n elif run_type == 'atom':\\n c_lambda1 = lambda p=pol, k=key: self.color_trace_by_atom_feature(p, k)\\n self.menuBar.addmenuitem('Color Trace','command','Color trace by atom '+key, command=c_lambda1, label='%s %s'%(pol.chain_name, key))\\n c_lambda2 = lambda p=pol, k=key: self.color_volume_by_atom_feature(p, k)\\n self.menuBar.addmenuitem('Color Volumes','command','Color volumes by atom '+key, command=c_lambda2, label='%s %s'%(pol.chain_name, key))\\n c_lambda3 = lambda p=pol, k=key: self.color_atoms_by_atom_feature(p, k)\\n self.menuBar.addmenuitem('Color Atoms','command','Color atoms by atom '+key, command=c_lambda3, label='%s %s'%(pol.chain_name, key))\\n key = old_key\\n #broken = 1\\n #break\\n if broken:\\n break\",\n \"def make_top_menus(self):\\n menubar = tk.Menu(self)\\n\\n # create a pulldown menu for languages, and add it to the menu bar\\n language_menu = tk.Menu(menubar, tearoff=0)\\n language_menu.add_command(label=self.translate(\\\"English\\\"), command=lambda: self.replace_language('english'))\\n language_menu.add_command(label=self.translate(\\\"Spanish\\\"), command=lambda: self.replace_language('spanish'))\\n language_menu.add_command(label=self.translate(\\\"Portuguese\\\"), command=lambda: self.replace_language('portuguese'))\\n menubar.add_cascade(label=self.translate(\\\"Languages\\\"), menu=language_menu)\\n \\n # create a pulldown menu for switching context areas, and add it to the menu bar\\n context_menu = tk.Menu(menubar, tearoff=0)\\n context_menu.add_command(label=self.translate(\\\"Chile\\\"), command=lambda: self.switch_context('Chile'))\\n context_menu.add_command(label=self.translate(\\\"Indonesia\\\"), command=lambda: self.switch_context('Indonesia'))\\n context_menu.add_command(label=self.translate(\\\"Luanda\\\"), command=lambda: self.switch_context('Luanda'))\\n context_menu.add_command(label=self.translate(\\\"Querétaro\\\"), command=lambda: self.switch_context('Querétaro'))\\n context_menu.add_command(label=self.translate(\\\"Rio de Janeiro\\\"), command=lambda: self.switch_context('Rio de Janeiro'))\\n context_menu.add_command(label=self.translate(\\\"Santiago\\\"), command=lambda: self.switch_context('Santiago'))\\n menubar.add_cascade(label=self.translate(\\\"Locations\\\"), menu=context_menu)\\n \\n # create a pulldown menu for arrangment, and add it to the menu bar\\n language_menu = tk.Menu(menubar, tearoff=0)\\n language_menu.add_command(label=self.translate(\\\"Graphs-Graphs\\\"), command=lambda: self.switch_arrangment(['Graph', 'Graph']))\\n language_menu.add_command(label=self.translate(\\\"Graphs-Map\\\"), command=lambda: self.switch_arrangment(['Graph', 'Map']))\\n language_menu.add_command(label=self.translate(\\\"Map-Graphs\\\"), command=lambda: self.switch_arrangment(['Map', 'Graph']))\\n language_menu.add_command(label=self.translate(\\\"Map-Map\\\"), command=lambda: self.switch_arrangment(['Map', 'Map']))\\n menubar.add_cascade(label=self.translate(\\\"Arrange\\\"), menu=language_menu)\\n \\n # create an exit command that closes the UI\\n menubar.add_command(label=self.translate(\\\"Exit\\\"), command=self.destroy)\\n \\n # display the menu\\n menubar.config(font=self.small_font)\\n self.config(menu=menubar)\\n \\n return menubar\",\n \"def _create_menu(self):\\n menu = ImageMenu(0.04, 0.16, 0.23, self.y_for_x(0.23) * 0.7)\\n menu.items_per_col = 2\\n menu.visible_rows = 2\\n menu.visible_cols = 4\\n\\n clips = self.video_library.get_video_clips()\\n clips_list = [[clip.thumbnail_url, clip] for clip in clips]\\n menu.async_add_clips(clips_list)\\n\\n # Create list indicator\\n self.list_indicator = ListIndicator(0.7, 0.8, 0.2, 0.045,\\n ListIndicator.HORIZONTAL)\\n self.list_indicator.set_maximum(len(clips))\\n self.list_indicator.show()\\n self.add(self.list_indicator)\\n\\n # Create information labels\\n self.clip_title = Label(0.042, \\\"title\\\", 0.15, 0.77, \\\"\\\",\\n font_weight=\\\"bold\\\")\\n self.clip_title.set_ellipsize(pango.ELLIPSIZE_END)\\n self.clip_title.set_line_wrap(False)\\n self.clip_title.width = 0.5\\n self.add(self.clip_title)\\n\\n self.clip_info = Label(0.034, \\\"subtitle\\\", 0.15, 0.85, \\\"\\\")\\n self.clip_info.set_ellipsize(pango.ELLIPSIZE_END)\\n self.clip_info.set_line_wrap(False)\\n self.clip_info.width = 0.5\\n self.add(self.clip_info)\\n\\n return menu\",\n \"def make_background_graphics(self):\\n # Title.\\n centered = self.graphics.center_just(1, \\\"CHOOSE DIFFICULTY\\\")\\n title = TextBox(*centered)\\n self.uielements.append(title)\\n\\n # Bars.\\n bar_a = TextBox(Point(0, 2), \\\"_\\\"*self.graphics.LENGTH)\\n self.uielements.append(bar_a)\\n\\n bar_b = TextBox(Point(0, 8), \\\"_\\\"*self.graphics.LENGTH)\\n self.uielements.append(bar_b)\\n\\n # Controls.\\n text = [\\n \\\"_\\\"*self.graphics.LENGTH,\\n \\\" wasd: Move | m: Select | q: Quit\\\"\\n ]\\n controls = LongTextBox(Point(0, self.graphics.HEIGHT-3), text)\\n self.uielements.append(controls)\",\n \"def mainmenu_background():\\n gameDisplay.fill((40, 0, 40))\",\n \"def mainmenu_background():\\n gameDisplay.fill((40, 0, 40))\",\n \"def setup_menu(self, surface, background_colour=BLACK):\\n # flood fill the surface with the background colour\\n surface.fill(background_colour)\\n\\n # set up the fixed items on the menu\\n # Add buttons and labels\\n menu_config = [\\n (\\\"Speed\\\", 6, 6, BLUE, WHITE), #, 62, 100, WHITE),\\n (\\\"Maze\\\", 122, 6, BLUE, WHITE), #, 62, 100, WHITE),\\n (\\\"Rainbow\\\", 6, 70, BLUE, WHITE), #, 62, 100, WHITE),\\n (\\\"Golf\\\", 122, 70, BLUE, WHITE), #, 62, 100, WHITE),\\n (\\\"Pi Noon\\\", 6, 134, BLUE, WHITE), #, 62, 100, WHITE),\\n (\\\"Obstacle\\\", 122, 134, BLUE, WHITE), #, 62, 100, WHITE),\\n (\\\"Shooting\\\", 6, 198, BLUE, WHITE), #, 62, 100, WHITE),\\n (\\\"RC\\\", 122, 198, BLUE, WHITE), #, 62, 100, WHITE),\\n (\\\"Exit\\\", 6, 262, BLUE, WHITE), #, 40, 210, WHITE),\\n (\\\"Calibrate\\\", 122, 262, BLUE, WHITE),\\n ]\\n\\n # perform list comprehension on menu_config, wherein we call\\n # make_button with the index, and individual item arguments\\n # note *item performs unpacking of the tuple and provides them\\n # as individual arguments to make_button\\n return [\\n self.make_button(index, *item)\\n for index, item\\n in enumerate(menu_config)\\n ]\",\n \"def mainmenu_background():\\n surface.fill((40, 0, 40))\",\n \"def draw_labels(self):\\n x = PygameUI.Label('Inactive Events')\\n x.frame = pygame.Rect(4, 4, 150, 30)\\n self.scene.add_child(x)\\n \\n x = PygameUI.Label('Active Events')\\n x.frame = pygame.Rect(Menu.scene.frame.w-150, 4, 150, 30)\\n self.scene.add_child(x)\",\n \"def menu_principal(self):\\r\\n print('=== Bem Vindo ao Sistema de Cadastro de Clientes ===')\\r\\n print('\\\\nEscolha uma das opções abaixo.')\\r\\n print('1. Visualizar contatos')\\r\\n print('2. Busca contatos')\\r\\n print('3. Adicionar contato')\\r\\n print('4. Remover contato')\\r\\n print('5. Alterar contato')\\r\\n print('6. Carregar lista Parte I') \\r\\n print('7. Visualizar grupo')\\r\\n print('8. Gestão de grupos')\\r\\n print('9. Sair')\",\n \"def draw_main(self, window, font, xpos, ypos):\\n\\n if not self.is_selected_main(xpos, ypos):\\n # Text\\n name_label = font.render(self.selected_option, 1, self.menu_colour1)\\n window.blit(\\n name_label,\\n (\\n self.x + (self.width // 2 - name_label.get_width() // 2),\\n self.y + (self.height // 2 - name_label.get_height() // 2),\\n ),\\n )\\n\\n else:\\n # Define label\\n # Generated first so it's width can be used in the background placement calculations\\n name_label = font.render(self.selected_option, 1, self.menu_colour2)\\n\\n # Box\\n # x defined in that way so the background is centered\\n # width is multiplied by 1.1 to give each option a bit more background on it's sides\\n pygame.draw.rect(\\n window,\\n self.menu_colour1,\\n (\\n self.x + (self.width // 2 - name_label.get_width() * 1.1 // 2),\\n self.y,\\n name_label.get_width() * 1.1,\\n self.height,\\n ),\\n 0,\\n )\\n # Place label\\n window.blit(\\n name_label,\\n (\\n self.x + (self.width // 2 - name_label.get_width() // 2),\\n self.y + (self.height // 2 - name_label.get_height() // 2),\\n ),\\n )\",\n \"def Crearmenu():\\n layoutM = [\\n [sg.T(\\\"ScrabbleAR\\\", size=(16, 1), justification=\\\"center\\\",\\n font=(\\\"Times New Roman\\\", 25))],\\n [sg.T(\\\" Bienvenido a ScrabbleAR!, el juego donde \\\")],\\n [sg.T(\\\" hay que armar palabras para ganar \\\")],\\n [sg.B(\\\"Iniciar nuevo juego\\\", size=(17, 1), key=\\\"inicio\\\"),\\n sg.B(\\\"Configuracion\\\", size=(17, 1), key=\\\"config\\\")],\\n [sg.B(\\\"Puntuaciones\\\", size=(17, 1), key=\\\"puntos\\\"),\\n sg.B(\\\"Salir\\\", size=(17, 1), key=\\\"exit\\\")]\\n ]\\n\\n if(os.path.isfile(\\\"Guardado.json\\\")):\\n layoutM += [[sg.B(\\\"Continuar partida\\\", size=(36, 1), key=\\\"continue\\\")]]\\n\\n window = sg.Window(\\\"ScrabbleAR - Menu\\\", layoutM)\\n\\n return window\",\n \"def menu_items():\\r\\n menu_name = \\\"Coloring\\\"\\r\\n algorithm_list = [[\\\"Brute Force\\\", board_brute_force_coloring],\\r\\n [\\\"separator\\\", \\\"separator\\\"],\\r\\n [\\\"Greedy Coloring\\\", board_greedy_coloring],\\r\\n [\\\"Tabu Coloring Search\\\", board_tabu_coloring],\\r\\n [\\\"Tabu Pre-Coloring Search\\\", board_tabu_precoloring]]\\r\\n \\r\\n return [menu_name, algorithm_list]\",\n \"def draw(self, context):\\n layout = self.layout\\n\\n pie = layout.menu_pie()\\n pie.operator(\\\"object.view_menu\\\", text=\\\"Node Editor\\\", icon='NODETREE').vp = \\\"NODE_EDITOR\\\"\\n pie.operator(\\\"object.view_menu\\\", text=\\\"UV Image Editor\\\", icon='IMAGE_COL').vp = \\\"IMAGE_EDITOR\\\"\\n pie.operator(\\\"object.view_menu\\\", text=\\\"Video Sequece Editor\\\", icon='SEQUENCE').vp = \\\"SEQUENCE_EDITOR\\\"\\n pie.operator(\\\"object.view_menu\\\", text=\\\"Movie Clip Editor\\\", icon='CLIP').vp = \\\"CLIP_EDITOR\\\"\",\n \"def create(self):\\n # Positioning background and pointer indicator for main menu\\n self.surface.blit(self.main_menu_background, (0, 0))\\n self.surface.blit(self.main_menu_greets, self.main_menu_greets_position)\\n self.show_mouse_position_with_px()\\n self.main_menu_buttons()\",\n \"def _createDisplayMenu(ned, menu):\\n pass\",\n \"def menu():\\n ferme_fenetre()\\n Menu()\",\n \"def create_frame_icons(self):\\n self.text = \\\"{}\\\".format(self.name)\\n self.y = self.startY - 10 if self.startY - 10 > 10 else self.startY + 10\\n self.colorIndex = LABELS.index(self.name)\",\n \"def Render(self, background):\\r\\n ui_back = rpg_image.Load('data/ui/board_500.png')\\r\\n rpg_gfx.Draw(ui_back, background, (70,55))\\r\\n \\r\\n ui_back_slot = rpg_image.Load('data/ui/board_500_slot_61.png')\\r\\n ui_back_slot_small = rpg_image.Load('data/ui/board_500_slot_35.png')\\r\\n \\r\\n # Print option background borders\\r\\n if len(self.options) >= 1:\\r\\n rpg_gfx.Draw(ui_back_slot, background, (70+30,55+75))\\r\\n if len(self.options) >= 2:\\r\\n rpg_gfx.Draw(ui_back_slot, background, (70+30,55+145))\\r\\n if len(self.options) >= 3:\\r\\n rpg_gfx.Draw(ui_back_slot, background, (70+30,55+215))\\r\\n if len(self.options) >= 4:\\r\\n rpg_gfx.Draw(ui_back_slot, background, (70+30,55+285))\\r\\n \\r\\n # Draw the Prompt\\r\\n rpg_image.DrawText(self.dialogue.prompt, 30, (255, 255, 255), (100,80),\\r\\n background, outline=1)\\r\\n \\r\\n # Print all the option texts\\r\\n for count in range(0, len(self.options)):\\r\\n #TODO(g): Make the show_always data option check and force this option\\r\\n # to be the last option, if we have it set to be on\\r\\n \\r\\n # Only drawing 4 now\\r\\n if count >= 4:\\r\\n break\\r\\n \\r\\n option = self.options[count]\\r\\n \\r\\n x = 70+30 + 15\\r\\n y = 55+75 + (70 * count) + 15\\r\\n \\r\\n # If this option is selected, highlight it\\r\\n if self.selected_option == count:\\r\\n color = (255, 0, 0)\\r\\n else:\\r\\n color = (255, 255, 255)\\r\\n \\r\\n rpg_image.DrawText(option['text'], 20, color, (x,y), background,\\r\\n outline=1)\",\n \"def buildKeyframeMenu(*args, **kwargs)->None:\\n pass\",\n \"def create_menu():\\n MenuData = [\\n (\\\"&Draw Variables\\\",drawable.ask),\\n (\\\"&Show Variables\\\",printall),\\n (\\\"&Print Variables\\\",printval),\\n (\\\"&Edit Variable\\\",edit),\\n (\\\"&Rename Variable\\\",rename),\\n (\\\"&Forget Variables\\\",forget),\\n (\\\"---\\\",None),\\n (\\\"&Create Plane\\\",\\n [(\\\"Coordinates\\\", \\n [(\\\"Point and normal\\\", createPlaneCoordsPointNormal),\\n (\\\"Three points\\\", createPlaneCoords3Points),\\n ]), \\n (\\\"Visually\\\", \\n [(\\\"Three points\\\", createPlaneVisual3Points),\\n ]),\\n ]),\\n (\\\"&Select Plane\\\",planes.ask),\\n (\\\"&Draw Selection\\\",planes.draw),\\n (\\\"&Forget Selection\\\",planes.forget),\\n (\\\"---\\\",None),\\n (\\\"&Pick Actors\\\",pick_actors),\\n (\\\"&Pick Elements\\\",pick_elements),\\n (\\\"&Pick Points\\\",pick_points),\\n (\\\"&Pick Edges\\\",pick_edges),\\n (\\\"---\\\",None),\\n ('&Selection',\\n [('&Create Report',report_selection),\\n ('&Set Property',setprop_selection),\\n ('&Grow',grow_selection),\\n ('&Partition',partition_selection),\\n ('&Get Partition',get_partition),\\n ('&Export',export_selection),\\n ]),\\n (\\\"---\\\",None),\\n ('&Query',\\n [('&Actors',query_actors),\\n ('&Elements',query_elements),\\n ('&Points',query_points),\\n ('&Edges',query_edges),\\n ('&Distances',query_distances),\\n ]),\\n (\\\"---\\\",None),\\n (\\\"&Close\\\",close_menu),\\n ]\\n return widgets.Menu('Tools',items=MenuData,parent=GD.gui.menu,before='help')\",\n \"def drawMenu(self):\\r\\n menuText, menuSize = self.menuView.draw() \\r\\n self.drawCenteredText(menuText, menuSize, .5, 11.0/16)\",\n \"def map_menu(self):\\n\\n # Set the window background\\n self.palette = QPalette()\\n gradient = QLinearGradient(0, 0, 0, 800)\\n gradient.setColorAt(0.0, QColor(227, 0, 77))\\n gradient.setColorAt(1.0, QColor(255, 255, 255))\\n self.palette.setBrush(QPalette.Window, QBrush(gradient))\\n self.setPalette(self.palette)\\n\\n for item in self.mapmenu_items:\\n item.show()\\n for item in self.mainmenu_items:\\n item.hide()\",\n \"def menu():\\n ########################## HELLO #############################\\n print(\\\"\\\"\\\" \\n \\n mm db 7MM db \\n MM MM \\n`7MMpdMAo. `7M' `MF' ,pW\\\"Wq. mmMMmm `7MM ,M\\\"\\\"bMM ,pP\\\"Ybd ,6\\\"Yb. 7MM \\n MM `Wb VA ,V 6W' `Wb MM MM ,AP MM 8I `\\\" 8) MM MM \\n MM M8 VA ,V 8M M8 MM MM 8MI MM `YMMMa. ,pm9MM MM \\n MM ,AP VVV YA. ,A9 MM MM `Mb MM L. I8 8M MM MM \\n MMbmmd' ,V `Ybmd9' `Mbmo .JMML. `Wbmd\\\"MML.M9mmmP' `Moo9^Yo..JMML.\\n MM ,V \\n.JMML. OOb\\\" \\n\\nVersion: 1.1 Autor: Paloma Sánchez y Juan Pablo Egido OS: Linux/Debian\\n\\\"\\\"\\\")\\n\\n ######################### MENU ############################\\n print(\\\"\\\\n [!] Bienvenid@ a Pyotidsai.\\\")\\n print('\\\\n [!] Introduce la opción deseada '\\n '\\\\n [!] (1) Crear reglas'\\n '\\\\n [!] (2) Detectar malware(SNORT)'\\n '\\\\n [!] (3) Machine Learning Classifier'\\n '\\\\n [!] (0) Salir')\",\n \"def makeTitle(self):\\n l1=Label(self.app, text=\\\"Asset Allocation Combinations\\\")\\n l1.grid(row=0, column=0)\",\n \"def __init__(self, MENUWIDTH, MENUHEIGHT):\\n \\n self._menu_items = []\\n self.menu_Text = []\\n #Background\\n self.menuRect = pygame.Rect((0, 0), (MENUWIDTH, MENUHEIGHT))\",\n \"def main():\\n os.makedirs(\\\"./img/event_generated/\\\", exist_ok=True)\\n for category in categories():\\n render_icon(category + \\\".png\\\")\",\n \"def draw_menu(self, context):\\n if context.engine == 'RPR':\\n layout = self.layout\\n layout.popover('RPR_VIEW3D_PT_panel')\",\n \"def _draw_main_menu(client, commands):\\n client.send(\\\"\\\\n\\\")\\n client.send_wrapped(\\\"~=\\\" * 20)\\n client.send(\\\" \\\" * 16 +\\\"Welcome to \\\" + PRODUCT_NAME + \\\"\\\\n\\\")\\n client.send_wrapped(\\\"~=\\\" * 20)\\n client.send(\\\"\\\\n\\\")\\n client.send(\\\" | \\\".join(commands))\\n client.send(\\\"\\\\n\\\")\",\n \"def printMenu():\\n print(\\\"\\\\nBienvenido\\\")\\n print(\\\"1- Cargar Datos\\\")\\n print(\\\"2- Cargar Catalogo de peliculas\\\")\\n print(\\\"3- Buscar productoras\\\")\\n print(\\\"0- Salir\\\")\",\n \"def createConnectionMarkingMenu(ned):\\n pass\",\n \"def menuBar(self):\\n\\n\\t\\tmenu = Menu(self.master)\\n\\t\\tself.master.config(menu=menu)\\n\\n\\t\\t#File Menu\\n\\t\\tfyle = Menu(menu)\\n\\t\\tfyle.add_command(label='New',command=self.newPad)\\n\\t\\tfyle.add_command(label='Open File',command=self.open_file)\\n\\t\\tfyle.add_command(label='Save', command=self.saveFile)\\n\\t\\tfyle.add_command(label='Save As',command=self.saveFileAs)\\n\\t\\tfyle.add_command(label='Exit', command=outer.destroy)\\n\\t\\tmenu.add_cascade(label='File',menu=fyle)\\n\\n\\t\\t#Edit Menu\\n\\t\\tedit = Menu(menu)\\n\\t\\tedit.add_command(label='Cut')\\n\\t\\tedit.add_command(label='Copy')\\n\\t\\tedit.add_command(label='Paste')\\n\\t\\tedit.add_command(label='Undo')\\n\\t\\tedit.add_command(label='Redo')\\n\\t\\tmenu.add_cascade(label='Edit',menu=edit)\\n\\n\\t\\t#View Menu\\n\\t\\tview = Menu(menu)\\n\\t\\tview.add_command(label='Line Numbers')\\n\\t\\tmenu.add_cascade(label='View', menu=view)\\n\\n\\n\\t\\t#Help Menu\\n\\t\\thelp = Menu(menu)\\n\\t\\thelp.add_command(label='About')\\n\\t\\tmenu.add_cascade(label='Help',menu=help)\",\n \"def __init__(self, gui, text, callback=None, shortcut=None):\\r\\n menu_text = pi3d.String(font=gui.font, string=text, is_3d=False,\\r\\n camera=gui.camera, justify='L')\\r\\n menu_text.set_shader(gui.shader)\\r\\n super(MenuItem, self).__init__(gui, [menu_text], 0, 0,\\r\\n callback=callback, shortcut=shortcut)\\r\\n self.child_menu = None\\r\\n self.own_menu = None\",\n \"def __init__(self, screen, bg_color=(0, 0, 0), font=None, font_size=35, font_color=(255, 255, 255)):\\n self.screen = screen\\n self.scr_width = self.screen.get_rect().width\\n self.scr_height = self.screen.get_rect().height\\n self.bg_color = bg_color\\n self.clock = pygame.time.Clock()\\n self.font = pygame.font.SysFont(font, font_size)\\n self.font_color = font_color\\n self.items = []\\n self.funcs = OrderedDict()\\n\\n self.text_color = (255, 255, 255)\\n\\n self.funcs[\\\"Back\\\"] = self.menu\\n self.funcs[\\\"Quit\\\"] = sys.exit\\n items = self.funcs.keys()\\n for index, item in enumerate(items):\\n menu_item = MenuItem(item)\\n t_h = len(items) * menu_item.height\\n pos_x = (self.scr_width / 2) - (menu_item.width / 2)\\n pos_y = (self.scr_height / 2) - (t_h / 2) + ((index * 2) + index * menu_item.height) + 120\\n menu_item.set_position(pos_x, pos_y)\\n self.items.append(menu_item)\\n\\n self.mouse_is_visible = True\\n self.cur_item = None\\n self.text_wall = TextWall()\\n self.text_wall.parse_text(instruction_text)\",\n \"def STARTMENU_LOOP():\\n pass\",\n \"def ibl_options_panel(self, context):\\r\\n \\r\\n AM = context.window_manager.asset_m\\r\\n node_group = bpy.context.scene.world.node_tree.nodes\\r\\n layout = self.layout\\r\\n \\r\\n box = layout.box()\\r\\n row = box.row()\\r\\n row.alignment = 'CENTER'\\r\\n row.label(\\\"IMAGE\\\")\\r\\n row = box.row(align = True)\\r\\n row.label(\\\"Rotation:\\\")\\r\\n col = row.column()\\r\\n col.prop(node_group[\\\"Mapping\\\"], \\\"rotation\\\", text = \\\"\\\")\\r\\n row = box.row(align = True)\\r\\n row.label(\\\"Projection:\\\")\\r\\n row.prop(AM, \\\"projection\\\", text = \\\"\\\")\\r\\n row = box.row(align = True)\\r\\n row.label(\\\"Blur:\\\")\\r\\n row.prop(node_group[\\\"ImageBlur\\\"].inputs[1], \\\"default_value\\\", text = \\\"\\\")\\r\\n row = box.row(align = True)\\r\\n row.label(\\\"Visible:\\\")\\r\\n row.prop(bpy.context.scene.world.cycles_visibility, \\\"camera\\\")\\r\\n row = box.row(align = True)\\r\\n row.label(\\\"Transparent:\\\")\\r\\n row.prop(bpy.context.scene.cycles, \\\"film_transparent\\\")\\r\\n \\r\\n \\r\\n \\r\\n box = layout.box()\\r\\n row = box.row(align = True)\\r\\n row.label(\\\"Gamma:\\\")\\r\\n row.prop(node_group[\\\"AM_IBL_Tool\\\"].inputs[0], \\\"default_value\\\", text = \\\"\\\")\\r\\n \\r\\n box = layout.box()\\r\\n row = box.row()\\r\\n row.alignment = 'CENTER'\\r\\n row.label(\\\"LIGHT\\\")\\r\\n row = box.row(align = True)\\r\\n row.label(\\\"Strength:\\\")\\r\\n row.prop(node_group[\\\"AM_IBL_Tool\\\"].inputs[2], \\\"default_value\\\", text = \\\"\\\")\\r\\n row = box.row(align = True)\\r\\n row.label(\\\"Saturation:\\\")\\r\\n row.prop(node_group[\\\"AM_IBL_Tool\\\"].inputs[3], \\\"default_value\\\", text = \\\"\\\")\\r\\n row = box.row(align = True)\\r\\n row.label(\\\"Hue:\\\")\\r\\n row.prop(node_group[\\\"AM_IBL_Tool\\\"].inputs[4], \\\"default_value\\\", text = \\\"\\\")\\r\\n row = box.row(align = True)\\r\\n row.label(\\\"Mix Hue:\\\")\\r\\n row.prop(node_group[\\\"AM_IBL_Tool\\\"].inputs[5], \\\"default_value\\\", text = \\\"\\\")\\r\\n \\r\\n box = layout.box()\\r\\n row = box.row()\\r\\n row.alignment = 'CENTER'\\r\\n row.label(\\\"GLOSSY\\\")\\r\\n row = box.row(align = True)\\r\\n row.label(\\\"Strength:\\\")\\r\\n row.prop(node_group[\\\"AM_IBL_Tool\\\"].inputs[7], \\\"default_value\\\", text = \\\"\\\")\\r\\n row = box.row(align = True)\\r\\n row.label(\\\"Saturation:\\\")\\r\\n row.prop(node_group[\\\"AM_IBL_Tool\\\"].inputs[8], \\\"default_value\\\", text = \\\"\\\")\\r\\n row = box.row(align = True)\\r\\n row.label(\\\"Hue:\\\")\\r\\n row.prop(node_group[\\\"AM_IBL_Tool\\\"].inputs[9], \\\"default_value\\\", text = \\\"\\\")\\r\\n row = box.row(align = True)\\r\\n row.label(\\\"Mix Hue:\\\")\\r\\n row.prop(node_group[\\\"AM_IBL_Tool\\\"].inputs[10], \\\"default_value\\\", text = \\\"\\\")\\r\\n \\r\\n layout.operator(\\\"wm.save_ibl_settings\\\", text = \\\"Save settings\\\", icon = 'FILE_TICK')\",\n \"def makeMenu(self):\\n\\t\\tself.fileMenu = self.menuBar().addMenu(self.tr(\\\"&Arquivo\\\"))\\n\\t\\tself.fileMenu.addAction(self.newAct)\\n\\t\\tself.fileMenu.addAction(self.openAct)\\n\\t\\tself.fileMenu.addAction(self.saveAct)\\n\\t\\tself.fileMenu.addAction(self.exportAct)\\n\\t\\tself.fileMenu.addSeparator() \\n\\t\\tself.fileMenu.addAction(self.exitAct)\\n\\n\\t\\tself.editMenu = self.menuBar().addMenu(self.tr(\\\"&Editar\\\"))\\n\\t\\t\\n\\t\\tself.helpMenu = self.menuBar().addMenu(self.tr(\\\"&Ajuda\\\"))\\n\\t\\tself.helpMenu.addAction(self.aboutAct)\",\n \"def __init__(self, parent_menu, label):\\n self._parent_menu = parent_menu\\n self._options = {}\\n self._label = label\\n self._menu = Menu(parent_menu)\\n self._key_underline = 0\",\n \"def controls_setup(self):\\n self.dashboard = element.Link(self, class_name='menu-dashboard', alias='Dashboard Menu')\\n # catalogue and sub-menus\\n self.catalogue = element.LinkButton(self, class_name='menu-catalogue', alias='Catalogue Menu')\\n self.products = element.Link(self, class_name='submenu-products', alias='Catalogue->Products Menu')\\n self.product_types = element.Link(self, class_name='submenu-product-types',\\n alias='Catalogue->Product Types Menu')\\n self.categories = element.Link(self, class_name='submenu-categories', alias='Catalogue->Categories Menu')\\n self.ranges = element.Link(self, class_name='submenu-ranges', alias='Catalogue->Ranges Menu')\\n self.stock_alerts = element.Link(self, class_name='submenu-low-stock-alerts',\\n alias='Catalogue->Low Stock Alerts Menu')\\n # fulfilment and sub-menus\\n self.fulfilment = element.LinkButton(self, class_name='menu-fulfilment', alias='Fulfilment Menu')\\n self.orders = element.Link(self, class_name='submenu-orders', alias='Fulfilment->Orders Menu')\\n self.statistics = element.Link(self, class_name='submenu-statistics', alias='Fulfilment->Statistics Menu')\\n self.partners = element.Link(self, class_name='submenu-partners', alias='Fulfilment->Partners Menu')\\n # customers and sub-menus\\n self.customers = element.LinkButton(self, class_name='menu-customers', alias='Customers Menu')\\n self.customers_submenu = element.Link(self, class_name='submenu-customers', alias='Customers->Customers Menu')\\n self.deleted_accounts = element.Link(self, class_name='submenu-deleted_accounts',\\n alias='Customers->Deleted Accounts Menu')\\n self.stock_alert_requests = element.Link(self, class_name='submenu-stock-alert-requests-customers',\\n alias='Customers->Stock Alert Requests Menu')\\n # offers and sub-menus\\n self.offers = element.LinkButton(self, class_name='menu-offers', alias='Offers Menu')\\n self.offers_submenu = element.Link(self, class_name='submenu-offers', alias='Offers->Offers Menu')\\n self.vouchers = element.Link(self, class_name='submenu-vouchers', alias='Offers->Vouchers Menu')\\n # content and sub-menus\\n self.content_menu = element.LinkButton(self, class_name='menu-content', alias='Content Menu')\\n self.pages = element.Link(self, class_name='menu-pages', alias='Content->Pages Menu')\\n self.announcements = element.Link(self, class_name='menu-announcements', alias='Content->Announcements Menu')\\n self.reviews = element.Link(self, class_name='menu-reviews', alias='Content->Reviews Menu')\\n\\n self.reports = element.Link(self, class_name='menu-reports', alias='Reports Menu')\\n self.store_info = element.LinkButton(self, css_selector='.dropdown.store > a', alias='Store Info Menu')\\n self.partner_store = []\",\n \"def side_menu(self, screen):\\n font = pg.font.Font('freesansbold.ttf', 30) \\n text = font.render('Menu', True, (120,130,135))\\n textRect = text.get_rect()\\n textRect.center = (1100, 180)\\n screen.blit(text, textRect)\",\n \"def draw_title(self):\\n title = text_helper.create_text(\\\"Indefinite Loop\\\", menu_fonts, 50, white)\\n self.main_menu_surface.blit(title, (center_horizontally(title, self.screen_dimensions), 50))\",\n \"def controls_setup(self):\\n\\n self.new_items = element.TopMenuLink(self, alias='New Items Menu',\\n css_selector='#browse > li:nth-child(1) > a')\\n if self.fixed:\\n self.jewelry = element.TopMenuLink(self, alias='Jewelry Menu',\\n css_selector='#browse > li:nth-child(2) > a')\\n self.artwork = element.TopMenuLink(self, alias='Artwork Menu',\\n css_selector='#browse > li:nth-child(3) > a')\\n self.homeware = element.TopMenuLink(self, alias='Homeware Menu',\\n css_selector='#browse > li:nth-child(4) > a')\\n self.clothing = element.TopMenuLink(self, alias='Clothing Menu',\\n css_selector='#browse > li:nth-child(5) > a')\\n self.vintage = element.TopMenuLink(self, alias='Vintage Menu',\\n css_selector='#browse > li:nth-child(6) > a')\\n self.food = element.TopMenuLink(self, alias='Food Menu',\\n css_selector='#browse > li:nth-child(7) > a')\\n self.rings = element.SubMenuLink(self, alias='Jewelry->Rings Submenu',\\n css_selector='#browse > li:nth-child(2) > ul > li:nth-child(1) > a')\\n self.necklaces = element.SubMenuLink(self, alias='Jewelry->Necklaces Submenu',\\n css_selector='#browse > li:nth-child(2) > ul > li:nth-child(2) > a')\\n self.bracelets = element.SubMenuLink(self, alias='Jewelry->Bracelets Submenu',\\n css_selector='#browse > li:nth-child(2) > ul > li:nth-child(3) > a')\\n self.earrings = element.SubMenuLink(self, alias='Jewelry->Earrings Submenu',\\n css_selector='#browse > li:nth-child(2) > ul > li:nth-child(4) > a')\\n self.broaches = element.SubMenuLink(self, alias='Jewelry->Broaches and Pins Submenu',\\n css_selector='#browse > li:nth-child(2) > ul > li:nth-child(5) > a')\\n self.wall_art = element.SubMenuLink(self, alias='Artwork->Wall Art Submenu',\\n css_selector='#browse > li:nth-child(3) > ul > li:nth-child(1) > a')\\n self.ceramics = element.SubMenuLink(self, alias='Jewelry->Ceramics Submenu',\\n css_selector='#browse > li:nth-child(3) > ul > li:nth-child(2) > a')\\n self.sculpture = element.SubMenuLink(self, alias='Jewelry->Sculpture Submenu',\\n css_selector='#browse > li:nth-child(3) > ul > li:nth-child(3) > a')\\n self.living = element.SubMenuLink(self, alias='Homeware->Living Submenu',\\n css_selector='#browse > li:nth-child(4) > ul > li:nth-child(1) > a')\\n self.furniture = element.SubMenuLink(self, alias='Homeware->Furniture Submenu',\\n css_selector='#browse > li:nth-child(4) > ul > li:nth-child(2) > a')\\n self.kitchen = element.SubMenuLink(self, alias='Homeware->Kitchen Submenu',\\n css_selector='#browse > li:nth-child(4) > ul > li:nth-child(3) > a')\\n self.lighting = element.SubMenuLink(self, alias='Homeware->Lighting Submenu',\\n css_selector='#browse > li:nth-child(4) > ul > li:nth-child(4) > a')\\n self.outdoor = element.SubMenuLink(self, alias='Homeware->Outdoor Submenu',\\n css_selector='#browse > li:nth-child(4) > ul > li:nth-child(5) > a')\\n self.organize = element.SubMenuLink(self, alias='Homeware->Organize Submenu',\\n css_selector='#browse > li:nth-child(4) > ul > li:nth-child(6) > a')\\n self.party = element.SubMenuLink(self, alias='Homeware->Party Submenu',\\n css_selector='#browse > li:nth-child(4) > ul > li:nth-child(7) > a')\\n self.sub_food = element.SubMenuLink(self, alias='Homeware->Food Submenu',\\n css_selector='#browse > li:nth-child(4) > ul > li:nth-child(8) > a')\\n self.pets = element.SubMenuLink(self, alias='Homeware->For Pets Submenu',\\n css_selector='#browse > li:nth-child(4) > ul > li:nth-child(9) > a')\\n self.women = element.SubMenuLink(self, alias='Clothing->Women Submenu',\\n css_selector='#browse > li:nth-child(5) > ul > li:nth-child(1) > a')\\n self.men = element.SubMenuLink(self, alias='Clothing->Men Submenu',\\n css_selector='#browse > li:nth-child(5) > ul > li:nth-child(2) > a')\\n self.kids = element.SubMenuLink(self, alias='Clothing->Kids Submenu',\\n css_selector='#browse > li:nth-child(5) > ul > li:nth-child(3) > a')\\n self.pet_lovers = element.SubMenuLink(self, alias='Clothing->Pet Lovers Submenu',\\n css_selector='#browse > li:nth-child(5) > ul > li:nth-child(4) > a')\\n self.antiques = element.SubMenuLink(self, alias='Vintage->Antiques Submenu',\\n css_selector='#browse > li:nth-child(6) > ul > li:nth-child(1) > a')\\n self.accessories = element.SubMenuLink(self, alias='Vintage->Accessories Submenu',\\n css_selector='#browse > li:nth-child(6) > ul > li:nth-child(2) > a')\\n self.sub_clothing = element.SubMenuLink(self, alias='Vintage->Clothing Submenu',\\n css_selector='#browse > li:nth-child(6) > ul > li:nth-child(3) > a')\\n self.sub_homeware = element.SubMenuLink(self, alias='Vintage->Homeware Submenu',\\n css_selector='#browse > li:nth-child(6) > ul > li:nth-child(4) > a')\\n self.toys = element.SubMenuLink(self, alias='Vintage->Toys Submenu',\\n css_selector='#browse > li:nth-child(6) > ul > li:nth-child(5) > a')\\n self.books = element.SubMenuLink(self, alias='Vintage->Books Submenu',\\n css_selector='#browse > li:nth-child(6) > ul > li:nth-child(6) > a')\\n else:\\n self.menus = []\",\n \"def get_menus():\\n\\n pass\",\n \"def main_menu():\\n \\n print(\\\"\\\"\\\"\\n ( \\n )\\\\ ) ( ) \\n (()/( ( ( ( )\\\\ ) ( ( ( /( ( ( \\n /(_)))\\\\))( ( )( (()/( ( ))\\\\ ))\\\\ )\\\\()) ))\\\\ )( \\n (_)) ((_)()\\\\ )\\\\ (()\\\\ ((_)) )\\\\ /((_)/((_)((_)\\\\ /((_)(()\\\\ \\n / __|_(()((_) ((_) ((_) _| | ((_)(_)) (_)) | |(_)(_)) ((_) \\n \\\\__ \\\\\\\\ V V // _ \\\\| '_|/ _` | (_-</ -_)/ -_) | / / / -_) | '_| \\n |___/ \\\\_/\\\\_/ \\\\___/|_| \\\\__,_| /__/\\\\___|\\\\___| |_\\\\_\\\\ \\\\___| |_| )\\\"\\\"\\\")\\n for i in range(3):\\n print()\\n print(\\\"\\\"\\\" Main menu:\\n\\n (1) Play\\n (2) Highscores\\n (3) Exit\\n \\n (0) About authors\\\"\\\"\\\")\",\n \"def printMenu():\\n print(\\\"\\\\nBienvenido\\\")\\n print(\\\"1- Cargar Datos\\\")\\n print(\\\"2- Contar los elementos de la Lista\\\")\\n print(\\\"3- Contar elementos filtrados por palabra clave\\\")\\n print(\\\"4- Consultar elementos a partir de dos listas\\\")\\n print(\\\"5- Consultar buenas peliculas\\\")\\n print(\\\"0- Salir\\\")\",\n \"def __init__(self, win):\\n Menu.__init__(self, None, FONT_ADV, win)\",\n \"def draw_menu(self, menu):\\n for but in menu.get_buttons():\\n self.win.blit(but.image, but.rect)\",\n \"def draw_menu_buttons():\\n global menu_leds\\n \\n def draw_menu_led(x, y, color):\\n \\\"\\\"\\\"Draws menu lights to launchpad and stores them in menu_leds\\n \\n :param x: x-coord of led\\n :param y: y-coord of led\\n :param color: color of led\\n :return: None\\n \\\"\\\"\\\"\\n lp.led_ctrl_xy(x, y, *color)\\n if y != 0:\\n menu_leds[7 + y] = color\\n else:\\n menu_leds[x] = color\\n \\n # Clear all circular buttons\\n for i in range(8):\\n draw_menu_led(i, 0, (0, 0))\\n draw_menu_led(8, i + 1, (0, 0))\\n \\n # Color all menu buttons\\n draw_menu_led(*BUT_LEFT, (3, 3)) # Select buttons yellow\\n draw_menu_led(*BUT_RIGHT, (3, 3))\\n draw_menu_led(*BUT_START, (0, 3)) # Start button green\\n draw_menu_led(*BUT_QUIT, (3, 0)) # Quit button red\",\n \"def addBgMarkupcallback(self):\\n if self.currentSeedNameComboBox.currentIndex != self.currentSeedNameComboBox.count - 1:\\n self.lastLabelIndex = self.currentSeedNameComboBox.currentIndex\\n self.currentSeedNameComboBox.setCurrentIndex( self.currentSeedNameComboBox.count - 1 )\\n interactionNode = slicer.app.applicationLogic().GetInteractionNode()\\n interactionNode.SetCurrentInteractionMode(interactionNode.Place)\",\n \"def MakeCustomMenu(content): #py:MakeCustomMenu\\n RUR._MakeCustomMenu_(content)\",\n \"def hlp(self):\\r\\n \\r\\n self.menubar.entryconfig(\\\"Help\\\", state = 'disabled')\\r\\n self.menubar.entryconfig(\\\"File\\\", state = 'disabled')\\r\\n self.hlpframe = LabelFrame(self.master, text = \\\"Help & About\\\", font = ('impact', 10), width = 580, height = 662)\\r\\n self.hlpframe.place(x = 5, y = 5)\\r\\n self.abt = LabelFrame(self.hlpframe, width = 566, height = 111)\\r\\n self.abt.place(x = 5)\\r\\n self.abtproj = Label(self.abt, text = \\\"A Program for citating web pages using APA 6th edition, commonly used in social sciences:\\\")\\r\\n self.abtproj.place(x=35, y = 20)\\r\\n self.projtitle = Label(self.abt, text = \\\"APA 6th Edition Web Citation Generator\\\", font = ('impact', 25))\\r\\n self.projtitle.place(x = 16, y = 40)\\r\\n self.how2use = LabelFrame(self.hlpframe, text = \\\"How to use?\\\", font = ('impact', 8), width = 566, height = 400)\\r\\n self.how2use.place(x = 5, y = 110)\\r\\n self.inst1 = Label(self.how2use, text = \\\"◾ Enter the required details first and click \\\\\\\"Create\\\\\\\":\\\", justify = LEFT)\\r\\n self.inst1.place(x = 1, y = 1)\\r\\n self.inst1auth = Label(self.how2use, text = \\\"▸ You can choose if the article's author is one or more author or a group of authors.\\\", justify = LEFT)\\r\\n self.inst1auth.place(x = 25, y = 20)\\r\\n self.inst1auth2 = Label(self.how2use, text = \\\"▸ By adding another author, enter the name of the first author to activate the \\\\\\\"+\\\\\\\" button to add \\\\n another one.\\\", justify = LEFT)\\r\\n self.inst1auth2.place(x=25, y = 40)\\r\\n self.inst1dates = Label(self.how2use, text = \\\"▸ Click the dropdown menus of Months, Days, and Years to choose for the article's date of \\\\n publication and the date of when did you retrieved it.\\\",justify = LEFT)\\r\\n self.inst1dates.place(x=25, y= 75)\\r\\n self.inst1datestip = Label(self.how2use, text = \\\"▸ If the article does not have a date of publication, just leave it blank or you can set it to none, \\\\n same goes for retrieval date if you don't want to include it.\\\",justify = LEFT)\\r\\n self.inst1datestip.place(x = 25, y = 113)\\r\\n self.inst2edit = Label(self.how2use, text = \\\"◾ In Editable result you can make changes on your created citation:\\\", justify = LEFT)\\r\\n self.inst2edit.place(x = 1, y = 160)\\r\\n self.inst2conf = Label(self.how2use, text = \\\"▸ Click \\\\\\\"Confirm\\\\\\\" to save changes and move on to Final result or \\\\\\\"Discard\\\\\\\" to go back to creating \\\\n a new citation.\\\",justify = LEFT)\\r\\n self.inst2conf.place(x=25, y = 180)\\r\\n self.inst3final = Label(self.how2use, text = \\\"◾ The Final result adds indents and handles the citations alphabetically:\\\",justify = LEFT)\\r\\n self.inst3final.place(x = 1, y = 225)\\r\\n self.inst3add = Label(self.how2use, text = \\\"▸ To add another citation to the reference list click \\\\\\\"Add another citation!\\\\\\\".\\\",justify = LEFT)\\r\\n self.inst3add.place(x = 25, y = 245)\\r\\n self.inst3dis = Label(self.how2use, text = \\\"▸ If you clicked \\\\\\\"Clear all\\\\\\\" it'll prompt you to clear the reference list, once confirmed it cannot \\\\n be undone.\\\", justify = LEFT)\\r\\n self.inst3dis.place(x = 25, y = 265)\\r\\n self.inst4sav = Label(self.how2use, text = \\\"◾ Saving the reference list by \\\\\\\".txt\\\\\\\" or \\\\\\\".doc\\\\\\\" type:\\\",justify = LEFT)\\r\\n self.inst4sav.place(x = 1, y = 310)\\r\\n self.inst4how = Label(self.how2use, text = \\\"▸ You can save the reference list by going to \\\\\\\"File\\\\\\\", \\\\\\\"Save as...\\\\\\\".\\\", justify=LEFT)\\r\\n self.inst4how.place(x = 25, y = 330)\\r\\n self.inst4note = Label(self.how2use, text = \\\"▸ Once the file has been saved, you can't make changes on it using the program.\\\", justify = LEFT)\\r\\n self.inst4note.place(x = 25, y = 350)\\r\\n self.lbl = LabelFrame(self.hlpframe, width = 566, height = 55)\\r\\n self.lbl.place(x = 5, y = 515)\\r\\n self.built = Label(self.lbl, text = \\\"Built using Python\\\", font = 'impact')\\r\\n self.built.place(x = 225)\\r\\n self.jp = Label(self.lbl, text = \\\"John Paul G. Zoleta Bachelor of Science in Computer Science College of Mary Immaculate\\\")\\r\\n self.jp.place(x =25, y = 25)\\r\\n\\r\\n self.ok = Button(self.hlpframe, text = \\\"Okay ⌐■_■\\\",activebackground = 'gray', command = self.hlpframeclear, width = 79, height = 3)\\r\\n self.ok.place(x = 6, y = 575)\",\n \"def create_menu(self, root):\\n menubar = Menu(root)\\n root['menu'] = menubar\\n\\n menu_file = Menu(menubar)\\n menu_run = Menu(menubar)\\n menu_folders = Menu(menubar)\\n menu_links = Menu(menubar)\\n menu_help = Menu(menubar)\\n menu_beta = Menu(menubar)\\n menubar.add_cascade(menu=menu_file, label='File')\\n menubar.add_cascade(menu=menu_run, label='Run')\\n menubar.add_cascade(menu=menu_folders, label='Folders')\\n menubar.add_cascade(menu=menu_links, label='Links')\\n menubar.add_cascade(menu=menu_help, label='Help')\\n menubar.add_cascade(menu=menu_beta, label='Experimental')\\n\\n menu_file.add_command(\\n label='Re-load param set', command=self.load_params,\\n accelerator='Ctrl+L')\\n menu_file.add_command(\\n label='Re-save param set', command=self.save_params,\\n accelerator='Ctrl+S')\\n menu_file.add_command(\\n label='Output log', command=lambda: LogWindow(self.root))\\n if sys.platform != 'darwin':\\n menu_file.add_command(\\n label='Exit', command=self.exit_program, accelerator='Alt+F4')\\n root.bind_all('<Control-l>', lambda e: self.load_params())\\n root.bind_all('<Control-s>', lambda e: self.save_params())\\n\\n menu_run.add_command(\\n label='Dwarf Fortress', command=self.lnp.run_df,\\n accelerator='Ctrl+R')\\n menu_run.add_command(\\n label='Init Editor', command=self.run_init, accelerator='Ctrl+I')\\n root.bind_all('<Control-r>', lambda e: self.lnp.run_df())\\n root.bind_all('<Control-i>', lambda e: self.run_init())\\n\\n menu_folders.add_command(\\n label='Savegame Folder', command=self.lnp.open_savegames)\\n menu_folders.add_command(\\n label='Utilities Folder', command=self.lnp.open_utils)\\n menu_folders.add_command(\\n label='Graphics Folder', command=self.lnp.open_graphics)\\n menu_folders.add_separator()\\n menu_folders.add_command(\\n label='Main Folder', command=self.lnp.open_main_folder)\\n menu_folders.add_command(\\n label='LNP Folder', command=self.lnp.open_lnp_folder)\\n menu_folders.add_command(\\n label='Dwarf Fortress Folder', command=self.lnp.open_df_folder)\\n menu_folders.add_command(\\n label='Init Folder', command=self.lnp.open_init_folder)\\n\\n menu_links.add_command(\\n label=\\\"DF Homepage\\\", command=self.lnp.open_df_web)\\n menu_links.add_command(label=\\\"DF Wiki\\\", command=self.lnp.open_wiki)\\n menu_links.add_command(label=\\\"DF Forums\\\", command=self.lnp.open_forums)\\n\\n menu_help.add_command(\\n label=\\\"Help\\\", command=self.show_help, accelerator='F1')\\n menu_help.add_command(\\n label=\\\"About\\\", command=self.show_about, accelerator='Alt+F1')\\n root.bind_all('<F1>', lambda e: self.show_help())\\n root.bind_all('<Alt-F1>', lambda e: self.show_about())\\n root.createcommand('tkAboutDialog', self.show_about)\\n\\n menu_beta.add_command(\\n label='Toggle graphics pack patching', command=self.toggle_patching)\",\n \"def create_menu(self):\\n about = gtk.ImageMenuItem(gtk.STOCK_ABOUT)\\n about.connect_object('activate', self.about, 'about')\\n about.show()\\n\\n# prefs = gtk.ImageMenuItem(gtk.STOCK_PREFERENCES)\\n# prefs.connect_object('activate', self.prefs, 'prefs')\\n# prefs.show()\\n\\n quit = gtk.ImageMenuItem(gtk.STOCK_QUIT)\\n quit.connect_object('activate', self.exit, 'quit')\\n quit.show()\\n\\n menu = gtk.Menu()\\n menu.append(about)\\n# menu.append(prefs)\\n menu.append(quit)\\n return menu\",\n \"def __admin_menu(self):\\n log.debug(\\\"Displaying __admin_menu\\\")\\n self.menu = TelegramMenu(\\\"config/comunda_admin_menu.bpmn\\\", self, \\\"MenuStart\\\")\\n self.menu.admin_menu(\\\"MenuStart\\\", \\\"menu_admin_main_txt\\\")\\n return\",\n \"def menu(self):\\n self.font = pygame.font.SysFont(\\\"comicsansms\\\", 72)\\n i = 0\\n self.window.fill((60,50,20))\\n self.menu_buttons = [0,0,0]\\n for i in range(3):\\n self.menu_buttons[i] = pygame.draw.rect(self.window, (255, 255, 255), (270, 200+i*120, 300, 100)) \\n text = self.font.render(\\\"H x H\\\", True, (0, 128, 0))\\n self.window.blit(text, (330, 200))\\n text = self.font.render(\\\"H x C\\\", True, (0, 128, 0))\\n self.window.blit(text, (330, 320))\\n text = self.font.render(\\\"C x C\\\", True, (0, 128, 0))\\n self.window.blit(text, (330, 440))\\n pygame.display.flip()\\n while True: \\n for event in pygame.event.get():\\n if event.type == pygame.QUIT:\\n self.quit = True\\n return\\n elif event.type == pygame.KEYDOWN:\\n if evenvt.key == pygame.K_r:\\n self.play()\\n elif event.key == pygame.K_t:\\n self.tip()\\n elif event.key == pygame.K_m:\\n self.__init__()\\n elif event.type == pygame.MOUSEBUTTONDOWN and event.button == 1:\\n pos = pygame.mouse.get_pos()\\n for i in range(len(self.menu_buttons)):\\n if self.menu_buttons[i].collidepoint(pos):\\n y_pos=pos[1]\\n difficulty=0\\n if y_pos<=300 and y_pos>=200:\\n return [1,difficulty]\\n elif y_pos<=420 and y_pos>=320:\\n difficulty=self.difficulty_menu(\\\"Computer difficulty:\\\")\\n return [2,difficulty]\\n elif y_pos<=540 and y_pos>=420:\\n difficulty_bot1=self.difficulty_menu(\\\"Computer 1 difficulty:\\\")\\n difficulty_bot2=self.difficulty_menu(\\\"Computer 2 difficulty:\\\")\\n return [3,difficulty_bot1, difficulty_bot2]\",\n \"def __init__(self, win):\\n Menu.__init__(self, ['START', 'QUIT'], FONT_LA_CASA, win)\",\n \"def build_labels():\\n l_title = GLabel('Which one is Karel?')\\n l_title.font = 'Courier-25'\\n l_title.color = 'black'\\n window.add(l_title, x=260, y=60)\\n l_num = GLabel('19')\\n l_num.font = 'Courier-50'\\n l_num.color = 'whitesmoke'\\n window.add(l_num, x=37, y=242)\\n l_skip = GLabel('skip')\\n l_skip.font = 'Courier-20'\\n l_skip.color = 'whitesmoke'\\n window.add(l_skip, x=726, y=152)\\n l_ans1 = GLabel('Answers')\\n l_ans1.font = 'Courier-20-italic'\\n l_ans1.color = 'black'\\n window.add(l_ans1, x=698, y=270)\\n l_ans2 = GLabel('0')\\n l_ans2.font = 'Courier-50-italic'\\n l_ans2.color = 'black'\\n window.add(l_ans2, x=722, y=252)\\n l_game_pin = GLabel('Game PIN: SC101')\\n l_game_pin.font = 'Courier-20'\\n l_game_pin.color = 'black'\\n window.add(l_game_pin, x=20, y=540)\\n l_1 = GPolygon()\\n l_1.add_vertex((210, 360))\\n l_1.add_vertex((197, 380))\\n l_1.add_vertex((221, 380))\\n l_1.filled = True\\n l_1.color = 'whitesmoke'\\n l_1.fill_color= 'whitesmoke'\\n window.add(l_1)\\n l_2_1 = GPolygon()\\n l_2_1.add_vertex((210+380, 359))\\n l_2_1.add_vertex((198+380, 370))\\n l_2_1.add_vertex((221+380, 370))\\n l_2_1.filled = True\\n l_2_1.fill_color = 'whitesmoke'\\n l_2_1.color = 'whitesmoke'\\n window.add(l_2_1)\\n l_2_2 = GPolygon()\\n l_2_2.add_vertex((210+380, 381))\\n l_2_2.add_vertex((198+380, 370))\\n l_2_2.add_vertex((221+380, 370))\\n l_2_2.filled = True\\n l_2_2.fill_color = 'whitesmoke'\\n l_2_2.color = 'whitesmoke'\\n window.add(l_2_2)\\n l_3 = GOval(23, 23, x=198, y=450)\\n l_3.filled = True\\n l_3.fill_color = 'whitesmoke'\\n l_3.color = 'whitesmoke'\\n window.add(l_3)\\n l_4 = GRect(20, 20, x=583, y=450)\\n l_4.filled = True\\n l_4.fill_color = 'whitesmoke'\\n l_4.color = 'whitesmoke'\\n window.add(l_4)\",\n \"def render(self):\\n self.delete()\\n self.__create_background(self._imfname)\\n # XXX must be last after successor implementation, but works without this line\\n #self.c.event_generate(\\\"<Configure>\\\")\\n #self.c.update_idletasks()\",\n \"def create_animation_menu(master: Widget) -> None:\\r\\n\\r\\n def create_gen_labels(master: Widget) -> None:\\r\\n \\\"\\\"\\\"Create generation labels with the parent MASTER.\\\"\\\"\\\"\\r\\n\\r\\n gen_label = Label(master, text='Gen:', font=self.FONT_NORMAL, bg=self.MAIN_BG)\\r\\n gen_label.pack(side=LEFT)\\r\\n self.gen_number = Label(master, text=0, font=self.FONT_NORMAL, bg=self.MAIN_BG)\\r\\n self.gen_number.pack(side=LEFT)\\r\\n\\r\\n def create_rule_labels(master: Widget) -> None:\\r\\n \\\"\\\"\\\"Create rule labels with the parent MASTER.\\\"\\\"\\\"\\r\\n\\r\\n rule_label = Label(master, text='Rule:', font=self.FONT_NORMAL, bg=self.MAIN_BG)\\r\\n rule_label.pack(side=LEFT, padx=(50,0))\\r\\n self.rule_name = Label(master, text=self.INITIAL_RULE, font=self.FONT_NORMAL,\\r\\n bg=self.MAIN_BG)\\r\\n self.rule_name.pack(side=LEFT)\\r\\n\\r\\n def create_anim_buttons(master: Widget) -> None:\\r\\n \\\"\\\"\\\"Create animation buttons with the parent MASTER.\\\"\\\"\\\"\\r\\n\\r\\n self.reset_button = Button(master, text='Reset', font=self.FONT_NORMAL,\\r\\n command=self.on_reset)\\r\\n self.reset_button.pack(side=RIGHT)\\r\\n self.step_button = Button(master, text='Step', font=self.FONT_NORMAL,\\r\\n command=self.on_step)\\r\\n self.step_button.pack(side=RIGHT, padx=self.WIDGET_PAD)\\r\\n self.play_button = Button(master, text='Play', font=self.FONT_NORMAL,\\r\\n command=self.on_play)\\r\\n self.play_button.pack(side=RIGHT)\\r\\n\\r\\n animation_menu = Frame(master, bg=self.MAIN_BG, pady=self.WIDGET_PAD)\\r\\n animation_menu.pack(side=TOP, fill=X)\\r\\n\\r\\n create_gen_labels(animation_menu)\\r\\n create_rule_labels(animation_menu)\\r\\n create_anim_buttons(animation_menu)\\r\\n TkState.disable([self.play_button, self.step_button, self.reset_button])\",\n \"def printMenu():\\n # tWelc = PrettyTable(['Welcome to the CLI-of the repository classifier'])\\n print('Welcome to the CLI of the repository classifier')\\n print(strStopper1)\\n t = PrettyTable(['Action', ' Shortcut '])\\n t.add_row(['Show Menu', '- m -'])\\n t.add_row([' Predict repositories form txt-file ', '- i -'])\\n t.add_row(['Input URL', '- u -'])\\n t.add_row(['Show Info', '- f -'])\\n t.add_row(['Train Model', '- t -'])\\n t.add_row(['set GitHub-Token', '- g -'])\\n t.add_row(['Help', '- h -'])\\n t.add_row(['Quit', '- q -'])\\n print(t)\\n print('')\",\n \"def display_menu(self):\\n menu_view = self.get_view.menu_view\\n menu_view.blit_background()\\n menu_view.draw_menu_view()\\n menu_view.update_display()\",\n \"def menu(self):\\n from mainmenu import Menu\\n gm = Menu(self.screen)\\n gm.run()\",\n \"def draw(self, context):\\n layout = self.layout\\n pie = layout.menu_pie()\\n\\n pie.operator(\\\"wm.call_menu_pie\\\", text=\\\"Images\\\", icon='IMAGE_COL').name = \\\"PieAreaViewsImage\\\"\\n pie.operator(\\\"wm.call_menu_pie\\\", text=\\\"Anim\\\", icon='IPO').name = \\\"PieAreaViewsAnim\\\"\\n pie.operator(\\\"screen.screen_full_area\\\", text=\\\"Full Screen\\\", icon='FULLSCREEN_ENTER')\\n pie.operator(\\\"object.view_menu\\\", text=\\\"3D View\\\", icon='VIEW3D').vp = \\\"VIEW_3D\\\"\\n\\n # DIAGONALS\\n pie.operator(\\\"wm.call_menu_pie\\\", text=\\\"Utils\\\", icon='BUTS').name = \\\"PieAreaViewsUtils\\\"\\n pie.separator()\\n pie.operator(\\\"area.joinarea\\\", text=\\\"Join\\\", icon='X')\\n pie.operator(\\\"wm.call_menu_pie\\\", text=\\\"Split\\\", icon='SPLITSCREEN').name = \\\"pie.split_viewport\\\"\",\n \"def create_main(self: object) -> None:\\n\\n welcome_msg = \\\"Welcome to dnazip! \\\\n A graphical \\\" + \\\\\\n \\\"representation of Burros-Wheeler and Huffman \\\" + \\\\\\n \\\"Coding algorithms\\\"\\n rand = \\\"You don't have a DNA sequence? we'll generate a small \\\" + \\\\\\n \\\"random one for you\\\"\\n Label(self, text=welcome_msg, bg='#ebebeb', font=(None, 15)).grid(row=0, columnspan=3, padx=5, pady=6)\\n Label(self, text=\\\"Compression\\\", bg='#ebebeb', borderwidth=2, relief=\\\"groove\\\", font=(None, 13, 'bold')).grid(row=1, columnspan=3, padx=5, pady=6)\\n Label(self, text=\\\"Decompression\\\", bg='#ebebeb', borderwidth=2, relief=\\\"groove\\\", font=(None, 13, 'bold')).grid(row=5, columnspan=3, padx=5, pady=6)\\n separator_one = ttk.Separator(self, orient='horizontal')\\n separator_one.grid(row=4, columnspan=3, ipadx=500, padx=5, pady=6) \\n Label(self, text=rand, bg='#ebebeb', font=(None, 12, 'bold')).grid(row=8, columnspan=3, padx=5, pady=6)\\n self.random = StringVar()\\n self.entry = Entry(self, textvariable=self.random, width=50)\\n self.entry.grid(row=9, columnspan=3, padx=5, pady=10)\\n separator_two = ttk.Separator(self, orient='horizontal')\\n separator_two.grid(row=7, columnspan=3, ipadx=500, padx=5, pady=6)\",\n \"def setup(self):\\n self.ui_manager.purge_ui_elements()\\n\\n button = buttons.MenuButton(\\n 'Menu',\\n center_x=self.window.width // 2,\\n center_y=self.window.height // 9,\\n width=200,\\n user=self.user.text\\n )\\n button.set_style_attrs(\\n bg_color_hover=(159, 255, 233),\\n bg_color_press=(51, 201, 166),\\n )\\n self.ui_manager.add_ui_element(button)\",\n \"def build_pcb(self, bg=None):\",\n \"def MainMenu():\\n\\n # You have to open an object container to produce the icons you want to appear on this page.\\n oc = ObjectContainer()\\n main_list = [('New Videos', ''), ('Best Videos', '/best'), ('Pornstars', '/pornstars')]\\n for pt, h in main_list:\\n oc.add(DirectoryObject(\\n key=Callback(ShowHTML, pTitle=pt, href=h),\\n title=pt, thumb=Callback(GetThumb, url=BASE_URL)))\\n oc.add(DirectoryObject(\\n key=Callback(ShowHTML, pTitle=\\\"Pornstars\\\", href='/channels'),\\n title=\\\"Channels\\\", thumb=Callback(GetThumb, url=BASE_URL)))\\n\\n oc.add(InputDirectoryObject(key=Callback(Search), title='Search Videos', prompt='Search Videos'))\\n\\n return oc\",\n \"def _createColorProfileMenuItems(node):\\n pass\",\n \"def _create_menu(self):\\n menubar = Menu(self.root)\\n # Game dropdown menu\\n gamemenu = Menu(menubar, tearoff=0)\\n gamemenu.add_command(\\n label='New Game', command=self.new_game)\\n gamemenu.add_separator()\\n gamemenu.add_command(label='Exit', command=self.root.quit)\\n menubar.add_cascade(label='Game', menu=gamemenu)\\n\\n # Options dropdown menu\\n optionmenu = Menu(menubar, tearoff=0)\\n optionmenu.add_command(label='Scattershot', command=self.game.spray)\\n optionmenu.add_command(label='All Ships Attack', command=None)\\n menubar.add_cascade(label='Options', menu=optionmenu)\\n\\n # Help dropdown menu\\n helpmenu = Menu(menubar, tearoff=0)\\n helpmenu.add_command(label='Help Index', command=None)\\n helpmenu.add_command(label='About...', command=None)\\n menubar.add_cascade(label='Help', menu=helpmenu)\\n\\n self.root.config(menu=menubar)\",\n \"def build_UI(self):\\n\\n #Common local coordinates to change the UI positions\\n common_x = 0\\n common_y = 5\\n\\n #Create the Main Title\\n self.titleFont = font.Font(family = FONTS[\\\"lucida grande\\\"], size = 30)\\n self.title = cGUIf.get_TextLabel(self,\\n \\\"File Converter App \\\",\\n self.titleFont,\\n 135 + common_x,\\n 40 + common_y)\\n\\n #Add the \\\"Main Icon\\\"\\n self.mainIcon = cGUIf.get_ImgLabel(self,\\n self.__pics[\\\"main_icon\\\"],\\n 280 + common_x,\\n 125 + common_y)\\n\\n\\n #Create a subtitle that says \\\"options\\\"\\n self.subtitleFont = font.Font(family = FONTS[\\\"courier new\\\"], size = 22)\\n self.subtitle = cGUIf.get_TextLabel(self,\\n \\\"Options\\\",\\n self.subtitleFont,\\n 240 + common_x,\\n 195 + common_y)\\n\\n #Create a label that says \\\"Image Conversion\\\"\\n self.conversionFont = font.Font(family = FONTS[\\\"times new roman\\\"], size = 15)\\n self.imageConversionLabel = cGUIf.get_TextLabel(self,\\n \\\" Image\\\\n Conversion\\\",\\n self.conversionFont,\\n 60 + common_x,\\n 285 + common_y)\\n\\n #Create a button for Image Conversion\\n self.imageButton = cGUIf.get_Button(self,\\n \\\"\\\",\\n lambda : self.switch_frames(\\\"image_frame\\\"),\\n 190 + common_x,\\n 270 + common_y)\\n self.imageButton.configure(image = self.__pics[\\\"image_icon\\\"])\\n\\n #Create a label that says \\\"Audio Conversion\\\"\\n self.audioConversionLabel = cGUIf.get_TextLabel(self,\\n \\\" Audio\\\\n Conversion\\\",\\n self.conversionFont,\\n 440 + common_x,\\n 285 + common_y)\\n\\n #Create a button for Audio Conversion\\n self.audioButton = cGUIf.get_Button(self,\\n \\\"\\\",\\n lambda : self.switch_frames(\\\"audio_frame\\\"),\\n 340 + common_x,\\n 270 + common_y)\\n self.audioButton.configure(image = self.__pics[\\\"audio_icon\\\"])\\n\\n #Create a label that says \\\"Doc Conversion\\\"\\n self.docConversionLabel = cGUIf.get_TextLabel(self,\\n \\\" Doc\\\\n Conversion\\\",\\n self.conversionFont,\\n 60 + common_x,\\n 410 + common_y)\\n\\n\\n #Create a button for Doc Conversion\\n self.docButton = cGUIf.get_Button(self,\\n \\\"\\\",\\n lambda : self.switch_frames(\\\"doc_frame\\\"),\\n 190 + common_x,\\n 400 + common_y)\\n self.docButton.configure(image = self.__pics[\\\"doc_icon\\\"])\\n\\n\\n #Create a label that says \\\"Video Conversion\\\"\\n self.videoConversionLabel = cGUIf.get_TextLabel(self,\\n \\\" Video\\\\n Conversion\\\",\\n self.conversionFont,\\n 440 + common_x,\\n 410 + common_y)\\n\\n #Create a button for Video Conversion\\n self.videoButton = cGUIf.get_Button(self,\\n \\\"\\\",\\n lambda : self.switch_frames(\\\"video_frame\\\"),\\n 340 + common_x,\\n 400 + common_y)\\n self.videoButton.configure(image = self.__pics[\\\"video_icon\\\"])\",\n \"def menu():\\n #On créer une nouvelle fenetre\\n win = createNewWin(curses)\\n\\n #On charge le logo et on l'affiche\\n logoLines = loadLogo()\\n i = 1\\n for line in logoLines:\\n win.addstr(i, 16, line, curses.color_pair(2))\\n i+=1\\n win.border(0)\\n\\n chooseMenu = 0 \\n\\n #On ajoute les entrées\\n win.addstr(1, 4, 'SnakePY', curses.color_pair(1))\\n win.addstr(2, 4, 'Choose option', curses.color_pair(1))\\n win.addstr(3, 4, '1. Launch game', curses.color_pair(1))\\n win.addstr(4, 4, '2. Show HighScore', curses.color_pair(1))\\n win.addstr(5, 4, '3. Credits', curses.color_pair(1))\\n win.addstr(6, 4, '4. Exit game', curses.color_pair(1))\\n win.addstr(7, 4, '')\\n\\n #On effectue une boucle\\n while chooseMenu != ord('4'):\\n #On récupere la touche appuyée par l'utilisateur\\n chooseMenu = win.getch()\\n #Si il s'agit du 1 ...\\n if chooseMenu == ord('1'):\\n #...on lance le jeu\\n destroyWin()\\n launchGame()\\n #Si il s'agit du 2 ...\\n elif chooseMenu == ord('2'):\\n #...on affiche le highscore\\n destroyWin()\\n showHG()\\n #Si il s'agit du 3 ...\\n elif chooseMenu == ord('3'):\\n #... on affiche les crédits\\n destroyWin()\\n showCredits()\\n #Si on sort de la boucle (4), alors on\\n #détruit les fenetres\\n destroyWin()\\n\\n #On ferme toutes les fenêtres de curses\\n closeCurse()\",\n \"def setup(self):\\n self.ui_manager.purge_ui_elements()\\n\\n button = buttons.MenuButton(\\n 'Menu',\\n center_x=self.window.width // 2,\\n center_y=self.window.height // 6,\\n width=200,\\n user=self.user.text\\n )\\n button.set_style_attrs(\\n bg_color=(255, 153, 204),\\n bg_color_hover=(255, 102, 178),\\n bg_color_press=(204, 0, 102),\\n )\\n self.ui_manager.add_ui_element(button)\",\n \"def menu(self):\\n ## This is a DICTIONARY, it's a list with custom index values. Python is cool.\\n # Please feel free to change the menu and add options.\\n print(\\\"\\\\n *** MENU ***\\\") \\n menu = {\\\"c\\\": (\\\"Calibrate\\\", self.calibrate),\\n \\\"d\\\": (\\\"Dance\\\", self.dance),\\n \\\"h\\\": (\\\"Hold position\\\", self.hold_position),\\n \\\"n\\\": (\\\"Navigate\\\", self.nav),\\n \\\"o\\\": (\\\"Obstacle count\\\", self.obstacle_count),\\n \\\"q\\\": (\\\"Quit\\\", self.quit),\\n \\\"v\\\": (\\\"Veer\\\", self.slither)\\n }\\n # loop and print the menu...\\n for key in sorted(menu.keys()):\\n print(key + \\\":\\\" + menu[key][0])\\n # store the user's answer\\n ans = str.lower(input(\\\"Your selection: \\\"))\\n # activate the item selected\\n menu.get(ans, [None, self.quit])[1]()\",\n \"def display_menu(self):\\n print(\\\"\\\"\\\"\\nLogistic System Menu\\n1. Add Vehicles\\n2. Add Item To The Cart\\n3. Complete The Order\\n4. Track The Order\\n5. Quit \\\"\\\"\\\")\",\n \"def _helpmenu_about():\\n self.helpindex = Toplevel(self.master)\\n self.helpindex.title(\\\"About\\\")\\n self.helpindex.geometry(\\\"500x300\\\")\\n self.helpindex.label()\",\n \"def CreateLayout(self):\\n # Defines the title of the Dialog\\n self.SetTitle(\\\"A Custom Dialog with a Top Menu\\\")\\n\\n # Flushes all the already existing menu to create our one. The content will be on the left.\\n self.MenuFlushAll()\\n\\n # Creates a Sub menu begin to insert new menu entry\\n self.MenuSubBegin(\\\"Left Menu\\\")\\n\\n # Adds a string with a given ID, so it will trigger a call to Command once clicked\\n self.MenuAddString(self.ID_LEFT_MENU_FIRST_ITEM, \\\"Close\\\")\\n\\n # Finalizes the Sub Menu\\n self.MenuSubEnd()\\n\\n # Finalizes the menu\\n self.MenuFinished()\\n\\n # Creates a Group in the Menu. The content will be on the right\\n if self.GroupBeginInMenuLine():\\n # Creates a BitmapButtonCustomGui with the find icon\\n settings = c4d.BaseContainer()\\n settings[c4d.BITMAPBUTTON_BUTTON] = True\\n settings[c4d.BITMAPBUTTON_BORDER] = False\\n settings[c4d.BITMAPBUTTON_TOGGLE] = True\\n settings[c4d.BITMAPBUTTON_ICONID1] = c4d.RESOURCEIMAGE_SCENEBROWSER_FIND2\\n settings[c4d.BITMAPBUTTON_ICONID2] = c4d.RESOURCEIMAGE_SCENEBROWSER_FIND1\\n\\n self.displayContentButtonDlg = self.AddCustomGui(self.ID_RIGHT_MENU_SHOW_CONTENT,\\n c4d.CUSTOMGUI_BITMAPBUTTON, \\\"\\\",\\n c4d.BFH_CENTER | c4d.BFV_CENTER, 0, 0, settings)\\n\\n self.GroupEnd()\\n\\n # Creates a group that will contain the content that will be hidden when the BitmapButton is pressed. It's\\n # important to have a parent group to the group that needs to be hidden since you need to redraw this parent\\n # group after the visibility definition.\\n if self.GroupBegin(self.ID_MAIN_GROUP, c4d.BFH_LEFT | c4d.BFV_CENTER):\\n\\n # The group that will be hidden\\n if self.GroupBegin(self.ID_HIDDEN_GROUP, c4d.BFH_LEFT | c4d.BFV_CENTER):\\n # Adds the content you want to toggle\\n self.AddStaticText(0, c4d.BFH_LEFT | c4d.BFV_CENTER, name=\\\"test\\\")\\n\\n self.GroupEnd()\\n self.GroupEnd()\\n\\n # Adds two buttons, Ok and Cancel\\n self.AddDlgGroup(c4d.DLG_OK | c4d.DLG_CANCEL)\\n\\n return True\",\n \"def get_all_menu():\",\n \"def create_menu_set(name):\\n\\t\\t\\tmenu = getattr(self, name + \\\"_menu\\\")\\n\\t\\t\\ttv = getattr(self, \\\"tv_\\\" + name)\\n\\t\\t\\tcid_index = getattr(self, \\\"cid_\\\" + name)\\n\\n\\t\\t\\t# bind menu helper\\n\\t\\t\\tdef bind_menu(label):\\n\\t\\t\\t\\tdef bind_menu_inner(func):\\n\\t\\t\\t\\t\\tmenu.add_command(label=label, command=func)\\n\\t\\t\\t\\t\\treturn func\\n\\t\\t\\t\\treturn bind_menu_inner\\n\\n\\t\\t\\t# add commands...\\n\\t\\t\\t@bind_menu(\\\"刪除\\\")\\n\\t\\t\\tdef tvdelete():\\n\\t\\t\\t\\tif messagebox.askyesno(\\\"Comic Crawler\\\", \\\"確定刪除？\\\"):\\n\\t\\t\\t\\t\\tselected = tv.selection()\\n\\t\\t\\t\\t\\tself.remove(name, *[cid_index[cid] for cid in selected])\\n\\n\\t\\t\\t@bind_menu(\\\"移至頂部\\\")\\n\\t\\t\\tdef tvlift():\\n\\t\\t\\t\\tselected = tv.selection()\\n\\t\\t\\t\\tself.downloader.mission_manager.lift(name, *[cid_index[cid] for cid in selected])\\n\\n\\t\\t\\t@bind_menu(\\\"移至底部\\\")\\n\\t\\t\\tdef tvdrop():\\n\\t\\t\\t\\tselected = tv.selection()\\n\\t\\t\\t\\tself.downloader.mission_manager.drop(name, *[cid_index[cid] for cid in selected])\\n\\n\\t\\t\\t@bind_menu(\\\"改名\\\")\\n\\t\\t\\tdef tvchangetitle():\\n\\t\\t\\t\\tselected = tv.selection()\\n\\t\\t\\t\\tmission = cid_index[selected[0]]\\n\\t\\t\\t\\tselect_title(self.root, mission)\\n\\n\\t\\t\\t@bind_menu(\\\"重新選擇集數\\\")\\n\\t\\t\\tdef tvReselectEP():\\n\\t\\t\\t\\ts = tv.selection()\\n\\t\\t\\t\\tmissions = [ cid_index[i] for i in s ]\\n\\t\\t\\t\\tfor mission in missions:\\n\\t\\t\\t\\t\\treselect_episodes(self.root, mission)\\n\\n\\t\\t\\t@bind_menu(\\\"開啟資料夾\\\")\\n\\t\\t\\tdef tvOpen():\\n\\t\\t\\t\\ts = tv.selection()\\n\\t\\t\\t\\tmissions = [ cid_index[i] for i in s ]\\n\\t\\t\\t\\tsavepath = setting[\\\"savepath\\\"]\\n\\t\\t\\t\\tfor mission in missions:\\n\\t\\t\\t\\t\\tfolder = os.path.join(savepath, safefilepath(mission.title))\\n\\t\\t\\t\\t\\tos.startfile(os.path.expanduser(folder))\\n\\n\\t\\t\\t@bind_menu(\\\"開啟網頁\\\")\\n\\t\\t\\tdef tvOpenBrowser():\\n\\t\\t\\t\\ts = tv.selection()\\n\\t\\t\\t\\tmissions = [ cid_index[i] for i in s ]\\n\\t\\t\\t\\tfor mission in missions:\\n\\t\\t\\t\\t\\twebbrowser.open(mission.url)\\n\\n\\t\\t\\tif name == \\\"view\\\":\\n\\t\\t\\t\\t@bind_menu(\\\"加入圖書館\\\")\\n\\t\\t\\t\\tdef tvAddToLib():\\n\\t\\t\\t\\t\\ts = tv.selection()\\n\\t\\t\\t\\t\\tmissions = [ cid_index[i] for i in s ]\\n\\t\\t\\t\\t\\ttitles = [ m.title for m in missions ]\\n\\t\\t\\t\\t\\tself.downloader.mission_manager.add(\\\"library\\\", *missions)\\n\\t\\t\\t\\t\\tsafeprint(\\\"已加入圖書館︰{}\\\".format(\\\", \\\".join(titles)))\\n\\n\\t\\t\\t# menu call\\n\\t\\t\\tdef tvmenucall(event):\\n\\t\\t\\t\\tmenu.post(event.x_root, event.y_root)\\n\\t\\t\\ttv.bind(\\\"<Button-3>\\\", tvmenucall)\",\n \"def build_schematic(self, bg=None):\",\n \"def __init__(self: object) -> None:\\n super().__init__()\\n self.title(\\\"dnazip\\\")\\n self.configure(bg='#ebebeb')\\n self.create_main()\\n self.create_menu()\\n self.create_buttons()\\n self.file = None\",\n \"def _render(self):\\n self._renderer.render_menu()\\n pg.display.update()\",\n \"def render(self,session,context):\\n #TODO: at some point this should actually get implemented, but to do so\\n # would need to convert self.menu_items from list to a dict, where the\\n # keys are the items that should be selected in the menu. \\n # to do so would need to update MenuItem as well.\\n menu = [] if self.title_str == '' else [\\\" %s\\\" % self.title_str]\\n for idx , item in enumerate(self.menu_items):\\n if item.hide_index: menu_text = str(item)\\n else:\\n menu_text = \\\"%s. %s\\\"%(item.custom_index, item)\\n menu.append( menu_text )\\n return \\\"\\\\n\\\".join(menu)\",\n \"def menuItem(*args):\\n\\toptionsWindow()\",\n \"def create_options(self, n):\\n f = Frame(n)\\n f.pack(side=TOP, fill=BOTH, expand=Y)\\n options = Labelframe(f, text='Options')\\n options.pack(side=TOP, fill=BOTH, expand=Y)\\n Grid.columnconfigure(options, 0, weight=1)\\n Grid.columnconfigure(options, 1, weight=1)\\n\\n population = Button(\\n options, text='Population Cap', command=self.set_pop_cap)\\n create_tooltip(population, 'Maximum population in your fort')\\n population.grid(column=0, row=0, sticky=\\\"nsew\\\")\\n self.controls['popcap'] = population\\n invaders = Button(\\n options, text='Invaders',\\n command=lambda: self.cycle_option('invaders'))\\n create_tooltip(\\n invaders, 'Toggles whether invaders (goblins, etc.) show up')\\n invaders.grid(column=1, row=0, sticky=\\\"nsew\\\")\\n self.controls['invaders'] = invaders\\n childcap = Button(\\n options, text='Child Cap', command=self.set_child_cap)\\n create_tooltip(childcap, 'Maximum children in your fort')\\n childcap.grid(column=0, row=1, sticky=\\\"nsew\\\")\\n self.controls['childcap'] = childcap\\n caveins = Button(\\n options, text='Cave-ins',\\n command=lambda: self.cycle_option('caveins'))\\n create_tooltip(\\n caveins,\\n 'Toggles whether unsupported bits of terrain will collapse')\\n caveins.grid(column=1, row=1, sticky=\\\"nsew\\\")\\n self.controls['caveins'] = caveins\\n temperature = Button(\\n options, text='Temperature',\\n command=lambda: self.cycle_option('temperature'))\\n create_tooltip(\\n temperature,\\n 'Toggles whether things will burn, melt, freeze, etc.')\\n temperature.grid(column=0, row=2, sticky=\\\"nsew\\\")\\n self.controls['temperature'] = temperature\\n liquid_depth = Button(\\n options, text='Liquid Depth',\\n command=lambda: self.cycle_option('liquidDepth'))\\n create_tooltip(\\n liquid_depth,\\n 'Displays the depth of liquids with numbers 1-7')\\n liquid_depth.grid(column=1, row=2, sticky=\\\"nsew\\\")\\n self.controls['liquidDepth'] = liquid_depth\\n weather = Button(\\n options, text='Weather',\\n command=lambda: self.cycle_option('weather'))\\n create_tooltip(weather, 'Rain, snow, etc.')\\n weather.grid(column=0, row=3, sticky=\\\"nsew\\\")\\n self.controls['weather'] = weather\\n varied_ground = Button(\\n options, text='Varied Ground',\\n command=lambda: self.cycle_option('variedGround'))\\n create_tooltip(\\n varied_ground,\\n 'If ground tiles use a variety of punctuation, or only periods')\\n varied_ground.grid(column=1, row=3, sticky=\\\"nsew\\\")\\n self.controls['variedGround'] = varied_ground\\n starting_labors = Button(\\n options, text='Starting Labors',\\n command=lambda: self.cycle_option('laborLists'))\\n create_tooltip(\\n starting_labors, 'Which labors are enabled by default:'\\n 'by skill level of dwarves, by their unit type, or none')\\n starting_labors.grid(column=0, row=4, columnspan=2, sticky=\\\"nsew\\\")\\n self.controls['laborLists'] = starting_labors\\n\\n modifications = Labelframe(f, text='Modifications', width=192)\\n modifications.pack(side=TOP, expand=Y, anchor=\\\"w\\\")\\n Grid.columnconfigure(modifications, 0, weight=1)\\n Grid.columnconfigure(modifications, 1, weight=1)\\n\\n aquifers = Button(\\n modifications, text='Aquifers',\\n command=lambda: self.cycle_option('aquifers'))\\n create_tooltip(\\n aquifers, 'Whether newly created worlds will have Aquifers in them '\\n '(Infinite sources of underground water, but may flood your fort')\\n aquifers.grid(column=0, row=0, sticky=\\\"nsew\\\")\\n self.controls['aquifers'] = aquifers\\n\\n keybindings = Labelframe(f, text='Key Bindings')\\n keybindings.pack(side=BOTTOM, fill=BOTH, expand=Y, anchor=\\\"s\\\")\\n Grid.columnconfigure(keybindings, 0, weight=2)\\n Grid.columnconfigure(keybindings, 1, weight=1)\\n Grid.columnconfigure(keybindings, 2, weight=1)\\n\\n keybinding_files = Listbox(\\n keybindings, height=4, listvariable=self.keybinds,\\n activestyle='dotbox')\\n keybinding_files.grid(column=0, row=0, rowspan=2, sticky=\\\"nsew\\\")\\n\\n load_keyb = Button(\\n keybindings, text='Load',\\n command=lambda: self.load_keybinds(keybinding_files))\\n load_keyb.grid(column=1, row=0)\\n create_tooltip(load_keyb, 'Load selected keybindings')\\n refresh_keyb = Button(\\n keybindings, text='Refresh', command=self.read_keybinds)\\n create_tooltip(refresh_keyb, 'Refresh keybinding list')\\n refresh_keyb.grid(column=2, row=0)\\n save_keyb = Button(keybindings, text='Save', command=self.save_keybinds)\\n create_tooltip(save_keyb, 'Save your current keybindings')\\n save_keyb.grid(column=1, row=1)\\n delete_keyb = Button(\\n keybindings, text='Delete',\\n command=lambda: self.delete_keybinds(keybinding_files))\\n create_tooltip(delete_keyb, 'Delete selected keybinding')\\n delete_keyb.grid(column=2, row=1)\\n return f\",\n \"def createMenubar(self):\\r\\n # Create menubar\\r\\n self.menubar = tk.Menu(tearoff=False)\\r\\n self.root.config(menu=self.menubar)\\r\\n filemenu = tk.Menu(self.menubar,tearoff=False)\\r\\n filemenu.add_command(label=\\\"Edit Video/fNIRS Sources\\\",command=self.launchImportWindow)\\r\\n filemenu.add_command(label=\\\"Synchronise Video/fNIRS\\\",command=self.launchSyncToolWindow)\\r\\n filemenu.add_command(label=\\\"Help\\\",command=self.launchHelpWindow)\\r\\n filemenu.add_command(label=\\\"Quit\\\",command=self.quit)\\r\\n self.menubar.add_cascade(label=\\\"Project\\\",menu=filemenu)\",\n \"def setupNewGame(self):\\r\\n self.level = 1\\r\\n self.num_cows = 2\\r\\n self.num_farmers = 1\\r\\n self.levelHeading = Text(self.gameDisplay, 120, 425, 175, self.light_orange, \\\"Farm 1\\\")\\r\\n self.shield_indicator.image = self.greenShield\\r\\n updatedHeading = self.levelHeading\\r\\n self.startUX[0] = updatedHeading\",\n \"def _context_menu_make(self, pos):\\n format = self._control.cursorForPosition(pos).charFormat()\\n name = format.stringProperty(QtGui.QTextFormat.ImageName)\\n if name:\\n menu = QtGui.QMenu()\\n\\n menu.addAction('Copy Image', lambda: self._copy_image(name))\\n menu.addAction('Save Image As...', lambda: self._save_image(name))\\n menu.addSeparator()\\n\\n svg = self._name_to_svg_map.get(name, None)\\n if svg is not None:\\n menu.addSeparator()\\n menu.addAction('Copy SVG', lambda: svg_to_clipboard(svg))\\n menu.addAction('Save SVG As...',\\n lambda: save_svg(svg, self._control))\\n else:\\n menu = super(RichJupyterWidget, self)._context_menu_make(pos)\\n return menu\",\n \"def _print_menu(self):\\n # Create header line.\\n header = \\\"%s Menu:\\\" % (self.__name)\\n header = header.title()\\n print(header)\\n\\n # Show the iterations counter.\\n iterations = self._status.get_value(\\\"iterations\\\")\\n print(\\\"(Iteration %d)\\\" % (iterations))\\n\\n self._print_custom()\\n\\n # Display the options alphabetically.\\n option_names = list(self.__options.keys())\\n option_names.sort()\\n for option in option_names:\\n desc, command = self.__options[option]\\n print(\\\"\\\\t%s: %s\\\" % (option, desc))\",\n \"def add_menus_and_status(self):\\r\\n# Adding the statusbar and menubar\\r\\n self.statusBar().showMessage(\\\"\\\")\\r\\n menubar = self.menuBar()\\r\\n# Adding the first menu to the menubar\\r\\n menu = menubar.addMenu(\\\"Menu\\\")\\r\\n# Adding the first \\\"choise\\\" in the menu\\r\\n contact_action = QAction(\\\"Contact\\\", self)\\r\\n contact_action.setStatusTip(\\\"Contact PyCrypt\\\")\\r\\n contact_action.triggered.connect(self.email)\\r\\n menu.addAction(contact_action)\\r\\n# Adding a separator line to the menu\\r\\n menu.addSeparator()\\r\\n# Adding the second \\\"choise\\\" to the menu\\r\\n exit_action = QAction(\\\"Exit\\\", self) # Create an exit action\\r\\n exit_action.setStatusTip(\\\"Click to exit the application\\\")\\r\\n exit_action.triggered.connect(self.close) # Close application when clicked\\r\\n exit_action.setShortcut(\\\"Ctrl+Q\\\") # Keyboard shortcut to exit app\\r\\n menu.addAction(exit_action)\",\n \"def add_menu():\\n\\n def _(*args, **kwargs):\\n args = (cast_str(i) if isinstance(i, six.text_type) else i for i in args)\\n kwargs = tuple(\\n {\\n k: cast_str(v) if isinstance(v, six.text_type) else v\\n for k, v in kwargs.items()\\n }.items()\\n )\\n return (args, kwargs)\\n\\n def _auto_comp():\\n try:\\n comp.Comp().create_nodes()\\n except comp.FootageError:\\n nuke.message(cast_str(\\\"请先导入素材\\\"))\\n\\n all_menu = [\\n {\\n _(\\\"工具\\\"): [\\n {\\n _(\\\"按素材名组装\\\"): [\\n _(\\\"对当前工程执行\\\", _auto_comp, icon=\\\"autocomp.png\\\"),\\n _(\\n \\\"批量执行\\\",\\n lambda: comp.panels.BatchCompPanel().showModalDialog(),\\n icon=\\\"autocomp.png\\\",\\n ),\\n _(\\n \\\"设置\\\",\\n lambda: comp.panels.CompConfigPanel().showModalDialog(),\\n icon=\\\"autocomp.png\\\",\\n ),\\n ],\\n },\\n {\\n _(\\\"转换为序列工程\\\"): [\\n _(\\\"对当前工程执行\\\", edit.script_use_seq.execute),\\n _(\\n \\\"批量执行\\\",\\n lambda: edit.script_use_seq.panels.BatchPanel().showModalDialog(),\\n ),\\n _(\\n \\\"设置\\\",\\n lambda: edit.script_use_seq.panels.ConfigPanel().showModalDialog(),\\n ),\\n ]\\n },\\n ]\\n }\\n ]\\n\\n # Add all menu.\\n def _add_menu(menu, parent=nuke.menu(cast_str(\\\"Nuke\\\"))):\\n # type: (..., nuke.Menu) -> None\\n assert isinstance(menu, dict)\\n\\n for k, v in menu.items():\\n m = parent.addMenu(*k[0], **dict(k[1]))\\n for i in v:\\n if i is None:\\n _ = m.addSeparator()\\n elif isinstance(i, dict):\\n _add_menu(i, m)\\n elif isinstance(i, tuple):\\n _ = m.addCommand(*i[0], **dict(i[1]))\\n\\n for menu in all_menu:\\n _add_menu(menu)\",\n \"def init_mapmenu(self):\\n\\n self.level1 = QPushButton('', self)\\n self.level1.setFixedSize(192, 192)\\n self.level1.setStyleSheet('''\\n color: black;\\n background-image: url(./pictures/level1.png);\\n border: none;\\n ''')\\n self.level1.move(80, 60)\\n self.level1.clicked.connect(self.start_level1)\\n self.mapmenu_items.append(self.level1)\\n\\n self.level2 = QPushButton('', self)\\n self.level2.setFixedSize(192, 192)\\n self.level2.setStyleSheet('''\\n color: black;\\n background-image: url(./pictures/level2.png);\\n border: none;\\n ''')\\n self.level2.move(330, 70)\\n self.level2.clicked.connect(self.start_level2)\\n self.mapmenu_items.append(self.level2)\\n\\n self.level3 = QPushButton('', self)\\n self.level3.setFixedSize(192, 192)\\n self.level3.setStyleSheet('''\\n color: black;\\n background-image: url(./pictures/level3.png);\\n border: none;\\n ''')\\n self.level3.move(580, 70)\\n self.level3.clicked.connect(self.start_level3)\\n self.mapmenu_items.append(self.level3)\\n\\n self.level_n = QPushButton('', self)\\n self.level_n.setFixedSize(192, 192)\\n self.level_n.setStyleSheet('''\\n color: black;\\n background-image: url(./pictures/level_n.png);\\n border: none;\\n ''')\\n self.level_n.move(80, 330)\\n self.level_n.clicked.connect(self.start_level_n)\\n self.mapmenu_items.append(self.level_n)\\n\\n self.cancel = QPushButton('Cancel', self)\\n self.cancel.setFixedSize(256, 128)\\n self.cancel.setStyleSheet('''\\n background-image: url(./pictures/cancel.png);\\n border: none;\\n ''')\\n self.cancel.move(578, 470)\\n self.cancel.clicked.connect(self.main_menu)\\n self.mapmenu_items.append(self.cancel)\",\n \"def BuildMenuTemplate(menuJSONFile, isCheeseburgerBao, isCoconutBao, isEggSausageBao, isIMPOSSIBLEBao, isBundles, isPotDumpCombined):\\n with open(menuJSONFile) as file:\\n data = json.load(file)\\n \\n template_menu = Menu(None, None)\\n for category in data['Categories']:\\n #have fun figuring out this part n00b\\n if(category.get(\\\"bundleDependency\\\", isBundles)==isBundles):\\n if(category.get(\\\"potDumpDependency\\\", isPotDumpCombined)==isPotDumpCombined):\\n template_menu.categories.append(category[\\\"Name\\\"])\\n \\n #setting standard modifier sets\\n #no use encoding the same 4 bao flavors over and over again in the json with hella dependencies\\n #modifier sets are the only parts of the menu you have to edit in the code\\n #IF YOU NEED TO CHANGE MODIFIERS DO IT HERE\\n\\n #base modifiers\\n bao_modifiers = [\\\"BBQ Berkshire Pork\\\", \\\"Teriyaki Chicken\\\", \\\"Spicy Mongolian Beef\\\", \\\"Whole Wheat Vegetable\\\"]\\n pot_dump_modifiers = [\\\"Ginger Chicken\\\", \\\"Green Vegetable\\\"]\\n bowl_modifiers = [\\\"Teriyaki Chicken Bowl\\\", \\\"Spicy Kung Pao Chicken Bowl\\\", \\\"Orange Chicken Bowl\\\"]\\n\\n #contingent modifiers (atm only bao)\\n if(isCheeseburgerBao):\\n bao_modifiers.append(\\\"Cheeseburger\\\")\\n if(isCoconutBao):\\n bao_modifiers.append(\\\"Coconut Custard\\\")\\n if(isEggSausageBao):\\n bao_modifiers.append(\\\"Egg & Sausage\\\")\\n if(isIMPOSSIBLEBao):\\n bao_modifiers.append(\\\"IMPOSSIBLE\\\")\\n\\n modifier_sets = {\\\"Bao\\\":bao_modifiers, \\\"Potsticker/Dumpling\\\":pot_dump_modifiers, \\\"Bowl\\\":bowl_modifiers}\\n \\n\\n\\n for i in data['Items']:\\n if(i.get(\\\"bundleDependency\\\", isBundles)==isBundles):\\n mgs = []\\n for m in i.get(\\\"ModifierGroups\\\", []):\\n mgs.append(ModifierGroup(m[\\\"Name\\\"], modifier_sets[m[\\\"ModifierSet\\\"]]))\\n item = Item(i[\\\"Name\\\"], i[\\\"Description\\\"], mgs)\\n template_menu.items.append(item)\\n \\n return template_menu\",\n \"def mainmenu(self):\\n\\n global con\\n global lt\\n global prg\\n global afi\\n global plv\\n \\n con = self.sql_conexion()\\n lt = lv.Lotes(con)\\n prg = prgva.Agenda(con)\\n afi = af.Afiliado(con)\\n plv = pl.Plan(con)\\n \\n afi.tabla_afiliados()\\n lt._Lotes__tabla_vacunas()\\n plv._Plan__tabla_plan()\\n prg._Agenda__tabla_prog()\\n\\n \\\"\\\"\\\"\\n este es el menu principal apartir de este se despliegan los submenus de cada modulo\\n \\\"\\\"\\\"\\n while True:\\n print('\\\\nS I S T E M A D E G E S T I Ó N D E V A C U N A C I Ó N ')\\n print(\\\"Selecciona una opción:\\\")\\n print(\\\"\\\\t1 - Administrar Afiliados\\\")\\n print(\\\"\\\\t2 - Administrar Vacunas\\\")\\n print(\\\"\\\\t3 - Plan Vacunacion\\\")\\n print(\\\"\\\\t4 - Programa Vacunacion\\\")\\n print(\\\"\\\\t5 - salir\\\")\\n\\n # Mostramos el menu\\n # solicituamos una opción al usuario\\n opcionmenu = input(\\\"Seleccione una opcion: \\\")\\n\\n if opcionmenu == \\\"1\\\":\\n self.menuafi()\\n elif opcionmenu == \\\"2\\\":\\n self.menulote()\\n elif opcionmenu == \\\"3\\\":\\n self.menuvac()\\n elif opcionmenu == \\\"4\\\":\\n self.provac()\\n elif opcionmenu == \\\"5\\\":\\n con.close()\\n break\\n else:\\n input(\\\"\\\\nNo has pulsado ninguna opción correcta...\\\\npulsa una tecla para continuar\\\")\",\n \"def make_control_frame(self):\\n \\n #Generate and place the frame for housing the controls\\n control_frame = tk.Frame(self, padx = 0,\\n bg=self.default_background)\\n control_frame.grid(column=0, row=4, columnspan = 1)\\n \\n #Generate the time indicator\\n time_label = tk.Label(control_frame, text=self.translate('Day')+': ',\\n bg=self.default_background)\\n time_label.grid(column=0, row=0)\\n self.timev = tk.StringVar()\\n self.timev.set(str(self.timeSeries[-1]))\\n self.time_value_label = tk.Label(control_frame, textvariable=self.timev,\\n bg=self.default_background)\\n self.time_value_label.grid(column=1, row=0, sticky='W')\\n \\n #set width of the control dropdowns\\n boxwidth = 30\\n \\n #Generate each of the policy control dropdowns\\n index = 0 \\n self.policy_option_vars = dict()\\n self.option_menus = []\\n fontsizelist = []\\n for policy in self.PolicyDicts.keys():\\n option1_label = self.DynamicLabel(400, control_frame, text=self.translate(policy)+': ',\\n bg=self.default_background)\\n option1_label.grid(column=0, row=index+1)\\n self.option1_label_list.append(option1_label)\\n fontsizelist.append(option1_label.font.actual(\\\"size\\\"))\\n option1_list = []\\n for entry in list(self.PolicyDicts[policy].keys()):\\n option1_list.append(self.translate(entry))\\n \\n self.policy_option_vars[policy] = tk.StringVar()\\n defaultpolicy = self.translate(self.PolicyDictsInv[policy][self.SD_Map.retrieve_ob(policy).value()])\\n \\n self.policy_option_vars[policy].set(defaultpolicy)\\n self.option_menus.append(tk.OptionMenu(control_frame, self.policy_option_vars[policy], \\n *option1_list, \\n command=lambda value, policy=policy: self.update_Policy(policy)\\n ))\\n self.option_menus[-1].config(width=boxwidth, anchor='w',\\n bg=self.button_color,\\n highlightbackground=self.highlight_color)\\n self.option_menus[-1]['menu'].config(bg=self.button_color)\\n self.option_menus[-1].grid(column=1, row=index+1, columnspan=2)\\n \\n index+=1\\n \\n #Resize the fontsize of all the labels to match that of the smallest\\n newsize = min(fontsizelist)\\n for policylabel in self.option1_label_list:\\n policylabel.font.configure(size=newsize)\\n\\n \\n button_font_sizes = []\\n \\n #Generate the Next Week simulation button\\n run_button = self.DynamicButton(300,control_frame, text=self.translate('Next Week'), \\n command = lambda: self.increment_time(),\\n bg=self.button_color,\\n highlightbackground=self.highlight_color)\\n run_button.grid(column=0, row=8, columnspan=1, sticky='E')\\n button_font_sizes.append(run_button.font.actual(\\\"size\\\"))\\n \\n #Generate the Run Autonomously button\\n automatic_button = self.DynamicButton(300,control_frame, text=self.translate('Run Autonomously'), \\n command = lambda: self.automatic_window(),\\n bg=self.button_color,\\n highlightbackground=self.highlight_color)\\n automatic_button.grid(column=1, row=8, columnspan=1)\\n button_font_sizes.append(automatic_button.font.actual(\\\"size\\\"))\\n \\n #Generate the Clear Simulation Button\\n clear_button = self.DynamicButton(300,control_frame, text = self.translate('Clear Simulation'),\\n command = lambda: self.clear_simulation(),\\n bg=self.button_color,\\n highlightbackground=self.highlight_color,)\\n clear_button.grid(column=2, row=8, columnspan=2)\\n button_font_sizes.append(clear_button.font.actual(\\\"size\\\"))\\n \\n #Resize all the button texts to match that of the smallest\\n new_button_font_size = min(button_font_sizes)\\n run_button.font.configure(size=new_button_font_size)\\n automatic_button.font.configure(size=new_button_font_size)\\n clear_button.font.configure(size=new_button_font_size)\\n \\n return control_frame\",\n \"def on_show(self): \\n arcade.set_background_color(arcade.color.BLACK)\",\n \"def main():\\n\\tmain_menu_title = txt_sns + \\\"\\\\nBIENVENIDO AL SANSANITO POKEMON. QUE DESEA HACER?\\\\n\\\"\\n\\t# Opciones de menu principal\\n\\tmain_menu_items = [\\\"Crear un registro (create)\\\", \\\"Ingresar un pokemon\\\", \\\"Buscar en tabla (read)\\\", \\\"Opciones especiales de busqueda\\\",\\\\\\n\\t\\t\\t\\t\\t\\t\\\"Cambiar datos de pokemon ingresado (update)\\\", \\\"Borrar registro (delete)\\\",\\\\\\n\\t\\t\\t\\t\\t\\t\\\"Ver la tabla Poyo\\\", \\\"Ver la tabla Sansanito Pokemon\\\", \\\"Capacidad actual\\\", \\\"Salir\\\"]\\n\\tmain_menu_cursor = \\\"> \\\"\\n\\tmain_menu_cursor_style = (\\\"fg_red\\\", \\\"bold\\\")\\n\\tmain_menu_style = (\\\"bg_purple\\\", \\\"fg_yellow\\\")\\n\\tmain_menu_exit = False\\n\\n\\tmain_menu = TerminalMenu(menu_entries=main_menu_items,\\n\\t\\t\\t\\t\\t\\t\\t title=main_menu_title,\\n\\t\\t\\t\\t\\t\\t\\t menu_cursor=main_menu_cursor,\\n\\t\\t\\t\\t\\t\\t\\t menu_cursor_style=main_menu_cursor_style,\\n\\t\\t\\t\\t\\t\\t\\t menu_highlight_style=main_menu_style,\\n\\t\\t\\t\\t\\t\\t\\t cycle_cursor=True,\\n\\t\\t\\t\\t\\t\\t\\t clear_screen=True)\\n\\n\\twhile not main_menu_exit:\\n\\t\\tmain_sel = main_menu.show()\\n\\t\\tsubmenu_flag = True\\n\\t\\t# Create de CRUD y Ingresar pokemon\\n\\t\\tif main_sel == 0 or main_sel == 1:\\n\\t\\t\\tsubmenu_flag = False\\n\\t\\t\\tprint(\\\"TABLA ORIGINAL:\\\")\\n\\t\\t\\tprint_sansanito()\\n\\t\\t\\tcreate()\\n\\t\\t\\tprint(\\\"\\\\nTABLA FINAL:\\\")\\n\\t\\t\\tprint_sansanito()\\n\\t\\t\\tprint(\\\"Ingrese X para volver al MENU PRINCIPAL.\\\")\\n\\t\\t\\tcondicion = input()\\n\\t\\t\\twhile(condicion != \\\"X\\\" and condicion != \\\"x\\\"):\\n\\t\\t\\t\\tcondicion = input()\\n\\n\\t\\telif main_sel == 2:\\n\\t\\t\\tmenu1_title = \\\"BUSQUEDA DE REGISTRO EN SANSANITO POKEMON\\\\n\\\"\\n\\t\\t\\tmenu1_items = [\\\"Buscar por ID\\\", \\\"Salir\\\"]\\n\\t\\t\\tmenu1 = TerminalMenu(menu_entries=menu1_items,\\n\\t\\t\\t\\t\\t\\t\\t title=menu1_title,\\n\\t\\t\\t\\t\\t\\t\\t menu_cursor=main_menu_cursor,\\n\\t\\t\\t\\t\\t\\t\\t menu_cursor_style=main_menu_cursor_style,\\n\\t\\t\\t\\t\\t\\t\\t menu_highlight_style=main_menu_style,\\n\\t\\t\\t\\t\\t\\t\\t cycle_cursor=True,\\n\\t\\t\\t\\t\\t\\t\\t clear_screen=True)\\n\\t\\t\\twhile submenu_flag:\\n\\t\\t\\t\\tmenu1_sel = menu1.show()\\n\\t\\t\\t\\tif menu1_sel == 0:\\n\\t\\t\\t\\t\\tread()\\n\\t\\t\\t\\t\\tprint(\\\"Ingrese X para volver al MENU PRINCIPAL.\\\")\\n\\t\\t\\t\\t\\tcondicion = input()\\n\\t\\t\\t\\t\\twhile(condicion != \\\"X\\\" and condicion != \\\"x\\\"):\\n\\t\\t\\t\\t\\t\\tcondicion = input()\\n\\t\\t\\t\\telif menu1_sel == 1:\\n\\t\\t\\t\\t\\tsubmenu_flag = False \\n\\n\\t\\telif main_sel == 3:\\n\\t\\t\\tmenu2_title = \\\"BUSQUEDA ESPECIAL EN SANSANITO POKEMON. ELIGA UNA OPCION.\\\\n\\\"\\n\\t\\t\\tmenu2_items = [\\\"10 pokemon con mayor prioridad\\\", \\\"10 pokemon con menor prioridad\\\",\\\\\\n\\t\\t\\t\\\"Pokemon con estado especifico\\\", \\\"Pokemon legendarios ingresados\\\",\\\\\\n\\t\\t\\t\\\"Pokemon que lleva mas tiempo ingresado\\\", \\\"Pokemon mas repetido\\\",\\\\\\n\\t\\t\\t\\\"Pokemon ingresados, ordenados por su prioridad\\\", \\\"Salir\\\"]\\n\\t\\t\\tmenu2 = TerminalMenu(menu_entries=menu2_items,\\n\\t\\t\\t\\t\\t\\t\\t\\ttitle=menu2_title,\\n\\t\\t\\t\\t\\t\\t\\t\\tmenu_cursor=main_menu_cursor,\\n\\t\\t\\t\\t\\t\\t\\t\\tmenu_cursor_style=main_menu_cursor_style,\\n\\t\\t\\t\\t\\t\\t\\t\\tmenu_highlight_style=main_menu_style,\\n\\t\\t\\t\\t\\t\\t\\t\\tcycle_cursor=True,\\n\\t\\t\\t\\t\\t\\t\\t\\tclear_screen=True)\\n\\t\\t\\twhile submenu_flag:\\n\\t\\t\\t\\tmenu2_sel = menu2.show()\\n\\t\\t\\t\\t# 10 con mayor prioridad\\n\\t\\t\\t\\tif menu2_sel == 0:\\n\\t\\t\\t\\t\\tmaxprio_sansanito()\\n\\t\\t\\t\\t\\tprint(\\\"Ingrese X para volver al MENU PRINCIPAL.\\\")\\n\\t\\t\\t\\t\\tcondicion = input()\\n\\t\\t\\t\\t\\twhile(condicion != \\\"X\\\" and condicion != \\\"x\\\"):\\n\\t\\t\\t\\t\\t\\tcondicion = input()\\n\\t\\t\\t\\t# 10 con menor prioidad\\n\\t\\t\\t\\telif menu2_sel == 1:\\n\\t\\t\\t\\t\\tminprio_sansanito()\\n\\t\\t\\t\\t\\tprint(\\\"Ingrese X para volver al MENU PRINCIPAL.\\\")\\n\\t\\t\\t\\t\\tcondicion = input()\\n\\t\\t\\t\\t\\twhile(condicion != \\\"X\\\" and condicion != \\\"x\\\"):\\n\\t\\t\\t\\t\\t\\tcondicion = input()\\n\\t\\t\\t\\t# Filtrado por estado\\n\\t\\t\\t\\telif menu2_sel == 2:\\n\\t\\t\\t\\t\\tprint(\\\"NOTA: Estados disponibles son: Envenenado, Paralizado, Quemado, Dormido, Congelado\\\")\\n\\t\\t\\t\\t\\tprint(\\\"Para ver pokemons sin estado, ingrese X.\\\\n\\\")\\n\\t\\t\\t\\t\\testado = input(\\\"Ingrese un estado para filtrar los datos: \\\")\\n\\t\\t\\t\\t\\tif estado.upper() == \\\"X\\\":\\n\\t\\t\\t\\t\\t\\testado = None\\n\\t\\t\\t\\t\\testado_sansanito(estado)\\n\\t\\t\\t\\t\\tprint(\\\"Ingrese X para volver al MENU PRINCIPAL.\\\")\\n\\t\\t\\t\\t\\tcondicion = input()\\n\\t\\t\\t\\t\\twhile(condicion != \\\"X\\\" and condicion != \\\"x\\\"):\\n\\t\\t\\t\\t\\t\\tcondicion = input()\\n\\t\\t\\t\\t# Los legendarios ingresados\\n\\t\\t\\t\\telif menu2_sel == 3:\\n\\t\\t\\t\\t\\tlegendarios_sansanito()\\n\\t\\t\\t\\t\\tprint(\\\"Ingrese X para volver al MENU PRINCIPAL.\\\")\\n\\t\\t\\t\\t\\tcondicion = input()\\n\\t\\t\\t\\t\\twhile(condicion != \\\"X\\\" and condicion != \\\"x\\\"):\\n\\t\\t\\t\\t\\t\\tcondicion = input()\\n\\t\\t\\t\\t# El mas antiguo\\n\\t\\t\\t\\telif menu2_sel == 4:\\n\\t\\t\\t\\t\\tantiguedad_sansanito()\\n\\t\\t\\t\\t\\tprint(\\\"Ingrese X para volver al MENU PRINCIPAL.\\\")\\n\\t\\t\\t\\t\\tcondicion = input()\\n\\t\\t\\t\\t\\twhile(condicion != \\\"X\\\" and condicion != \\\"x\\\"):\\n\\t\\t\\t\\t\\t\\tcondicion = input()\\n\\t\\t\\t\\t# Pokemon mas repetido\\n\\t\\t\\t\\telif menu2_sel == 5:\\n\\t\\t\\t\\t\\trepetido_sansanito()\\n\\t\\t\\t\\t\\tprint(\\\"Ingrese X para volver al MENU PRINCIPAL.\\\")\\n\\t\\t\\t\\t\\tcondicion = input()\\n\\t\\t\\t\\t\\twhile(condicion != \\\"X\\\" and condicion != \\\"x\\\"):\\n\\t\\t\\t\\t\\t\\tcondicion = input()\\n\\t\\t\\t\\t# Ordenados por prioidad\\n\\t\\t\\t\\telif menu2_sel == 6:\\n\\t\\t\\t\\t\\torden = input(\\\"Ingrese 1 si desea el orden DESCENDIENTE, 0 en caso contrario: \\\")\\n\\t\\t\\t\\t\\tif orden != \\\"1\\\" and orden != \\\"0\\\":\\n\\t\\t\\t\\t\\t\\tprint(\\\"Input invalido! Intente de nuevo.\\\")\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tif orden == \\\"1\\\":\\n\\t\\t\\t\\t\\t\\t\\tordenado_sansanito(\\\"DESC\\\")\\n\\t\\t\\t\\t\\t\\t\\tprint(\\\"Ingrese X para volver al MENU PRINCIPAL.\\\")\\n\\t\\t\\t\\t\\t\\t\\tcondicion = input()\\n\\t\\t\\t\\t\\t\\t\\twhile(condicion != \\\"X\\\" and condicion != \\\"x\\\"):\\n\\t\\t\\t\\t\\t\\t\\t\\tcondicion = input()\\n\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\tordenado_sansanito(\\\"ASC\\\")\\n\\t\\t\\t\\t\\t\\t\\tprint(\\\"Ingrese X para volver al MENU PRINCIPAL.\\\")\\n\\t\\t\\t\\t\\t\\t\\tcondicion = input()\\n\\t\\t\\t\\t\\t\\t\\twhile(condicion != \\\"X\\\" and condicion != \\\"x\\\"):\\n\\t\\t\\t\\t\\t\\t\\t\\tcondicion = input()\\n\\t\\t\\t\\telif menu2_sel == 7:\\n\\t\\t\\t\\t\\tsubmenu_flag = False\\n\\t\\telif main_sel == 4:\\n\\t\\t\\tupdate()\\n\\t\\t# Delete de CRUD\\n\\t\\telif main_sel == 5:\\n\\t\\t\\ta_borrar = int(input(\\\"Ingrese el ID de registro a borrar: \\\"))\\n\\t\\t\\tdelete(a_borrar)\\n\\t\\t\\ttime.sleep(1)\\n\\t\\t# Muestra la tabla de poyo\\n\\t\\telif main_sel == 6:\\n\\t\\t\\tprint_poyo()\\n\\t\\t\\tprint(\\\"Ingrese X para volver al MENU PRINCIPAL.\\\")\\n\\t\\t\\tcondicion = input()\\n\\t\\t\\twhile(condicion.upper() != \\\"X\\\"):\\n\\t\\t\\t\\tcondicion = input()\\n\\t\\t# Muestra la tabla de sansanito\\n\\t\\telif main_sel == 7:\\n\\t\\t\\tprint_sansanito()\\n\\t\\t\\tprint(\\\"Ingrese X para volver al MENU PRINCIPAL.\\\")\\n\\t\\t\\tcondicion = input()\\n\\t\\t\\twhile(condicion.upper() != \\\"X\\\"):\\n\\t\\t\\t\\tcondicion = input()\\n\\t\\t# Funcion extra que muestra la ocupacion actual de sansanito\\n\\t\\telif main_sel == 8:\\n\\t\\t\\tprint(\\\"La capacidad actual es: {}/50\\\".format(calcular_ocupacion()))\\n\\t\\t\\tprint(\\\"Ingrese X para volver al MENU PRINCIPAL.\\\")\\n\\t\\t\\tcondicion = input()\\n\\t\\t\\twhile(condicion != \\\"X\\\" and condicion != \\\"x\\\"):\\n\\t\\t\\t\\tcondicion = input()\\n\\t\\t# Salir\\n\\t\\telif main_sel == 9:\\n\\t\\t\\tmain_menu_exit = True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7138286","0.699841","0.65738827","0.65555376","0.6499045","0.64050287","0.63404286","0.6264091","0.6249606","0.6249606","0.6233549","0.62195414","0.6210253","0.61664957","0.6162381","0.6152219","0.61354536","0.60851127","0.6030847","0.60096836","0.59637636","0.59625095","0.59559757","0.5948254","0.59466136","0.5944072","0.59226525","0.58977675","0.5860068","0.58377594","0.58155924","0.58136225","0.578546","0.5781087","0.5755268","0.5744401","0.57417256","0.5729688","0.5720683","0.57165396","0.57109994","0.5707912","0.5705807","0.5705129","0.56832576","0.56695396","0.56612694","0.56596375","0.5655074","0.5652212","0.5651077","0.5645328","0.56332946","0.561512","0.5610265","0.5609353","0.5601566","0.55948055","0.5578365","0.55765855","0.55718905","0.55667794","0.5563319","0.5559733","0.5555491","0.5551828","0.55517113","0.5547984","0.5543775","0.5541092","0.5537509","0.55322564","0.5508072","0.55006975","0.5499003","0.5498318","0.54946244","0.54943985","0.54870296","0.54864615","0.54752356","0.54723066","0.54672873","0.5461856","0.5445042","0.5443906","0.5442527","0.5434035","0.54211944","0.54194707","0.5417089","0.541407","0.5409157","0.5409116","0.54058975","0.5405516","0.5401016","0.54002476","0.5398314","0.5392224"],"string":"[\n \"0.7138286\",\n \"0.699841\",\n \"0.65738827\",\n \"0.65555376\",\n \"0.6499045\",\n \"0.64050287\",\n \"0.63404286\",\n \"0.6264091\",\n \"0.6249606\",\n \"0.6249606\",\n \"0.6233549\",\n \"0.62195414\",\n \"0.6210253\",\n \"0.61664957\",\n \"0.6162381\",\n \"0.6152219\",\n \"0.61354536\",\n \"0.60851127\",\n \"0.6030847\",\n \"0.60096836\",\n \"0.59637636\",\n \"0.59625095\",\n \"0.59559757\",\n \"0.5948254\",\n \"0.59466136\",\n \"0.5944072\",\n \"0.59226525\",\n \"0.58977675\",\n \"0.5860068\",\n \"0.58377594\",\n \"0.58155924\",\n \"0.58136225\",\n \"0.578546\",\n \"0.5781087\",\n \"0.5755268\",\n \"0.5744401\",\n \"0.57417256\",\n \"0.5729688\",\n \"0.5720683\",\n \"0.57165396\",\n \"0.57109994\",\n \"0.5707912\",\n \"0.5705807\",\n \"0.5705129\",\n \"0.56832576\",\n \"0.56695396\",\n \"0.56612694\",\n \"0.56596375\",\n \"0.5655074\",\n \"0.5652212\",\n \"0.5651077\",\n \"0.5645328\",\n \"0.56332946\",\n \"0.561512\",\n \"0.5610265\",\n \"0.5609353\",\n \"0.5601566\",\n \"0.55948055\",\n \"0.5578365\",\n \"0.55765855\",\n \"0.55718905\",\n \"0.55667794\",\n \"0.5563319\",\n \"0.5559733\",\n \"0.5555491\",\n \"0.5551828\",\n \"0.55517113\",\n \"0.5547984\",\n \"0.5543775\",\n \"0.5541092\",\n \"0.5537509\",\n \"0.55322564\",\n \"0.5508072\",\n \"0.55006975\",\n \"0.5499003\",\n \"0.5498318\",\n \"0.54946244\",\n \"0.54943985\",\n \"0.54870296\",\n \"0.54864615\",\n \"0.54752356\",\n \"0.54723066\",\n \"0.54672873\",\n \"0.5461856\",\n \"0.5445042\",\n \"0.5443906\",\n \"0.5442527\",\n \"0.5434035\",\n \"0.54211944\",\n \"0.54194707\",\n \"0.5417089\",\n \"0.541407\",\n \"0.5409157\",\n \"0.5409116\",\n \"0.54058975\",\n \"0.5405516\",\n \"0.5401016\",\n \"0.54002476\",\n \"0.5398314\",\n \"0.5392224\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5933318"},"document_rank":{"kind":"string","value":"26"}}},{"rowIdx":84199,"cells":{"query":{"kind":"string","value":"A partir de un diccionario que representa un punto P devuelve una estructura de C."},"document":{"kind":"string","value":"def PyC_Punto(P):\n C = CPunto()\n\n C.x[0] = P['x'][0]\n C.x[1] = P['x'][1]\n C.x[2] = P['x'][2]\n \n C.y[0] = P['y'][0]\n C.y[1] = P['y'][1]\n C.y[2] = P['y'][2]\n\n C.D = P['D']\n \n C.n = P['n']\n C.a, C.b, C.c = P['a'], P['b'], P['c']\n C.d, C.e, C.f = P['d'], P['e'], P['f']\n\n return C"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def CPy_Punto(C, P):\n \n P['x'][0] = C.x[0]\n P['x'][1] = C.x[1]\n P['x'][2] = C.x[2]\n\n P['y'][0] = C.y[0]\n P['y'][1] = C.y[1]\n P['y'][2] = C.y[2]\n\n P['D'] = C.D\n P['a'], P['b'], P['c'] = C.a, C.b, C.c\n P['d'], P['e'], P['f'] = C.d, C.e, C.f","def findpc(self):\n u = -(-can.C.len() + self.P.len() + can.R)/self.V.len()\n if u >= 0:\n return self.P + self.V.scale(u), u\n else:\n u = (can.C.len() - self.P.len() + can.R)/self.V.len()\n return self.P + self.V.scale(u), u","def C_P(self):\n return self.generic_getter(\n get_heat_capacity_pressure, \"C_P\", \"convert_heat_capacity\"\n )","def init(self):\n # pC es un puntero a la imagen en C\n nP = len(self.puntos)\n self.pC = c_colision.malloc_tren(nP)\n # Estas magnitudes se inicializan en la imagen y\n # ya no se tocan desde python\n for i in range(nP):\n self.pC.contents.P[i]= pointer(PyC_Punto(self.puntos[i]))\n self.colision = self.pC.contents.colision = 0","def cov2pv(self, P):\n return P","def distancia_puntos(c, p):\n return sqrt((c[0] - p[0]) ** 2 + (c[1] - p[1]) ** 2)","def from_thermo(T_C, p):\n\ty = y_from_p(p)\n\tx = x_from_Tp(T_C+C_to_K, p)\n\treturn x, y","def convert_kicad_coor(edif_pt):\n scale = 10\n return [edif_pt[0] * scale, +edif_pt[1] * scale]","def DPc(R,Pc):\n return r2*(K2**B2/(K2**B2 + (A)**B2))*(S/(S + R*Pc + Pc)) \\\n *(R*M)/(K3 + R*M)*Pc - gc*Pc","def c_cd2cp(s, N):\n color, move = s.strip().split()\n # print('color:{} move:{}'.format(color,move))\n c = color2c(color)\n p = cd2p(move, N)\n return c, p","def COC(fp, csiz):\n lcoc = unpack('>H', fp.read(2))[0]\n print('Csiz', csiz)\n if csiz < 257:\n ccoc = unpack('B', fp.read(1))[0]\n else:\n ccoc = unpack('>H', fp.read(2))[0]\n scoc = unpack('B', fp.read(1))[0]\n\n _decomp_levels = unpack('B', fp.read(1))[0]\n _block_width = unpack('B', fp.read(1))[0]\n _block_height = unpack('B', fp.read(1))[0]\n _block_style = unpack('B', fp.read(1))[0]\n _transform = unpack('B', fp.read(1))[0]\n\n _precincts = []\n has_precincts = _get_bit(scoc, 7)\n if has_precincts == 1:\n for ii in range(_decomp_levels + 1):\n _precincts.append(unpack('B', fp.read(1))[0])\n\n info = {\n 'Lcoc' : lcoc,\n 'Ccoc' : ccoc,\n 'Scoc' : scoc,\n 'SPcoc' : {\n 'decomp_levels' : _decomp_levels,\n 'block_width' : _block_width,\n 'block_height' : _block_height,\n 'block_style' : _block_style,\n 'transform' : _transform\n }\n }\n\n if has_precincts:\n info['SPcoc']['precincts'] = _precincts\n\n return info","def calculate_cd_transpose(P,C):\n conf_deg_t = C.T.dot(P)\n a1 = P.sum(axis=0)\n B = np.tile(a1, (C.shape[1], 1))\n CD_t = 1. * conf_deg_t / (B + np.spacing(0))\n return CD_t","def t2c(x):\n dx = to_dlpack(x)\n return cp.fromDlpack(dx)","def cov2pv(self, P):\n raise NotImplementedError","def C(self):\n return None if self.pC is None else self.pC.buf","def ppl_convert(P):\n if isinstance(P, ppl.C_Polyhedron):\n return P\n gs = ppl.Generator_System()\n for v in P.vertices_list():\n gs.insert(ppl.point(sum(int(j) * ppl.Variable(i) for i,j in enumerate(v))))\n for r in P.rays_list():\n gs.insert(ppl.ray(sum(int(j) * ppl.Variable(i) for i,j in enumerate(r))))\n for l in P.lines_list():\n gs.insert(ppl.line(sum(int(j) * ppl.Variable(i) for i,j in enumerate(l))))\n return ppl.C_Polyhedron(gs)","def cima(pila):\n return pila.datos[pila.tope]","def polymul(c,P):\n return [c*x for x in P]","def project_p2c_points(R, C, H): #---- project to cylindrical\r\n Rc= H[0]/2; Cc= H[1]/2; # center coordinate\r\n phi = H[3]; S= H[4]; # rotation angle and sizing\r\n Tv = H[5]; Tu= H[6]; # displacement\r\n COSF= np.cos(phi); SINF= np.sin(phi); # \r\n U = Tu + S*( COSF*(C- Cc)- SINF*(R- Rc) ); \r\n V = Tv + S*( SINF*(C- Cc)+ COSF*(R- Rc) );\r\n return V, U","def translate( t, P ): \n assert t.shape[0]==P.n # Dimension match \n if P.type=='AH_polytope':\n return pp.AH_polytope(t=t+P.t,T=P.T,P=P.P)\n elif P.type=='zonotope':\n return pp.zonotope(x=t+P.x,G=P.G)\n elif P.type==\"H_polytope\":\n return pp.H_polytope(H=P.H,h=P.h+np.dot(P.H,t))\n else:\n return ValueError('Polytope type: ',P.type,\" Not recognized\")","def cp2p(np.ndarray[DTYPE_t, ndim=1] cp, DTYPE_t alpha, DTYPE_t rh, DTYPE_t rw):\n#def cp2p( cp, alpha, rh, rw):\n\tdirH=np.array([np.cos(alpha),np.sin(alpha)])\n\tdirW=np.array([np.sin(alpha),-np.cos(alpha)])\n\treturn np.array( [ _r+cp for _r in [rh*dirH+rw*dirW, -rh*dirH+rw*dirW, -rh*dirH-rw*dirW, rh*dirH-rw*dirW] ]).transpose()","def p2cd(p, N):\n a = p2a(p, N) # OK\n (r, c) = a2rc(a, N) # OK\n return rc2cd(r, c, N)","def projectPoint(self,p):\n a,b,c = self.a, self.b, self.c\n x,y = p\n return numpy.array( [ b*(x*b-y*a) - c*a, a*(y*a-x*b) - c*b ] )","def piece_coor(self):\n return self.piece_type[self.rotation]","def test_pid_ccs2():\n d = trivariates['anddup']\n pid = PID_CCS(d, [[0], [1], [2]], [3])\n for atom in pid._lattice:\n if atom == ((0,), (1,), (2,)):\n assert pid[atom] == pytest.approx(0.10375937481971094)\n elif atom in [((0,), (2,)), ((1,),)]:\n assert pid[atom] == pytest.approx(0.20751874963942191)\n elif atom in [((2,), (0, 1)), ((0,), (1, 2)), ((0, 1), (0, 2)), ((0, 1), (1, 2)), ((0, 2), (1, 2)), ((0, 1, 2),)]:\n assert pid[atom] == pytest.approx(0.14624062518028902)\n elif atom in [((2,),), ((0,),), ((0, 1), (0, 2), (1, 2)), ((0, 2),)]:\n assert pid[atom] == pytest.approx(-0.14624062518028902)\n else:\n assert pid[atom] == pytest.approx(0.0)","def get_copp(data):\n if data is None:\n raise EmptyDataError('[!] Invalid data value')\n\n result = TA.COPP(data)\n if result is None:\n raise IndicatorException\n return result","def c(self):\n pass","def c(self):\n pass","def __repr__(self):\n return \"<PID_onject P: %s I: %s D: %s>\"\\\n % (self.K[0], self.K[1], self.K[2])","def _Dp_table(cls, ci, hi, co, ho, inside_heating):\n cip, hip, cop, hop = ci.phase, hi.phase, co.phase, ho.phase\n dP_c = cls._get_dP(cip, cop)\n dP_h = cls._get_dP(hip, hop)\n if inside_heating:\n dP_in = dP_c\n dP_out = dP_h\n else:\n dP_in = dP_h\n dP_out = dP_c\n return dP_in, dP_out","def forward_cone(self, p):\n return to_rec(zip(p, self.top))","def carbon_prime(C,p,p0):\r\n \r\n if p > p0:\r\n return C\r\n else:\r\n return .03","def _cal_uct(node):\n uct = C_PUCT * node.p * math.sqrt(node.parent.N / (1 + node.N))\n uct += node.Q\n\n return uct","def __init__(self, P):\n self._n = len(P) # control point iterator\n self._P = P\n self._X, self._Y, self._Z, self._W = self.sep() \n self._bc = self._bn()","def uc(p) :\r\n return Components(p, Scale=2)","def __init__(self, p0: Point, p1: Point, c0: Point = None, c1: Point = None) -> None:\n self.p0 = p0\n self.p1 = p1\n self.c0 = c0 if c0 is not None else p0\n self.c1 = c1 if c1 is not None else p1","def c(self):\r\n return self.__c","def basis_group_oriented_p_chains(self, p):\r\n if ((p<0) or (p>self.dimension())):\r\n return 0\r\n else:\r\n c_p = P_chains([], [])\r\n faces = []\r\n values = []\r\n for x in self.p_simplex(p):\r\n if (is_int(x) == True):\r\n faces.append(tuple(tuple_sorted(x)))\r\n values.append(1)\r\n else:\r\n faces.append(tuple(x))\r\n values.append(1)\r\n c_p = P_chains(faces, values)\r\n return c_p","def getC(self):\n\t\treturn self.c","def test_pid_ccs1():\n d = bivariates['gband']\n pid = PID_CCS(d, ((0,), (1,)), (2,))\n assert pid[((0,), (1,))] == pytest.approx(0.81127812445913283)\n assert pid[((0,),)] == pytest.approx(0.5)\n assert pid[((1,),)] == pytest.approx(0.5)\n assert pid[((0, 1),)] == pytest.approx(0.0)","def pc_project(data, pcs):\n if not isinstance(pcs, pd.DataFrame):\n TypeError('Invalid input type: type(pcs) = {}'.format(type(pcs)))\n if isinstance(data, pd.DataFrame):\n res = {sym: {pc: data[sym].dot(pcs[pc]) for pc in pcs} for sym in data}\n return pd.DataFrame(res).T\n elif isinstance(data, pd.Series):\n res = {pc: data.dot(pcs[pc]) for pc in pcs}\n return pd.Series(res, name=data.name)\n else:\n TypeError('Invalid input type: type(data) = {}'.format(type(data)))","def prove_CP() -> Proof:\n # Optional Task 6.7d","def cov2pv(self, P):\n d = 2 * self._dimension\n with torch.no_grad():\n P_pv = torch.zeros(d, d, dtype=P.dtype, device=P.device)\n P_pv[: self._dimension, : self._dimension] = P\n return P_pv","def test_pid_ccs3():\n d = trivariates['anddup']\n pid = PID_CCS(d)\n for atom in pid._lattice:\n if atom == ((0,), (1,), (2,)):\n assert pid[atom] == pytest.approx(0.10375937481971094)\n elif atom in [((0,), (2,)), ((1,),)]:\n assert pid[atom] == pytest.approx(0.20751874963942191)\n elif atom in [((2,), (0, 1)), ((0,), (1, 2)), ((0, 1), (0, 2)), ((0, 1), (1, 2)), ((0, 2), (1, 2)), ((0, 1, 2),)]:\n assert pid[atom] == pytest.approx(0.14624062518028902)\n elif atom in [((2,),), ((0,),), ((0, 1), (0, 2), (1, 2)), ((0, 2),)]:\n assert pid[atom] == pytest.approx(-0.14624062518028902)\n else:\n assert pid[atom] == pytest.approx(0.0)","def factor(P):\n cd = P[-1]\n if P.deg == 0:\n return (cd, defaultdict(int))\n P = P * (1 / cd)\n return (cd, P.factor_unit())","def C ( self ) :\n lst = [ i for i in self.__nums_components ]\n if not lst : return () ## extended fit? no other components?\n elif 1 == len(lst) : return lst[0] ## single component?\n return tuple ( lst )","def C ( self ) :\n lst = [ i for i in self.__nums_components ]\n if not lst : return () ## extended fit? no other components?\n elif 1 == len(lst) : return lst[0] ## single component?\n return tuple ( lst )","def _complement(self, k, p):\n assert self._.d == 2, \"the complement is only defined for two classes\"\n if checkPos(self._.b[0] - self._.c[2]):\n return self._get_class()((k[2], p[2, 2, 1]),\n (Integer(1), p[1, 2, 2]),\n complement=self)\n else:\n return ASParameters._complement(self)","def catmull(P):\n N = P.shape[0]\n Q = np.zeros((2*N-1, 2), 'd')\n Q[0,:] = P[0,:]\n for i in range(0,N-1):\n if i > 0:\n Q[2*i,:] = (P[i-1,:]+6*P[i,:]+P[i+1,:])/8\n Q[2*i+1,:] = (P[i,:]+P[i+1,:])/2\n Q[-1,:] = P[-1,:]\n return Q","def covar(self):\n a, c, d, b = self.to_ccw()\n return a * d - b * c","def is_camp(p):\n return _board_graph[p].space_type == CAMP","def get_cape(temp,pres,dewpt,hght,startp,startt,startdp,totalcape=False): \n\n # Check units\n # Init temp is startt in C, Init dew point is stwrtdp,\n # pressure levels are in hPa \n temp = temp - 273.15 # convert temperature to celsius\n dewpt = dewpt - 273.15 # convert dewpoint to celsius\n pres = pres/100 # convert pressure to hPa\n \n \n inds = np.where( (pres < startp) ) \n tmp = pres[inds]\n del pres\n #pres = tmp[::-1]\n pres = tmp[:]\n del tmp \n startp = startp/100\n \n tmp = temp[inds]\n del temp\n #temp = tmp[::-1]\n temp = tmp[:]\n del tmp \n\n tmp = dewpt[inds]\n del dewpt\n #dewpt = tmp[::-1]\n dewpt = tmp[:]\n del tmp \n\n tmp = hght[inds]\n del hght\n #hght = tmp[::-1]\n hght = tmp[:]\n del tmp \n\n \n # Get Sub-LCL traces \n presdry,tempdry,tempiso=dry_ascent(startp,startt-degCtoK,startdp-degCtoK) \n \n\n # make lcl variables explicit\n P_lcl=presdry[-1]\n T_lcl=tempdry[-1]\n\n # Now lift a wet parcel from the intersection point\n # preswet=linspace(P_lcl,100,101)\n preswet,tempwet=moist_ascent(P_lcl,T_lcl)\n\n # tparcel is the concatenation of tempdry and \n # tempwet, and so on.\n \n tparcel=np.concatenate((tempdry,tempwet[1:]))\n pparcel=np.concatenate((presdry,preswet[1:]))\n\n # Interpolating the environmental profile onto the \n # parcel pressure coordinate\n # tempenv=interp(preswet,pres[::-1],temp[::-1])\n ## NEW, for total column:\n tempenv=interp(pparcel,pres[::-1],temp[::-1])\n\n\n # now solve for the equlibrium levels above LCL\n # (all of them, including unstable ones)\n # eqlev,stab=solve_eq(preswet[::-1],(tempwet-tempenv)[::-1])\n # NEW, for total column:\n # On second thought, we don't really want/need\n # any equilibrium levels below LCL\n # eqlev,stab=solve_eq(pparcel[::-1],(tparcel-tempenv)[::-1])\n # This is equivalent to the old statement :\n eqlev,stab=solve_eq(pparcel[pparcel<=P_lcl][::-1],\\\n (tparcel-tempenv)[pparcel<=P_lcl][::-1])\n\n aa = tparcel-tempenv\n\n # Sorting index by decreasing pressure\n I=np.argsort(eqlev)[::-1]\n eqlev=eqlev[I]; stab=stab[I]\n\n # temperatures at the equilibrium level\n # tempeq=interp(eqlev,preswet[::-1],tempenv[::-1])\n ## NEW, for total column:\n tempeq=interp(eqlev,pparcel[::-1],tparcel[::-1])\n\n # This helps with debugging\n # for ii,eq in enumerate(eqlev):\n # print \"%5.2f %5.2f %2d\"%(eq,tempeq[ii],stab[ii])\n\n # need environmental temperature at LCL\n tenv_lcl=interp(P_lcl,pparcel[::-1],tempenv[::-1])\n\n isstab=np.where(stab==1.,True,False)\n unstab=np.where(stab==1.,False,True) \n\n if eqlev.shape[0]==0:\n # no unstable layers in entire profile\n # because the parcel never crosses the tenv\n P_lfc=float('NaN')\n P_el=float('NaN')\n elif T_lcl>tenv_lcl:\n # check LCL to see if this is unstable\n P_lfc=P_lcl\n if totalcape:\n P_el=eqlev[isstab][-1]\n else:\n P_el=eqlev[isstab][0]\n elif eqlev.shape[0]>1:\n # Parcel is stable at LCL so LFC is the \n # first unstable equilibrium level and \n # \"EQ\" level is the first stable equilibrium \n # level\n P_lfc=eqlev[unstab][0]\n if totalcape:\n P_el=eqlev[isstab][-1]\n else:\n P_el=eqlev[isstab][0]\n else:\n # catch a problem... if there is only\n # one eqlev and it's stable (this is \n # unphysical), then it could be a vertical\n # resolution thing. This is a kind of \n # \"null\" option\n try:\n\t P_el=eqlev[isstab][0]\n P_lfc=eqlev[isstab][0]\n except:\n\t P_el=eqlev[unstab][0]\n P_lfc=eqlev[unstab][0]\t\n\t\n if np.isnan(P_lfc):\n return P_lcl,P_lfc,P_el,0,0\n\n # need to handle case where dwpt is not available \n # above a certain level for any reason. Most simplest \n # thing to do is set it to a reasonably low value; \n # this should be a conservative approach!\n \n #dwpt=dewpt.copy().soften_mask()\n [inds] = np.where(np.isnan(dewpt))\n dwpt = dewpt\n dwpt[inds] = dwpt.min()\n \n # raise ValueError\n #if dwpt[(pres>=P_el).data*(pres<P_lfc).data].mask.any():\n # print \"WARNING: substituting dwpt.min() for masked values of DWPT in this sounding\"\n #dwpt[dwpt.mask]=dwpt.min()\n # dwptenv=interp(preswet,pres[::-1],dwpt[::-1])\n # NEW:\n\n dwptenv=interp(pparcel,pres[::-1],dwpt[::-1])\n\n\n \n #if hght[(pres>=P_el).data].mask.any():\n # raise NotImplementedError, \"TODO: Implement standard atmosphere to substitute missing heights\"\n # hghtenv=interp(preswet,pres[::-1],self.soundingdata['hght'][::-1])\n # NEW:\n hghtenv=interp(pparcel,pres[::-1],hght[::-1])\n \n\n # Areas of POSITIVE Bouyancy\n # cond1=(tempwet>=tempenv)*(preswet<=P_lfc)*(preswet>P_el)\n # NEW:\n cond1=(tparcel>=tempenv)*(pparcel<=P_lfc)*(pparcel>P_el)\n # Areas of NEGATIVE Bouyancy\n # cond2=(tempwet<tempenv)*(preswet<=P_lcl)*(preswet>P_el)\n # NEW:\n if totalcape:\n cond2=(tparcel<tempenv)*(pparcel>P_el)\n else:\n cond2=(tparcel<tempenv)*(pparcel>P_lfc)\n # Do CAPE calculation\n # 1. Virtual temperature of parcel... remember it's saturated above LCL.\n # e_parcel=SatVap(tempwet)\n # Tv_parcel=VirtualTemp(tempwet+degCtoK,preswet*100.,e_parcel)\n # e_env=SatVap(dwptenv)\n # Tv_env=VirtualTemp(tempenv+degCtoK,preswet*100.,e_env)\n # NEW:\n e_parcel=SatVap(tparcel)\n Tv_parcel=VirtualTemp(tparcel+degCtoK,pparcel*100.,e_parcel)\n e_env=SatVap(dwptenv)\n Tv_env=VirtualTemp(tempenv+degCtoK,pparcel*100.,e_env)\n\n CAPE=trapz(9.81*(Tv_parcel[cond1]-Tv_env[cond1])/Tv_env[cond1],hghtenv[cond1])\n CIN=trapz(9.81*(Tv_parcel[cond2]-Tv_env[cond2])/Tv_env[cond2],hghtenv[cond2])\n\n return P_lcl,P_lfc,P_el,CAPE,CIN","def computeProp(self):\n self.chem = {}\n for key in self.config.C:\n if key in ['P', 'T', 'Z', 'DZ']:\n continue\n self.chem[key] = chemistry.ConstituentProperties(key)\n\n # nAtm = len(self.gas[self.config.C['P']])\n self.property = []\n for op in self.config.LP:\n self.property.append([])\n zOffset = 0.0\n iOffset = 0\n psep = 1.0E6\n for i, zv in enumerate(self.gas[self.config.C['Z']]): # find the nearest z value at p_ref\n P = self.gas[self.config.C['P']][i]\n if abs(P - self.config.p_ref) < psep:\n psep = abs(P - self.config.p_ref)\n iOffset = i\n zOffset = self.gas[self.config.C['Z']][iOffset]\n z_at_p_ref = self.config.Req\n\n for i, zv in enumerate(self.gas[self.config.C['Z']]):\n T = self.gas[self.config.C['T']][i]\n P = self.gas[self.config.C['P']][i]\n self.property[self.config.LP['P']].append(P)\n self.property[self.config.LP['Z']].append(zv)\n rr = z_at_p_ref + zv - zOffset\n # note that this is the \"actual\"z along equator referenced to planet center (aka radius)\n self.property[self.config.LP['R']].append(rr)\n # ##set mean amu\n amulyr = 0.0\n for key in self.chem:\n amulyr += self.chem[key].amu * self.gas[self.config.C[key]][i]\n self.property[self.config.LP['AMU']].append(amulyr)\n # ##set GM pre-calc (normalized further down) and get lapse rate\n if not i:\n self.property[self.config.LP['GM']].append(0.0)\n self.property[self.config.LP['LAPSE']].append(0.0)\n self.property[self.config.LP['LAPSEP']].append(0.0)\n else:\n rho = (amulyr * P) / (chemistry.R * T)\n dr = abs(zv - self.gas[self.config.C['Z']][i - 1])\n dV = 4.0 * np.pi * (rr**2) * dr\n dM = 1.0e11 * rho * dV\n GdM = self.property[self.config.LP['GM']][i - 1] + chemistry.GravConst * dM\n # in km3/s2\n # mass added as you make way into atmosphere by radius r (times G)\n self.property[self.config.LP['GM']].append(GdM)\n dT = abs(T - self.gas[self.config.C['T']][i - 1])\n dP = abs(P - self.gas[self.config.C['P']][i - 1])\n self.property[self.config.LP['LAPSE']].append(dT / dr)\n self.property[self.config.LP['LAPSEP']].append(dT / dP)\n # ##set refractivity and index of refraction\n refrlyr = 0.0\n for key in self.chem:\n refrlyr += self.chem[key].refractivity(T=T) * self.gas[self.config.C[key]][i]\n refrlyr = refrlyr * P * (293.0 / T)\n self.property[self.config.LP['REFR']].append(refrlyr)\n nlyr = refrlyr / 1.0E6 + 1.0\n self.property[self.config.LP['N']].append(nlyr)\n\n # ##Now need to normalize GM to planet and calculate scale height (H)\n GMnorm = self.property[self.config.LP['GM']][iOffset] # G*(Mass added by p_ref)\n for i, mv in enumerate(self.property[self.config.LP['GM']]):\n gm = self.config.GM_ref - (mv - GMnorm)\n self.property[self.config.LP['GM']][i] = gm\n little_g = gm / self.property[self.config.LP['R']][i]**2\n m_bar = self.property[self.config.LP['AMU']][i]\n T = self.gas[self.config.C['T']][i]\n self.property[self.config.LP['H']].append((chemistry.R * T) /\n (little_g * m_bar) / 1000.0)\n self.property[self.config.LP['g']].append(little_g)\n self.property = np.array(self.property)","def set_norm(self, P={}, C={}, c_t=\"pp\"):\n N = dict()\n if isinstance(P, list):\n Pl = P\n else:\n Pl = [P]\n if isinstance(C, list):\n Cl = C\n else:\n Cl = [C]\n if len(Pl) > 0:\n N['comp_dim'] = len(Pl)\n if len(Cl) > 0:\n if len(Cl) != len(Pl):\n raise ValueError(\"Need same number of principal parts and coefficients to set!\")\n keys = list(Cl[0].keys())\n for j in range(1, N['comp_dim']):\n if list(Cl[j].keys()) != keys:\n raise ValueError(\"Need to set the same coefficients! (or call the method more than once)\")\n else:\n Cl = []\n for j in range(N['comp_dim']):\n Cl.append(C)\n N['Vals'] = list()\n N['Vals'] = list()\n N['SetCs'] = list()\n for i in range(N['comp_dim']):\n N['Vals'].append({})\n N['Vals'].append({})\n N['SetCs'].append([])\n D = self.multiplier().D()\n for j in range(len(D)):\n a = D[j]\n x = self.multiplier().Qv[j]\n # N['Vals'][i][(0,j)]=dict()\n if x == 0:\n if c_t == \"pp\":\n # N['comp_dim']=N['comp_dim']+1\n N['SetCs'][i].append((j, 0))\n if (0, j) in Pl[i]:\n N['Vals'][i][(j, 0)] = Pl[i][(j, 0)]\n else:\n N['Vals'][i][(j, 0)] = 0 # P[(0,0)]\n elif x < 0 and self._holomorphic:\n N['SetCs'][i].append((j, 0))\n N['Vals'][i][(j, 0)] = 0 # P[(0,0)]\n\n for (r, n) in Cl[i].keys():\n if(N['SetCs'][i].count((r, n)) == 0):\n N['SetCs'][i].append((r, n))\n N['Vals'][i][(r, n)] = Cl[i][(r, n)]\n return N","def ocpc(self):\n return self._ocpc","def project_p2c_image(src, H): #---- project p to c (whole image)\r\n Z = H[2]; phi= H[3]; S= H[4]; TV= H[5]; TU= H[6];\r\n rows= src.shape[0]; cols= src.shape[1]; # get image size info\r\n diag= np.sqrt(rows**2+cols**2); # diagnol length\r\n radi= int(diag*S/2*1.1); # radius of new plot should be larger\r\n dest= np.zeros((radi*2,radi*2,3)) # projection result\r\n cosf= np.cos(phi); sinf= np.sin(phi); # rotation parameters\r\n u0 = radi-(TU-np.floor(TU)); # only process fractional part\r\n v0 = radi-(TV-np.floor(TV)); # of TU and TV\r\n kv = np.arange(0,radi*2); # \r\n #--- ---\r\n srcx= src.copy();\r\n srcx[0,:,:]=0; srcx[rows-2:rows,:,:]=0; \r\n srcx[:,0,:]=0; srcx[:,cols-2:cols,:]=0;\r\n #--- mapping ---\r\n for ku in range(0,radi*2): # scan each column\r\n UP = (ku-u0)/S; VP= (kv-v0)/S; # correct tu,tv,s\r\n RP =-sinf*UP + cosf*VP;\r\n CP = cosf*UP + sinf*VP; # correct rotation phi\r\n theta= CP/Z; # horizontal angle\r\n C = Z*np.tan(theta) + cols/2;\r\n R = RP/np.cos(theta) + rows/2;\r\n #--- interpolation ---\r\n C = np.minimum(np.maximum(C, 0), cols-2);\r\n R = np.minimum(np.maximum(R, 0), rows-2); \r\n C0 = np.floor(C).astype(int); C1= C-C0; \r\n R0 = np.floor(R).astype(int); R1= R-R0; \r\n for m in range(0,3):\r\n pixel = srcx[R0 ,C0 ,m]*(1-R1)*(1-C1);\r\n pixel+= srcx[R0 ,C0+1,m]*(1-R1)*( C1);\r\n pixel+= srcx[R0+1,C0 ,m]*( R1)*(1-C1);\r\n pixel+= srcx[R0+1,C0+1,m]*( R1)*( C1);\r\n dest[kv,ku,m]= pixel; \r\n return dest","def circpol(self):\n return self._circpol","def getpc(cls, attrib=None):\n return PC","def c(self):\n return self._c","def cp(temp,pres):\n g_tt = liq_g(2,0,temp,pres)\n cp = -temp * g_tt\n return cp","def npToPcd(np):\n\n pcd = o3d.geometry.PointCloud()\n pcd.points = o3d.utility.Vector3dVector(np)\n\n return pcd","def C(self,X):\n return(\n 0\n )","def p2c(v: Union[Tuple, List, np.ndarray]) -> np.ndarray:\n if not isinstance(v, np.ndarray):\n v = np.array(v)\n if len(v.shape) == 1:\n v = np.array([v])\n return (np.array([np.cos(v[:, 1]), np.sin(v[:, 1])]) * v[:, 0]).T","def crossSection(self, E_p):\n \n barn = 1e-24 # centimeter^2\n mb = 1e-3*barn\n \n L = np.log(E_p/1e3)\n \n sigma = 34.3 + 1.88*L + 0.25*L*L\n E_th = self.mp_c2 + self.mpi_c2 + self.mpi_c2**2./self.mp_c2\n \n return sigma * ( 1 - (E_th/E_p)**4. )**2. * mb","def to_compas(self):\n return Point(self.x, self.y, self.z)","def param2Conic(tParam):\r\n #Auxiliary vaiables\r\n cx, cy, l, w, tau = tParam\r\n s=np.sin(tau)\r\n c=np.cos(tau)\r\n\r\n # Conic parameters\r\n A = (w*c)**2+(l*s)**2\r\n B = 2*c*s*(l**2-w**2)\r\n C = (w*s)**2+(l*c)**2\r\n D = -2*A*cx - cy*B\r\n E = -2*C*cy - cx**B\r\n F = -(l*w)**2 + A*cx**2 + B*cx*cy + C*cy**2\r\n\r\n return (A, B, C, D, E, F)","def getConc(fileID, spc):\r\n\r\n dataKey = rmn.fstinf(fileID, nomvar=spc, ip1=ip1)['key']\r\n dataRec = rmn.fstluk(dataKey)\r\n concData = dataRec['d']\r\n return concData, dataKey, dataRec","def Q2C(self, q):\n\n #q = q.squeeze();\n C = np.empty((3,3));\n\tC[0,0] = (q[0]**2.0) + (q[1]**2.0) - (q[2]**2.0) - (q[3]**2.0);\n\tC[0,1] = 2.0 * ((q[1]*q[2]) + (q[0]*q[3]));\n\tC[0,2] = 2.0 * ((q[1]*q[3]) - (q[0]*q[2]));\n\n\tC[1,0] = 2.0 * ((q[1]*q[2]) - (q[0]*q[3]));\n\tC[1,1] = (q[0]**2.0) - (q[1]**2.0) + (q[2]**2.0) - (q[3]**2.0);\n\tC[1,2] = 2.0 * ((q[2]*q[3]) + (q[0]*q[1]));\n\n\tC[2,0] = 2.0 * ((q[1]*q[3]) + (q[0]*q[2]));\n\tC[2,1] = 2.0 * ((q[2]*q[3]) - (q[0]*q[1]));\n\tC[2,2] = (q[0]**2.0) - (q[1]**2.0) - (q[2]**2.0) + (q[3]**2.0);\n\n return C","def C(self, u, v):\n pass","def projective_point(p):\n from sage.rings.integer import GCD_list, LCM_list\n try:\n p_gcd = GCD_list([x.numerator() for x in p])\n p_lcm = LCM_list([x.denominator() for x in p])\n except AttributeError:\n return p\n scale = p_lcm / p_gcd\n return [scale * x for x in p]","def Pp(nccd):\n return (128.1-56.9) * (nccd - 1) / (6-1) + 56.9","def get_principal_parts(self, PP):\n if not isinstance(PP, (dict, list)):\n raise TypeError(\"Need principal part in form of dictionary! Got:{0}\".format(PP))\n if isinstance(PP, dict):\n Ptmp = [PP]\n else:\n Ptmp = PP\n P0 = list()\n for p in Ptmp:\n d = {}\n for t, c in p.items():\n if isinstance(t, tuple) and len(t) == 2:\n (r, m) = t\n if QQ(r).denominator() > 1:\n order = self.multiplier().weil_module().order() # = 2N\n r = (r * order) % (order)\n if r not in self.multiplier().weil_module().D():\n raise ValueError(f\"Need r either x/2N or r = x with x in {self.multiplier().D()}. Got:{r}\")\n d[(r, m)] = c\n elif isinstance(t, (int, Integer)):\n if not self.is_Heegner_disc(t):\n raise ValueError(\"Need discriminant satisfying Heegner condition, i.e. square mod {0}. Got: {1}\".format(self.multiplier().level(), t))\n r_and_n = rn_from_D(self.multiplier(), t)\n if not r_and_n:\n raise ValueError(f\"D={t} is not a compatible discriminant!\")\n (r, m) = r_and_n\n d[(r, m)] = c\n # Remember that there are two solutions to D = +- r^2 mod 44\n # if we are not using symmetry then we set this as part of the principal part\n minus_r = self.multiplier().weil_module().D()[-r]\n d = [(minus_r, m)] = self.multiplier().weil_module().sigma_invariant()**4 * c\n\n # Also check that principal part adheres to the symmetry if present\n if self.sym_type():\n minus_r = self.multiplier().weil_module().neg_index(r)\n if (minus_r, m) in p:\n if p[(minus_r, m)] != self.sym_type() * p[(r, m)]:\n raise ValueError(\"Need symmetric principal part! Got:{0}\".format(PP))\n P0.append(d)\n return P0","def p(self, p: ComType):\n if isinstance(p, complex):\n self._pwr = p\n else:\n self._pwr = complex(p, 0)","def convert_polygon_COCO_type(element: list, height: int, width: int) -> Dict:\n rles = maskUtils.frPyObjects(element, height, width)\n p = maskUtils.merge(rles)\n return p","def primitive(f):\n cont, F = dmp_ground_primitive(f.rep, f.lev, f.dom)\n return cont, f.per(F)","def FtoC(F):\n c = ((F-32)/9.)*5\n return c","def transformedRect(self, P):\n A = self.transformPointHeight(P, -1)\n B = self.transformPointHeight(P)\n C = self.transformPoint(P)\n D = self.transformPoint(P, -1)\n return A, B, C, D","def Cima(self):\n if(self.Pila_Vacia()=='true'):\n return \"Pila Vacia\"\n else:\n return self.pila[self.puntero]","def codonComposition(self):#works\n return {codon: self.countDicNuc.get(codon) for codon in self.rnaCodonTable.keys()}","def ceps_coeff(sig,coefNumber):\n\n est=lpc_coef(sig,coefNumber)\n cc=lpcar2cc(est)\n if len(cc)==1:\n cc=float(cc)\n else:\n cc=tuple(cc)\n\n return cc","def clogP(row):\n m = Chem.MolFromSmiles(row.SMILES)\n clogp = Descriptors.MolLogP(m)\n\n return clogp","def get_value(self, c_puct):\n self._u = (c_puct * self._P *\n np.sqrt(self._parent._n_visits) / (1 + self._n_visits))\n return self._Q + self._u","def get_value(self, c_puct):\n self._u = (c_puct * self._P *\n np.sqrt(self._parent._n_visits) / (1 + self._n_visits))\n return self._Q + self._u","def get_value(self, c_puct):\n self._u = (c_puct * self._P *\n np.sqrt(self._parent._n_visits) / (1 + self._n_visits))\n return self._Q + self._u","def get_value(self, c_puct):\n self._u = (c_puct * self._P *\n np.sqrt(self._parent._n_visits) / (1 + self._n_visits))\n return self._Q + self._u","def get_value(self, c_puct):\n self._u = (c_puct * self._P *\n np.sqrt(self._parent._n_visits) / (1 + self._n_visits))\n return self._Q + self._u","def _mps_decompose_CA(self, A):\n Dl, d, Dr = A.shape\n Q, C = qr(np.reshape(A, [Dl, d * Dr]).T)\n nC = nfactor(C)\n # nC = max(abs(C.min()), abs(C.max()))\n if C.shape == (1, 1):\n Q *= np.sign(C.flat[0])\n C = np.ones((1, 1))\n else:\n C = (C.T) / nC\n Dl = C.shape[1]\n Q = np.reshape(Q.T, [Dl, d, Dr])\n return C, Q, nC, Dl","def __init__(self, p) -> None:\n self._p = p\n self._delegate = TwoQubitAsymmetricDepolarizingChannel(p / 15, p / 15, p / 15,\n p / 15, p / 15, p / 15,\n p / 15, p / 15, p / 15,\n p / 15, p / 15, p / 15,\n p / 15, p / 15, p / 15)","def pca(self):\n self.pca_mean = self.X.mean(axis=1)\n X_meanC = self.X - self.pca_mean[:, None]\n (self.pca_U, self.pca_S, self.pca_V) = np.linalg.svd(X_meanC, full_matrices=False)\n self.pc_weights = np.dot(np.diag(self.pca_S), self.pca_V)\n self.pc_stdevs = np.std(self.pc_weights, axis=1)","def cpf(self):\n return self._cpf","def create_pf_coils(self) -> list:\n\n # from diagram\n lower_lefts = np.array(\n [\n (345.5429497568881, -788.0537547271744),\n (800.6482982171799, -703.3664235548353),\n (1160.9400324149108, -272.69381415451096),\n (1158.833063209076, 268.97622906537015),\n (798.5413290113453, 621.6369529983793),\n (345.5429497568881, 707.7795786061589),\n ]\n )\n\n top_rights = np.array(\n [\n (511.99351701782825, -690.4038357644516),\n (880.7131280388979, -611.8280659103187),\n (1222.0421393841166, -162.37506753106413),\n (1230.4700162074555, 379.2780929227446),\n (861.7504051863858, 681.9523230686117),\n (446.677471636953, 803.4508373851974),\n ]\n )\n\n outboard_pf_coils = []\n for counter, (top_right, lower_left) in enumerate(zip(top_rights, lower_lefts), 1):\n pf_coil = paramak.PoloidalFieldCoilFP(\n corner_points=(top_right, lower_left),\n rotation_angle=self.rotation_angle,\n name=f\"outboard_pf_coils_{counter}\",\n )\n outboard_pf_coils.append(pf_coil)\n\n x_inner, x_outer = 132.73905996758506, 202.26904376012968\n\n pf_coils_1 = paramak.RotateStraightShape(\n points=[\n (x_inner, -600),\n (x_outer, -600),\n (x_outer, -400),\n (x_inner, -400),\n ],\n rotation_angle=self.rotation_angle,\n name=\"pf_coil_1\",\n )\n\n pf_coils_2 = paramak.RotateStraightShape(\n points=[\n (x_inner, -400),\n (x_outer, -400),\n (x_outer, -200),\n (x_inner, -200),\n ],\n rotation_angle=self.rotation_angle,\n name=\"pf_coil_2\",\n )\n\n pf_coils_3 = paramak.RotateStraightShape(\n points=[\n (x_inner, -200),\n (x_outer, -200),\n (x_outer, 0),\n (x_inner, 0),\n ],\n rotation_angle=self.rotation_angle,\n name=\"pf_coil_3\",\n )\n\n pf_coils_4 = paramak.RotateStraightShape(\n points=[\n (x_inner, 0),\n (x_outer, 0),\n (x_outer, 200),\n (x_inner, 200),\n ],\n rotation_angle=self.rotation_angle,\n name=\"pf_coil_4\",\n )\n\n pf_coils_5 = paramak.RotateStraightShape(\n points=[\n (x_inner, 200),\n (x_outer, 200),\n (x_outer, 400),\n (x_inner, 400),\n ],\n rotation_angle=self.rotation_angle,\n name=\"pf_coil_5\",\n )\n\n pf_coils_6 = paramak.RotateStraightShape(\n points=[\n (x_inner, 400),\n (x_outer, 400),\n (x_outer, 600),\n (x_inner, 600),\n ],\n rotation_angle=self.rotation_angle,\n name=\"pf_coil_6\",\n )\n\n return outboard_pf_coils + [\n pf_coils_1,\n pf_coils_2,\n pf_coils_3,\n pf_coils_4,\n pf_coils_5,\n pf_coils_6,\n ]","def get_ppo(data):\n if data is None:\n raise EmptyDataError('[!] Invalid data value')\n\n result = TA.PPO(data)\n if result is None:\n raise IndicatorException\n return result","def algi(C):\n return np.array([ C[0,2], C[1,2], C[1,0] ])","def get_circle_3p(self, A, B, C):\n return circle_3p(A, B, C)","def structure_output(sequence, P):\n structure = [\".\" for _ in range(len(sequence))]\n for pair in P:\n structure[pair[0]] = \"(\"\n structure[pair[1]] = \")\"\n return \"\".join(structure)","def cep(self):\n return self._cep","def getGcd(self, P):\n if self.getDegree() == -1 and P.getDegree() == -1:\n raise PolynomialError(\"Impossible to get gcd of 2 zero polynomial\")\n a = Polynomial(self.__coefficients)\n b = Polynomial(P.__coefficients)\n temp = None\n while b.getDegree() >= 0:\n temp = a.divide(b)[1]\n a = b\n b = temp\n return a.divide(Polynomial([a.__coefficients[a.getDegree()]]))[0]","def algorithm_1_2(p, c, x):\n\n q = np.array(c, dtype=np.float64)\n\n for k in range(1, p + 1):\n for j in range(0, p - k + 1):\n q[j] = (1 - x) * q[j] + x * q[j + 1]\n return q[0]","def cp_to_gp(cp):\n if isinstance(cp, ExchangeForPopulation):\n return [\"population\", cp.species_index, cp.played_card_index]\n else:\n return None","def bpc_to_pc(pc):\n return float(pc) * 100 / 0x8000"],"string":"[\n \"def CPy_Punto(C, P):\\n \\n P['x'][0] = C.x[0]\\n P['x'][1] = C.x[1]\\n P['x'][2] = C.x[2]\\n\\n P['y'][0] = C.y[0]\\n P['y'][1] = C.y[1]\\n P['y'][2] = C.y[2]\\n\\n P['D'] = C.D\\n P['a'], P['b'], P['c'] = C.a, C.b, C.c\\n P['d'], P['e'], P['f'] = C.d, C.e, C.f\",\n \"def findpc(self):\\n u = -(-can.C.len() + self.P.len() + can.R)/self.V.len()\\n if u >= 0:\\n return self.P + self.V.scale(u), u\\n else:\\n u = (can.C.len() - self.P.len() + can.R)/self.V.len()\\n return self.P + self.V.scale(u), u\",\n \"def C_P(self):\\n return self.generic_getter(\\n get_heat_capacity_pressure, \\\"C_P\\\", \\\"convert_heat_capacity\\\"\\n )\",\n \"def init(self):\\n # pC es un puntero a la imagen en C\\n nP = len(self.puntos)\\n self.pC = c_colision.malloc_tren(nP)\\n # Estas magnitudes se inicializan en la imagen y\\n # ya no se tocan desde python\\n for i in range(nP):\\n self.pC.contents.P[i]= pointer(PyC_Punto(self.puntos[i]))\\n self.colision = self.pC.contents.colision = 0\",\n \"def cov2pv(self, P):\\n return P\",\n \"def distancia_puntos(c, p):\\n return sqrt((c[0] - p[0]) ** 2 + (c[1] - p[1]) ** 2)\",\n \"def from_thermo(T_C, p):\\n\\ty = y_from_p(p)\\n\\tx = x_from_Tp(T_C+C_to_K, p)\\n\\treturn x, y\",\n \"def convert_kicad_coor(edif_pt):\\n scale = 10\\n return [edif_pt[0] * scale, +edif_pt[1] * scale]\",\n \"def DPc(R,Pc):\\n return r2*(K2**B2/(K2**B2 + (A)**B2))*(S/(S + R*Pc + Pc)) \\\\\\n *(R*M)/(K3 + R*M)*Pc - gc*Pc\",\n \"def c_cd2cp(s, N):\\n color, move = s.strip().split()\\n # print('color:{} move:{}'.format(color,move))\\n c = color2c(color)\\n p = cd2p(move, N)\\n return c, p\",\n \"def COC(fp, csiz):\\n lcoc = unpack('>H', fp.read(2))[0]\\n print('Csiz', csiz)\\n if csiz < 257:\\n ccoc = unpack('B', fp.read(1))[0]\\n else:\\n ccoc = unpack('>H', fp.read(2))[0]\\n scoc = unpack('B', fp.read(1))[0]\\n\\n _decomp_levels = unpack('B', fp.read(1))[0]\\n _block_width = unpack('B', fp.read(1))[0]\\n _block_height = unpack('B', fp.read(1))[0]\\n _block_style = unpack('B', fp.read(1))[0]\\n _transform = unpack('B', fp.read(1))[0]\\n\\n _precincts = []\\n has_precincts = _get_bit(scoc, 7)\\n if has_precincts == 1:\\n for ii in range(_decomp_levels + 1):\\n _precincts.append(unpack('B', fp.read(1))[0])\\n\\n info = {\\n 'Lcoc' : lcoc,\\n 'Ccoc' : ccoc,\\n 'Scoc' : scoc,\\n 'SPcoc' : {\\n 'decomp_levels' : _decomp_levels,\\n 'block_width' : _block_width,\\n 'block_height' : _block_height,\\n 'block_style' : _block_style,\\n 'transform' : _transform\\n }\\n }\\n\\n if has_precincts:\\n info['SPcoc']['precincts'] = _precincts\\n\\n return info\",\n \"def calculate_cd_transpose(P,C):\\n conf_deg_t = C.T.dot(P)\\n a1 = P.sum(axis=0)\\n B = np.tile(a1, (C.shape[1], 1))\\n CD_t = 1. * conf_deg_t / (B + np.spacing(0))\\n return CD_t\",\n \"def t2c(x):\\n dx = to_dlpack(x)\\n return cp.fromDlpack(dx)\",\n \"def cov2pv(self, P):\\n raise NotImplementedError\",\n \"def C(self):\\n return None if self.pC is None else self.pC.buf\",\n \"def ppl_convert(P):\\n if isinstance(P, ppl.C_Polyhedron):\\n return P\\n gs = ppl.Generator_System()\\n for v in P.vertices_list():\\n gs.insert(ppl.point(sum(int(j) * ppl.Variable(i) for i,j in enumerate(v))))\\n for r in P.rays_list():\\n gs.insert(ppl.ray(sum(int(j) * ppl.Variable(i) for i,j in enumerate(r))))\\n for l in P.lines_list():\\n gs.insert(ppl.line(sum(int(j) * ppl.Variable(i) for i,j in enumerate(l))))\\n return ppl.C_Polyhedron(gs)\",\n \"def cima(pila):\\n return pila.datos[pila.tope]\",\n \"def polymul(c,P):\\n return [c*x for x in P]\",\n \"def project_p2c_points(R, C, H): #---- project to cylindrical\\r\\n Rc= H[0]/2; Cc= H[1]/2; # center coordinate\\r\\n phi = H[3]; S= H[4]; # rotation angle and sizing\\r\\n Tv = H[5]; Tu= H[6]; # displacement\\r\\n COSF= np.cos(phi); SINF= np.sin(phi); # \\r\\n U = Tu + S*( COSF*(C- Cc)- SINF*(R- Rc) ); \\r\\n V = Tv + S*( SINF*(C- Cc)+ COSF*(R- Rc) );\\r\\n return V, U\",\n \"def translate( t, P ): \\n assert t.shape[0]==P.n # Dimension match \\n if P.type=='AH_polytope':\\n return pp.AH_polytope(t=t+P.t,T=P.T,P=P.P)\\n elif P.type=='zonotope':\\n return pp.zonotope(x=t+P.x,G=P.G)\\n elif P.type==\\\"H_polytope\\\":\\n return pp.H_polytope(H=P.H,h=P.h+np.dot(P.H,t))\\n else:\\n return ValueError('Polytope type: ',P.type,\\\" Not recognized\\\")\",\n \"def cp2p(np.ndarray[DTYPE_t, ndim=1] cp, DTYPE_t alpha, DTYPE_t rh, DTYPE_t rw):\\n#def cp2p( cp, alpha, rh, rw):\\n\\tdirH=np.array([np.cos(alpha),np.sin(alpha)])\\n\\tdirW=np.array([np.sin(alpha),-np.cos(alpha)])\\n\\treturn np.array( [ _r+cp for _r in [rh*dirH+rw*dirW, -rh*dirH+rw*dirW, -rh*dirH-rw*dirW, rh*dirH-rw*dirW] ]).transpose()\",\n \"def p2cd(p, N):\\n a = p2a(p, N) # OK\\n (r, c) = a2rc(a, N) # OK\\n return rc2cd(r, c, N)\",\n \"def projectPoint(self,p):\\n a,b,c = self.a, self.b, self.c\\n x,y = p\\n return numpy.array( [ b*(x*b-y*a) - c*a, a*(y*a-x*b) - c*b ] )\",\n \"def piece_coor(self):\\n return self.piece_type[self.rotation]\",\n \"def test_pid_ccs2():\\n d = trivariates['anddup']\\n pid = PID_CCS(d, [[0], [1], [2]], [3])\\n for atom in pid._lattice:\\n if atom == ((0,), (1,), (2,)):\\n assert pid[atom] == pytest.approx(0.10375937481971094)\\n elif atom in [((0,), (2,)), ((1,),)]:\\n assert pid[atom] == pytest.approx(0.20751874963942191)\\n elif atom in [((2,), (0, 1)), ((0,), (1, 2)), ((0, 1), (0, 2)), ((0, 1), (1, 2)), ((0, 2), (1, 2)), ((0, 1, 2),)]:\\n assert pid[atom] == pytest.approx(0.14624062518028902)\\n elif atom in [((2,),), ((0,),), ((0, 1), (0, 2), (1, 2)), ((0, 2),)]:\\n assert pid[atom] == pytest.approx(-0.14624062518028902)\\n else:\\n assert pid[atom] == pytest.approx(0.0)\",\n \"def get_copp(data):\\n if data is None:\\n raise EmptyDataError('[!] Invalid data value')\\n\\n result = TA.COPP(data)\\n if result is None:\\n raise IndicatorException\\n return result\",\n \"def c(self):\\n pass\",\n \"def c(self):\\n pass\",\n \"def __repr__(self):\\n return \\\"<PID_onject P: %s I: %s D: %s>\\\"\\\\\\n % (self.K[0], self.K[1], self.K[2])\",\n \"def _Dp_table(cls, ci, hi, co, ho, inside_heating):\\n cip, hip, cop, hop = ci.phase, hi.phase, co.phase, ho.phase\\n dP_c = cls._get_dP(cip, cop)\\n dP_h = cls._get_dP(hip, hop)\\n if inside_heating:\\n dP_in = dP_c\\n dP_out = dP_h\\n else:\\n dP_in = dP_h\\n dP_out = dP_c\\n return dP_in, dP_out\",\n \"def forward_cone(self, p):\\n return to_rec(zip(p, self.top))\",\n \"def carbon_prime(C,p,p0):\\r\\n \\r\\n if p > p0:\\r\\n return C\\r\\n else:\\r\\n return .03\",\n \"def _cal_uct(node):\\n uct = C_PUCT * node.p * math.sqrt(node.parent.N / (1 + node.N))\\n uct += node.Q\\n\\n return uct\",\n \"def __init__(self, P):\\n self._n = len(P) # control point iterator\\n self._P = P\\n self._X, self._Y, self._Z, self._W = self.sep() \\n self._bc = self._bn()\",\n \"def uc(p) :\\r\\n return Components(p, Scale=2)\",\n \"def __init__(self, p0: Point, p1: Point, c0: Point = None, c1: Point = None) -> None:\\n self.p0 = p0\\n self.p1 = p1\\n self.c0 = c0 if c0 is not None else p0\\n self.c1 = c1 if c1 is not None else p1\",\n \"def c(self):\\r\\n return self.__c\",\n \"def basis_group_oriented_p_chains(self, p):\\r\\n if ((p<0) or (p>self.dimension())):\\r\\n return 0\\r\\n else:\\r\\n c_p = P_chains([], [])\\r\\n faces = []\\r\\n values = []\\r\\n for x in self.p_simplex(p):\\r\\n if (is_int(x) == True):\\r\\n faces.append(tuple(tuple_sorted(x)))\\r\\n values.append(1)\\r\\n else:\\r\\n faces.append(tuple(x))\\r\\n values.append(1)\\r\\n c_p = P_chains(faces, values)\\r\\n return c_p\",\n \"def getC(self):\\n\\t\\treturn self.c\",\n \"def test_pid_ccs1():\\n d = bivariates['gband']\\n pid = PID_CCS(d, ((0,), (1,)), (2,))\\n assert pid[((0,), (1,))] == pytest.approx(0.81127812445913283)\\n assert pid[((0,),)] == pytest.approx(0.5)\\n assert pid[((1,),)] == pytest.approx(0.5)\\n assert pid[((0, 1),)] == pytest.approx(0.0)\",\n \"def pc_project(data, pcs):\\n if not isinstance(pcs, pd.DataFrame):\\n TypeError('Invalid input type: type(pcs) = {}'.format(type(pcs)))\\n if isinstance(data, pd.DataFrame):\\n res = {sym: {pc: data[sym].dot(pcs[pc]) for pc in pcs} for sym in data}\\n return pd.DataFrame(res).T\\n elif isinstance(data, pd.Series):\\n res = {pc: data.dot(pcs[pc]) for pc in pcs}\\n return pd.Series(res, name=data.name)\\n else:\\n TypeError('Invalid input type: type(data) = {}'.format(type(data)))\",\n \"def prove_CP() -> Proof:\\n # Optional Task 6.7d\",\n \"def cov2pv(self, P):\\n d = 2 * self._dimension\\n with torch.no_grad():\\n P_pv = torch.zeros(d, d, dtype=P.dtype, device=P.device)\\n P_pv[: self._dimension, : self._dimension] = P\\n return P_pv\",\n \"def test_pid_ccs3():\\n d = trivariates['anddup']\\n pid = PID_CCS(d)\\n for atom in pid._lattice:\\n if atom == ((0,), (1,), (2,)):\\n assert pid[atom] == pytest.approx(0.10375937481971094)\\n elif atom in [((0,), (2,)), ((1,),)]:\\n assert pid[atom] == pytest.approx(0.20751874963942191)\\n elif atom in [((2,), (0, 1)), ((0,), (1, 2)), ((0, 1), (0, 2)), ((0, 1), (1, 2)), ((0, 2), (1, 2)), ((0, 1, 2),)]:\\n assert pid[atom] == pytest.approx(0.14624062518028902)\\n elif atom in [((2,),), ((0,),), ((0, 1), (0, 2), (1, 2)), ((0, 2),)]:\\n assert pid[atom] == pytest.approx(-0.14624062518028902)\\n else:\\n assert pid[atom] == pytest.approx(0.0)\",\n \"def factor(P):\\n cd = P[-1]\\n if P.deg == 0:\\n return (cd, defaultdict(int))\\n P = P * (1 / cd)\\n return (cd, P.factor_unit())\",\n \"def C ( self ) :\\n lst = [ i for i in self.__nums_components ]\\n if not lst : return () ## extended fit? no other components?\\n elif 1 == len(lst) : return lst[0] ## single component?\\n return tuple ( lst )\",\n \"def C ( self ) :\\n lst = [ i for i in self.__nums_components ]\\n if not lst : return () ## extended fit? no other components?\\n elif 1 == len(lst) : return lst[0] ## single component?\\n return tuple ( lst )\",\n \"def _complement(self, k, p):\\n assert self._.d == 2, \\\"the complement is only defined for two classes\\\"\\n if checkPos(self._.b[0] - self._.c[2]):\\n return self._get_class()((k[2], p[2, 2, 1]),\\n (Integer(1), p[1, 2, 2]),\\n complement=self)\\n else:\\n return ASParameters._complement(self)\",\n \"def catmull(P):\\n N = P.shape[0]\\n Q = np.zeros((2*N-1, 2), 'd')\\n Q[0,:] = P[0,:]\\n for i in range(0,N-1):\\n if i > 0:\\n Q[2*i,:] = (P[i-1,:]+6*P[i,:]+P[i+1,:])/8\\n Q[2*i+1,:] = (P[i,:]+P[i+1,:])/2\\n Q[-1,:] = P[-1,:]\\n return Q\",\n \"def covar(self):\\n a, c, d, b = self.to_ccw()\\n return a * d - b * c\",\n \"def is_camp(p):\\n return _board_graph[p].space_type == CAMP\",\n \"def get_cape(temp,pres,dewpt,hght,startp,startt,startdp,totalcape=False): \\n\\n # Check units\\n # Init temp is startt in C, Init dew point is stwrtdp,\\n # pressure levels are in hPa \\n temp = temp - 273.15 # convert temperature to celsius\\n dewpt = dewpt - 273.15 # convert dewpoint to celsius\\n pres = pres/100 # convert pressure to hPa\\n \\n \\n inds = np.where( (pres < startp) ) \\n tmp = pres[inds]\\n del pres\\n #pres = tmp[::-1]\\n pres = tmp[:]\\n del tmp \\n startp = startp/100\\n \\n tmp = temp[inds]\\n del temp\\n #temp = tmp[::-1]\\n temp = tmp[:]\\n del tmp \\n\\n tmp = dewpt[inds]\\n del dewpt\\n #dewpt = tmp[::-1]\\n dewpt = tmp[:]\\n del tmp \\n\\n tmp = hght[inds]\\n del hght\\n #hght = tmp[::-1]\\n hght = tmp[:]\\n del tmp \\n\\n \\n # Get Sub-LCL traces \\n presdry,tempdry,tempiso=dry_ascent(startp,startt-degCtoK,startdp-degCtoK) \\n \\n\\n # make lcl variables explicit\\n P_lcl=presdry[-1]\\n T_lcl=tempdry[-1]\\n\\n # Now lift a wet parcel from the intersection point\\n # preswet=linspace(P_lcl,100,101)\\n preswet,tempwet=moist_ascent(P_lcl,T_lcl)\\n\\n # tparcel is the concatenation of tempdry and \\n # tempwet, and so on.\\n \\n tparcel=np.concatenate((tempdry,tempwet[1:]))\\n pparcel=np.concatenate((presdry,preswet[1:]))\\n\\n # Interpolating the environmental profile onto the \\n # parcel pressure coordinate\\n # tempenv=interp(preswet,pres[::-1],temp[::-1])\\n ## NEW, for total column:\\n tempenv=interp(pparcel,pres[::-1],temp[::-1])\\n\\n\\n # now solve for the equlibrium levels above LCL\\n # (all of them, including unstable ones)\\n # eqlev,stab=solve_eq(preswet[::-1],(tempwet-tempenv)[::-1])\\n # NEW, for total column:\\n # On second thought, we don't really want/need\\n # any equilibrium levels below LCL\\n # eqlev,stab=solve_eq(pparcel[::-1],(tparcel-tempenv)[::-1])\\n # This is equivalent to the old statement :\\n eqlev,stab=solve_eq(pparcel[pparcel<=P_lcl][::-1],\\\\\\n (tparcel-tempenv)[pparcel<=P_lcl][::-1])\\n\\n aa = tparcel-tempenv\\n\\n # Sorting index by decreasing pressure\\n I=np.argsort(eqlev)[::-1]\\n eqlev=eqlev[I]; stab=stab[I]\\n\\n # temperatures at the equilibrium level\\n # tempeq=interp(eqlev,preswet[::-1],tempenv[::-1])\\n ## NEW, for total column:\\n tempeq=interp(eqlev,pparcel[::-1],tparcel[::-1])\\n\\n # This helps with debugging\\n # for ii,eq in enumerate(eqlev):\\n # print \\\"%5.2f %5.2f %2d\\\"%(eq,tempeq[ii],stab[ii])\\n\\n # need environmental temperature at LCL\\n tenv_lcl=interp(P_lcl,pparcel[::-1],tempenv[::-1])\\n\\n isstab=np.where(stab==1.,True,False)\\n unstab=np.where(stab==1.,False,True) \\n\\n if eqlev.shape[0]==0:\\n # no unstable layers in entire profile\\n # because the parcel never crosses the tenv\\n P_lfc=float('NaN')\\n P_el=float('NaN')\\n elif T_lcl>tenv_lcl:\\n # check LCL to see if this is unstable\\n P_lfc=P_lcl\\n if totalcape:\\n P_el=eqlev[isstab][-1]\\n else:\\n P_el=eqlev[isstab][0]\\n elif eqlev.shape[0]>1:\\n # Parcel is stable at LCL so LFC is the \\n # first unstable equilibrium level and \\n # \\\"EQ\\\" level is the first stable equilibrium \\n # level\\n P_lfc=eqlev[unstab][0]\\n if totalcape:\\n P_el=eqlev[isstab][-1]\\n else:\\n P_el=eqlev[isstab][0]\\n else:\\n # catch a problem... if there is only\\n # one eqlev and it's stable (this is \\n # unphysical), then it could be a vertical\\n # resolution thing. This is a kind of \\n # \\\"null\\\" option\\n try:\\n\\t P_el=eqlev[isstab][0]\\n P_lfc=eqlev[isstab][0]\\n except:\\n\\t P_el=eqlev[unstab][0]\\n P_lfc=eqlev[unstab][0]\\t\\n\\t\\n if np.isnan(P_lfc):\\n return P_lcl,P_lfc,P_el,0,0\\n\\n # need to handle case where dwpt is not available \\n # above a certain level for any reason. Most simplest \\n # thing to do is set it to a reasonably low value; \\n # this should be a conservative approach!\\n \\n #dwpt=dewpt.copy().soften_mask()\\n [inds] = np.where(np.isnan(dewpt))\\n dwpt = dewpt\\n dwpt[inds] = dwpt.min()\\n \\n # raise ValueError\\n #if dwpt[(pres>=P_el).data*(pres<P_lfc).data].mask.any():\\n # print \\\"WARNING: substituting dwpt.min() for masked values of DWPT in this sounding\\\"\\n #dwpt[dwpt.mask]=dwpt.min()\\n # dwptenv=interp(preswet,pres[::-1],dwpt[::-1])\\n # NEW:\\n\\n dwptenv=interp(pparcel,pres[::-1],dwpt[::-1])\\n\\n\\n \\n #if hght[(pres>=P_el).data].mask.any():\\n # raise NotImplementedError, \\\"TODO: Implement standard atmosphere to substitute missing heights\\\"\\n # hghtenv=interp(preswet,pres[::-1],self.soundingdata['hght'][::-1])\\n # NEW:\\n hghtenv=interp(pparcel,pres[::-1],hght[::-1])\\n \\n\\n # Areas of POSITIVE Bouyancy\\n # cond1=(tempwet>=tempenv)*(preswet<=P_lfc)*(preswet>P_el)\\n # NEW:\\n cond1=(tparcel>=tempenv)*(pparcel<=P_lfc)*(pparcel>P_el)\\n # Areas of NEGATIVE Bouyancy\\n # cond2=(tempwet<tempenv)*(preswet<=P_lcl)*(preswet>P_el)\\n # NEW:\\n if totalcape:\\n cond2=(tparcel<tempenv)*(pparcel>P_el)\\n else:\\n cond2=(tparcel<tempenv)*(pparcel>P_lfc)\\n # Do CAPE calculation\\n # 1. Virtual temperature of parcel... remember it's saturated above LCL.\\n # e_parcel=SatVap(tempwet)\\n # Tv_parcel=VirtualTemp(tempwet+degCtoK,preswet*100.,e_parcel)\\n # e_env=SatVap(dwptenv)\\n # Tv_env=VirtualTemp(tempenv+degCtoK,preswet*100.,e_env)\\n # NEW:\\n e_parcel=SatVap(tparcel)\\n Tv_parcel=VirtualTemp(tparcel+degCtoK,pparcel*100.,e_parcel)\\n e_env=SatVap(dwptenv)\\n Tv_env=VirtualTemp(tempenv+degCtoK,pparcel*100.,e_env)\\n\\n CAPE=trapz(9.81*(Tv_parcel[cond1]-Tv_env[cond1])/Tv_env[cond1],hghtenv[cond1])\\n CIN=trapz(9.81*(Tv_parcel[cond2]-Tv_env[cond2])/Tv_env[cond2],hghtenv[cond2])\\n\\n return P_lcl,P_lfc,P_el,CAPE,CIN\",\n \"def computeProp(self):\\n self.chem = {}\\n for key in self.config.C:\\n if key in ['P', 'T', 'Z', 'DZ']:\\n continue\\n self.chem[key] = chemistry.ConstituentProperties(key)\\n\\n # nAtm = len(self.gas[self.config.C['P']])\\n self.property = []\\n for op in self.config.LP:\\n self.property.append([])\\n zOffset = 0.0\\n iOffset = 0\\n psep = 1.0E6\\n for i, zv in enumerate(self.gas[self.config.C['Z']]): # find the nearest z value at p_ref\\n P = self.gas[self.config.C['P']][i]\\n if abs(P - self.config.p_ref) < psep:\\n psep = abs(P - self.config.p_ref)\\n iOffset = i\\n zOffset = self.gas[self.config.C['Z']][iOffset]\\n z_at_p_ref = self.config.Req\\n\\n for i, zv in enumerate(self.gas[self.config.C['Z']]):\\n T = self.gas[self.config.C['T']][i]\\n P = self.gas[self.config.C['P']][i]\\n self.property[self.config.LP['P']].append(P)\\n self.property[self.config.LP['Z']].append(zv)\\n rr = z_at_p_ref + zv - zOffset\\n # note that this is the \\\"actual\\\"z along equator referenced to planet center (aka radius)\\n self.property[self.config.LP['R']].append(rr)\\n # ##set mean amu\\n amulyr = 0.0\\n for key in self.chem:\\n amulyr += self.chem[key].amu * self.gas[self.config.C[key]][i]\\n self.property[self.config.LP['AMU']].append(amulyr)\\n # ##set GM pre-calc (normalized further down) and get lapse rate\\n if not i:\\n self.property[self.config.LP['GM']].append(0.0)\\n self.property[self.config.LP['LAPSE']].append(0.0)\\n self.property[self.config.LP['LAPSEP']].append(0.0)\\n else:\\n rho = (amulyr * P) / (chemistry.R * T)\\n dr = abs(zv - self.gas[self.config.C['Z']][i - 1])\\n dV = 4.0 * np.pi * (rr**2) * dr\\n dM = 1.0e11 * rho * dV\\n GdM = self.property[self.config.LP['GM']][i - 1] + chemistry.GravConst * dM\\n # in km3/s2\\n # mass added as you make way into atmosphere by radius r (times G)\\n self.property[self.config.LP['GM']].append(GdM)\\n dT = abs(T - self.gas[self.config.C['T']][i - 1])\\n dP = abs(P - self.gas[self.config.C['P']][i - 1])\\n self.property[self.config.LP['LAPSE']].append(dT / dr)\\n self.property[self.config.LP['LAPSEP']].append(dT / dP)\\n # ##set refractivity and index of refraction\\n refrlyr = 0.0\\n for key in self.chem:\\n refrlyr += self.chem[key].refractivity(T=T) * self.gas[self.config.C[key]][i]\\n refrlyr = refrlyr * P * (293.0 / T)\\n self.property[self.config.LP['REFR']].append(refrlyr)\\n nlyr = refrlyr / 1.0E6 + 1.0\\n self.property[self.config.LP['N']].append(nlyr)\\n\\n # ##Now need to normalize GM to planet and calculate scale height (H)\\n GMnorm = self.property[self.config.LP['GM']][iOffset] # G*(Mass added by p_ref)\\n for i, mv in enumerate(self.property[self.config.LP['GM']]):\\n gm = self.config.GM_ref - (mv - GMnorm)\\n self.property[self.config.LP['GM']][i] = gm\\n little_g = gm / self.property[self.config.LP['R']][i]**2\\n m_bar = self.property[self.config.LP['AMU']][i]\\n T = self.gas[self.config.C['T']][i]\\n self.property[self.config.LP['H']].append((chemistry.R * T) /\\n (little_g * m_bar) / 1000.0)\\n self.property[self.config.LP['g']].append(little_g)\\n self.property = np.array(self.property)\",\n \"def set_norm(self, P={}, C={}, c_t=\\\"pp\\\"):\\n N = dict()\\n if isinstance(P, list):\\n Pl = P\\n else:\\n Pl = [P]\\n if isinstance(C, list):\\n Cl = C\\n else:\\n Cl = [C]\\n if len(Pl) > 0:\\n N['comp_dim'] = len(Pl)\\n if len(Cl) > 0:\\n if len(Cl) != len(Pl):\\n raise ValueError(\\\"Need same number of principal parts and coefficients to set!\\\")\\n keys = list(Cl[0].keys())\\n for j in range(1, N['comp_dim']):\\n if list(Cl[j].keys()) != keys:\\n raise ValueError(\\\"Need to set the same coefficients! (or call the method more than once)\\\")\\n else:\\n Cl = []\\n for j in range(N['comp_dim']):\\n Cl.append(C)\\n N['Vals'] = list()\\n N['Vals'] = list()\\n N['SetCs'] = list()\\n for i in range(N['comp_dim']):\\n N['Vals'].append({})\\n N['Vals'].append({})\\n N['SetCs'].append([])\\n D = self.multiplier().D()\\n for j in range(len(D)):\\n a = D[j]\\n x = self.multiplier().Qv[j]\\n # N['Vals'][i][(0,j)]=dict()\\n if x == 0:\\n if c_t == \\\"pp\\\":\\n # N['comp_dim']=N['comp_dim']+1\\n N['SetCs'][i].append((j, 0))\\n if (0, j) in Pl[i]:\\n N['Vals'][i][(j, 0)] = Pl[i][(j, 0)]\\n else:\\n N['Vals'][i][(j, 0)] = 0 # P[(0,0)]\\n elif x < 0 and self._holomorphic:\\n N['SetCs'][i].append((j, 0))\\n N['Vals'][i][(j, 0)] = 0 # P[(0,0)]\\n\\n for (r, n) in Cl[i].keys():\\n if(N['SetCs'][i].count((r, n)) == 0):\\n N['SetCs'][i].append((r, n))\\n N['Vals'][i][(r, n)] = Cl[i][(r, n)]\\n return N\",\n \"def ocpc(self):\\n return self._ocpc\",\n \"def project_p2c_image(src, H): #---- project p to c (whole image)\\r\\n Z = H[2]; phi= H[3]; S= H[4]; TV= H[5]; TU= H[6];\\r\\n rows= src.shape[0]; cols= src.shape[1]; # get image size info\\r\\n diag= np.sqrt(rows**2+cols**2); # diagnol length\\r\\n radi= int(diag*S/2*1.1); # radius of new plot should be larger\\r\\n dest= np.zeros((radi*2,radi*2,3)) # projection result\\r\\n cosf= np.cos(phi); sinf= np.sin(phi); # rotation parameters\\r\\n u0 = radi-(TU-np.floor(TU)); # only process fractional part\\r\\n v0 = radi-(TV-np.floor(TV)); # of TU and TV\\r\\n kv = np.arange(0,radi*2); # \\r\\n #--- ---\\r\\n srcx= src.copy();\\r\\n srcx[0,:,:]=0; srcx[rows-2:rows,:,:]=0; \\r\\n srcx[:,0,:]=0; srcx[:,cols-2:cols,:]=0;\\r\\n #--- mapping ---\\r\\n for ku in range(0,radi*2): # scan each column\\r\\n UP = (ku-u0)/S; VP= (kv-v0)/S; # correct tu,tv,s\\r\\n RP =-sinf*UP + cosf*VP;\\r\\n CP = cosf*UP + sinf*VP; # correct rotation phi\\r\\n theta= CP/Z; # horizontal angle\\r\\n C = Z*np.tan(theta) + cols/2;\\r\\n R = RP/np.cos(theta) + rows/2;\\r\\n #--- interpolation ---\\r\\n C = np.minimum(np.maximum(C, 0), cols-2);\\r\\n R = np.minimum(np.maximum(R, 0), rows-2); \\r\\n C0 = np.floor(C).astype(int); C1= C-C0; \\r\\n R0 = np.floor(R).astype(int); R1= R-R0; \\r\\n for m in range(0,3):\\r\\n pixel = srcx[R0 ,C0 ,m]*(1-R1)*(1-C1);\\r\\n pixel+= srcx[R0 ,C0+1,m]*(1-R1)*( C1);\\r\\n pixel+= srcx[R0+1,C0 ,m]*( R1)*(1-C1);\\r\\n pixel+= srcx[R0+1,C0+1,m]*( R1)*( C1);\\r\\n dest[kv,ku,m]= pixel; \\r\\n return dest\",\n \"def circpol(self):\\n return self._circpol\",\n \"def getpc(cls, attrib=None):\\n return PC\",\n \"def c(self):\\n return self._c\",\n \"def cp(temp,pres):\\n g_tt = liq_g(2,0,temp,pres)\\n cp = -temp * g_tt\\n return cp\",\n \"def npToPcd(np):\\n\\n pcd = o3d.geometry.PointCloud()\\n pcd.points = o3d.utility.Vector3dVector(np)\\n\\n return pcd\",\n \"def C(self,X):\\n return(\\n 0\\n )\",\n \"def p2c(v: Union[Tuple, List, np.ndarray]) -> np.ndarray:\\n if not isinstance(v, np.ndarray):\\n v = np.array(v)\\n if len(v.shape) == 1:\\n v = np.array([v])\\n return (np.array([np.cos(v[:, 1]), np.sin(v[:, 1])]) * v[:, 0]).T\",\n \"def crossSection(self, E_p):\\n \\n barn = 1e-24 # centimeter^2\\n mb = 1e-3*barn\\n \\n L = np.log(E_p/1e3)\\n \\n sigma = 34.3 + 1.88*L + 0.25*L*L\\n E_th = self.mp_c2 + self.mpi_c2 + self.mpi_c2**2./self.mp_c2\\n \\n return sigma * ( 1 - (E_th/E_p)**4. )**2. * mb\",\n \"def to_compas(self):\\n return Point(self.x, self.y, self.z)\",\n \"def param2Conic(tParam):\\r\\n #Auxiliary vaiables\\r\\n cx, cy, l, w, tau = tParam\\r\\n s=np.sin(tau)\\r\\n c=np.cos(tau)\\r\\n\\r\\n # Conic parameters\\r\\n A = (w*c)**2+(l*s)**2\\r\\n B = 2*c*s*(l**2-w**2)\\r\\n C = (w*s)**2+(l*c)**2\\r\\n D = -2*A*cx - cy*B\\r\\n E = -2*C*cy - cx**B\\r\\n F = -(l*w)**2 + A*cx**2 + B*cx*cy + C*cy**2\\r\\n\\r\\n return (A, B, C, D, E, F)\",\n \"def getConc(fileID, spc):\\r\\n\\r\\n dataKey = rmn.fstinf(fileID, nomvar=spc, ip1=ip1)['key']\\r\\n dataRec = rmn.fstluk(dataKey)\\r\\n concData = dataRec['d']\\r\\n return concData, dataKey, dataRec\",\n \"def Q2C(self, q):\\n\\n #q = q.squeeze();\\n C = np.empty((3,3));\\n\\tC[0,0] = (q[0]**2.0) + (q[1]**2.0) - (q[2]**2.0) - (q[3]**2.0);\\n\\tC[0,1] = 2.0 * ((q[1]*q[2]) + (q[0]*q[3]));\\n\\tC[0,2] = 2.0 * ((q[1]*q[3]) - (q[0]*q[2]));\\n\\n\\tC[1,0] = 2.0 * ((q[1]*q[2]) - (q[0]*q[3]));\\n\\tC[1,1] = (q[0]**2.0) - (q[1]**2.0) + (q[2]**2.0) - (q[3]**2.0);\\n\\tC[1,2] = 2.0 * ((q[2]*q[3]) + (q[0]*q[1]));\\n\\n\\tC[2,0] = 2.0 * ((q[1]*q[3]) + (q[0]*q[2]));\\n\\tC[2,1] = 2.0 * ((q[2]*q[3]) - (q[0]*q[1]));\\n\\tC[2,2] = (q[0]**2.0) - (q[1]**2.0) - (q[2]**2.0) + (q[3]**2.0);\\n\\n return C\",\n \"def C(self, u, v):\\n pass\",\n \"def projective_point(p):\\n from sage.rings.integer import GCD_list, LCM_list\\n try:\\n p_gcd = GCD_list([x.numerator() for x in p])\\n p_lcm = LCM_list([x.denominator() for x in p])\\n except AttributeError:\\n return p\\n scale = p_lcm / p_gcd\\n return [scale * x for x in p]\",\n \"def Pp(nccd):\\n return (128.1-56.9) * (nccd - 1) / (6-1) + 56.9\",\n \"def get_principal_parts(self, PP):\\n if not isinstance(PP, (dict, list)):\\n raise TypeError(\\\"Need principal part in form of dictionary! Got:{0}\\\".format(PP))\\n if isinstance(PP, dict):\\n Ptmp = [PP]\\n else:\\n Ptmp = PP\\n P0 = list()\\n for p in Ptmp:\\n d = {}\\n for t, c in p.items():\\n if isinstance(t, tuple) and len(t) == 2:\\n (r, m) = t\\n if QQ(r).denominator() > 1:\\n order = self.multiplier().weil_module().order() # = 2N\\n r = (r * order) % (order)\\n if r not in self.multiplier().weil_module().D():\\n raise ValueError(f\\\"Need r either x/2N or r = x with x in {self.multiplier().D()}. Got:{r}\\\")\\n d[(r, m)] = c\\n elif isinstance(t, (int, Integer)):\\n if not self.is_Heegner_disc(t):\\n raise ValueError(\\\"Need discriminant satisfying Heegner condition, i.e. square mod {0}. Got: {1}\\\".format(self.multiplier().level(), t))\\n r_and_n = rn_from_D(self.multiplier(), t)\\n if not r_and_n:\\n raise ValueError(f\\\"D={t} is not a compatible discriminant!\\\")\\n (r, m) = r_and_n\\n d[(r, m)] = c\\n # Remember that there are two solutions to D = +- r^2 mod 44\\n # if we are not using symmetry then we set this as part of the principal part\\n minus_r = self.multiplier().weil_module().D()[-r]\\n d = [(minus_r, m)] = self.multiplier().weil_module().sigma_invariant()**4 * c\\n\\n # Also check that principal part adheres to the symmetry if present\\n if self.sym_type():\\n minus_r = self.multiplier().weil_module().neg_index(r)\\n if (minus_r, m) in p:\\n if p[(minus_r, m)] != self.sym_type() * p[(r, m)]:\\n raise ValueError(\\\"Need symmetric principal part! Got:{0}\\\".format(PP))\\n P0.append(d)\\n return P0\",\n \"def p(self, p: ComType):\\n if isinstance(p, complex):\\n self._pwr = p\\n else:\\n self._pwr = complex(p, 0)\",\n \"def convert_polygon_COCO_type(element: list, height: int, width: int) -> Dict:\\n rles = maskUtils.frPyObjects(element, height, width)\\n p = maskUtils.merge(rles)\\n return p\",\n \"def primitive(f):\\n cont, F = dmp_ground_primitive(f.rep, f.lev, f.dom)\\n return cont, f.per(F)\",\n \"def FtoC(F):\\n c = ((F-32)/9.)*5\\n return c\",\n \"def transformedRect(self, P):\\n A = self.transformPointHeight(P, -1)\\n B = self.transformPointHeight(P)\\n C = self.transformPoint(P)\\n D = self.transformPoint(P, -1)\\n return A, B, C, D\",\n \"def Cima(self):\\n if(self.Pila_Vacia()=='true'):\\n return \\\"Pila Vacia\\\"\\n else:\\n return self.pila[self.puntero]\",\n \"def codonComposition(self):#works\\n return {codon: self.countDicNuc.get(codon) for codon in self.rnaCodonTable.keys()}\",\n \"def ceps_coeff(sig,coefNumber):\\n\\n est=lpc_coef(sig,coefNumber)\\n cc=lpcar2cc(est)\\n if len(cc)==1:\\n cc=float(cc)\\n else:\\n cc=tuple(cc)\\n\\n return cc\",\n \"def clogP(row):\\n m = Chem.MolFromSmiles(row.SMILES)\\n clogp = Descriptors.MolLogP(m)\\n\\n return clogp\",\n \"def get_value(self, c_puct):\\n self._u = (c_puct * self._P *\\n np.sqrt(self._parent._n_visits) / (1 + self._n_visits))\\n return self._Q + self._u\",\n \"def get_value(self, c_puct):\\n self._u = (c_puct * self._P *\\n np.sqrt(self._parent._n_visits) / (1 + self._n_visits))\\n return self._Q + self._u\",\n \"def get_value(self, c_puct):\\n self._u = (c_puct * self._P *\\n np.sqrt(self._parent._n_visits) / (1 + self._n_visits))\\n return self._Q + self._u\",\n \"def get_value(self, c_puct):\\n self._u = (c_puct * self._P *\\n np.sqrt(self._parent._n_visits) / (1 + self._n_visits))\\n return self._Q + self._u\",\n \"def get_value(self, c_puct):\\n self._u = (c_puct * self._P *\\n np.sqrt(self._parent._n_visits) / (1 + self._n_visits))\\n return self._Q + self._u\",\n \"def _mps_decompose_CA(self, A):\\n Dl, d, Dr = A.shape\\n Q, C = qr(np.reshape(A, [Dl, d * Dr]).T)\\n nC = nfactor(C)\\n # nC = max(abs(C.min()), abs(C.max()))\\n if C.shape == (1, 1):\\n Q *= np.sign(C.flat[0])\\n C = np.ones((1, 1))\\n else:\\n C = (C.T) / nC\\n Dl = C.shape[1]\\n Q = np.reshape(Q.T, [Dl, d, Dr])\\n return C, Q, nC, Dl\",\n \"def __init__(self, p) -> None:\\n self._p = p\\n self._delegate = TwoQubitAsymmetricDepolarizingChannel(p / 15, p / 15, p / 15,\\n p / 15, p / 15, p / 15,\\n p / 15, p / 15, p / 15,\\n p / 15, p / 15, p / 15,\\n p / 15, p / 15, p / 15)\",\n \"def pca(self):\\n self.pca_mean = self.X.mean(axis=1)\\n X_meanC = self.X - self.pca_mean[:, None]\\n (self.pca_U, self.pca_S, self.pca_V) = np.linalg.svd(X_meanC, full_matrices=False)\\n self.pc_weights = np.dot(np.diag(self.pca_S), self.pca_V)\\n self.pc_stdevs = np.std(self.pc_weights, axis=1)\",\n \"def cpf(self):\\n return self._cpf\",\n \"def create_pf_coils(self) -> list:\\n\\n # from diagram\\n lower_lefts = np.array(\\n [\\n (345.5429497568881, -788.0537547271744),\\n (800.6482982171799, -703.3664235548353),\\n (1160.9400324149108, -272.69381415451096),\\n (1158.833063209076, 268.97622906537015),\\n (798.5413290113453, 621.6369529983793),\\n (345.5429497568881, 707.7795786061589),\\n ]\\n )\\n\\n top_rights = np.array(\\n [\\n (511.99351701782825, -690.4038357644516),\\n (880.7131280388979, -611.8280659103187),\\n (1222.0421393841166, -162.37506753106413),\\n (1230.4700162074555, 379.2780929227446),\\n (861.7504051863858, 681.9523230686117),\\n (446.677471636953, 803.4508373851974),\\n ]\\n )\\n\\n outboard_pf_coils = []\\n for counter, (top_right, lower_left) in enumerate(zip(top_rights, lower_lefts), 1):\\n pf_coil = paramak.PoloidalFieldCoilFP(\\n corner_points=(top_right, lower_left),\\n rotation_angle=self.rotation_angle,\\n name=f\\\"outboard_pf_coils_{counter}\\\",\\n )\\n outboard_pf_coils.append(pf_coil)\\n\\n x_inner, x_outer = 132.73905996758506, 202.26904376012968\\n\\n pf_coils_1 = paramak.RotateStraightShape(\\n points=[\\n (x_inner, -600),\\n (x_outer, -600),\\n (x_outer, -400),\\n (x_inner, -400),\\n ],\\n rotation_angle=self.rotation_angle,\\n name=\\\"pf_coil_1\\\",\\n )\\n\\n pf_coils_2 = paramak.RotateStraightShape(\\n points=[\\n (x_inner, -400),\\n (x_outer, -400),\\n (x_outer, -200),\\n (x_inner, -200),\\n ],\\n rotation_angle=self.rotation_angle,\\n name=\\\"pf_coil_2\\\",\\n )\\n\\n pf_coils_3 = paramak.RotateStraightShape(\\n points=[\\n (x_inner, -200),\\n (x_outer, -200),\\n (x_outer, 0),\\n (x_inner, 0),\\n ],\\n rotation_angle=self.rotation_angle,\\n name=\\\"pf_coil_3\\\",\\n )\\n\\n pf_coils_4 = paramak.RotateStraightShape(\\n points=[\\n (x_inner, 0),\\n (x_outer, 0),\\n (x_outer, 200),\\n (x_inner, 200),\\n ],\\n rotation_angle=self.rotation_angle,\\n name=\\\"pf_coil_4\\\",\\n )\\n\\n pf_coils_5 = paramak.RotateStraightShape(\\n points=[\\n (x_inner, 200),\\n (x_outer, 200),\\n (x_outer, 400),\\n (x_inner, 400),\\n ],\\n rotation_angle=self.rotation_angle,\\n name=\\\"pf_coil_5\\\",\\n )\\n\\n pf_coils_6 = paramak.RotateStraightShape(\\n points=[\\n (x_inner, 400),\\n (x_outer, 400),\\n (x_outer, 600),\\n (x_inner, 600),\\n ],\\n rotation_angle=self.rotation_angle,\\n name=\\\"pf_coil_6\\\",\\n )\\n\\n return outboard_pf_coils + [\\n pf_coils_1,\\n pf_coils_2,\\n pf_coils_3,\\n pf_coils_4,\\n pf_coils_5,\\n pf_coils_6,\\n ]\",\n \"def get_ppo(data):\\n if data is None:\\n raise EmptyDataError('[!] Invalid data value')\\n\\n result = TA.PPO(data)\\n if result is None:\\n raise IndicatorException\\n return result\",\n \"def algi(C):\\n return np.array([ C[0,2], C[1,2], C[1,0] ])\",\n \"def get_circle_3p(self, A, B, C):\\n return circle_3p(A, B, C)\",\n \"def structure_output(sequence, P):\\n structure = [\\\".\\\" for _ in range(len(sequence))]\\n for pair in P:\\n structure[pair[0]] = \\\"(\\\"\\n structure[pair[1]] = \\\")\\\"\\n return \\\"\\\".join(structure)\",\n \"def cep(self):\\n return self._cep\",\n \"def getGcd(self, P):\\n if self.getDegree() == -1 and P.getDegree() == -1:\\n raise PolynomialError(\\\"Impossible to get gcd of 2 zero polynomial\\\")\\n a = Polynomial(self.__coefficients)\\n b = Polynomial(P.__coefficients)\\n temp = None\\n while b.getDegree() >= 0:\\n temp = a.divide(b)[1]\\n a = b\\n b = temp\\n return a.divide(Polynomial([a.__coefficients[a.getDegree()]]))[0]\",\n \"def algorithm_1_2(p, c, x):\\n\\n q = np.array(c, dtype=np.float64)\\n\\n for k in range(1, p + 1):\\n for j in range(0, p - k + 1):\\n q[j] = (1 - x) * q[j] + x * q[j + 1]\\n return q[0]\",\n \"def cp_to_gp(cp):\\n if isinstance(cp, ExchangeForPopulation):\\n return [\\\"population\\\", cp.species_index, cp.played_card_index]\\n else:\\n return None\",\n \"def bpc_to_pc(pc):\\n return float(pc) * 100 / 0x8000\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68054706","0.63273036","0.59791553","0.58777106","0.58699834","0.5814331","0.574379","0.5730052","0.57189286","0.5685744","0.5653829","0.5597755","0.55749863","0.55708575","0.5543975","0.553301","0.55071986","0.5486988","0.5479547","0.54753065","0.54649866","0.5450474","0.54349464","0.543409","0.53587544","0.5357338","0.53497","0.53497","0.53135943","0.52956545","0.5294925","0.529245","0.5289099","0.52795213","0.5278726","0.5254653","0.5244426","0.523935","0.52305156","0.5222325","0.5220618","0.51869214","0.5175695","0.5175545","0.5174158","0.51485753","0.51485753","0.51455724","0.51130074","0.51103634","0.5108248","0.510457","0.5095313","0.5086118","0.508162","0.5080369","0.507671","0.50696105","0.5063405","0.50482416","0.50463116","0.5041028","0.50364816","0.5036402","0.5035952","0.50319797","0.502719","0.5025346","0.5020801","0.50053227","0.4998045","0.4993544","0.4970469","0.49591345","0.49331024","0.4932396","0.49304926","0.49302357","0.49167922","0.49161124","0.4915552","0.49130476","0.49130476","0.49130476","0.49130476","0.49130476","0.49087065","0.49036315","0.49000636","0.488097","0.48781303","0.4874943","0.4869173","0.4864026","0.48605168","0.48599246","0.48584533","0.48541334","0.48510277","0.48509207"],"string":"[\n \"0.68054706\",\n \"0.63273036\",\n \"0.59791553\",\n \"0.58777106\",\n \"0.58699834\",\n \"0.5814331\",\n \"0.574379\",\n \"0.5730052\",\n \"0.57189286\",\n \"0.5685744\",\n \"0.5653829\",\n \"0.5597755\",\n \"0.55749863\",\n \"0.55708575\",\n \"0.5543975\",\n \"0.553301\",\n \"0.55071986\",\n \"0.5486988\",\n \"0.5479547\",\n \"0.54753065\",\n \"0.54649866\",\n \"0.5450474\",\n \"0.54349464\",\n \"0.543409\",\n \"0.53587544\",\n \"0.5357338\",\n \"0.53497\",\n \"0.53497\",\n \"0.53135943\",\n \"0.52956545\",\n \"0.5294925\",\n \"0.529245\",\n \"0.5289099\",\n \"0.52795213\",\n \"0.5278726\",\n \"0.5254653\",\n \"0.5244426\",\n \"0.523935\",\n \"0.52305156\",\n \"0.5222325\",\n \"0.5220618\",\n \"0.51869214\",\n \"0.5175695\",\n \"0.5175545\",\n \"0.5174158\",\n \"0.51485753\",\n \"0.51485753\",\n \"0.51455724\",\n \"0.51130074\",\n \"0.51103634\",\n \"0.5108248\",\n \"0.510457\",\n \"0.5095313\",\n \"0.5086118\",\n \"0.508162\",\n \"0.5080369\",\n \"0.507671\",\n \"0.50696105\",\n \"0.5063405\",\n \"0.50482416\",\n \"0.50463116\",\n \"0.5041028\",\n \"0.50364816\",\n \"0.5036402\",\n \"0.5035952\",\n \"0.50319797\",\n \"0.502719\",\n \"0.5025346\",\n \"0.5020801\",\n \"0.50053227\",\n \"0.4998045\",\n \"0.4993544\",\n \"0.4970469\",\n \"0.49591345\",\n \"0.49331024\",\n \"0.4932396\",\n \"0.49304926\",\n \"0.49302357\",\n \"0.49167922\",\n \"0.49161124\",\n \"0.4915552\",\n \"0.49130476\",\n \"0.49130476\",\n \"0.49130476\",\n \"0.49130476\",\n \"0.49130476\",\n \"0.49087065\",\n \"0.49036315\",\n \"0.49000636\",\n \"0.488097\",\n \"0.48781303\",\n \"0.4874943\",\n \"0.4869173\",\n \"0.4864026\",\n \"0.48605168\",\n \"0.48599246\",\n \"0.48584533\",\n \"0.48541334\",\n \"0.48510277\",\n \"0.48509207\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7179086"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":841,"numItemsPerPage":100,"numTotalItems":95772,"offset":84100,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjAyODYxNCwianRpIjoiYmY0NDFiN2MtNDg3Yi00MmU4LTg5NDQtNDYyZDk0ZmRiZGJiIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYwMzIyMTQsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.cokI85BHZhY6WMDsek-vTy8w9LQyJiMVnNA9KIoOIF-RaedqrmVOzNkyk_TQysNg0EuSIw8a1TC_rWIgbU4aDg","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Get historical candle data fro a given time period. | def historical_candles(self, symbol, interval, start_time, end_time, **kwargs):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def history(self, t_minus=0):\n data = self.ohlcv_df[self.ohlcv_df.index <= utc_to_epoch(\n self.prior_time)]\n return OHLCVData(data[-t_minus:])",
"def get_candles(self, exchange: str, symbol: str, timeframe: str) -> np.ndarray:\n return store.candles.get_candles(exchange, symbol... | [
"0.6971255",
"0.6928205",
"0.69048023",
"0.68139064",
"0.6760691",
"0.6760632",
"0.6622193",
"0.65805143",
"0.6545956",
"0.6524185",
"0.6452969",
"0.64112544",
"0.6368604",
"0.6332906",
"0.62834126",
"0.6267579",
"0.62272704",
"0.62151915",
"0.6207075",
"0.6167549",
"0.616567... | 0.7047711 | 0 |
Place order to execute at a current market price. | def place_market_order(self, side, symbol, size, **kwargs):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def place_buy_order(self):\n price = request.form[\"price\"]\n stocks = request.form[\"stocks\"]\n trader_id = request.form[\"trader_id\"]\n self.market.place_buy_order(trader_id, price, stocks)\n return \"\"",
"def _simulate_market_order_execution() -> None:\n if jh.is_... | [
"0.69765216",
"0.69292",
"0.6865394",
"0.68172646",
"0.67815804",
"0.66491234",
"0.6612978",
"0.6596807",
"0.65942866",
"0.6575112",
"0.651982",
"0.6463076",
"0.6432228",
"0.64138097",
"0.6408965",
"0.6353235",
"0.6305847",
"0.63014066",
"0.6282573",
"0.6276069",
"0.6270481",... | 0.68720007 | 2 |
Place order to execute at a given price. | def place_limit_order(self, side, symbol, size, price, **kwargs):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def place(exchange: str, side: str, amount: float, pair: str, price: float):\n loop.run_until_complete(place_order(exchange, side, amount, pair, price))",
"def place_order(self, exchange, pair, type, amount, price = None):\n return self.ccxt.place_order(exchange, pair, type, amount, price)",
"def pla... | [
"0.74788094",
"0.7128345",
"0.70201325",
"0.689616",
"0.6730254",
"0.66365486",
"0.65407693",
"0.6537548",
"0.65215284",
"0.6474264",
"0.6464579",
"0.6443588",
"0.6407609",
"0.63726",
"0.63536555",
"0.6338436",
"0.6338136",
"0.6334442",
"0.63172585",
"0.6302584",
"0.62971854"... | 0.6710089 | 5 |
Query a given order. | def order(self, order_id, symbol, **kwargs):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def query_order(self, order_id: str):\n return self._call_txtrader_api('query_order', {'id': order_id})",
"def query_order(self, symbol, orderId):\n payload = {\n 'symbol': symbol,\n 'orderId': orderId\n }\n return self.signed_request('GET', '/api/v3/order', **pa... | [
"0.7486202",
"0.74333036",
"0.7157581",
"0.69978845",
"0.6756502",
"0.65135545",
"0.63157356",
"0.6278133",
"0.613502",
"0.60711783",
"0.60216105",
"0.60098916",
"0.599705",
"0.5977328",
"0.59540844",
"0.59491086",
"0.5931085",
"0.59158975",
"0.5893463",
"0.5887605",
"0.58468... | 0.532939 | 77 |
Get all open orders. | def open_orders(self, **kwargs):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_open_orders(self):\n self.__init_client()\n open_orders = retry(lambda: self.client\n .futures_get_open_orders(symbol=self.pair)) \n if len(open_orders) > 0:\n return open_orders\n else:\n return None",
"def open_orde... | [
"0.84299356",
"0.8342123",
"0.8311697",
"0.82279056",
"0.8223204",
"0.8068352",
"0.79213613",
"0.79142207",
"0.77187514",
"0.77005",
"0.76585364",
"0.76417845",
"0.76108724",
"0.7528356",
"0.7517391",
"0.7487796",
"0.74506944",
"0.73906827",
"0.7383538",
"0.73350686",
"0.7329... | 0.73145187 | 23 |
Get all open orders for a given symbol. | def open_orders_for(self, symbol, **kwargs):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def orders_for(self, symbol):\n\t\tif self._session:\n\t\t\tdata = {'symbol': symbol}\n\t\t\treturn self._session.get_open_orders(**data)\n\n\t\treturn []",
"async def get_open_orders(self, symbol):\n params = {\n \"symbol\": symbol,\n \"timestamp\": tools.get_cur_timestamp_ms()\n ... | [
"0.8801361",
"0.86804783",
"0.86483186",
"0.8514623",
"0.808615",
"0.797817",
"0.7919081",
"0.78487015",
"0.7825336",
"0.7795131",
"0.7702692",
"0.7692453",
"0.76745373",
"0.76318485",
"0.7543502",
"0.7499859",
"0.7396698",
"0.738475",
"0.7374423",
"0.72892445",
"0.72152036",... | 0.7731922 | 10 |
Get all orders for a given symbol. | def all_orders(self, symbol, **kwargs):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def get_all_orders(self, symbol):\n params = {\n \"symbol\": symbol,\n \"timestamp\": tools.get_cur_timestamp_ms()\n }\n success, error = await self.request(\"GET\", \"/api/v3/allOrders\", params=params, auth=True)\n return success, error",
"def orders_for(... | [
"0.871888",
"0.8511643",
"0.7882938",
"0.76025885",
"0.7352556",
"0.7308918",
"0.7204348",
"0.7117544",
"0.7101336",
"0.7085123",
"0.69951177",
"0.68210506",
"0.6699723",
"0.66776425",
"0.6673496",
"0.66453934",
"0.6642084",
"0.65410185",
"0.65242124",
"0.6509453",
"0.6493752... | 0.82315767 | 2 |
Get all trades executed by a user for a given symbol. | def user_trades(self, symbol, **kwargs):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def trades_for(self, symbol, from_id=None, timestamp=None, limit=None):\n\t\tif self._session:\n\t\t\tdata = {'symbol': symbol}\n\n\t\t\tif from_id:\n\t\t\t\tdata['fromId'] = from_id\n\n\t\t\tif timestamp:\n\t\t\t\tdata['startTime'] = int(timestamp * 1000.0)\n\n\t\t\tif limit:\n\t\t\t\tdata['limit'] = limit\n\n\t\... | [
"0.6786857",
"0.6675823",
"0.6539069",
"0.62613904",
"0.6163731",
"0.60968274",
"0.60843253",
"0.6073838",
"0.60372895",
"0.60368985",
"0.6019417",
"0.6006068",
"0.59857243",
"0.5983466",
"0.59783465",
"0.5852677",
"0.58494574",
"0.5848803",
"0.57797194",
"0.57628",
"0.572351... | 0.6612362 | 2 |
Sets the dimention of the stokes vector. | def set_stokes_dim(self, stokes_dim):
if stokes_dim not in [1, 2, 3, 4]:
raise ValueError(
"The dimention of the stokes vector can only be 1, 2, 3 or 4"
)
self.stokes_dim = stokes_dim | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setidd(self, iddinfo, iddindex, block, idd_version):\n self.idd_info = iddinfo\n self.block = block\n self.idd_index = iddindex\n self.idd_version = idd_version",
"def duns(self, duns: str):\n\n self._duns = duns",
"def set_stdeviations(self, sigma):\n stdeviations... | [
"0.632355",
"0.6193907",
"0.5888917",
"0.5645816",
"0.5536929",
"0.55124253",
"0.5478698",
"0.544167",
"0.5424394",
"0.54221827",
"0.53855914",
"0.5377502",
"0.5362815",
"0.53419864",
"0.53410035",
"0.53191084",
"0.53157526",
"0.5282019",
"0.52759916",
"0.5262627",
"0.5243309... | 0.6994929 | 0 |
Set the scattering type for the model Options are scattering with an constant scattering cross section and a phasefunction based on Henyey Greenstein (0) and rayleigh scattering with an wavelength dependent cross section (1). For full polarimetric simulation (stokes_dim > 1) rayleigh scattering will be used. | def set_scattering_type(self, scattering_type):
if scattering_type == "rayleigh":
scattering_type = 1
elif scattering_type == "henyey_greenstein":
scattering_type = 0
if scattering_type not in [0, 1]:
raise ValueError(
'Only "henyey_greenstein"... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def toggle_scattering(self, setting=1):\n if setting not in [0, 1, \"on\", \"off\"]:\n raise ValueError(\n \"The input for the toggle the us of scattering \"\n 'in the model must \"on\" (1) or \"off\" (0)'\n )\n self.use_scat = 1 if setting == \"on\... | [
"0.66796225",
"0.62938654",
"0.58174783",
"0.5719786",
"0.56479996",
"0.54444414",
"0.523036",
"0.52157414",
"0.5163354",
"0.5094794",
"0.50772446",
"0.50407004",
"0.50305027",
"0.49877325",
"0.49587128",
"0.4926275",
"0.4912892",
"0.48998433",
"0.48370242",
"0.48196423",
"0.... | 0.81008196 | 0 |
Toggle the use of planck radiation in the model. The default is on (1). | def toggle_planck_radiation(self, setting=1):
if setting not in [0, 1, "on", "off"]:
raise ValueError(
"The setting for the toggle the use of planck "
'Radiation must "on" (1) or "off" (0)'
)
self.use_planck = 1 if setting == "on" else 0 if setting... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def configOperationMode(self):\n\n if self.ui.checkShowAlignStar.isChecked():\n self.ui.checkPolarAlignment.setEnabled(True)\n else:\n self.ui.checkPolarAlignment.setEnabled(False)\n if self.ui.checkPolarAlignment.isChecked():\n self.ui.checkEditNone.se... | [
"0.62344855",
"0.62020314",
"0.6173377",
"0.6023187",
"0.6016536",
"0.5940673",
"0.5907294",
"0.5877835",
"0.58360946",
"0.58043313",
"0.57058173",
"0.56724185",
"0.56472945",
"0.56395024",
"0.5592563",
"0.55920243",
"0.55766785",
"0.5559038",
"0.55545807",
"0.5543908",
"0.55... | 0.79669845 | 0 |
Toggle the use of scattering in the model. (So k = abs_coef) The default is on (1). | def toggle_scattering(self, setting=1):
if setting not in [0, 1, "on", "off"]:
raise ValueError(
"The input for the toggle the us of scattering "
'in the model must "on" (1) or "off" (0)'
)
self.use_scat = 1 if setting == "on" else 0 if setting == ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clicked_checkbox_model_smoothing(self):\n if self.checkbox_model_smoothing.isChecked():\n self._get_selected_model().metadata[\"smoothing_kernel\"] = True\n self.edit_manual_smoothing.setEnabled(False)\n else:\n self._get_selected_model().metadata[\"smoothing_kern... | [
"0.52622956",
"0.52451706",
"0.51579946",
"0.5096683",
"0.5060119",
"0.50167304",
"0.49783123",
"0.49736682",
"0.49626586",
"0.4955168",
"0.49531534",
"0.4933673",
"0.49187443",
"0.49177575",
"0.4909981",
"0.4859031",
"0.48441973",
"0.48347628",
"0.47741818",
"0.4743281",
"0.... | 0.6945123 | 0 |
Sets the wavelength for the model instance. This is needed for the scattering cross section as well as the incoming sun intensity. | def set_wavelenth(self, wavelength):
if wavelength < 0:
raise ValueError("The wavelength cannot be negative")
self.wavelength = wavelength
RT_model_1D.set_scattering_cross_sec(self)
RT_model_1D.get_atmoshperic_profiles(self)
self.sun_intensity = f.sun_init_intensity(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setUpWavelength(self,wavelength):\n if wavelength == None:\n self.wavelength = getDefaultWavelength()\n else:\n self.wavelength = wavelength\n return self",
"def set_wavelength(self, wavelength: float) -> None:\n\n assert isinstance(wavelength, float), \"Inco... | [
"0.7543897",
"0.7506083",
"0.7441443",
"0.72564566",
"0.6959144",
"0.6959144",
"0.6941237",
"0.664906",
"0.6635105",
"0.65459937",
"0.64332366",
"0.62733537",
"0.62157166",
"0.61628973",
"0.60751235",
"0.6061872",
"0.6035115",
"0.6034717",
"0.6006406",
"0.59973127",
"0.593761... | 0.80278105 | 0 |
Sets the scattering cross section according to the wavelength of the model instance. | def set_scattering_cross_sec(self):
if self.scat_type == 1:
sigma = f.calc_rayleigh_scattering_cross_section(self.wavelength)
self.scattering_cross_sec = sigma
elif self.scat_type == 0:
self.scattering_cross_sec = self.SCATTERING_CROSS_SEC_HENYEY | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fun_set(self):\n\n self.type.set(self.xtl._scattering_type)\n # self.energy_kev.set(8)\n self.theta_offset.set(self.xtl._scattering_theta_offset)\n self.theta_min.set(self.xtl._scattering_min_theta)\n self.theta_max.set(self.xtl._scattering_max_theta)\n self.twotheta_m... | [
"0.5799355",
"0.57958126",
"0.5480301",
"0.52612436",
"0.5250689",
"0.5242964",
"0.51150775",
"0.5089051",
"0.50533265",
"0.50456446",
"0.49918267",
"0.49321675",
"0.49295756",
"0.49250093",
"0.4916542",
"0.49130252",
"0.48921743",
"0.48824984",
"0.48434013",
"0.48340112",
"0... | 0.6786386 | 0 |
Set the reflection type for the model Options are lambert (0) and specular (1) and a linear combination of those two. | def set_reflection_type(self, reflection_type=0):
if reflection_type < 0 or reflection_type > 1:
raise ValueError("Must be 0, 1 or inbetween")
self.reflection_type = reflection_type | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_type(self,number):\n if number == 0:\n self.Goblin()\n \n if number == 1:\n self.Ork()\n\n if number == 2:\n self.Skeleton()\n\n if number == 3:\n self.Troll()",
"def set_type_smart(self):\n self.update(type=\"smart\")"... | [
"0.52875787",
"0.52531016",
"0.5250906",
"0.5228072",
"0.5163002",
"0.5162343",
"0.50840604",
"0.50517833",
"0.5041647",
"0.50235975",
"0.5020327",
"0.4987726",
"0.49623233",
"0.49217913",
"0.49075902",
"0.4863573",
"0.48560688",
"0.4852885",
"0.48483944",
"0.48453522",
"0.48... | 0.5811174 | 0 |
Sets the position (closest to the model grid) and viewing angle of the receiver e.g. where and in which direction the radiation field is evaluated. The function converts the angle into the radiation transport direction. | def set_receiver(self, height, elevation, azimuth):
if height < 0:
raise ValueError("The height cannot be negative")
if elevation < 0 or elevation > 180:
raise ValueError("The elevation cannot be negative or greater 180")
if elevation == 90:
raise ValueError(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_robot_pos(self):\n\t\tx,y,z = self.geo2desiredENU(self.curr_lat, self.curr_lon, self.gpsAlt)\n\t\tself.robot_msg.point.x = x\n\t\tself.robot_msg.point.y = y\n\t\tself.robot_msg.point.z = z",
"def move(self, t):\n if self.visible == True:\n self.x += self.vx * t\n self.y += se... | [
"0.62159103",
"0.6057396",
"0.6008578",
"0.58065987",
"0.5802125",
"0.57931626",
"0.5757918",
"0.5615237",
"0.56146073",
"0.5603354",
"0.5600667",
"0.55741394",
"0.5569498",
"0.55332774",
"0.55239934",
"0.5523441",
"0.54987025",
"0.54981136",
"0.54925036",
"0.54598767",
"0.54... | 0.0 | -1 |
Sets the elevation and azimuth angle of the sun and the intensity. The function converts the angle into the radiation transport direction. | def set_sun_position(self, elevation, azimuth, intensity=None):
if elevation < 0 or elevation >= 90:
raise ValueError("The elevation cannot be negative or >= 90")
if azimuth < 0 or azimuth >= 360:
raise ValueError("The azimuth cannot be negative or >= 360")
if intensity ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_azimuth(self):\n self.azimuth = self.Calculations.convert_to_azimuth( self.declination, self.right_ascension, self.Latitude, self.LHA)\n if self.azimuth < 0:\n self.azimuth = self.azimuth + 360.0\n return self.azimuth\n else:\n pass\n return self... | [
"0.62218684",
"0.6024862",
"0.60163593",
"0.5670285",
"0.5291753",
"0.5291011",
"0.52647144",
"0.5253364",
"0.51982725",
"0.5173323",
"0.5167679",
"0.51630104",
"0.51583445",
"0.5147184",
"0.51196414",
"0.511552",
"0.51055723",
"0.5090999",
"0.5079181",
"0.506311",
"0.5036682... | 0.7400268 | 0 |
Sets the Grid for the atmosheric parameters. | def define_grid(self, atm_height=200, swiping_height=1):
if atm_height < 0:
raise ValueError("The height cannot be negative")
if swiping_height < 0:
raise ValueError("The swiping height must be positive")
atm_height = f.km2m(atm_height)
self.swiping_height = f.k... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_grid(self,ug):\n self.grd=ug\n self.set_topology()",
"def grid(self, grid):\n\n self._grid = grid",
"def grid(self):\r\n dimA = self.dimA ; dimC = self.dimA ; W_grid = self.W_grid\r\n \r\n self.tol = 10e-5\r\n self.Niter = 10000\r\n \r\n a0... | [
"0.7466827",
"0.69703215",
"0.65102744",
"0.6470333",
"0.6462259",
"0.6410897",
"0.639689",
"0.63504964",
"0.6303279",
"0.6245297",
"0.62265956",
"0.62132394",
"0.6193023",
"0.61890674",
"0.61395335",
"0.61229646",
"0.602347",
"0.6017699",
"0.6000968",
"0.59987503",
"0.597637... | 0.0 | -1 |
Returns atm fields of the absorption and scattering coefficent depending on the readin_densprofile | def get_atmoshperic_profiles(self):
self.absorption_coeff_field = np.zeros(
(len(self.height_array), self.stokes_dim, self.stokes_dim)
)
self.scattering_coeff_field = np.zeros(
(len(self.height_array), self.stokes_dim, self.stokes_dim)
)
dens_profile_heigh... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def atmprofileread(filename):\n f = open(filename, 'r')\n line1 = f.readline()\n Nst = int(line1.split()[-1])\n line = f.readline()\n Np = int(line.split()[1])\n atm = 0*numpy.ndarray(shape=(Nst, Np, 5), dtype=float)\n S = 0*numpy.ndarray(shape=(Nst), dtype=float)\n f = open(filename, 'r')\... | [
"0.5766092",
"0.57300544",
"0.5558378",
"0.5503394",
"0.5415505",
"0.5388779",
"0.5379009",
"0.53642666",
"0.53555596",
"0.53375286",
"0.53191704",
"0.53132015",
"0.5234382",
"0.5219888",
"0.5212103",
"0.51860213",
"0.518094",
"0.51677203",
"0.51662415",
"0.51586294",
"0.5126... | 0.51104105 | 22 |
Calculates the intensity of the suns beam at the given height with just extinction. This is needed for the Scattering source term. | def calc_direct_beam_intensity(self, height):
if height < 0:
raise ValueError("The height cannot be negative")
idx, height = f.argclosest(height, self.height_array, return_value=True)
angle, _ = f.convert_direction(self.sun_elevation, self.sun_azimuth)
tau = np.zeros((self.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def beam(xb,yb,zb,wx,wy,wavelen):\n\n zRx = np.pi * wx**2 / wavelen\n zRy = np.pi * wy**2 / wavelen \n \n sqrtX = np.sqrt( 1 + np.power(zb/zRx,2) ) \n sqrtY = np.sqrt( 1 + np.power(zb/zRy,2) ) \n intensity = np.exp( -2.*( np.power(xb/(wx*sqrtX ),2) \\\n + np.power(yb/(w... | [
"0.5935863",
"0.5868439",
"0.5751317",
"0.54317284",
"0.535781",
"0.53225744",
"0.5312339",
"0.5210378",
"0.5191353",
"0.5132932",
"0.5099186",
"0.50963634",
"0.5054381",
"0.50518787",
"0.5045105",
"0.50375617",
"0.5034523",
"0.50232214",
"0.501657",
"0.50001836",
"0.4996118"... | 0.72599554 | 0 |
Clalculates the extinction term based on the given intensity and the absorbtion and scattering coefficent at the given height. | def extinction_term(self, intensity, height):
id = f.argclosest(height, self.height_array)
k = (
self.absorption_coeff_field[id]
+ self.use_scat * self.scattering_coeff_field[id]
)
# np.exp calculates element wise not the matricexponential!
# but for diag... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extinction(pyrat):\n # Load parameters for that molecular species (in future pyrat.cond.names for condensate species) \n species = 'H2O'\n pyrat.haze.cloudBenneke.readVP(species) # do this outside once in beginning of code and call it??\n # Fill in Cloud Base Pressure and Temperature\n pyrat.h... | [
"0.5243953",
"0.5232212",
"0.52192557",
"0.52076006",
"0.5106173",
"0.5002402",
"0.49990737",
"0.497829",
"0.49478427",
"0.49193546",
"0.49118948",
"0.487345",
"0.48102024",
"0.4802348",
"0.47917363",
"0.47859445",
"0.477885",
"0.47580746",
"0.47495157",
"0.47404954",
"0.4739... | 0.74419826 | 0 |
creates an empty array where the field will be evaluated | def create_receiver_viewing_field(self):
height = self.receiver_height
# revert in viewing direct
angle, _ = f.convert_direction(self.receiver_elevation, self.receiver_azimuth)
if angle < 90:
# from rec (at idx) to TOA (len(h.a.))
height_at_rad_field = np.arange(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def new_array(cls):\n return cls.for_value({})",
"def type_array():\n return []",
"def __array__(self):\n return pa.column(\"dummy\", self.data).to_pandas().values",
"def _buildArray(self):\n if len(self.synth_spec.components) == 0:\n self.array = np.zeros(len(self.synt... | [
"0.6259808",
"0.62299883",
"0.6079102",
"0.5930863",
"0.5783783",
"0.5781425",
"0.5779135",
"0.5701756",
"0.558807",
"0.5547544",
"0.55269605",
"0.55269605",
"0.5511912",
"0.5511912",
"0.5511912",
"0.5511912",
"0.5480194",
"0.54765636",
"0.5469752",
"0.5431977",
"0.53989655",... | 0.0 | -1 |
Returns the height field for the it is seen from the receiver | def get_receiver_viewing_field(self):
field = RT_model_1D.create_receiver_viewing_field(self)
return np.flipud(field) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def height(self):\n return self[\"height\"]",
"def height(self):\n return self[\"height\"]",
"def get_height(self):\r\n return self.state['h']",
"def height(self):\n return (self.__height)",
"def height(self):\n return self.client.call('GET', self.name + 'height')",
"de... | [
"0.7799121",
"0.7799121",
"0.7622584",
"0.7476387",
"0.7450351",
"0.74150395",
"0.74150395",
"0.74150395",
"0.73988795",
"0.7388901",
"0.7386272",
"0.73818356",
"0.73818356",
"0.73818356",
"0.7372417",
"0.7299199",
"0.7299199",
"0.7299199",
"0.7299199",
"0.7275214",
"0.727521... | 0.0 | -1 |
Returns the starting value based on where the reciever is looking | def rad_field_initial_condition(self):
# revert in viewing direct
angle, _ = f.convert_direction(self.receiver_elevation, self.receiver_azimuth)
# Looking at the sky
if angle < 90:
I_init = (
self.sun_intensity
* f.delta_func(self.sun_elevatio... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def first_value(self):\n return self._start",
"def __findStartValue(self):\n\t\tself.minEvidence = 1e120*np.ones(self.nStartValues)\n\t\tfor idx1 in range(self.nStartValues):\n\t\t\tself.minEvidence[self.nStartValues-1-idx1] = self.__minBayesianEvidence(self.logLamStart[self.nStartValues-1-idx1])\n\t\t\t#... | [
"0.7167975",
"0.6875307",
"0.6829003",
"0.66621625",
"0.66621625",
"0.66621625",
"0.6571673",
"0.6541329",
"0.64761525",
"0.6409516",
"0.63821906",
"0.6366693",
"0.63498056",
"0.6266545",
"0.6245555",
"0.6236381",
"0.6226824",
"0.61588854",
"0.6126496",
"0.6062355",
"0.606235... | 0.0 | -1 |
prints an attribute which must be set here. It's for testing. | def print_testvals(self):
print(self.use_planck) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _print_attribute(self):\n print(vars(self))",
"def print_attr(self):\n return \"name : {0}\\nprice : {1}\\ndescription : {2}\".format(\n self.name, self.price, self.description\n )",
"def AttributeString(self) -> str:",
"def AttributeString(self) -> str:",
"def print_att... | [
"0.7736712",
"0.7399579",
"0.6852937",
"0.6852937",
"0.6824587",
"0.6807231",
"0.6670959",
"0.6625542",
"0.66095066",
"0.65378267",
"0.65056926",
"0.6415073",
"0.6340306",
"0.6311172",
"0.6304341",
"0.62643856",
"0.6244496",
"0.62075406",
"0.6147234",
"0.61126673",
"0.6112667... | 0.0 | -1 |
Extracts MFCCs from music dataset and saves them into a json file along witgh genre labels. | def save_mfcc(dataset_path, json_path, num_mfcc=13, n_fft=2048, hop_length=512, num_segments=5):
# dictionary to store mapping, labels, and MFCCs
data = {
"mapping": [],
"labels": [],
"mfcc": []
}
samples_per_segment = int(SAMPLES_PER_TRACK / num_segments)
num_mfcc_vectors_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_all_features(mp3_file):\n # Decode and read mp3\n audio, _ = librosa.load(mp3_file, sr=SR)\n\n # Compute mels\n mel = compute_melspecs(audio)\n\n # Save\n out_file = os.path.join(\n OUTPUT_DIR, os.path.basename(mp3_file).replace(\".mp3\", \"-mel.npy\"))\n np.save(out_file, m... | [
"0.5844222",
"0.5784501",
"0.569305",
"0.5653748",
"0.56458896",
"0.5638793",
"0.55832857",
"0.55445707",
"0.55068433",
"0.55017704",
"0.5487365",
"0.5436023",
"0.53990126",
"0.53845435",
"0.53288573",
"0.53150344",
"0.53050363",
"0.53041786",
"0.52813184",
"0.5267655",
"0.52... | 0.77331597 | 0 |
getArchive(conf=...) Fetch an Archive instance. If conf is passed will be used instead of the default configuration. Returns a Deferred which will fire with the Archive instance. | def getArchive(conf):
url = conf.get('url')
maxreq = conf.getint('maxrequests', 10)
maxq = conf.getint('maxquery')
proxy=Proxy(url, limit=maxreq, qlimit=maxq)
proxy.connectTimeout=3.0
info = proxy.callRemote('archiver.info').addErrback(_connerror)
archs= proxy.callRemote('archiver.arch... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_archive(*args, **kwargs):\n return get_archive_async(*args, **kwargs).get_result()",
"def get_archive_async(\n hostname, project, treeish, dir_path=None, **fetch_kwargs):\n _validate_args(hostname, project, treeish, dir_path)\n dir_path = (dir_path or '').strip('/')\n if dir_path:\n dir_path = ... | [
"0.69059646",
"0.58675325",
"0.57660156",
"0.53493696",
"0.53290415",
"0.53190356",
"0.5217525",
"0.51383114",
"0.50821906",
"0.5054611",
"0.48578238",
"0.48038158",
"0.47682074",
"0.47294822",
"0.4705755",
"0.46751308",
"0.46295667",
"0.46268368",
"0.46169835",
"0.4615044",
... | 0.7841836 | 0 |
Search for PV names matching the given pattern. If archs is None then all archives are searched. Otherwise archs must be a list of strings or integers specifing archive names or keys. Returns a Deferred which fires with a dictionary. If breakDown is False (the default) then the result is | def search(self, exact=None, pattern=None,
archs=None, breakDown=False,
rawTime=False):
if exact is None and pattern is None:
raise TypeError("Must provide 'exact' or 'pattern'")
if exact is not None:
pattern = '^%s$'%re.escape(exact)
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def search(regex, paths, args, ignore_case=False, verbose=False):\n printer = MultiLinePrinter()\n for path in paths:\n if os.path.isdir(path):\n for dirname, subdirs, files in os.walk(path):\n for filename in files:\n if not KNOWN_TYPES or any([filename.en... | [
"0.47382924",
"0.45816582",
"0.45443344",
"0.45098388",
"0.4450674",
"0.43616328",
"0.43554297",
"0.42992774",
"0.42957407",
"0.42762348",
"0.42538893",
"0.42345315",
"0.42316225",
"0.42286372",
"0.4218467",
"0.4217593",
"0.42083153",
"0.41941893",
"0.41695234",
"0.41633487",
... | 0.66993964 | 0 |
Fetch raw data for the given PV. Results are passed to the given callback as they arrive. | def fetchraw(self, pv, callback,
cbArgs=(), cbKWs={},
T0=None, Tend=None,
count=None, chunkSize=None,
archs=None, breakDown=None,
enumAsInt=False, displayMeta=False, rawTimes=False):
if breakDown is None:
breakDown ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def fetch_data(self) -> T:",
"def fetchplot(self, pv, callback,\n cbArgs=(), cbKWs={},\n T0=None, Tend=None,\n count=None, chunkSize=None,\n archs=None, breakDown=None,\n enumAsInt=False):\n\n delta = total_seconds(Tend-... | [
"0.55016786",
"0.5248723",
"0.52326065",
"0.50593275",
"0.493895",
"0.48849452",
"0.48460248",
"0.4819368",
"0.4760639",
"0.47135985",
"0.46557218",
"0.46315485",
"0.46160027",
"0.46151036",
"0.4607112",
"0.45550036",
"0.45491877",
"0.45421728",
"0.4500722",
"0.44971785",
"0.... | 0.58612335 | 0 |
Fetch raw data for the given PV. Results are passed to the given callback as they arrive. | def fetchplot(self, pv, callback,
cbArgs=(), cbKWs={},
T0=None, Tend=None,
count=None, chunkSize=None,
archs=None, breakDown=None,
enumAsInt=False):
delta = total_seconds(Tend-T0)
if delta<=0.0 or count<=0:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fetchraw(self, pv, callback,\n cbArgs=(), cbKWs={},\n T0=None, Tend=None,\n count=None, chunkSize=None,\n archs=None, breakDown=None,\n enumAsInt=False, displayMeta=False, rawTimes=False):\n if breakDown is None:\n ... | [
"0.5860869",
"0.55020386",
"0.52328575",
"0.50589204",
"0.49374425",
"0.4883425",
"0.48438007",
"0.48192716",
"0.47610128",
"0.4713053",
"0.4657324",
"0.46313772",
"0.46159387",
"0.46154013",
"0.46082655",
"0.4553728",
"0.4550245",
"0.45417523",
"0.4501568",
"0.44982013",
"0.... | 0.52498764 | 2 |
Fetch the value of all requested PVs at the given time | def fetchsnap(self, pvs, T=None,
archs=None, chunkSize=100,
enumAsInt=False):
pvs = list(pvs)
archs = self._archname2key(archs)
# values() request time range is inclusive, so Tcur==Tlast is a no-op
sec,ns = Tcur = timeTuple(makeTime(T))
ns+=10... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def poll(self) -> Tuple[np.ndarray]:\n t = time.time()\n try:\n v = self.controller.get(self.pvname)\n\n except TimeoutError:\n print(f\"No process variable found for {self.pvname}\")\n v = DEFAULT_SCALAR_VALUE[self.pvname]\n\n self.time = np.append(self... | [
"0.66055065",
"0.6367764",
"0.58524436",
"0.5844636",
"0.5721731",
"0.5689309",
"0.5669876",
"0.55845404",
"0.5539236",
"0.5535923",
"0.5528909",
"0.5492437",
"0.54848826",
"0.54116595",
"0.54083866",
"0.5388092",
"0.53508306",
"0.5335522",
"0.5291628",
"0.5287209",
"0.526849... | 0.48753047 | 93 |
A synchronous entrance to call self.fetch() | def sync_fetch(self, *args, **kwargs):
async def wrapper():
async with aiohttp.ClientSession() as session:
return await self.fetch(session=session, *args, **kwargs)
python_version = float(".".join(platform.python_version_tuple()[0:2]))
if python_version >= 3.7:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_fetch(self):\n pass",
"def fetch(self):\n pass",
"def fetch(self):\n pass",
"def fetch(self) -> None:\n pass",
"def run(self):\n results = self.fetch()\n return results",
"def fetch(self):\n raise NotImplementedError()",
"async def fetch_data(self... | [
"0.85556155",
"0.776958",
"0.776958",
"0.7708839",
"0.7366698",
"0.7306223",
"0.7268298",
"0.71274513",
"0.69972205",
"0.69962937",
"0.6940042",
"0.6909716",
"0.685237",
"0.68308353",
"0.68101543",
"0.6636323",
"0.66226596",
"0.6621362",
"0.6556455",
"0.6512855",
"0.64756656"... | 0.6501955 | 20 |
The version of the NDEF mapping. | def version(self):
return "%d.%d" % (self._vmajor, self._vminor) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def version():\n return _nfc.version()",
"def _get_mapVersion(self):\n return self.__mapVersion",
"def getVersion(self):\n return self.get('Version', type=\"numeric\")",
"def read_versionInfo(self):\n # PROTECTED REGION ID(SdpMasterLeafNode.versionInfo_read) ENABLED START #\n retur... | [
"0.72624695",
"0.7213992",
"0.7134265",
"0.7128217",
"0.7027476",
"0.7016305",
"0.7016305",
"0.6976598",
"0.69160473",
"0.68602616",
"0.67498976",
"0.673876",
"0.67278546",
"0.6727481",
"0.66671264",
"0.6664292",
"0.6662441",
"0.6656609",
"0.6631765",
"0.6629883",
"0.6629883"... | 0.7023342 | 5 |
The maximum number of user bytes on the NDEF tag. | def capacity(self):
return self._ndef_file_size - 2 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def len_max(self):\n return 16 + 16 + 8 + 8 + Tools.bin_to_dec(self.get_data_size()) + Tools.bin_to_dec(self.get_verification_size())",
"def maxsize(self):\r\n return self._maxsize",
"def maxMessageSize(self, appdata=None):\r\n return self.user.maxMessageSize",
"def max_length(self):\n\t... | [
"0.72259533",
"0.6949809",
"0.69376296",
"0.6894642",
"0.68714035",
"0.68587446",
"0.6839513",
"0.6792636",
"0.67583084",
"0.67414427",
"0.6697727",
"0.6697234",
"0.66827977",
"0.6665894",
"0.6654297",
"0.6569374",
"0.6536132",
"0.6513441",
"0.6513441",
"0.651174",
"0.6485859... | 0.632753 | 31 |
Is True if data can be read from the NDEF tag. | def readable(self):
return self._cc[13] == 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def has_data(self):\n return len(self.data) > 0",
"def has_data(self) -> bool:\n return bool(self.data)",
"def has_data(self) -> bool:\n raise NotImplementedError",
"def isSupportedData(self, data, info):\n return True",
"def is_present(self):\n try:\n self.rea... | [
"0.6675449",
"0.6558587",
"0.6554889",
"0.64809996",
"0.6385621",
"0.63179773",
"0.622701",
"0.6197048",
"0.6192307",
"0.6179339",
"0.6113737",
"0.60905915",
"0.6049415",
"0.6049415",
"0.59764844",
"0.58661574",
"0.58595383",
"0.5847148",
"0.5847148",
"0.58342046",
"0.5825380... | 0.55474675 | 39 |
Is True if data can be written to the NDEF tag. | def writeable(self):
return self._cc[14] == 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def has_data(self) -> bool:\n raise NotImplementedError",
"def can_insert(data):\n return hasattr(data, 'read')",
"def XPLMCanWriteDataRef(inDataRef):\n return bool",
"def has_data_appended(self):\n return self._incompat_flags[0] & 0x1",
"def has_data(self) -> bool:\n return ... | [
"0.6688601",
"0.6649141",
"0.6635758",
"0.6589478",
"0.65562564",
"0.65354025",
"0.65251344",
"0.6313288",
"0.62992036",
"0.6082002",
"0.60768175",
"0.6073173",
"0.6066335",
"0.6025633",
"0.5993792",
"0.5966116",
"0.5966116",
"0.59162486",
"0.58527404",
"0.58494073",
"0.58494... | 0.6650295 | 1 |
NDEF message data length. | def length(self):
return len(self.data) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def data_length(self):\n size = self.unpack_dword(0x4)\n if size >= 0x80000000:\n size -= 0x80000000\n return size",
"def Length(data):\n return len(data)",
"def length(self):\n return struct.unpack('<B', self.pkt.payload[2:3])[0]",
"def length(self):\n return... | [
"0.71663755",
"0.71121925",
"0.70100874",
"0.70100874",
"0.70045644",
"0.69800526",
"0.69625735",
"0.69608307",
"0.6957863",
"0.692996",
"0.6908471",
"0.690535",
"0.6900083",
"0.68991673",
"0.6883723",
"0.683814",
"0.68362767",
"0.6809234",
"0.6779064",
"0.6772033",
"0.671825... | 0.6781162 | 20 |
True if the message has changed since last read. | def changed(self):
if self.readable:
old_data = self.data[:]
data = self.tag.read_binary(0, self._max_le)
size = data[0] * 256 + data[1] + 2
tail = max(0, size - len(data))
while len(data) < size:
count = min(self._max_lc, size - len(da... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isUpdated(self):\n seq = self.readSeq()\n\n if (seq != self.seq):\n self.seq = seq\n return True\n else:\n return False",
"def has_changed(self):\n return bool(self.changed_data)",
"def has_been_modified(self):\n return self._has_been_modi... | [
"0.7590222",
"0.7515303",
"0.74319184",
"0.7383908",
"0.73827004",
"0.7348319",
"0.7306883",
"0.72848237",
"0.7212825",
"0.7155925",
"0.71495605",
"0.714205",
"0.7109246",
"0.7104731",
"0.7051768",
"0.70474595",
"0.6978085",
"0.6942784",
"0.69229704",
"0.6901572",
"0.6877428"... | 0.69910336 | 16 |
An NDEF message object (an empty record message if tag is empty). | def message(self):
try: return nfc.ndef.Message(str(self.data))
except nfc.ndef.parser_error: pass
return nfc.ndef.Message(nfc.ndef.Record()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_deserialize_empty(self):\n # Completely empty\n out = self.instance.deserialize(self._empty_msg)\n self.assert_result_equal(out[0], self.instance._empty_msg)\n self.assert_equal(out[1], dict(size=0, incomplete=False))\n # Empty metadata and message\n out = self.in... | [
"0.6054486",
"0.5856007",
"0.5810143",
"0.5708189",
"0.565236",
"0.56506103",
"0.55909777",
"0.5566611",
"0.55424976",
"0.5519104",
"0.54985887",
"0.54669297",
"0.54646397",
"0.5444558",
"0.54414916",
"0.5432448",
"0.5428145",
"0.5423365",
"0.5419152",
"0.54155207",
"0.541419... | 0.72136223 | 0 |
True if the tag is still within communication range. | def is_present(self):
try:
self.read_binary(0, 2)
return True
except:
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_active(self):\n now = self.sim.now\n return (now < self.stop) and (self._number < self.max_length) \\\n and (now >= self.start)",
"def is_open(self):\n return Intersection(self, self.boundary).is_empty",
"def is_bounded(self):\n return True",
"def __bool__(self):... | [
"0.6215045",
"0.61343145",
"0.61313975",
"0.60261875",
"0.59547496",
"0.590731",
"0.590731",
"0.5893823",
"0.5837768",
"0.5826704",
"0.579437",
"0.5790598",
"0.5758101",
"0.57309175",
"0.56994855",
"0.5659538",
"0.5656623",
"0.56554794",
"0.56545836",
"0.5650019",
"0.5639016"... | 0.0 | -1 |
Select a file or directory with parameters defined in ISO/IEC 78164 | def select_file(self, p1, p2, data, expected_response_length=None):
log.debug("select file")
cmd = bytearray([0x00, 0xA4, p1, p2])
if not data is None:
cmd += bytearray([len(data)]) + bytearray(data)
if not expected_response_length is None:
cmd += bytearr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def select_file(category, performer=None):\n\n files = File.get_files_by_category(category, performer=performer)\n files_ = []\n for file in files:\n if isinstance(file, str):\n files_.append(PyInquirer.Separator())\n continue\n file.category... | [
"0.6513869",
"0.6364568",
"0.63109976",
"0.6171725",
"0.61390656",
"0.609822",
"0.6077192",
"0.60535794",
"0.6029697",
"0.6011048",
"0.59819657",
"0.5981733",
"0.59141463",
"0.5894914",
"0.57887304",
"0.5788316",
"0.57765114",
"0.5741142",
"0.5687205",
"0.5656245",
"0.5642443... | 0.5514871 | 26 |
Read count bytes from selected file starting at offset | def read_binary(self, offset, count):
log.debug("read binary {0} to {1}".format(offset, offset+count))
cmd = bytearray([0x00, 0xB0, offset/256, offset%256, count])
rsp = self.transceive(cmd)
if rsp[-2:] != "\x90\x00":
raise Type4TagError(rsp[-2:])
return rsp[0:-2] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_count(buffer, offset, count):\n\n return buffer[offset:offset + count]",
"def chars(count):\n\n global offset\n\n bytes=midifile[offset:offset+count]\n offset+=count\n return bytes",
"def FileRead(offset, bytes):\r\n return _hiew.HiewGate_FileRead(offset, bytes)",
"def read_count(f... | [
"0.7175359",
"0.6825586",
"0.6784585",
"0.6660244",
"0.6636855",
"0.66041386",
"0.6511718",
"0.6443673",
"0.64226043",
"0.6303588",
"0.63005584",
"0.62887025",
"0.62636715",
"0.6233748",
"0.62070924",
"0.6184758",
"0.6153358",
"0.6116445",
"0.6106086",
"0.60951126",
"0.608945... | 0.6073786 | 21 |
Write data bytes to selected file starting at offset | def update_binary(self, offset, data):
log.debug("write binary {0} to {1}".format(offset, offset+len(data)))
cmd = bytearray([0x00, 0xD6, offset/256, offset%256, len(data)])
cmd = cmd + bytearray(data)
rsp = self.transceive(cmd)
if rsp[-2:] != "\x90\x00":
raise Type4T... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _write_at(self, data, offset):\n n_data = len(data)\n bytes_written = 0\n while bytes_written < n_data:\n available = min(\n n_data - bytes_written,\n self.channel.connection.credits * smb2.BYTES_PER_CREDIT,\n self.channel.connection.... | [
"0.7502533",
"0.73030454",
"0.7083179",
"0.67941546",
"0.66155225",
"0.6552972",
"0.64673847",
"0.64223176",
"0.6390805",
"0.6389237",
"0.6383939",
"0.6322789",
"0.6318765",
"0.6307246",
"0.6307246",
"0.62998325",
"0.6293559",
"0.6291714",
"0.62438726",
"0.62424064",
"0.62251... | 0.6530812 | 6 |
Get a ``sqlalchemy.orm.Session`` instance backed by a transaction. This function will hook the session to the transaction manager which will take care of committing any changes. When using pyramid_tm it will automatically be committed or aborted depending on whether an exception is raised. When using scripts you should... | def get_tm_session(session_factory, transaction_manager):
dbsession = session_factory()
transaction_manager.retry_attempt_count = 3 # TODO: Hardcoded for now
zope.sqlalchemy.register(
dbsession, transaction_manager=transaction_manager)
return dbsession | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def transaction_session(session=None, auto_commit=False):\n from decanter.database import db\n session = session or db.session\n try:\n yield session\n # Catch any and every exception and execute a rollback\n except Exception, e:\n session.rollback()\n # Now re-raise the excepti... | [
"0.7649063",
"0.7550382",
"0.7413695",
"0.73852265",
"0.7375639",
"0.7271203",
"0.71535194",
"0.71199584",
"0.71002847",
"0.70967805",
"0.69299304",
"0.69023323",
"0.68734854",
"0.6852362",
"0.6846709",
"0.6803013",
"0.6774827",
"0.6767137",
"0.6751748",
"0.66976035",
"0.6670... | 0.74530476 | 2 |
Initialize the model for a Pyramid app. Activate this setup using ``config.include('tm.models')``. | def includeme(config):
settings = config.get_settings()
settings['tm.manager_hook'] = 'pyramid_tm.explicit_manager'
# use pyramid_tm to hook the transaction lifecycle to the request
config.include('pyramid_tm')
# use pyramid_retry to retry a request when transient exceptions occur
config.inclu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_model(self):\n pass",
"def setup_models(self):\n pass",
"def init_model(engine):\n # Must use the same engine as the session fixture used by this module.\n initialize_model(model.mapper_registry, engine)",
"def initialize_model(self):\n pass",
"def init_model(engine):\n ... | [
"0.72130656",
"0.7058143",
"0.70304936",
"0.70059496",
"0.69764674",
"0.69742304",
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"0.63920647",
"0.6363125",
"0.6336152",
"0.62965196",
"0.6273022",
"0.62500... | 0.0 | -1 |
Create a grapher and stores its functions with its predefined colors. | def __init__(self,functions):
Plane.__init__(self)
self.functions=functions
self.colors=([RED,BLUE,GREEN,YELLOW]+[window.randomColor() for i in range(len(functions)-4)])[:len(functions)] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def new_gladiator(health, rage, damage_low, damage_high):\n return {\n 'health': health,\n 'rage': rage,\n 'damage low': damage_low,\n 'damage high': damage_high\n }",
"def __init__(self):\n self.function_dict = {\n \"Sphere\": self.draw_sphere,\n \"... | [
"0.58874846",
"0.5784521",
"0.5688146",
"0.56115836",
"0.56011266",
"0.5571135",
"0.5527934",
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"0.5186563",
"0.51737446",
"0.5167234",
"0.51666117",
"0.51666117",
"0.516... | 0.5603802 | 4 |
Show the elements on screen using the window. | def show(self,window):
self.showFunctions(window) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show(self, window):\r\n\r\n return",
"def show_window(self):\n self.show()",
"def show(self):\n # * displays the window, after using either the iconify or the withdraw methods\n self.wm_deiconify()\n # * this method can be called after the event which needs to happen befo... | [
"0.8284488",
"0.80970746",
"0.78321534",
"0.78083706",
"0.7644032",
"0.75353515",
"0.73609525",
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"0.6982777",
"0.6917495",
"0.69170654",
"0.69159263",
"0.69141114",
"0.6910... | 0.7856842 | 2 |
Overload of the update method of plane for the Grapher class in order to print the functions, takes window in parameter. | def showFunctions(self,window):
wsx,wsy=window.size
for i,function in enumerate(self.functions):
self.showGraph(function,window,self.colors[i])
window.print(str(function),[wsx-wsx/5,wsy-wsy/20*(i+2)],color=self.colors[i],size=25) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plane_update(self):\n self.plane.update()",
"def update_output_window(self):\n\n self.output_window.refresh(self.coordinate_dict['output_y'], 0, 0,\n self.coordinate_dict['output_x'] + 1,\n self.full_height_out - 1,\n ... | [
"0.6443978",
"0.58718014",
"0.5847557",
"0.57163036",
"0.5636099",
"0.55257374",
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"0.53994507",
"0.53977495",
"0.53960454",
"0.5395982",
"0.5382329",
"0.53... | 0.61533886 | 1 |
Show the graph of a function on screen using the function, the window and an optional color. | def showGraph(self,function,window,color=None):
if not color: color=window.randomColor()
wsx,wsy=window.size
points=[]
for X in range(0,wsx):
x,y=self.getFromScreen([X,0],window)
X,Y=self.getToScreen([x,function(x)],window)
points.append((X,Y))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def showFunctions(self,window):\n wsx,wsy=window.size\n for i,function in enumerate(self.functions):\n self.showGraph(function,window,self.colors[i])\n window.print(str(function),[wsx-wsx/5,wsy-wsy/20*(i+2)],color=self.colors[i],size=25)",
"def show(self, window, color=None, m... | [
"0.7177986",
"0.6613471",
"0.64047915",
"0.6390181",
"0.62344164",
"0.62344164",
"0.62344164",
"0.62308174",
"0.62073886",
"0.6182668",
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"0.59148264",
"0.58654755",
"0.58518666",
"0.5824023",
"0.5760816",
"0.57189447",
"0.5... | 0.8408193 | 0 |
Return the context data | def get_context_data(self, **kwargs):
context = super(IndexView, self).get_context_data(**kwargs)
context['version'] = settings.VERSION
return context | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_data(self, context):\n # Things to do\n return context",
"def get_context(self):\n return self.context.generate()",
"def get_context(self):\n return {}",
"def context():\n return dict()",
"def data_context(self):\n return self._data_context",
"def get_context... | [
"0.86183727",
"0.83535194",
"0.8277457",
"0.81600744",
"0.8112438",
"0.8018441",
"0.7926518",
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"0.7807353",
"0.7761825",
"0.77448404",
"0.7643687",
"0.7492722",
"0.74027705",
"0.7398663"... | 0.0 | -1 |
Return the context data | def get_context_data(self, **kwargs):
context = super(IngredientsView, self).get_context_data(**kwargs)
context['version'] = settings.VERSION
return context | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_data(self, context):\n # Things to do\n return context",
"def get_context(self):\n return self.context.generate()",
"def get_context(self):\n return {}",
"def context():\n return dict()",
"def data_context(self):\n return self._data_context",
"def get_context... | [
"0.86183727",
"0.83535194",
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"0.7761825",
"0.77448404",
"0.7643687",
"0.7492722",
"0.74027705",
"0.7398663"... | 0.0 | -1 |
Return the context data | def get_context_data(self, **kwargs):
context = super(IngredientCreate, self).get_context_data(**kwargs)
context['version'] = settings.VERSION
return context | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_data(self, context):\n # Things to do\n return context",
"def get_context(self):\n return self.context.generate()",
"def get_context(self):\n return {}",
"def context():\n return dict()",
"def data_context(self):\n return self._data_context",
"def get_context... | [
"0.86183727",
"0.83535194",
"0.8277457",
"0.81600744",
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"0.7761825",
"0.77448404",
"0.7643687",
"0.7492722",
"0.74027705",
"0.7398663"... | 0.0 | -1 |
Return the context data | def get_context_data(self, **kwargs):
context = super(IngredientUpdate, self).get_context_data(**kwargs)
context['version'] = settings.VERSION
return context | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_data(self, context):\n # Things to do\n return context",
"def get_context(self):\n return self.context.generate()",
"def get_context(self):\n return {}",
"def context():\n return dict()",
"def data_context(self):\n return self._data_context",
"def get_context... | [
"0.86183727",
"0.83535194",
"0.8277457",
"0.81600744",
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"0.8018441",
"0.7926518",
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"0.7761825",
"0.77448404",
"0.7643687",
"0.7492722",
"0.74027705",
"0.7398663"... | 0.0 | -1 |
Return the context data | def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
context['version'] = settings.VERSION
return context | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_data(self, context):\n # Things to do\n return context",
"def get_context(self):\n return self.context.generate()",
"def get_context(self):\n return {}",
"def context():\n return dict()",
"def data_context(self):\n return self._data_context",
"def get_context... | [
"0.86183727",
"0.83535194",
"0.8277457",
"0.81600744",
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"0.7926518",
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"0.7761825",
"0.77448404",
"0.7643687",
"0.7492722",
"0.74027705",
"0.7398663"... | 0.0 | -1 |
Return set of measurements. | def get_queryset(self):
return Measurement.objects.all() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def measurements(self):\n # get available measurement types for this node\n measurement_types = self.measurement_types()\n\n # retrieve measurement for each type\n return list(self.measurement(t) for t in measurement_types)",
"def measurements(self):\n return self._measurements... | [
"0.7974883",
"0.7895312",
"0.7810476",
"0.77818793",
"0.774465",
"0.77199775",
"0.7672082",
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"0.76322615",
"0.74093044",
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"0.68823",
"0.68502975",
"0.67978585",
"0.6783351",
"0.67077327",
"0.6688337",
"0.6600619... | 0.5979516 | 46 |
Return the context data | def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
context['version'] = settings.VERSION
return context | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_data(self, context):\n # Things to do\n return context",
"def get_context(self):\n return self.context.generate()",
"def get_context(self):\n return {}",
"def context():\n return dict()",
"def data_context(self):\n return self._data_context",
"def get_context... | [
"0.86183727",
"0.83535194",
"0.8277457",
"0.81600744",
"0.8112438",
"0.8018441",
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"0.7807353",
"0.7761825",
"0.77448404",
"0.7643687",
"0.7492722",
"0.74027705",
"0.7398663"... | 0.0 | -1 |
Return the context data | def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
context['version'] = settings.VERSION
return context | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_data(self, context):\n # Things to do\n return context",
"def get_context(self):\n return self.context.generate()",
"def get_context(self):\n return {}",
"def context():\n return dict()",
"def data_context(self):\n return self._data_context",
"def get_context... | [
"0.86183727",
"0.83535194",
"0.8277457",
"0.81600744",
"0.8112438",
"0.8018441",
"0.7926518",
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"0.7807353",
"0.7761825",
"0.77448404",
"0.7643687",
"0.7492722",
"0.74027705",
"0.7398663"... | 0.0 | -1 |
Defines which entries should be possible to prepare and run. Return True on error. | def setPossibleEntryList(self, redo = True):
self.entry_list_possible = NTlist()
if False: # DEFAULT: False
self.entry_list_possible += '1brv 1dum'.split()
return
# end if
if not self.entry_list_nmr:
if self.usePreviousPdbEntries and not redo:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def actionPrepare():\n \n #Do preparation that is common for all platforms. Pass true if ortc is one of targets\n result = Preparation.setUp('ortc' in Settings.targets)\n if result != NO_ERROR:\n #Terminate execution, because prepration common for all targets and platforms has failed.\n System.stopExecut... | [
"0.5940267",
"0.5919323",
"0.57682717",
"0.57292414",
"0.5697648",
"0.56928116",
"0.5680766",
"0.5666504",
"0.56541455",
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"0.55484563",
"0.5534304",
"0.5534304",
"0.5534304",
"0.55319035",
"0.55059725",
"0.5505... | 0.0 | -1 |
Bundled setups that require significant resources. | def setup(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _setup(self):\n pass",
"def _setup(self):\n pass",
"def _setup(self):\n pass",
"def _setup(self):\n pass",
"def _setup(self):\n pass",
"def _setup(self):",
"def _setup(self):",
"def setup_prod():\n setup_general()",
"def setup():\n _confirm_branch()\n ... | [
"0.70037156",
"0.70037156",
"0.70037156",
"0.70037156",
"0.70037156",
"0.69219625",
"0.69219625",
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"0.69045925",
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"0.66828656",
"0.66540056",
"0.66175795",
"0.66054",
"0.6593788",
"0.6... | 0.0 | -1 |
Returns True for error. This routine sets self.entry_list_todo Will remove entry directories if they do not occur in NRG up to a maximum number as not to whip out every one in a single blow by accident. If an entry has restraint data but is not in DOCR, it will be done from mmCIF until it does occur in DOCR. In NMR_RED... | def getEntryInfo(self):
showTimings = False # DEFAULT: False Enable for reporting.
nTmessage("Get the entries tried, todo, crashed, and stopped in %s from file system." % self.results_base)
crdb = NrgCingRdb(schema=self.schema_id) # Make sure to close it.
if not crdb:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setPossibleEntryList(self, redo = True):\n self.entry_list_possible = NTlist()\n if False: # DEFAULT: False\n self.entry_list_possible += '1brv 1dum'.split()\n return\n # end if\n\n if not self.entry_list_nmr:\n if self.usePreviousPdbEntries and not ... | [
"0.69043577",
"0.56096375",
"0.5511947",
"0.54344046",
"0.5384412",
"0.5379743",
"0.53401375",
"0.51841885",
"0.5162066",
"0.5123202",
"0.5095876",
"0.50184226",
"0.49978057",
"0.49429077",
"0.49403515",
"0.490497",
"0.4890484",
"0.48809263",
"0.48709345",
"0.48637065",
"0.48... | 0.5511539 | 3 |
Set the list of matched entries and the dictionary holding the number of matches. They need to be defined as globals to this module. Return True on error. Also searches the PDB and BMRB databases itself. | def searchPdbEntries(self, redo = False):
# modification_time = os.path.getmtime("/Users/jd/.cshrc")
# self.match.d[ "1brv" ] = EntryInfo(time=modification_time)
# nTmessage("Looking for entries in the different preparation stages.")
# for i, phaseData in enumerate(self.phaseDataList):
# ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def number_matches():\n ALL_FIXED_q = \"all_fixed_queries\" + str(17)\n ALL_FIXED_dbs = \"all_fixed_dbs\" + str(17)\n try:\n with open(\"matches\", \"rb\") as file_h:\n db_length = pickle.load(file_h)\n except:\n print \"reading files..\"\n t = time.time()\n \"\"\... | [
"0.58386177",
"0.5780435",
"0.5727033",
"0.56841534",
"0.5484644",
"0.53357863",
"0.53213334",
"0.5216295",
"0.51756287",
"0.5151685",
"0.5096588",
"0.4999635",
"0.4946475",
"0.4941619",
"0.49411833",
"0.49349916",
"0.49349916",
"0.49349374",
"0.49326298",
"0.49273935",
"0.48... | 0.64668524 | 0 |
Reformat the input HTML file content and return it. | def _format_html(self, file_content):
old_string = r"<!-- INSERT JUMP BOX HERE -->"
new_string = self._getJumpBoxHtml()
file_content = string.replace(file_content, old_string, new_string)
additional_head_string = '''
<link media="screen" href="dataTableMedia/css/demo_tab... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_html():\n return",
"def convert_content(self, html):\n\n try:\n dom = BeautifulSoup(html, 'html.parser')\n return self.parse_content(dom)\n except:\n return html",
"def parse_html(self):\n if self.file_extension == '.czm': # Caso de fichero c... | [
"0.6582659",
"0.6492117",
"0.6416533",
"0.63923806",
"0.63347447",
"0.629414",
"0.62634313",
"0.62472993",
"0.62427706",
"0.61941993",
"0.61035115",
"0.60052234",
"0.597376",
"0.5970281",
"0.5909006",
"0.5899246",
"0.5887682",
"0.5870766",
"0.58454716",
"0.5806536",
"0.578152... | 0.5648374 | 29 |
Update the plots on the front page except the pretty plot webpages. Return True on error. | def updateFrontPagePlots(self):
crdb = NrgCingRdb(schema=self.schema_id) # Make sure to close it.
if not crdb:
nTerror("In %s RDB connection was not opened" % getCallerName())
return True
# end if
for trending in [ 1, 0 ]: # DEFAULT: 1,0
# for trend... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def updateFrontPagePrettyPlots(self):\n for trending in [ 1, 0 ]: # DEFAULT: 1,0\n# for trending in [ 0 ]:\n inputDir = os.path.join(self.results_dir, PLOT_STR )\n outputDir = os.path.join(self.results_dir, PPLOT_STR )\n if trending:\n inputDir = os.pat... | [
"0.7287471",
"0.6297854",
"0.6288511",
"0.604255",
"0.58940005",
"0.5868938",
"0.5679945",
"0.562785",
"0.5612468",
"0.5590711",
"0.5510018",
"0.5443478",
"0.5443478",
"0.5443478",
"0.5443478",
"0.5443478",
"0.5441277",
"0.54403853",
"0.5436331",
"0.5414991",
"0.5399666",
"... | 0.7332676 | 0 |
Create an indexing page with all images linked. Return True on error. | def updateFrontPagePrettyPlots(self):
for trending in [ 1, 0 ]: # DEFAULT: 1,0
# for trending in [ 0 ]:
inputDir = os.path.join(self.results_dir, PLOT_STR )
outputDir = os.path.join(self.results_dir, PPLOT_STR )
if trending:
inputDir = os.path.join(self... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_index_file(\n root_dir, location, image_files, video_files, dirs, force_no_processing=False):\n # Put together HTML as a list of the lines we'll want to include\n # Issue #2 exists to do this better than HTML in-code\n header_text = 'imageMe: {0} [{1} image(s)] [{2} video(s)]'.format(\n... | [
"0.65096873",
"0.60325557",
"0.5879827",
"0.57953185",
"0.57412857",
"0.5682427",
"0.5672433",
"0.5629655",
"0.56278783",
"0.55914193",
"0.55709606",
"0.55628884",
"0.5557938",
"0.55475134",
"0.5511182",
"0.55088973",
"0.54807615",
"0.54618657",
"0.5436935",
"0.53450286",
"0.... | 0.0 | -1 |
Create an indexing page with all images linked. It is assumed here that the two directories are located in a shared parent directory | def createPrettyPlots(self, inputDir, outputDir, fnExtension = 'png'):
number_of_entries_per_row = 4
number_of_files_per_column = 2
imageWidth = 200 # 1600 org is four times as large
imageHeight = 150 # 1200
nTmessage("Updating index files for input directory: %s" % inputDir... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_index_file(\n root_dir, location, image_files, video_files, dirs, force_no_processing=False):\n # Put together HTML as a list of the lines we'll want to include\n # Issue #2 exists to do this better than HTML in-code\n header_text = 'imageMe: {0} [{1} image(s)] [{2} video(s)]'.format(\n... | [
"0.72902095",
"0.6531778",
"0.63808787",
"0.6137101",
"0.5913248",
"0.59064376",
"0.5902415",
"0.58962154",
"0.586412",
"0.581342",
"0.5738488",
"0.5735766",
"0.5680487",
"0.5666166",
"0.5649754",
"0.56297827",
"0.561287",
"0.5594195",
"0.558716",
"0.5577697",
"0.55735856",
... | 0.6073317 | 4 |
Dumps the relational database to about 1 Gb of CSV files. | def updateCsvDumps(self):
csvDumpDir = os.path.join( self.results_dir, 'pgsql' )
sqlFile = os.path.join( self.base_dir, 'sql', 'dumpNRG-CING.sql')
if runSqlForSchema(sqlFile, schemaId = self.schema_id, rootPath=csvDumpDir):
nTerror("Failed runSqlForSchema in updateCsvDumps")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def database_dump(self):\r\n print('=====Dumping database=====')\r\n self.database_table_dump(query.TABLE_STATS)\r\n print()\r\n self.database_table_dump(query.TABLE_TWEETS)\r\n print()\r\n self.database_table_dump(query.TABLE_POSTS)\r\n print()\r\n self.data... | [
"0.66528565",
"0.6367354",
"0.6322796",
"0.62614536",
"0.622275",
"0.6192562",
"0.61876374",
"0.61837506",
"0.61021197",
"0.609251",
"0.6038993",
"0.5967554",
"0.5927112",
"0.58945864",
"0.5880952",
"0.5828055",
"0.58182377",
"0.57567316",
"0.57287896",
"0.57047254",
"0.57025... | 0.59946346 | 11 |
Create a overall summary table that shows an overview of the tables. Created on Oct 13, 2011 | def updateFrontPages(self):
nTmessage("Starting %s" % getCallerName())
htmlDir = os.path.join(self.results_dir, "HTML")
if os.path.isdir(htmlDir):
# nTdebug("Removing original html directory for NRG-CING.")
rmdir(htmlDir)
# end if
nTmessage("Creating HTML d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_split_summary_table(all_df, train_df, test_df):\n table = PrettyTable()\n table.field_names = ['set', 'N total', 'N non-ICH', 'N ICH', 'frac non-ICH', 'frac ICH']\n for df, name in zip([all_df, train_df, test_df],['All', 'Train', 'Test']):\n table.add_row([name, len(df), len(df[df.Hemorrhag... | [
"0.7423239",
"0.73014647",
"0.7273784",
"0.70171285",
"0.699093",
"0.6793168",
"0.67701775",
"0.6736828",
"0.6680276",
"0.66769737",
"0.6619547",
"0.66166747",
"0.6596528",
"0.6585564",
"0.651712",
"0.64721656",
"0.64467716",
"0.6446023",
"0.6446023",
"0.64426595",
"0.6358988... | 0.0 | -1 |
Unzips the tgz. Copies the log Removes both tgz & log. Returns True on error. | def postProcessEntryAfterVc(self, entry_code):
nTmessage("Doing postProcessEntryAfterVc on %s" % entry_code)
doRemoves = 0 # DEFAULT 0 enable to clean up.
doLog = 1 # DEFAULT 1 disable for testing.
doTgz = 0 # DEFAULT 1 disable for testing.
doCopyTgz = 0
extractTgzOnly ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_zip_file (filename, path_unzip, outfile) :\n assert path_unzip is not None\n file,ext = os.path.splitext (filename)\n ext = ext.lower ()\n if ext == \".gz\" :\n \n import gzip\n \n if outfile is None :\n dest = filename.split (\"!\")\n d... | [
"0.6601055",
"0.6349917",
"0.6184881",
"0.61692077",
"0.61302775",
"0.60576636",
"0.6039221",
"0.5867746",
"0.58496195",
"0.58417505",
"0.5814407",
"0.5787656",
"0.577728",
"0.5739839",
"0.5729355",
"0.56869155",
"0.56007725",
"0.5594995",
"0.55871946",
"0.5576418",
"0.555929... | 0.0 | -1 |
On self.entry_list_todo. Return True on error. | def runCing(self):
nTmessage("Starting runCing")
if 0: # DEFAULT 0
nTmessage("Going to use non-default entry_list_todo in runCing")
# self.entry_list_todo = readLinesFromFile('/Users/jd/NRG/lists/bmrbPdbEntryList.csv')
self.entry_list_todo = "2l2e 2l2w 2l2x 2l2y 2l2z".... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_item(todo_list, todo_new_item):\n check = True\n try:\n todo_list.append(todo_new_item)\n except todo_list:\n print(\"Could not add new item to todo list\")\n check = False\n\n return check",
"def write_todo(self, todo):\n if todo != None:\n print 'added... | [
"0.65362954",
"0.6264628",
"0.62457657",
"0.5950381",
"0.59243155",
"0.591951",
"0.5868918",
"0.5857064",
"0.5763488",
"0.5735792",
"0.5725641",
"0.56935704",
"0.566834",
"0.56297255",
"0.55990285",
"0.5598494",
"0.55606014",
"0.5551346",
"0.5486843",
"0.54821986",
"0.5474715... | 0.0 | -1 |
Return True on error. | def postProcessAfterVc(self):
nTmessage("Starting postProcessAfterVc")
self.entry_list_nmr = readLinesFromFile(os.path.join(self.results_dir, 'entry_list_nmr.csv'))
self.entry_list_done = readLinesFromFile(os.path.join(self.results_dir, 'entry_list_done.csv'))
self.entry_list_todo = NTl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def has_errors(self) -> bool:",
"def check_errors(self) -> None:",
"def _check_error(self):\n\n if self.error_code_test != 0:\n return False\n else:\n return True",
"def prnt_error():\n print \"Error!\\n\"\n return False",
"def hasErrors(self):\n return Fals... | [
"0.79648924",
"0.78216517",
"0.7730106",
"0.7702022",
"0.76580405",
"0.7605078",
"0.7452465",
"0.7430146",
"0.7429136",
"0.7429136",
"0.7369654",
"0.7352932",
"0.72891927",
"0.7282168",
"0.72466207",
"0.7120696",
"0.70649666",
"0.7058519",
"0.7055884",
"0.70386034",
"0.700246... | 0.0 | -1 |
Return True on error. verbosity inputDir outputDir pdbConvention restraintsConvention archiveType projectType storeCING2db ranges filterTopViolations filterVasco singleCoreOperation | def createToposTokens(self, jobId = TEST_CING_STR):
# jobId = TEST_CING_STR # DEFAULT: TEST_CING_STR Set for testing.
# jobId = REFINE_ENTRY_STR
jobId = VALIDATE_ENTRY_NRG_STR
tokenListFileName = os.path.join(self.results_dir, 'token_list_todo.txt')
# Sync below code with valida... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_all_files_and_dirs(self):\n err = 0\n err_m = ''\n warning = 0\n warning_m = ''\n # Check the pdb file for refinement\n if self.refine_pdb_in == None:\n err = 1\n err_m += '\\nPdb file should be supplied'\n else:\n if self.... | [
"0.5354922",
"0.5183361",
"0.5134487",
"0.51090115",
"0.5005448",
"0.49695235",
"0.4932564",
"0.49020913",
"0.48695174",
"0.4862666",
"0.48543862",
"0.48481873",
"0.48474097",
"0.48417285",
"0.48415136",
"0.48409507",
"0.48312497",
"0.48222607",
"0.47857195",
"0.4769777",
"0.... | 0.0 | -1 |
On self.entry_list_todo. Return True on error. | def replaceCoordinates(self):
self.entry_list_todo = readLinesFromFile(os.path.join(self.results_dir, 'list', 'entry_list_recoord_nrgcing_shuffled.csv'))
self.entry_list_todo = self.entry_list_todo[:100]
entryListFileName = "entry_list_recoord_todo.csv"
writeTextToFile(entryListFileName,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_item(todo_list, todo_new_item):\n check = True\n try:\n todo_list.append(todo_new_item)\n except todo_list:\n print(\"Could not add new item to todo list\")\n check = False\n\n return check",
"def write_todo(self, todo):\n if todo != None:\n print 'added... | [
"0.6535864",
"0.6267024",
"0.6246162",
"0.5952014",
"0.59265065",
"0.5922316",
"0.58717424",
"0.5859177",
"0.5764468",
"0.5735906",
"0.5728612",
"0.56953245",
"0.56699693",
"0.56310666",
"0.55995315",
"0.55988985",
"0.5561708",
"0.555206",
"0.5489174",
"0.54824257",
"0.547576... | 0.0 | -1 |
Needs to be overriden by e.g. nmr_redo. Return True on error. | def refine(self): # pylint: disable=R0201
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def can_redo(self) -> bool:\n\n return self.position < len(self.history) - 1",
"def has_redo(self):\n return self._current_undo_command + 1 != len(self._commands)",
"def canRedo(self):\n return self._index < len(self._commands)",
"def retry(self):\n # XXX: check whether it is poss... | [
"0.7054411",
"0.701077",
"0.692074",
"0.65056694",
"0.64995927",
"0.6481644",
"0.6417906",
"0.6362988",
"0.6357255",
"0.6115446",
"0.61079174",
"0.60984766",
"0.60878116",
"0.5987586",
"0.5986594",
"0.59642196",
"0.5913475",
"0.5890314",
"0.5882373",
"0.5878642",
"0.58432573"... | 0.0 | -1 |
sync images to docker machine hosts | def cli(ctx, image_file):
if not image_file:
return
for pull_image in image_file.readline():
pull_image = pull_image.rstrip('\n')
if len(pull_image) == 0:
continue
docker.pull(pull_image)
push_image = '%s/%s/%s' % (DEFAULT_REGISTRY,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_remote_image(local_image_path, filename):\n for host in APP.config['REMOTE_HOSTS']:\n user = APP.config['REMOTE_HOST_USERNAME']\n directory = APP.config['REMOTE_IMAGE_DIRECTORY']\n ssh_command = f'''ssh -oStrictHostKeyChecking=accept-new -i /root/.ssh/id_rsa \\\n {user}@{... | [
"0.6557685",
"0.6444026",
"0.6244093",
"0.62162626",
"0.6192624",
"0.6067474",
"0.5999034",
"0.5986492",
"0.5871816",
"0.5861316",
"0.5789827",
"0.57858646",
"0.5764116",
"0.5740863",
"0.5707998",
"0.5635532",
"0.5630496",
"0.56185627",
"0.56116",
"0.55559105",
"0.54989785",
... | 0.60403055 | 6 |
Reset all lengths pose with init state | def ResetPos(self):
for idx in range(self.unFixJL):
self._p.resetJointState(self.uid, idx,
self.InitInfo["JPos"][idx],
self.InitInfo["JVel"][idx]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reset(self):\n self.steps = 0\n self.state = 0\n self.trajectory = []",
"def reset(self):\n self.position = np.zeros(self.ndegres)\n self.velocity = np.zeros(self.ndegres)\n self.state = np.zeros(2*self.ndegres)\n self.flag = 0\n self.h_ref = np.array([... | [
"0.7203299",
"0.7193509",
"0.7189836",
"0.71515554",
"0.71506715",
"0.702369",
"0.69588333",
"0.6910216",
"0.6836779",
"0.6830261",
"0.67455906",
"0.6727042",
"0.672369",
"0.672369",
"0.672369",
"0.67201173",
"0.66877395",
"0.66546106",
"0.66449106",
"0.66274893",
"0.6600268"... | 0.68099713 | 10 |
Robot Simulation Env, every cmd exec need to run (sim_freq / cmd_freq) sim_tims steps; | def Apply_Pos(self, act_cmd, record_joints = False):
# limit motor vel, if surplus, then clip
if record_joints:
cur_cmd_js = []
# cur_cmd_js.append(self.GetPosObs())
for _ in range(self.cmd_step):
# print("pos : ", self.GetMotorAng())
# print("v... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def runRobot():",
"def main():\n run_simulation(spectral=False, ml=False, num_procs=1)\n run_simulation(spectral=True, ml=False, num_procs=1)\n run_simulation(spectral=False, ml=True, num_procs=1)\n run_simulation(spectral=True, ml=True, num_procs=1)\n run_simulation(spectral=False, ml=True, num_p... | [
"0.638473",
"0.6331924",
"0.632148",
"0.61970353",
"0.61686397",
"0.60981554",
"0.6055782",
"0.6055782",
"0.59908384",
"0.5978672",
"0.59524536",
"0.595225",
"0.5946836",
"0.5919655",
"0.5914979",
"0.586384",
"0.5856052",
"0.5836727",
"0.58234084",
"0.58036256",
"0.5795908",
... | 0.0 | -1 |
Get a list of dates that contain memories for a user. | def list_memories(self, user_id, start_date=None, end_date=None):
raise NotImplementedError() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_memos():\n records = []\n for record in collection.find({\"type\": \"dated_memo\"}):\n record['date'] = arrow.get(record['date']).isoformat()\n del record['_id']\n records.append(record)\n # sorted() function use taken from Rob Murray:\n # https://stackoverflow.com/question... | [
"0.62519693",
"0.589958",
"0.5861902",
"0.5788278",
"0.5683896",
"0.5669883",
"0.5618058",
"0.5612521",
"0.5612506",
"0.55803084",
"0.55629635",
"0.5521892",
"0.5515748",
"0.54657376",
"0.5464114",
"0.54432034",
"0.54235256",
"0.5418274",
"0.54057574",
"0.5374128",
"0.5370449... | 0.7138492 | 0 |
Return whether a memory exists for a user at a date. | def has_memory(self, user_id, memory_date):
raise NotImplementedError() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def record_exists(self, date):\n for record in self.records:\n if self.date_str == record[\"date\"]:\n return True\n return False",
"def alreadyStored(self, date_):\r\n for day in self._repo:\r\n if day.date == date_:\r\n return True\r\n ... | [
"0.64419144",
"0.5881912",
"0.5841056",
"0.5658975",
"0.56083417",
"0.56036156",
"0.5585815",
"0.5566384",
"0.5522872",
"0.55008674",
"0.53935784",
"0.53619343",
"0.5356803",
"0.52772665",
"0.5258884",
"0.5247556",
"0.52393395",
"0.5212584",
"0.5209245",
"0.51901716",
"0.5186... | 0.8038259 | 0 |
Configure and return the Flask app | def create_app(self):
app.config.from_object('config.TestingConfig')
return app | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def app(self):\n\n ## set flask specific things that are non-optional\n error = lambda k: 'Fatal: You need to specify a \"flask\" section ' + \\\n 'with an entry like \"'+k+'=...\" in your .ini file'\n try: app_name = self['flask.app']\n except KeyError: raise SystemExit... | [
"0.85104823",
"0.81120455",
"0.8054589",
"0.80477077",
"0.7995264",
"0.7929426",
"0.7846994",
"0.7837908",
"0.7773335",
"0.7747373",
"0.7746391",
"0.7729097",
"0.77219594",
"0.7708146",
"0.77017826",
"0.76943517",
"0.7686414",
"0.7680069",
"0.76726705",
"0.76552176",
"0.76191... | 0.76463825 | 20 |
Copy description from manual page 79 | def h_o(self):
return self._h_o | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def description():",
"def description(self):",
"def get_description(self):",
"def help_description():\n pass",
"def description(self):\n pass",
"def description(self):\n pass",
"def get_description(self):\n return re.sub('\\n\\W+',' ', self.__doc__)",
"def Description(self... | [
"0.798952",
"0.74467456",
"0.7336333",
"0.72837377",
"0.71202844",
"0.71202844",
"0.7059274",
"0.70321923",
"0.70321923",
"0.6990859",
"0.696746",
"0.6894048",
"0.68891364",
"0.6875786",
"0.6862433",
"0.6778213",
"0.6750307",
"0.6750307",
"0.67375386",
"0.6713404",
"0.6671586... | 0.0 | -1 |
Returns bootstrap estimate of 100.0(1alpha) CI for statistic. | def bootstrap(data, num_samples, statistic, alpha):
n = len(data)
idx = npr.randint(0, n, (num_samples, n))
samples = x[idx]
stat = np.sort(statistic(samples, 1))
return (stat[int((alpha/2.0)*num_samples)],
stat[int((1-alpha/2.0)*num_samples)]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_bootstrap_CI(self, alpha, num_samples):\n\n # set random number generator seed\n numpy.random.seed(1)\n\n # initialize delta array\n delta = numpy.zeros(num_samples)\n\n # obtain bootstrap samples\n for i in range(num_samples):\n sample_i = numpy.random.... | [
"0.76049566",
"0.7369145",
"0.7369145",
"0.7200213",
"0.69077885",
"0.68503654",
"0.68281436",
"0.65225583",
"0.6516209",
"0.6500391",
"0.6483958",
"0.6221434",
"0.61406034",
"0.6129345",
"0.60075456",
"0.598997",
"0.5946004",
"0.59068424",
"0.5902341",
"0.5886393",
"0.586061... | 0.6811339 | 7 |
check to see if a site is live | def is_alive(self, site):
try:
return requests.get(site).status_code == 200
except Exception:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_website_online(host):\n try:\n socket.gethostbyname(host)\n except socket.gaierror:\n return False\n else:\n return True",
"def isonline():\n\n conn = httplib.HTTPConnection(\"www.google.com\", timeout=5)\n try:\n conn.request(\"HEAD\", \"/\")\n conn.close... | [
"0.7184357",
"0.6925616",
"0.69226384",
"0.6872171",
"0.68130124",
"0.680132",
"0.65696245",
"0.64950216",
"0.6441501",
"0.64284706",
"0.6418446",
"0.6374213",
"0.63635945",
"0.6330601",
"0.6329618",
"0.6314663",
"0.6310036",
"0.6304025",
"0.62810314",
"0.62600166",
"0.624670... | 0.7745489 | 0 |
method to inject the url with a query | def blind(self):
print("Performing blind sql injection on {}".format(self.domain))
return self.inject() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def url_replace(context, **kwargs):\n query = context['request'].GET.dict()\n query.update(kwargs)\n return urlencode(query)",
"def url_replace(context, **kwargs):\n query = context['request'].GET.dict()\n query.update(kwargs)\n return urlencode(query)",
"def url_with_querystring(url, **kwarg... | [
"0.70120484",
"0.70120484",
"0.7010469",
"0.699704",
"0.68464214",
"0.6821921",
"0.6809707",
"0.6648688",
"0.6613393",
"0.66075397",
"0.6583891",
"0.6579221",
"0.6572595",
"0.6557424",
"0.6557424",
"0.65356547",
"0.6489736",
"0.648267",
"0.64804256",
"0.6476738",
"0.6470362",... | 0.0 | -1 |
Run method for the start of the application, accepts the domain as an argument. | def run(self, domain):
url = re.findall('(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', domain)
if len(url) >= 1:
for r in url:
self.domain = r
else:
print("Provide a valid url ie www.example.com ")
self.blind # calling a m... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self):\n self.app.run()",
"def run(self):\n self.app.run()",
"def run():\n app = Application()\n #app.sentry_client = AsyncSentryClient(app.settings['sentry_url'])\n http_server = HTTPServer(app, xheaders=True)\n http_server.listen(options.port)\n print('Running on port %d'... | [
"0.6693647",
"0.6693647",
"0.6408186",
"0.6318336",
"0.62469375",
"0.6231638",
"0.6231638",
"0.62093973",
"0.6198194",
"0.61581784",
"0.6132528",
"0.6112055",
"0.6091389",
"0.60707957",
"0.60672754",
"0.60449064",
"0.6042374",
"0.60180104",
"0.6017973",
"0.60146195",
"0.60134... | 0.66517687 | 2 |
10 ms faster, than import uuid4 | def uuid4():
b = ''.join('%x' % x for x in os.urandom(16))
return "%s-%s-%s-%s-%s" % (b[0:8], b[8:12], b[12:16], b[16:20], b[20:]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def v4():\n return base64.urlsafe_b64encode(uuid.uuid4().bytes)[:-2] # Drop '==' padding",
"def test_uuid():\n for _ in range(1000):\n uuid = uuid_generator()\n assert len(uuid) == 36\n assert uuid.count('-') == 4",
"def _get_uuid():\n return str(uuid.uuid4())",
"def generate_id... | [
"0.749502",
"0.74192566",
"0.73969394",
"0.73628277",
"0.72862285",
"0.7247379",
"0.7240885",
"0.7184937",
"0.7184937",
"0.7184658",
"0.717396",
"0.71590453",
"0.71353364",
"0.711079",
"0.70460355",
"0.70416224",
"0.7026545",
"0.70200133",
"0.69982344",
"0.6930404",
"0.692983... | 0.7622199 | 0 |
Create an index of mapped letters (zip to dict). | def translation(first, second):
if len(first) != len(second):
raise WrongLengthException('The lists are not of the same length!')
return dict(zip(first, second)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mapping_letter(letters):\n my_list = list(map(lambda x: x.upper(), letters))\n return dict(zip(letters, my_list))",
"def create_dictionaries(chars):\n return dict((c, i) for i, c in enumerate(chars)), dict((i, c) for i, c in enumerate(chars))",
"def _create_subscript_mapping():\n # Create the no... | [
"0.7323739",
"0.6905413",
"0.68344617",
"0.6818953",
"0.6551249",
"0.6550341",
"0.6446107",
"0.64334625",
"0.64166945",
"0.63979185",
"0.63765055",
"0.6270741",
"0.62367254",
"0.62338865",
"0.6211539",
"0.619778",
"0.61915135",
"0.61460876",
"0.6141319",
"0.61408085",
"0.6139... | 0.0 | -1 |
Squeeze the given sequence by dropping consecutive duplicates. | def squeeze(word):
return ''.join(x[0] for x in groupby(word)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def removeDuplicates(seq):\n\n pass",
"def removeDuplicates(seq):\r\n seen = set()\r\n seen_add = seen.add\r\n return [x for x in seq if not (x in seen or seen_add(x))]",
"def uniq(seq):\r\n seen = set()\r\n seen_add = seen.add\r\n return [x for x in seq if x not in seen and no... | [
"0.7483001",
"0.7261245",
"0.65032953",
"0.6358768",
"0.6315142",
"0.6266054",
"0.6199574",
"0.61771053",
"0.6175709",
"0.61725605",
"0.61043745",
"0.60808164",
"0.60753167",
"0.605259",
"0.6049053",
"0.60409445",
"0.60155874",
"0.59284973",
"0.59154576",
"0.5809901",
"0.5727... | 0.0 | -1 |
Constructs the Linear module. | def __init__(
self,
output_size: int,
with_bias: bool = True,
w_init: tp.Optional[hk.initializers.Initializer] = None,
b_init: tp.Optional[hk.initializers.Initializer] = None,
name: tp.Optional[str] = None,
):
super().__init__(
output_size=output_s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, units, activation=None, use_bias=True, kernel_initializer=None, bias_initializer=None,\n trainable=True, dtype=tf.float32, name=None):\n # general init\n if name is None:\n name = \"Linear\"\n self.name = str(name)\n self.variable_scope = tf... | [
"0.6729752",
"0.64801776",
"0.6456392",
"0.63238543",
"0.63191384",
"0.621048",
"0.6181088",
"0.6168781",
"0.61668515",
"0.6132894",
"0.6119526",
"0.6119526",
"0.6091859",
"0.6075223",
"0.603353",
"0.6022447",
"0.60161537",
"0.5987431",
"0.5971014",
"0.5959086",
"0.595031",
... | 0.0 | -1 |
Initialize layers of the head. | def _init_layers(self):
self.relu = nn.ReLU(inplace=True)
self.cls_convs = nn.ModuleList()
self.reg_convs = nn.ModuleList()
for i in range(self.stacked_convs):
chn = self.in_channels if i == 0 else self.feat_channels
self.cls_convs.append(
ConvModu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _init_layers(self):\n self._init_predictor()\n if self.use_edge_fusion:\n self._init_edge_module()",
"def _init_layers(self) -> None:\n self.self_attn = MultiheadAttention(**self.self_attn_cfg)\n self.cross_attn = MultiheadAttention(**self.cross_attn_cfg)\n self.... | [
"0.7565452",
"0.73801965",
"0.7378758",
"0.70826614",
"0.70083666",
"0.68058515",
"0.6793121",
"0.6747672",
"0.6699219",
"0.6565106",
"0.6552534",
"0.6548868",
"0.6536724",
"0.6533556",
"0.65013635",
"0.64803135",
"0.6478405",
"0.6438143",
"0.6429889",
"0.6428964",
"0.6401287... | 0.67046845 | 8 |
Initialize weights of the head. | def init_weights(self):
for m in self.cls_convs:
normal_init(m.conv, std=0.01)
for m in self.reg_convs:
normal_init(m.conv, std=0.01)
bias_cls = bias_init_with_prob(0.01)
normal_init(self.retina_cls, std=0.01, bias=bias_cls)
normal_init(self.retina_reg, st... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _initialize_weights(self):\n pass",
"def init_weights(self):\n # Initialize weights\n self.apply(self._init_weights)",
"def setWeightInitializer(self,weights):\n self.init_w = weights",
"def init_weights(self):\n # Initialize weights\n self.apply(self._init_weigh... | [
"0.8231931",
"0.8066145",
"0.8041016",
"0.8037346",
"0.79638714",
"0.78825206",
"0.7794443",
"0.7718116",
"0.76905215",
"0.76740956",
"0.7660412",
"0.76257116",
"0.76194113",
"0.75636816",
"0.7554319",
"0.7554319",
"0.75442475",
"0.75423366",
"0.75423366",
"0.75423366",
"0.75... | 0.66495466 | 96 |
Forward feature of a single scale level. | def forward_single(self, x):
cls_feat = x
reg_feat = x
for cls_conv in self.cls_convs:
cls_feat = cls_conv(cls_feat)
for reg_conv in self.reg_convs:
reg_feat = reg_conv(reg_feat)
cls_score = self.retina_cls(cls_feat)
bbox_pred = self.retina_reg(reg... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def feature_forward(self, x):\n raise NotImplementedError",
"def forward(self, x):\n x = self.features(x)\n return x",
"def forward_single(self, x, scale):\n cls_feat = x\n reg_feat = x\n for cls_conv in self.cls_convs:\n cls_feat = cls_conv(cls_feat)\n ... | [
"0.74690366",
"0.68007946",
"0.65777415",
"0.6515914",
"0.6515914",
"0.6404764",
"0.6320513",
"0.6306763",
"0.6299315",
"0.6285584",
"0.6279949",
"0.6200305",
"0.6179189",
"0.6158268",
"0.6132828",
"0.6124821",
"0.61077636",
"0.61061907",
"0.6104554",
"0.6101124",
"0.6098391"... | 0.0 | -1 |
Compute losses of the head. | def loss(self,
cls_scores,
bbox_preds,
gt_bboxes,
gt_labels,
img_metas,
gt_bboxes_ignore=None):
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
assert len(featmap_sizes) == self.anchor_generator.num_levels
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def losses(self):\n pass",
"def compute_loss(self):",
"def losses(self):\n # compute all kinds of losses \n\n # 1. Logits losses for classification \n\n # 2. regression loss for bbox \n\n return classification_loss, bbox_reg_loss",
"def compute_loss(self, obs, returns):",
... | [
"0.7471189",
"0.6744396",
"0.6732741",
"0.66957474",
"0.6637872",
"0.65996355",
"0.65996355",
"0.6463765",
"0.6462264",
"0.6452444",
"0.6387869",
"0.63160586",
"0.6304027",
"0.6283066",
"0.6242887",
"0.6218127",
"0.61900795",
"0.617547",
"0.6174948",
"0.6159635",
"0.6144969",... | 0.0 | -1 |
Reads the discounts csv file & returns a dataframe | def read_discounts(self, file_name):
discounts = pd.read_csv(f'{file_name}.csv')
return discounts | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_extract(cryptocurrency):\n df = pd.read_csv(f'input_12mo/{cryptocurrency}.csv')\n df = df['Close'].copy()\n df = df[-183:].copy()\n return df",
"def loan_data():\n return pd.read_csv(data_path / \"credit_data.csv\")",
"def read_csv():",
"def read_data(filepath):\n df = pd.read_... | [
"0.67762727",
"0.66502154",
"0.657781",
"0.65579677",
"0.65579677",
"0.65579677",
"0.6523957",
"0.6514698",
"0.64671755",
"0.6462768",
"0.6447732",
"0.63911945",
"0.63872385",
"0.6359474",
"0.6340352",
"0.6323935",
"0.6310876",
"0.62966776",
"0.6278205",
"0.6277503",
"0.62688... | 0.7847184 | 0 |
Consumes discount dataframe & returns multiple metadata from it | def get_product_vs_offer_and_volume(self, df):
product_vs_min_volume = {}
product_offer = {}
base_product_vs_offer_product = {}
for i in df.itertuples():
product_vs_min_volume[i[4].lower()] = i[5]
product_offer[i[4].lower()] = i[1]
base_product_vs_of... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def metadata(self, df):\n raise NotImplementedError(\"missing metadata() method\")",
"def get_metadata(self):\n df_data = pd.read_csv(self.path_bin)\n # df_data = df_data[df_data['subject'] == 0] # limit data to certain subject.\n\n df = pd.DataFrame()\n df['mean'] = np.mean(df... | [
"0.6035103",
"0.5869",
"0.5865952",
"0.58466834",
"0.5742667",
"0.57354236",
"0.5657524",
"0.5648695",
"0.56444234",
"0.5626451",
"0.5606854",
"0.5591722",
"0.55566615",
"0.55493385",
"0.5523179",
"0.5518336",
"0.5508248",
"0.5500367",
"0.54583555",
"0.54141194",
"0.54134256"... | 0.0 | -1 |
Takes in the product volume dictionary & returns applicable discounts, any residuals (product codes) which doesnt have any offers | def get_applicable_discounts(self, actual_volume):
applicable_discounts = []
residual = {}
min_vol_criteria, product_vs_offer, base_product_vs_offer_product = self.get_product_vs_offer_and_volume(self.discounts)
for basket_item, volume in actual_volume.items():
offer_on_pr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_basket_with_discounts(self, actual_volume, applicable_discounts, residual):\n\n # product_vs_min_volume, product_offer = self.get_product_vs_offer_and_volume(self.discounts)\n pr = Product('items')\n product_prices = pr.product_meta\n\n basket = []\n\n for discount in app... | [
"0.64345753",
"0.6058145",
"0.57161295",
"0.5696214",
"0.5675615",
"0.56543845",
"0.5502182",
"0.54654807",
"0.544653",
"0.5391345",
"0.5367731",
"0.5360388",
"0.53320503",
"0.53056145",
"0.5287403",
"0.526012",
"0.52296513",
"0.52091134",
"0.52077895",
"0.5205086",
"0.519474... | 0.7132442 | 0 |
Computes all applicable offers if the offer type is Dicsount. Buy 1 get 1 or any other discounts | def get_basket_items_discount(self, offer_info, actual_volume, product_prices):
prod_code = offer_info.base_prod_code
base_prod_vol = actual_volume.get(prod_code.lower())
discount_basket = []
if base_prod_vol >= offer_info.min_vol:
offer_on_prod = offer_info.offer_on
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _compute_offers_to_send(self) -> Dict[Tuple[float, float], float]:\n partial_asgt = self._neighbors_values.copy()\n offers = dict()\n\n for limited_asgt in generate_assignment_as_dict([self.variable, self._partner]):\n partial_asgt.update(limited_asgt)\n cost = self._... | [
"0.55473804",
"0.5530387",
"0.55122185",
"0.5472553",
"0.5421471",
"0.5414957",
"0.53732795",
"0.53643686",
"0.53457385",
"0.5306167",
"0.52392435",
"0.523554",
"0.52336174",
"0.52059656",
"0.5203071",
"0.51802623",
"0.51542354",
"0.5132325",
"0.5119246",
"0.50583845",
"0.505... | 0.5732468 | 0 |
Computes all applicable offers if the offer type is PriceDrop. | def get_basket_items_pricedrop(self, offer_info, actual_volume, product_prices):
prod_code = offer_info.base_prod_code
base_prod_vol = actual_volume.get(prod_code.lower())
pricedrop_basket = []
if base_prod_vol >= offer_info.min_vol:
offer_on_prod = offer_info.offer_on
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def filter_viable_offers(want, have, offers) -> List:\n return [x for x in offers if is_offer_viable(want, have, x) is True]",
"def _compute_offers_to_send(self) -> Dict[Tuple[float, float], float]:\n partial_asgt = self._neighbors_values.copy()\n offers = dict()\n\n for limited_asgt in g... | [
"0.6091617",
"0.56822497",
"0.5510932",
"0.54817843",
"0.5454564",
"0.5445133",
"0.5424857",
"0.5380617",
"0.5372044",
"0.53682536",
"0.52954173",
"0.52618384",
"0.52528185",
"0.5248819",
"0.5241865",
"0.5238828",
"0.5230508",
"0.5212023",
"0.51742303",
"0.5160276",
"0.515464... | 0.55353993 | 2 |
Methd for invoking discount & price drop offer computation methods | def get_basket_with_discounts(self, actual_volume, applicable_discounts, residual):
# product_vs_min_volume, product_offer = self.get_product_vs_offer_and_volume(self.discounts)
pr = Product('items')
product_prices = pr.product_meta
basket = []
for discount in applicable_disco... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def discount(self, cart):",
"def get_discount(self, price):\r\n pass",
"def apply_discount(self, product):\n pass",
"def compute(self):\r\n \r\n if self.option_type == \"European call\":\r\n option_price = BS_call(self.S0, self.K, self.T_scaled, self.vola, self.div_yiel... | [
"0.69941723",
"0.6785887",
"0.66767174",
"0.6378575",
"0.6193816",
"0.6177383",
"0.61754054",
"0.60723627",
"0.5998024",
"0.59970456",
"0.5993197",
"0.59507436",
"0.59506613",
"0.59471047",
"0.59038407",
"0.58806396",
"0.5844756",
"0.58214176",
"0.58164537",
"0.57721055",
"0.... | 0.0 | -1 |
Generates background and labels for the Cafeteria Menu | def menu_handler(menu,fileout_path):
image = Image.open("icons/menu_template.jpg")
draw = ImageDraw.Draw (image)
if menu is None:
TITLE_X_OFFSET=0.05*image.size[0]
TITLE_Y_OFFSET=0.25*image.size[1]
huge_font=ImageFont.truetype("fonts\Ariblk.ttf",... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_menu():",
"def create_menus( self ):",
"def draw_main_menu():\n draw_cover()\n draw_menu_buttons()\n draw_border()",
"def main_menu(self):\n\n # Set the window background\n self.palette = QPalette()\n self.pixmap = QPixmap('./pictures/menu_cat.png').scaled(860, 640)\... | [
"0.7138286",
"0.699841",
"0.65738827",
"0.65555376",
"0.6499045",
"0.64050287",
"0.63404286",
"0.6264091",
"0.6249606",
"0.6249606",
"0.6233549",
"0.62195414",
"0.6210253",
"0.61664957",
"0.6162381",
"0.6152219",
"0.61354536",
"0.60851127",
"0.6030847",
"0.60096836",
"0.59637... | 0.5933318 | 26 |
A partir de un diccionario que representa un punto P devuelve una estructura de C. | def PyC_Punto(P):
C = CPunto()
C.x[0] = P['x'][0]
C.x[1] = P['x'][1]
C.x[2] = P['x'][2]
C.y[0] = P['y'][0]
C.y[1] = P['y'][1]
C.y[2] = P['y'][2]
C.D = P['D']
C.n = P['n']
C.a, C.b, C.c = P['a'], P['b'], P['c']
C.d, C.e, C.f = P['d'], P['e'], P['f']
return C | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def CPy_Punto(C, P):\n \n P['x'][0] = C.x[0]\n P['x'][1] = C.x[1]\n P['x'][2] = C.x[2]\n\n P['y'][0] = C.y[0]\n P['y'][1] = C.y[1]\n P['y'][2] = C.y[2]\n\n P['D'] = C.D\n P['a'], P['b'], P['c'] = C.a, C.b, C.c\n P['d'], P['e'], P['f'] = C.d, C.e, C.f",
"def findpc(self):\n u ... | [
"0.68054706",
"0.63273036",
"0.59791553",
"0.58777106",
"0.58699834",
"0.5814331",
"0.574379",
"0.5730052",
"0.57189286",
"0.5685744",
"0.5653829",
"0.5597755",
"0.55749863",
"0.55708575",
"0.5543975",
"0.553301",
"0.55071986",
"0.5486988",
"0.5479547",
"0.54753065",
"0.54649... | 0.7179086 | 0 |
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